Family Ownership and Performance in New Firms�

Sharon Belenzony Rebecca Zarutskiez

January 4, 2011

Abstract

We study the relation between family ownership and performance in a large sampleof new �rms. We �nd that new family-owned �rms exhibit better performance thannew non-family-owned �rms in terms of higher returns on assets, wider pro�t mar-gins and greater survival rates. These �ndings are especially strong in the earliestyears of the �rm life cycle and when two of the leading shareholders are married.A large part of the advantage of family ties between shareholders stems from anability of shareholders and managers to commit to keep cash within the �rm. Theperformance advantage of new family-owned �rms is also larger when managers holdsmaller percentages of equity. Our results suggest that family ties serve as a mech-anism to improve the monitoring of managers by shareholders and the enforcementof implicit contracts between shareholders and managers. Moreover, the additionalperformance advantage of �rms owned by married shareholders, even after control-ling for managerial equity ownership, suggests family ties, in particular the ties ofmarriage, serve as a mechanism to enhance �rm performance above and beyondmitigating the manager-owner con�ict. Overall, our results support e¢ ciency-basedexplanations for why family ownership, in particular the "mom and pop shop", is apredominant ownership structure in new �rms.

Keywords:

JEL Classi�cation: G32, G18, O16

�Acknowledgement: We thank Ashish Arora, Mark Schankerman, David Sraer, Margarita Tsout-soura, Belen Villalonga, John Van Reenen, and seminar participants at Duke University and the Universityof Virginia for helpful comments and suggestions. We express our special gratitude to Hadar Gafni foroutstanding research assistant. All remaining errors are our own.

yDuke University, Fuqua School of Business (sharon.belenzon@duke.edu)zDuke University, Fuqua School of Business (rebecca.zarutskie@duke.edu)

1

1 Introduction

The relation between ownership structure and �rm performance is a topic of debate in

the corporate governance literature. Beginning with the thesis of Berle and Means (1932)

that �rm performance should decline as its ownership becomes more di¤use due to lower

incentives of shareholders to monitor managers, the debate has largely focused on the

relation between ownership structure and performance in established, usually publicly

traded, �rms (e.g., Morck, Shleifer and Vishny (1988) and Claessens et al (2002)). Studies

have also examined whether the identity of the largest shareholders in a �rm matter for

performance, for example, looking at the impact of institutional shareholders and �nancial

investors on �rm performance (e.g., Del Guercio and Hawkins (1999) and Gillian and

Starks (2000)).1 In addition, a number of studies (e.g., Anderson and Reeb (2003) and

Villalonga and Amit (2006)) have examined the impact of large individual shareholders

who are members of the same family on �rm performance

In this paper, we take a di¤erent approach by examining the relation between family

ownership and performance in new �rms. We do this for several reasons. First, new �rms

are a major part of production, employment and growth in most economies, yet the relation

between ownership structure and performance in new �rms is relatively understudied.2

Second, family ownership is a prevalent ownership structure in new �rms, even more so

than for publicly traded �rms in some countries.3 Third, the nature of family ownership in

new �rms is di¤erent than that of publicly traded �rms. In particular, in the vast majority

of new family-owned �rms, family members own a majority of the �rm�s shares and several

large shareholders comprise the entire ownership base (of course, concentrated ownership

structure is also prevalent for new non-family-owned �rms, typically, several large, but

unrelated in this case, individual shareholders comprise the entire ownership base). In prior

1See Bebchuk and Weisbach (2010) for a more thorough discussion of this literature.2The existing studies that examine ownership and performance in new �rms have mainly focused on

performance di¤erences between �rms in which venture capitalists do and do not hold equity (e.g., Lerner(1995), Kaplan and Stromberg (2004) and Puri and Zarutskie (2010)). However, fewer than 1 percentof new �rms receive venture capital in the U.S. (Puri and Zarutskie (2010)), where the venture capitalmarkets are most active, making it useful to consider the other ownership structures and their relation toperformance in early stage �rms.

3For example, Anderson and Reeb (2003) report that family shareholders are present in one thirdof S&P 500 �rms. In Western Europe, La Porta et al (1999) �nd that in large public corporationsfamilies control 10 to 40 percent of �rms. The prevalence of family ownership in Western European publiccorporations is also established by Faccio and Lang (2002). In our data, around 40 percent of youngprivate �rms are controlled by families.

2

studies the focus has largely been on understanding whether family shareholders as a group

exacerbate or mitigate the expropriation of dispersed shareholders (e.g., Berle and Means

(1932), Shleifer and Vishny (1986), Burkhardt, Gromb and Panunzi (1997) and Burkhardt,

Panunzi and Shleifer (2003)). We, however, consider variation in the relationships between

multiple individual large shareholders and analyze how these relationships may mitigate

or exacerbate agency problems between shareholders and managers.

We employ a new panel dataset of �rms from four of Europe�s largest economies -

Great Britain, France, Germany, and Italy - that contains detailed information on the

shareholders, managers, boards of directors and �nancial statements of a large sample

of both public and private �rms. These data allow for an in depth examination of the

ownership structures and performance of a representative sample of new �rms over time.

Since we can observe both the identity of shareholders and their percentage ownership

shares, we can more richly characterize the ownership structures of a large sample of new

�rms.4

From our data we see that relative to established �rms, new �rms are smaller, have a

smaller number of shareholders and are more likely to have managers who are typically

also large owners of equity.5 New �rms face greater uncertainty surrounding the success of

their investment projects and ultimate survival, as evidenced by the higher documented

failure rates of new �rms relative to established �rms (e.g., Dunne, Roberts and Samuelson

(1989)). New �rms also face greater �nancial constraints (e.g., Petersen and Rajan (1995)

and Hadlock and Pierce (2010)). Greater uncertainty about �rm success may make the

enforcement of implicit contracts between managers and shareholders more di¢ cult (e.g.,

Bull (1987), Hart and Holmstrom (1987) and Baker, Gibbons and Murphy (2002)). At the

same time, greater �nancial constraints may make it more important for shareholders and

managers to agree to keep cash within the �rm and keep operating costs low in order for

the �rm to invest optimally, making the mitigation of the classic owner-manager con�ict

(e.g., Jensen and Meckling (1976)) even more important for new �rm performance and

survival.4Most large-scale research databases containing private �rms either contain no information about

ownership structure or contain basic information such as the number of shareholders, the ownership shareof the principal shareholder, and whether the manager is also a shareholder, such as the National Surveyof Small Business Finances.

5These characteristics are in line with those that have been documented for private �rms in the U.S.,e.g., Bitler, Moskowitz and Vissing-Jorgensen (2005) and Mach and Wolken (2006).

3

Family ties between shareholders and managers may enable better monitoring and en-

forcement of implicit contracts with the manager, and may reduce the incentives for a

controlling shareholder to expropriate the other shareholders, since there may be more in-

teractions as well as shared resources outside the �rm between shareholders and managers

by which enforcement may be applied (e.g., Bennedsen and Wolfenzon (2000)). Alter-

natively, to the extent that family ties between shareholders lead to decisions that are

based on maximizing family utility rather than �rm value, as has been argued to be the

case in several samples of established family-owned �rms (e.g., Morck, Strangeland and

Yeung (2000), Perez-Gonzalez (2006), Bennedsen et al (2007) and Bloom and Van Reenen

(2007)), we might expect new family-owned �rms to actually perform worse than those

with unrelated shareholders.

In fact, we �nd that new family-owned �rms earn higher returns on assets and higher

pro�t margins than non-family-owned �rms. Family-owned �rms earn an ROA that is

almost 2 percentage points greater than that of non-family-owned �rms and a pro�t margin

that is 1.3 percentage points greater than that non-family owned �rms. When �rms are

very young, i.e., age 3 years or younger or the 25th percentile of the �rm age distribution,

family-owned �rms earn an ROA that is over 3 percentage points higher and pro�t margin

that is 2 percentage points higher than non-family-owned �rms. We also �nd that new

family-owned �rms have greater survival rates than non-family-owned �rms. New family-

owned �rms are 30 percent less likely to fail relative to similar non-family-owned �rms.

This evidence is consistent with the hypothesis that family ties between shareholders and

managers enable better monitoring of and enforcement of implicit contracts with managers,

which may be especially important in the early years of the �rm lifecycle when uncertainty

and �nancial constraints are greatest.

We further examine whether the nature of the family ties between shareholders matters

for �rm performance. In particular, we distinguish between family-owned �rms in which

a husband and wife are leading shareholders and family-owned �rms in which the share-

holders are non-married but related family members. We make this particular distinction

since new �rms with married shareholders are the largest category of family-owned �rms

in our data and the nature of the family ties between married couples is arguably very dif-

ferent than the nature of the family ties between other family members. Because married

couples typically live together and jointly own many common resources outside the �rm,

4

they may be better able to monitor one another as well as be better able enforce implicit

contracts between themselves relative to other types of family shareholders.

We �nd that the performance advantage of family-owned �rms is strongest when two

of the leading shareholders are married. The average ROA of a family �rm owned by a

married couple is over 3 percentage points higher than the ROA for non-family-owned

�rms, compared to a 1 percentage point ROA advantage of family-owned �rms owned by

non-married shareholders relative to non-family-owned �rms. The advantage of married

shareholder ownership is again strongest in the youngest �rms. For �rms aged 3 years and

younger, those with married shareholders have ROAs that are almost 5 percentage points

higher than non-famiy-owned �rms, compared to a 1.3 percentage point advantage for

family-owned �rms with non-married shareholders. This evidence suggests that the nature

of the family ties between married shareholders is more e¤ective at enabling monitoring

and enforcement of implicit contracts.

We �nd evidence that the performance advantage of family-owned �rms is largely

driven by the ability of shareholders and managers to commit to keeping cash within

the �rm. Family-owned �rms have greater liquidity than non-family-owned �rms. In

particular, family-owned �rms have a cash to asset ratio that is almost 3 percentage points

greater than non-family-owned �rms; �rms with married shareholders have a liquidity ratio

that is over 4.5 percentage points higher than non-family-owned �rms. This di¤erence is

greatest when �rms are very young. For �rms aged three years and younger, family-owned

�rms with married shareholders have liquidity ratios that are between 12 and 9 percentage

points higher than non-family-owned �rms. While we cannot observe all operating costs,

we can observe wages, and �nd that family-owned �rms also pay lower wages than non-

family-owned �rms, especially when they are young. This evidence is also consistent with

family-owned �rms being better able to keep operating costs low and to enforce implicit

contracts with managers and employees.

We also examine whether the performance advantage of family-owned �rms varies with

managerial incentives. If family ties between managers and shareholders facilitates mon-

itoring of and enforcement of implicit contracts with managers, we should expect the

performance advantage of family-owned �rms to be stronger when the need to monitor

managers is stronger. We measure managerial incentives by the percentage of equity held

by the top management team of the �rm. We �nd that the performance advantage of

5

family-owned �rms vis-a-vis non-family-owned �rms is larger when managers own small

percentages of equity. However, even in the set of �rms in which managers hold a major-

ity of equity, we still �nd a performance advantage of family-owned �rms, in particular

those with married shareholders. This suggests that family ties, in particular marital ties,

between shareholders lend a performance advantage to new �rms above and beyond mit-

igating the classic manager shareholder con�ict, perhaps by mitigating agency problems

between large shareholders themselves.

Finally, we examine the robustness of our main �ndings to alternative explanations.

Because ownership structures are endogenously determined (e.g., Demsetz and Lehn (1985)

and Himmelberg et al (1999)), our results may not re�ect a causal role of family ownership

in new �rms but rather that families select into owning �rms with certain characteristics.

We, therefore, exploit geographical variation within each country in the likelihood that

�rms are family-owned by using the fraction of the population that is married and a¢ liated

with di¤erent religious groups as instruments for family ownership. We �nd that our results

are in fact strengthened when we account for endogeneity of new �rm ownership structure,

suggesting that families may select into starting safer, lower return, �rms relative to �rms

started by unrelated shareholders. We also �nd that our results are robust to di¤erent

sample periods and de�nitions of family ownership.

Overall, our �ndings are consistent with the theory that family ties between sharehold-

ers and managers enhance �rm performance by facilitating the monitoring and enforcement

of implicit contracts with managers necessary to keep cash in the �rm and to keep oper-

ating costs low. This mechanism is likely to be most important for �rms facing �nancial

constraints and greater uncertainty over the success of investment projects, key character-

istics of very young �rms like those in our sample. Our analysis provides a new perspective

on the role of family ownership in �rms by examining the performance e¤ects of family

ownership early in the �rm�s lifecycle. Our results point to e¢ ciency-based explanations

for why family ownership, in particular the "mom and pop shop" is such a predominant

ownership structure in new �rms.

The rest of the paper proceeds as follows. Section 2 describes the data we use. Sections

3 and 4 present our main empirical results. Section 5 presents the instrumental variables

results and other robustness tests. Section 6 concludes.

6

2 Related Literature and Empirical Predictions

The existing evidence on the performance e¤ects of family ownership suggests that the

impact of family ownership on �rm performance is context dependent. Anderson and

Reeb (2003) �nd that on average family-owned �rms in the S&P 500 perform better than

non-family-owned �rms. Sraer and Thesmar (2007) �nd similar evidence for publicly

traded French �rms. However, numerous studies also highlight the negative performance

e¤ects of family ownership in mature �rms. Perez-Gonzalez (2006) �nds that in the U.S.

when new CEOs are related to the �rm�s founders, ex-CEO or large shareholder that �rm

performance declines. Bloom and Van Reenen (2007) �nd that management practices

at �rms with descendent CEOs are worse. Bertrand et al (2008) and Bennedsen et al

(2007) �nd underperformance in �rms in which control is inherited in the context of Thai

and Danish family-owned �rms, respectively. Villalonga and Amit (2006) synthesize the

evidence for U.S. public �rms by distinguishing between di¤erent types of family-owned

�rms and �nd that family-owned �rms with founder CEOs or chairmen outperform non-

family-owned �rms, but that family-owned �rms with descendent CEOs or in which the

family owns a controlling stake underperform.6 Thus, the evidence to date suggests that

family ownership may be bene�cial when founders are involved, but not when subsequent

generations are involved with the �rm. This raises the question of the role family ownership

plays in the early part of �rms�life cycles.

In the context of the new private �rms, family ties between shareholders and managers

may enable better monitoring of and enforcement of implicit contracts (e.g., Hart and

Holmstrom (1987) and Baker, Gibbons and Murphy (2002)) with the manager since there

may be more interactions as well as shared resources outside the �rm between sharehold-

ers and managers. Greater �nancial constraints of young �rms (e.g., Hadlock and Pierce

(2010)) may make it more important for shareholders and managers to agree to keep cash

within the �rm and keep operating costs low in order for the �rm to invest optimally,

making the mitigation of the classic owner-manager con�ict (e.g., Jensen and Meckling

(1976)) even more important for new �rm performance and survival. Family ties between

shareholders may also reduce the incentives of a controlling shareholder or group of share-

holders to expropriate the other shareholders, e.g., Bennedsen and Wolfenzon (2000) since

6Villalonga and Amit (2009) also describe how di¤erent family ownership cash �ow rights in U.S. public�rms are related to their control rights.

7

related shareholders may have more interactions and shared resources outside the �rm.

This leads to the �rst hypothesis that we test in our data.

Hypothesis 1: If family ties between shareholders and managers facilitate better monitoring

of managers and enforcement of implicit contracts, then family ownership should exert a

positive impact on �rm performance.

Alternatively, to the extent that family ties between shareholders lead to decisions

that are based on nepotism rather than value maximization (e.g., Morck, Strangeland

and Yeung (2000), Perez-Gonzalez (2006), Bennedsen et al (2007) and Bloom and Van

Reenen (2007)), we might expect new family-owned �rms to perform worse than those

with unrelated shareholders. This performance decreasing mechanism could also be at

play in new �rms, in which controlling family members may use �rm resources directly for

family consumption or may hire less quali�ed family members to manage the �rm. Thus,

the alternative hypothesis to our �rst hypothesis above is that family ownership has a

negative impact on �rm performance.

It is also possible that the nature of the family ties between shareholders matters for

�rm performance. In particular, we might expect there to be di¤erences between family-

owned �rms in which a husband and wife are leading shareholders and family-owned �rms

in which the shareholders are non-married but related family members. Because married

couples typically live together and jointly own many common resources outside the �rm

(e.g., Becker (1973), Freiden (1974) and Lundberg and Pollak (1996)) they may be better

able to monitor one another as well as be better able enforce implicit contracts between

themselves relative to other types of family shareholders. Other family shareholders,

such as siblings or a parent and child(ren), may have more interactions and share more

resources outside of the �rm than unrelated shareholders but have fewer interactions and

shared resources than married shareholders.

Hypothesis 2: If marital ties between shareholders and managers are more e¤ective at

facilitating monitoring and enforcement of implicit contracts with managers relative to

other types of family ties, then performance of �rms owned by married shareholders should

be greater than performance of �rms owned by non-married but related family members.

In contrast, the alternative hypothesis to hypothesis 2 is that the likelihood of making

8

decisions based on family interest rather than �rm value maximization may be greater for

married shareholders. Since married couples often jointly produce children, they may be

more likely to make decisions in the interest of their children�s utility rather than in the

interest of the �rm relative to non-married related shareholders. However, it may also be

the case that �rms owned by siblings or by parent and child(ren) may be more susceptible

to in�ghting or disagreement than �rms owned by married shareholders if they disagree on

how to allocate �rm resources or whose opinion should prevail when making �rm decisions

(e.g., Bertrand et al (2008)).

We also examine whether the relation between family ownership and �rm performance

varies over the �rm lifecycle. Greater uncertainty about success and survival in new �rms

may make the enforcement of implicit contracts between managers and shareholders more

di¢ cult (e.g., Bull (1987), Hart and Holmstrom (1987) and Baker, Gibbons and Murphy

(2002))). At the same time, greater �nancial constraints in young �rms (e.g., Hadlock

and Pierce (2001)) may make it more important for shareholders and managers to agree

to keep cash within the �rm and keep operating costs low in order for the �rm to invest

optimally. This leads to our third testable hypothesis.

Hypothesis 3: If better monitoring of and ability to enforce implicit contracts with

managers are more important when �rms are younger due to greater �nancial constraints

and uncertainty, then family-owned �rms should exhibit greater performance relative to

non-family-owned �rms when �rms are young.

Further, if family ownership facilitates monitoring which is most useful for �rms that

need to conserve cash and keep costs low, we can postulate and test two related hypotheses.

Hypothesis 4: If family facilitated monitoring and contract enforcement leads to a

greater ability to keep cash within the �rm for future investment, then family-owned �rms

should have greater cash to asset ratios than non-family-owned �rms. Further, to the extent

that conserving cash is more important when �rms are young, the di¤erence in cash ratios

for family-owned �rms should be greater when �rms are younger.

Hypothesis 5: If family ownership leads to lower operating costs due to better monitor-

ing of managers or the ability to defer costs through implicit contracts, family-owned �rms

should have lower costs relative to non-family-owned �rms.

9

In most new private �rms, the manager is also an owner. This is true in our data

and has also been documented in other datasets (e.g., Bitler, Moskowitz and Vissing-

Jorgensen (2005)). If family ties between managers and shareholders facilitates monitoring

and enforcement of implicit contracts with managers, we should expect the performance

advantage of family-owned �rms to be stronger when the need to monitor managers is

stronger. Managerial incentives are argued to be more aligned when equity ownership of

managers is greater (e.g., Jensen and Murphy (1990) and Holmstrom and Tirole (1993))

This leads to our �nal testable hypothesis:

Hypothesis 6: If family ownership enhances performance by mitigating the manager-

owner con�ict, family-owned �rms should outperform non-family-owned more when man-

agerial ownership is lower.

3 Data

We obtain our data from Amadeus, a database maintained by Bureau van Dijk (BvD)

which contains ownership and �nancial statement information for European countries.

BvD obtains its data from regulatory �lings as well as from its own proprietary sources.

Amadeus includes both private and public �rms in its data collection which allows an in

depth examination of new �rms, which in most cases are private. Amadeus also contains

detailed ownership information, including the names of each shareholder, the number and

type of shares held, and detailed information on the board of directors and management

of each �rm.7

We build our base sample from �rms located in the four largest Western European

countries: Great Britain, France, Germany, and Italy. We focus on these countries since

they contain the most comprehensive accounting and ownership data for smaller private

�rms. We retain only those �rms for which we have ownership information. We exclude

any �rms for which we are unable to identify at least 90% of reported shareholders and

those whose annual sales are not reported. We retain the 332,474 �rms that are 20 years old

or younger and that report sales information and that have fewer than 150 employees and

7See also recent papers by Brav (2009) which uses data from BvD for the United Kingdom and Faccio,Marchica and Mura (2010) which uses data for all European countries contained in Amadeus for additionaldescriptive statistics for the �rms contained in BvD�s Amadeus database.

10

$100 million in annual sales. We restrict our analysis to �rms with multiple shareholders

since we are interested in comparing �rms that have two or more unrelated shareholders

with �rms that have two or more related shareholders. Excluding single-shareholder �rms

leaves us with an estimation sample of 183,537 �rms and a total of 932,801 �rm-year

observations for the period 1997-2006.

Firms are classi�ed as family-owned if at least two shareholders have the same last

name and hold a majority of the company shares. We further classify family-owned �rms

as having married shareholders if two shareholders with the same last name hold the

majority of the company�s shares and are of a di¤erent gender.8 We exclude �rms from

the married shareholder category if there are 15 years or more between the birth dates of

the two shareholders, to exclude �rms that are majority owned by a father and daughter

or by a mother and son.9 This classi�cation scheme for married shareholder family-owned

�rms may include some �rms in which the leading shareholders are not married, e.g.

those owned by a brother and sister, and may exclude some �rms in which the leading

shareholders are married but in which the wife does not take the husband�s last name. This

misclassi�cation will introduce measurement error into our measure of family-owned �rms

and those with married shareholders, mitigating against �nding any di¤erences between

di¤erent ownership structures. We examine whether our main �ndings are robust when

we examine a smaller sample of �rms. In this sample of �rms, we only include �rms in

which the shareholders are explicitly identi�ed as being married through their reported

names (e.g., Mr. John Smith is one shareholder and another shareholder is Mrs. John

Smith or Mrs. Mary Smith) or which have birthdates that are within the same year but

not the same day to exclude brother-sister pairings.

69,524 (38%) of �rms are classi�ed as family-owned �rms and the remaining 114,013

�rms are classi�ed as non-family-owned. Of the family �rms, 35,503 (51%) are classi�ed

as having married shareholders and 34,021 are classi�ed as having non-married but re-

lated shareholders. We examine how family shares distribute across the di¤erent family

8Gender information is taken from several sources. First, for a subset of shareholders (shareholdersthat also appear on the �rm�s manager or director lists) Amadeus provides a gender variable. Second,in many cases, names include "Mr." or "Mrs." pre�x which explicitly identi�es the person�s gender.Third, for the set of shareholders for whom Amadeaus does not identify a gender, we use several publicsources to learn about the most common male and female names for di¤erent countries (for example,http://names.mongabay.com/male_names.htm).

9Birthdate information is taken from the board of directors and manager lists.

11

members. The average �rm has 2.5 shareholders (a median of 2 and a 90th percentile of

4). This distribution of number of shareholders is even across ownership types (averages

of 2.6, 2.5, and 2.4 for family-married, family-non-married and non-family-owned). On

average, 80% of the shares are held by family members. Family shareholders of married

family �rms hold 90% of their company shares; family shareholders of non-married family

�rms hold 78% of the shares.

Table 1 reports summary statistics for our sample �rms ($ values are in nominal terms).

The average �rm in our sample holds $837,000 in total assets, generates $2.0 million in

annual sales, has 13 employees, 8 years old, and makes $129 thousand in pro�ts (EBITDA).

Returns on assets and pro�t margin are computed as earnings before depreciation, taxes

and interest over total assets, and earnings before depreciation, taxes and interest over

sales, respectively. Both variables are windsorized above and below 2 and -2, respectively.

Average returns on assets and pro�t margin are 0.157 and 0.080 respectively (medians of

0.123 and 0.059).

Insert Table 1 here

4 Family Ownership and New Firm Characteristics

In examining our hypotheses about the impact of family ownership and new �rm perfor-

mance we �rst examine whether there are di¤erences in raw correlations between mea-

sures of performance and family ownership. Table 2 reports mean comparison tests for

family-owned and non-family-owned �rms. Consistent with hypothesis 1, we see that

family-owned �rms unconditionally perform better than non-family-owned �rms. Family-

owned �rms generate higher returns on assets and are more pro�table, less leveraged, and

hold more cash than non-family �rms. family-owned �rms generate, on average, 4.7%

higher returns on assets than non-family-owned �rms. Evaluated at the sample mean,

this �gure implies that family-owned �rms generate $39,000 more in annual pro�ts than a

non-family-owned �rm (0:047�837). This di¤erence is particularly large given the sampleaverage pro�ts of $129,000. We reach an even higher estimate when comparing di¤erences

in pro�t margin. On average, family �rms enjoy a 2.3% higher margin than non-family

�rms. Evaluated at the sample mean, this di¤erence implies that family �rms generate

$47,000 more in annual pro�ts than non-family �rms (0:023� 2; 037).

12

Family �rms are also more liquid and less leveraged than non-family �rms. We de�ne

liquidity as the ratio of cash and cash equivalents to total assets. This ratio is signi�cantly

higher for family �rms, where family �rms are 19% more liquid than non-family �rms

(0:058=0:309). Family �rms are also less leveraged than non-family �rms. We de�ne

leverage as the ratio of current and non-current liabilities to total assets. On average,

this ratio is 0.768 for family �rms and 0.783 for non-family �rms. While the di¤erence

in means is signi�cant at the one percent level, the di¤erence is not quantitatively large

(3%).

Family �rms are slightly older and larger than non-family �rms (average age and

number of employees are 8.8 and 14 for family �rms, and 7.5 and 12 for non-family �rms,

respectively).

Insert Table 2 here

Table 3 explores in a greater detail unconditional di¤erences in means distinguishing

between family-owned �rms with leading married shareholders and those with non-married

leading shareholders. Consistent with hypothesis 2, family-owned �rms with married

shareholders earn the highest returns on assets as compared to the other ownership types.

family-owned �rms with married shareholders make 7.8% higher returns on assets than

non-family-owned �rms, and 5.8% higher returns than other family-owned �rms. The

di¤erence in returns on assets between family-owned �rms with non-married shareholders

and non-family-owned �rms is smaller: returns on assets for non-married family-owned

family are 2.0% higher than the returns for non-family-owned �rms. A similar pattern

holds for pro�t margin as well. An interesting pattern emerges for liquidity. Married

family �rms are substantially more liquid (hold more cash) than family-owned �rms with

non-married shareholders and non-family-owned �rms. The ratio of cash to assets for

married family �rms is 0.101 higher than the ratio for non-family �rms (33% higher), and

is 0.081 higher than the ratio for non-married family �rms (25% higher). The di¤erence in

liquidity between non-married family and non-family �rms is much lower at 0.020. Family

married and non-married family-owned �rms are less leveraged than non-family-owned;

however, the di¤erences are not large. The di¤erence in the ratio debt and assets for family

married and non-family-owned �rms is -0.021 (-2.6%). Married family-owned �rms have

13

fewer assets than non-family-owned �rms (-$103,000), whereas non-married family-owned

�rms hold more assets than non-family-owned �rms ($311,000).

Insert Table 3 here

Since we posit that the bene�cial aspects of family ownership are likely to be strongest

when �rms are young and uncertainty and �nancial constraints are largest, we next ex-

amine how the raw correlations between family ownership and performance vary by �rm

age. In particular, Table 4 examines how the di¤erences between married family and

non-family-owned �rms vary across �rm age. The results are consistent with hypotheses

3 and 4. While there is a very large di¤erence in performance between married family and

non-family �rms for very young �rms, this di¤erence in performance shrinks as �rms grow

older. The di¤erence in returns on assets between married family and non-family �rms is

0.146 for �rms less than three years in age. On average, this means that married family

�rms generate $75,000 more in pro�ts than a non-family �rm (based on total assets of

$511,000 for �rms in the same age bracket). For �rms 13 years and older, the di¤erence

in returns on assets is only 2.3%. This di¤erence is associated with $31,000 di¤erence in

pro�tability of family and non-family �rms in the same age bracket (based on total assets

of $1,363,000 for �rms in the same age bracket). A similar pattern holds for pro�tability

as well.

Family married �rms appear to be much more liquid than non-family especially early

in the �rm life cycle. For young �rms (less than 3 years), the di¤erence in the ratio of

cash to assets for married family and non-family �rms is 0.190. This di¤erence shrinks to

0.026 for the older �rms in our sample (13-20 years). This di¤erence means than young

�rms hold about $97,000 more in cash than non-family �rms (0:190 � 511; 000). For

the older �rms in our sample, married family �rms hold only $35,000 more in cash than

non-family �rms (0:026� 1; 363; 000). For leverage, we �nd that married-family �rms areless leveraged than non-family �rms, especially when they are young, but the di¤erence is

small (-0.022 for young �rms, versus -0.019 for older �rms).

Thus, the uniaxial correlations in the data support the notion that family ownership,

in particular ownership by married shareholders lends a performance advantage to new

�rms, especially early on in the �rm lifecycle. Moreover, this performance advantage may

be linked to an ability to commit to keep cash within the �rm.

14

Insert Table 4 here

5 Regression Analysis

While the di¤erences in means for the variables of interest presented in the previous

section are suggestive, they do not control for other possible di¤erences between �rms

with di¤erent ownership structures, such as industry, size, time period and geography. In

this section, we explore the relation between ownership and �rm performance controlling

more carefully for �rm characteristics in a regression framework.

5.1 Family Ownership, ROA and Pro�tability

We �rst estimate the following speci�cation for the relationship between our base measure

of �rm performance, ROA, and family ownership.

ROAit = �1 lnSalesit�1 + �2Ageit + �3FamilyNonmarriedi

+ �4FamilyMarriedi + 'j + cc + �t + �it (1)

ROAit is returns on assets, de�ned as EBITDA over assets (we also examine pro�t

margins, de�ned as EBITDA over sales), Salesit�1 is annual sales lagged by one period,

and Ageit is years from date of incorporation. FamilyNonmarried and FamilyMarried

are �rm ownership type dummies. The base category is non-family-owned �rms. 'j;

cc;and �t are complete sets of three-digit SIC codes, country, and year dummies. �it is

an iid error term. Our main interest is in the coe¢ cients �3 and �4: Standard errors are

always clustered by �rms. If family-owned �rms outperform non-family-owned �rms, we

expect b�3 > 0 and b�4 > 0, and if �rms with married shareholders outperform family-owned�rms with non-married shareholders we expect that b�4 > b�3:Table 5 reports the estimation results. The general pattern of results is consistent with

hypotheses 1 and 2. Column 1 includes a dummy for family ownership (receives the value

of 1 for family married or family non-married �rms and the value of zero for non-family

�rms). Consistent with hypothesis 1, the results indicate that family-owned �rms generate

a higher return on assets than non-family-owned �rms. The coe¢ cient on dummy for

family ownership is 0.0195 (a standard error of 0.0013). Evaluated at the sample mean, this

15

estimate implies that a family-owned �rm earns $17,600 in pro�ts more than a non-family-

owned �rm of similar characteristics (0:0131 � 1; 217; 000).10. Column 2 tests hypothesis2 by distinguishing between family-owned �rms with married shareholders and those with

non-married shareholders by including separate dummies for the di¤erent ownership types.

There are striking di¤erences in the e¤ect of married and non-married family-owned �rms,

where the e¤ect of married shareholders is much stronger that the e¤ect of non-married

related shareholders. The coe¢ cient on dummy for married shareholders is 0.0329 (a

standard error of 0.0019), and the coe¢ cient on non-married but related shareholders

�rms is 0.0087 (a standard error of 0.0015). These estimates imply that a family-owned

�rm with married shareholders generates, on average, $40,000 in pro�ts more than a non-

family �rm (0:0329 � 1; 217; 000), and that an family-owned �rm with non-married but

related shareholders generates only $11,000 in pro�ts more than a non-family-owned �rms

(0:0087 � 1; 217; 000). Column 3 focuses only on family owned �rms (thus, including

only married and non-married �rms). Married �rms earn a higher returns on assets than

non-married family-owned �rms. The coe¢ cient estimate on the dummy for married

family-owned �rm is 0.0175 (a standard error of 0.0022). Evaluated at the sample mean,

married family-owned �rms make $25,000 more pro�ts than those with non-married but

related shareholders (0:0175 � 1; 412; 000). Dummy for married family �rms can capturea multi-gender shareholder e¤ect, instead of a marriage e¤ect (this is because a married

family �rmmust have shareholders of di¤erent genders, where this restriction does not hold

for non-married �rms). Column 4 adds a dummy variable for multi-gender shareholders.

This dummy equals one if there is at least one shareholder from each gender, and that

there is at least one male and at least one female shareholder that together hold at least

30% of the �rm equity (results are robust to di¤erent thresholds). The e¤ects of married

family �rms remain robust.

Columns 5-8 report the estimation results for pro�t margin. Column 5 includes a

dummy for a family �rm. family-owned �rms have, on average, a higher pro�t margin

than non-family-owned �rms (a coe¢ cient of 0.0130 and a standard error of 0.0008). A

family-owned �rm generates $28,000 more than a non-family-owned �rm of similar size

and age (0:0130�2; 160; 000).11 Column 6 includes separate dummies for married and non-101; 217; 000 is the average value of assets for �rms with non-missing values of EBITDA and lagged

sales.112; 160; 000 is the average value of sales for �rms with non-missing values of EBITDA and lagged sales.

16

married family-owned �rms. Married family �rms remain the most pro�table ownership

form. Relative to non-family �rms, married family �rms generate an additional $39,000 in

pro�ts (0:0179� 2; 160; 000): non-married �rms, on the other hand, generate only $19,000in pro�ts more than non-family �rms (0:0090� 2; 160; 000).We test also the robustness of our results to di¤erent ownership structures. For ex-

ample, we exclude family �rms where the leading shareholders own more than 75% of the

shares (the average and median family shareholder holds 46% and 50% of equity, respec-

tively). The same pattern of results continues to hold. For example, the coe¢ cient on

dummy for married family (same speci�cation as in column 2) is 0.0247 (a standard error

of 0.0016). We also exclude family �rms that have non-family shareholders, or family �rms

where non-family members hold at least 10% of the �rm�s equity. The results continue to

be robust.

We also study the sensitivity of our �ndings to measurement error in classifying family

�rms to married versus non-married. We estimate the above speci�cation for a subsample

of �must be married��rms. In these speci�cations we classify family �rms to married,

only if the names are in the format " Mr. and Mrs. Smith", and if the age di¤erence

between the two is less than 15 years. The results continue to hold. The coe¢ cient

estimate of married family dummy is 0.0438 (a standard error of 0.0082), as compared to

0.0329 when we adopt the more general and less restrictive classi�cation of �rms married

versus non-married (consistent with attenuation bias due to measurement error in married

family dummy).

We also examine whether our results are robust to changes in ownership. For some

�rms in our sample, we lack historical ownership information. For these �rms, we use

cross-sectional (as of 2007) ownership data over the entire period for these �rms. As

a robustness test, we also estimate our regressions on the subsample of �rms for which

we have complete historical ownership information. In general, we see little change in

ownership structures in our sample of �rms. 94 percent of the �rms that start as married

family �rms experience no change in ownership structure; 81 percent of the non-married

family �rms experience no change in ownership structure. We �nd that our results are

very similar for the set of �rms for which we have complete historical ownership data.

Insert Table 5 here

17

5.2 Family Ownership and Performance over the Firm Life-cycle

As hypothesized earlier, if family ties between shareholders facilitate monitoring and en-

forcement of implicit contracts, this mechanism should be most important in the early

years of the �rm�s life when uncertainty about the success of investment projects and

�nancial constraints are highest. We examine how the e¤ect of family ownership on per-

formance varies across the �rm life-cycle by estimating the following speci�cation, which

includes interaction terms between family ownership dummies and �rm age:

ROAit = �1 lnSalesit�1 + �2Ageit + �3FamilyNonmarriedi

+ �4FamilyMarriedi + 1FamilyNonmarried� Ageit

+ 1FamilyMarried� Ageit + 'j + cc + �t + �it (2)

Here our interest is in the coe¢ cients 1 and 2; where we expect b 1 < 0 and b 2 < 0:Table 6 reports the estimation results. Column 1 focuses on the youngest �rms in our

sample (1st quartile, less than 3 years), and column 2 focuses on the mature �rms in our

sample (4th quartile, between 13 and 20 years old). The general pattern of results indicates

that the di¤erence in performance between family and non-family-owned �rms is especially

strong for young �rms. For young �rms, the coe¢ cient on family �rms dummy is 0.0309

(a standard error of 0.0029), and for mature �rms this coe¢ cient is 0.0073 (a standard

error of 0.0017). These estimates implies that for young �rms, family �rms generate

$20,000 in additional pro�ts than non-family �rms (0:0309�646; 000)12, relative to averagepro�ts of $84,000. For mature �rm, the additional pro�ts by family �rms amount to

$15,000 (0:0073 � 2; 036; 000), as compared to average pro�ts of $202,000.13 Column 3

adds an interaction between family-owned �rm dummy and �rm age. The coe¢ cient on

this interaction terms is negative and highly signi�cant (-0.0039 and a standard error of

0.0002), implying that the family ownership bene�t diminishes as the �rm grows older.

Based on the estimates in column 3, the di¤erence between family and non-family-owned

�rms completely disappears after 14 years from year of incorporation (0:0542�0:0039�14):12646,000 is the average value of total asstes for the estimation sample of young �rms with non-missing

values of EBITDA and lagged sales.132,036,000 is the average value of total asstes for the estimation sample of mature �rms with non-

missing values of EBITDA and lagged sales.

18

Columns 4-6 add separate dummies for family-owned �rms with married shareholders

and those without. Column 4 focuses on young �rms, and column 5 focuses on mature

�rms. Married family �rms have a strong e¤ect on returns on assets for young �rms, but

a much weaker e¤ect for mature ones. For young �rms, the coe¢ cient on married family

dummy is 0.0479 (a standard error of 0.0039). This implies that relative to non-family

�rms, married family �rms generate $31,000 in additional pro�ts (0:0479�646; 000), whichamounts to 37% of average pro�ts. For mature �rms, the coe¢ cient on married family

dummy is 0.0137 (a standard error of 0.0027). This means that family married �rms

generate $28,000 in additional pro�ts relative to non-family �rms (0:0137 � 2; 036; 000);which amounts to only 14% of average pro�ts. The same pattern of results hold when

comparing non-married family owned �rms to non-family-owned �rms, however, the e¤ects

are substantially lower. Column 6 adds interaction terms between �rm age and dummy for

married family and non-married family �rms. The coe¢ cient on the interaction between

age and married family �rms is -0.0059 (a standard error of 0.0003), and the coe¢ cient

on unmarred family dummy is -0.0020 (a standard error of 0.0003). This estimate implies

that the e¤ect of family �rms fully disappears after 14 years from year of incorporation

(0:0831 � 0:0059 � 14): The same pattern of results holds for family-owned �rms withnon-married but related shareholders.

In separate (not reported) speci�cations we examine the e¤ect of family ownership on

pro�ts margin. We reach the same conclusion on the diminishing e¤ect of family ownership

as the �rm grows older.

Insert Table 6 here

5.3 Survival

Our �nal measure of �rm performance is �rm survival. Since it is well documented that

the probability of failing is proportionally higher for younger �rms, survival is a relevant

measure of performance for the new �rms in our sample. If family ownership structure

lends a performance advantage to young �rms, we should expect to see that these �rms

are also less likely to fail.

Our panel dataset allows us to examine whether family-owned �rms have greater sur-

vival rates, since we compiled past editions of BvD�s Amadeus database to collect historical

19

information on �rms that exited the sample before the end of our sample period. We de-

�ne �rm failure in two ways. The �rst de�nition, which we call our restrictive de�nition,

labels �rms as exitors, or failing, if they report their last �nancial statement before year

2005. The second de�nition, which we call our broad de�nition of failure, labels �rms as

exitors if they either �le their last �nancial statement before 2005 or if they shrink in size

to having only one employee and less than one thousand dollars in annual sales.

Using these two de�nitions of �rm failure, we estimate Cox proportional hazard models

in which the exit event is either the restrictive or broad de�nition of �rm failure. Table

7 reports the hazard ratios for the family ownership dummies. Columns 1 and 2 report

estimates using the restrictive de�nition of failure. Columns 3 and 4 report estimates

using the broad de�nition of failure. We see that family-owned �rms are less likely to

fail, or more likely to survive, than non-family-owned �rms, as shown by the hazard ratios

of around 0.65 in both Columns 1 and 3. This maps to a 30 percent lower likelihood

of failure at any point in time for family-owned �rms, which is economically meaningful,

given the sample failure rate of 15 percent (restrictive) and 18 percent (broad). When we

examine the di¤erences in failure hazard ratios between family-owned �rms with married

and non-married leading shareholders in Columns 2 and 4 we see that both groups have

signi�cantly lower failure rates than non-family-owned �rms, and that family owned �rms

with non-married shareholders have a slightly lower failure hazard than those with married

shareholders.

Insert Table 7 here

5.4 Liquidity

We have so far seen that family ownership is associated with higher performance in new

�rms as measured by ROA, pro�t margin and probability of survival. This performance

advantage is particularly strong in very young �rms and when the leading family share-

holders are married. In this section, we next examine whether this performance advantage

may in part be due to an ability of shareholders and managers to commit to keep cash

within the �rm for future investment (and future payouts), rather than pay it out in some

form, e.g., dividend or wages, in the present. This commitment ability is likely to be

20

crucial when uncertainty about the future of investment projects (e.g., Bull (1987), Hart

and Holmstrom (1987) and Baker, Gibbons and Murphy (2002)))

To test whether family-owned �rms keep more cash internally we estimate regressions

of the following form:

Liquidityit = �1 lnSalesit�1 + �2Ageit + �3FamilyNonmarriedi

+ �4FamilyMarriedi + 'j + cc + �t + �it (3)

Liquidity is de�ned as cash and cash equivalents over total assets (we also estimate the

same speci�cation for leverage, which is de�ned as current and non-current liabilities over

total assets). According to the view that family owned �rms, especially those with married

shareholders, are better able to commit their personal funds or retained earnings to invest

in the �rm we expect b�3 > 0, b�4 > 0; and b�4 > b�3: In a separate speci�cation, we estimatethe life-cycle e¤ect of family ownership on liquidity by interacting family dummies with

�rm age. We expect the family e¤ect to diminish with �rm age.

Table 8 examines the relationship between family ownership and �rm liquidity and

leverage. Columns 1-3 examine the e¤ect of family ownership on liquidity. The general

pattern of results suggest that family-owned �rms, especially those with married sharehold-

ers, hold more cash than non-family-owned �rms. Column 1 includes the family-owned �rm

dummy. The coe¢ cient on family-owned �rm dummy is positive and signi�cant (0.0292

and a standard error of 0.0035). Based on this estimate, family-owned �rms hold $40,000

additional cash than non-family �rms (0:0292� 1; 352; 000);14 relative to an average cashholding of $287,000. As shown in column 2, the di¤erence in cash holding between family

and non-family-owned �rms is driven mostly by family-owned �rms with married share-

holders. The coe¢ cient on the married shareholder dummy is 0.0453 (a standard error of

0.0047), and the coe¢ cient on the non-married shareholder dummy is 0.0160 (a standard

error of 0.0034). These estimates imply that a family-owned �rmwith married shareholders

holds $61,000 additional cash than a non-family �rm (0:0453� 1; 352; 000): Other family-owned �rms, on the other hand, hold only $22,000 in additional cash (0:0160�1; 352; 000).Column 3 examines how the e¤ect of family ownership on liquidity varies by �rm age,

by adding interaction terms between family ownership and �rm age. The results indicate141,352,000 is the average value of total assets for the estimation sample that includes observations with

non-missing values of cash and lagged sales.

21

that family ownership, especially married shareholder ownership, is important for liquidity

especially early in the �rm life-cycle. For family-owned �rms with married shareholders,

the interaction with �rm age is negative and signi�cant (-0.0088 and a standard error of

0.0008), and the coe¢ cient on the linear married family dummy is positive and signi�cant

(0.1206 and a standard error of 0.0070). These estimates imply that the e¤ect of married

shareholders on liquidity disappears after 14 years (0:1206�0:0088�14). A similar patternholds for family �rms with non-married shareholders.

Columns 4-6 study the e¤ect of family ownership on leverage. We examine leverage

to test whether the better monitoring of family ownership perhaps lessens the need for

external �nance in the form of outside debt since more cash is available and debt is not

needed to discipline management (Jensen and Meckling (1976)). The general pattern of

results suggest that family-owned �rms are less leveraged than non-family-owned �rms.

Column 4 includes the family ownership dummy. The coe¢ cient on family ownership

dummy is negative and signi�cant (-0.0184 and a standard error of 0.0022). This estimate

implies that family-owned �rms hold $23,000 less debt than non-family �rms (�0:0184�1; 263; 000)15:This di¤erence is small relative to the average debt (current and non-current

liabilities) level of $895,000. This suggests that both family and non-family owned �rms

borrow as much from outside sources as possible even if family owned �rms are better at

keeping available cash within the �rm for investment. Column 5 examines the e¤ect of

married and non-married shareholder family-owned �rms. The di¤erence in the e¤ect of

the two �rm types is not statistically signi�cant. Column 6 adds interaction terms between

�rm age and married and non-married family ownership dummies. Both interaction e¤ects

are not statistically signi�cant.

Insert Table 8 here

Hypothesis 4 proposes greater liquidity as a mechanism through which family �rms

outperform non-family �rms, especially early in their life-cycle. To estimate the extent

to which this channel is indeed signi�cant, Table 9 reports the estimation results for

the speci�cations reported in Table 5, but controlling for liquidity. Our aim, thus, is to

examine by how much the coe¢ cients on family ownership drop when controlling for cash

151,263,000 is the average value of total assets for the estimation sample that includes observations withnon-missing values of current and non-current liabilities and lagged sales.

22

over assets. Column 1 includes dummy for family without controlling for liquidity, but

only for observations where the value of liquidity is non-missing (in order to compare this

estimate to other speci�cations where we control for liquidity. The coe¢ cient on dummy

for family is 0.0198 (a standard error of 0.0013).16 Computed at the sample average, family

ownership is associated with $26,000 additional pro�ts (0.0198�1; 314; 000):Column 2 adds cash over assets. The coe¢ cient on dummy for family sharply drops

from 0.0198 to 0.0059, but remains signi�cant (0.0001). Thus, controlling for liquidity,

family ownership is now associated with only $7,800 additional pro�ts - about a third of

the estimate when we do not control for liquidity. The coe¢ cient on cash over assets is

very high and signi�cant, highlighting the importance of liquidity for �rm performance (a

coe¢ cient of 0.5120 and a standard error of 0.0060). Columns 3 and 4 repeat the same

analysis, but now we distinguish between married and non-married family �rms. We reach

the same conclusion - controlling for liquidity sharply reduces the e¤ects of married and

non-married family ownership. When controlling for liquidity, the coe¢ cient on dummy for

married shareholders drops from 0.0329 to 0.0129 (but remains signi�cant with a standard

error of 0.0020), and the coe¢ cient on dummy for non-married related shareholders drops

from 0.0090 to 0.0003 (which is not signi�cant, with a standard error of 0.0011).

Columns 5 and 6 add the interaction between married and non-married family own-

ership dummies and �rm age. We �nd that liquidity also explains a substantial part of

the age-family ownership interaction. Controlling for the interaction between liquidity

and �rm age, the coe¢ cient on the age interaction of dummy for married shareholders

drops from -0.0027 to -0.0015, and the coe¢ cient on the age interaction for dummy for

non-married related shareholders drops from -0.0011 to -0.0009. However, both interaction

terms remain signi�cant also after controlling the age-liquidity interaction. Interestingly,

we �nd that the e¤ect of liquidity on performance diminishes with �rm age, as shown in

column 6. The coe¢ cient on the interaction term between cash over assets and �rm age

is -0.0197 (a standard error of 0.0007). This means that ten years after the �rm�s year of

incorporation the e¤ect of liquidity drops by close to 30% (0:6850�0:0197�100:6850

).

Insert Table 9 here

16This estimate is very similar to the estimate from Table 5 (0.0195), where a larger sample is used.

23

5.5 Wages

We next examine whether family ownership is associated with lower costs, as posited in

hypothesis 5. While we do not observe all cost measures, we do observe wages, a major

component of costs. We can also think of lower costs, in particular, lower wages as another

measure of the ability of family owners to keep cash within the �rm for future investment,

and payouts.

We estimate regressions of the following form:

ln(Wages)it = �1 lnSalesit�1 + �2Ageit + �1 lnEmpit�1 + �3FamilyNonmarriedi

+ �4FamilyMarriedi + 'j + cc + �t + �it (4)

Wages is annual wage bill of the �rm, and Emp is number of employees. If family-

owned �rms, especially married family, are more likely to agree on and support wage

deferral we expect b�3 > 0, b�4 > 0; and b�4 > b�3: In a separate speci�cation, we estimatethe life-cycle e¤ect of family ownership on employment costs by interacting family dummies

with �rm age. We expect the family e¤ect to diminish with �rm age.

Table 10 reports the estimation results. Results suggest that family �rms, especially

young family-owned �rms with married shareholders, have lower employment costs. Col-

umn 1 includes dummy for family-owned �rms. The coe¢ cient on family dummy is -

0.0329 (a standard error of 0.0038). Evaluated at the sample mean (employment costs of

$460,000), this means that employment costs are $15,000 less than employment costs by

non-family �rms (�0:0329� 460; 000): It is worth noting that family �rms in the estima-tion sample have, on average, 15 employees, as compared to 12 employees by non-family

�rms. Thus, the actual costs advantage per employee are even higher.17 Column 2 in-

cludes dummies for married and non-married shareholder family �rms. The coe¢ cient

on married shareholders �rms is -0.0389 (a standard error of 0.0056), and the coe¢ cient

on non-married shareholders is -0.0287 (a standard error of 0.0046). These estimates im-

ply that a family �rm with married shareholders spends $18,000 less on employee wages

(�0:0389�460; 000). Column 3 adds interactions between married and non-married share-holder family �rms with �rm age. Both interaction terms are positive, implying that as17We also estimate a speci�cation where the dependent variable is the log of wages per employee. Family

�rms have a costs advantage of about 4% over non-family �rms (a family dummy coe¢ cient of -0.0409and a standard error of 0.0034).

24

the �rm matures, the employment cost advantage by family �rms, especially married ones,

diminishes. For married family �rms, 10 years after the year of incorporation, employment

costs advantage drops by close to half (�0:076+ 10� 0:0034). A similar pattern holds forfamily-owned �rms with non-married shareholders.

Thus, as measured by both wages and liquidity ratios, family-owned �rms, in particular

young �rms and those owned by married shareholders keep more cash within the �rm. This

evidence is consistent with family ties facilitating monitoring and enforcement of implicit

contracts between shareholders and managers resulting in an ability to commit to keep cash

within the �rm when it is most needed, for example when uncertainty is high or when

�nancial constraints are greater. Prior research such as Sraer and Thesmar (2007) has

documented that in publicly traded �rms, family ownership is also associated with lower

wages. These lower wages have been argued to be the result of management being able to

credibly commit to implicit contracts with employees due to continuity in ownership and

management that is often seen in publicly held �rms.

An alternative interpretation of our �nding is that family �rms are better able to

smooth pro�ts from early years to later ones by deferring wages, and that this smoothing

has no real long-term e¤ect on �rm performance. This view is not likely to explain our

�ndings. First, we show that family �rms have much lower exit hazards than non-family

�rms, which is inconsistent with a pure pro�t smoothing story. Second, our estimates

suggest that while wage deferral explains a substantial part of the di¤erence in pro�ts

between family and non-family �rms, still more than 50% of the pro�t di¤erential cannot

be accounted to lower wages18. Our interpretation, therefore, is that lower wages is one

important mechanism through which young family �rms can enhance liquidity. Higher

liquidity leads to a strong increase in performance, as well as higher survival probability.

Insert Table 10 here

5.6 Management Equity

We next explore whether the performance advantage of family-owned �rms varies with

managerial incentives, as posited in hypothesis 6. If family ties between managers and

18On average, married family �rms spend $18,000 less on wages, out of a total of $40,000 higher pro�tsthan non-family �rms.

25

shareholders facilitates monitoring of and enforcement of implicit contracts with managers,

we should expect the performance advantage of family-owned �rms to be stronger when

the need to monitor managers is stronger. We measure managerial incentives by the

percentage of equity held by the top management team of the �rm (Morck, Shleifer and

Vishny (1988) and Jensen and Murphy (1990)).

For each �rm in our sample we extract a list of their top management team.19 The

average number of top managers listed per �rm is small, 1.24, as we might expect for

smaller new �rms. We match this list of managers by �rst and last name to the �rms�

shareholders �le. For the majority of family owned �rms, at least one manager is also

a family member. For each �rm we then compute the share of equity that is held by

management. Manager�s equity share does not very much by ownership type. For married

family, management holds on average 34% of �rm equity. This �gure is 30%, and 37% for

non-married family, and non-family �rms, respectively.

We then estimate regressions in which we allow the e¤ect of family ownership to vary

with the manager�s equity stakes:

ROAit = �1 lnSalesit�1 + �2Ageit + �3FamilyOwnedi

+ �1FamilyOwned�ManagerEquity + �2ManagerEquity + 'j + cc + �t + �it(5)

ManagerEquity is the share of equity that is held by the top management team (typically

the CEO). If family �rms outperform non-family �rms because family �rms induce stronger

monitoring and incentive alignment, we would expect the di¤erence in performance be-

tween family and non-family �rms to be especially large when the share of equity held by

management is low. Thus, we expect b�1 < 0:If high-powered incentives are important for�rm performance, we expect b�2 > 0: (e.g., Jensen and Murphy (1990) and Holmstrom andTirole (1993)).

Table 11 reports the estimation results. Columns 1 and 2 include �rms where managers

hold above-median (35%) equity stakes. The coe¢ cient on family dummy is 0.0081 (col-

umn 1), and the coe¢ cient on married family dummy is 0.0174 (column 2). Columns 3 and

4 include �rms where managers hold below-median equity. The e¤ect of family ownership

19Common titles of the top managers listed for the �rms in our sample are Chief Executive O¢ cer,Manager, Managing Director and General Manager

26

is much higher. The coe¢ cient on family dummy is 0.0272 (column 3), and the coe¢ cient

on married family dummy is 0.0410 (column 4). Columns 4 and 5 con�rms the pattern

that family �rms outperform non-family �rms especially when high-powered incentives in

the form of management equity are low. Column 4 includes an interaction term between

family dummy and manager equity. The coe¢ cient on this interaction term is negative

and highly signi�cant (a coe¢ cient of -0.0159 and a standard error of 0.0042). This esti-

mate means that moving from zero management equity to complete management equity

reduces the e¤ect of family ownership by 65%, from 0.0248 to 0.0089. We obtain similar

results when adding separate interaction for married and non-married �rms (column 6),

and when focusing separately on young and mature �rms (columns 7-8).

When we calculate the impact of family ownership on ROA at very high management

equity ownership levels, we still �nd a performance boost from family ownership, in par-

ticular ownership by married shareholders. This suggests that while family ties between

shareholders and managers facilitates monitoring and enforcement of implicit contracts be-

tween managers and shareholders, the existence of family ties between shareholders them-

selves contributes to �rm performance above and beyond mitigating the classic manager-

owner con�ict. It is possible that family ties between shareholders themselves may serve

to mitigate tendencies for larger shareholders to expropriate smaller shareholders (e.g.,

Bennedsen and Wolfenzon (2000)).

Insert Table 11 here

6 Instrumental Variables and Other Robustness Tests

6.1 Instrumental Variables Estimation

Our results are prone to classical unobserved heterogeneity concerns; ownership structures

may be chosen endogenously based on �rm characteristics (e.g., Demsetz and Lehn (1985)

and Himmelberg, Hubbard and Palia (1999)). Family �rms may be of higher quality

because families may be more reluctant than non-family �rms to invest in low-quality

projects. In other words, family �rms are more selective in the projects in which they

want to invest their limited family wealth. However, it might also be the case that family

shareholders select to own less risky �rms than unrelated shareholders due to a less di-

27

versi�ed family portfolio that results from joint ownership. In this case, we might expect

family-owned �rms to have even higher performance, once we account for the underlying

selection into less risky �rms or projects.

This section reports the results of an instrumental variable strategy that exploits ge-

ographical variation in marriage rates and religious composition of regional populations

within countries, controlling for economic fundamentals, such as GDP and unemployment.

Firms in our sample span a wide set of regions within Europe.20 These regions vary sub-

stantially in terms of the prevalence and stability of the marriage institution. We combine

data from several sources. Data on marriage status comes from the 2005-2008 wave of the

World Value Survey (WVS)21. The survey provides religion and marital status information.

Respondents are classi�ed into six martial categories: married, living together as married,

divorced, separated, widowed, and single/never married. We aggregate under married the

categories married, living together as married, and widowed.22 We aggregated under di-

vorced the categories divorced and separated.23 In addition to martial and religion data,

we collect extensive data from OECD and Eurostat24 on regional development (such as

GDP, GDP growth, unemployment rate, education level). Our �nal regional data includes

information on 107 unique regions (with the four countries in our sample), 119,779 �rms

(with non-missing lagged sales values), and 489,562 observations.

The idea behind our instruments is that the probability that a �rm has family owner-

ship will depend on the supply of families and in particular married couples within a region

of a given country. Note that we do not rely on cross-country variation in marriage rates

and religious composition, but rather regional variation in these measures since we include

country �xed e¤ects in all of our speci�cations. We should expect ownership by married

20For every country we have information on each city address, and for the majority of �rms the regionwithin the country. For example, �rm GB04158722 is located in the city of Wedmore which is in theBristol region in Great Britain. For �rms with missing region information we manually assign regioninformation based on the city location. We standardize region names and merge to the two concordanceregional OECD tables NUTS-level3 and NUTS-level2. For example, UKL17 BRIDGEND AND NEATHPORT TALBOT GB, UKK11 BRISTOL GB. These codes allow us to merge regional information to ouroriginal �rm sample.21http://www.wvsevsdb.com/wvs/WVSData.jsp22An example of a region with a high marriage rate is Marche, Italy, 76.47%, and an example of a region

with a relatively low marriage rate is SHROPSHIRE AND STAFFORDSHIRE, UK, 58.7%.23An example of a region with a high divorce rate is PROVENCE-ALPES-COTE D�AZUR France -

14.03%, and an example of a region with a low divorce rate is CAMPANIA, Italy - 2%.

24http://epp.eurostat.ec.europa.eu/portal/page/portal/region_cities/regional_statistics/data/main_tables

28

shareholders to be higher when there are more married people in a region. Moreover, we

might expect the cultural nature of marriage in a region to in�uence whether there is more

married shareholder ownership in a region. We use the share of people in a region who

identify themselves as Catholic, Protestant or Orthodox Christians to measure religious

di¤erences in the role of women within a marriage to also instrument for married family

ownership. To the extent that more permissive roles granted to women within a religion

might map to greater ownership rights of women within family �rms, we might expect

di¤erences in religious composition to in�uence the rates of married family ownership.

In this regard, we would expect that in heavily Protestant regions, women might play a

greater role within �rms than in heavily Catholic or Orthodox regions.

Tables 12 and 13 report the estimation results of treatment e¤ects models in which

we instrument for family ownership. The general pattern of results is consistent with

our previous �ndings. Moreover, our results are strengthened, which suggests that any

selection mechanism of family shareholders to �rms is one in which families gravitate to

lower risk/lower return �rms. Table 12 focuses on returns on assets. Columns 1 and 2

report OLS estimates for the sample of �rms for which we match regional information.

The pattern of results is consistent with our previous �ndings. Columns 3-7 report the in-

strumental variable estimation results. Column 3 reports the �rst-stage estimation results

for family dummy. Our instruments include share married within region, and the region

share of main religions (Orthodox, Protestant, and Catholic). As expected, share married

within region has a positive e¤ect on the probability that a �rm is family-owned (a coe¢ -

cient of 0.3887 and a standard error of 0.1435). The share of Orthodox and Catholic have

a negative e¤ect on family �rm probability, where �rms in regions with a higher share of

Protestants are more likely to be family-owned. This e¤ect is largely driven by the mar-

ried family-owned �rms when we instrument for married shareholders and non-married

shareholders separately. Columns 4-7 report the second-stage estimation results. The IV

estimates are larger in most cases that the OLS estimates. The IV estimate of the coe¢ -

cient on family dummy is 0.0417, as compared to an OLS estimate of 0.0318. Similarly,

the estimate of the coe¢ cient on family married �rms is 0.0615, as compared to an OLS

estimate of 0.0498. This pattern suggests that the OLS estimated are downward-biased.

Columns 6 and 7 report IV estimation results separately for young and mature �rms. The

results are consistent with our previous �ndings indicating the family ownership, especially

29

married �rms, a¤ects performance mostly very early in the �rm life-cycle.

Table 13 reports IV estimation results for pro�t margin, liquidity (cash over assets),

leverage (debt over assets), and wages. The general pattern of results is consistent with

the OLS �ndings, however, typically larger in magnitude, once again consistent with a

matching mechanism in which family shareholders match to lower risk/lower return �rms.

Insert Tables 12 and 13 here

6.2 Other Robustness Tests

Table 14 reports several robustness tests for alternative de�nitions of family �rms, and

ownership structures. Columns 1 to 3 study the sensitivity of our main results to the

de�nition of family �rms. Our main concern is that while a �rm may have two sharehold-

ers listed, the vast majority of �rm equity actually lies at the hands of only one party.

Thus, we exclude �rms where the leading shareholder holds more than 75% of equity. The

results continue to hold. In some cases, family �rms have additional non-family share-

holders. Columns 4 to 6 exclude such �rms, thus, study a sample of family �rms where

there are no non-family shareholders. The same pattern of results continue to hold, and

actually, the point estimate of family ownership are actually higher. Lastly, columns 7 to

12 study the extent to which di¤erences in the e¤ect of family ownership can be driven

by di¤erences in ownership concentration. We do not �nd supporting evidence, as the

e¤ect of family ownership remains robust across sub-samples of below and above-median

number of shareholders.

Insert Table 14 here

7 Conclusion

Using a large panel of European �rms, we examine the e¤ect of family ownership on the

performance of new �rms. We �nd that family-owned �rms exhibit higher returns on

assets, higher pro�t margins and higher survival rates than non-family-owned �rms. This

di¤erence between family and non-family �rms is greatest for family-owned �rms with

married shareholders and in the earliest years of he �rm life cycle.

30

Our �ndings suggest that family ties between shareholders, in particular marital ties,

enhance �rm performance by facilitating monitoring and enforcement of implicit contracts

between managers and shareholders. We �nd that family-owned �rms keep more cash in-

ternally and pay lower wages than non-family-owned �rms, consistent with the view that

family-owned �rms may have more managerial discipline and shareholder willingness to

keep cash within the �rm. This aspect of family ownership is especially strong when un-

certainty is high and �nancial constraints are greater. We also �nd that the performance

advantage of family ownership is greater when managerial incentives are lower, consistent

with the view that family ties between shareholders and managers may facilitate monitor-

ing. However, we continue to �nd a performance advantage of family ownership, especially

married shareholder ownership, even when managerial incentives are strong. Family ties,

in particular marital ties, between shareholders seem to lend a performance advantage to

new �rms above and beyond mitigating the classic manager shareholder con�ict.

Our analysis provides a new perspective on the impact of family ownership on �rm

performance by examining the performance e¤ects of family ownership early in the �rm�s

lifecycle. Our results support e¢ ciency-based explanations for why family ownership, in

particular the "mom and pop shop" is a predominant ownership structure in new �rms.

Our research also points to future research questions. How might shareholders of non-

family-owned new �rms create some of the performance enhancing mechanisms facilitated

by family ties between shareholders and management? When might the costs of fam-

ily ownership, e.g., nepotism or inheritance norms, begin to make a negative impact on

the performance of �rms, even those that remain private, and does this matter for the

particular nature of the family ownership structure?

31

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35

Variable Obs. Firms Mean Std. Dev. 10th 50th 90th

Returns on Assets 589,386 105,126 0.157 0.262 -0.018 0.123 0.395

Profit margin 591,718 105,375 0.080 0.155 -0.009 0.059 0.216

Assets ($,'000) 932,801 183,537 837 4,585 0 138 1,387

Sales t ($,'000) 932,801 183,537 2,094 5,303 155 676 4,264

Sales t-1 ($,'000) 750,166 169,583 2,037 24,408 147 651 4,061

EBITDA ($,'000) 592,797 105,411 129 1,088 -3 38 279

Cash /Assets 524,363 109,655 0.329 0.707 0.036 0.250 0.750

Debt/Assets 627,290 117,552 0.785 0.366 0.421 0.777 1.027

Wages ($,'000) 517,018 97,162 362 828 29 151 780

Firm age 932,801 183,537 8 5.4 1 7 16

Number of employees 525,659 139,437 13 19 2 6 30

Table 1. Summary StatisticsDistribution

Notes: This table provides summary statistics for the main firm-level variables used in the econometric analysis. Return on Assets is EBITDA over Assets , Profit Margin is EBITDA over Sales , and Debt is the sum of current and non-current liabilities. Firm age is years from date of incorporation.

VariableFamily - Non-

Family Obs. Mean Median Std. Dev. Obs. Mean Median Std. Dev.

Returns on Assets 0.047** 197,837 0.188 0.133 0.292 391,549 0.141 0.118 0.244

Profit margin 0.023** 199,220 0.095 0.065 0.165 392,498 0.072 0.056 0.149

Cash/Assets 0.058** 181,591 0.367 0.227 0.511 342,772 0.309 0.214 0.790

Debt/Assets -0.026** 214,686 0.768 0.766 0.368 412,604 0.794 0.783 0.364

Assets ($,'000) 105.1** 348,213 903 421 5,670 584,588 798 215 3793

Firm age 1.3** 348,213 8.8 5.6 5.6 584,588 7.5 5.2 5.2

Number of employees 2.1** 194,838 13.8 19.9 19.9 330,821 11.7 6 17.5

Table 2. Family- and Non-Family-Owned Firm Characteristics

Family-Owned Firms Non-Family-Owned Firms

Notes: This table reports mean comparison tests for family and non-family owned firms firms. A firm is classified as a family owned firm if it has at least two shareholders that have the same last name, and that these family shareholders hold together a majority of the firm equity. Return on Assets is EBITDA over Assets , Profit Margin is EBITDA over Sales , and Debt is the sum of current and non-current liabilities. Firm age is years from date of incorporation. ** denotes that the difference in means is significant at the 1 percent level.

Married - Unrelated

Non-Married -Unrelated

Married- Non-Married Obs. Mean Median Obs. Mean Median Obs. Mean Median

Returns on Assets 0.078** 0.020** 0.058** 91,836 0.219 0.148 106,001 0.161 0.123 391,549 0.141 0.118

Profit margin 0.034** 0.013** 0.021** 92,841 0.106 0.072 106,379 0.085 0.060 392,498 0.072 0.056

Cash /Assets 0.101** 0.020** 0.081** 85,227 0.410 0.287 96,364 0.329 0.250 342,772 0.309 0.266

Debt/Assets -0.021** -0.031 0.010** 100,753 0.773 0.771 113,933 0.763 0.758 412,604 0.794 0.764

Assets ($,'000) -103** 311** -414** 173,338 695 90 174,875 1,109 152 584,588 798 119

Firm age 1.0** 1.5** -0.46** 173,338 8.6 8 584,588 7.5 9 584,588 7.5 8

Number of employees 0.9** 3.2** -2.3** 94,986 12.6 6 99,852 14.9 8 330,821 11.7 7

Table 3. Married Family, Non-married Family and Non-Family Owned Firm Characteristics

Notes : This table reports mean comparison tests for family and non-family owned firms firms. A firm is classified as a family owned firm if it has at least two shareholders that have the same last name, and that these family shareholders hold together a majority of the firm equity. Return on Assets is EBITDA over Total Assets , Profit Margin is EBITDA over Sales , and Debt is the sum of current and non-current liabilities. Firm age is years from date of incorporation. ** denotes that the difference in means is significant at the 1 percent level.

Married Shareholders Non-Married

Family Owned Firms

Unrelated shareholders

Non-Family Owned FirmsDifference in means

VariableMarried - Unrelated Obs.

Married - Unrelated Obs.

Married - Unrelated Obs.

Married - Unrelated Obs.

Return on Assets 0.146** 142,177 0.083** 132,276 0.043** 112,626 0.023** 96,306

Profit Margin 0.059** 143,278 0.040** 132,817 0.019** 112,864 0.012** 96,380

Cash /Assets 0.190** 119,291 0.120** 116,034 0.055** 102,795 0.026** 89,879

Debt/Assets -0.022** 153,200 -0.023** 139,363 -0.009** 119,319 -0.019** 101,475

Table 4. Peformance, Liquidity and Leverage: Married Family-Owned vs Non-Family-Owned by Firm Age

Notes: This table reports mean comparison tests for family and non-family firms by firm age. Return on Assets is EBITDA over Assets , Profit Margin is EBITDA over Sales , and Debt is the sum of current and non-current liabilities. Firm age is years from date of incorporation. ** denotes that the difference in means is significant at the 1 percent level.

5 ≤ Firm Age ≤ 7 8 ≤ Firm Age ≤ 12 13 ≤ Firm Age ≤ 20Firm Age ≤ 3

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

Dependent variable:

Sample/Firms: All All Family-Owned All All All Family-Owned All

Dummy for Family-Owned 0.0195** 0.0130**(0.0013) (0.0008)

Dummy for Married Family-Owned 0.0329** 0.0175** 0.0316** 0.0179** 0.0055** 0.0167**(0.0019) (0.0022) (0.0020) (0.0012) (0.0014) (0.0012)

Dummy for Non-Married Family-Owned 0.0087** 0.0092** 0.0090** 0.0095**

(0.0015) (0.0015) (0.0010) (0.0010)

Dummy for Multiple Gender Shareholders 0.0022 0.0021**

(0.0012) (0.0008)

Firm Age -0.0033** -0.0032** -0.0046** -0.0032** -0.0005** -0.0005** -0.0009** -0.0005**(0.0001) (0.0001) (0.0002) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)

ln(Sales )t-1 -0.0088** -0.0086** -0.0166** -0.0085** -0.0087** -0.0086** -0.0127** -0.0086**(0.0005) (0.0005) (0.0010) (0.0005) (0.0004) (0.0004) (0.0007) (0.0004)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.0932 0.0940 0.1420 0.0940 0.0805 0.0808 0.1237 0.0808

Observations 487,565 487,565 163,565 487,565 489,367 489,367 164,619 489,367

Table 5. Family Ownership and Firm Performance OLS Regressions

Notes: This table reports the results of OLS regressions examining the relationship between family ownership and return on assets (EBITDA over total assets ) and profit margin (EBITDA over sales ). Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significant at the 1% level.

Return on Assets Profit Margin

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

Sample/Firms:

Age ≤ 3

(25 th pct.)

Age > 12

(75 th pct.) All

Age ≤ 3

(25 th pct.)

Age > 12

(75 th pct.) AllFamily Owned

Dummy for Family-Owned 0.0309** 0.0073** 0.0542**(0.0029) (0.0017) (0.0026)

Dummy for Married Family-Owned 0.0479** 0.0137** 0.0831** 0.0514**(0.0039) (0.0027) (0.0037) (0.0045)

Dummy for Non-Married Family-Owned 0.0129** 0.0034 0.0257**

(0.0036) (0.0019) (0.0031)

Dummy for Family-Owned × Firm Age -0.0039**

(0.0002)

Dummy for Married Family-Owned × Firm Age -0.0059** -0.0038**

(0.0003) (0.0004)

Dummy for Non-Married Family-Owned × Firm Age -0.0020**

(0.0003)

Firm Age -0.007** -0.0021** -0.0019** -0.0069** -0.0020 -0.0019 -0.0029**(0.0012) (0.0003) (0.0001) (0.0012) (0.0003) (0.0001) (0.0002)

ln(Sales )t-1 -0.0084** -0.0022** -0.0087** -0.0083** -0.0021** -0.0086** -0.0167**(0.0010) (0.0008) (0.0005) (0.0010) (0.0008) (0.0005) (0.0010)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes

R2 0.1495 0.0432 0.0948 0.1504 0.0435 0.0960 0.1433

Observations 103,259 117,123 487,565 103,259 117,123 487,565 163,565

Table 6. Family Ownership and Performance by Firm Age

Notes: This table reports the results of OLS regressions examining how the relation between family ownership and return on assets (EBITDA over sales) and profit margin (EBITDA over sales) varies across the firm life-cycle. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significance at the 1% level.

Dependent variable: Return on Assets

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

All AllFamily-Owned

Age ≤ Median

Age > Median All All

Family-Owned

Age ≤ Median

Age > Median

Dummy for Family-Owned 0.6503** 0.6546**(0.0075) (0.0069)

Dummy for Married Family-Owned 0.6944** 1.1495** 0.8581** 0.7209** 0.7041** 1.1669** 0.8738** 0.7353**

(0.0100) (0.0202) (0.0172) (0.0152) (0.0092) (0.0186) (0.0160) (0.0139)

Dummy for Non-Married Family-Owned 0.6109** 0.8493** 0.6276** 0.6110** 0.8547** 0.6298**

(0.0089) (0.0176) (0.0130) (0.0080) (0.0162) (0.0117)

ln(Initial Sales )t-1 0.7616** 0.7627** 0.7606** 0.8795** 0.8172** 0.7606** 0.7619** 0.7602** 0.8808** 0.8147**(0.0027) (0.0027) (0.0043) (0.0046) (0.0046) (0.0025) (0.0025) (0.0039) (0.0043) (0.0042)

Observations 232,423 232,423 100,254 120,715 111,708 264,749 232,423 100,254 120,715 111,708

% exit 14.97 14.97 13.45 16.74 13.06 18.18 18.18 16.49 19.98 16.23

Table 7. Firm Survival: Cox Hazard Models

Exit Hazard: Restrictive

Notes: This table reports the hazard ratios for Cox proportional hazard models that examine the effect of family ownership on survival. In columns 1-5 (flexible exit definition) we classify firms as exitors if the most recent year they submit their financial report is before 2005. In columns 6-10 (restrictive exit definition) exitors are firms that satisfy at least one of the following conditions: submit their most recent report before 2005 or shrink in terms of employment and sales to a level of employee and one thousand in annual sales. All regressions include a complete set of year, industry and country dummies. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significance at the 1% level.

Exit Hazard: Flexible

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

Dependent variable:

Dummy for Family-Owned 0.0292** -0.0184**(0.0035) (0.0022)

Dummy for Married Family-Owned 0.0453** 0.1206** -0.0211** -0.0251**(0.0047) (0.0070) (0.0030) (0.0048)

Dummy for Non-Married Family-Owned 0.0160** 0.0387** -0.0161** -0.0187**

(0.0034) (0.0063) (0.0028) (0.0048)

Dummy for Married Family-Owned × Firm Age -0.0088** 0.0005

(0.0008) (0.0005)

Dummy for Non-Married Family-Owned × Firm Age -0.0026** 0.0001

(0.0007) (0.0004)

Firm Age -0.0010 -0.0009 0.0010 -0.0074** -0.0074** -0.0076**(0.0007) (0.0007) (0.0009) (0.0002) (0.0002) (0.0002)

ln(Sales )t-1 -0.0424** -0.0421** -0.0422** -0.0216** -0.0217** -0.0217**(0.0049) (0.0049) (0.0049) (0.0010) (0.0010) (0.0010)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes

Observations 433,184 433,184 433,184 511,115 511,115 511,115

R2 0.0373 0.0375 0.0380 0.0430 0.0431 0.0431

Table 8. Liquidity and Leverage OLS Regressions

Notes: This table reports the results of OLS regressions examining the relation between family ownership, leverage and liquidity. Debts is the sum of current and non-current liabilities. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significance at the 1% level.

Debt/Total AssetsCash/Total Assets

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

Dummy for Family-Owned 0.0198** 0.0059**(0.0013) (0.0001)

Dummy for Married Family-Owned 0.0329** 0.0129** 0.0357** 0.0247**(0.0020) (0.0014) (0.0025) (0.0024)

Dummy for Non-Married Family-Owned 0.0090** 0.0003 0.0105** 0.0078**

(0.0015) (0.0011) (0.0011) (0.0021)

Dummy for Married Family-Owned × Firm Age -0.0027** -0.0015**

(0.0002) (0.0002)

Dummy for Non-Married Family-Owned × Firm Age -0.0011** -0.0009**

(0.0002) (0.0002)

Cash/Assets 0.5120** 0.5417** 0.5411** 0.6838**(0.0060) (0.0060) (0.0060) (0.0049)

Cash /Assets × Firm Age -0.0196**(0.0007)

Firm Age -0.0037** -0.0029** -0.0036** -0.0029** -0.0022** 0.0036**(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002)

ln(Sales )t-1 -0.0152** 0.0038** -0.0149** 0.0039** 0.0039** 0.0040**(0.0006) (0.0004) (0.0006) (0.0004) (0.0004) (0.0004)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes

Observations 418,726 418,726 418,726 418,726 418,726 418,726

R2 0.1098 0.5848 0.1106 0.5850 0.5854 0.6026

Table 9. Family Ownership, Liquidity, and Performance

Notes: This table reports the results of OLS regressions examining the relation between family ownership, liquidity, and return on assets. We include only observations with non-missing cash values. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significance at the 1% level.

Dependent variable: Return on Assets

(1) (2) (3)

Dummy for Family-Owned -0.0329**(0.0038)

Dummy for Married Family-Owned -0.0389** -0.0706**(0.0056) (0.0110)

Dummy for Non-Married Family-Owned -0.0287** -0.0447**

(0.0046) (0.0093)

Dummy for Married Family-Owned × Firm Age 0.0034**

(0.0010)

Dummy for Non-Married Family-Owned × Firm Age 0.0017*

(0.0008)

Firm Age 0.0019** 0.0018** 0.0011**(0.0003) (0.0003) (0.0004)

ln(Sales )t-1 0.3537** 0.3535** 0.3535**(0.0034) (0.0034) (0.0034)

ln(Employees ) 0.6775** 0.6774** 0.6774**(0.0037) (0.0037) (0.0037)

Three-digit SIC code dummies Yes Yes Yes

Country dummies Yes Yes Yes

Year dummies Yes Yes Yes

R2 0.8596 0.8596 0.8597

Observations 237,665 237,665 237,665

Notes: This table reports the results of OLS regressions examining the relation between family ownership and wages. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistically significance at the

1% level.

Table 10. Wages

Dependent variable: ln(Wages)

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

Sample:

Age ≤ 3

(25 th pct.)

Age > 12

(75 th pct.)

Dummy for Family-Owned 0.0081** 0.0272** 0.0248**(0.0019) (0.0017) (0.0018)

Dummy for Married Family-Owned 0.0174** 0.0410** 0.0394** 0.0621** 0.0211**(0.0028) (0.0026) (0.0028) (0.0057) (0.0039)

Dummy for Non-Married Family-Owned -0.0011 0.0172** 0.0151** 0.0256** 0.0075**

(0.0023) (0.0019) (0.0020) (0.0049) (0.0025)

Dummy for Family -Owned× % Manager's Equity -0.0159**

(0.0042)

Dummy for Married Family-Owned × % Manager's Equity -0.0028** -0.0504** -0.0290**

(0.0068) (0.0126) (0.0101)

Dummy for Non-Married Family-Owned × % Manager's Equity -0.0189** -0.0399** -0.0118

(0.0048) (0.0111) (0.0069)

% Manager's Equity 0.0287** 0.0281** 0.0480** 0.0217**(0.0020) (0.0020) (0.0037) (0.0034)

Firm Age -0.0027** -0.0027** -0.0036** -0.0035** -0.0032** -0.0031** -0.0069** -0.0020**(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0012) (0.0003)

ln(Sales )t-1 -0.0026** -0.0026** -0.0116** -0.0114** -0.0085** -0.0083** -0.0081** -0.0019*(0.0008) (0.0008) (0.0007) (0.0007) (0.0005) (0.0005) (0.0010) (0.0008)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes

R2 0.0938 0.0942 0.0992 0.0999 0.0941 0.0948 0.1518 0.0442

Observations 216,784 216,784 270,781 270,781 487,565 487,565 103,259 117,123

Table 11. Management Equity Ownership

Notes: This table reports the results of OLS regressions that examine the effect of managerial equity ownership on family-owned firm performance. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** and * indicate statistical significance at the 1% and 5% level, respectively .

Dependent variable: Return on Assets

% Manager's Equity > Median (35%)

% Manager's Equity ≤ Median (35%)

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

Dependent variable:Family Dummy

Sample:

Age ≤ 4

(25 th pct.)

Age > 12

(75 th pct.)

Dummy for Family-Owned 0.0318** 0.0417**(0.0021) (0.0043)

Dummy for Married Family-Owned 0.0498** 0.0615** 0.0817** 0.0299**(0.0030) (0.0038) (0.0065) (0.0056)

Dummy for Non-Married Family-Owned 0.0145** 0.0146** 0.0224** 0.0073*

(0.0025) (0.0025) (0.0049) (0.0032)

Share Married within Region 0.3887**(0.1435)

Share Orthodox within Region -2.6863**(0.5172)

Share Protestant within Region 1.6153**(0.0779)

Share Catholic within Region -0.1080**(0.0618)

Region GDP -0.0343** -0.0333** -0.0335** -0.0328** -0.0380** -0.0411**(0.0061) (0.0060) (0.0060) (0.0060) (0.0102) (0.0099)

Region Uemployment Rate 0.0021** 0.0021** 0.0021** 0.0020** -0.0380** 0.0012**(0.0003) (0.0003) (0.0003) (0.0003) (0.0102) (0.0004)

ln(Sales )t-1 -0.0180** -0.0177** -0.0180** -0.0177** -0.0201** -0.0084**(0.0008) (0.0008) (0.0008) (0.0008) (0.0014) (0.0013)

Firm Age -0.0049** -0.0048** -0.0049** -0.0048** -0.0098** -0.0025**(0.0002) (0.0002) (0.0002) (0.0002) (0.0013) (0.0004)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes

Observations 244,634 244,634 244,634 244,634 244,634 76,168 58,085

Instrumental Variables

Table 12. Instrumental Variables

Notes: This table reports the results of instrumental variables estimation of the effect of married family ownership and firm performance. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** and * indicate statistical significance at the 1% and 5% level, respectively.

OLS

Returns on assets Returns on assets

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

Dependent variable:

Sample: OLS OLS OLS OLS

Dummy for Family-Owned 0.0167** 0.0281** 0.0394** 0.0439** -0.0271** -0.0701** -0.0507** -0.0645**(0.0012) (0.0029) (0.0030) (0.0074) (0.0034) (0.0069) (0.0083) (0.0115)

Dummy for Married Family-Owned 0.0498** 0.0719** -0.0639** -0.1617**

(0.0030) (0.0055) (0.0063) (0.0220)

Dummy for Non-Married Family-Owned 0.0113** 0.0220** -0.0220** -0.0277**

(0.0015) (0.0037) (0.0043) (0.0102)

Region GDP -0.0247** -0.0237** -0.0238** 0.0167* 0.0171* 0.0185* 0.0601** 0.0565** 0.0583** -0.0071 -0.0564* -0.0123(0.0035) (0.0035) (0.0035) (0.0086) (0.0086) (0.0086) (0.0096) (0.0096) (0.0096) (0.0230) (0.0246) (0.0230)

Region Uemployment Rate 0.0013** 0.0012** 0.0012** 0.0001 0.0004 0.0003 -0.0010* -0.0009* -0.0009* 0.0020 0.0027** 0.0021(0.0002) (0.0002) (0.0002) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0004) (0.0011) (0.0011) (0.0011)

ln(Sales )t-1 -0.0113** -0.0113** -0.0112** -0.0437** -0.0438** -0.0434** -0.0250** -0.0251** -0.0251** 0.8120** 0.8112** 0.8114**(0.0006) (0.0006) (0.0006) (0.0011) (0.0011) (0.0011) (0.0014) (0.0014) (0.0014) (0.0037) (0.0037) (0.0037)

Firm Age -0.0008** -0.0008** -0.0008** -0.0027** -0.0027** -0.0027** -0.0079** -0.0079** -0.0079** 0.0100** 0.0097** 0.0099**(0.0001) (0.0001) (0.0001) (0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0003) (0.0007) (0.0007) (0.0007)

Three-digit SIC code dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Country dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Year dummies Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Observations 244,321 244,321 244,321 214,097 214,097 214,097 243,670 243,670 243,670 175,582 175,582 175,582

Notes: This table reports the results of instrumental variables estimation of the effect of married family ownership and firm performance. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** and * indicate statistical significance at the 1% and 5% level, respectively.

Cash/Total Assets Debt/Total Assets

Table 13. Instrumental Variables (Cont'd)

ln(Wages)

Instrumental Variables Instrumental Variables Instrumental Variables Instrumental Variables

Profit Margin

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

Dummy for Family-Owned 0.0194** 0.0305** 0.0215** 0.0221**(0.0013) (0.0018) (0.0017) (0.0019)

Dummy for Married Family-Owned 0.0326** 0.0808** 0.0452** 0.1086** 0.0366** 0.0953** 0.0333** 0.0721**

(0.0020) (0.0039) (0.0025) (0.0045) (0.0028) (0.0049) (0.0027) (0.0056)

Dummy for Non-Married Family-Owned 0.0090** 0.0248** 0.0132** 0.0545** 0.0101** 0.0285** 0.0124** 0.0318**

(0.0015) (0.0032) (0.0024) (0.0048) (0.0020) (0.0042) (0.0022) (0.0046)

Dummy for Married Family-Owned × Firm Age -0.0057** -0.0078** -0.0074** -0.0043**

(0.0003) (0.0004) (0.0004) (0.0005)

Dummy for Non-Married Family × Fir m Age -0.0018** -0.0047** -0.0022** -0.0022**

(0.0003) (0.0004) (0.0003) (0.0004)

ln(Sales )t-1 -0.0083** -0.0081** -0.0081** -0.0097** -0.0096** -0.0096** -0.0106** -0.0104** -0.0104** -0.0039** -0.0037** -0.0038**(0.0005) (0.0005) (0.0005) (0.0006) (0.0006) (0.0006) (0.0007) (0.0007) (0.0007) (0.0008) (0.0008) (0.0008)

Firm Age -0.0031** -0.0031** -0.0019** -0.0034** -0.0033** -0.0018** -0.0035** -0.0034** -0.0020** -0.0027** -0.0027** -0.0014**(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0002) (0.0002) (0.0002)

R2 0.0916 0.0923 0.0941 0.0950 0.0958 0.0990 0.1016 0.1024 0.1049 0.0796 0.0804 0.0819

Observations 476,086 476,086 476,086 416,601 416,601 416,601 317,828 317,828 317,828 169,737 169,737 169,737

Notes: This table reports the results of OLS regressions that examine the robustness of the effect of family shareholders on returns on assets. All regressions include complete sets of three-digit SIC codes, country and year dummies. Standard errors (in brackets) are robust to arbitrary heteroskedasticity and allow for serial correlation through clustering by firms. ** indicates statistical significance at the 1% level.

Firm has two (median) shareholders

Firm has more than two shareholders

Table 14. Robustness Checks

Dependent variable: Returns on Assets (Profits/Assets)

Leading family shareholder shares ≤ 75%

Family firms do not have non-family shareholders