Granular Income Inequality and Mobility using IDDA: Exploring Patterns across Race and Ethnicity
November 2023
Working Paper Number:
CES-23-55
Abstract
Shifting earnings inequality among U.S. workers over the last five decades has been widely stud ied, but understanding how these shifts evolve across smaller groups has been difficult. Publicly available data sources typically only ensure representative data at high levels of aggregation, so they obscure many details of earnings distributions for smaller populations. We define and construct a set of granular statistics describing income distributions, income mobility and con ditional income growth for a large number of subnational groups in the U.S. for a two-decade period (1998-2019). In this paper, we use the resulting data to explore the evolution of income inequality and mobility for detailed groups defined by race and ethnicity. We find that patterns identified from the universe of tax filers and W-2 recipients that we observe differ in important ways from those that one might identify in public sources. The full set of statistics that we construct is available publicly as the Income Distributions and Dynamics in America, or IDDA, data set.
Document Tags and Keywords
Keywords
Keywords are automatically generated using KeyBERT, a powerful and innovative
keyword extraction tool that utilizes BERT embeddings to ensure high-quality and contextually relevant
keywords.
By analyzing the content of working papers, KeyBERT identifies terms and phrases that capture the essence of the
text, highlighting the most significant topics and trends. This approach not only enhances searchability but
provides connections that go beyond potentially domain-specific author-defined keywords.
:
economist,
earnings,
minority,
ethnicity,
ethnic,
hispanic,
recession,
white,
percentile,
population,
racial,
race,
poverty,
irs,
distribution,
earner,
disparity,
income distributions
Tags
Tags are automatically generated using a pretrained language model from spaCy, which excels at
several tasks, including entity tagging.
The model is able to label words and phrases by part-of-speech,
including "organizations." By filtering for frequent words and phrases labeled as "organizations", papers are
identified to contain references to specific institutions, datasets, and other organizations.
:
Internal Revenue Service,
Social Security Administration,
Federal Reserve Bank,
Current Population Survey,
Federal Reserve System,
Decennial Census,
Department of Housing and Urban Development,
American Community Survey,
Protected Identification Key,
W-2,
Temporary Assistance for Needy Families,
Census Bureau Disclosure Review Board,
Federal Reserve Board of Governors,
ASEC,
Indian Health Service,
Adjusted Gross Income,
Opportunity Atlas
Similar Working Papers
Similarity between working papers are determined by an unsupervised neural
network model
know as Doc2Vec.
Doc2Vec is a model that represents entire documents as fixed-length vectors, allowing for the
capture of semantic meaning in a way that relates to the context of words within the document. The model learns to
associate a unique vector with each document while simultaneously learning word vectors, enabling tasks such as
document classification, clustering, and similarity detection by preserving the order and structure of words. The
document vectors are compared using cosine similarity/distance to determine the most similar working papers.
Papers identified with 🔥 are in the top 20% of similarity.
The 10 most similar working papers to the working paper 'Granular Income Inequality and Mobility using IDDA: Exploring Patterns across Race and Ethnicity' are listed below in order of similarity.
-
Working PaperMobility, Opportunity, and Volatility Statistics (MOVS): Infrastructure Files and Public Use Data🔥
April 2024
Working Paper Number:
CES-24-23
Federal statistical agencies and policymakers have identified a need for integrated systems of household and personal income statistics. This interest marks a recognition that aggregated measures of income, such as GDP or average income growth, tell an incomplete story that may conceal large gaps in well-being between different types of individuals and families. Until recently, longitudinal income data that are rich enough to calculate detailed income statistics and include demographic characteristics, such as race and ethnicity, have not been available. The Mobility, Opportunity, and Volatility Statistics project (MOVS) fills this gap in comprehensive income statistics. Using linked demographic and tax records on the population of U.S. working-age adults, the MOVS project defines households and calculates household income, applying an equivalence scale to create a personal income concept, and then traces the progress of individuals' incomes over time. We then output a set of intermediate statistics by race-ethnicity group, sex, year, base-year state of residence, and base-year income decile. We select the intermediate statistics most useful in developing more complex intragenerational income mobility measures, such as transition matrices, income growth curves, and variance-based volatility statistics. We provide these intermediate statistics as part of a publicly released data tool with downloadable flat files and accompanying documentation. This paper describes the data build process and the output files, including a brief analysis highlighting the structure and content of our main statistics.View Full Paper PDF
-
Working PaperRe-examining Regional Income Convergence: A Distributional Approach
February 2023
Working Paper Number:
CES-23-05
We re-examine recent trends in regional income convergence, considering the full distribution of income rather than focusing on the mean. Measuring similarity by comparing each percentile of state distributions to the corresponding percentile of the national distribution, we find that state incomes have become less similar (i.e. they have diverged) within the top 20 percent of the income distribution since 1969. The top percentile alone accounts for more than half of aggregate divergence across states over this period by our measure, and the top five percentiles combine to account for 93 percent. Divergence in top incomes across states appears to be driven largely by changes in top incomes among White people, while top incomes among Black people have experienced relatively little divergence.View Full Paper PDF
-
Working PaperEstimating Trends in U.S. Income Inequality Using the Current Population Survey: The Importance of Controlling for Censoring
August 2008
Working Paper Number:
CES-08-25
Using internal and public use March Current Population Survey (CPS) data, we analyze trends in US income inequality (1975'2004). We find that the upward trend in income inequality prior to 1993 significantly slowed thereafter once we control for top coding in the public use data and censoring in the internal data. Because both series do not capture trends at the very top of the income distribution, we use a multiple imputation approach in which values for censored observations are imputed using draws from a Generalized Beta distribution of the Second Kind (GB2) fitted to internal data. Doing so, we find income inequality trends similar to those derived from unadjusted internal data. Our trend results are generally robust to the choice of inequality index, whether Gini coefficient or other commonly-used indices. When we compare our best estimates of the income shares held by the richest tenth with those reported by Piketty and Saez (2003), our trends fairly closely match their trends, except for the top 1 percent of the distribution. Thus, we argue that if United States income inequality has been substantially increasing since 1993, such increases are confined to this very high income group.View Full Paper PDF
-
Working PaperRecent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data
September 2009
Working Paper Number:
CES-09-26
Although the vast majority of US research on trends in the inequality of family income is based on public-use March Current Population Survey (CPS) data, a new wave of research based on Internal Revenue Service (IRS) tax return data reports substantially higher levels of inequality and faster growing trends. We show that these apparently inconsistent estimates can largely be reconciled once one uses internal CPS data (which better captures the top of the income distribution than public-use CPS data) and defines the income distribution in the same way. Using internal CPS data for 1967'2006, we closely match the IRS data-based estimates of top income shares reported by Piketty and Saez (2003), with the exception of the share of the top 1 percent of the distribution during 1993'2000. Our results imply that, if inequality has increased substantially since 1993, the increase is confined to income changes for those in the top 1 percent of the distribution.View Full Paper PDF
-
Working PaperConsistent Cell Means for Topcoded Incomes in the Public Use March CPS (1976-2007)
March 2008
Working Paper Number:
CES-08-06
Using the internal March CPS, we create and in this paper distribute to the larger research community a cell mean series that provides the mean of all income values above the topcode for any income source of any individual in the public use March CPS that has been topcoded since 1976. We also describe our construction of this series. When we use this series together with the public use March CPS, we closely match the yearly mean income levels and income inequalities of the U.S. population found using the internal March CPS data.View Full Paper PDF
-
Working PaperMeasuring Inequality Using Censored Data: A Multiple Imputation Approach
April 2009
Working Paper Number:
CES-09-05
To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter's (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution.View Full Paper PDF
-
Working PaperTrends in the Relative Household Income of Working-Age Men with Work Limitations: Correcting the Record Using Internal Current Population Survey Data
March 2008
Working Paper Number:
CES-08-05
Previous research measuring the economic well-being of working-age men with work limitations relative to such men without work limitations in the public use March Current Population Survey (CPS) systematically understates the mean household income of both groups; overstates the relative household income of those with work limitations; and understates the decline in their relative household income over time. Using the internal March CPS, we demonstrate this by creating a cell mean series beginning in 1975 that provides the mean reported income of all topcoded persons for each source of income in the public use March CPS data. Using our cell mean series with the public use March CPS, we closely match the yearly mean income of working-age men with and without work limitations over the period 1987-2004 in the internal data and show that this match is superior to ones using alternative methods of correcting for topcoding currently used in the disability literature. We then provide levels and trends in the relative income of working-age men with work limitations from 1980-2006, the earliest year in the March CPS that such comparisons can be made.View Full Paper PDF
-
Working PaperThe Census Historical Environmental Impacts Frame
October 2024
Working Paper Number:
CES-24-66
The Census Bureau's Environmental Impacts Frame (EIF) is a microdata infrastructure that combines individual-level information on residence, demographics, and economic characteristics with environmental amenities and hazards from 1999 through the present day. To better understand the long-run consequences and intergenerational effects of exposure to a changing environment, we expand the EIF by extending it backward to 1940. The Historical Environmental Impacts Frame (HEIF) combines the Census Bureau's historical administrative data, publicly available 1940 address information from the 1940 Decennial Census, and historical environmental data. This paper discusses the creation of the HEIF as well as the unique challenges that arise with using the Census Bureau's historical administrative data.View Full Paper PDF
-
Working PaperInternal Migration in the U.S. During the COVID-19 Pandemic
September 2024
Working Paper Number:
CES-24-50
Survey and administrative internal migration data disagree on whether the COVID-19 pandemic increased or decreased mobility in the U.S. Moreover, though scholars have theorized and documented migration in response to environmental hazards and economic shocks, the novel conditions posed by a global pandemic make it difficult to hypothesize whether and how American migration might change as a result. We link individual-level data from the United States Postal Service's National Change of Address (NCOA) registry to American Community Survey (ACS) and Current Population Survey (CPS-ASEC) responses and other administrative records to document changes in the level, geography, and composition of migrant flows between 2019 and 2021. We find a 2% increase in address changes between 2019 and 2020, representing an additional 603,000 moves, driven primarily by young adults, earners at the extremes of the income distribution, and individuals (as opposed to families) moving over longer distances. Though the number of address changes returned to pre-pandemic levels in 2021, the pandemic-era geographic and compositional shifts in favor of longer distance moves away from the Pacific and Mid-Atlantic regions toward the South and in favor of younger, individual movers persisted. We also show that at least part of the disconnect between survey, media, and administrative/third-party migration data sources stems from the apparent misreporting of address changes on Census Bureau surveys. Among ACS and CPS-ASEC householders linked to NCOA data and filing a permanent change of address in their 1-year survey response reference period, only around 68% of ACS and 49% of CPS-ASEC householders also reported living in a different residence one year ago in their survey response.View Full Paper PDF
-
Working PaperUnited States Earnings Dynamics: Inequality, Mobility, and Volatility
September 2020
Working Paper Number:
CES-20-29
Using data from the Census Bureau's Longitudinal Employer-Household Dynamics (LEHD) infrastructure files, we study changes over time and across sub-national populations in the distribution of real labor earnings. We consider four large MSAs (Detroit, Los Angeles, New York, and San Francisco) for the period 1998 to 2017, with particular attention paid to the subperiods before, during, and after the Great Recession. For the four large MSAs we analyze, there are clear national trends represented in each of the local areas, the most prominent of which is the increase in the share of earnings accruing to workers at the top of the earnings distribution in 2017 compared with 1998. However, the magnitude of these trends varies across MSAs, with New York and San Francisco showing relatively large increases and Los Angeles somewhere in the middle relative to Detroit whose total real earnings distribution is relatively stable over the period. Our results contribute to the emerging literature on differences between national and regional economic outcomes, exemplifying what will be possible with a new data exploration tool'the Earnings and Mobility Statistics (EAMS) web application'currently under development at the U.S. Census Bureau.View Full Paper PDF
