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Table 4 Natural resources and particulate matter concentration: baseline results

From: Mineral wealth paradox: health challenges and environmental risks in African resource-rich areas

 

(1)

(2)

(3)

(4)

Mineral deposit

0.6349a

0.6219a

0.5932a

0.2583a

 

[0.1405]

[0.1406]

[0.1404]

[0.0654]

Petroleum field

0.6886a

0.5708a

0.4314a

0.1349a

 

[0.0868]

[0.0769]

[0.0675]

[0.0383]

Latitude

 

0.0167a

0.0218a

0.0063b

  

[0.0043]

[0.0041]

[0.0026]

Longitude

 

0.0204a

0.0239a

0.0027

  

[0.0057]

[0.0050]

[0.0036]

Mean elevation

 

0.0000

-0.0004a

-0.0000

  

[0.0000]

[0.0001]

[0.0001]

Ln(distance to coast)

 

-0.2106a

-0.2545a

-0.1196a

  

[0.0255]

[0.0317]

[0.0292]

Temperature

  

-0.0648a

-0.0043

   

[0.0109]

[0.0082]

Precipitation

  

0.0018

0.0036a

   

[0.0022]

[0.0013]

Vegetation

  

0.0003a

0.0001c

   

[0.0001]

[0.0000]

Night luminosity

   

2.0503a

    

[0.3130]

Observations

12,529

12,428

12,356

12,356

R-squared

0.1468

0.1731

0.1987

0.7205

Country FE

Yes

Yes

Yes

Yes

Clustering var.

Grid Cell

Grid Cell

Grid Cell

Grid Cell

  1. The table presents the coefficient estimates of the OLS regression with multiple fixed effects, which examines the association between natural resources and atmospheric particulate matter concentration. We define all variables in Appendix A. In columns (1) to (4), we include country-fixed effects, and all variables are clustered at the grid level. Heteroscedasticity-robust standard errors are reported in parentheses. a, b, and c indicate statistical significance at the 1%, 5%, and 10% levels (two-tailed), respectively