Journal of Corporate Finance 11 (2005) 107 – 127
www.elsevier.com/locate/econbase
Family ownership and productivity:
the role of owner-management
Erling Barth, Trygve Gulbrandsen, Pål Schøne *
Institute for Social Research, Pb. 3233 Elisenberg, 0208 Oslo, Norway
Received 28 January 2003; accepted 13 February 2004
Available online 16 April 2004
Abstract
This article analyses the relationship between family ownership and productivity, with special
focus on the role of owner-management. The results show that family-owned firms are less
productive than non-family-owned firms. This productivity gap is, however, explained by
differences in management regime. Family-owned firms managed by a person hired outside the
owner family are equally productive as non-family-owned firms, while family-owned firms managed
by a person from the owner family are significantly less productive. This finding is sustained after
controlling for endogeneity of management regime.
D 2005 Elsevier B.V. All rights reserved.
JEL classification: G32; G34
Keywords: Ownership structure; Management; Productivity
1. Introduction
During the last two decades, ownership of firms has received increased attention among
scholars as well as in the general public debate. Several researchers and observers have
claimed that ownership matters and that the economic behaviour of enterprises is
influenced by how property rights are allocated and by who their owners are (Blair,
1995; Schleifer and Vishny, 1997). But the issue is not settled. A summary of studies of
the effect of ownership structure on firm performance is given by Demsetz and Villalonga
(2001). This summary shows that empirical studies of the relation between ownership
structure and performance have yielded conflicting results.
* Corresponding author. Tel.: +47-23086182; fax: +47-23086101.
E-mail address: psc@socialresearch.no (P. Schøne).
0929-1199/$ - see front matter D 2005 Elsevier B.V. All rights reserved.
doi:10.1016/j.jcorpfin.2004.02.001
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
In this paper, we analyse how family ownership affects the performance of enterprises.
In line with Palia and Lichtenberg (1999), we use total factor productivity (TFP) as our
measure of corporate performance. We believe productivity is a more reliable measure of
performance than financial measures, as for instance operating profits, because the
accounting profit rates of an enterprise may be manipulated.1 In addition, as only a small
percentage of our firms are listed, performance measures based on market prices are not
possible.
It is not necessarily the mode of ownership in itself that may affect the economic
performance of a firm. Recent literature (Hart, 2001) has focused on the allocation of
decision rights. Accordingly, we go on to investigate the importance of owner-management versus professional management. In many family businesses, the owner or a member
of the owner family chooses to run the firm him/herself. On the one hand, ownermanagement provides a solution to the inherent agency problem involved in operating the
business. On the other, it has been claimed that this combination of the roles of owner and
top manager may have unfavourable consequences for the efficient operation of a firm. In
order to examine the validity of this claim, we compare family businesses managed by a
member of the owner family and family businesses where a professional manager is at the
helm.
In the empirical analyses presented below, we will distinguish accordingly between
three types of firms: (i) family firms run by a member of the family, a mode of governance
we choose to term ‘‘owner-management’’; (ii) family businesses where a professional
manager is in charge of the day-to-day running of the firm; and (iii) non-family firms,
which presumably are also run by a professional manager.
We first compare the productivity of family-owned firms to the productivity of nonfamily firms. This analysis provides us with an estimated productivity gap between these
two modes of ownership. We then control for management regime. Comparing the
productivity gap between family- and non-family-owned firms, with and without this
control, allows us to assess the extent to which this productivity gap is due to differences
in the allocation of decision rights rather than to the difference in ownership structure per
se. Furthermore, we obtain an estimate of the productivity gap between professionally and
owner-managed family firms.
Demsetz (1983) and Demsetz and Villalonga (2001) argue that ownership structure
should be thought of as an endogenous outcome. In a study of management succession in
family-controlled firms, Smith and Amoako-Adu (1999) found that the appointment of
non-family successors tended to follow periods of poor operating performance, indicating
that the management function is endogenous. Drawing on a recent theoretical analysis of
family firms by Burkart et al. (2002), we develop a simple empirical choice model to
account for the endogeneity of management regime. We extend their model by allowing
for private benefits of day-to-day control and use years in family ownership, year of
establishment, and preferences for family control as instruments for the choice between
hiring a professional manager or leaving management to the heir.
1
Of course, measures of productivity also come with errors. Our argument is only that this type of measure is
less exposed to manipulation errors than most financial measures.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
109
The paper is organized as follows. In Section 2, we briefly discuss theoretical issues
and some previous empirical evidence. Section 3 presents the data and empirical strategy.
We argue that one contribution from our study comes from the data. In contrast to many
other studies in this field, we have access to a data set containing information for a
representative sample of family firms. Section 4 provides the first analysis of the
productivity gap between family- and non-family-owned firms. Section 5 gives the
analysis of management regime. Conclusions are given in Section 6.
2. Theory and previous empirical studies
Previous literature has argued both in favour of and against the claim that concentrated
family ownership as well as owner-management may have beneficial economic consequences. Theoretically, there are reasons to expect that firms where ownership is
concentrated in the hands of a family will be more efficient than other firms. Firstly,
concentrated ownership gives the owners a particular incentive to monitor the managers,
thus reducing agency costs connected to hired management (Schleifer and Vishny, 1997).
Concentrated ownership may as well ease the task of monitoring.
Anderson et al. (2003) argue that founding families, representing a form of undiversified ownership, may mitigate the risk-shifting problem between shareholders and
bondholders (Jensen and Meckling, 1976) as the founding families are more interested
in firm survival than are other types of shareholders. Consequently, family firms may face
a lower cost of debt financing. Furthermore, the relations within a family are largely
characterized by altruism, loyalty, and trust. Pollack (1985) and Coleman (1990) have
emphasized that in a family business, these qualities may promote flexibility in operations,
ease decision-making and reduce shirking, all of which may have favourable effects upon
the productivity of the firm.
There are also reasons to believe that family-owned firms may be less efficient than
non-family firms. Concentration of ownership implies a limited diversification of financial
risk and a higher cost of capital due to higher risk premium (Demsetz and Lehn, 1985).
This situation may induce family owners to be cautious when making new investments
and reluctant to raise loans or to admit new investors (Agrawal and Nagarjan, 1990; Gallo
and Vilaseca, 1996). This caution may limit the introduction of productivity-enhancing
new technology.
Inadequate investment in R&D and new technology has also been attributed to an
alleged preference on the part of family owners for a continuous and stable cash flow in
order to finance a privileged lifestyle (Chandler, 1990). This illustrates that concentrated
ownership gives an owner opportunity to reap private benefits, which may be incompatible
with the efficient operation of the firm. Pollack (1985) has maintained that the same
qualities that promote mutual trust among owners may make them excessively tolerant
towards lax efforts by family members working in the firm.
As to the distinction between owner-management and professional management,
owner-management aligns the interests of owners and managers, thus providing a solution
to the agency problems connected with monitoring and motivating professional managers
(Jensen and Meckling, 1976; Fama and Jensen, 1983).
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
Owner-management may also have negative effects. In an owner-managed family
business, the top manager is taken from a much more restricted pool of talent than when
the manager is recruited from the general market for managers. According to Coleman
(1990) and Burkart et al. (2002), this situation generally leads to a lower quality among
owner– managers than among professional managers and may be detrimental to the
productivity of the firms.
In owner-managed family businesses, the middle managers know that they have few
hopes of achieving top management positions in the firm. Limited career prospects may
function as a disincentive to these middle managers with reduced efforts as a result.
Owner – managers have a strong preference for control. Often, this preference may
entail an inefficient concentration of decision-making authority in the hands of the ownermanager (Goffee and Scase, 1985; Hofer and Charan, 1984). Their control orientation may
also prevent them from adopting new and productivity-enhancing management principles
and personnel policies (Gulbrandsen, in press).
Empirical studies comparing the performance of family-owned, owner-managed, and
non-family-owned firms are rather sparse. Gorriz and Fumas (1996) have studied familyowned firms in Spain. They used both productivity and profitability as measures of firm
performance. They found that on an average, family firms have higher productivity than
non-family-owned firms, but they did not find any difference in profitability. In a study by
McConaughy et al. (1998), founding family-controlled firms turned out to be more
efficient and valuable than firms without founding family control.
Anderson and Reeb (2003) find that family firms perform better than non-family firms,
both as to return-on-assets, return on equity, and as measured by Tobin’s Q. Moreover, in
family firms where a family member serves as chief executive officer (CEO), performance
is better than in family firms with an outside CEO.
Wall (1998) has analysed the impact of family ownership on productivity among
private firms in Western New York. He found that family firms are less productive than
non-family firms after controlling for industry, labour input, and firm age. The productive
gap was estimated to be approximately 18%. Lauterbach and Vaninsky (1999) examined
the extent to which performance in Israeli firms was influenced by type of ownership and
by how the management function was organized. They distinguished between family
firms, firms controlled by a partnership of individuals, firms controlled by a group of
firms, and firms where blockholders had less than 50% of the votes. Across these modes of
ownership, they then distinguished between firms managed by a representative of the
owners and firms being led by a professional top manager. Their analyses demonstrate,
firstly, that family businesses are less efficient than firms with another form of ownership.
Secondly, firms managed by their owners perform worse than those run by a professional
manager. Perez-Gonzalez (2001) has analysed the impact of inherited control on firm’s
performance. He found that firms where control is inherited undergo a large decline in
return-on-assets and market-to-book ratios compared to firms that promote a chief
executive officer who is not related to the owner family. In a Canadian study (Morck et
al., 1998), heir-controlled firms showed low industry-adjusted financial performance
relative to other firms of same ages and sizes. Controlling for endogenous ownership,
Demsetz and Villalonga (2001) found no statistically significant relationship between
ownership structure and firm performance.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
111
3. Data, variables, and empirical strategy
We use an establishment level survey conducted in 1996 among firms associated with
the Confederation of Norwegian Business and Industry (NHO).2 The sample of establishments is representative for NHO firms with more than 10 employees, a net total of 438
firms. These firms were all operative in 1996 and they have non-missing values in all the
included variables.
Our information on ownership (whether the firm is family-owned or not) is based on
response to the following question: ‘‘Are at least 33% of the shares in the firm, owned by
one person or by one family?’’ If the respondent answers ‘yes’, the firm is classified as
family-owned. Owner-management is measured by response to the following question:
‘‘Does the manager come from the owner family?’’ This question identifies two kinds of
family-owned firms: family-owned firm run by a manager from the family (answer ‘yes’ to
the latter question), and family-owned firms run by a professional (from outside the
family) manager (answer ‘no’ to the latter question). The reference type of firm is nonfamily-owned firms.
In the empirical analyses, we estimate standard Cobb –Douglas production functions,
allowing for productivity differences between different ownership structures:
Y ¼ AecFAM La K b
where Y is value added, L is labour, and K is capital. Furthermore, a and b are the
corresponding elasticities.3
Value added is measured by the difference between sales of the firm and the value of
purchase of intermediary goods used in the production. Labour input is measured by the
number of employees. Capital is measured by the sum of equity plus debt.4 As control
variables, we include information on industry and stock exchange affiliation. Industry is
measured by three broad categories: manufacturing, service, and crafts.5 Stock exchange
affiliation is measured by a dummy variable, taking the value 1 if the firm is listed on the
stock exchange and 0 otherwise.
2
Statistics from 1997 show that approximately 70% of all private Norwegian establishments with more than
10 employees were associated with an employer’s organisation.
3
In studies of producer behaviour, there is a well-known potential endogeneity problem related to the input
variables, labour and capital. The input variables are chosen by the producer in some optimal or behavioural
fashion and cannot therefore treated as predetermined exogenous variables. There is a vast literature on different
approaches to overcome these problems (see, for instance, Griliches and Mairesse, 1995; Arellano and Bover,
1995; Blundell and Bound, 1999). Common to most of the proposed remedies is that they require panel data,
which unfortunately are not available for this study. Therefore, if there are unobservable firm-specific
characteristics correlated with labour and capital and if these unobservables are also correlated with the output
variable, our estimated coefficients for labour and capital will be biased. However, our estimated coefficients fall
within reasonable ranges. Perhaps more importantly, as reported below, our results are quite robust to variation in
the coefficients for labour and capital.
4
The book value of debt and equity is vulnerable to manipulation and different types of adaptation to
accounting and tax rules. Alternative proxies like replacements or market values were not available.
5
Crafts include construction, engineering, building, and installation.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
Table 1
Summary statistics for included variables
Family firms
Mean
values
Log value added
Log number of employees
Log capital
Listed firm
Manager from the
owner family
Industry
Manufacturing
Crafts
Services
N
Non-family firms
Standard
deviation
9.389
3.375
9.252
0.020
0.763
0.953
0.802
1.247
0.133
0.463
0.345
0.259
0.295
0.476
0.439
0.490
220
Median
Mean
values
Standard
deviation
9.113
3.178
9.139
10.064
3.873
10.027
0.069
1.277
1.074
1.607
0.254
0.486
0.096
0.417
0.501
0.295
0.494
218
Median
9.900
3.689
9.856
Family firms and non-family owned firms.
Median, means values and standard deviations.
Listed firm is a dummy variable taking the value 1 if listed on the stock exchange and 0 otherwise.
To measure the importance of ownership, we include a binary variable (FAM), taking
the value 1 if the firm is family owned and 0 if otherwise. Taking logs of the production
function, we get: ln Y = ln A + cFAM + a ln L + b ln K, where (after controlling for other
observable variables) c measures whether family-owned firms are more or less productive
than non-family-owned firms. To analyse the importance of management, we include a
dichotomous variable showing whether the firm is led by a member of the owner family.
We get ln Y = ln A + dFAMMAN + gFAM + a ln L + b ln K, where d measures whether
family firms managed by a member of the owner family (FAMMAN) are more or less
productive than non-family-owned firms.
So far, we have not addressed the problems related to endogeneity of management. If
modes of management are chosen in an optimising form, results from standard ordinary
least squares (OLS) will be biased. This problem and our suggestion on how to solve it are
dealt with in Section 5.
4. Results
Table 1 presents some summary statistics for family-owned and non-family-owned
firms. Approximately 50% of the firms in our sample are family owned.6 The familyowned firms have lower value added, fewer employees, and less capital. Regarding
industry location, approximately 35% of the family-owned firms are located in manufacturing industry. Compared to non-family-owned firms, family-owned firms are overrep-
6
Furthermore, (but not shown in the table), among the family-owned firms, 75% do not have any external
owners (100% family owned).
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
113
resented in the craft industry. Furthermore, we see that family firms are less represented
among firms established after 1980 and they are listed on the stock exchange to a lesser
extent.
Among the family-owned firms, 168 firms (76%) employ a manager from the owner
family, while 52 firms (24%) use an external manager.
Table 2 presents results from the multivariate analysis. We present four models.
Model 1 includes variables for family ownership, number of employees, and industry.
Model 2 adds information on whether the manager comes from the owner family. This
enables us to analyse the effect of management on productivity. Model 3 adds
measures of capital services. By adding capital services, we get a picture of how
sensitive the relationship between family ownership and productivity is to differences
in the level of capital. From Table 1, we know that family-owned firms have less
capital than non-family-owned firms. Finally, Model 4 adds information on whether the
firm is listed on the stock exchange.
The results in Model 1 show that on an average, family-owned firms are less
productive than non-family-owned firms. The difference is estimated to be 0.102 or
approximately 10%. Model 2 controls for the importance of owner-management.
Family-owned firms with a manager from the owner family are less productive than
Table 2
The relationship between family ownership and productivity under different model specifications
Intercept
Family-owned firm
Manager from the owner
family (FAMMAN)
Log number of employees
Model 1
Model 2
Model 3
Model 4
5.764***
(0.102)
0.102***
(0.046)
1.041***
(0.024)
5.808***
(0.1037)
0.056
(0.070)
0.215***
(0.073)
1.035***
(0.024)
3.825***
(0.1217)
0.056
(0.070)
0.155***
(0.052)
0.617***
(0.027)
0.370***
(0.018)
3.834***
(0.120)
0.060
(0.049)
0.153***
(0.052)
0.614***
(0.026)
0.374***
(0.018)
0.169**
(0.076)
0.365***
(0.066)
0.220***
(0.063)
0.848
438
0.343***
(0.066)
0.196***
(0.063)
0.851
438
0.097**
(0.048)
0.100**
(0.063)
0.925
438
0.096**
(0.048)
0.099**
(0.045)
0.926
438
Log capital
Listed firm
Industry (ref: Crafts)
Manufacturing
Services
R2 adj.
N
Estimates of production functions including indicators of family ownership (Model 1), family management
(Model 2), capital stock (Model 3) and whether the firm is listed on the stock exchange (Model 4). Estimated
coefficients and standard errors of coefficients in parentheses. Ordinary least squares. Dependent variable: log
value added.
Listed firm is a dummy variable taking the value 1 if listed on the stock exchange and 0 otherwise. The reference
alternative for industry is craft. Level of significance: ***1%; **5%.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
non-family-owned firms.7 These results suggest that difference in management regime
is the driving force behind the productivity gap of Model 1.
Adding capital in Model 3 reduces the negative effect of family management,
reflecting that family managed firms are less capital intensive. The productivity gap is
estimated to 0.155 or about 14% compared to non-family-owned firms. Finally, Model
4 reveals that listed firms are more productive compared to non-listed firms. The family
management relationship is not altered by the inclusion of this variable. The results
indicate that firms with family management are about 14% less productive than nonfamily-owned firms.
The lesson from Table 2 is that neither differences in labour input, capital services nor
listing on the stock exchange is sufficient to explain productivity differences between
family and non-family-owned firms. The ‘culprit’ is the management function. Ownermanagement is significantly less productive compared to external management. The
productivity difference is about 14%. An interpretation is that search for the best qualified
manager from a large pool of applicants characterized by open competition is more likely
to ensure a good firm – manager match and high productivity.
4.1. Alternative specifications and robustness checks
In Table 3, we report results from some alternative specifications. We present some
results based on regression analyses using all firms (columns 2 and 3) as well as results
based on the sample of family-owned firms only. The focus is on the coefficient of the
‘‘manager from own family’’ indicator. For the different specifications, the table only
presents the regression coefficients for the two variables ‘‘Manager from owner family’’
and ‘‘Family owned’’.
First, we present two models where we impose the restriction of constant returns to
scale with respect to capital and labour. The first model is obtained by dividing value
added by our measure of capital. Assuming constant returns to scale we have Y/K = A(L/
K)a, which is then estimated in logs allowing A to vary with industry, listed firms, and
family control variables. We report the coefficients of the family control variables only. We
find a negative and significant coefficient for the ‘‘manager from owner family’’ indicator
( 0.150), again indicating a productivity gap of about 14%.
In the second specification, we address the issue of potential endogeneity of inputs by
using the following estimator for the elasticity of production with respect to labour inputs:
a = WL/Y, averaged across firms. This estimator is taken from the first-order condition of
profit-maximising firms with constant returns to scale. In accordance with the assumption
of constant returns to scale, we proceed by estimating the elasticity of production with
respect to capital by (1 a). Next, we calculate the log of total factor productivity by
7
A White test for heteroskedasticity is performed. No evidence of heteroskedasticity is found. Robustness
tests reveal that we cannot reject unit elasticity of scale. The conclusions with respect to ownership and the
management regime remain unchanged. The coefficients for capital and labour are not significantly different
between family- and non-family firms. We have experimented with different input elasticities. For instance,
reducing the elasticity of production with respect to capital to 0.15 only strengthened our results by increasing the
difference between family- and professionally managed firms from 0.168 to 0.216.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
115
Table 3
Productivity effects under alternative specifications
Alternative specifications
All firms
Family-owned firms
Number of
Manager from
observations owner family
0.062 (0.050)
0.163*** (0.050)
0.134** (0.053)
0.064 (0.051)
0.134** (0.053)
0.129 (0.085)
0.186** (0.082)
0.109 (0.087)
0.058 (0.053)
0.033 (0.051)
0.104* (0.058)
0.101** (0.047)
0.053 (0.045)
0.134*** (0.048)
369/200
69/20
0.114** (0.053)
0.303* (0.167)
0.018 (0.052)
0.202 (0.133)
0.126** (0.051)
0.347 (0.240)
93/27
345/193
0.319** (0.122)
0.127** (0.057)
0.078 (0.095)
0.063 (0.057)
0.293* (0.159)
0.134** (0.055)
182/76
178/87
78/57
419/216
0.143* (0.081)
0.177** (0.083)
0.034 (0.092)
0.149*** (0.053)
438/220
0.174 (0.188)
0.216 (0.182)
0.140 (0.197)
438/220
0.193* (0.106)
0.213** (0.102)
0.187* (0.111)
Alternative forms of labour input
Number of employees
436/220
replaced by payroll costs
Hourly wage added to
436/220
number of employees
Return on capitala
Dependent variable:
value added divided
by total capital
Dependent variable:
return divided by
total capital
Manager from
owner family
0.150*** (0.052)
Dependent variable: ln (value 438/220
added per unit of capital)
Dependent variable:
438/220
total factor productivity
Dependent variable: ln (Sales) 438/220
Sub-samples
Number of employees
Less than 100 employees
100 employees or more
Capital
50 million NKr or more
Less than 50 million NKr
Industry
Manufacturing
Services
Crafts
Non-listed firms
Family owned
0.004
0.109
0.019
0.053
(0.076)
(0.078)
(0.099)
(0.051)
0.154* (0.089)
0.181** (0.079)
0.042 (0.097)
0.170*** (0.052)
Some results from production functions including indicators of family ownership and family management
estimated under alternative specifications. Dependent variable: log value added. The table reports estimated
coefficients of the variables ‘‘Manager from owner family’’ and ‘‘Family owned’’. Standard errors of coefficients
are in parentheses. All firms and family owned firms.
The set of explanatory variables used in the specifications are the same as the one used in Table 2 (model 4).
Estimated coefficients and standard errors. Level of significance: ***1%; **5%; *10%.
a
Return on capital is regressed on industry, listed firms, and family control variables only.
ln( Y) a ln L (1 a)ln K, which we then regress on industry, listed firms and family
control variables. Again, we find a significant negative effect of owner-management
( 0.134 or about 12.5%). These results show that applying the constant returns to scale
assumption and using another plausible estimator of a does not change our results in any
interesting way.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
In the third specification, we investigate a model where we use log sales as the
dependent variable instead of log value added. Qualitatively, we do not find large
differences to the model using log value added, that is, we find a negative coefficient
for the variable ‘‘manager from owner family’’ in both data sets. The coefficients are not
significantly different from zero. However, as the value of intermediate goods is an
obvious omitted variable in this regression, we definitely prefer the analysis presented in
Table 2.
Labour input may be of different quality, for instance, as a result of different levels of
human capital of the workers. If this is systematically related to family control, our results
may be biased. We present two tests to check for this potential problem. Assuming that the
wage rate equals marginal productivity, we may use total payroll (WL) as a measure of
labour input (in a specification otherwise identical to that of Table 2). This is done in the
first specification. We find a negative, but not significant coefficient of 0.058 in the
regression on all firms. In the regression on family firms only, we find a negative and
significant coefficient of 0.104. In the next specification, we allow for differential
effects of the wage rate (labour quality) and the number of workers employed by adding
the log of the wage rate to the standard equation of Table 2. We then find a significant
negative effect of family management in both analyses. Examination of the models reveals
that the restriction imposed on the first model is not valid and that the results from the
model with differential effects of the wage rate and the number of employees should be the
preferred. We still tend to trust the model presented earlier (in Table 2) more for the
following reason: if wages are in some way related to firms’ productivity through some
bargaining mechanism, for example, they will be correlated with the error term in the
production function and thus introduce a bias to the estimators of the equation. It is
comforting, therefore, to note that our results do not rely on the exclusion of measures of
labour quality and remain strong and significant also after controlling for human capital
and other quality differences.
One might be concerned that our sample is too broad and that our results arise from
some underlying heterogeneity rather than from differences in management regime. To
check for this, we split the sample into sub-samples along several dimensions. The first
split is according to employment level. We find a strong and significant effect among small
firms (smaller than 100 employees) in line with what we found in the full sample. For
large firms (100+), we find an even stronger effect, in the order of 0.303, indicating a
gap of more than 25%. For the group of family firms, we have only 20 firms with more
than 100 employees and fail to find a statistically significant effect. Given the sub-sample
size, this is not of particular concern and the point estimate of 0.347 is not significantly
different from 0.126 either. In the next specification, we split the book value of total
capital. The point estimates are again larger (negatively) in the larger firms than the smaller
firms and the effects are statistically significant from zero in all specifications. Again, we
fail to find statistically significant differences between the two sub-samples.
The split, according to industry, yields more interesting results. In manufacturing and
services, the management effect is significant. However, within the crafts industry, neither
ownership nor management matters significantly for productivity. This means that had we
removed the crafts observations from the sample in Table 2, we would have obtained even
stronger effects of the management variables. We suspect that within the crafts industry,
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
117
which is dominated by small firms, several of which probably resemble self-employment,
management is not such an important skill as they are in the two other industries. As a
further robustness check, we have removed the listed firms and estimated the model for the
non-listed firms only. The results remain large and significant.
Lastly, we have made three experiments related to the return to total capital. In this
exercise, we use two measures of earnings: one is sales minus intermediate goods
(value added) and the other is sales minus intermediate goods minus total wage bill,
i.e., a measure of operating profits. Both were then divided by the book value of total
capital. These two variables are regressed on industry, listed firms, and family control
variables. We find a negative relationship between return on capital, measured in terms
of value added per unit of capital, and family management, but which is statistically
insignificant.
A similar, and significant, relationship at the 10% level is found between operating
profits per unit of capital and family management ( 0.193 or about 17.5%). A third
experiment, not reported in the table, is conducted using operating profits per unit of
equity. This latter relation displays no significant relationship to any of the variables and is
not reported.
In our opinion, a production function approach represents the most appropriate test
of the hypothesis that professional managers are better. This is particularly true since
the measure of total capital, i.e., the book value of debt and equity, is vulnerable to
manipulation and different types of adaptation to accounting and tax rules (e.g., rules
on capital depreciation), which may well differ between ownership and management
structure. This source of error is more serious in the analyses of returns to capital than
in the production function approach, particularly as the estimates obtained for total
factor productivity appear highly robust to changes in the estimated coefficients for
capital and labour. However, our results also show that returns in terms of operating
profits per unit of total capital are lower for family managed firms than for
professionally managed firms.
We now turn to an analysis of questions related to endogeneity of management regime.
5. A simple model of ownership and control
In order to address the issue related to endogeneity of management, we need to
model the choice between owner-management and professional management. We use a
simplified and slightly modified version of the model developed by Burkart et al.
(2002). They model both the founder’s choice between professional and ownermanagement, and a subsequent choice of the proportion of shares to float on the
stock exchange. This unified framework allows them to combine the twin conflict
between the manager and the owners, and that between the family and other minority
shareholders. In our analysis here, we limit the discussion to the choice between
owner-management and professional management, assuming, for the sake of simplicity,
that the family maintains full ownership of the stocks.
A key assumption in Burkart et al. (2002) is that owner-management is inferior to
professional management. This assumption is tested below. We model this assumption
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
in the following way: consider a family-owned firm described by the following value
added
VF ¼ A
ð1Þ
V M ¼ Aed
ð2Þ
where superscript F denotes family managed firms and superscript M denotes
professionally managed firms. Furthermore, d z 0 is a measure of the professional’s
advantage. The owner’s utility under the two regimes is given by:
U F ¼ Aec
ð3Þ
U M ¼ Aed ð1 p wÞ k
ð1 pÞ2
2
ð4Þ
where c reflects the family’s preference for controlling the firm. We will return to a
discussion of c below. A positive c means that the owners have preferences for
controlling the firm, while a negative c could be a result of the working effort involved
in managing. The utility level in case of a professional manager is slightly more
complicated. In addition to the value added of a professionally run firm, the term
involves three costs: w is the wage rate paid to the manager, p is a private benefit
extraction of the professional manager, and k[(1 p)2/2] are monitoring costs involved
in keeping p down.
Consider first the choice of monitoring level by a family firm with professional
management. Firm profits are given by: P = Aed(1 p w) k[(1 p)2/2]. Maximising
profits with respect to p gives:
ð1 pÞ ¼
Aed
k
pVp̄
ð5Þ
where p̄ is an upper level of the private benefit extraction possible for the manager
(technologically or institutionally determined).
Next, the wage w is given by the professional manager’s outside option c:
Aed ðp þ wÞzc
ð6Þ
If VMp̄ is greater than c, the owner chooses to monitor the manager. As monitoring is
costly, the family will choose to set w = 0, and let the manager obtain his benefits through
p.8 Inserting w = 0 and Eq. (5) into Eq. (4) gives:
UM ¼
ðAed Þ2
;
2k
ð7Þ
8
Whether the manager is able to obtain a rent or not in this firm depends on the size of value added and
monitoring costs, relative to the outside option, c. See Burkart et al. (2002) for a more elaborate discussion.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
119
which is to be compared to the family’s utility under owner-management (Eq. (3)). We
obtain the following qualitative results. Choose professional management:
– in the more productive firms (the intuitive reason is that the monitoring costs are smaller
per value added);
– the higher is the professional manager’s advantage;
– the smaller are the monitoring costs;
– the smaller are the preferences for family control.
In addition, as discussed below in the framework of the empirical model, there may be
important patterns in the stochastic elements of the value added of the two regimes.
We now have a model framework that enables us to empirically model the choice
between family ownership and professional ownership. What we want to analyse is the
following empirical relationship:
m M ¼ a þ d þ uM
m F ¼ a þ uF
ð8Þ
where a is logs of A and u represents stochastic error terms with standard properties.
However, we observe the professional management regime only when the owners find
this profitable. Comparing (the logs of) Eqs. (3) and (7) gives us the following model.
Choose professional management when:
a þ 2d ln2 lnk czu ¼ uc þ uF 2uM
ð9Þ
c
We have introduced u to allow for stochastic variations in preferences for family control.
As our data is constructed under the selection equation (Eq. (9)), the expected value of the
observed value added in each group is given by (see, e.g., Maddala, 1990, p. 260):
EðmM Þ ¼ a þ d rMu
EðmF Þ ¼ a þ rFu
/
þ e1
U
/
þ e2
1U
ð10Þ
where riu = Cov(ui,u) = Cov(ui,uc + uF 2uM), and / and U are the density and cumulative
density functions of the standard normal. The error terms in Eq. (10) imply that OLS on
Eq. (8) produce biased results. To see this clearly, we may calculate the expected value
added of the establishment as:
/
/
ð1 DÞrMu
ð11Þ
EðmÞ ¼ ða þ dÞ dD þ DrFu
1U
U
An indicator of a family-run establishment is given by D = 1, while D = 0 for professionally run establishments. From Eq. (11), we find that by omitting the selection terms, the
OLS estimation of d may be considerably biased. In order to discuss the nature of the bias
consider the following simple example.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
Let uM = uF = uA; which means that there is a stochastic productivity factor uA, which is
identical regardless of management regime. Let also uc = 0, so that the common
productivity shifter is the only stochastic element of the model. In this case, riu = rA2
for i = F, M, and consequently:
EðmÞ ¼ ða þ dÞ dD r2A D
/
/
ð1 DÞ
1U
U
¼ ða þ dÞ dD r2A k
ð12Þ
Note that k is increasing in D and thus larger for family-owned establishments. Thus,
failure to account for the selection term produces a positive bias for the professionally run
establishment and a negative bias for the family run establishment and consequently an
upward bias in the coefficient d.
The intuition behind this result is simple. Because monitoring costs are increasing in the
share of A, that may be kept by the owner (1 p), rather than in the absolute value of the
profit, the optimal monitoring level is higher for more productive establishments and
owners gain more from professional management of productive establishments. When
comparing across management regimes, we are consequently comparing across establishments with different expected productivity levels. Inserting a weighted Heckman’s
lambda, as suggested by Eq. (12) then produces the necessary correction to obtain
unbiased estimates of d.
Consider next the introduction of a stochastic element in family preferences, uc.
Now rMu = rFu = rAu = rAc rA2.
We obtain:
EðmÞ ¼ ða þ dÞ dD þ ðrAc r2A Þk
ð13Þ
The coefficient attached to the weighted Heckman’s lambda is thus rAc rA2. If the
correlation between the productivity term and family preferences for running the
business is negative, the selection term still produces an upward bias in the OLS
estimator of d.
However, if the correlation is positive and large enough relative to the variance in
productivity, the selection term produces a negative bias. This case may occur if families
enjoy running well performing firms more than running inefficient firms. If the firm
performs well, the family prefers to keep control, but if the enterprise turns out to be
negative, they resort to outside management for help.
Obviously, different correlations between the productivity terms of the two regimes
may also induce similar results. If, for instance, managers are relatively more efficient than
the family in handling troubled times, a negative bias occurs. Note also that an absolute
bankruptcy level may also produce a similar bias as the productivity distribution is
truncated at a higher level for family-owned firms (who, by assumption is d units to the
left of the productivity distribution of professionally run firms). In all of these cases, we
are more likely to find professional managers in the poorer establishments, while the
family keeps on running the efficient ones. In the empirical section below, a standard
selection model of the type presented here is estimated.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
121
Another way of writing the expected value added is:
/
/
EðmÞ ¼ U a þ d rMu
þ ð1 UÞ a þ rFu
U
1U
¼ ða þ dÞ dð1 UÞ þ ðrMu rFu Þ/
ð14Þ
One modelling strategy is thus to estimate the log of value added including both / and U
in the model. This is a standard switching model. The coefficient for (1 U) (the
probability of being run by the family) gives us and estimate of d and the coefficient
for / gives us an estimate of (rMurFu) = rMcrFc + 2rM2 + rF23rFM. We also run this
switching model below.
5.1. Issues of identification
The switching model described above is identified by functional form, but also from the
fact that the switching regression includes variables that are not in the value-added
regression. Therefore, we do not rely solely on identification by functional form. The two
variables that enter the switching regression are the monitoring costs as well as the
family’s preferences for managing the firms themselves.
The variables we use to represent monitoring costs are firm size, whether the firm is
listed on the stock exchange, and industry. These variables should also be included in the
value added regressions and cannot be used for identification purposes.
Nevertheless, we have in our data variables that could reasonably represent (c) the
family’s preference for control. We have information on the number of years the firm
has been in the family’s ownership. Conditional upon the number years since the firm
was established, the number of years in family’s ownership could be a good proxy for
variables affecting the family’s preference for control. We also have a direct question to
the manager on the perceived importance of family control: ‘‘How important is it to
sustain control of the family firm?’’ We utilized this as an indicator of the family’s
preference for control.
We present both a two-stage least squares (2SLS) approach as well as the Heckman
procedure presented above, and the switching regression in the next section. First,
however, we take a closer look the first-stage results: What determines the choice of
management regime?
5.2. Family manager or professional manager?
In this section, we report from the first-stage regressions of the switching model.
What determines the choice of family versus professional manager? We limit the
analysis to family-owned firms, i.e., to the 220 firms that report that at least 33% of
the shares in the firm are owned by one person or one family. The dependent variable
is a dummy variable, taking the value 1 if the firm uses a manager from the owner
family and 0 otherwise.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
Table 4
Estimates of the probabilities of choosing owner-management
Model 1
Probit
coefficients
Intercept
Log capital
Log number of
employees
Listed firm
2.458***
(0.910)
0.269*
(0.141)
0.075
(0.196)
0.629
(0.687)
Industry (ref: Crafts)
Manufacturing
0.544*
(0.319)
Services
0.566*
(0.306)
Importance of
0.397
control
(0.242)
Established (ref. after 1990)
Pre-1900
0.364
(0.721)
1900 – 1919
1.576**
(0.709)
1920 – 1939
0.406
(0.712)
1940 – 1959
0.231
(0.653)
1960 – 1979
0.091
(0.649)
1980 – 1989
0.572
(0.608)
Marginal
probabilities
0.063
0.018
0.148
0.128
0.133
0.094
0.086
0.372
0.096
0.055
0.021
0.135
Model 2
Model 3
Model 4
LPM (OLS)
Instruments
only
Without
instruments
1.059***
(0.229)
0.054
(0.034)
0.002
(0.050)
0.229
(0.207)
0.519***
(0.110)
1.441***
(0.223)
0.058*
(0.035)
0.015
(0.053)
0.272
(0.213)
0.109
(0.074)
0.124*
(0.069)
0.077
(0.057)
0.081
(0.057)
0.114
(0.119)
0.448**
(0.189)
0.119
(0.184)
0.108
(0.175)
0.061
(0.175)
0.177
(0.168)
0.265
(0.186)
0.614***
(0.183)
0.276
(0.181)
0.233
(0.172)
0.142
(0.172)
0.254
(0.167)
How many years in family’s ownership ( ref. less than 6 years)
6 – 9 years
1.637***
0.386
0.479***
(0.599)
(0.146)
10 – 19 years
1.147**
0.271
0.377***
(0.528)
(0.135)
20 – 49 years
1.221**
0.288
0.399***
(0.531)
(0.133)
50+ years
1.958***
0.462
0.531***
(0.583)
(0.137)
Log likelihood
93.052
R2 adj.
0.179
N
220
220
0.119
(0.077)
0.122*
(0.072)
0.538***
(0.147)
0.441***
(0.134)
0.457***
(0.131)
0.618***
(0.137)
0.148
220
0.050
220
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
123
In Table 4, Model 1 displays a probit analysis of the probability of choosing a family
manager. As the probit model is a nonlinear regression model, the parameters cannot be
interpreted as marginal effects on the probability of choosing a family manager. To receive
a measure on the marginal effects on the probability, we must multiply the estimated
parameter with /; the standard normal density, evaluated at the mean of the sample.
Measures of the marginal effects are presented at the right hand side of the probit
coefficients. The specification in Model 1 is used to calculate the / and U. Model 2 reports
results from a simple linear probability model (LPM) that is used in the 2SLS
specification. The two last models are reported to give an account of the explanatory
power of the instruments used in the first-stage regression. These models are also
estimated by LPM models.
From the probit equation (Model 1), we find that owner-management is significantly
less likely in firms with more capital. A doubling of the capital stock decreases the
probability of owner-management by 6.3 percentage points, as judged by the marginal
effect in Model 1. This result is in accordance with our theoretical model. In the theoretical
model, it is more profitable for professionals to manage the more productive firms mainly
because the optimal level of monitoring is higher in these firms. The point estimate for
listed firms is negative, possibly indicating that professional managers are easier to
monitor in listed firms, but as the coefficient is insignificant, we cannot rely too heavily on
this result.
Model 1 shows that a young firm is more likely to be managed by the owner.
Conditioned on the firm’s age, however, a recently acquired firm is less likely to be family
managed. One reason for the positive relationship between owner-management and
number of years in family ownership is that some of the family firms were established
earlier and subsequently taken over by the present owners. Several of these acquired firms
were probably managed by professional managers at the time of acquirement. However,
after the first 5 years, the relationship between owner-management and years in family
ownership is quite stable.
Model 2 contains results from the LPM model. The LPM coefficients can be
interpreted as marginal effects on the probability. We see that estimated coefficients in
Model 2 are fairly comparable to the predicted marginal effects from the probit
estimation in Model 1.
In Model 3, we find that the variables reflecting the age of the firm, years in family
ownership and preferences for family control explains about 15% of the variation in
management regime, when entered alone. Comparing Models 4 and 2, we also find that
the instruments add about 10 percentage points to the explained variation, when added to
the full model. Even though these numbers suffer from the weakness of the linear
Notes to Table 4:
Family-owned firms only.
The table reports results from a probit model (Model 1) and three linear probability models (Models 2 – 4).
Estimated coefficients and standard errors of coefficients in parentheses. Dependent variable: dummy variable for
owner-management.
Level of significance: ***1%; **5%; *10%.
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
probability model, they clearly indicate that we have a set of instruments that contributes
significantly in explaining the choice between family and professional management.
5.3. Family ownership and productivity
In this section, we test the basic assumption that managers from the family are less
productive than professional managers. We estimate four models on the sample of familyowned firms only. The results are presented in Table 5. Model 1 is the OLS specification.
In this specification, we find that family managed firms are about 16% less productive than
firms managed by a manager outside of the family. Model 2 is the weighted Heckman
model as presented in Eq. (12). Model 3 is the switching model as presented in Eq. (14),
and Model 4 is the 2SLS specification.
In Model 2, the family manager disadvantage increases after controlling for endogeneity. This result indicates that there is a positive selection into family management; i.e.,
Table 5
The relationship between family management and productivity
Model 1 (OLS)
Intercept
Manager from the
owner family
Log number of
employees
Listed firm
Log capital
Model 2 (weighted Model 3
Model 4 (2SLS)
Heckman model) (Switching model)
4.166*** (0.183) 4.242*** (0.237) 4.333*** (0.255) 4.294*** (0.243)
0.169*** (0.051) 0.222* (0.115) 0.292** (0.134) 0.257*** (0.122)
0.641*** (0.040)
0.639*** (0.040)
0.634*** (0.041)
0.639*** (0.041)
0.244 (0.161)
0.338*** (0.027)
0.229 (0.164)
0.335*** (0.028)
0.279* (0.167)
0.341*** (0.027)
0.220 (0.165)
0.333*** (0.028)
0.047 (0.060)
0.092 (0.057)
0.037 (0.072)
0.077 (0.062)
0.128** (0.059)
0.043 (0.060)
0.088 (0.057)
Industry (ref: Crafts)
Manufacturing
0.052 (0.058)
Services
0.099 (0.055)
Heckman’s k
/
Hausman test
coefficient (standard error)
Basmann test for
overidentifying
restrictions, P-value
R2 adj.
0.893
N
220
0.448* (0.248)
0.141 (0.132)
0.413
0.892
220
0.890
220
0.891
220
Production functions estimated on the sample of family-owned firms only. All regressions include an indicator of
family management (‘‘Manager from the owner family’’). Models 2 – 4 include different types of control for the
endogeneity of family management; a weighted Heckman model, a switching regression model and a two-stage
least square model (2SLS). Estimated coefficients and standard errors of coefficients in parentheses. Dependent
variable: log value added.
Level of significance: ***1%; **5%; *10%.
Instruments in the first-stage regression include a survey question of the importance of control of the firm, the
establishment date of the firm and the length of time with family involvement in the firm. See Table 4 for the firststage regressions.
E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
125
that the family prefers to keep control of the good firms. However, as the coefficient for
the Heckman’s lambda is not significant, we should express some caution in interpreting
the results. In the next model, we present the results from the switching model. The effect
is now estimated to be 0.292, indicating a productivity gap of about 25%. We do not
obtain a significant coefficient at the 5% level for the selection term (/). This suggests we
cannot reject the OLS model to the advantage of the switching model.
In Model 4, where we instrument manager type by standard 2SLS, the Hausman test
fails to support a significant selection bias. However, in the 2SLS specification, we obtain
an even larger negative effect of owner-management on productivity. To test for overidentifying restrictions, we report results from a standard Basmann test (Basmann, 1960)
as well. The Basmann test performs satisfactorily, indicating that the instruments should
not be included in the productivity equation.
6. Conclusion
We compare the performance of family-owned versus non-family-owned firms. The
measure of performance is productivity. The results show that family-owned firms are
less productive than non-family-owned firms. The difference in productivity is
estimated to be approximately 10%. This productivity gap is explained by differences
in management regimes. Family-owned firms managed by a manager from outside the
owner family are equally productive as non-family-owned firms. However, familyowned firms managed by a person from the owner family, are found to be
significantly less productive than non-family-owned firms. We estimate this productivity gap to be about 14%. When we compare within the sample of family-owned
firms only, the difference between firms that are managed by a member of the family
and those operated by managers from outside the family is estimated to be 15 –16%.
When we try to correct for the endogeneity of management type, we obtain even larger
negative effects. From the discussion above, this indicates that there is positive (negative)
selection into family (professional) management. One reason could be that professional
managers are called for in difficult times, or similarly, that in good times or in good firms
family owners enjoy keeping control. As the selection terms are not significant in any of
our second-step specifications, we cannot put much weight on this particular result. What
remains is a strong and significant negative (positive) effect of family (professional)
management on productivity.
The productivity gap may be due to skill differences between professional and family
managers. After all, professional managers are chosen from a larger pool of talent. But, it
might also be the case that family managers choose to run the firm in less productive
manners. Owner – managers may retain the position as top manager because they enjoy
being in charge and manage their lifework, the family firm, in their own way.
At first sight, our results seem to be at odds with the recent results reported in Anderson
and Reeb (2003), who report that family firms perform better than non-family firms.
However, they find a concave relationship between family ownership and performance.
After about 30% ownership, further family ownership has a negative effect on performance and after about 60%, family firms perform worse than non-family-owned firms. In
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E. Barth et al. / Journal of Corporate Finance 11 (2005) 107–127
our sample, most of the family-owned firms have family ownership shares of more than
50%. In fact, 74% of the family-owned firms have no other owners, that is, they are 100%
owned by the owner family. Thus, our results are consistent with the findings of Anderson
and Reeb. But they also conclude that hired CEOs perform worse than managers from the
family. The difference shown in our results may be because our performance measure
differs, as we analyse productivity while they study the effect on returns-on-assets and
Tobin’s Q. Further, our analysis suggests a positive selection into family management.
Even if this effect is not statistically significant in our sample, such a selection may be
present in a sample of large listed firms.
In summary, we do not find support for the hypothesis that concentrated ownership per
se affects productivity. It does, however, matter who runs the firm. When choosing
between owner-management and professional management, the owner may have to make a
trade-off between skills and incentives. Owner-management ensures right incentives.
Nevertheless, it seems that professional managers hired in the market are more efficient in
operating the firm.
Acknowledgements
The authors thank Hege Torp, Tone Ognedal, an anonymous referee and the Editor for
valuable comments. The paper has been presented at the research seminar at the Frisch
Centre for Economic Research, and at the 2nd European Conference on Corporate
Governance, Brussels 27 – 29 November 2002. We thank seminar and conference
participants for fruitful discussions. The work is financed by the Norwegian Research
Council, grant # 136779. The financial support is gratefully acknowledged.
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