Emerging Trends in The Social and Behavioral Sciences

Intergenerational Mobility

Item

Title

Intergenerational Mobility

Author

Durlauf, Steve N.

Shaorshadze, Irina

Research Area

Class, Status and Power

Topic

Social Stratification

Abstract

This essay describes basic facts about intergenerational mobility as well as some of the mechanisms that have been proposed to explain levels of mobility or persistence of socioeconomic status across generations. Limits of current research are identified.

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Identifier

etrds0191

extracted text

Intergenerational Mobility
STEVE N. DURLAUF and IRINA SHAORSHADZE

Abstract
This essay describes basic facts about intergenerational mobility as well as some of
the mechanisms that have been proposed to explain levels of mobility or persistence
of socioeconomic status across generations. Limits of current research are identified.

INTRODUCTION
Intergenerational mobility refers to the degree to which individual socioeconomic characteristics are associated with the outcomes and characteristics of
their parents. Mobility can thus be either absolute or relative.
MEASUREMENT
Economics and sociology have traditionally focused on income and occupation, respectively, in measuring intergenerational mobility. Contemporary
social science includes theoretical and empirical studies of mobility that
account for the persistence of socioeconomic phenomena such as education,
occupations, family structure, and criminal activity.
The standard measure of intergenerational persistence of income is the
intergenerational elasticity (IGE) of offspring’s income with respect to
parental income. If the variance of the income distribution in two generations is the same, the IGE will be the same as the intergenerational correlation
of the logarithms of incomes. If the variance of the income distribution has
changed, then the IGE will be the intergenerational correlation weighted by
ratio of variances in the two generations. Both elasticities and correlations
intergenerational mobility have been widely used in literature (Black &
Devereux, 2011). The use of logarithm of income reflects the objective of
measuring mobility after accounting for secular income growth in a society.
Because of relatively limited labor force participation of women, most
studies look at the relationship of incomes of fathers and sons. We focus on
the IGE as it is the more commonly used measure.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

It has recently been claimed that countries that have higher cross-sectional
income inequality tend to have a lower rate of intergenerational income
mobility; this relationship is often called The Great Gatsby Curve (Corak,
2013). It does not necessarily imply causality from inequality to low mobility
or vice versa. Among the OECD countries, during the mid-1980s, Finland,
Sweden, Norway, and Denmark were the most equal in terms of income
distribution, whereas the United States and the United Kingdom the most
unequal. Intergenerational income mobility is found to be higher in the
Nordic countries and lower in the United Kingdom and the United States.
Low IGE for Nordic countries can be explained either by low return to
skills resulting in compressed income distribution, or higher reliance on
distributive social and educational policies (Black & Devereux, 2011).
Surveys of IGE of select countries gives some perspective of the magnitude
of cross-country differences in income mobility. Jantti et al. (2006) found that
the IGE was 0.071 in Denmark, 0.306 in the United Kingdom, and 0.0517 in
the United Status. They find that larger IGE in the United Status and United
Kingdom relative to that in the Nordic countries was almost entirely due to
the differences in the tails of income distribution. South America, other developing countries, and the Southern Europe also have high degree of income
inequality and intergenerational income mobility (Blanden, 2013). Some of
the estimates for the IGE in other countries include 0.4 for France (Lefranc &
Trannoy, 2005), 0.5 for Italy (Piraino, 2007; Mocetti, 2007), and 0.52 in Brazil
(Dunn, 2007).
There is a growing public perception in the United States that the IGE has
been declining in recent decades. However, the empirical evidence on the
trend in IGE has been mixed. Some studies have found that income mobility has declined in recent decades (Aaronson & Mazumder, 2008; Putnam,
Frederick, & Snellman, 2012), whereas other studies have found no trend in
income mobility (Hertz et al., 2007; Lee & Solon, 2009). Furthermore, due to
the difficulties in measuring permanent income accurately, different studies
have estimated vastly different magnitudes of IGE, while using different data
sources and different income aggregation methods for the same cohorts. Estimates of the IGE in the United States for the cohorts of children born in the
1950s and 1960s include 0.2 (Behrman & Taubman, 1985), 0.4 (Solon, 1992),
and 0.6 (Mazumder, 2005).
Chetty, Hendren, Kline, and Saez (2014) analyze intergenerational mobility
in the United States for the last three decades using the universe of actual tax
returns filed. The authors measured intergenerational mobility based on the
correlation between parental and child’s income percentile ranks rather than
the IGE between their actual incomes. By looking at the mobility measures of
the 1971 and 1993 birth cohorts, they conclude that the rank-based measures
of intergenerational mobility have not changed significantly over recent

Intergenerational Mobility

3

decades. Meanwhile, the authors find that the cross-sectional inequality has
increased during these years, but this increase came from the extreme upper
tails of the income distribution. The authors estimate that the rank-rank
based measure of intergenerational income elasticity is 0.3, half of the estimate found by Mazumder (2005) using data for the same years. Differences
in these estimates stem primarily from the fact that Mazumder did not have
the information for the parental incomes for 60% of observations in his
sample and had to impute it using education and race. Meanwhile, Chetty
et al. (2014) use direct measures of parental income.
Analysis in Chetty, Hendren, Kline, Saez, and Turner (2014) uncovered substantial spatial heterogeneity in intergenerational mobility rates across the
United States. They find that intergenerational mobility is strongly correlated
with the several factors: (i) residential segregation, (ii) income inequality, (iii)
school quality, and (iv) family structure. Comparing intergenerational mobility measures across the commuting zones in the United Status, intergenerational mobility appears to be lower in the areas characterized by higher residential segregation, high income inequality, lower school quality, lower rate
of engagement in the community organizations, and higher share of children
living in single-parent households. It reasons to be seen if these relationships
are causal.
Intergenerational income elasticities (and correlations) can mask important
features of the relationship in incomes across the generations as they measure
a single linear relationship between incomes across generations. In particular,
this single summary measure ignores differences in income mobility across
different parts of the income distribution. Such measures cannot capture the
possibility that the correlatedness of parents’ and children’s income differs
by parental income. Some studies employ Markov Chains to allow for such
nonlinearities (Bhattacharya & Mazumder, 2011; Chetty et al., 2014).
Linear measures of income mobility also do not take into account the fact
that the family dynasties may differ by ethnicity, and the persistence of
incomes across generations is partially the result of permanent differences in
opportunities between families of different ethnic groups. Chetty et al. (2014)
find that there is special variation across the United States in the rates of
intergenerational mobility, and the mobility is significantly lower in the areas
with the larger share of African-American population. They note that such
areas tend to be more segregated both by income and race. Bhattacharya and
Mazumder (2011) use a Markov chain approach to calculate the probability
that an offspring’s income falls in a higher percentile than a parent’s and
find substantially more upward mobility for whites than blacks.
Sociologists have a long history of studying intergenerational occupational
mobility (Blau & Duncan, 1967; Sewell & Hauser, 1975). The extent of intergenerational occupational mobility can shed additional light on the extent

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of intergenerational social and economic mobility. Hellerstein and Morrill
(2011) looked at the correlation of occupations between parents and their
children and found that in recent cohorts, 30% of sons and 20% of daughters
worked in the same occupation as their fathers. Using a continuous measure
of “occupation prestige,” Ermisch and Francesconi (2002) find that the
intergenerational occupational correlation in the British Household Panel
Survey ranges from 0.4 to 0.75 between fathers and offspring, and from 0.30
to 0.50 between mothers and offspring. The authors also find that the effect
is nonlinear, and that families of higher socioeconomic status have higher
elasticity. Corak and Piraino (2010) analyze Canadian data, and find that
by the age of 30, about 40% of men have worked for the same firm that had
previously employed their father. This correlation was stronger for higher
income earning fathers.
There is empirical evidence of intergenerational correlation of the welfare
receipt, but the economic literature has not found clear evidence of the
causality of the welfare receipts of parents on the welfare receipts by
children. Levine and Zimmerman (1996) exploit the variation of the welfare
receipts across states to identify the presence of the Welfare Trap—the
mechanical correlation of the means-tested welfare policy that leads to the
correlation of the welfare receipts between parents and children. They do
not find the evidence of such welfare traps, and conclude that most of the
correlation in the welfare receipts is attributable to the correlation in income.
Correlation of parental and child’s income across generations could
conceivably be partially the result of the intergenerational transmission of
attitudes. Some authors have examined the transmission of attitudes and
social behavior across generations. Dohmen, Falk, Huffman, and Sunde
(2011) show that the willingness to take risks and the extent of trust are
correlated across generations in Germany. Altonji and Dunn (2000) find that
the intergenerational persistence of hours worked are primarily due to the
intergenerational persistence of work preferences.
MECHANISMS
What sorts of mechanisms determine intergenerational income mobility? We
highlight four types of explanations that have received much attention.
FAMILY TRANSMISSION MODELS
For economists, the impact of family income on the educational attainment
of children is a natural way to explain intergenerational income patterns.
This perspective reflects the importance of human capital in economic
attainment. Becker and Tomes (1979) and Loury (1981) have proposed the

Intergenerational Mobility

5

classical intergenerational mobility models. These models have a common
logical structure. Parents invest in their children by “purchasing” education.
Since parents cannot legally obligate their children to pay for educational
loans, parental income determines the level of investment. Since human
capital, in conjunction with ability and labor market luck, determine the
offspring’s income, a potential causal relation exists between parental
income and child’s income.
The frontier in understanding family and other influences on individuals
moves away from equating human capital with formal education toward
a broader conception of cognitive skills and personality traits. The focus
is on the key personality traits (“the big five” of psychology): openness,
conscientiousness, extraversion, agreeableness, and neuroticism. Research
on the importance of personality traits, commonly called noncognitive skills,
in determining socioeconomic success has been pioneered by James Heckman. Borghans, Duckworth, Heckman, and ter Weel (2008) and Almlund,
Duckworth, Heckman, and Kautz (2011) are comprehensive surveys.
Studies have shown that there are critical and sensitive periods in the
technology of human capital formation and different capacities are malleable
at different stages of life (Cunha, Heckman, Lochner, & Masterov, 2006).
For instance, IQ is rank stable after the age of 10, whereas personality
skills are malleable through early adulthood. Besides, there is a dynamic
complementarity across both cognitive and noncognitive skills. Better
noncognitive skills, such as self-control, self-confidence, and discipline, help
future investments in cognitive skills be more productive. Higher stocks of
skills help beget more skills (Heckman, 2008). Thus, credit constraints for
parents of the very young are potentially important as they may limit the
ability of parents to provide such an environment.
In addition to providing human capital, family income has also been linked
to “health capital.” The “Fetal Origins” hypothesis argues that there is the
effect of prenatal environment (health of the mother) on subsequent human
capital formation of children (Currie & Almond, 2011). If parental income
helps determine parental health (a relationship that is well-established), then
it is possible that persistence in health status is the source of the transmission
of income status.
There is indeed positive correlation between parental income and child’s
achievement, and many studies interpret this correlation as evidence
that parental credit constraints impede investment in children. However,
empirical evidence on the importance of credit constraints for human capital
formation is not conclusive. Heckman and Mosso (2014) survey literature
on determinants of human capital and find that the importance of credit
constraints in shaping child outcomes are exaggerated in recent literature
compared to the importance of parenting and mentoring.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

SOCIAL-LEVEL TRANSMISSION MODELS
A second approach to understanding intergenerational mobility focuses on
social influences on children and adults. Social influences, abstractly, refer to
influences which act on individuals via some collective activities or environments. To understand what we mean, consider education. For most American children, education between 5 and 17 is publically provided without
direct charge. Education, in other words, is a public good whose level is determined by a political mechanism and depends on the incomes and preferences
of the adults in the school district (as well as any other levels of government
that provide funding for the schools).
How does parental income matter for publically provided education? The
answer is that parental income is one of the factors which determine which
school a child attends. For public schools, it is of particular interest to gauge
the extent to which equilibrium house prices and rent levels sustain stratification of communities by income. Models of neighborhoods and intergenerational mobility have been developed by Bénabou (1996a, 1996b), Durlauf
(1996a, 1996b), and Hoff and Sen (2005), among others. The Durlauf models
explicitly mimic those of Loury (1981) and Becker and Tomes (1979) and show
how persistent income inequality between family dynasties can be produced.
One can identify a range of reasons why neighborhoods function as intergenerational transmission mechanisms. One reason may involve role models.
If the education decisions of the young (effort in schools, years of schooling
completed) are determined by perceptions of future economic benefits, the
assessment of these benefits may depend on the distributions of educational
levels and incomes observed in a community. Stratification of communities
according to income will correspondingly mean that different locations produce different inferences about the value of education. This mechanism is
formalized in Streufert (2000).
A second reason involves the influence of self-identity on individual
choices (Akerlof & Kranton, 2000, 2002). Suppose that one effect of educational choices by an individual concerns how he relates his own identity
to that of others in his community. In a community where few parents are
well-educated, high education can render an individual feeling alienated
from those with whom he wants to share an identity. This argument has been
of long-standing importance in understanding racial inequality as a number
of authors have argued that black educational attainment is hampered by the
perception that academic success is a form of “acting white” (Fryer & Torelli,
2010; Ogbu & Davis, 2003). Durlauf (2004) is an overview of neighborhood
effects.
The third reason that neighborhoods can affect the transmission of socioeconomic status is through providing access to information about employment

Intergenerational Mobility

7

opportunities. Access to information about job openings can be low in a disadvantaged community because the disadvantage of each individual (e.g.,
because of unemployment) means that information is simply not available.
Bayer, Ross, and Topa (2008) provide empirical evidence of the importance
of interpersonal hiring networks for job market outcomes. Calvo-Armengol
and Jackson (2004, 2007) provide the theoretical framework illustrating how
the social structure could affect the wage inequality and employment participation of individuals belonging to different groups. Neighborhoods models
illustrate an important feature of social poverty traps, namely that it is the
interplay of strong social effects as well as the existence of distinct social
environments that affects mobility. In other words, the social provision of
education creates intergenerational persistence because communities stratify
by income. This leads to a “memberships” theory of intergenerational mobility (Durlauf, 1996c, 1999, 2006) in which various group memberships can
explain the transmission of advantage and disadvantage across generations.
This idea independently appears in Massey (2007) who terms it categorical
inequality.
GENETIC TRANSMISSION
A third source of intergenerational persistence of socioeconomic status is
via genes. Obviously, if an individual’s genotype affects socioeconomic
outcomes, then genes can create an intergenerational persistence in these
outcomes. In his study of the multigenerational persistence of surnames
in institutions such as elite colleges, Clark (2014) not only argues that parent/offspring persistence is far higher than found in IGE or Markov Chains,
but reflects some latent factor he calls moxie which, given his discussion,
is plausibly interpretable as genes. Empirical claims on the importance
of genes in inequality, however, are derivative from studies of genes in
intelligence, which is then linked to income. How is the genetic component
of intelligence measured? Behavioral genetics performs decompositions of
the variance of some characteristic or outcome, such as IQ into distinct roles
for nature and nurture. Jensen (1969) claimed that 80% of the variance in
IQ scores is genetic, launching a vast and continuing literature. Genomic
data has been used to claim that 50% of variation in IQ can be described
by measured variation of SNPs (single nucleotide polymorphisms) (Davies
et al., 2011).
In our judgment, there does not exist a strong basis for authoritative statements about the role of genes in intergenerational mobility. Clark’s work may
be rationalized by genes, but there is no basis for privileging genes over
institutions. (The British monarch is determined by genes, but the rule of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

succession is an institution.) Measurements of a genetic component to variation in IQ (or any other trait) are typically based on the studies of twins’
data, and exploit differences in correlations of traits between monozygotic
and dizygotic twins. As argued by Goldberger (1979), many of these decompositions assume that family environment and genes are uncorrelated. This is
clearly an untenable assumption. To see how it matters, note that the evidence
for the role of genes is derived from higher correlations between monozygotic twins than dizygotic ones with respect to IQ or some other measure.
If parents adjust childrearing to a child’s genotype, then the shared family
influence for monozygotic twins may be higher than that for dizygotic ones.
This problem is not resolved by genomic studies which employ algorithms
to isolate individuals with relative little DNA overlap such as Davies et al.
(2011).
To be clear, behavioral genetics research has fully recognized the importance of gene/environment interactions. Studies such as Neiderhiser and
Lichtenstein (2008) and Narusyte et al. (2008) look at the correlations between
children of twins and suggest strategies to allow for gene/environment correlations. See Johnson (2013) for nuanced views on how work on intelligence
should proceed. Our argument is that currently the magnitude of the role of
genes is simply unknown.
ASSORTATIVE MATING
A fourth explanation, which is linked to each of the previous explanations,
concerns marriage patterns. Assortative mating, with respect to income,
education, or other factors which affect children, can naturally enhance
intergenerational persistence. Early efforts to quantify the implications of
assortative mating for inequality led to disparate conclusions, see Kremer
(1997) and Fernandez and Rogerson (2001). Recently, Greenwood, Guner,
Kocharkov, and Santos (2014) have performed a detailed calibration analysis
of a socioeconomic environment which shows how assortative mating can
have first-order effects on cross-section inequality, which, given factors such
as credit constraints, can translate into intergenerational mobility. A promising topic, in our view, is the role of assortative mating by ethnicity and
persistent inequality across ethnic groups, see Kalmijn and Van Tubergen
(2010) for a study of intermarriage patterns.
There is no systematic account that addresses the relative salience of
these different mechanisms. A few analyses attempt to decompose the IGE
as derivative from distinct channels. Bowles and Gintis (2002) employ a
linear system of equations to decompose a 0.32 IGE of the United States
as the sum of four underlying mechanisms that link parents and children:
IQ conditional on schooling (treated as a proxy for genes) 0.04, education

Intergenerational Mobility

9

conditional on IQ 0.07, wealth 0.12, and personality and race 0.07. Blanden,
Gregg, and MacMillan (2007) engage in a similar exercise for the United
Kingdom. While suggestive, these analyses, in our judgment are limited
by their mechanical nature. By this, the decompositions do not derive from
behavioral models, but from ad hoc modeling choices of the linear system.
Nor do they address social mechanisms. That said, these studies are very
original and should be the basis for future work.
WHY DOES INTERGENERATIONAL MOBILITY MATTER?
Intergenerational mobility is commonly regarded as a measure of the degree
of equality of opportunity in a society, although rigorous philosophical
formulations of equality of opportunity (e.g., Roemer, 1998) focus not
on the degree of similarity between parents and children, but rather on
whether high (or low) similarities are due to factors for which individuals
are responsible. In other words, intergenerational mobility measures focus
on outcomes, rather than mechanisms, which are the relevant objects in
ethical evaluations of observed mobility patterns. A rare effort to explicitly
measure equality of opportunity is Bjorklund, Jantti, and Roemer (2012).
FUTURE DIRECTIONS
While there are rich research literatures on the measurement of intergenerational mobility as well as on the various mechanisms that are believed to
play major roles in determining rates of mobility, there does not exist an integrated analysis of intergenerational mobility which combines these mechanisms into a unified structure. As indicated by the work on early childhood
development, identity, and social interactions, such a unified model must
blend elements of neuroscience, psychology, sociology, and economics. While
a daunting task, an integrated framework is essential in the identification of
efficacious policies to promote mobility.
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Heckman, J. & Mosso, S. (2014). The economics of human development and social mobility.
National Bureau of Economic Research, Working Paper 19925. Cambridge, MA.
Hellerstein, J., & Morrill, M. (2011). Dads and daughters: The changing impact
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333–372.
Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., & Vereshchagina, A.
(2007). The inheritance of educational inequality: International comparisons and
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Hoff, K., & Sen, A. (2005). Homeownership, community interactions, and segregation. The American Economic Review, 95(4), 1167–1189.
Jantti, M., Bratsberg, B., Roed, K., Raaum, O., Naylor, R., Osterbacka, E., … & Eriksson, T. (2006). American exceptionalism in the new light: A comparison of intergenerational earnings mobility in the Nordic countries, the United Kingdom and the United
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Jensen, A. (1969). How much can we boost IQ and scholastic achievement? Harvard
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Johnson, W. (2013). Whither intelligence research? Journal of Intelligence, 1, 24–35.
Kalmijn, M., & Van Tubergen, P. (2010). A comparative perspective on intermarriage:
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Economics, 112(1), 115–139.
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Lefranc, A., & Trannoy, A. (2005). Intergenerational earnings mobility in France: Is
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Massey, D. (2007). Categorically unequal. New York, NY: Russell Sage Foundation
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Mazumder, B. (2005). Fortunate sons: New estimates of intergenerational mobility
in the United States using social security earnings data. Review of Economics and
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Mocetti, S. (2007). Intergenerational earnings mobility in Italy. The B.E. Journal of Economic Analysis and Policy, 7, 1–25.
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Ogbu, J., & Davis, A. (2003). Black American students in an affluent suburb: A study of
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Streufert, P. (2000). The effect of underclass isolation on school choice. Journal of Public
Economic Theory, 2(4), 461–482.

STEVEN N. DURLAUF SHORT BIOGRAPHY
Steven N. Durlauf is Vilas Research Professor and Kenneth J. Arrow Professor of Economics at the University of Wisconsin at Madison. He is a Fellow of
the Econometric Society and of the American Academy of Arts and Sciences.
IRINA SHAORSHADZE SHORT BIOGRAPHY
Irina Shaorshadze is a PhD student at Cambridge University and a visiting
fellow at the University of Wisconsin at Madison. Previously, she has worked
at the Social and Economic Development Unit at the World Bank.
RELATED ESSAYS
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David B. Grusky
Elites (Sociology), Johan S. G. Chu and Mark S. Mizruchi
Enduring Effects of Education (Sociology), Matthew Curry and Jennie E.
Brand
Stratification in Hard Times (Sociology), Markus Gangl
Social Class and Parental Investment in Children (Sociology), Anne H.
Gauthier
Changing Family Patterns (Sociology), Kathleen Gerson and Stacy Torres
The Future of Employment, Wages, and Technological Change (Economics),
Michael J. Handel
The Emerging Psychology of Social Class (Psychology), Michael W. Kraus
Education for Mobility or Status Reproduction? (Sociology), Karyn Lacy

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Stratification and the Welfare State (Sociology), Stephanie Moller and Joya
Misra
Public Opinion, The 1%, and Income Redistribution (Sociology), David L.
Weakliem
The Welfare State in Comparative Perspective (Sociology), Jill Quadagno et al.
Class, Cognition, and Face-to-Face Interaction (Sociology), Lauren A. Rivera
Family Income Composition (Economics), Kristin E. Smith
The Future of Class Analyses in American Politics (Political Science), Jeffrey
M. Stonecash
Recent Demographic Trends and the Family (Sociology), Lawrence L. Wu



Intergenerational Mobility
STEVE N. DURLAUF and IRINA SHAORSHADZE

Abstract
This essay describes basic facts about intergenerational mobility as well as some of
the mechanisms that have been proposed to explain levels of mobility or persistence
of socioeconomic status across generations. Limits of current research are identified.

INTRODUCTION
Intergenerational mobility refers to the degree to which individual socioeconomic characteristics are associated with the outcomes and characteristics of
their parents. Mobility can thus be either absolute or relative.
MEASUREMENT
Economics and sociology have traditionally focused on income and occupation, respectively, in measuring intergenerational mobility. Contemporary
social science includes theoretical and empirical studies of mobility that
account for the persistence of socioeconomic phenomena such as education,
occupations, family structure, and criminal activity.
The standard measure of intergenerational persistence of income is the
intergenerational elasticity (IGE) of offspring’s income with respect to
parental income. If the variance of the income distribution in two generations is the same, the IGE will be the same as the intergenerational correlation
of the logarithms of incomes. If the variance of the income distribution has
changed, then the IGE will be the intergenerational correlation weighted by
ratio of variances in the two generations. Both elasticities and correlations
intergenerational mobility have been widely used in literature (Black &
Devereux, 2011). The use of logarithm of income reflects the objective of
measuring mobility after accounting for secular income growth in a society.
Because of relatively limited labor force participation of women, most
studies look at the relationship of incomes of fathers and sons. We focus on
the IGE as it is the more commonly used measure.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

1

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

It has recently been claimed that countries that have higher cross-sectional
income inequality tend to have a lower rate of intergenerational income
mobility; this relationship is often called The Great Gatsby Curve (Corak,
2013). It does not necessarily imply causality from inequality to low mobility
or vice versa. Among the OECD countries, during the mid-1980s, Finland,
Sweden, Norway, and Denmark were the most equal in terms of income
distribution, whereas the United States and the United Kingdom the most
unequal. Intergenerational income mobility is found to be higher in the
Nordic countries and lower in the United Kingdom and the United States.
Low IGE for Nordic countries can be explained either by low return to
skills resulting in compressed income distribution, or higher reliance on
distributive social and educational policies (Black & Devereux, 2011).
Surveys of IGE of select countries gives some perspective of the magnitude
of cross-country differences in income mobility. Jantti et al. (2006) found that
the IGE was 0.071 in Denmark, 0.306 in the United Kingdom, and 0.0517 in
the United Status. They find that larger IGE in the United Status and United
Kingdom relative to that in the Nordic countries was almost entirely due to
the differences in the tails of income distribution. South America, other developing countries, and the Southern Europe also have high degree of income
inequality and intergenerational income mobility (Blanden, 2013). Some of
the estimates for the IGE in other countries include 0.4 for France (Lefranc &
Trannoy, 2005), 0.5 for Italy (Piraino, 2007; Mocetti, 2007), and 0.52 in Brazil
(Dunn, 2007).
There is a growing public perception in the United States that the IGE has
been declining in recent decades. However, the empirical evidence on the
trend in IGE has been mixed. Some studies have found that income mobility has declined in recent decades (Aaronson & Mazumder, 2008; Putnam,
Frederick, & Snellman, 2012), whereas other studies have found no trend in
income mobility (Hertz et al., 2007; Lee & Solon, 2009). Furthermore, due to
the difficulties in measuring permanent income accurately, different studies
have estimated vastly different magnitudes of IGE, while using different data
sources and different income aggregation methods for the same cohorts. Estimates of the IGE in the United States for the cohorts of children born in the
1950s and 1960s include 0.2 (Behrman & Taubman, 1985), 0.4 (Solon, 1992),
and 0.6 (Mazumder, 2005).
Chetty, Hendren, Kline, and Saez (2014) analyze intergenerational mobility
in the United States for the last three decades using the universe of actual tax
returns filed. The authors measured intergenerational mobility based on the
correlation between parental and child’s income percentile ranks rather than
the IGE between their actual incomes. By looking at the mobility measures of
the 1971 and 1993 birth cohorts, they conclude that the rank-based measures
of intergenerational mobility have not changed significantly over recent

Intergenerational Mobility

3

decades. Meanwhile, the authors find that the cross-sectional inequality has
increased during these years, but this increase came from the extreme upper
tails of the income distribution. The authors estimate that the rank-rank
based measure of intergenerational income elasticity is 0.3, half of the estimate found by Mazumder (2005) using data for the same years. Differences
in these estimates stem primarily from the fact that Mazumder did not have
the information for the parental incomes for 60% of observations in his
sample and had to impute it using education and race. Meanwhile, Chetty
et al. (2014) use direct measures of parental income.
Analysis in Chetty, Hendren, Kline, Saez, and Turner (2014) uncovered substantial spatial heterogeneity in intergenerational mobility rates across the
United States. They find that intergenerational mobility is strongly correlated
with the several factors: (i) residential segregation, (ii) income inequality, (iii)
school quality, and (iv) family structure. Comparing intergenerational mobility measures across the commuting zones in the United Status, intergenerational mobility appears to be lower in the areas characterized by higher residential segregation, high income inequality, lower school quality, lower rate
of engagement in the community organizations, and higher share of children
living in single-parent households. It reasons to be seen if these relationships
are causal.
Intergenerational income elasticities (and correlations) can mask important
features of the relationship in incomes across the generations as they measure
a single linear relationship between incomes across generations. In particular,
this single summary measure ignores differences in income mobility across
different parts of the income distribution. Such measures cannot capture the
possibility that the correlatedness of parents’ and children’s income differs
by parental income. Some studies employ Markov Chains to allow for such
nonlinearities (Bhattacharya & Mazumder, 2011; Chetty et al., 2014).
Linear measures of income mobility also do not take into account the fact
that the family dynasties may differ by ethnicity, and the persistence of
incomes across generations is partially the result of permanent differences in
opportunities between families of different ethnic groups. Chetty et al. (2014)
find that there is special variation across the United States in the rates of
intergenerational mobility, and the mobility is significantly lower in the areas
with the larger share of African-American population. They note that such
areas tend to be more segregated both by income and race. Bhattacharya and
Mazumder (2011) use a Markov chain approach to calculate the probability
that an offspring’s income falls in a higher percentile than a parent’s and
find substantially more upward mobility for whites than blacks.
Sociologists have a long history of studying intergenerational occupational
mobility (Blau & Duncan, 1967; Sewell & Hauser, 1975). The extent of intergenerational occupational mobility can shed additional light on the extent

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of intergenerational social and economic mobility. Hellerstein and Morrill
(2011) looked at the correlation of occupations between parents and their
children and found that in recent cohorts, 30% of sons and 20% of daughters
worked in the same occupation as their fathers. Using a continuous measure
of “occupation prestige,” Ermisch and Francesconi (2002) find that the
intergenerational occupational correlation in the British Household Panel
Survey ranges from 0.4 to 0.75 between fathers and offspring, and from 0.30
to 0.50 between mothers and offspring. The authors also find that the effect
is nonlinear, and that families of higher socioeconomic status have higher
elasticity. Corak and Piraino (2010) analyze Canadian data, and find that
by the age of 30, about 40% of men have worked for the same firm that had
previously employed their father. This correlation was stronger for higher
income earning fathers.
There is empirical evidence of intergenerational correlation of the welfare
receipt, but the economic literature has not found clear evidence of the
causality of the welfare receipts of parents on the welfare receipts by
children. Levine and Zimmerman (1996) exploit the variation of the welfare
receipts across states to identify the presence of the Welfare Trap—the
mechanical correlation of the means-tested welfare policy that leads to the
correlation of the welfare receipts between parents and children. They do
not find the evidence of such welfare traps, and conclude that most of the
correlation in the welfare receipts is attributable to the correlation in income.
Correlation of parental and child’s income across generations could
conceivably be partially the result of the intergenerational transmission of
attitudes. Some authors have examined the transmission of attitudes and
social behavior across generations. Dohmen, Falk, Huffman, and Sunde
(2011) show that the willingness to take risks and the extent of trust are
correlated across generations in Germany. Altonji and Dunn (2000) find that
the intergenerational persistence of hours worked are primarily due to the
intergenerational persistence of work preferences.
MECHANISMS
What sorts of mechanisms determine intergenerational income mobility? We
highlight four types of explanations that have received much attention.
FAMILY TRANSMISSION MODELS
For economists, the impact of family income on the educational attainment
of children is a natural way to explain intergenerational income patterns.
This perspective reflects the importance of human capital in economic
attainment. Becker and Tomes (1979) and Loury (1981) have proposed the

Intergenerational Mobility

5

classical intergenerational mobility models. These models have a common
logical structure. Parents invest in their children by “purchasing” education.
Since parents cannot legally obligate their children to pay for educational
loans, parental income determines the level of investment. Since human
capital, in conjunction with ability and labor market luck, determine the
offspring’s income, a potential causal relation exists between parental
income and child’s income.
The frontier in understanding family and other influences on individuals
moves away from equating human capital with formal education toward
a broader conception of cognitive skills and personality traits. The focus
is on the key personality traits (“the big five” of psychology): openness,
conscientiousness, extraversion, agreeableness, and neuroticism. Research
on the importance of personality traits, commonly called noncognitive skills,
in determining socioeconomic success has been pioneered by James Heckman. Borghans, Duckworth, Heckman, and ter Weel (2008) and Almlund,
Duckworth, Heckman, and Kautz (2011) are comprehensive surveys.
Studies have shown that there are critical and sensitive periods in the
technology of human capital formation and different capacities are malleable
at different stages of life (Cunha, Heckman, Lochner, & Masterov, 2006).
For instance, IQ is rank stable after the age of 10, whereas personality
skills are malleable through early adulthood. Besides, there is a dynamic
complementarity across both cognitive and noncognitive skills. Better
noncognitive skills, such as self-control, self-confidence, and discipline, help
future investments in cognitive skills be more productive. Higher stocks of
skills help beget more skills (Heckman, 2008). Thus, credit constraints for
parents of the very young are potentially important as they may limit the
ability of parents to provide such an environment.
In addition to providing human capital, family income has also been linked
to “health capital.” The “Fetal Origins” hypothesis argues that there is the
effect of prenatal environment (health of the mother) on subsequent human
capital formation of children (Currie & Almond, 2011). If parental income
helps determine parental health (a relationship that is well-established), then
it is possible that persistence in health status is the source of the transmission
of income status.
There is indeed positive correlation between parental income and child’s
achievement, and many studies interpret this correlation as evidence
that parental credit constraints impede investment in children. However,
empirical evidence on the importance of credit constraints for human capital
formation is not conclusive. Heckman and Mosso (2014) survey literature
on determinants of human capital and find that the importance of credit
constraints in shaping child outcomes are exaggerated in recent literature
compared to the importance of parenting and mentoring.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

SOCIAL-LEVEL TRANSMISSION MODELS
A second approach to understanding intergenerational mobility focuses on
social influences on children and adults. Social influences, abstractly, refer to
influences which act on individuals via some collective activities or environments. To understand what we mean, consider education. For most American children, education between 5 and 17 is publically provided without
direct charge. Education, in other words, is a public good whose level is determined by a political mechanism and depends on the incomes and preferences
of the adults in the school district (as well as any other levels of government
that provide funding for the schools).
How does parental income matter for publically provided education? The
answer is that parental income is one of the factors which determine which
school a child attends. For public schools, it is of particular interest to gauge
the extent to which equilibrium house prices and rent levels sustain stratification of communities by income. Models of neighborhoods and intergenerational mobility have been developed by Bénabou (1996a, 1996b), Durlauf
(1996a, 1996b), and Hoff and Sen (2005), among others. The Durlauf models
explicitly mimic those of Loury (1981) and Becker and Tomes (1979) and show
how persistent income inequality between family dynasties can be produced.
One can identify a range of reasons why neighborhoods function as intergenerational transmission mechanisms. One reason may involve role models.
If the education decisions of the young (effort in schools, years of schooling
completed) are determined by perceptions of future economic benefits, the
assessment of these benefits may depend on the distributions of educational
levels and incomes observed in a community. Stratification of communities
according to income will correspondingly mean that different locations produce different inferences about the value of education. This mechanism is
formalized in Streufert (2000).
A second reason involves the influence of self-identity on individual
choices (Akerlof & Kranton, 2000, 2002). Suppose that one effect of educational choices by an individual concerns how he relates his own identity
to that of others in his community. In a community where few parents are
well-educated, high education can render an individual feeling alienated
from those with whom he wants to share an identity. This argument has been
of long-standing importance in understanding racial inequality as a number
of authors have argued that black educational attainment is hampered by the
perception that academic success is a form of “acting white” (Fryer & Torelli,
2010; Ogbu & Davis, 2003). Durlauf (2004) is an overview of neighborhood
effects.
The third reason that neighborhoods can affect the transmission of socioeconomic status is through providing access to information about employment

Intergenerational Mobility

7

opportunities. Access to information about job openings can be low in a disadvantaged community because the disadvantage of each individual (e.g.,
because of unemployment) means that information is simply not available.
Bayer, Ross, and Topa (2008) provide empirical evidence of the importance
of interpersonal hiring networks for job market outcomes. Calvo-Armengol
and Jackson (2004, 2007) provide the theoretical framework illustrating how
the social structure could affect the wage inequality and employment participation of individuals belonging to different groups. Neighborhoods models
illustrate an important feature of social poverty traps, namely that it is the
interplay of strong social effects as well as the existence of distinct social
environments that affects mobility. In other words, the social provision of
education creates intergenerational persistence because communities stratify
by income. This leads to a “memberships” theory of intergenerational mobility (Durlauf, 1996c, 1999, 2006) in which various group memberships can
explain the transmission of advantage and disadvantage across generations.
This idea independently appears in Massey (2007) who terms it categorical
inequality.
GENETIC TRANSMISSION
A third source of intergenerational persistence of socioeconomic status is
via genes. Obviously, if an individual’s genotype affects socioeconomic
outcomes, then genes can create an intergenerational persistence in these
outcomes. In his study of the multigenerational persistence of surnames
in institutions such as elite colleges, Clark (2014) not only argues that parent/offspring persistence is far higher than found in IGE or Markov Chains,
but reflects some latent factor he calls moxie which, given his discussion,
is plausibly interpretable as genes. Empirical claims on the importance
of genes in inequality, however, are derivative from studies of genes in
intelligence, which is then linked to income. How is the genetic component
of intelligence measured? Behavioral genetics performs decompositions of
the variance of some characteristic or outcome, such as IQ into distinct roles
for nature and nurture. Jensen (1969) claimed that 80% of the variance in
IQ scores is genetic, launching a vast and continuing literature. Genomic
data has been used to claim that 50% of variation in IQ can be described
by measured variation of SNPs (single nucleotide polymorphisms) (Davies
et al., 2011).
In our judgment, there does not exist a strong basis for authoritative statements about the role of genes in intergenerational mobility. Clark’s work may
be rationalized by genes, but there is no basis for privileging genes over
institutions. (The British monarch is determined by genes, but the rule of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

succession is an institution.) Measurements of a genetic component to variation in IQ (or any other trait) are typically based on the studies of twins’
data, and exploit differences in correlations of traits between monozygotic
and dizygotic twins. As argued by Goldberger (1979), many of these decompositions assume that family environment and genes are uncorrelated. This is
clearly an untenable assumption. To see how it matters, note that the evidence
for the role of genes is derived from higher correlations between monozygotic twins than dizygotic ones with respect to IQ or some other measure.
If parents adjust childrearing to a child’s genotype, then the shared family
influence for monozygotic twins may be higher than that for dizygotic ones.
This problem is not resolved by genomic studies which employ algorithms
to isolate individuals with relative little DNA overlap such as Davies et al.
(2011).
To be clear, behavioral genetics research has fully recognized the importance of gene/environment interactions. Studies such as Neiderhiser and
Lichtenstein (2008) and Narusyte et al. (2008) look at the correlations between
children of twins and suggest strategies to allow for gene/environment correlations. See Johnson (2013) for nuanced views on how work on intelligence
should proceed. Our argument is that currently the magnitude of the role of
genes is simply unknown.
ASSORTATIVE MATING
A fourth explanation, which is linked to each of the previous explanations,
concerns marriage patterns. Assortative mating, with respect to income,
education, or other factors which affect children, can naturally enhance
intergenerational persistence. Early efforts to quantify the implications of
assortative mating for inequality led to disparate conclusions, see Kremer
(1997) and Fernandez and Rogerson (2001). Recently, Greenwood, Guner,
Kocharkov, and Santos (2014) have performed a detailed calibration analysis
of a socioeconomic environment which shows how assortative mating can
have first-order effects on cross-section inequality, which, given factors such
as credit constraints, can translate into intergenerational mobility. A promising topic, in our view, is the role of assortative mating by ethnicity and
persistent inequality across ethnic groups, see Kalmijn and Van Tubergen
(2010) for a study of intermarriage patterns.
There is no systematic account that addresses the relative salience of
these different mechanisms. A few analyses attempt to decompose the IGE
as derivative from distinct channels. Bowles and Gintis (2002) employ a
linear system of equations to decompose a 0.32 IGE of the United States
as the sum of four underlying mechanisms that link parents and children:
IQ conditional on schooling (treated as a proxy for genes) 0.04, education

Intergenerational Mobility

9

conditional on IQ 0.07, wealth 0.12, and personality and race 0.07. Blanden,
Gregg, and MacMillan (2007) engage in a similar exercise for the United
Kingdom. While suggestive, these analyses, in our judgment are limited
by their mechanical nature. By this, the decompositions do not derive from
behavioral models, but from ad hoc modeling choices of the linear system.
Nor do they address social mechanisms. That said, these studies are very
original and should be the basis for future work.
WHY DOES INTERGENERATIONAL MOBILITY MATTER?
Intergenerational mobility is commonly regarded as a measure of the degree
of equality of opportunity in a society, although rigorous philosophical
formulations of equality of opportunity (e.g., Roemer, 1998) focus not
on the degree of similarity between parents and children, but rather on
whether high (or low) similarities are due to factors for which individuals
are responsible. In other words, intergenerational mobility measures focus
on outcomes, rather than mechanisms, which are the relevant objects in
ethical evaluations of observed mobility patterns. A rare effort to explicitly
measure equality of opportunity is Bjorklund, Jantti, and Roemer (2012).
FUTURE DIRECTIONS
While there are rich research literatures on the measurement of intergenerational mobility as well as on the various mechanisms that are believed to
play major roles in determining rates of mobility, there does not exist an integrated analysis of intergenerational mobility which combines these mechanisms into a unified structure. As indicated by the work on early childhood
development, identity, and social interactions, such a unified model must
blend elements of neuroscience, psychology, sociology, and economics. While
a daunting task, an integrated framework is essential in the identification of
efficacious policies to promote mobility.
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STEVEN N. DURLAUF SHORT BIOGRAPHY
Steven N. Durlauf is Vilas Research Professor and Kenneth J. Arrow Professor of Economics at the University of Wisconsin at Madison. He is a Fellow of
the Econometric Society and of the American Academy of Arts and Sciences.
IRINA SHAORSHADZE SHORT BIOGRAPHY
Irina Shaorshadze is a PhD student at Cambridge University and a visiting
fellow at the University of Wisconsin at Madison. Previously, she has worked
at the Social and Economic Development Unit at the World Bank.
RELATED ESSAYS
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Brand
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Gauthier
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Michael J. Handel
The Emerging Psychology of Social Class (Psychology), Michael W. Kraus
Education for Mobility or Status Reproduction? (Sociology), Karyn Lacy

14

EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

Stratification and the Welfare State (Sociology), Stephanie Moller and Joya
Misra
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M. Stonecash
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Intergenerational Mobility
STEVE N. DURLAUF and IRINA SHAORSHADZE

Abstract
This essay describes basic facts about intergenerational mobility as well as some of
the mechanisms that have been proposed to explain levels of mobility or persistence
of socioeconomic status across generations. Limits of current research are identified.

INTRODUCTION
Intergenerational mobility refers to the degree to which individual socioeconomic characteristics are associated with the outcomes and characteristics of
their parents. Mobility can thus be either absolute or relative.
MEASUREMENT
Economics and sociology have traditionally focused on income and occupation, respectively, in measuring intergenerational mobility. Contemporary
social science includes theoretical and empirical studies of mobility that
account for the persistence of socioeconomic phenomena such as education,
occupations, family structure, and criminal activity.
The standard measure of intergenerational persistence of income is the
intergenerational elasticity (IGE) of offspring’s income with respect to
parental income. If the variance of the income distribution in two generations is the same, the IGE will be the same as the intergenerational correlation
of the logarithms of incomes. If the variance of the income distribution has
changed, then the IGE will be the intergenerational correlation weighted by
ratio of variances in the two generations. Both elasticities and correlations
intergenerational mobility have been widely used in literature (Black &
Devereux, 2011). The use of logarithm of income reflects the objective of
measuring mobility after accounting for secular income growth in a society.
Because of relatively limited labor force participation of women, most
studies look at the relationship of incomes of fathers and sons. We focus on
the IGE as it is the more commonly used measure.
Emerging Trends in the Social and Behavioral Sciences. Edited by Robert Scott and Stephen Kosslyn.
© 2015 John Wiley & Sons, Inc. ISBN 978-1-118-90077-2.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

It has recently been claimed that countries that have higher cross-sectional
income inequality tend to have a lower rate of intergenerational income
mobility; this relationship is often called The Great Gatsby Curve (Corak,
2013). It does not necessarily imply causality from inequality to low mobility
or vice versa. Among the OECD countries, during the mid-1980s, Finland,
Sweden, Norway, and Denmark were the most equal in terms of income
distribution, whereas the United States and the United Kingdom the most
unequal. Intergenerational income mobility is found to be higher in the
Nordic countries and lower in the United Kingdom and the United States.
Low IGE for Nordic countries can be explained either by low return to
skills resulting in compressed income distribution, or higher reliance on
distributive social and educational policies (Black & Devereux, 2011).
Surveys of IGE of select countries gives some perspective of the magnitude
of cross-country differences in income mobility. Jantti et al. (2006) found that
the IGE was 0.071 in Denmark, 0.306 in the United Kingdom, and 0.0517 in
the United Status. They find that larger IGE in the United Status and United
Kingdom relative to that in the Nordic countries was almost entirely due to
the differences in the tails of income distribution. South America, other developing countries, and the Southern Europe also have high degree of income
inequality and intergenerational income mobility (Blanden, 2013). Some of
the estimates for the IGE in other countries include 0.4 for France (Lefranc &
Trannoy, 2005), 0.5 for Italy (Piraino, 2007; Mocetti, 2007), and 0.52 in Brazil
(Dunn, 2007).
There is a growing public perception in the United States that the IGE has
been declining in recent decades. However, the empirical evidence on the
trend in IGE has been mixed. Some studies have found that income mobility has declined in recent decades (Aaronson & Mazumder, 2008; Putnam,
Frederick, & Snellman, 2012), whereas other studies have found no trend in
income mobility (Hertz et al., 2007; Lee & Solon, 2009). Furthermore, due to
the difficulties in measuring permanent income accurately, different studies
have estimated vastly different magnitudes of IGE, while using different data
sources and different income aggregation methods for the same cohorts. Estimates of the IGE in the United States for the cohorts of children born in the
1950s and 1960s include 0.2 (Behrman & Taubman, 1985), 0.4 (Solon, 1992),
and 0.6 (Mazumder, 2005).
Chetty, Hendren, Kline, and Saez (2014) analyze intergenerational mobility
in the United States for the last three decades using the universe of actual tax
returns filed. The authors measured intergenerational mobility based on the
correlation between parental and child’s income percentile ranks rather than
the IGE between their actual incomes. By looking at the mobility measures of
the 1971 and 1993 birth cohorts, they conclude that the rank-based measures
of intergenerational mobility have not changed significantly over recent

Intergenerational Mobility

3

decades. Meanwhile, the authors find that the cross-sectional inequality has
increased during these years, but this increase came from the extreme upper
tails of the income distribution. The authors estimate that the rank-rank
based measure of intergenerational income elasticity is 0.3, half of the estimate found by Mazumder (2005) using data for the same years. Differences
in these estimates stem primarily from the fact that Mazumder did not have
the information for the parental incomes for 60% of observations in his
sample and had to impute it using education and race. Meanwhile, Chetty
et al. (2014) use direct measures of parental income.
Analysis in Chetty, Hendren, Kline, Saez, and Turner (2014) uncovered substantial spatial heterogeneity in intergenerational mobility rates across the
United States. They find that intergenerational mobility is strongly correlated
with the several factors: (i) residential segregation, (ii) income inequality, (iii)
school quality, and (iv) family structure. Comparing intergenerational mobility measures across the commuting zones in the United Status, intergenerational mobility appears to be lower in the areas characterized by higher residential segregation, high income inequality, lower school quality, lower rate
of engagement in the community organizations, and higher share of children
living in single-parent households. It reasons to be seen if these relationships
are causal.
Intergenerational income elasticities (and correlations) can mask important
features of the relationship in incomes across the generations as they measure
a single linear relationship between incomes across generations. In particular,
this single summary measure ignores differences in income mobility across
different parts of the income distribution. Such measures cannot capture the
possibility that the correlatedness of parents’ and children’s income differs
by parental income. Some studies employ Markov Chains to allow for such
nonlinearities (Bhattacharya & Mazumder, 2011; Chetty et al., 2014).
Linear measures of income mobility also do not take into account the fact
that the family dynasties may differ by ethnicity, and the persistence of
incomes across generations is partially the result of permanent differences in
opportunities between families of different ethnic groups. Chetty et al. (2014)
find that there is special variation across the United States in the rates of
intergenerational mobility, and the mobility is significantly lower in the areas
with the larger share of African-American population. They note that such
areas tend to be more segregated both by income and race. Bhattacharya and
Mazumder (2011) use a Markov chain approach to calculate the probability
that an offspring’s income falls in a higher percentile than a parent’s and
find substantially more upward mobility for whites than blacks.
Sociologists have a long history of studying intergenerational occupational
mobility (Blau & Duncan, 1967; Sewell & Hauser, 1975). The extent of intergenerational occupational mobility can shed additional light on the extent

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

of intergenerational social and economic mobility. Hellerstein and Morrill
(2011) looked at the correlation of occupations between parents and their
children and found that in recent cohorts, 30% of sons and 20% of daughters
worked in the same occupation as their fathers. Using a continuous measure
of “occupation prestige,” Ermisch and Francesconi (2002) find that the
intergenerational occupational correlation in the British Household Panel
Survey ranges from 0.4 to 0.75 between fathers and offspring, and from 0.30
to 0.50 between mothers and offspring. The authors also find that the effect
is nonlinear, and that families of higher socioeconomic status have higher
elasticity. Corak and Piraino (2010) analyze Canadian data, and find that
by the age of 30, about 40% of men have worked for the same firm that had
previously employed their father. This correlation was stronger for higher
income earning fathers.
There is empirical evidence of intergenerational correlation of the welfare
receipt, but the economic literature has not found clear evidence of the
causality of the welfare receipts of parents on the welfare receipts by
children. Levine and Zimmerman (1996) exploit the variation of the welfare
receipts across states to identify the presence of the Welfare Trap—the
mechanical correlation of the means-tested welfare policy that leads to the
correlation of the welfare receipts between parents and children. They do
not find the evidence of such welfare traps, and conclude that most of the
correlation in the welfare receipts is attributable to the correlation in income.
Correlation of parental and child’s income across generations could
conceivably be partially the result of the intergenerational transmission of
attitudes. Some authors have examined the transmission of attitudes and
social behavior across generations. Dohmen, Falk, Huffman, and Sunde
(2011) show that the willingness to take risks and the extent of trust are
correlated across generations in Germany. Altonji and Dunn (2000) find that
the intergenerational persistence of hours worked are primarily due to the
intergenerational persistence of work preferences.
MECHANISMS
What sorts of mechanisms determine intergenerational income mobility? We
highlight four types of explanations that have received much attention.
FAMILY TRANSMISSION MODELS
For economists, the impact of family income on the educational attainment
of children is a natural way to explain intergenerational income patterns.
This perspective reflects the importance of human capital in economic
attainment. Becker and Tomes (1979) and Loury (1981) have proposed the

Intergenerational Mobility

5

classical intergenerational mobility models. These models have a common
logical structure. Parents invest in their children by “purchasing” education.
Since parents cannot legally obligate their children to pay for educational
loans, parental income determines the level of investment. Since human
capital, in conjunction with ability and labor market luck, determine the
offspring’s income, a potential causal relation exists between parental
income and child’s income.
The frontier in understanding family and other influences on individuals
moves away from equating human capital with formal education toward
a broader conception of cognitive skills and personality traits. The focus
is on the key personality traits (“the big five” of psychology): openness,
conscientiousness, extraversion, agreeableness, and neuroticism. Research
on the importance of personality traits, commonly called noncognitive skills,
in determining socioeconomic success has been pioneered by James Heckman. Borghans, Duckworth, Heckman, and ter Weel (2008) and Almlund,
Duckworth, Heckman, and Kautz (2011) are comprehensive surveys.
Studies have shown that there are critical and sensitive periods in the
technology of human capital formation and different capacities are malleable
at different stages of life (Cunha, Heckman, Lochner, & Masterov, 2006).
For instance, IQ is rank stable after the age of 10, whereas personality
skills are malleable through early adulthood. Besides, there is a dynamic
complementarity across both cognitive and noncognitive skills. Better
noncognitive skills, such as self-control, self-confidence, and discipline, help
future investments in cognitive skills be more productive. Higher stocks of
skills help beget more skills (Heckman, 2008). Thus, credit constraints for
parents of the very young are potentially important as they may limit the
ability of parents to provide such an environment.
In addition to providing human capital, family income has also been linked
to “health capital.” The “Fetal Origins” hypothesis argues that there is the
effect of prenatal environment (health of the mother) on subsequent human
capital formation of children (Currie & Almond, 2011). If parental income
helps determine parental health (a relationship that is well-established), then
it is possible that persistence in health status is the source of the transmission
of income status.
There is indeed positive correlation between parental income and child’s
achievement, and many studies interpret this correlation as evidence
that parental credit constraints impede investment in children. However,
empirical evidence on the importance of credit constraints for human capital
formation is not conclusive. Heckman and Mosso (2014) survey literature
on determinants of human capital and find that the importance of credit
constraints in shaping child outcomes are exaggerated in recent literature
compared to the importance of parenting and mentoring.

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

SOCIAL-LEVEL TRANSMISSION MODELS
A second approach to understanding intergenerational mobility focuses on
social influences on children and adults. Social influences, abstractly, refer to
influences which act on individuals via some collective activities or environments. To understand what we mean, consider education. For most American children, education between 5 and 17 is publically provided without
direct charge. Education, in other words, is a public good whose level is determined by a political mechanism and depends on the incomes and preferences
of the adults in the school district (as well as any other levels of government
that provide funding for the schools).
How does parental income matter for publically provided education? The
answer is that parental income is one of the factors which determine which
school a child attends. For public schools, it is of particular interest to gauge
the extent to which equilibrium house prices and rent levels sustain stratification of communities by income. Models of neighborhoods and intergenerational mobility have been developed by Bénabou (1996a, 1996b), Durlauf
(1996a, 1996b), and Hoff and Sen (2005), among others. The Durlauf models
explicitly mimic those of Loury (1981) and Becker and Tomes (1979) and show
how persistent income inequality between family dynasties can be produced.
One can identify a range of reasons why neighborhoods function as intergenerational transmission mechanisms. One reason may involve role models.
If the education decisions of the young (effort in schools, years of schooling
completed) are determined by perceptions of future economic benefits, the
assessment of these benefits may depend on the distributions of educational
levels and incomes observed in a community. Stratification of communities
according to income will correspondingly mean that different locations produce different inferences about the value of education. This mechanism is
formalized in Streufert (2000).
A second reason involves the influence of self-identity on individual
choices (Akerlof & Kranton, 2000, 2002). Suppose that one effect of educational choices by an individual concerns how he relates his own identity
to that of others in his community. In a community where few parents are
well-educated, high education can render an individual feeling alienated
from those with whom he wants to share an identity. This argument has been
of long-standing importance in understanding racial inequality as a number
of authors have argued that black educational attainment is hampered by the
perception that academic success is a form of “acting white” (Fryer & Torelli,
2010; Ogbu & Davis, 2003). Durlauf (2004) is an overview of neighborhood
effects.
The third reason that neighborhoods can affect the transmission of socioeconomic status is through providing access to information about employment

Intergenerational Mobility

7

opportunities. Access to information about job openings can be low in a disadvantaged community because the disadvantage of each individual (e.g.,
because of unemployment) means that information is simply not available.
Bayer, Ross, and Topa (2008) provide empirical evidence of the importance
of interpersonal hiring networks for job market outcomes. Calvo-Armengol
and Jackson (2004, 2007) provide the theoretical framework illustrating how
the social structure could affect the wage inequality and employment participation of individuals belonging to different groups. Neighborhoods models
illustrate an important feature of social poverty traps, namely that it is the
interplay of strong social effects as well as the existence of distinct social
environments that affects mobility. In other words, the social provision of
education creates intergenerational persistence because communities stratify
by income. This leads to a “memberships” theory of intergenerational mobility (Durlauf, 1996c, 1999, 2006) in which various group memberships can
explain the transmission of advantage and disadvantage across generations.
This idea independently appears in Massey (2007) who terms it categorical
inequality.
GENETIC TRANSMISSION
A third source of intergenerational persistence of socioeconomic status is
via genes. Obviously, if an individual’s genotype affects socioeconomic
outcomes, then genes can create an intergenerational persistence in these
outcomes. In his study of the multigenerational persistence of surnames
in institutions such as elite colleges, Clark (2014) not only argues that parent/offspring persistence is far higher than found in IGE or Markov Chains,
but reflects some latent factor he calls moxie which, given his discussion,
is plausibly interpretable as genes. Empirical claims on the importance
of genes in inequality, however, are derivative from studies of genes in
intelligence, which is then linked to income. How is the genetic component
of intelligence measured? Behavioral genetics performs decompositions of
the variance of some characteristic or outcome, such as IQ into distinct roles
for nature and nurture. Jensen (1969) claimed that 80% of the variance in
IQ scores is genetic, launching a vast and continuing literature. Genomic
data has been used to claim that 50% of variation in IQ can be described
by measured variation of SNPs (single nucleotide polymorphisms) (Davies
et al., 2011).
In our judgment, there does not exist a strong basis for authoritative statements about the role of genes in intergenerational mobility. Clark’s work may
be rationalized by genes, but there is no basis for privileging genes over
institutions. (The British monarch is determined by genes, but the rule of

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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES

succession is an institution.) Measurements of a genetic component to variation in IQ (or any other trait) are typically based on the studies of twins’
data, and exploit differences in correlations of traits between monozygotic
and dizygotic twins. As argued by Goldberger (1979), many of these decompositions assume that family environment and genes are uncorrelated. This is
clearly an untenable assumption. To see how it matters, note that the evidence
for the role of genes is derived from higher correlations between monozygotic twins than dizygotic ones with respect to IQ or some other measure.
If parents adjust childrearing to a child’s genotype, then the shared family
influence for monozygotic twins may be higher than that for dizygotic ones.
This problem is not resolved by genomic studies which employ algorithms
to isolate individuals with relative little DNA overlap such as Davies et al.
(2011).
To be clear, behavioral genetics research has fully recognized the importance of gene/environment interactions. Studies such as Neiderhiser and
Lichtenstein (2008) and Narusyte et al. (2008) look at the correlations between
children of twins and suggest strategies to allow for gene/environment correlations. See Johnson (2013) for nuanced views on how work on intelligence
should proceed. Our argument is that currently the magnitude of the role of
genes is simply unknown.
ASSORTATIVE MATING
A fourth explanation, which is linked to each of the previous explanations,
concerns marriage patterns. Assortative mating, with respect to income,
education, or other factors which affect children, can naturally enhance
intergenerational persistence. Early efforts to quantify the implications of
assortative mating for inequality led to disparate conclusions, see Kremer
(1997) and Fernandez and Rogerson (2001). Recently, Greenwood, Guner,
Kocharkov, and Santos (2014) have performed a detailed calibration analysis
of a socioeconomic environment which shows how assortative mating can
have first-order effects on cross-section inequality, which, given factors such
as credit constraints, can translate into intergenerational mobility. A promising topic, in our view, is the role of assortative mating by ethnicity and
persistent inequality across ethnic groups, see Kalmijn and Van Tubergen
(2010) for a study of intermarriage patterns.
There is no systematic account that addresses the relative salience of
these different mechanisms. A few analyses attempt to decompose the IGE
as derivative from distinct channels. Bowles and Gintis (2002) employ a
linear system of equations to decompose a 0.32 IGE of the United States
as the sum of four underlying mechanisms that link parents and children:
IQ conditional on schooling (treated as a proxy for genes) 0.04, education

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conditional on IQ 0.07, wealth 0.12, and personality and race 0.07. Blanden,
Gregg, and MacMillan (2007) engage in a similar exercise for the United
Kingdom. While suggestive, these analyses, in our judgment are limited
by their mechanical nature. By this, the decompositions do not derive from
behavioral models, but from ad hoc modeling choices of the linear system.
Nor do they address social mechanisms. That said, these studies are very
original and should be the basis for future work.
WHY DOES INTERGENERATIONAL MOBILITY MATTER?
Intergenerational mobility is commonly regarded as a measure of the degree
of equality of opportunity in a society, although rigorous philosophical
formulations of equality of opportunity (e.g., Roemer, 1998) focus not
on the degree of similarity between parents and children, but rather on
whether high (or low) similarities are due to factors for which individuals
are responsible. In other words, intergenerational mobility measures focus
on outcomes, rather than mechanisms, which are the relevant objects in
ethical evaluations of observed mobility patterns. A rare effort to explicitly
measure equality of opportunity is Bjorklund, Jantti, and Roemer (2012).
FUTURE DIRECTIONS
While there are rich research literatures on the measurement of intergenerational mobility as well as on the various mechanisms that are believed to
play major roles in determining rates of mobility, there does not exist an integrated analysis of intergenerational mobility which combines these mechanisms into a unified structure. As indicated by the work on early childhood
development, identity, and social interactions, such a unified model must
blend elements of neuroscience, psychology, sociology, and economics. While
a daunting task, an integrated framework is essential in the identification of
efficacious policies to promote mobility.
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STEVEN N. DURLAUF SHORT BIOGRAPHY
Steven N. Durlauf is Vilas Research Professor and Kenneth J. Arrow Professor of Economics at the University of Wisconsin at Madison. He is a Fellow of
the Econometric Society and of the American Academy of Arts and Sciences.
IRINA SHAORSHADZE SHORT BIOGRAPHY
Irina Shaorshadze is a PhD student at Cambridge University and a visiting
fellow at the University of Wisconsin at Madison. Previously, she has worked
at the Social and Economic Development Unit at the World Bank.
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