Archived Projects

Early Warning Model for At Risk Communities

Economic distress can stem from a variety of causes: downsizing, plant closing and macroeconomic downturns are the most common causes. But communities are at risk from other, sometimes unseen sources as well. The key to surviving economic restructuring is to have as much early warning as possible. That is what this model will provide. It will systematically identify communities that are at risk so that local leadership can begin reorganizing for the future. The model uses a variety of socioeconomic data including: dependence on single industry (LQ), product lifecycle stage, declining industry, plant closings, bankruptcy of customers or supply chain participants, migration, national cycles, and branch plant activity. More variables will be added as the model evolves. The model uses a combination of I/O, regressions as well as logit and probit models to calculate the risk coefficients. Following the completion of the initial design, we will test for temporal lags to see how many quarters of lead time we will be able to forecast. After the temporal dimension is established, we will plot the counties that are shown to be at risk and then use spatial autocorrelation techniques to help identify any contusion effects on surrounding communities. In the second year of funding, we will present our findings to the leadership of the communities identified as at risk. We will help the leadership prepare for the reorganization and put them in touch with professionals to help them in the transitions. It is anticipated that in the third year of UC funding, the model would be run for the entire EDA Chicago region and we would work with the other EDA university centers to help their distressed communities.

Locating Aspirational Clusters: A Spatial Method for Locating Ohio's Wind Energy Cluster 

New and emerging industries, such as the alternative energy sector, can only be established by developing a robust supply chain.  The State of Ohio is seeking inward investment in wind turbine manufacturing.  Identifying areas where pre-existing manufacturing capacity can be redirected to support production of turbines is of primary importance.  Ohio has a broad and deep manufacturing base orienting toward the automotive industry which could in some cases be easily redirected to alternative energy.  Ohio has in excess of 965 manufacturing establishments whose NAICS code is in the supply chain of wind turbines, but that does not mean they are producing for that market (County Business Patterns, 2006).  While one could approach all of those manufacturers to determine their interest in converting production from existing markets to wind turbines, a more efficient first step is to identify potential sites based on the supply chain characteristics of existing establishments.  Once such sites are identified, one can approach prospective firms in a more localized area.  Given this strategy, those studies that have addressed the identification of the spatial footprint of clusters and/or industrial agglomerations are relevant.

The purpose of this research was to develop a spatial methodology that will help in locating aspirational industrial clusters; that is, clusters which currently do not exist but have targeted for development by policy makers.  The method blends the work of Moah and Kanaroglou (2006), who argued that kernel estimated surfaces are useful in examining the geographical characteristics of clusters, coupled with the gravity model approach of Shearmur and Coffey (2002).  The model builds a location index that can aid policymakers to identify and locate existing or aspirational industrial clusters.  The wind energy cluster in Ohio is used as the illustrative case because the industry does not currently exist, but the state has placed a high priority on its development. 

An Economic Viability Analysis of a Proposed BGSU-Firelands Wind Turbine

This study examines the monetary costs and benefits of a proposed wind turbine on the BGSU-Firelands campus.  It finds that the present value of the cost of the project significantly outweighs the present value of the benefit.  Specifically, the project is estimated to have a net present value of negative 1.2 million dollars.  Thus, this analysis does not recommend that the project be undertaken.  The overall result is insensitive to changes in assumptions regarding the wind speed, the length of the project, the discount rate, salvage values, and a number of other parameters.

This result is found to be a consequence of the idiosyncratic way that BGSU-Firelands is billed for its electricity: Specifically that the amount of the bill depends a great deal on the peak load of the facility.  Because wind is somewhat lower and more intermittent at times of high peak load, a wind turbine would not have a dramatic effect on the billed amount.  This suggests that either the project should be modified to achieve a more positive result, or perhaps that a solar-based alternative energy project (which would likely provide more energy at times of peak load) should be considered.  Alternatively, BGSU-Firelands could explore changing its billing arrangements, or adopt a peak load management system in order to reduce its electric bill.

Alternative Energy Economic Analysis

The primary goals of this work is to perform an economic analysis of the existing utility-scale wind turbines in Bowling Green Ohio, and to determine whether additional turbines are economically justified at the same location.

The first portion of this work is a semi-retrospective analysis of the economics of the existing Bowling Green wind turbines. Because they have already been constructed and have been operating for several years, we can obtain reliable data on construction costs, maintenance costs, and electrical output.  These figures, with data on the pricing of the electricity generated (either with spot market pricing or special (green) joint venture agreements) and updated estimates of the turbines'slongevity, will be combined to construct a measure of the net monetary benefits of the turbines.

We will also include an analysis of the costs and benefits of the turbines that are not captured by markets. Specifically, we will attempt to estimate the dollar value of both the negative extrernalities associated with wind generation in this setting, (such as avian deaths, effects on residents's views, and noise pollution) and the negative externalities prevented by these wind projects (because they displace electricity generated primarily by coal.

The second potion of this work will seek to determine whether additional turbines can be justified at the site, and if so, what turbine models and configuration would be best. The presence of the existing turbines provides excellent data on wind speed in the area, which can be used to project the output of various turbines. Updated quotes for new turbines, towers, and construction costs (which have undoubtedly changed since the construction of the existing turbines) will be obtained. Projections of electricity pricing and the demand for special green power arrangements will be determined, to estimate the expected revenue from any expansion. Estimates of the external effects (see above) of additional turbines will be included. Finally, the complications caused by the introduction of more turbines (wake effects, more extensive electrical distribution infrastructure) will be determined and incorporated into the analysis.

Also RUC will explore the possibility of restating this project as a function of wind velocity. This will allow for general cost benefit analysis based on the existing wind maps in Ohio. It will look at site selection from a spatial analysis perspective.

Exploratory Space-Time Analysis of Local Economic Development

Many questions regarding local economic growth lie at the intersection of the disciplines of geography and economics. Research on local economic growth has shown that the complex interaction of economic well-being, the threat from globalization, and the formation of local institutional responses have driven economic development into new arenas. Issues of identifying communities at risk have been particularly important in Ohio. The U.S. lost a 19.8 percent of its manufacturing jobs over the past seven years but Ohio's losses totaled 23.3 percent during the same time period. According to the U.S. Department of Labor, Ohio had 209,400 fewer nonfarm jobs in December 2007 than it had in December 2000. Given that the data ends at the beginning of the current downturn only amplifies the concern.

Relevant work on local economic growth has attempted to incorporate developments in GIS and spatial analysis. In addition to the methodological advances, the importance of space to many socioeconomic theories has been gaining recognition. Goodchild and Janelle (2004) emphasize space as a source of insight and understanding and as a basis for prediction and the solution of problems. However, economic growth has a temporal dimension, and it is not clear that how spatial dependence and spatial heterogeneity might hold over time. Nearby economies might exhibit greater degrees of similarity (or dissimilarity) over time because of economic transition and globalization, depending on the selected industry. Spatial heterogeneity of the economic process with respect to location might also be unstable because local economic policies have been modified in the changing world. Hence, the nature of local economic growth is both spatial and temporal.

The purpose of this research is to explore the space-time dynamics of local economic development. It attempts to show how space-time analysis can help identify communities that are at risk from economic contractions. This research project is divided into two parts. The first part provides a brief overview of the space-time methods. It shows how exploratory data analysis (EDA) can provide insights on the temporal and spatial patterns of growth. The second part of the project two offers a space-time analysis of employment rates in Ohio counties over the time period 1969-2007.

The Cognitive Limits to Economic Cluster Formation

There has been increasing interest in the social dimensions of economic clusters The literature now includes select examples of social network analysis plus an extensive discussion of learning regions. Unfortunately, much of this work treats the network as the primary unit of analysis. It may be that network attributes such as density, centrality, and power are primarily dependent on human limitations and not instituted factors. In other words, a human's limited ability to process information may be a better determinant of cluster success than economic or network theory.

The purpose of this work is to highlight human limits in cluster formation. To do this, we draw on recent developments in the cognitive psychology and communications literatures. We show that many of the factors that lead to underperforming cluster policies are the result of a human's inability to develop and sustain a large number of social interactions. Any cluster policy must be cognizant of such limitations and carefully address these limits in the formation of the initial strategy.

An Open Source Toolbox for Cluster-Based Economic Development

Cluster-based economic development (CBED), as an alternative economic development strategy, has become more commonplace in recent years (Carroll et al 2008). This idea has been promoted by the work of academics, and has gained acceptance among practitioners. An assessment of industry location and density patterns becomes the first phase in the identification of potential cluster regions to be included in a cluster driven development policy. However, it is more interesting to policy-makers regarding the stability of spatial industry cluster. This toolbox employs the concept of industry cluster time path developed by Carroll and Ye (2009). Industry cluster time path extends the spatial pattern of industry cluster to a dynamic context. This toolbox can be used to conduct exploratory space-time analysis of industry cluster in a comparative framework using both simulated (for policy scenario) data and real world NAICS (The North American Industry Classification System) data. Written entirely in Python, this toolbox is cross-platform and easy to install (and expand).

City of Bowling Green LUCA Project

CRD contracted with the City of Bowling Green to be its agent for the Local Update of Census Addresses (LUCA) program for the 2010 Census. CRD verified addresses and made adjustments to ensure a complete and accurate count can be obtained through the census. CRD identified and made corrections to the boundaries, roads, or other physical features and submitted digital updates using data provided by the City of Bowling Green. Additionally, CRD assisted the city with the appeals process following the US Census Bureau’s full canvassing that will be completed prior to the 2010 census.

Impact of the Creative Industries

CRD expanded its analysis of the creative industries to include the southeast and southwest regions of Ohio. The impact study allow regional comparisons of the creative industries in the footprint of the Rural Universities Program (RUP) regions across the state. The results were released in March of 2009.

Liberty Center Master Plan

CRD partnered with DGL Consulting Engineers on completing a master plan for the Village of Liberty Center in Henry County. CRD focused its efforts on assisting with the projections of housing demand and residential growth, industrial development potential and site possibilities and a description of available funding sources and financing alternatives for community investments.

WSOS Internship

The Wood, Seneca, Ottawa, and Sandusky Community Action Commission (WSOS) partnered with Bowling Green State University and CRD on development of rural transportation plans for three counties in their service area. CRD provided an intern from the MBA program that to work for WSOS twenty hours a week assisting in the development of these plans. The project was funded in part with grants from the US Department of Transportation Federal Transit Administration.

Wood County Emergency Management Agency Internship

The Wood County Emergency Management Agency (WCEMA) was seeking assistance with plan revisions and project coordination. BGSU and the CRD provided a twenty hour a week intern to WCEMA to work on revisions to the All Hazard Plan to bring the county in compliance with the National Incident Management System. Additionally, the intern worked on the Community Emergency Response Team, updated and maintained the agencies website and developed a directory for emergency management responders.

Cluster-Based Economic Development

Dr. Michael Carroll worked with Dr. Neil Reid, Associate Professor of the Department of Geography and Planning at the University of Toledo (UT), and Sue Wuest, Research Associate with the Urban Affairs Center at UT on a project on cluster-based economic development. Clusters are geographic concentrations of interconnected companies, local suppliers, infrastructure providers, educational institutions, and other relevant agencies that work closely with each other for mutual and regional benefit. Through clusters, companies (and their region) can realize higher levels of competitiveness by looking beyond the limited capacity of one single company. By strategically partnering with other companies and support institutions, the companies can address challenges and solve problems that it would be less able to address alone. Cluster-based approaches to economic development also help companies identify new market opportunities, become aware of best practices, and become more innovative.

The research team seeks to identify clusters in the Toledo area and analyze how these clusters function in the community, as well as identify potential clusters and make policy recommendations to enhance cluster growth and economic development.

Economic Impact of Greenhouse Nursery Industry

A study by Dr. Michael C. Carroll (CRD) and Dr. Mark K. Kasoff (Canadian Studies) examined the ways in which the greenhouse industry impacts the economic region of NW Ohio and Southern Ontario. Economic impact in this case is the total economic value of the industry's output; the employment in the greenhouse and related industries, salaries and wages of the greenhouse and auxiliary employment, and the value added to a product or material at each stage of its manufacture or distribution (value added). The research team anticipates that the greenhouse industry will emerge as a significant component of the Ohio economy. Other agencies involved in the research project included the University of Toledo, the Ohio State University, and the Toledo Botanical Garden. The study incorporated issues such as technology, management practices, business costs, and evaluation of sales opportunities. By establishing researchable needs of the local industry, the study can improve competitiveness, increase revenues or reduce costs through improved production technology.