Abstract
Existing research inadequately explains the factors that drive temporary internal migration in China. Using data for 2005 drawn from 1,903 households in 43 rural villages, we calculate binomial and multinomial logit (BL, MNL) models of probabilities that an adult belongs to one of three categories of worker—on-farm, off-farm, or temporary migrant—as a function of individual and household characteristics. We control for village fixed effects, paying close attention to male/female differences. Nearly all coefficients—even for village dummies—vary significantly by sex. For two variables—age and schooling—the relationships are non-linear. There is an optimal age and amount of schooling that maximizes the probability that a worker will be employed away from the family farm. For schooling, this is low, suggesting that educated workers are underemployed. This might indicate that schooling beyond primary grades is poor quality, or at least inappropriate for the job market.
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Notes
The exception is Xia and Simmons (2004), who in lieu of a single gender dummy, include three dummy variables: Single male, single female, and married male, but no interaction terms.
The careful reader might calculate that this leaves more than 200 households without a head. The heads are lacking only because we excluded all individuals 60 years of age and older from our sample. Many of these excluded individuals head a household.
Yang and Guo (1999) estimate separate BL regressions for men and for women. With only individual/household variables, the pseudo R2 is 0.0266 for the men’s regressions and 0.1601 for the women’s. Adding four village variables (distance to a city, per capita income, population density, population growth) raises the pseudo R2 to 0.0856 for men and 0.1997 for women. The increase is greater for men than for women, but this suggests only that the four coefficients in a joint test are significant at a higher level in the men’s regression than in the women’s. One cannot conclude from this that village fixed effects are “larger” for men than for women, any more than one can conclude from looking only at t statistics that a coefficient is quantitatively important. Small coefficients, after all, can have large t statistics.
The turning point of each odds ratio can be calculated by setting the derivative of the logit equation with respect to AGE equal to zero and solving for AGE. For male off-farm work, for example, dz 1 /d AGE = 0.381 − 2*0.00463AGE, which equals zero when AGE = 0.381/0.00926, which is approximately 41 years.
Xia and Simmons (2004) rely on the variables experience and experience squared rather than age and age squared. They define experience as the number of years a person has lived following completion of his or her schooling. The other four MNL studies use the variables age and age squared.
“Both low- and high-skilled individuals are more likely to migrate but usually for different reasons: “surplus” low-skilled individuals have strong incentives to move to the city in search of a manual job they may not find in the rural area, while “scarce” educated workers may find that their human capital is better rewarded in cities than in rural areas” (Lall et al. 2006: 4).
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The authors would like to thank two anonymous referees for helpful suggestions on an earlier draft.
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Willmore, L., Cao, GY. & Xin, LJ. Determinants of off-farm work and temporary migration in China. Popul Environ 33, 161–185 (2012). https://doi.org/10.1007/s11111-011-0135-3
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DOI: https://doi.org/10.1007/s11111-011-0135-3