January 19, 2007 | General

Determining Economic Values Of Ethanol Production In Iowa

BioCycle January 2007, Vol. 48, No. 1, p. 51
A summary of findings gives an overview of the research problem and provides a tool to evaluate the potential economic benefits to rural areas from an expanding ethanol industry in the Midwest.
David Swenson and Liesl Eathington

LIMITED credible economic impact analysis of the emerging ethanol industry in Iowa and the nation is available. Indeed, much of the research that is relied upon by policy makers and advocates is based on poorly specified industrial accounts in modeling systems that were not designed to accommodate the modern and rapidly expanding ethanol industry. Coupled with this problem are other analytic issues and concerns:
Analysts often “created” new jobs in the corn producing sector of the economy, a sector that continues to produce a massive surplus of corn and which annually sheds workers as a result of technological innovations.
Economic activity in the transportation sectors was frequently boosted even though the haulage differences among surplus grain (or fed grain products) and ethanol were not articulated well, or evident at the outset.
A price premium often was injected into farmer incomes without determining the net regional effects on farm income or costs, or the uses to which that income might be put.
Cost impacts of higher corn prices locally on other corn users or on other industries that handle and distribute grain were often ignored.
All of the plant construction effects were allocated into the region of analysis unmindful that the vast majority of the components that capitalize an ethanol plant as well as the higher valued engineering, architectural, and specialized construction talent almost always come from outside of the region of study. In addition, temporary construction effects often were added to operational effects.
Most importantly, the cost and revenue structure of modern ethanol producing facilities had not been systematically reconciled with actual industrial impact modeling systems.
This research addresses all of these issues and more. It sifts through the analytic shortcomings of previous research and creates an ethanol impact modeling prototype for studying the regional effects of the placement of an ethanol plant. It also measures the potential regional economic impact gains (or potential losses) that might be attributed to different levels of local ownership of the plants. This comprehensive look at these topics should be helpful for regional economists, policy makers, advocates, and citizens as they evaluate the changes accumulating to rural areas as a result of the boom in ethanol plant construction and operations. Only the major findings are summarized in this short report.
Our research employs an economic impact definition that seeks to identify the net new economic product generated in an area as a result of ethanol industrial activity. Economic product represents simply and solely the value-added payments that are made by the industry – payments to workers as salaries and benefits, payments to investors (or investor-owners), and indirect tax payments to governments that are part of the production process. These are the first levels of economic effects that we seek to measure properly, and our modeling structure transparently specifies the values being measured and how we arrived at those amounts.
An ethanol plant has important commodity supply requirements. It needs new-to-the-region inputs to convert corn into ethanol. As the corn already exists and the plant is not altering the overall production of agricultural goods in the region, we do not count the corn as a net new product as many analysts and advocates mistakenly do. The plant does need important inputs to process the grain, however. These include natural gas or other fuels, electricity, water, enzymes and chemical inputs, perhaps a reconfigured rail distribution system locally, along with a host of financial, technical, mechanical, waste discharge, and service inputs that keep a modern plant running. All of these examples constitute net new input demands in the region that are directly attributable to the placement of the plant in the area. Hence, the plant creates an indirect impact on supplying industries and bolsters their sales and employment.
Last, when workers at the plant and in the supplying industries receive their pay, they convert it into household spending. This induces a third round of economic activity.
Table 1 demonstrates the basic economic impacts of a 50 million gallons/year (mgy) dry-milling ethanol plant in a three county region of Iowa (TriCo) in which it is assumed that there is no local ownership in the plant.
This prototypical ethanol plant generated $118.65 million in simulated sales for 2005 based on the labor of 35 workers. In so doing, it made payments to value-added of $18.4 million. It further stimulated $13.3 million in input sales in the region, which required 75 more jobs to produce and generated $6.01 million in value-added. It is clear that the job effects of this plant are greater in the supplying industries than in the capital-intensive direct industry. Last, as the workers convert their earnings into consumption, they induce $1.55 million in additional output in the region, which takes 23 jobs and sustains $942,326 in value-added.
We can add all of these categories of economic data together to get total economic impact estimates. Considering direct, indirect, and induced effects, this plant links to $133.5 million in regional sales, $25.4 million in value-added, and 133 jobs. The table also lists multipliers – the ratio of total impacts to direct impacts.
The output multiplier is 1.13 (remembering corn has been excluded from this analysis because the plant is not causing more regional agricultural commodity), the value-added multiplier is 1.38, and the jobs multiplier is 3.79. In order, these multipliers mean that for every $1 in output, an additional $.13 in (noncorn) purchases was made from the regional economy. For every $1 in value-added generated in the plant, $.38 in additional value-added was supported in the rest of the economy. And for every job in the plant, 2.79 jobs were sustained in the remaining economy. The jobs multiplier is relatively high compared to other industries because this industry is considered capital intensive relative to its labor demands. It is very atypical of most manufacturing firms in Iowa primarily because the labor needs of a modern plant are very low relative to the total value of production.
The previous example assumed complete external ownership. As the payments to local investors constitute enhancements of local household income, we get increases in area economic activity when those payments are re-entered into the region of study. Using modeled assumptions about the likelihood that those payments will be spent locally, we will get inducements or bumps in local spending by these recipients for household goods and services. As local ownership increases, and payments are shifted to local households, then their collective consumption boosts the amount of induced value-added generated in the region. In this modeling structure, a 25 percentage point increase in payments to local owners resulted in an increase of 29 jobs to the study region.
Having developed and documented this analytic capacity, we then looked at four ethanol plants in Iowa. Three of these plants actually have a local investor component, and one is completely externally owned. The locations, the industrial characteristics of the host regions, and the amount of local ownership differed markedly among our plants.
In each plant, we first calculated a baseline value where no local ownership is assumed. We next added the amount of local ownership actually residing in the study region that the plants provided us and allowed value-added payments to accrue to those local owner-investors to demonstrate the differences in the regional economic impacts. Local ownership was determined by calculating the amount of shares that existed within the postal codes that were in the region that primarily supplied the feedstocks to the plant, its labor, and where residents were likely to shop for everyday goods and services. Every example had a region of a different size.
The findings are displayed in Table 2 by plant measured. The titles are self explanatory and the impacts’ differentials are evident, as are the ranges in baseline values generated from these plants. All of these analyses were simulations based on the TriCo model research and were not based on actual plant revenues and costs. Job, payroll, and plant size characteristics from these firms were actual, however.
The three modeled comparisons had different levels of local ownership, using our definitions of the primary region affected by the plant: 27 percent, 63 percent, and 73 percent. They were located in geographically distinct and separate areas of the state, and contained a mix of primarily rural to primarily metropolitan area economies.
The no local ownership assumption was compared with the actual amounts of ownership identified in our research to gauge the job differences in the three instances attributable to returns accumulating to local investors. For the firm that had 27 percent local ownership, the local ownership dimension accounted for 47 more jobs over the baseline consideration; with 63 percent local ownership, 80 more jobs were added; with 73 percent local ownership, 53 more jobs were added.
These values will vary by level of local ownership and the overall characteristics of the local economy in which the plant resides. However, as long as returns to investors are robust and competitive with other investment alternatives, higher levels of local ownership yield higher job impacts for rural areas.
The information in Table 2 displays findings based on a range of plant configurations, different primary regions, and different overall industrial structures in the areas that we studied. Plant A, with no local ownership, had the lowest baseline jobs multiplier owing primarily to the configuration of its primary economic region. The simulated baseline job impact values of Plant D were much higher because its primary region consisted of two metropolitan counties with very extensive and diverse industrial structures. The values for the other two study plants fit between these extremes.
It is instructive to also note that the ostensible gains from local ownership can work in reverse if the fortunes of these plants wane. Robust local gains become robust local losses if plants are not able to produce future investor payments at levels simulated for the study year (2005) or expected for the current production year. Over time, as investors’ collective comfort with risk changes, they may divest their holdings, which lowers the local effects as well. Accordingly, local ownership is a fluid concept in an increasingly fluid industry.
This research was designed to provide policy makers, planners, and advocates credible baseline information on the economic impact dynamics of modern ethanol plants. The research also quantifies the obvious: higher local ownership levels yield higher economic impacts during a period where returns are strong, as they currently are. There are many dynamics of a changing biofuels economy that are not covered in this research, e.g., changes in returns to farmers who produce corn or purchase corn for animal feed; other handlers and warehousers of grain; “down-stream” economic activity that might accumulate to blenders and distributors of ethanol; local government fiscal impacts; or the net regional outcomes in light of all associated production subsidies at the local, state, and federal government levels.
These findings refer solely to the economic product produced directly by an ethanol plant in consideration of that plant’s linkages to regional industries, investors, and opportunities for household spending.
David Swenson and Liesl Eathington are in the Department of Economics, College of Agriculture at Iowa State University. This research was funded with major assistance from the W.K. Kellogg Foundation through the BioEconomy Working Group at Iowa State University [Grant: BIOE2006-01]. The longer paper is titled: “Input Outrageous: The Economic Impacts of Modern Biofuels Production.” Visit paper12644.pdf. This project benefited greatly by the coordinating and information procurement assistance provided by Jill Euken of ISU Extension. In addition, Extension Professor Robert Jolly, Economics, assisted our project immensely by sharing his existing research on ethanol plant costs and returns and through his wise and timely guidance.

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