crucial for agricultural production. A one standard deviation increase in monsoon rainfall in a district improves the yield of rice by 2.7 percent, improving total production by 4.5 percent and total rice cultivated area by 1.8 percent. Similarly, maize production increases by 5.9 percent and area of cultivation by 6.8 percent in response to one standard deviation higher rainfall in monsoon<sup>15</sup> .

Consequently, increased rainfall in the monsoon months increases farming income and consumption of farming households. I investigate the impact of rainfall directly on measures of income and consumption using three waves of cross-sectional surveys conducted between 1995 and 2010. I find that one standard deviation of rainfall increases farming income by Rs. 2,400 (about 16 percent of total farm income in 2010) and increases consumption in the subsequent year by almost the same amount (Rs. 2,300 or about 7 percent of total consumption in 2010) for households who do not receive any remittance income. Details of this exercise are described in Appendix B.

## **Data on rainfall**

The rainfall data that I use for this paper are provided by the Department of Hydrology and Meteorology (DHM) for years 1972 to 2010. The dataset contains average daily rainfall for each of the months from 1972-2010 collected from over 300 rainfall stations throughout the country. The distribution of these stations is shown in Figure 3. I interpolate these rainfall measures to each of the 452 villages in my dataset using inverse of distance from the stations as weights. For each of the villages in my sample, I compute normalized monsoon rainfall in standard deviation units using historic (spanning 1972-2002) mean and standard deviation of monsoon rainfall in the village. The measure that I use for my analysis is the normalized monsoon rainfall in the year preceding the survey.

There is considerable variation in rainfall between years as well as within a given year. Figure 5 plots the histogram of normalized rainfall for the three survey years. Within each year, the range is well over 2 standard deviations and across the three years, the range is more than 4 standard deviations with considerable overlap between the years. From this graph alone, it looks like rainfall is trending down in the 2000s, but this is driven by exceptionally high rainfall around year 2000. The trend does not hold if rainfall distribution from earlier periods (years, 1981 and 1991) is added.

# **3.3 Conflict: The Maoist insurgency**

When international migration from Nepal began to increase drastically in late 1990s and early 2000s, the ongoing Maoist insurgency took an unexpectedly sharp and violent turn. This led experts to believe that the conflict, which took over 13,000 lives and displaced many more, also pushed individuals outside the country (CBS, 2006; The World Bank, 2011, for instance). In this subsection, I discuss the context of the conflict and my data source.

The Maoist insurgency began, and gained momentum, as an anti-feudal and anti-elite movement. The insurgency, or the “People’s War” as they call it, formally began on February 13, 1996 by a splinter faction of the leftist communist parties that was dissatisfied with the lack of

> 15Author’s calculations from the historic rainfall data and historic production data for each of the districts for years 1975-2000. The estimated regression includes district fixed effects as well as year fixed effects. The identifying variation comes from geographic variation in rainfall measures within a given year and also intertemporal variation in rainfall within a given district.

11