Venables, Hugh Wenban-Smith, three anonymous referees, and the participants at the LSE SERC junior seminar, the Urban Economics Association conference in New York and the ISI Annual Conference on Economic Growth and Development in Delhi, who provided useful comments. We are also grateful to Petia Topalova and Pingali Parthasarathy Rao for sharing some of the data and to the Tata Institute of Social Science in Mumbai for their kind hospitality. Financial support by the Chronic Poverty Research Centre and the India Observatory at LSE is gratefully acknowledged. The views expressed here do not necessarily reflect those of the OECD or the Bank of Italy. The other usual disclaimers also apply. A supplemental appendix to this article is available at http://wber.oxfordjournals.org/.

1 In fact, the actual poverty line used by Ravallion et al. (2007) is $1.08/day; to simplify, we refer to it as the $1/day poverty line.

2 On the basis of calculations from UN (2008) data, developing countries are expected to become more urban than rural in 2018, as are least developed countries in 2045.

3 This estimate is based on the authors’ calculations using UN (2008) data.

4 The migrant’s family often provides economic support (monetary or in-kind) to the migrant during his initial stay in the urban area. This support, intended to cover the fixed costs of migration, can be interpreted as an investment whose main return is the counter urban-to-rural remittance flow that is received afterward (Stark 1980). This urban-to-rural remittance flow can somewhat reduce the net resources transferred to rural areas by urban workers.

5 To the extent that rural-urban migration is concentrated among the most productive rural workers, this may counter the productivity-enhancing effect of migration. However, the incentive for highly productive agricultural workers to move from rural to urban areas is partly softened because the skills that make them productive in agriculture are not easily transferable to the urban sector.

6 In addition, an expanding city can benefit agriculture productivity in the surrounding rural areas through spillover effects in technology and marketing (Dore 1987; Allen 2009).

7 Although each survey was conducted over two years, we refer to them with the first of the two years. 8 The “thick” surveys are conducted approximately every five years and sample a higher share of households than do the “thin” surveys, thus allowing inferences at the district level. We do not use the other “thick” survey for the period, the 43rd round (1987–88), because we only use census data for the population variables, which do not have a natural match with the 1987 poverty data.

9 In particular, Topalova (2010) follows the adjustment made in Deaton (2003a and 2003b) and imputes the distribution of total per capita expenditure for each district from the households’ expenditures on a subset of goods for which the new recall period questions were not used. The poverty and average consumption measures were derived from this corrected distribution of consumption from the detailed consumption schedule of the surveys.

10 Results are available upon request.

11 Available at www.bsos.umd.edu/socy/vanneman/districts/codebook/index.html.

12 The original source of these data is the Government of India, Directorate of Economic and Statistics, Ministry of Agriculture and Cooperation.

13 This finding is consistent with Topalova (2005), who finds limited labor mobility across Indian regions between 1983 and 2000.

14 In fact, Delhi and the urban Bangalore districts are automatically dropped because they do not have rural areas. In the following, we present robustness tests showing that the exclusion of Bangalore and Chennai increases precision but does not affect the main results of the analysis.

15 All of this evidence appears to be roughly consistent with Fafchamps and Shilpi (2003), who find that in Nepal, the effects of proximity on rural areas decrease beyond a four-hour radius (in travel time) around the cities. Using the boundaries of the Indian districts as in 1987, the average district size in our analysis is approximately 7,300 km<sup>2</sup> . If we approximate the district with a circle, a city located in its center would be approximately 50 km from the boundary of the district. It is plausible that in several districts, this distance could be covered in three to four hours on rural Indian roads during the period considered.

16 Appendix S1 (available online at http://wber.oxfordjournals.org) contains a detailed description of all of the variables included in the empirical analysis.

17 This does not consider the possibility of interdistrict migration or of urban-rural migration. The latter is relatively unimportant in influencing the rural-urban split of the population in a country such as 36