Mallon’s duties are centered on recruitment and expansion in health sciences, clean technology, and digital technology. The businesses he works with in these three sectors require skilled labor, and often an abundance of it. Says Mallon, “I use labor market data in virtually any serious or relevant business analysis or proposal that I do. Particularly in the three areas that I’m in, workforce is so critical because of the technology and the higher education requirements. It’s a core component of what we do.”

The workforce information that Mallon values most is data relative to wages (median, entry-level, etc.), availability, and educational attainment (from the associate’s to Ph.D. level). EMSI provides access to these needed figures in quick order, giving Mallon and his coworkers a leg up when they are inundated with research and proposal work.

“We did not anticipate being as busy as we were in August and September,” Mallon says. “And we got hit with so many requests for proposals that if I did not have a tool like that available, I would not have met timing deadlines. I mean it just wouldn’t have happened.”

For more on Greater Spokane Inc., visit its web site.

PDF of the case study: Mercury Marine retention effort

Nearly 1,900 Fond du Lac jobs at stake
By late summer 2009, Mercury Marine’s long-term future in Fond du Lac, Wisconsin, was very much in doubt. The world’s largest producer of outboard boat motors was challenged by the downturn of the boating industry and was looking for contract concessions from the local union. An option for Mercury Marine executives was to move their manufacturing operation from Fond du Lac to Stillwater, Oklahoma, where it already had a smaller-scale site. The company’s corporate offices would possibly follow later.

One part of the three-pronged process that was necessary to keep Mercury Marine in Fond du Lac was a “yes” vote by the union on some contract concessions. The other two parts—local and state incentive packages—were being formulated well before an initial union vote took place in late August. The Fond du Lac County Economic Development Corporation (FCEDC) and other county officials were exploring all options to persuade the company that east central Wisconsin was its best option.

Economic impact results used as educational piece
From the start of discussions with Mercury Marine, FCEDC President Brenda Hicks-Sorensen emphasized the magnitude of the company’s impact to the city, region and state to decision-makers. She produced multiple EMSI economic impact reports to convey that the relocation of Mercury Marine would not just mean the loss of 1,900 jobs and the estimated 500 others the company was planning to add when the economy improved. EMSI’s impact figures showed that the departure of the county’s largest employer would result in the loss of an estimated 5,900 net jobs in Fond du Lac County and almost 8,000 jobs in the seven-county region through indirect impact on suppliers and government and business. The financial hit would be $353 million to the county and $450 million to the larger region due to a loss of annual earnings.

The impact figures were also used to inform city, county and state officials who were deciding on various incentive packages to offer Mercury Marine.

“We wanted to make sure stakeholders were making informed decisions based on reliable data with a true understanding of the impacts. So these numbers were utilized a lot with them,” said Hicks-Sorensen. “In fact, it was a number of the county board supervisors who asked if they could share this information with their constituents. So they really relied on this information that we provided them.”

Understanding the ramifications of the company’s exit from Wisconsin added an element to the process that ultimately proved effective.

Result: Mercury stays, will move hundreds of jobs from Oklahoma
Once Mercury Marine went public with its potential relocation, FCEDC decided to release the impact numbers. Hicks-Sorensen and her staff wanted area residents to understand how damaging it would be to lose the manufacturer.
“Because this ended up being a very public discussion, it was very important for us to be able to show the impact on the economy,” says Linda Berlin, FCEDC’s Director of Research. “This was also helpful in discussions with state and local leadership.”

The city and county eventually unveiled a $53 million incentive package to Mercury Marine. Ultimately, the union accepted the concessions, paving the way for the company to announce it was staying in Wisconsin – and elevating manufacturing job totals to 1,400 to 1,600 jobs between those from Oklahoma and anticipated new hiring in the next seven years.
Looking back, Hicks-Sorensen says, the economic impact reports changed the dynamic of the retention effort, particularly when the staggering job-loss numbers were made public.

“I think it allowed the community to rally around something,” she said. “Before, everyone knew that Mercury was important to the community, but I think it was a little bit shocking and it provided people with something real to get their arms around.”

Links and Media Resources
Fond du Lac EDC

Impact of Mercury Marine on Fond du Lac (PDF)

“Mercury Marine agrees to keep Fond du Lac plant,” The Business Journal of Milwaukee, September 4, 2009.

About EMSI
Economic Modeling Specialists Inc. (EMSI) is a professional services firm that offers integrated regional data, web- based analysis tools, data-driven reports, and custom consulting services. EMSI has served thousands of workforce, education, economic development, and other policy professionals in the U.S., Canada, and the United Kingdom, and the company’s web-based Strategic Advantage research and analysis suite is used by over 2,500 professionals across the U.S. For more information, call (866) 999-3674 or visit www.economicmodeling.com.

For a PDF of the case study click here

Growing list of start-ups turn to Center for assistance, space
Housed at Spartanburg Community College’s Tyger River campus in Duncan, The Center for Business and Entrepreneurial Development opened in late 2006 and quickly became a key economic development engine for Spartanburg County. It has provided workforce services, soft landings, small business incubation, and assistance with special projects to four expanding companies and furnished facilities to five other firms inside an 180,000-square foot warehouse and 16,000 square feet of office space.

The first tenant to manufacture a product in the facility, Master Precision Global, was provided 42,000 square feet in the Center and is projected to bring 120 jobs to the region, and other manufacturers have followed. “So far the Center has been very successful,” says Mike Forrester, director of economic development at SCC.

Despite the current activity, the Center is in need of an extensive renovation to accommodate more start-ups and better serve existing tenants. With enhancements in mind, SCC is applying for two major economic development grants that will help fund a $1.7 million remodeling project. An integral part of making a case for the grants—as well as identifying the best industries to target for future growth—is a recently completed study that measured the economic impact of the Center on the county.

Study shows the Center has helped create 5,000 jobs
Gina Misch, former economic development project manager at SCC, had read where other incubators and agencies calculated their return on investment and economic impact, and she knew the most powerful evidence would come through hard data. She was already aware that the Center had assisted in creating more than 2,300 jobs from November 2007 to June 2009 in Spartanburg County. Yet to get the complete picture of the Center’s impact, she used EMSI’s input-output model to:

1. Estimate how many additional jobs had come about indirectly;
2. Calculate the total job loss in Spartanburg County due to businesses retracting or closing altogether; and
3. Analyze the economic base (i.e. the basic and non-basic industries) of the county to see what sectors the county relies on.

The IO tool showed the accelerator brought nearly 2,700 jobs to the region through indirect impacts on other industries—the most coming in transportation and warehousing—for a total of more than 5,000 direct and indirect jobs. Misch then accounted for layoffs and business closures in the county and estimated that the total job loss (including the indirect impact) was 3,386. With the initial level of analysis complete, Misch had the concrete results she was looking for: The Center helped generate 1,621 net jobs, resulting in $58.7 million in earnings. “We were quite delighted to see the other industries that are impacted, as well as the earnings, with EMSI’s model,” she says.

Misch also differentiated in SCC’s report between basic industries (those that depend on income from outside the region and thus bring money in the region) and non-basic industries (those that circulate money already present in the region). Her analysis showed that almost all of the companies represented at the Center fit into the basic industries category, and therefore are real drivers of the economy. As the report states, these companies represent “a greater economic impact because [they] bring new income into the region, while also supporting local businesses via consumption through its employees’ earnings.”

Center moves forward on grant applications, attracting new firms
With the data-driven study complete, the Center is moving forward on applying for two grants—an $850,000 EDA grant and $500,000 Appalachian Regional Commission grant that would go a long way toward funding the renovation. According to Misch, “The report is the meat of being able to prove that the renovation project is well worth the money.” Forrester and other county stakeholders are also actively seeking new companies to attract to the Center, and the study has helped inform decision-makers on what types of businesses to seek and the economic impacts those firms would have on Spartanburg County.

About EMSI
Economic Modeling Specialists Inc. (EMSI) is a professional services firm that offers integrated regional data, web-based analysis tools, data-driven reports, and custom consulting services. EMSI has served thousands of workforce, education, economic development, and other policy professionals in the U.S., Canada, and the United Kingdom, and the company’s web-based Strategic Advantage research and analysis suite is used by over 2,500 professionals across the U.S. For more information, call (866) 999-3674.

For a PDF of the case study click here

Dayton works to overcome economic downturn

With the departure of major companies such as Fortune 500 NCR Corp. to Georgia and downsizing of other firms, the Dayton area has lost high-paying jobs and seen its unemployment rate nearly double to more than 11% in the last year. The region’s economic losses and excess skilled workforce prompted airport and city officials to emphasize economic development in their airport master plan. One of the goals is to attract industries to property surrounding the Dayton International Airport to strengthen the regional economy. Airport Director Iftikhar Ahmad also believed that a key to future economic success is identifying industries that would bolster the Dayton area’s supply chain.

The airport contracted with Gresham Smith and Partners of Nashville, TN, to complete the master plan, and GSP in turn brought on Janus Economics, a business location and economic development consulting firm, to complete a thorough economic development assessment of the airport area and four-county Dayton MSA. Janus and EMSI came together to create an unique, three-tiered approach for the study:

• A SWOT analysis to gauge the strengths and weaknesses in the region, and a targeted industry report to ensure city and economic development officials focus on attracting or growing businesses that work best given Dayton’s unique assets.
• A supply gap analysis for all industries in the region to determine which supplier industries have the biggest gaps between the local demand for products and the percentage of their inputs produced in the area.
• A matching of the labor needs of the major supply gap industries with the available workforce in the region based on knowledge, skills, and abilities embedded in the occupations and the highest-developed skill ranges.

Study shows medical-related industries are good fit

EMSI’s data and tools provided the basis for the industry and occupational analysis, as well the supply gap study that relied on input-output modeling. Robert Pittman and Jennifer Tanner, Senior Principals at Janus, also looked at the “transportation intensity” of different industries (again using EMSI data), since Dayton has a strong transportation infrastructure and industries that rely on air, rail, and highway transportation would naturally be good fits to locate near the airport.

With the data collection complete, Janus created a weighted average index based on the aforementioned economic and labor force variables and came up with the top 15 supply chain industries that would work best in the airport property. The top of the list included several industries related to pharmaceutical and medical/biological product manufacturing, which matched Dayton’s economic base since the area has a medical school and active pharmaceutical/medical cluster.

The study was completed in the spring of 2009, and the next step is for the city’s economic development team to market the results to prospective businesses. As for the project itself, Pittman says, “Blending EMSI’s data expertise with our economic development expertise and coming up with new approaches is what really made the study unique. It’s a neat story.”

In the Media

“Dayton Airport details new business plan,” Airport Business.

About EMSI

Economic Modeling Specialists Inc. (EMSI) is a professional services firm that offers integrated regional data, web-based analysis tools, data-driven reports, and custom consulting services. EMSI has served thousands of workforce, education, economic development, and other policy professionals in the U.S., Canada, and the United Kingdom, and the company’s web-based Strategic Advantage research and analysis suite is used by over 2,500 professionals across the U.S. For more information, call (866) 999-3674.

PSG is currently working on an economic development project centered in the greater Los Angeles metro area. Using EMSI’s ZIP code-level data, Mark Schill, a PSG associate (pictured left), was able to gather the labor market, human capital, and demographic figures he needed for each corner of the affected counties without worrying about extraneous data coming into play.

Furthermore, Schill appreciates having quick, web-based access to the figures he needs. “I can do a lot of analysis in a hurry on the front end of a project and spend less time formatting numbers and more time thinking,” he says.

Strategic planning for PSG is based on a full range of personal interviews and time on the ground in communities, combined with different economic and demographic metrics and analysis of workforce shortages. Part of the process for the North Dakota-based firm is “taking a hard look at what the facts are about communities,” Schill says.

A large portion of PSG’s work focuses on small to mid-sized communities. One such current project is in Montana, where a five-county region is seeking to diversify its economy in the wake of a military base realignment. Schill and others at PSG used EMSI to get a basic understanding of the region’s labor market and demographic trends. With the information in hand, the consultants had a solid foundation to come up with a strategic plan and new target industries.

“It’s critical to look at data,” Schill says. “It’s always best to lead with the facts.”

For a PDF of the case study click here

More than 7,000 jobs lost in region in a year-plus

When General Motors announced in June 2008 that it was closing its enormous truck and SUV plant in Janesville, Wis., the repercussions for the region were severe. The longest-running GM plant in the nation—it opened in 1919—employed 2,500 people and fed business to a mountain of small suppliers throughout southwest Wisconsin and adjoining counties. The net effect of the closure and other shutdowns has been the loss of more than 7,000 jobs in a little over a year. That’s close to 10% of the workforce for the six-county area.

With so many jobseekers in the region (the unemployment rate in the Janesville-Beloit area is 13.5%), Bob Borremans, Executive Director of the Southwest Wisconsin Workforce Development Board, responded with a data-focused strategy that included:

1. Using EMSI’s input-output tool to model the impact of losing the auto-related manufacturing workers, and then projecting what would be the region’s main economic drivers in the near future outside of automaking.
2. Analyzing the competencies of the displaced workers with the Career Pathways tool to help them find new occupations that would meet their skill sets or training programs that would help them “up-skill” for in-demand fields.
3. Working with economic development professionals to use the thousands of laid-off workers to attract new businesses to the area.

Assessing economic impacts, labor market a key step

Borremans stresses that it will take “at least five to 10 years” to diversify southwest Wisconsin’s economy to the point where it will be better equipped to handle future economic downturns. Nevertheless, the groundwork laid by Borremans and other stakeholders has been a good first step. It was particularly helpful, he says, to measure the impact of the closures and assess regional industry/occupation trends and projections. “It allowed us to pinpoint some of the opportunities and give people some hope that there are some reasonable job opportunities for the future.”

After doing an analysis, Borremans found that 76% of dislocated workers from the first round of the auto industry worker layoffs earned $20 per hour or more. Most of those, of course, do not just need to find a new job—they are looking for a family-sustaining wage. “So using EMSI we were able to isolate what those jobs are in the Rock County-Janesville-Beloit area,” Borremans says. “We have used this information to advise area dislocated workers on what job skills will be needed in the area and what jobs they should be preparing for.”

With retraining suddenly a key emphasis, enrollment at Blackhawk Technical College has climbed by 33% from early 2008. The bulk of the new enrollees are former auto industry workers, and they are taking courses in everything from culinary arts to radiologic technology.

The medical, biotech, and information technology industries are all strong in Madison (about 40 miles northwest of Janesville), but it will take time to train laid-off auto workers for those fields. According to Borremans, “You do not take an assembly worker off an assembly line and put them into one of those settings. It takes some training for them to be successful. It takes retooling their skills in order to make them capable of handling the higher-technology needs of those industries. In order to take some of those jobs, you are a year or two years away from developing the kind skills and base background to even consider applying for those types of jobs.”

Another area of focus is working on business recruitment opportunities for the region. Borremans has partnered with economic development groups such as Forward Janesville, which is selling the region to prospective businesses on its central location between Milwaukee, Chicago, and Minneapolis, and its skilled workforce. “It’s the strength of our workforce that will attract new business to the area,” Borremans told The Janesville Gazette.

References

“Ex-GM Workers Try to Reboot Their Lives,” New York Times, February 12, 2009.

About EMSI

Economic Modeling Specialists Inc. (EMSI) is a professional services firm that offers integrated regional data, web-based analysis tools, data-driven reports, and custom consulting services. EMSI has served thousands of workforce, education, economic development, and other policy professionals in the U.S., Canada, and the United Kingdom, and the company’s web-based Strategic Advantage research and analysis suite is used by over 2,500 professionals across the U.S. For more information, call (866) 999-3674.

To read the case study in PDF format: New York city sketches out revitalization plan for brownfield sites

Rome becomes part of BOA program

Located on the Erie Canal, the City of Rome (pop. 34,950) was once a thriving industrial hub for upstate New York. The community’s industrial base, however, has dwindled and the little-used or vacant pockets of land are hindering the development of the city, especially its downtown.  To overcome these challenges, Rome became part of the state’s Brownfield Opportunity Area (BOA) program. It targets revitalization in communities affected by brownfield sites, which require clean up before any redevelopment efforts take place.

After initial success, local planners have more aggressively begun to use the vacant sites to spur economic growth. A crucial piece of the community development plan has been responding to regional labor market data and trends.

Consultants give picture of area’s economy

Camoin Associates, a consulting firm based in Saratoga Springs, NY, completed a market analysis of a city-owned property in Rome in 2007. Having done the reuse study, the firm was a good fit to be a contributing partner on the BOA project. Part of Camoin’s role involved analyzing current labor, retail, and residential market conditions in Rome. To start things off, Michael N’dolo, Associate Principal at Camoin, used EMSI’s comprehensive database and other sources to provide:

• An overview of industry and occupation trends for Rome, Oneida County, and upstate New York compared to state and national totals.
• A detailed analysis of demographic trends for Rome and Oneida County.
• And a breakdown of the region’s economic base, as well as the industries with the largest supply chain gaps.

After pinpointing the suggested industries that should be recruited into the region in order to reduce import dependence and those industries’ projected growth, N’dolo analyzed the likely office real estate space needs for those industries compared to what was currently available. EMSI’s “Suggested Industries” tool helped Camoin Associates identify possible reuse scenarios which would be marketable and successful in the existing economic climate. Camoin used its professional experience combined with the information gathered from EMSI to provide recommendations to the rest of the planning team and the city. The information was helpful in “engaging the community in terms of developing a vision,” N’dolo said. “The first step is putting the context together to see what’s possible. Then we go to the vision.”

About EMSI

Economic Modeling Specialists Inc. (EMSI) is a professional services firm that offers integrated regional data, web-based analysis tools, data-driven reports, and custom consulting services. EMSI has served thousands of workforce, education, economic development, and other policy professionals in the U.S., Canada, and the United Kingdom, and the company’s web-based Strategic Advantage research and analysis suite is used by over 2,500 professionals across the U.S. For more information, call (866) 999-3674 or visit www.economicmodeling.com.

By: M. Henry Robison, Founder and Senior Economist, EMSI

contact information: Rob Sentz (rob@economicmodeling.com)

For a PDF version of the paper: IO guidebook

Introduction

Our nation’s economic woes have led to many ideas for the best way to transform our economy and get it back on track. Many of these ideas are rooted in “Keynesian” economic theory, which advocates for monetary and fiscal policy responses by the government to stabilize and control the business cycle. The most significant instance of this is the current American Recovery and Reinvestment Act (aka the “stimulus package”), which aims to spend unprecedented amounts of money on everything from infrastructure to health care to educational programs in order to create demand and spur stagnant markets. There is much debate over the true effectiveness of this sort of intervention, which is beyond the scope of this paper. For now we want to provide a practical guide to make sure local planners know how to evaluate projects and programs based on their importance to the region and especially on their potential return on investment. Because of the sheer enormity and pervasive nature of the spending, the federal government would like to see a high level of accountability for organizations, institutions, and programs that will be receiving this money.

A primary tool for this accountability and evaluating where spending might produce the greatest regional benefit is the input-output (IO) model. This tool is often misunderstood and misused. However, if approached and applied correctly, IO can be a very powerful and helpful tool for informing decisions—allowing planners to determine where dollars will have their highest economic and workforce impacts.

In this paper we aim to provide an overview of IO for non-economists, provide some useful illustrations and definitions, and serve as a basic orientation for local, state, federal planners so they can take advantage of this resource. While some of the material is applicable to many economic models, we focus on the capabilities of EMSI’s Economic Impact model.

If you have more questions about using IO modeling for your region, or would like special assistance please feel free to contact us.

Hank Robison

About the author

Dr. Henry Robison is a senior economist and co-principal of Economic Modeling Specialists Inc. (EMSI). He has over 20 years of experience and a significant publications record in regional economic impact modeling and analysis. He is recognized for theoretical work blending regional input-output and spatial trade theory, and for development of community-level input-output modeling and analysis. He served 10 years as faculty member and consultant to the University of Idaho, where he secured a wide array of grants and contract research.

Dr. Robison’s major EMSI consulting projects include design of the Utah Multiregional Input-Output (UMRIO) Model; work in partnership with Rutgers and Princeton University for the U.S. Department of Commerce Economic Development Administration (EDA) to assess the effectiveness of EDA regional economic development grants; partner with New Jersey Institute of Technology and Rutgers University to develop the Federal Highway Administration’s TELUS transportation project impact model; and extensive work with the U.S. Forest Service in ten western states on measuring the economic impacts of federal land management planning. He is also currently working on a project to develop regional labor market analysis for the Middle East and North Africa.

A retrospective published in the journal of the Regional Science Association International listed Dr. Robison number eighteen on a list of the top one hundred “intellectual leaders of regional science” for the decade of the nineties (Papers in Regional Science, 83(1), 2004).

Table of Contents

I. Regional Economics 101

II. Practical Applications of IO

III. Understanding Multipliers

IV. Avoiding Impact Modeling Pitfalls

V. Using Input-Output for Economic Development Analysis

VI. Defining Appropriate Regions

Introduction
Before you begin to conduct input-output analysis it is important to establish “boundaries” that in some sense reflect functioning economic regions.  Though at first the concept of “region” or “community” might seem obvious, defining one for economic modeling purposes is not always so easy. This section will therefore serve as an introduction to understanding how to capture your functioning economy.

A. Functional Economic Regions
For the purpose of economic modeling what do we mean by communities or regions? We first might think of political regions such as states and counties. However, when trying to determine functional economies political and administrative boundaries are not sufficient because they are simply drawn to serve some narrow political or administrative task and do not necessarily represent any economic relationship. We might also think of areas that share some similar characteristic (e.g., Silicon Valley, North Carolina Research Triangle, Appalachia, the Rust Belt, or the Corn Belt). This is a little closer, but still not quite what we are looking for.

Basically, when working to accurately portray a regional economy, one must avoid being influenced by political boundaries and/or other shared-feature regions. Instead, the area should reflect a spatial exchange of goods, services, and labor which are, incidentally, all types of exchange tracked in input-output models (Fox and Kumar, 1965).  A region defined by such economic exchange is referred to as a functional economic region. The key characteristic of functional economic regions is that they have a cohesive, semi-closed market for goods, services, and labor.

B. Central Place Theory
So what is a “semi-closed market for goods, services and labor?” Perhaps the best way to understand that is through the concept of Central Place Theory (CPT), which provides a description of how communities (e.g. places) are economically linked. This spatial linkage is simple to understand and marks essentially what are functional economic areas. By tracing linkages, CPT also describes the trade hierarchy among the various places in a region. The idea is most easily conveyed with a simple graphic.

In its simplest form, a single “highest-order place” (i.e., Metropolitan areas, which offer the widest array of goods and services) “dominates” some collection of lower-order places. The figure presents a four-order hierarchy, with a single “metro area” economically dominating a pair of “cities,” which in turn dominate “small towns,” which dominate “hamlets.”  The analysis is completed by specifying that each place (or “node”) dominates peripheral areas populated with isolated homesteads. Notice that a political region (such as a state or county boundary) may cut through central place regions regardless of trade links. The figure below illustrates this idea and reinforces the difference between functional and non-functional regions.

An alternative way of presenting the concept of CPT is presented in the table below.
Here it is seen that the lowest-order places provide goods that are available everywhere (in our case, post offices and restaurants), and that additional goods and services are added as one ascends the hierarchy.  The highest-order place offers a unique subset of goods and services that is only regionally available.

It is important to note that the specific configuration of places and the specific collection of goods and services shown in these figures are illustrative only: real-world central place hierarchies will be far less tidy.  Yet the principles remain, in that larger places will exhibit market reach (dominance) over smaller places, and through this dominance the two places are spatially interlinked.  As noted above, this interlinking of places provides the basis for the economic cohesion that defines a functional economy, and provides impor-tant context for the community-level modeler.

C. Complicating Factors
Unfortunately for the would-be modeler, many situations in the real world are much more complex than the ideal cases. From the previous discussion, it should be no surprise that nearly all complications will arise from a lack of isolation.  In this section, we will discuss two basic situations that could raise serious obstacles to an appropriate community definition: (1) thin economies and overlapping market areas, and (2) interlinked economies.

“Thin Economies” and Overlapping Market Areas
As a general rule, it is fair to say that input-output and community level modeling has different applications based on the difference in the character and population density of your area. The population density west of the Mississippi is, of course, much lower than that of the east, even in rural regions.  Rural communities in the West are also much fewer in number and are generally spread further apart than in the East.  Moreover, larger cities and metro-places are far less plentiful, meaning that opportunities for shopping and work are less plentiful.

The following simple map shows a large collection of small towns and larger cities around Cornwall, PA.

The circular area in the figure approximates a one-hour commute from Cornwall. Residents of this community will travel in many directions to work and shop.  Now picture dozens of circles similar to the one drawn in the figure, each centered on the other nodes on the map, whether large or small.  The result would appear as an incomprehensible tangle of overlapping labor markets. Certainly a great many people who work in the circled area (i.e., Cornwall’s commuting field) live elsewhere.

If we compare Cornwall to another example of rural western town (Grangeville, ID) the contrast is stark (below).

Cornwall (approximate population 3,500) and Grangeville (approximate population 3,200) are comparable in size, but Grangeville is isolated while Cornwall is not. Whereas the total population within a one-hour commute of Grangeville is approximately 10,000, that figure might be upwards of 1 million in Cornwall’s radius (these are very rough estimates, but good enough to make the point). The density around Cornwall creates very open markets and a web of “thin” economies with uncertain inter-community/inter-industry linkages.  Even if a major event (the opening or closure of a large retail store, for example) occurred in one of these towns, the employment and other market effects would be too fleeting, easily leaking to surrounding areas.

In light of this, it does not make sense to speak of “Cornwall’s labor market”  in any closed sense. Workers from Cornwall compete with workers well beyond the commute field shown in map, and similarly, job opportunities within Cornwall’s commute field are affected by business conditions beyond that field. Similar market overlap occurs in consumer and business goods and services markets.

It is therefore difficult to define or otherwise make sense of a “Cornwall economy.” One might draw the commute field shown in the figure and call this “Cornwall’s labor market,” but it would be scarcely useful due to the fact that there are so many overlapping labor markets in this same field.  Narrow community boundaries, for instance Cornwall’s city limits or ZIP code, might be proposed, but this is an extremely open economy, with many goods (e.g., perhaps as common as hardware or supermarket) and services available in abundance in nearby communities.  Crosshauling, which is the inflow and outflow of the same goods or services, including labor, is doubtless very prevalent.

As a result, it makes more sense to speak of a large-scale regional economy such as “the South Central Pennsylvania economy,” or perhaps a metro-area economy such as “the Harrisburg area economy.” The lesson is that because of complex trade hierarchies input-output modeling is more well suited to either larger multi-county regions around a metropolitan area, or closed economies such as can be found in the rural western states.

Interlinked Economies
In the previous example, we saw that community definition for the sake of modeling purposes, can be impossible due to extreme regional interconnection.  For our next case study we turn to a much less radical situation which nonetheless exhibits a complicating lack of isolation.  Consider the two communities shown in the following map: Moscow, Idaho, and Pullman, Washington.

Moscow and Pullman are located just 8 miles apart on either side of the Idaho-Washington state line. Excluding each other, these two communities are considerably isolated—the nearest larger cities are Lewiston-Clarkston (35 minutes), Coeur d’Alene-Post Falls (2 hours), and Spokane (2 hours), and few residents commute to or from those places. Thus the case is easily made that Moscow and Pullman represent a single, albeit bi-nodal, functioning economy: there is considerable intercommunity commuting and head-to-head competition among like businesses, e.g., restaurants, retail stores, profes-sional services, consumer services, and so on.

That said, the existence of a state boundary means that these two communities could be modeled as a single functioning economy or as separate, though highly interlinked, communities. Commuting and other trade, which links these two communities, might be tracked using economic base principles, or they might be formally linked as a two-community interregional IO model (beyond the scope of this document). The choice would depend somewhat on the goals of the analysis, and how important political boundaries are in it (if taxes or local government policy are involved, modeling them separately would make more sense).

Conclusion
This chapter has presented some background theory and general advice about defining communities for modeling purposes. The advice can be summarized in three basic points:

1. Functional economic areas must take precedence over political regions when attempting to accurately model a regional economy;
2. The appropriate scale is that of a city-region; and
3. Be aware of complicating factors, which generally arise from lack of isolation.

It is hoped that this chapter provides a solid foundation for the community modeler, but it should be emphasized that community definition, like region definition, is by no means a simple task that can be accomplished with only a handful of all-purpose rules. Rather, the specific geography and economics of a community or area can greatly alter a community’s boundaries for modeling purposes. Moreover, the purpose of the model being built will certainly affect the appropriate scale, and therefore the way boundaries are drawn. A model intended to examine the impact of new road construction in Idaho will likely require a different focus than a model aimed at examining the impact of a mill closure in Wyoming or a commuter rail line in Pennsylvania.

Introduction
Input-output has not been used to its maximum potential in economic development. Often it is merely a political tool that helps to justify incentives for recruiting and retaining specific companies in a region. By running impact scenarios and estimating multipliers, an IO model will tell you approximately how much one employer is “worth” to the regional economy.

But IO has far more potential to help economic development efforts, because of its ability to capture and analyze the structure of a regional economy, which is invaluable for measuring the economy’s health and potential. IO can identify the region’s most important industries (not just those with a lot of jobs), regional industry clusters, and gaps in the regional supply chain, to name a few applications.

Much of the thought and investment that is going into current issues like green jobs (which are at this point primarily middle- to low-skill construction-related occupations) and energy efficiency really are not thought of as “basic” industries, or those industries that bring money into the region. So, before any of these development projects take place, and a lot of money is spent on systems or structures that need a lot of maintenance, it is vital for regions to understand which development projects could compliment the current base of the economy. Development professionals also need to make sure that investments make sense and are actually feasible for the region. If this is not done, projects with positive short term impacts might actually have a negative long term impacts (e.g. increased taxes, increase maintenance, lack of economic structure to support and utilize the improvements).

An important summary of economic structure is an economic base analysis. By taking into account key industries and their multiplier effects, rather than simply total jobs by industry, you can get a much clearer picture of the importance of various sectors in your economy. We cannot stress how important it is for you to know and understand these foundations of your regional economy before development decisions are made. Lately our nation has experienced many shifts in many well established industry sectors. For instance, manufacturing, which has historically been the backbone of many regional economies across the Unites States, has experienced radical employment decline due to globalization and technology. As a result, many local economies, particularly in places like Michigan and Ohio, have shaky foundations and risk losing the jobs and earnings that drive regional prosperity. Again, these manufacturing industries have very high multipliers—meaning that for every 1 job lost in this sector there could actually be as many as 5-10 other jobs in the region that are threatened. Regional planners should first understand what industries drive the local economy and understand how to incorporate this factor into broader development plans.

After structure is identified, you can begin to look for “gaps” and opportunities to strengthen the linkages among your region’s industries, which can lead to a short list of important industries you should try to attract into the area.

A. Analyzing Economic Base

Introduction: Basic vs. Non-Basic Industries
Discussion of economic base hinges on the difference between “basic” and “non-basic” industries, since the economic base is made up of contributions from basic industries only. Basic industries are those which export products/services, generating income from outside the region which they bring into the region. Non-basic industries are those which generally support or sell to residents and businesses that are already in the region. Economic development theory indicates that basic industries first take root in a region, followed by non-basic industries. For example, the California gold rush started with a basic industry—gold mining—but generated a lot of non-basic industries to supply the gold miners with food, clothing, tools, housing, entertainment, and so on. As time goes on, original basic industries may go into decline and be replaced by others. Economic development theory thus emphasizes the importance of basic industries as the “pillars” of a region’s economy that support non-basic industries. Basic industries are often natural resources or manufacturing based. However, basic industries can also be more intangible, like information services, financial services, tourism, and even government (e.g., in a university town or state capitol).

Going back to our original water-tank illustration, basic industries are like tanks with big inflow pipes that bring water directly into the room (the region). But the majority of pipes going into non-basic industries are coming from other tanks in the room—they mostly handle water that has already been brought in.

Note that many industries are partially basic and partially non-basic. A manufacturer that sells 10% of its products locally and 90% non-locally is one example. A restaurant that serves 80% locals and 20% visitors/tourists can also be considered partially basic and partially non-basic, because it is bringing in some outside income.

Using IO Data to Generate Economic Base Analysis
There are many ways of determining economic base, including
(1)    simple assumption (assuming certain industries are always basic to some degree—like agriculture, mining, or manufacturing),
(2)    location quotients* (LQs significantly higher than 1.0 indicate basic industries), and
(3)    using input-output models to see the multiplier effects of industries’ export sales. EMSI recommends and uses the IO method for economic base analysis.

* A location quotient measures the relative size of an industry in a region, compared to the relative size of the same industry in a larger area (such as the whole nation). If the LQ is greater than 1, it means that the industry has an above-average presence, or concentration, in the region’s economy. This usually also indicates that the industry has an export orientation.

Any economic base analysis should show which broad sectors, industries, and other sources are ultimately responsible for bringing income to the region. Industries generally do this by exporting products and services to non-regional purchasers, but there are other ways that a region can bring in money: for example, the income of out-commuters in a bedroom community, or residents’ Social Security payments from the federal government. (Where these “other” sources are significant, they should be identified as part of the region’s economic base.) To create our Economic Base report, EMSI’s EI model estimates how much of each industry’s jobs and earnings rely on its out-of-region exports and other outside income, then uses multiplier effects to attribute jobs and earnings from other industries to the original “basic” industry. Additional data is used to calculate non-industry sources of income.

So, an EMSI economic base report might show that the Manufacturing sector is “responsible” for 40% of the jobs and 37% of the earnings in your region. Note that this includes ripple effects: the 40% of jobs that Manufacturing supports are more than the jobs on the payroll of manufacturing establishments. This is because manufacturing workers take their pay home from factories and buy food, clothes, housing, entertainment, etc., which supports jobs in the industries that provide those goods and services. Those jobs are thus included in the Manufacturing sector of the region’s economic base because Manufacturing is “responsible” for those jobs through its jobs multiplier.

So, if all regional Manufacturing industries suddenly went out of business, 40% of the jobs and 37% of the earnings in your economy would be lost. This is because there would be less of nearly all goods and services (e.g., food, clothing, housing, entertainment, etc.) bought, and so layoffs would occur in those areas as well. Notice that the Manufacturing sector in our example supports slightly lower-than-average paying jobs—it drives 40% of the jobs but only 37% of the earnings in your region.

Moreover, note that the “Services” sector tends to play a smaller role in a region’s economic base than it does in terms of total jobs. Service industries may hold 30% of the region’s actual jobs, but only account for 10% of jobs in the region’s economic base. This is because service industries tend to be non-basic to a region’s economy—that is, they serve local residents and circulate money that was already brought into the region by another industry. For example, a large restaurant may be next door to a custom software development firm. Though both are in the “services” industry (food services and custom programming services), the consulting firm brings in large amounts of income from clients outside the region while the restaurant mostly serves residents of the region. This means that the software firm contributes to the region’s economic base while the restaurant does not, and the closure of the software firm would have a much more negative impact than the closure of the restaurant. In a tourism-heavy region, however, most of a restaurant’s sales may be to non-residents, which would make the restaurant contribute to the region’s economic base.

Defining Economic Base Sectors
When analyzing economic base, we need to create different categories or sectors of economic activity to represent basic activities. These major groups of industries are called economic base sectors—not to be confused with industry sectors (NAICS* definitions) or industry clusters. Economic base sectors are merely groupings of broadly related industries with no claims made about their inter-dependence; in contrast, NAICS sectors are grouped by similar products and production processes, and industry cluster definitions assume a much tighter supply chain and/or labor market inter-dependence. Economic base sectors are created for convenience to describe any broad type of activity that brings money into a region, for example, “Manufacturing,” or “Visitors.” As such, they are somewhat arbitrary and can be redefined using local knowledge: a town might even allocate an economic base sector to a single large employer. In addition, some of them are not “industries” at all, but various non-industrial sources of outside regional income.

*  North American Industry Classification System. All recent federal statistics use this system to categorize businesses.

In EMSI’s default economic base report, we use the following top-level economic sectors. Most of them are collections of NAICS industries.

• Agriculture, Mining, Manufacturing, Construction, Communications, Services, Finance, and Government: These super-sectors are familiar and fairly self-explanatory, since they closely match top-level NAICS categories. Note that the size of each of these sectors depends more on each one’s export orientation than on each one’s total employment.
• Residents’ Outside Income: This sector includes various sources of income from outside the region, which residents in turn spend in the regional economy. Examples of outside income include outside earnings (e.g., income of residents who commute or telecommute to an employer outside the region), capital or property income (investment dividends, royalties, rents), and transfer payments (unemployment benefits, welfare, Social Security payments, etc.)
• Exogenous Investment: Capital investments in the region from sources outside the region; e.g., federal transportation projects, new factory or plant, or a state government office complex.
• Visitors: Non-residents are an important source of income for many regions, and this sector helps quantify the jobs and earnings attributable to visitors in the region. Note that a region need not be centered on tourism to profit from visitors. For example, cities frequently draws visitors from surrounding towns and rural areas because they need to use the cities’ unique amenities, or “central functions” (shopping centers, airport, county courthouse, and so on).
• All Other: All other industries or other sources of money entering the region not included in other sectors.

Note again that these are somewhat arbitrary categories that can be rearranged to suit the area being analyzed. If, for example, we drill down in “manufacturing” and discover that one industry in that sector (Aluminum sheet, plate, and foil manufacturing) is responsible for 10% of the region’s economic base. Furthermore, we know that there is only one local employer in this industry (ACME Aluminum). We could then create a new economic base sector and call it “ACME Aluminum” to better inform community stakeholders about the area’s economic base—it means more to say that “ACME Aluminum is ultimately responsible for 10% of our jobs; the rest of our manufacturing accounts for 25%” than just to say “Manufacturing as a whole is responsible for 35% of our jobs.” To take another example, suppose a small town has a large public university. A researcher could estimate how much of each default sector’s jobs and earnings could be attributed to that single entity (e.g., the university may be responsible for 75% of the jobs currently assigned to the generic sector “Government”). The researcher could then create a new economic base sector called “The University,” and even subdivide it further into “University-related visitors,” “University-related real estate activity,” “Tech transfer spinoffs,” and so on. This can make analysis and presentation of data more customized and meaningful to the area.

Interpreting and Acting on Economic Base Analysis
Once broad sectors are identified, the region needs to make sure its broad economic development strategy and government policies are designed to support, retain, and recruit industries that make up and/or complement its economic base structure. Recent and projected job growth or decline in key industries should be monitored closely to detect potentially disastrous declines in basic industries. Emerging basic industries should also be identified as those having relatively fewer jobs but a relatively high share of economic base.

Finally, most of this discussion has focused on “region-building” industries, or industries that make up the economic base. Also important are “region-filling” industries, which support basic industries and residents’ needs. While the former are absolutely necessary for economic growth, the latter also play an important role. Region-filling industries can form a supplier network for basic industries, thus making them more competitive. They can also create higher quality of life for residents, by providing goods and services that they demand. If for some reason there are obstacles to region-filling industries, it can harm the region-building industries. So to that extent, region-filling industries can also be considered as targets for economic development efforts.

B. Using IO to Conduct Industry Gap Analysis
Because IO models capture inter-industry purchasing relationships, they can identify overall supply/demand gaps in a given region. For example, many regional industries might require commercial printing services, but the model may indicate that existing commercial printing services in the region do not have nearly enough output to satisfy the demand, so the other industries purchase these services elsewhere. If it is feasible and cost-effective to increase the in-region availability of commercial printing services, then more money would remain in the region rather than “leaking” out, contributing to economic growth.

Such information not only helps to identify the gap, but also provides additional persuasion when courting prospective employers to the region. The ability to say “there is $20 million in unmet demand for your services in our region” gives your economic develop-ment team an edge.

Scans like this can look at the requirements (demand) of the whole economy, or of one particular industry in particular. EMSI’s EI model interface has made some key innovations in this area, offering “push-button” reports that use its IO model to scan for gaps in regional industry requirements.

Remember: because IO models include many mathematically-generated estimates, it is always a good idea to confirm important parts of the data with some primary research.

Here are some issues you might run into:

• The size of input gaps depends on assumptions about how much of their inputs that industries will buy in-region versus outside the region. Cost of the inputs is the biggest factor.
• It may not be cost-effective or even feasible for a certain gap-filling industry to locate in the region. For example, key raw materials may not be located close by, or regional labor with the relevant skill sets may be too expensive, or lack of economies of scale may not make the venture profitable.
• Some firms may have specialized input needs not captured by the model, which uses average data for the whole industry.
• Because of these issues, it is advisable to use the data as a starting point for further survey-based investigation.

To read Section VI, click here