Inclusive growth: Empowering Women in India’s Growth Trajectory
1 October 2019 – 15 March 2020
Inclusive growth presumably includes gender equality. Two recent World Bank reports (Gender and Jobs 2011 and 2012) show the importance of women’s employment for outcomes such as agency in the household and better investments in childrens’ education and health (Chatterjee et al 2017). However, the Female Labour Force Participation Rate (FLPR) rate in India is unusually low for it’s level of development. It ranks 120 among 131 countries for which data is available (ILO, 2013). India’s per capita income grew from USD 375 to USD 1572 between 1990-2015 but the FLPR fell from 37% to 28% (Pande, (2017)). In this project, we are interested in the question of how to improve the FLPR in India. We undertook a detailed study of the causes of the low FLPR in a set of districts in Delhi.
We began our study of Female Labour Force participation by carrying out a survey in 5 districts of Delhi, namely, North Delhi, North East Delhi, North west Delhi, Shahdara, and West Delhi. From these districts, 10 assembly constituencies including wards which are roughly comparable to each other In terms of development indicators were chosen. From the 10 assembly constituencies 100 polling station units were randomly chosen to constitute the primary sampling unit (PSU). From each PSU, 15 households were randomly selected for the survey. From each household one married couple was interviewed. The couple to be interviewed was selected using the following criteria: one, the age of both individuals must be between 18 and 40; and second, if there are multiple couples for whom the age criterion is satisfied, the youngest couple is selected. Since we were specially interested in the networks of our respondents and whether they helped or hindered their employment prospects, we also carried out phone surveys for people named by respondents in the first part as friends/relatives of the couple- at least two friends were to be interviewed, conditional on sharing of phone numbers. The baseline survey started in May 2019 until 22 July 2019 and the phone survey started on 17 May 2019 to 16 Nov 2019. The total number of main respondents was 3052 (from 1544 households in 108 PSUs). Total couples surveyed were 1508 (Target=1613) out of which 1520 males and 1532 females have been surveyed. We find high employment inequality only 23.6% of women are employed vs 95.7% of males. Second, there is also high wage inequality: 63% of male workers earn more than Rs.10,000, while only 10% of women are in this bracket.
Third, we find that occupational segregation and gender pay gaps go together: Women are concentrated in low-end tasks in factories like packaging and machine cleaning, and are not assigned to operating machines. Among the constraints that women face in working: we find that attitudes and norms in the family are unfavourable: 60% of females agree that women should be able to work outside the home, but only 33% of men agree- underlying this statistic are concerns over safety in the job as well as customs. More men (87%) than women (80%) believe that the man should be the main achiever outside the home and women take care of the family. Overall the top three constraints for women to work as reported by women are: Childcare (83% of women), Low wages (79% of women), Non availability of safe jobs (76%). Both men and women prefer home based work for women – most women work on piece rates which are not regulated by any minimum wage legislation. Conditional on employed status, about 37% females get piece rates vs only 9% males.
In terms of their networks: Women report having larger networks and greater interaction with networks vis-à-vis men. The total number of friends/relatives – whom one may reach out to in an emergency or undertake activities with at home or work – of females is 5.6, on average, while the corresponding figure for men is 3.6. Men have known their first closest person for longer (16.4 years) however, vis-à-vis women (12 years) but the intensity of the relationship (meeting/talking on the phone/texting) is greater in the case of women.
To summarize the main findings of our baseline survey, we found high employment inequality, high wage inequality: 63% of male workers earn more than Rs.10,000, only 10% of women are in this bracket. The main question therefore for us was how to improve job opportunities for women where we can overcome these two issues?
We then carried out an intervention on both supply and demand side factors that inhibit women from working. Specifically, we address the employer-employee matching constraint by offering women (and their husbands) a chance to register with a job search aggregator. In another treatment, we offer the service to the woman’s friends as well, to study whether the response of the network differentially affects service take-up and employment status of the women. We then ask, what is the impact of the intervention on the employment status and the nature of work done by women? How do these changes interact with the husband’s, woman’s and their network’s gender attitudes or perceptions, as well as the network’s response to the intervention? In treatment 1 we have the control group (540 households in 36 PSUs): for these we simply update the employment information gathered in the survey (3052 individuals). In treatment 2 (540 HHs in 36 PSUs) we provide information on HNM to husband and wife using respondents in the baseline survey and facilitate on-boarding to HNM (i.e. photo ID, phone number etc. are inputted on the HNM interface by us). In treatment 3: (540 HHs in 36 PSUs), we do the same as treatment 2 but 2 Friends of the 540 wives are also contacted and given the same information/facilitation (1013 individuals).
After the informational treatments, the survey for the effects of the intervention started in November 2019 for the control group. The survey for the treatment group(T2) started on 4 Dec 2019 and for treatment group (T3) it started on 11 Dec 2019 – 17 Jan 2020.
The midline survey was started in March. Then it was supposed to be every three months until endline. Of course, as we now know the survey could not be completed due to the spread of Covid 19 infections and our team left the field. The initial findings from the survey shows that the percentage of individuals who qualified to be pre-boarded to the HNM portal was 63.13%. From the HNM Dashboard we calculated that out of 1811 workers who attended, 757 have been successfully registered (41.8%). Out of the registered workers, 58.2% are females and 41.8% are males even though we contact them equally. 9 individuals have been featured in an order till date and 8 (female) of them have received calls. From information available in the website of ‘Helpers Near Me’, we calculated that we can expect about 9% female employment to be generated by HNM out of all the women to whom the platform was introduced. Unfortunately, we have had to stop our household surveys and replace them with phone surveys. Given the situation with Covid 19 and lockdowns in India subsequent to March 24 2020, we included a short survey on reactions to the lockdown in our phone surveys. The results have been published in “Ideas for India” (https://www.ideasforindia.in/topics/poverty-inequality/how-has-covid-19-crisis-affected-the-urban-poor-findings-from-a-phone-survey.html)
Amrita Dhillon is Professor of Economics at King’s College London. Her research interests are political economy and theory and development economics. Her recent work has been on the use of social networks in developing countries to mitigate a host of market inefficiencies related to the lack of good institutions, how social networks can sometimes enhance labour productivity and on whether electoral competition is always a force for good in reducing corruption. She is an associate editor of the Journal of Public Economic Theory and International Tax and Public Finance. She is also on the advisory board of Economia Politica. She was an elected member of the council of the Royal Economic Society from 2014–2019.