Jobs in the Time of Corona

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By Anirban Mukherjee

Almost two years have passed since the first patient of Covid-19 was found in India and during this time, India experienced humanitarian and economic crises on an unprecedented scale. The direct effect of the pandemic comprising around forty million patients and half a million deaths is only the tip of the iceberg. A much bigger impact came through the indirect channel as people lost their livelihoods and lives because of economic lockdown. During the last two years, numerous reports detailing the plights of migrant laborers, small traders and factory workers bore evidence of this national tragedy. As much as these reports elaborate on how social and economic relations broke down during the pandemic, they leave space for objective analyses of the economy based on macroeconomic data. In this article, I shed light on how unemployment figures changed during the time of pandemic and offer a possible analysis of the underlying analysis.

The most perplexing thing about the unemployment figure in India is that it didn’t change much during the pandemic. The unemployment data collected by Centre for Monitoring of Indian Economy (CMIE) shows that the aggregate unemployment figure did not change much in these two years. In a pre-Covid time period between January-April, 2019 the unemployment rate was 6.87% which, contrary to our expectation, declined slightly to become 6.83% between January-April, 2021. Disaggregating the data across location and sex identity of the workers does not change the broad pattern either. We see that the rural unemployment rate was 6.5% in January-April, 2019 which decreased slightly to become 6.4% during January-April, 2021. Urban unemployment on the other increased from 7.6% to 7.7% between these two time periods. A similar pattern emerges when we look at the male and female unemployment rates between these two time periods; the male unemployment rate increased from 5.8% to 6.6% while the female unemployment rate came down from 20.8% to 18.4%. This pattern is perplexing as it does not match with the stories we hear or read in the media. To make sense of these figures we need to take a closer look at the data. But before that, we need to present a general understanding of the idea of unemployment.

Before digging deeper into the data, let us note a thing or two about unemployment. The first thing we should remember is that remaining unemployed is costly and not everyone can afford to do that. This is particularly true for a country like India where, the state does not provide any social security. In Indian case what is more rampant is underemployment when a worker is forced to accept a job below his skill level. So, it is not a big surprise that the aggregate unemployment rate largely remains unchanged during the pandemic. I instead, divide the unemployment rate across educational categories and study the movement of the unemployment rate for each of the educational categories. There is a reason behind disaggregating the data at the educational level is that education signals two things – skill level of the worker and their socio-economic position. Education level as proxy of skill is well known but the second point needs a bit of clarification. Education is costly to acquire. The costs of education do not only include direct costs such as tuition fee or costs of books. A large part of the cost of education comes in the form of foregone wage – the work opportunity one sacrifices to stay in the school. In a developing country like ours, education can be seen as a mark of privilege.

I collect the data from the CMIE reports which are published every four months. In the table below, I show the data from January, 2019 to August, 2021. The report for the last quarter of 2021 was not published when I was writing this article. The data is represented in the figure below.

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Figure 1: Time series of unemployment rates across educational categories

The starting period of our study is Jan-April, 2019. There are three data points in the year 2019 (Jan-April, May-August, Sep-Dec) which represent the time before the pandemic hit India. Hence, the unemployment rates prevailing in these periods can be thought as the normal unemployment rates and are used for benchmarking. We see in the pre-pandemic periods the unemployment rates go up with the level of education; the higher is the education level, the higher is the unemployment rate. Given the argument detailed above, this is not surprising. The candidates who manage to finish high school or complete college education are usually from more privileged backgrounds and can afford to wait until they get a suitable job.

The first period which fell under the lockdown phase, albeit partially, is January-April, 2020. Remember that the first lockdown was imposed in India from 25th March, 2020. During this period, we find that the rate of unemployment is higher than that in the pre-pandemic period and that is true for all the educational groups. The rates of the rise are, however, sharper for the lower educational groups. In fact, the steepest rise in the rate of unemployment is among the people with no education. The situation started to change from May, 2020 when the rate of unemployment started to decline for all groups except the graduates for whom the rate of unemployment kept rising. Between September-December, 2020 the unemployment rates for all groups below class 10 remained low – around 1%-2%. The rate of unemployment for workers who completed class 12 remained around 10% while that for the graduates remained around 20%. This trend persisted until May, 2021 when the country was hit by the second wave; from May, 2021, the unemployment rates for all educational groups starts rising even though the relative position of the groups remained the same; the unemployment rate for the graduates remained at the top, followed by that for the workers who completed class 12 while all other groups’ unemployment rate stayed at the bottom.

This disaggregated picture of unemployment rates reveals the underlying structure of the labor market. The people with no education were the first to lose their jobs at the beginning of the period but they were also the group to recover very fast. A steep rise followed by a sharp decline is common among all the educational groups, except the graduates which I will discuss separately. What exactly was going on here? Remember that the people in the non-graduate categories are the workers with less or no skill. At the beginning of the lockdown when business establishments were closed, they were the ones who can be easily dispensed with. Skilled workers on the other hand are difficult to find and therefore, firing them would be the last option. The unlock phases in India started from June 1, 2020 and we find that the unemployment rates for these groups started to fall from the period covering May-August, 2020. It is worth noting that the lower is the educational level of a group, the greater is their fall in the unemployment rate. Our earlier discussion on the cost of acquiring education suggests that the workers with higher education usually come from well-off families and therefore, if the wage or job profiles are not as good as they expected, they could wait a bit more. I believe when business establishments started to open in the unlock phases, they started offering lower wages than they were paying before the pandemic. The workers with no education were probably the ones from the weakest socio-economic background and they could not afford to remain unemployed for any longer. They were also the ones who had little or no savings and were the worst hit group during the pandemic. They started getting back to work early even at a lower wage than they were getting previously. The other educational category workers followed the suit soon.

A more interesting case is the group of workers with graduate degree. During the first phase of lockdown, unemployment rate in this group did not rise very sharply. However, it started to rise in the later phases, between May and August, 2020. Highly educated, skilled workers are difficult to find and the companies therefore took some time before firing them. Interestingly, unlike other educational groups, their unemployment rate did not fall after unlock phase started in June, 2020. I believe the key to this puzzle lies with the unobserved wage dynamics as well. This group of workers come from more privileged families and therefore, can wait for jobs commensurate with their skill levels and wage expectations. Most of these workers work in the organized sector and the salary quoted in their pay slips have long term consequence for their future salaries. Hence, they are probably the most reluctant to accept jobs paying much less than their skill level.

In this article I shed light on how pandemic has affected differently skilled laborers differently. Note that, even before the pandemic the unemployment rate for the graduates was around 15% which was much higher than the other groups. But pandemic has pushed the rate to approximately 20%. The scenario is quite the opposite for less educated people; pandemic has brought down their rates of unemployment from the pre-pandemic level. However, from the unemployment rate dynamics we cannot conclude anything about their welfare levels which are jointly determined by the employment status and wage rate. I suspect that this unemployment dynamics is being caused by the wage dynamic; employers are offering lower wages after pandemic and workers of different skill levels are reacting differently. The less educated segment of the work force was the worst hit in the pandemic which made them re-join the work force even at a wage rate lower than they were previously earning. The workforce with higher education, however, decided to stay out of the workforce rather than accepting a low wage. This mechanism, if in place, will have critical policy implications. However, without examining the wage data it is difficult to make any definitive conclusion or concrete policy suggestions.

Bio:
Anirban Mukherjee, Assistant Professor, Department of Economics, University of Calcutta.

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