Madheswaran (2011) show that age and age-squared are, respectively, positively and negatively related to the migration decision. This may be explained by the fact that age, which provides a proxy for work experience, is indicative of an individual’s earning potential but subject to diminishing returns. This quadratic formulation of age may also reflect several demographic considerations. For example, younger men may wish to use their home as a base while searching for work, while older men may be close to retirement or may choose to live at home to look after elderly parents. Marital status is also a relevant factor, as the recently married are more likely to migrate than their unmarried counterparts. Completion of below primary education, which corresponds to a minimal level of literacy, positively affects the likelihood of migration. And as the level of education increases, the tendency to migrate intensifies. The completion of secondary schooling has a particularly strong effect.

Household characteristics matter too and are perhaps more important than individual ones, according to the relevant literature on India. While a migration decision at the individual level is driven by the wage differential between the destination and the origin, other household considerations are also likely to matter for a family to decide who and how many should migrate to increase overall household welfare. Bhattacharyya (1983) finds that migration flows are governed by decisions at the family level rather than the individual level in the case of India.

The paper uses a panel data regression framework to explore the association between the decision to migrate and a range of household characteristics in the IHDS.<sup>6</sup> The sample under consideration includes households with no nonresident members in 2004-05 and the same households either with or without nonresident members in 2011-12. Therefore, the dependent variable is always zero in 2004-05 and binary in 2011-12, with a value of one if a household had at least one nonresident member in 2011-12 and zero otherwise. Following the literature on the subject, the decision to migrate at the household level is regressed on a range of potentially relevant variables in the IHDS. This includes household consumption, household size, social group, exposure to mass media, local trust and conflict, and a rural/urban dummy variable. It also includes household head characteristics (age, age-squared, sex, marital status, occupation dummy variable, industry dummy variable, and education level dummy variable) that may influence the migration decision. Last, but not least, to control for any unobserved time-invariant household characteristics, household fixedeffects are also included (see Appendix Table 2).<sup>7</sup> This analysis based on the IHDS shows that household income has a strong association with the probability of having a migrant member, and this conforms to the broader migration literature. The coefficient on the household consumption variable is positively and statistically significant, which suggests that household consumption (which is a proxy for household income) is positively associated with migration. The fact that additional income can induce migration is borne out by randomized experiments, for example, in rural Bangladesh. Bryan et al. (2013) show that an incentive of US$8.50 to migrate out of rural areas during the lean agricultural season offered to a random set of families induced 22 percent of the households to send a seasonal migrant, and their consumption increased significantly. In the context of international migration, some studies

> 6 The association need not be indicative of causality because some of the determinants of migration, in turn, might also be impacted by the migration decision.

> 7 Some of these household head characteristics, while highly correlated between the two periods, are not entirely timeinvariant and therefore do not drop out of the equation upon the inclusion of household-fixed effects.

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