mobility of workers across sectors is somehow impeded. An increase in the rate of workers’ mobility into high-wage sectors over the survey period would therefore indicate decrease in the degree of segmentation. In order to see if the rate of workers’ mobility has increased over the survey period, we compare sample transition matrices across survey waves. We also estimate dynamic binary sector choice models for four states, namely, unemployment, informal sector employment, self-employment, and public sector employment.<sup>9</sup> A decrease or an increase in the mobility of workers across sectors over time should be reflected in a decrease or an increase the degree of state dependence in sector choice, the parameter of state dependence being the coefficient of the initial state in the sorting equation. It is important in this context that the parameter of state dependence we estimate measures true state persistence rather than merely reflects unobserved individual heterogeneity. Even then true state dependence could be a consequence as much of state dependence in mobility costs, taste or skills as it could be of entry barriers, of which only the latter defines segmentation.<sup>10</sup> Still, if we can assume that mobility costs and the skill and taste distribution of workers remain the same over time, we can tell whether or not the market is getting more segmented by looking at what happens to the persistence parameter over the same period.<sup>11</sup> Given that the rate of workers’ mobility is usually highly correlated with the size of sectoral earnings gaps, the change in the earnings gap itself is a second (alternative) indicator of change in the degree of segmentation. A third alternative indicator is change in the sensitivity of sector choice to earnings gaps. The less sensitive is state (or sector) choice to pay gaps, the more likely it is that mobility into the higher paying sector has been impeded. To look at these second set of indicators, we compute sector earnings differentials based on estimated sector specific earnings equations. The sensitivity of

> 9 For completeness one could also estimate a separate model of binary choice into formal private sector employment. However, the estimation would not add information to that the models of choice into unemployment, informal sector employment and public sector employment provide between them. 10 Heckman and Hotz (1985) highlight the distinction  between mobility costs and entry barriers as an important issues in testing for segmentation.

> 11 Maloney (1999) is probably the first to make use of mobility data in conjunction with post transition changes in earnings to test for segmentation.  Gong and van Soest (2002) build on this idea to jointly model earnings and inter-sectoral mobility and estimate  state dependence in sector choice as one of the test parameters of segmentation. Although we borrow from these two papers the idea of using mobility data, our goal here is less ambitious than theirs since we are not seeking to test for segmentation directly. We are in fact side stepping the task of testing for segmentation,  while asking whether our data are consistent with becoming more or less pronounced over the period of observation.

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