Abstract
This paper examines China’s changing internal labor migration patterns between 1990 and 2005 as its household registration (hukou) system evolves. We document a drastic increase in the size of the migrant population, along with significant composition shifts in migrants’ characteristics, and geographic and employment distributions. Recent migrants are on average older, more educated, more likely to be female, more likely to be married, and more likely to have an urban hukou. Regression analysis shows that migration rates increased substantially during this period for all individuals regardless of their education, gender, age, marital or hukou status. By employing a simple migration location choice model, we investigate the relationship between hukou policy and migration behavior. We find that larger and more developed cities are more attractive to migrants but tend to set more stringent hukou restrictions. Rural migrants are significantly more deterred by hukou restrictions relative to urban migrants. These findings suggest that institutional factors, such as the hukou system, are important for understanding the observed patterns in China’s labor migration.
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Notes
All references to migrants in this study include individuals between the ages of 16 and 65, who live in counties other than where they register their hukou.
“Blue-stamp” urban hukou is granted by local authorities and is valid only locally. It is distinguished by a blue stamp, as compared to a red stamp that is carried by a formal urban hukou on the hukou registration book.
To obtain urban hukou, farmers have to give up their land-use rights, which may provide more financial benefits in an urbanizing region. Critics of this program argue that the government unfairly appropriates farmers’ property during the process.
We use a 1% sample of the 1990 census, a 0.095% sample of the 2000 census, and a 20% sample of the 2005 population survey. In the 2005 mini-census, certain provinces (such as Guangdong) were oversampled, and therefore the provincial population shares differ from the aggregate population statistics. To correct for this, we reweight the 2005 sample such that the population distribution across provinces after reweighting is consistent with the national and provincial statistics reported in the statistical yearbook for the year of 2005. More specifically, let \({n}_{sample}^{j}\) be province j’s population share implied by the 2005 population survey and \({n}_{agg}^{j}\) be province j’s population share reported in the statistical yearbook, we assign a weight \(w^{j}={n}_{agg}^{j}/{n}_{sample}^{j}\) to all individuals in province j in the 2005 sample. The reweighting does not change the descriptive patterns or empirical results in any significant way.
Migration is also commonly defined as the separation of hukou and residence across township boundaries. The 2000 and 2005 surveys kept track of whether a respondent registered his/her hukou in the township and county where they lived, but the 1990 survey only had information on whether a respondent lived in the county of their hukou registration. Therefore cross-county non-hukou migration can be defined most consistently over time. In addition, for those moving within a county or district, especially urban residents, some of them may be just living in a different neighborhood for work or for school and they may have nothing to do with migration.
In Appendix Table 10, we show changes in numbers of non-hukou, cross-township migrants over time. In 1990, only cross-county migration information was available, but the number of cross-township migrants was 106 million in 2000 and 119 million in 2005, much higher than the cross-county migration size presented in Table 1.
Using a cross-sectional migrant survey data in 2008, Ge (2018) shows that rural-to-urban migrants in the sample on average resided in cities for 8 years.
According to a 2013 report from the All-China Women’s Federation (2013), 30 million children were left behind and living in the coutryside without their parents. In the censuses and population survey, only women report the number of children they have. There is also no direct information on whether a respondent lives with his/her spouse or children in the data. We have attempted to analyze whether married migrants were traveling with their spouses by using the information on each respondent’s relation to the household head. As such, we limit this analysis to respondents who list themselves as the household head or the spouse. Furthermore, only a subset of all household members were interviewed for a large fraction of households in 2005, and thus we limit our analysis to households in which all household members were interviewed for the 2005 sample. For this sample of households, the proportion of married migrants living with their spouses has been roughly constant at 85% between 1990 and 2005, and female married migrants are more likely to live with their spouses than male married migrants.
The 1990 to 2005 surveys ask migrants about their reasons for coming to their current location. In all three years, most migrants migrate for work related reasons. However female migrants are much more likely than male migrants to move for family reasons such as moving with family or to be with relatives or moving through marriage, with 47%, 22% and 30% of female migrants moved for family reasons in 1990, 2000, and 2005, respectively, compared to 11%, 6% and 11% of men in those three years.
We group electricity, gas and water in the manufacturing sector. The basic service consists of wholesale and retail trade, hotels and restaurants, transportation, storage and communications. The advanced service sector consists of financial services and insurance, public administration and defense, real estate and business services, education, health and social work and other services.
We should interpret the regression results in Table 6 as correlations between individual characteristics and the propensity to migrate, instead of the causal effects of demographic characteristics on the decision to migrate. There may exist omitted variables that are correlated with both migration decision and individual characteristics, and migration decision may affect individual education and marriage outcomes.
More specifically, we use the apc-ie command in stata to estimate the model in Eq. 2 by using the intrinsic estimator method. An alternative method to estimate the age, time and cohort effects is to impose some explicit restrictions on one of the effects. For example, Deaton and Paxson (1994) identify age and cohort effects by imposing the constraint that the year effects must add up to zero and be orthogonal to a time trend. Yang et al. (2004) compares parameter estimates and model fit statistics produced by the two methods.
For example, (r, f, o) = (0, 0, 0) corresponds to males with urban hukou and below age 30.
Bao et al. (2011) use two alternative measures of hukou restrictions: (1) the fraction of previous migrants who secured a local hukou; and (2) the fraction of previous migrants who secured a local hukou and local employment. We believe our measure is more likely to reflect the actual policy differences across cities, as we do not rely on realized (migration) outcomes that respond to policy differences.
All regressions are weighted by city population in 1995. The unweighted results are qualitatively similar.
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Colas, M., Ge, S. Transformations in China’s Internal Labor Migration and Hukou System. J Labor Res 40, 296–331 (2019). https://doi.org/10.1007/s12122-019-9283-5
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DOI: https://doi.org/10.1007/s12122-019-9283-5