that individuals can observe in other people and can use to compare their own labor market status with the status of others. This is evidently a normative and also a conservative choice which may be seen as a common minimum denominator for all individuals. It is also a standard approach to the choice of variables for the selection of the reference group in relevant papers (see Van Praag _et al_ 1979, Woittiez and Kapteyn, 1998, Clark and Senik, 2010). We may argue that each individual has a different reference group and that many more factors come into play when people compare themselves with others. However, it is also true that certain criteria for the selection of the reference group are rather standard and apply to most people and that it would be arguably hard to construct individualized preference models. By being conservative, we hope to capture the standard and essential comparison criteria used by most people.

Table 6 reports results from the probit regressions used to estimate the probability of individuals being employed, i.e., being in the ‘good’ labor market state. Four different models are estimated: with all individuals (including and excluding the gender variable – columns 1 and 2), with males only (col. 3) and with females only (col. 4). Here we see how the researcher can make different assumptions about the reference group. If we want to compare the relative deprivation of men and women, we can either assume that both genders compare themselves with both genders, or alternatively that men compare themselves with men and women compare themselves with women. In the first case, the first-step estimation is carried out jointly for men and women as in column 2. In the second case, the first-step estimation is carried out separately for men and women as in columns 3 and 4.

A first insight into the gender divide can be gathered by comparing columns 1 and 2. The gender variable is clearly very significant in explaining labor status and its inclusion increases the pseudo-R squared dramatically - from 0.07 to 0.33 - indicating that gender is the major predictor of employment among the variables considered. In the two-steps model proposed, however, we have to remove the gender variable if our objective is to compare men and women. This is what we did in columns 2-4. Here we can see that - by splitting the sample across genders - the pseudo-R squared increases very significantly for both genders from, 0.07 to 0.15. We can also see how differently the same variables perform for men and women. For example, having primary education as compared to no education does not improve a female’s employment status but improves the male’s. Being married improves the likelihood of a good labor market status for men but reduces the likelihood for women. And urban women do not seem to have a comparative advantage in accessing a good labor status as compared to rural women while urban men do better than rural men. Therefore, changing assumptions about the reference group by shifting from universal to within-group reference group can make a very substantial difference in the first-step estimation and, consequently, in assessing relative labor deprivation.

The probit estimates from models 2, 3, and 4 are then used to construct individual relative labor deprivation indices as described in equation (4). Aggregating these individual indices we can obtain the societal relative deprivation measure as in equation (5).

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