rates. Similarly, increases in vegetation vigor as measured by NDVI resulted in poverty rate reductions in rural areas (see robustness check in appendix). The reasons for such significant and sizeable effects in Sub‐ Saharan Africa and rural areas has likely to do with livelihoods there being more dependent on the environment, among other reasons that are discussed below.

Using the data variation available and assuming a linear relationship, we find that the effects of NPP range between a reduction of the poverty headcount ratio by 4.8 percentage points (in times of extremely large increases in vegetation) and an increase in the poverty headcount ratio of around 3 percentage points (in times of a large decrease in vegetation vigor). In that sense, the effects we find are significantly larger than the effects suggested in Okwi et al. (2007). The interquartile range of the effects is ‐0.28 percentage points and +0.39 percentage points for the rural areas.

**_Table 2: Panel Specification ‐  Dependent Variable ‐ Poverty Headcount Ratio_**

|VARIABLES|(1)<br>OLS|(2)<br>OLS-SSA|(3)<br>OLS- rural|(4)<br>IV|(5)<br>IV-No outliers|(6)<br>IV- rural|(7)<br>IV-SSA|
|---|---|---|---|---|---|---|---|
|NPP|-4.176***|-2.658*|-3.468***|-0.961|0.0297|-6.703**|-13.08***|
|NPP * SSA|(1.214)|(1.360)<br>-6.195***<br>(2.178)|(1.325)|(2.466)|(3.300)|(2.756)|(4.641)|
|NPP * Rural|||-1.301<br>(0.990)|||||
|Precipitation|1.523<br>(1.323)|1.328<br>(1.330)|<br>1.509<br>(1.318)|||||
|Share Cropland|9.783***|7.941**|9.845***|8.623**|8.945*|6.755|22.99*|
||(3.481)|(3.544)|(3.490)|(3.836)|(4.584)|(5.396)|(12.31)|
|Share Urban|-0.540|-1.383|-1.191|0.207|2.643|-25.53***|-872.4***|
||(7.007)|(7.189)|(7.103)|(7.216)|(7.153)|(9.615)|(248.8)|
|Share Grassland|-1.584|-2.982|-2.383|-1.696|-2.761|13.78|-89.30*|
||(5.330)|(5.404)|(5.413)|(5.327)|(6.095)|(10.38)|(49.61)|
|Share Forest|3.666|1.670|2.226|0.557|-1.076|0.0652|20.22|
||(4.105)|(4.176)|(4.258)|(5.426)|(6.215)|(7.226)|(14.31)|
|Population|-1.850***<br>(0.570)|-1.812***<br>(0.574)|-1.897***<br>(0.569)|-1.825***<br>(0.554)|-2.030***<br>(0.627)|-1.102**<br>(0.546)|-9.812***<br>(1.613)|
|Ruggedness|1.875***<br>(0.417)|1.669***<br>(0.424)|1.925***<br>(0.418)|1.545***<br>(0.485)|1.498***<br>(0.521)|3.024***<br>(0.548)|-0.883<br>(2.133)|
|Road Density|<br>-2.897***<br>(0.801)|<br>-2.874***<br>(0.823)|<br>-2.904***<br>(0.808)|<br>-3.085***<br>(0.826)|<br>-3.115***<br>(0.969)|<br>-5.015***<br>(1.229)|<br>-0.323<br>(2.271)|
|Constant|47.60***<br>(10.13)|53.66***<br>(10.48)|49.01***<br>(10.16)|42.06***<br>(13.30)|53.31***<br>(14.37)|14.43<br>(12.65)|195.9***<br>(29.28)|
|Observations|2,736|2,736|2,736|2,736|1,478|1,360|189|
|R-squared|<br>0.744|<br>0.747|<br>0.745|<br>0.742|<br>0.759|<br>0.820|0.445|
|Country FE|YES|YES|YES|YES|YES|YES|YES|
|Year FE|YES|YES|YES|YES|YES|YES|YES|
|Countrytrend|YES|YES|YES|YES|YES|YES|YES|

Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1 The coefficients of the other control variables are in line with the effects suggested by the literature. Note however that all effects other than those of environmental quality are not ‘exogenized,’ meaning they may not be interpreted as causal. That said, important lessons can be drawn from correlations as well. For example, road density, our proxy for infrastructural development, is an important and strong predictor of poverty reduction. This is a long established and well‐known effect in development economics. For an 12