identification. Additional research efforts to compare the relative effects of urbanization, infrastructure development, and in‐situ development of environmental quality in rural areas are needed.

# **4. Conclusion and Discussion**

As expected, we find evidence that building roads is associated with reductions in poverty rates, as many studies have shown before (see e.g. Jacoby, 2000; Gibson and Rozelle 2003; Mu and van de Walle 2007; Jacoby and Minten, 2009; Khandker et al., 2009). What has not been shown conclusively so far however is whether in‐situ improvements of environmental quality significantly reduce poverty. Several authors have suggested that they do not. Okwi et al. (2007), for example conclude that if all of Kenya’s soil was raised to its highest quality, an only 1 percentage point reduction in poverty rates would ensue. Wantchekon and Stanig (2015) go even farther and conclude that in Sub‐Sharan Africa good soil may be a hindrance for poverty reduction.

We find that environmental quality is indeed important for poverty reduction. Using the unbalanced panel data from the Hidden Dimensions data set, we find that as environmental quality increases, the speed of poverty reduction accelerates. Using instrumental variable regressions and exploiting the exogenous time variations in annual rainfall, we find that increasing environmental quality causes significant accelerations in poverty reduction rates, particularly in rural areas and in Sub‐Saharan Africa.

Our analysis does not specifically address any mechanism through which environmental quality helps reduce poverty, which is a major research question that has yet to be tackled (see Andam et al., 2010). Agricultural productivity and higher environmental services are most likely to be the channels through which the poverty reduction effect operates. More research on this issue is required in the future, such as disentangling the different channels of irrigation schemes,<sup>14</sup> the importance of the primary sector, and the effect of high initial poverty rates.

We find a similar pattern in a cross‐sectional setting. Soil quality has significant and sizeable effects on poverty rates and on GDP per capita. We think that the underlying mechanism is that as environmental quality increases, agricultural and environmental output increase as well. This might be especially relevant for low income households that draw a larger share of their income from natural resources and the environment (Wunder, 2015).

Landscape restoration can reduce degradation and positively contribute to increasing environmental quality (and therefore also poverty reduction). In the Inner Mongolia region of China, Mu et al. (2013) examined NPP changes from 2001 to 2009 and found NPP increased by 21 percent. Of this increase, 80 percent of the NPP change was due to human activity – notably landscape restoration programs (returning cropland to grassland, converting desert to grassland, and better grazing management). These results 14 The coefficients for Sub‐Saharan Africa are usually larger than for the full sample specifications. An interesting idea to pursue for further research would therefore be to explore the mechanisms that lead to this result. It is hard to establish whether the effects are larger in SSA because of the extreme poverty in the region or lower irrigation. The irrigation channel could be instrumentalized using available subnational irrigation data sets, see for instance the AQUASTAT data set (Siebert et al., 2005). However, data quality is low especially in Southern and East Africa. For example, the highest indicated data quality in AQUASTAT is measured in North America with 1.03, whereas it is just 3.85 in Southern Africa (the worst measured quality is 4.00). As this means that the geospatial data in these regions rely on highly modeled estimates, we did not pursue this idea in this paper.

17