Research 350 - NZ Transport Agency

ECONOMIC DEVELOPMENT BENEFITS OF TRANSPORT INVESTMENT
For this reason I-O has traditionally been favoured as a measure of regional impacts. Thus,
ACG (2004) indicated that development of a regional CGE model for New Zealand was beyond
its project resources, it was able to use I-O as a means of estimating changes in ‘regional
GDP’.
However, ACG also noted the issues arising from the lack of constraints in I-O modelling,
particularly capital formation and labour constraints. Accordingly ACG’s preferred CGE model
(ESSAM) was used in conjunction with national I-O analysis as a method to scale down the
‘raw’ regional I-O results. Use of this methodology suggested that I-O analysis overestimated
‘regional GDP’ by between 6.2% - 7.7% (ACG 2004, pp. 68-70).
ACG’s recognition of the problem of I-O overestimation is notable. Theoretically, its assessed
degree of I-O overestimation may be ‘at the high end’, given that the modelled results were
derived from national level, where I-O’s lack of modelled constraints may be most serious.
However, this methodology implies access to a national CGE model. In many cases, analysts
are unlikely to have such an asset 28 .
A more fundamental problem concerns the derivation of multiplier values for regions. Official
sub-national I-O tables do not exist in Australia and New Zealand. Thus, the relevant tables
are often ‘synthetically created’, base d on reasoned conjecture. One example of this is the I-
O tables used in ACG’s study of the CityLink project (BTE 1999, p. 136).
Likewise, ACG’s survey of New Zealand road investment refers to the use of ‘regional inter
industry tables’ to derive changes in ‘regional GDP’ however, the source of these is unclear
(ACG 2004, p. 68). Previous New Zealand studies have estimated inter-industry tables by use
of methods such as GRIT (Generation of Regional Input Output Tables). However one such
study, focussing on Christchurch Tourism, indicated that GRIT tables had a margin of error of
+/- 20%. The authors advise using survey work to establish more reliable results (Butcher,
Fairweather & Simmons 2003, pp. 27-28).
Further, information problems connected with the use of multipliers increase as the scale of
the regional analysis becomes smaller and multipliers are applied to ‘regions within
regions’ 29 . Not surprisingly, Docwra and West (1999) note that in general the smaller the
region, the less reliable and available the data (Docwra & West 1999, p. 941).
In spite of these difficulties, I-O analysis would appear to have a role to play in measuring
regional economic impacts, given typical policy requirements for information on output and
employment. The UK Government appears to have recognised this in its acceptance of
28 Further, Dwyer, Forsyth and Spurr argue that I-O and CGE models will generally only give similar
answers if implausible inputs are fed into the CGE model. Thus, these authors argue that adjusting I-O
results downwards ‘misses the point’ (Dwyer, Forsyth and Spurr 2004, p. 312). Whether this comment
would apply in the case of ACG’s modelling is uncertain and would seem to require a detailed review of
both the I-O and CGE modelling undertaken. Again, this would seem to constitute part of the ‘black box’
problem encountered with such econometric modelling.
29 Recent studies of the Australian tourist industry also indicate the problems faced by applying
multipliers within small areas. For example, a recent study of Queensland tourism noted that the size of
multipliers is dependent on the size and complexity of the region in question. Nonetheless it applied
multipliers developed for tropical North Queensland to Fraser Island in the South-East (Kleinhardt 2002,
pp.30-31, 50). A recent review of the Victorian Alpine Region estimated regional economic impacts by
applying national direct value added and employment multipliers and halving the value of state level
indirect multipliers. It was indicated that this approach was consistent with the procedure for ‘special
events’(KPMG 2000, p. 54).
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