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Prospects from Efficiency Analysis of Exporting and Non-Exporting Pakistani Firms.

Byline: Mamona Sadaf and Arshia Ishaq

Abstract

In the wake of global competition with greater economic and regional integration, a country's economic growth is largely determined by industrial efficiency. Operating and staying within market under cut throat competition appears as an unprecedented challenge for exporting and non-exporting firms. A data of 403 firms from World Bank Enterprise Survey 2013 is used for estimation of technical and scale efficiencies between and within exporting and non-exporting firms. Data Envelopment Analysis (DEA) is applied for constant returns to scale (CRS) and variable returns to scale (VRS) efficiency scores.

It has been found that the average technical efficiency scores for non-exporting firms is slightly greater than the exporting firms. But majority of the firm's technical efficiency remained the same for both type of firms. Industries with highest technical efficiency comprise of chemicals, food, garments and non-metals. Efficiency scores of some firms depict interesting results and found efficient in either of exporting and non-exporting firms. The efficiency analysis can help to identify potential changes which industry may require for its consistent growth in current surge of global competition and nationalization.

Keywords: Efficiency, Economic Integration, Input-Output, Growth

Introduction

Across the developing countries, export-led growth is considered a prominent paradigm for industrial development and economic growth since decades. It is associated with potential benefits of globalization and trade openness. Generally, policies to promote economic growth are comprised of investment, monetary and exchange rate, fiscal, financial sector development and structural reforms related policies. Out of structural reforms, trade and commercial policies in Pakistan have been formed to promote export-led growth. Export promotion polices are deeply rooted in the development and growth of SMEs. As small and medium enterprises (SMEs) account for 90 per cent of business activities of the country, they are contributing approximately 25 per cent to the total exports earnings. SMEs can thus catalyze the process of industrial growth.

With reference to increasing growth and productivity, exporting and non-exporting firms are dealt under different sets of policies, where the former are more often promoted owing to their ability of earning higher incomes, compared to the latter. Similarly, factors used in the production of export related commodities earn more compared to the firms with non-exporting firms. Being its empirical validation, export led growth appeared as one of the most prominent policy to enhance economic growth. Furthermore, positive correlation has also been established between efficiency of the firms and their exports.

On performance, exporting firms are believed to be more efficient owing to their subjection to global competition. Hence, the attempts at specialization and accruing of benefits from economics of scale, they achieve more managerial, organizational and production efficiencies.5

On the other hand, importing firms are believed to operate less efficiently as they enjoy government protection from foreign competition and operate in limited market, which may not require state of the art standard of commodities, remaining less efficient in their operations.6 Against the background, international trade theories have also empirically and theoretically prioritized exporting firms over local demand based firms and considered them as an engine of economic growth. To see whether this theory proves in the case of Pakistan, it is important to empirically analyze the efficiency of exporting and non-exporting firms.

This study is exploratory in nature and aims at comparing efficiency scores between and within exporting and non-exporting firms, with a purpose to improve performance of the firms, in the light of identified gaps. Secondary data set provided by World Bank Enterprise Survey based on stratified sampling for industry, size, region and establishment, has been used. Exporting firms are selected on the basis of percentage of their direct sales for exports. And non-exporting firms are selected on the basis of percentage of their national sales. In both cases threshold for selection has been solely determined by national sales or export oriented firms. To see how efficiently exporting and non-exporting firms are transforming inputs to outputs, we have calculated technical and scale efficiency for both type of firms.

Technical efficiency for firms under the assumption of variable returns to scale for input oriented measure is calculated where technical efficiency measures firm's capacity to obtain maximum amount of output given inputs.7 This analysis helps to check the performance of decision making units (DMUs). Since performance is the measure of a firm's outputs in relation to inputs it uses, this measure of performance is called productivity ratio and it is termed as natural measure of performance.8 To assess the performance of the exporting and non-exporting firms Data Envelopment Analysis (DEA) has been used for large manufacturing firms of Pakistan.

Theoretical and Empirical Evidence towards the Framework of Efficiency

Comparative advantage theory of international trade by David Ricardo (1817) postulates that countries must export commodities which have least cost of production or are more efficiently produced. Over time, new trade theories emerged based on differences in opportunity costs, differences in factor endowments, factor intensities and with regard to inter and intra industry trade. All cases were built on empirical grounds, to varying extent. Trade in intra industry depicts that endowment-based demand can alter relative demand of factors and their rewards which leads to the specialization of resources intensively used in production and trade. However, intra-industry trade has also received importance where countries with similar factor endowments trade with each other and welfare gain arises from economies of scale and consumer preferences.9

Aggregate trade pattern can be successfully traced if differences in technologies, factor prices and logistics costs are considered as basis of trade between countries.10 Further, taking export-orientation of the firm will help us to see how trade channelizes and influence productivity. Endowment and factor driven trade doesn't depict that why some firms solely produce for domestic sales while others just export and how a firm's productivity and efficiency is linked to exports or domestic sales.

To compare productivity of exporting and non-exporting firms, efficiency analysis was built. A DMU is efficient if it cannot increase its output without increasing any of its inputs or if it cannot achieve the same level of output by reducing its input.11

Concept of productivity and efficiency are not much differentiated in literature.12 It is defined as the ratio of output to input.13 Some researchers use partial measures of productivity like fuel or labor, others use total factor productivity that include all inputs and outputs. Total factor productivity helps to avoid gain from one factor that may be attributed due to some other factor by considering all inputs and outputs.14 Whereas, efficiency is considered as a more suitable term to compare firms with each other as it is a measure of most efficient frontier for productivity. Efficiency is measured by comparing optimal level of output and input with observed output. It can be estimated in two ways by comparing ration of observed output to maximum obtainable output given inputs or ration of observed to minimum obtainable input subject to output constraint.15 Usually optimum efficiency is termed as technical efficiency.

An input and output vector is technically efficient if it cannot increase its output without decreasing some other output and it can't decrease its input without a decrease in some other input.16 But this measure doesn't offer degree of inefficiency of input or output vector for its improvement. For this purpose, radial measures of technical efficiency was introduced. And it explains input and output slacks. It measures maximum feasible expansion of all inputs and maximum feasible contraction of all inputs used in production process.17 But firms don't always operate at optimal scale. Scale efficiency is suggested to measure the difference in efficiency when firms operate under imperfect competition and financial constraints, etc. It calculates the difference in technical efficiency when a firm operates under constant returns to scale (CRS) and variable returns to scale (VRS).18 The difference in both defines inefficiency in that particular DMUs operations.19

Many studies have used efficiency analysis to compare different DMUs. Utility companies, banks, hospitals, agriculture farms, universities, manufacturing firms and etc., have been taken as DMUs. Studies identify factors contributing in the differences between efficiencies of DMUs as ownership structure,20 marketing productivity,21 firms age and size,22 foreign investment,23 firms location,24 research and development and exports.25

Plenty of literature is available on empirical investigation of firms' technical efficiency and export orientation behavior. It has been found that export has positive impact on technical efficiency of low value added industries as compared with the high value added industries.26 Positive link also exists between a firm's exports and efficiency. 27 However, few studies have also found no relationship between exports and firm's efficiency.28

While comparing foreign and domestic firms on technical efficiency, the former has been found as relatively more efficient.29 Same technique has been used to find technical efficiency of manufacturing firms.30 Exports and efficiency are found to have bidirectional relationship.31 Estimated and compared efficiency of exporting and non-exporting firms in Pakistan indicates no significance difference between both types of firms. Some studies have tested hypothesis of learning by exporting and found empirical grounds in support of it.32 It is very important to see how exporting and non-exporting firms in Pakistan behave in context of efficiency, manufacturing being the most important part of economic growth and development. Improvement in efficiency of these firms can prove as a catalyst for this growth process. For this purpose, efficiency comparison between and within exporting and non-exporting firms is made so that policy recommendations could be proposed for future adaptions.

Measuring Efficiency of Exporting and Non-Exporting Firms

There are two approaches available in literature for assessing efficiency of DMUs; Stochastic Frontier Analysis (SFA) and data envelopment analysis. These methods involve econometric and mathematical programing respectively. For structuring SFA there functional and distribution form should be specified in the model.33 However, multiple inputs and outputs cannot be easily handled with SFA.34 For this reason, DEA has been used in this paper for analyzing the efficiency of firms. DEA uses linear programming to create a non-parametric frontier on data and then related to that frontier efficiencies are measured. Data Envelopment Analysis (DEA) was first introduced by Charnes, Cooper, and Rhodes in 1978.35 It is a non-parametrical linear approach to measure the relative efficiency of a group of homogenous firms or decision making units (DMUs).

A DMU can be defined as an entity responsible for converting input(s) into output(s) for which performance is to be evaluated. DEA is frequently used measure for efficiency analysis. The popularity of DEA is due to its ability to measure relative efficiencies of multiple-input and multiple-output of DMUs without including prior weights for the inputs and outputs.

Proportion reduction in input with given output is known as input oriented measure and proportional increase in output keeping input as constant is known as output oriented measure. This study will consider input orientation measure to assess efficiency of firms. Choice of input orientation is based on the fact that inputs are controllable in firms' scenario as compared to output. Usually, input-oriented models are applicable in this case as the use of raw material, labour, expertise etc. are controllable then output as productive capacity and capacity utilization. This approach is initially explained by Farrell. If a firm wants to produce output y considering two inputs x1 and x2 under constant returns to scale.36 Concept of technical efficiency can be easily explained by Figure 1. Here fully efficient firm is presented to operate at SS' isoquant.

If any firm uses inputs to produce Q output then firm is technically inefficient. Technical efficiency is calculated as the ratio between 0Q by 0P. Here 0Q is efficient point of production. These efficiencies can be further explained by comparing efficiency with unit cost. AA gives unit cost of firm producing at Q' has more allocative efficiency as compared with the firm producing at point Q and firms at Q' produces with least cost. Using this intuition the current study will analyze efficiency of exporting and non-exporting under variable returns to scale and constant returns to scale as all firms cannot operate at optimal scale due to financial and imperfect competition related constraints. Scale efficiency captures the differences in technical efficiency under variable and constant returns to scale. These calculations are made using Data Envelopment Analysis (DEA).

DEA compares different decision making units (DMUs)'s efficiency by calculating inputs and outputs variable weights of these for maximization ratio. For these DMU's efficiency ratio can be calculated using DEA as follows:

(Equations)

Here, 'u' represents weight of outputs and 'v' represents weights of inputs. x and y represent the output and the input of i firms respectively. These weights can be found by DEA program that maximizes efficiency of decision making units subject to constraints that all efficiency measures are less than or equal to 1 and weights u and v must be greater or equal to 0.

This formulation gives infinite number of solutions; to overcome this problem constraint can be applied that is:

(Equations)

In order to alter the symbolization u and v are used instead of u and v. This form is called multiplier of liner programing problem. Using the duality in linear programing, one can drive equivalent envelopment form of this problem:

(Equations)

Where is scalar and is Nx1 vector of constants. This envelopment form has fewer constraints as compared to multiplier form (K + M < N+1), and hence is generally the preferred form to solve.

Evidence of Comparative Performance Exporting vs Non-Exporting Firms

The World Bank Enterprise Survey (for the latest available year 2013) is used to assess the efficiencies of exporting and non-exporting firms. Data was collected between May 2013 and May 2015.37 The World Bank collects this type of data for manufacturing firms from developed and developing countries to analyze the experience of private sector firms in these countries. Seven sectors of manufacturing firms are chosen for efficiency analysis, e.g. food, textile, motor vehicles, non-metals, chemicals, manufacturing and others etc. Selected data contributes 45 per cent of total manufacturing in the country.38 It is an important sector of the country; as it generates employment opportunities for 15.3 per cent of total labor force. Further, manufacturing contribution to GDP is 13.6 per cent in Pakistan as per Economic Survey of Pakistan 2015-16, selected industries contribute to 46 per cent of total weight in manufacturing sector's growth.

These industries are the highest growing industries in manufacturing sector with an average growth rate of 10 per cent in 2015-16. Firms are sub categorized based on their direct sales as exporting and non-exporting firms. Firms with direct sales solely for exports are considered as exporting or foreign demand-based firms and home demand-based firms or non-exporting firms otherwise.

There is a direct relationship between a manufacturing firm's growth and its value addition in GDP. Studies for assessment of manufacturing's contribution in GDP and firm's performance in different aspects are available. Many researchers have worked on efficiency analysis of manufacturing sector of Pakistan by taking different measures like output, capital, labor, growth market share and exports.39 But studies comprehensively measuring efficiencies of firms considering major inputs responsible for firms' growth as cost material cost, energy cost non-material costs are still lacking. Since selection of detailed variables may alter the results and consequently policy implications based on these results. Our study will try to include best possible variables which can affect firms' efficiency and growth.

We have considered capacity utilization (CU) as an output; CU shows many economic conditions under which firms operate. It is the gap between potential and actual output. This variable shows productive capacity of the firms, usually it is also called economic utilization rate, and thus, can be used as proxy of output of the firm as more capacity utilization shows more growth.40

We have taken usual input variables available in World Bank enterprise survey as labor cost that includes the cost of wages, salaries, bonuses, social security payment. Costs of raw material include intermediate goods used in production. The cost of fuel, electricity, vehicle and equipment's, machine and building rent are included as inputs to compare capacity utilization of the firms. Furthermore, we have categorized firms into exporting and non-exporting firms based on their national sales, directly or indirectly. Descriptive statistics for exporting and non-exporting firms is given in Table 1 in appendix. It has been explored that on average, capacity utilization of exporting firms is found slightly greater than their counter parts in non-exporting firms. Further, labor and raw material costs are the major costs heads where both kind of firms are spending.

Firms with missing data for selected variables from World Bank Enterprise micro data set were dropped. A total of 403 firms' data on our selected variables was made available for efficiency analysis. Out of total firms 56 and 347 firms are found as exporting and non-exporting firms respectively. Maximum capacity utilization of domestic firms is found as 78 per cent. Majority of the firms are found operating in the range of capacity utilization of 76 per cent and 67 per cent. Exporting firms are found utilizing their capacities in the range of 62 per cent to 92 per cent. Efficiency analysis can give a comprehensive picture of optimal utilization of inputs.

Labor, raw material and electricity are found as major costs heads in both exporting and domestic firms. Labor cost as a ratio of total cost and capacity utilization is graphed to have a glimpse to compare firms for their productive capacity. Graph 1 exhibits labor-total cost ratio in comparison with productive capacity of both types of firms.

Fig 1a shows labor cost and capacity utilization of the exporting firms while Fig 1b shows non-exporting firms' capacity utilization and labor cost. Mostly exporting and domestic firms' capacity utilization is above 60 per cent, having their labor cost below 60 per cent. There are few firms with almost same capacity utilization but their labor cost is above 60 per cent so these firms can minimize their labor cost to enhance their efficiency. Majority of domestic firms' cost is below 20 per cent with their capacity utilization above 60 per cent which demonstrate that domestic firms' labor cost is low compared to exporting firms.

Scores for technical and scale efficiency of exporting and non-exporting firms have also been calculated. It has been found that average technical efficiency scores for non-exporting firms are slightly greater than the exporting firms. It implies that exporting firms could have used 13 per cent less resources as compared to non-exporting firms. Moreover, technical efficiency scores under constant returns to scale for both type of firms are less as compared to variable's returns to scale efficiency. It means that both kind of firms can have more returns if they consider a change in their current use of inputs for production processes. Moreover, under VRS technical efficiency for exporting firms is 0.90 which means that these firms could have used 10 per cent less resources to have the same level of output.

Similarly, scale efficiency of non-exporting firms is 2.4 per cent greater than the scale efficiency of exporting firms.

Using DEA, we analyzed technical efficiency (TE) and scale efficiency (SE) of firms. Technical efficiency shows how effectively a firm can use its inputs for producing maximum output. Input oriented measure along with variable returns to scale is used as a measure of efficiency. Through input orientation measurement of efficiency firms can focus on reducing input use keeping the same output as compared with its peer firms. Initially we have separated firms into two categories, home demand based firms and foreign demand based firms to see efficiency within these firms.

SE of exporting firms is found at 0.914 which implies that on average the actual scale of productive capacity diverges from potential productive capacity by 8.6 per cent. And within exporting firms it varied by 6.4 per cent. Food, Garments and Non-metals are found completely efficient industries in non-exporting firms. Along these firms, chemical exporting firms are also found completely efficient in context of exporting firms. Returns to scale for the firms under considerations have been also given in Table 2. Majority of the firms in both kind of industries are found operating under decreasing returns to scale which implies that they have availed an opportunity to operate at an optimal scale.

To have a contingency of efficiency analysis, data is combined for home and exporting firms to compare efficiency results with initial findings. Comparative efficiencies of domestic and foreign demand-based firms were calculated and given in Table 3. Apparently, it was observed that home demand-based firms are more efficient compared to foreign demand based firms but when compared in aggregate terms, result are opposite. According to this domestic firms on aggregate are less efficient and should increase their returns to be efficient. Food and Garments technical efficiency is almost 60 per cent low than technical efficiency of their exporting counter parts.

Conclusion

This paper estimated the technical efficiencies and scale efficiencies for 403 Pakistani manufacturing firms using World Bank Enterprise Survey in order to contribute to the current preferred policy of export-led growth. Contrary to the recent analysis,41 most of the exporting firms are found technically more efficient compared to non-exporting firms.

Similarly, scale efficiency for exporting firms on average is found 6 times greater compared to non-exporting firms. Interesting results are found in case of textile, and other manufacturing firms. Interestingly, scale efficiencies for textile and other manufacturing are found substantially greater in case of non-exporting firms as compared with exporting firms. It means that given real world phenomenon of market behavior of imperfect competition and sub-optimality, these firms can perform better in contrast to the exporting counterparts. However, majority of the exporting firms are enjoying technical and scale efficiencies which partly validate learning by exporting phenomenon widely discussed in literature.

In conclusion, exporting firms are appeared more efficient. This can be due to the inherent nature of exporting industries of self-selection. But it doesn't mean that efficiency is only learning by exporting phenomenon. Rather non exporting firm can also be encouraged to achieve sustainable economic growth. Export led growth policy remained successful before East Asian Crisis. Currently, it is pertinent that global volume of trade has undergone a sharp reduction. In current scenario of global competition and falling world incomes, lessons must be learnt from neighboring countries like India and China, who shifting away from conventional wisdom to domestic demand based growth due to vagaries in global market.

Due to limited technologically advanced nature of our industrial commodities, there is a need to focus on growth of non-exporting firms. Since, home demand in Pakistan is based upon low-tech, low-quality and crude rural commodities, which can be easily targeted and fulfilled by increasing efficiency of non-exporting firms on the same lines as Japan and China have done to their current status of development. However, there is a need to bring balance between promotion of home-based demand growth and export led growth since there is no escape from competition, innovation and inventions as an ultimate source of growth.

Limitations of the Study

More judicious selection of output and input can change technical and scale efficiencies. Adding more relevant variables as protection, agglomeration, innovation, informalities, institutional strengthening, geographical locations, etc, in inputs can make efficiency score more efficient to cautiously use it for policy perspective. We have used aggregate values representing specific industries. Individual firm's data could have shown different results. Panel data analysis ay give a comprehensive picture of productivity of the firms which can better guide our concern.

Appendix

Table 1: Descriptive statistics for domestic and foreign demand based firms

###Non-Exporting Firms###Exporting Firms

###Variables###Mean###Median###SD###Mean###Median###SD

###Capacity

Output###Utilization###71.75###72.89###4.72###76.6###77.2###15.8

###Labour cost###224.3###61.1###397.9###131.2###25.5###179.1

###Electricity cost###90.6###18.4###155.6###49.0###13.4###89.2

###Raw material cost###287.7###160.4###242.5###633.3###245.3###743.7

Inputs###Fuel cost###12.6###9.1###8.5###5.0###2.5###6.6

###Machine rental

###cost###10.3###5.8###11.4###2.3###2.1###1.6

###Land and Build cost###2.9###3.3###0.8###2.3###1.7###2.0

###Other costs###42.8###37.4###18.8###7.3###6.6###3.8

Table:2 Technical and Scale Efficiency Scores of Home and Foreign Demand Based Firms

###Non-Exporting Firms###Exporting Firms

###VRST###CRST###VRST

Firms Type###CRSTE###E###SE###E###E###SE

Chemicals###0.78###1###0.78###DRS###1###1###1###-

###Food###1###1###1###-###1###1###1###-

Garments###1###1###1###-###1###1###1###-

Non-metal###1###1###1###-###1###1###1

Other Man###0.989###1###0.989###DRS###0.577###0.89###0.648###DRS

Textiles###0.784###0.929###0.844###DRS###0.455###0.544###0.837###DRS

Table 3: TE and SE Scores of Home and Foreign Demand Based Firms

###Firms###CRSTE###VRSTE###SE

Chemicals X###1###1###1###-

Food X###1###1###1.000###-

Garments X###1###1###1.000###-

Non-metal X###1###1###1.000###-

Other Man X###0.577###0.89###0.648###DRS

Textiles X###0.455###0.544###0.837###DRS

Chemicals H###0.447###0.492###0.908###DRS

Food H###0.414###0.5###0.827###IRS

Garments H###0.39###0.456###0.855###IRS

Non-metal H###0.728###1###0.728###IRS

Other Man H###0.339###0.375###0.904###IRS

Textiles###0.373###0.432###0.864###IRS

Notes

1 Matthew Mccartney, "Pakistan, Growth, Dependency, and Crisis," The Lahore Journal of Economics, (September 2011): 71-94, http://repec:lje:journl:v:16:y:2011:i:sp:p:71-94.

2 ibid.

3 Hallward-Driemeier, Mary, Giuseppe Iarossi, and Kenneth Sokoloff, "Exports and Manufacturing Productivity in East Asia: A Comparative Analysis with Firm-Level Data" NBER Working Paper no. 8894 (2002). doi:10.3386/w8894.

4 Hallward-Driemeier, Mary, Giuseppe Iarossi, and Kenneth Sokoloff. "Exports and Manufacturing Productivity in East Asia: A Comparative Analysis with Firm-Level Data" National Bureau of Economic Research, (April 2002). doi:10.3386/w8894.

5 Encarnacion Moral-Pajares, Adoracion Mozas-Moral, Enrique Bernal-Jurado, and Miguel Jesus Medina-Viruel, "Efficiency and Exports: Evidence from Southern European Companies," Journal of Business Research 68, no. 7 (July 2015): 1506-1511.

6 Traiq Mahmood, Ejaz Ghani, and Musleh Ud Din, "Are Our Export-Oriented Industries Technically More Efficient?," Pakistan Development Review 2, no. Summer (2015): 97-121, http://www.pide.org.pk/pdf/PDR/2015/Volume2/97-121.pdf.

7 Coelli, Tim J. Coelli and George E. Battese, "Identification Of Factors Which Influence The Technical Inefficiency Of Indian Farmers," Australian Journal of Agricultural Economics 40, no. 2 (August 1996): 103-128.

8 Ibid.

9 Paul Krugman, "Scale Economies, Product Differentiation, and the Pattern of Trade," The American Economic Review 70, no. 5 (1980): 950-59.

10 Elhanan Helpman, "The Structure of Foreign Trade," Journal of Economic Perspectives 1-13, no. 2 (1999): 121-44.

11 Abraham Charnes et al., "Foundations of Data Envelopment Analysis for Pareto-Koopmans Efficient Empirical Production Functions," Journal of Econometrics 30, no. 1-2 (1985): 91-107

12 William W. Cooper, Lawrence M. Seiford, and Joe Zhu. "A Unified Additive Model Approach for Evaluating Inefficiency and Congestion with Associated Measures in DEA," Socio-Economic Planning Sciences 34, no. 1 (2000): 1-25.

13 Fried, Harold O., C. A. Knox Lovell, and Shelton S. Schmidt, eds. The Measurement of Productive Efficiency and Productivity Change (London: Oxford University Press, February 1, 2008).

14 Cooper, William W., Lawrence M. Seiford, and Joe Zhu, eds. "Handbook on Data Envelopment Analysis," International Series in Operations Research and Management Science (2011).

15 Harold O., Fried, C. A. Knox Lovell, and Shelton S. Schmidt, eds. "The Measurement of Productive Efficiency and Productivity Change" (February 1, 2008).

16 Tjalling C. Koopmans, "Efficient Allocation of Resources," Econometrica 19, no. 4 (1951): 455.

17 Gerard Debreu, "The Coefficient of Resource Utilization," Econometrica 19, no. 3 (July 1951): 273.

18 R. D. Banker, A. Charnes, and W. W. Cooper, "Some Models for Estimating Technical and Scale Inefficiencies in Data Envelopment Analysis," Management Science 30, no. 9 (September 1984): 1078-1092.

19 Coelli and Tese, "Identification of Factors Which Influence the Technical Inefficiency of Indian Farmers." Australian Journal of Agricultural Economics, Vol. 40, No. 2 (August 1996), pp. 103-128

20 Zhong Qin, Vinod Mishra, and Smyth Russell, "An Empirical Examination of Endogenous Ownership in Chinese Private Enterprises," Journal of the Asia Pacific Economy 21, no. 4 (2016): 513-30.

21 Rizal Edy Halim, "Marketing Productivity and Profitability of Indonesian Public Listed Manufacturing Firms An Application of Data Envelopment Analysis(DEA)," Benchmarking: An International Journal 17, no. 6 (2010): 842-57,

22 Roberto Alvarez and Gustavo Crespi, "Determinants of Tehnical Efficiecny of Small Firms," Small Business Economics 20, no. 3 (2003): 233-44.

23 Fukunari Kimura and Kozo Kiyota, "Foreign-owned versus Domestically-owned Firms: Economic Performance in Japan.," Review of Development Economics 11, no. 1 (2007): 31-48.

24 Thi Bich Tran, R Quentin Grafton, and Tom Kompas, "Firm Efficiency in a Transitional Economy: Evidence from Vietnam," Asian Economic Journal 22, no. 1 (2008): 47-66, https://doi.org/10.1111/j.1467-8381.2008.00268.x.

25 Dilling-Hansen et al., "'Efficiency, RandD and Ownership-some Empirical Evidence.,'" International Journal of Production Economics 83, no. 1 (2003): 85-94.

26 Ehsan H. Feroz, And Sungsoo Kim, and Raymond L. Raab., "Financial Statement Analysis: A Data Envelopment Analysis Approach," The Journal of Operational Research Society 54, no. 1 (2013): 48-58.

27 Garrick Blalock and Paul J. Gertler, "'Learning from Exporting Revisited in a Less Developed Setting," Journal of Development Economics 75, no. 2 (2004): 397-416.

28 Alvarez and Crespi, "Determinants of Tehnical Efficiecny of Small Firms," Small Business Economics 20, no. 3 (2003): 233-244.

29 Arun Kumaraswamy and Ram Mudambi, "Catch-up Strategies in the Indian Auto Components Industry: Domestic Firms' Responses to Market Liberalization," Journal of International Business Studies 43, no. 4 (2012): 368-95, https://doi.org/10.1057/jibs.2012.4

30 Abid A Burki and Dek Terrell, "'Measuring Production Efficiency of Small Firms in Pakistan,'" World Development 26, no. 1 (1998): 155-69; Mahmood Ghani Ud Din, "Are Our Export-Oriented Industries Technically More Efficient?"; Mehran Ali Memon and Izah Mohd Tahir, "'Performance Analysis of Manufacturing Companies in Pakistan,'" Business Management Dynamics 1, no. 7 (2012): 12-21.

31 Mahmood Ghani, and Ud Din, "Are Our Export-Oriented Industries Technically More Efficient?" Pakistan Development Review, no.2 (2015): 91-121.

32 A Graner Mats and Anders Isaksson, "'Firm Efficiency and the Destination of Exports: Evidence from Kenyan Plant-level Data,'" The Developing Economies 47, no. 3 (2009): 279-306.

33 Coelli and Tese, "Identification of Factors Which Influence the Technical Inefficiency of Indian Farmers,"Australian Journal of Agriculture Economics 40, no. 2 (1996): 103-128.

34 Chuen Tse and Kuan Yew, "Procedia Computer Science Efficiency Assessment of Universities through Data Envelopment Analysis," Procedia Computer Science 3 (2011): 499-506.

35 Charnes, A., W.W. Cooper, and E. Rhodes. "Measuring the Efficiency of Decision Making Units," European Journal of Operational Research 2, no. 6 (November 1978): 429-444.

36 Adopted from Tim Coelli, "A Guide to Deap Version 2.1: A Data Envelopment Analysis (Computer) Program," Centre for Efficiency and Productivity Analysis, University of New England, Australia (1996).

37 World Bank, Enterprises Surveys. http://www.enterprisesurveys.org/

38 Farooq, O., and S. E. Wasti, "Agriculture, Pakistan Economic Survey 2014-15," Report by Ministry of Finance, Government of Pakistan (2015). URL: http://finance.gov.pk/survey/chapters_15/02_Agricultre.pdf

39 Traiq Mahmood, Ejaz Ghani, and Musleh Ud Din, "Are Our Export-Oriented Industries Technically More Efficient?" Pakistan Development Review 2, no. Summer (2015): 97-121, http://www.pide.org.pk/pdf/PDR/2015/Volume2/97-121.pdf; Tariq Mahmood Din, Musleh-ud, Ejaz Ghani, "'Technical Efficiency of Pakistan's Manufacturing Sector: A Stochastic Frontier and Data Envelopment Analysis,'" The Pakistan Development Review 46, no.1 (2007):01-18. https://www.jstor.org/stable/pdf/41260785.pdf

40 Marc Lavoie, "'The Kaleckian Model of Growth and Distribution and Its Neo-Ricardian and Neo-Marxian Critiques,'" Cambridge Journal of Economics 19, no. 6 (1995): 789-818.

41 Mahmood Ghani, Ud Din, "Are Our Export-Oriented Industries Technically More Efficient?The Pakistan Development Review, (Summer 2015) pp. 97-121
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