We thank for their excellent comments Todd Sorensen, Måns Söderbom, two anonymous refereess, and the participants to seminars at the University of Gothenburg and UC Riverside. A more detailed version of this paper is available as CEPR working paper DP7407. We are very grateful to Prem Sangraula and the Central Bureau of Statistics of the Nepal whose assistance with the data was essential for the success of this endeavor. Financial support for this research was provided by the World Bank.
Determinants of the Choice of Migration Destination*
Article first published online: 30 MAY 2012
© Blackwell Publishing Ltd and the Department of Economics, University of Oxford, 2012
Oxford Bulletin of Economics and Statistics
Volume 75, Issue 3, pages 388–409, June 2013
How to Cite
Fafchamps, M. and Shilpi, F. (2013), Determinants of the Choice of Migration Destination. Oxford Bulletin of Economics and Statistics, 75: 388–409. doi: 10.1111/j.1468-0084.2012.00706.x
- Issue published online: 12 APR 2013
- Article first published online: 30 MAY 2012
- Final Manuscript Received: July 2011
This paper examines migrants’ choice of destination conditional on migration. The study uses data from two rounds of Nepal Living Standard Surveys and a Population Census and examine how the choice of a migration destination is influenced by various covariates, including income differentials across possible destinations. We find that migrants move primarily to nearby, high population density areas where many people share their language and ethnic background. Better access to amenities is significant as well. Differentials in average income across destination districts are significant in univariate comparisons but not once we control for other covariates. Differentials in consumption expenditures are statistically significant but smaller in magnitude than other determinants. It is differentials in absolute, not relative, consumption between destination districts that are correlated with the destination of work migrants. Except for the latter, results are robust to different specifications and datasets.