14 destination characteristics were further added. _ɛid_ is an error term.<sup>31</sup> The coefficients are estimated using Ordinary Least Squares.

To capture high and low income potential (formal and informal employment) in the destination, Christiaensen, De Weerdt and Kanbur (2016) construct individual wealth indices in each of the destination districts, using the 2002 census. The 90th percentile value of the constructed wealth index at destination d (P90 wealth) and the 10<sup>th</sup> percentile are used to reflect the high and low income potential in each destination. Distance is the distance (in 1,000s of km) of the district to the baseline community (expressed in logs).

The results from Table 3 confirm the importance of distance for destination choice. The further away from the destination, the less likely it is that a migrant will end up there. Migrants to cities travel on average five times further than migrants to secondary towns (roughly 1,000 km versus 200 km). That difference represents 1.6 log points, which lowers the likelihood of migration by about 5 percentage points. The negative distance effect is only slightly mitigated by education (column 2), but not by wealth (column 3), suggesting that the effect of distance holds across the sample, irrespective of the individual characteristics. Town migrants also have only one year less of education on average than city migrants.

Higher earning potential at the lower end of the income distribution of the destination also exerts a positive influence. Contrary to the model’s predictions, the coefficients on higher top incomes (reflecting income polarization) are negative, but not statistically significant. A Shapley decomposition further shows that for urban migrants, distance accounts for 65% of the explained variation. Clearly, the econometric results suggest that at least in this sample, distance, a broad (and exogenous) proxy for the cost (and barrier) to migrate, plays a role in understanding migrant destination choice.

- 5 Unpacking Distance through Qualitative Investigation

To triangulate the findings, a selection of the respondents to the KHDS survey were also approached to talk about their migration experience in their own words (Ingelaere, et al., 2018). This life history method provides a rich complement to the more conventional economic approaches presented above and helps probe further into migrants’ motivation in choosing their destination. From the overall KHDS sample, 75 migrants were interviewed from six purposively selected villages that were pairwise similar in their socio‐economic characteristics in the early 1990s, but that displayed quite different migration patterns.

The overall insights from this qualitative investigation as to why more people may end up in towns than in cities, are well illustrated through the story of a single migrant, called R, here, for

> 31 Fafchamps and Shilpi (2013) point out that, by construction, each migrant is only able to choose one destination, which raises a concern about negative correlation between error terms of observations from the same individual for the selected district and the non‐selected districts and a positive correlation between all non‐selected districts of the same individual. We follow these authors by clustering the standard errors at the individual (or higher) level to address this concern.