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Table 6 COVID-19, labor market status, and telework availability

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

 

Employed

Unemployed

Non-active

Female

−0.0413\(^{***}\)

0.00300\(^{*}\)

0.0370\(^{***}\)

 

(0.00289)

(0.00164)

(0.00248)

CovInd

−0.0101\(^{***}\)

0.00346

0.00739\(^{**}\)

 

(0.00354)

(0.00252)

(0.00299)

Female \(\times\) CovInd

−0.00357

−0.000196

0.00336

 

(0.00490)

(0.00350)

(0.00415)

LowTele

−0.0137\(^{***}\)

0.00803\(^{***}\)

0.00508\(^{*}\)

 

(0.00358)

(0.00210)

(0.00306)

LowTele \(\times\) female

−0.0176\(^{***}\)

−0.00170

0.0195\(^{***}\)

 

(0.00479)

(0.00281)

(0.00410)

LowTele \(\times\) CovInd

−0.0159\(^{**}\)

0.0117\(^{***}\)

0.00433

 

(0.00623)

(0.00442)

(0.00527)

LowTele \(\times\) female \(\times\) CovInd

−0.00325

−0.0122\(^{*}\)

0.0163\(^{**}\)

 

(0.00890)

(0.00631)

(0.00753)

Constant

0.883\(^{***}\)

0.0230\(^{***}\)

0.0942\(^{***}\)

 

(0.00786)

(0.00445)

(0.00674)

Age FE

YES

YES

YES

Canton FE

YES

YES

YES

Education FE

YES

YES

YES

NOGA FE

YES

YES

YES

Observations

171218

171218

171218

\(R^2\)

0.0611

0.0787

0.0415

  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, LowTele dummy (1 for respondents in an occupation with low telework availability and 0 otherwise), and their interactions. Regressions estimated with linear probability model, including random effects. Robust standard errors in parentheses. The sample is restricted to respondents who share complete information on occupation type
  2. *\(p<0.1\), **\(p<0.05\), ***\(p<0.01\)