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The Journal of Economic Inequality (2023) 21:743–773
https://doi.org/10.1007/s10888-023-09599-1
Occupational dualism and intergenerational educational
mobility in the rural economy: evidence from China and India
M. Shahe Emran1·Francisco H. G. Ferreira2·Yajing Jiang3·Yan Sun4
Received: 19 August 2023 / Accepted: 2 October 2023 / Published online: 30 November 2023
© The Author(s) 2023
Abstract
We extend the Becker-Tomes model to a rural economy with farm-nonfarm occupational
dualism to study intergenerational educational mobility in rural China and India. Using
data free of coresidency bias, we find that fathers’ nonfarm occupation and education were
complementary in determining sons schooling in India, but separable in China. Sons faced
lower mobility in India irrespective of fathers’ occupation. Sensitivity analysis using the
Altonji et al. (J. Polit. Econ. 113(1), 151–84, 2005) approach suggests that genetic correlations
alone could explain the intergenerational persistence in China, but not in India. Farm-nonfarm
differences in returns to education, and geographic mobility are plausible mechanisms behind
the contrasting cross-country evidence.
Keywords Educational mobility ·Rural economy ·Occupational dualism ·Farm-nonfarm ·
Complementarity ·Coresidency Bias ·China ·India
1 Introduction
Intergenerational persistence in economic status in developing countries has attracted the
attention of policymakers and researchers in recent years, partly in response to growing
evidence that economic liberalization increased income inequality in many countries, despite
a significant reduction in poverty.1In the absence of reliable income data over the life-cycle
1Intergenerational persistence refers to the association between children’sand their parents’ lifetime economic
outcomes. A stronger intergenerational persistence implies a lower intergenerational mobility. For recent
contributions on intergenerational mobility in China, see, among others, Fan et al. (2021); Park and Zou
(2019); Sato and Li (2007); Emran and Sun (2015a); Emran et al. (2020b); on India see, among others, Azam
and Bhatt (2015); Emran and Shilpi (2015); Asher et al. (2018); Ahsan et al. (2022); Emran et al. (2021). For
cross-country analysis, see Behrman (2019); Hertz et al. (2007), and Neidhofer et al. (2018), among others.
Among the few contributions on intergenerational persistence in health, see Bhalotra and Rawlings (2013).
BFrancisco H. G. Ferreira
f.d.ferreira@lse.ac.uk
M. Shahe Emran
shahe.emran.econ@gmail.com
1IPD, Columbia University, New York, NY, USA
2London School of Economics, London, UK
3Charles River Associates, Boston, MA, USA
4World Bank, District of Columbia, DC, USA
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744 M.S. Emran et al.
of parents and children, the focus of the recent literature on developing countries has been on
intergenerational educational mobility.2However, most of the recent studies are devoted to
urban households, and intergenerational mobility in rural areas remains particularly under-
researched, even though the bulk of the poor live in villages in developing countries. This
paper provides a comparative analysis of intergenerational educational mobility in the rural
areas of the two most populous countries in the world: China and India, with more than 1.5
billion people living in villages in 2000/2001.3
While understanding the role of family background in the educational opportunities of 1.5
billion people is an important research agenda in itself, the policy differences between China
and India make such a comparative study especially interesting.4Perhaps, the most important
difference in the 1970s to 1990s, the period during which the children in our empirical
analysis went to school, was the restrictions on rural-urban migration in China because of
the Hukou registration system.5In contrast, there were no policy restrictions on rural-urban
migration in India. Returns to education in farm vs. nonfarm occupations are also likely to
be different in rural China because of policies such as the household responsibility system,
rural industrialization (township and village enterprises), and a lack of a well-functioning
labor market in the 1970s to 1990s.6Another important aspect in which rural India and rural
China differed in this period is their schooling systems, with private schools playing a much
more prominent role in rural India. In our analysis, we pay close attention to the implications
of these cross-country differences.
The standard model of intergenerational educational mobility where parent’s education
is the sole indicator of family background is not suitable for our analysis because it ignores
occupational dualism between farm and nonfarm sectors in the rural economy.7Children
born into a nonfarm household may face different educational opportunities compared to
the children born into a farming household even when the parents have similar educational
background. At a given level of education, the parent’s nonfarm occupation may affect invest-
ment in children’s education through two major channels. First, higher income from nonfarm
occupations may relax binding credit constraints on investment in schooling.8Second, the
2Educational mobility in this paper relates to schooling attainment. For a discussion on the broader concept
of education in and outside the classroom as input to human capital production, see, for example, Behrman
(2019).
3The rural population in India was 742.62 million in 2001 (72.16 percent of the total), according to the census
of India 2001. In China, the rural population in 2000 was 807.39 million (64 percent of the total), according
to the China Statistical Yearbook, 2011.
4China opened up its economy gradually from 1978, and the rural economy went through substantial policy
changes starting with the household responsibility system that introduced market incentives at the margin.
India liberalized its economy with a big bang in 1991, but the rural economy was relatively less affected as the
initial trade protections were focused on the industrial sector as part of a planned industrialization strategy.
5The Hukou registration system was implemented in the early 1950s in China. It is like an internal passport
system dividing the rural and urban population. This restricted rural-urban migration because a migrant without
an urban Hokou could not access the formal jobs, and their children were not eligible for the public schools
in a town or city, among other restrictions. Until recently, children inherited the Hukou status (rural vs. urban)
of their mother.
6The household responsibility system introduced market at the margin at the early phase of liberalization
after 1978 reform. A household was allowed to sell the surplus agricultural output to the market after meeting
the quota requirement by the government.
7Although there is a long tradition in development economics followingLewis (1954) that emphasizes dualism
as a central feature of underdevelopment, to the best of our knowledge, ours is the first paper to incorporate
occupational dualism in an analysis of intergenerational educational mobility.
8However, in some countries, low-skilled nonfarm activities might be occupations of last resort, with low
income, lower than the income of the farming households (Lanjouw and Lanjouw 2001).
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Occupational dualism and intergenerational mobility 745
probability of a child getting a nonfarm job may be higher when the parents themselves
are employed in nonfarm occupations because of network, referral, and role model effects
(Emran and Shilpi 2011), and this would increase the optimal investment when returns to
education are higher in nonfarm occupations.
There is substantial evidence that structural change in favor of non-farm occupations is an
important source of increasing income inequality in villages of many developing countries.9
According to the estimates of Lanjouw et al. (2013) based on the data from Palanpur in
the Indian state of Uttar Pradesh, the contribution of non-farm income to income inequality
was only 4 percent in 1974/75, which increased to 67 percent in 2008/09.10 The evidence
on China also suggests that nonfarm income contributes to income inequality in rural areas
(Rozelle 1994;YangandAn2002). Such a rise in inequality associated with the expansion
of the nonfarm sector is of special concern when it reflects lower intergenerational mobility.
We develop a theoretical model in the tradition of Becker and Tomes (1986) that incor-
porates the role played by parental farm and nonfarm occupations in shaping children’s
educational opportunities, and yields the almost universally used linear-in-levels estimating
equation.11 As emphasized by Mogstad (2017), in the absence of an explicit theoretical
model, it is difficult to understand and interpret the economic content of the estimated
intergenerational persistence. The theoretical analysis identifies a set of economic mecha-
nisms determining the intercept and the slope of the intergenerational educational persistence
equation. In particular, the household-level returns to education in the parental generation
determine relative mobility as measured by the slope of the regression function (called inter-
generational regression coefficient (IGRC, for short) in the literature). The intercept which
shows the expected schooling attainment of the children from the most disadvantaged fam-
ily background (fathers with no schooling) is determined by intergenerational persistence
in occupation, among other factors.12 A substantial literature shows that Hukou restrictions
affected intergenerational persistence in nonfarm occupations in rural China (Wu and Treiman
2007).
A credible empirical analysis of the role of occupational dualism in intergenerational
educational persistence needs to address two major challenges highlighted in the recent
literature: (i) truncation bias due to coresidency restrictions in household surveys, and (ii)
the role of genetic correlations in the observed intergenerational persistence. Most of the
available household surveys, especially in developing countries, suffer from serious sample
truncation as household membership is defined in terms of coresidency.13 As a result, the
9For an excellent survey of the literature on rural nonfarm economy in developing countries, see Lanjouw
and Lanjouw (2001). On rural China see Rozelle (1994); Yang and An (2002), and on rural India see Lanjouw
et al. (2013), and Ravallion and Datt (2002).
10 The Gini coefficient of income in Palanpur was 0.253 in 1974/75 and 0.427 in 2008/09.
11 The estimating equation used in the literature on intergenerational income mobility is log linear. In contrast,
all of the papers on intergenerational educational mobility we are aware of use a linear-in-levels estimating
equation. This is partly motivated by the fact that, in many developing countries, 20-40 percent of fathers have
zero schooling. However, we are not aware of any published work on developing countries that derives the
estimating equation from a theoretical model.
12 This deserves especial attention because the focus in much of the existing literature has been on the slope-
based measures of mobility such as the intergenerational regression coefficient (IGRC) and intergenerational
correlation (IGC).
13 For example, widely used surveys such as LSMS and DHS collect information only on the coresident
children.
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746 M.S. Emran et al.
nonresident children and parents are not included in the survey. Sample truncation occurs as
we lack information on both the dependent and independent variables in the regression for
these missing household members. Recent evidence indicates that the standard measures of
relative mobility, such as IGRC, suffer from substantial downward bias in coresident samples
(Emran et al. 2018). Our empirical analysis focuses on the father-son linkage in education, and
takes advantage of two exceptionally rich data sets: the rural sample from the China Family
Panel Studies (2010) for China and the Rural Economic and Demographic Survey (1999) for
India. Both of these surveys are unique in that they include all the children of the household
head irrespective of their residency status at the time of the survey.14 This is especially
important in a comparative study such as ours, because cross-country comparisons based on
coresident samples can lead to wrong conclusions (see Emran et al. 2018 on Bangladesh and
India).
A longstanding concern in the literature has been whether the observed correlations are
primarily mechanical, driven largely by genetic transmissions from parents to children (see,
for example, the discussion by Black and Devereux (2011)). We address this issue in two
ways. First, we develop a simple but plausible approach to check whether the estimated inter-
generational persistence could be due solely to genetic correlations. The approach combines
the recent evidence on intergenerational correlation in cognitive ability from the behavioral
genetics and economics literature with the Altonji et al. (2005) biprobit sensitivity analy-
sis (henceforth called augmented AET analysis). Second, the theoretical foundation for the
empirical specification allows us to use economic mechanisms as a test for the importance
of parental economic choices.
The substantive conclusions of this paper can be summarized as follows. Intergenerational
educational mobility was substantially lower for sons in rural India compared to sons in rural
China for the cohorts that went to school in the 1970s-1990s. The point estimates of inter-
generational persistence are larger in nonfarm households in both countries. The difference
between farm and nonfarm households is statistically significant in rural India, but not in rural
China, and this conclusion applies to both the slope and the intercept of the intergenerational
educational persistence regression. The evidence suggests that while parent’s education and
nonfarm occupation were complementary in determining a son’s education in rural India,
they were separable in rural China.15 The long-term variance in schooling was significantly
higher for the sons born into nonfarm households in rural India, and structural change from
agriculture to the nonfarm sector contributed to educational inequality. The evidence from
the augmented AET sensitivity analysis shows that the observed persistence in rural India
cannot be accounted for by genetic correlation alone, while the persistence in China can be
explained away by an ability correlation of plausible magnitude.16 An important advantage
of this approach is that the conclusions refer to the whole population of interest, rather than
a subset as usually is the case with other standard approaches such as instrumental variables
14 Although some surveys collect limited information on the non-resident parents of the household head and
spouse, we are aware of only a few surveys that include all children of the household head.
15 It is important to recognize that separability in rural China does not imply that nonfarm income did not play
any role in rural income inequality; in fact, we cite a substantial literature to the contrary. The evidence in this
paper suggests that intergenerational educational linkage is unlikely to be an important mechanism through
which the nonfarm sector influenced the observed inequality in rural China in the 1980s and 1990s.
16 This evidence on rural China is similar to the recent analysis on urban China based on a sample of
monozygotic twins by Behrman et al. (2020). They find that the estimated impact of father’s education can be
explained fully by genetic correlations and unobserved family endowments.
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Occupational dualism and intergenerational mobility 747
strategies.17 Under the null hypothesis that the observed persistence is due solely to genet-
ics, the economic mechanisms identified in the theory are unlikely to provide a coherent
explanation of the pattern of mobility across countries.
We use household income data from Chinese Household Income Project (CHIP 2002,
CHIP 1995) and National Sample Survey (NSS 1993) of India to explore the mechanisms
behind the pattern of relative mobility (the IGRCs) across farm and nonfarm households.
The evidence suggests an important role for the economic forces (returns to education and
occupational persistence) in explaining the pattern of educational persistence across farm
and nonfarm households, both in rural India and rural China. The theory also suggests that
we should observe changes in the intergenerational educational persistence in rural China for
the younger generation because of the effects of reform on the relevant mechanisms such as
higher returns to education in nonfarm occupations. In contrast to the separability observed
for the older cohorts, we indeed find evidence of emerging complementarity between father’s
education and nonfarm occupation in rural China for the educational attainment of the younger
generation (18-28 years old in 2010).
The rest of the paper is organized as follows. Section 2develops a model of intergen-
erational persistence with credit constraint that incorporates the salient features of farm vs.
nonfarm households relevant for educational attainment. The next section describes the esti-
mating equations derived from the theory and the empirical issues in understanding potential
complementarity between parent’s education and nonfarm occupation in determining chil-
dren’s schooling. Section 4discusses the data and Section 5reports the main empirical
estimates. The following section explores the evidence on the mechanisms identified by the
theory underlying the observed pattern of slope (IGRC) and intercept estimates for the cohorts
who went to school in the 1990s or earlier. Section 7provides evidence on possible changes
in the pattern of educational mobility in rural China for the younger generation (18-28 years
old in 2010). The paper concludes with a summary of the main results from the theoretical
and empirical analysis.
2 A theory of intergenerational educational persistence in a dualistic
rural economy
We develop an extension of the Becker-Tomes model with credit constraint (Becker and
Tome s 1986) to understand the role played by nonfarm occupations of parents in intergen-
erational educational mobility of children in a rural economy. The differences in expected
returns to education in farm vs. nonfarm households play an important role in this set-up.
Expected returns to education in our context depends on two factors: the probability of getting
a non-farm employment, and the difference between returns to education in farm vs. nonfarm
occupations. The goal in this section is to derive an estimating equation that incorporates
these differences in farm and nonfarm households.
The basic set-up
The economy consists of households with a father and a son. We couch the discussion in
terms of father and son given that our empirical analysis focuses on the father-son linkages
in schooling attainment. The father of child iis described by a pair SP
i,Op
iwhere Sp
iis the
education (years of schooling) and Op
i∈{f,n}with fdenoting farming occupation and n
17 It is well-understood that the instrumental variables strategy provides estimates of local average treatment
effect (LATE), and refers only to the subset of the population whose treatment status is affected by the
instrument.
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748 M.S. Emran et al.
denoting nonfarm occupation of the father. Given his education and occupation, the father’s
income is determined as follows:18
Yp
i=Ypj
0+Rpj Sp
i;j=f,n(1)
The income determination equation assumes that the fathers with zero years of schooling
working in occupation jearns Ypj
0>0,andthereturnstoeducationinoccupation jis Rpj for
the parental generation. The assumption that Ypj
0>0 is motivated by our empirical context
where a substantial proportion of fathers has zero years of schooling, but positive household
income. It is important to underscore that the focus is on how a household’s ability to invest
in education changes with the education of the father. The “returns to education” relevant
here thus relate to permanent household income, not an individual’s labor market earnings
in a given year which has been the focus of much of the literature on the Mincerian returns
to education.19 In general, the intercepts are likely to be different, but whether Ypn
0is larger
or smaller than Ypf
0will depend on the quality of the nonfarm activities at low education
level Sp
i=0which is likely to vary across countries. If low-end nonfarm activities have
low-productivity, then it is possible that Ypn
0<Ypf
0.
The father allocates Yp
ito own consumption Cp
iand investment in child’s education Ii;
thus the budget constraint is
Yp
iCp
i+Ii(2)
The educational investment is made from a father’s own income, as there is little or no
financing available from the credit market for such investments in developing countries. The
education production function assumes that years of schooling depends on father’s investment
(Ii) and a child’s cognitive ability (φi):
Sc
i=F(Ii)=θ0+θ1φi+θ2Ii(3)
The a priori sign restrictions are: θ0≥0, θ1,θ
2>0. We would expect θ0to be higher
when government policies such as free primary schooling (including free books and midday
meals etc) are in place so that a child can get a certain level of education, for example, primary
schooling without any significant investment by the parents. A higher φiimplies that child
ihas higher innate cognitive ability, and a higher ability produces more schooling, ceteris
paribus. We normalize the ability measure such that E(φi)=0.
The productivity of parental financial investment is represented by the parameter θ2which
captures, among other things, the quality of schools available (and affordable) to a family. For
example, there is evidence that children have better learning outcomes in rural India when
they attend private schools (Kingdon (2017)). The parents need to pay fees for private school
while the public schools do not charge any fees, but the quality of schooling is, in general,
low in the public schools. The productivity of financial investment is likely to be higher
when the private education market is well-developed and parents can buy better quality by
paying higher tuition and/or donations for admission. In contrast, when schooling is primarily
provided by the government free of charge (including free books and midday meals) and the
private market is thin or nonexistent, the role played by parent’s investment in children’s
18 This specification is similar to that in Solon (2004).
19 This point may be especially important in rural China during 1980s and early 1990s when the labor market
was still not functioning very well, and the labor market earnings would be a poor measure of a household’s
economic status.
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Occupational dualism and intergenerational mobility 749
education is expected to be rather limited, making θ2small.20 Note that the access to better
quality schools does not depend on a parent’s occupation in the formulation in Eq. 3(i.e., θ
is not indexed by j).
Parent’s optimization
The consumption sub-utility function of the parent is given by:
UCp=α1Cp−α2Cp2(4)
Denote the expected income of a child iwith education Sc
iat the time of the parental invest-
ment choice by EYc
i|Sc
i. The parent’s optimization problem is (denoting the Lagrange
multiplier on the budget constraint by λ):
MaxCp,IVp=UCp+σEYc
i|Sc
i+λYp
i−Cp
i−Ii(5)
subject to Eqs. 1and 3. In this formulation, the parameter σis the degree of parental altruism.
The expected income of the child EYc
i|Sc
idepends on the probability of getting a nonfarm
job, and is given as follows:
EYc
i|Sc
i=πnj
iRcn +1−πnj
iRcf Sc
i(6)
where πnj
i≥0 is the probability that child igets a nonfarm job when the father is employed
in occupation j=n,f. To simplify notation, we write πn
iOp
i=j≡πnj
i;j=n,f.
If there is intergenerational persistence in non-farm occupations then the probability that a
child gets a nonfarm job is higher when the parent is also in the nonfarm occupations, i.e.
πnn
i>π
nf
i. This may reflect learning by doing at the parent’s workplace through informal
apprenticeship, referral and network effects in the labor market, and role model effects (for
a discussion, see Emran and Shilpi (2011)). Government policies also affect the strength of
occupational persistence. As noted earlier, a major policy difference between rural India and
rural China during our study period is the restrictions on rural-urban migration in China.
These restrictions implied that many more children had to stay back in the rural areas in
China compared to the counterfactual of no such restrictions (the India case). A substantial
literature on occupational mobility in rural China shows that the Hukou restrictions reduced
the persistence in non-farm occupations, as many children of the nonfarm parents had to take
up farming activities because they could not migrate to the urban labor market. This implies
that we should expect πnn
i(India)>π
nn
i(China).
The first order conditions for parent’s optimization are:
α1−2α2Cp−λ=0
σθ
2πnj
iRcn +1−πnj
iRcf −λ=0(7)
20 However, the “free schooling” offered by the governments may not be free, especially for the poor house-
holds, because of corruption when the enforcement system is not unbiased and impersonal. Emran et al.
(2020a) show that, in Bangladesh, the poor parents are more likely to pay bribes for admission into “free”
public schools.
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750 M.S. Emran et al.
The first order conditions and the budget constraint together yield the following solution
for the optimal investment in a son’s education:21
I∗
i=χj
0+Rpj Sp
i(8)
where
χj
0=Ypj
0+1
2α2σθ
2πnj
iRcn +1−πnj
iRcf −α1(9)
Intergenerational persistence equation
Combining Eqs. 3and 8above, we get the following relationship between the education
of the father and that of a son, determined by the optimal investment decision:
Scj∗
i=ψj
0+ψj
1Sp
i+˜εi(10)
where
ψj
0i=θ0+θ2χj
0
ψj
1=θ2Rpj ;˜εi=θ1φi
(11)
Equation 10 is consistent with the almost universally used specification for intergen-
erational schooling persistence in the literature, but it allows for possible differences in
educational opportunities across the farm and nonfarm households. Some examples of stud-
ies that use this linear-in-levels specification are: Neidhofer et al. (2018); Narayan et al.
(2018) and Hertz et al. (2007) on cross-country analysis, Azam and Bhatt (2015) and Emran
and Shilpi (2015)onIndia,andEmranandSun(2015a) on China. However, we are not aware
of any studies on intergenerational educational mobility in developing countries that derive
the estimating equation from a theoretical model.
It is standard to assume homothetic functional forms in the analysis of intergenerational
income mobility, as the estimating equation is log-linear (see, for example, Solon (1999,
2004)). The reliance on the linear in levels (years of schooling) specification in the literature on
intergenerational educational persistence partly reflects the fact that a substantial proportion
of fathers have no schooling. This is especially relevant in the rural areas of developing
countries such as India where about 40 percent of fathers have no schooling (estimate based
on REDS 1999 data). The estimating Eq. 10 is also consistent with the common assumption
in the literature that the omitted ability is captured in the error term of the intergenerational
persistence regression, and can lead to ability bias in the OLS estimates of the parameters.22
An important implication of Eq. 10 is that both the slope and the intercept of the persistence
equation capture the differences in educational opportunities faced by the children in farm vs.
nonfarm households, although the existing literature focuses largely on the slope (IGRC).23
21 Note that the missing credit market is important for an interior solution given that the education production
function has constant returns to parental financial investment. If there is a credit market where parents can
borrow at a fixed interest rate to finance investments in children’s schooling, the solutions to the optimization
problem takes the bang-bang form (either zero or infinite). For a model where such a credit market exists, and
there is diminishing returns to investment in education production function, see Becker et al. (2018).
22 Although most of the existing studies on intergenerational mobility adopt this additively separable speci-
fication for the impact of ability, there is little evidence on the validity of this assumption. In a recent paper,
Ahsan et al. (2020) use measures of ability based on Raven’s test and two memory tests in Indonesia and find
evidence in favor of this assumption.
23 For example, none of the 13 studies on educational mobility in developing countries summarized in Emran
et al. (2018) report estimates of intercepts. Some of the more recent works report measures of absolute mobility
that combines both the slope and the intercept effects (following Chetty et al. (2014)), but do not report the
intercept estimates separately.
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Occupational dualism and intergenerational mobility 751
An interpretation of the intercept term in our context is that it provides an estimate of the
expected education of the children from the subset of households where the fathers have zero
schooling. Thus, the intercept estimate may be especially important in developing countries
where a significant proportion of the households have parents with zero schooling in the
data. In the empirical analysis, we thus pay close attention to the estimated intercepts across
farm and nonfarm households in addition to the standard relative mobility measures based
on slopes.
Equally important, Eqs. 10 and 11 help improve our understanding of the economic
mechanisms behind the observed pattern of mobility. For example, consider the factors
that determine the intercepts across farm and nonfarm households. The intercept is, ceteris
paribus, higher (lower) for the nonfarm children when the income of the parents with zero
schooling is higher (lower) in the nonfarm occupations. These nonfarm occupations are,
however, likely to be unskilled as they do not require any schooling. In some countries, the
low-skilled nonfarm occupations may yield very low income, lower than the income of the
farmers (Lanjouw and Lanjouw 2001, World Bank (2011)), making the intercept for the
nonfarm households smaller. Another important implication, noted before, is that intergen-
erational persistence in occupational choices is likely to affect the relative magnitudes of
the intercept terms. When ˆπnn
i>ˆπnf
i,itismorelikelytohave ˆ
ψn
0>ˆ
ψf
0,ceteris paribus,
assuming that Rcn >Rcf .Conversely,if ˆπnn
i>ˆπnf
ibut Rcn <Rcf , then it is more likely
to have ˆ
ψn
0<ˆ
ψf
0,ceteris paribus. This implies that the evidence on intergenerational occu-
pational persistence (farm vs. nonfarm) accumulated independently in a sub-strand of the
literature is necessary to understand the pattern of intergenerational educational mobility in
a rural economy. As emphasized by Emran and Shilpi (2019), the interactions between occu-
pational and educational mobility are not considered in the existing literature on developing
countries; there are two sub-strands of the literature that grew independently: one focusing
solely on education and the other focusing solely on occupation.
The relative magnitudes of the slope parameters (IGRC) across farm and nonfarm house-
holds depend on the household returns to education in the parental generation Rpjaccording
to Eq. 11. Thus, Rpn >Rpf generates complementarity between the parent’s education and
occupation in determining children’s schooling.24 This provides testable implications to
check the importance of economic forces in the observed differences in relative mobility
across farm and nonfarm households in China vs. India.25
3 Empirical approach
Equation 10 above suggests the following estimating equation for the combined farm and
nonfarm sample which we take as a benchmark:
Sc
i=ψ0+ψ1Sp
i+εi(12)
24 The theoretical analysis shows that different roles are played by the parental returns to education and
the expected returns to education in children’s generation, a point not adequately recognized in the current
literature. It is important to appreciate that the children’s expected returns to education at the time of the
investment decision do not affect the slope (IGRC), their effects are mediated only through the intercept of
the persistence regression.
25 Returns to education are identified as a major factor in changes in intergenerational income persistence
(see Becker and Tomes (1979,1986), Solon (1999,2004).
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752 M.S. Emran et al.
where εi=˜i+ηi=θ1φi+ηi,andηicaptures exogenous idiosyncratic shocks to children’s
schooling. We normalize so that E(ηi)=0.The corresponding estimating equation allowing
for different intercepts and slopes for the farm and nonfarm households is:
Sc
i=ψf
0+ψf
1Sp
i+λ0Dnp
i+λ1Sp
i∗Dnp
i+εi(13)
where Dnp
iis a dummy variable that takes on the value of 1 when the father of child i
is employed in nonfarm occupations, and zero otherwise. In this formulation, the measure
of relative mobility is IGRC: ψf
1for the farm households and ψf
1+λ1for the nonfarm
households; we denote ψf
1+λ1≡ψn
1. Similarly, the intercepts are given by ψf
0(farm) and
ψn
0≡ψf
0+λ0(nonfarm).
Note that we do not include any controls in Eqs. 12 and 13. As emphasized by Emran
and Shilpi (2019), including household or individual characteristics may bias the estimate in
our case because father’s education is a summary statistic for all family background factors
that determine children’s schooling. Following the seminal contribution of Solon (1992), it
is standard in the literature on intergenerational income mobility to include quadratic age
controls for both the parents and the children. This helps reduce the biases that arise from
life-cycle effects in estimating permanent income.26 The life-cycle bias is not likely to be a
concern in our application, as we chose the age cut-off to ensure that most of the children
completed schooling by the time the survey was done. Our main estimates thus do not include
any age controls; but, as a robustness check, we estimate Eq. 13 including age controls.
It is important to appreciate that a comparison of farming and nonfarming households
based solely on the most widely used measure of mobility, i.e., IGRC (ψf
1and ψn
1), may be
misleading. The caveat that IGRC or other measures of relative mobility such as intergener-
ational correlation (IGC) may be misleading in comparing mobility across groups has been
emphasized by Hertz (2005), Mazumder (2014), and Bhattacharya and Majumder (2011)
in their analysis of racial (black-white) differences in intergenerational income mobility in
United States of America. But it has not been adequately appreciated in the literature on
intergenerational educational mobility, both in economics and sociology.27 This is especially
so in developing countries, as is evident from the fact that most of the available studies on
China and India we are aware of focus exclusively on relative mobility measures such as
IGRC, and IGC (intergenerational correlation).
To see the pitfalls in relying on IGRC alone in our context, it is instructive to consider the
case where ψn
1>ψf
1so that intergenerational persistence is higher in nonfarm households.
However, whether this higher persistence leads to convergence or divergence in schooling
attainment of children born into farm and nonfarm households depends on the relative mag-
nitudes of the intercepts. When the intercepts are ψn
0>ψf
0, the expected schooling is higher
for children born into nonfarm households across the distribution of parental schooling, and
the gap between the two groups widens as parental education increases (please see Fig. 1).
On the other hand, we can have two sub-cases when the intercepts are: ψn
0<ψ
f
0(please
see Fig. 2). If the IGRC for the nonfarm group is high enough, the children born to lower
educated nonfarm households are disadvantaged compared to the children of low-educated
farmer parents, but at the higher end of parental education distribution they are relatively
advantaged (see nonfarm(a) line in Fig. 2). When the difference between IGRC estimates is
26 When data on many years spanning the appropriate phases of life-cycle are available, the age controls are
not necessary. Some recent contributions do not include any age controls, as it may wipe out the inter-cohort
differences in income mobility.
27 See the discussion on this point by Torche (2015) in the context of Sociological literature on mobility.
123
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Occupational dualism and intergenerational mobility 753
Fig. 1 Intergenerational schooling relationships when both the intercept and the IGRC are greater for nonfarm
households
small enough, the farmer’s children are better off in educational attainment over the entire
distribution, and only in this special case, the conclusion based on IGRC that nonfarm chil-
dren face lower relative mobility is consistent with the idea that they are at a disadvantage in
educational attainment (please see the nonfarm(b) line in Fig. 2).
Rank-based measures of intergenerational mobility
While most of the existing studies on intergenerational educational mobility in developing
countries rely on years of schooling as the indicator of educational attainment, following the
influential contribution of Chetty et al. (2014), the recent literature is increasingly adopting
the rank-based measures where the indicator of educational status is the percentile rank in the
relevant distribution. A growing literature suggests that the rank-based measures of mobility
are significantly more robust to data limitations compared to the measures based on years of
schooling (Nybom and Stuhler 2017; Emran and Shilpi 2018).
Denote rc
ias the percentile rank of child iin the over-all (including both farm and nonfarm)
schooling distribution of children, and rp
ithe percentile rank of the father of iin the over-all
schooling distribution in fathers generation.28 For the rank-based estimates, the estimating
equations are as follows:
rc
i=δ0+δ1rp
i+ξi∀i(14)
rc
i=δf
0+δf
1rp
i+λ2Dnp
i+λ3rp
i∗Dnp
i+ξi(15)
The slope parameters of regression Eqs. 14-15 represent intergenerational rank-rank slope
(IRRS, for short) which is a measure of relative mobility similar to IGRC. The IRRS is given
by δf
1for farm and δn
1≡δf
1+λ3for nonfarm. Similar relations hold for the intercepts.
However, there are important differences between IGRC and IRRS as measures of mobil-
ity, especially for education. A common argument is that IRRS is not affected by the growth
at the top of the distribution, or the changes in cross-sectional inequality across generations.
As noted recently by Ahsan et al. (2022), this argument is valid for a continuous variable with
large support such as income, but not for education (years of schooling) which is a discrete
variable with limited support. They report evidence that when ranks are calculated for years
of schooling using the standard mid-rank method, the cross-sectional schooling inequality
28 Following the statistics and economics literature, we calculate the ranks using the mid-rank method.
123
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754 M.S. Emran et al.
Fig. 2 Intergenerational schooling relationships when the IGRC is greater but the intercept is lower for nonfarm
households
across generations can be substantially different.29 The economic interpretation of these two
measures are also very different. IGRC captures the effects of changes in economic policies
such as school construction and trade liberalization as they primarily affect the marginal
distributions. IRRS, in contrast, captures primarily the effects of deep-seated institutional
structure and social norms, for example, racial bias, and caste discrimination. It is important
to appreciate these differences when comparing estimates of IGRC and IRRS.30
Interaction between parent’s education and occupation: complementary, substitutes
or separable?
An important advantage of the empirical models discussed above is that they provide
a straight-forward way to test the nature of interaction between parent’s occupation and
education in determining intergenerational persistence in schooling. Consider, for example,
the estimating Eq. 13 above; from the theoretical analysis in Section (2), it is easy to derive the
conditions under which parental education and non-farm occupation can be complementary,
i.e., λ1>0 implyingψn
1>ψf
1, substitutes, i.e., λ1<0 implying ψn
1<ψf
1,or
separable, i.e., λ1=0 implying ψn
1=ψf
1. The prevailing view among many observers
is that nonfarm occupation and education are likely to be complementary in determining
children’s education, leading to cumulative forces of inequality in educational attainment
and income in villages in developing countries (see, for example, Rama et al. (2015)). Yet,
to the best of our knowledge, there is no evidence in the literature on the existence and the
nature of the interaction between parent’s education and occupation in determining children’s
educational attainment. Also, without a formal model, the economic mechanisms behind the
hypothesized complementarity cannot be assessed. According to the theoretical model above,
such complementarity requires that returns to education for the parents is higher in non-farm
29 As a results, the rank-rank slope is different from the rank correlation in this case. For a continuous variable,
the rank-rank slope is equal to the rank correlation.
30 Recent evidence suggests that these two measures can lead to conflicting conclusions regarding the causal
effects of government policies. See, for example, the analysis of the INPRES primary schools (60,000 primary
schools constructed in the early 1970s in Indonesia) on intergenerational educational mobility by Ahsan et al.
(2023).
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Occupational dualism and intergenerational mobility 755
occupations, i.e., Rpn >Rpf .31 This is one of the predictions that we take to the data
as a test of the importance of economic mechanisms underlying the observed pattern of
intergenerational educational persistence.
4 Data
For our main empirical analysis, we use two exceptionally rich surveys that collected data
on children irrespective of their residency status at the time of the survey. The data for rural
India come from the Rural Economic and Demographic Survey (REDS) carried out by the
National Council for Applied Economic Research, and the source of the data for rural China
is the China Family Panel Studies (CFPS) implemented by the Institute of Social Science
Survey unit of Peking University.32
This is an important advantage for the empirical analysis, as most of the evidence on
intergenerational educational mobility in India and China currently available are based on
data that suffer from truncation due to coresidency restrictions used to define household
membership. Emran et al. (2018) summarize 13 studies on intergenerational educational
mobility in developing countries, only two of which use data not affected by coresidency
bias.33 While Emran et al. (2018) provide evidence of substantial downward bias (average 18
percent) in the IGRC estimate from the coresident sample in rural India, we are not aware of
any similar estimate for rural China. In online appendix A, we provide evidence on the extent
of coresidency bias in rural China in a widely used household survey: the Chinese Household
Income Project (CHIP). In particular, we compare the estimates from the CFPS (without any
sample truncation) with those from the CHIP 2002 for the overlapping age cohorts. The
evidence shows that the IGRC estimate from the CHIP 2002 is 25 percent smaller because
of truncation of the sample arising from coresidency restrictions (see Table A.2 in the online
appendix). A comparison of CHIP 2002 with the CFPS is also of independent interest, because
CHIP 1995 and 2002 have been used by many researchers to study intergenerational mobility
in China.34 For a more complete discussion, please see online Appendix A.
We use the 1999 round of the REDS and the first round of the CFPS in 2010. From
the REDS data, we obtain the relevant information for our analysis on all father-son pairs
irrespective of residency status at the time of the survey. For the CFPS data, we restrict to
rural communities subsample, given our focus on intergenerational mobility in rural areas,
and use the family roster to obtain a complete list of father-son pairs that includes all sons of
the household head irrespective of their residency status at the time of the survey.
31 If private school locations are motivated by higher income associated with nonfarm activities, then school
quality may also play a role in generating complementarity. In this case, the productivityof parental investment
θ2will be correlated with occupation, i.e., θn
2>θf
2.
32 One might wonder why we chose not to use the IHDS 2012 round survey for India which would provide a
survey year close to the survey year of CFPS in China. The CFPS and REDS are the most comparable in that
they provide a random sample of parents with information on all their children irrespective of the residency
status of a child at the time of the survey. The IHDS, in contrast, contains a random sample of children with
information on their parents irrespective of their residency status at the time of the survey.
33 The exceptions are Fan et al. (2021) and Azam and Bhatt (2015).
34 Two of the authors of this paper, M. Shahe Emran and Yan Sun, used CHIP 2002 data to analyze the effects
of farm and nonfarm occupations on intergenerational educational mobility in rural China (see Emran and Sun
(2015b)). We decided not to publish that paper because of the worry about the biases due to sample truncation
arising from coresidency. This paper replaces (Emran and Sun 2015b) and the conclusions on rural China here
supersede those in Emran and Sun (2015b).
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756 M.S. Emran et al.
The main samples for our analysis consist of children aged 18 - 54 in the 1999 REDS
survey, and 29 - 65 in the 2010 CFPS survey. This ensures that we focus on the same age
cohorts of children who went to school mostly during the 1980s and 1990s. It is important to
recognize that such an analysis for the overlapping age cohorts is meaningful for education,
as most of the children under focus (29-65 years old in 2010) in China have completed their
schooling by 1999, even though the information was gathered later in 2010. The observations
with fathers aged over 100 years or missing, or sons aged over 65 years are excluded from
the samples used in the empirical analysis.
In each data set, we observe the education level and an indicator of whether the main occu-
pation is agriculture or nonfarm activity for both the father and the son. Our main analysis of
educational mobility is based on years of schooling as the measure of educational attainment.
Father’s schooling is used as the indicator of parental education to avoid complications from
many missing observations on mother’s schooling. In our data sets, the maximum of parental
education coincides with the education level of the father in most of the cases. We define
the parental occupation dummy Dnp
i=0 when the father of child ireports agriculture as
the main occupation (corresponding to Op
i=fin the theoretical model), Dnp
i=1oth-
erwise. This means that the households who are primarily engaged in farming with some
nonagricultural sources of income are classified as agricultural occupation.
Online appendix Table A.1 shows the descriptive statistics of our main data samples from
the REDS and the CFPS. In the REDS sample, we have 6887 observations, and the children’s
are 29 years old on average in the survey year 1999. Fathers are 60 years old on average.
About half of the children’s main occupation is agriculture, while 60% of the fathers also
reported agriculture as their main occupation. The children attain significantly higher levels
of education than the fathers, when comparing their average years of schooling (6.26 vs.
4.13.)
In CFPS sample, a similar pattern is observed. We have 3,305 father-son pairs, and chil-
dren’s age is about 40 years in the survey year, 2010 (29 years in 1999, same as that for
India in 1999 REDS data). Fathers are aged 68 years on average in 2010. About half of the
fathers work in the agricultural sector. Children receive 6.31 years of schooling on average,
significantly higher than their fathers (less than 3.81 years).
While our main empirical analysis is based on the CFPS 2010 and REDS 1999, we
take advantage of a number of additional data sets for exploring the economic mechanisms
identified by the theoretical analysis. To understand how the relation between a father’s
education and household income varies by farm and nonfarm occupation in rural China we
utilize the data from the Chinese Household Income Project (CHIP) 1995 and 2002. To
estimate the relation between father’s education and household income in rural India, we use
the data on household total expenditure from the National Sample Survey 1993.
5 Empirical results
5.1 Evidence on relative mobility and test of complementarity
Tabl e 1reports the estimates of relative mobility using two measures: intergenerational regres-
sion coefficient (IGRC) and intergenerational rank-rank slope (IRRS). In addition to the
separate estimates for the farm and nonfarm households, we report the estimates from the
combined farm and nonfarm sample as a benchmark.
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Occupational dualism and intergenerational mobility 757
Table 1 Relative Mobility and Test of Complementarity: Rural China and Rural India
IGRC ψj
1IRRS ψj
1
CHINA INDIA CHINA INDIA
Combined Sample 0.313 0.518 0.337 0.456
(0.025) (0.020) (0.017) (0.014)
Farm 0.311 0.488 0.331 0.422
(0.033) (0.027) (0.032) (0.024)
Nonfarm 0.316 0.555 0.344 0.500
(0.029) (0.027) (0.031) (0.024)
Test of Complementarity (Farm/Nonfarm)
H0: Farm and Nonfarm Coefficients are Equal
IGRC ψn
1=ψf
1IRRS δn
1=δf
1
CHINA INDIA CHINA INDIA
F Statistic 0.014 3.803 0.134 6.421
P-Value 0.906 0.052 0.715 0.012
Notes: (1) IGRC stands for Intergenerational Regression Coefficient, and IRRS stands for Intergenerational
Rank-Rank Slope. (2) The numbers in parenthesis are robust standard errors clustered at the Primary Sampling
Unit level. (3) The number of observations for China: Combined (3305), Farm (1662), Nonfarm (1643), and
for India: Combined (6952), Farm (4035), Nonfarm (2917)
Table 2 Estimates of intercepts and test of equality
IGRC Intercept ψj
0IRRS Intercept δj
0
CHINA INDIA CHINA INDIA
Combined Sample 5.862 5.339 0.381 0.340
(0.221) (0.165) (0.010) (0.009)
Farm 5.713 5.612 0.371 0.364
(0.282) (0.219) (0.025) (0.017)
Nonfarm 6.012 4.980 0.391 0.307
(0.237) (0.193) (0.022) (0.015)
Test of Equality (Farm/Nonfarm)
H0: Farm and Nonfarm Intercepts are Equal
IGRC Intercepts ψn
0=ψf
0IRRS Intercepts δn
0=δf
0
CHINA INDIA CHINA INDIA
F Statistic 1.181 6.233 0.626 7.784
P-Value 0.279 0.013 0.430 0.006
Notes: (1) The numbers in bold in the upper panel are estimates of the intercepts from intergenerational
persistence regression using years of schooling (Called IGRC intercepts), and from rank-rank regressions
(called IRRS intercepts). (2) the numbers in parenthesis are robust standard errors clustered at the Primary
Sampling Unit level
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758 M.S. Emran et al.
Table 3 AET (2005) sensitivity analysis for ability bias
The effects of father’s higher education on the probability of higher education of sons
CHINA INDIA
Farm Nonfarm Farm Nonfarm
ρ=0 1.72 2.83 8.68 9.99
(0.38) (0.46) (0.44) (0.45)
ρ=0.10 1.17 2.14 7.64 9.46
(0.44) (0.51) (0.47) (0.48)
ρ=0.20 0.38 1.2 6.27 8.65
(0.50) (0.57) (0.51) (0.53)
ρ=0.30 −0.66 −0.02 4.53 7.48
(0.57) (0.63) (0.55) (0.58)
ρ=0.40 −1.99 −1.52 2.39 5.88
(0.64) (0.68) (0.58) (0.63)
Notes: (1) AET (2005) stands for Altonji, Elder and Taber (2005, Journal of Political Economy) Biprobit
sensitivity analysis. (2) ρstands for correlation in cognitive ability of father and son. Estimates in the first row
are the univariate Probit estimates. The upper bound ρ=0.40 is based on economics and behavioral genetics
literature. (3) The numbers in bold are percentage points increase in the probability of higher education of
sons when the father has higher education. (3) Higher education for parents implies more than primary, and
for sons in India more than 10 years of schooling, for sons in China more than 9 years of schooling. (4) The
numbers in parenthesis are standard errors clustered at the PSU level
The point estimates of IGRC show that, both in rural India and rural China, intergenera-
tional persistence in schooling is higher for the sons born into nonfarm households, but the
estimates for farm and nonfarm households are similar in magnitude in China. A son of a
father with 1 year more schooling in India is expected to gain 0.49 year of schooling on aver-
age if the father is a farmer, while the expected gain increases to 0.56 year of schooling when
the father is employed in a nonfarm occupation (column 2 of Table 1). The corresponding
estimates for rural China are 0.31 year (farm) and 0.32 year (nonfarm) of additional schooling
for the sons born to a father with 1 year of more schooling (column 1 of Table 1). Another
important conclusion from the evidence in Table 1is that all of the IGRC estimates in rural
China are smaller compared to the corresponding estimates in rural India, providing strong
evidence that the sons in rural China who went to school in the 1980s and 1990s enjoyed
substantially more relative mobility in schooling. The conclusions above remain valid when
we include age controls in the specifications (see Table A.3 in the online appendix ).35
The estimates of intergenerational rank-rank slope (IRRS) reported in columns 3 and 4 of
Tabl e 1also tell a similar story: the point estimates of the influence of a father’s schooling
rank on the son’s schooling rank are higher for the nonfarm households, both in China and
India. Again, the influence of parental education does not vary substantially between farm
and nonfarm household in rural China, but there is substantial difference in rural India. The
magnitudes of the IRRSs are consistently smaller in rural China compared to those in India,
reinforcing the conclusion from the IGRC estimates that the sons in rural India faced lower
educational mobility. These conclusions from the IRRS estimates remain intact when we
include age controls in the specification (see Table A.4 in the online appendix).
35 Since life-cycle bias is not likely to be a major issue in our context, our preferred estimates are from the
specification without age controls.
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Occupational dualism and intergenerational mobility 759
Table 4 Father’s education and household income
Panel A: Estimates for Rural China
Intercept Slope (Returns to Education)
Farm Nonfarm Farm Nonfarm
CHIP 2002 8344.64 8582.80 275.32 335.42
(670.34) (832.97) (91.38) (108.72)
CHIP 1995 4608.86 4457.28 34.21 107.45
(666.28) (383.14) (71.14) (43.19)
Test of Equality Between Farm and Nonfarm
H0: Intercepts are Equal H0: Slopes are Equal
CHIP 2002
F Statistic 0.08 0.30
P-value 0.78 0.58
CHIP 1995
F Statistic 0.06 0.96
P-value 0.81 0.33
Panel B: Estimates for Rural India
Intercept Slope (Returns to Education)
Farm Nonfarm Farm Nonfarm
NSS 1993 1199.27 1172.17 68.17 77.92
(9.67) (13.57) (2.52) (3.28)
Test of Equality Between Farm and Nonfarm
H0: Intercepts are Equal H0: Slopes are Equal
F Statistic 2.99 5.71
P-value 0.08 0.02
Notes: (1) The dependent variable for Rural China is the average household income (total). CHIP 2002 is
the average of the last 5 years of total household income, and CHIP 1995 is the average of the last 3 years
of household income. The dependent variable for India is total household expenditure. (2) The numbers in
parenthesis are standard errors. (3) H0stands for Null Hypothesis. (4) The number of observations for CHIP
1995: Farm (1709), Nonfarm (3893), and for CHIP 2002: Farm (4457), Nonfarm (4087). For NSS (1993), the
number of observations are Nonfarm (20535), and Farm (48196)
The contrasting evidence in China vs. India suggests that father’s education and nonfarm
occupation are likely to be complementary in India, but separable in China. We formally
test the null hypothesis of separability H0:ψf
1=ψn
1. The results are reported in the lower
panel of Table 1, with standard errors clustered at the primary sampling unit (village in REDS
data, and county in CFPS data). The evidence from both IGRC and IRRS estimates shows
that, in rural China, the null hypothesis of separability cannot be rejected at the 10 percent
significance level; the F statistic for IGRC estimates is 0.014 with a P-value of 0.90, and
the corresponding numbers for IRRS are 0.13 (F statistic) and 0.72 (P-value). In contrast, in
rural India, the null hypothesis of separability is rejected at the 10 percent level for IGRC
(F=3.80, P-value=0.052), and at the 5 percent level for IRRS (F=6.42, P-value=0.012). Since
the estimated influence of parental schooling is larger in the nonfarm households in rural
123
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760 M.S. Emran et al.
Table 5 Intergenerational persistence in education (18-28 Age Cohort, CFPS)
Panel A: Estimates Based on Years of Schooling
Intercept (ψ j
0)IGRC ψj
1
Farm Nonfarm Farm Nonfarm
6.997 7.173 0.274 0.337
(0.341) (0.308) (0.035) (0.032)
Test of Equality Between Farm and Nonfarm
H0: Intercepts are Equal (ψn
0=ψf
0)H0: IGRCs are Equal (ψn
1=ψf
1)
F Statistic 0.244 2.632
P-Value 0.622 0.107
Panel B: Estimates Based on Schooling Ranks
Intercept (δ j
0)IRRS (δ j
1)
Farm Nonfarm Farm Nonfarm
0.399 0.404 0.304 0.373
(0.027) (0.027) (0.034) (0.035)
Test of Equality Between Farm and Nonfarm
H0: Intercepts are Equal (δn
0=δf
0)H0: Slopes are Equal (δn
1=δf
1)
F Statistic 0.028 2.792
P-Value 0.868 0.097
India, the evidence suggests complementarity between nonfarm occupation and a father’s
education in determining a son’s schooling.
Relative mobility and long-term variance in schooling
When interpreted as a dynastic model of the evolution of schooling across generations, a
higher IGRC implies a higher long-term variance in schooling.36 To see this, note that for
the IGRC Eq. 12, we can write the long-term variance of education as:
σ2
s=1
1−ψ2
1σ2
ε(16)
where σ2
sis the long-term variance of education and σ2
εis the long-term variance of the
error term capturing all other factors unrelated to father’s schooling such as market luck, and
macro and trade shocks. 1
1−ψ2
1is called the ‘family background multiplier’ by Emran
and Shilpi (2019), which amplifies the impact of the shocks to education. Using Eq. 16 and
the estimates of ψf
1and ψn
1reportedinTable1, we have the following estimates for sons in
farm and nonfarm households in rural India:
σ2
s,If =1.31σ2
ε(farm)
σ2
s,In =1.45σ2
ε(nonf ar m)
36 For a discussion on the dynastic interpretation of the model and the implications for long-term variance,
see Acemoglu and Autor (undated).
123
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Occupational dualism and intergenerational mobility 761
0
2
4
6
8
10
12
14
16
18
0 1 2 3 4 5 6 7 8 9 1011121314151617181920
gniloohcSfosraeY'snoS
Fathers' Years of Schooling
Farm Non-Farm
Fig. 3 Regression of fathers’ years of schooling against sons’ years of schooling in rural India
The subscripts Iand sdenote India and schooling, respectively, and as before, n=nonfarm
and f=farm. The long-term variance of education of sons in the farming sample is 31 percent
higher than the variance due to idiosyncratic factors alone (i.e., σ2
ε), and is 45 percent higher
in the nonfarm sample. Thus the contribution of family factors to the long-term variance is
14 percentage points higher in the nonfarm households.
The long-term variances in schooling for the farm and nonfarm households in China are:
σ2
s,cf =1.107σ2
ε(farm)
σ2
s,cn =1.111σ2
ε(nonf ar m)
The multiplier effect of family background is much smaller in the case of China; the long-
term variance in schooling is only about 10 percent higher than the variance of idiosyncratic
shocks, and the estimates are virtually identical across the farm and nonfarm samples.
5.2 Intercepts and steady states
As noted earlier, measures of relative mobility give us an incomplete, and sometimes mislead-
ing, picture of intergenerational mobility across groups such as farm and nonfarm households.
A simple but important reason is that different groups may be converging to different steady
states due to different intercepts in the intergenerational persistence equations. Perhaps more
importantly, the theory in Section (2) suggests that factors such as persistence in occupa-
tion choices, and expected returns to investment in schooling for children work through the
intercept, leaving relative mobility as measured by IGRC and IRRS largely unaffected.
The estimated intercepts of Eqs. 12-13 and 14-15 above are reported in Table 2. The point
estimates show that the intercept of the IGRC equation in India is significantly higher for
the farm households (p-value 0.013). The evidence for the intercept of the IRRS equation is
similar (p-value 0.006). When considered along with the evidence that the slope estimates
(IGRC and IRRS) are smaller for the farm households in India, the evidence implies a set of
interesting conclusions. First, whether the sons born to fathers in farm or nonfarm occupation
enjoy educational advantage depends on the level of their fathers’ education with a switching
threshold of 9-10 years of schooling. Please see Fig. 3. An interpretation of the evidence
is that the national public examination administered at 10th grade (known as Matriculation
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762 M.S. Emran et al.
0
2
4
6
8
10
12
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16
gnilooh
cSfosraeY'
snoS
Fathers' Years of Schooling
Farm Non-Farm
Fig. 4 Regression of fathers’ years of schooling against sons’ years of schooling in rural China
examination, or all India Secondary School Examination (SSC)) represents a bifurcation
point. The children of non-farm fathers with Matriculation or more schooling are expected
to achieve better schooling attainment when compared to the children of farmer fathers with
similar educational credential, but the children of nonfarm fathers with lower education (and
probably employed in unskilled nonfarm jobs) are likely to be worse-off when compared
to the children of low educated farmer fathers (who likely own land). Second, the steady
state level of education is not substantially different across farm and nonfarm households:
10.81 years of schooling (farm) and 11.20 years of schooling (nonfarm). This reflects the
fact that the sons born into nonfarm households gain more from the higher schooling of a
father, although they start from a lower intercept.
The picture for rural China is different (please see Fig. 4). The evidence in Table 2shows
that there is no statistically significant difference across the farm and nonfarm households
in the intercepts of the intergenerational persistence regressions (p-values are 0.279 (IGRC
intercept) and 0.43 (IRRS intercept)). When combined with the evidence on IGRC and IRRS
in Table 1, this implies that the schooling attainment of the sons in rural China converges to
virtually the same steady state (8.53 years of schooling) irrespective of whether the father is
a farmer or is engaged in a nonfarm occupation.37
5.3 Structural change and cross-sectional schooling inequality
To understand the implications of the higher variance in the nonfarm households in India
for the cross-sectional variance in rural schooling, it is important to consider the structure of
the rural economy (i.e., proportion of fathers employed in the nonfarm sector) and both the
within group and between groups variances. Denote the proportion of nonfarm households
by ω, then we can write the long-term variance as:
Var (S)=ωσ 2
s,n+(1−ω)σ2
s,f+ω(1−ω)μf−μn2(17)
37 The estimate of the steady state is based on the combined farm and nonfarm sample. Although they are not
statistically different, the point estimates differ numerically across farm and nonfarm subsamples.
123
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Occupational dualism and intergenerational mobility 763
where μnand μfare the long-term means (the steady state) of a father’s education in farm
and nonfarm households, respectively. The effects of a marginal increase in the proportion
of nonfarm sector is given by:
dVar(S)
dω=σ2
s,n−σ2
s,f+(1−2ω)μf−μn2(18)
As discussed in Sections (5.1)and(5.2) above, there are no significant differences in the
long-term means or long-term variances across the farm and nonfarm households in rural
China. This implies that both terms in Eq. 18 are zero, indicating that structural change in
favor of the non-farm sector during the decades of 1970s-1990s is unlikely to contribute to the
cross-sectional variance of schooling. As noted earlier, this, however, does not imply that the
nonfarm sector did not play any role in the increasing income inequality in rural China during
this period, only that the nonfarm sector’s effect is not mediated through intergenerational
educational persistence.
In India, the evidence in Section (5.1) shows that the long-term variance is substantially
higher in the nonfarm households because of a large family background multiplier. The
estimates of the long-term (steady state) means also show a higher mean for the nonfarm
households. When we plug in the estimates from Sections (5.1)and(5.2) for rural India in
Eq. 18 above, we get (using ω=0.40 from the summary statistics table in online appendix):
dVar(S)
dω|India=0.078 +0.13σ2
ε>0
Thus, the evidence suggests that structural change in favor of the nonfarm sector con-
tributed to higher cross-sectional variance in rural India during our study period.
6 Economic mechanisms: towards an explanation of the differences
between rural China and rural India
A major concern in the literature has been whether the observed pattern of intergenerational
linkages is primarily driven by omitted variables bias due to unobserved genetic correlations
between parents and children. An obvious approach to this question is to try to correct
the estimates for possible positive bias due to genetic correlations in cognitive ability. We
develop a simple but plausible approach by taking advantage of the recent evidence on
intergenerational correlation in cognitive ability from economics and behavioral genetics.
There is substantial evidence that intergenerational correlation in cognitive ability (denoted as
ρ) falls in a narrow interval, ρ∈[0.20,0.40]; see, for example, Black et al. (2009); Bjorklund
et al. (2010) on economic literature, and Plomin and Spinath (2004) on behavioral genetics
literature. We use this information in a biprobit sensitivity analysis as developed by Altonji
et al. (2005) to check if the estimates of intergenerational persistence in schooling remain
positive and statistically significant for plausible values of intergenerational correlation in
ability. We call this approach augmented AET (AAET) sensitivity analysis, and it requires
binary indicators of educational attainment instead of years of schooling. We use a dummy
for higher than primary schooling for fathers. For sons in rural China a dummy for higher
than 9 years of schooling (higher middle school), and for sons in rural India, a dummy for
more than 10 years of schooling (called SSC or matriculation) are used. The details of this
approach are provided in online Appendix B, and the estimates are reported in Table 3.
The evidence from the AAET sensitivity analysis suggests that, the estimated intergen-
erational schooling persistence in India is very strong, and the estimates remain statistically
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764 M.S. Emran et al.
significant and numerically substantial even when we impose ρ=0.40 in the biprobit model.
In contrast, the estimates turn negative in the case of rural China when ρ=0.30, suggesting
that the observed positive effect of a father’s education could be explained away by abil-
ity correlation between parents and children.38 This evidence strengthens substantially the
conclusions that educational mobility was much lower in India in the 1970s-1990s, and that
economic forces are likely to be important in explaining the differences between India and
China. The advantage of this approach is that it is easily implementable, and thus could be
used fruitfully by other researchers. However, it is also important to appreciate the limita-
tions of such an a-theoretical approach. For example, the evidence that the persistence in
rural China could be explained by genetic correlations alone does not necessarily imply that
economic forces were not at play. The theoretical analysis in Section (2) provides us a way
to explore the question by focusing on the economic mechanisms behind the pattern of the
slope and intercept estimates across farm and nonfarm households. We turn to this exercise
next.
Under the null hypothesis that genetic transmission is the main force at work, we should
not expect the economic mechanisms identified in the model to offer a consistent explanation
of the observed pattern of intergenerational persistence across India and China. If economic
forces are important, the theory provides us with testable implications even in the case of
rural China; the equality of the slopes (IGRCs) across the farm and nonfarm households in
this case implies equality of the returns to education for the farm and nonfarm parents.
6.1 Differences in the IGRCs
The estimates of IGRC in Table 1imply the following (denoting an estimate by a hat):
(China)ˆ
ψf
1=ˆ
ψn
1⇒θ2ˆ
Rpf =θ2ˆ
Rpn
(India)ˆ
ψf
1<ˆ
ψn
1⇒θ2ˆ
Rpf <θ
2ˆ
Rpn
The theoretical analysis thus highlights the importance of household-level returns to school-
ing in the father’s generation (Rpj)across occupations for understanding the pattern of
relative mobility. We have ˆ
ψf
1−ˆ
ψn
1=θ2ˆ
Rpf −ˆ
Rpn,and the effects of a widening gap
in returns to education for household income between farm and nonfarm households would
be low if θ2, the productivity of financial investment in children’s education, is low. We
would expect θ2to be low when the private market for education is not well-developed in a
country.39 Since the expansion of private schooling has been much larger in India compared
to that in China during the study period, the value of θ2is likely to be higher in India.
It is important to recognize that the “returns to education” for the parents (i.e., Rpj)differ
from most of the available estimates of returns to education for three reasons. First, we are
interested in the total income of all household members rather than the individual income
(i.e., not only a father’s income). Second, the focus of the existing literature has been on labor
market returns, while our analysis requires both labor and non-labor income. Third, a father’s
education in our analysis is not only a measure of human capital, but a summary statistic
for a family’s socio-economic status and captures the effects of other correlated factors, for
38 These conclusions remain robust when we define the schooling cut-off to be the same in the two countries,
and also when age controls are included in the specification. The details are available from the authors.
39 To see this clearly, consider the polar case where schooling is provided only by the government free
of charge and there is no private schools (or private tutoring). In this case, the scope for parental financial
investment to improve a child’s educational attainment is effectively nonexistent, making θ2≈0.
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Occupational dualism and intergenerational mobility 765
example, a mother’s education due to assortative matching in the marriage market.40 Thus,
we need a measure of permanent household income.
The Chinese Household Income Project (CHIP) provides us with high quality household
income data for rural households for multiple years (5 years in CHIP 2002, and 3 years in
CHIP 1995).41 Unlike China, the data on household income in rural India are, however, more
limited; we are not aware of any household survey data set that has good quality income
information for consecutive multiple years, similar to the CHIP data on China. We thus take
household expenditure reported in the National Sample Survey as our measure of household
permanent income.
Tabl e 4(panel A) provides estimates of household-level returns to education, Rpn and Rpf ,
in rural China and tests the null hypothesis that Rpn =Rpf using data from two rounds (1995
and 2002) of the Chinese Household Income Project (CHIP) survey. The standard errors are
clustered at the primary sampling unit (county). The estimates based on the 5-year average
income of a household in CHIP 2002 data in the last two columns of Table 4show that the
null hypothesis cannot be rejected with a P-value equal to 0.58.42 The evidence from the 1995
data (three year average income) also delivers a similar conclusion:the null hypothesis cannot
be rejected with a P-value of 0.33.43 The conclusion that returns to education measured in
permanent household income do not differ significantly across farm and nonfarm households
in rural China during the study period is robust to inclusion of number of children in the
household as a control (see online appendix Table A.5). The evidence on returns to education
when put together with the evidence on complementarity discussed earlier in Table 1provide a
theoretically consistent explanation: a lack of difference in returns to education across farm
and non-farm occupations leads to separability between father’s education and nonfarm
occupation in determining son’s schooling in rural China.
Also, the magnitude of θ2is likely to be low in rural China during the relevant period
(the children who went to school during 1970s-1990s) which would reduce the impact of
any emerging advantage in favor of nonfarm households in returns to education. Recall that
θ2is the efficiency of parental investment, determined primarily by the supply side of the
education market such as availability of high-quality private schools. In China, the availability
of private schools was limited; in 1996, only 4 percent of the schools in China were private
(Kwong 1996). Most of the private schools in rural areas in the 1990s were primary schools
with limited facilities and equipment, and they catered to children from the low-income
households. At the secondary level, the private schools primarily met the demand by the
students who were unsuccessful in the admission test given after grade 9 to screen for the
senior secondary public schools (Lin 1999). This implies that, in contrast to many other
countries, any quality advantage in education in rural China is associated with better quality
public schools. While local financing and various types of fees increasingly played a role in
public schools after the fiscal decentralization, it is unlikely to create a significant impact on
the magnitude of θ2for the following reason: the share of private expenditure remained small
compared to the public expenditure during the 1980s and 1990s; for example, tuition and
40 The available estimates on Mincerian labor market returns to education at the individual level in China
show low returns in the early years after the reform, but there is evidence of increasing returns in the later
years, as one would expect with the deepening of the labor market. The evidence also suggests higher labor
market returns in nonfarm occupations (de Brauw and Rozelle 2008).
41 Note that the estimates of the effects of father’s education on household permanent income using CHIP
data do not suffer from truncation bias, unlike the estimates of intergenerational persistence; whether some of
the children were nonresident at the time of the survey is not relevant for this analysis.
42 The 5 year income data cover from 1998 to 2002 in the CHIP 2002.
43 The 3 years income data in CHIP 1995 cover 1991, 1993, and 1995.
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766 M.S. Emran et al.
other fees paid by the parents amount to only 4.42 percent of total educational expenditure
in 1991 and 10.72 percent in 1995 (see Table 7.2 in Hannum et al. 2008).
Rural India
For the estimates of IGRCs across farm and nonfarm households in India to be consistent
with the extended Becker-Tomes model of Section (2), the returns to schooling in nonfarm
households need to be higher than that in the farming households in the parental generation.
Tabl e 4(panel B) reports the estimates of household-level returns to education in rural
India using household expenditure data from the NSS 1993 survey (the employment and
unemployment round). The returns to education are, in fact, higher in nonfarm occupations
and the difference is significant at the 5 percent level (P-value= 0.02). The conclusion that
returns to education at the household level are higher for the nonfarm activities is robust; for
example, the null hypothesis of equality is rejected with a P-value=0.002 when we control
for number of children in the household (see the online appendix Table A.5).
The available evidence also suggests that the magnitude of θ2is likely to be much higher in
rural India when compared to that in rural China. A higher value of θ2would act as a multiplier
for higher returns to schooling in nonfarm activities for the parents, and amplify the difference
between farm and nonfarm slopes (IGRCs). This can lead to the complementarity we found
earlier in Table 1above. In India, private schools have historically been more important
than in rural China, and they have become more important over time, especially after the
liberalization in 1991. Muralidharan and Kremer (2008) report that, in 2003, 28 percent of
rural households had access to fee-charging private schools. They also provide evidence that
private schools are more likely to be established in places where public school quality is
low, and the students in private schools perform better academically.44 Thus, the relative
quality of private and public schools in rural India is opposite to that in rural China. This
suggests that the higher income (and better educated) households can take advantage of the
high-quality private schools making θ2higher. Since the private schools are more likely to
locate in villages where the public school quality is low, the differential effects of school
quality are likely to be strong in rural India, as the better educated nonfarm parents with high
income send children to private schools, and the other children (including the children of
low-educated and low-skilled nonfarm parents) go to low quality public schools.
6.2 Differences in the intercepts
According to the theory, the estimated intercepts in Table 2discussed above imply the fol-
lowing relations (using a hat to denote an estimate):
(China)ˆ
ψf
0≈ˆ
ψn
0⇒ Ypf
0+1
2α2σθ
2ERIcf −α1≈Ypn
0+1
2α2σθ
2E(RIcn )−α1
(India)ˆ
ψf
0>ˆ
ψn
0⇒ Ypf
0+1
2α2σθ
2ERIcf −α1>Ypn
0+1
2α2σθ
2E(RIcn )−α1
(19)
where ERIcj=πnj
iRcn +1−πnj
iRcf is the expected return to financial invest-
ment in son’s education when the father is employed in occupation j=n,f.
Rural China
44 Private schools have more teachers with college degree and teacher absenteeism is less of a problem
compared to the public schools. Azam et al. (2016) find that the students in private secondary schools in rural
Rajasthan scored about 1.3 standard deviation (SD) higher than their counterparts in the public schools in a
comprehensive standardized math test.
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Occupational dualism and intergenerational mobility 767
The following observations are important for understanding the role played by occupa-
tional persistence in educational mobility in rural China. First, when πnn
iπnf
i,wehave
E(RIcn)ERIcf , irrespective of whether Rcn >Rcf or Rcn ≤Rcf . Since expected
returns to education for the children do not vary significantly across farm and nonfarm house-
holds in this case, we would expect parental investment in education and thus educational
mobility to be similar also. The second observation is that when there is low or no intergen-
erational persistence in nonfarm (or farm) occupations, we have πnn
iπnf
i.
A substantial body of independent evidence, in fact, suggests that, for the relevant
cohorts, there was no significant intergenerational persistence in nonfarm occupation choices
(πnn
iπnf
i)in rural China. Wu and Treiman (2007) use the 1996 national probability sam-
ple of Chinese men and show that there is high degree of mobility into agriculture; the sons
of nonfarm parents also face a substantial probability of becoming a farmer. They identify
the geographic restrictions on mobility of rural people because of the Hukou registration
system as the primary factor behind this weak intergenerational persistence in nonfarm occu-
pations.45 Using CHIP 2002 data, Emran and Sun (2015a) report evidence supporting (Wu
and Treiman 2007) finding.
The evidence that E(RIcn )=ERIcf , along with Eq. 19, above implies that a sufficient
condition for the equality of the intercepts of the intergenerational persistence equations is that
Ypn
0=Ypf
0, i.e., the intercepts of the returns to education function in the parent’s generation
are the same across farm and nonfarm households. We would expect Ypn
0=Ypf
0when the
fathers with zero schooling have similar income (permanent income) and face similar credit
constraint, irrespective of their occupation.
The estimates in panel A of Table 4show that the null hypothesis Ypn
0=Ypf
0cannot be
rejected at the 10 percent level with a p-value of 0.78 for the CHIP 2002 data on five-year
average income. The evidence from 1995 data is also similar (p-value is 0.81). Again, these
conclusions from CHIP 2002 and CHIP 1995 remain intact when we include the number of
children in the household as a control (please see online appendix Table A.5).
The evidence above is also consistent with other available studies on the nonfarm sector
and rural industries (TVEs) in rural China. The income gap between the farm and nonfarm
households was mitigated in the early years of reform by two factors: the household respon-
sibility reform increased the farmer’s income, and, in many cases, people employed on the
farm were paid wages similar to the wages paid to workers in the township village enterprises
(TVEs), the growing TVE sector in effect subsidizing the agricultural employment (Peng,
1998). This also reflects in part the lingering effects of policies during the cultural revolu-
tion that were successful in eliminating any significant differences between the peasants and
non-peasants in rural China (Hannum et al. (2008)).
Rural India
In contrast to China, there were no restrictions on rural-urban migration in India during
the study period. A substantial body of independent evidence on occupational mobility in
rural India suggests strong intergenerational persistence in farm/nonfarm occupations (Reddy
2015; Motiram and Singh 2012; Azam and Bhatt 2015; Hnatkovska et al. 2013). Hnatkovska
et al. (2013) show that there is strong persistence in rural occupations both in 1983 and 2004-
45 The link between restrictions on geographic mobility of rural people and a lack of intergenerational
occupational persistence (farm/nonfarm) is, however, not unique to China, similar evidence is available on
Vietnam where the Ho Khau registration system has been in place since 1964; see the evidence and the analysis
in Emran and Shilpi (2011). This enhances the credibility of Wu and Treiman (2007) analysis that the Hukou
restrictions played an important role in the low occupational persistence in rural China.
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768 M.S. Emran et al.
2005; the son of a farmer is highly likely to be a farmer himself. Using the IHDS (2005)
survey, Motiram and Singh (2012) also provide similar evidence.
The fact that there was significant persistence in farm/nonfarm occupations implies that
the expected returns to investing in children’s education are likely to differ across farm
and nonfarm households. But whether the intercept in the nonfarm households would be
higher or lower depends partly on the expected relative returns to education in the children’s
generation, i.e., whether Rcn >Rcf or Rcn <Rcf . It is, however, much more difficult to
estimate expected returns to education for children. One can argue that a parent’s expectation
would depend on his/her information set, a salient element in which is his/her own returns to
education. In other words, the evidence of higher returns to education in nonfarm occupations
in the parental generation suggests that the parents would expect similarly higher returns for
children in nonfarm occupations. Note that even when Rcn >Rcf , the intercept for the
nonfarm households can be smaller as we find in the empirical analysis above (Table 2), if
the households with zero (or very low) parental schooling have sufficiently lower income in
nonfarm occupations, i.e., Ypn
0<Ypf
0.
The estimates of Ypn
0and Ypf
0, i.e., the intercepts of the income equation for parents, using
data from NSS 1993 round (employment and unemployment round), are reported in panel
BofTable4. The estimated intercept is larger for the farm households and the difference
is statistically significant at the 10 percent level (standard errors clustered at the PSU level).
The evidence in favor of a larger intercept in the farm households is stronger when we add
controls to the regression; for example, the difference is significant at the 1 percent level
when number of children is added to the specification (see online appendix Table A.5).
This conclusion is also supported by other available evidence on India; for example, the All
India Debt and Investment Survey 1991 (NSS 48th round) shows that the assets of farming
households (“cultivators”) are higher than those of the nonfarming households (see P. ii,
NSSO report No. 491, 1998).
The evidence in panel B of Table 4also accords well with a substantial body of related
evidence available on the nonfarm activities in India. Note that it is likely to have Ypn
0<Ypf
0
if the low-end nonfarm occupations are primarily low productivity residual activities and
provide the last resort for the poorest households. Lanjouw and Murgai (2009)usethree
rounds of NSS data (1983, 1993/94, and 2004/2005) and show that nonfarm employment is
positively associated with rural poverty in India, consistent with the observation that nonfarm
employment involves primarily low productivity economic activities (see also World Bank
(2011)).
This evidence on the intercepts of the income equation provides an explanation for the
higher intercept for farm households in the intergenerational mobility equation as discussed
earlier.
7 Evolution of mobility: evidence on the younger generation in rural
China
The rural economy and educational policies in China went through significant changes in
recent decades. The changes include gradual relaxation of Hukou restrictions, increasing
returns to education as the labor market matured, accelerated structural change in favor of
the nonfarm sector, and a more prominent role for private educational expenditure after the
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Occupational dualism and intergenerational mobility 769
fiscal decentralization (for an excellent discussion, see Fan et al. (2020)).46 There is credible
evidence that the economic changes have adversely affected intergenerational mobility of the
younger generation in China. Fan et al. (2020) find that intergenerational income persistence
has increased over time; they report an IGE estimate of 0.390 for the 1970-1980 birth cohorts,
which increased to 0.442 for the 1981-1988 birth cohorts. The extended Becker-Tomes model
suggests that the pattern of educational persistence across farm and nonfarm households
should also change for the younger generation because of the changes in the economic
mechanisms.
To check if the pattern of educational mobility across farm and nonfarm households has
changed in the younger generation, we estimate the intergenerational educational mobility
equation for 18-28 years age cohorts who were excluded from our main estimation sample.47
The estimates are reported in Table 5, with the upper panel containing the results for years
of schooling, and the lower for rank-based estimates. There is, in fact, evidence of emerging
divergence between farm and nonfarm households in relative mobility as measured by IGRC
and IRRS. For example, the IGRC estimate for the farm households has declined a bit from
0.31 (main sample) to 0.27 (younger sample), while the IGRC estimate has increased for the
nonfarm households from 0.316 (older) to 0.34 (younger). Similarly, the IRRS estimate for
farm households declined from 0.331 (main sample) to 0.304 (younger sample), and it has
increased from 0.337 (main sample) to 0.373 (younger sample) for the nonfarm households.
The difference between farm and nonfarm households is significant at the 10 percent level
in the case of IRRS estimates (P-value=0.097). While the difference in IGRC estimates is
not significant at the 10 percent level, the p-value in the younger sample is much smaller:
0.107 (younger sample) vs. 0.90 (main sample), providing evidence in favor of emerging
complementarity between a father’s education and nonfarm occupation.
8 Conclusions
This paper develops a model of intergenerational educational persistence in a rural econ-
omy taking into account the role of parental farm vs. nonfarm occupations, and derives a
theoretically-grounded estimating equation which we take to the data from rural India and
rural China. We use two unique data sets that include the required information for all the
children of the household head irrespective of their residency status at the time of the survey;
thus eliminating the truncation bias common in the existing studies based on the standard
surveys that use coresidency criteria to define household membership.
The empirical analysis delivers the following conclusions for the sons who went to school
in the 1990s or earlier: (i) the intergenerational educational mobility in rural China was
significantly higher compared to that in rural India, (ii) the farm/nonfarm occupations did not
play any significant role in the intergenerational schooling linkage in rural China, and this is
true for both the intercept and the slope of the intergenerational persistence regressions, (iii)
both the slopes and intercepts were significantly different across farm and nonfarm households
in rural India. The estimates suggest that a father’s education and nonfarm occupation were
complementary in determining son’s schooling in rural India, but separable in rural China.
46 The share of tuition and miscellaneous fees in educational expenditure rose from 4.42 percent in 1991 to
18.59 percent in 2004 (Hannum et al. (2008)). The spread of better quality public schools to the rural areas
has accelerated. All these changes would increase the magnitude of θ2in the extended Becker-Tomes model
for the younger generation.
47 Our main estimation sample consists of 29-65 age cohorts in 2010 to ensure that the sample of children in
China refers to the same age groups as in the data for India.
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770 M.S. Emran et al.
Structural change in favor of the nonfarm sector contributed to higher educational inequality
in rural India during the study period, in part due to the complementarity. In contrast, such
structural change was not an important factor in schooling inequality in rural China. Evidence
from an approach that combines the biprobit sensitivity analysis of Altonji et al. (2005) with
recent evidence on intergenerational correlation in cognitive ability suggests that the observed
educational persistence in rural India is unlikely to be due solely to mechanical transmission
of ability across generations, while the persistence in rural China could be explained by
genetic correlations alone.
We analyze whether the economic forces identified in the extended Becker-Tomes model
provide a coherent explanation of the observed pattern of educational mobility across coun-
tries (rural China vs. rural India) and over time (older vs. younger cohorts in rural China).
A lack of intergenerational persistence in nonfarm occupations in rural China because of the
Hukou restrictions seems to have played an important role in making the intercepts similar
in rural China, but strong intergenerational occupational persistence in rural India resulted
in significant differences between farm and nonfarm households. In rural India, the observed
complementarity can be explained by higher returns to education in nonfarm occupations in
the parental generation. The separability between a father’s education and occupation in rural
China was driven by the absence of any significant differences in the household-level returns
to education across farm and nonfarm occupations. However, because of economic forces
unleashed by the policy reform in China, the returns to education in nonfarm occupations for
parents have increased and Hukou restrictions have been relaxed progressively. According to
the theory, this should tighten the link between father’s education and son’s schooling in the
nonfarm sector. Indeed, we find evidence that the separability between a father’s education
and occupation broke down for the 18-28 years old sons, implying that structural change
in favor of the nonfarm sector is increasingly contributing to educational inequality in rural
China.
Supplementary Information The online version contains supplementary material available at https://doi.
org/10.1007/s10888- 023-09599-1.
Acknowledgements We are grateful to Forhad Shilpi for help with REDS data and insightful comments
throughout this project, and to Yang Huang for help with the CFPS data. Wewould like to thank the participants
in Equal Chances conference 2018, and LACEA conference 2019, and Matthew Lindquist, Guido Neidhofer,
Reshad Ahsan, Koen Decancq, and Hanchen Jiang for helpful comments on an earlier draft and Rakesh Gupta
Nichanametla Ramasubbaiah for excellent research assistance. An earlier version of the paper was titled
“Intergenerational Educational Mobility in the Rural Economy: Evidence from China and India”. This project
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