Jeff Larrimore

Recent Trends in Top Income Shares in the USA: Reconciling Estimates from March CPS and IRS Tax Return Data

Although the majority of research on US income inequality trends is based on public-use March CPS data, a new wave of research using IRS tax return data reports substantially higher levels of inequality and faster growing trends. We show that these apparently inconsistent estimates are largely reconciled if the inequality measure and the income distribution are defined in the same way. Using internal CPS data for 1967-2006, we closely match IRS data-based estimates of top income shares reported by Piketty and Saez (2003). Our results imply that any inequality increases since 1993 are concentrated among the top 1 percent of the distribution.

Levels and Trends in U.S. Income and its Distribution: A Crosswalk from Market Income towards a Comprehensive Haig-Simons Income Approach

Recent research on U.S. levels and trends in income inequality varies substantially based on how these studies measure income. We crosswalk (move between standards) from a market income of tax units to a more comprehensive measure of income including realized capital gains of households using a unified data set and replicate common findings in the literature. By using a comprehensive income definition in the spirit of Haig-Simons, considering yearly accrued capital gains rather than focusing on the delayed reporting of capital gains that appear in Internal Revenue Service tax return data, the observed growth in income inequality and top income shares since 1989 is dramatically reduced.

Measuring Inequality Using Censored Data: A Multiple Imputation Approach

To measure income inequality with right-censored (top-coded) data, we propose multiple-imputation methods for estimation and inference. Censored observations are multiply imputed using draws from a flexible parametric model fitted to the censored distribution, yielding a partially synthetic data set from which point and variance estimates can be derived using complete-data methods and appropriate combination formulae. The methods are illustrated using US Current Population Survey data and the generalized beta of the second kind distribution as the imputation model. With Current Population Survey internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using Current Population Survey public use data with cell mean imputations may lead to incorrect inferences. Multiply-imputed public use data provide an intermediate solution.

Measuring the Impact of Valuing Health Insurance on Levels and Trends in Inequality and How the Affordable Care Act of 2010 Could Affect Them

A substantial part of the U.S. inequality literature focuses on yearly levels and trends in pre‐tax, post‐transfer cash income and its distribution over time and finds that median income appears to be stagnating, with income growth primarily coming at higher income levels. When we use data from the Current Population Survey for 1995–2008 and add the value of employer‐ and government‐provided health insurance coverage, not only does it increase the upward trend in the level of resources controlled by Americans, but also reduces the level of inequality in these resources and its upward trend. We then provide a highly stylized example of this broader income measures value in capturing the impact of two key provisions of the Affordable Care Act of 2010 - an expansion in Medicaid and the provision of subsidies to lower‐income families for purchasing private coverage on state‐run exchanges. Even though these incremental expansions build on existing systems of government‐provided health insurance, we find that the vast majority of the benefits would still accrue to the bottom three deciles of the income distribution when we include the value of employer‐ and government‐provided health insurance in our expanded yearly income measure.

USING THE PARETO DISTRIBUTION TO IMPROVE ESTIMATES OF TOPCODED EARNINGS

Inconsistent censoring in the public-use March Current Population Survey (CPS) limits its usefulness in measuring labor earnings trends. Using Pareto estimation methods with less-censored internal CPS data, we create an enhanced cell-mean series to capture top earnings in the public-use CPS. We find that previous approaches for imputing topcoded earnings systematically understate top earnings. Annual earnings inequality trends since 1963 using our series closely approximate those found by Kopczuk, Saez, & Song (2010) using Social Security Administration data for commerce and industry workers. However, when we consider all workers, earnings inequality levels are higher but earnings growth is more modest

Trends in the Relative Household Income of Working-Age Men With Work Limitations

Policy makers relying on public-use Current Population Survey (CPS) data to measure the success of government policies in overcoming the gap in economic well-being between working-age men with and without disabilities will understate the mean income of both and overstate the relative economic well-being of the former. This understatement results from topcoding in the public-use CPS, which suppresses top incomes in the data set. Using cell means with the public-use CPS, the authors better correct for these topcoding problems than alternate methods and provide a relative economic well-being series (1980—2006) based on the mean incomes of working-age men with and without disabilities.

Measuring Labor Earnings Inequality Using Public-Use March Current Population Survey Data: The Value of Including Variances and Cell Means When Imputing Topcoded Values

Using the Census Bureaus internal March Current Population Surveys (CPS) file, we construct and make available variances and cell means for all topcoded income values in the public-use version of these data. We then provide a procedure that allows researchers with access only to the public-use March CPS data to take advantage of this added information when imputing its topcoded income values. As an example of its value we show how our new procedure improves on existing imputation methods in the labor earnings inequality literature.

Income and Earnings Mobility in U.S. Tax Data

We use a large panel of federal income tax data to investigate intragenerational income mobility in the United States. We have two primary objectives. First, we explore the determinants of two-year changes in individual labor earnings and family incomes, such as job or industry changes, marriage, divorce, and geographic mobility. Second, we evaluate how federal income taxes stabilize or destabilize post-tax income changes relative to pre-tax changes. We find a relatively high degree of income mobility, with almost half of workers exhibiting earnings increases or decreases of at least 25 percent, and two-fifths of tax units experiencing income changes of this magnitude. Male and female labor income mobility patterns are remarkably similar, though marriage is associated with earnings gains among men, but is associated with modest earnings declines among women. We also observe that large income gains are most likely among families that add workers - either through marriage or through a second family member entering the workforce.

Shedding Light on Our Economic and Financial Lives

In November and December of 2017, we interviewed over 12,000 individuals, representative of all adults in the United States, about their economic and financial lives. Here we discuss the responses on three important economic issues: the role of economic conditions in the opioid epidemic; jobs with irregular schedules and varying income as a potential barrier to full employment; and how low rates of geographic mobility may relate to family support networks.

Are Central Cities Poor and Non-White?

For much of the 20th century, Americas central cities were viewed as synonymous with economic and social hardship, often used as proxy for low-income communities of color. Since the 1990s, however, many metropolitan areas have seen a resurgence of interest in central city neighborhoods. Theoretical models of income sorting lead to ambiguous predictions about where households of different income levels will live within metropolitan areas. In this paper, we explore intra-city spatial patterns of income and race for U.S. metropolitan areas, focusing particularly on the locations of low-income and minority neighborhoods. Results indicate that, on average, income and white population shares increase with distance to city centers. However, many centrally located neighborhoods are neither low-income nor majority non-white, while low-income and minority neighborhoods are spatially dispersed across most metropolitan areas.