Droughts and Deluges: Effects of Climate Extremes on the Gender Gap in Labor Supply

TL;DR

The paper addresses how climate extremes influence gender gaps in labor supply, acknowledging nonlinearity and heterogeneity across contexts. It uses a collective-bargaining framework and a TWFE panel with $SPEI_{ct}$ to identify nonlinear relationships between drought/wetness and the gender gap in labor force participation, finding a U-shaped relation for drought and an inverted U-shaped relation for extreme wetness. The empirical analysis with 151 countries (1995–2019) shows that drought affects the gap mainly through employment, while excess wetness affects unemployment, with patterns varying by displacement risk, women’s empowerment, and net resilience. These findings highlight the need for intensity- and context-aware climate adaptation and gender-focused policies to mitigate adverse labor-market effects on women.

Abstract

Over the past three decades, extreme climate events have caused losses of worth USD 4.5 trillion. Using a panel of 151 countries (1995-2019), I examine how extreme climate conditions shape gender gap in labor force participation. Key results show that the gender gap in paid labor exhibits a U-shaped relationship with droughts and an inverted U-shaped relationship with extreme wet conditions. The drought pattern is primarily driven by gender gap in employment while wetness affects gender gap in participation through unemployment. These relationships vary with country characteristics. Countries with high disaster-displacement risk exhibit declining gender gaps in participation during excess wetness while moderate-risk economies experience expanded gaps during droughts. Furthermore, the drought U-shape is most pronounced in countries with low to moderate empowerment while the nonlinear wet responses is concentrated only in moderately empowered countries. Lastly, both droughts and excess wetness expands gender gap in countries with weak net resilience to climate shocks.

Figures (7)

• Figure 1: Distribution of 12-month SPEI during 1995-2019. 10th percentile and 90th percentile values are considered as thresholds for extreme drought and wet intensities.
• Figure 2: Non-linear Relationship Between Climate Extremes and Gender Gap in LFP. Plots are based on the predicted gender gap in LFP using eq \ref{['main_equation']}. Left panel captures the relationship with extreme drought and right panel shows the relationship with extreme wet conditions. Dashed vertical lines mark the estimated turning points: $D^*=0.278$ and $W^*=0.612$. Shaded areas indicate 95% confidence intervals and the horizontal dotted line denotes zero.
• Figure 3: Non-linear Relationship Between Climate Extremes and Gender Gap in LFP. Plots are based on the predicted gender gap in LFP using cubic spline specification. Left panel captures the relationship with extreme drought and right panel shows the relationship with extreme wet conditions. Dashed vertical lines mark the estimated turning points: $D^*=0.247$ and $W^*=0.338$. Shaded areas indicate 95% confidence intervals and the horizontal dotted line denotes zero.
• Figure 4: Non-linear Relationship Between Climate Extremes and Gender Gap in LFP. Plots are based on the predicted gender gap in LFP using linear piecewise specification. Left panel captures the relationship with extreme drought and right panel shows the relationship with extreme wet conditions. Dashed vertical lines mark the estimated turning points: $D^*=0.278$ and $W^*=0.612$. Shaded areas indicate 95% confidence intervals and the horizontal dotted line denotes zero.
• Figure 5: Heterogeneous Effects of Drought and Excess Wet Intensities on Gender Gap in LFP by Disaster Displacement Risk. Predicted gender gap in LFP are plotted for different drought intensity (left-side panel) and excess wet intensity (right-side panel). Shaded areas indicate 95% Confidence Intervals.