This report is one of the important ways that the Center for Business and Economic Research fulfills its mission to examine various aspects of Kentucky’s economy as directed by the Kentucky Revised Statutes (KRS 164.738). The analysis and data presented here cover a variety of topics that range from a discussion of Kentucky’s current economic climate to a broad presentation of factors affecting the economy. The report covers numerous dimensions of Kentucky’s economy including the effects of COVID-19. As the pandemic approaches its third year, COVID-19 continues to dominate the economic narrative. Many aspects of the economy have improved substantially since the early months of the pandemic. Consumer demand is strong. Where possible, businesses have expanded payrolls and ramped up production to meet this demand. As of October 2021, Kentucky had recovered 84 percent of the jobs lost during the initial months of the pandemic. The total value of goods and services produced in Kentucky reached pre-pandemic levels in 2021.

The average workforce education near firms’ research centers facilitates firms’ matching with innovation talents and acquisition of knowledge. This study documents a positive association between the average education level in the metropolitan statistical areas (MSAs) where firms’ research centers are located and the quantity and quality of innovation outputs. The results are confirmed by controlling for various measures of MSA-level economic, population, and employment conditions, as well as research-center level analyses which control for firm-year fixed effects. We further find that local workforce education is more important for firms that are large, less labor-intensive, in non-high-tech industries and located in low education regions. The evidence highlights the importance of having access to well-educated local workforce for corporate innovation.

For individuals in low-wage labour markets, an increase in the minimum wage can theoretically pull them into or push them out of the labour force. If increases raise expected wages beyond reservation wages, marginal individuals could enter the labour force and begin searching for employment. If increases lower expected wages, marginal individuals already in the labour force could exit. Leveraging revised individual identifiers in the U.S. Current Population Survey, this research estimates the contemporaneous effects of minimum wage increases on labour force participation. The use of within-person variation, short individual panels, and flexible controls for time create an empirical strategy that mitigates potential biases from unobserved constant individual-level heterogeneity and time-varying factors. This research finds that minimum wage changes tend to impact the youngest individuals, but there is substantial heterogeneity in responses by age, race/ethnicity, and sex. There is stronger evidence of pull effects amongst young white men and Latinos, and weaker evidence amongst young Black women and older Latinas. Weak evidence of push effects is observed amongst younger white women, younger Latinos, and older Latinas. This research highlights heterogeneous labour force participation responses to further inform our understanding of search behavior and labour market churn.

Disparities in health insurance coverage and access to care are well documented between sexual minority adults and their heterosexual peers. Much less research has examined whether marital status affects access to care by sexual orientation identity. This study used data on nonelderly gay/lesbian adults (n = 1740), bisexual adults (n = 1138), and their heterosexual counterparts (n = 80,329) from the 2015–2018 National Health Interview Survey. Multivariable logistic regression models estimated the differences in health insurance status and access to care between married and cohabiting adults to single adults by sexual orientation. Compared to single adults, married adults were more likely to have private health insurance and less likely to have public health insurance regardless of sexual orientation. Married heterosexual and gay/lesbian adults were more likely to have a usual source of care and less likely to experience delayed or unmet medical care and mental health care due to cost compared to their single counterparts. We found fewer statistically significant differences between married and single bisexual adults and between married and cohabiting adults of all sexual orientations. Our study demonstrates that married gay, lesbian, and bisexual adults are more likely to have private health insurance and fewer financial barriers to care compared to their single counterparts. Given the recent recognition of legal same-sex marriage for sexual minorities, much more research is needed to document if and how marriage affects access to care and health outcomes by sexual orientation.

Objective To standardize the implementation dates of various cannabis liberalization policies and determine whether previous research by Anderson et al. [D.M. Anderson, D.I. Rees, J.J. Sabia, American Journal of Public Health 104, 2369-2376] on medical marijuana access and population-level suicidality is robust to additional years of data and further cannabis liberalization in the form of recreational marijuana access. Design A state-level longitudinal (panel) analysis. Suicide mortality rates from the National Center for Health Statistics and mental health morbidity rates from the National Survey on Drug Use and Health were employed with the procedures outlined by Anderson et al., using weighted ordinary least squares for three different specifications with various combinations of control variables as a sensitivity analysis to test for robustness.

Many commentators have suggested that Donald Trump's 2016 election emboldened discrimination against racial minorities. We focus on changes in weekly work hours among hourly paid employees during the five months following the 2016 election (relative to 12 months prior). Using two-wave panel data from the Current Population Survey, we find that black workers suffered temporary work hours and earnings losses relative to white workers in areas where Trump received greater electoral support. There were no within-person declines among non-Hispanic whites in high-Trump-support areas or among any groups in lower-Trump-support areas. These patterns are not driven by seasonality, industrial composition, or pre-election trends, suggesting that Trump's victory exacerbated racial disparities where he received strong electoral support. The findings reveal how political events can catalyze surges of discriminatory behavior in labor markets over the short to medium term, and they provide new evidence about the effects of Trump's early presidency on U.S. race relations.

The association between multi-dimensional deprivation and public health is well established, and many area-based indices have been developed to measure or account for socioeconomic status in health surveillance. The Yost Index, developed in 2001, has been adopted in the US for cancer surveillance and is based on the combination of two heavily weighted (household income, poverty) and five lightly weighted (rent, home value, employment, education and working class) indicator variables. Our objectives were to 1) update indicators and find a more parsimonious version of the Yost Index by examining potential models that included indicators with more balanced weights/influence and reduced redundancy and 2) test the statistical consistency of the factor upon which the Yost Index is based. Despite the usefulness of the Yost Index, a one-factor structure including all seven Yost indicator variables is not statistically reliable and should be replaced with a three-factor model to include the true variability of all seven indicator variables. To find a one-dimensional alternative, we conducted maximum likelihood exploratory factor analysis on a subset of all possible combinations of fourteen indicator variables to find well-fitted one-dimensional factor models and completed confirmatory factor analysis on the resulting models. One indicator combination (poverty, education, employment, public assistance) emerged as the most stable unidimensional model. This model is more robust to extremes in local cost of living conditions, is comprised of ACS variables that rarely require imputation by the end-user and is a more parsimonious solution than the Yost index with a true one-factor structure.

Spatial income disparities have increased in the United States since 1980. Growth in this form of inequality is linked to major social, economic and political challenges. Yet, contemporary patterns, and how they relate to those of the past, remain insufficiently well understood. Building on population survey microdata spanning 1940-2019, this paper uses group-based trajectory modelling techniques to identify distinct sets of local labor markets based on the evolution of their income levels. We find that the increase in spatial inequality since 1980 is almost entirely driven by a small number of populous, economically-important, and resiliently high-performing ‘superstar’ city-regions. Meanwhile, since 1940, much of the rest of the urban system has continued to converge toward the mean. We examine the demographic, economic and social characteristics of these different trajectories, identifying catch-up regions, declining regions, long-term winners, and possible future superstars. There is considerable turbulence within the convergence process, consisting of regions that are moving both upward and downward in the system. We conclude by exploring implications for the American urban-regional system in the mid-21st century, considering the challenges in overcoming the growing split between superstar locations and the rest of the country.

Environmental justice and health research demonstrate unequal exposure to environmental hazards at the neighborhood-level. We use an innovative method—eco-intersectional multilevel (EIM) modeling—to assess intersectional inequalities in industrial air toxics exposure across US census tracts in 2014. Results reveal stark inequalities in exposure across analytic strata, with a 45-fold difference in average exposure between most and least exposed. Low SES, multiply marginalized (high % Black, high % female-headed households) urban communities experienced highest risk. These inequalities were not described by additive effects alone, necessitating the use of interaction terms. We advance a critical intersectional approach to evaluating environmental injustices.

Background: Diabetes and hypertension are common in Asian Americans and vary by subgroup. There may be further variation by social determinants of health (SDOHs), but few studies have examined this previously. Objective: To examine the associations of SDOHs and diabetes and hypertension within and across Asian subgroups in the USA Design: Series cross-sectional analyses Setting: National Health Interview Surveys (NHIS) from 1999 to 2018 Participants: Asian-American adults (Chinese, Filipino, Asian Indian, and Other Asian [Korean, Vietnamese, Japanese, and other]) Measurements: Self-reported diabetes and hypertension prevalence in pooled 5-year increments over 1999–2018 and multivariable regression models to assess the adjusted prevalence of diabetes or hypertension by poverty, marital status, education, and years in the USA, adjusting for age, sex, BMI, and health insurance status Results: From 1999–2003 to 2014–2018, the age- and sex-adjusted prevalence of diabetes increased for Other Asians (absolute change: 4.6%) but not for other subgroups; age- and sex-adjusted hypertension prevalence significantly increased for Asian Indians and Other Asians (absolute change: 5–7.5%). For Filipinos, high school education or less was associated with an increase in diabetes prevalence over time (difference from 1999–2003 to 2014–2018: +6.0 (95% CI: 2.0–10.0)), while for Asian Indians, college education or higher was associated with an increase in diabetes prevalence for the same period (difference: +2.7 (95% CI: 0.01–5.4). Differences over the 2 time periods (1999–2003 and 2014–2018) show that Filipino and Other Asians, who lived in the USA for ≥10 years, increased in diabetes prevalence. Similar variations in associations of SDOHs by Asian subgroup were seen for hypertension. Limitations: Self-reported primary outcomes and multi-year data were pooled due to small sample sizes. Conclusions: The influence of SDOHs on cardiometabolic risk is not uniform among Asian Americans, implying tailored strategies may be needed for different population subgroups. Primary Funding Source: NIH.

Provisional U.S. national COVID-19 mortality data for the year 2020 analyzed by the CDC in March 2021 indicated that non-Hispanic Asians fared markedly better overall than other racial/ethnic minority groups–and marginally better than non-Hispanic Whites–in terms of age-adjusted mortality rates. However, Asians in the United States are composed of diverse array of origin subgroups with highly varying social, economic, and environmental experiences, which influence health outcomes. As such, lumping all Asians together into a single category can mask meaningful health disparities among more vulnerable Asian subgroups. To date, there has not been a national-level analysis of COVID-19 mortality outcomes between Asian subgroups. Utilizing final multiple cause of death data for 2020 and population projections from the U.S. Census Bureau’s Current Population Survey Annual Social and Economic Supplement for 2020, crude and age-adjusted national COVID-19 mortality rates, both overall and stratified by sex, were calculated for the six major single-race Asian origin subgroups (Asian Indian, Chinese, Filipino, Japanese, Korean, and Vietnamese) and a catch-all seventh category that comprises the remaining Asian subgroups (Other Asians), contrasting them to the corresponding mortality rates of other racial/ethnic groups. A substantially more nuanced picture emerges when disaggregating Asians into its diverse origin subgroups and stratifying by sex, with Filipino males and Asian males outside of the six major Asian subgroups in particular experiencing markedly higher age-adjusted mortality rates than their White male counterparts, whether comparisons were restricted to their non-Hispanic subsets or not. During the COVID-19 pandemic and in the post-pandemic recovery, it is imperative not to overlook the health needs of vulnerable Asian populations. Public health strategies to mitigate the effects of COVID-19 must avoid viewing Asians as a monolithic entity and recognize the heterogeneous risk profiles within the U.S. Asian population.

We provide new evidence on the effect of education on later life health. Using variation in state compulsory schooling laws, we examine education's effect on a range of outcomes encompassing physical health, decision-making, and life expectancy. We employ under-utilized Health and Retirement Study data linked to restricted geographic identifiers, allowing us to match individuals more accurately to compulsory schooling laws. While positively related to educational attainment, compulsory schooling laws have no significant effect on later life health outcomes. Our results suggest that increased educational attainment has no significant causal effect on health.

Stable housing is critical for health, employment, education, and other social outcomes. Evictions reflect housing instability that is experienced by millions of Americans each year. Psychiatric disorders are proximate determinants of evictions. We estimate the effect of local access to psychiatric treatment on evictions. We combine data on the number of psychiatric treatment centers that offer outpatient and residential care within a county with eviction outcomes in a two-way fixed-effects framework. We find that ten additional psychiatric treatment centers in a county leads to a reduction of 2.1% in the eviction rate.

The discourse of economic development through culture and its applications have increasingly received more attention in geographic academia. However, there has been little insight into how the breakthroughs and paradigms of more successful experiments could be sensitively and carefully used for the benefit of less experienced areas. This paper presents an attempt to rectify this, by proposing the use of tangible data from the United States, a country with extensive experience in cultural development and governance. It presents a piece of that research, in the form of a trial methodology for assessing significant clusters of cultural development and identifying their causes. After briefly overviewing the development of the theory of cultural development and defining some basic terms-artists and their definition for quantitative research, (creative) clusters, and creative cities-, a methodology will be proposed and showcased. It will depend on exploratory spatial analysis and the concept of the "artistic dividend", a method of more directly measuring artists' contributions to their local economies by counting their numbers and aggregating their income. Data will be taken from the American Community Survey for its thematic and spatial detail, with visual artists being used as an example category. The decennial evolution of clusters will also be inspected and displayed. Finally, the methodology's further applications, possible evolutions (use of further literature review and regression methods for discovering factors), and distilled focus (improvements by qualitative methods) will be assessed, for its implementation in the final thesis. Highlights:-An empirical definition of the artist for quantitative research-Spatially-applied artistic dividend examined via exploratory spatial data analysis-Use of American Community Survey data-Discovery of hierarchy in centers of cultural development in the US-Discovery of good spatial unit for exploratory data analysis (PUMA, region-level)

Throughout the history of the United States, racialized forms of violence and social control have been used to enforce racial boundaries and the racial hierarchy of the nation. My dissertation offers a historically guided study of racial violence, resistance, and social control since the late 19th Century. I provide a detailed discussion of historical racial violence and how past violence has been embedded in more recent racially disparate forms of violence and formal criminal justice practices. Specifically, I examine two historical periods—the era of lynching and the era of resistance to racial injustices that was prominent through the 1960s and early 1970s— and consider how these periods may be related, as well as their potential legacy effects on more contemporary issues around policing (i.e., the use of lethal force by police measured through fatal shootings of Black Americans). We must grapple with forces of the past and attempt to understand how they may have transformed over time and may continue to influence contemporary social phenomena (e.g., criminal justice practices). The field of criminology has largely failed to acknowledge how the past may continue to influence contemporary issues. Thus, my dissertation offers a further step in understanding historical racial violence and resistance while addressing how this history can help us to understand contemporary forms of violence and social control.

Integrating ecosystem services (ESs), i.e., the benefits humans obtain from nature, into decision making is essential for the sustainable management of resilient urban landscapes, where people and nature are interdependent. However, ES spatial assessments often leave unanswered important questions about their relationship to social systems and their implications in management. In this study, we identified ES provisioning areas in the Chicago metropolitan region that have potential to produce ESs across multiple ES-categories (i.e., ES hotspots). We also identified areas lacking this capacity (i.e., ES coldspots). We then analyzed spatial linkages to local demographics. Specifically, we: (1) modelled the distribution of ES hotspots and ES coldspots at 30-m resolution; (2) compared the proportion of total ES-hotspot and ES-coldspot land area to that of racial groups and residents living below the poverty line; and (3) determined overlap of ES hotspots and ES coldspots with conservation, managed and public open land. We found that, at regional level, ES-hotspots correlate negatively with racial minorities. We also observed that a great number of ES hotspots overlap with conserved and government-managed land, but because these land types inversely correlate with racial minorities it is likely that these cannot serve to alleviate race-based ES inequality if targeted. Therefore, we made a number of suggestions on how to circumvent this issue, including acquiring land (for conservation) in areas where racial minorities live the most, particularly targeting vacant lots and grassland within the City of Chicago to address ES inequity in the region.

This article highlights the role of land and housing submarkets in the construction of aggregate house price indexes and in the estimated value of the housing stock. We document idiosyncratic house price appreciation and sales volumes across submarkets within metropolitan areas that result in a sample of sold homes that are representative neither of the housing stock nor its appreciation over time. Commonly used aggregate price indexes, like the Federal Housing Finance Agency (FHFA) and Case-Shiller indexes, fail to capture these local dynamics and produce indexes and estimates of the value of the stock that are biased. Our approach is to build “locally-pooled” indexes and then weight these local price indexes by their submarket's share of the housing stock to estimate our metropolitan-level index. This allows for more accurate movements in urban land prices, which is especially important in higher cost land markets. We show traditional globally pooled indexes exhibit significant bias. This may complicate research that makes use of the traditional house price indexes.

In the wake of the Affordable Care Act, some US states expanded Medicaid eligibility to low-income, working-age adults while others did not. This study investigates whether this divergence induces migration across state borders to obtain Medicaid, especially in border regions of expansion states. It compares border with interior regions’ in-migration in the concerned subgroup before and after the Medicaid expansion in linear probability difference-in-difference and triple difference regression frameworks. Using individual-level data from the American Community Surveys over 2012–2017, this study finds only a statistically significant increase in in-migration to border regions after the expansion in Arkansas. The differing results across states could stem from statistical power issues of the employed regression analysis but might also result from state peculiarities. In Arkansas, the odds of having migrated increase by about 48% in its border regions after the Medicaid expansion compared to before and control regions. If all additional migrants take up Medicaid, the number of Medicaid beneficiaries in these regions increases by approximately 4%. Thus, even if the induced migration is statistically significant, it appears unlikely to impose meaningful fiscal externalities at the regional level.

We provide new and computationally attractive methods, based on jackknifing by cluster, to obtain cluster-robust variance matrix estimators (CRVEs) for linear regression models estimated by least squares. These estimators have previously been com-putationally infeasible except for small samples. We also propose several new variants of the wild cluster bootstrap, which involve the new CRVEs, jackknife-based bootstrap data-generating processes, or both. Extensive simulation experiments suggest that the new methods can provide much more reliable inferences than existing ones in cases where the latter are not trustworthy, such as when the number of clusters is small and/or cluster sizes vary substantially.

We leverage variation in the adoption of coeducation by U.S. women’s colleges to study how exposure to a mixed-gender collegiate environment affects women’s human capital investments. Our event-study analyses of newly collected historical data find a 3.0-3.5 percentage-point (30-33%) decline in the share of women majoring in STEM. While coeducation caused a large influx of male peers and modest increase in male faculty, we find no evidence that it altered the composition of the female student body or other gender-neutral inputs. Extrapolation of our main estimate suggests that coeducational environments explain 36% of the current gender gap in STEM.