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Does Public Investment Promote Intergenerational Mobility? Who Really Benefits?

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Abstract

The conventional model of intergenerational income mobility suggests child’s income is a function of his human capital investment determined by parental income, which neglects the role of public investment. This paper aims to distinguish whether and how public investment affects intergenerational income mobility and who are the actual beneficiaries. Data used for analysis is from China Health and Nutrition Survey, and is precisely matched to provincial public investment data. To mitigate the potential biases, average income of at least three waves is adopted. Through investigations, this study finds public investment could promote intergenerational income mobility obviously but unequally. Middle-distributed income groups are found to benefit more from public investment than the upper and bottom tail of the income distribution. The opportunities for the middle-distributed income groups are fairly better, while it seems harder for individuals of the bottom income distribution to have a breakthrough. Positional changes between middle income groups and top income groups contribute largely to the intergenerational income mobility during the sample period. Regions with higher public investment present higher mobility as well. To promote intergenerational mobility, public investment should target more precisely at lower income groups.

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Notes

  1. A society with IGE of less than 0.2 is often regarded to be mobile, of 0.2 to 0.4 is median mobile, and of higher than 0.4 is less mobile (Dearden et al., 1997; Björklund & Jäntti, 1997; Mazumder 2005; Blanden & Macmillan, 2013; Corak, 2013; Chetty et al., 2014).

  2. The Data is from the National Bureau of Statistics of China and is available at http://www.stats.gov.cn/.

  3. The CHNS program re-interviews the same respondents every 2 or 3 years.

  4. According to China’s Education Law, nine years of compulsory education is essential for every teenager, which normally spans from 6 years old to14 years old.

  5. The weights are designated of q for situations when the predicted values are smaller than the true values and of (1-q) for the versa situations. See more detailed discussion in Koenker and Bassett (1978), Buchinsky (1994), Koenker and Hallock, (2001) and Cameron and Trivedi (2005).

  6. For example, the Median (equivalent to 0.5 quantile), lower quartile (equivalent to 0.25 quantile), 1 decile (equivalent to 0.1 quantile) and 40th percentile (equivalent to 0.4 quantile).

  7. Detailed information can be found at http://www.cpc.edu/china/.

  8. The nine provinces include Heilongjiang, Jiangsu, Shandong, Liaoning, Henan, Hubei, Hunan, Guangxi and Guizhou.

  9. Ten waves of survey are included in total: 1989, 1991, 1993, 1997, 2000, 2004, 2006, 2009, 2011, 2015. See Zhang and Eriksson (2010) for a detailed description of the data.

  10. 3 waves of survey contain at least 6-year income gap and 10 waves of income almost span the whole career time.

  11. The survey wave numbers for participation are summarized in appendix (Table 7).

  12. Government expenditure on education reflects government’s expenses on education affairs, including educational administration, preschool education, primary education, junior and senior high school, tertial education, and all other kinds of occupational and technical schools. Government expenditure on science includes the expenses on science and technology, and expenditure on culture items includes culture, sports, press and publication. Government expenditure on health items includes medical and health management expenses, expenditure on medical service, health care expenses, disease prevention and control expenditures, and expenditures on maternal and child health care and rural health care. Also see footnote.

  13. See SECH data of these two sources at http://data.cnki.net/yearbook/Single/N2010042091 and http://data.stats.gov.cn/easyquery.htm?cn=C01.

  14. since the child generation in our sample was born between 1958 and 1990, the annual data of public investment at provincial level between 1964 and 2005 is therefore required. But data of the student number can’t be tracked back for so long time. Data of SECH per capita is obtained by dividing the total SECH expenditures by the total population for each province over 1952–2017.

  15. We use father’s income instead of mother’s because women’s participation in the labor market is relatively lower and unstable during 1980s to early 2000s, which may provide a nosier measure (Chi & Li, 2014; Hare, 2016). Meanwhile, both sons and daughters are included in the sample for two reasons. A first one is female labor participation rate has been largely rising for child generation. A second reason is the consideration of expanding the sample size as possible. Chadwick and Solon (2002), Björklund, Jäntti and Solon (2007), Lee and Allen (2020), Cruz and Pero (2020), among the others all include daughters in sample.

  16. Also see Table 7 for sample distribution, which also provide information on the data attrition.

  17. See detailed comparison statistics and t-test statistics in Tables 8, 9 in the appendix.

  18. See the statistics of the public expenditure on SECH items for each province in the appendix (Table 10).

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  1. School of Public Administration, Huazhong University of Science and Technology, Wuhan, China

    Lijie Song

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Appendix

Appendix

Tables 7, 8, 9 and 10 presents the statistics of the child generation for three samples. Sample A consists of observations of all child generation (N = 19,630) aged 25–60. Sample B further restricts sample A to observations born between 1958 and 1990 to qualify the sample more comparable to our final sample, and sample C is our final sample consisting of 438 father-child pairs. Through comparison it is clear that the mean of urban is quite similar among the three samples (1.602, 1.607 and 1.616 respectively, and 1 indicates urban while 2 indicates rural). The mean of gender for sample C is slightly lower (1.23) compared with Sample A (1.502) or sample B (1.505), which may indicate male in our final sample is slightly over-represented (gender = 1 denotes male while gender = 2 denotes female). Compared with mean value of schooling years (denoting the completed years of formal education) in sample A (20.66) and sample B (23.40), mean value of schooling in sample C is 22.83. It seems different between sample A and Sample C. The main reason as we speculate is the great gap in age during which great changes occurred in China and affect some people’s education experience. This speculation is verified when birth years of sample A is restricted, as schooling for sample B and sample C have no signs of difference. We further conduct a T-test for difference between sample B and sample C. The result shows there’s no significant difference between sample B and sample C at 5% significant level (p value = 0.081).

Table 7 Number of survey waves participated by both generations
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Table 8 Statistical comparison among different samples
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Table 9 Two-sample T-test with equal variance
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Table 10 Distribution of subgroups according to the provincial public investment
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Song, L. Does Public Investment Promote Intergenerational Mobility? Who Really Benefits?. Soc Indic Res 158, 59–80 (2021). https://doi.org/10.1007/s11205-021-02708-5

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