10 It can now be shown that migration declines with _ti_ (with _i=c_ or _s_ ), but increases with _wi_ and _w0i_ .<sup>18</sup> Put differently, according to the model, in equilibrium, the size of the migration flow to a destination (city or town) depends on the number of formal (or high paying) jobs ( _Ei_ ), the wage gap between the formal and informal jobs at destination ( _wi‐woi_ ), the wage gap between the job at origin and the informal job at destination ( _wr‐woi_ ) as well as the cost of migration ( _ti_ ).

These equations give a handle on key factors that affect the number of people that end up in cities versus towns in equilibrium. Parameterization further allows to explore the empirical relevance of its insights. If empirical resolution of the right‐hand side of equations (8) and (9) yields migration patterns that correspond to those observed in reality ( _Nc, Ns_ ), then this would suggest that the model has explanatory power. It would support its use as an entry point in understanding the reasons why so many people end up in towns, while there is more to be gained from moving to the city.

Information on the population size ( _Ni_ ), the size of formal employment ( _Ei_ ) and the formal and informal wage rates or income in the three locations ( _wi, woi, wr_ ) can be obtained. Yet, empirical measures of the full migration cost ( _ti)_ (transport, settlement and jobs search costs as well as psycho‐ social adjustment) are hard to come by.<sup>19</sup> This makes it difficult to empirically verify (8) and (9) directly. Therefore, equations (8) and (9) are rewritten as a ratio: Given information on all parameters except _tc_ and _ts_ ,, assuming _wr+ti> woi_ (otherwise migration to _i_ would be zero), and assuming _tc>ts_ , the ratio of _tc/ts_ needed to solve (11) can then be derived and its empirical plausibility assessed. This will give a sense of the empirical importance of the cost differences between migrating to cities and towns in understanding why many more people moved to towns than to the city.

The long running KHDS panel provides an ideal opportunity to do so. The spatial and sectoral distribution of its population by 2010 could be seen as the long run equilibrium outcome of the migration process, represented by the model. Furthermore, virtually everybody of the KHDS sample living in the rural areas in 2010 worked in the informal sector (only 3.5 percent relied on a formal wage

> 18 By holding the number of formal or high paying jobs ( _Ec, Es_ ) fixed in each location, and thus independent of city size ( _Nc, Ns_ ), we abstract from the potential of agglomeration economies, and thus faster economic growth and potentially larger formal employment generation, induced by rural‐urban migration. If these effects are larger for cities than for towns, cities may in equilibrium attract more migrants than towns, which could affect the income distribution. While the evidence for the developed world clearly indicates that larger urban centers (by population size) also enjoy faster economic growth (Duranton, 2015), such a relationship has not been empirically established for urban centers in the developing world. In fact, if anything, as indicated above, the relationship may even be negative (Frick and Rodriguez‐Pose, 2016).

19 While transport costs are relatively easy to obtain, the costs of migration go well beyond that. They also include the monetary costs related to resettlement, jobs search as well as the psycho‐social costs associated with adjustment and being away from home.