Does Gender Diversity in the Boardroom Improve
Firm Performance?∗
Sven-Olov Daunfeldt† and Niklas Rudholm†
Abstract
The purpose of this paper is to investigate whether increasing gender
diversity on the board of directors improves firm perfomance, using a
data-set of 20,487 limited companies in Sweden during 1997-2005. We
use a random-effects random-coefficients model to account for unobserved firm heterogenity. More gender diversity in the boardroom is
found to have a negative impact on returns on total assets after two
years. Thus, legal requirements to increase gender diversity on the
board of directors might carry a cost in lower profitability.
Keywords: board of directors; board diversity; corporate governance
JEL-codes: G30; G34; J16.
∗
We would like to thank Erika Rosén for excellent research assistance, as well as David
Granlund, seminar participants at Ratio, Jönköping International Business School, and
KTH Royal Institute of Technology for valuable comments and suggestions. Financial
support from Stiftelsen Marcus och Amalia Wallenbergs Minnesfond is gratefully acknowledged.
†
Department of Economics, Dalarna University, SE-781 88 Borlänge, Sweden; and HUI
Research, SE-103 29 Stockholm, Sweden.
1
1
Introduction
In most countries the share of women on boards of directors is very low,
though increasing (Adams and Ferreira, 2009). This has led to a debate
whether governments should implement regulations to increase female participation. In 2006, the Norweigan government imposed a law that from
January 2008 the share of each gender on the board of directors of all listed
companies should be at least 40%, with dissolution the penalty for noncompliance. Spain, Iceland, and France soon imposed gender quotas as well,
while in Belgium, the Netherlands, and Italy such laws have passed at least
the first stage of the legaslative process.1 A rather intense debate has also
followed in other countries, such as Sweden, where politicians have threatened similar actions if firms do not voluntarily choose to include more women
on the boards.
One argument for regulation is that more women on the board of directors would improve firm performance. There are several theoretical reasons
why a higher share of women on boards of directors might be associated with
better performance (Bantel and Jackson, 1989; Murray, 1989; Carter et al.,
2003). A more diverse board of directors might lead to a better understanding of markets that are themselves diversified in terms of gender, increase
firm creativity and innovativness, improve decision-making as more alternatives are evaluated, select more productive board members, and improve
the image of the firm.
On the other hand, a more diverse board of directors might also have
1
Ahren and Dittmar (2012) provides a more detailed description of the legaslative
process regarding gender quota laws in different countries.
2
more conflict and slower decision-making (Lau and Murnighan, 1998; Miller
et al., 1998; Williams and Reilly, 1998). This argument is in line with the
social choice literature (Arrow, 1951), which predicts that costs associated
with collective decision-making will be higher when decision-makers are heteregenous.
Some empirical studies have found no influence on performance of gender
diversity in top managment (Shrader et al, 1997; Smith et al., 2005; Rose,
2007; Eklund et al., 2009). Other studies found that a higher proportion
of women in top managment have had a statistically significantly positive
effect (Carter et al., 2003; Erhardt et al., 2003; Campbell and Minquez-Vera,
2008). Still others found a negative effect (Bøhren and Strøm, 2007; Adams
and Ferreira, 2009; and Ahren and Dittmar, 2012).
However, most previous papers suffer from several methodological shortcomings. Most only used information on the largest firms in the economy,
so their results may not be representative for all firms. Problems with reversed causality and unobserved firm heterogenity have typically not been
addressed. Succesful companies might be more likely to choose more women
as board members, i.e., high returns might lead to more gender-equal boards,
rather than the reverse (Adams and Ferreira, 2009). Also, due to unobserved
firm heterogenity, firms with a higher share of women on the board of directors might have performed better even if they had fewer female board
members. Simple comparison between firms with a higher and lower shares
of female board members will most likely produce biased results.
The purpose of this paper is to investigate whether increased gender
diversity on boards of directors improves firm performance, using a unique
3
micro-level firm data-set covering 20,487 limited companies of all sized in
Sweden during 1997-2005. The data-set is thus larger in both detail and
extent than those that have been used previously.
We investigate if there is reversed causality by testing whether greater
gender diversity on boards of directors came before or after changes in firm
performance (Granger, 1969). To account for unobserved firm heterogenity,
a random-effects random-coefficients model is estimated. This method has
two main advantages: It captures unobserved heterogeneity in two dimensions, accounting for firm-specific unobserved heterogeneity in profits while
also allowing for unobserved heterogeneity in the effects of gender diversity
on firm performance. It also allows us to do a deeper cross-sectional analysis of industries and firms where the effects of changes in board composition
had the largest effect on firm profitability.
An increase in gender diversity on boards of directors is found to have a
negative impact on returns on total assets after two years, but not before.
This result is in accordance with Bøhren and Strøm (2007), Adams and
Ferreira (2009), and Ahren and Dittmar (2012), and supports predictions
that more gender diversity in the boardroom might carry a cost in worse
performance. Thus, our results do not support the argument that legal
requirements to increase gender diversity in board rooms would improve
firm performance.
The next section summarizes previous studies of whether greater gender
diversity on boards of directors improve firm performance, while Section 3
describes the data we used. Section 4 then describes the econometric specification, while the results are presented in Section 5. Section 6 summarizes
4
and draws conclusions.
2
Previous studies of gender diversity and firm
performance
In the corporate governance literature, the board of directors is treated
as an institution that is endogenously determined by agency problems. The
relation between board composition and firm performance has therefore been
investigated in many studies (reviewed by Hermalin and Weisbach, 2003),
on the share of insiders on the board (Agrawal and Knoeber, 1999), the size
of the board (Kini et al., 1995), and the tenure of directors and managers
(Hermelin and Weisbach, 1991). Board size seems to be negatively related to
firm performance, but in general the results suggest that board composition
does not influence firm performance.
The relationship between the share of women on boards of directors
and firm perfomance has recently received attention. There are theoretical
reasons why a higher share of women on boards of directors might be associated with better performance (Carter et al., 2003). Greater gender diversity
could lead to a better understanding of markets that are themselves diverse
in terms of gender (Robinson and Dechant, 1997), and could increase creativity and innovativness (Singh and Vinnicombe, 2004). Decision-making
process could also be improved since more alternatives and their consequences might be evaluated (Eisenhardt, 1989; Judge and Miller, 1991). It
could also have a positive impact on the image of the firm, and therefore
on performance (Smith et al., 2006). Agency costs associated with homoge5
nous boards could be reduced (Rosenstein and Wyatt, 1990). It could, for
example, lead to better career opportunities for women in lower positions,
thereby increasing the number of female candidates for top positions in the
future (Ely, 1990; Burke and McKeen, 1996; and Bell, 2005). Substantial
costs can arise if women are dissatisfied with opportunities for advancement
within a firm (Cox and Blake, 1991).
On the other hand, there are theoretical reasons why more women on
boards might have a negative impact on firm performance, through more
emotional conflicts (Tajfel and Turner, 1985; Williams and O´Reilly, 1998),
lack of communication (Miller et al., 1998; Adams and Ferreira, 2007),
and more time-consuming and thus less effective decision-making (Lau and
Murnighan, 1998). Similarly in the social choice literature, Arrow (1951) argued that costs associated with collective decision-making would be higher
if the decision-makers were heteregenous.
Empirical studies that investigated whether more women on boards of
directors was associated with better performance are summarized in Table
1 (range 63 - 2,500 firms, median 200). The findings are inconclusive. Three
studies (Carter et al., 2003; Erhardt et al., 2003; Campbell and MinguezVera, 2008) documented a positive relationship, suggesting that firms can
improve performance by appointing more female board members. However,
eight studies found no effect, and three (Bøhren and Strøm, 2007; Adams
and Ferreira, 2009; and Ahren and Dittmar, 2012) discovered a negative
relationship.
Table 1 about here
6
All studies except one are been based on U.S. or Nordic data. The
evidence from the U.S. is mixed, whereas no study using data from the
Nordic countries has found that greater gender diversity in the boardroom
improved firm performance. Theres is little evidence on effects in other
countries.
The studies reported in Table 1 focused on listed companies, usually large
firms. Thus, the results may not be representative, since only a minority of
firms are listed.
The ambigiuous evidence might also be explained by the use of different
empirical methods. Only one-third of the studies have controlled for both
unobserved firm heterogenity and reverse causality, meaning that most previous findings cannot be given causal interpretations. A simple comparison
between firms with higher and lower shares of women will most likely produce biased results, because firms with more women on boards of directors
are not a representive group of all firms. It is important to control for unobserved firm heterogenity. For example, an omitted (and unmeasurable)
variable such as firm culture might affect both firm performance and the
proportion of women on the board, thereby creating spurious correlation.
Firms that perform well might also choose more women as board members.
Thus, it is also important to control for reverse causality.
No previous study has explicitly allowed the effect of changes in board
gender-diversity on performance to differ between firms.
7
3
Data
To analyze whether greater gender diversity on boards of directors improves
firm performance, we use a micro-level longitudinal data-set of all Swedish
limited companies active at some point during 1997-2005. The data, collected from MM-Partner (now merged with PAR), a Swedish consulting
firm that gathers economic information from the Swedish Patent and Registration Office (PRV), is unique in both scope and detail and primarily used
by decision-makers and stakeholders in Swedish commercial life. We use
data on all continuing firms, i.e, those in existence during the whole period.
The data includes all variables in the annual reports, e.g., profits, number
of employees, salaries, fixed costs, and liquidity.
This data-set was merged with another data-set also from MM-Partner
on the characteristics of board members, including the gender of individual
members. This information was used to calculate the share of women on
boards of directors.
As a special case, we also analyze the effects of introducing a legislation
to increase gender diversity on boards. In accordance with a regulation
introduced by the Norweigan government in 2008, a board is defined as
"gender equal" if it has at least 40% of each gender. Companies with only
one board member were not included in the sample, and those with three
were classified as gender equal if there was at least one member of each
gender.
The merged sample covers 20,487 firms, almost ten-times more than any
8
previous study.2 The firms were observed over 9 years, which means that
our sample include 184,383 firm-years in total.
The inclusion of non-listed firms means that we cannot use performance
measures such as Tobin’s Q, market-to-book ratio, and abnormal returns,
since we do not observe any market value for these firms. Following Schrader
et al. (1997) and Adams and Ferreira (2009), we use returns on total assets
(ROA) as our measure of firm performance. To study whether gender diversity influence performance, we created a gender diversity index (Indexit )
based on the share of board members from the underrepresented gender. A
totally gender equal board gets an index of 100, while a board containing
only one gender gets an index of 0.
Figure 1 shows the trend in the share of women on boards in our sample
during the study period. Only 15.5% were women in 1997, increasing only
to 16.9% by 2005.3 These figures are not directly with the other studies
in Table 1 since those studied only large listed firms. Statistics from Upplysningscentralen (UC), similarly show the overall share of female board
members in Sweden as 16.7% in 2009, increasing approximately 0.3% per
year. In a study of only listed Swedish firms, Eklund et al. (2009) found
the share of female board members to be 14% in 2005, which is rather high
compared to most other countries (Adams and Ferreira, 2009).
Only 1.5% of the firms had a board of directors composed only of women
in 1997 (Figure 2), whereas 62.6% had only men. Women-only boards increased to 1.8% in 2005, while men-only boards decreased to 60.9%.
2
3
The median number of firms in previous studies was only 200 (Table 1).
At this rate, Sweden will have gender-equal boards in 2577, i.e., another 565 years.
9
1
0,9
0,8
0,7
0,6
0,5
0,4
0,3
0,2
0,169
0,155
0,1
0
1997
1998
1999
2000
2001
2002
2003
2004
2005
Figure 1: Average share of women on boards of directors, 1997-2005
1
0,9
0,8
0,7
0,626
0,609
0,6
0,5
Only women
0,4
Only men
0,3
0,2
0,1
0,018
0,015
0
1997 1998 1999 2000 2001 2002 2003 2004 2005
Figure 2: Share of boards of directors with only male or female members,
1997-2005
10
In estimations of our random-effects random-coefficients model presented
below, we took returns on total assets (πit ) as our dependent variable, and
included firm size (Sizeit ), firm age (Ageit ), board size (Boardsizeit ), and an
indicator variable equal to one if the firm was located in a metropolitan area
(Metroit ) as independent variables. In our most general specification, we
also include interaction terms between our index of gender diversity and firm
size, firm age and board size. Time-specific and industry-specific fixed effects
are also included in the estimated model to capture time - and industryinvariant heterogenity.
Table 2 reports means, standard deviations and definitions for the variables included in the analysis.
Table 2 about here
4
Empirical method
We want to estimate the causal impact of increased gender diversity on
boards of directors on firm performance, measured as returns on total assets. If increases in gender diversity on boards were independent random
events that varied in timing over firms, a correctly specified regression equation could capture any effects. However, firms that increased their gender
diversity were not randomly assigned, but rather chose it for themselves.
Thus, an empirical model has to account both for unobserved firm heterogenity and for reverse causality.
Unobserved firm heterogeneity has usually been dealt with by including
several covariates in the analysis, and adding fixed or random effects struc11
tures as well. The possibility of reverse causality has in most cases been
ignored, taking gender board composition as exogenous (Table 1).
Other studies have instead used instrumental variables (Bøhren and
Strøm, 2007; Adams and Ferreira, 2009), but a problem discussed in these
papers is lack of clearly exogenous instruments. Ahren and Dittmar (2012)
used the newly-legislated gender quota in Norway as a natural experiment to
solve this problem, with cross-sectional variation in the proportion of female
board members prior to the quota as an exogenous instrument.4
We include several covariates and a rather sophisticated fixed- and randomeffects structure to capture unobserved industry- and firm-characteristics
that could affect our results. In addition, we employed a test for causality
to verify that causes came before consequences, not vice versa (Granger,
1969).5
Firms are assumed to be forward-looking and boards, to the best of
their ability, to be trying to maximize present value of the sum of current
and future profits. We view the boards as the organizational unit responsible for long-run strategic decisions, with the CEO responsible for day-to-day
operational decisions. Thus we expect that, although changes in board com4
Adams and Ferreira (2009) used an innovative approach. They included the fraction
of male board members who sat on other boards with female members as an instrumetal
variable. This is probably a good instrument since it should be correlated with gender
diversity on the board, but not directly with firm performance. However, we do not have
the information that is needed to include such an instrument in our estimations. And
we cannot follow Ahren and Dittmar’s (2012) approach since no natural experiment that
changed the proportion of female board members occured during the study period.
5
Since we could not find any valid exogenous instruments in our dataset, we instead
employed the Granger test. Endogenity caused by other factors than reverse causality
(such as, for example, measurement errors in the data) would be a problem even if we had
been able to create instruments. The potential instruments would then be plagued by the
same type of endogeneity they were created to address.
12
position might have an immediate effect, they more likely have an effect with
some time-lag.
A general-to-specific estimation strategy is used. We will start first discuss our most general model and then how it is reduced to a more specific
econometric model, where the impact of potentially endogenous independent variables is reduced to a minimum. Our most general specification can
be written as
πit = β 0 + β 1 Indexit + β 2 Boardsizeit
(1)
+β 3 (Indexit ∗ Boardsizeit ) + β 4 Sizeit
+β 5 (Indexit ∗ Sizeit ) + β 6 Ageijt β 3 (Indexit ∗ Metroit )
+β 7 (Indexit ∗ Ageit ) + β 8 Metroit +
2005
β t Y eart
t=1998
+
15
j=1
β j Snij +
15
β tj (Snij ∗ T rendt ) + uit ,
tj=1
where πit measures returns on total assets of firm i (i = 1, 2, 3, ..., 20487) at
time t (t = 1997, 1998, 1999, .., 2005) measured as the return on total assets;
Indexit indicates the share of board members from the underrepresented
gender; Boardsizeit is the number of members on the board of directors;
Sizeit is the number of employees in the firm; Ageit is the number of years
that the firm has been active in Sweden; Metroit is one if the firm was located
in one of Swedens three large metropolitan areas (Stockholm, Gothenburg,
and Malmö), otherwise zero; while Indexit ∗ Boardsizeit , Indexit ∗ Sizeit ,
Indexit ∗ Ageit , and Indexit ∗ Metroit are interaction terms.
13
Time-specific fixed-effects (Y eart ) are included to capture time-variant
heterogeneity in firm profits (e.g., business-cycle movements or time-trends
in revenues or costs), while industry-specific fixed effects (Snij ) are included
to capture time-invariant heterogeneity across industries (e.g., differences in
cost-structure or number of competitors if unchanged).6 An interaction term
for industry-specific time trends (Snij ∗ T rendt ) is also included. Finally,
the residual (or heterogeneity) term is specified as
uit = vi + µi Indexit + εit
(2)
where vi ∼ iid N (0, σ2v ) are firm-specific random-effects included to capture time-invariant heterogeneity across firms, and µi ∼ iid N (0, σ2µ ) are
firm-specific random-coefficients (related to the gender-diversity index) included since there is no reason to believe that firms are affected equally when
increasing gender diversity of their board of directors.7 The specific randomeffects and random-coefficients are assumed independent of each other. The
most general model (Model I) estimated can thus be written as8
6
Firms are classified into industry j (j = 1, 2, ...15) according to the Standard Industrial Classification (SNI), a classification based on firm activity commonly employed
by Statistics Sweden (Statistiska Centralbyrån). The industries included are agriculture,
fishing, mining, manufacturing, electricity and heating, construction, wholesale and retail,
tourism, transportation, financial services, real estate rental, education, health care, and
community services. A final group consists of firms that did not report any SNI-code even
though they are obligated by law to do so.
7
These random-coefficients capture all changes affecting firm profits that are highly
correlated with changes in board composition, not just only effects of changes in board
composition on individual firm profits.
8
We test for autocorrelation by regressing the residual on lagged values of the residual
(5 lags) and all other independent variables used in the original estimations. In all estimated models, the autocorrelation coefficients lie below 0.21. Thus, we do not consider
autocorrelation an important problem.
14
πijt = β 0 + (β 1 + µi )Indexit + β 2 BoardSizeit
(3)
+β 3 (Indexit ∗ BoardSizeit ) + β 4 Sizeit
+β 5 (Indexit ∗ Sizeit ) + β 6 Ageit
+β 7 (Indexit ∗ Ageit ) + β 8 Metroit
2005
+β 3 (Indexit ∗ Metroit ) +
β t Y eart
t=1998
+
15
β j Snij +
j=1
15
β tj (Snij ∗ T rendt ) + vi + εit
tj=1
We also estimate several variants of this model. First, we remove the interaction terms (Model II). The index parameter (β 1 ) will be an unbiased
estimate of the average effect of changing gender diversity on boards if the
interaction variables are left out because it captures the linear relationship
between the index and them (e.g. Greene, 2003, Chapter 8).9
We also remove variables ( Sizeit , Ageit and Metroit ) with insignificant
parameter estimates (Model III).
Finally, due to low variation in the number of board members in most
firms during the period, Boardsizeit has most likely a very limited influence
since this effect is also captured by firm-specific random effects. It is also
likely that the bulk of the correlation between Boardsizeit and the index
for gender diversity is due to the effect that chages in board composition
has on the size of the board, with only a smaller fraction due to exogenous
9
One requirement here is that none of the other variables in the analysis are correlated
with the interaction terms.
15
variation in Boardsizeit . Therefore, not controlling for Boardsizeit might
give a better estimate of the total effect sought. The final specification
(Model IV) can thus be written as10
πit = β 0 + (β 1 + µi )Indexit +
2005
β t Y eart +
t=1998
+
15
15
β j Snij
(4)
j=1
β tj (Snij ∗ T rendt ) + vi + εit
tj=1
To control for reverse causality, we test whether "causes" happened before consequences or vice versa.11 Our model assumes that the composition of the board of directors actually causes profitability. A Granger-type
causality test then means that we should not be able to find any correlation
between the independent variable representing gender diversity in time t and
profitability in previous periods, while such a correlation should be present
when profits are measured at time t or subsequently.
10
This specification could also provide biased estimates if the index of gender diversity is
correlated with the random-effects and random-coefficients. In order to investigate if this
is a serious problem, we have also estimated standard fixed-effects (FE) and random-effects
(RE) models based on the specification in Equation (3) above. In all these specifications,
the impact of increased gender diversity on ROA is negative; the parameter estimates for
β 1 are: −0.013 (RE, RC); −0.011 (RE); and −0.009 (FE). Although the estimated impact
is somewhat smaller in the fixed-effects and random-effects models, these differences are
not statistically significant at conventional levels.
11
The Granger test gives necessary, but not sufficient conditions for causality. This
means there could exist some omitted variable sufficiently highly correlated to changes in
board composition which is the true causal factor driving our results. However, since our
model takes unobserved heterogeneity at industry, firm, and over time into account, we
find this possibility remote.
16
5
Results
We will first present the results from estimating Model I-IV (Table 3 below),
then those from our Granger causality test, then those from a test of the
relative profitability of firms fulfilling the suggested legal requirement that
at least 40% of board members be of the underrepresented gender, and
finally those from a cross-sectional analysis of the random-coefficients from
the estimation of our main model.
Based on likelihood-values, we choose to present the impact on firm
profits of increased gender diversity on the board of directors on firms profits
in period t + 3. This makes sense because, while CEOs are responsible
for day-to-day operational decisions, boards are responsible for long run
strategic decisions that mainly affect performance with a lag. Results related
to previous periods are presented in Tables A1-A6 in Appendix 1.
Table 3 about here
In all estimated models, a more gender diverse board has a negative
impact on firm performance (∂πijt /∂Indexijt ), a 10% increase in the participation of the underrepresented gender decreasing return on total assets by
0.13%. The random-effects and random-coefficients parameters (vi and µi )
are statistically significant for all models, indicating that not including them
would lead to biased estimates. The estimated random-coefficient term also
shows that there is considerable heterogeneity among firms regarding how
increased gender diversity on boards of directors affects performance. In accordance with previous studies (Hermelin and Weisbach, 2001), board size
is negatively related to performance.
17
The effect of increased gender diversity on firm performance in our most
specific model (Model IV) is illustrated in Figure 3 for all periods.
Figure 3 about Here
There is no apparent effect on performance before gender diversity on the
board increased, but an increasingly negative effect afterwards, suggesting
causality. The effect is statistically significant from period t + 2.12
Model IV might suffer from missing-variable bias since board size, although endogenously determined, seem to influence returns on total assets
(i.e., statistically significant results in Models I-III). It can be shown (e.g.
Studenmund, 2006: Ch. 6) that the effect of such bias can be expressed as
Bias = β Boardsizeit ∗ corr(Boardsizeit , Indexit )
(5)
where β Boardsizeit is the parameter estimate related to board size if it had
been available. Previous studies and our estimations indicate that β Boardsizeit
is negative. The correlation between board size and the gender diversity index is also negative, implying that the omitted variable bias is positive.
Thus, the estimated negative effect of increased gender diversity on the
board when board size variable has been excluded should be interpreted as
a lower bound of the actual negative effect; i.e., the actual effect may be
larger.
Results for all models and periods are summarized in Table 4. Our results
do not seem at all sensitive to the choice of model. Gender diversity on the
12
Previous studies have often estimated the immediate effect on firm performance of
gender diversity on the boards. Significant results might thus have been discarded, since
our results suggest that gender diversity influence firm performance with a lag.
18
board thus seems to have a clear negative impact on firm performance. This
is in accordance with results from Bøhren and Strøm (2007), Adams and
Ferreira (2009), and Ahren and Dittmar (2012), supporting theories that
more gender diverse boards are associated with costs (e.g., conflicts, slower
decision-making) which reduce profitability.
Table 4 about here
The Norweigan, Spanish, and French governments each imposed laws requiring gender balanced boards of directores. Politicians in other countries,
such as Sweden, have threatened similar actions if firms do not voluntarily choose to include more women on the boards. To study whether firms
fullfilling this legal requirement are more or less profitable, we deleted the
gender diversity index in our estimations and substituted an indicator variable with value one if at least 40% of the board members of firm i are of
the underrepresented gender. The results, measuring the impact on performance in period t + 3, are presented in Table 5. Results for all other periods
are presented in Table A7-A12 in Appendix 2.
Table 5 about here
The results suggest that the proposed requirement that at least 40% of
the underrepresented gender be represented on boards of directors would
reduce performance. Ceteris paribus, firms fulfilling this requirement had
0.48% and 0.82% lower return on total assets. Board size is again negatively
related to performance, and the random-effects and random-coefficients are
again statistically significant. The effect on performance of having at least
19
40% of each gender on boards of directors as measured by Model IV is
illustrated in Figure 4.
Figure 4 about Here
As with the results presented in Figure 3, there is no apparent effect on
performance of increasing gender diversity on boards before period t, but an
increasingly negative effect afterwards, suggesting a causal interpretation of
our results. Again, the effect is statistically significant from period t + 2 at
the 10% significance level.
Results for all models and periods are summarized in Table 6. The
effect appears more clearly and stronger in Model I-III. Firms that increase
gender diversity on their board of directors to at least the suggested legal
requirement had lower returns on total assets in periods t + 1, t + 2, and
t + 3, with the effect increasing over time.
Table 6 about here
In these random-effects random-coefficients models (I-IV), the effect of
a change in board composition on return on assets for an individual firm is
given by β 1 + µi . Although the effect on the average firm is negative, there
is considerable heterogenity on how more gender balanced boards influence
firm performance. To analyze the differences, we created a new indicator
variable taking the value one when incresed gender diversity on the board
had a positive effect on ROA, i.e., yi = 1 if β 1 + µi > 0, otherwise zero.
Frequencies and shares of the total population of firms for which yi =
1 are presented in Table 7 by 2-digit industry. Overall there were 2, 944
20
observations where yi = 1 and 16, 847 observations where yi = 0. In no
industry did the majority of firms experience a positive effect on ROA of
greater gender balance on their board. The shares varied from 0% (fishing)
to 33.5% (health care).
Table 7 about here
To analyze what determined the probability of observing a positive affect
on ROA of increasing gender diversity on the board (i.e., yi = 1 = β 1 +µi > 0
for the individual firm), we estimate the logistic regression model
Pr[yi = 1|Xi ] = f(M Board Si zeit , M Si zeit , MAgeit , Metroit , Snij )
(6)
where all variables are as above but the prefix M now indicates that all
firm-specific variables are measured at their mean during the study period.
The results are presented in Table 8. All available data are used in Model
1, while Model 2 excludes observations more than two standard deviations
above or below the mean of β 1 + µi , though this made almost no difference
in the results.
The odds-ratio for an independent variable X is given by
odds-ratioXi =
Pr[yi = 1|Xi = 1]/ Pr[yi = 1|Xi = 0]
.
Pr[yi = 1|Xi = 0]/ Pr[yi = 0|Xi = 0]
Hence, an odds-ratio of one means that the variable does not affect the
probability of yi = 1, whereas an higher odds-ratio means that the variable
increases that probability.
21
There is a statistically significant positive correlation between the size of
the board and the probability of a positive effect of increased gender diversity
on ROA, but the effect is small. There is also a statistically significant
positive correlation with firm size, but the effect is even smaller.
Table 8 about here
Finally, firms classified into 15 industries according to their two-digit
SNI-codes (as on Table 7) are compared to manufacturing. The estimated
industry-specific fixed effects (excluded from Table 8 to save space) indicate
that greater gender balance on the board is more likely to have a positive
effect on firm performance in wholesale and retailing, tourism, education,
health care, and community services, but more likely to have a negative
effect in electricity and heating.
6
Summary and conclusions
We studied whether greater gender diversity on the boards of directors was
associated with better firm performance.
There are several theoretical arguments why more women on boards
might improve firm performance. A more gender-diversified board might
lead to a better understanding of markets that are themselves diversified
in terms of gender; might increase firm creativity and innovativness; might
improve the decision-making as more alternatives and their consequences
are evaluated; might have a positive effect on consumer behavior by improving the image of the firm; and might have positive dynamic effects by
22
improving the career opportunities of women, thereby increasing the quality
of candidates for top positions in the future.
However, results from previous empirical studies are ambigous. They
also tend to suffer from three serious methodological problems. First, the
results have been based solely on data from the largest companies (e.g.,
Fortune 500 companies). Thus results have not been representative, since
most companies are small. We instead used longitudinal data from the
period 1997-2005 covering 20,487 Swedish limited firms of all sizes.
Second, firms that increased gender diversity on their board and improved their performance might have performed better even if they had not
done so. To account for unobserved firm heterogenity in profits while also allowing for unobserved heterogeneity in the effects on performance of greater
gender diversity in the boardroom, a random-effects and random-coefficients
model with time-specific and industry-specific fixed effects was estimated.
This also made it possible to study which industries and firms experienced
the largest effect on profitabillity of changes in the gender composition of
the board.
Finally, when estimating effect on firm performance of increased gender
diversity on the board, it is important to consider reverse causality. High
profitable firms might increase gender diversity on their boards (Adams and
Ferreira, 2009). An analysis that does not consider this might conclude
that gender diversity on boards affect firm performance, when it is firm
performance that affects the gender composition of the boards. Following
Granger (1969), we therefore tested whether "causes" came before or after
consequences.
23
An increase in gender diversity on the board is found to have a negative
effect on returns on total assets after two years, while not related to firm
performance in earlier years. In no industry did the majority of firms experience a positive effect on ROA of greater gender balance on their board.
The shares of firms with positive effect ranged from zero for the fishing industry to 33.5% for health care. Our result are in accordance with those
from Bøhren and Strøm (2007), Adams and Ferreira (2009), and Ahren and
Dittmar (2012), all supporting theoretical predictions that more gender diversity on boards would have a negative effect on performance. Our results
contradict previous studies that found either no effect of increased gender
diversity on performance or found a positive effect. However, all studies that
have controlled for both reverese causality and unobserved firm heterogenity
have found a negative effect or none.
We found no correlation between performance in previous periods and
greater gender diversity on boards in later periods. That is a necessary, but
not sufficient, condition for direct causality of gender balanced boards on
firm performance. For our findings not to be a causal effect of greater gender
diversity on boards there must be some other factor sufficiently correlated
with gender balance (in different firms at different times) that caused poorer
performance.
Advocates for laws to increase gender diversity on boards of directors
often argue that it would improve firms’ performance. Our results do not
support this claim. On the contrary, our results suggest that legal requirements to increase the gender balance of boards might reduce firm performance. We found the negative effect on firm performance of greater gender
24
diversified boards when firms changed their boards voluntarily. The negative effect could be even stronger if firms were forced to implement greater
gender balance on boards of directors.
25
References
Adams, R.B., and F. Ferreira (2007), "A Theory of Friendly Boards", Journal of Finance, 62, 217—250.
Adams, R.B., and F. Ferreira (2009), "Women in the Boardroom and Their
Impact on Governance and Performance", Journal of Financial Economics, 94, 291-309.
Ahren, K.R., and Dittmar, A (2012), "The Changing of the Boards: The
Impact on Firm Valuation of Mandated Female Board Representation",
The Quarterly Journal of Economics, 127, 137—197.
Agrawal, A., and C.R. Knoeber (1996). "Firm Performance and Mechanisms to Control Agency Problems between Managers and Shareholders", Journal of Financial and Quantitative Analysis, 31, 377-397.
Arrow, J. A. (1951), Social Choice and Individual Values, New haven: Yale
University Press.
Ayusu, S., M.A. Rodriquez., R. Garcia., and M.A. Ariño (2007), "Maximizing Stakeholders’ Interests: An Empirical Analysis of the Stakeholder
Approach to Corporate Governance", Working Paper No 670, IESE Business School, University of Navarra.
Bantel, K.A., and S.E. Jackson (1989), "Top Managment and Innovations in
Banking: Does the Composition of the Top Team Make a Difference?",
Strategic Managment Journal, 10, 107-124.
Bell, L.A. (2005), "Women-led Firms and the Gender Gap in Top Executive
Jobs", IZA Discussion Paper 1689, IZA, Bonn.
26
Burke, R.J., and C.A. McKeen (1996), "Do Women at the Top Make a Difference? Gender Proportions and Experiences of Managerial and Professional Women", Human Relations, 49, 1093-1104.
Bøhren, Ø., and R.Ø. Strøm (2007), "Aligned, Informed, and Decisive: Characteristics of Value-creating Boards", working paper, Norweigan School
of Managment BI, Oslo.
Campbell, K., and A. Minguez-Vera (2008), "Gender Diversity in the Boardroom and Firm Financial Performance", Journal of Business Ethics, 83,
435-451.
Carter, D.A., B.J. Simkins., and W.G. Simpson (2003), "Corporate Governance, Board Diversity, and Firm Value", Financial Review, 38, 33-53.
Cox, T.H., and S. Blake (1991), "Managing Cultural Diversity: Implications
for Organizational Competitiveness", Academy of Management Executive, 5, 45-56.
Eisenhardt, K.M. (1989), "Making Fast Strategic Decisions in High-Velocity
Environments", Academy of Managment Journal, 32, 543-576.
Eklund, J., J. Palmgren., and D. Wiberg (2009), "Ownership Structure,
Board Composition, and Investment Performance", working paper no
129. The Ratio Institute, Stockholm.
Ely, R (1990). "The Role of Men in Relationship among Professional Women",
Academy of Management Best Paper Proceedings, 64-368.
Erhardt, N.L., J.D. Werbel., and C.B. Schrader (2003), "Board of Directors
Diversity and Firm Financial Performance", Corporate Governance: An
27
International Review, 11, 102-111.
Farell, K.A., and P.L. Hersch (2005), "Additions to Corporate Boards: The
Effect of Gender", Journal of Corporate Finance, 11, 85-206.
Granger, C.W.J (1969), "Investigating Causal Relations by Econometric
Models and Cross-Spectral Methods", Econometrica, 37, 424-438.
Greene, W.E. (2003). Econometric Analysis, New Jersey: Prentice Hall.
Hermalin, B., and M.S. Weisbach (1991), "The Effect of Board Compisition and Direct Incentives on Corporate Performance", Financial Managment, 20, 101-112.
Hermalin, B., and M.S. Weisbach (2003), "Boards of Directors as an Endogenously Determined Institution: A Survey of the Economic Literature",
FRBNY Economic Policy Review, April.
Judge, W.O., and A. Miller (1991). "Antecedents and Outcomes of Decisions
Speed in Different Environmental Contexts", Academy of Managment
Journal, 34, 449-463.
Kini, O., W. Kracaw, and S. Mian (1995), "Corporate Takeovers, Firm
Performance, and Board Composition", Journal of Corporate Finance,
1, 383-412.
Lau, D.C., and J.K. Murnighan (1998), "Demographic Diversity and Faultlines: The Compositional Dynamics of Organizational Groups", Academy
of Managment Review, 23, 325-340.
Miller, C.C., L.M. Burker, and W. Glick (1998), "Cognitive Diversity among
Upper-echelon Executives: Implications for Strategic Decision Processes",
28
Strategic Managment Journal, 19, 39-58.
Murray, A.I. (1989), "Top Managment Group Performance and Firm Performance", Strategic Managment Journal, 20, 125-141.
Richard, O.C. (2000), "Racial Diversity, Business Strategy, and Firm Performance: A Resource-Based View", Academy of Managment Journal,
43, 164-177.
Robinson, G., and K. Dechant (1997), "Building a Business Case for Diversity", Academy of Managment Executive, 11, 21-30.
Rose, C. (2007), "Does Female Board Representation Influence Firm Performance? The Danish Evidence", Corporate Governance: An International Review, 15, 404-413.
Rosenstein, S., and J.G. Wyatt (1990), "Outside Directors, Board Independence, and Shareholder Wealth", Journal of Financial Economics, 26,
175-191.
Shrader, C.B., V.B. Blackburn., and P. Iles (1997), "Women in Managment
and Firm Financial Performance: An Explorative Study", Journal of
Managerial Issues, 9, 355-372.
Singh, V., and S. Vinnicombe (2004), "Why so Few Women Directors in Top
UK Boardrooms: Evidence and Theoretical Explanations", Corporate
Governance: An International Review, 12, 479-488.
Smith, N., V. Smith., and M. Verner (2006), "Do Women in Top Management Affect Firm Performance? A Panel Study of 2500 Danish Firms",
International Journal of Productivity and Performance Management, 55,
29
569-593.
Tajfel, H., and J. Turner (1985), "The Social Identity of Inter-group Behavior", in Worchel, S., and W. Austin (Eds.), Psychology and Intergroup
Relations, 7-24. Chicago: Nelson-Hall.
Williams, K., and C. O´Reilly (1998), "Forty Years of Diversity Research:
A Review", in Staw, B.M., and L.L. Cummings (Eds.), Research in Organizational Behavior, 77-140, Greenwich, CT: JAI Press.
30
Table 1: Summary of previous studies
Country
Firmsf
Dep. var.a
Period
Rev.?b
FE?c
RC?d
USA
1,939
TQ, ROA
1996-03
Yes
Yes
No
Ahren and Dittmar (2012)
.Norway
248
AR, TQ
2001-09
Yes
Yes
No
Ayuso et al. (2007)
31 count
946
ROE
2004
No
No
No
Norway
NA
TQ
1989-02
Yes
Yes
No
Spain
68
TQ
1995-00
Yes
Yes
No
+
USA
638
TQ
1999
Yes
No
No
+
Sweden
105
MQ
1999-05
No
No
No
Erhardt et al. (2003)
USA
112
ROA, ROI
1993, 98
No
No
No
Farell & Hersch (2004)
USA
111
AR
1990-99
No
No
No
Denmark
459
ROA,
2005
No
No
No
Study
Adams & Ferreira (2009)
Bøhren & Strøm (2007)
Campbell & M-V (2008)
Carter et al. (2003)
Eklund et al. (2009)
Randoj et al. (2007)
Norway
MV/BV
Sweden
Richard (2000)
Rose (2007)
Shrader et al. (1997)
USA
63
ROE, LP
1995
No
No
No
Denmark
NA
TQ
1998-01
No
No
No
USA
200
ROA, ROE
1992
No
No
No
1993-01
Yes
Yes
No
1997-05
Yes
Yes
Yes
ROI, ROS
Smith et al (2006)
Denmark
2,500
GP, CM
OI, NI
Daunfeldt & Rudholm (2010)
Sweden
20,487
ROA
a TQ=Tobin’s Q; ROA=return on total assets; ROE=return on equity; ROI=return on investments;
AR=abnormal returns; MV/BV=market value through book value; LP=labor productivity;
ROS=return on sales; GP=gross profits/net sales; CM=contribution margin/net sales;
OI=operating income/net assets; NI=net income after tax/net assets.
b Does the study control for reverse causality?
c Does the study use fixed or random-effects to control for unobserved firm heterogenity?
d Does the study use random-coefficients to control for unobserved heterogeneity in the effect of gender
diversity on firm performance?
e What’s the impact of greater gender diversity in the boardroom on firm performance?
f NA=not reported. Bøhren & Strøm (2007) has 1,515 and Rose (2007) 443 firm-year observations.
Res?
+
Table 2: Means, standard deviations (SD) and definitions of variables
Variable
πit
Indexit
Mean
SD
Definition
7.06
28.19
Returns on total assets for firm
25.82
38.00
(((Underrepit /Boardsizeit )∗100) ∗ 2), where
i in period t
Underrepit is the share of the underrepresented
gender on the board of directors in firm
Dequalit
0.26
0.44
i in period t
Indicator variable equal to one when the underrepresented gender has at least 40% participation on
the board of directors in firm
Boardsizeit
Indexit *Boardsizeit
Sizeit
Indexit *Sizeit
Ageit
3.29
83.11
28.95
748.76
17.98
1.63
i in period t
Number of board members in firm
i in period t
127.40
244.83
Number of employees in firm
i in period t
13840.06
8.69
Firm age. Year of observation minus the registered
start year. The data is truncated; the
earliest registered start year was 1972.
Indexit *Ageit
Metroit
459.73
0.12
768.11
0.32
Indicator variable equal to one if firm
i was located
in Stockholm, Gothenburg or Malmö in period t.
Number of observations
184,383
Table 3: Estimation results, all models, ROA, period t + 3
Variable (parameter)
Indexit (β 1 )
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
Model 1
Model 2
Model 3
Model 4
0.0053
-0.012∗∗∗
-0.012∗∗∗
-0.013∗∗∗
(0.010)
(0.0038)
(0.0038)
(0.0038)
-0.44∗∗∗
-0.49∗∗∗
-0.46∗∗∗
(0.088)
(0.072)
(0.071)
-0.0020
(0.0023)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
0.0012∗
0.00077
(0.00071)
(0.00048)
-0.000010
(0.000012)
Ageit (β 6 )
Indexit ∗ Ageijt (β 7 )
0.032∗
0.023
(0.018)
(0.016)
-0.00061
(0.00043)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
0.11
-0.084
(0.46)
(0.41)
-0.011
( 0.012)
Constant (β 0 )
1630.83
1626.60
1582.50
1557.84
(2163.55)
(2163.49)
(2163.22)
(2163.07)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: std. errors in parentheses.
14.06∗∗∗
14.07∗∗∗
14.07∗∗∗
14.10∗∗∗
(0.13)
(0.13)
(0.13)
(0.13)
0.17∗∗∗
0.17∗∗∗
0.17∗∗∗
0.17∗∗∗
(0.0044)
(0.0044)
(0.0044)
(0.0044)
-0.014∗∗∗
-0.012∗∗∗
-0.012∗∗∗
-0.013∗∗∗
(0.0039)
(0.0038)
(0.0038)
(0.0038)
Table 4. Estimation results, Indexijt (β 1 )
Lag/lead
Model 1
Model 2
Model 3
Model 4
t−3
-0.0022
-0.0014
-0.0014
-0.0017
(0.0034)
(0.0033)
(0.0033)
(0.0033)
-0.0026
-0.0018
-0.0017
-0.0020
(0.0032)
(0.0032)
(0.0032)
(0.0032)
-0.00051
-0.00046
-0.00045
-0.0011
(0.0032)
(0.0032)
(0.0032)
(0.0032)
-0.0012
-0.00067
-0.00065
-0.0013
(0.0031)
(0.0032)
(0.0032)
(0.0032)
-0.0057∗
-0.0048
-0.0048
-0.0050
(0.0032)
(0.0032)
(0.0032)
(0.0032)
-0.0092∗∗∗
-0.0086∗∗
-0.0085∗∗
-0.0089∗∗
(0.0036)
(0.0035)
(0.0035)
(0.0035)
-0.014∗∗∗
-0.012∗∗∗
-0.012∗∗∗
-0.013∗∗∗
(0.0039)
(0.0038)
(0.0038)
(0.0038)
t−2
t−1
t
t+1
t+2
t+3
Note: Std. errors in parentheses.
Table 5: Estimation results, all models, ROA, period t + 3
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
-0.54
-0.88∗∗∗
-0.87∗∗∗
-0.54∗
(0.91)
(0.31)
(0.31)
(0.31)
-0.55∗∗∗
-0.53∗∗∗
-0.50∗∗∗
(0.074)
(0.071)
(0.070)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
0.23
(0.23)
Sizeit (β 4 )
DLegislationijt ∗ Sizeit (β 5 )
0.00091∗
0.00078
(0.00054)
(0.00048)
-0.00054
(0.00091)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.033∗
0.024
(0.017)
(0.016)
-0.053
(0.035)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
0.051
-0.16
(0.43)
(0.40)
-1.35
(0.97)
Constant (β 0 )
-244.68
-242.78
-287.98
-321.12
(1249.71)
(1249.71)
(1249.30)
(1249.20)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πijt /∂DLegislationit
Note: Std. errors in parentheses.
14.34∗∗∗
14.34∗∗∗
14.34∗∗∗
14.38∗∗∗
(0.13)
(0.13)
(0.13)
(0.13)
13.59∗∗∗
13.58∗∗∗
13.57∗∗∗
13.55∗∗∗
(0.37)
(0.37)
(0.37)
(0.37)
-0.91∗∗∗
-0.88∗∗∗
-0.87∗∗∗
-0.54∗
(0.32)
(0.31)
(0.31)
(0.31)
Table 6: Estimation results, DLegislationit (β 1 )
Lag/lead
t−3
t−2
t−1
t
t+1
t+2
t+3
Model 1
Model 2
Model 3
Model 4
0.10
-0.050
-0.049
0.22
(0.30)
(0.29)
(0.29)
(0.29)
-0.14
-0.25
-0.25
0.0030
(0.28)
(0.27)
(0.27)
(0.27)
-0.17
-0.23
-0.23
0.062
(0.28)
(0.27)
(0.27)
(0.27)
-0.33
-0.30
-0.30
-0.024
(0.27)
(0.27)
(0.27)
(0.26)
-0.54∗∗
-0.49∗
-0.49∗
-0.17
(0.27)
(0.27)
(0.27)
(0.27)
-0.82∗∗∗
-0.76∗∗∗
-0.76∗∗∗
-0.48∗
(0.29)
(0.29)
(0.29)
(0.28)
-0.91∗∗∗
-0.88∗∗∗
-0.87∗∗∗
-0.54∗
(0.32)
(0.31)
(0.31)
(0.31)
Note: Std. errors in parentheses.
Table 7: Frequencies of firms for which yi = 1 and yi = 0 for 2-digit industries, and overall.
yi = 1
yi = 0
Share yi = 1
170
987
0.147
78
549
0.124
Fishing (SNI3 )
0
30
0.000
Mining (SNI4 )
12
78
0.133
496
2979
0.143
41
197
0.172
Construction (SNI7 )
143
1476
0.088
Wholesale and retail (SNI8 )
484
3296
0.128
Turism (SNI9 )
101
325
0.237
Transportation (SNI10 )
196
1147
0.146
Financial services (SNI11 )
54
305
0.150
Real estate rental (SNI12 )
874
4700
0.157
Education (SNI13 )
31
94
0.248
Health care (SNI14 )
113
224
0.335
Community and personal services (SNI15 )
151
460
0.247
2944
16847
0.149
Industry
Other (SNI1 )
Agriculture (SNI2 )
Manufacturing (SNI5 )
Electricity and heating (SNI6 )
All industries
Note: Other (SNI1 ) refer to a group of firms not reporting any SNI-code to the authorities
although obligated by law to do so.
Table 8: Estimation results, odds ratios
Variable (parameter)
Model 1
Model 2
MBoard Si zeit (γ 1 )
1.06∗∗∗
1.08∗∗∗
(0.013)
(0.014)
1.00∗∗
1.00∗∗
(0.000066)
(0.000066)
1.00
1.00
(0.0025)
(0.0025)
1.09
1.09
(0.066)
(0.066)
M Si zeit (γ 2 )
MAgeit (γ 3 )
MMetroit (γ 4 )
Note: Standard errors in parenthisis.
0,01
0,005
t-3
t-2
t-1
t
t+1
0
t+2
-0,005
t+3
-0,01
-0,015
-0,02
-0,025
Figure 3: Point estimates of β 1 (index of gender diversity effect on firm
performance) together with 95% confidence intervals, by period, Model IV
Estimate with 95% C.I.
1
t-3
0,5
t-2
t-1
t
t+1
t+2
t+3
0
-0,5
-1
-1,5
Figure 4: Point estimates of β 1 (legislation, 40% of underrepresentated gender represented on boards) together with 95% confidence interval, by period,
Model IV.
Estimate with 95% C.I.
Appendix 1. Additional results, gender diversity
index
Table A1: Estimation results, all models, period t − 3
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
Indexit (β 1 )
0.025∗∗
-0.0014
-0.0014
-0.0017
(0.0099)
(0.0033)
(0.0033)
(0.0033)
-0.39∗∗∗
-0.50∗∗∗
-0.49∗∗∗
(0.092)
(0.074)
(0.073)
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
-0.0048∗∗
(0.0021)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
0.0025∗∗∗
0.0010∗∗
(0.00072)
(0.00042)
-0.000028∗∗
(0.000011)
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.0045
-0.0047
(0.017)
(0.016)
-0.00053
(0.00037)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.048
-0.17
(0.45)
(0.40)
-0.0052
(0.010)
Constant (β 0 )
-729.88
926.40
935.82
923.19
(1159.05)
(1987.74)
(1987.47)
(1987.36)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂π it /∂Indexit
Note: Std. errors in parentheses.
14.29∗∗∗
14.31∗∗∗
14.31∗∗∗
14.34∗∗∗
(0.12)
(0.12)
(0.12)
(0.12)
0.12∗∗∗
0.12∗∗∗
0.12∗∗∗
0.12∗∗∗
(0.0046)
(0.0046)
(0.0046)
(0.0047)
-0.0022
-0.0014
-0.0014
-0.0017
(0.0034)
(0.0033)
(0.0033)
(0.0033)
Table A2: Estimation results, all models, period t − 2
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
Indexit (β 1 )
0.034∗∗∗
-0.0018
-0.0017
-0.0020
(0.0092)
(0.0032)
(0.0032)
(0.0032)
-0.32∗∗∗
-0.47∗∗∗
-0.46∗∗∗
(0.085)
(0.069)
(0.068)
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
-0.0061∗∗∗
(0.0020)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
0.0016∗∗
0.00057
(0.00069)
(0.00039)
-0.000020∗
(0.000010)
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.016
-0.00048
(0.017)
(0.015)
-0.00088∗∗
(0.00035)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.17
-0.26
(0.44)
(0.39)
-0.0034
(0.0098)
Constant (β 0 )
-576.87
508.60
509.45
488.96
(934.53)
(1605.72)
(1605.41)
(1605.31)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: Std. errors in parentheses.
14.06∗∗∗
14.07∗∗∗
14.07∗∗∗
14.10∗∗∗
(0.12)
(0.12)
(0.12)
(0.12)
0.12∗∗∗
0.12∗∗∗
0.12∗∗∗
0.12∗∗∗
(0.0043)
(0.0043)
(0.0043)
(0.0043)
-0.0026
-0.0018
-0.0017
-0.0020
(0.0032)
(0.0032)
(0.0032)
(0.0032)
Table A3: Estimation results, all models, at time t − 1
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
Indexit (β 1 )
0.033∗∗∗
-0.00046
-0.00045
-0.0011
(0.0089)
( 0.0032)
( 0.0032)
( 0.0032)
-0.42∗∗∗
-0.51∗∗∗
-0.52∗∗∗
(0.078)
(0.064)
(0.063)
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
-0.0036∗
(0.0019)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
-0.0011∗
-0.00033
(0.00063)
(0.00037)
0.000015
(0.000010)
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.027∗
0.0041
(0.016)
(0.015)
-0.0012∗∗∗
(0.00033)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.17
-0.19
(0.41)
(0.37)
-0.00086
(0.0097)
Constant (β 0 )
1475.85
1457.76
1449.78
1425.28
(1302.00)
(1302.06)
(1301.76)
(1301.72)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: Std. errors in parentheses.
13.52∗∗∗
13.52∗∗∗
13.51∗∗∗
13.54∗∗∗
(0.11)
(0.11)
(0.11)
(0.11)
0.15∗∗∗
0.15∗∗∗
0.15∗∗∗
0.15∗∗∗
( 0.0035)
( 0.0035)
( 0.0035)
( 0.0035)
-0.00051
-0.00046
-0.00045
-0.0011
(0.0032)
(0.0032)
(0.0032)
(0.0032)
Table A4: Estimation results, all models, period t
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
Indexit (β 1 )
0.026∗∗∗
-0.00067
-0.00065
-0.0013
(0.0086)
(0.0032)
(0.0032)
(0.0032)
-0.39∗∗∗
-0.46∗∗∗
-0.46∗∗∗
(0.074)
(0.061)
(0.060)
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
-0.0032∗
(0.0019)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
-0.00042
-0.000014
(0.00059)
(0.00036)
8.41E −06
(9.62E −06 )
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.024
0.0075
(0.015)
(0.014)
-0.00096∗∗∗
(0.00032)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.26
-0.17
(0.40)
(0.36)
0.0054
(0.0097)
Constant (β 0 )
935.97
923.91
908.95
890.28
(1139.97)
(1139.99)
(1139.65)
(1139.62)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: Std. errors in parentheses.
12.79∗∗∗
12.79∗∗∗
12.79∗∗∗
12.82∗∗∗
(0.10)
(0.10)
(0.10)
(0.10)
0.16∗∗∗
0.16∗∗∗
0.16∗∗∗
0.16∗∗∗
(0.0035)
(0.0035)
(0.0035)
(0.0035)
-0.0012
-0.00067
-0.00065
-0.0013
(0.0031)
(0.0032)
(0.0032)
(0.0032)
Table A5: Estimation results, all models, period t + 1
Variable (parameter)
Indexit (β 1 )
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
Model 1
Model 2
Model 3
Model 4
0.016∗
-0.0048
-0.0048
-0.0050
(0.0086)
(0.0032)
(0.0032)
(0.0032)
-0.47∗∗∗
-0.52∗∗∗
-0.50
(0.079)
(0.064)
(0.063)
-0.0020
(0.0019)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
0.0012∗
0.00069∗
(0.00063)
(0.00041)
-0.000010
(.000010)
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.028∗
0.011
(0.016)
(0.015)
-0.00086∗∗∗
(0.00034)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.18
-0.093
(0.42)
(0.38)
0.0041
(0.0098)
Constant (β 0 )
-147.39
-138.54
-159.57
-197.35
(793.90)
(793.89)
(793.33)
(793.30)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: Std. errors in parentheses.
13.74∗∗∗
13.74∗∗∗
13.74∗∗∗
13.78∗∗∗
(0.11)
(0.11)
(0.11)
(0.11)
0.13∗∗∗
0.13∗∗∗
0.13∗∗∗
0.13∗∗∗
(0.0041)
(0.0042)
(0.0042)
(0.0042)
-0.0057∗
-0.0048
-0.0048
-0.0050
(0.0032)
(0.0032)
(0.0032)
(0.0032)
Table A6: Estimation results, all models, period t + 2
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
Indexit (β 1 )
0.00074
-0.0086∗∗
-0.0085∗∗
-0.0089∗∗
(0.0095)
(0.0035)
(0.0035)
(0.0035)
-0.40∗∗∗
-0.41∗∗∗
-0.40∗∗∗
(0.083)
(0.068)
(0.067)
BoardSizeit (β 2 )
Indexit ∗ BoardSizeit (β 3 )
-0.00066
(0.0021)
Sizeit (β 4 )
Indexit ∗ Sizeit (β 5 )
0.0011
0.00068
(0.00066)
(0.00044)
-8.41E −06
(0.000011)
Ageit (β 6 )
Indexit ∗ Ageit (β 7 )
0.021
0.014
(0.017)
(0.015)
-0.00043
(0.00038)
Metroit (β 8 )
Indexit ∗ Metroit (β 9 )
-0.051
-0.025
(0.44)
(0.39)
0.0013
(0.011)
Constant (β 0 )
-10.91
-3.86
-29.96
-59.05
(993.87)
(993.84)
(993.36)
(993.27)
Random-effects/random-coefficients parameters (variable)
vi
µi (Indexit )
∂πit /∂Indexit
Note: Std. errors in parentheses.
13.79∗∗∗
13.79∗∗∗
13.80∗∗∗
13.82∗∗∗
(0.12)
(0.12)
(0.12)
(0.12)
0.16∗∗∗
0.16∗∗∗
0.16∗∗∗
0.16∗∗∗
(0.0043)
(0.0043)
(0.0043)
(0.0043)
-0.0092∗∗∗
-0.0086∗∗
-0.0085∗∗
-0.0089∗∗
(0.0036)
(0.0035)
(0.0035)
(0.0035)
Appendix 2. Additional results, legal requirement
dummy
Table A7: Estimation results, all models, period t − 3
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
0.94
-0.050
-0.049
0.22
(0.93)
(0.29)
(0.29)
(0.29)
-0.55∗∗∗
-0.53∗∗∗
-0.52∗∗∗
(0.076)
(0.073)
(0.072)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
0.16
(0.22)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
0.0012∗∗∗
0.0010∗∗
(0.00043)
(0.00040)
-0.0017∗
(0.00092)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.0067
-0.0053
(0.017)
(0.015)
-0.068∗∗∗
(0.032)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
-0.057
-0.17
(0.43)
(0.40)
-0.71
(0.89)
Constant (β 0 )
938.69
919.29
929.72
917.74
(1992.34)
(1992.32)
(1992.07)
(1991.97)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂π it /∂DLegislationit
Note: Std. errors in parentheses.
14.50
14.50
14.51
14.55
(0.12)
(0.12)
(0.12)
(0.12)
12.93
12.93
12.92
12.89
(0.38)
(0.38)
(0.38)
(0.38)
0.10
-0.050
-0.049
-0.024
(0.30)
(0.29)
(0.29)
(0.26)
Table A8: Estimation results, all models, period t − 2
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
1.67∗∗∗
-0.25
-0.25
0.0030
(0.85)
(0.27)
(0.27)
(0.27)
-0.48∗∗∗
-0.48∗∗∗
-0.47∗∗∗
(0.071)
(0.069)
(0.068)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
-0.0025
(0.20)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
0.00061
0.00054
(0.00040)
(0.00039)
-0.00065
(0.00087)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.016
-0.0015
(0.016)
(0.015)
-0.092∗∗∗
(0.030)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
0.0026
-0.19
(0.42)
(0.39)
-1.09
(0.82)
Constant (β 0 )
523.69
495.80
498.61
478.47
(1607.56)
(1607.56)
(1607.25)
(1607.15)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πit /∂DLegislationit
Note: Std. errors in parentheses.
14.55∗∗∗
14.55∗∗∗
14.55∗∗∗
14.59∗∗∗
(0.12)
(0.12)
(0.12)
(0.12)
11.18∗∗∗
11.19∗∗∗
11.19∗∗∗
11.16∗∗∗
(0.36)
(0.36)
(0.36)
(0.36)
-0.14
-0.25
-0.25
0.0030
(0.28)
(0.27)
(0.27)
(0.27)
Table A9: Estimation results, all models, period t − 1
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
1.82∗∗
-0.23
-0.23
0.062
(0.81)
(0.27)
(0.27)
(0.27)
-0.51∗∗∗
-0.51∗∗∗
-0.52∗∗∗
(0.065)
(0.063)
(0.063)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
-0.058
(0.19)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
-0.00043
-0.00035
(0.00038)
(0.00036)
0.00059
(0.00088)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.019
0.0027
(0.015)
(0.015)
-0.097∗∗∗
(0.028)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
-0.13
-0.22
(0.40)
(0.37)
-0.56
(0.82)
Constant (β 0 )
1425.43
1400.53
1395.43
1371.32
(1305.59)
(1305.61)
(1305.31)
(1305.29)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πit /∂DLegislationit
Note: Std. errors in parentheses.
14.01∗∗∗
14.01∗∗∗
14.00∗∗∗
14.04∗∗∗
(0.11)
(0.11)
(0.11)
(0.11)
13.00∗∗∗
13.00∗∗∗
13.00∗∗∗
12.98∗∗∗
(0.31)
(0.31)
(0.31)
(0.31)
-0.17
-0.23
-0.23
0.062
(0.28)
(0.27)
(0.27)
(0.27)
Table A10: Estimation results, all models, period t
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
2.42∗∗∗
-0.30
-0.30
-0.024
(0.77)
(0.27)
(0.27)
(0.26)
-0.46∗∗∗
-0.48∗∗∗
-0.47∗∗∗
(0.060)
(0.060)
(0.059)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
-0.22
(0.18)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
-0.00012
-0.000032
(0.00037)
(0.00035)
0.00042
(0.00079)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.025
0.0083
(0.014)
(0.014)
-0.11∗∗∗
(0.027)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
-0.030
-0.13
(0.38)
(0.36)
-0.64
(0.80)
Constant (β 0 )
-505.52
-497.25
-513.91
-550.70
(660.24)
(660.27)
(659.66)
(659.61)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πit /∂DLegislationit
Note: Std. errors in parentheses.
13.30∗∗∗
13.30∗∗∗
13.30∗∗∗
13.30∗∗∗
(0.10)
(0.10)
(0.10)
(0.10)
13.37∗∗∗
13.36∗∗∗
13.36∗∗∗
13.36∗∗∗
(0.29)
(0.29)
(0.29)
(0.29)
-0.33
-0.30
-0.30
-0.024
(0.27)
(0.27)
(0.27)
(0.26)
Table A11: Estimation results, all models, period t + 1
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
1.40∗
-0.49∗
-0.49∗
-0.17
(0.79)
(0.27)
(0.27)
(0.27)
-0.52∗∗∗
-0.53∗∗∗
-0.51∗∗∗
(0.065)
(0.063)
(0.062)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
-0.080
(0.19)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
0.00076∗
0.00070∗
(0.00043)
(0.00040)
-0.00038∗
(0.00080)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.025
0.014
(0.015)
(0.015)
-0.089∗∗∗
(0.029)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
-0.043
-0.13
(0.40)
(0.37)
-0.54
(0.83)
Constant (β 0 )
1552.90
1529.07
-147.93
-550.70
(1359.67)
(1359.69)
(786.31)
(659.62)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πit /∂DLegislationit
Note: Std. errors in parentheses.
13.88∗∗∗
13.88∗∗∗
13.88∗∗∗
13.34∗∗∗
(0.11)
(0.11)
(0.11)
(0.10)
12.05∗∗∗
12.04∗∗∗
12.04∗∗∗
13.36∗∗∗
(0.32)
(0.32)
(0.32)
(0.29)
-0.54∗∗
-0.49∗
-0.49∗
-0.17
(0.28)
(0.27)
(0.27)
(0.27)
Table A12: Estimation results, all models, period t + 2
Variable (parameter)
Model 1
Model 2
Model 3
Model 4
DLegislationit (β 1 )
0.41
-0.76∗∗∗
-0.76∗∗∗
-0.48∗
(0.84)
(0.29)
(0.29)
(0.28)
-0.47∗∗∗
-0.46∗∗∗
-0.44∗∗∗
(0.070)
(0.068)
(0.067)
BoardSizeit (β 2 )
DLegislationit ∗ BoardSizeit (β 3 )
0.017
(0.20)
Sizeit (β 4 )
DLegislationit ∗ Sizeit (β 5 )
0.00074
0.00067
(0.00048)
(0.00043)
-0.00033
(0.00084)
Ageit (β 6 )
DLegislationit ∗ Ageit (β 7 )
0.027∗
0.017
(0.016)
(0.015)
-0.061∗∗
(0.031)
Metroit (β 8 )
DLegislationit ∗ Metroit (β 9 )
0.22
-0.045
(0.42)
(0.39)
-1.58∗
(0.88)
Constant (β 0 )
1301.68
1284.47
1251.53
1228.07
(1703.93)
(1703.92)
(1703.63)
(1703.49)
Random-effects/random-coefficients parameters (variable)
vi
µi (DLegislationit )
∂πit /∂DLegislationit
Note: Std. errors in parentheses.
14.29∗∗∗
14.29∗∗∗
14.29∗∗∗
14.33∗∗∗
(0.12)
(0.12)
(0.12)
(0.12)
11.79∗∗∗
11.79∗∗∗
11.78∗∗∗
11.76∗∗∗
(0.36)
(0.36)
(0.36)
(0.36)
-0.82∗∗∗
-0.76∗∗∗
-0.76∗∗∗
-0.48∗
(0.29)
(0.29)
(0.29)
(0.28)