within the villages increased or decreased compared to five years ago. Information was also collected on whether the time to collect firewood from forests increased or decreased compared to five years ago. If forests were depleted faster than they were replenished through afforestation or regrowth, then given the reliance of these rural households on firewood as a primary source of energy, it is imaginable that the time to collect firewood would increase. In order to test the effect of remittances on forests that we found through our satellite data, we run a probit model with a binary variable indicating the increase and decrease of forest area as well as time to collect firewood. We restrict attention to responses in 2004 and run these specifications using 2004 levels as well as the change from 1995 to 2004 in the continuous independent variables. The marginal effects are reported in Table 8.

Controlling for the change in population, rainfall, road density and the presence or absence of community forestry user groups in the villages, we find that the remittances have a significant negative effect on the probability of forested area declining in villages. Similarly, point estimates also seem to indicate that remittances are positively associated with forest area increasing and time to collect firewood decreasing compared to five years ago. This gives us additional corroborative evidence on our earlier finding that rural economic growth caused by remittances may have ended up having a positive effect on forests.

An additional dimension that needs to be addressed given the time period we are looking at in Nepal is the possible biases due to conflict. Nepal experienced a Maoist insurgency that escalated into a low-level civil war between 1996 and 2004. A total of 13,000 people lost their lives and many more were displaced. To the extent that the intensity of this conflict was correlated with migration rates, the attendant inflow of remittances and forest area, failure to account for this may bias our results. In particular, we may be attributing to remittances what could actually be driven by conflict. There have been numerous efforts to study the proximate causes of conflict in Nepal and the overt political mobilization notwithstanding, there seems to be a general agreement that intensity was the highest in poorer, underserved areas (see Do and Iyer (2010) and Murshed and Gates (2005) for example).

However, in order for the unobserved conflict variable to be driving our results on remittances, it would have to be the case that it is either positively or negatively correlated with both remittances and forests; it cannot be positively correlated with one and negatively with the other. If it were, then the omitted conflict variable would actually be biasing our finding for remittance downwards. As it happens, this is something we can test for explicitly using a crude, albeit reasonably acceptable measure of conflict at the district level. Conflict in Nepal was manifest in three main forms: deaths (targeted killings as well as those killed in crossfires); abductions and ’disappearances’, and; displacements from home villages. A normalized weighted average of the frequency of each of these incidents can serve as an index of conflict intensity. We present the correlation between remittances and forests on this measure of in Table 9. The results show that conditional on the size of 26