IMPLAN Methodology

Introduction

Researchers generally address questions concerning the size of cooperative businesses or the contribution of cooperatives to the larger economy in three ways. The first and simplest is a "head-count" approach that focuses on assessing the relative size of the sector by inventorying the sales revenue generated by cooperatives, the number of cooperative employees, and the total wages, salaries, and patronage paid by cooperatives. The second approach uses scalar multipliers to assess the level of linkages between cooperatives and the larger economy. This approach enables the research to move from the simple head-count approach to the next step by capturing the "multiplier" effect. The third approach uses a complete model of the larger economy to capture not only the aggregate multiplier effect obtained in the scalar multiplier approach, but also to estimate specific industry-to-industry linkages. This latter research approach enables the researcher to decompose the scalar multiplier to the industry level.

The head-count approach reveals that cooperatives employ 500 persons and pay wages and salary of about $35K annually per employee ($17.5M total). If the scalar employment multiplier is 1.5 and the income multiplier is 1.6, then the total impact of cooperatives on the larger economy is 750 jobs (500 x 1.5) and $28M (17.5 x 1.6). Using the third approach, the research can identify which industries are affected by the multiplier effect and at what level. An important question is, If the 250 jobs generated through the multiplier effect, how many are in services, retail, construction, or the public sector? The third approach will provide insights into this question.

The most common and widely accepted methodology for measuring the economic impacts of cooperatives and other enterprises is input-output (I-O) analysis, a subset of a family of methods called social accounting models (Shaffer, et al. 2004; Hewings 1985). Input-output models attempt to describe an array of economic transactions between various sectors in a defined economy for a given period, typically a year. These models provide researchers not only with estimates of the scalar multipliers but also support a detailed decomposition of the multipliers (briefly described above).

Like any economic model, ours is an abstraction of the real world and depends on assumptions that may be imperfect. Unfortunately, most studies that document the impact of cooperatives seldom discuss these limitations. Regardless, this type of analysis, the results of which are frequently cited in newspapers and used in government testimonies, seems more prevalent than ever. Input-output models are used descriptively and analytically to demonstrate the relative importance of a business, industry, or sector (e.g., agriculture) in an economy, and prescriptively, to predict the economic responses from alternative actions (e.g., building a new sports stadium) (Hastings and Brucker 1996; Hewings and Jensen 1986). Input-output analysis is attractive in part because it provides (seemingly) straightforward results; for example, agriculture accounts for 20% of the local economy or a new stadium will generate $1M in additional income. Another appeal of I-O analysis is that it uses multiplier effect to calculate the total impact, which yields far larger values than would be obtained by any direct "head-count" method.

The usefulness of I-O analysis seems to naturally extend to the cooperative sector where such results would surely appeal to multiple groups. Trade associations, government agencies, and even university centers that rely on public funds use the figures to demonstrate the significance of cooperatives to the economy, and hence, the importance of their work. Individual cooperatives might also seek to know the impact of their organization on the local economy, to build support in the community, or to capture a marketing advantage. Using cooperative economic impact analysis would enable policy makers and community development practitioners to make more informed decisions regarding the support of alternative business development options.

Few studies have used I-O analysis to measure the economic impact of cooperatives (Folsom 2003; Zeuli, et al. 2002; Bhuyan and Leistritz 1996; Coon and Leistritz 2001; Herman and Fulton 2001). This dearth may stem from a lack of familiarity with this methodology and how it might be applied. A better understanding of I-O assumptions and data requirements, as related to cooperative studies, is also necessary to avoid “unused, underused, or misunderstood” results (Hastings and Brucker 1996; Zeuli and Deller 2007).

Input-Output Methodology

An I-O model offers a "snapshot" of the economy, detailing the sales and purchases of goods and services between all sectors of the economy for a given period of time within a conceptual framework derived from economic theory. The activities of all economic agents (industry, government, households) are divided into n production sectors. The transactions between the sectors are measured in terms of dollars and segmented into two broad categories: non-basic, which includes transactions between local industries, households and other institutions, and basic, which includes transactions between industries, households, and other institutions outside the economy being modeled (i.e., imports and exports).

One can think of an I-O model as a large "spreadsheet" of the economy where columns represents buying agents in the economy. These agents include industries within the economy buying inputs into their production processes, households and governments purchasing goods and services, as well as industries, households, and governments that are located outside the region of analysis. The latter group represents imports into the economy. Economic agents can import goods and services into the regional economy for two reasons. First, the good or service might not be available and must be imported. Second, local firms might produce or supply the imported good or service, but the local prices or specifications might not meet the needs of the purchasing economic agents. The columns represent economic demand. The rows of the “spreadsheet” represent selling agents in the economy or supply. These agents include industries selling goods and services to other industries, households, governments, and consumers outside the region of analysis. The latter group represents exports out of the economy. Households that sell labor to firms are also included as sellers in the economy.

Within the terminology of input-output modeling, this "spreadsheet of the economy" is referred to as a transactions table; an illustrative example is provided in Table A.1. In this example, the economy is composed of three industries including agriculture (Agr), manufacturing (Mfg) and services (Serv) along with households (HH). Reading down the agricultural column reveals the purchasing patterns of the agricultural industry. Here, agriculture purchases $10 worth of other agricultural goods, such as dairy farmers purchasing feed from other farmers. Farmers also purchase $4 from manufacturing, such as capital equipment such as tractors or milking equipment. Farmers purchase $6 worth of services such as accounting services or specialty crop services. Household supplies $16 worth of labor, such as the farmer or any hired hands. Finally, agriculture imports $14 worth of goods and services into the region. Total spending or costs of the agricultural industry (the input) is $50. Reading across a row identifies the particular industry or sector that sells goods or services. Continuing the agricultural industry example, agriculture sells $10 worth of product to other farmers, such as feed grain to dairy farmers. Agriculture sells $6 to manufacturing, such as milk sold to cheese plants. Agriculture sells $2 to the service sector, such as direct sales to restaurants. Agriculture sells $20 of product to households, and finally exports $12 out of the region. Total sales, or total industry revenue (the output) in this example, is $50.

Table A.1: Illustrative Transactions Table

Processing Sectors (Sellers)	Agriculture	Manufacturing	Service		Household	Exports	Output
	Purchasing Sectors (Demand, in $)				Final Demand, in $
Agriculture	10	6	2		20	12	50
Manufacturing	4	4	3		24	14	49
Service	6	2	1		34	10	53
Household	16	25	38		1	52	132
Import	14	12	9		53	0	88
Input	50	49	53		132	88	372

A key assumption in the construction and application of input-output modeling is that supply equals demand. In the framework of the "spreadsheet of the economy" outlined above, the row total (supply or industry revenue) for any particular industry equals the column total (demand or expenditures): the "spreadsheet of the economy" must be balanced. In the above agricultural example, total sales, or total revenue ("Output" in Table A-1) is $50 and total expenditures, or total costs, ("Input" in Table A-1) is also $50: Therefore, the supply of agricultural products exactly equals to the demand for agricultural products. This framework enables us to trace how shocks to one part of the economy affect the whole of the economy.

For example, consider an increase in the demand for agricultural products in our simple economy outlined above. Suppose that demand for U.S. milk products increases. To meet this new, higher level of demand, dairy farmers must increase production. Increasing production requires the purchase of additional feed from grain farmers, the purchase of additional capital equipment from manufacturing, purchase of additional professional services such as veterinarian services and more labor. These other sectors must also increase production, and their corresponding inputs, to meet the new level of demand created by an increase in milk production. The new labor hired by dairy, for example, has higher levels of income that it in turns spends in the regional economy, thus creating even higher levels of demand for milk. The increased milk demand creates a rippling effect throughout the whole of the economy. This rippling effect, the multiplier effect, can be measured and applied to assessment of how a change in one part of the economy affects the whole of the economy.

Input-Output Multipliers

We described an input-output model of an economy as a "spreadsheet of the economy" in which any change or shock in one part of the economy ripples across the entire economy. By manipulating the empirical I-O model, it is possible to compute a unique multiplier for each sector in the economy. Using these multipliers for policy analysis can provide insight and be useful in preliminary policy analysis to estimate the economic impact of alternative policies or changes in the local economy. In addition, the multipliers can identify the degree of structural interdependence between cooperatives and the rest of the economy. The output multiplier described here is among the simplest input-output multipliers available. By employing a series of fixed ratios from the input-output model, researchers can create a set of multipliers ranging from output to employment multipliers, as shown in Table A-2.

Table A-2: Understanding Multipliers

Type	Definition
Output Multiplier	The output multiplier for industry i measures the sum of direct and indirect requirements from all sectors needed to deliver an additional dollar-unit of output of i to final demand.
Income Multiplier	The income multiplier measures the total change in income throughout the economy from a dollar-unit change in final demand for any given sector.
Employment Multiplier	The employment multiplier measures the total change in employment due to a one-unit change in the employed labor force of a particular sector.

The income multiplier represents a change in total income (employee compensation plus proprietary income plus other property income) for every dollar change in income in any given sector. The employment multiplier represents the total change in employment resulting from the change in employment in any given sector. Thus, changes in economic activity can be measured three ways.

For example, consider a dairy farm that has $1M in sales or revenue (industry output), pays labor $100K inclusive of wages, salaries and retained profits, and employs three workers including the farm proprietor. Suppose that demand for milk produced at this farm increases by 10%, or $100K dollars. The traditional output multiplier could be used to determine the total impact on output. Alternatively, to produce this additional output the farmer will need to hire a part-time worker. The employment multiplier could be used to examine the impact of this new hire on total employment in the economy. In addition, the income paid to labor will increase by some amount and the income multiplier could be used to determine the total impact of this additional income on the larger economy.

Initial, Indirect, and Induced Effects

Construction of the multipliers allows us to decompose the multiplier effect into three parts: (1) the initial (or direct) effects; (2) the indirect effects; and (3) the induced effects. The initial effect is associated with the scenario that creates the impact on the economy. In the agricultural example above, this is the increased in agricultural (or milk) sales. To produce the additional output, the firm or industry must purchase additional inputs. The inputs take two forms: (1) purchases from other businesses and (2) labor. The first purchases from other businesses, creates the indirect effect, while the second form creates the induced effect. For a particular producing industry, multipliers estimate the three components of total change within the local area:

Direct effects represent the initial change in the industry in question (e.g., in the industry itself). Indirect effects are changes in inter-industry transactions when supplying industries respond to increased demands from the directly affected industries (e.g., impacts from non-wage expenditures). Induced effects reflect changes in local spending that result from income changes in the directly and indirectly affected industry sectors (e.g., impacts from wage expenditures).

Comparing and contrasting the indirect and induced effects can offer important insights. For example, industries that are more labor-intensive will tend to have larger induced effects and smaller indirect effects. In addition, industries that tend to pay higher wages and salaries will also tend to have larger induced effects. Decomposing the multiplier into its induced and indirect effects can provide a better understanding of the industry under examination and its relationship to the larger economy.

Data Requirements

Assessing the contribution of cooperatives to the larger US economy requires describing cooperatives in a way that is compatible with the input-output model. This study faces the challenge that cooperatives are a specific business structure not a particular industrial sector. Thus, the input-output model provides no "cooperative multiplier". A major component of this study is the creation of a consistent method for assessing the impact of cooperatives across the spectrum of cooperative types. We therefore, focused on the income generated by cooperatives through wages and salaries paid to employees plus patronage payments to cooperative members. However, we did not obtain quality data on non-labor-related expenditures. For labor-intensive cooperatives, such as credit unions, this approach adequately represents the scale and scope of the cooperative. Our analysis lacks business-to-business expenditures, such as office supplies or utilities.

Given the gap in our survey data, our study is limited to examining the employment and patronage side of cooperatives. Like any other business, cooperatives employ people and pay wages/salaries to those employees. Many cooperatives also make patronage payments to members, which is a form of income. The study examines the impact of those wages/salaries and patronage payments on the broader economy. Given the computed impact on the economy of cooperatives' wages/salary and patronage payments, we compute "implicit" multipliers for each type of cooperative. These implicit multipliers can then be used to assess the impact of any one type of cooperative in future analyses. Importantly, because we consider only the labor-related expenditures of cooperatives, the resulting impacts are conservative because they underestimate total impacts.

In some instances, we did not obtain data for all firms in a given sector. In these cases, we used the available survey data to compute a sample mean and then applied it to the population size to estimate of the population size. For example, if we had usable survey data from 50 cooperatives of a particular type and the total population is 200 cooperatives, we would use the data from the 50 cooperatives to compute an average, then multiply that average by 200 to estimate the total size of the cooperative sector. We then would enter this estimate into the input-output model.

Modeling System

The input-output modeling system used in this study is IMPLAN (Impact M for Planning), originally developed by the USDA Forest Service. A product of the Rural Development Act of 1972, IMPLAN is a system of county-level secondary data input-output models designed to meet the mandated need for accurate, timely economic impact projections of alternative uses of U.S. public forest resources. The Forest Service made IMPLAN as widely available as possible because it was developed using public funds. Moreover, a small investment by the USDA Cooperative Extension Service ensured that the IMPLAN modeling system became widely used by rural development researchers and Extension specialists in the Land Grant University System. The relationship among university-based researchers, Extension specialists, and the Forest Service quickly became bilateral-researchers and specialists questioned data and assumptions, made suggestions, and demanded changes. To accommodate this demand for services, the Forest Service privatized IMPLAN; it is now operated by the Minnesota IMPLAN Group (MIG). In addition to updating and improving the databases and software, MIG holds regular training sessions, biannual user conferences and maintains a collection of hundreds of papers that have used IMPLAN.

One advantage of the IMPLAN system is the open access philosophy instilled by the Forest Service. IMPLAN is designed to provide users with maximum access so that they can alter the underlying structure of the data, the model, or means of assessing impact. The combination of the detailed database, flexibility in application, and the open access philosophy has made IMPLAN one of the most widely used and accepted economic impact modeling systems in the U.S. IMPLAN has been accepted in the U.S. court system and in many regulatory settings.

To assess the economic impact of cooperatives, we employed the 2006 IMPLAN database and the model constructions for the U.S. economy. Labor and patronage payments were used to model the impact of each cooperative type on the whole of the U.S. economy. Given data on cooperative sales, employment, wages, and salary along with patronage refunds, we could assess the impact of cooperatives with a high level of confidence.

Data Collection

Population Discovery

The aim of the project was to create a complete census of U.S. cooperative businesses and measure their economic impact on the U.S. economy. The process of creating a census involved three distinct steps:

• Identifying cooperative business and relevant trade associations.
• Compiling business lists with contact information.
• Gathering data on key economic indicators to aid in the measurement of impacts.

Most businesses were identified with the help of key contacts in various trade associations, academic partners and collaborators, and primary population discovery conducted by the UWCC using business software. In the next section, we discuss each of these venues for population discovery.

Trade Associations and Public Organizations

For regulated industries such as credit unions [5], corporate credit unions [6], the farm credit system [7], and federal home loan banks [8], we used annual reports available at the regulatory Federal agencies' websites. The data for rural electrics [9] comes from NRECA [10]. Agricultural Marketing and Supply Co-ops data come from the USDA [11] 2006 annual survey.

Purchasing cooperative lists were provided by NCBA, and housing cooperative lists were provided by NCB. The EPA provided a list of water mutuals and associations which was supplemented with Guidestar data.

Primary Population Discovery

For many sectors we created primary lists with the assistance of undergraduate researchers. Online searches were conducted with key phrases such as "co-op", "cooperative", and "mutual" for each economic sector. Once cooperatives were identified, lists were created and downloaded into a database with appropriate contact information.

Childcare [12], Healthcare [13], Mutual Insurance [14], Transportation [15], Education [16], Water and Waste [17], and Telephones [18] lists were created using Google [19], Broadlook [20], Onesource [21], Dunn [22], and Guidestar [23]; UWCC purchased the software. Finally, for grocery and worker cooperatives, we used lists maintained by Professor Ann Hoyt and Professor Christina Clamp, respectively.

Data Collection and Survey Methodology

We used standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that would be comparable across economic sectors. The instruments were also designed to identify businesses and collect firm-level data that can be used for future longitudinal studies of cooperative performance.

Design, Sample Frame, and Implementation

Implementing a survey involved numerous separate tasks. These activities included:

• Designing a survey instrument
• Identifying and building an appropriate sample frame
• Hiring and training enumerators
• Piloting the survey
• Securing the participation of selected cooperative firms
• Sending out invitations for participation
• Making and tracking appointments, and tracking refusals to participate
• Implementing the questionnaire
• Tracking survey completion and quality control
• Entering data and quality control

The Instrument

The identical survey instrument was used for all economic sectors, except that adjustments were made as needed for inherent structural differences. The core instrument has four sections:

• Section I. Institutional Information
• Section II. Organizational Structure
• Section III. Financial Information
• Section IV. Governance & Taxation Information

A sample instrument is attached at the end of this section.

Selecting a sample frame

The cooperative business surveys were targeted to a particular set of firms in the following sectors the USDA [11] identified: Commercial Sales and Marketing [24]; Social and Public Services [25]' Financial Services [26]; and Utilities [27].

Our interest was to collect firm-level data. A firm may have one or many establishments. Financial information for the purposes of this study was collected at the aggregate level, so all reported financial data is consolidated unless otherwise specified.

Our sampling strategy was as follows: If the total number of firms were <400 in a given economic sector, then we interviewed all firms in the list. Our goal was to elicit a 50% survey response rate. The following sectors were surveyed using this approach: Grocery and other consumer retail [28]; Arts and Craft [29]; Education [16]; Healthcare [13]; (not Community Healthcare Centers) Transportation [15], Bio-fuels [30]; Telephone [18]; and Purchasing and Worker [31] cooperatives.

For economic sectors with >400 firms we selected a stratified random sample of 300 firms. We employed this approach for the following sectors: Mutual Insurance [14]; Water [17]; and Housing [32] Cooperatives. Our sampling unit for stratification was U.S. states. We followed this approach to ensure that the resulting sample represented underlying distribution within each state for a particular economic sector. To preserve the anonymity of firms, we excluded any state that had fewer than 5 firms in a particular economic sector.

Even following this sampling strategy, identifying telephone numbers for cooperatives was sometimes difficult particularly in the case of . housing [32], and water and waste [17]cooperatives. Most of these cooperatives are small, or without offices, and no one is available during regular business hours. To maximize data points, we redrew our stratified sample from firms with telephone numbers, preserving the population distribution.

Piloting the survey

We piloted the survey to pretest the questions to minimize question ambiguities, check for clarity and consistency, incorporate input from key participants, and allow survey modification to address sector-specific differences. Finally, piloting enabled better training of enumerators. Our piloting consisted of up to 20 interviews, depending on the number of firms in the sector.

Publicizing and Implementation of the Economic Impact Survey

Publicizing a survey increases participation. Because we were surveying multiple sectors simultaneously, we used various mediums to invite participants. To increase participation, we solicited help from trade associations to distribute invitations to member lists, on their websites, and in their newsletters. UWCC also posted an announcement about the survey on its website, mailed invitation letters and e-mails, and often extended direct invitations by telephone.

We intended to create a web form that firms could visit annually to update their profile. Although we followed this approach early in survey implementation, survey responses were not adequate. We therefore hired a staff of 12 students to conduct phone surveys to reach this desired 30% response rate. Calling individual firms and scheduling appointments with the CEO or accountant was more efficient, because this approach gave the respondents time to collect financial information before the phone survey.

Using supplementary data from Guidestar [23], and Onesource [21] we attained a response rate of 30% for all sectors except housing [32]. We surveyed the following sectors: healthcare [13]; childcare [12]; groceries [28]; purchasing [31]; worker [31]; transportation [15]; education [16]; telephones [18]; water and waste [17]; mutual insurance [14]; farm credit system [7](only employment information); arts and crafts [29]; housing [32]; and bio-fuels [30]. We contacted each firm at least three times. Specific response rates for each economic sector are provided in the sector analysis section under "population discovery".

Data Entry and Analysis

Although the data needed for this economic impact analysis was fairly straightforward, the reporting of financial information varies greatly by sector and posed challenges to standardizing data for analysis. This was especially true for defining a patronage refund. Further research needs to carefully document patronage practices across cooperatives.

Once the data was standardized, it was used to create the maps and the IMPLAN analysis.

Aggregate IMPLAN

The aggregate impact tables provided in this section compare with Tables 4-2, 4-3, 4-4, and 4-5. These tables show the Direct, Indirect Induced and Total Impacts for USDA sectors.

 

Table A4-2: Economic Impacts for Commercial Sales and Marketing

Economic Impact	Total Reporting	Direct	Indirect	Induced	Total
Number of firms	2,858	3,463	3,463	3,463	3,463
Revenues	175,593	190,617	4,734	5,856	201,209
Income	--	21,013	6,894	9,830	37,735
Wages	7,522	8,683	2,305	2,822	13,809
Industry Jobs	265,780	264,995	63,300	94,210	422,505

 

Table A4-3: Economic Impacts for the Public Services

Economic Impact	Total Reporting	Direct	Indirect	Induced	Total
Number of firms	841	11,311	11,311	11,311	11,311
Revenues	4,358	6,373	504	646	7,522
Total Income	--	1,235	410	568	2,213
Wages	605	987	311	392	1,690
Industry Jobs	9,160	278,607	58,584	87,314	424,505

 

Table A4-4: Comparing Economic Impacts for Financial

Economic Impact	Total Reporting	Direct	Indirect	Induced	Total
Number of firms	8,627	9,978	9,978	9,978	9,978
Revenues ($M)	267,701	312,006	36,522	45,836	394,362
Total Income ($M)	11,867	57,215	18,432	25,013	100,661
Wages ($M)	769,323	25,606	11,433	14,138	51,175
Industry Jobs	376,052	575,297	222,695	335,361	1,133,353

 

Table A4-5:Comparing Economic Impacts for Utilities

Economic Impact	Total Reporting	Direct	Indirect	Induced	Total
Number of firms	1,970	4,525	4,525	4,525	4,525
Revenues ($M)	36,399	38,857	4,876	6,076	49,810
Total Income ($M)	--	7,620	2,456	3,316	13,392
Wages ($M)	4,325	4,895	1,523	1,874	8,292
Industry Jobs	118,000	88,801	29,612	44,459	162,872

Acknowledgements

Core Research and Data Collection Team for the University of Wisconsin - Madison

This report was prepared by a core team of faculty and staff led by Brent Hueth, and comprising Steven Deller, Ann Hoyt, Matt Kures, Lynn Pitman, Anne Reynolds, and Reka Sundaram-Stukel. The team benefited greatly from excellent research assistance from Pilar Jano, Hedayat Moussavi, and Andrés Moya; and we are indebted to our two graduate-student IT-Gurus Badri Narayan Bhaskar and Mayank Maheshwari, who kept the project going. We would like to extend special acknowledgment to Catherine Levinten-Reid for her contributions early in the project. We would also like to thank our army of undergraduate research assistants Charity Bingham, Tracey Beechner, Christa Behnke, Katie Behnke, Kristen Degeneffe, Eugene Dreyster, Manju Gupta, Alia Jammaluddin, Elizabeth Johnson, Maren Maland, Chris McKim, Monica Sharma, Kevin Vandernaald, and Chris Wollum. Their cheerful telephone personalities and diligence made this analysis possible.

Industry Collaborators and Cooperative Community

First and foremost, we are extremely grateful to the survey participants who took the time from their busy schedules to participate in the study. Without their participation and feedback, this study would not have been possible.

Industry Collaborators

We would like to extend a special thanks to all agencies, organizations, and trade associations for their help in the population discovery, data collection, and survey promotion and for the many helpful comments and suggestions they provided. We are especially grateful to the following organizations for formally supporting our project proposal with letters of support, and for subsequently participating in our project: National Cooperative Business Association [33]; Department of Agriculture, Trade and Consumer Protection [34]; National Rural Electric Cooperative Association [10]; National Telecommunications Cooperative Association [35]; National Cooperative Bank [36]; Farm Credit Council [37]; CoopMetrics [38]; National Association of Housing Cooperatives [39]; Parent Cooperative Preschools International [40]; Filene Research Institute [41]; Wisconsin Federation of Cooperatives [42] and Minnesota Association of Cooperatives [42]; and CHS Foundation [43].

Individual Collaborators

Excellent and extensive advice was given, during various conference calls and meetings, by Paul Hazen, Christina Clamp, Herb Fisher, John Hays, George A. Hofheimer, Terry Lewis, Martin Lowery, Mike Schenck, Tim Size, Marc T. Smith, Walden Swanson, David Thompson, Roger Wilcox, and John von Seggern. We are grateful to all. We also consulted with Elizabeth Bailey, Spyros Heniadis, Melissa Hoover, Melanie Hovey, Anne Katz, Mary Ann Rothman, Stu Schneider, Rick Shadelbauer, Marc Shafroth, Dr. Tun Wei, and many more that we forget to list.

Cooperative Research Council and Boundaries Workshop Participants

We formed two advisory committees during the course of our research. The Cooperative Research Council served as a point of contact with the cooperative community and as a review panel for discussion paper proposals. The Boundaries Advisory Committee was formed to help us identify the legal, tax, and structural character of cooperatives to define our research population. We are deeply grateful to those who gave their time to these efforts.

• James Baarda, USDA Cooperative Programs [44]
• Dennis Bolling, CEO, United Producers [45]
  
• Ann Fedorchak, NCB [36]
• Gail Graham, General Manager, Mississippi Markets Grocery [46]
• Bill Hampel, Executive Vice President, CUNA [47]
• John Hayes, Executive Vice President, Farm Credit Council [37]
• Paul Hazen, CEO, National Cooperative Business Association [48]
• John Logue, Director, Ohio Center for Employee Ownership [49]
• Martin Lowery, Executive Vice President, National Rural Elecrtric Cooperative Association [10]
• Catherine Levinten-Reid, Postdoctoral fellow, Centre for Study of Cooperatives [50], University of Saskatchewan
• Rosemary Mahoney, Board Member, NCB [51]
• William Nelson, Executive Director, CHS Foundation [43]
• LeAnn Oliver, Director, USDA Cooperative Programs [44]
• Bruce Reynolds, USDA Cooperative Programs [44]
• David Swanson, Partner, Dorsey and Whitney LLP [52]
• Tom Schomisch, Board Member, Group Health Cooperative [53]
• Barry Silver, Executive Vice President, NCB [51]

Academic Collaborators and Discussion Paper Authors

• Ethan Ligon [54], Associate Professor, Dept. of Agricultural and Resource Economics, University of California, Berkeley.
• Philippe Marcoul [55], Associate Professor, Dept. of Rural Economy, University of Alberta.
• Brian Mayhew [56], Associate Professor, Wisconsin School of Business, University of Wisconsin, Madison.
• Jessican Gordon-Nembhard, Visiting Scholar, Centre for the Study of Cooperatives University of Saskatchewan.
• Greg Reilly [57], Assistant Professor, University of Connecticut School of Business.
• Richard Sexton [58], Professor, Department of Agricultural and Resource Economics, University of California, Davis.
• Gordon Smith [59], Professor, Brigham Young School of Law.
• Charlie Trevor [60], Associate Professor, Wisconsin School of Business, University of Wisconsin, Madison.

Funding Partners

The team gratefully acknowledges the generous support of the USDA's Cooperative Programs for providing us with the funding and opportunity to conduct this research project. We also gratefully acknowledge matching support from the members of the National Cooperative Business Association and the Wisconsin Department of Agriculture, Trade and Consumer Protection.

List of Acronyms

Acronym	Agencies, Organizations, and Trade Associations
ACA [61]	Agricultural Credit Associations
ACCU [62]	Association of Corporate Credit Unions
ADHS [63]	Arizona Department of Health Services
AESA [64]	Association of Educational Service Agencies
ASI [65]	American Share Insurance
CCHA [66]	Cooperative Home Care Associates
CCMA [67]	Consumer Cooperative Management Association
CCPPNS [68]	California Council of Parent Participation Nursery Schools
CDF [69]	Cooperative Development Foundation
CDSS [70]	California Department of Social Services
CFC [71]	National Rural Utilities Cooperative Finance Corporation
CUNA [47]	Credit Union National Association
DCD [72]	North Carolina Division of Child Development
DFPS [73]	Texas Department of Family and Protective Services
DHS [74]	Michigan Department of Human Services
DHSS [75]	Alaska Department of Health and Social Services
EBSA [76]	Employee Benefits Security Administration
EPA [77]	Environmental Protection Agency
FAC [78]	Farm Credit System Financial Assistance Corporation
FCA [79]	Farm Credit Administration
FCC [37]	Farm Credit Council
FCC [80]	Federal Communications Commission
FCSIC [81]	Farm Credit System Insurance Corporation
FHLBS [8]	Federal Home Loan Bank System
FHSC [82]	Federated Human Service Co-op
FLCA [61]	Federal Land Credit Associations
GDCNC [83]	The Greater Detroit Cooperative Nursery Council
HRSA [84]	Health Resources and Services Administration
ICA [85]	International Co-operative Alliance
ICPC [86]	Indiana Council of Preschool Cooperatives
NACHC [87]	National Association of Community Health Centers
NAFCU [88]	National Association of Federal Credit Unions
NASCUS [89]	National Association of State Credit Union Supervisors
NBCH [90]	National Business Coalition of Health
NCB [36]	National Cooperative Bank
NCBA [91]	National Cooperative Business Association
NCCUSL [92]	National Conference of Commissioners for Uniform State Law
NCHN [93]	National Cooperative of Health Networks Association
NCSC [94]	National Cooperative Services Corporation
NCUA [95]	National Credit Union Administration
NCUSIF [96]	National Credit Union Share Insurance Fund
NLAHCC [97]	National Labor Alliance of Health Care Coalitions
NRECA [10]	National Rural Electric Cooperative Association
NRTC [98]	National Rural Telecommunications Cooperative
NTCA [35]	National Telecommunications Cooperative Association
PCPI [40]	Parent Cooperative Preschools International
PHI [99]	Paraprofessional Healthcare Institute
REDLG [100]	Rural Economic Development Loan and Grant
RFA [101]	Renewable Fuels Association
RTB [102]	Rural Telephone Bank
RUS [103]	Rural Utilities Service
TDI [104]	Texas Department of Insurance
TLPA [105]	Taxicab, Limousine, and Paratransit Association
USDA [11]	United States Department of Agriculture
USFWC [106]	US Federation of Worker Cooperatives
WEP [107]	Water and Environmental Program
WSTB [108]	Water Science and Technology Board
DHS [109]	Wisconsin Department of Health and Human Services