journal of corporate finance · managerial personal diversiﬁcation and portfolio equity...

Managerial personal diversification and portfolio equity incentives

Mao-Wei Hung a,1, Yu-Jane Liu b,⁎, Chia-Fen Tsai a,2

a College of Management, National Taiwan University, Taipei, Taiwanb Guanghua School of Management, Peking University, Beijing, People's Republic of China

a r t i c l e i n f o a b s t r a c t

Article history:Received 25 March 2010Received in revised form 15 September 2011Accepted 17 September 2011Available online 25 September 2011

This paper examines the diversification choices of top managers and their implications for thelevels of portfolio equity incentives as well as for firms' financial policies. Standard portfoliotheory should also apply to corporate managers and therefore excessive risk exposures tothe firm should create portfolio diversification incentives for the managers. We use a uniquedataset from the Taiwan tax data center and construct the measures of the degree of diversi-fication in a manager's equity portfolio that is made up of equities of other firms to capturehis motives for diversifying his risk exposure to his own firm. We provide empirical evidencesupporting the view that managers have a risk-reduction motive when they trade in the equi-ties of other firms besides their own. Moreover, we document evidence that the degree of di-versification in such equity portfolios also significantly affects managerial equity incentives aswell as firms' financial policies. Overall, our findings confirm that managers' personal diversi-fication can help make up for the diversification that the managers would otherwise have lost,thereby reducing the agency cost of equity incentive contracts.

© 2011 Elsevier B.V. All rights reserved.

Keywords:Managerial personal diversificationEquity incentivesFirm financing policies

JEL classification:G11G32

1. Introduction

The cost of offering an incentive contract to top managers is that it imposes firm risk on their financial wealth and human cap-ital, which results in a loss of diversification on the part of the managers. Since modern portfolio theory should also apply to cor-porate managers, excessive risk exposures to the firm should create portfolio diversification incentives for the managers (e.g.,Bettis et al., 2001; Ofek and Yermack, 2000).

Stock markets include a rich array of individual equities whose returns are correlated with a manager's own-firm equityreturns. An equity portfolio made up of equities of other firms that have only a low degree of correlation with own-firm equityhas diversification advantages that could give the manager the opportunity to diversify away some risk of their exposure totheir own firms. In practice, there are no regulations or restrictions on managerial trading in the personal equity portfoliosmade up of shares of other firms. Hence, trading in such equity portfolios is a more direct indicator of whether managers perceivetheir wealth as incurring excessive risk exposure to the firm.

Analyzing the degree of diversification in top managers' equity portfolios is important, because it has important implicationsfor research on incentive contracts and firms' financing policy decisions. In the absence of equity portfolios outside the firm inwhich to diversify, the manager has a tendency to require a higher risk premium in his incentive contracts or to implementlower leverage in order to diversify his large ownership share. If a manager trades in the equities of other firms to diversifyaway some of his risk exposure to his own firm, he will be able to bear more firm risk and will require less of a risk premiumin his equity compensation, which in turn will allow the shareholders to write contracts or increase equity grants requiring

Journal of Corporate Finance 18 (2012) 38–64

⁎ Corresponding author. Tel.: +86 10 6275 7699; fax: +86 10 62761161.E-mail addresses: [email protected] (M.-W. Hung), [email protected] (Y.-J. Liu), [email protected] (C.-F. Tsai).

1 Tel.: +886 2 3366 4988; fax: 886 2 2369 0833.2 Tel.: +886 6 6900765.

0929-1199/$ – see front matter © 2011 Elsevier B.V. All rights reserved.doi:10.1016/j.jcorpfin.2011.09.006

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him to hold a higher level of equity incentives. In addition, if top managers maintain low debt levels due to their large holdings ofcompany stock, their diversifying by means of equity portfolios should raise the levels of debts.

However, due to the unavailability of top managers' personal investment portfolios, there is no empirical literature that exam-ines the top managers' equity portfolio choices as well as their implications for the different levels of equity incentives and thefirms' financial policies. The goal of this paper is therefore to bridge the gap. Specifically, we examine the personal portfoliochoices of the top five managers working in listed firms in Taiwan from 1998 to 2001.3 For each firm we obtain the stock returnsand financial statement data from the TEJ database and then combine the firm's data with information on the top managers' in-terest income, salary income, real estate, listed and non-listed stockholdings, and demographic characteristics from the FinancialData Center (FDC) of the Ministry of Finance in Taiwan.4

We measure managers' personal diversification as the degree of diversification that a manager has allocated for his equityportfolio (made up of shares in other companies) to diversify away his own-firm risk. The details regarding the individual share-holder data files provide precise information on the amount a manager invests in each individual stock (stock picking), includinghis or her employer's stocks. This enables us to measure the degree of managerial personal diversification that refers to the neg-ative of the correlation between the returns on the manager's listed equity portfolio made up of shares in other firms and thereturns of his own-firm equity. Due to data limitation, past studies on equity incentives pay very little attention to managers' per-sonal outside wealth. Our data enables us to measure managerial financial wealth as the sum of the total value of financial assetsinvested in stocks, bonds, savings accounts, real estate, and private company equities and define managerial equity incentive asthe percentage change in the dollar value of the manager's financial wealth for a 1% change in the stock price. To recognize thatour inferences are unaffected by our choice of measures of managerial personal diversification and portfolio equity incentives, weperform robustness tests using alternative diversification and equity incentives measures.

In our sample, we find that managers who hold large shares of financial wealth in their company stocks will hold more diver-sified equity portfolios. Moreover, managers of firms with more idiosyncratic risks will also tend to hold more diversified equityportfolios. The results indicate that managers self-select among different equities beyond those of their own firm, with managerswho are exposed to higher firm risk selecting into other equities whose returns exhibit a low degree of correlation with thereturns on their own-firm equity. Overall, the findings suggest that managers have risk-reduction motives for using equity port-folios in diversifying their excessive risk exposure to their own firm.

This paper further documents that managers who hold more diversified equity portfolios made up of equities of other firmswill hold a larger share of financial wealth in their company stock. This result supports the idea that the diversification cost mat-ters, indicating that equity incentives appear to vary with managerial personal diversification. In addition, we find that the level ofthe firms' debt ratio is negatively related to equity incentives, but is positively related to the degree of personal diversification.These results highlight the fact that managers have a tendency to achieve portfolio diversification through their firms' financingpolicy and suggest that top managers coordinate their personal portfolio diversifying decisions and equity incentive levels tomanage their firms' financial decisions.

Our contribution has three main aspects. First, this paper provides evidence of the importance of managerial personal diver-sification. To the best of our knowledge, we are among the first to uncover the relationship between the managers' equity port-folio selections and the levels of their wealth exposure to own-firm risk. Because top managers are usually restricted from sellingtheir own company stockholdings, they represent a source of background risk that can be expected to affect the managers' assetallocations. Our findings confirm that background risk is important in understanding a manager's portfolio allocations and diver-sification decisions (e.g., Guiso et al., 1996; Heaton and Lucas, 2000a).

Second, this paper goes beyond previous empirical research that the managerial personal diversification helps explain the dif-ferences in their equity incentives. Given that the stock markets have been highly developed and liquid in recent years, the man-agers' ability to trade equity portfolios to diversify should therefore become even higher. Consequently, optimal contractingmodels should take into account the managers' ability to engage in portfolio diversification.

Finally, the evidence offers empirical support for the view that managerial personal diversification can mitigate the agencycost due to the managers' personal risk reduction motives for keeping debts low. These findings may also confirm the argumentthat a manager's hedging ability is an important determinant in designing contracts and act as controlling mechanisms that re-duce agency conflicts.

The study most closely related to this paper is that of Bettis et al. (2001), who discuss the growing use of zero-cost collars andequity swaps by corporate insiders and document that insiders use these instruments primarily for risk-reduction purposes. Thispaper, however, makes two significant improvements. First, it strengthens the claim that managers have the ability to directlyalter the risk in their equity incentives by using personal equity portfolios. Second, we further document a direct relationship be-tween the degrees of diversification in managers' equity portfolios and firms' leverage levels.

The remainder of this paper is organized as follows. Section 2 discusses the literature and develops the hypotheses. Section 3describes the data sources, sample selection and the main variable measures. Section 4 specifies the empirical models. Section 5discusses the econometric issues and reports the empirical results. Section 6 discusses the robustness of the empirical findings.Section 7 concludes with a brief summary.

3 Because it takes time to process the data and because of the sensitive nature of the data, this is the most comprehensive period when such data is madeavailable.

4 The data center collects detailed employee information for tax filing and collecting purposes after the end of each calendar year. This is similar to the infor-mation that the Internal Revenue Service (IRS) collects in the United States for household tax filing.

39M.-W. Hung et al. / Journal of Corporate Finance 18 (2012) 38–64

2. Discussion of the literature and development of hypotheses

2.1. The use of equity portfolios and the diversification motives of top managers

To reduce agency conflicts with managers, shareholders use equity-based compensation to manage the sensitivity of man-agers' wealth to the prices of equities and induce managers to have the incentive to take actions that increase firm value (Jensenand Meckling, 1976). For example, by comparing the results in Hall and Liebman (1998) with those in Jensen and Murphy (1990),we can find that the growth in the use of equity-based compensation has led to a substantial increase in the sensitivity of CEOwealth to the company's stock price. Core and Guay (1999) find that firms use annual grants of options and restricted stock toCEOs to manage the optimal level of equity incentives. In addition, recent studies show that top managers typically allocate alarge fraction of their wealth to investments in their company's stock. As reported in Bryant (1997), top managers hold morethan one-third of their net worth in their firm's stock. Lee et al. (2008) use Taiwan investor data for 1997 and find that 83% ofsenior employees investing in listed stocks invest in their employer's stocks and these stocks make up on average 55% of the em-ployees' equity portfolios.5

Managers with a higher fraction of wealth invested in company stock will have more incentives to work harder or more effec-tively, but they will also be exposed to more risk in their firms. Given that managers are undiversified with respect to large stock-holdings in their own company, modern portfolio choice theory predicts that they will have incentives to adjust their riskexposure to the firm. For example, Ofek and Yermack (2000) provide empirical evidence that higher-ownership managers negatemuch equity compensation by selling previously owned shares. Bettis et al. (2001) provide evidence of executives using swapsand zero-cost collars to hedge their ownership position in the firm.

However, in reality, managers may not be allowed to hedge or sell company stock and are therefore subject to the idiosyncrat-ic risk in their firms' stock return. For example, managers may decide to hold on to their company stock for corporate control con-siderations (Denis et al., 1997). Firms may also write contracts or vary equity grants requiring executives to hold a target orminimum level of equity (Core and Guay, 1999, 2002). Because of the disclosure rules imposed on insider trading, managers con-cerned with signaling could find it costly to sell unrestricted stocks. Finally, Bettis et al. (2000) document that over 92% of theirsample companies have their own policies restricting trading by insiders, and 78% have explicit blackout periods during whichthe company prohibits trading by its insiders.

Another way for managers to reduce their risk exposure to the firm, other than through an outright sale of equity or a deriv-ative transaction, is through an equity portfolio transactions that involves equities other than those of the firm, such as trading inother stocks correlated with their firm risk.6 If a manager shares the shareholders' goal of tying his wealth more closely to firmvalue, for risk-reduction reasons, he will adjust his holdings of stocks outside of his company. Managers with larger exposureto their firms' equity risk will have more incentives to engage in trading in the equities of other firms to diversify their own-firm risk and hence hold more diversified equity portfolios. Specifically, we expect to observe that managers with larger sharesof financial wealth in their firms' stock will hold relatively more diversified equity portfolios. Similarly, managers in the firmwith relatively higher idiosyncratic risk will also hold relatively more diversified equity portfolios.

H1. The degrees of diversification in top managers' equity portfolios made up of equities of other firms should be positively re-lated to the levels of their portfolio equity incentives and their own firms' idiosyncratic risks.

2.2. The implications of the degree of equity portfolio diversification for levels of equity incentives for top managers

Managerial personal diversification has important implications for research on incentive contracting. To date, a few studies,from a theoretical perspective, have highlighted the important implications for incentive contracting of the ability of managersto engage in personal hedging. Lambert et al. (1991) suggest that the degree of diversification in managers' outside wealth affectstheir valuation of equity compensation and incentives. In the late 1990s, there was a dramatic increase in the development ofhedging instruments, including executive equity swaps, basket hedges and zero-cost collars. Schizer (2000) argues that the in-creasing availability of derivative instruments for managerial hedging and the growing importance of equity-based compensationhave occurred almost simultaneously.

Ozerturk (2006a) constructs a theoretical model that allows a manager to reduce his or her systematic risk exposure by trad-ing with a market portfolio. The model demonstrates that given that a manager's hedging ability depends on the liquidity of amarket, a manager's equilibrium incentive increases when the market is more liquid. Celen and Ozerturk (2007) and Ozerturk(2006b) use a model to show that a manager can use financial instruments to hedge firm-specific risk. A main prediction fromtheir model is that a managerial equity incentive is higher where financial markets are more developed and the costs of financialinnovations are lower.

5 In Lee et al. (2008), senior employees are defined as employees whose salaries fall into the top five percentile within respective employers in listed firms inTaiwan.

6 We acknowledge that other than through an equity portfolio, either real estate investments or private business ownership is an alternative way for managersto reduce the risk exposure to their own company's stock. We thank the referee for pointing this out. However, without observable and reliable data of housingincome (private business income) streams, it is difficult to explore the issues. Probably because of this reason, there have been few attempts to examine whethertop managers invest in real estate or private business to diversify their own firm risk. The data limitations also make it difficult for us to explore the issues.

40 M.-W. Hung et al. / Journal of Corporate Finance 18 (2012) 38–64

While the theoretical literature provides the considerable prediction that a higher degree of managerial personal diversifica-tion implies higher equity incentives (e.g., Celen and Ozerturk, 2007; Gao, 2010; Ozerturk, 2006a, 2006b), except for Gao (2010)and Garvey and Milbourn (2003), there are no studies that empirically estimate the relationships. Unfortunately, due to data lim-itations, the measurements used in the aforementioned studies are indirect proxies for the hedging costs that managers may en-counter. Specifically, the proxies for the hedging costs used in Garvey and Milbourn (2003) are managerial age and a proxy forfinancial wealth, while the ones used in Gao (2010) are listed equities options and associated trading volume. However, thesemeasurements are very indirect proxies for the hedging costs that managers may encounter. Hence, it is still necessary to deter-mine whether the managers' personal diversification has observable implications for equity incentives in practice.

Stock markets give managers the flexibility to reduce the sensitivity of the value of their wealth to fluctuations in their respec-tive firm's stock prices, without undermining the effort incentives induced by share ownership. If managers trade in the equitiesof other firms to diversify their own-firm risk, they may increase their tolerance for firm-specific risk and be more willing to sharethe shareholders' goal of tying in their wealth more closely to firm value. Thus, we expect to observe that managers who holdmore diversified equity portfolios will hold higher shares of their wealth in their company stock. This provides our second testablehypothesis:

H2. The levels of top managers' portfolio equity incentives should be positively related to the degrees of diversification in theirequity portfolios made up of equities of other firms.

2.3. The implications of the degree of equity portfolio diversification of top managers for the firm leverage level

Empirical evidence in the literature has long recognized that managers may prefer less leverage than optimal because of a de-sire to protect their undiversified human capital or large holding shares in their firm. For example, Friend and Lang (1988) doc-ument that the debt ratio is negatively related to management's shareholding, reflecting that capital structure decisions are inpart motivated by managerial self-interest. May (1995) also finds a negative relationship between CEO years vested and firmdebt ratios, and supports the view that managers reduce firm debt levels in order to mitigate their personal non-diversifiablerisk. Coles et al. (2006) and Lewellen (2006) provide evidence that the higher sensitivity of CEO wealth to stock prices (equityincentives) implements lower leverage.

However, if managers can use personal portfolios to diversify their exposure to their firms' risks, their tolerance for the firms'risks will increase and their motives for implementing a risk-reduction policy will be mitigated. From a theoretical perspective,Celen and Ozerturk (2007) study agency settings where the managers can trade a customized hedging security correlated withtheir firm-specific risk and predict that customized hedging opportunities reduce managers' inefficient risk-reduction policies.

The use of portfolios made up of equities of other firms allows CEOs or top management teams to diversify their firms' riskexposures without altering their effective share ownerships and while keeping their effort incentives intact.7 If CEOs or top man-agement teams keep their firm-level debts low due to their large holdings of company stock, their use of diversified equity port-folios should raise the level of debts. Hence, the hypothesis advanced in previous research and confirmed in the empirical analysisin this paper is that CEOs or top management teams that hold more diversified equity portfolios will be more likely to implementhigher levels of debt.

H3. The levels of firm leverage should be negatively related to the CEOs' or firms' top management teams' portfolio equity incen-tives and be positively related to the degree of diversification in the CEOs' or top management teams' equity portfolios made up ofequities of other firms.

3. Data, sample selection, and measurement of variables

3.1. Data and sample selection

This paper analyzes all top managers who work in firms listed on the Taiwan Stock Exchange (TSE) and Over-the-Counter(OTC) market in Taiwan. Complete firm-level information, such as stock price information and accounting variables obtainedfrom the Taiwan Economic Journal (TEJ) database, are used to construct the risk measure and control variables. The detailed in-formation on managers' characteristics, such as income, real estate, listed and non-listed stockholdings, and demographic charac-teristics, are compiled from the Financial Data Center (FDC) of the Ministry of Finance in Taiwan. The period for which the uniquedataset from the FDC is available extends from 1998 to 2001. Because it takes time to process the data and because of the sensitivenature of the data, this is the most comprehensive period when such data has been made available. For details of the tax systemand taxation data processing by the FDC in Taiwan, please refer to Appendix A. This appendix also discusses the limitations andpotential biases of the dataset.

The initial sample consists of all firms listed in Taiwan from 1998 to 2001; in total, there are 956 firms and 3087 firm-year ob-servations. Financial firms and firms with fiscal years ending in months other than December are excluded. This procedure re-duces the sample by 78 firms and 282 firm-years. Firm-years with negative sales and having fewer than 30 weekly stock

7 Much of the existing literature on the determinants of leverage levels focuses on the equity incentives for the chief executive officer (CEO). However, priorstudies also suggest that top management teams are likely to affect firms' policy decisions (e.g., Aggarwal and Samwick, 1999, 2003a, 2006; Coles et al., 2006).


return observations before the end of the test year as well as missing individual shareholder data files in the FDC are excluded.This procedure reduces the sample by 54 firms and 335 firm-years.

The income files from the FDC record different types of the employees' income, including salary income, interest income,dividend income, self-employment income, rentals and royalties, pension income and other income.8 Salary income isreported as the total value of basic salary, a year-end bonus, the employees' cash bonus and the fair value of the employees'stock bonus. From the information on salary income files, we have precise information about the employer of each individual.An individual whose annual salary income is within the top five of the employer's payroll is identified as a top manager.9 Inorder to measure the degree of the managers' portfolio diversification in the sample, 803 manager-firm years whose total fi-nancial wealth is less than NT $10,000, or whose listed stockholdings have fewer than 30 weekly return observations beforethe end of the year being tested are excluded. Our final sample consists of 824 firms, 2470 firm-years and 11,558 manager-firm years.

3.2. Measuring financial wealth

Firms are required to provide their individual shareholder records (including the identification numbers of shareholders,names of shareholders and the numbers of shares held) to the FDC at the end of the tax year when the firms declare profit-seeking enterprise income taxes. We combine the listed firms' individual shareholder records with the listed stocks' closing pricesat the year-end from the TEJ database. The market value of each listed stock holding is measured as the number of shares of stockheld times the stock price. Furthermore, according to summary statistics obtained from the TSE and the OTC market, we cross-check the accuracy of the information regarding listed firms' individual shareholder records in the FDC. Overall, the data providestockholder records for about 97.15% of the listed firms and 97.04% of the market capitalization of listed companies from 1998 to2001.

In addition, according to non-listed firms' individual shareholder records, information on managers' equity holdings in privatebusinesses due to earlier entrepreneurial activities or private investment projects is also available. Given that such companies areprivately held and not obligated to disclose financial information, we use either the net value per share as reported by companies(which should be consistent with any public transaction disclosed to the market) or the par value (tens in NT$) if the former isunavailable. We acknowledge that such records may be obsolete and inaccurate in the filing year.

Information regarding managers' other financial assets is also obtained from the FDC. Managers' bond-holdings at the end ofyear t are approximated by the annual interest income during year t+1 divided by the average annual interest rate on savingsdeposits during the test year t+1 as in Heaton and Lucas (2000a). Moreover, the FDC collects data on national property for tax-ation control and auditing purposes. These data files record the tax-assessed value of houses, land and farms at the individuallevel. The market value of housing and land for each manager in each year is estimated by using the averaged ratio of marketvalue to tax-assessed value provided by each county in each year by the Department of Land Administration. However, becausethe Department of Land Administration does not provide similar information for farms, the tax-assessed value of a farm is used asa proxy for the market value of the farm. To summarize, the imputed value of real estate is the sum of the market value of housing,the market value of land and the tax-assessed value of a farm. Finally, following Heaton and Lucas (2000a), we compute a man-ager's financial wealth as the sum of the market value of listed stock holdings, imputed bond holdings, real estate and equity hold-ings in private businesses.10

3.3. Measuring portfolio equity incentives

Although managers can receive incentives from various sources, Jensen and Murphy (1990) document that the vast majorityof incentives are related to ownership of company stock and stock options. Consistent with Jensen and Murphy (1990), muchprior research on equity incentive levels focuses only on stock ownership and stock options (e.g., Aggarwal and Samwick,2003a, 2003b, 2006; Core and Guay, 1999; Himmelberg et al., 1999). Since firms in Taiwan were not allowed to use stock optionsas a part of compensation schemes during our sample period (1998–2001), we compute managers' stockholdings only. Due todata limitations, past studies on managers' incentive levels pay very little attention to managers' total wealth. Himmelberget al. (1999) point out that agency theoretical models generally emphasize managerial ownership levels relative to the managers'wealth, and not simply the fraction of firm equity held by managers.

Our unique dataset has detailed information regarding a manager's financial wealth, including bonds, listed stocks, non-listed stocks and real estate. This detailed information enables us to define managerial portfolio equity incentives as the per-centage change in the dollar value of the manager's wealth for a 1% change in the stock price. The definition of portfolio equityincentives, denoted by Portfolio equity incentives, is therefore equal to the proportion of the manager's financial wealthinvested in his company's stock. In order to compare our results with prior research, we perform robustness tests using theCore and Guay (1999) and Jensen and Murphy (1990) measures of equity incentives and the inference with respect to our hy-potheses is unchanged.

8 Capital gains are not subject to taxation in Taiwan and are therefore not reported under tax filing obligations.9 Since the SEC reporting requirement relates to the top five managers ranked annually by salary and bonus, the identification in our study is similar to Stan-

dard and Poor's ExecuComp dataset.10 Appendix B also presents more details of the measurement of managers' financial assets.


3.4. Measuring personal diversification

As in Bodnaruk et al. (2008) and Goetzmann and Kumar (2008), the measure of managerial personal diversification, denotedby Personal diversification, is constructed as:

Personal diversification ¼ −corr Rfirm ; ∑i∈out firm

wiRi

!

where Rfirm is the stock return that a manager works for,wi is the weight of the stock in the managers' equity portfolio made up ofequities of other firms and∑i∈out firm wiRi is the return of the overall equity portfolio made up of equities of other firms. The mea-sure is computed by using weekly returns over 3 years prior to the end of the test year.

The idea behind this variable is straightforward. If a manager wants to diversify his risk exposure to his firm, he will take a longposition in an equity portfolio that is less positively or is negatively correlated with his firm's stock return, or else will take a shortposition in an equity portfolio that is more positively correlated with his firm's stock return. This implies that if a manager hasmore incentives to diversify his risk exposure to the firm, his equity portfolio made up of equities of other firms will be less pos-itively correlated than his own firm's stock return. In Section 5.2, we also perform robustness tests using alternative measures thatare constructed to capture the sensitivity of a manager's equity portfolio made up of equities of other firms to his exposure to thespecific industry. The inference with respect to our hypotheses is unchanged.

4. Empirical specifications

4.1. Managerial personal diversification equation

Clarifying the effect of the risk-reduction motive on the degree of diversification in a manager's equity portfolio, the level ofthe manager's wealth and alternative background risks need to be controlled. Following previous empirical studies on portfoliochoice, we use the logarithms of age (Log(Age)) and financial wealth (Log(Financial wealth)) as proxies for the relative magni-tudes of the wealth effect and the risk-aversion effect. However, the logarithms of age and financial wealth have ambiguous ef-fects on a manager's diversification decisions. On the one hand, if the risk-aversion effect dominates, the manager will preferto decrease firm risk and hold a more diversified equity portfolio. Goetzmann and Kumar (2008) find that portfolio diversificationincreases with age and wealth. On the other hand, if the wealth effect dominates, the manager will be poorly diversified with re-spect to firm risk in terms of his equity portfolio. Guiso and Paiella (2008) show that absolute risk tolerance is an increasing func-tion of consumer wealth.

In addition to age and wealth, managers' financial positions beyond investment in listed equities also play important roles inunderstanding the levels of diversification in managers' equity portfolios. Cocco (2005) and Heaton and Lucas (2000a, 2000b)have demonstrated that since real estate and private business are risk assets, and returns are correlated with the stock market,individuals who are exposed to entrepreneurial risk or home ownership risk may have a diversification motive for holdingfewer stocks relative to other liquid assets. They explain the reason is that these risks affect the consumption process and inducerisk aversion towards tradable financial risk. Existing empirical results in the literature (e.g., Campbell, 2006; Heaton and Lucas,2000a; Palia et al., 2009; Shum and Faig, 2006) support the idea and find that stock market participation and equity shares de-crease with stakes in real estate and private businesses. Since real estate is the most important asset class for many managersand private business investment is especially prevalent among wealthy managers, we include real estate and private business in-vestments to control the changes of managers' risk attitudes to tradable financial risk. Relative real estate is computed as the frac-tion of the value of real estate to financial wealth, while Relative private business is computed as the fraction of the value of non-listed stockholdings to financial wealth. Because own company stock return correlates more highly with a manager's labor in-come, holdings of one's own company stock are riskier than holdings of common equities in general (Heaton and Lucas, 2000a,2000b). Therefore, we might expect that, all else being equal, managers holding a relatively high fraction of their financial wealthin real estate and private business are more risk averse to the risk exposure to their own company stocks, and hence are morelikely to hold equity portfolios to diversify the risk of their own companies.

The substantial survey and experimental studies concerning financial decision-making document a well-known stereotypethat women tend to be more risk-averse than men (e.g., Eckel and Grossman, 2002; Jianakoplos and Bernasek, 1998; Powelland Ansic, 1997; Sundén and Surette, 1998). However, contradictory evidence also exists and this stereotype may not apply totop managers. For example, Schubert et al. (1999) finds that under controlled economic conditions, female subjects do not gen-erally make less risk financial choices than male subjects. Feng and Seasholes (2008) show that male and female investors exhibitsimilar investment behavior in the PRC. Johnson and Powell (1994) provide evidence that in the sub-population who have takenmanagement education males and female display similar risk propensity. Caliper Corporation (2005) finds that women leadersare more assertive and persuasive, and are more willing to take risks than male leaders.11 Except for gender status, a person'smarital status also appears to determine the risk attitude of individual investors (Säva-Söderbergh, 2005; Sundén and Surette,

11 A detailed description is available at http://calipercorp.com/cal_women.asp.


http://calipercorp.com/cal_women.asp

1998). To control for other potential determinants of a manager's risk attitude, we also include two indicator variables to controlfor managers' gender (Gender status dummy) and marital statuses (Marital status dummy). Finally, we include year dummy vari-ables to control for year fixed effects, and 17 industry indicator variables to control for industry fixed effects. Our empirical modelfor managerial personal diversification is summarized as:

Personal diversificationit ¼ α0 þ α1 Log Portfolio equity incentivesð Þit þα2 Idiosyncratic riskjt þ α3 Log Ageð Þitþα4 Log Financial wealthð Þit þα5 Relative real estateit þ α6 Relative private businessit

þα7 Gender status dummyit þ α8 Marital status dummyit þ uj þ yt þ εit

ð1Þ

where i, j, and t represent the manager, firm and time, respectively, uj is an industry effect, yt is a time effect and εit is a white-noise error term.

4.2. Managerial portfolio equity incentives equation

Besides managerial personal diversification, we include firm characteristics that influence top managers' portfolio equity in-centives in our empirical tests. Much of the empirical work on agency issues tests for the negative trade-off between risk and in-centives. Since higher managerial equity incentives imply less portfolio diversification for managers, the higher the firm'sidiosyncratic risk, the lower are the optimal equity incentives (Aggarwal and Samwick, 1999; Himmelberg et al., 1999; Jin,2002). However, Core and Guay (1999) provide another interpretation for this relationship, suggesting that noisier environmentsare evidence of more monitoring difficulty and thereby increase the equilibrium managerial ownership level. They use idiosyn-cratic risk as a proxy for noise that increases monitoring costs and find a positive association between this variable and thelevel of equity incentives. In this paper, a manager's portfolio equity incentives are measured as the share of his financial wealthinvested in his own company stock. We expect that these equity incentives decrease with the firm's idiosyncratic risk. The firm'sidiosyncratic risk (Idiosyncratic risk) is estimated as the standard deviation of the residual from a CAPMmodel, using weekly stockreturns (%) for the past 3 years from the end of the test year.

The existing literature suggests that monitoring and agency costs tend to be greater in larger firms, thereby increasing the op-timal level of managerial ownership (Core and Guay, 1999; Himmelberg et al., 1999). Following Himmelberg et al. (1999), we usethe logarithm of firm sales, Log(Sales), and its square, Log(Sales)2, to measure firm size and expect that the levels of equity incen-tives will increase at a decreasing rate with firm size. In addition, numerous studies hypothesize that firms with higher growthopportunities use more equity compensation to lower monitoring costs by tying a manager's wealth to firm value (Core andGuay, 1999; Himmelberg et al., 1999). They proceed to provide empirical support for this hypothesis by documenting a positiveassociation between proxies for growth opportunities and top managers' equity incentives. Following Core and Guay (1999), weuse the market value of total assets divided by the book value of total assets to measure firms' growth opportunities and expectthat managers who work in firms with higher market-to-book ratios (M/B) will have higher levels of equity incentives.

Finally, managers may decide to hold on to their company stock for corporate control considerations (Denis et al., 1997). Thus,this paper adds a dummy variable (Large shareholder dummy) that is equal to one if a manager owns more than 10% of his ownfirm. Core and Guay (1999, 2002) and Core and Larcker (2002) find that firms write contracts or vary equity grants requiring ex-ecutives to hold a target or minimum level of equity. In practice, many Taiwanese listed companies potentially restrict the dispos-al of stock bonuses by their employees, aiming to cut personal turnover and avoid the impact on their share prices.12 Methods ofstock restriction include centralized custody, which often involves the use of dummy accounts of some employees. In order to ex-plicitly control the substantial differences in equity-based compensation policies, we construct a dummy variable (Stock bonusdummy) that is equal to one if a firm awards an employee a stock bonus at fiscal t or t−1, otherwise 0 is used.13

Lambert et al. (1991) point out that managers' valuation of equity compensation and incentives depends on substantial differ-ences in their levels of wealth and degrees of risk aversion. Based on the same reasons discussed in the specification for the de-terminants of managerial personal diversification, we include the logarithm of a manager's age and financial wealth to proxy forthe relative magnitudes of the wealth effect and the risk-aversion effect. Real estate and private business are illiquid projects thatgenerate liquidity needs. Faig and Shum (2002) provide a theoretical model and empirical support that equity shares decreasewith stakes in real estate and private business. Moreover, relative real estate and relative private business are also proxies for il-liquidity risks (Heaton and Lucas, 2000a, 2000b). We add relative real estate and relative private business to control these effectsand expect that managers holding a relatively high fraction of their financial wealth in real estate and private business would bemore risk averse toward the risk exposure to their own company stock and hold a relatively low fraction of their financial wealthin their own company stock. Finally, we also include two indicator variables to control for the managers' gender and marital

12 Philip Liu, 4/9/2007, FSC proposes legalizing centralized custody of employee stock dividends, Taiwan Economic News.13 We then construct an alternative variable, the average of the market value of the aggregate employees' stock bonuses at fiscal t and t−1 to proxy for theextent of the market value of stock bonus grants. Attached, please find the regression results in Table A.1. Consistent with firms using stock-based compensationeffectively, the estimated coefficients on the proxy are positive and statistically significant. In addition, the main results are also unaffected by deleting managerswho receive stock bonus grants over the previous fiscal year, confirming that the results are not driven by managers to whom incentive grants are given.


status, year dummy variables to control for year fixed effects, and 17 industry indicator variables to control for industry fixed ef-fects. Our empirical model for the managerial portfolio equity incentives is summarized as:

Log Portfolio equity incentivesð Þit ¼ β0 þ β1Personal diversificationit þ β2Idiosyncratic riskjt þ β3 Log Salesð Þjtþβ4Log Salesð Þ2jt þ β5 M=Bð Þjt þ β6Stock bonus dummyjt þ β7Big shareholder dummyit

þβ8Log Ageð Þit þ β9Log Financial wealthð Þit þ β10Relative real estateit

þβ11Relative private businessitþβ12Gender dummyit þ β13Marital status dummyit

þuj þ yt þ ηit

ð2Þ

where i, j, and t represent the manager, firm and time, respectively, uj is an industry effect, yt is a time effect, and ηit is a white-noise error term.

4.3. Leverage equation

In this paper, we identify the CEO as the highest-paid manager at each firm in each year. In addition, the portfolio equity in-centive (personal diversification) for a top management team is calculated as the average portfolio equity incentive (personal di-versification) of all top managers for each firm in each sample year. The current study uses both market leverage and bookleverage as dependent variables. While market leverage is defined as the ratio of the book value of total debt to the book valueof total debt plus the market value of equity, book leverage is defined as the ratio of the book value of total debt to the bookvalue of total debt plus the book value of equity.

In order to isolate the effects of both managerial portfolio equity incentives and personal diversification on leverage ratios, thispaper adds to the capital structure literature by including controls for firm size, profitability, growth opportunities, asset tangibil-ity, the tax benefits of debt, dividend payouts, and asset uniqueness. Rajan and Zingales (1995) and Titman and Wessels (1988)find these variables hold for the United States and other developed economies. In addition, Antoniou et al. (2008), Booth et al.(2001) and DeJong et al. (2008) and find that the aforementioned determinants also work well in developing economies.

According to the static trade-off theory, leverage should be negatively related to the expected costs of financial distress. Largefirms are less likely to face financial distress, allowing large firms to be able to raise debt at a lower premium (Myers and Majluf,1984). In the case of bankruptcy and liquidation, tangible assets can serve as collateral and lower the risk for the lenders as well asreduce the direct costs of bankruptcy (Myers, 1984; Myers and Majluf, 1984). Titman (1984) argues that firms that produceunique or specialized products probably suffer relatively high costs in the event that they are liquidated. As a result, firms thatproduce unique products have lower debt ratios. Titman and Wessels (1988) provide supportive empirical evidence in this re-gard. Therefore, a positive relationship is expected between leverage and firm size, as well as the tangibility of assets, and a neg-ative relationship is expected between leverage and asset uniqueness. As in prior research, we use the logarithm of sales (Log(Sales)) as a proxy for firm size, property, and plant and equipment plus inventory scaled by total assets (PP&E) to proxy forthe tangibility of assets and advertising expense scaled by sales (Advertising) to capture asset uniqueness.

Agency conflicts between shareholders and bondholders can also affect capital structure choice. Since the bondholders canshare in any profitable future investment returns, the transfer of the net present value can cause the shareholders to foregosome good investment opportunities (Jensen and Meckling, 1976). According to the static trade-off theory, such agency conflictscould contribute to the costs of financial distress by distorting a firm's investment decisions and could be more severe for firmswith more investment opportunities. Therefore, high-growth opportunities lead to an increase in the agency costs of debt andto lower leverage. To account for the investment opportunities, we use the market-to-book ratio (M/B) and the ratio of R&D ex-pense to total assets (R&D) as proxies for growth opportunities.

Modigliani and Miller (1963) suggest that a major borrowing incentive is the tax advantage of interest payments and thus apositive relationship is anticipated between leverage and the effective tax rate. In addition, DeAngelo and Masulis (1980) suggestthat corporate tax deductions can be considered to be substitutes for the tax benefits of debt financing. In their model, a firm'snon-debt tax shields, such as R&D expenditure and depreciation, imply a lower expected corporate tax rate and a lower expectedgain from interest tax shields. Therefore, a negative relationship is expected between leverage and the level of non-debt taxshields. In order to control for the influence of taxation on leverage, we include the ratio of income tax expense to EBITDA(Tax) to proxy for the effective tax rate as well as the ratio of R&D expense to total assets (R&D) and the ratio of depreciation ex-pense to total assets (Depreciation) to proxy for the level of non-debt tax shields.

The relationship between firms' profitability and leverage can be expected to be positive or negative. According to thepeaking-order theory (Myers, 1984), if internal resources are sufficient, firms prefer to finance new investments by using retainedearnings to avoid issuing debt, this being due to the imperfect financial markets. The transaction costs and asymmetric informa-tion reduce the firm's ability to engage in new investment using external funds. Highly profitable firms have more ability to useretained earnings, and a negative relationship is expected between firms' profitability and leverage. In contrast, in the trade-offmodel, bankruptcy costs, agency costs, and taxes push more profitable firms toward higher leverage (Fama and French, 2002).First, since potential bankruptcy costs rise when firms' profitability declines, firms with more profitability tend to have higher le-verage levels. Second, since a firm's free cash flow is determined by the size of its profitability investment, the threat of agencycosts of free cash flow pushes firm with more profitability toward to higher leverage. Third, the DeAngelo and Masulis (1980)


model implies a more general prediction that the deductibility of corporate interest pushes more profitable firms toward higherleverage. In this paper, we add the return on assets (ROA) as earnings before interest, tax, depreciation and amortization scaled bytotal assets to proxy for profitability.

Rozeff (1982) suggests that dividend payments signal a firm's future performance and therefore firms with high dividend pay-outs have a lower equity cost for capital and, hence, have lower leverage levels. In addition, firms with a high payout policy arelikely to be classified as higher risk by creditors and thus it would be possible for the bondholders, by the inclusion of covenants inthe indenture provisions, to limit the managerial dividend decisions (Jensen and Meckling, 1976). Thus, this paper adds the div-idend payout ratio (Dividend) as the ratio of cash dividends to retained earnings and expects there to be an inverse relationshipbetween leverage and dividends.14

Finally, to control for other potential determinants of leverage, we include 17 industry indicator variables to control for indus-try fixed effects, and year dummy variables to control for year fixed effects. The foregoing argument suggests using the followingmodel for leverage to test the third hypothesis:

Leveragejt ¼ γ1 þ γ2 Personal diversificationjt þ γ3 Log Portfolio equity incentivesð Þjt þ γ4Log Salesð Þjt þ γ5 M=Bð Þjt þ γ6ROAjt

þγ7PP&Ejt þ γ8R&Djt þ γ9Advertisingjt þ γ10Depreciationjt þ γ11Taxjt þ γ12Dividendjt þ uj þ yt þ μjt ð3Þ

where j and t represent the firm and time, respectively, uj is an industry effect, yt is a time effect and μit is a white-noise errorterm.

5. Empirical results

5.1. Descriptive statistics

Table 1 reports the descriptive statistics for the sample. Panel A presents the frequency of the managers holding equity in theirown firms and in other firms. On average, 89.18% of the managers in the sample hold stocks in their firms in a given year, while75.40% of the managers hold equities in other firms in a given year. Panel A also provides descriptive statistics on managerial per-sonal diversification, portfolio equity incentives and the relative shares of other assets in their financial wealth. The value of mean(median) managerial personal diversification is−0.28 (−0.29), and ranges from−0.46 at the 25th percentile to zero at the 75thpercentile. These results indicate that there is significant cross-sectional variation in managerial personal diversification.

The mean (median) value of portfolio equity incentives is equal to 0.33 (0.21). The interpretation of this result is that if thevalue of shareholder wealth increases by 1%, then the value of the financial wealth of a manager will increase by 0.33%(0.21%). In our sample, while the mean (median) value of the top managers' own company stockholdings relative to total firmvalue is only 1.72% (0.16%), the mean (median) value of the top managers' own company stockholdings relative to their financialwealth is approximately 33% (21%). This result indicates that while the value of the managers' equity holdings is quite small rel-ative to total firm value, the managers' equity holdings are frequently large relative to their financial assets. Since the distributionof equity incentives across firms is skewed to the right, we use the logarithm of this measure plus one in our tests.

The relative shares of other assets vary considerably and are highly skewed. As for the top managers' financial allocations, onaverage, they hold few stocks in other firms (relative stockholdings in other firms=0.10), and real estate and non-listed stocksare important components of their financial wealth (relative real estate=0.33 and relative non-listed stocks=0.14). They alsohold much smaller bond holdings (0.11) than employees on average (0.30). A typical top manager is aged 49, is married (92%)and 93% of the managers are male. The mean annual salary income (including bonuses) is around NT$3,450,000, and the meanfinancial wealth is about NT$268 million. For a typical employee of a listed company, the salary income is around NT$765,000and financial wealth amounts to approximately NT$6.11 million. Four percent of the top managers in our sample are largeshareholders.

Panel B presents the descriptive statistics for the firm's characteristics of the firms sampled. The sample represents about 88%of all non-financial listed firms in Taiwan during the period 1998–2001. The mean of firm sales is around NT$6745 million, themarket-to-book ratio (M/B) is 1.35, and 35% of firms grant stocks as employee bonuses. All the variables used in this paper aredescribed in Appendix B.

Panel C reports a correlation matrix for the explanatory variables included in Eqs. (1) and (2). With the exception of the largenegative correlation between portfolio equity incentives and relative real estate, all of the correlations are below 0.4 in magni-tude. Real estate is the most important asset class for households and is perhaps the obvious source of background risk that is dif-ficult to diversify, is highly leveraged, and is costly to adjust. The strong negative relationship between portfolio equity incentivesand relative real estate is consistent with the intuitive view that managers who hold a higher fraction of their financial wealth inreal estate will reduce their tolerance for stock market risk and, hence, will have less willingness to hold their firms' stock (Heatonand Lucas, 2000a). The negative relationship could also result from the fact that, for a given level of wealth, managers who chooseto spend more on real estate have less to invest in other risky assets. These facts suggest that it is important to control for relativereal estate in the regression analysis.

14 It is also possible that the payout ratio is endogenous to the capital structure decisions. However, when we re-estimate the equation without the payout ratio,the main results remain qualitatively the same.


5.2. Managerial personal diversification

In our first set of tests, we define manager groups on the basis of financial wealth and measure the mean and median personaldiversification and portfolio equity incentive levels of managers in these groups. Panel A of Table 2 reports the results. Somewhatsurprisingly, the first row shows that wealthier managers are less likely to use portfolios made up of equities in other firms to di-versify their own-firm risk and instead allocate a higher fraction of their financial wealth to their own firm. These results may re-flect the existence of a positive wealth effect on managers' portfolio diversification decisions, which is consistent with the typicalassumption that managers' utility functions exhibit declining absolute risk aversion. On the other hand, these results strongly sug-gest that financial wealth is an important determinant of portfolio equity incentives and personal diversification decisions.

Table 1Descriptive statistics for the sample. The sample consists of 11,558 manager-year observations and is drawn from the Financial Data Center (FDC), Ministry ofFinance in Taiwan from 1998 to 2001. The first part of Panel A lists the fraction of manager-year observations during which managers hold their own companystock and portfolios of other listed equities. The listed stocks are traded in the Taiwan Stock Exchange (TSE) or the Over-the-Counter (OTC) market. The secondpart of Panel A presents descriptive statistics regarding the top managers' portfolio choices, financial wealth and demographic characteristics. Personal diversifi-cation is defined as the negative of the correlation between the returns on the manager's listed equity portfolio (made up shares in other firms) and his own-firmequity returns. Portfolio equity incentives is defined as the fraction of the financial wealth of the manager allocated to the firm in which he works. Financial wealth(FW) is defined as the sum of the value of listed stockholdings, bond-holdings, real estate and the imputed value of holdings in non-listed companies. Relativeholdings of other firms' stock is defined as the fraction of the financial wealth of the manager allocated to the equities of other listed firms. Relative real estate iscalculated as the fraction of the financial wealth of the manager allocated to real estate. Relative private business is defined as the fraction of the financial wealthof the manager allocated to non-listed stocks. Relative bond holdings is calculated as the fraction of the financial wealth of the manager allocated to bonds. Age isthe age of the manager. Large shareholder dummy is set to one if the manager's percentage ownership of the firm is more than 10%. Gender status dummy is set toone if the manager is male. Marital status dummy is set to one if the manager is married. Panel B reports the descriptive statistics of the sampling firms' charac-teristics. The sample consists of 2470 firm-year observations from 1998 to 2001. Sales are defined as gross sales, and are used to measure firm size. M/B is mea-sured as the ratio of the market value of total assets to the book value of total assets. The firm's Idiosyncratic risk is calculated as the standard deviation of theresiduals from a CAPM model estimated using the past three-year weekly stock returns (%) before the end of the test year. Stock bonus dummy is a dummy var-iable equal to one if the firm pays employees' stock bonuses over the fiscal year t or t−1. Panel C presents the correlation matrix of the variables used in Eqs. (1)and (2). Log(Sales) is the natural logarithm of sales. Log(Age) is the natural logarithm of age. Log(Financial wealth) is the natural logarithm of financial wealth. Log(Portfolio equity incentives) is the natural logarithm of the portfolio equity incentives plus one. The details of variable definitions are reported in Appendix B.

Panel A. Top managers' private portfolio choices, financial wealth, and demographic characteristics

Number of observations 11,558Percentage of all managersHolding their own company stock 89.18%Holding equity portfolios made up of equities of other firms 75.4%

Mean Standard deviation 25th percentile Median 75th percentile

Personal diversification −0.28 0.23 −0.46 −0.29 0.00Portfolio equity incentives 0.33 0.32 0.03 0.21 0.60Financial wealth (NT$ ten thousands) 26,833.97 106,631.51 1187.02 3325.17 14,113.14Relative out-firm stockholdings 0.10 0.17 0.00 0.02 0.11Relative real estate 0.33 0.31 0.03 0.26 0.59Relative private business 0.14 0.19 0.00 0.05 0.18Relative bond holdings 0.11 0.18 0.01 0.03 0.12Age 49.47 10.06 43.00 48.00 54.00Large shareholder dummy 0.04 0.19 0.00 0.00 0.00Gender status dummy (Male=1) 0.92 0.26 1.00 1.00 1.00Marital status dummy (Married=1) 0.93 0.26 1.00 1.00 1.00

Panel B. Firms' characteristics


Number of observations 2470Sales (NT$ millions) 6745.09 14,022.89 1249.92 2511.74 5822.40M/B 1.35 0.92 0.83 1.05 1.50Idiosyncratic risk (%) 6.77 2.14 5.28 6.44 7.94Stock bonus dummy 0.35 0.48 0.00 0.00 1.00

Panel C. Correlation matrix for the variables used in the study

(1) (2) (3) (4) (5) (6) (7) (8) (9)

Log(Sales) (1) 1.00M/B (2) 0.17 1.00Idiosyncratic risk (3) −0.31 0.11 1.00Log(Age) (4) −0.23 0.01 0.11 1.00Log(Financial wealth) (5) 0.10 0.39 0.06 0.10 1.00Personal diversification (6) 0.12 −0.15 −0.25 −0.08 −0.07 1.00Log(Portfolio equity incentives) (7) 0.26 0.22 −0.10 −0.16 0.28 0.26 1.00Relative real estate (8) −0.11 −0.23 −0.07 0.05 −0.58 0.04 −0.12 1.00Relative private business (9) −0.01 −0.17 0.05 −0.03 −0.24 0.03 0.10 −0.25 1.00


Furthermore, Panel B of Table 2 shows that when we focus on managers within the same financial wealth cohort, there is sig-nificant variability in terms of in the level of personal diversification across the various portfolios of equity incentive groups. Forthe low financial wealth group, in going from the low to the high portfolio of the equity incentive group, the mean (median) per-sonal diversification increases from −0.29 (−0.30) to−0.20 (−0.13). For the medium and high financial groups, the results arequite similar in either case. These results suggest that managers with higher portfolio equity incentives are more likely to usethem to diversify their portfolios among equities of other firms and will therefore hold more diversified equity portfolios.

Table 2Cross-sectional variation in personal diversification. The sample consists of 11,558 manager-year observations from 1998 to 2001. All manager-years are pooled,ranked by financial wealth and sorted into three groups. Panel A reports descriptive statistics on the personal diversification and portfolio equity incentives forlow, medium and high financial wealth managers. The t-test and Kologorov-Smirnov test of differences in the personal diversification and portfolio equity incen-tives between the low and high financial wealth groups are also reported. In Panel B, for low, medium and high financial wealth groups, managers are ranked byportfolio equity incentives and sorted into three groups. For different classes of managers, the mean and the median personal diversification as well as the t-testand Kologorov-Smirnov test of differences in personal diversification between the low and high portfolio equity incentive groups are reported.

Panel A. Personal diversification and portfolio equity incentives by financial wealth

Financial wealth t-Test Median test

Low Med High

Mean Median Mean Median Mean Median t-Stat p-Value KS p-Value

Personal diversification −0.25 −0.25 −0.29 −0.30 −0.31 −0.31 −11.88 b0.0001 5.21 b0.0001Portfolio equity incentives 0.22 0.09 0.29 0.17 0.48 0.49 36.59 b0.0001 15.33 b0.0001

Panel B. Personal diversification by financial wealth and portfolio equity incentives

Portfolio equity incentives t-test Median test

Low Med High

Mean Median Mean Median Mean Median t-Stat p-Value KS p-Value

Financial wealth Personal diversificationLow −0.29 −0.30 −0.25 −0.25 −0.20 −0.13 10.26 b0.0001 7.68 b0.0001Med −0.31 −0.32 −0.30 −0.32 −0.25 −0.24 5.94 b0.0001 4.40 b0.0001High −0.34 −0.36 −0.32 −0.32 −0.26 −0.25 8.83 b0.0001 4.38 b0.0001

Table 3ARegressions of managerial personal diversification on portfolio equity incentives and firm idiosyncratic risk. OLS and median regressions. The table reports OLSand median regression estimates of Eq. (1). The sample includes 11,558 manager-year and 2470 firm-year observations from 1998 to 2001. The dependent var-iable is the managerial personal diversification that is defined as the negative of the correlation between the returns of the manager's portfolio of listed equities(not his firm) and his own-firm equity returns. The independent variable definitions are given in Table 1. Panel A reports the results for individual managers, andPanel B reports the results for the average of the same variable across all managers in a given firm year. To save space, coefficient estimates of year and industryfixed effects as well as intercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients sig-nificant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *. t-Statistics for OLS are based on heteroskedas-ticity robust standard errors. R-squared is the adjusted R2 for OLS and the pseudo-R2 for the median regressions.

Independent variables Panel A. For individual managers Panel B. For firm-years averaged

Baseline regression OLS OLS Median OLS Median

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

Log(Portfolio equity incentives) 0.1527*** 0.3821*** 0.4486*** 0.5047*** 0.5962***(15.63) (29.26) (28.81) (17.96) (16.69)

Idiosyncratic risk 0.0208*** 0.0188*** 0.0260*** 0.0171*** 0.0216***(18.86) (17.58) (19.52) (11.33) (11.07)

Log(Age) −0.0341*** −0.0417*** −0.1073*** −0.0978***(−3.03) (−3.11) (−4.06) (−2.97)

Log(Financial wealth) −0.0262*** −0.0302*** −0.0372*** −0.0403***(−22.16) (−22.46) (−12.85) (−11.77)

Relative real estate 0.1717*** 0.1854*** 0.2713*** 0.2796***(18.84) (16.30) (12.06) (10.00)

Relative private business 0.1383*** 0.1636*** 0.1966*** 0.2248***(11.08) (10.54) (6.73) (6.12)

Gender status dummy 0.0335*** 0.0299*** −0.0261 −0.0432(4.26) (2.71) (−1.18) (−1.64)

Marital status dummy −0.0146* −0.0172* 0.0022 0.0129(−1.82) (−1.70) (0.10) (0.46)

Year fixed effect Yes Yes Yes Yes Yes YesIndustry fixed effect Yes Yes Yes Yes Yes YesR-squared 0.0586 0.0674 0.1525 0.1074 0.2816 0.1820Observations 11,558 2470


Tables 3A and 3B present the results of the regressions based on Eq. (1) pooled over the sample years from 1998 to 2001 withyear and industry indicator variables. The specification in column 1 of Table 3A includes only the portfolio equity incentives. Con-sistent with the univariate test results in Table 2, we observe a significantly positive coefficient on the logarithm of portfolio eq-uity incentives. In addition, the specification in column 2 includes only the firm's idiosyncratic risk. The coefficient estimate forthe idiosyncratic risk is significantly positive, as predicted by our hypothesis. Column 3 presents an OLS estimation of the fullspecification of Eq. (1) and shows that the coefficients for all of the explanatory variables are statistically significant and of thepredicted signs. Compared to the estimates in column 1, the estimated coefficient for the logarithm of portfolio equity incentivesis higher in magnitude but maintains its sign and significance levels in the full specification. This difference suggests that man-agers' age, financial wealth, and alternative background risks are correlated with their portfolio equity incentives. Thus excludingthese characteristics as control variables would bias downward the estimated coefficient on portfolio equity incentives in apooled regression.

Jenter (2005) points out that the trading decisions of managers in the same firm are likely to be dependent, giving rise to theconcern that the t-statistics reported in column 3 are inflated. Like Jenter (2005), we average the dependent and explanatory vari-ables across managers in the same firm and repeat the regression analysis on these firm-year averages. The results reported incolumn 5 of Table 3A are similar to the results in column 3. However, the effects of portfolio equity incentives are larger in abso-lute terms and the adjusted R-squared statistic is higher than in the manager-level regressions. These results more strongly sup-port our conjectures.

In order to provide evidence that our results are robust to various changes in the estimation procedure, we first present a me-dian regression15 estimation of Eq. (1) in column 4 for individual managers and in column 6 for firm-years averaged. The resultsare similar to the OLS regression. Second, to remove the effect of any firm-specific characteristics that may affect managers' per-sonal diversification in a way not specified in our model, we replace the industry indicators with an indicator for each firm.16 Thefirm fixed-effects results are reported in column 1 of Table 3B for individual managers and in column 4 for firm-years averaged.With the exception of the estimated coefficient for Log(Age), the coefficient estimates for all explanatory variables are consistentwith the results from the industry-effects specification in Table 3A.

Our inferences will be affected if there is potential cross-sectional dependence in our pooled time-series cross-sectional re-gression models. The results from the Fama–MacBeth regression procedure are presented in columns 2 and 5 of Table 3B,

Table 3BFirm fixed effects, Fama–MacBeth, and cross-sectional regressions. The table reports the firm fixed effects, Fama–MacBeth and cross-sectional regression esti-mates of Eq. (1). The sample includes 11,558 manager-year and 2470 firm-year observations from 1998 to 2001. The dependent variable is the managerial per-sonal diversification that is defined as the negative of the correlation between the returns of the manager's portfolio of listed equities (not his firm) and his ownfirm equity returns. The independent variable definitions are given in Table 1. Panel A reports the results for individual managers, and Panel B reports the resultsfor the average of the same variable across all managers in a given firm year. To save space, coefficient estimates of year, industry, or firm fixed effects as well asintercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients significant at the 1% level aredenoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.


Firm fixed effectsregressions

Fama–MacBethregressions

Time-averaged cross-sectional regressions




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

Log(Portfolio equityincentives)

0.2782*** 0.3821*** 0.3981*** 0.2548*** 0.5064*** 0.5654***(20.11) (59.85) (22.14) (6.77) (50.49) (11.19)

Idiosyncratic risk 0.0119*** 0.0204*** 0.0149*** 0.0113*** 0.0186*** 0.0143***(6.44) (7.83) (10.78) (6.47) (6.90) (5.62)

Log(Age) 0.0200* −0.0369 −0.0083 0.0457 −0.1091 −0.0562(1.76) (−2.04) (−0.54) (1.25) (−2.22) (−1.20)

Log(Financial wealth) −0.0205*** −0.0263*** −0.0254*** −0.0206*** −0.0375*** −0.0405***(−17.60) (−33.59) (−15.46) (−5.71) (−22.45) (−7.80)

Relative real estate 0.1145*** 0.1744*** 0.1842*** 0.0642** 0.2728*** 0.3495***(13.09) (17.75) (14.88) (2.55) (20.90) (8.10)

Relative private business 0.1033*** 0.1399*** 0.1528*** 0.0571* 0.1916*** 0.3049***(8.50) (16.13) (8.89) (1.78) (17.32) (5.60)

Gender status dummy 0.0502*** 0.0300** 0.0378*** 0.0429* −0.0375 −0.0423(6.88) (3.97) (3.80) (1.78) (−1.96) (−1.07)

Marital status dummy −0.0165** −0.0116 −0.0179* −0.0234 −0.0030 0.0101(−2.22) (−1.24) (−1.08) (−0.25) (−1.72) (0.24)

Year fixed effect Yes Yes No Yes Yes NoIndustry fixed effect No Yes Yes No Yes YesFirm fixed effect Yes No No Yes No NoObservations 11,558 2470

15 The median regression minimizes the sum of absolute errors and is particularly useful when the conditional distribution does not have a standard shape, suchas when there exist outliers, asymmetry, or fat-tail distribution (e.g., Koenker and Bassett, 1978; Koenker and Hallock, 2001).16 Himmelberg et al. (1999) point out that the firm effects model controls for unobserved firm characteristics that are correlated with the observed character-istics. In addition, the time-series component can be elicited by adding firm fixed effects to the regressions.


indicating that the inference from Eq. (1) is not affected by cross-correlation problems. Finally, in order to isolate the effects of thepure cross-sectional elements of the portfolio equity incentives on personal diversification, we employ Jenter's (2005) approachand re-test the specification of Eq. (1) by running the cross-sectional regressions. We average all annual observations from 1998to 2001 for either an individual or a firm. The results using individual managers as data points are reported in column 3, and theresults using firms as data points are reported in column 6. Again, the results are qualitatively similar.

To sum up, the positive signs on the Log(Portfolio equity incentives) and Idiosyncratic risk provide evidence that managers haveincentives to diversify the risk associated with their personal holdings of the firm's equity. The significantly estimated positivecoefficients for the portfolio equity incentives and idiosyncratic risk indicate that managers could engage in trading in the equitiesof other firms to diversify their own firm risk when they have a higher fractions of their financial wealth invested in the firm orwhen the firm's idiosyncratic risk is high. Our findings imply that managers are able to rebalance their portfolio levels of incen-tives through transactions in the equities of other firms. These findings implicitly confirm the results of Bettis et al. (2001), whofind that higher-ranking insiders use collars and swaps to cover a significant proportion of their holdings of the firm's stock. Fur-thermore, consistent with the results in Table 2, the Log(Financial wealth) has a significantly negative coefficient in our regressionframework, indicating that wealthier managers hold less diversified equity portfolios. The significantly positive coefficients forRelative real estate and Relative private business indicate that managers with exposure to higher levels of entrepreneurial risk orhome ownership risk will reduce their tolerance for their own-firm risk and will have more incentives to diversify.

5.3. Managerial portfolio equity incentives

In this section, we begin by estimating regressions based on Eq. (2) to test the second hypothesis in Section 2. Tables 4A and 4Bpresent the results. To provide a baseline for comparison, we first run a set of three baseline regressions. The specification in col-umn 1 of Table 4A includes only personal diversification. Consistent with our hypothesis, we observe a significantly positive

Table 4ARegressions of managerial portfolio equity incentives on personal diversification. OLS and median regressions. The table reports the OLS and median regressionestimates of Eq. (2). The sample includes 11,558 manager-year and 2470 firm-year observations from 1998 to 2001. The dependent variable is the natural log-arithm of the portfolio equity incentives plus one. The portfolio equity incentives are defined as the fraction of the financial wealth of the manager allocatedto the firm where he works. The independent variable definitions are given in Table 1. Panel A reports the results for individual managers, and Panel B reportsthe results for the average of the same variable across all managers in a given firm year. To save space, coefficient estimates of year and industry fixed effectsas well as intercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients significant atthe 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *. t-Statistics are based on heteroskedasticity-robust standarderrors. R-squared is the adjusted R2 for OLS regressions and the pseudo-R2 for the median regressions.


Baseline regressions OLS OLS Median OLS Median

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

Personal diversification 0.1358*** 0.1682*** 0.1488*** 0.2068*** 0.2244***(15.63) (27.20) (26.72) (15.93) (15.49)

Log(Sales) 0.1248*** 0.1032*** 0.0778*** 0.0969*** 0.1118***(5.48) (6.42) (3.89) (4.53) (3.56)

(Log(Sales))2 −0.0041*** −0.0036*** −0.0028*** −0.0035*** −0.0039***(−5.41) (−6.69) (−4.31) (−4.87) (−3.79)

M/B 0.0675*** 0.0222*** 0.0090*** 0.0068*** 0.0021(28.25) (12.60) (7.97) (2.64) (0.86)

Idiosyncratic risk −0.0076*** −0.0052*** −0.0038*** −0.0054*** −0.0068***(−6.82) (−6.48) (−5.43) (−4.94) (−5.21)

Stock bonus dummy 0.0513*** 0.0255*** 0.0197*** 0.0202*** 0.0127***(11.46) (8.04) (6.77) (4.62) (3.01)

Log(Age) −0.0586*** −0.0342*** −0.0326*** −0.0993*** −0.0979***(−7.42) (−4.44) (−5.13) (−5.56) (−4.65)

Log(Financial wealth) 0.0276*** 0.0286*** 0.0268*** 0.0440*** 0.0449***(33.59) (34.22) (27.91) (22.19) (22.46)

Relative real estate −0.4468*** −0.4368*** −0.5519*** −0.5195*** −0.5693***(−90.37) (−90.62) (−101.24) (−45.19) (−46.68)

Relative private business −0.4607*** −0.4349*** −0.5574*** −0.5489*** −0.5968***(−59.67) (−57.51) (−73.19) (−33.29) (−37.48)

Large shareholder dummy 0.0913*** 0.0755*** 0.0316*** 0.1250*** 0.0915***(11.91) (10.15) (6.90) (6.43) (5.29)

Gender status dummy −0.0020 −0.0062 −0.0036 0.0022 −0.0116(−0.35) (−1.17) (−0.74) (0.15) (−0.79)

Marital status dummy 0.0397*** 0.0370*** 0.0159*** 0.0646*** 0.0608***(7.00) (6.79) (2.97) (4.45) (3.86)

Year fixed effect Yes Yes Yes Yes Yes Yes YesIndustry fixed effect Yes Yes Yes Yes Yes Yes YesR-squared 0.2082 0.2581 0.5974 0.6315 0.4892 0.7747 0.5763Observations 11,558 2470


coefficient for the personal diversification. Columns 2 and 3 report the results that the specification includes only firms' charac-teristics and managers' characteristics, respectively. We find that the explanatory power in column 3 with an adjusted R2 of59.74% is larger than that in column 2 with an adjusted R2 of 25.81%. This finding reveals that the inclusion of variables that con-trol for managers' age, financial wealth and alternative background risks are important in explaining the cross-sectional variationin the level of managerial portfolio equity incentives.

Column 4 presents an OLS estimation of the full specification of Eq. (2) and shows that the coefficients for all of the explana-tory variables are statistically significant and of the predicted sign. Repeating the regression with firm-years averaged for both thedependent and the explanatory variables confirms this result in column 6. As discussed in Section 5.2, we consider alternative es-timation methods to re-test the full model of Eq. (2): median, firm fixed effect, Fama–MacBeth and cross-sectional regressions.Taken together, the results presented in Tables 4A and 4B show that the coefficient estimates on personal diversification are pos-itive and statistically significant in all different regression methods, respectively. Overall, such a relationship is economicallymeaningful: A one standard deviation increase in the level of personal diversification increases the level of equity incentives by3.87% in the OLS regression.

These results confirm the hypothesis that managers tend to hold a larger share of their company stock when they use a port-folio made up of equities of other firms to diversify their own-firm risk. These findings are consistent with Gao's (2010) findingsthat an optimal managerial incentive level increases with the decrease in executive hedging costs. Our findings implicitly confirmthe predictions of Celen and Ozerturk (2007) and Ozerturk (2006b), who demonstrate that the managerial equity incentive ishigher where financial markets are more developed and the costs of financial innovations are lower.

The control variables are mostly significant with the coefficients having the expected signs. The estimated coefficient for Log(Sales) is positive, which implies that managers' portfolio equity incentives increase at a rate that declines with firm size, as pre-dicted by Core and Guay (1999) and Himmelberg et al. (1999). The estimated coefficient for M/B is positive, which implies thatfirms with higher growth opportunities are associated with higher managerial equity incentives, consistent with Coles et al.(2006) and Core and Guay (1999). We find that higher idiosyncratic risk generally tends to reduce portfolio equity incentives,

Table 4BFirm fixed effects, Fama–MacBeth, and cross-sectional regressions. The table reports the firm fixed effect, Fama–MacBeth and cross-sectional regression estimatesof Eq. (2). The sample includes 11,558 manager-year and 2470 firm-year observations from 1998 to 2001. The dependent variable is the natural logarithm of theportfolio equity incentives plus one. The portfolio equity incentives are defined as the fraction of the financial wealth of the manager allocated to the firm wherehe works. The independent variable definitions are given in Table 1. Panel A reports the results for individual managers, and Panel B reports the results for theaverage of the same variable across all managers in a given firm year. To save space, coefficient estimates of year, industry or firm fixed effects as well as interceptsare not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients significant at the 1% level are denotedby ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.

Independentvariables

Panel A. For individual managers Panel B. For firm-years averaged







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

Personal diversification 0.1277*** 0.1722*** 0.1795*** 0.1053*** 0.2132*** 0.2136***(19.75) (23.49) (20.24) (6.71) (18.51) (9.87)

Log(Sales) −0.0720** 0.1158*** 0.0848*** −0.0546** 0.1021** 0.1199***(−2.36) (5.62) (3.60) (−2.02) (4.77) (3.19)

(Log(Sales))2 0.0032*** −0.0040** −0.0030*** 0.0024** −0.0036** −0.0043***(2.93) (−5.44) (−3.84) (2.48) (−4.86) (−3.43)

M/B 0.0157*** 0.0239** 0.0233*** 0.0121*** 0.0074 0.0024(5.46) (4.24) (9.76) (4.48) (1.85) (0.59)

Idiosyncratic risk −0.0007 −0.0053** −0.0043*** −0.0002 −0.0058*** −0.0036**(−0.48) (−3.25) (−4.06) (−0.16) (−7.15) (−2.03)

Stock bonus dummy 0.0172*** 0.0242** 0.0239*** 0.0146*** 0.0186** 0.0137*(3.64) (5.26) (5.14) (3.37) (3.80) (1.75)

Log(Age) 0.0261*** −0.0310* −0.0528*** −0.0332 −0.0981* −0.1265***(3.37) (−2.43) (−4.88) (−1.42) (−2.77) (−4.20)

Log(Financial wealth) 0.0201*** 0.0285*** 0.0299*** 0.0318*** 0.0440*** 0.0448***(24.40) (18.94) (25.29) (13.91) (21.21) (12.55)

Relative real estate −0.3808*** −0.4384*** −0.4351*** −0.3993*** −0.5216*** −0.5849***(−80.13) (−30.10) (−65.36) (−31.10) (−48.53) (−28.51)

Relative private business −0.3539*** −0.4405*** −0.4341*** −0.4390*** −0.5591*** −0.6011***(−46.69) (−17.79) (−40.79) (−25.02) (−16.79) (−20.38)

Large shareholder dummy 0.0655*** 0.0734*** 0.0747*** 0.0279 0.1227** 0.1075***(9.02) (6.45) (6.41) (0.91) (5.16) (3.52)

Gender status dummy −0.0054 −0.0056 −0.0042 −0.0266* 0.0072 0.0046(−1.08) (−1.00) (−0.61) (−1.72) (0.50) (0.18)

Marital status dummy 0.0216*** 0.0383*** 0.0324*** 0.0168 0.0693** 0.0680**(4.28) (8.25) (4.51) (1.21) (4.98) (2.52)

Year fixed effect Yes Yes No Yes Yes NoIndustry fixed effect No Yes Yes No Yes YesFirm fixed effect Yes No No Yes No NoObservations 11,558 2470


which is consistent with the prior literature's findings that managers have incentives to diversify firm risk (e.g., Aggarwal andSamwick, 1999; Himmelberg et al., 1999; Jin, 2002). The estimated coefficient on the Stock bonus dummy is positive and is con-sistent with Core and Guay's (1999) finding that firms use stock-based compensation to increase managerial equity incentives.

In addition, our findings indicate that managers with a higher level of financial wealth will have higher portfolio equity incen-tives. Consistent with the idea of induced risk aversion, the negative signs for Relative real estate and Relative private business in-dicate that managers with a higher fraction of financial wealth invested in real estate and private business are less willing to holdtheir own firm's stock. In Tables 3A and 3B, we find that the estimated coefficient on the Gender status dummy is positive but not

Table 5Descriptive statistics for a subsample of levels of leverage and their determinants. The subsample consists of 2326 firm-year observations from 1998 to 2001. Thesample is smaller because firm-year observations with data unavailable on R&D expense, advertising expense, or dividends are excluded. Panel A presents de-scriptive statistics regarding the levels of leverage and their determinants. Market leverage is defined as the ratio of the book value of total debt to the bookvalue of total debt plus the market value of equity. Book leverage is defined as the ratio of the book value of total debt to the book value of total debt plus thebook value of equity. Personal diversification is defined as the negative of the correlation between the returns of the manager's portfolio of listed equities ofother firms and his own firm equity returns. Portfolio equity incentives is defined as the fraction of the financial wealth of the manager allocated to the firmwhere he works. CEOs are identified as the highest paid manager at each firm in each year. The portfolio equity incentives (personal diversification) for thetop management team are calculated as the average portfolio equity incentives (personal diversification) of all top managers for each firm in each sampleyear. Log(Portfolio equity incentives) is defined as the natural logarithm of the portfolio equity incentives plus one. Log(Sales) is defined as the natural logarithmof gross sales. M/B is measured as the ratio of the market value of total assets to the book value of total assets. ROA is earnings before interest, tax, depreciation,and amortization (EBITDA) scaled by total assets. PP&E is property, plant, and equipment plus inventory scaled by total assets. R&D is defined as the ratio of R&Dexpense to total assets. Advertising is defined as the ratio of advertising expense to sales. Depreciation is measured as the ratio of depreciation expense to totalassets. Tax is used as the ratio of income tax expense to EBITDA. Dividend is the ratio of cash dividends to retained earnings. Panel B presents the correlation matrixof the variables used in Eq. (3). The details of the variable definitions are reported in Appendix B.

Panel A. Descriptive statistics for the variables used in the regressions of leverage ratio


CEOsPortfolio equity incentives 0.40 0.33 0.07 0.34 0.70Personal diversification -0.28 0.23 −0.47 −0.29 0.00

Top management teamPortfolio equity incentives 0.26 0.19 0.10 0.24 0.41Personal diversification −0.28 0.16 −0.40 −0.27 −0.16

Firms' characteristicsMarket leverage 0.41 0.24 0.21 0.38 0.58Book leverage 0.43 0.20 0.30 0.41 0.53Log(Sales) 14.87 1.23 14.05 14.74 15.58M/B 1.35 0.93 0.83 1.06 1.51R&D 0.01 0.02 0.00 0.00 0.02ROA 0.06 0.11 0.03 0.07 0.11PP&E 0.47 0.20 0.32 0.47 0.61Tax 0.07 0.81 −0.01 0.04 0.13Depreciation 0.03 0.02 0.01 0.02 0.04Advertising 0.01 0.02 0.00 0.00 0.01Dividend 0.15 0.48 0.00 0.00 0.22

Number of observations 2326

Panel B. Correlation matrix for the determinants of leverage ratio

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)

CEOsLog(Portfolio equityincentives) (1)

1.00

Personaldiversification (2)

0.13 1.00

Top management teamLog(Portfolio equityincentives) (3)

0.74 0.09 1.00

Personaldiversification (4)

0.10 0.82 0.10 1.00

Log(Sales) (5) −0.25 0.09 −0.32 0.12 1.00M/B (6) 0.01 0.40 0.03 0.49 0.16 1.00R&D(7) 0.00 0.33 −0.05 0.38 0.01 0.45 1.00ROA (8) −0.03 0.31 −0.04 0.38 0.19 0.36 0.18 1.00PP&E (9) 0.08 −0.24 0.12 −0.27 −0.20 −0.29 −0.28 −0.19 1.00Tax (10) 0.00 0.02 0.00 0.00 −0.03 0.00 −0.02 0.00 −0.02 1.00Depreciation (11) −0.08 −0.03 −0.10 −0.01 0.03 −0.04 0.01 0.10 0.29 −0.02 1.00Advertising (12) 0.07 −0.02 0.07 −0.07 −0.09 −0.07 −0.03 −0.09 0.08 0.00 −0.13 1.00Dividend (13) −0.05 −0.03 −0.05 −0.03 0.07 −0.03 −0.04 0.11 −0.02 0.01 −0.02 0.08 1.00


always significant. In Tables 4A and 4B, we cannot find a significant association between gender status and equity incentives. Thismay be due to the fact that women are under-represented in the senior ranks of corporate managers or have taken managementeducation, and display a similar risk propensity to men.

5.4. Firms' leverage ratio

The sample of leverage regressions is smaller because firm-years with missing data on R&D expense, advertising expense anddividends are excluded. The final sample, with data available for all control variables in Eq. (3), consists of 2326 firm-years. PanelA of Table 5 presents descriptive statistics for all variables that we analyze in Eq. (3), and Panel B presents a correlation matrix ofthe explanatory variables.

The first row of Panel A shows that the mean of portfolio equity incentives for CEOs is equal to 0.40. This number means that ifthe value of shareholder wealth increases by 1%, then the financial wealth of a CEO will increase by 0.40%. This also indicates thatthe company stock is the most important component of financial wealth for CEOs. The distribution of portfolio equity incentivesacross firms is skewed to the right, with median equity incentives substantially lower at 0.34. The presence of skewness motivatesus to use the natural logarithm of portfolio equity incentives in the regression analysis. The second row in Panel A shows that themean (median) value of personal diversification for CEOs is−0.28 (−0.29). For the top management team, the mean andmedianof the portfolio equity incentives are lower than those for CEOs, but the mean and median of the personal diversification are sim-ilar to those for CEOs.

The results for the market leverage regressions are reported in Tables 6A and 6B. In Panel A, the independent variables are theCEOs' personal diversification and portfolio equity incentives. In Panel B, the computations of portfolio equity incentives and per-sonal diversification for each CEO are replaced by those for the top management team. To provide a baseline for comparison, wefirst run a set of four baseline regressions. Consistent with our hypothesis, we observe a significantly negative coefficient on theLog(Portfolio equity incentives) and a positive coefficient on the Personal diversification. Column 5 presents an OLS estimation of thefull specification of Eq. (3) and shows that by controlling firms' characteristics, the coefficients on the Log(Portfolio equity incen-tives) and Personal diversification are statistically significant and of the predicted sign. Column 7 of Panel B presents the estimates

Table 6ARegressions of market leverage on CEO (top management team) personal diversification and portfolio equity incentives. OLS and median regressions. The tablereports OLS and median regression estimates of Eq. (3). The sample consists of 2326 firm-year observations from 1998 to 2001. The dependent variable is marketleverage that is defined as the ratio of the book value of total debt to the book value of total debt plus the market value of equity. The independent variable def-initions are given in Table 5. In Panel A, the independent variables are the CEOs' personal diversification and portfolio equity incentives. In Panel B, the indepen-dent variables are the top management teams' personal diversification and portfolio equity incentives. To save space, coefficient estimates of year and industryfixed effects as well as intercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients sig-nificant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *. t-Statistics are based on heteroskedasticity-robust standard errors. R-squared is the adjusted R2 for OLS regressions and the pseudo-R2 for the median regressions.

Independent variables Panel A. CEOs Panel B. Top management teams

Baseline regressions OLS OLS Median OLS Median

(1) (2) (3) (4) (5) (6) (7) (8)

Log(Portfolio equity incentives) −0.2918*** −0.3085*** −0.1368*** −0.1367*** −0.2490*** −0.2184***(−15.83) (−16.59) (−8.76) (−7.27) (−11.46) (−8.25)

Personal diversification 0.0420** 0.0902*** 0.0766*** 0.0670*** 0.1424*** 0.1319***(2.36) (5.29) (5.49) (3.91) (6.66) (4.89)

Log(Sales) 0.0194*** 0.0231*** 0.0274*** 0.0264*** 0.0281***(7.10) (8.39) (9.81) (9.43) (9.79)

M/B −0.0634*** −0.0590*** −0.0631*** −0.0557*** −0.0601***(−14.64) (−13.74) (−7.35) (−12.93) (−7.25)

R&D −1.0608*** −0.9660*** −0.7735*** −0.8423*** −0.6585***(−5.36) (−4.96) (−3.75) (−4.37) (−3.34)

ROA −0.7584*** −0.7073*** −0.8174*** −0.6704*** −0.7941***(−24.37) (−22.72) (−12.45) (−21.49) (−13.08)

PP&E 0.1952*** 0.1898*** 0.2065*** 0.1891*** 0.2116***(9.86) (9.76) (8.55) (9.83) (8.54)

Tax −0.0066* −0.0061* −0.0021 −0.0067* −0.0018(−1.76) (−1.65) (−0.21) (−1.85) (−0.18)

Depreciation −0.2458 −0.2636* −0.4798** −0.2419 −0.5153**(−1.58) (−1.73) (−2.15) (−1.60) (−2.33)

Advertising −0.4387** −0.3446** −0.4415* −0.3992** −0.4734*(−2.48) (−1.98) (−1.80) (−2.32) (−1.88)

Dividend −0.0443*** −0.0435*** −0.1013** −0.0424*** −0.0991**(−6.67) (−6.69) (−2.56) (−6.59) (−2.46)

Year fixed effect Yes Yes Yes Yes Yes Yes Yes YesIndustry fixed effect Yes Yes Yes Yes Yes Yes Yes YesR-squared 0.1371 0.1077 0.1473 0.3616 0.3765 0.4567 0.392 0.4642Observations 2326


for the top management team. As in the case of the CEOs, the coefficients on the Log(Portfolio equity incentives) have the predictedsigns and are even more statistically significant. These results show that managerial personal diversification motives for CEOs aswell as other top managers can explain the observed differences in firms' financing policies.

As discussed in Section 5.2, we consider alternative estimation methods to re-test the full model of Eq. (2): the median, firmfixed effect, Fama–MacBeth and cross-sectional regressions. Taken together, the results presented in Tables 6A and 6B show thathigher equity incentives lead to lower leverage, a finding that is consistent with prior studies (e.g., Barclay et al., 2006; Coles et al.,2006; Lewellen, 2006; Rajan and Zingales, 1995). In addition, the coefficient estimates for personal diversification are positive andstatistically significant for all different regression methods. This finding confirms our third hypothesis that managers with higherpersonal diversification are more willing to implement a higher leverage ratio. Managerial personal diversification can mitigatethe agency cost due to the managers' personal risk-reduction motives for keeping debts low.

With the exception of the tax rate, the coefficients on all of the explanatory variables are statistically significant and of the pre-dicted signs. Consistent with prior studies (e.g., Booth et al., 2001; Friend and Lang, 1988; Lewellen, 2006; Rajan and Zingales,1995; Rozeff, 1982; Titman andWessels, 1988), larger firms and firms with higher tangible asset ratios have higher market lever-age ratios. In addition, firms with higher growth opportunities, profitability, unique asset ratios, non-debt tax shields, and divi-dend payout ratios have lower market leverage ratios. The regressions in Tables 6A and 6B support the profitability predictionof the pecking order theory. Empirical studies by Titman and Wessels (1988), Rajan and Zingales (1995), and Fama and French(2002) using data from the U.S., as well as Antoniou et al. (2008) and Booth et al. (2001) using data from developing economies,also supported the results. Interestingly, the estimated coefficient on Tax is negative and insignificant, which goes against the pre-diction of the static trade-off theory. This discrepancy might be due to the asymmetry of the corporate taxation. Faig and Shum(1999) provide a theoretical model and show that the effects of the asymmetry of the corporate taxation on firms' debt financingdepend on each firm's profitability. Moreover, this variable itself could also measure profitability and, according to the peckingorder theory, profitable firms have lower leverage (Lewellen, 2006).

To check for robustness, we re-estimate Eq. (3) using book leverage as the dependent variable. With the exception of firmfixed-effect regressions, all our major findings remain.17 As for the firm fixed-effect regressions, the estimated coefficients for

17 To save space, the results are not reported and such results are available from the authors upon request.

Table 6BFirm fixed effects, Fama–MacBeth, and cross-sectional regressions. The table reports firm fixed effects, Fama–MacBeth and cross-sectional regression estimates ofEq. (3). The sample consists of 2326 firm-year observations from 1998 to 2001. The dependent variable is market leverage that is defined as the ratio of the bookvalue of total debt to the book value of total debt plus the market value of equity. The independent variable definitions are given in Table 5. In Panel A, the in-dependent variables are the CEO's personal diversification and portfolio equity incentives. In Panel B, the independent variables are the top management team'spersonal diversification and portfolio equity incentives. To save space, coefficient estimates of year, industry or firm fixed effects as well as intercepts are notreported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients significant at the 1% level are denoted by***, at the 5% level are denoted by **, and at the 10% level are denoted by *.

Independentvariables

Panel A. CEOs Panel B. Top management teams







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

Log(Portfolio equity incentives) −0.1779*** −0.1117*** −0.0967*** −0.4247*** −0.2115*** −0.1533***(−9.24) (−18.22) (−3.69) (−13.27) (−9.84) (−4.40)

Personal diversification 0.0447*** 0.0640*** 0.0670*** 0.1113*** 0.1200*** 0.1482***(2.74) (8.01) (2.97) (4.07) (9.74) (4.30)

Log(Sales) −0.0499*** 0.0252*** 0.0205*** −0.0391*** 0.0277*** 0.0242***(−6.50) (7.51) (5.03) (−5.18) (9.40) (5.78)

M/B −0.0428*** −0.0635*** −0.0558*** −0.0361*** −0.0604** −0.0557***(−9.16) (−6.21) (−8.14) (−7.81) (−5.58) (−8.12)

R&D −0.3448 −0.8401*** −0.8281*** −0.3582 −0.7209** −0.7269***(−0.71) (−6.33) (−3.38) (−0.76) (−4.98) (−2.98)

ROA −0.1917*** −0.6984*** −0.7580*** −0.1863*** −0.6628** −0.7347***(−6.44) (−4.35) (−13.48) (−6.46) (−4.38) (−12.91)

PP&E 0.2069*** 0.2038*** 0.1437*** 0.1850*** 0.2018*** 0.1444***(5.83) (24.66) (4.79) (5.36) (21.96) (4.85)

Tax −0.0010 −0.0075 −0.0223** −0.0016 −0.0087 −0.0223**(−0.40) (−0.85) (−2.06) (−0.62) (−0.95) (−2.08)

Depreciation 0.1753 −0.3605 −0.0188 0.0462 −0.3440* 0.0097(0.67) (−2.14) (−0.08) (0.18) (−2.38) (0.04)

Advertising −0.2107 −0.3226 −0.3467 −0.1888 −0.3626 −0.4201(−0.99) (−1.42) (−1.17) (−0.91) (−1.53) (−1.43)

Dividend −0.0101** −0.1406** −0.1047*** −0.0090** −0.1383** −0.1014***(−2.16) (−3.54) (−5.50) (−1.99) (−3.50) (−5.37)

Year fixed effect Yes Yes No Yes Yes NoIndustry fixed effect No Yes Yes No Yes YesFirm fixed effect Yes No No Yes No NoObservations 2326


managerial personal diversification are not statistically significant. Compared to the corresponding coefficients in the market le-verage regressions, the estimated coefficients for managerial personal diversification are positive and statistically significant andsupport our prediction. The difference could be a response to market leverage being the most appropriate for testing the impor-tance of personal diversification, for market leverage affects the managers' incentives through its effect on equity risk (Lewellen,2006).

6. Robustness checks

6.1. Simultaneous equations

Taken together, the results presented in Tables 3A and 3B and 4A and 4B strongly suggest that causation is likely to run in bothdirections for managerial personal diversification and portfolio equity incentives. These findings give rise to the concern that ourresults are spurious. To account for how the managerial personal diversification and portfolio equity incentives are jointly deter-mined, following Coles et al.'s (2006) approach, we first model a system of simultaneous equations and estimate the system usingthree-stage least squares (3SLS).18 We hypothesize that a positive relationship exists between personal diversification and port-folio equity incentives given that personal diversification and equity incentives are jointly determined.

Table 7 provides findings on simultaneous Eqs. (1) and (2) as discussed in Sections 2.3 and 2.4.19 The first column pro-vides the predictions regarding the determinants of personal diversification. The positive associations between Personal

18 To deal with this endogeneity problem, we also introduce a one-year lag between the dependent and all the explanatory variables following Hermalin andWeisbach (1991) and Smith and Watts (1992). The results are similar. We do not, however, report the results due to space limitations. However, Himmelberget al. (1999) point out that if these explanatory variables change slowly over time, then lagged explanatory variables will suffer as much from endogeneity prob-lems as will contemporaneous ones.19 In order to impose identifying restrictions in the simultaneous equation specifications of equity incentives and personal diversification, the sets of controlvariables in these two equations are not identical.

Table 7Simultaneous equations: managerial personal diversification and portfolio equity incentives. The table reports the results of the simultaneous regressions of man-agers' personal diversification and portfolio equity incentives. For the equation of personal diversification, the definitions of the dependent and independent vari-ables are the same as those of Tables 3A and 3B. For the equation of log(portfolio equity incentives), the definitions of the dependent and independent variablesare the same as those of Tables 4A and 4B. Panel A reports the results for individual managers, and Panel B reports the results for the average of the same variableacross all managers in a given firm year. We estimate the system using three-stage least squares (3SLS). To save space, coefficient estimates of year and industryfixed effects as well as intercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and estimated coefficients sig-nificant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.


Personal diversification Log(Portfolio equity incentives) Personal diversification Log(Portfolio equity incentives)

Log(Portfolio equity incentives) 0.8706*** 1.0741***(12.98) (9.83)

Personal diversification 0.4565*** 0.7605***(3.37) (4.69)

Idiosyncratic risk 0.0188*** −0.0108*** 0.0173*** −0.0140***(16.63) (−6.18) (10.55) (−5.61)

Log(Sales) 0.0590*** 0.0074(5.17) (0.74)

(Log(Sales))2 −0.0022*** −0.0004(−5.09) (−0.90)

M/B 0.0154*** 0.0022(5.00) (0.82)

Stock bonus dummy 0.0171*** 0.0034(4.86) (0.91)

Log(Age) −0.0305*** −0.0273(−2.94) (−0.88)

Log(Financial wealth) −0.0403*** 0.0339*** −0.0608*** 0.0541***(−17.26) (14.00) (−11.27) (14.76)

Relative real estate 0.3930*** −0.4424*** 0.5745*** −0.5325***(12.57) (−86.69) (9.42) (−33.50)

Relative private business 0.3686*** −0.4324*** 0.5408*** −0.5142***(10.96) (−52.49) (7.62) (−20.22)

Large shareholder dummy 0.0539*** 0.0267(4.27) (0.89)

Gender status dummy 0.0124* −0.0039(1.69) (−0.69)

Marital status dummy 0.0183*** 0.0099(4.85) (0.94)

Observations 11,558 2470


diversification and Log(portfolio equity incentives) and Idiosyncratic risk are consistent with our prediction. In column 2, forthe equation of Log(portfolio equity incentives), the estimated coefficient for Personal diversification is positive and statisti-cally significant. The results from repeating the analysis of firm-year averages for the dependent and explanatory variablesare reported in columns 3 and 4 and are similar to the manager-level regression. In sum, when we control the causationproblems, the results provide empirical evidence that a positive relationship exists between personal diversification andportfolio equity incentives.

Since high levels of debts will increase the risk of bankruptcy, debt ratios will affect equity portfolio choices through raisingfirms' idiosyncratic risks. Furthermore, agency cost theory suggests that debt and equity ownership are endogenous because ofa limited cash flow problem (Garvey, 1997).20 Coles et al. (2006) provide empirical evidence of a strong causal relationship be-tween CEO pay-performance sensitivity (equity incentives) and debt policy. Therefore, to control the possible interdependenceof managerial equity incentives and leverage, we model the following system of simultaneous equations and follow Coles et al.(2006) to estimate the system using three-stage least squares (3SLS).

Personal diversification ¼ α0 þα1Log Portfolio equity incentivesð Þ þ α2Other control varialesþ ε ð4Þ

Log Portfolio equity incentivesð Þ ¼ β0 þ β1Personal diversificationþ β2Leverageþ β3Other control variablesþ η ð5Þ

Leverage ¼ γ0 þ γ1Personal diversificationþ γ2Log Portfolio equity incentivesð Þ þ γ3Other control variablesþ μ ð6Þ

DIV ¼ α0 þ α1IC þα2IDOR LEVð Þ þ α3Y1þ ε:

Eqs. (5) and (6) reflect the simultaneous determinations of portfolio equity incentives and leverage. Eq. (4) reflects the factthat portfolio equity incentive is one of the determinants of managerial personal diversification. Therefore, managerial personaldiversification is also endogenous. All other control variables in Eqs. (4), (5), and (6) are defined in the same way as in Eqs. (1),(2), and (3), respectively.

Again, the results in Table 8 confirm that CEOs (top management teams) with higher equity incentives are more likely to keepfirm-level leverage ratios low. However, CEOs (top management teams) that hold more diversified equity portfolios are morewilling to implement higher leverage ratios. The results confirm the robustness of the hypothesis that managers have risk-reduction motives to implement firms' financial policies.

6.2. Alternative measures and interpretations of personal diversification

To recognize that our inferences are unaffected by our choice of measures of managerial personal diversification, we re-testEqs. (1) and (2) using alternative measures of managerial personal diversification. We consider two different measures thatare derived from Bodnaruk et al. (2008) and Goetzmann and Kumar (2008).

The first measure is constructed as the negative of the correlation between the industry return to which the firm belongs andthe return of the manager's portfolio made up equities of other firms. We define the industry return as the weighted average ofreturns of all the listed traded firms that fall into the same industry category, weighted by their market capitalization. In addition,the second measure is constructed as the negative of the fraction of a manager's equity portfolio (consisting of shares in otherfirms) allocated to the industry to which his own firm belongs. As a manager increases the fraction of his equity portfolio (sharesin other firms) that is allocated to the industry to which his own firm belongs, the diversification measure goes down. Again, ourunreported results show that our main results remain unchanged. Such results are not reported to conserve space and are avail-able from the authors upon request.

This paper's measure of personal diversification may be interpreted as a measure of attitudes towards risk, inside informa-tion or the degree of managerial optimism (Goetzmann and Kumar, 2008). A manager may be less personally diversified be-cause he is less risk-averse or because he has some inside information or has an optimistic opinion regarding the industry ofhis firm. A less risk-averse manager should have a higher portfolio equity incentive since these managers are more willing tohold more of the stock of their own firm. Similarly, managers may decide not to decrease their exposure to firm risk and tobe more willing to hold a larger position in the stock of their own firm due to the availability of either private informationor to having an optimistic opinion regarding future stock prices in their employer's industry. Hence, the expected relationshipbetween managerial personal diversification and portfolio equity incentives is both negative and significant. However, our find-ings strongly suggest that the relationship between managerial personal diversification and portfolio equity incentives is posi-tive and significant. These results provide evidence that goes against the three aforementioned interpretations of personaldiversification.

20 Debt mitigates the shareholder-manager agency problem by reducing the free cash flow available to managers (e.g., Jensen, 1986; Stulz, 1990), leading tohigher debt being substituted for high-powered incentives that are costly to under-diversified managers.


6.3. Alternative measures of managers' equity incentives

Many previous researchers who have worked on equity incentive levels have focused on the Jensen and Murphy (1990)measure of equity incentives (e.g., Aggarwal and Samwick, 1999, 2003a, 2003b, 2006). Jensen and Murphy (1990) define equityincentives as the dollar value of a manager's wealth change relative to a 1000 dollar change in shareholders' value. When com-puted for stockholdings only, the dollar change in a manager's wealth for a dollar change in firm value is proportional to thefraction of shares outstanding owned by the manager. In addition, Core and Guay (1999) define portfolio equity incentives asthe dollar value of a manager's wealth change relative to the percentage change in shareholders' value. When computed

Table 8Simultaneous equations: market leverage, managerial personal diversification, and portfolio equity incentives. The table reports the results of simultaneous re-gressions of the market leverage ratio, managerial portfolio equity incentives, and personal diversification. For the equation of market leverage, the dependentand independent variables are the same as those of Table 5. For the equation of personal diversification, the definitions of the dependent and independent vari-ables are the same as those of Tables 3A and 3B. For the equation of log(portfolio equity incentives), the definitions of the dependent and independent variablesare the same as those of Tables 4A and 4B. In Panel A, the independent variables are the CEO's personal diversification and portfolio equity incentives. In Panel B,the independent variables are the top management team's personal diversification and portfolio equity incentives. We estimate the system using three-stage leastsquares (3SLS). To save space, coefficient estimates of year and industry fixed effects as well as intercepts are not reported. t-Statistics are reported beneath theestimated coefficient within parentheses and estimated coefficients significant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10%level are denoted by *.

Independent variables Panel A. CEOs Panel B. Top management team

Personaldiversification


Marketleverage

Personaldiversification


Marketleverage

Log(Portfolio equity incentives) 1.0893*** −0.1884*** 1.1256*** −0.2888***(9.96) (−8.50) (14.08) (−9.91)

Personal diversification 0.7791*** 0.2605*** 0.7435*** 0.4036***(8.88) (3.60) (13.01) (4.27)

Market leverage −0.0686*** −0.0407***(−3.34) (−2.69)

Log(Sales) 0.1493*** 0.0290*** 0.1499*** 0.0347***(8.07) (7.18) (12.70) (7.38)

(Log(Sales))2 −0.0047*** −0.0049***(−7.79) (−12.62)

M/B −0.0026 −0.0600*** −0.0021 −0.0553***(−0.88) (−13.79) (−0.95) (−11.72)

R&D −1.0687*** −1.0646***(−5.59) (−5.66)

ROA −0.6934*** −0.6862***(−21.98) (−21.57)

PP&E 0.1812*** 0.1720***(9.17) (8.81)

Tax −0.0073** −0.0070**(−2.04) (−2.03)

Depreciation −0.2123 −0.2196(−1.34) (−1.40)

Advertising −0.3507** −0.2356(−2.06) (−1.44)

Dividend −0.0332*** −0.0322***(−5.22) (−5.28)

Idiosyncratic risk 0.0253*** −0.0104*** 0.0201*** −0.0101***(10.09) (−6.21) (12.82) (−8.99)

Stock bonus dummy 0.0091** 0.0078***(2.02) (3.39)

Log(Age) 0.0135 −0.0039(1.08) (−0.21)

Log(Financial wealth) −0.0403*** 0.0325*** −0.0619*** 0.0520***(−11.48) (13.23) (−14.92) (22.87)

Relative real estate 0.5287*** −0.4771*** 0.5802*** −0.5055***(8.96) (−36.60) (12.35) (−37.82)

Relative private business 0.5433*** −0.4858*** 0.6086*** −0.5469***(8.57) (−27.63) (11.00) (−26.83)

Large shareholder dummy 0.0088 0.0204(0.98) (1.50)

Gender status dummy −0.0073 −0.0087(−0.65) (−0.89)

Marital status dummy 0.0162*** 0.0185**(2.60) (2.35)

Observations 2326 2326


for stockholdings only, the measure of portfolio equity incentives is proportional to the dollar value of managerialstockholdings.

In order to compare our results with those of prior research, we re-estimate Eq. (2) using the Core and Guay (1999) and Jensenand Murphy (1990) measures of equity incentives. Consistent with our hypothesis, we find that the equity incentives remain sig-nificantly positively associated with personal diversification. However, whenwe perform the test again using the Jensen andMur-phy (1990) measure of equity incentives, the estimated coefficient for idiosyncratic risk is now significantly positive, consistentwith the findings of Core and Guay (1999).

6.4. Information on household level financial wealth

If a manager does indeed know that his personal financial information will be made public, he will have an incentive to shiftpart of his holdings to his spouse and/or other family members. However, this is less of a problem for the top managers working atlisted firms in Taiwan because the Taiwanese Securities and Exchange Act (Articles 22-2 and 25) requires listed firms to announceto the public the class and the number of the shares held not only by top managers but also by their spouses and minor children.Moreover, the changes in the number or the transfer of shares by top managers or their family members will be disclosed andreviewed in accordance with the regulations by a competent authority.

To alleviate the concerns that a manager has an incentive to shift part of his holdings to his spouse and/or other family mem-bers, we combined the information about the financial wealth and equity portfolios of top managers with their spouses and otherfamily members.21 We replicate our analysis at the household level in Tables A.2 and A.3 and confirm the robustness of our mainresults. Again, the estimated coefficients on managerial personal diversification in Table A.2 and portfolio equity incentives inTable A.3 are statistically significant and of the predicted sign.

6.5. Insider-trading restrictions

According to the Taiwanese Securities and Exchange Act and Operating Rules of the Taiwan Stock Exchange Corporation, topmanagers also face many insider-trading regulations. Bettis et al. (2000) find evidence that corporate policies restrict trading byinsiders. Roulstone (2003) uses a trading-window proxy for the existence of firm-level insider-trading restrictions and finds thatfirms restricting insider trading use more incentive-based compensation and their insiders hold larger equity incentives relativeto firms that do not restrict insider trading.

Here, following Roulstone (2003), we construct two proxies of firm-level insider-trading restrictions and then relate manage-rial portfolio equity incentives to the insider-trading restrictions, while controlling for other variables used in the paper. By gath-ering the insiders' stockholding data in the TEJ database from 1998 to 2001, the trading volume of each insider is computed fromend-of-month changes in shares of each insider's holding. The first proxy is measured as the percentage of shares traded by CEOsfrom 1998 to 2001 that are traded within the one-month period following an earnings announcement. We refer to this percent-age value as the variable Restrict. The second proxy, Restrict dummy, is calculated as a dummy variable equal to 1 if Restrict isgreater than or equal to 75%, otherwise 0 is used. In Panels A and B of Table A.4, these robustness tests suggest that twoinsider-trading restrictions proxies do not drive the coefficient on the personal diversification in the managerial portfolio equityincentives regressions. It is interesting that the insider-trading restrictions proxy has a negative effect on the managerial portfolioequity incentives. This result may be due to insider trading restrictions motivating managers to diversify their firm risk withintheir total financial wealth.

6.6. Further robustness checks

Himmelberg et al. (1999) emphasize the need to identify observable variables that are related to potential moral hazard andthat influence optimal managerial stakes. They document a positive association between the proxies of the scope for managerialdiscretion and top managers' equity incentives. Following their specification, we add the ratio of property, plant and equipmentto sales, K/S, and its square, (K/S)2, as proxies of the scope for managerial discretionary spending. All of the results are also ro-bust to including K/S and (K/S)2 in Eq. (2). The coefficients have the same signs as the corresponding coefficients in Tables 4Aand 4B, and the estimated coefficients for K/S are significantly negative, which is consistent with the findings of Himmelberget al. (1999).

Finally, the results do not change if we delete managers who do not invest in equities in other firms. The inferences are alsounaffected by deleting managers who receive no stock bonus grants over the previous fiscal year, confirming that the resultsare not driven by managers to whom no incentive grants are given. In addition, it is possible that the payout ratio is endogenousto the capital structure decisions. However, when we re-estimate Eq. (3) without the payout ratio, the main results remain qual-itatively the same.

21 The data on the financial wealth and equity portfolio of managers' minor children is unavailable. However, deleting managers having minor children does notaffect the results.


7. Conclusion

This paper obtains a unique dataset that contains detailed and high-quality managerial information on portfolio holdings,wealth and income information. The unique dataset allows us to measure the degree of diversification that a manager allocatesto his portfolio of equities in other firms' portfolios and measure the managerial equity incentive by identifying the actualvalue of the manager's financial wealth.

We first study whether top managers use their investment in portfolios made up of equities of other firms to diversify theirrisk exposure to the firm. The empirical results support the view that managers do engage in diversifying. We then investigatewhether the extent of the diversification in the managers' equity portfolios can explain the difference in their equity incentives.The results indicate that a more diversified manager is willing to hold a larger share of financial wealth in his own firm stock com-pared to a less diversified manager. Finally, this paper examines the effect of the degree of the manager's equity portfolio diver-sification on the firms' leverage ratio. Our findings suggest that the degree of diversification in the top management team's equityportfolios (in the shares of other firms) does have an influence on corporate financial leverage. Given that managers with a higherfractions of their financial wealth invested in their company stock keep their debt levels low, managers who hold more diversifiedequity portfolios will implement higher levels of firm debt.

These results in this paper are consistent across alternative empirical specifications, diversification and equity incentives mea-sures as well as various regressionmethods. Furthermore, we use simultaneous equations to remedy endogenous feedback effectsof equity incentives on personal diversification and the effects of a firm's leverage ratio level on equity incentives. Our results pro-vide empirical evidence of strong causal relationships between managerial personal diversification, equity incentives and debtpolicy. The results of simultaneous equations are consistent with our previous findings. Finally, controlling for household levelinformation, our results remain the same.

Our results shed light on an unexplored empirical dimension of a manager's incentive for personal diversification of equity.The degree of personal diversification incentives is particularly important given the use of equity-based compensation. The evi-dence gathered in this paper offers empirical support for the view that firm-level risk-reduction decisions are affected by mana-gerial equity incentives and personal diversification. A manager's diversification ability is an important determinant in designingincentive mechanisms.

Acknowledgments

We thank an anonymous referee and the editor for their valuable comments and suggestions. Liu gratefully acknowledge fi-nancial support from the National Natural Science Foundation of China (grant no.71021001 and grant no.71172026).

Appendix A. The taxation data processing and limitations from the FDC

In Taiwan, employees are subject to individual income tax, land value tax, land value increment tax, house tax, vehicle licensetax and other miscellaneous taxes. In January of each year, tax withholding companies, including government agencies, organiza-tions, financial institutions, business entities and state-owned utilities, submit their declaration of income tax withholding or ex-emption to local revenue services. This information is then transferred to the FDC, which uses it to identify taxpayers. Later inJanuary, the FDC processes the data files of personal particulars entered into the household registrations, and married couplefiles and household relationships are created that include the spouse and dependents.

In February, the taxpayers receive a tax return on which all the data supplied by employers and financial institutions have al-ready been submitted to the FDC. The taxpayer checks the figures and corrects errors and adds information or claims for deduc-tions, if necessary. Individuals with income equal to or greater than NT$201,000 are required to file tax returns. Finally, the FDCwill audit tax filings by comparing them with records of withholding tax and taxpayer information to produce lists of abnormal-ities which are transferred to local revenue services for verification. The processing of individual income tax returns in the FDC hasbeen computerized since 1971. For more than three decades now the FDC has been successful both in planning and programming,and we believe the FDC data is of high quality (FDC Annual Report, (2003)).

A limitation of the data is that it does not include managers' holdings in mutual funds (Calvet et al., 2007). However, mu-tual funds are underdeveloped during our sample period. In addition, according to summary statistics from the central bank,mutual funds make up between 1.37% and 3.26% of the household's domestic financial assets during our sample years. There-fore, we feel that such a lack of information on mutual fund holdings should not have a directional impact on our results.Nonetheless, readers should interpret our results with due care. Another potentially important component of a manager'sportfolios is holdings in foreign assets. According to the Bureau of Statistics of Taiwan,22 foreign assets make up between1.49% and 2.19% of the average household's total assets. Therefore, foreign assets should not have much impact on ourmain findings.

22 http://www.dgbas.gov.tw/lp.asp?ctNode=3104&CtUnit=394&BaseDSD=7.


http://www.dgbas.gov.tw/lp.asp?ctNode=3104&CtUnit=394&BaseDSD=7

Appendix B. Variable descriptions

1. Managers' characteristicsPersonal diversification The negative of the correlation between the returns on the manager's listed equity portfolio made up of shares

in other firms and the returns of his own-firm equity. The measure is estimated using weekly returns over3 years prior to the end of the fiscal year.

Portfolio equity incentives The fraction of the financial wealth of the manager allocated to the firm in which he works.Log(Portfolio equity incentives) The natural logarithm of the portfolio equity incentives plus one.Financial wealth The sum of the value of imputed bond holdings, listed stock holdings, non-listed stockholdings, and real estate.

Imputed bond holdings The imputed bond holdings Bit at end of year t for the manager i are approximated measured asBit=(Iit+1/rt+1), where Iit+1 is the sum of taxable interest income during the test year t+1 for the manager i,and rt+1 is his average annual interest rate on savings' deposits during the test year t+1.

Listed stockholdings Listed stockholdings are the sum of the market value of direct public listed stocks at the end of year for a manager.Public listed stocks are traded in the Taiwan Stock Exchange (TSE) or in the Over-the-Counter (OTC) market. Themarket value of the listed stock is estimated by multiplying year-end stock value.

Non-listed stockholdings Given that such companies are privately held and do not have to disclose financial information, we use either netvalue per share as reported by companies (which should be consistent with any public transaction disclosed to themarket) or the par value (tens in NT$) if the former is unavailable.

Real estate We impute the market value of housing and land for each manager each year by using the averaged ratio ofmarket value to tax-assessed value provided by each county, each year by the Department of Land Administration.However, because the Department of Land Administration does not provide similar information about farms, weuse the tax-assessed value of farms as a proxy for the market value of farms. The value of real estate is the sum ofthe imputed market value of land, houses and the tax-assessed value of farms.

Log(Financial wealth) The natural logarithm of financial wealth.Log(Age) The natural logarithm of age.Relative real estate The ratio of the value of real estate to financial wealth.Relative private business The ratio of the value of non-listed stockholdings to financial wealth.Large shareholder dummy Dummy variable is set to one if a manager's ownership is more than 10%.Gender status dummy Dummy variable is set to one if a manager is a male.Marital status dummy Dummy variable is set to one if a manager is married.2. Firms' characteristicsLog(Sales) The natural logarithm of sales, used to proxy for firm size.M/B The market value of total assets to the book value of total assets, used to proxy for firms' growth opportunities.

Market value of total assets Book value of total assets−book value of equity+market value of equityMarket value of equity Market capitalizationBook value of equity Book value of total assets− total liabilities+miscellaneous long-term liabilities−preferred stocks

Idiosyncratic risk For each firm, we calculate a firm's idiosyncratic risk as the standard deviation of the residuals from a CAPMmodel estimated using the past three-year weekly stock return (%) before the end of the test year. The CAPM isperformed as below:(Rit−Rf)=αi+βi(Rmt−Rf)+εitwhere Rit−Rf and Rmt−Rf, respectively, denote the excess returns on firm i and on the market portfolio. Rmt is theweekly return on the value-weighted Taiwan market index. Rf is estimated by using the series of one-monthdeposit rates of the First Commercial Bank taken from Financial Statistics Monthly, Taiwan District, R.O.C., and iscompiled by the Central Bank of China.

Stock bonus dummy Dummy variable equal to one if the firm pays employees' stock bonus over the fiscal year t or t−1.Book leverage The ratio of the book value of total debt to the book value of total debt plus the book value of equity.Market leverage The ratio of the book value of total debt to the book value of total debt plus the market value of equity.ROA The ratio of EBITDA (earnings before interest, tax, depreciation, and amortization) to total assets.PPE The ratio of property, plant, and equipment plus inventory to total assets.R&D The ratio of R&D expense to total assets.Depreciation The ratio of depreciation expense to total assets.Tax rate The ratio of income tax expense to EBITDA.Advertising The ratio of advertising expense to net sales.Dividend The ratio of cash dividends to retained earnings.


Table A.1This table investigates the effect of the stock-based compensation schemes in the OLS andmedian regression estimates of Eq. (2). In this table, the Stock bonus dummy isreplaced by Stock bonus value,which is calculated as the logarithm of the average of themarket value of aggregate employees' stock bonuses at fiscal t and t−1. To savespace, coefficient estimates of year, industry or firm fixed effects aswell as intercepts are not reported. t-Statistics are reported beneath the estimated coefficientwithinparentheses and estimated coefficients significant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.

Independent variables Dependent variables: Log(Portfolio equity incentives)

For individual managers For firm-years averaged

OLS Median OLS Median

Personal diversification 0.1711*** 0.1577*** 0.2254*** 0.2395***(23.91) (24.35) (14.82) (13.77)

Log(Sales) 0.1452*** 0.0933*** 0.1491*** 0.1565***(6.26) (3.94) (4.79) (5.04)

(Log(Sales))2 -0.005*** -0.0033*** -0.0051*** -0.0052***(-6.54) (-4.33) (-5.01) (-5.13)

M/B 0.0212*** 0.0102*** 0.0076** 0.0034(9.42) (6.01) (2.36) (1.15)

Idiosyncratic risk -0.0036*** -0.0035*** -0.0041*** -0.0061***(-3.29) (-3.38) (-2.73) (-3.44)

Stock bonus value 0.0037*** 0.0024*** 0.0023*** 0.0012*(9.27) (5.46) (4.26) (1.81)

Log(Age) -0.0227** -0.0252*** -0.0944*** -0.0859***(-2.53) (-2.64) (-4.47) (-3.43)

Log(Financial wealth) 0.0277*** 0.0259*** 0.0385*** 0.0413***(28.65) (27.48) (16.63) (16.77)

Relative real estate -0.4367*** -0.5496*** -0.5244*** -0.5656***(-77.28) (-104.94) (-38.08) (-34.85)

Relative private business -0.4357*** -0.5546*** -0.5529*** -0.6003***(-46.58) (-68.95) (-26.36) (-30.16)

Large shareholder dummy 0.0796*** 0.0391*** 0.1035*** 0.0833***(9.37) (7.55) (4.79) (3.99)

Gender status dummy -0.0097 -0.0035 -0.0109 -0.018(-1.54) (-0.49) (-0.62) (-0.93)

Marital status dummy 0.0439*** 0.022*** 0.0971*** 0.0949***(6.71) (2.82) (5.52) (5.36)

R-squared 0.6176 0.5309 0.7601 0.61128Observations 11,558 2470

Table A.2In this table, we report the OLS andmedian regression estimates of Eq. (1) by combining the information of the financial wealth and equity portfolios ofmanagerswiththeir spouses and other family members. For each regression in Panel A, managerial personal diversification, portfolio equity incentive, financial wealth, relative realestate, and relative private business are re-computed by aggregating the data on the financial wealth and equity portfolios of managers and their spouses. In PanelB, according to the tax identification number, we furthermore aggregate the data on the financial wealth and equity portfolios of managers' other family members.To save space, coefficient estimates of year, industry or firm fixed effects aswell as intercepts are not reported. t-Statistics are reported beneath the estimated coefficientwithin parentheses and estimated coefficients significant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.

Independent variables Dependent variables: Personal diversification

Panel A Panel B

For individual managers For firm-years averaged For individual managers For firm-years averaged

OLS Median OLS Median OLS Median OLS Median

Log(Portfolio equity incentives) 0.2942*** 0.2963*** 0.4006*** 0.4593*** 0.2886*** 0.2853*** 0.4030*** 0.4628***(22.22) (18.46) (12.96) (12.45) (21.37) (17.12) (12.81) (11.75)

Idiosyncratic risk 0.0222*** 0.0255*** 0.0198*** 0.0222*** 0.0222*** 0.0255*** 0.0197*** 0.0221***(21.99) (20.57) (13.30) (12.24) (22.06) (20.95) (13.32) (12.20)

Log(Age) -0.0239** -0.0255* -0.1220*** -0.1282*** -0.0235** -0.0267** -0.1200*** -0.1272***(-2.23) (-1.84) (-4.50) (-4.04) (-2.21) (-1.99) (-4.49) (-4.02)

Log(Financial wealth) -0.0262*** -0.0271*** -0.0385*** -0.0418*** -0.0265*** -0.0276*** -0.0404*** -0.0419***(-20.63) (-15.96) (-11.65) (-10.19) (-20.20) (-14.95) (-11.84) (-10.05)

Relative real estate 0.1134*** 0.1077*** 0.2089*** 0.2277*** 0.1112*** 0.1042*** 0.2087*** 0.2346***(11.78) (9.62) (8.30) (6.83) (11.29) (9.00) (8.15) (6.59)

Relative private business 0.0976*** 0.1160*** 0.1362*** 0.1667*** 0.0949*** 0.1086*** 0.1382*** 0.1741***(7.39) (6.91) (4.21) (3.97) (7.03) (6.62) (4.18) (3.81)

Gender status dummy 0.0092 0.0037 -0.0465** -0.0515* 0.0065 -0.0024 -0.0534** -0.0595**(1.24) (0.39) (-2.12) (-1.93) (0.87) (-0.25) (-2.45) (-2.18)

Marital status dummy -0.0389*** -0.0354*** -0.0185 -0.0192 -0.0380*** -0.0345*** -0.0184 -0.0141(-5.12) (-4.10) (-0.85) (-0.71) (-5.01) (-4.08) (-0.85) (-0.51)

R-squared 0.1673 0.2060 0.3053 0.1985 0.1642 0.2100 0.3064 0.2013Observations 11,558 2470 11,558 2470


Table A.3In this table, we report the OLS andmedian regression estimates of Eq. (2) by combining the information of the financial wealth and equity portfolios ofmanagerswiththeir spouses and other family members. For each regression in Panel A, managerial personal diversification, portfolio equity incentive, financial wealth, relative realestate, and relative private business are re-computed by aggregating the data on the financial wealth and equity portfolios of managers and their spouses. In PanelB, according to the tax identification number, we furthermore aggregate the data on the financial wealth and equity portfolios of managers' other family members.To save space, coefficient estimates of year, industry or firm fixed effects aswell as intercepts are not reported. t-Statistics are reported beneath the estimated coefficientwithin parentheses and estimated coefficients significant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.


Panel A Panel B



Personal diversification 0.1271*** 0.1293*** 0.1365*** 0.1444*** 0.1192*** 0.1173*** 0.1301*** 0.1369***(20.29) (19.80) (10.89) (9.92) (19.34) (20.20) (10.52) (9.81)

Log(Sales) 0.0962*** 0.0922*** 0.0909*** 0.0917*** 0.0871*** 0.0936*** 0.0829*** 0.0737***(6.38) (4.61) (4.61) (3.97) (5.89) (4.59) (4.30) (3.30)

(Log(Sales))2 -0.0033*** -0.0032*** -0.0032*** -0.0033*** -0.0030*** -0.0033*** -0.0030*** -0.0027***(-6.59) (-4.83) (-4.94) (-4.36) (-6.11) (-4.85) (-4.63) (-3.66)

M/B 0.0256*** 0.0179*** 0.0112*** 0.0078*** 0.0249*** 0.0178*** 0.0108*** 0.0077***(15.47) (11.04) (4.73) (3.52) (15.35) (11.19) (4.65) (3.17)

Idiosyncratic risk -0.0040*** -0.0050*** -0.0038*** -0.0065*** -0.0036*** -0.0045*** -0.0035*** -0.0059***(-5.31) (-5.47) (-3.82) (-5.21) (-4.86) (-5.20) (-3.59) (-4.66)

Stock bonus value 0.0221*** 0.0225*** 0.0181*** 0.0126*** 0.0213*** 0.0219*** 0.0177*** 0.0152***(7.43) (7.23) (4.50) (2.80) (7.32) (7.32) (4.51) (3.56)

Log(Age) -0.0298*** -0.0320*** -0.1198*** -0.1416*** -0.0251*** -0.0202*** -0.1157*** -0.1343***(-4.08) (-3.82) (-7.06) (-7.72) (-3.53) (-2.67) (-7.02) (-7.55)

Log(Financial wealth) 0.0284*** 0.0310*** 0.0435*** 0.0472*** 0.0287*** 0.0321*** 0.0440*** 0.0479***(32.27) (28.94) (20.83) (19.63) (32.31) (29.00) (20.81) (18.78)

Relative real estate -0.4267*** -0.5069*** -0.5121*** -0.5430*** -0.4338*** -0.5116*** -0.5179*** -0.5408***(-81.79) (-73.25) (-42.01) (-40.77) (-84.21) (-72.95) (-42.98) (-39.97)

Relative private business -0.4115*** -0.5013*** -0.5209*** -0.5411*** -0.4024*** -0.4858*** -0.5049*** -0.5173***(-50.37) (-52.46) (-29.60) (-32.21) (-50.33) (-52.57) (-29.34) (-32.56)

Large shareholder dummy 0.0970*** 0.0613*** 0.1198*** 0.0921*** 0.0979*** 0.0625*** 0.1224*** 0.0998***(13.90) (11.90) (6.67) (5.24) (14.31) (11.61) (6.95) (6.14)

Gender status dummy -0.0066 -0.0008 0.0049 -0.0035 -0.0061 -0.0017 0.0052 -0.0013(-1.32) (-0.16) (0.36) (-0.25) (-1.25) (-0.34) (0.39) (-0.09)

Marital status dummy 0.0112** -0.0125** 0.0362*** 0.0254 0.0111** -0.0115** 0.0350*** 0.0314**(2.18) (-2.07) (2.69) (1.49) (2.20) (-2.07) (2.66) (2.09)

R-squared 0.6202 0.5050 0.7674 0.6059 0.6278 0.5147 0.7693 0.6142Observations 11,558 2470 11,558 2470

Table A.4This table investigates the effect of the insider-trading regulations in the OLS and median regression estimates of Eq. (2). By gathering the insiders' stockholdingdata in the TEJ database from 1998 to 2001, the trading volume of each insider is computed from end-of-month changes in the shares of each insider's holding.We calculate the percentage of shares traded by CEOs from 1998 to 2001 that are traded within the one-month period following an earnings announcement. InPanel A, we refer to this percentage value as the variable Restrict and add this variable to the set of characteristics in Tables 3A and 3B. In Panel B, we add Restrictdummy that is calculated as an dummy variable equal to 1 if Restrict is greater than or equal to 75%; otherwise 0. Because in formulating Restrict we delete firmswith no trading by CEOs or little data with which to estimate Restrict, the samples in Panel A and Panel B are even smaller. To save space, coefficient estimates ofyear, industry or firm fixed effects as well as intercepts are not reported. t-Statistics are reported beneath the estimated coefficient within parentheses and esti-mated coefficients significant at the 1% level are denoted by ***, at the 5% level are denoted by **, and at the 10% level are denoted by *.


Panel A Panel B



Personal diversification 0.1692*** 0.1458*** 0.2218*** 0.2282*** 0.1694*** 0.1467*** 0.2218*** 0.2278***(24.72) (24.87) (15.37) (13.13) (24.76) (24.32) (15.39) (13.29)

Log(Sales) 0.1339*** 0.0869*** 0.1351*** 0.1136*** 0.134*** 0.092*** 0.1361*** 0.119***(6.01) (4.19) (4.54) (3.04) (6.02) (4.43) (4.58) (3.04)

(Log(Sales))2 -0.0046*** -0.0031*** -0.0047*** -0.0039*** -0.0046*** -0.0033*** -0.0047*** -0.0041***(-6.24) (-4.61) (-4.75) (-3.15) (-6.25) (-4.86) (-4.80) (-3.17)

M/B 0.021*** 0.007*** 0.0067** 0.0028 0.021*** 0.007*** 0.0067** 0.0024(10.75) (5.83) (2.38) (0.90) (10.73) (5.68) (2.38) (0.77)

Idiosyncratic risk -0.0013 -0.0019** -0.0022 -0.0047*** -0.0012 -0.0019** -0.0021 -0.0042***(-1.26) (-2.52) (-1.61) (-3.50) (-1.17) (-2.43) (-1.53) (-2.99)


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Table A.4 (continued)


Panel A Panel B



Stock bonus value 0.0225*** 0.0141*** 0.0168*** 0.0065 0.0227*** 0.0141*** 0.017*** 0.0061(6.34) (4.33) (3.49) (1.31) (6.40) (4.38) (3.54) (1.21)

Log(Age) -0.0327*** -0.0302*** -0.1079*** -0.095*** -0.0322*** -0.0307*** -0.1057*** -0.095***(-3.79) (-4.46) (-5.39) (-4.51) (-3.73) (-4.53) (-5.29) (-4.38)

Log(Financial wealth) 0.0285*** 0.0259*** 0.0396*** 0.0411*** 0.0284*** 0.026*** 0.0395*** 0.0409***(30.48) (28.00) (17.87) (17.29) (30.46) (27.23) (17.86) (16.92)

Relative real estate -0.4492*** -0.568*** -0.5345*** -0.5817*** -0.4492*** -0.5683*** -0.5345*** -0.5852***(-82.39) (-110.50) (-40.54) (-38.30) (-82.42) (-111.97) (-40.57) (-38.76)

Relative private business -0.4538*** -0.5788*** -0.5649*** -0.6131*** -0.4543*** -0.5779*** -0.567*** -0.6133***(-50.21) (-79.10) (-28.14) (-30.98) (-50.29) (-77.73) (-28.30) (-30.76)

Large shareholder dummy 0.0707*** 0.0291*** 0.0981*** 0.0838*** 0.0706*** 0.0301*** 0.0971*** 0.0766***(8.82) (6.63) (4.78) (3.71) (8.81) (6.85) (4.74) (3.41)

Gender status dummy -0.0104* -0.0066 -0.0195 -0.0365** -0.0103* -0.0075 -0.0182 -0.0286(-1.71) (-1.06) (-1.16) (-2.02) (-1.69) (-1.20) (-1.08) (-1.54)

Marital status dummy 0.0469*** 0.0231*** 0.1046*** 0.1114*** 0.0468*** 0.0236*** 0.1037*** 0.1108***(7.41) (3.64) (6.17) (6.02) (7.40) (3.63) (6.13) (5.95)

Restrict -0.0131** -0.0102** -0.0185** -0.0157*(-2.33) (-1.99) (-2.45) (-1.83)

Restrict dummy -0.0221*** -0.0144** -0.0261*** -0.0233**(-3.36) (-2.30) (-2.97) (-2.34)

R-squared 0.6394 0.6124 0.7789 0.6867 0.6396 0.6125 0.7792 0.687Observations 9001 1961 9001 1961


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