Results

THE IMPACT OF COVID ON EMPLOYMENT AND INCOME

The regressions confirm the descriptive analysis, that is, the peak of the crisis in April 2020 coincides with the most stringent phase of India’s lockdown. It was associated with a large loss of jobs. Most importantly, the probability of job loss was greater among informal workers than among formal workers.

The regression estimates of equation 7.1 are presented in annex 7A, table 7A.2. Column 1 of the table corresponds to a specification with only individual fixed effects. Columns 2–4 successively add district-time, industry-time, and wave-time fixed effects. Column 3, with individual, district-wave, and industry-wave fixed effects, is the preferred specification.

In the regression specification without any time-specific fixed effects (column 1), one may calculate that, in April 2020, there is a sharp rise in the probability of job loss (and a corresponding large reduction in the employment rate) for each employment group relative to December 2019. Because formal workers are the reference group, the decline among formal workers corresponds to the value of the βApr 2020 coefficient, which is 15 percentage points. The additional penalty for informal workers (the value of the coefficient γApr 2020) is 21 percentage points, which, summed with the 15 percentage points, means that the total loss of employment among informal workers is 36 percentage points.

The magnitude of the differential impact of the pandemic falls as more fixed effects are added in columns 2–4 of table 7A.2, but it remains statistically significant and economically large: informal workers are more vulnerable even after one controls for a variety of fixed effects. The initial difference of 21 percentage points is reduced to 19 percentage points if district-wave fixed effects are added as controls (table 7A.2, column 2). These fixed effects control for factors such as the time-varying intensity of COVID-19 or the lockdown across Indian districts, and they also control for any district-time variation in the ability of local governments to contain the virus. A mere 2 percentage point reduction indicates that these factors do not play a major role in explaining the vulnerability of informal jobs during the pandemic. The magnitude of the differential impact of the pandemic falls by over two-thirds, to 6 percentage points, if both industry-time and district-wave fixed effects are controlled for (table 7A.2, column 3, also plotted in figure 7.4), and to 3.4 percentage points if occupation-time and district-wave fixed effects are controlled for (table 7A.2, column 4). This means that the COVID shock and informality are somewhat more intense in certain vulnerable industries and occupations. Controlling for these characteristics explains a significant share of the difference between informal and formal job vulnerability during the pandemic but not all of it. Even within industries or occupations, informal worker status is a significant disadvantage relative to formal worker status.