a sensitivity analysis of the impact of cap reform - associazione ...

A SENSITIVITY ANALYSIS
OF THE IMPACT OF CAP REFORM
Alternative Methods of Constructing
Regional Input-Output Tables
Andrea Bonfiglio
associazioneAlessandroBartola
studi e ricerche di economia e di politica agraria
PhD Studies 1

associazioneAlessandroBartola
A SENSITIVITY ANALYSIS OF THE IMPACT OF
CAP REFORM. ALTERNATIVE METHODS OF
CONSTRUCTING REGIONAL I-O TABLES
Andrea Bonfiglio
Department of Economics
Polytechnic University of Marche
Ancona, Italy
PhD Studies 1

Associazione “Alessandro Bartola”
Studi e ricerche di economia e di politica agraria
Department of Economics
Polytechnic University of Marche
Piazzale Martelli, 8
60121 Ancona, Italy
PhD Studies Series: Volume 1, 2005

I give my thanks to Francesco Chelli, Professor of Statistics at the Polytechnic
University of Marche, for a precious contribution towards developing the statistical
analysis. I also thank Roberto Esposti, Researcher at the Polytechnic University
of Marche, for giving me advice when carrying out the econometrical analysis.
Finally, I must thank my family, in particular my father Roberto, my mother
Liliana and my aunt Assunta, for all sacrifices they made to give me a good education.

Contents
1 INTRODUCTION..................................................................................................................9
2 SURVEY VS. NON-SURVEY METHODS ....................................................................... 15
2.1 SURVEY METHODS ...................................................................................................... 15
2.1.1 Full survey ....................................................................................................... 15
2.1.2 Rows-only method............................................................................................ 16
2.1.3 Columns-only method ...................................................................................... 16
2.2 NON-SURVEY METHODS.............................................................................................. 17
2.3 ONE-REGION NON-SURVEY METHODS ......................................................................... 19
2.3.1 Regionalizing technical coefficients................................................................. 19
2.3.1.1 Use of unadjusted national coefficients ...................................................................20
2.3.1.2 Price modifications ..................................................................................................20
2.3.1.3 Aggregation techniques ...........................................................................................21
2.3.1.4 Fabrication effects....................................................................................................22
2.3.2 Estimating regional input coefficients ............................................................. 23
2.3.2.1 Location quotient approach......................................................................................24
2.3.2.2 Regional supply percentages....................................................................................31
2.3.2.3 Supply-demand pool approach.................................................................................32
2.3.2.4 Regional purchase coefficients ................................................................................33
2.3.3 Short-cut methods ............................................................................................ 34
2.3.4 Ready-made models ......................................................................................... 35
2.4 INTERREGIONAL AND MULTIREGIONAL NON-SURVEY METHODS ................................. 38
3 HYBRID METHODS .......................................................................................................... 41
3.1 INTRODUCTION ........................................................................................................... 41
3.2 TOP-DOWN APPROACH ................................................................................................ 43
3.2.1 Institutional approach...................................................................................... 46
3.2.2 One-region institutional approach................................................................... 46
3.2.2.1 Constrained matrix techniques.................................................................................47
3.2.2.2 Import-survey-based method ...................................................................................49
3.2.2.3 Export-survey-based method ...................................................................................49
3.2.2.4 The family of GRIT-based methods.........................................................................49
3.2.2.4.1 The original version .............................................................................................50
3.2.2.4.2 GRIT II ................................................................................................................55
3.2.2.4.3 The Aberdeen version ..........................................................................................56
3.2.2.4.4 The REAPBALK version.....................................................................................57
3.2.2.5 GRITSSIC................................................................................................................59
3.2.2.6 TDA.........................................................................................................................60
v

vi
3.2.2.7 Lahr’s strategy .........................................................................................................61
3.2.2.8 Distributive commodity balance method .................................................................64
3.2.3 Multiregional institutional approach............................................................... 65
3.2.3.1 GRIT III...................................................................................................................66
3.2.3.2 DEBRIOT................................................................................................................67
3.2.4 Make and Use approach .................................................................................. 72
3.2.4.1 Jackson’s regionalization method ............................................................................74
3.2.4.2 Lahr’s contribution ..................................................................................................77
3.2.4.3 The Austrian experience ..........................................................................................79
3.3 BOTTOM-UP APPROACH .............................................................................................. 80
3.4 HORIZONTAL APPROACH............................................................................................. 80
4 PERFORMANCES OF REGIONALIZATION METHODS .......................................... 83
4.1 INTRODUCTION ........................................................................................................... 83
4.2 MEASURES FOR COMPARING INPUT-OUTPUT MATRICES .............................................. 84
4.2.1 Traditional statistics ........................................................................................ 85
4.2.2 General distance statistics ............................................................................... 88
4.2.3 Information-based statistics............................................................................. 93
4.3 OVERVIEW OF EMPIRICAL STUDIES COMPARING INDIRECT METHODS.......................... 93
4.4 TEACHING FROM EMPIRICAL STUDIES ......................................................................... 98
5 AN ANALYSIS OF IMPACT OF CAP REFORM USING DIFFERENT
REGIONALIZATION METHODS .................................................................................... 101
5.1 INTRODUCTION ......................................................................................................... 101
5.2 THE SITUATION OF THE CEREAL SECTOR IN THE MARCHE REGION ............................ 101
5.3 THE COMMON AGRICULTURAL POLICY (CAP) AND THE CEREAL MARKET: A BRIEF
OVERVIEW .......................................................................................................................... 106
5.3.1 From CAP’s institution to the Mac Sharry Reform ....................................... 106
5.3.2 Agenda 2000 .................................................................................................. 106
5.3.3 The Mid-term Review of Agenda 2000........................................................... 108
5.4 METHODOLOGY TO ASSESS POLICY IMPACT.............................................................. 110
5.4.1 Farmers’ responsiveness to price variations ................................................. 111
5.4.1.1 Theoretical economic model..................................................................................111
5.4.1.2 Functional form......................................................................................................112
5.4.1.3 Elasticities..............................................................................................................114
5.4.1.4 Estimation..............................................................................................................115
5.4.1.5 Data........................................................................................................................116
5.4.1.6 Results ...................................................................................................................116
5.4.2 A closed mixed-variable I-O model ............................................................... 119
5.4.3 Construction of regional I-O tables by different methods.............................. 124
5.4.3.1 Methods used and common data............................................................................124
5.4.3.2 Hybrid methods......................................................................................................126
5.4.3.3 Non-survey methods ..............................................................................................132
5.5 IMPACT SENSITIVITY ANALYSIS USING ALTERNATIVE REGIONALIZATION METHODS .133
5.5.1 Direct policy impact on farmers’ output........................................................ 133
5.5.2 Assessing overall impact generated by European Policy .............................. 135
5.5.3 Assessing overall sectoral impact produced by European Policy ................. 137
5.5.4 Analysing relationships among regionalization methods .............................. 138
6 CONCLUDING REMARKS............................................................................................. 147
REFERENCES......................................................................................................................153

APPENDIX A – REGIONAL I-O TABLES CONSTRUCTED BY 16 DIFFERENT
REGIONALIZATION METHODS .................................................................................... 171
APPENDIX B – TYPE II OUTPUT-TO-OUTPUT MULTIPLIERS ACCORDING TO
THE KIND OF REGIONALIZATION METHOD ........................................................... 205
vii

1 Introduction
Construction of input-output tables still represents an important objective for
numerous types of research. The increasing need for dealing with economic problems
at sub-national scale (whether urban or regional) is the fundamental reason
for leading economists, geographers, urbanists, and in general scholars of regional
science or analysts involved in regional planning, to invent, to adapt or to develop
tools and techniques of “regional” analysis. Among these techniques, success of
the input-output approach essentially depends on the possibility, which this approach
offers, to measure impact in the regional economy from local or national
policy for which a differentiated impact in regions can be foreseen. But the possibility
of evaluating impacts at sub-national level is not the only advantage related
to the construction of regional I-O models. Other advantages are (Gerking et al.,
2001): (a) the entire modelling process, including data development, may improve
the knowledge of a regional economy as well as the theoretical and practical sides
of regional modelling that otherwise would not be apparent; (b) further model applications
and extensions may come out. For instance, a natural extension of regional
input-output models is the construction of social accounting matrices,
which are the basis for computable general equilibrium modelling.
However, these advantages clash with a series of restrictive hypotheses on
which regional I-O models are based: rigid production and consumption assumptions
on the structure of a regional model; linearity, fixed prices and zerosubstitution
elasticities in consumption and production; value-added inputs, such
as land, labour and capital are treated as completely mobile between regions
(Gerking et al., 2001). Moreover, the construction of an input-output transactions
table implies the knowledge of all flows of goods and services among intermediate
and final sectors expressed in a disaggregated form and related to a given time
period (Hewings, 1985). That means the need for collecting a considerable volume
of information. In this respect, while the preparation of national tables is facilitated
by the existence of detailed national accounts and commodity data, at a
regional level, analysts, facing the issue of preparing regional tables, are less advantaged
both with reference to data availability and access to research resources.

Introduction
In fact, data collection by official collection institutions is primarily projected for
aims other than the construction of regional tables and in most cases provides only
some of the basic control totals. For this reason, different approaches for the
preparation of regional input-output tables have been developed in an attempt to
contrive methods capable of providing satisfactory results. These approaches can
be divided into three main categories: “survey”, “non-survey” and “hybrid” approaches.
“Historically, the relative emphasis given to survey and non-survey techniques
by regional analysts has tended to vary. In the early 1950s, when attention was
first turned to applying the Leontief input-output system to sub-national areas,
non-survey techniques were applied. Throughout the sixties, analysts concentrated
on constructing survey-based regional input-output tables. However, in the late
sixties and early seventies, attention has focused on the non-survey techniques as
the demand has grown for a wider application of the input-output approach in regional
and urban planning” (Jensen et al., 1979, pp. 28-29). High costs in terms of
time and resources associated to survey methods and doubts inherent to the accuracy
of survey-based tables due to possible undervalued sampling errors were
other reasons why non-survey methods imposed themselves.
The seventies were dominated by non-survey methods. The theoretical apparatus
of the first methods was controversial and their degree of reliability in representing
the regional economy was not clear. Therefore, efforts to improve the
reputation of non-survey methods were made both theoretically and empirically.
Modifications and new methods were introduced to make methods more acceptable
from a theoretical standpoint. Moreover, empirical studies were accomplished
to verify how far results from non-survey methods were from surveybased
results. In this regard, Morrison and Smith (1974) contributed substantially
to an understanding of the non-survey approach.
In the late seventies, the possibility of calculating multipliers without the need
for constructing regional tables was suggested, by proposing short-cut techniques
(Davis, 1978; Burford and Katz, 1977). They would have made non-survey methods
and all techniques aimed at reducing national coefficients unnecessary. However,
this possibility raised a heated debate and, in the middle of the 1980s, was
definitively abandoned.
In the meantime, some researchers suggested a hybrid approach as a compromise
between non-survey and survey approaches, in order to gain the advantages
of both and to avoid the main disadvantages. Richardson (1972) referred to the
possibility of using non-survey methods to estimate regional transactions and to
replace some entries with survey-based estimates. However, the real change happened
with the introduction of GRIT (Generating Regional I-O Tables) from Jensen
and his Australian colleagues (Jensen et al., 1979). This method, which can be
defined as the first real hybrid method, extended the idea of replacing some en-
10

Introduction
tries with exogenous information, inserting “superior data” systematically into the
regional table in order to improve its overall accuracy. The other innovative characteristic
was to split the whole regionalization procedure into separated, removable
and substitutable modules giving the procedure a high degree of flexibility
and adaptability. Moreover, whilst non-survey methods basically focused on intersectoral
transactions, GRIT shifted attention to the entire table. The general
principle was not to create as an accurate cell-by-cell table as possible but to derive
a faithful representation of the overall regional economy, concentrating research
efforts on the larger coefficients. The validity of this principle was confirmed
by empirical studies showing that multipliers were mostly affected by larger
coefficients (Jensen and West, 1980). GRIT was largely successful in the Australian
context. In the 1980s, further versions of GRIT aimed at improving its
characteristics of reliability and theoretical validity were introduced (West, 1980;
Phibbs and Holsman, 1982; West et al., 1984; Johns and Leat, 1987) and the theoretical
system behind hybrid models was formulated (Greenstreet, 1989; West,
1990). Moreover, further attempts to improve non-survey methods were made
(West, 1980; Stevens et al., 1983; Sawyer and Miller, 1983).
Starting from the middle of the 1980s, thanks to the development of computers,
ready-made models were developed. They are pre-packaged models, fundamentally
based on non-survey methods. Moreover, they are cheap and do not require
particular technical competencies. These advantages widely favoured their diffusion
especially in the US context.
In the 1990s, two main directions were followed by most analysts: hybrid
methods and ready-made models. However, interest in pure non-survey methods
did not disappear completely and attempts aimed at improving non-survey techniques
were made (Flegg et al., 1995; Oude Wansink and Maks, 1998). At the end
of the 1990s, a new hybrid approach was suggested: the “make and use” approach
(Jackson, 1998). Until that time, attention was focused on the institutional approach
based on national industry-based accounts to derive regional tables. On the
contrary, the “make and use” approach shifts the attention to the industry-bycommodity
framework so that three kinds of regional tables are derived (make table,
use table and final use table) instead of only one symmetric table. Currently,
ready-made models and hybrid methods represent the most applied methods. As
for hybrid methods, the institutional approach is the most widespread: most tables
are produced using this approach and new related methods have been continuing
to be introduced (Johnson, 2001; Lahr, 2001a; Mattas et al., 2003). However, the
increasing number of studies oriented to the “make and use” approach (Madsen
and Jensem-Butler, 1999; Lahr, 2001b; Fritz et al., 2002) and the more and more
frequent utilization of the make-use format at national level induce us to think that
future research could move towards the industry-by-commodity framework.
11

Introduction
The main objective of this dissertation is to evaluate impact sensitivity using
alternative approaches for deriving regional I-O tables. In other words, the aim is
to verify how different or similar results in terms of impact are when the I-O
model, used to estimate impact, is constructed on the basis of different regionalization
methods. The dissertation is articulated in the following way.
Chapter 2 is aimed at illustrating survey and non-survey methods of regionalization.
More attention is paid to describing non-survey methods and the latest
techniques are presented. Non-survey methods are classified in methods focusing
on one region (one-region non-survey approach) and methods oriented to the construction
of tables for more than one region (the multiregional non-survey approach).
The former category comprises methods estimating regional technical
coefficients, methods estimating regional input coefficients, short-cut methods
and ready-made models.
Chapter 3 is dedicated to the description of hybrid methods. Three approaches
are identified and illustrated: top-down, horizontal and bottom-up approaches.
Within the former, two sub-categories are presented: institutional and make and
use methods. Each category includes both methods deriving tables for one region
and methods deriving tables for more than one region.
Chapter 4 is finalized to illustrate empirical studies that have been carried out
to evaluate performances of indirect methods of construction comparing these latter
ones with survey-based methods. Furthermore, the main statistical measures
that have been used towards this aim are described. These are classified as traditional
statistics, general distance statistics and, finally, information-based statistics.
Chapter 5 represents the core of this work. It attempts to evaluate how the use
of different approaches for deriving regional I-O tables affects results in terms of
impact. Impacts to be evaluated are effects in terms of output, income and employment
on the overall economy of the Marche region, which come from the
Common Agricultural Policy (CAP) during the period 2000-2006. In this regard,
policy analysis is limited to the effects from intervention-price reduction on the
cereal market. After pointing out the importance of the cereal market in the
Marche region, policy reform effects of Agenda 2000 and the mid-term review are
examined. Impact generated by price reduction is estimated by two different kinds
of methods that are applied sequentially: an econometric model and an Input-
Output model. The former is a multi-input and multi-output model of profit
maximization aimed at estimating responsiveness of farmers to price changes to
evaluate variation of output produced by a reduction of intervention price. The latter
is a modification of the traditional I-O model aimed at estimating overall impact
(indirect, direct and induced effects) induced by a variation of output. This is
referred to as a closed mixed-variable I-O model. Different from the Leontief
model, the model developed here is able to measure the impact from output
12

Introduction
changes, rather than final demand variations, taking into account also effects related
to a variation of household expenditure (induced effects). The closed mixed
I-O model is applied using 16 different regional I-O tables which are obtained
from 16 different regionalization methods: eight non-survey methods based on location
quotients and the supply-demand pool (SLQ, PLQ, WLQ, CILQ, RLQ,
SCILQ, FLQ, SDP) and eight hybrid methods based on GRIT methodology
(original GRIT, GRIT II, Aberdeen version of GRIT, REAPBALK version of
GRIT, GRIT incorporating SDP, GRIT incorporating SCILQ, GRIT incorporating
RLQ and GRIT incorporating PLQ). In order to analyse impact sensitivity with
respect to different approaches for regionalising tables, methods are compared in
terms of both overall impact and impact by sector in terms of output, income and
employment. For this objective, besides classical analyses (range, variability and
ranking), two well-known statistical procedures are applied: factor analysis and
the multidimensional scaling procedure 1 . Finally, results of these analyses are illustrated
and commented on.
In chapter 6, some concluding notes, summarising results from the comparison
of alternative regionalization methods, are provided.
1 Both procedures were applied using the Software Package SPSS 11.5. Factor analysis was carried out using
FACTOR procedure, while multidimensional scaling was carried out using PROXSCAL procedure.
13

2 Survey vs. Non-survey Methods
2.1 Survey methods
The 1960s can be considered the “golden age” of survey-based models
(Richardson, 1985). Regional models were built for Washington State (Bourque et
al., 1967), West Virginia (Miernyk et al., 1970), Philadelphia (Isard et al., 1966-
68) and Kansas (Emerson, 1971). More recently, other examples of survey-based
approaches are provided by Pullen and Proops (1983), McGregor and McNicoll
(1992), Chase et al. (1993), Garhart et al. (1997). As for the Italian situation, survey-based
tables were constructed, for example, for the Marche region (Santeusanio,
1978) and the Toscana region (Casini Benvenuti and Grassi, 1986).
The survey-based approach attempts to identify the elements of the transactions
table from the collection of primary data by surveys of industries and final consumers
which document both sales and purchases. With reference to the kind of
information requested, three alternative approaches can be identified: full-survey,
“rows-only” method, “columns-only” method.
2.1.1 Full survey
In the case of full survey methods, each sampled firm should be asked to give
information about (Hewings, 1985):
• its sales to regional industries and to final users (households, government
and for investment purchases) inside the region and outside the region
(exports);
• its purchases from regional industries, from industries outside the region
(imports) and their final payments to primary resource owners in the form
of wages and salaries, profits, depreciation allowances, taxes, etc..

Survey vs. Non-survey Methods
These purchases and final payments outline the production technology of each
industry in a usable format, already separated into a regional flows matrix and imports
matrix.
The potential advantage of the full survey-method is to allow constructing tables
with a high level of accuracy. However, this approach has three main drawbacks:
firstly, it is highly expensive and time-consuming; secondly, there is the
risk of producing unsatisfactory results due to possible sources of non-sampling
errors such as bias in estimates owing to non-response and in case the conventions
of sampling theory in selecting samples and preparing schedules are not correctly
respected (Jensen et al., 1979; Bulmer-Thomas, 1982). Thirdly, there could be a
serious problem of balancing 2 in that, when only few firms are sampled, row totals
and column totals will tend to vary widely (Schaffer, 1999).
2.1.2 Rows-only method
An alternative to the full-survey approach is the “rows-only” method (Hansen
and Tiebout, 1963). By this method, only information about sales is requested.
The reason for this is that, being inputs very varied and complex and considering
that firms are more concerned with the destination of their products, information
about origins of inputs is more difficult to obtain. This asymmetry becomes more
apparent when the final demand sector, especially consumption, is considered; it
is easier to ask firms what they sell to consumers than to trace back the industrial
source of consumer purchases. This method produces only one entry per cell in
the transactions tables avoiding a complex data reconciliation that is necessary in
the case of the full-survey approach.
2.1.3 Columns-only method
Another alternative is the “columns-only” method (Harmston and Lund, 1967).
Different from the rows-only method, this technique only requires information
about purchases, adducing that firms have a detailed knowledge of the pattern of
expenditure for control and tax purposes. This approach seems to be suited for use
in small regions characterized by a relatively large number of locally-owned firms
(Shaffer, 1999).
2 As for problems of reconciliation, refer for instance to Bourque et al. (1967), Miernyk et al. (1970), Isard
and Langford (1971), Jensen and McGaurr (1976, 1977), Gerking (1976, 1979), Miernyk (1979), Bulmer-
Thomas (1982).
16

Survey vs. Non-survey Methods
2.2 Non-survey methods
Increasing need for studying regional economies, lack of information at regional
level and high costs associated to the survey-based methods are the main
reasons for the development of non-survey methods during the 1970s. Boster and
Martin (1972) found that the ratio of total budget outlay for the survey table to the
non-survey table was 20:1. For this reason and in consideration of the fact that the
alternative is no table when regional information is lacking, Boster and Martin
strongly supported this approach.
Non-survey techniques attempt to derive the elements of the transactions table
from other (usually national) tables by various modification techniques.
All non-survey methods make certain assumptions about how the economy of a
place differs from the national economy (Gerking et al., 2001). A common approach
is to assume that the same technology is used there as in the nation. The
main differences between the national economy and most sub-national ones are
that: (1) the nation is more self-sufficient because it produces a higher proportion
of the inputs to its production and (2) the nation has a larger set of industries.
Non-survey modellers typically recognize these two factors by using a measure of
self-sufficiency to scale downward the input supplied by the sub-national area to
its industries and by eliminating the rows and columns for industries that exist in
the nation but not in the sub-national area.
Given the assumption of identical regional and national technology, almost all
developed methods concentrate on the estimation of input and trade coefficients
rather than technical coefficients. Stevens and Trainer (1980) demonstrated that
the regional purchase coefficients used to scale down the inputs are far more important
in determining the accuracy of the model than is the assumption of identical
technology. However, Harrigan and McNicoll (1981), after some experiments,
found that error reduction is maximum using information on a combination
of trade and technical coefficients rather than exclusively on either on or the other.
A common characteristic of all non-survey methods suggested in the literature
is that they are unable to generate zero intraregional coefficients when there is
non-zero technical linkage, providing of course the relevant industries are represented
in the region (Round, 1983).
Moreover, non-survey methods tend to focus on estimation of intraregional or
interregional coefficients, often neglecting the other portions of a regional I-O table
represented by final demand and primary inputs.
The methods that have been used for construction of regional I-O tables can be
regrouped into two main categories: one-region non-survey methods and interregional
and multiregional non-survey methods.
17

Estimating
Technical coefficients
- Unadjusted coefficients
- Price modifications
- Weighted Aggregation
- Fabrication effects
- Location Quotients
- Regional Supply Perc.
- Supply-demand Pool
- Regional Purchase Coeff.
Fig. 2.1 – Non-survey methods’ scheme
Non-survey methods
One-region methods More-regions methods
Estimating
Input coefficients
Short-cut methods Ready-made models
- RIMS
- Column-sum
Multipliers
Source: Author’s elaboration
- ADOTMAR
- ISAMIS
- INSIGHT
- IMPLAN
- REMI
- RIMS II
Location Quotient’s
Extensions
Supply-demand Pool’s
Extensions
Chenery-Moses model
Polenske model
Gravity models

Survey vs. Non-survey Methods
2.3 One-region non-survey methods
This category of methods includes a series of methods that have been used to
construct Input-Output tables only for one region. They can be regrouped into
four categories:
a) Methods estimating regional technical coefficients
b) Methods estimating regional input coefficients
c) Short-cut methods
d) Ready-made models
2.3.1 Regionalizing technical coefficients
These techniques are concerned with estimating regional technical coefficients
3 . One simple way is to suppose that regional coefficients are perfectly identical
to the national ones, therefore regional coefficients are borrowed from the
national matrix. In reality, the national matrix is only a weighted average of a set
of regional matrices. The more a regional economic structure differs from the average
characteristics of the whole economic system in terms of prices, sectoral
composition and technological path, the more the relevant regional matrix differs
from the national one.
Richardson (1972) has identified four main reasons for explaining differences
existing between regional and national coefficients. First, there may be an industry-mix
and a product-mix problem. A sector at the regional level may contain different
industries from that in the nation. Moreover, even if a sector at regional
level has the same industries as that at national level, regional industries are likely
to produce a different and probably smaller range of products or the same range
but in different proportions. Thus, regional technical coefficients will vary from
national coefficients. However, these differences will be smaller the higher the
degree of sector disaggregation and the bigger and the more diversified the region
under study. Second, different from a nation, a region is characterized by a greater
“openness” since the regional propensity to import includes imports from other
regions besides imports from abroad. For this reason, regional technical coefficients,
given by the sum of input and import coefficients, cannot be equal to national
technical coefficients. Third, there are differences in regional price levels
largely explained by wage differentials that affect regional coefficients. Four, regional
technical production functions may differ from those observed at a national
level.
3
Regional technical coefficients express the amount of inputs (both produced locally and imported) used to
produce one unit of output.
19

Survey vs. Non-survey Methods
Basically, procedures of regionalization are aimed at removing from the initial
matrix of technical coefficients all possible causes of distortion which are attributable
to the following factors: (i) territorial variability of prices; (ii) differences related
to sector composition in terms of both industry mix and product mix; (iii)
territorial variability of technology. These procedures may be applied separately
or sequentially. However, if vectors of intermediate purchases and sales were
available, adjustment, taking account of technological and sector composition differences,
might be made globally, without removing separately the causes of distortion,
by bi-proportional adjustment procedures (Strassoldo, 1988).
2.3.1.1 Use of unadjusted national coefficients
The use of unmodified national technical coefficients represents one of the earliest
and simplest methods used to represent the structure of a regional economy.
This method has been adopted in the early regional studies (see for example Isard
and Kuenne, 1953; Miller, 1957). More recently, it has been adopted by Zaitseva
(2002) to derive seven 1997 regional tables for the Russian Federation.
Since regional coefficients are known to vary considerably from national coefficients,
the use of unadjusted national coefficients has been strongly questioned.
Harrigan et al. (1980b) compared the Scottish economic structure to that of UK
confronting the 1973 table for Scotland with the 1973 table for the United Kingdom.
They found that, in spite of the existence of a common fundamental structure
of production at an aggregated level, at more disaggregated level there were
significant differences between the region and the nation, probably due to different
industry mix. They concluded that the assumption of identical industry technologies
at regional and national level was unjustified and that any regionalization
method (both non-survey and hybrid) based on this assumption would have failed
in estimating regional coefficients.
2.3.1.2 Price modifications
The territorial variability of prices of goods and services used for production
becomes important only in regional economic systems characterized by significant
wage differentials and by spatial variability of remuneration rates of other
primary inputs, or by local rigidity in raw material and intermediate goods markets
due to a high incidence of transport costs.
Such differentials of prices affect both the value of intermediate flows and substitution
process due to substitutive relationships among goods and services.
However, there could be some compensatory mechanisms keeping regional coefficients
at levels which are not very different from the national ones. To take account
of price differentials, it is necessary to have a vector of regional prices of
20

Survey vs. Non-survey Methods
each class of intermediate goods and a corresponding vector of national prices. In
this case, consequences from price differentials can be eliminated in the following
way (Strassoldo, 1988):
p
A = P A P
( ) -1
r r n n
where P is a diagonalized vector of prices, r indicates the region, n indicates the
nation, p means that the national matrix is regionalized with respect to price differentials,
A is a matrix of technical coefficients.
Unfortunately, this procedure is not feasible since it requests the availability of
two vectors of prices or, at least, of sufficient data to estimate prices for all sectors.
And also when the national price vector is available or may be easily estimated,
there is always the problem of estimating regional prices since prices indices
at regional level and at a proper level of sector disaggregation are often unavailable.
2.3.1.3 Aggregation techniques
Differences in terms of sector composition determine a high degree of divergence
between regional and national technical coefficients. Problems posed by
these differences are faced by proper procedures of aggregation of national coefficients,
which are weighted to reflect the sector structure of the regional economy
(Shen, 1960). This procedure is accomplished in the following way:
s
A =G A W
r r n r
r
where sA is a m× m regional matrix regionalized with respect to the regional
sector composition;
n
A is a n× n national matrix of technical coefficients
r
( n > m);
G is a m× n matrix of aggregation, reflecting the number and the
r
structure of regional sectors and with unitary and null elements; W is a n× m
weighting matrix whose weights are regional output ratios; s means that the national
matrix is regionalized with respect to the regional sector composition.
Boudeville (1966) observed that this long procedure can be avoided, obtaining the
same results, multiplying the national transactions table by a diagonalized vector
of ratios between regional and national output and, successively, aggregating the
weighted matrix at a desired level.
This procedure requests the availability of national matrix having a high level
of sector disaggregation and a regional vector of output at a sufficient disaggregation
level. It is evident that this procedure needs for a considerable volume of re-
21

Survey vs. Non-survey Methods
gional information which is often unavailable. For this, modellers often use proxies
for weighting the national matrix, such as value added, wages and salaries,
sales, etc.. These latter are approximations producing errors which are correlated
to the intensity and to the nature of relationships between regional output and the
variable used as a proxy.
This approach has been also used by Czamanski and Malizia (1969) to model
regional economies. Walderhaug (1972) tested the differences between surveybased
regional coefficients for the Washington State economy and regional coefficients
obtained by aggregation using estimates of regional gross outputs from
the US 1963 national table. He found that, in most cases, differences could be reasonably
accepted and the largest discrepancies were essentially due to the poor
quality of the survey coefficients.
2.3.1.4 Fabrication effects
Technology differences are certainly those mostly affecting regional technical
coefficients and explaining differences between regional and national coefficients.
These differences may be faced using several ways: for instance, adjustments
based on engineering evaluations or full use of several kinds of statistical information.
It has been noted (Round, 1978) and empirically demonstrated (Harrigan et
al., 1980b) that differences between regional and national coefficients should be
attributed to different value added coefficients. Consequently, the regionalization
might be effected by multiplying each technical coefficient of a given sector by a
scaling factor, the so-called “fabrication effect”, modifying the entire column vector.
Fabrication effects are measures suggested by Round (1972, 1978) to adjust national
coefficients in order to derive regional technical coefficients.
The regional fabrication effect for sector j and region R takes the following
form:
22
R ( Wj R
X j )
( Wj X j )
⎡1−⎤ R
ρ j =
⎣ ⎦
N N
⎡1−⎤ ⎣ ⎦
⎡ ⎤
⎣ ⎦
is the
proportion of total output related to inputs from the processing sectors (including
imports). In other words, it measures the dependence of sector j on inputs from
the same sector and the others. So the fabrication effect is an estimate of the relative
dependence of a given regional sector on intermediate inputs compared to the
where W represents the value added and N is the nation. 1− ( Wj X j)

Survey vs. Non-survey Methods
same sector at national level. Regional technical coefficients are estimated as follows:
r = ρ a
R N
ij j ij
N
where a ij is the national technical coefficient. The national matrix is thus modified
uniformly across columns. The idea is that if importance of intermediate inputs
for a given sector at regional level is relatively less than that at national level
R
R
( ρ j < 1),
national coefficients are scaled downwards. Similarly, if ρ j > 1,
national
coefficients are scaled upwards. This kind of adjustment is similar in spirit
to the column adjustments in the RAS updating procedure aimed at taking account
of what was defined by Stone as “fabrication effect”, that is the possibility of
changes in the proportion of value added in a sector’s output over time (see par.
3.2.2.1).
2.3.2 Estimating regional input coefficients
All non-survey techniques aimed at estimating regional input coefficients assume
that regional and national technologies are identical 4 . All attempt to face this
problem: how to modify each matrix row, expressing the regional pattern of productive
allocation, to take account of the fact that each regional productive sector
may satisfy, exactly or to a lower or upper extent, the regional demand of goods
produced by the same sector. The main hypothesis is that regional purchasers prefer
to buy from regional producers and decide to import only when regional production
is not sufficient to satisfy local requirements. Moreover, it is assumed that
regional producers export only the quantity exceeding the regional demand. This
implies that spatial differences, in terms of transport and communication, are such
that regional producers do not compete with extra-regional producers. The consequence
is that all techniques tend to overestimate the volume of local transactions
and thus the value of regional input coefficients and to underestimate imports and
exports.
Methods included in this category are: location quotient approach, regional
supply percentages, supply-demand pool approach and regional purchase coefficients.
4 Regional input coefficients express the amount of locally produced goods and services used to produce one
unit of output. The regional technical coefficient is supposed to be the sum of the regional input coefficient
and the regional import coefficient expressing the amount of goods and services imported from other regions
and from abroad and used to produce one unit of output. Supposing that regional technical coefficient equals
the national one, regional import coefficient is estimated subtracting regional input coefficient from national
(regional) technical coefficient.
23

Survey vs. Non-survey Methods
2.3.2.1 Location quotient approach
This approach is borrowed from economic base theory. The regional input co-
R N
efficient is estimated in the following way: aij = qijaij , where q ij represents the
location quotient and it results that 0< qij
≤ 1.
Regional input coefficients and regional
import coefficients ( R
t ij ) are estimated as follows:
24
a
t
N
⎧ ⎪aij
qij if qij
< 1
= ⎨
⎪⎩ aij if qij≥1
R
ij N
R
ij
( )
N ⎧⎪ aij ⋅ 1− qijif qij
< 1
= ⎨
⎪⎩ 0 if qij
≥ 1
There are several variants of location quotients that estimate in different ways
the value of q ij . Here, we will consider the following ones: simple location quotient,
purchases-only location quotient, West’s location quotient, cross industry
location quotient, symmetric cross industry location quotient, semilogarithmic
quotient and Flegg’s location quotient.
Simple Location Quotient. The first location quotient that has been adopted is
the Simple Location Quotient (SLQ) (Shaffer and Chu, 1969a, 1969b; Morrison
and Smith, 1974; Round, 1978; Sawyer and Miller, 1983; Robison and Miller,
1988; Harris and Liu, 1998; Flegg and Webber, 2000; Beyers, 2000; Gerking et
al., 2001; Parrè et al., 2002).
It takes the following form:
X X
SLQ
X X
R R
i = i
N
i
N
where i indexes a given sector, X represents the output, R and N indicate the
region and the nation, respectively. The SLQ compares the relative importance of
an industry in a region and its relative importance in the nation. It is a measure of
regional concentration of production.
The logic is that if the relative importance at a regional level of a given sector
is equal or greater than that of the same sector at a national level ( SLQi ≥ 1),
the
sector is considered to be self-sufficient (it does not need for imports) and the national
coefficient is not reduced. Otherwise ( SLQ i < 1),
the sector is supposed not

Survey vs. Non-survey Methods
to be self-sufficient. So, the national coefficient is reduced and the import coefficient
is estimated as difference between the national coefficient and the regional
input coefficient.
The SLQ method is often followed by further adjustments. This is because estimates
of regional industry output that are obtained using SLQ-based coefficients
may exceed actual output for some industries; therefore, coefficients produced by
this method are rescaled to avoid overestimating regional output 5 (Miller and
Blair, 1985).
According to Round (1978), the size of regional input coefficients can be expressed
in the following way:
R ⎛ R R
R X X i j X ⎞ R
aij = f ⎜ , , , k
⎜ N N N ij ⎟
Xi X j X ⎟
⎝ ⎠
The function establishes that regional input coefficients depend on: (a) the relative
size of the regional selling sector i compared to that national; (b) the relative
size of the regional purchasing sector j compared to that national; (c) the relative
size of the region compared to the nation; (d) additional unspecified factors. It is
easy to note that SLQ incorporates the first and the third property.
The SLQ-based approach has several drawbacks:
(a) It assumes for a region the same consumption functions, production techniques
and industry mixes than those at national level (Richardson, 1972).
(b) It is an asymmetric method since it allows resizing national coefficients
downwards but not upwards.
(c) Data related to regional output vector are often unavailable, therefore analysts
are forced to use proxies of output such as employment or value
added (Strassoldo, 1988).
(d) It establishes import needs in constant proportions for the appropriate rows
since rows of the national table are modified uniformly. In this way, imports
are redistributed within sectors in the same way as regional production
is distributed (Richardson, 1972).
5 Miller and Blair (1985) present a method, which, according to them, is usually adopted to adjust coefficients.
Practically, output of each sector is estimated summing intermediate sales, obtained multiplying regional
coefficients by actual regional output, to final demand, obtained multiplying final demand coefficients
by actual regional final demand. Final demand coefficients are ratios between national components of final
demand and total national final demand, scaled down if SLQ is less than one. If ratio between estimated and
actual output is greater than one (output is thus overestimated) regional coefficients are scaled down multiplying
these latter by the inverse of output ratio. Otherwise, regional coefficients remain unchanged.
25

Survey vs. Non-survey Methods
26
(e) It tends to overestimate regional input coefficients and underestimate regional
imports. The first source of error comes from neglecting the relative
size of purchasing sectors (the second Round’s property). In effect, the
SLQ implies that a sector with a bigger relative importance than the national
one can sell goods to a sector, whose relative importance is less than
that at a national level, to the extent suggested by the national coefficient
so overestimating the regional input coefficient and thus the value of multipliers
6 (Johns and Leat, 1987). The second source of error derives from
ignoring cross-hauling of commodities since imports and exports of the
same commodity are denied. Norcliffe (1983) showed that cross-hauling is
particularly present when industries are characterized by product differentiation
and brand preference. The tendency to overestimate multipliers
and underestimating imports is generally recognized as the main limit of
SLQ. However, “knowing the direction of bias is often helpful when drawing
policy and planning inferences from an input-output study” (Gerking
et al., 2001, p. 382).
Variants of SLQ are contained in Morrison and Smith (1974) and Sawyer and
Miller (1983). Morrison and Smith apply the SLQ to a national table where intrasectoral
flows for some sectors (all manufacturing sectors and construction) are
set to zero to avoid overestimating regional intrasectoral flows.
Sawyer and Miller adjust SLQ to account for differences in the regional “fabrication
effect”. For each sector, column coefficients derived by SLQ are multiplied
by the Round’s “regional fabrication effect”.
Purchases-Only Location Quotient. This location quotient was suggested by
Tiebout (Consad, 1967) and explored by Morrison and Smith (1974). For sector i
and region R, it takes the following form:
X X
PLQ
X X
R * R
i = i
N
i
* N
6 The systematic tendency to overstate multipliers is even more marked when SLQ is applied to small economies
in which transport costs are relatively low (Harris and Liu, 1998) or to economies which do not coincide
with self-sufficient areas (Robison and Miller, 1987). Robison and Miller, in a study of the timber economy
of the West-central Idaho Highlands, have shown that the location quotient (but also the supply-demand pool)
approach should not be applied when the area under study does not coincide with a functional economic area,
in which businesses and households purchase most of their services in the same area. This is because, in that
case, interregional trade would be more intense and location quotients would fail in capturing this volume of
trade, overestimating the intraregional one.

Survey vs. Non-survey Methods
*
where X is the output of only those industries that use i as input. The logic behind
is simply that if input i is not used by a given sector, the size of this sector in
terms of output is uninfluential in determining if a region can or cannot satisfy all
local requirements about input i.
Costa (1973) notes that, rewriting the PLQ, this location quotient can be read
as a ratio between the concentration of sector i’s output in the region and the concentration
of commodity i’s demand (only for intermediate uses), estimated by the
regional concentration of output of sectors using commodity i. In fact, PLQ can be
rearranged as:
X X
PLQ
X X
R N
i = i
* R
i
* N
Costa argues that PLQ could be improved in approximating the regional structure,
if final demand along with intermediate demand was taken into consideration.
Thus, he proposes the following modification:
( *
+ )
( *
+ )
X X FD
cLQ
X X FD
R R R
i =
i
N
i
N N
West’s Location Quotient. West (1980), in order to overcome some deficiencies
of SLQ (assumption of uniformity in production and demand/consumption
patterns throughout the nation) proposed the following location quotient:
( θθ)(
)
WLQ = SLQ C C
i i i i
where
r ( E
r n
X ) ( E
n
X )
nation ( E is employment and X refers to output); i
r ( Ei r n
Xi ) ( Ei n
Xi
)
θ = is the productivity ratio of the region relative to the
θ = is
the productivity ratio of the industry in the region relative to the same industry at
r
national level; C = C
n
C refers to the level of regional per capita consumption
r
relative to the nation; C = C
n
C is the level of regional per capita consumption
i i i
of commodity i relative to the nation. The first factor ( )
i
θ θ is an attempt to consider
labour productivity differences between corresponding regional and national
industries and between the region and the nation. The productivity is approximated
by labour-output ratios. If regional productivity of sector i is higher than
27

Survey vs. Non-survey Methods
that at national level (the ratio between employment and output is lower), the
WLQ will be higher and accordingly regional imports will be relatively lower. On
the contrary, the second factor ( CCi) is an attempt to take account of demand
and consumption pattern differences throughout the nation. If local per capita consumption
for commodity i is higher than the corresponding national per capita
consumption, the WLQ will be lower, resulting in relatively higher regional imports.
Comparing input-output tables for the South Australian region derived using
both SLQ and WLQ, West concluded that the WLQ performs much better
than the SLQ in estimating regional trade coefficients especially when regions are
relatively more distant from the national “average”.
Cross Industry Location Quotient. To overcome drawbacks related to SLQ, the
Cross Industry Location Quotient (CILQ) has been introduced (Schaffer and Chu,
1969a, 1969b; Morrison and Smith, 1974; Brand, 1997; Flegg and Webber, 2000;
Oude Wansink, 2000).
The CILQ takes the following form:
28
CILQ
X X
R N
ij = i
R
X j
i
N
X j
The CILQ compares the proportion of national output of selling industry i in the
region to that of purchasing industry j. Different from SLQ, the CILQ enables import
proportions to vary within the rows since it allows for differing cell-by-cell
adjustments rather than uniform adjustments along each row. The logic behind
this method is that if sector i at the regional level is relatively smaller than sector j
at regional level ( CILQ ij < 1 ) then it is assumed that some inputs for sector j will
be imported. Otherwise, if CILQij ≥ 1then
all sector j ’s needs in terms of inputs
will be supplied from within the region. Compared to SLQ, CILQ takes account
of the importance of both purchasing and selling sectors at regional level. However,
it has three new drawbacks:
(a) it does not consider the size of the region. This causes that regional imports
coefficients of a small region will be equal to those of a great region while
imports of a small region will be bigger than imports of a greater region.
Therefore, imports tend to be underestimated.
(b) Midmore and Harrison-Mayfield (1996) point out that, since CILQ does not
consider the weighting of industries relative to the output of the region, it
fails in attempting to represent the economy of a rural region. In fact, in ru-

Survey vs. Non-survey Methods
ral areas, agriculture is far more important to employment and this would
not be recognised using CILQ, differently from SLQ.
(c) CILQ ii = 1.
This brings about that the intraregional input coefficients (on the
principal diagonal) are overestimated for two reasons. Firstly, it implies that
a sector is always able to satisfy all requirements of the same sector even
when the local industry is small (Morrison and Smith, 1974). Secondly, the
intrasectoral trade at the national level is mostly represented by interregional
trade, so the regional input coefficient, remaining equal to the national coefficient,
would incorporate trade among regions (Flegg et al., 1995).
Variants of CILQ are contained in Morrison and Smith (1974). They apply CILQ
to all non-on-diagonal national coefficients and SLQ to on-diagonal national coefficients
to avoid the problem of unity along the principal diagonal associated to
CILQ. They also apply CILQ after zeroing on-diagonal national coefficients for
some sectors to avoid overestimating regional intrasectoral flows.
Symmetric Cross Industry Location Quotient. One variant of the traditional
CILQ is the symmetric CILQ (Oude Wansink and Maks, 1998). This method has
been adopted to derive 1992 I-O tables of twelve Dutch regions. The conclusion
of this study is that multipliers obtained by the symmetric CILQ (SCILQ) are
lower than the ones obtained by adopting the traditional CILQ. The SCILQ is designed
to take into consideration the possibility of deriving regional coefficients
that exceed the national ones, overcoming the problem of asymmetric adjustments.
It takes the following form:
SCILQ
ij
2
= 2 −
CILQ
ij
+ 1
The logic behind is the following one. If CILQ equals zero or one, then SCILQ
equals zero or one. If CILQ goes to infinity, SCILQ goes to two. In so doing, the
regional coefficients not only take account of the fact that sectors may be less
concentrated in a region, but also that sectors may be more concentrated. Oude
Wansink and Maks, after applying the SCILQ, make two further adjustments.
First, the regional coefficients are successively rescaled to a fixed sum of intermediary
transactions obtained by subtracting from output, the sum of value added
and international and interregional imports (previously estimated on the basis of
information related to transportation of goods and movements of individuals
across regions). Second, it is admitted the possibility that in some cells, because of
aggregation, regional transactions may be wrongly bigger than national transactions,
so a further adjustment is made.
29

Survey vs. Non-survey Methods
Semilogarithmic Quotient. A further location quotient is the semilogarithmic
quotient (Round, 1972, 1978; Morrison and Smith, 1974; Batey et al., 1993; Flegg
et al., 1995; Brand, 1997). Variants of this method appear in Morrison and Smith
(1974). The location quotient takes the following form:
30
RLQ
ij
SLQi
=
log 1
2
( + SLQj
)
According to Round, this method incorporates the properties of both the SLQ and
CILQ methods. He recognizes that its form is arbitrary but it maintains properties
of the other methods without further parametrization.
However, Flegg et al. (1995) argued that this method does not really take account
of the size of the region since it fails in attributing larger input coefficients
(smaller import coefficients) to larger regions.
Flegg Location Quotient. This location quotient is a modification of the Semilogarithmic
Quotient (Flegg et al. 1995; Flegg and Webber, 1996a, 1996b,
1997; Brand, 1997; McCann and Dewhurst, 1998; Flegg and Webber, 2000). The
FLQ takes the following form:
E E
FLQ CILQ
R R
ij =
i
N
Ei j
N
Ej
*
⋅ λ =
*
ij ⋅ λ
*
R N
where = log2 ( 1+
E E )
δ
λ ⎡ ⎤
⎣ ⎦
, 0 1 δ
*
≤ < ; 0≤λ≤ 1.
The FLQ seems to be theoretically the most appropriate. In effect, it is designed to
incorporate the properties of CILQ and to take account of the size of the region.
The larger the regional size, the greater the regional input coefficients and the
smaller the regional import coefficients. The use of FLQ requires estimating the
δ parameter. If the value of δ is bigger, the adjustment for regional imports will
be greater. So, this parameter is inversely related to the size of the region. On the
basis of studies concerning the small English town of of Peterborough in 1968
(Morrison and Smith, 1974) and Scotland in 1989 (Flegg and Webber, 1996a,
1996b), Flegg and Webber (1997) find that an approximate value for δ of 0.3 allows
deriving closer multipliers to those obtained by surveys than multipliers obtained
by the conventional cross industry location quotients.
However, the authors remind that more empirical studies are needed to confirm
the value of δ . Furthermore, it has to be kept in mind that the choice of the parameter
significantly affects regional coefficients when regional employment is
relatively little important (Fig. 2.2).

Fig. 2.2 – The function
*
λ - Flegg Location Quotient
Survey vs. Non-survey Methods
Note: TRE and TNE are total regional employment and total national employment, respectively.
Source: Author’s elaboration
2.3.2.2 Regional supply percentages
Analogous to the LQ approach, this method (Miller, 1957; Miller and Blair,
1985) assumes that the technology of production of a given regional sector is the
same as that at national level. Regional supply percentages are defined as follows:
p
=
R R ( Xi − Ei
)
( Xi − Ei + Mi
)
R
i R R R
where E are exports and M are imports. The ratio represents the share of total
production available in the region that is locally produced. These percentages uniformly
adjust rows of national coefficients to derive estimates of regional input
coefficients. One evident empirical disadvantage related to this method is the requirement
of data concerning regional output, exports and imports by sector that
are often unavailable at regional level.
31

Survey vs. Non-survey Methods
2.3.2.3 Supply-demand pool approach
The supply-demand pool technique was derived from the concept of regional
commodity balance developed by Isard (1953). The term “supply-demand pool”
was given by Schaffer and Chu (1969a). The commodity balance is the total regional
output produced by a specific sector less the regional demand of that sector
represented by the local production needs (as input) and local consumption needs.
Regional demand is estimated as sum of the product between each national coeffi-
cient ( N
ij
portions of final demands ( N
if
32
a ) and actual regional output ( X ) and the product between national pro-
∑ ∑
D = a X + c Y
R N R N
i ij j if f
j f
R
j
c ) and regional final demands ( Y f ). That is:
The regional commodity balance for a given sector is calculated as difference be-
R R
tween regional output and regional demand ( bi = Xi − Di
).
Regional input coefficients and regional import coefficients are derived as follows:
a
( ) if 0
N R R
⎧ ⎪aij
XiDi bi<
= ⎨
⎪⎩ aij if bi≥0
R
ij N
R ⎧ N ⎛ X ⎞ i
ij 1 if i 0
R ⎪a ⎜ − b
R ⎟ <
tij = ⎨ ⎝ Di
⎠
⎪
⎩ 0 if bi
≥ 0
The system above implies that if the balance is positive or zero, all inputs can
be supplied by local producers, imports are set to zero and exports are assumed
equal to the surplus. In this case, national coefficients remain unmodified. Instead,
if the balance is negative, some inputs must be imported and national coefficients
are scaled down by the amount necessary to make the regional balance exactly
zero.
The supply-demand pool approach has several analogies to location quotients.
Round (1972) points out that the technique is the same as the cross-industry location
quotient with the difference that the output-demand ratios are substituted for
gross output proportions. Like location quotients, this approach suffers from the
same weaknesses. However, it does not require additional steps in terms of ad-

Survey vs. Non-survey Methods
justments for correcting negative balancing and it allows determining regional exports
as a residual. Moreover, its need for information is not prohibitive. Nevertheless,
it requires much more information that is the regional vector of output and
final demand net of exports in addition to a national matrix.
Studies related to this technique are for instance those of Moore and Petersen
(1955), Schaffer and Chu (1969a), Morrison and Smith (1974), Sawyer and Miller
(1983), Mattas et al. (1984), Jin (1991), Tzouvelekas and Mattas (1995), Jackson
(1998).
Some variants of this method have been developed. For example Schaffer and
Chu (1969a) developed an iterative procedure as a refinement to the SDP technique,
referred to as Regional Input-Output Table Simulator (RIOT). Other modifications
can be found in Kokat (1966), Nevin et al. (1966), Morrison and Smith
(1974), Vanwynsberghe (1976), Alward and Palmer (1981), Valma (1993), Robison
et al. (1993), Robison (1997), Roy (1999).
2.3.2.4 Regional purchase coefficients
This approach was developed by Stevens and his colleagues at the Regional
Science Research Institute (Stevens et al., 1983). Regional purchase coefficients
are proportions of regional demand for sector outputs that are satisfied by regional
production. Formally, for region R and good i, they take the following form:
RPC
z
R
i = R
R
i
R
( zi + mi
)
R
where z i represent sales of good i from local producers to local buyers (exports
R
are not considered) while m i are imports from outside region R to local buyers. It
R
results that 0 < RPCi
≤ 1 . As can be easily noted, regional purchase coefficients
are identical to regional supply percentages and they are applied in the same way.
RPCs are estimated via regression methods. They are expressed as a function
of a series of independent variables. Then, the parameters of the function are estimated
on the basis of cross-section data mostly taken from censuses. For manufacturing
industries, the regression function takes the following form:
( ) 1 β β2
RPC = Q D P
i i i i
where i Q is the amount of good i produced in the region, D i is total use of good
i in the region and P i is the proportion of good i produced in the region and
33

Survey vs. Non-survey Methods
shipped to intraregional destinations. For the other industries, the function is more
complex:
34
r n r n r n β4
r n
( ) ( ) ( ) ( ) ( )
β β β β
RPC = β w w e e W V SLQ A A
1 2 3 5
i 0 i i i i i i i
where w are wages; e is employment; W is tonnage of shipments; V is the value
of shipments; A is land; r and n are superscripts for the region and the nation,
respectively. This is a simplified version of another equation which would include
other relative costs, if available, relative output (rather than the proxy of relative
employment) and relative transport costs, for which the weight-value ratio, location
quotient and relative regional size are a complicated proxy.
Although Stevens et al. (1983) found that this technique performed quite well
after comparing a survey-based table for the state of Washington to a table produced
using RPCs, the possibility of applying this technique outside the USA context
appears to be extremely limited because of insufficient availability of data
(Strassoldo, 1988).
2.3.3 Short-cut methods
These methods have been conceived to derive regional multipliers without the
need for constructing a full regional I-O table. The inclusion of these techniques
within non-survey methods is justified by the common objective to reduce costs
deriving from the construction of regional tables. Noteworthy contributions to this
research field come from Davis (1976, 1978), Drake (1976), Miernyk (1976),
Latham and Montgomery (1979), Burford and Katz (1977, 1981, 1985), Phibbs
and Holsman (1980, 1981), Jensen and Hewings (1985a, 1985b).
One short-cut technique is the RIMS (regional industrial multiplier system)
method developed by Drake (1976) and subsequently extended by Cartwright et
al. (1981). It involves computing the direct component of the regional multiplier
by applying the location quotient to adjust the national input-output coefficient,
and using this along with data on the proportions of earnings derived from agriculture
and manufacturing in the region to estimate the indirect component of the
multiplier. This is achieved by using coefficients derived from a regression equation
fitted to indirect and direct multiplier components from survey-based regional
models and the national model. Latham and Montgomery (1979) found that this
method generated multipliers which were closer to survey-based multipliers than
those estimated by the simple location method. Nevertheless, they defined this
method as a crude approximation of usual multipliers derived from tables.
The alternative is given by the “column-sum multiplier”, so called by Richardson
(1985), developed by Burford and Katz (1977, 1981, 1985). They suggest that

Survey vs. Non-survey Methods
the gross output multiplier for a sector, that is the column sum of the Leontief inverse,
can be approximated by:
⎛ 1 ⎞
K = 1+
⎜ ⎟V
⎝1−V⎠ j j
n
where V = ∑ a and V = ( 1 n) ∑ V . V is thus the mean of the column sums of
j ij
i
n
i
i
the A matrix. There are some questions related to this technique. First, the method
implicitly assumes that the expected value of each coefficient in a column is equal
to the column mean, ignoring structural features of regional matrices, that are
generally characterized by many small or zero coefficients and a few large ones
(Harrigan, 1982). Second, column sums of intermediate inputs are quite difficult
to obtain without a full input-output table. Third, there is a profound difficulty in
assessing accuracy or defining acceptable error (Round, 1983). Finally, regional
input-output tables can serve for aims other than computing sectoral output multipliers
whose knowledge does not necessarily imply anything about sectoral linkages
(Conway, 1977). Although these approaches initially seemed to be promising,
there was some agreement on feeling that they were unreliable (Miernyk,
1976; Latham and Montgomery, 1979; Phibbs and Holsman, 1981; Jensen and
Hewings, 1985a, 1985b) and the possibility of deriving multipliers without constructing
a regional I-O table was abandoned.
2.3.4 Ready-made models
Ready-made models are pre-packaged regionalized input-output models that
implement a given non-survey technique (location quotients, supply-demand pool,
regional purchase coefficient approach) for the mechanical derivation of regional
tables and for the calculation of linkages, multipliers and impacts starting from
national tables. Their development has been favoured by the wide diffusion of
personal computers. At the end of the 80’s, Brucker et al. (1987), when illustrating
the main characteristics of some of these models, noted that the supply of
ready-made models was already quite broad and diversified.
ADOTMAR, ISAMIS, INSIGHT, IMPLAN, REMI and RIMS II are among
the growing number of computer-generated equilibrium models. However, the
more popular models among state and local governments are IMPLANPro (Lindall
and Olson, 1998) and REMI Policy Insight (Treyz et al., 1992).
IMPLANPro is the product of a joint modelling effort between the University
of Minnesota and the U.S. Forest Service. The package software continues to be
maintained and updated by the Minnesota IMPLAN Group, a private firm based
35

Survey vs. Non-survey Methods
in Minnesota. IMPLANPro is a simple straightforward Input-Output model which
can use alternatively location quotients, supply-demand pool and regional purchase
coefficients. The results it produces are static in nature. IMPLANPro calculates
impacts from a new business location or relocation on a county economy for
one period in time. This model provides data, primarily, on employment, wages
and output.
REMI (Regional Economic Models, Inc.) is an Amherst-based consulting firm
that began developing computer-generated policy decision-making models in the
1980s. REMI Policy Insight is an economic modelling software package that forecasts
the economic and demographic effects that an event such as the construction
of a new facility or a policy initiative may cause on the regional economy. REMI
is a unique model. It combines input-output metrics with a statistical econometric
module. The econometric component allows REMI to forecast changes into the
future. This model also provides a great deal of detail on how a proposed change
impacts the economy. It provides data on changes in employment, income, industry
demand, industry output, gross regional product, household consumption,
population and economic migration.
These models present the following advantages:
36
• They are cheap and fast and, for their use, do not require specific competence
(Brucker et al. 1987);
• They produce reliable results for regional industries that do not largely differ
from the national average structure (Jensen, 1987);
• They can capture the regularities within the “Fundamental Economic
Structure” (FES) of the economic system under analysis (Jensen, 1987).
The FES notion refers to that part of the regional table that is directly
(mostly linearly) related to total gross output and thus “predictable” in statistical
terms. Jensen et al. (1988), analysing input-output tables for ten
regions of Queensland (Australia), came to three main conclusions. First,
this portion of table concerns the economic relationships between secondary
and tertiary sectors. Second, as we move from smaller regions to larger
regions, the FES portion increases. Third, most of the analytically significant
cells of the regional table are located on the FES portion of the table.
This would lead to state that ready-made models produce reliable results
in measuring impact especially for larger regions and when the
analysis is concerned with non-primary sectors (agriculture, mining and
some manufacturing activities).

The main disadvantages can be summarized as follows:
Survey vs. Non-survey Methods
• They are based on methods whose reliability and accuracy is suspicious
(Round, 1987).
• They are inadequate in representing regional industries characterized by
specificity and uniqueness (Jensen, 1987).
• They cannot capture the “Non-fundamental Economic Structure” (NFES)
represented by the economic relationships among primary sectors and between
the latter and the FES (Jensen, 1987). If primary sectors are significant
in the local economy or have significant linkages with the rest of the
local economy, the use of ready-made models could introduce large unacceptable
errors into the analysis.
Studies aimed at evaluating performances of ready-made models are for example
those of Brucker and Hastings (1990), Crihfield and Campbell (1991),
Richman and Schwer (1993a, 1995a, 1995b), Rickman (1995).
Brucker and Hastings (1990) evaluated the performances of five regional inputoutput
ready-made models (RIMS II, ADOTMATR, RSRI, IMPLAN,
SCHAFFER). They estimated impacts of seven hypothetical regional development
scenarios in terms of change in final demand for different regions by using
the five models and compared the results with those obtained from the Texas
semi-survey input-output table for 1979. They found that the models provided estimates
of impact that were similar to each other and also to the semi-survey
Texas model but only for total output and total income estimates. In fact, employment
estimates were very disparate as were the estimates for the impact on
disaggregated sectors.
Rickman and Schwer (1995a) compared IMPLAN, REMI and RIMS II. They
found that the default versions of the models revealed that IMPLAN estimated the
largest multipliers. All comparisons of pairs of methods showed significant differences
in terms of multipliers. However, a correlation related to employment multipliers
among all methods was found as well as a correlation related to output
multipliers but limited to REMI and RIMS II models. These differences were attributed
to the different characteristics of models and in particular to the specific
closure rules. To work on common benchmark, these differences were smoothed
creating alternative versions of models. Then, multipliers were calculated and
compared again. Results showed statistically indistinguishable differences on the
whole, but more marked differences at the level of single industry. The authors
concluded that models, in a benchmarked version, were very similar to each other
and differences found by previous studies were mainly due to the diverse formulation
of models.
37

Survey vs. Non-survey Methods
These models are used extensively in regional policy analysis. For instance,
IMPLAN was adopted by Bergstrom et al. (1990), Bairak and Hughes (1996),
Hughes and Litz (1996), Tanjuakio et al. (1996), Sills et al. (1996), Raymond and
Lichty (1997, 2002), Hughes (1999), English (2000), Lazarus et al. (2002), Brown
et al. (2002).
REMI was adopted for instance by Treyz et al. (1992), Cassing and Giarratani
(1992), Richman and Schwer (1993b), Gazel and Schwer (1997).
2.4 Interregional and multiregional non-survey methods
In this category, there are included those methods that attempt to estimate interregional
as well as intersectoral flows in order to construct input-output models
for two (interregional or bi-regional) or more regions (multiregional). Round
(1983) notes that this kind of models offers more scope for non-survey methods
than does a single region system, in spite of the apparent sharp increase in the
number of elements that require to be estimated. This is explained arguing that in
a closed system one region’s exports are another region’s imports so that the interregional
framework contains some inherent accounting constraints which can be
of special value in calibrating estimates of intraregional as well as interregional
commodity flows.
The full interregional model proposed by Isard (1951) and its closely related
derivatives (multiregional models) have rarely been implemented empirically for
the enormous requirement of data. For this, techniques reducing need for data
have been introduced. They are: extensions of location quotients (Round, 1972,
1978, 1983; Bonfiglio, 2002a); extensions of commodity balance methods (Nevin
et al., 1966; Vanwynsberghe, 1976); Chenery-Moses model (or the column coefficient
model) (Chenery, 1953; Moses, 1955); Polenske model (or the row coefficient
model) (Polenske, 1970); gravity models (Leontief and Strout, 1963, Batten,
1982; Nagy, 1997; Boyce, 1998; Cho et al., 1998; Domingues, 2001; Paradigm
Consulting Group, 2002; Okubo T., 2003).
Polenske (1970) tested the Chenery-Moses, the row coefficient and the Leontief-Strout
models on Japanese data and found that the row coefficient model performed
worse than the other two.
Below, we will briefly illustrate extensions suggested by Round (1972, 1978,
1983) and the general principles of gravity models.
Round proposed a two-stage estimation method based on SLQ for the regional
requirement coefficients. The first stage involves a preliminary estimation of intraregional
and interregional flows by means of location quotients. Consider two
R
regions R and S . The location quotient approach establishes that if qij ≥ 1,
then
region R is supposed to be self-sufficient and the surplus is exported. In the case
38

Survey vs. Non-survey Methods
of one region, this amount is unknown. However, in a closed system with two re-
R
S
gions, if q ≥ 1,
it results that q < 1.
It means that region S will import goods
ij
and services by an amount of ( 1 )
ij
S N
− qij aij.
This quantity is supposed to be imports
from the other region (imports from abroad are therefore excluded) and consequently
exports from region R . Thus, the use of location quotients within an interregional
model allows estimating exports (only interregional) and offers a
higher degree of internal consistency than single region applications.
The second stage involves the adjustment of initial estimates to conform to
known vectors of intermediate output. By adopting a two-region framework, any
adjustment in region R must be accommodated by a compensating adjustment in
region S . Therefore, the procedure ensures that total flows within the system sum
to known values.
A problem associated to this technique is that there is no obvious extension of
the approach based on quotients to multiregional input-output tables involving
three or more regions (Hewings and Janson, 1980).
Of the other methods that estimate multiregional models, the gravity approach
has received more attention in the literature. The logic behind this approach is that
interaction between any two zones is proportional to the number of activities in
each zone and inversely proportional to the frictions impeding movement between
them (Jin et al., 2003). More specifically, the basic idea is that the flow of good i
from a region R to a region S can be looked upon as a function of total output of
R
S
good i in region R ( X i ), total purchases of good i in region S ( P i ) and the
RS
distance between the two regions ( D ). One simple function, taking inspiration
from Newton’s observations on gravity (that gave the name to this class of models),
to measure the flow of good i from regions R to region S is the following:
( i i )( i i )
( )
z
c X d P
k
X P
D D
where
R R S S R S
RS
i =
RS
ei =
RS
i
i i
RS
ei
R
c i ,
S
d i (alternatively,
( ) ( )
RS
k i ) and e i are parameters to be estimated (in the
strictest Newtonian form, e i = 2 ) (Miller and Blair, 1985).
39

3 Hybrid Methods
3.1 Introduction
The hybrid approach is currently considered the most feasible method to derive
regional I-O tables (Lahr, 2001a; Fritz, 2002). It combines non-survey techniques
for estimating regional direct requirements tables with superior data, which are
obtained from experts, surveys and other reliable sources (primary or secondary).
It is therefore a compromise between the survey and non-survey approaches in order
to gain the advantages of both and to avoid the main disadvantages. Therefore,
hybrid methods should be less costly than survey methods and more accurate than
non-survey methods in generating regional I-O tables. Phibbs and Holsman
(1982) estimated that hybrid tables can be produced at about one-tenth of the cost
of survey-based tables and in three months instead of two years which is the usual
time necessary to construct survey-based tables.
A further advantage lies in the modular separability and thus in a major possibility
of revision and updating. This advantage will be enhanced if interregional
trade is represented separately since technical and trade coefficients can be revised
independently of one another. The separability characterizing hybrid models also
gives analysts the opportunity of choosing whether deriving a full regional table
or just a coefficient matrix 7 (Greenstreet, 1989).
7 According to Greenstreet (1989), albeit construction of complete accounts allows for consistency checks
and reconciliations, it could be preferable to keep the coefficient matrix, if superior data were of low quality
and largely differed from estimates coming from non-survey models, and also in consideration of the studies
of Jensen et al. (1988) on “fundamental economic structure”, about which large portions of a regional table,
involving secondary and tertiary sectors, would seem to resemble national structure.

Hybrid Methods
42
However, some criticisms to this approach have been raised:
(a) A priori information and local knowledge of the economy are relatively
subjective (Imansyah, 2000).
(b) It is not well-established how much information should be inserted and
which parts of a table have to be replaced (Imansyah, 2000). In this regard,
Dewhurst (1992) found that probable substantial gains in terms of accuracy,
measured by induced income and output multipliers, could be derived
from limited amounts of superior information related to more important
transactions (identified measuring effects of changes in coefficients on
the output multipliers) and that these gains were decreasing as more information
was inserted. However, fewer gains were obtained when superior
data were collected and incorporated for entire sectors 8 . These conclusions
were partly confirmed by Lahr (2001a). Lahr, comparing a “prototype”
of hybrid method (see par. 3.2.2.10) to a survey-based table, noted
that as more information related to entire sectors (identified on the basis of
their impact on multipliers) was inserted, there was not a significant improvement
in the table. Moreover, contrary to Dewhurts’ results, he noted
that the hypothesis of decreasing marginal returns to accuracy from superior
data was not strictly true. Two studies are not sufficient to give a definitive
response to the problem of the quantity of exogenous information
to be inserted in order to improve accuracy. Nevertheless, both agree with
the conclusion that a limited amount of information focused on sectors
8 Dewhurst (1992) attempted to test the importance of inserting superior information in hybrid models for deriving
accurate regional tables using the RAS technique as an instrument for the test. He tried to update a survey-based
1973 Scotland I-O table to derive a regional table for 1979 utilizing superior data from a surveybased
1979 Scotland I-O table. For this objective, he made three types of experiments: insertion of an increasing
number of data for single cells, insertion of single rows and insertion of single columns. From the first
experiment (replacing single cells), he found that, for induced income multipliers, the error between multipliers
derived from the table created by RAS and survey-based multipliers tended to decrease monotonically.
For induced output multipliers, the error decreased but in a less marked way. For example, replacing 11% of
the total number of cells, the error, measured by mean absolute relative difference, diminished by 44% for
income multipliers and by 45% for output multipliers. Replacing 26% of all cells, the error decreased by 79%
for income effects and by 54% for output effects. Moreover, there appeared to be decreasing returns to additional
superior information. The cells to be replaced step by step were identified by ranking the elements of
the 1973 input-output table on the basis of the relative effects of changes in coefficients on the output multipliers.
The disadvantage of this ranking method is that zero cells, which could become over time regionally
important, are ranked last in terms of importance. The second experiment consisted of replacing an increasing
number of columns instead of single cells. This was defined as a more realistic situation in which researchers
usually are. The columns to be replaced were identified ranking column sums of effects of changes in coefficients
on output multipliers. The replacement of entire columns eliminated the problem of non-inclusion of
zero cells. However, the results showed that the error did not decrease at the same rate as that found in the
case of replacing single cells. The third experiment consisted of replacing an increasing number of rows. The
rows to be replaced were identified ranking rows sums of effects of changes in coefficients on output multipliers.
In this case, results were more encouraging but they were attributed to the change in regional structure
over time.

Hybrid Methods
having the highest impact on multipliers is sufficient to improve considerably
the general level of accuracy.
(c) The role of non-survey techniques into a hybrid model is often undervalued
(Lahr, 1993).
(d) There may be a discrepancy between local experts’ opinions and other
secondary data when constructing input-output tables since process of data
collection is often unclear (Jackson et al., 1992).
(e) As Midmore (1991) also underlines, most studies following the hybrid approach,
unfortunately, do not have the resources to insert any superior data
at all. For this reason, many regional I-O tables that are told to be derived
by a hybrid method are ultimately derived using non-survey methods, raising
problems of accuracy and reliability.
Hybrid methods that have been developed starting from the end of the 1970s
can be regrouped into three general categories: the “top-down”, the “horizontal”
and the “bottom-up” approaches.
3.2 Top-down approach
The “top-down” approach is the most used and it was proven that it gives reliable
results (West, 1990). The general characteristics of this approach were
widely described by Greenstreet (1989) and West (1990).
The construction procedure can be distinguished in a sequence of intermediate
models: source, foundation, prototype and final hybrid models (Fig. 3.1). A source
model is given by a pre-existing table, often national, and provides the initial
broad basis for constructing regional tables. Foundation models are substantially
non-survey or synthetic methods aimed at deriving a crude matrix of coefficients.
In this phase, systematic secondary data (such as sectoral employment data) are
utilized. Specific secondary data and preliminary primary data are used to modify
individually some cells or sectors to come to prototype models, expressing a crude
representation of a full regional I-O table. Prototype models are already hybrid.
They may or may not be partial-survey. Ultimately, the final hybrid model arises
from combining primary survey data and experts’ opinions on local economy with
the prototype model.
43

Hybrid Methods
44
Fig. 3.1 – Conceptual framework for hybrid model construction
SOURCE
FOUNDATION
PROTOTYPE
FINAL HYBRID
Source: Greenstreet (1989)
Systematic Secondary Data
Specific Secondary Data
Preliminary Primary Data
Primary Data
Experts’ Comments
The main aim of hybrid methods is to maximize the use of available and reliable
data, concentrating resources on the most critical and significant regionspecific
elements of the table and neglecting the less significant ones.
The top-down approach can be subdivided into two further categories: institutional
approach (including methods deriving regional tables from industry-based
national accounts) and make and use approach (including methods producing regional
tables from commodity-by-industry-based national I-O accounts).

Hybrid
Top-down
Horizontal
Bottom-up
Fig. 3.2 – Hybrid methods’ scheme
Institutional
Make and Use
Modified RAS
FES-concept-based
Exchanging
One region
Multiregional
Interregional
One region
Source: Author’s elaboration
GRIT
Constrained matrix
Import-survey based
Export-survey based
GRITSSIC
TDA
DBC
Lahr's Strategy
GRIT III
Stone-estimator-based
DEBRIOT
Random-utility-based
Madsen et al. (1999)
Eding et al. (1996)
Jackson (1998)
Lahr (2001b)
Fritz et al. (2002)
Original
GRIT II
Aberdeen version
REAPBALK version

Hybrid Methods
3.2.1 Institutional approach
The institutional approach derives regional I-O tables mainly starting from the
national quadratic interindustry transactions matrix (plus, in some cases, the quadrants
related to final demand and primary inputs). This approach is surely the most
widespread for a series of factors (Jackson, 1998). First, it can be partly explained
by simple inertia considering that industries have been dominant units of analysis
for as long as economies have been studied. Second, it depends on the industrybased
statistical reporting systems still employed by many government agencies.
Another factor may be the relevance and intuitive appeal of the industry as a
meaningful agent in a regional economy. Moreover, impact analysis and several I-
O applications (such as key-sector analyses, industrial complex and clustering
analysis, analyses of regional economic structural change) can be carried out
without the need for estimating values outside the interindustry quadrant of traditional
accounts (final demand and primary inputs). Madsen and Jensen-Butler
(1999) claim that other reasons explaining the wide use of the institutional approach
are the many researchers’ perception of greater requirements of data associated
to the make and use approach and the analytical elegance of the Leontief
solution.
Within the institutional approach, two main classes of methods can be identified:
methods oriented to derive one-region I-O tables (one-region approach) and
methods deriving regional tables for more than one region (multiregional approach).
3.2.2 One-region institutional approach
Regional I-O tables are constructed for only one region. Within this approach,
many methods can be included. They are: constrained matrix techniques; exportsurvey
based method; import-survey based method; the family of GRIT-based
methods; GRITSSIC; TDA; Lahr’s strategy; Distributive Commodity Balance
Method. Other methods have been adopted. However, many of them are mostly
variants of the methodologies above categorized (Jackson et al., 1989; Robison,
1997; Harris and Liu, 1998; Hubacek and Sun, 2001; Aroca, 2001; Hughes and
Zaricki, 2001; Secretario et al., 2002).
46

Hybrid Methods
3.2.2.1 Constrained matrix techniques
Constrained matrix techniques are iterative adjustment procedures applied to
reconcile input-output matrices or estimate tables, given row and column totals
and an input-output table that is taken as initial information with respect to the internal
structure of the required matrix. Within this class of methods, we find the
well-know RAS or bi-proportional method (Stone and Leicester, 1966). Other
techniques are the method of Langrangian multipliers (Morrison and Thuman,
1980; Van der Ploeg, 1982), linear programming techniques (Matuszewski et al.,
1974; Malte et al., 1987), quadratic programming methods (Harrigan and Buchanan,
1984), iterative procedures (Friedlander, 1961) and more innovative
methods like the Backpropagation Neural Network (Papadas and Hutchinson,
2002).
Of the constrained matrix techniques, RAS has received more attention in the
literature. RAS is a procedure that iteratively adjusts both columns and rows of a
given matrix until this matrix converges to a new matrix respecting constraints in
terms of row and column totals. Initially, RAS was proposed to update national
tables. Successively, it was extended to updating and estimation of regional tables
from national tables (Czamanski and Malizia, 1969; Morrison and Smith, 1974;
Malizia and Bond, 1974; McMenamin and Haring, 1974; Hewings, 1977; Harrigan
et al., 1980a, Dewhurst, 1992, Junius and Oosterhaven, 2003). Among variants
of this technique, it is worth mentioning RAS with exogenous information
(Allen and Lecomber, 1975), ERAS (Extended RAS) method (Israelevich, 1991)
and the so-called TRAS (three-stage RAS) proposed by Gilchrist and Louis
(1999).
The RAS technique derives regional technical coefficients as follows:
A r =R An S
where R and S are diagonal matrices of multipliers r i and s j used to regionalize
national technical coefficients and obtained in such a way that the following conditions
are satisfied:
RAn S1=x s
1RA ′ ′
n S=x p
where s x and ′ x p are respectively the column vector of intermediate sales and the
row vector of intermediate purchases, while 1 represents the unit vector. The economic
interpretation of multipliers is well known. Row multipliers would take ac-
47

Hybrid Methods
count of the substitution effect since a proportional increase or decrease of all row
coefficients represents substitution of inputs caused by price differences. Instead,
column multipliers would take account of the so-called fabrication effect, reported
by Stone and Brown (1962), since they reduce or increase needs for primary inputs
and, therefore, value added by modifying uniformly needs for intermediate
goods and services.
Properties and conditions of convergence and uniqueness related to the RAS
technique have been widely discussed in several studies (see for example
Bacharach, 1970; Costa, 1973; Lecomber, 1975; Filippucci and Gardini, 1978). In
particular, it has been noted that performances of RAS depend on the plausibility
of the following assumptions:
48
(a) price variations and specific sector composition affect uniformly all sectors
that arrange for substituting inputs proportionally;
(b) the regional specificity in terms of mix and technology affects uniformly all
sectors that adjust proportionally their needs for intermediate and primary
inputs;
(c) national coefficients and data on intermediate production do not contain errors
in that these errors tend to be far larger in the final matrix owing to the
iterative procedure.
Since these assumptions are too stringent, RAS should not be applied mechanically
without modifying the national matrix. This latter should be first regionalized
by using other methods so as to take account of relative prices, specific sector
composition and technology (Strassoldo, 1988).
The application of RAS requires the availability of information related to vectors
of intermediate sales and purchases. Since this information is not usually provided
at a regional level, these vectors have to be estimated by procedures that
construct vectors of regional production, value added and final demand (Strassoldo,
1988). Intermediate purchases and intermediate sales are finally estimated
by subtracting value added from total output and final demand from total output,
respectively. In many cases, the difficulty of obtaining estimates of these vectors
or some components of final demand, especially inventory changes and exports,
forces to carry out surveys to collect missing data. For this reason, RAS method is
classified as a semi-survey method.

Hybrid Methods
3.2.2.2 Import-survey-based method
Su (1970) proposed a technique emphasising the direct estimation of imports.
He suggested carrying out a survey of regional firms finalized to identify the proportion
of required inputs which are imported into the region. Applying this matrix
of import proportions to the national technical coefficient matrix, it is possible
to derive the matrix of regional import coefficients and, by difference, the matrix
of regional input coefficients.
3.2.2.3 Export-survey-based method
The export-survey method was suggested by Schaffer (1972). It firstly requires
a survey aimed at collecting information from firms in the region about the value
of sales for a given year and the proportion of sales going to out-of-region purchasers
(exports).
Then a supply-demand pool approach is applied by using an adjusted commodity
balance:
b = X −E −D
( )
R R R
i i i i
R
R
R
where i is a sector, X is total regional production, D is local demand, E are
regional exports estimated by survey and b expresses commodity balance.
This approach is motivated by the consideration that regional trade represents a
substantial part of a regional I-O table due to the greater openness of regions.
Since estimated exports tend to vary considerably from survey-based exports, the
correction of these discrepancies might lead to better estimates of regional transactions
(Schaffer, 1999). This technique was used, for example, to derive an I-O
table for the Italian Liguria region (Amato, 1978).
3.2.2.4 The family of GRIT-based methods
Methods based on the GRIT methodology are: the original version of GRIT,
GRIT II, the Aberdeen version and the REAPBALK version. In the following
sub-paragraphs, these methods will be described in more detail.
49

Hybrid Methods
3.2.2.4.1 The original version
The GRIT (Generating Regional Input-Output Tables) methodology was developed
by Jensen et al. (1979) to derive regional input-output tables and multipliers
for the regions and State of Queensland in Australia. This methodology has
been widely used in the Australian context. However, the original version has not
been largely successful outside the Australian continent. Recent work based on
this methodology in its standard version is that of Psaltopoulos and Efstratoglou
(2000) who have built a 1988 I-O table for the Greek rural region of Evrytania
and that of the Centre for Agricultural Strategy (Reading University UK) (2000),
commissioned by the European Commission to study employment and the level of
dependency on fishing in the European Union.
GRIT is a system producing variable-interference non-survey based tables. It
relies on a series of mechanical steps to derive regional coefficients from a national
matrix, but provides the possibility at different stages for the insertion of
superior data, which analysts consider to be more reliable than those obtained by
mechanical processes. So, the system is defined as variable-interference in that the
analyst interferes with the mechanically produced tables by inserting exogenous
information. Estimates that should be replaced with superior data are those related
to larger coefficients in the technical coefficient matrix. These cells are considered
analytically important for their bigger impact produced on multipliers. Instead, the
smaller coefficients are assumed to have a low impact on multipliers; therefore,
their method of calculation can be considered unimportant. This would allow the
calculation of tables to the degree of accuracy definable as “free from significant
error”. To confirm the different impact of coefficients, Jensen and West (1980)
carried out some experiments to verify that which happens when smaller coefficients
are removed from the technical coefficient matrix. They found that, estimating
output, income and employment multipliers in 14 input-output models,
when 45 percent of small coefficients were removed, multipliers only decreased
on average by a one percent, while when 70 percent were removed, reduction was
by 5 percent.
The objective of GRIT is to derive a regional table that has to be accurate in a
holistic sense and not in a partitive sense (Jensen, 1980). The partitive accuracy is
reached when each cell of the table is as close as possible to the “true” amount of
intersectoral transactions. In this sense, the table is viewed of a set of regional accounts.
Conversely, the holistic accuracy is guaranteed when the regional table is
a faithful representation of the overall regional economic structure in analytical
and descriptive terms. A method to measure this level of accuracy consists of analysing
the forecasting capabilities of the regional I-O model. A difference of 5-
10% between forecasted and known values of output at both sectoral and overall
level could be the limit over which the holistic accuracy is no more preserved.
50

Hybrid Methods
The system is composed of 15 steps which are arranged into 5 phases. However,
it has been studied for allowing analyst the maximum flexibility. In effect,
much freedom is given to analyst in choosing a different combination of procedural
components or in replacing a module with one preferred by the analyst.
Phase I: Adjustments to National I-O table
The objective of the phase I is the derivation of an appropriate technology matrix
from which regional coefficients will be estimated.
Step 1: Selection of a national I-O table. The national table should be as disaggregated
as practically possible. Jensen et al. (1979), when developing GRIT,
chose a 109-sector table in basic values 9 with direct allocation of imports 10 and
net of intrasectoral transactions. They expressed preference for a national matrix
with flows evaluated in purchasers’ prices, but this solution was impracticable because
it would have requested the availability of a regional matrix of marketing
and distribution costs to correct the national matrix. Non-competitive and competitive
imports 11 were aggregated since there would have been a problem in distinguishing
these imports at a regional level. All imports were directly allocated
into the national table. Lastly, the national table was taken net of intrasectoral
flows to avoid overestimating regional coefficients since intrasectoral transactions
contain interregional trade. However, other formats could be adopted considering
objectives and availability of data.
Step 2: Adjustments for updating. Whether the national table is considered too
old for economic events, this may be adjusted or updated to take account of
changes in relative prices, of changes in industry structure or also of appearance
(or disappearance) of new industries.
Step 3: Adjustments for international trade. This step derives a technology matrix
that expresses the technical requirements for commodity i per unit of output
j , regardless of the geographical source of supply. This matrix is estimated by allocating
imports over those sectors which could supply imported commodities if
the latter ones were produced locally.
9 Basic values are producers’ prices net of commodity taxes. Transactions presented in basic values are net of
commodity taxes; these are shown in a separate row and paid by the purchaser of the commodities on which
the taxes are levied.
10 Imports are allocated directly when they are recorded in the column of purchasing sector. Imports are allocated
indirectly when they are recorded in the column of the sector which would have produced them within
the region and distributed within the row of that sector.
11 Non-competitive imports are those for which no suitable substitute is produced locally. Competitive imports
are those which are close substitute for locally produced commodities and are normally recorded as an
addition to the local value of output.
51

Hybrid Methods
52
Phase II: Adjustment for Regional Imports
This phase derives, from national technical coefficients, both regional input coefficients
and regional import coefficients.
Step 4: Calculation of non-competitive imports. If national sectors do no exist
at a regional level, the corresponding rows of coefficients are removed from the
table and allocated to the regional import row. The existence of a sector at the regional
level can be verified using employment data supplemented by local knowledge.
Step 5: Calculation of competitive imports. Regional input coefficients and regional
(competitive) import coefficients are estimated by using SLQ, given its
presumed superiority demonstrated by empirical studies in comparison with other
non-survey methods. The import coefficients are allocated to the import row.
Phase III: Definition of Regional Sectors
Phase III contemplates sector aggregation and insertion of superior data at different
levels of aggregation.
Step 6: Insertion of disaggregated superior data. Superior data available at a
disaggregated level are inserted prior to aggregation.
Step 7: Aggregation of sectors. Sector aggregation is made to represent, more
faithfully, the regional economy, which is usually simpler than the national one.
Jensen et al. (1979) adjusted regional input and import coefficients using employment-based
weights before proceeding to aggregation.
Step 8: Insertion of aggregated superior data. Further superior data available at
a more aggregated level can be inserted at this stage.
Phase IV: Derivation of Prototype Transactions Table
In this phase, regional coefficients are converted into transactions and estimates
of final demand and primary inputs are derived.
Step 9: Derivation of initial transactions table. Coefficients in each column are
multiplied by estimates of gross regional output to derive first estimates of transactions.
Step 10: Adjustments to prototype table. Final demand and primary inputs
quadrants are added to complete first estimated table. Three components of final
demand are considered: household consumption, exports and other final demands.
The first two components are frequently used in the regional analysis therefore
they should be kept separately while the other components can be aggregated into
one category. Two different approaches to the estimation of final demand can be
adopted depending on available information. First, final demand can be estimated

Hybrid Methods
as a residual if no relevant information is available. Second, reliable estimates of
final demand can be incorporated into the table. The latter procedure would
probably produce an inconsistent table where column and row totals of intermediate
sectors are not equal. For ensuring consistency, adjustments can be made
manually or using some iterative constrained matrix techniques like RAS technique.
The residual-based procedure calculates final demand by subtracting intermediate
sales from regional output (Fig. 3.3). Final demand components can be
estimated by using allocators taken from national or other regional tables and then
constrained to the previously calculated value of final demand. If no allocator is
available or considered reliable for one component (for example exports), this latter
can be estimated as a residual by subtracting the sum of the other two components
from total final demand. It can occur that final demand is negative for a
given sector (regional output is less than intermediate sales). In this case, final
demand components should be estimated by using allocators and in order to reconcile
the row and column totals, the difference between regional output and the
sum of final demand components should be removed proportionally from the row
of intermediate sales and allocated to the import row 12 .
As for primary inputs, two categories are identified: imports, already estimated
in previous steps, and value added. It is assumed that superior data about value
added are available.
Step 11: Aggregation if uniform tables are required. If more than one regional
table is constructed and the objective is to produce uniform tables, regional tables
are aggregated to the same level of sector detail.
Step 12: Derivation of inverse and multipliers for prototype table. This step derives
from the prototype table the Leontief inverse and related output, income and
employment multipliers.
Phase V: Derivation of Final Transactions Table
In this phase, analyst has to use further reliable data and expert’s advice to produce
as an accurate regional table as possible in at least a holistic sense. This
means that the final table should be a faithful representation of the general economic
structure of the region so as to provide a robust basis for analytical applications.
Step 13: Final superior data insertions and other adjustments. Reliable data related
to transactions, final demand and primary inputs have to be inserted in this
step.
12
In GRIT, the possibility that the difference between total output and final demand is negative is not mentioned.
53

Hybrid Methods
Step 14: Derivation of final transactions table. Lastly, the table is adjusted for
ensuring consistency and the resulting table represents the final transactions table.
Step 15: Calculation of inverse and multipliers for final table. This step derives,
from the final table, the Leontief inverse and related output, income and
employment multipliers.
54
Fig. 3.3 – Final demand residual-based adjustment – GRIT methodology
FALSE
Calculation of final demands
using national allocators
Constraining final demands to
total FD
FD=X-IS
FD ≤ 0
Note: FD – Final Demand; IS – Intermediate Sales; X – Total Output
Source: Author’s elaboration
TRUE
Calculation of final demands
using national allocators
Re-calculation of intermediate
sales: IS(1)=X-FD
Attribution of differences
IS(1)-IS(0) to import vector

Tab. 3.1 – The GRIT Methodological Sequence
Phase Step Description
I. Adjustments to National I-O table 1 Selection of a national I-O table
2 Adjustment for updating
3 Adjustment for international trade
II. Adjustment for regional imports 4 Calculation of non-competitive imports
5 Calculation of competitive imports
III. Definition of Regional Sectors 6 Insertion of disaggregated superior data
7 Aggregation of sectors
8 Insertion of aggregated superior data
Hybrid Methods
IV. Derivation of Prototype transactions table 9 Derivation of initial transactions table
10 Manual or iterative adjustments to derive prototype table
11 Aggregation if uniform tables are required
12 Derivation of inverse and multipliers for prototype table
V. Derivation of final transactions table 13 Final superior data insertions and other adjustments
14 Derivation of final transactions table
15 Calculation of inverse and multipliers for final table
Source: Jensen et al. (1979)
3.2.2.4.2 GRIT II
West (1980) proposes a modified version of the GRIT methodology that is
named GRIT II. The main differences between the old and the new methodology
are related to: (a) the choice of the non-survey method; (b) the aggregation
scheme; (c) superior data; (d) stability analysis.
As for the non-survey method to be adopted in the phase II – step 5, West uses
a modified version of the SLQ (WLQ) (see par. 2.3.2.1) to take account of the
consumption and demand pattern differences between nation and region. The
WLQ requires the availability of data related to employment, output and consumption.
If data on consumption are lacking, West suggests using a simpler version
of WLQ, that is: WLQi = SLQi( θ θi)
. If data on output are also unavailable,
the WLQ becomes the SLQ.
As for the aggregation scheme, output weighting is adopted instead of that
based on employment. According to West, output weighting would produce more
reliable transactions figures and would eliminate the balancing problem that
emerges if employment is used. However, there would not be differences between
the two aggregation schemes if the labour-output ratios were identical for all sectors.
With regard to the collection and insertion of superior data (steps 6, 8 and 13),
for identifying critical cells for which more data research effort is necessary, a
sensitivity analysis is effected (West, 1981). This analysis attempts to rank coefficients
on the basis of their relative importance by evaluating the effects that
changes in each individual coefficient produce on the multipliers values. The co-
55

Hybrid Methods
efficients that significantly affect multipliers are those for which superior data
should be collected.
In step 15, a stability analysis is also introduced. This kind of analysis is aimed
at verifying the stability of multipliers to minor individual cell variations. Fixed
and random shocks of various size and types are generated simultaneously in all
the coefficients and the new multipliers compared to the originals. If differences
are not large, the regional table can be considered acceptable.
3.2.2.4.3 The Aberdeen version
Johns and Leat (1987) proposed a modified version of the GRIT methodology.
Three main modifications can be identified:
56
• A new step, between steps 1 and 2 of the original GRIT, is inserted: the
national table is aggregated when the level of sector detail of employment
data (necessary for applying the location quotient approach) is lower than
that of the national table.
• The CILQ is used instead of the SLQ owing to stronger theoretical foundations
related to the CILQ;
• Components of final demand are not estimated using national or other allocators
but using employment ratios, SLQ and residual-based techniques.
More specifically, household consumption is estimated by a two stage
process. First, national household consumption is multiplied by the ratio
between regional and national employment. Then, if a sector has an importance
that, at regional level, is lower than that at national level (SLQ is less
than one), the initial estimate is reduced by multiplying it by the SLQ.
Other final demands (excluding exports) are estimated applying employment
ratios to the corresponding national components. Lastly, exports are
obtained as a residual subtracting from regional output, the sum of intermediate
output, regional household consumption and other final demand.
In GRIT, exports are estimated by difference only if no allocator is available
o considered reliable.
Johns and Leat applied this modified version of GRIT to derive a regional table
for Grampian Region in North-East Scotland. They found that multipliers obtained
using their version incorporating the SLQ were higher than those derived
using their modified version incorporating the CILQ. They concluded that the use
of the CILQ was to be preferred. However, it would seem that they did not use
any superior data for the derivation of the regional table. Therefore, even if, in
general, the SLQ tends to overstate multipliers more than CILQ, the insertion of

Hybrid Methods
superior data could attenuate this tendency and reduce differences existing between
the two methods 13 .
Versions of GRIT incorporating CILQ have been also applied for instance by
Psaltopoulos and Thomson (1993) to construct the 1989 64-sector I-O table for
the whole “rural” Scotland area; by Doyle et al. (1997) to derive the 1989 inputoutput
table for the Scotland region of Dumfries and Galloway; by Mattas et al.
(1999) to construct input-output tables for the Greek regions of Macedonia and
Thrace; by Tzouvelekas and Mattas (1999) to construct a 1988 I-O table for the
Greek island of Crete; by Ciobanu et al. (2002) to construct 1980 and 1997 inputoutput
tables for the Greek regions of East Macedonia and Thrace.
3.2.2.4.4 The REAPBALK version
REAPBALK is an acronym of a European research project, entitled “Rural
Employment and Agricultural Perspective in the Balkan Applicant Countries” 14 .
Within this project, a procedure for deriving regional table, which is fundamentally
based on GRIT, was developed (Bonfiglio, 2002b, 2002c; Mattas et al.,
2003). By this procedure, 5 regional I-O tables were created: the 1998 table for
Thessalia (Greece), the 2000 table for “peripheral” Slovenia, the 1997 table for
the North East region (Bulgaria), the 1999 table for the North West region (Romania),
the 1997 table for the county of Bielovar-Bilogora (Croatia). The system
differs from GRIT for the following aspects:
(1) Selection of a national I-O table. The national table to be selected should
satisfy the following requisites: (a) symmetric; (b) expressed in current prices; (c)
with total flows (domestic plus import flows) expressed in basic values; (d) constructed
using the industry-based technology assumption. Two points deserve to
be discussed. First of all, the choice of a national table with total flows does not
go against the principles of GRIT. On the contrary, one of the GRIT’s characteristics
is to calculate regional coefficients starting from a national technology matrix
regardless the origin of products. Since an import matrix was not available for
Australia, Jensen and his colleagues decided to insert the step related to the adjustment
for international trade, allocating proportionally all national imports to
sectors that could have produced those commodity locally. This step was aimed at
deriving a national technology matrix. However, if an import matrix is available,
there is no reason why a national table with total flows cannot be employed.
13 This hypothesis will be verified in the fifth chapter.
14 This project began in October 2001 and will last for the next three years. The project is financed by EU
Fifth Framework Research Programme: “Quality of Life and Management of Living Resources”, Key Action
1.1.1.-5.5: “New tools and models for the integrated development of rural and other relevant areas”. It is coordinated
by the University of Ancona (Italy) and involves a further six countries: Bulgaria, Croatia, Greece,
Romania, Slovenia and the United Kingdom. The project focuses on the development perspectives of rural
regions of five countries of Balkan area: Bulgaria, Croatia, Greece, Romania and Slovenia. Four of the case
study countries are applying for accession to the EU, while Greece is already a Member-State.
57

Hybrid Methods
Secondly, the choice of the industry-based technology assumption was conditioned
by the core of the project in which this regionalization procedure was developed.
Since the objective was that to study rural regions and considering that
studies oriented to agriculture tend to use the industry technology assumption as it
is more applicable to farm systems (Midmore and Harrison-Mayfield, 1996), this
choice was judged plausible.
(2) National sector aggregation. The national sector aggregation step is inserted
between the steps 1 and 2 of the original GRIT.
(3) Treatment of non-existing sectors at regional level. The step 4 of GRIT
(calculation of non-competitive imports) establishes that if national sectors do no
exist at regional level, the corresponding rows of coefficients have to be removed
from the table and allocated to the regional import row. In the REAPBALK’s version,
following suggestions from Mattas et al. (1984), also the corresponding columns
of coefficients are removed and, in this case, allocated to the regional export
column. The step 4 can be thus renamed as: calculation of non-competitive imports
and exports.
(4) Derivation of regional coefficients. Regional input and import coefficients
are estimated using the FLQ-based method. The parameter δ , without which FLQ
cannot be applied, should be estimated on the basis of the relative importance of
the economic activity in the region. Practically, the parameter is fixed at a value
that makes final demand positive.
(5) Estimation of regional output and final demands components. Regional
output is estimated using employment ratios and SLQ. Like in GRIT, final demand
is first estimated as a residual. The use of FLQ, choosing accurately the
value of its parameter, guarantees that final demand, obtained by difference, is
always positive. Regional household consumption and exports are estimated like
output while other final demands are calculated as a residual by subtracting the
sum of exports and consumption from regional final demand.
(6) Disaggregation and estimation of primary inputs. Three components of
primary inputs are considered: household income, imports and other final payments.
Elements of value added different from household income are thus included
into the “other final payments” category. If superior data for household income
are not available, these are estimated by employment ratios and SLQ. Other
final payments are estimated as a residual subtracting the sum of intermediate
purchases, imports and household income from total output.
Unfortunately, neither superior data nor expert’s opinions were available for all
regions under study. Accordingly, all regional tables were derived mechanically
and this hybrid method was reduced to being a non-survey method.
58

Hybrid Methods
3.2.2.5 GRITSSIC
Phibbs and Holsman (1982) proposed a hybrid input-output technique named
GRITSSIC (Generalized Regional Input-Output Tables with Survey-based Sums
of Intermediate Coefficients). This method derives regional Input-Output tables
by adjusting a rough approximation of regionalized tables on the basis of survey
data just related to the sum of intermediate inputs for each sector. This technique
was developed starting from the ascertainment that in many cases reliable I-O
multipliers can be generated with the sum of the intermediate coefficients alone.
To validate the goodness of the methodology, first, a survey-based table was
compared with several randomly-generated-coefficient matrices imposing that the
column sums of intermediate coefficients had to equal those of the survey-based
table. It was found that, when the maximum deviation allowed between the “true”
direct coefficients and simulated coefficients was 100%, the maximum error for
output multipliers was about 16%, while for type I income multipliers, the maximum
error was unacceptable (91%). However, acceptable errors for most sectors
were obtained when deviations were set to 50%. Phibbs and Holsman concluded
that the GRITSSIC could be a valid alternative to both survey and non-survey
methods.
Furthermore, they tested the hypothesis that multipliers for any one sector are
largely independent of the direct coefficient entries for the remaining sectors. Towards
this aim, they again compared several randomly-generated-coefficient matrices
with the survey-based table imposing that the column sum of intermediate
coefficients related to a given sector had to equal that of the survey-based table.
They found that the lower level of accuracy for the non-interest sectors had only a
minimal impact on the accuracy of the multiplier estimates. According to the authors,
this experiment showed that it is not necessary to obtain survey-based sums
of intermediate coefficients for all sectors but only for the sectors of special interest
to the analyst.
Lastly, the performances of this method were compared with those of GRIT.
First of all, a regional table was derived by using GRIT. Then, GRITSSIC was
applied collecting and inserting superior data related to the sum of intermediate
coefficients into the regional table obtained by GRIT. The results were clearly in
favour of GRITSSIC. In fact, multipliers obtained by this procedure were much
closer to those derived from a survey-based table than multipliers obtained by
GRIT. They concluded that GRITSSIC outperformed GRIT. However, the comparison
between the two methodologies cannot be considered valid. This is because,
although Jensen’s methodology is strongly based on the use and insertion
of superior data, they considered GRIT as a non-survey method, simply based on
the application of the simple location quotient. Therefore, the comparison was, in
substance, between GRITSSIC and the simple location quotient, rather than between
GRITSSIC and GRIT.
59

Hybrid Methods
Nevertheless, the results obtained by using GRITSSIC were encouraging in
terms of both accuracy and costs. As for accuracy, for output multipliers, the
maximum difference was under 11%, for income multipliers, it was under 10%,
whereas for employment multipliers it was under 13%. In the case of “GRIT” or
rather the simple location quotient, the differences were 69%, 171% and 192%,
respectively.
3.2.2.6 TDA
TDA is the acronym for the “Table Disaggregation and Adjustment” technique
proposed by Jackson and Comer (1993) and explored successively by Comer and
Jackson (1997). It is composed of two main steps:
60
1. Derivation of a regional aggregated table from a national aggregated table
using the regionalization method proposed by Jackson et al. (1989);
2. Disaggregation of the regional aggregated table using an old national (or,
preferably, regional) highly disaggregated table. Formally, considering
that each cell in an aggregated table corresponds to a block of cells in the
disaggregated table, the ij − th cell of the aggregated table is disaggregated
in the following way:
ij
1Zrs ij
1Z ij
0Zrs k z
ij
0Zrs
r= 1 s=
1
= ⋅ ∑∑ r = 1, …, k; s = 1, … , z
where the subscripts 0 and 1 refer to the old disaggregated table and the
recent aggregated table, respectively; k is the number of sub-sectors
within the aggregated sector i while z is the number of sub-sectors within
the aggregated sector j , which are present in old disaggregated table;
ij
therefore, 1Z
represents the value of the cell ij in the aggregated table,
ij
0 Zrs is the cell rs of the old disaggregated table within the block of cells
k× z,
which corresponds to the cell ij of the aggregated table and, lastly,
Z is the cell rs of the disaggregated version of the recent table.
1
ij
rs
This method is based on the consideration that high levels of bias and error can
result from using an aggregated table for regionalization and that greater levels of
disaggregation in the regionalization process improve table accuracy. Comer and
Jackson (1997) attempted to test the performances of the TDA method. Three
types of tables were developed for five U.S. regions. First, they derived a regional
disaggregated table from a 1982 disaggregated U.S. table using the regionalization

Hybrid Methods
method mentioned above. They calculated output vector using a 1977 national
vector of final demand and then they aggregated the output vector. The regional
table was considered the “true” table and the relevant output vector was considered
a comparison base. Second, they derived a regional aggregated table from a
1982 aggregated U.S. table. The relevant output vector was calculated using an
aggregated vector of final demand. Third, they derived a regional disaggregated
table by TDA, disaggregating the regional aggregated table on the basis of the
structure of a 1977 disaggregated U.S. table. They calculated the output vector
that was after aggregated. Using several statistics, output vectors of the latter two
tables were compared to the output vector of the first table that was considered
“true”. Results showed that the TDA method reduced the error in terms of predicted
output deriving from using aggregated data. However, it has to be noted
that the test was based on a regionalized table and not on a survey-based regional
table. Therefore, results cannot be considered conclusive.
3.2.2.7 Lahr’s strategy
Lahr (1993, 2001a) has proposed a procedure to derive hybrid regional I-O tables
which is partially based on the GRIT II methodology. The differences are
substantial and can be summarized as follows:
• the location quotient approach is rejected in favour of a regionalization
scheme that allows for cross-hauling;
• sector aggregation is avoided;
• a different and a more specified approach to recognize critical portions of
input-output tables ripe for superior data is adopted;
• the procedure only focuses on the regional transactions matrix.
Below, single steps of the procedure will be illustrated in more detail.
Step 1: Preparation of Initial Nonsurvey Regional Direct Requirements. This
step comprises the first two phases of GRIT II: selection of a national I-O table,
adjustments for updating and international trade, adjustment for regional imports.
The main difference lies in the choice of the regionalization method. The modified
location quotient approach suggested by West (1980) is rejected in favour of
a regionalization scheme that allows for cross-hauling, although it is not very
clear what method should be applied. Moreover, to avoid aggregation bias, regional
sector aggregation is skipped and a regional prototype table is thus produced.
61

Hybrid Methods
Step 2: Identifying Sectors for Superior-data Collection. Sectors to which top
consideration should be given automatically for superior data collection are:
household-labour sector, resource production sectors (i.e. agriculture, forestry,
fishing and mining) and any aggregate sectors such as those denoted as “miscellaneous”
or “not elsewhere classified” or others which are severely aggregated.
These sectors are considered critical in representing correctly a regional economy
for a series of reasons. As for household-labour sector, some studies (such as:
Stevens and Trainer, 1976; Garhart and Giarratani, 1987) have ascertained the
importance of this sector especially in models closed with respect to households 15 .
As far as resource production sectors are concerned, because of particular soil,
climate and geologic conditions that characterize a given region, these sectors
have technology that is likely to be very different from the national average technology.
Lastly, the sectors that are highly aggregated have wide variance in their
technology not because of their specific location but because of the wide mix of
establishments that they embrace.
Moreover, further superior data are collected for sectors that make the regional
table more sensitive to changes in trade. Different from GRIT II, the aim is not to
identify single cells to be replaced with exogenous information, but entire sectors.
For this objective, a combined-coefficient sensitivity concept of West (1982) is
adopted.
First, a more practical variant of the Evan’s (1954) formula is calculated:
62
( )
-1
E= ⎡ I-BP -I⎤B ⎣ ⎦
where B is the Leontief inverse, I is an identity matrix and P is the matrix of
perturbations of the direct-requirements matrix. Therefore, E represents the matrix
of errors produced in a Leontief inverse by a perturbation in a directrequirements
matrix (change in regional trade). To calculate the effect that a percentage
change in a sector’s information has on the rest of the economy, all cells
of P should be zeroed except for the column and row of sectors for which sensitivity
to imports has to be tested. By letting P = ⎡
⎣ pijaij ⎤
⎦ (where a ij are the elements
of the direct-requirements matrix), the values of nonzero p ij are set to proportions
that represent likely deviations of each of the sector’s direct-requirement
coefficient from its true value. Since the comparison of E matrices for each sec-
15 First, the local labour income coefficient generally is the largest coefficient for any given column. Second, labour
income is largely used to buy goods from non-manufacturing sectors, which tend to purchase large proportions
of their demands (including labour services) locally. This means that household sector produces a nonnegligible
multiplier effect in any given model.

Hybrid Methods
tor would be complicated, each sector’s error matrix is then translated into a scalar
by this formula:
E ′ i =1EX i
where i represents the sector, E is the scalar, E is the error matrix, ′
1 is a transposed
unit vector, X is the vector of regional sector output (or other economic
weights such as final demand, earnings, employment, value added).
By this scalar, sectors are ranked and those with higher sensitivity are chosen
as targets for inserting superior data. Since rankings of these sectors are altered
with any coefficient correction, the process of identifying sectors is performed recursively
after the model has been enhanced by each sector’s survey data. The
criticism that could be raised to this approach is that critical sectors, for which further
data should be collected, are identified by using a prototype table derived by
non-survey methods. However, Lahr demonstrated that non-survey tables can be a
good basis from which to identify critical sectors. Towards this aim, he used the
survey-based 1972 Washington State Input-Output table to compare sector rankings
obtained using the error measure with those obtained using the total linkage
difference (Meller and Marfan, 1981) between the non-survey and survey-based
models. He found that rankings were very similar and therefore he concluded that
non-survey models might be used to identify critical sectors.
Step 3: Identifying Individual Cells for Data Collection. In order to keep datacollection
costs relatively low, for all sector defined as being critical, information
should be collected only about proportions of intermediate inputs and outputs,
proportions of intrasectoral flows (since they would tend to be the largest cells in
indirect requirements matrices of high order) and total regional outputs. Moreover,
apart from intrasectoral cells, not all cells of the selected sectors should be
surveyed. For targeting cells for superior data, West’s (1981) single-coefficient
change estimates, weighted using value added, are applied.
Step 4: Insertion of Superior Data. The data for cells identified in step 3 are inserted
into the region’s technology table.
Step 5: Biproportional Regionalization. The RAS technique is employed to
reconcile flows. It is known that convergence is achieved when estimated margin
totals are within a “reasonable” measure of tolerance. In the Lahr’s procedure,
these tolerances are set to measures of relative reliabilities of data. If data are survey-based,
tolerance is set to 1 unit (i.e. 1 million dollars of shipments) for intermediate
inputs and outputs; if data are non-survey-based and sectors are manufacturing,
tolerance is set to 100% of the initial estimate for intermediate inputs and
to 30% for intermediate outputs (this is because the used regional purchase coefficients
were supposed to be more accurate in the case of manufacturing sectors);
lastly, if data are non-survey-based and sectors are not manufacturing, the toler-
63

Hybrid Methods
ance is set to 100% of the initial estimate for both intermediate inputs and intermediate
outputs.
The steps from 2 to 5 are iteratively applied until funds for superior data collection
are depleted or the difference between the survey and non-survey results appears
to be very small.
As already mentioned in the paragraph 3.1, Lahr tested this procedure comparing
the survey-based 1972 Washington State Input-Output table with the table
produced using this hybrid method. He repeated the procedure recursively through
13 “surveyed” sectors of the 52 sectors. He found that hypotheses that each additional
sector “surveyed” reduces error and there are decreasing marginal returns to
accuracy from superior data were not strictly true. However, he noted that error
did not tend to increase as more sectors were “surveyed” and there were general
improvements in the various components of the tables as the process proceeded.
3.2.2.8 Distributive commodity balance method
The “distributive commodity balance” method (DCB) was introduced by Johnson
(2001) who employed it to derive a 1994-95 table for the Kimberley region of
Western Australia. The DCB method is an iterative approach to the development
of regional input-output tables. It is an eight-step process which allows the incorporation
of regional data. Its main peculiarities are:
64
• the derivation of three kinds of tables prior to the calculation of the final
table: supply table, demand table, minimum table;
• the use of a regionalization method that allows for cross-hauling;
• a significant interference in the mechanical process from the analyst in
terms of local knowledge and judgement.
Below, single stages of the procedure will be illustrated in more detail.
Stage 1: Select and adjust the base table. In this step, a highly disaggregated
national table is first selected and then adjusted. Adjustments are mainly related to
sector disaggregation.
Stage 2: Derive the preliminary regional supply and demand tables. The aim
of this stage is to derive a preliminary estimate of regional demand and supply.
CILQs are applied to scale the rows and columns of the national table obtaining
the first estimate of regional supply (regional supply table) and demand (regional
demand table) respectively. The values in these separate tables represent what the
industries would like to supply and demand if they were able to follow the national
sales and consumption patterns. In other words, elements of each row in the
supply table represent hypothetical supply to sectors, while those in the demand
table represent hypothetical demand.

Hybrid Methods
Stage 3: Add regional specific data. Regional data are added to the estimates
obtained in the previous step to derive the “augmented” regional supply and demand
tables.
Stage 4: Determine excess supply and demand. The two tables are compared
element by element to determine excess supply and demand. For all sectors, all
instances of excess supply in a given row (industry) have to be summed to give
total excess supply, as well as all values of excess demand in a given row have to
be summed to derive total excess demand. Moreover, a “minimum” table is created.
This table is based on the assumption that trade can only occur at the minimum
values for each element of the supply and demand tables. These two tables
are compared element by element and the lower value is put into the “minimum”
table.
Stage 5: Redistribute excess supply. The analyst has to decide how much of the
total excess supply is made available to satisfy the total excess demand. In this
step, local knowledge and judgement are crucial. The “available” excess supply is
distributed to those industries with excess demand for that industry’s output. Any
remaining excess supply forms additional exports and unsatisfied demand represents
additional imports. In so doing, cross-hauling is taken into consideration,
since both exports and imports can be generated for a given industry.
Stage 6: Derive the preliminary regional input-output table. The redistributed
amounts are added to the minimum table to determine a preliminary table.
Stage 7: Adjust industry shares. If some sectors are known to be “local” industries
but significant trade is predicted in the stage 5 calculations, CILQs have to be
adjusted until trade is eliminated.
Stage 8: Apply the RAS method to determine the final table. The addition of regional
data in stage 3 could have produced inconsistencies or imbalances. Therefore,
the RAS technique is applied to balance the table and to obtain the final regional
table.
3.2.3 Multiregional institutional approach
Within the multiregional approach, methods attempting to estimate regional tables
for two or more regions are included. In next sub-paragraphs, we will describe
the following procedures: GRIT III and DEBRIOT. Further methods are,
for instance, techniques based on the estimator proposed by Stone et al. (1942)
and random-utility-based models. The Stone-estimator-based technique, which is
a constrained matrix technique for balancing, requires information on reliability of
first estimates of flows in terms of origin and quality of data. It was applied to derive
a 1998 multiregional I-O model for Italy (Paniccià and Casini Benvenuti,
2002). Random-utility-based models are models of new generation joining spatial
I-O theories with principles of random utility. They estimate internal trade flows
65

Hybrid Methods
and external trade flows by a function of disutility of acquiring some commodity
from a given origin zone and consuming it in the destination (or export) zone. Parameters
of this function are estimated by nested logit models of input origin and
shipping-mode choice by zone and sector using commodity flow survey data (Jin
et al., 2003).
3.2.3.1 GRIT III
GRIT III is an extension of GRIT II methodology, finalized to derive interregional
input-output tables (West et al., 1984). This system is based on a set of regional
input-output tables constructed by GRIT or GRIT II. The export vector of
each regional table is decomposed to derive the matrix of interregional exports.
The matrix of interregional imports is just derived indirectly. This is justified with
the major availability of superior data related to exports. GRIT III has been used
to derive an interregional input-output model for the State of Queensland (Australia)
including ten regions. The system is made up of four phases and twelve steps.
In the Phase I, the regional tables are selected and, in case, adjusted to ensure
accounting conformity.
Phase II is concerned with the identification of those cells that are expected to
contribute significantly to the interregional multipliers. For these cells, superior
data should be collected and inserted.
Phase III is aimed at estimating those cells for which superior data are not
available and which are the less significant cells of the table. First, cells that
should be set to zero are identified. The first source of zeros is related to sectors
for which exports are null in the regional table. The second source results from
those flows that are presumed to be exports to households in other regions (i.e.
community services sector). These flows are entered as zeros in the interregional
matrices and entered as household consumption for the exporting sector and region.
A third source of zeros are exports whose amount is of no possible consequence
in an analytical sense (less than about 1% of regional exports). The relevant
row cells are set to zero and the amount of exports is allocated to those cells
which are considered to be the most appropriate (i.e. household consumption column
or exports outside the interregional economy). In the successive step, nonzero
cells are estimated allocating regional trade estimates by using the Leontief-
Strout model.
The last phase concerns the preparation of the final version of the interregional
table. Towards this aim, the regional trade balance and general consistency of the
table are ensured. A sensitivity analysis is carried out to verify if high levels of
accuracy have been observed for critical coefficients in multiplier formation. Finally,
inverses and multipliers for the interregional table are calculated.
66

Tab. 3.2 – The GRIT III Methodological Sequence
Hybrid Methods
Phase Step Description
I. Selection and adjustment of regional tables 1 Determination of the interregional set
2 Adjustment for accounting uniformity
II. Identification of significant trade flows 3 Identification of significant regional trade components
4 Identification of significant interregional trade components
5 Insertion of superior data
III. Estimation of remaining trade flows 6 Identification of zero cells
7 Allocation methods
8 Preparation of preliminary interregional table
IV. Derivation of final tables and multipliers 9 Ensuring the regional trade balance
10 Consistency checks
11 Analysis of sensitivity and coefficient significance
12 Derivation of inverses and multipliers for final transactions
table
Source: West et al. (1984)
3.2.3.2 DEBRIOT
DEBRIOT is an acronym identifying a double-entry method for the construction
of bi-regional input-output tables (Boomsma and Oosterhaven, 1992; Oosterhaven
et al., 2003) where the two regions are, on one hand, the region under study
and, on the other hand, the rest of the country. This method has been widely used
during the 1980s in the Netherlands for the derivation of interregional and regional
input-output tables. One of the main features is that it is based on a survey
for collecting interregional export data and some interregional import data. The
second main feature is that, by means of two different non-survey methods, it derives
two separate tables (domestic sales and domestic use tables) as a basis for
deriving the final transactions matrix. The method to derive the domestic sales table
is labelled as RSC (intra-Regional Sales Coefficients).
DEBRIOT is made up of six phases and twenty steps which will be below illustrated
in more detail.
Phase I. Adaptation of Given Data.
In this phase, a national highly disaggregated table (both for domestic and for
competitive foreign inputs) is selected. Moreover, total data for each sector of region
r have to be collected (value added, total intermediate use, foreign imports,
foreign exports, total production and household consumption). Since data for the
region r and data for the rest of the country (region s ) do not always add to the
corresponding national total, a confrontation of any regional and national data is
made to reach consistency (step 1). Missing regional data are estimated by secondary
data (step 2).
67

Hybrid Methods
68
Phase II. Limited Regional Trade Survey.
In this phase, a survey is carried out to collect interregional export data as well
as some interregional import data. Regional sectoral totals and the Leontief inverse
of the national input-output table are used to identify sectors and the potentially
inverse-important cells for which data should be collected. Wholesale sector
is automatically considered a sector to be surveyed (step 3). Per each interest sector,
firms are selected. They are all dominant companies and a random selection
from the smaller companies (step 4). In step 5, sampled firms are requested to
give information at least about: (a) total production; (b) total exports to the rest of
the country and total foreign exports; (c) exports to specific sectors in the rest of
the country (major cells); (d) imports from specific sectors in the rest of the country
(major cells) and from foreign countries. If the ultimate destination of sales is
unknown: (e) sales through the wholesale sector in the region and (f) sales
through wholesale in the rest of the country (in this case sales are distributed spatially
according to the distribution of demand).
The survey also involves the wholesale sector so as to cover all output that is
sold through this sector. In this case, collected information serves to allocate the
gross trade margin of the regional wholesale sector, the sales of firms that sold
through wholesale sector and the sales of non-surveyed firms (mainly smaller) to
their region of destination.
If firms are unwilling to give information, to supplement the information from
the survey, expert opinions are called upon.
Finally, individual firms’ coefficients are weighted on the basis of total production.
As for experts’ coefficients, weights are guesstimated reflecting the analysts’
own judgements of the relative importance and reliability of the information concerned.
Phase III. Construction of the Regional Domestic Use Table.
This phase is finalised to derive a regional domestic use table and regional foreign
imports by means of adjustments of national table. The total use of products
(both domestically produced and imported from abroad) from sector i by sector
j in region r is estimated as follows:
r r ( x j v j)
( x j −
v j)
−
⋅r ⋅n ij = ij ⋅ n n
z z

where n and r indicate the nation and the region, respectively. ( x j v j)
Hybrid Methods
− are total
intermediate uses of sector j in region r calculated as difference between total
n n
production and value added; ( x j − v j)
are total intermediate uses of national sector
j . Therefore regional intermediate costs are estimated adjusting proportionally
national columns by means of the ratio of total intermediate uses (step 6).
The estimates of intermediate uses are then compared to regional data related
to the most specific sector to identify eventual discrepancies (step 7). However, in
the event that remarkable differences are found, how to proceed is not mentioned.
In step 8, missing data about regional household consumption and data related
to other categories of final demand (i.e. investments) per sector are estimated by
using information from budget surveys.
In step 9, regional foreign imports are estimated using the share of national imports
on total intermediate uses. Foreign imports from sector i by sector j are defined
as:
m
m z
r
ij
n
ij
= ⋅n
zij
⋅r
ij
The availability of estimates related to foreign imports and total uses allows deriving
a preliminary version of the domestic use or purchase table. Domestic uses
of products from sector i by sector j are calculated by difference, i.e.:
z = z − m
nr ⋅r
r
ij ij ij
If the share of national imports on total intermediate uses differs from the regional
share estimated on the basis of data emerging from the trade survey, reconciliation
is needed (step 10).
Phase IV. Construction of the Regional Domestic Sales Table.
This phase is aimed at constructing a regional domestic sales table. First, official
regional domestic export coefficients are compared to those emerging from
the trade survey. If survey-based coefficients are considered to be sufficiently
“hard”, the official ones are preferred; otherwise if no significant differences exist,
survey-based coefficients are used (step 11).
69

Hybrid Methods
The regional domestic export coefficient for region r and sector i , expressing
the proportion of total domestic output of sector i that is sold to the rest of the
country s , is calculated as:
70
( ⋅ ⋅ ) ( )
t = z + y x − e
where
rs rs rs r r
i i i i i
rs
zi⋅ are total intermediate sales of sector i sold by region r to region s ,
rs
yi⋅ are total sales from region r to final demand sectors of region s ,
r
xi is total
r
production of sector i in region r and ei are exports of sector i in region r outside
the country.
In step 12, regional domestic sales coefficients are estimated (RSC’s method).
These are calculated as the weighted average of the demand structure of the rest of
the country and the demand structure of the region. The regional domestic sales
coefficient for sector i related to sales to sector j is defined as follows:
z z
s t t
ns nr
rn
ij =
rs
i ⋅
ij
ns ns
zi⋅ + yi⋅ + −
rs
i ⋅
ij
nr nr
zi⋅ + yi⋅
( )
( 1 )
( )
nr
ns
where zij are domestic purchases of commodity i from sector j in region r , z ij
are domestic purchases of commodity i from sector j in region s (they are esti-
nr
mated subtracting domestic uses of region r , z ij , from national domestic uses,
nn ns
nr
z ij ), yi⋅ are final uses of domestically produced commodity i in region s , yi⋅ are final uses of domestically produced commodity i in region r .
In step 13, the RSC’s method is adopted. By multiplying domestic output by
domestic sales coefficients, a preliminary domestic sales table is created. Formally,
regional domestic sales of sector i to sector j in region r is derived as
follows:
( )
z = s x − e
rn rn r r
ij ij i i
Phase V. Construction of the Intra-Regional Transactions Table.
This phase is aimed at deriving the intraregional transactions table. After constructing
two separate tables, one for domestic sales, the other one for domestic
uses, two different estimates of each cell are available. In step 14, intraregional

Hybrid Methods
transactions are estimated choosing the minimum between domestic sales and
domestic purchases, i.e.:
( )
z = min z , z
rr nr rn
ij ij ij
whereas domestic exports and imports are estimated respectively as:
z = z − z ; z = z − z
rs rn rr sr nr rr
ij ij ij ij ij ij
These estimated trade flows are compared to data from the survey and eventual
inconsistencies have to be resolved.
In step 15, corrections to previous estimates are made. If cell-specific export
rs
coefficients are available from the trade survey ( t ij ), these coefficients are applied
to the domestic sales table and domestic exports are recalculated as follows:
rs rs rn
z (spec) = t z .
ij ij ij
This allows obtaining cell-specific intra-regional transactions:
z = z − z
rr rn rs
ij ij ij
(spec)
The remaining intraregional cells, for which cell-specific export coefficients are
unavailable, are reduced proportionally by a factor r
hi until the interregional exports
reach a level consistent with the trade survey’s overall domestic export coefficient
( t ), i.e.:
rs
i
r +
rn rr rs rs r r
= ( − ) ∀{ hi ∈Ζ } / ∑ ( zij − zij ) + yij = ti ( xi −ei
)
z (non-s) 1 h z
rr r rr
ij i ij
The interregional exports are obtained endogenously by adding the subtracted part
of the equation above to interregional exports previously estimated:
( non-s)
z = z + h z
rs rs r rr
ij ij i ij
In step 16, domestic imports are first derived as follows:
z = z −
z
sr nr rr
ij ij ij
j
71

Hybrid Methods
Afterwards, domestic imports coefficients are calculated by dividing domestic
imports by domestic purchases:
72
c
z
sr
sr ij
ij = nr
zij
These coefficients are then compared to survey data and analysts’ judgment to
reconcile eventual differences.
In step 17, this final reconciliation together with the earlier ones in steps 10 and
14 may require the collection of additional data before the preliminary intraregional
transactions table can be made final.
Phase VI. Construction of the Bi-Regional Input-Output Table.
In this phase, the bi-regional table is completed by estimating intraregional
transactions for the rest of the country (steps from 18 to 20). Domestic uses of
product from sector i by sector j in region s are obtained by difference as follows:
z = z −z −z − z
ss nn rr rs sr
ij ij ij ij ij
3.2.4 Make and Use approach
In this approach, the derivation of regional input-output tables does not focus
on the quadratic national input-output table but on the three matrices underlying
that table: the make table (industry-by-product), providing information on what
type of commodities are produced by the different sectors in the economy; the intermediate
use table (product-by-industry), accounting for the use of (domestically
produced or imported) commodities in the production process of these sectors;
and the final use table, containing the value of all commodities delivered to different
final demand categories (Fritz et al., 2002). An exhaustive explanation of this
approach and related models can be found in Oosterhaven (1984), Mattas et al.
(1984), Miller and Blair (1985).
The Make and Use approach has received less attention in the literature for the
reasons that explain the major diffusion of the institutional approach. However, in
the recent years, this tendency has been changing (Madsen and Jensen-Butler,
1999). Jackson (1998) argues that commodity-by-industry accounts systems
should be preferred.

Tab. 3.3 – The DEBRIOT Methodological Sequence
Hybrid Methods
Phase Step Description
I. Adaptation of Given Data 1 Confrontation of the national input-output table with regional
(sectoral) totals
2 Estimation of lacking regional (household consumption)
totals
II. Limited Regional Trade Survey 3 Identification of relatively and absolutely large regional
sectors and sectoral peculiarities
4 Selection of firms per sector and determination of questions
to be asked per sector
5 Survey of firms and sector specialists and weighing of
III. Construction of the Regional Domestic
Use Table
IV. Construction of the Regional Domestic
Sales Table
V. Construction of the Intra-Regional Transactions
Table
VI. Construction of the Bi-Regional Input-
Output Table
the regional trade data
6 Application of national technology coefficients to regional
total use
7 Confrontation with available regional technology data
8 Estimation of missing (household consumption and private
and public investments) “technology” data
9 Application of national foreign import coefficients per
cell
10 Confrontation with regional foreign import data from the
trade survey
11 Confrontation of official regional foreign export data with
foreign export coefficients from the trade survey
12 Determination of the regional domestic sales coefficients
13 Application of regional domestic sales coefficients to
regional total domestic sales
14 Determination per cell of maxima for intra-regional
transactions and minima for regional domestic imports
and regional domestic exports, and confrontation of
these minima with data from the trade survey (consistency
checks)
15 Application of cell-specific domestic export coefficients
to the domestic sales table and reduction of remaining
cells from the maximum intra-regional transactions table
to reach the trade survey’s overall regional domestic
export coefficients per sector
16 Plausibility verification of the preliminary regional domestic
import coefficients and confrontation with the
import coefficients available from the trade survey
17 Determination of the final intra-regional transactions
table through selective collection of additional data and
revision of earlier estimates
18 Calculation of the regional domestic exports table
19 Calculation of the regional domestic imports table
20 Calculation of the intra-regional transactions table for
the rest of the country
Source: Boomsma and Oosterhaven (1992)
73

Hybrid Methods
This is because the traditional focus on processing sectors neglects some important
attributes that distinguish regional economies. In effect, final demand activities
tend to vary widely from region to region, for reasons that include the
presence or absence of universities, state capitals, prisons, etc.. If these peculiarities
are not taken into consideration, there is a serious risk of overestimating or
underestimating the regional industry’s ability to supply goods and services for
local demand. Accordingly, focusing only on the interindustry transactions may
lead to biased coefficient estimates.
Madsen and Jensen-Butler (1999) argue that “make and use approaches relate
more directly to the theoretical framework that underlies data collection for national
and regional accounts and, as a consequence, to the data themselves.”
(Madsen and Jensen-Butler, 1999, p. 278). This is defined as an advantage since it
is preferable to use directly the data which were collected in deriving regional
data using non-survey methods, instead of using data that have been transformed
as is the case with institutionally based accounts. Other advantages are: the closeness
to reality, the possibility of adopting this approach in a broader field of application
such as environmental modelling and, finally, the same requirement of data
as in the case of the institutional approach.
Recent contributions to this approach are provided by Jackson (1998), Lahr
(2001b) and Fritz et al. (2002). These studies focus on one-single-region models.
Regarding attempts to extend the make and use approach to interregional models,
we refer to Eding and Oosterhaven (1996) and Madsen and Jensen-Butler (1999).
3.2.4.1 Jackson’s regionalization method
Jackson (1998) suggests a general method to regionalize commodity-byindustry
accounts, in the context of the US reporting system. This method contemplates
the possibility of inserting any superior data to replace mechanically obtained
estimates. The national accounts on which Jackson bases his regionalization
method are regrouped as in Tab. 3.4. He notes that this accounting system
differs from the SNA for two reasons: first, use table entries are expressed in producers’
prices; second, imports are differently treated. The first deviation is
judged advantageous since it facilitates the regionalization procedure considering
that data on margins are generally non-existent or scarce at sub-national level. On
the contrary, the second deviation is felt to complicate the transition from accounting
to modelling frameworks. For this reason, national accounts are modified
prior to regionalization so as to emphasize imports accounting which at subnational
level holds a crucial importance. Specifically, national imports are removed
from the final demand column and inserted in an import row vector (Tab.
3.5). Below, regionalization procedure for main aggregates will be described in
more detail.
74

Hybrid Methods
Regional output. Regional output is estimated multiplying national output by
coefficients expressing relationships between regional and national industry outputs.
These indices are employment ratios multiplied (if they are available) by reliable
estimates of regional-to-national productivity ratios.
Make and use tables. The Make and Use tables are estimated multiplying the
corresponding national tables by a diagonal matrix of coefficients used to estimate
output. This implies that regional and national industry technologies are identical.
Tab. 3.4 – Schematic layout of commodity-by-industry accounts with a negative entry for
competitive imports in final demand
Commodities Industries Final Demand Total Output
Commodities U Fx( -m )
Industries V g
Value Added W
Total Inputs q ′
g ′
Note: F is the matrix of domestic final demand, x is a vector of exports, m is a vector of imports, U is the Use
matrix and V is the Make matrix
Source: Jackson (1998)
Tab. 3.5 – Schematic layout of commodity-by-industry accounts with imports shown as a
commodities source
Commodities Industries Final Demand Total Output
Commodities U Fx q+m=s
Industries V g
Imports m ′
mi ′
Value Added W
Total Inputs s ′
′
g
Note: F is the matrix of domestic final demand, x is a vector of exports, m is a vector of imports, U is the Use
matrix and V is the Make matrix, s is the vector of total output including imports
Source: Jackson (1998)
q
75

Hybrid Methods
76
Formally, it results that:
R
R
V = τV ˆ U = Uτ ˆ
where V represents the make matrix, U is the use matrix, R indicates the region
and ˆτ is the diagonal matrix of coefficients expressing relationship between national
and regional output. More precisely, τ j = ερ j j where ε j is the ratio between
regional and national employment of sector j , while ρ j is a measure of
productivity ratio for sector j . However, it is not specified how to measure productivity
ratios.
Final demand. Components of final demand are distinguished into local supply-dependent
activities and local demand-dependent activities. The former are
those for which commodity final demand is determined primarily by the relative
size of the local production sector. They are rest-of-the-world exports, gross private
fixed investments (GPFI) and changes in business inventory (CBI). These
components are estimated for all processing sectors multiplying national components
by output ratios. A different treatment is reserved to sectors defined as special
such as government industry (GI), rest-of-world industry (ROW), household
industry (HI), inventory valuation adjustment (IVA) and non-comparable imports
(NCI). As for IVA, ROW and NCI, values of exports, GPFI and CBI are estimated
multiplying national ratio between the component considered and total final
demand for processing sectors by total regional final demand for processing sectors.
As for GI and HI, values for local supply-dependent activities retain their
zero value as in the national accounts. The local demand-dependent activities are
instead more directly functions of the size of the final demand activity sector
within the region. They are personal consumption expenditure and public expenditure.
These components are derived multiplying the corresponding national
component by variable control total ratios. These ratios can concern different aggregates
according to the sector considered. For instance, as for food consumption,
the ratio is represented by total regional personal income divided by total national
personal income. As for public expenditure, a total could be total government
education expenditure and so on. This scaling procedure can be extended to
special sectors.
Imports. Regional imports are treated as a vector aggregating national and restof-nation
imports. They are estimated using a supply-demand pool approach. This
choice is justified arguing that location quotients, that have demonstrated to be
superior to supply-demand techniques, have been used to estimate regional coefficients
from national interindustry coefficient tables and not from entire tables.
According to Jackson, this is one of the reasons for which location quotients tend
to overestimate sectoral multipliers. Other methods like regional purchase coeffi-

Hybrid Methods
cient are rejected because they are less simple and conceptually less clear than
supply-demand pool approach. Definitively, regional imports are calculated as
difference between the sum of purchases (from use table), final demand and exports
and the sum of sales (from make matrix). In other words, imports are derived
as difference between demand and supply. Negative values of imports are
accommodated introducing a new final demand activity to represent regional exports
to the rest of the nation. These values are transferred to this new column, reversed
in sign, while values of imports are replaced by zeros. The analyst is made
free to consider an aggregated vector of exports instead of two vectors. This can
be made removing the exports from the calculation of regional imports. Imports as
well as interregional exports are net because they do not take account of crosshauling.
To consider this latter, the estimated quantity of regional output that is
cross-hauled (obtained as proportion of regional output) has to be added to exports
to other regions and subtracted from imports. A different treatment is reserved to
three special sectors: NCI, scrap and ROW. The NCI value for imports is defined
to be equal to the negative sum of other NCI. The scrap value is computed as the
difference between total scrap production, derived as the sum of scrap column of
the regionalized make matrix, and the sum of the regionalized scrap row of the
Use table (excluding the imports column). Imports for ROW are estimated by national
share on imports.
Value Added. As for value added, it is supposed that value added is proportionate
to industry size and that value added per dollar of output is invariant across
sub-national regions. Accordingly, value added is estimated multiplying national
value added by coefficients used for deriving estimates of output.
3.2.4.2 Lahr’s contribution
Lahr (2001b) analyzes Jackson’s regionalization method (Jackson, 1998) and
proposes some extensions to the regional accounting approach formulated by
Jackson. Moreover, he offers some guidelines to be followed in producing regional
accounts from national accounts when a limited amount of secondary regional
data is available.
As for regional output, he suggests estimating it using the region’s shares of
labour income by industry, being measures of the relative regional productivity.
This is because, according to economic theory, gross wages paid to a worker
should equal the value of the worker’s marginal product. Thus, dividing national
gross wages by regional gross wages is like dividing marginal products. Resuming
the notation used by Jackson, the labour income share coincides with the productivity-enhanced
value of τ .
With reference to the regional make table, Lahr notes that Jackson estimates
the make table (as well as the use table) substantially using output ratios. This im-
77

Hybrid Methods
plies that the hypothesis of spatially invariant mix of commodities is assumed.
Moreover, he presumes that Jackson refers to data on output by industry because
those by commodities are not generally available. If output data by commodities
were available, the same adjustment proposed by Jackson on the national make
matrix would be made. If both industry and commodity output are available, he
suggests a bi-proportional adjustment or mathematical programming procedure to
balance the regional make matrix. This latter approach would permit some variation
in the mix of commodities produced by an industry but would constrain it to
being as close as possible to that of the nation.
With regard to value added, Lahr notes that statistical offices also publish regional
data on value added albeit at a more aggregated level. Therefore, it would
need to take account of available data in deriving estimates of value added. For
this, he suggests calculating regional value added multiplying initially national
value added by output shares and then proportionally adjusting groups of estimates
on the basis of the available corresponding totals.
As for labour income, since this component is considered crucial, being the
biggest one within value added and for its impact on multipliers, detailed data at
local level regarding wage and salaries disbursement by industry should be collected
or estimated accurately. Then, these data should be proportionally adjusted
on the basis of official data on regional labour income which are available at a
more aggregated level.
The regional use matrix is derived scaling columns of national use matrix in
order to assure that the sum of columns equals the difference between regional
output and value added. This adjustment is the so-called fabrication adjustment.
As for final demand (excluding exports), he mentions the possibility of estimating
final demand activities econometrically. However, he notices that most
analysts generally follow an approach that is not far from the one used by Jackson.
Moreover, he reminds that much more attention should be paid to the region’s
household consumption pattern. In this regard, regional data on personal
consumption expenditure, that are available in most OECD countries, should be
collected and implemented.
Once an estimate of net final demand has been produced, one can proceed to
estimate exports. If data on interregional trade are missing, Lahr reminds that developers
of regional accounts generally regionalize exports applying output ratios
to exports. However, if data on interregional trade are available on a commodity
basis, these data can be used to distinguish interregional exports from international
exports.
With estimates by commodity of regional output, final demand, exports and
imports (these latter obtained as Jackson suggested) and by industry of regional
output and value added, regional use accounts could be re-estimated using biproportional
adjustment like RAS where total rows are differences between sums
78

Hybrid Methods
of regional output by commodity and imports and sums of net final demand and
exports, whilst total columns are differences between total output by industry and
value added.
3.2.4.3 The Austrian experience
Fritz et al. (2002) illustrate the procedure they adopted to derive 1995 Austrian
regional tables starting from a 1995 national table in make and use format.
Regional output, make and use tables. Data on regional outputs were available
from Austrian statistics. As for the regional make matrix, data by sector on produced
commodity shares were taken from survey or other sources. For sectors for
which survey data were not available, they applied national shares. Then they reconcile
commodity shares in order to assure the sum over all shares for each activity
was one. The regional intermediate use matrix was derived using the same
procedure used for producing the regional make table.
Regional Final Use Table. With regard to the regional final use, components of
final demand were derived using value added, sectoral output and population data.
In particular, private consumption for each commodity was estimated by percapita
value added ratio. Investments were calculated multiplying total regional
investment demand by national shares in total investment. Total regional investment
demand was estimated as a sum of regional output multiplied by national ratios
of investment to output. Changes in regional inventories were approximated
for each activity multiplying regional output by national ratios of change in inventories
to output. Public consumption for each commodity was derived multiplying
total regional public consumption by national shares of public consumption to
output. Total regional public consumption was estimated multiplying total regional
value added by national ratio of government consumption and total value
added. Data on exports were collected conducting surveys among regional firms.
Firms were requested to give information on the ratio of regional exports on total
revenues.
Imports. Intermediate and final uses were gross of commodities imported from
other regions or from abroad. To separate uses of locally produced commodity
from uses of imported goods, an import ratio was applied uniformly along rows to
the intermediate and final uses tables. Import ratio for each commodity was calculated
dividing total imports by total uses (both intermediate and final). These ratios
were estimated using qualitative information and, in case, were adjusted using
national commodity import ratios.
After completing both the regional make table and regional use table, they derived
a sector-by-sector input-output table using the industry-technology assumption.
79

Hybrid Methods
3.3 Bottom-up approach
The “bottom-up” approach attempts to build the table from local data. Studies
based on this approach are not many because of unavailability of regional data.
Examples are provided by: Smith (1983) who applied the so-called ASSET system
(A System for Small Economy Table) to derive a regional table for the town
of Cooroy (Queensland, Australia); Martins (1993) who constructed a regional table
for Illinois in 1982 using establishment-level data and Piispala (2000) who derived
all 1995 regional tables for Finland using information from a survey over
9600 establishments. The drawback of the bottom-up approach is in the estimation
of regional imports because a small region does not usually record export and import
flows (Imansyah, 2000).
3.4 Horizontal approach
In this approach, methods that use existing regional tables to derive further regional
tables are included. They are: the “modified” RAS (based on insertion of
further information in addition to column and row totals) applied to an existing
regional table; the FES-concept-based method (Imansyah, 2000) and the use of
representative regional coefficients.
The FES-concept-based method is a technique developed from intuitions of
Van der Westhuizen (1992). It is based on the concept of “fundamental economic
structure” (Jensen et al., 1988) and it uses regression techniques requiring the
availability of set of regional input-output tables for similar regions. The onerosity
of data requirements significantly reduces application possibilities of this method.
The method based on exchanging regional coefficient consists of “borrowing”
survey-based input-output coefficients of a given region to construct a table for
another region (Tiebout, 1969; Czamanski and Malizia, 1969; Hewings, 1977;
Thumann, 1978; Hewings and Janson, 1980; Antille, 1990). It is clear that a necessary
condition for applicability of this method is that economic structure of the
two regions has to be as similar as possible. Tiebout (1969) used this idea for his
forecasting model of the state of Washington. Czamanski and Malizia (1969) argued
that the use of unadjusted national coefficients is the less appropriate for
primary industries and industries in which the region specializes. Thus, they suggested
using coefficients of a different region for these sensitive and important
sectors. Hewings (1977) carried out an experiment aimed at verifying the effectiveness
of using representative coefficients. He used a survey-based table for
Washington State for 1963 to estimate Kansas interindustry structure in 1965 and
used a survey-based table for Kansas in 1965 to estimate the table for Washington
Sate in 1963. He found, multiplying actual final demand by the borrowed coefficients
matrix, that, for some sectors, outputs were predicted rather accurately
whereas, for others, the estimated outputs were far from the known values. On the
80

Hybrid Methods
contrary, applying the RAS procedure to the Washington survey-based table in
conjunction with Kansas survey-based information on total intermediate inputs
and outputs, he achieved far superior results in terms of predicted sectoral output
for Kansas. However, from the analysis of single coefficients, he noted that RAS
did not produce more improvements in terms of accuracy than those achieved
with the simple coefficients matrix exchange.
81

4 Performances of Regionalization Methods
4.1 Introduction
Considering the existence of numerous indirect techniques finalized to avoid
constructing regional tables through survey methods, it becomes important to verify
not only theoretical foundations of the various indirect methods, but also their
real performances in representing accurately a given productive structure. In the
literature, evaluation of performances of indirect methods has been carried out by
measuring deviations between indirectly constructed tables and survey-based tables.
However, there are four major obstacles to a sound evaluation of these techniques.
Firstly, it is assumed that the survey models generate true values, an assumption
which may be wrong considering the possibility of sampling errors as well as
reconciliation adjustments, indirect estimation of some quantities and various arbitrary
procedures used in constructing survey-based tables. Accordingly, comparisons
are aimed at evaluating the extent to which indirect methods are able to
reproduce the survey-based matrix which, in some cells, can suffer from errors
that might be bigger than those of the indirectly derived matrix.
Secondly, the distance between two matrices may be measured using a multiplicity
of indices having different properties and characteristics. Therefore, the
choice among methods becomes rather difficult (Richardson, 1985).
Thirdly, it needs to clear what purpose the input-output matrix must serve.
There is in fact a choice between partitive accuracy (precision of estimates) and
holistic accuracy (faithful representation of the overall economy in spite of possible
errors) (Jensen, 1980; Hewings and Syversen, 1982; Hewings, 1984). In the
first case, a direct comparison between indirectly and directly constructed matrices
might be sensible, assuming that the survey-based matrix is a “true” table. On
the contrary, in the second case, a more satisfactory test might be an ex post
analysis of the predictive power of indirect models with regard to real impacts.
Nevertheless, there may be serious problems in separating out the impact from

Performances of Regionalization Methods
everything else and which happened during the period of analysis (Isserman and
Merrifield, 1982).
Finally, only indirect methods using same statistical information should be
compared, since more satisfactory results coming from some methods may not
depend on their characteristics but on major volume of information used for their
application (Strassoldo, 1988).
In next paragraphs, the main measures that have been employed so far to test
accuracy of indirect methods will be discussed. Then, empirical studies that were
carried out in the literature to verify the validity of indirect methods will be illustrated.
Finally, some concluding notes, summarising results from empirical studies,
will be given.
4.2 Measures for comparing input-output matrices
In the literature, measures that have been used to estimate deviations between
survey-based and indirect-method-based tables can be divided into two main categories:
those comparing direct coefficients and those comparing actual and surrogate
vectors of exports, imports, gross outputs and sectoral multipliers. Thumann
(1978), in commenting on the use of gross output comparisons, notes that there
are infinite alternative matrices that are consistent with the inherent accounting
constraints. This conclusion has been later confirmed by Hewings and Janson
(1980). Moreover, gross outputs include components exogenous to the system,
therefore they make gross output comparisons look better than they really are
(Round, 1983).
Overall, many measures have been employed to test accuracy of indirect methods.
Lahr (2001a) states to have found 14 different measures to determine the accuracy
of I-O models (on the contrary, we counted 23 different measures). Lahr
claims that this can be explained by the fact that “regional tables are unlike many
other matrix-based models. The structure of I-O tables and the possible uses toward
which they can be applied require a very stringent set of properties for a
general-use comparison measure.” (Lahr, 2001a, p. 236).
Butterfield and Mules (1980) argue that only one measure is not sufficient to
reach a satisfactory judgement when comparing more techniques because of limits
associated to each measure. For this reason, they propose a interesting multipletesting
procedure involving non-parametric tests, regression analysis, chi-square
contingency table of size distribution of coefficients and mean and mode of absolute
and standardized absolute difference.
In spite of a wide use of distance and similarity measures, studies discussing
their properties or justifying their use are not many. This lack of investigation is
even more worrying if one considers that the relationship between error and the
value of the measure adopted must be known in order to draw conclusions regard-
84

Performances of Regionalization Methods
ing model performances. Instead, this relationship is often unknown and is assumed
to be linear, whereby if the value of a given distance measure for one
model is twice that measured for another model, it is assumed that the first model
is twice less accurate than the other (Knudsen and Fotheringham, 1986).
Studies, discussing characteristics of distance and similarity measures, are for
example Morrison and Smith (1974), Butterfield and Mules (1980), Knudsen and
Fotheringham (1986), Asami and Smith (1995), Flegg and Webber (2000), Lahr
(2001a). Unfortunately, apart from the research of Knudsen and Fotheringam, the
existing studies do not seem to face the problem of errors in an extensive and scientific
way (Lahr, 2001a).
Measures used to compare survey with indirectly constructed tables can be regrouped
into three main classes: traditional statistics, general distance statistics
and information-based statistics.
4.2.1 Traditional statistics
Within this category, there can be included chi-square statistic, contingency table,
regression and correlation analysis, non-parametric tests.
Chi- Square Statistic. This statistic takes the following form:
n n
2
χ = ∑∑
( ) 2
aij − bij
b
i= 1 j= 1 ij
where ij a are estimated coefficients whilst b ij are survey-based coefficients.
Clearly, the lower the chi-square the better the estimate is. A merit of this statistic
is that it is based on proportionate error which also allows for comparisons among
different studies. However, there are some related problems. First, a problem happens
when aij ≠ 0 and b ij = 0 , which makes the standardization meaningless. In
this case, the approach often used is to omit such pairs of coefficients in the calculation
of the statistic or to assign some arbitrarily chosen very low value to each
zero b ij (Butterfield and Mules, 1980). Second, results could be distorted in cases
where coefficients are close to zero. Third, the squaring of the simulation error
may take the statistic oversensitive to outliers (Flegg and Webber, 2000).
To overcome the problem of meaningless standardization, Butterfield and
Mules proposed to calculate the statistic in terms of the frequency distribution of
size of coefficients, arranging coefficients in class intervals and calculating for
each class the frequency of occurrence.
85

Performances of Regionalization Methods
86
Thus, the statistic becomes:
n
2
χ = ∑
( ) 2
fi − fi
f
i= 1 i
where i is a given interval of coefficients, n is the total number of intervals, f i is
the frequency of occurrences related to estimated coefficients within the class i
and f i is the frequency of occurrences related to “true” coefficients within the
class i . Regrouping coefficients into intervals makes it unlikely that f i = 0 and
fi ≠ 0 .
Flegg and Webber introduced a modified version of the statistic that was
called: weighted chi square. It takes the form:
( ) 2
n n aij − bij
= ∑ j∑
WCS w
b
j= 1 i= 1 ij
It is weighted on the basis of proportions of employment of purchasing sector j
( w j ) in order to take account of the differing relative importance of sectors when
aggregating the results across sectors.
To overcome the drawback associated to coefficients close to zero, Flegg and
Webber excluded from calculation cases for which coefficients were smaller than
0.001.
Contingency table. Butterfield and Mules (1980) proposed to construct a chisquare
contingency table for frequency distribution of coefficients, i.e.: F = ⎡
⎣fij ⎤
⎦
where i are class intervals for the estimated matrix while j are class intervals for
the survey matrix. Therefore f ij expresses how many estimated coefficients are in
the interval i and how many true coefficients are in the interval j . If estimated
coefficient matrix is a good estimate, the cells on the principal diagonal, f ii , will
contain high frequencies while the other cells will have low frequencies.

Performances of Regionalization Methods
Regression and correlation analysis. The regression approach was suggested
by Butterfield and Mules (1980). It may be applied if there are sufficient elements
in a row or column to give a satisfactory number of observations. The regression
equation is:
a = α + βb
ij ij
From this equation, three test coefficients can be calculated: (i) the squared corre-
2
lation coefficient ( R ) which would be close to unity for a good fit; (ii) an intercept
or constant term (α ) which would be close to zero for a good fit and (iii) the
slope coefficient or regression coefficient ( β ) which would be close to unity for a
good fit. The advantages related to the application of this technique are: (a) zero
values do not pose any problem; (b) regression analysis is readily usable thanks to
computer packages; (c) the analysis can be limited to some coefficients (for instance
larger coefficients having major impact on multipliers). The main disadvantage
is possible ambiguity which can arise when intercept is significantly different
from zero and a slope coefficient significantly differs from unity. In this
case, it becomes hard to express a judgement particularly when comparing two or
2
more indirect techniques. Moreover, R could have a high value also in the cases
of underestimation or overestimation, if the errors followed some consistent pattern.
Therefore, Butterfield and Mules advise calculating further measures.
Non-parametric tests. Non-parametric tests can be used to test if an estimated
matrix consistently underestimates or overestimates a survey-based matrix. In this
context, their use is aimed at examining the size rankings of pairs of coefficients.
Acceptance of the null hypothesis of no significant difference between the two
rankings means that one is not consistently above or below the other. However,
one would come to the same conclusion even in the case in which there were large
errors and positive errors compensated negative errors (Butterfield and Mules,
1980).
87

Performances of Regionalization Methods
4.2.2 General distance statistics
General distance statistics are: Euclidean metric distance, index of inequality,
index of relative change, similarity index, mean absolute difference, mean absolute
relative difference, mean weighted absolute error, mean weighted error, mean
weighted relative error, standardised total error, weighted absolute difference.
Euclidean Metric Distance (EMD). The Euclidean Metric Distance or root
mean squared error was introduced by Harrigan et al. (1980b) in the input-output
literature. It takes the following form:
88
1 n n
2
n i= 1 j=
1
( ) 2
∑∑ ij ij
EMD = a −b
This measure does not yield any idea of the relative difference between two
matrices, but rather only the average total difference. Hence, one cannot really determine
how bad or good an estimated matrix is when compared to the actual
(Lahr, 2001a).
Index of Inequality (Theil’s U). This measure was originally developed in Theil
et al. (1966). It was used in the input-output literature by Stevens and Trainer
(1976). This measure, commonly called Theil’s index of inequality, can be
broadly interpreted as a standardized root mean squared error. It takes the following
form:
U =
( ) 2
n n
∑∑ aij − bij
i= 1 j=
1
n n
∑∑
i= 1 j=
1
b
2
ij
It has the advantage of yielding an overall distance proportion as well as three
other proportions: bias, variance and covariance. These extra error proportions are
valuable in showing the researcher the patterns of the differences between two
matrices.

Performances of Regionalization Methods
Index of relative change. The index of relative change was introduced by Isard
and Romanoff (1968). It holds an unconventional feature since it ranges in value
from zero to two. It has the following structure:
RC
ij
=
1 2
a − b
ij ij
( aij + bij
)
Similarity index (SI). In order to remove the potentially confusing property of
the index of relative change, Isard and Romanoff modified this index to range
from zero to unity, calling the next index a similarity index whose form is:
SI
ij
= 1−
a − b
ij ij
( aij + bij
)
It is a standardized measure that takes one value when aij = bij
and zero for either
a ij or b ij being zero when the other is non-zero. There are problems of skewness
and extreme values associated to this measure. Therefore Butterfield and Mules
(1980) suggest comparing a modal value of the SI ij with their mean to assess this.
Another problem is that over-unity and under-unity values of SI ij will cancel out
in the averaging process, giving the impression of a mean close to unity. If the
distribution of SI ij is uni-modal, the mode may help to overcome this problem.
Mean Absolute Difference (MAD). It was introduced by Morrison and Smith
(1974). It has the following form:
n n 1
MAD = a −b
∑∑
2
n i= 1 j=
1
ij ij
This measure gives an idea about average error. However, it suffers from some
weaknesses. First, it is affected by any skewness in the distribution of errors. For
instance, one large error in a set of very small errors will pull the mean upwards
and give an impression of greater error than exists on the whole. According to
Butterfield and Mules (1980), this can be considered either a disadvantage or an
advantage according to the use to which the matrix is bound. In fact, if pursued
accuracy is holistic, this will be an advantage since larger coefficients affect multipliers.
Otherwise, if the objective is to construct an accurate system of regional
89

Performances of Regionalization Methods
accounts, this will be a disadvantage. In this case, Butterfield and Mules propose
to calculate the modal value of each of the absolute differences and to compare
these latter with the respective means. Second, given the importance of larger coefficients,
this measure is not sufficiently sensitive to errors related to large coefficients
(Lahr, 2001). Third, its magnitude changes with the order (size) of the I-O
tables being evaluated. Forth, it does not consider the relative importance of coefficients.
Mean Absolute Relative Difference (MARD). This index is a variant of MAD
that considers the relative dimension of coefficients. It is sometimes called as
standardised mean absolute difference (SMAD). It takes the following form:
90
1
a − b
n n
ij ij
MARD = 2 ∑∑ n i= 1 j= 1 bij
Most considerations made for MAD are worth for this measure as well.
Mean Weighted Absolute Error. The mean weighted absolute error was employed
by Flegg and Webber (2000). It takes this form:
n n 1
MWAE = w a −b
∑ ∑
j ij ij
n j= 1 i=
1
It is very similar to MAD with the difference that employment ratios are used as
weights and it is less sensitive to the size of the input-output matrices under study.
Mean Weighted Error (MWE). The mean weighted error was used by Flegg
and Webber (2000). It takes the following form:
1
MWE = w a −b
n n
∑ j∑ ( ij ij)
n j= 1 i=
1
This index is the mean of the weighted column sums of differences between the
simulated and survey-based coefficients. The main disadvantage is that large positive
and negative weighted column sums can offset each other giving a misleading
impression of a good overall simulation.
Flegg and Webber argued that this index is able to capture in someway any
systematic tendency towards overestimation or underestimation. However, it dis-

Performances of Regionalization Methods
regards the intersectoral variation in simulation errors. To take account of this factor,
they developed the following statistic:
( ) 2 2
MWE 0
ω = − + σ
MWE MWE
n
σ is the standard deviation of wj ( aij − bij)
where MWE
∑
i=
1
. By minimizing ω MWE
rather than MWE , both bias and variance are considered when selecting a method
of estimation.
Mean Weighted Relative Error. The mean weighted relative error was used by
Flegg and Webber (2000). It is:
n 1
MWRE = ∑ w
n
j=
1
n
∑(
aij − bij
)
i=
1
j n
∑
i=
1
b
ij
It was conceived to overcome limits of MWE taking account of two factors:
(a) the relative size of the simulation error for each coefficient; (b) the relative
a − b b .
size of the coefficient in question. The first factor is represented by ( )
n
ij ij ij
The second factor is measured by the ratio bij ∑ bij
expressing each survey-
i=
1
based coefficient as a proportion of the sum of intermediate inputs for a given
purchasing sector j . This second adjustment is intended to be an attempt to take
account of West’s (1981) considerations, according to which it needs to focus on
the largest coefficients because errors related to these coefficients have the greatest
impact in the estimated sectoral multipliers. Analogous to MWE, Flegg and
Webber, in order to take account of both bias and variance, developed the following
statistic:
( ) 2 2
MWRE 0
ω = − + σ
MWRE MWRE
n n
σ is the standard deviation of wj∑( aij − bij) ∑ bij
.
where MWRE
i= 1 i=
1
91

Performances of Regionalization Methods
Standardized Total Error (STE). The name of this measure was given by Miller
and Blair (1982, 1983). However, it was first used by Leontief (1966) with a different
name. It has the following structure:
92
STE =
n n
∑∑
ij ij
i= 1 j=
1
n n
∑∑
i= 1 j=
1
a − b
b
ij
The major drawback is that it may not be exceptionally sensitive to high-valued
cells.
Weighted Absolute Difference (WAD). Lahr (2001a) was the first one to introduce
it. It takes the following form:
WAD =
n n
∑∑
i= 1 j=
1
( )
a − b a + b
n n
∑∑(
aij + bij)
i= 1 j=
1
ij ij ij ij
It is designed to make up for problems of other measures: low sensitivity to errors
in large cells and indefinite values. As for the first problem, the term ( aij + bij
)
weights the absolute difference term so that the errors of large cells are emphasized.
In this way, the measure is extremely sensitive to error in large cells. With
regard to the second problem, if either of the matrices is nonzero for a cell, the
measure’s value is never undefined. However, there is one evident problem with
this measure: it does not necessarily express proportional error.

Performances of Regionalization Methods
4.2.3 Information-based statistics
Of information-based statistics, information content index was used in the input-output
context. This index was borrowed by Czamanski and Malizia (1969)
from the field of information theory. The survey-based matrix is considered as a
forecast of the non-survey estimate and the additional information contained in
the latter is quantified by the following index:
n n
ij
: = ∑∑ ij log
i= 1 j= 1 bij
( ) 2
I A B a
a
The lower the information content thus measured, the closer the estimate. The
information content has the disadvantage that it cannot handle a situation in which
the survey estimate is zero and the estimated value is nonzero. Moreover, it is difficult
to judge if a particular value of this measure is high or low, and so the
measure would appear only to be useful in ranking different methods.
4.3 Overview of empirical studies comparing indirect methods
The lack of sufficient and reliable survey data have limited the possibility of
comparing performances of indirect methods (Miernyk, 1976). In effect, studies
oriented towards this aim are relatively few. Here, we will examine the following
ones: Shaffer and Chu (1969a, 1969b), Morrison and Smith (1974), Butterfield
and Mules (1980), Eskelinen and Suorsa (1980), Sawyer and Miller (1983), Mogorovich
(1987), Willis (1987), Harris and Liu (1998), Gilchrist and Louis (1999)
and Flegg and Webber (2000).
Schaffer and Chu (1969a) compare simulated I-O tables with the Washington
survey-based matrix by using chi-square statistics for each column of the tables.
They find that the best method able to estimate closer coefficients to those of the
survey-based matrix is SLQ, followed by CILQ, RIOT simulation and Supply-
Demand Pool. They also find that income multipliers obtained by using SLQ are
the closest to survey-based income multipliers. In every case, the non-surveybased
multipliers overestimate those derived from the survey table.
In a later paper, Shaffer and Chu (1969b) include in their study three states
(Washington, Utah and New Mexico) and they use the index of relative change,
the regression and information contents procedure as methods of comparison. The
best simulation results are obtained by SLQ, followed by Supply-Demand Pool,
iterative RIOT procedure and CILQ.
93

Performances of Regionalization Methods
Tab. 4.1 – Some measures used to compare survey-based tables with indirect-methodbased
tables
94
TRADITIONAL STATISTICS
( ) Chi-square statistic 2
n n a 2
ij − bij
χ j = ∑∑
b
i= 1 j= 1 ij
( ) Weighted chi-square 2
n n aij − bij
WCS = ∑wj∑ Contingency Table
Regression
Correlation coefficient
R =
b
j= 1 i= 1 ij
F
⎡ f ⎤
= ⎣ ij ⎦
a = α + βb
ij ij
n n
∑∑(
aij −a)( bij −b)
i= 1 j=
1
GENERAL DISTANCE STATISTICS
n n n n
∑∑( aij −a) ∑∑(
bij −b)
i= 1 j= 1 i= 1 j=
1
1 n n
n i= 1 j=
1
2 2
Euclidean metric distance ( ) 2
EMD = 2 ∑∑ aij −bij
Index of inequality
Index of relative change
Similarity index
Mean Absolute difference
U =
( ) 2
n n
∑∑ aij − bij
i= 1 j=
1
n n
∑∑
i= 1 j=
1
b
2
ij
aij − bij
RCij
=
1
2
SIij
= 1−
aij − bij
a + b
( aij + bij
)
( ij ij )
n n 1
MAD = a − b
Note: ij a are estimated coefficients whilst b ij are survey-based coefficients
∑∑
2
n i= 1 j=
1
ij ij

Performances of Regionalization Methods
Tab. 4.1 – Some measures used to compare survey-based tables with indirect-methodbased
tables (continued)
Mean Absolute relative difference
Mean weighted absolute error
Mean weighted error
Mean weighted relative error
Standardized total error
Weighted absolute difference
GENERAL DISTANCE STATISTICS
1
a − b
n n
ij ij
MARD = 2 ∑∑ n i= 1 j= 1 bij
n n 1
MWAE = w a − b
∑ ∑
j ij ij
n j= 1 i=
1
1
MWE = w a −b
n n
∑ j∑ ( ij ij)
n j= 1 i=
1
( ) 2 2
MWE 0
ω = − + σ
MWE MWE
n 1
MWRE = ∑ w
n
j=
1
n
∑(
aij − bij
)
i=
1
j n
∑
i=
1
b
( ) 2 2
MWRE 0
ω = − + σ
MWRE MWRE
WAD =
STE =
n n
∑∑
i= 1 j=
1
INFORMATION-BASED STATISTICS
n n
∑∑
ij ij
i= 1 j=
1
n n
∑∑
i= 1 j=
1
a − b
b
ij
ij
( )
a − b a + b
n n
∑∑(
aij + bij)
i= 1 j=
1
ij ij ij ij
n n
ij
: = ∑∑ log
i= 1 j= 1 bij
I A B a
Information content index ( ) ij 2
Note: ij a are estimated coefficients; ij b are survey-based coefficients; w j are proportions of employment of
sector j .
a
95

Performances of Regionalization Methods
Morrison and Smith (1974), on the basis of the I-O table constructed by survey
for the City of Peterborough (UK), compare the effectiveness of several mechanical
methods to derive regional coefficients (RAS, SLQ, CILQ, Supply-Demand
Pool, PLQ, RLQ). By calculating several statistical tests (mean absolute difference,
correlation coefficient, mean similarity index, chi-square test, information
content) measuring differences between matrices, they demonstrate, that the RAS
technique produces a superior simulation. Among the purely nonsurvey methods,
they note that SLQ is the best of the methods, followed by PLQ, Supply-demand
pool, RLQ and, lastly, CILQ, producing the poorest simulations. They also find
that RAS allows deriving income multipliers of type I and II which are much
closer to the survey-based multipliers, followed by SLQ, CILQ and Supplydemand
pool. In every case, all methods tend to overestimate the value of multipliers.
Therefore, they conclude that the RAS technique, being based on survey
data, provides superior results both in terms of coefficients and multipliers. However,
in the absence of survey material, they note that SLQ is the best way to derive
a regional I-O table.
Butterfield and Mules (1980) test performances of three methods: RAS, RAS
applied to the entire table and a naïve method consisting of adjusting national
flows so that their totals agree with sectoral outputs. They compare a Western
Australia survey table with three tables estimated from Australian table using the
previously mentioned methods on a column by column basis. For this objective,
they adopt a specific testing procedure. First, they make a non-parametric test to
see if there is consistent over- or under-estimation. Then, they perform a test
based on regression analysis. Finally, they use both the chi-square contingency table
test of size distribution of coefficients and mean and mode of absolute distance
measure (including standardised absolute difference). They find that all
methods produce acceptable estimates for larger coefficients rather than smaller
coefficients. However, RAS performs better than the others. They also compare
output multipliers using the mean absolute difference (MAD) and Pearson’s correlation
coefficient. They find that RAS is superior to “full” RAS and the naïve
method, again.
Eskelinen and Suorsa (1980) evaluate performances of SLQ, CILQ and an iterative
procedure based on the knowledge of regional aggregate data, comparing
these methods with a 1970 survey input-output table of the North Karelian in
Finland. Measuring distances among matrices by the ratio between the sum of the
absolute values of the deviations of individual flows and survey-based total flows,
they find that SLQ performs better, followed by the iterative procedure and CILQ.
Comparing matrices also on a row and a column basis, they conclude that: (a)
non-survey methods do not systematically overestimate the local input use; (b) the
SLQ deviates from the survey-based table less than the other non-survey methods;
96

Performances of Regionalization Methods
(c) distances among methods are smaller than those existing between non-surveybased
and survey-based tables.
Sawyer and Miller (1983) compare the 1972 Washington survey-based table
with regional tables derived from the 1967 US table using SLQ, SDP and RAS.
They employ the mean absolute difference (MAD), the mean absolute difference
as a percentage of the mean coefficient and the mean absolute relative difference
(MARD) as comparison measures. First, they find that RAS provides the best estimates
of direct coefficients, inverse coefficients and type I and II value-added
multipliers. Second, both SDP and SLQ tend to overestimate multipliers. Third,
SDP provides less accurate estimates than SLQ. Fourth, estimates that are much
nearer the survey-based ones are achieved applying, prior to the aggregation, a
version of SLQ adjusted by the “fabrication effect” and balanced by using survey
export data. In addition, using survey-based coefficients of value added, more improvements
in terms of multipliers are gained. They conclude that, in order to derive
as reliable estimates as those obtained by hybrid methods like RAS, the SLQ
approach has to be applied at an high level of sectoral disaggregation, using reliable
estimates of value added for calculating multipliers and a version of SLQ adjusted
to take account of the regional fabrication effect and balanced by reliable
export and import data.
Willis (1987) compares two survey-based input-output tables of Wales (dated
from 1968) and Staffordshire (dated from 1977) with non-survey tables obtained
by using three methods: the original version of GRIT without superior data, GRIT
with superior data related to imports and GRIT with superior data related to final
demand (in this case adjustment is made by RAS). From comparisons, it emerges
that the versions of GRIT incorporating superior data produce average multipliers
which are closer to survey-based multipliers than those obtained by GRIT without
exogenous information. However, at a sector level, the insertion of superior data
does not improve the closeness to survey-based multipliers. In all cases, there is a
general tendency to overestimate impacts, which is attributed to the use of SLQ
within GRIT.
Strassoldo (1988) mentions a study of Mogorovich (1987) who compares a
1978 direct matrix constructed for the Italian Toscana region with various matrices
constructed by indirect methods: RAS, SDP, PLQ, SLQ, RND, CILQ. From
this study, it emerges that RAS is superior, but this superiority is not very evident.
SDP outperforms location quotients, ranking on the second position. Among location
quotients, PLQ is the best whilst CILQ ranks on the last position. Indices that
are used as comparison tools are mean absolute difference, chi-square statistic, information
content, correlation coefficient.
Harris and Liu (1998) compare the survey-based 1989 Scottish table with tables
obtained by using SLQ and a hybrid method that substantially coincides with
the RAS technique. Survey data are borrowed from the survey-based table and are
97

Performances of Regionalization Methods
concerned with total intermediate inputs, total sales, imports, exports and income
from employment. They show that the hybrid approach produces estimates of exports
which are 0.9% smaller and estimates of imports which are 2.4% larger than
the survey-based figures. The LQ approach produces estimates that are 38% and
49% of the survey-based figures, respectively. They deduce that the LQ approach
considerably overestimates interindustry transactions and subsequently any estimates
of multipliers. By using regression techniques to compare the different tables,
they find that the hybrid method reproduces the survey-based table much
better than the LQ approach. Ultimately, they conclude that the hybrid approach
has to be preferred.
Gilchrist and Louis (1999) compare coefficients, obtained applying RAS and
an extended version of RAS (TRAS) to a national table, with 1984 Canadian provincial
I-O tables. Using regression test, they find that TRAS, incorporating more
information than that used by RAS, demonstrates to be superior.
Flegg and Webber (2000), testing the hypothesis on the importance of regional
specialization in deriving regional coefficients, compare coefficients derived by
adjusted (taking account of regional specialization) and unadjusted (original) versions
of FLQ, CILQ and SLQ to survey-based regional coefficients calculated
from the input-output table for Scotland in 1989. For this purpose, they apply
some statistical tests to measure differences between matrices (mean weighted error,
mean weighted absolute error, mean weighted relative error, weighted chisquare
statistic, bias and variance of both mean weighted error and mean weighted
relative error). They show that FLQ outperforms both SLQ and CILQ but it also
emerges that the standard version of SLQ almost always outperforms the standard
version of CILQ.
4.4 Teaching from empirical studies
Results from empirical evidence cannot be considered conclusive since empirical
studies are still very few and the existing ones take only account of different
batteries of indirect methods and different measures of comparison. However, a
common result from most of these studies is that the RAS technique is the best
method to simulate a regional I-O table derived from survey, albeit it is not certain
that RAS is the best one among hybrid methods. Instead, of purely non-survey
methods, SLQ seems to be the most effective method, despite there is not full
judgement unanimity among studies. FLQ is a promising variant since it demonstrated
to outperform the traditional location quotients. Nevertheless, little work
has been done to validate the value of the parameter on which FLQ is based and
therefore the value suggested by the authors could not be appropriate for all regions.
98

Performances of Regionalization Methods
Anyway, the fact that empirical evidence has shown that the simplest version
of location quotients performs better than other methods requiring more information,
like Supply-demand pool, is an advantage. This is because data requested by
traditional location quotients (employment data) are often the only data that are
available at the highest level of sector disaggregation and at both regional and national
level.
99

5 An Analysis of Impact of CAP Reform Using
Different Regionalization Methods
5.1 Introduction
The main objective of this chapter is to evaluate impact sensitivity to the approach
used for deriving regional tables. In other words, the aim is to verify how
different or similar results in terms of impact are when the I-O model, used to estimate
impact, is constructed on the basis of different regionalization methods.
Impact to be evaluated will be impact in terms of output, income and employment
on the overall economy of the Marche region coming from the Common Agricultural
Policy (CAP) during the period 2000-2006. In this regard, policy analysis
will be limited to the effects from intervention-price reduction on the cereal market.
The analysis will be articulated in the following way. In the second paragraph,
importance of the cereal market in the Marche region will be pointed out. The
third paragraph will be dedicated to illustrate briefly the history of CAP focusing
on policy reform effects of Agenda 2000 and the mid-term review on the cereal
market. In the forth paragraph, methodology to assess policy impact will be described.
Lastly, in the sixth paragraph, an analysis of impact sensitivity will be
carried out.
5.2 The situation of the cereal sector in the Marche region
From the agricultural standpoint, the Marche region has been defined as a region
based on cereals (Solustri, 2000). In effect, on the basis of 1999 data, it
emerges that cereals absorb the highest share of land used for agricultural purposes
and show a high index of specialization compared to the national situation.
Within cereals, durum wheat represents the most important crop (Tab. 5.1).

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
102
Tab. 5.1 – Land distribution among different crops, 1999
Marche Italy
Hectares (000) % Hectares (000) %
Marche SI*
Cereals 236 40.7 3,959 23.3 1.8
Soft wheat 40 6.9 699 4.1 1.7
Durum wheat 137 23.7 1,691 10.0 2.4
Maize 15 2.6 1,028 6.1 0.4
Forage 201 34.8 6,716 39.5 0.9
Industrial crops 77 13.4 791 4.7 2.9
Sugar beet 42 7.2 283 1.7 4.4
Soya 0 0.1 247 1.5 0.0
Sunflower 35 6.1 210 1.2 4.9
Rapeseed 0 0.0 51 0.3 0.0
Potatoes and vegetables 20 3.5 1,043 6.1 0.6
Permanent crops 39 6.8 3,368 19.8 0.3
TOTAL 574 100.0 16,984 100.0 1.0
* SI = index of specialization obtained as ( S
R
i
tares of land used and i is the type of crop
S
R) N ( S
i
S
N)
where R is the Marche region N is Italy, S expresses hec-
Source: Author’s elaboration on data from ISTAT
Factors that have contributed to the development of cereals in the Marche region
can be identified in the following ones: (a) climatic and morphological conditions;
(b) the European agricultural policy by means of compensations and price
support; (c) wide presence of structures for trading and processing cereals
throughout the regional territory; (d) production of quality durum wheat giving
regional farmers a strong competitive advantage in satisfying requirements from
processing industry in comparison with other regions (Regione Marche, 2000).
However, in spite of emphasis given to cereals by regional studies, cereals are
not the only prevailing crop. In fact, in terms of specialization related to both used
land and production, industrial crops are the most important ones. Among industrial
crops, sugar beet and sunflower are the predominant ones (Tab. 5.1, Tab.
5.2). Moreover, in terms of weight of production, livestock has the highest share
on total output. In any case, both in terms of specialization and weight of production,
cereals represent the second more important crops.

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Tab. 5.2 – Production at basic prices*, 1999 (billion of Lire)
Marche Italy
Output % Output %
Marche SI**
Cereals 439 29.0 8,820 14.9 1.9
Soft wheat 71 4.7 1,448 2.4 1.9
Durum wheat 262 17.3 2,173 3.7 4.7
Barley 63 4.1 599 1.0 4.1
Rice 0 0 906 1.5 0.0
Local maize 0 0 2 0.0 0.0
Hybrid maize 43 2.8 3,693 6.2 0.5
Potatoes and vegetables 193 12.7 8,431 14.2 0.9
Industrial crops 226 14.9 2,753 4.6 3.2
Sugar beet 156 10.3 1,129 1.9 5.4
Tobacco 1 0.1 632 1.1 0.1
Sunflower 68 4.5 377 0.6 7.1
Soya 1 0.1 614 1.0 0.1
Permanent crops 186 12.3 15,563 26.3 0.5
Livestock 470 31.1 23,659 39.9 0.8
TOTAL*** 1,513 100.0 59,226 100.0 1.0
* Production at basic prices includes subsidies and excludes taxes
** SI = index of specialization obtained as ( P
R R N N
i
P ) ( P
i
P ) where R is the Marche region N is Italy, P is production
and i is the type of crop
*** Forage is not considered
Source: Author’s elaboration on data from ISTAT
From 1990 to 1999, hectares destined to cereals kept rather constant, although
a slight decrease can be noticed (Fig. 5.1). The biggest decrease occurred in the
period 1994-1996, after that a slight recovery happened. This latter was probably
due to the increasing diminution of aids to oilseeds, which led farmers to move to
cereals. The trend noticed for cereals did not regard all crops within cereals uniformly.
The land used for durum wheat increased whilst that destined to soft
wheat considerably decreased. This opposed tendency may be explained by advantages
related to farming durum wheat which benefited from both bigger compensations
than other cereals and a favourable price trend. As for the other cereals,
a small increase of barley-used land occurred, whilst the land used for the remaining
cereals kept nearly constant.
In terms of production, during the 90s, the trend of the cereal output was about
constant until 1994 (Fig. 5.2). From 1994 to 1997, output decreased, after that a
sudden rise occurred. This increase depended on the growth of durum wheat and,
103

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
to a less extent, of maize and barley, which was partly balanced by a decrease in
soft wheat.
As far as yield is concerned, the yield of all cereals, in spite of fluctuation
mostly regarding sorghum and maize, kept constant (Fig. 5.3). Maize and sorghum
showed the highest yields, whilst oat registered the lowest one. Durum
wheat, barley, soft wheat were instead characterized by similar and constant yield
values. This stagnation of yields can be related to policy orientation to supply restraint.
Definitively, from the analysis of weight on local production, land used for agricultural
purposes and specialization indices, it emerges that the cereal production
holds a non-neglectable importance in the Marche region. Accordingly, any
policy or policy change aimed at influencing or regulating this market can produce
significant effects.
Hectares (000)
104
300
250
200
150
100
50
0
Fig. 5.1 – Trend of the land used for cereals, Marche, 1990-1999
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Source: Author’s elaboration on data from ISTAT
Cereals
Durum wheat
Soft wheat
Barley
Oat
Maize
Sorghum

Billion of Lire
quintals per hectare
An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Fig. 5.2 – Trend of the cereal output, Marche, 1990-99 (1990 constant prices)
600
500
400
300
200
100
90
80
70
60
50
40
30
20
10
0
0
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Source: Author’s elaboration on data from ISTAT
Fig. 5.3 – Trend of the cereal yield, Marche, 1990-99
1990 1991 1992 1993 1994 1995 1996 1997 1998 1999
Source: Author’s elaboration on data from ISTAT
Cereals
Soft wheat
Durum wheat
Barley
Maize
Durum wheat
Soft wheat
Barley
Maize
Oat
Sorghum
105

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
5.3 The Common Agricultural Policy (CAP) and the cereal market: a
brief overview
5.3.1 From CAP’s institution to the Mac Sharry Reform
The Common Agricultural Policy was instituted in the 1960s for different reasons:
reducing disparities among European countries, increasing agricultural productivity,
improving farmers’ incomes, stabilising markets, guaranteeing autosupplying
in order to reduce dependence from outside and assuring reasonable
prices to consumers (Vieri, 1994). These objectives were substantially pursued by
protectionist mechanisms and aids incorporated in prices and proportional to production.
Coupling of support with production led to accumulation of surplus, distortion
effects on internal and international markets and increasing disparities
among farmers since proportional support mainly favoured bigger farmers in addition
to landowners. Moreover, support was concentrated on high-capital-intensity
production (such as cereals and industrial crops) leading farmers to increase production
of these commodities. Even regions which were not naturally suited to
this kind of production (the Italian Marche region is one of them) were pushed to
specialize in capital-intensive production systems, raising environmental problems
and increasing regional disparities (Bonfiglio, 2000).
The problems related to expenses due to surplus, market distortion and a manifest
incoherence with objectives of Rome Treaty were the main reasons for the introduction
of Mac Sharry reform in 1992. This reform significantly reduced price
support but, to compensate consequent income losses, compensations per hectare
were introduced, distinguishing a regime for small producers, who could directly
accede to aids, from a regime for big producers, for whom accessibility to aids
was subordinated to retirement of part of used land from production (set-aside).
Unfortunately, the reform did not produce the desired effects. Compensations and
growth of international prices induced farmers to increase production of supported
commodities and expenditure related to surplus continued to rise.
5.3.2 Agenda 2000
In 1999, a further agricultural reform, contained within Agenda 2000, was introduced.
Agenda 2000 is an action programme whose objectives are to strengthen
Community policies and to give the European Union a new financial framework
for the period 2000-06 with a view to enlargement (European Commission, 1999).
It covers four main, closely related areas: the reform of the common agricultural
policy, structural policy reform, the pre-accession instruments and the new financial
framework.
106

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
The main objective of the agricultural reform is to continue the reform process
along the lines of the changes made in 1992, improving the European competitiveness
on markets, taking great account of environmental considerations, ensuring
fair income for farmers, simplifying legislation and decentralising the application
of legislation, improving food safety, strengthening the Union’s position in
the new round of WTO negotiations and stabilising agricultural spending.
These objectives are pursued by two measures: modifications of the common
organisations of the markets in wine, arable crops, beef and veal and milk and,
secondly, measures of a more horizontal nature (cross-compliance by which direct
aids are bounded to the respect of environmental requirements and decentralized
finance administration in favour of Member States).
As for cereals 16 , modifications are related to a reduction of intervention price
(guaranteed minimum price at which commodities are sold if market price goes
under a given level) by 15% in two equal steps of 7.5%, starting in the 2000/2001
marketing year, bringing it down from 119.19€ to 110.25€ per tonne in the
2000/01 marketing year and to 101.31€ per tonne from the 2001/02 marketing
year.
To compensate the loss of income caused by the reduction in the market support
prices, regionally differentiated compensatory payments are introduced. Their
amount is fixed at 58.67 € per tonne (multiplied by the historical regional reference
yield for cereals expressed in tonnes per hectare) in the 2000/01 marketing
year and at 63 € per tonne from the successive year marketing onwards. For Italy,
the historical reference yield for cereals has been increased and fixed at 3.9 tonne
per hectare. All Italian regions are divided in homogenous zones with different
yields for maize and other cereals. With reference to the Marche region 17 , the average
yield for maize is 6.5 tonne per hectare, while for other cereals is 3.7.
A special treatment is reserved to durum wheat in particular zones. It is established
that these crops benefit from a supplementary aid of 344.5 € per hectare in
traditional zones (the Marche region is included in this category) and from a specific
aid of 138.9 € per hectare in zones where the production is “wellestablished”
within the limit of the Maximum Guaranteed Areas (MGA). MGA
has been fixed at 1,646,000 hectares for Italy. Supplements are conditioned on the
use of certified seeds and on the respect of MGA. Overcoming of MGA leads to a
decrease of premium proportional to the entity of overcoming itself. The possibility
of dividing national MGA into regional areas is given to member states. Italy
has taken this possibility and a limit of 125,172 hectares has been established for
the Marche region. So, in the case of overcoming of national MGA, penalties are
16 Regulation (EC) no. 1253/1999.
17 Ministerial Act 4 th April 2000.
107

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
attributed to regions overcoming their limits after distributing surfaces, which do
not reach regional limits, among regions that do not respect these limits.
For all arable crops 18 , the reform establishes a rate of compulsory set-aside
fixed at 10% for all period of Agenda 2000. The percentage of land to be set aside
by producers is to be calculated as a proportion of their areas under arable crops.
The payment of direct aids is subject to the obligation of set-aside and is also
granted to area set aside. The average reference yield for calculating direct payments
related to set-aside in favour of farms of the Marche region is fixed at 3.9
tonne per hectare. No set-aside requirements are imposed on small producers
whose claim for area payments is below a certain level (equivalent to production
of 92 tonnes of cereals).
5.3.3 The Mid-term Review of Agenda 2000
In 2003, the Council of Agriculture Ministers of the European Union approved
a reform of Agenda 2000, known as Mid-term Review and based on the Commission
proposals. In line with the overall objectives of Agenda 2000, this reform
will be introduced from 2004 and 2005, completing the reform process started
with Agenda 2000 (European Commission, 2003). The key elements of the reform
are:
• Helping EU farmers to become more market-oriented and competitive on
markets, while receiving reasonable income support. This is done by
i. the introduction of a single payment scheme for EU farmers, independent
(“decoupled”) from production. This payment will replace
most of the direct aid payments to farmers currently offered. The
relevant amount will be calculated dividing direct aids which a
farmer received in a reference period (from 2000 to 2002) by the
number of hectares which gave rise to this amount (including forage
area) in the reference years. The single payments scheme will come
into operation in 2005, but Member States may delay implementation
up to 2007. But, by 2007 at the latest, all Member States should
introduce the single payment scheme. Member States may decide to
maintain a proportion of direct aids to farmers (partial decoupling),
in the case in which there might be disturbance to agricultural markets
or abandonment of production as a result of the move to the single
payment scheme.
ii. linking the single payment scheme to environmental and quality
conditions (“cross-compliance”).
18 Regulation (EC) no. 1251/99.
108

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
• Strengthening rural development policy by shifting more money to this
policy; by introducing new measures to promote environment and quality;
by helping farmers to meet new EU standards; by a reduction in direct
payments (“modulation”) for bigger farms to finance the new rural development
policy.
• Revising the market support parts of the CAP by modifications in the intervention
mechanism of sectors of structural imbalance (butter, rye and
rice); by adjusting support mechanisms in other sectors (durum wheat,
drying aids, starch potatoes, dired fodder, nuts); by a mechanism for financial
discipline ensuring that the farm budget fixed until 2013 is not overshot.
As for the cereal sector, the Mid-term Review establishes that intervention
price and direct payment of 63€ per ton will be retained, but monthly increments
will be reduced by 50%. Rye has been excluded from the intervention system but
Member States whose rye production is very significant can receive an additional
10% of the modulation money raised in the Member State concerned in order to
assist, within the framework of rural development measures, rye producing regions.
From 2005, or at the latest from 2007, a single payment scheme will be applied.
However, Member States may retain 25% of the single payment scheme or,
alternatively, up to 40% of the supplementary durum wheat aid in order to continue
the existing coupled per hectare payments up to the above-mentioned percentage
levels.
With reference to durum wheat, the supplement in traditional production zones
will be paid independently from production from 2005. As mentioned before,
there is the possibility of linking 40% of this premium to production. It will be
fixed at 313€/ha in 2004, 291€/ha in 2005 and 285€/ha from 2006 onwards and
included in the single payment scheme. The specific aid for other regions where
durum wheat is supported, currently set at 138.9 €/ha, will be phased out. The cuts
will be implemented over three years, starting in 2004 (93€/ha in 2004, 46€/ha in
2005 and zero thereafter). From 2004/05, in order to improve quality of durum
wheat, it has been decided to introduce a special premium of 40€/ton to be given
to farmers in traditional zones. The premium will be conditioned on the use of
certified seeds.
As for set-aside, farmers will receive set-aside entitlements calculated on the
basis of historic references. Set-aside entitlements will be activated only if accompanied
by an eligible hectare put into set-aside. Set-aside areas must cover at
least 0.1 hectares in size and be at least 10 metres wide. For justified environmental
reasons a width of 5 metres may be accepted.
109

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Definitively, the mid-term review does not modify decisions taken with
Agenda 2000 with regard to intervention price on the cereal market. A value of
101.31 € per tonne is maintained from 2003 to 2006.
110
Tab. 5.3 – Summary of Agenda 2000 and the Mid-term Review – the cereal market
2000 2001 2002 2003 2004 2005* 2006
Intervention price (Euro per tonne) 110.25 101.31 101.31 101.31 101.31 101.31 101.31
Direct Payments (Euro per tonne) 58.67 63.00 63.00 63.00 63.00 63.00 63.00
DURUM WHEAT
Aid for traditional zones (Euro per ha) 344.50 344.50 344.50 344.50 313 291 285
Specific Aid for other zones (Euro per ha) 138.90 138.90 138.90 138.9 93 46 0
Special quality premium (Euro per tonne) - - - - 40 40 40
Set Aside (Euro per tonne) 58.67 63.00 63.00 63.00 63.00 63.00 63.00
* In 2005 or at the latest in 2007, direct payments will be decoupled and included in a single payment scheme.
Alternatively, a Member State may decide either keeping 25% of these payments coupled or keeping 40% of the
supplement granted to durum wheat in traditional zones linked to production.
Source: European Commission, 1999, 2003
5.4 Methodology to assess policy impact
To estimate policy impact produced by a reduction of intervention price in the
cereal market for the period 2000-2006 in the Italian Marche region, we will adopt
two models to be applied sequentially: an econometric model and an I-O model.
The econometric model based on a profit function will be developed to evaluate
the farmers’ responsiveness in terms of production levels to price changes. By
means of price elasticities calculated by econometric model, it will be possible to
assess for the period 2000-2006, direct impact on farmers’ output, through price
reduction.
Estimation of the overall impact on the regional economy induced by policy
will be made by a modified version of the traditional I-O model (the closed mixedvariable
I-O model) in order to evaluate total effects (indirect, direct and induced)
from variation of the cereal output 19 .
19 It would have been also possible to estimate impact generated by total payments to farmers in terms of direct
payments and supplementary aids. One way to model by I-O analysis effects produced by total payments
is to translate payments into corresponding consumption. This could be made multiplying total payments (income)
by ratio between consumption and income (deduced from regional I-O tables) and distributing proportionally
the resulting consumption among sectors. Consumption is a component of final demand in I-O
framework. Therefore, impact from payments can be assessed as impact from final demand variation using a
traditional I-O model. This impact in terms of output, income and employment would have been positive and

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Overall impact will be determined starting from 16 different regional I-O tables
constructed by various regionalization methods, both non-survey and hybrid
methods. Then, in order to analyse impact sensitivity using different approaches
for regionalising tables, methods will be compared in terms of both overall impact
and impact by sector in terms of output, income and employment. Towards this
aim, besides classical analyses (range, variability and ranking), two well-known
statistical procedures will be applied: factor analysis and the multidimensional
scaling procedure.
5.4.1 Farmers’ responsiveness to price variations
The objective is to evaluate the responsiveness of farmers towards variation of
intervention price related to cereals. In other words, we intend to verify how
farmers react in terms of productive choices following to a reduction of institutional
prices. For this objective, following the example of Doyle et al. (1997), we
will develop a multi-input and multi-output model of profit maximization aimed
at deriving price elasticities of demand for inputs and supply of outputs for farms
of the Marche region.
5.4.1.1 Theoretical economic model
The basis of the methodology used is the duality existing between the profit
function, the transformation function and the production possibility set. This duality
is widely discussed in the literature (see for instance Diewert, 1974 and Lau,
1978) and will only be briefly described here. The profit function Π is defined in
terms of variable input and output prices and levels of fixed input, i.e.:
( , )
Π= f PZ
(5.1)
Where P and Z are vectors of prices and fixed inputs, respectively. The basic
behavioural assumption required for using the profit function to model production
possibilities is that farmers are profit maximisers. In addition, it is required that
output and variable input markets are competitive (i.e. prices are exogenous). If
the profit function satisfies certain regularity conditions, it is dual of the transformation
function and its parameters contain sufficient information to describe the
farm’s production technology at profit maximising points in the production possi-
would have compensated negative impact produced by a decrease in output of the cereal sector. Since our
objective was not to model the whole policy but to evaluate impact sensitivity using different approaches for
regionalizing I-O tables and considering that the implementation of a further and different model could have
complicated the reading of results, we decided to omit impact analysis related to payments. In any case, this
part will be object of further research.
111

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
bility set. The regularity conditions are that the profit function be non-negative,
continuous, twice differentiable, linear homogenous in prices and fixed inputs,
convex in prices, concave in fixed inputs, increasing in output prices, decreasing
in input prices and non-decreasing in fixed inputs (Diewert, 1974).
Output supply and input demand functions, commonly referred to as netput functions
(Varian, 1984), can be obtained by differentiating the profit function with respect
to netput prices (Hotelling’s Lemma):
112
∂Π
Qi= ( P, Z)
∂P
i
(5.2)
where Q i are positive for outputs and negative for variable inputs. After choosing
a functional form for the profit function, which satisfies the regularity conditions
for duality between the profit and transformation functions, the parameters of
equations (5.1) and (5.2) can be empirically estimated. These estimated parameters
can be used to derive the elasticities describing production relationships of
multiple-input, multiple-output farms.
5.4.1.2 Functional form
The functional form used should not impose arbitrary restrictions on the parameters
which describe production technology. The so-called flexible functional
forms meet this requirement. A function, that is a second-order Taylor series approximation
to an arbitrary function, is flexible. In this study, the transcendental
logarithmic functional form (Christensen et al., 1973) is used. This is one of the
most commonly used flexible functional forms for the profit function.
It takes the following form:
1
ln Π ( PZ , ) = + ln P+ (ln P)(ln P)
+
α0 v1 ∑αi i= 1
i
v1 v1
∑∑αij
2 i= 1 j=
1
i j
v2 ∑βkln v1 v2
Zk + ∑∑βik(ln
Pi)(ln Zk)
+
k= 1 i= 1 k=
1
v2 v2
1
+ ∑∑γ
hk (ln
2 h= 1 k=
1
Zh)(ln Zk)
where 1 v denotes the total number of inputs and outputs, while 2
number of quasi-fixed inputs.
(5.3)
v is the total

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
In order that the profit function is dual of the production function, equation (5.3)
must satisfy the following conditions:
v
1
∑
i=
1
v
1
∑
j=
1
v
1
∑
i=
1
α = 1
i
α = 0,
β
ij
ik
=
0,
∀i
∀k
These conditions impose linear homogeneity in netput prices. This implies that
profit will remain unchanged if all variable inputs and output prices increase by
the same proportion.
For the profit function to be twice differentiable in netput prices the following
symmetry restrictions must apply:
α = α
ij
ji
∀i,
j
Differentiating the profit function with respect to netput prices, we obtain a set of
output supply and input demand equations:
∂ln Π ∂Π
=
∂ln Pi ∂Pi i = 1, …,
v
Pi QP i i = = Si Π Π
v1 v2
= αi + ∑αij(ln Pj) + ∑βik(ln
Zk)
j= 1 k=
1
1
(5.4)
Where S i is share of netput i in total profit; it is positive for outputs and negative
for inputs and it results that
v1
∑
i=
1
S = 1.
i
113

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
5.4.1.3 Elasticities
The own-price and cross-price elasticities of demand for inputs and supply of
outputs can be derived from equation (5.4) (Sidhu and Baanante, 1981). Equation
(5.4) can be rewritten as:
114
Π⎛∂ln Π⎞
Qi = ⎜ ⎟
Pi ⎝∂ln Pi
⎠
Thus,
⎛∂ln Π⎞
lnQi = ln Π− ln Pi
+ ln ⎜ ⎟
⎝∂ln Pi
⎠
The price elasticities of demand for inputs or supply of outputs are therefore:
∂lnQi ∂ln Π ∂ln ⎛∂ln Π⎞
ηii
= = − 1+
⎜ ⎟
∂ln Pi ∂ln Pi ∂ln Pi ⎝∂ln Pi
⎠
αii
= Si
− 1+
S
The cross-price elasticities are:
i
∂lnQi ∂ln Π ∂ln ⎛∂ln Π⎞
ηij
= = + ⎜ ⎟
∂ln Pj ∂ln Pj ∂ln Pj ⎝∂ln Pi
⎠
αij
= S j +
S
i
(5.5)
(5.6)
Where j
i ≠ . The estimated elasticities are short-run in nature, because they are
derived assuming that some of the factor inputs are fixed. Thus, the predicted
price responses do not take into account the ability of farms to adjust the fixed inputs
in the long run.

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
5.4.1.4 Estimation
Since the sample of firms used for estimation is made up of cross-section and
time-series data 20 , to take account of possible structural shifts, a time trend (T ) is
incorporated in the profit function. Ultimately, for a situation involving n outputs
and inputs and m quasi-fixed factors, the translog profit function estimated takes
the following form:
1
ln Π = + ln + (ln )(ln ) +
n n n
t α0 ∑αiPit , ∑∑αijPit
, Pjt
,
i= 1 2 i= 1 j=
1
m n m
∑βkln Zk, t + ∑∑βik(ln
Pi, t)(ln Zk,
t)
+
k= 1 i= 1 k=
1
m m 1
+ ∑∑γ
hk (ln Zh, t )(ln Zk,
t ) +
2 h= 1 k=
1
n
1 2
+ δtT + δttT + ∑εi,
t(ln Pi, t)
T +
2
i=
1
m
1
+
2
∑
k=
1
ε (ln Z ) T
kt , kt ,
The corresponding share equations are:
n m
it , = αi+ αij(ln jt , ) + βik(ln kt , ) + εit
,
j= 1 k=
1
S P Z T
(5.7)
∑ ∑ (5.8)
We estimate required parameters from the system of share equations. Since the
profit shares sum to one, the variance covariance matrix of the system of share
equations will be singular. One of the share equations must be dropped in order to
make estimation of the parameters of the system of equations possible. The parameters
of the excluded equation can be obtained from the symmetry and homogeneity
restrictions which are imposed on the system, while the others can be derived
by maximum likelihood estimation (Berndt, 1991). The cross-equation
symmetry restrictions and the possibility that the error terms of different share
equations may be correlated make the Zellner SURE estimator the most appropriate.
In order to make the estimates invariant to the choice of equation excluded, an
20 The model here adopted is a pooled model which does not take account of differences among firms. Further
models able to take account of these differences could be employed. However, our objective was not to study
problems of efficiency. Therefore we decided to apply the TOTAL regression model.
115

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
iterative Zellner SURE procedure (known as IZEF) is used (Maietta, 2000).
Kmenta and Gilbert (1968) have shown that the parameter estimates from the iterative
Zellner SURE procedure converge to maximum likelihood estimates.
5.4.1.5 Data
The data used are taken from the Farm Accounting Data Network (FADN) 21 .
Data refer to a constant sample made up of 573 regional farms oriented to production
of cereals and industrial crops; the period covered is 1990-1998. The total
number of observations is thus 5,157. For each farm, two only outputs are considered:
cereals and industrial crops, being the prevailing crops in the region (see par.
5.2). The variable inputs are: fertilizers, pesticides and seeds. We only considered
inputs used for producing the two outputs considered. The quasi-fixed factors are:
land, capital and labour. Land was expressed in terms of quantity of hectares
farmed, while labour was expressed in terms of hours worked. For the capital inputs,
it was assumed that the service flow from the stock of capital was proportional
to the stock, so the stock values (net of depreciation) were used as proxies
for the service flow (Higgins, 1986). All fixed costs were expressed in terms of
indices having the 1990 values as bases.
Profit was defined as the value of output minus the value of variable inputs.
The shares of profit were the values of the outputs and inputs divided by profit.
Regional variable input prices are not available in the FADN. So, they were
taken as national indices of prices having 1990 as a base. Instead, regional output
prices were calculated as Paasche indices using data from INEA 22 .
5.4.1.6 Results
Parameter estimates of share equations are shown in Tab. 5.4. The excluded
share equation was that of seeds. Therefore, the relevant parameters were estimated
from the symmetry and homogeneity conditions.
Elasticities of output supply and input demand were calculated from parameters
estimated using the sample mean 23 (Tab. 5.5). Results obtained are consistent
21 It needs to remind that the sample of farms contained into FADN is neither random nor statistically representative.
Accordingly, any estimate obtained using FADN as data source may be affected by this distortion
(Sckokai, 2001).
22 Used output prices may reflect both intervention price (when market prices are below intervention price)
and market prices (when the latter are above intervention price). For this reason, calculated own price elasticities
with respect to the cereal output do not exactly represent farmers’ responsiveness to price intervention
and they could overestimate impact from intervention-price reduction (De Muro and Salvatici, 2001).
23 From this model, further and interesting information can be derived (i.e. elasticities with respect to quasifixed
factors). However, we were not interested in extracting all possible information. Therefore, we decided
to present only information related to output and variable inputs.
116

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
with the assumption of profit maximising behaviour since own price elasticities of
output supply are positive and own price elasticities of input demand are negative
24 .
Demand for inputs is particularly elastic, especially for pesticides, while supply
of products is not very elastic. Among products, the cereal output would seem to
react more than industrial crops. Expected results are related to consequences on
inputs deriving from price variations of outputs. In fact, it can be noted that an increase
in prices of outputs will generate an increase in quantities of inputs used.
This should be due to a rise in the demand of inputs necessary to fulfil the higher
level of production. A further interesting outcome concerns the relationship between
cereals and industrial crops. It emerges that a variation of cereal (industrial
crop) prices leads to a variation of opposite sign of output of industrial crops (cereals).
This points out an high capability of farmers in adjusting production
choices according to market conveniences. This flexibility is partly explained by
the characteristics of outputs considered since cereals and industrial crops mostly
require the same technology.
Towards our aims, the figure in which we are interested is concerned with cereals.
The table shows that elasticity of quantity of cereals with respect to their
prices is 0.25. Assuming invariance throughout period 2000-2006, we will use this
elasticity to estimate the cereal output variation due to price reduction established
by CAP.
24 The reliability and the meaningfulness of results can also be assessed by checking if the estimated parameters
of the share equations satisfy the symmetry, convexity, monotonicity and homogeneity conditions (Higgins,
1986). If it is true then the assumption that farmers are profit maximisers and the profit function is continuous
and twice differentiable will be true. Monotonicity will be satisfied if the predicted output shares are
positive and input shares are negative. This condition was checked at the average levels of prices and fixed
inputs and was satisfied. Symmetry and linear homogeneity were tested using F statistic. At 1% level of significance,
both assumptions were not rejected. Convexity is satisfied if the bordered Hessian matrix of second
order derivatives of the profit function with respect to netput prices is positive semi-definite. This was
checked at the average levels of prices and fixed inputs and was found to be true.
117

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
118
Tab. 5.4 – Parameter estimates of share equations
Cereals Industrial crops Fertilizers Pesticides Seeds*
Intercept -0.4645 -1.2946 1.3480 0.6787 0.7323
(18.2) (3.5) (63.0) (30.4) -
Output prices
Cereals 0.0128 0.3678 -0.2067 0.0538 -0.2277
(2.2) (2.5) (3.7) (4.4) -
Industrial crops 0.3678 0.0535 -0.1926 -0.1184 -0.0033
(2.5) (3.4) (5.5) (8.0) -
Input prices
Fertilizers -0.2067 -0.1926 -0.3030 0.1713 0.5310
(3.7) (5.5) (2.3) (4.1) -
Pesticides 0.0538 -0.1184 0.1713 -0.1354 -0.2419
(4.4) (8.0) (4.1) (5.8) -
Seeds* -0.2277 -0.0033 0.5310 -0.2419 -0.0581
- - - - -
Fixed inputs
Land -0.3215 0.0655 0.2174 -0.0028 0.0415
(2.2) (3.6) (1.9) (2.4) -
Labour -0.2722 0.0061 0.2183 0.0201 0.0277
(2.0) (3.5) (2.1) (1.8) -
Capital 0.0752 -0.0517 -0.0283 0.0060 -0.0011
(0.7) (4.0) (0.4) (0.7) -
Trend 0.0277 0.0041 0.0231 0.0034 -
(5.8) (4.5) (0.7) (2.4) -
2
R
Absolute t values are in parentheses
0.27 0.28 0.88 0.45 -
* Seeds coefficients are obtained from the symmetry homogeneity restrictions
Source: Author’s elaboration on FADN data, 1990-98
Tab. 5.5 – Elasticities of outputs and variable inputs with respect to prices changes, calculated
on a sample of farms of the region Marche, Italy
Quantity
Prices
Cereals Industrial crops Fertilizer Pesticides Seeds
Cereals 0.25 -0.08 0.57 0.07 0.44
Industrial crops -0.04 0.17 0.77 0.34 0.26
Fertilizer -1.76 -1.01 -0.64 -0.54 -0.59
Pesticides -0.77 -1.56 -0.90 -1.30 0.87
Seeds -1.14 -0.54 -0.48 0.83 -1.03
Source: Author’s elaboration on FADN data, 1990-98

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
5.4.2 A closed mixed-variable I-O model
In spite of some restrictive assumptions (Gerking et al., 2001), I-O analysis
still represents an effective analytical tool to quantify the impact on output and, by
a little extension, on income and employment, resulting from a change in the final
demand relative to a given sector. I-O methodology permits, in fact, to capture not
only the effects produced in the sector involved by final demand variation but also
those induced by backward linkages among sectors (Leontief, 1966).
However, our aim is to evaluate the effects generated by a change in output
whilst the traditional Leontief model is not able to analyse this kind of effects
unless a change in output is converted into a corresponding change in final demand
using sectoral multiplier (Bonfiglio, 2002a). Models that were conceived to
study impact from output variation are the mixed-variable I-O model (Miller and
Blair, 1985) and the mixed-variable SAM-based model (Roberts, 1994) which is
an extension of the former. Both allow analysing the effects induced by a variation
of output, treating the latter as a final demand component, i.e. exogenous to
the system. Compared to the mixed-variable I-O model, the mixed-variable SAMbased
model is able to measure also induced effects since household sector is endogenous
to the system. However, as this model was defined using a SAM as a
reference base, it could not be directly used.
Since our analysis is oriented to the mid-long term and in consideration of the
importance of household sector in regional I-O models (Costa, 1973), we decided
to modify the mixed-variable I-O model closing it with respect to household sector.
The resulting model, which can be called closed mixed-variable I-O model, is
conceptually very similar to the approach suggested by Roberts but it slightly differs
from this latter in terms of construction.
To illustrate the model, consider an economy with n + 1 sectors: n productive
sectors and one endogenous household sector. The basic balance equations of this
system can be written as:
⎧(1 − a11) X1+ …( − ) a1jX j + …+
( −) a1nXn − k1y = Y1
⎪
⎪…
⎪
⎪−
aj1X1 + …+ (1 − ajj) X j + …+
( −) ajnXn − kjy = Yj
⎨
⎪…
⎪− an1X1 + …+ ( − ) anjX j + …+
(1 −ann) Xn − kny = Yn
⎪
⎪− ⎩ hX + …+ ( − ) hX + …+
( − ) hX + (1 − h ) y = y
1 1 j j n n n+ 1
x
(5.9)
119

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
where a ij is the regional input coefficient (local products of sector i purchased as
inputs from sector j per unit of output of sector j ); k i is a consumption coefficient,
expressing the share of income expended by households for purchasing
commodity i ; h i is a labour income coefficient, expressing the share of labour
income on sector i ’s output; y is total labour income; h n+
1 is the share of labour
income paid by households for services offered by households themselves (i.e.
domestic services); y x is labour income paid by other institutions for services offered
by households; i X is sector i ’s output and Y i is final demand of sector i .
The last equation states that total income of households less the sum of returns
from factor earnings equals the exogenous income of households paid by other institutions.
The others simply present the accounting identity that gross output less
intermediate sales equals final demand for a commodity. In matrix notation, the
system (5.9) can be reformulated as follows:
120
⎡(1 −a11) −a1j −a1n −k1⎤
⎡X1⎤ ⎡Y1 ⎤
⎢
⎥ ⎢ ⎥ ⎢ ⎥
⎢ ⎥ ⎢
⎥
⎢ ⎥
⎢ −aj1(1 −ajj) −ajn −kj
⎥ ⎢X⎥ ⎢Y j j ⎥
⎢ ⎥ ⎢ ⎥ = ⎢ ⎥
⎢
⎥ ⎢ ⎥ ⎢
⎥
⎢ −a −a (1 −a ) −k
⎥ ⎢X⎥ ⎢Y ⎥
n1nj nn n n n
⎢ ⎥ ⎢ ⎥ ⎢ ⎥
⎢ −h1 −hj −hn (1 −hn+ 1)
⎥ ⎢ y ⎥ ⎢⎣ yx
⎥⎦
⎣ ⎦
⎣ ⎦
Now suppose that sector j is exogenous. The system (5.9) becomes:
(5.10)
⎧(1
− a11) X1+ …+ ( −) a1, j−1X j− 1 − a1, j+ 1X j+ 1 + …+
( −) a1nXn − k1y = Y1+ a1jX j
⎪
⎪…
⎪
⎪−
a X + …+ ( −) a X −Y − a X + …+
( −) a X − k y = −(1 −a
) X
⎨
⎪…
⎪−
an1X1 + …+ ( −) an, j−1X j− 1 − an, j+ 1X j+ 1+
…+
(1 −ann) Xn − kny = Yn + anjX j
⎪
⎪
⎩−
hX 1 1 + …+ ( −)
hj−1X j−1−
hj+ 1X j+ 1+ …+
( − ) hnXn + (1 − hn+ 1 ) y = yx + hjX j
j1 1 j, j−1 j− 1 j j, j+ 1 j+ 1
jn n j jj j
(5.11)

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
The solution of the system, in matrix notation, is thus:
⎡X1⎤ ⎡(1 −a11) 0 −a1n−k1⎤ ⎡ Y1+ a1jX j ⎤
⎢ ⎥ ⎢ ⎥ ⎢ ⎥
⎢
⎥
⎢ ⎥ ⎢ ⎥
⎢Y⎥ ⎢ −a (1 )
j1−1−a a j
jn −kj
⎥ ⎢− − jj X j ⎥
⎢ ⎥ = ⎢ ⎥ ⎢ ⎥
⎢ ⎥ ⎢
⎢
⎥
⎥
⎢X⎥ ⎢ −an1 0 (1 −ann) −k
⎥ ⎢ Y n
n
n + anjX ⎥ j
⎢ ⎥ ⎢ ⎥ ⎢ ⎥
⎣⎢ y ⎦⎥ ⎣⎢ −h1 0 −hn (1 −hn
1)
yx + hjX + ⎦⎥
⎢⎣ j ⎥⎦
−1
(5.12)
The system (5.12) represents the modified Leontief model, capable of investigating
the impact of an exogenous change in output of sector j . Three points deserve
to be illustrated. First, the inverse matrix of the modified model differs from
that of the traditional model. Second, the system suggests that in order to measure
the effects of a change in gross output of sector j , it is necessary to translate the
output effect into derived demand effects on the input and factor suppliers using
the vector of direct inputs coefficients. Third, final demand of the sector whose
output is exogenous is solved endogenously. Following the example of Miller and
Blair (1985), to make the relationships between exogenously determined variables
and endogenous variables more evident, the system can be rewritten as:
⎡X1⎤ ⎡Y1⎤ ⎢ ⎥ ⎢ ⎥
⎢
⎥
⎢ ⎥
⎢Y⎥ ⎢X j
j ⎥
-1
⎢ ⎥ = M N ⎢ ⎥
⎢ ⎥ ⎢
⎥
⎢X⎥ ⎢Y⎥ n
n
⎢ ⎥ ⎢ ⎥
⎢ y ⎥ ⎢⎣ yx
⎥⎦
⎣ ⎦
Where:
M
⎡(1 −a11) ⎢
⎢
⎢ −a
0
−1
−a1n
−a −k1
−k
⎤
⎥
⎥
⎥
⎢
⎢
−a
0
(1 −a )
−k
⎥
⎥
j1jn j
= ⎢ ⎥
n1nn n
⎢ ⎥
⎢⎣ −h1 0
−hn (1 −h
n+
1)
⎥⎦
(5.13)
121

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
122
⎡1 a1
j 0 0⎤
⎢
⎥
⎢ ⎥
⎢0 −(1 −ajj)
0 0⎥
N = ⎢ ⎥
⎢
⎥
⎢0 anj
1 0⎥
⎢ ⎥
⎢⎣0 h j 0 1⎥⎦
The model also allows determining impact in terms of income. y registers the
overall variation of income generated by a change in exogenous variables. However,
the sectoral impact in terms of income is not known. To determine how income
impact is distributed among sectors, we first modify the system of equations
(5.9) converting it into income relationships.
The system (5.9) can be rewritten as follows:
⎧(1 − a11) a1
j a1nk1 ⎪ y1 + …− yj + …−
yn − yn+ 1 = Y1
⎪
h1 hj hn hn+
1
⎪…
⎪
⎪ a (1 − a ) a k
⎪−
y + …+ y + …−
y − y = Y
⎪ h h h h
⎨
⎪…
⎪ a a (1 − a ) k
⎪− y + …− y + …+
y − y = Y
⎪ h h h h
⎪
⎪ h h h (1 − h )
− y + …− y + …−
y + y = y
⎪
⎩ h h h h
j1jj jn j
1 j n n+ 1 j
1 j n n+
1
n1nj nn n
1 j n n+ 1 n
1 j n n+
1
1 j n n+
1
1 j n n+ 1 x
1 j n n+
1
(5.14)
where hi = yi Xi
is the previously illustrated income input coefficient. In particular,
it results that hn+ 1 = yn+ 1 y , where y n+
1 equals income received by
households for services offered to households themselves. The existence of a linear
relationship between wages and salaries and production is assumed.

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Then, we proceed as above, making output of sector j exogenous to the system.
Considering that yi hi = Xi
, we obtain:
⎡ y ⎤ 1
⎡Y1⎤ ⎢ ⎥ ⎢ ⎥
⎢
⎥
⎢ ⎥
⎢ Y ⎥ -1 X
j ( ˆ
⎢ j ⎥
-1
⎢ ⎥ = Mh ) N ⎢ ⎥
⎢ ⎥ ⎢
⎥
⎢ y ⎥ ⎢Y⎥ n
n
⎢ ⎥ ⎢ ⎥
⎢y y
n+
1 ⎥ ⎢⎣ x ⎥⎦
⎣ ⎦
(5.15)
where N and M have been previously defined. ˆ h is diagonalized income coefficient
vector and it results that h = ⎡
⎣h1, …, hj− 1,1, hj+ 1, … , hn, hn+
1⎤
⎦ .
Note that the model does not give information on variation of income in sector
whose output is exogenous. To find the corresponding change in income, it is sufficient
to apply the following formula: ∆ yj = ∆ X j hj
.
To determine the impact in terms of employment, the procedure to be followed
is analogous to that applied to income. The substantial difference lies in the use of
the labour input coefficient instead of the income input coefficient. The labour in-
e = E X and represents the quantity of labour
put coefficient is defined as i i i
needed to produce one output unit in sector i . It expresses the relationship existing
between employment and output of a given sector, which is, also in this case,
hypothesized to be linear 25 . Finally, employment model results to be:
⎡ E ⎤ 1
⎡Y1⎤ ⎢ ⎥ ⎢ ⎥
⎢
⎥
⎢ ⎥
⎢ Y ⎥ ⎢X j
j ⎥
-1
-1
⎢ ⎥ = ( Meˆ ) N ⎢ ⎥
⎢ ⎥ ⎢
⎥
⎢ E ⎥ ⎢Y⎥ n
n
⎢ ⎥ ⎢ ⎥
⎢E y
n+
1 ⎥ ⎢⎣ x ⎥⎦
⎣ ⎦
(5.16)
25 Ideally, this relationship could be expressed by a function that can be approximated by techniques of simple
linear regression. However, since data for estimating this function are not readily available, a point approximation
based on a single pair of observations is normally used. This might lead to an overestimation of
the employment effects of any change in output (Harrison-Mayfield, 1996).
123

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Where ê is a diagonalized employment coefficient vector and it results that
e = ⎡
⎣e1, …, ej− 1,1, ej+ 1, … , en, en+
1⎤
⎦.
Like income model, even employment model
does not give information about sector whose output is exogenous. To find the
corresponding change in employment, the following formula is applied:
∆ E j =∆ X j ej
.
It is undoubted that this sort of model requires much more calculations than the
traditional model. However, there is the possibility of reducing considerably the
quantity of operations, by constructing an output-to-output multiplier matrix. This
kind of matrix is obtained by dividing all elements of a traditional inverse matrix
( b ij ) by on-diagonal elements. i.e: *
bij = bij bjj
. It has been demonstrated that this
alternative approach may be valid only for evaluating impact from output variation
of one sector at once (Miller and Blair, 1985; Roberts, 1994).
5.4.3 Construction of regional I-O tables by different methods
5.4.3.1 Methods used and common data
Different regional I-O tables are derived using 16 methods: eight non-survey
methods and eight hybrid methods. Non-survey methods are SLQ, PLQ, WLQ,
CILQ, RLQ, SCILQ, FLQ, SDP, while hybrid methods are versions of GRIT incorporating
th e mentioned non-survey methods. Three of these versions are wellknown:
they are the original GRIT (with SLQ), GRIT II (with WLQ) and the Aberdeen
version of GRIT (with CILQ). One has been recently used: it is the RE-
APBALK version (with FLQ). The other four coincide with the original version
incorporating SDP, SCILQ, RLQ and PLQ, respectively 26 .
First requirement of all used methods is a national I-O table as a starting point.
We took a national I-O table dated from 1997 and constructed by Rampa (2001).
It is symmetric, containing 42 sectors, valued at basic prices, expressed in total
flows (domestic plus import flows), in current values and with imputed banking
services allocated among intermediate costs. The table is consistent with the
ESA1979 European accounting methodology 27 . The final demand quadrant is
composed of the following entries: household consumption, public expenditure
(distinguished into expenditure of public administration and of social and other
institutions), gross fixed capital formation, change in inventories and exports. The
primary inputs’ quadrant is composed of: gross wages and salaries, social contri-
26 For an illustration of the methods adopted, see chapters two and three.
27 As known, the accounting system has been reformed introducing the so-called ESA1995 (ISTAT, 1996).
For Italy, the only table constructed by this new system is dated from 1992. For this, it was preferred to employ
a more recent table even if constructed by the previous methodology.
124

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
bution, gross operating surplus, V.A.T. (Value Added Tax) on products, other indirect
taxes on production, subsidies on products, product transfers, imports CIF,
VAT on imports and other indirect taxes on imports.
We decided not to update the national table for two main reasons. First, employment
data at disaggregated sectoral level, necessary to regionalize the national
table, were available only for 1996. Second, empirical evidence has shown
that an input-output table can remain valid for a certain number of years 28 (Tilanus
and Rey, 1964; Carter, 1970; Conway, 1980).
Second requirement is the availability of employment data if other data (such
as output or value added data) are not available. Employment data at both regional
and national level were available for 24 sectors from 1996 middle Census of industry
and services and ISTAT regional accounting (as for agriculture). At national
level, employment data were also available for 42 sectors corresponding to
those of the national I-O table (Rampa, 2001). One possibility was to aggregate
national sectors up to 24 sectors for which regional data were available and then
to regionalize. However, this could have caused significant error due to aggregation
in terms of multipliers (Lahr and Stevens, 2002). To minimize error, practice
suggests aggregating after regionalizing. If detailed regional data are lacking, regional
data could be disaggregated using national ratios. So, we decided to estimate
regional employment of missing sectors by national weights represented by
employment ratios 29 . In so doing, it was possible to obtain employment data at
regional level for all sectors represented in the national I-O table. In order to ensure
consistency, national employment was derived using census data and disaggregating
these latter on the basis of national shares calculated from employment
data available for 42 sectors.
For all methods, a full 1997 regional I-O table was derived, where primary inputs
were value added, imports and other final payments (including all the other
categories such as taxes, subsidies and transfers) 30 and, following the example of
GRIT, final demand was disaggregated into household consumption, exports and
other final demands (including public expenditure, investments and changes in inventory).
Afterwards, all regional tables were first aggregated into 24 sectors to fit the I-
O table to the simpler regional economic structure (Tab. 5.6). Then, regional ta-
28
Notwithstanding, one can object that updating could take place after regionalization directly to the regional
table using regional relative prices. Unfortunately, information on prices at regional level was missing and the use
of national price indices was considered improper.
29
For real estate, renting and business services, output ratios were used for lacking of employment data even
at national level.
30
In GRIT, only two categories of final payments are considered: value added and imports. However, there
exist other components that do not fall into the mentioned categories. We classified them as “other final payments”.
125

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
bles were disaggregated to represent the cereal sub-sector within agricultural sector
31 .
5.4.3.2 Hybrid methods
In all versions of GRIT, the national intrasectoral flows were not zeroed since
level of sectoral detail was not high. First estimates of regional coefficients for 42
sectors were obtained using non-survey methods incorporated into each version.
As for the REAPBALK version, the parameter δ was fixed at the value of 0.9
since lower values generated a negative difference between total output and intermediate
sales. For all versions, regional output was estimated by labour income
ratio (Lahr, 2001b).
Superior data at regional level were collected from two information sources:
ISTAT (regional accounting and COEWEB database 32 ) and INEA (Tab. 5.7).
From ISTAT, the following data were taken: value added, labour income, exports
outside Italy 33 , household consumption and other final demands. Data on value
added and labour income were available for 24 sectors. The former were used directly
within GRIT while the latter were used successively (after aggregation) to
calculate income input coefficients.
Information on exports was available for 18 sectors (16 producing goods and 2
producing services). Since regional exports include interregional flows, exports
outside the nation were used as inferior limits of total exports. Data on household
consumption were overall available for 12 expenditure categories and they were
also distinguished into durable goods, non-durable goods and services. As for
other final demands, only investments presented a high level of sectoral detail (24
sectors). In fact, changes in inventory were available as a total, while data on public
consumption were available for 10 expenditure categories. Therefore, it was
decided to consider an aggregated value of other final demands.
From INEA, the following data were taken: total intermediate costs related to
agricultural sector (including hunting, forestry and fishing) and seed, fertilizer and
pesticide consumption in agricultural sector.
31
1997 25-sector Marche Regional I-O tables constructed by several methods are shown in appendix A.
32
COEWEB is an Italian on-line database managed by ISTAT and providing information on international
trade.
33
Also data on imports were available but they were not taken into consideration because they are not significantly
tied to the regional demand but they are located amongst regions according to the role of trader and the
degree of multi-location of the region (Paniccià and Benvenuti, 2002).
126

Tab. 5.6 – Connection table between 42-sector regional I-O table and 24-sector regional I-O table
Aggregated sectors
Disaggregated sectors
01 - Agriculture
02 - Mining
03 - Food and tobacco
04 - Textile products and apparel
05 - Leather and shoes
06 - Timber and furniture
07 - Paper, printing, publishing
08 - Coke and nuclear fuel
09 - Chemicals
10 - Rubber and plastic products
11- Non-metal mineral products
12 - Metal products
13 - Machinery (except electricity)
14 - Electrical and electronic equipment
15 - Transportation equipment
16 - Other manufacturing
17 - Energy and water
18 - Construction
19 - Trade
20 - Hotels and businesses
21 - Transport and communication
22 - Credit and insurance
23 - Real estate, renting, research, business services
24 - Other services
01-Agriculture, forestry, hunting, fishery
03-Coal mining
13-Ferrous minerals and metals
31-Fresh and canned meat
33-Milk products
35-Other foodstuffs
37-Alcohol and soft drinks
39-Tobacco manufactures
41-Textile products and apparel
43-Leather and shoes
45-Timber and furniture
47-Paper, printing, publishing
05-Coke
11-Nuclear fuel
07-Petroleum and natural gas
17-Chemicals
49-Rubber and plastic products
15-Non-metal minerals
19-Metal products
21-Machinery (except electricity)
23-Office machinery and optical equipment
25-Electrical equipment
27-Motor-vehicles and motors
29-Other means of transportation
51-Other manufacturing
09-Electricity, gas and water
53-Construction
55-Goods recovered
57-Trade
59-Hotels and businesses
61-Internal transportation
63-Water and air transportation
65-Other transportation activities
67-Communications
69-Credit and insurance
71-Business services
73-Real estate and rental
75-Private research and educational services
77-Private health services
79-Entertainment and cultural services
81-Public administration services
93-Domestic services
Source: Author’s elaboration on data from Rampa (2001)

Tab. 5.7 – Regional superior data (flows) utilized in the 42-sector regional I-O table, Marche, 1997
Data Sectoral Detail Correspondence with the 42-sector regional table
Total intermediate costs of Agriculture
Total intermediate costs of Forestry
Total intermediate costs of Fishing
} ∑ Z
i,01
i
Intermediate costs
Value added
Labour income
Seed consumption from agriculture ≤ Z
35,01
Fertilizer consumption from agriculture
Pesticide consumption from agriculture } ≤ Z
17,01
Agriculture, hunting, forestry
Fishing and related services
} 01
Mining 03+13
Food and tobacco 31+33+35+37+39
Textile products and apparel 41
Leather and shoes 43
Timber, rubber, plastic products and other manufacturing 45+49+51
Paper, printing, publishing 47
Coke, nuclear fuel and chemicals 05+07+11
Non-metal mineral products 15
Metal products 19
Machinery, electronic and transportation equipment 21+23+25+27+29
Energy and water 09
Construction 53
Trade 55+57
Hotels and businesses 59
Transport and communication 61+63+65+67
Credit and insurance 69
Real estate, renting, research, business services
Public Administration and defence; social security
Education
71+73+75
} 81
Health and social work 77
Other community, social and personal services 79
Domestic services 93
Other final demands (Public consumption,
investments, changes in
inventories)
TOTAL All sectors
Note: Z
ij
represents intermediate flows from sector i to sector j .

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Tab. 5.7 – Regional superior data (flows) utilized in the 42-sector regional I-O table, Marche, 1997 (continued)
Data Sectoral Detail Correspondence with the 42-sector regional table
Agriculture, hunting, forestry
Fishing and related services
} 01
Mining 03+13
Food and tobacco 31+33+35+37+39
Textile products and apparel 41
Leather and shoes 43
Timber and furniture 45
Paper, printing, publishing 47
Exports outside nation
Coke and nuclear fuel
Chemicals
05+11+07
17
Rubber and plastic products 49
Non-metal mineral products 15
Metal products 19
Machinery (except electricity) 21
Electrical and electronic equipment 23+25
Transportation equipment 27+29
Other manufacturing 51
Informatics’, professional and business activities 71+73+75
Other community, social and personal services 77+79+81+93
Household consumption
Foodstuffs and non-alcoholic beverages
Alcoholic beverages, tobacco and narcotics
} 01+31+33+35+37+39
Clothes and footwear 41+43
Housing expenses, electricity, gas, other fuels unused
Furniture, household appliances, housewares and house services unused
Health expenses unused
Transport 61+63+65
Communication 67
Recreational and cultural services 79
Education unused
Hotels and businesses 59
Other goods and services unused
Durable goods unused
Non-durable goods unused
Services 55+…+93
TOTAL All sectors
Source: ISTAT and INEA
129

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Total intermediate costs were used as a control total for agricultural sector (in
the wider definition) ( ∑ Zi,01
) while purchases of seeds and purchases of fertiliz-
i
ers and pesticides were used as inferior limits related to purchases by agriculture
from the sub-sector of other foodstuffs (within food and tobacco industry) ( Z 35,01 )
and chemical industry ( Z 17,01),
respectively.
Since exports, consumption, other final demands, intermediate costs were expressed
in market prices, they were converted into basic prices using information
from the national matrices.
After inserting superior data, the full table was balanced using a non-linear
programming technique 34 . In all versions of GRIT, the possibility of further adjustments
based on expert’s opinions is contemplated in order to derive a table
that is as reliable as possible. However, this was not our objective and, therefore,
this phase was skipped.
The final table was successively aggregated into 24 sectors. Then, agricultural
sector, including agriculture, livestock, forestry and fishing, was disaggregated
into sub-sector of cereals and other sectors (other agricultural sub-sectors, livestock,
forestry and fishing).
Unfortunately, disaggregation problem is not faced by GRIT and, in general,
studies which face the practical problem of disaggregation, proposing techniques
finalized to disaggregate a regional table, are relatively few. They are for example
that of Fanfani (1978) and of Aislabie and Gordon (1990).
The former study offers some suggestions to disaggregate among more regions
the national intermediate costs related to agriculture, livestock and hunting, processing
of grape and olives and storage of fruits for drying, forestry and, finally,
fishing. Apart from some entries, the general adopted criterion is based on production
ratios. Instead, the latter study presupposes a survey to reconstruct the entire
input structure of the sub-sector incorporated in the aggregated sector.
Considering our aim and the availability of data, both studies could not be directly
applied in this research. However, it was decided to use the Fanfani’s general
principle based on production ratios.
34 The balancing was made by minimizing a function measuring the distance between the non-balanced matrix
and the objective matrix under some constraints. As a measure of the distance, the Friedlander’s minimand,
adjusted for negative values, was used (Friedlander, 1961; Bulmer-Thomas, 1982). Constraints were
related to: (a) the imposition of non-negative values for intermediate flows, imports, consumption, exports.
Other final demands and other final payments were made free to take negative values. Actually, the former
may be negative because of subsidies on products and product transfers while the latter may take negative
values because of de-structuring (negative investments) and changes in inventory; (b) row and column balance;
(c) superior data, expressed as totals, punctual estimates and inferior limits; (d) keeping of zeros for initial
zero-cells. It was supposed that superior data were free from error. The minimization problem was solved
using the MINOS solver of the GAMS package.
130

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Output was disaggregated by regional marketable gross production (MGP) 35 .
Data on MGP were taken from INEA. Intermediate costs of the cereal sector were
initially obtained multiplying costs by ratio between the cereal and agricultural
output (including livestock, forestry and fishing). Superior data for bigger and
more important entries were taken from Farm Accounting Data Network
(FADN) 36 . We used the proportion of seed re-uses on gross output to estimate
purchases of the cereal sector from itself ( a cc , ). The proportion of expenses for
mechanical services on output was used for estimating purchases from other agri-
a ). The share related to consumption of seeds was utilized to
cultural sectors ( nc, c
calculate purchases from food and tobacco sector ( a 3,c ). Finally, the share relative
to consumption of pesticides and fertilizer was employed to derive purchases from
chemical sector ( a 9,c )(Tab. 5.8). Intermediate costs of remaining sectors were estimated
by difference. We supposed that remaining sectors did not purchase from
the cereal sector (the relevant flow was given equal to zero).
Intermediate sales of the cereal sector and remaining sectors were simply estimated
by MGP ratios.
Primary inputs and final demand were disaggregated by production ratios, as
well. We assumed that the cereal sector did not directly sell to households. Therefore,
household consumption of cereals was given equal to zero. This hypothesis
was considered plausible and is coherent with the consumption matrix attached to
the 1992 national table, in which consumption of cereals from agriculture is zero.
Disaggregation raised a problem of balancing for sub-sectors within agriculture,
since total input was not equal to total output. Flows were reconciled by attributing
discrepancies to other final demands.
Data on labour income and employment were estimated from FADN. Labour
income for the cereal sector was obtained using proportion of total labour income
of farms oriented to cereals on gross output. As for employment relevant to the
cereal sector, the first intention was to multiply the cereal output by the proportion
of labour units on gross output pertinent to farms oriented to cereals. However,
this proportion was judged non-significant. Therefore, it was decided to estimate
employment by multiplying regional employment in agriculture in 1996 by ratio
between employment in farms oriented to cereals and total employment of the
FADN sample in 1996. Datum on regional employment in agriculture was taken
from ISTAT regional accounting.
35 Regional marketable gross production does not take account of re-uses of products.
36 In 1997, 1274 regional farms, producing arable crops, vegetables, fruit, floriculture and permanent crops
were surveyed within FADN. 402 out of 1274 were farms oriented to cereals.
131

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Tab. 5.8 – Regional superior data (coefficients) utilized in the 25-sector regional I-O table,
Marche (Italy), 1997
Superior data
Share of re-uses on gross output
Share of expenses for mechanical services on
gross output
Share of seed consumption on gross output 3,c
Share of pesticides and fertilizer on gross output
132
Coefficient
replaced
Description
a cc ,
Purchases by the cereal sector from itself
a nc, c
Purchases by the cereal sector from other
agricultural sectors
a Purchases by the cereal sector from food and
tobacco industry
a 9,c
Source: Author’s elaboration on data from FADN
Purchases by the cereal sector from chemical
industry
5.4.3.3 Non-survey methods
With reference to SLQ, PLQ, CILQ, RLQ, SCILQ and FLQ, regional coefficients
derived by these methods were adjusted using a modified version of the
method mentioned by Miller and Blair (1985) and described in chapter two. The
main modification is related to the possibility of scaling coefficients upwards if
the sum of intermediate sales and final demand is less than estimated output and
not only downwards. Outputs were estimated by employment ratios adjusted
downwards if SLQ was less than one (Mattas et al., 2003). Components of final
demand, value added and labour income were estimated multiplying regional outputs
by ratios between national components and outputs (henceforth national allocators).
Other primary inputs were simply derived by difference. Specific considerations
have to be made with reference to FLQ and SCILQ. As far as FLQ is
concerned, the value of the parameter that was used was 0.3 which is the value
suggested by FLQ’s authors. As for SCILQ, because of the structure of this location
quotient, import coefficients for some sectors (obtained by difference between
national input coefficients and regional input coefficients) were negative. In
these cases, since we were not interested in estimating imports precisely and considering
that imports did not affect the structure of regional input coefficients, we
estimated imports using the absolute value of import coefficients.
With regard to WLQ, data requirements were major. In fact, besides employment
data, reliable estimates for output and consumption were requested. Therefore,
outputs were estimated by income ratios while superior data were used to
balance consumption vector initially obtained by national allocators. As for final
demand, apart from consumption, which was separately estimated, exports and
other final demands were estimated by national allocators. Further coefficient adjustment
was made using the method adopted for the other location quotients.
As for SDP, components of final demand different from exports were initially
estimated by national allocators. If commodity balance was negative, final de-

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
mands as well as intermediate sales were adjusted downwards. Output, value
added, labour income and other primary inputs were estimated as already specified
for location quotients.
Again, once a full table was obtained, this was aggregated into 24 sectors and
agriculture sector was successively disaggregated into the cereal sector and remaining
sectors. All components were disaggregated using MGP ratios. Moreover,
it was assumed that flows of goods and services from the cereal sector to itself
and to other sub-sectors were null. Household consumption of cereals was
given equal to zero.
As already noted for hybrid methods, disaggregation raised a balancing problem
related to sub-sectors within agriculture that was analogously solved by attributing
discrepancies to other final demands.
5.5 Impact sensitivity analysis using alternative regionalization methods
5.5.1 Direct policy impact on farmers’ output
In order to assess the overall policy impact, the first step is to estimate direct
effects produced by policy reform on the cereal sector output, through price reduction.
We assume that if there had not been any policy reform, the cereal output, during
the period 2000-2006, would have followed the trend (derived by linear regression)
noticed in the period going from 1993 to 1999 which substantially coincides
with the period in which “Mac Sharry” reform policy was operating. Output
is expressed in constant 1997 prices since the regional I-O table is expressed in
the same prices (Fig. 5.4).
The total variation of the cereal production generated by policy reform through
price reduction (7.5% in 2000 and 15% in the other years) is estimated multiplying
percentage output variation induced by a decrease in intervention prices by
forecasted output in the absence of reform. Output percentage variation is obtained
multiplying price elasticity with respect to output (0.25) by percentage
price reduction.
In Tab. 5.9, results from analysis are shown. As clearly emerges from the table,
overall output for all the period 2000-2006 is expected to be lower by 3.5% than
forecasted output in the absence of reform. The negative variation of output in the
cereal sector due to policy reform is minus 124 billion of Lire.
133

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Fig. 5.4 – Past and forecasted trend of the cereal output, Marche (Italy), 1993-2006 (1997
constant prices)
Billion of Lire
134
600
500
400
300
200
100
0
1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
Source: Author’s elaboration
Past
Forecasted
(no reform)
Forecasted
(with reform)
Tab. 5.9 – Direct effects of policy reform on the cereal sector output, Marche (Italy), 2000-
2006 (1997 constant prices – billion of Lire)
2000 2001 2002 2003 2004 2005 2006 TOT
Output without reform 443 465 486 507 528 550 571 3,550
∆Output due to price reduction -8 -17 -18 -19 -20 -21 -21 -124
Output with reform 435 447 468 488 509 529 550 3,426
% Variation -1.9 -3.9 -3.9 -3.9 -3.9 -3.9 -3.9 -3.5
Source: Author’s elaboration

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
5.5.2 Assessing overall impact generated by European Policy
Different versions of the closed mixed-variable I-O model were developed using
the 16 different regionalization methods previously illustrated 37 . These models
were applied to estimate overall impact generated by negative output variation in
the cereal sector induced by the European policy with reference to the Marche region
and for the period 2000-2006. Results of this application are shown in Tab.
5.10.
On average, output variation estimated by various methods is minus 333 billion
of Lire, income variation is minus 49 billion of Lire and employment variation is
minus 6,141 units of labour. These results show that the European policy will
generate in the Marche region for the period 2000-2006 a decrease in overall output
of the region around by 3 times the initial variation of output in the cereal sector
(10% of forecasted output without reform) and a decrease in overall employment
by 1% of employment surveyed in 1999.
Substantially, regionalization methods do not produce particularly different results.
As for output, the predicted overall impact goes from a minimum value of
minus 225 billion of Lire to a maximum value of minus 399 billion of Lire. As for
income, the minimum value is minus 30 billion of lire whilst the maximum one is
minus 63 billion of Lire. Finally, with regard to employment, the interval is from
minus 2,460 to minus 8,628 units of labour. The biggest discrepancy can be noted
for employment (the maximum value is by 3.5 times the minimum), followed by
income (2.1 times) and output (1.7 times).
Similarity among methods is also confirmed by the analysis of variation coefficient.
Results show that variability among methods in terms of results is contained.
Higher values are measured with reference to employment (about 42%),
followed by income (about 22%), and, finally, output (about 16%).
Variability is different if one considers hybrid and non-survey methods separately.
In fact, it results that variability within hybrid methods is quite low, while
the degree of diversity within the group of non-survey methods appears to be
higher. This would induce us to think that the considered hybrid methods represent
a homogenous group while the non-survey methods taken into consideration
are characterized by a bigger degree of heterogeneity.
In terms of impact extent, it emerges that, on average, hybrid methods produce
impacts which are higher than those derived by non-survey methods. This may be
due to the fact that superior data used were generally bigger than mechanically
obtained estimates.
More information can be obtained ranking methods on the basis of their related
impacts (Tab. 5.11).
37 Type II output-to-output multipliers are provided in the appendix B.
135

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Tab. 5.10 – Policy impact measured using 16 different regionalization methods, Marche
(Italy), 2000-2006
IMPACT
136
Negative
output variation
(billion of Lire)
Negative income variation
(billion of Lire)
Negative employment
variation (units)
ALL METHODS
Minimum 225 30 2,460
Maximum 399 63 8,628
Average 333 49 6,141
VC* (%) 15.7 21.5 41.7
NON-SURVEY METHODS
Minimum 225 30 2,460
Maximum 371 50 4,431
Average 307 41 3,703
VC (%) 20.0 20.9 18.5
HYBRID METHODS
Minimum 325 51 8,547
Maximum 399 63 8,628
Average 359 57 8,579
VC (%) 6.6 8.5 0.3
*VC=Variation Coefficient calculated as percentage ratio between standard deviation and average.
Source: Author’s elaboration
Tab. 5.11 – Regionalization methods sorted by impact (negative variation)
Output Income Employment
Methods Bill. Lire Methods Bill. Lire Methods Units
GRIT-SCILQ (H) 399 GRIT-SLQ (H) 63 GRIT-SCILQ (H) 8,628
GRIT-SDP (H) 384 GRIT-SCILQ (H) 63 GRIT-FLQ (H) 8,602
PLQ (NS) 371 GRIT-SDP (H) 61 GRIT-RLQ (H) 8,599
SLQ (NS) 371 GRIT-RLQ (H) 57 GRIT-SLQ (H) 8,573
GRIT-RLQ (H) 362 GRIT-WLQ (H) 56 GRIT-SDP (H) 8,565
GRIT-SLQ (H) 359 GRIT-PLQ (H) 54 GRIT-CILQ (H) 8,563
GRIT-WLQ (H) 354 GRIT-CILQ (H) 51 GRIT-PLQ (H) 8,558
RLQ (NS) 350 GRIT-FLQ (H) 51 GRIT-WLQ (H) 8,547
GRIT-CILQ (H) 350 PLQ (NS) 50 WLQ (NS) 4,431
GRIT-PLQ (H) 339 SLQ (NS) 50 PLQ (NS) 4,223
CILQ (NS) 339 RLQ (NS) 47 SLQ (NS) 4,223
GRIT-FLQ (H) 325 CILQ (NS) 45 RLQ (NS) 3,961
SCILQ (NS) 309 SCILQ (NS) 41 CILQ (NS) 3,854
FLQ (NS) 257 FLQ (NS) 34 SCILQ (NS) 3,512
WLQ (NS) 231 SDP (NS) 30 FLQ (NS) 2,958
SDP (NS) 225 WLQ (NS) 30 SDP (NS) 2,460
Note: NS – Non-survey method; H – Hybrid methods
Source: Author’s elaboration

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
The following considerations can be made:
• As for output, SLQ, PLQ, RLQ and CILQ produce results included in the
impact interval of hybrid methods, while FLQ, SDP and WLQ produce
much lower results. With regard to income, two different groups can be
identified: hybrid methods and non-survey methods. These latter produce
lower impacts. This clear distinction is due to used income coefficients
which take bigger values in the case of hybrid methods. Therefore, differences
among non-survey and hybrid methods are emphasised. As for employment,
a clear distinction between non-survey and hybrid methods can
be noted, again. Also in this case, non-survey methods produce lower impact.
The reason for this can be found in the bigger value of employment
coefficients used by hybrid methods. In the case of both employment and
income, SLQ and PLQ confirm to yield results similar to hybrid methods,
while SCILQ, FLQ, SDP tend to produce much lower impacts. In terms of
employment, also WLQ produces results closer to those of hybrid method,
but this depends on the bigger value of employment coefficients used in
the case of WLQ.
• The ranking among methods confirms that which emerges from literature
with reference to the general tendency of SLQ to overestimate multipliers
if compared to other non-survey methods. However, this conclusion has
often been interpreted as a declaration of failure of SLQ in estimating reliable
multipliers (Johns and Leat, 1987). But if it is true that hybrid methods
are the best ones to produce reliable results, results show that SLQ and
its variants (PLQ and WLQ, this latter in the case of employment), are the
best ones among non-survey methods, followed by RLQ, CILQ, SCILQ,
FLQ and, finally, SDP. Therefore, SDP and all location quotients based on
cell-by-cell adjustment would perform worse. Notwithstanding, this outcome
may be affected by relationship between superior data and mechanical
estimates. If superior data had been smaller than mechanical estimates,
results could have been different. In any case, that which emerges is that
the general tendency of SLQ (and its variants) to overestimate multipliers
may be, in some cases, an advantage and not a defect.
5.5.3 Assessing overall sectoral impact produced by European Policy
A great advantage of I-O analysis is the possibility of analysing impact sector
by sector. As emerges from Tab. 5.12, on average, the cereal sector registers the
highest negative variation of output, income and employment. As for output and
income, other sectors that suffer from the negative variation of output in the cereal
sector are household sector (but only in terms of output), other services, other ag-
137

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
ricultural sub-sectors and trade. In terms of employment, other agricultural subsectors
along with food and tobacco, transport and communication and trade are
those that, after the cereal sector, undergo major losses.
While variability among methods in terms of overall impact is not particularly
high, variability in terms of sectoral impact is instead quite high. Methods demonstrate
to yield very disparate estimates at sectoral level. Sectors registering the
highest variability (over 200%) are mining and household sector.
As results from the comparison of Tab. 5.13 and Tab. 5.14, the high sector
variability among all methods depends, above all, on a higher degree of sector
dispersion among non-survey methods. On the contrary, hybrid methods show a
relatively low level of sector variability albeit there is a considerable divergence
for some sectors such as mining and household sector, explaining high variability
among all methods about these sectors.
5.5.4 Analysing relationships among regionalization methods
The target of this section is to attempt to identify relationships existing among
regionalization methods. Towards this aim, two statistical procedures are applied:
factor analysis and the multidimensional scaling procedure. These are analyses
aimed at reducing original dimensions of a problem, using variance an covariance
matrix (factor analysis) and matrix of distances (the multidimensional scaling procedure).
Both are useful to identify clusters of methods.
Factor analysis 38 is used to analyse correlation among methods examining results
in terms of output, income and employment separately and trying to extract
the underlying components for facilitating clustering. Matrices of correlation
among methods are calculated using impacts by sector as input data.
For all kinds of impact, it emerges that two factors are sufficient to explain relationships
among methods. This demonstrates that methods are highly correlated
to each other and is consistent with the analysis of variability among methods. Extracted
factors are transformed by rotation to make them more interpretable. Results
from factor analysis are shown in Fig. 5.5, Fig. 5.6, Fig. 5.7.
38 Factor analysis is a statistical technique used to identify (or extract) a relatively small number of factors
that can be used to represent relationships among sets of many interrelated variables. The basic assumption is
that underlying dimensions, or factors, can be used to explain complex phenomena. Observed correlations
between variables result from their sharing these factors. The procedure was applied using the Software
Package SPSS 11.5 (FACTOR procedure).
138

Tab. 5.12 – Negative average variation due to policy impact by sector and degree of dispersion among regionalization methods,
Marche (Italy), 2000-2006
Sectors
Output
Bill. Lire VC* (%)
Income
Bill. Lire VC (%)
Employment
Units VC (%)
Cereals 124.8 0.0 23.8 26.9 5,040.5 59.9
Other agricultural sub-sectors 39.8 87.1 5.5 91.0 710.5 63.1
Agriculture 164.6 21.1 29.3 15.4 5,751.0 47.6
Mining 1.7 290.5 0.1 290.5 8.0 126.6
Food and tobacco 13.1 64.1 1.1 64.1 115.5 124.7
Textile products and apparel 1.7 67.1 0.3 67.1 1.4 146.6
Leather and shoes 2.9 43.9 0.4 43.9 3.0 51.4
Timber and furniture 0.8 84.2 0.1 87.0 3.2 118.1
Paper, printing, publishing 1.5 91.9 0.2 91.9 8.0 113.2
Coke and nuclear fuel 5.5 148.4 0.1 154.3 3.0 130.2
Chemicals 13.9 100.8 1.5 100.3 4.6 101.5
Rubber and plastic products 1.3 110.0 0.2 114.2 0.3 131.7
Non-metal mineral products 0.5 120.7 0.1 116.6 0.4 93.4
Metal products 1.0 100.5 0.2 105.4 1.2 103.2
Machinery (except electricity) 0.1 88.5 0.0 89.5 0.5 195.2
Electrical and electronic equipment 1.5 98.4 0.2 96.1 2.2 73.8
Transportation equipment 2.0 121.3 0.3 118.9 1.2 105.3
Other manufacturing 0.9 82.0 0.1 81.7 3.7 54.1
Energy and water 2.4 126.8 0.4 123.2 0.7 111.4
Construction 1.2 48.3 0.2 48.3 3.0 65.8
Trade 30.0 32.2 4.9 32.2 61.7 94.6
Hotels and businesses 5.2 21.9 0.5 21.9 27.8 103.3
Transport and communication 7.2 64.6 1.7 64.6 71.5 114.1
Credit and insurance 5.5 97.5 1.3 97.5 17.1 111.1
Real estate, renting, research, business services 12.0 51.6 1.7 43.2 23.3 31.9
Other services 7.1 35.7 2.9 36.6 2.0 47.7
Household sector 48.9 21.5 0.9 200.5 26.9 21.5
* Variation coefficient obtained as percentage ratio between standard deviation and average
Source: Author’s elaboration

Tab. 5.13 – Negative average variation due to policy impact by sector and degree of dispersion among regionalization hybrid
methods, Marche (Italy), 2000-2006
Sectors
Output
Bill. Lire VC* (%)
Income
Bill. Lire VC (%)
Employment
Units VC (%)
Cereals 124.8 0.0 30.0 0.0 7,921.6 0.0
Other agricultural sub-sectors 16.4 2.5 2.0 2.5 449.0 2.5
Agriculture 141.2 0.3 32.0 0.2 8,370.6 0.1
Mining 3.3 212.9 0.2 212.9 0.8 212.9
Food and tobacco 20.3 20.2 1.7 20.2 6.9 20.2
Textile products and apparel 2.5 43.3 0.4 43.3 1.4 43.3
Leather and shoes 3.9 26.9 0.5 26.9 2.1 26.9
Timber and furniture 0.3 167.1 0.0 167.1 0.1 167.1
Paper, printing, publishing 2.6 46.2 0.4 46.2 1.2 46.2
Coke and nuclear fuel 10.7 84.7 0.3 84.7 0.1 84.7
Chemicals 27.4 8.7 3.0 8.7 1.6 8.7
Rubber and plastic products 2.4 55.1 0.4 55.1 0.4 55.1
Non-metal mineral products 1.0 65.1 0.2 65.1 0.5 65.1
Metal products 1.7 60.0 0.3 60.0 0.9 60.0
Machinery (except electricity) 0.1 106.1 0.0 106.1 0.0 106.1
Electrical and electronic equipment 2.6 58.9 0.4 58.9 1.0 58.9
Transportation equipment 3.6 64.7 0.5 64.7 0.2 64.7
Other manufacturing 1.1 81.8 0.1 81.8 2.6 81.8
Energy and water 4.5 65.2 0.7 65.2 0.1 65.2
Construction 1.2 55.5 0.2 55.5 4.0 55.5
Trade 30.0 22.2 4.9 22.2 109.0 22.2
Hotels and businesses 5.9 13.6 0.6 13.6 5.3 13.6
Transport and communication 11.3 23.8 2.7 23.8 10.5 23.8
Credit and insurance 8.9 61.4 2.1 61.4 2.9 61.4
Real estate, renting, research, business services 8.0 18.7 1.3 18.7 24.3 18.7
Other services 7.6 43.1 2.5 43.1 1.6 43.1
Household sector 56.8 8.5 1.1 230.0 31.2 8.5
* Variation coefficient obtained as percentage ratio between standard deviation and average
Source: Author’s elaboration

Tab. 5.14 – Negative average variation due to policy impact by sector and degree of dispersion among regionalization nonsurvey
methods, Marche (Italy), 2000-2006
Sectors
Output
Bill. Lire VC* (%)
Income
Bill. Lire VC (%)
Employment
Units VC (%)
Cereals 124.8 0.0 17.6 0.0 2,159.3 35.5
Other agricultural sub-sectors 63.3 57.5 8.9 57.5 972.1 53.9
Agriculture 188.1 19.3 26.5 19.3 3,131.4 20.2
Mining 0.2 27.7 0.0 27.7 15.2 64.8
Food and tobacco 5.8 63.7 0.5 63.7 224.1 59.0
Textile products and apparel 0.9 36.3 0.1 36.3 1.3 217.8
Leather and shoes 1.9 19.8 0.3 19.8 4.0 43.7
Timber and furniture 1.4 25.4 0.2 32.6 6.2 47.8
Paper, printing, publishing 0.4 64.4 0.1 64.4 14.8 55.8
Coke and nuclear fuel 0.3 253.9 0.0 121.7 5.8 62.4
Chemicals 0.5 248.4 0.1 253.1 7.6 68.0
Rubber and plastic products 0.2 126.7 0.0 62.7 0.2 236.0
Non-metal mineral products 0.1 84.1 0.0 138.3 0.2 108.8
Metal products 0.3 55.5 0.0 26.6 1.6 105.0
Machinery (except electricity) 0.1 71.0 0.0 77.2 1.0 128.2
Electrical and electronic equipment 0.5 78.1 0.1 108.8 3.3 40.7
Transportation equipment 0.4 271.1 0.1 274.9 2.3 45.6
Other manufacturing 0.6 66.4 0.1 42.9 4.7 24.4
Energy and water 0.2 277.7 0.1 279.6 1.2 59.5
Construction 1.1 42.0 0.2 42.0 2.0 58.3
Trade 30.1 41.4 5.0 41.4 14.3 278.1
Hotels and businesses 4.5 22.9 0.5 22.9 50.4 49.3
Transport and communication 3.1 32.7 0.7 32.7 132.5 57.3
Credit and insurance 2.1 111.2 0.5 111.2 31.2 56.4
Real estate, renting, research, business services 15.9 41.1 2.2 39.4 22.3 43.7
Other services 6.6 23.0 3.2 29.5 2.4 42.6
Household sector 41.1 20.9 0.7 39.4 22.6 20.9
* Variation coefficient obtained as percentage ratio between standard deviation and average
Source: Author’s elaboration

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
142
Factor 2 - Impact
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Fig. 5.5 – Plot of rotated factor matrix – output impact
TRADITIONAL LQ
HYBRID & WLQ
0
0 0.1 0.2 0.3 0.4 0.5
Factor 1 - Accuracy
0.6 0.7 0.8 0.9 1
(1) Explained variance by Factor 1 and Factor 2 is 91.9% and 6.4%, respectively.
(2) Hybrid cluster contains GRIT-SLQ, GRIT-PLQ, GRIT-CILQ, GRIT-RLQ, GRIT-SCILQ, GRIT-FLQ, GRIT-WLQ, GRIT-SDP.
(3) Traditional-LQ cluster contains : SLQ, PLQ, CILQ, RLQ.
Factor 2 - Impact
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
SCILQ
Source: Author’s elaboration
Fig. 5.6 – Plot of rotated factor matrix – income impact
TRADITIONAL LQ
0
0 0.1 0.2 0.3 0.4 0.5
Factor 1 - Accuracy
0.6 0.7 0.8 0.9 1
SDP
SCILQ
SDP
FLQ
FLQ
HYBRID & WLQ
(1) Explained variance by Factor 1 and Factor 2 is 91.9% and 7.3%, respectively.
(2) Hybrid cluster contains GRIT-SLQ, GRIT-PLQ, GRIT-CILQ, GRIT-RLQ, GRIT-SCILQ, GRIT-FLQ, GRIT-WLQ, GRIT-SDP.
(3) Traditional-LQ cluster contains : SLQ, PLQ, CILQ, RLQ.
Source: Author’s elaboration

Factor 2 - Impact
An Analysis of Impact of CAP Reform Using Different Regionalization Methods
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
Fig. 5.7 – Plot of rotated factor matrix – employment impact
TRADITIONAL LQ
HYBRID & WLQ
0
0 0.1 0.2 0.3 0.4 0.5
Factor 1 - Accuracy
0.6 0.7 0.8 0.9 1
(1) Explained variance by Factor 1 and Factor 2 is 93.2% and 6.8%, respectively.
(2) Hybrid cluster contains GRIT-SLQ, GRIT-PLQ, GRIT-CILQ, GRIT-RLQ, GRIT-SCILQ, GRIT-FLQ, GRIT-WLQ, GRIT-SDP.
(3) Traditional-LQ cluster contains : SLQ, PLQ, CILQ, RLQ.
Source: Author’s elaboration
Five clusters of methods can be identified: traditional location quotients (SLQ,
PLQ, CILQ, RLQ); SCILQ; FLQ; SDP and hybrid methods along with WLQ. For
all kinds of impact, results show that hybrid methods and WLQ are more correlated
to factor 1, while traditional location quotients (SLQ, PLQ, CILQ, RLQ) are
more correlated to factor 2. SCILQ, FLQ and SDP are located between traditional
LQ and hybrid methods.
Assuming that hybrid methods tend to produce reliable I-O tables, factor 1
could be interpreted as degree of accuracy. Therefore, WLQ would perform better
than other non-survey methods, followed by SDP and FLQ. While SCILQ and
traditional location quotients would perform worse.
On the contrary, factor 2 could be interpreted as extent of impact. This would
confirm that which emerges from the literature about the general tendency of location
quotients (in particular SLQ) to overestimate multipliers.
SCILQ
SDP
FLQ
143

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
The multidimensional scaling procedure 39 is employed to identify the overall
degree of dissimilarity existing among regionalization methods, taking into consideration
all the three kinds of impact (output, income and employment) and all
sectors simultaneously. First, matrices of distances among regionalization methods
in terms of output, income and employment are calculated. Distances among
methods are derived as Euclidean distances 40 . Afterwards, the multidimensional
scaling procedure is applied on the three distance matrices jointly. Graphical results
are shown in Fig. 5.8.
Dimension 2 - Impact
144
Fig. 5.8 – Plot of two-dimensional solution – output, income and employment impacts
(Multidimensional Scaling Procedure)
0
-1 -0,8 -0,6 -0,4 -0,2 0 0,2 0,4 0,6 0,8
FLQ
TRADITIONAL LQ
SDP
SCILQ
WLQ
0,4
0,3
0,2
0,1
-0,1
-0,2
-0,3
-0,4
-0,5
-0,6
-0,7
Dimension 1 - Accuracy
HYBRID
(1) Measures of Fit – S-Stress: 0.06; Explained dispersion: 0.96.
(2) Hybrid cluster contains GRIT-SLQ, GRIT-PLQ, GRIT-CILQ, GRIT-RLQ, GRIT-SCILQ, GRIT-FLQ, GRIT-WLQ, GRIT-SDP.
(3) Traditional-LQ cluster contains : SLQ, PLQ, CILQ, RLQ.
Source: Author’s elaboration
39 This procedure attempts to find a structure from a set of distance measures among objects. This operation is
carried out assigning observations to specific positions within a reduced conceptual space, in order to make
distances among points on the space correspond to specified dissimilarities as much as possible. In this way,
it is possible to obtain a representation of least-squares of objects within the space, which mostly helps to understand
data in a better way. The procedure was applied using the Software Package SPSS 11.5 (PROX-
SCAL procedure).
40 Several distance measures could be adopted (see chapter four). However, in order to avoid complicating the
reading of results, we decided to adopt only one measure, choosing one of the most used.

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
Explained dispersion is very high, being near 100%. This means that the synthesized
structure of distances among methods well reproduces structures associated
to each kind of impact.
On the basis of results from applying the multidimensional scaling procedure,
six categories of methods can be identified: hybrid methods; WLQ; SDP; FLQ;
SCILQ and, finally, traditional LQs. Differently from factor analysis, the multidimensional
scaling procedure allows highlighting differences existing between
WLQ and hybrid methods.
It results that, with respect to dimension 1, traditional LQs and hybrid methods
are very distant to each other, while, with respect to dimension 2, WLQ is the
most distant non-survey method from hybrid methods. Again, assuming that hybrid
methods are the most reliable methods among indirect regionalization methods,
dimension 1 can be interpreted as degree of accuracy. Therefore, traditional
LQs would be less accurate, while WLQ would reproduce better the structure of
regional tables incorporating superior data. On the contrary, dimension 2 can be
interpreted as extent of impact. Accordingly, WLQ, followed by SDP and FLQ,
would tend to understate impacts. This is substantially consistent with the ranking
of methods based on the magnitude of impact.
Conclusions from factor analysis and the multidimensional scaling procedure
are basically coherent with each other and can be summarised as follows:
• The used methods of regionalization can be regrouped into a smaller number
of classes of methods. Accordingly, some methods can be neglected
since they are very similar to each other. Six categories can be identified:
hybrid methods; WLQ; SDP; FLQ; SCILQ and traditional location quotients
(SLQ, PLQ, RLQ, CILQ).
• Hybrid methods (here represented by GRIT versions) are substantially invariant
with respect to the non-survey method used within the procedure.
In other words, the kind of the employed non-survey method does not affect
significantly results. This depends on insertion of superior data and
successive balancing which tend to level considerably the initial differences
characterizing different methods in order to make the structure of
the regional table consistent with the system of superior data.
• If it is true that hybrid methods better replicate the survey-based ones and
therefore produce more reliable results, then, performances of non-survey
methods can be evaluated by comparing these latter with hybrid methods.
Once accepted this, it results that WLQ is the most accurate among nonsurvey-methods.
That can be partially explained by the use of some superior
data (consumption and output) associated to WLQ and by an attempt
implicit to this LQ to take account of the different pattern of consumption
and production existing between nation and region. However, a relevant
145

An Analysis of Impact of CAP Reform Using Different Regionalization Methods
146
disadvantage is that it tends to underestimate sectoral impacts. Instead,
traditional location quotients (in particular SLQ) are those which perform
worse: they are the less accurate and, at the same time, tend to overestimate
impacts. However, as the analysis of magnitude of overall impact
shows, this tendency cannot always be considered as a disadvantage. In
fact, if the objective is to estimate the overall impact and superior data are
generally bigger than mechanical estimates, then SLQ or other traditional
LQs can produce estimates which are closer to “real” impacts when compared
to other non-survey methods. As for the other methods analysed, between
WLQ and traditional location quotients, we find SDP, FLQ and
SCILQ. Therefore, both SCILQ and, above all, FLQ may be considered
improved versions of traditional LQs. Moreover, SDP would seem to perform
better than all location quotients (except for WLQ).

6 Concluding Remarks
Construction of regional I-O tables implies the collection of a considerable
volume of information that, at a local level, is often unavailable. For this reason,
different approaches for deriving regional tables have been introduced during the
course of time. Historically, in the early 1950s, when the main interest was in applying
regionally the Leontief input-output system, the first applied approach was
that based on non-survey techniques. The 1960s can be considered the “golden
age” of survey-based models: many tables were derived by carrying out surveys.
However, in the late sixties and in the seventies, because of high costs and doubts
in terms of accuracy related to survey-based tables and considering the increasing
interest in studying regional economies, non-survey methods were widely used.
They allowed deriving tables easily and at a low cost. Nevertheless, they posed
problems in terms of reliability and theoretical validity. In the late seventies, there
emerged the possibility of measuring multipliers without constructing regional tables
by the use of short-cut techniques. That would have made all techniques
aimed at deriving regional tables useless. However, this possibility was so widely
contested that, in the middle of the 1980s, it was definitively abandoned.
Meanwhile, the idea of a hybrid approach capable of joining advantages associated
to survey and non-survey methods, eliminating related disadvantages, was
emerging. At the end of the seventies, the first real hybrid method, based on systematic
insertion of exogenous information, appeared with the introduction of
GRIT methodology (Jensen et al., 1979). In the 1980s, further GRIT versions
were introduced and the theoretical system behind hybrid models was formulated.
Moreover, improvements of non-survey methods were suggested by introducing,
for instance, the West’s SLQ and regional purchase coefficients. Starting from the
middle of the 1980s, pre-packaged models based on non-survey models were developed
and increasingly used. They were referred to as ready-made models. The
1990s were dominated by two different approaches: the hybrid approach and
ready-made models. However, efforts to improve the attractiveness of non-survey
methods did not cease and variants of location quotients were introduced (i.e.
symmetric cross industry location quotient and the Flegg location quotient). In the

Concluding Remarks
late nineties, the “make and use” approach, opposed to the institutional approach,
was suggested. At present, ready-made models and hybrid techniques are the most
supported. The institutional hybrid approach is preferred to the make and use format,
but the increasing attention to this latter makes us think that there could be a
movement of interest towards this approach.
Along with the construction of regional tables, a further problem about which
researchers worried during the course of time was to evaluate the performances of
methods of regionalization. This problem raises several questions. Firstly, a comparison
base is absolutely necessary. Analysts generally used survey-based tables
for this objective. However, these tables could be even less accurate than nonsurvey
models in estimating some cells, given the possibility of sampling errors,
reconciliation adjustments, indirect estimation of some aggregates and various arbitrary
procedures adopted to construct survey-based tables. Secondly, appropriate
statistics are needed to measure distances between matrices. Unfortunately, there
are a lot of statistics than can be used, each of them with different properties and
characteristics. Therefore, any comparison is affected by used statistics. Thirdly,
evaluation of performances is conditioned by the purpose that an input-output table
must serve. Whether the aim was to produce accurate regional accounts, the
comparison could be made with a survey-based table. But if the aim was only to
represent the structure of the overall regional economy, a better comparison would
be between estimated impacts and real impacts, although it has to be considered
that unpredictable changes affecting real impacts could alter the validity of this
comparison.
Empirical studies carried out to evaluate performances of regionalization
methods are relatively few, mainly because of lack of sufficient and reliable survey
data. Some of them are aimed at establishing the effectiveness of only one
method which often coincides with the method suggested within these studies.
Others are more general since they try to evaluate characteristics of more methods
simultaneously. In any case, results from empirical evidence cannot be considered
conclusive since they take account of different batteries of methods and different
measures of comparison. However, a common result would seem to be the superiority
of the RAS technique in comparison with other methods, although we are
not sure that the RAS technique is the best one among hybrid methods. Of purely
non-survey methods, SLQ appears to be the most effective method. FLQ is a
promising technique since it demonstrated to outperform traditional location quotients.
Nevertheless, the value of the parameter in which FLQ is based could be
inadequate for regions other than those for which the value was estimated. Accordingly,
further work is necessary. Anyway, the fact that empirical evidence has
shown that the simplest version of location quotients performs better than other
methods requiring more information, like the Supply-Demand Pool, can be an advantage.
This is because data requested by traditional location quotients (em-
148

Concluding Remarks
ployment data) are often the only data that are available at the highest level of sector
disaggregation and at both regional and national levels.
This research has attempted to compare a large battery of methods of regionalization
both hybrid methods and non-survey methods in terms of prediction of impact,
without having a survey-based table as a comparison base. More specifically,
the purpose was to verify the impact sensitivity using alternative approaches
for regionalizing tables. For this objective, a concrete case was considered. Impacts
to be evaluated were effects in terms of output, income and employment
produced by CAP’s decisions related to a reduction of intervention price in the cereal
market, during the period 2000-2006, on the overall economy of the Italian
Marche region.
To estimate the overall impact generated by the variation of the cereal output
induced by policy, first, a multi-output and multi-input model of profit maximization
was estimated. Through this model, it was possible to derive own price elasticity
with respect to the cereal output as a measure of degree of farmers’ responsiveness
towards price changes. Using price elasticity, we found that negative
variation of output induced by policy for all the period 2000-2006 will be minus
124 billion of Lire (3.5% of forecasted output in the absence of policy reform).
Then, a closed mixed-variable I-O model was applied using 16 different regionalization
methods. Related results can be analysed from two different standpoints:
policy information and methodological information.
In terms of policy information, results show that, on average, output variation
estimated by various methods is minus 333 billion of Lire, income variation is
minus 49 billion of Lire and employment variation is minus 6,141 units of labour.
These results show that the European policy will generate in the Marche region
for the period 2000-2006 a decrease in overall output of the region around by 3
times the initial variation of output in the cereal sector (10% of the forecasted output
in the absence of policy reform) and a decrease in overall employment by 1%
of employment surveyed in 1999. As is logical to expect, the cereal sector will
register the highest negative variation of output, employment and income. As for
output and income, other sectors that will suffer from the negative variation of
output in the cereal sector will be the household sector (but only in terms of output),
other services, other agricultural sub-sectors and trade. In terms of employment,
other agricultural sub-sectors along with food and tobacco, transport and
communication and trade will be those that, after the cereal sector, will undergo
major losses.
From the methodological point of view, methods were compared in terms of
both overall impact and impact sector by sector.
149

Concluding Remarks
150
As for overall impact, two main considerations can be made:
(a) Substantially, regionalization methods do not produce particularly different
results. This is confirmed by the analysis of impact range and variability
among methods (measured by a variation coefficient). Hybrid methods are
more similar to each other while non-survey methods present a higher degree
of heterogeneity.
(b) The ranking among methods based on the extent of overall impact confirms
that which emerges from literature with reference to a general tendency of
SLQ to overestimate multipliers if compared to other non-survey methods.
However, this conclusion has often been interpreted as a declaration of failure
of SLQ in estimating reliable multipliers (Johns and Leat, 1989). But if it
is true that hybrid methods are the best ones to produce reliable results, results
show that SLQ and its variants, producing results similar to those related
to hybrid methods, are the best ones among non-survey methods, followed
by RLQ, CILQ, SCILQ, FLQ and, finally, SDP. Therefore, SDP and
all location quotients based on cell-by-cell adjustments would perform
worse. Notwithstanding, this outcome may be affected by a relationship between
superior data and mechanical estimates. In effect, superior data were
generally bigger than mechanically obtained estimates. If superior data had
been smaller than mechanical estimates, results could have been different. In
any case, that which emerges is that the general tendency of SLQ (and its
variants) to overestimate multipliers may be, in some cases, an advantage
and not a defect.
In terms of sectoral impacts, it emerged that variability is much higher than that
observed in terms of overall impact. Non-survey methods show a much higher
level of sector dispersion than hybrid methods, confirming the characteristics of
bigger homogeneity existing among hybrid methods. The relationships exiting
among methods were studied by applying factor analysis and the multidimensional
scaling procedure. These analyses allowed identifying two only dimensions
or factors capable of explaining relationships between methods: extent of impact
and degree of accuracy. On the basis of these factors, it was possible to visualize
methods on a two-dimensional plot.

Results showed that:
Concluding Remarks
(a) The used methods of regionalization can be regrouped into a smaller number
of classes of methods. Accordingly, some methods can be neglected since
they are very similar to each other. Six categories can be identified: hybrid
methods (here represented by GRIT versions), WLQ, SDP, FLQ, SCILQ
and traditional location quotients (SLQ, PLQ, RLQ, CILQ).
(b) Hybrid methods, being very close to each other, demonstrate to be substantially
invariant with respect to the non-survey method used within the procedure.
In other words, the kind of non-survey method employed would seem
not to affect results significantly. This can be attributed to the insertion of
superior data and successive balancing which tend to level considerably the
initial differences characterizing different methods in order to make the
structure of the regional table consistent with the system of superior data.
This result contrasts with the hypothesis put forward by some researchers
(Johns and Leat, 1987; Lahr, 1993) about which the non-survey method employed
within the hybrid procedure affects results considerably.
(c) As for non-survey methods, WLQ, being closer to hybrid methods, would be
the most accurate among non-survey-methods. That can be partially related
to the use of some superior data associated to WLQ and to the attempt implicit
to this LQ to take account of the different pattern of consumption and
production existing between nation and region. However, a relevant disadvantage
is that it tends to underestimate sectoral impacts. Instead, traditional
location quotients (in particular SLQ) are those which perform worse: they
are the less accurate and, at the same time, tend to overestimate impacts.
However, this tendency cannot always be considered as a disadvantage. In
fact, if the objective is to estimate the overall impact and the superior data
are generally bigger than mechanical estimates, then SLQ or other traditional
LQs can produce estimates which are closer to “real” impacts when
compared to other non-survey methods, even if the latter ones are considered
more accurate. As far as the other methods are concerned, SDP, FLQ
and SCILQ are located between WLQ and traditional location quotients.
This means that SDP performs better than location quotients (except for
WLQ). Moreover, both SCILQ and, above all, FLQ may be considered improved
versions of traditional LQs.
All these considerations have to be taken with caution. Firstly, results could be
affected by the national matrix taken as an initial base, so that a different starting
matrix could produce different results. This requires further experiments to verify
the sensitivity of results with respect to the choice of the starting matrix. Secondly,
results could be also influenced by regional data. Accordingly, the same
151

Concluding Remarks
experiment should be extended to further regions to validate the results obtained.
Thirdly, we limited the analysis of some methods. Inclusion of further methods (in
particular further hybrid methods like RAS) could change some considerations for
example those related to the hypothetical capability of non-survey methods, but
also of hybrid methods themselves, to replicate survey-based models. Fourthly,
we used sectoral impacts generated by a variation in the cereal sector to identify
relationships between different methods of regionalization. Therefore, more definitive
results could be obtained if the analysis was extended to all sectors and
not only to the cereal sector. Finally, we focused on a full regional I-O table. This
requested adjusting coefficients once these latter ones were derived from nonsurvey
model. However, non-adjustment, which means focusing only on the
transactions matrix, could stress differences among non-survey methods and alter
some conclusions.
Nevertheless, in spite of the limitations described above, we feel that results of
this analysis contain some original elements and are encouraging for development
of further research in this direction.
152

References
Aislabie C. and Gordon M. (1990), “Input-Output in Practice: Sector Disaggregation
in Regional Input-Output Tables”, Economic Systems Research, 2, pp.
407-419.
Allen R. I. G. and Lecomber J. R. C. (1975), “Some Tests on a Generalized
Version of RAS” in Allen R.I.G. and Gossling W.F. (eds), Estimating and Projecting
Input-Output Coefficients, Input-Output Publishing, London, pp. 43-56.
Amato A. (1978), “La tavola intersettoriale dell’economia ligure: prime valutazioni
da una analisi degli effetti moltiplicativi” in Costa P. (eds), Interdipendenze
industriali e programmazione regionale, Franco Angeli, Milan, pp. 309-333.
Antille G. (1990), “Input-Output Tables and Scarcity of Data”, Economic Systems
Research, 2, pp. 147-155.
Aroca P. (2001), “Impacts and development in local economies based on mining:
The Case of the Chilean II Region”, Resources Policy, 27, pp. 119-134.
Asami Y. and Smith T. E. (1995), “Additive-ratio Measures of Interactivity in
Input-Output Systems”, Journal of Regional Science, 35, pp. 85-115.
Bacharach M. (1970), Biproportional Matrices and Input-Output Change,
Cambridge University Press, London.
Bairak R. I. and Hughes D. W. (1996), “Evaluating the Impacts of Agricultural
Exports on a Regional Economy”, Journal of Agricultural and Applied Economics,
28, pp. 393-407.
Batey P. W. J., Madden M. and Scholefield G. (1993), “Socio-economic impact
assessment of large-scale projects using input-output analysis: a case study of
an airport”, Regional Studies, 27, pp. 179-191.
Batten D. F. (1982), “The Interregional Linkages Between National and Regional
Input-Output Models”, International Regional Science Review, 7, n. 1, pp.
53-67.
Bergstrom J. C., Cordell H. K., Ashley G. A. and Watsonet A. E. (1990), “Economic
Impacts of State Parks on State Economies in the South”, Southern Journal
of Agricultural Economics, 22, pp. 69-77.

References
Berndt E. R. (1991), The Practice of Econometrics: classic and contemporary,
Addison-Wesley, Reading.
Beyers W. B. (2000), “King County International Airport Economic Impact
Study”, unpublished paper, Department of Geography, University of Washington,
Seattle.
Bonfiglio A. (2000), “Lo scenario comunitario. Politiche dei mercati” in Arzeni
A., Esposti R., Solustri A. and Sotte F., Il sistema agricolo e alimentare nelle
Marche – Rapporto 2000, FrancoAngeli, Milan, pp. 20-25.
Bonfiglio A. (2002a), “Input-Output Analysis and Rural Development Policies:
An Application to Areas of Objective 5b” in Arzeni A., Esposti R. and Sotte F.
(2002), European Policy Experiences with Rural Development, Wissenschaftsverlag
Vauk Kiel KG, Kiel, pp. 207-227.
Bonfiglio A. (2002b), Regionalization Methodology, Working Paper 3-02,
European Research Project REAPBALK, Polytechnic University of Marche, Ancona,
Italy.
Bonfiglio A. (2002c), R.A.P. 1.0 – Regionalization Automatized Procedure,
Software Package, European Research Project REAPBALK, Polytechnic University
of Marche, Ancona, Italy.
Boomsma P. and Oosterhaven J. (1992), “A Double entry method for the construction
of bi-regional input-output tables”, Journal of Regional Science, 32, pp.
269-284.
Boster R. C. and Martin W. E. (1972), “The Value of Primary Versus Secondary
Data in Interindustry Analysis: A Study in the Economics of Economic
Models.”, Annals of Regional Science, 6, pp. 35-44.
Boudeville J. R. (1966), Problems of Regional Economic Planning, University
Press, Edimburgh.
Bourque P., Chambers E., Chiu J., Denman F., Dowdle B., Gordon G., Thomas
M., Tiebout C. and Weeks E. (1967), The Washington Economy: An Input-Output
Study, University of Washington, Washington.
Boyce D. (1998), Formulation of a Transportation Network Model for Earthquake
Impact Analysis, Discussion Paper, Department of Civil and Environmental
Engineering, University of Illinois, Chicago.
Brand S. (1997), “On the Appropriate Use of Location Quotients in Generating
Regional Input-Output Tables: A Comment”, Regional Studies, 31, pp. 791-794.
Brown M. D., Var T. and Lee S. (2002), “Messina Hof Wine and Jazz Festival:
An Economic Impact Analysis”, Tourism Economics, 8, pp. 273-279.
Brucker S. M. and Hastings S. E. (1990), “The variation of estimated impacts
from five regional input-output models”, International Regional Science Review,
13, pp. 119-139.
154

References
Brucker S. M., Hastings S. E. and Latham W. R. (1987), “Regional inputoutput
analysis: a comparison of five ‘ready-made’ model systems”, The Review
of Regional Studies, 17, pp. 1-16.
Bulmer-Thomas V. (1982), Input-Output Analysis in Developing Countries.
Source, Methods and Applications, John Wiley & Sons Ltd.
Burford R. L. and Katz J. L. (1977), “Regional input-output multipliers without
a full input-output table”, Annals of Regional Science, 11, pp. 21-36.
Burford R. L. and Katz J. L. (1981), “A Method for estimation of input-outputtype
multipliers when no I-O model exists”, Journal of Regional Science, 21, pp.
151-161.
Burford R. L. and Katz J. L. (1985), “Shortcut formulas for input-output multipliers:
Alive and well”, Environment and Planning A, 17, pp. 1541-1549.
Butterfield M. and Mules T. (1980), “A Testing routine for evaluating cell by
cell accuracy in short-cut regional input-output tables”, Journal of Regional Science,
20, pp. 293-310.
Carter A. P. (1970), Structural Change in the American Economy, Cambridge,
Harvard University Press.
Cartwright J. V., Beemiller R. M and Gustely R. D. (1981), Regional Input-
Output Modelling System, Department of Commerce, Bureau of Economic Analysis
Washington, D.C..
Casini Benvenuti S. and Grassi M. (1986), Matrici e modelli I/O regionali. Il
caso della Toscana, Franco Angeli, Milan.
Cassing S. and Giarratani F. (1992), “An Evaluation of the REMI Model for
the South Coast Air Quality Management District”, Environment and Planning A,
24, pp. 1549-1564.
Centre for Agricultural Strategy (Reading University UK) (2000), Methodological
Guidelines for Regional Studies, Regional Socio-Economic Studies on
Employment and the Level of Dependency on Fishing, DGXIV European Commission.
Chase R. A., Bourque P. J. and Conway R. S. (1993), The 1987 Washington
State Input-Output Study, Graduate School of Business Administration, University
of Washington.
Chenery H. B. (1953), “Regional Analysis” in Chenery H. B., Clark P. G. and
Pinna V. C. (eds.), The Structure and Growth of the Italian Economy, United
States Mutual Security Agency, Rome, pp. 97-129.
Cho S., Gordon P., Moore J. E., Richardson H. W., Shinozuka M. and Chang
S. E. (1998), “Integrating Transportation Network and Regional Economic Models
to Estimate the Costs of a Large Earthquake”, Final Report to the National
Science Foundation, University of Southern California, Los Angeles.
Christensen L. R., Jorgenson D. W. and Lau L. J. (1973), “Transcendental
logarithmic frontiers”, Review of Economics and Statistics, 55, pp. 28-45.
155

References
Ciobanu C., Mattas K. and Psaltopoulos D. (2002), “Structural Changes in
Less Developed Regions” in Arzeni A., Esposti R and Sotte F. (ed.), European
Policy Experiences with Rural Development, Wissenschftsverlag Vauk Kiel KG,
Kiel, pp. 271-297.
Comer J. and Jackson R. W. (1997), “A Note on Adjusting National Input-
Output Data for Regional Table Construction”, Journal of Regional Science, 37,
pp. 145-153.
Consad Research Corp. (1967), Research Federal Procurement Study, Office
of Economic Regional, US Department of Commerce, Washington DC.
Conway R. S. (1977), “The Stability of Regional Input-Output Multipliers”,
Environment and Planning A, 9, pp. 177-214.
Conway R. S. (1980), “Changes in Regional Input-Output Coefficients and
Regional Forecasting”, Regional Science and Urban Economics, 10, pp. 158-71.
Costa P. 1973 (eds.), “La regionalizzazione di un modello input-output nazionale:
rassegna delle tecniche”, Archivio di Studi Urbani e Regionali, 4, pp. 61-90.
Crihfield J. B. and Campbell H. S. (1991), “Evaluating Alternative Regional
Planning Models”, Growth and Change, 22, pp. 1-16.
Czamanski S. and Malizia E. E. (1969), “Applicability and limitations in the
use of national input-output tables for regional studies”, Regional Science Association
Papers and Proceedings, 23, pp. 65-77.
Davis H. C. (1976), “Regional Sectoral Multipliers with reduced data requirements”,
International Regional Science Review, 1, pp. 18-29.
Davis H. C. (1978), “A synthesis of two methods of estimating regional sector
multipliers”, Growth and Change, 9, pp. 9-13.
De Muro P. and Salvatici L. (2001), “La Politica Agricola Comune nei Modelli
Multisettoriali” in Anania G. (eds.), Valutare gli effetti della Politica Agricola
Comune, INEA, Edizioni Scientifiche Italiane, Napoli.
Dewhurst J. H. L. (1992), “Using the RAS Technique as a Test of Hybrid
Methods of Regional Input-Output Table Updating”, Regional Studies, 26, pp. 81-
91.
Diewert W. E. (1974), “Applications of Duality Theory” in Intriligator M. P.
and Kendricks D. A. (eds.), Frontiers of Quantitative Economics, North-Holland,
Amsterdam, p. 106-176.
Domingues E. P., Haddad E. A., Hewings G. J. D. and Perobelli F. (2001),
Structural Changes in the Brazilian Interregional Economic System, 1985-1997:
holistic matrix interpretation, Discussion Paper 01-T-7, Regional Economics Applications
Laboratory, University of Illinois.
Doyle C. J., Mitchell M. and Topp K. (1997), “Effectiveness of farm policies
on social and economic development in rural areas”, European Review of Agricultural
Economics, 24, pp. 530-546.
156

References
Drake R. E. (1976), “A Short-cut to estimates of regional input-output multipliers:
methodology and evaluation”, International Regional Science Review, 1,
pp. 1-17.
Eding G. J. and Oosterhaven J. (1996), “Towards a new, rectangular approach
in the construction of regional input-output tables”, Paper presented to the American
Regional Science Association Congress, Washington, DC.
Emerson M. J. (1971), “The 1969 Kansas Input-Output Study”, Kansas Department
of Economic Development, Kansas.
English D. B. K. (2000), “Calculating Confidence Intervals for Regional Economic
Impacts of Recreation by Bootstrapping Visitor Expenditures”, Journal of
Regional Science, 40, pp. 523-539.
Eskelinen H. and Suorsa M. (1980), “A note on estimating interindustry
flows”, Journal of Regional Science, 20, pp. 261-266.
Evans W. D. (1954), “The Effect of Structural Matrix Errors on Interindustry
Relations Estimates”, Econometrica, 22, 461-480.
Fanfani R. (1978), “La disaggregazione regionale dell’agricoltura nelle tavole
delle interdipendenze industriali” in Costa P. (eds.), Interdipendenze industriali e
programmazione regionale, Angeli, Milan, pp. 155-165.
Filippucci C. and Gardini A. (1978), “Il modello input-output regionale e alcune
considerazioni sul metodo biproporzionale per la sua costruzione” in Costa P.
(eds.), Interdipendenze industriali e programmazione regionale, Franco Angeli,
Milan, pp. 136-153.
Flegg T. A. and Webber C.D. (1996a), “Using location quotients to estimate
regional input-output coefficients and multipliers”, Local Economy Quarterly, 4,
pp. 58-86.
Flegg T. A. and Webber C.D. (1996b), “The FLQ formula for generating regional
input-output tables: an application and reformation”, Working Papers in
Economics, 17, University of the West of England, Bristol.
Flegg T. A. and Webber C.D. (1997), “On the Appropriate Use of Location
Quotients in Generating Regional Input-Output Tables: Reply”, Regional Studies,
31, pp. 795-805.
Flegg T. A. and Webber C.D. (2000), “Regional Size, Regional Specialization
and the FLQ Formula”, Regional Studies, 34, pp. 563-69.
Flegg T. A., Webber C.D. and Elliot M. V. (1995), “On the appropriate use of
location quotients in generating regional input-output tables”, Regional Studies,
29, pp. 547-561.
Friedlander D. (1961), “A Technique for Estimating a Contingency Table
Given the Marginal Row and Column Totals and Some Supplementary Data”,
Journal of the Royal Statistical Society Series A, 124, pp. 412-420.
157

References
Fritz O., Kurzmann R., Streicher G. and Zakarias G. (2002), “Constructing Regional
Input-Output Tables in Austria”, Working Paper Series, 5, Joanneum Research,
Vienna, Austria.
Garhart R. E., Moloney R., O’Leary E. and Donnellan T. (1997), “An Input-
Output Model of South-West Ireland”, Department of Economics, University College
Cork, Ireland.
Garhart, R. E. and Giarratani F. (1987), “Nonsurvey Input-Output Estimation
Methods: the Importance of the Household Sector”, Paper presented at the 34th
North American meetings of the Regional Science Association, Baltimore.
Gazel R. and Schwer R. K. (1997), “Beyond Rock and Roll: The Economic
Impact of the Grateful Dead on a Local Economy”, Journal of Cultural Economics,
21, pp. 41-55.
Gerking S. (1976), “Reconciling ‘Rows Only’ and ‘Columns Only’ Coefficients
in an Input-Output Model”, International Regional Science Review, 1, pp.
30-46.
Gerking S. (1979), “Reconciling Reconciliation Procedures in Regional Input-
Output Analysis”, International Regional Science Review, 4, pp. 23-36.
Gerking S., Isserman A., Hamilton W., Pickton T., Smirnov O. and Sorenson
D. (2001), “Anti-Suppressants and the Creation and Use of Non-Survey Regional
Input-Output Models” in Lahr M. L. and Miller R. E. (eds), Regional Science Perspectives
in Economic Analysis, North-Holland, Amsterdam, pp. 379-406.
Gilchrist D. A. and St Louis L. V. (1999), “Completing Input-Output Tables
Using Partial Information, with an Application to Canadian Data”, Economic Systems
Research, 11, pp. 185-193.
Greenstreet D. (1989), “A conceptual framework for construction of hybrid regional
input-output models”, Socio-Economic Planning Sciences, 23, pp. 283-289.
Hansen W. L. and Tiebout, C. M. (1963), “An Intersectoral Flows Analysis of
the California Economy”, Review of Economics and Statistics, 45, pp. 409-418.
Harmston F. and Lund L. (1967), Application of An Input-Output Framework
to a Community Economic System, University of Missouri, Columbia.
Harrigan F. J. (1982), “The Estimation of input-output type output multipliers
when no input-output model exists: a comment”, Journal of Regional Science, 22,
pp. 375-381.
Harrigan F. J. and Buchanan I. (1984), “A Quadratic Programming Approach
to Input-Output Estimation and Simulation”, Journal of Regional Science, 24, pp.
339-358.
Harrigan F. J., McGilvray J. W. and McNicoll I. H. (1980a), “Simulating the
structure of a regional economy”, Environment and Planning A, 12, pp. 927-936
Harrigan F. J., McGilvray J. W. and McNicoll I. H. (1980b), “A comparison of
regional and national technical structures”, Economic Journal, 90, pp. 795-810.
158

References
Harrigan F. J. and McNicoll I. H. (1981), “Superior information and nonsurvey
location quotient input-output simulations: observations on method and practice”,
Discussion Paper No. 19, Strathclyde: Fraser of Allander Institute, U.K..
Harris R. I. D. and Liu A. (1998), “Input-Output Modelling of the Urban and
Regional Economy: The Importance of External Trade”, Regional Studies, 32, pp.
851-862.
Hewings G. J. D. (1977), “Evaluating the Possibility for Exchanging Regional
Input-Output Coefficients”, Environment and Planning A, 8, pp. 927-944.
Hewings G. J. D. (1984), “The Role of Prior Information in Updating Regional
Input-Output models”, Socio-Economic Planning Sciences, 18, pp. 319-336.
Hewings G. J. D. (1985), Regional Input-Output Analysis, Sage Publications,
Beverly Hills, California.
Hewings G. J. D. and Janson B. N. (1980), “Exchanging regional input-output
coefficients: a reply and further comments”, Enviroment and Planning A, 12, pp.
843-854.
Hewings G. J. D. and Syversen W. M. (1982), “A Modified Bi-proportional
Method for Updating Regional Input-Output Matrices: Holistic Accuracy Evaluation”,
Modeling and Simulation, 13, pp. 115-120.
Higgins J. (1986), “Input Demand and Output Supply on Irish farms – A micro-economic
approach”, European Review of Agricultural Economics, 13, pp.
477-493.
Hubacek K. and Sun L. (2001), “A Scenario Analysis of China’s Land Use and
Land Cover Change: Incorporating Biophysical Information into Input-Output
Modelling”, Structural Change and Economic Dynamics, 12, pp. 367-397.
Hughes D. W. (1999), “The Contribution of the Pet Turtle Industry to the Louisiana
Economy”, Aquaculture Economics and Management, 3, pp. 205-214.
Hughes D. W. and Litz V. N. (1996), “Rural-Urban Economic Linkages for
Agriculture and Food Processing in the Monroe, Louisiana, Functional Economic
Area”, Journal of Agricultural and Applied Economics, 28, pp. 337-355.
Hughes D. W. and Zaricki S. N. (2001), “Growing the Economy of Clay
County through Industry Targeting: A Preliminary Analysis”, Research Paper, 8,
West Virginia University.
Imansyah M. H. (2000), “An Efficient Method for Constructing Regional Input-Output
Table: A Horizontal Approach in Indonesia”, Paper presented at the
XIII International Conference on Input-Output Techniques, University of Macerata,
Italy, August 21-25 th .
Isard W. H. (1951), “Interregional and Regional Input-Output Analysis: A
Model of a Space Economy”, Review of Economics and Statistics, 33, pp. 318-
328.
Isard, W. H. (1953), “Regional Commodity Balances and Interregional Commodity
Flows”, American Economic Review, Vol. 43, pp. 167-180, May.
159

References
Isard W. H. and Langford T. W. (1971), “Regional Input-Output Study: Recollections,
Reflections and Diverse Notes on the Philadelphia Experience”, Cambridge,
M.I.T. Press.
Isard W. H., Langford T. W. and Romanoff E. (1996-1968), “Philadelphia Region
Input-Output Study”, Working Papers, 3 vols, Regional Science Research Institute,
Philadelphia.
Isard, W. H. and Keunne R. (1953), “The Impact of Steel upon the Greater
New York - Philadelphia Industrial Region”, Review of Economics and Statistics,
34, pp. 289-301.
Isard W. H. and Romanoff E. (1968), “The Printing and Publishing Industries
of Boston SMSA, 1963: And Comparison with the Corresponding Philadelphia
Industries”, Technical Paper No. 7, Regional Science Research Institute, Cambridge.
Israelevich P. R. (1991), “The Construction of Input-Output Coefficients with
Flexible Functional Forms” in Dewhurst J., Hewings G. and Jensen R. (eds), Regional
Input-Output Modelling, Avebury.
Isserman A. M. and Merrifield J. (1982), “The Use of Control Groups in
Evaluating Regional Economic Policy”, Regional Science and Urban Economics,
12, pp. 43-58.
Istat (1996), “Verso il nuovo sistema di contabilità nazionale”, Annali di Statistica,
serie X, vol. 11.
Jackson R. W. (1998), “Regionalizing national commodity-by-industry accounts”,
Economic Systems Research, 10, pp. 223-238.
Jackson R. W. and Comer J. C. (1993), “An Alternative Base Tables in Input-
Output Table Regionalization”, Growth and Change, 24, pp. 191-205.
Jackson R. W., Israilevich P. R. and Comer J. C. (1992), “A Note on the Role
of Survey Data and Expert Opinion in Constructing Input-Output Tables”, Papers
in Regional Science, 71, pp. 87-93.
Jackson R. W., Miller H. J. and Hynes M. (1989), “A Formal Procedure for
Generating Regional Product Accounts for U.S. Regions”, Socio-Economic Planning
Science, 23, pp. 271-281.
Jensen R. C. (1980), “The concept of accuracy in regional input-output models”,
International Regional Science Reviews, 5, pp. 139-154.
Jensen R. C. (1987), “On the concept of ready-made regional input-output
models”, The Review of Regional Studies, 17, pp. 20-24.
Jensen R. C. and Hewings G.J.D. (1985a), “Shortcut input-output multipliers: a
requiem”, Environment and Planning A, 17, pp. 747-759.
Jensen R. C. and Hewings G.J.D. (1985b), “Shortcut input-output multipliers:
the resurrection problem (a reply)”, Environment and Planning A, 17, pp. 1551-
1552.
160

References
Jensen R.C., Mandeville T.D. and Karunarante N.D. (1979), Regional Economic
Planning: Generation of Regional Input-Output Analysis, Croom Helm,
London.
Jensen R. C. and McGaurr A. D. (1976), “Reconciliation of Purchases and
Sales Estimates in Input-Output Tables”, Urban Studies, 13, pp. 59-65.
Jensen R. C. and McGaurr A. D. (1977), “Reconciliation Techniques in Input-
Output Analysis: Some Comparisons and Implications”, Urban Studies, 14, pp.
327-337.
Jensen R. C. and West G. R. (1980), “The Effects of Relative Coefficient Size
on Input-Output Multipliers”, Environment and Planning, 12, pp. 659-670.
Jensen R. C., West G. R. and Hewings G. J. D. (1988), “The Study of regional
economic structure using input-output tables”, Regional Studies, 22, pp. 209-220.
Jin L., Kockelman K. M. and Zhao Y. (2003), “Tracking Land Use, Transport,
and Industrial Production using Random-Utility Based Multizonal Input-Output
Models: Applications for Texas Trade”, Paper presented at the 82nd Annual
Meeting of the Transportation Research Board, Washington, D.C..
Jin Y. (1991), “Estimating Regional Input-Output Tables from Available
Data”, Economic Systems Research, 3, pp. 391-397.
Johns P. M. and Leat P. M. K. (1987), “The Application of Modified GRIT Input-Output
Procedures to Rural Development Analysis in Grampian Region”,
Journal of Agricultural Economics, 38, pp. 245-256.
Johnson P. L. (2001), “An Input-Output Table for the Kimberley Region of
Western Australia”, Economic Research Centre, University of Western Australia.
Junius T. and Oosterhaven J. (2003), “The Solution of Updating or Regionalizing
a Matrix with both Positive and Negative Entries”, Economic Systems Research,
15, pp. 87-96.
Kmenta J. and Gilbert R. F. (1968), “Small sample properties of alternative estimators
of seemingly unrelated regression”, Journal of the American Statistical
Association, 63, pp. 1180-1200.
Knudsen D. C. and Fotheringham A. S. (1986), “Matrix Comparison, Goodness-of-Fit,
and Spatial Interaction Modeling”, International Regional Science
Review, 10, pp. 127-147.
Kokat R. (1966), “The Economic Component of a Regional Socioeconomic
Model”, IBM Technical Report, pp. 17-210.
Lahr M. L. (1993), “A Review of Literature Supporting the Hybrid Approach
to Constructing Regional Input-Output Models”, Economic Systems Research, 5,
pp. 277-293.
Lahr M. L. (2001a), “A Strategy for Producing Hybrid Regional Input-Output
Tables”, in Lahr M. L. and Dietzenbacher (eds.), Input-Output Analysis: Frontiers
and Extensions, Palgrave, London, pp. 1-31.
161

References
Lahr M. L. (2001b), “Reconciling Domestication Techniques, the Notion of
Re-exports, and Some Comments on Regional Accounting”, Economic Systems
Research, 13, pp. 166-179.
Lahr M. L. and Stevens B. H. (2002), “A Study of the Role of Regionalization
in the Generation of Aggregation Error in Regional Input-Output Models”, Journal
of Regional Science, 3, pp. 477-507.
Latham W. R and Montgomery M. (1979), “Methods for calculating regional
industry impact multipliers”, Growth and Change, 10, pp. 2-9
Lau L. J. (1978), “Application of profit functions” in Fuss M. and McFadden
D. (eds.), Production Economics: A Dual Approach to Theory and Applications 1,
North-Holland, Amsterdam.
Lazarus W. F., Platas D. E. and Guess-Murphy S. (2002), “Evaluating the Economic
Impacts of an Evolving Swine Industry: The Importance of Regional Size
and Structure”, Review of Agricultural Economics, 22, pp. 458-473.
Lecomber J. R. C. (1975), “A Critique of Methods of Adjusting, Updating and
Projecting Matrices” in Allen R. I. G. and Gossling W. F. (Ed.), Estimating and
Projecting Input-Output Coefficients, Input-Output Publishing Company, London,
pp. 1-25.
Leontief W. W. (1966), Input-Output Economics, Oxford University Press,
New York.
Leontief W.W. and Strout A. (1963), “Multiregional Input-Output Analysis”,
in Barna T. (eds), Structural Interdependence and Economic Development, Macmillan,
London.
Lindall S. A. and Olson D. C. (1998), The IMPLAN Input-Output System,
Stillwater, MN, Minnesota IMPLAN Group, Inc.
Madsen B. and Jensen-Butler C. (1999), “Make and Use Approaches to Regional
and Interregional Accounts and Models”, Economic Systems Research, 11,
pp. 277-299.
Maietta O. W. (2000), “The Decomposition of cost inefficiency into technical
and allocative components with panel data of Italian dairy farms”, European Review
of Agricultural Economics, 27, pp. 473-495.
Malizia E. and Bond D. L. (1974), “Empirical Tests of the RAS method of interindustry
coefficient adjustment”, Journal of Regional Science, 14, pp. 355-365.
Malte M., Crown W. H. and Polenske K. R. (1987), “A Linear Programming
Approach to Solving Infeasible RAS Problems”, Journal of Regional Science, 27,
pp. 587-603.
Martins E. (1993), “Construction of regional input-output tables from establishment-level
microdata: Illinois, 1982”, Discussion Papers, Center for Economic
Studies, Washington.
162

References
Mattas K., Fotopoulos C., Tzouvelekas V., Loizou S. and Polymeros K.
(1999), “The Dynamics of Crop Sectors in Regional Development: The Case of
Tobacco”, International Advances in Economic Research, 5, pp. 255-268.
Mattas K., Pagoulatos A. and Debertin D.L. (1984), Building Input/Output
Models using Non-Survey Tecniques: an application to Kentucky, Southern Rural
Development Centre, Series No. 72, Mississippi.
Mattas K., Tzouvelekas V., Loizou S. and Tsakiri M. (2003), “Regional Input-
Output Tables”, Deliverable n. 9, European Research Project REAPBALK.
Matuszewski T., Pitts P. R. and Sawyer J. A. (1974), “Linear Programming Estimates
of Changes in Input-Output Coefficients”, Canadian Journal of Economics
and Political Science, 30, pp. 203-210.
McCann P. and Dewhurst J.H.L. (1998), “Regional Size, Industrial Location
and Input-Output Expenditure Coefficients”, Regional Studies, 32, pp. 435-444.
McGregor P.G. and McNicoll I.H. (1992), “The impact of forestry on output in
the UK and its member countries”, Regional Studies, 26, pp. 69-79.
McMenamin D. G. and Haring J. E. (1974), “An Appraisal of nonsurvey techniques
for estimating regional input-output models”, Journal of Regional Science,
14, pp. 191-203.
Meller P. and Marfan M. (1981), “Small and Large Industry: Employment
Generation, Linkages and Key Sectors”, Economic Development and Cultural
Change, 29, pp. 263-274.
Midmore P. (1991), “Input-output in agriculture: a review” in Midmore P.
(eds), Input-Output Models in the Agricultural Sector, Avebury, Aldershot, England,
pp. 5-20.
Midmore P. and Harrison-Mayfield L. (1996), “Rural Economic Modelling:
Multi-Sectoral Approaches” in Midmore P., Harrison-Mayfield L. (eds), Rural
Economic Modelling. An input-output approach, CAB INTERNATIONAL, Wallingford,
pp. 19-33.
Miernyk W. H. (1976), “Comments on Recent Developments in Regional Input-Output
Analysis”, International Regional Science Review, 1, pp. 47-55.
Miernyk W. H. (1979), “Reconciling Reconciliation Procedures in Regional
Input-Output Analysis: A Comment”, International Regional Science Review, 1,
pp. 36-38.
Miernyk W., Shellhammer K., Brown D., Coccari R., Gallagher C. and Wineman
W. (1970), Simulating Regional Economic Development, Heath Lexington
Books, Lexington.
Miller R. E. (1957), “The Impact of the Aluminium Industry on the Pacific
Northwest: A Regional Input-Output Analysis”, Review of Economics and Statistics,
34, pp. 20-209.
163

References
Miller R. E. and Blair P.D. (1982), “State-level Technology in the U.S. Multiregional
Input-Output Model”, Working Paper No. 65, Regional Science Department,
University of Pennsylvania.
Miller R. E. and Blair P. D. (1983), “Estimating State-Level Input-Output Relationships
from U.S. Multiregional data”, International Regional Science Review,
8, pp. 233-254.
Miller R.E. and Blair P.D. (1985), Input-Output Analysis: foundations and extensions,
Prentice-Hall, Inc., Englewood Cliffs, New Jersey.
Mogorovich L. (1987), “Tavole input-output regionali: tecniche indirette e sensibilità
agli errori”, Dissertation in Statistics, University of Trieste, Italy.
Moore F. T. and Petersen J. W. (1955), “Regional Analysis and Interindustry
model of Utah”, Review of Economics and Statistics, 37, pp. 368-380.
Morrison W. I. and Smith P. (1974), “Nonsurvey Input-Output Techniques at
the Small Area Level: An Evaluation”, Journal of Regional Science, 14, pp. 1-14.
Morrison W. I. and Thuman R. G. (1980), “A Langrangian Multiplier Approach
to the Solution of a Special Constrained Matrix Problem”, Journal of Regional
Science, 20, pp. 279-292.
Moses L. N. (1955), “The Stability of Interregional Trading Patterns and Input-
Output Analysis”, American Economic Review, 5, pp. 803-832.
Nagy T. (1997), “A generalization of the Gravity Model”, Paper presented at
the International Symposium on Mathematical Programming, August 24-29,
1997, Lausanne, EPFL.
Nevin E. T., Roe A. R. and Round J. I. (1966), The Structure of the Welsh
Economy, University of Wales Press, Cardiff.
Norcliffe G. B. (1983), “Using Location quotients to estimate the economic
base and trade flows”, Regional Studies, 17, pp. 61-168.
Okubo T. (2003), “The Border Effect in the Japanese Market: A Gravity Model
Analysis”, Research Seminar in International Economics, Discussion Paper No.
494, University of Michigan.
Oosterhaven J. (1984), “A Family of Square and Rectangular Interregional Input-Output
Tables and Models”, Regional Science and Urban Economics, 14, pp.
565-582.
Oosterhaven J., Van der Knijff E. C. and Eding G. J. (2003), “Estimating Interregional
Economic Impacts: An Evaluation of Nonsurvey, Semisurvey, and Full-
Survey Methods”, Environment and Planning A, 35, pp. 5-18.
Oude Wansink M. J. (2000), “A Transactions Table for the Euregion Meuse-
Rhine: A Second Attempt”, OWP research, Maastricht.
Oude Wansink M. J. and Maks J. A. H. (1998), “Constructing Regional Input-
Output Tables”, Working Paper, Maastricht University.
164

References
Paniccià R. and Casini Benvenuti S. (2002), “A Multi-regional Input-Output
Model for Italy: Methodology and First Results”, Paper presented at the 14 th International
Conference on Input-Output Techniques, Montreal, Canada, 10-15 th
October.
Papadas C. T. and Hutchinson W. G. (2002), “Neural Network Forecasts of Input-Output
Technology”, Applied Economics, 34, pp. 1607-1615.
Paradigm Consulting Group (2002), “Economic Impact Analysis of the 2002
North American Indigenous Games, Winnipeg, Manitoba”, Report prepared for
Canadian Sport Tourism Alliance.
Parré J. L., Alves A. F. and Sordi J. C. (2002), “Input-Output Matrix for Metropolitan
Areas Using Local Census Data: The Case of Maringà, Brazil”, Paper
presented at the 14 th International Conference on Input-Output Techniques, Montreal,
Canada, 10-15 th October.
Phibbs P. J. and Holsman A. (1980), “A Shortcut method for computing final
demand multipliers for small regions”, Environment and Planning A, 12, pp.
1001-1008.
Phibbs P. J. and Holsman A. (1981), “An Evaluation of the Burford-Katz
Short-Cut Technique for Deriving Input-Output Multipliers”, Annals of Regional
Science, 15, pp. 11-19.
Phibbs P. J. and Holsman A. (1982), “Estimating Input-Output Multipliers – A
New Hybrid Approach”, Environment and Planning A, 14, pp. 335-342.
Piispala J. (2000), “On Regionalising Input/Output Tables – Experiences from
Compiling Regional Supply and Use Tables in Finland”, Paper presented at the
XIII International Conference on Input-Output Techniques at University of Macerata,
Italy, August 21-25 th .
Polenske K. R. (1970), “An Empirical Test of Interregional Input-Output Models:
Estimation of 1963 Japanese Production”, American Economic Review, 60,
pp. 76-82.
Psaltopoulos D. and Efstratoglou S. (2000), “An Empirical Evaluation of EU
and National Structural Policies in Remote Rural Areas: The Case of Evrytania”,
Agricultural Economics Review, 1, pp. 7-18.
Psaltopoulos D. and Thomson K.J. (1993), “Input-Output Evaluation of Rural
Development: a Forestry-centred Application”, Journal of Regional Studies, 9, pp.
351-358.
Pullen M. J. and Proops J. L. R. (1983), “The North Staffordshire regional
economy: an input-output assessment”, Regional Studies, 17, pp. 191-200.
Rampa G. (2001), “Yearly Series of Input-Output Tables (ESA1979) for the
Italian Economy, 1959-1997”, unpublished paper, Department of Juridical Culture
- Economic Section, Genova.
165

References
Raymond R. L. and Lichty R. W. (1997), “An Efficiency Analysis of Minnesota
Counties: A Data Envelopment Analysis Using 1993 IMPLAN Input-Output
Analysis”, Journal of Regional Analysis and Policy, 27, pp. 75-93.
Raymond R. L. and Lichty R. W. (2002), “Identifying Subareas That Comprise
a Greater Metropolitan Area: The Criterion of County Relative Efficiency”, Journal
of Regional Science, 42, pp. 579-594.
Regione Marche (2000), Piano di Sviluppo Rurale della Regione Marche
2000-2006.
Richardson H. W. (1972), Input-Output and Regional Economics, Weidenfeld
and Nicolson, London, UK.
Richardson H. W. (1985), “Input-Output and Economic Base Multipliers:
Looking Backward and Forward”, Journal of Regional Science, 25, pp. 607-771.
Rickman D. S. (1995), “A Bayesian Analysis of the Use of Pooled Coefficients
in a Structural Regional Economic Model”, International Journal of Forecasting,
11, pp. 477-490.
Richman D. S. and Schwer R. K. (1993a), “A Systematic Comparison of the
REMI and Implan Models: The Case of Southern Nevada”, The Review of Regional
Studies, 23, pp. 143-161.
Rickman D. S. and Schwer R. K. (1993b), “Regional Definition in Regional
Economic Impact Models: The Case of the REMI Model and Clark County, Nevada”,
Economic Systems Research, 5, pp. 419-431.
Richman D. S. and Schwer R. K. (1995a), “A comparison of the multipliers of
IMPLAN, REMI and RIMS II: Benchmarking Ready-Made Models for Comparison”,
Annals of Regional Science, 29, pp. 363-374.
Richman D. S. and Schwer R. K. (1995b), “Multiplier Comparisons of the IM-
PLAN and REMI Models Across Versions: Illuminating Black Boxes”, Environment
and Planning A, 27, pp. 143-151.
Roberts D. (1994), “A Modified Leontief Model for Analysing the Impact of
Milk Quotas on the Wider Economy”, Journal of Agricultural Economics, 45, pp.
90-101.
Roberts D. (eds) (1999), Scottish Forestry: An Input-Output Analysis, University
of Aberdeen.
Robison M. H. (1997), “Community Input-Output Models for Rural Area
Analysis with an Example from Central Idaho”, The Annals of Regional Science,
31, pp. 325-351.
Robison M. H. and Miller J. R. (1988), “Cross-hauling and Nonsurvey Input-
Output Models: Some Lessons from Small-Area Timber economies”, Environment
and Planning A, 20, pp. 1523-1530.
Round J. I. (1972), “Regional Input-Output Models in the U.K.: A Reappraisal
of Some Techniques”, Regional Studies, 6, pp. 1-9.
166

References
Round J. I. (1978), “An Interregional Input-Output Approach to the Evaluation
of non-survey methods”, Journal of Regional Science, 18, pp. 179-94.
Round J. I. (1983), “Nonsurvey techniques: a critical review of the theory and
the evidence”, International Regional Science Review, 8, pp. 189-212.
Round J. I. (1987), “A note on ‘Ready-Made’ Regional Input-Output Models”,
The Review of Regional Studies, 17, pp.26-27.
Roy J. R. (1999), “Regional Input-Output, Leontief-Strout and Uncertainty”,
Australasian Journal of Regional Studies, 5, pp. 393-404.
Santeusanio A. (1978), “Sulla costruzione ‘diretta’ della tavola economica intersettoriale
delle Marche” in Costa P. (eds), Interdipendenze industriali e programmazione
regionale, Franco Angeli, Milan, pp. 205-212.
Sawyer C. and Miller R. (1983), “Experiments in regionalization of a national
input-output table”, Environment and Planning A, 15, pp. 1501-1520.
Schaffer W. (1972), “Estimating regional input-output coefficients”, Review of
Regional Studies, 2, pp. 57-71.
Schaffer W. (1999), Regional Impact Models, Regional Research Institute,
West Virginia University.
Schaffer W. and Chu K. (1969a), “Nonsurvey Techniques for Constructing
Regional Interindustry Models”, Papers and Proceedings of the Regional Science
Association, 23, pp. 83-101.
Schaffer W. and Chu K. (1969b), “Simulating Regional Interindustry Models
for Western States”, A Program on Regional Industrial Development, Discussion
Paper 14, Georgia Institute of Technology, Georgia.
Secretario F., Kim K. and Goce-Dakila C. (2002), “The Metro-Manila Inter-
Regional Input-Output Table: Its Attempt of Compilation by the Hybrid Approach”,
Paper presented at the 14 th International Conference on Input-Output
Techniques, Montreal, Canada, 10-15 th October.
Shen T. Y. (1960), “An Input-Output Table with Regional Weights”, Papers
and Proceedings of the Regional Science Association, 6, pp. 113-199.
Sidhu S. S and Baanante C. (1981), “Estimating Farm-Level Input Demand and
Wheat Supply in the Indian Punjab Using a Translog Profit Function”, American
Journal of Agricultural Economics, 63, pp. 237-246.
Sills E. O., Alwang J. and Driscoll P. J. (1994), “Migrant Farm Workers on
Virginia’s Eastern Shore: An Analysis of Economic Impacts”, Journal of Agricultural
and Applied Economics, 26, pp. 209-223.
Skokai P. (2001), “La Politica Agricola Comune nei Modelli Econometrici” in
Anania G. (eds), Valutare gli effetti della Politica Agricola Comune, INEA, Edizioni
Scientifiche Italiane, Napoli.
167

References
Smith C. (1983), “An Initial Exploration into an Alternative System for Small
Economy Input-Output Table Construction”, Paper presented at the Input-Output
Workshop of the 8th Meeting of the Regional Science Association (Australia &
New Zealand Section), Armidale, 30 th November-2 nd December.
Solustri A. (2000), “Le colture cerealicole” in Arzeni A., Esposti R., Solustri
A. and Sotte F., Il sistema agricolo e alimentare nelle Marche – Rapporto 2000,
Franco Angeli, Milan, pp. 91-92.
Stevens B. H. and Trainer G. A. (1976), “The Generation of Error in Regional
Input-Output Impact Models”, Working Paper No. A1-76, Regional Science Research
Institute, Peace Dale, Rhode Island.
Stevens B. H. and Trainer G. A. (1980), “Error generation in regional inputoutput
analysis and its implications for non-survey models”, in Pleeter S. (eds),
Economic Impact analysis: methodology and applications, Martin Nijhoff, Boston,
pp. 66-84.
Stevens B. H., Treyz G. I., Ehrlich D. J. and Bower J. R. (1983), “A New
Technique for the Construction of Non-Survey Regional Input-Output Models and
Comparison with Two Survey-Based Models”, International Regional Science
Review, 8, pp. 271-286.
Stone R. and Brown J. A. C. (1962), “A Long-term Growth Model for the British
Economy” in Geary J. A. C. (eds.), Europe’s Future in Figures, North-
Holland, Amsterdam.
Stone R., Champernowne D. G. and Meade J. E. (1942), “The Precision of National
Income Estimates”, The Review of Economic Studies, 9, pp. 111-125.
Stone R. and Leicester C. S. (1966), An Exercise in Projecting Industrial Needs
for Labour, Cambridge.
Strassoldo M. (1988), Contabilità macroeconomica e tavole input-output regionali,
Giuffrè Editore, Milan.
Su T. T. (1970), “A Note on Regional Input-Output Models”, Southern Economic
Journal, 36, pp. 325-327.
Tanjuakio R. V., Hastings S. E. and Tytus P. J. (1996), “The Economic Contribution
of Agriculture in Delaware”, Agricultural and Resource Economics Review,
25, pp. 46-53.
Theil H., Beerens G. A. C., DeLeeuw C. G. and Tilanus C. B. (1966), Applied
Economic Forecasting, American Elsevier Publishing Co., New York, pp. 15-43.
Thumann R. G. (1978), “A Comment on ‘Evaluating the possibilities for exchanging
regional input-output coefficients’”, Environment and Planning A, 10,
pp. 321-325.
Tiebout C. M. (1969), “An empirical regional input-output projection model,
the state of Washington, 1980”, Review of Economics and Statistics, 51, pp. 334-
340.
168

References
Tilanus C. B. and Rey G. (1964), “Input-Output Volume and Value Predictions
for the Netherlands, 1948-1958”, International Economic Review, 5, pp. 34-45.
Treyz G. I., Rickman D. S. and Shao G. (1992), “The REMI Economic Demographic
Forecasting and Simulation Model”, International Regional Science Review,
14, pp. 221-253.
Tzouvelekas V. and Mattas K. (1995), “Revealing a Region’s Growth Potential
through the Internal Structure of the Economy”, International Advances in Economic
Research, 1, pp. 304-313.
Tzouvelekas V. and Mattas K. (1999), “Tourism and Agro-Food as a Growth
Stimuls to a Rural Economy: The Mediterrean Island of Crete”, Journal of Applied
Input-Output Analysis, 5, pp. 69-81.
Valma E. (1993), “Interregional multipliers for the Greek Economy: An Input-
Output Approach”, Rivista Internazionale di Scienze Economiche e Commerciali,
40, pp. 575-586.
Van der Ploeg F. (1982), “Reliability and Adjustment of Sequences of Large
Economic Accounting Matrices”, Journal of the Royal Statistical Society Series A,
145, pp. 169-194.
Van der Westhuizen M. (1992), “Towards Developing a Hybrid Method for
Input-Output Table Compilation and Identifying a Fundamental Economic Structure
(Regional Economics)”, Ph.D. Dissertation, University of Pennsylvania.
Vanwynsberghe D. (1976), “An Operational Nonsurvey Technique for Estimating
a Coherent Set of Interregional Input-Output Tables” in Polenske K. R.
and Skolka J. V. (eds), Advances in Input-Output Analysis, Ballinger, Cambridge,
pp. 279-294.
Varian H. R. (1984), Microeconomic Analysis, Norton International, New
York.
Vieri S. (1994), La Politica Agricola Comune. Dal Trattato di Roma alla riforma
Mac Sharry, Edagricole, Bologna.
Walderhaug A. J. (1972), “State Input-Output Tables Derived from National
Data”, 1971 Proceedings of the Business and Economics Statistics Section of the
American Statistical Association, pp. 77-86.
West, G. R. (1980), “Generation of regional input-output tables (GRIT): An introspection”,
Economic Analysis and Policy, 10, pp. 71-86.
West G. R. (1981), “An Efficient Approach to the Estimation of Regional Input-Output
Tables”, Environment and Planning A, 13, pp. 857-867.
West G. R. (1982), “Sensitivity and Key Sector Analysis in Input-Output Models”,
Australian Economic Papers, 21, pp. 365-378.
West, G. R. (1990), “Regional Trade Estimation: A Hybrid Approach”, International
Regional Science Review, 13, 103-118.
169

References
West G. R., Morison J. B. and Jensen R. C. (1984), “A method for the estimation
of hybrid interregional input-output tables”, Regional Studies, 18, pp. 413-
422.
Willis K. G. (1987), “Spatially Disaggregated Input-Output Tables: An Evaluation
and Comparison of Survey and Nonsurvey Results”, Environment and Planning
A, 19, pp. 107-116.
Zaitseva I. (2002), “Multiregional Analysis on the base of Input-Output Tables”,
Paper presented at the 14 th International Conference on Input-Output Techniques,
Montreal, Canada, 10-15 th October.
170

APPENDIX A – Regional I-O tables constructed by
16 different regionalization methods

Tab. A.1 – 1997 25-sector Marche I-O table constructed by SLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 9 50,931 2,636 6,227 4,652 344 0 6 274 52 1 1 16 1
2 Other agriculture 131,606 797,107 53 308,480 15,968 37,712 28,174 2,080 0 39 1,657 314 4 4 98 8
3 Mining 78 473 8,774 609 53 388 703 139 252 7 200 710 8,788 2,034 3,633 102
4 Food and tobacco 6,524 39,512 0 12,573 34 13,743 107 31 0 14 2 0 0 0 0 0
5 Textile products and apparel 259 1,570 52 157 41,718 11,154 3,933 172 0 2 361 60 126 29 116 11
6 Leather and shoes 56 341 13 0 1,467 246,299 3,702 93 0 1 70 1 50 13 81 2
7 Timber and furniture 62 379 70 631 738 3,215 180,267 165 0 5 130 389 710 137 371 18
8 Paper, printing, publishing 33 198 91 1,566 208 1,065 893 7,278 0 19 164 239 189 123 481 6
9 Coke and nuclear fuel 46 277 19 41 13 46 48 7 8 1 3 15 8 2 5 0
10 Chemicals 105 636 14 25 117 180 124 31 0 9 92 15 15 4 21 0
11 Rubber and plastic products 64 387 33 513 269 7,084 2,314 107 0 9 756 78 163 240 772 37
12 Non-metal mineral products 21 124 155 410 9 96 781 34 0 12 23 1,011 135 14 231 4
13 Metal products 72 435 271 641 177 1,499 2,832 77 2 5 180 140 1,990 2,038 1,331 104
14 Machinery (except electricity) 79 480 76 121 82 225 99 64 1 2 23 76 201 1,313 498 29
15 Electrical and electronic equipment 21 130 110 107 45 95 128 67 1 4 71 47 276 709 12,434 61
16 Transportation equipment 3 16 0 0 0 0 0 0 0 0 0 0 0 1 0 4
17 Other manufacturing 3 17 158 137 344 490 68 148 0 5 72 204 67 87 1,638 5
18 Energy and water 6 35 5 10 7 11 14 3 0 0 2 3 3 1 2 0
19 Construction 154 935 709 712 454 2,131 793 343 5 13 168 439 410 215 732 14
20 Trade 27,489 166,496 17,825 46,425 32,963 154,056 58,153 14,103 13 436 7,011 10,171 18,373 8,685 22,681 662
21 Hotels and businesses 20 124 377 709 710 1,936 1,739 199 11 18 119 341 499 367 1,204 21
22 Transport and communication 1,977 11,977 2,814 6,715 2,659 13,325 7,382 1,474 32 92 745 1,671 2,835 1,399 3,366 84
23 Credit and insurance 1,676 10,150 199 1,213 968 3,678 3,715 306 7 8 129 212 613 261 604 10
24 Real estate, renting, research, business services 2,956 17,901 6,758 18,328 18,115 48,087 31,293 7,640 45 459 3,325 4,540 10,661 5,622 21,727 502
25 Other services 180 1,092 314 2,924 784 1,127 602 803 1 30 194 155 399 230 976 50
Total intermediate costs 173,491 1,050,791 38,899 453,978 120,538 553,869 332,516 35,708 378 1,196 15,771 20,883 46,516 23,529 73,018 1,735
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 43,850 265,590 26,971 60,285 65,258 147,183 84,110 27,523 961 1,497 17,712 15,931 33,664 20,492 50,545 1,533
Other primary inputs -76,594 -463,910 12,302 -179,975 -14,452 91,726 -10,636 2,345 5,816 1,050 -1,265 -2,216 -7,782 4,829 57,586 1,609
PRIMARY INPUTS 165,643 1,003,258 56,692 -53,120 125,067 482,494 233,470 58,785 7,274 3,447 28,870 31,079 64,048 41,401 148,988 4,219
INPUT 339,133 2,054,050 95,591 400,858 245,605 1,036,363 565,986 94,493 7,652 4,643 44,641 51,962 110,564 64,930 222,006 5,954
Source: Author’s elaboration

Tab. A.1 – 1997 25-sector Marche I-O table constructed by SLQ (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 592 0 295 36 25,350 166 5 49 3,985 95,626 0 28,756 214,751 243,507 339,133
2 Other agriculture 3,585 0 1,789 215 153,538 1,007 28 299 24,133 1,507,900 677,397 174,165 -305,412 546,150 2,054,050
3 Mining 14,494 103 26,086 7,010 16 99 2 25 1,858 76,636 2 12,697 6,256 18,955 95,591
4 Food and tobacco 24 0 0 94 59,102 174 0 0 6,852 138,786 217,638 32,968 11,465 262,071 400,858
5 Textile products and apparel 258 0 1,749 2,725 1,772 813 24 885 4,688 72,634 99,131 71,089 2,757 172,977 245,605
6 Leather and shoes 250 0 0 8,957 1 57 31 9 2,330 263,824 305,066 431,019 36,453 772,538 1,036,363
7 Timber and furniture 1,222 0 36,764 13,212 3,542 537 88 5,276 6,190 254,118 181,159 120,654 10,055 311,868 565,986
8 Paper, printing, publishing 263 0 1,895 21,707 2,916 1,305 303 10,031 16,782 67,755 14,561 10,037 2,138 26,736 94,493
9 Coke and nuclear fuel 14 1 189 796 153 424 3 244 572 2,935 1,452 564 2,704 4,720 7,652
10 Chemicals 19 0 225 113 65 9 1 81 744 2,645 1,007 895 91 1,993 4,643
11 Rubber and plastic products 367 0 4,873 5,594 163 1,316 2 1,711 2,079 28,931 3,885 10,798 1,029 15,712 44,641
12 Non-metal mineral products 297 0 31,457 389 819 20 0 139 590 36,771 1,548 10,528 3,116 15,192 51,962
13 Metal products 1,215 1 19,380 23,641 677 540 56 2,981 3,136 63,421 3,913 24,205 19,024 47,142 110,564
14 Machinery (except electricity) 53 1 3,091 3,372 0 413 22 763 2,989 14,073 207 33,143 17,511 50,861 64,930
15 Electrical and electronic equipment 424 3 20,414 28,863 492 915 70 3,069 8,972 77,528 25,010 64,437 55,034 144,481 222,006
16 Transportation equipment 0 0 0 746 0 47 0 13 213 1,043 1,681 1,621 1,605 4,907 5,954
17 Other manufacturing 1,757 0 600 907 180 420 22 2,598 9,517 19,444 97,253 67,806 -15,646 149,413 168,856
18 Energy and water 2 0 11 91 43 8 2 47 79 385 162 1 0 163 549
19 Construction 144 19 59,979 19,855 9,339 10,362 1,377 161,625 103,126 374,053 24,830 55,624 1,466,736 1,547,190 1,921,242
20 Trade 12,916 10 132,642 613,819 194,255 59,462 2,748 141,999 153,348 1,896,741 3,261,853 326,677 280,650 3,869,180 5,765,921
21 Hotels and businesses 372 1 7,790 44,440 0 7,195 1,021 31,601 25,889 126,703 923,963 220 0 924,183 1,050,886
22 Transport and communication 2,030 3 35,940 110,701 13,391 51,228 1,703 26,776 43,460 343,779 156,056 101,233 15,196 272,485 616,260
23 Credit and insurance 545 1 18,804 59,092 2,205 3,425 1,968 11,127 67,054 187,970 8,674 10,685 0 19,359 207,332
24 Real estate, renting, research, business services 10,423 34 193,989 511,564 85,854 53,349 94,339 467,445 450,179 2,065,135 2,051,401 135,007 71,490 2,257,898 4,323,036
25 Other services 273 1 2,996 105,174 21,674 7,084 1,948 43,081 126,708 318,800 1,223,541 10,576 3,436,044 4,670,161 4,988,962
Total intermediate costs 51,539 178 600,958 1,583,113 575,547 200,375 105,763 911,874 1,065,473 8,037,636 9,281,390 1,735,405 5,333,047 16,349,842 24,387,475
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 42,726 162 534,118 861,633 199,821 129,982 15,004 196,726 723,851 3,567,128
Other primary inputs 42,578 -33 -27,351 -9,658 -194,212 -55,758 -21,041 57,590 -136,737 -924,189
PRIMARY INPUTS 117,317 371 1,320,284 4,182,808 475,339 415,885 101,569 3,411,162 3,923,489 16,349,839
INPUT 168,856 549 1,921,242 5,765,921 1,050,886 616,260 207,332 4,323,036 4,988,962 24,387,475
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.2 – 1997 25-sector Marche I-O table constructed by PLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 9 50,931 2,636 6,227 4,652 344 0 6 274 52 1 1 16 1
2 Other agriculture 131,606 797,107 53 308,480 15,968 37,712 28,174 2,080 0 39 1,657 314 4 4 98 8
3 Mining 78 473 8,774 609 53 388 703 139 252 7 200 710 8,788 2,034 3,633 102
4 Food and tobacco 6,524 39,512 0 12,573 34 13,743 107 31 0 14 2 0 0 0 0 0
5 Textile products and apparel 259 1,570 52 157 41,718 11,154 3,933 172 0 2 361 60 126 29 116 11
6 Leather and shoes 56 341 13 0 1,467 246,299 3,702 93 0 1 70 1 50 13 81 2
7 Timber and furniture 62 379 70 631 738 3,215 180,267 165 0 5 130 389 710 137 371 18
8 Paper, printing, publishing 33 198 91 1,566 208 1,065 893 7,278 0 19 164 239 189 123 481 6
9 Coke and nuclear fuel 46 277 19 41 13 46 48 7 8 1 3 15 8 2 5 0
10 Chemicals 105 636 14 25 117 180 124 31 0 9 92 15 15 4 21 0
11 Rubber and plastic products 64 387 33 513 269 7,084 2,314 107 0 9 756 78 163 240 772 37
12 Non-metal mineral products 21 124 155 410 9 96 781 34 0 12 23 1,011 135 14 231 4
13 Metal products 72 435 271 641 177 1,499 2,832 77 2 5 180 140 1,990 2,038 1,331 104
14 Machinery (except electricity) 79 480 76 121 82 225 99 64 1 2 23 76 201 1,313 498 29
15 Electrical and electronic equipment 21 130 110 107 45 95 128 67 1 4 71 47 276 709 12,434 61
16 Transportation equipment 3 16 0 0 0 0 0 0 0 0 0 0 0 1 0 4
17 Other manufacturing 3 17 158 137 344 490 68 148 0 5 72 204 67 87 1,638 5
18 Energy and water 6 35 5 10 7 11 14 3 0 0 2 3 3 1 2 0
19 Construction 154 935 709 712 454 2,131 793 343 5 13 168 439 410 215 732 14
20 Trade 27,489 166,496 17,825 46,425 32,963 154,056 58,153 14,103 13 436 7,011 10,171 18,373 8,685 22,681 662
21 Hotels and businesses 20 124 377 709 710 1,936 1,739 199 11 18 119 341 499 367 1,204 21
22 Transport and communication 1,977 11,977 2,814 6,715 2,659 13,325 7,382 1,474 32 92 745 1,671 2,835 1,399 3,366 84
23 Credit and insurance 1,676 10,150 199 1,213 968 3,678 3,715 306 7 8 129 212 613 261 604 10
24 Real estate, renting, research, business services 2,956 17,901 6,758 18,328 18,115 48,087 31,293 7,640 45 459 3,325 4,540 10,661 5,622 21,727 502
25 Other services 180 1,092 314 2,924 784 1,127 602 803 1 30 194 155 399 230 976 50
Total intermediate costs 173,491 1,050,791 38,899 453,978 120,538 553,869 332,516 35,708 378 1,196 15,771 20,883 46,516 23,529 73,018 1,735
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 46,685 282,763 26,872 64,613 65,368 137,932 86,059 27,516 959 1,501 17,708 15,995 33,564 20,358 49,883 1,520
Other primary inputs -79,429 -481,083 12,401 -184,303 -14,562 100,977 -12,584 2,351 5,819 1,046 -1,261 -2,280 -7,683 4,963 58,247 1,622
PRIMARY INPUTS 165,643 1,003,258 56,692 -53,120 125,067 482,494 233,471 58,784 7,275 3,447 28,870 31,079 64,047 41,401 148,987 4,219
INPUT 339,133 2,054,050 95,591 400,858 245,605 1,036,363 565,987 94,492 7,653 4,643 44,641 51,962 110,563 64,930 222,005 5,954
Source: Author’s elaboration

Tab. A.2 – 1997 25-sector Marche I-O table constructed by PLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 592 0 295 36 25,350 166 5 49 3,985 95,626 0 28,756 214,751 243,507 339,133
2 Other agriculture 3,585 0 1,789 215 153,538 1,007 28 299 24,133 1,507,900 677,397 174,165 -305,412 546,150 2,054,050
3 Mining 14,494 103 26,086 7,010 16 99 2 25 1,858 76,636 2 12,697 6,256 18,955 95,591
4 Food and tobacco 24 0 0 94 59,102 174 0 0 6,852 138,786 217,638 32,968 11,465 262,071 400,858
5 Textile products and apparel 258 0 1,749 2,725 1,772 813 24 885 4,688 72,634 99,131 71,089 2,757 172,977 245,605
6 Leather and shoes 250 0 0 8,957 1 57 31 9 2,330 263,824 305,066 431,019 36,453 772,538 1,036,363
7 Timber and furniture 1,222 0 36,764 13,212 3,542 537 88 5,276 6,190 254,118 181,159 120,654 10,055 311,868 565,987
8 Paper, printing, publishing 263 0 1,895 21,707 2,916 1,305 303 10,031 16,782 67,755 14,561 10,037 2,138 26,736 94,492
9 Coke and nuclear fuel 14 1 189 796 153 424 3 244 572 2,935 1,452 564 2,704 4,720 7,653
10 Chemicals 19 0 225 113 65 9 1 81 744 2,645 1,007 895 91 1,993 4,643
11 Rubber and plastic products 367 0 4,873 5,594 163 1,316 2 1,711 2,079 28,931 3,885 10,798 1,029 15,712 44,641
12 Non-metal mineral products 297 0 31,457 389 819 20 0 139 590 36,771 1,548 10,528 3,116 15,192 51,962
13 Metal products 1,215 1 19,380 23,641 677 540 56 2,981 3,136 63,421 3,913 24,205 19,024 47,142 110,563
14 Machinery (except electricity) 53 1 3,091 3,372 0 413 22 763 2,989 14,073 207 33,143 17,511 50,861 64,930
15 Electrical and electronic equipment 424 3 20,414 28,863 492 915 70 3,069 8,972 77,528 25,010 64,437 55,034 144,481 222,005
16 Transportation equipment 0 0 0 746 0 47 0 13 213 1,043 1,681 1,621 1,605 4,907 5,954
17 Other manufacturing 1,757 0 600 907 180 420 22 2,598 9,517 19,444 97,253 67,806 -15,646 149,413 168,856
18 Energy and water 2 0 11 91 43 8 2 47 79 385 162 1 0 163 548
19 Construction 144 19 59,979 19,855 9,339 10,362 1,377 161,625 103,126 374,053 24,830 55,624 1,466,736 1,547,190 1,921,242
20 Trade 12,916 10 132,642 613,819 194,255 59,462 2,748 141,999 153,348 1,896,741 3,261,853 326,677 280,650 3,869,180 5,765,921
21 Hotels and businesses 372 1 7,790 44,440 0 7,195 1,021 31,601 25,889 126,703 923,963 220 0 924,183 1,050,886
22 Transport and communication 2,030 3 35,940 110,701 13,391 51,228 1,703 26,776 43,460 343,779 156,056 101,233 15,196 272,485 616,261
23 Credit and insurance 545 1 18,804 59,092 2,205 3,425 1,968 11,127 67,054 187,970 8,674 10,685 0 19,359 207,332
24 Real estate, renting, research, business services 10,423 34 193,989 511,564 85,854 53,349 94,339 467,445 450,179 2,065,135 2,051,401 135,007 71,490 2,257,898 4,323,036
25 Other services 273 1 2,996 105,174 21,674 7,084 1,948 43,081 126,708 318,800 1,223,541 10,576 3,436,044 4,670,161 4,988,961
Total intermediate costs 51,539 178 600,958 1,583,113 575,547 200,375 105,763 911,874 1,065,473 8,037,636 9,281,390 1,735,405 5,333,047 16,349,842 24,387,473
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 42,588 161 535,696 857,374 221,184 129,395 14,978 195,916 722,268 3,598,856
Other primary inputs 42,716 -33 -28,929 -5,399 -215,575 -55,170 -21,015 58,400 -135,155 -955,919
PRIMARY INPUTS 117,317 370 1,320,284 4,182,808 475,339 415,886 101,569 3,411,162 3,923,488 16,349,837
INPUT 168,856 548 1,921,242 5,765,921 1,050,886 616,261 207,332 4,323,036 4,988,961 24,387,473
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.3 – 1997 25-sector Marche I-O table constructed by CILQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 9 50,931 2,636 6,227 4,652 344 0 6 274 52 1 1 16 1
2 Other agriculture 131,606 797,107 53 308,480 15,968 37,712 28,174 2,080 0 39 1,657 314 4 4 98 8
3 Mining 19 113 17,391 776 77 104 427 236 335 14 399 1,279 16,012 4,067 5,856 204
4 Food and tobacco 2,885 17,475 0 29,544 86 8,537 114 71 0 32 6 0 0 0 0 0
5 Textile products and apparel 59 359 72 191 57,885 2,847 2,297 239 0 3 501 83 175 40 160 15
6 Leather and shoes 50 305 13 0 1,467 246,338 3,703 93 0 1 70 1 50 13 81 2
7 Timber and furniture 25 152 72 651 759 1,446 185,474 169 0 5 134 401 731 141 382 18
8 Paper, printing, publishing 11 65 214 2,746 416 392 752 17,044 1 44 385 560 443 287 1,067 13
9 Coke and nuclear fuel 16 97 44 78 29 18 43 18 130 17 9 47 21 10 11 4
10 Chemicals 38 231 43 47 258 73 115 80 2 152 296 51 40 19 54 6
11 Rubber and plastic products 24 147 105 1,036 618 2,994 2,238 288 1 29 2,540 262 470 807 1,967 124
12 Non-metal mineral products 8 45 471 795 20 39 727 87 0 38 74 3,348 374 46 565 14
13 Metal products 26 155 739 1,222 386 599 2,589 196 6 14 491 381 5,415 5,547 3,204 283
14 Machinery (except electricity) 22 133 178 178 139 70 70 127 2 5 58 191 425 4,204 932 92
15 Electrical and electronic equipment 7 42 239 180 87 34 106 147 1 7 154 104 603 1,545 27,096 134
16 Transportation equipment 1 7 0 0 0 0 0 0 0 0 1 1 2 4 1 70
17 Other manufacturing 1 7 172 149 373 220 70 160 0 5 78 221 73 95 1,776 5
18 Energy and water 2 13 16 20 16 5 13 7 0 2 6 9 9 4 4 1
19 Construction 66 400 743 746 476 1,021 832 360 5 14 176 461 429 226 767 14
20 Trade 15,251 92,370 18,917 51,374 36,337 95,298 63,700 15,383 15 477 7,700 11,159 20,015 9,594 25,056 733
21 Hotels and businesses 9 54 473 890 892 944 1,940 250 14 23 150 428 626 461 1,510 27
22 Transport and communication 751 4,551 5,245 12,544 5,004 5,531 6,910 2,770 61 170 1,397 3,148 5,237 2,572 6,241 156
23 Credit and insurance 690 4,178 642 2,661 2,422 1,692 3,910 896 23 27 414 682 1,920 839 1,676 33
24 Real estate, renting, research, business services 1,363 8,253 7,049 19,171 18,966 24,812 32,744 7,998 46 475 3,443 4,751 11,153 5,861 22,450 519
25 Other services 180 1,092 345 3,210 861 541 661 882 1 33 213 170 438 253 1,072 55
Total intermediate costs 153,110 927,351 53,245 487,620 146,178 437,494 342,261 49,925 643 1,632 20,626 28,104 64,666 36,640 102,042 2,531
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 62,461 378,313 5,527 20,453 19,549 237,390 80,887 5,660 12 34 9,888 4,240 6,688 1,116 9,360 32
Other primary inputs -74,824 -453,193 19,402 -173,786 5,621 117,897 -17,157 9,992 6,501 2,074 1,705 2,256 1,044 11,098 69,745 2,317
PRIMARY INPUTS 186,023 1,126,699 42,348 -86,763 99,431 598,872 223,726 44,569 7,010 3,008 24,016 23,860 45,898 28,294 119,962 3,426
INPUT 339,133 2,054,050 95,593 400,857 245,609 1,036,366 565,987 94,494 7,653 4,640 44,642 51,964 110,564 64,934 222,004 5,957
Source: Author’s elaboration

Tab. A.3 – 1997 25-sector Marche I-O table constructed by CILQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 592 0 295 36 25,350 166 5 49 3,985 95,626 0 28,756 214,751 243,507 339,133
2 Other agriculture 3,585 0 1,789 215 153,538 1,007 28 299 24,133 1,507,900 677,397 174,165 -305,412 546,150 2,054,050
3 Mining 9,359 137 15,271 3,361 7 116 2 11 1,060 76,633 2 12,697 6,256 18,955 95,593
4 Food and tobacco 36 0 0 85 71,453 381 0 0 8,082 138,787 217,638 32,968 11,465 262,071 400,857
5 Textile products and apparel 159 0 977 1,248 1,165 932 33 458 2,735 72,633 99,131 71,089 2,757 172,977 245,609
6 Leather and shoes 250 0 0 8,958 1 57 31 9 2,331 263,824 305,066 431,019 36,453 772,538 1,036,366
7 Timber and furniture 1,257 0 36,180 10,657 3,644 553 91 4,812 6,366 254,120 181,159 120,654 10,055 311,868 565,987
8 Paper, printing, publishing 234 1 1,526 14,326 2,762 2,157 710 7,484 14,119 67,759 14,561 10,037 2,138 26,736 94,494
9 Coke and nuclear fuel 13 11 162 560 153 729 8 200 512 2,940 1,452 564 2,704 4,720 7,653
10 Chemicals 18 1 199 82 68 15 3 67 690 2,648 1,007 895 91 1,993 4,640
11 Rubber and plastic products 375 1 4,507 4,242 177 2,501 5 1,467 2,009 28,934 3,885 10,798 1,029 15,712 44,642
12 Non-metal mineral products 292 0 27,986 284 858 36 0 115 550 36,772 1,548 10,528 3,116 15,192 51,964
13 Metal products 1,172 3 16,948 16,946 697 969 151 2,416 2,866 63,421 3,913 24,205 19,024 47,142 110,564
14 Machinery (except electricity) 40 3 2,100 1,878 0 575 47 481 2,122 14,072 207 33,143 17,511 50,861 64,934
15 Electrical and electronic equipment 372 7 16,177 18,748 460 1,491 152 2,246 7,385 77,524 25,010 64,437 55,034 144,481 222,004
16 Transportation equipment 0 0 0 635 0 97 0 13 213 1,045 1,681 1,621 1,605 4,907 5,957
17 Other manufacturing 1,905 0 590 730 195 456 24 2,367 9,773 19,445 97,253 67,806 -15,646 149,413 168,856
18 Energy and water 2 3 10 66 45 14 5 39 74 385 162 1 0 163 549
19 Construction 151 20 62,878 17,062 9,790 10,862 1,443 157,002 108,110 374,054 24,830 55,624 1,466,736 1,547,190 1,921,243
20 Trade 14,263 10 146,019 677,294 215,472 63,519 2,886 148,382 165,515 1,896,739 3,261,853 326,677 280,650 3,869,180 5,765,919
21 Hotels and businesses 438 2 8,310 38,859 0 9,031 1,281 31,238 28,856 126,706 923,963 220 0 924,183 1,050,886
22 Transport and communication 2,058 7 32,983 78,611 14,243 84,360 3,380 23,129 42,720 343,779 156,056 101,233 15,196 272,485 616,257
23 Credit and insurance 606 3 18,934 48,770 2,612 7,084 6,332 10,382 70,545 187,973 8,674 10,685 0 19,359 207,327
24 Real estate, renting, research, business services 10,909 34 203,105 476,332 90,158 55,923 98,767 488,407 472,446 2,065,135 2,051,401 135,007 71,490 2,257,898 4,323,045
25 Other services 300 1 3,148 90,582 23,795 7,777 2,138 41,946 139,108 318,802 1,223,541 10,576 3,436,044 4,670,161 4,988,965
Total intermediate costs 48,386 244 600,094 1,510,567 616,643 250,808 117,522 923,019 1,116,305 8,037,656 9,281,390 1,735,405 5,333,047 16,349,842 24,387,494
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 41,307 0 541,688 1,001,721 180,879 65,036 1,283 220,478 717,211 3,611,213
Other primary inputs 47,150 63 -34,056 -77,202 -216,366 -41,248 -19,084 22,702 -180,926 -968,275
PRIMARY INPUTS 120,470 305 1,321,149 4,255,352 434,243 365,449 89,805 3,400,026 3,872,660 16,349,838
INPUT 168,856 549 1,921,243 5,765,919 1,050,886 616,257 207,327 4,323,045 4,988,965 24,387,494
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.4 – 1997 25-sector Marche I-O table constructed by RLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 9 50,931 2,636 6,227 4,652 344 0 6 274 52 1 1 16 1
2 Other agriculture 131,606 797,107 53 308,480 15,968 37,712 28,174 2,080 0 39 1,657 314 4 4 98 8
3 Mining 29 175 15,405 734 71 153 476 214 366 16 373 1,217 14,355 4,545 5,339 228
4 Food and tobacco 3,913 23,700 0 24,436 78 10,266 112 69 0 31 6 0 0 0 0 0
5 Textile products and apparel 94 567 82 185 54,735 4,300 2,610 258 0 4 572 95 200 45 165 17
6 Leather and shoes 56 341 13 0 1,467 246,299 3,702 93 0 1 70 1 50 13 81 2
7 Timber and furniture 35 210 72 648 756 1,907 184,022 169 0 5 134 399 729 141 380 18
8 Paper, printing, publishing 16 95 209 2,439 361 544 785 14,480 1 46 397 588 399 301 913 13
9 Coke and nuclear fuel 23 140 38 68 25 25 45 15 96 14 7 38 17 8 10 3
10 Chemicals 55 331 35 41 221 100 119 67 2 112 241 41 33 15 45 5
11 Rubber and plastic products 34 206 84 888 519 4,015 2,257 236 1 29 2,026 214 382 813 1,626 125
12 Non-metal mineral products 11 64 383 690 17 53 742 73 0 39 60 2,696 308 46 473 14
13 Metal products 36 218 650 1,046 323 802 2,606 161 6 14 456 362 4,393 5,609 2,643 286
14 Machinery (except electricity) 33 203 155 165 127 101 77 113 2 6 50 165 374 3,539 835 101
15 Electrical and electronic equipment 10 62 246 160 76 48 110 132 1 7 162 109 575 1,625 23,266 140
16 Transportation equipment 2 9 0 0 0 0 0 0 0 0 1 1 2 3 1 49
17 Other manufacturing 2 9 171 148 371 289 69 159 0 5 78 220 72 94 1,765 5
18 Energy and water 3 18 13 18 13 6 14 6 0 2 5 8 8 3 4 0
19 Construction 90 546 730 734 468 1,328 818 354 5 14 173 453 422 222 754 14
20 Trade 20,253 122,670 18,463 49,293 34,920 120,872 61,372 14,846 15 459 7,411 10,745 19,327 9,212 24,058 704
21 Hotels and businesses 12 72 459 864 865 1,212 1,873 243 13 22 145 415 607 447 1,465 25
22 Transport and communication 1,034 6,260 5,244 11,247 4,995 7,315 6,909 2,765 60 169 1,394 3,137 5,252 2,586 6,254 154
23 Credit and insurance 948 5,744 539 2,232 1,988 2,220 3,858 719 22 26 367 618 1,526 831 1,355 32
24 Real estate, renting, research, business services 1,855 11,232 6,922 18,801 18,589 32,200 32,109 7,840 46 468 3,392 4,658 10,936 5,756 22,136 513
25 Other services 180 1,092 337 3,141 842 701 644 863 1 32 209 166 428 247 1,048 54
Total intermediate costs 160,329 971,072 50,312 477,389 140,431 478,695 338,155 46,299 637 1,566 19,660 26,712 60,400 36,106 94,730 2,511
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 56,847 344,308 10,842 31,483 32,134 208,795 81,786 11,615 273 370 11,566 6,676 13,716 2,788 20,469 189
Other primary inputs -76,429 -462,909 17,018 -174,583 -1,218 105,290 -13,950 7,663 6,245 1,802 995 1,212 -1,719 9,957 65,950 2,174
PRIMARY INPUTS 178,805 1,082,977 45,279 -76,530 105,177 557,670 227,832 48,195 7,015 3,072 24,984 25,252 50,163 28,825 127,276 3,440
INPUT 339,133 2,054,050 95,591 400,859 245,608 1,036,365 565,987 94,494 7,652 4,638 44,644 51,964 110,563 64,931 222,006 5,951
Source: Author’s elaboration

Tab. A.4 – 1997 25-sector Marche I-O table constructed by RLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 592 0 295 36 25,350 166 5 49 3,985 95,626 0 28,756 214,751 243,507 339,133
2 Other agriculture 3,585 0 1,789 215 153,538 1,007 28 299 24,133 1,507,900 677,397 174,165 -305,412 546,150 2,054,050
3 Mining 10,261 150 17,201 4,018 8 110 2 12 1,175 76,633 2 12,697 6,256 18,955 95,591
4 Food and tobacco 32 0 0 89 68,002 315 0 0 7,739 138,788 217,638 32,968 11,465 262,071 400,859
5 Textile products and apparel 178 0 1,124 1,524 1,282 910 38 538 3,110 72,633 99,131 71,089 2,757 172,977 245,608
6 Leather and shoes 250 0 0 8,957 1 57 31 9 2,330 263,824 305,066 431,019 36,453 772,538 1,036,365
7 Timber and furniture 1,253 0 36,346 11,363 3,632 550 90 4,941 6,317 254,117 181,159 120,654 10,055 311,868 565,987
8 Paper, printing, publishing 241 1 1,614 16,082 2,796 1,936 654 8,092 14,752 67,755 14,561 10,037 2,138 26,736 94,494
9 Coke and nuclear fuel 13 12 170 621 153 650 6 212 528 2,937 1,452 564 2,704 4,720 7,652
10 Chemicals 18 1 208 91 68 14 3 72 711 2,649 1,007 895 91 1,993 4,638
11 Rubber and plastic products 373 1 4,603 4,597 173 2,166 4 1,531 2,028 28,931 3,885 10,798 1,029 15,712 44,644
12 Non-metal mineral products 293 0 28,935 311 849 32 0 121 560 36,770 1,548 10,528 3,116 15,192 51,964
13 Metal products 1,163 3 17,272 18,329 679 837 126 2,517 2,885 63,422 3,913 24,205 19,024 47,142 110,563
14 Machinery (except electricity) 43 3 2,325 2,207 0 540 41 544 2,321 14,070 207 33,143 17,511 50,861 64,931
15 Electrical and electronic equipment 383 7 17,156 21,100 467 1,341 149 2,436 7,750 77,518 25,010 64,437 55,034 144,481 222,006
16 Transportation equipment 0 0 0 670 0 83 0 13 213 1,047 1,681 1,621 1,605 4,907 5,951
17 Other manufacturing 1,857 0 591 777 194 453 24 2,423 9,669 19,445 97,253 67,806 -15,646 149,413 168,855
18 Energy and water 2 2 10 73 45 12 4 41 77 387 162 1 0 163 550
19 Construction 148 20 61,809 17,940 9,624 10,678 1,419 159,020 106,272 374,055 24,830 55,624 1,466,736 1,547,190 1,921,242
20 Trade 13,697 10 140,401 650,629 206,553 61,828 2,830 145,749 160,424 1,896,741 3,261,853 326,677 280,650 3,869,180 5,765,923
21 Hotels and businesses 417 2 8,125 40,324 0 8,761 1,243 31,224 27,870 126,705 923,963 220 0 924,183 1,050,886
22 Transport and communication 2,011 7 33,120 85,618 13,789 75,183 3,343 23,706 42,221 343,773 156,056 101,233 15,196 272,485 616,260
23 Credit and insurance 589 3 18,914 51,707 2,497 6,003 5,018 10,602 69,612 187,970 8,674 10,685 0 19,359 207,332
24 Real estate, renting, research, business services 10,696 34 199,068 497,143 88,142 54,752 96,808 478,917 462,129 2,065,142 2,051,401 135,007 71,490 2,257,898 4,323,034
25 Other services 293 1 3,103 94,755 23,269 7,605 2,091 42,264 135,437 318,803 1,223,541 10,576 3,436,044 4,670,161 4988957
Total intermediate costs 48,388 257 594,179 1,529,176 601,111 235,989 113,957 915,332 1,094,248 8,037,641 9,281,390 1,735,405 5,333,047 16,349,842 24,387,475
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 41,699 17 539,583 954,379 186,113 83,646 4,561 212,922 719,062 3,575,839
Other primary inputs 46,755 34 -26,037 -48,465 -206,068 -45,036 -18,792 37,934 -160,728 -932,905
PRIMARY INPUTS 120,467 293 1,327,063 4,236,747 449,775 380,271 93,375 3,407,702 3,894,709 16,349,834
INPUT 168,855 550 1,921,242 5,765,923 1,050,886 616,260 207,332 4,323,034 4,988,957 24,387,475
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.5 – 1997 25-sector Marche I-O table constructed by FLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 17 96,705 5,006 3,651 6,241 652 0 12 520 98 1 1 31 3
2 Other agriculture 69,041 418,163 102 585,718 30,318 22,114 37,800 3,950 0 73 3,147 597 8 8 187 15
3 Mining 18 110 16,746 755 75 101 413 229 602 51 412 1,211 15,551 5,448 5,687 718
4 Food and tobacco 2,882 17,457 0 29,513 98 8,529 114 94 0 116 11 0 0 0 0 0
5 Textile products and apparel 59 356 98 189 57,493 2,827 2,281 277 1 12 728 124 218 75 176 54
6 Leather and shoes 47 283 44 0 4,934 228,846 7,870 313 0 3 237 3 169 43 271 5
7 Timber and furniture 24 148 229 1,316 1,753 1,405 180,283 458 0 18 452 1,386 2,115 497 978 64
8 Paper, printing, publishing 11 64 248 2,717 411 388 744 16,865 3 158 475 710 470 460 1,055 45
9 Coke and nuclear fuel 16 97 44 77 29 18 43 17 129 18 9 47 21 10 11 4
10 Chemicals 38 231 43 47 258 73 115 80 2 151 296 51 40 19 54 6
11 Rubber and plastic products 24 143 102 1,013 605 2,928 2,188 281 4 102 2,484 263 459 1,024 1,923 437
12 Non-metal mineral products 7 45 469 792 20 39 724 87 1 138 74 3,335 373 58 563 51
13 Metal products 24 147 764 1,152 363 565 2,440 185 21 47 540 429 5,103 7,912 3,020 965
14 Machinery (except electricity) 22 131 175 174 137 69 69 125 9 18 57 188 418 4,134 916 300
15 Electrical and electronic equipment 7 41 289 176 86 34 104 151 6 26 199 137 667 2,584 26,501 471
16 Transportation equipment 1 7 0 0 0 0 0 0 0 0 1 1 2 4 1 70
17 Other manufacturing 1 5 415 229 657 172 54 330 0 15 201 583 160 267 3,462 14
18 Energy and water 2 13 16 20 16 5 13 7 0 2 6 9 9 4 4 1
19 Construction 61 371 2,350 1,507 1,095 945 805 969 18 47 590 1,544 1,237 757 1,956 48
20 Trade 11,062 66,999 47,739 98,634 79,296 68,983 58,196 38,960 38 1,236 20,008 28,970 51,650 25,044 60,708 1,920
21 Hotels and businesses 8 47 1,205 1,446 1,653 829 1,703 543 43 72 406 1,188 1,453 1,464 3,103 83
22 Transport and communication 689 4,172 8,069 12,285 5,619 5,116 6,426 3,640 203 568 2,282 5,266 7,365 5,497 7,768 522
23 Credit and insurance 687 4,159 683 2,650 2,411 1,685 3,893 892 82 96 468 791 1,911 1,231 1,669 116
24 Real estate, renting, research, business services 1,018 6,167 19,016 33,848 38,393 18,579 27,682 18,943 124 1,261 9,181 12,971 28,164 15,853 48,961 1,375
25 Other services 180 1,092 1,040 6,179 1,889 500 611 2,267 5 110 684 559 1,204 844 2,607 182
Total intermediate costs 85,928 520,449 99,903 877,142 232,615 368,401 340,812 90,315 1,291 4,350 43,468 60,461 118,768 73,238 171,612 7,469
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 97,388 589,857 27,952 85,513 81,632 462,644 254,483 31,157 668 889 17,815 15,930 36,697 18,101 60,293 445
Other primary inputs -42,570 -257,834 -49,684 -628,374 -142,897 -38,266 -189,302 -55,898 5,196 -1,498 -29,060 -41,790 -83,066 -42,487 -50,757 -3,036
PRIMARY INPUTS 253,205 1,533,601 -4,313 -476,291 12,996 667,963 225,177 4,176 6,361 291 1,178 -8,496 -8,203 -8,306 50,393 -1,514
INPUT 339,133 2,054,050 95,590 400,851 245,611 1,036,364 565,989 94,491 7,652 4,641 44,646 51,965 110,565 64,932 222,005 5,955
Source: Author’s elaboration

Tab. A.5 – 1997 25-sector Marche I-O table constructed by FLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 838 0 379 37 38,274 316 9 59 5,343 158,192 0 28,756 152,185 180,941 339,133
2 Other agriculture 5,075 0 2,296 227 231,819 1,911 53 355 32,359 1,445,337 677,397 174,165 -242,849 608,713 2,054,050
3 Mining 9,090 246 14,832 3,263 4 109 1 9 957 76,638 2 12,697 6,256 18,955 95,590
4 Food and tobacco 36 0 0 85 71,397 381 0 0 8,072 138,785 217,638 32,968 11,465 262,071 400,851
5 Textile products and apparel 158 0 970 1,239 1,157 926 42 455 2,717 72,632 99,131 71,089 2,757 172,977 245,611
6 Leather and shoes 561 0 0 14,928 1 193 105 17 4,950 263,823 305,066 431,019 36,453 772,538 1,036,364
7 Timber and furniture 1,289 0 35,168 10,359 3,986 1,055 270 4,677 6,187 254,117 181,159 120,654 10,055 311,868 565,989
8 Paper, printing, publishing 231 2 1,510 14,175 2,733 2,134 771 7,405 13,970 67,755 14,561 10,037 2,138 26,736 94,491
9 Coke and nuclear fuel 13 23 162 557 152 724 8 199 509 2,937 1,452 564 2,704 4,720 7,652
10 Chemicals 18 1 199 82 68 15 3 67 690 2,647 1,007 895 91 1,993 4,641
11 Rubber and plastic products 367 2 4,408 4,148 173 2,446 5 1,435 1,965 28,929 3,885 10,798 1,029 15,712 44,646
12 Non-metal mineral products 290 1 27,871 282 854 36 0 115 548 36,773 1,548 10,528 3,116 15,192 51,965
13 Metal products 1,104 11 15,970 15,969 656 913 146 2,277 2,700 63,423 3,913 24,205 19,024 47,142 110,565
14 Machinery (except electricity) 39 9 2,065 1,847 0 566 46 473 2,087 14,074 207 33,143 17,511 50,861 64,932
15 Electrical and electronic equipment 364 24 15,821 18,335 450 1,457 173 2,198 7,226 77,527 25,010 64,437 55,034 144,481 222,005
16 Transportation equipment 0 0 0 635 0 97 0 13 213 1,045 1,681 1,621 1,605 4,907 5,955
17 Other manufacturing 1,488 1 461 571 162 663 54 1,847 7,632 19,444 97,253 67,806 -15,646 149,413 168,858
18 Energy and water 2 3 10 66 45 14 5 39 74 385 162 1 0 163 546
19 Construction 154 68 58,213 15,796 10,665 20,658 4,269 145,354 104,579 374,056 24,830 55,624 1,466,736 1,547,190 1,921,244
20 Trade 13,840 27 128,122 488,047 223,822 111,619 7,218 115,347 149,262 1,896,747 3,261,853 326,677 280,650 3,869,180 5,765,923
21 Hotels and businesses 385 5 7,296 34,116 0 13,843 3,054 27,425 25,335 126,705 923,963 220 0 924,183 1,050,885
22 Transport and communication 1,900 22 30,417 74,884 13,368 81,096 4,996 21,618 39,985 343,773 156,056 101,233 15,196 272,485 616,257
23 Credit and insurance 603 12 18,850 48,554 2,600 7,052 6,304 10,336 70,232 187,967 8,674 10,685 0 19,359 207,328
24 Real estate, renting, research, business services 9,755 89 165,399 382,347 89,476 95,084 256,807 371,333 413,310 2,065,136 2,051,401 135,007 71,490 2,257,898 4,323,039
25 Other services 292 2 2,906 83,621 24,735 14,116 6,036 38,722 128,417 318,800 1,223,541 10,576 3,436,044 4,670,161 4,988,961
Total intermediate costs 47,892 548 533,325 1,214,170 716,597 357,424 290,375 751,775 1,029,319 8,037,647 9,281,390 1,735,405 5,333,044 16,349,839 24,387,481
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 64,533 78 872,226 1,697,861 399,616 185,483 18,655 599,127 1,285,945 6,904,988
Other primary inputs 24,420 -322 -297,824 -476,941 -535,058 -268,311 -209,308 -184,709 -662,678 -4,262,054
PRIMARY INPUTS 120,966 -2 1,387,919 4,551,753 334,288 258,833 -83,047 3,571,264 3,959,642 16,349,834
INPUT 168,858 546 1,921,244 5,765,923 1,050,885 616,257 207,328 4,323,039 4,988,961 24,387,481
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.6 – 1997 25-sector Marche I-O table constructed by SDP (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 2 13,050 676 1,595 1,192 88 0 2 70 13 0 0 4 0
2 Other agriculture 33,722 204,248 14 79,043 4,091 9,664 7,219 533 0 9 425 81 1 1 26 3
3 Mining 95 572 10,361 736 64 469 836 168 17 9 242 620 10,638 2,462 4,398 124
4 Food and tobacco 11,875 71,922 0 23,434 67 35,655 194 58 0 26 5 0 0 0 0 0
5 Textile products and apparel 389 2,353 78 235 62,563 16,727 5,899 258 0 3 542 90 190 43 173 16
6 Leather and shoes 48 294 11 0 1,263 212,057 3,187 80 0 1 61 1 43 11 69 2
7 Timber and furniture 57 345 64 577 672 2,930 164,304 150 0 4 119 355 648 125 339 16
8 Paper, printing, publishing 43 259 119 2,046 272 1,392 1,167 9,511 0 25 215 313 247 160 629 7
9 Coke and nuclear fuel 71 429 25 62 21 72 75 11 13 2 4 23 12 3 7 0
10 Chemicals 184 1,111 25 43 205 314 217 54 0 15 160 27 25 8 39 1
11 Rubber and plastic products 96 580 50 769 403 10,611 3,466 160 1 13 1,133 117 244 360 1,157 55
12 Non-metal mineral products 28 173 216 570 13 133 1,087 47 0 16 32 1,406 188 19 321 6
13 Metal products 114 688 429 1,015 281 2,374 4,484 122 3 8 286 222 3,150 3,226 2,107 164
14 Machinery (except electricity) 294 1,781 284 446 306 834 366 240 3 6 87 281 746 4,878 1,849 107
15 Electrical and electronic equipment 44 263 224 222 99 211 272 146 1 7 145 99 567 1,489 26,152 128
16 Transportation equipment 11 68 0 0 0 0 0 0 0 0 1 2 3 5 3 25
17 Other manufacturing 1 9 80 69 174 248 34 75 0 2 37 103 34 44 827 2
18 Energy and water 8 51 7 13 10 16 21 4 0 0 3 4 5 1 3 0
19 Construction 120 728 552 555 354 1,660 618 268 4 10 131 342 319 168 570 10
20 Trade 12,624 76,461 7,414 21,094 14,858 70,386 25,868 6,218 6 194 3,136 4,540 8,079 3,932 10,268 302
21 Hotels and businesses 17 105 319 600 601 1,639 1,472 169 9 15 101 289 422 311 1,018 18
22 Transport and communication 2,790 16,895 3,937 9,247 3,707 18,287 10,017 2,060 45 131 1,050 2,338 3,954 1,910 4,692 117
23 Credit and insurance 1,837 11,129 219 1,330 1,061 4,033 4,073 336 8 9 141 232 672 286 663 11
24 Real estate, renting, research, business services 1,332 8,066 2,961 8,444 8,550 22,052 14,384 3,594 16 173 1,290 2,125 4,933 2,466 8,135 184
25 Other services 466 2,823 255 2,373 636 915 489 652 1 24 158 126 324 187 792 40
Total intermediate costs 66,265 401,354 27,646 165,973 100,947 414,274 250,941 25,002 127 704 9,574 13,749 35,444 22,095 64,241 1,338
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 44,457 269,266 21,816 56,157 29,070 134,872 79,820 26,947 977 1,500 17,449 15,839 27,668 14,414 33,879 1,289
Other primary inputs 30,025 181,852 28,712 112,150 41,331 243,633 75,231 13,628 6,053 1,535 5,199 5,013 9,286 12,340 83,029 2,250
PRIMARY INPUTS 272,868 1,652,697 67,947 234,877 144,662 622,090 315,047 69,492 7,527 3,935 35,071 38,216 75,120 42,834 157,765 4,616
INPUT 339,133 2,054,051 95,593 400,850 245,609 1,036,364 565,988 94,494 7,654 4,639 44,645 51,965 110,564 64,929 222,006 5,954
Source: Author’s elaboration

Tab. A.6 – 1997 25-sector Marche I-O table constructed by SDP (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 152 0 76 9 6,496 43 1 13 1,021 24,503 0 197,763 116,867 314,630 339,133
2 Other agriculture 918 0 458 55 39,342 257 7 76 6,184 386,377 677,397 1,197,805 -207,529 1,667,673 2,054,051
3 Mining 17,546 7 31,579 8,468 1 109 0 15 1,823 91,359 2 1,159 3,069 4,230 95,593
4 Food and tobacco 62 0 0 170 104,934 344 0 0 13,914 262,660 131,690 95 6,407 138,192 400,850
5 Textile products and apparel 387 0 2,623 4,086 2,658 1,219 36 1,328 7,029 108,925 99,131 34,798 2,757 136,686 245,609
6 Leather and shoes 215 0 0 7,712 1 49 27 8 2,007 227,147 305,066 467,698 36,453 809,217 1,036,364
7 Timber and furniture 1,114 0 33,508 12,042 3,228 490 80 4,808 5,642 231,617 181,159 143,157 10,055 334,371 565,988
8 Paper, printing, publishing 344 0 2,477 28,364 3,810 1,704 396 13,108 21,930 88,538 5,194 0 763 5,957 94,494
9 Coke and nuclear fuel 21 1 293 1,231 234 619 4 337 883 4,453 21 413 2,765 3,199 7,654
10 Chemicals 32 0 393 197 113 15 2 143 1,301 4,624 15 0 1 16 4,639
11 Rubber and plastic products 550 0 7,299 8,378 244 1,970 3 2,563 3,116 43,338 1,032 0 273 1,305 44,645
12 Non-metal mineral products 413 0 43,764 542 1,140 28 0 193 822 51,157 267 0 538 805 51,965
13 Metal products 1,924 2 30,683 37,429 1,072 854 88 4,721 4,965 100,411 1,732 0 8,421 10,153 110,564
14 Machinery (except electricity) 196 3 11,479 12,526 0 1,531 81 2,835 11,102 52,261 148 0 12,524 12,672 64,929
15 Electrical and electronic equipment 862 6 42,507 60,117 998 1,917 141 6,558 19,546 162,721 18,360 0 40,923 59,283 222,006
16 Transportation equipment 0 0 0 4,416 1 233 0 78 933 5,779 91 0 82 173 5,954
17 Other manufacturing 888 0 303 458 91 213 11 1,313 4,808 9,824 97,253 77,426 -15,646 159,033 168,856
18 Energy and water 3 0 16 128 61 12 2 67 113 548 0 0 0 0 548
19 Construction 112 15 46,728 15,469 7,276 8,072 1,073 125,916 80,342 291,412 24,830 138,264 1,466,736 1,629,830 1,921,243
20 Trade 5,843 5 59,617 277,218 88,687 25,082 1,118 57,099 66,130 846,179 3,261,853 1,377,240 280,650 4,919,743 5,765,920
21 Hotels and businesses 315 1 6,595 37,624 0 6,092 864 26,755 21,918 107,269 923,963 19,653 0 943,616 1,050,887
22 Transport and communication 2,828 5 50,048 141,235 17,919 60,314 2,367 38,588 61,529 456,010 108,072 40,673 11,508 160,253 616,258
23 Credit and insurance 598 1 20,617 64,790 2,417 3,755 2,158 12,200 73,519 206,095 1,233 0 0 1,233 207,329
24 Real estate, renting, research, business services 4,834 11 91,428 295,393 45,568 26,552 44,365 220,120 233,210 1,050,186 2,051,401 1,149,957 71,490 3,272,848 4,323,034
25 Other services 222 0 2,432 85,370 17,593 5,749 1,581 34,969 102,849 261,026 1,221,524 70,365 3,436,044 4,727,933 4,988,960
Total intermediate costs 40,379 57 484,923 1,103,427 343,884 147,223 54,405 553,811 746,636 5,074,419 9,111,434 4,916,466 5,285,151 19,313,051 24,387,473
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 34,984 156 521,558 862,559 193,035 118,707 17,238 192,763 781,367 3,497,787
Other primary inputs 61,480 93 101,245 469,101 44,238 8,667 28,080 419,614 124,582 2,108,367
PRIMARY INPUTS 128,477 491 1,436,320 4,662,493 707,003 469,035 152,924 3,769,223 4,242,324 19,313,054
INPUT 168,856 548 1,921,243 5,765,920 1,050,887 616,258 207,329 4,323,034 4,988,960 24,387,473
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.7 – 1997 25-sector Marche I-O table constructed by SCILQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 13 69,247 3,655 5,307 5,400 486 0 10 397 76 1 1 23 2
2 Other agriculture 106,253 643,550 77 419,410 22,141 32,140 32,705 2,945 0 61 2,406 457 6 6 138 13
3 Mining 27 162 13,938 761 72 147 525 205 414 19 330 1,051 13,448 3,784 5,204 269
4 Food and tobacco 3,734 22,618 0 22,660 68 9,959 116 62 0 44 6 0 0 0 0 0
5 Textile products and apparel 93 566 77 187 53,236 4,423 2,973 237 0 5 548 92 179 48 155 24
6 Leather and shoes 52 314 23 0 2,421 240,551 5,032 157 0 2 122 2 86 23 135 3
7 Timber and furniture 35 215 108 861 1,047 1,972 181,821 244 0 9 204 613 1,073 227 543 33
8 Paper, printing, publishing 14 86 173 2,361 337 509 831 12,812 1 58 321 473 345 267 824 16
9 Coke and nuclear fuel 17 105 41 77 28 20 45 17 72 10 8 43 20 9 11 2
10 Chemicals 42 252 41 48 255 79 122 77 1 85 278 47 39 17 52 4
11 Rubber and plastic products 30 182 75 896 509 3,685 2,408 221 1 34 1,760 184 351 631 1,550 139
12 Non-metal mineral products 9 54 329 671 16 46 759 66 0 43 50 2,240 273 35 434 15
13 Metal products 32 195 547 1,018 304 740 2,744 144 7 17 374 294 3,833 4,729 2,408 334
14 Machinery (except electricity) 29 177 149 176 131 92 83 114 3 6 47 154 369 3,033 848 103
15 Electrical and electronic equipment 9 57 202 156 71 45 118 116 1 10 135 91 491 1,496 20,803 174
16 Transportation equipment 1 7 0 0 0 0 0 0 0 0 1 1 2 3 1 39
17 Other manufacturing 2 9 233 177 464 279 64 208 0 9 108 306 96 138 2,274 8
18 Energy and water 2 14 15 20 15 5 14 7 0 1 6 9 9 4 4 0
19 Construction 90 542 1,096 976 647 1,336 811 511 10 25 263 693 620 355 1,073 26
20 Trade 17,645 106,873 26,458 64,733 47,379 106,102 61,917 20,811 26 793 10,894 15,860 27,473 14,171 33,481 1,208
21 Hotels and businesses 11 70 611 1,004 1,052 1,181 1,785 310 22 37 197 565 789 641 1,838 42
22 Transport and communication 973 5,890 5,129 10,115 4,272 7,092 7,221 2,533 84 240 1,392 3,163 4,957 2,822 5,607 216
23 Credit and insurance 822 4,981 446 2,127 1,832 1,991 3,961 631 26 31 296 494 1,307 672 1,210 36
24 Real estate, renting, research, business services 1,699 10,291 9,965 24,188 24,863 29,858 31,194 10,917 80 814 4,932 6,847 15,458 8,810 30,201 882
25 Other services 180 1,092 493 4,059 1,133 704 618 1,214 3 58 310 248 613 387 1,453 95
Total intermediate costs 131,803 798,300 60,239 625,928 165,948 448,263 343,267 55,045 751 2,421 25,385 34,003 71,838 42,309 110,270 3,683
Value Added 198,386 1,201,579 17,419 66,570 74,261 243,585 159,996 28,917 497 900 12,423 17,364 38,166 16,080 40,857 1,077
Imports 54,712 331,375 3,630 61,701 470 196,296 56,828 2,206 136 788 4,174 2,590 4,471 8,594 5,625 1,457
Other primary inputs -45,768 -277,204 14,303 -353,341 4,931 148,220 5,895 8,328 6,269 531 2,663 -1,993 -3,911 -2,052 65,249 -263
PRIMARY INPUTS 207,330 1,255,750 35,352 -225,070 79,662 588,101 222,719 39,451 6,902 2,219 19,260 17,961 38,726 22,622 111,731 2,271
INPUT 339,133 2,054,050 95,591 400,858 245,610 1,036,364 565,986 94,496 7,653 4,640 44,645 51,964 110,564 64,931 222,001 5,954
Source: Author’s elaboration

Tab. A.7 – 1997 25-sector Marche I-O table constructed by SCILQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 697 0 339 38 30,378 224 7 55 4,625 120,980 0 28,756 189,398 218,153 339,133
2 Other agriculture 4,224 0 2,050 232 183,992 1,356 40 335 28,011 1,482,549 677,397 174,165 -280,062 571,501 2,054,050
3 Mining 11,356 184 18,962 4,351 8 115 2 13 1,288 76,635 2 12,697 6,256 18,955 95,591
4 Food and tobacco 33 0 0 95 70,875 296 0 0 8,220 138,786 217,638 32,968 11,465 262,071 400,858
5 Textile products and apparel 203 0 1,281 1,725 1,454 938 34 613 3,542 72,633 99,131 71,089 2,757 172,977 245,610
6 Leather and shoes 345 0 0 11,233 1 91 53 12 3,167 263,825 305,066 431,019 36,453 772,538 1,036,364
7 Timber and furniture 1,265 0 36,257 11,712 3,783 718 134 5,001 6,242 254,117 181,159 120,654 10,055 311,868 565,986
8 Paper, printing, publishing 255 1 1,707 16,802 2,956 1,900 558 8,532 15,615 67,754 14,561 10,037 2,138 26,736 94,496
9 Coke and nuclear fuel 14 8 170 591 158 722 7 209 534 2,938 1,452 564 2,704 4,720 7,653
10 Chemicals 19 0 211 87 71 15 3 72 729 2,646 1,007 895 91 1,993 4,640
11 Rubber and plastic products 398 1 4,897 4,795 186 2,214 4 1,620 2,163 28,934 3,885 10,798 1,029 15,712 44,645
12 Non-metal mineral products 301 0 29,518 311 872 32 0 123 574 36,771 1,548 10,528 3,116 15,192 51,964
13 Metal products 1,225 4 18,160 18,991 716 826 109 2,636 3,037 63,424 3,913 24,205 19,024 47,142 110,564
14 Machinery (except electricity) 46 3 2,499 2,308 0 578 41 579 2,506 14,074 207 33,143 17,511 50,861 64,931
15 Electrical and electronic equipment 408 9 18,216 22,176 494 1,310 126 2,581 8,233 77,528 25,010 64,437 55,034 144,481 222,001
16 Transportation equipment 0 0 0 667 0 94 0 13 217 1,046 1,681 1,621 1,605 4,907 5,954
17 Other manufacturing 1,709 0 555 752 181 533 32 2,307 9,003 19,447 97,253 67,806 -15,646 149,413 168,857
18 Energy and water 2 2 10 67 46 14 5 40 77 388 162 1 0 163 546
19 Construction 151 37 59,969 17,884 10,095 13,935 2,097 155,440 105,369 374,051 24,830 55,624 1,466,736 1,547,190 1,921,245
20 Trade 14,189 17 139,150 587,238 220,220 78,736 3,984 136,542 160,844 1,896,744 3,261,853 326,677 280,650 3,869,180 5,765,921
21 Hotels and businesses 393 3 7,807 39,805 0 9,980 1,627 30,371 26,563 126,704 923,963 220 0 924,183 1,050,887
22 Transport and communication 2,089 9 34,613 89,826 14,209 70,071 3,124 24,579 43,546 343,772 156,056 101,233 15,196 272,485 616,259
23 Credit and insurance 606 4 19,389 52,187 2,567 5,797 4,257 10,821 71,478 187,969 8,674 10,685 0 19,359 207,331
24 Real estate, renting, research, business services 10,659 61 189,722 460,920 91,448 69,599 137,768 440,692 453,267 2,065,135 2,051,401 135,007 71,490 2,257,898 4,323,037
25 Other services 288 1 3,009 94,943 23,572 9,642 3,014 41,580 130,092 318,801 1,223,541 10,576 3,436,044 4,670,161 4,988,967
Total intermediate costs 50,875 344 588,491 1,439,736 658,282 269,736 157,026 864,766 1,088,942 8,037,651 9,281,390 1,735,405 5,333,044 16,349,839 24,387,490
Value Added 32,013 242 813,517 3,330,833 469,730 341,661 107,606 3,156,846 3,336,375 13,706,900
Imports 29,916 93 414,064 802,231 88,124 32,813 26,768 170,613 548,643 2,848,318
Other primary inputs 56,053 -133 105,173 193,121 -165,249 -27,951 -84,069 130,812 15,007 -205,379
PRIMARY INPUTS 117,982 202 1,332,754 4,326,185 392,605 346,523 50,305 3,458,271 3,900,025 16,349,839
INPUT 168,857 546 1,921,245 5,765,921 1,050,887 616,259 207,331 4,323,037 4,988,967 24,387,490
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.8 – 1997 25-sector Marche I-O table constructed by West’s SLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 0 0 5 83,541 5,539 5,972 3,089 778 0 273 2,192 130 3 4 34 88
2 Other agriculture 6,635 40,187 29 505,985 33,545 36,171 18,707 4,713 0 1,652 13,275 786 20 26 207 534
3 Mining 14 88 17,198 3,671 410 1,367 1,684 1,155 1,511 1,119 5,873 4,688 150,287 46,122 28,144 25,342
4 Food and tobacco 10,688 64,737 0 644,658 2,096 236,009 1,931 2,164 52 17,439 672 0 0 0 0 0
5 Textile products and apparel 181 1,095 393 3,554 1,212,954 148,061 36,147 5,391 64 1,380 40,054 2,074 8,142 2,451 3,368 10,001
6 Leather and shoes 23 136 57 0 24,490 1,877,044 19,532 1,678 32 289 4,482 20 1,862 625 1,351 893
7 Timber and furniture 26 161 322 8,718 13,033 25,934 1,006,819 3,138 2 1,773 8,773 8,194 27,810 7,126 6,580 10,018
8 Paper, printing, publishing 20 121 602 30,968 5,266 12,324 7,151 198,867 166 9,742 15,870 7,221 10,627 9,130 12,230 4,314
9 Coke and nuclear fuel 801 4,853 2,753 23,311 9,809 15,460 11,121 5,528 110,000 16,937 7,916 12,652 12,481 5,165 3,457 7,322
10 Chemicals 2,016 12,209 2,948 15,386 93,718 65,558 31,425 26,709 1,998 144,918 280,018 14,585 25,752 10,414 17,502 11,134
11 Rubber and plastic products 60 362 336 15,651 10,496 126,175 28,531 4,501 265 6,848 112,451 3,625 14,102 27,526 30,231 46,134
12 Non-metal mineral products 27 166 2,251 17,844 508 2,440 13,763 2,028 106 13,183 4,857 67,120 16,684 2,285 12,899 7,676
13 Metal products 43 258 1,748 12,388 4,390 16,936 22,145 2,058 1,018 2,537 17,011 4,124 109,036 148,105 33,037 82,683
14 Machinery (except electricity) 68 413 715 3,366 2,960 3,684 1,119 2,498 479 1,188 3,207 3,232 15,979 138,586 17,943 33,253
15 Electrical and electronic equipment 10 64 575 1,647 903 874 815 1,463 253 1,380 5,441 1,142 12,359 42,013 251,814 39,693
16 Transportation equipment 72 437 23 0 0 0 0 61 0 25 667 332 1,128 1,812 426 104,539
17 Other manufacturing 0 2 144 374 1,204 782 75 557 7 342 962 849 520 896 5,745 530
18 Energy and water 566 3,426 5,382 31,713 28,717 20,848 18,449 11,367 2,587 10,323 28,635 13,572 30,397 11,159 8,057 6,953
19 Construction 20 121 994 2,997 2,447 5,243 1,351 1,996 508 1,473 3,448 2,821 4,890 3,410 3,957 2,381
20 Trade 5,874 35,575 37,429 319,298 288,058 623,033 158,516 130,013 2,095 76,724 231,782 104,844 346,983 223,905 199,764 188,048
21 Hotels and businesses 8 49 1,610 9,081 11,661 14,509 9,022 3,529 3,236 6,026 7,473 6,663 18,130 17,704 19,817 10,996
22 Transport and communication 586 3,549 9,079 64,702 34,519 75,687 29,033 20,527 7,153 23,104 35,504 24,911 77,821 51,771 42,365 33,677
23 Credit and insurance 6,013 36,419 7,767 141,596 144,731 251,049 175,523 49,337 19,101 25,241 73,315 37,759 202,869 114,488 90,675 48,346
24 Real estate, renting, research, business services 417 2,525 10,263 87,355 114,980 130,547 59,124 52,080 4,595 50,591 70,298 34,197 145,390 98,810 118,346 85,023
25 Other services 218 1,319 1,286 35,935 12,335 8,100 2,995 13,635 386 9,617 11,651 2,901 13,903 10,640 15,413 25,149
Total intermediate costs 34,387 208,271 103,909 2,063,739 2,058,769 3,703,807 1,658,067 545,771 155,614 424,124 985,827 358,442 1,247,175 974,173 923,362 784,727
Value Added 92,306 559,076 87,934 1,007,733 1,439,762 2,156,121 980,418 604,594 173,357 356,380 918,146 401,008 1,639,171 915,799 794,842 671,591
Imports 7,873 47,682 141,031 453,824 357,849 1,034,850 309,997 509,789 229,236 428,805 965,006 308,693 1,396,913 1,063,552 945,258 822,544
Other primary inputs 23,228 140,687 149,678 2,542,713 905,442 2,278,707 519,762 315,500 1,007,612 628,814 430,384 131,918 465,331 744,524 1,655,528 1,433,187
PRIMARY INPUTS 123,407 747,445 378,643 4,004,270 2,703,053 5,469,678 1,810,177 1,429,883 1,410,205 1,413,999 2,313,536 841,619 3,501,415 2,723,875 3,395,628 2,927,322
INPUT 157,793 955,717 482,552 6,068,009 4,761,822 9,173,485 3,468,244 1,975,654 1,565,819 1,838,123 3,299,363 1,200,061 4,748,590 3,698,048 4,318,990 3,712,049
Source: Author’s elaboration

Tab. A.8 – 1997 25-sector Marche I-O table constructed by West’s SLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 437 1 67 7 17,502 164 13 8 1,357 121,203 0 13,380 23,211 36,591 157,793
2 Other agriculture 2,650 7 405 39 106,006 995 77 50 8,216 780,918 159,158 81,036 -65,396 174,798 955,717
3 Mining 39,369 4,937 21,727 4,753 2 329 1 8 1,345 361,144 21 64,099 57,285 121,405 482,552
4 Food and tobacco 311 0 0 580 1,244,958 4,130 0 0 52,372 2,282,797 3,422,898 499,047 -136,731 3,785,214 6,068,009
5 Textile products and apparel 2,641 0 5,487 6,974 16,936 11,105 910 2,033 29,608 1,551,004 970,514 1,378,256 862,047 3,210,817 4,761,822
6 Leather and shoes 1,469 0 0 13,161 3 449 684 12 4,922 1,953,214 1,222,451 3,815,215 2,182,611 7,220,277 9,173,485
7 Timber and furniture 7,596 0 70,087 20,551 20,568 4,461 2,044 6,799 22,531 1,283,064 502,549 739,343 943,289 2,185,181 3,468,244
8 Paper, printing, publishing 2,347 1,073 5,181 48,412 24,278 15,536 10,064 19,647 69,485 520,642 786,656 209,852 458,503 1,455,011 1,975,654
9 Coke and nuclear fuel 3,543 75,413 14,868 51,014 36,108 136,692 2,663 12,452 92,588 674,907 1,310,482 70,322 -489,888 890,916 1,565,819
10 Chemicals 5,216 4,378 19,391 7,930 17,055 3,150 1,128 4,942 190,654 1,010,134 1,030,849 354,408 -557,267 827,990 1,838,123
11 Rubber and plastic products 5,044 1,035 20,508 19,210 2,089 24,135 93 5,222 13,947 518,577 741,863 797,984 1,240,933 2,780,780 3,299,363
12 Non-metal mineral products 5,828 593 189,160 1,910 15,008 521 0 569 4,557 381,983 92,384 243,139 482,555 818,078 1,200,061
13 Metal products 10,575 4,370 51,725 51,483 5,504 6,275 1,804 4,650 11,708 605,611 434,220 1,039,570 2,669,186 4,142,976 4,748,590
14 Machinery (except electricity) 666 4,138 11,976 10,663 0 6,960 1,022 2,167 11,300 277,582 30,398 1,887,604 1,502,464 3,420,466 3,698,048
15 Electrical and electronic equipment 3,019 8,969 44,484 51,318 3,274 8,695 1,847 4,805 24,825 511,682 1,257,214 1,253,572 1,296,524 3,807,310 4,318,990
16 Transportation equipment 15 0 0 50,743 34 21,576 0 765 22,241 204,896 2,708,267 1,010,597 -211,710 3,507,154 3,712,049
17 Other manufacturing 2,161 99 226 279 207 690 100 715 4,921 22,387 1,714,160 462,524 -1,047,247 1,129,437 1,151,822
18 Energy and water 2,869 57,944 4,932 32,158 56,771 14,285 8,944 12,234 76,092 498,380 1,157,021 2,145 -140,098 1,019,068 1,517,448
19 Construction 273 14,785 34,869 9,419 16,539 26,249 9,725 47,152 64,796 261,864 134,195 116,344 3,506,094 3,756,633 4,018,494
20 Trade 39,855 11,796 124,696 473,104 565,091 228,611 28,406 75,783 246,463 4,765,746 1,985,769 557,511 2,531,168 5,074,448 9,840,197
21 Hotels and businesses 2,151 2,959 13,795 64,210 0 55,525 21,959 40,990 153,568 494,671 2,845,000 1,401 3,354,924 6,201,325 6,695,999
22 Transport and communication 8,981 6,768 48,352 131,139 59,418 287,239 37,211 29,575 103,614 1,246,285 3,815,000 922,688 -367,076 4,370,612 5,616,891
23 Credit and insurance 28,692 22,807 303,289 777,661 108,321 240,709 385,737 112,250 1,289,347 4,693,042 78,635 271,542 225,762 575,939 5,268,981
24 Real estate, renting, research, business services 22,359 24,126 132,163 422,808 226,783 178,209 777,219 220,970 779,777 3,848,955 772,049 205,534 601,055 1,578,638 5,427,597
25 Other services 1,513 1,308 5,086 145,694 112,097 52,408 40,178 40,664 562,954 1,127,385 3,738,548 89,711 15,360,633 19,188,892 20,316,279
Total intermediate costs 199,580 247,506 1,122,474 2,395,220 2,654,552 1,329,098 1,331,829 644,462 3,843,188 29,998,073 30,910,301 16,086,824 34,282,831 81,279,956 111,278,027
Value Added 218,372 668,987 1,701,561 5,684,439 2,993,008 3,114,066 2,734,659 3,856,790 13,802,821 47,572,941
Imports 299,678 337,391 922,982 878,588 534,438 1,029,351 138,473 264,435 1,276,705 14,704,943
Other primary inputs 434,192 263,564 271,477 881,950 514,001 144,376 1,064,020 661,910 1,393,565 19,002,070
PRIMARY INPUTS 952,242 1,269,942 2,896,020 7,444,977 4,041,447 4,287,793 3,937,152 4,783,135 16,473,091 81,279,954
INPUT 1,151,822 1,517,448 4,018,494 9,840,197 6,695,999 5,616,891 5,268,981 5,427,597 20,316,279 111,278,027
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.9 – 1997 25-sector Marche I-O table constructed by GRIT-SLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 5 81,307 5,398 5,415 4,204 692 0 260 1,902 124 3 4 33 86
2 Other agriculture 16,103 74,158 30 492,454 32,692 32,795 25,465 4,191 0 1,573 11,517 751 20 26 201 523
3 Mining 0 0 21 0 0 0 1 0 1,431 1 0 78 0 0 0 0
4 Food and tobacco 12,850 169,521 0 572,098 1,738 124,653 2,089 1,917 40 14,915 558 0 0 0 0 0
5 Textile products and apparel 332 2,012 291 2,496 856,046 99,463 32,507 3,575 44 958 26,123 1,444 5,792 1,742 2,367 6,988
6 Leather and shoes 56 340 74 0 29,819 2,131,392 32,914 1,870 39 344 4,877 24 2,293 768 1,639 1,082
7 Timber and furniture 0 0 0 0 0 0 228,046 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 6 35 445 20,513 3,487 7,156 8,048 111,333 108 6,191 8,471 4,610 7,183 6,147 8,002 2,817
9 Coke and nuclear fuel 5,062 30,658 67 468 200 112 1,055 22 2,013 268 0 206 269 107 66 134
10 Chemicals 27,874 74,937 274 1,255 7,620 4,525 4,754 1,760 158 11,202 17,345 1,134 2,148 864 1,402 883
11 Rubber and plastic products 47 286 246 10,560 7,029 76,268 29,696 2,646 174 4,440 63,750 2,359 9,599 18,681 20,053 30,338
12 Non-metal mineral products 30 185 1,797 13,718 374 1,645 15,136 1,335 77 9,452 3,097 48,275 12,489 1,707 9,437 5,589
13 Metal products 58 348 1,679 11,117 3,899 13,754 29,257 1,631 892 2,190 13,070 3,572 98,251 133,103 29,104 72,704
14 Machinery (except electricity) 0 0 15 7 0 0 290 0 0 0 0 0 0 0 0 0
15 Electrical and electronic equipment 13 80 585 1,540 849 746 1,164 1,216 233 1,257 4,364 1,043 11,767 39,911 234,231 36,802
16 Transportation equipment 13 81 4 0 0 0 0 7 0 3 71 42 165 240 58 13,551
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 Energy and water 77 466 470 2,568 2,326 1,553 2,177 828 207 814 2,027 1,074 2,496 914 648 557
19 Construction 83 501 2,905 8,091 6,613 12,963 5,423 4,817 1,355 3,870 8,067 7,434 13,406 9,324 10,615 6,354
20 Trade 19,113 115,763 52,559 447,869 401,381 823,755 293,294 165,427 2,834 104,012 291,569 142,753 481,335 315,307 277,515 261,185
21 Hotels and businesses 12 72 1,552 8,156 10,444 11,953 11,767 2,843 2,860 5,256 5,848 5,828 16,459 16,031 17,620 9,766
22 Transport and communication 264 1,601 11,085 68,480 39,978 72,561 52,772 20,666 7,595 26,360 32,632 25,840 91,234 61,876 48,608 37,812
23 Credit and insurance 3,327 20,153 2,757 46,761 47,735 76,165 84,305 14,635 6,219 8,108 21,127 12,163 67,822 38,180 29,686 15,815
24 Real estate, renting, research, business services 641 3,882 10,621 84,372 110,579 114,274 82,976 44,903 4,476 47,974 59,633 32,134 141,459 96,398 114,716 82,203
25 Other services 369 2,232 1,396 36,465 12,440 7,515 4,399 12,368 384 9,446 10,266 2,858 14,212 10,850 15,434 25,006
Total intermediate costs 86,462 497,311 88,878 1,910,295 1,580,647 3,618,663 951,739 398,682 31,139 258,894 586,314 293,746 978,402 752,180 821,435 610,195
Value Added 240,847 1,458,753 39,733 526,084 562,583 2,332,200 1,020,555 723,907 273,851 555,452 1,181,054 398,000 1,285,100 1,058,012 901,106 761,982
Imports 191,944 1,162,556 469,113 934,995 1,296,358 1,227,930 769,065 530,283 339,529 591,885 1,171,843 368,188 1,501,627 1,209,118 999,961 968,305
Other primary inputs -357,722 -2,166,641 -115,177 2,696,628 1,322,235 1,994,695 726,886 322,785 921,298 431,887 360,150 140,129 983,458 678,734 1,596,490 1,371,573
PRIMARY INPUTS 75,068 454,669 393,669 4,157,707 3,181,176 5,554,825 2,516,506 1,576,975 1,534,678 1,579,224 2,713,047 906,317 3,770,185 2,945,864 3,497,557 3,101,860
INPUT 161,530 951,980 482,547 6,068,002 4,761,823 9,173,488 3,468,245 1,975,657 1,565,817 1,838,118 3,299,361 1,200,063 4,748,587 3,698,044 4,318,992 3,712,055
Source: Author’s elaboration

Tab. A.9 – 1997 25-sector Marche I-O table constructed by GRIT-SLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 408 1 67 6 14,910 209 12 13 1,299 116,489 0 15,437 29,604 45,041 161,530
2 Other agriculture 2,469 6 404 37 90,303 1,267 74 78 7,871 795,009 129,117 93,498 -65,644 156,971 951,980
3 Mining 0 4,671 0 0 2 2 1 3 12 6,223 497,582 27 -21,284 476,325 482,547
4 Food and tobacco 174 0 0 487 969,125 4,440 0 0 45,033 1,919,638 2,586,071 1,119,648 442,646 4,148,365 6,068,002
5 Textile products and apparel 1,807 0 3,929 4,779 10,912 10,797 636 2,009 20,635 1,097,684 707,953 2,844,802 111,381 3,664,136 4,761,823
6 Leather and shoes 1,713 0 0 15,650 3 735 821 22 5,909 2,232,384 1,725,000 4,805,771 410,332 6,941,103 9,173,488
7 Timber and furniture 0 0 0 0 0 1,619 0 3,160 0 232,825 0 3,254,227 -18,806 3,235,421 3,468,245
8 Paper, printing, publishing 1,434 705 3,561 29,519 12,502 19,121 6,497 26,322 44,725 338,938 1,208,394 352,517 75,809 1,636,720 1,975,657
9 Coke and nuclear fuel 43 1,468 363 619 0 15,170 47 1,606 1,577 61,600 1,547,654 244,003 -287,438 1,504,219 1,565,817
10 Chemicals 384 352 1,651 604 1,008 495 89 907 14,832 178,457 1,226,454 392,813 40,396 1,659,663 1,838,118
11 Rubber and plastic products 3,172 689 14,147 12,195 1,153 27,972 61 6,216 9,135 350,912 1,167,175 1,624,884 156,391 2,948,450 3,299,361
12 Non-metal mineral products 4,068 435 143,275 1,356 9,348 550 0 750 3,300 287,425 157,224 581,640 173,775 912,639 1,200,063
13 Metal products 8,889 3,858 47,157 44,265 4,132 8,821 1,575 7,580 10,206 551,112 594,420 2,008,970 1,594,084 4,197,474 4,748,587
14 Machinery (except electricity) 0 0 0 0 0 2,397 0 802 0 3,511 15,122 3,762,952 -83,539 3,694,535 3,698,044
15 Electrical and electronic equipment 2,666 8,353 42,904 46,558 2,560 14,047 1,699 7,830 22,864 485,282 1,095,759 1,476,012 1,261,938 3,833,709 4,318,992
16 Transportation equipment 2 0 0 6,532 3 5,003 0 192 3,359 29,325 1,942,067 876,696 863,963 3,682,726 3,712,055
17 Other manufacturing 0 0 0 0 0 0 0 140 0 140 406,170 1,070,293 -324,779 1,151,684 1,151,822
18 Energy and water 221 4,668 410 2,473 3,933 1,870 713 1,764 6,037 41,291 2,578,813 7,048 -1,109,706 1,476,155 1,517,447
19 Construction 698 39,718 96,720 24,130 37,819 117,453 25,829 247,450 172,212 863,850 90,324 110,943 2,953,375 3,154,642 4,018,492
20 Trade 53,605 16,300 176,640 636,049 709,350 441,612 36,634 155,357 330,531 6,751,749 1,975,347 591,550 521,550 3,088,447 9,840,199
21 Hotels and businesses 1,830 2,635 12,662 54,502 0 79,056 19,348 59,951 134,799 491,250 2,845,000 1,491 3,358,258 6,204,749 6,695,997
22 Transport and communication 8,847 8,481 52,637 170,878 61,304 602,724 52,410 66,801 124,491 1,747,937 3,815,000 1,039,830 -985,882 3,868,948 5,616,895
23 Credit and insurance 8,989 7,480 102,513 243,507 30,553 124,146 125,162 64,499 418,222 1,620,029 500,805 2,074,363 1,073,787 3,648,955 5,268,984
24 Real estate, renting, research, business services 20,295 23,932 130,244 399,852 190,439 276,770 734,052 380,548 760,112 3,947,485 406,013 9,165,794 -8,091,692 1,480,115 5,427,596
25 Other services 1,449 1,311 5,257 139,242 96,678 82,064 39,863 74,289 556,658 1,162,451 3,692,833 308,947 15,152,051 19,153,831 20,316,284
Total intermediate costs 123,163 125,063 834,541 1,833,240 2,246,037 1,838,340 1,045,523 1,108,289 2,693,819 25,312,996 30,910,297 37,824,156 17,230,570 85,965,023 111,278,024
Value Added 298,639 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,058
Imports 283,654 455,153 1,173,092 1,439,242 1,115,639 1,852,960 384,367 2,875,255 2,394,586 25,706,648
Other primary inputs 446,366 288,831 -289,341 359,817 1,410,821 888,895 1,465,394 -759,548 6,369,679 21,088,322
PRIMARY INPUTS 1,028,659 1,392,384 3,183,951 8,006,959 4,449,960 3,778,555 4,223,461 4,319,307 17,622,465 85,965,028
INPUT 1,151,822 1,517,447 4,018,492 9,840,199 6,695,997 5,616,895 5,268,984 5,427,596 20,316,284 111,278,024
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.10 – 1997 25-sector Marche I-O table constructed by GRIT-CILQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 0 6,879 502 94 772 10 0 11 11 11 0 0 2 5
2 Other agriculture 16,103 44,723 0 41,663 3,041 570 4,678 61 0 68 66 68 2 2 10 28
3 Mining 13 76 5,432 1,713 206 71 688 372 1,685 552 2,033 3,182 83,856 26,814 11,700 12,762
4 Food and tobacco 12,850 133,714 0 739,853 2,815 112,812 1,873 1,846 70 18,329 561 0 0 0 0 0
5 Textile products and apparel 329 1,990 349 3,632 1,392,880 25,592 24,223 5,023 59 1,395 36,435 2,353 8,904 2,628 3,454 9,870
6 Leather and shoes 216 1,310 49 0 27,316 1,702,060 30,459 1,508 28 283 3,930 22 1,975 650 1,312 880
7 Timber and furniture 0 0 0 0 0 0 135,242 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 87 526 1,517 75,871 14,621 5,162 11,554 525,630 437 27,904 40,965 23,189 32,910 27,725 33,625 12,344
9 Coke and nuclear fuel 5,062 30,658 1,038 9,694 4,625 1,109 3,010 2,509 302,983 50,827 4,394 8,697 6,736 4,064 1,622 19,010
10 Chemicals 27,874 74,937 1,356 5,886 40,176 4,260 7,792 10,956 4,976 425,782 140,133 9,261 13,193 7,922 7,467 26,130
11 Rubber and plastic products 225 1,364 876 33,524 25,112 46,238 38,950 10,417 746 21,301 317,954 12,536 40,410 90,366 72,301 141,214
12 Non-metal mineral products 94 566 5,116 34,574 1,085 784 17,198 4,117 270 37,163 12,013 212,526 42,564 6,837 27,311 21,422
13 Metal products 179 1,082 4,523 29,663 11,619 6,790 33,920 5,210 2,708 7,432 45,020 13,514 344,767 459,417 86,778 241,746
14 Machinery (except electricity) 173 1,045 1,189 4,780 4,723 872 1,060 3,729 1,150 3,135 5,823 7,628 30,313 389,630 28,131 88,867
15 Electrical and electronic equipment 46 276 1,314 3,949 2,425 346 1,313 3,478 612 3,603 12,734 3,381 35,319 117,039 667,123 103,141
16 Transportation equipment 63 384 14 0 0 0 0 33 0 78 445 277 761 1,821 241 323,371
17 Other manufacturing 0 1 0 28 0 0 87 0 0 0 0 0 0 0 0 0
18 Energy and water 87 528 308 2,634 2,620 197 1,367 664 1,192 5,197 1,910 1,802 3,076 1,623 596 2,778
19 Construction 55 332 0 1,836 1,389 0 2,449 0 4 268 0 1,492 2,024 1,207 843 374
20 Trade 24,997 151,403 24,289 279,919 253,430 231,480 210,574 87,226 1,392 57,300 149,205 91,154 289,282 179,596 150,747 136,790
21 Hotels and businesses 14 84 351 4,475 5,570 1,516 7,389 921 765 2,049 1,786 3,091 7,667 7,108 6,982 3,747
22 Transport and communication 1,223 7,409 4,076 69,781 41,766 7,902 36,276 12,233 3,757 23,574 18,341 26,735 87,863 53,096 38,580 27,544
23 Credit and insurance 3,785 22,923 1,691 45,178 53,757 9,660 52,939 11,724 4,520 8,581 17,521 17,515 83,573 45,963 27,583 16,501
24 Real estate, renting, research, business services 1,683 10,196 6,674 83,308 109,538 46,749 83,664 37,718 2,702 35,756 43,270 31,943 129,211 83,142 84,881 59,244
25 Other services 419 2,538 280 17,728 5,892 953 2,760 3,559 90 3,270 2,784 1,346 5,879 4,272 5,416 8,163
Total intermediate costs 95,708 488,066 60,441 1,496,568 2,005,108 2,205,217 710,237 728,944 330,146 733,858 857,334 471,723 1,250,285 1,510,922 1,256,705 1,255,931
Value Added 240,847 1,458,753 28,899 548,466 551,035 2,332,200 1,020,555 730,554 252,319 576,984 1,196,170 398,000 1,285,100 1,079,504 892,720 748,876
Imports 253,759 1,536,956 1,494,126 411,942 497,881 2,321,946 871,197 129,811 4,633 16,049 785,575 127,265 361,577 78,624 216,847 23,272
Other primary inputs -419,538 -2,541,041 -1,100,917 3,611,032 1,707,800 2,314,125 866,258 386,346 978,723 511,228 460,283 203,069 1,851,629 1,028,998 1,952,718 1,683,967
PRIMARY INPUTS 75,068 454,668 422,108 4,571,440 2,756,716 6,968,271 2,758,010 1,246,711 1,235,675 1,104,261 2,442,028 728,334 3,498,306 2,187,126 3,062,285 2,456,115
INPUT 170,776 942,734 482,549 6,068,008 4,761,824 9,173,488 3,468,247 1,975,655 1,565,821 1,838,119 3,299,362 1,200,057 4,748,591 3,698,048 4,318,990 3,712,046
Source: Author’s elaboration

Tab. A.10 – 1997 25-sector Marche I-O table constructed by GRIT-CILQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 0 0 4 0 734 45 1 2 63 9,279 0 10,501 150,996 161,496 170,776
2 Other agriculture 0 0 23 3 4,446 272 6 15 384 116,230 1,302,214 63,599 -539,309 826,504 942,734
3 Mining 3,783 3,720 3,546 546 1 307 1 4 260 163,323 239,245 6,929 73,053 319,227 482,549
4 Food and tobacco 343 0 0 220 695,824 8,566 0 0 31,204 1,760,880 943,810 3,213,947 149,372 4,307,129 6,068,008
5 Textile products and apparel 1,028 0 2,332 2,463 8,123 17,129 1,027 1,270 12,335 1,564,823 414,501 2,743,603 38,898 3,197,002 4,761,824
6 Leather and shoes 1,239 0 0 12,747 3 738 750 19 5,171 1,792,665 1,656,957 5,515,158 208,710 7,380,825 9,173,488
7 Timber and furniture 0 0 0 0 0 1,706 0 1,429 0 138,377 68,043 3,254,227 7,599 3,329,869 3,468,247
8 Paper, printing, publishing 2,214 2,545 5,327 41,781 28,188 55,718 32,163 29,569 73,725 1,105,297 81,561 753,242 35,555 870,358 1,975,655
9 Coke and nuclear fuel 578 192,962 2,612 7,478 7,064 81,720 1,578 3,133 16,074 769,237 12,539,726 964,732 -12,707,874 796,584 1,565,821
10 Chemicals 765 10,703 3,085 1,002 3,067 1,670 611 1,139 28,591 858,734 5,729 963,461 10,194 979,384 1,838,119
11 Rubber and plastic products 4,188 2,710 18,283 13,989 2,110 74,026 282 6,534 12,803 988,459 22,898 2,265,519 22,483 2,310,900 3,299,362
12 Non-metal mineral products 4,217 1,378 149,655 1,205 13,398 1,352 0 684 3,829 599,358 8,588 526,430 65,682 600,700 1,200,057
13 Metal products 9,566 10,648 50,784 39,635 6,107 21,043 5,882 7,006 12,036 1,457,075 22,221 2,926,062 343,230 3,291,513 4,748,591
14 Machinery (except electricity) 353 8,880 7,030 4,848 0 14,958 2,059 1,881 7,478 619,735 689 3,762,952 -685,328 3,078,313 3,698,048
15 Electrical and electronic equipment 2,726 19,150 44,340 39,698 3,690 32,572 5,476 7,125 26,228 1,137,104 132,330 2,232,384 817,174 3,181,888 4,318,990
16 Transportation equipment 3 0 0 8,339 8 14,581 0 223 5,251 355,893 9,988 3,194,451 151,718 3,356,157 3,712,046
17 Other manufacturing 0 0 0 0 0 1,302 0 997 0 2,415 166,324 1,070,293 -87,210 1,149,407 1,151,823
18 Energy and water 50 26,640 146 827 1,756 2,667 1,007 958 1,947 62,577 540 207,724 1,246,609 1,454,873 1,517,448
19 Construction 0 0 10,736 2,741 3,098 66,251 4,989 111,908 29,928 241,924 59,938 0 3,716,635 3,776,573 4,018,494
20 Trade 24,164 6,708 98,276 380,809 417,985 359,356 25,508 113,676 187,375 3,932,641 3,479,156 1,029,699 1,398,706 5,907,561 9,840,197
21 Hotels and businesses 414 485 4,514 18,484 0 64,302 10,062 31,408 41,176 224,360 2,845,000 875,034 2,751,601 6,471,635 6,695,996
22 Transport and communication 1,310 2,757 18,639 60,679 27,742 880,507 48,523 38,998 50,094 1,589,405 3,814,999 506,023 -293,534 4,027,488 5,616,896
23 Credit and insurance 2,035 3,741 36,550 81,330 13,642 174,827 176,749 34,732 175,048 1,122,068 3,866 482,872 3,660,175 4,146,913 5,268,980
24 Real estate, renting, research, business services 14,808 10,433 113,048 342,695 189,288 294,949 724,143 344,676 652,789 3,536,508 2,505,717 83,170 -697,804 1,891,083 5,427,590
25 Other services 311 215 1,874 47,263 38,336 58,125 18,410 40,583 179,046 449,512 586,261 1,887,265 17,393,244 19,866,770 20,316,286
Total intermediate costs 74,095 303,675 570,804 1,108,782 1,464,610 2,228,689 1,059,227 777,969 1,552,835 24,597,878 30,910,301 38,539,277 17,230,574 86,680,152 111,278,024
Value Added 276,876 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,058
Imports 293,444 269,050 1,383,391 2,095,323 1,357,629 1,163,527 42,203 2,629,969 2,825,587 21,191,583
Other primary inputs 507,408 296,323 -235,901 428,192 1,950,257 1,187,980 1,793,850 -183,948 7,079,664 26,318,505
PRIMARY INPUTS 1,077,728 1,213,773 3,447,690 8,731,415 5,231,386 3,388,207 4,209,753 4,649,621 18,763,451 86,680,146
INPUT 1,151,823 1,517,448 4,018,494 9,840,197 6,695,996 5,616,896 5,268,980 5,427,590 20,316,286 111,278,024
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.11 – 1997 25-sector Marche I-O table constructed by GRIT-PLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 5 81,273 5,348 5,386 4,160 688 0 258 1,888 123 3 4 33 86
2 Other agriculture 16,103 80,266 30 492,254 32,395 32,620 25,194 4,165 0 1,564 11,436 746 20 26 199 518
3 Mining 0 0 21 0 0 0 1 0 1,432 1 0 78 0 0 0 0
4 Food and tobacco 12,850 151,761 0 472,719 1,559 188,786 1,762 1,538 40 12,617 458 0 0 0 0 0
5 Textile products and apparel 368 2,227 294 2,554 869,079 101,374 32,761 3,642 44 975 26,617 1,469 5,892 1,773 2,408 7,111
6 Leather and shoes 61 367 74 0 29,750 2,135,237 32,727 1,872 39 344 4,879 24 2,294 769 1,641 1,083
7 Timber and furniture 0 0 0 0 0 0 224,988 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 6 37 438 20,493 3,425 7,071 7,916 109,810 107 6,112 8,336 4,548 7,091 6,083 7,903 2,784
9 Coke and nuclear fuel 5,062 30,658 58 413 159 59 1,002 2 1,606 208 0 160 226 91 54 108
10 Chemicals 27,874 74,937 269 1,252 7,439 4,446 4,662 1,724 155 11,003 16,942 1,113 2,110 851 1,377 867
11 Rubber and plastic products 51 310 244 10,611 6,970 76,056 29,385 2,636 174 4,423 63,419 2,348 9,556 18,632 19,974 30,227
12 Non-metal mineral products 32 193 1,728 13,300 359 1,588 14,496 1,287 75 9,117 2,985 46,542 12,039 1,648 9,102 5,392
13 Metal products 62 378 1,672 11,191 3,881 13,759 29,023 1,631 890 2,189 13,051 3,567 98,108 133,128 29,079 72,661
14 Machinery (except electricity) 0 0 15 8 0 0 298 0 0 0 0 0 0 0 0 0
15 Electrical and electronic equipment 14 86 590 1,637 926 827 1,217 1,307 243 1,318 4,443 1,081 11,929 41,591 244,229 38,452
16 Transportation equipment 13 78 4 0 0 0 0 9 0 4 90 53 174 305 65 16,636
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 Energy and water 84 507 469 2,589 2,320 1,557 2,164 830 208 815 2,029 1,075 2,497 916 649 559
19 Construction 90 545 2,897 8,158 6,589 12,983 5,384 4,820 1,355 3,871 8,065 7,432 13,401 9,336 10,618 6,357
20 Trade 20,522 124,299 52,387 449,268 399,662 822,979 291,305 165,222 2,829 103,853 290,967 142,485 480,449 315,022 277,054 260,792
21 Hotels and businesses 13 79 1,550 8,227 10,420 11,986 11,697 2,848 2,862 5,263 5,854 5,835 16,472 16,070 17,646 9,782
22 Transport and communication 302 1,829 11,060 69,235 39,853 72,781 52,479 20,711 7,596 26,435 32,643 25,838 91,411 62,152 48,730 37,906
23 Credit and insurance 3,620 21,923 2,752 47,116 47,618 76,357 83,783 14,661 6,222 8,118 21,145 12,173 67,864 38,263 29,723 15,839
24 Real estate, renting, research, business services 696 4,215 10,589 84,937 110,174 114,408 82,380 44,926 4,475 47,989 59,624 32,124 141,381 96,506 114,760 82,249
25 Other services 159 965 1,416 37,348 12,608 7,654 4,441 12,587 391 9,608 10,440 2,905 14,448 11,047 15,700 25,445
Total intermediate costs 88,114 495,660 88,562 1,814,583 1,590,534 3,687,914 943,225 396,916 30,743 256,085 585,311 291,719 977,365 754,213 830,944 614,854
Value Added 240,847 1,458,753 39,521 531,972 556,907 2,332,200 1,020,555 724,261 273,723 555,580 1,180,835 398,000 1,285,100 1,058,580 900,698 761,821
Imports 192,638 1,166,760 480,060 1,010,134 1,295,365 1,153,640 782,012 531,095 338,999 593,998 1,172,614 369,952 1,498,093 1,203,838 987,996 961,126
Other primary inputs -358,417 -2,170,846 -125,595 2,711,317 1,319,014 1,999,735 722,455 323,384 922,354 432,456 360,599 140,389 988,034 681,417 1,599,351 1,374,247
PRIMARY INPUTS 75,068 454,667 393,986 4,253,423 3,171,286 5,485,575 2,525,022 1,578,740 1,535,076 1,582,034 2,714,048 908,341 3,771,227 2,943,835 3,488,045 3,097,194
INPUT 163,182 950,327 482,548 6,068,006 4,761,820 9,173,489 3,468,247 1,975,656 1,565,819 1,838,119 3,299,359 1,200,060 4,748,592 3,698,048 4,318,989 3,712,048
Source: Author’s elaboration

Tab. A.11 – 1997 25-sector Marche I-O table constructed by GRIT-PLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 405 1 66 6 15,003 208 12 13 1,292 116,393 0 15,327 31,462 46,789 163,182
2 Other agriculture 2,452 6 402 36 90,867 1,260 73 78 7,823 800,533 124,836 92,830 -67,872 149,794 950,327
3 Mining 0 4,671 0 0 2 2 1 3 12 6,224 498,610 27 -22,312 476,325 482,548
4 Food and tobacco 264 0 0 403 789,376 4,277 0 0 39,457 1,677,867 2,580,398 1,299,957 509,785 4,390,140 6,068,006
5 Textile products and apparel 1,839 0 3,996 4,865 11,219 10,918 647 2,031 20,995 1,115,098 623,774 2,908,984 113,963 3,646,721 4,761,820
6 Leather and shoes 1,713 0 0 15,669 3 735 822 22 5,914 2,236,039 1,722,488 4,804,491 410,467 6,937,446 9,173,489
7 Timber and furniture 0 0 0 0 0 1,642 0 3,182 0 229,812 2,512 3,254,227 -18,304 3,238,435 3,468,247
8 Paper, printing, publishing 1,413 695 3,515 29,116 12,630 18,988 6,408 26,142 44,156 335,223 1,218,293 347,389 74,751 1,640,433 1,975,656
9 Coke and nuclear fuel 30 1,192 309 432 0 14,745 37 1,567 1,237 59,415 1,551,325 244,003 -288,922 1,506,406 1,565,819
10 Chemicals 376 346 1,621 593 1,019 488 87 898 14,548 176,997 1,236,589 384,921 39,608 1,661,118 1,838,119
11 Rubber and plastic products 3,155 686 14,081 12,147 1,170 27,900 61 6,199 9,095 349,510 1,178,214 1,616,008 155,630 2,949,852 3,299,359
12 Non-metal mineral products 3,920 419 138,085 1,308 9,166 528 0 723 3,182 277,214 160,570 586,842 175,433 922,845 1,200,060
13 Metal products 8,873 3,853 47,079 44,250 4,197 8,814 1,571 7,569 10,194 550,670 600,403 2,005,355 1,592,163 4,197,921 4,748,592
14 Machinery (except electricity) 0 0 0 0 0 2,519 0 842 0 3,682 15,356 3,762,952 -83,943 3,694,365 3,698,048
15 Electrical and electronic equipment 2,669 8,416 44,166 48,015 2,601 14,543 1,693 8,322 24,632 504,947 1,100,466 1,463,392 1,250,187 3,814,045 4,318,989
16 Transportation equipment 3 0 0 7,880 4 5,026 0 240 3,089 33,673 1,955,459 867,705 855,209 3,678,373 3,712,048
17 Other manufacturing 0 0 0 0 0 0 0 124 0 124 409,182 1,070,293 -327,776 1,151,699 1,151,820
18 Energy and water 221 4,671 410 2,476 3,999 1,871 713 1,764 6,042 41,435 2,605,573 7,048 -1,136,607 1,476,014 1,517,448
19 Construction 698 39,709 96,663 24,132 38,453 117,431 25,813 247,332 172,264 864,396 91,252 110,825 2,952,019 3,154,096 4,018,493
20 Trade 53,482 16,269 176,263 634,897 715,608 441,178 36,580 155,201 329,986 6,758,559 1,971,281 589,957 520,392 3,081,630 9,840,195
21 Hotels and businesses 1,831 2,638 12,669 54,566 0 79,109 19,359 59,968 134,871 491,615 2,845,000 1,491 3,357,890 6,204,381 6,695,994
22 Transport and communication 8,837 8,484 52,597 171,901 62,670 608,531 52,443 66,857 124,539 1,757,820 3,815,000 1,036,336 -992,270 3,859,066 5,616,888
23 Credit and insurance 8,993 7,485 102,553 243,759 31,064 124,204 125,209 64,502 418,710 1,623,656 501,795 2,074,363 1,069,168 3,645,326 5,268,980
24 Real estate, renting, research, business services 20,278 23,938 130,148 400,059 193,530 276,735 733,458 380,395 760,162 3,950,136 406,720 9,140,897 -8,070,160 1,477,457 5,427,591
25 Other services 1,473 1,334 5,343 141,605 99,869 83,409 40,515 75,480 565,806 1,181,996 3,695,204 308,473 15,130,607 19,134,284 20,316,281
Total intermediate costs 122,925 124,813 829,966 1,838,115 2,082,450 1,845,061 1,045,502 1,109,454 2,698,006 25,143,033 30,910,300 37,994,093 17,230,568 86,134,961 111,277,998
Value Added 298,503 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,056
Imports 282,860 453,362 1,177,018 1,433,843 1,255,594 1,845,659 383,831 2,873,578 2,388,423 25,832,488
Other primary inputs 447,532 290,873 -288,691 360,337 1,434,450 889,468 1,465,947 -759,041 6,371,652 21,132,421
PRIMARY INPUTS 1,028,895 1,392,635 3,188,527 8,002,080 4,613,544 3,771,827 4,223,478 4,318,137 17,618,275 86,134,965
INPUT 1,151,820 1,517,448 4,018,493 9,840,195 6,695,994 5,616,888 5,268,980 5,427,591 20,316,281 111,277,998
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.12 – 1997 25-sector Marche I-O table constructed by GRIT-RLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 4 76,639 4,941 5,111 4,070 693 0 278 1,868 123 3 4 33 89
2 Other agriculture 16,103 117,850 27 464,184 29,923 30,958 24,653 4,196 0 1,681 11,314 746 19 27 198 540
3 Mining 0 0 13 0 0 0 0 0 1,328 1 0 59 0 0 0 0
4 Food and tobacco 12,850 116,789 0 895,541 2,801 16,653 1,872 3,713 73 30,016 1,076 0 0 0 0 0
5 Textile products and apparel 239 1,449 603 3,989 1,502,079 52,303 30,434 7,628 105 2,273 57,953 3,240 12,810 4,082 4,778 15,968
6 Leather and shoes 91 553 70 0 28,985 2,139,481 33,172 1,983 43 386 5,089 25 2,350 846 1,691 1,168
7 Timber and furniture 0 0 0 0 0 0 230,630 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 11 64 1,060 36,441 6,644 4,255 8,256 286,172 366 21,524 25,707 14,238 18,272 20,637 18,800 9,129
9 Coke and nuclear fuel 5,062 30,658 176 1,267 525 179 1,225 334 70,428 11,326 420 1,326 1,137 916 290 3,523
10 Chemicals 27,874 74,937 610 2,117 13,635 2,551 4,762 4,428 2,390 184,385 50,984 3,431 5,279 3,590 3,206 10,759
11 Rubber and plastic products 44 269 564 17,705 12,726 42,379 29,295 6,228 680 17,569 177,442 6,794 22,876 71,618 43,907 112,424
12 Non-metal mineral products 28 173 4,286 23,885 703 945 15,197 3,209 310 38,665 8,845 142,019 30,507 6,613 21,131 21,650
13 Metal products 52 313 3,936 18,887 7,193 7,750 29,032 3,850 2,953 7,368 36,603 10,327 235,959 437,689 64,020 232,245
14 Machinery (except electricity) 0 0 0 3 0 0 287 0 67 174 0 0 0 20,921 0 2,861
15 Electrical and electronic equipment 12 71 1,351 2,587 1,541 414 1,148 2,849 682 3,737 11,677 2,820 28,025 115,899 511,020 103,468
16 Transportation equipment 11 69 8 0 0 0 0 15 0 44 178 111 373 871 120 144,141
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 Energy and water 74 449 1,160 4,593 4,529 924 2,231 2,048 2,919 12,498 5,970 3,245 6,274 3,615 1,489 6,630
19 Construction 77 467 2,603 7,723 6,138 7,563 5,340 4,995 1,492 4,322 8,183 7,607 13,339 10,181 10,737 6,726
20 Trade 20,705 125,404 49,574 444,678 390,055 607,372 294,523 173,423 3,094 114,704 301,271 148,180 489,899 342,099 285,993 277,147
21 Hotels and businesses 11 70 1,668 9,325 11,625 7,108 12,065 3,511 3,719 6,924 7,091 7,091 19,495 20,665 21,211 12,285
22 Transport and communication 129 783 17,631 103,540 68,071 36,338 50,205 40,264 15,697 56,781 59,967 48,164 169,321 130,563 92,356 75,804
23 Credit and insurance 3,205 19,413 6,821 83,895 92,935 45,292 86,435 36,176 21,949 29,003 62,234 36,759 170,485 133,633 68,247 54,031
24 Real estate, renting, research, business services 677 4,099 9,928 84,120 107,072 75,009 84,426 48,388 5,091 55,354 63,027 34,142 146,225 108,833 120,620 90,489
25 Other services 355 2,150 1,332 37,028 12,297 4,469 4,509 13,567 444 11,050 11,055 3,087 14,950 12,419 16,492 27,787
Total intermediate costs 87,743 496,029 103,425 2,318,147 2,304,418 3,087,054 953,767 647,670 133,830 610,063 907,954 473,534 1,387,598 1,445,721 1,286,339 1,208,864
Value Added 240,847 1,458,753 34,635 542,766 550,998 2,332,200 1,020,555 735,933 271,439 557,864 1,182,102 398,000 1,285,100 1,093,482 861,230 766,388
Imports 195,140 1,181,920 1,290,216 488,160 621,088 1,761,674 757,629 241,634 109,292 168,641 811,019 164,079 633,445 185,354 426,458 129,978
Other primary inputs -360,919 -2,186,004 -945,733 2,718,928 1,285,323 1,992,561 736,297 350,422 1,051,260 501,552 398,285 164,446 1,442,447 973,488 1,744,960 1,606,818
PRIMARY INPUTS 75,068 454,669 379,118 3,749,854 2,457,409 6,086,435 2,514,481 1,327,989 1,431,991 1,228,057 2,391,406 726,525 3,360,992 2,252,324 3,032,648 2,503,184
INPUT 162,811 950,698 482,543 6,068,001 4,761,827 9,173,489 3,468,248 1,975,659 1,565,821 1,838,120 3,299,360 1,200,059 4,748,590 3,698,045 4,318,987 3,712,048
Source: Author’s elaboration

Tab. A.12 – 1997 25-sector Marche I-O table constructed by GRIT-RLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 380 1 63 6 14,271 201 11 13 1,226 110,160 0 14,472 38,179 52,651 162,811
2 Other agriculture 2,300 7 381 34 86,436 1,219 70 76 7,428 800,370 141,828 87,655 -79,155 150,328 950,698
3 Mining 0 4,862 0 0 1 0 1 0 3 6,268 508,640 22 -32,383 476,279 482,543
4 Food and tobacco 37 0 0 370 943,714 6,324 0 0 37,732 2,069,561 2,740,976 894,330 363,139 3,998,445 6,068,001
5 Textile products and apparel 1,686 0 3,457 3,630 10,849 17,208 1,380 1,740 18,679 1,758,562 699,335 2,213,140 90,787 3,003,262 4,761,827
6 Leather and shoes 1,696 0 0 15,576 3 741 824 23 5,920 2,240,716 1,725,000 4,780,142 427,629 6,932,771 9,173,489
7 Timber and furniture 0 0 0 0 0 1,660 0 2,931 0 235,221 0 3,254,227 -21,200 3,233,027 3,468,248
8 Paper, printing, publishing 1,486 2,646 3,493 25,074 14,430 33,187 16,293 24,736 45,411 638,332 1,030,151 250,688 56,484 1,337,323 1,975,659
9 Coke and nuclear fuel 83 86,992 525 1,010 471 29,284 198 1,653 2,675 251,683 1,323,942 244,003 -253,807 1,314,138 1,565,821
10 Chemicals 375 8,949 1,547 485 1,108 839 213 837 14,236 423,527 1,103,533 280,803 30,256 1,414,592 1,838,120
11 Rubber and plastic products 3,095 2,921 13,106 9,733 1,227 46,850 144 5,653 8,716 653,965 1,183,543 1,327,938 133,913 2,645,394 3,299,360
12 Non-metal mineral products 4,111 1,890 136,706 1,118 10,279 926 0 692 3,253 477,141 143,741 441,098 138,077 722,916 1,200,059
13 Metal products 8,815 13,811 44,231 35,855 4,451 14,849 3,757 6,909 9,876 1,240,731 577,713 1,599,975 1,330,165 3,507,853 4,748,590
14 Machinery (except electricity) 0 1,664 0 0 0 4,060 0 737 0 30,774 16,868 3,762,952 -112,546 3,667,274 3,698,045
15 Electrical and electronic equipment 2,611 26,577 39,796 37,246 2,728 23,535 4,012 7,117 21,870 952,793 1,096,694 1,194,955 1,074,549 3,366,198 4,318,987
16 Transportation equipment 2 0 0 4,736 3 8,066 0 168 3,090 162,006 2,098,194 716,338 735,512 3,550,044 3,712,048
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 451,952 1,070,293 -370,424 1,151,821 1,151,820
18 Energy and water 231 141,601 403 2,113 4,479 3,240 1,788 1,652 6,133 220,288 2,727,866 6,347 -1,437,057 1,297,156 1,517,445
19 Construction 663 47,171 93,135 20,234 37,187 116,300 24,978 232,346 166,174 835,681 105,325 106,518 2,970,973 3,182,816 4,018,493
20 Trade 52,944 18,773 175,802 631,403 718,719 444,244 36,551 155,679 328,805 6,631,041 2,039,706 607,934 561,513 3,209,153 9,840,195
21 Hotels and businesses 1,915 3,670 12,467 46,557 0 91,794 22,364 56,391 136,733 515,755 2,845,000 1,488 3,333,755 6,180,243 6,695,999
22 Transport and communication 7,683 20,056 43,962 140,882 64,200 1,024,520 104,071 60,593 116,027 2,547,608 3,814,999 747,550 -1,493,261 3,069,288 5,616,899
23 Credit and insurance 9,409 28,289 100,942 207,998 34,793 215,257 314,090 60,458 426,256 2,348,005 418,811 1,730,375 771,791 2,920,977 5,268,980
24 Real estate, renting, research, business services 20,084 29,197 130,500 378,350 195,860 282,175 739,117 386,085 762,129 3,960,997 409,867 7,225,655 -6,168,918 1,466,604 5,427,595
25 Other services 1,465 1,622 5,177 118,947 101,362 84,765 40,918 69,490 565,056 1,161,793 3,706,618 305,274 15,142,601 19,154,493 20,316,286
Total intermediate costs 121,071 440,699 805,693 1,681,357 2,246,571 2,451,244 1,310,780 1,075,979 2,687,428 30,272,977 30,910,302 32,864,172 17,230,572 81,005,046 111,278,017
Value Added 285,566 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,058
Imports 275,405 59,858 1,190,677 1,596,153 1,072,302 1,220,841 118,038 2,812,576 2,390,977 19,902,554
Other primary inputs 469,778 368,488 -278,077 354,785 1,453,626 908,114 1,466,462 -664,560 6,379,681 21,932,428
PRIMARY INPUTS 1,030,749 1,076,746 3,212,800 8,158,838 4,449,428 3,165,655 3,958,200 4,351,616 17,628,858 81,005,040
INPUT 1,151,820 1,517,445 4,018,493 9,840,195 6,695,999 5,616,899 5,268,980 5,427,595 20,316,286 111,278,017
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.13 – 1997 25-sector Marche I-O table constructed by GRIT-FLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 2 22,206 1,679 333 394 276 0 641 971 59 1 2 11 168
2 Other agriculture 16,103 44,723 12 134,499 10,167 2,016 2,386 1,672 0 3,883 5,880 357 8 15 70 1,015
3 Mining 49 300 2,268 309 39 24 68 130 676 833 824 670 18,079 8,398 2,995 15,268
4 Food and tobacco 12,850 41,174 0 14,236 63 2,794 25 61 15 3,655 24 0 0 0 0 0
5 Textile products and apparel 99 600 664 65 1,497,312 574 321 133 10 226 1,235 66 215 98 78 1,323
6 Leather and shoes 74 450 9 0 2,808 39,589 943 225 27 257 751 4 268 136 171 642
7 Timber and furniture 49 300 22 383 653 239 21,235 184 1 689 643 615 1,746 677 365 3,151
8 Paper, printing, publishing 25 150 42 20,309 266 114 152 11,757 61 3,814 1,171 546 672 874 685 1,367
9 Coke and nuclear fuel 5,062 30,658 31 167 80 23 38 52 6,503 1,067 94 154 127 80 31 373
10 Chemicals 27,874 74,937 31 102 715 92 101 238 111 8,567 3,120 167 246 151 148 533
11 Rubber and plastic products 74 450 19 555 425 939 486 213 78 2,148 6,648 220 715 2,112 1,356 11,709
12 Non-metal mineral products 25 150 122 617 20 18 229 94 30 4,032 280 3,968 825 171 564 1,900
13 Metal products 49 300 113 512 207 147 440 114 349 928 1,173 292 6,446 13,253 1,728 24,480
14 Machinery (except electricity) 49 300 31 92 92 21 15 91 109 287 146 151 624 8,193 620 6,505
15 Electrical and electronic equipment 25 150 2,496 444 48 9 18 92 99 571 424 91 826 4,250 14,887 13,285
16 Transportation equipment 25 150 0 0 0 0 0 1 0 2 9 4 13 32 4 6,180
17 Other manufacturing 0 0 26 43 160 19 4 87 7 353 187 169 87 226 848 443
18 Energy and water 74 450 1,862 112 116 15 31 54 76 322 169 82 153 85 36 175
19 Construction 74 450 201 386 359 142 84 343 544 1,677 740 621 900 950 644 2,194
20 Trade 40,710 246,568 3,289 214,838 18,414 7,325 4,264 9,729 976 38,028 21,654 10,044 27,789 27,142 14,154 75,422
21 Hotels and businesses 0 0 99 355 519 119 169 184 1,049 2,081 486 445 1,012 1,495 978 3,072
22 Transport and communication 1,238 7,498 926 4,212 2,562 1,032 907 1,783 3,865 13,295 3,852 2,770 7,235 7,286 3,485 15,680
23 Credit and insurance 2,847 17,247 175 72,605 2,374 757 1,211 947 2,281 3,210 1,757 928 4,168 3,561 1,648 4,975
24 Real estate, renting, research, business services 990 5,999 1,162 75,162 9,373 2,030 2,097 4,975 2,395 30,027 7,878 4,158 15,010 15,065 9,918 40,797
25 Other services 322 1,949 89 1,580 619 75 63 801 141 3,739 854 218 874 1,012 857 7,912
Total intermediate costs 108,821 474,951 13,691 563,789 1,549,070 58,446 35,681 34,236 19,403 124,332 60,970 26,799 88,039 95,264 56,281 238,569
Value Added 240,847 1,458,753 0 91,700 1,036,700 2,332,200 1,020,555 1,020,555 274,219 555,084 955,733 398,000 1,285,100 1,046,069 907,907 767,124
Imports 51,044 309,160 240,922 3,154,426 2,443,409 4,802,374 1,991,151 1,051,896 367,320 749,120 1,936,009 656,491 2,622,564 1,984,170 1,852,585 1,399,518
Other primary inputs -216,823 -1,313,245 227,938 2,258,088 -267,355 1,980,464 420,861 -131,034 904,881 409,585 346,648 118,772 752,888 572,544 1,502,218 1,306,838
PRIMARY INPUTS 75,068 454,668 468,860 5,504,214 3,212,754 9,115,038 3,432,567 1,941,417 1,546,420 1,713,789 3,238,390 1,173,263 4,660,552 3,602,783 4,262,710 3,473,480
INPUT 183,889 929,619 482,551 6,068,003 4,761,824 9,173,484 3,468,248 1,975,653 1,565,823 1,838,121 3,299,360 1,200,062 4,748,591 3,698,047 4,318,991 3,712,049
Source: Author’s elaboration

Tab. A.13 – 1997 25-sector Marche I-O table constructed by GRIT-FLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 59 3 8 1 2,512 41 5 1 173 29,678 0 28,756 125,456 154,211 183,889
2 Other agriculture 356 15 50 4 15,217 249 30 5 1,047 239,779 1,708,176 174,165 -1,192,502 689,840 929,619
3 Mining 1,678 4,489 840 151 0 27 0 0 54 58,169 12,593,773 12,697 -12,182,088 424,382 482,551
4 Food and tobacco 9 0 0 5 15,343 2,727 0 0 662 93,643 594,018 764,786 4,615,557 5,974,361 6,068,003
5 Textile products and apparel 25 0 47 48 169 1,159 25 961 262 1,505,715 249,976 71,089 2,935,044 3,256,109 4,761,824
6 Leather and shoes 75 0 0 498 0 42 101 1 237 47,308 1,082,295 3,031,805 5,012,076 9,126,176 9,173,484
7 Timber and furniture 169 0 1,414 340 488 184 132 127 475 34,281 642,705 3,254,227 -462,966 3,433,966 3,468,248
8 Paper, printing, publishing 53 795 105 807 581 648 654 21,764 1,475 68,887 48,493 64,069 1,794,203 1,906,765 1,975,653
9 Coke and nuclear fuel 13 8,987 49 137 139 9,574 28 2,245 316 66,028 4,835 244,003 1,250,956 1,499,794 1,565,823
10 Chemicals 18 490 60 20 62 19 11 781 611 119,205 3,354 214,358 1,501,204 1,718,916 1,838,121
11 Rubber and plastic products 91 615 334 257 40 807 5 5,026 236 35,558 12,938 438,226 2,812,638 3,263,802 3,299,360
12 Non-metal mineral products 102 343 3,004 25 280 17 0 395 75 17,286 5,155 119,245 1,058,376 1,182,776 1,200,062
13 Metal products 222 3,026 982 801 123 245 109 2,638 232 58,909 13,032 914,265 3,762,384 4,689,681 4,748,591
14 Machinery (except electricity) 9 1,893 150 110 0 179 41 25 148 19,881 689 3,762,952 -85,476 3,678,165 3,698,047
15 Electrical and electronic equipment 71 7,021 955 903 82 5,218 127 4,507 557 57,156 83,292 480,267 3,698,277 4,261,836 4,318,991
16 Transportation equipment 0 0 0 157 0 168 0 0 88 6,833 130,818 292,403 3,281,996 3,705,217 3,712,049
17 Other manufacturing 128 193 12 13 13 75 17 36 275 3,421 323,887 1,070,293 -245,777 1,148,403 1,151,824
18 Energy and water 5 3,426 8 43 108 48 46 9 129 7,634 95,170 1 1,414,643 1,509,814 1,517,448
19 Construction 18 31,867 2,061 457 1,150 3,187 1,837 139,712 4,003 194,601 82,693 55,624 3,685,577 3,823,894 4,018,495
20 Trade 1,131 11,066 3,208 9,978 17,107 318,469 2,335 29,733 6,625 1,159,998 1,489,295 326,677 6,864,227 8,680,199 9,840,196
21 Hotels and businesses 43 1,934 247 943 0 2,045 1,258 681 2,877 22,091 2,845,000 220 3,828,681 6,673,901 6,695,992
22 Transport and communication 296 7,372 1,445 3,211 2,088 17,627 3,552 13,530 3,234 129,981 3,815,000 101,233 1,570,678 5,486,911 5,616,892
23 Credit and insurance 209 5,489 2,002 4,208 841 3,265 8,136 28,847 8,893 182,581 13,434 10,685 5,062,284 5,086,403 5,268,984
24 Real estate, renting, research, business services 828 23,824 4,353 9,405 7,775 11,317 81,992 6,174 22,488 395,192 3,177,235 135,007 1,720,164 5,032,406 5,427,597
25 Other services 34 962 103 2,411 2,662 64,309 2,591 19,729 11,873 125,779 1,895,036 42,890,497 -24,595,042 20,190,491 20,316,272
Total intermediate costs 5,642 113,810 21,437 34,933 66,780 441,646 103,032 276,927 67,045 4,679,594 30,910,299 58,457,550 17,230,570 106,598,419 111,278,015
Value Added 227,312 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,600 8,858,200 39,170,058
Imports 508,442 477,768 2,083,733 3,320,250 3,354,331 2,367,747 1,717,673 966,282 5,231,715 45,640,100
Other primary inputs 410,428 277,470 -386,875 277,113 1,351,381 1,770,799 1,074,579 1,980,788 6,159,312 21,788,263
PRIMARY INPUTS 1,146,182 1,403,638 3,997,058 9,805,263 6,629,212 5,175,246 5,165,952 5,150,670 20,249,227 106,598,421
INPUT 1,151,824 1,517,448 4,018,495 9,840,196 6,695,992 5,616,892 5,268,984 5,427,597 20,316,272 111,278,015
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.14 – 1997 25-sector Marche I-O table constructed by GRIT-SDP (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 6 93,271 5,970 6,355 5,180 917 0 321 2,439 163 4 6 40 110
2 Other agriculture 16,103 63,735 37 564,919 36,160 38,491 31,373 5,552 0 1,946 14,770 987 22 33 245 667
3 Mining 0 0 10 0 0 0 1 0 514 1 0 40 0 0 0 0
4 Food and tobacco 12,850 171,603 0 913,571 2,471 22,504 2,512 3,361 48 24,047 969 0 0 0 0 0
5 Textile products and apparel 443 2,680 618 5,132 1,698,378 205,448 70,404 8,041 92 2,058 57,352 3,239 11,993 3,893 5,021 15,223
6 Leather and shoes 47 283 80 0 30,027 2,277,777 34,642 2,211 41 381 5,637 28 2,418 884 1,724 1,220
7 Timber and furniture 0 0 0 0 0 0 234,813 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 26 155 1,083 44,738 6,923 15,766 20,375 312,376 239 15,285 22,270 12,757 16,137 16,811 19,129 7,306
9 Coke and nuclear fuel 5,062 30,658 1,292 9,509 3,112 4,532 14,737 2,678 44,770 8,073 2,973 7,640 5,382 3,211 1,649 4,055
10 Chemicals 27,874 74,937 2,813 11,795 63,809 43,332 48,534 22,036 1,512 119,421 204,639 13,642 20,488 10,177 14,495 9,827
11 Rubber and plastic products 178 1,081 873 35,658 22,126 261,524 103,929 10,641 595 16,178 247,032 9,282 32,602 72,526 71,466 113,477
12 Non-metal mineral products 73 439 5,493 39,909 991 4,679 48,003 4,481 221 29,100 9,917 161,443 35,987 5,667 28,506 17,786
13 Metal products 163 990 5,232 33,449 11,006 41,860 89,503 5,688 2,686 7,006 44,156 12,204 295,584 449,319 91,056 238,477
14 Machinery (except electricity) 0 0 217 79 0 0 1,514 186 0 87 0 875 356 47,834 1,464 7,265
15 Electrical and electronic equipment 26 160 1,369 3,328 1,704 1,616 2,793 3,137 509 2,960 10,781 2,675 25,916 101,567 537,365 89,837
16 Transportation equipment 86 524 29 0 0 0 0 22 0 9 134 201 1,057 1,211 323 60,258
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 Energy and water 2,350 14,234 15,761 83,998 72,691 52,167 69,988 31,169 6,759 28,292 74,574 39,402 82,042 33,083 21,900 19,715
19 Construction 76 462 2,970 7,908 6,121 12,929 5,454 5,503 1,340 4,058 8,930 8,331 13,228 10,326 10,822 6,827
20 Trade 9,868 59,771 44,331 339,167 293,579 627,195 240,785 143,559 2,229 84,621 243,044 122,342 377,311 266,412 219,667 213,625
21 Hotels and businesses 15 88 2,047 10,456 12,802 15,746 14,844 4,197 3,709 7,163 8,441 8,390 21,222 22,763 23,451 13,570
22 Transport and communication 427 2,587 18,665 97,770 61,220 107,665 94,244 41,609 11,353 48,375 58,706 47,057 151,193 123,525 85,033 69,542
23 Credit and insurance 10,833 65,614 9,873 161,324 158,891 272,445 288,795 58,674 21,890 30,002 82,809 47,545 237,472 147,205 107,407 59,679
24 Real estate, renting, research, business services 620 3,754 11,718 88,562 109,031 124,061 91,020 54,300 4,536 53,119 69,378 38,046 149,666 113,150 123,289 93,558
25 Other services 392 2,377 1,635 41,505 13,542 8,790 4,928 16,215 443 11,431 13,160 3,653 16,274 13,681 18,210 30,799
Total intermediate costs 87,645 496,131 126,152 2,586,048 2,610,554 4,144,882 1,518,371 736,553 103,486 493,934 1,182,111 539,942 1,496,354 1,443,284 1,382,262 1,072,823
Value Added 240,847 1,458,753 44,508 546,749 537,142 2,332,200 1,020,555 746,475 263,787 565,516 1,186,673 398,000 1,285,100 1,095,911 858,941 766,248
Imports 8,967 54,309 131,032 1,052,828 298,336 503,746 227,635 109,991 135,519 282,852 531,441 109,959 1,069,325 410,144 277,557 249,650
Other primary inputs -174,745 -1,058,391 180,858 1,882,383 1,315,790 2,192,659 701,683 382,633 1,063,030 495,819 399,137 152,158 897,810 748,708 1,800,228 1,623,326
PRIMARY INPUTS 75,068 454,672 356,398 3,481,960 2,151,268 5,028,605 1,949,873 1,239,099 1,462,336 1,344,187 2,117,251 660,117 3,252,235 2,254,763 2,936,726 2,639,224
INPUT 162,713 950,803 482,550 6,068,008 4,761,822 9,173,487 3,468,244 1,975,652 1,565,822 1,838,121 3,299,362 1,200,059 4,748,589 3,698,047 4,318,988 3,712,047
Source: Author’s elaboration

Tab. A.14 – 1997 25-sector Marche I-O table constructed by GRIT-SDP (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 462 1 84 7 17,331 250 13 20 1,460 134,543 0 10,501 17,670 28,171 162,713
2 Other agriculture 2,797 8 509 45 104,969 1,515 80 124 8,846 893,932 35,961 63,599 -42,690 56,870 950,803
3 Mining 0 2,116 0 0 1 1 0 0 4 2,688 496,676 3,843 -20,657 479,862 482,550
4 Food and tobacco 11 0 0 812 1,414,560 5,554 0 0 64,858 2,639,731 2,707,642 499,741 220,894 3,428,277 6,068,008
5 Textile products and apparel 3,645 0 8,568 10,135 22,356 23,459 1,234 5,577 40,774 2,205,763 893,956 1,581,925 80,181 2,556,062 4,761,822
6 Leather and shoes 1,771 0 0 18,635 3 764 808 31 6,108 2,385,520 1,725,000 4,643,645 419,322 6,787,967 9,173,487
7 Timber and furniture 0 0 0 0 0 1,631 0 6,822 0 243,266 0 3,254,227 -29,248 3,224,979 3,468,244
8 Paper, printing, publishing 2,952 1,723 9,153 67,836 26,460 48,256 11,971 99,555 89,484 868,766 1,023,709 64,069 19,109 1,106,887 1,975,652
9 Coke and nuclear fuel 994 38,363 9,137 19,730 6,046 207,409 622 34,724 24,328 490,686 1,049,335 244,003 -218,199 1,075,139 1,565,822
10 Chemicals 3,385 3,697 18,137 7,250 9,455 4,957 689 13,773 117,280 867,954 747,997 214,358 7,811 970,166 1,838,121
11 Rubber and plastic products 10,347 2,484 52,505 45,322 3,935 98,785 185 30,818 28,762 1,272,311 1,032,270 937,453 57,327 2,027,050 3,299,362
12 Non-metal mineral products 11,053 1,330 454,970 4,473 25,938 1,697 0 3,177 8,922 904,255 126,922 119,245 49,636 295,803 1,200,059
13 Metal products 25,913 12,228 153,062 154,470 12,562 26,686 4,296 27,956 29,164 1,774,716 623,873 1,600,357 749,645 2,973,875 4,748,589
14 Machinery (except electricity) 0 452 3,240 3,738 0 12,353 0 7,856 0 87,516 34,393 3,762,952 -186,815 3,610,530 3,698,047
15 Electrical and electronic equipment 5,548 19,593 104,266 123,725 5,466 33,791 3,269 27,262 46,930 1,155,593 1,718,042 735,655 709,698 3,163,395 4,318,988
16 Transportation equipment 1 0 0 41,680 0 51,348 0 3,059 27,819 187,761 3,197,639 292,403 34,241 3,524,283 3,712,047
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 640,100 1,070,293 -558,570 1,151,823 1,151,821
18 Energy and water 7,129 160,541 14,320 84,055 133,210 59,288 21,719 66,415 186,896 1,381,698 1,429,065 0 -1,293,313 135,752 1,517,451
19 Construction 667 41,282 102,984 24,834 37,654 117,301 23,109 272,494 165,394 891,004 192,720 127,266 2,807,504 3,127,490 4,018,495
20 Trade 39,877 13,143 145,426 501,514 534,185 375,361 28,022 167,537 247,309 5,339,880 1,957,818 2,029,848 512,648 4,500,314 9,840,192
21 Hotels and businesses 2,318 3,556 17,370 72,145 0 98,658 23,128 100,681 159,614 646,374 2,845,000 307,171 2,897,452 6,049,623 6,695,995
22 Transport and communication 11,927 16,855 87,533 362,184 113,821 1,224,283 103,108 183,399 209,450 3,331,531 3,815,000 342,554 -1,872,194 2,285,360 5,616,896
23 Credit and insurance 30,920 27,406 381,887 882,413 110,235 421,188 406,264 267,020 1,427,260 5,715,051 82,694 347,709 -876,471 -446,068 5,268,985
24 Real estate, renting, research, business services 20,941 25,236 147,385 382,590 187,567 279,130 703,188 474,081 666,488 4,014,414 600,633 127 812,422 1,413,182 5,427,594
25 Other services 1,630 1,572 6,404 163,613 114,078 90,149 42,316 94,503 591,284 1,302,584 3,933,855 2,146,670 12,933,171 19,013,696 20,316,275
Total intermediate costs 184,288 371,586 1,716,940 2,971,206 2,879,832 3,183,814 1,374,021 1,886,884 4,148,434 38,737,536 30,910,300 24,399,614 17,230,574 72,540,488 111,278,017
Value Added 270,452 648,400 2,300,200 6,207,900 1,923,500 1,036,700 2,373,700 2,203,599 8,858,200 39,170,056
Imports 245,788 157,968 279,492 335,705 487,779 449,389 365,950 271,165 731,136 8,777,663
Other primary inputs 451,293 339,497 -278,137 325,381 1,404,884 946,993 1,155,314 1,065,946 6,578,505 24,592,762
PRIMARY INPUTS 967,533 1,145,865 2,301,555 6,868,986 3,816,163 2,433,082 3,894,964 3,540,710 16,167,841 72,540,481
INPUT 1,151,821 1,517,451 4,018,495 9,840,192 6,695,995 5,616,896 5,268,985 5,427,594 20,316,275 111,278,017
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.15 – 1997 25-sector Marche I-O table constructed by GRIT-SCILQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 5 86,684 5,737 3,633 3,874 843 0 370 2,354 155 4 6 39 116
2 Other agriculture 16,103 94,262 32 525,023 34,751 22,002 23,465 5,109 0 2,242 14,261 942 22 34 235 703
3 Mining 0 0 9 0 0 0 0 0 1,204 1 0 44 0 0 0 0
4 Food and tobacco 12,850 140,713 0 949,739 2,766 7,979 2,288 3,885 113 49,008 1,235 0 0 0 0 0
5 Textile products and apparel 309 1,869 636 4,739 1,704,725 62,280 41,156 8,340 177 3,854 66,336 3,788 13,570 5,197 5,296 26,813
6 Leather and shoes 103 622 121 0 49,941 2,168,892 46,857 3,584 86 779 9,525 48 4,222 1,619 2,970 2,341
7 Timber and furniture 0 0 0 0 0 0 234,894 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 20 122 1,176 50,617 8,885 5,644 12,401 380,192 653 39,139 31,742 17,508 22,984 27,429 24,757 16,262
9 Coke and nuclear fuel 5,062 30,658 410 3,660 1,558 390 2,560 1,129 125,570 19,572 1,661 4,188 3,217 2,514 826 7,231
10 Chemicals 27,874 74,937 1,284 4,868 31,427 4,019 9,434 10,849 3,764 282,387 127,693 8,485 12,383 8,455 7,541 18,716
11 Rubber and plastic products 67 408 667 25,487 17,634 54,242 44,852 8,602 1,126 29,369 229,138 8,795 30,310 82,889 60,504 185,706
12 Non-metal mineral products 44 265 5,310 35,615 1,010 1,249 23,744 4,566 520 65,551 11,780 188,891 41,733 7,875 30,041 35,885
13 Metal products 78 469 4,426 26,118 9,516 9,959 43,315 5,015 5,056 12,764 44,174 12,444 294,633 542,201 83,476 392,465
14 Machinery (except electricity) 0 0 0 16 0 0 483 0 158 423 0 196 13 46,933 0 7,094
15 Electrical and electronic equipment 17 106 1,402 3,343 1,904 517 1,649 3,463 1,169 6,531 13,573 3,349 32,579 149,610 622,143 177,433
16 Transportation equipment 18 107 14 0 0 0 0 27 0 50 333 217 725 1,651 230 190,670
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
18 Energy and water 134 810 2,662 10,697 10,663 1,428 4,483 5,023 5,609 24,203 14,938 8,144 15,162 8,961 3,566 13,818
19 Construction 94 570 3,801 10,561 8,699 7,722 5,444 7,644 2,864 8,327 13,315 12,546 20,382 17,390 15,893 12,984
20 Trade 18,382 111,332 63,881 559,313 504,347 503,295 295,929 236,703 5,170 194,255 432,900 218,215 674,434 527,416 387,483 472,643
21 Hotels and businesses 16 94 2,468 12,629 16,432 8,008 13,201 5,362 7,350 13,740 11,598 11,700 29,766 35,462 31,272 24,179
22 Transport and communication 105 639 18,884 108,603 70,634 40,370 64,758 46,781 26,779 99,994 75,127 61,828 196,758 184,151 102,346 131,791
23 Credit and insurance 5,190 31,436 8,270 122,448 131,464 61,982 134,574 50,171 39,267 52,383 80,142 46,955 226,493 170,786 94,574 94,876
24 Real estate, renting, research, business services 633 3,834 11,746 92,720 124,434 57,702 68,431 60,347 8,808 90,838 88,650 45,868 179,904 152,225 156,710 147,557
25 Other services 466 2,825 2,029 52,188 18,030 4,863 4,658 21,407 883 22,073 18,592 5,235 23,550 21,802 25,151 55,878
Total intermediate costs 87,696 496,079 129,233 2,685,068 2,754,557 3,026,176 1,082,450 869,042 236,326 1,017,853 1,289,067 659,541 1,822,844 1,994,606 1,655,053 2,015,161
Value Added 240,847 1,458,753 33,423 546,326 548,650 2,332,200 1,020,555 736,748 271,530 557,773 1,189,852 398,000 1,285,100 1,110,448 850,112 760,540
Imports 368,675 2,232,979 1,799,958 993,036 9,471 1,714,163 541,438 49,206 57,094 377,679 316,952 69,094 217,233 612,671 123,083 1,075,026
Other primary inputs -534,454 -3,237,062 -1,480,063 1,843,569 1,449,147 2,100,952 823,808 320,660 1,000,872 -115,184 503,483 73,426 1,423,420 -19,677 1,690,745 -138,687
PRIMARY INPUTS 75,068 454,670 353,318 3,382,931 2,007,268 6,147,315 2,385,801 1,106,614 1,329,496 820,268 2,010,287 540,520 2,925,753 1,703,442 2,663,940 1,696,879
INPUT 162,764 950,749 482,551 6,067,999 4,761,825 9,173,491 3,468,251 1,975,656 1,565,822 1,838,121 3,299,354 1,200,061 4,748,597 3,698,048 4,318,993 3,712,040
Source: Author’s elaboration

Tab. A.15 – 1997 25-sector Marche I-O table constructed by GRIT-SCILQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 373 2 61 5 14,484 222 14 10 1,172 120,295 0 14,975 27,494 42,469 162,764
2 Other agriculture 2,258 9 366 31 87,726 1,347 82 61 7,099 838,165 90,682 90,699 -68,797 112,584 950,749
3 Mining 0 4,988 0 0 0 0 0 0 3 6,249 517,114 25 -40,837 476,302 482,551
4 Food and tobacco 9 0 0 451 1,131,759 6,672 0 0 44,180 2,353,647 2,733,736 685,952 294,668 3,714,356 6,067,999
5 Textile products and apparel 2,222 0 4,638 4,804 14,346 21,365 1,435 2,078 24,429 2,024,402 781,599 1,874,984 80,840 2,737,423 4,761,825
6 Leather and shoes 2,430 0 0 20,506 6 1,233 1,439 26 8,294 2,325,644 1,725,000 4,682,567 440,277 6,847,844 9,173,491
7 Timber and furniture 0 0 0 0 0 2,359 0 1,489 0 238,742 0 3,254,227 -24,720 3,229,507 3,468,251
8 Paper, printing, publishing 2,229 4,764 5,352 37,745 22,176 46,847 19,180 30,107 66,794 874,725 883,552 175,757 41,622 1,100,931 1,975,656
9 Coke and nuclear fuel 226 123,373 1,318 2,666 1,792 69,286 513 2,409 6,261 418,050 1,138,756 244,003 -234,989 1,147,770 1,565,822
10 Chemicals 771 11,647 3,168 949 2,400 1,863 467 1,273 27,654 684,308 917,411 214,358 22,040 1,153,809 1,838,121
11 Rubber and plastic products 4,616 4,958 19,977 14,417 1,863 70,709 179 6,997 12,765 916,277 1,204,024 1,066,061 112,991 2,383,076 3,299,354
12 Non-metal mineral products 6,346 3,281 214,164 1,708 16,148 1,398 0 931 4,963 703,018 115,023 287,451 94,573 497,047 1,200,061
13 Metal products 12,936 24,213 66,137 52,635 6,631 21,021 4,441 9,101 14,314 1,697,538 586,492 1,315,282 1,149,281 3,051,055 4,748,597
14 Machinery (except electricity) 0 2,240 0 0 0 7,171 0 668 0 65,395 18,909 3,762,952 -149,206 3,632,655 3,698,048
15 Electrical and electronic equipment 3,660 46,513 57,143 52,700 3,878 31,531 4,392 8,392 30,342 1,257,339 1,136,848 987,958 936,852 3,061,658 4,318,993
16 Transportation equipment 3 0 0 7,387 5 16,088 0 222 5,363 223,110 2,314,304 567,176 607,456 3,488,936 3,712,040
17 Other manufacturing 0 0 0 0 0 0 0 0 0 0 489,172 1,070,293 -407,643 1,151,822 1,151,822
18 Energy and water 473 201,384 816 4,037 9,458 7,464 4,179 2,867 12,162 373,141 2,905,417 5,742 -1,766,849 1,144,310 1,517,450
19 Construction 684 88,016 93,471 20,730 40,187 158,393 36,768 215,913 166,164 968,562 117,260 96,740 2,835,931 3,049,931 4,018,495
20 Trade 51,741 31,317 168,286 543,253 727,168 575,580 46,710 128,978 305,778 7,784,509 1,326,176 369,318 360,190 2,055,684 9,840,196
21 Hotels and businesses 2,084 7,108 14,009 53,608 0 121,569 33,200 52,930 146,968 654,753 2,845,000 1,463 3,194,779 6,041,242 6,695,996
22 Transport and communication 9,070 35,888 54,426 191,802 83,743 1,225,247 126,955 66,163 146,245 3,169,087 3,815,001 521,591 -1,888,783 2,447,809 5,616,890
23 Credit and insurance 14,745 52,000 159,811 323,439 55,431 319,364 398,989 82,237 661,128 3,418,155 371,286 1,258,959 220,579 1,850,824 5,268,982
24 Real estate, renting, research, business services 16,849 51,410 108,452 353,818 196,987 331,266 884,458 287,556 729,792 4,250,995 411,257 3,588,336 -2,822,988 1,176,605 5,427,604
25 Other services 1,556 3,153 5,498 130,216 112,883 116,291 62,633 66,294 574,667 1,352,821 4,466,282 281,370 14,215,811 18,963,463 20,316,283
Total intermediate costs 135,281 696,264 977,093 1,816,907 2,529,071 3,154,286 1,626,034 966,702 2,996,537 36,718,927 30,910,301 26,418,239 17,230,572 74,559,112 111,278,040
Value Added 277,000 648,400 2,300,200 6,207,900 1,923,500 1,036,699 2,373,700 2,203,601 8,858,200 39,170,057
Imports 204,296 319,247 957,164 1,403,196 533,793 499,876 703,654 4,291,476 1,910,381 21,380,841
Other primary inputs 535,245 -146,461 -215,962 412,193 1,709,632 926,029 565,594 -2,034,175 6,551,165 14,008,215
PRIMARY INPUTS 1,016,541 821,186 3,041,402 8,023,289 4,166,925 2,462,604 3,642,948 4,460,902 17,319,746 74,559,113
INPUT 1,151,822 1,517,450 4,018,495 9,840,196 6,695,996 5,616,890 5,268,982 5,427,604 20,316,283 111,278,040
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

Tab. A.16 – 1997 25-sector Marche I-O table constructed by GRIT-WLQ (million of Lire)
Sectors 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
1 Cereals 132 0 5 82,140 5,600 5,113 4,872 590 0 232 1,670 115 3 4 29 78
2 Other agriculture 16,103 75,469 29 497,506 33,918 30,969 29,512 3,570 0 1,403 10,116 696 17 23 179 470
3 Mining 32 195 18,875 4,142 460 1,323 2,845 1,004 1,456 1,075 5,132 4,677 147,969 46,840 27,354 24,874
4 Food and tobacco 12,850 142,870 0 682,111 2,233 221,205 3,219 1,706 46 15,635 540 0 0 0 0 0
5 Textile products and apparel 402 2,433 430 3,975 1,358,985 142,948 60,973 4,673 60 1,323 34,900 2,063 7,996 2,483 3,263 9,644
6 Leather and shoes 53 320 65 0 28,710 1,909,716 33,961 1,542 32 292 4,138 21 1,925 666 1,365 920
7 Timber and furniture 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
8 Paper, printing, publishing 45 272 665 34,745 5,955 12,026 12,137 174,453 157 9,438 13,993 7,259 10,546 9,344 11,972 4,250
9 Coke and nuclear fuel 5,062 30,658 2,005 23,346 9,928 13,213 17,572 4,190 91,229 14,184 6,045 11,041 10,432 4,386 2,971 6,307
10 Chemicals 27,874 74,937 3,128 16,732 101,830 61,076 51,944 22,249 1,803 133,990 234,508 14,017 24,418 10,197 16,377 10,412
11 Rubber and plastic products 138 838 381 18,184 12,155 126,566 49,200 4,071 258 6,821 102,212 3,743 14,379 28,920 30,431 46,352
12 Non-metal mineral products 60 365 2,446 20,081 565 2,335 23,101 1,741 99 12,525 4,192 66,221 16,245 2,296 12,393 7,386
13 Metal products 88 536 1,806 13,264 4,649 15,310 35,993 1,657 891 2,276 13,776 3,850 100,374 140,928 29,963 75,483
14 Machinery (except electricity) 0 0 0 0 0 0 525 0 0 0 0 0 0 0 0 0
15 Electrical and electronic equipment 22 133 597 1,738 953 786 1,325 1,176 222 1,240 4,430 1,069 11,436 40,063 228,738 36,252
16 Transportation equipment 147 888 23 0 0 0 0 49 0 22 535 307 1,024 1,709 383 93,611
17 Other manufacturing 0 2 78 211 685 324 85 176 3 139 308 376 224 414 2,345 226
18 Energy and water 1,171 7,095 5,552 33,510 30,373 18,821 29,963 9,141 2,264 9,247 23,154 12,652 27,944 10,604 7,282 6,324
19 Construction 33 201 857 2,650 2,172 3,842 1,965 1,266 357 1,069 2,202 2,150 3,665 2,661 2,907 1,759
20 Trade 12,067 73,090 36,846 330,160 300,598 557,013 254,355 101,544 1,773 67,217 183,436 95,836 310,574 209,990 178,452 168,770
21 Hotels and businesses 15 92 1,535 8,858 11,413 11,953 13,941 2,559 2,572 4,922 5,453 5,686 15,237 15,427 16,370 9,172
22 Transport and communication 1,257 7,612 9,602 69,127 37,237 69,659 47,507 16,970 6,473 21,439 29,708 23,861 73,354 49,907 39,284 31,399
23 Credit and insurance 11,309 68,496 7,404 135,931 141,659 206,826 271,216 35,780 15,179 20,617 53,498 32,220 170,492 99,764 74,924 40,338
24 Real estate, renting, research, business services 836 5,062 10,286 89,157 117,910 114,383 94,425 40,211 3,797 43,361 54,120 30,757 129,231 90,592 102,346 74,425
25 Other services 356 2,158 1,226 35,067 12,073 6,673 4,628 9,888 307 7,855 8,502 2,475 11,684 9,272 12,725 20,788
Total intermediate costs 90,054 493,720 103,841 2,102,635 2,220,061 3,532,080 1,045,264 440,206 128,978 376,322 796,568 321,092 1,089,169 776,490 802,053 669,240
Value Added 240,847 1,458,753 39,951 526,970 561,479 2,332,200 1,020,555 716,747 267,387 561,916 1,198,516 398,000 1,285,100 1,072,153 889,525 759,421
Imports 164,059 993,666 169,851 546,441 432,876 1,091,491 550,577 487,415 235,111 449,260 926,967 334,682 1,496,502 1,172,328 999,623 873,262
Other primary inputs -329,838 -1,997,751 168,908 2,891,958 1,547,406 2,217,712 851,849 331,287 934,347 450,627 377,313 146,285 877,819 677,076 1,627,794 1,410,124
PRIMARY INPUTS 75,068 454,668 378,710 3,965,369 2,541,761 5,641,403 2,422,981 1,535,449 1,436,845 1,461,803 2,502,796 878,967 3,659,421 2,921,557 3,516,942 3,042,807
INPUT 165,121 948,389 482,551 6,068,004 4,761,822 9,173,483 3,468,245 1,975,655 1,565,823 1,838,125 3,299,364 1,200,059 4,748,590 3,698,047 4,318,995 3,712,047
Source: Author’s elaboration

Tab. A.16 – 1997 25-sector Marche I-O table constructed by GRIT-WLQ (million of Lire) (continued)
Sectors 17 18 19 20 21 22 23 24 25 TIS HC EXP OFD TFD OUTPUT
1 Cereals 340 1 61 6 15,647 208 11 13 1,221 118,091 0 10,723 36,307 47,030 165,121
2 Other agriculture 2,057 6 371 38 94,768 1,260 69 82 7,394 806,025 159,158 64,946 -81,740 142,364 948,389
3 Mining 34,972 4,889 22,328 4,884 2 500 1 16 1,411 357,256 21 74,318 50,957 125,296 482,551
4 Food and tobacco 267 0 0 577 1,191,941 5,233 0 0 50,540 2,330,973 3,422,898 1,992,599 -1,678,460 3,737,037 6,068,004
5 Textile products and apparel 2,339 0 5,625 7,324 16,992 18,486 913 3,381 29,289 1,720,900 970,514 1,964,140 106,272 3,040,926 4,761,822
6 Leather and shoes 1,377 0 0 14,173 3 666 722 20 5,350 2,006,037 1,222,451 5,323,690 621,304 7,167,445 9,173,483
7 Timber and furniture 0 0 0 0 0 0 0 0 0 0 502,549 3,254,227 -288,532 3,468,244 3,468,245
8 Paper, printing, publishing 2,103 1,050 5,365 51,541 24,610 24,894 10,199 32,906 71,060 530,985 786,656 507,528 150,484 1,444,668 1,975,655
9 Coke and nuclear fuel 2,760 64,959 13,654 48,185 31,843 203,774 2,365 19,368 81,598 721,075 1,310,482 244,003 -709,739 844,746 1,565,823
10 Chemicals 4,444 4,090 19,221 7,920 16,534 4,679 1,093 8,054 178,345 1,049,872 1,030,849 310,778 -553,376 788,251 1,838,125
11 Rubber and plastic products 4,658 1,041 21,794 20,766 2,174 40,702 97 8,750 14,606 559,237 741,863 1,765,341 232,917 2,740,121 3,299,364
12 Non-metal mineral products 5,114 569 192,332 1,960 14,932 739 0 979 4,648 393,324 92,384 505,328 209,025 806,737 1,200,059
13 Metal products 8,717 3,962 49,843 49,733 5,184 9,495 1,699 7,955 11,225 588,657 434,220 1,763,582 1,962,132 4,159,934 4,748,590
14 Machinery (except electricity) 0 0 0 0 0 2,931 0 895 0 4,351 30,398 3,762,952 -99,653 3,693,697 3,698,047
15 Electrical and electronic equipment 2,507 8,181 43,006 49,647 3,105 13,974 1,751 7,594 23,987 483,932 1,257,214 1,175,563 1,402,283 3,835,060 4,318,995
16 Transportation equipment 12 0 0 48,415 32 30,093 0 1,194 21,965 200,409 2,708,267 347,073 456,300 3,511,640 3,712,047
17 Other manufacturing 722 41 107 138 94 723 45 854 2,311 10,631 1,714,160 1,070,293 -1,643,262 1,141,191 1,151,822
18 Energy and water 2,361 52,462 4,746 31,857 53,397 22,717 8,410 20,910 70,273 502,230 1,157,021 1,819 -143,622 1,015,218 1,517,448
19 Construction 178 10,874 27,633 7,709 12,746 37,261 7,492 77,287 52,735 263,671 134,195 89,753 3,530,875 3,754,823 4,018,492
20 Trade 32,297 10,478 118,098 461,547 526,409 346,590 25,071 121,394 223,456 4,747,061 1,985,769 1,345,128 1,762,238 5,093,135 9,840,194
21 Hotels and businesses 1,601 2,446 12,188 58,661 0 82,539 18,916 61,055 127,347 489,958 2,845,000 2,581,394 779,650 6,206,044 6,696,000
22 Transport and communication 7,604 6,297 47,687 127,888 56,075 400,861 35,274 47,528 100,695 1,364,305 3,815,000 1,168,180 -730,596 4,252,584 5,616,894
23 Credit and insurance 21,351 18,850 267,948 708,867 93,336 351,084 332,276 178,892 1,152,294 4,510,551 78,635 382,483 297,311 758,429 5,268,983
24 Real estate, renting, research, business services 17,759 20,565 123,006 394,255 202,129 269,549 706,523 361,145 679,930 3,775,760 772,049 489,577 390,212 1,651,838 5,427,596
25 Other services 1,126 1,081 4,493 133,120 96,590 75,202 34,610 67,469 472,299 1,031,667 3,738,548 4,374,784 11,171,283 19,284,615 20,316,284
Total intermediate costs 156,666 211,842 979,506 2,229,211 2,458,543 1,944,160 1,187,537 1,027,741 3,383,979 28,566,958 30,910,301 34,570,202 17,230,570 82,711,073 111,278,033
Value Added 288,337 648,400 2,300,200 6,207,900 1,923,500 1,036,701 2,373,700 2,203,601 8,858,200 39,170,059
Imports 292,191 356,750 1,028,534 994,436 583,899 1,686,113 151,253 2,757,059 1,393,257 20,167,603
Other primary inputs 414,628 300,456 -289,748 408,647 1,730,058 949,920 1,556,493 -560,805 6,680,848 23,373,413
PRIMARY INPUTS 995,156 1,305,606 3,038,986 7,610,983 4,237,457 3,672,734 4,081,446 4,399,855 16,932,305 82,711,075
INPUT 1,151,822 1,517,448 4,018,492 9,840,194 6,696,000 5,616,894 5,268,983 5,427,596 20,316,284 111,278,033
Note: TIS – Total intermediate sales; HC – Household consumption; EXP – Exports; OFD – Other final demands; TFD – Total final demand.
Source: Author’s elaboration

APPENDIX B – Type II output-to-output
multipliers according to the kind of regionalization
method

Tab. B.1 – Type II output-to-output multipliers calculated for non-survey methods
Sectors
Output
SLQ
Income Employment Output
WLQ
Income Employment Output
PLQ
Income Employment Output
CILQ
Income Employment
Cereals 2.97 2.85 2.24 1.85 1.69 1.09 2.97 2.85 2.24 2.71 2.58 2.04
Other agricultural sub-sectors 1.70 1.63 1.28 1.76 1.61 1.04 1.70 1.63 1.28 1.58 1.50 1.19
Mining 1.97 2.86 1.05 1.58 2.04 1.04 1.97 2.86 1.05 2.13 3.22 1.08
Food and tobacco 4.25 6.20 1.46 1.66 2.00 1.91 4.25 6.20 1.46 4.19 6.12 1.47
Textile products and apparel 2.33 2.11 18.78 1.81 1.60 61.29 2.33 2.11 18.78 2.41 2.19 21.25
Leather and shoes 2.21 2.15 4.97 1.81 1.67 7.30 2.21 2.15 4.97 1.94 1.86 3.93
Timber and furniture 2.15 2.07 2.49 1.63 1.97 2.33 2.15 2.07 2.49 2.15 2.07 2.45
Paper, printing, publishing 2.28 2.05 1.13 1.83 1.60 1.49 2.28 2.05 1.13 2.39 2.16 1.16
Coke and nuclear fuel 1.16 2.29 1.02 1.06 7.74 1.13 1.16 2.29 1.02 1.23 2.88 1.03
Chemicals 1.98 2.21 1.03 1.68 1.62 3.29 1.98 2.21 1.03 2.16 2.45 1.05
Rubber and plastic products 2.40 2.10 27.42 1.73 2.30 243.52 2.40 2.10 27.42 2.58 2.25 39.13
Non-metal mineral products 2.62 2.03 6.12 2.74 1.46 30.17 2.62 2.03 6.12 2.88 2.22 8.80
Metal products 2.54 2.12 4.49 1.63 2.60 14.12 2.54 2.12 4.49 2.92 2.41 7.19
Machinery (except electricity) 2.38 2.08 2.10 2.13 1.56 11.46 2.38 2.08 2.10 2.86 2.48 3.08
Electrical and electronic equipment 2.11 2.03 1.71 1.93 1.48 3.69 2.11 2.03 1.71 2.30 2.22 2.07
Transportation equipment 2.13 2.17 1.04 1.90 1.51 5.55 2.13 2.17 1.04 2.49 2.58 1.07
Other manufacturing 2.04 2.32 2.60 1.53 1.94 2.06 2.04 2.32 2.60 2.02 2.31 2.33
Energy and water 2.28 1.99 1.08 1.94 1.37 12.93 2.28 1.99 1.08 2.64 2.27 1.13
Construction 2.33 1.87 7.02 2.18 1.78 3.49 2.33 1.87 7.02 2.34 1.88 6.76
Trade 1.72 1.56 706.77 1.83 1.62 293.11 1.72 1.56 706.77 1.68 1.52 634.67
Hotels and businesses 2.75 3.41 2.23 2.03 2.24 2.61 2.75 3.41 2.23 2.82 3.50 2.32
Transport and communication 2.35 1.74 1.09 1.97 1.44 1.18 2.35 1.74 1.09 2.42 1.78 1.10
Credit and insurance 2.83 2.02 1.25 1.98 1.47 2.27 2.83 2.02 1.25 2.91 2.07 1.29
Real estate, renting, research, business services 1.59 1.59 3.95 1.63 1.42 2.01 1.59 1.59 3.95 1.59 1.58 3.86
Other services 2.90 1.38 30.66 2.18 1.36 23.25 2.90 1.38 30.66 2.91 1.38 30.67
Household sector 2.53 17.18 21.35 2.35 72.66 6.58 2.53 17.18 21.35 2.53 17.09 21.10
Source: Author’s elaboration

Tab. B.1 – Type II output-to-output multipliers calculated for non-survey methods (continued)
Sectors
Output
RLQ
Income Employment Output
FLQ
Income Employment Output
SCILQ
Income Employment Output
SDP
Income Employment
Cereals 2.80 2.68 2.10 2.06 1.95 1.57 2.47 2.36 1.86 1.80 1.71 1.30
Other agricultural sub-sectors 1.62 1.55 1.21 1.55 1.48 1.18 1.60 1.53 1.21 1.58 1.50 1.14
Mining 2.11 3.16 1.08 3.23 5.45 1.13 2.36 3.67 1.08 1.58 2.10 3.45
Food and tobacco 4.22 6.16 1.47 5.53 8.29 1.66 4.79 7.08 1.55 1.89 2.43 2.29
Textile products and apparel 2.39 2.17 20.92 3.10 2.82 28.23 2.61 2.36 22.96 1.78 1.62 1.31
Leather and shoes 2.03 1.96 4.25 1.82 1.74 3.52 1.97 1.90 4.03 1.80 1.73 1.80
Timber and furniture 2.14 2.06 2.46 2.14 2.06 2.45 2.16 2.08 2.49 1.72 1.65 2.23
Paper, printing, publishing 2.37 2.14 1.16 3.30 2.99 1.23 2.60 2.34 1.17 1.82 1.65 1.38
Coke and nuclear fuel 1.23 2.90 1.04 1.59 6.30 1.07 1.29 3.47 1.04 1.08 1.66 1.17
Chemicals 2.14 2.43 1.05 3.83 4.75 1.14 2.59 3.04 1.07 1.63 1.76 1.12
Rubber and plastic products 2.56 2.23 37.53 3.73 3.25 56.58 2.84 2.48 40.55 1.89 1.68 1.35
Non-metal mineral products 2.84 2.19 8.52 4.34 3.25 12.56 3.17 2.42 8.77 2.11 1.68 1.38
Metal products 2.83 2.35 6.60 4.19 3.45 8.22 3.10 2.56 6.44 2.10 1.77 1.20
Machinery (except electricity) 2.84 2.47 3.12 4.51 3.93 4.38 3.09 2.69 3.01 2.04 1.79 1.29
Electrical and electronic equipment 2.25 2.18 1.99 3.04 2.98 2.34 2.43 2.36 2.01 1.76 1.69 1.47
Transportation equipment 2.48 2.56 1.08 5.27 5.73 1.24 2.98 3.13 1.09 1.84 1.86 1.14
Other manufacturing 2.02 2.30 2.38 2.09 2.41 2.34 2.07 2.37 2.47 1.75 1.90 1.19
Energy and water 2.67 2.29 1.14 4.76 4.13 1.26 3.16 2.71 1.16 1.68 1.54 1.04
Construction 2.33 1.87 6.79 2.33 1.87 6.70 2.34 1.88 6.92 2.04 1.68 1.70
Trade 1.69 1.54 653.33 1.67 1.51 621.85 1.69 1.53 659.13 1.61 1.47 1.38
Hotels and businesses 2.79 3.47 2.29 2.98 3.72 2.45 2.90 3.62 2.38 1.91 2.21 1.83
Transport and communication 2.39 1.77 1.10 2.79 2.02 1.12 2.53 1.85 1.11 1.96 1.50 1.45
Credit and insurance 2.89 2.05 1.28 4.46 3.00 1.46 3.29 2.29 1.32 2.14 1.61 1.52
Real estate, renting, research, business services 1.59 1.58 3.87 1.57 1.55 3.76 1.59 1.58 3.88 1.48 1.46 1.53
Other services 2.91 1.38 30.58 2.89 1.38 30.35 2.91 1.38 30.77 2.53 1.28 8.35
Household sector 2.53 17.12 21.14 2.51 16.94 20.98 2.52 17.08 21.15 2.27 14.61 14.62
Source: Author’s elaboration

Tab. B.2 – Type II output-to-output multipliers calculated for hybrid methods
Sectors
Output
GRIT-SLQ
Income Employment Output
GRIT-WLQ
Income Employment Output
GRIT-PLQ
Income Employment Output
GRIT-CILQ
Income Employment
Cereals 2.87 2.09 1.08 2.83 1.86 1.08 2.71 1.79 1.08 2.80 1.72 1.08
Other agricultural sub-sectors 2.21 2.34 1.05 2.17 2.08 1.04 2.09 1.98 1.05 2.13 1.89 1.05
Mining 1.63 2.36 5.50 1.57 1.99 4.69 1.57 2.00 5.21 1.41 1.57 3.83
Food and tobacco 1.72 2.31 13.17 1.69 2.04 12.95 1.66 1.98 13.08 1.46 1.55 3.83
Textile products and apparel 1.85 1.81 3.49 1.82 1.59 3.34 1.75 1.54 3.31 1.66 1.37 2.76
Leather and shoes 1.78 1.84 3.34 1.77 1.63 3.21 1.71 1.58 3.24 1.46 1.25 2.22
Timber and furniture 1.93 2.01 4.35 2.04 1.93 4.58 1.83 1.71 4.11 1.69 1.47 3.37
Paper, printing, publishing 1.84 1.78 3.79 1.75 1.51 3.34 1.74 1.52 3.55 1.63 1.33 3.09
Coke and nuclear fuel 1.13 1.77 16.51 1.13 1.58 15.85 1.11 1.50 14.71 1.09 1.33 14.23
Chemicals 1.64 1.91 16.99 1.58 1.61 14.68 1.57 1.62 15.58 1.61 1.53 14.34
Rubber and plastic products 1.89 1.79 9.33 1.91 1.60 8.46 1.79 1.52 8.64 1.78 1.41 7.38
Non-metal mineral products 2.14 1.83 3.94 2.05 1.57 3.53 2.00 1.56 3.67 1.97 1.44 3.58
Metal products 1.98 1.84 3.75 1.94 1.60 3.40 1.87 1.57 3.50 1.83 1.44 3.42
Machinery (except electricity) 2.00 1.91 5.26 1.93 1.64 4.73 1.89 1.63 4.88 2.04 1.64 5.27
Electrical and electronic equipment 1.75 1.81 3.73 1.68 1.54 3.35 1.66 1.54 3.48 1.64 1.43 3.39
Transportation equipment 1.75 1.89 21.40 1.67 1.60 18.28 1.67 1.62 19.63 1.84 1.68 21.37
Other manufacturing 1.57 1.86 1.34 1.55 1.63 1.29 1.50 1.58 1.31 1.39 1.33 1.23
Energy and water 1.67 1.64 29.74 1.63 1.40 25.24 1.58 1.40 26.51 1.71 1.30 25.17
Construction 2.05 1.82 1.38 2.07 1.66 1.37 1.92 1.55 1.34 1.81 1.37 1.33
Trade 1.75 1.71 1.20 1.80 1.58 1.22 1.66 1.46 1.18 1.53 1.25 1.18
Hotels and businesses 1.98 2.42 3.60 2.00 2.16 3.66 1.85 2.02 3.37 1.62 1.58 2.07
Transport and communication 2.11 1.71 2.63 2.11 1.52 2.53 1.99 1.46 2.50 1.96 1.35 2.56
Credit and insurance 2.12 1.73 5.65 1.96 1.45 5.18 1.99 1.47 5.20 1.92 1.34 5.49
Real estate, renting, research, business services 1.81 1.77 1.32 1.71 1.49 1.26 1.71 1.51 1.29 1.55 1.27 1.24
Other services 2.20 1.51 8.08 2.15 1.33 7.78 2.04 1.29 7.24 2.00 1.21 7.77
Household sector 2.29 2.81 4.26 2.35 70.95 4.66 2.29 83.27 4.21 2.30 39.68 5.50
Source: Author’s elaboration

Tab. B.2 – Type II output-to-output multipliers calculated for hybrid methods (continued)
Sectors
Output
GRIT-RLQ
Income Employment Output
GRIT-FLQ
Income Employment Output
GRIT-SCILQ
Income Employment Output
GRIT-SDP
Income Employment
Cereals 2.90 1.89 1.09 2.60 1.69 1.09 3.20 2.09 1.09 3.08 2.03 1.08
Other agricultural sub-sectors 2.13 2.04 1.05 1.90 1.78 1.05 2.35 2.30 1.06 2.38 2.34 1.05
Mining 1.68 2.25 5.66 1.17 1.20 2.36 1.89 2.68 7.01 1.91 2.72 6.31
Food and tobacco 1.73 2.11 13.03 1.35 1.42 5.30 1.92 2.45 14.67 1.90 2.43 14.62
Textile products and apparel 1.83 1.62 3.44 1.40 1.19 2.23 2.03 1.82 3.98 1.99 1.78 3.61
Leather and shoes 1.63 1.51 2.85 1.34 1.18 1.98 1.68 1.57 2.80 1.96 1.88 3.50
Timber and furniture 1.89 1.77 4.28 1.35 1.18 2.23 2.09 2.00 4.67 2.43 2.36 5.34
Paper, printing, publishing 1.88 1.65 3.93 1.42 1.20 2.53 2.12 1.88 4.76 2.07 1.83 4.25
Coke and nuclear fuel 1.17 1.87 21.18 1.08 1.26 13.12 1.27 2.50 33.95 1.18 1.95 21.67
Chemicals 1.83 1.97 20.95 1.36 1.30 12.52 2.34 2.68 33.75 1.88 2.05 20.64
Rubber and plastic products 1.98 1.70 9.99 1.44 1.20 5.66 2.33 2.00 13.20 2.24 1.91 11.03
Non-metal mineral products 2.24 1.73 4.16 1.54 1.20 2.56 2.64 2.02 5.29 2.50 1.91 4.42
Metal products 2.06 1.74 3.93 1.46 1.20 2.43 2.36 1.99 4.79 2.23 1.88 4.04
Machinery (except electricity) 2.36 2.05 6.47 1.45 1.21 3.23 2.87 2.52 8.81 2.53 2.21 6.68
Electrical and electronic equipment 1.81 1.70 3.92 1.36 1.19 2.49 2.03 1.94 4.78 1.95 1.86 4.12
Transportation equipment 1.98 1.98 26.13 1.41 1.28 14.13 2.58 2.68 42.16 2.07 2.09 26.46
Other manufacturing 1.54 1.63 1.33 1.25 1.19 1.16 1.63 1.78 1.36 1.75 1.96 1.39
Energy and water 1.82 1.58 33.87 1.49 1.27 28.37 2.19 1.92 50.57 1.84 1.63 34.23
Construction 1.99 1.60 1.36 1.49 1.18 1.23 2.23 1.80 1.43 2.69 2.16 1.55
Trade 1.68 1.47 1.19 1.39 1.17 1.15 1.81 1.60 1.21 2.10 1.88 1.28
Hotels and businesses 1.97 2.16 3.59 1.25 1.19 1.53 2.17 2.45 4.01 2.33 2.65 4.31
Transport and communication 2.10 1.53 2.63 1.62 1.21 2.16 2.37 1.70 3.01 2.39 1.71 2.82
Credit and insurance 2.08 1.53 5.57 1.59 1.20 3.85 2.26 1.66 6.35 2.19 1.60 5.81
Real estate, renting, research, business services 1.75 1.54 1.30 1.45 1.23 1.23 1.83 1.62 1.32 2.15 1.92 1.41
Other services 2.13 1.33 7.75 1.77 1.15 6.91 2.26 1.39 8.42 2.40 1.46 8.87
Household sector 2.41 91.08 4.53 2.07 18.99 5.76 2.57 85.50 4.82 2.56 78.87 4.74
Source: Author’s elaboration

This research faces the problem of constructing regional I-O tables which still
represents an important objective for numerous types of research. The main interest
in the I-O approach lies in the possibility, which this approach offers, of evaluating
impacts generated by local or extra-local policy, at sub-national and disaggregated
sectoral levels. Construction of regional I-O tables implies the need to collect a
considerable volume of information, which, at local level, is very difficult to obtain. For
this reason, starting from the 1950s, different approaches have been developed,
namely: survey, non-survey and hybrid approaches. In front of the existence of
numerous regionalization techniques, researchers have also worried to evaluate
performances of methods, comparing survey-based tables with indirectly constructed
tables. Towards this aim, many statistical measures have been employed.
This research is articulated into two parts.
The first one proposes a survey, from the 1960s, of regionalization methods and
empirical studies carried out to analyse the validity of these methods. Moreover, a
review of statistical measures used to compare methods is provided.
The second part is centred on an empirical analysis whose objectives are twofold.
The main objective is to evaluate policy impact sensitivity to the use of alternative
approaches for deriving regional I-O tables. In other words, the aim is to estimate the
degree of similarity or difformity characterizing several regionalization methods in
terms of prediction of impact. In this connection, 16 alternative methods of
constructing regional I-O tables are investigated. A further objective is to assess the
impact generated by the CAP reform for the period 2000-2006 on the overall
economy of the Marche region in terms of output, employment and income. Towards
this aim, two models are developed and sequentially applied: an econometric model
of profit maximization and a closed mixed-variable I-O model. Finally, impact results
related to the explored regionalization methods are compared using univariate and
multivariate statistical techniques.
Andrea Bonfiglio is a Ph.D. in Agricultural Economics and Politics at the Department of
Economics of the Marche Polytechnic University.