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Published on November 21, 2022.

Section I: Women’s Entrepreneurship in India

Introduction and problem statement

Over the last two decades, the labour force participation of women in India has been steadily declining, despite an increase in their educational attainment. Deshpande (2021) provides an overview of this phenomenon and notes that while an extensive academic literature revolves around the supply-side constraints to women’s employment particularly due to the embedded patriarchal norms, there has been limited research on demand-side constraints to generating more female employment in both salaried work and in self-employment.

According to World Bank estimates, female labour force participation in India is abysmally low at just above 20% (See figure 1, Panel A). Of those women who work, over three-quarters are self-employed (See figure 1, Panel B) (World Bank, 2019). According to government data, 20% of the 633.88 lakh unincorporated non-agricultural Micro, Small & Medium Enterprises in India are owned by women (Ministry of Micro, Small & Medium Enterprises, 2021). In addition to the gender gap in prevalence of entrepreneurship, women entrepreneurs are systematically less productive than men. Self-employed women in India largely operate through micro enterprises which have a low turnover and are mostly in the informal sector. Additionally, the presence of women in medium sized enterprises is almost negligible (Deshpande 2021).

Figure 1. Female labour force participation rate vs. share of self-employed women

Panel A

Panel B

Source: World Bank

The existing literature cites factors such as women’s education and skill levels, access to critical resources such as credit, marketing and distribution networks, technical and managerial know-how; the nature of the business environment and how welcoming it is to women; and prevailing socio-cultural norms that limit whether and what type of paid work women can engage in. The poor participation and productivity of women in self-employment leads us to ask the following questions:

Why do women decide to become entrepreneurs?
What sectors do they decide to enter?
What barriers do they face as entrepreneurs/aspiring entrepreneurs?
What works to increase the participation of women entrepreneurs and the sustainability of their businesses?

This policy brief reviews existing evidence around the aforementioned factors and in each section, we try to address one of the questions stated above. Section 2 discusses the motivators for women’s entrepreneurship, Section 3 provides a brief overview of sectoral choices made by women entrepreneurs, Section 4 discusses the main barriers to women’s entrepreneurship, Section 5 provides a review of successful interventions to boost women’s entrepreneurship, and Section 6 provides an overview of the gig economy, its features, and potential growth opportunities for women. Finally, Section 7, concludes the brief with further areas of research that can be explored.

Why do women choose self-employment/entrepreneurship?

Before we get into the motivators that drive women to start their own businesses, it is critical to first define entrepreneurship. Self-employment encompasses all individuals who do not work under a fixed contract and salary: this category includes a diverse set of individuals ranging from owners of businesses with hired labour, individuals operating own-account enterprises with no hired workers, and unpaid family labour. Self-employed women disproportionately fall into the category of unpaid family workers (30%) compared to men (10%) according to the Periodic Labour Force Survey (PLFS) 2020-21 report. Even among business owners, the overwhelming share (close to 96%) of all micro, small and medium enterprises in India are proprietary concerns (Ministry of Micro, Small & Medium Enterprises, 2021), and most enterprises (84%) are own-account enterprises with no additional hired workers (National Sample Survey 73rd round, 2017). This reflects the fact that most enterprises in India are small, limited in scale and operational scope, and informal in nature. Women are poorly represented across all types of enterprises but particularly so among larger enterprises: 22% of own-account enterprises are women-owned while only 5% of establishments are women-owned (Chakraborty and Mukherjee 2021). Similarly, 95% of women-owned proprietors have fewer than six workers, compared to 72.5% of men-owned proprietors (Chakraborty and Mukherjee 2021).

Within this category of business owners, entrepreneurs are those self-employed individuals who exemplify the image of a risk-taker who is committed to the growth and prosperity of their businesses (Fields 2019). The reality of entrepreneurship in India is that most enterprise owners do not closely resemble this image, and the disparity is particularly acute for women. In this section, we will try to understand why women chose to become entrepreneurs. Are they driven into self-employment by economic conditions, or do they want to become entrepreneurs because they find joy in working and improving their skills and building their businesses? This is a crucial subject to explore if we want to understand not just the reasons behind the gender disparity in self-employment, but also the gender disparity in successful business creation.

Opportunity or necessity

In India, the majority of women entrepreneurs/self-employed women work in the informal sector and own low-turnover enterprises. As they are unable to find better-paying jobs in the formal sector, many of these women turn to self-employment (Chen 2016). As a result, self-employment is a need for many Indian women in order to survive and offer financial assistance to their families. EdelGive (2021) conducted a study in 13 Indian states, with 1,235 women entrepreneurs, to discover the factors that influence their success as entrepreneurs. They discovered that 61% of those who responded to their survey started their own business in order to contribute financially to their family but 55% of those surveyed said that entrepreneurship was born out of their own personal needs and goals. Lastly, 27% of respondents said they established their own business to use talents they already had. Hence, while the majority of women entrepreneurs started their businesses to provide for their families, a considerable proportion of women established their businesses to use their skills and abilities.

Inability to find wage employment

One of the reasons why women prefer self-employment is that they confront major hiring biases in wage labour and cannot find a way to avoid them. Choi and Kim (2021) conducted a study among female law professionals in the United States to assess their movement from wage employment to self-employment due to “pull and push” factors. The pull factor suggests that women choose entrepreneurship due to their own desires and see entrepreneurship as a more profitable and lucrative option whereas the push factor postulates that women choose self-employment over wage employment for a variety of reasons, including discontent with their existing job, job loss, or career setbacks.

In India, women are more likely to opt for self-employment due to push factors. Despite a huge increase in women’s educational attainment in India, obtaining work, particularly one which complements their education and talents, remains a difficulty for women. We learnt through a non-profit organisation focused on women’s entrepreneurship that firms frequently outsource recruiting to micro contractors, who typically have preconceived views about women’s efficiency for certain occupations. A multi-state survey of 2610 former skilling trainees discovered that women are less likely to receive employment offers from placement agencies after training, highlighting potential hiring discrimination (Artiz Prillaman et al. 2017).

Sectoral choice: Which sectors do women enter?

Women entrepreneurs in both developed and developing economies tend to operate in sectors which are traditionally feminine or female dominant. Businesses in these sectors often tend to be smaller in size and less profitable (Carranza, Dhakal and Love 2018). Klapper and Parker (2011) review evidence from both industrialised and developing countries to find that women entrepreneurs are engaged in less capital-intensive sectors and have low average returns to capital. This phenomenon is more conspicuous in developing countries. Campos et al. (2015) conducted a survey and focus group discussions in Africa to find that the sector of operations of African women entrepreneurs continues to be one of the prominent reasons behind their low growth. Prominent factors which prohibit women from entering male-dominant industries are lack of information about growth and profitability of these sectors and low technical skills. On the contrary, factors such as presence of male role models and handed-down family businesses are some of the reasons why some women entrepreneurs participate in traditionally non-feminine sectors. In India as well, women-led enterprises tend to be concentrated in sectors which are traditionally female dominated such as domestic care, childcare, and beauty, and are often regulated by stereotypical gender roles (Chaudhary 2020). Additionally, according to the survey conducted by EdelGive (2021), the top three categories of enterprises owned by women are food and edibles, beauty and household items (Figure 2).

Figure 2. Preferred types of enterprises by women entrepreneurs

Source: EdelGive Landscape Study on Women Entrepreneurship

What holds women back from becoming entrepreneurs?

An expansive literature has assessed various barriers faced by women in order to become entrepreneurs. Some of the important barriers include:

Limited access to finance which leads them to choose low investment options or not enter entrepreneurship at all (Carranza, Dhakal and Love 2018; Klapper and Parker 2011)
Limited access to distribution and marketing networks and credit that can allow them to scale up their businesses to a sustainable level (EdelGive, 2021)
Insufficient skills and business training that does not allow them to make the best of the economic opportunities available to them
Importance of caregiving responsibilities that disproportionately fall on women, preventing them from working sufficient hours necessary to run a business (Bago and Dessy 2020)
Other sociocultural norms that prevent women from working at all or starting businesses in productive sectors (Jayachandran 2021)

We discuss each of these barriers in some detail below.

Access to finance

Access to finance is a critical resource for starting and sustaining businesses and women often face barriers to secure financial support from formal sources to grow their businesses. Carranza, Dhakal and Love (2018) note that women are apprehensive of seeking financial credit from external sources and prefer borrowing from friends and family. Having less assets in their name and often no collateral to offer, women’s opportunities to seek help from financial institutions become more restricted.

Access to finance is also restricted due to the lack of independence among women to make financial decisions. A recent estimate of women’s decision-making from the National Family Health Survey (NFHS)-5 (2019-21) data reveals that only 18% women make independent decisions on how to manage their own earnings, while 67% women make such decisions along with their husbands. Furthermore, 14% of women responded that their husbands have control over their earnings.

Moreover, even where finance has been offered to women, it has failed to transform female-run enterprises. Banerjee et al. (2010), conducted a randomised control trial in the urban slums of Hyderabad to evaluate the impact of offering microcredit to women. After 1.5 years of offering credit, the loan take-up among eligible households in treated areas was merely 27% as compared to 18.3 % in control areas. While the intervention did lead to increase in small business activities, the profit margins increased for businesses which were already successful before the intervention. Moreover, the experiment did not find statistically significant improvement in owning or starting new businesses among the treated households. While women participated more in managing self-employment activities in the treated areas, the intervention had no significant impact on their decision-making autonomy with respect to both financial and non-financial decisions in the short or long run.

It is, therefore, unsurprising that even as awareness of microcredit programs has increased in recent years from 41% (of all women being aware of a microcredit program) in 2015-2016 compared to 51% during 2020-2021, actual take up of microcredit remains abysmally low, increasing from only 8% to 11% over the same period (NFHS-4, NFHS-5).

Access to markets and distribution networks

Considering that women-owned enterprises are often home-based, especially owing to women’s need to balance their businesses along with domestic responsibilities, access to markets in order to sell products is often more constrained than men. Factors such as lack of mobility, certification and documents which allows entrepreneurs to trade in specific markets and low volume of output can be an impediment of smooth access to markets (United States Agency for International Development (USAID), 2005) One of the survey results of the EdelGive report indicates that women’s access to the market is also hindered due to the nature of their business and unpaid care work. For instance, the EdelGive report found that in Jammu and Kashmir, 76% of the women entrepreneurs surveyed reported that they find it challenging to manage both family and business. Women entrepreneurs in the state are mostly engaged in embroidery work, which is labour-intensive in nature. Having spent considerable time on making embroidered products, as well as managing household chores, leaves these entrepreneurs no choice but to conduct business through middlemen, which eats into their profit margins.

Caregiving responsibilities: Balancing home and work

The primary responsibility for unpaid care work falls squarely on women, preventing them from participating in the economy to full capacity. 48% of female respondents across the country in the Edelgive survey reported that they find it challenging to balance unpaid care work and their businesses. The social construct of women being the primary caregiver, prohibits them from contributing significant hours into paid work and as a result, they are deprived of several job opportunities which require a specific time commitment. Bago and Dessy (2020) conducted a study on Nigerian women that found that having young children in the household may prevent women from pursuing wage employment and choose self-employment as an alternative. Being a country with the highest fertility rate poses a persistent challenge for women to be gainfully employed in their most productive years due to childcare responsibilities. In India as well, women undertake most of the domestic responsibilities which leaves them with little feasibility to grow their businesses (De, Soni and Upadhyay 2021). Self-employment and entrepreneurship provide an avenue for attaining some level of flexibility in balancing both work and family. However, given the significant burden of caregiving work, women entrepreneurs’ participation in income-generating activities is subdued.

Socio-cultural barriers

The existing literature finds socio-cultural norms as a persistent barrier to women’s contribution in self-employment. Jayachandran (2021) notes that at least some of the barriers to women’s employment are due to the cultural norms of different societies. She specifically mentions the social norms around countries in the Middle East, North Africa (MENA region) and India which have low female labor force participation rates, where women’s employment outside home is seen as a violation of her reputation. Along with household restrictions on movement, women’s concerns over sexual harassment in public places impedes women’s ability to participate in the labor market. These restrictions can be both self-imposed and household imposed.

In 2019, Bain & Company conducted a survey across India, with more than 1,100 women and found that approximately 50% rural solo entrepreneurs struggle with socio-cultural barriers and permissions to work which sink their self-confidence. Overall, 69% of the women surveyed agree that social barriers are one of the most pertinent barriers to successful entrepreneurship.

Improving outcomes for women entrepreneurs: What works?

The barriers to women’s entrepreneurship, discussed in the previous section, have provided a scope for researchers and experts to develop and implement interventions which can ease these barriers for women. Some of these interventions have increased accessibility to resources and equipped women with necessary skills and capacity and have led them to pursue successful entrepreneurship. In this section, we take a look at interventions and recommendations that have proven successful in boosting entrepreneurship among women in developing countries.

Increased access to finance: While access to finance is widely acknowledged as the most significant barrier to women’s entrepreneurship, interventions aimed at increasing access to finance have yielded varied results. Aragón et al. (2020) conducted a study in Maharashtra, India, where a new flexible credit line was offered to the treatment group and standard term loans to the control group, in order to determine the impact of repayment and borrowing flexibility on vendor profits. The results show that during the intervention period, the treatment group with the credit line and flexibility of repayment and borrowing, experienced a 7% higher growth in vendor profits as compared to the control group. Furthermore, the vendor profits continued to increase with time, post-loan disbursal.
Increased access to markets: As discussed in the previous section, women’s access to market has been more restricted as compared to men, often due to lack of mobility, restricted movements due to social norms and less familiarity with marketplaces (USAID, 2005). However, potential interventions to increase access to market could include computerised supplier databases and e-commerce platforms, improved technology, skills, and manufacturing processes that better incorporate women-owned businesses into value chains. (Burga et al. 2021). The Canadian Trade and Investment Facility for Development in 2021, provided technical assistance to a small women-led enterprise in Bangladesh in increasing their access to the market of leather goods by linking them to e-commerce platforms. Prior to their intervention, the enterprise was struggling to sell and export their goods due to the Covid-19 pandemic.
Presence of role models: Identification and creation of role models, especially among women who have similar backgrounds and struggles can motivate women to challenge existing barriers to entrepreneurship. Bain (2019) recommends interventions targeted towards identifying and celebrating role models, especially led through central and state government campaigns, via traditional and digital platforms. These platforms can be used to share success stories of entrepreneurs and highlight positive outcomes of pursuing entrepreneurship which can motivate women to adopt better business practices.
Building peer networks: Field et al. (2015) experimentally assessed an intervention where women entrepreneurs in Ahmedabad, Gujarat were given access to training and counselling sessions alongside their peers. Half of the participants of the training sessions were randomly allowed to attend sessions with a peer or friend of their choice. Women who attended the training with a peer displayed a higher likelihood of borrowing from banks, and dedicating money borrowed solely to business activities. Furthermore, these women reported a higher volume of business and had more detailed business plans to increase revenue. This illustrates the importance of peer learning in boosting the adoption of successful business strategies.
Business training programs: McKenzie and Puerto (2017) conducted a randomised experiment in Kenya to evaluate the effectiveness of business training programs for female entrepreneurs on growth of markets. The study specifically tries to assess the impact of receiving business training, followed by one-on-one mentorship training, on profitability, sustainability and growth of women owned businesses. They find that the firms which completed the training were found to be more profitable and sustainable for more than three years. The intervention led to improvement in the mental health of the entrepreneurs as well as their standard of living. The intervention also led to growth in the market, solely attributed to improved customer service, better product portfolio and better business practices.
Multidirectional approaches: Buvinic et al. (2020) conducted a study to assess the impact of a combination of interventions which included banking support, financial literacy, and skill training, on women’s entrepreneurship in Java, Indonesia. The study found that easing banking services, along with appropriate mentorship on managing finances, planning and implementing good business practices helped women entrepreneurs alleviate the skills constraint and promote savings development. The study found that the intervention led to a 15.2% increase in the women’s profits and also empowered them to make decisions related to household purchases. The results set an example of the efficacy of a well-designed combination of interventions.

Opportunities for growth: The gig economy

In this section we look at the opportunities for growth of women entrepreneurs in a new emerging ecosystem – the gig economy. The gig economy aims at pooling human capital through digital platforms or e-commerce platforms in order to provide services to their clients. A report by Boston Consulting Group released in 2021 stated that the gig economy can generate 90 million jobs in India’s non-farm economy and can add up to 1.25% to gross domestic product (GDP).

The existing literature around the gig economy is scant and anecdotal to some extent, as many of the studies revolve around one or more specific platforms. In the subsequent section, we provide an overview of existing evidence on the features of the gig economy and prospects of growth for self-employed women in the gig economy.

Preferring flexibility over stability

The increase in demand of gig workers along with the flexibility of working on one’s own terms makes the gig/platform economy a desirable alternative to more traditional forms of work. Some of the major factors which are driving women in particular to join the gig economy are as follows:

Self-regulated, flexible working hours allowing women to balancing household work and labour market work
Contributing to household income
Increased agency and autonomy within the household with respect to decision making

Women’s ability to balance unpaid care work with paid work has emerged to be one of the most desired attributes of gig work. In a survey conducted by Asia Foundation with women service providers of Urban Company, Chaudhary (2020) finds that 80% of the respondents are married and almost half of them have young children. The survey finds that women, especially with young children prefer gig work as it allows them to balance domestic duties and paid work. This finding is consistent with other studies in this field. Berg, et al, (2018) studied crowdworking platforms [1] and found that more women prefer remote work and flexible timings as the most appealing attribute of crowd work platforms in comparison to men.

Gig platforms have given rise to a new form of informality since gig workers typically are nor covered under employee benefits. However, more and more people, especially from rural areas continue to join the gig economy (Chaudhary, 2020). BCG (2021) finds that workers in gig platforms consider gig work as a secondary source of income, especially for contingencies, medical expenses and repaying debts. On the contrary, for women, working in the gig economy has provided them with the status of breadwinners in their respective households. Moreover, women being engaged with the gig economy has helped them achieve some level of financial autonomy and decision-making capabilities within the household (Chaudhary, 2020). While studies have shown the positive outcomes of the gig economy on women’s financial autonomy, it must be noted that there is a significant gap in income between males and females. Foong et al. (2018) conducted a study in the US on Upwork, which allows gig-workers to set their hourly wages. The study found that after controlling for factors such as education, job category, offline and online education, per hour women made $6.80 less than men.

Conclusion: Areas for future research

To summarise, the existing research emphasises the prevalence of women entrepreneurs in sectors that are naturally prone to low growth, as well as interconnections between various hurdles to women’s successful entrepreneurship, with patriarchal norms of society serving as the nodal point. Furthermore, strategies devised and executed to overcome these barriers have yielded mixed outcomes. In our research, we plan to expand research into the following questions:

Examine drivers of occupational and sectoral choices by women to better understand how women decide what sectors to work in. This will be enabled through a survey of young women and their families on their occupational preferences and eliciting their subjective expectations of outcomes conditional on investing in different kinds of occupation-specific skills. Our target population will be a sample of women aged 15-25 years since these are important years for these human capital decisions. Speaking to their families is important since they play an important role in determining their choices.
Conduct interventions that can encourage women to enter more productive sectors and assess their impact on occupational preferences and choice. These interventions could include:
1. Providing information on returns to investing in different sectors
2. Providing access and exposure to female role models who have already successfully entered these sectors, and who come from similar socioeconomic backgrounds as our target population
3. Providing access to networks of similarly placed female job seekers to examine whether access to colleagues in one’s network can increase retention at work
Survey female users of a popular e-retail platform to examine their motivation for participating in digitally enabled paid work on the same platform.
Conduct interventions to see if more women retailers can be encouraged to participate in paid work. Such interventions could include:
1. Providing information about returns to platform-based employment
2. Providing access to financial literacy and business training programs

References

Ministry of Micro, Small and Medium Enterprises. (2021). Annual Report 2020-21. Government of India.
Periodic Labour Force Survey (PLFS). (2022). Quarterly Bulletin (January – March 2021). Ministry of Statistics and Programme Implementation.
Ministry of Statistics and Program Implementation. (2017). Key Indicators of Unincorporated Non-Agricultural Enterprises (Excluding Construction) in India. National Sample Survey Office.
Deshpande, A. (2021). Intersecting Identities, Livelihoods and Affirmative Action: How Social Identity Affects Economic Opportunity for Women in India. IWWAGE. Retrieved from https://iwwage.org/wp-content/uploads/2021/04/Intersecting-Identities-Livelihoods-and-Affirmative-Action.pdf
Chen, M. (2016). Expanding the economic potential of women informal workers. Background Paper, UN General Secretary’s High Level Panel on Women’s Economic Empowerment.
Prillaman, S. A., Pande, R., Singh, V., & Moore, C. T. (2017). What constrains young Indian women’s labor force participation? Evidence from a survey of vocational trainees. Working paper.
Klapper, L. F., & Parker, S. C. (2011). Gender and the business environment for new firm creation. The World Bank Research Observer, 26(2), 237-257.
Jayachandran, S. (2021). Social norms as a barrier to women’s employment in developing countries. IMF Economic Review, 69(3), 576-595.
Carranza, E.; Dhakal, C.; Love, I. Female Entrepreneurs: How and Why Are They Different? World Bank: Washington, DC, USA, 2018. Working Paper.
Banerjee, A., Duflo, E., Glennerster, R., & Kinnan, C. (2015). The miracle of microfinance? Evidence from a randomized evaluation. American Economic Journal: Applied Economics, 7(1), 22-53.
Chaudhary, R. (2020). India’s Emerging Gig Economy: The Future of Work for Women Workers. IWWAGE. Retrieved from https://asiafoundation.org/wp-content/uploads/2020/07/Indias-Emerging-Gig-Economy_The-Future-of-Work-for-Women_update7.24.20.pdf
Berg, J., Furrer, M., Harmon, E., Rani, U., & Silberman, M. S. (2018). Digital labour platforms and the future of work. Towards Decent Work in the Online World. Rapport de l’OIT.
Augustinraj, R., & Bajaj, S. (2021). Unlocking the potential of the gig economy in India. Boston Consulting Group and Michael and Susan Dell Foundation.
Mckenzie, D. J., & Puerto, S. (2017). Growing markets through business training for female entrepreneurs: A market-level randomized experiment in Kenya. World Bank Policy Research Working Paper, (7993).
Bain & Company. (2019). Powering the economy with her: women entrepreneurship in India. Bain & Company. Retrieved from https://www.bain.com/contentassets/dd3604b612d84aa48a0b120f0b589532/report_powering_the_economy_with_her_women_entrepreneurship_in-india.pdf
Field, E., Jayachandran, S., Pande, R., & Rigol, N. (2016). Friendship at work: Can peer effects catalyze female entrepreneurship?. American Economic Journal: Economic Policy, 8(2), 125-53.
Bago, J. L., & Dessy, S. E. (2020). Motherhood and Female Self-Employment: Theory and Evidence. Centre de recherche sur les risques les enjeux économiques et les politiques publiques.
De, A., Soni, S., & Upadhyay, S. (2021). Moving the Needle: The Women Entrepreneurship Platform. NITI Aayog. Retrieved from https://www.niti.gov.in/sites/default/files/2021-03/MovingTheNeedle_08032021-compressed.pdf
Buvinić, M., Johnson, H. C., Perova, E., & Witoelar, F. (2020). Can Boosting Savings and Skills Support Female Business Owners in Indonesia?: Evidence from a Randomized Controlled Trial (No. 530). Center for Global Development.
Choi, S. Y., & Kim, S. J. (2021). What Brings Female Professionals to Entrepreneurship? Exploring the Antecedents of Women’s Professional Entrepreneurship. Sustainability, 13(4), 1765.
Aragón, F. M., Karaivanov, A., & Krishnaswamy, K. (2020). Credit lines in microcredit: Short-term evidence from a randomized controlled trial in India. Journal of Development Economics, 146, 102497.
Campos, F., Goldstein, M., McGorman, L., Boudet, A. M. M., & Pimhidzai, O. (2018). Breaking the Metal Ceiling: Female Entrepreneurs who Succeed in Male-Dominated Sectors. In Towards Gender Equity in Development (pp. 167-191). Oxford University Press.
Canadian Trade and Investment Facility for Development (C-TIF), E-Commerce, Sustainability, and Market Access Support to Women-led Leather Goods Enterprise. (2022). Technical Report. Retrieved 13 June 2022, from https://c-tif.ca/portfolio/projects/e-commerce-sustainability-and-market-access-support-to-women-led-leather-goods-enterprise/
Foong, E., Vincent, N., Hecht, B., & Gerber, E. M. (2018). Women (still) ask for less: Gender differences in hourly rate in an online labor marketplace. Proceedings of the ACM on Human-Computer Interaction, 2(CSCW), 1-21.
Gammage, S., Diamond, N., & Packman, M. (2005). Enhancing women’s access to markets: An overview of donor programs and best practices. USAID Greater Access to Trade Expansion Project. Arlington, VA: Development and Training Services, Inc.
Fields, G. S. (2019). Self-employment and poverty in developing countries. IZA World of Labor.
Chakraborty, S., & Mukherjee, R. (2022). Women’s Entrepreneurship in India. Retrieved from https://iwwage.org/wp-content/uploads/2021/03/ISST-and-IWWAGE-Position-Paper.pdf
Burga, L., Gimelli, N., Muradyan, S., Robakowski, A., Miller, M., Rawlins, M., & Snyder, G. (2021). Using Digital Solutions to Address Barriers to Female Entrepreneurship (No. 35993). The World Bank Group.

[1] Crowdworking platforms include web-based tasks where the client can access a large workforce for the completion of clerical tasks that can be completed via an internet connection and a computer. (Berg et al. 2018).

Section II: Women and Work in India: Evidence and Gaps

Despite rapid economic growth and increase in female education over the past four decades in India, female labour force participation (FLFP) rates remain low and stagnant in the urban areas of the country. Figure 1 below shows that among the urban population aged 25-60, while almost 95% men participate in the labour market, the corresponding figure for women is only 20%. More importantly, this proportion has not changed over the past 30 years. In a cross-country context, Afridi, Bishnu and Mahajan (2019) find that among countries with similar per capita incomes, India has one of the lowest FLFP rates. Both supply- and demand-side factors can plausibly explain the low FLFP rates in India and we discuss the evidence around these below.

Figure 1. Labour force participation rate in urban India by gender

Source: National Sample Surveys, Employment and unemployment rounds (1993, 2004, 2011) and Periodic Labour Force Surveys (2017-18, 2018-19).

Supply-side factors

Existing literature has largely focused on supply-side factors as determinants of the low FLFP rates in India. Among these factors, gender norms in performing household duties (Afridi, Bishnu and Mahajan 2019; Fletcher, Pande and Moore 2017), status considerations for families when women work outside home (Eswaran, Ramaswami and Wadhwa 2013), stigma against women who work in the labour market on account of purity concerns as women interact with men outside their family (Jayachandran 2021), lack of female mobility to access non-farm jobs due to lack of proximity of such jobs to their homes (Afridi, Mahajan and Sangwan 2021; Chatterjee and Sircar 2021) and lack of social networks for women which restricts their access and hiring in jobs (Afridi and Dhillon 2021) are some of the most commonly examined channels.

Existing evidence from skilling programs in India shows that women are less likely to be placed after undergoing skilling (85% for men versus 72% for women), they are less likely to accept jobs if placed (70% for men versus 56% for women) and less likely to be in a job nine months after training is completed (Prillaman et al. 2017). Women state family concerns as the primary reason for not working or leaving work after training, again pointing at supply-side constraints. However, access to post-migration support increased job attachment rates, showing that alleviating mobility constraints can increase FLFP.

Among other factors, evidence for countries such as the United States shows that labour force participation rates of married women are negatively correlated with the commuting time in metropolitan areas (Black, Kolesnikova and Taylor 2014). Analysing French data, Barbanchon, Rathelot and Roulet (2021) find that gender differences in commute valuation account for a 0.5 log point hourly wage deficit for women since women have a higher willingness to pay for shorter commute times. Further, Gu et al. (2021) show that households in China derive a larger disutility from the wife’s commute as compared to the husband’s commute, and that on average, a newly purchased home is closer in distance to the wife’s workplace. Other factors which have been shown to affect women’s preferences are flexibility at the workplace (Bustelo et al. 2020; Mas and Pallais 2017; Wiswall and Zafar 2018).

Apart from the above channels, another factor at the intersection of supply and demand that can possibly affect FLFP rates is if women are skilled and have comparative advantage in jobs for which demand is decreasing over time. Evidence shows that fall in agriculture jobs unaccompanied by an increase in jobs in the non-farm sector, which are near home or provide regular part-time employment, may contribute to the observed stagnation of FLFP in India (Klasen and Pieters 2015; Afridi, Bishnu and Mahajan 2019). However, this hypothesis has not been examined in the Indian context, for the structure of growth within the non-farm sector. For instance, it is possible that within the non-farm sector itself the growth has been in segments where female presence has been low traditionally. Prillaman et al.(2017) find that women register for different skills than men in vocational training programs, and while women are less likely to receive job offers, the differential is not the same across skills. For instance, women and men trained in BPO (business process outsourcing) skills are equally likely to get an offer while women trained in tailoring are 20% less likely to get an offer. This is also partly driven by the fact that women are more likely to be enrolled for tailoring skills. Thus, the skills chosen for investment by women can play a role in affecting their job prospects. Among all the supply-side factors, this remains the least researched.

Demand-side factors

While the supply-side factors have been shown to contribute to the low FLFP in the country,

significant latent demand by women to participate in the labour force is indicated in the National Sample Survey (2011-12). Of women engaged solely in domestic duties and hence, currently out of the labour force, almost a third report they would like to work. Table 1 presents the proportion of women aged 25-60 who are willing to accept suitable work by education, and among those who are currently married vis-à-vis those who are single. Clearly, there is substantial demand among both married and single women and this demand increases with education. Almost 42% of women who are highly educated would like to work if such work was available near home. We now examine existing evidence around the demand-side factors.

Table 1. Latent demand for work by urban women: By education

	Married	Never married
Illiterate	29.45	32.93
Less than Primary	28.19	18.88
Primary	36.56	30.47
Middle	38.4	41.65
Higher Secondary	36.56	59.72
Graduate and above	41.98	49.65

Source: National Sample Survey, Employment and Unemployment round (2011-12).

Hiring stage

Academic literature suggests that discrimination in hiring is an important factor contributing to the poor labour market integration of vulnerable groups (Altonji and Blank 1999). Studies focusing on discrimination in hiring have relied on the use of correspondence experiments – in which fictitious job applications that are identical except for a randomised trait – to measure discrimination at the point of hire. A characteristic that is dominant in the labour market and in the literature on discrimination in the hiring of workers is gender, indicating a deep-rooted gender imbalance in preferences and stereotypes about gender-specific skills and roles (Glick, Zion and Nelson 1988; Bertrand 2020).[1] At the time of hiring, gender discrimination by the employer may be reflected at various stages – systematically hiring one gender over the other irrespective of qualifications, in requesting a certain gender through the job advertisement and offering unequal wages to men and women. We discuss the literature around each of these below.

Experiments show that women are less likely to receive call backs when they apply to jobs as compared to men, holding other characteristics constant. Baert, Pauw and Deschacht (2016), in a study conducted in the Belgian labour market, find that women receive 33% fewer callbacks for job interviews. Zhou, Zhang and Song (2013) find similar results for China and additionally show that the lower call-back rates are in male-dominated occupations while female candidates are more likely to receive a call back for occupations that are female-dominated. It is found that female applicants need to send 55% more applications to receive the same number of call backs (Zhang et al. 2021). This is consistent with Booth and Leigh’s (2010) results for experiments conducted in Australia, Berson (2012) in France, Carlsson (2011) in the Swedish labour market and Riach and Rich (2006) in the UK. In conjunction with gender, other attributes like age of the female candidates (Lahey 2008; Capeau et al. 2012 in Belgium; Petit 2007 in France and Albert, Escot and Fernandez-Cornejo 2011 in Spain), whether she is married (Arceo-Gomez and Campos-Vazquez 2014 for Mexico) or has a child (Correll, Benard and Paik 2007 for US) are seen to contribute to the difference in call-back rates.

Notably, while correspondence experiments have been conducted in India to study labour market discrimination through the lens of caste and religion (Banerjee et al. 2008; Thorat and Attewell 2007), to the best of our knowledge no such study has been conducted to examine the extent of gender discrimination in the hiring process in the Indian context. Hence, whether gender discrimination in India at hiring stage is larger than other countries – contributing to lower FLFP – remains unknown.

The second strand of literature which has demonstrated bias against women at the hiring stage uses text mining across job postings. Within hiring decisions, gender is often seen to interact with prejudices such as ethnicity, age, marital status and motherhood. For instance, Ningrum et al. (2020) use 9,000 job advertisements from Indonesia to show that jobs targeting female candidates also required other criteria to be fulfilled such as young age, single and good appearance. Other studies for China (Kuhn and Shen 2011) and India (Chowdhury et al. 2018; Chaturvedi, Mahajan and Siddique 2021) find direct gender preference in the posted job ads with employers stating their explicit gender preference in 35% of jobs when this field is allowed and in 8% job ads through job description and titles when this field is not present.

Using data from online job ads in India from 2018-2019, we find that the explicit preference for women tend to be larger for roles such as teachers, receptionists, beauticians, tele-callers while that for men tend to be greater in roles such as engineering and IT (information technology), sales/marketing and delivery executives (Figure 2).

Another set of studies find that use of gendered language in the text also has consequences for application rates by gender. Kuhn, Shen and Zhang (2020) find that female application rates are larger in job ads that request for women, and removal of such explicit requests through a ban in China resulted in more comparable application rates by men and women across job roles (Kuhn and Shen 2021), thus, generating equity at the application stage. These explicit gender preferences have consequences for gender wage gaps. For instance, Chaturvedi, Mahajan and Siddique (2021), applying text analysis on data from a leading job portal in India, find that wages were lowest for jobs in which employers implicitly or explicitly preferred women leading to larger gender wage gaps at the application stage as women tend to apply to jobs that explicitly request women.

Apart from explicit references for gender, Gaucher, Friesen and Kay (2011) use archival and experimental analysis to show that ‘gender wording’ is commonly employed in job recruitment advertisements. ‘Masculine’ wording (such as, leader, competitive and dominant) reinforced gender stereotypes and maintained inequality in hiring. Text mining for Indian job ads shows that ‘feminine’ skills tend to increase the female applicant share, while those related to reduced job flexibility decrease the female applicant share (Chaturvedi, Mahajan and Siddique 2021).

The last set of studies in this literature uses online job ads to examine whether employers exhibit differential hiring for women. Here, the literature is sparse. Looking at a Swiss online recruitment platform, Hangartner, Kopp and Siegenthaler (2021) find that female profiles are 7% less likely to be contacted by recruiters in professions that are dominated by men, and vice versa. Thus, even when on average there are no differences in female contact rates, there exist clear differences across stereotyped occupations. Also, women do not receive less demand from employers requiring longer commute times (Barbanchon, Rathelot and Roulet 2021 for France). Apart from these two studies, there is no research looking at hiring by employers on online platforms.

Retention

Evidence for other countries shows that the proportion of women is higher in entry-level jobs but that female workers are lost from the pipeline as they approach mid-senior-levels by almost one to two-thirds varying by occupations.[2] While family responsibilities and other supply-side factors can act as barriers, firm-side factors can also affect female application rates to jobs as well as retention. For instance, workplace sexual harassment is considered a form of sex discrimination, imposing costs on female employees. Victims are more likely to leave the workplace after harassment, a transition that lends to sex segregation and a larger wage gap (McLaughlin, Uggen and Blackstone 2017; Folke and Rickne 2020). Employees who experienced sexual harassment reported a lower overall job satisfaction and a greater intention to quit (Laband and Lentz 1998; Antecol and Cobb-Clark 2006). This holds true for developing and middle-income countries such as Brazil, Argentina, Chile and Pakistan (Merkin 2008; Merkin and Shah 2014), as well as developed countries such as the US (Moore 2010). However, there is almost no evidence on this aspect for India.

Given women’s concerns around workplace flexibility and childcare, working in firms with maternity benefits helps reduce the wage penalty for women transitioning to motherhood (Glass and Riley 1998; Hotz, Johansson and Karimi 2018). Sequerah and Singh (2019) find that perceived flexibility in work hours and maternity benefits significantly improved female employee retention.

Lower support by subordinates can also lead to lower retention and women occupying less leadership positions. Abel and Buchman (2020) find that workers do not respond differentially to feedback provided by male and female managers, thus, showing limited role of discrimination by workers in female occupying managerial positions in India. In contrast, Husain, Matsa and Miller (2021) find that male teachers are about 12% more likely to leave their schools when they work under female principals in the US. Thus, mixed evidence based on context and methodology shows that there is greater need to generate evidence in this sphere.

Demand-side interventions

A few studies examine what interventions can work to hire more women, which seek to target female applicants. Flory et al. (2018) find little effect of Equal Opportunity Statements in job ads and content in recruiting materials signalling explicit interest in employee diversity in the US, for attracting more female applicants. Anonymizing (or gender-blind) job applications within correspondence experiments in the initial screening stage of recruitment has also been examined (Rinne 2018) with mixed results. Behaghel, Crépon and Le Barbanchon (2015) find that the interview rates of minority candidates fall in France. Firms are more likely to shortlist female applicants, compared to male applicants when they are identifiable from their resumes (Krause, Rinne and Zimmermann 2012; Ball, Hiscox and Oliver 2017). This can in part be driven by the fact that firms that sign up for these experiments are self-selected and perhaps already have policies in place to increase female representation. However, dual-anonymization[3] was found to have a significantly positive impact in reducing bias towards women during hiring (Johnson and Kirk 2020). Again, there is no evidence in the Indian context around these issues.

Way forward

The above review suggests that prevalence of big data from online job portals is increasing in the field of labour economics. It has the potential to generate multidimensional, granular, and real-time information that can be used to provide new insights and aid policy development (Nomura et al. 2017). There is emerging evidence that analyses of online job-search engines and other employment services may be used to address information asymmetry and coordination failures in the labour market. Against the evidence presented above, we aim to focus on the below research questions under this project:

What is the role of frictions in the labour market, like preferences over location of workplace, in affecting application preferences by gender?

We aim to examine whether men and women differ in their applications to jobs by distance using the unique dataset we have acquired from a job portal that provides details on applications made by each job seeker. Any gender differences in such preferences, have not been analysed in the Indian context. We specifically also plan to look at whether locational characteristics matter more for women than men. For instance, metro cities may be perceived as safe or less safe, affecting application rates by women to locations where demand is high or low, thus affecting their chances of getting hired.

How do employers in India shortlist female applicants?

To answer this question, we have already gained access to data from a large online job portal for entry-level workers in India. For a subset of job ads, we can observe shortlisting undertaken by employers. Using this data, we aim to examine whether female applicants in India face any penalty after applying to a job and if so, what are the dimensions that affect this penalty. Is it the nature of job roles or other factors affecting employers’ perceptions like distance to various job characteristics?
We will also engage with the portal to brainstorm the possibilities around conducting experiments to understand what increases application rates and shortlisting rates for female job seekers, depending on funding availability under the grant since conducting experiments is more expensive.

Can any demand-side factors explain stagnant FLFP in India? We would like to explore two hypotheses here:

Is there a change in demand for skills and occupations in India such that occupations and jobs that are traditionally female-dominated show lower growth? We plan to use either nationally representative surveys or ongoing data collected from job boards to answer this question.
Role of firm level size-based policies and their impact on gender composition of workforce in India.

Sexual harassment:

We aim to understand the reported incidence of sexual harassment against women across industries in India. For this, we have partnered with a firm which has provided us these statistics for listed firms in India. We will analyse these and look at factors that can explain the observed patterns. We may also conduct a pilot, depending on funding availability under the grant, to understand the preferences of prospective female employees in India and how they perceive sexual harassment policies. This would provide insights on the extent to which these issues serve as barriers on female hiring and retention in the workplace in India.

Figure 2. Explicit requests for men and women across job roles: India online job ads (N=196,000)

References

Abel, M., & Buchman, D. (2020). The effect of manager gender and performance feedback: Experimental evidence from India.
Afridi, F., & Dhillon, A. (2021). Social networks and the labour market.
Afridi, F., Bishnu, M., & Mahajan, K. (2019). What Determines Women’s Labor Supply? The Role of Home Productivity and Social Norms.
Afridi, F., Mahajan, K., & Sangwan, N. (2021). The gendered effects of climate change: Production shocks and labor response in agriculture.
Albert, R., Escot, L., & Fernández-Cornejo, J. A. (2011). A field experiment to study sex and age discrimination in the Madrid labour market. The International Journal of Human Resource Management, 22(02), 351-375.
Altonji, J. G., & Blank, R. M. (1999). Race and gender in the labor market. Handbook of labor economics, 3, 3143-3259.
Antecol, H., & Cobb-Clark, D. (2006). The sexual harassment of female active-duty personnel: Effects on job satisfaction and intentions to remain in the military. Journal of Economic Behavior & Organization, 61(1), 55-80.
Arceo-Gomez, E. O., & Campos-Vazquez, R. M. (2014). Race and marriage in the labor market: A discrimination correspondence study in a developing country. American Economic Review, 104(5), 376-80.
Baert, S. (2018). Hiring discrimination: An overview of (almost) all correspondence experiments since 2005. Audit studies: Behind the scenes with theory, method, and nuance, 63-77.
Baert, S., De Pauw, A. S., & Deschacht, N. (2016). Do employer preferences contribute to sticky floors?. ILR Review, 69(3), 714-736.
Ball, S., Hiscox, M., & Oliver, T. (2017). Starting a behavioural insights team: three lessons from the Behavioural Economics Team of the Australian Government. Journal of Behavioral Economics for Policy, 1(Special Issue), 21-26.
Banerjee, A., Bertrand, M., Datta, S., & Mullainathan, S. (2009). Labor market discrimination in Delhi: Evidence from a field experiment. Journal of comparative Economics, 37(1), 14-27.
Behaghel, L., Crépon, B., & Le Barbanchon, T. (2015). Unintended effects of anonymous resumes. American Economic Journal: Applied Economics, 7(3), 1-27.
Berson, C. (2012). Does competition induce hiring equity?.
Bertrand, M. (2020, May). Gender in the twenty-first century. In AEA Papers and proceedings (Vol. 110, pp. 1-24).
Bertrand, M., & Mullainathan, S. (2004). Are Emily and Greg more employable than Lakisha and Jamal? A field experiment on labor market discrimination. American economic review, 94(4), 991-1013.
Black, D. A., Kolesnikova, N., & Taylor, L. J. (2014). Why do so few women work in New York (and so many in Minneapolis)? Labor supply of married women across US cities. Journal of Urban Economics, 79, 59-71.
Booth, A., & Leigh, A. (2010). Do employers discriminate by gender? A field experiment in female-dominated occupations. Economics Letters, 107(2), 236-238.
Bustelo, M., Diaz, A. M., Lafortune, J., Piras, C., Salas, L. M., & Tessada, J. (2020). What is the price of freedom? Estimating women’s willingness to pay for job schedule flexibility.
Capéau, B., Eeman, L., Groenez, S., & Lamberts, M. (2012). Two concepts of discrimination: Inequality of opportunity versus unequal treatment of equals. Ecore Discussion Papers, 58, 127.
Carlsson, M. (2011). Does hiring discrimination cause gender segregation in the Swedish labor market?. Feminist Economics, 17(3), 71-102.
Chatterjee, D., & Sircar, N. (2021). Why Is Female Labour Force Participation So Low in India?. Urbanisation, 6(1_suppl), S40-S57.
Chaturvedi, S., Mahajan, K., & Siddique, Z. (2021). Words matter: Gender, jobs and applicant behaviour.
Chowdhury, A. R., Areias, A. C., Imaizumi, S., Nomura, S., & Yamauchi, F. (2018). Reflections of employers’ gender preferences in job ads in India: an analysis of online job portal data. World Bank Policy Research Working Paper, (8379).
Correll, S. J., Benard, S., & Paik, I. (2007). Getting a job: Is there a motherhood penalty?. American journal of sociology, 112(5), 1297-1338.
Eswaran, M., Ramaswami, B., & Wadhwa, W. (2013). Status, caste, and the time allocation of women in rural India. Economic Development and Cultural Change, 61(2), 311-333.
Fletcher, E., Pande, R., & Moore, C. M. T. (2017). Women and work in India: Descriptive evidence and a review of potential policies.
Flory, J. A., Gneezy, U., Leonard, K. L., & List, J. A. (2018). Gender, age, and competition: A disappearing gap?. Journal of Economic Behavior & Organization, 150, 256-276.
Folke, O., & Rickne, J. K. (2020). Sexual harassment and gender inequality in the labor market.
Galarza, F. B., & Yamada, G. (2014). Labor market discrimination in Lima, Peru: Evidence from a field experiment. World Development, 58, 83-94.
Gaucher, D., Friesen, J., & Kay, A. C. (2011). Evidence that gendered wording in job advertisements exists and sustains gender inequality. Journal of personality and social psychology, 101(1), 109.
Glass, J. L., & Riley, L. (1998). Family responsive policies and employee retention following childbirth. Social forces, 76(4), 1401-1435.
Glick, P., Zion, C., & Nelson, C. (1988). What mediates sex discrimination in hiring decisions?. Journal of Personality and Social Psychology, 55(2), 178.
Gu, Y., Guo, N., Wu, J., & Zou, B. (2021). Home Location Choices and the Gender Commute Gap. Journal of Human Resources, 1020-11263R2.
Hangartner, D., Kopp, D., & Siegenthaler, M. (2021). Monitoring hiring discrimination through online recruitment platforms. Nature, 589(7843), 572-576.
Hotz, V. J., Johansson, P., & Karimi, A. (2018). Parenthood, family friendly workplaces, and the gender gaps in early work careers (No. w24173). National Bureau of Economic Research.
Husain, A. N., Matsa, D. A., & Miller, A. R. (2021). Do male workers prefer male leaders? An analysis of principals’ effects on teacher retention. Journal of Human Resources, 1118-9838R2.
Jayachandran, S. (2021). Social norms as a barrier to women’s employment in developing countries. IMF Economic Review, 69(3), 576-595.
Johnson, S. K., & Kirk, J. F. (2020). Dual-anonymization yields promising results for reducing gender bias: A naturalistic field experiment of applications for Hubble Space Telescope time. Publications of the Astronomical Society of the Pacific, 132(1009), 034503.
Klasen, S., & Pieters, J. (2015). What explains the stagnation of female labor force participation in urban India?. The World Bank Economic Review, 29(3), 449-478.
Krause, A., Rinne, U., & Zimmermann, K. F. (2012). Anonymous job applications of fresh Ph. D. economists. Economics Letters, 117(2), 441-444.
Kuhn, P. J., & Shen, K. (2011). Gender discrimination in job ads: Theory and evidence (No. w17453). National Bureau of Economic Research.
Kuhn, P. J., & Shen, K. (2021). What Happens When Employers Can No Longer Discriminate in Job Ads? (No. w29116). National Bureau of Economic Research.
Kuhn, P., K. Shen, and S. Zhang (2020): Gender-targeted job ads in the recruitment process: Facts from a Chinese job board,” Journal of Development Economics, 102531.
Laband, D. N., & Lentz, B. F. (1998). The effects of sexual harassment on job satisfaction, earnings, and turnover among female lawyers. ILR Review, 51(4), 594-607.
Lahey, J. N. (2008). Age, women, and hiring an experimental study. Journal of Human resources, 43(1), 30-56.
Le Barbanchon, T., Rathelot, R., & Roulet, A. (2021). Gender differences in job search: Trading off commute against wage. The Quarterly Journal of Economics, 136(1), 381-426.
Mas, A., & Pallais, A. (2017). Valuing alternative work arrangements. American Economic Review, 107(12), 3722-59.
McLaughlin, H., Uggen, C., & Blackstone, A. (2017). The economic and career effects of sexual harassment on working women. Gender & Society, 31(3), 333-358.
Merkin, R. S. (2008). The impact of sexual harassment on turnover intentions, absenteeism, and job satisfaction: Findings from Argentina, Brazil and Chile. Journal of International Women’s Studies, 10(2), 73-91.
Merkin, R. S., & Shah, M. K. (2014). The impact of sexual harassment on job satisfaction, turnover intentions, and absenteeism: findings from Pakistan compared to the United States. SpringerPlus, 3(1), 1-13.
Moore, B. L. (2010). Effects of Sexual Harassment on Job Satisfaction, Retention, Cohesion, Commitment and Unit Effectiveness: The Case of the Air Force. State Univ Of New York Coll At Buffalo Buffalo United States.
Ningrum, P. K., Pansombut, T., & Ueranantasun, A. (2020). Text mining of online job advertisements to identify direct discrimination during job hunting process: A case study in Indonesia. Plos one, 15(6), e0233746.
Nomura, S., Imaizumi, S., Areias, A. C., & Yamauchi, F. (2017). Toward labor market policy 2.0: the potential for using online job-portal big data to inform labor market policies in India. World Bank Policy Research Working Paper, (7966).
Oreopoulos, P. (2011). Why do skilled immigrants struggle in the labor market? A field experiment with thirteen thousand resumes. American Economic Journal: Economic Policy, 3(4), 148-71.
Petit, P. (2007). The effects of age and family constraints on gender hiring discrimination: A field experiment in the French financial sector. Labour Economics, 14(3), 371-391.
Prillaman, S. A., Pande, R., Singh, V., & Moore, C. T. (2017). What constrains young Indian women’s labor force participation? Evidence from a survey of vocational trainees. Working paper.
Riach, P. A., & Rich, J. (2006). An experimental investigation of sexual discrimination in hiring in the English labor market. The BE Journal of Economic Analysis & Policy, 6(2).
Rinne, U. (2018). Anonymous job applications and hiring discrimination. IZA World of Labor.
Sequerah, J., & Singh, J. S. K. (2019) The Influence of Work-Life Balance, Perceived Flexibility and Maternity Benefits Towards the Retention of Working Mothers in Kuala Lumpur.
Thorat, S., & Attewell, P. (2007). The legacy of social exclusion: A correspondence study of job discrimination in India. Economic and political weekly, 4141-4145.
Wiswall, M., & Zafar, B. (2018). Preference for the workplace, investment in human capital, and gender. The Quarterly Journal of Economics, 133(1), 457-507.
Zhang, J., Jin, S., Li, T., & Wang, H. (2021). Gender discrimination in China: experimental evidence from the job market for college graduates. Journal of Comparative Economics, 49(3), 819-835.
Zhou, X., Zhang, J., & Song, X. (2013). Gender discrimination in hiring: Evidence from 19,130 resumes in China. Available at SSRN 2195840.

[1] There is a vast literature that deals with discrimination toward ethnic and racial minorities in hiring practices, suggesting a lower call-back rate to applicants from these minorities (Bertrand and Mullainathan 2004; Oreopoulus 2011; Arceo-Gomez and Campos-Vazquez 2014; Galarza and Yamada 2014; Baert 2018). The ethnic origin in these studies is revealed by means of the names of the applicants (Baert 2018).

[2] In India less than 5-7% of top management positions are occupied by women. There is no direct evidence but given the average female presence of 20% in the workforce across all employed persons, the pipeline appears leaky in the country. See https://news.careers360.com/only-5-percent-women-in-top-management-positions-in-indian-companies-iima-indian-institute-of-management-ahmedabad-study.

[3] Anonymity of a specific minority characteristic through all stages of the hiring process.