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Temporary Migration and Savings Rates: Evidence from China

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Abstract

We study the effect of temporary migration on migrants' savings rates in China. This is done by developing a simple theoretical model and conducting empirical analyses using propensity score matching and two-stage least square models; the latter addresses an endogeneity problem associated with the migration intention variable. Data used in this paper are primarily from the 2017 China Migrants Dynamic Survey. Results show that temporary migration has a significant effect on migrants' savings rates. On average, migrants with a temporary intention save 3.41% points more than their permanent counterparts. Precautionary motives, permanent income, and asset specificity are the potential mechanisms affecting savings rates. A series of robustness checks show that our results are robust. Results have implications for rural and urban development and the structuring of future social safety net programs in China.

Resume

Nous étudions l'effet de la migration temporaire sur les taux d'épargne des migrant·e·s en Chine. Nous réalisons cela en développant un modèle théorique simple et en effectuant des analyses empiriques grâce à la méthode d'appariement des scores de propension et à des modèles des moindres carrés en deux étapes ; ce dernier répond à un problème d'endogénéité associé à la variable de l’intention de migration. Les données utilisées dans cet article proviennent principalement de l'enquête dynamique sur les migrant·e·s en Chine, conduite en 2017. Les résultats montrent que la migration temporaire a un effet significatif sur le taux d'épargne des migrant·e·s. En moyenne, les migrant·e·s qui prévoient une migration temporaire épargnent 3,41 % de points de plus que leurs homologues qui migrent de façon permanente. Les motifs de précaution, la permanence des revenus et la spécificité des actifs sont les mécanismes potentiels affectant les taux d'épargne. Une série de contrôles de robustesse révèle que nos résultats sont robustes. Les résultats ont des implications pour le développement rural et urbain et pour la structuration des futurs programmes de protection sociale en Chine.

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Fig. 1

Data source: https://databank.worldbank.org/reports.aspx?source=2&series=NY.GNS.ICTR.ZS&country=CHN#

Fig. 2

Data source: 2017 CMDS

Fig. 3
Fig. 4
Fig. 5

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Data availability

Data used in this paper are freely available from the corresponding author.

Notes

  1. The OECD (2008) estimates that, depending on the countries and time periods considered, 20–50% of immigrants leave the host country within the first 5 years after arrival. In 2011, foreign-born outflows to the inflow of migrants stood at 21%, 41%, 64%, and 76% in Australia, the United Kingdom, Germany, and Spain, respectively (see Dustmann and Görlach 2016).

  2. Date source: China Statistical Yearbook 2019, available at http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm.

  3. https://data.worldbank.org/indicator/NY.GNS.ICTR.ZS?locations=CN.

  4. Date source: China Statistical Yearbook 2019, available at http://www.stats.gov.cn/tjsj/ndsj/2019/indexch.htm.

  5. The real earning amount may be different. However, our main results would not change.

  6. The per capita income of urban residents is about three times that of rural residents in China.

  7. Survey can be accessed here: http://www.chinaldrk.org.cn/wjw/#/home.

  8. We exclude observations from Xinjiang Construction Corps. We also exclude migrants who moved into cities for joining the army, going to college, or visiting their friends and relatives. We have also removed observations with outlier values, for instance, observations with zero household consumption and income.

  9. This value is larger than 3485 yuan reported by the National Bureau of statistics in 2017; because we have adjusted the household income by including OECD (2008) board allowances provided by migrants’ employers. Without room and board allowances, the monthly household income per capita is 3553 yuan.

  10. Blue-collar workers refer to the manual or industrial laborers working in the primary industries of mining, construction, manufacturing, and service.

  11. The proportion of married migrants accompanied by a spouse in the city is 84.19%.

  12. If we do not consider the perceived monetary values provided by employer, migrant consumption will be underestimated, as shown in robustness checks in Table 18. The saving amount gets impacted the same way whether migrants get free room or board or a cash allowance.

  13. In the result section, we also show how total consumption differs based on temporary or permanent migration. In that case, consumption is a dependent variable.

  14. We also match our sample using the caliper value 0.025, as well as other matching methods such as radius matching and kernel matching, etc. The results presented in Table 3 are very similar. We also estimated alternative models such as IPW, regression adjustment, and doubly robust. Results are similar to the results presented here. Please see Appendix Table 16 for results obtained from other models.

    Table 3 The average treatment effect for savings rates
    Full size table

  15. Also the difference in household consumption per capita between the two groups is 7.20% (vs. 8.10% in the OLS model), which is presented in Table 17.

  16. We also specify a Gaussian distribution for γ, and the results are similar (see Appendix Fig. 1A). By choosing a much higher direct effect of urban segregation as the upper range of γ, we want to find the sensitivity of results.

  17. Preschool children indicate that the child is under 6 years old.

  18. Education is a categorical variable. There are five categories: elementary education (6 years of schooling or lower), junior high school (up to 9 years of education), high school education (up to 12 years of education), associate degree (up to 15 years of education), and a college degree (16 years of education).

  19. As the 2017 CMDS data do not provide the sub-consumption level information, we use the 2015 CMDS to test our Hypothesis 3.

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Acknowledgements

The authors are grateful for the research grants provided by the Natural Science Foundation of Guangdong Province (No. 2020A1515110797), Guangdong Planning Office of Philosophy and Social Science (No. GD18XYJ26), the Department of Education of Guangdong Province (No. 2018WQNCX005) and the China Scholarship Council (CSC) Programs. Paudel’s time in this paper was supported by the USDA Hatch Projects #94382 and #94483. This research was completed when Wen was a visiting scholar at Louisiana State University.

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  1. College of Economics & Management, Research Center for Green Development of Agriculture, South China Agricultural University (SCAU), Guangzhou, 510642, People’s Republic of China

    Le Wen & Qinying He

  2. Department of Agricultural Economics and Agribusiness, Louisiana State University (LSU) and LSU Agricultural Center, Baton Rouge, LA, USA

    Krishna P. Paudel

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Correspondence to Krishna P. Paudel.

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Appendix

Appendix

See Fig. 6; Tables 15, 16, 17, 18 and 19.

Fig. 6
figure 6

Conley et al. (2012)'s LTZ results. The figure presents the point estimate and the corresponding 95% confidence interval for the effect of temporary migration on migrants’ savings rates using the local-to-zero (LTZ) method. Following Conley et al. (2012), we assume a Gaussian prior for \(\gamma ,\) a direct effect of the instrument (i.e., urban segregation) on the dependent variable (i.e., saving rates). As shown in the figure, the estimate of the coefficient of Temporary becomes larger if the degree of the violation of the exogeneity of the instrument increases, indexed by the parameter \(\delta\). At the same time, the confidence interval becomes wider with a larger variance (\({\delta }^{2}/10\)). Nonetheless, the results suggest that there is still a significantly positive effect of temporary migration on saving rates, even with substantial departures from a perfect instrumental variable

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Table 15 The distribution of standardized bias between the pre- and after-matching
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Table 16 The average treatment effect for savings rates (2)
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Table 17 Overcoming the endogeneity: IV results
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Table 18 Robustness checks: dependent variable is savings rates
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Table 19 Robustness Checks with different waves of CMDS
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Wen, L., Paudel, K.P. & He, Q. Temporary Migration and Savings Rates: Evidence from China. Eur J Dev Res 34, 2810–2849 (2022). https://doi.org/10.1057/s41287-021-00491-0

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