Intergenerational Mobility: A Cross‐National Comparison
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Intergenerational Mobility:
A Cross-National Comparison
BHASHKAR MAZUMDER
Abstract
A goal in many societies is to ensure that individuals have the same opportunities for
success irrespective of their circumstances at birth. While equality of opportunity is
an elusive concept to measure, social science researchers have developed measures
of intergenerational mobility to serve as a rough barometer. Presumably, societies
in which where there is a high likelihood that families can improve their relative
socioeconomic standing over generations are likely to be ones characterized by more
widespread opportunity. In recent decades, a large and growing body of research that
has used a variety of approaches to study intergenerational mobility with respect to
income, education, and occupation has emerged. At this stage, the requisite data to
conduct this kind of analysis is not available for all countries. Nevertheless, a few
key patterns of results have emerged. First, intergenerational mobility appears to be
most rapid in Nordic countries. Second, the United States and by some measures
the United Kingdom appear to have lower rates of intergenerational mobility than
other industrialized countries. Third, intergenerational mobility seems to be lower
in developing countries, particularly those in Latin America. These conclusions are
still tentative and may be revised as new and better data and more creative methods
arise in future research.
INTRODUCTION
One of the fundamental ways to judge the fairness of a society is to ask
whether there is equality of opportunity. Do all children have the same
opportunity for success in life irrespective of their family background?
Are children in some societies more likely to be condemned to lives of
poverty simply because of the circumstances of their birth? If there are
differences in equality of opportunity across countries, what accounts for
these differences?
While these are pretty weighty questions, it might be surprising to learn
just how difficult it is to answer them. One of the key challenges for
researchers has been conceptual. How exactly should equality of opportunity be measured? The primary approach that researchers have used is to
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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construct measures of “intergenerational mobility,” that is, to measure how
much families move up or down the distribution of income, education, or
occupation over the course of a generation. Although these measures are not
likely to perfectly capture equality of opportunity, the idea is that the societies in which there is more fluid movement of families over generations are
likely to be the same societies in which the opportunities for advancement
are more widespread. With solid measures of intergenerational mobility
in hand, one can then see how they compare across countries and perhaps
begin to try to understand exactly what characteristics of countries allow
them to experience greater or lesser rates of mobility.
As this entry highlights, however, a major limitation to this kind of an analysis has been the lack of adequate data. To study intergenerational mobility, ideally one needs to track a large sample of families over at least two
generations with good measures of long-term socioeconomic status in each
generation. Access to good longitudinal data on one generation can be challenging enough, collecting data for two generations can be especially daunting. Ensuring that these samples and data sources are reasonably comparable
across countries is an even more onerous challenge.
Yet, despite these obstacles, a picture is beginning to emerge of how intergenerational income mobility differs across societies, at least for a set of countries where good income data for multiple generations is readily available.
There appears to be pretty compelling evidence that Nordic countries appear
to have the most income mobility. There is also fairly strong evidence that
the United States and perhaps the United Kingdom have much less income
mobility, with countries in Continental Europe somewhere in between. It has
been much more challenging to establish how countries in other parts of the
world fare on this yardstick because of limitations on collecting multigenerational income data. The studies that do exist seem to provide fairly good
evidence that income mobility tends to be lower in developing countries. But
it is probably too early to draw strong conclusions about the specific relative
rankings of countries with respect to income mobility.
An alternative approach to using income data is to measure intergenerational educational mobility because multigenerational data on completed
schooling is accessible for many more countries. Recent work has found that
Nordic countries also experience the most rapid intergenerational mobility
with respect to education. It also appears that rates of intergenerational
educational mobility are particularly low in many Latin American countries.
Over the coming decades, new research from many more countries is
expected to build on the existing results in this nascent literature to fill
in the gaps in our knowledge. Recent studies are already beginning to
consider more innovative methods for inferring rates of intergenerational
mobility across the world. This should also allow us to better understand
Intergenerational Mobility: A Cross-National Comparison
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what it is about some countries that allow them to exhibit greater equality
of opportunity and may provide powerful insights to policy makers about
what kinds of policies can be used to promote intergenerational mobility.
FOUNDATIONAL RESEARCH
How social status is passed down over generations has been a question of
interest dating back to at least the pioneering work of the Victorian era social
scientist, Sir Francis Galton. Galton, for example, studied how height was
transmitted across generations and, in the process, developed statistical tools
such as regression analysis and correlations that have formed the building
blocks of empirical methods used today.
In the modern era, the study of intergenerational mobility was truly
advanced by the seminal work of the sociologists Peter Blau and Otis
Dudley Duncan in their 1967 book entitled “The American Occupational
Structure.” The authors conducted the first large-scale survey of intergenerational mobility among US workers, finding a significant amount of upward
occupational mobility. They also showed the importance of intergenerational
links in education and occupation, finding that fathers often influenced son’s
education and occupational trajectory. One negative finding was that Blacks
are often stuck in a bad cycle: As education yields fewer career options, they
get less education, and remain in low-class jobs.
Building on this work, many studies have used measures based on occupation to understand cross-country differences in intergenerational mobility.
Looking at a set of countries with comparable coding of occupations, Emily
Beller and Michael Hout have argued that occupational mobility is highest
in nations such as Sweden, Canada, and Norway and lower in nations such
as Germany, Ireland, and Portugal. They find that occupational mobility in
the United States falls squarely in the middle of this set of countries. They
also cite other research suggesting that occupational mobility is quite low in
Italy, France, and Great Britain.
Economists have developed a largely separate strand of research that has
focused on intergenerational mobility with respect to economic outcomes
such as wages, earnings, and income. In addition to extending the literature from “social mobility” to “economic mobility,” these studies have made
important contributions to the measurement of mobility and in the process
helped dramatically alter the conventional view that intergenerational mobility was higher in the United States than in other countries.
Economists have primarily focused on measuring the “intergenerational
elasticity” in income. The intergenerational elasticity is estimated by running
a regression of the log of income of the child on the log of income of parents.
The coefficient from that regression expresses what the effect of a 1% change
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in income of a parent would be on the income of the child, in percentage
terms. For example, a coefficient of 0.4 would mean that a 1% increase in
parent’s income is associated with a 0.4% increase in the child’s income. The
elasticity also provides an approximation of how much of the gap in income
between any two families, in percentage terms, is expected to persist into the
next generation.
One minus the intergenerational elasticity can then be used to infer the rate
of intergenerational mobility because it indicates approximately what percentage of the gap in income between families, on average, is wiped away in
a generation. The first studies typically used just a single year of income in
each generation and estimated the intergenerational elasticity in the United
States to be about to 0.2. This led observers to conclude that intergenerational
mobility was quite rapid because it implied that most economic differences
between families would be wiped out in about three generations.
Subsequent studies by economists, most notably work by Gary Solon using
longitudinal data from the Panel Study of Income Dynamics, showed that the
use of a single year of income dramatically overstated the degree of intergenerational mobility in the United States relative to using short-term averages
of income. He argued that after correcting for this bias, the intergenerational
elasticity in the United States was actually 0.4 or higher. In a paper that further built on this insight, I used confidential social security earnings data containing even longer windows of earnings information to better approximate
long-term economic status, and estimated the intergenerational elasticity in
earnings in the United States to be about 0.6. This suggested that even using
short-term averages of income could still substantially overstate intergenerational income mobility and that income gaps in the United States could take
much longer to equalize. Other notable work by Steven Haider and Gary
Solon has also highlighted how estimates of the mobility can be prone to a
“life cycle” bias. This can occur if income is measured at ages which tend to
be a poor reflection of the long-term status of either parents or children.
For countries that have very good longitudinal data on income for multiple
generations, researchers have been able to produce estimates of intergenerational income mobility that address these forms of measurement related
bias. Some of the best research has used data on Nordic countries where
access to population and tax registers are readily available. In one of the first
comprehensive reviews of the international evidence, Miles Corak assembled estimates of the intergenerational elasticity for nine countries that were
harmonized to address comparability issues. He found the lowest rates of
intergenerational earnings mobility in the United States and the United Kingdom, where the estimated intergenerational elasticity was around 0.5. The
most rapid rates of mobility were found to be in Denmark, Norway, Finland,
and Canada, where the intergenerational elasticity was under 0.2. In between
Intergenerational Mobility: A Cross-National Comparison
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these two groups of countries were France, Germany, and Sweden, which had
intergenerational elasticities of 0.41, 0.32, and 0.27, respectively.
A number of studies have estimated intergenerational income elasticities
for a variety of other countries such as Australia, Brazil, Chile, China,
Ecuador, Italy, Germany, Malaysia, Nepal, Pakistan, Peru, Singapore, South
Africa, Spain, and Switzerland. Unfortunately, owing to many differences
in methodology and data quality, it is difficult to accurately rank countries
without using very careful procedures. Nevertheless, estimates suggest that
intergenerational income mobility tends to be low in China as well as most
developing countries, and are in a roughly similar range to estimates produced for the United States. Estimates for Central and Southern European
countries tend to fall somewhere between the high rates of mobility found
in Nordic countries and the low rates found in the United Kingdom and
United States.
A limitation of using the intergenerational elasticity is that it is not informative about how mobility might differ at different points in the income
distribution or provide insight about the relative importance of upward versus downward mobility. An alternative approach to studying income mobility that addresses these issues has been to divide the income distribution in
each generation into equally sized groups such as quintiles and then calculate a “transition matrix” of movement across the quintiles. For example, if
one is concerned about upward mobility from the bottom, then one could
focus on the fraction of individuals who start as children in families in the
bottom quintile in one generation but who end up in the top quintile as
adults. A study by Markus Jantti and coauthors compared patterns in intergenerational mobility using transition matrices for six countries (Denmark,
Finland, Norway, Sweden, United States, and United Kingdom). Among the
key findings were that the United States had an exceptionally high degree of
persistence in the bottom quintile and a low degree of upward mobility out
of the bottom quintile. The United States and the United Kingdom also had
a low degree of downward mobility out of the top quintile of the income distribution. The authors found a remarkably similar degree of mobility across
the middle quintiles of the income distribution.
An alternative approach to estimating intergenerational mobility for a
broad range of countries is to use educational outcomes rather than occupation or income. Many countries have multigenerational datasets that collect
information on completed schooling for two generations within a family.
In a study that was impressive in the sheer breadth of countries examined,
Tom Hertz and his coauthors assembled estimates of the intergenerational
correlation for 42 countries including 10 countries in Asia, 7 countries in
Latin America, 4 countries in Africa, 8 countries in the Eastern bloc, and 13
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Western capitalist economies which include the United States, United Kingdom, along with 4 Nordic countries. The authors found the lowest degree of
intergenerational educational mobility in Latin American countries, where
the correlation averaged 0.60. Next lowest was the Eastern bloc countries,
which averaged 0.41. The Asian nations and the group of Western nations
both averaged 0.39. Finally, the intergenerational education correlation in
the small sample of African countries averaged 0.36. Within the group of
Western nations, Nordic countries had lower estimates of intergenerational
persistence.
An interesting and powerful conclusion that appears robust to all types
of measures of socioeconomic status is that the Nordic countries appear to
have the highest rates of intergenerational mobility. There is also suggestive
evidence that mobility is lowest in Latin American countries based on high
estimates of intergenerational persistence in education and, in some cases,
income. However, much more evidence is needed before we can draw especially sharp conclusions. Given the diversity of approaches in the literature
an important lesson is that we may wish to consider a variety of estimates
that may eventually yield more nuanced conclusions about which countries
are more mobile than others.
CUTTING-EDGE RESEARCH
ARE INTERGENERATIONAL ASSOCIATIONS TRULY CAUSAL
Until recently, most research on intergenerational mobility was purely
descriptive in nature and simply presented statistics that could be used to
broadly characterize different societies in terms of inequality of opportunity. The measures could be used the same way that broad measures of
living standards or inequality are used to compare countries. A major new
avenue of research has tried to push further and to try to understand if the
intergenerational associations are actually causal. In other words, if parents
were given additional income or years of schooling, would this actually
cause their offspring to also have higher incomes or more schooling? Or, is
it merely the case that high-status parents who have certain characteristics
(e.g. patience, strong cognitive skills) transfer these characteristics to their
children through either nature or nurture (or both), and that it is these
characteristics rather than income or years of schooling per se that explain
intergenerational associations?
Perhaps the most innovative research in this field has focused on the
extent to which the intergenerational association in years of schooling is
causal. In an excellent review paper, Helena Holmlund, Mikael Lindahl,
and Erik Plug describe the three main approaches that have been used
Intergenerational Mobility: A Cross-National Comparison
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in the literature. They then apply all three methods themselves on an
intergenerational sample in Sweden. The first method uses identical twins
and compares the difference in their schooling levels with those of their
offspring. The second method compares the offspring of natural born siblings with adoptive siblings. Both of these approaches attempt to eliminate
genetic factors from influencing the intergenerational association. The third
approach uses a compulsory schooling reform that gradually spread across
Swedish municipalities starting in 1949 and through the early 1960s. As
this reform can be viewed as an “exogenous” source of variation in years of
schooling of the parents, the effects on the schooling of the offspring may be
viewed as causal. While there are some differences in the estimates across
the three methods, all three approaches suggest that some portion of the
intergenerational association is causal.
USING SURNAMES
One of the most creative new approaches to studying intergenerational
mobility has been the use of surnames as a way of identifying common
lineages. Maia Guell, Jose V. Rodriguez Mora, and Christopher Tellmer
have developed a sophisticated model that demonstrates how using
population-wide data containing rare surnames along with socioeconomic
outcomes can provide insight on rates of intergenerational mobility. This
approach overcomes the problem of relying on multigenerational data in
order to measure mobility. They apply this approach to studying intergenerational mobility across birth cohorts in Spain and find that mobility has
declined over the course of the twenthieth century.
In a series of papers, Gregory Clark has similarly used surnames to study
intergenerational mobility in a wide variety of characteristics over long periods of time across a number of societies. Clark’s approach does not utilize
an elaborate model but instead uses a more simple approach to infer the rate
at which differences in socioeconomic status by groups of surnames erode
over time. Clark consistently finds that the relative advantages of having a
high-status surname erodes only very slowly over time. His results are very
provocative and suggest that rates of intergenerational mobility are remarkably slow in all countries and in all time periods.
Models based on surnames essentially use a “group”-based approach to
inferring rates of intergenerational mobility and thereby rely on certain
assumptions in order for these findings to be strictly comparable to the
individual-level estimates typically estimated in past research. Further
research is needed both to verify the empirical findings of these early
surname studies and to validate the underlying assumptions of the models.
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
Nevertheless, the use of surnames offers a promising new approach that
could potentially allow for cross national comparisons.
TIMING OF INCOME
A newer strand of the literature on intergenerational income mobility
has focused on when exactly in the lifecycle of children that parental
income might matter most. In part, this has been motivated by the growing
cross-disciplinary literature that has focused on the importance of early life
events in determining long-term success. The highly influential work by the
epidemiologist David Barker has shown that birth weight is highly correlated with measures of long-term health such as heart disease. Economists
have contributed to the literature by using “natural experiments” such as
early life exposure to famines and disease environments to estimate the
causal effects.
Economic theory might also suggest that the timing of income could matter
particularly if parents are financially constrained and cannot borrow from
their anticipated future income (or from their children’s future income). A
few studies have started to focus on whether income earned when children
are particularly young might have a particularly strong effect. While some
early studies in this area have found evidence that this might be the case,
there are many thorny issues such as life cycle bias that could complicate the
interpretation of results and so more work is needed in this area.
MOBILITY DIFFERENCES WITHIN SOCIETIES
In many countries that have a segment of the population that has been
disadvantaged for historical reasons, policy makers are concerned about
differences in rates of intergenerational mobility across subgroups of the
population and the causes of those differences. In the United States, for
example, policies such as affirmative action have been motivated by a
desire to redress the historical legacy of slavery and segregation of Blacks.
Unfortunately, commonly used measures of intergenerational mobility
such as the intergenerational elasticity cannot be used to measure group
differences in mobility with respect to the overall income distribution. In
recent work, Debopam Bhattacharya and I have developed new measures
that can be used to study group differences in intergenerational mobility. We
have also supplemented existing mobility measures with statistical methods
that may allow future researchers to better understand the sources of group
differences in both upward and downward intergenerational mobility.
In subsequent research using these methods, I have shown that there would
be virtually no further progress in reducing Black–white income differences
Intergenerational Mobility: A Cross-National Comparison
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in the United States if the especially low rates of intergenerational mobility experienced by recent cohorts of Blacks were to continue into the future.
It would be useful for further research to show if similar levels of extreme
rigidity exist or have existed in the past for disadvantaged groups in other
countries. The ability to take into account group differences, in general, may
also provide a richer view of cross-national differences in intergenerational
mobility.
KEY ISSUES FOR FUTURE RESEARCH
MEASURING INEQUALITY OF OPPORTUNITY
Although studies of intergenerational mobility have been largely motivated
by a desire to measure the degree of inequality of opportunity, many
researchers have argued that, at best, such measures only imperfectly capture inequality of opportunity. It has also been argued that certain policies
that could reduce the intergenerational transmission of advantages may
be so intrusive on families as to be clearly undesirable even if they would
increase mobility. Given these issues, how should we think about efforts
to promote equality of opportunity? Political philosophers such as John
Roemer and Adam Swift have argued that a distinction should be drawn
between factors that are outside of the control of families and those that are
due to choices or effort. It may be more appropriate for policy makers to try
to equalize opportunities that are purely due to chance.
A few studies have tried to create empirical analogues to this concept by
measuring the degree of intergenerational persistence after accounting for
measures of effort. This literature is still at a very early stage and probably
requires much more refinement before these alternative measures are more
readily accepted. In order to make more progress, however, it is likely that
future research will require ongoing feedback loops between the experts in
political philosophy and the applied researchers who work on either developing measures or on implementing statistical estimators.
TRENDS IN INTERGENERATIONAL MOBILITY AND GROUP-BASED APPROACHES
One very active area of research has been in trying to understand trends in
intergenerational mobility. Typically, these studies have looked at one country at a time and have focused primarily on documenting whether there have
been any pronounced changes over time. Secondarily, these studies then try
to understand the causes of any changes in intergenerational mobility over
time. This line of research can be viewed as an alternative approach for trying to understand the underlying drivers of mobility differences by looking
at different time periods rather than different countries.
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Because it is challenging enough to find satisfactory data to measure mobility at any one point in time, it is even more difficult to measure changes
over time. Researchers have attempted to use various strategies to overcome
this challenge. For example, in work with Daniel Aaronson, I have tried to
use historical Census data for the United States to measure trends in mobility. Although one cannot directly link parents to their adult children across
Censuses, we created “synthetic families” by linking children born in a particular state and year to the average income of a group of parents in an earlier Census who had kids born in the same state and in the same year. For
example, we can link a 42-year-old in the 2000 Census who was born in Minnesota in 1958 to the average income of parents in Minnesota in the 1960
Census who had children who were two years old. Under some assumptions, this approach of using synthetic families can be used to infer trends
in intergenerational economic mobility. The study found that trends in intergenerational mobility in the United States closely matched contemporaneous
trends in cross-sectional inequality, a topic to which I return later. It should
be noted that other research on trends in the United States using the Panel
Study of Income Dynamics have not found evidence of significant changes in
intergenerational mobility, but that data is only ideally suited for measuring
mobility starting in the 1980s.
Grouping methods such as this approach or the use of surnames hold
potential both for measuring trends and cross-national differences in
intergenerational mobility. In order for these approaches to reach their
full potential, there is also the need for harmonizing large datasets in as
many countries as possible. The IPUMS-I database of international censuses
developed at the University of Minnesota’s population center might be
useful in this regard. One could imagine using the synthetic family approach
to estimate trends in intergenerational income or educational mobility
for a large set of countries. A similar effort to collect large administrative
databases containing surnames and socioeconomic outcomes for many
countries could be fruitful but will likely require strong cross-country
collaborations and efforts to maintain confidentiality and data security. An
important caveat is that there is also a need for future research in this area to
further explore the possible limitations of these group-based approaches.
FORWARD-LOOKING MEASURES OF MOBILITY
Another challenge for future research studying trends in intergenerational
mobility concerns how to interpret an estimate at a given point of time.
For example, if we find that the rate of intergenerational mobility is low for
individuals who are around 40 years old today, is it because of their family
Intergenerational Mobility: A Cross-National Comparison
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circumstances in the early 1970s or is it because of their labor market opportunities in the early 2010s? This highlights an important point, which is that
measures of intergenerational mobility to some degree are inherently “backwards looking.” This is important for forecasting what we might expect intergenerational mobility to look like in the future. It is also relevant to thinking
about cross-country differences because what we might think of as big differences today actually reflect factors that were only relevant a generation ago.
One approach that holds promise to produce a more forward-looking
indicator of intergenerational mobility is to look at “gradients” in children’s
outcomes by measures of family socioeconomic status. For example, in a
recent provocative study, Sean Reardon has argued that the gap in children’s
tests scores between families at the 90th percentile of the income distribution
and those at 10th percentile has grown sharply since the 1970s and is no
higher that the black–white gap in achievement. One recent comparative
study has shown that the gradients in child capacities by family background
are largest in the United States and the United Kingdom than in Australia
and especially Canada. The potential to further develop cross-national
research along these lines might provide for a potentially more policy relevant set of findings. Here, efforts to harmonize datasets tracking childhood
development information since birth for many countries would be fruitful.
This would likely be facilitated by greater cross-disciplinary collaborations
between groups such as medical researchers, developmental psychologists,
sociologists, and economists.
HOW DOES INEQUALITY AFFECT INTERGENERATIONAL MOBILITY
Perhaps the most intriguing idea that has emerged from the early research on
cross-national comparisons is that countries with high inequality also tend
have lower rates of intergenerational mobility. Alan Krueger, the Chair of
the US Council of Economic Advisors, in 2012 referred to a graph which
plots the intergenerational elasticity for a sample of countries against a measure of inequality as the “Great Gatsby” curve. Krueger’s drew the inference
that further growth in inequality in the United States may imply reduced
intergenerational mobility going forward. Some of the research on the United
States described in the earlier section on “Trends in Intergenerational Mobility” that used a grouping strategy based on synthetic families also found
a remarkable correspondence between increases in inequality in the United
States and reductions in intergenerational mobility.
Perhaps the most salient question for future research on cross-national differences in intergenerational mobility is to address whether this relationship between inequality and mobility is robust, and if so, how it should be
interpreted. One straightforward argument is that there simply might be a
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EMERGING TRENDS IN THE SOCIAL AND BEHAVIORAL SCIENCES
causal relationship between inequality and mobility. Perhaps, unequal societies tend to create political structures that perpetuate these inequalities and
therefore reduce mobility. Alternatively, it could be that broad-based forces
such as technological change that create larger economic returns for workers
with certain skills tend to create both higher inequality and lower intergenerational mobility.
To make further progress on this topic, it may be useful for researchers
to not only consider careful cross-country comparisons but to also gather
more historical data to compare time periods within countries and across
geographic areas within countries. Unraveling the reasons for the possible
relationship between inequality and mobility will also require imaginative
thinking and perhaps new methods to carefully tease out which explanations
make the most sense.
BIOMARKERS AND GENETIC DATA
The use of data on biomarkers and genetic data has not yet made its way into
the research on intergenerational mobility but may have the potential to have
transformative effects. Suppose it was possible for researchers in the future to
identify particular sequences of DNA that map into key capabilities on the
part of children such as the ability to delay gratification. Such information
may be able to help researchers incorporate such information into statistical
models of intergenerational persistence of socioeconomic outcomes.
There are at least two reasons to be highly cautious about how fruitful
this line of research will be. First, the field of epigenetics has highlighted
that important role of environmental factors in influencing the expression
of genes, preventing any simple interpretations of statistical decomposition
models. Second, initial research has suggested that data mining is a real
concern with the use of genetic data and that many initially provocative
findings have not held up under closer scrutiny. Nevertheless, it is hard to
predict the potential disruptive effects of the growing use of new biological
data on this literature.
FURTHER READING
Beller, E., & Hout, M. (2006). Intergenerational social mobility: The United States in
comparative perspective. The Future of Children, 16(2), 19–36.
Black, S. E., & Devereux, P. J. (2011). Recent developments in intergenerational mobility. In Handbook of labor economics. Elsevier.
Corak, M. (2006). Do poor children become poor adults? Lessons from a crosscountry comparison of generational earnings mobility. In J. Creedy & G. Kalb
(Eds.), Dynamics of inequality and poverty (pp. 143–188 (Research on Economic
Inequality, Volume 13)). Emerald Group Publishing Limited.
Intergenerational Mobility: A Cross-National Comparison
13
Hertz, T., Jayasundera, T., Piraino, P., Selcuk, S., Smith, N., & Verashchagina, A. (2007,
Article 10). The inheritance of educational inequality: International comparisons
and fifty-year trends. The B.E. Journal of Economic Analysis & Policy 7:2 (Advances)
October.
BHASHKAR MAZUMDER SHORT BIOGRAPHY
Bhashkar Mazumder is a senior economist in the economic research department and executive director of the Chicago Census Research Data Center
at the Federal Reserve Bank of Chicago. His research has been focused in
four key areas: intergenerational economic mobility; the long-term effects of
poor health early in life; black–white gaps in human capital development,
and consumer financial decision making.
Mazumder’s research has been published in academic journals such as
the Journal of Political Economy, American Economic Journal: Applied
Economics, Quantitative Economics, Journal of Human Resources, and the
Review of Economics and Statistics.
Mazumder previously worked at the Conference Board in New York and
oversaw the transfer of the leading economic indicators from the US Commerce Department to the Conference Board.
Mazumder received a BA in political science from New York University, an
MA in economics from New York University, and a PhD in economics from
the University of California at Berkeley.
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