Since the 1970s, the U.S. has experienced dramatic increases in income inequality. Although this macro-level trend is well-established in research literature, less is known about subnational patterns of income inequality in the U.S., particularly as they vary between and within rural and urban localities. Using Census and ACS data, this study produces Gini estimates of within-county income inequality and examines these trends across a six-strata urban-rural typology from 1970 to 2016. This study finds the following. Income inequality has remained consistently higher in nonmetropolitan counties than metropolitan counties throughout the study period. However, levels of inequality have converged by 2016, a convergence that has been driven by increases in metropolitan counties. There are notable exceptions to the secular trend of increasing inequality. The central Plains region has experienced decreasing levels of inequality, and inequality in large, peripheral metropolitan counties lags noticeably behind other types of counties. Almost all lowinequality counties in 1970 have shifted to moderate-or high-inequality, such that almost no one lives in low-inequality places by 2016. This increase in exposure to inequality has been particularly dramatic among residents of large, central metropolitan counties. As the only county-level analysis to track income inequality across the rural-urban continuum from 1970 to 2016, this study lays the foundation for more sophisticated analyses that explain spatial variation in income inequality and that account for the demographic and economic diversity of the rural and urban United States.

This paper examines the effects of population growth and decline on county-level income inequality in the rural United States from 1980 to 2016. Findings from previous research have shown that population growth is positively associated with income inequality. However, these studies are largely motivated by theories of urbanization and growth in metropolitan areas, and do not explicitly test for differences between the impacts of population growth and decline. Examining the effects of both forms of population change on income inequality is particularly important in rural counties of the United States, the majority of which are experiencing population decline. We analyze county-level data (N=11,320 county-decades) from the U.S. Decennial Census and American Community Survey, applying fixed-effects regression models to estimate the respective effects of population growth and decline on income inequality within rural counties. We find that both forms of population change have significant effects on income inequality relative to stable growth. Population decline is associated with increases in income inequality, while population growth is marginally associated with decreases in inequality. These relationships are consistent across a variety of model specifications, including models that account for counties' employment, sociodemographic, and ethno-racial composition. We also find that the relationship between income inequality and population change varies by counties' geographic region, baseline level of inequality, and baseline population size, suggesting that the links between population change and income inequality are not uniform across rural America.

Each decade since the 1950s, demographers have generated high-quality net migration estimates by age, sex, and race for US counties using decennial census data as starting and ending populations. The estimates have been downloaded tens of thousands of times and widely used for planning, diverse applications, and research. Census 2020 should allow the series to extend through the 2010–2020 decade. The accuracy of new estimates, however, could be challenged by differentially private (DP) disclosure avoidance techniques in Census 2020 data products. This research brief estimates the impact of DP implementation on the accuracy of county-level net migration estimates. Using differentially private Census 2010 demonstration data, we construct a hypothetical set of DP migration estimates for 2000–2010 and compare them to published estimates, using common accuracy metrics and spatial analysis. Findings show that based on demonstration data released in 2020, net migration estimates by five-year age groups would only be accurate enough for use in about half of counties. Inaccuracies are larger in counties with populations less than 50,000, among age groups 65 and over, and among Hispanics. These problems are not fully resolved by grouping into broader age groups. Moreover, errors tend to cluster spatially in some regions of the country. Ultimately, the ability to generate accurate net migration estimates at the same level of detail as in the past will depend on the Census Bureau’s allocation of the privacy loss budget.

This paper examines the effects of population growth and decline on county-level income inequality in the United States from 1980 to 2016. Findings from previous research have shown that income inequality is positively associated with population change, but these studies have not explicitly tested for differences between the impacts of population growth and decline. Understanding the implications of population dynamics is particularly important given that many rural areas are characterized by population decline. We analyze county-level data (n=15,375 county-decades) from the Decennial Census and American Community Survey (ACS), applying fixed effects models to estimate the respective effects of population growth and decline on income inequality, to identify the processes that mediate the links between population change and inequality, and to assess whether these effects are moderated by county-level economic and demographic characteristics. We find evidence that population decline is associated with increased levels of income inequality relative to counties experiencing stable and high rates of population growth. This relationship remains robust across a variety of model specifications, including models that account for changes in counties’ employment, sociodemographic, and ethnoracial composition. We also find that the relationship between income inequality and population change varies by metropolitan status, baseline level of inequality, and region.