13 If we are to understand destination choice among migrants, then there is not only information in knowing where the migrant migrated to, but also in knowing the potential destinations the migrant did not migrate to. Information about both the chosen and foregone destinations can thus be exploited to identify the effect of the characteristics of the destination, such as distance, earnings potential, income polarization (while controlling for selectivity by controlling for the individual characteristics of the migrants). As towns are typically more nearby, with lower earnings potential and lower income polarization, the relative empirical importance of these different factors in determining why more migrants end up in towns can thus be explored.<sup>30</sup> To begin, note that internal KHDS migrants moved to 57 different districts, within which further moves can be distinguished to rural or urban areas, to arrive at a total of 78 locations. We are particularly interested in the 967 migrants who migrated to the 31 urban locations. We assume that these 31 urban locations are the potential urban destinations for our sample of urban migrants. Taking the choice of moving to an urban (as opposed to a rural) location as given, each of the migrants in the sample has thus chosen to move to one urban destination; and has therefore also chosen not to move to the 30 other potential urban destinations. To understand the factors determining that choice better, a dyadic data set was created that contains 31 observations for each urban migrant _i_ ; one observation for each potential urban destination _d_ . The dependent variable _Yid_ is a dummy equal to one if _i_ was found in location _d_ during the last survey round and zero otherwise. The correlates of _Yid_ were then examined by estimating the following equation: ��� ����� ������ ��� ���� (12) Destination choice _Yid_ will depend on the observed and unobserved characteristics of the individual (including those related to household and community circumstances), the destination (such as the standard of living the migrant would expect to achieve at destination) (denoted by vector _Dd_ ) and the relation between the individual and the location (such as distance to destination). Motivated by the extended H‐T model (equations (8) and (9)), the key variables of interest are the characteristics of the destination district (especially the income potential in their formal and informal sectors) and the relational variables (denoted by vector _Rid_ ) that are specific to the _i−d_ pair, such as distance of _d_ to the _i_ ’s baseline village.

A key econometric concern relates to selectivity bias. If the more entrepreneurial (a feature which is hard to observe) systematically move further away (say to the city because of higher earning potential) (Young, 2013; Diamond, 2016), then the estimated coefficients of _D_ will be biased. To control for this, equation (12) includes an individual fixed effect _αi_ . This also controls for all household and community characteristics at the location of origin of _i_ . To explore the differential effect of distance and location characteristics on destination choice by individual characteristics (Bryan, Chowdhury, Mobarak, 2014), interaction effects of individual characteristics such as education and poverty status with

> 30 This section draws heavily on Christiaensen, De Weerdt, and Kanbur (2016).  The application only analyzes those who have actually migrated, in order to not confound the destination choice with the migration choice. Herein, Christiaensen, De Weerdt and Kanbur (2016) follow Fafchamps and Shilpi (2013) who have used this kind of dyadic analysis before to study destination choice. We also analyze separately those who have moved to rural areas and those who have moved to urban areas.