Peer Firms and Board Appointments in Family Firms

Peer firms and board appointments in family firms

Mario Daniele Amore Bocconi University

I show that a family firm’s decision to appoint non-family directors is strongly influenced by neighboring companies. Confirming the role of geographic proximity, I find that this effect becomes smaller when the distance between a firm and its peers increases, and insignificant when firms are randomly allocated to geographic areas. Studying the mechanisms at play, I suggest that peer effects are driven by imitation of leading firms and social interactions within the local community.

Keywords: boards; corporate governance; geography; family firms; peer effects

JEL classification: G34; D21; D23; Z13

______Email address: [email protected]. I am grateful to Ana Albuquerque, Guido Corbetta, Alfonso Gambardella, Giovanni Valentini, Hong Zhang as well as seminar participants at the Universidade Católica Portuguesa, Royal Economics Society Annual Conference (Brighton), Workshop on Economics of Entrepreneurship and Innovation (Trier) and Asian Finance Association Conference (Changsha) for useful comments and suggestions. 1. Introduction The recent wave of corporate governance reforms following the scandals of the early 2000s has fueled an intense debate on the role of independent directors and their effect on firm outcomes (see e.g. Armstrong et al. 2014; Duchin et al. 2010; Laux 2008; Nguyen and

Nielsen 2010; Schmidt 2015). Building on Fama and Jensen (1983), a prevalent approach in the literature suggests that independent directors contribute to their companies by providing monitoring service and curtailing agency conflicts (Adams and Ferreira 2007; Harris and

Raviv 2008).

Family firms, which represent a major economic force in both developed and developing economies (Claessens et al. 2002; Faccio and Lang 2002; La Porta et al. 1999), represent an ideal context to study board monitoring. Dating back to Demsetz and Lehn

(1985), it has been argued that concentrated ownership mitigates problems of managerial expropriation typical in companies with small and fragmented shareholders. However, a different agency conflict arises in that families can exploit their decision-making power to reap private benefits at the expenses of non-family shareholders (e.g. via nepotistic managerial appointments) or even channel directly resources from minorities via tunneling

(Morck and Yeung 2003; Villalonga and Amit 2006). Empirical studies confirm that family firms (especially those led by family heirs) have a strong tendency to adopt poor managerial practices (Bloom and Van Reenen 2007), potentially detrimental for non-family stakeholders

(Bennedsen et al. 2007; Cucculelli and Micucci 2008).

A corporate governance mechanism that can restrain families’ tendency to engage in wasteful managerial decisions is represented by non-family independent board members, who not only contribute to executive decisions providing expert counseling, but are also key to limit expropriation threats and increase minorities’ power vis-à-vis controlling owners.

2 Consistent with a beneficial effect on the quality of family business governance, Anderson and Reeb (2004) find that non-family independent directors increase family firms’ performance. Wu and Sorensen (2013) find that independent directors mitigate asymmetric information problems – and may thus alleviate problems of opaqueness (Anderson et al.

2009) and weak disclosure (Chen et al. 2008) induced by a tight family control. Ansari et al.

(2014) document that independent directors reduce the likelihood of appointing family

CEOs, which are often selected from a small pool of heirs on the basis of kinships rather than talent (e.g. Mehrotra et al. 2013; Perez-Gonzalez 2006). Confirming these results, I show that firms with non-family directors are more likely to be run by non-family CEOs, less likely to have family CEO-Chairman duality, and exhibit less concentrated ownership structures.

Despite such importance for the quality of family firms’ corporate governance, the determinants of non-family directorship remain surprisingly underexplored. Going beyond the role of traditional family and firm-specific factors (e.g. Voordeckers et al. 2007), this paper studies the influence of local peer companies on a family firm’s decision to appoint non-family board members. I conduct the empirical investigation on a rich longitudinal dataset of Italian private and listed family firms for the period 2000-2012. Italy provides an interesting context for this study given its strong presence of family firms in parallel with a wide geographic heterogeneity in local community values, which is useful to explore the mechanisms through which peer firms influence corporate governance arrangements.

I start by showing qualitative evidence that non-family directorship in the board of family firms displays agglomeration across geographic areas. For instance, the average fraction of non-family directors among family firms located in Udine (a medium-sized city

3 in Northern Italy) is around 41%, whereas for family firms located in Lecco (a similarly- sized city located 300 Km away from Udine and with the same number of sample firms) this average is just 19%. These marked differences are somewhat surprising given that such traditional determinants of corporate governance as legal systems, managerial supply, economic development and corporate laws are similar in these two cities. Generalizing the analysis to the full sample, I conduct statistical tests confirming that non-family directorship is significantly agglomerated at the local level. Regression analyses further show that local fixed effects are as important as industry fixed effects in explaining the variation in non- family directorship. Next, I conduct regression analyses using local peer averages to predict the presence of non-family directors at a focal family firm.1 After controlling for standard variables used in the corporate governance literature, such as firm size, age, capital structure and performance, I find that the presence of boards open to non-family directors in a given province significantly increases a focal firm’s likelihood to appoint non-family members in its own board of directors.

To corroborate the notion that the effect of local peers is driven by geographic proximity, I explore variations in the distance between a focal firm and its peers. First, rather than considering peer firms within the same province of a focal firm, I estimate the effect using peer firms located in the bordering provinces around the focal firm’s province. As expected, the effect of these “more distant” peers is weaker than the baseline result. Second,

I find insignificant effects from peer firms in the most distant bordering province, and from a random assignment of firms to provinces.

1 Notice that I exclude the focal firm in the computation of the peer average.

4 A well-known challenge in the identification of peer effects is the so-called reflection problem, which arises from regressing individual outcomes on group-level averages (Manski

1993). To alleviate this concern, I employ a quasi-natural experimental approach. In 2011,

The Italian Stock Exchange passed a revision to its “code of best practices” which strongly encouraged listed firms to appoint at least one third of independent directors. Combining the number of listed firms for each given province with the temporal variation provided by this regulatory change, I construct an instrument based on the idea that a larger number of listed firms (i.e. those directly affected by the regulatory change) in a given province represents a bigger exposure to the regulatory change and thus a larger (exogenous) variation in the province-average of non-family directors. The main effect becomes almost 3 times larger after controlling for endogeneity concerns with the above-mentioned approach. I conduct additional checks such as: (1) restricting the identification to the within-area level to mitigate the scope for omitted factor bias; (2) reducing the effect of possible outliers; (3) excluding the largest (or smallest) areas; (4) controlling for differences in the industrial composition of local economic activities; and (5) computing standard errors in alternative ways to account for heteroskedasticity and serial autocorrelation at the firm level.

Next, I investigate the potential mechanisms at play. Bouwman (2014) outlines a number of reasons why peer firm characteristics may correlate with a focal firm. In my context, the finding can be explained by an imitation mechanism whereby companies adopt the same board practices of leading (i.e. the largest and best-known) family firms in a given area. Alternatively, a social influence mechanism suggests that given community preferences and values could foster stronger social interactions, which make family companies more susceptible to local peers. Finally, a supply mechanism suggests that the correlation of board

5 governance between local firms may be explained by differences in the underlying labor markets, e.g. all firms in a given area are more likely to appoint non-family directors because they can draw from a larger pool of directors (as suggested by Knyazeva et al. 2013 for US listed firms).

To assess the importance of each mechanism, I investigate the heterogeneity behind the main result. Exploiting variations in the exposure to local influences, I find that the companies most affected by peer firms are small and young, i.e. those that are likely to have stronger ties and interactions with the local community and more limited geographic scope of operations. Moreover, I find that local peer effects are particularly strong when they stem from leading neighboring firms in terms of operating returns. Collectively, these results are in line with an imitation mechanism whereby less-established family firms are influenced by their most successful peers.

I then explore the similarity between a focal firm and its local peers using individual traits. Drawing on insights from demography and psychology, management scholars have argued that age similarity increases the frequency of communication interactions (Zenger and Lawrence 1989) and can thereby provide a driving force of group identification between executives (e.g. Zajac and Westphal 1995). I expect that, by making social interactions less frequent/effective, the demographic dissimilarity between local firm CEOs should weaken the peer effects. To test this hypothesis, I measure the age distance between a family firm’s

CEO and the average age of the other family firm CEOs in the same local area. Results show that peer effects are economically and statistically stronger when age similarity is particularly high. Moreover, exploiting geographic variations in cultural values from the

World Value Survey, I show that peer effects are stronger for companies that operate in local

6 environments characterized by a strong individual attachment to the local community.

Finally, peer effects are stronger for firms in provinces characterized by high population density, which is expected to make social interactions more likely. Taken together, these results suggest that the social influence mechanism plays a major role in determining the governance effect of local peers.

Finally, I test the importance of the supply mechanism by employing various empirical proxies for the vibrancy of local managerial labor markets. Results indicate that controlling for these supply-side variables affects only marginally the economic and statistical significance of local peer effects.

Several works have documented that social interactions affect a broad array of economic outcomes. This paper specifically contributes to a growing organizational and corporate finance literature that explores how peer effects shape firm outcomes such as financial policies (Leary and Roberts 2014), investment and growth (Beatty et al. 2013; Focault and

Fresard 2014; Dougal et al. 2015), CEO compensation (Albuquerque et al. 2013; Shue 2013;

Bouwman 2014), innovation (Matray 2015), stock splits (Kaustia and Rantala 2015), and financial misconduct (Parsons et al. 2014). Showing the importance of peer effects for board appointments, I contribute to the literature by better explaining the wide variations in governance arrangements even within areas subject to the same legal framework and structural characteristics. As such, my results highlight a novel determinant of the adoption and persistence of bad (or good) corporate governance practices. Along this line, I also expand existing works on the importance of cultural values for organizations (e.g. Bertrand and Schoar 2006; Bloom et al. 2012) by providing specific channels through which community values can shape family business’ organization and conduct. Finally, the findings

7 of this study lend empirical support to recent theoretical works that highlight the importance of corporate governance externalities and strategic complementarities across firms’ governance arrangements (Acharya and Volpin 2010; Levit and Malenko 2015).

2. Data and variables

I conduct the empirical analysis using representative longitudinal dataset of listed and private family firms in Italy for the period 2000-2012.2 Family firms are defined as those private companies in which a family owns at least 50% of equity; the threshold is reduced to

25% if the firm is listed in the stock market. This definition assures that families play a key role in the appointment of board members. Each sample firm had sales exceeding €50 million in any year between 2000 and 2010, a threshold that corresponds to a typical large or medium-sized family firm in Italy.

The dataset is assembled from two sources. Information on corporate ownership and governance structures comes from official public filings at the Italian Chamber of

Commerce. The family identity of CEOs and board members is identified by surname affinity with that of the controlling family.3 Thus by non-family director I mean a director that has no direct affiliation with the controlling family. Notice that this measure captures as important dimension of the supervisory role of boards: boards represented entirely by family

2 This section follows other works on the same dataset (e.g. Amore et al. 2011, 2014; Miller et al. 2013, 2014; Minichilli et al. 2014).

3 To prevent the potential misclassification of spouses, the sample identified as family members the wife or husband of the major controlling owners using national government fiscal codes and other personal data (such as residence, change in address etc.) This enabled to detect “cohabitation” by married individuals and made it possible to count as family members those with surname affinity to an identified wife (or husband) of a controlling owner. However, these remedies were at times insufficient in cases such as divorce, and where different family branches assumed control in later generations.

8 directors are essentially boards without outside monitoring role; the entire variation of supervisory role by independent directors lies in the sample region of firms with at least one non-family director. To be more precise about this aspect, in a robustness check I employ a finer definition closer to the concept of “independence”, using directors neither affiliated to the controlling family nor with executive power.4 Accounting data come from AIDA, the

Italian provider of the Bureau van Dijk databases. Such accounting data is merged with hand-collected ownership and governance data coming from the Italian Chambers of

Commerce, which also provide information on the location of firm headquarters.5

From the resulting sample, I exclude firms with negative or zero book value of total assets, firms with missing information on the province of headquarter, as well as firms without a formal governance and executive structure (i.e. firms with “Amministratore

Unico” or without any formal CEO).

The public administration in Italy is organized in four different levels: central State, regions (20), provinces (110), and municipalities (8,057 as of 2014). I elect provinces, broadly equivalent to US counties, as the relevant local unit to measure peer effects.6 This choice is motivated by the fact that, on the one hand, regions are too broad and heterogeneous and thus comparing them would suffer from a high risk of omitted factor bias; on the other hand, municipalities are too many and fragmented, and these features increase

4 This data is only available for the later sample years and thus I do not use it for the baseline analyses.

5 Changes in the province of headquarter are extremely rare in the sample. While this is a desirable feature (as it suggests that endogenous relocations are not a concern), it prevents the use of relocations as test to vary the exposure to peer groups over time.

6 While I assign firms to provinces based on the location of headquarter, companies may have plants or operations in more than one province. This, however, is not a major concern given that board governance structures are largely determined at the headquarter level.

9 the risk of not having enough observations within each unit. Selecting provinces as the level of aggregation represents a good compromise between the region-level and the municipality- level. Moreover, this approach is consistent with existing studies in the Italian context (e.g.

Guiso and Schivardi 2007; Guiso et al. 2014a). In any case, I will validate the empirical results employing finer constructions of peer groups at the municipality level, and at the ZIP code level, which relies more precisely on the geographic location of a firm’s headquarter.

I operationalize the peer average in non-family directorship as the fraction of firms with at least one non-family director in a given province (where provinces refer to the firm headquarter), after excluding the focal firm to avoid a mechanical correlation between a firm and its group average. In robustness checks, I verify the results using the average local firms’ fraction of non-family directors to the total number of directors (family plus non-family).

Next, I construct a number of variables used as controls in the empirical analysis. These controls are apt to capture structural firm characteristics that may correlate with the presence non-family directors. Specifically, I take the logarithm of a firm’s book value of total assets as a proxy for firm size; the logarithm of a firm’s age (measured in years) 7; the ratio of total debt to the book value of total assets, to measure the use of debt in the capital structure; the ratio of operating profits to total assets (ROA), to measure a firm’s profitability; a dummy for listing status; the fraction of equity capital held by the family; and the logarithm of all

(family plus non-family) board members, to control for the effect of board size. I exclude values of the debt to assets ratio greater than 1 or smaller than 0. Similarly, to avoid the effect of outliers, I drop 1% of observations in the right and left tails of the ROA

7 Notice that controlling for firm age is important as it captures differences in family business life-cycle (e.g. founder vs. heir managed) that, according to the literature (e.g. Villalonga and Amit 2006; Miller et al. 2007) are crucial for the scope of managerial inefficiency as well as family firms’ under- or over-performance.

10 distribution.8 After removing observations with missing values in the key explanatory variables, the sample contains 17,495 observations for 2,222 unique firms. Table 1 reports summary statistics for the main variables used in the empirical analysis.

[[ INSERT Table 1 about Here ]]

Focusing on board characteristics, I find that, on average, firms have 5 board members

(ranging from a minimum of 2 to a maximum of 24); 66% of firms have at least one non- family director and the average fraction of non-family to total directors is 33%. In untabulated tests (e.g. isolating top and bottom-5 provinces in the ratio of non-family directors), I also find that non-family directorship displays a wide geographic heterogeneity.

3. Empirical findings

This section illustrates the main results from a variety of empirical tests. First, it provides correlational evidence indicating that non-family directorship is aggregated at the geographic level. Second, it confirms that geography matters by documenting weaker effects as the physical proximity between peers decreases. Third, it provides quasi-experimental evidence to tease out the causal effect, along with robustness checks that overcome various empirical concerns.

3.1. Preliminary evidence

I start by applying the method developed by Rysman and Greenstein (2005) to establish whether agents in discrete locations are more agglomerated or dispersed than predicted by independent random choices. I report Rysman and Greenstein’s (2005) original examples to 8 I check that the results are largely unaffected by these exclusions.

11 give an intuition of the method. Consider four firms choosing between two locations.

Dispersion would imply two firms in each location, whereas agglomeration would imply all four firms in either location. Consider now the combinatory expression () where x represents the number of firms in the first location. If each firm selects randomly between locations with equal probability, the expression gives an expected value of 4.375. The dispersed arrangement generates ()=6, whereas the agglomerated arrangements generate ()= ()=1.

Thus, whether the combinatory statistic is above or below its expectation tells us whether the data displays more or less agglomeration than would be expected under independent random choice.

Adapting this method to my context, I test whether firms with at least one non-family director are agglomerated or not at the province level. I do this using the pooled full sample, or alternatively by using each annual year as a separate cross-section (which is more restrictive as it avoids repeating the same firm over time) and simulating random allocation using 250 draws.

As shown in Table 2 (which reports evidence on the full sample, in Column 1, and on the year 2010, i.e. the annual cross-section with most observations, in Column 2), the test always rejects at the 1% level the null hypothesis of independent random assignment and instead finds strong evidence that non-family directorship is agglomerated at the province level.

Following a procedure similar to Parsons et al. (2014), I also estimate various OLS models to test the importance of year, industry and province fixed effects in explaining non- family directorship. The dependent variable in Table 3 is a dummy equal to one if at least

12 one non-family director sits in the board of a focal firm, and zero otherwise. In Column (1), I explain the variation of this dependent variable only including year dummies, which account for time changes in the presence of non-family directors common to all firms (e.g. due to macroeconomic conditions, common regulatory changes or profitability shocks). As reported, year fixed effects are jointly significant around the 5% level, though the explanatory power indicated by the R2 is very small (0.1%).

Next, I include 2-digit industry fixed effects, which account for industry cross-sectional variation in non-family directorship (e.g. due to some sectors being more open than others to external managerial talents because of greater competitive pressures). As shown in Column

(2), such fixed effects are jointly highly significant and account for more than 5% of the overall variation in the dependent variable. Column (3), which includes province fixed effects, suggests that local fixed effects are as important as industry fixed effects in explaining variations in non-family directorship. This result thus confirms the initial insight on the relevance of local peers for the decision to appoint non-family board members. I then combine different sets of year, industry and province fixed effects. As expected, adding industry or province fixed effects (Columns 4 and 5) to the specification of Column (1) significantly increases the explanatory power of the model.

Finally, Column (6) presents a full specification with year, industry and province fixed effects jointly included, and test the significance of such model against the model with year and industry fixed effects only (Column 4). Reinforcing the idea that local peers matter for the likelihood of non-family board appointments, I find that the inclusion of province fixed effects to such model is jointly statistically significant and delivers an R2 above 10%.

13 3.2. Multivariate regressions

I start by estimating, in Panel A of Table 4, a model in which the dependent variable is a dummy for having any non-family member sitting in the board of directors, and explanatory variables are the average peer variable together with industry and year fixed effects. While I adopt linear probability models, the results are consistent to the use of Logit or Probit regressions. Standard errors are clustered at the province level, i.e. the level of aggregation of the key explanatory variable. As shown in Column (1), the local peer variable displays a positive and 1% statistically significant coefficient, suggesting that the higher the fraction of firms with non-family directors in a given area, the higher the likelihood that a firm in such area appoints a non-family director. Notice that this effect is established using time-t values of the main explanatory variable, but results are robust to the use of 1-year lagged values.

In Columns (2)-(3), I add sequentially the firm-level controls. Results indicate that firms with larger boards, with lower equity stake in the hands of families, with a larger asset base and with more debt in their capital structure are more likely to have non-family directors.

Yet, even accounting for these factors, the local peer coefficient remains positive and statistically significant at the 5% level. In Column (4), I adopt a more restrictive specification that includes area dummies (for northern, central and southern Italy), which restricts the identification within more homogeneous geographic areas and thus helps mitigate concerns of omitted factor bias. As expected, the inclusion of these additional dummies leads to a reduction in the peer coefficient, which however remains positive and significant at the 5% level. Finally, in Column (5), I include two peer province-level characteristics, namely the average firm size (as measured by the logarithm of total assets), to account for structural firm differences across provinces, and the logarithm of the number

14 of sample firms within a given province, to account for differences in the size of peer groups. In unreported tests, I also verify the robustness to controlling for the geographic size of provinces, measured as the logarithm of the surface area expressed in kilometers or the logarithm of total inhabitants (as of December 2010). Again, the main result is robust to the inclusion of these additional controls. To offer an interpretation of the economic magnitude of peer effects, I re-estimate the model of Column (5) using a probit regression and computing marginal effects. Results indicate that a 10% increase in the average fraction of non-family directors among local peers increases a focal firm’s likelihood of having any non-family director by 14%.

Panel B of Table 4 replicates these results adopting different dependent and peer average variables. Rather than considering a dummy for having any non-family director as dependent variable (and its province average as main explanatory variable), I use the ratio of non-family directors to the total number of directors. As shown, the main result is robust to this continuous operationalization of non-family directorship.

3.2.1 Intensive vs. extensive margins

The findings so far are consistent with the notion that peer effects increase the focal firm’s propensity to appoint directors from outside the family. To shed light on whether this effect is driven by extensive margins (i.e. appointing one outside director as opposite to none) or by intensive margins as well, in Column (1) of Table 5 I re-estimate the regression of Table

4 only using firms that have a positive number of non-family directors. As shown, the peer

15 coefficient remains statistically significant, and economically marginally smaller than the one in Table 4.

Finally, in Column (2), I adopt a subsample of firms that experience a change from one year to another in the number of non-family directors. Despite the drastically smaller sample size, the peer coefficient remains significant at the 5% level. This evidence suggests that peer effects matter not just statically but also at the intensive margins for new non-family board appointments.9

3.2.2 The role of geographic proximity

Next, I test the importance of distance between a focal firm and its peers. I posit that a firm should be influenced by other companies headquartered nearby (i.e. within the same province) but the effect should become weaker as the distance between the firm and its peers increases. To conduct the test, I gather from the Italian National Statistics Office (ISTAT) data on the borders between each Italian province.

[[ INSERT Figure 1 about Here ]]

As first test, I replace the baseline peer variable (i.e. computed using firms within the same province of the focal firm) with a peer variable computed using firms in the bordering provinces around the focal firm’s province. Figure 1 provides an illustration of this approach using as example the province of Alessandria (located in the yellow area of the figure).

Alessandria shares borders with seven provinces (Piacenza, Genova, Savona, Asti, Pavia,

9 This evidence also reduces the concern that the statistical significance is inflated by the stickiness of board characteristics both at the focal-firm and peer level. Notice that in robustness checks I further take this concern into account by clustering standard errors at the firm level.

16 Torino and Vercelli). Rather than computing the peer average using firms in Alessandria, for a focal firm headquartered in Alessandria I compute the peer average using firms headquartered in the seven bordering provinces. As expected, Table 6, Column (2) shows that the effect of these “more distant” peers is weaker than the baseline result of Table 4

(reported in Column 1 for comparison purposes). I strengthen the notion that geographic distance shapes the economic magnitude of peer effects by focusing on the most distant firms. Going back to the above example, I now compute the peer average using firms headquartered in the province that shares the shortest border with the Alessandria province

(i.e. Turin, with which Alessandria shares just 7.3 km). The lack of significance reported in

Column (3), supports the notion that geography shapes the effect of local peers on boards.

3.2.3 Evidence from a quasi-natural experiment

While the previous section increases confidence in the robustness of the main finding, the identification of peer effects is complicated by the so-called reflection problem (Manski

1993). To mitigate this concern, I adopt a quasi-natural experimental approach. In 2011, The

Italian Stock Exchange promoted a revision of its “code of best practices” for the corporate governance of listed firms (“Codice di Autodisciplina per la Corporate Governance delle

Società Quotate”), which put forward important recommendations for the board of directors.

17 In particular, the code strongly encouraged listed firms to appoint in their boards at least one third of independent directors.10

I construct an empirical test by combining: (1) the number of listed firms for each province and year; and (2) the temporal variation in the pressure to appoint independent directors as provided by the 2011 regulatory change. This test draws on the idea that a larger number of listed firms (i.e. those directly affected by the regulatory change) in a given province corresponds to a bigger exposure to the regulatory change and thus to a larger

(exogenous) variation in the relevant peer average of non-family directors.

Empirically, I employ an instrument equal to the interaction between the logarithm of one plus the number of listed firms in a given province-year and a dummy equal to one for the years from 2011 (and zero otherwise).11 I then use this variable as instrument in a 2SLS model (excluding listed firms given that they were directly affected by the shock). The first stage regression explains changes in the province-level average of non-family directors by using the interaction between the number of listed firms and the temporal dummy. The second stage employs the predicted values of the province averages of non-family directorship to explain a focal firm’s non-family directorship.

Results from this additional test are reported in Table 7, Column (1), which indicates that the instrumented local peer coefficient is statistically significant.

10 Notice that the 2011 edition of the “Codice di Autodisciplina per la Corporate Governance delle Società Quotate” is a revision of the 2006 edition, which however did not contain any indication on the minimum representation of independent directors in the board of listed firms. By contrast, the 2011 edition specifically refers to one third as the minimum representation of independent directors.

11 I take the logarithm of one plus listed firms to avoid losing the several observations with zero listed firms in the province.

18 One concern with the exclusion restriction necessary for this identification is that non- listed firms may have reacted to the policy shock not via their peer listed firms but in anticipation of becoming listed themselves (and thus in need to satisfy the minimum ratio of independent director recommended by the reform). To mitigate this concern, in Column (2) I exclude listed firms from the analysis, whereas in Column (3) I exclude firms that changed listing status during the sample period.

3.2.4 Additional tests

Table 8 presents a number of checks to confirm the validity of the main result so far. I start by including to the baseline model of Table 4, Column (4), additional controls that may correlate with both the local peer variable and the firm-specific dependent variable. For instance, in Column (1) I include a measure of the strength of family values, which is a significant determinant of the presence of family firms (Bertrand and Schoar 2006) as well as the openness towards non-family members in corporate positions (Miller et al. 2014).

Thus, family values can be thought as a common factor affecting both the peer and focal firm board, features potentially leading to endogeneity concerns. To control for family values, I rely on existing studies (e.g. Alesina and Giuliano 2010; Bertrand and Schoar 2006) that have adopted the World Value Survey (WVS), a comprehensive large-scale survey aimed at measuring cultural values around the world. Using data from the 2005 wave in

19 Italy, I focus on five questions related to family values (Miller et al. 2014).12 Exploiting the information on the location of respondents, I take the first principal component of these variables and construct a region-level measure of family values, which I then include as further control in the model.13 Results indicate that the peer coefficient displays a statistical significance at the 5% level. In Column (2), I replace the baseline control for the fraction of family equity shares with a measure of ownership concentration computed using the

Herfindahl–Hirschman index (HHI) of shares owned by the seven largest shareholders. In

Column (3), I control for another relevant aspect of family involvement, i.e. the presence of family members in CEO positions. As shown, results are robust to the inclusion of such additional control, as well as to controlling for the share of executive directors (Column 4).

In Column (5), I employ as dependent variable, and thus for the computation of the peer average, a finer definition of outside director using a dummy equal to one if the firm has any non-family director without executive power, and zero otherwise (notice that due to data availability the sample used in this check is only from 2009 onwards; it includes observations prior to 2009 only when it was possible to establish that the board had no independent members using secondary criteria, e.g. when the board was entirely controlled by family members). In Column (6), I remove extreme values in the average peer variable,

12 The first asks the respondent how much she/he trusts his/her family members: it ranges from “trust them completely” (taking the value of 1) to “don’t trust parents at all” (a value of 4). The second question asks to agree with the statement: “one of the main goals in my life is to make parents proud”, where values range from 1 (strongly agree) to 4 (strongly disagree). The third asks the respondent to rate “how important is the family in your life” from 1 (very important) to 4 (not important at all). The fourth asks to agree with one of the two statements: “regardless of what the qualities and faults of one’s parents are, one must always love and respect them” (a value of 1) and “one does not have the duty to respect and love parents who have not earned it” (a value of 2). The fifth question asks to agree with one of the two statements: “It is the parents’ duty to do their best for their children even at the expense of their own well-being” (a value of 1) and “parents have a life of their own and should not be asked to sacrifice their own well being for the sake of their children” (value of 2).

13 Unfortunately the WVS does not provide the province identifiers of respondents.

20 e.g. due to provinces having a small number of firms of the same type, by excluding 2.5% of observations in the left and right tails of the distribution of the average peer non-family directorship. In Columns (7)-(8), I provide estimates obtained clustering standard errors at the firm and firm-year level, rather than at the province level as done in the baseline models.

Social capital, a widely studied determinant of economic development and institutional quality, varies greatly across Italian provinces. To capture variations in social capital, I follow a long-standing tradition (from Putnam 1993 to Guiso et al. 2014b) and employ as additional control in Column (9) the number of non-profit organizations per capita in each province (time-invariant, due to data availability). In Column (10) I include a measure of non-sport newspaper diffusion (data as of 2001 are drawn from Cartocci 2007) to also capture differences in political accountability. These additional controls are useful to mitigate the concern that peer effects are driven by omitted factors related to the institutional quality and social capital of the province.

Next, I establish that no significant effect arises from randomly allocating firms to provinces (Column 11), that the effect remains significant to the exclusion of the three largest provinces in terms of total observations (Column 12) and the smallest provinces

(Column 13), classified as those in the lowest tertile of the distribution in terms of sample firms. While I adopt province-level peers in the baseline analyses, I show that results are significant to finer levels of aggregation. To this end, I employ a municipality-level peer factor (only taking those municipalities with at least 3 sample firms in a given year, which explains the substantially smaller sample size). Column (14) shows that municipality peer effects are statistically significant and economically similar to the baseline province effect in

Table 4, Column (4). Alternatively, in Column (15) I estimate the effect of peers computed

21 at the ZIP code (again, taking ZIP codes with at least 3 sample firms in a given year). Even using this narrower definition of local peers delivers a coefficient close to 5% significance level. I also deal with potential industry effects. I start by controlling for industry peer effects, computed as the year average of the dependent variable of each 2-digit industry after excluding the firm in question. As shown in Column (16), controlling for the potential influence of rivals’ governance on the focal board reduces only slightly the effect of local peers, which remains positive and statistically significant. In an additional test, I attempt at controlling for the industrial specialization of given provinces. Italy is indeed well known for its long-running presence of “industrial clusters”, i.e. agglomeration in given areas of firms with very similar specialization. This feature is important to take into account given that the local peer effects documented so far may stem from (or be specific to) industry similarity among local firms. To accommodate this concern, I compute an index measuring the 2-digit industry concentration of revenues for each province and year (with lower values indicating that the revenues of firms headquartered in a given province come from a wider array of industries). Column (17) shows that the baseline local peer effects remain significant to the inclusion of this additional control. Finally, in Column (18), I allow for the fact that non- family directorship may have exhibited industry-specific time variations by augmenting the specification with the interaction between year and 2-digit industry dummies.

4. Mechanisms

Although the influence of local peers on a focal firm’s non-family directorship is robust to various checks and estimation methods, there are several channels through which this result

22 can manifest. By showing variations in the economic magnitude of peer effects, this section explores the possible mechanisms at play.

I start the analysis by distinguishing between old and young family firms. I expect older firms to have more established routines and corporate culture, and thus be less sensitive to local peers; by contrast, young companies are expected to be more open to local environmental influences. Estimating the baseline model on the subsamples of young and old firms (created splitting the sample around the average age cutoff computed for a given province and year excluding the firm in question), I find that the local peer effect is significant (and economically stronger) for young firms (Table 9, Columns 1-2). Next, I estimate the effect depending on firm size. Similar to the arguments used for firm age, I expect larger firms to be less sensible to local peers; larger firms may have operate at a scale larger than the province itself and thus be less prone to local influences. Moreover, large firms may be able to appoint from a broader pool of directors, e.g. due to higher corporate visibility and prestige or because they are exposed to a larger managerial labor market.

Estimating the model on the subsamples of small and large firms (classified using the same procedure used for firm age), I find an economically larger peer coefficient for small companies. However, the peer effect remains statistically and economically significant also for large companies, suggesting that the influence of peers is strong enough to affect established firms as well.

I then check whether the influence of local peers varies with a focal firm’s profitability.

On the one hand, one can expect that low-profitability firms should be more prone to alter their board if observing low returns provides a feedback on the effectiveness of their corporate governance. On the other hand, low-profitability firms may be entrenched

23 organizations in which family ties prevail regardless of merit, and those firms should be the least open to external influences. Splitting the sample around the median ROA for a given province and year, Columns (5)-(6) show that peer coefficients are statistically significant at the 5% and 10% level, and only slightly larger for the low-profitability firms.

Next, I examine the effect of demographic similarity. Here the argument is that the influence of peers should be stronger when peer and a focal firm’s key decision-maker share common characteristics. To this end, I focus on age as key demographic characteristics, and compute the distance between the age of a focal firm’s family CEO and the average age of peer family CEOs. I focus on family CEOs given that they almost always represent the most powerful individuals (being also the largest owners and, often, company founders) with key power on board appointments. I then split the sample around the median cutoff and estimate the model on the two subsamples. Results in Columns (7)-(8) indicate that peer effects are economically strong and statistically significant when the age distance between local family

CEOs is small.

To reinforce the notion that the neighboring companies affect a focal firm’s governance as result of social interactions, I exploit geographic variations in the strength of individual attachment to the local community. To this end, I rely again on the WVS and focus on a question that asks individuals how much they see themselves as members of their local community. The working hypothesis is that stronger identification with the local community should make corporate decision-making more sensitive to local peer influences. I take the region average of this variable and estimate the baseline model separately for areas characterized by high or low attachment with the local community. Consistent with the importance of social interactions between community members as potential driver for my

24 result, results in Columns (9)-(10) show that peer effects are statistically and economically stronger when the attachment with the local community is high. Finally, I establish that peer effects are stronger in areas with higher population density, which makes social interactions more likely. To this end, I compute as the ratio of province inhabitants per squared kilometers (in 2011) and then I split the sample around the average value. As shown in

Columns (11)-(12), results are stronger and statistically significant where population density is higher. In untabulated tests verified that the same variation holds using the ratio of sample directors per squared kilometers.

I proceed by I examining whether the result is driven by focal firms imitating the governance practices of “leading” peers, which are expected to have higher visibility and be more higher appreciated by other local business owners. To this end, in Table 10, I employ two variables that separately measure peer effects based on firms that are in the top or bottom 10% in terms of ROA in a given province and year. As shown, although peer effects are significant in both cases, the effect stemming from “leading” firms is substantially larger in economic magnitude.

The last mechanism outlined relates to geographic differences in managerial labor markets, i.e. whether the local correlation of non-family directorship is due to a larger supply of professional managers in that given area. This argument is line with Knyazeva et al.

(2013) who show that the local supply of directors increases the probability of appointing independent members in the board of US listed companies. To assess the importance of this mechanism, I start by using a proxy for the vibrancy of local managerial labor markets.

25 Specifically, in Column (1) of Table 11, I augment the baseline specification with a dummy equal to one if in the province where the firm is headquartered there is at least one university or educational institution granting MBA degrees, and zero otherwise (as of 2014). In

Column (2), I follow a different approach and augment the specification with the logarithm of total director seats (computed using all sample firms) in a given province and year. As shown, including these additional controls affects only marginally the coefficient of interest, which remains significant at the 5% level.

5. Non-family directors and corporate governance quality

Taken together, the results so far suggest that peer effects influence the propensity to appoint directors from outside the controlling family. The importance of this result lies in that non- family directors can improve the corporate governance of family firms by reducing the risk of minority expropriation and wasteful managerial practices (e.g. Anderson and Reeb 2004;

Ansari et al. 2014). I reinforce this notion by providing Correlational evidence on the relationship between non-family directorship and governance and ownership characteristics.

To this end, I estimate OLS regressions in which the key explanatory variable is a dummy equal to one if the firm has any non-family director, and the dependent variables are, alternatively, (1) a dummy equal to one for firms with any non-family (i.e. professional)

CEO; (2) a dummy equal to one for firms with family CEO-chairman duality; and (3) the share of equity capital held by the family. Each regression controls for a host of firm

26 characteristics, year and industry effects, and clusters standard errors at the firm level. As shown in Table 12, non-family directorship is significantly associated with the presence of non-family CEO, non-family board chairman, and with a less concentrated family ownership, all factors pointing to superior corporate governance quality.

6. Conclusion

Far from operating in isolation, companies are embedded in their institutional environment and make corporate decisions being subject to external influences stemming from community logics and interpersonal relationships. This paper has focused on local peers as a previously unexplored factor that can potentially shape the governance arrangements of family firms. Understanding the drivers of good governance in the context of family firms is crucial given that family-controlled companies tend to engage in practices that generate private benefits at the expense of minority investors and family-unrelated stakeholders.

Conducting the empirical investigation on a comprehensive dataset of family firms in

Italy for the period 2000-2012, I provided evidence that companies operating in local areas with a denser network of non-family directors are significantly more likely to appoint directors from outside the controlling family. To validate this finding, I documented that peer effects become smaller for geographically more distant peers, and that peer effects constructed from randomly allocating firms to provinces do not have any significant effect.

Testing the potential mechanisms at play, I found that the firms most influenced by local peers are young and small, i.e. those firms that more likely to be tied to local communities in terms of firm operations and interpersonal connections. Moreover, peer

27 effects are stronger when the demographic similarity between local executives is low, and in areas characterized by high population density and strong individual attachment with the local community.

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34 Table 1. Summary statistics

This table reports summary statistics for the main variables used in the analysis. Presence of non-family directors is a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Average of non-family directors is the average ratio of non-family directors to the total number of directors. Family equity shares is the fraction of a firm’s equity that belongs to the family. Presence of non-family CEOs is a dummy equal to one if the firm has any non-family CEO, or zero otherwise. Ln (Board size) is the logarithm of the number of directors. Ln (Assets) is the logarithm of the book value of a firm’s total assets. Ln (Firm age) is the logarithm of firm age measured in years. Leverage is the ratio of total debt to the book value of a firm’s assets. ROA is the ratio of earnings before interest and taxes divided by total assets.

Observ- Mean s.d. 25th Median 75th ations Perc. Perc.

Presence of non-family directors 17,495 0.6648 0.4720 0 1 1 Average of non-family directors 17,495 0.3337 0.2992 0 0.3333 0.5714 Presence of non-family CEOs 17,459 0.3532 0.4780 0 0 1 Family equity shares 17,495 0.9181 0.1520 0.900 1 1 Ln (Board size) 17,495 1.4726 0.4410 1.0986 1.3862 1.7918 Ln (Assets) 17,495 11.2471 1.2155 10.5035 11.145 11.8289 Ln (Firm age) 17,495 3.0969 0.7374 2.7081 3.2189 3.5835 Leverage 17,495 0.6560 0.1920 0.5385 0.6866 0.8042 ROA 17,495 0.0524 0.057 0.0215 0.0432 0.0761

35 Table 2. Agglomeration/dispersion tests

The table reports evidence from the multinomial test-statistic for agglomeration or dispersion developed by Greenstein and Rysman (2005). In Column (1), the test is performed on the full sample, whereas in Column (2) it is performed using the year 2010. The mean and standard deviation of the sample likelihood under random allocation is calculated using 250 random draws.

Full sample Single year

(1) (2) Observed likelihood: -9.447 -2.182 Expected likelihood -2.965 -1.860 s.d. 0.078 0.064 z -83.249 -5.000 P>|z| 0.00 0.00

36 Table 3. Fixed effects analysis

The upper part of this table presents results from OLS regression models estimated that include various combinations of year, 2-digity industry and province dummies. The bottom part of the table presents the result from F-tests to assess the joint significance of additional dummy sets (as compared to models 1 and 4).

Dependent variable: Presence of non-family directors

(1) (2) (3) (4) (5) (6) Year fixed effects Yes Yes Yes Yes Industry fixed effects Yes Yes Yes Province fixed effects Yes Yes Yes

Observations 17,495 17,495 17,495 17,495 17,495 17,495 R2 0.001 0.056 0.062 0.058 0.063 0.110 Adjusted R2 0.001 0.053 0.058 0.053 0.058 0.102

Statistical tests: Year fixed effects F statistics 1.71 p value 0.058

Industry fixed effects vs. (1) F statistics 15.95 16.02 p value 0.000 0.000

Province fixed effects vs. (1) vs. (4) F statistics 14.18 14.17 12.55 p value 0.000 0.000 0.000

Table 4. Local peers and non-family board appointments 37 This table reports results from OLS regressions. In Panel A, the dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. In Panel B, the dependent variable is the ratio of non-family directors to the total number of directors. Peer non-family directorship is computed as the province-level average of the dependent variable after excluding the firm in question. Ln (Board size) is the logarithm of the number of directors. Ln (Assets) is the logarithm of the book value of a firm’s total assets. Ln (Firm age) is the logarithm of firm age measured in years. Leverage is the ratio of total debt to the book value of a firm’s assets. ROA is the ratio of earnings before interest and taxes divided by total assets. Family equity shares is the fraction of a firm’s equity that belongs to the family. Depending on the specification, the regression includes 2-digit industry dummies, year dummies, macro-area (i.e. north, center and south) dummies, and province-level average firm size, computed as the average of the book value of total assets, and number of firms. Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Panel A. Dependent variable: Presence of non-family directors

(1) (2) (3) (4) (5) Peer non-family directorship 0.2393*** 0.1565** 0.1499** 0.1457** 0.1451** (0.0774) (0.0600) (0.0601) (0.0588) (0.0664) Ln (Board size) 0.4403*** 0.3829*** 0.3825*** 0.3823*** (0.0215) (0.0214) (0.0214) (0.0215) Ln (Assets) 0.0189*** 0.0190*** 0.0189*** (0.0064) (0.0064) (0.0066) Ln (Firm age) -0.0254** -0.0256*** -0.0256*** (0.0096) (0.0096) (0.0096) Leverage 0.1169*** 0.1162*** 0.1160*** (0.0390) (0.0390) (0.0392) ROA -0.1261 -0.1295 -0.1312 (0.1207) (0.1221) (0.1218) Family equity shares -0.4554*** -0.4546*** -0.4547*** (0.0832) (0.0833) (0.0832) Industry fixed effects Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Area fixed effects Yes Yes Province controls Yes Observations 17,495 17,495 17,495 17,495 17,495

Panel B. Dependent variable: Fraction of non-family directors

38 (1) (2) (3) (4) (5) Peer non-family directorship 0.3573*** 0.2692*** 0.2508*** 0.2527*** 0.2254*** (0.0952) (0.0723) (0.0729) (0.0709) (0.0581) Ln (Board size) 0.2821*** 0.2052*** 0.2054*** 0.2051*** (0.0165) (0.0165) (0.0165) (0.0160) Ln (Assets) 0.0316*** 0.0317*** 0.0311*** (0.0040) (0.0040) (0.0040) Ln (Firm age) -0.0249*** -0.0251*** -0.0250*** (0.0067) (0.0066) (0.0066) Leverage 0.0607** 0.0616** 0.0606** (0.0277) (0.0278) (0.0283) ROA -0.1144 -0.1147 -0.1201 (0.0863) (0.0860) (0.0831) Family equity shares -0.4849*** -0.4850*** -0.4857*** (0.0529) (0.0531) (0.0529) Industry fixed effects Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Area fixed effects Yes Yes Province controls Yes Observations 17,495 17,495 17,495 17,495 17,495

39 Table 5. Intensive margins

This table reports results from OLS regressions. The dependent variable is the ratio of non-family directors to the total number of directors. Peer non-family directorship is computed as the province- level average of the dependent variable after excluding the firm in question. In Column (1), I use the subsample of firms with a positive number of non-family directors. In Column (2), I use the subsample of firms that experience any change from one year to another in the number of non-family directors. All controls of Table 5, Column (4), are included but the coefficients are not reported for brevity. Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Dependent variable: Fraction of non-family directors

(1) (2) Peer non-family directorship 0.2181*** 0.2510** (0.0777) (0.0980) Industry fixed effects Yes Yes Year fixed effects Yes Yes Area fixed effects Yes Yes Controls (unreported) Yes Yes Observations 11,632 2,145

40 Table 6. The role of geographic distance

This table reports results from OLS regressions. The dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. In Column (1), I report for comparison purposes the baseline estimate from Column (4) of Table 4. In Column (2), I replace the baseline peer variable with a peer variable computed using all firms headquartered in the provinces that share borders with the province of a focal firm. In Column (3), I replace the baseline peer variable with a peer variable computed using all firms headquartered in the province that share the shortest border with the province of the focal firm. All controls of Table 4, Column (4), are included but the coefficients are not reported for brevity. Standard errors are clustered by province. . *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Baseline Bordering Most distant provinces bordering province (1) (2) (3) Peer non-family directorship 0.1457** 0.1104* 0.0388 (0.0588) (0.0615) (0.0354) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Area fixed effects Yes Yes Yes Controls (unreported) Yes Yes Yes Observations 17,495 17,401 17,401

41 Table 7. Quasi-experimental findings

This table reports second-stage results from a 2SLS regression. In the first stage (unreported for brevity), the dependent variable is the peer presence of non-family directors, i.e. the province-level average of the dummy equal to one if the firm reports any non-family director, computed after excluding the firm in question. The instrument used is the interaction between a dummy equal to one from 2011 onwards (and zero prior to 2011) and a variable equal to the logarithm of one plus the number of listed firms in a given province and year. In addition to this instrument, the first stage includes all the controls used in Table 4, Column (4). In the second stage, the dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. The second stage regression uses as explanatory variable the predicted peer fraction of non-family directors from the first stage together again with all the controls used in Table 5, Column (4), not reported for brevity. In Column (1), I use the full sample, in Column (2) I exclude listed firms, and in Column (3) I exclude firms that went public during the sample period. Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

(1) (2) (3) 0.3975** Peer non-family directorship 0.3110* * 0.3965*** (0.1729) (0.1485) (0.1485) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Area fixed effects Yes Yes Yes Controls (unreported) Yes Yes Yes Observations 17,495 16,560 16,509

42 Table 8. Robustness checks

This table reports various robustness checks. The dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Peer non-family directorship is computed as the province-level average of the dependent variable after excluding the firm in question. All controls of Table 4, Column (4), are included but the coefficients are not reported for brevity. Column (1) controls for region-level family values, computed as the first principle component of a number of questions from the World Value Survey (refer to Section 3.2.3 for details). Column (2) controls for the equity concentration (HHI) computed using the seven largest shareholders. Column (3) includes a dummy equal to one if in firm has any non-family CEO (and zero otherwise). Column (4) controls for the share of executive directors. Column (5) uses as dependent variable, and thus for the computation of the peer average, a dummy equal to one if the firm has any non-family director without executive power, and zero otherwise (in this case, the sample is from 2009 onwards). Column (6) excludes 2.5% of observations in the left and right tails of the distribution of the average peer fraction of firms with any non-family director. Column (7) provides estimates obtained clustering standard errors at the firm level. Column (8) provides estimates obtained clustering standard errors at the firm-year level. Column (9) includes the number of non- profit organizations per capita in a given province (data as of 1999), whereas Column (10) includes a measure of non-sport newspaper diffusion (data as of 2001). Column (11) shows results obtained after randomly allocating firms to provinces. Column (12) excludes observations in the three largest provinces in terms of sample observations. Column (13) excludes provinces within the lowest tertile of the distribution in terms of sample firms. Column (14) replaces the province-level peer variable with a city-level variable, computed at the municipality level (only using municipalities with at least 3 firms in a given year) and clustering standard errors by municipality. Column (15) replaces the province-level peer variable with a ZIP code-level variable, computed at the ZIP code level (only using ZIP codes with at least 3 firms in a given year) and clustering standard errors by ZIP code. Column (16) includes industry peer effects, computed as the annual average of the dependent variable of each 2-digit industry excluding the firm in question. Column (17) includes the 2-digit industry concentration of revenues for each province and year. Column (18) includes the interaction between 2-digit industry and year dummies. Unless differently specified, standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Control C Control Control Use Remove Firm- Firm-year Non-profit for family o for family for executive independent possible clustered clustered organizations values n CEOs directors directors outliers residuals residuals per capita tr o l f o r e q u

43 it y H H I ( 2 (1) ) (3) (4) (5) (6) (7) (8) (9) 0 . 1 2 6 4 * Peer non-family directorship 0.1329** * 0.1331** 0.1432** 0.1718* 0.1770** 0.1457*** 0.1421*** 0.1436** ( 0 . 0 5 7 2 (0.0611) ) (0.0558) (0.0586) (0.0976) (0.0773) (0.0509) (0.0498) (0.0617) Y e Industry fixed effects Yes s Yes Yes Yes Yes Yes Yes Yes Y e Year fixed effects Yes s Yes Yes Yes Yes Yes Yes Yes Area fixed effects Yes Y Yes Yes Yes Yes Yes Yes Yes e

44 s Y e Controls (unreported) Yes s Yes Yes Yes Yes Yes Yes Yes 1 7 , 4 8 Observations 17,495 1 17,459 17,495 10,748 16,506 17,495 17,495 17,495 (Continued from the previous page)

Random Exclude Exclude Use Use ZIP Control for Control for Control for Newspaper local largest smallest city-level code-level industry industry industry diffusion assignment provinces provinces peers peers peer effects composition trends (10) (11) (12) (13) (14) (15) (16) (17) (18) Peer non- family directorship 0.1261** -0.0299 0.1401** 0.2277* 0.1311** 0.0863* 0.1415** 0.1423** 0.1405** (0.0618) (0.0622) (0.0573) (0.1053) (0.0626) (0.0469) (0.0571) (0.0575) (0.0603) Industry fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Area fixed effects Yes Yes Yes Yes Yes Yes Yes Yes Yes Controls (unreported) Yes Yes Yes Yes Yes Yes Yes Yes Yes

45 Observations 17,495 17,487 14,748 11,572 7,392 8,284 17,403 17,495 17,495

46 Table 9. Heterogeneity depending on focal firm characteristics

This table reports results from OLS regressions for various subsamples. The dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Peer non- family directorship is computed as the province-level average of the dependent variable after excluding the firm in question. All controls of Table 4, Column (4), are included but the coefficients are not reported for brevity. Old (young) firms are firms below (above) the median age in a given province (computed excluding the firm in question). Large (small) firms are firms below (above) the average book value of total assets in a given province (computed excluding the firm in question). High (low) ROA firms are firms above (below) the average ROA in a given province (computed excluding the firm in question). High (low) similarity firms are firms below (above) the median age distance from a focal firm’s family CEO and the average age of peer firms’ CEOs. High (low) local attachments are firms located in regions above (below) the median value of the answers to the World Value Survey question “Do you see yourself as member of your local community?”. High (low) density are firms above (below) the average population density (as of December 2010). Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Old Young Large Small High-ROA Low-ROA firms firms firms firms firms firms (1) (2) (3) (4) (5) (6) Peer non-family directorship 0.1153* 0.1876*** 0.1285** 0.1820** 0.1261* 0.1463** (0.0681) (0.0666) (0.0568) (0.0815) (0.0647) (0.0669) Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Area fixed effects Yes Yes Yes Yes Yes Yes Controls (unreported) Yes Yes Yes Yes Yes Yes Observations 10,059 7,436 8,019 9,476 7,350 10,145

Low local attac High age Low age High local hme Low similarity similarity attachment nt High density density (7) (8) (9) (10) (9) (10) 0.11 Peer non-family directorship 0.1956*** 0.0893 0.1891** 78 0.2798*** 0.0698 (0.09 (0.0713) (0.0715) (0.0834) 01) (0.0856) (0.0709) Industry fixed effects Yes Yes Yes Yes Yes Yes Year fixed effects Yes Yes Yes Yes Yes Yes Area fixed effects Yes Yes Yes Yes Yes Yes Controls (unreported) Yes Yes Yes Yes Yes Yes 7,13 Observations 8,742 8,753 10,365 0 6,276 11,219

47 48 Table 10. Heterogeneity depending on peer firm characteristics

This table reports results from OLS regressions. The dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Peer non-family directorship is computed as the province-level average of the dependent variable after excluding the firm in question and only using firms in: the top-10% of ROA (in Column 1); the bottom-10% of ROA (in Column 2). All controls of Table 4, Column (4), are included but the coefficients are not reported for brevity. Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

(1) (2) (3) Peer non-family directorship (top performer) 0.5828*** 0.5260*** (0.0314) (0.0437) Peer non-family directorship (bottom performer) 0.0949*** 0.0751*** (0.0251) (0.0163) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Area fixed effects Yes Yes Yes Controls (unreported) Yes Yes Yes Observations 17,454 12,031 12,024

49 Table 11. Controlling for directors supply

This table reports results from OLS regressions. The dependent variable is the presence of non-family directors, i.e. a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Peer non-family directorship is computed as the province-level average of the dependent variable after excluding the firm in question. All controls of Table 4, Column (4), are included but the coefficients are not reported for brevity. Additionally, Column (1) includes a dummy equal to one if there is any institution granting MBA degrees in a given province (and zero otherwise). Column (2) includes the logarithm of total director seats in a given province and year. Standard errors are clustered by province. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

(1) (2) Peer non-family directorship 0.1398** 0.1432** (0.0610) (0.0595) MBA granting institution 0.0109 (0.0172) Ln (total directors) 0.0018 (0.0043) Industry fixed effects Yes Yes Year fixed effects Yes Yes Area fixed effects Yes Yes Controls (unreported) Yes Yes Observations 17,495 17,495

50 Table 12. Non-family directorship and corporate governance quality

This table reports results from OLS regressions. Non-family CEOs is a dummy equal to one if the firm has any non-family CEO, or zero otherwise. Family duality is a dummy equal to one if the firm has a family CEO and board chairman, or zero otherwise. Family equity shares is the fraction of a firm’s equity that belongs to the family. Presence of non-family directors is a dummy equal to one if the firm has any non-family member in its board of directors, or zero otherwise. Ln (Board size) is the logarithm of the number of directors. Ln (Assets) is the logarithm of the book value of a firm’s total assets. Ln (Firm age) is the logarithm of firm age measured in years. Leverage is the ratio of total debt to the book value of a firm’s assets. ROA is the ratio of earnings before interest and taxes divided by total assets. Standard errors are clustered by firm. *, ** and *** denote significance at 10%, 5% and 1%, respectively.

Dependent variable: Non-family CEO-board Family CEOs duality equity stake

(1) (2) (3) Presence of non-family directors 0.4766*** -0.5386*** -0.0522*** (0.0169) (0.0186) (0.0072) Industry fixed effects Yes Yes Yes Year fixed effects Yes Yes Yes Observations 17,459 17,442 17,459