Capita Personal Income Research Articles

Impacts of Shocks to U.S. Macroeconomic Variables on the Regional Economy of Georgia

This paper develops a way to analyze a regional economy and a large economy within a model. Using structural autoregression with block exogeneity and mutual independence assumptions as well as Cholesky factorization, we quantify the impacts of shocks to U.S. macroeconomic variables on the regional economy of Georgia. We found that the largest impact on Georgia's aggregate variables - consumer prices, real per capita personal income, and total employment - is the federal funds rate. For other shocks, Georgia's personal income is most affected by an increase in commodity prices, while Georgia's consumer prices are most affected by U.S. inflation. At the industry level, we found that the impact of real GDP is the largest and the impact of M2 is the smallest on output and employment in Georgia's industries, although the magnitude of the impact on employment is smaller and more short-lived than on output.

The impact of Medicaid expansion on state expenditures through the COVID-19 era.

To investigate the impact of Medicaid expansion on state expenditures through the end of 2022. We used data from the National Association of State Budget Officers (NASBO)'s State Expenditure Report, Kaiser Family Foundation (KFF)'s Medicaid expansion tracker, US Bureau of Labor Statistics data (BLS), US Bureau of Economic Analysis data (BEA), and Pandemic Response Accountability Committee Oversight (PRAC). We investigated spending per capita (by state population) across seven budget categories, including Medicaid spending, and four spending sources. We performed a difference-in-differences (DiD) analysis that compared within-state changes in spending over time in expansion and nonexpansion states to estimate the effect of Medicaid expansion on state budgets. We adjusted for annual state unemployment rate, annual state per capita personal income, and state spending of Coronavirus Relief Funds (CRF) from 2020 to 2022 and included state and year fixed effects. We linked annual state-level data on state-reported fiscal year expenditures from NASBO with state-level characteristics from BLS and BEA data and with CRF state spending from PRAC. Medicaid expansion was associated with an average increase of 21% (95% confidence interval [CI]: 16%-25%) in per capita Medicaid spending after Medicaid expansion among states that expanded prior to 2020. After inclusion of an interaction term to separate between the coronavirus disease (COVID) era (2020-2022) and the prior period following expansion (2015-2019), we found that although Medicaid expansion led to an average increase of 33% (95% CI: 21%-45%) in federal funding of state expenditures in the post-COVID years, it was not significantly associated with increased state spending. There was no evidence of crowding out of other state expenditure categories or a substantial impact on total state spending, even in the COVID-19 era. Increased federal expenditures may have shielded states from substantial budgetary impacts.

Per Capita Personal Income and Economic Decline in the Midwestern States, 1929-2023: A Note

Per Capita Personal Income and Economic Decline in the Midwestern States, 1929-2023: A Note

A comprehensive analysis of the societal impact of industrial diversity on sustainable economic development

PurposeThis paper aims to explore the interplay between industrial diversity and sustainable economic development in US counties.Design/methodology/approachAmong other popularly used measures, this study uses an underused measure, Hachman index, to gauge the degree of industrial diversity in the models. To capture the impact of industrial diversity on the local community, this study estimates the relationship of two diversity measures to four traditional socioeconomic indicators: per capita personal income growth, gross domestic product per worker, income inequality ratio and poverty rate.FindingsStatistical results suggest that industrial diversity, which is measured by Hachman index, is significantly related to the four socio-economic indicators. Industrial diversity can positively contribute to regional per capita personal income growth and mitigate income inequality and poverty stress; however, it is negatively related to the gross domestic product (GDP) per worker, which means industrial specialization may contribute to GDP per worker growth.Originality/valueThe findings of this study show that there is a nonlinear relationship between industrial diversity and all socioeconomic indicators. Most of the control variables, human capital variables and business and industry profile variables also display significant and positive impacts on economic development.

Small Firm Electricity Demand in Las Cruces, New Mexico, USA

Research examining small commercial and industrial electricity usage patterns have historically received less attention than residential electricity consumption patterns. This study examines electricity as an input to small firm commercial and industrial (CIS) production in Las Cruces, the second largest metropolitan economy in the state of New Mexico, using annual frequency data from 1978 to 2018. Those data include labor, per capita personal income, price measures for electricity and natural gas, and weather variables. The long-run and short-run elasticities of the data are then estimated using an autoregressive distributed lag model (ARDL). In the long-run, the CIS derived-demand curve is found to be upward sloping, and Las Cruces CIS customers use natural gas as a complementary input. Real per capita income is also found to have a positive impact in the long-run, while weather impacts are found to be ambiguous. In the short-run, the Las Cruces CIS derived-demand curve is downward sloping, CIS customers use natural gas as a substitute factor, and weather extremes are found to be positively correlated with small firm electricity usage.

Demographic and geographic variations in the access time of pediatric patients presenting with staring spells

ObjectiveTo assess the demographic and geographic variations in access time – defined as years between the date of symptom onset and initial date of neurological care – in pediatric patients presenting with staring spells. Materials and methodsWe conducted a secondary analysis of a retrospective chart review study from 2011 to 2021. A total of 1,353 staring spell patients, aged 0 to 17.9 years, were analyzed for age, sex, race/ethnicity, insurance, county, average county annual per capita personal income, and access time. ResultsPatients aged 0–2.9 years had the shortest median access time of 0.3 years, compared to 1.2 years in patients aged 3–12.9 years and 1.0 year in patients aged 13–17.9 years. Statistically significant differences were seen based on race/ethnicity and insurance with White patients having shorter access time of 0.5 years compared to Black patients with 1.0 year and self-pay patients having the shortest access time of 0.4 years compared to patients with private insurance (0.7 years). Warren County had the largest annual per capita personal income of $65,855 and access time of 0.5 years compared to Preble county with the least annual per capita personal income of $45,016 and access time of 1.1 years. ConclusionDemographic parameters of age, race/ethnicity, insurance, and annual county per capita personal income appeared to be associated with access time to initial neurological care in patients with staring spells. These associations need to be investigated further to ensure timely access to neurological care and to ensure equity in health care.

Comparison of Stock Price Prediction in Context of ARIMA and Random Forest Models

With the gradual improvement of the stock market system, coupled with the increase of per capita personal income, speculating in the stock market will become the main way for residents to invest in the future. Being able to predict the return on stocks is a highly desirable thing for investors. Predicting the future trend of stocks is an essential part when investing in stocks. There are many categories of stock prediction. The purpose of this paper is to compare the ability of the ARIMA and the random forest model to predict the stock market by introducing these two models’ theoretical knowledge. The results turn out that the ARIMA model is more suitable for short-term forecasting although it's essentially only valid for linear relationships. As for the random forest model, it has higher accuracy but it's more complex and computationally expensive. Overall, these results shed light on guiding further exploration of stock price prediction based on the state-of-art machine learning scenarios.

Ahead of the pain: Where we stand after a decade of growth in United Council for Neurologic Subspecialties-certified headache subspecialists.

To characterize the geographic distribution of United States (US) headache subspecialists in 2021 compared to 2012 and analyze trends in distribution of and growth by geography. Headache disorders are the second-highest cause of disease burden worldwide. Historically, headache disorders have been treated by primary care and emergency physicians, often suboptimally. Our 2012 study identified further disparities in geographic distribution of United Council for Neurologic Subspecialties (UCNS)-certified headache subspecialists. This correlational population study explores the current geographic distribution of and changes in subspecialists by aggregating data from the UCNS, National Health Interview Survey, US Census, Bureau of Economic Analysis, and American Community Survey. In 2021, there were 692 UCNS-certified headache subspecialists. There continued to be higher subspecialist density in the Northeast (1:34,678) and Midwest (1:55,005), with the highest density in Vermont (1:12,510) and Connecticut (1:20,419). The highest absolute subspecialist increases were in the South (99), California (35), and Texas (25). The highest relative subspecialist increases were in the West (105%), Vermont (500%), and Mississippi (500%). The univariable regressions showed significant associations between number of subspecialists and adult headache population (R2 =0.797, p < 0.001), between subspecialist density and personal income (R2 =0.935, p=0.033), and between growth in subspecialists and poverty rate (R2 =0.553, p=0.022). However, the multivariable regressions showed that only the adult population was significantly associated with number of subspecialists (adjusted R2 =0.806, p < 0.001). Overall, there has been substantial growth in the number of UCNS-certified headache subspecialists in the US, especially relative to growth in adult headache population. Subspecialist density continues to be highest in the Northeast and is associated with higher per capita personal income. However, there has been encouraging growth in geographies that previously had little to no access. These findings represent positive progress, but there is more to be done to both increase access to optimal headache care by subspecialists nationally and decrease geographic disparities in access to care.

Distribution dynamics: a spatial perspective

ABSTRACT It is quite common in cross-sectional convergence analyses that data exhibit spatial dependence. Within the literature adopting the distribution dynamics approach, authors typically opt for spatial prefiltering. We follow an alternative route and propose a procedure based on an estimate of the mean function of a conditional density for which we develop a two-stage non-parametric estimator that allows for spatial dependence estimated via a spline estimator of the spatial correlation function. The finite sample performance of this estimator is assessed via Monte Carlo simulations. We apply the procedure that incorporates the proposed spatial non-parametric estimator to data on per capita personal income in US states and metropolitan statistical areas.

Spatial and Non-Spatial Panel Data Estimators: Simulation Study and Application to Personal Income in U.S. States

The spatial analysis aims to understand and explore the nature of entanglements and interactions between spatial units’ locations. The analysis of models involving spatial dependence has received great attention in recent decades. Because ignoring the presence of spatial dependence in the data is very likely to lead to biased or inefficient estimates if we use traditional estimation methods. Therefore, this paper is an attempt to assess the risks involved in ignoring the spatial dependence that characterizes the panel data by using a Monte Carlo simulation (MCS) study for two of the most common spatial panel data (SPD) models; Spatial lag model (SLM) and spatial error model (SEM), by comparing the performance of two estimators; i.e., spatial maximum likelihood estimator (MLE) and non-spatial ordinary least squares (OLS) within-group estimator, across two levels of analysis; Parameter-level in terms of bias and root mean square error (RMSE), and model-level in terms of goodness of fit criteria under different scenarios of spatial units N, time-periods T, and spatial dependence parameters, by using two different structures of spatial weights matrix; inverse distance, and inverse exponential distance. The results show that the non-spatial bias and RMSE of β ̂ are functions of the degree of spatial dependence in the data for both models, i.e., SLM and SEM. If the spatial dependence is small, then the choice of the non-spatial estimator may not lead to serious consequences in terms of bias and RMSE of β ̂. On the contrary, the choice of the non-spatial estimator always leads to has disastrous consequences if the spatial dependence is large. On the other hand, we provide a general framework that shows how to define the appropriate model from among several candidate models through application to a dataset of per capita personal income (PCPI) in U.S. states during the period from 2009 to 2019, concerning three main aspects: educational attainment, economy size, and labour force type. The results confirm that PCPI is spatially dependent lagged correlated.

Franchising variation across US states

PurposeThe purpose of this study is to examine whether variation in franchising across US states can be explained by differences in state regulatory burdens.Design/methodology/approachThree years of US state-level panel data is used on measures of franchising activity published by the International Franchise Association. The authors measured variation in regulatory burdens across state governments using the regulatory freedom index, developed by the Cato Institute. Multiple regression analysis was the statistical technique used.FindingsControlling for state-level per capita personal income, educational attainment, unemployment and share of population identifying as non-white, the authors find states with fewer regulatory burdens for business owners have more franchises and franchise jobs per 100,000 residents, higher franchise output per capita and a larger share of small businesses are franchises. These results were robust to alternative econometric specifications. The results support our hypothesis that states with lower regulatory burdens will have more franchising activity.Research limitations/implicationsOnly three years of data are currently available; however, our research provides some practical avenues for managers and policy makers to explore when considering new franchise opportunities or developing policies that impact regulatory burdens for small businesses.Originality/valueThis study contributes to the literature by providing supporting evidence for the relationship between US state institutional factors and franchised small businesses, and it adds a cross-state study to the existing literature using cross-country and cross-city data.

National Trends in Gender-Affirming Surgical Procedures: A Google Trends Analysis.

Background: There has been a significant increase in the volume of gender-affirming surgical (GAS) procedures over the past decade. The objective of this paper is to use online search data from Google Trends (GT) to describe national search trends for GAS procedures.Methods: GT was queried for search terms relating to GAS from January 2004 to February 2021. The 19 selected keywords covered a broad range of GAS topics. United States (US) search interest was collected as relative search volumes (RSVs) and then analyzed by geographic region. The number of plastic surgery providers offering GAS and academic surgery centers was collected from the World Professional Association for Transgender Health (WPATH) and Trans-health.com. RSVs were analyzed by metro area to determine the relationship between search demand and personal income. State Medicaid policies for transgender health services were also collected.Results: All search terms demonstrated a positive increase in RSVs over time except “sex reassignment surgery” and “penectomy”. The Mountain/Pacific and East South Central/West South Central had the greatest search volume for GAS and most providers offering care. The East South Central/West South Central region​​ ranked last for providers offering care, despite the relatively high search interest. This region also had no states with explicit Medicaid policies covering gender-affirming care. Metro areas in the top five for RSV but bottom quartile for per capita personal income were identified.Conclusions: Online search interest for GAS-related terms has increased. Search interest for GAS has regional variation and did not show a specific pattern with provider availability.

Testing for Trend Specifications in Panel Data Models

This article proposes a consistent nonparametric test for common trend specifications in panel data models with fixed effects. The test is general enough to allow for heteroscedasticity, cross-sectional and serial dependence in the error components, has an asymptotically normal distribution under the null hypothesis of correct trend specification, and is consistent against various alternatives that deviate from the null. In addition, the test has an asymptotic unit power against two classes of local alternatives approaching the null at different rates. We also propose a wild bootstrap procedure to better approximate the finite sample null distribution of the test statistic. Simulation results show that the proposed test implemented with bootstrap p-values performs reasonably well in finite samples. Finally, an empirical application to the analysis of the U.S. per capita personal income trend highlights the usefulness of our test in real datasets.

A new approach to the relative convergence test

ABSTRACT This article proposes a new approach to the relative convergence test. The original relative convergence test proposed by Phillips and Sul (2007) recommends discarding the first 1/3 of the time-series data to achieve the best performance in terms of the size and power of the test. My new approach to the relative convergence test does not require discarding any time-series data by assuming the unobservable data are already discarded. In this article, I compare the new approach to the Phillips and Sul (2007)’s test and determine, through a Monte-Carlo simulation, at what fraction the new approach achieves the best performance in terms of size and power of the test. I confirm that the new approach performs better than the original approach when the fraction is set 1/10 of a penalty function. To compare my new method to the Phillips and Sul (2007) method, I provide an empirical example using the U.S. Per Capita Personal Income data and how the new approach affects the convergence club classification.

The Local Economic Impact of Natural Disasters

We use county panel data to study the dynamic responses of local economies after natural disasters in the U.S. Specifically, we estimate disaster impulse response functions for personal income per capita and a broad range of other economic outcomes, using a panel version of the local projections estimator. In contrast to some recent cross-country studies, we find that disasters increase total and per capita personal income over the longer-run (as of 8 years out). The effect is driven initially largely by a temporary employment boost and in the longer run by an increase in average weekly wages. We then assess the heterogeneity of disaster impacts across several dimensions. We find that the longer-run increase in income per capita rises with disaster severity, as measured by monetary damages. Hurricanes, tornados, and fires yield longer run increases in income, while floods do not. The longer run increase in income tends to rise with recent disaster experience and is absent for counties with no recent experience. Finally, a spatial spillover analysis suggests that, while over the short- to medium-run, the regional and local impacts of disasters on personal income are similar, over the longer run the net regional effect may be negative, in contrast to the positive local effect.

Determinants of Per Capita Personal Income in the US: Spatial Fixed Effects Panel Data Modeling

Over the last decades, the Per Capita Personal Income (PCPI) variable was a common measure of the effectiveness of economic development policy. Therefore, this paper is an attempt to investigate the determinants of personal income by using spatial panel data models for 48 U.S. states during the period from 2009 to 2017. We utilize the three following models: spatial autoregressive (SAR) model, Spatial Error (SEM) Model, and Spatial Autoregressive Combined (SAC) model, with individual (or spatial) fixe deffects according to three different known methods for constructing spatial weights matrices: binary contiguity, inverse distance, and Gaussian transformation spatial weights matrix. Additionally, we pay attention for direct and indirect effects estimates of the explanatory variables for SAR, SEM, and SAC models. The second objective of this paper is to show how to select the appropriate model to fit our data. The results indicate that the three used spatial weights matrices provide the same result based on goodness of fit criteria, and the SAC model is the most appropriate model among the models presented. However, the SAC model with spatial weights matrix based on inverse distance is better compared to other used models. Also, the results indicate that percentage of individuals with graduate or professional degree, real Gross Domestic Product (GDP) per capita,and number of nonfarm jobs have a positive impact on the PCPI, while the percentage of individuals without degree or bachelor’s degree have a negative impact on the PCPI.

Proportions of macronutrients, including specific dietary fats, in prospective anti-Alzheimer\u2019s diet

Here we present a novel life-long whole-population study, which enabled us to predict a diet that, in terms of macronutrient proportions, may be prophylactic against Alzheimer’s Disease (AD). The method is based on the existence of oscillations in the correlation between historical per capita personal income (PCPI) and age-adjusted death rates (AADR) for AD for each state of the USA in 2005. These oscillations can be explained by changing proportions of macronutrients in the average American diet between 1929 and 2005. We assumed that reducing future correlation of PCPI with AADR will reduce the population’s susceptibility to AD. Based on the results of fitting macronutrient availabilities to the variability of Roriginal, using Generalized Additive Models (GAM) analysis, we constructed four “Calculator” equations. The Calculator allowed for prediction of an optimal diet characterized by low correlation of PCPI with AADR (Rpredicted) and minimum energy difference from the historical average macronutrient consumption for each corresponding period of life. We predict that protein consumption should be reduced by half in early middle age and late middle age, whereas in late age it should increase. Our predictions are in line with results on humans and simpler organisms in the context of prolonging life.

Do Targeted Business Subsidies Improve Income and Reduce Poverty? A Synthetic Control Approach

Interstate competition for economic development has led many states to adopt targeted economic development incentive programs known as deal-closing funds. Deal-closing funds allow state officials to provide discretionary cash grants to select businesses to attract and retain economic development projects. However, whether these targeted business subsidies increase prosperity in the local economy remains unclear. The authors use evidence from Arkansas’s Quick Action Closing Fund to analyze how effective deal-closing funds are at increasing incomes and decreasing poverty. Specifically, the causal effects of the Quick Action Closing Fund on Arkansas’s county-level per capita personal income and poverty rates are estimated using a synthetic control approach. The results largely suggest that the business subsidy program fails to increase incomes and lower poverty rates over the long term, at least at the county level. These findings should serve as a caution to policy makers who wish to improve incomes and poverty rates with targeted business subsidies.

Spatiotemporal downscaling of global population and income scenarios for the United States.

Downscaled climate projections need to be linked to downscaled projections of population and economic growth to fully develop implications for land, natural resources, and ecosystems for future scenarios. We develop an empirical spatiotemporal approach for jointly projecting population and income at the county scale in the United States that is consistent with neoclassical economic growth theory and overlapping labor markets and that accounts for labor migration and spatial spillovers. Downscaled projections generated for the five Shared Socioeconomic Pathways used to support global scenario analysis generally show growth focused around relatively few centers especially in the southeast and western regions, with some areas in the Midwest and northeast experiencing population declines. Results are consistent with economic growth theory and with historical trends in population change and convergence of per capita personal income across US counties.

Is real per capita state personal income stationary? New nonlinear, asymmetric panel‐data evidence

AbstractThis paper re‐examines the stochastic properties of U.S. state real per capita personal income, using new panel unit‐root procedures. The new developments incorporate non‐linearity, asymmetry, and cross‐sectional correlation within panel‐data estimation. Including nonlinearity and asymmetry finds that 43 states exhibit stationary real per capita personal income whereas including only nonlinearity produces 42 states that exhibit stationarity. Stated differently, we find that two states exhibit nonstationary real per capita personal income when considering nonlinearity, asymmetry, and cross‐sectional dependence.