Chen et al “the effect of the political connections of government bank CEOs on bank performance during the financial crisis”, j financial stability (2018)

Accepted Manuscript Title: The Effect of the Political Connections of Government Bank CEOs on Bank Performance during the Financial Crisis Authors: Hung-Kun Chen, Yin-Chi Liao, Chih-Yung Lin, Ju-Fang Yen PII: DOI: Reference: S1572-3089(17)30525-9 https://doi.org/10.1016/j.jfs.2018.02.010 JFS 609 To appear in: Journal of Financial Stability Received date: Revised date: Accepted date: 26-7-2017 9-12-2017 28-2-2018 Please cite this article as: Chen, Hung-Kun, Liao, Yin-Chi, Lin, Chih-Yung, Yen, Ju-Fang, The Effect of the Political Connections of Government Bank CEOs on Bank Performance during the Financial Crisis.Journal of Financial Stability https://doi.org/10.1016/j.jfs.2018.02.010 This is a PDF file of an unedited manuscript that has been accepted for publication As a service to our customers we are providing this early version of the manuscript The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain The Effect of the Political Connections of Government Bank CEOs on Bank Performance during the Financial Crisis✩ Hung-Kun Chen Department of Banking and Finance Tamkang University Email: hkchen@mail.tku.edu.tw SC R IP T Yin-Chi Liao Department of Management and Marketing Western Illinois University Email: yinchi.liao@gmail.com N A M ED Ju-Fang Yen Department of Statistics National Taipei University Email: jfyen@mail.ntpu.edu.tw U Chih-Yung Lin* College of Management Yuan Ze University Email: d95723009@ntu.edu.tw ✩ PT * Corresponding author Tel.: (886) 3-463-8800#6370; Fax: +886-3-4557040 E-mail address: d95723009@ntu.edu.tw (C.-Y Lin) A CC E We are especially grateful for constructive comments from Yan-Shing Chen, Yehning Chen, Tse-Chun Lin, Yanzhi Wang and seminar participants at National Taiwan University for helpful comments and suggestions Chih-Yung Lin appreciates financial support from the Taiwan Ministry of Science and Technology Any remaining errors are ours Abstract SC R IP T This study investigates how the political connections of government bank CEOs affected their banks’ performance during the 2007-2009 financial crisis Examination of global data shows that government banks with politically connected CEOs experienced significantly higher loan default rates and worse operating performance during the crisis than those without politically connected CEOs However, these politically connected CEOs were less likely than others to be penalized for the poor performance of their banks Our evidence suggests that politically connected CEOs of government banks can influence a bank’s lending decisions by using their political power and influence to relax lending standards and to reap private benefits that thus raise their banks’ sensitivity to a crisis Keywords: Political connections, government banks, financial crisis, institutional ownership, country corruption and governance U JEL classification: G01, G21, G28, G34 N Introduction A In August 2007, the credit markets froze after two hedge funds run by New York- M based Bear Stearns Co collapsed because of the plummeting values of their subprime mortgage holdings The inability to set a price on such securities paralyzed the market ED At that time, most government-owned banks were encouraged by their governments to increase lending to prevent the collapse of business and to stabilize and promote PT economic recovery (Laeven and Valencia, 2010, 2013), which resulted in banks CC E acquiring many nonperforming loans that weakened their capital reserves.1 However, not all government banks suffered equally While researchers have been intrigued by the heterogeneous performance of banks during the most recent financial crisis (2007 A to 2009) (e.g., Fahlenbrach and Stulz, 2011; Beltratti and Stulz, 2012; Berger and Bouwman, 2013; Ellul and Yerramilli, 2013; Ho, Huang, Lin, and Yen, 2016), we contribute to the literature by providing a new perspective to explore why some government banks’ performance was worse than others during the crisis period For instance, Iannotta, Nocera, and Sironi (2013) find that some European government banks became insolvent following the onset of the global financial crisis We propose that the political connections of government banks CEOs contributed to risky lending decisions by their banks that influenced the banks’ subsequent performance during the crisis We focus on CEOs because they are the primary influence on government bank lending standards, which affect the banks’ sensitivity to a crisis (Sapienza, 2004; Khwaja and Mian, 2005; Shen and Lin, 2012) In a theoretical IP T model, Acharya and Naqvi (2012) also show that a manager’s incentives to take excessive risks can induce over-lending decisions and thus sow the seeds of an SC R impending crisis We follow the previous literature and define government banks as banks with at U least 20 percent government ownership (La Porta, Lopez-de-Silanes, and Shleifer, N 2002) We divide these banks into two groups by year: we designate banks with CEOs A who served as politicians as political banks and those without these types of executives M as non-political banks (Faccio, Masulis, and McConnell, 2006; Fan, Wong, and Zhang, 2007) CEOs with political backgrounds may retain their political connections, even as ED executives To pursue these political affiliations, such as a future political career, politically connected CEOs of government banks tend to follow the interests of other PT politicians Further, with the support that politically connected CEOs obtain from other CC E politicians, they may ignore market pressures to report low-quality accounting information or poor operating performance (Chaney, Faccio, and Parsley, 2011) Hence, we conjecture that political banks may have carried more low-quality loans because of A political connections than did non-political banks, either before or during the crisis (the political-connection hypothesis) To investigate the issue, we compare the loan quality and performance of political and non-political government banks during the global financial crisis by using data from 41 countries We obtain these data from Bankscope The period of the global financial crisis ran from 2007 to 2009 and the period from 2004 to 2006 we term the pre-crisis period (Ivashina and Scharfstein, 2010; Beltratti and Stulz, 2012) Empirically, the results support the political-connection hypothesis Political banks significantly approved more low-quality loans than did non-political banks IP T before or during the crisis, such that they were confronted with a higher ratio of nonperforming to gross loans during the crisis This ratio indicates that politically SC R connected banks became increasingly inefficient and pursued riskier lending behavior Furthermore, these lower-quality loans caused significant underperformance, as measured by return on assets, return on equities, net interest income to total assets, and U the cost-to-income ratio during the crisis years N Our research design, based on the recent global financial crisis, can mitigate A endogeneity concerns The financial crisis represents an exogenous shock with a M negative effect on all individual firms The crisis resulted in a systematic decrease in loan quality and in the subsequent performance of the banks and thus allows us to ED employ a difference-in-differences (DiD) analysis We also include bank fixed effects PT in our regression models to control for any potential endogeneity concerns arising from omitted variables or measurement error (Roberts and Whited, 2013) However, CC E government banks may choose politically connected CEOs for unobserved bank characteristics or political reasons (Cooper, Gulen, and Ovtchinnikov, 2010), leading to a self-selection bias.2 We use Heckman’s (1979) two-stage approach to address the A self-selection bias that might result from a ruling party choosing politically connected CEOs for government banks (Cooper, Gulen, and Ovtchinnikov, 2010) As a result, our main results remain unchanged after controlling for the potential selection bias In another view, the ruling party can choose politically connected CEOs for government banks (Shen and Lin, 2012), whereby hiring politically connected CEOs for government banks is an endogenous assignment We report the results in Table based on the endogenous assignment assumption We also find that the negative influence of political connections on government banks, which we term the PC effect, can be reduced by the presence in those countries of institutional ownership and superior institutional factors We find that the PC effect is diminished if a government bank has institutional ownership We also find that the underperformance of political banks is not observed in countries with strong IP T governance systems or low levels of corruption, which is consistent with the findings of previous studies that institutional factors in countries have a significantly positive SC R influence on the lending behavior of banks (Qian and Strahan, 2007; Bae and Goyal, 2009; Haselmann, Pistor, and Vig, 2009) This evidence also shows that these U institutional factors may in fact exercise sufficient influence to protect banks from N political intervention Thus, the inefficient allocation of resources by political banks A can be partly controlled by institutional ownership and the specific country’s M institutional factors Moreover, we investigate possible motives for the performance-destructive ED behavior of government banks with politically connected CEOs, i.e., whether these CEOs grant more low-quality loans for their own benefit, such as enhancing their own PT future political careers By studying employment renewal as well as whether the CEOs CC E of government banks were offered a political position after the crisis, we find positive evidence of such motives We find that 22.50% of politically connected CEOs remained at the same government bank from 2010 to 2013, as compared to only 8.79% of non- A politically connected CEOs; 28.75% of the politically connected CEOs of government banks were offered a political position from 2010 to 2013, as compared to only 4.40% of non-politically connected CEOs Therefore, the CEOs of government banks with political connections and poor operating performance are less likely to be penalized by the bank or by the political system These CEOs even had the potential for a successful political career after the crisis This evidence is consistent with our political-connection hypothesis, that the CEOs of government banks used their political power and influence to relax lending standards and to reap private benefits Research studies have typically used a macro-level measure, election years, to IP T represent the political factor and to analyze the influence of political ties (Sapienza, 2004; Dinỗ, 2005; Brown and Dinỗ, 2005; Micco, Panizza, and Yaňez, 2007; Shen and SC R Lin, 2012; Jackowicz, Kowalewski, and Kozłowski, 2013) The literature shows that politicians obtain more benefits during major elections (Dinỗ, 2005; Micco, Panizza, and Yaňez, 2007; Iannotta, Nocera, and Sironi, 2013) Different from previous studies, U we focus on a bank-level political factor by considering the previous role of a CEO as N a politician and investigate whether the PC effect is stronger in a major election year A We not find evidence showing that a major countrywide election aggravates the M negative influence of political connections on government banks’ performance, however, indicating that the PC effect still exists after controlling for a major election ED year PT The contributions of our study to the literature are threefold First, we complement the literature on political connections of bank CEOs by investigating their negative CC E influence on government banks The influence of political connections on corporate finance has recently attracted critical attention within industrial firms Most of this literature indicates that the political connections of CEOs add value to firms.3 While A these studies have shown that borrowing firms usually use their own political connections to attract favorable loans from government banks (Sapienza, 2004; Dinỗ, That is, politically connected firms are more likely to obtain preferential treatment when applying for bank loans (Khwaja and Mian, 2005; Charumilind, Kali, and Wiwattanakantang, 2006) to gain an increase in stock returns during the elections (Goldman, Rocholl, and So, 2009; Cooper, Gulen, and Ovtchinnikov, 2010), to be informed in advance on future policy directions (Belo, Gala, and Lin, 2013), to be the first to be bailed out (Faccio, Masulis, and McConnell, 2006), and so on 2005; Carvalho, 2014), we provide evidence that the political connections of lenders could also affect their lending decisions Using global data, we propose a novel viewpoint to show the dark side of political connections from the perspective of the supply side of the financial system.5 This paper thus complements the literature by showing that government banks with politically connected CEOs suffer from lower IP T lending standards.6 SC R Second, we relate political connections to the banking literature on corporate governance and institutional factors We show that the PC effect can be partly eliminated when government banks have institutional ownership This finding U contributes to the field of corporate governance in which the presence of institutional N ownership reduces the agency problem (e.g., Weisbach 1988; Bhojraj and Sengupta, A 2003; Henry, 2008) In addition, previous studies reveal the far-from-ideal track record M of government banks regarding the efficiency of their capital allocations (Sapienza, 2004; Khwaja and Mian, 2005; Iannotta, Nocera, and Sironi, 2007; Ho, Chen, Lin, and ED Chi, 2016) We find that the influence of the political connections of government bank PT CEOs is not as strong in countries with better governance and lower corruption levels A CC E Government banks tend to charge lower interest rates to firms associated with the ruling party than to those without such an affiliation (Sapienza, 2004) In addition, politicians can use government banks to distribute incentives to their supporters by increasing lending during election periods (Dinỗ, 2005) and to use lending to expand employment in politically attractive regions (Carvalho, 2014) Only two studies show the dark side of political connection, that is, Fan, Wong, and Zhang (2007) and Chaney, Faccio, and Parsley (2011) They find that politically connected firms underperform in comparison with non-politically connected firms in terms of post-IPO stock returns and the quality of accounting information reports, respectively Our study complements Fan, Wong, and Zhang (2007) and Chaney, Faccio, and Parsley (2011) by showing that banks with political CEOs would perform worse Hung, Jiang, Liu, Tu, and Wang (2017) find that banks with politically connected CEOs outperform their non-connected counterparts, which is in contrast with ours Although both studies focus on the political view of lenders, we use a sample of global government banks whereas Huang et al (2017) use a sample of commercial banks in China, which includes both government-owned and privatelyowned banks Our results are in line with prior literature, which has argued that political connections hurt the value of government-owned banks (Sapienza, 2004; Khwaja and Mian, 2005; Iannotta, Nocera, and Sironi, 2007; Shen and Lin, 2012; Shen, Hasan, and Lin, 2014) Hung et al (2017) is consistent with the literature that political connections enhance the value of privately owned firms (Cooper, Gulen, and Ovtchinnikov, 2010) These findings complement those in recent studies that investigate the important role of corruption in bank lending (Beck, Demirgỹỗ-Kunt, and Levine, 2006; Barth, Lin, Lin, and Song, 2009; Houston, Lin, and Ma, 2011) Third, this paper complements the literature on bank lending during global IP T financial crises (e.g., Ivashina and Scharfstein, 2010; Puri, Rocholl, and Steffen, 2010; Acharya and Naqvi, 2012; Chen, Chen, Lin, and Sharma, 2016) Although these studies SC R show a substantial decline in the loan supply during crisis periods, little attention has been given to the lending behavior of government banks During the 2007-09 crisis period, we find that the political connections of government banks led to the U deterioration of their lending quality and a consequent decline in their operating N performance These findings are consistent with the view that the political intervention A of politicians leads to inefficient lending by government banks (Dinỗ, 2005; Shen and M Lin, 2012; Iannotta, Nocera, and Sironi, 2013) The remainder of this paper is organized as follows We develop our hypotheses ED in Section and describe our data and present basic statistics in Section We examine PT the relationship between the PC effect and bank performance in Section and in Section we examine that between the PC effect and institutional factors Section presents a CC E discussion of the results, and Section concludes the paper Hypotheses development A Government banks played an important role in preserving the stability of financial markets during the global financial crisis (Laeven and Valencia, 2010, 2013) However, not all government banks suffered equally during the crisis This difference raises the question of why some government banks performed worse than others during crisis period The political view suggests that the operations of government banks are constantly being used by politicians to pursue their individual political goals, such as the provision of jobs, resources, or subsidies to their friends and supporters (Shleifer and Vishny, 1998) Therefore, the maximization of the political benefits to politicians becomes their main objective For instance, several studies have shown that industrial firms often use IP T their political connections to attract favorable loans from government banks (Sapienza, 2004; Dinỗ, 2005; Faccio, Masulis, and McConnell, 2006; Carvalho, 2014) No studies SC R have as yet taken into account, however, whether the types of political connection of some government bank CEOs might differ from others, so that some banks are aligned U with politicians’ interests, but others are not N When government bank CEOs have previously served as politicians, they can A retain their political ambitions On the one hand, politically connected bank CEOs are M willing to align themselves and their banks with the interests of other politicians to facilitate their future political careers To ensure the success of these careers, politically ED connected CEOs can grant more low-quality loans that lead to poor operating PT performance, especially during financial crises On the other hand, politically connected CEOs of government banks are not CC E penalized for high loan default rates as long as their politician friends can protect them from market pressures To some extent, politicians provide protection to the companies with which they associate by preventing them from being penalized for low-quality A accounting information (Chaney, Faccio, and Parsley, 2011) If politically connected government bank CEOs care less about loan quality, they are more likely to lend money to low-quality borrowers to gain political influence, resulting in an increase in default loan rates and a reduction in operating performance during crisis years Non-politically connected CEOs of government banks tend to care more about Ho, P H., Huang, C W., Lin, C Y., Yen, J F., 2016 CEO overconfidence and financial crisis: Evidence from bank lending and leverage Journal of Financial Economics, 120, 194-209 Houston, J F., Lin, C., Ma, Y., 2011 Media ownership, concentration and corruption in bank lending Journal of Financial Economics 100, 326–350 Hung, C H D., Jiang, Y., Liu, F H., Tu, H., Wang, S., 2017 Bank political connections and performance in China Journal of Financial Stability, 32, 57–69 IP T Iannotta, G., Nocera, G., Sironi, A., 2007 Ownership structure, risk and performance in the European banking industry Journal of Banking and Finance 31, 2127–2149 SC R Iannotta, G., Nocera, G., Sironi, A., 2013 The impact of government ownership on bank risk Journal of Financial Intermediation 22,152–176 Ivashina, V., Scharfstein, D., 2010 Bank lending during the financial crisis of 2008 Journal of Financial Economics 97, 319–338 U Jackowicz, K., Kowalewski, O., Kozłowski, Ł., 2013 The influence of political factors on commercial banks in Central European countries Journal of Financial Stability 9, 759–777 A N Kaufmann, D., Kraay A., Mastruzzi, M., 2007 Governance matters VI: Aggregate and individual governance indicators for 1996–2006 World Bank Policy Research Working Paper No 4280 ED M Khwaja, A., Mian A., 2005 Do lenders favor politically-connected firms? Rent provision in an emerging financial market Quarterly Journal of Economics 120, 1371–1411 La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 1999 Corporate ownership around the world Journal of finance, 54, 471–517 PT La Porta, R., Lopez-de-Silanes, F., Shleifer, A., 2002 Government ownership of banks Journal of Finance 57, 265–301 CC E Laeven, L., Valencia, F., 2010 Resolution of banking crises: The good, the bad, and the ugly, IMF Working Paper No 10/146 Washington: International Monetary Fund Laeven, L., Valencia, F., 2013 The real effects of financial sector interventions during crises Journal of Money, Credit and Banking 45, 147–177 A Liu, C., Ryan, S G., 2006 Income smoothing over the business cycle: changes in banks’ coordinated management of provisions for loan losses and loan chargeoffs from the pre-1990 bust to the 1990s boom Accounting Review 81, 421–441 Liu, C., Ryan, S G., Wahlen, J M., 1997 Differential valuation implications of loan loss provisions across banks and fiscal quarters The Accounting Review 72, 133– 146 Megginson, W L., 2005 The economics of bank privatization Journal of Banking and Finance 29, 1931–1980 30 Micco, A., Panizza, U., Yaňez, M., 2007 Bank ownership and performance Does politics matter? Journal of Banking and Finance 31, 219–241 Parrino, R., 1997 CEO turnover and outside succession a cross-sectional analysis Journal of Financial Economics 46, 165–197 Persson, T., Tabellini, G., 2003 The economic effects of constitutions MIT Press, Massachusetts IP T Petersen, M., 2009 Estimating standard errors in finance panel data sets: Comparing approaches Review of Financial Studies 22, 435–480 SC R Puri, M., Rocholl, J., Steffen, S., 2010 Global retail lending in the aftermath of the US financial crisis: Distinguishing between supply and demand effects Journal of Financial Economics 100, 556–578 Qian, J., Strahan, P.E., 2007 How laws and institutions shape financial contracts: The case of bank loans Journal of Finance 62, 2803–2834 N U Roberts, M R., Whited, T M., 2013 Endogeneity in empirical corporate finance In: George M Constantinides, Milton Harris and Rene M Stulz (Eds.), Handbook of the Economics of Finance: Empirical Corporate Finance 2A Elsevier, North Holland, Amsterdam, 493–572 A Sapienza, P., 2004 The effects of government ownership on bank lending Journal of Financial Economics 72, 357–384 M Shen C H., Hasan, I., Lin, C Y., 2014 The Government’s Role in Government-owned Banks Journal of Financial Service Research 45, 307–340 ED Shen, C H., Lin, C Y., 2012 Why government banks underperform: A political interference view Journal of Financial Intermediation 21, 181–202 PT Shleifer, A., Vishny, R W., 1998 The grabbing hand: Government pathologies and their cures Harvard University Press, Cambridge, MA CC E Wahlen, J M., 1994 The nature of information in commercial bank loan loss disclosures The Accounting Review 455, 455–478 Warner, J B., Watts, R., Wruck, K., 1988 Stock prices and top management changes Journal of Financial Economics 20, 461–492 A Weisbach, M S., 1988 Outside directors and CEO turnover Journal of Financial Economics 20, 431–460 White, H., 1980 A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity Econometrica 48, 817–838 31 Table Definitions of dummy, control, and performance variables Variable Definition Data Source Political connection variables PB A dummy variable that equals one if the government bank is a political Bankscope, bank whose chief executive (CEO or president) has previously served as a Factivaa and politician and that equals zero if the government bank is a non-political by usb bank IP T Loan quality variable Ratio of nonperforming loans to gross loans Nonperforming loans are Bankscope loans with payments of interest and principal more than 90 days overdue NPL (%) This proxy measures the portion of the banks’ loan portfolios that are in default or close to default Performance variables Ratio of net income to total assets Bankscope ROE (%) Ratio of net income to total equities NIM (%) Ratio of net interest income to total assets C/I (%) Ratio of cost to income SC R ROA (%) Bankscope U Bankscope A N Corporate governance variable INSTIT A dummy variable that equals one if government banks have more than 10% private institutional ownership, and zero otherwise A dummy variable that equals one if a major election is occurring in the country, and zero otherwise Major elections include presidential elections in countries with a presidential system and parliamentary elections in countries with a parliamentary system CC E Election PT Election variable ED M Institutional variables Country The corruption index is obtained from the International Country Risk Corruption Guide The scale of the index is from to 6, in which a high number implies low corruption level The value of is used as the basis for classifying countries into low- and high-corruption countries Country The country governance index is obtained from the Worldwide Governance Governance Indicators compiled by Kaufmann, Kraay, and Mastruzzi (2007) We classify the sample countries as either having strong or weak governance based on the theoretical median of zero Bankscop e Bankscope and Factivaa International Country Risk Guide Kaufmann, Kraay, and Mastruzzi (2007) Persson and Tabellini, (2003) and Factivaa Bank characteristic control variables Natural logarithm of total assets Bankscope D/E Ratio of total debts to total equities Bankscope LOANDEP Ratio of average balance of loan to average balance of deposit Bankscope LIQUID Ratio of current assets to total assets Bankscope A Asset Macroeconomic control variables GDP Natural logarithm of GDP of the country per capita GDP growth GDP growth rate of the country World Bank World Bank 32 Budget surplus Government budget surplus of the country as a percentage of GDP Inflation rate Inflation rate of the country Exchange rate World Bank World Bank Change in the exchange rate of the domestic currency against the US dollar Datastream from the previous year A CC E PT ED M A N U SC R IP T Note: a: Factiva: databases including Dow Jones News, Reuters News, and Wall Street Journal b: By us: the variables are contrasted by authors 33 Table Number of government banks (41 countries) This table lists the basic statistics of political and non-political government banks Government banks with chief executives (CEOs/presidents) who previously served as politicians are classified as political government banks; otherwise, they are classified as non-political government banks Table only shows the sample in 2006 for brevity PT CC E A IP T Non-political government banks 10 18 1 3 1 1 1 2 2 1 111 U SC R Political government banks 2 10 2 1 1 1 2 1 96 N Argentina Australia Bahrain Belarus Belgium Brazil Cameroon China Egypt Ethiopia France Hungary India Israel Kenya Madagascar Malaysia Mexico Morocco Norway Pakistan Philippines Poland Portugal Romania Russian Rwanda Senegal Serbia Sierra Leone Slovenia South Africa South Korea Swaziland Switzerland Taiwan Thailand Turkey United Arab Emirates Uzbekistan Vietnam Total A 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 Number of government banks 12 5 12 20 2 4 3 5 4 207 M Country name ED Country ID Table Descriptive statistics and correlation coefficient matrix of the variables This table presents the descriptive statistics and the correlation coefficient matrix of the variables The sample covers data in 41countries and 207 government banks from 2004 to 2009 The definitions of all variables are defined in Table Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively 34 PB Descriptive NPL ROA ROE NIM C/I Asset D/E LOAND LIQUI GDP EP D 13.867 58.223 12.303 4.2727 8.5059 13.436 23.260 S.D 0.4991 7.2066 1.7191 3.1050 2.2496 8.7243 44.000 25th 0.0000 1.8175 0.5600 6.5300 2.3350 7.0144 6.7047 13.820 54.600 10.650 Median 0.0000 3.3200 1.1200 3.2550 8.6805 0 20.450 67.255 10.169 15.684 75th 1.0000 7.8875 2.2950 5.5300 0 Mean 0.4658 6.2276 1.4595 17.389 6.1500 0.2381 66.256 0.9161 0.1516 1.4538 2.1319 4.6831 0.4226 0.5240 0.1129 7.0089 0.1027 3.1590 0.0227 5.1690 0.8180 0.2280 8.3064 0.3639 0.6719 0.1797 8.3199 0.1716 0.7810 5.7367 0.1137 0.8439 0.3034 9.4442 0.2472 Panel B: Correlation coefficient matrix D/E LOAND EP LIQUID SC R U CC E GDP N Asset A C/I M NIM ED ROA ROE GDP growth A Budget surplus Inflation rate Exchang e rate 12.482 8.9140 0.2723 1.00 0.12** 1.00 * -0.01 -0.08* 1.00 0.67** 1.00 0.14** 0.18** * * * -0.05 0.19** 0.41** 0.25** 1.00 * * * -0.05 0.26** 0.15** 1.00 * 0.36** 0.42** * * * 0.08** 1.00 0.21** 0.29** 0.09** 0.57** 0.30** * * * * * -0.02 -0.06 -0.05 -0.06* 0.44** 1.00 0.45** 0.36** * * * 0.08** 0.00 0.12** 0.11** 0.05 1.00 * 0.10** * 0.14** 0.20** * * 0.03 0.26** 0.17** 0.16** 0.33** 0.14** 1.00 * * * * * 0.38** 0.18**0.16*** * * 0.15** 0.02 0.02 0.17** 0.08** 1.00 * 0.31** 0.22** 0.24** * 0.20** 0.09** * * * * 0.01 0.11** -0.07* 0.10** 0.15**-0.08** -0.02 1.00 0.09** 0.08** * 0.11** * * 0.21** * * 0.21** -0.05 -0.02 0.23** 0.17**-0.04 -0.02 0.05 -0.02 1.00 * 0.10** 0.11** 0.14** * * * * 0.15** 0.21** 0.20** 0.34** 0.06* 0.00 0.26** 0.18** 1.00 0.09** * * * * 0.33** 0.16** * 0.38** * 0.11** * * * * 0.00 0.03 -0.04 -0.06* 0.00 0.01 0.02 0.45** -0.06* 1.00 0.10** 0.12** 0.14** * 0.07** 0.16** * * * PT PB NPL GDP Budget Inflatio Exchan growth surplus n rate ge rate IP T Panel A: statistics 35 Table Comparison of bank characteristics and performance between political and nonpolitical banks This table presents a comparison of the bank characteristics and performance between political and nonpolitical banks during the pre-crisis period (2004–2006) and crisis period (2007–2009) The definition of all variables: see Table The Diff represents the differences between the two groups: political banks (PB=1) and non-political banks (PB=0) Superscripts *, **, and *** denote significance at the 10%, 5%, and 1% levels respectively Pre-Crisis Period (2004–2006) 2004 2005 2006 t= Crisis Period (2007–2009) 2007 2008 2009 IP T Panel A: Bank Characteristics Asset PB =1 PB =0 Diff 8.4921 8.0889 0.4032 8.4551 8.1388 0.3163 8.7119 8.3776 0.3343 8.8286 8.4217 0.4069 8.7579 8.4118 0.3461 D/E PB =1 PB =0 Diff 13.0612 11.9610 1.1002 13.2729 12.6028 0.6700 12.5007 13.2834 -0.7828 10.8627 12.3628 -1.5001 11.9608 12.0677 -0.1068 LOANDEP PB =1 PB =0 Diff 0.8072 0.6946 0.1126 0.8379 0.6123 0.2257* 0.8795 0.7909 0.0886 0.9126 0.8056 0.1071 0.9480 0.8743 0.0736 0.9936 0.7175 0.2761* LIQUID PB =1 PB =0 Diff 0.2279 0.2368 -0.0089 0.2347 0.2210 0.0137 0.2368 0.2378 -0.0009 0.2391 0.2275 0.0116 0.2254 0.2136 0.0118 0.2353 0.2032 0.0321 0.0877 0.0908 -0.0031 1.5546 1.4675 0.0872 0.0788 0.0816 -0.0029 1.9012 1.2987 0.6025* 0.0816 0.0582 0.0234 1.6554 1.7002 -0.0447 11.8791 13.9567 -2.0776 14.1756 13.3528 0.8228 15.2956 18.5429 -3.2473 4.2246 4.7154 -0.4909 4.4497 4.5451 -0.0954 56.0986 62.2215 -6.1229* 52.3027 58.1806 -5.8778* PB =1 PB =0 Diff NIM (%) PB =1 PB =0 Diff C/I (%) PB =1 PB =0 Diff N U SC R 10.6633 12.8268 -2.1635* 0.0664 0.0394 0.0270** 0.9719 1.1360 -0.1641 15.2874 17.7462 -2.4587 7.4379 15.6092 -8.1713*** 8.3111 13.0222 -4.7111** 4.1488 4.2042 -0.0554 4.0246 4.1557 -0.1311 4.1006 4.3632 -0.2626 3.5589 4.5158 -0.9570** 53.0948 55.8809 -2.7861 52.0223 56.5270 -4.5047 61.5538 62.5695 -1.0157 61.4219 59.0631 2.3588 M A 0.0665 0.0360 0.0305** 0.9189 1.5517 -0.6328** A CC E PT ROE (%) ED Panel B: Bank performances NPL (%) PB =1 PB =0 Diff ROA (%) PB =1 PB =0 Diff 8.9197 8.6639 0.2558 36 0.0702 0.0454 0.0248* 1.8153 1.6255 0.1898 Table PC effect with bank loan quality changes on bank performance This table presents the estimated results of the PC effect on the performances of government banks The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝜷′ 𝒁𝒊𝒋𝒕 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , Asset D/E LOANDEP ED LIQUID GDP PT GDP growth Budget surplus CC E Inflation rate Exchange rate A Control For Bank fixed effect Obs Adj-R2 Yes 559 0.7377 Yes 758 0.5245 Yes 758 0.4604 37 C/I (5) -44.2229 (-0.75) 68.8823*** (4.24) 4.1331 (1.52) -1.9080 (-0.89) -11.8809*** (-2.88) 0.4291*** (2.96) 3.2946 (1.17) 10.8475 (0.99) 19.0750*** (2.71) -0.6649* (-1.85) -0.0541*** (-2.70) -0.4956* (-1.86) -4.1685 (-0.38) SC R NIM (4) 30.8826*** (6.07) -5.4153*** (-3.02) -0.4352** (-2.09) 0.5688*** (3.02) -1.0927*** (-4.37) -0.0336*** (-3.21) -0.0073 (-0.03) -1.9855* (-1.85) -1.7133*** (-3.01) 0.0085 (0.35) 0.0045*** (3.46) 0.1229*** (4.59) 0.5494 (0.48) U Crisis N PB × Crisis ROE (3) 65.7835* (1.77) -29.0744*** (-2.78) -4.6081** (-2.50) 0.8671 (0.54) 5.6039** (2.37) -0.3436 (-1.16) 1.3939 (1.15) 5.0306 (0.74) -8.1491* (-1.91) 0.2429 (1.18) 0.0077 (0.45) 0.0757 (0.39) 0.2863 (0.04) A PB ROA (2) 11.0916*** (2.60) -2.8040** (-2.25) -0.6076*** (-2.83) 0.0912 (0.48) 0.1790 (0.65) -0.0797*** (-4.14) 0.3267 (1.22) 0.4525 (0.53) -0.9663* (-1.94) 0.0515** (2.42) -0.0013 (-0.82) 0.0510** (2.33) 0.0055 (0.01) M Constant NPL (1) 73.9827*** (4.24) -16.3914*** (-3.49) 1.2948* (1.77) -0.8187 (-1.30) 1.2485 (1.05) 0.2146** (2.18) 0.1355 (0.29) -11.3364* (-1.78) -7.6329*** (-3.67) -0.0337 (-0.30) 0.0094 (1.16) 0.0495 (0.47) 3.7578 (1.59) IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; Zijt-1 represents a vector of control variables from bank i in country j at year t-1; 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes 751 0.8653 Yes 783 0.5366 38 A ED PT CC E IP T SC R U N A M Table PC effect: Heckman’s two-stage regression IMR Asset D/E LOANDEP ED LIQUID GDP PT GDP growth Budget surplus CC E Inflation rate Exchange rate A Control For Bank fixed effect Obs Adj-R2 Yes 559 0.7377 Yes 758 0.5236 Yes 758 0.4595 39 C/I (5) -43.2135 (-0.74) 94.8825*** (2.87) 4.4296 (1.55) -2.0201 (-0.93) -16.1282 (-1.01) -12.1632*** (-2.88) 0.4603*** (3.02) 1.9946 (0.62) 5.3388 (0.41) 18.0048*** (2.65) -0.0001 (-0.00) -0.0883** (-2.16) -0.5313* (-1.96) -2.3364 (-0.21) SC R NIM (4) 30.9435*** (6.09) -2.8990 (-1.01) -0.4074* (-1.90) 0.5561*** (2.93) -1.5603 (-1.16) -1.1189*** (-4.50) -0.0310*** (-2.97) -0.1305 (-0.48) -2.5097** (-2.24) -1.8130*** (-3.12) 0.0720 (1.41) 0.0012 (0.36) 0.1194*** (4.53) 0.7228 (0.62) U Crisis N PB × Crisis ROE (3) 66.0491* (1.77) -21.0201 (-0.80) -4.5187** (-2.43) 0.8287 (0.51) -5.0062 (-0.32) 5.5228** (2.37) -0.3354 (-1.17) 0.9985 (0.60) 3.3448 (0.41) -8.4771* (-1.90) 0.4471 (0.66) -0.0031 (-0.07) 0.0642 (0.32) 0.8393 (0.12) A PB ROA (2) 11.0775*** (2.60) -3.2512 (-1.19) -0.6125*** (-2.77) 0.0934 (0.49) 0.2779 (0.20) 0.1835 (0.66) -0.0802*** (-4.15) 0.3486 (1.26) 0.5460 (0.57) -0.9482* (-1.93) 0.0402 (0.72) -0.0007 (-0.18) 0.0516** (2.31) -0.0253 (-0.03) M Constant NPL (1) 74.8396*** (4.28) -6.0472 (-0.52) 1.4547* (1.96) -0.8930 (-1.39) -6.5461 (-0.94) 1.1537 (0.97) 0.2228** (2.22) -0.3891 (-0.55) -13.5449** (-1.99) -8.1170*** (-3.77) 0.2393 (0.75) -0.0056 (-0.25) 0.0360 (0.34) 4.4828* (1.91) IP T This table presents the results of Heckman’s two-stage regression of the PC effect on government banks In the first stage, we perform a probit regression using PBij as the dependent variable Four bank characteristics (Asset, D/E, LOANDEP, and LIQUID) and country dummies are used as independent variables to assess the possible motives behind government bank CEOs building political connections The resulting inverse Mill’s ratio (IMR) is inserted into the second-stage regressions to correct for any potential bias In the second-stage regression, the dependent variables are PERFORMijt, which is substituted by NPL, ROA, ROE, NIM, and C/I The sample period is t=2004 to 2009 The definitions of all variables are in Table The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes 751 0.8653 Yes 783 0.5361 Table PC effect: Control government ownership This table presents the estimated results of the PC effect on government banks by controlling government ownership The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼5 𝐺𝑂𝑖𝑗 + 𝜷′ 𝒁𝒊𝒋𝒕 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , GO Asset D/E ED LOANDEP LIQUID GDP growth PT GDP CC E Budget surplus Inflation rate Exchange rate A Control For Bank fixed effect Obs Adj-R2 Yes 559 0.7594 Yes 758 0.5292 Yes 758 0.4937 40 C/I (5) 21.3905 (0.20) 65.7482*** (4.74) 2.4004 (1.09) -1.7191 (-0.90) -2.9123 (-0.55) -9.9108*** (-2.92) 0.3716*** (2.85) 3.7358 (1.41) 10.4489 (1.14) 16.5376*** (2.85) -0.5931* (-1.86) -0.0507*** (-2.63) -0.4140* (-1.82) -1.8108 (-0.21) SC R NIM (4) 25.2208*** (7.20) -5.2149*** (-2.97) -0.4046** (-1.99) 0.5496*** (2.96) 0.2420** (2.10) -1.0811*** (-4.47) -0.0332*** (-3.21) 0.0112 (0.05) -1.8094* (-1.74) -1.6526*** (-2.98) 0.0102 (0.43) 0.0045*** (3.47) 0.1168*** (4.53) 0.5725 (0.50) U Crisis N PB × Crisis ROE (3) 119.9246** (2.28) -26.3230*** (-2.78) -3.6981** (-2.35) 0.4073 (0.29) -3.3450*** (-2.72) 4.3122** (2.25) -0.2112 (-0.95) 1.1767 (0.99) 5.2861 (0.83) -5.9031 (-1.57) 0.2183 (1.14) 0.0093 (0.62) 0.1098 (0.64) -0.7872 (-0.13) A PB ROA (2) 15.7640** (2.42) -2.7763** (-2.36) -0.5245*** (-2.59) 0.0732 (0.40) -0.2221 (-1.40) 0.1242 (0.49) -0.0756*** (-4.13) 0.3730* (1.70) 0.5362 (0.64) -0.9508** (-2.02) 0.0509** (2.55) -0.0011 (-0.77) 0.0496** (2.35) -0.0115 (-0.01) M Constant NPL (1) 115.8554*** (4.06) -16.0357*** (-3.67) 1.1278* (1.67) -0.6793 (-1.17) -2.1134*** (-2.85) 1.2310 (1.10) 0.2065** (2.09) 0.1740 (0.39) -10.2882* (-1.84) -7.6037*** (-3.95) -0.0341 (-0.31) 0.0087 (1.09) 0.0392 (0.40) 3.7040 (1.60) IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; 𝐺𝑂𝑖𝑗 is the government ownership of the bank i in country j; Zijt-1 represents a vector of control variables from bank i in country j at year t-1; 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes 751 0.8687 Yes 783 0.6216 Table PC effect and institutional ownership This table presents the estimated results regarding the influence of private institutional ownership on the PC effect To test this issue, we divide our sample into two subgroups: banks with no institutional ownership versus banks with institutional ownership The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝜷′ 𝒁𝒊𝒋𝒕 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , NIM M CC E PT ED (4) 38.0712*** (6.38) -4.5368** (-2.30) -1.0864*** (-3.03) 1.0246*** (3.34) 375 0.8491 (5) 33.7090 (0.50) 28.6766 (1.52) 7.0703* (1.78) 0.0268 (0.01) 395 0.5643 (8) 134.7705** (2.25) -24.3561*** (-2.67) -0.6516 (-0.29) 0.9266 (0.39) 378 0.4444 (9) 21.1714*** (2.76) -6.3318*** (-6.13) 0.2902 (1.64) -0.0858 (-0.38) 376 0.9113 (10) -191.4255** (-2.05) 181.3012*** (3.79) -0.2623 (-0.08) -3.3229 (-1.30) 388 0.4839 U Panel B: GOBs with institutional ownership (6) (7) Constant 83.6300** 17.2539*** (2.43) (3.04) -14.7509 3.7011** PB (-0.86) (2.17) 1.4254 -0.3342 PB × Crisis (0.91) (-1.50) -1.0776 0.0987 Crisis (-0.90) (0.50) Obs 307 378 Adj-R2 0.7120 0.6437 Yes Yes Yes Yes Yes Yes A Control For Control variables Bank fixed effect 41 C/I (3) 17.1566 (0.39) -9.3754 (-0.71) -7.0976*** (-2.67) -0.2290 (-0.11) 380 0.5014 A Panel A: GOBs with no institutional ownership (1) (2) Constant 42.3898* 8.0626 (1.95) (1.44) -0.7511 -1.6707 PB (-0.09) (-1.04) 0.8514 -0.7132** PB × Crisis (0.68) (-2.01) -0.7510 -0.0266 Crisis (-0.75) (-0.08) 252 380 Obs Adj-R2 0.7639 0.4439 ROE SC R ROA N NPL IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; Zijt-1 represents a vector of control variables from bank i in country j at year t-1; 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes Yes Yes Yes Table PC effect in different country governance levels This table presents the estimated results of the influence of political connection on government banks in countries with different country governance levels To test this issue, we divide our sample into two subgroups: countries with country governance values of less than zero are classified as weak country governance; countries with values above zero are strong country governance The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝜷′ 𝒁𝒊𝒋𝒕 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , ROE M CC E PT ED Panel B: Strong country governance countries (6) (7) Constant 51.5872*** 17.4362** (4.24) (2.12) 5.5380 3.6650 PB (1.27) (1.57) 1.1244*** -1.0316*** PB × Crisis (2.72) (-2.81) -0.9018** 0.3603 Crisis (-2.13) (1.41) 201 246 Obs Adj-R 0.8724 0.6495 Yes Yes Yes Yes (8) 38.8933 (0.59) -30.5774** (-2.03) -2.8936 (-1.01) 1.1140 (0.36) 246 0.4208 (9) (10) 28.3403*** 110.0667 (2.97) (1.43) -4.7601*** -29.8932 (-2.78) (-1.34) 0.0045 -5.3094* (0.02) (-1.88) 0.2415 3.2114 (0.97) (1.21) 241 244 0.8941 0.7608 U (4) (5) 22.6917*** -29.3007 (5.00) (-0.47) 2.9714*** 53.9611*** (3.05) (3.78) -0.8810*** 7.6778** (-3.01) (1.99) 0.6355** -3.2385 (2.49) (-1.11) 510 539 0.8560 0.4763 Yes Yes A Control For Control variables Bank fixed effect 42 C/I (3) -9.2226 (-0.24) 19.1018* (1.66) -5.7963** (-2.21) 0.1078 (0.05) 510 0.4567 A Panel A: Weak country governance countries (1) (2) Constant 41.8242** 7.4177 (2.36) (1.62) 18.5149** -1.2142 PB (2.25) (-0.94) 1.8480 -0.4672* PB × Crisis (1.59) (-1.66) -0.6424 0.0158 Crisis (-0.66) (0.06) 358 512 Obs Adj-R2 0.7034 0.4679 NIM SC R ROA N NPL IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; Zijt-1 represents a vector of control variables from bank i in country j at year t-1; 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes Yes Yes Yes Table 10 PC effect in different corruption levels This table presents the estimated results of the influence of political connection on government banks in countries with different corruption levels To test this issue, we divide our sample into two subgroups: countries with corruption values of less than three are classified as high-corruption; countries with values above three are low corruption The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝜷′ 𝒁𝒊𝒋𝒕 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , (7) 26.3522* (1.71) 4.4918** (2.63) -1.0941* (-1.74) 0.5097 (1.54) 242 0.6799 PT CC E Yes Yes Yes Yes 43 SC R C/I (4) 32.0482*** (5.94) -3.1029* (-1.77) -0.8597*** (-3.00) 0.7348*** (2.83) 514 0.8550 (8) 111.7279 (1.43) -36.2531** (-2.42) -3.8622 (-1.04) 2.5774 (0.77) 242 0.4859 (9) (10) 9.8919 -1.4720 (1.48) (-0.02) -4.4148*** -19.5928 (-2.81) (-0.82) 0.2703 -4.5036 (0.76) (-1.48) -0.1096 1.1831 (-0.62) (0.53) 237 239 0.8836 0.7538 U (3) -17.3989 (-0.45) 18.6319 (1.53) -5.6469** (-2.20) -0.2741 (-0.14) 516 0.4478 Yes Yes A Control For Control variables Bank fixed effect NIM A (2) 6.5330 (1.45) -1.2606 (-0.94) -0.4507* (-1.74) -0.0228 (-0.08) 516 0.4581 ED Panel B: Low-corruption countries (6) Constant 67.9628*** (5.60) 2.4811 PB (0.79) 0.9866 PB × Crisis (1.50) -0.7303 Crisis (-1.35) Obs 207 Adj-R2 0.8629 ROE M Panel A: High-corruption countries (1) Constant 40.8536** (2.25) 18.4082** PB (2.18) 1.9835* PB × Crisis (1.74) -0.6317 Crisis (-0.63) Obs 352 Adj-R2 0.7046 ROA N NPL IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; Zijt-1 represents a vector of control variables from bank i in country j at year t-1; 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes Yes (5) -3.4582 (-0.06) 59.3555*** (2.99) 7.3235* (1.88) -1.6467 (-0.59) 544 0.4833 Yes Yes Table 11 PC effect in election years This table presents the estimated results of the influence of political connection in government banks during the election period The sample period is t=2004 to 2009 The econometric model is 𝑃𝐸𝑅𝐹𝑂𝑅𝑀𝑖𝑗𝑡 = 𝛼1 + 𝛼2 𝑃𝐵𝑖𝑗 + 𝛼3 𝑃𝐵𝑖𝑗 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼4 𝑃𝐵𝑖𝑗 × 𝐸𝐿𝐸𝐶𝑇𝐼𝑂𝑁𝑗𝑡 + 𝛼5 𝑃𝐵𝑖𝑗 × 𝐸𝐿𝐸𝐶𝑇𝐼𝑂𝑁𝑗𝑡 × 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛼6 𝐸𝐿𝐸𝐶𝑇𝐼𝑂𝑁𝑗𝑡 + 𝛼7 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 + 𝛽 ′ 𝑍𝑖𝑗𝑡−1 + 𝜈𝑖 + 𝜀𝑖𝑗𝑡 , PB ×Crisis×ELECTION ELECTION Crisis Yes Yes 559 0.7371 Yes Yes 758 0.5267 A CC E PT Control For Control variables Bank fixed effect Obs Adj-R2 44 NIM (4) 30.5686*** (5.92) -5.4028*** (-3.03) -0.4548** (-2.00) -0.0998 (-0.37) 0.1234 (0.36) 0.0993 (0.69) 0.5551*** (2.90) U N PB × ELECTION ROE (3) 58.7306 (1.57) -27.2101** (-2.57) -5.2994*** (-2.77) -5.4197** (-2.55) 3.6643 (1.26) 1.0855 (0.90) 0.5979 (0.37) A PB × Crisis M PB ROA (2) 10.3820** (2.45) -2.6018** (-2.10) -0.7323*** (-3.12) -0.6442** (-2.39) 0.6374* (1.67) 0.0884 (0.67) 0.0671 (0.36) ED Constant NPL (1) 73.7446*** (4.08) -16.6483*** (-3.49) 1.3960* (1.72) 0.9172 (0.91) -0.4260 (-0.37) 0.1209 (0.23) -0.8258 (-1.30) SC R IP T where PERFORMijt is substituted by NPL, ROA, ROE, NIM, and C/I; PBij is a dummy variable that equals one if bank i in country j is a political bank and zero, if otherwise; 𝐶𝑟𝑖𝑠𝑖𝑠𝑡 is a dummy variable that equals one if the end of a bank’s fiscal year, t, is within the crisis period (2007–2009), and zero otherwise; ELECTIONjt is a dummy variable that equals one if there is a major election in country j at year t (equals zero for all other cases) Major elections include presidential elections in countries with a presidential system and parliamentary elections in countries with a parliamentary system; Zijt-1 represents a vector of control variables from bank i in country j at year t-1, 𝜈𝑖 capture the bank fixed effect; and 𝜀𝑖𝑗𝑡 is the random error The t-statistics based on standard errors are adjusted for heteroskedasticity and clustered at the country level (White, 1980; Petersen, 2009) Superscripts *, **, and *** denote significance at 10%, 5%, and 1% levels, respectively Yes Yes 758 0.4628 Yes Yes 751 0.8648 C/I (5) -38.6446 (-0.65) 67.5582*** (4.14) 2.5527 (0.86) 0.1809 (0.06) 7.6427 (1.41) -1.1135 (-0.65) -1.6186 (-0.76) Yes Yes 783 0.5374 .. .The Effect of the Political Connections of Government Bank CEOs on Bank Performance during the Financial Crisis✩ Hung-Kun Chen Department of Banking and Finance Tamkang University Email: hkchen@mail.tku.edu.tw... CEOs of government banks were offered a political position from 2010 to 2013, as compared to only 4.40% of non-politically connected CEOs Therefore, the CEOs of government banks with political connections. .. low-quality loans because of A political connections than did non -political banks, either before or during the crisis (the political- connection hypothesis) To investigate the issue, we compare the