fixed location-specific effect. As for the last term ( εij + φij ), it is an error independently distributed across households and has zero mean, so it does not affect the estimation.

Models A, B, C and D are estimated as Probit regressions using data from the 61<sup>st</sup> (2004-05) and 68<sup>th</sup> (2011-12) rounds of the NSS employment and unemployment surveys. Observations are at the individual level, but some variables (those indexed by “ _j_ ” as opposed to “ _ij_ ”) need to be constructed at a more aggregate level.

The potential misclassification of urban areas as rural ( Gj ) is measured at the district level, as the difference between the true share of the urban population in the district the household lives in and the urban share estimated based on the NSS. The true urban share is computed on the basis of Census data for 2001 and 2011, treating all villages with more than 5,000 inhabitants as urban, to take into account the fact that the Census uses a rather restrictive definition of urban areas compared to standard international practice.

To capture the diversity of job opportunities in the local labor market, the employment variable ( Lj ) is computed at the substratum level (below district). An innovation of this paper is the approach used to construct indicators below the district level and at the level of the village or town, along six ranks in the rural-urban gradation.

Both the 61<sup>st</sup> and the 68<sup>th</sup> rounds of the NSS use the Census 2001 as the sampling frame for rural areas.<sup>5</sup> The rural sample is representative at the sampling substratum (below district) level. Linking the Census to the NSS at the substratum level allows us to reclassify rural areas in every district based on the average village size of the substratum. By doing so, we can construct employment variables for each substratum for rural areas and thereby control for the local labor market structure. We reclassify rural areas into three ranks based on the average population size of a village in a substratum: 0-999, 1,000-4,999 and 5,000 and above. We are able to match 571 districts in the NSS 61<sup>st</sup> round and 595 districts in the NSS 68<sup>th</sup> round with the Census 2001 districts.<sup>6</sup> This procedure cannot be replicated for the other three ranks considered in this paper, as the NSS uses the Urban Frame Survey (UFS) for urban areas, instead of the Census. Moreover, in urban areas the NSS data is not representative below the district level. For the urban sample we therefore use the population size of the location (below 50,000, between 50,000 and one million, and above one million) to attribute the rank, but construct the employment variables at the district level.

The description of the variables used in the regressions and their descriptive statistics are in Tables 2 and 3 respectively. As noted there, local employment ( Lj ) is disaggregated by type of

> 5 The exception is Kerala, where the panchayat wards are used as the sampling frame.

> 6 For the districts that were created after 2001, we merge the data of the new district with its parent to ensure comparability over time. Also, due to missing substrata information we drop two states (Delhi and Nagaland) and two Union Territories (Daman and Diu and Andaman and Nicobar Islands).

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