# **4 Empirical Strategy**

## **Push shocks**

Ordinary Least Squares estimates of the effect of shocks on migration in a cross-sectional data are fraught with problems. For instance, places with fertile land that is responsive to rainfall might also have more entrepreneurial people who are more likely to migrate. Similarly, places with extremely unequal land ownership might invite conflict as well as force people to migrate due to lack of economic opportunities. These characteristics, which are not always observable, will bias the OLS estimates obtained from cross-sectional data. Panel data solve this problem if such characteristics are time invariant by comparing changes in migration rates with changes in shocks within the same village over different periods.

Given the structure of my migration data, I estimate the following model to study the impact of ‘push’ shocks on migration: where _Mit_ is the migration rate in village _i_ observed in time _t_ , _γt_ represents the survey year fixed effects which absorbs any national trends common to all villages, _μi_ is the village fixed effect which absorbs all time invariant characteristics of the village, _Xit_ is the shock that affects village _i_ at time _t_ and _εit_ represents the error term. I allow the error term to be correlated between different villages within a district as well as over time. _Xit_ are either conflict intensity in the district as defined above, or normalized rainfall in very recent past (one or two years ago). The identifying variation for this specification comes from variation within village over time as well as variation across villages in a given year.

Further, my measure of rainfall shock is in standard deviation units relative to the average historic rainfall in the village and is likely to be uncorrelated with any village specific trends in any of the related observed and unobserved variables. More importantly, individuals cannot predict rainfall shocks and engage in anticipatory migration. Hence, the variation in rainfall measure is likely to be uncorrelated with the error term.

Conflict intensity could, however, be driven by village or region specific trends. This could bias the estimates of the effect of conflict intensity. However, the overall trajectory of this conflict, as discussed earlier, suggests that the two big factors that drove conflict intensity were quite unanticipated. The deployment of Nepali Army, which escalated the death toll stemmed from the massacre of the royal family and coronation of a new king who was not in the accession line. Similarly, the end of the conflict was brought together by an unlikely alliance between the mainstream political parties and the Maoists to overthrow the king.

## **Pull shocks**

The regression model in Equation (1) cannot be used to estimate the effect of ‘pull’ shocks from the destination countries, as they will be completely subsumed by the year fixed effects. To look at whether migration responds to demand from the destination countries, I estimate a slightly model: where _Mijt_ is the migration rate from village _i_ to country _j_ in time _t_ , _γij_ is the destinationvillage fixed effect, _μit_ is the village year fixed effect and _Djt_ is the recent growth rate in 14