In 2020, the average social mobility score in Brazil reached 52.1 points, out of a maximum of 100. The higher the score, the more a particular economy provides equal opportunities for social advancement. Among the different areas analyzed to construct this index, Brazil scored best in technology access, where it was awarded 67.8 points out of 100. However, this score was relatively lower than that received in the same area by other thriving Latin American economies such as Costa Rica or Uruguay. As one of the most unequal Latin American countries, fair wage distribution was the social mobility area in which Brazil received the lowest score, at only 35.9 points.

In 2020, the average social mobility score in Argentina reached 57.3 points, out of a maximum of 100. The higher the score, the more a particular economy provides equal opportunities for social advancement. Among the different areas analyzed to construct this index, Argentina scored best in technology access, where it was awarded 70.8 points out of 100. Compared to other regional counterparts, this country also ranked particularly high in education access, at 69.2 points, though it ranked worst at education quality and equity, area in which it only received 38.4 points. Uruguay, Costa Rica and Chile were some of the Latin American countries with the best social mobility scores.

This dataset contains replication files for "Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States" by Raj Chetty, Nathaniel Hendren, Patrick Kline, and Emmanuel Saez. For more information, see https://opportunityinsights.org/paper/land-of-opportunity/. A summary of the related publication follows. We use administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States. First, we characterize the joint distribution of parent and child income at the national level. The conditional expectation of child income given parent income is linear in percentile ranks. On average, a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child’s income. Second, intergenerational mobility varies substantially across areas within the U.S. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose. Third, we explore the factors correlated with upward mobility. High mobility areas have (1) less residential segregation, (2) less income inequality, (3) better primary schools, (4) greater social capital, and (5) greater family stability. While our descriptive analysis does not identify the causal mechanisms that determine upward mobility, the publicly available statistics on intergenerational mobility developed here can facilitate research on such mechanisms. The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department. This work is a component of a larger project examining the effects of tax expenditures on the budget deficit and economic activity. All results based on tax data in this paper are constructed using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures: Evidence from Spatial Variation across the U.S.,” approved under IRS contract TIRNO-12-P-00374 and presented at the National Tax Association meeting on November 22, 2013.

The tables contain statistics describing how the income of taxfilers change, relative either to income they made in the past or to income made by other taxfilers. The first group of statistics illustrate absolute income mobility while the second group illustrate relative income mobility. This table provides five-year income mobility statistics. Table 11-10-0061 provides one-year mobility statistics.

This dataset contains replication files for "The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility" by Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya R. Porter. For more information, see https://opportunityinsights.org/paper/the-opportunity-atlas/. A summary of the related publication follows. We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population. For each tract, we estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender. These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up. We find that children’s outcomes vary sharply across nearby tracts: for children of parents at the 25th percentile of the income distribution, the standard deviation of mean household income at age 35 is $5,000 across tracts within counties. We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications. First, we show that the estimates permit precise targeting of policies to improve economic opportunity by uncovering specific neighborhoods where certain subgroups of children grow up to have poor outcomes. Neighborhoods matter at a very granular level: conditional on characteristics such as poverty rates in a child’s own Census tract, characteristics of tracts that are one mile away have little predictive power for a child’s outcomes. Our historical estimates are informative predictors of outcomes even for children growing up today because neighborhood conditions are relatively stable over time. Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes. We then identify high-opportunity neighborhoods that are affordable to low-income families, providing an input into the design of affordable housing policies. Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets. Click here to view the Opportunity Atlas Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319.

This paper studies the strength of the relationship between parental income and child income over the period 1300 to 1900, when many social changes, such as the erosion of hereditary class barriers, took place. The rich information in genealogies are used to examine social mobility across a representative socioeconomic population of ranging from commoners to elites. The results indicate, first, that intergenerational mobility in this sample population changed over time. Second, the changes correspond to a substantially higher level of mobility in the 19th century compared to the 17th century. Third, an inverse correlation between mobility and inequality can be seen in the time-series, implying social mobility for birth cohorts characterized by high inequality tends to be low, and vice versa.

The social mobility index of England sets out the differences between where children grow up and the chances they have of doing well in adult life.

More details available at: https://www.gov.uk/government/publications/social-mobility-index

The Social Mobility Index compares the chances that a child from a disadvantaged background will do well at school and get a good job across each of the 324 local authority district areas of England. It examines a range of measures of the educational outcomes achieved by young people from disadvantaged backgrounds and the local job and housing markets to shed light on which are the best and worst places in England in terms of the opportunities young people from poorer backgrounds have to succeed.

Please take a look at our interactive atlas spine chart, where you can discover how all the English districts, unitaries and boroughs ranked, as well as the data scores behind the ranks. http://atlas.cambridgeshire.gov.uk/SocialMobilityIndex/atlas.html

The 2001 Chilean Social Mobility Survey examined inter-generational and intra-generational mobility in Chile. The data contain information on adult Chilean men's education, migration, current job, first job, social origins (parents' education, occupation, assets and living standards when the respondent was 14 years old), wife/partner, inter-generational transfers, household income and assets, respondent's siblings and focal brother, and respondent's opinions about inequality and determinants of economic well-being. Demographic variables include sex, age, education level, and socio-economic status.

In 2020, the average social mobility score in Mexico reached 52.6 points, out of a maximum of 100. The higher the score, the more a particular economy provides equal opportunities for social advancement. Among the different areas analyzed to construct this index, Mexico scored best in work opportunities, where it was awarded 74.4 points out of 100. Fair wage distribution was the social mobility area in which Mexico received the lowest score, at only 37.1 points. Uruguay, Costa Rica and Chile were some of the Latin American countries with the best social mobility scores.

Americans have long believed in upward mobility and the narrative of the American Dream. Even in the face of rising income inequality and substantial empirical evidence that economic mobility has declined in recent decades, many Americans remain convinced of the prospects for upward mobility. What explains this disconnect? I argue that their media diets play an important role in explaining this puzzle. Specifically, contemporary Americans are watching a record number of entertainment TV programs that emphasize “rags-to-riches” narratives. I demonstrate that such shows have become a ubiquitous part of the media landscape over the last two decades. Online and lab-in-the-field experiments as well as national surveys show that exposure to these programs increases viewers’ beliefs in the American Dream and promotes internal attributions of wealth. Media exemplars present in what Americans leisurely consume everyday can powerfully distort economic perceptions and have important implications for public preferences for redistribution.

Social mobility and life opportunities across different generations in Great Britain by personal characteristics; indicators from the Opinions and Lifestyle Survey (OPN).

OBJECTIVE To analyze the association between social mobility, lifestyle and body mass index in adolescents. METHODS A cohort study of 1,716 adolescents aged 10 to 17 years of both sexes. The adolescents were participants in a cohort study and were born between 1994 and 1999. The adolescents, from public and private schools, were assessed between 2009 and 2011. Lifestyle was assessed by interview and anthropometry was used to calculatebody mass index. For the economic classification, both at pre-school age and in adolescence, the criteria recommended by the Brazilian Association of Research Companies were used. Upward social mobility was categorized as an increase by at least one class in economic status within a 10-year-period. Poisson regression was used to estimate the association between upward social mobility and the outcomes assessed. RESULTS Among all respondents (71.4% follow-up of the cohort), 60.6% had upward social mobility. Among these, 93.6% belonged to socioeconomic class D and 99.9% to economy class E. Higher prevalence of social mobility was observed for students with black skin (71.4%) and mulatto students (61.9%) enrolled in public schools (64.3%) whose mothers had less schooling in the first evaluation (67.2%) and revaluation (68.7%). After adjustment for confounding variables, upward social mobility was associated only with sedentary behavior (p = 0.02). The socioeconomic class in childhood was more associated with the outcomes assessed than was upward mobility. CONCLUSIONS Upward social mobility was not associated with most of the outcomes evaluated, possibly as it is discreet and because the period considered in the study may not have been sufficient to reflect substantial changes in lifestyle and body mass index in adolescents.

We quantitatively identify the factors that drive wealth dynamics in the United States and are consistent with its skewed cross-sectional distribution and with social mobility. We concentrate on three critical factors: (i) skewed earnings, (ii) differential saving rates across wealth levels, and (iii) stochastic idiosyncratic returns to wealth. All of these are fundamental for matching both distribution and mobility. The stochastic process for returns which best fits the cross-sectional distribution of wealth and social mobility in the United States shares several statistical properties with those of the returns to wealth uncovered by Fagereng et al. (2017) from tax records in Norway.

ABSTRACT: Objective: To assess the effect of socioeconomic position (SEP) in childhood and social mobility on linear growth through adolescence in a population-based cohort. Methods: Children born in Cuiabá-MT, central-western Brazil, were evaluated during 1994 - 1999. They were first assessed during 1999 - 2000 (0 - 5 years) and again during 2009 - 2011 (10 - 17 years), and their height-for-age was evaluated during these two periods.Awealth index was used to classify the SEP of each child’s family as low, medium, or high. Social mobility was categorized as upward mobility or no upward mobility. Linear mixed models were used. Results: We evaluated 1,716 children (71.4% of baseline) after 10 years, and 60.6% of the families showed upward mobility, with a higher percentage among the lowest economic classes. A higher height-for-age was also observed among those from families with a high SEP both in childhood (low SEP= -0.35 z-score; high SEP= 0.15 z-score, p < 0.01) and adolescence (low SEP= -0.01 z-score; high SEP= 0.45 z-score, p < 0.01), whereas upward mobility did not affect their linear growth. Conclusion: Expressive social mobility was observed, but SEP in childhood and social mobility did not greatly influence linear growth through childhood in this central-western Brazilian cohort.

This dataset contains replication files for "The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects" and "The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates" by Raj Chetty and Nathaniel Hendren. For more information, see https://opportunityinsights.org/paper/neighborhoodsi/ and https://opportunityinsights.org/paper/neighborhoodsii/. A summary of the related publications follows. To what extent are children’s opportunities for upward economic mobility shaped by the neighborhoods in which they grow up? We study this question using data from de-identified tax records on more than five million children whose families moved across counties between 1996 and 2012. The study consists of two parts. In part one, we show that the area in which a child grows up has significant causal effects on her prospects for upward mobility. In part two, we present estimates of the causal effect of each county in the United States on a child’s chances of success. Using these results, we identify the properties of high- vs. low-opportunity areas to obtain insights into policies that can increase economic opportunity. The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department. This work is a component of a larger project examining the effects of tax expenditures on the budget deficit and economic activity. All results based on tax data in this paper are constructed using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures: Evidence from Spatial Variationacross the U.S.,” approved under IRS contract TIRNO-12-P-00374.

We estimate intergenerational income mobility in Italy using administrative data from tax returns. Our estimates of mobility in Italy are higher than prior work using survey data and other indirect methods. The rank-rank slope of parent-child income in Italy is 0.22, compared to 0.18 in Denmark and 0.34 in the United States. The probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 0.11. Upward mobility is higher for sons and first-born children. We uncover substantial geographical variation: upward mobility rates are much higher in Northern Italy, where provinces have higher measured school quality, more stable families, and more favorable labor market conditions.

Research on race in Brazil has for a long time recognized that racial categories are based on skin color distinctions along a black-white continuum. However, quantitative evidences about racial inequality are mostly based on the white versus non-white (brown and black) dichotomy or on the threefold categorization (white, brown, and black). This way of using the variable contributed to show the high levels of racial inequality. This finding, however, has often been questioned because of another aspect: the high ambiguity in racial classification and the possibility of “whitening” with money or with upward mobility. If this last feature is true, it is hard to make a reliable measure of racial inequality. In order to deal directly with this dilemma, I measure the “skin color continuum” combining answers to an open question (respondents free choice) and to a closed question (census categories) about skin color. I implement counterfactual simulations to access the possible effects of “whitening with money” on educational, occupational and economic attainments.

The sample was drawn by means of the central population register (CPR) of Statistics Sweden. CPR contains basic demographic and social data on every individual born on the 15th of any month, any year, and irrespective of place of birth or place of residence. Thus CPR forms, in effect, a 3.3 probability sample of the entire Swedish population. From CPR were drawn all men born in any of the years 1899, 1902, 1905, and so on, down to and including 1923. Thus there are nine birth cohorts, spaced with three-year intervals. Information about occupation in the present (son's) generation was taken from CPR. The method for gathering information on occupation in the previous (father's) generation was a different one. In CPR parish of birth (if in Sweden) and date of birth is always stated. Consequently every person can be located in the copies of the parish birth registers filed in Stockholm, and in these registers the father's occupation is stated (if the father is known). Other data collected from the CPR: place of birth and current place of residence, marital status, age of the parents, and information on income based on the tax assessments.

Studies of economic mobility summarize the relationship between parent and offspring incomes. At each level of parent income there is an entire distribution of possible incomes for offspring. Mitnik and Grusky (2020, hereafter MG) highlight that the conventional intergenerational elasticity targets the geometric mean and propose a parametric strategy for estimating the arithmetic mean. We decompose each proposal into two choices an analyst must make: (1) the summary statistic for the conditional distribution and (2) whether to assume or learn a functional form. These choices lead us to a different strategy---visualizing several quantiles of the offspring income distribution as smooth, nonparametric functions of parent income. Our proposal solves the problems MG highlight with geometric means, avoids the sensitivity of arithmetic means to top incomes, and provides more information about the conditional distribution than any single-number summary can provide. Our proposal has broader implications for regression analyses, which often collapse a distribution to its mean although this summary can be sensitive to skew.