




































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

  

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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

  

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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

  

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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

  

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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. 

  



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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.                                                                                                                                                                                                                                   

  



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