Gendered Impact on Unemployment: A Case Study of India during the COVID-19 Pandemic

India witnessed one of the worst coronavirus crises in the world. The pandemic induced sharp contraction in economic activity that caused unemployment to rise, upheaving the existing gender divides in the country. Using monthly data from the Centre for Monitoring Indian Economy on subnational economies of India from January 2019 to May 2021, we find that a) unemployment gender gap narrowed during the COVID-19 pandemic in comparison to the pre-pandemic era, largely driven by male unemployment dynamics, b) the recovery in the post-lockdown periods had spillover effects on the unemployment gender gap in rural regions, and c) the unemployment gender gap during the national lockdown period was narrower than the second wave.


The coronavirus disease (COVID-19) has adversely impacted labour markets all around the world. According to the International Labour Organization, the working hours lost in 2020 were equal to 255 million full-time jobs, which translated into labour income losses worth US$3.7 trillion (International Labour Organization 2021). Due to the existing gender inequalities, women were more vulnerable to the economic impact of COVID-19 (Madgavkar et al. 2020). The sudden closure of schools and daycare centres due to the Great Lockdown exacerbated the burden of unpaid care on women (Collins et al. 2020; Power 2020; Czymara et al. 2020; Seck et al. 2021). Women also disproportionately represented the accommodation, food services, and retail and wholesale trade sectors, which were worst-hit by the COVID-19 pandemic (Alon et al. 2020; Adams-Prassl et al. 2020; Bonacini et al. 2021). In most countries, women often work in these sectors without any work protection or job guarantee (United Nations Women 2020), leading them to loose their livelihoods faster than men while also dealing with their deteriorating mental health. India is an interesting case study with one of the lowest female labour force participation rates (LFPRs) globally to analyse how the COVID-19 pandemic exacerbated the pre-existing gender disparities in unemployment. According to the World Bank data, India’s female LFPRs was approximately 21% in 2019, the lowest among the BRICS nations (Brazil, Russia, India, China, and South Africa) and 26 percentage points lower than the global average. An even more troubling fact is that women’s LFPRs has been falling since the mid-2000s (Ghai 2018; Andres et al. 2017; Sarkar et al. 2019). Since the onset of the pandemic, women in India have been increasingly dropping out of the labour force. As seen in Figure 1, the greater female labour force, which comprises unemployed females who are active and inactive job seekers, has been lower than the pre-pandemic average since April 2020. The number of unemployed women actively looking for jobs has also been lower than the pre-pandemic average barring the months of April, May, and December in 2020. On the contrary, the number of women who are unemployed but inactive in their job search has risen drastically, albeit with minor fluctuations, during this period (Figure 2). A recent survey by Deloitte (2021) identified that the burden of household chores and responsibility for childcare and family dependents increased exponentially for women worldwide and more so in India due to the pandemic. The surveyed women mentioned increase in work and caregiving responsibilities as the main reasons for considering leaving the workforce.

Figure 1: Percent Change in Female Greater Labour Force and Unemployed Active Job Seekers Compared to the Pre-pandemic Average

Source: Centre for Monitoring Indian Economy April 2020 - May 2021


Figure 2: Percent Change in Female Unemployed and Inactive Job Seekers Compared to the Pre-pandemic Average

Source: Centre for Monitoring Indian Economy April 2020 - May 2021

Figure 3: Unemployment Rate in India (Percent)

Source: Centre for Monitoring Indian Economy Jan 2020 - May 2021 

This study analyses the effect of the COVID-19 pandemic on the gender unemployment gap from its onset until the second wave using the subnational-level monthly data from the Centre for Monitoring Indian Economy (CMIE). The gender unemployment gap is defined as the difference between male and female unemployment rates (Albanesi and Şahin 2018). We assess the gender unemployment gap during the COVID-19 pandemic compared to the pre-pandemic era using a difference-in-differences (DID) model. A preliminary investigation of the gender unemployment gap based on the raw data reveals that the gap declined in the lockdown period compared to the pre-lockdown period (Figure 3). We find the gender gap to widen during the second wave, albeit smaller than the pre-pandemic level.

Although a large number of national-level studies were conducted on the impact of the COVID-19 pandemic on unemployment (Estupinan and Sharma 2020; Estupinan et al. 2020; Bhalotia et al. 2020; Chiplunkar et al. 2020; Afridi et al. 2021; Deshpande 2020; Desai et al. 2021), this study is among the very first to assess the impact of the second wave of COVID-19 on the unemployment gender gap in India. A previous study found the rise in male unemployment during the lockdown period contributing to a smaller gender gap (Zhang et al. 2021). In this study, we take one step further to assess the effect of the second COVID-19 wave on the unemployment gender gap in India.

The remainder of the article is organised as follows. In Sections 2 and 3, we present the data sources and some facts on the unemployment trend in India. The effects of first and second COVID-19 waves on unemployment disaggregated by gender are discussed in Section 4. Section 5 delves into the gendered impact on unemployment dynamics across urban and rural regions. The concluding remarks are presented in Section 6.

Data and Methodology

In this study, we use the subnational-level monthly employment data from the CMIE from the period of

January 2019 to May 2021. Starting from January 2016, the CMIE has been conducting household surveys in India on a triennial basis, covering the periods of January to April, May to August, and September to December. This is the only nationally representative employment data in the absence of official government data (Abraham and Shrivastava 2019) and has been used by several employment studies on India (Beyer et al. 2020; Deshpande 2020; Deshpande and Ramachandran 2020).

The employment data are classified into three categories—the number of persons employed, the number of persons unemployed and actively seeking jobs, and the number of persons unemployed and not actively seeking jobs. The sum of these three categories constitutes the greater labour force. The data are also disaggregated by gender (male and female) and residence (rural and urban).[1] For the analysis, we focus on five time periods as indicated in Table 1.

Table 1: Time Periods

For state[2] i at time t, we construct the unemployment rate as given below:

Unemployment rate = Number of persons unemployed and seeking jobs/Greater labour force                                                                                                    (1)

Stylised Facts on Unemployment

This section describes some stylised facts based on the subnational unemployment data from February 2019 to May 2021. To this end, we estimate the regression model below:


where Unempit is the unemployment rate of state i in time t. To see the unemployment dynamics over the period of study, we use a binary variable Months that takes the value one for month s and 0, otherwise. The model takes into consideration the impact of past unemployment rates, represented by Unempit−1. Additionally, the state fixed effects δi are included to account for unobserved, time-invariant state-level characteristics that may potentially confound our estimates.

Figure 4: Trends in Unemployment Rate

Our coefficient of interest is β1s which depicts the time trend in unemployment. The results from the model estimation are shown in Figure 4, in which we can see the dynamics of aggregate unemployment in India from February 2019 to May 2021. The vertical axis pertains to coefficient β1s, and the horizontal axis corresponds to the respective months. In Figure 4, the aggregate unemployment rate is found to be relatively stable during the pre-pandemic era. This trend faces an overhaul during the national lockdown (April–May 2020) with a structural upward shift in the unemployment rate. The shock to the unemployment rate does not persist as economic recovery during the post-lockdown period enables unemployment to fall steadily from June 2020 onwards. The unemployment rate becomes stable from January to March 2020 as the country returned to a sense of normalcy with the continued resumption of economic activity.[3] However, the economic impact from the onset of the second wave of the COVID-19 pandemic caused the unemployment rate to rise again in April and May 2021.

Next, we estimate Equation (3) separately for the female and male unemployment rates to assess the gender differential impacts of the COVID-19 pandemic on unemployment in India.[4]


where binary variable Quartertakes the value one for quarter s in the time period of our sample. The model also accounts for lagged unemployment effects through Unempit−1.

Figure 5: Trends in Unemployment Rate by Gender

Figure 5 shows that a stark gender gap in the unemployment rate (distance between the red and blue lines) exists in the pre-pandemic era as the male unemployment rate is consistently lower than that of the female. Figure 5 also shows that the gender gap dynamics are primarily driven by male unemployment. The sharp rise in male unemployment during the national lockdown causes the gender gap to close in Q2 2020. The post-lockdown recovery (Q3–Q4 2020) is found to have a favourable impact on male unemployment, causing gender gap to revert to the pre-pandemic levels. Although both males and females lost jobs during the onset of the second wave (Q2 2021), the gender gap narrowed as males are found to lose more jobs in absolute terms.

Figure 6: Trends in Urban and Rural Unemployment Rate by Gender

6 a Urban

Figure 6 shows the estimates of β1s  (see Equation [3]) for urban and rural unemployment in Panels (a) and (b), respectively. During the national lockdown, the sharp rise in male unemployment is more evident in urban areas than rural. In fact, the national lockdown period dynamics in aggregate male and female unemployment in Figure 5 largely resemble the effects seen in the urban region (see Figure 6, Panel [a]). The post-lockdown recovery suits male unemployment, both in rural and urban areas. Female unemployment remains stable in rural areas during the pandemic.

Figure 7: Trends in Regional Unemployment Rate by Gender

7 c                                                                                                                                                                         



The subsample regression estimates of β 1s  pertaining to the north, east, west and south regions are shown in Figure 7. All regions witnessed a rise in male unemployment during the national lockdown period. On the contrary, the female unemployment dynamics differ between regions. During the national lockdown period, female unemployment rose in the west and south regions (Panels [c] and [d] in Figure 7). The north region shows an interesting anomaly (Panel [a] in Figure 7). Contrary to other regions, female unemployment dipped steeply in the north during the national lockdown period. East region alone did not 

experience any strong movements in female unemployment throughout the pandemic (Panel [b] in Figure 7).

Impact of COVID-19 on Unemployment

Section 3 discussed how the overall unemployment and unemployment gender gap witnessed structural breaks during the COVID-19 pandemic. To further investigate the gender aspect of the COVID-19 unemployment dynamics in India, we begin our empirical exercise by examining the unemployment changes during the COVID-19 pandemic compared to the pre-pandemic era. We use the following model:



where Period1, Period2, Period3, and Periodpertain to lockdown, post-lockdown, post-lockdown normalcy, and second wave time periods, respectively. Besides the overall unemployment, we also estimate Equation (4) for male and female unemployment separately. The results are shown in Table 2. We can see from Column (1) of Table 2 that the overall unemployment rate (β11) witnessed an increase of 0.066 (statistically significant at one percent level) during the lockdown period in comparison to the pre-pandemic period. This effect was primarily driven by the rise in the male unemployment that shot up by 0.082 during the lockdown period (Column [3]).

The uneven distributional effects of the post-lockdown recovery are seen from β12 estimates. Male unemployment rose by 0.01, while female unemployment fell by 0.036 in comparison to the pre-pandemic era. The fall in female unemployment does not necessarily indicate that the overall labour conditions improved for women during this period. Equation (1) shows that the unemployment rate is driven by two components. Figure 1 validates that the female unemployment rate fell over time due to the decline in the number of unemployed females actively seeking jobs being higher than the decline in the female labour force.[5]

β14 estimate in Column (1) indicates that the total unemployment rose by 0.019 (statistically significant at 10 percent level) during the second wave compared to the pre-pandemic period. A comparison between β14 and β11 estimates reveals an interesting policy highlight that the second wave’s impact on unemployment was smaller than the nationwide lockdown. Finally, the rise in unemployment during the second wave is primarily driven by male unemployment.

Table 2: Impact of COVID-19 on Unemployment

Note: *** p<0.01, ** p<0.05, and * p<0.1. The robust standard errors are in parentheses.

Unemployment Gender Gap in Urban and Rural Regions

This section delves further into the gendered impact of lockdown on the unemployment dynamics across urban and rural regions. As defined in Section 1, the unemployment gender gap measures the difference between female and male unemployment rates. To identify the effect of the first and second COVID-19 waves on the unemployment gender gap, we estimate the regression model below:



where Female is a binary variable that takes the value 1 for female unemployment and 0, otherwise.

Table 3 shows the estimation results of Equation (5). We discuss the coefficient estimates that are found to be significant. The significant β1 coefficient reiterates that the unemployment gender gap was an existential problem in India even before the COVID-19 pandemic. The β31 estimates reveal that the urban region dynamics drove the narrow unemployment gender gap during the lockdown period. Although the magnitude of the narrowing gap during the lockdown did not persist to the post-lockdown period (β32), rural regions experienced a narrow unemployment gender gap (marginally significant at 10%). This trend continues even in the post-lockdown normalcy period (β33) as the unemployment gender gap is narrower than the pre-pandemic level by 0.047 in the rural region. This highlights the possibility that the post-lockdown recovery process had a spillover effect on the unemployment gender gap in rural regions. Finally, β34 estimates show that the narrowing gender gap trend persists only in the urban region during the second wave.

Table 3: Impact of COVID-19 on Unemployment across Urban and Rural Regions during the post-lockdown and post-lockdown normalcy periods.

Note: *** p<0.01, ** p<0.05, and * p<0.1. The robust standard errors are in parentheses.


This article analyses the impact of the COVID-19 pandemic vis-à-vis the pre-pandemic period on the gender unemployment gap. Our findings indicate that the gender gap in unemployment narrowed during the COVID-19 pandemic, primarily driven by male unemployment dynamics. Interestingly, we find that female unemployment declined during the post-lockdown period. Such a decline was likely driven by women dropping out of the labour force rather than a dip in the absolute number of unemployed persons. Further, the region-wide subsample analysis finds the unemployment gender gap in urban regions to narrow across all periods of the COVID-19 era. In contrast, the rural regions witness narrowing gender gap during the post-lockdown normalcy. This indicates that the rural regions’ unemployment gender gap witnessed spillover effects from recovery associated with the economic reopening. Finally, the narrow gender gap (compared to the pre-pandemic level) is smaller during the second wave.

There is a looming uncertainty whether the impending third wave will further narrow the gender unemployment gap at the expense of increasing male unemployment and females being pushed out of the workforce. Further research is required with a more extended period of assessment and focussed on household-level data to understand the difference in the impact of COVID-19 on the gender unemployment gap across the different parts of the country and income strata.

The authors thank Paul Cheung and the anonymous referee for their valuable comments and feedback. They also thank Rohanshi Vaid for her excellent research assistance.

Must Read

Do water policies recognise the differential requirements and usages of water by women and the importance of adequate availability and accessibility?
Personal Laws in India present a situation where abolishing them in the interest of gender justice also inadvertently benefits the reactionary side.   
Back to Top