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