||(0.137)|(0.179)|(0.321)|(0.270)|(0.172)||
|---|---|---|---|---|---|---|
|soil_type = 12, Spodosol|0.200*|0.395**|0.764**||-0.00456||
||(0.111)|(0.155)|(0.301)||(0.173)||
|soil_type = 13, Ultisol|0.0905|0.0799|0.117|-0.0599|0.0488|-0.0875|
||(0.0819)|(0.0783)|(0.108)|(0.125)|(0.0819)|(0.129)|
|soil_type = 14, Vertisol|0.126|0.129|0.119||0.139||
||(0.115)|(0.119)|(0.200)||(0.125)||
|Constant|10.53***|8.818***|10.17***|8.959***|4.060***|4.588|
||(0.506)|(1.076)|(2.965)|(3.369)|(1.409)|(3.301)|
|Observations|635|635|419|46|338|32|
|R-squared|0.964|0.960|0.942|0.836|0.978|0.915|
|CountryFE|YES|YES|YES|NO|YES|NO|
||Rob<br>*|ust standard e<br>** p<0.01, **|rrors in paren<br>p<0.05, * p<|theses<br>0.1|||

Aside from the positive effects of environmental quality on poverty reduction, and effect of the intensive margin, there are also interesting associations displayed by measures of the extensive margin such as land use changes. Infrastructure (road density) development shows significant positive relationships with GDP per capita and significant negative relationships with poverty ratios in the panel estimation and the cross‐ sectional specifications.

Not in line with our expectations, crop land expansion is not positively associated with poverty reduction, quite to the contrary (in both the panel and the cross‐sectional OLS specifications). The direction of the effects may be an indication that it has not been the poor who have been benefitting from land conversion (such as forest‐to‐crop conversion) in the overall sample. In fact, poverty rates might increase after areas are converted into cropland according to our results. However, much more research into the reasons for this unexpected positive correlation between poverty rates and cropland is necessary.

Urbanization (urban expansion) is significantly linked to increasing GDP per capita, and has in fact the largest coefficient among any land use change measures, by a large margin, attesting to the powerful links between urbanization and economic development. However, the effects on poverty rates are not significant. This is a puzzling observation. As we had mentioned before, only environmental quality is ‘exogenized’ and therefore all other relationships can be interpreted merely as correlative. That said, the absence of a correlation between urbanization and poverty, especially despite such strong and significant relationships with GDP per capita, suggests that urbanization is not an inclusive demographic process. On the other hand, the Stable Unit Treatment Variable Assumption (SUTVA) may be violated, meaning that it could be that those in rural areas with the most talent moved to the cities (in other provinces), contributing to economic growth there, but being absent from contributing to the economic development process in the rural origin provinces, which would have helped to reduce poverty (and not sent back home enough remittances to counteract that demographic effect).

The relative strength of the effects of the different explanatory variables on poverty need to be assessed with caution, as it is hard to grasp at what expense each of these changes happen. For example, looking at the original OLS specification reveals that although a 10 percent change in NPP is associated with a 0.4 percentage point change in poverty rates, a 1 percent change in road density only leads to a 0.3 percentage point change in poverty rates. This could indicate that improving environmental quality has larger ties to poverty reduction than improvements of road density. However, the relative costs for pursuing one over the other are not part of the modeling, and this specification does not provide causal 16