Value Chain Analysis and Economic Diversification

Transcription

1 Value Chain Analysis and Economic Diversification A Case Study Prepared for Northern Illinois University s Executive Education Series Regional Economic Development Leadership Course PSPA 692: Value Chair Analysis and Economic Diversification Dr. Brian Richard, Instructor February 2014 This report was prepared by Northern Illinois University s Center for Governmental Studies under award from the Economic Development Administration, U.S. Department of Commerce. The statements, findings, conclusions and recommendations are those of the author(s) and do not necessarily reflect the views, opinions, or policies of the Economic Development Administration or the U.S. Department of Commerce or the officers and/or trustees of Northern Illinois University.

2 Value Chain Analysis for the State of Ingenuity Region The State of Ingenuity (SOI) region including six counties in Illinois and Wisconsin (figure 1) has suffered from both natural disasters such as flooding and from serious economic setbacks in the Great Recession that closed one automobile factory and caused serious cutbacks in another. While some employment has returned, it is important that efforts continue to revitalize and transform the economic base for long term prosperity. This case study focuses on identifying value chain and industrial cluster opportunities to guide local economic development practices. Figure 1. State of Ingenuity Region A previous case study, Regional Economic Analysis Tools for the State of Ingenuity Region, employed various techniques to analyze the economy of the SOI region. Trend analysis, location quotients, and shift share analysis were employed. It found that the manufacturing sector has a higher than average concentration in the region. However, manufacturing is losing jobs at a faster rate than the nation as a whole. This case study will start by exploring connections between counties within the SOI region using commuting data. Then, techniques for identifying clusters will be presented. This process will dig deeper into the employment data, examining data for subsectors within the broad manufacturing sector, along with linkages to other sectors. 2

3 Commuting Data Commuting data from the U.S. Census Bureau identifies linkages between counties. Commuters represent economic linkages between places. These linkages come in the form of spending patterns and also in the distribution of skills and knowledge. Winnebago County has the largest population of the six SOI counties. Table 1 displays data on the work location of Winnebago County, IL residents and the residence locations of Winnebago County workers. Winnebago County has slightly more jobs (134,742) than resident workers (130,501). It has a relatively low commuting level few residents commute to jobs in other counties and few jobs are filled by residents of other counties. The left side of the table shows that over 83 percent of employed Winnebago County residents work within the county. Four percent commute to Boone County, IL directly to the east, with another 2.8 percent commuting to Rock County, WI directly to the north. No more than 2 percent of Winnebago County residents commute to jobs in any other county. A total of about 7 percent of Winnebago County residents work in other SOI counties (highlighted in bold in the table). The right side of Table 1 show that almost 81 percent of Winnebago County workers live in the county. Similar to out commuters, Boone County, IL (5.6 %) is home to the next largest group of county employees. Ogle County, directly to the south is home to about 4 percent of Winnebago County employees. About 9 percent of Winnebago County workers live in other parts of the SOI region. Table 1. Winnebago County, IL Commuting County State County Number State County County Number Winnebago Illinois Winnebago County 108, % Illinois Winnebago County Winnebago 108, % Winnebago Illinois Boone County 5, % Illinois Boone County Winnebago 7, % Winnebago Wisconsin Rock County 3, % Illinois Ogle County Winnebago 5, % Winnebago Illinois Cook County 2, % Wisconsin Rock County Winnebago 4, % Winnebago Illinois Ogle County 1, % Illinois Stephenson County Winnebago 2, % Winnebago Illinois McHenry County 1, % Illinois Cook County Winnebago % Winnebago Wisconsin Walworth County % Wisconsin Walworth County Winnebago % Winnebago Wisconsin Kenosha County % Wisconsin Racine County Winnebago % Winnebago Wisconsin Racine County % Wisconsin Kenosha County Winnebago 3 0.0% All Other 6, % All Other 5, % 130, ,742 Source: Census Bureau, year ACS County to County Worker Flow Files Adjacent to the east, Boone County, IL is more rural than Winnebago County. There are many more workers than jobs in the county (Table 2). Boone County is highly dependent on Winnebago, both for jobs and workers. Almost one third of Boone County employed residents are employed in Winnebago. Over 35 percent of Boone County employees commute from Winnebago. 3

4 Table 2. Boone County, IL Commuting County State County Number County State County Number Boone County Illinois Boone County 7, % Illinois Boone County Boone County 7, % Boone County Illinois Winnebago County 7, % Illinois Winnebago County Boone County 5, % Boone County Illinois McHenry County 2, % Wisconsin Rock County Boone County % Boone County Illinois Cook County 2, % Illinois Ogle County Boone County % Boone County Illinois Kane County 1, % Illinois McHenry County Boone County % Boone County Illinois DuPage County % Illinois DeKalb County Boone County % Boone County Illinois DeKalb County % Illinois Stephenson County Boone County % Boone County Wisconsin Rock County % Illinois Cook County Boone County % Boone County Wisconsin Walworth County % Wisconsin Walworth County Boone County % Boone County Wisconsin Kenosha County % Wisconsin Racine County Boone County % All Other % All Other % Total 23,346 14,818 Source: Census Bureau, year ACS County to County Worker Flow Files Rock County, WI has more employed residents than jobs (Table 3). It has a relatively low level of commuting, although more workers commute out and in to the county. Commuters in and out of Rock County primarily travel north and south. Dane County, home to the state capital of Madison is directly north and Winnebago County to the south. Interstate 90 runs north south, facilitating the commute to these employment centers. Table 3. Rock County, WI Commuting County State County Number State County County Number Rock Wisconsin Rock County 55, % Wisconsin Rock County Rock 55, % Rock Wisconsin Dane County 8, % Illinois Winnebago County Rock 3, % Rock Illinois Winnebago County 4, % Wisconsin Dane County Rock 1, % Rock Wisconsin Walworth County 2, % Wisconsin Walworth County Rock 1, % Rock Wisconsin Jefferson County 1, % Wisconsin Green County Rock 1, % Rock Wisconsin Green County % Wisconsin Jefferson County Rock 1, % Rock Illinois Boone County % Illinois Boone County Rock % Rock Wisconsin Racine County % Wisconsin Kenosha County Rock % Rock Wisconsin Kenosha County % Wisconsin Racine County Rock % All Other 1, % All Other 1, % 75,781 66,172 Source: Census Bureau, year ACS County to County Worker Flow Files Kenosha County, WI is on the eastern edge of the SOI region, bordering Illinois to the south. The county is home to more workers than jobs (Table 4). Kenosha has a higher level of commuting than Winnebago or Rock counties. Kenosha County workers and residents are more likely to commute north or south along Interstate 94. Racine County, part of the SOI region just to the north, is the place of work for about 10 percent of Kenosha County residents and is home to over 11 percent of Kenosha County workers. 4

5 Table 4. Kenosha County, WI Commuting County State County Number State County County Number Kenosha Wisconsin Kenosha County 42, % Wisconsin Kenosha County Kenosha 42, % Kenosha Illinois Lake County 17, % Wisconsin Racine County Kenosha 6, % Kenosha Wisconsin Racine County 7, % Illinois Lake County Kenosha 4, % Kenosha Illinois Cook County 3, % Wisconsin Milwaukee County Kenosha 1, % Kenosha Wisconsin Milwaukee County 3, % Wisconsin Walworth County Kenosha 1, % Kenosha Illinois McHenry County 1, % Wisconsin Waukesha County Kenosha % Kenosha Wisconsin Waukesha County 1, % Illinois McHenry County Kenosha % Kenosha Wisconsin Walworth County % Illinois Cook County Kenosha % Kenosha Co Illinois DuPage County % Illinois Winnebago County Kenosha % Kenosha Wisconsin Rock County % Wisconsin Rock County Kenosha % Kenosha Wisconsin Winnebago County % Illinois Boone County Kenosha % All Other 1, % All Other % 78,252 58,244 Source: Census Bureau, year ACS County to County Worker Flow Files Racine County, WI has similar commuting patterns to Kenosha County to the south. There are more workers than jobs and commuting tends to be to the north and south (Table 5). Over 7 percent of employed Racine County residents work in Kenosha County and about 10 percent of the Racine County workforce live in Kenosha. Table 5. Racine County, WI Commuting County State County Number State County County Number Racine Wisconsin Racine County 59, % Wisconsin Racine County Racine 59, % Racine Wisconsin Milwaukee County 15, % Wisconsin Kenosha County Racine 7, % Racine Wisconsin Kenosha County 6, % Wisconsin Milwaukee County Racine 4, % Racine Wisconsin Waukesha County 4, % Wisconsin Walworth County Racine 2, % Racine Illinois Lake County 1, % Wisconsin Waukesha County Racine 2, % Racine Wisconsin Walworth County 1, % Illinois Lake County Racine % Racine Illinois Cook County % Wisconsin Rock County Racine % Racine Wisconsin Rock County % Illinois Winnebago County Racine % Racine Illinois Winnebago County % Wisconsin Washington County Racine % Racine Illinois Boone County % Wisconsin Jefferson County Racine % All Other 1, % All Other 1, % 90,836 78,301 Source: Census Bureau, year ACS County to County Worker Flow Files Walworth County, WI also has more workers than jobs (Table 6). It has commuting levels similar to its neighbors to the east, but the commuting is more diffuse. Between 5 and 8 percent of employed Walworth County residents are commuting to the northeast along Interstate 43 to Waukesha and Milwaukee counties, directly to the south to McHenry County, IL. Less than 5 percent commute to each of the SOI region counties directly to the east (Racine and Kenosha) and west (Rock). Only Rock County to the west and Jefferson County to the east are home to greater than 5 percent of Walworth County employees. 5

6 Table 6. Walworth County, WI Commuting County State County Number State County County Number Walworth Wisconsin Walworth County 32, % Wisconsin Walworth County Walworth 32, % Walworth Wisconsin Waukesha County 3, % Wisconsin Rock County Walworth 2, % Walworth Wisconsin Milwaukee County 2, % Wisconsin Jefferson County Walworth 2, % Walworth Illinois McHenry County 2, % Wisconsin Racine County Walworth 1, % Walworth Wisconsin Racine County 2, % Wisconsin Waukesha County Walworth 1, % Walworth Wisconsin Rock County 1, % Wisconsin Kenosha County Walworth % Walworth Wisconsin Kenosha County 1, % Illinois McHenry County Walworth % Walworth Wisconsin Jefferson County % Wisconsin Milwaukee County Walworth % Walworth Illinois Cook County % Wisconsin Dane County Walworth % Walworth Illinois Lake County % Illinois Lake County Walworth % Walworth Wisconsin Dane County % Illinois Winnebago County Walworth % Walworth Illinois Winnebago County % Illinois Boone County Walworth % Walworth Illinois Boone County % Illinois Cook County Walworth % All Others 1, % All Others % 51,133 45,142 Source: Census Bureau, year ACS County to County Worker Flow Files Overall, there is more out commuting from SOI counties than in commuting. All but one county (Winnebago) have more resident workers than jobs. The out commuters are largely working in the Milwaukee area and northeast Illinois. With some exceptions, commuting patterns show a relatively low level of economic integration between SOI counties. The major exception to that is Boone County s dependence on Winnebago County. About one third of employed Boone County residents work in Winnebago County and about one third of jobs are filled by Winnebago County residents. Kenosha and Racine counties are somewhat interconnected with about 10 percent of each counties workforce commuting to the other county. Industry Clusters There are many different definitions of industry clusters in the regional economic development literature. Porter ( 2001, p.7) defines clusters as geographic concentrations of interconnected companies, specialized supplies, service providers, firms in related industries, and associated institutions. Barkley and Henry (2005, p. 3) elaborate on this definition. Firms in an industry cluster may interact through purchase sale relationships; interfirm collaboration in product development, marketing, or research; or a shared reliance on specialized services and labor markets. Barkley and Henry (2005) go on to list five advantages of targeting industry clusters as an economic development strategy: Clustering strengthens localization economies o Input suppliers, workers, and infrastructure become specialized and more productive as the cluster develops in the region. A large number of nearby 6

7 firms within the cluster learn from each other through technology transfers and movement of employees. Clustering facilities industrial reorganization o It is easier for small, specialized manufacturers to thrive in clusters. Adoption of new technologies is easier because of the concentration of similar firms. The proximity of specialized suppliers and markets increases the flow of goods through the system. Clustering encourages networking among firms o Firms cooperate to take advantage of complementarities, access markets, integrate activities, or pool resources. Clustering results in larger local economic impacts o Because of a well developed supplier network and pool of skilled employees, there are higher multiplier effects. Clustering facilitates entrepreneurial activity o Clusters & entrepreneurial activity reinforce one another by fostering innovation and facilitating commercialization of new products. This is especially important in small clusters or in declining industries. There are also disadvantages of targeting industry clusters (Barkley and Henry, 2005). The most prominent is the difficulty in picking winners. The process requires projections of future competitive advantage, but this is not an exact science. Some of the pitfalls of this process will be discussed later in this study. Identifying Industry Clusters There are two broad approaches to identifying industry clusters using benchmarks. Benchmarking refers to a process of comparing a local region to a larger benchmark region, such as comparing a county or group of counties to a state or multi state region. Comprehensive benchmarks use a region focused approach while focused benchmarks use an industry focused approach. Comprehensive benchmarks apply industry groupings for a relevant reference area (i.e. nation) to regional data to identify potential clusters. Focused benchmarks apply industry groupings for a relevant reference area (i.e. nation) to selected industries within a region to identify potential clusters. This case study will employ a focused benchmark approach. Barkley and Henry (2005) lay out a methodology for targeting industry clusters for regional economic development. It begins with identifying industry concentrations with recent regional employment growth. Following that identification, value chains are constructed for the industries selected Finally, the selected industry clusters are ranked by expected economic impacts on the local economy. 7

8 Industry Concentration Screening The first step in identifying industry clusters using this process is to apply a series of screening criteria to industries in the region. Potential screening criteria include measures of industry type, size and growth: Industry sector (export oriented) Minimum number of establishments Minimum employment Employment growth LQ > 1 or LQ growth 1 Positive industry competitiveness measure o Regional Shift portion of shift share analysis For the purposes of this case study, the following screening criteria will be used on industries at the 4 digit NAICS code level: Limited to manufacturing industries Sector employment of at least 500 Above average industry concentration (LQ > 1) Positive regional share portion of shift share analysis A couple of exceptions to these criteria were made to capture especially prominent industries in the region. If an industry sector had employment above 2,000 or an LQ > 2 they were included. Potential pitfalls with including these exceptions will be discussed later in the case. Table 1 displays sectors that result from the screening process. They range from machine shops, with total employment of over 6,000 in the region to other rubber product manufacturing which employs just over 600. The sectors list in italics indicate those that had a negative regional share component of the sift share analysis but were included because of their size or concentration in the region. 1 Location Quotients (LQs) and Shift Share Analysis are discussed in the previous SOI Case Study: Regional Economic Analysis Tools for the State of Ingenuity Region. 8

9 Table 1. Prominent SOI Manufacturing Sectors SOI Employment NAICS IMPLAN Industry 2012 Total 317, Machine shops 6, Fluid power process machinery 5, Cutting tool and machine tool accessory manufacturing 3, Other plastics product manufacturing 2, Motor vehicle parts manufacturing 2, Other fabricated metal manufacturing 1, Coating, engraving, heat treating and allied activities 1, Snack food manufacturing 1, Other major household appliance manufacturing 1, Other commercial and service industry machinery manufacturing 1, Other engine equipment manufacturing 1, Plate work and fabricated structural product manufacturing 1, Printing 1, Automatic environmental control manufacturing 1, Crown and closure manufacturing and metal stamping 1, Surgical appliance and supplies manufacturing Ferrous metal foundries Other rubber product manufacturing 618 Source: Census Bureau, 2011 County Business Patterns Cluster Value Chains Following the industry sector screening process, a value chain analysis is conducted. Barkley and Henry (2005) suggest using IMPLAN data to identify the top five input supplier industries and the top five product markets for each identified industry. Important linked industries are identified using counts of industry that appear in this process. Table 2 displays the sectors most linked to the prominent SOI manufacturing sectors identified above. Wholesale trade is in the top five inputs of 17 and 3 markets out of the 18 industries. This is not surprising because it is an important part of the distribution system of any industry. Motor vehicle parts manufacturing is also prominent, especially on the markets side. Beyond that, most industries appear as either inputs or markets. 9

10 Table 2. SOI Prominent Industry Linkages IMPLAN Commodity Inputs Markets 319 Wholesale trade businesses Motor vehicle parts manufacturing Iron and steel and ferroalloy products Management of companies and enterprises Securities, commodity contracts, investments, and related services 8 36 Construction of other new nonresidential structures 7 38 Construction of other new residential structures Plastics materials and resins Construction machinery manufacturing Semiconductor and related device manufacturing Construction of new nonresidential commercial and health care structures Machine shops Automobile manufacturing Mining and oil and gas field machinery manufacturing Other engine equipment manufacturing Private hospitals 3 37 Construction of new residential permanent site single and multi family structures Steel products from purchased steel Plates and fabricated structural products Valve and fittings other than plumbing manufacturing Farm machinery and equipment manufacturing Heavy duty truck manufacturing Food services and drinking places 2 In addition to being used to generate counts of important supplier and market industries, IIMPLAN data can be used for a more detailed investigation of linkages. Backward linkages (linkages to industries supplying inputs) can be identified through the industry balance sheet in IMPLAN. The industry balance sheet identifies intermediate inputs into the production process of a given industry. Machine shops industry balance sheet for the United States is shown in Table 3. SOI region firms in this sector purchase about $344 million in inputs annually (this does not include labor). The values in the Gross Inputs column represent estimates of inputs from various sectors based on national averages. Machined products (the output produced by machine shops) comprise the largest portion of inputs at almost 8 percent of the total. This indicates that firms buy components of their products from other specialized producers in the same NAICS sector. The next two input sectors (iron and steel products and plates & fabricated structural products) provide the typical raw materials to machined products. The fourth most important input sector to machine shops (based on national averages) might be surprising to some. Securities, commodity contracts, and investments are not the types of inputs that would normally come to mind when one thinks about machine shops. 10

11 However, many large businesses that purchase commodities such as steel as an input use financial products such as futures contracts to stabilize the costs of those commodities. The second column of data indicates the Regional Purchase Coefficient (RPC) for each input. The RPC is an estimate of the percentage of input that is purchased from within the region. The RPC of.070 for machined products indicates that firms in this sector purchase about 7 percent of their inputs from with the SOI region and import about 93 percent. The Regional Inputs column represents the dollar value of inputs purchased locally within the region (simply the Gross Inputs multiplied by the RPC). Finally, the Imports column indicates the purchases of inputs being shipped in from outside the region. Table 3. Backward Linkages: Machine Shops SOI Industry Balance Sheet Industry Sector Gross Inputs RPC Regional Inputs Imports Total Commodity Demand 344,073, ,615, ,458,087 Machined products 26,906, ,881,788 25,024,846 Iron and steel and ferroalloy products 22,300, ,686,279 20,614,660 Plates and fabricated structural products 18,309, ,918,515 16,390,694 Securities, commodity contracts, investments, and related 16,544, ,588,875 9,955,353 Management of companies and enterprises 15,731, ,134,994 11,596,970 Wholesale trade distribution services 12,073, ,475,442 4,598,037 Real estate buying and selling, leasing, and related 9,835, ,684,819 2,150,819 All other professional, scientific, and technical services 9,833, ,624,212 5,209,065 Nonferrous metals 9,370, ,136,676 8,233,436 Monetary authorities and depository credit services 8,476, ,709,375 1,767,143 Aluminum products from purchased aluminum 7,147, ,621 7,048,989 Source: IMPLAN 2012 Data. Forward linkages are those to industries that purchase the output of firms. These can be identified through the IMPLAN commodity balance sheet (Table 4). The commodity balance sheet identifies where an industry s production is sold. Over 14 percent of the $323 million in output of machine shops is sold to motor vehicle parts manufacturers. Since the output of the industry is relatively homogenous, the RPCs for forward linkages are the same for all sectors. Just under 7 percent of the output in the sector is sold to firms in the SOI region. 11

12 Table 4. Forward Linkages: Machine Shops SOI Commodity Balance Sheet Industry Sector Gross Inputs RPC Regional Inputs Total Industry Demand 322,970,496 NA 22,587,812 Motor vehicle parts manufacturing 46,241, % 3,234, % Fluid power process machinery manufacturing 33,251, % 2,325, % Machine shops 26,906, % 1,881, % Automobile manufacturing 16,818, % 1,176, % Soap and cleaning compound manufacturing 14,048, % 982, % Other industrial machinery manufacturing 9,952, % 696, % Other engine equipment manufacturing 8,572, % 599, % Turned product and screw, nut, and bolt mfg 7,787, % 544, % Pump and pumping equipment manufacturing 7,731, % 540, % Other commercial and service industry machinery mfg 7,325, % 512, % Source: IMPLAN 2012 Data. Location Quotients can be used to determine the concentration of forward and backward linked industries. As discussed earlier, machine shops (which produce machined products) have a high concentration in the SOI region, indicated by an LQ of almost 5 (Table 5). Other input sectors with above average concentration in the region include plates and fabricated structural products and nonferrous metals. Many input sectors have low concentrations in the region. Iron, steel, and ferroalloy products, securities, commodity contracts, and investments, management of companies, and aluminum products all have low LQs. These low LQs may indicate potential economic development targeting opportunities. However, these policies should be approached with caution. It may simply be that the region cannot be competitive in these industry sectors. Table 5. Location Quotients of Top Upstream Industries Industry Sector Gross Inputs SOI Empl LQ Total 344,073, , Machined products 26,906,634 3, Iron and steel and ferroalloy products 22,300, Plates and fabricated structural products 18,309, Securities, commodity contracts, investments, and related 16,544,228 2, Management of companies and enterprises 15,731,963 3, Wholesale trade distribution services 12,073,479 18, Real estate buying and selling, managing, and related svcs 9,835,638 15, All other professional, scientific, and technical services 9,833, Nonferrous metals 9,370, Monetary authorities and depository intermediation svc 8,476,518 5, Aluminum products from purchased aluminum 7,147, Source: IMPLAN 2012 Data. 12

13 As shown in Table 6, most of sectors with downstream linkages to machine shops have high concentrations in the region. All of the top 10 input sectors to machine shops have LQs above 3. Fluid power process machinery manufacturing is almost 33 times more concentrated in the SOI region as compared to the United States average. Table 6. Location Quotients of Top Downstream Industries Industry Sector Gross Inputs SOI Empl LQ Total 322,970, , Motor vehicle parts manufacturing 46,241,650 4, Fluid power process machinery manufacturing 33,251,964 3, Machine shops 26,906,634 3, Automobile manufacturing 16,818,831 3, Soap and cleaning compound manufacturing 14,048,678 2, Other industrial machinery manufacturing 9,952, Other engine equipment manufacturing 8,572,660 1, Turned product and screw, nut, and bolt mfg 7,787,777 2, Pump and pumping equipment manufacturing 7,731, Other commercial and service industry machinery mfg 7,325,573 1, Source: IMPLAN 2012 Data. Tables 5 and 6 indicate high industry concentration of downstream linked industries and relatively low concentrations of upstream linked industries. This indicates that machine shops are a supporting sector in a larger cluster, rather than leading a cluster. The high concentration of downstream industries suggests that there is a large local market for their output, rather than export markets driving their production. Intermediate inputs (purchases from other firms) are only a portion of the impact that a firm has on a regional economy. The more important impact (for most industry sectors) comes from value added. Value added is the difference between the value of intermediate inputs and the value of the output of a firm. It primarily consists of employee compensation, but also includes proprietor income (profits) and taxes. Table 7 displays value added for machine shops in the SOI region. The Coefficient column indicates the percentage of total output represented by each category. Employee compensation comprises almost 37 percent of the total output for this sector. Other income (proprietor and property) add another 7 percent. This means that for every $1 million increase in sales in this sector, and additional $440,000 in income is added in the region. 13

14 Table 7 SOI Region Machine Shops Value Added Description Coefficient Value Added Total Value Added ,834,528 Employee Compensation ,540,497 Proprietor Income ,090,937 Other Property Type Income ,284,359 Tax on Production and Imports ,918,708 Source: IMPLAN 2012 Data. Methodology Pitfalls The methods presented in this case study can be used to examine industry sectors to indicate their potential for successful economic development targeting efforts. The results of the quantitative analyses should be used with care, however, and followed up with a more qualitative analysis of industries that are identified. Reasons that these methods may not yield definitive results include measures being too broad, data suppression, and data quality issues. Overly Broad Measures One of the important industries identified with the initial value chain analysis was wholesale trade distribution services. This sector was in the top 5 inputs for 17 out of 18 sectors identified through the industry screening process. IMPLAN data were used for this identification process. The IMPLAN sector (319) is the equivalent of NAICS sector 42. Other data sources, such as County Business Patterns, publish data for a much more detailed set of sub industries in this sector. Table 8 displays LQs for the 4 digit wholesaler subsectors. It is clear that within this broad sector, SOI firms are concentrated in a relatively small number of sub sectors. Motor vehicle and parts wholesalers, lumber and construction wholesalers, and metal and mineral wholesalers all have LQs above 1.9. A few others have LQs between 1 and 1.5, but half of them have LQs below 0.5. This more detailed data, not available in IMPLAN, provides policymakers with a better picture of conditions in the region. 14

15 Table 8. Location Quotients of Wholesaler Subsectors SOI LQ NAICS Industry Motor vehicle and parts merchant wholesalers Furniture and furnishing merchant wholesalers Lumber and const. supply merchant wholesalers Commercial equip. merchant wholesalers Metal and mineral merchant wholesalers Appliance and electric goods merchant whls Hardware and plumbing merchant wholesalers Machinery and supply merchant wholesalers Misc. durable goods merchant wholesalers Paper and paper product merchant wholesalers Druggists' goods merchant wholesalers Apparel and piece goods merchant wholesalers Grocery and related product wholesalers Farm product raw material merch. whls Chemical merchant wholesalers Petroleum merchant wholesalers Alcoholic beverage merchant wholesalers Misc. nondurable goods merchant wholesalers Wholesalers 1.00 Source: Census Bureau, 2011 County Business Patterns The measures used in this case study also cover a wide geography. The SOI region consists of six diverse counties. Using data for the region as a whole may suggest policies that are not appropriate in some parts of the region. Table 9 displays LQs for the Wholesale subsectors at the county level. The high concentration of motor vehicle and parts wholesalers is being driven by concentrations in Rock, Racine, and Kenosha counties. Lumber and construction supply wholesalers has the highest LQ of and of the wholesaler sub sectors. However, this is being driven almost exclusively the employers in Rock County. Even sectors with relative low LQs for the region can show significant concentrations in individual counties. Commercial equipment wholesaling has an LQ below 0.5 for the region, but a relatively high concentration in Kenosha County (LQ = 1.13). 15

16 Table 9. County Level Location Quotients of Wholesaler Subsectors NAICS Merchant Wholesaler Sector Winnebago Walworth Rock Racine Kenosha Boone SOI 4231 Motor vehicle and parts Furniture and furnishing Lumber and const. supply Commercial equipment Metal and mineral Appliance and electric goods Hardware and plumbing Machinery and supply Misc. durable goods Paper and paper product Druggists' goods Apparel and piece goods Grocery and related product Farm product raw material Chemical Petroleum Alcoholic beverage Misc. nondurable goods Wholesalers Source: Census Bureau, 2011 County Business Patterns Data Suppression A single company, Woodward, employs 1,400 in the region in the airplane parts manufacturing sector (NAICS 3364). Employment at this single firm put the industry among the largest in the region. However, the industry didn t show up in the data analysis conducted to identify important sectors. Frequently when a single firm dominates employment in a county, government data providers will suppress the data for that sector to avoid reveal information about that individual company. When data is not available, the analytical techniques presented are obviously not useful in identifying that sector as being prominent. This may cause an analyst not familiar with a region to miss important assets. Data Quality Issues No source of data is perfect. For example, IMPLAN (and other) input output models are based on national datasets. Firm production technology in each region is assumed to be the same as for the US economy. Grain farmers in Illinois are assumed to use the same inputs (seed, fertilizer, etc.) as farmers in Mississippi or Washington State. If the regional economy differs significantly from the national economy, IMPLAN may overor underestimate local purchases versus imports. If the firm operations differ significantly from the national industry sector, IMPLAN may misidentify upstream and downstream 16

17 linkages. The potential problems with this can be seen in the analysis of machine shops above. Because firm output in an individual sector is assumed to be consistent across the United States, the estimates of regional purchase coefficients are identical across sectors (Table 4). However, the differing concentrations of downstream linked industries (Table 6) might suggest that that the output of machine shops is not homogenous. It s likely that the RPC for products sold to fluid power process machinery manufacturing firms (LQ = 33) is greater than the RPC for motor vehicle parts manufacturing firms (LQ = 3.1). Summary This case study walks through a methodology for analyzing industry concentrations and value chains for the purposes of economic development planning. Backward linkages (suppliers to prominent industry sectors) and forward linkages (customers of firms in prominent industry sectors) can be identified using IMPLAN input output data. An investigation of these sectors can point to potential opportunities for economic development targeting efforts. These quantitative methods are no substitute for local knowledge. Because of potential data availability and quality issues, the results of these techniques should be used with care. Further boots on the ground investigation of local conditions should always occur before committing resources to industrial targeting/recruiting efforts. REFERENCES Barkley, David & Mark S. Henry (2005). Targeting Industry Clusters for Regional Economic Development: The REDRL Approach. In Goetz, Stephan J., Steven C. Deller, and Thomas R. Harris. Targeting Regional Economic Development (pp ). New York: Routledge. Blakely, Edward J. and Ted K. Bradshaw (2002). Planning Local Economic Development. Thousand Oaks, CA: Sage Publications Porter, Michael (1990). The Competitive Advantage of Nations. New York: The Free Press. Porter, Michael (2001). Clusters of Innovation: Regional Foundations of U.S. Competitiveness. Washington, DC: Council on Competitiveness. Stimson, Robert J., Roger R. Stough, and Brian H. Roberts (2006). Regional Economic Development: Analysis and Planning Strategy. New York: Springer. 17