PAPER

1. Gender in Management: An International Journal
Does gender make a difference in business performance? Evidence from a large enterprise survey data
Jabir Ali, Sana Shabir,
Article information:
To cite this document:
Jabir Ali, Sana Shabir, (2017) "Does gender make a difference in business performance? Evidence from a large enterprise
survey data", Gender in Management: An International Journal , Vol. 32 Issue: 3, doi: 10.1108/GM-09-2016-0159
Permanent link to this document:
http://dx.doi.org/10.1108/GM-09-2016-0159
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2. 1
Does gender make a difference in business performance? Evidence from a large enterprise
survey data of India
Abstract
Purpose - This paper aims at analyzing the difference in business performance and obstacles across
the male-owned versus female-owned enterprises in India.
Design/methodology/approach - This study is based on a comprehensive enterprise survey of 9281
Indian firms operating in different regions of the country, conducted under the World Bank’s Enterprise
Survey, 2014. The survey contains information on a variety of enterprise characteristics such as
ownership, type of firms, size of firms, locations and age, performance indicators and information on 16
parameters of business obstacles. Business performance indicators have been derived from data in the
form of growth in sales, employment, labour productivity and capability utilization by gender ownership
of the firms. Simple statistical tools such as descriptive statistics, chi-square test and the independent
samples t-test have been used to analyse the data. Further, an ordered probit regression model has
been estimated to identify the relative importance of parameters affecting female-owned enterprises.
Findings - Out of total 9281 firms surveyed under the World Bank’s Enterprise Survey, about 8 percent
were being managed by a top female manager and about 15 percent firms reported to have atleast one
female owner. Among the female owners, about 36 percent were reported to own 50 percent and
above share of the firm. Chi-square statistics indicate that there is significant difference in enterprise
characteristics of male versus female-owned firms in terms of location, size, type and age. Result of the
independent samples t-test indicates a significant difference in business performance across male and
female owned businesses in term of annual sales growth, labour productivity growth and capacity
utilization of the firms. Similarly, the perception of male and female owned firms significantly vary on 10
obstacles out of total 16 business obstacle parameters. Overall, females perceives comparatively less
business obstacles as compared to males. An ordered probit regression model has revealed the
relative importance of enterprise characteristics, performance indicators and extent of business
obstacles among female-owned enterprises.
Practical implications - This study provides insight on differences in firms’ performance across gender
ownership based on a large survey data. This study can be helpful in designing policies for promoting
gender-based business enterprises in a focused manner.
Originality/value – There are limited empirical evidences on difference in organisational profile,
business performance and understanding business obstacles across male-owned versus female-
owned firms in India based on a large survey data.
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3. 2
Does gender make a difference in business performance? Evidence from a large enterprise
survey data of India
Introduction
The importance of business development, particularly small and medium enterprises as an engine of
economic growth has been widely recognized across the developing and developed nations (Tanabe
and Watanabe, 2005; Forsman and Temel, 2011). Dethier et al. (2011) highlighted various
infrastructure, finance, security, competition, and regulation factors that have a significant impact on
enterprise performance. The antecedents to business performance can be categorized into human and
non-human factors (Chittithaworn et al., 2011). The non-human factors include technology, finance,
demand level, competition and organization structure, whereas business entities make appropriate
investment to achieve the business goals. The human factor is another important resource influencing
the performance of business enterprises, which may vary in certain respects like motivation, education
level, skill level and training of workforce and is significantly correlated with gender in a given socio-
cultural setting (Belfield, 2005; Fitzenberger and Muehler, 2015). It is also evident in literature that
gender has been prominent among the demographic variables influencing the business performance
and decision-making (Chirwa, 2008; Rodriguez-Dominguez et al., 2012; Khalife and Chalouhi, 2013;
Lee and Marvel, 2014).
Chen (2009) argued that the competitiveness of an enterprise derives from the performance of its
business units in the form of input-output relationship. Nichter and Goldmark (2009) highlighted four
types of factors affecting the growth and performance of business enterprises (1) individual
entrepreneur characteristics; (2) firm characteristics; (3) relational factors such as social networks or
value chains; and (4) contextual factors such as the business obstacles. The differences in business
performance across male versus female-owned enterprises have been rigorously researched
particularly in the developed nations (Westhead, 2003; Khalife and Chalouhi, 2013). The differences in
business performance across male versus-female owned firms have been analyzed in terms of cost
(Watson, 2002), employment (Rosa et al., 1996; Menzies et al., 2004; Coleman, 2005; Chirwa, 2008),
revenue and profitability (Kalleberg and Leicht, 1991; Du Rietz and Henrekson, 2000; Menzies et al.,
2004), risk (Jianakoplos and Bernasek, 1998; Robb and Watson, 2012) and business survival
(Kalleberg and Leicht, 1991; Boden and Nucci, 2000; Basyith et al., 2014).
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4. 3
Many researchers believe that there is performance gap between male and female-led organizations
but empirical evidences do not offer very strong support (Coad and Tamvada, 2012). However, a
number of studies conducted across various countries identified that there have been significant
difference in performance of female-led businesses (Watson, 2011; Diaz-Garcia and Brush, 2012; Lee
and Marvel, 2014). Lerner et al. (1997) discussed that factors such as social learning, human capital,
network affiliation, motivations and goals, demographics and environmental factors are expected to
have an impact on the performance of the female-led businesses. Lee and Stearns (2012) argued that
critical success factors in the performance of female-owned businesses as family support and
knowledge, communication skills, knowledge of business, product competency, business capability,
and availability of resources.
Increasing participation of women in businesses has attracted the attention of researchers, practitioners
and policymakers for promoting economic activity and growth among women in developing countries
(Hisrich and Fülöp, 1997; Klapper and Parker, 2011). However, the under-representation of women on
boards of business organizations is increasingly becoming an important issue of discussion across the
world (Campbell and Minguez-Vera, 2007; Rai, 2012; Joecks et al., 2013). Several empirical studies
have indicated the importance of gender diversity at workplaces and its implication on business
performance (Rodriguez-Dominguez et al., 2012; Al-Mamun et al., 2013; Lazzaretti et al., 2013). Indian
government has taken an active step in ensuring women participation in the boards of companies as a
mandatory provision under the new Companies Act of 2013 to improve the corporate governance with
gender diversity (Shrivastava and Chakraborty, 2015). In the process of implementing the new
provision, the Securities and Exchange Board of India (SEBI) has announced the implementation of
gender diversity in boardrooms among all organizations listed with the stock exchanges in a time bound
manner. Under such a scenario, a study on analyzing the difference in business performance across
male versus female-owned enterprises becomes important for better policy initiatives.
Conceptual Framework and Research Hypotheses
Empirical evidences indicate that gender has been prominent among the demographic variables
influencing the business performance and decision-making (Chaganti and Parasuraman, 1996; Chirwa,
2008; Fairlie and Robb, 2009; Davis et al., 2010; Rodriguez-Dominguez et al., 2012; Lee and Marvel,
2014). However, the relationship between gender and firm performance is a relatively less researched
area (Bardasi et al., 2011; Khan and Vieito, 2013) and most studies on gender differences in business
performance have been undertaken in the developed countries (Boohene et al., 2008). There have
been studies indicating gender gap in business performance, whereas there are researchers who have
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5. 4
found mixed or no relationships between gender and business performance (Collins-Dodd et al., 2004;
Johnsen and McMahon, 2005; Watson, 2012; Diaz-Garcia and Brush, 2012; Lee and Marvel, 2014).
Majority of literature indicates that men usually occupy dominant positions in the economy and labor
force (Kalleberg and Leicht, 1991) and have generally reported that female-owned firms underperform
as compared to male-owned firms (Chell and Baines, 1998; Lee and Marvel, 2014).
Rosa et al. (1996) suggested that the relationship between gender and small business performance is
complex, but that gender still appears to be a significant determinant of small business performance in
Britain. Chirwa (2008) empirically analyzed that though there is no significant difference in profit
margins, female-owned enterprises tend to grow more rapidly in terms of employment than male-owned
ones. Davis et al. (2010) argued that female-led Small and Medium Enterprises (SMEs) perform
significantly better due to their strong market orientation as compared to those led by males. Robb and
Watson (2012) argued that women who are contemplating to start a new venture are not discouraged
from the false belief that new ventures initiated by women are less likely to succeed than those initiated
by men. Alowaihan (2004) analyzed the relationship between gender and business performance of
Kuwaiti small firms and concluded that female-owned firms suffered significantly in their financial
performance due to the liability of newness. Based on the association of percentage of female directors
on corporate boards and financial performance of firm, (Pletzer et al., 2015) concluded non-significant
linkage. It is further corroborated by Post & Byron (2015) who highlighted nearly zero relationship
between female board representation and performance. Moreover, Johnsen & McMahon (2005)
reported that there is no statistically significant difference in financial performance and business growth
across female and male owner-managed organizations.
Gender diversity in board of management has been an important area of research across the globe
(Dwyer et al., 2003; Adams and Ferreira, 2009; Joecks et al., 2013; Ahrens et al., 2015; Low et al.,
2015; Pletzer et al., 2015; Marinova et al., 2016). Campbell and Minguez-Vera (2007) argued on the
importance of gender diversity issue, not only in terms of board diversity, but also in terms of female
participation in economic activities in the society. Several researchers have analyzed gender diversity
at workplaces and its implications on business performance across the world and found a mixed
response (de Luis-Carnicer, 2008; Rodriguez-Dominguez et al., 2012; Al-Mamun et al., 2013; Badal
and Harter, 2014). Marinova et al. (2016) concluded in their study in Europe that there is no effect of
gender diversity in board on firm performance, whereas Adams and Ferreira (2009) analysed negative
average effect of gender diversity on firm performance in the United States. Kılıç and Kuzey (2016)
investigated the relationship between board gender diversity and firm performance in Turkey and
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6. 5
concluded that the inclusion of female directors in the board is positivity related to the financial
performance of the firms. Joecks et al. (2013) assessed the relationship between the magnitude of
gender diversity in board and firm performance and argued that linkage between gender diversity and
firm performance follows a U-shape i.e. gender diversity in board first negatively affect the firm
performance and after reaching a threshold of about 30% female directors in the board, the firm
performance becomes positive.
Several researchers have analysed the relationship between gender leadership and firm performance
(Jalbert et al., 2013; Khan and Vieito, 2013; Peni, 2014; Lam et al., 2013; Ahrens et al., 2015;
Singhathep and Pholphirul, 2015). Singhathep and Pholphirul (2015) investigated the impact of female
CEOs on both short-term performance and long-term firm development in Thailand and suggested that
promoting highly educated and experienced women in management positions should be a priority for
enhancing the short- term and long-term performances of their companies. Khan and Vieito (2013)
argued that ROA of a firm increases much more if the firm is managed by a female CEO instead of a
male CEO. Peni (2014) assessed a positive relationship between the firm led by a female CEOs or
Chairs and firm performance. India was ranked at 120th position in female labour participation among
the 131 nations surveyed by International Labour Organization (ILO) in the year 2013. Under the
new Companies Act of 2013, India has tightened the corporate governance norms by making it
mandatory for firms to hire women directors (Shrivastava and Chakraborty, 2015). The Securities and
Exchange Board of India (SEBI) has announced the measure to boost gender diversity in boardrooms
among all organizations listed with the stock exchanges. Therefore, an analysis of business
performance across the male-owned versus female-owned enterprises become imperative. This paper
aims at analyzing the difference in business performance and obstacles across the male-owned versus
female-owned firms in India. Following three hypotheses have been formulated and tested in the study
(Fig. 1):
H1: There is no difference in enterprise characteristics of female versus male owned firms
H2: There is no difference in business performance of female-owned enterprises versus male-owned
enterprises.
H3: There is no difference in business obstacles across male versus female owned firms.
H4: There is no causal implications of enterprise characteristics, performance indicators and business
obstacles for female-owned firms.
Please Insert “Figure 1” about here
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7. 6
Data and methods
Data source
This study is based a comprehensive survey of 9281 Indian firms operating in different regions of the
country, conducted under the World Bank’s Enterprise Survey in 2014. A female owned firms have
been categorized if firms reported to have atleast one female owner. Further, relevant indicators have
been identified from the dataset that influence the business performance and demonstrate that gender
has a bearing on business performance in Indian context. The survey contained information on a
variety of enterprise characteristics such as ownership, type of firms, size of firms, locations and age;
performance indicators such as growth in sales, employment, labour productivity and capacity
utilization of the firms; and information on 16 business obstacles as reported by the firms on a 5-points
scale rating, where 1= No obstacle, 2=Minor obstacle, 3=Moderate obstacle, 4=Major obstacle, 5= Very
severe obstacle on issues related to electricity, telecommunications, transport, practices of competitors
in informal sector, access to land, crime, theft and disorder, access to finance, tax rates, business
licensing and permits, corruption, labor regulations and inadequately educated workforce.
Description of variables
Ownership of the firm by gender has be defined based on the survey response collected in the
Enterprise Survey whereby firms have been asked to report weather there are females among the
owners. All those firms having female as an owner have been categorized as female-owned firms.
Business performance has been measured in terms of annual sales growth, growth in employment and
labour productivity and capacity utilization of the firms. The annual sales growth has been calculated as
the change in sales reported in the current fiscal year from a previous period. Similarly, annual
employment growth is defined as change in number of full-time employment in the current fiscal year
from a previous period. Further, labour productivity has been calculated as annual sales divided by
number full-time employees and growth in labour productivity the change in labour productivity in the
current fiscal year from a previous period. Finally, capacity utilization of firms has been defined as
proportion of the current output produced by as establishment with the maximum output possible while
using all the current inputs available with the firms.
Data analysis
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8. 7
This large primary survey data has been analyzed using simple statistical tools such as descriptive
statistics, cross-tabulation, chi-square test and the independent-samples t-test have been used to
understand the business performance differences between male-owned and female-owned firms in
India. The difference in enterprise characteristics across female versus male owned firms has been
analysed using the chi-square test statistics as follows:
χ2
= O−E
( )
2
/E
∑
with df=(r-1) (c-1), where r and c are the number of possible values for the two variables under
consideration.
The difference in business performance and perceived business obstacles has been analyzed using
the independent samples t-test. The independent samples t-test is the single most widely used test in
statistics to compare differences between two group means, which is calculated as follows:
Where, ܺ
ത = mean; n = sample size; s2
=variance
Further, an ordered probit regression model has been developed to analyze the relative importance of
factors determining the female-ownership of firms. An ordered probit model is used to estimate
relationships between an ordinal dependent variable and a set of independent variables. The
regression model is defined as:
∑
=
+
+
=
n
i
i
i
i
i X
y
1
*
ε
β
α
Where Y is an unobserved dependent variable; and i
X is vector of explanatory variables, which are
likely to influence the ownership of the firm; α is the intercept; i
β is coefficient of regression for i
X
and i
ε is the standard error.
Results and Discussion
Enterprise Characteristics in India by Gender
1
1 2
2
1
2
2
1
2
1
−
+
−
Χ
−
Χ
=
n
S
n
S
t
x
x
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9. 8
Several studies have analyzed the difference in enterprise characteristics by gender, which have shown
varied evidences based on socio-demographic configuration of geographic locations and business
environment (Elizabeth and Baines, 1998; Alowaihan, 2004; Danes et al., 2007; Lee and Marvel, 2014;
Robichaud et al., 2015). Out of total 9281 firms surveyed in India under the World Bank’s Enterprise
Survey, about 8 percent were being managed by a top female manager and about 15 percent firms
reported to have atleast one female owner. In this study, the enterprise where there is atleast one
female owner has been categorized as female-owned firm. It is important to note that among the
female-owners, about 36 percent have reported to own 50 percent and above of the firm’s resources.
Table 1 provides details of enterprise characteristics in India by gender in terms of location, size, type
and age of the firms. The results of chi-square statistics clearly indicate that male versus female-owned
firms significantly differ in terms of their characteristics.
Please Insert “Table 1” about here
It is also evident from the analysis that there is significant difference in distribution of male versus
female-owned firms across various regions of the country(Χଶ
= 195.740, p < 0.01). Female-owned firms
are comparatively more in central, eastern and northern regions, whereas southern and western
regions have more male-owned firms. Similarly, male-owned enterprises are mainly located in
comparatively bigger cities as compared to female-owned enterprises (Χଶ
= 63.774, p < 0.01). Most of
the firms surveyed were small and medium enterprises and female ownership is comparatively higher
in large firms (Χଶ
= 61.053, p < 0.01). The result of chi-square test for type of firms indicates significant
difference across male and female owned enterprises (Χଶ
= 73.177, p < 0.01). Most of the male-owned
enterprises are mainly sole proprietory firms whereas female-owned enterprises are mostly partnership
firms. The distribution of enterprises by age across gender also vary significantly(Χଶ
= 55.709, p <
0.01). This implies that share of female-owned firms increases with age of the firms whereas male
owned-firms are comparatively younger in age.
Therefore, Hypothesis H1, which assumes that there is no difference in enterprise characteristics of
female versus male owned firms is not accepted, and male and female owned firms significantly vary
on their enterprise characteristic parameters. In a nutshell, female owned firms generally belong to
central, eastern and northern regions with concentration in smaller cities and female-ownership
increases with size of the firms. Similarly, partnership firms are having more of female ownership, which
increases with age of the firms.
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10. 9
Difference in business performance between male and female owned enterprises
There has been an increase in the number of female-managed and female-owned firms across the
globe (Mukhtar, 2002; Sandberg, 2003; Chirwa, 2008). The difference in business performance across
male versus female-owned enterprises has been analyzed using a variety of indicators such as inputs
and outputs (Watson, 2002); profit margins and employment (Chirwa, 2008); closure rates, return on
assets (ROA) and risk (Robb and Watson, 2012); and return on assets (ROA), business growth and
survival (Basyith et al., 2014). Under this study, business performance of enterprises has been
analyzed in terms of annual growth in sales, employment and labour productivity and capacity
utilization. Table 2 provides the difference in business performance across male versus female-owned
enterprises. The independent-samples t-test has been used to analyze the difference between mean
values of business performance indicators across male versus female-owned enterprises. It is evident
from the analysis that female-owned and male-owned enterprises have grown similarly in terms of
annual sales at about 5 percent rate and there is no significant difference in annual sales growth by
gender-based ownership of the enterprises. However, analysis of mean difference for sales growth
between gender-based ownership by controlling with the level of growth i.e. firms with increasing sales
growth and firms with decreasing sales growth, clearly indicate that there is no difference in sales
growth across gender among the firms with declining rate of sales growth whereas female-owned firms
differ significantly from male-owned firms among the firms with increasing rate of sales (t=22.869,
p<0.01), i.e. female-owned firms are growing faster than male-owned firms in terms of sales turnover.
Employment generation is another key indicator to measure the performance of business enterprises.
Chirwa (2008) argued that female-owned enterprises tend to grow more rapidly in terms of employment
than male-owned enterprises. However, result of the analysis under this study indicates that female-
owned enterprises have been experiencing slightly lower annual employment growth than male-owned
enterprises. It is interesting to note that annual labour productivity growth in female-owned firms are
higher than male-owned firms, which is not significant. After controlling the analysis of mean difference
for labour productivity, firms with increasing labour productivity growth differs across gender (t=5.868,
p<0.05). Further, performance of enterprises has been measured based on capacity utilization of the
firms. It is evident from the analysis that capacity utilization of female-owned firms is significantly lower
than male-owned firms (t=5.028, p<0.01).
Therefore, Hypothesis H2, which assumes that there is no difference in business performance of
female-owned versus male-owned enterprises, is partially accepted, and male and female owned firms
significantly vary on business performance parameters annual sales growth and capacity utilization.
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11. 10
Please Insert “Table 2” about here
Business obstacles across male versus female owned firms
In the changing business environment, there are several factors that hinder the performance of
business enterprises (Watson, 2006; Roomi et al., 2009; Mbonyane and Ladzani, 2011; Kwong et al.,
2012). Formal institutional arrangements in the form of government legislations, policies and programs
play an important role in facilitating the growth and development of business enterprises (Aidis, 2005;
Roxas et al., 2013). Under this study, information on business obstacles were collected from the firms
on a 5-points rating scale where 1= No obstacle, 2=Minor obstacle, 3=Moderate obstacle, 4=Major
obstacle, 5= Very severe obstacle, on 16 parameters related to electricity, telecommunications,
transport, practices of competitors in informal sector, access to land, crime, theft and disorder, access
to finance, tax rates, business licensing and permits, corruption, labour regulations and inadequately
educated workforce. Table 3 provides analysis results on difference in business obstacles across male
versus female-owned enterprises. The survey results shows that firms’ responses on obstacles faced
by them in their business operations varies significantly by male versus female-owned enterprises for
10 business obstacles out of total 16 indicators included in the study. Lower mean value of the
responses on business obstacles indicators depict that firms are mainly facing minor or moderate level
of obstacles in undertaking business operations.
Please Insert “Table 3” about here
Among the business obstacles with significant t-test statistics values, female-owned enterprises
perceive comparatively low level of obstacles than male-owned enterprises for 8 indicators of business
obstacles such as transport (t=4.531, p<0.01), practices of competitors (t=4.799, p<0.01), access to
land (t=2.436, p<0.05), crime, theft and disorder (t=3.746, p<0.01), access to finance (t=3.932, p<0.01),
business licensing and permits (t=2.037, p<0.05), courts (t=5.080, p<0.01) and labor regulations
(t=4.252, p<0.01). Similarly, male-owned enterprises perceive comparatively low level of business
obstacles than female-owned enterprises for 2 indicators of business obstacles such as tax rates (t=-
2.681, p<0.01) and corruption (t=-2.148, p<0.01). Overall, female perceive comparatively less business
obstacles as compared to male. Therefore, Hypothesis H3, which assumes that there is no difference in
business obstacles across male versus female-owned firms, is largely accepted, and male and female
owned firms significantly vary on 10 obstacles out of total 16 business obstacle parameters.
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12. 11
Implications of enterprise characteristics, performance indicators and business obstacles for
female-owned firms
Analysis of survey data has clearly revealed the difference in enterprise characteristics, business
performance and perception towards business obstacles between female-owned and male-owned firms
in India. It is further evident from the analysis that performance of female-owned enterprises is better in
terms of growth in sales and labour productivity and female-owned enterprises show comparatively less
perceived obstacles in doing business in India than male-owned firms. Therefore, an ordered probit
regression model has been estimated to identify the relative importance of enterprise characteristics,
performance indicators and extent of business obstacles among female-owned enterprises (Table 3).
Out of 29 explanatory variables, 14 variables are estimated to be significant for female-owned
enterprises. Result of the regression analysis indicates that female-owned firms are more likely to be
influenced by a number of firm characteristic indicators such as age of the firm, type of firm, locality,
access to export market, participation in formal training, quality certification and use of credit. The result
of marginal effect associated with each explanatory variable indicate the relative significance. Firms
with higher age are 0.1% more likely to be a female-owned firm whereas a sole proprietorship firm is
16.2% less likely to be owned by a female entrepreneur. Female-owned firms are more likely to operate
in comparatively smaller localities with an objective of accessing the export market. Moreover, female-
owned firms are 4.0% more likely to organize formal training for its employees and 5.5% more likely to
avail credit for running the business. However, female-owned firms are 2.2% less likely to adopt quality
certification.
Out of 4 explanatory variables on business performance, only capacity utilization is estimated to be
inversely significant for female-owned enterprises. This implies that capacity utilization is comparatively
lower among female-owned enterprises. The regression analysis indicate that out of 16 explanatory
variables on business obstacles, 6 variables are estimated to be significant for female-owned
enterprises. A closer look on the analysis clearly reveals that the incidence of female-owned enterprise
and the level of business obstacles are inversely related on most of the parameters. Female-owned
enterprises are 2.4% less likely to perceive obstacle in accessing finance for their businesses. Similarly,
tax administration is 3.1% less likely to affect the functioning of business operations of female-owned
enterprises. Female-owned enterprises are also less likely to face business obstacles related to labour
relations and handling of business cases in the court of law. It is also evident from the analysis that
female-owned enterprises are less likely to face business obstacles in the form of corruption. Thus
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13. 12
Hypothesis H4, which assumes that there is no causal implications of enterprise characteristics,
performance indicators and business obstacles for female-owned firms, is partially accepted.
Please Insert “Table 4” about here
Conclusion and Managerial Implications
The participation of women in businesses has been significantly at an increasing rate, which has
attracted the attention of researchers, practitioners and policymakers for promoting business
enterprises through gender diversity at workplaces and ensuring the representation of women director
in the boards of the firms. The recent amendment in the Companies Act, 2013 has mandated the
government to ensure gender diversity in boards of organizations for improving the corporate
governance. In continuation to this, the Securities and Exchange Board of India (SEBI) has announced
the implementation of gender diversity in boardrooms among all the public limited companies in a
phased manner. An analysis of business performance across male-owned versus female-owned
enterprises is critical to substantiate the on-going agenda of the government in promoting gender
diversity in the boards of business enterprises.
This study provides insight on differences in firms’ performance across gender, based on a large survey
data. Firstly, business characteristics of enterprises significantly vary with gender ownership. It is
evident from the analysis that female owned firms comparatively belong to central, eastern and
northern regions with concentration in smaller cities and number of female-owned firms increases with
the size of the firms. Similarly, female ownership is comparatively more in partnership firms and number
of female-owned firms increases with age of the enterprises. Analysis on firm’s performance across
gender of ownership depicts that annual sales growth and labour productivity growth in female-owned
enterprises are comparatively higher than male-owned enterprises. An analysis of difference in
business obstacles across male versus female-owned enterprises indicate that female owned
enterprises perceive comparatively low level of obstacles than male-owned enterprises, which implied
that female-owned enterprise are comparative more positive and able to manage business obstacles
effectively. Further, the relative importance of a set of indicators have been identified related to
enterprise characteristics, performance indicators and extent of business obstacles for female-owned
enterprises.
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14. 13
This study is useful for researchers, bankers, entrepreneurs and policymakers and provides insights for
designing effective business models for promoting gender-based business enterprises in a focused
manner. The difference in enterprise characteristics across gender suggests prescriptions on nature of
male and female-owned enterprises. Similarly, promotion of female-owned enterprises seems a better
option for generating additional employment due to high labour productivity growth. Besides, female-
owned enterprises perceive less business obstacles than male-owned enterprises. Although this study
is based on a large survey data, the secondary source has provided limited choices in selecting the
performance indicators of business enterprises and well as business obstacles faced by these
enterprises. Future research can be conceptualized based on theoretical models with suitable
indicators by incorporating in-depth interviews of respondents and their characteristic variables.
Acknowledgments
Earlier version of the paper was presented in the 57th Annual Conference Indian Society of Labour
Economics at Central University of Kashmir, Srinagar during October 10-12, 2015. Authors are thankful
to the conference participants for their invaluable comments and feedback.
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Author Biographies
Dr. Jabir Ali is Professor at Institute of Cooperative and Corporate Management, Research & Training (ICCMRT),
Lucknow. He has more than 15 years of academic experience in organizations of repute. His areas of interest in
teaching and research include Supply Chain Management, International Trade Logistics, Risk Management,
Agricultural Commodity Futures and Options, Social Marketing and Social Entrepreneurship. Dr. Ali has published
more than 30 research papers in referred national and international journals, book chapters and has many
conference presentations to his credit.
Ms. Sana Shabir is pressuring her Doctoral Research in the Department of Management Studies, School of
Business Studies, Central University of Kashmir, Srinagar, Jammu and Kashmir. Her area of research interest is
Human Resource Management and Organizational Behaviour.
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20. 19
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21. Fig. 1: Conceptual Research Framework and Hypothesis Testing
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22. Table 1: Enterprise characteristics by gender of ownership
Firm Characteristics Male-Owned
Enterprises
(N=7909)
Female-Owned
Enterprises
(N=1372)
Chi
square
df p
N % N %
Regional location 195.740** 5 .000
Central 620 7.8 151 11.0
Eastern 1114 14.1 321 23.4
North-eastern 424 5.4 127 9.3
Northern 2262 28.6 405 29.5
Southern 1961 24.8 191 13.9
Western 1528 19.3 177 12.9
Locality size of the firms 63.774** 3 .000
City with population over 1
million
3198 40.4 402 29.3
Over 250.000 to 1 million 2501 31.6 491 35.8
50.000 to 250.000 1787 22.6 381 27.8
Less than 50.000 423 5.3 98 7.1
Size of the firms 61.053** 2 .000
Small >=5 and <=19 2748 34.7 372 27.1
Medium >=20 and <=99 3474 43.9 586 42.7
Large >=100 1687 21.3 414 30.2
Type of firm 73.177** 3 .000
Limited Company 694 8.8 278 20.3
Sole Proprietorship 4210 53.2 188 13.7
Partnership 2819 35.6 815 59.4
Others 186 2.4 91 6.6
Age of firms 55.709** 3 .000
<10 years 2460 31.3 351 25.7
10-20 years 2714 34.6 408 29.9
21-30 years 1350 17.2 289 21.2
> 30 years 1329 16.9 317 23.2
**significant at 0.01 level, *significant at 0.05 level
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23. Table 2: Difference in business performance between male and female owned firms
Business Performance
variables
Mean t-test for Equality of Means
Male-Owned
Enterprises
Female-
Owned
Enterprises
t df Sig. (2-
tailed)
Annual sales growth (%) 5.3 5.5 -.913 9279 .361
Firms with declining rate -9.7 -10.6 1.445 1 .230
Firms with increasing rate 8.1 9.4 22.869** 1 .000
Annual Employment Growth (%) 2.7 2.5 1.099 9279 .272
Firms with declining rate -8.2 -8.1 .002 1 .965
Firms with increasing rate 7.9 7.7 .272 1 .602
Annual labour productivity growth
(%)
2.8 3.2 -1.405 9279 .160
Firms with declining rate -7.1 -7.8 2.317 1 .128
Firms with increasing rate 8.2 8.9 5.868* 1 .015
Capacity Utilization (%) 80.1 77.1 5.028** 7163 .000
**significant at 0.01 level, * significant at 0.05 level
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24. Table 3: Responses on business obstacles across male versus female owned firms
Business Obstacles Male-Owned
Enterprises
Female-Owned
Enterprises
t df Sig. (2-
tailed)
N Mean N Mean
Electricity 7348 2.35 1297 2.36 -.407 8643 .684
Telecommunications 7795 1.40 1357 1.41 -.719 9150 .472
Transport 7706 2.05 1330 1.92 4.531** 9034 .000
Access to Land 7537 1.83 1313 1.76 2.436* 8848 .015
Access to Finance 7551 2.05 1304 1.93 3.932** 8853 .000
Customs and Trade
Regulations
7120 1.70 1173 1.71 -.407 8291 .684
Tax Rates 7339 2.53 1229 2.62 -2.681** 8566 .007
Tax Administrations 7495 2.24 1323 2.24 -.162 8816 .871
Business Licensing and
Permits
7655 2.01 1318 1.95 2.037* 8971 .042
Labour Regulations 7730 2.06 1332 1.93 4.252** 9060 .000
Courts 7526 1.71 1272 1.58 5.080** 8796 .000
Crime, Theft and Disorder 7747 1.56 1357 1.48 3.746** 9102 .000
Practices of competitors 7534 1.99 1310 1.84 4.799** 8842 .000
Political Instability 7474 1.95 1305 1.95 -.018 8777 .986
Corruption 6365 2.68 1077 2.76 -2.148* 7440 .032
Inadequately Educated
Workforce
7633 1.92 1321 1.87 1.944 8952 .052
**significant at 0.01 level, * significant at 0.05 level
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25. Table 4: Ordered probit estimates for female-owned firms
Variables Code Estimated coefficient Marginal
effects
Coef. Std. Err. z P>z
Age of the firm (years) Age 0.004* 0.001 2.96 0.003 0.001
Type of Firm (1, Sole Proprietorship, 0,
Otherwise)
TYPE
1
-0.869* 0.048 -17.98 0.000 0.162
Size of locality (1, Population over
250000; 0, Population below 250000)
LOC
1
-0.222* 0.043 -5.14 0.000 0.045
SMEs (1, SMEs; 0, Large) SMEs
1
-0.024 0.048 -0.49 0.622 0.005
Direct exports (%) EXPORT 0.003* 0.001 3.23 0.001 0.001
Formal Training (1, Yes, 0, Otherwise) FT1
0.202* 0.043 4.72 0.000 0.040
Quality Certification (1, Yes, 0,
Otherwise)
CERT
1
-0.117* 0.043 -2.73 0.006 0.022
Credit (1, Yes, 0, Otherwise) CREDIT
1
0.268* 0.042 6.31 0.000 0.055
Innovation (1, Yes, 0, Otherwise) INNOV1
0.043 0.043 0.99 0.324 -0.008
Annual sales growth (%) ASG 0.006 0.008 0.82 0.413 0.001
Annual Employment Growth (%) AEG -0.001 0.007 -0.17 0.862 0.000
Annual labour productivity growth (%) ALPG -0.004 0.008 -0.52 0.606 0.001
Capacity Utilization (%) CU -0.004* 0.001 -3.75 0.000 0.001
Electricity (1, Obstacle, 0, No Obstacle) O1
1
0.100 0.052 1.92 0.055 -0.019
Telecommunications (1, Obstacle, 0,
No Obstacle)
O2
1
0.123 0.049 2.51 0.012 -0.024
Transport (1, Obstacle, 0, No Obstacle) O31
-0.074 0.052 -1.42 0.156 0.015
Access to Land (1, Obstacle, 0, No
Obstacle)
O4
1
0.060 0.054 1.11 0.266 -0.012
Access to Finance (1, Obstacle, 0, No
Obstacle)
O5
1
-0.122** 0.050 -2.45 0.014 0.024
Customs and Trade Regulations (1,
Obstacle, 0, No Obstacle)
O61
0.096** 0.046 2.08 0.038 0.018
Tax Rates (1, Obstacle, 0, No Obstacle) O7
1
0.095 0.063 1.51 0.131 -0.018
Tax Administrations (1, Obstacle, 0, No
Obstacle)
O8
1
-0.154* 0.058 -2.66 0.008 0.031
Business Licensing and Permits (1,
Obstacle, 0, No Obstacle)
O91
-0.036 0.054 -0.66 0.507 0.007
Labour Regulations (1, Obstacle, 0, No
Obstacle)
O10
1
-0.155* 0.055 -2.81 0.005 0.031
Courts (1, Obstacle, 0, No Obstacle) O111
-0.095** 0.048 -2.00 0.046 0.018
Crime, Theft and Disorder (1, Obstacle,
0, No Obstacle)
O121
-0.100 0.053 -1.89 0.058 0.019
Practices of competitors (1, Obstacle, 0,
No Obstacle)
O13
1
-0.047 0.046 -1.00 0.316 0.009
Political Instability (1, Obstacle, 0, No
Obstacle)
O141
0.052 0.048 1.07 0.284 -0.010
Corruption (1, Obstacle, 0, No
Obstacle)
O15
1
-0.169** 0.068 -2.50 0.012 0.035
Inadequately Educated Workforce (1,
Obstacle, 0, No Obstacle)
O16
1
0.040 0.052 0.77 0.441 -0.008
Number of obs=7124, LR chi2(29)=851.34, Prob > chi2=0.000, Log likelihood = -2625.998, Pseudo R2=0.1395
(1
) Marginal effect for discrete change of dummy variable
*significant at 0.01 level, **significant at 0.05 level
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