India. The stock of urban-rural migrants represented less than 1.4 percent of the total population in the majority of Indian districts in 1991, with a mean equal to 1.7 percent (based on the Indian districts database at the University of Maryland; see below). The interdistrict migration represents a substantial share of the total migration, particularly rural-urban migration. In 1991, it accounted for less than 34 percent of the total migration for the majority of Indian districts (with a mean equal to 37 percent). However, the empirical analysis below rejects the relevance of this type of migration in determining rural poverty.

18 In fact, the National Sample Survey contains socio-demographic information on intradistrict ruralurban migrants, but we cannot use this information for two reasons. First, the number is not large enough to be representative at the district level. Second, the survey only asks about migration in the last 365 days, whereas we would need information on migrants in the last five years. Although we cannot use these data in the regressions, we nevertheless refer below to the national-level expenditure comparison between rural-urban migrants and stayers (Singh 2009).

19 This pattern receives strong support in the empirical analysis that follows, which suggests that rural poverty is negatively associated with the rural young adult population and with the share of literates in this group.

20 This assumption is supported by the results of regressing the 1981–91 change in the urban population in the 15–34 year age group ( ∆ _P_ 15 _U_ − 34 ) on the change in the rural population in the same age group ( ∆ _P_ 15 _R_ − 34 controlling for changes in the district’s total population and the total population in 1981). The

> coefficient of ∆ _P_ 15 _R_ − 34 is not statistically different from −1, indicating that changes in the rural population are reflected in mirrored changes in the urban population through either rural-urban migration or a rural-to-urban change in the status of villages (results available from the authors upon request).

21 In any instance, the results are not sensitive to the change in the time lag (i.e., applying a one-year lag as well) (results available upon request).

22 We show these tests only for the entire period (1983–99) and for the specification with the total urban population, but the results of these tests are analogous for the first period (1981–93) and the nonpoor urban population (results available upon request).

23 We are aware that the quantity of agricultural goods produced in a district might also be endogenous to rural poverty even after controlling for a district fixed effect. However, this concern should not be significant for two reasons: first, the degree of endogeneity should be small given the large number of exogenous factors that affect agricultural productivity (e.g., climate); second, the variable is an accessory control that enters only a few specifications with almost no effect.

24 This nonsignificant result is due to the negative and significant effect of the total cultivated area, that is, the denominator, which compensates for the negative effect of the total production value, that is, the numerator (results available upon request).

25 His finding emerges in the context of the response of rural schooling decisions to the possibility of employment in urban areas, which tends to be the largest amongst landless households.

26 It is worth noting that the IV estimation can also correct eventual biases arising from errors in the measurement of the urban population. This correction occurs if the measurement error of the instrument and that of the instrumented variable are independent.

27 Not shown here; results available upon request.

28 To obtain the covariance matrix of orthogonality conditions of full rank, which allows the calculation of clustered standard errors, year-state dummies are “partialled out,” and their coefficient is not calculated. By the Frisch-Waugh-Lovell theorem, in IV, the coefficients for the remaining regressors are the same as those that would be obtained if the variables were not partialled out (Baum et al. 2008). 29 For each of the 24 such goods for which we have data, we regress the district’s land cultivated with that crop over the urban population, all other control variables and the district and state-year fixed effects.

30 The census divides employment into four broad sectors: agricultural laborers, cultivators, household industry, and other nonagricultural workers. We use the last category to construct the variable, although the subsequent results are also very similar when using the total nonfarm employment share, obtained by adding the household industry category.

31 In fact, the coefficient becomes positive and very small but with a large standard error.

32 Results available upon request.

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