Technical efficiency and its determinants the case of manufacturing firms in vietnam

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 TECHNICAL EFFICIENCY AND ITS DETERMINANTS: THE CASE OF MANUFACTURING FIRMS IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By TRAN VAN KHUE Academic Supervisor: DR NGUYEN TRONG HOAI DR PHAM LE THONG HO CHI MINH CITY, DECEMBER 2011 ABBREVIATIONS AEC Allocative Efficiency Change DEA Data Envelopment Analysis E&E Electrical and Electronics FDI GDP Foreign Direct Investment Fixed Effects Model Gross Domestic Product GSO General Statistic Office ICT Information and Communication Technology MDE Master of Development Economics POLS Pooled Ordinary Least Squares R&D Research and Develop REM Random Effects Model SEC SEC Scale Economies Scale Efficiency Change SFPF Stochastic Frontier Production Function SMEs Small and Medium Enterprises SOEs State-Owned Enterprises FEM TE Technical Efficiency TEC Technical Efficiency Change Total Factor Productivity Technical Progress Time Trend TFP TP TT III TABLE OF CONTENTS CHAPTER 1: INTRODUCTION I.I The problem statement 1.2 Objectives of the research 1.3 Research questions 1.4 Research methodology 1.5 Thesis structure CHAPTER 2: LITERATURE REVIEW 2.1 Introduction 2.2 Basic Concepts and Theoretical Review .8 2.2.1 The Production Function 2.2.2 Cobb-Douglas production function 2.2.3 Technical Efficiency 11 2.2.4 Technical efficiency measurement 12 2.2.5 The stochastic frontier production function (SFPF) 13 2.3 Empirical Studies 16 2.3.1 Studies in advanced countries .16 2.3.2 Studies in developing countries 19 2.3.3 Studies in Vietnam 22 2.4 Analytical framework for the research 29 CHAPTER 3: RESEARCH METHODOLOGY AND DATA COLLECTION 3.1 Introduction 31 3.2 Research methodology 31 3.2 The stochastic frontier model 31 3.2.2 The technical efficiency model 34 3.3 Testing Hypothesis 37 3.3.1 The stochastic frontier model 37 ' 3.3.2 The technical efficiency model 37 3.4 Data Collection 38 CHAPTER 4: ANALYSIS RESULTS 4.1 Sample profile 39 4.2 Technical efficiency .41 4.3 Comparison of technical efficiency .44 4.4 Technical efficiency model 46 4.4 Testing for the most appropriate model 46 4.4.2 Testing for heteroskedasticity .47 4.4.3 Determinants of technical efficiency .47 4.5 Chapter Summary .50 CHAPTER 5: CONCLUSIONS, RECOMMENDATION AND LIMITATIONS 5.1 The conclusions 51 5.2 The recommendations 54 5.3 Limitations 55 REFERENCES 56 APPENDICES .60 LIST OF TABLES & GRAPHS Table 2.1: Summary of Empirical Studies Table 3.1: Summary of variables in the frontier production function Table 3.2: Summary of variables in the technical efficiency model Table 4.1: Descriptive statistics of output, capital and labour of manufacturing firms in the period 2000-2004 Table 4.2: Estimates of ti model and tvd model Table 4.3: The statistical tests of some hypothesis Table 4.4: Summary of technical efficiency between ti model and tvd model Table 4.5: Determinants of technical efficiency Graph 1: The structure of 1,645 manufacturing firms from other sectors VI LIST OF FIGURES Figure 1.1: The share of manufacturing enterprises in all industries of Vietnam Figure 1: Illustration of Technical Efficiency Figure 2.2: Analytical Framework VII CHAPTER 1: INTRODUCTION 1.1The problem statement Since the launch of renovation in 1986, Vietnam has successfully transformed the centrally-planned economy into a market economy and made great achievements in social and economic aspects In the period of 2000 — 2010, the country’s economic growth was relatively high and stable at an annual average rate of 7.2% In 2010, the real GDP was recorded 3.4 times as much as that in 2000; the state budget collection was times; and the GDP per capita stood at US$1,168 (GSO, 2011) By achieving these, Vietnam has moved from the group of poorest countries to the group of middle-income countries In addition, Vietnam has been successful in poverty reduction, close to achieving universal primary education, improving maternal health, reducing child mortality, obtaining much progress in gender equality and empowering women, and etc In contribution to economic and social development, Vietnamese enterprises play a crucial role Business activities of enterprises have made significant progress In 1995, enterprises contributed about 45.3% of GDP; in 2001 this share increased to 53.2% and in 2007 was over 60% (GSO, 2008) The development of enterprises in many different sectors and localities lead to the change of economy’s structure which reduces the share of agriculture and increases those rates of industry and services With regards to manufacturing enterprises, they made important contribution to dealing with social matters such as creating more new jobs, increasing income for employees, contributing more to the state budget, and etc In more details, manufacturing enterprises create 2.203 million jobs, accounting for 47.3% of total jobs in all enterprises (GSO, 2007) However, many weaknesses are found in the process of the development of the economy in general and the manufacturing sector in particular The infrastructure has not been completed and needs to be improved comprehensively The shortage of electricity and water which are common may reduce the productivity (Klause et a1., 2005) So, the efficiency and competitiveness of the economy still is lower than its potential Moreover, the performance of enterprises has different results because of their resource, types of ownership, type and scale of business, location and some other reasons Although the business environment has been more transparent and flexible for business operation, the business results of each enterprise might not grow steadily In general, Vietnam enterprises expose their own features Firstly, the number of new enterprises especially private companies has grown sharply since 2000 when the Enterprise Law came into effect In three years after the issue of the Law, more than 72,600 new private enterprises were established, creating around 1.6 — million new jobs (CIEM, 2004) These figures are very impressive when compared with just 26,000 private enterprises operating by the end of 1998 Secondly, enterprises located in big cities such as Hanoi; Hochiminh city may enjoy many favorable conditions such as ideal geographical location; advantage of telecommunication, transportation; abundant labor supply with high skill to apply new technology in production Consequently, the number of enterprises in these cities increases very fast and accounts for about 47% of total number of enterprises and 45% of total revenue of the whole country (GSO, 2007) On the other hand, these enterprises are still facing with a lot of problems such as nonsynchronous infrastructure, un-skilled labor Especially, each enterprise in big cities has to compete fiercely with many other local and domestic companies located at the same city These problems in association with improper policies might cause the companies to slowly increase their effectiveness Thirdly, as a multi-sector market model operating according to the market mechanism and the state regulations, Vietnam’s enterprises include state, private and foreign-invested sectors where the former plays a leading role in the economy The government uses the state enterprises as an important tool to stabilize the micro- economic environment and market prices of essential commodities such as electricity, coal, transport, rice and rubber So, state enterprises have received a lot of support, priorities, and subsidies from government Therefore, the performance of state enterprises is questioned about the efficiency relative to other sectors in the economy For above reasons, some issues need to be clarified such as the performance of firms in Vietnam; the production efficiency level of firms located in former Hanoi, Hochiminh cities and other places; firms of the state, foreign and other sectors; and the factors influencing the technical efficiency of firms The purpose of this thesis is to identify the above issues And, the manufacturing sector is selected to research because of following reasons: The share of manufacturing enterprises in all industries accounts more than 20 percent of all kind of activity (GSO, 2006) However, manufacturing enterprises contribute important shares of revenue (more than 30 percent), number of employees (about 50 percent) and export value (22 percent) The thesis applies a stochastic frontier production model and technical efficiency model to analyze the technical efficiency of manufacturing firms and try to find the determinants that affect firms’ technical efficiency APPENDIX 4: Results of summary of other provinces’ manufacturing firms in the period 2000-2004 Variable Mean overall 63505.03 267503.3 overall | 291.855 807.8368 overall | 24907.01 101464.9 97229.9 between | within | between | within | k Std Dev between | within | 253709.8 84952.44 Min Max | 8467547 | N - 9340 5775797 | 2755255 | n T - 1868 19.4 -2132360 Observations 20028 | N = 9340 13234.8 | 7085.055 | n T = 1868 13 2444251 | N - 9340 33.8 1659482 | n - 1868 784.9994 191.4162 -4518.945 29077.85 -755072.8 1055495 | T - APPENDIX 5: Results of summary of State’s manufacturing firms in the period 2000-2004 Variable k Mean Dev Std overall | 87121.53 between | within | 263628.8 253133.2 74074.62 overall | 464.1109 885.7782 between | within | 872.8569 153.2206 overall | 30944.01 132021.6 between | within | Min Max | 19.4 -952958.9 4731648 | 3822103 | 1951564 | -1194.289 10 128895.3 28846.73 10.8 -813227 11101 | Observations N n = T - 4050 810 N = 4050 10288.8 | 3214.111 | n T - 810 3433053 | N - 4050 n - 810 2488468 | 975529 | T - APPENDIX 6: Results of summary of Foreign’s manufacturing firms in the period 2000-2004 Variable Mean overall | between | within | l overall | 520.6596 between | within k 169699 | overall | between | within | 81711.16 Std Dev Min 461699.2 30 434221.6 157669.3 305.8 -2026166 1965.861 1868.639 9.2 614.2256 -17075.94 191266 181725.1 60008.65 147 852.2 -1027957 61 Max | Observations N = 3120 5775797 | 2861449 | n = 624 49756 | 35192.6 | N - 3120 n T - 624 2724186 | N - 3120 n = T - 624 8467547 | 15084.06 | 2024304 | 1112299 | T - “ APPENDIX 7: Results of summary of Other sector’s manufacturing firms in the period 2000-2004 Variable Mean overall | between | within 19823.51 | overall | 167.5474 between | within | k overall | Std Dev Min 49685.92 45528.58 28.2 19921.02 -349597.1 495.271 480.9453 118.7329 -3239.253 6029.695 between | within | Max | Observations 1004170 | 485267.2 | 615872.9 | n T - 1645 11663 | 9510 | 2676.947 | N = n T = 8225 1645 N = n = 8225 1645 T - 15868.91 11 287106 | 14462.39 6539.36 53.2 -150830.9 213077.6 | 199135.9 | APPENDIX 8: Results of time-invariant inefficiency model of all 3,079 manufacturing firms in the period 2000-2004 Time-invariant inefficiency model Number of obs Number of groups Group variable: i = = 15395 3079 ’ Obs per group: - avg - max = Log likelihood lny | lnk | lnl | locl | loc2 staetp foretp cons /mu | sigma2 | gamma | siqina_u2 si9ma_v2 | | Wald chi2(6) Prob > chi2 14678.43 - 0.0000 Std Err .4666343 5999489 0091367 010475 51.07 57.27 0.000 0.000 5794184 4487266 484542 6204795 0404067 0428747 0498907 1.424144 3.55 -1.77 3.89 4.61 0.000 0.076 0.000 0.000 0641063 -.160077 0965347 3.774058 2224975 0079887 2921029 9.3566 4.087004 1.423711 2.87 0.004 1.296582 6.877425 1.190591 0241283 1.144228 1.238834 84 61555 3444359 0240005 0044189 6976963 91154 335775 7233697 1534861 1744502 8987952 7107019 0542879 0202658 0318594 2.83 8.61 28.21 0065504 62 0.005 0.000 0.000 [95% Conf - Coef | 1433019 | -.0760441 | 1943188 | 6.565329 /lnsigma2 | /ilgtgamma | ’ - -17621.917 0470839 13473 836352 Interval] 2598884 2141704 9612384 8931957 3530968 APPENDIX 9: Results of time-varying decay inefficiency model of all 3,079 manufacturing firms in the period 2000-2004 15395 Number of obs Time-varying decay inefficiency model Group variable: i 3079 Number of groups Obs per group: Time variable: t avg max Log likelihood -17364.974 Wald chi2(6) = = 13114.67 0.0000 Prob > chi2 Interval] Coef lnk | 4119543 0094253 43.71 0.000 6064609 1753239 1820449 -.0264891 3529977 6.540999 0103161 0552174 0411297 0435719 0511649 2308662 58.79 3.18 4.43 -0.61 6.90 28.33 0.000 0.001 0.000 0.543 0.000 0.000 5862418 0670997 1014321 -.1118885 2527163 6.08851 6266801 2835481 2626577 0589103 4532792 6.993489 220003 16.13 0.000 3.117247 3.979643 0176294 0960532 8565087 0233593 1782926 9852113 1.100818 1.195175 7733245 3183977 8674218 3349089 lnl locl loc2 staetp foretp cons | | | | | | /mu | /eta | /lnsigma2 | /ilgtgamma | sigma2 | gamma | sigma u2 | sigma_v2 | 3.548445 Std Err [95% Conf lny | 0204943 1371729 92086 0014617 0209798 0328329 1.147026 7152173 8203731 0240644 0066875 0240048 3266533 14.02 6.54 28.05 7019307 0042121 APPENDIX 10: Hypothesis test whether y=0 - ti model 1) [ilgtgamma] cons - chi2( 1) - 795.88 Prob > chi2 = 0.0000 - tvd model ( 1) 0.000 0.000 0.000 [ilgtgamma]_cons - chi2( 1) - 786.63 Prob > chi2 = 0.0000 63 4304276 728141 APPENDIX 11: Hypothesis test whether the manufacturing sector having constant returns to scale - ti model: 1) [lny]lnk + [lny]lnl - chi2( 1) 45.22 Prob > chi2 0.0000 - tvd model ( 1) [lny]lnk + [lny]lnl = chi2( 1) 3.28 Prob > chi2 = 0.0702 APPENDIX 12: Hypothesis test whether ti model nested in tvd model Likelihood-ratio test (Assumption: ti nested in tvd) LR chi2(1) = Prob > chi2 - df AIC BIC -17621.92 10 35263.83 35340.25 -17364.97 11 34751.95 34836.01 Model | Obs 11(null) 11(model) ti | tvd | 15395 15395 Note: N=Obs used in calculating BIC; see [R] BIC note APPENDIX 13: Hypothesis test whether rJ=0 in tvd model 1) 513.89 0.0000 [eta]_cons = chi2( 1) = Prob > chi2 - 196.57 0.0000 64 APPENDIX 14: Results of ti and tvd models of 378 former Hanoi’s manufacturing firms in the period 2000-2004 - ti model Time-invariant inefficiency model - Number of obs — Number of groups Group variable: i 1890 378 Obs per group: - avg max - Log likelihood Coef lny | lnk | lnl | 4208586 7167271 cons | /mu | /Insigma2 | /ilgtgamma | [95% Conf Std Err .0219787 0284312 6.020406 1.973859 3.533922 1.972964 1.084867 090063 -.0418204 - Wald chi2(2) Prob > chi2 - -1863.4897 0600638 2106.99 0.0000 Intervall 463936 7724513 19.15 25.21 0.000 0.000 3.05 0.002 2.151713 9.889098 1.79 0.073 0.486 -.3330163 -.1595434 7.400861 0759025 -0.70 12.05 0.000 6610029 9083472 1.261388 sigma2 | 959042 0576037 852533 1.078857 gamma | sigma u2 | sigma_v2 | 747414 71680l4 2422406 0170027 0574433 0088739 7126618 7792649 6042145 2248481 8293883 259633 - tvd model - Time-varying decay inefficiency model Group variable: i Time variable: t Log likelihood -1805.1087 lny | lnk | lnl | _cons | /inu /eta | /lnsigma2 | /ilgtgamma | sigma2 | gamma | siqma_u2 ] sigma v2 J - Number of obs Number of groups Obs per group: 1890 378 avg = max Wald chi2(2) - - - 1479.90 0.0000 Prob > chi2 Coef Std Err .3234155 0242868 7301529 529.6796 0279339 52 634 [95% Conf 13.32 26.14 0.000 00 67 54 35 Interval] 3710168 9024 194 97 13 9 18 0.000 525.8813 526.6455 000175 0000158 0.000 0001441 0002059 1.030946 7885861 0679275 0159547 2179563 008121 0304769 1.316424 8129897 0658885 0956989 11.09 0.46 13.76 0683548 644 000 — 0986622 128857 9060487 1.17306 755628 8181688 6790168 2020394 65 1596159 1.50399 9469626 2338732 ' APPENDIX 15: Results of ti and tvd models of 833 Hochiminh city’s manufacturing firms in the period 2000-2004 - ti model: Number of obs Time-invariant inefficiency model Number of groups Group variable: i - = 4165 833 Obs per group: = max avg = Log likelihood - -4066.442 = 4107.45 Prob > chi2 - 0.0000 [95% Conf Interval] lny | Coef lnk | lnl | cons | 4350849 583741 6.137818 0143764 0180179 7192152 30.26 32.40 8.53 0.000 0.000 /mu | /lnsigma2 | /ilgtgamma | 3.124665 -.0557448 1.09398 7177722 0407693 0608903 4.35 -1.37 17.97 si9ma | 94 57 80 038 558 7491303 7085128 0114434 0384504 gamma | sigma_u2 | sigma_v2 | ' Wald chi2(2) Std Err .2372676 4069078 4632621 5484265 4.728182 6190555 7.547453 0.000 1.717857 4.531473 0.172 0.000 -.1356513 9746366 0241616 1.213322 8731471 7260427 6331515 1.024456 7708863 7838742 0.000 2257969 0058525 2487383 tvd model: Time-varying decay inefficiency model Group variable: i Time variable: t Log likelihood lny | = -3915.1078 Coef Number of obs = 4165 Number of groups = 833 Obs per group: = Wald chi2(2) - 3321.41 = 0.0000 Prob » chi2 Std Err [958 Conf Interval] lnk | 3609855 0148397 lnl | 5932986 0174288 3031636 24.33 34.04 21.26 0.000 0.000 0.000 3319003 5591388 5.850652 3900707 6274584 7.039032 2853867 9.27 0035119 0430941 063546 9.20 -2.55 18.40 0.000 0.000 0.011 2.08524 0254362 -.1941441 3.203935 0392026 -.0252184 8235392 9750969 cons | 6.444842 /mu | 2.644587 /eta | 0323194 /lnsigma2 | -.1096812 /ilgtgamma | 1.169187 sigma2 | gamma sigma u2 sigma_v2 896l197 0386175 7629981 6837376 212382l 0114912 0386702 0052841 0.000 1.044639 7397442 6079454 2020254 66 1.293735 7847787 7595298 2227388 APPENDIX 16: Results of ti and tvd models of 1,868 other provinces manufacturing firms in the period 2000-2004 - ti model: Number of obs Time-invariant inefficiency model Number of groups Group variable: i 9340 - = 1868 Obs per group: avg - Log likelihood Coef lnk | 5108214 lnl | cons | 5792951 6.290339 /mu | 4.060638 /ilgtgamma | 7978541 /lnsigma2 | sigma2 | gamma | sigma_u2 | sigma_v2 | ' 8312.06 0.0000 Prob > chi2 Std Err .0116519 0135667 1.2681 1.26732 2882112 0255523 1.334039 0340878 6895153 9198403 4141988 - Wald chi2(2) - -11465.588 lny | max - 5 0413597 [95% Conf Interval] 5336587 43.84 0.000 42.70 4.96 0.000 0.000 5527048 3.804909 3.20 0.001 1.576737 6.54454 0.000 0.000 2381296 7167906 3382928 8789175 1.268874 6718999 1.402551 7065979 4008205 4275771 11.28 19.29 0088544 0338691 0068258 6058854 8.77577 853458 9862225 tvd model: Time-varying decay inefficiency model Number of obs Number of groups i Time variable: Obs per group: = avg - Group variable: 9340 1868 - max - t Log likelihood -11367.285 lny | lnk | lnl | _cons | Wald chi2(2) Prob > chi2 - Coef - - 7635.84 0.0000 [95% Conf Interval] Std Err .4799547 5849678 6.070122 0119003 013442 2527411 40.33 43.52 24.02 0.000 0.000 0.000 66 622 27 3137 747 486 /inu | 8 37 S 00 53 0.000 3.017955 3.958795 /eta | 0179619 2495572 7894952 001724 0260966 0423517 10.42 0.000 0.000 0.000 0145829 1984088 7064873 021341 3007057 872503 sigma2 | 1.283457 0334939 1.219461 1.350812 6696245 7052663 0066287 8173299 3878023 /lnsigma2 | /ilgtgamma | qaiama I sigma_u2 | sigma v2 | 68 7 2 8826628 4007942 9.56 18.64 00 90 55 0333337 67 9479957 4137862 ' APPENDIX 17: Results of ti and tvd models of 810 State’s manufacturing firms the period 2000-2004 Time-invariant inefficiency model Number of obs Number of groups Group variable: i Obs per group: Log likelihood lny | Coef ink | lnl | 4139825 7143291 cons | 5.555988 Std Err .014516 0196425 8264937 28.52 36.37 0.000 0.000 6.72 0.000 4050 810 = avg max = Wald chi2(2) Prob > chi2 = -3722.2865 - = = 4504.25 - 0.0000 [95% Conf IntervalJ 4 333 6758306 38 55317 7528276 9360 17 588 /mu | 3.235821 824795 3.92 0.000 1.619253 /lnsigma2 | /ilgtgamma | -.0890141 1.235721 0432702 0631796 -2.06 19.56 0.040 0.000 -.1738222 1.111891 -.0042061 1.359551 sigma2 | 9148327 8404 63 9958028 6310942 19582 98 2161774 gamma | sigma_u2 | sigma_v2 | 7748183 7088291 2060036 039585 4.85239 7524 815 0110233 0396614 0051908 95 68 67 7865639 "-tvd model: , Time-varying decay inefficiency model Number of obs Number of groups Group variable: i - 4050 810 Obs per group: = Time variable: t Log likelihood -3581.7732 - avg = Wald chi2(2) - max - 3474.06 0.0000 Prob > chi2 lny | Coef lnk | 3456468 0151282 22.85 0.000 lnl | 7429418 56 68 0194884 54 651 38.12 2.94 0.000 003 19068 S 15 68 6.656491 3.223679 2.06 0.039 3381963 12.97479 cons ! /mu | Std Err .0010609 -.1790878 1.243455 7811383 sigma2 | gamma | 9242303 7998295 047294 011581 8360325 7761648 1.021733 8215674 sigma_v2 | l850037 0047297 1757337 1942737 047665 0.032 0.124 0.000 3752975 0124029 -.078794 1.385229 7392266 2.14 -1.54 19.15 7047454 Intervall /eta | /lnsigma2 | /ilgtgamma | sigma_u2 | 0057868 0511712 072335 [95% Conf .645805 68 0237449 0214998 1.527003 8326483 APPENDIX 18: Results of ti and tvd models of 624 Foreign’s manufacturing firms in the period 2000-2004 - ti model: Time-invariant inefficiency model = - Number of obs Number of groups Group variable: i 3120 624 Obs per group: = avg - Log likelihood -3418.7066 max - 5 - 2530.66 0.0000 - wald chi2(2) Prob > chi2 Coef lny | lnk | 517498 0219783 lnl | cons | 6886674 3.974811 0276141 2850278 2.191179 2230413 /mu | /lnsigma2 | /ilgtgamma | 2032021 1.103179 sigma2 | 1.22532 gamma | sigma_u2 | sigma_v2 | • 7508552 920038 3052821 [95% Conf Std Err .0604187 0874541 23.55 24.94 13.95 000 000 0 000 6345447 4744214 416167 9.82 000 54 0.001 0.000 0847836 9317718 3.36 12.61 0740323 0163602 0741083 0088528 Interval] 560574 74279 533455 62 3.3216207 32 1.274586 1.088481 1.379361 7l74346 7747884 287931 7815267 1.065288 3226332 tvd model: Time-varying decay inefficiency model Number of obs Number of groups Group variable: i Time variable: t - 3120 = 624 Obs per group: avg max - Log likelihood - = Wald chi2(2) -3318.35 Prob > chi2 - 2042.62 0.0000 [95% Conf Interval] lny | Coef lnk | lnl | cons | 4806874 6001234 4.705053 0219915 0262357 21.86 22.87 0.000 0.000 0.000 4375849 5487023 4.18627 5237899 6515445 5.223837 /mu | 1.889422 1620078 11.66 0459233 0493185 9990252 0042802 056654 0847781 10.73 0.87 11.78 0.0 0.0 92 37 2.2 69 43 1.050555 0595181 7308669 0166759 /eta | /lnsigma2 | /ilgtgamma | sigma2 | gamma | sigma_u2 | sigma_v2 | 7678158 2827391 Std Err .2646902 17.78 05957 00816 - 72 0.000 8328632 1.165187 940145 69696 6510608 1.173931 762274 8845708 2667458 69 2987324 APPENDIX 19: Results of ti and tvd models of 1,645 other sector’s manufacturing firms in the period 2000-2004 - ti model: Number of obs Time-invariant inefficiency model = = Number of groups Group variable: i Obs per group: = avg - Log likelihood -10170.127 8225 1645 5 max - = 5067.70 - Wald chi2(2) - 0.0000 Prob > chi2 lny | Coef Std Err lnk | 4623057 0l33328 cons | 6.704487 1.088427 3.931725 1.086141 lnl | 5466299 /mu | /lnsigma2 | 2935622 7730087 0437251 sigma2 | gamma | sigma_u2 | 1.341197 6841714 9176083 03610L 0094482 0357877 /ilgtgamma | sigma_v2 | , 0141404 4235883 026917 [95% Conf Interval] 39 76 74 446 34.67 38.66 0000 0 09 8377 3.62 0.000 0.000 1.802927 2408059 6.060523 5463l85 1.272274 1.413853 8474657 40904l6 987751 438135 6.16 10.91 17.68 0.000 89 52 6873092 8587083 6653681 0074219 7023907 tvd model: Time-varying decay inefficiency model Number of obs Number of groups Group variable: i lny | lnk | lnl | cons | /mu /eta /lnsigma2 /ilgtgamma | | | | sigma2 | gamma l sigma_u2 | sigma_v2 | - 8225 1645 Obs per group: - Time variable: t Log likelihood -10090.637 = avg max - Wald chi2(2) - - 44 8.3 Prob > chi2 Coef Std Err [95% Conf Interval] 802 64 0138538 0140413 36 55 51 29.74 39.72 000 000 0 000 384 9125 530194 08625 4392185 5852348 519043 3.568853 0174894 2604628 7714557 3494309 0020841 0275077 0446684 10.21 8.39 9.47 17.27 0.000 0.000 0.000 2.883981 0134046 2065487 4.253725 0215743 314377 1.29753 0356921 1.229428 1.369406 6646102 8178548 7024526 9567405 4120655 5577144 6838357 8872977 4102328 0096575 0354307 0072112 18 0.000 6839072 3960991 8590042 4243666 70 ” Appendix 20: Descriptive statistic of efficiency factors Variable i Mean overall | between | within size staetp foretp 2002 574124 492.5163 8148946 | 244.4477 -3430.017 overall | 11.06301 between | 10.16148 10.06389 1.414259 9.063007 overall | 225.6392 446.5064 | between | within | overall | between | 2.577785 | overall | between | within | 3.575912 overall | between | within | 2630724 444.5529 11 42.33237 -88.36083 1.228521 3405397 l77785 41.7722 21.18007 36.00609 024 -854.6141 4403157 4403729 0 2630724 40l9967 4020489 overall | 1227671 3281804 328223 within | overall | 2705424 between I within | 15395 n - 3079 2004 | T = 16183 | N - 15395 N = 15395 n - 3079 1540 | 11528.39 | 12897.89 | 79 13.06301 l 0 0 2026632 1227671 4442545 4443122 0 2705424 71 T - n T = T = 3079 5 395 6243 | 543.6392 | 1.180533 overall | 2026632 between | | N = 2004 | 2oo2 549.7854 between | loc2 142 15395 3079 2000 2002 2000 1540 Observations N n = 3079 | 3079 | overall | 233.7034 within locl Max | 1 between | within liq 888.8596 888.9751 1.414259 within age2 Min between | within | within age 1540 | overall | k2l Std Dev n309 T - N = n = 15395 3079 T - 4258.33 | 858.52 | N = 15395 3403.386 | T = | N = 15395 N - 15395 T - 5 I 577 85 I 2630724 | | | 2026632 | 1227671 | 270542 n = n T - n = 3079 3079 3079 Nn - 15395 3079 N - 15395 n = 3079 T - Appendix 21: Testing OLS vs REM and testing REM vs FEM • - Testing OLS vs REM xttest0 Breusch and Pagan Lagrangian multiplier test for random effects te[i,t] = Xb + u[i] + e[i,t] Var Estimated results: te | e | u | Test: Var(u) = sd = sqrt(Var) 2.856562 0858215 5711535 1.690137 2929531 755747 chi2(1) - 19437.05 Prob > chi2 = 0.0000 - Testing REM vs FEM hausman fem rem Coefficients k21 | (b) (B) fem rem —.0000309 (b-B) Difference 000052 -.000083 sqrt(diag(V_b-V_B)) S.E aqe ! 0830454 0686352 0144102 0008111 age2 | -.0008323 -.0010049 0001726 000 -.1601798 size | 4227513 5829311 liq | -.000015 -.0000217 002 6.69e-06 b - consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under No; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(5) - (b-B)' [(V_bV_B)’(-1)](b-B) (V b-V B is not positive definite) 72 Appendix 22: Test for heteroskedasticity for FEM xttest3 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)’2 - sigma’2 for all i chi2 (3079) - l.0e+07 Prob>chi2 = 0.0000 Appendix 23: The best model of determinant on technical efficiency xtreg te k2l age age2 size liq staetp foretp locl loc2, fe vce(robust) Fixed-effects (within) regression Group variable: i Number of obs Number of groups R-sq: within 0.3343 between 0.3999 = - 15395 3079 Obs per group: - 5.0 avg max - overall 0.3962 F(5,3078) corr(u_i, Xb) - 0.1986 - 39 Prob > F (Std Err adjusted for 3079 clusters in i) te | k2l | age age2 size liq staetp foretp locl loc2 _cons Coef -.0000309 Robust Std Err [95% Conf Interval] 000051 -0.61 0.544 -.0001309 | 0830454 | -.0008323 | 4227513 | -.000015 0038507 0001051 0l17802 0000529 21.57 -7.92 35.89 -0.28 0.000 0.000 0.000 0.777 0754952 -.0010383 3996534 -.0001187 0905956 -.0006263 4458492 0000888 | | | | | 0395404 274.70 0.000 10.78408 10.93914 (omitted) (omitted) (omitted) (omitted) 10.86161 sigma u | 1.3132341 sigma_e | 29295314 rho | 9525954 (fraction of variance due to u i) 73 0000691 Appendix 24: Test determinant on technical efficiency × testpam k2l age age2 size liq ( 1) k2l - ( 2) age - ( 3) age2 - ( 4) size - F( 5, 3078) - 485.39 Prob > F = 0.0000 74 ... efficiency of the firms In summarizing the technical efficiency of manufacturing firms and its determinants, according to the theoretical and empirical evidence, the analytical framework is presented in. .. caused the changes in the proportion of inputs Elina (2006) estimates the technical efficiency and determinants of inefficiency in Finnish information and communication technology (ICT) manufacturing. .. - The firms in determinants of Vietnam in technical 2002 efficiency 27 11 12 Viet and Charles (2010) Minh and Dong (2005) Data of 926 - SFPF model firms in 2002; 2,228 - The firms in 2005 determinants