Social capital and payout policies

Journal of Contemporary Accounting and Economics 16 (2020) 100183

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Social capital and payout policies q Mostafa Monzur Hasan a,⇑, Ahsan Habib b a b

Department of Accounting and Corporate Governance, Macquarie Business School, Macquarie University, NSW 2109, Australia School of Accountancy, Massey University, Private Bag 102904, Auckland, New Zealand

a r t i c l e

i n f o

Article history: Received 25 June 2019 Revised 3 November 2019 Accepted 4 November 2019 Available online 13 January 2020 JEL classifications: G30 G32 G35 Keywords: Social capital Dividends Stock repurchases Corporate social responsibility

a b s t r a c t In this paper, we investigate the relationship between regional social capital and corporate payout policies. Using a large sample of US data, we find a positive relationship between regional social capital and both the likelihood and the amount of cash dividend payouts. However, we find that social capital has no bearing on the likelihood and amount of stock repurchases. The results from additional analyses show that the relationship between social capital and dividends is more pronounced for less geographically dispersed firms. We also find that the network component of social capital has a greater effect on dividends than the social norm component. Our results are robust to alternative specifications of dividends and social capital and to the use of a two-stage least squares (2SLS) analysis to alleviate endogeneity concerns. Overall, we document that regional social capital plays an important role in influencing cash dividend payout policies. Ó 2020 Elsevier Ltd. All rights reserved.

1. Introduction Payout policies are a topic of immense research interest in the corporate finance literature because of their effects on many other corporate policies, such as cash holdings, investment policies, financing and capital structure decisions as well as management compensation and its signaling role in conveying future prospects relative to peers (Farre-Mensa et al., 2014). A plethora of research identifies various determinants of payout policies, including firm-level fundamentals, corporate governance variables and macro-economic conditions (Allen and Michaely, 2003; DeAngelo et al., 2006; Farre-Mensa et al., 2014). However, no studies investigate how regional non-religious social capital (social norms and networks) affects corporate payout policies.1 Motivated by this gap in the literature, we examine the extent to which county-level social capital in the US influences corporate payout policies. This study draws motivation from prior literature suggesting that social capital (e.g., the strength of cooperative norms and the density of social networks) limits managerial opportunistic behaviors and has enduring effects on corporate investment, financing and working capital decisions (Habib and Hasan, 2017; Hasan and Habib, 2019a, 2019b; Hasan et al., 2017a, 2017b; Hoi et al., 2019; Jha and Chen, 2015; Jha and Cox, 2015). We aim to extend this literature by linking social capital with corporate payouts.

q We would like to thank Robert Durand and Grantley Taylor for encouragement, helpful comments and suggestions for initiating this project. We also thank Andrew Ferguson and an anonymous reviewer many helpful comments and suggestions. The usual disclaimer applies. ⇑ Corresponding author. E-mail addresses: [email protected] (M.M. Hasan), [email protected] (A. Habib). 1 In this paper, we define payouts as both cash dividends and stock repurchases.

https://doi.org/10.1016/j.jcae.2020.100183 1815-5669/Ó 2020 Elsevier Ltd. All rights reserved.

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

In a seminal paper, Miller and Modigliani (1961) argue that a firm’s dividend policy does not affect its value in perfect capital markets. However, real-world financial markets are plagued by various market imperfections, including agency conflicts between shareholders and management. The free cash flow problem represents one of the most severe agency conflicts (Jensen, 1986). Self-serving managers have incentives to retain free cash flows for maximizing personal benefits (e.g., empire building) instead of returning them to investors. Paying dividends mitigates the free cash flow problem by forcing them to raise external funds more often: a phenomenon that subjects them to capital market scrutiny (Easterbrook, 1984). Dividend signaling models argue that firms’ managers can use dividends to signal the quality of the firm to the market (e.g., Bhattacharya, 1979; Miller and Rock, 1985). Accordingly, an increase in dividends typically signals a better future performance for the firm, while a decrease in dividends indicates a worse one. The dividend clientele literature (Miller and Modigliani, 1961) suggests that investors have diverse preferences toward dividend income, based on their risk aversion and tax bracket. Such differences give rise to a desire for different dividend levels among different groups. Accordingly, investors select firms with payout policies that are consistent with their preferences. Farre-Mensa et al. (2014) note that ‘‘The accumulated evidence on payout and agency indicates that firms use payouts to reduce potential overinvestment by management . . . the market ‘appreciates’ more dividends and repurchases paid by firms with more free cash flow. There is less evidence that signaling plays a significant role in dividend policy decisions or in the decision to repurchase shares” (p. 77). The authors also note that ‘‘. . . dividend tax clientele effects are not a dominant force in the determination of portfolio holdings. Perhaps other forces that interact with dividends, such as agency, are at work” (p. 98). DeAngelo et al. (2009) support this view too by arguing that ‘‘The available evidence . . . supports the view that the need to distribute [free cash flows] is a first-order determinant of the overall value and timing of payouts” (pp. 97–98). Therefore, in this paper, we use agency theory in linking social capital with corporate payouts. Social capital encompasses a certain set of informal values, norms and networks that foster cooperation and facilitate collective action (Fukuyama, 1997; Woolcock, 2001). Studies also characterize social capital as the existence of a mutual level of trust (Guiso et al., 2004), social networks and the associated norms of reciprocity (Putnam, 2000). The strong cooperative norms and the dense social networks in high social capital regions foster honest behavior as well as imposing punishment for deviant behavior (Coleman, 1994; Spagnolo, 1999). Human beings, including managers, take into account the costs, such as reputation losses, associated with deviating from the accepted norms (Cialdini et al., 1991; Milgram et al., 1969). Therefore, social capital is shown to serve as a mechanism to discipline managers (Hoi et al., 2019; Jha, 2019). Recent finance and accounting studies investigating the implications of social capital suggest that regional social capital is associated with pro-CSR corporate behavior (Jha and Cox, 2015), less restrictive loan terms and lower levels of bank loan spreads (Hasan et al., 2017a), lower audit fees (Jha and Chen, 2015), less firm-level tax avoidance (Hasan et al., 2017b), lower levels of cash holdings (Habib and Hasan, 2017), lower idiosyncratic return volatility (Hasan and Habib, 2019a) and less usage of trade credit (Hasan and Habib, 2019b). In this study, we investigate whether payout policies vary depending on the county-level variations in social capital in the US. We hypothesize that firms headquartered in high social capital regions will pay more dividends for at least two reasons. First, prior research finds that firms operating in high social capital regions can access external funds at a lower cost than firms headquartered in low social capital counties (Hasan et al., 2017a), thus lessening the need for the former group to hold more cash (Habib and Hasan, 2017). Consequently, firms headquartered in high (low) social capital counties can distribute more (fewer) cash dividends. Second, managers of firms headquartered in high social capital regions are more ethical and hence are expected to serve the interests of the shareholders (Coleman, 1994; Spagnolo, 1999). Accordingly, managers of firms headquartered in high social capital counties are less likely to squander cash, for example through overinvesting despite poor investment opportunities. Thus, the agency perspective also suggests that firms headquartered in high social capital counties tend to distribute excess cash as dividends. We take the position that social capital complements rather than substitutes payout decisions. The substitutive view argues that dividend payments can be used as a reputation-building tool by signaling that firms will not squander free cash flows. Such a reputation will be instrumental in raising external capital at a lower cost. According to this perspective, strong governance (high social capital in our case) mitigates the free cash flow problem and thus can substitute the need to use dividends as a tool for building reputation (La Porta et al., 2000; Myers, 2000; Rozeff, 1982).2 Therefore, managers of firms operating in high social capital regions will find the marginal benefit of paying dividends to be smaller. This perspective therefore predicts a negative relationship between social capital and dividend payouts. The complementary argument, on the other hand, proposes that strong governance (social capital in our case) can discipline management by imposing a dividend policy. As argued earlier, managers tend to comply with the prevalent social norms and consider the costs associated with deviating from such prevalent norms. Since disbursing cash for the benefit of shareholders is a valued social norm in counties with high social capital, we expect managers of firms headquartered in high social capital counties to pay more dividends. A well-documented phenomenon in the payout landscape is the fact that stock repurchases have outpaced cash dividends over a substantial period of time. It is not clear ex ante whether firms headquartered in high social capital counties will mechanically switch to repurchases from cash dividends. As pointed out by Farre-Mensa et al. (2014), the traditional agency theory has been less successful in explaining secular changes in the mode of payouts, that is, the dramatic increase in repur2 Officer (2011) uses board size and CEO/chairman duality as proxies for internal governance, and John and Knyazeva (2006) use Gompers et al.’s (2003) index as a proxy for external governance. Both studies find that firms with good governance pay lower levels of dividends. Grinstein and Michaely (2005) find that firms with high institutional holdings (an external governance proxy) generally pay lower dividends.

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

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chases. Three alternative arguments are advanced in the literature to explain the increases in stock repurchases: (i) behavioral theories arguing for time-changing preferences for particular forms of payouts among investors; (ii) the use of repurchases for maximizing managerial bonus compensation; and (iii) the increased use of option compensation for executives. The second argument is compelling in that, if executives’ compensation is benchmarked against earnings per share (EPS), then managers have incentives to use repurchases to decrease the number of outstanding shares, thereby increasing the EPS and bonus compensation if all else remains the same. Cheng et al. (2015) and Young and Yang (2011) find support for this argument. The argument relating to option compensation is also well supported in the literature. This is because dividends reduce the per-share value of the stock, which has a detrimental effect on the value of executive options (Dittmar, 2000; Fenn and Liang, 2001; Lambert et al., 1989). Therefore, firms with more executive stock options are more likely to substitute cash dividends with repurchases. In a recent study, Hoi et al. (2019) show that executives of firms headquartered in high social capital regions have less total and equity-based compensation. They also document that social capital reduces the opportunistic option grant awards that favor the executives unduly. To the extent that managers of firms in high social capital counties are trustworthy and behave honestly and that social capital mitigates managerial rent extraction, we expect firms headquartered in high social capital areas to be less likely to use stock repurchases in this manner to maximize their private gains. Accordingly, we predict that social capital is related negatively to stock repurchases. We use the social capital measure of Rupasingha et al. (2006), who construct a social capital index at the US county level with two variants of social norms and two variants of networks. Following this methodology, we perform a principal component analysis of four indicators for each year (1997, 2005, 2009 and 2014) and use the first component as the regional social capital index (see Section 2.2 for details). This procedure is consistent with contemporary studies on the implications of social capital related to accounting and finance outcomes (e.g., Habib and Hasan, 2017; Hasan et al., 2017a, 2017b; Jha, 2019). Using the US data from 1997 to 2015, we document a positive and statistically significant relation between regional social capital and the likelihood of cash dividends after controlling for firm characteristics and county-level demographic factors as well as year and industry effects. Our estimates are also economically significant. To put this coefficient into perspective, our reported coefficient indicates that a 1 unit increase in social capital is related to a 2.54%–3% increase in the probability of cash dividend payments for an average firm. We continue to find a positive and statistically significant relation between social capital and the amount of cash dividends. In terms of economic significance, we show that a 1 standard deviation increase in social capital is predicted to increase cash dividends by 64.8%–68.05% relative to the mean, after controlling for firm characteristics and county-level features as well as industry and year effects. Then, we examine the relationship between social capital and both the likelihood and the amount of stock repurchases but find no significant association between county-level social capital and stock repurchases. We show that our results remain robust when alternative measures of social capital and corporate payouts are used in the regression models. Our inferences from the analysis also remain unaltered when additional controls are included and county fixed effects are used in the regression to address omitted variable bias as well as when two-stage least squares regression is used to address endogeneity concerns. Our additional analysis reveals that the positive association between social capital and cash dividends holds for both the ‘‘norm” and the ‘‘network” components of social capital, but the effect of the network component on cash dividends is more pronounced. Finally, we provide some evidence that the relationship between social capital and the amount of cash dividends is more pronounced for less geographically dispersed firms. Our study contributes to the literature on corporate payouts and social capital in a number of important ways. First, our paper is, to the best of our knowledge, the first that comprehensively examines the relationship between regional social capital and corporate payouts policies. We examine not only dividend payouts but also stock repurchases. The extant literature shows that both firm-level factors (e.g., the corporate life cycle, information environment, profitability, stock liquidity and corporate social responsibility) and region-level features affect payout policies (Bodnaruk and Östberg, 2013; Cheung et al., 2018; Farre-Mensa et al., 2014; Jiang et al., 2017; Ucar, 2016). Our study relates to the latter stream of literature, which suggests that local variations in the attitudes of managers (and firms) are important in explaining payout policies. For example, Kumar et al. (2011) document the effect of the local culture on investor behavior and corporate decision making. Becker et al. (2011) show local dividend clienteles. Ucar (2016) finds that the local religious culture affects the payout decisions of firms. Our study contributes to this literature by showing that firms headquartered in high social capital regions pay more cash dividends. Second, our study also contributes to the growing literature on social capital. Recent studies show that regional social capital has an important effect on the corporate outcome, including the cost of bank loans (Hasan et al., 2017a), tax planning (Hasan et al., 2017b), cash holdings (Habib and Hasan, 2017), the probability of committing fraud by misrepresenting financial information (Jha, 2019), access to trade credit (Hasan and Habib, 2019b) and socially responsible behavior (Jha and Cox, 2015) as well as bank failures and financial trouble during a financial crisis (Jin et al., 2017). We contribute to this growing literature by linking social capital with corporate payouts. Overall, we show that ‘‘informal institutional factors” play an important role in payout decisions. Our paper is close in spirit to those by Cheung et al. (2018) and Ucar (2016). Using CSR measures from Kinder, Lydenberg and Domini from 1991 to 2010, Cheung et al. (2018) show that firms with higher corporate social responsibility (CSR) are associated with higher cash dividend payouts. In this paper, instead of investigating the effect of CSR on cash dividends, we focus on the role of regional social capital in influencing both cash dividends and stock repurchases. Note that CSR is conceptually different from social capital, the latter being defined as a set of informal values, norms and networks that promotes cooperation and facilitates collective action in a region (Fukuyama, 1997). Untabulated results show that the correlation

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183 Table 1 Sample and industry distribution. Panel A: Sample selection procedure Filtering

Observations

Firm-year observations from 1997 to 2015 in COMPUSTAT Less: Observations pertaining to SIC #60–69 Less: Observations pertaining to SIC #49 Less: Firms incorporated outside USA Firm-year observations available for matching with social capital data Less: Non-matched firm-year observations with social capital data file Less: Missing cash dividends and control variable data Final Sample for the social capital and cash dividends analysis Number of unique firms

218,021 61,604 7,659 40,382 108,376 26,716 26,965 54,695 7,962

Panel B: Industry distribution Industry

Freq.

Percent

Consumer non-durables Consumer Manufacturing durables Oil, Gas, and Coal Extraction and Products Chemicals and Allied Products‘‘ Computers, Software, and Electronic Equipment Telephone and Television Transmission Wholesale, Retail, and Some Services Healthcare, Medical Equipment, and Drugs Other Total

3360 1281 6272 2912 1404 14,060 1772 6575 8734 8325 54,695

6.14 2.34 11.47 5.32 2.57 25.71 3.24 12.02 15.97 15.22 100

between social capital and CSR is 0.07, implying that social capital and CSR capture different region- and firm-level attributes, respectively. Finally, our analysis indicates that the relationship between social capital and payouts remains qualitatively similar even after controlling for the CSR score of the firm. Using data from 1990 to 2010, Ucar (2016) finds that the local religion affects corporate payout policies. The author argues that this finding is consistent with the risk aversion and dividend clientele arguments. We, on the other hand, use an agency framework as our argument. We extend the study of Ucar (2016) by showing that norms and networks that promote cooperation and facilitate collective action in a region also matter for corporate payout decisions. We also investigate the association between social capital and stock repurchases, whilst Ucar (2016) examines only the association between local religion and cash dividends.3 The remainder of the paper is organized as follows. We discuss our data and key variables in Section 2. The research methodology is discussed in Section 3. We present our empirical results in Section 4 and conclude the paper in Section 5.

2. Data and key variables 2.1. Data and sample construction We use several data sources to construct the sample. We obtain financial data from Standard & Poor’s Compustat database, stock return data from the Center for Research in Security Prices (CRSP) database, social capital data from the Northeast Regional Center for Rural Development (NRCRD) database of Pennsylvania State University and county-level demographic data from the Bureau of Economic Analysis. Our sample period begins in 1997, since this is the first year for which the new dataset on county-level social capital is available. The initial sample consists of 218,021 firm-year observations spanning the period 1997–2015. We exclude 61,604 and 7659 firm-year observations pertaining to the financial industry (Standard Industrial Classification (SIC) codes 6000–6999) and the utility industry (SIC codes 4900–4999), respectively. We further exclude 40,382 firm-years that are incorporated outside the US. We also exclude 26,716 firm-year observations that do not match the county-level social capital data. Finally, after excluding the firm-year observations (26,965) with missing cash dividends and control variables, our final sample size reduces to 54,695 firm-year observations (7962 unique firms) for social capital and cash dividend analysis.4 Panel A of Table 1 reports the sampling procedure. To reduce the influence of outliers, we winsorize all the continuous variables at the 1% level on both sides. 3 Note that, of the four factors used to estimate social capital, the association (ASSN) component includes the number of religious organizations in a county along with other types of associations (see Section 2.2), implying that our estimations take religions into consideration in estimating the relationship between social capital and payouts. 4 Our final sample contains 49,240 firm-year observations (7615 unique firms) for social capital and repurchase analysis. Overall, our sample is comparable with those of prior studies (e.g., Hasan and Habib, 2017; Hasan et al., 2017a, 2017b).

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

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Panel B of Table 1 presents the sample distribution across the Fama–French twelve industry groups. We observe considerable representation of computers, software and electronic equipment (25.71%); healthcare, medical equipment and drugs (15.97%); and other industries (15.22%). 2.2. Measures of social capital We use the county-level social capital index (SC) developed by Rupasingha et al. (2006) as our main independent variable. The NRCRD provides the data for estimating county-level social capital in the US. Rupasingha et al. (2006) use the census mail response rate (RESPN) and the votes cast in presidential elections (PVOTE) as the two constructs capturing social norms and the numbers of associations (ASSN)5 and non-profit organizations (NCCS) as the two variants of networks. A principal component analysis is used for each year (1997, 2005, 2009 and 2014), and the first component capturing the most common variance for each year is used as the proxy for social capital. We use three different specifications of this index as proxies for social capital. First, we estimate the social capital index (SC_RES) for each of the years (i.e., 1997, 2005, 2009 and 2014) for which the NRCRD provides the data required for constructing social capital. Second, we fill in the data for the missing years using the estimated social capital index in the preceding year for which a social capital index is available (SC_PREV): a procedure that is consistent with the previous literature (e.g., Habib and Hasan, 2017; Hasan et al., 2017a, 2017b). For example, we fill in the missing data from 2010 to 2013 using the social capital data in 2009. Third, we interpolate social capital (SC_IPOL) linearly to fill in the missing social capital index in the years 1998–2004, 2006–2008, 2010–2013 and 2015, which is consistent with the aforementioned literature. In the sensitivity analysis, we employ state-level organ donation (SC_ORGAN) as an additional measure of social capital. 2.3. Measure of corporate payouts We construct the following set of dependent variables to examine the relationship of social capital with the propensity and amount of dividend payments and stock repurchases. DIV_D is a dummy variable that takes the value of one if the firm pays dividends and zero otherwise. DIV_D is used in logistic regressions. For tobit regressions, we use two measures of dividend payments: DIV/TA is the ratio of dividends to total assets; and DIV/NI is the ratio of dividends to net income. Since payout ratios are not meaningful when the denominator (net income) is negative, we use left-censored tobit regression. In the sensitivity analysis, we also scale dividends by the market value of equity (DIV/MVE) and cash flow (DIV/CF). We define stock repurchases as common and preferred stock repurchases adjusted for any decreases in preferred stock (Cuny et al., 2009; Desai and Jin, 2011). Prior studies (e.g., Fenn and Liang, 2001; Grullon and Michaely, 2002; Stephens and Weisbach, 1998) also use this measure of repurchases.

REP ¼

purchase of common and preferred stock þ min ð0; change in preferred stock total assets

v alueÞ

ð1Þ

For the logistic regressions, we use a dummy variable, REP_D, which takes a value of one if the firm repurchases stocks and zero otherwise. For the tobit regressions, we scale repurchases by total assets (REP/TA) and net income (REP/NI). In the sensitivity analysis, we also define stock repurchases as an increase in treasury shares using annual Compustat data (TSTKC). We replace a decrease in treasury stock with zero (Banyi et al., 2008). 2.4. Control variables We use a set of control variables to capture firm- and county-level characteristics that may affect dividend payouts and stock repurchases (Akhigbe and Whyte, 2012; Bodnaruk and Östberg, 2013; Grullon and Michaely, 2002; Hoberg et al., 2014; von Eije and Megginson, 2008).6 Size affects firms’ ability to make dividend payments or sustain stock repurchases. Therefore, we control for firm size (SIZE) in the regressions. Truong and Heaney (2007) show that firms are more likely to pay dividends when the investment opportunities are limited. Furthermore, firms are likely to repurchase stocks when stocks are undervalued. To control for investment opportunities and equity valuation effects, we use the market-to-book ratio (MTB), R&D ratio (R&D) and capital expenditure to assets ratio (CAPEX). Extant studies (e.g., DeAngelo et al., 2006; Dittmar, 2000) document that debt contracts typically limit the extent of corporate payouts; therefore, we control for the leverage ratio (LEV). DeAngelo et al. (2006) show that mature firms pay more dividends. We include firm age (AGE_LN) to control for firms’ maturity. A firm’s performance affects its ability to make dividend payments or sustain stock repurchases. We include profitability (ROA) and stock return (RET) to control for performance. Firms may retain cash to finance investment opportunities. Nonetheless, firms with excess cash may pay more dividends and repurchase stocks to reduce agency costs. Therefore, we control for cash to assets (CASH). Jagannathan et al. (2000) show that firms with volatile returns (RET_SD) tend to replace dividends with stock repur5 Association (ASSN) includes religious organizations, civic and social associations, business associations, political organizations, professional organizations, labor organizations, bowling centers, physical fitness facilities, public golf courses, and sports clubs. Note that the inferences from our analyses remain qualitatively similar when we exclude religious organizations in constructing the social capital index. 6 Interestingly, a similar set of variables is used to explain dividends and stock repurchases (see Akhigbe and Whyte, 2012; Skinner, 2008; von Eije and Megginson, 2008).

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

chases. Hoberg et al. (2014) indicate that product market competition reduces firms’ propensity to make payouts through dividends and repurchases. We include industry concentration (IND_CON) to control for such competition. Tangible assets may either increase dividend payouts by improving firms’ access to external financing or decrease dividend payouts by limiting the availability of cash flows (Koo et al., 2017). We include property, plant and equipment scaled by total assets (TANG) in the regression. In addition to the firm-specific characteristics, we include some county-level demographic factors that recent studies include to examine the effects of social capital on corporate outcomes (Hasan et al., 2017a, 2017b; Jha and Chen, 2015; Jha and Cox, 2015). In particular, we control for the natural log of the median household income per capita in a county in a given year (MEDIAN_INC_LN), the natural log of the population in a county in a given year (POPULATION_LN), the median age of the residents in a county in a given year (MEDIAN_AGE_LN) and the percentage of persons aged 25 years and over with at least 1 year of college education in a county in a given year (EDU). Finally, to control for unobserved heterogeneity, we also use a set of dummy variables capturing industry effects at the two-digit SIC level and year effects. 3. Methodology Following prior studies (Brockman et al., 2014; Chay and Suh, 2009; Wang, 2012), we use logit and tobit models to test our hypotheses. In particular, we use the following multivariate logit regressions with county-level clustered standard errors to examine the relationship between social capital and the likelihood of either paying dividends or repurchasing stocks:

Prob ðDIV D ¼ 1Þ ¼ a0 þ b1 SC þ c Controls þ e

ð2:1Þ

Prob ðREP D ¼ 1Þ ¼ a0 þ b1 SC þ c Controls þ e

ð2:2Þ

0

0

where the dependent variable, DIV_D, is a dummy variable that takes the value of one if a firm pays dividends in a given year and zero otherwise and REP_D is a dummy variable that takes the value of one if a firm repurchases stock in a given year and zero otherwise. Our main independent variable is county-level social capital (SC), as discussed in Section 2.1, and the regression model controls for firm characteristics, county-level features and industry and year dummies (see Section 2.4). We expect the coefficient for SC (i.e., b1) to be positive and significant in Eq. (2.1). We hypothesize that the coefficient for SC (i.e., b1) will be negative and significant in Eq. (2.2). Next, we use the following multivariate tobit regressions with county-level clustered standard errors to test the relation between social capital and the amount of dividend payouts as well as the amount of stock repurchases: 0

ð3:1Þ

0

ð3:2Þ

DIV=TA ¼ a0 þ c1 SC þ h Controls þ e REP=TA ¼ a0 þ c1 SC þ h Controls þ e  where DIV=TA ¼  and REP=TA ¼

DIV=TA; if DIV=TA > 0 0; otherwise

REP=TA; if REP=TA > 0 0; otherwise

ð3:3Þ

ð3:4Þ

where the dependent variable, DIV/TA, is the amount of common dividends scaled by the total assets. In addition to this, we use the ratios of dividends to net income (DIV/NI), dividends to market value of equity (DIV/MVE) and dividends to cash flow (DIV/CF) as dependent variables to measure the amount of dividend payouts. REP/TA refers to the amount of stock repurchases scaled by total assets. We also use repurchases to net income (REP/NI) as a dependent variable. Other variables have been described earlier. Our key variable of interest is the county-level social capital (SC). We predict that the coefficient for SC (i.e., c1) will be positive and significant in Eq. (3.1) but negative and significant in Eq. (3.2). 4. Empirical results 4.1. Summary statistics results Table 2 presents summary statistics of the key variables used in this study. The table shows that the proportion of sample firms repurchasing stocks (38.2%) is larger than that for firms paying cash dividends (24.8%). In addition, the average cash payout is 0.7% of the total assets and 9.8% of the net income. However, the sample firms show an average stock repurchase of 2.1% of the total assets and 19.5% of the net income. The dominance of stock repurchases over cash payouts is consistent with the extant studies, which indicate that repurchases have become the prime vehicle for corporate payouts (Farre-Mensa et al., 2014). Our statistics relating to dividends and repurchases are very close to those in prior studies (Farre-Mensa et al., 2014). The means of social capital, as proxied by SC_PREV and SC_IPOL, are 0.604 and 0.56, which are similar to those found in other prior studies (Hasan and Habib, 2019b; Hasan et al., 2017a, 2017b). The average firm may be characterized

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 2 Descriptive statistics. This table presents summary statistics of the variables used in the study (Panel A) and state-level distribution of social capital, dividends, stock repurchases, and sample (Panel B). Variable definitions are provided in the Appendix. Panel A: Summary statistics Variable

N

DIV_D 54,695 DIV/TA 54,695 DIV/NI 54,694 REP_D 49,240 REP/TA 49,240 REP/NI 49,240 SC_RES 11,745 SC_PREV 54,695 SC_IPOL 54,690 SIZE 54,695 MTB 54,695 LEV 54,695 R&D 54,695 ROA 54,695 CASH 54,695 CAPEX 54,695 AGE_LN 54,695 RET 54,695 RET_SD 54,695 TANG 54,695 IND_CON 54,695 MDN_INC_LN 54,695 POPULATION_LN 54,695 MEDIAN_AGE_LN 54,695 EDU 54,695 Variables used in sensitivity analysis DIV/MVE 54,695 DIV/CF 54,673 SC_ORGAN 54,695 SC_NORM 54,695 SC_NETWORK 54,695

Mean

S.D.

0.25

Median

0.75

0.248 0.007 0.098 0.382 0.021 0.195 0.579 0.604 0.560 5.501 1.932 0.216 0.078 0.005 0.230 0.055 2.334 0.121 0.043 0.239 0.081 10.905 13.772 3.575 0.359

0.432 0.022 0.353 0.486 0.054 0.836 0.777 0.794 0.795 2.154 1.945 0.234 0.177 0.307 0.250 0.065 0.957 0.735 0.026 0.227 0.072 0.260 1.073 0.080 0.106

0.000 0.000 0.000 0.000 0.000 0.000 1.154 1.234 1.188 3.950 0.847 0.007 0.000 0.018 0.034 0.016 1.662 0.320 0.024 0.067 0.041 10.687 13.228 3.517 0.279

0.000 0.000 0.000 0.000 0.000 0.000 0.528 0.550 0.537 5.482 1.284 0.155 0.005 0.094 0.130 0.033 2.412 0.004 0.035 0.160 0.052 10.887 13.785 3.575 0.344

0.000 0.000 0.000 1.000 0.012 0.081 0.019 0.019 0.006 6.947 2.181 0.341 0.086 0.156 0.352 0.066 3.030 0.355 0.053 0.340 0.086 11.095 14.332 3.626 0.435

0.007 0.066 4.28 0.179 0.68

0.017 0.199 0.767 0.87 0.696

0.000 0.00 3.779 0.473 1.183

0.000 0.000 4.105 0.182 0.85

0.000 0.000 4.685 0.831 0.354

Panel B: State-level distribution of social capital, dividends, stock repurchases, and sample State DIV_D REP_D DIV/TA REP/TA SC_PREV Freq. State DIV_D AL AR AZ CA CO CT DE FL GA HI IA ID IL IN KS KY LA MA MD ME MI MN MO MS MT

0.369 0.510 0.127 0.119 0.171 0.314 0.377 0.173 0.302 0.222 0.565 0.158 0.362 0.425 0.361 0.408 0.294 0.158 0.141 0.077 0.402 0.294 0.497 0.540 0.039

0.466 0.619 0.337 0.327 0.295 0.426 0.391 0.364 0.461 0.222 0.401 0.300 0.449 0.501 0.367 0.478 0.376 0.319 0.267 0.407 0.463 0.412 0.427 0.478 0.078

0.016 0.010 0.005 0.004 0.006 0.010 0.010 0.005 0.009 0.004 0.015 0.003 0.011 0.010 0.015 0.011 0.007 0.005 0.003 0.003 0.009 0.010 0.015 0.012 0.001

0.023 0.024 0.019 0.025 0.014 0.021 0.009 0.020 0.021 0.002 0.027 0.023 0.021 0.020 0.014 0.025 0.013 0.020 0.018 0.011 0.020 0.027 0.021 0.012 0.011

0.469 0.577 1.707 1.170 0.102 0.191 0.264 1.078 0.826 1.003 1.068 0.156 0.929 0.247 0.210 0.144 0.550 0.338 0.189 0.774 0.130 0.793 0.054 0.524 0.743

320 210 771 11,796 1594 1330 207 2110 1259 9 237 120 1864 567 294 316 367 3354 744 91 935 1729 199 113 51

NC ND NE NH NJ NM NV NY OH OK OR PA RI SC SD TN TX UT VA VT WA WI WV WY

0.374 0.158 0.549 0.176 0.211 0.063 0.177 0.253 0.489 0.398 0.214 0.350 0.400 0.323 0.537 0.301 0.274 0.207 0.285 0.083 0.198 0.483 0.292 0.043

REP_D

DIV/TA

REP/TA

SC_PREV

Freq.

0.347 0.105 0.477 0.383 0.326 0.208 0.385 0.404 0.522 0.353 0.344 0.421 0.468 0.420 0.299 0.423 0.405 0.422 0.437 0.375 0.334 0.441 0.319 0.232

0.008 0.001 0.019 0.005 0.006 0.001 0.007 0.008 0.012 0.018 0.004 0.009 0.007 0.009 0.029 0.007 0.009 0.006 0.010 0.003 0.006 0.012 0.009 0.001

0.016 0.000 0.019 0.019 0.018 0.002 0.021 0.020 0.021 0.015 0.014 0.019 0.020 0.014 0.014 0.025 0.018 0.031 0.026 0.019 0.022 0.020 0.011 0.008

0.314 0.701 0.231 0.117 0.566 0.801 1.776 0.164 0.058 0.208 0.036 0.151 0.311 0.866 0.928 0.103 1.534 1.306 0.236 0.908 0.052 0.407 0.120 0.889

948 19 277 227 2570 48 509 5334 1717 561 593 2326 205 226 67 810 5572 521 1059 72 1166 714 72 69

as being moderately large (SIZE = 5.501), somewhat profitable (ROA = 0.50%, RET = 12.1%), less investment intensive (CAPX = 5.5%) and moderately innovative (R&D = 7.8%) with low risk (RET_SD = 4.3%), low leverage (LEV = 21.6%) and moderate growth opportunities (MTB = 1.932). The table also shows that the average of cash holdings (CASH) is 23% of the total assets, whilst the proportion of PPE to total assets (TANG) is 24%.

8

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Panel B of Table 2 presents a state-level distribution of the proportion of sample firms paying dividends and repurchasing stocks, the amount of dividends and stock repurchases, social capital and firm-year observations. Consistent with the prior studies (Bradley et al., 2016; Hasan and Habib, 2019b), firms headquartered in California (CA), New York (NY) and Texas (TX) constitute around 40 per cent of the sample. Firms headquartered in Iowa (IA) and South Dakota (SD) have the highest social capital scores of 1.068 and 0.928, respectively, with corresponding DIV_D (REP_D) values of 0.565 and 0.537 (0.401 and 0.299) and DIV/TA (REP/TA) of 0.015 and 0.029 (0.027 and 0.014), respectively. On the other hand, firms headquartered in Nevada (NV) and Arizona (AZ) have the lowest social capital scores of 1.776 and 0.1707, respectively. Note that the DIV_D and REP_DIV (DIV/TA and REP/TA) of firms headquartered in Nevada are 0.177 and 0.385 (0.007 and 0.021), while those for firms headquartered in Arizona are 0.127 and 0.337 (0.005 and 0.019). Overall, Panel B suggests that there is considerable variation in the state-level distributions of dividends, stock repurchases, social capital and number of firm-year observations.

4.2. Univariate tests Table 3 reports the univariate test of differences in the variables between the high and the low social capital regions. We define a firm headquartered in a high (low) social capital county if county-level social capital score (SC_PREV) is higher (lower) than the median social capital across the sample years. The tabulated results show that the propensity for paying cash dividends (DIV_D) and repurchasing stocks (REP_D) is significantly higher (p < 0.01) for firms headquartered in the high social capital counties. Moreover, although the level of cash dividends is significantly higher (p < 0.01) for firms headquartered in the high social capital counties, the level of stock repurchases of firms headquartered in high social capital counties is not statistically different (at p < 0.05 or better) from that of low social capital counties. In addition, firms from high social capital counties are significantly larger and more mature, profitable and levered while being less innovative, holding less cash and having more tangible assets and fewer growth opportunities.

4.3. Correlation matrix Table 4 shows the correlations between the variables used in the baseline regression. First, we find that social capital (SC_PREV) is positively and significantly (p < 0.01) correlated with DIV_D (q = 0.10) and DIV/TA (q = 0.05). In addition, the correlation of SC_PREV with REP_D (q = 0.03) and REP/TA (q = 0.02) is positive and significant, although the latter correlation coefficients are weak relative to the cash dividends. Second, the correlation between cash dividends and stock repurchases is positive and significant, indicating that dividends and stock repurchases are not mutually exclusive. Third, both the propensity for and the levels of cash dividends are correlated positively with firm size, profitability, age, tangibility and industry concentration but negatively with R&D, cash and return volatility. Finally, both the propensity for and the levels of stock repurchases are correlated positively with firm size, profitability, firm age, tangibility and industry concentration but negatively with leverage, R&D, capital expenditures and return volatility.

Table 3 Univariate test. This table presents univariate test of difference of variables between high and low social capital groups. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Variable definitions are provided in the Appendix. Variable

SC > median

SC < Median

t-value

DIV_D DIV/TA DIV/NI REP_D REP/TA REP/NI SIZE MTB LEV R&D ROA CASH CAPEX AGE_LN RET RET_SD TANG IND_CON MDN_INC_LN POPULATION_LN MEDIAN_AGE_LN EDU

0.288 0.008 0.115 0.393 0.021 0.200 5.569 1.894 0.221 0.074 0.014 0.222 0.052 2.393 0.118 0.041 0.236 0.081 10.929 13.358 3.604 0.395

0.208 0.006 0.081 0.372 0.021 0.190 5.433 1.969 0.211 0.081 0.004 0.238 0.057 2.275 0.123 0.044 0.243 0.082 10.88 14.185 3.545 0.323

21.64*** 9.92*** 11.33*** 5.053*** 0.26 1.73* 7.33*** 4.51*** 4.72*** 4.58*** 6.77*** 7.45*** 8.52*** 14.43*** 0.817 13.33*** 3.57*** 1.11 22.00*** 97.63*** 93.46*** 84.56***

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

9

4.4. Main results 4.4.1. Social capital and the likelihood of cash dividend payouts: Logit analysis Table 5 (Panel A) presents multivariate logit regressions that test the relationship between social capital and the likelihood of cash dividend payouts. We predict that firms headquartered in high social capital counties are more likely to pay cash dividends. We estimate Eq. (2.1) with county-level clustered standard errors to test the hypothesis. In Column (1), we estimate the logit regression only for firm-year samples with available social capital (SC_RES) reported by the NRCRD for the years 1997, 2005, 2009 and 2014 and control for firm characteristics and industry and year effects. The coefficient for SC_RES is 0.218 (significant at p < 0.01). The marginal effect estimated from this regression suggests that a 1 unit increase in SC_RES will increase the probability of cash dividend payments by 2.55% for an average firm. In Column (2), we include the county-level controls along with the firm-level controls and industry and year effects. We continue to find a positive and significant coefficient (coefficient = 0.252; p < 0.01) as well as economically meaningful marginal effects (=2.92%) on SC_RES. In Columns (3) and (4), we use SC_PREV as a social capital measure. Recall that SC_PREV is constructed by filling in data for the missing years with the social capital index in the preceding year for which social capital data are available. In Column (3), we list the estimates for SC_PREV as positive and significant (coefficient = 0.228; p < 0.01 with marginal effects = 2.54%) after controlling for firm characteristics and industry and year effects. In Column (4), we repeat the regression after including county-level control variables. We continue to document positive, significant (coefficient = 0.272; p < 0.01) and economically meaningful marginal effects (=3.0%) of SC_PREV on the likelihood of cash dividend payouts. Taken together, the results reported in Table 5 suggest that firms headquartered in high social capital counties are more likely to pay cash dividends. The logit results in Table 5 (Panel A) further show that the likelihood of paying cash dividends is higher for large (SIZE), profitable (ROA) and mature (AGE_LN) firms with more tangible assets (TANG). In contrast, the likelihood of dividend payments is lower for levered (LEV), research-intensive (R&D), capital-intensive (CAPEX) and risky firms (RET_SD). 4.4.2. Social capital and level of cash dividends: Tobit analysis Panel B of Table 5 employs multivariate tobit regressions (Eq. (3.1)) with county-level clustered standard errors to test the relationship between social capital and the level of dividend payouts. Our dependent variables in Columns (1) to (4) are common dividends scaled by total assets (DIV/TA), while those in Columns (5) to (8) are common dividends scaled by net income (DIV/NI). Column (1) uses firm-year samples with available social capital (SC_RES) and controls for firm characteristics and industry and year effects. The coefficient for SC_RES in Column (1) is positive and significant (coefficient = 0.004, p < 0.01). In Column (2), we repeat the regression after including the county-level controls and continue to find a positive and significant relation between social capital and the level of payouts (coefficient = 0.006, p < 0.01). In Columns (3) to (4), we use SC_PREV as the social capital measure and find the estimates for SC_PREV to be positive and significant (coefficient = 0.004 in Column (3) and 0.006 in Column (4); p < 0.01). When we repeat the regression estimates for the DIV/NI measure of dividends in Columns (5) to (8), we find that the magnitudes of the coefficients range from a low of 0.056 in Column (6) (p < 0.05) to a high of 0.080 in Column (8) (p < 0.01). In addition to the statistical significance, the estimates in Panel B are economically meaningful. Focusing on the estimates in Column (4), for example, for a 1 standard deviation increase in SC_PREV (=0.794), the predicted increase in the payout ratio (measured as DIV/TA) is 68.05% relative to the mean (calculated as (0.794*0.006)/0.007). Similarly, in Column (8), a 1 standard deviation increase in SC_PREV leads to an increase in the payout ratio (measured as DIV/NI) of 6.4%, which translates into a 64.8% increase in payouts (i.e., DIV/NI) relative to the mean. Thus, the findings from our tobit regressions are statistically significant and economically meaningful. Overall, our results documented in Table 5 confirm that both the propensity for and the level of cash dividends are higher for firms headquartered in high social capital counties. 4.4.3. Social capital and likelihood of stock repurchases: Logit analysis Table 6 (Panel A) presents multivariate logit regression results that examine the relationship between social capital and the likelihood of stock repurchases. We estimate Eq. (2.2) with county-level clustered standard errors to test the relationship. The results tabulated in Columns (1) to (4) show that there is no statistically significant relationship between social capital and the propensity for stock repurchases. This statistically insignificant result holds irrespective of the controls and sample periods used in the regression analysis. The logit results in Table 6 show that the likelihood of stock repurchases is higher for large (SIZE), profitable (ROA) and mature (AGE_LN) firms. In contrast, the likelihood of stock repurchases is lower for growth (MTB and R&D), levered (LEV), capital-intensive (CAPEX) and risky firms (RET_SD). 4.4.4. Social capital and amount of stock repurchases: Tobit analysis Panel B of Table 6 employs multivariate tobit regressions (Eq. (3.2)) with county-level clustered standard errors to test the relationship between social capital and the level of stock repurchases. The dependent variable in Columns (1) to (4) is stock repurchases scaled by total assets (REP/TA), while that in Columns (5) to (8) is stock repurchases scaled by net income (REP/ NI). Irrespective of the repurchase and social capital proxies, none of the coefficients for social capital is statistically significant across Columns(1) to (8). This insignificant relation holds even with the use of different samples and controls. As an additional sensitivity analysis, we measure stock repurchases as an increase in treasury shares using annual Compustat data

10

Variables

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

DIV_D [1] DIV/TA [2] REP_D [3] REP/TA [4] SC_PREV [5] SIZE [6] MTB [7] LEV [8] R&D [9] ROA [10] CASH [11] CAPEX [12] AGE_LN [13] RET [14] RET_SD [15] TANG [16] IND_CON [17] MDN_INC_LN [18] POPULATION_LN [19] MEDIAN_AGE_LN [20] EDU [21]

1.00 0.60 0.26 0.08 0.10 0.39 0.10 0.04 0.20 0.27 0.24 0.00 0.37 0.01 0.38 0.16 0.12 0.08 0.09 0.03 0.06

1.00 0.12 0.08 0.05 0.22 0.05 0.00 0.11 0.19 0.08 0.00 0.16 0.01 0.23 0.08 0.08 0.00 0.03 0.02 0.00

1.00 0.45 0.03 0.28 0.09 0.04 0.16 0.24 0.11 0.02 0.22 0.00 0.27 0.01 0.04 0.01 0.01 0.02 0.01

1.00 0.02 0.19 0.12 0.06 0.02 0.12 0.05 0.02 0.03 0.01 0.12 0.06 0.02 0.06 0.02 0.00 0.06

1.00 0.03 0.01 0.00 0.00 0.02 0.02 0.05 0.07 0.01 0.06 0.04 0.02 0.10 0.51 0.39 0.38

1.00 0.19 0.00 0.16 0.37 0.06 0.05 0.22 0.16 0.59 0.06 0.05 0.13 0.03 0.05 0.13

1.00 0.12 0.36 0.28 0.40 0.01 0.20 0.24 0.07 0.17 0.12 0.10 0.05 0.01 0.10

1.00 0.10 0.02 0.38 0.12 0.03 0.06 0.05 0.32 0.08 0.13 0.04 0.03 0.10

1.00 0.67 0.45 0.09 0.14 0.05 0.25 0.22 0.18 0.19 0.05 0.04 0.16

1.00 0.40 0.06 0.23 0.15 0.47 0.17 0.11 0.11 0.07 0.03 0.10

1.00 0.20 0.25 0.04 0.15 0.42 0.18 0.29 0.10 0.06 0.25

1.00 0.09 0.05 0.01 0.64 0.11 0.18 0.04 0.17 0.14

1.00 0.04 0.31 0.07 0.07 0.01 0.04 0.12 0.03

1.00 0.07 0.02 0.00 0.02 0.00 0.00 0.01

1.00 0.08 0.08 0.06 0.04 0.09 0.04

1.00 0.23 0.27 0.11 0.15 0.24

1.00 0.11 0.04 0.04 0.10

1.00 0.09 0.39 0.73

1.00 0.31 0.13

1.00 0.23

1.00

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 4 Correlation matrix. This table presents correlations between variables used in this study. Bold correlation coefficients are significant at p < 0.01. Variable definitions are provided in the Appendix.

11

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183 Table 5 Social capital and cash dividends. Panel A. Social capital and the probability of cash dividends (1)

(2)

(3)

(4)

Restricted Sample DIV_D 0.218*** [0.04] –

Restricted Sample DIV_D 0.252*** [0.07] –

Full Sample DIV_D –

Full Sample DIV_D –

MEDIAN_INC_LN

0.222*** [0.03] 0.051 [0.03] 0.853*** [0.19] 10.197*** [1.25] 3.455*** [0.63] 0.297 [0.24] 5.782*** [0.89] 0.590*** [0.06] 0.095* [0.05] 49.003*** [4.82] 1.645*** [0.24] 0.069 [0.80] –

POPULATION_LN

–

MEDIAN_AGE_LN

–

EDU

–

Year effects Industry effects Constant

Yes Yes 0.737 [0.74] 11,745 6,539 0.371

0.240*** [0.03] 0.063* [0.03] 0.829*** [0.19] 9.546*** [1.32] 3.339*** [0.63] 0.153 [0.23] 5.897*** [0.91] 0.590*** [0.06] 0.101** [0.05] 48.241*** [4.84] 1.520*** [0.25] 0.093 [0.82] 0.312 [0.27] 0.083** [0.04] 1.079* [0.56] 0.912 [0.68] Yes Yes 7.586*** [2.84] 11,676 6,505 0.375

0.228*** [0.04] 0.230*** [0.03] 0.009 [0.02] 0.801*** [0.17] 8.811*** [0.91] 2.982*** [0.43] 0.587*** [0.22] 5.764*** [0.60] 0.663*** [0.05] 0.223*** [0.03] 50.162*** [3.11] 1.770*** [0.20] 0.211 [0.73] –

0.272*** [0.06] 0.244*** [0.03] 0.017 [0.02] 0.778*** [0.17] 8.233*** [0.96] 2.899*** [0.43] 0.440** [0.22] 5.868*** [0.61] 0.663*** [0.05] 0.233*** [0.03] 49.887*** [3.11] 1.668*** [0.20] 0.055 [0.75] 0.388 [0.24] 0.053 [0.04] 0.959* [0.54] 0.822 [0.63] Yes Yes 6.734** [2.62] 54,695 7,962 0.388

Dep. Var. = SC_RES SC_PREV SIZE MTB LEV R&D ROA CASH CAPEX AGE_LN RET RET_SD TANG IND_CON

Observations No. of unique firms Pseudo R2

– – – Yes Yes 1.546** [0.70] 54,984 7,989 0.384

Panel B: Social capital and the level of cash dividends

Dep. Var. = SC_RES SC_PREV SIZE MTB LEV R&D ROA

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Restricted Sample DIV/TA

Restricted Sample DIV/TA

Full Sample DIV/TA

Full Sample DV/TA

Restricted Sample DIV/NI

Restricted Sample DIV/NI

Full Sample DIV/NI

Full Sample DV/NI

0.004*** [0.00] –

0.006*** [0.00]

–

–

–

0.002** [0.00] 0.003*** [0.00] 0.014*** [0.00] 0.235*** [0.03] 0.113*** [0.02]

0.002*** [0.00] 0.003*** [0.00] 0.013*** [0.00] 0.221*** [0.03] 0.111*** [0.02]

0.006*** [0.00] 0.002*** [0.00] 0.004*** [0.00] 0.012*** [0.00] 0.162*** [0.02] 0.092*** [0.01]

0.056** [0.03] –

–

0.004*** [0.00] 0.002*** [0.00] 0.004*** [0.00] 0.013*** [0.00] 0.172*** [0.02] 0.094*** [0.01]

0.066*** [0.02] – 0.041*** [0.01] 0.014 [0.01] 0.323*** [0.09] 4.044*** [0.49] 2.189*** [0.21]

0.046*** [0.01] 0.019 [0.01] 0.332*** [0.09] 3.832*** [0.50] 2.157*** [0.22]

0.077*** [0.02] 0.042*** [0.01] 0.004 [0.01] 0.267*** [0.07] 3.342*** [0.36] 1.946*** [0.17]

0.080*** [0.02] 0.047*** [0.01] 0.002 [0.01] 0.265*** [0.07] 3.168*** [0.36] 1.920*** [0.17]

(continued on next page)

12

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 5 (continued) Panel B: Social capital and the level of cash dividends (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

MEDIAN_INC_LN

0.011 [0.01] 0.147*** [0.02] 0.008*** [0.00] 0.004*** [0.00] 1.050*** [0.13] 0.037*** [0.01] 0.003 [0.02] –

0.003 [0.01] 0.143*** [0.01] 0.010*** [0.00] 0.007*** [0.00] 1.081*** [0.08] 0.040*** [0.00] 0.011 [0.02] –

MEDIAN_AGE_LN

–

EDU

–

Year effects Industry effects Constant

Yes Yes 0.018 [0.02] 11,754 6,547

– – Yes Yes 0.030** [0.01] 54,978 7,989

– – Yes Yes 0.165 [0.25] 11,754 6,547

0.116 [0.10] 2.882*** [0.40] 0.122*** [0.02] 0.030 [0.03] 25.167*** [2.48] 0.667*** [0.11] 0.488 [0.36] 0.128 [0.10] 0.035** [0.01] 0.189 [0.22] 0.083 [0.24] Yes Yes 2.309** [1.04] 11,685 6,513

0.078 [0.09] 2.718*** [0.25] 0.158*** [0.02] 0.049*** [0.01] 24.539*** [1.64] 0.718*** [0.08] 0.241 [0.27] –

–

0.005 [0.01] 0.145*** [0.01] 0.010*** [0.00] 0.007*** [0.00] 1.067*** [0.08] 0.038*** [0.00] 0.015 [0.02] 0.005 [0.00] 0.000 [0.00] 0.017* [0.01] 0.023* [0.01] Yes Yes 0.089* [0.05] 54,695 7,962

0.083 [0.11] 2.862*** [0.40] 0.122*** [0.02] 0.026 [0.03] 25.663*** [2.48] 0.709*** [0.11] 0.555 [0.36] –

POPULATION_LN

0.013* [0.01] 0.148*** [0.02] 0.008*** [0.00] 0.004*** [0.00] 1.025*** [0.13] 0.035*** [0.01] 0.007 [0.02] 0.003 [0.01] 0.001 [0.00] 0.018 [0.01] 0.027* [0.02] Yes Yes 0.089 [0.06] 11,685 6,513

– – Yes Yes 0.325 [0.23] 54,977 7,989

0.041 [0.08] 2.751*** [0.25] 0.155*** [0.02] 0.053*** [0.01] 24.283*** [1.65] 0.678*** [0.08] 0.178 [0.27] 0.088 [0.08] 0.024* [0.01] 0.188 [0.18] 0.269 [0.21] Yes Yes 1.609* [0.92] 54,694 7,962

3.206

3.177

4.088

4.112

0.267

0.268

0.282

0.284

CASH CAPEX AGE_LN RET RET_SD TANG IND_CON

Observations No. of unique firms Pseudo R2

– –

– –

– –

Note: This table reports logit regression results of the relation between social capital and the likelihood of cash dividends (Panel A) and Tobit regression results of the relation between social capital and the amount of cash dividends (Panel B). Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Variables are defined in the Appendix.

(TSTKC) in which we replace a decrease in treasury stock with zero (Banyi et al., 2008). Untabulated results confirm the insignificant relation between social capital and stock repurchases. Overall, the results documented in Table 6 indicate that county-level social capital has no bearing on either the likelihood or the level of stock repurchases. One plausible explanation for this insignificant relation could be the fact that, unlike dividends, shareholders do not expect share repurchases to occur on a regular basis. The latter is viewed as a one-time cash distribution whereby the CEO can exercise much more discretion over the size and the timing of a share repurchase (Chintrakarn et al., 2018). 4.5. Sensitivity analyses In this section, we conduct additional sensitivity tests to examine the robustness of the results. Since our above analyses show a statistically significant relationship between social capital and cash dividends, we limit our sensitivity analyses to this group. 4.5.1. Alternative measures of common dividends In our main analysis, we scale cash dividends by total assets and net income. To test the sensitivity of our documented results, we re-estimate the relationship between social capital and the level of cash dividends using an alternative measure of cash dividends. In particular, we scale the amount of common dividends by the market value of equity (DIV/MVE) and the cash flow from operations (DIV/CF). The results tabulated in Panel A of Table 7 show that the coefficients for SC_PREV remain positive and significant (coefficient = 0.005; p < 0.01 and coefficient = 0.048; p < 0.01 for the DIV/MVE and DIV/CF measures of common dividends, respectively). Thus, our inference from the main analysis remains robust with the alternative scaling of common dividends. 4.5.2. Alternative measures of social capital Recall that, in our main analysis, we used the county-level social capital data of Rupasingha et al. (2006) and filled in the data for the missing years using the social capital index in the preceding year for which social data are available (SC_PREV). In the sensitivity analysis, we use linearly interpolated social capital (SC_IPOL) to fill in the social capital value in the years

13

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183 Table 6 Social capital and stock repurchases. Panel A. Social capital and the probability of stock repurchases (1)

(2)

(3)

(4)

Restricted Sample REP_D

Restricted Sample REP_D

Full Sample REP_D

Full Sample REP_D

0.031 [0.03] –

0.043 [0.04] –

–

–

MEDIAN_INC_LN

0.203*** [0.02] 0.113*** [0.02] 0.419*** [0.14] 0.715* [0.38] 1.394*** [0.22] 0.185 [0.18] 0.966* [0.50] 0.300*** [0.03] 0.074* [0.04] 13.723*** [2.07] 0.410** [0.20] 0.110 [0.62] –

POPULATION_LN

–

MEDIAN_AGE_LN

–

EDU

–

Year effects Industry effects Constant

Yes Yes 1.577*** [0.58] 11,761 6,549 0.149

0.204*** [0.02] 0.112*** [0.02] 0.391*** [0.14] 0.707* [0.37] 1.412*** [0.22] 0.232 [0.16] 0.913* [0.50] 0.305*** [0.03] 0.077** [0.04] 13.698*** [2.12] 0.445** [0.20] 0.093 [0.63] 0.065 [0.19] 0.009 [0.03] 0.348 [0.36] 0.076 [0.42] Yes Yes 0.433 [2.35] 11,692 6,515 0.149

0.003 [0.03] 0.208*** [0.01] 0.123*** [0.01] 0.662*** [0.10] 0.345 [0.25] 1.201*** [0.14] 0.206* [0.12] 0.354 [0.31] 0.291*** [0.02] 0.116*** [0.02] 12.604*** [1.02] 0.416*** [0.13] 0.408 [0.49] –

0.002 [0.04] 0.209*** [0.01] 0.122*** [0.01] 0.639*** [0.10] 0.339 [0.24] 1.196*** [0.14] 0.238** [0.12] 0.351 [0.31] 0.295*** [0.02] 0.117*** [0.02] 12.620*** [1.03] 0.444*** [0.13] 0.379 [0.49] 0.110 [0.14] 0.018 [0.03] 0.386 [0.30] 0.047 [0.38] Yes Yes 0.481 [1.88] 49,240 7,615 0.141

Dep. Var. = SC_RES SC_PREV SIZE MTB LEV R&D ROA CASH CAPEX AGE_LN RET RET_SD TANG IND_CON

Observations No. of unique firms Pseudo R2

– – – Yes Yes 2.227*** [0.52] 55,045 7,996 0.14

Panel B: Social capital and the amount of stock repurchases

Dep. Var. = SC_RES SC_PREV SIZE MTB LEV R&D ROA

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Restricted Sample REP/TA

Restricted Sample REP/TA

Full Sample REP/TA

Full Sample REP/TA

Restricted Sample REP/NI

Restricted Sample REP/NI

Full Sample REP/NI

Full Sample REP/NI

0.000 [0.00] –

0.000 [0.00] –

–

–

–

0.009*** [0.00] 0.002* [0.00] 0.012* [0.01] 0.018 [0.02] 0.063***

0.009*** [0.00] 0.002* [0.00] 0.011 [0.01] 0.019 [0.02] 0.063***

0.000 [0.00] 0.010*** [0.00] 0.002 [0.00] 0.018*** [0.00] 0.014 [0.01] 0.064***

0.032 [0.03] –

–

0.001 [0.00] 0.010*** [0.00] 0.001 [0.00] 0.019*** [0.00] 0.015 [0.01] 0.064***

0.000 [0.03] – 0.150*** [0.01] 0.158*** [0.03] 0.404*** [0.13] 0.639 [0.53] 4.417***

0.148*** [0.02] 0.155*** [0.03] 0.387*** [0.13] 0.707 [0.55] 4.411***

0.014 [0.03] 0.146*** [0.01] 0.160*** [0.01] 0.500*** [0.08] 0.204 [0.33] 4.973***

0.046 [0.03] 0.145*** [0.01] 0.158*** [0.01] 0.483*** [0.08] 0.275 [0.33] 4.967***

(continued on next page)

14

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 6 (continued) Panel B: Social capital and the amount of stock repurchases (1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

MEDIAN_INC_LN

[0.01] 0.021** [0.01] 0.049* [0.03] 0.006*** [0.00] 0.007*** [0.00] 0.489*** [0.09] 0.023** [0.01] 0.028 [0.03] –

[0.01] 0.027*** [0.01] 0.002 [0.02] 0.007*** [0.00] 0.009*** [0.00] 0.407*** [0.05] 0.026*** [0.01] 0.022 [0.02] –

MEDIAN_AGE_LN

–

EDU

–

Year effects Industry effects Constant

Yes Yes 0.082*** [0.03] 10,567 5,985

Yes Yes 0.128*** [0.02] 49,513 7,637

Yes Yes 0.936** [0.43] 10,567 5,985

[0.51] 0.555*** [0.17] 1.163** [0.55] 0.093*** [0.03] 0.019 [0.04] 20.557*** [2.50] 0.614*** [0.17] 0.964 [0.64] 0.027 [0.17] 0.028 [0.02] 0.141 [0.30] 0.335 [0.39] Yes Yes 0.095 [1.82] 10,504 5,955

[0.24] 0.556*** [0.10] 1.011*** [0.33] 0.101*** [0.02] 0.045** [0.02] 20.472*** [1.43] 0.559*** [0.11] 0.223 [0.37] –

–

[0.01] 0.027*** [0.01] 0.001 [0.02] 0.007*** [0.00] 0.009*** [0.00] 0.413*** [0.05] 0.026*** [0.01] 0.021 [0.02] 0.002 [0.01] 0.000 [0.00] 0.025* [0.01] 0.010 [0.01] Yes Yes 0.021 [0.08] 49,240 7,615

[0.51] 0.555*** [0.17] 1.218** [0.55] 0.089*** [0.03] 0.021 [0.04] 20.494*** [2.46] 0.584*** [0.18] 0.993 [0.63] –

POPULATION_LN

[0.01] 0.022*** [0.01] 0.049* [0.03] 0.006*** [0.00] 0.007*** [0.00] 0.495*** [0.10] 0.023** [0.01] 0.028 [0.03] 0.003 [0.01] 0.001 [0.00] 0.005 [0.02] 0.009 [0.02] Yes Yes 0.047 [0.11] 10,504 5,955

Yes Yes 1.259*** [0.38] 49,513 7,637

[0.24] 0.558*** [0.10] 0.990*** [0.33] 0.104*** [0.02] 0.046** [0.02] 20.586*** [1.44] 0.568*** [0.10] 0.241 [0.37] 0.031 [0.15] 0.026 [0.02] 0.065 [0.25] 0.318 [0.29] Yes Yes 0.469 [1.51] 49,240 7,615

1.505

1.511

1.087

1.095

0.139

0.14

0.144

0.144

CASH CAPEX AGE_LN RET RET_SD TANG IND_CON

Observations No. of unique firms Pseudo R2

– – –

– – –

– – –

Note: This table reports logit regression results of the relation between social capital and the likelihood of stock repurchase (Panel A) and Tobit regression results of the relation between social capital and the amount of stock repurchase (Panel B). Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Variables are defined in the Appendix.

when social capital data are missing (Hasan et al., 2017a; Jha and Chen, 2015; Jha and Cox, 2015). Furthermore, we use per capita registered organ donor data, which are available from the Organ Procurement and Transplantation Network (OPTN), to construct an alternative proxy for social capital (SC_ORGAN) (Habib and Hasan, 2017; Hasan and Habib, 2019b; Hasan et al., 2017a, 2017b). Columns (1) to (3) in Panel B of Table 7 show that the coefficients for SC_IPOL are positive and statistically significant for the DIV_D (coefficient = 0.294; p < 0.01), DIV/TA (coefficient = 0.006; p < 0.01) and DIV/NI (coefficient = 0.087; p < 0.01) measures of common dividends. Moreover, the results tabulated in Columns (4) to (6) show positive and significant (p < 0.01) coefficients for all three variants of common dividends when we use SC_ORGAN as the alternative measure of social capital. Thus, our analysis suggests that the documented positive and significant relationship between social capital and common dividends is not driven by a specific measure of social capital. 4.6. Endogeneity test Our documented positive relationship between social capital and dividend payouts may be biased because of endogeneity problems relating to omitted variable bias and/or reverse causality. In this section, we use several approaches to mitigate such endogeneity concerns. 4.6.1. Omitted variable bias Although we included a number of controls in our regression analyses, one may argue that our analysis may have omitted some other firm-specific characteristics that are associated with both dividends and other included variables. For example, Cheung et al. (2018) show that corporate social responsibility (CSR) increases dividend payouts. Jiang et al. (2017) find that stock liquidity increases dividend payouts. Studies also indicate that firms with better financial reporting quality and strong corporate governance pay higher dividends (Adjaoud and Ben‐Amar, 2010; Koo et al., 2017). Bodnaruk and Östberg (2013) argue and show that firms with a large shareholder base and fewer financing constraints have lower payout levels. To address the concern with the potential omitted variable bias problem, we re-estimate the regressions in Table 5 (both Panel A and Panel B) after incorporating corporate social responsibility (CSR), stock illiquidity (ILLIQUIDITY), the discretionary

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

15

Table 7 Sensitivity analysis. Panel A: Alternative measure of cash dividends

Dep. Var. = SC_PREV Other controls Year effects Industry effects Constant Observations Pseudo R2

(1)

(2)

DIV/MVE 0.005*** [0.00] Yes Yes Yes 0.073* [0.04] 54,695 2.034

DIV/CF 0.048*** [0.01] Yes Yes Yes 0.961* [0.54] 54,673 0.35

Panel B: Alternative measures of social capital (1)

(2)

(3)

(4)

(5)

(6)

Dep. Var. =

DIV_D

DIV/TA

DIV/NI

DIV_D

DIV/TA

DIV/NI

SC_IPOL

0.294*** [0.06] –

0.006*** [0.00] –

0.087*** [0.02] –

–

–

–

Yes Yes Yes No 7.225*** [2.55] 54,691 0.389

Yes Yes Yes No 0.098* [0.05] 54,690 4.117

Yes Yes Yes No 1.742* [0.90] 54,689 0.285

0.171*** [0.06] Yes Yes Yes Yes 2.392*** [0.72] 54,984 0.391

0.004*** [0.00] Yes Yes Yes Yes 0.043*** [0.01] 54,978 4.1712

0.062*** [0.02] Yes Yes Yes Yes 0.591*** [0.23] 54,977 0.286

SC_ORGAN Other controls Year effects Industry effects State effects Constant Observations Pseudo R2

Note: This table reports the regression results of the relation between social capital and alternative measures of cash dividends (DIV/MVE and DIV/CF) (Panel A) and the relation between alternative measures of social capital (SC_IPOL and SC_ORGAN) and dividends (Panel B). Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Variable definitions are provided in the Appendix.

accrual measure of financial reporting quality (|DAC|), corporate governance measures (TAKEOVER_INDEX and INST_HOLD), shareholder base (SHARE_BASE), financing constraints (FC) and free cash flow (FCF). We define the variables in the appendix. The results reported in Panel A of Table 8 show that the relation between social capital (SC_PREV) and the probability of cash dividends (DIV_D) remains unaffected in terms of sign, significance and magnitude (the coefficients vary between 0.264 and 0.387; all significant at p < 0.01), indicating that the results reported in the main analysis are not driven by omitted, correlated variables. Similarly, the results presented in Panel B provide evidence that the sign, significance and magnitude of SC_PREV remain qualitatively similar (the coefficients range from 0.004 to 0.006; all significant at p < 0.01) when additional controls are incorporated into the regression models. In Panel B, we present the results using the DIV/TA measure of cash dividends. Our inference remains similar even if we use other measures of cash dividends (results untabulated). Additionally, the coefficients for the majority of the additional control variables are significant and largely consistent with the prediction. 4.6.2. Use of county fixed effects In our main regression analysis, we controlled for county-level demographic factors to mitigate concerns about the county effects on our estimates. Nonetheless, one may argue that these controls are not adequate to isolate the potential effects arising from county-specific characteristics. To address this concern, we re-estimate our regressions after including county fixed effects that control for county-level differences. Panel C of Table 8 presents the results from this regression analysis. We continue to find a positive and significant relation between social capital and both the likelihood and the amount of cash dividends (coefficient = 0.211, p < 0.05 for the DIV_D, 0.002, p < 0.01 for the DIV/TA and 0.050, p < 0.01 for the DIV/NI measures of cash dividends). Thus, we provide evidence that our documented results are not driven by county-specific effects. 4.6.3. Instrumental variable estimation results Since we lack a natural experiment that exhibits an exogenous shock to the county-level social capital, we use an instrumental variable estimation approach to mitigate further the endogeneity problem stemming from omitted variable bias and reverse causality. In the spirit of Jha and Cox (2015), we use two instruments: (i) the yearly mean social capital for firms headquartered in the same state and (ii) the yearly mean social capital for firms within the same two-digit SIC code.

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 8 Omitted variable bias. Panel A: Social capital and the probability of cash dividends – Omitted variable bias (1) DIV_D

(2) DIV_D

(3) DIV_D

(4) DIV_D

(5) DIV_D

(6) DIV_D

(7) DIV_D

(8) DIV_D

(9) DIV_D

0.273*** [0.06] –

0.277*** [0.07] –

0.288*** [0.07] –

0.290*** [0.06] –

0.265*** [0.06] –

0.264*** [0.07] –

0.277*** [0.06] –

ILLIQUIDITY

0.313*** [0.09] 0.058*** [0.02] –

–

–

–

–

–

–

|DAC|

–

0.016*** [0.00] –

–

–

–

–

–

TAKEOVER_INDEX

–

–

0.200 [0.29] –

–

–

–

–

FCF

–

–

–

2.156*** [0.61] –

–

–

–

SHARE_BASE

–

–

–

–

1.385** [0.57] –

–

– –

INST_HOLD

–

–

–

–

–

0.178*** [0.04] –

FC

–

–

–

–

–

–

1.477*** [0.19] –

Other controls Year effects Industry effects Constant

Yes Yes Yes 9.509*** [3.64] 21,185 0.358

Yes Yes Yes 6.792*** [2.63] 54,681 0.389

Yes Yes Yes 6.553** [2.78] 52,544 0.392

Yes Yes Yes 5.850** [2.79] 50,876 0.394

Yes Yes Yes 7.848*** [2.66] 49,687 0.388

Yes Yes Yes 6.617*** [2.56] 53,500 0.392

Yes Yes Yes 7.976*** [2.76] 40,641 0.383

0.118*** [0.06] Yes Yes Yes 6.718*** [2.66] 53,222 0.387

0.387*** [0.10] 0.044** [0.02] 0.031* [0.02] 0.653 [0.61] 2.314*** [0.81] 2.051* [1.19] 0.135*** [0.05] 1.763*** [0.23] 0.105 [0.09] Yes Yes Yes 8.462** [3.93] 15,959 0.380

Dep. Var. = SC_PREV CSR

Observations Pseudo R2

Panel B: Social capital and the level of cash dividends - Omitted variable bias (1) DIV/TA

(2) DIV/TA

(3) DIV/TA

(4) DIV/TA

(5) DIV/TA

(6) DIV/TA

(7) DIV/TA

(8) DIV/TA

(9) DIV/TA

0.006*** [0.00] –

0.005*** [0.00] –

0.006*** [0.00] –

0.006*** [0.00] –

0.006*** [0.00] –

0.005*** [0.00] –

0.005*** [0.00] –

ILLIQUIDITY

0.004*** [0.00] 0.001*** [0.00] –

–

–

–

–

–

–

|DAC|

–

0.000*** [0.00] –

–

–

–

–

–

TAKEOVER_INDEX

–

–

0.003 [0.01] –

–

–

–

–

FCF

–

–

–

0.048*** [0.01] –

–

–

–

SHARE_BASE

–

–

–

–

0.039** [0.02] –

–

–

INST_HOLD

–

–

–

–

–

0.001 [0.00] –

–

FC

–

–

–

–

–

–

0.004*** [0.00] –

Yes Yes Yes 0.089* [0.05] 54,680 4.067

Yes Yes Yes 0.082 [0.06] 52,499 4.178

Yes Yes Yes 0.073 [0.06] 50,877 4.482

Yes Yes Yes 0.099* [0.05] 49,705 2.955

Yes Yes Yes 0.082 [0.05] 53,227 3.493

Yes Yes Yes 0.085 [0.05] 53,520 3.993

0.004*** [0.00] 0.001*** [0.00] 0.000* [0.00] 0.021** [0.01] 0.033*** [0.01] 0.014 [0.02] 0.001 [0.00] 0.003*** [0.00] 0.033*** [0.00] Yes Yes Yes 0.100* [0.06] 15,950 0.623

Dep. Var. = SC_PREV CSR

Other controls Year effects Industry effects Constant

Yes Yes Yes 0.125** [0.06] Observations 21,285 Pseudo R2 0.666 Panel C: Control for county effects Dep. Var. = SC_PREV SIZE MTB

0.037*** [0.00] Yes Yes Yes 0.098** [0.05] 40,655 2.184

(1) DIV_D

(2) DIV/TA

(3) DIV/NI

0.211** [0.10] 0.260*** [0.03] 0.011 [0.02]

0.002*** [0.00] 0.002*** [0.00] 0.004*** [0.00]

0.050*** [0.00] 0.050*** [0.00] 0.004*** [0.00]

17

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183 Table 8 (continued) Panel C: Control for county effects LEV R&D ROA CASH CAPEX AGE_LN RET RET_SD TANG IND_CON Year effects Industry effects County effects Constant Observations Pseudo R2

(1) 0.816*** [0.17] 8.294*** [1.00] 2.789*** [0.45] 0.342 [0.23] 5.614*** [0.62] 0.661*** [0.06] 0.231*** [0.03] 50.325*** [3.30] 1.598*** [0.21] 0.888 [0.79] Yes Yes Yes 3.165*** [0.96] 53,411 0.416

(2) 0.011*** [0.00] 0.148*** [0.00] 0.086*** [0.00] 0.007*** [0.00] 0.135*** [0.00] 0.009*** [0.00] 0.007*** [0.00] 1.022*** [0.00] 0.035*** [0.00] 0.003*** [0.00] Yes Yes Yes 0.038*** [0.00] 54,978 4.558

(3) 0.258*** [0.00] 3.081*** [0.04] 1.804*** [0.01] 0.015** [0.01] 2.512*** [0.02] 0.145*** [0.00] 0.051*** [0.00] –23.344*** [0.05] 0.609*** [0.01] 0.313*** [0.02] Yes Yes Yes 0.463*** [0.00] 54,977 0.313

Note: This table reports the regression results of the relation between social capital and the probability of cash dividends after including additional controls (Panel A), the relation between social capital and the level of cash dividends after including additional controls (Panel B), and the relation between social capital and the probability as well as level of cash dividends after including county effects (Panel C). Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Variable definitions are provided in the Appendix.

Habib and Hasan (2017) and Hasan and Habib (2019b) use a similar set of instruments, arguing that the spatially sticky nature of social capital makes industry- and state-level social capital ideal instruments for county-level social capital (Rutten et al., 2010). Baptista and Swann (1998) document that industries tend to concentrate in the same geographic region. Therefore, it is reasonable to contend that the social capital of firms in the same industry might be homogeneous (Jha and Cox, 2015). Furthermore, it is likely that state-level social capital is highly correlated with the social capital of the counties nested in the same state. Given the available evidence and argument, it is rational to expect that county-level social capital (i.e., our endogenous variable) is highly positively correlated with both state-level and industry-level social capital (i.e., our selected instruments). It is very unlikely that dividend payouts affect industry- and state-level social capital. Thus, our selected instruments satisfy the essential requirements. Table 9 reports the results from the instrumental variable estimation results. Column (1) reports the first-stage regression, in which the dependent variable is the county-level social capital. The explanatory variables include our selected instruments and the same control variables as used in the main regressions. Consistent with our expectation, the coefficient estimates for the instruments in Column (1) are statistically significant at the 1% level, implying the validity of our instruments. A weak IV test suggests that the instrument is not subject to a weak-instrument problem. The Smith–Blundell exogeneity test reported in Table 9 shows that we fail to reject the exogeneity of our instrument. Columns (2) to (4) report the second-stage regression results. Since the dependent variable in Column (2) is a dummy variable, and those in Columns (3) and (4) are left-censored, we use the IV probit and IV tobit methods, respectively (see, e.g., Hellmann et al., 2008; Jiang et al., 2017). The results from the instrumental variable estimates show that the relationship between fitted social capital (SC_PREV_FIT) and both the likelihood and the amount of dividends remains robust after accounting for the endogeneity problem. In particular, the estimated coefficients (and p values) for SC_PREV_FIT are 0.200 (p < 0.01), 0.006 (p < 0.01) and 0.083 (p < 0.01) for the DIV_D, DIV_TA and DIV_NI measures of dividends, respectively. These results thus indicate that our documented positive relationship between social capital and dividends is not driven by an endogeneity problem. 4.7. Additional analysis 4.7.1. Effect of civic norms and social networks on the likelihood and amount of dividends: An exploratory analysis Recall that we used two variants of social norms (viz. the census mail response rate (RESPN) and the votes cast in presidential elections (PVOTE)) and two variants of social networks (viz. the numbers of associations (ASSN) and the number of non-profit organizations (NCCS)) to form our social capital variable for the main analysis. One may argue that the norm and

18

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Table 9 Instrumental variable estimation results. This table reports the two-stage least square regression results of the relationship between social capital and likelihood and amount of cash dividends. Column (1) presents the first-stage regression results and columns (2) to (4) present the second-stage regression results, with propensity to pay dividends (DIV_D), dividend-to-assets ratio (DIV/TA), dividend-to-net income ratio (DIV/NI), respectively. Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively. Other variable definitions are provided in Appendix. First Stage

Second Stage

(1)

(2)

(3)

(4)

Dep. Var. = SC_PREV_FIT

SC_PREV –

SIZE

0.006*** [0.00] 0.001 [0.00] 0.006 [0.01] 0.051*** [0.01] 0.014* [0.01] 0.016* 0.01] 0.018 [0.03] 0.006*** [0.00] 0.002 [0.00] 0.186** [0.08] 0.073*** [0.01] 0.046 [0.05] 0.962*** [0.01] 0.187*** [0.00] 1.252*** [0.00] 3.826*** [0.02] 0.692*** [0.00] 0.186*** [0.02] Yes Yes 7.065*** [0.12] 54,695 0.82 0.00 –

DIV_D 0.200*** [0.02] 0.151*** [0.00] 0.002 [0.01] 0.428*** [0.04] 4.089*** [0.21] 1.246*** [0.08] 0.272*** [0.05] 3.167*** [0.18] 0.374*** [0.01] 0.122*** [0.01] –23.079*** [0.67] 0.919*** [0.06] 0.089 [0.25] 0.179*** [0.06] 0.016 [0.01] 0.623*** [0.12] 0.721*** [0.16] –

DIV/TA 0.006*** [0.00] 0.002*** [0.00] 0.004*** [0.00] 0.012*** [0.00] 0.161*** [0.01] 0.092*** [0.00] 0.005** [0.00] 0.145*** [0.01] 0.010*** [0.00] 0.007*** [0.00] 1.065*** [0.03] 0.038*** [0.00] 0.014 [0.01] 0.003 [0.00] 0.000 [0.00] 0.020*** [0.00] 0.028*** [0.01] –

DIV/NI 0.083*** [0.02] 0.047*** [0.00] 0.002 [0.01] 0.265*** [0.03] 3.165*** [0.18] 1.919*** [0.07] 0.040 [0.04] 2.750*** [0.14] 0.155*** [0.01] 0.053*** [0.01] 24.272*** [0.60] 0.677*** [0.04] 0.178 [0.19] 0.082* [0.04] 0.022*** [0.01] 0.197** [0.09] 0.291** [0.12] –

–

–

–

Yes Yes 3.396*** [0.62] 54,695

Yes Yes 0.080*** [0.02] 54,695

Yes Yes 1.573*** [0.47] 54,694

0.12

0.14

0.74

MTB LEV R&D ROA CASH CAPEX AGE_LN RET RET_SD TANG IND_CON MEDIAN_INC_LN POPULATION_LN MEDIAN_AGE_LN EDU SC_STATE SC_IND Year effects Industry effects Constant Observations Adj. R-squared Wald statistic of weak IV (p-value) Smith–Blundell exogeneity test (p-value)

network facets could have distinct relationships with dividends (Hasan et al., 2017a). Therefore, in the additional analysis, we examine the relationship of social norms and social networks with both the likelihood and the amount of cash dividend payouts. Following Hasan and Habib (2019b) and Hasan et al. (2017a), we use the first principal component from a factor analysis based on ASSN and NCCS to construct our proxy for social networks (SC_NETWORK). Similarly, we employ the first principal component from a factor analysis based on PVOTE and RESPN to construct our proxy for social norms (SC_NORM). We report the results from this analysis in Panel A of Table 10. Our analysis reveals that both SC_NORM and SC_NETWORK have strong positive relationships with the likelihood and amount of cash dividends. However, the positive relationship is more pronounced for the SC_NETWORK component than for the SC_NORM component. An F-test suggests that the difference in the coefficients between SC_NETWORK and SC_NORM is also statistically significant. We obtain qualitatively similar results if we include both SC_NETWORK and SC_NORM in the same regression models.

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4.7.2. Social capital, the geographic dispersion of firms and dividends Earlier, we argued that the cooperative norms and dense network prevalent in high social capital regions would be likely to provide the incentives for both a greater likelihood and higher levels of cash dividend payouts. This argument suggests that the positive relationship between social capital and dividends is accentuated for firms that are less geographically dispersed. To test this conjecture, we use the material subsidiary disclosures in Exhibit 21 in the 10 K filings as required by the SEC.7 We adopt two approaches to divide the sample into two groups: (i) firms with material subsidiaries in more than one state and (ii) firms with material subsidiaries in more than three states. We present the results from this analysis in Panel B of Table 10. The results in Columns (1) to (4) show that both more and less geographically dispersed firms are likely to pay dividends and that the difference between more and less dispersed firms in terms of the likelihood of dividend payments is statistically insignificant. Columns (5) to (8) report the results for the same analysis but for dividend amounts measured as DIV/TA, and Columns (9) to (12) report the dividend amounts measured as DIV/NI. We find that firms headquartered in high social capital counties pay more dividends, irrespective of their geographical diversification. However, the relationship between social capital and the level of dividends is more pronounced for less diversified firms. An F-test suggests that the difference in coefficients for social capital between high and low numbers of subsidiaries is significant at the conventional level. 4.7.3. Social capital, corporate governance and dividends While developing our prediction, we argued that social capital reduces managerial opportunistic behavior and therefore the managers of firms headquartered in high social capital counties are less likely to hold cash for self-serving purposes (e.g., overinvestment in negative NPV projects). Consequently, firms headquartered in high social capital counties distribute excess cash as dividends. If this argument is valid, one would expect the relation between social capital and dividends to be more pronounced for firms with poor corporate governance. In this sub-section, we test this proposition empirically.8 We use two proxies to capture the quality of corporate governance (i.e., agency problems) of the firm. Our first measure of corporate governance is board independence. Prior studies suggest that independent corporate boards provide effective monitoring of managerial activities, which in turn reduces agency problems (Jaggi et al., 2009). Therefore, we expect the role of social capital in affecting dividends to be more pronounced when a firm’s board has relatively few independent directors. We also use CEO age (CEO_AGE) as the second measure of corporate governance/agency problems. Andreou et al. (2017) argue that CEO age is a source of agency problems in that firms managed by younger CEOs are more likely to suffer from agency problems. We expect social capital to play a dominating role in affecting dividends when firms are managed by young CEOs. We report the results from the above analysis in Panel C of Table 10. Columns (1) and (2) show that the relation between social capital and cash dividends is positive and significant only when the proportion of independent directors on the board is smaller than the sample median. In Columns (3) and (4), we find that the relation between social capital and cash dividends is positive and significant for firms managed by both young (CEO_AGE < median) and old (CEO_AGE > median) CEOs. An F-test suggests that the difference in coefficients for social capital between young and old CEOs is statistically insignificant at the conventional level. Thus, we find some evidence that the relationship between social capital and cash dividends is stronger in the presence of agency problems. 5. Conclusion In this paper, we examine the relationship between social capital and both dividend payouts and stock repurchases. We predict a positive relationship between social capital and both the likelihood and the level of cash dividend payouts. Using a large sample of US firms, we find support for our conjecture. In particular, we document that firms headquartered in high social capital counties have a higher propensity to pay cash dividends and a higher level of cash dividend payouts than firms headquartered in low social capital counties. However, we find no relationship between social capital and either the likelihood or the levels of stock repurchases. We show that our findings are robust to the use of alternative measures of dividends, stock repurchases and social capital and to controls for endogeneity concerns. Our additional analysis shows that both the social norm and the network dimension of social capital affect the propensity for, and amount of, cash dividend payments. We also reveal that the relationship between social capital and cash dividend payments is more pronounced for firms with a lower level of geographical diversification. Finally, we find some evidence that the role of social capital in influencing cash dividends is more pronounced when firms are subject to more agency problems. Our findings have implications for investors who prefer to receive current investment income in the form of cash dividends while foregoing anticipated, long-term capital gains. Our findings suggest that such investors might buy stocks of companies domiciled in high social capital counties. This is important, because the county-level characteristics may be a better predictor of dividend payouts, given that they are sticky in nature compared with firm-specific characteristics, which might exhibit volatility. Our findings also provide evidence supporting the ‘‘dividend clientele theory.” Moreover, we find 7 We obtain state-level data for material subsidiaries from Dyreng Scott’s personal website at https://sites.google.com/site/scottdyreng/Home/data-andcode. 8 We thank the anonymous reviewer for suggesting this analysis.

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Table 10 Additional analysis. Panel A: Norm vs network Dep. Var. = SC_NORM

(1) DIV_D

(2) DIV_D

0.123*** [0.04]

(5) DIV/NI

0.249*** [0.08] Yes Yes Yes 3.821 [3.08]

Yes Yes Yes 11.237*** [2.64] 11.97***

[0.00] 54,696 0.387

0.042*** [0.01] 0.005*** [0.00] Yes Yes Yes 0.022 [0.06]

Yes Yes Yes 0.178*** [0.05] 15.52***

[0.00] 54,695 4.098

54,696 0.387

(6) DIV/NI

0.068** [0.03] Yes Yes Yes 0.881 [1.06]

Yes Yes Yes 3.040*** [0.94] 2.86*

[0.09] 54,694 0.284

54,695 4.105

54,694 0.284

Panel B: Geographical diversification

Dep. Var. = SC_PREV Other controls Year effects Industry effects Constant Difference in coefficients of SC_PREV (v2) p-value Observations Pseudo R2 Panel C: Agency problem

(1) # of Seg. >1 DIV_D

(2) # of Seg. =1 DIV_D

(3) # of Seg. >3 DIV_D

(4) # of Seg. <=3 DIV_D

(5) # of Seg. >1 DIV/TA

(6) # of Seg. =1 DIV/TA

(7) # of Seg. >3 DIV/TA

(8) # of Seg. <=3 DIV/TA

(9) # of Seg. >1 DIV/NI

(10) # of Seg. =1 DIV/NI

(11) # of Seg. >3 DIV/NI

(12) # of Seg. <=3 DIV/NI

0.301*** [0.09] Yes Yes Yes 5.895 [4.05]

0.382*** [0.08] Yes Yes Yes 6.115* [3.53] 0.86

0.381*** [0.13] Yes Yes Yes 11.498** [4.85]

0.290*** [0.07] Yes Yes Yes 3.826 [3.25] 2.12

0.004*** [0.00] Yes Yes Yes 0.038 [0.07]

0.009*** [0.00] Yes Yes Yes 0.039 [0.07] 10.65***

0.003*** [0.00] Yes Yes Yes 0.025*** [0.00]

0.007*** [0.00] Yes Yes Yes 0.055 [0.07] 22.01***

0.072*** [0.02] Yes Yes Yes 1.189 [1.25]

0.114*** [0.04] Yes Yes Yes 1.489 [1.61] 3.29*

0.079*** [0.00] Yes Yes Yes 0.826*** [0.01]

0.097*** [0.03] Yes Yes Yes 0.771 [1.35] 1.54

21,318 0.353

[0.35] 7,626 0.402

10,403 0.348

[0.15] 39,157 0.382

21,412 0.755

[0.00] 28,220 1.7694

10,475 0.450

[0.00] 39,157 3.449

21,412 0.244

[0.07] 28,219 0.307

10,475 0.233

[0.21] 39,156 0.293

(1) IND_DIR>Median

(2) IND_DIR
(3) IND_DIR>Median

(4) IND_DIR
(5) CEO_AGE>Median

(6) CEO_AGE
(7) CEO_AGE>Median

(8) CEO_AGE
Dep. Var. =

DIV/TA

DIV/TA

DIV/NI

DIV/NI

DIV/TA

DIV/TA

DIV/NI

DIV_NI

SC_PREV

0.002 [0.00] 0.001 [0.00] 0.003***

0.005*** [0.00] 0.001 [0.00] 0.005***

0.026 [0.03] 0.021 [0.01] 0.011

0.086*** [0.03] 0.014 [0.01] 0.003

0.003*** [0.00] 0.002*** [0.00] 0.003***

0.005*** [0.00] 0.000 [0.00] 0.004***

0.068*** [0.01] 0.044*** [0.01] 0.006

0.058*** [0.02] 0.022*** [0.01] 0.002

SIZE MTB

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

Difference in coefficients of SC_NORM vs. SC_NETWORK (v2) p-value Observations Pseudo R2

(4) DIV/TA

0.002*** [0.00]

SC_NETWORK Other controls Year effects Industry effects Constant

(3) DIV/TA

LEV R&D ROA CASH CAPEX

RET RET_SD TANG IND_CON MEDIAN_INC_LN POPULATION_LN MEDIAN_AGE_LN EDU Constant Difference in coefficients of SC_PREV (v2) p-value Observations Year effects Industry effects Pseudo R2

6,308 Yes Yes 0.34

[0.00] 0.004 [0.01] 0.100*** [0.03] 0.087*** [0.02] 0.002 [0.01] 0.099*** [0.02] 0.016*** [0.00] 0.007*** [0.00] 1.008*** [0.15] 0.017*** [0.01] 0.001 [0.02] 0.000 [0.01] 0.001 [0.00] 0.043*** [0.02] 0.023 [0.02] 0.173** [0.08] 7.47*** 0.006 7,180 Yes Yes 0.48

[0.01] 0.077 [0.08] 1.982*** [0.46] 0.785*** [0.20] 0.190* [0.11] 1.352*** [0.47] 0.194*** [0.02] 0.063 [0.04] 18.353*** [2.47] 0.299** [0.14] 0.671* [0.37] 0.205** [0.09] 0.015 [0.01] 0.007 [0.21] 0.225 [0.21] 1.964** [0.97]

6,308 Yes Yes 0.22

[0.01] 0.206** [0.10] 1.861*** [0.58] 0.979*** [0.20] 0.060 [0.11] 1.615*** [0.35] 0.246*** [0.02] 0.044** [0.02] 20.704*** [2.20] 0.247** [0.10] 0.409 [0.42] 0.024 [0.12] 0.029* [0.02] 0.740*** [0.27] 0.374 [0.26] 3.128** [1.28] 7.37*** 0.006 7,180 Yes Yes 0.22

[0.00] 0.008*** [0.00] 0.105*** [0.01] 0.080*** [0.01] 0.009*** [0.00] 0.114*** [0.01] 0.009*** [0.00] 0.009*** [0.00] 0.852*** [0.05] 0.025*** [0.00] 0.024** [0.01] 0.006** [0.00] 0.001*** [0.00] 0.018*** [0.01] 0.001 [0.01] 0.114*** [0.03]

9,387 Yes Yes 0.32

[0.00] 0.000 [0.00] 0.125*** [0.01] 0.111*** [0.01] 0.003 [0.00] 0.118*** [0.01] 0.014*** [0.00] 0.007*** [0.00] 1.030*** [0.06] 0.020*** [0.00] 0.001 [0.02] 0.008** [0.00] 0.000 [0.00] 0.022*** [0.01] 0.004 [0.01] 0.144*** [0.04] 1.12 0.29 9,710 Yes Yes 0.79

[0.01] 0.194*** [0.05] 1.988*** [0.26] 0.795*** [0.12] 0.037 [0.07] 1.525*** [0.24] 0.140*** [0.01] 0.084*** [0.02] 20.052*** [1.04] 0.384*** [0.07] 0.738*** [0.23] 0.002 [0.05] 0.026*** [0.01] 0.377*** [0.12] 0.247* [0.13] 1.224* [0.64]

9,387 Yes Yes 0.20

[0.01] 0.056 [0.05] 2.185*** [0.26] 1.436*** [0.14] 0.183** [0.07] 1.851*** [0.26] 0.224*** [0.01] 0.075*** [0.02] 21.714*** [1.18] 0.357*** [0.08] 0.402 [0.32] 0.188*** [0.06] 0.026** [0.01] 0.273** [0.14] 0.214 [0.16] 3.093*** [0.70] 0.25 0.62 9,710 Yes Yes 0.27

Note: Panel A presents the regression results for the relationship of social network and social norms with likelihood and amount of cash dividend payouts. Following Hasan et al. (2017a), we use the first principal component from a factor analysis based on ASSN and NCCS to capture the density of social networks (SC_NETWORK) and first principal component from a factor analysis based on PVOTE and RESPN to capture the strength of civic norms (SC_NORM) in a county. In Panel B we examine whether the relation between social capital and likelihood and amount of cash dividends differs based on geographical diversification. Finally, in Panel C we test whether the relation between social capital and likelihood and amount of cash dividends differs based on agency problem. We define other variables in the Appendix. Robust standard errors clustered by county are in brackets. *, **, *** denote a two-tailed p-value of <0.10, 0.05, and 0.01, respectively.

M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

AGE_LN

[0.00] 0.003 [0.01] 0.081*** [0.03] 0.086*** [0.02] 0.006 [0.01] 0.097*** [0.02] 0.011*** [0.00] 0.006*** [0.00] 0.746*** [0.11] 0.020*** [0.01] 0.020 [0.02] 0.010* [0.01] 0.000 [0.00] 0.003 [0.01] 0.012 [0.01] 0.075 [0.06]

21

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M.M. Hasan, A. Habib / Journal of Contemporary Accounting and Economics 16 (2020) 100183

support for the argument that corporate governance and social capital are substitutes for each other. Therefore, our study suggests that shareholders need to ensure more effective corporate governance mechanisms for firms headquartered in regions with low levels of social capital to mitigate concerns about self-serving managerial behavior. Finally, given the beneficial role of social capital in enhancing stockholders’ interests, governments should attempt to enhance, or at least preserve, regional social capital. Appendix Table A1

Variables

Definition and measurement

Dependent variables DIV_D Dummy variable that takes the value of one if the firm pays dividends (DVC > 0) and zero otherwise. DIV/TA Dividend payments measured as the ratio of dividends (DVC) to total assets (AT). DIV/NI Dividend payments measured as the ratio of dividends (DVC) to net income (NI). REP_D Dummy variable that takes the value of one if the firm repurchases stocks and zero otherwise. We define stock repurchases as common and preferred stock repurchases adjusted for any decreases in preferred stock (Cuny et al., 2009; Desai and Jin, 2011). REP/TA Stock repurchases measured as the amount of stock repurchases scaled by total assets (AT). See Eq. (1) for details. REP/NI Stock repurchases measured as the amount of stock repurchases scaled by net income (NI). Independent variables SC_RES The social capital index for each of the years (i.e., 1997, 2005, 2009 and 2014) for which the NRCRD provides required data for constructing social capital. Following Rupasingha et al. (2006), we use the census mail response rate (RESPN) and the votes cast in presidential elections (PVOTE) as the two constructs capturing social norms and the number of associations (ASSN) and the number of non-profit organizations (NCCS) as the two network variants. A principal component analysis is used for each year (1997, 2005, 2009 and 2014), and the first component capturing the most common variance for each year is used as the proxy for social capital. SC_PREV Social capital index, for which we fill in the data for the missing years using the estimated social capital index in the preceding year for which a social capital index is available (SC_PREV). SC_IPOL Linearly interpolated social capital, in which we fill in the missing social capital index in the years 1998–2004, 2006–2008, 2010– 2013 and 2015. Control variables SIZE Natural log of market value of equity (PRCC_F*CSHO). MTB Market-to-book ratio calculated as the market value of assets ((PRCC_F*CSHO) + (DLTT + DLC)) divided by the book value of assets (AT). LEV Leverage measured as the ratio of the sum of short-term and long-term debt (DLC + DLTT) over total assets (AT). R&D Research and development expenses measured as R&D (XRD) over total assets (AT). We replace missing R&D with zero. ROA Return on assets measured as operating income before depreciation scaled by total assets. CASH Cash and marketable securities (CHE) scaled by total assets (AT). CAPEX Capital expenditure (CAPX) scaled by total assets (AT). AGE_LN Firm age, measured as the number of years since the firm was first covered by the Center for Research in Securities Prices (CRSP). We measure AGE as the natural log of (1 + age of the firm). RET Yearly stock return. RET_SD Standard deviation of daily stock returns over the year. TANG Asset tangibility measured as the net property, plant and equipment scaled by total assets. IND_CON Industry concentration measured as the sum of the squared market share of each firm in the same industry (two-digit SIC codes) during a year. The market share is defined as the total sales of the firm in a given year divided by the total sales of the industry in that year. MDN_INC_LN Natural log of the median household income per capita in a county in a given year. Source: Census Bureau. POPULATION_LN Natural logarithm of the population in the county. Source: Census Bureau. MEDIAN_AGE_LN Natural logarithm of the median age of residents in a county during a year. Source: Census Bureau. EDU Percentage of persons aged 25 years and over with at least one year of college education in a county in a given year. Source: Census Bureau. Variables used in the sensitivity analysis and additional analysis DIV/MVE Dividend payments measured as the ratio of dividends (DVC) to the market value of equity (PRCC_F*CSHO). DIV/CF Dividend payments measured as the ratio of dividends (DVC) to the operating cash flow (OANCF). SC_ORGAN State-level per capita registered organ donation multiplied by 1000. Data available from the Organ Procurement and Transplantation Network (OPTN). SC_NORM Social norm captured by the census mail response rate (RESPN) and the votes cast in presidential elections (PVOTE). SC_NETWORK Social network captured by the number of associations (ASSN) and the number of non-profit organizations (NCCS).

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