Determinants underlying incidence and amount of bribery an evidence from 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 DETERMINANTS UNDERLYING INCIDENCE AND AMOUNT OF BRIBERY: AN EVIDENCE FROM MANUFACTURING FIRMS IN VIETNAM BY VU THI THUONG MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, November 2015 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 DETERMINANTS UNDERLYING INCIDENCE AND AMOUNT OF BRIBERY: AN EVIDENCE FROM MANUFACTURING FIRMS IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By VU THI THUONG Academic Supervisor: DR LE VAN CHON HO CHI MINH CITY, November 2015 Declaration “I declare that this thesis entitled “Factors influencing the propensity to bribe and size of bribe payments: evidence from manufacturing firms in Vietnam”, which is submitted by me in fulfillment of the requirements for the degree of Master of Arts in Development Economics to the Vietnam – The Netherlands Programme (VNP) The thesis was done only my original work and under the guidance of my supervision and acknowledgement has been made in the text to all materials used.” Vu Thi Thuong Acknowledgments This thesis could not have been completed without the support and encouragement that I have received from many people First of all, I would like to express my deepest gratitude to my advisor, Dr Le Van Chon for his enthusiastic guidance and mentorship He has oriented and supported me to break a deadlock in times of finding new ideas for a new topic He also always listens to my ideas and discussions with me to figure out the way to solve problems Moreover, he provided me with essential skills for doing research, especially the skills of using econometric tools Besides, I truly appreciate the time he devotes to this thesis to carefully review my final thesis draft and help me rearrange thesis structure reasonably as well as correct errors and inappropriate words usages It must take me a longer time to complete this project without his invaluable sources of help From my sincere heart, I would like to thank all lecturers in VNP who equipped me with useful knowledge to conduct this thesis as well as to serve my job in the future Especially, I would like to express my special thanks to Prof Nguyen Trong Hoai, Dr Pham Khanh Nam and Dr Truong Dang Thuy who always travel with me during the two years of my Master program, share their practical profound insights and inspire the love of scientific research Next, I would also like to thank all my friends here at VNP, especially Vu Thi Khanh, Le Huu Nhat Quang for their enthusiasm of sharing data processing techniques with me; Bui Minh Khoi, Nguyen Huong Nguyen for their spiritual encouragement during these stressful and difficult moment I also want to say thanks to VNP officers for their restless assistance I would like to express my special thanks to Da Nang University Campus in Kon Tum who has given me advantages to accomplish my Master program I also want to thank my colleague – Nguyen Thi Phuong Thao for her great support me during my writing of this thesis I am deeply and forever indebted to my parents and my sister for their love, support and encouragement throughout my entire life Abbreviations CIEM Central Institute for Economic Management CPI Corruption Perceptions Index OLS Ordinary Least Squares PCI Provincial Competitiveness Index SMEs Small and medium enterprises TI Transparency International WB The World Bank UNDP United Nations Development Programme Abstract Using a panel data set from the Small and Medium Enterprise (SME) Surveys from 2005 2013, this study investigates the factors which influence the incidence of bribery and the size of bribe payment among formal and informal firms in Vietnam Due to censored nature of the data on bribes and sample selection bias, this paper applies Heckman two step procedure which was proposed by Heckman (1979) to correct these problems This study finds strong evidence that the propensity to bribe as well as the variation in the amount of bribe is highly positively correlated with the interaction level with public officials, the firm‟s ability to pay and the burden of regulation that firms face In this paper, the interaction with public officials is looked at different types of interaction Besides, this study also points out that company without official business registration licenses are more likely to avoid paying the informal costs These results are robust when the lagged values of profit are used as instruments for profit Keywords: firms, bribery, Vietnam Table of contents Chapter 1: Introduction 1.1 Problem statement 1.2 Research objectives 1.3 Research questions 1.4 The scope of the study 1.5 The structure of the study Chapter 2: Literature review 2.1 Corruption 2.1.1 The definition of corruption 2.1.2 The Forms of Corruption 2.1.3 Measuring corruption 2.2 Empirical studies 11 2.2.1 Factors influencing the propensity to bribe 11 2.2.2 How much must graft-paying firms pay? 16 2.3 Basic framework to estimate the incidence and level of bribe 16 2.4 Conceptual framework 18 2.5 Chapter summary 18 Chapter 3: Corruption in Vietnam 20 Chapter 4: Data and Econometric Model 26 4.1 Data 26 4.1.1 Data source 26 4.1.2 Data description 27 4.2 Descriptive statistical analyses 28 4.3 Econometric model 33 Chapter 5: Empirical results 37 5.1 Factors influencing the propensity to bribe 39 5.2 Factors influencing the size of bribe payment 42 5.3 Robustness 43 Chapter 6: Conclusion 47 6.1 Main findings 47 6.2 Policy implications 47 6.3 Limitations and further research 48 6.3.1 Limitations 48 6.3.2 Directions for further studies 49 References 49 Appendices 55 List of tables Table 1: Definitions of corrupt activities Table 2: Summary of features of measures of corruption Table 1: Vietnam‟s annual CPI result 21 Table 2: Average cost of bribes paid, by sector 23 Table 1: Panel data structure 26 Table 2: Data definition 27 Table 3: The descriptive statistics of the size of bribe payment by location 28 Table 4: The descriptive statistics of the size of bribe payment by the legal ownership form and sector 29 Table 5: The purpose of bribe payment 30 Table 6: The summary statistics of key variables 30 Table 7: Pairwise Correlation 32 Table 8: The Expected Variables in Heckman two step 36 Table 1: Heckman two-step regression analyses the incidence of bribery and bribe amount 38 Table 2: Heckman two-step regression analyses the incidence of bribery and bribe amount, using instrument variable 44 List of figures Figure The most serious Economic & Social Issues for Vietnam 20 Figure Perceptions of the prevalence of corruption across sectors 21 Figure 3 Key Indicators of Informal Charges (2006 to 2014) 22 Figure The purpose of bribe payment 23 Figure Awareness about the government's anti-corruption efforts 24 Figure Willingness to report an incident of corruption (Southeast Asia) 25 List of appendix Appendix 1: Heckman two-step regression on the incidence of bribery and the size of bribe 55 Appendix 2: Heckman two-step regression on the incidence of bribery and the size of bribe (corrects errors for heteroscedasticity) 57 Appendix 3: Heckman two-step regression on the incidence of bribery and the size of bribe (using instrument variable) 59 Appendix 4: Heckman two-step regression on the incidence of bribery and the size of bribe (correct heteroscedasticity and using instrument variable) 61 Appendix 5: Heteroskedasticity test using Breusch-Pagan / Cook-Weisberg - the Heckman two-stage model 63 Appendix 6: Heteroskedasticity test using Breusch-Pagan / Cook-Weisberg - the Heckman two-stage model when using instrument variable 63 to lack of the information about firm‟s competitiveness, this paper cannot examine whether a number of competitors for the firm‟s principal product have an effect on corruption 6.3.2 Directions for further studies Deriving from the mentioned limitations; there are some suggestions for further studies Firstly, it is necessary to examine the effect of firm‟s competitor‟s paying informal cost on firm‟s profitability and propensity to pay a bribe This may contribute to literature about the supply of bribe Secondly, the investigation of the impact of informal payment on future profitability and competitiveness of companies would allow us to have a better interpretation of the impact of profit on the incidence of bribery and the size of bribe payment Besides, the Heckman Model is especially sensitive to the choice of variables included in the selection equation (Briggs, 2004) Hopefully, there will be a better model to solve the issue of sample selection bias in the future 49 References Ades, A., & Di Tella, R (1999) Rents, competition, and corruption American economic review, 982-993 Ades, A., & Tella, R 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Arbitrariness kills (No w6255) National Bureau of Economic Research World Bank (2009): Anticorruption, http://go.worldbank.org/K6AEEPROC0 World Bank (2013) Corruption from the Perspective of Citizens, Firms, and Public Officials - Results of Sociological Surveys National Political Publishing House, Hanoi Retrieved from http://towardstransparency.vn/wp-content/uploads/2014/07/GCB-Full- Report_FINAL.8.7.2013_VN.pdf 54 Appendices Appendix 1: Heckman two-step regression on the incidence of bribery and the size of bribe Heckman selection model two-step estimates Number of obs = 7139 (regression model with sample selection) Censored obs Uncensored obs = = 5236 1903 Wald chi2(23) Prob > chi2 = = 270.17 0.0000 -| Coef Std Err z P>|z| [95% Conf Interval] -+ -ebribe_ln | eprofit_ln | 3096538 0310097 9.99 0.000 2488759 3704316 sunkcost_ln | 0137625 0103616 1.33 0.184 -.0065459 0340709 instate_eln | 0560113 0089144 6.28 0.000 0385395 0734832 tax_percentage | 0691537 0137358 5.03 0.000 0422319 0960754 regulations | 0035161 0019079 1.84 0.065 -.0002234 0072555 estate_ln | 0288157 0095338 3.02 0.003 0101297 0475016 eimp_ln | -.0096063 0140449 -0.68 0.494 -.0371338 0179212 eexp_ln | 0014625 0112637 0.13 0.897 -.020614 023539 informalr | -.7984671 1401055 -5.70 0.000 -1.073069 -.5238653 gov_ass | 2457989 0884271 2.78 0.005 0724849 4191129 foods | -.7766669 232093 -3.35 0.001 -1.231561 -.321773 textiles | -1.05734 2792185 -3.79 0.000 -1.604598 -.510082 wearing | -.912864 2738085 -3.33 0.001 -1.449519 -.3762092 leather | -.9060265 2298544 -3.94 0.000 -1.356533 -.4555202 wood | -.4425156 2370452 -1.87 0.062 -.9071155 0220844 paper | -.2412782 2717521 -0.89 0.375 -.7739027 2913462 printing | -.1417555 2712699 -0.52 0.601 -.6734348 3899238 chemicals | 0835925 2707723 0.31 0.758 -.4471114 6142964 rubber_plas | -.2236627 2496569 -0.90 0.370 -.7129812 2656559 non_metallic | -.6300206 2609058 -2.41 0.016 -1.141387 -.1186546 fabricated | -.4592049 2268947 -2.02 0.043 -.9039104 -.0144994 machi_equip | 3083286 2354916 1.31 0.190 -.1532265 7698837 furniture | -.0920933 2378933 -0.39 0.699 -.5583555 3741689 _cons | 0771708 4221353 0.18 0.855 -.7501992 9045408 -+ -bribe | eexp_ln | -.0183494 006753 -2.72 0.007 -.0315851 -.0051138 eimp_ln | -.0061714 008599 -0.72 0.473 -.0230252 0106823 regulations | 0028176 0008815 3.20 0.001 0010899 0045454 estate_ln | 0184929 0052906 3.50 0.000 0081235 0288622 instate_eln | 014512 0046716 3.11 0.002 0053558 0236683 informalr | -.5669477 0462326 -12.26 0.000 -.6575619 -.4763335 employment_ln | 3509695 021133 16.61 0.000 3095496 3923894 gov_ass | -.0238107 0461862 -0.52 0.606 -.114334 0667126 sunkcost_ln | 0115754 0057141 2.03 0.043 000376 0227748 eprofit_ln | 1343599 0174865 7.68 0.000 100087 1686328 tax_percentage | 0264855 0074203 3.57 0.000 0119419 0410291 foods | -.5111635 1173744 -4.35 0.000 -.7412132 -.2811139 textiles | -.6195506 1431126 -4.33 0.000 -.9000462 -.3390551 wearing | -.5493971 1434945 -3.83 0.000 -.8306412 -.268153 leather | -.2741424 1181033 -2.32 0.020 -.5056206 -.0426642 wood | -.388044 1231304 -3.15 0.002 -.6293751 -.1467129 paper | -.3457245 147539 -2.34 0.019 -.6348957 -.0565533 55 printing | -.3061911 1458867 -2.10 0.036 -.5921238 -.0202584 chemicals | -.0929435 1442997 -0.64 0.520 -.3757657 1898788 rubber_plas | -.4552019 1334526 -3.41 0.001 -.7167642 -.1936397 non_metallic | -.4359481 1380127 -3.16 0.002 -.706448 -.1654482 fabricated | -.3702301 1182907 -3.13 0.002 -.6020756 -.1383846 machi_equip | 1133831 1268782 0.89 0.372 -.1352936 3620597 furniture | -.0025583 1276278 -0.02 0.984 -.2527042 2475876 _cons | -2.433177 2032883 -11.97 0.000 -2.831615 -2.03474 -+ -mills | lambda | 1.575631 1641866 9.60 0.000 1.253831 1.897431 -+ -rho | 0.88797 sigma | 1.7744197 56 Appendix 2: Heckman two-step regression on the incidence of bribery and the size of bribe (corrects errors for heteroscedasticity) Heckman selection model (regression model with sample selection) Log pseudolikelihood = -6572.269 Number of obs Censored obs Uncensored obs Wald chi2(23) Prob > chi2 = = = = = 7139 5236 1903 278.24 0.0000 (Std Err adjusted for 1955 clusters in id) -| Robust | Coef Std Err z P>|z| [95% Conf Interval] -+ -ebribe_ln | eprofit_ln | 2949298 0530753 5.56 0.000 1909042 3989554 sunkcost_ln | 0120249 0114757 1.05 0.295 -.0104671 034517 instate_eln | 0515046 0088462 5.82 0.000 0341663 0688429 tax_percentage | 0622559 0144024 4.32 0.000 0340276 0904841 regulations | 0023955 0017498 1.37 0.171 -.0010341 005825 estate_ln | 0227754 0104432 2.18 0.029 002307 0432438 eimp_ln | -.0122819 0166737 -0.74 0.461 -.0449617 0203979 eexp_ln | -.002478 0123732 -0.20 0.841 -.0267291 0217731 informalr | -.5910952 1023991 -5.77 0.000 -.7917938 -.3903966 gov_ass | 2155655 0879815 2.45 0.014 0431249 3880061 foods | -.696418 2149552 -3.24 0.001 -1.117722 -.2751137 textiles | -1.002008 2618025 -3.83 0.000 -1.515131 -.4888842 wearing | -.8587136 2516049 -3.41 0.001 -1.35185 -.365577 leather | -.8856976 2205103 -4.02 0.000 -1.31789 -.4535054 wood | -.4107226 2191212 -1.87 0.061 -.8401923 0187471 paper | -.254062 275038 -0.92 0.356 -.7931266 2850026 printing | -.1224595 2397702 -0.51 0.610 -.5924003 3474814 chemicals | 0609792 2603722 0.23 0.815 -.4493408 5712993 rubber_plas | -.1853487 2487343 -0.75 0.456 -.6728591 3021616 non_metallic | -.6165925 2425263 -2.54 0.011 -1.091935 -.1412497 fabricated | -.4016482 2126667 -1.89 0.059 -.8184673 0151709 machi_equip | 2928165 2193289 1.34 0.182 -.1370602 7226932 furniture | -.1041654 2174173 -0.48 0.632 -.5302954 3219646 _cons | 6283917 5119354 1.23 0.220 -.3749833 1.631767 -+ -bribe | eexp_ln | -.019555 0072804 -2.69 0.007 -.0338242 -.0052857 eimp_ln | -.0097871 0106172 -0.92 0.357 -.0305963 0110222 regulations | 0028938 0007707 3.75 0.000 0013833 0044043 estate_ln | 0202486 0053778 3.77 0.000 0097084 0307888 instate_eln | 0120282 0049878 2.41 0.016 0022523 021804 informalr | -.5423607 0457624 -11.85 0.000 -.6320534 -.452668 employment_ln | 384042 0194993 19.70 0.000 345824 42226 gov_ass | -.0146338 045682 -0.32 0.749 -.1041689 0749014 sunkcost_ln | 0112819 0068576 1.65 0.100 -.0021587 0247224 eprofit_ln | 1436064 0233009 6.16 0.000 0979374 1892753 tax_percentage | 0265839 0074701 3.56 0.000 0119428 0412249 foods | -.4550889 1207823 -3.77 0.000 -.6918179 -.2183598 textiles | -.5923944 1443172 -4.10 0.000 -.8752509 -.3095379 wearing | -.5268295 1493445 -3.53 0.000 -.8195393 -.2341198 leather | -.2270549 1195385 -1.90 0.058 -.461346 0072361 wood | -.3459677 1230163 -2.81 0.005 -.5870753 -.1048602 paper | -.3200471 1449899 -2.21 0.027 -.6042222 -.035872 printing | -.2756782 1479138 -1.86 0.062 -.565584 0142276 chemicals | -.0881631 1432293 -0.62 0.538 -.3688873 1925611 rubber_plas | -.4310629 1406373 -3.07 0.002 -.706707 -.1554188 non_metallic | -.4058361 1415601 -2.87 0.004 -.6832889 -.1283834 57 fabricated | -.32898 1199554 -2.74 0.006 -.5640884 -.0938717 machi_equip | 1349563 1274952 1.06 0.290 -.1149296 3848422 furniture | 0158357 1248862 0.13 0.899 -.2289368 2606082 _cons | -2.606149 2395696 -10.88 0.000 -3.075697 -2.136601 -+ -/athrho | 1.04534 0715835 14.60 0.000 9050389 1.185641 /lnsigma | 4646773 0340876 13.63 0.000 3978668 5314877 -+ -rho | 779988 0280334 7187425 829222 sigma | 1.591501 0542504 1.488646 1.701462 lambda | 1.241351 0820975 1.080443 1.402259 -Wald test of indep eqns (rho = 0): chi2(1) = 213.25 Prob > chi2 = 0.0000 58 Appendix 3: Heckman two-step regression on the incidence of bribery and the size of bribe (using instrument variable) Heckman selection model two-step estimates (regression model with sample selection) Number of obs Censored obs Uncensored obs = = = 7139 5236 1903 Wald chi2(23) Prob > chi2 = = 310.02 0.0000 -| Coef Std Err z P>|z| [95% Conf Interval] -+ -ebribe_ln | eprofitln1hat | 8850884 0818181 10.82 0.000 7247278 1.045449 sunkcost_ln | 006388 0097191 0.66 0.511 -.0126611 0254371 instate_eln | 0248723 0092154 2.70 0.007 0068104 0429342 tax_percentage | 0602692 0126307 4.77 0.000 0355134 085025 regulations | 0028043 001803 1.56 0.120 -.0007295 006338 estate_ln | 0350587 0089199 3.93 0.000 017576 0525414 eimp_ln | -.021355 0129937 -1.64 0.100 -.0468221 0041121 eexp_ln | 0100326 0104895 0.96 0.339 -.0105264 0305916 informalr | -.617875 1314774 -4.70 0.000 -.8755659 -.360184 gov_ass | 339119 0840563 4.03 0.000 1743716 5038663 foods | -.5024561 2163863 -2.32 0.020 -.9265654 -.0783467 textiles | -.5725753 2646433 -2.16 0.030 -1.091267 -.053884 wearing | -.511341 2577811 -1.98 0.047 -1.016583 -.0060993 leather | -.6290291 2151266 -2.92 0.003 -1.050669 -.2073886 wood | 0172384 2241995 0.08 0.939 -.4221845 4566612 paper | -.123882 2535467 -0.49 0.625 -.6208243 3730603 printing | -.0109621 2531885 -0.04 0.965 -.5072024 4852781 chemicals | 1618052 2533729 0.64 0.523 -.3347964 6584069 rubber_plas | -.0948224 2328791 -0.41 0.684 -.551257 3616122 non_metallic | -.2095785 2473813 -0.85 0.397 -.6944369 2752799 fabricated | -.2145171 2120087 -1.01 0.312 -.6300466 2010123 machi_equip | 502201 2203836 2.28 0.023 070257 934145 furniture | 0626513 2225813 0.28 0.778 -.3736 4989026 _cons | -4.673835 7720103 -6.05 0.000 -6.186948 -3.160723 -+ -bribe | eexp_ln | -.0161249 0067419 -2.39 0.017 -.0293388 -.002911 eimp_ln | -.012622 0087161 -1.45 0.148 -.0297054 0044613 regulations | 0027611 0008816 3.13 0.002 0010332 004489 estate_ln | 0232808 005347 4.35 0.000 0128009 0337607 instate_eln | 001128 0054475 0.21 0.836 -.009549 0118049 informalr | -.5491197 0465152 -11.81 0.000 -.6402879 -.4579515 employment_ln | 3876285 0228593 16.96 0.000 3428251 4324319 gov_ass | 0178335 0467811 0.38 0.703 -.0738558 1095229 sunkcost_ln | 0093017 0057498 1.62 0.106 -.0019677 0205711 eprofitln1hat | 4218776 054906 7.68 0.000 3142639 5294913 tax_percentage | 0248367 0073873 3.36 0.001 0103578 0393156 foods | -.4334766 1177843 -3.68 0.000 -.6643295 -.2026236 textiles | -.4521657 1448781 -3.12 0.002 -.7361215 -.1682099 wearing | -.4136038 1447411 -2.86 0.004 -.6972911 -.1299165 leather | -.1705656 118513 -1.44 0.150 -.4028468 0617156 wood | -.207245 1258005 -1.65 0.099 -.4538093 0393194 paper | -.3255377 1475417 -2.21 0.027 -.6147142 -.0363612 printing | -.2737196 1458672 -1.88 0.061 -.5596141 012175 chemicals | -.0670686 1443445 -0.46 0.642 -.3499785 2158414 rubber_plas | -.4389888 1334008 -3.29 0.001 -.7004495 -.1775281 59 non_metallic | -.2806446 1400248 -2.00 0.045 -.5550881 -.0062012 fabricated | -.2903278 1188004 -2.44 0.015 -.5231724 -.0574833 machi_equip | 2177297 1275445 1.71 0.088 -.032253 4677124 furniture | 0730714 1281066 0.57 0.568 -.1780128 3241557 _cons | -5.067833 5190649 -9.76 0.000 -6.085182 -4.050485 -+ -mills | lambda | 1.25517 1505806 8.34 0.000 9600375 1.550302 -+ -rho | 0.79044 sigma | 1.5879429 60 Appendix 4: Heckman two-step regression on the incidence of bribery and the size of bribe (correct heteroscedasticity and using instrument variable) Heckman selection model Number of obs = 7139 (regression model with sample selection) Censored obs Uncensored obs = = 5236 1903 Wald chi2(23) Prob > chi2 = = 280.57 0.0000 Log pseudolikelihood = -6567.933 (Std Err adjusted for 1955 clusters in id) -| Robust | Coef Std Err z P>|z| [95% Conf Interval] -+ -ebribe_ln | eprofitln1hat | 8818281 1457241 6.05 0.000 596214 1.167442 sunkcost_ln | 0057808 0113289 0.51 0.610 -.0164234 027985 instate_eln | 0234901 0110742 2.12 0.034 0017852 0451951 tax_percentage | 0581756 0139427 4.17 0.000 0308485 0855027 regulations | 0022778 0017191 1.33 0.185 -.0010915 0056471 estate_ln | 0322493 0104475 3.09 0.002 0117725 0527261 eimp_ln | -.0217534 0161482 -1.35 0.178 -.0534032 0098965 eexp_ln | 0080388 0122342 0.66 0.511 -.0159398 0320174 informalr | -.5471651 1051576 -5.20 0.000 -.7532702 -.3410599 gov_ass | 3198313 0881136 3.63 0.000 1471318 4925307 foods | -.5042254 2094418 -2.41 0.016 -.9147239 -.093727 textiles | -.5782828 2659665 -2.17 0.030 -1.099568 -.0569981 wearing | -.5123877 2526717 -2.03 0.043 -1.007615 -.0171603 leather | -.6478846 2159862 -3.00 0.003 -1.07121 -.2245595 wood | 0032901 2240275 0.01 0.988 -.4357957 442376 paper | -.1493191 2686552 -0.56 0.578 -.6758737 3772354 printing | -.0242165 2348832 -0.10 0.918 -.4845792 4361461 chemicals | 1473632 2534488 0.58 0.561 -.3493872 6441137 rubber_plas | -.1010095 2425579 -0.42 0.677 -.5764142 3743953 non_metallic | -.2285925 2476707 -0.92 0.356 -.7140182 2568332 fabricated | -.2121181 2090807 -1.01 0.310 -.6219087 1976725 machi_equip | 4880634 2148549 2.27 0.023 0669555 9091712 furniture | 0483837 2115383 0.23 0.819 -.3662238 4629911 _cons | -4.479822 1.280195 -3.50 0.000 -6.988958 -1.970687 -+ -bribe | eexp_ln | -.0158691 0072776 -2.18 0.029 -.0301329 -.0016053 eimp_ln | -.015786 0106601 -1.48 0.139 -.0366794 0051073 regulations | 0028626 000771 3.71 0.000 0013514 0043737 estate_ln | 0252141 0054655 4.61 0.000 0145019 0359263 instate_eln | -.0016981 0060798 -0.28 0.780 -.0136143 010218 informalr | -.535521 0461881 -11.59 0.000 -.6260481 -.4449939 employment_ln | 4078376 0214447 19.02 0.000 3658066 4498685 gov_ass | 0317423 0467687 0.68 0.497 -.0599227 1234073 sunkcost_ln | 0087916 0068666 1.28 0.200 -.0046667 0222499 eprofitln1hat | 44813 0707831 6.33 0.000 3093977 5868623 tax_percentage | 0251381 0074013 3.40 0.001 0106318 0396443 foods | -.383215 1209359 -3.17 0.002 -.6202449 -.1461851 textiles | -.4126085 1471962 -2.80 0.005 -.7011079 -.1241092 wearing | -.3810781 1519294 -2.51 0.012 -.6788543 -.0833019 leather | -.125751 1200564 -1.05 0.295 -.3610573 1095553 wood | -.1587594 1271418 -1.25 0.212 -.4079528 0904339 paper | -.2967856 1454504 -2.04 0.041 -.5818632 -.011708 printing | -.2433042 1482412 -1.64 0.101 -.5338517 0472433 chemicals | -.0594006 1438301 -0.41 0.680 -.3413024 2225013 61 rubber_plas | -.4150369 1407839 -2.95 0.003 -.6909683 -.1391056 non_metallic | -.2397216 1450476 -1.65 0.098 -.5240096 0445664 fabricated | -.2526914 1204776 -2.10 0.036 -.4888231 -.0165597 machi_equip | 2401982 1287744 1.87 0.062 -.0121951 4925914 furniture | 0923224 1256761 0.73 0.463 -.1539983 3386431 _cons | -5.361826 6480471 -8.27 0.000 -6.631975 -4.091677 -+ -/athrho | 9610621 0819019 11.73 0.000 8005373 1.121587 /lnsigma | 4254585 0381801 11.14 0.000 3506268 5002902 -+ -rho | 7447503 0364748 6643371 8081202 sigma | 1.530292 0584268 1.419957 1.6492 lambda | 1.139685 0951066 9532799 1.326091 -Wald test of indep eqns (rho = 0): chi2(1) = 137.69 Prob > chi2 = 0.0000 62 Appendix 5: Heteroskedasticity test using Breusch-Pagan / Cook-Weisberg - the Heckman two-stage model Ho: Constant variance Variables: fitted values of ebribe_ln Result: chi2 (1) = 35.51 Prob > chi2 = 0.0000  reject Ho Appendix 6: Heteroskedasticity test using Breusch-Pagan / Cook-Weisberg - the Heckman two-stage model when using instrument variable Ho: Constant variance Variables: fitted values of ebribe_ln Result: chi2(1) = 36.43 Prob > chi2 = 0.0000  reject Ho 63 ... UNDERLYING INCIDENCE AND AMOUNT OF BRIBERY: AN EVIDENCE FROM MANUFACTURING FIRMS IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT...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 DETERMINANTS UNDERLYING INCIDENCE. .. such as Kaufmann and Wei (1998) and Hellman et al (2000) Kaufmann and Wei (1998) examined the nexus between informal cost in the form of time spent by managers with bureaucrats and cost of capital