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Who Rewards Diversity? Institutional Context, Board Gender Diversity, and Analyst Evaluation

Job Market Paper

Letian Zhang Harvard University

Although many studies have explored the consequences of diversity, few have considered how they are affected by social context. This paper develops an institutional framework for understanding how a board’s gender diversity influences its organization’s analyst ratings. I propose that evaluators reward gender-diverse organizations with higher ratings when gender diversity is more institutionalized and that female evaluators do so more than male evaluators. Using a unique longitudinal dataset of 290,281 stock ratings by 5,069 securities analysts, I find that an organization’s analyst rating increases as women’s representation on its board increases, but only when industry peers also have women on their boards or when news about the industry frequently mention board diversity, both of which reflect normative acceptance of female board membership. This pattern is more pronounced when the analyst is female; as board diversity gains normative acceptance, female analysts’ reward for gender-diverse boards increases more than that of male analysts. These findings contribute to the diversity literature by showing the importance of social context in shaping the consequences of diversity. They also extend institutional theory by demonstrating gender heterogeneity in the enforcement of institutional norms. INTRODUCTION An organization’s board composition has important consequences for its evaluation by analysts and investors (Pfeffer and Salancik 1978; Westphal and Graebner 2010). As more women enter the boardroom, there is a growing interest in understanding how their presence influences evaluation. However, findings so far have been highly inconsistent. In some studies, board diversity appears to benefit evaluation, while in others it has a limited or even a negative effect (Dobbin and Jung 2011; Hoobler et al. 2016; Post and Byron 2015). But this literature has given little consideration to social context. Social norms can shape the way people perceive diversity (Dobbin, Kim, and Kalev 2011; Kelly and Dobbin 1998); rather than being uniform, the effect of board diversity on evaluation may depend on characteristics of the surrounding social environment. In this paper, I develop an institutional framework for understanding how a board’s gender composition influences organizational evaluators. Institutional theory suggests that people are more likely to value institutionalized practices, defined as those that have been widely accepted as desirable and appropriate (DiMaggio and Powell 1983; Meyer and Rowan 1977; Scott 1995; Suchman 1995). Institutional context could

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influence evaluators’ perceptions of board diversity in two ways. First, institutionalization leads people to associate a practice with performance benefits (Briscoe, Gupta, and Anner 2015; Zajac and Westphal 2004). As having women on boards becomes more institutionalized, evaluators should believe more in its performance benefits. Second, in a context in which board diversity is institutionalized, having more women on the board signals an organization’s awareness of its social environment and evaluators may attribute that awareness to more competent management. These two processes suggest that, as board gender diversity becomes more institutionalized, having more women on a given board should increase that organization’s rating.

This hypothesis is consistent with a core tenet of institutional theory: organizations are rewarded for conforming to institutional norms (Zuckerman 1999). But the institutional literature has not closely examined those who are doing the rewarding (Deephouse and Suchman 2008). I suggest that there may in fact be considerable heterogeneity in the enforcement of institutional norms. Evaluators may reward an organization more for its conformity when the institutionalized practice is more consistent with their own preferences and interests. In this case, the institutionalization of board gender diversity should have a greater influence on female than on male evaluators. I expect female evaluators to more readily accept the value of gender diversity and use it as signal of quality. Therefore, as board gender diversity becomes more institutionalized, their reward of gender-diverse boards should increase more than that of male evaluators.

I examine these theoretical propositions in the context of equity securities analysts. Securities analysts make stock ratings for public firms and their views have significant effects on a firm’s market price (Giorgi and Weber 2015; Loh and Stulz 2011; Zuckerman 1999). Understanding analyst behavior has long been a key focus in studies of financial markets and a large body of literature has examined how analysts evaluate firms (Clement and Tse 2005; Cohen, Frazzini, and Malloy 2010; Loh and Stulz 2011; Stickel 1992). However, this rich literature has seldom considered social context and gender dynamics in the evaluation process (Bosquet, de Goeij, and Smedts 2014; Fang and Huang 2015; Green, Jegadeesh, and Tang 2009; Kumar 2010). By introducing institutional environments and gender demographics, my study also demonstrates the importance of social factors in the workings of financial markets. AN INSTITUTIONAL FRAMEWORK Given the board’s symbolic significance, a number of studies have examined the relationship between board gender diversity and organizational evaluation. This literature suggests that evaluators may perceive organizations with gender-diverse boards in several positive ways. For example, a board with more women may make better decisions due to its diverse perspectives and resources (Dezsö and Ross 2012; Miller and Triana 2009), do a better job of monitoring corporate governance (Adams and Ferreira 2009; Carter, Simkins, and Simpson 2003; Hillman and Dalziel 2003), and demonstrate the firm’s ability to engage a diverse customer base (Miller and Triana 2009). Such performance advantages should encourage evaluators to reward an organization with a gender-diverse board. But empirical evidence has been far from consistent. Although some found a positive effect of board gender diversity on a firm’s rating and valuation (Carter, Simkins, and Simpson 2003; Dezsö and Ross 2012), others found a null or even a

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negative effect (Adams and Ferreira 2009; Carter et al. 2010; Dobbin and Jung 2011; Lee and James 2007; Miller and Triana 2009; Ntim 2015; O’Reilly and Main 2012). It therefore remains unclear whether or not board gender diversity contributes to organizational evaluation (Dobbin and Jung 2011; Hoobler et al. 2016; Post and Byron 2015). Social context may influence how evaluators treat board diversity, since actors are embedded in their social environments and norms influence the way people perceive a given organizational practice. In the following sections, I develop an institutional framework for understanding the consequences of board diversity. The Consequences of Institutional Conformity

Institutional theory suggests that organizations are located in their institutional fields, which are defined as “the communities of organizations that share a common meaning system” (Scott 1995) and are conventionally based on industries (Wooten and Hoffman 2016). An organizational practice is considered institutionalized when it has been widely accepted as desirable, proper, and appropriate (Suchman 1995). The theory posits that, once a practice becomes institutionalized, people perceive it as valuable and thus reward those organizations that adopt it (DiMaggio and Powell 1983; Meyer and Rowan 1977; Suchman 1995). For example, firms that follow institutionalized practices receive higher valuations from investors (Zajac and Westphal 2004) and higher ratings from securities analysts (Zuckerman 1999).

One case of institutionalization is gender diversity on corporate boards (Edelman, Fuller, and Mara-Drita 2001; Fondas 2000; Hillman, Shropshire, and Cannella 2007; Kelly and Dobbin 1998; Miller and Triana 2009). In the 1990s, the rhetoric emerged that diversity could improve an organization’s underlying performance by introducing diverse perspectives and offering a better understanding of customer needs (Edelman, Fuller, and Mara-Drita 2001; Kalev, Dobbin, and Kelly 2006). Although academic research has found only mixed evidence that diversity benefits performance (Post and Byron 2015), this rhetoric has gained increasing acceptance in many industries (Edelman, Fuller, and Mara-Drita 2001; Kelly and Dobbin 1998). For example, the number of news articles that mention a business case for board diversity has increased exponentially in the last decade, as shown in figure 1. Corresponding to this trend, the percentage of women on major US corporate boards has risen from a little under 6 in 1990 to about 20 in 2015, as shown in figure 2 (Catalyst 2015; Farrell and Hersch 2005).

[Insert figure 1 here] [Insert figure 2 here]

For two reasons, I expect organizations with gender-diverse boards to receive higher ratings as board gender diversity becomes more institutionalized. First, evaluators may expect board diversity to improve performance. Because the performance implication of a given organizational practice is not always clear, normative acceptance could strongly influence its perceived value (Briscoe, Gupta, and Anner 2015). People tend to take for granted a practice’s functional benefit when it has been institutionalized, but are more skeptical otherwise (Westphal and Zajac 1998; Zajac and Westphal 2004). As diversity becomes more institutionalized, people should be more likely to believe in

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its business benefits and expect gender-diverse boards to perform better. This should lead to higher ratings for organizations with gender-diverse boards.

Second, gender diversity on a board may serve as a positive signal of an organization’s management quality (Connelly et al. 2011; Lamkin Broome and Krawiec 2008; Miller and Triana 2009). When diversity is institutionalized, managements that embrace it demonstrate both their awareness of a changing social norm and their willingness to adapt to it. Organizations that adhere to these norms of diversity will gain legitimacy and credibility. In contrast, managements that do not follow a diversity norm, even when it has been institutionalized, may be taken as backward and overly conservative. Evaluators may question such an organizations’ ability to cope with technological and other environmental changes (Bilimoria 2006; Miller and Triana 2009). This signaling effect also suggests that board gender diversity should more positively contribute to organizational evaluation in more institutionalized contexts.

Hypothesis 1: Evaluators’ ratings of gender-diverse boards increase as board gender diversity becomes more institutionalized.

Heterogeneity in Institutional Enforcement

Hypothesis 1 suggests that evaluators can serve as institutional enforcers: those who are in positions to reward and punish organizations for their conformity or deviance. Enforcers play a central role in the institutional framework by incentiving organizations to follow institutionalized practices (Zajac and Westphal 2004; Zuckerman 1999). But the literature has paid only limited attention to these actors, generally treating them as a homogeneous group whose members reward institutional conformity in a similar fashion (Deephouse and Suchman 2008).

This account may have underplayed the role of individual agency, as there may be considerable heterogeneity among evaluators in their institutional enforcement. In evaluating an organization, analysts and investors tend to select criteria that meet their individual needs (Cowen, Groysberg, and Healy 2006; Hayward and Boeker 1998). As a practice becomes institutionalized, some evaluators may find it consistent with their preferences and interests, showing more enthusiasm for it and paying closer attention to it. Evaluators who do not find it appealing may underplay its importance and focus on other organizational attributes. Thus, even in the same institutional field, evaluators may enforce an institutionalized practice to different extents.

For two reasons, I expect female evaluators to enforce board gender diversity more than male evaluators do as it becomes more institutionalized. First, people tend to believe rhetorics that present a favorable image of their ingroups and reject rhetorics that do the opposite (Reskin 2000; Stainback, Tomaskovic-Devey, and Skaggs 2010). The institutionalization of board diversity brings more opportunities for evaluators to encounter rhetorics that emphasize the value of female leadership, which should appeal more to women than men. I therefore expect female evaluators to be more enthusiastic in embracing these rhetorics and accepting evidence that support them. For example, while male evaluators may interpret the increasing number of women in boardrooms as driven by moral conscience, female evaluators may perceive the same trend as an indication that diversity does benefit organizations. As board gender diversity becomes more

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institutionalized, female evaluators should believe more in its organizational value than their male counterparts do.

Second, I expect female evaluators to use board gender diversity as a signal of an organization’s management quality more than male evaluators do. Although following institutionalized practices could positively contribute to evaluation by signaling the management team’s awareness of the social context, male and female evaluators may observe this signal to different extents. As a disadvantaged group in many contexts, women tend to be more attentive to gender diversity in organizations (Cohen and Huffman 2007). For example, female managers show much more awareness than male managers do of gender diversity in workforces (Dobbin, Kim, and Kalev 2011; Skaggs, Stainback, and Duncan 2012). Therefore, I expect female evaluators to pay closer attention to an organization’s board gender composition. As board gender diversity becomes more institutionalized, they should use it as a signal of an organization’s management quality more than male evaluators to. Based on these two processes, I hypothesize:

Hypothesis 2: As board gender diversity becomes more institutionalized, female evaluators’ ratings of gender-diverse boards increase more than that of male evaluators.

A Strategic Setting: Securities Analysts’ Evaluations

One important locus of firm evaluation is stock analysts who work in independent research firms and the research departments of investment banks (Giorgi and Weber 2015; Phillips and Zuckerman 2001; Rao, Greve, and Davis 2001; Zuckerman 1999). They specialize by industry and their primary role is to maintain active stock rating for firms in their industry. Analysts’ stock ratings predict a firm’s long-term prospect relative to its current market value and recommend to investors whether they should buy, hold, or sell its stock. As market experts, their ratings have important consequences for a firm’s market value (Zuckerman 1999). For example, a stock can move as much as 5 to 10 percent following an analyst’s rating change (Loh and Stulz 2011). Consequently, this context is an important one, as evidenced by a voluminous literature in finance and accounting that centers on analyst behavior (Bradshaw 2004, 2011; Clement and Tse 2005; Cohen, Frazzini, and Malloy 2010; Kumar 2010; Loh and Stulz 2011; Malmendier and Shanthikumar 2014; Stickel 1992).

The analyst context offers three advantages. First, it provides detailed information on individual evaluators. This allows me not only to know evaluators’ gender, but also to carry out more rigorous analysis. Because a given analyst typically follows the same set of firms for some time, I can use analyst-by-firm dyadic fixed effects to observe changes in each analyst-firm relationship. This model shows how the same analyst rates a firm differently as its board gender composition changes, eliminating unobserved time-invariant heterogeneity at both the firm and evaluator levels.

Second, board composition has important consequences for analyst evaluation, as analysts tend to place significant weight on a firm’s management and closely scrutinize its board members (Cohen, Frazzini, and Malloy 2010; Fang and Huang 2015; Westphal and Graebner 2010). In a large-sample survey of financial analysts and institutional

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investors, three-quarters of the participants mentioned that board choices are just as important to them as financial performance (Coombes and Watson 2000). Consistent with this, Westphal and Graebner (2010) used a large sample of US firms to show that board appointments have a significant impact on analyst ratings. Some qualitative evidence also suggests the importance of board composition in analyst evaluation. For instance, after issuing a positive rating, an analyst noted the following in her report (Morgan Stanley Analyst Reports 2016):

The board structure and officer compensation mix lend us a good amount of confidence with regards to the operations of the company. The company's board is also among the most gender-balanced boards in the industry, with four women on board. We believe that gender diversity leads to better business decision-making.

In contrast, another analyst gave a different firm a negative rating for, among other faults, its lack of board diversity: “Only two out of the thirteen-member board are women, though we believe that gender diversity can contribute positively towards business decisions” (Morgan Stanley Analyst Reports 2016). Given analysts’ attention to board composition, they offer an ideal setting to examine the influence of board diversity on evaluation.

The third advantage of this context is analysts’ industry-based division of labor. Each industry has its networks and a set of task environments, which lead to a particular set of institutional norms (Wooten and Hoffman 2016). Analysts, because of their industry specialization, spend most of their time with actors in the same industry. They are therefore much more likely to be influenced by their particular industry’s norms than a typical investor, who may cover a range of industries and be exposed to a variety of norms. Thus, this context is also well suited for examining how institutional norms in an industry influence evaluation. DATA AND METHOD I obtained analyst recommendations for US stocks listed on Thomson Reuters’s Institutional Brokers’ Estimate System (I/B/E/S) from 2001 to 2014. The I/B/E/S database has “the most comprehensive information on analysts following firms” (Rao, Greve, and Davis 2001). Although some ratings may be missing, financial economists and accounting researchers generally treat these data as if they reflected complete coverage of the analyst ratings available to market participants (Zuckerman 1999). Because analysts continuously maintain their ratings on a stock, the database records only an analyst’s initial rating of a stock and then subsequent revisions. Each database record includes the stock name, the analyst’s updated rating, the date of the announcement, and the analyst’s surname, first initial, and brokerage firm. With this information, I calculated analysts’ annual ratings for each of their stocks, such that each observation is a unique analyst-stock-year combination. If an analyst revised a stock’s rating midyear, I averaged the old and the revised rating, pro-rated by duration. The I/B/E/S database provides only analysts’ surnames and first initials, so to determine their genders, I obtained Bosquet, de Goeij, and Smedts’s (2014) and Kumar’s (2010) databases that contain analysts’ full names and matched them to my dataset. For analysts not found in these two datasets, I hand-collected full names using Nelson’s Directory of Investment Research, which contains annual information on each analyst’s

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full name and brokerage house. To determine the gender of each first name, I gathered the complete list of baby names from the Social Security Administration for Americans born between 1950 and 1990. To safeguard privacy, that list is restricted to those names with at least five occurrences. I determined whether an analyst is male or female based on the proportion of births with the analyst’s name that were male or female. I excluded gender-neutral names, deemed so if the minority gender exceeds 20 percent. In the end, I identified the gender for 91.8 percent of the analysts in the sample.

An analyst could be missing from my sample for any of the following reasons: the I/B/E/S database does not have a record on his or her surname and first initial; Nelson’s Directory does not have his or her information; the name is an uncommon foreign name not included in the Social Security database; or the name is gender-neutral. The analysts with missing gender information tend to have shorter tenures and cover fewer stocks, but they do not differ in coverage choice and stock ratings. I used several databases to identify the annual gender composition of the boards of firms listed in the I/B/E/S database. The two most commonly used databases for board diversity are Institutional Shareholder Services (ISS, formerly known as RiskMetrics) and BoardEx, which jointly cover 88.4 percent of the firms in the I/B/E/S database. For the remaining firms, I used Bloomberg, a financial database widely used by practitioners. In all three databases, the data source on board composition is firms’ proxy statements. By these means, I identified board gender composition for 93.1 percent of the firm-year observations in the sample. I used eight-digit CUSIP identification numbers to merge these databases with the I/B/E/S database. Firms with missing data are generally covered by fewer analysts and are less likely to be listed on major exchanges.

For controls, I used Compustat, I/B/E/S, and Bloomberg databases to download annual financial measures for each firm, including stock prices, earnings, and total assets. Earnings data come from the Detail History file of the I/B/E/S database. Stock prices and total assets come from Compustat and otherwise from Bloomberg. Of the I/B/E/S firms, 8.3 percent are missing in both the Compustat and Bloomberg databases. For these cases, I impute their financial controls using ordinary least squares regression with industry, earnings, and number of analysts covering its stock as covariates. Omitting cases with imputed data does not substantively change the findings. To control for analyst quality, I collected Institutional Investor’s annual lists of “all-star” analysts from 2001 to 2014. The all-star recognition is widely used as a measure of an analyst’s status and quality. Each year, Institutional Investor asks more than 300 institutional investors to name the best analysts in each industry. Those with the most votes are considered all-star analysts for that year and their names are listed in the October issue.

The final sample includes 290,281 ratings issued by 5,069 analysts, covering 7,285 firms in 43 industries. The analyst gender distribution in the sample is shown in table 1. Of the 5,069 analysts, only 675 (13.3 percent) are women, reflecting women’s underrepresentation in important positions in finance (Roth 2004).

[Insert table 1 here]

Dyadic Fixed-effects Models This study uses linear panel models. To minimize unobserved heterogeneity, I used analyst-by-firm dyadic fixed effects. These models focus on variation within each

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analyst-firm dyad; they estimate how the same analyst rates a firm differently as its board gender composition changes. Of the sample observations, 77.2 percent saw some change in board gender composition during the study period. By treating each analyst-firm dyad as a dummy variable, the models can rule out any time-invariant firm- and individual-level heterogeneity that may influence evaluation (Halaby 2004). A Hausman test (p<0.05) indicates that dyadic fixed effects are a more appropriate specification than random effects for this sample. I also included calendar-year fixed effects to account for macro-level changes in the market. Because fixed effects cannot account for time-varying within-cluster correlation, I clustered standard errors at both the firm and analyst levels. Variables

I used an analyst’s stock rating as the dependent variable. The I/B/E/S database standardizes stock ratings on a five-point scale. A rating of 1 reflects a strong buy, 2 a buy, 3 a hold, 4 a sell, and 5 a strong sell. For a more intuitive interpretation, I reverse-coded the scale so that 5 is the most favorable rating and 1 the least favorable. Analysts are known to exhibit an optimistic bias, meaning that they are more likely to give optimistic than pessimistic ratings. As table 2 shows, the average rating in the sample is 3.6, indicating that there are many more buy ratings than hold and sell ratings. The main independent variable is the percentage of women on a board, which is updated annually and I lagged it by one year. 1 Results are substantively similar when this variable is not lagged.Much like the trend in figure 2, the average percentage of women on boards in my sample increased from only 7.9 percent in 2001 to 12.0 percent in 2014.

My other independent variables are the level of institutionalization of board gender diversity in an industry and whether the analyst is female. I categorized industries using the Global Industry Classification Standard (GICS), a recommended classification system for analyst research because it corresponds well to the industry categories used in most brokerages (Bradshaw 2011). I used the Bloomberg database to identify each firm’s main corresponding GICS industry. I then measured the level of institutionalization in each industry-year using two approaches. The first is the average percentage of women on boards among industry peers. In the institutional literature, it has been conventional to measure the institutionalization of a practice by its acceptance by industry peers (Briscoe, Gupta, and Anner 2015; Deephouse and Suchman 2008; Hannan and Carroll 1992). I updated this variable annually and lagged it by one year.

The second measure is the proportion of published news articles that discuss the value of board gender diversity, sorted by year and industry. A number of researchers have used media coverage as an indicator of society-wide institutional norms in addition to peer-based measures of institutionalization (Abrahamson and Fairchild 1999; Baum and Powell 1995; Deephouse and Suchman 2008; Pollock and Rindova 2003). Using the Factiva news database, I searched for all English-language news articles in North America and then searched them for articles that discuss the value of board gender 1An alternative measure of gender diversity is Blau’s index of heterogeneity. In this sample, the percentage of women on a board and Blau’s index are highly correlated at 0.98 and do not show a substantive difference in results. For ease of interpretation, I use the percentage of women on a board to measure board gender diversity.

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diversity. Factiva tags each article with a publication date and a relevant set of industries. I sorted these articles by their publication years and industries; an article could belong to multiple industries. I divided the number of news articles that discuss board gender diversity in each industry-year by the total number of articles in that industry-year. I then multiplied this number by 10,000 for readability, logged it, and lagged it by one year. There is a 0.3 correlation between this measure and the average percent of women on boards.

I included a number of variables to control for changes in a firm’s quality over time. First, to control for a firm’s performance each year, I included its annual earnings per share, P/E ratio, total assets, and the amount of analyst coverage. Previous studies have identified these as the most influential factors in analysts’ evaluations (Bradshaw 2011). These financial measures are updated annually and I lagged them by one year. The P/E ratio measures how much a firm is valued in the market relative to its earnings, with a higher P/E ratio often indicating that the firm is overvalued. If a firm has negative earnings for the year, the formula produces a negative P/E ratio, even though the firm should be considered highly valued relative to its earnings. Therefore, in such cases I replaced the firm’s P/E ratio with the highest P/E ratio in its industry that year. Excluding cases with negative earnings as a robustness check does not substantively change my results. Because P/E ratios can be very large, I divided them by 1,000 for readability.

Second, I included a set of standard control variables on analysts and their brokerages. These include an analyst’s tenure, all-star status, and portfolio size (measured by the number of stocks under coverage). Because analysts in high-status brokerages tend to issue more pessimistic forecasts than those in low-status brokerages (Cowen, Groysberg, and Healy 2006), I also included a control for whether or not a brokerage is considered a bulge bracket bank, which typically refers to the world’s nine largest and most prestigious investment banks.

Table 1 presents descriptive information on gender differences in these covariates. Male analysts have slightly longer tenure than female analysts (7.5 years vs. 6.0 years), are somewhat less likely to be all-stars (9.1 percent vs. 9.8 percent), and are less likely to work in bulge bracket firms (27.8 percent versus 36.3 percent). Table 2 presents variable summary statistics and correlations. In particular, the percentage of women on a board is only weakly correlated with analyst gender (cor=0.07), suggesting that male and female analysts cover industries with relatively comparable diversity norms.

[Insert table 2 here] RESULTS My results show that analysts’ ratings of gender-diverse boards increase with the greater institutionalization of board gender diversity in an industry. Moreover, female analysts’ ratings of gender-diverse board increases more than those of male analysts.

Before turning to the main analysis, I examined factors that determine an organization’s board gender composition in order to show the institutionalization of board diversity in this period. Table 3 uses a linear model with firm fixed effects to predict the percentage of women on a board. I lagged all independent variables by one year and multiplied the outcome variable by 100 for readability. As model 1 shows, earnings, P/E ratio, and analyst rating are not significantly associated with a firm’s

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percentage of women on board, suggesting no direct association between a firm’s performance and its board gender diversity. At the same time, the model suggests that larger firms and firms that have a large analyst following have more women on their boards. This seems intuitive: such firms tend to be more visible and – given the symbolic importance of board diversity to outside audiences – should therefore be more likely to embrace board diversity (Hillman, Shropshire, and Cannella 2007). In model 2, I added two measures of the institutionalization of board diversity in an industry: the average percentage of women on boards and the proportion of news articles that discuss board gender diversity. Both have a significant positive impact on a board’s gender diversity. For example, when industry peers increase their average percentage of women on boards by 10 percent, a firm is predicted to increase its percentage of women on the board by 5.8 percent. These results are consistent with a story of institutionalization; as gender diversity on boards becomes normatively accepted, firms tend to place more women on their boards (Kelly and Dobbin 1998; Miller and Triana 2009).

[Insert table 3 here] I next examined the consequences of board diversity for analyst rating. In table 4, the percentage of women on a board has a slightly positive effect on analyst rating, but this effect is strongly contingent on the institutional environment. In models 2 and 3, respectively, the association between board diversity and analyst rating is more positive when the industry has a higher average percentage of women on boards and when a higher percentage of news about the industry discusses the value of diversity. These two results support hypothesis 1 that the institutionalization of board gender diversity positively moderates the effect of that diversity on firm rating. These findings are not driven by between-industry effects. By fixing each analyst-firm pair, variation in institutional context comes from changes within an industry as diversity becomes more institutionalized within it.

[Insert table 4 here] In table 5, I split the sample by gender to examine heterogeneity between male and female analysts. First, without considering the institutional environment, female analysts generally reward board gender diversity, while male analysts appear quite indifferent to it. As shown in models 1 and 4, the slope for the percentage of women on the board is relatively high (b=0.54) and statistically significant for female analysts, but quite low (b=0.04) and statistically insignificant for male analysts. In a supplementary analysis, I interacted board gender diversity with analyst gender and found this difference between male and female analysts to be statistically significant, confirming that female analysts generally reward board gender diversity with much higher ratings than their male counterparts do.

[Insert table 5 here] I then explored differences between male and female analysts in their reception

of the institutional context. In model 2 of table 5, I found that female analysts reward a gender-diverse firm more when the industry has a higher level of board gender diversity. As shown in figure 3, when boards in an industry are, on average, 15 percent women, a given firm’s percentage of women on the board has a statistically significant slope of 1.08 on female analysts’ ratings. In other words, a female analyst in this context is predicted to give a firm a 0.21 points higher in rating when it increases the percentage of women on its board by 20 percent. To put that in perspective, 0.21 is almost one-third of

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the standard deviation in rating. Thus, this slope of 1.08 indicates that women’s representation on the board has a significant impact on analyst rating. In contrast, when the boards in an industry are, on average, only 5 percent women, the slope becomes 0.2 and statistically insignificant. Model 3 shows that female analysts’ treatment of board gender diversity is also moderated by the amount of media discourse on diversity in an industry. They tend to give higher ratings to a gender-diverse firm when more news about the industry discusses board gender diversity. In all, these results suggest that institutional context significantly moderates female analysts’ response to board gender diversity.

[Insert figure 3 here] The institutionalization of board gender diversity has less impact on male

analysts. In models 5 and 6 of table 5, although the two measures of institutionalization also show positive moderating effects on male analysts’ response to board gender diversity, these effects have much smaller magnitudes than those of the female analysts and are not statistically significant at the 0.05 level. In a supplementary analysis, a three-way interaction between the percentage of women on a firm’s board, the average percentage of women on boards in that firm’s industry, and the analyst’s gender shows a statistically significant difference, suggesting that female analysts embrace the institutionalization of gender diversity on boards significantly more than male analysts do. Figure 4 illustrates this gender difference visually. Drawing from models 2 and 5 of table 5, the figure shows that institutionalization influences female analysts’ emphasis on gender diversity much more than it influences that of male analysts. At the lower levels of institutionalization, female and male analysts value board gender diversity to a similar extent. But as the level of institutionalization rises, women’s representation on boards becomes much more important for female than for male analysts. These results support hypothesis 2: as board gender diversity becomes more institutionalized, female analysts’ reward for it increases much more than that of male analysts.

[Insert figure 4 here] Controls are generally in line with previous research. Analysts from higher-status

brokerages tend to issue more pessimistic ratings. As Cowen, Groysberg, and Healy (2006) show, lower-status brokerages have more need to issue optimistic ratings to boost their trading revenues. Also consistent with previous research, analysts tend to increase their ratings as they become more senior in the industry. Senior analysts tend to have closer relationships with firm management and may be less willing to risk those relationships with a negative rating (Cowen, Groysberg, and Healy 2006). The financial measures are also in the expected directions. Better earnings indicate that a firm is performing well, thus increasing its rating. In contrast, a higher P/E ratio suggests that a firm’s stock value is high relative to its earnings, which signals overvaluation and leads to negative ratings. Additional Analyses I examined whether having more women on boards is associated with a firm’s accounting-based performance. One alternative to the institutional explanation is that analysts’ reward may reflect observable improvements in a diverse firm’s immediate underlying performance. In model 1 of table 6, I ran firm-fixed-effects linear models to show that the percentage of women on the board has a small and statistically insignificant

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effect on a firm’s immediate earnings. In models 2 and 3, I found that the institutional context has an insignificant moderating effect on board gender diversity in predicting earnings. These results show that a firm’s board gender diversity has little direct association with its immediate financial performance, which is consistent with previous research (Post and Byron 2015). They support an institutional explanation: securities analysts think more highly of gender-diverse firms as board gender diversity becomes more institutionalized, even though there is no directly observable change in these firms’ underlying performance.

[Insert table 6 here] It is quite possible, however, that board gender diversity has a longer-term impact

that is more difficult to observe and identify. Therefore, the lack of a relationship between board diversity and short-term performance does not imply that those securities analysts who value board diversity are biased in their evaluations; it simply highlights the unclear performance implications of board diversity, which could contribute to the heterogeneity in analysts’ responses.

CONCLUSION This study develops an institutional framework for understanding how board gender diversity influences an organization’s rating. Using a unique, fine-grained dataset of 290,281 analyst ratings, I found that institutional context significantly shapes the effect of diversity on evaluation. A firm’s board gender diversity has a more positive influence on its rating when board gender diversity is more institutionalized in that firm’s industry. Moreover, this institutional influence differs significantly between male and female analysts. As board gender diversity becomes more institutionalized, female analysts’ reward for gender-diverse boards increases much more than that of male analysts.

This study makes several contributions. First, although there is a rich literature on the consequences of diversity, few studies have considered the role of social context. This neglect is unfortunate, because the meaning of diversity is socially constructed and the social environment influences how people interpret and approach it. For example, studies have found that, as social norms gradually shift over time, people’s perception of diversity also changes significantly (Edelman, Fuller, and Mara-Drita 2001; Kelly and Dobbin 1998). Therefore, it is difficult to fully understand the impact of diversity without taking into account social context. This study makes an important contribution in this direction by highlighting the role of the institutional environment in shaping the consequences of diversity for the evaluation of organizations.

By focusing on social context, I emphasize the significance of field-level diversity; the amount of gender diversity in an organizational field determines whether diversity benefits a given organization. In this case, female presence on the board benefits an organization’s rating more when both its peers and its evaluators also have a greater female presence. This suggests that diversity, unlike many other organizational practices, does not give a few organizations exclusive advantages. Diversity creates an organizational advantage only when other organizations in the field are also diverse. More studies on the effect of diversity should take into account the overall diversity in an organizational field rather than focusing just on individual organizations.

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Second, this study contributes to institutional theory. A central proposition of the theory is that institutional conformity benefits an organization by increasing its appeal to outside audiences. But while there is a rich literature on institutional diffusion, the consequences of institutional conformity have received considerably less attention (Zajac and Westphal 2004; Zuckerman 1999). In particular, it is not clear who rewards organizations for their institutional conformity. This study highlights important gender differences in the enforcement of institutional norms; as board gender diversity becomes more institutionalized, female analysts’ reward for gender-diverse boards increases much more than that of male analysts. Thus, when facing an institutional norm, there is considerable heterogeneity in the enforcers’ behaviors. Some are more likely than others to embrace a particular institutional norm and reward organizations for conforming to it. Others, however, could downplay the importance of such a norm because it does not align with their identities and preferences.

This heterogeneity in the enforcement of institutional norms is consistent with recent work depicting organizational environments as complex. Early institutional theory treated the organizational field as unitary and its individual actors as passive recipients of institutional norms. This view, however, has been gradually replaced by a more agentic perspective, which suggests that organizational managers respond to institutional norms using various strategies, such as decoupling and resistance (Oliver 1991). My study contributes to this tradition by showing that it is not only organizational managers, but also institutional enforcers whose responses to institutional pressures vary. Thus, organizations do not always experience a reward for conformity and a punishment for deviation; it depends on enforcer preferences. To take this study setting as an example, if most analysts covering a firm are male, then the firm’s conformity to a gender-diversity norm won’t matter much to its ratings. If most are female, it will matter a lot.

Finally, this study contributes to a well-established literature in finance and accounting on analysts as important market intermediaries (Bradley et al. 2014; Clement and Tse 2005; Cohen, Frazzini, and Malloy 2010; Loh and Stulz 2011; Stickel 1992). For example, Bradshaw (2011) estimates that more than 500 articles were published on this topic between 1995 and 2005. Understanding analyst behavior is important because it informs us about the production and use of information in the financial market and the process by which market actors make market valuations. The dominant perspective in the literature is that analysts are rational information processors who objectively evaluate the available information and calculate future earnings, but this has been increasingly challenged by behavioral finance, which argues that analysts are subjected to psychological influences (Clement and Tse 2005). My study adds to the alternative perspectives by demonstrating the importance of social influences. Contrary to the orthodox view, analysts’ conceptions of market value are influenced by social norms and by their own gender.

Some limitations in this study provide opportunities for further exploration. First, this study focuses on diversity at the board level. Board diversity, being highly visible and transparent, is observable to analysts (Certo 2003; Cohen, Frazzini, and Malloy 2010; Fang and Huang 2015; Westphal and Graebner 2010). But it is not clear if analysts would also pay attention to an organization’s workforce diversity, which tends to be much less accessible. If not, then we may expect decoupling, whereby some organizations arrange for high gender diversity on their boards but leave it low in their workforces. Additional

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studies could explore whether this decoupling process occurs and, if it does, its consequences for evaluation. The second limitation, common to research at an organizational level, is that the data do not allow researchers to directly observe the fine-grained psychological processes underlying analysts’ behaviors. For example, it is not clear if analysts reward gender-diverse boards because of diversity’s perceived performance benefits, its signaling effect, or both. Qualitative studies and laboratory experiments may serve to better delineate the psychological mechanisms.

In conclusion, this study opens up two important avenues for future research. One is that studies of organizational diversity should pay closer attention to various social contexts. While this study shows the importance of the normative environment, it is possible that rules and regulations could also influence how investors and analysts value an organization’s diversity practices. Similarly, in addition to evaluation by external audiences, future research could examine whether social norms influence the way managers and workers interact in a diverse workplace. These directions could offer a more thorough understanding of how social context shapes the consequences of diversity. The second avenue would help us better understand conditions for the enforcement of institutional norm, a valuable contribution given the considerable heterogeneity in norm enforcement. Future studies could further explore consequences of institutional conformity and, in particular, which actors are more likely to reward conforming organizations and punish deviating ones. This would help us understand in what contexts institutional conformity has consequences. REFERENCES Abrahamson, Eric, and Gregory Fairchild. 1999. “Management Fashion: Lifecycles,

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Tables and Figures Figure 1: The Proportion of News Articles that Discuss Board Gender Diversity

Data source: I counted the annual number of news articles in the Factiva database that mention board gender diversity and divided it by the total number of articles that appeared in the database that year. To improve readability, I multiplied this proportation by 10,000 and logged it. Figure 2: The Percentage of Women on Boards in Major US Corporations

Data sources: 1990–1994: Fortune 500 firms (Farrell and Hersch 2005) 1995–2013: Fortune 500 firms (Official Catalyst Reports) 2014–2015: S&P 500 firms (Official Catalyst Reports)

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Figure 3 Predicted Effect of Board Gender Diversity on Ratings: by Level of Institutionalization

Very low institutionalization (industry average pct of women on board 1%)

female analysts slope = -0.16, male analysts slope = -0.11 Low institutionalization (industry average pct of women on board 5%)

female analysts slope = 0.20, male analysts slope = -0.02 High institutionalization (industry average pct of women on board 10%)

female analysts slope = 0.64**, male analysts slope = 0.10 Very high institutionalization (industry average pct of women on board 15%)

female analysts slope = 1.08**, male analysts slope = 0.22 Note. Slopes are calculated based on models 2 and 5 of table 5. ** p<0.01, * p<0.05, + p<0.1 Figure 4: Importance of Board Gender Diversity in Rating: Comparison of Female and Male Analysts.

Note. Bars represent coefficient values for Pct Women on Board in models 2 and 5 of table 5. A higher bar implies that analysts give a greater reward for gender-diverse boards.

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Table 1 Analyst Distribution by Gender Variable Female analysts Male analysts All analysts Number of analysts 675 4,394 5,069 Pct of all analysts (%) 13.32 86.68 100 Avg rating 3.63 3.67 3.66 Avg total years in the sample 5.97 7.45 7.24 Avg portfolio size 11.91 13.29 13.14 All-star (%) 9.80 9.10 9.16 Bulge bracket (%) 36.30 27.70 28.72 Note. The gender composition in this table is different from that in table 2a because here each analyst is counted only once, regardless of how many ratings given. Table 2a Variable Summary Variable Mean SD Min Max Annual Stock Rating 3.66 0.87 1 5 Pct Women on Board 0.10 0.10 0 0.67 Avg Pct Women on Board among

Industry Peers 0.09 0.03 0.00 0.24

Proportion of News Articles on Board Diversity (log)

0.50 0.45 0 6.91

Female Analyst 0.11 NA 0 1 Analyst Tenure (years) 7.55 4.65 1 21 Analyst Portfolio Size 11.02 7 1 73 All-star Analyst 0.13 NA 0 1 Bulge Bracket Brokerage 0.30 NA 0 1 Num of Analysts Covering the Stock 13.75 8.5 1 57 Earnings 1.53 2.55 -8.75 11.83 Price-to-earnings Ratio 17.58* 97.85 0.01 399.5 Total Assets (log) 7.91 1.88 0.82 18.35 Note. * median value Table 2b Correlation Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 1 Annual Stock Rating 1 2 Pct Women on Board -0.04 1 3 Avg Pct Women on Board

in Industry -0.01 0.30 1

4 Proportion of News Articles on Diversity (log)

-0.01 0.10 0.33 1

5 Female Analyst -0.01 0.07 0.09 0.01 1 6 Analyst Tenure (years) 0.03 0.08 0.19 0.19 -0.05 1 7 Analyst Portfolio Size -0.06 0.02 -0.01 -0.03 -0.06 0.20 1 8 All-Star Analyst -0.10 0.08 0.02 -0.03 0.01 0.20 0.20 1 9 Bulge Bracket Brokerage -0.19 0.07 -0.01 -0.02 0.04 0.01 0.13 0.47 1 10 Num of Analysts Covering

the Stock -0.06 0.20 -0.01 -0.01 0.01 0.08 0.01 0.09 0.21 1

11 Earnings 0.01 0.11 0.05 0.10 -0.00 -0.07 0.05 0.06 -0.12 -0.39 1 12 Price-to-earnings Ratio -0.02 -0.09 -0.09 -0.09 -0.00 0.12 -0.05 -0.04 0.53 0.36 -0.26 1 13 Total Assets (log) -0.09 0.28 0.07 0.08 0.00 0.19 0.11 0.15 -0.01 0.05 -0.09 0.07 1

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Table 3 Panel Linear Models with Firm Fixed Effects: Predicting Pct Women on Board, 2001-2014 Variable Model 1 Model 2 Avg Pct Women on Boards among Industry Peers 58.301** (4.180) Proportion of News Articles on Diversity (log) 0.620** (0.097) Earnings 0.034 0.021 (0.027) (0.027) Price-to-earnings Ratio -0.346 -0.307 (0.506) (0.500) Total Assets (log) 1.643** 0.628** (0.153) (0.157) Num of Analysts Covering the Stock 0.102** 0.064** (0.017) (0.017) Pct Female Analysts -1.426** -0.876** (0.348) (0.339) Mean Analyst Rating -0.104 0.068 (0.075) (0.074) SD in Analyst Ratings 0.127 0.037 (0.104) (0.104) Constant -3.041** -0.106 (1.092) (1.073) Observations 37,956 37,956 R-squared 0.033 0.063 Number of firms 6,386 6,386 Firm fixed-effects Yes Yes Year fixed-effects Yes Yes Note. Robust standard errors in parentheses; ** p<0.01, * p<0.05, + p<0.1

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Table 4 Panel Linear Models with Dyadic Fixed Effects: Predicting Analyst Rating 2001-2014 Variable Model 1 Model 2 Model 3 Pct Women on Board 0.099+ -0.170 0.033 (0.057) (0.110) (0.064) Pct Women on Board x Avg Pct Women on Boards among

Industry Peers 3.522**

(1.231) Pct Women on Board x Proportion of News Articles on

Diversity (log) 0.122*

(0.050) Analyst Tenure (years) 0.160** 0.159** 0.159** (0.008) (0.008) (0.008) Analyst Portfolio Size -0.001** -0.001** -0.001** (0.001) (0.001) (0.001) All-star Analyst -0.011 -0.011 -0.011 (0.010) (0.010) (0.010) Bulge Bracket Brokerage -0.119** -0.118** -0.119** (0.015) (0.015) (0.015) Num of Analysts Covering the Stock -0.000 -0.000 -0.000 (0.001) (0.001) (0.001) Earnings 0.022** 0.021** 0.021** (0.002) (0.002) (0.002) Price-to-earnings Ratio -0.249** -0.250** -0.251** (0.032) (0.032) (0.032) Total Assets (log) 0.060** 0.061** 0.061** (0.010) (0.010) (0.010) Avg Pct Women on Boards among Industry Peers -0.812* -1.300** (0.331) (0.370) Proportion of News Articles on Diversity (log) 0.001 (0.008) Constant 3.414** 3.435** 3.380** (0.079) (0.079) (0.078) Observations 290,281 290,281 290,281 R-squared 0.035 0.036 0.035 Number of analyst-firm dyads 99,057 99,057 99,057 Analyst-firm dyadic fixed-effects Yes Yes Yes Year fixed-effects Yes Yes Yes Note. Robust standard errors in parentheses; ** p<0.01, * p<0.05, + p<0.1

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Table 5: Panel Linear Models with Dyadic Fixed Effects: Predicting Analyst Rating, 2001-2014 (Split Sample)

Variable

Model 1: Female analysts

Model 2: Female analysts

Model 3: Female analysts

Model 4: Male

analysts

Model 5: Male

analysts

Model 6: Male

analysts Pct Women on Board 0.537** -0.244 0.404** 0.042 -0.131 -0.005 (0.145) (0.278) (0.154) (0.058) (0.114) (0.066) Pct Women on Board x Avg Pct Women on

Boards among Industry Peers 8.801** 2.309+

(2.898) (1.304) Pct Women on Board x Proportion of News

Articles on Diversity (log) 0.247* 0.087

(0.114) (0.054) Analyst Tenure (years) 0.088** 0.085** 0.086** 0.168** 0.167** 0.168** (0.023) (0.023) (0.023) (0.009) (0.009) (0.009) Analyst Portfolio Size -0.000 -0.000 -0.000 -0.002** -0.002** -0.002** (0.002) (0.002) (0.002) (0.001) (0.001) (0.001) All-star Analyst -0.062* -0.062* -0.060* -0.006 -0.006 -0.006 (0.027) (0.027) (0.027) (0.010) (0.010) (0.010) Bulge Bracket Brokerage -0.251** -0.251** -0.255** -0.108** -0.107** -0.108** (0.048) (0.047) (0.047) (0.016) (0.016) (0.016) Num of Analysts Covering the Stock -0.000 -0.000 -0.000 -0.000 -0.000 -0.000 (0.002) (0.002) (0.002) (0.001) (0.001) (0.001) Earnings 0.029** 0.029** 0.029** 0.021** 0.021** 0.021** (0.004) (0.004) (0.004) (0.002) (0.002) (0.002) Price-to-earnings Ratio -0.260** -0.263** -0.263** -0.248** -0.249** -0.250** (0.091) (0.091) (0.091) (0.033) (0.033) (0.033) Total Assets (log) 0.057+ 0.061* 0.056+ 0.061** 0.061** 0.061** (0.029) (0.029) (0.029) (0.011) (0.011) (0.011) Avg Pct Women on Boards among Industry

Peers -1.147 -2.650** -0.730* -1.038**

(0.754) (0.929) (0.344) (0.382) Proportion of News Articles on Diversity (log) 0.016 0.001 (0.021) (0.008) Constant 3.550** 3.628** 3.514** 3.396** 3.409** 3.366** (0.222) (0.224) (0.221) (0.081) (0.081) (0.080) Observations 31,061 31,061 31,061 259,220 259,220 259,220 R-squared 0.041 0.042 0.042 0.035 0.035 0.035 Number of analyst-firm dyads 11,002 11,002 11,002 88,055 88,055 88,055 Analyst-firm dyadic fixed-effects Yes Yes Yes Yes Yes Yes Year fixed-effects Yes Yes Yes Yes Yes Yes Note. Robust standard errors in parentheses; ** p<0.01, * p<0.05, + p<0.1

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Table 6 Panel Linear Models with Firm Fixed Effects: Predicting Earnings per Share: 2001-2014

Variable Model 1 Model 2 Model 3 Pct Women on Board -0.155 -0.027 -0.253 (0.292) (0.559) (0.340) Pct Women on Board x Avg Pct Women on Boards in

Industry -1.657

(5.557) Pct Women on Board x Proportion of News Articles on

Diversity (log) 0.164

(0.259) Total Assets (log) 0.316** 0.316** 0.316** (0.053) (0.053) (0.052) Num of Analysts Covering the Stock 0.013* 0.013* 0.014* (0.006) (0.006) (0.006) Pct Female Analysts -0.028 -0.028 -0.019 (0.115) (0.115) (0.115) Mean Analyst Rating 0.383** 0.384** 0.387** (0.030) (0.030) (0.029) SD of Analyst Ratings -0.049 -0.049 -0.050 (0.038) (0.038) (0.038) Avg Pct Women on Boards in Industry -5.742** -5.526** (1.449) (1.655) Proportion of News Articles on Diversity (log) 0.030 (0.045) Constant -2.575** -2.583** -2.808** (0.376) (0.379) (0.372) Observations 37,956 37,956 37,956 R-squared 0.052 0.052 0.052 Number of firms 6,386 6,386 6,386 Firm fixed-effects Yes Yes Yes Year fixed-effects Yes Yes Yes Note. Robust standard errors in parentheses; ** p<0.01, * p<0.05, + p<0.1