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International Journal of Contemporary Business Studies 

Vol: 4, No: 6 JUNE, 2013 ISSN 2156-7506 

Available online at http://w w w.akpinsight.webs.com 

Higher Productivity of Workers Leads to Higher Profit 

for the Firms: Evidence from Large Scale Manufacturing 

Industries in Pakistan 

Liaqat Ali 

PhD Scholar, Al-Khair University, Pakistan 

Page | 6 

Dr. Zekeriya Nas 

National University of Modern Languages, Islamabad, Pakistan 

Dr. Muhammad Ismaeel Ramay 

Head Graduate School of Business, 

Al-Khair University, Pakistan 

ABSTRACT 

Aim of this study is to compute various measures of productivity like labor productivity both per worker 

and per hour, capital productivity, combined measure of labor & capital productivity, total factor 

productivity based on output as well as on revenue and profitability for the firms operating in the large 

scale manufacturing industries in Pakistan and to ascertain the relationship between various measures of 

productivity and profitability using census data of manufacturing industries. A disaggregated analysis has 

been carried out by form of ownership of the firm, legal and listing status of the firm and by type of major 

industry. Data has been analyzed through descriptive, correlation measures as well as by regression 

analysis. Results shows that average output, productivity and profitability for listed companies is greater 

than the non-listed companies and foreign controlled firms operating are more efficient as compared to 

both private and public sector companies. Public limited companies have been emerges as more efficient 

and profitable as compared to private limited companies. Further, firms operating in petroleum and 

tobacco industries have been found as more productive, efficient and higher in profitability as compared 

to firms in other industries. All the predictors like labor, capital, materials and energy has been found as 

having a positive and significant relationship with profitability. Further, measures of productivity and 

total factor productivity have also been emerged as significant predictors of profitability. The data 

evidence suggests that higher productivity of workers leads to higher profit for the firms. This study 

recommends investment in human capital through education, training and development to enhance the 

productivity of workers and profitability for the firms in long run. 

Key Word: Firm, industry, productivity, total factor productivity, profitability, CMI, Pakistan, 

INTRODUCTION 

After its independence in 1947 from British Rule, Pakistan has pursued an import substitution strategy 

and speedy industrial growth has been followed as one of the important objective of the public policies. In 

the beginning, the policy of import substitution was initially focused on industries producing consumer 

goods only. However, later on it was also extended to industries producing raw materials and investment 

goods as well (Irfan, 2010). Resultantly, major structural changes have been occurred since then and share 

of agriculture towards Gross Domestic Product (GDP) fell from 53.2 percent in 1950 to 21.1 percent in 

C opyright © 2 0 13. A cademy of Knowledge P rocess




2012 and those of the industry and services stood at 25.4 percent and 53.5 percent during 2012 

respectively (Handbook of Statistics, 2010; PBS, 2011-12). At the time of its independence from British 

Rule in 1947, Pakistan was a poor country, lacking in all types of physical infrastructure including the 

industrial one. In spite of absence of requisite resources, Pakistan’s economic performance has been 

satisfactory during 1950 to 1990 except in the late 1990s when it was slowed down a bit. However, 

economic recovery was witnessed in the early 2000’s and average growth rate of GDP was recorded as 7 

percent between 2003 and 2007 (Economic Survey, 2011-12). Pakistan has faced a massive capital flight 

to other countries primarily due to unfavorable internal business conditions particularly poor law and 

order situation and worsening energy crises. Coupled with global financial crises and commodity price 

hike particularly of petroleum products, Pakistan's economy witnessed a widening trade deficits and 

higher rate of inflation which was recorded as high as 21 percent during the fiscal year 2008. However, it 

was slowed down a bit and was reduced to 13.81 percent and 10.84 percent during the fiscal years 2011 

and 2012 respectively (Economic Survey, 2011-12). Both internal weaknesses and external threats have 

hindered the economic performance of the country and economy could only manage to grow at meager 

rate of 3.7 percent during the fiscal year 2012 (Economic Survey, 2011-12). 

The industrial sector whose share in the economy of Pakistan is 25 percent is comprised of mining & 

quarrying, manufacturing 1 which includes three sub-sector namely large scale industries, small scale& 

household industries and slaughtering, construction and electricity & gas distribution. Manufacturing 

sector alone contributes 18.6 percent towards GDP of Pakistan whereas the share of Large Scale 

Manufacturing Industries 2 (LSMI) is 11.9 percent (PBS, 2012).13.65 percent of the employed persons 

were engaged in manufacturing sector during 2010-11 (Labor Force Survey, 2010-11). Manufacturing is a 

key sector for the economy of Pakistan and determines the direction and pace of overall economic growth 

of the country. The periods of higher growth rate in manufacturing sector have witnessed higher growth 

rate of GDP in Pakistan (Figure 1).A large number of studies have been undertaken to analyze the 

behavior and performance of firms by taking advantage of information available at the micro level. 

However, most of these studies have concentrated on manufacturing industries because of easily available 

and accessible databases (Kremp & Mairesse, 1991). 

The variability of profitability at firm level has been an attractive topic of research in finance, accounting, 

economics and strategic management during recent years. The strategic management literature stresses the 

role of firm’s internal resources as the major source of variability in their profitability. According to the 

resource-based view of strategic management, the performance of firms varies from each other due to 

differences in organizational structures and management practices (Goddard, Tavakoli, & Wilson, 2005). 

Keeping in view the importance of manufacturing sector both from the stand point of its contribution in 

economic growth and employment generation, it is relevant to study the relationship between productivity 

of the workers and profitability of the firms working in LSMI of Pakistan. This paper attempts to 

ascertain the relationship between various measures of productivity of worker and profitability of the 

firms operating in LSMI using the data from Census of Manufacturing Industries (CMI) 2005-06.A 

Page | 7 

1 Manufacturing is a process of chemical or mechanical transformation of inorganic and organic substances into new 

compounds whether the work is carried out by hand or through machine inside a factory or in the house of a 

worker. Manufacturing activities include treating, processing, assembling, repairing and services. Source: Census of 

Manufacturing Industries, 2005-06 

2 Large Scale Manufacturing Industries covers the establishments registered or qualified for registrations under the 

Factories Act-1934, having 10 or more employees. Source: Census of Manufacturing Industries, 2005-06 





disaggregated analysis has been carried out by form of ownership of the firm, legal and listing status of 

the firm and by type of major industry. The study will help the investors and firms operating in LSMI to 

improve their productivity and profitability as well as the policy makers to shape right kind of policies. 

LITERATURE REVIEW 

Productivity has been variously defined by different authors. According to Joseph (1987) productivity is 

the link between output produced and the input provided to produce this output. According to the same 

author, productivity means proficient use of inputs such as labor, capital, material, energy and information 

change. Ha, Strappazzon, & Fisher (2001) defined productivity as a measurement of the physical units of 

output produced when physical inputs are given. Productivity is a proportion of a quantity measure of 

output to a quantity measure of input (OECD, 2001). In the Business Economy Overview (2006) the 

definition of productivity has been given as a measurement of how efficiently inputs or resources are 

converted into useful outputs, products, or results. Productivity is finding ways of doing things smarter 

and better (Domingo, 2012). Productivity is a measure of the competence of the labor force. It can be 

calculated by per worker output or output per worker hour (Riley, 2012). According to Clarke (2012) 

firms producing more output with a smaller amount of raw material and smaller number of workers will 

have higher productivity of labor. 

Another measure of labor productivity is a ratio of productivity adjusted for wages. This measure of 

productivity is a ratio of firm’s value added to its personnel costs and afterward adjusted by the proportion 

of paid employees in the total employed persons. In other words it will be equal to productivity of labor 

alienated by mean personnel costs articulated as a ratio in percentage forms (Business economy overview, 

2006). 

According to OECD (2001), real cost savings, benchmarking production processes, assessment of living 

standards technology and efficiency are the major objectives of measurement of labor productivity. 

Higher productivity of workers can lead to lower average costs, improved competitiveness and trade 

performance, higher profits, higher wages and economic growth (Riley, 2012). 

Productivity can be measured in many ways. However, the choice between different methods of 

productivity measurement depends on its purpose as well as on the availability of data. Value added (VA) 

and gross output (GO) are two most common types of measures of output used in the measurement of 

productivity ( 

Table 1).The distinction between using VA or GO as measure of output is 

of particular relevance for productivity measurement of firms or industries (OECD, 2001). The 

productivity measures based on input can be classified into single factor productivity or multifactor 

productivity (MFP). The measures of single factor productivity establish relationship between a single 

measure of output to a single measure of input whereas MFP measures establish relationship between 

single measure of output to a bundle of inputs (OECD, 2001).For example labor and productivities based 

either on VA or GO, are the single factor measures of productivity. Capital-labor productivity based on 

VA or GO is an example of MFP measure. The productivity measure combining capital, labor, energy, 

materials and services (KLEMS) is also an example of MFP measure ( Table 1). 

Page | 8 





Measure of 

output 

GO 

VA 

Table 1 Summary of productivity measures 

Measure of input 

Labor Capital Capital and 

labor 

Productivity of 

labor 

(based on GO) 


labor 

(based on VA) 

Single factor measures of 

productivity 

Source: OECD, 2001 


capital 

(based on GO) 


capital 

(based on VA) 

Capital-labor 

MFP 

(based on GO) 

Capital-labor 

MFP 

Capital, labor and 

intermediate inputs 

(energy, materials, 

services) 

KLEMS multifactor 

Productivity 

(based on VA) 

Multifactor measures of productivity 

(MFP) 

Page | 9 

Kremp and Mairesse, (1991) constructing a panel sample data on 2300 large French firms from 1984 to 

1987 compared the mean productivity ratios of labor and profitability margins in levels as well as in 

growth rates by industries. They used VA per person and sales per person as labor productivity measures 

and ratios of operating income to sales and VA to sales as measures of profitability margins. 

Productivity is the black box for a firm which is used to convert the capital into sales and profits 

(Domingo, 2012). Profits are generally considered as essential feature of market economy. Low profit 

pause innovation and leads to a decline in the rate of investment which ultimately results to sluggish 

growth in output and capacity. Low growth may also lead to low profits (James, Lee & Sutch, 1985). 

According to James, et al., (1985) profit can be viewed at from the perspective of firm, industry, or 

economy. It can be gross or net, pre or after tax, before or after the deduction of payments of factor to 

capital; planned or realized. Profit can also be treated in relation to production or, more generally, as the 

excess of the total current revenue over current expenses. According to Ha, Strappazzon, & Fisher, (2001) 

profit is defined as receipts minus costs. 

Profit of a firm is generally affected by prices of inputs or outputs but productivity is not. However, both 

productivity and profit are affected by technical changes occurred through research. Therefore, both profit 

and productivity, being closely related and different concepts, are the highly concerned areas for 

managers and research administrators. Continuous improvement in productivity is a prerequisite for an 

industry to survive in international competition (Ha, et al., 2001). 

Prasad and Harker (1997) presented productivity and profitability of banking industry in the U.S with 

focus on importance of IT labor. Their investigation was focused on bank’s characteristics leading 

towards effectual use of IT labor. They recommended a continuous need for investment for the 

enhancement of knowledge specific to each industry and technical skills of the labor. They found process 

of procurement and IT labor as the main determinants of effectiveness and efficiency in each industry. 

According to Stierwald (2010) performance of a firm, from the view point of profitability, can be 

explained either through firm effect models or structure-conduct-performance (SCP).In the SCP models 

firm behavior and profitability is determined by exogenously given market structure in contrast to firm 





effect models where structure of the market is treated as endogenous and is the result of characteristics of 

a firm itself. Firm effect models are also known as heterogeneity, revisionist or resource-based models. 

In Pakistan, a number of studies have been carried out to measure productivity of manufacturing sectors. 

Mahmood and Siddiqui (2000) estimated total factor productivity(TFP), by applying the growth 

accounting framework pioneered by Solow (1956) for Pakistan’s manufacturing industries covering the 

period 1972 to 1997. Their estimates of TFP growth rate was 2.37 percent per annum. 

Khan (2006) calculated the TFP in Pakistan for the period 1960 to 2003, by utilizing the growth 

accounting framework. His estimates of TFP were 2.4 percent during 1960’s, 0.73 percent during 1970’s, 

2.1 percent during 1980’s and 0.6 percent during 1990’s. He also noted a high degree of correlation i.e. 88 

percent between TFP and GDP growth rate. He also found development in financial sector, foreign direct 

private investment and macroeconomic stability as significant determinants of TFP. 

Din, Ghani, and Mahmood (2007) examined the competence of LSMI in Pakistan by using Stochastic 

Frontier and Data Envelopment Analysis techniques for the year 1995-96 and 2000-01. They computed 

the efficiency scores under the assumptions of constant as well as variables returns to scale. With constant 

returns to scale, their average score of efficiency increased from 0.23 in 1996 to 0.42 in 2001. However, 

with variable returns to scale, average efficiency score was 0.31 in 1996 and 0.49 in 2001 which shows an 

improvement in technical efficiency of LSMI sector. 

Raheman, Afza, Qayyum and Bodla (2008) calculated TFP and its components for LSMI in Pakistan. 

They used Data Envelopment Analysis to estimate TFP growth in sub sectors of manufacturing for eleven 

chosen industries for the period of 1998 to2007. According to their study overall TFP registered an 

increase of 0.9 percent in the selected period. 

Ahmed, Chaudry and Ilyas (2008) calculated the TFP for Pakistan agriculture sector for the sample period 

of 1966 to 2005 following growth accounting approach. Their results show that the yearly average growth 

rate ofTFP in agriculture sector of Pakistan was 0.28 percent between 1966 and 2005. 

Hamid and Pichler (2009) analyzed the important variables responsible for growth in productivity and 

value added in the manufacturing sector of Pakistan for the period of 1972 to 2005. They used Translog 

Production Technology technique. According to their results one third of growth in value added of 

manufacturing sector is determined collectively by human capital and productivity. They also found that 

labor and capital like traditional factors of production were still the major determinants of growth in 

valued added of manufacturing sector in Pakistan. 

APO Productivity Data book (2011) presented per worker and per hour labor productivity for 20 Asian 

countries. According to APO data book, mean yearly growth rate of per worker productivity of labor, 

using 2005 PPPs for Pakistan were 3.5 percent, 0.40 percent, 1.9 percent and 0.20 percent for the periods 

1990–1995, 1995–2000, 2000–2005, and 2005–2008 respectively whereas mean yearly growth rate of per 

hour productivity of labor for Pakistan for the periods 1970–2008, 1970–1990, and 1990–2008 were 2.2 

percent, 2.6 percent, and 1.7 percent respectively. 

Page | 10 

Although a number of studies have measured productivity and TFP in the manufacturing sector of 

Pakistan but there is no studies which have linked productivity with profitability. The present study is 





aimed at to establish link between productivity of workers and profitability of the firms operating in the 

LSMI of Pakistan. 

METHODOLOGY AND SOURCE OF DATA 

Theoretical Framework 

Page | 11 

The objective of this paper is to ascertain the relationship between various measures of productivity and 

profitability of firms. We measure productivity of labor (per worker and per hour), productivity of 

capital, and productivity of labor & capital based on gross output and total factor productivity (TFP) 

based on gross output and revenue. We also compute gross margin for each firm in order to establish 

relationship between various measure of productivity and profitability. The over theoretical framework of 

the model used in the paper is presented below:- 

Independent Variabless 

Dependent Variabless 

1. Labor 

Revenues 

2. Capital 

Output Productivity Profitability 

3. Materials 

4. Energy 

Employment and 

material's cost 

Methodology 

Productivity 

Following the OECD (2001) and Riley (2012), the productivity of labor; per worker and per hour worked, 

capital and labor & capital has been calculated by using the formula given below:- 

Productivity= Volume measure of output/volume measure of input (OECD, 2001). 





where input can be a measure of labor (number of workers or total hours worked), capital or both 

depending on the measure of productivity 

Measures of output as well as inputs used in this paper are described as under:- 

Output 3 (Y): Output has been computed as sum of value of sales from own produced finished goods& Page | 12 

semi-finished goods, own account capital formation, sales revenue from electricity generated and receipts 

from other activities such as contract & commission work done for others, repair & 

maintenance/installation work done for others, receipts from industrial waste, value of sales of goods 

purchased for resale, receipts from rental and lease of buildings & equipment & warehouse, receipts from 

storage of goods in cold storage, receipts from transport service rendered to others and receipts from 

agency commissions. Further, changes in inventories in finished goods and by-products have also been 

accounting by adding their opening stock and deducting closing stock. Hereafter output in this paper 

means gross output. 

Labor (L): Labor means worker engaged in production, maintenance, and repair activities. It also includes 

those engaged in non-production activities, family workers, partners as well as proprietors. CMI reports 

number of workers on pay role on last working day of each quarter. The average number of workers of 

four quarters has been used as labor (L). However, according to OECD (2001) most appropriate measure 

of labor input is the total number of hours worked. In this study number of hours worked has been 

computed as total number of employees on pay role on last working day of a quarter times number of 

days a firm worked during that quarter times number of shifts worked during that quarter. A shift has been 

assumed as equal to 8 hours. Total number of hours thus generated have been used to compute per hour 

productivity of labor. 

Capital (K): Capital stock has been computed as sum of fixed assets as on July 1 st 2005 (the first day of 

the reference period of the census), purchases of fixed assets during the year and own account capital 

formation less sales of fixed assets during the year. The fixed assets covered in the census are land, costs 

of transfers & its improvements, building residential as well as non-residential, construction, machinery& 

equipment, transport, office equipment, furniture and other assets. 

Materials (M): Cost of materials has been worked out as sum of cost incurred for purchase of packing 

materials, components, parts, chemicals & dyes and raw materials during the reference period plus 

opening stock on input materials less their closing stock. 

Energy (E): The variable energy has been computed as sum of payments made for fuels purchased and 

payments for electricity purchased. 

Total Factor Productivity (TFP) 

We have measured TFP based on output as well as on revenue for each firm. The production function can 

be written as:- 

3 In OECD (2001) gross output is defined as the goods or services which are produced within a producer unit and 

that become available for use outside the unit. 





where Y refers either to output or revenue, A is the technology factor, L is labor, K is capital, M is 

materials and E is energy inputs. The subscript i refer to firm. The functional form is usually assumed to 

be a Cobb-Douglas function. 

Page | 13 

In this case taking logs we get 

(3) 

Following Foster, et al., (2005) TFP values for each firm based on output and total revenue have been 

derived from the following formula:- 

where, , , , , and .The letters in lower-case denote 

logarithms of establishment-level TFP, gross output or total revenue, number of labor hours, stocks of 

capital, materials, and energy inputs, and and are the factor elasticitiesfor the respective 

inputs. 

Profitability 

Following Ha, et al., (2001) we have used gross margin as measure of profitability. In order to keep the 

calculation simple gross margin has been used instead of economic profit. Further, use of gross margin 

has also been preferred over other measures of profitability because no restriction was required for this 

measure. The formula to calculate gross margin as used by Ha, et al., (2001) can be expressed in algebraic 

form as under:- 

where GM is the gross margin, TR is the total revenue, w is the wage rate and q is the price of materials 

and L & M are the quantities of labor and materials consumed by an individual firm respectively. 

For empirical reasons total revenue (TR) in this paper means total value of local and export sales during 

the reference period. According to Business economy overview (2006) employment cost of production 

workers include costs incurred on personnel remuneration both cash and kind of temporary, home and 

permanent employees plus voluntary and compulsory social contribution of employers plus taxes and 

social security contributions employees retained by firm. Data set used for this paper does not provide the 

wage rate for various categories of employees at firm level, however it does provide the total employment 

cost which has been treated as equivalent to and for this paper is equal to sum of wages & salaries, 

other cash payments and payments in kind made those qualified to be included in the labor above. The 

third item on the right hang side of equation (5) i.e. qM has been used as the same as defined under 

productivity. 

In this paper, analysis of the determinants of profitability of the firms has also been carried out. For this 

purpose, we have used following equation:- 

where , , , and . The letters in lower-case 

denote logarithms of establishment-level, gross margin, number of labor hours, stock of capital, materials, 

and energy inputs, and is the intercept and are the factor elasticities for the respective 

inputs. 





Source of Data 

Pakistan Bureau of Statistics (PBS) responsible for collection, compilation, analysis and publication of 

statistical data relating to various sectors of economy. Data relating to various socio-economic sectors is 

collected through primary, secondary sources and administrative records of the government on price, 

labor force, demographic, household income &expenditure, population &housing, agriculture &livestock, 

manufacturing industries, education, health, sports &culture, mining, electricity, business 

&communication, trade and public finance (PBS, 2012). Present study is based on Census of Large-Scale 

Manufacturing Industries (CMI) 2005-06. CMI covers manufacturing establishments registered under 

Factories Act, 1934. Separate returns collected are for establishments engaging in more than one activity. 

However, government workshops and defense establishment are not covered in the CMI. The data of the 

CMI 2005-06 was collected from July 2005 to June 2006 (CMI, 2005-06). 

The main objective of the CMI is to capture the changing trends in production and structures of LSMI 

over time. In CMI data is provided on inputs & outputs, value addition, contribution towards GDP, assets, 

inventories, employment and its cost as well as taxes paid during the reporting period at establishment 4 

level. New developments in the industrial field are taking into account in the CMI. It also captures new 

industrial products and establishments and is used to derive new weights for Quantum Index of 

Manufacturing Industries (CMI, 2005-06). 

CMI 2005-2006 was the result of joint efforts made by Provincial Bureaus of Statistics (BOS) and 

Directorates of Industries of Sindh, Punjab, KPK 5 and Baluchistan provinces and PBS. The 

questionnaires 6 were issued to the manufacturing establishments by the provincial Directorates of 

Industries in accordance with the list of establishments maintained by Provincial Directorates of Labor 

Welfare. In order to enhance the response rate, CMI 2005-06 was conducted through mail enquiry and 

also was supplemented by field visits. The filled-in questionnaires were collected by officers appointed by 

provincial governments. The returns collected by Directorates of Industries of provinces were forwarded 

to Provincial Bureaus of Statistics (BOS) for more dispensation (CMI 2005-06). 

In CMI 2005-06 all establishments falling within the scope of manufacturing activity, have been included 

in a specific industry 7 on the basis of market value of their main products. For this purpose Pakistan 

Standard Industrial Classification (PSIC-2007), which has been adopted from International Standard 

Industrial Classification, ISIC Rev3.1 at 4-digit level has been used. The classification consists of 

different sections, divisions, groups, classes and sub-classes (CMI, 2005-06). The coverage position of 

CMI 2005-06 is given in table 2. 

Page | 14 

4 UN System of National Accounts (SNA) 2008 defines establishment as―an enterprise, or part of an enterprise, that 

is situated in a single location and in which only a single productive activity is carried out or in which the principal 

productive activity accounts for most of the value added‖ (2008SNA, para 5.2). 

5 Khyber-Pakhtoonkhwa (KPK); formerly was known as North Western Frontier Province (NWFP). 

6 The questionnaire of CMI, 2005-06 is available on the official website of Pakistan Bureau of Statistics: 

www.pbs.gov.pk 

7 An industry consists of a group of establishments engaged in the same, or similar, kinds of activity (SNA 2008, 

para 5.2) 





Total No. establishments in the mailing list for CMI 2005-06 were 13146 out of which 8239, 3288, 972, 

309 and 372 were located in Punjab, Sindh, NWFP, Baluchistan and Islamabad Capital Territory (ICT) 

respectively (Table 2). Filled-in questionnaires were received from 7236 (55%) establishments including 

4072 (49%) from Punjab, 2093 (64%) from Sindh, 731 (75%) from NWFP, 221 (72%) from Baluchistan 

and 119 (35%) from ICT (Table 2). However, 819 (6%) returns were rejected for irrelevant activities. 

Among the rejected returns 482 (6%) came from Punjab, 268 (8%) from Sindh, 58 (6%) from NWFP, 9 

(3%) from Baluchistan and 2 (1%) from ICT (Table 2). Thus total No. of establishments qualified for 

tabulation and final analysis were 6417 (49%), 3590 (44%), 1825 (56%), 673 (69%), 212 (69%) and 117 

(35%) from all over Pakistan, Punjab, Sindh, NWFP, Baluchistan, and ICT respectively (Table 2). 24%, 

30%, 13%, 8%, 24% and 62% establishments from overall Pakistan, Punjab, Sindh, NWFP, Baluchistan, 

and ICT respectively, were non-respondents (Table 2). 

Table 2 Coverage Position of CMI 2005-06 

Page | 15 

S.No Items Pakistan Punjab Sindh NWFP Baluchistan Islamabad 

1 No. of 

establishments on 

Mailing List 

2 Filled-in 

questionnaires 

received 

3 Filled-in 

questionnaires 

received (%) 

4 No. of 

establishments 

qualified for 

tabulation 

5 %age of 

establishments 

qualified for 

tabulation 

6 No. of returns 

rejected for 

activities 

irrelevant 

7 %age returns 

rejected for 

irrelevant 

activities (% of 

total) 

8 %age of returns 

rejected for 

activities 

13,146 8,239 3,288 972 309 338 

7,236 4,072 2,093 731 221 119 

55 49 64 75 72 35 

6,417 3,590 1,825 673 212 117 

49 44 56 69 69 35 

819 482 268 58 9 2 

6 6 8 6 3 1 

11 12 13 8 4 2 





irrelevant (% of 

filled-in) 

9 Duplicate in the 333 333 0 0 0 0 

frame 

10 Closed or ceased 2,364 1,403 770 165 14 12 

to exit as reported 

by Provincial 

Directorates of 

Industries 

11 No. of nonresponding 

3,213 2431 423 76 74 209 

factories 

12 %age of nonresponding 

24 30 13 8 24 62 

factories 

Source: CMI, 2005-06, Authors Calculations 

Page | 16 

Micro data sets are not in general immediately fit for econometric analyses; first, they have to be 

thoroughly "cleaned" from extreme values. If this is not done, such observations, even if few, can 

influence the estimates (and statistical tests) to a very large extent (and wrongly so, significant 

correlations possibly arising from them only, or being masked by them) (Kremp & Mairesse, 1991). Thus 

in order to get a satisfactory balanced sample, providing necessary information for all the variable of our 

interest, we have cleaned the data set by eliminating establishments reporting zero or negative values 

output, labor, capital, materials, energy, employment cost as well as revenue. We finally obtained a 

sample of 5178 establishments which amounts to about 81 percent of the total establishments covered in 

the census. 

Properties of the Sample 

The properties of the final sample are given in table 3. Establishments listed with the stock exchange in 

the sample were 401 which account for 7.74 percent as compared to the non-listed establishments which 

were 92.26 percent (Table 3). Form of ownership, which is distinguished as corporate firms and noncorporate 

firms, has been used as an indicator of specialization as was used by Kremp & Mairesse (1991) 

for example. In the sample majority of the establishments i.e. 88.57 percent were private owned followed 

by public sector (6.47 percent), public sector with foreign collaboration (2.59 percent), private with 

foreign collaboration (2.05 percent) and foreign controlled enterprise (0.33 percent) (Table 3). From legal 

point of view, 37.76 percent of the establishments in the sample were private limited companies, 30.48 

percent were partnership, 19.47 percent were in individual ownership and 10.83 percent were public 

limited companies. Cooperative societies, state owned and others collectively form about 2.00 percent of 

the sample (Table 3). 





Table 3 Properties of the Sample 

Properties Frequency Percent 

Establishment listed with stock exchange 

Listed 401 7.74 

Non-listed 4777 92.26 

Total 5178 100.00 

Form of Ownership 

Public sector 335 6.47 

Page | 17 

Public sector with foreign collaboration 134 2.59 

Private owned 4586 88.57 

Private with foreign collaboration 106 2.05 

Foreign controlled 17 0.33 

Legal Organization 

Total 5178 100.00 

Individual ownership 1008 19.47 

Partnership 1578 30.48 

Private limited 1955 37.76 

Public limited 561 10.83 

Cooperative Society 6 0.12 

State owned 4 0.08 

Others 66 1.27 

Source: CMI 2005-06, Pakistan Bureau of Statistics 

Total 5178 100.00 

A number of firms involves only in one kind of production activity. Most of the production is produced 

by a fewer number of large corporations which produce more than one type of products in order to 

diversify their operations. If firms are classified according to their major activities, some of the resulting 

groups may be very diverse in terms of their products and production techniques and methods. Therefore, 

for the purpose of analyses of production where technology plays an important role, it is essential to work 

with group of firms which involves in same kind of activities (2008 SNA). Further, industry-wise 

measures of productivity are subject to change with choice of statistical units which are concerned with 

dividing the enterprises into similar and smaller parts aiming to generate industry groups of same 

activities (OECD, 2001). In the present study analysis of productivity of the firms has been carried out by 

type of industries as well. The detail of number of firms operating in each type 8 of industry groups 

covered in CMI 2005-06 is given in (Table 4). 

8 CMI 2005-06 provide detail of firms operating in each type of industry following Pakistan Standard Industrial 

Classification (PSIC, 2007), Pakistan Bureau of Statistics, which have been adopted from International Standard 

Industrial Classification (ISIC 3.1). 





Table 4 Properties of the sample 

Name of industry group Frequency Percent 

PSIC 

Food Products and Beverages 

1553 29.99 

Tobacco Products 12 0.23 

Manufacture of Textiles 888 17.15 

Wearing Apparel 245 4.73 

Tanning and Dressing of Leather, 

Handbags, Footwear etc. 

109 2.11 

Wood & Wood Products 55 1.06 

Paper & Paper Products 113 2.18 

Publishing, Printing & Reproduction 32 0.62 

Coke, Petroleum 23 0.44 

Chemicals & Chemical Products 450 8.69 

Rubber & Plastic Products 155 2.99 

Other Non-Metallic Mineral Products 436 8.42 

Basic Metals 261 5.04 

Fabricated Metal Products 125 2.41 

Machinery & Equipment n.e.c 320 6.18 

Electrical Machinery & Apparatus n.e.c 60 1.16 

Radio, TV & communication equipment 10 0.19 

Medical, Precision & optical instruments 63 1.22 

Motor vehicles & trailers 104 2.01 

Other transport equipment 40 0.77 

Furniture, Manufacturing n.e.c 105 2.03 

Recycling 19 0.37 

Total 5178 100.00 


Page | 18 

According to table 4, most of the LSMI firms operating in Pakistan were engaged in the manufacturing of 

food products and beverages (30 percent), followed by those engaged in the manufacture of textile (17 

percent), chemicals & chemicals products (8.69 percent), other non-metallic mineral products (8.42 

percent), machinery & equipment (6.18 percent), basic metals (5.04 percent), wearing apparel (4.73 

percent), fabricated metal products (2.41). 

Data Analysis 

Descriptive Analysis 

Manufacturing sector is one of the key determinants of the direction as well pace of overall economic 

growth in advanced and developing countries of the world including Pakistan. The relationship between 

growth rates of manufacturing sector and GDP is presented in the figure 1. In the economic history of 

Pakistan highest ever growth rate in GDP was observed during the period 1961-1965 and was recorded at 





6.79 percent. The growth rate of manufacturing sector was also highest during that period and stood at 

11.74 percent. During 1966 and 1970 the growth rates of GDP and manufacturing were recorded at 6.74 

percent and 8.12 percent respectively. Between 1996 and 2000 the growth rate of GDP was just 3.98 

percent which was mainly due to the lowest growth rate of manufacturing sector during that period (3.24 

percent). The performance of manufacturing sector with growth rate of 10.04 percent during 2001 to 2005 

leads to a remarkable recovery of the economy of Pakistan. The growth rates of GDP and manufacturing 

sector during 1951 to 2012 were recorded as 4.98 percent and 6.98 percent respectively. The positive 

correlation can be observed between growth rate of GDP and manufacturing sector. This also advocates 

for studying the factors contributing towards improvement of the productivity of the manufacturing sector 

in Pakistan (Figure 1). 

Figure 1 Growth Rates of GDP and Manufacturing Sector 

Page | 19 

Source: Pakistan Bureau of Statistics 

The mean output, labor, capital, materials, energy, employment cost, revenue and profitability by listing 

status, form of ownership and by type of legal organization is presented in (Table 5). According to results 

the mean performance of listed companies in terms of inputs like labor, capital, materials and employment 

cost as well as output, revenue and profitability is better than non-listed companies. The mean 

profitability in the listed companies is Rs.858434 as compared to the non-listed companies where it is 

Rs.115809. Similarly output and revenue in listed companies are Rs.2672881 and Rs.2392366 as 

compared to the non-listed companies where they are Rs.333731 and 313828 respectively. Mean number 

of workers is highest in the public sector i.e. 469 per firm followed by the foreign controlled firms where 

it is 429. However, significant differences exist between mean profitability and revenue of the public 

sector and foreign controlled firm, indicating prevalence of inefficiency in the public sector enterprises in 

Pakistan. Private firms with firm collaboration have been emerged as second highest performer in terms 

average output, revenue and profitability. Profitability, revenue and output of firms in individual 

ownership and partnership is far behind as compared to private and public limited companies and state 

owned enterprises. From legal organization’s point of view, performance in terms of output, revenue and 

profitability of public limited companies and state owned enterprises is surprisingly better than private 

limited companies that generally are considered as more efficient. 





Table 5: Mean Factor Inputs, Output & Profitibility by Form of ownership and Legal and Listed Status 

Output Labor Capital Materials Energy Employment Revenue GM 

Characteristics 

cost 

Listing Status 

Listed 2672881 634 1359314 1440812 157224 93121 2392366 858434 

Non-listed 333731 113 128872 183067 16846 14951 313828 115809 Page | 20 

Form of 

Ownership 

Public sector 994943 469 710044 571152 95431 67267 919472 281053 

Public sector 

with foreign 773384 116 462281 435019 63363 25056 729318 269243 

collaboration 

Private owned 412811 126 166249 225504 20610 15266 379058 138288 

Private with 

foreign 2111855 357 653784 1073580 57062 90933 1924067 759554 

collaboration 

Foreign 

controlled 

6594700 429 1716298 3216992 146603 189517 6496025 3089516 

Type of Legal 

Organization 

Individual 

ownership 

146021 43 68121 69762 9196 5975 142165 66428 

Partnership 184544 41 35985 129655 7562 4668 173832 39509 

Private limited 

company 

448961 142 152415 234820 17901 17926 417639 164893 

Public limited 

company 

2295834 684 1260950 1236839 143699 96004 2104254 771410 

Cooperative 

Society 

766117 362 143593 334888 19487 42024 714765 337853 

State owned 9477358 4316 3632831 3026335 1176357 1347003 5370796 997457 

Others 295028 89 219714 156187 28536 12588 274907 106132 


The average output, labor, capital, materials, energy, employment cost, revenue and profitability of the 

firms by major industry group are presented in (Table 6). According to table 6, firms operating in the 

industry group coke and petroleum and tobacco products are capital intensive where average capital has 

been estimated as Rs. 593464 and Rs. 798258 respectively. Resultantly, firms in these two groups 

outweigh other firms operating in less capital intensive or in other words more labor intensive industries 

in terms of mean output, revenue and profitability. Contrarily, firms engaged in the manufacture of textile 

have been emerged as the most labor intensive where mean workers per establishment has been calculated 

as 410. Firms engaged in the manufacturing of furniture and recycling have been emerged as the bottom 

line performers from the view point of output, revenue and profit. Generally, firms operating in more 

capital intensive industries such as chemicals, motor vehicles, transport equipment, paper products, 

electrical machinery etc. produce more output and consequently earn more revenue and profit (Table 6). 





Table 6 Mean Factor Inputs, Output & Profitability by Major Industry Groups 

Output Labor Capital Materials Energy Employment Revenue GM 


cost 

PSIC 

Food Products and 

346577 73 94630 234239 7132 9163 338542 95140 

Beverages 

Page | 21 

Tobacco Products 4262553 361 798258 957756 23010 142411 4302618 3202452 

Manufacture of 

Textiles 

742346 410 488409 438981 57109 40366 705637 226290 

Wearing Apparel 398048 198 92651 164189 11622 24160 320159 131810 

Tanning and 

Dressing of 

Leather, 

203386 135 40862 115078 6507 16955 189954 57921 

Handbags, 

Footwear etc. 

Wood & Wood 

Products 

209801 67 138595 96308 13519 4710 201950 100932 

Paper & Paper 

Products 

476199 118 267181 192935 42500 17652 364382 153795 

Publishing, 

Printing & 

276675 112 273583 158249 5503 24097 245877 63531 

Reproduction 

Coke, Petroleum 11789904 138 593464 6296217 86346 87186 11382685 4999283 

Chemicals & 

Chemical Products 

727410 145 417680 313681 39405 38910 602321 249731 

Rubber &Plastic 

Products 

197083 56 93853 121846 9481 8764 193411 62801 

Other Non- 

Metallic Mineral 303980 85 400304 43255 80005 14168 272442 215019 

Products 

Basic Metals 454507 110 128932 263493 42771 27076 389374 98805 

Fabricated Metal 

Products 

151033 61 39269 91445 4385 7877 153233 53910 

Machinery & 

Equipment n.e.c 

167944 61 29608 106145 3634 9856 159278 43277 

Electrical 

Machinery & 

977529 210 162883 478248 20914 22192 727728 227288 

Apparatus n.e.c 

Radio, TV & 

communication 375503 244 167453 173426 11311 44286 351108 133396 

equipment 

Medical, Precision 

& optical 

143191 117 96021 75567 3669 11948 136500 48985 

instruments 

Motor vehicles & 

trailers 

1482524 170 276374 969774 9245 31245 1393370 392351 

Other transport 

equipment 

870124 229 205731 574005 8594 32849 791683 184829 

Furniture, 

Manufacturing 

n.e.c 

96996 79 33116 51171 3114 7767 89148 30210 

Recycling 7551 17 3833 167 42 1291 7166 5708 






Excessive variability is the common happening when micro data is analyzed. Environmental and 

historical factor and differences in precise activities partly explain this variation whereas large part of this 

variation is due to inherent factors and is considered as true dispersion (Kremp & Mairesse, 1991). The 

considerable variation in average measures of inputs, output, revenue and profitability by listed and legal 

status, form of organization and type of industries presented the table 5 & 6 support the finding of Kremp 

& Mairesse (1991) for manufacturing firms in Pakistan. 

The total factor productivity (TFP) has been computed using equation (4). For this purpose equation (3) 

was estimated twice with output and total revenue as the dependent variables to derive the factor 

elasticities. The adjusted R 2 for the model with output as dependent variable was found as 0.918 

indicating a good fit of the model. The elasticities and the t-ratios for the labor, capital, materials and 

energy were found as 0.080, 0.085, 0.651 & 0.170 and 9.67, 12.89, 120.62, & 23.7 respectively. Hence, 

all the factor inputs were to be as the highly significant predictors of the output. Resulting equation to 

derive the output based TFP was:- 

Page | 22 

The value of adjusted R 2 With total revenue as the dependent variable was found as 0.932. The higher 

value of adjusted R 2 signifies the collective explanatory power of the independent variables. The factor 

elasticities and t-ratios found for labor, capital, materials, & energy were 0.078, 0.076, 0.661 & 0.167 and 

10.31, 12.69, 134.57 & 25.64. The four factor inputs were also found to have a significant and positive 

relationship with total revenue. The equation used to calculate the TFP based on total revenue was equal 

to:- 

The various measures of productivity and TFP by form of ownership and by type of legal and listed status 

are summarized in ( 

Table 7). According to results per worker productivity, per hour productivity and TFP based on output as 

well as revenue are in listed companies as compared to non-listed companies where as capital productivity 

and labor & capital productivity is higher in non-listed companies Per worker productivity in listed and 

non-listed companies has been emerged as Rs.10206 and Rs.4831 respectively. Output based TFP in 

listed companies is 1.42 as compared to non-listed companies where it is 1.21. Similarly, revenue based 

TFP in listed companies is 1.39 as compared to 1.20 of non-listed companies. Average per worker 

productivity is highest in foreign controlled firms and stands at Rs. 29217, followed by public sector 

(Rs.8695), Public sector with foreign collaboration (Rs.7919), Private with foreign collaboration 

(Rs.7726) and private owned firms (Rs.4771). Foreign controlled firm’s leads in per worker productivity, 

per hour productivity and TFP based on output. Output and revenue based TFP in foreign controlled firms 

is 1.74 and 1.73 respectively. Capital productivity is highest in public sector firm (62.8), followed by 

Public sector with foreign collaboration (52.11) and private firms (16.69). Per hour productivity is highest 





in foreign firms (8.0) followed by firms in public sector with foreign collaboration (3.92) and public 

sector (3.32) (Table 7). 

Table 7 Productivity & TFP by Form of Ownership, Listed & Legal Status of the Firm 


Listing Status 

Per worker 

productivity 

Capital 

productivity 

Per hour 

productivity 

Labor& 

capital 9 

productivity 

TFP 

(output) 

TFP 

(revenue) 

Mean Mean Mean Mean Mean Mean 

Listed 10205.78 13.49159 2.613061 1.347742 1.422789 1.391607 

Non-listed 4830.771 21.05271 2.154687 1.436034 1.212401 1.206543 

Form of 

Ownership 

Page | 23 

Public sector 8694.756 62.80628 3.322012 2.617744 1.220359 1.238207 

Public sector 

with foreign 

collaboration 

7918.842 52.11933 3.92501 2.633805 1.441858 1.419194 

Private owned 4770.96 16.69668 2.016766 1.289532 1.214661 1.206768 

Private with 

foreign 

7725.826 10.61511 2.990508 1.783943 1.509218 1.444134 

collaboration 

Foreign 

controlled 

29216.71 15.2125 8.004033 3.977313 1.74929 1.729574 

Type of Legal 

Organization 

Individual 

ownership 

2865.414 25.1198 1.342671 1.006207 1.180671 1.182436 

Partnership 5830.436 28.74273 2.915484 2.232791 1.221533 1.219421 

Private limited 

company 

5564.098 15.88199 2.211814 1.184403 1.272174 1.249644 

Public limited 

company 

7075.109 5.823693 1.705747 0.823608 1.19033 1.200156 

Cooperative 

Society 

2180.228 5.083103 0.921816 0.746099 1.329568 1.331208 

State owned 1494.828 7.87336 0.273109 0.243172 1.223973 1.342917 

Others 3247.527 13.99605 1.501317 1.20873 1.162629 1.149257 


Foreign firm also leads in combined measure of labor and capital productivity (3.98) followed by Public 

sector with foreign collaboration (2.63). Per worker productivity of Public limited company is the highest 

stands at Rs. 7075, followed by partnership Rs. 5830, private limited company (5564). Capital 

productivity is highest in partnership (28.7) followed by individual ownership (25.1) and private limited 

9 The total number of hours worked and capital stock have been added to obtain a combined measure of labor & 

capital. 





company (15.9). TFP output and revenue based is highest in cooperative societies (1.33) followed by 

private limited companies (1.27 & 1.25) (Table 7). 

The various measures of productivity, and total factor productivity (TFP) by major industry groups are 

summarized in ( 

Page | 24 

Table 8). Firms operating in coke and petroleum, being capital intensive, industries are predominantly 

ahead in terms of per worker and per hour productivity. Both measures of TFP also prove these firms 

more productive as compared to firms in most of other industries. This is in conformity of the findings of 

the Business Economy Review (2006) according to which labor productivity is likely to be higher 

intensive industries. Per worker productivity for the firms in food industries is Rs.8014, followed by 

wearing apparel (Rs.7468), basic metals (Rs.7532), tobacco products (Rs.5347) and electrical machinery 

(Rs.3818). Productivity of capital is highest for the firms operating in food industries, followed by those 

in basic metals, petroleum, textile, wearing apparel and wood products. Per hour productivity is highest in 

food (3.8), wearing apparel (3.1) and basic metals (2.7) and it is lowest in recycling (0.15), non-metallic 

mineral products (0.46). Combined measure of labor & capital also suggest food and basic metal as 

dominant industries productivity is 2.9 and 2.1 respectively (Table 8). Output and revenue based TFP is 

highest for firms in recycling (3.5 each), followed by those in tobacco products (1.93 & 1.94), petroleum 

(1.93 & 1.83) (Table 8). Firms in the industry groups like machinery & equipment and rubber products 

have been emerged as the least efficient in terms of TFP. 


Table 8 Productivity & TFP by Major Industry Groups 

Per worker 

productivity 

Capital 

productivity 

Per hour 

productivity 

Labor& capital 

productivity 

TFP 

(output) 

TFP 

(revenue) 

Mean Mean Mean Mean Mean Mean 

PSIC 

Food Products and 

Beverages 

8014.10 44.1599 3.7514 2.8821 1.2784 1.2724 

Tobacco Products 5346.87 6.1491 1.4532 0.9909 1.9296 1.9436 

Manufacture of 

Textiles 

4469.24 11.4030 1.6194 0.9844 1.1160 1.0929 

Wearing Apparel 7467.80 10.7174 3.1756 0.5694 1.3270 1.2997 

Tanning and 

Dressing of Leather, 

Handbags, Footwear 

1717.04 8.3768 0.7038 0.5417 1.1311 1.0677 

etc. 

Wood & Wood 

Products 

4299.70 10.0067 1.5909 1.1292 1.1869 1.2238 

Paper & Paper 

Products 

2361.32 7.8322 0.6956 0.4579 1.1341 1.1031 

Publishing, Printing 

& Reproduction 

2195.93 10.3419 0.8545 0.5813 1.2746 1.1828 

Coke, Petroleum 85866.64 22.2302 30.3277 8.5908 1.9263 1.8273 

Chemicals & 

Chemical Products 

3058.36 9.0591 1.0269 0.6569 1.3108 1.2943 

Rubber & Plastic 

Products 

2589.90 7.0565 0.9348 0.5757 1.0550 1.0635 

Other Non-Metallic 

Mineral Products 

1465.22 3.2997 0.4584 0.2538 1.3236 1.3558 

Basic Metals 7532.50 33.7065 2.7360 2.1471 1.1413 1.1500 





Fabricated Metal 

Products 

Machinery & 

Equipment n.e.c 

Electrical 

Machinery & 

Apparatus n.e.c 

Radio, TV & 

communication 

equipment 

Medical, Precision 

& optical 

instruments 

Motor vehicles & 

trailers 

Other transport 

equipment 

Furniture, 

Manufacturing n.e.c 

1569.23 7.1644 0.6089 0.4679 1.0496 1.0878 

1751.57 5.4763 0.9499 0.4629 1.0315 1.0435 

3817.70 10.5804 1.6827 1.0742 1.2166 1.1615 

4587.46 7.9657 2.2571 1.2438 1.4716 1.4924 

1302.64 13.2433 0.5787 0.4441 1.3260 1.3102 

3728.87 7.1771 1.5559 0.6841 1.2940 1.2499 

2047.09 5.7917 0.7319 0.5714 1.1567 1.2034 

1429.95 7.1619 0.5588 0.4365 1.2195 1.1951 

Recycling 477.41 2.5840 0.1541 0.1419 3.4712 3.4733 


Page | 25 

Correlation Analysis 

Productivity and profitability are interrelated concepts. For example Ha, et al., (2001) presented the 

graphical relationship between productivity and profitability. According to them change in productivity is 

linked with changes in output, use of inputs as well as with progress in technology ( 

Figure 2). Changes in productivity, input & output prices and marker conditions are directly related with 

changes in profit of the firm (Figure 2). 

Figure 2 Graphical Presentation of the Relationship between Productivity and Profitability 

Source: Copied from Arthur Ha; Loris Strappazzon and William Fisher, 2001 

The statistical measure of correlation i.e. Pearson correlation matrix, for inputs & output, and for 

productivity measures is presented in the (Table 9). The correlation between measure of profitability i.e. 





GM and inputs such as labor, capital, materials and energy stands at 0.22, 0.38, 0.45 and 0.37 

respectively. The correlation between profitability and output is highly significant and stands at 0.80. The 

correlation between per worker and per hour productivity of labor and profitability is 0.59 and 0.53. The 

relationship between profitability and capital productivity is only 0.02. The correlation between output 

based TFP and revenue based TFP and of profitability is 0.17 and 0.18 respectively. The value of 

correlation between output and per worker measure of productivity is 0.47. Generally, the relationship 

between factor inputs and various measures of productivity with that of profitability has been emerged as 

positive and significant. When a Cobb-Douglas production function is estimated, high correlation is 

expected between explanatory variables (Prasad and Harker, 1997). This is also true with our results as 

correlation between per worker labor productivity and per hour labor productivity is 0.963. The value of 

correlation labor & capital productivity and per hour labor productivity is 0.605. Our values of correlation 

between labor and other inputs i.e. capital, materials and energy are at 0.46, 0.31 and 0.57 respectively. 

The correlation between capital and energy is 0.711 and between materials and output is 0.85. Further, 

correlation between two measures of TFP i.e. output and total revenue is highly significant at 0.01 level 

and stands at 0.933. The relationship between output and various measures of productivity and TFP is 

positive and significant at 0.01 percent. Similarly, the correlation between gross margin and various 

inputs and measures of productivity and TFP is also positive and significant at 0.01 percent except for 

capital productivity where it is not significant (Table 9). 

Table 9 Correlation Analysis 

Page | 26 

Variables Labor Capital Materials Energy Output L_prdy K_ prdy 

PH_ 

prdy 

LK_ 

prdy 

tfp_y 

tfp_tr 

Labor 1.000 

Capital 0.461** 1.000 

Materials 0.312** 0.360** 1.000 

Energy 0.570** 0.711** 0.251** 1.000 

Output 0.377** 0.536** 0.858** 0.449** 1.000 

L_prdy -0.019 0.079** 0.215** 0.096** 0.467** 1.000 

K_ prdy -0.033* -0.028* 0.032* -0.015 0.026 0.172** 1.000 

PH_ prdy -0.027 0.040** 0.133** 0.063** 0.388** 0.963** 0.219** 1.000 

LK_ prdy -0.048** -0.023 0.147** 0.004 0.217** 0.523** 0.527** 0.605** 1.000 

tfp_y -0.023 0.038** 0.047** 0.052** 0.135** 0.181** 0.178** 0.187** 0.245** 1.000 

tfp_tr -0.027* 0.028* 0.054** 0.043** 0.124** 0.170** 0.180** 0.170** 0.224** 0.933** 1.000 

GM 0.227** 0.378** 0.469** 0.370** 0.798** 0.592** 0.018 0.532** 0.255** 0.167** 0.182** 

**. Correlation is significant at the 0.01 level (2-tailed). 

*. Correlation is significant at the 0.05 level (2-tailed). 

where L_prdy= per worker productivity of labor, K_ prdy = capital productivity, PH_ prdy = per worker productivity of labor, LK_ prdy = combined measure of 

productivity of labor & capital, tfp_y = total factor productivity based on output, tfp_tr = total factor productivity based on total revenue and GM = gross margin. 

Regression Analysis 

Although correlation between independent variables in our data set is on higher side in some cases; 

however it does not create a high value of variance for the coefficient estimates (Prasad & Harker, 1997). 

We have also performed regression analysis. The results of equation (6) are presented in Table 10, 





Table 11 and Error! Reference source not found.. The value of adjusted R 2 

for the models is 0.748 which shows that about 75 percent of the total variation in the gross margin i.e. 

profitability is collectively explained by labor, capital, materials and energy ( Table 10). 

Table 10 Model Summary 

Page | 27 

Model R R Square 

Adjusted R 

Square 

Std. Error of 

the Estimate 

1 .865 a .748 .748 1.08166 

a. Predictors: (Constant), e, l, m, k 

Higher value of F-statistics presented in the analysis of variance (ANOVA), Table 11, shows the 

existence of significant differences among the factor inputs. 

Table 11 ANOVA 

Model 

Sum of 

Squares df Mean Square F Sig. 

1 Regression 17945.836 4 4486.459 3.835E3 .000 a 

Residual 6052.316 5173 1.170 

Total 23998.151 5177 

a. Predictors: (Constant), e, l, m, k 

b. Dependent Variable: g_m 

The estimated coefficients of factor inputs along with the values of t-ratios are presented in the Table 12. 

The value of coefficients and t-ratios of labor, capital, materials and energy are 0.24, 0.158, 0.303 & 

0.353 and 15.696, 13.025, 30.445, 26.724 respectively. The elasticity’s of all four explanatory variables 

have been emerged as positive and significant depicting a strong and positive relationship with the 

measure of profitability. 

As evident from Table 9, various measures of productivity and TFP are highly correlated with each other. 

The effect of independent variables cannot be shown in multiple regression analysis when there is 

multicollinearity among explanatory variables (Nas, 2011). Therefore, to avoid this potential problem the 

actual effect of various measures of productivity and TFP on profitability has been shown by applying 

simple linear regression analysis separately for all variables. Results of separate regressions for various 

measures of labor productivity and total factor productivity are summarized in adjusted R2 is 0.346 when 

per worker productivity of labor is used as an independent variable. 20 percent, 17 percent, 15 percent and 

13 percent variation in the profitability is explained by per hour productivity of workers, labor & capital 

productivity, TFP based on total revenue and output based TFP respectively. However, capital 

productivity explains only 0.03 percent of the variation in profitability (Table 13). 





Table 13 

Table 12 Coefficients 

Page | 28 

Model 

Unstandardized 

Coefficients 

Standardized 

Coefficients 

B Std. Error Beta 

1 (Constant) -.770 .115 -6.671 .000 

l .240 .015 .181 15.696 .000 

k .158 .012 .157 13.025 .000 

m .303 .010 .322 30.445 .000 

e .353 .013 .321 26.724 .000 

a. Dependent Variable: g_m 

Summary of separate regressions is presented in adjusted R2 is 0.346 when per worker productivity of 

labor is used as an independent variable. 20 percent, 17 percent, 15 percent and 13 percent variation in the 

profitability is explained by per hour productivity of workers, labor & capital productivity, TFP based on 

total revenue and output based TFP respectively. However, capital productivity explains only 0.03 percent 

of the variation in profitability (Table 13). 

Table 13. According to results the value is adjusted R 2 is 0.807 when output is used as a predictor. 

The value of adjusted R 2 is 0.346 when per worker productivity of labor is used as an independent 

variable. 20 percent, 17 percent, 15 percent and 13 percent variation in the profitability is explained by per 

hour productivity of workers, labor & capital productivity, TFP based on total revenue and output based 

TFP respectively. However, capital productivity explains only 0.03 percent of the variation in profitability 

(Table 13). 

Table 13 Summary of Separate Regressions 

Predictors 

R 

R 

Square 

Adjusted 

R 

Square 

t 

Std. 

Error of 

the 

Estimate 

(Constant), y .899 a 0.807 0.807 0.94484 

(Constant), l_p .588 a 0.346 0.346 1.74177 

(Constant), k_p .169 a 0.029 0.028 2.1222 

(Constant), 

ph_p 

.443 a 0.196 0.196 1.93045 

(Constant), lk_p .408 a 0.167 0.167 1.9654 

(Constant), 

tfp_tr 

.390 a 0.152 0.152 1.98264 

Sig. 





(Constant), 

.362 

tfp_y 

a 0.131 0.131 2.0071 


where y = ouput, l_p = per worker productivity of labor, k_p =capital productivity, ph_p = per hour 

productivity of labor, lk_p = labor & capital productivity, tfp_tr = total factor productivity based on total 

revenue and tfp_y = total factor productivity based on output. 

Summary of coefficients for separate regressions is presented in ( 

Table 14). The coefficients and t-ratios of output, labor productivity, capital productivity, per hour 

productivity and labor & capital productivity have been found as 0.943, 0.855, 0.235, 0.619 & 0.602 and 

147.329, 52.29, 12.35, 35.55, & 32.20 respectively. According to the results, an increase in output and 

labor productivity will cause greater increase in the profitability as compared to other measures of 

productivity. However, all the measures of productivity have emerged as having positive and significant 

impact on profitability. Further, measures of TFP based on revenue as well as output have emerged as 

significant because both have high values of t-ratio stands at 30.48 and 27.95 respectively. Results in the 

form of positive elasticity’s also suggest that an increase in TFP will also lead to an increase in 

profitability (Table 14). 

Page | 29 

Table 14 Summary of Coefficients for Separate Regressions 

Predictors 

Unstandardized 

Coefficients 

Standardized 

Coefficients 

t 

Sig. 

Std. 

B 

Beta 

Error 

(Constant) -0.99 0.072 -13.709 0 

y 0.943 0.006 0.899 147.329 0 

(Constant) 3.199 0.122 26.123 0 

l_p 0.855 0.016 0.588 52.291 0 

(Constant) 9.115 0.041 219.886 0 

k_p 0.235 0.019 0.169 12.35 0 

(Constant) 9.813 0.028 344.755 0 

ph_p 0.619 0.017 0.443 35.547 0 

(Constant) 9.939 0.031 321.843 0 

lk_p 0.602 0.019 0.408 32.197 0 

(Constant) 7.551 0.069 109.62 0 

tfp_tr 1.576 0.052 0.39 30.481 0 

(Constant) 7.839 0.065 120.92 0 

tfp_y 1.331 0.048 0.362 27.95 0 






where y = ouput, l_p = per worker productivity of labor, k_p =capital productivity, ph_p = per hour 

productivity of labor, lk_p = labor & capital productivity, tfp_tr = total factor productivity based on total 

revenue and tfp_y = total factor productivity based on output. 

CONCLUSION AND RECOMMENDATIONS 

Page | 30 

The objective of this study was two fold. Firstly, computation of a variety of measures of productivity 

including per worker labor productivity, labor productivity per hour worked, capital productivity, 

combined measure of labor & capital productivity, two measures of total factor productivity based on 

output as well as on revenue and profitability for the firms operating in the large scale manufacturing 

industries in Pakistan. Second objective of this study was to establish and analyze the relationship 

between various measures of productivity and that of the profitability. Census data of manufacturing 

industries has been used to carry out the firm level analysis disaggregated by form of ownership of the 

firm, legal and listing status of the firm and by type of major industry. Analysis of the data has been 

carried out in three stages. In the first stage, averages of the measures of factor inputs like labor, capital, 

materials and energy as well as of the output measures such as gross output, revenue, productivity and 

profitability were calculated. In the second stage, correlation analysis was carried out and in the third and 

final stage regression analysis between profitability and factor inputs as well as between profitability and 

various measures of productivity was conducted. 

According to results average output, productivity and profitability for listed companies is greater than the 

non-listed companies. Foreign controlled firms operating in the large scale manufacturing industries are 

more efficient in terms of output, productivity and profitability as compared to both private and public 

sector companies. Public limited companies have been emerges as more efficient and profitable as 

compared to private limited companies. Firms operating in the industry groups like petroleum and tobacco 

have been found as more productive, efficient and higher in profitability as compared to firms in other 

industries. All the four explanatory variables i.e. labor, capital, materials and energy has been found as 

having a positive and highly significant relationship with profitability. Further, log measures of 

productivity and total factor productivity have also been emerged as significant predictors of profitability. 

Data analysis carried out by descriptive, correlation and regression suggest a positive, strong and 

significant relationship between various measures of productivity and profitability. In sum, the data 

evidence from large scale manufacturing industries in Pakistan suggests that higher productivity of 

workers leads to higher profit for the firms. Therefore, firms in these industries should make human 

capital investment through education, training and development to enhance the productivity of their 

workers, which in turn will result higher profit for the firms in long run. 

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