Human Development

Does non-farm growth in rural areas reduce intergenerational educational mobility?

  • Blog Post Date 04 January, 2021
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Francisco Ferreira

London School of Economics

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Yajing Jiang

Charles River Associates

While the growth of the non-farm sector in a rural economy is known to reduce poverty, it may also exacerbate inequality. Comparing rural India and rural China the study finds that there is lower intergenerational education mobility in the former. Farmers’ sons in India attain higher schooling relative to non-farm sons when the father has low education, but the advantage flips when the father has over 10 years of education.


Structural change from agriculture to the non-farm sector has been an important feature of economic transformation in many developing countries over the last few decades. Evidence from India suggests that the non-farm rural economy – including activities such as household manufacturing, handicrafts, trade, and so on – has absorbed most of the workforce that exited the agricultural sector, with little employment generation in a stagnant industrial sector (Binswanger-Mkhize 2012,Lanjouwet al. 2013). There is substantial evidence that the non-farm sector reduces poverty but increases income inequality in a rural setting (Lanjouwet al. (2013) on India, and Rozelle (1994) on China). According to the estimates of Lanjouwet al. (2013) based on data from Palanpur village in Uttar Pradesh, the contribution of non-farm income to income inequality was only 4% in 1974-75, which increased to 67% in 2008-09. This raises the concern of whether structural change in the rural economy reduced intergenerational economic mobility by adversely affecting the opportunities of children from poor socioeconomic backgrounds.

Access to skilled, non-farm occupations can be a ladder for upward social mobility as these in general, yield higher income. A growing rural non-farm economy may increase returns to education substantially by expanding skill-intensive economic activities. This may create a positive feedback loop where parents with relatively more education, work in skilled non-farm occupations and earn higher income, which in turn boosts investments in children’s education by relaxing credit constraints. Children born into educated non-farm families may reap double advantages – they acquire greater education, and then get better access to high-paying non-farm jobs through referrals and parental networks.

Studying intergenerational education mobility

We provide a comparative analysis of intergenerational educational mobility of men in rural India versus rural China (Emranet al. 2020). We compare rural India and rural China because of important policy differences between the two in the 1970s and 1990s. For example, rural-urban migration was restricted in China because of “hukou1 household registration system, but there were no such restrictions on geographical mobility in India.

For credible empirical analysis, we need datasets where information on children is available even when the children are no longer living with parents in the same household. Recent analysis shows that the estimates of intergenerational educational mobility are likely to be substantially biased if data on only the co-resident parents and children are used (Emranet al. 2018). Hence, we use data from the Rural Economic and Demographic Survey (REDS) 1999 survey for rural India and Chinese Family Panel Studies (CFPS) 2010 for rural China – both of which contain information on the sons irrespective of the residency status at the time of the survey.

A distinguishing feature of our study is that the empirical analysis is grounded in an explicit theoretical model of intergenerational educational mobility, which is based on the assumption that parents cannot borrow to invest in children’s education because of credit market imperfections (Becker and Tomes 1986,Beckeret al. 2015, 2018). We extend the model to incorporate the role played by parental farm and non-farm occupations in shaping children's educational opportunities. The extended model helps us understand and interpret the underlying economic mechanisms. In particular, higher household-level returns to education in the parental generation reduce relative mobility. For example, when the returns to education are higher in non-farm occupations, an educated non-farmer earns higher income relative to an educated farmer. The higher income leads to higher investment in son’s education. This reduces intergenerational mobility by strengthening the link between father’s education and son’s schooling in the non-farm households relative to the farm households. The expected schooling attainment of the children from the most disadvantaged family background (fathers without any education) is determined by intergenerational persistence in non-farm occupations and the expected returns to children’s education, among other factors.

Intergenerational education mobility among rural men in India vs. China

Our findings suggest substantially lower intergenerational education mobility for sons in rural India as compared to sons in rural China, for the cohorts that went to school between the 1970s and 1990s. The son of a father with five years of schooling (henceforth ‘ educated father’) in rural India attained 2.6 years of more schooling, on average, relative to the son of a father with no schooling. The corresponding gain in rural China is only 1.6 years of schooling. Thus, the family background – as captured by father’s education – mattered much more for the educational attainment of sons in rural India relative to rural China.

The impact of father’s education was especially strong among the non-farm households in rural India – the son of a educated father in non-farm households gained 2.8 years of additional schooling, on average, while the corresponding estimate is 2.4 years for the farm households. These results suggest that the positive feedback loop described above was important for the non-farm households in rural India during the study period. In contrast, there was no significant difference between farm and non-farm households in rural China, suggesting the absence of such a mechanism.

Further, we find that among the households where the father has no schooling, the sons of farmers enjoy an educational advantage as compared to the sons of non-farmers in rural India. This reflects the fact that low-skilled, non-farm jobs are occupations of last resort, and these generate lower incomes than that of farm households with no schooling. Our analysis identifies 10 years of schooling (Secondary School Certificate or Matriculation) as an important threshold in rural India. The sons of fathers with 10 or more years of education attained greater years of schooling if the father was in a non-farm occupation as compared to farming. The relative advantage flipped in favour of the farm households when the father had less than 10 years of education.

An important concern is whether the estimated intergenerational persistence is primarily due to genetic transmissions of cognitive ability from father to sons, with little room for economic forces that can potentially be manipulated by policy interventions. We develop a methodology based on the estimates of intergenerational correlation in genetic ability in economics and behavioural genetics literature. The evidence suggests a maximum correlation of 0.4 in cognitive ability of parents and children (for example, Plomin and Spinath 2004). Incorporating this estimate in our study, we find dramatic differences between India and China. While the intergenerational persistence in schooling in rural China could be completely accounted for by genetic transmission of ability alone, in rural India, genetics alone cannot explain the observed persistence, indicating a much stronger role for economic forces (and potentially for public policies).

Underlying economic mechanisms

Insights from the theoretical model help us understand the underlying economic mechanisms. We estimate household-level returns to education in the parental generation which, according to the theory, is a salient mechanism behind intergenerational persistence in schooling. Higher returns to education are expected to lower mobility by tightening the link between the education of father and sons. If the model developed for our analysis is a good description of the economic forces determining intergenerational educational persistence in farm versus non-farm households, returns to education in the non-farm sector should be significantly higher in rural India. However, we should not observe any such difference between farm and non-farm sectors in rural China. Indeed, we find that household-level returns to education were significantly higher in non-farm occupations in rural India, and there are no significant differences in rural China.

Our analysis also suggests that the dramatic expansion of better-quality private schools in rural India played a role by increasing the returns to private investment in education. More educated, non-farm parents employed in skilled occupations could take advantage of the better-quality private schools because of their high income. The educated farmers, however, could not avail of the same advantages because of low income given the low returns to education in farming. The sons of the farmers were left behind in the free but low-quality public schools plagued by issues such as teacher absenteeism. In contrast, in rural China, private schooling was limited and generally of lower quality (compared to free public schools) during the study period.

The Hukou registration system in rural China weakened the intergenerational occupational persistence in the-non farm sector, as many children of non-farm parents had to take up farming due to difficulties in urban migration. The lack of intergenerational persistence in non-farm occupations equalised the expected returns to investment in children’s education across farm and non-farm households in rural China. In contrast, there were significant intergenerational linkages in non-farm occupations in rural India, which increased the expected returns to and investment in son’s education in non-farm households. This was an important factor behind the positive feedback loop observed in the non-farm households in India.

The evidence and analysis presented in our study suggest that structural change in favour of the non-farm sector increased educational inequality in rural India by lowering intergenerational mobility during the 1970s-1990s.

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  1. China’s hukou (household registration) system has imposed strict limits on ordinary Chinese citizens changing their permanent place of residence, since it was instituted in the 1950s.

Further Reading

  • Becker, Gary S and Nigel Tomes (1986), “Human Capital and the Rise and Fall of Families”,Journal of Labor Economics, 4(3): 1-39.
  • Becker, Gary S, Scott Duke Kominers, Kevin M Murphy and Jörg L Spenkuch (2018), “A Theory ofIntergenerational Mobility”,Journal of Political Economy, 126(S1): 7-25.
  • Binswanger-Mkhize, HP (2012), ‘India 1960‐2010: Structural Change, the Rural Non-farm Sector, and the Prospects for Agriculture’, Department of Agricultural and Resource Economics, University of California, Berkeley.
  • Emran, M Shahe, William Greene and Forhad Shilpi (2018), “When Measure Matters: Coresidency, Truncation Bias, and Intergenerational Mobility in Developing Countries”,Journal of Human Resources, 53(3): 589-607.
  • Emran, MS, F Ferreira, Y Jiang and Y Sun (2020), ‘Occupational Dualism and Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India’, IZA Discussion Paper No. 13550.
  • Himanshu, Peter Lanjouw, Rinku Murgai and Nicholas Stern (2013), “Nonfarm diversification, poverty, economic mobility, and income inequality: a case study in village India”,Agricultural Economics, 44(4-5): 461-473.
  • Plomin, Robert and Frank M Spinath (2004), “Intelligence: genetics, genes, and genomics”,Journal of Personality and Social Psychology, 86(1): 112-129.
  • Rozelle, Scott. 1994. “Rural Industrialization and Increasing Inequality: Emerging Patterns in Chinas Reforming Economy.” Journal of Comparative Economics, Vol. 19(3): 362-391.
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