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 I~ By TRAN VAN KHUE Academic Supervisor: DR NGUYEN TRONG HOAI DR PHAM LE THONG HO CHI MINH CITY, DECEMBER 2011 I I : -~ ~ ABBREVIATIONS AEC Allocative Efficiency Change DEA Data Envelopment Analysis E&E Electrical and Electronics FDI Foreign Direct Investment FEM Fixed Effects Model GDP 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 Scale Economies SEC Scale Efficiency Change SFPF Stochastic Frontier Production Function SMEs Small and Medium Enterprises SOEs State-Owned Enterprises TE Technical Efficiency TEC Technical Efficiency Change TFP Total Factor Productivity TP Technical Progress TT Time Trend III TABLE OF CONTENTS CHAPTER 1: INTRODUCTION 1.1 The problem statement 1.2 Objectives of the research S 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 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 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 3.2.1 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 IV 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 4.4.1 Testing for the most appropriate model .46 4.4.2 Testing for heteroskedasticity 47 4.4.3 Determinants of technical efficiency 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 v LIST OF TABLES & GRAPHS Table 2.1: Summary of Empirical Studies Table 3.1: Summary ofvariables 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 ofti 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 oftechnical efficiency Graph 4.1: 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 ofVietnam Figure 2.1: Illustration of Technical Efficiency Figure 2.2: Analytical Framework VII CHAPTER 1: INTRODUCTION 1.1 The 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 al., 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 carne 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 (ClEM, 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; Hochirninh 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 non-synchronous 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 microeconomic 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 Figure 1.1: The share of manufacturing enterprises in all industries of Vietnam 60 50 tn ,-= Q) ·c 40 30 Employment C'G Export 20 ';te 10 2000 2001 2002 2003 2004 2005 2006 Source: GSO 1.2 Objectives of the research Basically, this thesis aims at four objectives as follows: (1) To measure the level of technical efficiency of manufacturing firms in the period 2000 to 2004 (2) To compare the difference in technical efficiency between manufacturing firms located in former Hanoi and Hochiminh City and those located in other provinces; between firms of state-owned, foreign firms and other firms (3) To identify factors influencing the technical efficiency of manufacturing firms (4) To suggest appropriate policies for improving technical efficiency of manufacturing firms *Note: Former Hanoi: Because the data applied in the thesis from 2000 to 2004 Since August 1, 2008 Hanoi has merged with Hatay province and parts of neighboring of Vinhphuc and Hoabinh provinces • APPENDICES APPENDIX 1: Results of summary of all manufacturing firms in the period 2000-2004 Variable Mean Std Dev Min Max I Observations -+ + -y k overall I between I within I I overall I between I within I I overall I between I within I 67902.01 257335.4 244018.2 81805.12 19.4 -2127963 8467547 I 5775797 I 2759652 I 1070.73 1027.906 300.2434 -17279.47 49756 I 35192.6 I 14880.53 I 113911.9 109579.7 31166.15 10 10.8 -1081747 3433053 I 2488468 I 1058510 I N I 317.1279 N = n = T = I 27921.81 = n = T = N = n = T = 15395 3079 15395 3079 15395 3079 APPENDIX 2: Results of summary of former Hanoi's manufacturing firms in the period 2000-2004 Variable I Mean overall I between I within I I overall I between I within I 52505.08 Std Dev Min Max I Observations -+ + -y 221.6233 138473.2 129852.5 48464.9 15 47.6 -506259.5 1656817 I 1503653 I 821746.9 I N 411.7016 401.7696 91.76708 4.2 -1370.177 5549 I 4707.8 I 1121 623 I N overall I between I within I 22747.46 74032.9 71624.63 19016.97 11 53.2 -261905.1 997436 I 741554.6 I 278628.9 I = n = T = I k = n = T = N = n = T = 1890 378 1890 378 1890 378 APPENDIX 3: Results of summary ofHochiminh city's manufacturing firms in the period 2000-2004 Variable Mean Std Dev Min Max I Observations -+ + -I overall between within 84749.05 overall between within 417.1405 k overall between within 37030.51 y 274212.4 260324.9 86536.09 153 -1493375 4748254 I 3822103 I 2081994 I 1637.762 1561.198 497.2594 4 -17179.46 49756 I 35192.6 I 14980.54 I 149262.3 144113.3 39122.1 10 10.8 -1072638 3433053 I 2488468 I 981615.5 I N I N ~ = n = T = I 60 = n = T = N = n = T = 4165 833 4165 833 4165 833 - APPENDIX 4: Results of summary of other provinces' manufacturing firms in the period 2000-2004 ~ Variable Mean Std Dev Min Max I Observations -+ + -y overall between within 63505.03 267503.3 253709.8 84952.44 19.4 -2132360 8467547 I 5775797 I 2755255 I overall between within 291.855 807.8368 784.9994 191.4162 -4518.945 20028 I 13234.8 I 7085.055 I k overall between within 24907.01 101464.9 97229.9 29077.85 13 33.8 -755072.8 2444251 I 1659482 I 1055495 I I N = n = T = 9340 1868 N = n = T = 9340 1868 N = n = T = 9340 1868 APPENDIX 5: Results of summary of State's manufacturing firms in the period 2000-2004 Variable I Mean Std Dev Min Max I Observations -+ + -y I overall I between I within I I overall I between I within I I N = n = T = 4050 810 11101 I 10288.8 I 3214.111 I N = n = T = 4050 810 3433053 I 2488468 I 975529 I N = n = T = 4050 810 87121.53 263628.8 253133.2 74074.62 19.4 -952958.9 4731648 I 3822103 I 1951564 I 464.1109 885.7782 872.8569 153.2206 -1194.289 30944.01 132021.6 128895.3 28846.73 10 10.8 -813227 I ~ k overall I between I within I APPENDIX 6: Results of summary ofF oreign' s manufacturing firms in the period 2000-2004 Variable I Mean Std Dev Min Max I Observations -+ + -y k I overall I between I within I I overall I between I within I I overall I between I within I I 169699 461699.2 434221.6 157669.3 30 305.8 -2026166 8467547 I 5775797 I 2861449 I 520.6596 1965.861 1868.639 614.2256 9.2 -17075.94 49756 I 35192.6 I 15084.06 I 81711.16 191266 181725.1 60008.65 147 852.2 -1027957 2724186 I 2024304 I 1112299 I I 61 N = n = T = 3120 624 N = n = T = 3120 624 N = n = T = 3120 624 , APPENDIX 7: Results of summary of Other sector's manufacturing firms in the period 2000-2004 Variable Mean Std Dev Min Max I 49685.92 45528.58 19921.02 28.2 -349597.1 167.5474 495.271 480.9453 118.7329 -3239.253 1004170 I 485267.2 I 615872 I I 11663 I 9510 I 2676.947 6029.695 15868.91 14462.39 6539.36 53.2 -150830.9 Observations -+ + -y k overall I between I within I I overall I between I within I I overall I between I within I 19823.51 11 287106 I 213077 I 199135.9 I N = n = T = 8225 1645 N = n = T = 8225 1645 N = n = 8225 1645 T = APPENDIX 8: Results of time-invariant inefficiency model of all 3,079 manufacturing firms in the period 2000-2004 Time-invariant inefficiency model Group variable: i Log likelihood lny I = 15395 3079 Obs per group: = avg = max = 5 Wald chi2(6) Prob > chi2 -17621.917 Coef = = Number of obs Number of groups Std Err z P>lzl = 14678.43 0.0000 = [95% Conf Interval] -+ -lnk lnl locl loc2 staetp foretp cons I I I I I I I 4666343 5999489 1534861 1433019 -.0760441 1943188 6.565329 0091367 010475 0542879 0404067 0428747 0498907 1.424144 51.07 57.27 2.83 3.55 -1.77 3.89 4.61 0.000 0.000 0.005 0.000 0.076 0.000 0.000 4487266 5794184 0470839 0641063 -.160077 0965347 3.774058 484542 6204795 2598884 2224975 0079887 2921029 9.3566 -+ -/mu I /lnsigma2 I /ilgtgamma I 4.087004 1744502 8987952 1.423711 0202658 0318594 2.87 8.61 28.21 0.004 0.000 0.000 1.296582 13473 836352 6.877425 2141704 9612384 -+ -sigma2 gamma sigma_u2 sigma_v2 1.190591 7107019 8461555 3444359 0241283 0065504 0240005 0044189 1.144228 6976963 7991154 335775 62 1.238834 7233697 8931957 3530968 ~ APPENDIX 9: Results of time-varying decay inefficiency model of all 3,079 manufacturing firms in the period 2000-2004 Time-varying decay inefficiency model Group variable: i Number of obs Number of groups = Time variable: t Obs per group: avg max = Wald chi2(6) Frob > chi2 = Log likelihood lny I = -17364.974 Coef Std Err z F>lzl = = = = 15395 3079 5 13114.67 0.0000 [95% Conf Interval] -+ -lnk lnl locl I loc2 staetp I foretp cons I 4119543 6064609 1753239 1820449 -.0264891 3529977 6.540999 0094253 0103161 0552174 0411297 0435719 0511649 2308662 43.71 58.79 3.18 4.43 -0.61 6.90 28.33 0.000 0.000 0.001 0.000 0.543 0.000 0.000 3934811 5862418 0670997 1014321 -.1118885 2527163 6.08851 4304276 6266801 2835481 2626577 0589103 4532792 6.993489 -+ -/mu I /eta /lnsigma2 I /ilgtgamma I 3.548445 0204943 1371729 92086 220003 0014617 0209798 0328329 16.13 14.02 6.54 28.05 0.000 0.000 0.000 0.000 3.117247 0176294 0960532 8565087 3.979643 0233593 1782926 9852113 -+ -I I I I sigma2 gamma sigma_u2 sigma_v2 1.147026 7152173 8203731 3266533 0240644 0066875 0240048 0042121 1.100818 7019307 7733245 3183977 APPENDIX 10: Hypothesis test whether y=O - ti model : ( 1) [ilgtgamma]_cons chi2( 1) = Frob > chi2 = = 795.88 0.0000 = 786.63 0.0000 - tvd model ( 1) [ilgtgamma]_cons chi2( 1) Frob > chi2 = • 63 1.195175 728141 8674218 3349089 APPENDIX 11: Hypothesis test whether the manufacturing sector having constant returns to scale - ti model: ( 1) [lny]lnk + [lny]lnl = chi2( 1) 45.22 Frob > chi2 = 0.0000 - tvd model : ( 1) [lny]lnk + [lny]lnl = chi2 ( 1) 28 Frob > chi2 = 0.0702 APPENDIX 12: Hypothesis test whether ti model nested in tvd model LR chi2(1) Frob > chi2 Likelihood-ratio test (Assumption: ti nested in tvd) Model I Obs 11 (null) 11 (model) df AIC 513.89 0.0000 BIC -+ ti I tvd I -17621.92 -17364.97 15395 15395 Note: 10 11 N=Obs used in calculating BIC; see [R] BIC note APPENDIX 13: Hypothesis test whether 11=0 in tvd model ( 1) 35263.83 34751.95 [eta] cons chi2( 1) Frob > chi2 196.57 0.0000 64 35340.25 34836.01 • APPENDIX 14: Results ofti and tvd models of 378 former Hanoi's manufacturing firms in the period 2000-2004 - ti model Time-invariant inefficiency model Group variable: i Log likelihood = lny I = Obs per group: avg max = = Wald chi2(2) Prob > chi2 -1863.4897 Coef Number of obs Number of groups Std Err z P>lzl = = = = 1890 378 5 2106.99 0.0000 [95% Conf Interval] -+ -lnk I lnl _cons 4208586 7167271 6.020406 1 0219787 0284312 1.973859 19.15 25.21 3.05 0.000 0.000 0.002 3777812 6610029 2.151713 463936 7724513 9.889098 -+ -/mu /lnsigma2 /ilgtgamma I 3.533922 -.0418204 1.084867 1.972964 0600638 090063 1.79 -0.70 12.05 0.073 0.486 0.000 -.3330163 -.1595434 9083472 7.400861 0759025 1.261388 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 959042 747414 7168014 2422406 0576037 0170027 0574433 0088739 852533 7126618 6042145 2248481 1.078857 7792649 8293883 259633 - tvd model Time-varying decay inefficiency model Group variable: i Number of obs Number of groups = 1890 378 Time variable: t Obs per group: = avg = max = 5 Log likelihood lny I = Wald chi2(2) Prob > chi2 -1805.1087 Coef Std Err z P>lzl = = = 1479.90 0.0000 [95% Conf Interval] -+ -lnk lnl I cons I 3234155 7301529 529.6796 0242868 0279339 13.32 26.14 0.000 0.000 2758143 6754035 3710168 7849024 -+ -/mu /eta /lnsigma2 /ilgtgamma I I I I 526.2634 000175 0304769 1.316424 1949713 0000158 0658885 0956989 2699.18 11.09 0.46 13.76 0.000 0.000 0.644 0.000 525.8813 0001441 -.0986622 1.128857 526.6455 0002059 1596159 1.50399 -+ -sigma2 gamma I sigma_u2 I sigma_v2 I 1.030946 7885861 8129897 2179563 0679275 0159547 0683548 008121 9060487 755628 6790168 2020394 • 65 1.17306 8181688 9469626 2338732 ii APPENDIX 15: Results ofti and tvd models of833 Hochiminh city's manufacturing firms in the period 2000-2004 ti model: Log likelihood = lny I 4165 833 Obs per group: = avg = max = 5 = = 4107.45 0.0000 Wald chi2(2) Prob > chi2 -4066.442 Coef = = Number of obs Number of groups Time-invariant inefficiency model Group variable: i Std Err z P>lzl [95% Conf Interval] -+ -lnk lnl I _cons I 4350849 583741 6.137818 0143764 0180179 7192152 30.26 32.40 8.53 0.000 0.000 0.000 4069078 5484265 4.728182 4632621 6190555 7.547453 -+ -/mu I /lnsigma2 I /ilgtgamma 3.124665 -.0557448 1.09398 7177722 0407693 0608903 4.35 -1.37 17.97 0.000 0.172 0.000 1.717857 -.1356513 9746366 4.531473 0241616 1.213322 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 9457804 7491303 7085128 2372676 0385588 0114434 0384504 0058525 8731471 7260427 6331515 2257969 1.024456 7708863 7838742 2487383 tvd model: Time-varying decay inefficiency model Group variable: i Number of obs Number of groups = = 4165 833 Time variable: t Obs per group: = avg = max = 5 = = 3321.41 0.0000 Log likelihood lny I = Wald chi2(2) Prob > chi2 -3915.1078 Coef Std Err z P>lzl [95% Conf Interval] 0.000 0.000 0.000 3319003 5591388 5.850652 -+ -lnk I lnl I _cons I 3609855 5932986 6.444842 0148397 0174288 3031636 24.33 34.04 21.26 3900707 6274584 7.039032 -+ -/mu /eta /lnsigma2 /i1gtgamma I I I I 2.644587 0323194 -.1096812 1.169187 2853867 0035119 0430941 063546 9.27 9.20 -2.55 18.40 0.000 0.000 0.011 0.000 2.08524 0254362 -.1941441 1.044639 3.203935 0392026 -.0252184 1.293735 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 8961197 7629981 6837376 2123821 0386175 0114912 0386702 0052841 8235392 7397442 6079454 2020254 66 9750969 7847787 7595298 2227388 • APPENDIX 16: Results ofti and tvd models of 1,868 other provinces manufacturing firms in the period 2000-2004 ti model: Time-invariant inefficiency model Group variable: i Log likelihood = lny I -11465.588 Coef Std Err z Number of obs Number of groups = Obs per group: avg max = = Wald chi2(2) Prob > chi2 = P>lzl = = = 9340 1868 5 8312.06 0.0000 [95% Conf Interval] -+ -lnk I lnl I _cons I 5108214 5792951 6.290339 0116519 0135667 1.2681 43.84 42.70 4.96 0.000 0.000 0.000 4879841 5527048 3.804909 5336587 6058854 8.77577 -+ -/mu I /lnsigma2 /ilgtgamma 4.060638 2882112 7978541 1 1.26732 0255523 0413597 3.20 11.28 19.29 0.001 0.000 0.000 1.576737 2381296 7167906 6.54454 3382928 8789175 -+ -sigma2 gamma sigma_u2 sigma_v2 , ~ 1.334039 6895153 9198403 4141988 0340878 0088544 0338691 0068258 1.268874 6718999 853458 4008205 1.402551 7065979 9862225 4275771 tvd model: Time-varying decay inefficiency model Group variable: i Number of obs Number of groups Time variable: t Obs per group: avg max Log likelihood lny I = Wald chi2(2) Prob > chi2 -11367.285 Coef Std Err z P>lzl = = = = = = = 9340 1868 5 7635.84 0.0000 [95% Conf Interval] -+ -lnk lnl I _cons 4799547 5849678 6.070122 0119003 013442 2527411 40.33 43.52 24.02 0.000 0.000 0.000 4566305 558622 5.574759 503279 6113137 6.565486 -+ -/mu /eta I /lnsigma2 I /ilgtgamma I 3.488375 0179619 2495572 7894952 2400146 001724 0260966 0423517 14.53 10.42 9.56 18.64 0.000 0.000 0.000 0.000 3.017955 0145829 1984088 7064873 3.958795 021341 3007057 872503 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 1.283457 6877229 8826628 4007942 0334939 0090955 0333337 0066287 1.219461 6696245 8173299 3878023 ,, 67 1.350812 7052663 9479957 4137862 t APPENDIX 17: Results ofti and tvd models of810 State's manufacturing firms in the period 2000-2004 ti model: 4050 810 Number of obs Number of groups Time-invariant inefficiency model Group variable: i 5 Obs per group: avg max Log likelihood = lny I Coef 4504.25 0.0000 Wald chi2(2) Prob > chi2 -3722.2865 Std Err z P>lzl [95% Conf Interval] -+ -lnk I lnl I cons 4139825 7143291 5.555988 014516 0196425 8264937 28.52 36.37 6.72 0.000 0.000 0.000 3855317 6758306 3.93609 4424333 7528276 7.175886 -+ -/mu /lnsigma2 /ilgtgamma 1 3.235821 -.0890141 1.235721 824795 0432702 0631796 3.92 -2.06 19.56 0.000 0.040 0.000 1.619253 -.1738222 1.111891 4.85239 -.0042061 1.359551 -+ -sigma2 gamma I sigma_u2 sigma_v2 9148327 7748183 7088291 2060036 r· 039585 0110233 0396614 0051908 8404463 7524815 6310942 1958298 9958028 7956867 7865639 2161774 tvd model: Time-varying decay inefficiency model Group variable: i Number of obs Number of groups = Time variable: t Obs per group: avg max = Wald chi2(2) Prob > chi2 = Log likelihood lny I = -3581.7732 Coef Std Err z P>lzl = = = = 4050 810 5 3474.06 0.0000 [95% Conf Interval] -+ -lnk I lnl I _cons I 3456468 7429418 9.569684 0151282 0194884 3.254651 22.85 38.12 2.94 0.000 0.000 0.003 315996 7047454 3.190685 3752975 7811383 15.94868 -+ -/mu /eta /lnsigma2 /ilgtgamma I I I I 6.656491 0124029 -.078794 1.385229 3.223679 0057868 0511712 072335 2.06 2.14 -1.54 19.15 0.039 0.032 0.124 0.000 3381963 0010609 -.1790878 1.243455 12.97479 0237449 0214998 1.527003 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 9242303 7998295 7392266 1850037 047294 011581 047665 0047297 8360325 7761648 645805 1757337 a 68 1.021733 8215674 8326483 1942737 i APPENDIX 18: Results ofti and tvd models of624 Foreign's manufacturing firms in the period 2000-2004 ti model: 3120 624 Number of obs Number of groups Time-invariant inefficiency model Group variable: i Obs per group: avg max Log likelihood = lny I 2530.66 0.0000 Wald chi2(2) Prob > chi2 -3418.7066 Coef 5 Std Err z P>lzl [95% Conf Interval] -+ -lnk lnl I _cons 517498 6886674 3.974811 0219783 0276141 2850278 23.55 24.94 13.95 0.000 0.000 0.000 4744214 6345447 3.416167 5605746 74279 4.533455 -+ -/mu I /lnsigma2 I /ilgtgamma I 2.191179 2032021 1.103179 2230413 0604187 0874541 9.82 3.36 12.61 0.000 0.001 0.000 2.628332 3216207 1.274586 754026 0847836 9317718 -+ -sigma2 gamma I sigma_u2 sigma_v2 1.22532 7508552 920038 3052821 1 • 0740323 0163602 0741083 0088528 1.088481 7174346 7747884 287931 1.379361 7815267 1.065288 3226332 tvd model: Time-varying decay inefficiency model Group variable: i Number of obs Number of groups = = 3120 624 Time variable: t Obs per group: = avg = max = 5 Log likelihood lny I = Wald chi2(2) Prob > chi2 -3318.35 Coef Std Err z P>lzl = = 2042.62 0.0000 [95% Conf Interval] -+ -lnk lnl I _cons I 4806874 6001234 4.705053 0219915 0262357 2646902 21.86 22.87 17.78 0.000 0.000 0.000 4375849 5487023 4.18627 5237899 6515445 5.223837 -+ -/mu /eta /lnsigma2 /ilgtgamma I I I I 1.889422 0459233 0493185 9990252 1620078 0042802 056654 0847781 11.66 10.73 0.87 11.78 0.000 0.000 0.384 0.000 1.571892 0375342 -.0617212 8328632 2.206951 0543125 1603582 1.165187 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 1.050555 7308669 7678158 2827391 0595181 0166759 05957 00816 940145 69696 6510608 2667458 a 69 1.173931 762274 8845708 2987324 ~ APPENDIX 19: Results ofti and tvd models of 1,645 other sector's manufacturing firms in the period 2000-2004 ti model: Time-invariant inefficiency model Group variable: i Log likelihood = lny I = Obs per group: avg max = Wald chi2(2) Prob > chi2 -10170.127 Coef Number of obs Number of groups Std Err z P>lzl = = = = = 8225 1645 5 5067.70 0.0000 [95% Conf Interval] -+ -lnk I lnl _cons 4623057 5466299 6.704487 1 0133328 0141404 1.088427 34.67 38.66 6.16 0.000 0.000 0.000 4361739 5189152 4.571209 4884376 5743446 8.837765 -+ -/mu I /lnsigma2 I /ilgtgamma 3.931725 2935622 7730087 1.086141 026917 0437251 3.62 10.91 17.68 0.000 0.000 0.000 6.060523 3463185 8587083 1.802927 2408059 6873092 -+ -sigma2 gamma sigma_u2 sigma_v2 ~ I I I I 1.341197 6841714 9176083 4235883 036101 0094482 0357877 0074219 1.272274 6653681 8474657 4090416 1.413853 7023907 987751 438135 tvd model: Time-varying decay inefficiency model Group variable: i Number of obs Number of groups Time variable: t Obs per group: avg max Log likelihood lny I Wald chi2(2) Prob > chi2 = -10090.637 Coef Std Err z P>lzl = = = = = = = 8225 1645 5 4488.30 0.0000 [95% Conf Interval] -+ -lnk lnl _cons 1 4120655 5577144 6.802646 0138538 0140413 3655151 29.74 39.72 18.61 0.000 0.000 0.000 3849125 530194 6.08625 4392185 5852348 7.519043 -+ -/mu I /eta /lnsigma2 /ilgtgamma 1 3.568853 0174894 2604628 7714557 3494309 0020841 0275077 0446684 10.21 8.39 9.47 17.27 0.000 0.000 0.000 0.000 2.883981 0134046 2065487 6839072 4.253725 0215743 314377 8590042 -+ -sigma2 gamma sigma_u2 sigma_v2 I I I I 1.29753 6838357 8872977 4102328 0356921 0096575 0354307 0072112 1.229428 6646102 8178548 3960991 • 70 1.369406 7024526 9567405 4243666 :- Appendix 20: Descriptive statistic of efficiency factors Variable Min Mean Std Dev 1540 888.8596 888.9751 1540 2002 414259 414259 2000 2002 2000 233.7034 549.7854 492.5163 244.4477 574124 8148946 -3430.017 11.06301 10.16148 10.06389 1.414259 9.063007 Max I Observations -+ + -i t k21 age •' ~ overall I between I within I I overall I between I within I I overall I between I within I I overall I between I within 1 age2 overall between within 225.6392 446.5064 444.5529 42.33237 11 -88.36083 size overall between within 2.577785 1.228521 1.180533 3405397 1 177785 liq overall between within 3.575912 41.7722 21.18007 36.00609 024 -854.6141 staetp overall between within 2630724 4403157 4403729 0 2630724 foretp overall between within 2026632 4019967 4020489 0 2026632 loc1 overall between within 1227671 3281804 328223 0 1227671 loc2 overall between 2705424 4442545 4443122 0 2705424 within • 71 3079 I 3079 I 1540 I I 2004 I 2002 I 2004 I I 16183 I 11528.39 I 12897.89 I I 81 I 79 I 13.06301 I I 6561 I 6243 I 543.6392 I I I I 5.577785 I I 4258.33 I 858.52 I 3403.386 I I I I 2630724 I N = n = T = 15395 3079 N = n = T = 15395 3079 N = n = T = 15395 3079 N = n = T = 15395 3079 N = n = T = 15395 307 N = n = T = 15395 3079 N = n = T = 15395 3079 N = n = T = 15395 307 I I 2026632 I I I I 1227671 I I I I N = n = T = 15395 3079 N = n = T = 15395 3079 N = n = 15395 3079 2705424 T = Appendix 21: Testing OLS vs REM and testing REM vs FEM - Testing OLS vs REM xttestO Breusch and Pagan Lagrangian multiplier test for random effects = Xb + u[i] + e[i,t] te[i,t] Estimated results: Var I sd = sqrt(Var) -+ - Test: te e I u Var(u) 856562 • 0858215 5711535 690137 2929531 755747 chi2(1) Prob > chi2 19437.05 0.0000 -Testing REM vs FEM hausman fern rem Coefficients • (b) (B) fern rem (b-B) sqrt(diag(V_b-V_B)) Difference S.E -+ -k21 -.0000309 000052 -.000083 age 0830454 0686352 0144102 0008111 age2 -.0008323 -.0010049 0001726 0000565 size 4227513 5829311 -.1601798 0024743 1iq -.000015 -.0000217 6.69e-06 b = consistent under Ho and Ha; obtained from xtreg B Test: Ho: inconsistent under Ha, efficient under Ho; obtained from xtreg difference in coefficients not systematic chi2(5) (b-B) I [(V_b-V_B)A(-1)] (b-B) 3996.32 Prob>chi2 0.0000 (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 chi2 (3079) Prob>chi2 l.Oe+07 = 0.0000 Appendix 23: The best model of determinant on technical efficiency xtreg te k2l age age2 size liq staetp foretp loc1 loc2, fe vce(robust) Fixed-effects (within) regression Number of obs Group variable: i Number of groups R-sq: 15395 3079 within 0.3343 Obs per group: between 0.3999 avg 5.0 overall 0.3962 max corr(u_i, Xb) 0.1986 F(5,3078) 485.39 Prob > F 0.0000 (Std Err adjusted for 3079 clusters in i) • Robust te Coef Std Err t P>ltl [95% Conf Interval] -+ -k2l -.0000309 000051 -0.61 0.544 -.0001309 0000691 age 0830454 0038507 21.57 0.000 07 54 952 0905956 age2 -.0008323 0001051 -7.92 0.000 -.0010383 -.0006263 size 4227513 0117802 35.89 0.000 3996534 4458492 0000529 -0.28 0.777 - 0001187 0000888 liq -.000015 staetp (omitted) foretp (omitted) loc1 (omitted) 1oc2 (omitted) 10.93914 10.78408 274.70 0.000 10.86161 0395404 - cons -+ • • sigma- u 1.3132341 sigma - e 29295314 rho 9525954 (fraction of variance due to u - i) 73 Appendix 24: Test determinant on technical efficiency testparm k21 age age2 size liq 1) k21 =0 2) age =0 3) age2 =0 4) size =0 5) liq =0 F( 5, 3078) Prob > F = => 485.39 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. .. model and technical efficiency model to analyze the technical efficiency of manufacturing firms and try to find the determinants that affect firms'' technical efficiency Figure 1.1: The share of manufacturing. .. 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