ABSTRACT. Small area estimation is widely used by national statistical offices to provide local information on issues of urgent policy concern such as childhood poverty or the incidence of illnesses, which typically would be prohibitively expensive to collect. It does this by having a smaller dataset with details on the phenomenon of interest “borrow strength” from much larger, general datasets lacking this information—relying instead on the ability of variables common to both datasets to predict the phenomenon of interest. This paper examines the feasibility of extending this method to business surveys. The Annual Business Survey (ABS) administered jointly by the Census Bureau and the National Center for Science and Engineering Statistics is the primary source of information on innovation. Despite a large sample size of 850,000 in Economic Census (EC) years (ending in 2 or 7) corresponding to a sampling fraction of roughly 1 in 6, accurately estimating an innovation rate from a binomial distribution in a small area requires a sampling fraction of 50% in an area with 500 firms or 84% in an area with 100 firms. The sampling fraction for the EC is roughly 66% that could potentially provide accurate innovation rate estimates for small areas. The empirical question is whether the variables that are common to both ABS and the EC provide accurate predictions of innovative behavior in ABS. The other sources of common information are the local contextual factors in each business location. If the combination of business characteristics and local characteristics can accurately predict various types of innovation using Empirical Bayes techniques, then new research questions pertaining to the meso-level of innovation can finally be addressed. To wit: the role that innovation plays in reallocation of productive assets at the local level.

ABSTRACT. Public use files (PUF) of Census microdata on individuals have been available for decades. However, large increases in computing power and the greater availability of Big Data have dramatically increased the probability of re-identifying anonymized data, potentially violating the pledge of confidentiality given to survey respondents. The same data science tools that increase the risk of disclosure can also be used to produce synthetic data that preserve critical moments of the empirical data but do not contain the records of any existing individual or business respondent. These synthetic data tools open new possibilities for producing microdata that will allow producing informative descriptive or multivariate analyses using PUFs that currently require access to confidential microdata. Developing public use establishment data from surveys presents unique challenges from demographic data, because there is a lack of anonymity and certain industries can be easily identified in a given geographic area. The presentation will briefly describe an algorithm used to construct a synthetic public use file based on the 2019 Annual Business Survey (ABS) and discuss various quality metrics. The ABS is conducted jointly by the Census Bureau and the National Center for Science and Engineering Statistics (NCSES) within the National Science Foundation (NSF), and collects data on R&D expenditures, innovation-related data, globalization, and business owner characteristics from businesses operating in the U.S. Various use cases—either as a substitute for or supplement to accessing confidential data—will be discussed in the context of tiered access.. A central objective of the presentation is to get feedback from session attendees regarding the ABS variables of greatest research interest to be considered in the public use file that aims to balance privacy and utility.

ABSTRACT. Synthetic public use files have the potential to greatly increase awareness of the types of microlevel business data available. However, addressing many research and policy questions of interest will require access to the full set of confidential empirical data. This is the case for analyses tracking firm performance through time by linking to longitudinal employment, payroll, or export data; or for the evaluation of programs where accurate identification of participation may be required for statistically powerful tests. The Standard Application Process, authorized under the Evidence Act, was developed to streamline the task of getting access to confidential data by data users and researchers. This presentation will discuss the process for applying for confidential data through the SAP, outline the various steps in the review process, and present metrics on applications to date, and discuss resources to assist users in applying for data access. Guidance on factors that impact the review process will be discussed, with ample time reserved for answering questions from session participants.

ABSTRACT. Tropical cyclones, significant natural disasters known for regional economic disruptions, have incurred an average cost of $51.5 billion annually over the past decade (Krichene et al., 2023) and are a threat to human well-being. In mitigating these significant impacts, government-initiated alarms, warnings, and mandatory orders play a crucial role. Yet, the effectiveness and necessity of mandatory orders remain uncertain and contentious, particularly when cyclone magnitudes and trajectories deviate from initial projections. Since the 1980s, the Taiwanese government has implemented typhoon day-off orders and issued typhoon alarms to help alleviate fatalities and economic impacts. These measures provide critical information and guidelines for the public, leading to more informed disaster avoidance decisions. Previous studies have primarily examined avoidance behavior by analyzing individual tropical cyclone events, making it challenging to assess policy impacts comprehensively. The present paper utilizes long-term aggregate data to evaluate avoidance behaviors during tropical cyclones under typhoon warnings and mandatory day-off policies. The hypothesis is that when the government announces day-off orders, individuals are more likely to stay at home, thereby reducing exposure to tropical cyclones, relative to the issuing of alarms. Consequently, it is expected that public transportation, which operates regularly during tropical periods, will experience decreased usage rates, implicitly reflecting avoidance behavior. To test this hypothesis, time-series daily data from Taipei and Kaohsiung, both cities with reliable underground transportation systems in Taiwan, spanning the period from 2009 to 2019, were analyzed. The results demonstrate a statistically significant decrease in the number of passenger rides following the issuance of alarms and government-mandated orders. Specifically, the findings indicate a substantial response to typhoon day-off orders, with a reduction of 83.9% in Taipei and 58.1% in Kaohsiung in metro passenger trips upon the announcement of such orders. However, it remains unclear whether the observed effect is solely attributable to the policy or whether individuals take avoidance actions even without a day-off order. Due to data limitations, we were unable to compare the effects before and after the implementation of the day-off order policy in Taiwan. To address this gap, we examine the case of Miami-Dade County, where hurricane alarms are issued but no day-off orders are implemented during hurricanes. Similar to Taiwan, Miami-Dade County experiences regular hurricanes each year and shares similar characteristics with Kaohsiung in terms of weather, population size, and rates of public transportation usage. Interestingly, our analysis of Miami-Dade County reveals a comparable pattern and magnitude of avoidance behavior: individuals respond to hurricane alarms by reducing passenger trips by 70.7%. Although the results suggest similar avoidance responses to day-off orders and warnings, this comparison should be interpreted cautiously. The study observed similar avoidance patterns in two tropical cyclone regions under comparable severity conditions. The findings suggest that when individuals have access to tropical cyclone information and are aware of potential exposure, they take precautions that are on average similar to the issuance of day-off orders.

Krichene, H., Vogt, T., Piontek, F. Geiger, T., Schotz C., and Otto C. (2023) The social costs of tropical cyclones. Nature Communications, 14, available online: https://doi.org/10.1038/s41467-023-43114-4.

ABSTRACT. The growing US population, in addition to climate change and associated extreme weather events, pose increasing threats to food production and supply chains. Society’s capacity to address these challenges hinges on the ability of trade to guarantee the resiliency of supply chains from producers to the agri-food industry and final consumers. As domestic agri-food trade networks consist of a complex and geographically dispersed network of supply and demand, research on these issues increasingly requires the use of trade data recorded at detailed spatial resolutions. Based on gravity model techniques and data from the Bureau of Transportation Statistics (BTS) on state-to-state flows of grains, this manuscript will create a novel dataset of trade flows of grains from county-to-county. In most markets, domestic trade represents the overwhelmingly largest use of agricultural production. In the United States, the world's second largest agricultural producer and a major exporter of agricultural products, domestic uses still account for approximately 85% of total agricultural production. Yet, while there is an abundance of data that give a detailed accounting of trade and agricultural supply chains at the international level, there is a general lack of readily available, systematic data on domestic (intranational) agricultural trade. Naturally, because production and consumption (intermediate or final) in a large country such as the United States tend to take place across different regions, domestic trade reflects a complex network of bilateral trading relationships that link supplying and demanding regions at a fine (sub-state) geographical resolution. And while government statistical agencies have clear incentives to record comprehensive data on foreign trade flows, including the source, destination, and a detailed description of the traded product, there is less impetus for the collection of such data at the domestic level. Nonetheless, there has been a recent and gradual increase in the availability of suitable data sources in developed economies. For instance, interstate trade data in the US are available from the Commodity Flow Survey or the Freight Analysis Framework developed by the Bureau of Transportation Statistics (BTS). They capture the domestic movement of agricultural products, which has offered researchers increasing opportunities to trace the domestic network of bilateral trading relationships in agriculture for the purpose of addressing research questions relating to domestic food supply chains. A key shortcoming of these datasets, however, is their geographical detail beyond the state-to-state level, which therefore inhibits their usefulness in exploring research questions that necessitate analyses at the sub-regional level. Examples are disruptions due to disasters (e.g. earthquakes) or extreme weather events (e.g. 2022 drought in the Mississippi river) or infrastructure planning efforts (e.g. Build Back Better Act) that are highly location-specific. Consequently, this paper we present our empirical approach to imputing the network of county-to-county agricultural trade flows for the contiguous United States, and offer a detailed description of the resulting dataset. To this end, we first recount the available data sources on interstate agricultural trade which we use to impute these trade flows, discussing the features of these primary datasets and the challenges associated with their use (challenges which we subsequently address in our imputational methodology). We then use the econometric estimates from a gravity-modeling approach that links observed bilateral state-to-state trade to origin- and destination-specific supply, demand, and bilateral factors, which we use to calibrate our imputed county-to-county bilateral trade dataset to observed data moments from the primary sources on state-to-state trade flows. Put differently, we use our gravity model approach to analyze the “push” and “pull” factors that shape the volume of trade between any two regions, and then use the associated parameter estimates to impute trade volumes between any two county pairs in the United States, using state-to-state trade volumes to constrain our imputed data to match the features of observed interstate trade. This approach is complemented with a logistics model that eliminates flows with a very low probability to occur. The resulting dataset that we generate provides a full account of county-to-county flows that can be compared with other attempts at disaggregating similar flows. Other approaches do not always use gravity model techniques and when they do, they do not account for differences in climate between origin and destination places or the presence of extreme weather events that affect trade. In addition, we compare our results with a small sample of county-to-county flows recently released by BTS. Our novel dataset afford researchers the opportunity to explore a multitude of critical research questions relating to logistics, food system, food security and vulnerability to system disruption, questions the investigation of which has heretofore been limited by data availability challenges. Furthermore, by basing our approach on gravity modeling techniques, we align our methodology with one of the most successful empirical frameworks in empirical economics, one which is furthermore well grounded in economic theory. Our dataset also improves on similar data sources describing sub-regional U.S. trade by explicitly accounting for the shortcomings of the primary trade data (for instance, the treatment of ostensibly large domestic trade flows which actually reflect intermediate shipments before international export). Finally, we believe our results help guide future storage and transportation investments and improve the resiliency of the nation-wide food production and supply chain network. These elements will contribute to enhancing the nation’s food security. Ongoing developments consist in extending this downscaling technique to other agricultural and food commodities and in developing a web application that will allow users to visualize trade flows, query the data, and create scenarios simulating perturbations to the domestic trade network. The application will be freely available to the public. Our dataset will enable researchers and planners to address questions related to food supply chain, logistics, food security and system disruptions at the county level.

ABSTRACT. In the United States, like in other countries around the world, agri-food security is challenged by a growing population and less predictable weather conditions. Extreme weather events such as drought and extreme rainfall increase the volatility of agricultural yield for producers. This means changes in comparative advantages and the domestic trade of agricultural products and, in turn, in the manufacturing of food products since the former are necessary inputs in the production of the latter. This challenge highlights the need to understand how locally sourced agricultural inputs are substituted with inputs imported from other states. Following the gravity model, this paper starts with a structural framework for manufactured food production where there is constant elasticity of substitution (CES) between agricultural inputs that are sourced locally and imported. We start with a gravity model of trade to highlight that interstate exports of agricultural products (crops, fruits, vegetables, livestock) increase as the destination state experiences more drought and inversely in the origin state. Next, state-level estimates of the food manufacturing production function indicate the extent to which food processing in one state depends on agricultural inputs grown locally and in other states so that a drought event in any state perturbates the entire domestic food supply chain. Overall, the findings provide details on the key linkages in the domestic food supply chain and are informative for the design of policies aiming at mitigating the impacts of climate change on the U.S. food and agricultural sector.

ABSTRACT. We analyze aspects of economic growth in a stylized, high-tech region A with two distinct features. First, the residents of this region are high-tech because they possess skills. Using the language of Richard Florida, these residents comprise the region’s creative class and hence they possess creative capital. Second, the region is high-tech because it uses an artificial intelligence (AI)-based technology and we explicitly model the use of this technology. In this setting, we first derive expressions for three growth related metrics. Second, we use these metrics to show that the economy of high-tech region A converges to a balanced growth path (BGP). Third, we compute the growth rate of output per effective creative capital unit on this BGP. Fourth, we study how heterogeneity in initial conditions influences outcomes on the BGP by introducing a second high-tech region B into the analysis. At time t=0, two key savings rates in region A are twice as large as in region B. We compute the ratio of the BGP value of income per effective creative capital unit in region A to its value in region B. Finally, we compute the ratio of the BGP value of skills per effective creative capital unit in region A to its value in region B.

ABSTRACT. In the past ten years, AI has become prevalent in everyday use. From 2010-2018, AI exposed firms experienced considerable growth in AI workers (Alekseeva et al., 2021). Due to this growth, people have deemed AI as the next industrial revolution. However, growth has not been uniform across industries. The manufacturing sector experienced one of the most sizeable increases in demand for AI (Alekseeva et al., 2021). This paper presents the result from a primary survey of manufacturing firms in the US and focuses on their experiences with AI. This study is one of first of its kind that attempts to capture the determinates of AI use as well as understanding potential barriers. The findings reveal the spatial patterns within the use of AI and the industrial variation among those that have adopted this technology. Further, the study provides insight into possible implications these technological advances have on the future workforce.

ABSTRACT. Time zones represent an unexplored discontinuity in spatial equilibrium models in the form of increased barriers to the amenities that come with agglomeration economies. In this paper I show that sorting along time zone boundaries is not in equilibrium, and that productivity gains are possible in counties on the "wrong" side of the boundary in certain cases. To accomplish this, I first exploit the change of Mercer County, North Dakota from Mountain Time to Central Time to show that there were gains in physical productivity among participants in the Bismarck Marathon and Half-Marathon using a difference-in-differences framework. I then build on this result by applying the same framework to industry-level job count data and find that changing time zones caused an increase in job creation in Mercer County. Finally, I provide evidence of a spatial disequilibrium around time zones by estimating the effect of being on the "good" side of a time zone boundary on total migration inflows as well as net migration within metro areas in four metro areas near the boundary between Eastern Time and Central Time. I find that people prefer to move to the "good" side of the boundary in both scenarios. Collectively these results imply that counties on the western side of a time zone boundary stand to make significant economic gains by moving to the eastern side of the time zone boundary whenever the county is part of a metro area whose urban core is on the eastern side of the boundary, and that people are actively leaving these counties for others across the time zone boundary.

ABSTRACT. This study compares competing definitions of “banking deserts” and their applicability in characterizing access to physical financial institutions such as banks, credit unions, or farm credit lenders. Geostatistical techniques are used to locate and spatially analyze financial institutions in census tracts across the lower 48 United States. Logistic regression is used to identify the demographic, economic, and geographic determinants of access to financial institutions. Mapping results indicate that a significant majority of the U.S. population resides near at least one financial institution, challenging the suitability of the “desert” metaphor. Regression models instead measure the extent to which brick-and-mortar financial institutions serve a given area, finding that poverty, lower educational attainment, and lower population density, were consistently associated with being underserved by financial institutions.

ABSTRACT. This study investigates the impact of the Dodd-Frank Act on mortgage lending practices, specifically focusing on the state of Ohio. Utilizing loan origination data on individual housing sales and employing a spatial difference-in-differences approach, we estimate Systemically Important Financial Institutions (SIFIs) adopt more risk-averse strategies when originating mortgages post Dodd-Frank than smaller institutions. While smaller institutions saw an increase in proportion of loans with a loan to sale value of over 80%, SIFIs remained constant. Our results suggest the passage of the Dodd-Frank Act led to loans originated by SIFIs to have less than 80% loan to sale value on 7 percentage points fewer than would have originated otherwise.

ABSTRACT. This article investigates the effect of financial literacy on entrepreneurship in China using China Family Panel Studies (CFPS) data. Parents’ financial literacy and education levels have been used as instrumental variables for individual financial literacy to uncover the causal effect. We find that individuals with more financial literacy tend to be more likely to become entrepreneurs and individuals who are risk-averse tend less likely to become entrepreneurs. Once we add the interaction between financial literacy and risk-aversion into the regression model, the results suggest that risk attitude tends to mitigate the positive effects of financial literacy on entrepreneurship. The heterogenous analysis shows that financial literacy affects various demographics differently.

ABSTRACT. Health, closely tied to overall well-being, is defined by the WHO as "a state of complete physical, mental, and social well-being, not merely the absence of disease or infirmity." This paper explores various approaches to leading a healthy life in Colombia, aiming to shape public policies for its enhancement. Recent years have seen significant advancements in Colombian health coverage, yet access challenges remain. We present an analysis of health services received by citizens, focusing on their timeliness and quality. A novel methodology, incorporating both objective and subjective aspects of individuals' experiences, was developed to evaluate these services. This methodology employs a composite index derived from fuzzy set analysis. Our findings reveal noticeable variations in health conditions across Colombia, often linked to the prosperity of different departments. The Andean region, for instance, typically exhibits better health standards compared to others. Notably, departments like Chocó, Amazonas, Vaupés, Vichada, and La Guajira, located in the country's periphery, consistently rank lower in health indicators. A significant aspect of our analysis compared health outcomes based on social security affiliation types. Results indicate that the subsidized regime, to which nearly half the population belongs, consistently underperforms relative to contributory and special regimes. This is particularly concerning in departments where health challenges are most acute and where a larger segment of the population is enrolled in the subsidized regime. Overall, this paper underscores the need for targeted attention to these disparities, particularly in peripheral departments and within the subsidized health regime.

ABSTRACT. In the late 1980s, mortality rates in metro and nonmetro areas of the United States started diverging, indicating that the health of rural residents was worsening in comparison to their urban counterparts. This discrepancy is particularly apparent among prime working-age adults (ages 25-54), whose mortality rate from natural causes (i.e., omitting external causes such as accidents, overdoses, and suicide) was 43 percent higher in nonmetro areas than metro areas in 2019. However, the factors influencing this trend are too varied and complex to be addressed in a single paper. In this paper, I address the role health care access has played in the growing spatial discrepancies in overall population health, particularly among the prime working-age population. This is accomplished by addressing two research questions. First, has access to health services changed over time to the disadvantage of rural residents? And second, because health is a combination of personal, cultural, and economic factors: Does health care access vary across rural racial and ethnic groups or areas with differing poverty levels? To explore these questions, I analyze changes in factors affecting health care access, such as the availably of doctors, hospitals, and health insurance, and their possible correlation with the growing rural disadvantage in natural cause mortality.

ABSTRACT. The Zika virus epidemic in Brazil had significant repercussions on maternal behavior and child health outcomes. This paper delves into the causal relationship between the Zika virus incidence and preventive maternal behaviors, particularly focusing on immunization practices for children. By employing a staggered difference-in-differences approach, I identified statistically significant effects of the Zika epidemic on the immunization of children in specific age groups. Specifically, the epidemic had a discernible effect on the immunization behavior of mothers, particularly in the 0-30-day-old and 4-year-old age groups and especially in the two years following the onset of the epidemic, indicating a delayed but significant influence on maternal immunization decisions. For the 4-year-old age group, the analysis revealed that the increase in final vaccine doses was primarily driven by the Diphtheria, Tetanus, and Pertussis, as well as Varicella (Chickenpox) vaccines. These findings emphasize a prevailing trend of prioritizing children's protection against the Zika outbreak through vaccination.

ABSTRACT. Maritime piracy is a prominent form of violence in countries that face struggling institutions (e.g. Somalia), often attracting international wartime resources to combat violence on international waterways. An unintended consequence of anti-piracy operations are reduced opportunity costs of violence on land. This paper examines how reducing piracy impacts land conflict. To overcome endogeneity, we exploit variation from ocean wind speeds using two stage least squares. We find one fewer pirate attack increases conflict by 27.3\% (5.9 events) and causes 9.11 more land conflict deaths. The results imply stopping piracy saves shipping companies \$343,318,180 at the cost of 565 Somalian land fatalities annually.

ABSTRACT. The USDA Economic Research Service (ERS) has developed and maintained county-level classifications of rurality since the 1970s for use by researchers, policy analysts, and program administrators. The Rural-Urban Continuum Codes (RUCC) classify counties on a continuum from most urban to most rural based on size of the metropolitan area, urban population, and adjacency to metropolitan areas. The Urban Influence Codes (UIC) follow a similar framework but place greater emphasis on adjacency to metropolitan and micropolitan areas when classifying nonmetropolitan counties. The criteria used to create these classifications have changed over time to reflect changes in data availability, definitions used by other federal agencies, and changes in our understanding of how peripheral regions interact with urban cores. The recent releases of 2020 Decennial Census data, 2023 Core Based Statistical Areas, and updates to the U.S. Census Bureau’s definition of ‘urban’ have significant implications for the updates to ERS’ county classifications. This presentation will cover the evolution of ERS’ rural classifications, the effects of the recent data releases and definition changes on the 2024 RUCC, and considerations for updating the UIC.

ABSTRACT. One important factor that has long been recognized to improve economic opportunities for residents in smaller urban and rural areas is their ability to access larger regional markets. Distance isn't necessarily the biggest factor in how connected locations are to their regional economies. Some may be significantly connected, while others that are no farther may tend to be more isolated.

Densely populated areas can offer broader ranges of local labor opportunities, goods and services. Locations with higher concentrations of people also attract businesses and can provide functions such as health services, education services and transportation more efficiently than more sparsely populated areas. Thus, residents in smaller urban and rural communities who interact with these areas can benefit from access to a larger labor market and a wider array of local goods and services.

And these benefits don't necessarily flow only one direction: Larger urban areas can benefit from their economic interactions with surrounding smaller urban towns and rural areas. As markets expand, urban areas become more productive, increase their production capabilities and gain further advantages of concentrating economic activities at their locations.

Taking advantage of these benefits may require tailored policies, as the ability of smaller urban and rural areas to benefit is far from uniform. Thus, it is important to understand how connected these smaller urban and rural areas are to regional economies.

One way of assessing economic linkages across areas is by examining intercounty commuting flows. This article classifies urban and rural areas based on observed commuting patterns and uses this information to establish the degree of spatial interactions or connectivity across areas for the United States.

ABSTRACT. This paper documents the creation of new 'commuting zones' for the United States based on 2020 geographies and data. Commuting zones originated in the late 1980's as the result of an effort by the Economic Research Service of the U.S. Department of Agriculture to provide a county-based delineation of functional regions that covered the entire U.S. and linked rural areas to their nearest economic center. The commuting zones presented here update the 2010 era definitions available at https://sites.psu.edu/psucz/ which were themselves an update of delineations based on data from 2000, 1990, and 1980. Additionally, these new delineations incorporate fit statistics and measures of quality to facilitate comparison with earlier decades and to account for the fact that the quality of commuting-based delineations may have been substantially affected by changing patterns of work and residence during and after the covid-19 pandemic. The methodology used here seeks to replicate as nearly as possible the method employed in earlier versions of this delineation using hierarchical clustering on a proportional flows matrix generated from county to county commuting flows. The data and all scripts used to generate them and this paper are available at https://github.com/csfowler/CommutingZones2020.

ABSTRACT. Variation in the definitions of spatial units substantially impacts the validation of causal relationships between individual and ecological contexts. Acknowledging this significant influence of neighborhood definition on research outcomes, our study seeks to introduce a scalable methodological innovation. We adapt Tolbert and Killian's (1987) proportional flow methodology, originally used to create the county-level Commuting Zones, to create Census-tract-level Commuting Zones. This solution allows researchers to account for the scale of spatial units and their internal and inter-unit mobility patterns. Our methodology leads to forming clusters that define neighborhood areas based on strong commuting patterns between tracts using the public LEHD-LODES data. Through clustering at the Census tract level, we aim to show how administrative microdata can be repurposed to better approximate the spatial distribution of spatial ecology contexts. This approach aims to reduce ecological fallacies in measuring individuals' daily geographies, encompassing their travel to work, grocery stores, and recreational areas. These new neighborhood definitions will be invaluable for researchers studying the effects of ecological processes on individual outcomes, enhancing the precision of social science spatial research.

ABSTRACT. In the United States (U.S.), the primary labor market policies used by state and local governments to promote job opportunities are called economic development incentives (EDI). At the same time, industry and occupational stratification by race and sex are pervasive features of U.S. labor markets often tied to structural racism and gender bias, with minorities and women generally overrepresented in lower wage industries and occupations. This has resulted in significant variation in (real) wage inequalities by race and gender both across locations and within the same location by industry. There is also substantial variation in the use of EDI across industries and locations. To examine the role that EDI may play in perpetuating (or alleviating) these inequities, we explore the relationship between industry-specific EDI and its effects on wages with an emphasis on racial and gender disparities. We also explore how racial and gender segregation influences the allocation of EDI across industries.

ABSTRACT. This paper explores the complex relationship between Tax Increment Finance (TIF) and school aid formulas in Oklahoma. TIF is popular tool used for economic development projects across US states, counties, and municipalities (Kriz and Johnson, 2019). TIF allows jurisdictions to divert a portion of property and/or sales tax revenue growth collected from a designated geographic area away from original taxing jurisdictions, including school districts. The impact of TIF on school aid depends on whether the TIF projects generate net new taxable property values and if TIF impacts the funding that school districts receive via state aid formula during the increment collection period. The few empirical studies investigating the impact of TIF on education finance find negative or negligible impacts in the context of just a few states: Indiana (Lehnen and Johnson 2001), Illinois (Weber 2003, Weber Hendrick and Thompson 2008) and Iowa (Nguyen-Hoang 2014). Nguyen-Hoang (2019) uses conceptual models to predict school funding with and without TIF, finding negative impacts for South Dakota and Texas, and positive impacts for Illinois, Iowa, and Wisconsin. We build on Nuguyen-Hoang’s approach by investigating the TIF-school funding relationship in the context of Oklahoma. Oklahoma is known to have a “fair” equalization formula and treats TIF apportionment in a very complicated manner. We show that TIF can help or hurt the school district’s bottom line and each one will have a different threshold for when TIF increases revenues during the increment collection period. We also show how TIF creates a beggar-thy-neighborhood situation where TIF leads to less overall state funding and lower distributions to non-TIF school districts.

ABSTRACT. This study offers valuable insights into the evolving landscape of workforce dynamics and economic development strategies in the wake of the COVID-19 pandemic. Specifically, we provide a comprehensive review of Remote Worker Incentives (RWIs) with a focus on well-documented programs. We systematically summarize and classify these programs, shedding light on their heterogeneity with respect to important features, including geographic level of delivery, funding sources, eligibility criteria, value, and other features. Remote work employment removes geographic access to place of employment from an individual's residential location decision both within and across communities. We apply a basic model of migration and residential location choice to show how monetary RWIs mitigate moving costs and non-monetary RWIs complement locational amenities. We also discuss how the emergence of remote work impacts interjurisdictional competition in the context of the Tiebout-Tullock Hypothesis and yardstick competition. Our conclusions contribute to a deeper understanding of the landscape of RWIs and their potential to influence regional development and economic growth.

ABSTRACT. With the widespread adoption of remote work opportunities, communities have implemented incentive programs to induce remote workers to relocate. This paper investigates the impact of Tulsa Remote, a prominently known incentive program aimed at attracting remote workers to the City of Tulsa, Oklahoma. Tulsa Remote is one of the most well-known and generous remote worker incentive programs, offering $10,000 in cash for an individual from out of state to move to Tulsa. There is little research investigating the efficacy of remote worker incentive programs. I address this gap by analyzing the impact of the Tulsa Remote program on in-migration patterns using county-to-county migration data and conventional regression analysis. I estimate county-level models, including gravity models of migration inflows and outflows, and difference-in-difference models of net migration. In addition, I estimate individual-level conditional logistic regression models grouped by MSA. The estimates fail to show that Tulsa Remote altered the city's attractiveness to migrants. Further, placebo tests suggest that the effects of the Tulsa Remote Program cannot be isolated from other economic policies in Tulsa.

ABSTRACT. In this session, an expert panel will discuss attributes needed for an exhaustive regional classification that could be adopted by the Office of Management in 2030 as an update/alternative to its existing core-based statistical area delineations that includes metropolitan, micropolitan and non-core areas. Issues such as the use of counties as core building blocks, the role of rural/urban classification in larger regional taxonomies, and functional priorities of an exhaustive classification.

Panelists:

Todd Gardner, US Census Bureau Kyle Hood, US Bureau of Economic Analysis Sarah Young, Health Resources and Services Administration Mark Partridge, The Ohio State University

ABSTRACT. We propose that neighborhoods have a measurable effect on housing prices. In theory, searching for houses by neighborhood can be seen as a heuristic process, reducing the time and effort needed to evaluate a plethora of particular local attributes. To test this hypothesis, we estimate the neighborhood's implicit value within the framework of a hedonic housing price model. Our data base encompasses more than 50,000 housing market transactions in Charleston, South Carolina. We estimate the model with neighborhood fixed effects, picking up the time-invariant quality of the area. Our regression fits the data extremely well, with the fixed effect exerting a pronounced effect on regression results. Neighborhoods outperform alternative local areal units used as fixed effects. The fixed effect estimates are then used to measure the implicit price of neighborhoods. The analysis reveals a wide variation in neighborhood values, from high-priced historic districts and ocean-side communities to low-price, poverty-stricken areas damaged by urban redevelopment. In addition, our approach to analyzing spatial fixed effect estimates as implicit prices can be used to assess the value of regional identity in other contexts like industry location and migration.

The second important contribution we make to the literature is a new method of estimating the Moran's I in large data sets. Based on our simplified Moran's I tests, we show that spatial dependence is effectively eliminated when the neighborhood fixed effects are added to the model. This original econometric tool can be applied to a wide range of urban and regional research

ABSTRACT. The pandemic caused considerable population shifts with the South adding almost 4 million people in the span of 4 years. During the same span, the Northeast and West coast experienced significant outflows. This reshuffling of people and money raises questions about how the recipient communities changed. We examine the extent to which migration affected the trajectory of housing prices. Specifically, we focus on whether the share of migrants over 65 has put upward pressure on prices. In addition to our OLS estimation, we augment our analysis with a shift share instrument that leverages the initial share of the over 65 population at the county level. We find that the share of migrants over the age of 65 is positively associated with the rise in housing prices across specifications and samples.

ABSTRACT. This study explores the often overlooked relationship between the avian population and residential property values. While conventional determinants such as location, amenities, and economic conditions typically shape housing prices, the ecological characteristics of a neighborhood, including its bird species, can also exert a significant impact. By integrating novel Ebird data with housing sales records in the Hampton Roads region of Virginia, we aim to elucidate the correlation between bird populations and property values. Our analysis reveals a statistically significant positive association between bird populations and house prices, with the most pronounced effects observed in close proximity to the properties. Although avian presence at greater distances from residences still exerts an influence, its impact diminishes accordingly. Moreover, we employ unconditional quantile regression techniques to examine whether the effects of bird populations on property values differ across higher- and lower-priced housing segments. Our findings indicate that the number of birds exerts a more substantial impact on higher-priced residences compared to their lower-priced counterparts.

ABSTRACT. In this study, the relationship between institutions, resource abundance and inequality is investigated in the U.S. Spatial models are applied on a panel of 3,144 counties and annual data between 2010 and 2018. Preliminary findings from a Spatial Autoregressive (SAR) Panel model indicate that an increase in resource-based employment in one county (i) increases inequality outcomes in its own county i and neighboring county j. Findings also show that an increase in the number of non-rent seeking institutions in a county increase the own county’s inequality. The analysis then focuses on 10 regions across the country, employing time series models assess these relationships at the state level. Findings should be of interest to policy makers and practitioners interested in policies aimed at for reducing inequality at the regional and national level.

ABSTRACT. Theoretical support for convergence of per capita income across economies dates to the work of Ramsey (1928), Solow (1956) and Cass (1965). More recent evidence points to changes in PCPI convergence trends across US states and regions (Crain, 2003; Berry and Glaeser, 2005; DiCecio and Glascon, 2010). Ganong and Shoag (2017) report convergent rates for the period 1990-2010 at roughly half the US historical norm with virtually no convergence occurring in the period immediately prior to the Great Recession. This paper examines the converging and diverging trend in PCPI and GDP for the 12 southern states during the 20th and 21st centuries. In addition, this paper expands the analysis to include the converging and diverging trends in a more comprehensive measure, output per worker. Data suggest the over the past two decades convergence has not just slowed but reversed as southeast population has increase for 24 percent of the U.S. to just under 26 percent of the U.S. During the same period southeast RGDP fell from 22.3 percent of U.S. RGDP to 20. 8 percent of U.S. RGDP.

ABSTRACT. We use data to find deeply disadvantaged rural communities showing signs of progress and economic growth amidst systemic inequities associated with the intersection of race and place. We call these communities thriving as they appear to be on a path toward progress and economic prosperity. Informed by previous research, we then explore the commonalities among these communities that may suggest why they are outperforming expectations. We find that these thriving rural communities, compared to similarly deeply disadvantaged places, exhibit growth from the ground up driven by enhanced infrastructure supporting entrepreneurship, greater industry diversity, healthcare and education access, and higher estimated quality of life. We find that their growth has been largely inclusive with higher income growth for the lowest quintiles and lower poverty rates leading to lower inequality in the community.

ABSTRACT. In a Europe suffering from the ‘geography of discontent’, the future of the Cohesion Policy and the European growth model point to the need of deeper integration of place-based and people-based approaches, in accordance with the spatial justice desideratum (IMAJINE, 2022) as well as to the ambition “to bring EU closer to citizens and to leave no one behind” (EC, 2023, p.5). The concern with considering the social dimension of regional disparities at the same time with increasing the geographical granularity makes it possible to ‘zoom in’ on territorial specificities (RELOCAL, 2018) and to outline place-sensitive combined with people-sensitive solutions (Iammarino et al., 2017). When social cohesion challenges are focused on, the attention is drawn to a large share of the EU population that is at risk of poverty or social exclusion “often in the poorest regions of the EU but also in and around rich urban agglomerations” (EC, 2023, p.5). Starting from these overall considerations, this paper brings into discussion the question of the poverty pockets in the EU’s capital regions with a spotlight on the Bucharest-Ilfov region – the capital region of Romania, which represents a relevant case taking into consideration that, on the one hand, it is one of richest NUTS 2 regions in the EU (166% GDP in PPS per capita percentage of the EU average in 2021, ex equo with Warsaw, only below Prague (203%) and higher than many other capital regions: Budapest (156%), Bratislava (149%), Vienna and Helsinki-Uusimaa (143%), Madrid (114%), Lazio (109%), Lisbon (96%), Attica (86%), etc. whereas, on the other hand, when it comes to the share of people at risk of poverty or social exclusion, Bucharest-Ilfov has a 16% rate, which, even if it is below the EU average (21.6%) and much lower than the average in Romania, it is by far higher than in other capital regions in Central and Eastern Europe (e.g. Prague – 5.9%, Bratislava – 8.1%) The research methodology is based on an in-depth analysis which combines the interpretation of the available statistical data with the examination of relevant national and EU documents, reports, the ex-post evaluations of previous regional operational programmes and a critical assessment of the on-going regional programme and, last but not least, interviews with decision-makers, experts, researchers, etc.. The results can provide useful lessons to be shared with other capital regions confronted with the concerning issue of the poverty pockets and, on this basis, can contribute to enriching the empirical evidence for the orientation towards a Cohesion Policy able “to deliver improved well-being for all” (Rodriguez-Pose, 2022).

Acknowledgement. This paper draws on the research funded from the European Union’s Horizon Europe research and innovation programme under grant agreement No. 101061104/2022, ”ESSPIN - Economic, Social and Spatial Inequalities in Europe in the Era of Global Mega-Trends” project.

ABSTRACT. The student loan burden continues to increase, reaching $1.75 trillion in 2022. With an average of roughly $30,000 per borrower, the presence of student loan debt may alter individual decision-making in the years after college graduation. Utilizing restricted data from the U.S. Department of Education's Baccalaureate & Beyond Surveys, we seek to understand the relationship of student loans with employment decisions. Specifically, how do student loans impact rates of entrepreneurship among recent college graduates? On one hand, the presence of student loans may reduce entrepreneurship, as individuals seek to reduce income volatility and risk so they may pay off their student loan burden. Conversely, student loans may increase entrepreneurship in some borrowers if they believe repayment is not feasible with other work agreements.

While our analysis primarily focuses on the relationship between student loans and entrepreneurship through time, we also explore the relationship spatially to discover any differences between rural and urban graduates. Given entrepreneurship’s critical role in economic growth, ascertaining the relationship between self-employment and student loans is critical for continued economic progress.

ABSTRACT. This research investigates the relationship between quality of life and business dynamism. We hypothesize that higher estimated quality of life fosters more business startups. As higher quality of life places attract people, these places may also attract entrepreneurs looking to start and grow their business in places that are easier to keep and attract talent. Recent work by Conroy and Watson (2023) suggests that high natural amenity places see an increase in entrepreneurship as they attract more highly educated workers than the current labor force demands. High quality of life places are associated with more than just natural amenities. They may also be associated with more “third places,” spaces for community members to meet to exchange ideas and build relationships. Communities with higher estimated quality of life may have more third places that can increase the flow of ideas and increase business startups. Our results suggest that policies to address declining business dynamism may need to focus more on investments in quality of life rather than ineffective and controversial traditional economic development approaches with sector-specific fiscal incentives.

ABSTRACT. Design is everywhere—there is no such thing as an undesigned product. Yet despite its ubiquity, previous research suggests that only a small share of firms approach design as a structured, creative process while the great majority of firms have no systematic approach to design—a largely ad hoc process. This paper uses newly collected Design Module data in the 2022 Annual Business Survey administered jointly by the Census Bureau and the National Center for Science and Engineering Statistics to 1) differentiate the design orientation of firms using the design ladder construct from Statistics Denmark to self-classify as a) having no systematic approach to design, b) using design as styling or a last finish before launch on the market, c) using design functionally as integrated throughout the product development process, or d) using design strategically as a defining feature of the firm’s business model; 2) apply latent class analysis (LCA) to variables believed to be associated with different rungs of the design ladder to corroborate or refute the self-classification; and 3) examine the distribution of LCA corrected design ladder classifications across the US settlement hierarchy. The research has implications for the economic geography of innovation due to the widespread assumption that design as a nontechnological input to innovation is largely ignorable. Previous research identifying a strong association between design orientation and firm performance suggests that observed differences in economic dynamism may be partly attributable to the concentration or absence of creative design processes. However, evidence that a high capacity for design exists in unexpected places, combined with the highly footloose character of design competency, suggests that any such disadvantages may be amenable to policy.

ABSTRACT. The last few years have been filled with examples of transportation route disruptions affecting both the global trade system and the U.S. interstate trade. In this manuscript, our focus is on the latter and more especially on the disruption of agricultural and food trade flows across the country. Given that 86.8% of the agricultural and food production of the U.S. is destined for its own domestic market, the literature is short of studies focusing on disruption to the agricultural and food trade system in the country. A small number of studies have focused on disruption to the agricultural and food trade when disruption, such as extreme weather, affects the places of origin and/or destination. Examples include Dall’erba et al. (2021) and Nava et al. (2023) which have focused on the U.S. domestic trade system. However, these contributions do not include impact estimates of disruptions happening along the transportation link. Furthermore, their approach does not differentiate impacts across agricultural commodities. We believe it is important because the same disruption, in the latter case weather, does not affect uniformly. In this study, we remedy this gap by focusing on three objectives. Firstly, we estimate the relationship between segment disruption and trade through an econometric gravity model of domestic trade estimated over each of the nine agricultural and manufactured food commodities and in the aggregate. We rely on inter- and intrastate trade data from the most recent Freight Analysis Framework Version 5 (FAF5). Second, we identify which road segments would cause the largest reduction in domestic trade value and national welfare if impacted by a negative shock. A set of counterfactual scenario experiments is conducted to measure by implementing the features of multilateral resistance terms (MRTS) used in Yotov et al. (2016). While our simulations do not correspond to any specific form of shock, hence accommodating a wide variety of disruptions, this process allows us to provide a ranking of the road segments that would need to be rebuilt more urgently where several segments were disrupted simultaneously. Lastly, we will aggregate the analysis from the transportation segment-level to the state-level to assess the degree of spatial heterogeneity present in the vulnerability to disruption. The choice of the state as the unit of analysis is driven by the fact that states provide 60% of highway and road spending with only 25% of the overall funding coming from the federal government, leaving more of the task to state and local governments. Our overall objective is to identify the “key” transportation segments and states by commodities, i.e., those that lead to the largest perturbation throughout the nation’s trade and welfare system. Based on the interstate trade gravity model that encompasses all nine agricultural and manufactured food commodities, our results reveal that any increase in transportation cost would reduce trade but that the elasticity varies across agrifood commodities. It is the trade of livestock and fish that is the most cost-sensitive while grains rank second. Next, we implement a simulated transportation cost increase of all transportation links and split the results between i) state-to-state segment disruptions and ii) disruptions across all the links of a specific state. In the first case, we find that the link between New York and New Jersey generates the largest nationwide trade loss when disrupted. The second simulation highlights which states matter the most in the nationwide trade of a specific SCTG. We find that Nebraska is the top key state in the trade of livestock and fish while for the other 8 remaining sectors it is MI, CA, IA, TX, PA, CA, TX and PA. The results of our analysis are important because they contribute to our understanding of food security and food trade volatility across the nation while changing climate conditions keep exerting pressure on the food supply chain. In addition, they will contribute to prioritizing the upgrading process of the nation’s critical road infrastructures in the frame of the “Build Back Better” initiative since the identification of these trade links is crucial for the guidance of preemptive investments necessary to mitigate the impact of extreme weather events, or any other forms of disruptions, and thus promote resilience in the food supply chain of the country.

ABSTRACT. A growing body of literature focuses on the disproportionate and unequal risks that climate change is projected to have on communities that are least able to anticipate, cope with, and recover from adverse impacts, but few have quantified disproportionate risks to socially vulnerable groups across multiple impacts and levels of global warming. Intra-urban heat variation offers an opportunity to evaluate the distribution of population vulnerability to rising temperatures in urban places. However, there are limited instances available to assess population vulnerability. The COVID-19 pandemic exposed all persons, providing a unique opportunity to explore population vulnerability based on the distribution of severity (measured by COVID-19 deaths/100,000 people). Using detailed heat variation within urban centers acquired from remotely sensed land surface temperature, we investigate whether the spatial distribution of inter-urban heat variation over place and persons coincides with increased COVID-19 severity to assess whether intra-urban heat severity can be used as an indicator of likely locations of vulnerable populations. Our analysis will help provide insight into the linkages between environment and climate vulnerable populations.

ABSTRACT. Three things are necessary for advancement of knowledge, public education, and informed policymaking on any issue: 1) an improved scientific understanding of the issue, 2) collaborative research processes with continuous engagement of relevant parties to ensure that research products are driven by user needs, and 3) effective translation of scientific information for a wide variety of audiences to ensure usability. The University of Florida Economic Impact Analysis Program (EIAP), housed within the Food and Resource Economics Department, collaborates with the Florida Cooperative Extension system and multiple levels (department, college, and university) of communications experts to engage with new audiences and increase the likelihood that research results inform decision making processes. Members of the Florida Cooperative Extension system help translate research results for use in real world contexts and gather and convey feedback from their audiences, which often leads to new research questions and the development of innovative methods to answer them. Experts in graphic design, science communication, and media relations ensure that non-technical summaries of research results are accessible to audiences such as policymakers, representatives of regulatory agencies, and the general public. This presentation will describe how these resources have been leveraged on specific projects related to the economic contributions of agriculture, natural resource, and food industries in Florida, the COVID-19 pandemic, Hurricane Idalia, and others to ensure that our research is policy-relevant, engaging, and impactful.

ABSTRACT. This paper aims to evaluate the effects of the Georgia Entertainment Industry Investment Act, commonly referred to as the Film Tax Credit (FTC), enacted in 2005 to promote investment in film, television, and digital media projects. We seek to determine whether tax incentives impact wages, employment, and business creation. Using data collected by the U.S. Bureau of Labor Statistics and the Studio System database, preliminary results were estimated using a synthetic control approach. We find that 11–30 percent of new establishments are attributable to the FTC. On the other hand, the FTC appears to induce the bulk of film activity and, more recently, television projects. The average is approximately 65 percent, with the general trend suggesting that an increasing share of all outcomes would not have occurred without the FTC.

ABSTRACT. Cooperative Extension can be a valuable partner for regional scientists working to disseminate research and best practices to policy makers. Using our work supporting outdoor recreation economy development as an example, we showcase how Extension and campus faculty partners are conducting transdisciplinary research that connects communities and the research and technical assistance resources of the university. These multi-directional relationships facilitate network building that supports rural innovation and knowledge sharing networks that enhance local and regional economies. Our work has generated peer-reviewed academic studies (Arbogast, Deng, & Maumbe, 2017; Arbogast et al., 2020; Eades & Arbogast, 2022; Goetz, et al., 2022; Han et al., 2022; Deng et al., 2023), informed state-wide Extension teaching efforts that engage local, regional, and state economic and tourism development entities, generated significant grant dollars to support multi-state, integrated research-Extension activities, and influenced national policy development (USDA Recreation Economy Resource Guide, 2017). Most importantly, the work has produced documented impacts in communities including changes in behavior and practice, new investment, and improvements to communities’ built environments.

ABSTRACT. The COVID-19 pandemic and corresponding economic disruptions greatly increased the prevalence of work-from-home. However, increased work-from-home was much more pronounced in metropolitan than non-metropolitan areas. This study uses the American Community Survey to examine work-from-home trends and divergence among metro and non-metro areas. We consider differences by worker characteristics and the possible role that these play in metro-non-metro differences. Worker demographics do not play a major role. However, education, occupation, and industry differences do play important roles in explaining the lower rates of work-from-home in non-metro areas. Thus, non-metro areas may still have untapped potential in increasing work-from-home rates to improve labor market outcomes for non-metro residents and strengthen local economies. Improved education and high-speed internet access are likely key policy goals to help achieve this.

ABSTRACT. In the case of large-scale solar facilities (LSS), some practices associated with economic impact assessment have become methodologically outdated. This is particularly true when researchers or consultants attempt to estimate local impacts at the county or city level using local input-output multipliers in three key ways. First, as the regional scale decreases and the underlying input-output tables cover smaller geographical regions, the models produced are more sensitive to the use of national data and rely on strong assumptions in estimating the local consumption of inputs, the local provision of labor and commuting flows, the leakage of capital to other regions, or the local fiscal policy and its complementary effects on local government expenditures. Secondly, most studies have simply neglected the impact of trade-offs with other land-intensive activities (like agriculture or forestry) that support the economic base of many rural economies. Finally, since solar development is still a relatively new technology in a phase of rapid expansion, national production technology data might not accurately reflect the impacts of the operation phase and instead overestimate the need for labor and other inputs primarily used in the construction phase. Unfortunately, many of the aspects above are absent from studies that use standardized applications of IMPLAN, REMI, or other top-down regional input-output models. Given this, we start by summarizing the recent literature that addresses the local economic impact of solar development and highlight how they have been overestimating the impacts both at the state and local levels, with the impacts of solar sometimes representing almost 10% of the total employment of some US states or local counties. Next, we highlight the potential methodological aspects that lead to these results and suggest different ways to improve the estimations and the data sources that can be used for this purpose. For example, in the case of IMPLAN, one particular aspect is that the coefficient of employment per unit of output is significantly outdated and ignores the differences between the construction and operation phases. Finally, we will list several areas where best practices should be implemented to increase the accuracy of local impact assessments. Regarding regional policy, our goal is that this work will open avenues in the debate on how local economic impact studies can effectively inform localities of the economic benefits associated with solar investment and instill trust and confidence to strengthen the solar transition process.

ABSTRACT. Debt level has increased in most African countries in recent years; furthermore, in some countries at a worrisome pace. However, the continent is not yet experiencing a systemic debt crisis risk. On a regional level, several countries in the Economic Community of West African States (ECOWAS) experienced major episodes of financial crisis characterized by unsustainable fiscal deficits several decades after independence. During this period, however, current account deficits were considered normal. Therefore, ECOWAS countries were encouraged to borrow from abroad to finance their deficits and to create a conducive environment that attracts foreign investment to boost economic growth. Meanwhile, little attention was paid to the individual countries’ absorptive capacities and ability to repay the borrowed funds. Suma (2007) posited that external funding has been crucial in developmental projects, financing capital, and budgetary support for developing countries. This research will build upon the framework of Suma (2007), covering the ECOWAS countries from 1970-2022 via spatial regression methods. The general objective of this research is to examine the impact of external debt on economic growth in ECOWAS countries. To achieve this general objective, the specific objectives of this research are  Investigating the link between external debt and economic growth of ECOWAS countries;  Examining the structure, type and composition of ECOWAS’ external debt;  Identifying the transmission mechanism of external debt influences on economic growth of the ECOWAS countries.

ABSTRACT. The goal of this study is to analyze the role of healthcare as a determinant of local economic development in rural/urban communities. While healthcare itself is an important driver of local economic growth in rural communities, we believe that healthcare is also a significant factor in establishment location decisions. Through this research, we will better understand how a change in the number and type of healthcare businesses impacts employment and wages within both healthcare and non-healthcare related industries in rural/urban communities across the United States. For this project, the main research questions address three basic areas of interest in relationship to the healthcare industry: 1. Urban versus rural dynamics: Comparing rural and urban areas, what is the economic activity and establishment dynamics of the hospital industry? How does this industry impact the local economy? 2.Economic Development and Hospitals: Building on question one, what impact does the hospital industry have on other healthcare and non-healthcare industries?

Our analysis uses restricted US Census data to estimate the relationship between hospital closures or significant reductions in hospital employment on local economic outcomes.

ABSTRACT. The behavior of state governments in the United States with respect to their defined-benefit pension plans can be rationalized with the use of a buffer-stock saving model with a portfolio choice component. We document that, on average over the 1993 to 2019 sample period, the ratio of total assets in pension plans to expected future liabilities plus current tax revenue (or cash-on-hand) is approximately 73 percent, and that plans invest about 68 percent of funds into risky assets. We also estimate what we call a marginal propensity to not contribute to the pension plan of between 0.45 and 0.50, so that, for every percentage point above the target level of assets in the fund, state governments contribute close to half of a percentage point less. Simulating the model reveals that this behavior can be rationalized by a state government with a rate of time discount of 0.87 and a degree of relative risk aversion of 2.70.

ABSTRACT. The rapid expansion of large-scale solar projects across the U.S. presents opportunities and challenges for rural communities, influencing land use patterns and agricultural operations. Continued growth in large-scale solar projects is expected due to falling levilized costs, federal and state incentives for solar energy, and state-level renewable energy requirements. States have continued to increase their renewable energy portfolios by requiring that energy production comes from a mix of renewable energy sources. As state policies evolve to emphasize further the enhancement of renewable energy portfolios, particularly in the domain of solar energy, there is a consequential emergence of concerns regarding heightened pressures on agricultural operations to make additional land resources available. These concerns are particularly pertinent as renewable energy companies seek to develop undeveloped farmland. As states like Maryland pursue ambitious renewable energy goals and prioritize utility-scale solar development, understanding the spatial implications and potential conflicts becomes crucial. This paper delves into a frequently overlooked aspect within the literature—specifically, the unmet requirements of rural landowners and attorneys in comprehending complex utility-scale solar energy contracts. Given the protracted durations of these contracts span decades, their implications on existing and prospective land uses, particularly in the agricultural domain, can be substantial. This research involves focus group data from Maryland and New York involving rural landowners and attorneys to assess the demand for educational resources for solar energy developments. For rural landowners in the Northeast, a comprehensive understanding of potential concerns at the outset of the contracting process is paramount. Although focused on the Northeast, our findings have implications across the South as well. Our preliminary findings highlight the critical need for spatially informed decision-making at the individual landowner level. Understanding potential zoning and land-use conflicts necessitates available educational resources to inform negotiation and minimize disruptions to existing and future agricultural practices. Simultaneously, our research recognizes that many attorneys practicing in rural areas may need more background information to effectively address rural landowners' nuanced needs within the intricate landscape of solar energy contracts. We aim to contribute to a more equitable and spatially mindful solar development process for rural communities. Our research findings can inform policy interventions and targeted educational resources tailored to local contexts, addressing the specific needs of rural landowners and attorneys. This effort is crucial to fostering sustainable and responsible solar development that prioritizes clean energy goals and the spatial realities of rural communities.

ABSTRACT. This study examines the rural energy burden, a key metric for assessing affordability and accessibility in households, by calculating the ratio of total energy costs to household income. In the United States, rural residents typically face higher energy expenses than their urban counterparts due to the elevated infrastructure installation and maintenance costs resulting from low population density. Low-income households also experience a high energy burden, with the Department of Energy (DOE) reporting that the national average energy burden for low-income households is three times higher than for non-low-income households. Given the prevalence of low-income or economically vulnerable households in rural areas, addressing and alleviating the energy burden becomes imperative. In recognition of this issue, the United States Department of Agriculture (USDA) initiated the Rural Energy Pilot Program in 2022 and launched the Rural Energy for America Program. These initiatives aim to enhance energy accessibility and efficiency in rural areas by promoting renewable energy adoption. Consequently, it becomes essential to comprehensively analyze temporal, regional, and urban-rural disparities in energy burden, considering the diverse and multifaceted factors that influence this phenomenon. This study utilizes data from Low-Income Energy Affordability, U.S. Department of Energy, Annual Electric Power Industry Report, U.S. Energy Information Administration, and Other publicly-accessible data. By implementing a fixed effects model, the study investigates energy costs (both overall and for each energy type) while accounting for weather conditions, household and housing characteristics, and market and socio-economic factors. Preliminary findings indicate that rural households pay higher energy costs than urban households, assuming comparable conditions. Furthermore, these disparities exhibit heterogeneity across different time periods and geographical locations.

ABSTRACT. Shifting away from coal and addressing declining jobs in the coal industry has been the subject of extensive discussions and research for years. While the shale revolution, lower natural gas prices, and improved access to natural gas primarily drive this shift, the transition to cleaner energy presents an opportunity for dislocated coal industry workers to pivot towards other jobs within the energy sector that require similar skills and knowledge. Solar energy, particularly, has become more cost-effective in recent years and holds the potential to occupy a larger share of the US energy sector. Currently, only six states have a higher number of employees in coal-based electricity generation than in solar-based energy generation with Ohio being one of them. This study aims to identify occupations within the solar industry and the requisite skills that could be filled by those who have lost their jobs in coal-related occupations. To assess this, we utilize the most recent publicly available data to measure dissimilarities between occupations. Taking into account factors such as median hourly wage and employment projections, we identify occupations that offer relatively straightforward transitions as well as those that may require more reskilling and retraining efforts.

ABSTRACT. The era of mass immigration into the US ended with the onset of WWI and the passage of restrictive immigration laws in the 1920s. To understand the impact of this disruption to immigration on low-skilled labor wages, we analyze newly digitized wage data from 1910 through 1929. The laws restricted immigration from certain countries more than others, which affected local labor markets differently. Our findings suggest that industries and regions with more exposure to these restrictions experienced larger reductions in immigration flows, leading to relatively higher wages for low-skilled labor during the 1920s.

ABSTRACT. The biotechnology industry saw huge academic interest among regional scholars in the early 2000s. Following the growth of the cluster literature the sector was synonymous as the target for regional policy makers and planners. Biotechnology was seen as a “must-have industry” bringing high value-added jobs and increased output. However, in the past decade, this sector has received far less attention. In addition to this, few have attempted to follow up on earlier studies or explore how the industry has fared in more recent times. This paper seeks to add a valuable contribution to the literature by exploring the changing industrial landscape of the biotechnology industry across the US. The paper then wants to explore how these changes have impacted local labor markets both on the demand side (job postings) and the supply side (wages). The results of the work indicate there has been much consolidation in the biotechnology sector and that for many regions it has not been a vehicle of greater regional economic development. Additionally, as the sector has evolved the perceived positive labor market dynamics have not generated the economic impact once promised.

ABSTRACT. This paper develops a model of superstar employers, those with significant within-market employment relative to others in the county, to explain sector-specific labor shares across regions in the United States. In-so-doing, we uniquely contribute to the models of monopsonistic labor markets at a spatial unit of analysis that is often overlooked in previous studies. Models that use County Business Patterns data for 2-digit and 3-digit NAICS sectors suggest that superstar firms in key industries do affect labor shares. Our results have real political implications, particularly for smaller regions looking to join the growing trend of areas that use policies to attract bigger firms as a method for increasing employment opportunities.

ABSTRACT. Using a two-step process we estimate Roback-type measures of quality of life using simple rent and earnings equations in the first step for US counties. In the second step we use Spatial Bayesian Model Averaging methods to explore community (county) level characteristics associated with quality-of-life estimates from the first stage. Particular interest is paid to difference between: (1) global estimates using a spatial Durbin specification and local estimates using Geographic Weighted Regression (GWR) and (2) urban-rural differences. We find that the spatial patterns using the spatial Durbin “disappear” with the GWR results. In the second step, unlike other studies we find few community characteristics are associated with either measure of quality of life.

ABSTRACT. Building off the theoretical work of Wrede (2015) we expand the two equation Roback model of quality of life to include unemployment. Using the notion of compensating differentials that are fundamental to the Roback approach to estimating quality of life, we argue that people are willing to accept higher rates of unemployment to live in a high quality of life region but unwilling to accept risks of unemployment to live in a low quality of life region. By introducing unemployment into the Roback framework we argue that the quality of life estimates will be more efficient and robust because of additional information entered into the formulation. In this preliminary analysis we compare and contrast the two vs three equation quality of life estimates with a focus on urban-rural differences.

ABSTRACT. The concept of the “third place” describes any aspect of the built environment in which individuals spend their time (and money) outside of their home or workplace. These spaces, found in neighborhoods and business districts in both urban and rural communities, play a crucial role in enriching community life. However, it remains uncertain whether a diverse mix of “third place” establishments—cafes, bookstores, arts venues, pubs, and so forth—can truly contribute to residents' satisfaction and perception of quality of life. Using a combination of business establishment and home value index data, this study aims to determine the relationship between the prevalence of “third places” and local (i.e., neighborhood) measures of quality of life, focusing specifically on rural areas. By examining this relationship, this study provides a more tangible understanding of the economic vitality and vibrancy brought forth by attempts to attract and retain so-called high amenity business establishments.

ABSTRACT. We describe the results of the first validation study of the U.S. Census Bureau’s new Community Resilience Estimates (CRE), which uses Census microdata to develop a tract-level vulnerability index for the United States. Using administrative microdata to link Social Security Administration mortality records to CRE, we show that CRE quartiles provide more stable predictions of COVID-19 excess deaths than single demographic categorizations like race or age, as well as other vulnerability measures including the U.S. Center for Disease Control’s Social Vulnerability Index (SVI) and the Federal Emergency Management Agency’s National Risk Index (NRI). We also use machine learning techniques to show that CRE provides more predictive power of COVID-19 excess deaths than standard socioeconomic predictors of vulnerability like poverty and unemployment, as well as SVI and NRI. We find that a 10 percentage point increase in a key CRE risk measure is associated with one additional death per neighborhood during the initial outbreak of COVID-19 in the United States. We conclude that, compared to alternative measures, CRE provides a more accurate predictor of community vulnerability to a disaster such as a pandemic.

ABSTRACT. Empirical studies have been conducted on the increase in the frequency and intensity of various natural disasters due to climate change, and the frequency and scale are predicted to become increasingly intense (Banholzer et al., 2013). As the scale and urgency of these threats increase, local communities are likely to suffer direct property damage as well as overall local economies. Accordingly, we emphasize the need to build a vulnerability index that can identify the extent to which individuals and communities are vulnerable to disturbance or change. The main purpose of this study is to develop a vulnerability index that can be generally applied to various natural disasters in the United States.

Currently, the most representative index measuring regional vulnerability is Social Vulnerability established by the US Census Bureau. It is designed to measure vulnerability and construct estimates of community resilience, including 10 possible components based on individual and household-level components of social vulnerability. In this study, based on data provided by the US Census Bureau's American Community Survey (ACS), we construct a regional vulnerability index using available data not only at the household level but also on local government and local Business and Economy topics. In order to consider the fact that local governments may be more vulnerable to external shocks when indicators such as economic size, employment capacity, and per capita gross regional product are low, and to consider the diversification of vulnerability construction, the index construction was categorized as follows. This study includes approximately 35 indicators representing eight categories of vulnerability: Business and Economy, Education, Employment, Government, Health, Income and Poverty, Populations and People, and Race and Ethnicity. Depending on the characteristics and form of the data, each indicator can be assigned scores in descending order of percentiles, scores through five qualitative categories, and Z-score scores assuming normal distribution. Additionally, in this study, the categorized indicators are assumed to be linearly independent, and principal components are constructed through dimensionality reduction using principal component analysis.

The significance of this study is that it considers the need to operationalize the concept of disaster vulnerability to external shocks. This vulnerability index is a tool for large-scale, comprehensive assessment of a community's vulnerability to natural disasters. Based on the preliminary results, the following will be presented and discussed at SRSA: First, the eight categories presented in this abstract are organized and the index values for each category are specified. Next, areas with high vulnerability by category will be specified. We will also discuss whether Principal Component 1 (PC1) using PCA analysis can be used independently. Lastly, among the eight categories that make up the vulnerability index, this study will be evaluated to determine which categories can be added and which categories can be deleted in the future. It is expected that various discussions on the construction of a more integrated and complex regional vulnerability index will be discussed by SRSA colleagues.

ABSTRACT. Health research is undergoing a paradigm shift. Rather than look at middle age health as a product of socioeconomic status, for instance, it has started to look at health “over the life course,” as a product of health and other past experiences. One finding, for instance, is that growing up in a low-income family in a low-income community tends to create stresses that can lead to chronic health problems later in life. This paper reports on our attempt to apply this model to the rise in middle-age disease-related mortality in many rural areas of the U.S. over the past 20 years, even as middle-age mortality in major metropolitan areas has fallen.

ABSTRACT. Congressional districts are political entities with heterogeneous trade policy preferences due to their diverse economic structures. Representation of these interests in Congress is a crucial aspect of trade policymaking that is missing in canonical political economy models of trade. In this paper, we underscore the influence of districts by developing a political economy model of trade with region-specific factors. Using 2002 data from U.S. Congressional Districts, we first characterize the unobserved district-level demand for protection. Extending the model beyond the small country assumption to account for export interests as a force countering protection, we develop a model of national tariff-setting. The model predictions are used to estimate the welfare weights implied by tariff and non-tariff measures enacted nationally. Our supply-side explanation for trade policy, while complementing Grossman and Helpman (1994), reveals district and industry-level patterns of winners and losers, central to understanding the political consequences of trade and the backlash against globalization.

ABSTRACT. *This paper is in the Operations Research/Location Modelling genre. I know we don't get a lot of those at SRSA!

Burkey, Bhadury and Eiselt (2012, Socio-Economic Planning Sciences) - showed that in 4 Southeastern states (NC, TN, VA and SC) the hospitals are located sub-optimally, albeit not by much except in TN. The optimization criteria used were efficiency (minimize weighted time to closet hospital or p-median model) and equity (maximize coverage of patients within 30 mins driving distance or p-cover). In both cases, hospitals were assumed to be uncapacitated and estimated travel time using existing roads (and assumed average speeds by type of roadway) was used.

In this new paper, we derive new methods for quantifying the inefficiency of locations, and explore the vulnerability of the hospital network to hospital closures. In brief: • We measure how many fewer optimally-located hospitals could provide the same level of service in terms of providing the same mean driving time for consumers. • We measure how many fewer optimally-located hospitals could provide the same level of service in terms of providing the coverage (% of people living within 30 minutes of the fastest-available hospital). • We measure the vulnerability of the hospital networks in the four states by deriving the distribution of impacts seen if one or more of the existing hospitals were to close. For example, we close each of the n=132 hospitals in North Carolina, and measure the impact (and similarly for the other three states). • We then close every possible subset of 2 and 3 hospitals, and measure the impacts. • Since the number of combinations of 4 and 5 hospitals are too large to exhaustively measure, we measure the impacts of 20,000 random samples of subsets of 4 and 5 hospital closures on both the average travel time and the coverage.

ABSTRACT. We present an overlapping generations model with two asymmetric regions and individuals who have heterogeneous preference for having children, and examine what types of population distribution emerge in the economy. Nakagawa, et al.(2023) mentions, regional studies refer to the diversity and mobility of people among regions as important perspectives in explaining characteristics of regional society and economic development. This paper discusses how diversity of people as of having children relates to migration behavior of people between regions, regional fertility, and total fertility of an economy. Despite extensive research on public policies to increase low fertility, such low or declining fertility in many countries are still observed suggesting limitations in previous theoretical models that have explored the mechanism behind low fertility. Although such theoretical models work well with a set of useful assumptions, some assumptions could lose the performance of theoretical models to discover unknown mechanism behind low fertility.

One of such assumptions is that people will all become parents, even though in reality there is a good number of people who do not: There are childless couples in reality. Moreover, most theoretical studies in the literature of population economics have been conducted with single-region economic models to explain low fertility. In such the single-region models, the region's fertility rate immediately implies the total fertility rate of the economy, as the economy is exactly the region itself. However, real economies are composed of multiple regions; fertility rates vary by region and the total fertility rate of an economy can be calculated as a weighted-sum of the fertility rates in each region. Another assumption to be removed is the single region assumption. In fact, Hashimoto and Naito (2023) used a two-region model to show that population concentration in urban areas brings about the decline in the total fertility rate. However, they also assume that all people have children a priori.

Extending the two-region model of Hashimoto and Naito (2021), this study shows that if it is assumed that there are individuals who do not have children and that they live and work in higher-wage region, the economy has three population distribution patterns and reaches one of them in the long run. The results on inter-regional population distribution indicate a surprising result that the presence of childless individuals in urban region could lead to a population concentration in the urban.

ABSTRACT. Inequalities in the distribution of bus services are important to understand. This paper adds to previous literature by exploring why inequalities exist. Specifically, does voting for elected officials affect the delivery of bus services? This study explores this question using a quantitative approach as part of a quasi-experimental research design focusing on GoRaleigh in North Carolina and the Milwaukee County Transit System in Wisconsin. The analysis provides evidence of a relationship between voting behavior and bus service. This finding is observed across cities and elections with the relationships holding even when controlling for factors associated with a bureaucratic explanation for changing bus service, like changes to population or jobs. However, the strength of the relationship can change between elections, the type of elected official, and cities. Overall, this work provides more evidence of the politics behind transit service planning, especially the political influences of voting behavior in representative democracies.

ABSTRACT. This study delves into the discourse surrounding the implementation and expansion of New York City’s speed enforcement program, which was initiated in 2014 and expanded to 750 school zones by 2020. While current data suggests the program’s effectiveness, disentangling its impact from other factors is challenging. The study employs a staggered difference-in-differences method to test the hypothesis: Did the reduction in accidents result from the camera monitoring program? Building on previous research, the paper contributes methodological rigor, advancing understanding of camera-based enforcement policies’ impact on traffic safety.

ABSTRACT. Trade flows between different geographical areas are fundamentally shaped by transportation costs. Research suggests that transport costs contribute a 21% markup to the prices of goods in industrialized countries, with potentially higher implications for developing countries (Anderson and van Wincoop, 2003). While the impact of fuel cost changes on freight costs has been extensively studied (Hummels 2007), the influence of alterations in driver compensation has received comparatively less attention. However, driver compensation constitutes a significant portion ranging from 27% to 41% of operating expenses in the trucking industry in the United States, according to SONAR, a data platform used in the trucking sector. This implies that the advent of self-driving technology is poised to generate cost savings for trucking companies, despite the initial higher costs associated with implementing this technology compared to conventional trucks. The ongoing advancements in driverless technology, potentially leading to a fleet of autonomous trucks in the United States (Fagnant and Kockelman, 2015), raise the question of how transportation costs are likely to decrease due to this technological shift. Consequently, it prompts an exploration of the resulting benefits in terms of trade and economic activity for individual states and the nation as a whole. Given that the trucking industry accounted for 70.1% of the total US domestic tonnage shipped in 2015, the introduction of driverless trucks is anticipated to significantly reduce transportation costs by mitigating labor expenses, thereby influencing the landscape of US interstate trade. However, the specific extent to which each state will benefit from this technological development remains unclear. In this manuscript, we address this gap by employing a calibrated structural gravity model with recent domestic trade data and a general equilibrium framework that incorporates feedback effects and multilateral effects such as trade creation and trade diversion. This approach introduces heterogeneous effects arising from the characteristics of each state’s trading partners including the composition of their trade and their interdependence through substitution. We rely on inter- and intrastate trade data from the most recent Freight Analysis Framework Version 5 (FAF5), a dataset compiled by the Bureau of Transportation Statistics (BTS), and the only official source of domestic trade data. In addition, we use annual average freight cost per Ton-Mile data from the United States Department of Transportation when it comes to transportation cost data. The first research objective is to quantify how driverless technology will reduce the impact of distance between trading regions on interstate trade volumes as mediated by reductions in transport costs. It is expected that the same per-mile changes in driver compensation heterogeneously affect trade flows because of the differences in the distance between trading partners. In other words, trade flows between more proximate partners are likely to experience different impacts from trading partners separated by greater distances when implementing driverless technology. To address this question, our statistical model allowed for the functional relationship between bilateral trade and distance between trading partners to depend on shipping costs so that a common price shock will have differential effects across geography yet still act as an incentive to adopt the driverless technology. We assume that recent developments in the technology will lead to a 35% reduction in transportation costs considering that drivers’ compensation represents between 27% and 41% of operating expenses. The second research objective is to measure the differential impact of reducing transportation costs on 42 different types of commodities, commodities which have their own value-to-weight ratio. Such a commodity-specific approach differs from conventional trade theory that would suggest that a uniform percentage change in trade costs across all partners would yield no change in the volume of trade because there is no change in relative prices (Anderson, 2011). The effect of transport costs on trade is intrinsically linked to the value of the goods being shipped relative to their weight (Hummels, 2007; Irarrazabal et al., 2015). In particular, goods that have a lower value relative to their weight (i.e., “low-valued goods”), such as gravel, typically travel shorter distances than high-valued goods, such as electronics, because per-unit transport costs are higher for the former than the later (Irarrazabal et al., 2015). The final objective consists of quantifying the change in both state and national trade levels that will occur under a decrease in transportation costs. Reduced costs mean that consumers will be able to expand their bundle of goods given their budget constraint has loosened. With falling transport costs, consumers are expected to purchase more goods from less-proximate sources as the relative prices of such goods decline in relation to goods from closer sources. However, goods from different origins are imperfect substitutes. The reallocation of trade patterns will thus vary by state. We find that the anticipated decrease in transportation costs will lead to a 64.13% increase in the value of interstate trade flows based on 2017 levels, the most recent year for which data on interstate trade was available, and that the main beneficiaries in terms of interstate exports will be Texas, Illinois and New York. Meanwhile, the reduction in costs will give rise to the largest increase in interstate imports for Texas, New York and Virginia. Intrastate trade flows are expected to increase by 45.67%, with the largest impacts found for California, Texas and Illinois. Under the general equilibrium, heterogeneity in the trade impacts arises from differences in each state’s trade composition with trading partners and in the multilateral costs associated with such trade. These results contradict the basic assumption that a uniform shock should not affect trade because it does not change relative prices (Anderson, 2011). Moreover, our results show that the states that benefit most from the transportation cost reduction provided by driverless technology are states that engage in relatively more trade in high-value-to-weight commodities.

ABSTRACT. The Regional Input-Output Modeling System (RIMS II) is a tool for assessing the economic impacts of projects and policies for a specific region. The system produces multipliers that relate the initial effect of the project or policy to the eventual effects on all sectors of the regional economy. Users of RIMS II include government agencies, economic development organizations, and businesses.

This session will aim to provide potential data users with an overview of the RIMS II product, focusing on the following: 1. What is RIMS II, what kinds of studies can it be used for, what are the limitations, and who should consider using RIMS II? 2. A more detailed description of the RIMS II framework and its assumptions and information/data that are used in its construction. 3. A guide on how to use RIMS II, with an illustrative example/demonstration. 4. Details on how to obtain RIMS II multipliers, including costs; details on how to obtain more information.

ABSTRACT. The introduction of a factory in an area has effects on home prices within certain distances of the factory. Past research indicates that perceived environmental quality and other changes from the establishment of a factory can be represented monetarily through home values. Environmental degradation typically negatively affects house prices. The magnitude to which this happens is also dependent on the perception of pollution. If people can see a factory, they often think that there is more pollution. Most people do not know exact levels of pollution. There is no exact research on the distance from a factory that these value changes will be felt. We use data from Corelogic on 8,304 home sales that occurred in Jefferson County, West Virginia from 2012 to 2023 to analyze how distance from and the stage of production of the Rockwool factory affects home values. We utilize the hedonic pricing model to complete this analysis. We find that there are significant negative effects on home prices within 3 kilometers after the announcement of the factory, significant negative effects on home prices within 3 kilometers of the factory after construction began, and significant negative effects on home prices within 2 kilometers of the factory after production began. These results indicate that Rockwool may have positive impacts of the economy of the whole county, but there are more localized negative impacts in the surrounding neighborhoods.

ABSTRACT. Previous research has highlighted difficulties people have accessing mental healthcare. A consistent barrier to care is the out-of-pocket cost and varied acceptance rates of insurance. Using data from the Agency for Healthcare Research and Quality's Medical Expenditure Panel Survey, we examined how the share patients pay out of pocket for healthcare varies across specialties and regions. There were no significant regional variations in the share of cost born directly by the patient when visiting a general practitioner, or another non-mental health specialty: all four of our regions are close to 15% for general practice and around 19% for all other specialties aside from what was studied. However, there was significant regional variation for mental health counselors and psychiatrists. We observed around a 22% out-of-pocket rate for mental health counselors in the Northeast and Midwest, which is contrasted by a 39% and 31% rates for the South and West, respectively. For psychiatrists, the West had the highest rate of out-of-pocket payment with 44% followed by the Midwest at 28%, with the South and Northeast both around 19%. These substantial regional differences indicate that access issues are not uniform, and point to needed future research into the underlying drivers of these differing levels.

ABSTRACT. Historical practices, such as redlining, have left a lasting imprint on urban landscapes, perpetuating disparities in access to various environmental benefits. It is also recognized that faith-based institutions such as churches may provide essential services for communities. However, whether faith-based institutions can help mitigate the adverse impacts of discriminatory practices is an open question. This research aims to assess the role of faith-based institutions in providing urban green space in Baltimore City and offer policy implications for fostering equitable outdoor environments. Baltimore City serves as an ideal case study given its unique history shaped by socioeconomic disparities, racial segregation, and urban development patterns.

We compiled and digitized a comprehensive list of 216 faith-based institutions within all 31 zip codes in Baltimore City. Utilizing the spatially explicit tax assessor parcel database from the Maryland Department of Assessment and Taxation, we identified the boundaries of above-grade living areas and property land areas of these faith-based institutions. We then computed the total outdoor space owned by these places of worship, amounting to approximately 250 acres. The calculated greenspace owned by faith-based institutions presents a significant quantity, yet further analysis is needed to comprehend its distribution across different zip codes and redlined neighborhoods. The initial findings underscore the potential role of faith-based institutions in contributing to urban green space. The implications of our findings extend to urban planning and community development, emphasizing the importance of recognizing and leveraging the contributions of faith-based communities for a more equitable and accessible outdoor environment.

ABSTRACT. This project works to identify suitable sites for photovoltaic (PV) development in Goochland, Hanover, and Powhatan Counties surrounding Richmond, Virginia. With rapid increase in the development of renewable energy, strategic siting of PV parks is important to avoid diminishing quality agricultural land and ensure long-term food security. Through the use of geospatial data, we analyze land suitability, road access, and energy infrastructure proximity at the parcel level across each of these three counties. We leverage these insights to identify parcels with quality characteristics for PV parks as well as those which aren’t situated on prime agricultural land.

ABSTRACT. Climate change and environmental degradation highlights the need for sustainable agricultural practices. The issue of carbon emissions from livestock production cannot be overlooked. Agriculture accounts for 10% of total US greenhouse gas emissions, and 4.5% of this stems from livestock (Mayerfeld et al., n.d.). Despite the potential of carbon sequestration in agricultural practices, little is known of its economic viability and social benefits within the Appalachian region, where unique environmental and economic conditions prevail. Farmers engaging in carbon sequestration produce positive externalities enjoyed by society and by the farmers themselves. Farmers, however, bear solely the costs of these sustainable practices. Hence, our objective is to evaluate private and social costs and benefits of adopting carbon sequestration practices. Data on livestock production costs and revenues come from estimates provided by extension agencies. Information on payments from government programs are compiled from official websites and complemented by past peer-reviewed studies. Currently a cow calf livestock producer faces costs of ~$456.00/cow (Halich et al., 2022). Havens et al. (2023) show that producers can store 0.76 to 1.85 ton/CO2/ac/year with the difference between private costs and benefits ranging from $26.82 to $37.20 per ton/CO2, depending on the implemented practice. We use these among other information to estimate cost and benefit from adopting CO2 sequestration practices in livestock production under different herd sizes. Findings will offer insights into the economic viability of carbon sequestration practices for livestock producers in Appalachia and motivate discussions around the scalability of these practices beyond the region.