Table 1 Regression results for the effects of climate conditions on water collection time

From: Climate change to exacerbate the burden of water collection on women’s welfare globally

Dependent variable

Daily water collection time (min)

Model

(1)

(2)

(3)

(4)

(5)

(6)

Variables

Temperature (C)

3.834***

4.065***

1.343

4.824

1.971

3.744**

 

(1.441)

(1.520)

(6.233)

(4.141)

(2.247)

(1.502)

Precipitation (mm)

−0.2923***

−0.0976**

−0.2919***

−0.1046*

−0.302**

−0.1007**

 

(0.0832)

(0.0382)

(0.0828)

(0.0416)

(0.0534)

(0.111)

Employment (%)

     

−0.0446

      

(0.0357)

Mean years of education

     

−0.8300**

      

(0.4197)

Education × precipitation

   

0.001

  
    

(0.0092)

  

Education × temperature

   

−0.145

  
    

(0.8823)

  

Employment × precipitation

    

0.004*

 
     

(0.002)

 

Employment × temperature

    

−0.0446

 
     

(0.0507)

 

Precipitation × precipitation

0.0005***

 

0.0005***

   
 

(0.0002)

 

(0.0002)

   

Temperature × temperature

  

0.0567

   
   

(0.1365)

   

Fixed effects

Region

Yes

Yes

Yes

Yes

Yes

Yes

Year

Yes

Yes

Yes

Yes

Yes

Yes

Fit statistics

Observations

1,355

1,355

1,355

1,298

1,298

1,355

R2

0.76847

0.76733

0.76850

0.76875

0.76329

0.76340

Within R2

0.01592

0.01106

0.01606

0.01710

0.01574

0.01618

  1. Driscoll–Kraay standard errors in parentheses.
  2. *P < 0.1, **P < 0.05, ***P < 0.01.
  3. Column (1) shows our preferred specification. Column (2) shows a basic model with only linear terms. Column (3) tests nonlinearities in the impacts of temperature and precipitation. Columns (4) and (5) explore the effect of socio-economic variables in mitigating the impacts of climate conditions. Column (6) demonstrates the robustness of the main results to the direct effects of socio-economic variables.