Corporate social responsibility, local seniors, and

Corporate social responsibility, local seniors, and corporate

dividend policy*

First draft: July 20, 2019

This draft: March 20, 2020

Xiang Dai, [email protected]

College of Business and Economics, Australian National University

Canberra, ACT, Australia

Jin Roc Lv**, [email protected]



Emma Schultz, [email protected]



* We would like to thank Tao Chen, Meijun Qian, Michael Weisbach, Qiaoqiao Zhu, and seminar participants at the Australian National University and the 2019 New Zealand Finance Meeting for their helpful comments and suggestions. We also gratefully acknowledge financial support received from the Australian Research Council [DP160103037].

** Corresponding author.

mailto:[email protected]



1

Corporate social responsibility, local seniors, and corporate

dividend policy

Abstract

The dividend irrelevance theorem predicts that a firm’s dividend policy is irrelevant to firm value

in a frictionless economy. Dividend clienteles create a friction that induces firms to pay dividends.

Existing literature documents that firms pay dividends in response to local seniors’ dividend

demand. However, the motivation for a firm to meet local seniors’ dividend demand is unclear.

We conjecture that a firm’s goodwill toward local seniors motivates the firm to pay dividends.

Consistent with our conjecture, we find that the response to local seniors’ dividend demand among

high-corporate social responsibility (CSR) firms is 34.0% greater than in low-CSR firms. The

positive relation between a firm’s CSR and its response to local seniors’ dividend demand is robust

to matched sample, instrumental variable, and alternative measurement analyses. Supplement tests

show that lower investor turnovers stemming from local seniors are unlikely to be a motivation for

dividend payment.

JEL Classification: G32; G35; M14; M41

Keywords: Corporate social responsibility (CSR); Local seniors; Dividends; Home bias

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1. Introduction

Ever since Miller and Modigliani (1961) first showed that a firm’s dividend policy is

irrelevant to firm value in a frictionless economy (dividend irrelevance theorem), financial

economists have puzzled over what frictions induce firms to pay dividends and create the cross-

sectional variations in corporate dividend policy in reality. Previous studies have investigated the

effects of various frictions on dividend policy, such as taxes, agency relationships, and information

asymmetry.1 Compared to other frictions, the investors’ preference of dividends relative to capital

gains is less investigated. A group of investors who share a similar preference for dividends form

a dividend clientele. The dividend demand hypothesis predicts that firms adjust their dividend

policy to satisfy dividend clienteles. To test this hypothesis, Becker et al. (2011) exploit the

demographic variation in the fraction of seniors, namely, the residents who are 65 years old or

older, across counties in the US and use the fraction of local seniors (Local Senior) to measure

dividend preference of local investors in a county. Consistent with the dividend demand hypothesis,

they find that firms headquartered in counties with greater Local Senior are more likely to pay

dividends and pay more dividends. However, the motivation for a firm to meet local seniors’

dividend demand is still unclear.

We conjecture that a firm’s goodwill toward local seniors motivates the firm to respond to

local seniors’ dividend demand. However, it is empirically difficult to measure a firm’s goodwill.

We propose to use a firm’s corporate socially responsibility (CSR) performance as a proxy for its

goodwill towards local seniors and hypothesize that in high-CSR firms, which are more engaged

in CSR activities, the effect of local seniors on dividend payment is stronger than in low-CSR

1 Allen and Michaely (2003), Kalay and Lemmon (2008), and DeAngelo et al. (2009) provide a comprehensive list

of studies in this literature.

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firms. Empirically, we use the MSCI ESG KLD STATS2 dataset to compute the adjusted CSR

score (CSR Adj) for each firm (e.g., Deng et al. 2013; Servaes and Tamayo 2013; Bereskin et al.

2018; Cao et al. 2019). High Adj = 1 identifies a high-CSR firm whose CSR Adj is in the highest

quartile in a year. Then we regress a firm’s dividend payment on Local Senior and the interaction

between Local Senior and High Adj. A positive coefficient of this interaction term will provide

empirical support for our conjecture that a firm’s goodwill toward local seniors motivates the firm

to pay dividends.

We use a sample of 36,173 firm-year observations in the US during 1991 – 2016 to test our

central hypothesis. In our baseline regression, both coefficients of Local Senior and the interaction

between Local Senior and High Adj are positive (0.456 and 0.155) and statically significant. While

the former confirms the dividend demand theory (Becker et al. 2011), the latter is consistent with

our central hypothesis that in high-CSR firms, the effect of local seniors on dividend payment is

stronger. Economically, the response to local seniors’ dividend demand among firms in the highest

CSR Adj quartile (High Adj = 1) is 34.0% (= 0.155/0.456 * 100%) greater than its counterpart

among firms in the lowest three quartiles (High Adj = 0).

We show that the positive relation between a firm’s CSR and its response to local seniors’

dividend demand is robust to a battery of additional tests. First, we conduct matched sample

analyses to address the selection bias problem. We use two methods to create the matched sample,

propensity score matching (PSM) and firm size and market-to-book ratio (MB) matching. Second,

we conduct instrumental variable (IV) analyses to alleviate the concern of potential endogeneity.

It is possible that some omitted variables of firm characteristics, such as financial constraints, lead

to both worse CSR performance and a lower effect of local seniors on dividend payment. We

2 Formerly KLD. MSCI acquired KLD in 2010.

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exploit the staggered adoption of universal demand (UD) laws at the state-level in the US to design

an IV for CSR performance. Third, we repeat our baseline regressions with alternative measures

of dividend payment and CSR performance. In all these robustness tests, the relation between a

firm’s CSR and its response to local seniors’ dividend demand remains positive, underscoring our

conjecture that a firm’s goodwill toward local seniors motivates the firm to pay dividends.

In supplemental tests, we first show that when firms have higher CSR performance in the

dimensions concerning stakeholders outside the firms, such as environment, community, human

rights, and corporate governance, the effect of local seniors on dividend payment is stronger. On

the other hand, higher CSR performance in the dimensions concerning stakeholders inside the

firms, such as employees, diversity, and product safety and quality, have a marginal impact on or

even weaken the local dividend clientele effect. This finding is consistent with the notion that local

seniors, as the firm outsiders, have a stronger effect on the firm dividend policy if the firm are

more responsible to outsiders.

Second, we investigate an alternative motivation for a firm’s response to local seniors’

dividend demand. Becker et al. (2011) document that holding periods of local senior investors is

longer than that of the other investors and suggest that one motivation for firms to respond to local

seniors’ demand for dividends is the benefit from lower investor turnovers. We examine two

channels through which firms might benefit from lower investor turnovers: investments in research

and development (R&D) and mergers and acquisitions (M&As). Both investments in R&D and

M&As might cause firms to sacrifice short-term earnings for better performance in a long run. If

the motivation for a firm’s response to local seniors’ dividend demand is the benefit from lower

investor turnovers, we predict that firms that expose to a great dividend demand from local seniors

and meet their demand invest more in R&D and/or M&As. However, our empirical results

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demonstrate a negative relation between dividend payment among firms that expose to a great

dividend demand and the investments in R&D and M&As, inconsistent with the notion that firms

respond to local seniors’ dividend demand in return for lower investor turnovers. Although we

cannot rule out all the benefits that dividend-paying firms could obtain from lower investor

turnovers, our empirical results on the R&D and M&A investments reinforces our argument that

a firm’s response to local seniors’ dividend demand stems from the firm’s CSR.

Our study contributes to the literature in three important ways. First, we lend support to the

dividend demand hypothesis. The dividend demand hypothesis predicts that firms pay dividends

to meet investors’ demand for dividends. Miller and Modigliani (1961) point out that a group of

investors who share a similar preference for dividends form a dividend clientele. Graham and

Kumar (2006) further conjecture that divided clienteles could affect firms’ financial and

managerial decisions. Becker et al. (2011) exploit the demographic variation in the fraction of local

seniors across counties in the US to test the dividend demand hypothesis and show that firms

headquartered in counties with greater factions of local seniors pay more dividends. We further

identify the goodwill toward local seniors as a motivation for firms to respond to the local seniors’

demand for dividends, providing additional support for the dividend demand hypothesis. More

broadly, our empirical results that support the dividend demand hypothesis also help to explain

why firms pay dividends and the cross-sectional variation in corporate dividend policy among

firms.

Second, we add to the research on CSR. Previous studies conduct economic analyses on

the outcomes of a firm’s CSR activities. For example, CSR activities can promote firms’ market

valuations (Clarkson et al. 2004; Servaes and Tamayo 2013; Albuquerque et al. 2018), increase

merger returns (Deng et al. 2013), and reduce the stock price crash risk (Kim et al. 2014).

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Complementing previous studies on economic outcomes, we document the influence of a firm’s

CSR on its managerial decisions. Specifically, we highlight the effect of CSR toward a particular

stakeholder in the community, local seniors, and through local seniors, a firm’s CSR increases its

dividend payment. Moreover, our empirical findings evidence the costs of CSR activities: firms

that meet local seniors’ dividend demand invest less in R&D and M&As.

Third, we contribute to the growing literature on the local seniors’ investment behaviors

and their impacts. Becker (2007) show that because seniors prefer low-risk investments, the high

fractions of seniors in a county generate higher volumes of bank deposits, which in turn promote

the number of firms in the same county, particularly the numbers of manufacturing firms and new

firms. Lv et al. (2020) argue that lower turnovers caused by local senior investors result in lower

benefits from earnings management, so firms that face higher fractions of local seniors manage

earnings less. Becker et al. (2011) demonstrate that local seniors, due to their preferences for

dividends, increase the likelihood that firms in the local areas pay dividends. We further show that

the same fractions of local seniors may not have the same impacts on local firms. The impacts of

local seniors on local firms vary depending on the local firms’ CSR, i.e., how much local firms

feel goodwill towards seniors in their communities.

The remainder of the paper is organized as follows: Section 2 presents hypothesis

development; Section 3 describes our empirical method; Section 4 explains our baseline results on

the effect of a firm’s CSR on its response to local seniors’ dividend demand; Section 5

demonstrates the robustness of our main finding; Section 6 discusses the results of supplemental

tests; and Section 7 concludes.

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2. Related literature and hypothesis development

2.1. Dividend irrelevance theorem and dividend clienteles

Miller and Modigliani (1961) show that a firm’s dividend policy is irrelevant to firm value

in a frictionless economy. Firm value is determined by investments, and investments and dividends

are independent and separable. Investors can create “homemade” dividends through appropriate

purchases and sales of equity, so they should have no preference between cash dividends and

capital gains and should not pay a premium for a firm with particular dividend policy. Therefore,

“homemade” dividends should not affect a firm’s investment policy or the firm value. However,

no economy is frictionless. Existing literature has investigated the effects of various frictions on

dividend policy, such as taxes, agency relationships, and information asymmetry.

Compared to other frictions, the investors’ preference of dividends relative to capital gains

is less investigated. In their original paper that develops the dividend irrelevance theorem, Miller

and Modigliani (1961) have also suggested a two-sided matching process between firms, which

set dividend policy, and investors with different preferences for dividends in the imperfect market.

A group of investors who share a similar preference for dividends form a dividend clientele.

Graham and Kumar (2006) provide direct evidence for retail investor dividend clienteles. They

use a panel data of a sample of retail investors to examine the individual retail investors’ portfolio

choices and trading behaviors. They find that cross-sectionally older and low-income investors

prefer dividend paying stocks, suggesting the age- and tax- induced retail investor dividend

clienteles. Moreover, the age-induced clientele is stronger than the tax-induced clientele.

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2.2. Local senior dividend clientele and corporate dividend policy

In addition to providing evidence for dividend clienteles, Graham and Kumar (2006)

further conjecture that divided clienteles could affect firms’ financial and managerial decisions.

Becker et al. (2011) investigate the effect of dividend clienteles on corporate dividend policy. An

empirical difficulty to identify the dividend clientele effect lies in the measurement of dividend

clienteles in individual firms. The identification strategy in Becker et al. (2011) is based on two

notions. First, seniors prefer dividend paying stocks (Miller and Modigliani 1961; Shefrin and

Thaler 1988; Graham and Kumar 2006). Second, investors prefer equity in local firms within a

domestic market (Coval and Moskowitz 1999, 2001; Grinblatt and Keloharju 2001; Huberman

2001; Ivkovic and Weisbenner 2005; Massa and Simonov 2006). These two notions, taken together,

induce a local dividend clientele that in a county with a higher fraction of senior residents, who

are 65 years old or older, investors overall have a stronger preference for dividends.

Becker et al. (2011) find that firms headquartered in counties with higher fraction of seniors

are more likely to pay dividends and pay higher dividends. This finding provides evidence for the

effect of local senior dividend clientele on corporate dividend policy, which is referred to the

dividend demand hypothesis. Nevertheless, the motivation for a firm to meet local seniors’

dividend demand is still unclear. One of the most straightforward explanations for a firm to satisfy

local seniors is the firm’s goodwill towards local seniors. However, it is empirically difficult to

measure a firm’s goodwill towards local seniors and to investigate the interplay among firms’

goodwill, local seniors, and corporate dividend policy.

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2.3. Corporate social responsibility, local seniors, and corporate dividend policy

We propose to use a firm’s CSR to proxy for its goodwill towards local seniors and

investigate whether a firm’s CSR affects its response to the local seniors’ demand for dividends.

Previous studies offer two explanations for the firms’ CSR activities: shareholder expense and

shareholder wealth maximization (Deng et al. 2013). According to the shareholder expense

explanation, firms engage in CSR activities at the expense of shareholders, so managers gain

reputation or popularity from other firm stakeholders, such as government, politicians, employees,

and universities (e.g., Garriga and Mele 2004; Pagano and Volpin 2005; Cespa and Cestone 2007;

Surroca and Tribo 2008; Cronqvist et al. 2009; Di Giuli and Kostovetsky 2014). The stakeholder

value maximization explanation suggests that implicit contracts exist between firms and

stakeholders, who supply critical resources or effort for firm development. A firm’s CSR activities

that benefit stakeholders motivate stakeholders to contribute more to the firm and eventually

increase the firm value.3 Previous studies demonstrate that CSR activities not only increase firms’

market valuations (Clarkson et al. 2004; Edmans 2011; Servaes and Tamayo 2013; Albuquerque

et al. 2018) and merger returns (Deng et al. 2013), but also reduce the stock price crash risk (Kim

et al. 2014) and the cost of capital (Dhaliwal et al. 2011; El Ghoul et al. 2011).

The two explanations for CSR activities are not mutually exclusive. It is possible that firms

engage in CSR activities at the expense of shareholders in short terms but enjoy other stakeholders’

contributions in long terms. Moreover, both explanations lead to the firms’ goodwill towards local

seniors, regardless of local seniors’ contribution to firm development. If in a county with a high

fraction of local seniors no firm pays dividends, local seniors have to either search for equity

investment opportunities outside their local areas, which will incur the searching cost (Coval and

3 We remark that the stakeholders who benefit from the CSR activities and the stakeholders who facilitate the firm

value increase are not necessarily the same.

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Moskowitz 1999, 2001; Ivkovic and Weisbenner 2005), or trade stocks without dividends, which

will induce self-control cost (Thaler and Shefrin 1981; Shefrin and Thaler 1988; Graham and

Kumar 2006). Considering these costs, firms that are more socially responsible and feel more

goodwill toward local seniors, namely, high-CSR firms, will be more responsive to their demand

for dividends. Therefore, we propose our central hypothesis as follows:

Hypothesis: The effect of local seniors on dividend payment is stronger in the high-CSR

firms compared to low-CSR firms.

3. Empirical method

3.1. Research design

To empirically examine how a firm’s CSR affects its response to the dividend demand

from local seniors, we estimate the following regression model.

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖 ,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐿𝑜𝑐𝑎𝑙 𝑆𝑒𝑛𝑖𝑜𝑟𝑖,𝑡 + 𝛽2 ∗ 𝐿𝑜𝑐𝑎𝑙 𝑆𝑒𝑛𝑖𝑜𝑟𝑖,𝑡 ∗ 𝐻𝑖𝑔ℎ 𝐶𝑆𝑅𝑖,𝑡

+ ∑ 𝛾𝑗 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡,𝑗

𝐽

𝑗=1

+ 𝜀𝑖,𝑡

(1)

where Dividend represents dividend payment in a firm. In our baseline regressions, Dividend is

Payer, which is a dummy variable equal to one if a firm pays dividends in a year. In a robustness

test, Dividend is Yield, which is the total dividend paid by a firm in a year, in a percentage of the

market value. The dividend measure is constructed at the end of a fiscal year. Local Senior is the

fraction of residents who are 65 years old or older in the county in which the firm is headquartered.

The interaction term, Local Senior * High CSR, is the variable of interest. High CSR is a dummy

variable equity to one if a firm’s CSR score is in the highest quartile in a year. Our central

hypothesis predicts that 𝛽2 > 0.

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We follow previous studies (e.g., Deng et al. 2013; Servaes and Tamayo 2013; Bereskin et

al. 2018; Cao et al. 2019) to compute the adjusted CSR score (CSR Adj). MSCI evaluates a firm’s

social performance along seven major dimensions: environment (ENV), community (COM),

human rights (HUM), employee relations (EMP), diversity (DIV), product safety and quality

(PRO), and corporate governance (CGOV). In each dimension, MSCI identifies the firm’s

strengths and concerns. The firm gains (loses) one point for each strength (concern) indicator. In

each demission, the raw (adjusted) CSR score is equal to the raw (adjusted) strength score minus

the raw (adjusted) concerns score. While the raw strength (concern) score is equal to the number

of indicators, the adjusted strength (concern) score is equal to the raw CSR strength (concern)

score divided by the number of strength (concern) indicators. CSR Raw (CSR Adj) is equal to the

sum of raw (adjusted) CSR scores in all seven dimensions. In our baseline regression, we use CSR

Adj. In robustness tests, we also use CSR Raw and the adjusted CSR score in each of the seven

dimensions.

Furthermore, we include such control variables as Ln(Assets), Ln(1 + Age), ROA, Cash,

MB, Leverage, Inst. Holdings, Lagged Return, and Volatility. Most of them, except Inst. Holdings,

have been used to explain dividend payment in Becker et al. (2011). However, the variable

definitions are not exactly the same in our study and in Becker et al. (2011). For example, to

mitigate potential problems caused by autocorrelations, we avoid to use information more than

one years before a fiscal year end when constructing control variables. Therefore, we use the daily

returns within one year before a fiscal year end to construct Lagged Return and Volatility. 4

Institutional investors play a monitoring role in the firm’s operation (Edmans 2009; Edmans and

Manso 2011; McCahery et al. 2016), which will in turn affect the firm’s dividend decision;

4 Becker et al. (2011) construct Lagged Return and Volatility using monthly returns during two years before the

fiscal year end when the dividend measure is constructed.

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therefore, we include Inst. Holdings as a control variable. More details on the variable definitions

are in Appendix A. In addition, we include the year and industry fixed effects in all regressions,

and the industry fixed effects are based on two-digit Standard Industry Classification (SIC) codes.

3.2. Sample and variables

First, we sample all firms in Compustat from 1991-2016. In this study, we focus on public

firms, so we eliminate firm-year observations when a firm does not appear in the CRSP database.

Second, to construct our key independent variable, Local Senior, we obtain the population data at

the county level from the US Census Bureau. We use the firm zip code in Compustat to identify

the county where the firm is headquartered. Firm-year observations without valid US zip codes

contain missing values of Local Senior and are excluded from our sample. Third, we require

observations to have data available in the MSCI database. Finally, our sample consists 36,173

firm-years in 7,434 county-years.

Table 1 presents statistics for county-level variables. During our sample period of 1991 –

2016, the numbers of counties and firms in a year both demonstrate increasing trends, mainly

because the coverage of MSCI increases over time. In particular, both numbers exhibit jumps in

the years of 2001 and 2003, consistent with Albuquerque et al. (2018).5 The variable of Local

Senior also increases over time because the aging population in the US increases over time (e.g.,

Poterba 2014; Maestas et al. 2016). For the same reason, the mean and median of Local Senior in

this study (0.131 and 0.123) are greater than those (0.116 and 0.115) in Becker et al. (2011).

[Place Table 1 here]

5 In robustness tests (untabulated), we partition our sample into two subsamples of pre-2002 and post-2003

(inclusive) periods and repeat the regressions on dividends. All results based on the two subsamples remain

qualitatively similar to those based on the full sample.

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Table 2 presents statistics for firm-level variables. The mean and median of Local Senior

(0.121 and 0.122) are greater than their counterparts at the county level, suggesting that on average

firms are more likely to be headquartered in counties with lower Local Senior, consistent with

Maestas et al. (2016) who document a negative relationship between aging population and the

growth rate of gross domestic product (GDP) per capita. The medians of CSR Adj and CSR Raw

are -0.075 and 0.000, comparable to the statistics (-0.083 and 0.000) in Deng et al. (2013). CSR

Dimension, where Dimension = ENV, COM, HUM, EMP, DIV, PRO, or CGOV, is the adjusted

CSR score in a particular dimension. The medians and 75th percentiles of these adjusted CSR

scores in a dimension are equal to zero, suggesting the adjusted CSR scores in a dimension are

equal to zero in at least a quarter of the observations in our sample, consistent with the statistics in

Cao et al. (2019). Nevertheless, CSR Adj, CSR Raw, and the adjusted CSR scores in different

dimensions are highly correlated with each other. Appendix B provides correlations of all CSR

measures used in empirical analyses. The correlations between any two of the CSR measures,

expect the one between CSR HUM and CSR DIV, are positive and statistically significant.


4. Baseline results

To test our central hypothesis, we regress Payer on Local Seniors, Local Seniors * High

Adj, and control variables, and Table 3 reports the associated regression results. In Columns (1) –

(3), we estimate ordinary least squares (OLS) models, while in Columns (4) – (6), we estimate

Probit models. The results from the OLS models and from the Probit models are qualitatively

similar. In Columns (1) and (4), we include Local Senior plus control variables. The coefficient of

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Local Senior is positive and statistically significant, confirming the local dividend clienteles

documented in Becker et al. (2011). In Columns (2) and (5), we include both Local Senior and its

interaction with High Adj. The coefficient of Local Senior is still positive and statistically

significant; however, its magnitude becomes smaller compared to Columns (1) and (4). The

coefficient of Local Senior * High Adj is positive and statistically significant, suggesting that a

firm’s CSR increases its response to the dividend demand from local seniors. Economically, the

coefficient of 0.155 in Column (2) means the response to local seniors’ dividend demand among

firms in the highest CSR Adj quartile (High Adj = 1) is 34.0% (= 0.155/0.456 * 100%) greater than

its counterpart among firms in the lowest three quartiles (High Adj = 0). In Columns (3) and

Column (6), we further add CSR Adj to the regressions. The coefficient of CSR Adj is positive but

statistically insignificant. By construction, CSR Adj and Local Seniors * High Adj are highly

correlated, so the coefficient of Local Seniors * High Adj in Columns (3) and (6) becomes smaller

compared to Columns (2) and Column (5). Nevertheless, the coefficient of Local Seniors * High

Adj is still positive and statistically significant. Therefore, we conclude that the firms’ CSR is a

main reason for a firm’s response to the dividend demand from local seniors.


The coefficients of all other variables in Table 3 are consistent with the intuition and

previous studies. For example, the coefficients of Ln(Assets) and Ln(1 + Age) are significantly

positively, suggesting that larger and more mature firms are more likely to pay dividends. The

coefficient of ROA is significantly positive, consistent with Jensen et al. (1992) who document a

positive relation between profitability and dividend payout. Firms that hold more cash are less

likely to pay dividends, so the coefficient of Cash is negative. The coefficient of MB is significantly

15

negative in the Probit models, suggesting that firms with higher growth opportunities are less likely

to pay dividends (Fama and French 2001; Becker et al. 2011). The coefficient of Inst. Holding is

significantly negative, consistent with Rubin and Smith (2009) and Chang et al. (2016). Finally,

the coefficients of Lagged Return and Volatility are both significantly negative. Lagged Return

proxies for the growth rate in the past, and the negative relationship between growth and dividend

payment agrees with previous studies (e.g., Rozeff 1982; Holder et al. 1998; Becker et al. 2011).

A greater Volatility suggests a higher uncertainty on the future earnings, leading to the lower

propensity that a firm pays dividends (Becker et al. 2011; Chang et al. 2016).

[Insert Table 3 here]

5. Robustness tests

5.1. Matched sample analyses

Our conclusion regarding the effect of a firm’s CSR on its response to local seniors’

dividend demand and its dividend policy suffers from potential selection biases. The dummy

variable High CSR Adj splits the sample into treatment and control groups, with High CSR Adj =

1 representing the treatment group. By construction, the number of firms with adjusted CSR scores

in the highest quartile is much smaller than that in the lowest three quartiles. We compare the effect

of Local Senior on Payer in these two groups of firms to identify the positive relation between a

firm’s CSR and its response to the dividend demand. However, any conclusion based on the

comparison of two groups with unbalanced sample sizes is very sensitive to the outliers in the

smaller group. To mitigate the effect of this section bias, we conduct matched sample analyses to

evaluate the effect of a firm’s CSR on its response to the dividend demand.

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We use two methods to create the matched sample. First, we employ PSM and calculate

propensity scores with the following Logit model:

𝐻𝑖𝑔ℎ 𝐴𝑑𝑗 = 𝛽0 + 𝛽1 ∗ 𝐿𝑜𝑐𝑎𝑙 𝑆𝑒𝑛𝑖𝑜𝑟 + ∑ 𝛾𝑗 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑗

𝐽

𝑗=1

+ 𝜀 (2)

where we include the same set of control variables used in Table 3. The sample used to estimate

the Logit model consists of firms with the adjusted CSR scores in the highest (treatment group)

and the lowest two (control group) quartiles. The Logit model produces a propensity score for each

observation in the sample. Based on propensity scores, we create a one-to-one match for each

observation in the treatment group without replacement. As a result, the sample based on PSM

consists of 16,612 observations.

Second, we create a matched sample based on the firm size and MB. For each observation

in the treatment group, we search for its matching candidates in the same year and in the same

industry from the control group. We require matching candidates’ sizes (the book value of total

assets) to be within the range of 75 – 125% of the treatment observation. Next, we choose the firm

that has the closest MB with the treatment observation that meets the size requirement (one-to-one

match). Due to the positive relation between firm size and CSR scores (e.g., Dhaliwal et al. 2011;

Benlemlih and Bitar 2018; Bae et al. 2019), we cannot find matches for some large firms in the

treatment group. As a result, the sample based on such a size-MB matching process consists of

11,454 observations.6 Table 4 reports the regression results on the matched sample analysis.

Regardless of the matching method, the coefficient of Local Senior * High Adj is always positive

6 In robustness tests (untabulated), we change the matching process by searching for candidates whose MBs are within

the range of 75 – 125% of the treatment observation first. Such a MB-size matching process generates a greater sample

size (15,402 observations) than the size-MB matching process. However, the regression results are qualitatively similar

regardless of the matching process.

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and statistically significant, highlighting the effect of a firm’s CSR on its response to local seniors’

dividend demand after controlling for the potential selection bias.


5.2. Instrumental variable analysis

In addition to selection biases, endogeneity is another concern that might affect the

conclusion regarding the effect of a firm’s CSR on its response to local seniors’ dividend demand.

The difference in the response to local seniors’ dividend demand between high CSR firms and low

CSR firms could stem from omitted variables of firm characteristics. For example, financially

constrained firms have significantly lower payout ratios (e.g., Fazzari et al. 1988; Almeida et al.

2004; Whited and Wu 2006; Almeida and Campello 2010), exhibiting a lower response to local

seniors’ dividend demand; meanwhile, a firm’s financial constraints reduce its investment in CSR

activities (Goetz 2018). These two effects of financial constraints generate the difference in the

response to local seniors’ dividend demand between high CSR and low CSR firms, which,

however, does not reflect the effect of a firm’s CSR on its dividend response.

To alleviate the endogeneity concern, we exploit the staggered adoption of UD laws at the

state-level in the US to design IV regressions. In the US, derivation actions allow shareholders to

initiate lawsuits against managers on behalf of a corporation. UD laws impose obstacles against

shareholders filing such derivative lawsuits and accordingly reduce firms’ litigation risk. Since

1989, 23 states and the District of Columbia have adopted UD laws (e.g., see Boone et al. 2018;

Bourveau et al. 2018; Li et al. 2018; Nguyen et al. 2018; Lin et al. 2019). Previous studies

document the effect of UD laws on various corporate operations, such as corporate disclosure

(Boone et al. 2018; Bourveau et al. 2018), corporate cash policy (Nguyen et al. 2018), R&D

18

expense (Lin et al. 2019), and selling, general, and administrative expense (Li et al. 2018). In

addition, Koh et al. (2014) suggest that firms use CSR activities as an insurance mechanism against

litigation risk. Taken together, we predict that under UD laws, firms face lower litigation risk and

are less engaged in CSR activities. On the other hand, the adoption of UD laws is unlikely to

directly affect a firm’s response to local seniors’ dividend demand. Therefore, we use the adoption

of UD laws as an IV for CSR scores and estimate the following IV regression model:

𝐶𝑆𝑅 𝐴𝑑𝑗𝑖,𝑡 = 𝛽0 + 𝛽1 ∗ 𝑈𝐷 𝐿𝑎𝑤𝑖,𝑡 + ∑ 𝑆𝑡𝑎𝑡𝑒𝑘

𝐾

𝑘=1

+ ∑ 𝛾𝑗 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡,𝑗 +

𝐽

𝑗=1

𝜀𝑖,𝑡 (3)

𝑃𝑟𝑒𝑑[𝐻𝑖𝑔ℎ 𝐴𝑑𝑗𝑖,𝑡] = {10

𝑖𝑓 𝑃𝑟𝑒𝑑[𝐶𝑆𝑅 𝐴𝑑𝑗𝑖,𝑡] > 𝑃𝑟𝑒𝑑[𝑃75]𝑡

𝑂𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 (4)

𝐷𝑖𝑣𝑖𝑑𝑒𝑛𝑑𝑖,𝑡 = 𝛽0 + 𝛽1 ∗ 𝐿𝑜𝑐𝑎𝑙 𝑆𝑒𝑛𝑖𝑜𝑟𝑖,𝑡 + 𝛽2 ∗ 𝐿𝑜𝑐𝑎𝑙 𝑆𝑒𝑛𝑖𝑜𝑟𝑖,𝑡

∗ 𝑃𝑟𝑒𝑑[𝐻𝑖𝑔ℎ 𝐴𝑑𝑗𝑖,𝑡] + ∑ 𝛾𝑗 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑖,𝑡,𝑗

𝐽

𝑗=1

+ 𝜀𝑖,𝑡

(5)

In the first-stage regression (Equation (3)), we employ a difference-in-differences method

to predict a firm’s adjusted CSR scores. UD Lawi,t is a dummy variable equal to one if a firm i is

incorporate in a state where a UD law is effective in year t. Statek controls for the fixed effect of

the state of incorporation. In addition, we include the same set of control variables used in Table

3. Column (1) in Table 5 reports the associated regression results. The coefficient of UD Law is

statistically negative at the significance level of 1%. This negative coefficient is consistent with

the notion that firms use CSR activities as an insurance mechanism against litigation risk,

suggesting that UD Law is a valid IV for CSR scores.

19


In the second-stage regression (Equation (5)), we replace High Adj in our baseline

regressions with the predicted High Adj from the first-stage regression (Pred[High Adj]). Equation

(4) presents a nonlinear relation between Pred[CSR Adj] from the first stage and Pred[High Adj]

used in the second stage. Pred[High Adj] = 1 when a firm’s Pred[CSR Adj] is greater than the 75th

percentile of Pred[CSR Adj] of all firms. Due to this nonlinear relation, we estimate the first-stage

and second-stage regressions separately. To correct the standard errors in the second-stage

regressions, we bootstrap observations 200 times and estimate bootstrap standard errors. Columns

(2) and (3) in Table 5 report the associated regression results.7 In both the OLS and Probit models,

the coefficient of Local Senior * High Adj is positive and statistically significant. Moreover, the

relative difference between the two coefficients of Local Senior * High Adj and Local Senior is

even greater in IV regressions than in baseline regressions, suggesting that omitted variables affect

a firm’s CSR score and its response to the dividend demand in opposite ways. Endogeneity creates

biases against us finding a positive relation between the two. To sum up, our conclusion regarding

the effect of a firm’s CSR on its response to local seniors’ dividend demand is robust to the control

for potential endogeneity.

5.3. Alternative measurements of dividends and CSR

We have used Payer and CSR Adj to establish the effect of a firm’s CSR on its response to

local seniors’ dividend demand. In this subsection, we use alternative measures of Payer and CSR

7 In robustness tests (untabulated), we estimate the OLS and Probit models without bootstrapping, and the coefficients

of all independent variables are almost the same to the reported coefficients. However, both the standard errors and

the associated p-values are much smaller.

20

Adj to evaluate this effect. First, we follow Becker et al. (2011) and use Yield, the total dividend

in the percentage of the market value, as an alternative for Payer. Table 6 reports the regression

results with Yield as the dependent variable. Due to the large number of firms that do not pay

dividends, we estimate Tobit models (Columns (2) and (4)) in addition to OLS models (Columns

1 and 3). In all four columns, the coefficients of Local Seniors and Local Seniors * High Adj are

positive, confirming both local dividend clienteles and the effect of a firm’s CSR on the dividend

response. However, these coefficients are not as significant as its counterparts in Table 3 and Table

4 based on the associated p-values, suggesting that local seniors affect the firm’s decision on

whether to pay dividends more than the dividend level (Becker et al. 2011). In addition, the

coefficient of Local Seniors * High Adj in the regressions using the full sample has smaller

magnitudes and greater p-values that that using the PSM sample. In the PSM sample, we exclude

firms with adjusted CSR scores in the second highest quartile. As a result, the difference in CSR

between firms with High Adj = 0 and High Adj = 1 becomes greater, and the regression analyses

are more likely to identify the difference in firms’ responses to local seniors’ dividend demand

between these two groups of firms.


Second, we follow Deng et al. (2013) and use CSR Raw, the raw CSR scores, as an

alternative for CSR Adj. Table 7 reports the associated regression results. A potential drawback in

the CSR Raw measure is lack of comparability across years and dimensions. The numbers of

strengthen and concern indicators vary significantly over time even in a dimension (Manescu

2011). However, this drawback should have a much smaller impact on the empirical results in our

study than others (e.g., Manescu 2011; Deng et al. 2013; Cao et al. 2019), due to our research

21

design that requires only a dummy variable indicating whether a firm have a high CSR score.

Indeed, we find in both columns of Table 7 that the coefficient of Local Seniors * High Raw is

positive and statistically significant, and its magnitudes and p-values are very close to those of the

coefficient of Local Seniors * High Adj in Table 3. The regression results in Table 6 and Table 7,

taken together, demonstrate that our conclusion regarding the effect of a firm’s CSR on its response

to local seniors’ dividend demand is robust to the alternative measures for both dividend and CSR.


6. Supplemental tests

6.1. CSR dimensions

We further explore the relation between adjusted CSR scores in seven individual

dimensions and firms’ responses to local seniors’ divided demand. The seven dimensions reflect

a firm’s CSR toward different stakeholders. We approximately classify these stakeholders into

outsiders and insiders of a firm. CSR activities in the dimensions of environment, community,

human rights, and corporate governance demonstrate a firm’s CSR toward outsiders, while CSR

activities in employees, diversity, and product safety and quality reflect a firm’s CSR toward

insiders.8

We conjecture that firms that demonstrate higher CSR toward outsiders are more likely to

feel goodwill toward local seniors and pay dividends to meet the local seniors’ need. To this end,

we predict that when a firm has a higher adjusted CSR score in the dimensions concerning

outsiders, the effect of Local Seniors on Payer is stronger. To test this prediction, we replace High

8 We acknowledge that such a classification is not accurate. For example, the CSR activities in the corporate

governance dimension could be perceived as a mechanism for mitigating the principal-agent conflict (Shleifer and

Vishny 1997; Cao et al. 2019). At the same time, the CSR activities in the corporate governance dimension include

controversial investments that cause negative social and environmental impacts.

22

Adj in our baseline regression in Column (2) of Table 3 with the adjusted CSR score in each of the

seven dimensions, High Dimension. Table 8 reports the associated regression results. Consistent

with our prediction, when High Dimension is constructed in the dimensions concerning outsiders

(i.e., environment, community, human rights, and corporate governance), the coefficient of Local

Seniors * High Dimension is positive and statistically significant. On the other hand, when High

Dimension is constructed in the dimensions concerning insiders (i.e., employees, diversity, and

product safety and quality), the coefficient of Local Seniors * High Dimension is insignificant or

even significantly negative. The different results using High Dimension based on outsider-

concerning and insider-concerning dimensions suggest a substitutive relationship between a firm’s

CSR toward outsiders and insiders. Such a substitutive relationship could be due to the firm’s

budget constraints on the CSR expense or the firm’s preference. In summary, the results in Table

8 demonstrate that when a firm exhibits higher CSR toward outsiders, its response to local seniors’

dividend demand is greater.


6.2. Alternative motivation for a firm’s response to local seniors’ dividend demand

Becker et al. (2011) document that holding periods of local senior investors is longer than

that of the other investors and suggest that one motivation for firms to respond to local seniors’

demand for dividends is the benefit from lower investor turnovers. In this subsection, we

investigate two channels through which firms might benefit from lower investor turnovers. The

first channel is R&D expense. Previous studies (e.g., Baber et al. 1991; Bushee 1998; Asker et al.

2014; Edmans et al. 2017) show that firms cut R&D expense to boost the short-term earnings/stock

prices. Firms with lower investor turnovers bear lower pressures to meet short-term earnings goals

23

and are engaged in more R&D activities. Indeed, Bushee (1998) and Harford et al. (2018)

document that lower investor turnovers lead to higher R&D expense and corporate innovations.

When firms that expose to a great dividend demand from local seniors and meet their demand

attract more local senior investors and enjoy lower turnovers, we predict that these firms invest

more in R&D. To test this prediction, we regress R&D expense on the dividend payment in a

sample of firms that expose to a great dividend demand from local seniors.

We construct the sample of firms that expose to a great local dividend demand in two ways.

First, we conjecture that firms with Local Senior in the highest quartile have pressure to pay

dividends to local seniors, and these firms form a high Local Senior sample. Second, we regress

Payer on Local Senior along with control variables and use the predicted Payer to identify whether

a firm is expected to meet local seniors’ dividend demand. The firms with predicted Payer equal

to one form the predicted Payer sample. We regress R&D expense on Payer in these two samples,

respectively. Table 9 reports the associated regression results. In all four columns, the coefficient

of Payer is negative and statistically significant, inconsistent with our prediction that firms that

meet local seniors’ demand for dividends invest more in R&D. Moreover, the negative relation

between Payer and R&D expense suggests that firms that meet local seniors’ demand for dividends

are subject to greater financial constraints and reduce R&D expense.


In addition to the channel of R&D expense, we investigate whether firms benefit from

lower investor turnovers through M&As. M&As are among the largest investments in a firm

(Betton et al. 2008). M&As provide firm with a unique opportunity for fast and inorganic growth

(e.g., Alexandridis et al. 2017; Renneboog and Vansteenkiste 2018). However, compared to

24

organic growth, inorganic growth adds greater risk to firms and might upset a firm’s short-term

earnings. Accordingly, short-term shareholders are more tolerant toward inorganic growth and

growth risk than long-term (low-turnover) shareholder. Therefore, we predict that firms that (1)

expose to a great dividend demand from local seniors and meet the demand are more likely to

conduct M&As, and (2) the uncertainty of their M&A performance is greater.

To test these predictions, we develop three dependent variables on M&As for regression

analyses. First, M&A Dummy, which is equal to one if a firm conducts an M&A during a year,

captures the likelihood of M&As. The second (third) measure, Low CAR (High CAR), is a dummy

variable equal to one if a firm conducts an M&A whose three-day announcement return is smaller

(greater) than the 10th (90th) percentile of all M&A announcement returns in a year.9 Low CAR

and High CAR together capture the M&A performance uncertainty. We regress these three

variables on dividend payment in a sample of firms that expose to a great dividend demand from

local seniors. A positive relation between dividend payment and M&A Dummy (Low CAR or High

CAR) will provide empirical evidence for our first (second) prediction on M&As.

We use the Thomson Financial Securities Data Corporation (SDC) Mergers and

Acquisitions database to identify an initial sample of M&As. We follow the M&A literature (e.g.,

Dhaliwal et al. 2016; Eckbo et al. 2018) and require a minimum deal size (relative size) of $10

million (1% of the acquirer’s total book assets).10 Table 10 reports the regression results of Probit

models on M&As. Similar to Table 9, we estimate all the regression models on M&As using both

the high Local Senior sample (Columns (1) – (3)) and the predicted Payer sample (Columns (4) –

9 It is possible that a firm conducts multiple M&As during a year. As long as one of them has an announcement return

smaller (greater) than the 10th (90th) of all M&A announcement returns in the same year, Low CAR (High CAR) is

assigned a value of one. 10 The final sample consists of 19,952 M&As from 1991 – 2016. The number of M&As divided by the total number

of firm-year observations (36,173) in our baseline regressions is greater than the mean of M&A Dummy (0.157) for

two reasons. First, not all acquirers have CSR scores available. Second, a firm could conduct multiple M&As during

a year.

25

(6)). In all columns, the coefficient of Payer is negative, inconsistent with our predictions that

firms that meet local seniors’ demand for dividends conduct more M&As and that their M&A

performance uncertainty is greater. Moreover, in Columns (1), (4), and (2), the coefficient of Payer

is statistically negative at the significance level of 1%, 1%, and 5%, suggesting that firms that meet

local seniors’ demand for dividends are subject to greater financial constraints and are less likely

to conduct M&As and risky M&As particularly.


Taken together, the results in Table 9 and Table 10 show that firms that pay dividends

cannot benefit from lower turnovers through the channel of R&D expense or M&As, which

reinforces our argument that a firm’s response to local seniors’ dividend demand stems from the

firm’s CSR. Moreover, as a CSR activity, a firm’s dividend payment in response to local seniors’

demand is costly and results in a lower investment in R&D and M&As.

7. Conclusion

Becker et al. (2011) demonstrate that a firm headquartered in a county with a higher

fraction of senior residents is more likely to pay dividends. However, the motivation for firms to

respond to local seniors’ dividend demand remains a puzzle. We conjecture that one motivation

stems from the firm’s CSR and hypothesize that in the high-CSR firms, the effect of local seniors

on dividend payment is stronger. We test our hypothesis in a sample of 36,173 firm-year

observations from 1991 – 2016. Our regression analyses provide empirical support for our

hypothesis. Economically, the response to local seniors’ dividend demand among firms in the

highest CSR quartile is 34.0% greater than its counterpart among firms in the lowest three quartiles.

26

The positive relation between a firm’s CSR and its response to local seniors’ dividend demand is

robust to a battery of additional tests, such as matched sample, IV, and alternative measurement

analyses.

In supplemental tests, we first show that when a firm has higher CSR for stakeholders

outside the firm (firm outsiders), the effect of local seniors on dividend payment is stronger. On

the other hand, higher CSR for stakeholders inside the firm (firm insiders) has a marginal effect

on or even weaken the firm’s response to local seniors’ dividend demand. This finding is consistent

with the notion that local seniors, as firm outsiders, have a stronger effect on a firm’s dividend

policy if the firm are more concerned with firm outsiders. Second, we show that firms that meet

local seniors’ demand for dividends do not benefit from lower investor turnovers. Our results

suggest that a firm’s dividend payment in response to local seniors’ demand is a CSR activity,

which is costly and results in a lower investment in R&D and M&As. The failure to identify

empirical evidence for a dividend payer’s benefits from lower turnovers, however, underscores

our argument that a firm’s response to local seniors’ dividend demand stems from the firm’s CSR.

Our study contributes to the literature in three important ways. First, we lend support to

the dividend demand hypothesis by identifying the goodwill toward local seniors as a motivation

for firms to respond to local seniors’ demand for dividends. Second, we add to the research on

CSR by highlighting a firm’s CSR toward a particular stakeholder in the community, local seniors.

Third, we extend the literature on local seniors’ investment behaviors and their impacts. However,

many issuers on local dividend clienteles remain open. For example, when high-CSR firms meet

local seniors’ demand for dividends, the cost for local seniors to search for dividend-paying stocks

is reduced. It will be interesting for future research to examine whether the variation in firms’ CSR

and the associated stock-searching costs for investors could create arbitrage opportunities.

27

References

Albuquerque, R., Koskinen, Y., Zhang, C., 2018. Corporate social responsibility and firm risk:

Theory and empirical evidence. Management Science, Forthcoming.

Alexandridis, G., Antypas, N., Travlos, N., 2017. Value creation from M&As: New evidence.

Journal of Corporate Finance 45, 632-650.

Allen, F., Michaely, R., 2003. Payout policy. In: Constantinides GM, Harris M & Stulz RM

(eds.) Handbook of the Economics of Finance. Elsevier, pp. 337-429.

Almeida, H., Campello, M., 2010. Financing frictions and the substitution between internal and

external funds. Journal of Financial and Quantitative Analysis 45, 589-622.

Almeida, H., Campello, M., Weisbach, M.S., 2004. The cash flow sensitivity of cash. The

Journal of Finance 59, 1777-1804.

Asker, J., Farre-Mensa, J., Ljungqvist, A., 2014. Corporate investment and stock market listing:

A puzzle? The Review of Financial Studies 28, 342-390.

Baber, W.R., Fairfield, P.M., Haggard, J.A., 1991. The effect of concern about reported income

on discretionary spending decisions: The case of research and development. The

Accounting Review 66, 818-829.

Bae, K.-H., El Ghoul, S., Guedhami, O., Kwok, C.C.Y., Zheng, Y., 2019. Does corporate social

responsibility reduce the costs of high leverage? Evidence from capital structure and

product market interactions. Journal of Banking & Finance 100, 135-150.

Becker, B., 2007. Geographical segmentation of US capital markets. Journal of Financial

Economics 85, 151-178.

Becker, B.O., Ivkovic, Z., Weisbenner, S., 2011. Local dividend clienteles. The Journal of

Finance 66, 655-683.

Benlemlih, M., Bitar, M., 2018. Corporate social responsibility and investment efficiency.

Journal of Business Ethics 148, 647-671.

Bereskin, F., Byun, S.K., Officer, M.S., Oh, J.-M., 2018. The effect of cultural similarity on

mergers and acquisitions: Evidence from corporate social responsibility. Journal of

Financial and Quantitative Analysis 53, 1995-2039.

Betton, S., Eckbo, B.E., Thorburn, K.S., 2008. Chapter 15 - Corporate takeovers. In: Eckbo BE

(ed.) Handbook of Empirical Corporate Finance. Elsevier, San Diego, pp. 291-429.

Boone, A., Fich, E., Griffin, T., 2018. Shareholder litigation and the information environment.

Working Paper.

28

Bourveau, T., Lou, Y.U.N., Wang, R., 2018. Shareholder litigation and corporate disclosure:

Evidence from derivative lawsuits. Journal of Accounting Research 56, 797-842.

Bushee, B.J., 1998. The influence of institutional investors on myopic R&D investment

behavior. The Accounting Review 73, 305-333.

Cao, J., Liang, H., Zhan, X., 2019. Peer effects of corporate social responsibility. Management

Science, Forthcoming.

Cespa, G., Cestone, G., 2007. Corporate social responsibility and managerial entrenchment.

Journal of Economics & Management Strategy 16, 741-771.

Chang, K., Kang, E., Li, Y., 2016. Effect of institutional ownership on dividends: An agency-

theory-based analysis. Journal of Business Research 69, 2551-2559.

Clarkson, P.M., Li, Y., Richardson, G.D., 2004. The market valuation of environmental capital

expenditures by pulp and paper companies. The Accounting Review 79, 329-353.

Coval, J.D., Moskowitz, T.J., 1999. Home bias at home: Local equity preference in domestic

portfolios. The Journal of Finance 54, 2045-2073.

Coval, J.D., Moskowitz, T.J., 2001. The geography of investment: Informed trading and asset

prices. Journal of Political Economy 109, 811-841.

Cronqvist, H., Heyman, F., Nilsson, M., Svaleryd, H., Vlachos, J., 2009. Do entrenched

managers pay their workers more? The Journal of Finance 64, 309-339.

DeAngelo, H., DeAngelo, L., Skinner, D.J., 2009. Corporate payout policy. Foundations and

Trends® in Finance 3, 95-287.

Deng, X., Kang, J.-k., Low, B.S., 2013. Corporate social responsibility and stakeholder value

maximization: Evidence from mergers. Journal of Financial Economics 110, 87-109.

Dhaliwal, D.S., Lamoreaux, P.T., Litov, L.P., Neyland, J.B., 2016. Shared auditors in mergers

and acquisitions. Journal of Accounting and Economics 61, 49-76.

Dhaliwal, D.S., Li, O.Z., Tsang, A., Yang, Y.G., 2011. Voluntary nonfinancial disclosure and the

cost of equity capital: The initiation of corporate social responsibility reporting. The

Accounting Review 86, 59-100.

Di Giuli, A., Kostovetsky, L., 2014. Are red or blue companies more likely to go green? Politics

and corporate social responsibility. Journal of Financial Economics 111, 158-180.

Eckbo, B.E., Makaew, T., Thorburn, K.S., 2018. Are stock-financed takeovers opportunistic?

Journal of Financial Economics 128, 443-465.

Edmans, A., 2009. Blockholder trading, market efficiency, and managerial myopia. The Journal

of Finance 64, 2481-2513.

29

Edmans, A., 2011. Does the stock market fully value intangibles? Employee satisfaction and

equity prices. Journal of Financial Economics 101, 621-640.

Edmans, A., Fang, V.W., Lewellen, K.A., 2017. Equity vesting and managerial myopia. The

Review of Financial Studies 30, 2229–2271.

Edmans, A., Manso, G., 2011. Governance through trading and intervention: A theory of

multiple blockholders. The Review of Financial Studies 24, 2395-2428.

El Ghoul, S., Guedhami, O., Kwok, C.C.Y., Mishra, D.R., 2011. Does corporate social

responsibility affect the cost of capital? Journal of Banking & Finance 35, 2388-2406.

Fama, E.F., French, K.R., 2001. Disappearing dividends: changing firm characteristics or lower

propensity to pay? Journal of Financial Economics 60, 3-43.

Fazzari, S.M., Hubbard, R.G., Petersen, B.C., 1988. Financing constraints and corporate

investment. Brookings Papers on Economic Activity 1988, 141-206.

Garriga, E., Mele, D., 2004. Corporate social responsibility theories: Mapping the territory.

Journal of Business Ethics 53, 51-71.

Goetz, M.R., 2018. Financial constraints and corporate environmental responsibility. SAFE

Working Paper No. , 241.

Graham, J.R., Kumar, A., 2006. Do dividend clienteles exist? Evidence on dividend preferences

of retail investors. The Journal of Finance 61, 1305-1336.

Grinblatt, M., Keloharju, M., 2001. How distance, language, and culture influence stockholdings

and trades. The Journal of Finance 56, 1053-1073.

Harford, J., Kecskés, A., Mansi, S., 2018. Do long-term investors improve corporate decision

making? Journal of Corporate Finance 50, 424-452.

Holder, M.E., Langrehr, F.W., Hexter, J.L., 1998. Dividend policy determinants: An

investigation of the influences of stakeholder theory. Financial Management 27, 73-82.

Huberman, G., 2001. Familiarity breeds investment. The Review of Financial Studies 14, 659-

680.

Ivkovic, Z., Weisbenner, S., 2005. Local does as local is: Information content of the geography

of individual investors' common stock investments. The Journal of Finance 60, 267-306.

Jensen, G.R., Solberg, D.P., Zorn, T.S., 1992. Simultaneous determination of insider ownership,

debt, and dividend policies. Journal of Financial and Quantitative Analysis 27, 247-263.

Kalay, A., Lemmon, M., 2008. Chapter 10 - Payout policy. In: Eckbo BE (ed.) Handbook of

Empirical Corporate Finance. Elsevier, San Diego, pp. 3-57.

30

Kim, Y., Li, H., Li, S., 2014. Corporate social responsibility and stock price crash risk. Journal

of Banking & Finance 43, 1-13.

Koh, P.-S., Qian, C., Wang, H., 2014. Firm litigation risk and the insurance value of corporate

social performance. Strategic Management Journal 35, 1464-1482.

Li, L., Monroe, G.S., Coulton, J.J., 2018. Litigation Risk and Cost Behavior: Evidence from

Derivative Lawsuits. Working Paper.

Lin, C., Liu, S., Manso, G., 2019. Shareholder Litigation and Corporate Innovation. Working

Paper.

Lv, J., Schultz, E., Twite, G., 2020. The effect of local seniors on earnings management.

Working Paper.

Maestas, N., Mullen, J.K., Powell, D., 2016. The effect of population aging on economic growth,

the labor force and productivity. NBER Working Papers, 22452.

Manescu, C., 2011. Stock returns in relation to environmental, social and governance

performance: Mispricing or compensation for risk? Sustainable Development 19, 95-118.

Massa, M., Simonov, A., 2006. Hedging, familiarity and portfolio choice. The Review of

Financial Studies 19, 633-685.

McCahery, J.A., Sautner, Z., Starks, L.T., 2016. Behind the scenes: The corporate governance

preferences of institutional investors. The Journal of Finance 71, 2905-2932.

Miller, M.H., Modigliani, F., 1961. Dividend policy, growth, and the valuation of shares. The

Journal of Business 34, 411-433.

Nguyen, H.T., Phan, H.V., Sun, L., 2018. Shareholder litigation rights and corporate cash

holdings: Evidence from universal demand laws. Journal of Corporate Finance 52, 192-

213.

Pagano, M., Volpin, P.F., 2005. Managers, workers, and corporate control. The Journal of

Finance 60, 841-868.

Poterba, J.M., 2014. Retirement security in an aging population. The American Economic

Review 104, 1-30.

Renneboog, L., Vansteenkiste, C., 2018. What goes wrong in M&As? On the long-run success

factors in M&As. Working Paper.

Rozeff, M.S., 1982. Growth, beta and agency costs as determinants of dividend payout ratios.

Journal of Financial Research 5, 249-259.

Rubin, A., Smith, D.R., 2009. Institutional ownership, volatility and dividends. Journal of

Banking & Finance 33, 627-639.

31

Servaes, H., Tamayo, A., 2013. The impact of corporate social responsibility on firm value: The

role of customer awareness. Management Science 59, 1045-1061.

Shefrin, H.M., Thaler, R.H., 1988. The behavioral life-cycle hypothesis. Economic Inquiry 26,

609-643.

Shleifer, A., Vishny, R.W., 1997. A Survey of corporate governance. The Journal of Finance 52,

737-783.

Surroca, J., Tribo, J.A., 2008. Managerial entrenchment and corporate social performance.

Journal of Business Finance & Accounting 35, 748-789.

Thaler, R.H., Shefrin, H.M., 1981. An economic theory of self-control. Journal of Political

Economy 89, 392-406.

Whited, T.M., Wu, G., 2006. Financial Constraints Risk. The Review of Financial Studies 19,

531-559.

32

Table 1: County-level variables

This table reports statistics of key county-level variables during 1991-2016. Local Senior is the fraction of residents who are 65 years old or older in a county. Number of Firms is the number of firms in a county. The sample consists 36,173 firm-years in 7,434 county-years.

Year Number of counties Local Senior Number of Firms

mean median mean median total

1991 121 0.122 0.124 2.322 1 281

1992 125 0.122 0.124 2.320 1 290

1993 131 0.122 0.124 2.290 1 300

1994 130 0.122 0.124 2.292 1 298

1995 130 0.122 0.123 2.346 1 305

1996 133 0.122 0.122 2.444 1 325

1997 137 0.121 0.121 2.504 1 343

1998 143 0.121 0.119 2.441 1 349

1999 154 0.120 0.118 2.435 1 375

2000 153 0.119 0.117 2.595 1 397

2001 211 0.122 0.119 3.483 2 735

2002 217 0.121 0.119 3.571 2 775

2003 422 0.126 0.123 4.841 1 2043

2004 403 0.124 0.123 5.288 2 2131

2005 402 0.125 0.123 5.361 2 2155

2006 403 0.125 0.123 5.449 2 2196

2007 392 0.125 0.124 5.594 2 2193

2008 430 0.130 0.128 5.451 2 2344

2009 430 0.131 0.128 5.553 2 2388

2010 436 0.133 0.130 5.679 1 2476

2011 423 0.135 0.132 5.681 2 2403

2012 424 0.140 0.137 5.762 1 2443

2013 368 0.142 0.139 5.924 2 2180

2014 358 0.146 0.143 5.696 2 2039

2015 374 0.150 0.147 5.893 2 2204

2016 384 0.154 0.151 5.742 2 2205

Full sample 7434 0.131 0.128 4.866 1 36173

33

Table 2: Descriptive statistics

This table reports statistics of firm-level variables. The sample consists 36,173 firm-years during 1991-

2016. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st

and 99th percentiles. “sd”, “p25”, and “p75” stands for standard deviation, 25th percentile, and 75th

percentile, respectively.

Variables count mean sd p25 p50 p75

Variables of dividends and population

Payer 36173 0.582 0.493 0.000 1.000 1.000

Yield 36173 1.632 2.451 0.000 0.679 2.423

Local Senior 36173 0.123 0.027 0.105 0.122 0.138 Variables of CSR

CSR Adj 36173 -0.050 0.651 -0.417 -0.075 0.167

CSR Raw 36173 -0.240 2.114 -1.000 0.000 0.000

CSR ENV 36173 0.024 0.139 0.000 0.000 0.000

CSR COM 36173 0.022 0.165 0.000 0.000 0.000

CSR HUM 36173 -0.004 0.077 0.000 0.000 0.000

CSR EMP 36173 0.005 0.169 0.000 0.000 0.000

CSR DIV 36173 -0.073 0.309 -0.333 0.000 0.000

CSR PRO 36173 0.005 0.208 0.000 0.000 0.000

CSR CGOV 36173 -0.021 0.228 -0.143 0.000 0.000 Variables of firm characteristics

Ln(Assets) 36173 7.508 1.738 6.232 7.440 8.629

Ln(1 + Age) 36173 2.663 0.895 2.079 2.833 3.401

ROA 36173 0.021 0.129 0.007 0.036 0.076

Cash 36173 0.110 0.134 0.018 0.058 0.151

MB 36173 1.983 1.364 1.134 1.495 2.246

Leverage 36173 0.231 0.210 0.046 0.196 0.353

Inst. Holdings 36173 68.963 25.777 53.489 73.612 88.541

Lagged Return 36173 0.070 0.159 -0.013 0.067 0.149

Volatility 36173 2.610 1.341 1.648 2.271 3.189

Instrumental variable

UD Law 35767 0.140 0.347 0.000 0.000 0.000

Supplemental test variables

R&D Dummy 36173 0.412 0.492 0.000 0.000 1.000

R&D (%AT) 36173 3.445 7.557 0.000 0.000 3.154

M&A Dummy 36173 0.157 0.364 0.000 0.000 0.000

Low CAR 5688 0.095 0.293 0.000 0.000 0.000

High CAR 5688 0.107 0.309 0.000 0.000 0.000

34

Table 3: CSR, local seniors, and dividend policy

This table reports regression results of ordinary least squares (OLS) and Probit models on dividends. The dependent variable is the Payer, indicating whether a firm pays dividends. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and the associated p-values are reported in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6)

Variables OLS Probit

Local Senior 0.485*** 0.456*** 0.463*** 1.478*** 1.364*** 1.388***

(0.000) (0.000) (0.000) (0.001) (0.003) (0.002)

Local Senior 0.155*** 0.100* 0.709*** 0.511**

* High Adj (0.000) (0.066) (0.000) (0.027)

CSR Adj 0.007 0.025

(0.155) (0.227)

Ln(Assets) 0.046*** 0.045*** 0.045*** 0.166*** 0.160*** 0.160***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Ln(1 + Age) 0.105*** 0.104*** 0.104*** 0.378*** 0.377*** 0.377***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

ROA 0.225*** 0.224*** 0.224*** 1.064*** 1.064*** 1.064***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Cash -0.262*** -0.263*** -0.262*** -0.918*** -0.922*** -0.922***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

MB -0.000 -0.001 -0.001 -0.017** -0.019** -0.020**

(0.922) (0.724) (0.689) (0.038) (0.018) (0.016)

Leverage -0.025* -0.023 -0.023 -0.066 -0.059 -0.057

(0.094) (0.117) (0.124) (0.233) (0.291) (0.303)

Inst. Holdings -0.002*** -0.002*** -0.002*** -0.008*** -0.008*** -0.008***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Lagged Return -0.048*** -0.047*** -0.047*** -0.185*** -0.177*** -0.177***

(0.004) (0.005) (0.005) (0.005) (0.007) (0.007)

Volatility -0.076*** -0.076*** -0.076*** -0.272*** -0.271*** -0.271***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Year FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

N 36173 36173 36173 36083 36083 36083

Adj. R-squared 0.394 0.394 0.394

Pseudo R-squared 0.351 0.352 0.352

35

Table 4: Matched sample analyses

This table reports the regression results of OLS and Probit models on dividends. All models include the same set of control variables used in Table 3. The dependent variable is the Payer, indicating whether a firm pays dividends. While Columns (1) and (2) use a sample of 16,612 firm-years based on propensity score matching (PSM), Columns (3) and (4) use a sample of 11,454 firm-years based on size-MB matching. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and the associated p-values are reported

in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

PSM Size-MB Matching

Variables OLS Probit OLS Probit

Local Senior 0.504*** 1.548** 0.466** 1.316

(0.001) (0.020) (0.039) (0.170)

Local Senior * High Adj 0.132*** 0.706*** 0.150** 0.795***

(0.005) (0.001) (0.032) (0.009)

Controls Yes Yes Yes Yes

Year FE Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes

N 16612 16576 11462 11454

Adj. R-squared 0.403 0.411

Pseudo R-squared 0.375 0.374

36

Table 5: Instrumental variable analyses

This table reports the regression results of instrumental variable analyses. All models include the same set of control variables used in Table 3. In the first stage, an OLS model is estimated with CSR Adj as the dependent variable. In the second stage, both an OLS and a Probit model are estimated with Payer as the dependent variable. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. While in the first stage, standard errors are clustered at the county-year level, in the second stage, bootstrap standard errors are estimated. The associated p-values are reported in

parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3)

Variables CSR Adj Payer

OLS Probit

UD Law -0.079***

(0.007)

Local Senior 0.383*** 1.130**

(0.003) (0.027)

Local Senior * High Adj 0.191*** 0.812***

(0.001) (0.001)

Controls Yes Yes Yes

Year FE Yes Yes Yes

Industry FE Yes Yes Yes

State FE Yes No No

N 35767 35767 35767


Pseudo R-squared 0.353

37

Table 6: Alternative dividend measure

This table reports regression results of OLS and Tobit models on dividends. All models include the same set of control variables used in Table 3. The dependent variable is the Yield, the dividends in a percentage of the firm’s market value. Columns (1) and (2) use the full sample of firm-year observations, while Columns (3) and (4) use the subsample based on propensity score matching (PSM). Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and the associated p-values are reported in parentheses. ***,

**, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

Full Sample PSM Sample

Variables OLS Tobit OLS Tobit

Local Seniors 1.497*** 3.269*** 1.508** 2.931***

(0.010) (0.000) (0.036) (0.006)

Local Seniors * High Adj 0.287 0.658** 0.717*** 0.997***

(0.190) (0.039) (0.005) (0.006)




N 36173 36173 16612 16612



38

Table 7: Alternative CSR measure

This table reports the regression results of OLS and Probit models on dividends. All models include the same set of control variables used in Table 3. The dependent variable is the Payer, indicating whether a firm pays dividends. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and the associated p-values are reported in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2)

Variables OLS Probit

Local Seniors 0.464*** 1.406***

(0.000) (0.002)

Local Seniors * High Raw 0.163*** 0.658***

(0.000) (0.000)

Controls Yes Yes

Year FE Yes Yes

Industry FE Yes Yes

N 36173 36083

Adj. R-squared 0.394

Pseudo R-squared 0.351

39

Table 8: CSR dimensions

This table reports regression results of Probit models on dividends. All models include the same set of control variables used in Table 3. The dependent variable is the Payer, indicating whether a firm pays dividends. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and the associated p-values are reported in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4) (5) (6) (7)

Variables ENV COM HUM EMP DIV PRO CGOV

Local Seniors 1.342*** 1.399*** 1.474*** 1.486*** 1.449*** 1.501*** 1.399***

(0.003) (0.002) (0.001) (0.001) (0.002) (0.001) (0.002)

Local Seniors * High Dimension 1.330*** 1.339*** 1.987*** -0.076 0.328 -0.788*** 0.687***

(0.000) (0.000) (0.007) (0.694) (0.115) (0.001) (0.001)

Controls Yes Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes Yes

N 36083 36083 36083 36083 36083 36083 36083

Pseudo R-squared 0.352 0.352 0.351 0.351 0.351 0.351 0.351

40

Table 9: Dividend payer and R&D expense

This table reports the regression results of OLS and Probit models on R&D expense. All models include the same set of control variables used in Table 3. The dependent variable is R&D (%AT) in Columns (1) and (3) and R&D Dummy in Columns (2) and (4). Columns (1) and (2) use the subsample of firms in which Local Senior is in the highest quartile during a year, while Columns (3) and (4) use the subsample of firms that are predicted to be dividend payers. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year

level, and associated p-values are reported in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

(1) (2) (3) (4)

High Local Senior Sample Predicted Payer Sample

Variables OLS Probit OLS Probit

Payer -1.094*** -0.357*** -0.958*** -0.487***

(0.000) (0.000) (0.000) (0.000)




N 8290 5513 21487 18545



41

Table 10: Dividend payer and M&As

This table reports the regression results of Probit models on M&As. All models include the same set of control variables used in Table 3. Columns (1) – (3) use the subsample of firms in which Local Senior is in the highest quartile during a year, while Columns (4) – (6) use the subsample of firms that are predicted to be dividend payers. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Standard errors are clustered at the county-year level, and associated p-values are reported in parentheses. ***, **, and * represent significance at the 1%, 5%, and 10% levels,

respectively.

(1) (2) (3) (4) (5) (6)

High Local Senior Sample Predicted Payer Sample

Variables M&A Dummy Low CAR High CAR M&A Dummy Low CAR High CAR

Payer -0.164*** -0.311** -0.127 -0.198*** -0.103 -0.037

(0.000) (0.035) (0.359) (0.000) (0.297) (0.681)

Controls Yes Yes Yes Yes Yes Yes

Year FE Yes Yes Yes Yes Yes Yes

Industry FE Yes Yes Yes Yes Yes Yes

N 8218 1231 1137 21486 3373 3302 Pseudo

R-squared 0.073 0.223 0.129 0.063 0.196 0.078

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Appendix A: Variable definitions

Variables Definitions

Dividends and population

Payer A dummy variable equal to 1 if a firm pays dividends in a year.

Yield Dividend, in the percentage of the market value.

Local Seniors The fraction of residents who are 65 years old or older in the county in which a firm is headquarter.

Corporate social responsibility

CSR Adj Adjusted CSR score, which is equal to the sum of the adjusted CSR score in seven dimensions.

CSR Raw Raw CSR score, which is equal to the sum of the raw CSR score in seven dimensions. The raw CSR score in a dimension is equal to the sum of strength

indicators minus the sum of concern indicators.

CSR ENV Adjusted CSR score in the dimension of environment, which is equal to the adjusted strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

CSR COM Adjusted CSR score in the dimension of community, which is equal to the

adjusted strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

CSR HUM Adjusted CSR score in the dimension of human rights, which is equal to the adjusted strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by

the number of strength (concern) indicators.

CSR EMP Adjusted CSR score in the dimension of employee relation, which is equal to the adjusted strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

CSR DIV Adjusted CSR score in the dimension of diversity, which is equal to the adjusted

strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

CSR PRO Adjusted CSR score in the dimension of product quality, which is equal to the adjusted strength score minus the adjusted concern score. The adjusted strength

43

(concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

CSR CGOV Adjusted CSR score in the dimension of corporate governance, which is equal

to the adjusted strength score minus the adjusted concern score. The adjusted strength (concern) score is equal to the sum of strength (concern) indicators divided by the number of strength (concern) indicators.

High Adj A dummy variable equal to 1 if CSR Adj in a firm is in the highest quartile (greater than the 75th percentile) in a year.

High Raw A dummy variable equal to 1 if CSR Raw in a firm is in the highest quartile

(greater than the 75th percentile) in a year.

High Dimension A dummy variable equal to 1 if the adjusted CSR score in a dimension in a firm is in the highest quartile (greater than the 75th percentile) in a year.

Firm characteristics

Ln(Assets) The natural log of total assets (book value).

Ln(1 + Age) The natural log of 1 + age. Age is in the number of years from the time when a firm appears in the CRSP database.

ROA Return on assets, which is equal to net income divided by total assets (book value).

Cash Cash, normalized by total assets (book value).

MB Market-to-book ratio, which is equal to the market value of the firm’s equity

plus the difference between the book value of the firm’s assets and the book value of the firm’s equity, divided by total assets (book value).

Leverage The ratio of total debt (short-term and long-term debt) to total assets (book value).

Inst. Holdings Institutional shareholdings, in the percentage of total shares outstanding in a firm.

Stock Return The mean of daily returns in the past year. The daily return is in a percent change compared to the stock price in the previous day.

Volatility The standard deviation of daily returns in the past year. The minimum number

of 100 observations is required to compute the standard deviation.

Instrumental variable

UD Law A dummy variable equal to 1 if a firm is incorporated in a state that has adopted the universal demand law.

44

Supplemental test variables

R&D Dummy A dummy variable equal to 1 if there is any research and development expense in a firm during a year.

R&D (%AT) Research and development expense, in the percentage of total assets (book value).

M&A Dummy A dummy variable equal to 1 if a firm conducts an M&A during a year.

Low CAR A dummy variable equal to 1 if a firm conducts an M&A whose three-day

announcement return is smaller than the 10th percentile of all M&A announcement returns in a year. An announcement return is equal to the three-day cumulative returns of the acquirer minus the three-day cumulative return of the CRSP value-weighted portfolio.

High CAR A dummy variable equal to 1 if a firm conducts an M&A whose three-day announcement return is greater than the 90th percentile of all M&A announcement returns in a year. An announcement return is equal to the three-day cumulative returns of the acquirer minus the three-day cumulative return of the CRSP value-weighted portfolio.

45

Appendix B: Correlations of CSR measures

This table reports the correlations of CSR measures. Variable definitions are provided in Appendix A. Continuous variables are winsorized at the 1st and 99th percentiles. Pearson correlation coefficients are reported, and ***, **, and * represent significance at the 1%, 5%, and 10% levels, respectively.

CSR Adj CSR Raw CSR ENV CSR COM CSR HUM CSR EMP CSR DIV CSR PRO CSR CGOV

CSR Adj 1.000

CSR Raw 0.786*** 1.000

CSR ENV 0.512*** 0.466*** 1.000

CSR COM 0.466*** 0.418*** 0.217*** 1.000

CSR HUM 0.265*** 0.156*** 0.118*** 0.073*** 1.000

CSR EMP 0.498*** 0.481*** 0.224*** 0.162*** 0.075*** 1.000

CSR DIV 0.603*** 0.536*** 0.157*** 0.190*** -0.027*** 0.150*** 1.000

CSR PRO 0.477*** 0.282*** 0.196*** 0.085*** 0.088*** 0.160*** 0.013* 1.000

CSR CGOV 0.502*** 0.294*** 0.129*** 0.050*** 0.142*** 0.078*** 0.083*** 0.136*** 1.000

Documents

Corporate social responsibility, local seniors, and