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Local culture and dividends.

I empirically investigate whether geographical variations in local culture, as proxied by local religion, affect dividend demand and corporate dividend policy for a large sample of US firms. Firms located in Protestant counties are more likely to be dividend payers, initiate dividends, and have higher dividend yields, while firms located in Catholic counties are less likely to be dividend payers and have lower dividend yields. There is a geographically varying dividend clientele effect consistent with the variations in risk aversion among different cultural groups. My results suggest that firms largely held by local investors determine their corporate policies in line with local culture.

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I examine the impact of culture, defined as the customary beliefs, ideas, values, social forms, and customs of a social or religious group, on dividend demand and payout policy. (1) Culture plays a major role in individuals' lives. Differences in cultural attributes can lead to variations in attitudes or actions. Culture has an influence on decision making via its impact on attitudes, beliefs, and values (Guiso, Sapienza, and Zingales, 2006; Grullon, Kanatas, and Weston, 2010). More importantly, recent studies indicate that cultural groups have different attitudes with respect to financial and economic decisions (La Porta et al., 1998,1999; Stulz and Williamson, 2003). Religion, as a common measure of culture, is particularly useful in shedding additional light on some phenomena related to investor behavior and corporate decisions (Stulz and Williamson, 2003; Kumar, Page, and Spalt, 2011; Pantzalis and Ucar, 2014). Payout policies or dividend demand are closely related to individuals' attitudes regarding financial outcomes. The dividend clientele literature (Miller and Modigliani, 1961) argues that investors' characteristics and attitudes toward dividend income vary across investor groups, leading to differences in dividend demand. I posit that culturally induced variations in attitudes are important in explaining issues related to payout policies.

Following the prior literature, 1 use religion as a proxy for culture and empirically investigate whether geographical variations in local religious affiliation affect dividend policy in a large sample of US firms. There is a significant variation in local religious affiliations across the United States, and my results suggest that this variation is an important determinant of dividend demand, as well as payout policies. Focusing on the two major religious affiliations in the United States, I find that there are significant differences between the dividend policies of firms located in predominantly Protestant areas and those located in predominantly Catholic areas. Firms headquartered in counties with large Protestant populations are more likely to be dividend payers, whereas firms from counties with large Catholic populations are less likely to be dividend payers. Similarly, dividend yields are higher (lower) for firms located in predominantly Protestant (Catholic) counties. Furthermore, nondividend payer firms headquartered in Protestant areas are more likely to initiate dividends.

My results can be interpreted based on risk aversion and dividend clientele arguments. Prior literature suggests that some investors prefer dividends because they perceive dividends as safe available income for current consumption compared to uncertain future income from equity investments. Firms cater to these demands through dividend payments. The clientele effect argument suggests that investors have different preferences and characteristics such that investors select firms whose payout policies are consistent with their preferences. Prior literature also highlights differences in investor attitudes and preferences by suggesting that "different cultures have different attitudes towards finance" (Stulz and Williamson, 2003) and indicates a significant variation in risk aversion across different religious affiliations. Protestant culture is more risk-averse than Catholic culture in terms of financial and economic outcomes (Barsky et al., 1997; Hilary and Hui, 2009; Benjamin, Choi, and Fisher, 2010; Kumar et al., 2011; Shu, Sulaeman, and Yeung, 2012). Kumar et al. (2011) demonstrate this point through individual investors' risk-taking behavior, as well as through corporate decisions and stock returns. In addition, Shu et al. (2012) support this point by analyzing mutual fund risk-taking behavior. My results suggest that this difference in risk aversion plays the largest role in the geographically varying local dividend clientele effect demonstrated in this paper.

After controlling for other firm characteristics, I find that geographical variation in local culture, as proxied by local religious affiliation, has an impact on corporate dividend policies. The findings indicate a statistically and economically significant positive (negative) relation between dividend policies and firms headquartered in counties with a large Protestant (Catholic) population. For example, holding other variables constant, a firm headquartered in an area with an approximately 13% (14%) Protestant (Catholic) population has a significantly higher (lower) likelihood of being a dividend payer than a similar firm from an area with no Protestant (Catholic) population. There are similar findings for dividend yield and initiation. My empirical findings hold after a series of robustness checks. For example, my results remain strong after controlling for other local economic, demographic, and cultural factors. In addition, using a sample of firms with location information confirmed by the Compact Disclosure data provides even stronger results. My findings are also robust after accounting for the impact of a greater preference for lottery-type stocks by Catholics as suggested by Kumar et al. (2011) or after controlling for an alternative set of control variables. Additional tests suggest that my results emerge through the local culture channel. The results are stronger for firms largely held by local investors when I examine different local ownership measures. Furthermore, a matched sample test indicates that local religious affiliations lead to a variation in dividend policies. An analysis of corporate relocations also provides additional evidence and suggests a change in dividend policies in line with local cultures in new firm locations. Chief executive officers (CEOs) play an important role in corporate policies. Therefore, I investigate whether local CEO or local culture is the primary determinant of my findings and determine that local culture, as proxied by local religious affiliation, is the main driver of the geographically varying dividend clientele effect in my tests.

Dividend policy has attracted a considerable amount of attention in the finance literature. An important strand of the prior literature focuses on cross-sectional variations in payout policies. The dividend clientele (or demand side) perspective, dating back to Miller and Modigliani (1961), investigates the determinants of a firm's propensity to pay dividends. Miller and Modigliani (1961) argue that imperfections, such as transaction costs or taxes, lead dividend clienteles to prefer dividends through variations in investor preferences. Recent studies find results consistent with dividend clienteles based on investor characteristics, such as investor age or income (Graham and Kumar, 2006; Becker, Ivkovic, and Weisbenner, 2011). My results suggest a correlation between geographical variation in dividend demand and investors' local cultural or religious characteristics. I highlight a new type of geographically varying clientele effect induced by local culture consistent with variations in risk aversion among different cultural groups. My paper is also related to Baker and Wurgler (2004a, 2004b) who propose that firms cater to investors' demands for dividends through dividend payouts when investors consider dividend payer firms more valuable. Moreover, earlier studies suggest that investor risk aversion can lead investors to prefer dividends over future capital gains (Lintner, 1962; Gordon, 1963). My paper suggests that local religion affects investor demand for dividends through its impact on investor risk attitudes and characteristics and firms cater to this dividend demand through their dividend payouts.

The paper is also related to the recent literature regarding local bias. Ivkovic and Weisbenner (2005) find that individual investors have a higher propensity to invest in local firms due to an information advantage. Pirinsky and Wang (2006) determine a strong comovement in stock returns for same-location firms. Gao, Ng, and Wang (2011) examine the effect of location on capital structure. The locality of investors plays an important role in my results. My findings highlight the local component of dividends by suggesting a geographically varying dividend clientele effect induced by local culture. Moreover, I note the stronger impact of local culture on dividend policies for firms held primarily by local shareholders. This implies that firms with greater local shareholder bases determine their dividend policies consistent with the investor risk aversion induced by the local culture.

Recent studies indicate that culture has an impact on economic and financial outcomes (Grinblatt and Keloharju, 2001; Stulz and Williamson, 2003; Guiso et al., 2006; Eun, Wang, and Xiao, 2014). Eun et al. (2014) suggest that "culture is an important omitted variable in the literature." Hilary and Hui (2009) demonstrate the impact of local religious characteristics on corporate risk-taking behavior. Kumar et al. (2011) confirm the effect of local culture on investor behavior and corporate decision making. Pantzalis and Ucar (2014) demonstrate how investor inattention to firm news can be traced to local religious characteristics. My paper expands on this literature by presenting the effect of local culture, as proxied by local religious affiliation, on dividend demand and corporate dividend policies.

My paper is closer in spirit to Becker et al. (2011) who examine local dividend clienteles. Becker et al. (2011) argue that firms from areas with a higher proportion of senior citizens have a greater likelihood of being dividend payers due to the dividend clientele based on age. The main distinction between my paper and their paper is that my paper focuses on the impact of local culture, as measured by local religion, on payout policies and suggests a new dividend clientele effect based on local culture. Moreover, my empirical findings are robust to the local senior population effect for a larger sample and suggest a stronger economic impact of local culture on geographically varying dividend policies.

The remainder of the paper is organized as follows. The next section presents a short summary of the data and the sample selection method in addition to the summary statistics. Section II provides the empirical results. Section 111 presents the empirical results of additional tests and robustness checks, while Section IV provides my conclusions.

I. Data, Sample Selection, and Summary Statistics

A. Data and Sample Selection

I follow the sample selection criteria used by recent studies (Grullon et al., 2011) in the related literature. My sample includes US firms with available dividend data and accounting information from Compustat and stock price information from CRSP in the period from 1990 to 2010. The sample excludes utilities and financials categories (SIC codes 4900-4999 or SIC codes 6000-6999). The sample also requires sample firms to have CRSP issue codes of 10 or 11 and available headquarters location information. For some variables, the empirical tests use lagged and leading year information. Thus, the main regression sample loses some firm-year observations (i.e., 80,049 observations in the propensity to pay dividends regressions.) I use firm address information from Compustat in the main tests. I also use firm address information from Compact Disclosure for some robustness tests. I obtain local religious affiliation information from the Association of Religion Data Archives (ARDA) using the 1990,2000, and 2010 county-level data sets in order to construct the local religious affiliation variables. County-level economic and demographic variables are from the 1990 and 2000 Censuses, as well as the other available statistics on the US Census website for the years following 2000. I also use the interpolations of the ARDA or Census data in order to construct the local variables for years without available data.

My empirical model builds on Becker et al.'s (2011), while keeping the dividend policy and firm characteristics variable definitions consistent with the recent literature (Becker et al., 2011; Grullon et ah, 2011). 1 define my dividend policy variables as follows. Dividend Payer is an indicator variable that takes a value of one if the total amount of dividends is greater than zero for a given year, and zero otherwise. Dividend Yield is the ratio of total dividends to the lagged market value. Dividend Initiation is an indicator variable that takes a value of one if a current nondividend payer firm becomes a dividend payer in the following year, and zero if a current nondividend payer firm stays as nondividend payer in the following year.

I define the local religious affiliation variables similar to Kumar et ah (2011) by using the ARDA data sets. Prot (Cath) is the fraction of Protestants (Catholics) in a given county where a firm is located. I also use a variable Cpratio, which is the ratio of Catholics to Protestants (ratio of Cath to Prot). Other main county-level control variables for headquarter locations are as follows. Local Seniors is the proportion of individuals who are 65 years old or older in a given county. Income is the county median household income. Median House Value provides the median house price for a given county. Education is the proportion of the population holding college degrees in a given county. I also use the logarithm of county population in the tests that include county-level variables.

I define the firm characteristic variables of the main empirical model as follows. Net Income is net income scaled by total assets for a given year. Cash is cash scaled by total assets. I define Q as the sum of the market value of equity and the book value of liabilities scaled by total assets for a given year. Debt is long-term debt scaled by total assets. Log of MV is the logarithm of a firm's market value for a given year. Log of Assets is the logarithm of total assets. Volatility is the standard deviation of monthly stock returns for the previous two-year period. Lagged Return is the monthly stock returns for the previous two-year period. In calculating Volatility and Lagged Return, I require stock return information for at least the previous 12 months to be nonmissing if a firm has stock return information available less than for 24 months. Asset Growth is the logarithm of the total assets growth rate calculated using both the current and the previous year's figures. My tests also include the following firm age group indicator variables consistent with Becker et al. (2011): Age 1-5, Age 6-10, Age 11-15, and Age 16-20. Age 21 and Over is the dropped category in my regressions. I measure firm age based on the time between the date that a firm is listed on the CRSP and the current year. My empirical tests also include state, industry, and year dummy variables. I use the Fama-French (1997) 48 industry classifications.

B. Summary Statistics

In Table I, I report the summary statistics for dividend policy variables and firm characteristics, as well as the main local economic and demographic variables. Panel A indicates that, on average, 25% of the sample firms pay dividends in a given year. Average dividend yield is 0.6% for all of the sample firms. On average, 2.1% of the firms that do not pay dividends in the current year pay dividends in the following year. These dividend policy variables have values consistent with previous studies. Some of the summary statistics for local county characteristics are as follows. The average proportion of Catholics (Protestants) is 26.3% (20.1%) for a firm's headquartered county in a given year. The average local senior citizen population is approximately 11.6% and the average fraction of the local population with a college degree is approximately 32%. County-level variables display summary statistics similar to that of previous studies. Panel C reports the summary statistics for some important firm characteristics. For example, an average firm in the sample has an equity value that is equal to the 24th percentile of the NYSE equity size distribution for a given year. Thus, an average firm in my study is not a very big firm. The average market-to-book ratio is approximately 2.2, the average return on assets (ROA) is approximately 0.03, and the average sales growth is approximately 22.3% for the sample. The characteristics of the sample firms display a pattern consistent with the prior literature.

II. Empirical Results

A. Main Tests

My main empirical model is similar to the one used by Becker et al. (2011). I control for Net Income, Cash, Q, Debt, Volatility, Lagged Return, Log of MV, Log of Assets, and Asset Growth, and indicator variables for firm age groups, as well as state, industry, and year dummy variables. I also adjust standard errors for heteroskedasticity and cluster them at the firm level in all empirical tests. I use my payout policy variables, Dividend Payer, Dividend Yield, and Dividend Initiation, as the dependent variables in the regression models. I use a Logit regression model in order to analyze the impact of local culture on a firm's propensity to pay dividends and to initiate dividends. I use an ordinary least square (OLS) regression model in the dividend yield tests. I use Cath, Prot, and Cpratio to measure local religious affiliation in my tests.

Columns 1-3 of Table II indicate that local religious affiliation has statistically significant coefficients. The coefficient of Prot (Cath) is 2.259 (-1.220), while the coefficient of Cpratio is -0.089. These results suggest that there is a positive (negative) relation between the likelihood of being a dividend payer and firm locations with higher fractions of Protestants (Catholics). In Logit regression models, it can be difficult and misleading to interpret the economic importance of variables based on coefficient magnitudes. A better way to interpret the economic importance of the results in a Logit regression model is to focus on the change in odds for the dependent variable when there is a one standard deviation change in an independent variable. Consistent with this method, the first three columns suggest economically significant effects. A one standard deviation greater value in the proportion of Protestants in a firm's county is associated with a 34% greater likelihood in that a firm will pay dividends compared to another firm in a county with a lower fraction of Protestants. Alternatively, the same increase in the proportion of Catholics (Cpratio) in a county decreases the odds that a firm will pay dividends by approximately 15.7% (15.5%). The control variables have the expected coefficient values with a pattern consistent with the prior literature (Becker et al., 2011). The findings suggest that, holding all other variables constant, a firm located in a county with an approximately 13% (14%) Protestant (Catholic) population has a significantly greater (lower) likelihood of being a dividend payer firm than a similar firm in a county with no Protestant (Catholic) population.

Columns 4-6 report a similar difference toward dividend policies across US counties by focusing on dividend yields. All of the three religious affiliation coefficients suggest statistically and economically significant effects. A one standard deviation higher value in the proportion of Protestants (Catholics) in a firm's county is associated with a 0.107 (0.053) standard deviation increase (decrease) in the dividend yield compared to another firm located in a different county with a lower proportion of Protestants (Catholics). The final three columns of Table II focus on the impact of the local religious composition on dividend initiation across locations within the United States. In this analysis, only Prot is statistically significant, while Cath and Cpratio are statistically insignificant. This finding suggests a stronger role for Protestants in dividend initiation when compared to Catholics, consistent with my earlier findings. Dividend initiation tests indicate that a one standard deviation higher value in the proportion of Protestants in a firm location produces a 20.8% greater likelihood of dividend initiation compared to another firm in a different county with a lower proportion of Protestants. Overall, the dividend yield and initiation tests of Table II support the results displayed in the dividend payer tests.

The results in Table II are consistent with my conjecture that Protestants (Catholics) are associated with a higher (lower) demand for dividends. Protestants are associated with a greater degree of risk aversion and, as such, investors in Protestant areas prefer safe dividend income compared to uncertain future income from their equity investments. Alternatively, Catholics are associated with a lower degree of risk aversion (such as greater risk-taking behavior indicated by Kumar et al., 2011.) Thus, Catholic areas prefer fewer dividends. Table II also suggests that firms determine their dividend policies consistent with local investors' preferences induced by the local culture.

B. Empirical Tests with County-Level Factors

In this section, I investigate whether other county-level demographic and economic variables have an influence on my results. In the recent literature, Becker et al. (2011) examine age-based dividend clientele and suggest a positive relation between the proportion of local seniors and dividend demand, along with dividend policies. Therefore, I repeat the main tests and include a variable that indicates the proportion of local senior citizens in Panel A of Table III. The results are very similar to the results reported in Table II. In addition, the local religion coefficients have a stronger effect on dividends than the effect induced by local seniors. For example, Column 2 indicates that a one standard deviation difference in the proportion of Protestants between two firms located in two different counties is associated with a 31% difference in the odds that a firm will pay dividends, while the same difference in the proportion of local seniors is only associated with a 8% difference in the odds that a firm will pay dividends. Similarly, Columns 1 and 3 present a greater impact of Cath and Cpratio on dividends compared to the impact of Local Seniors on dividends. The dividend yield and initiation tests in Table III also present similar evidence. For example, the economic significance of Prot is almost four times that of Local Seniors in my dividend initiation tests. Overall, local culture, as proxied by religious composition, presents a stronger local dividend clientele effect than the age-based local clientele effect demonstrated by Becker et al. (2011).

Next, I also include county-level control variables in the empirical tests. Specifically, I include some local demographic and economic factors consistent with the prior literature (median household income, median house value, local education, and the logarithm of the county population, along with the proportion of local senior citizens) in Panel B of Table III. My results still hold and display a pattern similar to the one in Panel A. Although there is some decrease in the magnitude of the religious affiliation coefficients, their economic significance remains robust, while there is a larger decrease in the impact of Local Seniors. For example, Prot has a stronger coefficient in Panel B in the dividend initiation tests and the economic significance of Prot is almost nine times that of Local Seniors. Overall, my findings are robust to other local factors that can affect local dividend clienteles and dividend policies. In unreported tests, I re-examine the main tests after controlling for county-fixed effects. These tests also provide findings similar to the ones presented in Table II. This section suggests that other local characteristics, such as county-level economic and demographic characteristics, do not eliminate the role that local religious affiliation plays in dividend demand and corporate dividend policies.

C. Role of Local Investors

My findings demonstrate a geographically varying clientele effect induced by local culture and firms maintain dividend policies consistent with this effect. A more direct way to investigate this effect is to focus on the role of local investors. In this section, I examine the differences in the dividend effect between firms with greater local ownership and other firms by using different measures of local stock ownership. First, I focus on empirical tests that highlight the differences between firms located in metropolitan areas and firms from other areas. Previous studies suggest that there are differences between firms located in large metropolitan areas and those located in smaller cities. For example, firms from big cities have an advantage in accessing firm information compared to those from smaller cities or rural areas (Loughran and Schultz, 2005; Loughran, 2008). Furthermore, the only-game-in-town effect demonstrated by Hong, Kubik, and Stein (2008) is expected to be weak in large cities with a higher number of firms and stronger in smaller cities with a lower number of firms. Hong et al. (2008) find that firms located in areas with a smaller number of local firms are associated with greater local ownership. One could expect a stronger local bias and, as such, a stronger impact of local culture on dividends for a subsample of firms excluding large metropolitan cities. I investigate whether the dividend effect is different between a subsample of firms located in the three largest metropolitan areas and another subsample that excludes these metropolitan areas in Panels Al and A2 of Table IV. All of the empirical models in Table IV are the same as the ones in the main tests in Table II.

The empirical findings in Panel Al of Table IV are very similar to the main findings in Table II. The results become stronger for firms located in smaller areas or areas excluding the largest metropolitan regions. This finding is consistent with the higher local bias effect associated with smaller areas. Panel A2 provides the results for firms located in large metropolitan areas and these results are not statistically significant. Most of the results are also weaker in Panel A2. In unreported tests, I also examine the 10 largest metropolitan areas versus other areas. My findings still hold when the 10 largest metropolitan areas are used instead of the 3 largest metropolitan areas. Overall, this analysis provides support to the notion that local religious affiliations affect dividend policies, particularly for firms where local ownership plays an important role.

Hong et al. (2008) find that firms located in areas with a lower number of firms experience relatively little competition for local investors' dollars (the only-game-in-town effect) and firms located in areas with a smaller number of local firms hold a greater fraction of local ownership. Therefore, examining the number of local firms per capita provides a more direct measure of the local bias effect induced by the only-game-in-town effect. In Panels B1 and B2,1 use the Census data and construct a variable by dividing the number of local firms in a given county by county population to identify the number of local firms per capita. Panel B1 of Table IV presents the findings for the sample firms in the lowest tercile of the number of firms per capita. In other words, Panel B1 reports the findings for the sample firms with a higher local bias induced by the only-game-in-town effect. Panel B1 indicates that firms located in areas with a lower number of firms per capita have a pattern similar to the main findings in Table II. There are also stronger coefficient values in Panel B1. Firms in the highest tercile of the number of local firms per capita are expected to have a lower local bias effect and Panel B2 reports the findings for these firms. As opposed to Panel B1, Panel B2 indicates that firms located in areas with a lower bias have a pattern different than the one in the main tests. The religious affiliation coefficients in Panel B2 are not statistically significant for the sample firms where local investors play a smaller role. The tests using the only-game-in-town effect, as measured by the number of local firms per capita, supports the results of the analysis in Panels Al and A2. The dividend effect demonstrated in this paper is stronger for those firms located in areas associated with greater local ownership. This result also suggests that firms located in areas where local investors hold a significant fraction of ownership in local firms consider local investors' culturally induced dividend preferences in shaping their dividend policies.

Another way of examining the importance of local investor bases for the impact of local culture on corporate dividend policies is to focus on local stock market participation. Prior literature suggests that there are differences in stock market participation rates among demographic groups. Hong, Kubik, and Stein (2004) find that stock market participation rates are much higher for white households compared to other racial groups. Hong et al. (2004) find that stock market participation rates for white households are almost three times greater than the rates for other racial groups. Hong et al. (2004) also indicate that differences in stock market participation between white households and other racial groups remain robust after controlling for wealth.

Thus, I focus on the demographic characteristics provided by the Census data and use the fraction of local white households as a proxy for local retail stock market ownership. This method helps to identify the dividend effect for different levels of local stock participation, particularly for retail investors.

Panel C1 of Table IV reports that firms located in areas with higher local market participation rates, as measured by the highest tercile of the percentage of local white households, elicit stronger findings than the main findings of Table II for most of the tests in Panel C1. However, the results for firms located in areas with lower local market participation rates, as measured by the lowest tercile of the percentage of local white households, are weaker and statistically insignificant in almost all of the tests in Panel C2. This table underlines the notion that the impact of local religion on dividend policies is stronger for firms headquartered in counties with high local ownership measured by high stock market participation. These tests suggest that firms consider local shareholder preferences on corporate policies in order to attract local investors' money when local investors play an important role.

Another way of investigating the importance of the locality of investors for my findings is to examine firms with different levels of institutional ownership. Prior literature indicates that firms with lower institutional ownership are expected to have higher levels of local and retail investor ownership. Coval and Moskowitz (1999) suggest that individual investors are more likely to own local stocks when compared to institutional investors. I examine the differences between firms with high and low institutional holdings. For every firm without missing information, I use institutional ownership figures from 13F filings in order to calculate the average annual institutional ownership. I then rank the sample into terciles based on this variable. Firms in the lowest (highest) tercile constitute the low institutional holding subsample serving as the high (local) local ownership subsample in the empirical tests. I rerun all of the main regressions for these two subsamples in Panels D1 and D2 conjecturing that my results are expected to be stronger for firms where local retail investors constitute a greater fraction of the investor bases. (2)

The results in Panel D1 are similar to my earlier findings. Consistent with my assumption, the results are more pronounced and stronger for the subsample of firms with low institutional holdings. I find more pronounced results for firms with higher local ownership, as measured by lower institutional ownership. On the other hand, the results for the subsample of firms with high institutional holdings are either weaker or statistically insignificant in Panel D2. (3) This table provides additional evidence regarding the dividend clientele effect induced by local culture, particularly for dividend payer and dividend yield tests. I also examine the local religious affiliation coefficient differences across groups of firms with different levels of local ownership for all the tests of Table IV in the appendix. The results of the tests that focus on the local religious affiliation coefficient and statistical significance of differences in the appendix provide support to the findings of Table IV. Overall, Table IV helps to shed additional light on the channel through which local culture affects geographically varying dividend policies. Local cultural or religious characteristics aid in measuring local investors' preferences and Table IV suggests that firms largely held by local investors cater to local investors' preferences and demands by providing dividend policies in line with the local culture.

III. Additional Tests

A. Matched Sample Tests

In this section, I present a series of additional tests and robustness checks that highlight the notion that local culture, as proxied by religious affiliation, is the main driver for the dividend effect demonstrated in this paper. First, I use a matched sample analysis that examines two groups of the sample firms with very similar characteristics except local religious affiliation. Specifically, I present empirical results based on pair-wise comparisons between firms from predominantly Protestant areas and a matched sample of firms with similar firm characteristics from predominantly Catholic areas.

I divide the sample in two based on Cpratio in order to determine those firms from predominantly Protestant and Catholic areas. The first group includes Protestant area firms. These are firms located in counties with at least 50% more Protestants than Catholics (or a Cpratio of 0.67.) The second group includes Catholic area firms. This is a sample of firms whose headquartered counties have at least 50% more Catholics than Protestants (or a Cpratio of 1.5.) Next, for each firm-year observation from Protestant area firms, I determine a firm-year observation with the same year, industry, and age group from Catholic areas. I then follow a matching process based on the firm characteristics including total assets, market value, net income, cash, Q value, debt, volatility, and lagged return. I match every firm-year observation of Protestant area firms with a firm-year observation from a Catholic area firm from the same year, industry, and age group, as well as determining the closest matched values for asset size, market value, net income, cash, Q values, debt, volatility, and lagged return. Finally, I include all of the firms from predominantly Protestant areas with a match and its matched firm observation from predominantly Catholic areas in the tests. Table V reports the mean values for the matched sample analysis.

Table V indicates that, on average, 33.84% of firms from predominantly Protestant areas are dividend payers and 2.71% of those firms initiate dividends during the sample period. In addition, on average, 0.75% is the dividend yield for these firms. On the other hand, firms from predominantly Catholic areas have lower values for all three payout policy variables. The differences in Dividend Payer and in Dividend Yield between Protestant and Catholic areas are also statistically significant. However, the difference in Dividend Initiation is not statistically significant. (4) This is similar to my earlier dividend initiation tests that only have pronounced findings for Prot. One reason for this point may be the smaller number of observations for Dividend Initiation compared to the other payout variables due to its definition. Table V displays some economically important results. A 6.26% difference in Dividend Payer is approximately equal to 25% of the full sample average. Similarly, a 0.11% difference in Dividend Yield is approximately 20% of the full sample average. Moreover, unreported median value differences also have findings similar to the ones in Table V. Overall, Table V supports the earlier results. This table highlights the point that local culture, as proxied by religious affiliation, is an important determinant of investors' demand for dividends and plays a significant role in the geographical variation in corporate dividend policies.

B. Headquarters Relocation

In the previous section, the matched sample tests present some evidence as to whether an omitted variable affects the effect of local religion on dividend policy. This section takes this concept a step further and uses another test to investigate the main driver of the findings. In particular, I focus on a group of firms that move headquarters to a different location and examine the impact of changes in local religious affiliation on changes in dividend payer status. Local investor bases change after corporate relocations. Therefore, if a firm's dividend policy in its new location is in line with the local affiliation of its new location, then this provides additional evidence as to the impact of local affiliation on dividends and supports my earlier results.

In this corporate relocation analysis, I follow a structure similar to the one used by Becker et al. (2011). I identify a group of firms that move their headquarters to another state from the Compact Disclosure data. The relocation sample includes firms that moved their headquarters from 1992 to 2005 in order to provide enough pre- and post-move information. Table VI analyzes the impact of changes in local religious affiliation on changes in dividend payer status in three different tests by focusing on the changes during post-move years as compared to one year prior to the corporate headquarters relocation. The first two columns specifically examine the change in dividend paying behavior status in the year following the headquarters' move compared to one year prior to the move. Columns 3 and 4 (Columns 5 and 6) report the same change during the second (third) year following the headquarters' move as compared to one year prior to the move. The dependent variable is the Change in Dividend Payer indicating a change in dividend payer status. Table VI highlights the results for changes in the local religious affiliation variables (Change in Cath and Change in Prof). Table VI also includes the corresponding changes in the main control variables, as well as age indicator variables from the post-move year for each analysis.

Changes in the local religious affiliation variables have coefficient signs consistent with the earlier findings. Change in Prot reports positive signs, while Change in Cath exhibits negative signs in all of the tests. These coefficients have statistically significant results for the final two columns that analyze the change in a firm's dividend payment behavior in the third year after the move compared to one year pre-move. Considering the small number of observations in the corporate relocation sample, observing statistically significant results in one test is somewhat expected. (5) The final two columns indicate that if the fraction of Protestants (Catholics) is one standard deviation higher in the new location than the old headquarters location, then there is a 5.34% (12.15%) greater (less) likelihood of being a dividend payer firm in the new location. Overall, Table VI provides additional evidence regarding the role of geographical variation in local culture on dividend demand and corporate dividend policies. These results also suggest that firms shape their dividend policies consistent with their new local cultures after corporate relocations.

C. Empirical Tests with Compact Disclosure's Firm Location Data

I use Compustat firm address information in my empirical tests. The Compustat data provide the most recent addresses for all firm years. Pirinsky and Wang (2006) suggest that there are a relatively small number of these headquarters relocation cases. Similarly, my corporate relocation tests include a small number of observations in the previous section. Alternatively, one might suggest that using Compustat addresses may affect the test results. Therefore, as a further robustness check, I investigate whether my results are robust to different firm location information in this step. Specifically, I use the firm headquarter location information from the Compact Disclosure data. I re-examine my main regressions for the years from 1990 to 2006 due to the availability of the Compact Disclosure data set. Considering my earlier findings, Table VII presents very similar and stronger results. The results in Table VII provide additional support to my earlier findings and suggest that my findings are robust to firm location information from alternative sources.

D. Local Culture, Dividend Policy, and Lottery Stocks

Recent studies indicate that there is a significant variation between two major US religious affiliations in terms of gambling investment types. Kumar et al. (2011) find that differences in attitudes toward gambling among Catholics and Protestants produces a variation in investments in lottery-type stocks. Catholics have a greater likelihood of investing in lottery-type investments due to their lower risk aversion and greater tolerance for gambling-type investments. Furthermore, Kumar et al. (2011) and Kumar (2009) also suggest that lottery stocks are those with a lower propensity to pay dividends. Therefore, as a further robustness check, I examine whether the lottery characteristics of a firm's stocks affect the results.

I include additional variables that help to identify lottery-type stocks and a propensity to invest in these stocks in my main empirical model and re-examine the tests. The first variable is a lottery index measuring lottery characteristics. Following Kumar et al. (2011) and Kumar (2009), I construct a lottery index based on the following three factors: 1) stock price, 2) idiosyncratic volatility, and 3) idiosyncratic skewness. Kumar et al. (2011) and Kumar (2009) suggest that stocks with low stock prices, high idiosyncratic volatility, and high idiosyncratic skewness are lottery-type stocks. For each year, I divide the sample firms into 50 groups based on these three lottery features, rank them, and construct a lottery index. (6)

The existence of high unemployment rates in an area can motivate investors to invest in lottery-type investments. Following Kumar et al. (2011), I also add county employment rates provided by the Bureau of Labor Statistics' website to the tests. Panel A in Table VIII presents the main regressions with lottery-type investment identifiers, while Panel B reports an empirical model with additional county-level control variables. Panel A presents empirical results that are very similar to the results in Table II. Similarly, Panel B provides empirical findings that are very similar to the findings of the tests using county-level factors in Table III. The coefficient magnitudes and statistical significance are similar to the ones in the earlier tests. The local dividend clientele effect induced by local religious affiliation is not subsumed by stocks with lottery characteristics. My findings are robust to the differences in lottery-type investments between Catholics and Protestants.

E. Empirical Tests with Alternative Control Variables

Some prior studies employ an alternative set of control variables when analyzing the propensity to pay dividends and dividend yields. In this step, I examine whether my results are robust to this alternative set of firm characteristics. Following Fama and French (2001) and Grullon et al. (2011), I include market-to-book ratio, ROA, sales growth, and NYSE equity percentiles for the corresponding periods. I also include local-level control variables including the proportion of local seniors. I present the findings for Dividend Payer and Dividend Yield in Table IX. When I consider the main tests, these findings have the same pattern and stronger coefficient values. Economic significance is also stronger using this set of alternative control variables. These tests indicate that my findings remain robust after controlling for an alternative set of firm characteristics used in the prior literature. Table IX provides additional evidence regarding the impact of local religious affiliations on local dividend clienteles and dividend policy.

F. Local Culture, Dividend Policy, and Local CEOs

Top management, particularly CEOs, are important decision makers in determining corporate policies. One plausible explanation may suggest that the locality of CEOs, rather than local culture, is the main driver of the dividend effect shown in the paper. In order to shed more light on my findings, I investigate the impact of the locality of CEOs on my findings. I identify CEO names for the sample firms after matching the sample with the ExecuComp database. Next, I manually collect birthplace information for these CEOs by using Marquis Who's Who, LexisNexis, Google search, and other publicly available websites that include birthplace information. In order to determine whether CEO locality is the main driver of the dividend effect, I re-examine the main tests using a subsample of observations with CEO birthplace information by including an indicator variable for local CEOs (Local CEO) in Table X. (7)

Local religious affiliation exhibits similar findings to the main results, especially for the dividend payer and dividend yield tests in the first six columns. These tests also have statistically significant coefficients. The final three columns provide dividend initiation tests. The religious affiliation coefficients have the expected signs and values in the final three columns although these coefficients are statistically insignificant. This result can be attributed to the small number of observations in these dividend initiation tests. Alternatively, patterns and signs of the local religious affiliation coefficients in these tests are consistent with the ones in the main tests. In contrast, Local CEO produces mostly statistically insignificant and weak results. Overall, Table X suggests that my findings remain robust after controlling for CEO locality. In addition, Table X provides additional support to the notion that local religious affiliation is the main driver of the geographically varying local dividend clientele effect demonstrated in this paper.

In a similar spirit, Baxamusa and Jalal (2016) examine CEO religious affiliation and managerial conservatism by focusing on other corporate policies including diversification, investment, and leverage. They suggest that CEO religion is important in determining these corporate policies. In order to examine whether there is any further CEO effect on my findings, I examine the impact of CEO religion on dividend policies along with local religion. (8) In unreported results, I find that local religion remains robust especially in the dividend payer tests, while CEO religion is not statistically significant for any of the tests. (9) This result also provides further support to the notion that local religion is the main driver of the dividend effect shown in my findings. While the recent literature suggests a role of CEO religion in other corporate policies, I find that the religion of local investors appears to play a much larger role in dividend policy. Prior literature highlights the importance of investor preferences on corporate dividend policies. For example, the dividend clientele literature suggests that investors select firms whose payout policies are consistent with their preferences and characteristics. Previous studies present results consistent with dividend clienteles based on investor characteristics (Graham and Kumar, 2006; Becker et al., 2011). Baker and Wurgler (2004a) suggest that firms consider investor demand when deciding to pay dividends. Consistent with all these points, my findings suggest that the role of local investors, as measured by local religion, in dividend policy is more important than the locality of CEOs or CEO religion.

IV. Conclusion

The role of culture in finance has attracted attention in recent studies. Prior literature suggests that cultural differences lead to a variation in attitudes with respect to financial and economic decisions. I contribute to this literature by demonstrating that geographical variation in local culture, as proxied by religion, is an important determinant of dividend demand and corporate dividend policies. My findings suggest a geographically varying clientele effect induced by local culture.

Firms located in areas with a higher proportion of Protestants are more likely to be dividend payers and have higher dividend yields, while firms located in areas with a greater proportion of Catholics are less likely to be dividend payers and have lower dividend yields. Firms located in Protestant areas are also more likely to initiate dividends. My results suggest that differences in attitudes with respect to finance among different religious affiliations, specifically differences in risk aversion, have an impact on dividend demand and, as such, corporate payout policies. My conclusions remain robust to a series of robustness checks. Furthermore, I demonstrate that the dividend effect induced by local culture is more pronounced for firms that are largely held by local investors. Firms with high local ownership rates appear to cater to local investors' dividend demand by instituting dividend policies consistent with investors' dividend preferences as suggested by the local culture.

Appendix: Main Coefficient Differences for Local Ownership Subsamples and Additional Local Factors

In this section, I examine the local religious affiliation coefficient differences across groups of firms with different levels of local ownership. Table A1 presents the Wald test results to determine whether the local religious affiliation coefficients in Table IV are the same across groups of firms with different levels of local ownership. This table reports the chi-square values provided by this test. I also use the method suggested by Hoetker (2004, 2007). Hoetker's (2004, 2007) method helps to present an accurate comparison of the local religious affiliation coefficients across groups of firms with different institutional ownership levels in Logit regressions. Hoetker (2004) finds that even small differences in residual variation across groups can lead to problematic coefficient comparisons across groups in Logit regressions. He proposes a method that allows residual variances to be different across groups, and later focuses on the test of equality of coefficients across the compared groups. Table A1 employs Hoetker's (2004, 2007) method in Wald tests for the dividend payer and dividend initiation Logit regressions. These results can be interpreted similarly to the tests used for the dividend yield OLS regressions. Overall, all of the panels in Table A1 indicate a stronger effect of local culture on dividends for those firms largely held by the local investors examined in Table IV, particularly for dividend payer and dividend yield tests.

Next, I report the corporate headquarters relocation tests using a detailed set of local and cultural factors in order to overcome any geographical omitted variable problem. (10) I add a large set of local ethnicity, political affiliation and voting patterns, industry concentrations, and education variables from the Census data, along with local seniors, and rerun the corporate headquarter relocation regressions in Table A2. In Panel A, the changes in local ethnicity variables indicate changes in the fraction of local ethnicity variables (White, Hispanic, African-American, and Asian racial groups). In Panel B, the change in local political affiliation reports a change in the fraction of local Republican votes in Presidential elections. Furthermore, I include the changes in local industry variables that represent changes in local industry compositions in shares of all available industries as a result of relocation in Panel C. In Panel D, the changes in local education variables demonstrate changes in the fraction of local education variables (the percentage of the population having an education level less than high school, a high school diploma, or a university diploma and higher). Moreover, Panel E includes the impact of local seniors by adding a change in local seniors. Table A2 indicates that the results for the corporate headquarters relocation tests remain robust after controlling for a large set of cultural and local factors, suggesting that any omitted geographical variable problem does not affect my findings.

I also include this detailed set of geographical factors in the main tests and re-examine them as a further robustness test. Table A3 reports robust results after controlling for this detailed set of additional cultural and local factors. In Panel E, I also report the main tests after controlling for local seniors. These tests are same as the ones in Panel A of Table III and these findings are provided for comparison in this table. Overall, I provide additional evidence in this section and highlight the notion that local religious affiliation is the main driver of the effect of local culture, as proxied by local religious affiliation, on dividends.

Table A1. Tests of Significance of Difference between Religious
Affiliation Coefficients of Table IV

A Wald test of whether the coefficients of local religious affiliation
variables in the tests in Table IV are same or not across groups of
firms with different levels of local ownership is provided in this
table. This table reports chi-square values provided by this test and
p-values for chi-square values in parentheses. Dividend Yield tests
use OLS regressions, while Dividend Payer and Dividend Initiation
tests use Logit regressions. In order to have an accurate comparison
of the coefficients of local religious affiliation variables across
groups of firms with different local ownership levels in Logit
regressions and measure the difference between these coefficients, I
use the method suggested on studies by Hoetker (2004, 2007).

Dependent          (1)            (2)            (3)
Variable:
                              Dividend Payer

Panel A. Test of Difference of Coefficients for Metropolitan Areas
versus Other Areas

Cath               1.16
                 (0.282)
Prot                             15.77
                              (0.000) ***
Cpratio                                         15.24
                                             (0.000) ***

Panel B. Test of Difference of Coefficients for Tests with Number of
Local Firms per Capita

Cath               3.16
                (0.075) *
Prot                              6.35
                               (0.012) **
Cpratio                                          9.42
                                             (0.002) ***

Panel C. Test of Difference of Coefficients for Tests with Stock
Market Participation

Cath               9.68
                (0.002) **
Prot                              6.35
                               (0.012) **
Cpratio                                         11.21
                                             (0.001) ***

Panel D. Test of Difference of Coefficients for Tests with
Institutional Ownership

Cath               3.17
                (0.075) *
Prot                             20.04
                              (0.000) ***
Cpratio                                         11.30
                                             (0.001) ***

Dependent          (4)            (5)            (6)
Variable:
                             Dividend Yield

Panel A. Test of Difference of Coefficients for Metropolitan Areas
versus Other Areas

Cath               0.32
                 (0.569)
Prot                             0.001
                                (0.995)
Cpratio                                          0.28
                                               (0.597)

Panel B. Test of Difference of Coefficients for Tests with Number of
Local Firms per Capita

Cath               3.97
                (0.046) **
Prot                              4.81
                               (0.028) **
Cpratio                                          5.44
                                              (0.020) **

Panel C. Test of Difference of Coefficients for Tests with Stock
Market Participation

Cath              28.16
               (0.000) ***
Prot                             12.40
                               (0.000) **
Cpratio                                         18.49
                                             (0.000) ***

Panel D. Test of Difference of Coefficients for Tests with
Institutional Ownership

Cath               0.06
                 (0.806)
Prot                              1.87
                                (0.171)
Cpratio                                          0.01
                                               (0.917)

Dependent          (7)            (8)            (9)
Variable:
                            Dividend Initiation

Panel A. Test of Difference of Coefficients for Metropolitan Areas
versus Other Areas

Cath               0.02
                 (0.886)
Prot                              0.44
                                (0.509)
Cpratio                                          0.36
                                               (0.548)

Panel B. Test of Difference of Coefficients for Tests with Number of
Local Firms per Capita

Cath               0.71
                 (0.400)
Prot                              0.34
                                (0.559)
Cpratio                                          2.39
                                               (0.122)

Panel C. Test of Difference of Coefficients for Tests with Stock
Market Participation

Cath               2.15
                 (0.143)
Prot                              0.57
                                (0.449)
Cpratio                                          0.79
                                               (0.375)

Panel D. Test of Difference of Coefficients for Tests with
Institutional Ownership

Cath               2.84
                 (0.092)*
Prot                              1.01
                                (0.314)
Cpratio                                          3.32
                                              (0.068) *

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table A2. Dividend Payout and Corporate Relocation with Geographical
Factors

This table analyzes the impact of change in local religious
affiliation on change in dividend payer status by focusing on the
change post-move years compared to one year before a corporate
headquarters' move. This table runs the same tests as the corporate
relocation tests presented in the text. The details about the
dependent variable and main control variable definitions can be found
in Table VI. In Panel A, Changes in Local Ethnicity variables report
the changes in the fraction of local ethnicity variables (White,
Hispanic, African-American, and Asian) in a firm's headquartered
county after the move compared to one year before the move. Local
ethnicity variables are from the Census. In Panel B, Change in Local
Political Affiliation indicates the change in the fraction of local
Republican votes in a firm's headquartered county after a move
compared to one year before the move. Local Republican presents the
fraction of Republican votes in the Presidential elections in a
county. The data are from the Census. This table also uses the
interpolations of the Census data to construct the variables for the
years without available Census data. Local industry variables provide
the share of each industry in a county. Local industry variables
(Manufacturing, Construction, Services, Wholesale, Retail, Finance,
Transportation, and Agriculture) are from the Census. In Panel C,
Changes in Local Industry Variables report the changes in the share of
industries in a firm's headquartered county after a move compared to
one year before the move. Changes in Local Industry Variables
represent the changes in local industry compositions or
concentrations. In Panel D, Changes in Local Education Variables
include the changes in the fraction of local education variables
(percentage of population having education less than high school,
percentage of population with high school diploma, percentage of
population having university diploma and higher) in a firm's
headquartered county after a move compared to one year before the
move. Local education variables are from the Census. In Panel E,
Change in Local Seniors provides the change in the fraction of local
seniors in a firm's headquartered county after a move compared to one
year before the move. In this table, only the local religious
affiliation variables are displayed for brevity. Standard errors are
adjusted for heteroskedasticity and clustered at firm level. Robust p-
values are in parentheses.

                                  (1)            (2)

                                 1 Year Pre-Move versus
                                   1 Year Post-Move

Dependent Variable:           Change in Dividend Payer

Panel A.

Change in Cath                   -0.041
                                (0.363)
Change in Pro                                   0.025
                                               (0.662)
Changes in Local                  Yes            Yes
Industry Variables
Changes in Main Controls          Yes            Yes
Number of Observations            299            299
R square                         0.081          0.080

Panel B.

Change in Cath                   -0.089
                               (0.072) *
Change in Prot                                  0.098
                                               (0.128)
Change in Local Political         Yes            Yes
Affiliation
Changes in Main Controls          Yes            Yes
Number of Observations            299            299
R square                         0.094          0.092

Panel C.

Change in Cath                   -0.024
                                (0.632)
Change in Prot                                  0.022
                                               (0.712)
Changes in Local Industry         Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            292            292
R square                         0.120          0.119

Panel D.

Change in Cath                   -0.056
                                (0.228)
Change in Prot                                  0.050
                                               (0.401)
Changes in Local Education        Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            289            289
R square                         0.085          0.083

Panel E.

Change in Cath                   -0.060
                                (0.180)
Change in Prot                                  0.030
                                               (0.557)
Change in Local Seniors           Yes            Yes
Changes in Main Controls          Yes            Yes
Number of Observations            301            301
R square                         0.082          0.085

                                  (3)            (4)

                                 1 Year Pre-Move versus
                                    2 Year Post-Move

Dependent Variable:           Change in Dividend Payer

Panel A.

Change in Cath                   -0.038
                                (0.248)
Change in Pro                                   0.145
                                               (0.180)
Changes in Local                  Yes            Yes
Industry Variables
Changes in Main Controls          Yes            Yes
Number of Observations            259            259
R square                         0.007          0.009

Panel B.

Change in Cath                   -0.076
                                (0.412)
Change in Prot                                  0.225
                                               (0.237)
Change in Local Political         Yes            Yes
Affiliation
Changes in Main Controls          Yes            Yes
Number of Observations            259            259
R square                         0.007          0.009

Panel C.

Change in Cath                   -0.146
                                (0.416)
Change in Prot                                  0.434
                                               (0.177)
Changes in Local Industry         Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            209            209
R square                         0.160          0.179

Panel D.

Change in Cath                   0.018
                                (0.811)
Change in Prot                                  0.174
                                               (0.247)
Changes in Local Education        Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            256            256
R square                         0.010          0.012

Panel E.

Change in Cath                   -0.014
                                (0.777)
Change in Prot                                  0.173
                                               (0.223)
Change in Local Seniors           Yes            Yes
Changes in Main Controls          Yes            Yes
Number of Observations            259            259
R square                         0.006          0.008

                                  (5)            (6)

                                1 Year Pre-Move versus
                                   3 Year Post-Move

Dependent Variable:           Change in Dividend Payer

Panel A.

Change in Cath                   -2.591
                              (0.000) ***
Change in Pro                                   3.708
                                             (0.000) ***
Changes in Local                  Yes            Yes
Industry Variables
Changes in Main Controls          Yes            Yes
Number of Observations            224            224
R square                         0.211          0.216

Panel B.

Change in Cath                   -1.911
                              (0.008) ***
Change in Prot                                  2.717
                                             (0.004) ***
Change in Local Political         Yes            Yes
Affiliation
Changes in Main Controls          Yes            Yes
Number of Observations            224            224
R square                         0.177          0.182

Panel C.

Change in Cath                   -2.836
                              (0.000) ***
Change in Prot                                  3.965
                                             (0.000) ***
Changes in Local Industry         Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            201            201
R square                         0.211          0.217

Panel D.

Change in Cath                   -2.723
                               (0.092) *
Change in Prot                                  2.972
                                              (0.057) *
Changes in Local Education        Yes            Yes
  Variables
Changes in Main Controls          Yes            Yes
Number of Observations            224            224
R square                         0.182          0.186

Panel E.

Change in Cath                   -2.825
                              (0.000) ***
Change in Prot                                  3.982
                                             (0.001) ***
Change in Local Seniors           Yes            Yes
Changes in Main Controls          Yes            Yes
Number of Observations            224            224
R square                         0.175          0.178

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table A3. Main Tests with Other Geographical Factors

The dependent variable for Columns 1-3 is Dividend Payer. The
dependent variable for Columns 4-6 is Dividend Yield. The dependent
variable for Columns 7-9 is Dividend Initiation. Columns 1-3 and
Columns 7-9 use Logit regressions. Columns 4-6 use OLS regressions.
All of the main variable definitions are provided in Table II. Local
ethnicity variables indicate the fraction of local ethnicity variables
(White, Hispanic, African-American, and Asian) in a firm's
headquartered county. Local Political Affiliation reports the fraction
of local Republican votes in a firm's headquartered county. Local
Republican presents the fraction of Republican votes in the
Presidential elections in a county. This table also uses the
interpolations of the Census data to construct the variables for the
years without available Census data. Local Industry Variables report
the share of each industry with available data in a county. Local
industry variables (Manufacturing, Construction, Services, Wholesale,
Retail, Finance, Transportation, and Agriculture) are from the Census.
Local Education Variables indicate fractions of different local
education statistics (percentage of population having education less
than high school, percentage of population with high school diploma,
percentage of population having university diploma and higher) in a
firm's headquartered county. Local Seniors Variable provides the
proportion of people who are 65 years old or older in a firm's
headquartered county. Panel E is reported for comparison and it
provides the results from Table III. Local cultural and demographic
information data are from the Census. All of the tests include the
following firm age group indicator variables: Age 1-5, Age 6-10, Age
11-15, and Age 16-20. Age 21 and over is the dropped category in the
regressions. All of the tests include state, industry, and year dummy
variables. In this table, only the local religious affiliation
variables are displayed for brevity. Standard errors are adjusted for
heteroskedasticity and clustered at firm level. Robust p-values are in
parentheses.

                                  (1)           (2)           (3)

Dependent Variable:                       Dividend Payer

Panel A.

Cath                            -1.363
                              (0.002) ***
Prot                                           2.330
                                            (0.000) ***
Cpratio                                                     -0.095
                                                          (0.015) **
Local Ethnicity Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          79,250        79,250        79,250
R square                         0.434         0.435         0.433

Panel B.

Cath                            -1.161
                              (0.008) ***
Prot                                           2.078
                                            (0.000) ***
Cpratio                                                     -0.082
                                                          (0.034) **
Local Political Affiliation       Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          79,250        79,250        79,250
R square                         0.433         0.434         0.433

Panel C.

Cath                            -0.931
                              (0.038) **
Prot                                           2.271
                                            (0.000) ***
Cpratio                                                     -0.093
                                                          (0.018) **
Local Industry Variables          Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          74,676        74,676        74,676
R square                         0.434         0.435         0.434

Panel D.

Cath                            -1.215
                              (0.006) ***
Prot                                           1.812
                                            (0.001) ***
Cpratio                                                     -0.095
                                                          (0.014) **
Local Education Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          75,575        75,575        75,575
R square                         0.431         0.431         0.431

Panel E.

Cath                            -1.364
                              (0.002) **
Prot                                           2.084
                                            (0.000) **
Cpratio                                                     -0.097
                                                           (0.013) *
Local Seniors                     Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          80,049        80,049        80,049
R square                        0.4335        0.4339        0.4332

                                  (4)           (5)           (6)

Dependent Variable:                      Dividend Yield

Panel A.

Cath                            -0.005
                              (0.006) ***
Prot                                           0.011
                                            (0.000) ***
Cpratio                                                     -0.000
                                                          (0.004) ***
Local Ethnicity Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          79,170        79,170        79,170
R square                         0.270         0.271         0.270

Panel B.

Cath                            -0.005
                              (0.010) ***
Prot                                           0.010
                                            (0.000) ***
Cpratio                                                     -0.0003
                                                          (0.006) ***
Local Political Affiliation       Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          79,170        79,170        79,170
R square                         0.270         0.271         0.269

Panel C.

Cath                            -0.003
                                (0.112)
Prot                                           0.011
                                            (0.000) ***
Cpratio                                                     -0.000
                                                          (0.039) **
Local Industry Variables          Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          74,601        74,601        74,601
R square                         0.272         0.274         0.273

Panel D.

Cath                            -0.005
                              (0.006) ***
Prot                                           0.009
                                            (0.000) ***
Cpratio                                                     -0.000
                                                          (0.002) ***
Local Education Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          75,511        75,511        75,511
R square                         0.264         0.265         0.264

Panel E.

Cath                            -0.005
                              (0.004) **
Prot                                           0.010
                                            (0.000) **
Cpratio                                                     -0.000
                                                          (0.002) **
Local Seniors                     Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          79,964        79,964        79,964
R square                        0.2703        0.2712        0.2701

                                  (7)           (8)           (9)

Dependent Variable:                     Dividend Initiation

Panel A.

Cath                            -0.4811
                                (0.346)
Prot                                           1.790
                                            (0.003) ***
Cpratio                                                      0.003
                                                            (0.950)
Local Ethnicity Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          51,853        51,853        51,853
R square                         0.110         0.111         0.110

Panel B.

Cath                            -0.278
                                (0.581)
Prot                                           1.881
                                            (0.002) ***
Cpratio                                                      0.010
                                                            (0.774)
Local Political Affiliation       Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          51,853        51,853        51,853
R square                         0.110         0.110         0.110

Panel C.

Cath                            -0.185
                                (0.746)
Prot                                           2.146
                                            (0.003) ***
Cpratio                                                      0.014
                                                            (0.747)
Local Industry Variables          Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          48,774        48,774        48,774
R square                         0.109         0.110         0.109

Panel D.

Cath                            -0.282
                                (0.576)
Prot                                           1.137
                                             (0.062) *
Cpratio                                                      0.013
                                                            (0.760)
Local Education Variables         Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          49,621        49,621        49,621
R square                         0.111         0.111         0.111

Panel E.

Cath                            -0.210
                                (0.681)
Prot                                           1.446
                                             (0.013) *
Cpratio                                                      0.014
                                                            (0.734)
Local Seniors                     Yes           Yes           Yes
Main Controls                     Yes           Yes           Yes
Number of Observations          52,363        52,363        52,363
R square                        0.1098        0.1104        0.1098

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.


I would like to thank an anonymous referee, Raghavendra Rau (Editor), Christos Pantzalis, M. Sinan Goktan, Ayca Altintig, Aslihan G. Korkmaz, and seminar participants at the Financial Management Association 2013 Annual Meeting and the Southern Finance Association 2013 Annual Meeting for their helpful comments. Any remaining errors are mine.

References

Baker, M. and J. Wurgler, 2004a, "A Catering Theory of Dividends," Journal of Finance 59, 1125-1165.

Baker, M. and J. Wurgler, 2004b, "Appearing and Disappearing Dividends: The Link to Catering Incentives," Journal of Financial Economics 73, 271-288.

Barsky, R.B., F.T. Juster, M.S. Kimball, and M.D. Shapiro, 1997, "Preference Parameters and Behavioral Heterogeneity: An Experimental Approach in the Health and Retirement Study," Quarterly Journal of Economics 112, 537-579.

Baxamusa, M. and A. Jalal, 2016, "CEO's Religious Affiliation and Managerial Conservatism," Financial Management 45, 67-104.

Becker, B., Z. Ivkovic, and S. Weisbenner, 2011, "Local Dividend Clienteles," Journal of Finance 66, 655-684.

Benjamin, D., J.J. Choi, and G. Fisher, 2010, "Religious Identity and Economic Behavior," NBER, Working paper 15925.

Berger, R, G. Davie, and E. Fokas, 2008, Religious America, Secular Europe? A Theme and Variations, Burlington, VT, Ashgate.

Coval, J.D. and T.J. Moskowitz, 1999, "Home Bias at Home: Local Equity Preference in Domestic Portfolios," Journal of Finance 54, 2045-2073.

Eun, C.S., L. Wang, and S.C. Xiao, 2014, "Culture and [R.sup.2]," Journal of Financial Economics 115, 283-303.

Fama, E.F. and K.R. French, 1997, "Industry Costs of Equity," Journal of Financial Economics 43,153-193.

Fama, E.F. and K.R. French, 2001, "Disappearing Dividends: Changing Firm Characteristics or Lower Propensity to Pay?" Journal of Financial Economics 60, 3-43.

Gao, W., L. Ng, and Q. Wang, 2011, "Does Corporate Headquarters Location Matter for Firm Capital Structure?" Financial Management 40, 113-138.

Gordon, M.J., 1963, "Optimal Investment and Financing Policy," Journal of Finance 18, 264-272.

Graham, J.R. and A. Kumar, 2006, "Do Dividend Clienteles Exist? Evidence on Dividend Preferences of Retail Investors," Journal of Finance 59, 1125-1165.

Grinblatt, M. and M. Keloharju, 2001, "How Distance, Language and Culture Influence Stockholdings and Trades," Journal of Finance 56, 1053-1073.

Grullon, G., G. Kanatas, and J.P. Weston, 2010, "Religion, and Corporate (Mis)behavior," Rice University Working paper.

Grullon, G., P. Bradley, S. Underwood, and J. Weston, 2011, "Has the Propensity to Pay out Declined?" Journal of Financial and Quantitative Analysis 46, 1-24.

Guiso, L., P. Sapienza, and L. Zingales, 2006, "Does Culture Affect Economic Outcomes?" Journal of Economic Perspectives 20, 49-72.

Hilary, G. and K.W. Hui, 2009, "Does Religion Matter in Corporate Decision Making in America?" Journal of Financial Economics 93, 455-473.

Hoetker, G., 2004, "Confounded Coefficients: Accurately Comparing Logit and Probit Coefficients across Groups," University of Illinois at Urbana-Champaign Working paper.

Hoetker, G., 2007, "The Use of Logit and Probit Models in Strategic Management Research: Critical Issues," Strategic Management Journal 28, 331-343.

Hong, H., J.D. Kubik, and J.C. Stein, 2004, "Social Interaction and Stock Market Participation," Journal of Finance 59, 137-163.

Hong, H., J.D. Kubik, and J.C. Stein, 2008, "The Only Game in Town: Stock-Price Consequences of Local Bias Journal of Financial Economics 90, 20-37.

Ivkovic, Z. and S.J. Weisbenner, 2005, "Local Does as Local Is: Information Content of the Geography of Individual Investors' Common Stock Investments," Journal of Finance 60, 267-306.

Kumar, A., 2009, "Who Gambles in the Stock Market?" Journal of Finance 64, 1889-1933.

Kumar, A., J.K. Page, and O.G. Spalt, 2011, "Religious Beliefs, Gambling Attitudes, and Financial Market Outcomes," Journal of Financial Economics 102, 671-708.

La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R.W. Vishny, 1998, "Law and Finance," Journal of Political Economy 106, 1113-1155.

La Porta, R., F. Lopez-de-Silanes, A. Shleifer, and R.W. Vishny, 1999, "The Quality of Government," Journal of Law, Economics, and Organization 15, 222-279.

Lintner, J., 1962, "Dividends, Earnings, Leverage, Stock Prices and Supply of Capital to Corporations," Review of Economics and Statistics 64, 243-269.

Loughran, T., 2008, "The Impact of Firm Location on Equity Issuance," Financial Management 37, 1-21.

Loughran, T. and P. Schultz, 2005, "Liquidity: Urban versus Rural Firms," Journal of Financial Economics 78, 341-374.

Miller, M.H. and F. Modigliani, 1961, "Dividend Policy, Growth, and the Valuation of Shares," Journal of Business 34, 411-433.

Pantzalis, C. and E. Ucar, 2014, "Religious Holidays, Investor Distraction, and Earnings Announcement Effects," Journal of Banking & Finance 47, 102-117.

Pirinsky, C. and Q. Wang, 2006, "Does Corporate Headquarters Location Matter for Stock Returns?" Journal of Finance 61, 1991-2015.

Renneboog, L. and C. Spaenjers, 2012, "Religion Economic Attitude and Household Finance," Oxford Economic Papers 64, 103-127.

Shu, T., J. Sulaeman, and P.E. Yeung, 2012, "Local Religious beliefs and Mutual Fund Risk-Taking Behaviors," Management Science 58, 1779-1796.

Stulz, R. and R. Williamson, 2003, "Culture, Openness, and Finance," Journal of Financial Economics 70, 313-349.

(1) See the following dictionary websites or Guiso, Sapienza, and Zingales (2006): http://www.merriam-webster. com/dictionary/culture, http://oxforddictionaries.com/definition/english/culture?q=culture.

(2) The logit regression model for dividend initiation regressions in the last three columns of Panels D1 and D2 does not converge when industry-fixed effects based on Fama and French's (1997) 48 industry classifications are used. This result can be attributed to the small number of observations in the dividend initiation compared to the dividend payer and the dividend yield tests. Thus, I use industry-fixed effects based on one-digit SIC codes in in the last three columns of Panels D1 and D2. The first six columns of Panels D1 and D2 have industry-fixed effects based on Fama and French's (1997) 48 industry classifications as in the other tests. When I employ one-digit SIC code classifications in the first six columns of Panels D1 and D2,1 find results similar to the ones reported in this table.

(3) In addition, in unreported tests, I divide the sample into subsamples based on time and compare the results between the earlier and later subperiods. I thank an anonymous referee for this suggestion. Most of the results are more pronounced for the earlier subperiod in this analysis. This finding is consistent with the expectation of a reduced level of local individual investor ownership with the rise of institutions over time. These results can be provided on request.

(4) When I use different Cpratio cutoff points in the matched sample comparisons, I find very similar results. In addition, using an alternative Cpratio cutoff points--such as using Cpratio 2 and 0.5 in determining Catholic and Protestant are firms, respectively--gives statistically significant results for Dividend Initiation.

(5) The small number of observations in the corporate relocation sample is consistent with the earlier literature (Pirinsky and Wang, 2006; Becker et al., 2011). Also note that Becker et al. (2011) have stronger results for the third year post-move compared to the first and second years after a headquarters' move.

(6) Using a lottery index based on different classifications, such as 33 groups or 20 groups, produces results similar to the ones demonstrated in Table VIII.

(7) I thank the Editor, Raghavendra Rau, for this suggestion. Also note that the number of observations in Table X is smaller than the number of observations in the main tests because the matching process does not provide CEO names for all the firm-year observations in the sample. In addition, Table X includes all the observations with available CEO birthplace information after the CEO birthplace information collection step.

(8) I thank Mufaddal Baxamusa and Abu Jalal for providing me with their CEO religion data set.

(9) These results can be provided upon request. Also note that although Baxamusa and Jalal (2016) examine the impact of religion on corporate policies, there are some differences between Baxamusa and Jalal (2016) and my paper. I examine the impact of local investor bases on corporate dividend policies as opposed to the impact of CEOs. They focus on other corporate policies. The way my paper classifies religion is based on local investor religion, while they focus on CEO religion. In addition, my paper only includes Catholics and Protestants and does not include other Christians or non-Christians in its analysis, whereas their paper's definition of religion examines Catholics versus non- Catholics. There is a group of studies that consider the differences in risk perceptions between Catholics and non-Catholics. These studies suggest different implications in risk perceptions, such as lower risk tolerance for Catholics, especially for pure risk aversion. Some of the evidence in these studies comes from European data (Renneboog and Spaenjers, 2012). Alternatively, similar evidence from US data indicates lower risk tolerance for Protestants (Barsky et al., 1997; Kumar et ah, 2011; Hilary and Hui, 2009; Shu et ah, 2012). In addition, the perception of religion notes some important differences between the United States and Europe due to differences in the development of religious structures over time (Berger, Davie, and Fokas, 2008). Some recent studies suggest that overall risk aversion can be examined under different categories, such as speculative risk and pure risk aversion. They demonstrate that Protestant culture is more risk- averse in terms of speculative risk (Shu et ah, 2012). Shu et ah (2012) report that recent studies indicate that Protestant and Catholic beliefs have "differences in speculative risk but share a common aversion to pure risk." Alternatively, Baxamusa and Jalal (2016) suggest that when the impact of managerial religion on some corporate policies is considered, Catholics have less risk tolerance when compared to non-Catholics in terms of pure risk. As Hilary and Hui (2009) suggest; the impact of religious phenomena on economic outcomes can be complex. In this literature, my paper focuses on the impact of local investor religion on dividend policy through the overall perception of risk and finds that the Protestant culture is associated with a stronger demand for dividends and consistent corporate dividend policies, while the Catholic culture is associated with weaker dividend demand and consistent dividend policies. This finding is in line with the notion that Protestants are more risk-averse toward dividends when compared to Catholics.

(10) I thank an anonymous referee and the Editor, Raghavendra Rau, for this suggestion.

Erdem Ucar is an Assistant Professor of Finance in the Barowsky School of Business at the Dominican, University of California in San Rafael, CA.

Table I. Summary Statistics

Dividend Payer is an indicator variable that takes a value of one if
the total amount of dividends is greater than zero for a given year
and zero otherwise. Dividend Yield is the ratio of total dividends to
the lagged market value. Dividend Initiation is an indicator variable
that takes a value of one if a current nondividend payer firm becomes
a dividend payer during the following year, and zero if a current
nondividend payer firm stays as a nondividend payer during the
following year. Cath is the fraction of Catholics in the county where
a firm is located. Prot is the fraction of Protestants in the county
where a firm is located. Cpratio is the ratio of Catholics to
Protestants in the county where a firm is located (ratio of Cath to
Prot). Local variables in this table are as follows. Local Seniors is
the proportion of people who are 65 years old or older in the firm's
headquartered county. Income is the median household income in the
firm's headquartered county. Education is the proportion of the
population with college in the firm's headquartered county. NYE is
measure of firm size based on the NYSE equity percentiles for the
corresponding period. M/B is the ratio of the market value of assets
to the book value of assets. Total Assets provides the total asset
value in millions of dollars. ROA is the return on assets as measured
by income before depreciation divided by total assets for a given
year. Sales Growth is the growth rate of the sales calculated by using
the current and previous year figures. Firm Age is based on the number
of years between the date a firm initially listed on CRSP and the
current year.

                          Mean        25th      Median
                                   Percentile

Panel A. Payout Policy Variables (in %)

Dividend Payer           24.84%      0.00%       0.00%
Dividend Yield           0.57%       0.00%       0.00%
Dividend Initiation      2.14%       0.00%       0.00%

Panel B. County-Level Variables

Cath (%)                 26.13%      15.98%     24.31%
Prot (%)                 20.11%      10.41%     15.62%
Local Seniors (%)        11.63%      9.73%      11.43%
Income ($)               48,557      38,133     46,030
Education (%)            31.95%      25.25%     30.40%

Panel C. Firm Characteristics

NYE                      24.19        3.00       12.00
M/B                       2.19        1.11       1.52
ROA                       0.03        0.00       0.10
Sales Growth             21.28%      -3.25%      8.63%
Total Assets ($ mil)    1,726.58     32.22      126.38
Firm Age                 13.26        3.47       8.58

                           75th       Standard
                        Percentile   Deviation

Panel A. Payout Policy Variables (in %)

Dividend Payer            0.00%        43.21%
Dividend Yield            0.25%        1.23%
Dividend Initiation       0.00%        14.47%

Panel B. County-Level Variables

Cath (%)                  36.85%       14.00%
Prot (%)                  28.40%       12.80%
Local Seniors (%)         13.07%       2.97%
Income ($)                56,113       13,986
Education (%)             38.68%       9.85%

Panel C. Firm Characteristics

NYE                       38.00        27.02
M/B                        2.41         1.97
ROA                        0.17         0.27
Sales Growth              25.54%       67.42%
Total Assets ($ mil)      583.54     12,491.93
Firm Age                  18.19        14.33

Table II. Dividend Payout and Religious Affiliation

The dependent variable for Columns 1 -3 is Dividend Payer. The
dependent variable for Columns 4-6 is Dividend Yield, while the
dependent variable for Columns 7-9 is Dividend Initiation. Columns 1-
3 and Columns 7-9 use Logit regressions. Columns 4-6 use OLS
regressions. Dividend Payer is an indicator variable that takes a
value of one if the total amount of dividends is greater than zero for
a given year and zero otherwise. Dividend Yield is the ratio of total
dividends to the lagged market value. Dividend Initiation is an
indicator variable that takes a value of one if a current nondividend
payer firm becomes a dividend payer during the following year, and
zero if a current nondividend payer firm stays as a nondividend payer
during the following year. Cath (Prot) is the fraction of Catholics
(Protestants) in the county where a firm is located. Cpratio is the
ratio of Catholics to Protestants in the county where a firm is
located. Net Income is the net income scaled by total assets for a
given year. Cash is the cash scaled by total assets for a given year.
Q is defined as the sum of the market value of equity and the book
value of liabilities scaled by total assets for a given year. Debt is
long-term debt scaled by total assets for a given year. Volatility is
the standard deviation of monthly stock returns for the previous two-
year period. Lagged Return is the monthly stock returns for the
previous two-year period. Log of Assets is the logarithm of total
assets. Log of MV is the logarithm of a firm's market value for a
given year. Asset Growth is the logarithm of the growth rate of total
assets calculated using the current and previous year's figures. All
of the tests include the following firm age group indicator variables:
Age 1-5, Age 6-10, Age 11-15, and Age 16-20. Age 21 and Over is the
dropped category in the regressions. All of the tests include state,
industry, and year dummy variables. Intercept, firm age indicators,
state, industry, and year dummy variables are not displayed for
brevity. Standard errors are adjusted for heteroskedasticity and
clustered at firm level. Robust p-values are in parentheses.

                              (1)             (2)             (3)

Dependent Variable:                       Dividend Payer

Cath                      -1.220
                          (0.005) ***
Prot                                       2.259
                                          (0.000) ***
Cpratio                                                   -0.089
                                                          (0.021) **
Net Income                 3.466           3.459           3.465
                          (0.000) ***     (0.000) ***     (0.000) ***
Cash                      -0.908          -0.890          -0.897
                          (0.000) ***     (0.000) ***     (0.000) ***
Q                         -0.140          -0.138          -0.141
                          (0.000) ***     (0.000) ***     (0.000) ***
Debt                      -0.979          -0.999          -0.984
                          (0.000) ***     (0.000) ***     (0.000) ***
Volatility               -14.354         -14.249         -14.340
                          (0.000) ***     (0.000) ***     (0.000) ***
Lagged Return             -0.017          -0.018          -0.017
                          (0.421)         (0.376)         (0.400)
Log of MV                  0.380           0.380           0.383
                          (0.000) ***     (0.000) ***     (0.000) ***
Log of Assets              0.036           0.042           0.034
                          (0.498)         (0.433)         (0.523)
Asset Growth              -0.635          -0.633          -0.634
                          (0.000) ***     (0.000) ***     (0.000) ***
Number of Observations      80,049          80,049          80,049
R square                     0.433           0.434           0.433

                              (4)             (5)             (6)

Dependent Variable:                      Dividend Yield

Cath                     -0.005
                         (0.008) ***
Prot                                      0.010
                                         (0.000) ***
Cpratio                                                  -0.000
                                                         (0.004) ***
Net Income               -0.001          -0.001          -0.001
                         (0.001) ***     (0.001) ***     (0.0013) ***
Cash                      0.000           0.000           0.000
                         (0.430)         (0.341)         (0.366)
Q                        -0.000          -0.000          -0.000
                         (0.000) ***     (0.000) ***     (0.000) ***
Debt                     -0.004          -0.004          -0.004
                         (0.000) ***     (0.000) ***     (0.000) ***
Volatility               -0.019          -0.018          -0.019
                         (0.000) ***     (0.000) ***     (0.000) ***
Lagged Return            -0.000          -0.000          -0.000
                         (0.378)         (0.324)         (0.355)
Log of MV                 0.001           0.001           0.001
                         (0.000) ***     (0.000) ***     (0.000) ***
Log of Assets             0.000           0.000           0.001
                         (0.431)         (0.309)         (0.431)
Asset Growth             -0.001          -0.001          -0.001
                         (0.000) ***     (0.000) ***     (0.000) ***
Number of Observations      79,964          79,964          79,964
R square                     0.270           0.271           0.70

                              (7)             (8)             (9)

Dependent Variable:                   Dividend Initiation

Cath                     -0.113
                         (0.822)
Prot                                      1.547
                                         (0.008)***
Cpratio                                                   0.019
                                                         (0.647)
Net Income                3.303           3.304           3.301
                         (0.000) ***     (0.000) ***     (0.000) ***
Cash                      0.669           0.681           0.667
                         (0.001) ***     (0.000) ***     (0.001) ***
Q                        -0.137          -0.137          -0.137
                         (0.003) ***     (0.030) ***     (0.003) ***
Debt                     -0.723          -0.734          -0.723
                         (0.001) ***     (0.001) ***     (0.001) ***
Volatility               -3.494          -3.460          -3.495
                         (0.000) ***     (0.000) ***     (0.000) ***
Lagged Return             0.142           0.142           0.142
                         (0.000) ***     (0.000) ***     (0.000) ***
Log of MV                 0.174           0.174           0.174
                         (0.010) ***     (0.009) ***     (0.009) ***
Log of Assets             0.002           0.004           0.002
                         (0.982)         (0.951)         (0.976)
Asset Growth             -0.443          -0.442          -0.443
                         (0.000) ***     (0.000) ***     (0.000) ***
Number of Observations      52,363          52,363          52,363
R square                     0.110           0.110           0.110

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table III. Dividend Payout, Religious Affiliation, Local Seniors, and
Other Local Factors

The dependent variable for Columns 1/3 is Dividend Payer. The
dependent variable for Columns 4/6 is Dividend Yield, and the
dependent variable for Columns 7/9 is Dividend Initiation. Columns 1/
3 and Columns 7/9 use Logit regressions, while Columns 4/6 use OLS
regressions. County/level local control variables are as follows:
Local Seniors, Income, Median House Value, Education, and Log
of Population. Local Seniors is the proportion of people who are 65
years old or older in a firm's headquartered county. Income is the
median household income in the county in which a firm is located.
Median House Value is the median house value in the county in which a
firm is located. Education is the proportion of the population with
college degrees in the county in which a firm is located. Log of
Population is the logarithm of the county population. All of the other
variable definitions are provided in Table II. All of the tests
include the following firm age group indicator variables: Age 1/5, Age
6/10, Age 11/15, and Age 16/20. Age 21 and Over is the dropped
category in the regressions. All of the tests include state, industry,
and year dummy variables. In this table, only the local religious
affiliation variables are displayed for brevity. Standard errors are
adjusted for heteroskedasticity and clustered at firm level. Robust /
^-values are in parentheses.

                               (1)            (2)            (3)

Dependent Variable:                       Dividend Payer

Panel A.

Cath                          -1.364
                            (0.002) **
Prot                                          2.084
                                           (0.000) **
Cpratio                                                     -0.097
                                                          (0.013) *
Main Controls                  Yes            Yes            Yes
Local Seniors                  Yes            Yes            Yes
Number of Observations        80,049         80,049         80,049
R square                      0.434          0.434          0.433

Panel B.

Cath                       -1.082
                           (0.024) *
Prot                                       1.652
                                          (0.003) ***
Cpratio                                                  -0.071
                                                         (0.088) *
Main Controls                  Yes            Yes            Yes
Local Control Variables        Yes            Yes            Yes
Number of Observations        80,049         80,049         80,049
R square                      0.436          0.436          0.435

                               (4)            (5)            (6)

Dependent Variable:                       Dividend Yield

Panel A.

Cath                          -0.005
                           (0.004) ***
Prot                                          0.001
                                           (0.000) **
Cpratio                                                     -0.000
                                                         (0.002) ***
Main Controls                  Yes            Yes            Yes
Local Seniors                  Yes            Yes            Yes
Number of Observations        79,964         79,964         79,964
R square                      0.270          0.271          0.270

Panel B.

Cath                       -0.005
                           (0.009) ***
Prot                                       0.009
                                          (0.000) ***
Cpratio                                                   0.000
                                                         (0.003) ***
Main Controls                  Yes            Yes            Yes
Local Control Variables        Yes            Yes            Yes
Number of Observations        79,964         79,964         79,964
R square                      0.272          0.272          0.272

                               (7)            (8)            (9)

Dependent Variable:                    Dividend Initiation

Panel A.

Cath                          -0.201
                             (0.681)
Prot                                          1.446
                                           (0.013) **
Cpratio                                                      0.014
                                                           (0.734)
Main Controls                  Yes            Yes            Yes
Local Seniors                  Yes            Yes            Yes
Number of Observations        52,363         52,363         52,363
R square                      0.110          0.110          0.110

Panel B.

Cath                       -0.287
                           (0.608)
Prot                                       1.645
                                          (0.010) ***
Cpratio                                                   0.008
                                                         (0.867)
Main Controls                  Yes            Yes            Yes
Local Control Variables        Yes            Yes            Yes
Number of Observations        52,363         52,363         52,363
R square                      0.111          0.111          0.111

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table IV. Dividend Payout, Local Religious Affiliation, and Local
Stock Ownership

The dependent variable for Columns 1-3 is Dividend Payer. The
dependent variable for Columns 4-6 is Dividend Yield, while the
dependent variable for Columns 7-9 is Dividend Initiation. Columns 1-
3 and Columns 7-9 use Logit regressions, while Columns 4-6 use OLS
regressions. Panel A focuses on the differences between firms located
in big metropolitan areas and firms located in other areas. Panel A1
repeats the main tests for firms located in areas excluding the three
largest metropolitan areas (New York, Chicago, and Los Angeles), while
Panel A2 repeats the main tests for the subsample of firms that are
headquartered in the three largest metropolitan areas. In Panel B,
local ownership is proxied by the only-game-in-town effect by
following Hong et al. (2008) and it is measured by the number of firms
per capita. Panel B1 provides results for the subsample of firms that
are located in areas with a lower number of local firms per capita and
Panel B2 presents the results for the subsample of firms that are
located in areas with a higher number of local firms per capita. Panel
C uses the fraction of local white households to measure the local
retail stock market participation rate by following Hong et al.
(2004). Panel Cl (C2) provides the test results for firms that are
located in areas with a higher (lower) fraction of white households.
Panel D uses institutional ownership to investigate the role of local
ownership. Panel D1 (D2) includes firms with lower (higher)
institutional holdings. The main regression control variables are Net
Income, Cash, Q, Debt, Volatility, Lagged Return, Log of Assets, and
Asset Growth. All of the variable definitions are provided in Table
II. All of the tests include the following firm age group indicator
variables: Age 1-5, Age 6-10, Age 11-15, and Age 16-20. Age 21 and
Over is the dropped category in the regressions. All of the tests
include state, industry, and year dummy variables. In this table, only
the local religious affiliation variables are displayed for brevity.
Standard errors are adjusted for heteroskedasticity and clustered at
firm level. Robust p-values are in parentheses.

                              (1)            (2)            (3)

Dependent Variable:                      Dividend Payer

Panel A. Metropolitan Areas versus Other Areas

Panel Al. Firms in Other Areas

Cath                         -1.624
                          (0.001) ***
Prot                                        2.269
                                         (0.000) ***
Cpratio                                                    -0.137
                                                        (0.003) ***
Main Controls                 Yes            Yes            Yes
Number of Observations       71,708         71,708         71,708
R square                     0.434          0.435          0.434

Panel A2. Firms in Metropolitan Areas

Cath                         -3.832
                            (0.117)
Prot                                        -2.885
                                           (0.479)
Cpratio                                                    0.109
                                                          (0.573)
Main Controls                 Yes            Yes            Yes
Number of Observations       8,216          8,216          8,216
R square                     0.469          0.468          0.468

Panel B. Number of Local Firms per Capita

Panel B1. Firms Located in Areas with Low Number of Local Firms per
Capita

Cath                         -1.631
                          (0.007) ***
Prot                                        3.275
                                         (0.000) ***
Cpratio                                                    -0.178
                                                        (0.006) ***
Main Controls                 Yes            Yes            Yes
Number of Observations       26,504         26,504         26,504
R square                     0.476          0.479          0.476

Panel B2. Firms Located in Areas with High Number of Local Firms per
Capita

Cath                         -0.077
                            (0.923)
Prot                                        0.035
                                           (0.973)
Cpratio                                                    0.018
                                                          (0.751)
Main Controls                 Yes            Yes            Yes
Number of Observations       26,402         26,402         26,402
R square                     0.411          0.411          0.411

Panel C. Local Stock Market Participation

Panel C1. Firms in Areas with High Fraction of White Households

Cath                         -2.242
                          (0.001) ***
Prot                                        2.229
                                         (0.006) ***
Cpratio                                                    -0.206
                                                        (0.002) ***
Main Controls                 Yes            Yes            Yes
Number of Observations       27,493         27,493         27,493
R square                     0.424          0.423          0.424

Panel C2. Firms in Areas with Low Fraction of White Households

Cath                         0.849
                            (0.358)
Prot                                        1.277
                                           (0.299)
Cpratio                                                    0.063
                                                          (0.406)
Main Controls                 Yes            Yes            Yes
Number of Observations       26,512         26,512         26,512
R square                     0.452          0.452          0.452

Panel D. Institutional Ownership

Panel D1. Firms with Low Institutional Holdings

Cath                         -1.767
                           (0.021) *
Prot                                        2.799
                                          (0.000) **
Cpratio                                                    -0.152
                                                         (0.031) *
Main Controls                 Yes            Yes            Yes
Number of Observations       26,928         26,928         26,928
R square                     0.341          0.342          0.341

Panel D2. Firms with High Institutional Holdings

Cath                         -1.162
                            (0.086)*
Prot                                         2.28
                                          (0.005) **
Cpratio                                                    -0.048
                                                          (0.420)
Main Controls                 Yes            Yes            Yes
Number of Observations       25,680         25,680         25.680
R square                      0.39          0.391           0.39

                              (4)            (5)            (6)

Dependent Variable:                      Dividend Yield

Panel A. Metropolitan Areas versus Other Areas

Panel Al. Firms in Other Areas

Cath                         -0.007
                          (0.002) ***
Prot                                        0.010
                                         (0.000) ***
Cpratio                                                    -0.001
                                                        (0.0005) ***
Main Controls                 Yes            Yes            Yes
Number of Observations       71,631         71,631         71,631
R square                     0.273          0.274          0.273

Panel A2. Firms in Metropolitan Areas

Cath                         -0.001
                            (0.879)
Prot                                        0.010
                                           (0.516)
Cpratio                                                    0.000
                                                          (0.910)
Main Controls                 Yes            Yes            Yes
Number of Observations       8,333          8,333          8,333
R square                     0.293          0.293          0.293

Panel B. Number of Local Firms per Capita

Panel B1. Firms Located in Areas with Low Number of Local Firms per
Capita

Cath                         -0.008
                          (0.003) ***
Prot                                        0.013
                                         (0.000) ***
Cpratio                                                    -0.001
                                                        (0.002) ***
Main Controls                 Yes            Yes            Yes
Number of Observations       26,490         26,490         26,490
R square                     0.346          0.348          0.346

Panel B2. Firms Located in Areas with High Number of Local Firms per
Capita

Cath                         -0.008
                            (0.786)
Prot                                        0.003
                                           (0.480)
Cpratio                                                    -0.000
                                                          (0.852)
Main Controls                 Yes            Yes            Yes
Number of Observations       26,405         26,405         26,405
R square                     0.212          0.212          0.212

Panel C. Local Stock Market Participation

Panel C1. Firms in Areas with High Fraction of White Households

Cath                         -0.008
                           (0.037) **
Prot                                        0.009
                                          (0.031) **
Cpratio                                                    -0.001
                                                         (0.019) **
Main Controls                 Yes            Yes            Yes
Number of Observations       27,511         27,511         27,511
R square                     0.272          0.271          0.271

Panel C2. Firms in Areas with Low Fraction of White Households

Cath                         0.001
                            (0.621)
Prot                                        0.017
                                         (0.000) ***
Cpratio                                                    -0.001
                                                          (0.443)
Main Controls                 Yes            Yes            Yes
Number of Observations       26,496         26,496         26,496
R square                     0.263          0.265          0.263

Panel D. Institutional Ownership

Panel D1. Firms with Low Institutional Holdings

Cath                         -0.003
                           (0.082) *
Prot                                        0.008
                                          (0.001) **
Cpratio                                                    0.000
                                                         (0.070) *
Main Controls                 Yes            Yes            Yes
Number of Observations       26,966         26,966         26,966
R square                     0.1243         0.126          0.124

Panel D2. Firms with High Institutional Holdings

Cath                         -0.003
                            (0.193)
Prot                                        0.006
                                          (0.076) *
Cpratio                                                    0.000
                                                          (0.178)
Main Controls                 Yes            Yes            Yes
Number of Observations       25,756         25,756         25,756
R square                     0.339          0.339          0.339

                              (7)            (8)            (9)

Dependent Variable:                    Dividend Initiation

Panel A. Metropolitan Areas versus Other Areas

Panel Al. Firms in Other Areas

Cath                         -0.391
                            (0.489)
Prot                                        1.430
                                          (0.015) **
Cpratio                                                    0.010
                                                          (0.831)
Main Controls                 Yes            Yes            Yes
Number of Observations       47,188         47,188         47,188
R square                     0.111          0.111          0.111

Panel A2. Firms in Metropolitan Areas

Cath                         3.306
                            (0.338)
Prot                                        0.452
                                           (0.916)
Cpratio                                                    0.199
                                                          (0.300)
Main Controls                 Yes            Yes            Yes
Number of Observations       4,763          4,763          4,763
R square                     0.165          0.164          0.165

Panel B. Number of Local Firms per Capita

Panel B1. Firms Located in Areas with Low Number of Local Firms per
Capita

Cath                         0.660
                            (0.448)
Prot                                        1.791
                                          (0.046) **
Cpratio                                                    0.077
                                                          (0.357)
Main Controls                 Yes            Yes            Yes
Number of Observations       16,054         16,054         16,054
R square                     0.118          0.119          0.118

Panel B2. Firms Located in Areas with High Number of Local Firms per
Capita

Cath                         -0.519
                            (0.472)
Prot                                        0.741
                                           (0.502)
Cpratio                                                    -0.066
                                                          (0.182)
Main Controls                 Yes            Yes            Yes
Number of Observations       18,079         18,079         18,079
R square                     0.107          0.107          0.108

Panel C. Local Stock Market Participation

Panel C1. Firms in Areas with High Fraction of White Households

Cath                         -1.857
                           (0.032) **
Prot                                        2.807
                                         (0.001) ***
Cpratio                                                    -0.093
                                                          (0.176)
Main Controls                 Yes            Yes            Yes
Number of Observations       16,935         16,935         16,935
R square                     0.124          0.126          0.123

Panel C2. Firms in Areas with Low Fraction of White Households

Cath                         1.675
                           (0.095) *
Prot                                        1.263
                                           (0.460)
Cpratio                                                    0.092
                                                          (0.171)
Main Controls                 Yes            Yes            Yes
Number of Observations       17,812         17,812         17,812
R square                     0.136          0.136          0.136

Panel D. Institutional Ownership

Panel D1. Firms with Low Institutional Holdings

Cath                         -0.772
                            (0.358)
Prot                                        2.332
                                          (0.019) *
Cpratio                                                    -0.050
                                                          (0.496)
Main Controls                 Yes            Yes            Yes
Number of Observations       20,442         20,442         20,442
R square                     0.118          0.119          0.118

Panel D2. Firms with High Institutional Holdings

Cath                         -0.257
                            (0.749)
Prot                                        -0.092
                                           (0.930)
Cpratio                                                    0.005
                                                          (0.934)
Main Controls                 Yes            Yes            Yes
Number of Observations       13,994         13,994         13,994
R square                     0.104          0.104          0.104

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table V. Dividend Payout and Religious Affiliation, with Matched
Characteristics

This table presents the mean values for payout policy variables for
firms that are located in predominantly Protestant areas (ProtArea)
and a matched sample of firms that are located in predominantly
Catholic areas (Matched CathArea). Payout Policy variables (Dividend
Payer, Dividend Yield, and Dividend Initiation) definitions are
provided in Table II. ProtArea firms are firms that are located in a
county in which county has at least 50% more Protestant than Catholics
or a Cpratio of 0.67. Matched CathArea firms are firms that are
located in a county in which the given county has at least 50% more
Catholics than Protestants or a Cpratio of 1.5. Matched CathArea firms
are determined after matching each firm-year observation of a
Protestant area firm with a firm-year observation of a Catholic area
firm that is from the same year, industry, and age group, and that
includes the asset size, market value, net income, cash, Q values,
debt, volatility, and lagged return. All of the variables that are
used in matching are defined in Table II.

                       [N.sub.      ProtArea    [N.sub.Matched
                      ProtArea]                   CathArea]

Dividend Payer          12,163       33.84%         12,163
Dividend Yield          12,163       0.75%          12,163
Dividend Initiation     7,183        2.71%          7,868

                       Matched                      p-value
                       CathArea    Difference    (difference)

Dividend Payer          27.58%        6.26%      (0.000) ***
Dividend Yield          0.64%         0.11%      (0.000) ***
Dividend Initiation     2.40%         0.31%      (0.382)

*** Significant at the 0.01 level.

Table VI. Dividend Payout and Corporate Relocation

This table analyzes the impact of change in local religious
affiliation on change in dividend payer status by focusing on the
change in the post-move years compared to one year before a corporate
headquarters' move. In particular, Columns 1-2 focus on the change in
a firm's dividend payer behavior one year after a headquarters' move
compared to one year before the move. Columns 3-4 provide the change
in dividend paying behavior two years after a headquarters' move
compared to one year before a move. Similarly, Columns 5-6 focus on
the change in dividend payment status in the three years after a
headquarters' move compared to one year before a move. The dependent
variable is Change in Dividend Payer that reports the change in
dividend payer status. Change in Cath indicates a change in the
fraction of Catholics in a firm's headquartered county after a
headquarters' move compared to one year before a move. Change in Prot
presents the change in the fraction of Protestants in firm's
headquartered county after a headquarters' move compared to one year
before a move. This table presents OLS regressions in its analysis.
This table also includes changes in the control variables that are
used in the main tests (Table II) along with the age group indicator
variables post-move. Only the changes in local religious affiliation
variables are displayed for brevity. Standard errors are adjusted for
heteroskedasticity and clustered at firm level. Robust values are in
parentheses.

                                (1)            (2)

                                       1 Year
                                   Pre-Move versus
                                  1 Year Post-Move

Dependent Variable:

Change in Cath                 -0.043
                              (0.295)
Change in Prot                                0.031
                                             (0.548)
Changes in Main Controls        Yes            Yes
Number of Observations          301            301
R square                       0.075          0.073

                                (3)            (4)

                                       1 Year
                                   Pre-Move versus
                                  2 Year Post-Move

Dependent Variable:           Change in Dividend Payer

Change in Cath                 -0.029
                              (0.411)
Change in Prot                                0.140
                                             (0.173)
Changes in Main Controls        Yes            Yes
Number of Observations          259            259
R square                       0.008          0.006

                                (5)            (6)

                                       1 Year
                                   Pre-Move versus
                                  3 Year Post-Move

Dependent Variable:

Change in Cath                 -2.571
                            (0.000) ***
Change in Prot                                3.598
                                           (0.000) ***
Changes in Main Controls        Yes            Yes
Number of Observations          224            224
R square                       0.177          0.173

*** Significant at the 0.01 level.

Table VII. Dividend Payout and Religious Affiliation, with Compact
Disclosure Address Information

The dependent variable for Columns 1-3 is Dividend Payer. The
dependent variable for Columns 4-6 is Dividend Yield and the dependent
variable for Columns 7-9 is Dividend Initiation. Columns 1-3 and
Columns 7-9 use Logit regressions, while Columns 4-6 use OLS
regressions. The main regression control variables are Net Income,
Cash, Q, Debt, Volatility, Lagged Return, Log of Assets, and Asset
Growth. All of the variable definitions are provided in Table II.
Panel B presents the following local control variables including Local
Seniors, Income, Median House Value, Education, and Log of Population.
Local control variable definitions are provided in Table III. All of
the tests include the following firm age group indicator variables:
Age 1-5, Age 6-10, Age 11-15, and Age 16-20. Age 21 and Over is the
dropped category in the regressions. All of the tests include state,
industry, and year dummy variables. In this table, only the local
religious affiliation variables are displayed for brevity. Standard
errors are adjusted for heteroskedasticity and clustered at firm
level. Robust p-values are in parentheses.

                             (1)            (2)            (3)

Dependent Variable:                     Dividend Payer

Panel A. Main Tests

Cath                        -1.713
                         (0.001) ***
Prot                                       2.523
                                        (0.000) ***
Cpratio                                                   -0.143
                                                       (0.001) ***
Main Controls                Yes            Yes            Yes
Number of Observations      44,751         44,751         44,751
R square                    0.454          0.454          0.454

Panel B. Main Tests with Local Controls

Cath                        -1.445
                          (0.012) **
Prot                                       1.585
                                         (0.018) **
Cpratio                                                   -0.109
                                                        (0.018) **
Main Controls                Yes            Yes            Yes
Local Controls               Yes            Yes            Yes
Number of Observations      44,751         44,751         44,751
R square                    0.457          0.457          0.457

                             (4)            (5)            (6)

Dependent Variable:                     Dividend Yield

Panel A. Main Tests

Cath                        -0.006
                         (0.007) ***
Prot                                       0.011
                                         (0.000)***
Cpratio                                                   -0.001
                                                       (0.001) ***
Main Controls                Yes            Yes            Yes
Number of Observations      44,767         44,767         44,767
R square                    0.316          0.317          0.316

Panel B. Main Tests with Local Controls

Cath                        -0.007
                          (0.017) **
Prot                                       0.009
                                        (0.002) ***
Cpratio                                                   -0.001
                                                       (0.003) ***
Main Controls                Yes            Yes            Yes
Local Controls               Yes            Yes            Yes
Number of Observations      44,767         44,767         44,767
R square                    0.319          0.319          0.318

                             (7)            (8)             0)

Dependent Variable:                   Dividend Initiation

Panel A. Main Tests

Cath                        -0.323
                           (0.611)
Prot                                       1.092
                                          (0.111)
Cpratio                                                   -0.037
                                                         (0.441)
Main Controls                Yes            Yes            Yes
Number of Observations      28,151         28,151         28,151
R square                    0.129          0.129          0.129

Panel B. Main Tests with Local Controls

Cath                        -0.712
                           (0.310)
Prot                                       1.244
                                          (0.104)
Cpratio                                                   -0.046
                                                         (0.370)
Main Controls                Yes            Yes            Yes
Local Controls               Yes            Yes            Yes
Number of Observations      28,151         28,151         28,151
R square                    0.131          0.131          0.131

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table VIII. Dividend Payout, Religious Affiliation, and Lottery Stocks

The dependent variable for Columns 1-3 is Dividend Payer. The
dependent variable for Columns 4-6 is Dividend Yield and the dependent
variable for Columns 7-9 is Dividend Initiation. Columns 1-3 and
Columns 7-9 are Logit regressions, while Columns 4-6 are OLS
regressions. The main regression control variables are Net Income,
Cash, Q, Debt, Volatility, Lagged Return, Log of Assets, and Asset
Growth. All of the variable definitions are provided in Table II.
Panel B includes the following local control variables including Local
Seniors, Income, Median House Value, Education, and Log of Population.
Local control variable definitions are provided in Table III. Lottery
Index is an index that determines lottery-type stocks-based stock
price, idiosyncratic volatility, and idiosyncratic skewness. Local
Unemp. Rate provides the county unemployment rate. All of the tests
include the following firm age group indicator variables: Age 1-5, Age
6-10, Age 11-15, and Age 16-20. Age 21 and Over is the dropped
category in the regressions. In this table, only the local religious
affiliation variables are displayed for brevity. Standard errors are
adjusted for heteroskedasticity and clustered at firm level. Robust p-
values are in parentheses.

                              (1)            (2)            (3)

Dependent Variable:                      Dividend Payer

Panel A. Main Regression Model

Cath                         -1.215
                           (0.006) **
Prot                                        2.230
                                          (0.000) **
Cpratio                                                    -0.087
                                                        (0.0239) **
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Number of Observations       79,927         79,927         79,927
R square                     0.436          0.437          0.436

Panel B. Main Regression Model with Local Controls

Cath                         -1.000
                           (0.036) *
Prot                                        1.534
                                         (0.007) ***
Cpratio                                                    -0.064
                                                          (0.128)
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Local Controls                Yes            Yes            Yes
Number of Observations       79,927         79,927         79,927
R square                     0.439          0.439          0.439

                              (4)            (5)            (6)

Dependent Variable:                      Dividend Yield

Panel A. Main Regression Model

Cath                         -0.005
                          (0.0086) **
Prot                                        0.010
                                          (0.000) **
Cpratio                                                    -0.000
                                                         (0.004) **
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Number of Observations       79843          79,843         79,843
R square                     0.272          0.273          0.272

Panel B. Main Regression Model with Local Controls

Cath                         -0.005
                           (0.012) *
Prot                                        0.009
                                         (0.000) ***
Cpratio                                                    -0.000
                                                        (0.0047) ***
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Local Controls                Yes            Yes            Yes
Number of Observations       79.843         79,843         79,843
R square                     0.274          0.275          0.274

                              (7)            (8)            (9)

Dependent Variable:                    Dividend Initiation

Panel A. Main Regression Model

Cath                         -0.134
                            (0.791)
Prot                                        1.558
                                          (0.007) **
Cpratio                                                    0.018
                                                          (0.661)
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Number of Observations       52,250         52,250         52,250
R square                     0.110          0.111          0.110

Panel B. Main Regression Model with Local Controls

Cath                         -0.261
                            (0.641)
Prot                                        1.579
                                          (0.015) *
Cpratio                                                    0.008
                                                          (0.861)
Main Controls                 Yes            Yes            Yes
Lottery Index                 Yes            Yes            Yes
Local Unemp. Rate             Yes            Yes            Yes
Local Controls                Yes            Yes            Yes
Number of Observations       52,250         52,250         52,250
R square                     0.111          0.112          0.111

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table IX. Dividend Payout and Religious Affiliation with an
Alternative Set of Control Variables

The dependent variable for Columns 1-3 is Dividend Payer, while the
dependent variable for Columns 4-6 is Dividend Yield. Columns 1-3 are
Logit regression. Columns 4-6 are OLS regressions. The dependent
variable definitions are provided in Table II. The control variables
are NYE, M-B, ROA, and Sales Growth. NYE is a measure of firm size
based on the NYSE equity percentiles for the corresponding period. M-
B is the ratio of the market value of assets to the book value of
assets. The market value of assets is measured as the market value of
equity plus total assets minus total equity in the M-B definition. ROA
is the return on assets as measured by income before depreciation
divided by total assets for a given year. Sales Growth is the growth
rate of sales calculated by using the current and previous year
figures. County-level local control variables include Local Seniors,
Income, Median House Value, Education, and Log of Population. Local
control variable definitions are provided in Table III. All of the
tests include state, industry, and year dummy variables. Intercept,
state, industry, and year dummy variables are not displayed for
brevity. Standard errors are adjusted for heteroskedasticity and
clustered at firm level. Robust p-values are in parentheses.

                              (1)            (2)            (3)

Dependent Variable:                     Dividend Payer

Cath                      -1.146
                          (0.011) **
Prot                                      2.496
                                         (0.000) ***
Cpratio                                                 -0.085
                                                        (0.027) **
NYE                        0.037          0.037          0.037
                          (0.000) ***    (0.000) ***    (0.000) ***
M/B                       -0.375         -0.373         -0.374
                          (0.000) ***    (0.000) ***    (0.000) ***
ROA                        5.704          5.672          5.701
                          (0.000) ***    (0.000) ***    (0.000) ***
Sales Growth              -1.055         -1.047         -1.053
                          (0.000) ***    (0.000) ***    (0.000) ***
State, Industry,              Yes            Yes            Yes
and Year Indicators
Local Controls                Yes            Yes            Yes
Number of Observations       79,722         79,722         79,722
R square                     0.336          0.337          0.336

                              (4)            (5)            (6)

Dependent Variable:                     Dividend Yield

Cath                      -1.044
                          (0.035)**
Prot                                      1.970
                                         (0.000)***
Cpratio                                                 -0.073
                                                        (0.072) *
NYE                        0.038          0.038          0.038
                          (0.000) ***    (0.000) ***    (0.000) ***
M/B                       -0.369         -0.368         -0.369
                          (0.000) ***    (0.000) ***    (0.000) ***
ROA                        5.599          5.581          5.595
                          (0.000) ***    (0.000) ***    (0.000) ***
Sales Growth              -1.051         -1.045         -1.050
                          (0.000) ***    (0.000) ***    (0.000) ***
State, Industry,              Yes            Yes            Yes
and Year Indicators
Local Controls                Yes            Yes            Yes
Number of Observations       79,722         79,722         79,722
R square                     0.340          0.340          0.340

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.

Table X. Local CEOs, Dividend Payout, and Religious Affiliation

The dependent variable for Columns 1-3 is Dividend Payer, while the
dependent variable for Columns 4-6 is Dividend Yield. The dependent
variable for Columns 7-9 is Dividend Initiation. Columns 1-3 and
Columns 7-9 are Logit regressions, while Columns 4-6 are OLS
regressions. Local CEO is an indicator variable that takes a value of
one if the CEO is a local CEO and zero otherwise. This table uses all
of the other variables in the main tests reported in Table II. All of
the other variable definitions are provided in Table II. All of the
tests include the following firm age group indicator variables: Age 1-
5, Age 6-10, Age 11-15, and Age 16-20. Age 21 and Over is the dropped
category in the regressions. All of the tests include state, industry,
and year dummy variables. In this table, only the local religious
affiliation variables are displayed for brevity. Standard errors are
adjusted for heteroskedasticity and clustered at firm level. Robust p-
values are in parentheses.

                              (1)            (2)            (3)

Dependent Variable:                      Dividend Payer

Cath                         -2.875
                          (0.023) **
Prot                                        3.646
                                          (0.029) **
Cpratio                                                    -0.136
                                                          (0.326)
Local CEO                     Yes            Yes            Yes
Main Controls                 Yes            Yes            Yes
Number of Observations       8,456          8,456          8,456
R square                     0.408          0.408          0.406

                              (4)            (5)            (6)

Dependent Variable:                      Dividend Yield

Cath                         -0.010
                           (0.031) **
Prot                                        0.009
                                           (0.121)
Cpratio                                                   -0.0008
                                                         (0.022) **
Local CEO                     Yes            Yes            Yes
Main Controls                 Yes            Yes            Yes
Number of Observations       8,452          8,452          8,452
R square                     0.399          0.398          0.399

                              (7)            (8)            (9)

Dependent Variable:                    Dividend Initiation

Cath                         -1.975
                            (0.202)
Prot                                        2.622
                                           (0.215)
Cpratio                                                    -0.084
                                                          (0.500)
Local CEO                     Yes            Yes            Yes
Main Controls                 Yes            Yes            Yes
Number of Observations       3,208          3,208          3,208
R square                     0.199          0.199          0.198

*** Significant at the 0.01 level.

** Significant at the 0.05 level.

* Significant at the 0.10 level.
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Date:Mar 22, 2016
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