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Table 17 COVID-19, labor market status, and individual telework availability

From: Gender effects of the COVID-19 pandemic in the Swiss labor market

 

Employed

Unemployed

Non-active

Female

−0.00297\(^{**}\)

0.000690

0.00228\(^{**}\)

 

(0.00128)

(0.000831)

(0.00102)

CovInd

−0.0352\(^{***}\)

0.0140\(^{***}\)

0.0212\(^{***}\)

 

(0.00262)

(0.00171)

(0.00209)

Female \(\times\) CovInd

−0.00757\(^{**}\)

−0.00260

0.0102\(^{***}\)

 

(0.00365)

(0.00238)

(0.00292)

Remote

0.00472\(^{***}\)

−0.00291\(^{***}\)

−0.00181

 

(0.00152)

(0.000989)

(0.00121)

Remote \(\times\) female

0.00141

0.000791

−0.00220

 

(0.00216)

(0.00141)

(0.00172)

Remote \(\times\) CovInd

0.0216\(^{***}\)

−0.00501\(^{*}\)

−0.0166\(^{***}\)

 

(0.00420)

(0.00274)

(0.00335)

Remote \(\times\) female \(\times\) CovInd

0.00195

0.00322

−0.00517

 

(0.00611)

(0.00398)

(0.00488)

Constant

0.944\(^{***}\)

0.00796\(^{***}\)

0.0485\(^{***}\)

 

(0.00353)

(0.00230)

(0.00282)

Age FE

YES

YES

YES

Canton FE

YES

YES

YES

Education FE

YES

YES

YES

NOGA FE

YES

YES

YES

Observations

133926

133926

133926

\(R^2\)

0.164

0.0135

0.182

  1. We show estimates from regression (2) of labor market status on a constant, female dummy (1 for women and 0 otherwise), COVID-19 stringency index, remote dummy (1 for respondents who can perform remote working occasionally or regularly and 0 otherwise), and their interactions. Regressions estimated with linear probability model, including random effects. Robust standard errors in parentheses
  2. *p<0.1, **p<0.05, ***p<0.01