ABSTRACT. Opioid abuse has become a major public health issue in the US. While opioid misuse has direct consequences for addicts, children may also be affected. To prevent misuse of the prescription opiate OxyContin, Purdue Pharma released an abuse-deterrent version in 2010. Unintentionally, this reformulation caused many addicted to OxyContin to substitute more harmful opioids such as heroin and fentanyl in its place. This study estimates the effect of opioid abuse on child out-of-home placements, the removal of a child from home due to maltreatment, using data on opioid abuse and OxyContin’s reformulation. Using the reformulation as an instrumental variable for opioid abuse rates, I estimate that a 10% increase in the opioid abuse treatment admission rate caused an additional 2.4 foster care entries per 100,000 children due to drug abuse – equivalent to 1,778.4 child foster care out-of-home placements nationally. An average 27.21% increase in the opioid abuse treatment admission rate over the study period caused an estimated 4,840 additional drug-related child out-of-home placements or $26M in additional costs to the US foster care system from 2006-2016.

ABSTRACT. In this paper, we study the impact of the oil and gas industry on local overdose deaths. We employ an instrumental variable approach that allow us to identify the causal impact of county revenues from new oil and gas wells on county overdose death rates. Our results show an increase in overdose deaths among working age population as a result of the oil and gas activity. On average every additional million dollar of oil and gas revenues led to an increase of 38.05 overdose deaths per 100,000 people. We conduct the analysis separately for different demographic subgroups, we find that the impact of oil and gas revenues on overdose deaths is more pronounced among working age males and whites.

ABSTRACT. To address the opioid epidemic, improved access to buprenorphine |a partial opioid agonist and one of three FDA-approved medications for opioid use disor- der (OUD) treatment| and limiting the supply of prescription opioids is vital. We merge the retail transactions of buprenorphine from the Automated Reports and Consolidated Ordering System (ARCOS) database, SAMSHA's Buprenorphine Practitioner Locator data, and the University of Kentucky Center for Poverty Research's National Welfare Data from 2005 to 2017. We employ dierence-in- dierence frameworks to estimate the impact of Medicaid expansion on buprenor- phine access. We nd that Medicaid expansion is associated with increased access to buprenorphine treatment among expansion states that also employed more strin- gent supply-side policies, including \pill mill" laws and mandatory prescription drug monitoring programs (PDMPs), compared to expansion states that did not imple- ment policies targeting opioid supply. Together, Medicaid expansion and policies limiting access to prescription opioids show promise for improving the accessibility of evidence-based medication treatment for OUD.

ABSTRACT. Deaths from drugs have become a major issue in the United States over the past two decades, with particular focus on opioid drug mortality. However, focus on the opioid epidemic alone has led to a lack of research on other types of drugs; and to a lack of understanding on the polysubstance nature of drug abuse. This leaves several questions unanswered. How similar is the methamphetamine drug problem compared to the opioid problem? If certain regions are “immune” from opioids, does this mean there is no drug problem or just there is a different drug problem? Is the opioid epidemic really a polysubstance prescription drug problem? To answer these questions, our analysis identifies drug mortality clusters by type of drug at the county level using latent profile analysis. To operationalize the drug problem, we examine any cause of death where a commonly abused drug was a contributing condition. This differs from extant drug mortality research that only considers drug overdose cause of deaths. This wider definition allows us to better capture the role drugs play in mortality, not just from overdoses, but from other causes like accidents or suicides. Data are pooled estimates for 2000-01, 2008-10, and 2016-18 using CDC microdata on the following drug mortality rates: prescription opioids, illicit opioids, cocaine, psychostimulants (methamphetamine), hallucinogens, central nervous system sedatives, antidepressants, and antipsychotics and other psychotropic drugs. We identify eight distinct drug epidemics: (1) hallucinogen epidemic, with methamphetamine co-abuse; (2) methamphetamine epidemic; (3) illicit opioid and cocaine polydrug epidemic, with sedatives and antidepressant co-abuse; (4) prescription opioid and sedatives polydrug epidemic, with antidepressants and methamphetamine co-abuse; and (5) a polydrug syndemic. Other classes include places with no drug problem, as well as an emerging hallucinogen cluster and an emerging illicit opioid and cocaine cluster. We then use multinomial logistic regressions to model membership in opioid epidemic classes using socioeconomic correlates for metropolitan and non-metropolitan counties. Preliminary results suggest that methamphetamine and hallucinogen abuse occurs in places distinct from the opioid epidemic. Further, the “opioid” epidemic is in reality two general drug problems. First, there is a prescription drug epidemic anchored by prescription opioids; and second, an illicit drug epidemic of heroin, synthetic opioids, and cocaine.

ABSTRACT. This study provides a first glimpse into the early impacts of the COVID-19 pandemic on house prices by applying a hedonic price model to house sale data from January 2015 to June 2020 in the Fargo Metropolitan Statistical Area. We estimate that early housing market effects of the pandemic resulted in price increases in the study area. Using an unconditional quantile regression, we find that these price impacts were most substantial among the lower priced segments of the market and were likely driven by a decline in housing supply.

ABSTRACT. Over the last two decades several stadiums for professional teams have been built or modified, with no sign of a slowdown. For the National Football League, since 2000 sixteen NFL stadiums have been constructed. The literature on the effects of new professional stadiums provides mix evidence. While there is some evidence of no effects of new stadiums in the local economy, especially after subsidization cost are taken into account, there are reports of significant effects on local crime rates, real estate properties, among others. We examine the effect of National Football League’s Raiders organization relocation from Oakland, California to Las Vegas, Nevada on real estate properties. In particular, we focus on residential properties in Las Vegas, and look at two key dates: the announcement of relocation (March 27,2017) and inauguration of the Allegiant stadium (July 31, 2020). Using data from the Clark County Assessor’s Office, we use a hedonic pricing model and focus on single-family residential properties close to the Allegiant stadium. In addition, we explore potential differential effects by comparing properties located in rings within different distances to the Allegiant stadium. If property prices increase, homebuyers see the stadium as an amenity and value being close to it.

ABSTRACT. Because of their reliance on large samples of micro-level housing and wage data, quality of life studies using Rosen-Roback models have focused almost exclusively on metropolitan areas, largely ignoring non-metropolitan areas. Although understandable given data constraints, this dominant focus on metropolitans has limited the data-driven approaches available to policymakers concerned with community and economic development in small cities, or micropolitan areas. To address this gap, we develop an aggregate approach to estimate both quality of life and quality of the business environment in micropolitan areas utilizing county-level housing and wage data that can be used when large samples of micro-level data are unavailable. Specifically, we use the county residuals from wage and housing regressions to replace the fixed effects typically estimated from the micro-level estimations in quality of life studies. We find compelling evidence that higher quality of life is not only associated with higher employment and population growth and lower poverty rates, but that it is more important than quality of the business environment in determining the success of micropolitan areas.

ABSTRACT. In June 2006, the South Carolina governor signed a bill to provide property tax relief to state residents. Among other provisions, Act 388 of 2006 aimed to reduce the tax burden on property owners by adding a homestead exemption with regard to school operating millage for properties classified as owner-occupied primary residences equal to 100 percent of fair market value, thereby restricting school districts from raising general fund revenue through taxation of owner-occupied homes. In order to compensate school districts for lost revenue, the act implemented an additional one percent statewide sales tax. Revenue from this sales tax was to provide funding to be distributed to each school district to be determined in each year based upon the previous year’s distribution and a formula including the 135-day average daily membership (135 ADM, a measure of enrollment), with a minimum guaranteed reimbursement of $2.5 million. Additionally, the act placed a cap on reassessment, limiting the growth in assessed property value to 15 percent per five-year reassessment cycle.

Act 388 might be anticipated to have a positive effect on home values in South Carolina, as it effectively lowers the cost of owning a home in the state. This analysis will examine housing values within South Carolina counties; it will utilize an interrupted time series to estimate the impact of Act 388 on home values, holding constant for economic growth and other factors. The analysis will indicate whether such a tax policy does indeed attract new homeowners to the state, all else equal, or whether other factors outweigh the cost savings, such as the amenity value of high-quality schools.

ABSTRACT. Among the lasting images of the SARS-Cov-19 pandemic in the U.S., long lines of individuals and cars at food banks will likely be prominent. Public media accounts as well as a growing number of academic studies have documented dramatic short-term increases in household food insecurity associated with the economic collapse caused by the pandemic. This raises the question of whether entities such as food banks, food pantries, and related aggregators can play a role in reducing food insecurity during a pandemic in a high-income country such as the U.S. While the causes and outcomes of food insecurity have received considerable attention over the past decade in the academic literature, the role of Community Food Services (CFS) such as food banks, food pantries, and soup kitchens in contributing to food security have received less consideration, especially in the U.S.

In this paper, we use state-level Census House Pulse Survey (HPS) data, which is a new timely data product of the Census Bureau for monitoring the impact of the pandemic on households, to examine the effect of CFS on reducing household food insecure conditions. We construct a panel data set from the HPS, of which the original individual-level variables are aggregated to the state level over weeks of the survey. The dependent variable is the change in food insufficiency status before and after the pandemic to control for unobserved individual and state-specific effect. We also compute the change in food insufficiency status by income class, and our descriptive analyses show that food insufficiency increased for all income classes during the pandemic, especially for the low and middle classes.

A feature of the panel data model in this paper is that the main regressor of interest, the number of CFS organizations per capita obtained from the County Business Patterns, is time invariant. Neither a typical fixed effects nor a random effects panel data model can render a consistent estimate of the coefficient on the time-invariant regressor. So we adopt a recently proposed fixed effects filtered (FEF) estimator to estimate the coefficient on the CFS variable. The estimation results confirm the contribution of community food services to mitigating food insufficiency, which is especially significant for the middle-class.

ABSTRACT. Child food security is a major concern. This paper uses the fracking era oil and gas boom to examine the importance of state economic conditions for child food security. The fracking boom was a large and unexpected economic shock that significantly improved labor market conditions in states with oil and gas resources but not in states without these resources. We find that increases in oil and gas labor income in a state reduce child poverty and improve child food security, especially for children in more disadvantaged households. A strong labor market improves food security for some children but not all.

ABSTRACT. Crossroads Community Services (CCS) is a pioneer in the non-profit (charitable) food provision sector and plays a major role in providing food to low-income households. Crossroads pioneered a system that allocated households points based on age, gender and physical demands of employment. These points were allocated based on USDA nutrition guidelines to provide each household that visits with a 21-meal supply of food for each household member. Recently Crossroads abandoned the food categories restrictions in order to understand if the clients would benefit from fewer restrictions. Under the new regime, points are allocated as a lump sum, thus making clients eligible to select foods starting with any category and exhausting points across any categories. Using fixed effects and random effects model, we study changes in client choices post policy. These changes include whether client choices are different regarding selection of various food options post policy. We also study whether these changes are indicative of healthier and economical food choices.

ABSTRACT. Information and knowledge spillovers are critical for innovation creation. However, innovation creation is clumpy across regions and innovation creation gaps between rural and urban areas are due in large part, to the agglomeration effect in urban areas. Can broadband access produce information and knowledge spillovers for rural areas that contribute to innovation creation in urban areas? To test this question, we examine the influence of broadband access, using a newly constructed Census tract-level data set, on firms achieving phase II Small Business Innovation Research (SBIR) awards after receiving a phase I award. We develop a spatial econometric model that incorporates the new broadband access data matched to firm-level Dun and Bradstreet data (similar to NETS), Small Business Innovation Research (SBIR) awards, and other regional-level secondary data. Implications of results may help policy makers develop new ways to mitigate the innovation creation gap between regions and encourage economic growth in rural areas.

ABSTRACT. The Bureau of Economic Analysis provide both real GDP by state and total state employment data from 19977 to 2019. Combining these two measures allows for a time series comparison of state real GDP/Worker measurement and changes over time. Labor productivity provides a good indicator of changes in wages and living standards. Thus examining differences among states and changes over time in state productivity can provide a window on what can produce productivity growth and decline. The paper analyses state productivity levels and growth rates between 1977 and 2019 and tests for factors that have influenced state productivity growth and decline over time. The paper attempts to generate a conversation around state level good and bad practices that can influence changes in state productivity levels and growth over time.

ABSTRACT. Previous research suggests that entrepreneurs can contribute to higher levels of economic growth in rural areas. However, entrepreneurship in certain industries may be more beneficial to growth than others. Other research has linked industrial and occupational diversity to entrepreneurship and regional growth, especially in urban areas, due to the cross-fertilization of ideas. However, the relationship between industrial and occupational diversity and entrepreneurship in rural areas is less clear. Using detailed industry-level self-employment data, we examine whether entrepreneurs from certain industries or occupations may contribute more to regional growth. Additionally, we analyze the impact of industrial and occupational diversity on entrepreneurs in rural areas, in order to understand why some regions have more self-employment than others. Overall, our analysis can help rural regions better target scarce resources to support regional prosperity.

ABSTRACT. After the airline industry was deregulated in 1978, there was an effort by the federal government to provide subsidies to rural airports so that these areas would not lose commercial air service. In this paper, we consider the impact of these subsidies, allocated through the Essential Air Service Act (EAS), on rural growth. There is a possible endogeneity issue if we considered the impact of the subsidy on growth, as the subsidies may have been targeted towards growing areas. To address this concern, we instrument an instrumental variable strategy, where congressional membership on key committees is used to predict EAS subsidy amounts. We find some evidence that the EAS subsidy initially helped reduce poverty rates in rural counties, but we do not find evidence that this impact continued to later periods. We also do not find a significant effect on population density or median income. These findings suggest that the EAS program did not have as strong of an impact on rural areas as policy makers intended.

ABSTRACT. Political economy explanations of the politics of trade argue that policy-makers are politically motivated, and choose trade policy in response to the demands by voters and privileged groups most affected by trade flows. Empirical approaches often rely on reduced-form estimates of parameters derived from different variants of traditional political economy models of trade to explain the influence of winners and losers from trade on the formation of trade policy. Yet there is an important mismatch between theoretical and empirical contributions, which is reflected in our inability to reconcile a strong empirical regularity: a lack of correspondence between legislative voting patterns on trade policy and district level predictors of trade policy stance, as reflected in the political backlash to the China-shock.

In this paper we take a first stab at a framework linking theory and empirics: we develop a structural estimation of a formal model inspired by the Grossman and Helpman's (GH) model of trade politics. Our modeling strategy differs from GH in a fundamental way: we model the vector of tariffs that would be chosen by a local decision-maker representing a district (or region) within a larger polity populated by numerous regions. We are, thus, able to compare the vector of tariffs enacted by a central planner from the solution that would be preferred by the local decision-maker. Ultimately the policy enacted will reflect the implicit weights that reflect the preference aggregation protocol resulting from the institutional structure in the polity. Using data for the 435 Congressional districts in the U.S. and the vector of sectoral tariffs enacted by Congress, we are able to estimate the implicit weights placed by the federal government on different actors, sectors and regions. We are also able to document sizable differences between the legislated tariffs and the implicit demand for protection at the Congressional District level.

ABSTRACT. Regional and multi-regional economic analyses require accurate data and information about intra- and inter-regional production and consumption. Subnational input–output (IO) tables capture industry- and region-specific production, uses and trade of commodities and serve as a common basis for regional impact analysis. However, the absence of annual subnational input–output (IO) tables in the US provided by a statistical office requires alternate, non-survey-based approaches to estimate them. Estimation approaches for US state IO models have not been transparent enough to verify that the resulting tables follow reported methods, nor have the tools been available to the public to automatically update or reproduce them.

We describe a robust economic method to develop two-region IO and trade models for all fifty states in the US using annual national IO tables and a series of state economic data from reliable sources including the Bureau of Economic Analysis, Census Bureau, and Freight Analysis Framework. The source data acquisition and implementation of this method are consolidated in an R package stateior to ensure streamlined and consistent calculations. In stateiorv0.2, we regionalize 2012-2017 national models to generate state IO models at the BEA summary level, i.e. 71 industries, 73 commodities and 10 final demand sectors. We validate the state models by confirming that the sum of state production and consumption equals the respective reported national totals. We then utilize reported state international trade data and the Freight Analysis Framework model to develop two-region trade models for each state-of-interest (SoI) and its corresponding rest-of-US (RoUS). For each SoI, four matrices are produced representing intermediate consumption and final consumption. Two represent the local consumption (SoI-to-SoI or RoUS-to-RoUS) and two represent the interdependencies between regions (SoI-to-RoUS or RoUS-to-SoI). We present a 2012-2017 time series of macro indicators for these stateior state IO models, such as total gross value added, employment, international trade, and regional purchase coefficients for 11 states, including Arizona, California, Delaware, Florida, Georgia, Iowa, Maine, Minnesota, New York, Oregon, and Washington. We also inspect interregional trade between each SoI and its corresponding RoUS. We compare selected indicators to an existing popular regional IO model, IMPLAN, as a benchmark.

stateior can provide both two-region and single state Make and Use tables in formats comparable to those released by BEA at the national level, and are planned for use in building subnational multi-regional versions of the US environmentally-extended input-output (USEEIO) model.

ABSTRACT. Economic impact models are powerful tools for the assessment of policy changes in regional economies. Computable General Equilibrium (CGE) models have grown in popularity, becoming the dominant choice of practitioners and academics in this field. This popularity has been at the expense of an older class of model, the Econometric Input Output (EIO). The present paper demonstrates how both models, using the same input data, may yield different outcomes. However, the paper suggests that EIO has been underutilized even though it provides a strong complementary tool accompany that enhance analyses using a CGE approach. This paper urges regional economists to rediscover the EIO model, especially two variants that are described in the paper, and bring them to the forefront of their research agenda.

ABSTRACT. Background In 2019, the Oklahoma’s Gross Domestic Product (GDP) was $202 billion in constant 2012 dollars. Oklahoma’s largest economic sector in terms of output is mining, quarrying, and oil and gas extraction. These activities account for 23% of the state’s GDP. Trade, transportation, and public utilities account for the next highest share of GDP at 18%. Oklahoma’s agricultural sector accounts for 2% of the state’s GDP and is an important industry that affects related industries by producing and exporting major grains and livestock. In 2019, Oklahoma had 77,300 farms and 34.4 million acres of land in farms. About $2,578 million worth of cattle and calves, $965 million worth of hogs and pigs were produced. Oklahoma accounts for about 5% of the country’s beef and pork exports, and exports of wheat, coarse grains and soybeans are also high (NASS, 2020). During the same year, regional flooding caused the closure of the McClellan-Kerr Arkansas River Navigation System (MKARNS). The MKARNS is an important transportation artery linking Kansas, Oklahoma, Arkansas, and Colorado to the Mississippi River and to global markets. The 2019 flood disrupted MKARNS port operations for nine months. The popular press reported daily losses of two million dollars during the system’s closure. Economic losses stemmed from the direct closure of the port, but also through indirect costs borne by industries whose supply logistics depend on the navigation system. What economic losses followed the closure of the MKARNS? How were these losses absorbed throughout Oklahoma’s economy?

Objectives

This study quantifies the economic impact of the 2019 MKARNS waterway closure on Oklahoma’s economy using a regional computable general equilibrium (CGE) model.

Methods and Data

We extend Stodic, Holland, and Devadoss (2006)’s Idaho-Washington State University CGE model (IW-CGE) to Oklahoma’s regional economy. The IW-CGE model is a standard CGE structure revised to incorporate inter-regional trade flow. The regional CGE includes inter-institutional transfers, including sector-specific make/use transfers, transfers originating from indirect business taxes, and import/export of production factors and institutional products. Government investment, as well as households, receive payments from the primary factors of production through (respectively) taxes and wage labor.

Oklahoma’s SAM is from the IMPLAN (Impact Analysis for Planning) database (Minnesota IMPLAN Group, Inc., 2020). The SAM is used to calibrate the Oklahoma CGE model (OK-CGE). The IMPLAN SAM data contains 546 sectors representing all private industries in the United States as defined by the North American Industry Classification System (NAICS) codes. For this study, the 546 IMPLAN sectors are aggregated into 37 distinct industries most representative of Oklahoma’s economy. The SAM includes three primary factors of production; one indirect business tax sector; nine household sectors differentiated by income strata; six government sectors; one corporate sector; savings and investment sectors; and exporting/importing activities. The primary factors of production are labor and capital. IMPLAN classifies households based on nine income categories. Government sectors are divided into federal government defense spending, federal government non-defense spending, federal government investment, state government non-education spending, state government education spending, and state government investment. Savings and investment sectors include gross private fixed investment and inventory additions/deletions. Trading sectors are separated into foreign trade and domestic trade.

OK-CGE requires three sets of elasticities. These include 1) the elasticities of substitution between production factors; 2) income and price elasticities of household consumption; 3) the elasticities of transformation. The elasticities of substitution, transformation, and income values are all free parameters and obtained from published sources. The model’s sensitivity to these assumptions is investigated using Monte Carlo methods by randomly varying the parameters between lowest, most likely, and highest values reported in the literature.

The OK-CGE model is separated into blocks factors of production, commodities produced, factors and commodity prices, institutions, and demand/supply constraints to ensure market clearing. This model ensures that factor and commodity markets are in equilibrium at optimality such that macroeconomic identities obtain. Using this CGE model, the effect of the waterway closure on the Oklahoma economy can be able to evaluate the value of the waterway system and allow it to simulate alternatives of waterway closure. Factor and product prices and the foreign exchange rate are endogenous to the model. Government expenditure, investment, and the import price from the rest of the world are assumed to be exogenous. The consumer price index is also fixed as a constant. This is one type of closure and other scenarios are possible, depending on the research question.

There will be two types of shocks used to estimate the impacts of the OK-MKARNS closure. The first will be a reduction to the water transportation sector indicating the closure for a period of 2-, 4-, 6-, 9-, and 12-months. This is calculated by taking the water transportation sector’s output from IMPLAN, calculating the output for each scenario, then reducing it by the respective output amount indicating a full closure for that scenario. For example, the output for the 2-month scenario would be 2/12 multiplied by the total output from IMPLAN. Then the water transportation sector would be shocked by a reduction of that amount.

The second shock will be the delay cost to all the industries that ship commodities on the OK-MKARNS. The delay costs will be calculated by using delay costs and multiplying the cost per ton by the total tonnage of each commodity that would be delayed. Tonnages of the commodities are obtained from the OK-Department of Transportation and are the tonnages that would normally flow through the system in the time frame for each delay scenario.

ABSTRACT. This panels session will discuss updated aims and scopes and publishing tips.

ABSTRACT. Recent research finds evidence that a large-scale forest conservation plan in the U.S. Pacific Northwest increased median household income in communities close to the protected forestland. In this paper, a simple theoretical model is sketched out to propose that natural amenity-induced skill sorting in a non-metropolitan context could help explain this finding. Then, using community-level data in the state of Oregon, we test this hypothesis along with several competing mechanisms. Our empirical results support the mechanism of natural amenity induced skill sorting. The other mechanisms like the out-migration of the low-income population and the in-migration of the elderly population are not supported.

ABSTRACT. Using college enrollment data and nationally representative population census data, this paper examines the impact of college education and college location on later-life migration in China. We take advantage of the in- and out-of-province variation in college enrollment driven by China's massive higher education expansion to identify the effects. Our results show that an increase in either in- or out-of-province college enrollment leads to a higher probability of college attendance. Only out-of-province college expansion increases the likelihood of attending college out-of-province. Using a two-step instrument variable approach, we find a modest negative effect of college education per se and a positive effect of college location per se on later-life migration. We further document significant heterogeneous effects by regional wage differential, housing price gap, and gender to justify the weakly negative college education effect in the Chinese context.

ABSTRACT. This study evaluates the spatial agglomeration of talent across U.S. regions. We identify "talent" based on the academic field of the individuals' undergraduate education using U.S. Census micro data from 2009 to 2018. Specifically, we investigate the agglomeration propensity of STEM fields relative to other higher education majors. Surprisingly, previous research has not looked into measuring the degree of talent agglomeration by urban area, based on higher education fields such as STEM. Recent studies that consider the impact of talent on regional economic development assert that such individuals sort themselves in cities with higher levels of agglomeration but do not measure the extent of and differences among clusters of academic fields. Thus, this study aims to fill the gap in the literature regarding the inter-urban distribution of talent concentration. To measure the revealed comparative advantages in talent agglomeration, we examine location quotients to discern whether there are superstar regions in STEM agglomeration. The regional location quotients for STEM degrees appear to be highly skewed, above all for the computer engineering STEM field. We then regress urban-level STEM agglomeration against population size, employment density, and urban area fixed effects. Examining the fixed effect estimates we clearly identify the talent "superstar" regions. In addition, we test the effect of STEM and non-STEM talent agglomeration on regional productivity (captured by the urban wage premium). Our results confirm that STEM agglomeration has a strong positive relationship with urban wages, whereas non-STEM agglomeration does not exhibit any association with regional productivity.