Debt tax shield and firm value empirical evidence from listed companies in vietnam

UNIVERSITY OF ECONOMICS HO CHI MINH CITY VIETNAM ERASMUS UNVERSITY ROTTERDAM INSTITUTE OF SOCIAL STUDIES THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DEBT TAX SHIELD AND FIRM VALUE: EMPIRICAL EVIDENCE FROM LISTED COMPANIES IN VIETNAM BY NGUYEN THI HONG HOA MASTER OF ARTS IN DEVELOPMENT ECONOMICS HO CHI MINH CITY, OCTOBER 2017 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 DEBT TAX SHIELD AND FIRM VALUE: EMPIRICAL EVIDENCE FROM LISTED COMPANIES IN VIETNAM A thesis submitted in partial fulfilment of the requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By NGUYEN THI HONG HOA Academic Supervisor: VU VIET QUANG HO CHI MINH CITY, OCTOBER 2017 ACKNOWLEDGEMENT I would first like to thank my thesis supervisor Dr Vu Viet Quang of the Vietnam – The Netherlands Programme (VNP) at Ho Chi Minh City University of Economics He consistently allowed this paper to be my own work, but steered me in the right the direction whenever he thought I needed it I would like to express my gratitude to the VNP officers who were involved in my thesis process by updating thesis schedule and providing good condition for my research process Without their passionate participation, the thesis process could not have been successfully conducted Finally, thanks are also due to my classmates for providing me with unfailing support and continuous encouragement throughout my years of study and through the process of researching and writing this thesis This accomplishment would not have been possible without them Thank you Nguyen Thi Hong Hoa Ho Chi Minh City, October 2017 Page i ABSTRACT In the present study, panel data in fiscal year from 2008 to 2015 has been collected to reveal the interaction between debt tax shield and firm value The main purpose is to examine the value of debt tax shield and its effect on firm value toward taxation The reverse approach is employed in which the future profitability is regressed on firm value and debt using non-linear least square The advantage of reverse method is to shift measurement bias in future operating income to the regression residual and to enhance the usefulness of market factors to control for risk and expected growth This way also includes nontax information in the market value variable As a result, debt tax shield has negative effect on firm value The predicted value for debt tax shield approximately gets 37 percent of debt or gets 9.5 percent of firm value Page ii TABLE OF CONTENT Chapter Page Acknowledgement i Abstract ii Table of content iii List of tables v List of figures vi Introduction 1.1 Research problem 1.2 Research objective 1.3 Scope of study 1.4 Thesis structure Literature review 2.1 Theoretical review .4 2.1.1 Modigliani and Miller and capital structure theory (MM Model) .4 2.1.2 Trade-off theory 2.1.3 Theory of Agency costs 2.2 Empirical review 11 2.3 Hypothesis development 18 Research methodology 21 3.1 Conceptual framework 21 3.2 Estimation method 22 3.3 Variables and measures 29 3.4 Data Collection 36 Empirical result and discussions 37 4.1 The statistic descriptions of variables 37 4.2 Empirical result 41 Page 4.2.1 Linear estimation 41 4.2.2 Nonlinear estimation 46 Conclusion .55 Reference vii Appendix xi LIST OF TABLES Table 3.1 Variable description 36 Table 4.1 Descriptive statistics 37 Table 4.2 Correlation 39 Table 4.3 Correlation (divided by total assets) 40 Table 4.4 Summary statistics from linear regression explaining the Value of firm (un-deflated intercept) 42 Table 4.5 Summary statistics from linear regression explaining the Value of firm with deflated intercept 43 Table 4.6 Valuation of debt tax shield ( β ) from reverse regression, No Control for Capitalization Rates .44 Table 4.7 Valuation of debt tax shield ( β ) from quantile regression according to industry effect .45 Table 4.8 Valuation of the debt tax shield ( β ) from nonlinear Regression 49 Table 4.9 Summary statistics from Nonlinear regression with interest expense 50 Table 4.10 State ownership and firm performance from nonlinear Regression 51 Page LIST OF FIGURES Figure 2.1 The optimal capital structure and the value of the firm .8 Figure 3.1 Conceptual framework 22 Figure 4.1 Distribution of future operating income 38 CHAPTER 1: INTRODUCTION 1.1 Problem statement In corporate finance’s perspective, one of the most important decisions of a particular firm is to determine the optimal level of its capital structure or financial leverage However, the issue of firm’s capital structure has been controversially argued among researchers (Akhtar & Oliver, 2009) In addition, financial leverage has become more important since there are a large number of corporations using debt as a main instrument to raise its capital The relationship between taxation and capital structure has been empirically examined from a large number of developed countries such as the U.S and European countries with many institutional similarities It is necessary for a research about enterprise taxation influences to operating income in Asian countries Vietnam context would be selected for analysis because Vietnam is an Asian developing country with a low income and fresh stock exchange compared to other economies in Asia At the aim of maximizing benefit and minimizing risk, a firm will choose the suitable capital structure to balance the costs and the benefits Therefore, the notion of deciding the ratio between debt and equity is always concerned at high level It is believed that the tax policy affect the firm’s financing Indeed tax is an essential component in firm’s activities and affects firm’s debt policy basing on deduction from interest expense It seems like that the only channel for firms to obtain funds is through bank borrowing in Vietnam Discovering how big magnitude tax affects firm profitability to find out the relationship between firm value, debt and corporate tax By this investigation, it is hopeful that there is appropriate guidance for effective application of debt Thus, above research context creates two research questions: (1) Does debt give impacts on performance of Vietnamese firms? (2) How big does the magnitude of net debt tax shield affect firm value? Page 1.2 Research objective This first purpose aims to value the magnitude of debt tax shield, besides that there is another tendency to test the effect of tax to debt ratio in the scope of this research of Vietnamese enterprise in the stock market Many researches build firm value as function of debt and unrecognized measures of future operating income, yet this study is based on an approach by regressing future operating income on firm value, debt and controlling for firm-level capitalization rates (Kemsley & Nissim, 2002) According to Kemsley and Nissim (2002) relying on reversed approach of future operating income, any unexpected result of profitability is collected to the regression residual without effecting on debt; simultaneously, the market value as independent variable hold nontax information from debt In addition, considering the market value as market-based variable is useful to control for the risk and expected growth by Kemsley and Nissim (2002) We use interest expense to investigate the magnitude of debt In case enterprises receive benefit from corporate tax of debt, it is expected that there will have useful measures from revealing this relationship It is essential to find out the limitation sourcing from debt to restrict this limitation of debt Therefore, the main objectives of this study are: (1) Examining the impact of debt on firm performance (2) Value the magnitude of net debt tax shield Giving some implications for Vietnamese firms to improve their performance (3) Revealing the role of state ownership on firm performance 1.3 Scope of study This study examines the effects of taxation on firm performance in the context of Vietnamese companies The firm data is collected from 262 companies in Ho Chi Minh Stock Exchange in fiscal year 2008 to 2015 on the following required variables: total assets, net operating assets, interest expense, debt, future operating income, total market value The firm performance in this research only focuses on financial performance The panel dataset is collected from Orbit database and Ho Chi Minh Stock Exchange database and data from Vietstock This study attempts to follow quantitative analysis by applying nonlinear least square regression model on the panel data of Vietnamese firms, which are listed in Ho Chi Minh Stock Exchange (HOSE) The panel data would be employed to review the operation of firm performance when putting tax across years At the aspect of econometric model, this study utilizes the nonlinear least square regression model to examine the value of debt tax shield relative to firm value 1.4 Thesis structure The remaining of this study includes four chapters First, chapter discusses the theoretical and empirical literature related to taxation and its relationship with firm performance This section primarily introduces the definitions of key concepts in this study, main theories about taxation and debt and lists out main empirical findings of prominent studies on taxation relationship This background would be the basis to form the conceptual framework utilized in this study Second, chapter reveals the research methodology including conceptual framework, estimation method and variable description to establish the econometric models based on the conceptual framework Third, chapter presents data and the descriptive statistics, regression results and discussions on the main findings of the study Finally, chapter expresses the conclusions, policy implementations based on the main findings In addition, this part also discusses the research limitations and future development of the topic _cons | 0314106 0086009 3.65 -+ -q50 | IND | -18407.56 783675.9 -0.02 MARKET_VALUE | 0223561 024504 0.91 DEBT | 005643 0122751 0.46 _cons | 0583462 01693 3.45 -+ -q75 | IND | -29302.53 1145369 -0.03 MARKET_VALUE | 0590126 0230566 2.56 DEBT | -.013689 0183443 -0.75 _cons | 0826817 0206238 4.01 0.000 0145401 0482811 0.981 0.362 0.646 0.001 -1555571 -.025708 -.0184344 0251384 1518756 0704201 0297204 091554 0.980 0.011 0.456 0.000 -2275919 0137876 -.0496708 0422286 2217314 1042376 0222929 1231347 // Non-linear regression for all years / is not a valid command name r(199); nl (FOI2 = ({a1} + {a2}*beta_u1 + {a3}*(MARKET_VALUE - DEBT*{b0} )/NOA + {a4} * ln_NOA + {a5} * ln_OL )*(MARKET_VALUE - DEBT*{b0} ) + {g2} > *industry_beta1 + {g3}*(MARKET_VALUE - DEBT*{b0} )/NOA + {g4} * ln_NOA + {g5} * ln_OL ), robust (obs = 1,572) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = 9.663606 9.63266 9.630258 9.630059 9.630042 9.630041 9.630041 9.630041 9.630041 9.630041 9.630041 Nonlinear regression -| Robust FOI2 | Coef Std Err t -+ -/a1 | 0624039 0146767 4.25 /a2 | -.0129191 0089856 -1.44 /a3 | -.0000893 0000237 -3.77 /b0 | -.3739859 1833228 -2.04 /a4 | 1116539 0448495 2.49 /a5 | 0020023 0013605 1.47 /g2 | 0070054 0038626 1.81 /g3 | 0281212 0170313 1.65 /g4 | -.0424047 0078807 -5.38 /g5 | -.0049639 0011657 -4.26 Number of obs = 1,572 Rsquared = 0.6102 Adj Rsquared = 0.6077 Root MSE = 0785188 Res dev = -3548.538 P>|t| 0.000 0.151 0.000 0.042 0.013 0.141 0.070 0.099 0.000 0.000 [95% Conf Interval] 0336157 -.0305443 -.0001358 -.7335706 0236823 -.0006663 -.0005711 -.0052854 -.0578625 -.0072504 091192 004706 -.0000428 -.0144011 1996256 0046709 0145818 0615277 -.0269468 -.0026774 Page 111 nl (FOI2 = ({a1} + {a2}*beta_u1 + {a3}*(MARKET_VALUE - INTEREST*{b0} )/NOA + {a4} * ln_NOA + {a5} * ln_OL )*(MARKET_VALUE - INTEREST*{b0} > ) + {g2}*industry_beta1 + {g3}*(MARKET_VALUE - INTEREST*{b0} )/NOA + {g4} * ln_NOA + {g5} * ln_OL ), robust (obs = 1,572) Iteration 0: residual SS = 9.663606 Page 112 Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = 9.494636 9.485742 9.485205 9.485169 9.485167 9.485166 9.485166 9.485166 9.485166 9.485166 Nonlinear regression Number of obs = 1,572 Rsquared = 0.6161 Adj Rsquared = 0.6136 Root MSE = 0779259 Res dev = -3572.366 -| Robust FOI2 | Coef Std Err t -+ -/a1 | 0604862 0132828 4.55 /a2 | -.0103164 0081835 -1.26 /a3 | -.0000937 0000226 -4.15 /b0 | -7.047326 1.835356 -3.84 /a4 | 1009602 0410699 2.46 /a5 | 0015508 0013116 1.18 /g2 | 0050862 0037389 1.36 /g3 | 0244256 0153092 1.60 /g4 | -.0399009 0083573 -4.77 /g5 | -.0050268 001017 -4.94 P>|t| 0.000 0.208 0.000 0.000 0.014 0.237 0.174 0.111 0.000 0.000 [95% Conf Interval] 0344323 -.0263682 -.000138 -10.64735 0204022 -.0010219 -.0022475 -.0056032 -.0562936 -.0070216 0865402 0057354 -.0000494 -3.447304 1815182 0041234 0124199 0544544 -.0235082 -.003032 nl (FOI2 = ({a1} + {a2}*beta_u1 + {a3}*(MARKET_VALUE - DEBT*{b0}*SC )/NOA + {a4} * ln_NOA + {a5} * ln_OL )*(MARKET_VALUE - DEBT*{b0}*SC ) > + {g2}*industry_beta1 + {g3}*(MARKET_VALUE - DEBT*{b0}*SC )/NOA + {g4} * ln_NOA + {g5} * ln_OL + {g6} * SC), robust (obs = 1,572) Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration Iteration 0: 1: 2: 3: 4: 5: 6: 7: 8: 9: 10: residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = residual SS = 9.484166 9.478361 9.477876 9.477836 9.477833 9.477832 9.477832 9.477832 9.477832 9.477832 9.477832 Nonlinear regression -| Robust FOI2 | Coef Std Err t Number of obs = 1,572 Rsquared = 0.6164 Adj Rsquared = 0.6137 Root MSE = 0779207 Res dev = -3573.582 P>|t| [95% Conf Interval] Page 113 -+ -/a1 | 0602014 0145908 4.13 /a2 | -.0155037 009189 -1.69 /a3 | -.0000818 0000238 -3.44 /b0 | 3592515 3305737 1.09 /a4 | 1197633 0459043 2.61 /a5 | 0018889 0013533 1.40 /g2 | 0094472 0036229 2.61 /g3 | 0324668 0173928 1.87 /g4 | -.0313325 0076024 -4.12 0.000 0.092 0.001 0.277 0.009 0.163 0.009 0.062 0.000 0315817 -.0335277 -.0001286 -.2891638 0297227 -.0007656 002341 -.0016489 -.0462445 088821 0025203 -.0000351 1.007667 2098039 0045434 0165534 0665824 -.0164205 Page 114 /g5 | -.0046459 0009879 -4.70 /g6 | 0285787 0058087 4.92 0.000 0.000 -.0065835 0171849 -.0027082 0399724 // Regression for each year / is not a valid command name r(199); keep if year == 2008 (1,834 observations deleted) reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression -| Robust FOI2 | Coef Std Err t -+ -12926.49 1.49 0.137 MARKET_VALUE | 0006431 0008906 0.72 DEBT | 3370419 0367342 9.18 Number of obs F(3, 259) Prob > F R-squared Root MSE = = = = = 262 34.08 0.0000 0.3312 13214 P>|t| [95% Conf Interval] ITA | 19300.41 -6153.977 44754.8 0.471 -.0011105 0023968 0.000 2647062 4093777 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap FOI2 | Coef Std Err t -+ -q25 | IND | -19043.66 965327.7 -0.02 MARKET_VALUE | -.0001871 014217 -0.01 DEBT | 0119686 0262857 0.46 _cons | 0597666 0132065 4.53 -+ -q50 | 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = P>|t| 0.984 0.990 0.649 0.000 262 0.0122 0.0077 0.0050 [95% Conf Interval] -1919968 -.0281832 -.0397932 0337604 1881881 027809 0637304 0857729 IND | -37039.29 1250778 -0.03 MARKET_VALUE | -.0003515 027294 -0.01 DEBT | -.0015276 0295671 -0.05 _cons | 0999614 024433 4.09 -+ -q75 | IND | -61005.18 1543640 -0.04 0.976 0.990 0.959 0.000 -2500074 -.0540989 -.0597512 0518479 2425995 0533958 0566959 148075 0.969 -3100744 2978734 MARKET_VALUE | -.0005979 0381386 -0.02 DEBT | 0128401 0525218 0.24 _cons | 1534684 0403521 3.80 0.988 0.807 0.000 -.0757005 -.0905859 074007 0745048 116266 2329298 clear use data.dta keep if year == 2009 (1,834 observations deleted) reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE -| Robust FOI2 | Coef Std Err t P>|t| -+ ITA | 2.09 0.038 458660.1 MARKET_VALUE | 0316063 0120853 2.62 0.009 DEBT | 1754081 0353255 4.97 0.000 = = = = = 262 91.95 0.0000 0.6039 09034 [95% Conf Interval] 7908118 3783053 1.54e+07 0078083 0554043 1058464 2449697 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap Coef t FOI2 | Std Err -+ -q25 | IND | 790918.5 1648257 0.48 MARKET_VALUE | 0079683 0187301 0.43 DEBT | 0300548 0274481 1.09 _cons | 0415188 0187464 2.21 -+ -q50 | IND | 1174819 1618624 0.73 MARKET_VALUE | 0492111 0162112 3.04 DEBT | 0626295 0329429 1.90 _cons | 0379477 0144724 2.62 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = 262 0.0223 0.1009 0.1989 P>|t| [95% Conf Interval] 0.632 0.671 0.275 0.028 -2454831 -.028915 -.023996 0046033 4036669 0448516 0841055 0784343 0.469 0.003 0.058 0.009 -2012577 0172879 -.0022417 0094487 4362215 0811342 1275008 0664468 -+ -q75 | IND | 1230869 3248008 0.38 MARKET_VALUE | 0494652 0113155 4.37 DEBT | -.0062465 0430355 -0.15 _cons | 0997236 0205527 4.85 0.705 0.000 0.885 0.000 -5165114 0271826 -.090992 0592511 7626851 0717478 0784991 140196 clear use data.dta keep if year == 2010 (1,834 observations deleted) reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE -| Robust FOI2 | Coef Std Err t P>|t| -+ ITA | 4.22 0.000 80895472.23e+07 MARKET_VALUE | 0260482 0110634 2.35 0.019 DEBT | 1341359 0219554 6.11 0.000 = = = = = 262 120.86 0.0000 0.5397 08355 [95% Conf Interval] 1.52e+07 3598124 0042626 090902 0478339 1773698 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap Coef t FOI2 | Std Err -+ -q25 | IND | -1995538 2525742 -0.79 MARKET_VALUE | -.0009343 0091978 -0.10 DEBT | 0591668 0211245 2.80 _cons | 0297198 014227 2.09 -+ -q50 | IND | -320895.2 3303790 -0.10 MARKET_VALUE | 0226727 0233071 0.97 DEBT | 0369697 0346364 1.07 _cons | 0509894 0191656 2.66 -+ -q75 | IND | 7144452 4576504 1.56 MARKET_VALUE | 0905988 0259518 3.49 DEBT | 0054242 0220433 0.25 _cons | 0448104 0229063 1.96 - clear 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = P>|t| 262 0.0282 0.0156 0.1358 [95% Conf Interval] 0.430 0.919 0.005 0.038 -6969232 -.0190466 0175683 001704 2978157 017178 1007653 0577356 0.923 0.332 0.287 0.008 -6826723 -.0232237 -.0312362 0132484 6184933 0685691 1051757 0887303 0.120 0.001 0.806 0.052 -1867606 0394945 -.0379834 -.0002967 1.62e+07 1417031 0488318 0899175 Page xxii use data.dta keep if year == 2011 (1,834 observations deleted) reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE -| Robust FOI2 | Coef Std Err t P>|t| -+ ITA | 2.95 0.003 35788071.79e+07 MARKET_VALUE | 0740219 0146696 5.05 0.000 DEBT | 0456952 0227319 2.01 0.045 = = = = = 262 50.85 0.0000 0.5362 0744 [95% Conf Interval] 1.07e+07 3641727 0451351 0009324 1029087 0904581 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap FOI2 | Coef Std Err t -+ -q25 | IND | 1145074 2009579 0.57 MARKET_VALUE | 0283686 0181202 1.57 DEBT | 0372189 0171499 2.17 _cons | 0032199 0091798 0.35 -+ -q50 | IND | 313869.6 4148999 0.08 MARKET_VALUE | 0628791 0299716 2.10 DEBT | -.0016071 0254391 -0.06 _cons | 0346063 0136023 2.54 -+ -q75 | IND | 4321403 5280328 0.82 MARKET_VALUE | 1115634 0349484 3.19 DEBT | 0103316 0356067 0.29 _cons | 0426015 018251 2.33 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = P>|t| 262 0.0285 0.0415 0.1367 [95% Conf Interval] 0.569 0.119 0.031 0.726 -2812193 -.0073138 0034474 -.0148571 5102341 064051 0709904 0212969 0.940 0.037 0.950 0.012 -7856346 003859 -.0517017 0078206 8484085 1218993 0484875 061392 0.414 0.002 0.772 0.020 -6076626 042743 -.0597851 0066616 1.47e+07 1803838 0804483 0785413 Page 121 clear use data.dta Page 122 keep if year == 2012 (1,834 observations deleted) reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE -| Robust FOI2 | Coef Std Err t P>|t| -+ ITA | 1.81 0.072 -586320.7 MARKET_VALUE | 0764732 0166445 4.59 0.000 DEBT | 0394226 0205807 1.92 0.057 = = = = = 262 52.62 0.0000 0.5521 06918 [95% Conf Interval] 6492632 3594900 1.36e+07 0436973 1092491 -.0011042 0799495 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap FOI2 | Coef Std Err t -+ -q25 | IND | -1435019 1994772 -0.72 MARKET_VALUE | 0316607 016076 1.97 DEBT | 0393071 0265213 1.48 _cons | 0023586 0098682 0.24 -+ -q50 | IND | -334575.5 1992652 -0.17 MARKET_VALUE | 0915574 0310832 2.95 DEBT | 0063569 025336 0.25 _cons | 0172245 0170399 1.01 -+ -q75 | IND | -3244050 2746070 -1.18 MARKET_VALUE | 1237737 0299148 4.14 DEBT | -.0053518 037604 -0.14 _cons | 0387584 0179 2.17 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = P>|t| 262 0.0628 0.1089 0.1982 [95% Conf Interval] 0.473 0.050 0.140 0.811 -5363127 3.68e-06 -.0129187 -.0170739 2493090 0633176 091533 0217911 0.867 0.004 0.802 0.313 -4258509 0303482 -.0435348 -.0163305 3589358 1527666 0562486 0507795 0.239 0.000 0.887 0.031 -8651616 0648654 -.0794018 0035098 2163515 182682 0686981 074007 clear Page 123 use data.dta keep if year == 2013 (1,834 observations deleted) Page 124 reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE -| Robust FOI2 | Coef Std Err t P>|t| -+ ITA | 0.77 0.439 -4267157 MARKET_VALUE | 0855302 0107033 7.99 0.000 DEBT | 0342534 0201279 1.70 0.090 = = = = = 262 67.82 0.0000 0.6362 06298 [95% Conf Interval] 2767271 3572289 9801699 0644537 1066067 -.0053819 0738887 sqreg FOI2 IND MARKET_VALUE DEBT, quantiles(0.25 0.5 0.75) reps(100) (fitting base model) Bootstrap replications (100) + - -+ - -+ - -+ - -+ - Simultaneous quantile regression bootstrap(100) SEs -| Bootstrap FOI2 | Coef Std Err t -+ -q25 | IND | -4129910 3456017 -1.19 MARKET_VALUE | 0604361 0147877 4.09 DEBT | 0401991 0146578 2.74 _cons | -.0069618 007535 -0.92 -+ -q50 | IND | -2261426 2870655 -0.79 MARKET_VALUE | 1011352 0211181 4.79 DEBT | 0339778 0138367 2.46 _cons | -.0055954 0116956 -0.48 -+ -q75 | IND | 6225554 4935652 1.26 MARKET_VALUE | 133802 0212655 6.29 DEBT | 0143195 0286756 0.50 _cons | 0051754 0172895 0.30 - clear exit, clear 50 100 Number of obs = 25 Pseudo R2 = 50 Pseudo R2 = 75 Pseudo R2 = P>|t| 262 0.1325 0.2047 0.2777 [95% Conf Interval] 0.233 0.000 0.007 0.356 -1.09e+07 031316 0113348 -.0217997 2675684 0895561 0690633 0078761 0.432 0.000 0.015 0.633 -7914323 0595495 0067305 -.0286263 3391471 1427208 0612252 0174356 0.208 0.000 0.618 0.765 -3493740 091926 -.0421484 -.0288711 1.59e+07 175678 0707875 0392219 ... CHI MINH CITY VIETNAM INSTITUTE OF SOCIAL STUDIES THE HAGUE THE NETHERLANDS VIETNAM - NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS DEBT TAX SHIELD AND FIRM VALUE: EMPIRICAL EVIDENCE FROM. .. risk and expected growth This way also includes nontax information in the market value variable As a result, debt tax shield has negative effect on firm value The predicted value for debt tax shield. .. statistics from linear regression explaining the Value of firm (un-deflated intercept) 42 Table 4.5 Summary statistics from linear regression explaining the Value of firm with deflated intercept