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ABSTRACT. This paper examines the impact of migration on earnings inequality using 1940–2015 data from the U.S. census and American Community Survey.  Upon addressing measurement challenges, I replicate existing findings regarding national trends in earnings inequality and migration, and subsequently analyze regional and state patterns.  Using 1940 birthplace information to instrument for migration, I find that recent immigration mildly increases the top decile earnings share, while recent in-migration and out-migration have no significant effects on such inequality.  I estimate that immigration contributed 5.8 percent to the observed rise in U.S. earnings inequality from 1950 to 2015, primarily through a non-migrant channel.

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ABSTRACT. Recent research finds that childhood neighborhoods affect adult economic outcomes, especially for children of low-income parents. Because places are shaped by both contemporary and historical factors, it is important to understand regional differences in opportunity both today and in the past. Using 1940 Census data linked to 1040 tax returns, we examine geographic differences in child outcomes experienced by cohorts born roughly 50 years apart – revealing how intergenerational persistence of status has changed over time both at the national level and at smaller geographic levels. In studying these changes, we hope to shed light on the causes of intergenerational persistence in status and inequality of opportunity.

ABSTRACT. This study empirically tests the effectiveness of two main aid modalities, conventional project-type aid and modern program-type aid, on a country’s economic growth and its budget expenditures. With respect to growth, neither type of aid showed a significant positive effect. In terms of fiscal response, however, both modalities indeed appeared to increase public expenditures in partner countries. This study specifically examines the case of Tanzania, among the countries where program-based approach (PBA) is most active. Tanzanian data support the abovementioned empirical findings; the country’s PBA, a specifically result-based approach (RBA) as an advanced version of PBA, is indeed positively related to government expenditures.

ABSTRACT. We study pollution cleanup in the Ganges in Varanasi, India. Voters elect politicians to office in each of two periods and elected politicians decide how much pollution to clean up. Between periods, there is an election. Politicians are sincere or insincere with probability p and (1-p). The marginal cost of public funds ζ measures how efficiently elected politicians transform tax receipts into pollution cleanup. All voters have identical per period utility functions. First, we ascertain the equilibrium outcome and per period voter welfare. Second, we show that an increase in ζ reduces the equilibrium cleanup of pollution and voter welfare. Third, we allow an insincere politician to borrow funds and thereby delay the revelation of his insincerity. Thus, an insincere politician can appear sincere and this affects his chance of getting elected to office. We solve for the equilibrium outcome and show that there exists a critical value of ζ,ζ^*, such that the insincere incumbent separates and loses the election if and only if ζ>ζ^* and that he pools and is re-elected otherwise. Finally, we note that an increase in ζ can raise voter welfare when politicians are more likely to be insincere.

ABSTRACT. The unprecedented shale boom in the U.S. since early 2000s has been widely studied. Many works find positive effects of the shale boom on labor market. On the other hand, people are aware of some side effects of the shale boom. For example, environmental issues like water and noise pollution ramped up in fracking rich areas; crime rates increased in places with more fracking activities. This paper examines effects of the U.S. shale boom on local patenting activities at commuting zone (CZ) level. Other geographical levels are used as robustness checks. Using direct and indirect measures of the shale boom, I find significant crowding out effects of the shale boom on local patents. When time lags are controlled, I find the strongest crowding-out effects are the contemporaneous effects. To alleviate endogeneity issues due to fracking activities are not randomly assigned in different commuting zones across the U.S. I use two IVs based on shale deposits and geological formation. IV results confirm the crowding-out effects. I also discuss possible mechanisms through labor market. This paper contributes to a strand of literature focusing on “natural resources curse”. I provide new evidence based on patent innovation, which is one of the main sources for economic growth.

ABSTRACT. Concern regarding rural economic growth policy in the United States rages on, making policy effectiveness of seminal importance. While policymakers increasingly focus on innovation-based economic development as particularly important, new business startups are not always innovative and not all job growth is created equal. We explore one potential influence of innovation policy by empirically testing the idea that government R&D investment can encourage rural innovative activity. We constructed our data by matching establishment-level Small Business Innovation Research (SBIR) award details (such as award amount, project phase, and funding agency) to the Rural Establishment Innovation Survey (REIS) and adding selected regional-level controls. The REIS data include about 260 different variables and at least 40 innovative activity measures. Our first test revealed that rural and urban establishment data should be modeled separately (as rural only and urban only). Using propensity scores to develop comparable samples of REIS firms with and without SBIR awards, our results reveal that the SBIR program appears to encourage innovation creation by rural establishments. It also appears that SBIR awards are more impactful on awarded rural establishments’ innovation creation, relative to urban establishments, in terms of the parameter magnitude and number of innovation measures potentially influenced. We conclude with a discussion of policy implications and next steps to advance this line of research.

ABSTRACT. Introduction and Motivation

A notable phenomenon across US labor markets is the visibility and growth of the on-demand or “gig economy,” representing self-employed workers performing on-demand tasks or “gigs” directly for clients, often on a short-term basis. Economists have struggled to analyze this sector given limited data, and appropriate job classifications. Yet, understanding the on-demand economy requires one to consider a number of underlying drivers, such as technological change and disruption, intergenerational workplace preferences and the rising interest in and support for entrepreneurship. In a 2019 New York Times article, Rothwell noted, “Even after a long economic expansion, America is still the land of the side hustle. . . involving around a quarter of workers — (but only) about one-third of people with multiple jobs say they do them out of financial necessity.” So, before we begin to “fix” this market innovation, we should better understand kay drivers. For example, some workers consider gigs the most effective way to supplement traditional wage income, yet others seek independence or have personal circumstances that prohibit traditional employment, so want to remain with on demand work. Since gig workers are largely self-employed, it is possible to get a sense of the trajectory of the gig economy by noting that the growth of nonemployer establishments outpaced the growth of traditional employer establishments by a factor of 8:1 between 2010 and 2015. These trends are particularly apparent in Colorado, making it an interesting state in which to pilot focus groups on this sector. Although 25 to 30 percent of Americans engage with on-demand work in some capacity (McKinsey, Pew, Federal Reserve), the exempt labor market has remained underexplored (Kacher and Weiler, 2017). Of particular note, and an important discussion point for our work, is rethinking the systematic division in access to policy between those working in standard and nonstandard work arrangements, but first, more insights on incentives and tradeoffs that employers and workers consider is needed.

Approach and Methods

To address the lack of place-based context to explore what drivers underlie the growth in on-demand employment, the Colorado Department of Labor and Employment, in cooperation with Colorado State University and the National Governors Association, framed a pilot study using employer and employee focus groups in the following three locations in Colorado: 1) The state’s dominant metro area in terms of size and economic growth, Denver; 2) The Southern edge of the Colorado metropolitan region, Pueblo, that is unique in that its economic prosperity has lagged in a state that has otherwise outperformed the national economy; 3) Grand Junction, the only metro area on the West side of the state, but surrounded by rural areas, thereby allowing more active recruitment and participation from rural areas

Recruitment was facilitated through CDLE and CSU networks in each of the focus group locations. There were significant challenges in securing RSVPs, so the team quickly assessed that potential participants perhaps did not see themselves as being part of the gig economy. To add clarity to our recruitment process and partners, a one-page document describing categories of on-demand work was distributed. Focus groups were digitally recorded and transcribed before being coded. The coding process began with open coding, which is designed to allow themes to emerge rather than beginning with a predetermined set of ideas or themes. This first stage of coding encourages variety and creativity in assigning codes to small units of text (Glaser 1978). Multiple codes for a single unit of text were used when appropriate to avoid early bias towards specific concepts or themes (Charmaz 2006). The next stage of coding, focused coding, zeroed in on specific codes that appear to be the most useful or relevant (Glaser 1978). After focused coding, the theoretical coding stage related codes that have remained relevant to each other (Charmaz 2006; Glaser 1978). According to Charmaz, theoretical codes are “integrative” and “lend form” to relevant codes (Charmaz 2006: 63).

Findings and Key Points for Discussion

Even with recruitment challenges (13 total participants across the three locations) a diverse set of participants allowing for inferences on different aspects of the on-demand economy. All participants had multiple jobs: some out of necessity to supplement income, others strategically to diversify projects and income sources. Most participants (10) identified their on-demand work as their main job or source of income; just three said their on-demand work provided supplemental income. It should be noted this is relatively aligned with secondary data related to on-demand work. Income generated was frequently the determining factor for which of their multiple jobs was their “main job”, but some participants also said the number of hours they worked or the benefits they received determined which was their “main job.” Four participants (just under one-third) said they were currently mixing traditional and on-demand employment. One participant used traditional part-time hourly work to supplement his business income; three participants used on-demand work to supplement traditional employment.

Participants noted both “push” and “pull” factors that led them to join the on-demand economy. For a few, the push into the gig economy was financial. One participant was laid off; another was seeking supplemental income to meet financial needs. Other participants pointed to “pull” factors that led them to choose on-demand work, including flexibility, discretionary income, and the ability to pursue work they are passionate about. But, it was also clear preferences for more flexible work was also key: participants placed a high value on control of their schedules, business decisions, and incomes. Several mentioned feeling “stifled” by the rules and bureaucracy of traditional employment arrangements. In the absence of formal or professional support systems, partners and families became essential. A major conversation in current literature on the on-demand economy centers around the lack of employer-provided benefits for independent workers. Despite the prevalence of this concern in previous studies, benefits and insurance came up organically in only the Denver focus group. Although the on-demand economy may provide new economic opportunities, policymakers have legitimate concerns about tradeoffs between the positives of on demand work, supplemental income or more flexible work schedules, but also, evidence that safety risks and financial uncertainties are high among such workers. Our findings, that most participants wanted to continue or increase their participation in the on-demand economy, demonstrates the importance of this sector of the workforce into the future. Our focus group methodology and lessons learned from the recruitment process, including the importance of defining the on-demand sector, will inform similar research conducted in other states through collaborations with the National Governor’s Association. Specifically, our findings highlight how place-based aspects are key to the development of policy around on-demand work.

References

Glasner, B. 2019. The Minimum Wage and the Gig Economy. University of Washington Working paper. 2019.

Kacher, N. and S. Weiler. Inside the Rise of the Gig Economy. CSU Regional Economic Development Institute (REDI) report. April 2017. Online at: https://redi.colostate.edu/wp-content/uploads/sites/50/2017/06/REDI-report-April-gig-economy.pdf

National Governor’s Association. Understanding the On-Demand Economy: Synthesizing Research & Policy. Working paper. 2019. 19 pp Rothwell, J. “Earning income on the side is a large and growing slice of American life.” The New York Times. Dec 18, 2019, 2:33pm.

ABSTRACT. This study attempts to explain productivity differences across regions in Mexico. Data shows differences in education quality, school attendance, and occupational choices across regions in Mexico. We argue that these differences influence the aggregate productivity of the regions. The study builds a general equilibrium model with frictions in the labor markets, education quality, and school attendance to quantify their influence on regional economic development. Education quality, proxied by test scores and school attendance, explains the substantial regional productivity differences. The study finds that regions with a higher quality of education and school attendance have higher productivity: the model predicts that improvement in education quality and increase in school enrollment rates increase the productivity of the regions on average by 7% and 17%, respectively. Regional differences in occupational choice, caused by frictions in labor markets, contribute to productivity differences as well. Regions with occupational distribution skewed toward higher-skilled occupations are, on average, 3%–4% more productive than the lowest-productive regions. The study also notes that a significant proportion of women work in the home sector. The counterfactual analysis shows that reducing barriers for women to work in the market sector would increase the aggregate regional productivity by 9%–12%.

ABSTRACT. We introduce a new method for modeling distributional impacts of regional shocks in spatial CGE models. Specifically, we de-compose a regional economy (Memphis, TN) into 8 sub-regions that are heterogeneous in both economic and household (distributional) composition. Sub-regions are linked with each other and the rest of the world via commodity, capital and labor flows (i.e., commuting). When aggregated, the sub-regions form an economy identical to the larger region. We refer to the aggregated model as an “aspatial” model and the disaggregated one as a “spatial” model.

While there is an extensive literature on multi-regional CGE models (linked primarily though intermediate input markets), we offer two, intertwined contributions to the CGE literature. Conceptually, we provide a more accurate modeling of distributional impacts by explicitly modeling the spatial disconnect between worker, proprietor and firm locations: what happens to firms located in one region can impact households located in others. For modelers, our second contribution is to increase awareness about relatively under-utilized, publicly available, spatially precise data for both households and firms that allows for greater spatial resolution in impact modeling.

Frankly, building the spatial model was very time intensive. In this paper we share our experience in order to help others decide if such a modeling approach is worthwhile. We do so by running a series of geospatially defined economic shocks--specifically a natural hazard, a tariff and a sales tax increase--in both the spatial and aspatial models and compare the results on a variety of economic indicators. Overall, we find that the additional effort of building a highly spatialized model may not be worth it when looking at typical measures of interest, such as regional output and employment. However, we also find that an aspatial model can obscure important distributional differences across sub-regions that are revealed in a more spatially defined model.

ABSTRACT. Multi-region CGE models have widely adopted Amington's assumption of substitutability between imports from different countries, and used a two level nested CES composition function. However, there are very few readily available 'micro' Armington elasticity estimations that are suitable for large-scale multi-region CGE models in the literature. This paper uses WIOD dataset to estimate Armington elasticity with and without adding domestic supplies in the commodity bundle. A modified Arellano-Bond GMM estimation shows that the micro elasticity which governs the easiness of substitution between different origins of imports is slightly larger than the macro one, which governs domestic supplies and all the imports. Long run elasticities are in average five times larger than short run elasticities. Firms are relatively inelastic to importing origins in the short run, but get more elastic than consumers in the long run.

ABSTRACT. Brazil has different types of police by its territorial division: federal police, state police and municipal police. The latter represented by the Municipal Guards, had its presence increased in Brazilian cities in the last twenty years. We assessed the impact of non-armed and armed municipal police on crime rates in Brazil. The quasi-experiment promoted by the amendment in the Disarmament Statue (Federal Law, 2004) and the increase in the number of cities creating local polices is explored trough an empirical strategy based on a two-step LASSO difference-in-differences regression controlling for spatial dependence. The findings reveal that there is no evidence of impact on homicide rate due to number of local police officers by 100,000 inhabitants or the use of firearms by them.

ABSTRACT. The U.S. Environmental Protection Agency (EPA) has developed and continues to enhance the US Environmentally Extended Input-Output Model (USEEIO). USEEIO is a national life cycle model that combines economic and environmental data to characterize environmental and economic effects associated with the production and consumption of goods and services in the United States. It tracks ~400 commodities and >2000 resources, emissions, and waste types to characterize 20+ environmental and economic indicators. USEEIO is widely used in industry, the non-profit sector, in government and academia for applications such as footprinting and sector-based environmental assessment.

From 2016-2018, the EPA developed a preliminary multi-regional input-output model in a two-region form. The methodology used was described (Yang et al. 2018) and the results were presented over this period to organizations and experts in the state of Georgia with an interest in sustainable materials management. The EPA now has plans to develop like models initially for 3 other interested states and eventually be able to construct them for all US states.

We present a refinement of this methodology integrating additional datasets to provide additional information on state industry output, implement model constraints, and apply balancing algorithms to produce balanced IO tables linked with corresponding environmental satellite tables. This methodology is being integrating into the USEEIO modeling framework to enable transparent and reproducible creation of state models for all states and for multiple recent years of interest. The resulting Georgia model for 2016 is described.

ABSTRACT. The U.S. Environmental Protection Agency (EPA) has developed and continues to enhance the US Environmentally Extended Input-Output Model (USEEIO). USEEIO is a national life cycle model that combines economic and environmental data to characterize environmental and economic effects associated with the production and consumption of goods and services in the United States. It tracks ~400 commodities and >2000 resources, emissions, and waste types to characterize 20+ environmental and economic indicators. USEEIO is widely used in industry, the non-profit sector, in government and academia for applications such as footprinting and sector-based environmental assessment. EPA is also developing regionalized versions of USEEIO and has developed a model for the state of Georgia. The EPA, GA Department of Economic Development, and Georgia Tech are now working with interested Georgia communities on using the model to evaluate the potential economic and environment consequences of new development directions through the creation of web applications that specifically address community concerns using the USEEIO model along with local data in the background to support the evaluation. One strong interest in a Georgia community is related to innovative recovery or alternative use of available materials. This project team will be working with stakeholders in Southeast Georgia to collect data on material generation and new technological improvements involving increased material efficiency to evaluate their consequences using the GA version of USEEIO.

The proposed method for consideration involves creating linkages between these material data and the regionalized IO model, which requires knowledge of: 1. local industries or final uses that are generating the materials, 2. how the materials are currently used, disposed of, or handled and in what region, 3. possible uses of the material to substitute for current industry material or energy requirements, 4. estimates of export of the new material or new products, 5. other technological and environmental changes related to material use and substitution, and 6. the market consequences of scaling up material cycling changes. We propose the creation of a model in the form of a multi-regional waste input-output (WIO) model, an extended form of the IO model, that retains attributes and thus functionality of an USEEIO model. This methodology will be presented using an example material of interest to the SE Georgia partners.

ABSTRACT. Environmentally extended input-output (EEIO) databases have been widely used by both input-output and life cycle analysis practitioners to study the environmental effects of products and processes in different sectors and regions. More recently, efforts have been focused on developing harmonized time-series of EEIO tables at both national and global levels, allowing a better understanding of the dynamics of such impacts. Based on the USEEIO framework, we develop a novel harmonized time-series of EEIO tables for the United States covering the benchmark Make-Use Tables from 2002, 2007 and 2012, and the same set of comprehensive physical accounts as the original USEEIO. We discuss the methodology employed, possible applications, and present the evolution of national aggregated environmental indicators over these years.

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ABSTRACT. The United States has experienced a boom in the construction of sports facilities over the past two decades. Proponents of these projects claim that sports facilities provide many benefits to local residents, such as income increases, job creation, and tax-revenue increases. However, a new stadium or arena may also generate negative externalities, such as increased traffic, noise, pollution, and crime, that make living near the venue less desirable. One way to assess the impact of a new sports facility on the surrounding area is to examine how it is capitalized into nearby property values. Using a hedonic difference-in-differences framework, we examine how proximity to the Staples Center, a large sports and entertainment venue in downtown Los Angeles, California, is capitalized into house prices. The Staples Center is located in a very populated area and is the most intensely utilized professional sports facility in the United States, which makes understanding the intangible benefits or costs of the arena of considerable importance.

ABSTRACT. The paper separately analyzes the effects on property values of being in a historic district that becomes listed on the National Register and being in one that is designated as a local historic district. Using detailed data on district boundaries and parcel-level transactions data from 1990-2015 for Fulton and DeKalb counties, this research documents the change in property values by type of historic district. To strengthen identification, the estimated effects are obtained by comparing the change in property values of districts newly listed in the National Register and locally designated districts with the change in historic districts that were proposed for the National Register and met the eligibility criteria. The estimates suggest single-family residential property values increased by 13-14 percent in historic districts after becoming listed on the National Register and by approximately 7 percent in historic districts after being designated as a local historic district. The estimated effects in this report suggest fears of negative property value effects associated with local historic designation or listing on the National Register are unwarranted.

ABSTRACT. Globalization, migratory phenomena, and transformations of production systems reflect processes driving change and evolution in cities. However, despite these ongoing disturbances, urban hierarchies maintain a stable state over time (Duranton, 2007). This stable state of urban systems is studied through many works on a national scale (see among others: Davis and Weinstein, 2002, Bosker et al., 2008, Gonzalez-Val, 2010). These studies provide an interesting but reductive approach to the processes at work in these systems. Indeed, it is accepted that two laws summarizing their functioning govern national urban systems. Urban hierarchies converge towards a stable state in time (Zipf's Law) thanks to a stochastic process (Gibrat's Law) (Gabaix, 1999). Zipf's law assumes that the city size distribution (that is, the ranking of cities from largest to smallest) is linear and continuous . Gibrat’s Law seeks to explain the linear and continuous shape of the city size distribution by describing urban growth processes (Gibrat, 1931). Gibrat's law is a stochastic process that implies that growth rates depend on a number of factors and that the effect of each of these factors is marginal growth. Simon (1955) and Ijiri and Simon (1977) conclude that Gibrat's law is confirmed if the long-term growth rate of cities is the same and does not depend on the size of urban areas.

This approach of organization of urban hierarchies assumes thus that, for an urban system to be stable, all the cities that comprise it follow the same stochastic process without differentiation (Lalanne and Zumpe, 2015). It implies that the diversity of growth processes depending territorial and regional specificities are not integrated into this approach (Lalanne, 2014). The linearity depicted at aggregate national scales actually obscures variation observed in a multi-scale regional analysis (Bessey, 2002). Cities are complex systems structured by key processes that occur at distinct spatial and temporal scales. In other words, systems such as cities cannot be understood at one scale of analysis, but as a nested set of structures and processes occurring at multiple spatial and temporal scales, where each scale is structured by different key processes (Allen et al., 2006). From that perspective, regional scale as national sub-system is relevant to better apprehend different urban growth processes. Actually, considering the Gibrat’s law mentioned above, we expect that big, medium and small sized cities within an urban system don’t respond to the same urban growth processes and to the same stochastic processes.

For all this reasons, this paper explores the different growth processes within a regional urban system among big, medium and small sized cities. France offers a very interesting field of application. In fact, the increasing skills of the French regions in terms of economic development since their new delimitation in 2015 and the importance of European regional policies reveal the need to develop new knowledge on the functioning of these large regions. The objective of this paper is therefore to explore and create a new field of knowledge on the organization of French regional urban systems. For this purpose, we use French censuses data from 1800 to 2016 for a french south-west region, the Nouvelle-Aquitaine. Moreover, we use methodological improvements to detect stochastic processes (Lalanne and Zumpe, 2020) as the unit root tests and the cointegration tests.

ABSTRACT. Police in many cities throughout the world are now fighting urban crime with new and sometimes controversial technologies such as automatic license plate readers, drones, and facial recognition software. After concisely describing some key issues, we construct a theoretical model with three cities and shed light on three broad questions. First, should police in each of these three cities have access to such new and potentially controversial crime fighting technologies? Second, if police are to have access to such technologies then what are the properties of a policy regime in which the technologies are made available in each of the three cities in a decentralized manner? Finally and once again if police are to be provided with these technologies then what are the properties of a policy regime in which the technologies are made available in a centralized manner with equal cost sharing by the three cities?

ABSTRACT. Global trade has increased dramatically over the past 50 years to represent a significant share of gross domestic product in many countries and enhanced interdependence between countries. Satisfying final demands of global consumers could necessitate demand for intermediate inputs sourced from all around the world. Even when imported inputs are not directly involved, indirect dependence on goods and services sourced from abroad is growing as countries capitalize on comparative advantage and trade barriers decline (Yamano, 2016). The continuation of these trends and interest in the ability to accurately model these relationships highlights the importance of inter-country input-output tables that combine data on production, intermediate consumption, demand, and trade disaggregated for different industries and distinct types of final demand. Inter-country I-O tables, such as the World Input-Output Database (WIOD) are already widely used to perform value chain analysis. WIOD contains annual input-output tables for 44 economies with 56 industries for the period 2000 to 2014. Such a database allows for the bi-lateral combination of trade balances among the most relevant economies in the world, for a relevant period of time, and the ability to distinguish between intermediate use and final demand. We first construct a panel data set that can be used to econometrically estimate the determinants of trade balances between 2000 and 2014. Based on economic theory and the data availability, several variables are tested including nominal exchange rate between countries, price deflators within the country, price deflators of imports, and national intra-industry trade, among others. By using the concept of the Leontief inverse and multipliers, we also test the relevance of variables as the weight of imports in output or the national value-added contribution by industry. The results represent a first attempt at understanding how such variables can be used to update estimations of inter-country trade or run distinct scenarios. Results also contribute to a better understanding of how trade balances are influenced by macroeconomic variables in various economies and industries. Specifically, policies that influence different macroeconomics variables might be more effective in some countries than in others depending on the type of production and its uses. Finally, in a world that has become more interconnected and where the future of foreign policy and trade policy is uncertain, understanding the possible impacts of future exchange rates or price fluctuations is also a way to better predict and potentially mitigate their consequences. References Yamano, N. (2016). OECD Inter-Country Input–Output Model and Policy Implications. In Uncovering value added in trade: New approaches to analyzing global value chains (pp. 47-59).

ABSTRACT. Consumer interest in locally grown food has been increasing dramatically in the United States via food hubs, farmers markets, and other venues. The number of farmers markets has grown significantly from 1,755 in 1994 to more than 8,600 markets as currently registered in the USDA Farmers Market Directory. This increase can be attributed to the increased demand for fresh, locally produced products. We developed an IMPLAN-based SAM model of 22 counties surrounding and including the District of Columba to evaluate the direct, indirect, and induced economic impacts of farmers' markets on the study region. To supplement the input of IMPLAN, this research incorporates robust data collected from a consumer survey of shoppers in Maryland, Virginia, and the District of Columbia conducted in 2017. The empirical results from IMPLAN show the direct gross sales and income figures into an estimate of the number of jobs in the study region’s economy that were tied to farmers' market activities. The analysis also provided jobs created from indirect and induced effects. The average income multiplier is 1.51 indicating that a $1 increase in personal income for a farmers market translates to $1.51 in personal income across the economy of the study region.

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This study provides a detailed analysis of the operation of the Jacksonville Clay Target Sports Club [JCTSC], and the effects of the club’s community involvement in providing an accurate estimate of the overall economic impact of the club on the four primary metropolitan counties of Jacksonville, including Duval, Clay, Nassau and St. Johns. Jacksonville Clay Target Sports Club is an independent and private sports club located on nearly 170 acres in Jacksonville, Florida. In its 84th year, Jacksonville Clay Target Sports Club is one of the most premier and successful sports clubs in Northeast Florida.

This report analyzes the economic contribution of the Jacksonville Clay Target Sports Club during 2019, including the club’s operating expenses, capital expenses, visitor spending and the combined contributions of the club’s community service and volunteerism. Using government-sourced economic data, models were constructed using the IMPLAN system to estimate the economic impact of the combined activities of the JCTSC on the region. IMPLAN, a PC-based social accounting and impact analysis software, is based on national economic data, and the models were designed to determine the effect of the club’s operations on the greater community. This study will determine the total effects of labor income, the number of jobs created and the overall economic impact that Jacksonville Clay Target Sports Club was responsible for in 2019.

Keywords: Operating budget, Capital budget, IMPLAN, Multiplier, Income effect, Employment effect, Output effect

ABSTRACT. A multivariate singular spectrum analysis (MSSA) of housing starts in the City of Toronto and national/regional socioeconomic time series data is presented. The analysis is based on a systems approach to estimate and understand the dynamic interactions between housing starts and other macroeconomic factors. Recognizing the importance of a holistic view of the relationship between the interacting variables temporal movements supports research to integrate methods from different analytical domains, or “silos”. The research strategy is to fuse the nonparametric MSSA of the selected time series collection with Granger bidirectional causality and feedback tests to build statistically significant causal loops that can virtually explain the oscillatory components in the time series system. The hypothesis is that the separate time series do not have common structure and therefore a trajectory matrix, of window length ‘L’, for the time series assembly can be constructed. The trajectory matrix is decomposed into its respective eigenvalues (spectrum) and eigenvectors to separate the embedded signals (trend and oscillation trajectories) in the series system from the different levels of noise associated with each series.

The cross-correlograms of some bivariate are also presented for comparison with the Granger causality and feedback tests of the corresponding time series. It is mostly true that causation and feedback between socioeconomic factors do not occur in practice contemporaneously. The leads and lags ranges of the bidirectional interaction between variables are estimated from the rejected (non)causality null hypothesis of Granger test for the respective lags. The systems view of the time series collection is constructed from the MSSA spectrum decomposition and Granger causality tests. To further identify the fingerprint of the system’s dynamism, the fishbone diagrams (the genetics) of the intra-causalities of the selected variables are used to explore the nonlinear complex relationships between the variables. It is evident that each cause (genetic) has its unique effect and vice versa. The system’s components interactions lags, which are mostly related to the non-common structure of the separate time series, can not float without absorption by some elements of the system. Therefore, ‘stock’ components are introduced to simulate the nonlinear system dynamics behaviour.

ABSTRACT. Introduction

On June 28, 2010, the Office of Management and Budget (OMB) released OMB Bulletin No 13-01, the 2010 Standards for Delineating Metropolitan and Micropolitan Statistical Areas. As a part of this delineation, the core population of an urbanized area from which a metropolitan statistical area (MSA) is created was maintained at 50,000. This cutoff has been maintained since the inception of the metropolitan statistical area definition in 1950 and its precursor, the 1930s era Metropolitan District (OMB 1998). The current metropolitan statistical area is a combination of a central county or counties that contain this urbanized area threshold and a commuting threshold that adds additional outlying counties to incorporate functional labor market linkages. While work has evaluated the sensitivity of the commuting cutoff to the MSA population (cf. LaHaye 2019), less work in recent years has been performed to evaluate the static nature of the 50,000 population threshold for defining metropolitan areas applied in academic research. In one of the few papers providing a recent overview of the threshold, Goetz, Partridge and Stephens (2018; p. 102) stated “In 1950 it may have been reasonable to assume that an urban core of 50,000 people represented sufficient agglomeration economies to be ‘urban.’ Yet, agglomeration thresholds appear to be increasing over time as economic functions that were found in smaller locales are now only found in larger places (Partridge et al. 2008; 2010) and the current definitions may not accurately reflect today’s concept of urban versus rural.” They call for research into these agglomeration thresholds.

Going back further in time, the Metropolitan Area Standards Review Project (MASRP) provided a comprehensive review of U.S. Metropolitan definitions dating back to the early 1900s and highlighted that the United States had more than doubled in population since 1930 (OMB 1998). They considered either doubling the core base population to 100,000 or maintaining the 50,000 base but with new ranges of Metropolitan population bands between 50,000 and 250,000, 250,000 and 1 million, and greater than 1 million. In the end, the 50,000 base was maintained for sake of definition continuity despite the cutoff having “decreased significance” toward metropolitan character (OMB 1998). Evaluating Metropolitan Character for Modern Core Thresholds MASRP delineated metropolitan definition strategies at the intersection of structural and functional characteristics. The functional approach used commuting as the key characteristic for incorporating outlying counties, while the structural approach used population density and threshold population and as a proxy for spatial equilibrium of labor supply and demand in geographic location. To address the growing agglomeration thresholds argued by Goetz, Partridge and Stephens (2018), we apply demand threshold analysis (based on Central Place Theory) as an approach to considering alternative threshold populations for urbanized areas. This type of threshold analysis may be well-suited for evaluating changing structural and functional characteristics because, along with total population, commuting patterns and population density influence the size of demand thresholds. Hence, demand thresholds may provide a proxy for the unobservable “metropolitan character.”

Specifically, we evaluate the distribution of county-level retail sector establishments by Core Based Statistical Areas using establishment count estimates from the W.E. Upjohn Institute’s WholeData between 1998 and 2016 (Bartik et al. 2018). Next, we measure the average establishments in various retail sectors for counties within 10% bands of key CBSA population thresholds (10,000 and 50,000) in 1998. In the 2016 data, we then identify the average population for that retail sector required to support the same number of establishments in 1998. We then generalize this approach by averaging the 2016 population across all retail sectors analyzed (weighted by the national size of the retail sector) to identify the 2016 population required to hold the retail establishment counts constant. When applied to the population thresholds analyzed, they could be considered the population change required to make the “metropolitan character” of urbanized areas constant.

This approach is analogous to the consumer price index in evaluating a bundle of consumer items for measuring consumer inflation. Here, we examine a bundle of retail sector population thresholds for measuring “metropolitan character.” The population required to maintain establishment counts would serve as an agglomeration threshold index allowing for population inflation in the CBSA core population cutoffs. Such an approach could be used to index the CBSA around a constant “metropolitan character.” References

Timothy J. Bartik, Stephen C.Y. Biddle, Brad J. Hershbein, and Nathan D. Sotherland. WholeData: Unsuppressed County Business Patterns Data: Version 1.0 [dataset]. Kalamazoo: W. E. Upjohn Institute for Employment Research, 2018. For availability: https://upjohn.org.

Goetz, S., M.D. Partridge, and H.M. Stephens. 2018. The Economic Status of Rural America in the President Trump Era and Beyond. Applied Economic Perspectives and Policy. 40(1): 97-118.

LaHaye, J. 2019. Commuting Error: Traversing Thresholds to Improve Rural and Urban Area Definitions. Selected Poster Presented at the 2019 Southern Regional Science Association Meetings, Arlington, VA, Apr 2-4.

Office of Management and Budget (1998). Alternative Approaches to Defining Metropolitan and Nonmetropolitan Areas. Federal Register. 63(244): 70526-70561. December.

Partridge, M.D., D.S. Rickman, K. Ali, and M.R. Olfert. 2008. Lost in Space:Population Growth in the American Hinterlands and Small Cities. Journal of Economic Geography 8: 727–57.

Partridge, M.D., D.S. Rickman, K. Ali, and M.R. Olfert. 2010. The Spatial Dynamics of Factor Price Differentials: Productivity or Consumer Amenity Driven? Regional Science and Urban Economics 40: 440–52.

ABSTRACT. This paper presents an application of structural equation models (SEM) to answer a set of complex, multi-faceted, and important questions for regional economic development that have long gone unanswered. In answering the question on the role of transportation in regional economic resilience, stakeholders, primarily from transportation agencies and regional planning organizations were first asked to identify the primary socioeconomic and infrastructure components and possible relationships the components had with regional economic resilience. Their responses were then used to construct a preliminary model. Afterwards, an SEM was fine-tuned using county-level data from multiple sources. Using the shift-share framework, total employment was selected as a response variable. In this way, the SEM accounts for stakeholders’ opinions (analogous to stated preferences) while being data-driven (analogous to revealed preferences). A case study in Indiana indicates that, while transportation accessibility does not directly affect the response variable (total employment), it has a positive and significant effect on industrial diversity, which in turn has a weakly significant and positive effect on total employment. These results may be useful to planners who are interested in the role and paths of transportation and regional economic resilience.

ABSTRACT. Political economy explanations of the politics of trade argue that policy-makers are politically motivated, and choose trade policy in response to the demands by voters and privileged groups most affected by trade flows. Empirical approaches often rely on reduced-form estimates of parameters derived from different variants of traditional political economy models of trade to explain the influence of winners and losers from trade on the formation of trade policy. Yet there is an important mismatch between theory and empirics, which is reflected in our inability to reconcile a strong empirical regularity: a lack of correspondence between legislative voting patterns on trade policy and district level predictors of trade policy stance. In this paper we take a first stab at framework linking theory and empirics: we develop a structural estimation of a formal model inspired by the Grossman and Helpman’s model of trade politics. Our modeling strategy differs from G&H in a fundamental way: we model the vector of tariffs that would be chosen by a local decision-makers representing a district (or region) within a larger polity populated by numerous regions. We are, thus, able to compare the vector of tariffs enacted by a central planner from the solution that would be preferred by the local decision-maker. Ultimately the policy enacted will reflect the implicit weights that reflect the preference aggregation protocol that results from institutional structure in the polity. Using data for the 435 Congressional districts in the U.S. and the vector of sectoral tariffs enacted by Congress, we are able to estimate the implicit weights placed by the federal government on different actors, sectors and regions.