Jason P. Brown

Jeremy G. Weber

Timothy R. Wojan

Economic

Research

Service

Economic 

Research

Report

Number 159

November 2013

United States Department of Agriculture

Emerging Energy Industries 

and Rural Growth
Economic Research Service 

www.ers.usda.gov

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Brown, Jason P., Jeremy G. Weber, and Timothy R. Wojan. Emerging Energy Industries 

and Rural Growth, ERR-159. U.S. Department of Agriculture, Economic Research Service, 

November 2013.

Cover image: Shutterstock.
United States Department of Agriculture

Economic 

Research 

Service

Economic

Research

Report

Number 159

November 2013

Abstract

Expansion of emerging energy industries—unconventional natural gas extraction, 

wind power development, and corn-based ethanol production—in rural areas of the 

United States during the last decade has led, on average, to net gains in local employment. Unconventional natural gas (so-called because it uses unconventional extraction 

methods—hydraulic fracturing and horizontal drilling—to reach gas trapped in relatively impermeable shale and sandstone) had the biggest employment effect, largely 

reflecting the scale of the activity. Despite its relatively large employment effect, the 

effect of natural gas development is smaller than what prior simulation models projected. 

Estimates of employment impacts for wind turbines and ethanol plants, in contrast, are 

consistent with some earlier projections. This report synthesizes and builds on findings 

from recent studies led by U.S. Department of Agriculture, Economic Research Service 

researchers investigating the local economic effects of these energy industries. 

Keywords: natural gas, wind power, ethanol, economic growth

Acknowledgments

The authors thank Todd Kuethe and Jayson Beckman of U.S. Department of Agriculture, 

Economic Research Service, as well as Harry Baumes, USDA, Office of the Chief 

Economist; Anthony Crooks, USDA, Rural Development; Tim Kelsey, Pennsylvania State 

University; and David Swenson, Iowa State University, for their reviews. The authors also 

thank ERS editor Priscilla Smith and ERS designer Cynthia A. Ray.

Jason P. Brown, Jeremy G. Weber, 

and Timothy R. Wojan

Emerging Energy Industries 

and Rural Growth
ii

Emerging Energy Industries and Rural Growth, ERR-159

Economic Research Service/USDA

Contents

Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii

Introduction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Three Emerging Energy Industries and Their Contributions to Rural Growth. . . . . . . . . . . . 2

Technological Innovations Spurred Growth in Unconventional Natural Gas Extraction . . . . . 5

Higher Electricity Prices, Supportive Policies Encouraged Wind Power Expansion. . . . . . . . 11

Gains in Economies of Scale, High Energy Prices Propelled Ethanol Production. . . . . . . . . . 16

Simulation Approach to Modeling Impacts. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21

Possible Future Job Growth Constraints. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28

References. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30

Appendix. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
www.ers.usda.gov

What Is the Issue?

The production of natural gas (primarily from unconventional sources), wind power, and cornbased ethanol in each case more than doubled from 2000 to 2010, with most of the growth 

occurring in rural areas with abundant land for drilling pads, wind turbines, and corn fields. 

(Unconventional natural gas is differentiated from regular natural gas by its unconventional 

extraction methods—hydraulic fracturing and horizontal drilling—that are used to reach gas 

trapped in relatively impermeable shale and sandstone). Previous studies have projected the new 

industries’ contributions to local and regional economies. After industry expansion, it is useful to 

compare the projections with what actually happened.

What Did the Study Find?

For two industries, the overall employment impacts were statistically significant. For counties in 

Colorado, Texas, and Wyoming that experienced a large increase in natural gas production, we find 

that natural gas development was associated with a 12-percent increase in total employment over 

8 years. For a 12-State region stretching from Texas to North Dakota, counties with expansion in 

wind power experienced a 0.6-percent increase in average total employment over a similar period. 

For ethanol production, statistically significant employment growth can be confirmed only among 

closely linked industries such as trucking and natural gas distribution. The entrance of an ethanol 

plant in Midwestern counties led on average to a 0.9-percent increase in employment within industries that previous studies suggest are closely linked to ethanol production.

The contribution of each of these three energy industries to local employment growth varied. For 

both natural gas and wind counties, the average increase in county employment from all sources 

was about 3,000 jobs. The 1,780 new jobs associated with natural gas development therefore 

represented about half of the average increase in local employment. For wind, the 60 new jobs 

associated with wind power development represented roughly 2 percent of the average increase in 

county employment from all sources. For counties with an ethanol plant, the average increase in 

local employment from all sources was smaller, at 254 jobs. The effect of 1 ethanol plant on local 

employment in closely linked industries, at 82 jobs, therefore represented a large share of employment growth in the typical ethanol plant county. 

The effect of natural gas development, despite its relatively large employment effect, is smaller 

than what prior studies projected. Estimates for wind turbines and ethanol plants, in contrast, are 

consistent with some earlier projections. 

United States Department of Agriculture

A report summary from the Economic Research Service November 2013

ERS is a primary source 

of economic research and 

analysis from the U.S. 

Department of Agriculture, 

providing timely information on economic and policy 

issues related to agriculture, 

food, the environment, and 

rural America.

Find the full report 

at www.ers.usda.

gov/publications/erreconomic-researchreport/err-159.aspx

Jason P. Brown

Jeremy G. Weber

Timothy R. Wojan

Economic

Research

Service

Economic 

Research

Report

Number 159

November 2013

United States Department of Agriculture

Emerging Energy Industries 

and Rural Growth

Jason P. Brown, Jeremy G. Weber, and Timoth R. Wojan

Emerging Energy Industries and 

Rural Growth
Overall, our findings suggest that expansion of unconventional gas drilling (and with similar technology, oil 

drilling) will contribute the most to short-term economic growth across rural areas, while the wind and ethanol 

industries will have more modest effects. Looking ahead, the growth potential of the three industries and their 

contribution to local economies may be quite different than what these short-term economic effects suggest. Gas 

reserves in specific locations will eventually decline, ethanol plants will have to compete with other end users for 

feedstocks, and variable winds pose increasing challenges for the electrical grid as their role as a power source 

increases. In addition, the environmental impacts associated with further development of each of these energy 

industries could lead to countervailing contractions in other local industries in the long run.

Our analysis provides a limited view of how the industries affect life in rural communities. The costs and benefits 

related to the industry can be unevenly distributed among local residents. Furthermore, the net economic benefits 

to an area may be quite different than gross private monetary gains measured by employment or personal income. 

A review of the literature suggests that the environmental impacts from wind power development are fewer 

compared with extracting natural gas and producing corn-based ethanol. However, each industry brings its own 

challenges for local communities: groundwater and road traffic concerns with natural gas; disruption of the landscape by wind turbines; and use of wastewater or water from ethanol plants. 

How Was the Study Conducted?

This report synthesizes and builds on recent studies by ERS economists that used empirical approaches to 

estimate causal effects based on a model of what would have happened in a county if expansion of the energy 

industry in question had never occurred. The industry-specific studies discussed in this report employed a combination of matching and regression analysis, including difference-in-difference and instrumental variable estimation. Despite differences in statistical details, the three studies share the same empirical thrust. They compare the 

growth in counties where the energy industry expanded with the growth in counties with less or no expansion, 

while controlling for other potential differences between counties. The studies draw primarily on data from the 

U.S. Department of Commerce’s Bureau of Economic Analysis Local Area Personal Income and Employment 

estimates, the Bureau of Labor Statistics Quarterly Census of Employment and Wages, State agencies that monitor 

oil and gas development, Lawrence Berkeley National Laboratory, and National Renewable Energy Laboratory. 

www.ers.usda.gov

Source: USDA, Economic Research Service.

Employment gains from the emergence of energy industries in selected regions

Percent

Industry-related employment gain relative 

to initial total employment

Industry-related employment gain relative to 

total change in employment from all sources

0

10

20

30

40

50

60

Natural gas Wind Ethanol
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Emerging Energy Industries and 

Rural Growth 

Jason P. Brown, Jeremy G. Weber, and Timothy R. Wojan

Introduction

Expansion of three emerging energy industries—unconventional natural gas extraction, wind power 

development, and corn-based ethanol production—in rural areas of the United States during the last 

decade has led, on average, to net gains in local employment. Unconventional natural gas (so-called 

because it uses unconventional extraction methods—hydraulic fracturing and horizontal drilling—to 

reach gas trapped in relatively impermeable shale and sandstone) had the biggest employment effect, 

largely reflecting the scale of the activity. 

From 2000 to 2010, domestic production of unconventional natural gas grew by 116 percent, causing 

U.S. natural gas production to reach historic highs (U.S. DOE/EIA, 2012b). Over the same period, 

installed wind power capacity increased from 2.5 to 40 gigawatts (GW), and total ethanol production, 

most of which is derived from corn, saw a sevenfold increase (U.S. DOE/EIA, 2012b). Much of the 

growth occurred in rural areas with abundant land for wind turbines, corn fields, and drilling pads.

In addition to reducing energy imports and offering potential environmental benefits like reducing 

particulate emissions by replacing coal with natural gas in electricity generation, proponents of 

the three industries highlight the economic opportunities that they create. Studies using simulation 

models projected the economic contributions of these new industries to local and regional economies 

before much, if any, industry growth actually occurred. Most studies used an input-output model 

approach to simulating employment and income impacts. When used appropriately, input-output 

and other simulation models can provide realistic projections of the economic activity supported by 

expansion of an industry. Because of their timeliness, the projections are often the only source of 

quantitative information on economic impact and can influence policy debates. 

After expansion has occurred, however, causal empirical studies can estimate the actual economic 

impact to local economies. (“Causal” implies that the estimated effect is the consequence of industry 

expansion and not a reflection of other changes in the economy). Studies of this kind have been rare 

due to the small sample size characteristics of emerging industries, limited data availability, and the 

sophisticated statistical techniques often required for definitive findings. 

In this report, we synthesize recently published studies led by ERS economists that assess how 

natural gas production, wind power development, and new corn-based ethanol plants affected 

economic growth at the county level using statistical approaches (for more details on those 

approaches, see appendix section “Empirical Approaches To Estimating Causal Effects”). We use 

the findings to assess the importance of each energy industry to growth in select counties in the first 

decade of the 2000s. 
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Three Emerging Energy Industries and Their 

Contributions to Rural Growth

This chapter describes the geography of energy industry growth and summarizes its economic 

contributions to specific regions. Unconventional natural gas extraction, wind power development, 

and corn-based ethanol production are most likely to occur in rural areas because of resource availability. The growth in gas production largely follows the location of unconventional gas formations, 

covering large rural areas in Colorado, Wyoming, Arkansas, Louisiana, Oklahoma, and Texas, as 

well parts of the Appalachian region such as areas in New York, Pennsylvania, Ohio, and West 

Virginia. The Great Plains of the central United States have the greatest onshore wind potential, 

and the Midwest and Great Plains grow most of the country’s corn. Consequently, most installation 

of wind turbines and construction of ethanol refineries to date have occurred in the Midwest and 

the Great Plains. Within areas that have the resource potential for energy development, many other 

factors can affect location decisions, including the cost of land and access to infrastructure like gas 

pipelines, railways, and electrical grids (fig. 1).

For the areas and periods considered, growth in natural gas production from the exploitation of 

unconventional sources made substantial contributions to employment growth, while the contributions from wind energy and ethanol plants were smaller (table 1). The empirical analysis of natural 

gas focused on counties in Colorado, Texas, and Wyoming and found that for counties that experienced considerable growth in production, the increase in employment from natural gas development caused a 12-percent increase in employment from 1999 to 2007. The wind empirical analysis 

focused on a 12-State region in the middle of the United States. In counties with some wind turbine 

installation, development caused a less-than-1-percent increase in employment from 2000 to 2008. 

For the same period, an estimate that draws on empirical methods suggests that the typical employment effect for a county with an ethanol plant was similar to that found for wind power development 

(with employment growth of 0.9 percent in closely linked industries).

For natural gas counties, the average change in employment from all sources from 1999 to 2007 was 

about 3,000 jobs. From 2000 to 2008, wind counties experienced a similar change in employment 

from all sources. The 1,780 new jobs associated with natural gas development therefore represented 

about half of the total change in local employment; for wind, the roughly 60 new jobs associated 

with wind power development represented roughly 2 percent of the total change in local employment. In contrast to wind and natural gas counties, the average total change in employment from 

2000 to 2008 for counties with an ethanol plant was only 254 jobs. The employment effect of the 

typical ethanol plant within closely linked industries, at 82 jobs, therefore, represented a large share 

of employment growth (32 percent) in the typical ethanol plant county (fig. 2). 

The net effect of each emerging energy industry on total employment at the county level in part 

reflects differences in the intensity of labor use, the strength of the linkages to the other sectors in 

the local economy, and the scale of activities. Over the study period, the average gas-boom county 

saw the annual value of gas production increase by $757 million, part of which reflected an increase 

in well-head natural gas prices from roughly $2 to $6 per 1,000 cubic feet. At $2.32 a gallon—the 

price in the 2006/2007 marketing year—a 60-million-gallon ethanol plant would, if used at full 

capacity, produce $139 million in ethanol. Similarly, the average county developing wind power had 

123 megawatts of installed capacity by the end of 2008, which translates into $30 million in electricity a year (assuming production at 30 percent of capacity, a 2008 price of $97 per megawatt hour, 
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and deflating to 2007 dollars) (U.S. DOE/EIA, 2012b). The ranking of the three industries by their 

total local employment effect, therefore, follows their ranking by growth in the value of production.

Source: U.S. Department of Energy, 

National Renewable Energy Laboratory.

Figure 1

Expansion in wind power, corn-based ethanol capacity, and natural gas production, 2000-10

Change in megawatts

0

1-70

71-446

447-1,339

1,340-9,906

Change in million gallons

0

1-100

101-249

250-538

539-2,931

Wind Ethanol

Source: Renewable Fuels Association.

Change in billion cubic feet

Decline

0

1-156

157-423

424-1,883

Natural gas

Source: U.S. Department of Energy, Energy Information Administration.
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Source: USDA, Economic Research Service.

Figure 2

Employment gains from the emergence of energy industries in selected regions

Percent

Industry-related employment gain relative 

to initial total employment

Industry-related employment gain relative to 

total change in employment from all sources

0

10

20

30

40

50

60

Natural gas Wind Ethanol

Table 1

Average employment effects for study counties experiencing energy industry expansion

Industry

Industry growth in 

the average county

 ($ million)

Average total 

jobs created 

Percent of base 

year employment

Mean change in 

total employment 

from any source

Natural gas 757 1,780 12 3,199

Wind 30 59 0.6 3,047

Ethanol 139 82 0.9 254

Notes: The estimated employment effect and the average total jobs created correspond to the same period used when 

measuring the change in production in the average energy county. For natural gas, the change is from 1999 to 2007. The 

average total jobs created in energy counties refers to the average jobs created in the 61counties that experienced substantial increases in gas production. For wind, the change is from 2000 to 2008. Wind energy counties are the 128 counties with 

installed wind power capacity. For ethanol, the change is from 2000 to 2008. The employment multiplier was estimated from 

a set of closely linked industries accounting for 75 percent of the indirect jobs created by an ethanol facility, as found by Low 

and Isserman (2007). Ethanol energy counties are 46 counties that received a single ethanol plant during the study period. 

Natural gas production was valued at State-level wellhead prices in the beginning and end year of the study period. The 

123 megawatts of installed capacity for the average wind county translates into $30 million in electricity a year, assuming 

production at 30 percent of the available capacity, a price of $97 per megawatt hour in 2008, and deflating to 2007 dollars. 

The change in ethanol production is for a plant producing 60 million gallons of ethanol a year at $2.32 per gallon. 

Source: Weber (2012); Brown et al. (2012).
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Technological Innovations Spurred Growth in 

Unconventional Natural Gas Extraction

From 2000 to 2010, production of conventional gas declined by more than 4 trillion cubic feet. 

Production of unconventional gas, by contrast, increased by almost 7 trillion cubic feet (U.S. DOE/

EIA, 2012b). The growth reflects improvements in technology that made it easier to extract unconventional gas together with higher well-head gas prices. Natural gas prices averaged $5.87 per 1,000 

cubic feet over the early 2000s in real terms, well above the average price for the 1990s of just $2.78 

(U.S. DOE/EIA, 2012d). Even after a large drop in 2009, real prices have generally exceeded those 

of the 1990s. 

The two primary innovations in drilling technology are hydraulic fracturing and horizontal drilling. 

Hydraulic fracturing involves injecting a mix of chemicals and water deep into the ground to open 

fissures in rocks and has been used in some form for decades. Innovation in drilling horizontally 

has occurred more recently, partly because of the Federal Government’s investment in research on 

extracting gas from hard rock formations, which lowered the cost of drilling horizontal wells (King, 

2010; U.S. DOE/NETL, 2011). Though generally more expensive than vertical wells, horizontal 

wells can draw gas from larger areas, thereby reducing the pipelines and wells needed to extract and 

transport gas from an area.

Reservoirs of unconventional gas are distributed throughout much of the United States, but most 

large-scale development first occurred in three of the leading producing States of onshore natural 

gas: Colorado, Texas, and Wyoming. From 1995 to 2000, production changed little in Wyoming and 

Texas and increased in Colorado. In subsequent years, production in all three States grew rapidly, 

with gross withdrawals of natural gas increasing by more than 80 percent in Colorado and Wyoming 

from 2000 to 2008. Though smaller in percent terms, the largest absolute increase in production 

occurred in Texas (Weber, 2012). 

More recently, several other States have experienced dramatic increases in production. Arkansas experienced a tripling of production from 2007 to 2010 from development of the Fayetteville Shale. Despite 

lower prices after 2008, production in Pennsylvania (Marcellus Shale) and Louisiana (Haynesville 

Shale) both spiked from 2009 to 2010, with production in Pennsylvania more than doubling. Other 

States showing trends towards greater production include Oklahoma and Utah (U.S. DOE/EIA, 2012c). 

Table 2 shows the top 10 producing States for onshore natural gas production as of 2010.

Boom Counties Experience Increased Employment, Income 

Total natural gas production increased in the early 2000s in Colorado, Texas, and Wyoming, but the 

location of reservoirs meant that some counties participated in the production boom while others did 

not. The counties that did not participate in the boom provided a business-as-usual scenario: what 

would have happened in boom counties had development not occurred. 

Ranking all counties in the three States by their change in gas production from 1998/1999 to 

2007/2008 reveals that only about 20 percent of counties saw a substantial increase in production, with 

most counties experiencing little or no change. Figure 3 shows that the 20 percent of counties with a 

substantial increase, referred to as boom counties, saw steady growth in production, with production 

increasing by 125 percent. Other counties, referred to as nonboom counties, had almost no change. 
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In high-population counties, normal churning of the economy, like plant closures and openings, can 

overshadow the effect of gas activity on the economy. Furthermore, the boom happened primarily 

in counties without large cities. We therefore exclude the 10 percent of counties that were the most 

populous (more than 130,000 people) as well as counties adjacent to gas-boom counties that did not 

experience a boom in production themselves. Adjacent counties may not represent a business-asusual scenario since their proximity to boom counties could have led to greater economic activity 

due to commuters or sourcing of inputs used in production not found in the boom counties. 

Table 2

Top 10 States for onshore production of natural gas 

State Bcf 2000 Bcf 2010 Change (2010-2000)

Texas* 5,682 7,565 1,883

Louisiana 1,343 2,969 1,626

Alaska 3,265 2,827 –438

Wyoming* 1,326 2,515 1,189

Oklahoma 1,613 1,827 214

Colorado* 760 1,590 829

New Mexico 1,714 1,341 –372

Arkansas 172 927 756

Pennsylvania 150 573 423

Utah 281 437 156

*States used in the natural gas analysis.

Bcf = billions of cubic feet.

Source: U.S. Department of Energy, Energy Information Administration, http://www.eia.gov/dnav/ng/ng_prod_sum_dcu_

NUS_m.htm

Source: USDA, Economic Research Service tabulations of data from the Colorado Oil and Gas Conservation Commission,

the Texas Railroad Commission, and the Wyoming Oil and Gas Conservation Commission.

Figure 3

Gas production more than doubled in gas-boom counties, 1999-2007 

Gas production, billion cubic feet

0

20

40

60

80

100

120

1999 2000 2001 2002 2003 2004 2005 2006 2007

Gas-boom counties

Nonboom counties
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A straightforward comparison of the percent increase in employment from 1993 to 1999 and from 1999 

to 2007 for boom and nonboom counties shows that employment gains accompanied natural gas development (fig. 4). From 1993 to 1999, both groups of counties experienced similar growth in employment 

and wage and salary income, but from 1999 to 2007, employment in boom counties grew faster by 1.4 

percentage points annually, and wage and salary income grew 3.4 percentage points faster relative to 

growth in the prior period.1 The larger increase in wage and salary income is likely because naturalgas-sector jobs pay higher-than-average wages (IHS Global Insight, 2011). A doubling of jobs in the 

natural gas sector would then imply a more-than-doubling of wages and salaries. 

A similar comparison yields an employment estimate of 962 jobs and $59 million in wage and 

salary income, on average. Differences between gas-boom counties and nonboom counties unrelated 

to natural gas production, however, may have caused the two groups to have different changes in 

employment and income from 1999-2007 relative to the period 1993-1999 (table 3). Boom counties, 

for example, may have been less dependent on sectors that grew more slowly during the early 2000s. 

Consequently, we control for differences in the 1992 values of several variables, like the share 

of earnings accounted for by manufacturing and each county’s per capita income and population 

density and those of adjacent counties. 

Because unobserved factors could still affect the results, we use an instrumental variable approach 

that uses only the boom counties statistically linked to the percent of the county covering an 

unconventional gas reservoir (see appendix section “Empirical Approaches To Estimating Causal 

Effects”). When the instrumented and noninstrumented approaches give results that are statistically different from each other, the more robust instrumented estimate is preferred. When the results 

are statistically indistinguishable, the noninstrumented estimate is preferred because it gives more 

1The 1.4-percentage-point greater employment growth is calculated as: (“percent change 1999-2007, boom counties” 

– “percent change 1993-1999, boom counties”) – (“percent change 1999-2007, nonboom counties” – “percent change 

1993-1999, nonboom counties”). The wage and salary growth is calculated in the same manner.

Source: USDA, Economic Research Service tabulations of data from the Colorado Oil and Gas Conservation Commission,

the Texas Railroad Commission, and the Wyoming Oil and Gas Conservation Commission.

Figure 4

Boom counties experienced higher growth in employment and wage and salary income 

during the boom period but not in the prior period

Percent change over the period

0

5

10

15

20

25

30

35

40

45

50

1993-1999 1999-2007 1993-1999 1999-2007

Employment Wage and salary income

Nonboom county

Boom county
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precise estimates. The row labeled “Best estimate” in table 2 applies this decision rule to the two 

sets of estimates. 

The best estimates suggest that natural gas extraction added 1,780 jobs and $69 million in wage and 

salary income to the average boom county economy. The estimates imply that natural gas development increased employment by 12.0 percent from 1999 to 2007 for the average boom county; for 

wage and salary income the increase is even larger, at 20.9 percent. Because a few counties have 

dramatically higher production than other counties, the employment and income effects for the mean 

boom county are greater than for the median county (table 4). (The median gas-boom county is the 

county at which half of the boom counties had a larger increase in production and half had a smaller 

increase). Details of the analysis are available in the appendix and in Weber (2012).

Rapid and Unequal Growth, Environmental and Public 

Infrastructure Costs, and Boom-Bust Cycles

Development of unconventional natural gas has implications for the quality of life of rural residents 

that extend beyond local aggregate employment and income effects. Issues associated with rapid 

development of unconventional gas include: the social consequences of rapid and unequal growth; 

environmental costs; public infrastructure costs; and boom-bust cycles of extractive industries. 

Table 3

Changes in employment and wage and salary income for natural-gas boom and 

nonboom counties

Employment (jobs)

Wage and salary income 

($ million)

Boom counties

Change 1999-1993 2,342 68

Change 2007-1999 3,199 132

Difference (1) 857 63

Nonboom counties

Change 1999-1993 1,572 46

Change 2007-1999 1,467 50

Difference (2) –106 4

Triple difference: (1) minus (2) 962 59

(314) (17)

Regression results

Conditional triple difference, 

noninstrumented approach

510 23

(364) (15)

Conditional triple difference, 

instrumented approach 

1,780 69

(820) (31)

Best estimate 1,780 69

Annualized increase relative to 

1998 level 1.5% 2.6%

Source: The first two sets of numbers are calculated using data from the U.S. Department of Commerce, U.S. Census 

Bureau and the U.S. Department of Labor, Bureau of Economic Analysis. Boom and nonboom county designations are 

based on growth in natural gas production from 1999 to 2007 and are the same counties used in Weber (2012). The 

regression results are from Weber (2012).
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Social Consequences

Rapid increases in gas drilling can change life quickly in rural communities. The influx of gas 

company workers and contractors increased the demand for housing in Pennsylvania, leading to 

higher rental rates, especially in low population counties (Williamson and Kolb, 2011). In Sublette 

County, WY, the expansion of drilling came with a 12-percent increase in population, and police 

arrests more than doubled (Jacquet, 2005). Aside from potential tension between long-term residents 

and gas industry newcomers, the distribution of costs and benefits also can undermine social cohesion. A landowner without mineral rights may receive almost nothing from drilling pads on his property while neighbors receive large royalty payments. Likewise, rising food and housing costs may 

strain the budget of low-income residents without the skills to compete for higher paying jobs in the 

new local industry.

Environmental Consequences

The environmental consequences of unconventional natural gas drilling have drawn attention 

mostly to hydraulic fracturing, the method commonly used to extract unconventional gas. Among 

other potential risks, the U.S. Environmental Protection Agency is investigating the potential for 

fluids (including gas) to migrate through the subsurface (U.S. EPA, 2012). Methane venting or 

flaring, volatile organic compounds from wastewater, and diesel exhaust from heavy truck traffic 

can lower air quality (Kargbo, Wilhelm, and Campbell, 2010). Pumping stations, drilling pads, and 

pipelines can give the landscape an industrial look. (For an overview of potential environmental 

consequences, see Resources for the Future, Risk Matrix for Shale Gas Development, 2012). Likely 

related to environmental or aesthetic issues, Boxall et al. (2005) found that in Alberta, Canada, properties located closer to natural gas facilities had lower values than properties located further away. 

Similarly, Muehlenbachs et al. (2012) found that the value of properties dependent on wellwater in 

one county in Pennsylvania declined with the properties’ proximity to unconventional gas wells. 

Table 4

Empirical estimates of natural gas development, 1999-2007

Employment (jobs)

Change in gas 

production 

($ million) 

Estimated effect 

per $1 million in 

production Total effect (jobs)

Percent of 1998 

mean value of 

employment

Percent

25th percentile 124

2.35 jobs

292 2.0 

50th percentile 382 898 6.1

75th percentile 946 2,224 15.0

Mean 757 1,780 12.0

Wage and salary income

Total effect 

($ million)

25th percentile 124

0.09 dollars in wage 

and salary income

11 3.4 

50th percentile 382 35 10.6

75th percentile 946 86 26.1

Mean 757 69 20.9

Source: The estimated effects are from Weber (2012). The change in gas production uses county-level production data from 

State agencies and wellhead natural gas prices from the U.S. Department of Energy, Energy Information Administration. 

Employment in 1998 is from the U.S. Department of Labor, Bureau of Economic Analysis.
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Public infrastructure costs

One unconventional gas well can require thousands of truckloads of materials, which cause roads to 

deteriorate faster, and up to 10 million gallons of water, much of which will return to the surface and 

require proper management in impoundments or wastewater treatment plants (U.S. Dept of Interior, 

National Park Service, 2009; Kargbo, Wilhelm, and Campbell, 2010). In the absence of State and 

local revenue policies tailored to the costs and revenues of the industry, a deterioration of public 

finances and infrastructure may mean that private monetary gains overstate the net economic gain 

to a community. If absentee landownership is common, much of the monetary gain may accrue to 

residents outside the community who are insulated from how industry development is affecting the 

quality of life in producing areas.

Boom-bust cycles

When natural gas drilling causes an economic boom in an area, the severity of the economic bust 

that may follow is unknown. Volatility in natural resource prices and its destabilizing effect on the 

broader economy is cited as a reason why local or national economies more dependent on natural 

resources often grow more slowly in the long run (van der Ploeg, 2011). Because of export restrictions and infrastructure constraints, most domestic production of natural gas is sold on the domestic 

market. Consequently, greater production of natural gas through unconventional means has caused a 

decline in domestic prices, slowing development in some areas. Permits issued in the Barnett Shale 

in Texas, for example, declined by more than 50 percent from 2008 to 2009. 

Even though exporting natural gas may help to limit how much prices decline on the domestic 

market, global energy markets have shown much volatility in the last 40 years. Integration with 

broader energy markets may therefore do little to stabilize domestic prices. Another dynamic consideration is that development of a natural gas formation may occur in defined stages, with large yearover-year increases in production for several years followed by a plateau and then a long, steady 

decline. The production curve for specific formations cannot be known until the drilling process has 

begun, creating another source of uncertainty for State and local public revenues in addition to the 

volatility in natural gas prices. 

Declines in natural gas prices reduce drilling activity in producing areas and decrease royalty 

payments and tax revenues from the industry as well as lower the cost of energy consumption by 

households and businesses throughout the economy. The decline in prices from roughly $8 per 1,000 

cubic feet in 2008 to around $4 per 1,000 cubic feet in the ensuing 3 years meant lower heating bills 

for households and higher profits for industries using natural gas. 
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Higher Electricity Prices, Supportive Policies Encouraged 

Wind Power Expansion

Wind power capacity has expanded dramatically in the United States. From 2000 to 2011, the cumulative installed capacity increased from 2,500 to 46,800 megawatts (fig. 5). Wind contributed 36 percent 

of all new electric generation capacity added to the U.S. power system from 2007 to 2010 (Wiser and 

Bolinger, 2011), and it is technically feasible for a fifth of the U.S. electric supply to come from wind 

power by 2030 (U.S. DOE, 2008). Despite recent growth in the wind power industry, wind accounts 

for less than 3 percent of the electricity generated in the United States (fig. 6). 

Higher electricity prices and supportive Federal and State Government policies have encouraged 

growth in wind development in the past decade (Bolinger and Wiser, 2009; Wiser et al., 2011). The 

national average electricity price in 2010 was 9.83 cents per kilowatt-hour, representing a 16-percent 

increase since 2000 in real terms (U.S. DOE/EIA, 2011a; and U.S. DOE/EIA, 2011b). At the Federal 

level, production-based tax credits have reduced the cost of wind energy to purchasers (Lu et al., 

2011), and the more recent ability to convert the credits to an upfront cash grant has helped the wind 

industry weather the 2008-10 financial crisis (Bolinger et al., 2010).2 Established by the Energy 

Policy Act of 1992, the credit reduces taxable income for qualified wind developers by 2.2 cents per 

kilowatt-hour of electricity produced for the first 10 years of operation.3 Wiser (2007) estimates that 

the credit offsets the cost of developing wind power by approximately one-third. States have also 

supported the industry through a combination of policies (Bird et al., 2005). Recently, State-specific 

renewable portfolio standards requiring electricity suppliers to use a certain amount of renewable 

energy in their supply mix have been a primary method of support for the emerging wind power 

industry (Wiser and Barbose, 2008). 

2Tax-exempt entities such as municipalities, publicly owned utilities, rural electrical cooperatives, nonprofit educational institutions, or other governmental entities are not eligible for the production tax credit or cash grant.

3The credit has been adjusted upward over time to account for inflation.

Source: U.S. Department of Energy, Energy Information Agency.

Figure 5

Wind power capacity in the continental United States, 2000-11

Cumulative installed 

capacity (megawatts)

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

8,000

9,000

10,000

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

2000 01 02 03 04 05 06 07 08 09 10 11

Additional annual U.S. capacity (right axis)

Cumulative U.S. capacity (left axis)

Additional annual 

capacity (megawatts)
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Great Plains and Midwest Are Sites for Most Wind Power 

Development

Most installations of wind power have occurred in the Great Plains and parts of the Midwest where 

wind resources abound and there is ample land for wind turbines (U.S. DOE, 2012). Of the top 10 

States in wind power capacity growth between 2000 and 2010, Texas led the way, followed by Iowa 

(table 5). By 2010, installed wind power capacity was capable of providing more than 5 percent 

of the electricity supply in 13 States, with 4 States—Iowa, Minnesota, North Dakota, and South 

Dakota—obtaining more than 10 percent of their electrical supply from wind power. 

Figure 7 illustrates that wind resources vary substantially in the 12-State study region. And among 

areas with large wind resources, the profitability of developing wind varies across the region. Long 

distances to population centers and making capacity available on transmission lines increase the cost 

of transmission, thereby affecting which wind-rich areas experience development. State policies vary, 

which affects development incentives. By 2011, 29 States had renewable portfolio standards, including 

States with significant wind development such as Iowa, Minnesota, and Texas (U.S. EPA, 2011).

Wind power development also has varied over time. Figure 5 shows that less development occurred in 

the years when the production-based tax credit was set to expire (2000, 2002, and 2004). Consistent 

with the pattern in the figure, Barradale (2010) argues that the renewal and expiration of the credits has 

created boom and bust cycles of development. Although the data are not yet available, development in 

2012 is expected to be greater than in 2010 or 2011 because of the expectation that the credits would 

expire at the end of the year. However, in January 2013, Congress passed legislation that extended the 

credits through 2013. The bill also changed the provision for projects to be eligible for the credit as 

long as construction began before the end of 2013 rather than needing to be completed. 

Source: U.S. Department of Energy, Energy Information Administration (2011a).

Figure 6

Wind-generated electricity in the United States, 2000-10

Percent of electricity 

generated by wind (percent)

Electricity generated by wind 

(thousands of megawatt hours)

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

0.0

0.5

1.0

1.5

2.0

2.5

2000 01 02 03 04 05 06 07 08 09 10

U.S. wind-generated electricity (right axis)

Percentage of total electricity produced (left axis)
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MW = megawatts.

Source: U.S. Department of Energy, National Renewable Energy Laboratory.

Figure 7

Technical wind resource potential for wind capacity 

Effective wind power capacity (MW)

0

1 - 8,900

8,901 - 15,964

15,965 - 25,979

25,980 - 43,898

43,899 - 85,492

State boundary

Cities - population 50k+

Table 5

Top 10 States with new installed wind power capacity

State MW in 2000 MW in 2010 Change (2010-2000)

Texas* 184 10,089 9,906

Iowa* 242 3,675 3,433

Washington - 2,104 2,104

Oregon 25 2,104 2,079

Illinois - 2,045 2,045

Minnesota* 291 2,205 1,914

California 1,616 3,253 1,637

Oklahoma* - 1,482 1,482

North Dakota* 0.4 1,424 1,423

Indiana - 1,339 1,339

*States used in the wind power development analysis.

MW = megawatts. 

Source: U.S. Department of Energy, http://www.windpoweringamerica.gov/wind_installed_capacity.asp
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Estimates of Employment and Income Effects of Wind Power Development 

Similar to unconventional natural gas development, the large expansion in wind power development 

in the Great Plains and parts of the Midwest provided an opportunity to study the consequences of 

wind power development on local economies. Although total installed capacity increased in several 

States, the location of wind turbines within each State meant that some counties experienced development while others did not. Similar to the natural gas analysis, the wind empirical analysis uses 

variation in wind development across space and time to estimate the effect of installed capacity on 

employment and income. To isolate the effect of greater installed capacity from confounding factors, 

the wind analysis also takes an instrumental variable approach, using measures of a county’s wind 

resource potential as instruments for changes in installed capacity (see appendix section, “Empirical 

Approaches To Estimating Causal Effects”).

Brown et al. (2012) estimated the effects of wind power installation on personal income and total 

employment at the county level for a 12-State region. Figure 8 shows the main variable of interest, 

the total installed wind power capacity from 2000 to 2008. The counties in the study region with the 

highest installed wind power capacity from 2000 to 2008 are in north-central/west Texas, southern 

Minnesota, and northern Iowa. Controlling for a county’s initial socioeconomic and demographic 

characteristics, each megawatt of installed capacity from 2000 to 2008 increased personal income 

by $11,150 and added 0.48 jobs over the same period (see table 6 for a summary of the findings and 

appendix A2 for more details). 

To gauge the economic significance of the estimates, a total effect for each county can be calculated by 

multiplying the marginal effect ($11,150 for personal income or 0.48 for employment) by the installed 

wind power capacity of each county. Doing so for all counties in the 12-State region reveals that wind 

development added $1,371,450 in personal income and 59 jobs to the mean county from 2000-08. 

MW = megawatts.

Source: U.S. Department of Energy, Lawrence Berkeley National Laboratory.

Figure 8

Total installed wind power capacity (megawatts) 2000-08

Installed capacity (MW)

0

1 - 113

114 -214

215 - 410

411 - 711

712 - 1,873

State boundary

Cities - population 50k+
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The increases represent 0.14 and 0.6 percent of initial mean income and employment for counties with 

wind development. Similar to the case of unconventional natural gas, much installed wind capacity is 

concentrated among relatively few counties. The mean employment and income effects are therefore 

higher than the effects for the median wind county (in terms of installed capacity).

Beyond Employment and Income: Environmental Benefits and 

Local Nuisances

Prior research has suggested that wind could be used to achieve large reductions in carbon dioxide 

emissions and fossil fuel use (DeCarolis and Keith, 2006). Wind turbines produce no particulate 

emissions like coal-fired electric power, which increases the incidence of mercury or other heavy 

metals in streams and lakes.

Wind power also uses much less water than corn-based ethanol production or unconventional natural 

gas extraction. Still, wind turbines can impose costs on nearby residents. 

Wind turbines can interrupt rural landscapes, harm birds, and create noise (National Wind 

Coordinating Collaborative, 2010). A review of epidemiological studies conducted near turbines 

in the United States and Europe by the Massachusetts Department of Environmental Protection 

(MDEP) reported that a typical modern utility-scale wind turbine creates about 103 decibels in noise 

(40 decibels is associated with annoyance), but can vary with the design and power of the turbine. 

In most cases the noise decreases rapidly with distance, dropping below the level associated with 

annoyance at 400 meters (MDEP, 2012). The turning of turbine blades can also cause a flickering 

of light when the blades come between the observer and the sun. During winter, ice can form on the 

blades and be thrown a good distance. The MDEP report concluded that light flickering does not 

pose a risk for causing seizures but that falling ice could be physically harmful and recommended 

measures be taken to mitigate the risk. 

Table 6

Empirical estimates of wind power development 

Employment (jobs)

Change in wind 

power capacity 

(MW) 

Estimated effect 

per MW of installed 

capacity Total effect (jobs)

Percent of 2000 

mean value of 

employment

Percent

25th percentile 15

0.48 jobs

7 0.1 

50th percentile 70 34 0.3

75th percentile 154 74 0.7

Mean 123 59 0.6

County personal income 

Total effect ($)

25th percentile 15

$11,150 per MW of 

installed capacity

167,250 0.02

50th percentile 70 780,500 0.08

75th percentile 154 1,717,100 0.18

Mean 123 1,371,450 0.14

MW = megawatts.

Source: Brown et al. (2012) estimated the impacts from cumulative installations of wind power from 2000 to 2008 at the 

county level for a 12-State region of Iowa, Kansas, Minnesota, Nebraska, North Dakota, South Dakota, New Mexico, 

Oklahoma, Texas, Colorado, Montana, and Wyoming. 
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Gains in Economies of Scale, High Energy Prices 

Propelled Ethanol Production

Since 2000, ethanol has become an important source of fuel in the United States, with production 

increasing from 1.6 billion gallons to 13.9 billion gallons in 2011 (fig. 9). Most of that production 

was derived from more than 5 billion bushels of corn (USDA, 2013). By the end of 2011, there were 

209 ethanol biorefineries in 29 States (RFA, 2012). Growth of the ethanol industry stems from high 

energy prices, State and Federal policies, and gains in economies of scale as the ethanol market has 

expanded (Tyner, 2008; Low and Isserman, 2009).

Substituting ethanol for methyl tertiary butyl ether (MTBE) as a fuel additive in gasoline increased 

demand for ethanol. In 2005, the Energy Policy Act created the Renewable Fuel Standard (RFS) 

program. A 2007 expansion and 2010 revision to that act now mandate that 15 billion gallons of 

biofuels be used in gasoline annually by 2015, representing a 100-percent increase from the original 

quantity required by 2012 (US EPA, 2010). Other Federal policies that supported the industry 

include the Reformulated Gasoline Program,4 the Winter Oxygenated Fuels Program, the Small 

Ethanol Producer Tax Credit of 10 cents per gallon for plants producing less than or equal to 60 

million gallons of ethanol per year, the 45-cents-per-gallon ethanol blender’s credit (RFA, 2010), 

and a 54-cents-per-gallon tariff applying to imported ethanol (Zhang, 2007). Blending requirements 

remain, but the blender’s credit and the tariff expired in January 2012.

4Reformulated gasoline (RFG) conforms to pollution reduction requirements established by the EPA in the 1990 Clean 

Air Act. RFG may include some level of ethanol that is an “oxygenate” used to supply additional oxygen to increase 

octane levels, enhance combustion, and reduce emissions.

Source: U.S. Department of Energy, Energy Information Administration (2011a).

Figure 9

U.S. ethanol fuel production, 2000-11 

Millions of gallons of ethanol

1,000

3,000

5,000

7,000

9,000

11,000

13,000

15,000

2000 01 02 03 04 05 06 07 08 09 10 11
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State policies also have supported the ethanol industry through: producer incentives (grants or preferential tax treatment); retailer/infrastructure incentives for ethanol blends; State use mandates; 

retail pump-label requirements; and State fleet fuel-use requirements.

Increases in plant scale have helped to increase innovation and lower production costs (Shapiro and 

Gallagher, 2005; Gallagher et al., 2007; Tyner, 2008). Figure 10 shows the number of ethyl alcohol 

manufacturing establishments by employment size between 2000 and 2010. The temporary bust 

that the sector experienced in 2008 occurred mostly in the smallest size class of 5 to 19 employees. 

In contrast, the number of plants in the largest category (100 to 249 employees) has remained fairly 

constant, with 7 establishments as of 2010, and the number of midsized plants has grown steadily. 

Ethanol Is Produced Primarily in the Midwest and Great Plains

Most of the increase in ethanol production capacity has occurred in rural areas where supplies of 

corn and other feedstock are readily available, and users of byproducts (e.g., dried distillers grain) 

like livestock producers are located nearby (Lambert et al., 2008). One 2006 study suggested that it 

is not economical to transport corn to an ethanol facility more than 50 miles away given the price of 

corn, ethanol, and fuel at the time (Swenson and Eathington, 2006). Table 7 reports the top 10 States 

with the most ethanol production capacity added between 2000 and 2010. All of the States listed are 

in the Midwest or the Great Plains (see fig. 1). By 2010, Iowa alone had approximately 3.2 billion 

gallons per year in production capacity.

Ethanol plants are not evenly distributed across corn-growing areas. Railroad, highway, and water 

access also affect where plants are built (Low and Isserman, 2009). Lambert et al. (2008) found 

that the supply of corn and railroad access influence plant location, while Haddad et al. (2010) 

found that within major corn-producing areas, population density and proximity to blending 

terminals also matter.

Note: The data are for Ethyl Alcohol Manufacturing (NAICS 325193), a manufacturing sector that predominantly 

manufactures nonpotable ethyl alcohol, including ethanol, for fuel as well as other uses.

Source: USDA, Economic Research Service calculations using County Business Patterns (U.S. Census Bureau, 2012). 

Figure 10

Number of ethanol plants by number of jobs 

Number of plants

0

10

20

30

40

50

60

70

80

90

100

110

2000 01 02 03 04 05 06 07 08 09 10

5-19 20-49 50-99 100-249

Employment size (number of jobs)
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The Employment Effects of Ethanol Capacity

Blanco and Isenhouer (2010) studied the link between ethanol production capacity at the county 

level and employment and wages, finding a very small but statistically significant relationship in the 

major corn-producing States. Their study did not estimate how opening a new ethanol plant would 

affect employment in the local economy. 

We looked at changes in employment in a county 1 year before and 2 years after construction of an 

ethanol plant to estimate the effect of a plant in operation. We then used a matching algorithm to 

select control counties (those without an ethanol facility) that closely resembled counties with an 

ethanol facility, so that comparisons could be refined further (fig. 11). This approach estimates the 

net number of jobs generated in the local economy as a result of the ethanol plant operating. We 

considered a 12-State region and plant operations beginning between 2000 and 2006 and find an 

imprecise (i.e., not statistically different from 0) estimate for total employment. This is unsurprising 

because the large variation in annual employment can statistically mask a relatively small employment effect, especially in small samples.

The analysis therefore focused on a subset of industries that accounted for approximately threequarters of the indirect and induced jobs that the IMPLAN analysis by Low and Isserman (2007; 

2009) projected would be created from a new ethanol facility. Considering only employment in these 

particularly “sensitive” industries gave a more statistically precise effect: each job in an ethanol 

plant created 2.6 jobs in the subset of industries (table 8). The estimate is likely a lower bound of the 

true employment effect since it only covers three-quarters of the total number of jobs that Low and 

Isserman projected would be created. Accounting for the additional 25 percent of the indirect and 

induced jobs that they projected would be created in industries believed to be less sensitive to the 

effects of ethanol production implies a total employment multiplier of 3.2 jobs (2.55 × 1.25 = 3.2) 

created for every ethanol job. This estimated multiplier, which combines empirical and model-based 

results, assumes that creation of an ethanol production facility does not cause a decline in employment in the industries excluded from the empirical analysis, either through displacement of lower 

valued activities or increases in wages or land prices (Steininger and Wojan, 2011). While possible, 

Table 7

Top 10 States with new installed corn-based ethanol production capacity

State MGY in 2000 MGY in 2010 Change

Number of plants 

in 2010

Iowa 362 3,293 2,931 39

Nebraska 304 1,507 1,203 25

South Dakota 27 1,005 978 14

Minnesota 198 1,118 921 21

Indiana 85 908 823 11

Illinois 631 1,383 752 12

Ohio 0 538 538 7

Wisconsin 0 498 498 9

Kansas 41 444 403 11

North Dakota 28 353 325 6

MGY = million gallons per year. 

Source: USDA, Economic Research Service calculations derived from published tables from the Renewable Fuels 

Association (2010). All of the top 10 States were used in the ethanol plant analysis.
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the total employment of a typical ethanol plant makes it unlikely that its creation would cause local 

wages to increase and other industries to become less competitive and shrink. Details of the analysis 

may be found in the appendix. 

Note: Treatment counties are counties where an ethanol plant began operation between 2000 and 2006. Control counties 

are counties with characteristics similar to treatment counties but where an ethanol plant was not established. 

Source: USDA, Economic Research Service tabulation using U.S. Department of Labor, Bureau of Labor Statistics, 

Quarterly Census of Employment and Wages.

Figure 11

Treatment and selected control counties in the ethanol analysis

Not used

Treated

Control

Table 8

Empirical estimates of corn-based ethanol impacts

Employment (jobs)

Change in ethanol 

employment

Estimated effect of 

ethanol employment Total effect (jobs)

Percent of mean 

employment prior to 

plant entry

Percent

25th percentile 21

2.6 jobs per 

ethanol job

55 0.6

50th percentile 33 86 0.9

75th percentile 39 101 1.1

Mean 31 82 0.9

Total effect ($)

25th percentile 21

3.2 jobs per 

ethanol job

68 0.7

50th percentile 33 106 1.1

75th percentile 39 124 1.3

Mean 31 101 1.1

Notes: The employment multiplier 2.6 was estimated from a set of industries comprising 75 percent of the indirect jobs 

created by an ethanol facility as found by Low and Isserman (2007). If another 25 percent of the jobs created are in other 

industries, it would imply an upper bound of the total employment multiplier of 3.2 (2.55 × 1.25 = 3.2). The higher estimated 

effect could not be empirically confirmed.

Source: USDA, Economic Research Service analysis.
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The estimated multiplier can be used to calculate estimates for the total jobs created for 

counties in the sample with an ethanol facility (see table 8). Using the empirically estimated 

multiplier of 2.6, which only considers industries thought to be linked to the ethanol industry, 

implies the creation of 82 jobs. Using the multiplier that combines the empirical estimate 

with an estimate based on IMPLAN gives a total local employment effect of 101 jobs. These 

estimates represent 0.9 and 1.1 percent of the initial mean employment in ethanol plant counties, respectively. Ethanol plant employment, which is used to determine the total employment effects, is more evenly distributed across counties compared to natural gas production or 

installed capacity for wind. The mean and median employment effects for ethanol are therefore very similar. 

All of the ethanol plants in the analysis were built after 2000 and before 2008. Because the 

jobs created by the plant were expected to remain at similar levels at least through 2008, the 

estimated effect approximates the change in employment from 2000 to 2008 attributable to the 

entry of an ethanol plant. 

Beyond Employment: Water Use and Air Quality

Water usage and discharge are common environmental concerns for ethanol plants. Depending 

on the plant type, water use ranges from 1.5 to 4 gallons for each gallon of ethanol produced, 

with an industry average between 3.0 and 3.5 gallons (Ethanol Across America, 2009). Plants 

discharge wastewater with brine from purifying water or from cleaning salts that accumulate in cooling towers and boilers, but the EPA does not consider corn ethanol plants to cause 

major effluent quality issues (U.S. EPA, 2010). Many local governments require ethanol 

plants to purchase water rights from other users so that the plants have no net effect on water 

demand. The cost of acquiring clean water and disposing of wastewater has encouraged innovation in plant water management, such as using municipal wastewater, supplying discharge 

water to farmers for crop irrigation, and developing zero-discharge technology. 

Emissions from ethanol production may vary slightly depending on the feedstock and emission 

controls used. Dust control equipment monitors particulate matter that is less than 10 microns 

in diameter created during corn delivery, handling, milling, and drying. Fermentation, distillation, and drying create volatile organic compounds, which arise regardless of the biofuel technology used. Combustion from a plant’s boilers generates carbon monoxide, nitrogen oxides, 

and sulfur oxides. Although the total amounts emitted are small compared to a power plant, 

local residents may still notice the emissions (U.S. EPA, 2010).
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Simulation Approach To Modeling Impacts

Academic and nonacademic authors and institutions have used simulation models, principally 

input-output models such as IMPLAN, to project the economic effects of expansion of the unconventional natural gas, wind, and ethanol industries. The input-output approach consists of creating 

a mathematical representation of an economy by specifying linkages between sectors, for example, 

assuming that each dollar in output from the automobile industry uses 20 cents of output from the 

steel manufacturing industry as an input. When combined with location-specific industry information, the relationships between sectors permit projections of how expansion of one industry would 

affect output in the entire economy. The total effect is the combination of direct, indirect, and 

induced effects. Direct effects come from the value of production and employment in the industry 

under study. Indirect effects are from the industry purchasing goods and services from local firms 

and from any additional local purchases by those firms. Induced effects come from consumption expenditures by industry employees, or those of its suppliers and their suppliers, in the local 

economy. Examples of input-output analysis applied to energy industries include:

Industry Study

Unconventional natural gas CBER (2006); L.C. Scott and Associates (2010)

Wind power Lantz and Tegen (2009); Slattery et al. (2011)

Ethanol Swenson (2006); Low and Isserman (2009)

Ex-ante simulation approaches have several advantages and limitations. Empirical approaches often 

require smaller geographic units to have sufficient observations for statistical analysis, whereas 

simulation approaches can model effects over a large economic region. Most importantly, they can 

provide projections before much industry growth has happened. 

At the same time, ex-ante simulation approaches often use employment and expenditure projections 

reported by companies engaged directly in the industry. If companies benefit by being perceived as 

making large contributions to local economies, they may have an incentive to overstate the scale of 

their operations or projected activities. Assuming that companies report reliable information, the 

approach often involves untested assumptions about fixed relationships between inputs and outputs. 

Input-output approaches, in particular, often assume that greater demand for an input will not 

cause the price of the input to increase, such as greater demand for labor causing wages to increase 

(Kilkenny and Partridge, 2009). Most simulation approaches also use information derived from 

national accounts data rather than information about the unique organization of the local economy 

(Rickman and Schwer, 1995). If the assumptions poorly approximate the economy under study, the 

models likely will make inaccurate projections. Evidence from two empirical studies suggests that 

input-output models, in particular, tend to overestimate the contribution of new industrial development to local economic growth (e.g., Edmiston, 2004; Fox and Murray, 2004), but our results 

indicate that input-output based projections can approximate actual impacts when the models are 

properly designed. 

Natural Gas

Multiple studies have used input-output models to project the number of jobs that would be created 

from developing natural gas resources. These projections have been cited often by newspapers, 

policymakers, and industry groups. Their broad use underscores the need to verify if the studies 
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have overstated or understated the economic contributions of natural gas development. The estimated employment effects from natural gas extraction in Colorado, Texas, and Wyoming combined 

with the change in the value of gas production from 1999 to 2007 imply that each $1 million in gas 

production created 2.35 jobs. We survey nine economic impact studies involving the natural gas 

industry. Some look only at natural gas and some combine oil and gas. From each study, we use the 

projected level or change in jobs and the corresponding level or change in the value of production to 

calculate the number of jobs for each $1 million in production (table 9). 

All of the simulation studies differ in some way from our empirical study, but some are similar in 

many aspects. One study looks at the county-level effects from shale gas development in Arkansas; 

two studies look at a multicounty region within Texas; and two other studies look at State-level 

effects for Colorado and Wyoming. We also consider four more State-level studies for Arkansas, 

Oklahoma, Louisiana, and Pennsylvania. Combined, the nine simulation studies provide a sense of 

how model estimates vary.

Estimates of the jobs created for each $1 million in production vary from about 3.5 jobs to more 

than 20 jobs. Some of the differences across the studies may reflect the level of analysis (State 

versus region), the scope (natural gas only or all oil and gas); the specifics of the area studied such 

as whether most wells are on public or private land; or the years considered, since natural gas prices 

were substantially lower in 2009 and later. But a close look at the nine studies reveals that such 

differences appear to explain little of the variation in estimates. None of the three studies that look 

below the State level provide the smallest estimate. Studies covering oil and gas do not always give 

larger or smaller results. Wyoming has more drilling on Federal lands than any other State, which 

reduces the royalty payments paid directly to local residents, yet its employment estimate is larger 

than that of Arkansas. Three of the four highest employment impacts correspond to 2009 and later. 

Because of the decline in prices, a dollar in production in 2007 involved much less gas than a dollar 

of gas in 2010. If a given quantity of gas requires a certain amount of labor, we would expect higher 

employment effects per dollar in times of low prices. This explanation, however, is also not fully 

consistent with results: two of the three studies with the smallest employment effect correspond to 

periods of lower prices. 

It is not surprising that obvious differences across input-output studies (State versus county, natural 

gas or oil and gas) seem to explain little of the variation in their results. Details of the modeling 

approach used in each study, which we do not explore, may provide a better explanation. For 

example, a change to an industry can be modeled as a change in capacity or as an expansion in sales, 

with each approach using different data and assumptions. 

The University of Arkansas Center for Business and Economic Research study of the Fayetteville 

Shale in Arkansas is the most comparable to our empirical estimate because it estimates the number 

of jobs created in producing counties (CBER, 2006). Taking the difference between the employment 

supported by shale gas development in 2005 and 2008 in Fayetteville Shale counties and dividing it 

by the change in the value of gas production implies that 3.57 jobs are created for each $1 million in 

production. Another study comparable in several aspects is from the University of Texas Center for 

Community and Business Research (CCBR, 2011), which looks at the multicounty region covering 

the Eagle-Ford Shale in southeastern Texas. It suggests that each $1 million in shale oil and gas 

production creates 4.17 jobs. Despite looking at Statewide effects, the 2012 CBER study of the 

Fayetteville Shale gives an estimate similar to the county-level analysis in the 2006 CBER study. 

The remaining input-output studies all estimate larger employment effects. Four of them imply that 

$1 million in production creates more than 10 jobs. 
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Table 9Input-output estimates of the employment sustained by oil and gas developmentStudy Period Location Geographic level Model

Jobs per 

$1 million of production Jobs/Bcf Scope

CBER (2006) 2005-2008 Fayetteville Shale Counties, AR County IMPLAN 3.57 23 Natural gas onlyCBER (2012) 2008-2011 Arkansas State IMPLAN 3.47 9 Natural gas onlyCCBR (2011) 2010 Eagle-Ford Shale Counties, TX Multicounty region IMPLAN 4.19 NA Oil and gasPerryman Group (2008) 2006-2007 Barnett Shale, TX Multicounty region Proprietary I-O Model 17.97 134 Natural gas onlyLSB (2011) 2011 Colorado State IMPLAN 12.69 NA Oil and gasBooz Allen Hamilton (2008) 2007 Wyoming State IMPLAN 5.60 NA Oil and gasS.C.A. ERPI (2012) 2011 Oklahoma State IMPLAN 6.38 NA Oil and gas

L.C. Scott & Associates (2010) 2009-2010 Louisiana State RIMS II 11.51 57 Natural gas onlyKelsey et. al (2011) 2009 Pennsylvania State IMPLAN 20.35 86 Natural gas only

I-O Min 3.47 9

I-O Max 20.35 134

I-O Median 6.38 57

Weber (2012) 1999-2007

Counties in TX, 

WY, CO County Empirical 2.35 27

Bcf = billions of cubic feet; CBER = Center for Business and Economic Research; CCBR = Center for Community and Business Research; LSB = Leeds School of Business; S.C.A. ERPI = Steven C. Agee Economic Research Policy Institute; NA = not applicable.Notes: The value of production is in real terms, using 2007 as the base year as in Weber (2012). Natural gas production for the two CBER studies is taken directly from the studies. Oil, gas, and gas condensate production for Eagle-Ford Shale counties is from the Texas Railroad Commission. Gas production for Barnett Shale counties is also from the Texas Railroad Commission. The total value of oil and gas production for the Colorado and Wyoming studies comes directly from the studies. Total oil and gas production for Oklahoma is from the U.S. Department of Energy, Energy Information Administration as is production for Louisiana and Pennsylvania. Prices used to value production are also from DOE/EIA. For the studies that span more than 1 year, the employment effect was calculated by differencing the first-year and last-year employment and production levels. Source: USDA, Economic Service analysis.
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An alternative way to express employment effects is per billion cubic feet of gas. An increase in 

natural gas prices explains part of the increase in the value of production in the empirical study. In 

contrast, the changes in the value of production used to understand the simulated projections come 

primarily from changes in quantities. Higher gas prices should increase the economic effect of a 

given scale of production since they will affect lease and royalty payments. Ignoring prices would 

then cause the empirical study employment effect expressed in the quantity of gas to be larger 

because, in reality, an increase in prices accompanied the increase in production. 

Our empirical study implies that each billion cubic feet of gas is associated with 27 jobs. We calculate a similar effect for the five input-output studies that only considered natural gas. The empirical 

employment effect is slightly larger than the effect from the two CBER studies and less than that 

of the other three studies. Thus, even when expressing the effect in a way that may exaggerate our 

empirical employment effect, our estimate is still on the lower end of impacts projected earlier by 

simulation models (see table 9). The disparity between our employment effects and theirs suggests 

that the projected local economic benefits of gas development may have been overstated. More 

empirical studies, however, would permit firmer conclusions. 

Wind Power

Most studies of the economic effects of wind power development have relied on project-level case 

studies of the direct effects of individual wind power plants. Their employment, cost, and revenue 

data come from project developers or operators, or simulation model estimates of the potential 

impacts of one wind power plant or an aggregate amount of assumed wind development (e.g., 

Pedden, 2006; Lantz and Tegen, 2009). Table 10 summarizes several studies of the local economic 

effects of wind power development. The estimated effects on employment in a county or group of 

counties range from 0.1 to 0.6 jobs per megawatt during the operations period.5

The same simulation studies found that during the long-term operations period, each megawatt 

creates between $5,000 and $18,000 in wage and salary income (in 2010 dollars). Some of the 

studies also examined the effects on total economic output and found that for plants in the operating 

phase, each megawatt supports between $13,000 and $55,000 in output (GAO, 2004) (see table 10).

The empirical estimates from Brown et al. (2012) are not strictly comparable to these earlier simulation studies because the estimates correspond to personal income rather than the narrower category 

of labor income (or the even broader category of total economic output). Furthermore, Brown et al. 

were unable to separate construction period impacts from operating impacts for installations occurring in 2008, while all of the input-output studies in table 10 consider only operating effects. Both 

differences imply that the empirical approach would yield higher impacts than the input-output 

approach applied to labor income and employment. But, in contrast to the input-output approach, the 

empirical approach allows wind development to discourage or displace other economic activities, 

which would lower empirical estimates compared to input-output estimates. Differences aside, the 

estimated impact on personal income and employment of approximately $11,000 and 0.5 jobs per 

megawatt are bounded by the minimum and maximum estimates from input-output models, which 

range from $5,000 to $18,000 in labor income per megawatt and from 0.1 to 0.6 jobs per megawatt 

for plants in operation. The estimated effects based on actual employment and income levels are also 

close to the median projections from earlier studies (see table 10).

5Power is measured as a unit of energy over time—a megawatt-hour can power about 1,000 homes for 1 hour.
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Table 10Input-output estimates of wind power developmentStudy Year Location Geographic level MW Methodology Jobs/MW

Income/MW

 (2010 dollars)

Output/MW 

(2010 dollars)

DanMar & Associates 1996 Southwest, MN

Six counties 

(Phase 1) 100 Proprietary I-O model 0.19 5,849 12,650

DanMar & Associates 1996 Southwest, MN

Six counties 

(Phase 2) 100 Proprietary I-O model 0.54 7,209 12,723

NEA 2003 Lincoln, MN County 107 IMPLAN 0.29 11,019 NRNEA 2003

Morrow & Umatilla, 

OR County 25 IMPLAN 0.24 5,396 NR

NEA 2003 Culberson, TX County 30 IMPLAN 0.37 14,959 NREcoNorthwest 2002 Kittitas, WA County 390 IMPLAN 0.14 13,385 NRTorgerson et al. 2006 Umatilla, OR County 5 JEDI 0.24 10,734 26,084Kildegaard & Myers 2006 Stone, MN County 10.5 IMPLAN 0.41 NR NRSlattery et al.* 2011 West Texas Four counties 1,398 JEDI 0.16 6,867 22,246GAO 2004 Multiple locations County 40 JEDI 0.30 – 0.62 8,704 – 16,933 18,591 – 51,360

I-O min 0.14 $5,396 $12,650

I-O max 0.62 $17,759 $55,488

I-O median 0.38 $11,019 $31,586

Brown et al. (2012)*

2000-2008

Counties in IA, KS, 

MN, NE, ND, SD, 

NM, OK, TX, CO, 

MT, and WY County Empirical 0.48 $11,150

MW = megawatts; NEA = Northwest Economic Associates; I-O model = input-output model; IMPLAN = Impact Analysis for Planning; JEDI = Jobs and Economic Development Impact; GAO = Government Accountability Office; NR = not reported. * Indicates publication in a peer-reviewed journal. For more information on the JEDI model, see http://www.nrel.gov/analysis/jedi/about_jedi.htmlSource: USDA, Economic Service analysis.
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Ethanol 

Similar to wind power development and natural gas production, most studies of the economic 

effects of ethanol facilities have used input-output models. The studies typically report direct, indirect, and induced employment from developing the capacity to produce a given amount of ethanol. 

Projections are typically reported as employment multipliers: the number of jobs created for each 

job at an ethanol plant. Table 11 summarizes several studies and shows that the reported employment multiplier ranges from 1.8 to 17.9 jobs for each ethanol job, with the median multiplier of 4.3. 

The multiplier of 17.9 comes from an early study by Urbanchuk and Kappell (2002). Perhaps due to 

more or better information, over time the estimated impacts have become more conservative, with 

the more conservative estimates often found in peer-reviewed journal articles. The study by Low and 

Isserman (2009) is of particular interest because they studied two different-sized plants (producing 

60 versus 100 million gallons per year) in two mainly rural counties and two counties with sizeable 

rural and urban areas. The largest impacts occurred in the counties with sizeable rural and urban 

areas, with an employment multiplier of 5.6 versus 3.9 in the rural counties. The point estimate of 

the multiplier for local employment in closely linked industries, based on our empirical analysis is 

somewhat below the median of the multipliers reported in table 11, but certainly within a 95-percent 

confidence interval. This suggests that similar to the case of wind, carefully constructed input-output 

studies of ethanol can generate impacts in line with empirical estimates. It should be noted that the 

empirical estimate of the employment multiplier on closely linked industries, however, is also noticeably higher than the smallest input-output multiplier. 

The Role of Simulation and Empirical Approaches 

A key finding is that for wind and ethanol, employment projections from carefully constructed simulation 

models are consistent with empirical estimates based on actual changes in local employment over time. 

One limitation of empirical methods is that they often require multiple years of data on many counties 

to yield precise and credible estimates. Simulation models like input-output models, on the other hand, 

can estimate the effects of a proposed activity well before an empirical study would be possible. Based 

on their different strengths and weaknesses, the two methods complement rather than substitute for each 

other, with empirical methods playing an important role in verifying earlier input-output results. 

In the case of natural gas, the input-output employment effects are generally larger than the empirical estimate. Subsequent empirical studies, potentially looking at different regions or periods, may 

show larger employment effects; nonetheless, there are at least three potential reasons for inputoutput studies to provide larger employment effects than the empirical study of county-level effects 

in Texas, Wyoming, and Colorado. First, most input-output models are static and do not allow for the 

expansion of one industry to crowd out other industries: for example, greater natural gas extraction 

increases wages and, consequently, labor costs for other industries. The assumption is most appropriate when employed in the study of small changes to an economy, which better describes placement of a wind turbine or construction of an ethanol plant than a boom in natural gas extraction. 

Evidence from the Eagle Ford Shale in Texas, for example, suggests that drilling activity has caused 

weekly wages to increase by as much as a third (Gilmer et al., 2012). 

A second possible explanation for the difference is that input-output models reflect outdated technology 

while empirical estimates implicitly reflect the technology in use during the study period. Empirical 

estimates, for example, may capture labor-saving innovations in drilling not yet captured in inputoutput models. Finally, some of the industry expenditures incorporated into input-output models may 

go to businesses outside of the study region, such as equipment manufacturers in other States. 
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Table 11Input-output estimates of ethanol plant developmentStudy Year Location

Geographic 

level MGY Methodology Direct jobs Indirect jobs Induced jobs Multiplier

Gallagher, Otto, and Dikeman*,† 2000 Midwest Multiple States 376 IMPLAN 900- 1,200 5,499 4.4-5.9Urbanchuk and Kappell 2002 n/a

Not specified 40 IMPLAN 41 694 17.9

Petersan 2003 NE State 24 IMPLAN 31 73 3.4

Swenson 2005 IA State 41 IMPLAN 32 135 5.2Swenson 2006 IA State 50 “ 35 75 23 3.8Parcell and Westoff* 2006 n/a

Not specified 60 IMPLAN 54 156 3.9

Flanders et al. 2007 GA State 100 IMPLAN 46 362 8.9Low and Isserman* 2009

Kankakee County, 

IL County 100 IMPLAN 39 152 29 5.6

“ “ Hamilton County, IL County 100 “ 39 97 17 3.9“ “ Coles County, IL County 60 “ 35 83 34 4.3“ “ Harlan County, NE County 60 “ 35 50 15 2.9Guerro et al.* 2011 Hockley County, TX County 40 IMPLAN 35 18 9 1.8“ “

Southern High 

Plains TX County 40 “ 35 86 48 4.8

I-O min 1.8

I-O max 17.9

I-O median 4.3

ERS analysis 2000 - 2008

Counties in IA, NE, 

KS, MN, ND, SD, 

IL, IN, WI, MO, CO, 

and OH County

Combination 

of Empirical 

and IMPLAN

2.6 – 

3.2

n/a = data not available; MGY = million gallons per year; IMPLAN = Impact Analysis for Planning. IMPLAN was the most commonly used model to determine the impacts. * Indicates publication in peer-reviewed journal. † The study analyzed a hypothetical expansion of 376 MGY across multiple facilities. Their assumptions were that a wet mill generating 90 to 120 MGY employs 300 workers and that a dry mill producing 100 MGY employs about 300 workers. We have calculated the range of implicit multipliers from these assumptions.Source: USDA, Economic Service analysis.
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Possible Future Job Growth Constraints

This study provides empirical estimates of the local economic effects of three emerging energy 

industries. The contribution of each emerging industry to local economies varies because of the 

nature of the regional economy where the new industry occurs, differences in the scale of the 

industry, the intensity of labor use, and the linkages to other sectors in the local economy. Our 

analysis provides a limited view of how the industries affect life in rural communities. Each industry 

brings its own challenges for local communities: groundwater concerns with natural gas; disruption of the landscape by wind turbines; and use of wastewater or water from ethanol plants. The net 

economic benefits to an area may be quite different than gross private monetary gains. For example, 

a landowner’s gas royalty payments may overstate the net economic gain to the local economy if 

wellwater must be replaced by purchased water. The interaction between the emerging industries and 

other local activities remains largely understudied.

The distribution of costs and benefits of a new energy industry varies among local residents. Some 

residents may experience increases in costs and enjoy few or none of the benefits brought by the 

industry. Someone who owns a home near several wind turbines but otherwise has no connection to 

them may perceive a decrease in quality of life by having the view disrupted. Negative perceptions 

of wind turbines or natural gas wells could lower the property values of nearby residents. In contrast, 

owners of mineral rights can benefit substantially from natural gas lease and royalty payments. More 

broadly, greater traffic congestion from natural gas development or transporting corn could lower 

the quality of life for many rural residents, which may or may not be offset by greater tax collections 

and subsequent declines in tax rates or increases in public services.

Many rural residents enjoy natural amenities and aspects of rural communities, such as uncongested 

roads. For some residents not directly benefiting from the industry, such as retirees, truck traffic 

associated with natural gas drilling can create congestion on rural roads, encouraging people to 

retire elsewhere. More jobs and public revenues may or may not fully counteract such forces.

Our report focuses on short-term economic effects, which are likely to change over time. The effects 

of the drilling phase of natural gas extraction, for example, are likely larger than the effects of the 

production phase. Perhaps more importantly, the three industries all face distinct constraints over 

the medium- and long-term, with natural gas, being a nonrenewable energy source, facing the most 

severe constraint. Gas production is expected to expand through 2035, but reserves in specific areas 

will no doubt decline. After strong growth in the mid-2000s, production from the Barnett Shale in 

Texas leveled off from 2008 to 2012 (Texas Railroad Commission, 2013). As reserves decline in 

specific areas, the industry will develop new formations until those new drilling areas’ productivity 

is too low to warrant extraction. The industry’s long-term effect on a local economy, therefore, will 

depend on how people and governments in gas-producing areas manage any negative effects caused 

by the drilling and how they use revenues generated from extraction. 

Ethanol may have greater capacity than the natural gas industry to maintain or expand production 

in the long term, but the supply of arable land used to produce ethanol feedstocks is largely fixed 

(Hertel, 2010). Although investments in agricultural research may increase land productivity, a 

growing global population and rising incomes will likely keep agricultural commodity prices high 

(Westcott and Trostle, 2012). An increase in the price of feedstocks larger than the price of oil will 

reduce ethanol producer profit margins (Hurt et al., 2006). Of course, technological improvements 

that allow plants to produce more ethanol with the same amount of feedstock or that increase the 
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feasibility of using lower cost feedstocks, such as corn stover or switchgrass, will improve the industry’s competitiveness. 

Wind development also faces constraints, although they may be less rigid than those faced by the 

natural gas and ethanol industries. Electricity production depends on when winds blow rather than 

when consumers need power, which encourages utilities to use wind as a supplementary source of 

electricity instead of the primary source. The variability of wind means that integrating wind power 

into the electricity grid becomes more costly as wind accounts for a greater share of an area’s electricity supply (Wiser and Bolinger, 2007). Many areas typically need costly new transmission lines 

to send wind-generated electricity from windswept rural areas where it is produced to areas of peak 

electricity demand in suburbs and cities (Logan and Kaplan, 2008). 

Growth of the ethanol industry will depend heavily on feedstock and oil prices, innovation, and 

public policies. Global energy markets and policies such as the production tax credits will affect 

expansion of wind power. In addition, greater availability of natural gas is expected to substantially decrease electricity prices over the next 20 years, making natural gas a direct competitor with 

wind (Palmer et al., 2012). Despite public pressure to limit hydraulic fracturing in several States, 

the abundance of natural gas and the profitability of extraction under normal market conditions 

suggest that natural gas development will continue to expand across the United States. Our findings 

suggest that expansion of unconventional gas drilling (and with similar technology, oil drilling) will 

contribute the most to economic growth across rural areas in the short term, while the wind and 

ethanol industries will have more modest effects. 
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Appendix 

Empirical Approaches To Estimating Causal Effects

Most empirical approaches to estimating causal effects, like the effect of natural gas extraction on 

income, seek to mimic a randomized experiment in which membership in the treatment group (experiencing a natural gas extraction boom) and control group (not having a boom) is randomly assigned. 

In practice, social scientists rarely can assign treatment randomly and must use other approaches 

to estimating causal effects. We briefly describe three general approaches: difference-in-difference 

estimation, instrumental variables, and matching. 

Difference-in-Difference: To continue with the natural gas boom example, the difference-in-difference requires a group of counties that experiences a boom and a group that does not. The approach 

involves taking: (1) the difference in income from the beginning and end of the boom period for 

each group of counties; and (2) the average difference for boom counties minus the average difference for nonboom counties. In the absence of other trends that affect income and that affect the two 

groups differently, the difference-in-difference approach provides an unbiased estimate of the mean 

effect of a gas boom on income. 

Because income in boom counties may have been growing more (or less) than in nonboom counties in the period prior to the boom, taking differences in growth instead of levels provides a robust 

version of the basic difference-in-difference approach. In the income example, it involves taking the 

difference in income from before and after, subtracting from it the difference in the period prior to 

the boom, and then comparing boom and nonboom counties. Consider three points in time, where 

the boom happens between the second and third point. A difference-in-difference estimate of the 

effect of a gas boom on income (y) would then be

where b and nb indicate boom and nonboom counties.

Instrumental Variables: Gas companies choose where to drill, and their decisions may reflect 

factors such as the willingness of landowners to lease their land, which may be related to local 

income levels. Growth in drilling and production also may be related to a characteristic of the 

county that is unlikely to be related to income, like the presence of unconventional gas formations 

deep under a county’s surface. Without drilling, the presence of unconventional gas formations 

should not affect income growth in a county. The instrumental variable approach involves using 

one or more variables that affect where extraction occurs but that are otherwise unrelated to income 

growth. Applied to the gas example, an instrumental variables approach estimates the effect of 

greater gas extraction for counties where their growth in gas production was related to having unconventional gas formations. 

Matching: Matching involves pairing a treatment county with one or more control counties. 

Continuing with the example of the natural gas boom, matching would involve matching a boom 

county with a nonboom county that had similar characteristics such as demographics and economic 

composition. The key assumption is that after appropriately matching counties, any difference 

between them in employment growth, for example, reflects the natural gas boom and not because the 

boom and nonboom counties are different in ways that have not been accounted for. 

y y y y y y y y p

b

p

b

p

b

p

b

p

nb

p

nb

p

nb

p

nb ( 3 2 − ) − − ( 2 1 ) 3 2 2 1 

  − − ( ) − − ( )  
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Many modifications of the basic difference-in-difference, instrumental variable, or matching 

approaches exist, and the methods can be combined with one another or with other techniques. The 

tie that binds them is the focus on providing the best estimate of causal effects by creating a credible 

estimate of what would have happened in a gas-boom county had drilling never occurred. 

Selection of Empirical Approaches 

The natural gas analysis combines the difference-in-difference approach with an instrumental variable. Three points in time are used in the differencing to allow for the possibility that boom counties 

had a prior employment trend different from that of nonboom counties. We may expect a different 

prior trend because, prior to the boom, the conventional natural gas industry had a greater presence 

in boom counties as evidenced by greater natural gas production and greater dependence on mining 

employment than nonboom counties. The percent of the county covering an unconventional gas 

formation is used as an instrument for a county’s status as a boom county. Most growth in drilling 

occurred in unconventional gas formations, so the instrument is related to a county’s status as a 

boom county. At the same time, it is unlikely that the location of unconventional gas formations is 

correlated with economic activity outside of the correlation caused by greater gas extraction. 

The relationship between wind resource potential and wind development enabled use of an instrumental variable approach in the wind analysis as well. Measures of a county’s wind resources are used 

as an instrument for changes in installed wind power capacity over the study period. It is unlikely 

that counties with more wind systematically have better or worse economic performance than other 

counties, yet wind turbines are often placed in areas with greatest wind potential. Only two points in 

time were used in differencing because, unlike the natural gas case, counties where most wind power 

capacity was installed tended to have characteristics similar to other counties. Moreover, counties with 

wind turbines were not previously dependent upon the industry, as it was largely nonexistent prior to 

the study period. A look at prior trends confirms that growth in wind power capacity in the early 2000s 

was not correlated with income or employment growth in the 1990s.

Like the wind analysis, the ethanol analysis looks at changes before and after development. In the 

ethanol analysis, however, we used a matching approach because of a lack of a convincing instrument for whether a county received an ethanol plant. And whereas natural gas and wind power 

development grew over time, building an ethanol plant is a discrete event. Instead of looking at 

changes over a fixed period such as 2000 to 2008, we calculated changes in employment from 1 

year prior to the beginning of plant construction and 2 years after completing construction. Doing so 

helps to distinguish changes in employment caused by construction of the plant from other economic 

events or trends.

Sources of Data

We use multiple sources of county-level data. Total employment, population, and income come from 

the U.S. Department of Commerce, Bureau of Economic Analysis. Gas production data by county 

by year for Texas, Wyoming, and Colorado come from the State agencies that monitor oil and gas 

development (Colorado Oil and Gas Conservation Commission, Texas Railroad Commission, and 

Wyoming Oil and Gas Conservation Commission). Information on installed annual county-level 

wind capacity is from the U.S. Department of Energy’s Lawrence Berkeley National Laboratory, 

while wind resource potential data were provided by the U.S. Department of Energy’s National 

Renewable Energy Laboratory (NREL). Both sources cover a 12-State region with abundant onshore 

wind resources. State-level information on installed wind capacity for the entire country is from 
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the U.S. Department of Energy (2012). Direct employment for the ethanol industry is from county 

files of the Quarterly Census of Employment and Wages compiled by the U.S. Department of Labor, 

Bureau of Labor Statistics. This information was compared to plant listings from the Renewable 

Fuels Association to identify corn-based ethanol facilities. State-level production of fuel ethanol 

came from the U.S. Department of Energy’s Energy Information Administration (2012a). 

Natural Gas Empirical Details

Differencing growth over the boom period with growth in the preboom period, a triple difference 

approach calculates how boom counties grew over the boom period relative to their trend in the 

preboom period and compares it with the same outcome for nonboom counties. The approach can be 

implemented by defining the dependent variable yi as 

(1A) y (y y y y i i = 2007 −−− i i 1999 1999 i1993 ) ( )

Another triple-difference approach would be to use the year-to-year change in employment, for 

example, as the outcome variable and regress it on a boom-county variable, a boom-period variable, and the interaction between the boom-county and boom-period variables. This panel approach 

is not taken because the instrumental variable used for identification is time invariant. Letting 

y (y y y y i i = 2007 −−− i i 1999 1999 i1993 ) ( ) converts the model into a cross-sectional form while retaining 

the advantage of the triple difference approach, namely allowing for different growth trends for 

boom and nonboom counties. An even more robust specification would be to include control variables that allow counties with different characteristics to have different outcomes in yi

. This is the 

empirical approach taken, with the full specification being

(2A) ( ) 1 92 2 ( )92 ( ) = α + β + δ + δ +θ + ε i i n i s i i y Boom County X C

A county’s initial conditions are captured in Xi92, as are the characteristics of neighboring counties 

(Cn(i)92) in the initial period, and a State fixed effect (qs(i)). Included as control variables are the 

county’s initial population density, per capita wage and salary income, and the percent of total earnings accounted for by the agricultural, mining, manufacturing, and construction sectors. To avoid 

confounding geographic characteristics with the effects of being a boom county, we also control for 

the average population density and per capita income in adjacent counties in the initial period. The 

following table shows the ordinary least squares (OLS) and instrumental variable (IV) estimation 

results (appendix table 1).

Wind Power Development Empirical Details

Brown et al. (2012) assume that changes in annual per capita personal income and employment (y) at 

the county level are affected by the counties’ own socio-economic and demographic characteristics 

(X), the county’s own wind power development (D) (measured in megawatts of capacity per capita), 

and State-level fixed effects (S), as shown by:

(1) y = Z (X, D)β +αS + μ

where Z is vector containing X and D, and μ is a vector of residuals. 
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The location of wind power development (D) may be endogenous to the outcome variables of 

interest. This could be because increases in per capita income or employment encourage wind development (e.g., if increased income enables local investors to invest in wind development), or because 

unobserved factors affecting income and employment are also correlated with wind development 

(e.g., if wind development is more likely to take place in communities that have fewer alternative 

economic opportunities or less ability to invest in such opportunities because of the quality of local 

resources or local leadership or entrepreneurial capacity). 

A common approach for dealing with endogenous regressors is instrumental variables (IV) estimation. Availability of a high-quality wind resource (i.e., high-speed wind) is likely a primary factor 

affecting the location and amount of wind power development and is unlikely to be directly related to 

Appendix table 1

Employment and wage and salary income effects from a natural gas boom

Employment Wage and salary income

Variables OLS IV OLS IV

Boom county 509.751 1,780.152** 22.693 69.027**

(364.102) (819.705) (15.295) (30.570)

Per capita income, 1993 -30.738 -38.115 1.293 1.024

(40.264) (37.979) (1.059) (1.083)

Population density, 1993 454.755 359.314 -10.526 -14.007

(920.816) (973.018) (42.537) (42.685)

Agriculture share of earnings, 1993 2,949.848 4,447.750* 53.342 107.974

(2,070.898) (2,444.322) (61.824) (74.136)

Mining share of earnings, 1993 2,536.754*** 1,529.984 149.463*** 112.744**

(879.979) (995.343) (56.696) (56.611)

Manufacturing share of earnings, 1993 2,267.435 2,255.311 81.995 81.553

(1,919.027) (1,888.647) (71.882) (72.155)

Construction share of earnings, 1993 625.841 499.340 23.178 18.564

(967.506) (1,008.886) (36.088) (39.672)

Population density of contiguous counties, 

1993 -4,033.341** -3,868.310** -114.304 -108.285

(1,816.576) (1,829.850) (78.017) (75.901)

Per capita income of contiguous counties, 

1993 108.524 -128.958 18.650 9.989

(557.495) (600.859) (21.096) (21.085)

Texas 852.856** 791.229** 21.456 19.208

(418.396) (403.097) (19.210) (17.602)

Wyoming 3,522.734*** 2,931.236*** 186.801*** 165.228**

(938.787) (958.270) (68.163) (67.477)

Intercept 2,016.595 2,026.847 45.870 46.244

(1,813.452) (1,771.150) (75.232) (72.752)

Observations 188 188 188 188

Adjusted R-squared 0.126 0.047 0.268 0.218

OLS = ordinary least squares, IV = instrumental variables.

Source: Weber (2012).
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the outcome measures in question (change in income per capita and employment from 2000 to 2008). 

Consequently, to instrument actual wind power development, we ultimately use two instrumental 

variables related to wind resource conditions: (1) the presence of wind resource potential across power 

classes 3-7 in a county (where 3 is toward the low end of feasible power classes for economic wind 

energy development and 7 represents areas with the highest wind speeds), and (2) the cumulative technical potential for wind power development in a county, measured in megawatts, based on the amount 

of class 3-7 winds available. The following tables show the descriptive statistics for variables used in 

the analysis as well as the OLS and IV estimation for wind power development effects on changes in 

per capita income and employment. (See appendix table 2, appendix table 3, and appendix table 4.)

Ethanol empirical details

We estimated an employment growth equation at the county level by using a difference-in-difference 

approach combined with matching treated counties that received an ethanol plant with similar 

control counties. The study was restricted to areas with a high probability of attracting an ethanol 

plant based on Stewart and Lambert’s (2011) probabilistic model of ethanol facility location. The 

entire States of Iowa, Nebraska, Kansas, Minnesota, North Dakota, South Dakota, Illinois, Indiana, 

Wisconsin, and Missouri were included in the study along with western Ohio counties and the nonmountainous counties of Colorado. Data for Wyoming and Michigan were not available. Forty-six 

plants with five or more employees started operations between 2000 and 2006 in the study area. 

Each plant has a unique study period that begins 1 year prior to the plant’s full operation and extends 

for 2 years of full operation.6 While the 3-year study period may not capture all import substitution possibilities that are eventually exploited, extending the study period would introduce additional variation decreasing the probability of detecting a significant effect. The empirical findings 

should be interpreted as the short- or intermediate-term impacts of a new ethanol plant on local 

employment. 

The selection of matching variables was based on meeting one of two criteria: (1) salience to 

ensuring that control counties are similar and comparable to their paired treatment county and (2) 

strong theoretical justification for inclusion in an employment-growth model. To satisfy the first 

criterion, county population in 2000 and a county’s urban influence code were selected to ensure 

that treatment and control counties are roughly the same size and have comparable interaction with 

urban agglomerations. A third variable captures information on the relative attractiveness of the 

county based on whether the county lost or gained population between 1970 and 1980—a period 

of widespread rural growth. Eight variables were selected to satisfy the theoretical justification 

criterion. These include 5 industrial structure variables (share of employment in farming, mining, 

manufacturing, and services related to recreation), a measure of the endowment of natural amenities (Partridge et al., 2008), and a human-capital variable (share of the population aged 24-44 with 

at least a college degree). Finally, employment-growth equations typically include the initial level of 

employment to account for convergence phenomena. A stronger rationale in the present case is the 

need to control for regression to the mean since we are examining a small subset of the economy. 

6The ideal base period would start right before construction of the new ethanol facility to ensure that construction 

employment is not counted in the baseline, but this information is not generally available. Construction of an ethanol 

plant may extend for more than 1 year in particular cases, so our 3-year interval will underestimate the true impact of a 

new plant if construction employment is included in the treatment county baseline. Tests were done with a 4-year interval 

model, beginning 2 years prior to full operation and including the first 2 years of operation. Unfortunately, the 4-year 

study interval was unsatisfactory as variance in employment growth increases substantially. Estimates using the 4-year 

interval were not significant, and the test had significantly lower power.
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Appendix table 2

Variables used in the growth models

Variable Label

Change in per capita income 2000–20081 ($/capita) dpci

Change in per capita employment 2000–20081 (jobs/capita) demp

Change in installed wind capacity 2000–2008 (MW/capita) mwcap

Technical wind resource potential (power class 3-7, MW) twrp

Per capita income ($)1 pci

Population (thousands)1 pop

Poverty rate (percent)2 poverty

Natural amenity scale3 nascale

Agriculture, forestry, fishing, and hunting share of employment1 agffh

Construction share of employment1 const

Manufacturing share of employment1 manuf

Retail & trade share of employment1 retrade

Adult population (25 yrs >) with associates degree (percent)2 pedas

Adult population (25 yrs >) with bachelors degree (percent)2 pedbs

Adult population (25 yrs >) with masters degree (percent)2 pedms

Population density (persons per square mile)2 popdens

Amount of Interstate highway (miles)4 interst

Distance to nearest urban population of 25,000(miles)5 d25k

Distance to nearest urban population of 100,000 (miles)5 d100k

Distance to nearest urban population of 250,000 (miles)5 d250k

Distance to nearest urban population of 500,000 (miles)5 d500k

Distance to nearest urban population of 1,000,000 (miles)5 d1000k

Unemployment rate (percent)6 uer

Farmland share of total acres7 farmland

Population weighted distance to highway on-ramp (km)5 hwyaccess

Rural population share2 rurpopsh

Farmer population share2 frmpopsh

African American population share2 afrpopsh

Child population share2 chdpopsh

Elderly population share2 eldpopsh

Share of adult men working full time2 wfullmsh

Share of adult women working full time2 wfullwsh

Metro county (yes/no)8 metro

Notes: N = 1,009; Source: 1 U.S. Department of Labor, Bureau of Economic Analysis, REIS; 2 U.S. Census Bureau, 2000 

Census; 3 USDA, Economic Research Service; 4 U.S. Department of Transportation; 5 ERS Geographic Information 

Systems team calculations; 6 U.S. Department of Labor, Bureau of Labor Statistics; 7 U.S. Census Bureau, 

U.S. Counties; 8 U.S. Office of Management of Budget.

Source: Brown et al. (2012).
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Appendix table 3

Change in per capita income 2000-08

OLS IV Estimation

Variable Coefficient Robust S.E. Coefficient Robust S.E.

mwcap 9,326.30** 4,858.10 11,150.05** 5,410.78

pci 457.74*** 159.19 458.89*** 162.51

pop -2.05 1.27 -2.05 1.30

poverty 3.65 105.96 6.69 108.20

nascale 150.71 131.31 151.06 134.11

agffh -17,346.00*** 6,018.60 -17,383.61*** 6,149.17

const -12,938.00 9,232.60 -12,648.34 9,432.33

manuf -24,799.00*** 2,881.70 -24,740.39*** 2,944.06

retrade -7,169.80 9,242.90 -7,069.71 9,440.70

pedas 215.86** 95.44 215.64** 97.52

pedbs -47.69 91.53 -48.18 93.47

pedms -400.69*** 153.90 -399.62** 157.24

popdens -0.09 0.92 -0.09 0.94

metro -1,467.80*** 366.43 -1,458.50*** 374.42

uer 261.29 170.40 259.04 174.95

interst 10.22 8.54 10.20 8.73

farmland 1,245.50 915.45 1,254.78 935.34

hwyaccess 10.25* 5.49 10.28* 5.61

d25k -3.74 4.51 -3.78 4.61

d100k 3.14 3.20 3.15 3.27

d250k -1.89 2.57 -1.90 2.63

d500k 3.21*** 1.13 3.24*** 1.16

d1,000k -0.35 0.71 -0.36 0.73

rurpopsh 2,206.10*** 828.51 2,194.24*** 846.41

frmpopsh 1,0340.00 6,336.60 1,0331.45 6,469.94

afrpopsh 2,427.70 3,134.50 2,519.21 3,200.89

chdpopsh -19,243.00** 8,823.80 -19,247.39** 9,013.07

eldpopsh -3,740.70 8,182.10 -3,592.00 8,353.54

wfullmsh 20,288.00*** 5,134.90 20,409.65*** 5,246.88

wfullwsh -17,788.00*** 4,961.00 -17,810.95*** 5,068.54

constant 6,011.40 6,330.20 5,832.22 6,464.29

Adj. R2 0.38 0.41

F-test (IVs) – 9.26***

Hansen J – 7.30

Note: “Variable” column terms are defined in appendix table 2.

Source: Brown et al. (2012).
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Appendix table 4

Change in per capita employment 2000-08

OLS IV Estimation

Variable Coefficient Robust S.E. Coefficient Robust S.E.

mwcap -0.0655 0.1000 0.4817* 0.2812

pci 0.0028* 0.0016 0.0031* 0.0016

pop -0.00002 0.00001 -0.00002 0.00001

poverty -0.0001 0.0012 0.0008 0.0013

nascale 0.0009 0.0015 0.0010 0.0016

agffh -0.0989 0.0784 -0.1101 0.0819

const -0.2971*** 0.1128 -0.2102* 0.1183

manuf -0.2200*** 0.0387 -0.2023*** 0.0403

retrade -0.2195* 0.1207 -0.1895 0.1237

pedas -0.0006 0.0014 -0.0007 0.0015

pedbs -0.0019 0.0014 -0.0021 0.0014

pedms -0.0012 0.0033 -0.0009 0.0034

popdens -0.00002 0.00001 -0.00002 0.00001

metro -0.0181*** 0.0053 -0.0153*** 0.0054

uer 0.0019 0.0023 0.0013 0.0026

interst 0.00002 0.0001 0.00001 0.0001

farmland -0.0363*** 0.0114 -0.0335*** 0.0119

hwyaccess 0.00001 0.00006 0.00002 0.0001

d25k -0.00006 0.00005 -0.0001 0.0001

d100k 0.00002 0.00004 0.00002 0.00004

d250k -0.00002 0.00003 -0.00002 0.00003

d500k 0.00001 0.00001 0.00002 0.00001

d1,000k 0.000001 0.00001 -0.000003 0.00001

rurpopsh 0.0047 0.0102 0.0011 0.0107

frmpopsh 0.0333 0.0675 0.0308 0.0778

afrpopsh -0.0561 0.0467 -0.0287 0.0475

chdpopsh -0.2001* 0.1183 -0.2015* 0.1216

eldpopsh 0.0413 0.0868 0.0859 0.0902

wfullmsh 0.2839*** 0.0778 0.3205*** 0.0849

wfullwsh -0.1262* 0.0678 -0.1330* 0.0712

constant 0.0037 0.0808 -0.0500 0.0829

Adj. R2 0.21 0.21

F-test (IVs) – 9.26***

Hansen J – 1.08

Note: “Variable” column terms are defined in appendix table 2; OLS = ordinary least squares; IV = instrumental variables.

Source: Brown et al. (2012).
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The final specification from which the matching variables were selected is provided in appendix 

table 5.

Matching protocols are sensitive to possible contamination of candidate control counties by treatment counties. To minimize this possibility, we remove all counties that are adjacent to counties with 

ethanol plants as possible controls. Adjacent counties with no ethanol plant may still see employment 

growth related to a new plant if they supply a large share of feedstock or a large share of employees. 

Our dataset utilizes a place of employment geography that assigns direct employment effects to 

treatment counties correctly, but induced effects may occur in neighboring counties where workers 

reside or spend income. Removing these adjacent counties as potential controls ensures that employment growth differences are not diluted by uncontrolled spillover effects.

Our matching algorithm minimizes the Mahalanobis distance between treatment and selected control 

observations (Rubin, 1979). In contrast to the conventional algorithm that optimizes matches across all 

observations, we utilize a “greedy” match algorithm that better suits the highly variable nature of the 

“treatment” or direct employment across counties (Mayo Clinic, 2003). Sorting plants by descending 

employment size (from 80 to 5 employees), the algorithm assigns the best matches to treatment 

counties with the highest probability of generating detectable indirect and induced employment 

impacts. The match is greedy because the counties with the largest plants retain their best match even 

if their paired control counties would provide closer matches with counties with smaller plants. After 

the match is complete, differences between the variables (dependent and independent) are calculated 

for each matched pair. The employment growth (y) is estimated by ordinary least squares (OLS) using 

the differenced values as shown by:

where superscripts t and c represent treated and control counties, i is the ith matched pair, p1 and p2 are 

the time periods 1 year before and 2 years after the ethanol plant became operational, X is a vector of 

explanatory variables measured in 2000 levels, β is a vector of coefficients, and ε is an error term.

= ( , 2 − , 1 ) − ( , 2 − , 1 ) = ( − )β + ε,   t t c c t c

i p i p i p i p i i y y y y y X X

Appendix table 5

County matching variables

Share of college graduates 25-441

Natural amenity scale2

Employment share of farming3

Employment share of manufacturing3

Employment share of mining3

Employment share of recreation3

Indicator of population loss county from 1970 to 19801

Urban influence code2

Level of population in 20003

Selected industry employment in 20004

Source: 1U.S. Census Bureau, Decennial Census; 2USDA, Economic Research Service; 3County Business Patterns;

4U.S. Department of Labor, Bureau of Labor Statistics, QCEW.
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This approach helps ensure that any remaining differences in employment growth between the 

treated (counties that received an ethanol plant) and the control (counties without an ethanol plant) 

are due to the ethanol plant. The following table shows the descriptive statistics of the variables used 

in the differenced employment growth equation as well as the estimation results (see appendix table 

6 and appendix table 7).

Counties With Multiple Emerging Industries

The employment estimates for each energy industry did not consider the possibility that multiple 

industries may be emerging in the county. This could affect the empirical estimates if, for example, 

natural-gas boom counties were systematically more or less likely than other counties to have wind 

power. Here we briefly assess the potential for industry overlap to affect our estimates. 

Of the three States considered in the natural gas study, Wyoming had no ethanol plants and 

Colorado and Texas had 3 and 4, indicating little overlap. In considering wind-natural gas overlap, 

figure 1a shows that Texas was a leader in wind power development in the early 2000s. Texas counties in the top quartile for the change in gas production (the definition of a boom county) were less 

likely to experience wind development. Of Texas boom counties, 4 percent were also wind counties, 

while 13 percent of nonboom counties were wind counties. The positive effect of wind development 

on employment combined with the greater prevalence of wind in nonboom counties would tend to 

lower the employment estimates associated with a natural gas boom.

Appendix table 6

Descriptive statistics of variables used in growth equation

Mean StdDev

Growth rate treatment 0.0461 0.1561

Growth rate control -0.0276 0.1566

Diff employment growth 35.761 105.698

Ethanol employment 31.41 14.82

Diff initial selected employment 209.869 470.22

Diff college graduates 2.49 7.51

Source: USDA, Economic Research Service tabulations.

Appendix table 7

Differences in employment growth for selected industries

Coefficient S.E. t - value Pr(>|t|)

95% Conf. 

Interval

Intercept -46.37 35.35 -1.31 0.197

Ethanol employment 2.559 1.006 2.54 0.015 0.529 - 4.589

Diff initial selected employment -0.030 0.0351 -0.87 0.391

Diff college graduates 3.258 2.195 1.48 0.145

F-stat 2.82 Adj. R2 0.1083

F p-value 0.050 N = 46

Source: USDA, Economic Research Service tabulations.
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Wind development and ethanol production may also overlap. However, of the 1,009 counties in the 

wind study region, only 18 had an ethanol plant, one of which was in a wind county. The few ethanol 

plants in the wind study region make it unlikely that any overlap would affect estimates of the wind 

employment effect.