UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS PERFORMANCE OF MANUFACTURING ENTERPRISES – VIETNAM CASE STUDY BY NGUYEN VIET CUONG MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, DECEMBER 2013 UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS PERFORMANCE OF MANUFACTURING ENTERPRISES – VIETNAM CASE STUDY A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN VIET CUONG Academic Supervisor: PHAN DINH NGUYEN HO CHI MINH CITY, DECEMBER 2013 Acknowledgement This thesis would not have been possible without the support of many people I wish to express my gratitude to my supervisor, Dr Phan Dinh Nguyen who was abundantly helpful and offered invaluable assistance, support and guidance Deepest gratitude to Vietnam-Netherlands programme for sharing the literatures, invaluable assistance and providing me a big opportunity to complete this study I wish to express my love and gratitude to my beloved families for their understanding and endless love, through the duration of my studies Table of content Chapter 1: Introduction 1.1 Reason of chosen topic 1.2 Research objectives 1.3 Research questions 1.4 Research methodology 1.5 Scope of the study 10 1.6 Justification of study 10 1.7 Structure of thesis 10 Chapter 2: Literature review 11 2.1 Measurements of firm performance 11 2.2 Stochastic frontier analysis in transition countries 11 2.3 Vietnamese technical efficiency analysis 13 2.4 Factors impact firm efficiency 15 2.5 Conceptual framework 21 Chapter 3: Methodology overview 22 3.1 Efficiency measurement concepts 22 3.2 The stochastic production frontier 24 3.3 Production functions accounting for technical change 25 3.4 Decomposition of productivity change 26 3.5 Stochastic production frontier with panel data 29 3.6 The stochastic frontier model using a single-stage estimation 31 3.7 Analytical framework 33 3.8 Variables are used in production function 33 3.9 Stochastic frontier production function 36 3.10 Variables are used in inefficiency model 37 3.11 Testing hypotheses 40 Chapter 4: Data and empirical results 43 4.1 Data issues 43 4.2 Testing hypothesis 46 4.3 Empirical result 47 4.4 Sources of technical inefficiency 48 4.5 The estimate technical efficiency 50 4.6 Total factor productivity decomposition 52 Chapter 5: Conclusions and policy implications 56 5.1 Main findings 56 5.2 Discussions and policy recommendations 56 5.3 Limitation and further studies 57 Appendix 59 Reference 65 List of tables Table 1-Average of some variables by ownership 44 Table 2-Pairwise correlation of continuous variables 44 Table 3-Summary of hypothesis testing 47 Table 4-Estimation of stochastic frontier function and inefficiency model 48 Table 5-Distribution of production efficiency by year 51 Table 6-Frequency distribution of efficiency estimated by year 51 Table 7-The average of productivity by ownership 52 Table 8-Summary of factors impact on technical efficiency .55 List of figures Figure 1-Conceptual framework 21 Figure 2-Technical and allocative efficiencies from an input orientation 22 Figure 3-Scale efficiency 23 Figure 4-The stochastic frontier 25 Figure 5-Estimation and decomposition of productivity change 27 Figure - Distribution of continuous variables 45 Figure 8-Scatter plot correlation of inefficiency explanatory variables 46 Figure 9-Technical efficiency in period 2000-2008 53 Figure 10-Technical progress in Vietnamese manufacturing firms .53 Figure 11- Change in technical efficiency by ownership 54 Figure 12 - Total factor productivity growth 54 Figure 13-Total factor productivity growth account return to scale 55 Abbreviations SFA: Stochastic frontier analysis DEA: Data envelopment analysis ML: Maximum likelihood OLS: Ordinary least squares GLS: Generalized least squares TFP: Total factor productivity HHI: Herfindahl index GSO: General Statistics Office SME: Small and medium enterprises FDI: Foreign direct investment SOE: State owned enterprises CSO: Central state owned enterprises LSO: Local state owned enterprises LTD: Private, private limited or private joint-stock enterprises COO: Cooperative, collective or partnership enterprises FIO: Foreign invested ownership enterprises RRD: Red River Delta NMM: Northern Midlands and Mountain Areas NSCC: North Central Coast and South Central Coast CH: Central Highlands SE: South East MRD: Mekong River Delta Chapter 1: Introduction Nowadays, macroeconomic developments, particularly macroeconomic stabilization is focused in many analyses of economies undergoing transition Recent studies suggest macroeconomic reforms through improving enterprise performance plays important role in sustaining macroeconomic stability for transition economies Besides, firms are assumed importance of accelerating economic growth in developing countries They promote capital formation, create wealth in the country, and help to reduce unemployment and poverty Studying the enterprise’s efficiency always plays an important role in economic research, especially in developing countries 1.1 Reason of chosen topic Vietnam began re innovation in 1986 and transited from planned economy to market oriented economy Besides achieving many successes in economic development and reduce poverty, Vietnam still confronts with unsustainable development issues and middle income trap Specifically, the comparative advantages of Vietnamese manufacturing firms remain heavily upon cheap labor and foreign direct investment, without enhancing their productivity A low level of productivity has been observed in this sector, since they lack new technology, product and process innovation, financial access , skilled labor, raw materials, high value added production and managerial skills (UNIDO, 2011) The practices demonstrated that the sustainable development can be maintained only if increasing productivity is engine of growth rather than accumulation of resources Moreover, structural evolution and the input productivity, which compose the quality of economic growth, are solutions for the middle income trap problem Through identifying sources affecting firm inefficiency, direct impact on the overall growth of the economy can be revealed The appropriate policies and recommendation can be learned from these analyses As a result, measuring technical efficiency to improve productivity and competitiveness over the long term is urgently needed especially for the manufacturing sector For this kind of study, there are two approaches to measure firm efficiency and examine firm inefficiency effects The former is the parametric stochastic frontier analysis and the latter is non parametric data envelopment analysis However, the stochastic frontier analysis approach is more relevant in this context The reasons are the stochastic frontier approach closer to reality while it considers both factors beyond the control of the firm and firm-specific factors Moreover, the stochastic frontier method separates effect of inefficiency and other random shocks Whereas, the data envelopment approach does not differentiate technical efficiency and statistical noise and it is a non-statistical technique Despite of the stochastic frontier analysis advantages, the SFA studies about Vietnamese enterprise’s efficiency are still inefficient, scatter and rare For example, Vu (2002) analyzed focus on SOE with a database of 164 manufacturing SOE for 1996-1998 and using two stage stochastic frontier analyses He revealed the skilled workers, engaged in exports activities impact positively on SOE performance Nguyen (2005) estimated technical efficiency of 32 manufacturing sector in Hanoi and Ho Chi Minh cities using 2000-2002 industrial data with both SFA and DEA approaches He found that Vietnamese industries operate with laborintensive way Nguyen et al (2007) studied panel data of 1,492 firms in 2000-2003 using both SFA and DEA In this study, they found Vietnamese manufacturing firms improve productivity by capital accumulation rather than increase productivity Tran et al (2008) investigated 800 SME in 1996 and 1,500 SME in 2001 using cross sectional stochastic frontier model They claimed that SME lack of management skill through their firm’s age and size affect negatively on performance Le and Harvie (2010) used the 2002, 2005 and 2007 SME database and cross sectional stochastic frontier model to estimate firm efficiency They observed that the cooperation, subcontract and product improvement are positive factors impact on technical efficiency Nguyen et al (2012) estimated the enterprise’s efficiency and decomposed TFP growth into technical progress and technical efficiency change However, they did not examine the inefficient effects in their model In brief, “Performance of manufacturing enterprises – Vietnam case study” topic is chosen for revealing Vietnamese manufacturing firms’ technical efficiency over the period 2000-2008 which can answer the question “Did Vietnam develops sustainable?” Although there were various literatures about Vietnamese manufacturing firms technical efficiency but these studies still incoherent and failed to investigate the impact of ownership, finance, technology on technical efficiency This thesis tries to overcome the shortcoming of previous studies 1.2 Research objectives The main objectives addressed in this thesis are: First, this thesis estimates the technical efficiency of Vietnamese manufacturing enterprises in the period 2000-2008 Further, this analysis tries to identify firm-specific and business environment factors, which significantly affect the inefficiency of Vietnamese manufacturing firms? Finally, total factor productivity growth of in Vietnamese manufacturing firms is decomposed to find which source mainly contributes 1.3 Research questions The following research questions are posed to complete objectives of the thesis: How Vietnamese manufacturing enterprises perform in term of technical efficiency? Which factors significantly contribute to the technical efficiency performance of Vietnamese manufacturing enterprises? Which sources contribute to total factor productivity growth of Vietnamese manufacturing firms? 1.4 Research methodology This study uses a stochastic frontier method introduced by Battese and Coelli (1995) to estimate firms’ technical efficiency and examine inefficiency factors increasing in TFP is mainly due to changes in technical efficiency rather than Total factor productivity change account return to scale technical progress 2000 2001 2002 2003 2004 2005 2007 -50 2008 CSO LSO LTD COO FIO -100 -150 -200 -250 Figure 12-Total factor productivity growth account return to scale When account return of scale, the picture of TFP growth is completely different All types of ownership have negative TFP growth in study period In this figure, LSO has highest TFP growth trend, follow by COO, LTD, CSO and FIO In summary, the technical efficiency of Vietnamese manufacturing enterprises increases over the period 2000-2008 The low level of technical efficiency shows Vietnamese manufacturing firms still operate ineffectiveness The change in technical efficiency is an essential source of TFP growth rate The impact of inefficiency factors on technical efficiency are summarized in Table Variable H1 Central state ownership Local state ownership Private, private limited enterprises and private joint-stock enterprises Effect Base Positive Positive H2 H3 H4 Domestic cooperative, collective, or partnership enterprises No significant Foreign invested ownership No significant H5 H6 Variable Firm size Capital intensity Labor quality Female ratio Leverage Liquidity Liquidity >75 Effect Positive Negative Positive Negative Negative No significant Negative Export dummy HHI index No significant No significant Table – Summary of factors impact on technical efficiency Chapter 5: Conclusions and policy implications 5.1 Main findings The main purpose of this study has been to measure technical efficiency and determine factors affecting it in production during the transition process to a market economy Using firm level data for Vietnam from 2000 to 2008, stochastic frontier analysis shows that average efficiency levels of the manufacturing industries are 0.49 and increase during the 2000–2008 periods The analysis also investigates determinants of technical efficiency and the result indicates that ownership is one of the important determinants Other determinants which have significant impacts are industry identity, business scale, capital intensity, female ratio, labor quality, municipal location and economic endowment amongst different regions Furthermore, the study also decomposes the efficiency of manufacturing companies to examine the growth source of TFP It finds that the TFP growth mostly driven by technical efficiency changes (moving toward frontier production) rather than from technical progress (shifting of the frontier production) The passive role of technical progress can be partly explained by the negative contribution of capital intensity on technical efficiency, which is considered as a determinant of growth in technical progress 5.2 Discussions and policy recommendations This thesis demonstrates that enterprises of all ownership types increase in technical efficiency in the period 2000-2008 Among them, FIO has the highest score, follow by CSO, LTD, LSO and COO The study also suggests that CSO lags behind the other forms organization in capital, labor and material productivity Although CSO has second high level capital intensity and business, the estimated return to scale of state owned enterprises (CSO and LSO) has the lowest score Thus, it is possible to argue that the state ownership had a relatively poor performance of governance problems and ambiguous responsibility which reduce their incentives to maximize production efficiency The similar evidence also provided by Hoang et al (2008), who used the DEA approach to estimate the production productivity of manufacturing firms in 2001-2005 The policy recommends here is the government should provide a stronger legal framework to improve the CSO performance For example, continuously strengthen the rules and responsibilities of the board of directors Illegal activities caused by firms should have strong punishment The study points out that the LTD has a positive effect on firm efficiency and second best return to scale score However, LTD still has operated under a decreasing return to scale and relied on labor intensive The estimated low level technical efficiency of LTD suggests the large room for LTD to improve performance and get more profits than their present achievements The same related result also provided by Nguyen et al (2007) when studied 1,492 firms from the period 2000-2003 The suggestion to these firms is they should focus on upgrading their production technology The suggested policy is that government should strongly support the private sector The analysis also remarks that firm with high leverage, capital intensity appear to be less technically efficient It is possible implies that financially constrained enterprises difficult in their financial liability and these companies invested in inappropriate technology These results are contradicted with analysis of Amornkitvikai and Harvie (2011), who studied the Thai listed manufacturing enterprises over the period 2000-2008 The suggested policy is that government should increase and improve accessibility to capital markets, especially for private sector In addition, government should promote and facilitate the enterprises productive investment fundraising (e.g, upgrading production technology) which generate firm’s higher future and sustainable revenues 5.3 Limitation and further studies This study uses the assumption that all manufacturing firms have the same production function with capital, labor and intermediate material inputs The difference between manufacturing industries will absorb by the industries control dummy in inefficiency model So the regression result state too general and not for specific industries This empirical study is limited to the period 2000-2008 due to data availability and the data 2006 were eliminated For this reason, the repeat study with correct data of year 2006 is worth For future research, it is also interesting to measure the technical efficiency of period 2009-2013 which is beyond the scope of this study As this study points out, the inefficiency model explains only 5.9 percent of variation in production efficiency The reason is the omitted important variable in the model, such as firm age, manager identification and social network…Some of them can not derive from our data sample, some only presented from the 2009 survey data A study on social network gain advantages from a political connection which affect business performance is worth further examination However, this is beyond the scope of this study In addition, there are many studies about enterprise efficiency in the ASEAN region, ASIA region recent years For instance, Radam et al (2008), Jung and Pyo (2008), Gao (2010), Amornkitvikai and Harvie (2011), Prabowo and Cabanda (2011) using micro data and stochastic frontier analysis for Malaysia, Korea, China, Thailand, Indonesia, respectively Hence, the comparative analysis of technical efficiency performance for ASEAN region, ASIA region and international should conduct for further research Appendix Result of stochastic frontier analysis Inefficiency effects model (truncated-normal) Group variable: madn Time variable: year Number of obs Number of groups Obs per group: avg max Prob > chi2 Wald chi2(14) Log likelihood = 14177.1873 lny_3 Coef Std Err z P>|z| = = = = = 40386 16507 2.4 = 0.0000 = 738227.18 [95% Conf Interval] Frontier k_1 l_1 im_1 t k_1sq l_1sq im_1sq tsq k_1l_1 k_1im_1 k_1t l_1im_1 l_1t im_1t _cons 0389304 3993548 5850654 0156922 0055458 0339517 0410224 -.0028925 0093302 -.0149391 0019148 -.0761087 0041448 -.0045293 2.01415 0049838 005344 0042037 0047638 0004362 0006599 0004008 0007234 00079 000661 0002862 0007013 0003997 0002743 108492 7.81 74.73 139.18 3.29 12.71 51.45 102.36 -4.00 11.81 -22.60 6.69 -108.52 10.37 -16.51 18.56 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0291624 3888808 5768262 0063554 0046909 0326584 040237 -.0043104 0077818 -.0162347 0013539 -.0774832 0033614 -.005067 1.80151 0486984 4098289 5933045 025029 0064007 0352451 0418079 -.0014747 0108786 -.0136435 0024758 -.0747341 0049281 -.0039916 2.22679 owner -.0111943 -.0305063 0044237 0002134 0055339 0044093 0068727 0046926 -2.02 -6.92 0.64 0.05 0.043 0.000 0.520 0.964 -.0220406 -.0391484 -.0090465 -.0089838 -.000348 -.0218642 0178939 0094107 region -.0072691 -.0209116 010296 -.0079814 -.0346111 0045081 0034402 0074622 0024665 003766 -1.61 -6.08 1.38 -3.24 -9.19 0.107 0.000 0.168 0.001 0.000 -.0161048 -.0276543 -.0043298 -.0128157 -.0419924 0015666 -.0141688 0249217 -.0031471 -.0272298 central -.0067737 0025571 -2.65 0.008 -.0117855 -.001762 icode 11 13 14 15 16 17 20 22 23 24 25 26 -.0494058 -.0039369 0243831 0083064 0247601 0078337 -.000517 -.0120195 -.0736849 0322637 0260061 0683383 0056858 0056783 005366 0065182 004827 0043056 0049946 0039155 0043558 0073185 0047265 0091478 -8.69 -0.69 4.54 1.27 5.13 1.82 -0.10 -3.07 -16.92 4.41 5.50 7.47 0.000 0.488 0.000 0.203 0.000 0.069 0.918 0.002 0.000 0.000 0.000 0.000 -.0605499 -.0150662 0138659 -.0044691 0152994 -.0006051 -.0103063 -.0196938 -.0822222 0179197 0167423 050409 -.0382618 0071924 0349003 0210819 0342209 0162725 0092723 -.0043453 -.0651476 0466076 0352698 0862676 Mu 27 0272936 0064272 4.25 0.000 0146964 0398908 0044237 0068727 0.64 0.520 -.0090465 0178939 0002134 0046926 0.05 0.964 -.0089838 0094107 -.0072691 0045081 -1.61 0.107 -.0161048 0015666 -.0209116 0034402 -6.08 0.000 -.0276543 -.0141688 010296 0074622 1.38 0.168 -.0043298 0249217 -.0079814 0024665 -3.24 0.001 -.0128157 -.0031471 -.0346111 003766 -9.19 0.000 -.0419924 -.0272298 -.0067737 0025571 -2.65 0.008 -.0117855 -.001762 11 -.0494058 0056858 -8.69 0.000 -.0605499 -.0382618 13 -.0039369 0056783 -0.69 0.488 -.0150662 0071924 14 0243831 005366 4.54 0.000 0138659 0349003 15 0083064 0065182 1.27 0.203 -.0044691 0210819 16 0247601 004827 5.13 0.000 0152994 0342209 17 0078337 0043056 1.82 0.069 -.0006051 0162725 20 -.000517 0049946 -0.10 0.918 -.0103063 0092723 22 -.0120195 0039155 -3.07 0.002 -.0196938 -.0043453 23 -.0736849 0043558 -16.92 0.000 -.0822222 -.0651476 24 0322637 0073185 4.41 0.000 0179197 0466076 25 0260061 0047265 5.50 0.000 0167423 0352698 26 0683383 0091478 7.47 0.000 050409 0862676 27 0272936 0064272 4.25 0.000 0146964 0398908 28 0208652 0074149 2.81 0.005 0063323 035398 29 0005253 0077132 0.07 0.946 -.0145922 0156429 30 0119779 0063094 1.90 0.058 -.0003883 024344 31 0867886 0047893 18.12 0.000 0774017 0961754 32 0769956 0190042 4.05 0.000 039748 1142432 -.0178785 region central icode size -.021442 0018181 -11.79 0.000 -.0250055 female_ratio 0119882 0047815 2.51 0.012 0026166 0213598 lnlabor -.1655617 0017891 -92.54 0.000 -.1690683 -.1620551 liquidity -2.67e-06 9.09e-06 -0.29 0.769 -.0000205 0000151 leverage 0231436 0035692 6.48 0.000 016148 0301391 lncap 0233799 0027373 8.54 0.000 0180149 0287449 liqdum 019913 007482 2.66 0.008 0052486 0345774 HHI 0004125 0003073 1.34 0.179 -.0001897 0010148 FDI -.0001753 0001151 -1.52 0.128 -.0004009 0000502 SOE 0001413 0000856 1.65 0.099 -.0000265 000309 cpi -.0007607 0002994 -2.54 0.011 -.0013475 -.000174 dexp 0036159 0026802 1.35 0.177 -.0016373 0088691 d2005 -.0256327 0051299 -5.00 0.000 -.0356871 -.0155782 _cons 1.38154 1139862 12.12 0.000 1.158131 1.604949 _cons -6.375032 9482754 -6.72 0.000 -8.233618 -4.516447 _cons -3.599632 0595172 -60.48 0.000 -3.716284 -3.482981 sigma_u sigma_v 0412743 1653293 0195697 00492 2.11 33.60 0.035 0.000 0162964 1559621 104536 175259 lambda 2496488 0244585 10.21 0.000 2017111 2975865 Usigma Vsigma Source: Author’s estimated result testparm k_1sq l_1sq im_1sq tsq k_1l_1 k_1im_1 k_1t l_1im_1 l_1t im_1t ( 1) [Frontier]k_1sq = ( 2) [Frontier]l_1sq = ( 3) [Frontier]im_1sq = ( 4) [Frontier]tsq = ( 5) [Frontier]k_1l_1 = ( 6) [Frontier]k_1im_1 = ( 7) [Frontier]k_1t = ( 8) [Frontier]l_1im_1 = ( 9) [Frontier]l_1t = (10) [Frontier]im_1t = chi2( 10) =17377.23 Prob > chi2 = 0.0000 testparm t ( 1) ( 2) ( 3) ( 4) ( 5) tsq k_1t l_1t im_1t [Frontier]t = [Frontier]tsq = [Frontier]k_1t = [Frontier]l_1t = [Frontier]im_1t = chi2( 5) = 355.45 Prob > chi2 = 0.0000 testparm k_1t l_1t im_1t ( 1) [Frontier]k_1t = ( 2) [Frontier]l_1t = ( 3) [Frontier]im_1t = chi2( 3) = 316.69 Prob > chi2 = 0.0000 test( size female_ratio lnlabor liquidity leverage lncap central 1.owner 2.owner 3.owner 4.owner 5.owner 1.region 2.region region 4.region 5.region 6.region HHI dexp ) ( 1) [Mu]size = ( 2) [Mu]female_ratio = ( 3) [Mu]lnlabor = ( 4) [Mu]liquidity = ( 5) [Mu]leverage = ( 6) [Mu]lncap = ( 7) [Mu]central = ( 8) [Mu]1b.owner = ( 9) [Mu]2.owner = (10) [Mu]3.owner = (11) [Mu]4.owner = (12) [Mu]5.owner = (13) [Mu]1b.region = (14) [Mu]2.region = (15) [Mu]3.region = (16) [Mu]4.region = (17) [Mu]5.region = (18) [Mu]6.region = (19) [Mu]HHI = (20) [Mu]dexp = chi2( 18) =10960.64 Prob > chi2 = 0.0000 Industry classification in data sample 10 Manufacture of food products 11 Manufacture of beverages 13 Knitting 14 Manufacture of wearing apparel 15 Manufacture of leather 16 Manufacture of wood and bamboo products 17 Manufacture of pulp and paper 20 Manufacture of chemicals and chemical products 22 Manufacture of rubber and plastics products 23 Manufacture of non metallic products 24 Manufacture of metal 25 equipment) Manufacture of fabricated metal products (except machinery 26 Manufacture of computer, electronic and optical products 27 Manufacture of electrical equipment 28 Manufacture of machinery and equipment n.e.c 29 Manufacture of motor vehicles 30 Manufacture of other transport equipment 31 Manufacture of furniture 32 Other manufacturing and The most efficiency firms of sample Business name Beer and alcohol manufacturing Cement manufacturing Beer manufacturing Cement manufacturing Plastic & steel frame manufacturing Wearing apparel manufacturing Roll paper manufacturing Mechanical manufacturer and equipment installation Mechanical Year Central Region owner 2000 2001 2002 2003 Red river delta 2004 2005 2007 2008 2000 2001 2002 North and south 2003 central coast 2004 2007 2008 2000 2007 South east 2008 2000 2001 2002 2004 South east 2005 2007 2008 2008 Red river delta 2004 2007 South east 2008 2003 2004 South east 2005 North and south 2007 2008 central coast 2008 South east Source: Author compiles from the result te 0.51 0.51 0.52 0.59 0.61 0.67 0.80 0.82 0.59 0.61 0.62 0.69 0.65 0.72 0.82 0.63 0.83 0.87 0.67 0.59 0.72 0.62 0.70 0.76 0.80 0.83 0.44 0.50 0.95 0.51 0.82 0.44 0.46 0.83 0.86 0.01 0.01 0.00 0.00 0.00 -0.01 -0.01 -0.02 0.01 0.00 0.00 0.00 0.00 -0.01 -0.01 0.01 -0.02 -0.03 0.01 0.00 0.00 0.00 -0.01 -0.01 -0.02 -0.04 0.01 0.00 -0.02 -0.01 0.00 -0.01 -0.01 -0.01 -0.03 tec 0.02 0.02 0.13 0.03 0.08 0.09 0.02 tfp -13.02 -9.86 -31.62 15.58 -79.90 -14.08 0.03 17.49 0.01 -106.16 0.11 2.14 -0.06 -13.04 0.13 -11.33 0.04 -105.63 -0.13 0.20 2.68 81.29 0.11 0.04 0.06 -25.99 0.64 16.60 -38.49 0.47 38.45 -0.62 -179.72 0.58 17.84 Herfindahl Indexes by (VSIC), (percent) Source: Ramstetter and Phan (2011) Reference Admassie A and Matambalya F.A.S.T (2002) Technical efficiency of small and medium scale enterprises: evidence from a survey of enterprises in Tanzania Eastern Africa Social Science Research Review, Vol 18 (2), pages 1-29 Aigner D J., Lovell C A K and Schmidt P (1977) Formulation and Estimation of Stochastic Frontier Production Function Models Journal of Econometrics, Vol 6(1), pages 21-37 Amornkitvikai, Y and Harvie, C (2011) Finance, ownership, executive remuneration, and technical efficiency: a stochastic frontier analysis of Thai listed manufacturing enterprises, Australasian Accounting Business and Finance Journal, Vol 5(1), pages 35-55 Batra G and Tan H (2003) 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