From Playground to Boardroom, CEO’s Family Background and

Managerial Style∗

Fangfang Du

January 17, 2017

Abstract

By matching a CEO’s place of residence at her formative years with U.S. Census survey and the

1940 Census report, I obtain an estimate of the CEO’s family wealth and study the link between

the CEO’s professional performance and her endowed social status. I find that CEOs born to

poor families outperform those born to wealthy families, as measured by their firms’ operating

return on assets. In addition, CEOs endowed with low social status tend to join highly levered

firms, but firm leverage quickly decreases over CEO tenure. The results suggest that CEO’s

endowed social status can be a valuable signal of her ability, but it is not recognized as such by

the labor market. Conditional on the same performance, CEOs from poor backgrounds are less

likely to get promoted than their peers from wealthy backgrounds.

Keywords: CEO endowed social status, risk taking, managerial style, professional performance

JEL codes: G30, G32

∗The author is in the Department of Finance, Arizona State University, fangfang.du@asu.edu, phone 480-965-7281;P.O. BOX 873906, Tempe, AZ 85287-3906.**I am grateful to Ilona Babenko, Sreedhar Bharath and Denis Sosyura for helpful comments. All errors are my own.

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1 Introduction

There is an extensive theoretical literature that postulates a relation between parents’ and their

children’s social status. The economics literature has documented that the behavioral, and economic

outcomes of parents are correlated with those of their children (see Blau and Duncan (1967), Solon,

1992; Zimmerman, 1992). Researchers also perform correlations among sibling to measure how

much variation of earnings are explained by family background factors. The empirical results

indicate that family origins pay a substantial role in accounting for earnings inequality.

Individuals are endowed with unequal opportunity to receive education, wealth and access to

social networks. Chuprinin and Sosyura (2016) find mutual fund managers from poor families

outperform managers from rich families by delivering higher risk-adjusted returns. They argue

that the barriers to entry into high status positions for individuals endowed with low social status

is much higher and only the most skilled individuals can successfully build a career in a management

profession.

The primary objective of this paper is two-fold. First, I investigate the link between CEOs’

family background and their professional performance. Children of wealthy families are generally

highly educated and benefit from their inherited social status. Therefore, they are more likely to get

promoted to the leadership position. Alternatively, CEOs from poor families face higher barriers

to enter the leadership positions and only the most talented individual can successfully build a

career. To examine this question, I test the relation between CEO’s endowed family wealth and her

professional performance. Second, I explore the channels through which CEOs make a difference in

performance. Extensive literatures suggest that the CEO’s managerial style has a large influence

on firm decisions and outcomes. Prior studies further show that CEOs’ individual life experience

explain a large heterogeneity in their managerial styles. One important channel is by affecting

managers’ attitude towards risk. From one hand, poverty at childhood may results in lower risk

tolerance of CEOs and they may be more conservative in decision-making. Alternatively, it might

increases CEOs’ ability to deal with risk and thus their risk taking capacities. Ultimately, it is an

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empirical question how CEO’s endowed social status impact her subsequent risk taking behavior.

For the empirical study, I start with the ExecuComp database to collect the names of CEOs

in the S&P 1500 firms in US between 1992 and 2015, and I drop all financial and utility firms as

they are subject to regulations. I hand-collect parents’ name and parents’ home address for each

CEO using several data sources such as Nexis online database of public records. For CEOs who

were born before 1940 or 5 years after 1940, I locate their parents’ 1940 US Census data and find

out the household income (see Chuprinin and Sosyura (2016)). For CEOs who were born after

1945, I collect their high school name and locations from U.S. school yearbooks and public resource

whenever available.

After identifying the district of a CEO’s high school, I match the county name of the high

school location with county level data in US Census of Population and Housing file. US Census

of population and Housing reports include important variables such as characteristics of the total

population in the county, median home value, median income of families and persons. I estimate

a CEOs’ family wealth at the formative years using the median income in the county reported in

the nearest US Census survey.

My main finding is that CEOs who were born to poor families outperform CEOs from wealthy

backgrounds by producing higher operating return on assets. For example, an increase in the CEO’s

family earning of 1,000 dollars translates to a decrease in the firm’s OROA of 0.017. The negative

relation between a CEO’s endowed family wealth and her professional performance is stronger in

industries which allow high level of managerial discretion and research intensive firms. In addition,

the negative relation is weaker in large firms with high uncertainty and complexity, as CEOs have a

weaker impact on firm strategy and performance in large organizations which usually involve many

executives in decision-making process.

I investigate whether CEOs from wealthier background pursue different managerial styles from

CEOs from poorer families. To test this hypothesis, I examine the relation between the CEO’s

family wealth at formative years and the firm’s corporate policies. I find that a CEO’s endowed

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social status is positively associated with the firm’s financial leverage, stock return volatility and

idiosyncratic volatility. The evidence consistently suggests a positive relation between a CEO’s

endowed social status and her risk taking behavior.

A potential concern of my study is that CEOs from poor family tend to join highly levered

firms since they face higher barrier to enter a management position. To better investigate the link

between CEOs’ family background and their attitude towards risk, I study firm policy changes over

CEO tenure. I compute the changes in firms’ book leverage, cash holding, stock return volatility

and idiosyncratic volatility from pre-turnover period to fiscal years after promotion to CEO. The

evidence supports the view that a CEO’s endowed social status at formative years has an impact

on her risking taking behavior. CEOs from poorer backgrounds tend to join higher levered firm

and decrease the leverage over their tenure.

Last but not the least, I examine whether the labor market observe the CEO’s endowed social

status as a signal of her ability. I calculate the average difference in compensation components

and total compensation for the wealthiest CEOs and poorest CEOs. The preliminary evidence

shows that CEOs who were born in wealthy families are compensated more than CEOs from poor

background. The results are not surprising given that CEOs with high endowed social status are

more likely to be hired by large firms which tend to pay more than small firms. Additionally, I

use propensity score matching to compare the compensation of poor CEOs with wealthy CEOs.

The analysis also suggest that the labor market doesn’t recognize CEO’s endowed family wealth

as a signal of her ability. CEOs from poor backgrounds are less likely to get promoted than their

wealthy peers conditional on the same performance.

The rest of the paper proceeds as follows. Section 2 discusses the motivations for this paper

and develops testable hypotheses. Section 3 describes data and sample construction. The main

empirical evidence is presented in Section 4. And section 5 concludes.

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2 Hypothesis Development

The economics literature has documented that the behavioral, and economic outcomes of parents

are correlated with those of their children. For example, Solon (1992) suggests that children whose

parents are highly educated and economically successful are more likely to complete higher levels of

education and earn more. Studies on the effect of family background on the incomes of American

adults recognize that children whose parents have ”all the advantages” are more likely to prosper

than children whose parents lack these advantages. (see Bowles and Herbert (2002)). Researchers

also estimate sibling correlations and measure how much proportion of earnings variation can be

explained by family background factors. The study suggests a positive correlation (Solon, 1999).

This results support the view that family origins play a substantial role in explaining earnings in-

equality. Individuals are endowed with unequal opportunity to receive education, wealth and access

to social networks. Chuprinin and Sosyura (2016) find mutual fund managers from poor families

outperform managers from rich families by delivering higher risk-adjusted returns. They argue that

the barriers to entry into high status positions for individuals endowed with low social status is

much higher and only the most skilled individuals can successfully build a career in a management

profession. The above arguments also apply to the studies in corporate managers since the demand

and supply condition in CEO’s labor market is mainly driven by human capital. It indicates that

CEOs from poor families have higher barriers to enter the prestigious positions and only the most

talented individual can succeed. Therefore, I develop the following hypothesis

Hypothesis 1 (H1): There is a negative relation between a CEO’s endowed social status and her

professional performance

Executives impact firm outcomes through their influences on decision-making process. CEO power

varies across firms. Compared to a firm where decisions are made among top executives, in a firm

where the CEO make all the major decisions, the effect of the CEO’s ability on firm performance

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is relatively larger. Hambrick and Finkelstein (1987) suggest that environmental, organizational,

and managerial characteristics constrain CEO’s influence. Finkelstein and Hambrick (1996) argue

that managerial discretion varies across industries due to factors such as product differentiability,

market growth, industry structure and etc. Therefore, the influence of a CEO on firm perfor-

mance is greater in industries where environments allow more variety and change in managerial

discretion. If a CEO’s endowed with lower social status is more talented than his wealthy peers

by delivering good professional performance. The effect should be more pronounced in industries

where managerial decision faces few environmental constraints. Building on a large literature on

managerial discretion, I identify the distribution of ratings of managerial discretion in industries.

Thus, I summarize the following:

Hypothesis 1.1 (H1.1): The negative relation between a CEO’s endowed social status and her

professional performance is stronger in industries which allow high managerial discretion.

Internal organizational inertia of the firm is also associated with managerial discretion. Ham-

brick and Finkelstein (1987) suggest that firm size is a determinant of organizational inertia. The

CEO has a weaker impact on firm strategy and performance when the firm is larger with high

uncertainty and complexity rather than smaller (see Finkelstein and Hambrick (1990)). In large

firms, the decision-making process usually involves many executives, and thus the CEO’s influence

on decision making is weaker. In one word, the larger the firm, the less the scope for managerial

discretion.

Hypothesis 1.2 (H1.2): Firm size and organization complexity weakens the negative relation be-

tween CEO’s endowed social status and her professional performance.

In research intensive firms, R&D expense is also highly influenced by CEOs’ operation. Heeley, Ma-

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tusik, and Jain( 2007 ) suggest that greater R&D expenditure may increase the CEO’s power and

discretion by increasing the information asymmetry between shareholders and managers. Firms

with more R&D expenditure allow their CEOs more discretion. Therefore, I propose the next

hypothesis:

Hypothesis 1.3 (H1.3): The negative relation between CEO’s endowed social status and her pro-

fessional performance is more pronounced in firms with high R&D expense.

Boards of directors are responsible for strategic leadership in developing firm’s risk management

framework. High performed boards of directors can effectively monitor and measure the CEO’s

performance. Good corporate governance practices is positively correlated with shareholder value

through strong internal controls. The positive effect of talented CEOs on performance is enhanced

in firms with strong corporate governance. Thus, I propose the next hypothesis.

Hypothesis 1.4 (H1.4): Strong corporate governance strengths the negative relation between CEO’s

endowed social status and her professional performance.

I next study the mechanism through which CEOs make a difference in performance. Exten-

sive literatures suggest that CEO’s managerial styles have a large influence on firm decisions and

outcomes. Prior studies further show that CEOs’ individual life experience explain a large het-

erogeneity in their managerial styles. For instance, Barsky, Juster, Kimball, and Shapiro (1997)

show a positive relation across individuals between measured risk tolerance and risky behaviors,

including smoking, drinking, failing to have insurance, and holding stocks rather than treasury

bills. Malmendier and Tate (2005) find that CEOs who are overconfident in their personal security

portfolios also display overconfidence when making corporate decisions. Cronqvist et al., (2012)

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suggest that the CEOs who are more conservative in terms of their personal investment manage

firms that choose more conservative corporate capital structures.

Family background may affect a CEO’s managerial styles in several ways. One important chan-

nel is by affecting her attitude towards risk. From one hand, poverty at childhood may results in

lower risk tolerance of CEOs and they may be more conservative in decision-making. Alternatively,

it might increases CEOs’ ability to deal with risk and thus their risk taking capacities. Therefore,

the effect of a CEO’s endowed social status on her subsequent behavior is unclear.

Hypothesis 2 (2): There is a positive (or negative) relation between a CEO’s endowed social status

and her risk preference.

3 Data and Sample Construction

3.1 CEO Sample Construction

The starting point for the data set is a panel of all companies in the S&P 1500 index covered by

the COMPUSTAT Executive compensation database from the period of 1992 to 2015. I exclude

financial services (SIC code between 6000 and 6999) and utility firms (SIC code between 4900

and 4999) from the sample because CEOs in these regulated industries have operation functions

different from CEOs in unregulated industries. Specifically, I first identify all executives with the

CEOANN field is labeled as ’ CEO’ and then check those with ’annual title’ variable in the Executive

Compensation database containing the key words ’ceo,’ ’chief executive officer,’ or ’ceo’. I also

manually check the titles for potential misclassification. All interim or acting CEOs are eliminated.

In the next step, I match the companies with Compustat database and the Center for Research in

Securities Prices (CRSP). Firms’ financial data in my study is obtained from Compustat. Stock

return and volatility are computed from CRSP. The definitions of all variables are provided in the

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appendix.

I use company CUSIP and ticker information to establish a match between the initial sample

and BoardEx database which contains biographical information on most executives for over 800,000

global organizations. I then screen these initial matches by requiring that executive names are the

same in both databases. I obtain the basic biographical descriptions of executives such as their

employment histories, educational institutions and degrees earned. When these biographical data

is not immediately available from my primary sources, I compile a new data set from a diverse set

of public sources including Wikipedia, NNDB, etc. Finally, I cross-check each CEO’s information

obtained from the public sources for accuracy against the primary database. This leaves me with

a sample of of 7,488 firm-year observations. There are 1,902 unique firms and 2,604 unique CEOs.

3.2 CEO family backgrounds

Following the method of Yonker (2010) and Pool, Stoffman, and Yonker (2012), I identify CEOs

in the LexisNexis Public Records database. The data base draws public records from nationwide

records. I use CEO’s name and current age as inputs when searching Lexis. For most CEOs from

Execucomp, I am able to locate them in Lexis Public Records. For CEOs with incomplete informa-

tion from Execucomp, the searching process becomes complicated. Because simply searching by age

and name will generate multiple records, I therefore cross-check the state of the firm’s headquarters

against the list of addresses associated with each person. This narrows the search to only those

individuals I am interested in.

In addition to a list of current and historical addresses where the executive lived, the public

records database also provide the birth date of an CEO, the year of social security number issuance

to the CEO and the state of issuance. Since social security numbers were issued to people when

they were born at United States, the data enables me to determine the state at which the CEO

was born1.

1I identify CEOs who were born outside of United States by searching the Internet using Google.

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The output from the people search in Lexis public records typically includes a list of associated

persons such as a CEO’s parents and the spouse. Following Chuprinin and Sosyura (2016), I am

able to locate a group of CEOs and their parents in the 1940 Census records with this available

information. The 1940 Census records were available to the public in 20122. It records the detailed

personally identifiable information about an individual. The information from 1940 Census records

allows me to identify an CEO’s family descent with high level accuracy. I focus on CEOs who were

born by 1940 or will be born within the next 5 years3 and search either for the CEO names or their

parents’ names in the Census data.

The 1940 Census data contains plenty information for individuals and households. In particular,

I am interested in the following: the residence address of the household, the parents’ years of

education4: highest level of education , household annual income, parents’ occupation, number

of siblings, whether the individual is an immigrant, religious background and party affiliation.

Moreover, I also track when a CEO’s parents passed away, because it provides conjecture on whether

CEOs got support from parents in early life or they develop the careers with their own talent. This

procedure leaves me with 91 unique CEOs for 188 firm-year observations. Panel A of Table 1

provides summary statistics for the CEO’s household data collected from 1940 census records in

my sample. This sample includes generally older CEOs who have been at the management position

for a long time, and majority of them are covered by the 1940 Census records which will provide

CEO’s family background information with high level of accuracy. The average value of houses

where sample CEOs’ parents owned at 1940 is $7,621 whereas the median value is $4,125. The

average annual income of an CEO’s father is $2,282 and the median income is $1,944. This incidence

is comparable to Chuprinin and Sosyura (2016), they use a similar procedure and report that the

average (median) annual income is $2,273 ($2,000) for mutual fund managers’ fathers at 1940.The

2US 1940 Census records are available at http://1940census.archives.gov/3Chuprinin and Sosyura (2016) identify parents that give birth to the manager with the next 10 years after 1940.

I keep CEOs who were born within next 5 years to reduce the noises added to the measurement.4Census data reports the highest grade completed by an individual such as high school, 3rd year, college, 4th year,

etc. I convert the level of grade to the number of years of education. The methodology is explained in Appendix.

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mean and median year of education for both parents is about 12. I also find that 97.14% (97.22%) of

CEOs’ fathers (mothers) attended elementary school, 74.29% (80.56%) of them attended high school

and only 28.57% (22.22%) of them attended college. The frequency is comparable to Chuprinin

and Sosyura (2016) as well.

Moreover, it is difficult to the identify the family background for younger CEOs because there

is no household information for them as of 1940. In this case, I compile data on the CEO’s family

wealth by estimating the median household income in the district where a CEO resided during

his/her teenage years. By utilizing an executive’s age and state of origin, I identify the name,

year and location of the high school that a CEO attended from high school yearbooks and a set

of public resources whenever available. This procedure yields me a sample of 348 unique CEOs.

Each individual record reports the county name of the high school which can me matched to county

level data in US Census of Population and Housing file. I obtain the US Census of population and

Housing file from US Census Bureau which conducts the survey and reports it every ten years. The

reports include important variables such as characteristics of the total population in the county,

median home value, median income of families and persons. I estimate a CEOs’ family wealth at the

time of formative years using the median income in the county reported in the nearest US Census

survey. All amounts are adjusted to 1940 constant dollars using the consumer price index. Panel

B of Table 1 reports the top 10 states where CEOs in my sample attended high schools or resided

during their formative years and the distribution of the estimated family wealth from US Census

survey. 44 CEOs resided in New York, which accounts for XX% of unique CEOs in may sample. I

use two measures to proxy for CEO’s parents’ income. One is the median household income (HH

Income) in this district and the other one is the median family income (F Income). According

to US census of population survey, a household include family members and unrelated individuals

living in one house. The estimated average (median) inflation adjusted household income in this

state is $2,885 ($2,419) dollars and the variation is quite considerable (the standard deviation is

$1,060). The distribution of estimated income in other states shows a similar pattern. There is

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substantial variation in the household/family income from the same state.

3.3 Firm Characteristics

Over the reporting period, I obtain 3,950 firm-year observations to conduct the study. In this

paper, I examine risk taking behavior of executives and the firm performance. In terms of risk

taking, I consider the following variables which proxy for the firm policy: (1) Cash Holdings,

defined as cash to asset ratio; (2) CAPEX, defined as net capital expenditures scaled by assets;

and (3) Financial Leverage, defined as total book debt scaled by book value of assets; Following

the extensive corporate literate (i.e. Denis and Denis (1995), Hochberg and Lindsey (2010)), I use

the operating return on assets (ROA) as the main measure for firm performance. It is calculated

as the ratio of operating income to book value of assets. It captures the current profitability of the

firm and is not impacted by the firms capital structure decisions. In addition, I compute industry-

adjusted operating return on assets to control for influences on operating income that are driven by

industry-wide trend. I define an industry-adjusted ROA as the ratio of operating income to total

assets for the sample firm minus the average ratio for COMPUSTAT firms in the same two-digit

SIC industry.

Panel A of Table 2 reports summary statistics concerning financial variables, CEO characteris-

tics and firm characteristics used in my study. Firms in my sample exhibit average (median) assets

of $17.49 billion ($2.43 billion). The average (median) book leverage for sample firms is 21.72%

(19.05%). The ratio mirrors many empirical studies in corporate finance. The average operating

return on asset is 0.149 whereas the median value is 0.145, the ratio drops to 0.074 (0.051) once I

adjust it for industry trend. Since I am not able to locate the places to grow up for all CEOs in

Execucomp, I compare the firms in my sample and the firms from Compustat universes and assess

the differences between two samples. The statistics for typical Compustat firms are reported in

panel B of Table 2. My sample represents the relative larger firms covered by COMPUSTAT. These

sample firms use more financial leverage and hold less cash than the Compustat firms. Moreover,

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on average, firms in my sample is more likely to pay dividends. It is not surprising to see such dif-

ferences because it is more likely for me to locate the biography information from public resources

for CEOs of larger firms. Given the disperse distribution of family income for CEOs in my sample,

I don’t see an obvious reason why the implications regarding the effects of CEOs’ family descent

would be biased within my sample.

4 Main Results

In this section, I test the hypothesis. My hypothesis has implications on the effect of managers on

the variation of performance across firms. It also provides conjectures on the within-firm variability.

4.1 Univariate Results

Here I present univariate results on the relation between CEOs’ family income and the firm charac-

teristics, as well as the CEO compensation obligation. As can be seen from Table 3, the operation

return on asset is significantly lower for firms operated by CEOs from a relative rich family. These

firms also take lower financial leverage, hold more cash and invest less capital expenditure. These

preliminary results are consistent with the story that a CEO’s family backgrounds signal his be-

havior and ability as a manager.

It is also interesting to observe from the table that total compensation of a CEO is positively

correlated with his parents’ income. CEOs who grew up in a poor family is not immediately

compensated for his good performance. The result is not surprising given that the CEOs who

were born rich is more likely to join larger firms which usually pay more. The negative correlation

between CEOs’ social status at birth and the average age when they work as corporate managers

also suggest that individuals endowed with high status is more likely to get promoted to industries

with high barriers to entry at an younger age than their low-status peers.

In the next step, I divide the entire sample into deciles based on the distribution of the CEO’s

household income (HH Income). I report the summary statistics in table 4. D1 refers to the group of

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firms operated by CEOs with lowest household income and D10 is for the group of firms whose CEOs

are endowed with highest social status. The results are consistent with the Pearson correlation test.

The professional performance measured by both operation return on asset and industry adjusted

ROA of CEOs who fall in the lowest decile is greater than the performance of CEOs in the highest

decile, with the difference being statistically significant between two samples. Moreover, compared

to CEOs who were born rich, low endowed social status CEOs is associated with riskier corporate

policies, as measured by financial leverage, cash holding and capital expenditures. The preliminary

results support the view that on average, CEOs from poorer backgrounds outperform CEOs that

come from wealth families, and they also tend to take more risk. The results are consistent when I

divide the sample into quintiles and/or split them based on the distribution of CEO’s family income

(F Income).

4.2 CEO Family Backgrounds and Professional Performance

To further investigate whether a CEO’s family backgrounds signal his managerial ability to create

value for shareholders, I perform OLS regressions to examine the relation. The left hand side of the

regression is the operating return on asset. I estimated models similar to those estimated by prior

literatures. 5 The right hand side are variables which measure the CEO’s family wealth during

her formative years, a vector of controls for the manager’s biographic information such as gender,

age and education level, and a vector of controls which measures firm characteristics. The control

measures are all lagged one year. I include year and industry (two-digit SIC codes) fixed-effects

in all estimations to control for any variation in firm performance across industries and over time.

The robust t-statistics are adjusted for firm-level clustering.

Table 5 shows the results for the regressions. In column (1) and column (2), I use the vari-

ables HH Income and F Income to proxy for CEO’s family earnings at his/her childhood years

respectively. Both measures are strongly negatively related to the operating return on assets, with

5see Chuprinin and Sosyura (2016) and Adams, Almeida and Ferreira(2005)

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the coefficients statistically significant. The results indicate that an increase in the CEO’s family

earning of 1,000 dollars (when measured by household income) translates to a decrease in the firm’s

OROA of 0.017. In column (3) and column (4), I focus on the relative measures of CEO’s family

wealth. The main variables of interest are HHIncomeRank, computed as the decile rank of house-

hold income, and FamilyIncomeRank which is defined as the decile rank of family income among

all sample CEOs. The results are consistent with those reported in column (1) and (2). Higher

HHIncomeRank is associated with lower firm performance, an increase in CEO’s household income

at childhood of 1 decile predicts a decrease in OROA of 0.5%. The estimation with FamilyIncom-

eRanks confirm the conclusion. The baseline tests in this section suggest that CEOs from the rich

families underperform those from poor families.

In my original tests, I infer CEOs’ family background based on the county they resided at her

formative years. One empirical challenge is that I don’t directly observe a CEO’s family income.

In model 5, I address this concern by restricting my samples to CEOs whose family information

were released by US census bureau. I keep the same control variables as in the previous estimations

and add the education level of CEOs’ parents as available in 1940 Census survey. I use the value

of house to estimate the family wealth of the CEO. The estimation result is consistent with other

specifications.

4.3 Variability of CEO Effects on Firm Performance

Finkelstein and Hambrick (1996) argue that managerial discretion varies across industries due to

factors such as product differentiability, market growth, industry structure and etc. Therefore,

the influence of a CEO on firm performance is likely to be more pronounced in industries where

environments allow more variety and change in managerial discretion. Hambrick and Abraham-

son (1995) studies seventy selected industries (four digit SIC codes) and compute the degree of

managerial discretion for them. To maximize the matches with my data, I follow the methods

proposed by Adams, Almeida and Ferreira (2005) and average the measures by two-digit SIC code

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industry. Next I take firms in industries at the top 40% of the distribution of ratings of managerial

discretion as high discretion firms. Firms in industries at the bottom 40% of the same distribution

are recorded as low discretion firms.

I next perform estimations incorporating the ”high-discretion industry” indicator in my anal-

ysis. To test the conjecture that the influence of CEO’s ability on performance should be more

pronounced in industries where managerial decision faces few environmental constraints, I add in-

teraction between the measures of CEO family earnings and the ”high-discretion industry” dummy.

Model 1 in table 6 report the results. The coefficients on interaction terms are negative and sig-

nificant, which suggests that CEOs who were born rich underperform CEOs from poor families,

and the result is more pronounced when firms operate in industries in which CEOs have more

discretion.

In addition, Hambrick and Finkelstein (1987) suggest that environmental, organizational, and

managerial characteristics constrain CEO’s influence. I have examined the effect of environment

factors by incorporating the ”high-discretion industry” indicator in my analysis. Next, I study

how a CEO’s effect varies with internal organization of the firm. The organizational factors that I

considered are a firm’s inertia and I used the number of divisions and firm size indicator as proxies.

I create a dummy variable ’Big’ which takes one for firms at the top 40% of the size distribution and

takes zero for firms at the bottom 60% of the same distribution. CEOs has a weaker impact on firm

strategy and performance when the firm is larger with high uncertainty and complexity rather than

smaller. Moreover, in research intensive firms, R&D expense is also highly influenced by CEOs’

operation.6 Heeley, Matusik, and Jain( 2007 ) suggest that CEO’s power and discretion will be

higher in firms with greater R&D expenditure due to the larger information asymmetry between

managers and shareholders. Therefore, I test the following implications: firm size and organization

complexity weaken the negative relation between CEO’s social status at formative years and his/her

professional performance, while firm R&D intensity strengthens the negative relation. Moreover,

6see Burgelman, 1983, 2002; Burgelman and Grove, 2007

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Models 2, 3, and 4 show results of my tests on the three implications, respectively. I add

interaction terms between the measure of CEO household income and firm size dummy in model

2. The coefficient of the interaction term is positive and statistically significant. Model 3 tests

the interaction of the CEO family background and number of divisions in a firm. The interaction

term coefficient is also positive and significant. The results support the conjectures that firm size

and complexity weaken the negative relationship between a CEO’s enddowed social status and

firm performance. In model 4, the coefficient of interaction term between firm R&D intensity and

measure of CEO family wealth is negative and significant, which indicates that the CEOs from rich

families underperform CEOs from poor families and the result is more pronounced in firms with

high R&D intensity.

High performed boards of directors can effectively monitor and measure the CEO’s performance.

They are also responsible for strategic leadership in developing firm’s risk management framework.

Good corporate governance practices is positive correlated with shareholder value through strong

internal controls. The positive effect of talented CEOs on performance is enhanced in firms with

strong corporate governance. I perform estimation on this conjecture and use the ratio of indepen-

dent directors in the board to measure the quality of corporate governance. The result presented

in model 5 suggest that CEOs endowed with low social status outperform CEOs who born rich and

the effect is more pronounced in firms with strong corporate governance.

4.4 CEO Family Backgrounds and Firm Policies

In this section, I investigate whether CEOs from different family backgrounds pursue different

managerial styles. Prior studies show that CEOs’ individual life experience explain a large het-

erogeneity in their managerial styles. One important channel is by affecting managers’ attitude

towards risk. From one hand, poverty at childhood may results in lower risk tolerance of CEOs and

they may be more conservative in decision-making. Alternatively, it might increases CEOs’ ability

to deal with risk and thus their risk taking capacities. Ultimately, it is an empirical question how

17

a CEO’s endowed social status impact her subsequent behavior. Therefore, the effect of a CEO’s

endowed social status on subsequent behavior is unclear. To test this hypothesis, I need to examine

the relation between a CEO’s family wealth at formative years and firm’s subsequent corporate

policies.

A potential concern of my study is that CEOs from poor family is more likely to join highly

levered firms since they face higher barrier to enter a management position. To better investigate

the link between CEOs’ family background and their attitude towards risk, I study firm policy

dynamics over CEO tenure. I follow the CEO turnover literature (e.g. Denis and Denis (1995),

Huson et al. (2004), PrezGonzlez (2006), Bennedsen et al. (2007)) to identify the CEO turnover

events. For each firm in year t, if the executive with the CEO title (i.e. Execucomp variable

CEOANN takes the value ”CEO”) does not hold the CEO title in year t - 1, I record a turnover

event for that firm in year t. I put the following restrictions on the initial turnover sample to obtain

the events for analysis. The first restriction is that the turnover is not directly related to special

corporate events, such as merger and acquisitions or spin offs. Second, the new CEO stays in the

position for at least two years after the turnover year. Eventually, I obtain a sample consisting of

253 turnover events for my analysis.

First of all, I calculate the changes in firms’ corporate policy from pre-turnover period to one

year and two years after the event. The turnover year is denoted as year 0 while the year before

ternover event is coded as year -1. I divide the samples into quintiles based on the distribution of

HHIncome and then test whether the changes in these variables which measure firm polices around

a CEO turnover event is consistent with my prediction. The evidence reported in table 7 supports

the view that a CEO’s attitude towards risk is shaped by her endowed social status at formative

years. For example, the median difference in the change from pre-turnover year to year 1 in book

leverage around turnover event between CEOs from poor families and CEOs from wealthy families

is- 0.016 and statistically significant. Moreover, the changes in cash holding report similar results,

but they are not statistically significant. The changes from pre-turnover to year 2 mirrors the

18

patten but most statistics are significant. I also calculate the industry adjusted measures to rule

out the effect on CEO turnover from the industry or year trends. The median difference in the

change of industry-adjusted variables confirm the findings.

Next, I calculate the changes in firms’ corporate policy from pre-turnover period to fiscal years

over the CEO tenure. I perform multivariate analysis where dependent variables are changes in

book leverage, cash holding, annual return volatility and idiosyncratic volatility respectively. All

variables are adjusted to rule out the industry or year effects. Table 8 reports estimates from OLS

regressions. Specially, the evidence in model 1 shows that on average over his tenure, the higher

family wealth that a CEO is endowed with, the more leverage they will increase after promotion to

CEO position. The estimates reported in model 2, model 3 and model 4 confirm results reported in

column (1). Across all model specifications, the evidence consistently suggests a positive relation

between a CEO’s endowed social status and her risk taking behavior. In addition, the results are

robust to replacing the measures of CEO’s household income with the decile indicators used in

previous analysis. I also plot the leverage of firms from one year prior to the turnover event to

fiscal years over CEO’s tenure. Figure 1 show that, on average, CEOs from poor families tend to

join highly levered firms and the firm leverage quickly decrease over their tenure.

4.5 CEO Family Backgrounds and Compensation Obligations

If a CEO’s endowed social status signals her ability, does the labor market recognize it as such?

To study this question, I focus on the compensation obligations of CEOs. I first divide the CEO

samples into quintiles based on the distribution of HHIncome and calculate the average difference

in compensation components and total compensation for the wealthiest CEOs and poorest CEOs.

The statistics are reported in table 9. Q1 refers to the group of poorest CEOs and Q5 refers to

the group of wealthiest CEOs. The average difference in Total compensation between Q1 and Q5

is negative (-4,428) and statistically significant. It is interesting to note that average cash compen-

sation for Q1 is greater than the cash pay for Q5 while the equity pay exhibits an opposite pattern.

19

The preliminary evidence shows that CEOs who were born in a wealthy family is compensated

more than CEOs from poor background. The results are not surprising given that CEOs with high

endowed social status are more likely to be hired by large or profitable firms which tend to pay

more.

To further investigate this question, I use propensity score matching to compare the compensa-

tion of poor CEOs with wealthy CEOs. The two types of CEOs are from firms with no observable

differences and the CEOs are not significantly different in individual characteristics either. The

treatment group contains a list of CEOs whose family wealth is below the annual median. The

control group of CEOs is constructed using the stratification matching with scores are computed

by a logit model where a dummy variable takes one for poor CEOs and zero for wealthy CEOs.

The estimation of logit regression model is reported in Panel A of table 10. CEOs endowed with

lower social status tend to be older and work in relative younger firms than CEOs from wealthy

families. Not surprisingly, wealthy CEOs tend to join firms with more investment opportunities.

Panel B of Table 10 reports the average treatment effects. The results are consistent with

those reported in table 9. The cash compensation for poor CEOs is 24% higher than that for

wealthy CEOs, however, the total compensation for CEOs from poor families is 12% lower than

their wealthy peers. Overall, the results suggest that the labor market doesn’t recognize a CEO’s

endowed family wealth as a signal of her ability. CEOs from poor families are not compensated

more than CEOs from wealthy background even though they performed better.

5 Conclusion

Theory suggests a relation between parents’ and their children’s social status, but empirical studies

provide mixed results. By matching a CEO’s place of residence at her formative years with US

Census survey and 1940 Census data, I obtain an estimate of the CEO’s family wealth and study

the link between the CEO’s professional performance and her endowed social status.

The results show that CEOs who were born to poor families outperform CEOs from wealthy

20

backgrounds by producing higher operating return on assets. The negative relation between a

CEO’s endowed family wealth and her professional performance is stronger in industries which

allow high level of managerial discretion and research intensive firms. In addition, the negative

relation is weaker in large firms with high uncertainty and complexity. Moreover, I find that CEOs

from wealthier backgrounds pursue different managerial styles than CEOs from poor families .

CEOs endowed with low social status tend to join higher levered firm and decrease the leverage

over their tenure.

My study indicates that a CEO’s endowed social status is a signal of her ability, but the labor

market doesn’t observe this signal. CEOs from poor families are not compensated more than CEOs

from wealthy background even they performed better. CEOs from poor backgrounds are less likely

to be promoted than their wealthy peers conditional on same performance.

21

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24

6 Appendicies:Variable Definitions

Panel A: Household characteristics

Variable Names Description

House Value The value of the house where the household resided. It is coded as a missing value

if the house is rented by the family (1940 Census record).

Monthly Rent The monthly rent paid by an CEO’ household. It is coded as a missing value if the

house is owned by the family (1940 Census record).

Father Income The annual income of the father of an CEO (1940 Census record).

Mother Income The annual income of the mother of an CEO (1940 Census record).

Parent Income It takes an CEO’s father’s income when the mother’s income is not available. Oth-

erwise, it takes the average income of the parents (1940 Census record).

Father Education The years of education of an CEO’s father. I assume 8 years for the elementary

school, 4 years for high school and 4 years for college when constructing the measure

(1940 Census record).

Mother Education The years of education of an CEO’s mother. I assume 8 years for the elementary

school, 4 years for high school and 4 years for college when constructing the measure

(1940 Census record).

HH Income The median household income in the country that CEO resided at formative years,

adjusted for 1940 dollars. (Public resources and US cnesus of population and hous-

ing).

F Income The median family income in the country that CEO resided at formative year, ad-

justed for 1940 dollars. (Public resources and US cnesus of population and housing).

25

Panel B: Firm characteristics

Variable Description

Firm size Logarithm of the book value of assets (COMPUSTAT).

Firm Age Number of years since first date of incorporation (COMPUSTAT).

ROA Operating Income Before Depreciation (COMPUSTAT OIBDP) scaled by assets.

Industry adj-ROA ROA for the sample firm minus the average ROA for COMPUSTAT firms in the

same two-digit SIC industry.

Leverage Total book debt scaled by assets (COMPUSTAT).

CAPEX Net capital expenditures (COMPUSTAT CAPX) scaled by assets.

Cash Holdings Cash to asset ratio(COMPUSTAT).

MTB Market value of assets divided by assets (COMPUSTAT).

RD Research and development expense divided by sales (COMPUSTAT).

No. of segments The number of business segments in the firm (COMPUSTAT).

Panel C: CEO Characteristics

Age An CEO’s age at the fiscal year (Execucomp).

Gender An indicator variable which takes one if the CEO is female (Execucomp).

Graduate An indicator variable which takes one if the CEO has a graduate degree (Boardex).

MBA An indicator variable which takes one if the CEO has a MBA degree (Boardex).

PHD An indicator variable which takes one if the CEO has a PHD degree (Boardex).

Total pay Total Compensation in thousands for an CEO (Execucomp).

Cash Pay The fraction of cash compensation (Execucomp) .

Equity Pay The fraction of equity compensation (Execucomp) .

26

Table 1. Summary Statistics.

This table presents summary statistics for the final sample. CEOs’ education, compensation and per-

sonal information are from Execucomp database and Lexis-Nexis public records. Firm characteristics are

from Compustat and CRSP. A CEO’s family background data is obtained from U.S. 1940 Census records.

HHIncome and FIncome are estimated by matching CEOs’ residing county and the county level data in US

Census of Population and Housing file, the variables are adjusted for 1940 dollars.The definitions of variables

are described in Appendix.

Panel A: CEO household

Variable Mean Std. 10th 25th 50th 75th 90th

House Value (in$000) 7.62 11.31 0.70 2.00 4.13 6.88 12.00

Monthly Rent 120.47 393.52 10.00 22.00 37.50 62.50 125.00

Father Income(in$000) 2.28 1.52 0.5 1.1 1.944 3.3 5

Father Education 11.89 3.23 8 10 12 15 16

Mother Income(in$000) 1.20 0.74 0.354 0.45 1.31 1.675 2.4

Mother Education 11.60 2.85 8 10 12 13 16

Parent Income(in$000) 2.33 1.53 0.5 1.2 1.972 3.3 5

HH Income 2608.19 862.49 1546.97 2019.25 2593.25 3067.70 3742.64

F Income 3104.58 951.71 1939.38 2462.77 3081.42 3697.37 4341.15

27

Panel B: Top 10 CEO Residing States

Std. 10th 25th 50th 75th 90th

State No. Of CEOs HHIncome FIncome HHIncome FIncome HHIncome FIncome

NY 44 2.89 3.36 2.42 2.98 1.06 1.12

CA 26 2.74 3.41 2.75 3.33 0.78 0.78

OH 21 2.48 2.95 2.59 3.05 0.48 0.55

MA 20 2.83 3.53 2.96 3.62 0.71 0.67

IL 19 2.98 3.53 2.91 3.45 1.04 1.19

TX 19 2.44 2.92 2.55 2.93 0.86 0.93

PA 14 2.43 2.81 2.36 2.74 0.76 0.85

CT 12 3.02 3.43 2.80 3.18 0.59 0.73

IN 10 2.74 3.16 2.87 3.21 0.36 0.44

MI 10 3.12 3.52 2.73 3.12 0.80 0.90

28

Table 2. Summary Statistics.

This table presents summary statistics for the final sample firms and Compustat Universe firms. Firm

characteristics are from Compustat and CRSP.The definitions of variables are described in Appendix.

Panel A: Sample Firm characteristics

Variable Mean Std. 10th 25th 50th 75th 90th

CEO Age 58.58 9.56 46.00 52.00 59.00 65.00 70.00

Assets (in$B) 17.49 58.16 0.31 0.73 2.43 10.72 35.37

Firm Size 7.99 1.83 5.72 6.59 7.79 9.28 10.47

Firm Age 27.31 16.43 8.00 14.00 23.00 41.00 53.00

No. of Segment 2.85 1.95 1.00 1.00 3.00 4.00 6.00

ROA 0.15 0.12 0.06 0.10 0.15 0.20 0.26

Ind.-adj ROA 0.07 0.13 -0.04 0.00 0.05 0.12 0.23

Leverage 0.22 0.20 0.00 0.05 0.19 0.33 0.47

CAPEX 0.05 0.05 0.01 0.02 0.04 0.07 0.11

Cash Holding 0.16 0.17 0.01 0.03 0.09 0.23 0.39

Panel B: COMPUSTAT Universe Firm characteristics

Assets (in$B) 2.77 19.74 0.00 0.02 0.12 0.69 3.31

Firm Size 4.71 2.77 1.34 3.00 4.80 6.54 8.10

ROA -1.26 65.36 -0.54 -0.07 0.07 0.15 0.22

Leverage 1.26 67.08 0.00 0.01 0.18 0.38 0.64

CAPEX 0.07 0.99 0.00 0.01 0.03 0.08 0.16

Cash Holding 0.20 0.25 0.01 0.03 0.09 0.29 0.60

29

Table

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30

Table 4. Summary Statistics.

This table compares the descriptive statistics for firms operated by wealthiest CEOs and Poorest CEOs.

D1 refers to the group of firms ran by CEOs from poorest families, and D10 refers to the firms ran by CEOs

endowed with highest social status. Panel A reports the summary statistics for firms when I split the sample

into deciles based on the distribution of HH Income and panel B reports the data when the sample is divided

based on the distribution of FIncome. *, **, and *** indicate statistical significance at the 10%, 5%, and

1% levels, respectively. The definitions of variables are described in Appendix.

Panel A

Variable D1 D10 Difference (D1-D10) t value

ROA 0.171 0.120 0.051*** 4.79

Ind-Adj ROA 0.097 0.047 0.050*** 4.13

Levergae 0.242 0.203 0.040** 2.45

Cash Holding 0.133 0.216 -0.083*** -5.90

CAPEX 0.058 0.044 0.014*** 3.86

Panel B

Variable D1 D10 Difference (D1-D10) t value

ROA 0.182 0.131 0.051*** 4.98

Ind-Adj ROA 0.109 0.056 0.053*** 4.53

Levergae 0.240 0.176 0.064*** 4.04

Cash Holding 0.133 0.216 -0.083*** -5.67

CAPEX 0.065 0.045 0.020*** 4.95

31

Table 5: CEO Family Background and Firm Performance.

This table reports OLS regression estimates of models where the dependent variables is ROA at the end of

fiscal year. Other control variables in each estimation include Firm Size, Firm Age, No. of Segments, Lever-

age, RD, CAPEX and CEO Age. Model 5 also include CEO parent education measure. All models include

fixed effects for industry (SIC 2 digit) and year. Standard errors are clustered at the firm level. T statistics

are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

32

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

Gender 0.012 0.019 0.013 0.020

(0.47) ( 0.72) (0.48) ( 0.75)

Graduate 0.009 0.008 0.007 0.007 0.034

( 0.37) (0.31) ( 0.29) ( 0.27) (0.43)

MBA 0.002 0.005 0.004 0.006 0.124

( 0.1) (0.24) ( 0.21) ( 0.29) (1.11)

PHD -0.013 -0.010 -0.013 -0.011 0.065

( -0.56) (-0.43) ( -0.6) ( -0.5) (0.41)

HH Income -0.017**

(-2.11)

F Income -0.015*

( -1.92)

HHIncomeRank -0.005*

( -1.87)

FIncomeRank -0.005*

( -1.76)

House Value -0.005**

( -2.01)

Other Controls YES YES YES YES YES

No of Obs 2,458 2,440 2,437 2,419 296

Adj R-squared 23.01 22.35 22.19 21.58 62.93

Year FE YES YES YES YES YES

Industry FE YES YES YES YES YES

33

Table 6: Variability of CEO Effects on Firm Performance.

This table reports OLS regression estimates of models where the dependent variables is ROA at the end of

fiscal year. Other control variables include Firm Size, Firm Age, No. of Segments, Leverage, R&D, CAPEX

and CEO Age. Big is a dummy variable which takes one for firms at the top 40% of the size distribution and

takes zero for firms at the bottom 60% of the same distribution. High Discretion Industry is an indicator

variable which takes one for industries at the top 40% of the distribution of ratings of managerial discretion

and takes zero for industries at the bottom 40% of the same distribution Standard errors are clustered at

the firm level. T statistics are in parentheses. *, **, and *** indicate statistical significance at the 10%, 5%,

and 1% levels, respectively.

34

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

Graduate -0.039** 0.007 0.016 0.016 -0.004

(-2.07) (0.32 ) (0.63) (0.70) (-0.21)

MBA 0.024 -0.001 -0.004 -0.004 0.010

( 1.47) (-0.08) (-0.22) (-0.21) (0.75)

PHD 0.004 -0.019 -0.017 -0.008 -0.002

(0.20) (-0.92) ( -0.75) (-0.46) (-0.13)

HH Income -0.030*** -0.066** -0.032 -0.001 0.029

( -0.75) (-2.75) (-2.54) (-0.08) (1.32)

Gender -0.004 0.009 0.016 -0.024 -0.036**

( -0.52) ( -0.17) ( 0.64) ( -1.50) (-2.32)

High-Discretion Industry * HH Income -0.024*

(-1.91)

Big * HH Income 0.028**

(2.45)

No. of Segment * HH Income 0.005*

( 1.92)

R&D * HH Income -0.213***

(-2.84)

Independent Board * HH Income -0.054*

(-1.90)

Other Controls YES YES YES YES YES

NO. of Obs 1,845 2,881 2,458 2,458 2,045

Adj R-squared 24.75 22.38 23.33 29.22 27.47

Year FE YES YES YES YES YES

Industry FE NO YES YES YES YES

35

Table 7. Changes in firm performance and policy around CEO turnover

This table presents the changes in corporate policies and firm performance around CEO turnover events.

Q1 refers to the group of CEOs from poorest families, and Q5 refers to the group of CEOs endowed with

highest social status. I divide the CEO sample into quintiles based on the distribution of HH Income. (Pre,1)

refers to the changes in variables from pre-turn over period to one year post the event. (Pre,2) refers to the

changes in variables from pre-turn over period to two years post the event. P value is based on Wilcoxon

rank sum test. *, **, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively.

The definitions of variables are described in Appendix.

(Pre, 1) Q1 Q5 P Value

ROA 0.007 -0.006 0.135

Ind-Adj ROA 0.006 0.000 0.261

Leverage -0.013 0.003 0.048

Ind-Adj Leverage -0.009 0.020 0.157

Cash Holding 0.000 0.002 0.111

Ind-Adj Cash Holding -0.006 -0.017 0.127

CAPEX 0.002 -0.001 0.319

Ind-Adj CAPEX 0.001 0.000 0.454

(Pre, 2) Q1 Q5 P Value

ROA 0.009 -0.007 0.072

Ind-Adj ROA 0.011 0.002 0.064

Leverage -0.013 0.008 0.041

Ind-Adj Leverage 0.004 0.030 0.094

Cash Holding 0.022 0.001 0.058

Ind-Adj Cash Holding -0.002 -0.008 0.088

CAPEX -0.003 -0.009 0.213

Ind-Adj CAPEX -0.001 -0.006 0.300

36

Table 8: Dynamics of Firm policy over CEO tenure.

This table reports OLS regression estimates of models where the dependent variables are changes in book

leverage, Cash Holding, stock return volatility and idiosyncratic volatility, respectively from pre-turnover pe-

riod to fiscal years over CEO tenure. All dependent variables are adjusted to rule out the effect from the

industry or year trends Standard errors are clustered at the firm level. T statistics are in parentheses. *, **,

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

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

HH income 0.0217* -0.0003 0.0233** 0.0017**

(1.69) (-0.03) (2.03) (2.33)

Firm Size -0.0087 -0.0027 0.0038 0.0003

(-1.50) (-0.58) (0.62) (0.80)

Gender -0.0183 -0.1231** -0.0752 -0.0045

(-0.69) (-2.13) (-1.21) (-1.09)

Graduate 0.0394 0.0127 0.0063 0.0009

( 1.02) (0.47) (0.16) (0.36)

MBA -0.0217 -0.0152 -0.0281 -0.0017

(-0.66) ( -0.55) (-0.93) (-0.89)

PHD -0.0229 -0.0316 0.0020 -0.0004

(-0.59) (-0.83) ( 0.06) (-0.21)

R&D -0.0001 -0.0001

(-1.06) (-0.62)

NO. of Obs 1,897 1,897 1,639 1,639

R-squared 0.016 0.053 0.021 0.022

37

Table 9. CEO Family Background and Compensation Obligation.

This table compares the descriptive statistics of compensation obligation for wealthiest CEOs and Poorest

CEOs. Q1 refers to the group of CEOs from poorest families, and Q5 refers to the group of CEOs endowed

with highest social status. I divide the sample into quintiles based on the distribution of HH Income *,

**, and *** indicate statistical significance at the 10%, 5%, and 1% levels, respectively. The definitions of

variables are described in Appendix.

Variable Q1 Q5 Difference (Q1-Q5) t value

ROA 0.160 0.149 0.012* 1.79

Ind-Adj ROA 0.089 0.072 0.017** 2.16

Cash Pay 0.435 0.380 0.054*** 2.96

Equity Pay 0.402 0.468 -0.066*** -2.63

Total Pay 7,115 11,543.2 -4,428.2*** -3.54

38

Table 10: Propensity Score Matching.

This table report estimates of difference in compensation obligation between the poor CEOs (treatment

group) and the wealthy CEOs (control group). The matched sample is constructed using a propensity score

match with scores given by a logit model in which the dependent variable is a dummy variable that takes a

value of one if a CEOs family wealth is above the median in a given year. Standard errors are reported in

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

39

Panel A : Logit Model

Log Sales -0.065

(0.046 )

Fixed Assets Ratio 1.037

( 0.671)

ROA 0.104

( 0.506)

Cash Holding -0.240

(0.518 )

Leverage 0.550

(0.401)

Capital Expenditure 0.013

(2.453)

Return 0.241**

(0.116)

Market to Book -0.109**

(0.048)

CEO Age 0.206***

(0.012)

Firm Age -0.013**

(0.005)

Gender 0.632*

(0.327)

NO. of Obs 2,774

Year FE YES

Industry FE YES

Panel B: Average Treatment Effect

log (1 + total pay) log (1+ cash pay)

-0.120 * 0.242***

(0.066) (0.062)

40

Figure 1: Leverage Dynamics Over CEO Tenure41