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 2.1.1 Modigliani and Miller and capital structure theory (MM Model) 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 iii 4.2.1 Linear estimation .41 4.2.2 Nonlinear estimation 46 Conclusion 55 Reference vii Appendix xi Page iv 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 (  ) from reverse regression, No Control for Capitalization Rates 44 Table 4.7 Valuation of debt tax shield (  ) from quantile regression according to industry effect 45 Table 4.8 Valuation of the debt tax shield (  ) 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 v LIST OF FIGURES Figure 2.1 The optimal capital structure and the value of the firm Figure 3.1 Conceptual framework 22 Figure 4.1 Distribution of future operating income 38 Page vi 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 Page Behavior, Agency Costs and Ownership Structure." 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International Business Research 7, no (2014): 64 Yu, M (2013) State ownership and firm performance: Empirical evidence from Chinese listed companies China Journal of Accounting Research, 6(2), 75-87 Website: http://finance.vietstock.vn/tai-chinh.htm; https://www.vndirect.com.vn/portal/home.shtml; http://www.cophieu68.com Page xiv http://www.cafef.vn.vn/; www.thesaigontimes.vn/ https://en.wikipedia.org/wiki/ Density APPENDIX -.5 Ratio of future operating income to total assets 1.5 LOG FILE -name: log: D:\Thesis final\Log file.log log type: text opened on: 17 Apr 2017, 16:21:48 set more off cd "D:\Thesis final" D:\Thesis final use data.dta Page xv sum foi2 market_value1 debt1 noa1 ol1 beta_u1 Variable | Obs Mean Std Dev Min Max -+ foi2 | 1,572 2.30e+08 8.87e+08 -1.99e+08 1.55e+10 market_val~1 | 2,096 2.41e+09 1.16e+10 119.56 2.95e+11 debt1 | 2,096 4.04e+08 8.80e+08 427 1.25e+10 noa1 | 2,096 1.08e+09 2.13e+09 403721 2.69e+10 ol1 | 2,096 9.04e+08 3.87e+09 1.06e+11 -+ beta_u1 | 2,096 1867535 6852749 -5.162238 5.318604 STATE | 2,096 21.38822 24.84722 98.11 SC | 2,096 2609733 4392702 pwcorr foi2 market_value1 debt1 noa1 ol1 beta_u1, star(5) | foi2 market~1 debt1 noa1 ol1 beta_u1 -+ -foi2 | 1.0000 market_val~1 | 0.7957* 1.0000 debt1 | 0.4953* 0.3437* 1.0000 noa1 | 0.7631* 0.6330* 0.7749* 1.0000 ol1 | 0.5689* 0.8155* 0.3509* 0.5804* 1.0000 beta_u1 | 0.0747* 0.1023* -0.0974* 0.0436* 0.0237 1.0000 sum FOI2 MARKET_VALUE DEBT NOA OL beta_u1 STATE SC Variable | Obs Mean Std Dev Min Max -+ FOI2 | 1,572 0890746 0882454 -.2632051 1.141078 MARKET_VALUE | 2,096 8789003 4.183719 0000203 186.7337 DEBT | 2,096 2226501 1904772 9.79e-08 9705139 NOA | 2,096 7240788 2108822 0921839 OL | 2,096 2759212 2108822 1.16e-09 9078161 -+ beta_u1 | 2,096 1867535 6852749 -5.162238 5.318604 STATE | 2,096 21.38822 24.84722 98.11 SC | 2,096 2609733 4392702 pwcorr FOI2 MARKET_VALUE DEBT NOA OL beta_u1, star(5) | FOI2 MARKET~E DEBT NOA OL beta_u1 -+ -FOI2 | 1.0000 MARKET_VALUE | 0.0721* 1.0000 DEBT | -0.0370 -0.0355 1.0000 NOA | 0.2189* 0.0028 0.4243* 1.0000 OL | -0.2189* -0.0028 -0.4243* -1.0000* 1.0000 beta_u1 | 0.2198* 0.1036* -0.2181* 0.2013* -0.2013* 1.0000 // Regression for all years / is not a valid command name r(199); reg MARKET_VALUE FOI2 DEBT, robust Linear regression Number of obs F(2, 1569) = = 1,572 43.14 Page xvi Prob > F R-squared Root MSE = = = 0.0000 0.0059 4.8054 -| Robust MARKET_VALUE | Coef Std Err t P>|t| [95% Conf Interval] -+ -FOI2 | 3.883264 1.445418 2.69 0.007 1.048109 6.718419 DEBT | -.7409398 2427241 -3.05 0.002 -1.217038 -.2648421 _cons | 7274486 2746364 2.65 0.008 1887557 1.266142 - reg MARKET_VALUE ITA FOI2 DEBT, nocon robust Linear regression Number of obs F(3, 1569) Prob > F R-squared Root MSE = = = = = 1,572 84.39 0.0000 0.0673 4.739 -| Robust MARKET_VALUE | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 2.07e+07 2.81e+07 0.74 0.462 -3.44e+07 7.57e+07 FOI2 | 6.058586 7760273 7.81 0.000 4.536426 7.580746 DEBT | 6862473 283043 2.42 0.015 131065 1.24143 - reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 1569) Prob > F R-squared Root MSE = = = = = 1,572 211.06 0.0000 0.2942 10542 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 17858.21 13725.53 1.30 0.193 -9064.109 44780.52 MARKET_VALUE | 0029982 0029622 1.01 0.312 -.0028121 0088085 DEBT | 2418825 0115935 20.86 0.000 2191422 2646228 - 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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 1,572 0.0036 0.0112 0.1019 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | -6347.683 750782.5 -0.01 0.993 -1478991 1466296 MARKET_VALUE | -.0000692 0107763 -0.01 0.995 -.0212066 0210683 DEBT | 0192598 0102893 1.87 0.061 -.0009225 0394421 Page xvii _cons | 0314106 0086009 3.65 0.000 0145401 0482811 -+ -q50 | IND | -18407.56 783675.9 -0.02 0.981 -1555571 1518756 MARKET_VALUE | 0223561 024504 0.91 0.362 -.025708 0704201 DEBT | 005643 0122751 0.46 0.646 -.0184344 0297204 _cons | 0583462 01693 3.45 0.001 0251384 091554 -+ -q75 | IND | -29302.53 1145369 -0.03 0.980 -2275919 2217314 MARKET_VALUE | 0590126 0230566 2.56 0.011 0137876 1042376 DEBT | -.013689 0183443 -0.75 0.456 -.0496708 0222929 _cons | 0826817 0206238 4.01 0.000 0422286 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 Number of obs R-squared Adj R-squared Root MSE Res dev = = = = = 1,572 0.6102 0.6077 0785188 -3548.538 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -/a1 | 0624039 0146767 4.25 0.000 0336157 091192 /a2 | -.0129191 0089856 -1.44 0.151 -.0305443 004706 /a3 | -.0000893 0000237 -3.77 0.000 -.0001358 -.0000428 /b0 | -.3739859 1833228 -2.04 0.042 -.7335706 -.0144011 /a4 | 1116539 0448495 2.49 0.013 0236823 1996256 /a5 | 0020023 0013605 1.47 0.141 -.0006663 0046709 /g2 | 0070054 0038626 1.81 0.070 -.0005711 0145818 /g3 | 0281212 0170313 1.65 0.099 -.0052854 0615277 /g4 | -.0424047 0078807 -5.38 0.000 -.0578625 -.0269468 /g5 | -.0049639 0011657 -4.26 0.000 -.0072504 -.0026774 - 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 xviii 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 R-squared Adj R-squared Root MSE Res dev = = = = = 1,572 0.6161 0.6136 0779259 -3572.366 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -/a1 | 0604862 0132828 4.55 0.000 0344323 0865402 /a2 | -.0103164 0081835 -1.26 0.208 -.0263682 0057354 /a3 | -.0000937 0000226 -4.15 0.000 -.000138 -.0000494 /b0 | -7.047326 1.835356 -3.84 0.000 -10.64735 -3.447304 /a4 | 1009602 0410699 2.46 0.014 0204022 1815182 /a5 | 0015508 0013116 1.18 0.237 -.0010219 0041234 /g2 | 0050862 0037389 1.36 0.174 -.0022475 0124199 /g3 | 0244256 0153092 1.60 0.111 -.0056032 0544544 /g4 | -.0399009 0083573 -4.77 0.000 -.0562936 -.0235082 /g5 | -.0050268 001017 -4.94 0.000 -.0070216 -.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 = Nonlinear regression 9.484166 9.478361 9.477876 9.477836 9.477833 9.477832 9.477832 9.477832 9.477832 9.477832 9.477832 Number of obs R-squared Adj R-squared Root MSE Res dev = = = = = 1,572 0.6164 0.6137 0779207 -3573.582 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -/a1 | 0602014 0145908 4.13 0.000 0315817 088821 /a2 | -.0155037 009189 -1.69 0.092 -.0335277 0025203 /a3 | -.0000818 0000238 -3.44 0.001 -.0001286 -.0000351 /b0 | 3592515 3305737 1.09 0.277 -.2891638 1.007667 /a4 | 1197633 0459043 2.61 0.009 0297227 2098039 /a5 | 0018889 0013533 1.40 0.163 -.0007656 0045434 /g2 | 0094472 0036229 2.61 0.009 002341 0165534 /g3 | 0324668 0173928 1.87 0.062 -.0016489 0665824 /g4 | -.0313325 0076024 -4.12 0.000 -.0462445 -.0164205 Page xix /g5 | -.0046459 0009879 -4.70 0.000 -.0065835 -.0027082 /g6 | 0285787 0058087 4.92 0.000 0171849 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 Number of obs F(3, 259) Prob > F R-squared Root MSE = = = = = 262 34.08 0.0000 0.3312 13214 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 19300.41 12926.49 1.49 0.137 -6153.977 44754.8 MARKET_VALUE | 0006431 0008906 0.72 0.471 -.0011105 0023968 DEBT | 3370419 0367342 9.18 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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.0122 0.0077 0.0050 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | -19043.66 965327.7 -0.02 0.984 -1919968 1881881 MARKET_VALUE | -.0001871 014217 -0.01 0.990 -.0281832 027809 DEBT | 0119686 0262857 0.46 0.649 -.0397932 0637304 _cons | 0597666 0132065 4.53 0.000 0337604 0857729 -+ -q50 | IND | -37039.29 1250778 -0.03 0.976 -2500074 2425995 MARKET_VALUE | -.0003515 027294 -0.01 0.990 -.0540989 0533958 DEBT | -.0015276 0295671 -0.05 0.959 -.0597512 0566959 _cons | 0999614 024433 4.09 0.000 0518479 148075 -+ -q75 | IND | -61005.18 1543640 -0.04 0.969 -3100744 2978734 Page xx MARKET_VALUE | -.0005979 0381386 -0.02 0.988 -.0757005 0745048 DEBT | 0128401 0525218 0.24 0.807 -.0905859 116266 _cons | 1534684 0403521 3.80 0.000 074007 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 = = = = = 262 91.95 0.0000 0.6039 09034 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 7908118 3783053 2.09 0.038 458660.1 1.54e+07 MARKET_VALUE | 0316063 0120853 2.62 0.009 0078083 0554043 DEBT | 1754081 0353255 4.97 0.000 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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.0223 0.1009 0.1989 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | 790918.5 1648257 0.48 0.632 -2454831 4036669 MARKET_VALUE | 0079683 0187301 0.43 0.671 -.028915 0448516 DEBT | 0300548 0274481 1.09 0.275 -.023996 0841055 _cons | 0415188 0187464 2.21 0.028 0046033 0784343 -+ -q50 | IND | 1174819 1618624 0.73 0.469 -2012577 4362215 MARKET_VALUE | 0492111 0162112 3.04 0.003 0172879 0811342 DEBT | 0626295 0329429 1.90 0.058 -.0022417 1275008 _cons | 0379477 0144724 2.62 0.009 0094487 0664468 -+ -q75 | IND | 1230869 3248008 0.38 0.705 -5165114 7626851 MARKET_VALUE | 0494652 0113155 4.37 0.000 0271826 0717478 DEBT | -.0062465 0430355 -0.15 0.885 -.090992 0784991 _cons | 0997236 0205527 4.85 0.000 0592511 140196 Page xxi 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 = = = = = 262 120.86 0.0000 0.5397 08355 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 1.52e+07 3598124 4.22 0.000 8089547 2.23e+07 MARKET_VALUE | 0260482 0110634 2.35 0.019 0042626 0478339 DEBT | 1341359 0219554 6.11 0.000 090902 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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.0282 0.0156 0.1358 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | -1995538 2525742 -0.79 0.430 -6969232 2978157 MARKET_VALUE | -.0009343 0091978 -0.10 0.919 -.0190466 017178 DEBT | 0591668 0211245 2.80 0.005 0175683 1007653 _cons | 0297198 014227 2.09 0.038 001704 0577356 -+ -q50 | IND | -320895.2 3303790 -0.10 0.923 -6826723 6184933 MARKET_VALUE | 0226727 0233071 0.97 0.332 -.0232237 0685691 DEBT | 0369697 0346364 1.07 0.287 -.0312362 1051757 _cons | 0509894 0191656 2.66 0.008 0132484 0887303 -+ -q75 | IND | 7144452 4576504 1.56 0.120 -1867606 1.62e+07 MARKET_VALUE | 0905988 0259518 3.49 0.001 0394945 1417031 DEBT | 0054242 0220433 0.25 0.806 -.0379834 0488318 _cons | 0448104 0229063 1.96 0.052 -.0002967 0899175 - clear 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 = = = = = 262 50.85 0.0000 0.5362 0744 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 1.07e+07 3641727 2.95 0.003 3578807 1.79e+07 MARKET_VALUE | 0740219 0146696 5.05 0.000 0451351 1029087 DEBT | 0456952 0227319 2.01 0.045 0009324 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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.0285 0.0415 0.1367 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | 1145074 2009579 0.57 0.569 -2812193 5102341 MARKET_VALUE | 0283686 0181202 1.57 0.119 -.0073138 064051 DEBT | 0372189 0171499 2.17 0.031 0034474 0709904 _cons | 0032199 0091798 0.35 0.726 -.0148571 0212969 -+ -q50 | IND | 313869.6 4148999 0.08 0.940 -7856346 8484085 MARKET_VALUE | 0628791 0299716 2.10 0.037 003859 1218993 DEBT | -.0016071 0254391 -0.06 0.950 -.0517017 0484875 _cons | 0346063 0136023 2.54 0.012 0078206 061392 -+ -q75 | IND | 4321403 5280328 0.82 0.414 -6076626 1.47e+07 MARKET_VALUE | 1115634 0349484 3.19 0.002 042743 1803838 DEBT | 0103316 0356067 0.29 0.772 -.0597851 0804483 _cons | 0426015 018251 2.33 0.020 0066616 0785413 - clear use data.dta Page xxiii 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 = = = = = 262 52.62 0.0000 0.5521 06918 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 6492632 3594900 1.81 0.072 -586320.7 1.36e+07 MARKET_VALUE | 0764732 0166445 4.59 0.000 0436973 1092491 DEBT | 0394226 0205807 1.92 0.057 -.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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.0628 0.1089 0.1982 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | -1435019 1994772 -0.72 0.473 -5363127 2493090 MARKET_VALUE | 0316607 016076 1.97 0.050 3.68e-06 0633176 DEBT | 0393071 0265213 1.48 0.140 -.0129187 091533 _cons | 0023586 0098682 0.24 0.811 -.0170739 0217911 -+ -q50 | IND | -334575.5 1992652 -0.17 0.867 -4258509 3589358 MARKET_VALUE | 0915574 0310832 2.95 0.004 0303482 1527666 DEBT | 0063569 025336 0.25 0.802 -.0435348 0562486 _cons | 0172245 0170399 1.01 0.313 -.0163305 0507795 -+ -q75 | IND | -3244050 2746070 -1.18 0.239 -8651616 2163515 MARKET_VALUE | 1237737 0299148 4.14 0.000 0648654 182682 DEBT | -.0053518 037604 -0.14 0.887 -.0794018 0686981 _cons | 0387584 0179 2.17 0.031 0035098 074007 - clear use data.dta keep if year == 2013 (1,834 observations deleted) Page xxiv reg FOI2 ITA MARKET_VALUE DEBT, nocon robust Linear regression Number of obs F(3, 259) Prob > F R-squared Root MSE = = = = = 262 67.82 0.0000 0.6362 06298 -| Robust FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -ITA | 2767271 3572289 0.77 0.439 -4267157 9801699 MARKET_VALUE | 0855302 0107033 7.99 0.000 0644537 1066067 DEBT | 0342534 0201279 1.70 0.090 -.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 50 100 Number of obs 25 Pseudo R2 50 Pseudo R2 75 Pseudo R2 = = = = 262 0.1325 0.2047 0.2777 -| Bootstrap FOI2 | Coef Std Err t P>|t| [95% Conf Interval] -+ -q25 | IND | -4129910 3456017 -1.19 0.233 -1.09e+07 2675684 MARKET_VALUE | 0604361 0147877 4.09 0.000 031316 0895561 DEBT | 0401991 0146578 2.74 0.007 0113348 0690633 _cons | -.0069618 007535 -0.92 0.356 -.0217997 0078761 -+ -q50 | IND | -2261426 2870655 -0.79 0.432 -7914323 3391471 MARKET_VALUE | 1011352 0211181 4.79 0.000 0595495 1427208 DEBT | 0339778 0138367 2.46 0.015 0067305 0612252 _cons | -.0055954 0116956 -0.48 0.633 -.0286263 0174356 -+ -q75 | IND | 6225554 4935652 1.26 0.208 -3493740 1.59e+07 MARKET_VALUE | 133802 0212655 6.29 0.000 091926 175678 DEBT | 0143195 0286756 0.50 0.618 -.0421484 0707875 _cons | 0051754 0172895 0.30 0.765 -.0288711 0392219 - clear exit, clear Page xxv ... 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