County Employment Research Articles

Treatment of substance use disorders in adolescence and early school leaving.

To examine early school leaving in a longitudinal cohort of all high school students treated for substance use disorder (SUD) and their demographic counterparts in Norway. From the National Patient and National Population Registries, we extracted (a) all high school students born in 1991-1992 who received SUD treatment during 2009-2010 (N = 648; nalcohol = 95, ncannabis = 327, and nother drugs = 226) and (b) their age-and-gender matched counterparts (n = 647). From the National Educational Database, we obtained enrollment and graduation status for these two cohorts throughout the designated school period of 5 years. We estimated the hazards of early school leaving as a function of students' treatment for alcohol, cannabis, and other drug use disorders and other known risk factors. Nine out of 10 adolescents receiving SUD treatment left high school early (89%) compared with one in four (27%) from the matched cohort; 422 (73.5%) of these left high school during or after the treatment year. Multivariate discrete-time models revealed significant and ordered associations between receiving SUD treatment and early school leaving, HRalcohol = 3.09 [1.96, 4.89], HRcannabis = 3.83 [2.64, 5.56], HRother drugs = 5.16 [3.32, 8.03], even after accounting for individual-level (sex, immigrant background, criminal charges, and mental health treatment), family-level (family structure, parental education, and family income), and structural risk factors (municipal size, county employment, and dropout rates). Adolescents receiving SUD treatment remain especially vulnerable for early school leaving. These findings underscore the importance of improving and coordinating health and educational services for youth in SUD treatment. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Local Financial Structure and the Pandemic’s Effect on the Distribution of Employment

Abstract Does the structure of financial systems influence the employment respo0nse to adverse shocks? Using (a) county-industry employment data differentiated by firm size and (b) high-frequency county employment data differentiated by income, we find that employment at small firms, employment of low-income workers, and overall employment fell less in counties with higher proportions of small banks in response to the pandemic. Furthermore, small banks lend more to small businesses than large banks, above and beyond government-guaranteed PPP loans. Evidence suggests that small banks cushioned small firms and low-income workers from the adverse effects of the pandemic. (JEL G21, E24, E32, J21, H12) Received: 15 April 2021; Editorial decision: 13 December 2023 Editor: Andrew Ellul Authors have furnished an Internet Appendix,which is available on the Oxford University Press Web site next to the link to the final published paper online.

Long-run effects on county employment rates of demand shocks to county and commuting zone employment

This paper estimates the long-run effects on a county's prime-age employment rate of labor demand shocks to both the county and its overlying commuting zone (CZ). These effects are allowed to vary with local “distress” (a low baseline employment rate of the county or CZ), and with the size of the demand shock. In more distressed CZs, a county's employment rate is more affected by county or CZ shocks. As a result, targeting or reallocating jobs to more distressed CZs will tend to raise employment rates. If a county is relatively distressed compared to its CZ, targeting job shocks at that county has greater effects on county employment rates. Reallocating CZ jobs or job shocks towards more distressed counties within a CZ results in greater effects on the CZ's average employment rate. In addition, a CZ shock's effects on a county's employment rate tend to be higher if the CZ's baseline demand-driven expected growth trend is below average. This is particularly true in CZs whose baseline distress was average or low.

Forest-Based Employment in the Southern United States amidst the COVID-19 Pandemic: A Causal Inference Analysis

Abstract In response to the COVID-19 pandemic, some governments instituted mandatory stay-at-home policies. As these policies made exceptions for essential industries such as the forest sector, it is not clear a priori whether and how these policies would affect forest-based employment. This study examined the effect of mandatory stay-at-home orders on employment in the forest sector in forest dependent counties in eleven southern states in the United States. We estimated panel event study models in addition to difference-in-difference models to evaluate the policy effects with respect to initial treatment as well as average effects to the treated counties during treatment. We found that employment in wood product manufacturing was lower on average in counties under a lockdown order compared with employment in counties without an active order. We also found that employment in the aggregate forest sector in counties that implemented the policy was significantly lower than employment in counties without the policy up to 9 months following initial enactment compared with the underlying difference between treated and control counties one month prior to treatment. Overall, our findings suggest that lockdown policies had a limited effect on employment in essential industries such as wood-based manufacturing sectors in the southern states. Study Implications: This study uses causal inference techniques to assess the effect of lockdown orders on forest-based employment in 11 southern states. Results suggest effects of lockdown orders were limited to the aggregate forest sector and wood product manufacturing, likely due to the rapid designation of the sector as essential and the reduced production of sawmills. The other industries within the sector were not significantly affected, implying that industries designated as essential may be more resilient to short-term shocks. These findings can be used to inform discussions on potential policy responses to future disasters, including how different interventions interact with one another.

Inequality and Social Distancing during the Pandemic

We study how pre-pandemic inequalities in the United States influenced social distancing over the course of the COVID-19 pandemic. Richer counties tended to see more protective mobility responses in the initial (pre-pharmaceutical) phase, but less protective responses later. Near linearity of this income effect implies that inequality between counties contributed very little to overall mobility reductions. By contrast, higher within-county inequality and/or poverty came with substantially larger attenuations to non-residential mobility at given average incomes. There were also significant effects of the county’s racial and age composition. County employment composition is found to be additionally important, with reductions in mobility in counties with a higher share of professional workers.

THE DYNAMICS OF UNEMPLOYMENT DURING THE PANDEMIC. CASE STUDY IN BIHOR COUNTY

The first part of the article presents a short theoretical approach of unemployment, and the more consistent part is based on the statistical analysis of unemployment at the level of Bihor County. The analyzed period is represented by the years 2019-2021. For the current analysis, we have chosen the problem of unemployment during the pandemic, from a theoretical but also concretely applied perspective, since unemployment represents a complex psychosocial and economic phenomenon, and its dimensions and dynamics highlight the state of a society, but also the concerns for the implementation of active policies, which aim at increasing the employment rate, but also the quality of life. This paper analyzes the dynamics of unemployment in the period 2019-2021, but also the job protection measures implemented by the public employment system. Thus, the objectives of the research consist in the analysis of the unemployment indicators in Bihor County in the pre-pandemic period and in the pandemic period and of the special measures adopted in order to solve the problems generated by the health crisis. The used research method was the analysis of documents; we have analyzed the Activity Reports of Bihor County Agency for Employment (AJOFM Bihor) for the years 2020 and 2021 and the legal regulations adopted in order to protect employees and employers during this period. The analysis of statistical data revealed that the maximum number of unemployed people in the records of Bihor County Agency for Employment (AJOFM Bihor) was recorded in March 2021 (6,280 people), and the unemployment rate on this date was 2.36%. Two measures adopted by the government and implemented by the National Employment Agency through the County Employment Agencies had a major impact in maintaining jobs during the pandemic: technical unemployment and reduced working hours. Thus, in 2021 compared to 2020, the number of employees who benefited from technical unemployment decreased by 5,000 people.

Oil and gas boomtowns and occupations: What types of jobs are created?

Innovation in hydraulic fracturing methods and micro-seismic technology, along with higher energy prices until 2014, led to oil and gas booms in various U.S. shale plays. While this appears to be a positive event for the country, it is unclear whether and how local communities benefit in the long-term from unconventional gas development. This paper evaluates the impact of oil and gas development on occupation-specific job growth in resource-intensive economies using a unique dataset with annual county employment at the 2- and 5-digit 2010 Standard Occupation Classification (SOC) code combined with education, experience, and training requirements from the O*NET database. Using a first-difference methodology, we find that oil and gas booms have a significant positive impact on occupational job growth in derrick, rotary drill, and service unit operators, truck drivers, secretaries, and engineers, with human capital requirements ranging from less than a high school diploma to at least a bachelor's degree. While occupations associated with oil and gas development and their human capital requirements vary, the results suggest that many of these occupations require workers to have vocational and technical training in addition to a high school diploma, thereby indicating greater demand for workers with intermediate skills in affected areas. Changes in human capital composition resulting from oil and gas development could have significant impacts on a region's ability to respond to energy busts and affect its economic resilience.

Bayesian Dependent Functional Mixture Estimation for Area and Time-Indexed Data: An Application for the Prediction of Monthly County Employment

The U.S. Bureau of Labor Statistics (BLS) publishes employment totals for all U.S. counties on a monthly basis. BLS use the Quarterly Census of Employment and Wages, where responses are received on a 6–7 month lagged basis and aggregated to county, and apply a time series forecast model to each county and project forward to the current month, which ignores the dependence among counties. Our approach treats these by-county employment time series as a collection of area indexed noisy functions that we co-model. Our model includes predictor, trend and seasonality terms indexed by county. This application is among the first in the U.S. Federal Statistical System to address the joint modeling of a collection of time series expressing heterogenous seasonality patterns between them. We demonstrate that use of a Fourier basis to model seasonality outperforms a locally-adaptive, intrinsic conditional autoregressive construction on our collection of time series where the degree of expressed seasonality varies. County-indexed parameters of the 3 terms are drawn from a dependent Dirichlet process (DDP) prior to allow the borrowing of information. We show that employment of both spatial and industry concentration predictors into the prior probabilities for co-clustering among the counties produces better prediction accuracy. Our DDP prior accounts for the possibility that nearby counties may express distinct underlying economic structures. A feature of our joint modeling framework is that it computes efficiently to support the monthly BLS production cycle. We compare the performances of alternative formulations for the dependent Dirichlet process prior on monthly, county employment data from 2002–2016.

The Impact of Foreign-Trade Zones on County Level Development: A Temporal Analysis

In 1934, the Foreign-Trade Zone Act was passed in order to ease restrictions on internationally traded goods, provide domestic firms with a competitive advantage over foreign companies, boost jobs in import/export businesses, increase exports, and promote economic development. The growth in the number of foreign-trade zones (FTZs) across the United States since 1934 has resulted in the ever-expanding reach of the zone program across the landscape. Even with the growth of FTZs, these zones have remained largely obscure to academia and the public at-large. While various scholars have analyzed the impact of FTZs on exports and trade volumes, few have focused on spatial and temporal impacts on neighboring communities. This study analyzed short- and long-term percentage changes in median household income, unemployment rates, number of manufacturing firms, and manufacturing employment after FTZs were added to a county. Results indicate a significant long-term increase in manufacturing employment in counties adding an FTZ but a significant increase in unemployment rates and decline in manufacturing firms in neighboring counties. These findings suggest FTZs may provide manufacturing employment benefits for a county in the long-term but costs on overall employment and number of firms in neighboring counties may outweigh these benefits.

The Racialized Consequences of Jail Incarceration on Local Labor Markets

As a racialized labor market institution, the criminal justice system shapes racial patterns in local labor markets through processes of exclusion and marginalization. How do local county jails contribute to these dynamics? To examine that question, the relationship between county-level jail and employment rates is examined across the U.S. between 2007 and 2017. The study uses a System Generalized Method of Moments (GMM) dynamic panel model approach that structurally controls for simultaneous determination and other sources of endogeneity. A racial stratification analysis identifies a negative relationship between jail and employment in the urban counties with the highest percentage of Black residents aged 15 to 64, whereas areas with the lowest percentage of Black residents have a positive relationship between jail and employment. These racially differential spillover effects suggest that the impact of jail incarceration on employment is significantly racialized at this level of analysis.

The Racialized Consequences of Jail Incarceration on Local Labor Markets

As a racialized labor market institution, the criminal justice system shapes racial patterns in local labor markets through processes of exclusion and marginalization. How do local county jails contribute to these dynamics? To examine that question, the relationship between county-level jail and employment rates is examined across the U.S. between 2007 and 2017. The study uses a System Generalized Method of Moments (GMM) dynamic panel model approach that structurally controls for simultaneous determination and other sources of endogeneity. A racial stratification analysis identifies a negative relationship between jail and employment in the urban counties with the highest percentage of Black residents aged 15 to 64, whereas areas with the lowest percentage of Black residents have a positive relationship between jail and employment. These racially differential spillover effects suggest that the impact of jail incarceration on employment is significantly racialized at this level of analysis.

Spatial Dimensions of Regional Innovativeness in Romania

Abstract This paper contains the analysis of regional innovation performance in the NUTS 2 regions of Romania, based on the European Innovation Scoreboard evaluation for 2011 and 2019, and the identification of the most significant influencing factors of county-level R&D activity, for the 1997–2018 period. The Regional Innovation Index (RII) provides a profound characterization for Romanian regions regarding their innovation performance, which was studied using the GIS (Geographic Information System) methodology in relation with the regional R&D activity, GDP per capita, and entrepreneurship innovation. In the following, the Romanian R&D activity is analysed at the county level. The influencing factors of R&D expenditures and employment in Romanian counties were studied based on two panel regression models and using exogenous variables, for economic development, entrepreneurship, education, and infrastructure.

The Contribution of the Arts and Creative Economy to Social Capital: Evidence from the COVID-19 Pandemic

While there is a large literature about the importance of social capital and its impact on economic and social flourishing, much less is known about the relevance of the arts and creative communities as inputs to social capital. Using cross-sectional variation in the exposure of different counties to the arts and creative communities prior to the pandemic, we quantify how their employment declines are associated with social capital. We find that a 1 percentage point decline in arts employment growth between February and May 2020 is associated with a 0.16 standard deviation decline in social capital. We find even stronger effects when we focus on the creative economy at large, concentrated in the association with collective efficacy and shared norms within a community. Our estimates are robust to the inclusion of median household income, overall county employment growth, and state fixed effects. These results suggest that there is an open window for state and local policy interventions that promote economic development and social capital through the arts and creative communities.

Association of Individual and Community Factors With Hepatitis C Infections Among Pregnant People and Newborns

The opioid crisis has increasingly affected pregnant people and infants. Hepatitis C virus (HCV) infections, a known complication of opioid use, grew in parallel with opioid-related complications; however, the literature informing individual and community risks associated with maternal HCV infection is sparse. To determine (1) individual and county-level factors associated with HCV among pregnant people and their newborn infants, and (2) how county-level factors influence individual risk among the highest risk individuals. This time-series analysis of retrospective, repeated cross-sectional data included pregnant people in all US counties from 2009 to 2019. We constructed mixed-effects logistic regression models to explore the association between HCV infection and individual and county-level covariates. Analyses were conducted between June 2019 and September 2021. Individual-level: race and ethnicity, education, marital status, insurance type; county-level: rurality, employment, density of obstetricians. Hepatitis C virus as indicated on the newborn's birth certificate. Between 2009 and 2019, there were 39 380 122 pregnant people who met inclusion criteria, among whom 138 343 (0.4%) were diagnosed with HCV. People with HCV were more likely to be White (79.9% vs 53.5%), American Indian or Alaska Native (AI/AN) (2.9% vs 0.9%), be without a 4-year degree (93.2% vs 68.6%), and be unmarried (73.7% vs 38.8%). The rate (per 1000 live births) of HCV among pregnant people increased from 1.8 to 5.1. In adjusted analyses, the following factors were associated with higher rates of HCV: individuals identified as White (adjusted odds ratio [aOR], 7.37; 95% CI, 7.20-7.55) and AI/AN (aOR, 7.94; 95% CI, 7.58-8.31) compared with Black individuals, those without a 4-year degree (aOR, 3.19; 95% CI, 3.11-3.28), those with Medicaid vs private insurance (aOR, 3.27; 95% CI, 3.21-3.33), and those who were unmarried (aOR, 2.80; 95% CI, 2.76-2.84); whereas, rural residence, higher rates of employment, and greater density of obstetricians was associated with lower risk of HCV. Among individuals at the highest risk of HCV, higher levels of county employment, accounting for other factors, were associated with less of a rise in HCV infections over time. In this cross-sectional study, maternal and newborn HCV infections increased substantially between 2009 and 2019, disproportionately among White and AI/AN people without a 4-year degree. County-level factors, including higher levels of employment, were associated with lower individual risks of acquiring the virus.

Who benefits more? The heterogeneous impact of highways on employment growth

ABSTRACT This paper explores the relationship between the stock of interstate highways on employment growth of counties in Texas between 1983 and 2012. Using the Chernozhukov-Hansen instrumental variables quantile regression (IVQR) method, we examine the heterogeneous impact that highways have on employment growth at different quantiles of the conditional distribution while at the same time controlling for potential endogeneity. The results show that the employment growth effect monotonically increases as one shifts from the lower tail to the upper tail of the distribution. The range is 0.15 to 0.44, with the highest at the 95th quantile, compared to 0.189 for OLS and 0.213 for 2SLS. A 10 percent increase in interstate highway kilometers in 1983 led to about a 1.5 to 4.4 percentage point increase in county employment over a 29-year period. Our results also indicate that the counties with low initial levels of employment grew faster than those with a high initial level of employment and that this convergence monotonically decreases from the lower tail to the upper tail of the growth distribution.

The Urban–Rural Divide: The Effects of the Small Business Innovation Research Program in Small and Nonmetro Counties

This study assesses the efficacy of the Small Business Innovation Research Program on innovation, employment, and business formation in small and nonmetropolitan counties. The primary goal is to further understand rural innovation and growth process through government intervention on small business development. Using panel data and employing fixed-effect models, the results suggest that, compared with large metropolitan counties, the Small Business Innovation Research Program may not be an effective program to generate employment and entrepreneurship opportunities in small and nonmetropolitan regions, but it could be a viable source to improve rural innovation outcome. The geographical effects of the program are heterogeneous across implementation years and award agencies. This study contributes to the innovation policy agenda in small and nonmetropolitan regions, which is an important and emerging topic in the field.

Job creation or job relocation? Identifying the impact of China's special economic zones on local employment and industrial agglomeration

Since the early 1980s, special economic zones (SEZs) in China have benefited from targeted place-based policies intended to promote local employment and economic growth. What remain poorly understood is whether SEZs serve to give birth to new firms, or rather attract and support the re-establishment of firms from other places. To address this question, this paper examines the impact of SEZs on employment growth in rural counties in China. Using ASIF panel data representing the activity of manufacturing firms for the period 1999 to 2008, this paper assesses the employment effects of SEZs according to firm births, relocation, expansion, and firm closure. By matching counties with future SEZs as comparison groups, the difference-in-differences estimates show that SEZs significantly increase employment in rural counties due to the creation of new firms and the expansion of existing large firms; in contrast, SEZs fail to promote firms to move in and restrain firms from moving out. Further, data analysis reveals significant regional heterogeneity, with the employment effects of SEZs on firm entry strongest for the eastern coastal region. Finally, we confirm that SEZs tend to reduce local industrial agglomeration in the eastern and central regions.

National transportation networks, market access, and regional economic growth

I estimate interregional transportation’s effect on local economic activity by studying the Interstate Highway System. To estimate transportation’s effects on county employment and wages, I develop a new instrumental variables strategy: isolating market access growth caused by incidental connections to rural counties. I find that through market access highways increased employment, had small and delayed wage effects, and that instruments correct for downward bias. A structural model interprets reduced-form results as agglomeration and congestion forces strengthening after 1980. Counterfactual simulations suggest that Interstates’ effects were highly heterogeneous and that additions to early Interstate plans were less valuable than the system’s core.

How Long-Run Effects of Local Demand Shocks on Employment Rates Vary with Local Labor Market Distress

This paper estimates that long-run changes in a county’s prime-age employment rate are significantly affected by labor demand shocks to both the county and its overlying commuting zone (CZ). The overall benefits of labor demand shocks are due more to CZ demand shocks than county demand shocks. A lower preexisting county employment rate increases the effects of CZ demand shocks. Simulations suggest that low prior employment rate CZs, versus higher-rate CZs, will have much larger employment rate effects from demand shocks. Targeting jobs at more distressed counties within a CZ has modest effects, much lower than the effects of targeting jobs at more distressed CZs.

Econometric Method for Estimating the Impact of Tourism Spending on County Employment in South Carolina

This article analyzes their university backgrounds. Tourism can promote job growth and income growth in regional economies. Policymakers in the regional government promote tourism to bring outside money into the local economy. Using accommodation tax revenue data published by the South Carolina Department of Revenue, this paper estimates the employment impact of tourism spending in South Carolina counties. The OLS regression results show that increased tourism spending, as measured by the accommodation tax, leads to increased total county employment, increased county employment in the accommodation sector (NAICS 721), increased county employment in full-service restaurant sector (NAICS 7221) and increased county employment in arts, entertainment, and recreation sector (NAICS 71).