Abstract
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 areas 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.
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Notes
We define rural in terms of population size and integration with metropolitan counties, per the definition provided by the U.S. Office of Management and Budget (OMB). We use the terms “rural” (“urban”) and “non-metropolitan” (“metropolitan”) interchangeably throughout this paper.
See Johnson and Lichter (2019) for a more detailed discussion on what constitutes depopulation.
Although our analytical sample is only comprised of non-metropolitan counties, we include Table 4 in the online appendix to highlight the differential distribution of metropolitan and non-metropolitan county-decade observations across population change categories. Approximately 41% of non-metropolitan counties experienced population decline during the time period, while just over 27% experienced stable growth and 32% experienced high growth. In contrast, only 15% of metropolitan counties experienced population decline, with 29% experiencing stable growth, and the majority of metropolitan counties, over 56%, experiencing high growth. Among counties experiencing population decline, 88% are non-metropolitan, and only 12% are metropolitan.
One exception is Parrado and Kandel (2010), whose analysis on population growth and income inequality in rural America includes a category representing “slow growth and decline” counties. Their findings have informed our study. However, Parrado and Kandel focus on changes in income inequality over the course of one decade (1990 to 2000), and their research motivation and modeling strategy emphasizes differences between counties experiencing varying degrees of Hispanic growth. In contrast, our study examines population change over a 46-year period and focuses on decadal patterns of population growth and decline in the total population (while controlling for variability in ethno-racial composition).
Although Kuznets emphasizes the changing income distribution in urbanizing economies, he also briefly addresses the characteristics of rural economies, which he describes as being smaller, with a lower per capita income, and a narrower income distribution due to the organization of agricultural production around small enterprises. Kuznets also acknowledges, however, that the process of farm consolidation was already underway in the 1950s (1955: 16). He thus draws attention to the process of economic restructuring and sustained depopulation in agricultural-dependent counties that occurred throughout the second half of the twentieth century (Johnson and Lichter 2019).
We ran a sensitivity analysis using an alternative OMB 2013 delineation, and the results support our main conclusions. Results of this analysis are available upon request.
The fixed approach ensures that we do not confound estimates of rural–urban differences over time with the metropolitanization process as counties experiencing population growth transition from non-metropolitan to metropolitan, or as counties experiencing population decline transition from metropolitan to non-metropolitan (Fuguitt, Heaton, and Lichter 1988).
The last time interval between the 2010 census and the 2012–2016 ACS is less than a decade. For ease of interpretation, however, we use “period” and “decade” interchangeably when referring to our county observations and our fixed effects models.
The standard deviation of population change in non-metropolitan counties is 14.5 across all periods. The minimum and maximum values are − 44.5% and 232%, respectively.
Services is a broad category that consists of several sub-industries: Business and repair services; Arts, entertainment, recreation, accommodation and food services; Professional, scientific, management, administrative, and waste management services; Educational services, health care, and social assistance; Personal services; and Other services, except public administration.
This category encompasses Agriculture, Forestry, Fishing, Hunting, and Mining.
County characteristics are likely to be spatially correlated with the characteristics of neighboring counties. Although this issue is typically addressed by adjusting for spatial autocorrelation in spatial regression analyses, our analytic sample is exclusively comprised of non-metropolitan counties. As such, counties are non-contiguous in our model, and it is therefore not necessary to test for spatial autocorrelation.
Consistent with this paper’s theoretical emphasis on the relationship between population change and income inequality, our discussion of the analytical results focuses on the coefficients for population change (decline and growth relative to stability) rather than the significance and direction of the compositional variables.
Of note, the full model explains considerably more variance than the naïve or intermediate models; the overall R2 increases from 0.030 in Model 1 to 0.382 in Model 5.
We measure baseline population in 1970, which represents the start of the first inter-censal period over which we measure population change. We also run Model 8 using 1980 as the baseline population year. The interaction term for Decline x Baseline-pop-1980 is statistically significant at the 0.05 level rather than the 0.10 level. The results of the interaction terms for baseline population change are generally robust, however, to using year 1980 or 1970.
The full, non-rounded, value cut points for the three categories are as follows: -.4451219 to -.0143492 (bottom tercile); -.0143454 to .0655099 (middle tercile); and .0655247 to 2.320075 (upper tercile).
This model employs an entropy measure of economic diversity (Brown and Greenbaum 2016), which captures the distribution of civilian workers across the ten industries designated by the census (Table 6, Model 12). The entropy index has a minimum value of 0, which would correspond to a county with only one industry, and is positively associated with the relative diversification of a county’s economy. Following Brown and Greenbaum (2016), the economic diversity index for county i in a given year is the sum of the absolute value of the product of the proportion employed in each industry (s) and the natural log of the proportion employed in each industry:
$$\sum_{s=1}^{S}\left|\left(\frac{{e}_{is}}{{e}_{i}}\right)ln\left(\frac{{e}_{is}}{{e}_{i}}\right)\right|$$The shift to rural manufacturing stimulated demographic and economic growth in these areas. In more recent decades, however, even rural counties with a prominent manufacturing sector have experienced population loss during periods of economic recession as rural residents have sought employment in more urbanized areas (ERS 2017; Johnson and Lichter 2019).
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Acknowledgements
This research is supported by USDA-AFRI grant 2018-67023-27646. The authors acknowledge Yosef Bodovski for programming support and assistance provided by the Population Research Institute at Penn State University, which is supported by an infrastructure grant from the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025). Thiede’s work was also supported by the USDA National Institute of Food and Agriculture and Multistate Research Project #PEN04623 (Accession #1013257).
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Butler, J., Wildermuth, G.A., Thiede, B.C. et al. Population Change and Income Inequality in Rural America. Popul Res Policy Rev 39, 889–911 (2020). https://doi.org/10.1007/s11113-020-09606-7
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DOI: https://doi.org/10.1007/s11113-020-09606-7