Abstract
This paper examines the causes and effects of migration in Uganda. It is the first to do so using household level data. The data are unusually detailed, chronicling the movements of household heads from birth to time of enumeration. Using Poisson regression analysis, we are able to investigate the characteristics that influence the number of moves undertaken by the household head. Using the Poisson results, we find that more moves in search of employment lead to less wealth accumulation. Although this result appears to be counterintuitive, it supports theories of migration in less developed countries. We also find that households headed by men are in general less likely to move than those headed by women, which could reflect differential ownership rights implicit in Ugandan law. This finding is reversed, however, when moves are limited to those in search of employment. This suggests that men, being the primary providers when they are the household head, are likely to move more in search of employment.
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Notes
Suppose a migrant has some probability, p, of obtaining a job at his destination with a reward of $R, and a probability, 1 − p, of being jobless with a cost of $ − C (drawing down his wealth). Alternatively, he has a certain reward of $S if he does not move. An actuarially unfair move is then defined as one where pR − (1 − p)C < S. That is, the migration gamble is unfair if the expected reward from migrating is less than the wager (the $S sacrificed by moving).
Katz and Stark also show that risk-averse individuals may engage in an actuarially unfair move for other reasons. Namely, capital market imperfections such as asymmetric bargaining power in loan negotiations may induce someone to migrate to avoid undervaluation of the wealth available as collateral.
Goddard et al. (1975), p. 27
Uganda National Household Survey II, Data Documentation
For more detail on the exposure term and the Poisson regression in general, see Cameron and Trivedi (1998).
Primary includes post-primary vocational/certificate training, and olevel includes post-secondary vocational/certificate training.
The likelihood ratio χ 2 is the nonlinear equivalent of the F statistic in a linear regression.
For perspective, in Table 3, $1 = 1,738 Ushs on January 1, 2003.
The total number of moves undertaken is not a perfect instrument. It is correlated with jobmoves (r = 0.503) because jobmoves is a subset of total moves. So if jobmoves is correlated with the error term, total moves will be as well. But Table 2 suggests that any remaining endogeneity bias would be lessened because 69.5% of our sample did not undertake a job move. In other words, total moves would have a smaller correlation with the error term because 69.5% of them are for reasons not likely correlated with the error term in a regression that explains wealth.
Interestingly, using total household wealth as the dependent variable yields a positive and significant coefficient for hhnum, suggesting that total wealth increases less than proportionally with additional household members.
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Herrin, W.E., Knight, J.R. & Balihuta, A.M. Migration and Wealth Accumulation in Uganda. J Real Estate Finan Econ 39, 165–179 (2009). https://doi.org/10.1007/s11146-008-9111-9
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DOI: https://doi.org/10.1007/s11146-008-9111-9