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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
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). For the analysis, we focus on five time periods as indicated in Table 1.
Table 1: Time Periods
For state 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 Unemp it 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 Month s that takes the value one for month s and 0, otherwise. The model takes into consideration the impact of past unemployment rates, represented by Unemp it −1. Additionally, the state fixed effects δ i 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 β 1 s 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 β 1 s , 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. 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. 
where binary variable Quarter s takes the value one for quarter s in the time period of our sample. The model also accounts for lagged unemployment effects through Unemp it −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
Figure 6 shows the estimates of β 1 s (see Equation ) 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
The subsample regression estimates of β 1 s 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 Period 1 , Period 2 , Period 3 , and Period 4 pertain 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 ( β 11 ) 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 ).
The uneven distributional effects of the post-lockdown recovery are seen from β 12 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. 
β 14 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 β 14 and β 11 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 β 1 coefficient reiterates that the unemployment gender gap was an existential problem in India even before the COVID-19 pandemic. The β 31 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 ( β 32 ), rural regions experienced a narrow unemployment gender gap (marginally significant at 10%). This trend continues even in the post-lockdown normalcy period ( β 33 ) 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, β 34 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.
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 data are not available for Jammu and Kashmir, Andaman and Nicobar Islands, Arunachal Pradesh, Dadra and Nagar Haveli, Daman and Diu, Lakshadweep, Manipur, Mizoram, Nagaland, and Sikkim. Hence, the main analysis focuses on only 26 subnational economies.
 The terms “state” and “subnational economy” are used interchangeably throughout the article.
 According to the official data, power consumption grew by 10.2% in January 2021; the highest growth rate in three months, which was indicative of higher commercial and industrial demand (Press Trust of India 2021).
 In order to obtain the unemployment dynamics on a quarterly basis, Equation (2) is revised to Equation (3) with dummies pertaining to quarter instead of month.
 This reason is also validated by CMIE who found the female labour participation in urban regions to fall to 7.2% in October 2020, the lowest since the organisation started measuring this indicator in 2016 (Centre for Monitoring Indian Economy 2020).
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Grappling With the Indian Problem of Unemployment
Since India got independence, unemployment has been one of the biggest banes of India. Unlike many factors that affect the economy, the impact of unemployment on a nation is holistic.
It is because unemployment not only affects one particular person and their family but will also impact the supply and demand of all forms of businesses, inflation, logistics, overall development, health, and whatnot. It is because of all these reasons that the Indian government, like any other economy, has put in a lot of effort to mitigate the issue of unemployment.
However one needs to understand that unemployment can never be completely erased. There will always be at least a small proportion of unemployment prevalent in any kind of society and all that we can do is to ensure that this rate of unemployment is always below a certain point so that it does not adversely affect the economy.
History of Employment Pandemic and Unemployment in India The Problem of Informal Sector Implications of Unemployment Types of Unemployment Causes of Unemployment Government Initiative to Control Unemployment FAQ
History of Employment
We have all know that employment has evolved as a significant part of human society. In the olden days, things were different. Human settlements were largely for security from external threats. This is because a group of food gatherers can protect themselves against wild animals rather than solitary ones.
As society evolved to be more complex and wide, the nature of society also changed. From food gathering and hunting, it reached into agriculture. However, agriculture was only for subsistence for a large period. One can trace the expansion of agriculture to the later Vedic phase. From agriculture, employment expanded to trade, artworks, metallurgy, defense, administration, and so on.
As kingdoms flourished, the job opportunities created by them also increased significantly. The fact that taxes were introduced was in itself indicative of the health and nature of employment in the respective kingdoms. In earlier times, a lot depended on employment. We have evidence from various instances of history where kingdoms collapsed when their tax revenue decreased due to unemployment and how the population withdrew to rural areas as employment opportunities became skewed in the city.
Clear evidence of the presence of unemployment can be seen during the reign of Firuz Shah Tughlaq who reigned the Delhi Sultanate in the 14th century. It was the time when the Delhi Sultanate was on the verge of collapsing. However, the king made arrangements to take account of the unemployed people in the kingdom and tried to devise state policies to help them.
An unforgettable blow to employment in the history of humanity was the Great Depression of 1929. Spreading across the USA and Europe it crippled the world economy. It resulted in large-scale unemployment, discrepancies in economic activities, demand, and large-scale shutdown of industries.
It was a time when the world realized the extent to which things can go wrong in the face of depression. Rather than abetting employment, one must say that all economies have been trying their level best to avoid another economic depression.
Pandemic and Unemployment in India
Although not a surprise, the pandemic has aggravated the situation of unemployment in India significantly. The Indian economy continues to wriggle out of the hands of unemployment since March 2020 when all economic activities came to a standstill.
According to the Centre for Monitoring Indian Economy (CMIE), the unemployment rate in India as of June 2021 stands at 12.8% . In March 2021 the unemployment rates were at 6.5% from where they rose to 8% by April 2021.
To understand the graveness of the issue one must understand that the rate of unemployment in India during 2018 was only 6.1%. India's economy was already slowing down before the pandemic and the worst came along with the lockdown.
The Problem of Informal Sector
One of the main reasons for the booming rates of unemployment in India even without the pandemic was the large population that is dependent on the informal sector for employment.
Apart from all sorts of uncertainties and disguised unemployment that is rampant here, the worst part is that there is no proper data regarding the number of people, the kind of job, and the implications of such jobs with the government.
Even as the productivity of India grew over time the rate of employment generation was inadequate. This means that a very small population contributes to the growth of the Indian economy while more than 75 percent of the working population is confined to the informal sectors like agriculture, small enterprises , construction, etc.
Implications of Unemployment
Unemployment as we all know is a state where a person who is actively searching for a job with all necessary qualifications is still unable to get a proper job. It is an indicator of the health of an economy.
As mentioned earlier unemployment will result in lower demand for goods and will lead to a decrease in purchasing power of citizens. These inadvertently affected the overall business and employment generation of the Indian economy. It further stresses the necessity of the government to be watchful of the rate of unemployment in their respective nations.
Types of Unemployment
There are different kinds of unemployment and each one of them is equally dangerous and requires the supervision of an independent organization to prevent the numbers from going out of hand. A few of them are mentioned below
This form of unemployment as the name suggests is in disguise and almost invisible to the eyes of the observer. Disguised unemployment refers to a situation where an excess number of people are employed for a particular task. In this case, they look employed but in fact, it is a form of unemployment.
Technological unemployment refers to those forms of unemployment that are a result of technological advances that humanity achieves. According to certain surveys, job losses due to technological advances are increasing by 30% every year.
It refers to those kinds of unemployment where the labourers are unemployed for a certain period of the year while they find work during the rest of the year.
A person is said to be under vulnerable unemployment when they have a job but they are appointed without any job contracts or securities. It is one of the most prevalent forms of unemployment in India.
Causes of Unemployment
Various factors cause unemployment. Although a small amount of unemployment is inevitable, a large fraction of it can be avoided through careful planning and efficient implementation. Here are a few causes of unemployment in India.
Lack of Skilled Population
Due to the lackadaisical state of most of the educational institutions in India, there is a significant deficit in the number of skilled population for doing a particular job.
Most of the companies share their experience where they have to additionally train the employees to make them understand and adjust to the job that they are supposed to do. India can mitigate this problem only if the quality of education right from school to higher education is improved .
The population in India is the second-largest in the world. And it is anticipated that in a few years they will surpass the population of China. The population is a boon to a nation. But in India, the problem is that this valuable resource is not properly used.
We must not forget that India has the biggest working population in the world. And imagine the impact that India could have had over the world economy if it actually put enough effort to develop each individual in the best way possible. Since that is not happening, the large population continues to be a burden that worsens the situation of unemployment in India.
India is an economy whose prime moving force in agriculture. Ideally, India was supposed to slowly switch from an agricultural economy to an industrial economy or a service sector economy. However, India is caught in a unique situation where more than half of the population is dependent on agriculture but with only a minuscule contribution to the economy.
The lack of productivity in the agricultural sector and the unavailability of enough alternatives have also resulted in rampant unemployment especially in rural India.
Proper infrastructure and adequate investments in the manufacturing and service sectors are integral parts of generating employment in any nation. But things were grim for India in this regard and the situation had contributed its part in increasing unemployment in India.
It is mainly because of the lack of proper infrastructure and investment that the growth of industries in secondary sectors especially is restricted.
Regressive Social Norms
Social norms that deter Women and marginalized groups from taking decisions regarding employment and access to education have kept a large part of the Indian population in the darkness of unemployment.
Although a lot of changes are coming up in this regard, there is still a lot to be done to improve the situation of women and other marginalized communities in society.
Government Initiative to Control Unemployment
- TRYSEM – Training of Rural Youth for Self-Employment - 1979
- IRDP – Integrated Rural Development Programme (IRDP) - 1980
- RUDSETI — Rural Development And Self Employment Training Institute - 1982
- MNREGA – Mahatma Gandhi National Rural Employment Guarantee Act - 2005
- PMKVY – Pradhan Mantri Kaushal Vikas Yojana - 2005
- National Skill Development Mission - 2014
- Start-Up India Scheme - 2016
- Stand Up India Scheme - 2016
- PMGKY - Pradhan Mantri Garib Kalyan Yojana - 2016
What is the cause of unemployment in India?
The major causes of unemployment in India are Large population, low educational levels of the working population, Inadequate growth of infrastructure and low investments in the manufacturing sector.
Which state in India has highest unemployment?
With a 26.4% unemployment rate, Haryana has recorded the highest unemployment rate in the country, as per the data released by the Centre for Monitoring Indian Economy for February 2021.
What is the unemployment rate in India?
India's unemployment rate sharply rose to 7.11 per cent in 2020 from 5.27 per cent in 2019.
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