Gender diversity in corporate boardroom and tax avoidance the evidence in hose listed firms

UNIVERSITY OF ECONOMICS HO CHI MINH CITY ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS GENDER DIVERSITY IN CORPORATE BOARDROOM AND TAX AVOIDANCE THE EVIDENCE IN HOSE LISTED FIRMS BY DAO THI HAN MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, December 2016 i UNIVERSITY OF ECONOMICS HO CHI MINH CITY ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS GENDER DIVERSITY IN CORPORATE BOARDROOM AND TAX AVOIDANCE THE EVIDENCE IN HOSE LISTED FIRMS A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS BY DAO THI HAN Academic Supervisor: Nguyen Thi Thuy Linh HO CHI MINH CITY, December 2016 ii ABSTRACT Using data set of 296 publicly listed firms in Ho Chi Minh Stock Exchange from 2010 to 2015, the study analyses the engagement of female board members, also in case of being leader of board and executive manager, on tax avoidance activities, measured by three proxies The fixed effect regression results indicate gender diversity in boardroom is negatively associated with tax avoidance measured by effective tax rate but chairwomen are more engaged in tax avoidance measured by book-tax difference As a result, the presence of women in boardroom of HOSE listed firms is important to shareholders who consider about firms’ transparency or profit iii TABLE OF CONTENT LIST OF ABBREVIATIONS iv LIST OF FIGURE v LIST OF TABLE vi CHAPTER ONE INTRODUCTION 1.1 Vietnam overview 1.1.1 Female labor and pay gap in Vietnam 1.1.2 Women participation in firm management in Vietnam 1.1.3 Tax avoidance in Vietnam 1.2 Research objective 1.3 Research design CHAPTER TWO LITERATURE REVIEW 2.1 Gender diversity and corporate governance 2.1.1 Resource-dependence theory 11 2.1.2 Agency theory 11 2.1.3 Gender equality reaction in corporate boardroom 12 2.2 Tax avoidance and corporate governance 13 2.2.1 Tax avoidance 13 2.2.2 Tax avoidance and corporate governance 14 2.3 Gender diversity in boardroom and tax avoidance 15 2.3.1 Women’ participation in boardroom and tax avoidance 16 2.3.2 Chairwomen and tax avoidance 17 2.3.3 Female executive in boardroom and tax avoidance 18 i 2.3.4 Summary 19 CHAPTER THREE METHODOLOGY 20 3.1 Analytical framework 20 3.2 Data and data source 20 3.3 Research model 21 3.3.1 Baseline model 21 3.3.2 Variable explanation 23 3.4 Research methodology 26 3.4.1 Regression models 26 3.4.2 Robust standard Errors 27 CHAPTER FOUR EMPIRICAL RESULT 28 4.1 Descriptive statistic 28 4.1.1 Summary descriptive statistic 28 4.1.2 Women board directors in HOSE listed firms 30 4.1.3 Tax expense in HOSE listed firms having women board directors 32 4.2 Empirical result 33 4.2.1 GAAP effective tax rate 34 4.2.2 CASH effective tax rate 35 4.2.3 Book-tax differences 37 4.3 Summary results 38 CHAPTER FIVE CONCLUSION 40 4.1 Conclusions 40 4.2 Implications 41 4.3 Limitations 41 ii REFERRENCE 43 APPENDIX A SELECTED FIRMS LISTED IN HOSE 48 APPENDIX B CORRELATION MATRIX 50 APPENDIX C MULTICOLLINEARITY TEST 52 APPENDIX D PANEL DATA REGRESSION RESULTS – FEM 54 APPENDIX E PANEL DATA REGRESSION RESULTS – REM 59 APPENDIX F HETEROSKEDASTICITY AND AUTOCORRELATION TEST 64 APPENDIX G REGRESSION WITH ADJUSTED STANDARD ERRORS 66 iii LIST OF ABBREVIATIONS BTD: book-tax different CEO(s): Chief of Executive officer(s) CFO(s): Chief of Finance officer(s) CIT: Corporate Income Tax ETR: effective tax rate EU: European Union FDI: Foreign directed investment FEM: Fixed effect model GAAP: Generally Accepted Accounting Principle GSO: General Statistics Office HNX: Hanoi Stock Exchange HOSE: Ho Chi Minh Stock Exchange ILO: International Labor Organization OECD: Organization for Economic Co-operation and Development REM: Random effect model ROA: Return on asset ROE: Return on equity ROI: Return on investment SG&A: Selling, general and administration VAT: Value-added tax VCCI: Vietnam Chamber of Commerce and Industry VWEC: Vietnam Women Entrepreneurs Council iv LIST OF FIGURE FIGURE 1.1 LABOR FORCE PARTICIPATION RATES (%) FIGURE 1.2: NATIONWIDE FEMALE EMPLOYED POPULATION FIGURE 2.1: THE SCOPE OF CORPORATE GOVERNANCE 10 FIGURE 3.1: ANALYTICAL FRAMEWORK 20 FIGURE 4.1: GRAPH HISTOGRAM OF TAX AVOIDANCE MEASURES 28 FIGURE 4.2: GRAPH HISTOGRAM OF CORPORATE BOARD SIZE AND WOMEN MEMBERS 30 FIGURE 4.3: HOSE LISTED FIRMS HAVING WOMEN PARTICIPATING BOARDROOM 31 FIGURE 4.4: NUMBER OF EXECUTIVE AND NON-EXECUTIVE FEMALE BOARD DIRECTOR 32 FIGURE 4.5: CURRENT TAX EXPENSE IN HOSE LISTED FIRMS (BILLION VND) 32 FIGURE 4.6: CASH TAX PAID IN FIRMS IN HOSE LISTED FIRMS (BILLION VND) 33 v LIST OF TABLE TABLE 1.1: CORPORATE INCOME TAX SINCE 2013 TABLE 2.1: SUMMARY HYPOTHESES AND RESEARCH QUESTIONS 19 TABLE 3.1: VARIABLE CONSTRUCTION 21 TABLE 3.2: EXPLANATORY VARIABLES 22 TABLE 4.1: SUMMARY STATISTICS OF VARIABLES 29 TABLE 4.2: RESULTS OF FEM WITH TAX AVOIDANCE MEASURED BY GAAPETR 34 TABLE 4.3: RESULTS OF FEM WITH TAX AVOIDANCE MEASURED BY CASHETR 36 TABLE 4.4: RESULTS OF FEM WITH TAX AVOIDANCE MEASURED BY BTD 37 TABLE 4.5: SUMMARY HYPOTHESIS TEST RESULTS 39 vi CHAPTER ONE INTRODUCTION 1.1 Vietnam overview 1.1.1 Female labor and pay gap in Vietnam According to Worldbank data, Vietnam keeps a very high female labor participation rate as high as male’s, not lower than 72 percent since 2000, shown in Figure 1.1 (while male rate is around 82 percent)1 General Statistics Office (GSO) of Vietnam also reports more than 40 percent of the nationwide labor force are women (see Figure 1.2) However, the International Labor Organization (ILO) identifies that gender pay gap in Vietnam has been widened Vietnamese women earn less than men thirteen percent in 2011 and twenty to thirty percent in 2012 while global average gender pay gap is around 17 percent The latest Labor Force Survey Report (2012) shows that women earn less than male counterparts in all economic sectors, even the favor-female industries like healthcare, social works Hence, the remuneration seems to reflect gender of worker instead of content of work It is clear that the principle of “equal pay for work of equal value” stipulated in the Labor Code need to be implemented Figure 1.1 Labor force participation rates (%) male rate female rate 100 90 80 70 60 50 40 30 20 10 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 (Source: worldbank.org) (female/male) labor force participation rate = % of (female/male) population ages 15+ having a job sigma_u | 08211323 sigma_e | 1013749 rho | 39616915 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1231) = 2.99 Prob > F = 0.0000 Figure D.3: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.0202 between = 0.0244 overall = 0.0142 Number of obs = 1532 Number of groups = 290 Obs per group: = avg = 5.3 max = F(11,1231) = 2.31 corr(u_i, Xb) = -0.2566 Prob > F = 0.0084 -gaapetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | 0082993 0408708 0.20 0.839 -.0718849 0884835 chairwm | 0042962 0217163 0.20 0.843 -.0383088 0469012 wmexe | 010462 0116425 0.90 0.369 -.0123793 0333033 roa | -.1445993 0520358 -2.78 0.006 -.2466879 -.0425107 size | -.0006112 0109152 -0.06 0.955 -.0220255 0208032 mb | -.0134158 0081951 -1.64 0.102 -.0294938 0026621 slev | 0035104 0193179 0.18 0.856 -.0343893 0414102 llev | -.0589038 0287853 -2.05 0.041 -.1153774 -.0024302 fasset | -.0100873 0259382 -0.39 0.697 -.0609753 0408008 sga1 | 2404527 0969188 2.48 0.013 0503084 4305971 cash | 0335956 0319265 1.05 0.293 -.0290408 096232 _cons | 2134198 0850949 2.51 0.012 0464727 3803669 -+ -sigma_u | 08219503 sigma_e | 10137481 rho | 39664608 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1231) = 2.98 Prob > F = 0.0000 Figure D.4: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.0429 between = 0.0139 overall = 0.0154 Number of obs = 1524 Number of groups = 290 Obs per group: = avg = 5.3 max = F(11,1223) = 4.98 corr(u_i, Xb) = -0.3563 Prob > F = 0.0000 -cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -wob | 0245138 015495 1.58 0.114 -.0058859 0549135 chairwm | 0362048 02872 1.26 0.208 -.0201413 0925508 wmexe | -.0071236 0149761 -0.48 0.634 -.0365052 022258 roa | -.4349692 0707956 -6.14 0.000 -.5738634 -.2960749 size | 0127518 0146723 0.87 0.385 -.0160338 0415374 mb | -.027712 0110635 -2.50 0.012 -.0494175 -.0060065 slev | -.0065324 0259899 -0.25 0.802 -.0575221 0444574 llev | -.0415756 0401246 -1.04 0.300 -.1202963 0371451 fasset | 0616296 0362373 1.70 0.089 -.0094645 1327238 sga1 | 2157719 1297726 1.66 0.097 -.0388296 4703735 cash | 0666806 0432169 1.54 0.123 -.0181068 151468 _cons | 0869656 1143225 0.76 0.447 -.1373244 3112556 -+ 55 sigma_u | 11917692 sigma_e | 13606129 rho | 43413677 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1223) = 3.29 Prob > F = 0.0000 Figure D.5: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.0412 between = 0.0150 overall = 0.0142 Number of obs = 1524 Number of groups = 290 Obs per group: = avg = 5.3 max = F(11,1223) = 4.78 corr(u_i, Xb) = -0.3637 Prob > F = 0.0000 -cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -nowob | -.0055259 0095485 -0.58 0.563 -.0242592 0132075 chairwm | 0443276 0294888 1.50 0.133 -.0135266 1021818 wmexe | 0004294 0156777 0.03 0.978 -.0303289 0311876 roa | -.4397202 0708192 -6.21 0.000 -.5786609 -.3007795 size | 012411 0146842 0.85 0.398 -.0163979 04122 mb | -.0300034 0110406 -2.72 0.007 -.051664 -.0083429 slev | -.0081416 0260033 -0.31 0.754 -.0591576 0428745 llev | -.0444867 0401194 -1.11 0.268 -.1231971 0342238 fasset | 0665253 0361995 1.84 0.066 -.0044948 1375454 sga1 | 2155136 129897 1.66 0.097 -.039332 4703592 cash | 0697052 043261 1.61 0.107 -.0151688 1545792 _cons | 1069837 1139869 0.94 0.348 -.1166478 3306152 -+ -sigma_u | 11930206 sigma_e | 1361818 rho | 43421746 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1223) = 3.27 Prob > F = 0.0000 Figure D.6: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.0410 between = 0.0152 overall = 0.0149 Number of obs = 1524 Number of groups = 290 Obs per group: = avg = 5.3 max = F(11,1223) = 4.75 corr(u_i, Xb) = -0.3554 Prob > F = 0.0000 -cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | 0144568 0547423 0.26 0.792 -.0929425 1218561 chairwm | 0382429 0296196 1.29 0.197 -.019868 0963538 wmexe | -.0040406 0155927 -0.26 0.796 -.0346319 0265508 roa | -.4388128 0708254 -6.20 0.000 -.5777655 -.2998601 size | 0122897 0146839 0.84 0.403 -.0165188 0410981 mb | -.0293143 0110485 -2.65 0.008 -.0509904 -.0076381 slev | -.0077861 026004 -0.30 0.765 -.0588035 0432312 llev | -.0444882 0401241 -1.11 0.268 -.1232079 0342315 fasset | 0654313 0362003 1.81 0.071 -.0055902 1364528 sga1 | 2149999 1299133 1.65 0.098 -.0398777 4698775 cash | 0686326 0432445 1.59 0.113 -.016209 1534742 _cons | 1023916 1141446 0.90 0.370 -.1215493 3263325 56 -+ -sigma_u | 11888016 sigma_e | 13619656 rho | 43242438 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1223) = 3.27 Prob > F = 0.0000 Figure D.7: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.4209 between = 0.3361 overall = 0.3388 Number of obs = 1623 Number of groups = 290 Obs per group: = avg = 5.6 max = F(11,1322) = 87.36 corr(u_i, Xb) = -0.3080 Prob > F = 0.0000 -btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -wob | -.0011336 0039306 -0.29 0.773 -.0088444 0065772 chairwm | 0098971 0073751 1.34 0.180 -.004571 0243653 wmexe | -.0004423 0038928 -0.11 0.910 -.008079 0071944 roa | 4615561 0175159 26.35 0.000 4271942 4959181 size | -.0134106 0038029 -3.53 0.000 -.020871 -.0059502 mb | -.0022329 0028821 -0.77 0.439 -.007887 0034212 slev | -.0151105 0066055 -2.29 0.022 -.0280689 -.0021522 llev | 0304894 0099422 3.07 0.002 0109853 0499936 fasset | -.0088462 0089069 -0.99 0.321 -.0263194 0086271 sga1 | -.243621 0327202 -7.45 0.000 -.3078102 -.1794319 cash | -.0336769 0112012 -3.01 0.003 -.055651 -.0117027 _cons | 1072556 029628 3.62 0.000 0491326 1653786 -+ -sigma_u | 04510992 sigma_e | 03624544 rho | 60768156 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1322) = 5.40 Prob > F = 0.0000 Figure D.8: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.4209 between = 0.3370 overall = 0.3395 Number of obs = 1623 Number of groups = 290 Obs per group: = avg = 5.6 max = F(11,1322) = 87.35 corr(u_i, Xb) = -0.3071 Prob > F = 0.0000 -btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -nowob | -.0004339 0024144 -0.18 0.857 -.0051704 0043026 chairwm | 0099829 0075243 1.33 0.185 -.0047779 0247437 wmexe | -.0004249 0040556 -0.10 0.917 -.008381 0075312 roa | 4616434 017512 26.36 0.000 4272891 4959976 size | -.0133768 0038031 -3.52 0.000 -.0208376 -.005916 mb | -.0021862 0028748 -0.76 0.447 -.0078259 0034535 slev | -.0151084 0066058 -2.29 0.022 -.0280673 -.0021495 llev | 030623 0099289 3.08 0.002 0111449 0501011 fasset | -.0089441 0088967 -1.01 0.315 -.0263972 0085091 sga1 | -.2434076 0327389 -7.43 0.000 -.3076336 -.1791817 cash | -.0336705 0112045 -3.01 0.003 -.055651 -.0116901 57 _cons | 106678 0295285 3.61 0.000 0487502 1646059 -+ -sigma_u | 0450601 sigma_e | 03624614 rho | 60714535 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1322) = 5.42 Prob > F = 0.0000 Figure D.9: FEM regression result of model Fixed-effects (within) regression Group variable: id R-sq: within = 0.4210 between = 0.3365 overall = 0.3390 Number of obs = 1623 Number of groups = 290 Obs per group: = avg = 5.6 max = F(11,1322) = 87.38 corr(u_i, Xb) = -0.3074 Prob > F = 0.0000 -btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | -.0063406 0137351 -0.46 0.644 -.0332857 0206044 chairwm | 0105032 0075491 1.39 0.164 -.0043064 0253127 wmexe | -.0000599 0040345 -0.01 0.988 -.0079745 0078548 roa | 4615425 0175112 26.36 0.000 4271897 4958953 size | -.0133887 0038021 -3.52 0.000 -.0208475 -.0059299 mb | -.0022449 0028754 -0.78 0.435 -.0078857 0033959 slev | -.0151136 0066051 -2.29 0.022 -.0280712 -.002156 llev | 0306421 0099277 3.09 0.002 0111664 0501178 fasset | -.0088865 0088948 -1.00 0.318 -.026336 0085629 sga1 | -.2433677 0327225 -7.44 0.000 -.3075614 -.179174 cash | -.0336716 0112 -3.01 0.003 -.0556433 -.0116999 _cons | 1072217 0295509 3.63 0.000 04925 1651935 -+ -sigma_u | 0450792 sigma_e | 03624366 rho | 60738019 (fraction of variance due to u_i) -F test that all u_i=0: F(289, 1322) = 5.42 Prob > F = 0.0000 58 APPENDIX E PANEL DATA REGRESSION RESULTS – REM Figure E.1: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1532 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0116 between = 0.0975 overall = 0.0427 Wald chi2(11) = 42.92 corr(u_i, X) = (assumed) Prob > chi2 = 0.0000 -gaapetr | Coef Std Err z P>|z| [95% Conf Interval] -+ -wob | -.0126885 0083821 -1.51 0.130 -.0291172 0037402 chairwm | -.005862 0132698 -0.44 0.659 -.0318703 0201462 wmexe | 0091046 0074746 1.22 0.223 -.0055454 0237546 roa | -.1159501 0397171 -2.92 0.004 -.1937942 -.038106 size | 0025748 0037878 0.68 0.497 -.004849 0099987 mb | -.0080779 0036947 -2.19 0.029 -.0153195 -.0008364 slev | 022029 0131578 1.67 0.094 -.0037599 0478179 llev | -.0030046 0217052 -0.14 0.890 -.0455459 0395368 fasset | -.0452626 0163085 -2.78 0.006 -.0772267 -.0132985 sga1 | 1112625 0433011 2.57 0.010 0263939 1961311 cash | -.0002726 0259819 -0.01 0.992 -.0511962 050651 _cons | 2022988 0282361 7.16 0.000 146957 2576407 -+ -sigma_u | 06078511 sigma_e | 10137269 rho | 26445947 (fraction of variance due to u_i) Figure E.2: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1532 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0122 between = 0.0880 overall = 0.0397 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 40.70 0.0000 -gaapetr | Coef Std Err z P>|z| [95% Conf Interval] -+ -nowob | -.0031525 0051134 -0.62 0.538 -.0131747 0068696 chairwm | -.0074321 0135292 -0.55 0.583 -.0339489 0190847 wmexe | 0078103 008275 0.94 0.345 -.0084083 0240289 roa | -.1162348 039791 -2.92 0.003 -.1942237 -.0382459 size | 0025269 0038137 0.66 0.508 -.0049477 0100016 mb | -.0076989 0036983 -2.08 0.037 -.0149475 -.0004503 slev | 0225224 0131889 1.71 0.088 -.0033273 0483721 llev | -.0036685 021761 -0.17 0.866 -.0463193 0389822 fasset | -.0450841 0163765 -2.75 0.006 -.0771815 -.0129867 59 sga1 | 1092841 0435003 2.51 0.012 024025 1945431 cash | 0005574 0260193 0.02 0.983 -.0504395 0515543 _cons | 1983424 0282639 7.02 0.000 1429461 2537386 -+ -sigma_u | 06154313 sigma_e | 1013749 rho | 26930043 (fraction of variance due to u_i) Figure E.3: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1532 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0119 between = 0.0888 overall = 0.0400 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 40.70 0.0000 -gaapetr | Coef Std Err z P>|z| [95% Conf Interval] -+ -womanratio | -.0170738 0294661 -0.58 0.562 -.0748263 0406788 chairwm | -.0073366 0136321 -0.54 0.590 -.034055 0193818 wmexe | 0074909 0081384 0.92 0.357 -.0084601 0234419 roa | -.1164035 0397797 -2.93 0.003 -.1943703 -.0384367 size | 0023707 0038051 0.62 0.533 -.0050872 0098286 mb | -.0076908 0036965 -2.08 0.037 -.0149358 -.0004458 slev | 0227013 0131784 1.72 0.085 -.0031278 0485304 llev | -.0035264 0217603 -0.16 0.871 -.0461758 039123 fasset | -.0452609 0163815 -2.76 0.006 -.0773681 -.0131538 sga1 | 1081908 0434443 2.49 0.013 0230415 19334 cash | 0003062 0260238 0.01 0.991 -.0506996 051312 _cons | 199441 028411 7.02 0.000 1437565 2551256 -+ -sigma_u | 06142783 sigma_e | 10137481 rho | 26856345 (fraction of variance due to u_i) Figure E.4: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1524 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0336 between = 0.0704 overall = 0.0426 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 60.54 0.0000 -cashetr | Coef Std Err z P>|z| [95% Conf Interval] -+ -wob | 0087186 0115065 0.76 0.449 -.0138337 0312708 chairwm | 0062567 0183479 0.34 0.733 -.0297044 0422179 wmexe | -.0036301 0102575 -0.35 0.723 -.0237346 0164743 roa | -.3276917 0541854 -6.05 0.000 -.4338931 -.2214903 60 size | 0162773 0052999 3.07 0.002 0058897 026665 mb | -.0004813 0051162 -0.09 0.925 -.0105088 0095462 slev | 0014747 0181138 0.08 0.935 -.0340276 036977 llev | -.085758 0305445 -2.81 0.005 -.1456242 -.0258919 fasset | 0359314 0229472 1.57 0.117 -.0090442 0809071 sga1 | 1819691 0602822 3.02 0.003 0638182 30012 cash | 0845742 0355902 2.38 0.017 0148187 1543297 _cons | 0380107 0394536 0.96 0.335 -.0393169 1153383 -+ -sigma_u | 08748018 sigma_e | 13606129 rho | 29247652 (fraction of variance due to u_i) Figure E.5: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1524 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0323 between = 0.0738 overall = 0.0427 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 59.94 0.0000 -cashetr | Coef Std Err z P>|z| [95% Conf Interval] -+ -nowob | -.0003762 0069444 -0.05 0.957 -.0139871 0132346 chairwm | 0091169 0186645 0.49 0.625 -.0274649 0456987 wmexe | -.0006107 0112867 -0.05 0.957 -.0227322 0215108 roa | -.327564 0542145 -6.04 0.000 -.4338224 -.2213056 size | 0164005 0053076 3.09 0.002 0059978 0268033 mb | -.000827 0051039 -0.16 0.871 -.0108304 0091764 slev | 0008662 0181153 0.05 0.962 -.0346391 0363715 llev | -.0853485 0305675 -2.79 0.005 -.1452597 -.0254374 fasset | 0358101 0229659 1.56 0.119 -.0092022 0808224 sga1 | 1842009 0602925 3.06 0.002 0660298 302372 cash | 0841615 0355982 2.36 0.018 0143904 1539327 _cons | 0415733 0392795 1.06 0.290 -.0354132 1185598 -+ -sigma_u | 08766409 sigma_e | 1361818 rho | 29297955 (fraction of variance due to u_i) Figure E.6: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1524 290 R-sq: Obs per group: = avg = max = 5.3 within = 0.0324 between = 0.0733 overall = 0.0429 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 60.01 0.0000 -cashetr | Coef Std Err z P>|z| [95% Conf Interval] 61 -+ -womanratio | 0102981 0403386 0.26 0.798 -.0687642 0893603 chairwm | 0074587 0188277 0.40 0.692 -.029443 0443603 wmexe | -.0023495 0111136 -0.21 0.833 -.0241318 0194329 roa | -.3278376 0542046 -6.05 0.000 -.4340766 -.2215986 size | 0164127 0053005 3.10 0.002 006024 0268014 mb | -.0007636 0051026 -0.15 0.881 -.0107645 0092372 slev | 0009728 0181041 0.05 0.957 -.0345105 0364562 llev | -.0857105 0305691 -2.80 0.005 -.1456248 -.0257963 fasset | 0360908 022975 1.57 0.116 -.0089394 0811211 sga1 | 1841331 0602384 3.06 0.002 066068 3021983 cash | 0844251 0356053 2.37 0.018 0146399 1542103 _cons | 0401961 0395107 1.02 0.309 -.0372434 1176355 -+ -sigma_u | 08759327 sigma_e | 13619656 rho | 29259996 (fraction of variance due to u_i) Figure E.7: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1623 290 R-sq: Obs per group: = avg = max = 5.6 within = 0.4115 between = 0.4733 overall = 0.4251 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 1168.88 0.0000 -btd | Coef Std Err z P>|z| [95% Conf Interval] -+ -wob | 0022604 0033081 0.68 0.494 -.0042233 0087442 chairwm | 0139666 0055802 2.50 0.012 0030297 0249035 wmexe | -.0024971 0030756 -0.81 0.417 -.0085251 0035309 roa | 4619549 0146746 31.48 0.000 4331931 4907167 size | -.0015478 0017616 -0.88 0.380 -.0050003 0019048 mb | -.0017003 0016195 -1.05 0.294 -.0048744 0014739 slev | -.0118145 0052228 -2.26 0.024 -.022051 -.001578 llev | 0190481 0085036 2.24 0.025 0023814 0357148 fasset | 0026275 0067292 0.39 0.696 -.0105615 0158165 sga1 | -.1234646 0193142 -6.39 0.000 -.1613198 -.0856094 cash | -.028198 0099774 -2.83 0.005 -.0477534 -.0086426 _cons | 0087944 0132831 0.66 0.508 -.01724 0348288 -+ -sigma_u | 03457435 sigma_e | 03624544 rho | 47641676 (fraction of variance due to u_i) Figure E.8: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1623 290 R-sq: Obs per group: = avg = max = 5.6 within = 0.4117 between = 0.4723 overall = 0.4245 62 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 1168.35 0.0000 -btd | Coef Std Err z P>|z| [95% Conf Interval] -+ -nowob | 0009234 0019977 0.46 0.644 -.0029921 0048389 chairwm | 0139456 0056787 2.46 0.014 0028156 0250756 wmexe | -.002561 0033143 -0.77 0.440 -.009057 003935 roa | 4619445 0146773 31.47 0.000 4331775 4907115 size | -.0015691 0017654 -0.89 0.374 -.0050291 001891 mb | -.0017548 0016168 -1.09 0.278 -.0049236 001414 slev | -.0118296 0052256 -2.26 0.024 -.0220717 -.0015876 llev | 0189671 0085055 2.23 0.026 0022966 0356376 fasset | 0026867 0067326 0.40 0.690 -.0105088 0158823 sga1 | -.1235122 0193381 -6.39 0.000 -.1614142 -.0856103 cash | -.0282964 0099781 -2.84 0.005 -.0478532 -.0087396 _cons | 0095052 0132513 0.72 0.473 -.0164668 0354772 -+ -sigma_u | 03466806 sigma_e | 03624614 rho | 47775776 (fraction of variance due to u_i) Figure E.9: REM regression result of model Random-effects GLS regression Group variable: id Number of obs Number of groups = = 1623 290 R-sq: Obs per group: = avg = max = 5.6 within = 0.4117 between = 0.4717 overall = 0.4242 corr(u_i, X) = (assumed) Wald chi2(11) Prob > chi2 = = 1168.01 0.0000 -btd | Coef Std Err z P>|z| [95% Conf Interval] -+ -womanratio | 0021976 0115232 0.19 0.849 -.0203874 0247826 chairwm | 0142899 0057163 2.50 0.012 0030863 0254936 wmexe | -.0021214 0032778 -0.65 0.517 -.0085459 004303 roa | 4619981 0146781 31.48 0.000 4332294 4907667 size | -.0015325 0017643 -0.87 0.385 -.0049905 0019256 mb | -.0017706 0016175 -1.09 0.274 -.0049409 0013996 slev | -.0118796 0052252 -2.27 0.023 -.0221209 -.0016383 llev | 0189814 0085077 2.23 0.026 0023065 0356562 fasset | 0026904 0067354 0.40 0.690 -.0105107 0158916 sga1 | -.1232284 0193285 -6.38 0.000 -.1611115 -.0853452 cash | -.0282694 0099801 -2.83 0.005 -.04783 -.0087088 _cons | 0095493 0133102 0.72 0.473 -.0165383 0356368 -+ -sigma_u | 03469394 sigma_e | 03624366 rho | 47816415 (fraction of variance due to u_i) 63 APPENDIX F HETEROSKEDASTICITY AND AUTOCORRELATION TEST Table F.1: Heteroskedasticity and Autocorrelation test result for model gaapetr wob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 261) = 13.903 Prob > F = 0.0002 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 9.0e+34 Prob>chi2 = 0.0000 Table F.2: Heteroskedasticity and Autocorrelation test result for model gaapetr nowob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 261) = 13.889 Prob > F = 0.0002 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 8.2e+34 Prob>chi2 = 0.0000 Table F.3: Heteroskedasticity and Autocorrelation test result for model gaapetr womanratio chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 261) = 13.955 Prob > F = 0.0002 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 1.3e+35 Prob>chi2 = 0.0000 Table F.4: Heteroskedasticity and Autocorrelation test result for model cashetr wob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 256) = 0.028 Prob > F = 0.8667 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 3.9e+33 Prob>chi2 = 0.0000 Table F.5: Heteroskedasticity and Autocorrelation test result for model cashetr nowob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 256) = 0.036 64 Prob > F = 0.8488 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 1.6e+34 Prob>chi2 = 0.0000 Table F.6: Heteroskedasticity and Autocorrelation test result for model cashetr womanratio chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 256) = 0.033 Prob > F = 0.8560 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 1.7e+34 Prob>chi2 = 0.0000 Table F.7: Heteroskedasticity and Autocorrelation test result for model btd wob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 273) = 22.445 Prob > F = 0.0000 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 3.6e+28 Prob>chi2 = 0.0000 Table F.8: Heteroskedasticity and Autocorrelation test result for model btd nowob chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 273) = 21.775 Prob > F = 0.0000 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 1.2e+31 Prob>chi2 = 0.0000 Table F.9: Heteroskedasticity and Autocorrelation test result for model btd womanratio chairwm wmexe roa size mb slev llev fasset sga1 cash Wooldridge test for autocorrelation in panel data H0: no first-order autocorrelation F( 1, 273) = 22.223 Prob > F = 0.0000 Modified Wald test for groupwise heteroskedasticity in fixed effect regression model H0: sigma(i)^2 = sigma^2 for all i chi2 (290) = 4.5e+30 Prob>chi2 = 0.0000 65 APPENDIX G REGRESSION WITH ADJUSTED STANDARD ERRORS Figure G.1: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1532 Number of groups = 290 F( 11, 289) = 90.96 Prob > F = 0.0000 within R-squared = 0.0203 -| Drisc/Kraay gaapetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -wob | -.003496 0039246 -0.89 0.374 -.0112205 0042284 chairwm | 0059888 0065379 0.92 0.360 -.0068791 0188566 wmexe | 0119845 0029514 4.06 0.000 0061756 0177934 roa | -.1449786 0167288 -8.67 0.000 -.1779044 -.1120529 size | -.0007178 0087929 -0.08 0.935 -.0180241 0165885 mb | -.0138124 0030571 -4.52 0.000 -.0198293 -.0077955 slev | 0032788 0086832 0.38 0.706 -.0138115 0203692 llev | -.0594447 0251223 -2.37 0.019 -.1088906 -.0099987 fasset | -.009408 017808 -0.53 0.598 -.0444578 0256419 sga1 | 2396895 0755389 3.17 0.002 0910133 3883657 cash | 0339472 0103885 3.27 0.001 0135004 054394 _cons | 2171895 0732823 2.96 0.003 0729548 3614243 Figure G.2: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1532 Number of groups = 290 F( 11, 289) = 131.99 Prob > F = 0.0000 within R-squared = 0.0202 -| Drisc/Kraay gaapetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -nowob | -.0014235 003082 -0.46 0.645 -.0074896 0046425 chairwm | 0064036 0076065 0.84 0.401 -.0085677 0213748 wmexe | 0121045 0032935 3.68 0.000 0056222 0185868 roa | -.1447999 0168735 -8.58 0.000 -.1780104 -.1115895 size | -.0006222 0087327 -0.07 0.943 -.0178099 0165655 mb | -.0136704 0031552 -4.33 0.000 -.0198804 -.0074603 slev | 0033729 0086701 0.39 0.698 -.0136917 0204374 llev | -.0591132 0250824 -2.36 0.019 -.1084807 -.0097458 fasset | -.0097231 0178857 -0.54 0.587 -.0449258 0254796 sga1 | 2400424 075216 3.19 0.002 0920019 388083 cash | 0339827 0106931 3.18 0.002 0129363 055029 _cons | 2155102 0727295 2.96 0.003 0723635 358657 Figure G.3: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number Number F( 11, Prob > within 66 of obs of groups 289) F R-squared = = = = = 1532 290 9.05 0.0000 0.0202 -| Drisc/Kraay gaapetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | 0082993 0201367 0.41 0.681 -.0313338 0479325 chairwm | 0042962 0081078 0.53 0.597 -.0116616 0202541 wmexe | 010462 0032269 3.24 0.001 0041108 0168132 roa | -.1445993 0169979 -8.51 0.000 -.1780547 -.1111439 size | -.0006112 008784 -0.07 0.945 -.0179 0166776 mb | -.0134158 0031825 -4.22 0.000 -.0196796 -.0071521 slev | 0035104 008691 0.40 0.687 -.0135953 0206162 llev | -.0589038 0251333 -2.34 0.020 -.1083713 -.0094363 fasset | -.0100873 0178472 -0.57 0.572 -.0452143 0250397 sga1 | 2404527 0756928 3.18 0.002 0914737 3894318 cash | 0335956 0104604 3.21 0.001 0130073 0541839 _cons | 2134198 0735652 2.90 0.004 0686284 3582112 Figure G.4: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1524 Number of groups = 290 F( 11, 289) = 346.46 Prob > F = 0.0000 within R-squared = 0.0427 -| Drisc/Kraay cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -wob | 0235068 0053883 4.36 0.000 0129015 0341121 chairwm | 0363675 0274953 1.32 0.187 -.0177489 0904838 wmexe | -.0069384 016807 -0.41 0.680 -.040018 0261412 roa | -.435302 029558 -14.73 0.000 -.4934782 -.3771258 size | 0126296 0044018 2.87 0.004 003966 0212933 mb | -.027781 0051541 -5.39 0.000 -.0379252 -.0176367 slev | -.0067089 0155427 -0.43 0.666 -.0373001 0238823 llev | -.041835 0550354 -0.76 0.448 -.1501561 0664861 fasset | 0619556 0224495 2.76 0.006 0177704 1061408 sga1 | 214784 2062794 1.04 0.299 -.1912164 6207845 cash | 0671313 0183363 3.66 0.000 0310417 1032209 _cons | 0884628 0291591 3.03 0.003 0310716 145854 Figure G.5: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1524 Number of groups = 290 F( 11, 289) = 269.23 Prob > F = 0.0000 within R-squared = 0.0412 -| Drisc/Kraay cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -nowob | -.0055259 0086023 -0.64 0.521 -.022457 0114053 chairwm | 0443276 0264279 1.68 0.095 -.0076879 0963431 wmexe | 0004294 0205244 0.02 0.983 -.0399669 0408257 roa | -.4397202 0299982 -14.66 0.000 -.4987628 -.3806776 size | 012411 0044891 2.76 0.006 0035756 0212465 mb | -.0300034 0053139 -5.65 0.000 -.0404624 -.0195445 slev | -.0081416 0149741 -0.54 0.587 -.0376136 0213305 67 llev | -.0444867 0561963 -0.79 0.429 -.1550925 0661192 fasset | 0665253 0230301 2.89 0.004 0211974 1118533 sga1 | 2155136 2060349 1.05 0.296 -.1900056 6210328 cash | 0697052 0179299 3.89 0.000 0344155 1049949 _cons | 1069837 0297421 3.60 0.000 0484451 1655223 Figure G.6: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1524 Number of groups = 290 F( 11, 289) = 108.58 Prob > F = 0.0000 within R-squared = 0.0410 -| Drisc/Kraay cashetr | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | 0144568 0395274 0.37 0.715 -.0633413 0922549 chairwm | 0382429 026727 1.43 0.154 -.0143613 0908471 wmexe | -.0040406 0195195 -0.21 0.836 -.0424589 0343778 roa | -.4388128 0297837 -14.73 0.000 -.4974332 -.3801924 size | 0122897 0045092 2.73 0.007 0034147 0211646 mb | -.0293143 0052146 -5.62 0.000 -.0395777 -.0190509 slev | -.0077861 0152625 -0.51 0.610 -.037826 0222537 llev | -.0444882 0557186 -0.80 0.425 -.1541538 0651774 fasset | 0654313 0225518 2.90 0.004 0210446 109818 sga1 | 2149999 2040558 1.05 0.293 -.186624 6166238 cash | 0686326 0179614 3.82 0.000 0332809 1039843 _cons | 1023916 0301307 3.40 0.001 0430881 1616951 Figure G.7: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1623 Number of groups = 290 F( 11, 289) = 1483.82 Prob > F = 0.0000 within R-squared = 0.4209 -| Drisc/Kraay btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -wob | -.0011336 0030061 -0.38 0.706 -.0070502 004783 chairwm | 0098971 0033319 2.97 0.003 0033393 0164549 wmexe | -.0004423 0022549 -0.20 0.845 -.0048805 0039959 roa | 4615561 0291708 15.82 0.000 404142 5189703 size | -.0134106 0017989 -7.45 0.000 -.0169513 -.0098699 mb | -.0022329 0024245 -0.92 0.358 -.0070049 0025391 slev | -.0151105 0057768 -2.62 0.009 -.0264804 -.0037406 llev | 0304894 011846 2.57 0.011 007174 0538049 fasset | -.0088462 0114398 -0.77 0.440 -.0313622 0136698 sga1 | -.243621 0450053 -5.41 0.000 -.3322008 -.1550413 cash | -.0336769 0121925 -2.76 0.006 -.0576741 -.0096796 _cons | 1072556 0124948 8.58 0.000 0826633 1318479 Figure G.8: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id Number of obs Number of groups F( 11, 289) 68 = = = 1623 290 1390.38 maximum lag: Prob > F = 0.0000 within R-squared = 0.4209 -| Drisc/Kraay btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -nowob | -.0004339 0015302 -0.28 0.777 -.0034457 0025778 chairwm | 0099829 002933 3.40 0.001 0042101 0157557 wmexe | -.0004249 0025409 -0.17 0.867 -.0054258 0045761 roa | 4616434 0292671 15.77 0.000 4040398 519247 size | -.0133768 0017723 -7.55 0.000 -.0168651 -.0098885 mb | -.0021862 0024199 -0.90 0.367 -.0069492 0025767 slev | -.0151084 0057812 -2.61 0.009 -.0264869 -.0037298 llev | 030623 0116077 2.64 0.009 0077767 0534694 fasset | -.0089441 0113208 -0.79 0.430 -.0312258 0133376 sga1 | -.2434076 0456846 -5.33 0.000 -.3333243 -.153491 cash | -.0336705 012206 -2.76 0.006 -.0576945 -.0096466 _cons | 106678 0127245 8.38 0.000 0816335 1317225 Figure G.9: Regression Driscoll – Kraay Standard Errors result for model Regression with Driscoll-Kraay standard errors Method: Fixed-effects regression Group variable (i): id maximum lag: Number of obs = 1623 Number of groups = 290 F( 11, 289) = 1293.37 Prob > F = 0.0000 within R-squared = 0.4210 -| Drisc/Kraay btd | Coef Std Err t P>|t| [95% Conf Interval] -+ -womanratio | -.0063406 005989 -1.06 0.291 -.0181282 0054469 chairwm | 0105032 0032562 3.23 0.001 0040943 016912 wmexe | -.0000599 0022132 -0.03 0.978 -.0044158 0042961 roa | 4615425 0293626 15.72 0.000 4037509 5193341 size | -.0133887 0017725 -7.55 0.000 -.0168773 -.0099 mb | -.0022449 0024169 -0.93 0.354 -.0070019 0025121 slev | -.0151136 0057407 -2.63 0.009 -.0264126 -.0038146 llev | 0306421 0115253 2.66 0.008 007958 0533262 fasset | -.0088865 011227 -0.79 0.429 -.0309837 0132106 sga1 | -.2433677 0453968 -5.36 0.000 -.332718 -.1540174 cash | -.0336716 0121286 -2.78 0.006 -.0575432 -.0098 _cons | 1072217 0127867 8.39 0.000 0820548 1323886 69 ... 2.2 Tax avoidance and corporate governance 13 2.2.1 Tax avoidance 13 2.2.2 Tax avoidance and corporate governance 14 2.3 Gender diversity in boardroom and tax avoidance. .. and tax avoidance Assuming, all firms may involve in minimizing corporate tax expense if there are few costs associated with tax avoidance However, there are different tax positions between firms. .. approaching determinants and consequence of it Tax accounting literature continues investigating the variation in tax avoidance and motivation for different tax planning level, such as incentivizing policy