State Unemployment Rates Research Articles

Iowa

The Governor’s Budget Recommendations for Fiscal Year 2024 are focused on the following prioritie ( Iowa Department of Management [DOM], 2023 ). Supporting Success at School, Promoting Healthy Families and Communities, Building Workforce Through Apprenticeships, Aligning Government to Better Serve Iowans, Combating the Fentanyl Crisis, and Protecting State Investments. Iowa’s unemployment rate has historically been below the national average. As of November 2022, the state unemployment rate was 3.1%, up from a recent low of 2.5% reached in July 2022, but still down 0.8% from one year ago. The national unemployment rate was 3.7%. Iowa’s labor force remains below its pre-pandemic level, albeit it is improving.

Unemployment and the direction of technical change

I construct and analyze a growth model in which technical change can increase unemployment. I first analyze the forces that deliver a constant steady state unemployment rate in this setting. Labor-saving technical change increases unemployment, which lowers wages and creates incentives for future investment in labor-using technologies. In the long run, this interaction generates a balanced growth path that is observationally equivalent to that of the standard neoclassical growth model, except that it also incorporates a positive steady state level of unemployment and a falling relative price of investment. I also study the effects of a permanent increase in the ability of R&D to improve labor-saving technologies. In the long run, this change leads to faster growth in output per worker and wages, but it also yields higher unemployment and a lower labor share of income. In the short run, this change exacerbates existing inefficiencies and slows economic growth.

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.

Unemployment Insurance benefit reduction and food hardship

AbstractWe leverage the sharp drop in unemployment insurance (UI) benefits following the expiration of the Federal Pandemic Unemployment Compensation program to estimate the consumption smoothing effect of UI. The $600/week decline in supplemental UI benefits is estimated to reduce total food spending by 9.7% and the odds of having food sufficiency by 6.0%. The estimate for food spending translates to a marginal propensity to spend on food out of UI benefits of 0.06, or a 1.2% reduction in food spending for every 10 percentage point decline in UI replacement rate (calculated as the ratio of UI benefits to pre‐unemployment wages). We find that the consumption effect of UI is countercyclical, greater when economic conditions are weak. The UI effect is also heterogeneous over respondents differentiated by race and ethnicity, income, homeownership, presence of children, state unemployment rates, and state UI generosity. The estimated effect of UI on self‐assessed food sufficiency and confidence about future food sufficiency is largely consistent with the food spending results.

Salary history bans and healing scars from past recessions

In a recession, increased competition forces inexperienced job market entrants to accept lower wages than those who start their careers during an economic boom. Despite years of improvement in labor market conditions following a recession, a wage disparity, known as scarring, persists between these cohorts. Recently implemented Salary History Ban laws (SHBs) are intended to reduce wage disparities between advantaged and disadvantaged groups. In this study, I test how these laws affect a unique and often less salient disadvantaged group – scarred workers. For scarred workers who began their careers during a moderate-to-severe recession, or a five percentage point higher state unemployment rate, I find SHBs increase job mobility by 0.6%, hourly wages by 2.65%, and weekly earnings by 5% relative to cohorts who graduated in baseline labor market conditions. These estimates represent a substantial reduction in the original scarring effect and provide a broader understanding of the mechanisms behind both scarring and SHB laws.

Handling multi-collinearity using principal component analysis with the panel data model

When designing a statistical model, applied researchers strive to make the model consistent, unbiased, and efficient. Labor productivity is an important economic indicator that is closely linked to economic growth, competitiveness, and living standards within an economy. This paper proposes the one-way error component panel data model for labor productivity. One of the problems that we can encounter in panel data is the problem of multi-collinearity. Therefore, multi-collinearity problem is considered. This problem has been detected. After that, the principal component technique is used to get new good unrelated estimators. For the purposes of our analysis, the multi-collinearity problem between the explanatory variables was examined, using principal component techniques with the application of the panel data model focused on the impact of public capital, private capital stock, labor, and state unemployment rate on gross state products. The analysis was based on three estimation methods: fixed effect, random effect, and pooling effect. The challenge is to get estimators with good properties under reasonable assumptions and to ensure that statistical inference is valid throughout robust standard errors. And after application, fixed effect estimation turned out to play a key role in the estimation of panel data models. Based on the results of hypothesis testing, the real data result showed that the fixed effect model was more accurate compared to the two models of random effect and pooling effect. In addition, robust estimation was used to get more efficient estimators since heteroscedasticity has been confirmed

Efekti poreza na dohodak na stopu nezaposlenosti u Sjedinjenim Američkim Državama

This study investigates the correlation between state income tax and unemployment rates across the United States. Using panel data in 50 states pertaining to the period from 2006 to 2022 with different regression models, the results suggest that the state corporate and personal income tax rates are positively correlated with the state unemployment rate. Specifically, a 1% decrease in the personal income tax rate may lead to a 0.712% decrease in the state unemployment rate, and a 1% decrease in the corporate income tax rate may cause a drop of 0.328% in the state unemployment rate. In addition, the results show that a personal income tax hike is associated with an increase of 1.532% in the state unemployment rate, and a corporate income tax hike may increase the state unemployment rate by 0.78%. The results of this study are relevant in the context of increasing government spending in the US and the world. Policymakers and government officials may not want to abuse the tax policy to fund the budget deficits.

Telework during the pandemic: Patterns, challenges, and opportunities for people with disabilities

BackgroundTelework has benefits for many people with disabilities. The pandemic may create new employment opportunities for people with disabilities by increasing employer acceptance of telework, but this crucially depends on the occupational structure. ObjectiveWe compare people with and without disabilities in the expansion of telework as the pandemic began, and the evolution of telework during the pandemic. MethodsWe use U.S. data from the American Community Survey from 2008 to 2020 and the Current Population Survey over May 2020 to April 2022 period. Prevalence and trends are analyzed using linear probability and multinomial logit regressions. ResultsWhile workers with disabilities were more likely than those without disabilities to telework before the pandemic, they were less likely to telework during the pandemic. The occupational distribution accounts for most of this difference. Tight labor markets, as measured by state unemployment rates, particularly favor people with disabilities obtaining telework jobs. While people with cognitive/mental health and mobility impairments were the most likely to telework during the pandemic, tight labor markets especially favored the expansion of telework for people with vision impairments and difficulty with daily activities inside the home. ConclusionsMany people with disabilities benefit from working at home, and the pandemic has increased employer acceptance of telework, but the current occupational distribution limits this potential. Tighter labor markets during the recovery offer hope that employers will increasingly hire people with disabilities in both telework and non-telework jobs.

Association between state unemployment rate and inpatient hospitalizations for tobacco use disorder.

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Identifying factors that promote successful vocational rehabilitation case closure: Comparison of individual characteristics, VR experiences and state economic conditions

BACKGROUND: Despite the series of public policies being implemented to strengthen vocational rehabilitation (VR) services, individuals with disabilities remained at the substantial deficit in employment outcomes compared with individuals without disabilities. OBJECTIVE: We aimed to examine effects of vocational rehabilitation services on the employment outcomes for people with disabilities while accounting for individual characteristics and state economic conditions. Findings had implications for VR program improvement. METHOD: We used the RSA-911 data covering cases from 2015 to 2017, in addition to state unemployment rate and poverty rate in 2017 to address the research questions. A logistic regression model was estimated with Generalized Estimating Equations to predict the probability of successful case closure by individual demographic characteristics, number of VR services received, and state economic conditions. RESULTS: State economic conditions were not associated with employment outcomes among VR participants. Being female, Black, having physical disabilities, and not having a job at application were disadvantageous in employment outcomes. The more varieties of vocational services that individuals received, the higher odds of successful case closure. CONCLUSIONS: VR services have significant beneficial impact on individual employment outcomes. The strongest beneficial impact may be achieved by providing a combination of different types of services that are designed to help individuals find or sustain employment.

The effects of welfare time limits on access to financial resources: Evidence from the 2010s

AbstractThe Personal Responsibility and Work Opportunity Reconciliation Act of 1996 established the Temporary Assistance for Needy Families (TANF) program within the United States. TANF mandated 60‐month lifetime time limits for federal cash assistance dollars. Because states reserve the right to set their own stricter or more generous time limits, the 60‐month lifetime limit did not bind in all cases. In recent years, however, several states imposed TANF time limits for the first time or made existing time limits more stringent. Using administrative and survey data, I find that stricter time limits decrease TANF participation within the past 12 months by 26%. Unlike previous research, I find heterogeneous effects of time limits on labor supply. I show that expected effects on employment and earnings decrease—and eventually become negative—as a state's unemployment rate increases. Evidence suggests that removing welfare recipients from TANF during periods of high unemployment inhibits their access to financial resources.

College football attendance in the long run: The Football Championship Subdivision

A balanced panel (61 Football Championship Subdivision (FCS) football teams over 38 years) is used to estimate four regression models using time series and panel methods and identify variables that drive per‐game season attendance. Variables are either stationary or co‐integrated. Most coefficients are consistent across models: season win percentage, lifetime win percentage, undergraduate enrollment, and real gas price per mile driven were positive while real state per capita income exerted a negative impact. Unexpectedly, FCS games are inferior goods and greater travel costs raise attendance. Other coefficients displayed inconsistent results: playoff appearances in the last 10 years, county population, and state unemployment rate.

Teacher Attrition and Mobility Over Time

We use 35 years of data on public school teachers in Washington to calculate several different measures of teacher attrition and mobility. We explore how these rates vary over time and their relationship with the state unemployment rate. Annual rates of teacher attrition from the workforce have been between 5% and 8% for each of the past 35 years, and there is a strong negative relationship between unemployment rates and these rates of attrition. This history suggests that teacher attrition is likely to increase as the economy recovers after the pandemic, but this increase is likely to be modest.

Estimating the uncertainty of traffic forecasts from their historical accuracy

• Past traffic forecast accuracy as estimator of uncertainty in future forecasts. • Quantile regression to quantify effect of project attributes on expected deviation. • Measures the range 90% of traffic volumes are expected to fall, given a forecast. Traffic forecasters may find value in expressing the uncertainty of their forecasts as a range of expected outcomes. Traditional methods for estimating such uncertainty windows rely on assumptions about reasonable ranges of travel demand forecasting model inputs and parameters. Rather than relying on assumptions, we demonstrate how to use empirical measures of past forecast accuracy to estimate the uncertainty in future forecasts. We develop an econometric framework based on quantile regression to estimate an expected (median) traffic volume as a function of the forecast, and a range within which we expect 90% of traffic volumes to fall. Using data on observed versus forecast traffic for 3912 observations from 1291 road projects, we apply this framework to estimate a model of overall uncertainty and a full model that considers the effect of project attributes. Our results show that the median post-opening traffic is 6% lower than forecast. The expected range of outcomes varies significantly with the forecast volume, the forecast method, the project type, the functional class, the time span and the unemployment rate at the time forecast is made. For example, consider a 5-year forecast for an existing arterial roadway made in 2019 when the state unemployment rate was 4% using a travel model. If a travel model predicted 30,000 Average Daily Traffic (ADT) on this road, our results suggest that 90% of future traffic volumes would fall between 19,000 and 36,000 ADT. A forecaster can apply the resulting equations to calculate an uncertainty window for their project, or they can estimate new quantile regression equations from locally collected forecast accuracy data. Aided by decision intervals, such uncertainty windows can help planners determine whether a forecast deviation would change a project decision.

Association of WIC Participation and Electronic Benefits Transfer Implementation

The Special Supplemental Nutrition Program for Women, Infants, and Children (WIC) is an important source of nutritional support and education for women and children living in poverty; although WIC participation confers clear health benefits, only 50% of eligible women and children currently receive WIC. In 2010, Congress mandated that states transition WIC benefits by 2020 from paper vouchers to electronic benefits transfer (EBT) cards, which are more convenient to use, are potentially less stigmatizing, and may improve WIC participation. To estimate the state-level association between transition from paper vouchers to EBT and subsequent WIC participation. This economic evaluation of state-level WIC monthly benefit summary administrative data regarding participation between October 1, 2014, and November 30, 2019, compared states that did and did not implement WIC EBT during this time period. Difference-in-differences regression modeling allowed associations to vary by time since policy implementation and included stratified analyses for key subgroups (pregnant and postpartum women, infants younger than 1 year, and children aged 1-4 years). All models included dummy variables denoting state, year, and month as covariates. Data analyses were performed between March 1 and June 15, 2020. Statewide transition from WIC paper vouchers to WIC EBT cards, specified by month and year. Monthly number of state residents enrolled in WIC. A total of 36 states implemented WIC EBT before or during the study period. EBT and non-EBT states had similar baseline rates of poverty and food insecurity. Three years after statewide WIC EBT implementation, WIC participation increased by 7.78% (95% CI, 3.58%-12.15%) in exposed states compared with unexposed states. In stratified analyses, WIC participation increased by 7.22% among pregnant and postpartum women (95% CI, 2.54%-12.12%), 4.96% among infants younger than 1 year (95% CI, 0.95%-9.12%), and 9.12% among children aged 1 to 4 years (95% CI, 3.19%-15.39%; P for interaction = .20). Results were robust to adjustment for state unemployment and poverty rates, population, and Medicaid expansion status. In this study, the transition from paper vouchers to WIC EBT was associated with a significant and sustained increase in enrollment. Interventions that simplify the process of redeeming benefits may be critical for addressing low rates of enrollment in WIC and other government benefit programs.

Mass Incarceration and Children's Health: A State-Level Analysis of Adverse Birth Outcomes and Infant, Child, and Teen Mortality.

Children's health indicators such as mortality and adverse birth outcomes are poorer in the United States than in comparable nations. These measures also show racial inequities within the United States, with Black children experiencing the highest levels. Mass incarceration may partially explain these findings. High incarceration rates can disrupt community functioning, influencing behavior and health. The purpose of the current study was to conduct a macro (state)-level analysis examining whether yearly state incarceration rates predict health outcomes including infant, child, and teen mortality as well as preterm birth and low birth weight. It was hypothesized that prior year incarceration rates would show positive relationships with all outcomes and that relationships would be stronger for Black than for white children. Yearly state-level panel data were gathered from 1990 to 2017. Weighted least squares regression used states' prior year incarceration rates to predict child health outcomes, using controls for overall state effects and year-to-year effects. Time-varying covariates such as state unemployment rate were also included to address the possibility of spurious relationships. Results indicated that as hypothesized, incarceration rates positively predicted infant mortality, child mortality (for Black children only), preterm births, and low-weight births. Relationships tended to be stronger for Black than for white children.

Unemployment rate, opioids misuse and other substance abuse: quasi-experimental evidence from treatment admissions data

BackgroundThe relationship between economic conditions and substance abuse is unclear, with few studies reporting drug-specific substance abuse. The present study examined the association between economic conditions and drug-specific substance abuse admissions.MethodsState annual administrative data were drawn from the 1993–2016 Treatment Episode Data Set. The outcome variable was state-level aggregate number of treatment admissions for six categories of primary substance abuse (alcohol, marijuana/hashish, opiates, cocaine, stimulants, and other drugs). Additionally, we used a broader outcome for the number of treatment admissions, including primary, secondary, and tertiary diagnoses. We used a quasi-experimental approach -difference-in-difference model- to estimate the association between changes in economic conditions and substance abuse treatment admissions, adjusting for state characteristics. In addition, we performed two additional analyses to investigate (1) whether economic conditions have an asymmetric effect on the number of substance use admissions during economic downturns and upturns, and (2) the moderation effects of economic recessions (2001, 2008–09) on the relationship between economic conditions and substance use treatment.ResultsThe baseline model showed that unemployment rate was significantly associated with substance abuse treatment admissions. A unit increase in state unemployment rate was associated with a 9% increase in treatment admissions for opiates (β = 0.087, p < .001). Similar results were found for other substance abuse treatment admissions (cocaine (β = 0.081, p < .001), alcohol (β = 0.050, p < .001), marijuana (β = 0.036, p < .01), and other drugs (β = 0.095, p < .001). Unemployment rate was negatively associated with treatment admissions for stimulants (β = − 0.081, p < .001). The relationship between unemployment rate and opioids treatment admissions was not statistically significant in models that adjusted for state fixed effects and allowed for a state- unique time trend. We found that the association between state unemployment rates and annual substance abuse admissions has the same direction during economic downturns and upturns. During the economic recession, the negative association between unemployment rate and treatment admissions for stimulants was weakened.ConclusionThese findings suggest that economic hardship may have increased substance abuse. Treatment for substance use of certain drugs and alcohol should remain a priority even during economic downturns.

Examining the Social Safety Net Response to the COVID-19 Pandemic

TANF and SNAP cases increased by 3.4 million between March and June 2020, their largest quarterly increase ever. This paper explores trends on the caseload growth and pandemic policies during the COVID-19 pandemic. TANF and SNAP caseloads initially rose sharply by 10 and 18 percent, but afterwards TANF cases declined to pre-pandemic levels while SNAP caseloads leveled off. Comparing the caseload response relative to the unemployment shocks, each percentage point increase in unemployment is associated with a 1.1 percent increase in TANF cases and a 1.2 percent increase in SNAP cases. Considering the caseload response to pandemic policies, I find that combined unemployment insurance pandemic programs increased SNAP cases by 5 percent and by 3 percent for TANF. I also find that states which adopted pandemic-friendly policies had larger TANF and SNAP caseload growth during the pandemic, especially in states with less restrictive pre-pandemic policies.

Does greater unemployment make people thinner in Brazil?

The study seeks to analyze the impact of macroeconomic conditions on weight measures, such as BMI, overweight, obesity, and severe obesity in Brazil. We examine this relationship in the specific context of a middle-income country that differs in many aspects from the high-income countries usually considered in the literature. The study uses the microdata of VIGITEL in the period from 2006 to 2014 and the state unemployment rate as a proxy for macroeconomic conditions. The results showed that the relationship is robust and presents a procyclical pattern-increases in the unemployment rate reduce BMI, and this reduction is observed throughout the entire distribution, with statistically significant effects for measures of overweight, obesity, and severe obesity. These results agree with the findings for the United States but contradict the results found for Finland and Canada.

Some Factors in State TANF Participation by Immigrants: An Examination Using the Group Threat Hypothesis

ABSTRACT This study asked what happens as the proportion of immigrants on states’ Temporary Assistance for Needy Families (TANF) rolls changes, employing the group threat hypothesis to analyze data dating 2000–2017. The data examined were extracted from compilations of information by federal agencies indicating states’ population by race/ethnicity, poverty and unemployment rates, and TANF participation by immigrants (as a percentage of total participation). Results showed states with relatively larger African American populations often had a lower percentage of immigrants enrolled in their TANF programs. Results also showed a negative association between percentage of TANF participation by immigrants and state unemployment rate. Policy implications are discussed.