ASIAN FINANCE & BANKING REVIEW 9(1) (2025), 9-17 9 FINANCE & BANKING REVIEW ASFBR VOL 9 NO 1 (2025) P-ISSN 2576-1161 E-ISSN 2576-1188 Journal homepage: https://www.cribfb.com/journal/index.php/asfbr Published by Asian Finance & Banking Society, USA WOMEN AT THE TABLE: ASSESSING THE EFFECT OF GENDER DIVERSITY ON FIRM PERFORMANCE Imtiaz Chowdhury (a)1 (a) PhD Student, Ivy College of Business, Iowa State University, USA, E-mail: imtiaz@iastate.edu A R T I C L E I N F O Article History: Received: 10th March 2024 Reviewed & Revised: 10th March 2025 to 20th October 2025 Accepted: 20th October 2025 Published: 24th October 2025 Keywords: Corporate Governance, Board Diversity, Firm Performance, Gender Diversity, Boardroom, Bangladesh JEL Classification Codes: G30, G34, J16, M14 Peer-Review Model: External peer-review was done through double-blind method. A B S T R A C T This study aims to assess the relationship between female representation on corporate boards and firm financial performance in the context of Bangladesh's emerging economy. While global research presents mixed findings on board gender diversity effects, empirical evidence from developing countries remains limited, particularly in South Asian contexts where cultural and economic factors may influence governance-performance relationships differently than in developed markets. This study employs panel data collected from published annual reports of 74 companies listed on the Dhaka Stock Exchange (DSE) spanning the period from 2019 to 2022, utilizing both the www.dsebd.org database and individual company websites for data verification. Panel data regression techniques including fixed effect models, random effect models, and Panel Corrected Standard Error (PCSE) models examine gender diversity effects measured through proportion of women directors, binary presence variables, and Blau heterogeneity index on firm performance proxied by return on assets (ROA) and Tobin's Q ratios. The results reveal that female representation on board shows a significant negative relationship with ROA and Tobin’s Q, with regression coefficients of -0.02 and -0.40 respectively, indicating deteriorating performance effects as women's board participation increases. Large firms show insignificant relationship between gender diversity and performance metrics, while smaller firms demonstrate significant negative impacts when female family members comprise board positions. The findings of this study suggest that increased female representation in boardrooms may signal negative market perceptions to shareholders in Bangladesh's developing economy context, particularly for smaller firms where family-based appointments are more prevalent. © 2025 by the authors. Licensee Asian Finance & Banking Society, USA. This article is an open- access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). INTRODUCTION Corporate boards are pivotal for strategic decision-making and firm oversight; yet female representation on these bodies remains disproportionately low in many emerging economies, despite broader advances in gender equality. Bangladesh, for instance, has achieved notable gains in narrowing gender gaps across education, politics, health, and economic participation over the past decade (See Table 1), but women continue to occupy only 17.16 percent of board seats on average across publicly listed companies. This disparity is stark, given the mounting evidence that diverse boards can enhance organizational resilience, innovation, and stakeholder trust in developed markets (Martínez‐García et al., 2021). However, empirical findings on board gender diversity and firm performance remain inconclusive due to methodological heterogeneity and underexplored contextual factors in South Asian settings (Singhania et al., 2022). In this circumstance, the scientific problem addressed in this study is whether female board representation influences firm financial performance in the specific institutional and cultural context of Bangladesh. The purpose of this research is to investigate the relationship between gender diversity on corporate boards and firm performance, proxied by return on assets (ROA) and Tobin’s Q, while controlling for firm size and industry effects. This study employs panel data regression techniques on 74 listed firms from 2019 to 2022, isolating the impact of three gender-diversity measures: the proportion of women on the board, the presence of women (a binary variable), and the Blau index of gender heterogeneity. 1Corresponding author: ORCID ID: 0009-0009-8480-6901 © 2025 by the authors. Hosting by Asian Finance & Banking Society. Peer review under responsibility of Asian Finance & Banking Society, USA. https://doi.org/10.46281/asfbr.v9i1.2674 To cite this article: Chowdhury, I. (2025). WOMEN AT THE TABLE: ASSESSING THE EFFECT OF GENDER DIVERSITY ON FIRM PERFORMANCE. Asian Finance & Banking Review, 9(1), 9-17. https://doi.org/10.46281/asfbr.v9i1.2674 http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) http://creativecommons.org/licenses/by/4.0/) https://www.openaccess.nl/en https://doi.org/10.46281/asfbr.v9i1.2674 https://orcid.org/0009-0009-8480-6901 Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 10 Table 1. Global gender gap index-Bangladesh Index Name 2018 2025 Rank Score Rank Score Global gender gap score 48 0.721 24 0.775 Sub-index 1: Economic participation and opportunity 133 0.441 141 0.457 Sub-index 2: Educational attainment 116 0.95 115 0.960 Sub-index 3: Health and Survival 117 0.969 123 0.960 Sub-index 4: Political Empowerment 5 0.526 3 0.721 Rank out of 149 148 Source: Global Gender Gap Report, World Economic Forum The aim of this research is to determine whether greater female representation on boards has a positive, negative, or neutral correlation with financial outcomes in an emerging economic context. Briefly, Chapter 2 reviews relevant literature and theoretical frameworks; Chapter 3 formulates hypotheses; Chapter 4 details data sources and empirical methodology; Chapter 5 presents the regression and robustness findings; and Chapter 6 concludes with implications for board governance research and directions for future study. LITERATURE REVIEW Gender Diversity in the Boardroom Gender diversity in boardrooms has gathered significant attention in recent years due to its potential implications for organizational effectiveness and governance dynamics. The proportion of women on boards has been widely studied as a key indicator of gender diversity. R. B. Adams and Ferreira (2009) and Carter et al. (2003) demonstrated that higher levels of female representation on boards are linked to improved financial performance, better decision-making processes, and increased stakeholder value. The presence of women on boards was analyzed dichotomously by Carter et al. (2010) to determine the influence on decision-making processes, inclusive discussions, and governance practices. Campbell and Mínguez-Vera (2007) suggest that higher levels of gender heterogeneity, measured through the Blau index, are associated with enhanced firm performance and innovation. Blau index refers to a measure of group heterogeneity or diversity across a specific attribute, such as ethnicity, religion, or occupation (Akram et al., 2020). Blau index is computed as: Blau index = 1 – ∑ 𝑝𝑖 2𝑛 𝑖=1 ............................... (1) Where 𝑝𝑖 2 refers to the square of the proportion of n groups in the board. Only two groups (i.e., male and female) are relevant for this study. The summation of all the squared proportions of relevant groups is subtracted from one to derive the Blau index. Firm Performance Firm performance is a central focus of corporate governance research and is often assessed through various financial indicators, including Return on Assets (ROA) and Tobin’s Q. Return on Assets (ROA) measures a firm’s profitability relative to its total assets, serving as a metric for operational efficiency and financial health. Gompers et al. (2003) found positive associations between board gender diversity and ROA and concluded that firms with higher levels of female representation tend to achieve better financial performance. Tobin’s Q assesses a firm’s market value relative to its book value. Tobin’s Q serves as a proxy for market valuation and investment efficiency. Rose (2007) studied investors’ perceptions of corporate governance practices and long-term value creation, demonstrating that gender diversity on the board is associated with higher Tobin’s Q ratios. Empirical Studies and Hypothesis Development This section reviews empirical studies on board gender diversity and firm performance, and highlights the key findings, methodological variations, and gaps that motivated the current research. Research on the proportion of women directors and firm performance yields mixed outcomes. Erhardt et al. (2003) examined 127 U.S. firms using ROA and ROI measures for the period from 1993 to 1998 and found a positive association between financial performance and the proportion of female board members. Similarly, Lückerath-Rovers (2011) analyzed 99 Dutch companies using OLS regression and reported superior performance in firms with a greater number of women on their boards. Smith et al. (2006) studied 2,500 Danish firms from 1993 to 2001 and observed positive effects of women directors on firm performance. In contrast, Marinova et al. (2015) found no significant relationship between women proportion on the board and firms’ performance in Scandinavian and other markets. Studies on the binary presence of women directors also report divergent findings. Munira (2020) examined 259 firms listed on the DSE across 18 sectors and identified a positive association between women directors and ROA. Sobhan (2021) studied 20 nonbank financial institutions using OLS regression and concluded that female directors significantly enhance ROA. The Blau heterogeneity index also provides insights into the effects of gender distribution on firm performance. Dwyer et al. (2002) reported positive relationships between Blau index values and firm outcomes. Joecks et al. (2012) argue that performance benefits only emerge when a critical mass of 30 percent women is reached. On the other hand, Darmadi (2010) investigated 354 Indonesian firms using panel regression and found negative relationships between the Blau index and ROA and Tobin’s Q. Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 11 Contradictory findings across various contexts and methodologies, with differing sample sizes, cultural settings, and control variables, highlight unresolved issues in the literature. Moreover, only a few studies integrate all three measures of gender diversity, such as proportion, presence, and heterogeneity, or focus on emerging economies or South Asian contexts. Thereby, the purpose of this study is to investigate the relationship between three measures of board gender diversity, covering the proportion of women directors, the presence of women directors, and the Blau heterogeneity index, and firm performance measured by ROA and Tobin’s Q in the context of an emerging economy. The following are the hypotheses of the study: H1: There is a positive relationship between the proportion of women in the boardroom and the firm’s performance. H2: There is a positive relationship between the presence of women on boardroom and the firm’s performance. H3: There is a positive relationship between the gender heterogeneity (Blau index) and the firm’s performance. MATERIALS AND METHODS Sample Data This study uses cross cross-sectional data set of 74 companies out of 319 listed companies in the Dhaka Stock Exchange (DSE) as a sample. Industry-wise, the random sampling method is used to maintain the same proportion of companies in their corresponding sectors in the Dhaka Stock Exchange (DSE). The cross-sectional data set comprises data from selected companies from 2019 to 2022, accounting for the impact of the pandemic. Due to inconsistent and insufficient data availability, 11 companies were excluded from the analysis. Moreover, the insurance industry was excluded from this study to account for the riskier nature of the business and the inconsistent reporting practices of relevant variables, which differ from those of other companies. Therefore, the final sample data comprise 296 firm-year observations, spanning 74 firms from 2019 to 2022. Data related to ROA, market value, book value, board members, participation of women on the board, and firm asset size are collected from the published annual reports of the respective firms available on their official websites. Variables This study uses variables for the regression models, aligning with the empirical studies. ROA and Tobin’s Q have been used as a proxy for a firm’s performance (Adams et al., 2008). The percentage of women on the board reflects the proportion of female directors on the board. A dichotomous variable is used for understanding the presence of women on the board (Dummy variable 1 means at least one female member on the board, and 0 represents no female member on the board), and the Blau heterogeneity index is used as a proxy for gender heterogeneity on the board (Darmadi, 2010). This study also incorporated some firm-specific control variables into the model, including firm size, board size, firm age, and the number of board meetings held. Since these variables vary significantly from firm to firm, the natural logarithm is used to control for them. Table 2. Measurement of Variables Variables Types of Variables Measurement Scale ROA Dependent EBIT/ Total Assets Tobin’s Q Dependent Market value of firm/Book value of firm Percentage of Women Independent No. of women/ No. of board members Dummy Variable Independent 1 for at least one woman in board else 0 Blau Index Independent Gender heterogeneity index Firm Size Control Variable Total assets of a firm Board Size Control Variable Numbers of board members Firm Age Control Variable Year of operation No. of Board Meetings Independent No. of board meetings held Methodology Descriptive statistics are used to summarize the data set and assess the nature and characteristics of the variables. The pairwise correlation coefficient matrix is used to observe any possible relationships among the variables. This study employed the fixed effects (FE) model and the random effects (RE) model to run the regression models (Bell & Jones, 2014). The pooled OLS method is overlooked in this study, aligning with the results of the Breusch-Pagan LM test (Breusch & Pagan, 1980). The heteroskedasticity and autocorrelation problems have been dealt with by using the Panel Corrected Standard Errors (PCSE) model to run the regression (Zidi & Hamdi, 2024). Moreover, modified Wald test and MLE Random-Effect test are also conducted to determine group-wise heteroskedasticity for the fixed effect (FE) model and the random effect (RE) model, respectively (Baum, 2006). The serial autocorrelation and cross-sectional dependencies issues were tested by using the Wooldridge test (Drukker, 2003) and the Pesaran test (Pesaran, 2004), respectively. These issues were also resolved in the panel-corrected standard error (PCSE) model by using robust standard errors. The random effect (RE) model is found to be the appropriate model by the Hausman test (Baltagi et al., 2003). Model Specification The model can be theoretically specified as a panel data regression model, which explains the extent to which the performance of selected firms listed on the Dhaka Stock Exchange is influenced by the representation of women on their boards. As we have chosen three proxies for gender diversity in the board members, along with some control variables thus our model will theoretically explain how and to what extent women proportion, women’s participation, and gender heterogeneity in the board members affect the firm’s performance, measured by ROA, accounting-based performance, and Tobin's Q, market-based performance. The model can theoretically be written as: Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 12 Yit = αo+ β1itX1it+ β2itX2it+ β3itX3it+ β4itX4it+ β5itX5it+ Cit+µit ……………………................................. (2) Where, Yit = Measure of the firm’s performance αo = Intercept coefficient β = Coefficient of gender diversity and other control variables X = Measures of gender diversity and other control variables i & t = ‘i’ denotes each firm and ‘t’ denotes year C = Unit-specific error component µ = Remaining error component Since this study uses two measures for a firm’s performance, ROA and Tobin’s Q, while three measures for a firm’s boardroom gender diversity, it sums up to six models, which can be rewritten as follows: ROAit = αo + β1itPERCENTAGEOFWOMENit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit …………………………................................. (3) ROAit = αo + β1itDUMMYit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit …………………………….................................. (4) ROAit = αo + β1it BLAU_INDEXit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit …………………………….................................. (5) LNTOBINQit = αo + β1itPERCENTAGEOFWOMENit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit ……………………………….............................. (6) LNTOBINQit = αo + β1itDUMMYit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit …………………………….................................. (7) LNTOBINQit = αo + β1it BLAU_INDEXit + β2itLNBSIZEit + β3itLNASSETit + β4itLNAGEit + β5it NOOFBOARDMEETINGit + Cit + µit …………………………….................................. (8) Where, ROA is a measure of the firm’s accounting-based performance; LNTOBINQ is natural log of the firm's market-based performance; αo is intercept coefficient; PERCENTAGEOFWOMEN is proportion of women on board; DUMMY is presence of women on board; BLAU_INDEX is a measure of gender heterogeneity; LNBSIZE is natural log of the number of board members; LNASSET is natural log of total asset of the firm; LNAGE is natural log of the firm’s age; NOOFBOARDMEETING is number of board meetings held yearly; Cit is unit specific error component; µit is remaining error component. These six models were tested separately for the FE model, the RE model, and the PCSE method. RESULTS AND DISCUSSIONS Descriptive Statistics Table 3 provides a summary of the descriptive statistics for our selected variables, which show that, on average, ROA is 3.76%, with an average number of board members being 8. Some firms have board members as many as 21, while the minimum number of board members is 4. On average, 1 female member holds a position on the board, which shows that Bangladesh has yet not become free from gender discrimination in the workplace. The average proportion of women on the board is 16%. While some firms have 13 females on their boards, some have no female representation on their boards of directors. Firm total assets range from Tk. 0.04 billion to Tk. 998 billion, having a Tobin’s Q of 1.32 on average. The average age of the firms is 27 years, and firms hold an average of 11 meetings a year, both of which have a significant effect on the firm’s financial performance. Table 3. Descriptive Analysis Variable Count Mean StdDev Min Max ROA 296 0.038 0.052 -0.120 0.280 Board Size 296 8.568 3.606 4.000 21.000 No. of Women 296 1.365 1.768 0.000 13.000 Firm Size (Billion) 296 61.500 135.000 0.040 998.000 Tobin’s Q 296 1.329 1.270 0.183 7.706 Firm Age 296 27.081 13.208 5.000 64.000 Percentage of Women 296 0.160 0.162 0.000 0.632 Dummy Variable 296 0.652 0.477 0.000 1.000 Blau Index 296 0.217 0.186 0.000 0.500 No. of Board Meetings 295 11.620 8.180 4.000 58.000 Source: Published annual reports of 74 companies listed in the Dhaka Stock Exchange (DSE) Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 13 Correlation Results Table 4 shows the coefficient correlation matrix illustrating the relationships among all the variables used in this study. Since this study is conducted on a panel dataset, the pairwise correlation coefficient matrix is used to determine the correlations among variables. It has been found that ROA and Tobin’s Q are significantly positively correlated, indicating that accounting-based performance has a strong impact on a firm’s market-based performance. Moreover, firm performance is significantly negatively related to LNASSET, indicating that large firms incur higher costs, which in turn result in lower profitability. The proportion of women, the Blau index, and the presence of women have positive correlations with each other because they all represent gender diversity on the board. The proportion of women has a significant negative relationship with LNASSET, which indicates that smaller firms have a higher proportion of women on their boards. Again, LN_BSIZE has a significant positive relationship with LNASSET and Noofboardm~g, which means that large firms have more members on their boards and large boards tend to hold frequent board meetings. Vatcheva et al. (2016) suggest that multicollinearity exists when correlation coefficients exceed 0.80; however, from the correlation matrix, it is observed that none of the independent variables in the six corresponding models exceed this level. Table 4. Correlation Coefficient Matrix ROA LNTOBBINQ Percenta~men Dummy BLAU_INDEX LNBSIZE LNASSET LNAGE Noofboardm~g ROA 1 LNTOBBINQ 0.45*** 1 Percenta~men 0.0301 0.0442 1 Dummy -0.0937 -0.0348 0.72*** 1 BLAU_INDEX 0.0019 0.0403 0.95*** 0.85*** 1 LNBSIZE -0.1341 0.0214 -0.0186 0.1067 -0.0369 1 LNASSET -0.24** -0.25** -0.23** -0.0666 -0.20* 0.59*** 1 LNAGE -0.0589 0.1086 0.1059 0.0572 0.0822 0.1935 0.131 1 Noofboardm~g 0.0234 0.1121 -0.0545 -0.0608 -0.0799 0.32*** 0.51*** 0.0519 1 N.B. Asterisk (*),(**), and (***) indicate significance at 10%, 5% and 1% level respectively Regression Analysis and Discussion Six regression models were run for each of the three panel data regression methods (FE, RE, PCSE). The dependent variables, ROA and Tobin’s Q, were regressed on three different independent variables representing gender diversity and four control variables. The Hausman test has been conducted to determine the appropriate model between FE and RE (See Table 5). It has been found that the random effect (RE) model is appropriate for the regression of ROA, and the fixed effect (FE) model is appropriate for the regression of Tobin’s Q on women’s representation on the board. To run a more robust and significant regression model, the Panel Corrected Standard Error (PCSE) model was employed, which addressed the heteroskedasticity and autocorrelation issues in the model. Table 5. Hausman Tests for FE vs. RE and Autocorrelation Model Name ROA Tobin’s Q H1 H2 H3 H1 H2 H3 Hausman Test [chi²(5)] 4.04 4.40 4.03 24.52 24.08 24.51 (0.54) (0.49) (0.54) (0.00) (0.00) (0.00) Wooldridge Test (F-stat) 1.86 1.92 1.90 51.50 62.19 52.79 (0.18) (0.17) (0.17) (0.00) (0.00) (0.00) N.B. Probabilities of chi2 and F-statistics are in parentheses Table 6 and Table 7 show that the F-statistic probability is less than the 5% significance level in all six models, indicating that all six models are statistically significant. The PCSE regression results indicate that all three gender diversity measures have negative impacts on firm performance. Specifically, the proportion of women on boards is significantly and negatively related to ROA at the 5% level and to Tobin’s Q at the 1% level, leading to the rejection of H1. Similarly, the presence of at least one female director exhibits a strong negative association with both performance metrics, ROA and Tobin’s Q, at the 1% significance level, in contrast to the findings of Nguyen et al. (2014), leading to the rejection of H2. Finally, the Blau heterogeneity index also shows significant negative relationships with ROA (at the 1% level) and Tobin’s Q (at the 5% level), consistent with the work of He and Huang (2011), which leads to the rejection of H3. These negative relationships across all three gender diversity measures suggest that, in Bangladesh’s context, female representation on the board signals a lack of professional expertise among directors, especially when women directors are predominantly drawn from founding families or sponsors rather than appointed on merit (Biswas et al., 2021). The scarcity of independent female directors, whose participation is shown elsewhere to enhance firm performance (Ruigrok et al., 2006), further exacerbates these negative perceptions. Control variables provide additional insights, with board size exhibiting no significant relation with ROA but a positive relationship with Tobin’s Q at the 1% level, which suggests that investors prefer larger boards (Darmadi, 2010). Firm age has a negative impact on ROA, yet a positive influence on Tobin’s Q at the 1% significance level, which provides evidence that established firms command greater market confidence despite lower accounting returns. The importance of frequent board engagement for strategic decision-making is justified as the number of board meetings correlates positively with both ROA and Tobin’s Q at 1% significance. However, larger firms (LNASSET) demonstrate significant negative Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 14 performance effects. It highlights that indirect costs from asset growth may outweigh revenue gains, contrary to the findings of Julizaerma and Sori (2012). Table 6. Regression of ROA on women’s representation in the boardroom Independent Variables Fixed Effect Random Effect PCSE Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Percentage of Women -0.02 -0.02 -0.02 (-0.63) (-0.77) (-2.53)** Dummy 0 -0.01 -0.01 (-0.54) (-1.04) (-3.96)*** BLAU_INDEX -0.01 -0.01 -0.02 (-0.35) (-0.66) (-3.14)*** LNBSIZE -0.01 -0.01 -0.01 0.00 0.00 0.00 0.00 0.01 0.00 (-0.64) (-0.61) (-0.62) (0.02) (0.06) (0.01) (1.03) (1.79)* (0.99) LNASSET -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 -0.01 (-1.48) (-1.48) (-1.48) (-2.92)*** (-2.88)*** (-2.89)*** (-9.79)*** (-11.29)*** (-10.4)*** LNAGE -0.03 -0.03 -0.03 -0.01 -0.01 -0.01 0.00 0.00 0.00 (-1.30) (-1.31) (-1.33) (-0.85) (-0.87) (-0.88) (-1.08) (-1.07) (-1.07) No. of Board Meetings 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 (0.09) (0.08) (0.08) (1.31) (1.27) (1.29) (6.81)*** (6.64)*** (6.76)*** No. of Observations 295 295 295 295 295 295 295 295 295 R-square 0.05 0.05 0.05 0.08 0.09 0.08 0.09 0.10 0.09 F-statistics / Wald chi² 1.83 1.81 1.78 12.83 13.38 12.67 2314.13 937.27 1778.64 Wald Test χ² (Prob.) 0.00 0.00 0.00 0.00 0.00 0.00 Pesaran’s Test (Prob.) 0.92 0.92 0.97 0.53 0.64 0.54 N.B. Z values and t values are in parentheses, and asterisk (*),(**), and (***) indicate significance at 10%, 5% and 1% level respectively Table 7. Regression of Tobin’s Q on women’s representation in the boardroom Independent Variables Fixed Effect Random Effect PCSE Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Percentage of Women -0.82 -0.62 -0.40 (-1.91)* (-1.96)** (-4.35)*** Dummy -0.14 -0.13 -0.13 (-1.57) (-1.72) (-2.70)*** BLAU_INDEX -0.50 -0.38 -0.22 (-1.62) (-1.54) (-2.18)** LNBSIZE -0.14 -0.11 -0.12 0.37 0.38 0.36 0.44 0.45 0.43 (-0.56) (-0.45) (-0.50) (2.23)** (2.27)** (2.19)** (6.76)*** (6.62)*** (6.58)*** LNASSET -0.65 -0.65 -0.65 -0.23 -0.22 -0.23 -0.20 -0.20 -0.20 (-6.13)*** (-6.10)*** (-6.13)*** (-6.05)*** (-5.87)*** (-5.93)*** (-15.31)*** (-16.05)*** (-15.29)*** LNAGE 0.52 0.50 0.51 0.16 0.15 0.15 0.18 0.17 0.17 (1.56) (1.50) (1.54) (1.30) (1.16) (1.20) (3.45)*** (3.41)*** (3.33)*** No. of Board Meetings 0.01 0.01 0.01 0.02 0.02 0.02 0.03 0.03 0.03 (1.41) (1.38) (1.38) (3.60)*** (3.49)*** (3.53)*** (10.23)*** (9.94)*** (9.93)*** No. of Observations 295 295 295 295 295 295 295 295 295 R-square 0.08 0.08 0.08 0.19 0.19 0.18 0.21 0.21 0.20 F-statistics / Wald chi² 9.56 9.27 9.31 41.09 40.15 39.42 5599.30 6361.55 5714.81 Wald Test χ² (Prob.) 0.00 0.00 0.00 0.00 0.00 0.00 Pesaran’s Test (Prob.) 0.00 0.00 0.00 0.00 0.00 0.00 N.B. Z values and t values are in parentheses, and asterisk (*),(**), and (***) indicate significance at 10%, 5% and 1% level respectively Table 8 reveals that the negative impact of gender diversity is insignificant in large firms but significant at 10% and 5% levels in smaller firms (asset size < BDT 200 billion). Since this study focuses primarily on smaller DSE-listed firms, their pronounced negative outcomes drive the overall model. Together, these results and their interpretations illustrate both empirical outcomes and theoretical implications, providing a comprehensive understanding of the effects of gender diversity on corporate performance in an emerging market setting. Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 15 Table 8. Individual Regression of Small Firms and Large Firms Independent Variables Small Firms Large Firms ROA Tobin's Q ROA Tobin's Q Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Model 1 Model 2 Model 3 Percenta~men -0.02 -0.60 -0.01 0.19 (-0.74) (-2.40)** (-1.51) -0.24 Dummy -0.01 -0.16 -0.00 0.09 (-1.65)* (-1.86)* (-1.09) -0.50 BLAU_INDEX -0.01 -0.36 -0.01 0.30 (-0.79) (-1.67)* (-1.43) -0.52 LNBSIZE 0.01 0.01 0.01 0.49 0.49 0.46 -0.01 -0.01 -0.01 0.08 0.08 0.12 -0.72 -0.94 -0.69 (3.80)*** (3.74)*** (3.60)*** (-2.03)** (-1.65)* (-1.96)** -0.27 -0.29 -0.40 LNASSET -0.01 -0.01 -0.01 -0.23 -0.21 -0.22 0.00 0.00 0.00 0.05 0.07 0.03 (-3.35)*** (-3.52)*** (-3.38)*** (-7.91)*** (-7.71)*** (-7.67)*** -0.85 -0.38 -0.78 -0.19 -0.29 -0.12 LNAGE 0.00 0.00 0.00 0.26 0.23 0.24 -0.01 -0.01 -0.01 -0.15 -0.15 -0.10 -0.17 -0.11 -0.16 (3.30)*** (2.98)*** (3.11)*** (-2.94)*** (-2.73)*** (-2.88)*** (-0.48) (-0.6) (-0.31) Noofboardm~g 0.00 0.00 0.00 0.04 0.03 0.03 0.00 0.00 0.00 0.01 0.01 0.01 (4.08)*** (3.84)*** (4.03)*** (5.65)*** (5.21)*** (5.44)*** (1.73)* -1.46 (1.71)* -0.56 -0.50 -0.39 No. of Observations 261 261 261 261 261 261 34 34 34 34 34 34 R-square 0.08 0.09 0.08 0.24 0.24 0.24 0.40 0.38 0.39 0.03 0.04 0.04 F-statistics/Wald chi² 4.60 5.07 4.61 16.45 15.86 15.69 3.72 3.38 3.65 0.18 0.22 0.23 N.B. Z values and t values are in parentheses, and asterisk (*),(**), and (***) indicate significance at 10%, 5% and 1% level respectively CONCLUSIONS The purpose of this study was to investigate the impact of gender diversity in corporate boardrooms on firm financial performance in Bangladesh. Panel regression results demonstrate that all three diversity measures (proportion of women directors, presence of women, and the Blau heterogeneity index) are significantly negatively related to both ROA and Tobin’s Q. The study indicates that increased female board representation corresponds with deteriorated firm performance in this emerging market context. This research makes a unique contribution to the literature by simultaneously analyzing multiple measures of gender diversity and controlling for firm size, board size, firm age, and meeting frequency within a developing-economy framework. It offers empirical evidence that, contrary to findings in many developed markets, gender diversity may signal shareholder concerns about director qualifications when women are predominantly appointed through familial or ownership ties rather than on merit. Theoretical implications of this study underscore the importance of integrating cultural and governance factors when assessing the relationship between diversity and performance. Managerially, firms and policymakers should consider merit-based board appointments, emphasizing professional expertise and independence over inheritance-based placements, to harness the potential benefits of board diversity. Additionally, findings suggest that expanding board size and increasing meeting frequency might improve performance through broader deliberation and oversight. Limitations of this study include its focus on a four-year period (2019–2022) and exclusion of other diversity dimensions, such as ethnic, educational, and experiential, due to scope constraints. The reliance on publicly reported annual data may also overlook qualitative aspects of director contributions. Future research should extend the temporal scope and incorporate additional board composition variables, such as director independence, tenure, and educational background, to provide a more comprehensive portrait of the effects of diversity. Comparative analyses across South Asian markets and qualitative examinations of board nomination processes would further elucidate the contextual mechanisms driving the gender diversity and firm performance. Author Contributions: Conceptualization, I.C.; Methodology, I.C.; Software, I.C.; Validation, I.C.; Formal Analysis, I.C.; Investigation, I.C.; Resources, I.C.; Data Curation, I.C.; Writing – Original Draft Preparation, I.C.; Writing – Review & Editing, I.C.; Visualization, I.C.; Supervision, I.C.; Project Administration, I.C.; Funding Acquisition, I.C. Authors have read and agreed to the published version of the manuscript. Institutional Review Board Statement: Ethical review and approval were waived for this study, as the research does not involve vulnerable groups or sensitive issues. Funding: The authors received no direct funding for this research. Acknowledgments: I would like to acknowledge Sabnaz Amin, Associate Professor at the University of Dhaka, who provided advice and guidance throughout the research process. Also, I would like to acknowledge Md Shikdarul Moin, University of Dhaka, for helping in data collection from annual reports. Thanks to all for your unwavering support Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available due to restrictions. Conflicts of Interest: The authors declare no conflict of interest. Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 16 REFERENCES Adams, R., Almeida, H., & Ferreira, D. (2008). Understanding the relationship between founder–CEOs and firm performance. Journal of Empirical Finance, 16(1), 136–150. https://doi.org/10.1016/j.jempfin.2008.05.002 Adams, R. B., & Ferreira, D. (2009). Women in the boardroom and their impact on governance and performance☆. Journal of Financial Economics, 94(2), 291–309. https://doi.org/10.1016/j.jfineco.2008.10.007 Akram, F., Haq, M. a. U., Natarajan, V. K., & Chellakan, R. S. (2020). Board heterogeneity and corporate performance: An insight beyond agency issues. Cogent Business & Management, 7(1), 1809299. https://doi.org/10.1080/23311975.2020.1809299 Baltagi, B. H., Bresson, G., & Pirotte, A. (2003). Fixed effects, random effects or Hausman–Taylor? Economics Letters, 79(3), 361–369. https://doi.org/10.1016/s0165-1765(03)00007-7 Baum, C. F. (2006). Stata Tip 38: Testing for groupwise heteroskedasticity. The Stata Journal Promoting Communications on Statistics and Stata, 6(4), 590–592. https://doi.org/10.1177/1536867x0600600412 Bell, A., & Jones, K. (2014). Explaining Fixed effects: random effects modeling of Time-Series Cross-Sectional and panel data. Political Science Research and Methods, 3(1), 133–153. https://doi.org/10.1017/psrm.2014.7 Biswas, P. K., Roberts, H., & Whiting, R. H. (2021). Female directors and CSR disclosure in Bangladesh: the role of family affiliation. Meditari Accountancy Research, 30(1), 163–192. https://doi.org/10.1108/medar-10-2019-0587 Breusch, T. S., & Pagan, A. R. (1980). The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics. The Review of Economic Studies, 47(1), 239–253. https://doi.org/10.2307/2297111 Campbell, K., & Mínguez-Vera, A. (2007). Gender diversity in the boardroom and firm financial performance. Journal of Business Ethics, 83(3), 435–451. https://doi.org/10.1007/s10551-007-9630-y Carter, D. A., D’Souza, F., Simkins, B. J., & Simpson, W. G. (2010). The gender and ethnic diversity of US boards and board committees and firm financial performance. Corporate Governance an International Review, 18(5), 396– 414. https://doi.org/10.1111/j.1467-8683.2010.00809.x Carter, D. A., Simkins, B. J., & Simpson, W. G. (2003). Corporate governance, board diversity, and firm value. Financial Review, 38(1), 33–53. https://doi.org/10.1111/1540-6288.00034 Darmadi, S. (2010). Do Women in Top Management Affect Firm Performance? Evidence from Indonesia. Corporate Governance, 13(3), 288–304. https://doi.org/10.2139/ssrn.1728572 Drukker, D. M. (2003). Testing for serial correlation in linear panel-data models. The Stata Journal Promoting Communications on Statistics and Stata, 3(2), 168–177. https://doi.org/10.1177/1536867x0300300206 Dwyer, S., Richard, O. C., & Chadwick, K. (2002). Gender diversity in management and firm performance: the influence of growth orientation and organizational culture. Journal of Business Research, 56(12), 1009–1019. https://doi.org/10.1016/s0148-2963(01)00329-0 Erhardt, N. L., Werbel, J. D., & Shrader, C. B. (2003). Board of Director Diversity and Firm Financial Performance. Corporate Governance an International Review, 11(2), 102–111. https://doi.org/10.1111/1467-8683.00011 Gompers, P., Ishii, J., & Metrick, A. (2003). Corporate governance and equity prices. The Quarterly Journal of Economics, 118(1), 107–156. https://doi.org/10.1162/00335530360535162 He, J., & Huang, Z. (2011). Board informal hierarchy and firm financial performance: Exploring a tacit structure guiding boardroom interactions. Academy of Management Journal, 54(6), 1119–1139. https://doi.org/10.5465/amj.2009.0824 Joecks, J., Pull, K., & Vetter, K. (2012). Gender diversity in the boardroom and firm performance: What exactly constitutes a “Critical mass?” Journal of Business Ethics, 118(1), 61–72. https://doi.org/10.1007/s10551-012-1553-6 Julizaerma, M., & Sori, Z. M. (2012). Gender diversity in the boardroom and firm performance of Malaysian public listed companies. Procedia - Social and Behavioral Sciences, 65, 1077–1085. https://doi.org/10.1016/j.sbspro.2012.11.374 Lückerath-Rovers, M. (2011). Women on boards and firm performance. Journal of Management & Governance, 17(2), 491–509. https://doi.org/10.1007/s10997-011-9186-1 Marinova, J., Plantenga, J., & Remery, C. (2015). Gender diversity and firm performance: evidence from Dutch and Danish boardrooms. The International Journal of Human Resource Management, 27(15), 1777–1790. https://doi.org/10.1080/09585192.2015.1079229 Martínez‐García, I., Terjesen, S., & Gómez‐Ansón, S. (2021). Board Gender Diversity Codes, Quotas and Threats of Supranational Legislation: Impact on director Characteristics and Corporate Outcomes. British Journal of Management, 33(2), 753–783. https://doi.org/10.1111/1467-8551.12517 Munira, S. (2020). Gender diversity and the firm financial performance of DSE listed companies in Bangladesh. Global Disclosure of Economics and Business, 9(2), 89–96. https://doi.org/10.18034/gdeb.v9i2.513 Nguyen, T., Locke, S., & Reddy, K. (2014). Does boardroom gender diversity matter? Evidence from a transitional economy. International Review of Economics & Finance, 37, 184–202. https://doi.org/10.1016/j.iref.2014.11.022 Pesaran, M. H. (2004). General diagnostic tests for cross section dependence in panels. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.572504 Rose, C. (2007). Does female board representation influence firm performance? The Danish evidence. Corporate Governance an International Review, 15(2), 404–413. https://doi.org/10.1111/j.1467-8683.2007.00570.x Ruigrok, W., Peck, S., Tacheva, S., Greve, P., & Hu, Y. (2006). The determinants and effects of board nomination committees*. Journal of Management & Governance, 10(2), 119–148. https://doi.org/10.1007/s10997-006-0001- 3 Chowdhury, Asian Finance & Banking Review 9(1) (2025), 9-17 17 Singhania, S., Singh, J., & Aggrawal, D. (2022). Board committees and financial performance: exploring the effects of gender diversity in the emerging economy of India. International Journal of Emerging Markets, 19(6), 1626– 1644. https://doi.org/10.1108/ijoem-03-2022-0491 Smith, N., Smith, V., & Verner, M. (2006). Do women in top management affect firm performance?A panel study of 2,500 Danish firms. International Journal of Productivity and Performance Management, 55(7), 569–593. https://doi.org/10.1108/17410400610702160 Sobhan, R. (2021). Board Characteristics and Firm Performance: Evidence from the Listed Non-Banking Financial Institutions of Bangladesh. DOAJ (DOAJ: Directory of Open Access Journals), 8(1), 25–41. https://doi.org/10.5281/zenodo.4589504 Vatcheva, K. P., Lee, M., McCormick, J. B., & Rahbar, M. H. (2016). Multicollinearity in regression analyses conducted in epidemiologic studies. Epidemiology Open Access, 6(2). https://doi.org/10.4172/2161-1165.1000227 Zidi, M., & Hamdi, H. (2024). A Panel-corrected Standard Error (PCSE) Framework to Estimate Capital Structure and Banking Performance within the Tunisian Context. 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