Journal of Corporate Finance - RMBS

Journal of Corporate Finance 42 (2017) 295–314

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Journal of Corporate Finance

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Stock liquidity and dividend payouts

Fuxiu Jiang ⁎, Yunbiao Ma, Beibei ShiSchool of Business, Renmin University of China, 59 Zhongguancun Street, Haidian District, Beijing, 100872, China

a r t i c l e i n f o

⁎ Corresponding author.E-mail addresses: [email protected] (F. Jiang), mayunb

http://dx.doi.org/10.1016/j.jcorpfin.2016.12.0050929-1199/© 2016 Elsevier B.V. All rights reserved.

a b s t r a c t

Article history:Received 9 June 2016Received in revised form 12 December 2016Accepted 14 December 2016Available online 15 December 2016

This study investigates the informational effect of stock liquidity on dividend payouts. Using asample of Chinese listed firms during 2000–2014, we find a positive relation between stock li-quidity and dividend payouts. This result is robust to the use of alternative measures of liquid-ity, and holds after we control for endogeneity concerns. In accord with our hypothesis thatstock liquidity provides information and increases insiders' incentive to pay out dividends,we find that the positive relation between stock liquidity and dividend payouts is more pro-nounced when the information environment is opaque, and when conflict between controllingshareholders and minority investors is severe. Further, market reactions to regulatory stipula-tions requiring dividend payouts are more favorable for firms with low stock liquidity, suggest-ing that legal provisions and regulations are substitutes for stock liquidity. Finally, we rule outseveral alternative explanations concerning the governance of non-controlling blockholdersand the alleviation of manager-shareholder agency conflict.

© 2016 Elsevier B.V. All rights reserved.

JEL classification:G14G30G35

Keywords:Stock liquidityDividend payoutsControlling shareholderInformational effectChina

1. Introduction

The traditional clientele transaction cost view predicts a negative relation between stock liquidity and dividend payouts. Millerand Modigliani (1961) advance the proposition that dividends are irrelevant—in a frictionless world, shareholders' wealth is de-termined solely by the firm's investment opportunities and is independent of payout policy. However, in actuality, trading frictionpervades financial markets. As stock liquidity can enable investors who need cash to create homemade dividends at no cost byselling an appropriate amount of their holdings, an implication of Miller and Modigliani's work is that, other things beingequal, firms with more liquid shares should pay fewer dividends. Using a sample of U.S. listed companies, Banerjee et al.(2007) find that firms with less liquid stock are more likely to pay cash dividends, supporting the view that stock market liquidityand dividends are substitutes.

However, this argument neglects the informational effect of stock liquidity on payout policy. It has been well established thatliquidity can reduce information asymmetry between insiders and outsiders by producing more information. In standard informedtrading models, liquidity can help an informed party to disguise private information that is not reflected in the price (Kyle, 1984).In this way, the marginal value of information is high when stock liquidity is high (Holmström and Tirole, 1993). To earn moretrading gains, the speculator will spend more time on gathering information.

This informational effect of stock liquidity may shape corporate insiders' payout policies. According to Easterbrook (1984) andJensen (1986), dividend payouts lower retained earnings that insiders can divert for personal use or invest in unprofitable projectsthat provide private benefits. As a consequence, insiders prefer retaining earnings to paying dividends. However, all insiders face a

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296 F. Jiang et al. / Journal of Corporate Finance 42 (2017) 295–314

cost-benefit analysis of whether or not to pay dividends. When the information environment is opaque, insiders have a strongerincentive to keep more cash for private benefit, as they can more easily seize private benefits without being detected (Stiglitz,2000; Leuz et al., 2003). But higher transparency associated with more informed trading makes insiders' expropriation more likelyto be detected and legally riskier (Li and Zhao, 2008; Petrasek, 2012), and thus increases the cost of expropriating retained earn-ings. Moreover, under higher transparency, keeping surplus earnings instead of distributing them to outside investors will damageinsiders' reputation for a lack of implicit commitment to limit expropriation (Gomes, 2000), resulting in an unfavorable valuation(Gomes, 2000; Kalcheva and Lins, 2007; Karpavicius and Yu, 2015) and poorer access to external finance (Gomes, 1996). Suchinformal enforcement further increases the cost of expropriating retained earnings. Thus, the net benefit of paying dividends in-creases with stock liquidity, which decreases insiders' incentives for expropriation and increases their incentives to pay out div-idends (La Porta et al., 2000a, 2000b).

In this paper, we investigate the informational effect of liquidity on corporate dividend payouts using Chinese data. There areseveral reasons why we use Chinese data. First, the informational effect of liquidity on dividends may be more significant in Chinathan in other countries, especially developed countries. It is well known that in western countries, such as the United States, div-idend policy is more stable, and firms seldom change it. Outsiders also have stable expectations of corporate dividend policy. Atthe same time, corporate governance and law and regulations are well established, so dividend policy may be more reasonable. Inthis case, even though liquidity can bring some information, it is hard to change the corporate dividend payout policy. However,in China, law and regulations and corporate governance are relatively weak, and dividend policy depends largely on insiders' will.They can pay more in one year, and pay less or nothing in other years. There is huge room for stock liquidity to influence corpo-rate dividend payouts in China. The information from stock liquidity helps outsiders understand the real reasons for dividend pay-outs, and pushes insiders to make the right dividend payout decision, or at least not a random one. So China provides a goodsetting to check the informational role of stock liquidity.

Second, Chinese listed firms have more opaque information environments. According to Allen et al. (2005), China's disclosureregulations and accounting standards are less developed than those of most of the countries studied by La Porta et al. (1997,1998). In addition, unlike their Anglo-American counterparts, Chinese firms typically have corporate ownership concentrated inthe hands of controlling shareholders (Jiang and Kim, 2015). In the relatively weak investor protection environment, there are se-vere agency problems between controlling shareholders and minority investors. Controlling shareholders often use their discre-tion on the production and disclosure of the firm's accounting information to withhold private information from outsideinvestors so as to disguise their opportunistic behavior, exacerbating the information asymmetry between controlling share-holders and outside shareholders (Liu and Lu, 2007). Moreover, information intermediaries, including analysts and externalauditing, are less effective (see, e.g., Firth et al., 2013; Gu et al., 2013; Ke et al., 2015). As a result, the informed trading facilitatedby liquidity should deliver more significant information content in China than in developed markets. This may make the informa-tional effect of liquidity outweigh the effect of clientele transaction cost, leading to a positive relation between stock liquidity anddividend payout.

Third, in order to promote the development of the stock market and protect the interests of minority shareholders, from 2005,China initiated a split share structure reform to transform all non-tradable shares into tradable shares,1 and this reform providesplausibly exogenous variation in liquidity. Moreover, the China Securities Regulatory Commission (CSRC) issued several stipula-tions to encourage firms to pay dividends (we give some details in the second part of this article). These events can serve as ex-ogenous shocks to test the effect of liquidity on dividend payouts.

Using a sample of Chinese listed firms during 2000–2014, we find a positive relation between stock liquidity and dividendpayouts. This result is robust to alternative measures of stock liquidity, and holds after we control for endogeneity concerns. Tovalidate stock liquidity's informational effect on dividend payouts, we test whether this effect differs among firms with differentextents of information asymmetry, different extents of conflict between controlling shareholders and minority investors, and dif-ferent degrees of excess cash flow. All these results are consistent with our expectation.

In addition, we find that stock liquidity and legal provisions and regulations are substitutes. At least, we rule out several alter-native explanations, for example, that our results might be driven by the role of liquidity in helping form another blockholder be-sides the controlling shareholder or in helping non-controlling blockholders threaten to exit, or in mitigating agency conflictsbetween managers and shareholders.

Our study contributes to the literature on how stock liquidity affects dividend payouts. The first mention of this link dates backto Miller and Modigliani (1961). According to their traditional clientele transaction cost view, the relation between stock liquidityand corporate dividend payouts is negative; and Banerjee et al. (2007) find that U.S. firms with less liquid stock are more likely topay cash dividends. In contrast, we find that the relation is positive: stock liquidity increases dividend payouts by mitigating in-formation asymmetry between controlling shareholders and minority investors. To our knowledge, our study is the first to showthis mechanism.

Our paper also contributes to the literature on dividends in an agency framework. It is well acknowledged that agency prob-lems shape dividend payout policies. Easterbrook (1984) and Jensen (1986) suggest that insiders have a preference for retainingearnings over paying dividends. La Porta et al. (1998, 2000a) document that dividends are low in countries where legal systems

1 It is well known that only recently have themajority of listed firms' shares become tradable in China. When China's stock exchanges were inaugurated in the early1990s, the government established a split share structure. Specifically, therewere two classes of shareswith otherwise identical rights, non-tradable shares and tradableshares. In April 2005, the Chinese government initiated the split share reform to transform all non-tradable shares into tradable shares. By the end of 2009, almost allfirms had finished the split share reform.

297F. Jiang et al. / Journal of Corporate Finance 42 (2017) 295–314

and accounting standards do not strongly protect minority shareholders from insider expropriation. Several studies empiricallyshow that strong corporate governance makes corporate insiders pay more dividends (Mitton, 2004; Francis et al., 2011; He,2012; Petrasek, 2012). We extend this strand of research and show that stock liquidity can alleviate agency problems betweeninsiders and outsiders by mitigating information asymmetry, and thus increase insiders' incentives to pay dividends.2

This paper also contributes to the fast-growing empirical research on the impact of liquidity on governance. A number ofstudies suggest that higher stock liquidity can increase share price informativeness and enhance governance by enabling ablock to form (Kyle and Vila, 1991; Kahn and Winton, 1998; Maug, 1998) or by amplifying threat of exit (Admati andPfleiderer, 2009; Edmans, 2009; Edmans and Manso, 2011). In contrast, Coffee (1991) and Bhide (1993) view higher liquidityas an impediment to governance because it allows large shareholders to sell without incurring large trading costs. Recently,researchers have begun to study the governance effect of liquidity on many dimensions of firm outcomes, such as firm value(Fang et al., 2009; Bharath et al., 2013), innovation (Fang et al., 2014), mergers and acquisitions (Roosenboom et al., 2013),earnings management (Chen et al., 2015), shareholder activism (Norli et al., 2015), and price crash risk (Chang et al., 2016).These studies provide mixed evidence on whether stock liquidity enhances or impedes governance. We extend this strand ofresearch to dividend policy and document that higher stock liquidity increases dividend payouts, suggesting that it can restraincontrolling shareholders from reserving cash for private benefits by mitigating the information asymmetry between them andminority investors.

The remainder of the paper is organized as follows. We introduce the institutional background in the next section. Section 3describes the data and our research design. Section 4 presents the main regression results. Section 5 reports robustness tests.Section 6 tests the possible channels that could lead to our results. Section 7 presents further tests. Section 8 presents the resultson responses to CSRC stipulations, and section 9 rules out several alternative explanations. Section 10 concludes.

2. Dividend policies in China

The corporate ownership of Chinese firms is highly concentrated. According to Jiang and Kim (2015), the average shareholdingof controlling shareholders was 39.36% for Chinese listed companies during 1998–2012. With significant ownership, the control-ling shareholder usually dominates the board of directors and the executive management team (Chen et al., 2006). Under theseconditions, the reasons underlying dividend payouts differ from those in developed capital markets.

First, Chinese listed companies underpay dividends. Under the corporate structure described above, controlling shareholdersoften divert cash and other resources for their private interests at the expense of minority shareholders (Berkman et al., 2009;Cheung et al., 2009; Jiang et al., 2010; Jiang et al., 2015). As Easterbrook (1984) and Jensen (1986) have noted, controlling share-holders have strong incentives to underpay dividends and to accumulate cash holdings that can be spent on their personal inter-ests (e.g., high compensation, related party transactions, and transfer of assets from the firm by controlling shareholders at below-market prices).

Second, Chinese controlling shareholders are less threatened by disciplinary takeovers if they rarely distribute cash dividends.Previous studies suggest that CEOs who underpay dividends and accumulate cash holdings will leave their firms more vulnerableto hostile takeovers. Accordingly, Dickerson et al. (1998) find that firms that want to avoid takeover pay more dividends. Franciset al. (2011) find that dividend payout propensities and payout ratios fall when managers are insulated from takeovers. However,this is not the case in China. As the corporate ownership of Chinese firms is highly concentrated, outside investors face a signif-icant cost to obtain a block big enough to take over. As a result, the market for corporate control is currently almost nonexistent inChina (Jiang and Kim, 2015), and controlling shareholders can keep dividends low.

Third, share repurchasing is not an alternative payout policy in China. With several advantages over cash dividends, stockrepurchases, as an alternative mechanism to return capital to stockholders, have become more popular and more importantaround the world (Allen and Michaely, 2003; Brav et al., 2005). However, this is not the case in China. China's Companies Lawstipulates that a company may not purchase its own shares except in one or more of the following circumstances: “decreasingthe registered capital of the company,” “merging with another company that holds its shares,” “rewarding the employees withshares,” and “responding to a request by any shareholder who objects to a resolution of the General Shareholders' Meeting onmerger or division of the company.” As a result, share repurchasing is rare in China's stock market.

However, controlling shareholders' costs and benefits of dividend payouts are changing. In order to improve corporate gover-nance and protect minority shareholders, the government has issued many new laws and securities regulations. The CompanyLaw and the Security Law became effective in 1994 and 1999, respectively; both were revised in 2004 and the revisions becameeffective in 2006. In 2007, the Property Law became effective. Beyond that, starting from 2000, the China Securities RegulatoryCommission (CSRC) was actively engaged in promoting minority shareholder protection and shareholder activism. In 2002, theCSRC issued the Code of Corporate Governance for Listed Companies in China, and mandated that listed companies with ablockholder holding N30% of the common stock must add a clause in their Articles of Association to require the use of a cumu-lative voting system. In 2004 and 2006, the CSRC introduced and revised rules allowing minority shareholders to vote remotelyvia the Internet. These laws and regulations give shareholders, especially minority shareholders, the power to extract cashdividends.

2 For many Chinese firms, the personwho is actively in charge of the business is neither the general manager nor the CEO, but the board chairperson (Jiang and Kim,2015); therefore, here the term “insiders” refers to controlling shareholders, and “outsiders” refers to minority shareholders.


Moreover, to further force controlling shareholders to disgorge cash dividends, the CSRC linked dividend payouts to SEO. Spe-cifically, on March 8, 2001, the CSRC issued rules requiring firms to have paid dividends (either cash or stock) at least once in thethree years preceding an SEO. On December 7, 2004, the CSRC issued rules requiring firms to have distributed cash dividends atleast once during the preceding three years before being allowed to issue additional shares or convertible debt. Later, on May 6,2006, the CSRC issued new regulations requiring that the cumulated dividend payout (either cash or stock) over the three yearsbefore the firm is allowed to issue public securities should be ≥ 20% of the average realized annual distributable profits of thethree years. Finally, on October 9, 2008, the CSRC issued a regulation that increased the dividend payouts ratio from 20% to30% and restricted the payment to cash dividends.

Along with formal systems, informal systems in China also encourage insiders to pay out dividends. Allen et al. (2005) pointout that informal systems, such as those based on reputation and relationships, act as an alternative mechanism to protect invest-ments against expropriation by controlling shareholders. Outside investors evaluate the share prices in light of their expectationsof expropriation by the controlling shareholders. Distributing excess cash to outside investors can act as an implicit commitmentthat the controlling shareholders will not divert corporate assets. Therefore, paying dividends helps controlling shareholders earna reputation and thus enjoy a favorable valuation. In line with this view, Eun and Huang (2007) find that Chinese investors pay asignificant premium for dividend-paying stocks.

3. Data, variables, methods, and descriptive statistics

To construct our sample, we start with all Chinese A-share listed companies between 2000 and 2014. The sample period startsfrom 2000 because China adopted a consistent and unified set of accounting standards for publicly traded firms from that year.We obtain our empirical sample from the China Securities Market and Accounting Research (CSMAR) database. Following thesample selection criteria in related literature, our sample excludes firms in the financial sector and firms with incomplete financialdata. Our tests use lagged and forward year information, and therefore our main regression sample loses some firm-year obser-vations. In addition, we delete firms with negative dividend-to-earnings ratio or negative dividend-to-cash-flow ratio (i.e., caseswhere a dividend has been paid even though earnings or cash flow is negative). Our full sample consists of 19,074 firm-year ob-servations for 2473 non-financial firms over a period of 15 years from 2000 to 2014.

We use three proxies for payouts in our main analyses. The first is DVE, cash dividends scaled by earnings. The second is DVC,cash dividends scaled by cash flow from operating activities. The third is DVP, an indicator variable that takes the value of one ifthe total number of dividends is greater than zero for a given year, and zero otherwise.

Our measure of liquidity is the Amihud (2002) illiquidity ratio. While the market microstructure literature has proposed ahandful of liquidity measures, the theories imply that the effect of liquidity on dividend mostly involves price impact and priceinformativeness, which are well captured by Amihud's (2002) measure (Amihud, 2002; Goyenko et al., 2009). We computeAmihud as the average ratio of daily stock returns (absolute value) to daily RMB volume for each firm i in fiscal year t:

3 Thepanies mfore,weratio as

Amihudi;t ¼ 1=Di;t � ∑D

d¼1Reti;t;d��

��=Volumei;t;d;

where Ret and Volume are, respectively, the daily stock returns multiplied by 100, and trading volume in million RMB on day d forfirm i, and D is the number of trading days in fiscal year t for firm i.

Since the raw Amihud measure is highly skewed, following Edmans et al. (2013), we log-transform (one plus) the measure inour analysis. Because a higher value of this measure corresponds to a lower level of liquidity, for ease of interpretation, we mul-tiply it by −1. Therefore, our liquidity measure is Liquidityi,t = − ln (1 + Amihudi,t).

It has been well documented that large, profitable, and less leveraged firms with few investment opportunities and rich cashare more likely to pay dividends. Accordingly, we include these firm characteristics in our regression analyses. We use Log of as-sets, the logarithm of total assets, as a measure of firm size. Our proxy for growth opportunities is Q, which is defined as the sumof the market value of equity and the book value of liabilities scaled by total assets. We use return on assets (ROA) to measureprofitability and compute ROA as net income divided by total assets. We compute Cash as cash holding scaled by total assets.We measure Lev as total liabilities scaled by total assets. We also control for the effect of ownership structure and board indepen-dence (corporate governance) on the firm's payouts. Top1 is the percentage of shares held by the largest shareholder. Indepen-dence is the number of independent directors on the board.3 We further include an industry dummy to control for time-invariant industry effects on dividend payouts, and a year dummy to remove any aggregate time effect.

We test the effect of liquidity on corporate payouts using the following basic regression model:

Payouti;tþ1 ¼ αi;t þ βi;tLiquidityi;t þ γ0Controlsi;t þ εi;t ð1Þ

CSRC requires that at least one-third of the board members of a listed firm be independent directors. Jiang and Kim (2015) find that most Chinese listed com-aintain the minimum one-third independence ratio, which means that the independence ratio may represent merely compliance with the regulation. There-use thenumber of independent directors as theproxy for board independence. To test robustness, as previous studies have suggested,weuse the independencethe proxy for board independence and get similar results. For brevity, details of the tests are not tabulated.

Table 1Summary statistics.This table reports the statistics of the main variables used in the paper. The sample comprises 19,074 firm-year observations representing 2473 unique firms over theperiod 2000–2014. Definitions of variables are provided in Appendix 1.

Mean S.D. P10 P25 P50 P75 P90 N

DVE 0.214 0.287 0 0 0.104 0.338 0.590 19,074DVC 0.245 0.556 0 0 0.050 0.260 0.583 19,074DVP 0.540 0.498 0 0 1 1 1 19,074Liquidity –0.195 0.218 –0.495 –0.262 –0.109 –0.049 –0.025 19,074Size (million yuan) 4843.7 10,953 562.7 935.2 1771.4 3821.8 9358.5 19,074ROA 0.030 0.070 –0.019 0.011 0.034 0.062 0.095 19,074Q 1.796 1.045 1.060 1.182 1.451 1.973 2.880 19,074Lev 0.225 0.170 0 0.076 0.213 0.346 0.455 19,074Cash 0.184 0.148 0.041 0.080 0.141 0.242 0.392 19,074Top1 38.299 16.199 18.560 25.410 36.160 50.510 61.360 19,074Independence 2.912 1.133 2 3 3 3 4 19,074


Because our first two dependent variables, DVE and DVC, are left-censored, we follow Chay and Suh (2009) in using the Tobitmethod to estimate the model. However, all our inferences basically hold if we use ordinary least squares instead. Since our thirddependent variable, DVP, is a dummy variable, we estimate the model using the Logit method. To account for the possibility thaterrors are not independently and identically distributed, we use robust standard errors clustered at the firm level (Petersen,2009).

Summary statistics for the sample are reported in Table 1. On average, only 54% of the sample firm-years pay dividends. Themeans (medians) of dividend-to-earnings ratio (DVE) and dividend-to-cash flow ratio (DVC) for the sample firms are 21.4%(10.4%) and 24.5% (5%), respectively, indicating that the distribution of corporate payouts is skewed to the right. Using the divi-dend-to-earnings ratio as a proxy for dividend payouts, as Allen et al. (2005) found, we find that on average Chinese firms tend tounderpay dividends to their shareholders compared with firms in countries studied by La Porta et al. (2000a).

Table 2 shows the Pearson correlations among the variables. Not surprisingly, the three dividend payout measures (DVE, DVC,and DVP) are highly correlated. The positive correlations between Liquidity and dividend payout measures indicate that firms withmore liquid stock tend to have more dividend payouts throughout the sample period of 2000–2014. Dividend payouts are alsopositively correlated with firm size, profitability, cash holding, controlling shareholders' shareholding, and board independence(only the DVP measure), and negatively correlated with leverage and Tobin's Q. Moreover, stock liquidity is also positively corre-lated with firm size, profitability, Tobin's Q, cash holding, and board independence, and negatively correlated with leverage andcontrolling shareholders' shareholding. Finally, correlations among other control variables suggest no serious concern overmulticollinearity. The highest correlation coefficient (in magnitude) is between Cash and Lev (−0.472, p = 0.000).

4. Empirical evidence

4.1. Univariate analysis

We begin our analysis with univariate comparisons between dividend payouts of firms with high stock liquidity and thosewith low stock liquidity. Firms are classified into high stock liquidity and low stock liquidity groups according to the median li-quidity in each year. Observations with stock liquidity above the median are called the high stock liquidity group, while those

Table 2Pearson correlation matrix.This table reports the Pearson correlations for all the variables. The sample comprises 19,074 firm-year observations representing 2473 unique firms over the period2000–2014. Definitions of variables are provided in Appendix 1.

DVE DVC DVP Liquidity Log of size ROA Q Lev Cash Top1 Independence

DVE 1DVC 0.441⁎⁎⁎ 1DVP 0.688⁎⁎⁎ 0.406⁎⁎⁎ 1Liquidity 0.118⁎⁎⁎ 0.104⁎⁎⁎ 0.256⁎⁎⁎ 1Log of size 0.119⁎⁎⁎ 0.018⁎⁎ 0.264⁎⁎⁎ 0.424⁎⁎⁎ 1ROA 0.255⁎⁎⁎ 0.201⁎⁎⁎ 0.414⁎⁎⁎ 0.261⁎⁎⁎ 0.155⁎⁎⁎ 1Q –0.097⁎⁎⁎ –0.0110 –0.092⁎⁎⁎ 0.160⁎⁎⁎ –0.352⁎⁎⁎ 0.086⁎⁎⁎ 1Lev –0.194⁎⁎⁎ –0.204⁎⁎⁎ –0.242⁎⁎⁎ –0.128⁎⁎⁎ 0.216⁎⁎⁎ –0.377⁎⁎⁎ –0.208⁎⁎⁎ 1Cash 0.193⁎⁎⁎ 0.227⁎⁎⁎ 0.250⁎⁎⁎ 0.162⁎⁎⁎ –0.129⁎⁎⁎ 0.307⁎⁎⁎ 0.086⁎⁎⁎ –0.472⁎⁎⁎ 1Top1 0.162⁎⁎⁎ 0.064⁎⁎⁎ 0.158⁎⁎⁎ –0.053⁎⁎⁎ 0.208⁎⁎⁎ 0.118⁎⁎⁎ –0.193⁎⁎⁎ –0.030⁎⁎⁎ –0.004 1Independence 0.006 0.003 0.083⁎⁎⁎ 0.111⁎⁎⁎ 0.295⁎⁎⁎ 0.042⁎⁎⁎ –0.073⁎⁎⁎ 0.021⁎⁎⁎ 0.015⁎⁎ –0.103⁎⁎⁎ 1

⁎ Denotes significance at the 10% level.⁎⁎ Denote significance at the 5% level.⁎⁎⁎ Denote significance at the 1% level.

Table 3Univariate analysis.This table reports univariate tests on the difference in dividend payouts between firms with high stock liquidity and those with low stock liquidity. Firms are classifiedinto high stock liquidity and low stock liquidity groups according to themedian liquidity in each year. Observationswith stock liquidity above themedian are called thehigh stock liquidity group,while those below form the low stock liquidity group. Definitions of variables are provided inAppendix 1. The sample comprises 19,074 firm-year observations representing 2473 unique firms over the period 2000–2014. Definitions of variables are provided in Appendix 1.

High stock liquidity Low stock liquidity Test of difference

Mean Median Mean Median t-Stat. z-Stat.

DVE 0.244 0.178 0.184 0.000 14.5⁎⁎⁎ 22.8⁎⁎⁎

DVC 0.278 0.107 0.212 0.000 8.3⁎⁎⁎ 24.6⁎⁎⁎

DVP 0.639 1.000 0.442 0.000 27.8⁎⁎⁎ 27.2⁎⁎⁎

N 9542 9532



below form the low stock liquidity group. The results are presented in Table 3. First, consider the relation between liquidity anddividend-to-earnings ratio (DVE). Firms with high stock liquidity pay out 24.4% of their earnings on average, while firms with lowstock liquidity pay out 18.4%. The t-statistic for the difference in means is 14.4, and the Wilcoxon z-statistic for the difference inmedians is 22.8, indicating that these differences are both statistically significant at the 1% level. We obtain consistent results forthe relations between liquidity and dividend-to-cash-flow ratio (DVE) and liquidity and the propensity to dividend (DVP).

4.2. Multivariate regression analysis

Although univariate tests show that firms' dividend payouts depend on stock liquidity, they omit a number of determinantsthat could be correlated with both liquidity and dividend payout policy. Therefore, we use multivariate analysis to formally eval-uate the magnitude of the informational effect of stock liquidity after controlling for other determinants of dividends. The mainmultivariate results are shown in Table 4. In the first three columns, we do not include any controls. The coefficients of liquidityare significant and positive for DVE (0.4976, t = 16.32), DVC (0.9913, t = 18.16), and DVP (2.6683, t = 21.91), and are consistentwith our univariate analysis. Next, in columns 4, 5, and 6, we include firm financial characteristics along with industry and yearfixed effects. The coefficients on liquidity are still significantly positive, though their magnitudes become smaller (0.2670, t = 6.82in column 4 for DVE, 0.4475, t = 7.27 in column 5 for DVC, and 1.1576, t = 6.38 in column 6 for DVP). We then estimate a fullmodel described in Eq. (1) that incorporates additional firm corporate governance characteristics in columns 7, 8, and 9. In allthree columns, we find results consistent with those estimated earlier. In particular, the coefficient on stock liquidity is positiveand significant when we examine DVE (0.2901, t = 7.35), DVC (0.5056, t = 7.66), and DVP (1.2848, t = 6.98) respectively.After accounting for other firm characteristics, we find that dividend levels are positively associated with stock liquidity, and sois the likelihood that a firm will pay dividends. The results are not only statistically significant but also economically significant.For example, the expected increase in dividend payouts associated with a one-standard-deviation increase in stock liquidity isabout 11.82% of the mean DVM of 0.214.

The coefficients on control variables are generally consistent with findings in previous studies. Dividends decrease with invest-ment opportunities and leverage, and increase with firm size, profitability, and cash holding. In accord with Mitton's (2004) find-ings that firms with less severe agency problems or strong corporate governance pay more dividends, the largest shareholder'sownership and board independence have a positive effect on dividends.

5. Robustness tests

In the previous section, we show that there is a positive relation between stock liquidity and dividend payouts after we controlfor other factors that have been shown to affect dividend payouts. In this section, we examine the robustness of our results acrossalternative measures of stock liquidity and to potential endogeneity concerns.

5.1. Alternative measures of liquidity

The documented positive relation between stock liquidity and dividend payouts may be driven by our choice of stock liquiditymeasure. To alleviate this concern, we consider the following two alternative measures of stock liquidity: Lesmond et al.'s (1999)percentage of zero daily returns measure and Corwin and Schultz's (2012) high-low impact spread estimator. These two measureshave been widely used in studies on both U.S. and emerging markets (e.g., Bekaert et al., 2007; Chen et al., 2015; Fong et al., 2016;McLean and Pontiff, 2016). Since a higher value of these two measures corresponds to a lower level of liquidity, we multiply eachmeasure by −1 for ease of interpretation.

4 As the coefficient estimates of Tobit regressions are not exactly the same asmarginal effects, we use the transformation suggested by Greene (2008, p.873) to eval-uate the economic significance of the impact of liquidity on dividend payouts.

Table 4Main regression results.This table reports the results ofmain regressions of stock liquidity on dividend policy. The dependent variables are dividend-to-earnings ratio (DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variable is stock liquidity (Liquidity). Definitions of variables are provided inAppendix 1. The samplecomprises 19,074 firm-year observations representing 2473 uniquefirms over the period 2000–2014. All regressions include industry- and year-fixed effects. The t-sta-tistics reported in parentheses are based on robust standard errors clustered by firm.

(1) (2) (3) (4) (5) (6) (7) (8) (9)DVE DVC DVP DVE DVC DVP DVE DVC DVP

Liquidity 0.4976⁎⁎⁎ 0.9913⁎⁎⁎ 2.6683⁎⁎⁎ 0.2670⁎⁎⁎ 0.4775⁎⁎⁎ 1.1576⁎⁎⁎ 0.2901⁎⁎⁎ 0.5056⁎⁎⁎ 1.2848⁎⁎⁎

(16.32) (18.16) (21.91) (6.82) (7.27) (6.38) (7.35) (7.66) (6.98)Log of size 0.0454⁎⁎⁎ 0.0517⁎⁎⁎ 0.4228⁎⁎⁎ 0.0314⁎⁎⁎ 0.0335⁎⁎⁎ 0.3655⁎⁎⁎

(7.37) (5.19) (11.66) (4.90) (3.20) (9.67)ROA 2.9446⁎⁎⁎ 5.7903⁎⁎⁎ 22.9533⁎⁎⁎ 2.8712⁎⁎⁎ 5.7101⁎⁎⁎ 22.6540⁎⁎⁎

(24.44) (23.65) (24.45) (24.19) (23.69) (24.33)Q −0.0975⁎⁎⁎ −0.1628⁎⁎⁎ −0.4653⁎⁎⁎ −0.0936⁎⁎⁎ −0.1584⁎⁎⁎ −0.4501⁎⁎⁎

(−15.64) (−13.80) (−12.30) (−15.13) (−13.52) (−12.00)Lev −0.3671⁎⁎⁎ −0.6123⁎⁎⁎ −1.6250⁎⁎⁎ −0.3408⁎⁎⁎ −0.5813⁎⁎⁎ −1.5294⁎⁎⁎

(−8.78) (−8.95) (−7.74) (−8.21) (−8.48) (−7.32)Cash 0.3395⁎⁎⁎ 0.7816⁎⁎⁎ 1.9216⁎⁎⁎ 0.3323⁎⁎⁎ 0.7736⁎⁎⁎ 1.9016⁎⁎⁎

(8.97) (11.13) (8.99) (8.89) (11.07) (8.95)Top1 0.0023⁎⁎⁎ 0.0028⁎⁎⁎ 0.0103⁎⁎⁎

(6.06) (4.40) (5.38)Independence 0.0267⁎⁎⁎ 0.0402⁎⁎⁎ 0.1137⁎⁎⁎

(3.65) (3.35) (2.98)Intercept 0.1425⁎⁎⁎ 0.0985⁎⁎⁎ 0.6671⁎⁎⁎ −0.7306⁎⁎⁎ −0.9052⁎⁎⁎ −8.6826⁎⁎⁎ −0.5600⁎⁎⁎ −0.6743⁎⁎⁎ −8.0107⁎⁎⁎

(16.66) (7.45) (17.71) (−5.14) (−3.91) (−10.74) (−3.87) (−2.84) (−9.75)Industry effect No No No Yes Yes Yes Yes Yes YesYear effect No No No Yes Yes Yes Yes Yes YesN 19,074 19,074 19,074 19,074 19,074 19,074 19,074 19,074 19,074Pseudo R2 0.028 0.022 0.050 0.203 0.130 0.280 0.208 0.131 0.283


Table 5Alternative liquidity measure regression results.This table reports the results of alternative liquidity measure regression of stock liquidity on dividend policy. The dependent variables are dividend-to-earnings ratio(DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). Liquidity measures are Lesmond et al.'s (1999) percentage of zero daily returns measure andCorwin and Schultz's (2012) high-low impact spread estimator. Other variable definitions are provided in Appendix 1. The sample comprises 19,074 firm-year obser-vations representing 2473 unique firms over the period 2000–2014. All regressions include industry- and year-fixed effects. The t-statistics reported in parentheses arebased on robust standard errors clustered by firm.

Panel A: Percentage of zero daily returns Panel B: High-Low impact spread estimator

(1)DVE

(2)DVC

(3)DVP

(4)DVE

(5)DVC

(6)DVP

Liquidity 0.0113⁎⁎⁎ 0.0295⁎⁎⁎ 0.1072⁎⁎⁎ 0.1973⁎⁎⁎ 0.1899⁎⁎⁎ 0.6906⁎⁎⁎

(4.56) (6.77) (8.36) (7.81) (4.11) (5.14)Log of size 0.0530⁎⁎⁎ 0.0726⁎⁎⁎ 0.4864⁎⁎⁎ 0.0457⁎⁎⁎ 0.0616⁎⁎⁎ 0.4478⁎⁎⁎

(8.65) (7.24) (13.55) (7.47) (6.13) (12.43)ROA 2.9125⁎⁎⁎ 5.7165⁎⁎⁎ 22.5046⁎⁎⁎ 2.9289⁎⁎⁎ 5.8492⁎⁎⁎ 22.8743⁎⁎⁎

(24.47) (23.71) (24.18) (24.78) (23.97) (24.36)Q −0.0903⁎⁎⁎ −0.1527⁎⁎⁎ −0.4299⁎⁎⁎ -0.0840⁎⁎⁎ −0.1466⁎⁎⁎ −0.4043⁎⁎⁎

(−14.55) (−13.00) (−11.38) (−13.57) (−12.35) (−10.51)Lev −0.3613⁎⁎⁎ −0.6175⁎⁎⁎ −1.6332⁎⁎⁎ −0.3383⁎⁎⁎ −0.5925⁎⁎⁎ −1.5457⁎⁎⁎

(−8.72) (−9.05) (−7.91) (−8.17) (−8.58) (−7.42)Cash 0.3434⁎⁎⁎ 0.7776⁎⁎⁎ 1.9036⁎⁎⁎ 0.3668⁎⁎⁎ 0.8274⁎⁎⁎ 2.0730⁎⁎⁎

(9.09) (11.10) (8.91) (9.84) (11.84) (9.76)Top1 0.0021⁎⁎⁎ 0.0024⁎⁎⁎ 0.0086⁎⁎⁎ 0.0021⁎⁎⁎ 0.0024⁎⁎⁎ 0.0089⁎⁎⁎

(5.46) (3.77) (4.56) (5.43) (3.76) (4.72)Independence 0.0273⁎⁎⁎ 0.0415⁎⁎⁎ 0.1174⁎⁎⁎ 0.0265⁎⁎⁎ 0.0402⁎⁎⁎ 0.1132⁎⁎⁎

(3.74) (3.46) (3.10) (3.62) (3.33) (2.98)Intercept −1.0194⁎⁎⁎ −1.4818⁎⁎⁎ −10.4252⁎⁎⁎ −0.7194⁎⁎⁎ −1.1555⁎⁎⁎ −9.2762⁎⁎⁎

(−7.41) (−6.53) (−13.43) (−5.15) (−4.94) (−11.66)Industry effect Yes Yes Yes Yes Yes YesYear effect Yes Yes Yes Yes Yes YesN 19,074 19,074 19,074 19,074 19,074 19,074Pseudo R2 0.204 0.130 0.283 0.206 0.129 0.281




The results are reported in Table 5. The coefficients of stock liquidity are positive and significant for each liquidity measure forall three payout measures. This suggests that our findings are robust across alternative measures of stock liquidity.

5.2. Endogeneity

The documented positive relation between stock liquidity and dividend payouts may be biased by endogeneity concerns relat-ed to omitted variables and reverse causality. We use several approaches to mitigate endogeneity concerns.

5.2.1. Endogeneity—omitted variablesTo make sure that our results are not driven by omitted variables bias, we first perform a firm-fixed effects regression analysis.

The inclusion of firm-fixed effects in the regression models controls for time-invariant firm-specific characteristics that may becorrelated with omitted explanatory variables and removes any purely cross-sectional correlation between stock liquidity and div-idend payouts, reducing the risk of omitted variables bias.

The results are reported in Table 6. The coefficient on stock liquidity is positive and significant in all three columns, suggestingthat firms with more liquid stock tend to have higher dividend payouts even after we control for unobservable time-invariantfirm-specific characteristics.

Second, we use an instrumental variable estimation approach to further alleviate concern about omitted variables. One benefitof this method is that the unobservable item can be inconstant across time. Following Fang et al. (2009), we use the mean liquid-ity of the two firms in firm i's industry with closest size to firm i as an exogenous instrument.5 Fang et al. (2009) point out thatthe portion of firm i's liquidity that is correlated with the average liquidity of its two similarly sized competitors is less likely to becorrelated with unobservable factors that affect the outcome variable, which is firm i's dividend payouts in our case. Because ourfirst two dependent variables, DVE and DVC, are left-censored and our third dependent variable, DVP, is a dummy variable, we usethe IV-Tobit and IV-Probit methods respectively (see, e.g., Hellmann et al., 2008).6

Table 7 reports the results from the instrumental variable estimation. Column 1 reports the first stage with liquidity as the de-pendent variable. As we expected, our instrumental variable is positive. And a weak IV test suggests that the instrument is notsubject to a weak-instrument problem. Columns 2, 3, and 4 report our second-stage regression results, with DVE, DVC and DVPas dividend payouts measures. The main finding is that stock liquidity continues to be positively correlated with dividend payouts.Table 7 also reports p-values for the Smith–Blundell exogeneity test that suggest that we fail to reject the exogeneity of our in-strument. In sum, these findings suggest that omitted variables do not seem to affect our main result that stock liquidity increasesdividend payouts.

5.2.2. Endogeneity—reverse causalityThe results above may be subject to reverse causality. For example, investors may prefer firms that pay more dividends and

simply trade more in them. To address this concern, we explore a quasi-natural experiment, the split share structure reform in2005 in China (hereafter, the share reform or the reform), which provides a plausibly exogenous liquidity shock. Taking advantageof this unique setting, we attempt to examine the causal effect of liquidity on firm dividend payouts.

From 2005, the CSRC mandated a reform to eliminate the two-tier share structure in Chinese listed companies.7 Before the re-form, listed shares were divided into non-tradable shares and tradable shares. The non-tradable shares held by the state and bylegal persons, which had exactly the same voting and cash flow rights as the tradable ones,8 accounted for approximately two-thirds of total shares. Tradable shares were issued to institutional and individual investors through the IPO subscription process.The share reform allows previously non-tradable shares to be freely traded on stock exchanges. More specifically, the non-trad-able shares were locked up for 1 year after the completion of the reform. Within 1 (2) year(s) after the lockup period, holdersof non-tradable shares could transfer up to 5% (10%) of total shares outstanding. Starting from the fourth year after the comple-tion of reform, the non-tradable shares became fully tradable.

Table 8 shows that the split share reform significantly reduces the market friction associated with non-tradable shares andprovides a significant and permanent shock to share liquidity. Thus, it can be used in a difference-in-differences (DID) frameworkto examine the causal effect of liquidity on dividend payouts.

Besides the liquidity shock, the reform aligns the interests of controlling shareholders and minority shareholders, which mayalso affect dividend payouts. Chen et al. (2012) find that the reform increases dividend payouts by improving governance andrelaxing financial constraints. However, Pan et al. (2015) find that cash dividends decrease significantly after the reform, andthey attribute this result to the improvements in firms' governance and liquidity.

To remove the influence of the reform on corporate governance and isolate the liquidity effect on dividend payout, we followFang et al.’s (2014) approach. We first define a treatment group and a control group of firms by change in annual liquidity. Wethen conduct propensity score matching on ownership structure, matching each firm in the treatment group with one in the

5 Besidesmean liquidity of competitors, Fang et al. (2009) also use lagged liquidity as another exogenous instrument. However, Roberts andWhited (2012) point outthat lagged values are also likely correlatedwith the error term.Moreover, an untabulated exogeneity test rejects the null hypothesis that lagged liquidity is exogenous.Therefore, we do not use lagged liquidity as another exogenous instrument in our study.

6 In untabulated tests, our results are robust to the use of two stage least squares procedures.7 See Li et al. (2011) and Liao et al. (2014) for more details.8 The state can be central or local governments. The legal persons are typically affiliated firms of central or local governments, founders, managers, strategic investors,

etc.

Table 6Firm-fixed effect regression results.This table reports the results of firm-fixed effect regression of stock liquidity on dividend policy. The dependent variables are dividend-to-earningsratio (DVE), dividend-to-cash-flowratio (DVC), and propensity to pay (DVP). The key explanatory variable is stock liquidity (Liquidity). Definitions ofvariables are provided in Appendix 1. The sample period is from 2000 to 2014. For column 3, sample size is reduced because 1187 firms (6617 ob-servations) lack variation in propensity to pay (DVP). All regressions include firm-fixed and year-fixed effects. The t-statistics reported in parenthe-ses are based on robust standard errors clustered by firm.

(1) (2) (3)DVE DVC DVP

Liquidity 0.1298⁎⁎⁎ 0.2339⁎⁎⁎ 0.7824⁎⁎⁎

(3.58) (3.37) (3.50)Log of size 0.0402⁎⁎⁎ 0.0593⁎⁎⁎ 0.3986⁎⁎⁎

(3.46) (2.60) (4.95)ROA 1.4962⁎⁎⁎ 3.6274⁎⁎⁎ 14.1355⁎⁎⁎

(12.83) (14.37) (15.55)Q −0.0396⁎⁎⁎ −0.0581⁎⁎⁎ −0.1176⁎⁎

(−7.01) (−4.70) (−2.36)Lev −0.3048⁎⁎⁎ −0.6057⁎⁎⁎ −1.6265⁎⁎⁎

(−6.28) (−6.73) (−5.20)Cash 0.2081⁎⁎⁎ 0.7098⁎⁎⁎ 1.2477⁎⁎⁎

(5.02) (7.82) (4.14)Top1 0.0034⁎⁎⁎ 0.0055⁎⁎⁎ 0.0201⁎⁎⁎

(5.31) (4.60) (5.00)Independence 0.0104 0.0133 0.0767

(1.37) (0.95) (1.49)Intercept −0.6009⁎⁎ −0.6294

(−2.21) (−1.18)Firm effect Yes Yes YesYear effect Yes Yes YesN 19,074 19,074 12,413Pseudo R2 0.538 0.309 0.134



control group that has the same ownership structure, and thus removing the influence of the reform on corporate governance.After that, we test whether the treatment and control groups experience different levels of increase in dividend payouts.

Specifically, to construct a treatment group and a control group, we require that our sample firms were listed before December31, 2004, the year before the share reform, to make sure that that all the firms experienced the liquidity shock. Also, we focus onthe great majority of firms (76.8%) that had finished the reform by the end of 2009.9 We then measure the change in liquidity(ΔLiquidity) from the pre-shock year (year −1) to the post-shock year (year +1), where year zero indicates the lockup period.We then divide our sample firms into 3 groups according to their increase in liquidity (ΔLiquidity(−1 to 1)) surrounding the liquid-ity shock. The top group—the treatment group—consists of firms experiencing the largest increase in liquidity surrounding liquid-ity shock, while the bottom group—the control group—consists of firms experiencing the smallest increase in liquidity.

We use a six-year window (from year −3 to year +3) to perform our DID analysis. The choice of a six-year window reflects atrade-off between relevance and accuracy. Choosing too wide a window could incorporate too much noise irrelevant to the eventsand could bias our matching and thus lower the validity of our test. But using a relatively narrow window surrounding the reformleaves limited time for the change in liquidity to affect firm dividend policy. We have qualitatively similar but statistically weakerresults if we use a two-year or four-year window.

We then conduct propensity score matching to match each firm in the treatment group (top group) with a firm in thecontrol group (bottom group) on the arithmetic mean of all control variables computed over the three-year period beforethe liquidity shock. Specifically, we estimate a probit model in which the dependent variable is equal to one if the firm-yearbelongs to the treatment group (top group) and zero otherwise. We also control for industry and year dummies in the probitmodel.

We report the probit model results in column 1 of Table 9, Panel A (labeled “Pre-Match”). The estimation results suggest firmcharacteristics across the treatment and control groups differ statistically significantly. This indicates that it is indeed necessary tomatch the treatment and control groups to provide an accurate estimate of the effect of stock liquidity on dividend payouts.

We then use the predicted probabilities, or propensity scores, from column 1 and perform a nearest-neighbor propensity scorematching procedure. Specifically, we match each firm-year observation in the top group (the treatment group) to a firm-year

9 According to Liao et al. (2014), there were 1315 firms with split-share structure before the reform. By the end of 2009, 1011 firms in our sample had finished thereform (themajority of excluded firms are excluded because of negative earnings or cash flows andmissing information for control variables). Most of the late-movershad some unusual difficulties in carrying out the reform. Nevertheless, our inferences remain the same if we include the firms that finished the reform after 2009.

Table 7Instrumental variable estimation results.This table reports the results of instrumental variable estimation of stock liquidity on dividend policy. The instrument variable, Ind_Mean_Liquidity, is themean liquidityof the two firms in firm i's industry that are closest in size to firm i. Column 1 presents the first-stage regression results and columns 2–4 present the second-stage re-gression results, with dividend-to-earnings ratio (DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP) as dependent variables, respectively. Other var-iable definitions are provided in Appendix 1. The sample period is from 2000 to 2014. Sample size is reduced because of missing values for Ind_Mean_Liquidity. Allregressions include industry- and year-fixed effects. The t-statistics reported in parentheses are based on robust standard errors clustered by firm.

First StageSecond Stage

(1) (2) DVE (3) DVC (4) DVP

Fitted liquidity 0.3816⁎⁎⁎ 0.5943⁎⁎⁎ 0.7772⁎⁎

(3.02) (2.69) (2.22)Log of size 0.0569⁎⁎⁎ 0.0246⁎⁎ 0.0268 0.2174⁎⁎⁎

(28.59) (2.32) (1.45) (6.47)ROA 0.2006⁎⁎⁎ 2.8524⁎⁎⁎ 5.6922⁎⁎⁎ 11.9722⁎⁎⁎

(7.89) (23.53) (23.20) (24.33)Q 0.0142⁎⁎⁎ −0.0951⁎⁎⁎ −0.1598⁎⁎⁎ −0.2466⁎⁎⁎

(7.90) (−14.34) (−12.92) (−11.41)Lev −0.0789⁎⁎⁎ −0.3326⁎⁎⁎ −0.5733⁎⁎⁎ −0.9480⁎⁎⁎

(−6.08) (−7.79) (−8.07) (−7.54)Cash 0.1007⁎⁎⁎ 0.3223⁎⁎⁎ 0.7639⁎⁎⁎ 1.1691⁎⁎⁎

(8.85) (8.26) (10.47) (9.02)Top1 −0.0009⁎⁎⁎ 0.0024⁎⁎⁎ 0.0029⁎⁎⁎ 0.0062⁎⁎⁎

(−7.71) (6.11) (4.36) (5.29)Independence 0.0012 0.0267⁎⁎⁎ 0.0402⁎⁎⁎ 0.0683⁎⁎⁎

(0.57) (3.65) (3.35) (3.01)Ind_Mean_Liquidity 0.2982⁎⁎⁎

(17.26)Intercept −1.2538⁎⁎⁎ −0.4067⁎ −0.5255 −4.7277⁎⁎⁎

(−29.01) (−1.71) (−1.26) (−6.32)Industry effect Yes Yes Yes YesYear effect Yes Yes Yes YesN 19,070 19,070 19,070 19,070Adj.R2 0.6066Model Wald statistic 1844.2 1844.2 1844.2Wald statistic of weak IV (p-value) 0.0007Smith–Blundell exogeneity test (p-value) 0.6716 0.3983 0.9735



observation in the bottom group (the control group) with the closest propensity score. We end up with 130 one-to-one pairs ofmatched firms (260 observations).10

After matching, we re-estimate the probit model using the matched sample and report the estimation results in column 2 ofTable 9, Panel A (labeled “Post-Match”). All of the matching variables are statistically insignificant, and the χ2 test fails to rejectthe null hypothesis that all of the coefficient estimates of matching variables in column 2 are zero. Panel B of Table 9 shows dif-ferences in pre-reform firm characteristics between the treatment and control groups, indicating that our matching procedure ac-curately matches treatment and control firms on observables and thus has removed observable differences in pre-shockcharacteristics between the treatment and control groups.

To capture the causal effect of liquidity on dividend payouts, we perform the DID test in a multivariate regression framework.Specifically, we estimate the following model with our matched samples:

10 In asignificament ancost of m

Payouti;tþ1 ¼ αi;t þ β1Treati;t � Afteri;t þ β2Treati;t þ β2Afteri;t þ γ0Controlsi;t þ εi;t ; ð2Þ

where Treat is a dummy variable that takes the value of one for treatment firms and zero for control firms, and After is a dummyvariable that takes the value of one for firm-year observations after a firm experiences the liquidity shock and zero otherwise.Controls are the same as in Eq. (1).

Table 10 reports the results. The coefficient on Treat is negative and significant, suggesting that dividend payouts are lower forthe treatment group before the reform. In accord with Chen et al. (2012), we find that the coefficient on Post is positive and sig-nificant (marginal for DVE), suggesting that dividend payouts increase after the reform. As we predicted, Table 10 shows that thecoefficients of Treat × After for all three columns are positive and significant, suggesting that the treatment group experiences alarger increase in dividend payouts after the reform.

nuntabulated robustness check, to obtainmorematched pairs,we re-perform thematchingwith replacement, and theDID estimates are evenmore statisticallynt than thosewithout replacement. However,matchingwith replacement results in differences infirm characteristics in the pre-reformperiod across the treat-d control groups, yielding an inaccurate estimate of the effect of stock liquidity on dividend payouts.Matchingwith replacement increasesmatched pairs at theatching precision. To ensure the validity of our DID test, we choose to stay on the conservative side and sacrifice statistical power of our tests.

Table 8Distribution of change in liquidity.This table reports the distribution of change in liquidity from one year before the reform to one year after the reform.

Mean S.D. P10 P25 P50 P75 P90 N

Δ Liquidity 0.356 0.285 0.037 0.131 0.316 0.543 0.759 1011Test ΔLiquidity N 0 t-Value = 39.75 p-Value b 0.0001


However, the increase in dividend payout may be capturing the effects of other market-wide confounding events or continuingtime trends rather than the effect of the reform per se or the effects of the reform on other firm characteristics rather than liquid-ity. To alleviate this concern, we first repeat the analysis, falsely assuming that the reform occurred in 2004, to test whether theestimated treatment effect is significant or not. Given the placebo nature of this test, the estimated treatment effect should be in-significant, and indeed our results (untabulated) show that the coefficients on all three estimated treatment effect indicators areinsignificant. This falsification test indicates that the increase in dividend payouts is capturing the effect of the reform and is notattributable to the effects of other market-wide confounding events or time trends. We then examine the changes in firm size,profitability, growth opportunity, leverage, cash holding, top1 shareholding, and board independence in our DID framework tomake sure that our control variables are unaffected by the reform. Our results (untabulated) demonstrate that treatment firmsand control firms do not differ statistically significantly in changes in our control variables (though there is a marginally

Table 9Matching diagnostic and post-matching differences.This table reports the matching diagnostic. Sample selection begins with all firms with non-missing matching variables in the year before the liquidity shock and theyear after the shock. Wematch firms using one-to-one nearest-neighbor propensity score matching, without replacement, on a host of observable firm characteristics.Panel A reports parameter estimates from the probit model used to estimate propensity scores for firms in the treatment and control groups. Panel B reports the uni-variate comparisons between the treatment and controlfirms' characteristics and their corresponding t-statistics. Definitions of variables are provided inAppendix 1. Allregressions include industry- and year-fixed effects.

Panel A: Pre-match propensity score regression and post-match diagnostic regression

(1) (2)Pre-match Post-match

Log of size (− 3 to − 1) −0.9388⁎⁎⁎ 0.0709(−8.87) (0.47)

ROA (− 3 to − 1) −4.5958⁎⁎⁎ −3.5975(−2.78) (−1.58)

Q (− 3 to − 1) −0.3433⁎⁎ 0.0882(−2.06) (0.39)

Lev (− 3 to − 1) 0.3008 0.1878(0.57) (0.29)

Cash (− 3 to − 1) −0.5089 0.5131(−0.71) (0.57)

Top1(− 3 to − 1) 0.0064 0.0010(1.44) (0.18)

Independence (− 3 to − 1) −0.0128 0.0377(−0.13) (0.30)

Intercept 19.6340⁎⁎⁎ −2.1419(8.41) (−0.64)

Industry effect Yes YesYear effect Yes YesN 630 259Pseudo R2 0.447 0.038

Panel B: Post-match differences

Treatment Control Difference t-Statistic

Log of size (− 3 to − 1) 21.255 21.198 0.057 0.570ROA (− 3 to − 1) 0.019 0.026 −0.006 −0.932Q (− 3 to − 1) 1.389 1.434 −0.044 −0.667Lev (− 3 to − 1) 0.272 0.254 0.018 0.987Cash (− 3 to − 1) 0.147 0.148 −0.001 −0.055Top1 (− 3 to − 1) 42.810 42.435 0.375 0.189Independence (− 3 to − 1) 3.241 3.203 0.038 0.454


Table 10DID results.This table reports the results of main regressions of stock liquidity on dividend policy. The dependent variables are dividend-to-earnings ratio (DVE),dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variable is the interaction term between Treat and After. Defi-nitions of variables are provided in Appendix 1. The sample uses a six-year window (from year−3 to year +3) around the reform. All regressionsinclude industry- and year-fixed effects. The t-statistics reported in parentheses are based on robust standard errors clustered by firm.

(1) (2) (3)DVE DVC DVP

Treat −0.0941⁎ −0.1238 −0.4747⁎⁎

(−1.81) (−1.54) (−2.06)After 0.3692 0.7012⁎ 2.7689⁎⁎

(1.63) (1.95) (2.03)Treat⁎After 0.1471⁎⁎⁎ 0.1778⁎⁎ 0.8012⁎⁎⁎

(2.69) (1.98) (3.04)Log of size 0.0738⁎⁎⁎ 0.0955⁎⁎ 0.5310⁎⁎⁎

(2.65) (2.02) (3.71)ROA 4.5001⁎⁎⁎ 6.4863⁎⁎⁎ 28.1171⁎⁎⁎

(8.42) (6.98) (7.32)Q −0.0834⁎⁎⁎ −0.1234⁎⁎⁎ −0.3376⁎⁎

(−3.10) (−2.72) (−2.25)Lev -0.0080 -0.0438 0.6201

(−0.06) (−0.20) (0.99)Cash 0.3647⁎ 1.2941⁎⁎⁎ 1.9079⁎⁎

(1.89) (3.32) (2.07)Top1 0.0022 0.0013 0.0027

(1.55) (0.61) (0.44)Independence 0.0589⁎⁎ 0.0731⁎ 0.2406⁎

(2.14) (1.73) (1.91)Intercept −2.1151⁎⁎⁎ −2.8161⁎⁎⁎ −13.5022⁎⁎⁎

(−3.51) (−2.75) (−4.36)Industry effect Yes Yes YesYear effect Yes Yes YesN 1446 1446 1413Pseudo R2 0.221 0.147 0.265



significant difference in change in firm size), suggesting that our results are not driven by the effects of the reform on other firmcharacteristics.

Taken together, our results suggest a positive causal relation running from stock liquidity to dividend payouts.

6. Information asymmetry, stock liquidity, and dividend payouts

Our findings thus far suggest a positive relation between stock liquidity and dividend payouts. We argue that higher stock li-quidity enables informed investors to trade on private information and impound more information about the controlling share-holders' actions in the stock price, and thus makes controlling shareholders pay more dividends.

To validate stock liquidity's informational effect on dividend payouts, we test whether there is any difference in the effect ofliquidity on dividend payouts among firms with different extents of information asymmetry. If it is true that stock liquidity in-creases dividend payouts by mitigating information asymmetry between controlling shareholders and minority investors, decreas-ing controlling shareholders' propensity to expropriation and increasing their incentive to pay more dividends, then the relationbetween liquidity and dividend payouts should be stronger when the information environment is more opaque.

Following the literature, we use two proxies to measure the extent of information asymmetry. First, we look into analyst fol-lowings. More analyst coverage supplies more information and thereby lessens information asymmetry (see, e.g., Yu, 2008; Bowenet al., 2008). Second, we focus on the quality of the firm's auditor. Good auditors improve financial statements by reducing bothintentional and unintentional measurement errors in historical earnings (e.g., Becker et al., 1998), so as to decrease the informa-tion asymmetry between controlling shareholders and outside investors.

Empirically, we use Analyst and Big4 to proxy for information asymmetry. Analyst is the logarithm of (one plus) the number ofanalysts following the firm, and Big4 is a dummy variable that equals one if a firm is audited by a Big 4 Auditor firm and zerootherwise. We implement our tests by augmenting our baseline model to include each factor under study and its interactionwith stock liquidity.

Panel A of Table 11 reports the results on analyst following. The coefficient estimates of the interaction term between Liquidityand Analyst are significantly negative in all three columns, which means that the positive relation between stock liquidity and div-idend payouts is more pronounced when there is less analyst following. Panel B of Table 11 reports the results on types of auditor

Table 11Tests for information asymmetry.This table reports the impact of information asymmetry on the relation between liquidity and dividendpayouts. The dependent variables are dividend-to-earnings ratio(DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variables are the interaction term between Liquidity and Analyst and theinteraction term between Liquidity and Big4_audit. Definitions of variables are provided in Appendix 1. The sample period is from 2000 to 2014. All regressions includeindustry- and year-fixed effects. The t-statistics reported in parentheses are based on robust standard errors clustered by firm.

Panel A: Analyst Panel B: Big4_audit

(1)DVE

(2)DVC

(3)DVP

(4)DVE

(5)DVC

(6)DVP

Liquidity 0.4812⁎⁎⁎ 0.2937⁎⁎⁎ 1.0999⁎⁎⁎ 0.3011⁎⁎⁎ 0.5244⁎⁎⁎ 1.3053⁎⁎⁎

(7.23) (7.29) (5.93) (7.55) (7.83) (6.99)Liquidity ∗ Analyst −0.2834⁎⁎⁎ −0.2540⁎⁎⁎ −0.7538⁎⁎⁎

(−4.66) (−7.58) (−3.97)Analyst 0.0819⁎⁎⁎ 0.0261⁎⁎⁎ 0.4068⁎⁎⁎

(7.02) (4.09) (10.47)Liquidity ∗ Big4_audit −0.2269⁎⁎ −0.3689⁎⁎ −0.1618

(−2.27) (−2.11) (−0.32)Big4_audit −0.0129 −0.0078 0.1476

(−0.53) (−0.19) (0.83)Log of size 0.0064 0.0245⁎⁎⁎ 0.2263⁎⁎⁎ 0.0309⁎⁎⁎ 0.0315⁎⁎⁎ 0.3540⁎⁎⁎

(0.56) (3.49) (5.60) (4.56) (2.85) (9.10)ROA 5.0091⁎⁎⁎ 2.5613⁎⁎⁎ 19.2010⁎⁎⁎ 2.8715⁎⁎⁎ 5.7108⁎⁎⁎ 22.6491⁎⁎⁎

(21.89) (22.69) (22.09) (24.18) (23.67) (24.33)Q −0.1579⁎⁎⁎ −0.0909⁎⁎⁎ −0.4511⁎⁎⁎ −0.0939⁎⁎⁎ −0.1591⁎⁎⁎ −0.4535⁎⁎⁎

(−13.51) (−14.76) (−12.15) (−15.16) (−13.52) (−12.08)Lev −0.5775⁎⁎⁎ −0.3406⁎⁎⁎ −1.5752⁎⁎⁎ −0.3370⁎⁎⁎ −0.5731⁎⁎⁎ −1.5059⁎⁎⁎

(−8.54) (−8.30) (−7.70) (−8.09) (−8.36) (−7.18)Cash 0.6764⁎⁎⁎ 0.2876⁎⁎⁎ 1.5448⁎⁎⁎ 0.3328⁎⁎⁎ 0.7747⁎⁎⁎ 1.9042⁎⁎⁎

(9.68) (7.71) (7.26) (8.92) (11.10) (8.96)Top1 0.0027⁎⁎⁎ 0.0022⁎⁎⁎ 0.0097⁎⁎⁎ 0.0023⁎⁎⁎ 0.0028⁎⁎⁎ 0.0103⁎⁎⁎

(4.24) (5.86) (5.12) (6.05) (4.40) (5.38)Independence 0.0339⁎⁎⁎ 0.0238⁎⁎⁎ 0.0874⁎⁎ 0.0263⁎⁎⁎ 0.0395⁎⁎⁎ 0.1108⁎⁎⁎

(2.85) (3.30) (2.34) (3.61) (3.29) (2.90)Intercept −0.0539 −0.3876⁎⁎ −4.9273⁎⁎⁎ −0.5468⁎⁎⁎ −0.6295⁎⁎ −7.7627⁎⁎⁎




firms. The coefficient estimates of the interaction term between Liquidity and Big4 are significantly negative in column 4 and col-umn 5, but insignificantly negative in column 6, implying that the positive relationship between stock liquidity and dividend pay-outs is stronger in firms audited by non-Big4 auditor firms. These findings are consistent with our conjecture that liquidity affectsdividend payouts by improving share price informativeness.

7. Impact of agency conflicts on the liquidity effect

To further support our hypothesis, we test whether the positive relation between stock liquidity and dividend payouts is re-inforced when conflicts between controlling shareholders and minority investors are potentially more pronounced. We consider afirm's agency problems along the following two dimensions: expropriation incentive of controlling shareholders and surplus cashflows.

7.1. Impact of expropriation incentives on the relation between liquidity and dividend payouts

When conflict between controlling shareholders and minority investors is more pronounced, the controlling shareholders havestronger incentives to retain earnings for personal interests and pay out less in dividends. If stock liquidity performs an informa-tional role and forces controlling shareholders to pay more dividends, then we could expect that the positive relation betweenstock liquidity and dividend payouts is reinforced when conflict between controlling shareholders and minority investors is po-tentially more pronounced.

Following the literature, we construct two measures to gauge the extent of these incentives. First, we look into the wedge be-tween controlling shareholders' voting rights and cash flow rights (Claessens et al., 2002). A wedge suggests that controlling

Table 12The effect of expropriation incentive on the relation between stock liquidity and dividend payouts.This table reports the impact of expropriation incentive on the relation between liquidity and dividend payouts. The dependent variables are dividend-to-earnings ratio(DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variables are the interaction term between Liquidity and Wedge and theinteraction termbetween Liquidity and SOE. Definitions of variables are provided in Appendix 1. The sample period is from2000 to 2014. Sample size is reduced becauseofmissing values forWedge and SOE. All regressions include industry- and year-fixed effects. The t-statistics reported inparentheses are based on robust standard errorsclustered by firm.

Panel A: Wedge Panel B: SOE

(1)DVE

(2)DVC

(3)DVP

(4)DVE

(5)DVC

(6)DVP

Liquidity 0.2331⁎⁎⁎ 0.4184⁎⁎⁎ 1.1799⁎⁎⁎ 0.7216⁎⁎⁎ 0.4453⁎⁎⁎ 1.6970⁎⁎⁎

(4.97) (5.17) (5.39) (8.16) (8.72) (7.01)Liquidity ∗ Wedge 0.1735⁎⁎⁎ 0.2692⁎⁎⁎ 0.5194⁎⁎

(3.28) (2.85) (2.12)Wedge 0.0020 −0.0082 −0.0659

(0.16) (−0.34) (−0.90)Liquidity ∗ SOE −0.3326⁎⁎⁎ −0.2368⁎⁎⁎ −0.6518⁎⁎⁎

(−3.57) (−4.57) (−2.73)SOE −0.1047⁎⁎⁎ −0.0682⁎⁎⁎ −0.2167⁎⁎⁎

(−3.87) (−4.60) (−2.68)Log of size 0.0272⁎⁎⁎ 0.0323⁎⁎⁎ 0.3454⁎⁎⁎ 0.0406⁎⁎⁎ 0.0352⁎⁎⁎ 0.3782⁎⁎⁎

(4.22) (2.99) (8.68) (3.72) (5.32) (9.63)ROA 2.6204⁎⁎⁎ 5.4422⁎⁎⁎ 21.4347⁎⁎⁎ 5.6170⁎⁎⁎ 2.8189⁎⁎⁎ 22.4849⁎⁎⁎

(22.10) (21.85) (22.02) (23.20) (23.86) (24.00)Q −0.0843⁎⁎⁎ −0.1528⁎⁎⁎ −0.4179⁎⁎⁎ −0.1544⁎⁎⁎ −0.0920⁎⁎⁎ −0.4446⁎⁎⁎

(−13.69) (−12.80) (−10.87) (−13.05) (−14.77) (−11.78)Lev −0.2622⁎⁎⁎ −0.5336⁎⁎⁎ −1.2500⁎⁎⁎ −0.5904⁎⁎⁎ −0.3441⁎⁎⁎ −1.5424⁎⁎⁎

(−6.13) (−7.28) (−5.46) (−8.55) (−8.25) (−7.34)Cash 0.3484⁎⁎⁎ 0.7685⁎⁎⁎ 1.9855⁎⁎⁎ 0.7532⁎⁎⁎ 0.3209⁎⁎⁎ 1.8400⁎⁎⁎

(9.02) (10.18) (8.36) (10.72) (8.54) (8.61)Top1 0.0020⁎⁎⁎ 0.0027⁎⁎⁎ 0.0095⁎⁎⁎ 0.0031⁎⁎⁎ 0.0025⁎⁎⁎ 0.0107⁎⁎⁎

(5.03) (3.94) (4.47) (4.69) (6.41) (5.44)Independence 0.0295⁎⁎⁎ 0.0402⁎⁎⁎ 0.1160⁎⁎ 0.0456⁎⁎⁎ 0.0302⁎⁎⁎ 0.1244⁎⁎⁎

(3.41) (2.79) (2.45) (3.79) (4.13) (3.25)Intercept −0.6088⁎⁎⁎ −0.8728⁎⁎⁎ −7.8546⁎⁎⁎ −0.7601⁎⁎⁎ −0.5961⁎⁎⁎ −8.1198⁎⁎⁎




shareholders bears a lower cost to divert cash and other resources from the firm, implying higher agency conflict between con-trolling shareholders and outside investors. Second, we consider the characteristics of the controlling shareholders (Jiang et al.,2010; Jiang and Kim, 2015). Among controlling shareholders, state parents have less incentive to tunnel assets or cash becauseno one can directly benefit from tunneling (Jiang and Kim, 2015),11 which means SOEs have less severe agency conflicts betweencontrolling shareholders and outside investors.

Empirically, we use Wedge and SOE to proxy for expropriation incentives. Wedge is a dummy variable that equals one if thereis a wedge between controlling shareholders' voting rights and cash flow rights and zero otherwise. SOE is a dummy variable thatequals one if the firm is a state-owned enterprise and zero otherwise. We implement our tests by augmenting our baseline modelto include each factor and its interaction with stock liquidity.

Panel A of Table 12 reports the results on divergence between controlling shareholders' voting rights and their cash flowrights. The coefficient estimates of the interaction term between Liquidity and Wedge are significantly positive in all three columns,which means that the positive relation between stock liquidity and dividend payouts is more pronounced when there is a wedgebetween controlling shareholders' voting rights and their cash flow rights. Panel B of Table 12 reports the results on type of con-trolling shareholder. The coefficient estimates of the interaction term between Liquidity and SOE are significantly negative in allthree columns, implying that the relation between stock liquidity and dividend payouts is stronger in non-SOEs. In sum, the re-sults reported in Table 12 show that the positive relation between liquidity and corporate dividend payouts is more pronouncedwhen controlling shareholders' expropriation incentive is stronger.

11 Jiang andKim (2015) argue that the state parent has less incentive to tunnel assets or cash because noone candirectly benefit from tunneling. Jiang et al. (2010)findthat controlling shareholders in China use intercorporate loans to siphon money from their firms but that this occurs less often in SOEs.

Table 13The effect of surplus cash flow on the relation between stock liquidity and dividend payouts.This table reports the impact of surplus cashflowon the relation between liquidity and dividendpayouts. Thedependent variables are dividend-to-earnings ratio (DVE),dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variables are the interaction term between Liquidity and SCI and the interactionterm between Liquidity and RER. Definitions of variables are provided in Appendix 1. The sample period is from 2000 to 2014. Sample size is reduced because ofmissingvalues for RER. All regressions include industry- and year-fixed effects. The t-statistics reported in parentheses are based on robust standard errors clustered by firm.

Panel A: SCI Panel B: RER

(1)DVE

(2)DVC

(3)DVP

(4)DVE

(5)DVC

(6)DVP

Liquidity 0.2814⁎⁎⁎ 0.4710⁎⁎⁎ 1.2627⁎⁎⁎ 0.2331⁎⁎⁎ 0.3639⁎⁎⁎ 0.6029⁎⁎⁎

(7.08) (7.21) (6.90) (4.97) (4.71) (2.73)Liquidity ∗ SCI 0.6564⁎⁎⁎ 0.7443⁎ 5.7551⁎⁎⁎

(2.89) (1.67) (5.46)SCI −0.0765 −0.5117⁎⁎⁎ 0.4531

(−1.32) (−4.67) (1.39)Liquidity ∗ RER 0.0657 0.1937⁎⁎ 1.0088⁎⁎⁎

(1.17) (2.02) (3.87)RER 0.3185⁎⁎⁎ 0.5512⁎⁎⁎ 1.8291⁎⁎⁎

(10.70) (9.77) (10.81)Log of size 0.0341⁎⁎⁎ 0.0432⁎⁎⁎ 0.3773⁎⁎⁎ 0.0248⁎⁎⁎ 0.0228⁎⁎ 0.3287⁎⁎⁎

(5.27) (4.12) (9.87) (3.97) (2.20) (8.84)ROA 2.9038⁎⁎⁎ 5.8362⁎⁎⁎ 22.7973⁎⁎⁎ 2.6321⁎⁎⁎ 5.2956⁎⁎⁎ 21.5726⁎⁎⁎

(24.38) (24.06) (24.75) (23.56) (23.17) (23.69)Q −0.0914⁎⁎⁎ −0.1492⁎⁎⁎ −0.4453⁎⁎⁎ −0.0697⁎⁎⁎ −0.1158⁎⁎⁎ −0.2934⁎⁎⁎

(−14.72) (−12.84) (−11.83) (−11.01) (−9.57) (−6.99)Lev −0.3566⁎⁎⁎ −0.6376⁎⁎⁎ −1.5746⁎⁎⁎ −0.2801⁎⁎⁎ −0.4804⁎⁎⁎ −1.1834⁎⁎⁎

(−8.55) (−9.18) (−7.50) (−6.73) (−6.97) (−5.41)Cash 0.3422⁎⁎⁎ 0.8136⁎⁎⁎ 1.9331⁎⁎⁎ 0.2405⁎⁎⁎ 0.6229⁎⁎⁎ 1.4820⁎⁎⁎

(9.10) (11.66) (9.06) (6.39) (8.87) (6.79)Top1 0.0023⁎⁎⁎ 0.0028⁎⁎⁎ 0.0102⁎⁎⁎ 0.0022⁎⁎⁎ 0.0026⁎⁎⁎ 0.0089⁎⁎⁎

(6.02) (4.38) (5.31) (5.83) (4.10) (4.69)Independence 0.0261⁎⁎⁎ 0.0384⁎⁎⁎ 0.1104⁎⁎⁎ 0.0270⁎⁎⁎ 0.0403⁎⁎⁎ 0.1117⁎⁎⁎

(3.58) (3.22) (2.90) (3.81) (3.43) (2.97)Intercept −0.6241⁎⁎⁎ −0.9143⁎⁎⁎ −8.2696⁎⁎⁎ −0.6354⁎⁎⁎ −0.8270⁎⁎⁎ −8.5608⁎⁎⁎




7.2. Impact of surplus cash flow on the relation between liquidity and dividend payouts

Agency problems are likely to be more severe when a firm has excess cash flow (Jensen, 1986), which increases the resourcesthat the controlling shareholders can divert from the firm. Therefore, we expect that the positive relation between liquidity anddividend payouts will be stronger when a firm has more surplus cash flow. We construct two measures of surplus cash flow. First,we use the surplus between cash flows from operating activities and investment (SCI). A larger surplus suggests that internalfunds are more than sufficient to finance the firm's investments. Second, we use the ratio of retained earnings to equity. Accordingto DeAngelo et al. (2006), firms with internal funds exceeding growth opportunities have cumulative profits and therefore ahigher retained-earnings-to-equity ratio (RER). Higher SCI and RER suggest severe agency conflicts between controlling share-holders and outside investors.

In accord with our expectations, the results reported in Table 13 show that the positive association between liquidity and cor-porate payouts is more pronounced when there is more surplus cash.

8. Market responses to CSRC stipulations

We further test whether stock liquidity and legal provisions and regulations are substitutes. La Porta et al. (2000a) proposethat effective legal and regulatory systems can constrain insiders' expropriation and force them to disgorge cash. Therefore,legal provisions and regulations may substitute for the informational effect of liquidity in affecting dividend payouts.

On March 28, 2001, December 7, 2004, May 6, 2006, and October 9, 2008, the CSRC stipulated that firms could not have issuerights without continuously paying cash dividends. We test whether the market responses to these stipulations differ with stockliquidity. As these stipulations were issued to encourage firms to pay dividends in the hope of protecting minority shareholders,they impose more pressure on firms with low stock liquidity to distribute cash. Accordingly, the market reactions to the stipula-tions should be more favorable for firms with low stock liquidity.

Table 14Market responses to CSRC stipulations.This table reportsmarket responses to CSRC stipulations. The dependent variable is the cumulated stock return on stipulation day. The standardmarketmodel is used tocompute the cumulative abnormal returns in the relevant event window: (−5, 5). The estimation window is 220 days (−230,−11). The key explanatory variable isLiquidity. Definitions of variables are provided in Appendix 1. All regressions include industry- and year-fixed effects. The t-statistics reported in parentheses are basedon robust standard errors clustered by firm.

(1) (2) (3) (4)2001 stipulation 2004 stipulation 2006 stipulation 2008 stipulation

Liquidity −1.6722⁎⁎⁎ −0.0536⁎⁎ −0.1088⁎⁎ 0.6031(−5.30) (−2.02) (−1.97) (1.25)

Log of size 0.0179⁎⁎⁎ 0.0029 −0.0032 0.0004(4.97) (1.45) (−0.71) (0.09)

ROA 0.0582⁎ −0.0406 −0.0063 −0.0969(1.84) (−1.07) (−0.15) (−1.49)

Q 0.0107⁎⁎⁎ 0.0089⁎ −0.0401⁎ −0.0027(3.35) (1.96) (−1.65) (−0.72)

Lev −0.0126 −0.0051 −0.0147 0.0013(−1.04) (−0.50) (−0.69) (0.05)

Cash −0.1328⁎⁎⁎ −0.0083 0.0320 −0.0645⁎

(−6.08) (−0.61) (1.11) (−1.95)Top1 −0.0004⁎⁎⁎ -0.0001 0.0003 −0.0001

(−3.43) (−0.78) (1.63) (−0.49)Independence −0.0058⁎ 0.0020 0.0016 −0.0020

(−1.71) (1.18) (0.40) (−0.38)Intercept −0.4053⁎⁎⁎ −0.0645 0.1119 0.0388

(−4.85) (−1.42) (1.00) (0.38)Industry effect Yes Yes Yes YesYear effect Yes Yes Yes YesN 919 1071 1179 1157Pseudo R2 0.192 0.082 0.097 0.123



We use the standard market model and value-weighted market return to compute the cumulative abnormal return (CAR). Therelevant event window is (−5, 5), and the estimation period is 200 days12 (−210, −11) before the announcement dates. Ourliquidity measure here is computed over a period of 210 trading days, ending 11 days before the announcements.

Table 14 reports our results. Columns 1 to 4 report our regression results for the 2001, 2004, 2006, and 2008 stipulations re-spectively. We find that the coefficient on liquidity is negative and significant for the 2001, 2004, and 2006 stipulations. We alsofind that the coefficient on liquidity for 2008 is positive but insignificant. Perhaps because the CSRC's 2001 stipulation was theleast anticipated, the relation between liquidity and market responses to that stipulation is strongest both in magnitude and insignificance. As time passed, the informational effects of the 2004 and 2006 stipulations may have been partially anticipatedand reflected in the market. As a result, the relation between market responses and liquidity is weaker for the 2004 and 2006stipulations. Eventually, for the 2008 stipulation, this relation disappears. Collectively, our results show that the market reactionsto the stipulations are more favorable for firms with low stock liquidity, suggesting that stock liquidity and legal provisions andregulations are substitutes.

9. Alternative explanations

In this section, we run several tests to rule out several alternative explanations that might also predict a positive relation be-tween stock liquidity and dividend payouts.

9.1. Does liquidity help shareholders to intervene?

Theory suggests that liquidity can help shareholders discipline insiders by helping non-blockholders to acquire a block holding(Kyle and Vila, 1991; Kahn and Winton, 1998; Maug, 1998) or by helping non-controlling blockholders threaten to exit (Admatiand Pfleiderer, 2009; Edmans, 2009; Edmans and Manso, 2011), and thus mitigating agency problems between corporate insidersand outside shareholders and making controlling shareholders pay more dividends. To explore whether higher liquidity increasesdividend payouts by helping form another blockholder in addition to the controlling shareholder, we run the following Logit re-gression:

12 We

Nc blockholdersi;tþ1 ¼ αi;t þ βi;tLiquidityi;t þ γ0Controlsi;t þ εi;t ; ð3Þ

require that there be at least 30 trading days before the announcement dates for each stock.

Table 15Tests for the impact of liquidity on non-controlling blockholders' formation and effectiveness of exit threat.This table reports the impact of liquidity on the formation of non-controlling blockholders and the effectiveness of non-controlling blockholders' exit threat. The depen-dent variable in column 1,Nc_blockholders, is a dummy variable that equals one if there is another blockholder (other than the controlling shareholder)who holds N10%of total shares infiscal year t + 1, and zero otherwise. The dependent variables in columns 2, 3, and 4 are dividend-to-earnings ratio (DVE), dividend-to-cash-flow ratio(DVC), and propensity to pay (DVP). The key explanatory variables are Liquidity and the interaction term between Liquidity andNc_blockholders. Definitions of variablesare provided in Appendix 1. The sample period is from 2000 to 2014. All regressions include industry- and year-fixed effects. The t-statistics reported in parentheses arebased on robust standard errors clustered by firm.

(1) (2) (3) (4)Nc_blockholders DVE DVC DVP

Liquidity −1.5168⁎⁎⁎ 0.2863⁎⁎⁎ 0.5302⁎⁎⁎ 1.3540⁎⁎⁎

(−8.20) (6.60) (7.45) (6.84)Liquidity ∗ Nc_blockholders 0.0441 −0.0180 −0.0453

(0.79) (−0.19) (−0.18)Nc_blockholders 0.0406⁎⁎⁎ 0.0342 0.1238

(2.84) (1.36) (1.56)Log of size −0.1931⁎⁎⁎ 0.0323⁎⁎⁎ 0.0339⁎⁎⁎ 0.3689⁎⁎⁎

(−3.71) (4.97) (3.20) (9.59)ROA 1.4753⁎⁎⁎ 2.8316⁎⁎⁎ 5.6529⁎⁎⁎ 22.5207⁎⁎⁎

(3.32) (23.89) (23.32) (24.03)Q −0.1837⁎⁎⁎ −0.0917⁎⁎⁎ −0.1542⁎⁎⁎ −0.4323⁎⁎⁎

(−4.27) (−14.78) (−13.02) (−11.54)Lev 0.0189 −0.3435⁎⁎⁎ −0.5786⁎⁎⁎ −1.5271⁎⁎⁎

(0.07) (−8.25) (−8.41) (−7.26)Cash 1.3361⁎⁎⁎ 0.3219⁎⁎⁎ 0.7695⁎⁎⁎ 1.8886⁎⁎⁎

(5.51) (8.51) (10.88) (8.75)Top1 −0.0545⁎⁎⁎ 0.0026⁎⁎⁎ 0.0032⁎⁎⁎ 0.0115⁎⁎⁎

(−19.22) (6.46) (4.74) (5.51)Independence 0.0452 0.0271⁎⁎⁎ 0.0394⁎⁎⁎ 0.1126⁎⁎⁎

(0.91) (3.67) (3.27) (2.93)Intercept 5.2114⁎⁎⁎ −0.6001⁎⁎⁎ −0.7167⁎⁎⁎ −8.2042⁎⁎⁎

(4.52) (−4.09) (−2.97) (−9.79)Industry effect Yes Yes Yes YesYear effect Yes Yes Yes YesN 18,892 18,902 18,902 18,902Pseudo R2 0.131 0.207 0.131 0.282



where Nc_blockholders is a dummy variable that equals one if there is a secondary blockholder (other than the controlling one)who holds N10% of total shares in fiscal year t + 1, and zero otherwise. Liquidity and Controls are the same as in Eq. (1).

Column 1 of Table 15 shows that the coefficient on liquidity is negative and significant at the 1% level, indicating that liquidityimpedes the formation of non-controlling blockholders.

We next explore whether higher liquidity increases dividend payouts by helping non-controlling blockholders effectivelythreaten to exit. Empirically, we add the dummy variable Nc_blockholders and its interaction with stock liquidity to our Eq. (1).The implication here is that if liquidity mitigates agency problems between controlling shareholders and minority investors byhelping non-controlling blockholders threaten to exit, the interaction term between Liquidity and Nc_blockholders should be sig-nificantly positive.

The results from columns 2–4 of Table 15 show that for all three measures of dividend payouts, the coefficient estimates of theinteraction term between Liquidity and Nc_blockholders are positive but insignificant.

In sum, the results from Table 15 suggest that the effect of liquidity on dividends does not work by facilitating either the for-mation or the exit threat of non-controlling blockholders.

9.2. Agency conflicts between managers and shareholders

Although agency problems mainly rest between the controlling shareholder and minority shareholders, there are also agencyconflicts between managers and shareholders in Chinese listed firms. Since liquidity can increase share price informativeness andpromote more efficient management compensation (Holmström and Tirole, 1993), and thus reduce agency problems betweenmanagers and shareholders, our findings may be attributable to the mitigation of such conflicts. To rule out this possible expla-nation, we split the sample into two subsamples according to whether managers own shares of the firm. Share ownership alignsa manager's incentive with that of other shareholders, thus reducing agency costs (Jensen and Meckling, 1976). If the positive re-lation between liquidity and dividend payouts is caused by the mitigation of agency conflicts between managers and shareholders,

Table 16Tests for agency conflicts between managers and shareholders.This table reports the impact of agency conflicts between management and shareholders on the relation between liquidity and dividend payouts. The dependent var-iables are dividend-to-earnings ratio (DVE), dividend-to-cash-flow ratio (DVC), and propensity to pay (DVP). The key explanatory variable is Liquidity. Definitions ofvariables are provided in Appendix 1. The sample period is from 2000 to 2014. All regressions include industry- and year-fixed effects. The t-statistics reported in pa-rentheses are based on robust standard errors clustered by firm.

DVE DVC DVP

(1) (2) (3) (4) (5) (6)With Without With Without With Without

Liquidity 0.2511⁎⁎⁎ 0.3513⁎⁎⁎ 0.4621⁎⁎⁎ 0.5421⁎⁎⁎ 1.0560⁎⁎⁎ 1.6343⁎⁎⁎

(5.26) (5.33) (5.91) (4.63) (4.86) (5.11)Log of size 0.0274⁎⁎⁎ 0.0333⁎⁎⁎ 0.0257⁎⁎ 0.0391⁎⁎ 0.3333⁎⁎⁎ 0.4071⁎⁎⁎

(3.78) (2.79) (2.08) (2.09) (7.67) (5.80)ROA 2.9290⁎⁎⁎ 2.6418⁎⁎⁎ 5.9007⁎⁎⁎ 5.1249⁎⁎⁎ 24.0511⁎⁎⁎ 19.8717⁎⁎⁎

(20.60) (12.62) (19.63) (13.18) (20.08) (13.82)Q −0.0854⁎⁎⁎ −0.1026⁎⁎⁎ −0.1454⁎⁎⁎ −0.1698⁎⁎⁎ −0.4024⁎⁎⁎ −0.4966⁎⁎⁎

(−12.00) (−7.69) (−10.43) (−6.99) (−8.76) (−7.12)Lev −0.3872⁎⁎⁎ −0.2263⁎⁎⁎ −0.6662⁎⁎⁎ −0.3664⁎⁎⁎ −1.5107⁎⁎⁎ −1.4555⁎⁎⁎

(−8.22) (−2.96) (−8.24) (−3.00) (−6.32) (−3.75)Cash 0.2664⁎⁎⁎ 0.4033⁎⁎⁎ 0.6674⁎⁎⁎ 0.8578⁎⁎⁎ 1.8447⁎⁎⁎ 1.6922⁎⁎⁎

(6.36) (5.20) (8.06) (6.68) (7.26) (4.20)Top1 0.0020⁎⁎⁎ 0.0039⁎⁎⁎ 0.0026⁎⁎⁎ 0.0047⁎⁎⁎ 0.0095⁎⁎⁎ 0.0152⁎⁎⁎

(4.57) (5.45) (3.38) (4.10) (4.15) (4.43)Independence 0.0225⁎⁎⁎ 0.0430⁎⁎⁎ 0.0286⁎⁎ 0.0767⁎⁎⁎ 0.0837⁎ 0.2291⁎⁎⁎

(2.68) (3.18) (1.97) (3.67) (1.90) (3.32)Intercept −0.4974⁎⁎⁎ −0.5529⁎⁎ −0.4934⁎ −0.8294⁎ −7.6006⁎⁎⁎ −8.1355⁎⁎⁎

(−3.04) (−2.01) (−1.76) (−1.92) (−8.03) (−5.28)χ2 with vs. without managerial ownership diff. 0.20 0.56 0.12Industry effect Yes Yes Yes Yes Yes YesYear effect Yes Yes Yes Yes Yes YesN 13,186 5244 13,186 5244 13,186 5229Pseudo R2 0.189 0.269 0.124 0.16 0.274 0.327



we should find that firms without managers' ownership exhibit a stronger effect of liquidity on dividends. Table 16 shows thatthere is no difference between the subsamples in the effect of liquidity on firms' dividend payouts.

Collectively, our evidence suggests that the mitigation of agency conflicts between managers and shareholders resulting fromhigher stock liquidity does not explain the positive relation between liquidity and dividend payouts.

10. Conclusion

Using a sample of Chinese listed firms, this paper explores the informational effect of liquidity on dividend payouts. We findthat firms with high stock liquidity have higher dividend payments, and higher propensity to pay dividends, than firms with lowstock liquidity. This result is robust to the use of different measures of liquidity, and holds after we control for endogeneity con-cerns. The relation is stronger when information asymmetry is higher and when controlling shareholders have greater incentivesto expropriate minority investors. Further, we find that the market reactions to the China Securities Regulatory Commission stip-ulations requiring cash dividend payments are more favorable for firms with low stock liquidity. We rule out the explanationsthat liquidity increases dividend payouts by enhancing the formation and exit threat of non-controlling blockholders or by miti-gating agency conflicts between managers and shareholders.

In general, we find that stock liquidity can mitigate agency problems between insiders and outsiders by reducing informationasymmetry, and thus can increase dividend payments. Hence, our paper contributes to the literature on how stock liquidity affectsdividend payouts, and contributes to the literature by emphasizing the beneficial role of stock liquidity in reducing agencyproblems.

Acknowledgements

We are grateful for helpful comments from an anonymous reviewer, Yunsen Chen, Junkoo Kang, Jeffry Netter (editor), ChenkaiNi, Xue Wang, Yanchao Wang, Tusheng Xiao, Chun Yuan, and Chengxi Yin. Fuxiu Jiang acknowledges the financial support fromthe China National Natural Science Foundation (nos. 71432008 and 71172179). All errors are ours.


Appendix 1. Variable definitions

Definition of variables

Variables Definition

DVE Dividend as a percentage of earnings. Dividends are defined as total cash dividends paid in a given fiscal year. Earnings are measured by netincome.

DVC Dividend as a percentage of cash flows. Dividends are defined as total cash dividends paid in a given fiscal year. Cash flows are measured bycash flows from operating activities.

DVP An indicator variable that takes the value of one if the total amount of dividends is greater than zero for a given fiscal year, and zerootherwise.

Liquidity The natural logarithm of one plus the firm's Amihud (2002) illiquidity ratio multiplied by −1, where the Amihud illiquidity ratio iscalculated as the daily price response associated with one million RMB of trading volume, averaged over the fiscal year.

Log of size The natural logarithm of total assets, as a measure of firm size.ROA Profitability measure computed as net income divided by total assets.Q Market value of equity plus book value of total assets minus book value of equity, divided by book value of total assets.Lev The ratio of total liabilities to total assets.Cash Cash holding scaled by total assets.Top1 Percentage of shares held by the controlling shareholder of the firm.Independence Numbers of independent directors on board.Analyst The logarithm of (one plus) the number of analysts who made forecasts about the firm's earnings in any given year.Big4_audit A dummy variable that equals one if the firm is audited by a Big 4 auditor firm, and zero otherwise.Wedge A dummy variable that equals one if there is a wedge between controlling shareholders' voting rights and cash flow rights, and zero

otherwise.SOE A dummy variable that equals one if the firm is a state-owned enterprise, and zero otherwise.SCI The surplus between cash flows from operating activities and investment.RER The ratio of retained earnings scaled by beginning book value of equity.CAR Cumulated stock return on stipulation day. The standard market model is used to compute the cumulative abnormal returns in the

relevant event window: (−5, 5). The estimation window is 220 days (−230, −11), where day 0 is the stipulation day.Nc_blockholders A dummy variable that equals one if there is another blockholder (other than the controlling shareholder) who holds N10% of total shares,

and zero otherwise.

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