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Like Father, Like Son? Intergenerational Educational Mobility in India

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Demography

Abstract

Using the nationally representative India Human Development Survey (IHDS), we create a unique son–father matched data set that is representative of the entire adult male population (aged 20–65) in India. We use these data to document the evolution of intergenerational transmission of educational attainment in India over time, among different castes and states for the birth cohorts of 1940–1985. We find that educational persistence, as measured by the regression coefficient of father’s education as a predictor of son’s education, has declined over time. This implies that increases in average educational attainment are driven primarily by increases among children of less-educated fathers. However, we do not find such a declining trend in the correlation between educational attainment of sons and fathers, which is another commonly used measure of persistence. To understand the source of such a discrepancy between the two measures of educational persistence, we decompose the intergenerational correlation and find that although persistence has declined at the lower end of the fathers’ educational distribution, it has increased at the top end of that distribution.

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Notes

  1. According to the Pew Research Center (2009:106), 87 % of respondents agree with the following statement: “Our society should do what is necessary to make sure that everyone has an equal opportunity to succeed.” Similarly, in an opinion poll on Economic Mobility and the American Dream by Pew Research Center conducted in March 2009, 71 % favored ensuring that everyone had a fair chance of improving their economic standing. In the same survey, only 21 % favored reducing inequality as a more important goal (Breen 2010). “In policy and political discourse, ‘equality of opportunity’ is the new motherhood and apple pie. In its strongest form, the position is that equality of outcomes should be irrelevant to policy; what matters is equality of opportunity” (Kanbur and Wagstaff 2014:2).

  2. See Haveman and Wolfe (1995), Black and Devereux (2011), and Blanden (2013) for reviews of research in this area.

  3. See Béteille (2002) for a discussion on caste system and affirmative action from the Government of India.

  4. The Indian constitution defines the power distribution between the federal (center) government and its states. Both the central and the state governments have power to legislate in the areas mentioned under the concurrent list.

  5. In Online Resource 1, we discuss the issue of coresidence in the existing studies on India and the resulting decline in the sample size, and the sample selection issue that can arise from the use of coresidence for matching children with parents. In Online Resource 1, we demonstrate that one can identify fathers’ information for merely 27 % of adult males in the age group 20–65 based on coresidence. In addition, the majority of the resulting sample consists of individuals in the age group 20–30 (roughly 80 %).

  6. Intergenerational transmission among women is another interesting question; however, we are unable to address this issue because of non-availability of data. The majority of married women in India reside in different households (husbands’ households) than their parents, and household surveys typically collect information on members residing in the same household (through household roster) at the time of survey. The IHDS survey used in this article collected fathers’ information of male household head separately, which helped us to identify fathers for adult males only.

  7. Their sample does not include India.

  8. Munshi and Rosenzweig (2006) used a survey for 4,900 households residing in Bombay and investigated the effect of caste-based labor market networks on occupational mobility. They found strong effects of traditional networks for males on occupational choice. However, for females, they found relatively greater mobility in occupational choices. Munshi and Rosenzweig (2009) used a panel data of rural households: namely, the 1982 Rural Economic Development Survey, which covered 259 villages in 16 states in India. They reported low rates of spatial and marital mobility in rural India, and related these to the existence of caste networks that provide mutual insurance to their members.

  9. The survey covered all the states and union territories of India except Andaman and Nicobar, and Lakshadweep. These two account for less than 0.05 % of India’s population. The data is publicly available from the Data Sharing for Demographic Research program of the Inter-university Consortium for Political and Social Research (ICPSR).

  10. We use a lower limit of 20 because most individuals in India finish college (about 15 years of education) around this age. In our data, only 10 % (1 %) of individuals in the age group 20–24 (25–29) are still in school and have not completed their education. Following Behrman et al. (2001), we use an upper age limit of 65 years. All the analyses in this article use the survey weights provided in the data.

  11. In our analysis, we adopt 5- and 10-year age-bands, and do not examine results under alternative aggregation schemes. As suggested by Hertz et al. (2007), such an aggregation scheme, although essentially arbitrary, should not bias the trend estimates unless it is chosen with a particular set of results in mind.

  12. We do not have information on mother’s education for the entire sample. We also carried out our analysis using average education for both parents: 44 % of the observations in our sample have information on mother’s education. We find a similar correlation coefficient but a larger regression coefficient at the all-India level. For brevity, we do not report these results in this article, but they are available upon request from the authors.

  13. Muslims are the largest minority religious group in India, and according to the Government of India (2006), their performance on many economic and education indicators is comparable with that for SC/ST. Certain differences exist among ST and SC. However, because of small sample sizes of ST after we divide the data into cohorts, we group SC and ST.

  14. It is common among economists to refer to both intergenerational regression coefficients and correlation coefficients as inverse measures of intergenerational mobility (Solon 1999). Hertz et al. (2007) provided comparable estimates of the two measures of status persistence for 42 countries.

  15. A weak intergenerational association (closer to 0) would have indicated that the opportunity to get any level of education is open to all, regardless of their fathers’ education.

  16. A chi-square test of equality of \( \widehat{\upbeta} \) for cohorts 1940–1945 and 1981–1985 rejects the null (p value = .000). A chi-square test of equality of \( \widehat{\upbeta} \) for successive cohorts rejects the null for 1956–1960 versus 1961–1965; 1961–1965 versus 1966–1970; 1966–1970 versus 1971–1975; 1971–1975 versus 1976–1980; and 1976–1980 versus 1981–1985. However, it fails to reject the null for 1940–1945 versus 1946–1950; 1946–1950 versus 1951–1955; and 1951–1955 versus 1956–1960.

  17. A chi-square test of equality of \( \widehat{\uprho} \) for cohorts 1940–1945 and 1981–1985 fails to reject the null (p value = .60). However, a chi-square test of equality of \( \widehat{\uprho} \) for successive cohorts rejects the null for 1951–1955 versus 1956–1960; 1961–1965 versus 1966–1970; 1966–1970 versus 1971–1975; 1971–1975 versus 1976–1980; and 1976–1980 versus 1981–1985. It fails to reject the null for 1940–1945 versus 1946–1950; 1946–1950 versus 1951–1955; and 1956–1960 versus 1961–1965.

  18. If the sons of better-educated fathers are the first to take advantage of new educational opportunities, the persistence measured by \( \widehat{\upbeta} \) will increase (Hertz et al., 2007).

  19. Note that it is important to account for preference for mobility when analyzing the issue of equality of opportunity based on realized outcomes in data. Immigrants, for instance, may have lower opportunity but greater observed mobility due to stronger preference for mobility. Because preferences are not uniform across social groups, one has to be careful in interpreting the extent of equal opportunity based on observed mobility (Breen and Goldthorpe 1997). We thank an anonymous referee for bringing this point to our notice.

  20. To investigate the persistence in education, or term B, we collapse our years of schooling into stages of schooling achieved by sons and fathers. We group the years of schooling into five achievement levels: years of schooling 0--4: below primary, 5--7: primary, 8--9: middle, 10--11: secondary, and 12--15: senior secondary or above.

  21. Hertz et al. (2007) also formed the simple average across cohorts. They argued that the advantage of this approach compared with running a single regression for all ages is that it does not give more weight to larger cohorts.

  22. Daude (2011) found that the countries in Latin America that show a high persistence using the beta coefficient measure also present a high correlation-coefficient persistence, with the correlation between the two measures being .75. Hertz et al. (2007) found a correlation of .51 between the two measures.

  23. We are interested in analyzing only the cohort trend in intergenerational educational persistence within each group, where we identify a group by caste membership and by state of residence. This analysis is based on intergenerational educational persistence estimated separately from a subsample of observations belonging to a particular group. Such an analysis is useful only in describing the extent of intergenerational mobility in educational attainment within a group. However, these intergenerational educational persistence estimates are not very informative for comparisons across groups because the estimated persistence for any group provides only an estimate of the rate to regression to the mean for that particular group and not for the overall education distribution. See Hertz (2005, 2008) and Mazumder (2011) for a detailed discussion of group-specific measures of intergenerational persistence.

  24. This is because the estimated persistence for any group provides only an estimate of the rate to regression to the mean for that particular group and not for the overall education distribution.

  25. For space considerations, we present only the probability of achieving higher level of education.

  26. The estimates for sons of fathers with secondary education or above among Muslims are quite imprecise because of a small sample size in this group.

  27. The non-convergence is in contrast to Hnatkovskay et al. (2013), who found convergence in mobility among caste groups based on education switches and average size of education switch, where education switch is defined as sons and fathers having different education levels. Hnatkovskay et al. (2013) looked at SC/ST versus non–SC/ST, thus grouping OBCs and Muslims with HHC as non–SC/ST. Further, they looked at the educational switches (son–father having different education levels), and thus their results are not directly comparable with ours.

  28. According to the Planning Commission of India (2008:5) the dropout rate in primary classes—which has been decreasing at a very low average rate of 0.5 % per annum since the 1960s—showed a steeper decline of 10.03 % over the first three years of the Tenth Plan (29 % in 2004–2005 compared with 39.03 % in 2001–2002). However, the dropout rate at the elementary level (classes I–VIII) has remained very high, at 50.8 %. In our sample, the most recent birth cohort is 1981–1985, who should have attended the schools in the 1990s. Hence, the dropout rates representing individuals in our sample would be even more severe than those reported in the Planning Commission report for 2004–2005.

  29. This is Question 2.8, on page 4 of the Household Questionnaire. In both the NSS and the NFHS, the analogous identification is achieved by utilizing the “relationship to the household head” question in the household roster (see Online Resource 1 for a discussion of such identification in the NSS data).

  30. This is Question 1.20, “How many standards/years of education had the household head’s father/husband completed?,” on page 3 of the Household Questionnaire.

  31. We also identify fathers’ years of education for some of the remaining adult males (who are not the household heads and whose fathers are not identified through coresidence) by exploiting relation to the head. A STATA .do file used to construct the son–father sample is available from the authors. Maitra and Sharma (2009) also used the IHDS data in their analysis but used coresidence to identify parental educational attainment. As a result, their sample is restricted to only 27.7 % and 6.4 % of the total adult male and female sample interviewed in the IHDS. For instance, in table 4 of their paper, they reported a sample size of 5,789 and 11,515 for males in urban and rural area, respectively, although the total adult male (20 and older) sample is 22,071 and 40,460 in urban and rural areas, respectively. Similarly, they used a sample of only 1,886 and 2,078 adult females living in urban and rural areas, whereas the total adult female sample is 21,790 and 40,378 in urban and rural areas, respectively.

  32. In the supplement to their paper, Hnatkovskay et al (2013: table S2) reported the sample sizes for each round of the NSS. They reported the number of observations (son–father pair) as 24,119 in 1983; 28,149 in 1987–1988; 25,716 in 1993–1994; 25,994 in 1999–2000; and 27,051 in 2004–2005. The actual number of males aged 16–65 surveyed in these cross-sections are 177,008; 196,412; 173,182; 183,732; and 188,585, respectively.

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Acknowledgments

The authors would like to thank the participants of 10th Midwest International Economic Development Conference, 2013 Pacific Conference for Development Economics (PacDev), 2012 Northeast Universities Development Consortium (NEUDC) Conference, 7th IZA/World Bank Conference on Employment and Development, and 2014 IHDS Users Conference for their comments and suggestions.

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Authors and Affiliations

  1. Department of Economics, Oklahoma State University, 326 Business Building, Stillwater, OK, 74078, USA

    Mehtabul Azam

  2. Department of Economics, James Madison University, MSC 204 ZSH, Room 442, Harrisonburg, VA, 22807, USA

    Vipul Bhatt

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  1. Mehtabul Azam
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Correspondence to Mehtabul Azam.

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Appendix: Identification of Father’s Educational Attainment

Appendix: Identification of Father’s Educational Attainment

This section highlights the additional information in IHDS that is not available in the NSS or the NFHS that allows us to identify father’s schooling for almost every adult male respondent in the age group 20–65. Table 8 presents our sample selection process and the loss of observations at each stage.

Table 8 Construction of son–father sample
Full size table

The first variable that we use is the ID of father in the household roster, which helps to link individuals to their fathers directly if the father is living in the household.Footnote 29 Using this information by default imposes the coresidence condition, which severely reduces the sample size. The last row of Table 8 in the appendix shows that using only this variable, we were able to extract father’s educational attainment for 34 % of the male respondents in the 20–65 age group.

In contrast to the NSS and the NFHS, the IHDS has another question regarding the education of the household head’s father (irrespective of the father living in the household or not).Footnote 30 Combining this variable with the ID of father variable, we are able to identify fathers’ schooling for about 97 % of the adult male respondents.Footnote 31 In comparison, Hnatkovskay et al. (2013), who used five rounds of the NSS, were able to identify fathers’ education for less than 15 % of males aged 16–65 interviewed in the NSS.Footnote 32

This measurement issue is of practical as well as theoretical importance: using only coresidence to identify parents’ educational attainment may cause a severe sample-selection problem. The issue of sample selection and the resulting non-randomness in survey data has been extensively documented in the literature (see Francesconi and Nicoletti 2006). Further, a sample of son–father pairs achieved through coresidence may be misleading because it may not be a representative sample of the entire adult population of interest. For example, in our sample, almost 86 % of respondents whose father is identified through coresidence are aged 20–35. Hence, the coresidence condition generates a sample that effectively overrepresents younger adults, which is expected as these individuals are more likely to be living with their parents. The distribution of sons’ and fathers’ years of schooling is very different in the coresidence sample versus the total sample (see Table 3).

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Azam, M., Bhatt, V. Like Father, Like Son? Intergenerational Educational Mobility in India. Demography 52, 1929–1959 (2015). https://doi.org/10.1007/s13524-015-0428-8

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