County-Level Drivers of Prime-Age Population Growth or Decline in the United States

County-Level Drivers of Prime-Age Population Growth or Decline in the United States | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article County-Level Drivers of Prime-Age Population Growth or Decline in the United States Roberto Gallardo, Zuzana Bednarik, Indraneel Kumar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9225189/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract The availability of prime-age workers has emerged as a central challenge for communities and economic developers across the United States, intensified by an aging population, demographic shifts, and the disruptions of the COVID-19 pandemic. This study examines the factors associated with growth or decline in prime-age working residents at the county level between 2013 and 2023, using first-differenced OLS regressions with state-clustered standard errors across 3,095 U.S. counties. Drawing on data from the American Community Survey and the USDA Economic Research Service, the model incorporates twelve variables spanning structural characteristics and time-varying local conditions, including three innovative metrics: digital distress, entrepreneurial efficiency, and a caring stress index. Results indicate that metropolitan status, housing cost pressures, work-from-home prevalence, and natural amenities are positively associated with prime-age population growth, whereas an aging population and high caring stress are the strongest negative predictors. By introducing the caring stress index, this study extends spatial equilibrium theory and advances the empirical understanding of prime-age labor supply dynamics. For practitioners, the findings suggest that, as communities compete for a shrinking pool of prime-age workers amid accelerating demographic change and significant place-based investments, workforce development strategies need to broaden to include caring infrastructure and remote-work access, in addition to traditional metrics such as educational attainment. This broader focus more effectively interconnects the drivers of community competitiveness. prime-age workers county first-differenced OLS regression factors 1. Introduction Availability of workers continues to be the number one workforce and economic development issue affecting communities across the United States. Demographic trends (e.g., aging populations or immigration policy), coupled with severe but short disruptions during the COVID-19 pandemic that rippled across the labor market, have left communities struggling to find workers to fill available job vacancies. According to the U.S. Chamber of Commerce Worker Shortage Index, as of December 2025, 31 states had more job openings than unemployed workers[1]. Furthermore, data from the St. Louis Federal Reserve Bank shows that many states have a labor force participation rate of less than 60 percent[2]. The lack of workers can be compounded by two factors: 1) the decline in prime-age working populations[3] and 2) the mismatch between skill requirements of existing jobs and existing skillsets in the labor force. The shortage of prime-age working populations coincides with a period of significant place-based investments across the country, mainly in advanced manufacturing, artificial intelligence, and pharmaceuticals. State economic development agencies, chambers of commerce, and local economic development organizations are increasingly prioritizing talent attraction and retention. This article examines the factors that impact the growth or decline of prime-age working populations during the 2013-2023 period using first-differenced OLS regressions with state-clustered standard errors. While work has been done on this topic (Krugman, 1991; Moretti, 2012; Lichter & Johnson, 2025), this article contributes to literature primarily by utilizing a different unit of analysis (counties), methodology (first-differenced OLS regression), and incorporating innovative variables: digital distress, entrepreneurship efficiency, and a caring stress index that might contribute to the structural shifts of prime-age residents. This study is structured as follows. A brief review of previous work on population changes and economic growth is discussed in the next section. Work from labor economics, regional science, demography, and economic geography is discussed, highlighting the innovative contributions of this work to these disciplines. The methodology section provides an overview of the data used and the methods employed. State-clustered errors were selected to account for shared state-level policy environments and labor market conditions that create spatial dependence. The results section interprets these results, explains how they contribute to the existing literature on this topic, and provides important insights to inform non-academic audiences, specifically economic developers, industries, and community leaders. Finally, the concluding section discusses the limitations of this study and opportunities for future research. [1] America Works Data Center: The U.S. Workforce by the Numbers | U.S. Chamber of Commerce [2] Dec 2025, Release Tables: Labor Force Participation Rate | FRED | St. Louis Fed [3] Defined as the population ages 25 to 54 2. Literature Review Factors affecting changes in prime-age workers have been studied across disciplines such as labor economics, regional science, demography, and economic geography, mostly grounded in spatial equilibrium theory. The literature has delved into factors including labor demand shocks, demographic changes (including geographic mobility and migration dynamics), spatial misallocation, natural amenities and quality of life, and human capital sorting. Frameworks for understanding spatial equilibrium and regional adjustments in labor market outcomes draw on several strands of economic literature. The Rosen–Roback spatial equilibrium model shows that wages and rents adjust to compensate for differences in local amenities and productivity (Roback, 1982 ). Research on agglomeration economies highlights how economic activity can generate self-reinforcing growth dynamics within regions (Moretti, 2012 ). Studies of regional adjustment dynamics suggest that local labor market distress is more likely to manifest as declines in employment-to-population ratios than as large population outflows (Blanchard & Katz, 1992 ; Dao et al., 2017 ). In addition, new economic geography explains why economic activity tends to concentrate spatially (Krugman, 1991 ), while work on housing supply elasticity emphasizes its role in shaping population growth across regions (Glaeser & Gottlieb, 2009 ). These frameworks have identified a series of variables affecting population and employment growth associated with the metro-rural divide, natural amenities and quality of life, the digital divide, demographic forces, human capital, earnings, occupational structure, remote work, and housing markets. Studies have shown a persistent growth advantage in metro counties or in proximity to metro counties (Partridge et al., 1991; Moretti, 2012 ). In addition, natural amenities and quality of life affect population change and employment levels. Natural amenities drive population change, particularly in rural areas (McGranahan, 1999 ; McGranahan et al., 2011 ) while also affecting employment and income growth (Deller et al., 2001 ). However, other research has found that employment growth driven by natural amenities primarily results in low-wage service jobs (Hunter et al., 2005 ). Recently, a study estimating quality of life in micropolitan (non-metro) areas found that higher quality of life is associated with higher employment, population growth, and lower poverty (Weinstein et al., 2023 ). For these reasons, the natural amenity scale developed by the United States Department of Agriculture Economic Research Service (USDA ERS) was included in the analysis. Changing demographics also affects population and employment levels. Population decline due to natural decrease is well documented, particularly in rural counties (Johnson, 2021 ). In addition, rural counties are being reshaped by experiencing four interconnected demographic forces, ranging from long-term depopulation caused by decades of young-adult out-migration, which left behind an aging population, to the recent influx of immigrants and refugees (Lichter & Johnson, 2025 ). Research also concluded that falling fertility—not out-migration—is the primary driver of county declines (Asquith et al., 2025 ). Variables measuring population aging and the share of the Hispanic population, the fastest growing and largest minority group in the country, were included in the model. The sorting of human capital—measured by the share of higher educated individuals—and its impact on brain hubs, rural brain drains, and spillover effects are well documented (Berry & Glaeser, 2005 ; Moretti, 2012 ; Carr & Kefalas, 2009 ; Waldorf, 2009 ). Therefore, the share of those ages 25 or older with a bachelor’s degree or higher needs to be factored in when assessing the factors driving changes in the prime-age working population. The literature on population and economic growth has also identified changes in earnings and occupational structure as factors impacting the increase or decrease of residents of prime-working age. The literature shows that globalization, including the China trade shock, employer concentration, job polarization, and labor demand are associated with depression of earnings (Autor et al., 2025 ), suppressing wages (Azar et al., 2022 ; Benmelech et al., 2022 ), employment-population ratio declines (Abraham & Kearney, 2020 ), routine employment shares (Autor & Dorn, 2013 ), and depressed prime-age participation (Valletta & Barlow, 2018 ). For these reasons, the change in real median earnings and an occupation fractionalization variable (Alesina et al., 2003 ) were factored into the model. Housing costs and affordability have also been identified in the literature as factors impacting population or economic growth. Some research concluded that housing supply restrictions in high-productivity cities limit workers’ access to high-productivity jobs, resulting in lower GDP (Hsieh & Moretti, 2019 ). Furthermore, a century-long convergence in incomes across the U.S. has disappeared, primarily due to rising housing costs (Ganong & Shoag, 2017 ), affecting the flow of prime-age working residents. For this article, the assumption was that an increase in housing affordability would lead to a decline in prime-age working residents, not the other way around. In other words, we treated housing affordability as an independent variable. Results may help confirm or weaken the endogeneity issue implied in the direction of this relationship. Broadband and remote work have created a structural shift in geographic reallocation, affecting population and economic growth. This reallocation accelerated during and immediately after the COVID-19 pandemic, driven by significant investments in digital infrastructure and an increase in remote work. Research documented that broadband speed impacts unemployment (Lobo et al., 2020 ), while broadband availability and adoption affect business location decisions (Kim & Orazem, 2016 ), rural entrepreneurship (Strover et al., 2024 ), job productivity (Gallardo et al., 2021 ), and contribute to rural economic growth (Whitacre et al., 2014 ). In addition, research found that remote work or telecommuting positively impacted median household income and created regional spillovers (Gallardo & Whitacre, 2018 ). This article contributes to this discussion by analyzing the interaction among prime-age residents, digital distress, an innovative metric developed by the Purdue Center for Regional Development (Gallardo, 2022 ), and remote work. Lastly, an innovative variable examining caring stress (Montenovo et al., 2026 ) was also included. Research points to a relationship between informal care provision and deteriorating labor market outcomes, including lower employment (Van Houtven & Norton, 2004 ; Van Houtven et al., 2013 ; Lilly et al., 2007 ). In addition, childcare desserts are associated with lower female labor force participation rates (Malik et al., 2018 ) while informal caregiving for elders reduces employment and wages cumulatively (Bauer & Sousa-Poza, 2015 ; Skira, 2015 ). As the population continues to age, childcare access remains unreliable, and elderly care facilities decline, it is important to consider how caring, or a lack of, shapes differences in the prime-age working population. In summary, this article contributes to the field of regional economy by 1) relying on county-level data when most studies have focused on states and commuting zones; 2) focusing on the uniquely dynamic period from 2013 to 2023, when the post Great Recession recovery, pre-pandemic expansion, the COVID-19 pandemic, and the remote work revolution took place; 3) simultaneously modeling the differential effects of aging and Hispanic population change, identified in the literature as impactful to prime-working age residents; 4) incorporating a robust digital distress measure; and 5) contributing to the caring and labor market literatures with the inclusion of the novel caring stress index. 3. Data & Methods 3.1 Data The data for this article came primarily from the 5-Year American Community Survey, the United States Department of Agriculture Economic Research Service (USDA ERS), and the Purdue Center for Regional Development (PCRD). The geographical units of analysis were counties, and all variables, except three, measured the differences between 2013 and 2023. 3.2 Variables Based on both the literature review, which describes different factors affecting prime-age working individuals, and data availability, twelve variables were identified as control variables. The dependent variable was defined as the percentage change in population ages 25 to 54 (first row in Table 1 ). Baseline characteristics – Time-invariant changes Time-invariant changes in metropolitan status, natural amenities, and digital infrastructure were captured by three variables. A dummy variable for the metropolitan status of counties is based on the 2013 Rural Urban Continuum Code (RUCC), developed by the USDA ERS. The RUCC distinguishes metropolitan from nonmetropolitan counties based on their population size, degree of urbanization, and adjacency to a metro area. These codes range from 1 to 9, with the first three representing metro counties, and coded accordingly for our analysis. A 2023 version of RUCC is available, but 2013 was used as the baseline measure because it aligned better and avoided endogeneity issues. The 2017 digital distress variable (Gallardo, 2022 ) flags counties with a higher share of households without internet access or relying solely on cellular data for internet access, as well as those that rely only on mobile devices or have no computing devices. The earliest year available for this variable was 2017. This year precludes significant investments in broadband and devices taking place during and immediately after COVID-19. A 1999 natural amenities scale variable was developed by the USDA ERS and ranges from 1 to 7, with higher numbers indicating more natural amenities. This scale includes mean temperatures, mean hours of sunlight, mean relative humidity, and percent water surface area, among other variables. A more recent natural amenities variable is not available, so the assumption was made that the underlying variables did not change significantly during the period analyzed. First-differenced county-level covariates These covariates included the remaining nine variables. The occupation fractionalization (Alesina et al., 2003 ) used the six occupations available from the ACS and calculated a value from 0 to 1, where a higher number indicates a more heterogeneous distribution of jobs by occupation, while a number closer to 0 indicates a more homogeneous distribution. In other words, a value of 0 signals that all jobs were in a single occupation. A higher entrepreneurship efficiency value (Gallardo & Kumar, 2020 ) indicates that a higher share of the population aged 16 or older and of workers aged 16 or older was self-employed. The housing cost ratio was calculated by dividing the median cost of homes by the median household income. The caring stress index (Montenovo et al., 2026 ) ranges from 0 to 100 and looks at both caring demand and supply. A higher number denotes a higher caring stress based on caring demand outpacing supply due to demographic changes, access to primary care physicians, and other variables. The percentage change in real median earnings (2023 dollars) was also included. Demographic variables included the percentage-point change in the share of the population aged 65 or older, the share of the Hispanic population, the share of the population aged 25 or older with a bachelor’s degree or higher, and the share of workers aged 16 or older who worked from home. 3.3 Regression Analysis The analysis exploits variation in changes across U.S. counties between the 2013 and 2023 American Community Survey 5-year estimates. Because the data consists of two cross-sections observed at two points in time, the natural specification is a c, which is algebraically equivalent to a county fixed-effects model with two periods (Wooldridge, 2010 ). By expressing all time-varying variables as changes (Δ), the specification eliminates time-invariant unobserved county characteristics — such as geographic location, historical settlement patterns, and fixed institutional features — that might otherwise confound the estimates. The estimating equation is: $$\:{\Delta\:}Pop{2554}_{i}={\beta\:}_{0}+{\gamma\:}_{1}Metr{o}_{i}+{\gamma\:}_{2}NaturalAmenitie{s}_{i}+{\gamma\:}_{3}DigitalDistres{s}_{i}+{\sum\:}_{k}{\beta\:}_{k}{\Delta\:}{X}_{ki}+{\epsilon\:}_{is}$$ where ΔPop2554 i is the percent change in prime-age (25–54) population in county i between 2013 and 2023; Metro i , NaturalAmenities i , and DigitalDistress i are time-invariant or cross-sectional county characteristics (metropolitan status based on 2013 Rural-Urban Continuum Codes, the USDA ERS Natural Amenities Scale, and a 2017 digital distress indicator, respectively); ΔX ki is a vector of first-differenced county-level variables including changes in the share of population aged 65 and older, the Hispanic population share, the bachelor's degree attainment share, real median earnings, occupation fractionalization, the work-from-home share, entrepreneurial efficiency, the housing cost ratio, and the caring stress index; and \(\:{\epsilon\:}_{is\:}\) represents standard errors clustered at the state level. The inclusion of cross-sectional level variables alongside first-differenced variables is standard in two-period differenced specifications (Autor, Dorn, and Hanson, 2013 ; Bartik, 2024 ). These variables capture whether time-invariant endowments — natural amenities, metropolitan status, digital infrastructure — predict differential trajectories in prime-age population change over the study period. Standard errors are clustered at the state level to account for arbitrary within-state correlation of residuals across counties. Counties within the same state share policy environments (e.g., tax structures, Medicaid expansion decisions, right-to-work laws), labor market regulations, and regional economic shocks that induce spatial dependence in the error term. The cluster-robust variance estimator takes the form: $$\:\widehat{V}={\left({X}^{{\prime\:}}X\right)}^{-1}\left({\sum\:}_{g}{X}_{g}^{{\prime\:}}\widehat{{u}_{g}\widehat{{{u}^{{\prime\:}}}_{g}}}{X}_{g}\right){\left({X}^{{\prime\:}}X\right)}^{-1}\times\:\left[\frac{N-1}{N-K}\right]$$ where g indexes the 48 state clusters, X g and û g are the regressor matrix and residual vector for counties in state g , and the final terms are the standard finite-sample correction (Cameron & Miller, 2015 ). With 48 clusters, inference based on a t-distribution with G-1 = 47 degrees of freedom is appropriate. Counties with missing natural amenities values, as well as harmonization issues, were removed (Alaska, Hawaii, and Connecticut) due to limited data availability. The natural amenities scale developed by the USDA ERS does not include these states. In addition, some extreme values (outliers) were observed in the dependent variable — percent change in population aged 25–54. These counties (n = 63) with modest absolute population changes produced large percentage swings (e.g., Perkins County, SD registered a 3,662% increase from a base of fewer than 200 prime-age residents). To address this, the dependent variable is winsorized (modified for extreme values) at the 1st and 99th percentiles, clipping values below − 31.96% to − 31.96% and above 32.33% to 32.33%. The result was 3,095 counties included in the analysis. Sensitivity analyses using trimmed samples (dropping observations beyond these thresholds) produced identical results. Connecticut was excluded due to changes from counties to planning regions, which made harmonization impossible. Table 1 provides a descriptive statistical summary of the variables used in the regression analysis. Table 1 Descriptive Statistics of Variables Used in Regression Analysis Variable N Mean Std. Dev. Min Max Source Δ Population 25–54 3,095 -5.529 11.027 -31.960 32.326 ACS Baseline characteristics – Time-invariant changes Metro (2013 RUCC, dummy) 3,095 0.372 0.483 0.000 1.000 USDA Natural Amenities Scale (1999, 1–7) 3,095 3.488 1.042 1.000 7.000 USDA Digital Distress (2017, dummy) 3,095 0.254 0.435 0.000 1.000 Gallardo; ACS First-differenced county-level covariates Δ Share 65+ 3,095 3.647 2.203 -9.068 35.687 ACS Δ Share Hispanic 3,095 1.578 2.104 -22.861 22.380 ACS Δ Share Bachelor's Degree+ 3,095 4.339 3.195 -18.064 22.477 ACS Δ Real Median Earnings (%) 3,095 13.262 11.701 -47.657 101.204 ACS Δ Occupation Fractionalization 3,095 -1.281 1.757 -19.497 10.919 Alesina; ACS Δ Share of Work from Home 3,095 3.992 4.500 -27.847 28.213 ACS Δ Entrepreneurial Efficiency 3,095 -0.005 0.066 -0.515 0.701 Gallardo & Kumar; ACS Δ Housing Cost Ratio 3,095 0.289 0.573 -5.052 5.059 ACS Δ Caring Stress Index 3,095 0.000 0.089 -0.660 0.706 Montenovo et al. 4. Results The results of the first-differenced OLS regression with state-clustered standard errors are shown in Table 2 . Overall, the model explained about 45 percent of the variability in the change in the prime-age working population (adjusted R 2 = 0.444). Nine of the twelve variables were statistically significant at the p < 0.05 level. The first-differenced regression results indicate that both structural characteristics and changes in local conditions are strongly associated with variation in the percentage change in the population ages 25 to 54 across counties. Metro status and higher levels of natural amenities are positively and significantly related to growth in the prime-age working population, suggesting that place-based advantages persist over time. Among the time-varying factors, demographic shifts are particularly influential: increases in the share of the population aged 65 and older are strongly associated with declines in the prime-age working population, while growth in the share of the Hispanic population is positively associated with the change. Table 2 First-Differenced OLS Regression Results Variable Coefficients Std. Beta Std. Error t-statistic p-value Baseline characteristics – Time-invariant changes Metro (2013 RUCC) 5.055 0.222 0.730 6.927 0.000 Natural Amenities Scale 1.984 0.187 0.260 7.627 0.000 Digital Distress (2017) -0.433 -0.016 0.529 -0.820 0.416 First-differenced county-level covariates Δ Share 65+ -1.493 -0.298 0.137 -10.903 0.000 Δ Share Hispanic 0.790 0.151 0.096 8.200 0.000 Δ Share Bachelor's Degree+ 0.489 0.142 0.101 4.854 0.000 Δ Real Median Earnings (%) 0.093 0.099 0.018 5.085 0.000 Δ Occupation Fractionalization -0.066 -0.011 0.159 -0.419 0.677 Δ Work from Home Share 0.503 0.205 0.068 7.445 0.000 Δ Entrepreneurial Efficiency -6.136 -0.037 3.285 -1.868 0.068 Δ Housing Cost Ratio 4.236 0.220 0.821 5.163 0.000 Δ Caring Stress Index -5.427 -0.044 2.526 -2.149 0.037 Intercept -16.743 — 1.210 -13.840 0.000 Observations (N) 3,095 State Clusters 48 R² 0.446 Adjusted R² 0.444 Residual Std. Error 8.222 Dep. Var. Mean -5.529 Dep. Var. Std. Dev. 11.027 Improvements in human capital and economic conditions, captured by increases in the share of residents with a bachelor’s degree and in real median earnings, also contribute positively. Notably, increases in remote work, likely associated with the COVID-19 pandemic, are among the strongest predictors of growth, underscoring its importance in shaping recent population dynamics. In contrast, digital distress and occupational fractionalization are not statistically significant, while entrepreneurial efficiency shows only marginal effects. Finally, the rising housing cost ratio is positively associated with growth, reflecting housing demand pressure from an influx rather than the effect of rising costs on population attraction (as we assumed). This interpretation is consistent with the simultaneity between housing markets and migration documented by Glaeser and Gottlieb ( 2009 ). 5. Discussion & Conclusions Workforce development has always been a critical element in industry attraction, business retention, and expansion efforts. Given demographic trends, such as an aging population, retirements, or out-migration, coupled with significant place-based investments in advanced manufacturing, clean energy, and semiconductors, the ability to fill new jobs and/or replace leaving employees is crucial for any community. Understanding the factors that drive changes in prime-age working residents is crucial, especially in times of tight labor markets. This article draws on existing research examining labor market outcomes and demographic change to add novel variables to broaden our understanding of this dynamic. Results aligned with those documented previously: higher educational attainment, more natural amenities, metro counties, higher earnings, more remote workers, and an influx of immigrants (Lichter & Johnson, 2025 ) led to an increase in prime-age working residents. An aging population had a negative impact, as documented by Johnson ( 2021 ) and Lichter and Johnson ( 2025 ). In fact, the aging of the population was the single most important predictor, followed by the housing cost ratio (which reflects demand-side dynamics rather than a supply-side effect), remote work, the share of Hispanics, and educational attainment. It is important to note that some of these variables are beyond the control of community leaders and/or economic developers, such as an aging population or immigration policy affecting Hispanics. However, housing affordability, remote work availability, access to child and adult care, and educational attainment can and should be influenced by local/state policies. The important contribution of this article was that the lack of caring infrastructure negatively impacted prime-age working residents. Since the caring stress variable considers both the demand and supply sides of caring, it provides a deeper understanding of its impact. It also bridges the childcare (Malik & Hamm, 2017) and informal eldercare burden (Van Houtven & Norton, 2004 ; Bauer & Sousa-Poza, 2015 ) by demonstrating their combined geographic effect, a relationship not previously quantified at the county level. This implies that policymakers and community leaders need to start considering child and elderly care as intrinsic to workforce development. Meta research has generally revealed that jobs follow people, not the other way around (Hoogstra et al., 2017 ). It is known that people prioritize amenities, facilities, and safety, and the findings of this article point to important place-based interventions that local decision-makers can consider. This article has some limitations. Counties in Alaska, Hawaii, and Connecticut were not included due to data availability and harmonization issues. The natural amenities variable needs to be updated. The endogeneity of housing and earnings, which are plausibly both causes and consequences of changes in prime-age working residents. In addition, more than half of the variance in the change in prime-age working residents was affected by unknown variables. Nonetheless, as communities compete for a shrinking pool of prime-age working residents, it is important to recognize that caring infrastructure, alongside traditional economic factors, should inform a broader, more integrated approach to workforce and community development. Future research can explore the impact of industrial fractionalization, particularly given that this study finds no statistically significant impact of occupational fractionalization on changes in the number of prime-age working population. This dynamic between industry and occupations will contribute insights at a time when artificial intelligence is impacting different industries and occupations. Another direction for future research could include different digital divide variables, such as speed tests. Lastly, future research can delve deeper into why an aging county affects growth in prime-age working residents, when both groups are sorting. Is it because the amenities and services tailored for an ageing population are not useful and/or crowd out services and amenities for a younger demographic group? Are prime-age working residents avoiding aging counties, or are aging counties not attracting younger residents? Declarations Funding Declaration: No funding was utilized for this research. References Abraham KG, Kearney MS (2020) Explaining the decline in the US employment-to-population ratio: A review of the evidence. J Econ Lit 58(3):585–643. https://doi.org/10.1257/jel.20191480 Alesina A, Devleeschauwer A, Easterly W, Kurlat S, Wacziarg R (2003) Fractionalization. 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RSF: Russell Sage Foundation J Social Sci 11(2):88–114. https://doi.org/10.7758/RSF.2025.11.2.05 Lilly MB, Laporte A, Coyte PC (2007) Labor market work and home care’s unpaid caregivers: A systematic review of labor force participation rates, predictors of labor market withdrawal, and hours of work. Milbank Q 85(4):641–690. https://doi.org/10.1111/j.1468-0009.2007.00504.x Lobo BJ, Alam MR, Whitacre BE (2020) Broadband speed and unemployment rates: Data and measurement issues. Telecomm Policy 44(1):101829. https://doi.org/10.1016/j.telpol.2019.101829 Malik R, Hamm K, Lee WF, Davis EE, Sojourner A (2018) America’s child care deserts in 2018. Center for American Progress. https://www.americanprogress.org/article/americas-child-care-deserts-2018/ McGranahan DA (1999) Natural amenities drive rural population change. Agricultural Economic Report No. 781. U.S. Department of Agriculture, Economic Research Service McGranahan DA, Wojan TR, Lambert DM (2011) The rural growth trifecta: Outdoor amenities, creative class and entrepreneurial context. J Econ Geogr 11(3):529–557. https://doi.org/10.1093/jeg/lbq007 Montenovo L, Gallardo R, Marshall MI (2026) ; Working Paper). The caring stress index: Measuring county-level caring burden in the United States Moretti E (2012) The new geography of jobs. Houghton Mifflin Harcourt Partridge MD, Rickman DS, Ali K, Olfert MR (2008) Lost in space: Population growth in the American hinterlands and small cities. J Econ Geogr 8(6):727–757. https://doi.org/10.1093/jeg/lbn038 Roback J (1982) Wages, rents, and the quality of life. J Polit Econ 90(6):1257–1278. https://doi.org/10.1086/261120 Skira MM (2015) Dynamic wage and employment effects of elder parent care. Int Econ Rev 56(1):63–93 Strover S, Choi Y, Schrubbe A (2024) Broadband and rural entrepreneurship: Evidence from U.S. counties. Telecomm Policy 48(6):102778. https://doi.org/10.1016/j.telpol.2024.102778 Valletta RG, Barlow N (2018) The prime-age workforce and labor market polarization. FRBSF Economic Letter , 2018-21 Van Houtven CH, Coe NB, Skira MM (2013) The effect of informal care on work and wages. J Health Econ 32(1):240–252. https://doi.org/10.1016/j.jhealeco.2012.10.006 Van Houtven CH, Norton EC (2004) Informal care and health care use of older adults. J Health Econ 23(6):1159–1180. https://doi.org/10.1016/j.jhealeco.2004.04.008 Waldorf BS (2009) Brain drain in rural America [Selected paper]. Agricultural and Applied Economics Association Annual Meeting Weinstein AL, Hicks M, Wornell E (2023) An aggregate approach to estimating quality of life in micropolitan areas. Ann Reg Sci 70(2):447–476. https://doi.org/10.1007/s00168-022-01155-5 Whitacre B, Gallardo R, Strover S (2014) Broadband’s contribution to economic growth in rural areas: Moving towards a causal relationship. Telecomm Policy 38(11):1011–1023. https://doi.org/10.1016/j.telpol.2014.05.005 Wooldridge JM (2010) Econometric analysis of cross section and panel data, 2nd edn. MIT Press Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 29 Apr, 2026 Reviewers agreed at journal 27 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 28 Mar, 2026 Submission checks completed at journal 28 Mar, 2026 First submitted to journal 25 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9225189","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":619001974,"identity":"68ba40ea-8260-4e65-a5e3-1441fb28e982","order_by":0,"name":"Roberto Gallardo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYFACHgjFj2AnEKMFqEiygWQtBgeI1WLe3nvwceUPG7vNt3vMPrxts2HgZ88xwKtF5sy5ZMMzCWnJ2+6cMZ45ty2NQbLnDX4tEhI5ZpINCYeTzW7kGDPzth1mMLhBwBagFvOfIC3GM8Ba/jPYE6HFjBGoxc5AAqzlAAOQQUALz7lkyYa0tASJO8eKGeecS+aROPOsAL8W9t6DHxtsbOz5ZzdvZnhTZifH3568Aa8WGEhskIAweIhSDgL2DBJEqx0Fo2AUjIKRBgCAPUJ2/5IYZQAAAABJRU5ErkJggg==","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":true,"prefix":"","firstName":"Roberto","middleName":"","lastName":"Gallardo","suffix":""},{"id":619001975,"identity":"c0124f2f-471c-4e15-9289-ce7d56cb710c","order_by":1,"name":"Zuzana Bednarik","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Zuzana","middleName":"","lastName":"Bednarik","suffix":""},{"id":619001980,"identity":"87e20557-909b-4af8-8f75-e005d27dc323","order_by":2,"name":"Indraneel Kumar","email":"","orcid":"","institution":"Purdue University West Lafayette","correspondingAuthor":false,"prefix":"","firstName":"Indraneel","middleName":"","lastName":"Kumar","suffix":""}],"badges":[],"createdAt":"2026-03-25 15:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9225189/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9225189/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106408033,"identity":"cff6c1a5-66c3-4a7f-87cd-d79f7f95d4d2","added_by":"auto","created_at":"2026-04-08 09:40:22","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":550652,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9225189/v1/612f3758-9dec-4a2c-b9d1-590254e45679.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"County-Level Drivers of Prime-Age Population Growth or Decline in the United States","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAvailability of workers continues to be the number one workforce and economic development issue affecting communities across the United States. Demographic trends (e.g., aging populations or immigration policy), coupled with severe but short disruptions during the COVID-19 pandemic that rippled across the labor market, have left communities struggling to find workers to fill available job vacancies. According to the U.S. Chamber of Commerce Worker Shortage Index, as of December 2025, 31 states had more job openings than unemployed workers[1]. Furthermore, data from the St. Louis Federal Reserve Bank shows that many states have a labor force participation rate of less than 60 percent[2]. \u003c/p\u003e\n\u003cp\u003eThe lack of workers can be compounded by two factors: 1) the decline in prime-age working populations[3] and 2) the mismatch between skill requirements of existing jobs and existing skillsets in the labor force. The shortage of prime-age working populations coincides with a period of significant place-based investments across the country, mainly in advanced manufacturing, artificial intelligence, and pharmaceuticals. State economic development agencies, chambers of commerce, and local economic development organizations are increasingly prioritizing talent attraction and retention. \u003c/p\u003e\n\u003cp\u003eThis article examines the factors that impact the growth or decline of prime-age working populations during the 2013-2023 period using first-differenced OLS regressions with state-clustered standard errors. While work has been done on this topic (Krugman, 1991; Moretti, 2012; Lichter \u0026amp; Johnson, 2025), this article contributes to literature primarily by utilizing a different unit of analysis (counties), methodology (first-differenced OLS regression), and incorporating innovative variables: digital distress, entrepreneurship efficiency, and a caring stress index that might contribute to the structural shifts of prime-age residents.\u003c/p\u003e\n\u003cp\u003eThis study is structured as follows. A brief review of previous work on population changes and economic growth is discussed in the next section. Work from labor economics, regional science, demography, and economic geography is discussed, highlighting the innovative contributions of this work to these disciplines. The methodology section provides an overview of the data used and the methods employed. State-clustered errors were selected to account for shared state-level policy environments and labor market conditions that create spatial dependence. The results section interprets these results, explains how they contribute to the existing literature on this topic, and provides important insights to inform non-academic audiences, specifically economic developers, industries, and community leaders. Finally, the concluding section discusses the limitations of this study and opportunities for future research.\u003c/p\u003e\n\u003cp\u003e[1] America Works Data Center: The U.S. Workforce by the Numbers | U.S. Chamber of Commerce\u003c/p\u003e\n\u003cp\u003e[2] Dec 2025, Release Tables: Labor Force Participation Rate | FRED | St. Louis Fed\u003c/p\u003e\n\u003cp\u003e[3] Defined as the population ages 25 to 54\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cp\u003eFactors affecting changes in prime-age workers have been studied across disciplines such as labor economics, regional science, demography, and economic geography, mostly grounded in spatial equilibrium theory. The literature has delved into factors including labor demand shocks, demographic changes (including geographic mobility and migration dynamics), spatial misallocation, natural amenities and quality of life, and human capital sorting.\u003c/p\u003e \u003cp\u003eFrameworks for understanding spatial equilibrium and regional adjustments in labor market outcomes draw on several strands of economic literature. The Rosen\u0026ndash;Roback spatial equilibrium model shows that wages and rents adjust to compensate for differences in local amenities and productivity (Roback, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1982\u003c/span\u003e). Research on agglomeration economies highlights how economic activity can generate self-reinforcing growth dynamics within regions (Moretti, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Studies of regional adjustment dynamics suggest that local labor market distress is more likely to manifest as declines in employment-to-population ratios than as large population outflows (Blanchard \u0026amp; Katz, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Dao et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, new economic geography explains why economic activity tends to concentrate spatially (Krugman, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), while work on housing supply elasticity emphasizes its role in shaping population growth across regions (Glaeser \u0026amp; Gottlieb, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese frameworks have identified a series of variables affecting population and employment growth associated with the metro-rural divide, natural amenities and quality of life, the digital divide, demographic forces, human capital, earnings, occupational structure, remote work, and housing markets.\u003c/p\u003e \u003cp\u003eStudies have shown a persistent growth advantage in metro counties or in proximity to metro counties (Partridge et al., 1991; Moretti, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). In addition, natural amenities and quality of life affect population change and employment levels. Natural amenities drive population change, particularly in rural areas (McGranahan, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; McGranahan et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) while also affecting employment and income growth (Deller et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, other research has found that employment growth driven by natural amenities primarily results in low-wage service jobs (Hunter et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Recently, a study estimating quality of life in micropolitan (non-metro) areas found that higher quality of life is associated with higher employment, population growth, and lower poverty (Weinstein et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). For these reasons, the natural amenity scale developed by the United States Department of Agriculture Economic Research Service (USDA ERS) was included in the analysis.\u003c/p\u003e \u003cp\u003eChanging demographics also affects population and employment levels. Population decline due to natural decrease is well documented, particularly in rural counties (Johnson, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In addition, rural counties are being reshaped by experiencing four interconnected demographic forces, ranging from long-term depopulation caused by decades of young-adult out-migration, which left behind an aging population, to the recent influx of immigrants and refugees (Lichter \u0026amp; Johnson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Research also concluded that falling fertility\u0026mdash;not out-migration\u0026mdash;is the primary driver of county declines (Asquith et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Variables measuring population aging and the share of the Hispanic population, the fastest growing and largest minority group in the country, were included in the model.\u003c/p\u003e \u003cp\u003eThe sorting of human capital\u0026mdash;measured by the share of higher educated individuals\u0026mdash;and its impact on brain hubs, rural brain drains, and spillover effects are well documented (Berry \u0026amp; Glaeser, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Moretti, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Carr \u0026amp; Kefalas, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2009\u003c/span\u003e; Waldorf, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Therefore, the share of those ages 25 or older with a bachelor\u0026rsquo;s degree or higher needs to be factored in when assessing the factors driving changes in the prime-age working population.\u003c/p\u003e \u003cp\u003eThe literature on population and economic growth has also identified changes in earnings and occupational structure as factors impacting the increase or decrease of residents of prime-working age. The literature shows that globalization, including the China trade shock, employer concentration, job polarization, and labor demand are associated with depression of earnings (Autor et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2025\u003c/span\u003e), suppressing wages (Azar et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Benmelech et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), employment-population ratio declines (Abraham \u0026amp; Kearney, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), routine employment shares (Autor \u0026amp; Dorn, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), and depressed prime-age participation (Valletta \u0026amp; Barlow, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). For these reasons, the change in real median earnings and an occupation fractionalization variable (Alesina et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) were factored into the model.\u003c/p\u003e \u003cp\u003eHousing costs and affordability have also been identified in the literature as factors impacting population or economic growth. Some research concluded that housing supply restrictions in high-productivity cities limit workers\u0026rsquo; access to high-productivity jobs, resulting in lower GDP (Hsieh \u0026amp; Moretti, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Furthermore, a century-long convergence in incomes across the U.S. has disappeared, primarily due to rising housing costs (Ganong \u0026amp; Shoag, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), affecting the flow of prime-age working residents. For this article, the assumption was that an increase in housing affordability would lead to a decline in prime-age working residents, not the other way around. In other words, we treated housing affordability as an independent variable. Results may help confirm or weaken the endogeneity issue implied in the direction of this relationship.\u003c/p\u003e \u003cp\u003eBroadband and remote work have created a structural shift in geographic reallocation, affecting population and economic growth. This reallocation accelerated during and immediately after the COVID-19 pandemic, driven by significant investments in digital infrastructure and an increase in remote work. Research documented that broadband speed impacts unemployment (Lobo et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), while broadband availability and adoption affect business location decisions (Kim \u0026amp; Orazem, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), rural entrepreneurship (Strover et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), job productivity (Gallardo et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), and contribute to rural economic growth (Whitacre et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, research found that remote work or telecommuting positively impacted median household income and created regional spillovers (Gallardo \u0026amp; Whitacre, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This article contributes to this discussion by analyzing the interaction among prime-age residents, digital distress, an innovative metric developed by the Purdue Center for Regional Development (Gallardo, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), and remote work.\u003c/p\u003e \u003cp\u003eLastly, an innovative variable examining caring stress (Montenovo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) was also included. Research points to a relationship between informal care provision and deteriorating labor market outcomes, including lower employment (Van Houtven \u0026amp; Norton, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Van Houtven et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lilly et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In addition, childcare desserts are associated with lower female labor force participation rates (Malik et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) while informal caregiving for elders reduces employment and wages cumulatively (Bauer \u0026amp; Sousa-Poza, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Skira, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As the population continues to age, childcare access remains unreliable, and elderly care facilities decline, it is important to consider how caring, or a lack of, shapes differences in the prime-age working population.\u003c/p\u003e \u003cp\u003eIn summary, this article contributes to the field of regional economy by 1) relying on county-level data when most studies have focused on states and commuting zones; 2) focusing on the uniquely dynamic period from 2013 to 2023, when the post Great Recession recovery, pre-pandemic expansion, the COVID-19 pandemic, and the remote work revolution took place; 3) simultaneously modeling the differential effects of aging and Hispanic population change, identified in the literature as impactful to prime-working age residents; 4) incorporating a robust digital distress measure; and 5) contributing to the caring and labor market literatures with the inclusion of the novel caring stress index.\u003c/p\u003e"},{"header":"3. Data \u0026 Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Data\u003c/h2\u003e \u003cp\u003eThe data for this article came primarily from the 5-Year American Community Survey, the United States Department of Agriculture Economic Research Service (USDA ERS), and the Purdue Center for Regional Development (PCRD). The geographical units of analysis were counties, and all variables, except three, measured the differences between 2013 and 2023.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Variables\u003c/h2\u003e \u003cp\u003eBased on both the literature review, which describes different factors affecting prime-age working individuals, and data availability, twelve variables were identified as control variables. The dependent variable was defined as the percentage change in population ages 25 to 54 (first row in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eBaseline characteristics \u0026ndash; Time-invariant changes\u003c/em\u003e \u003c/p\u003e \u003cp\u003eTime-invariant changes in metropolitan status, natural amenities, and digital infrastructure were captured by three variables. A dummy variable for the metropolitan status of counties is based on the 2013 Rural Urban Continuum Code (RUCC), developed by the USDA ERS. The RUCC distinguishes metropolitan from nonmetropolitan counties based on their population size, degree of urbanization, and adjacency to a metro area. These codes range from 1 to 9, with the first three representing metro counties, and coded accordingly for our analysis. A 2023 version of RUCC is available, but 2013 was used as the baseline measure because it aligned better and avoided endogeneity issues.\u003c/p\u003e \u003cp\u003eThe 2017 digital distress variable (Gallardo, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) flags counties with a higher share of households without internet access or relying solely on cellular data for internet access, as well as those that rely only on mobile devices or have no computing devices. The earliest year available for this variable was 2017. This year precludes significant investments in broadband and devices taking place during and immediately after COVID-19.\u003c/p\u003e \u003cp\u003eA 1999 natural amenities scale variable was developed by the USDA ERS and ranges from 1 to 7, with higher numbers indicating more natural amenities. This scale includes mean temperatures, mean hours of sunlight, mean relative humidity, and percent water surface area, among other variables. A more recent natural amenities variable is not available, so the assumption was made that the underlying variables did not change significantly during the period analyzed.\u003c/p\u003e \u003cp\u003e \u003cem\u003eFirst-differenced county-level covariates\u003c/em\u003e \u003c/p\u003e \u003cp\u003eThese covariates included the remaining nine variables. The occupation fractionalization (Alesina et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2003\u003c/span\u003e) used the six occupations available from the ACS and calculated a value from 0 to 1, where a higher number indicates a more heterogeneous distribution of jobs by occupation, while a number closer to 0 indicates a more homogeneous distribution. In other words, a value of 0 signals that all jobs were in a single occupation. A higher entrepreneurship efficiency value (Gallardo \u0026amp; Kumar, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) indicates that a higher share of the population aged 16 or older and of workers aged 16 or older was self-employed. The housing cost ratio was calculated by dividing the median cost of homes by the median household income.\u003c/p\u003e \u003cp\u003eThe caring stress index (Montenovo et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2026\u003c/span\u003e) ranges from 0 to 100 and looks at both caring demand and supply. A higher number denotes a higher caring stress based on caring demand outpacing supply due to demographic changes, access to primary care physicians, and other variables. The percentage change in real median earnings (2023 dollars) was also included. Demographic variables included the percentage-point change in the share of the population aged 65 or older, the share of the Hispanic population, the share of the population aged 25 or older with a bachelor\u0026rsquo;s degree or higher, and the share of workers aged 16 or older who worked from home.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Regression Analysis\u003c/h2\u003e \u003cp\u003eThe analysis exploits variation in changes across U.S. counties between the 2013 and 2023 American Community Survey 5-year estimates. Because the data consists of two cross-sections observed at two points in time, the natural specification is a c, which is algebraically equivalent to a county fixed-effects model with two periods (Wooldridge, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). By expressing all time-varying variables as changes (Δ), the specification eliminates time-invariant unobserved county characteristics \u0026mdash; such as geographic location, historical settlement patterns, and fixed institutional features \u0026mdash; that might otherwise confound the estimates.\u003c/p\u003e \u003cp\u003eThe estimating equation is:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:{\\Delta\\:}Pop{2554}_{i}={\\beta\\:}_{0}+{\\gamma\\:}_{1}Metr{o}_{i}+{\\gamma\\:}_{2}NaturalAmenitie{s}_{i}+{\\gamma\\:}_{3}DigitalDistres{s}_{i}+{\\sum\\:}_{k}{\\beta\\:}_{k}{\\Delta\\:}{X}_{ki}+{\\epsilon\\:}_{is}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eΔPop2554\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e is the percent change in prime-age (25\u0026ndash;54) population in county \u003cem\u003ei\u003c/em\u003e between 2013 and 2023; \u003cem\u003eMetro\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, \u003cem\u003eNaturalAmenities\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e, and \u003cem\u003eDigitalDistress\u003c/em\u003e\u003csub\u003e\u003cem\u003ei\u003c/em\u003e\u003c/sub\u003e are time-invariant or cross-sectional county characteristics (metropolitan status based on 2013 Rural-Urban Continuum Codes, the USDA ERS Natural Amenities Scale, and a 2017 digital distress indicator, respectively); \u003cem\u003eΔX\u003c/em\u003e\u003csub\u003e\u003cem\u003eki\u003c/em\u003e\u003c/sub\u003e is a vector of first-differenced county-level variables including changes in the share of population aged 65 and older, the Hispanic population share, the bachelor's degree attainment share, real median earnings, occupation fractionalization, the work-from-home share, entrepreneurial efficiency, the housing cost ratio, and the caring stress index; and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\epsilon\\:}_{is\\:}\\)\u003c/span\u003e\u003c/span\u003erepresents standard errors clustered at the state level.\u003c/p\u003e \u003cp\u003eThe inclusion of cross-sectional level variables alongside first-differenced variables is standard in two-period differenced specifications (Autor, Dorn, and Hanson, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Bartik, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). These variables capture whether time-invariant endowments \u0026mdash; natural amenities, metropolitan status, digital infrastructure \u0026mdash; predict differential \u003cem\u003etrajectories\u003c/em\u003e in prime-age population change over the study period. Standard errors are clustered at the state level to account for arbitrary within-state correlation of residuals across counties. Counties within the same state share policy environments (e.g., tax structures, Medicaid expansion decisions, right-to-work laws), labor market regulations, and regional economic shocks that induce spatial dependence in the error term. The cluster-robust variance estimator takes the form:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:\\widehat{V}={\\left({X}^{{\\prime\\:}}X\\right)}^{-1}\\left({\\sum\\:}_{g}{X}_{g}^{{\\prime\\:}}\\widehat{{u}_{g}\\widehat{{{u}^{{\\prime\\:}}}_{g}}}{X}_{g}\\right){\\left({X}^{{\\prime\\:}}X\\right)}^{-1}\\times\\:\\left[\\frac{N-1}{N-K}\\right]$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ewhere \u003cem\u003eg\u003c/em\u003e indexes the 48 state clusters, \u003cem\u003eX\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e and \u003cem\u003e\u0026ucirc;\u003c/em\u003e\u003csub\u003e\u003cem\u003eg\u003c/em\u003e\u003c/sub\u003e are the regressor matrix and residual vector for counties in state \u003cem\u003eg\u003c/em\u003e, and the final terms are the standard finite-sample correction (Cameron \u0026amp; Miller, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). With 48 clusters, inference based on a t-distribution with G-1\u0026thinsp;=\u0026thinsp;47 degrees of freedom is appropriate.\u003c/p\u003e \u003cp\u003eCounties with missing natural amenities values, as well as harmonization issues, were removed (Alaska, Hawaii, and Connecticut) due to limited data availability. The natural amenities scale developed by the USDA ERS does not include these states. In addition, some extreme values (outliers) were observed in the dependent variable \u0026mdash; percent change in population aged 25\u0026ndash;54. These counties (n\u0026thinsp;=\u0026thinsp;63) with modest absolute population changes produced large percentage swings (e.g., Perkins County, SD registered a 3,662% increase from a base of fewer than 200 prime-age residents). To address this, the dependent variable is winsorized (modified for extreme values) at the 1st and 99th percentiles, clipping values below \u0026minus;\u0026thinsp;31.96% to \u0026minus;\u0026thinsp;31.96% and above 32.33% to 32.33%.\u003c/p\u003e \u003cp\u003eThe result was 3,095 counties included in the analysis. Sensitivity analyses using trimmed samples (dropping observations beyond these thresholds) produced identical results. Connecticut was excluded due to changes from counties to planning regions, which made harmonization impossible. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a descriptive statistical summary of the variables used in the regression analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics of Variables Used in Regression Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Population 25\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-5.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-31.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e32.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline characteristics \u0026ndash; Time-invariant changes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetro (2013 RUCC, dummy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSDA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Amenities Scale (1999, 1\u0026ndash;7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eUSDA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Distress (2017, dummy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.254\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.435\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGallardo; ACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c7\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirst-differenced county-level covariates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.647\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-9.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.687\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.578\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-22.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share Bachelor's Degree+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.339\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-18.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.477\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Real Median Earnings (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e13.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-47.657\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e101.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Occupation Fractionalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-1.281\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-19.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eAlesina; ACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share of Work from Home\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-27.847\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Entrepreneurial Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGallardo \u0026amp; Kumar; ACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Housing Cost Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.573\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-5.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eACS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Caring Stress Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.089\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMontenovo et al.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cp\u003eThe results of the first-differenced OLS regression with state-clustered standard errors are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Overall, the model explained about 45 percent of the variability in the change in the prime-age working population (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.444). Nine of the twelve variables were statistically significant at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level.\u003c/p\u003e \u003cp\u003eThe first-differenced regression results indicate that both structural characteristics and changes in local conditions are strongly associated with variation in the percentage change in the population ages 25 to 54 across counties. Metro status and higher levels of natural amenities are positively and significantly related to growth in the prime-age working population, suggesting that place-based advantages persist over time.\u003c/p\u003e \u003cp\u003eAmong the time-varying factors, demographic shifts are particularly influential: increases in the share of the population aged 65 and older are strongly associated with declines in the prime-age working population, while growth in the share of the Hispanic population is positively associated with the change.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFirst-Differenced OLS Regression Results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Beta\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003et-statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBaseline characteristics \u0026ndash; Time-invariant changes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetro (2013 RUCC)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.222\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.730\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNatural Amenities Scale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.627\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Distress (2017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.416\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFirst-differenced county-level covariates\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share 65+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1.493\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.790\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Share Bachelor's Degree+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.489\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.854\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Real Median Earnings (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Occupation Fractionalization\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.066\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Work from Home Share\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Entrepreneurial Efficiency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-6.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.285\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.868\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.068\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Housing Cost Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eΔ Caring Stress Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.427\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.037\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-16.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.210\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-13.840\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eObservations (N)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" morerows=\"6\" nameend=\"c6\" namest=\"c3\" rowspan=\"7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eState Clusters\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidual Std. Error\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.222\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDep. Var. Mean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-5.529\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDep. Var. Std. Dev.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eImprovements in human capital and economic conditions, captured by increases in the share of residents with a bachelor\u0026rsquo;s degree and in real median earnings, also contribute positively. Notably, increases in remote work, likely associated with the COVID-19 pandemic, are among the strongest predictors of growth, underscoring its importance in shaping recent population dynamics.\u003c/p\u003e \u003cp\u003eIn contrast, digital distress and occupational fractionalization are not statistically significant, while entrepreneurial efficiency shows only marginal effects. Finally, the rising housing cost ratio is positively associated with growth, reflecting housing demand pressure from an influx rather than the effect of rising costs on population attraction (as we assumed). This interpretation is consistent with the simultaneity between housing markets and migration documented by Glaeser and Gottlieb (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e"},{"header":"5. Discussion \u0026 Conclusions","content":"\u003cp\u003eWorkforce development has always been a critical element in industry attraction, business retention, and expansion efforts. Given demographic trends, such as an aging population, retirements, or out-migration, coupled with significant place-based investments in advanced manufacturing, clean energy, and semiconductors, the ability to fill new jobs and/or replace leaving employees is crucial for any community.\u003c/p\u003e \u003cp\u003eUnderstanding the factors that drive changes in prime-age working residents is crucial, especially in times of tight labor markets. This article draws on existing research examining labor market outcomes and demographic change to add novel variables to broaden our understanding of this dynamic. Results aligned with those documented previously: higher educational attainment, more natural amenities, metro counties, higher earnings, more remote workers, and an influx of immigrants (Lichter \u0026amp; Johnson, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) led to an increase in prime-age working residents. An aging population had a negative impact, as documented by Johnson (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and Lichter and Johnson (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn fact, the aging of the population was the single most important predictor, followed by the housing cost ratio (which reflects demand-side dynamics rather than a supply-side effect), remote work, the share of Hispanics, and educational attainment. It is important to note that some of these variables are beyond the control of community leaders and/or economic developers, such as an aging population or immigration policy affecting Hispanics. However, housing affordability, remote work availability, access to child and adult care, and educational attainment can and should be influenced by local/state policies.\u003c/p\u003e \u003cp\u003eThe important contribution of this article was that the lack of caring infrastructure negatively impacted prime-age working residents. Since the caring stress variable considers both the demand and supply sides of caring, it provides a deeper understanding of its impact. It also bridges the childcare (Malik \u0026amp; Hamm, 2017) and informal eldercare burden (Van Houtven \u0026amp; Norton, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Bauer \u0026amp; Sousa-Poza, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) by demonstrating their combined geographic effect, a relationship not previously quantified at the county level. This implies that policymakers and community leaders need to start considering child and elderly care as intrinsic to workforce development. Meta research has generally revealed that jobs follow people, not the other way around (Hoogstra et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). It is known that people prioritize amenities, facilities, and safety, and the findings of this article point to important place-based interventions that local decision-makers can consider.\u003c/p\u003e \u003cp\u003eThis article has some limitations. Counties in Alaska, Hawaii, and Connecticut were not included due to data availability and harmonization issues. The natural amenities variable needs to be updated. The endogeneity of housing and earnings, which are plausibly both causes and consequences of changes in prime-age working residents. In addition, more than half of the variance in the change in prime-age working residents was affected by unknown variables. Nonetheless, as communities compete for a shrinking pool of prime-age working residents, it is important to recognize that caring infrastructure, alongside traditional economic factors, should inform a broader, more integrated approach to workforce and community development.\u003c/p\u003e \u003cp\u003eFuture research can explore the impact of industrial fractionalization, particularly given that this study finds no statistically significant impact of occupational fractionalization on changes in the number of prime-age working population. This dynamic between industry and occupations will contribute insights at a time when artificial intelligence is impacting different industries and occupations. Another direction for future research could include different digital divide variables, such as speed tests.\u003c/p\u003e \u003cp\u003eLastly, future research can delve deeper into why an aging county affects growth in prime-age working residents, when both groups are sorting. Is it because the amenities and services tailored for an ageing population are not useful and/or crowd out services and amenities for a younger demographic group? Are prime-age working residents avoiding aging counties, or are aging counties not attracting younger residents?\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding Declaration:\u0026nbsp;\u003c/strong\u003eNo funding was utilized for this research.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbraham KG, Kearney MS (2020) Explaining the decline in the US employment-to-population ratio: A review of the evidence. 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MIT Press\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"prime-age workers, county, first-differenced OLS regression, factors","lastPublishedDoi":"10.21203/rs.3.rs-9225189/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9225189/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe availability of prime-age workers has emerged as a central challenge for communities and economic developers across the United States, intensified by an aging population, demographic shifts, and the disruptions of the COVID-19 pandemic. This study examines the factors associated with growth or decline in prime-age working residents at the county level between 2013 and 2023, using first-differenced OLS regressions with state-clustered standard errors across 3,095 U.S. counties. Drawing on data from the American Community Survey and the USDA Economic Research Service, the model incorporates twelve variables spanning structural characteristics and time-varying local conditions, including three innovative metrics: digital distress, entrepreneurial efficiency, and a caring stress index. Results indicate that metropolitan status, housing cost pressures, work-from-home prevalence, and natural amenities are positively associated with prime-age population growth, whereas an aging population and high caring stress are the strongest negative predictors. By introducing the caring stress index, this study extends spatial equilibrium theory and advances the empirical understanding of prime-age labor supply dynamics. For practitioners, the findings suggest that, as communities compete for a shrinking pool of prime-age workers amid accelerating demographic change and significant place-based investments, workforce development strategies need to broaden to include caring infrastructure and remote-work access, in addition to traditional metrics such as educational attainment. This broader focus more effectively interconnects the drivers of community competitiveness.\u003c/p\u003e","manuscriptTitle":"County-Level Drivers of Prime-Age Population Growth or Decline in the United States","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-08 09:17:00","doi":"10.21203/rs.3.rs-9225189/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-29T21:15:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"72911786031662757722569151852348768312","date":"2026-04-27T16:13:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"293774926618984976803574669479377005803","date":"2026-04-14T15:34:29+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T11:19:04+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-28T13:58:21+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-28T07:44:38+00:00","index":"","fulltext":""},{"type":"submitted","content":"The Annals of Regional Science","date":"2026-03-25T15:37:47+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"the-annals-of-regional-science","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"arsc","sideBox":"Learn more about [The Annals of Regional Science](https://link.springer.com/journal/168)","snPcode":"168","submissionUrl":"https://submission.springernature.com/new-submission/168/3","title":"The Annals of Regional Science","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"5c468846-1d25-4e2e-ae68-cd0098cc4ce7","owner":[],"postedDate":"April 8th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-04-29T21:15:04+00:00","index":20,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-08T09:17:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-08 09:17:00","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9225189","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9225189","identity":"rs-9225189","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}