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 D (1997) National Champions and Corruption: Some Unpleasant Interventionist Arithmetic The Economic Journal, 107(443), 1023-1042 Aidt, T S (2003) Economic analysis of corruption: a survey The Economic Journal, 113(491), F632-F652 Amundsen, I (1999) Political corruption: An introduction to the issues CMI Working Paper Awartani, H (2009) The Sources of Corruption Center for International Private Enterprise (CIPE), Development Institute, 1-5 Bardhan, P (1997) Corruption and development: a review of issues Journal of economic literature, 1320-1346 Batra, G., Kaufmann, D., & Stone, A (2003) Investment climate around the world Washington, DC: World Bank Bennedsen, M., Feldmann, S E., & Lassen, D D (2009) Strong firms lobby, weak firms bribe: A survey-based analysis of the demand for influence and corruption Weak Firms Bribe: A Survey-Based Analysis of the Demand for Influence and Corruption (November 9, 2009) Bliss, C., & Tella, R D (1997) Does competition kill corruption? 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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 (regression model with sample selection) Number of obs Censored obs = = 7139 5236 Uncensored obs Wald chi2(23) = = 1903 270.17 Prob > chi2 = 0.0000 ebribe_ln eprofit_ln sunkcost_ln instate_eln tax_percentage regulations estate_ln eimp_ln eexp_ln informalr gov_ass foods textiles wearing leather wood paper printing chemicals rubber_plas non_metallic fabricated machi_equip furniture _cons - bribe eexp_ln eimp_ln regulations estate_ln instate_eln informalr employment_ln gov_ass sunkcost_ln eprofit_ln tax_percentage foods textiles wearing leather wood paper | Coef Std Err + -| | 3096538 0310097 | 0137625 0103616 | 0560113 0089144 | 0691537 0137358 | 0035161 0019079 | 0288157 0095338 | -.0096063 0140449 | 0014625 0112637 | -.7984671 1401055 | 2457989 0884271 | -.7766669 232093 | -1.05734 2792185 | -.912864 2738085 | -.9060265 2298544 | -.4425156 2370452 | -.2412782 2717521 | -.1417555 2712699 | 0835925 2707723 | -.2236627 2496569 | -.6300206 2609058 | -.4592049 2268947 | 3083286 2354916 | -.0920933 2378933 | 0771708 4221353 + -| | -.0183494 006753 | -.0061714 008599 | 0028176 0008815 | 0184929 0052906 | 014512 0046716 | -.5669477 0462326 | 3509695 021133 | -.0238107 0461862 | 0115754 0057141 | 1343599 0174865 | 0264855 0074203 | -.5111635 1173744 | -.6195506 1431126 | -.5493971 1434945 | -.2741424 1181033 | -.388044 1231304 | -.3457245 147539 55 z P>|z| [95% Conf Interval] 9.99 1.33 6.28 5.03 1.84 3.02 -0.68 0.13 -5.70 2.78 -3.35 -3.79 -3.33 -3.94 -1.87 -0.89 -0.52 0.31 -0.90 -2.41 -2.02 1.31 -0.39 0.18 0.000 0.184 0.000 0.000 0.065 0.003 0.494 0.897 0.000 0.005 0.001 0.000 0.001 0.000 0.062 0.375 0.601 0.758 0.370 0.016 0.043 0.190 0.699 0.855 2488759 -.0065459 0385395 0422319 -.0002234 0101297 -.0371338 -.020614 -1.073069 0724849 -1.231561 -1.604598 -1.449519 -1.356533 -.9071155 -.7739027 -.6734348 -.4471114 -.7129812 -1.141387 -.9039104 -.1532265 -.5583555 -.7501992 3704316 0340709 0734832 0960754 0072555 0475016 0179212 023539 -.5238653 4191129 -.321773 -.510082 -.3762092 -.4555202 0220844 2913462 3899238 6142964 2656559 -.1186546 -.0144994 7698837 3741689 9045408 -2.72 -0.72 3.20 3.50 3.11 -12.26 16.61 -0.52 2.03 7.68 3.57 -4.35 -4.33 -3.83 -2.32 -3.15 -2.34 0.007 0.473 0.001 0.000 0.002 0.000 0.000 0.606 0.043 0.000 0.000 0.000 0.000 0.000 0.020 0.002 0.019 -.0315851 -.0230252 0010899 0081235 0053558 -.6575619 3095496 -.114334 000376 100087 0119419 -.7412132 -.9000462 -.8306412 -.5056206 -.6293751 -.6348957 -.0051138 0106823 0045454 0288622 0236683 -.4763335 3923894 0667126 0227748 1686328 0410291 -.2811139 -.3390551 -.268153 -.0426642 -.1467129 -.0565533 printing chemicals rubber_plas non_metallic fabricated machi_equip furniture _cons - mills - | | | | | | | | + | lambda | + rho | sigma | -.3061911 -.0929435 -.4552019 -.4359481 -.3702301 1133831 -.0025583 -2.433177 1458867 1442997 1334526 1380127 1182907 1268782 1276278 2032883 -2.10 -0.64 -3.41 -3.16 -3.13 0.89 -0.02 -11.97 0.036 0.520 0.001 0.002 0.002 0.372 0.984 0.000 -.5921238 -.3757657 -.7167642 -.706448 -.6020756 -.1352936 -.2527042 -2.831615 -.0202584 1898788 -.1936397 -.1654482 -.1383846 3620597 2475876 -2.03474 1.575631 1641866 9.60 0.000 1.253831 1.897431 0.88797 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) Number of obs Censored obs Uncensored obs Wald chi2(23) Prob > chi2 Log pseudolikelihood = -6572.269 = = = = = 7139 5236 1903 278.24 0.0000 (Std Err adjusted for 1955 clusters in id) ebribe_ln eprofit_ln sunkcost_ln instate_eln tax_percentage regulations estate_ln eimp_ln eexp_ln informalr gov_ass foods textiles wearing leather wood paper printing chemicals rubber_plas non_metallic fabricated machi_equip furniture _cons - bribe eexp_ln eimp_ln regulations estate_ln instate_eln informalr employment_ln gov_ass sunkcost_ln eprofit_ln tax_percentage foods textiles wearing leather wood paper printing chemicals rubber_plas non_metallic | Robust | Coef Std Err + -| | 2949298 0530753 | 0120249 0114757 | 0515046 0088462 | 0622559 0144024 | 0023955 0017498 | 0227754 0104432 | -.0122819 0166737 | -.002478 0123732 | -.5910952 1023991 | 2155655 0879815 | -.696418 2149552 | -1.002008 2618025 | -.8587136 2516049 | -.8856976 2205103 | -.4107226 2191212 | -.254062 275038 | -.1224595 2397702 | 0609792 2603722 | -.1853487 2487343 | -.6165925 2425263 | -.4016482 2126667 | 2928165 2193289 | -.1041654 2174173 | 6283917 5119354 + -| | -.019555 0072804 | -.0097871 0106172 | 0028938 0007707 | 0202486 0053778 | 0120282 0049878 | -.5423607 0457624 | 384042 0194993 | -.0146338 045682 | 0112819 0068576 | 1436064 0233009 | 0265839 0074701 | -.4550889 1207823 | -.5923944 1443172 | -.5268295 1493445 | -.2270549 1195385 | -.3459677 1230163 | -.3200471 1449899 | -.2756782 1479138 | -.0881631 1432293 | -.4310629 1406373 | -.4058361 1415601 57 z P>|z| [95% Conf Interval] 5.56 1.05 5.82 4.32 1.37 2.18 -0.74 -0.20 -5.77 2.45 -3.24 -3.83 -3.41 -4.02 -1.87 -0.92 -0.51 0.23 -0.75 -2.54 -1.89 1.34 -0.48 1.23 0.000 0.295 0.000 0.000 0.171 0.029 0.461 0.841 0.000 0.014 0.001 0.000 0.001 0.000 0.061 0.356 0.610 0.815 0.456 0.011 0.059 0.182 0.632 0.220 1909042 -.0104671 0341663 0340276 -.0010341 002307 -.0449617 -.0267291 -.7917938 0431249 -1.117722 -1.515131 -1.35185 -1.31789 -.8401923 -.7931266 -.5924003 -.4493408 -.6728591 -1.091935 -.8184673 -.1370602 -.5302954 -.3749833 3989554 034517 0688429 0904841 005825 0432438 0203979 0217731 -.3903966 3880061 -.2751137 -.4888842 -.365577 -.4535054 0187471 2850026 3474814 5712993 3021616 -.1412497 0151709 7226932 3219646 1.631767 -2.69 -0.92 3.75 3.77 2.41 -11.85 19.70 -0.32 1.65 6.16 3.56 -3.77 -4.10 -3.53 -1.90 -2.81 -2.21 -1.86 -0.62 -3.07 -2.87 0.007 0.357 0.000 0.000 0.016 0.000 0.000 0.749 0.100 0.000 0.000 0.000 0.000 0.000 0.058 0.005 0.027 0.062 0.538 0.002 0.004 -.0338242 -.0305963 0013833 0097084 0022523 -.6320534 345824 -.1041689 -.0021587 0979374 0119428 -.6918179 -.8752509 -.8195393 -.461346 -.5870753 -.6042222 -.565584 -.3688873 -.706707 -.6832889 -.0052857 0110222 0044043 0307888 021804 -.452668 42226 0749014 0247224 1892753 0412249 -.2183598 -.3095379 -.2341198 0072361 -.1048602 -.035872 0142276 1925611 -.1554188 -.1283834 fabricated | machi_equip | furniture | _cons | -+ /athrho | /lnsigma | -+ rho | sigma | lambda | -.32898 1349563 0158357 -2.606149 1199554 1274952 1248862 2395696 -2.74 1.06 0.13 -10.88 0.006 0.290 0.899 0.000 -.5640884 -.1149296 -.2289368 -3.075697 -.0938717 3848422 2606082 -2.136601 1.04534 4646773 0715835 0340876 14.60 13.63 0.000 0.000 9050389 3978668 1.185641 5314877 779988 1.591501 1.241351 0280334 0542504 0820975 7187425 1.488646 1.080443 829222 1.701462 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 Wald chi2(23) = = = = 7139 5236 1903 310.02 Prob > chi2 = 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 fabricated machi_equip furniture _cons - mills - | -.2806446 1400248 | -.2903278 1188004 | 2177297 1275445 | 0730714 1281066 | -5.067833 5190649 + -| lambda | 1.25517 1505806 + -rho | 0.79044 sigma | 1.5879429 -2.00 -2.44 1.71 0.57 -9.76 0.045 0.015 0.088 0.568 0.000 -.5550881 -.5231724 -.032253 -.1780128 -6.085182 -.0062012 -.0574833 4677124 3241557 -4.050485 8.34 0.000 9600375 1.550302 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 (regression model with sample selection) Log pseudolikelihood = -6567.933 Number of obs Censored obs = = 7139 5236 Uncensored obs Wald chi2(23) = = 1903 280.57 Prob > chi2 = 0.0000 (Std Err adjusted for 1955 clusters in id) ebribe_ln eprofitln1hat sunkcost_ln instate_eln tax_percentage regulations estate_ln eimp_ln eexp_ln informalr gov_ass foods textiles wearing leather wood paper printing chemicals rubber_plas non_metallic fabricated machi_equip furniture _cons - bribe eexp_ln eimp_ln regulations estate_ln instate_eln informalr employment_ln gov_ass sunkcost_ln eprofitln1hat tax_percentage foods textiles wearing leather wood paper printing chemicals | Robust | Coef Std Err + -| | 8818281 1457241 | 0057808 0113289 | 0234901 0110742 | 0581756 0139427 | 0022778 0017191 | 0322493 0104475 | -.0217534 0161482 | 0080388 0122342 | -.5471651 1051576 | 3198313 0881136 | -.5042254 2094418 | -.5782828 2659665 | -.5123877 2526717 | -.6478846 2159862 | 0032901 2240275 | -.1493191 2686552 | -.0242165 2348832 | 1473632 2534488 | -.1010095 2425579 | -.2285925 2476707 | -.2121181 2090807 | 4880634 2148549 | 0483837 2115383 | -4.479822 1.280195 + -| | -.0158691 0072776 | -.015786 0106601 | 0028626 000771 | 0252141 0054655 | -.0016981 0060798 | -.535521 0461881 | 4078376 0214447 | 0317423 0467687 | 0087916 0068666 | 44813 0707831 | 0251381 0074013 | -.383215 1209359 | -.4126085 1471962 | -.3810781 1519294 | -.125751 1200564 | -.1587594 1271418 | -.2967856 1454504 | -.2433042 1482412 | -.0594006 1438301 61 z P>|z| [95% Conf Interval] 6.05 0.51 2.12 4.17 1.33 3.09 -1.35 0.66 -5.20 3.63 -2.41 -2.17 -2.03 -3.00 0.01 -0.56 -0.10 0.58 -0.42 -0.92 -1.01 2.27 0.23 -3.50 0.000 0.610 0.034 0.000 0.185 0.002 0.178 0.511 0.000 0.000 0.016 0.030 0.043 0.003 0.988 0.578 0.918 0.561 0.677 0.356 0.310 0.023 0.819 0.000 596214 -.0164234 0017852 0308485 -.0010915 0117725 -.0534032 -.0159398 -.7532702 1471318 -.9147239 -1.099568 -1.007615 -1.07121 -.4357957 -.6758737 -.4845792 -.3493872 -.5764142 -.7140182 -.6219087 0669555 -.3662238 -6.988958 1.167442 027985 0451951 0855027 0056471 0527261 0098965 0320174 -.3410599 4925307 -.093727 -.0569981 -.0171603 -.2245595 442376 3772354 4361461 6441137 3743953 2568332 1976725 9091712 4629911 -1.970687 -2.18 -1.48 3.71 4.61 -0.28 -11.59 19.02 0.68 1.28 6.33 3.40 -3.17 -2.80 -2.51 -1.05 -1.25 -2.04 -1.64 -0.41 0.029 0.139 0.000 0.000 0.780 0.000 0.000 0.497 0.200 0.000 0.001 0.002 0.005 0.012 0.295 0.212 0.041 0.101 0.680 -.0301329 -.0366794 0013514 0145019 -.0136143 -.6260481 3658066 -.0599227 -.0046667 3093977 0106318 -.6202449 -.7011079 -.6788543 -.3610573 -.4079528 -.5818632 -.5338517 -.3413024 -.0016053 0051073 0043737 0359263 010218 -.4449939 4498685 1234073 0222499 5868623 0396443 -.1461851 -.1241092 -.0833019 1095553 0904339 -.011708 0472433 2225013 rubber_plas | -.4150369 1407839 non_metallic | -.2397216 1450476 fabricated | -.2526914 1204776 machi_equip | 2401982 1287744 furniture | 0923224 1256761 _cons | -5.361826 6480471 -+ -/athrho | 9610621 0819019 /lnsigma | 4254585 0381801 -+ -rho | 7447503 0364748 sigma | 1.530292 0584268 lambda | 1.139685 0951066 -2.95 -1.65 -2.10 1.87 0.73 -8.27 0.003 0.098 0.036 0.062 0.463 0.000 -.6909683 -.5240096 -.4888231 -.0121951 -.1539983 -6.631975 -.1391056 0445664 -.0165597 4925914 3386431 -4.091677 11.73 11.14 0.000 0.000 8005373 3506268 1.121587 5002902 6643371 1.419957 9532799 8081202 1.6492 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 ... Economic Management (CIEM), The Ministry of Planning and Investment (MPI), the Institute of Labour Science and Social Affairs (ILSSA) - the Ministry of Labour, Invalids and Social Affairs of Vietnam. .. 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