metadata: authors: 0: author: Guo,Yiming
1: author: Narayan,Ambar
2: author: Olinto,Pedro
3: author: Sinha Roy,Sutirtha
4: author: Sarma,Nayantara
count: India
docna: 0: docna: Synthesis of the Impacts of Covid-19 on
            India’s Labor Market : Looking at People, Places and Policies
docty: Policy Research Working Paper
abstracts: cdata!: This paper examines the impact of
            Covid-19 on a host of labor market outcomes in India, using
            a continuous household survey. Cross-sectional trends of
            headline indicators like unemployment and labor force
            participation indicate a quick recovery after the
            unprecedented shock of the pandemic. However, women,
            marginalized social groups, and youth were more adversely
            affected, largely due to the nature of their employment
            before the pandemic. Next, a fixed-cohort analysis with a
            difference-in-differences regression framework is used to
            follow the labor market trajectories of a Covid-affected
            cohort and a pre-Covid cohort for comparison, over a span of
            12-16 months. A story of downward transitions emerges, with
            movements out of the labor force, into more informal and
            lower-paid types of work in agriculture and rural areas.
            Higher education and being located in dense, urban areas
            partially mediated the adverse shocks of Covid-19. Lastly,
            India&#x2019;s extensive vaccination campaign had
            significant positive impacts on employment recovery.
display_title: Synthesis of the Impacts of Covid-19
            on India’s Labor Market : Looking at People, Places and Policies
listing_relative_url: /research/2023/03/34022828/synthesis-impacts-covid-19-india’s-labor-market-looking-people-places-policies
topicv3: Labor Markets m1326488 855,Economic Shocks and Vulnerability m1326506 702,Labor Mobility m1582535 4713,Coronavirus (COVID-19) m1939478 4828
publishtoextweb_dt: 2023-03-16T00:00:00Z
dois: 10.1596/1813-9450-10366
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
author_keys: 0: author_key: 2291566
1: author_key: 167324
2: author_key: 142620
3: author_key: 1312512
4: author_key: 1812473
model_extractions: Policy Research Working Paper 10366 Synthesis of the Impacts of Covid-19 on India’s Labor Market Looking at People, Places and Policies _Yiming He Ambar Narayan Pedro Olinto Sutirtha Sinha Roy Nayantara Sarma_ Poverty and Equity Global Practice March 2023
Policy Research Working Paper 10366

# **Abstract**

This paper examines the impact of Covid-19 on a host of labor market outcomes in India, using a continuous household survey. Cross-sectional trends of headline indicators like unemployment and labor force participation indicate a quick recovery after the unprecedented shock of the pandemic. However, women, marginalized social groups, and youth were more adversely affected, largely due to the nature of their employment before the pandemic. Next, a fixed-cohort analysis with a difference-in-differences regression framework is used to follow the labor market trajectories of a Covid-affected cohort and a pre-Covid cohort for comparison, over a span of 12-16 months. A story of downward transitions emerges, with movements out of the labor force, into more informal and lower-paid types of work in agriculture and rural areas. Higher education and being located in dense, urban areas partially mediated the adverse shocks of Covid-19. Lastly, India’s extensive vaccination campaign had significant positive impacts on employment recovery.

This paper is a product of the Poverty and Equity Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at heyiming23@gmail.com; anarayan@worldbank.org; polinto@worldbank.org; ssinharoy@worldbank.org; nsarma@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Synthesis of the Impacts of COVID-19 on India’s Labor Market: Looking at People, Places and Policies

Yiming He, Ambar Narayan, Pedro Olinto, Sutirtha Sinha Roy, Nayantara Sarma

## **1 Introduction**

The coronavirus pandemic has had an unprecedented impact on the Indian labor market. To curb the virus, India implemented nationwide stringent lockdowns at the onset of the pandemic and subsequently, localized restrictions during the second and third waves. During the first set of lockdowns in April 2020, 20-25 percent of the labor force became unemployed as most economic activity was halted.<sup>1</sup> . Headline labor market indicators recovered once the lockdowns were relaxed but workers faced many transitions, poorer job quality and reduced earnings.

This paper examines the impact of Covid-19 on a host of labor market outcomes in India using a continuous panel survey of around 170,000 households conducted three times a year. The survey is called the Consumer Pyramids Household Survey (CPHS) and is conducted by the Centre for Monitoring the Indian Economy (CMIE). We first look at cross-sectional trends of headline indicators, disaggregate them based on demographic characteristics, and then analyze the nature and determinants of labor market transitions using the panel structure of the data. As a last step, we look at the policy

> 1Based on CPHS data using the rural and urban samples from April 2020 and the Periodic Labour Force Survey using the urban quarterly sample from April-June 2020.
implications of India’s vaccination campaign on individual employment outcomes. Due to the limitations of the data and the scope of this paper, we do not attempt to capture the grave health impacts of the pandemic (Jha et al., 2022), which are likely to have affected the labor market.<sup>2</sup> For the panel data analysis, we follow two fixed cohorts from the CPHS data – a Covid-affected cohort and a pre-Covid cohort for comparison – over a span of 12-16 months. Keeping each self-contained cohort fixed allows us to track the same individuals at different points in time. Further, the pre-Covid cohort acts as a plausible counterfactual for the Covid-cohort so that we have a baseline reference for “normal” labor market dynamics. The Covid-cohort consists of individuals interviewed between November 2019 and February 2020, right before the pandemic, and we track their outcomes every 4 months until March–June 2021. The pre-Covid cohort consists of individuals interviewed either between November 2017 and February 2018 or November 2018 and February 2019 as baseline, and then followed for 12-16 months. The outcomes of these two cohorts are then compared with reference to their own baselines using a differencein-difference regression framework, to assess the impact of the pandemic on labor market dynamics. We further examine whether individual circumstances like education and location mitigated the impacts of pandemic-related labor shocks.

Unemployment peaked at an unprecedented 24.3 percent in April 2020 and recovered quickly to 7.3 percent by July 2020. It rose again in May 2021 to 11.4 percent during India’s devastating second wave of Covid but came back to around 7 percent between July and December 2021. The quick recoveries in unemployment however mask an overall decline in labor force participation. Moreover, while Covid-19 was an economy-wide shock, women, youth, and historically disadvantaged castes experienced relatively more adverse effects, partly due to the vulnerable and informal nature of their pre-pandemic occupations. Covid-19 has induced further shifts into insecure forms of employment. In the immediate aftermath of the national lockdown, the Covid-cohort who were employed at baseline were more likely to shift into self-employment from casual-wage and salaried work by 3.6 percentage points relative to the pre-Covid cohort. In the longer run (16 months after baseline), these workers were 3.9 percentage points more likely to be in casual wage work, the most vulnerable form of employment. Transitions into more insecure jobs were also accompanied by downward transitions into lower paying jobs even among salaried workers. Salaried workers in the Covid-cohort shifted into jobs earning a median income of INR 250 less per month than the pre-Covid cohort up to 16 months after the pandemic.

We also find significant transitions from industry into agriculture for the Covid-cohort 4 months after baseline relative to the pre-Covid cohorts. However, the differences between the two cohorts become insignificant when we follow them 16 months afterwards,

> 2Refer to Malani and Ramachandran (2021) for an attempt at measuring excess mortality during the pandemic using CPHS data.

2
which suggests that the sectoral shift induced by the pandemic was temporary in nature. The short-term shift into agriculture is accompanied by return-migration, primarily into rural areas. The migrant crisis that was extensively covered by the media saw almost 4 percent of the Indian population (roughly 50 million migrants) move back to their native households. Another potential crisis, albeit less documented, concerns the long-term adverse effects of unemployment spells on later employment and wage outcomes of young people. Youth of age 16-24 years in the Covid-cohort, who were not employed at baseline were 9.5 percentage points more likely to remain out of the labor force even 16 months afterwards relative to their counterparts in the pre-Covid cohort. This abstention from the labor market is not compensated by acquiring more education, as the Covid cohort is also more likely to be out of school by 9.6 percentage points, and consequently face greater learning losses relative to the pre-Covid cohort.

Our study contributes to the emerging literature on the impacts of Covid-19 on India and other emerging economies. Gupta et al. (2021) document findings similar to ours, of downward labor market churn into jobs that yield lower earnings. While casual workers faced higher relative losses in earnings vis-`a-vis salaried workers, losses were also relatively higher for individuals from richer households, with mixed implications for overall income inequality. Bussolo et al. (2021) look at short-term effects of Covid-19 indicating a large shift into self-employment. Researchers have also focused on the gendered impacts of the pandemic in India and have found that conditional on being employed, women were more likely to lose their jobs and face greater domestic burdens due to the pandemic (Abraham et al., 2021; Deshpande et al., 2020). Our results confirm early findings on the differential impacts of Covid-19 along caste lines, driven by the overrepresentation of marginalized castes in informal work and their lower levels of human capital (Deshpande and Ramachandran, 2020).

Our paper also adds to emerging evidence in developing economies on how labor markets fared during the “recovery” period that followed the first wave of Covid-19 and associated lockdowns in developing countries. In the early days of the pandemic between April and June 2020, the recession inflicted by the pandemic caused severe labor market disruptions in developing countries. The World Bank’s High-Frequency Phone Survey (HFPS) data collected in a large sample of developing countries show that work stoppage, pay-cuts and income loss were common consequences (Khamis et al., 2021) and occurred at a disproportionately high rate among those with low education, informal jobs in urban areas, women and youth (Bundervoet et al., 2022). Sectors hit hardest by Covid included accommodation and food services, non-food manufacturing, retail and wholesale, and travel and transport (ITC Business Impact Survey 2020).

Evidence on labor market recovery in developing countries since the summer of 2020 is rather sparse. The limited and indicative evidence that exists suggests that even as economic activity started reviving in many countries since May/June 2020, the job market recovery was slow and uneven in most countries. In a sample of 17 developing 3
countries where policies became more conducive to economic activity between May and September 2020, employment among those who suffered larger initial shocks – women, non-college-educated, and urban workers – did not recover enough to close the gaps caused by initial disparities in losses. There are indications that self-employment, which is often lower-quality employment in developing countries, is accounting for a high share of the employment that is coming back (Narayan et al., 2022).

More detailed evidence on labor market dynamics available for a few countries indicates continued signs of distress in nonfarm labor markets even as economies recover, but with important differences across countries. For example, in South Africa, where employment rates were already low before the pandemic, Covid-19 led to a further decline of 13.6 percent in overall employment followed by a slow rebound whereby just 40 percent of the jobs lost were recovered by the end of 2020. Employment losses were four times as large among the bottom quintile of workers than among the top quintile, and strongly concentrated among young workers, those with lower skills or education, people of color, and those in the informal sector or in small firms. These gaps remained largely unchanged even as jobs started coming back (World Bank, 2021 _b_ ).

Evidence from Nigeria suggests that labor market impacts of the pandemic can persist even as aggregate employment numbers recover. After a sharp fall during the initial Covid-19 lockdown in the share of the Nigerian population who were working, employment recovered to exceed pre-pandemic levels by August 2020. Most of the increase was among women and the poor, in a pattern that is consistent with an “added worker effect” whereby households increase their overall labor-market participation in order to cope with economic shocks. Many Nigerians have shifted to retail and trade jobs in nonfarm household enterprises, most of which are lower productivity jobs that do not yield secure earnings (Lain et al., 2021). Vietnam offers another example of a country where signs of weaknesses have persisted amid a robust recovery in aggregate employment. High initial losses in jobs, hours worked and wages – and large gaps by gender, skill level and sector of workers – largely dissipated by the fourth quarter of 2020. However, the highest rate of job growth was occurring in more precarious forms of off-farm selfemployment rather than in wage work (World Bank, 2021 _a_ ). Job quality, as measured by the probability of having employment with a labor contract, is estimated to have declined as a result of the pandemic in second and third quarters of 2020 (Dang and Nguyen, 2020).

Our paper adds to this body of evidence by deepening the understanding of the shortterm as well as longer-term impacts of the shock, such as on labor market outcomes for youth, for a developing economy. Access to several rounds of panel data with detailed information on labor market outcomes and individual characteristics, spanning several years before and during the pandemic, offers us the unique opportunity to explore causal impacts of the shock on labor market dynamics, after accounting for the effects of confounding factors like seasonality, time trends and worker characteristics to a reasonable 4
degree. To the best of our knowledge, this is the first paper to analyze the causal impact of the Covid shock on labor market dynamics in a developing economy, over a fairly long (16 months) time horizon since the end of the initial lockdowns in the summer of 2020. The findings are also likely to have relevance for other developing countries whose labor markets share some similarities with India.

Our paper also relates to the broader literature on the impact of economic crises in general on labor outcomes, the longer-term impacts of which can be particularly harmful for inequality and socioeconomic mobility. There is evidence to suggest that loss of jobs can leave lasting impacts on vulnerable workers, and particularly on young entrants to the labor market. For example, Guvenen et al. (2017) use data from the Social Security Administration in the United States to show that individuals who go through a long period of non-employment suffer large and long-term earnings losses compared to individuals with similar age and previous earnings histories. Because of this effect, a cohort that enters the labor market during a recession is likely to face long-term disadvantages that lead to lower lifetime earnings. Moreover, employment shocks that are unexpected in nature can be particularly harmful – studies have shown workers who are displaced by unexpected firm closures to experience significant and long-lasting reductions in earnings. A crisis also tends to affect workers unequally because of differences in their socioeconomic background, widening existing inequalities. Youth from disadvantaged backgrounds may be forced to enter the labor market at a time when few economic opportunities are available, compared with youth in households that are more well off or enjoy better access to credit, who are more likely to postpone labor market entry, accumulate more schooling or unpaid work experience, and improve their prospects of upward mobility.

This paper does not attempt a comprehensive analysis of the policy response by the Indian government but only focuses on the extensive vaccination campaign and its role in helping labor markets return to normal. For a review of India’s relief package - the Prandhan Mantri Garib Kalyan Yojana (PMGKY) - refer to Bhattacharya and Sinha Roy (2021) and Chatterjee et al. (2022). In the next sections of this paper, we describe the data and methods used for our main analysis. Section 4 presents our main results on labor market impacts looking at transitions across employment status, earning levels, employment types, sector and location. Section 4 also examines how education and location may have mitigated some of these transitions and contains some additional results on the effect of vaccination on individual employment outcomes. The last section concludes.

# **2 Data and descriptive statistics**

The Consumer Pyramids Household Survey (CPHS) is a continuous panel survey of approximately 170,000 households in each wave. The surveys are conducted by the Centre for Monitoring the Indian Economy (CMIE), a private think tank and data collection 5
<!-- Start of picture text --> 50<br>45<br>40<br>>E 35<br>><br>® = 30<br>Pyz5 24.3\<br>B 20<br>E<br>2 45<br>2<br>10<br>5<br>0<br>Jan19 Apr19 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>— Male Female Total<br>‘Source: CMIE People of India (15 yrs above)<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>Ss<br>2£ 45<br>= 235<br>g3s2©8 « ° > . : . 40.3<br>.<br>2<br>Re}<br>5 35 1<br>4<br>30<br>Jan19 Apri9 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>—= Urban Rural Total<br>‘Source: CMIE People of India (15 yrs above)<br><!-- End of picture text -->
<!-- Start of picture text --> 50<br>45<br>& 40<br>2©35<br>s2§30<br>s&3 2<br>a©8 20<br>S15<br>ss2 510<br>0<br>Jani9 Apr19 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>—= Youth (15-24 yrs.) Adult (25+ yrs.)<br>Source: CMIE People of India (15 yrs above)<br><!-- End of picture text --> <!-- Start of picture text --> 50<br>45<br>40<br>== 35<br>2£3S 30<br>=<br>© 25<br>=<br>2 2<br>E<br>2 15<br>—) 10<br>5<br>0<br>Jan19 Apri9 Jul19 Oct19 Jan20 Apr20 Jul20 Oct20 Jan21 Apr21 Jul21 Oct21 Jan22<br>— General oBc sT — sc<br>‘Source: CMIE People of india (15 yrs above)<br><!-- End of picture text -->
Due to the flexible nature of casual work, they also saw a quick recovery to pre-pandemic shares of employment. Section 4 discusses how transitions between employment types during the pandemic were likely a distress signal in the labor market.

# **3 Methodology: Fixed cohort analysis using CMIE panel structure to follow the same cohorts over time**

In this section, we introduce a fixed-cohort analysis that allows us to study the same cohort of individuals over time. This approach has two advantages. First, it eliminates the concern over different composition of individuals in different snapshots of time from the cross-sectional analysis, which confounds any analysis of trends in labor market outcomes. Second, it allows us to follow the labor market trajectory of individuals up to 16 months afterwards.

We define a Covid-affected cohort as the group of workers employed at the baseline period (Nov 2019-Feb 2020), right before the pandemic. The CPHS revisits households once every 4 months, therefore we can track this group of workers 4 months later (Mar 2020-Jun 2020), 8 months later (Jul 2020-Oct 2020), 12 months later (Nov 2020-Feb 2021), and 16 months later (Mar 2021-Jun 2021). We consider only workers who can be tracked consistently over all four periods so that the composition of workers stays the same for each time period.

We create a pre-Covid cohort for comparison. It consists of workers who were employed in Nov 2017-Feb 2018 and those who were employed in the Nov 2018-Feb 2019 period as baseline and follow them for 16 months and 12 months respectively (12 months for the second group to avoid overlap with the Covid period). The goal of having this cohort, for a period that is as close as possible to (and yet distinct from) the Covid period, is to have a comparator group with a likely ‘normal’ labor market trajectory for workers in India during a time period without Covid. If we observe any difference in terms of labor market trajectory between the Covid cohort and pre-Covid cohort, these differences can reasonably be attributed to the Covid shock.

We formulate the above comparison in a difference-in-difference regression framework, estimating the change in labor market outcomes with respect to baseline for each time period separately for both the Covid and the pre-Covid cohorts. For worker _i_ from cohort _k_ (pre-Covid vs Covid cohort) at time period _t_ , we run the following regression specification: _Outcomeikt_ = _βkt × CovidCohortik × Periodt_ + _Fixed Effects_ + _εikt_ We estimate _βkt_ separately for each of the 4 post-baseline periods. We are particularly interested in the difference in the coefficients between the pre-Covid and Covid 8
<!-- Start of picture text --> Employed baseline: employed afterwards<br>05<br>@ -.067<br>@-.09<br>-1 1<br>=: bo * 11<br>: bess<br>3 15 .<br>2<br>a<br>E<br>Ww<br>-2<br>: 23 ® Pre-covid cohort<br>725 @ Covid cohort<br>4 months later*** 8 months later"** 12 months later*** 16 months later"**<br>EachT-tast coeff on d i cientfferances estimatesbetweenthe differencetwo coefficients from baselinefor aach periperi od forreportad each cohorton the atx-axis Bachlabp e riod.ls.<br>Significance levele: " pefl1 * paf.05 "** pef.04<br><!-- End of picture text -->
<!-- Start of picture text --> Male employed baseline: employed afterwards<br>042<br>05 rs-.059 -067 -07<br>Eo 089 Ih 096<br>3<br>z ote<br>3E 15<br>Ww<br>“2 “2 ® Pre-covid cohort<br>®@ Covid cohort<br>4 months later"** 8 months later"** 12 months later***16 months later"**<br>‘SigniticanceEach‘Ttost costicienton diferencealevels:estates* betweenp<0.1 the** ctterencetwop<0.05cosficients*** tromp<0.01baselinefor each periodperiod reportedfor each cohorton the atx-axiseachlabels,period<br><!-- End of picture text --> <!-- Start of picture text --> Female employed baseline: employed afterwards<br>+2<br>$2<br>= +3 $a<br>3oA $-28<br>2 ~43<br>WwE -5 28 -48~ -5<br>~56 ® Pre-covid cohort<br>-6 ®@ Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later**<br>‘SignificanceEach‘testcoeticienton ciflerenceslevels:estates* betweenp<0.1 the** twoditarencep<0.05cosficlents*** tromp<0.01baselineforeach periodperiod reportedfor each cohortonthe atx-axiseachlabels,period<br><!-- End of picture text -->
<!-- Start of picture text --> Employed baseline: out of labor force afterwards<br>16<br>14<br>14<br>Pal<br>E<br>E<br>=]<br>oD 12<br>oD<br>o<br>fs]- a 4 ° ah)<br>Fs A ° $ 028<br>isZ + 088<br>08 ¢ O77<br>@ Pre-covid cohort<br>06 $ 058 @ Covid. cohort<br>4 months later" 8 months later*** 12 months later"** 16 months later"**<br><!-- End of picture text -->
<!-- Start of picture text --> Employed baseline: employed with positive wage afterwards<br>1 ; 08<br>- 0 =<br>3E -0037 ow ope $-013<br>2<br>23<br>=g “A<br>8<br>a<br>-19 Pre-covid cohort<br>“2 ®@ Covid5 cohort<br>4 months later*** 8 months later 12 months later 16 months later***<br><!-- End of picture text --> <!-- Start of picture text --> Employed baseline: log earning afterwards<br>1 091 + 7<br>> 037 > 039<br>0<br>£o “A<br>€ 12<br>§ hers u 13 ;<br>agz 2<br> Pre-covid cohort<br>“4 ® Covid. cohort<br>4 months later***8 months later***12 months later"*16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> Median 2018 income of occupation among the employed<br>100<br>2 34 45 27<br>2 0 a7<br>= .<br>a<br>:<br>ae) -100<br>Oo<br>:<br>2 200 -186<br>o -217<br>I -251<br>@ -300 “286<br>a=]<br>o<br>=<br>@ Pre-covid cohort<br>“400 B® Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> Employed baseline: being self-employed afterwards<br>.06<br>.047<br>> .04<br>—<br>—£<br>=]<br>no]<br>B 024<br>se 2<br>a 015<br>— on on $ ou<br>=2® °<br>Q o<br>-.0062<br>-.0099<br> Pre-covid cohort<br>-.02 ®@ Covid cohort<br>4 months later*** 8 months later 12 months later*** 16 months later***<br><!-- End of picture text --> <!-- Start of picture text --> Employed baseline: being casual worker afterwards<br>04<br>.027<br>024<br>02<br>> Jot .<br>—<br>—<br>=]<br>no)<br>So<br>3<br>g2 -.015<br>6<br>-.031<br>04 @ Pre-covid cohort<br>® Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text --> <!-- Start of picture text --> Employed baseline: being salaried worker afterwards<br>0<br>-.0025 0045 $0 .0083<br>> -.01<br>££<br>3<br>} -.017 -.017 -.018<br>8 -.02<br>3<br>=Ss<br>& -03<br>-.032<br> Pre-covid cohort<br>-.04 ® Covid cohort<br>4 months later** 8 months later***. 12 months later" 16 months later*<br><!-- End of picture text -->
We also look at the employment sectors that workers switch to. We define three industries: agriculture, industry, and services. At baseline, 37% of workers are in agriculture, 26% in industry, and 37% are in services. In the short run, we see a large switch into the agriculture sector (4.7 ppts) at the expense of industry and service (Figure 9, top). In the long run we see a slight but steady decrease in service sector jobs for the Covid cohort (1.8 ppts 12 months later and 2.1 ppts 16 months later). The decrease is statistically significant at 95 percent confidence level only 16-months after baseline (Figure 9, bottom).

15
<!-- Start of picture text --> Work in agricultural sector for the employed<br>08<br>E> 6<br>3E .051<br>2<br>=]<br>S 04<br>oS<br>=<br>S<br>£ .028<br>. .026<br>= 02 ° 015 017 015<br>0087 |<br>0042 ® Pre-covid cohort<br>9 ®@ Covid cohort<br>4 months later*** 8 months later 12months later 16 months later<br><!-- End of picture text -->

<!-- Start of picture text --> Work in industry sector for the employed<br>02<br>0<br>2 .0027 0046<br>€ ~0044 0034 .0059<br>3 oo13<br>FS ~<br>3I} -.02<br>£<br>£<br>x<br>S<br>= 04 -.039<br>@® Pre-covid cohort<br>-.06 a Covid. cohort<br>4 months later*** 8 months later 12 months later 16 months later<br><!-- End of picture text --> <!-- Start of picture text --> Work in service sector for the employed<br>01<br>2e -ot0 . .000046 005 t= -.0085<br>g3 -.012 -.015<br>3 -.019<br>=<br>5<br>=<br>-.03 -.03<br>@ Pre-covid cohort<br>~.04 ®@ Covid cohort<br>4 months later 8 months later 12 months later**16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> 10<br>Ss<br>§<br>35&°<br>3a<br>o<br>c4<br>5 22 z<55 NNO<br>fo)<br>Apri9— Jul19 Oct19 Jan20 Apr20 Jul20 Oct20. Jan21 Apr21 Jul21 Oct21 Jan22<br>—— Emigrant (leaving) Immigrant (returning)<br>source: CME People of nla<br><!-- End of picture text --> <!-- Start of picture text --> 4<br>2°<br>s&5<br>ag?a<br>xo}<br>@<br>ga4Ff<br>0<br>Apr19, Jul19: Oct19 Jan20 Apr20 Jul20 Oct20) Jan21 Apr21 Jul21 Oct21 Jan22<br>— Return migrant Return migrantto rural Return migrantto urban<br>source: CMI People ot nla<br><!-- End of picture text -->
<!-- Start of picture text --> Youth not employed baseline: out of labor force later<br>05<br>E on 3 02 0073<br>= 0 0028<br>a<br>=!<br>oO<br>2<br>&<br>E5 > -035<br>“= -.05<br>o<br>5<br>ie) $058 & -.068<br>@ Pre-covid cohort<br>“1 $086 ®@ Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> Male youth not employed baseline: out of labor force later<br>05<br>Oo : 0048<br>E -.0072 0052 -.0054<br>3<br>288<br>8 $-056<br>8<br>Ss + -.088<br>fo}a4 + a<br> Pre-covid cohort<br>“15 ~ 1s @ Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text --> <!-- Start of picture text --> Female youth not employed baseline: out of labor force later<br>05<br>' .026 * 027<br>> 6 ’ ore 2 071<br>E -01<br>3 -024 -025<br>2 + -.038<br>2-058<br>8<br>Ss<br>Oo3 -A<br> Pre-covid cohort<br>“45 ® Covid cohort<br>4 months later*** 8 months later*** 12 months later*** 16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> Youth not employed baseline: in school later<br>|<br>063 064<br>05 ° O72 ° ° 069<br>=<br>—E<br>—<br>=]<br>=)<br>Oo f8]<br>o<br>E<br>c ~.05 mi -.041 ; -.04 e -.027<br>: ~074 @ Pre-covid cohort<br>B Covid cohort<br>4<br>4 months later*** 8 months later*** 12 months later***16 months later***<br><!-- End of picture text -->
<!-- Start of picture text --> Employed post baseline (compared to workers with <5 schooling)<br>08<br>06 0<br>E O83<br>§ .04 .0B2<br>=)<br>B<br>e<br>a”<br>w 0064<br>a -.0033 0914+<br>¢ “o<br>“op @ Pre-covid cohort<br>-.02 Ps § Covid cohort<br>5~7 years” 8-9 years’ 10-11 years*** >12 years***<br><!-- End of picture text -->
<!-- Start of picture text --> Log earning post baseline (compared to workers with <5 schooling)<br>8<br>48<br>6<br>Pa<br>c<br>I<br>oO<br>i=)<br>Se 4 7<br>O7ap4 |+ .¢<br>O11<br>i]<br>@ Pre-covid cohort<br>®@ Covid cohort<br>-<br>5-7 years 8~9 years 10~11 years >12 years *<br><!-- End of picture text -->
<!-- Start of picture text --> Upward mobility of baseline non-salaried workers (compared to rural)<br>14<br>12 2<br>=34<br>zof<br>Boe 082<br>073<br>06 061 ° .059<br>° 49 Pre-covid cohort<br>os ® Covid cohort<br>Small city* Large city** Very large city***<br><!-- End of picture text --> <!-- Start of picture text --> Downward mobility of baseline salaried workers (compared to rural)<br>0<br>-.05 + ons<br>a£<br>QPo-a3°<br>3= -12 “13<br>a<br>-15<br>@Pre-covid cohort<br>“2 TM Covid cohort<br>Small city* Large city** Very large city***<br><!-- End of picture text -->
by the OECD Weekly Tracker. They find that a 1pp increase in the share of people vaccinated would increase weekly GDP by between 0.042 - 0.061 pp one month later. The economic impact of vaccination has not yet been measured in a developing country, and no study has examined impacts on micro-level outcomes in a developing country either.

Using CPHS data, we can examine the relationship between vaccination and individual labor market outcomes. In a Covid Special Module fielded by the World Bank, individuals were asked about their vaccination status and the number of doses they have taken. This information was collected during the 24th Wave of CPHS between September - December 2021 when all adults were eligible for vaccination. India’s vaccination campaign took place in a phased manner. Vaccination of frontline workers started on January 16, 2021; individuals above 60 years and above 45 years with co-morbidities on March 1, 2021; for all individuals above 45 years on April 1, 2021; and finally, for all adults on May 1, 2021. However, survey data on individual vaccination status was not collected until September 2021. Due to the panel structure of CPHS, we are able to observe outcomes for the 18-45 years age-group both before they were eligible for vaccines (January – April 2021) and after (September – December 2021). Hence, the results in this section exploiting the panel structure of the data restrict the sample to adults aged between 18-45 years.

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Table 1: Individual vaccination status and employment outcomes

||OL|S _yi_|First-Dife|renced ∆_yit_|||Within|_yit_||
|---|---|---|---|---|---|---|---|---|---|
||(1)<br>Wav<br>All|(2)<br>e 24<br> ages|(3)<br>Wav|(4)<br>e 23-24|(5)|(6)<br>Wave 22, 24<br>18-45 years|(7)<br>|(8)<br>Wav|(9)<br>es 16-24|
|At least 1 dose|0.096***||0.006***||0.009***|0.004***||||
||(0.006)||(0.002)||(0.001)|(0.002)||||
|doses=1||0.068***||0.006***|||0.004**|||
|||(0.006)||(0.002)|||(0.002)|||
|doses=2||0.130***||0.006***|||0.004***|||
|||(0.008)||(0.002)|||(0.002)|||
|At least 1 dose * WAVE24||||||||0.022***<br>(0.002)||
|Fully vaccinated * WAVE24|||||||||0.028***<br>(0.002)|
|Controls|Age, g|ender, religi|on, caste, e|ducation||||wave#vaci|nnation status|
|Individual fxed efects|||||yes|yes|yes|yes|yes|
|District fxed efect|yes|yes|yes|yes||||||
|Month fxed efects|yes|yes|yes|yes||||||
|Wave fxed efects||||||yes|yes|yes|yes|
|Observations|394088|394088|234121|234121|363864|363864|363864|1719948|1719948|
|R-squared|0.422|0.424|0.016|0.016|0.953|0.953|0.953|0.853|0.853|
|Dependent var. mean|0.386|0.386|0.010|0.010|0.387|0.387|0.387|0.378|0.378|

Note:<sup>_∗_</sup> _p <_ 0 _._ 10 _,_<sup>_∗∗_</sup> _p <_ 0 _._ 05 _,_<sup>_∗∗∗_</sup> _p <_ 0 _._ 01. Standard errors in parentheses are clustered at the district level for Columns 1-4 and at the individual level for Columns 5-9. Columns 1 and 2 estimate the specification _Employedi_ = _α_ + _βV accinationi_ + _φControlsi_ + _Month FE_ + _District FE_ + _εi_ where Vaccination refers to either the individual being vaccinated with at least 1 dose or the number of doses, and the dependent variable is a dummy variable indicating whether the person is employed or not. In Columns 3 and 4, the dependent variable is first-differenced, so that _β_ estimates the effect of vaccination on changes in employment status; ∆ _Employedit_ = _α_ + _βV accinationi_ + _φControlsi_ + _Month FE_ + _District FE_ + _εi_ . In Columns 5-7 we allow both employment and vaccination to vary over time to partial out individual fixed effects and estimate _Employedit_ = _α_ + _βV accinationit_ + _Individual FEi_ + _Wave FEt_ + _εit_ . Finally, in Columns 8- 9 we take individual vaccination status as fixed over time and interact with all survey-waves: _Employedit_ = _α_ + _βV accinationi∗Wavet_ + _φControlit_ + _Individual FEi_ + _Wave FEt_ + _εit_ .

Columns 1 and 2 of Table 1 present the OLS estimates of the correlation between individual vaccination and employment status using cross-sectional data between SeptemberDecember 2021 (wave 24). Getting vaccinated with at least 1 dose is associated with a 9.6 pp increase in a person’s likelihood of being employed (Column 1). In Column 2, when we compare the marginal effects of 1 dose (6.8 pp) and 2 doses (13 pp), the 6.2 pp difference is significant at 99% confidence levels suggesting that completing the full vaccination course has stronger effects on employment outcomes. In Columns 3 and 4, we estimate the same specification, but use the first-differenced employment status instead. This allows the coefficient on vaccination status to measure the effect on changes in employment status in wave 24 from employment status in the previous wave. Coefficient estimates reduce substantially to a 0.6 pp increase in the likelihood of being employed and the differences in marginal effects between 1 and 2 doses are not significant.

In the previous estimations, although we control for individual characteristics like 25
<!-- Start of picture text --> Among HHs where not all eligible members get vaccinated, who gets priority<br>60 56.07<br>52.67<br>49.52<br>50<br>40<br>oD 34.22<br>© 30.32<br>=gg 30 27.84<br>20<br>10<br>0<br>Unvaccinated At least 1 dose<br>Whether vaccinated with at least 1 dose<br>G9 Male (%) GH Employed(%) Hil Age (yrs)<br>Source: CMIE People of India, September-December2021 for individuals between 18-45 years<br><!-- End of picture text -->
available, individuals’ safety and health concerns might be assuaged and therefore, they are more willing to supply their labor. Alternatively, vaccination can lead to increases in labor demand as the economy recovers and particularly, for those workers who are vaccinated to mitigate spread of the virus at the workplace. While it is not possible to quantitatively distinguish whether supply- or demand-side drivers are behind the findings in Table 1, the supporting evidence seems to be in favor of the latter. Firstly, labor force participation has been declining consistently and has not shown signs of an uptick since India’s vaccine campaign was launched. Unemployment, however, has recovered. This suggests that instead of additional workers joining the labor force, vaccination may have allowed existing job seekers to match with employers. Secondly, there are anecdotal reports that employers are requesting new hires to be vaccinated and are prominently displaying signs stating that all staff are vaccinated. This is corroborated by survey data when we look at the individuals who get prioritized for vaccination in households where not all, but only some eligible adults are vaccinated. In Figure 17, we see that male, employed, and prime-working age adults are more likely to get priority for being vaccinated. This may be due to costs or behavioral constraints in accessing vaccination despite it being freely provided at government health centers. The priority of ‘primeworking’ members over others in the household suggests that they have greater returns from vaccination, likely through the labor market.

# **5 Discussion**

Covid-19 was an unprecedented health crisis across the world and in India. This paper examines the impact of the crisis on labor market outcomes in India, which has mostly been in the form of various downward transitions. Individuals affected by Covid19 transitioned out of employment or the labor force altogether. People who remained employed moved into more vulnerable employment arrangements like casual-wage work or self-employment. This is also accompanied by greater volatility in the probability of positive wages and reduced earnings for the Covid cohort. The reduction in median wages holds even for salaried workers, who transitioned into lower-paying jobs. There was a short-term shift into agriculture at the expense of industry, which coincided with India’s migrant crisis. The large movement of people to rural areas was not compensated by a return to cities in the same magnitude, and in fact rates of out-migration steadily decreased since the pandemic.

Women, marginalized castes, and the youth have fared worse due to the pandemic. The former two groups faced greater likelihoods of unemployment conditional on being employed before the pandemic, partly due to characteristics of their pre-pandemic jobs. Youths (aged 15-24 years) were more likely to withdraw or abstain from entering the labor force, and were also less likely to be enrolled in an educational institution relative to their pre-Covid counterparts. This finding is worrying because higher education helped mitigate adverse effects of the pandemic. Individuals with tertiary education retained their earnings premium vis-a-vis those with no education even after Covid-19, while lower 27
education groups did not. Since schools and universities in India were closed for almost 2 years, the ability of young people to recover their losses and cope with future shocks may be at risk. Being located in denser urban areas offered more opportunities for informal workers (self-employed and casual-wage) to transition into salaried jobs, but this channel for upward mobility may also be at risk if people choose not to migrate in future. This paper further looks at the relationship between India’s extensive vaccination campaign and labor market outcomes. Vaccinations allowed a return to normalcy in the Indian economy and we find positive and statistically significant effects on individual employment status. However, the magnitudes are small and based on findings in other sections of the paper, unlikely to compensate for the losses in job quality during the pandemic.

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