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University of Wisconsin Center for Cooperatives
Research on the Economic Impact of Cooperatives
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).
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.
|Processing Sectors (Sellers)||Agriculture||Manufacturing||Service||Household||Exports||Output|
|Purchasing Sectors (Demand, in $)||Final Demand, in $|
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.
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.
|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.
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.
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.
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.