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Contract No. DE-AC36-08GO28308

Assessment of the Value, 

Impact, and Validity of the Jobs 

and Economic Development 

Impacts (JEDI) Suite of Models

L. Billman and D. Keyser

National Renewable Energy Laboratory

Technical Report

NREL/TP-6A20-56390

August 2013
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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC.

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Assessment of the Value, 

Impact, and Validity of the 

Jobs and Economic 

Development Impacts (JEDI) 

Suite of Models

L. Billman and D. Keyser

National Renewable Energy Laboratory

Prepared under Task No. SA12.0335

Technical Report

NREL/TP-6A20-56390

August 2013
NOTICE

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

 Printed on paper containing at least 50% wastepaper, including 10% post consumer waste. 
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Acknowledgments 

This work was funded by the U.S. Department of Energy, Office of Energy Efficiency and 

Renewable Energy. The authors wish to thank the rest of the National Renewable Energy 

Laboratory’s (NREL) Jobs and Economic Development Impacts (JEDI) team (Eric Lantz, 

Suzanne Tegen, Barry Friedman, Yimin Zhang, Chad Augustine, and Jim Leyshon of NREL, 

and Marshall Goldberg of MRG Associates) for their assistance. Gian Porro, David Kline, and 

Robin Newmark of NREL provided management review and important guidance. The authors 

also thank the following individuals for their review of the JEDI models, this report, or both: 

Jason Brown, Christopher Goldsberry, Scott Lindall, Carl Mas, Douglas Meade, Richard 

Morgenstern, Constantine Samaras, and Mike Scott. 
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Executive Summary

The Jobs and Economic Development Impacts (JEDI) models, developed by the National 

Renewable Energy Laboratory (NREL) for the U.S. Department of Energy (DOE) Office of 

Energy Efficiency and Renewable Energy (EERE), use input-output methodology to estimate 

gross

1

 jobs and economic impacts of building and operating selected types of renewable 

electricity generation and fuel plants. Input-output analysis is a technique for preparing a rough 

estimate of economic activity, including gross jobs. Other analytical models and approaches are 

also used for rough estimates of gross impacts, and for more detailed estimates of net impacts, 

depending on the needs of the analyst. 

As of July 2013, 13 JEDI models for varying energy technologies were available to the public,2

and three additional models were in development.3 JEDI models can be accessed at 

http://www.nrel.gov/analysis/jedi. The website provides assistance in interpretation of results, a 

summary of inherent limitations of the input-output methodology, JEDI citations, and other 

valuable information for appropriate application of the models. 

This analysis provides the DOE with an assessment of the value, impact, and validity of the JEDI 

suite of models. While the models produce estimates of jobs, earnings, and economic output, this 

analysis focuses only on jobs estimates. This validation report includes three topics:

• An introduction to JEDI models, including the input-output modeling methodology, the 

data used by JEDI, the process for developing JEDI models, and the current status of the 

suite of JEDI models 

• An analysis of the value and impact of the JEDI models, through independent expert 

reviewer comments, citation and use in published studies, number of users, and 

international interest

• An analysis of the validity of job estimates generated by JEDI model through: 

o Comparison to other modeled estimates

o Comparison to empirical, observed jobs data as reported or estimated for a 

commercial project, a state, or a region. 

JEDI models are developed using cost information researched from interviews with project 

developers and other experts, and are reviewed by other industry experts before being released. 

The technology cost data, and the input-output coefficients relating outputs of one sector to 

inputs from a different sector, are updated from new industry information periodically for all 

models. 

 

1 Gross job estimates do not take into account net effects on jobs, such as displacement of other jobs by the 

construction of a renewable energy plant, or economic impacts of potentially changing electricity or fuel prices, etc.

2 Available: Coal, Geothermal (hydrothermal and enhanced geothermal systems), Marine Hydrokinetic (wave, tidal, 

ocean current, river hydrokinetic), Natural Gas Combined Cycle, Land-based and Offshore Wind (utility scale), 

Solar Concentrating Solar Power (trough), Project and Scenario Solar Photovoltaics (four system capacities), 

Biofuels (cellulosic ethanol), Biofuels (corn ethanol), Biopower, and Petroleum. 3 In development: Conventional Hydropower, Land-based Wind (community scale), and Transmission.
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Value and impact of JEDI models were assessed through the solicitation of expert reviews, the 

study of citations, the analysis of the numbers of users and model downloads, and the 

documentation of interest in the tool from other countries. Three economic modeling experts 

reviewed the JEDI models and their methodology. These reviewers commented positively on 

various aspects of the JEDI model and provided helpful recommendations for improvements. 

JEDI has been used and cited in more than 70 public studies from 2004 to August 2012, 

including 12 studies in five different peer-reviewed journals. Currently, about 1,700 individuals 

(as measured by unique emails used during registration) download one or more JEDI models 

each year. Unique downloads (one model by one user one time during one year) for fiscal years 

2010 through 2012 range from 2,700 to 3,300 per year. Lastly, although designed solely for use 

in the United States, NREL has received inquiries about JEDI from seven foreign countries in the 

last two years, and studies have been published regarding six additional countries where JEDI 

has been modified for foreign use. 

The validity of JEDI estimates was assessed through comparison to both published modeled 

estimates and data on empirical observations of jobs associated with renewable energy projects. 

For comparison to published modeled estimates, three technologies were examined. Comparison 

of modeled vs. JEDI job results for a solar photovoltaics (PV) study indicated that JEDI results 

were within 10%–12% lower than the modeled study for direct4

 jobs for the sum of construction 

and operation and maintenance (O&M) phases. Comparison of an econometric county-level 

analysis of wind jobs with JEDI results showed that JEDI results were similar: the JEDI results 

[0.7 full-time equivalents (FTEs)/megawatt (MW) for construction and 0.3 FTEs/MW for O&M] 

bracket the econometric calculation (0.5 FTEs/MW reflecting both construction and O&M 

phases) by +/- 40%. Compared to modeled job results for O&M of several corn ethanol plants, 

JEDI results ranged from 20% lower to 28% higher. 

Comparing results between modeled and empirical employment data continues to be problematic 

due to the many differences between how actual employment data are collected and reported in 

the United States collectively or by individual companies, and the type of data required by JEDI 

and other jobs-estimating models. For example, using actual employment data, this analysis 

found an annual average of 0.7 ongoing jobs/MW at eight wind farms. JEDI estimated an 

average of 0.05 O&M FTEs/MW for wind farms with the same nameplate capacity and location, 

which is lower than observed employment. This difference may be due to employment counts 

often including part-time employees. Comparison of JEDI estimates with solar installation jobs 

survey data showed that results from JEDI were lower than the surveyed results for residential 

systems (8 FTEs/MW for new and 11 FTEs/MW for retrofit for JEDI, compared to 33 jobs/MW 

for all residential installations surveyed). For larger PV systems, JEDI results were very close 

and slightly higher than the installer survey (15 FTEs/MW to 23 FTEs/MW for JEDI, compared 

to 12 jobs/MW to 21 jobs/MW for the installer survey). 

 

4 Direct impacts refer to changes in jobs, economic activity, and earnings associated with the on-site or immediate 

impacts created by the project scenario. Indirect impacts refer to changes in jobs, economic activity, and earnings 

associated with upstream linked sectors in the economy, such as suppliers of hardware or equipment. Induced 

impacts refer to further changes in economic activity and earnings created by changes in household, business, or 

government spending patterns. These impacts occur when the earnings generated from the direct and indirect 

impacts are re-spent in the local economy. An example would be increased spending at local restaurants or grocery 

stores as a result of direct and indirect impacts.
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Comparison of JEDI U.S.-based results for wind plants to a recent global study of the wind 

industry indicated that JEDI results on an FTE/MW basis were lower for construction and higher 

for O&M. Comparison of JEDI U.S.-based results for installation of solar PV to a recent global 

study of the solar installation industry indicated that JEDI results on an FTE/MW basis were 

close to, but lower than, the industry-wide study. Results for distributed PV installations ranged

from 7 FTEs/MW to 11 FTEs/MW for JEDI’s U.S. results depending on system size, compared 

to 11 FTEs/MW (no system size specified) for the global study. JEDI results for utility-scale PV 

installations were almost twice as high as the global study, but the JEDI methodology counted 

labor hours for engineering, marketing and sales activities while the global study did not. 

Comparison of several empirical estimates for O&M jobs at corn ethanol plants showed that 

JEDI results ranged from 9% higher to 21% lower than the empirical estimates.

Based on the assessment of expert review, citations, user download data, and inquiries from 

foreign countries, the JEDI suite of models appears to be a credible and well-used estimation or 

screening tool for gross job estimates for the construction and operation of renewable energy 

power and fuel plants in the United States. Further, based on the above comparisons, subject to 

the limitations and challenges inherent to any comparisons of jobs estimates, JEDI results are 

reasonably comparable to these other modeled results and empirical observations. 

Expert reviewers and users have pointed out areas for improvement in the models. Several 

improvement ideas are offered in the concluding section of the report to address further 

documenting the models’ methodology and assumptions, validating default values incorporated 

in the models, continuing to compare estimates generated by the models to estimates from other 

models or empirical observations, and improving the user experience. 
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List of Abbreviations and Acronyms

BEAR Berkeley Energy and Resource 

BNEF Bloomberg New Energy Finance 

CGE computable general equilibrium 

CSP concentrating solar power

DOE U.S. Department of Energy 

EERE Energy Efficiency and Renewable Energy

EPA Environmental Protection Agency

FTE full-time equivalent 

FY fiscal year

IMPLAN impact for planning 

I/O input-output 

JEDI Jobs and Economic Development Impacts 

kW kilowatts

MGY millions of gallons per year

MIG Minnesota IMPLAN Group 

MW megawatts

NEMS National Energy Modeling System 

NYSERDA New York State Energy Research and Development Authority 

O&M operation and maintenance

PV photovoltaics 

REMI PI Regional Economic Models Inc. Policy Insight 

RIMS II Regional Input-Output Modeling System

TCU Texas Christian University

UI unemployment insurance 

US GAO United States Government Accountability Office 

USDA United States Department of Agriculture

USDA ERS United States Department of Agriculture Economic Research Service
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Table of Contents

Executive Summary.................................................................................................................................... ii

List of Abbreviations and Acronyms ........................................................................................................ v

List of Figures ........................................................................................................................................... vii

List of Tables ............................................................................................................................................ viii

Background ................................................................................................................................................. 1

Model Development Process and Status ................................................................................................ 3

JEDI Value and Impact................................................................................................................................ 6

Independent Expert Reviewer Comments.............................................................................................. 6

Citations in Published Studies................................................................................................................ 6

International Interest............................................................................................................................... 7

Numbers of Users................................................................................................................................... 8

JEDI Validity: Comparisons to Other Jobs Estimates (Modeled) ........................................................ 10

Comparison 1: JEDI vs. REMI PI+ (a Macroeconomic Model) for Proposed Solar Photovoltaics 

Capacity Additions......................................................................................................................... 10

Methodology.................................................................................................................................. 10

Results............................................................................................................................................ 11

Comparison 2: JEDI vs. an Econometric Analysis of Actual Wind Power Plant Additions................ 13

Methodology.................................................................................................................................. 13

Results............................................................................................................................................ 14

Comparison 3: JEDI vs. Published Sources of Modeled Jobs Estimates for Corn Ethanol Plants ...... 15

Methodology.................................................................................................................................. 15

Results............................................................................................................................................ 15

JEDI Validity: Comparison with Observed Direct Employment ........................................................... 17

Comparison 4: JEDI vs. Empirical Land-based Wind Farm Data........................................................ 17

Methodology.................................................................................................................................. 17

Results............................................................................................................................................ 18

Comparison 5: JEDI vs. Empirical Solar Photovoltaic Installation Data............................................. 19

Methodology.................................................................................................................................. 20

Comparison 6: JEDI U.S.-Based vs. Bloomberg Global Wind Industry Study ................................... 22

Comparison 7: JEDI U.S.-Based vs. Bloomberg Global Solar PV Industry Study.............................. 23

Methodology.................................................................................................................................. 23

Results............................................................................................................................................ 23

Comparison 8: JEDI vs. Published Engineering Estimates and Actual Counts of Corn Ethanol Jobs 24

Summary, Conclusions, and Possible Improvements.......................................................................... 26

Summary .............................................................................................................................................. 26

Conclusion............................................................................................................................................ 27

Possible Improvements......................................................................................................................... 28

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

Bibliography .............................................................................................................................................. 32
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List of Figures

Figure 1. Selected published studies discussing or using a JEDI model............................................. 7

Figure 2. Number of unique JEDI users each year.................................................................................. 8

Figure 3. JEDI downloads by model, Oct. 1, 2011 through Sep. 31, 2012............................................. 9

Figure 4. Phase-specific annual direct job impacts calculated in the base PV scenario case........ 11
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List of Tables

Table 1. Selected Methods for Jobs and Economic Impact Analysis ................................................... 2

Table 2. JEDI Models Summary and Status (July 2013).......................................................................... 5

Table 3. JEDI Model Downloads, Oct. 1, 2011 through Sep. 31, 2012 ................................................... 9

Table 4. Detailed Solar PV JEDI Results Using the REMI PI+ Inputs................................................... 12

Table 5. Comparison of Jobs Impacts for REMI PI+ and JEDI (Direct Only, Construction plus O&M 

Phases)................................................................................................................................................ 13

Table 6. Comparison of Jobs and Earnings Impacts for an Econometric Study and JEDI............... 14

Table 7. Comparison of JEDI and Published Results (Modeled) for Annual Corn Ethanol Plant 

O&M Jobs............................................................................................................................................ 16

Table 8. Analysis of Land-based Wind Facilities for Direct, On-Site O&M Jobs................................ 18

Table 9. Differences Between Observed Wind Employment and JEDI FTE Estimates for Direct O&M 

Phase (Observed Minus JEDI On Site)............................................................................................. 19

Table 10. Values Assigned to Survey Ranges for Percent of Revenue from Solar PV Installations 21

Table 11. Values Assigned to Survey Ranges for How Much Solar PV Capacity Was Installed ...... 21

Table 12. Comparison of Direct, On-Site Solar PV Installation Jobs Reported by Companies vs. 

JEDI Estimates.................................................................................................................................... 22

Table 13. Comparison of Bloomberg Global Wind Study vs. JEDI U.S.-based Direct Impact 

Estimates for Both Construction Phase and O&M Phase.............................................................. 23

Table 14. Comparison of Bloomberg Global Solar PV Study and JEDI U.S.-Based Direct Impact 

Estimates for Installation Labor ........................................................................................................ 24

Table 15. Comparison of JEDI vs. Empirical Measures of Direct, O&M Corn Ethanol Jobs............. 25
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Background

In 2002, the Wind Program of the U.S. Department of Energy (DOE), Office of Energy 

Efficiency and Renewable Energy (EERE), funded the development of a new spreadsheet-based 

model to calculate jobs impacts using input-output (I/O) methodology. I/O models apply 

historical relationships between demand (i.e., specific expenditures within a given sector) and the 

resulting economic activity to estimate how new expenditures will affect economic development 

metrics. These metrics include jobs, earnings (wages and employer paid benefits), and economic 

output, a general measure of economic activity. I/O models are static–they represent 

relationships between modeled sectors of the economy at a given time period. They also assume 

that any change in demand, regardless of magnitude, has the same proportional result. However, 

the structure and relationships between sectors of the economy tend to change gradually over 

time. Despite this, I/O modeling is a commonly used methodology for measuring economic 

development activity (Lantz and Tegen 2011). 

I/O models produce gross impact estimates, as opposed to net estimates–they do not take into 

account far-reaching potential changes, such as impacts of changes in utility rates as a result of 

development, greenhouse gas emissions, health care costs, and property values. 

I/O analysis of jobs, earnings, and economic output is one of several modeling approaches that 

can be used to estimate economic impacts. Each of these approaches has advantages and 

disadvantages. Table 1 summarizes some of the better known basic and sophisticated 

approaches, including Jobs and Economic Development Impacts (JEDI). 
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Table 1. Selected Methods for Jobs and Economic Impact Analysis

Basic Methods Moderate Methods Complex Methods

Approach 

Rule of thumb

Meta-analysis

Input-output or based on

input-output

Computable General Equilibrium 

(CGE)

Econometric

System Dynamics—Linear and 

Non-Linear Programming

Examples

Rule-of-thumb estimates 

(i.e., “5 jobs/MW”)

Screening models

Impact Analysis for Planning 

(IMPLAN)5

Regional Input-Output 

Modeling System (RIMS II)6

Jobs and Economic 

Development Impacts 

(JEDI)

National Energy Modeling System 

(NEMS)7

Berkeley Energy And Resource 

(BEAR) model8

U.S. Regional Energy Policy 

(USREP) Model9

Regional Economic Models Inc.

Policy Insight (REMI PI)10

RAND econometric model11

Benefits

Easy to use

Minimal time requirement

Transparent

Inexpensive

Easy to moderately easy to 

use

Time requirement can be 

minimal but varies

Can be inexpensive

Widely used, accepted

More comprehensive than inputoutput; can model more scenarios, 

retrieve more information

Able to incorporate changes over 

time (i.e., dynamic versus static)

Flexible 

Drawbacks

Results can be limited

Often overly simplistic 

assumptions

Inflexible

Not very transparent

Many restrictive 

assumptions (i.e., constant 

prices)

Scenarios limited to 

changes in demand

Difficult or moderately 

difficult to develop

Can be expensive

Not very transparent

Assumptions vary

Often difficult to operate or modify

Most require expensive software 

or licenses

Difficult, expensive to build

Data intensive

Table data from EPA 2010 (modified by NREL) 

 

5 IMPLAN is a commercial model that uses input-output analysis techniques, social accounting matrices, and 

publicly available data that is widely used for analysis of jobs and economic impacts; IMPLAN is typically used for 

gross job estimates.

6 RIMS II is an input-output-based model that uses regional multipliers to help users estimate gross jobs, developed 

by the Department of Commerce/Bureau of Economic Analysis.

7 NEMS is a computer-based model that estimates the energy supply and demand to 2035 with regional projections, 

developed by the DOE Energy Information Administration and used for energy projections; it has been used with 

input-output methodologies to project jobs and economic impacts. 8 BEAR is a state-level computable general equilibrium model developed by the Lawrence Berkeley National 

Laboratory, which can account for many different factors affecting jobs, producing net jobs estimates.

9 USREP is a computable general equilibrium model developed and maintained by the Massachusetts Institute of 

Technology (MIT). The model is national, but splits the United States in to multiple regions. 

10 REMI PI is a commercial model that uses hybrid techniques, combining aspects of input-output, econometric, and 

computable general equilibrium techniques, and produces net jobs estimates. 11 The RAND econometric model is a commercial tool that uses sets of related equations, and mathematical and 

statistical techniques to analyze economic conditions over time, generally producing net jobs estimates.
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JEDI models rely on two different types of data sets and I/O matrix calculations. First, for 

industry economic I/O relationships, JEDI models utilize economic data (multipliers and 

household expenditure patterns) derived from IMPLAN software and state data files purchased 

from the Minnesota IMPLAN Group (MIG). MIG compiles, and aggregates national and 

regional economic and demographic data from the U.S. Census Bureau (Department of 

Commerce), Bureau of Economic Analysis (Department of Commerce), and the Bureau of Labor 

Statistics (Department of Labor) to calculate inter-industry linkages and the relationships 

between changes in demand for goods and services, along with the associated economic activity 

at the local, state, and regional levels. 

Second, JEDI provides default costs for each technology, including detail for about one hundred 

cost categories.12 The default values represent a reasonable expenditure pattern for constructing and 

operating the technology in the United States, including the share of expenditures spent locally. 

However, actual project spending on goods and services can vary significantly by project and 

location. Therefore, each model has the flexibility for the user to override these default values. 

The basic JEDI models are designed to calculate the jobs and economic impacts by state. The 

expenditure data are adjusted to account for the typical percentage of various project costs that 

are provided locally (within the state) as opposed to imported from outside the state. Users can 

adjust these local shares to account for variability among projects and specific locations.13

Model Development Process and Status

During the design and development phase of each JEDI model, NREL researchers or affiliates

gather and synthesize technology cost data through a literature review, and interviews of project 

developers, industry representatives, state tax representatives, and others. This discovery 

approach ensures the use of the most current and accurate technology information available from 

renewable energy practitioners. Once a draft model is complete, it undergoes internal review by 

NREL and DOE technology experts and then review by independent experts. External review is 

typically performed by individuals who have direct project experience and familiarity with 

project costs and key development and operating parameters. Lastly, JEDI models are reviewed 

and validated by an expert from the appropriate industry. 

JEDI models are publicly available. However, because appropriate application of the models by 

the user is the prime determinant of the credibility of the resulting estimates, neither NREL nor 

DOE endorses the results of JEDI analyses performed by others. This caveat is included in the 

spreadsheet for each model.

The first JEDI model, developed in 2002 and first cited in 2004 (U.S. GAO 2004), assessed the 

economic impacts of utility-scale land-based wind projects; the initial version was known as the 

“Wind Impact Model.” The models were made publicly available in 2007. Over time, new JEDI 

models were developed for additional technologies. As of September 2012, JEDI models are 

 

12 Examples from the wind model include construction materials such as concrete, rebar, construction equipment, 

roads, and site prep; electrical transformers; electrical cables and wire; and high-voltage line extension; and labor 

cost categories such as foundation, erection, electrical, management, and miscellaneous. Cost categories reflect what 

is needed to construct and operate projects and vary among technologies. 

13 Other regional configurations can be analyzed (counties, groups of states) if multipliers for these regions are 

provided by the user. 
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available for nine energy technologies (seven in the electricity sector and two in the 

transportation fuels sector), and seven more are in development (Table 2).

The websites for the JEDI models provide additional information on the models and contact 

information for additional support. NREL has published numerous reports of JEDI-based 

analyses, which are always peer reviewed, and have given numerous webinars and other public 

presentations about JEDI and its results. These interactions with experts and model users offer 

valuable insights that allow NREL to continuously refine and improve the models after they are 

published. Table 2 summarizes the status of JEDI model development. JEDI models can be 

accessed at http://www.nrel.gov/analysis/jedi. 
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Table 2. JEDI Models Summary and Status (July 2013) 

Year of Last Update to Default Values 

JEDI Module Energy Sector

Current 

Version or 

Status

Year Original 

Model 

Developed 

IMPLAN 

Multipliers 

(2010 Dataset) 

Capital 

and O&M 

Cost 

Data 

Cost 

Input 

Mix 

Local 

Share

Biopower Electricity In development N/A 2012 N/A N/A N/A 

Coal Electricity 

C1.11.1— 

Update 

planned 2005 2012 2011 2011 2011 

Conventional 

Hydropower Electricity In development N/A N/A N/A N/A N/A 

Geothermal 

(hydrothermal and 

enhanced 

geothermal 

systems) Electricity GT08.02.12 2012 2012 2011 2011 2011 

Marine 

Hydrokinetic (wave, 

tidal, ocean current, 

and river 

hydrokinetic) Electricity MHK01.11.01 2011 2012 2011 2011 2011 

Natural Gas 

Combined Cycle Electricity 

NG1.11.01—

Update 

planned 2005 2012 2012 2011 2011 

Offshore Wind Electricity OSW 02.04.13 2012 2013 2012 2012 2012 

Land-based Wind 

(community scale) Electricity In development N/A N/A N/A N/A N/A 

Land-based Wind 

(utility scale) Electricity 

W1.10.03—

Update 

planned 2002 2012 2012 2011 2011 

Concentrating 

Solar Power 

(trough) Electricity CSP1.10.02 2006 2012 2010 2010 2010 

Solar Photovoltaics 

(four system 

capacities) Electricity PV1.17.11 2006 2012 2011 2011 2011 

Solar Photovoltaics 

(policy version) Electricity PVS4.5.13 2012 2012 2012 2012 2012 

Transmission Electricity 

In 

development; 

Wyomingspecific done N/A 2012 2011 N/A N/A 

Biofuels (cellulosic 

ethanol) Transportation C1.10.02 2007 2012 2010 2008 2008 

Biofuels (corn 

ethanol) Transportation 

CE1.10.02—

currently being 

updated 2006 2012 2010 2010 2010 

Conventional Fuels 

(Petroleum) Transportation P3.09.13 2013 2013 2013 2013 2013 
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JEDI Value and Impact

The value and impact of the JEDI models were assessed through feedback from independent 

expert reviewers, citations, and usage in published studies, numbers of users and downloads, and 

international interest.

Independent Expert Reviewer Comments

In March 2012, the authors asked three experts on economic impact modeling to provide a 

technical review of the JEDI models and information available on the JEDI website. The 

complete reviews are available on request. 

The following is a synthesis of observations. 

• All the reviewers offered a number of positive comments, including that JEDI is a 

“valuable resource,” applied a “credible method,” and is an “excellent addendum to the 

toolbox” of models and calculators available for jobs analysis for renewable energy 

technologies. 

• JEDI suffers from the same limitations and criticisms common to all I/O techniques in 

general. 

• Uncertainties introduced by the use of a variety of assumptions can introduce errors in 

particular situations. 

• Inaccuracies arise from the approximations of the I/O relationships built on the 2002 

benchmark tables of the Bureau of Economic Analysis, and aggregating specific costs for 

these renewable energy technologies into a few industry sectors is difficult. 

• The JEDI website provides documentation and caveats on the shortcomings inherent in 

the methodology. 

• Reviewers recommended:

o The sources for default data should be documented wherever possible. 

o The default data based on actual power plant projects should be updated and more 

extensively surveyed.

o Models should be more frequently validated against observed jobs data. Some 

inconsistencies in terminology should be addressed. 

o All JEDI models should have an accompanying user guide (only two of the nine 

models have user guides). 

Citations in Published Studies

The bibliography at the end of this report includes a selection of published, English-language 

studies that use or discuss one or more of the JEDI models. An examination of this bibliography 

indicates:
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• Seventy-one different published studies use or discuss JEDI models for the period 2004 

to June 2012, as shown in Figure 1. 14 The number of publications increased in 2010 and 

2011 to twelve per year. In the first half of 2012, 15 new studies have been published. 

• Twelve studies were published in peer-reviewed journals including Energy Policy, 

Energies, Energy Economics, American Institute of Chemical Engineers Journal, and 

Renewable and Sustainable Energy Reviews. 

• Authors of these studies include representatives of all major stakeholder groups. 

Examples include: 

o Private sector: General Electric, The Brattle Group

o Federal agencies and national laboratories: U.S. Environmental Protection 

Agency, U.S. Department of Agriculture, Lawrence Berkeley National 

Laboratory, Argonne National Laboratory, and U.S. Air Force 

o Universities: University of California Berkeley, University of Texas, Michigan 

State University, Utah State University, Arizona State University, Texas Tech 

University, Rennselaer Polytechnic Institute, Syracuse University, and 

Northwestern University

o International institutions: World Bank, University of Spain, National Technical 

University of Athens, and Fraunhofer Institute. 

Figure 1. Selected published studies discussing or using a JEDI model 

International Interest

JEDI models have been modified and results published for Greece (Tourkolias and Mirasgedis 

2011) and North Africa (including Morocco, Algeria, Tunisia, Egypt, and Jordan) (World Bank 

 

14 The Bibliography section of this paper contains a more complete listing of studies and publications that have used 

JEDI. 

 -

 500

 1,000

 1,500

 2,000

 2,500

 3,000

 3,500

 4,000

 4,500

FY 08 FY 09 FY 10 FY 11 FY 12

Number of Unique Users In Each Year
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2011). In addition, NREL has received requests for assistance in adapting and using JEDI in 

Argentina, Turkey, Canada, Puerto Rico, United Arab Emirates, the Netherlands, South Africa, 

Greece, and Germany (personal communications 2012). 

Numbers of Users

In order to download any model, JEDI model users must provide their name and email address. 

This number of registered downloads serves as a proxy for the number of users. Figure 2 shows 

that the number of users when JEDI was first publicly launched has declined from 2,400 in fiscal 

year 200915 to about 1,700 in fiscal years 2011 and 2012. The high level of interest in 2009 may 

have been triggered by the American Recovery and Reinvestment Act of 2008 and its focus on 

new renewable energy projects. 

Figure 2. Number of unique JEDI users each year

The JEDI website tracks both the total number of model downloads and the number of unique 

downloads. Unique downloads are the number of times each model is downloaded, excluding 

models that are downloaded more than once by the same user. Unique downloads may be 

understood to represent the level of interest in the JEDI models, but we cannot verify that 

downloading the model results in someone actually using the model for analysis. 

The JEDI website logged 3,389 unique downloads in fiscal year (FY) 2010 (October 2009 

through September 2010), 2,787 in FY 2011, and 2,908 in FY 2012. The most downloaded 

models were wind and solar photovoltaics (PV), followed by concentrated solar power (CSP) 

trough (Figure 3). 

 

15 Collection of download data was begun in late fiscal year 2008. 

 -

 500

 1,000

 1,500

 2,000

 2,500

 3,000

 3,500

 4,000

 4,500

FY 08 FY 09 FY 10 FY 11 FY 12

Number of Unique Users In Each Year
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Table 3. JEDI Model Downloads, Oct. 1, 2011 through Sep. 31, 2012

All 

Downloads

Unique 

Downloads

Total 3,916 2,908

Land-based Wind 1,259 909

Solar Photovoltaics 1,008 661

Concentrating Solar Power 444 354

Natural Gas (Combined Cycle) 293 239

Biofuels (Cellulosic Ethanol) 284 237

Marine Hydrokinetic 224 182

Coal 171 126

Biofuels (Corn Ethanol) 166 142

Figure 3. JEDI downloads by model, Oct. 1, 2011 through Sep. 31, 2012

2% 5%

5%

6%

8%

8%

12%

23%

31%

Geothermal

Coal

Biofuels (Corn Ethanol)

Marine Hydrokinetic

Biofuels (Cellulosic

Ethanol)

Natural Gas (Combined

Cycle)

Concentrating Solar

Power

Solar Photovoltaic
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JEDI Validity: Comparisons to Other Jobs Estimates 

(Modeled)

Studies of modeled jobs estimates have been published by various authors using JEDI, other 

models or manual calculations based on I/O tables, and other models or manual calculations that 

do not use I/O tables.

16 Three recent studies provided sufficient detail to allow comparison to an 

equivalent analysis using one of the JEDI models.

Comparison 1: JEDI vs. REMI PI+ (a Macroeconomic Model) for 

Proposed Solar Photovoltaics Capacity Additions

The Power New York Act of 2011 directed the New York State Energy Research and 

Development Agency (NYSERDA) to evaluate the costs and benefits of increasing the use of 

solar PV in New York to 5,000 megawatts (MW) by 2025. The impacts of meeting the goals on 

New York’s economy (measured by changes in employment and gross state product) were 

developed using a Regional Economic Models Inc. Policy Insight (REMI PI+) model. This is an 

advanced macroeconomic model that combines an I/O model with a dynamic ability to forecast 

shifts in prices and competitiveness factors over time (NYSERDA 2012). 

Methodology

The NYSERDA report contains data that can be used as JEDI inputs for four types of PV 

systems: residential, small commercial, large commercial, and utility scale. The input parameters 

included installed project costs, capacity and number of systems installed, financing parameters, 

percent purchased in-state, percent manufactured in-state, and sales tax assumptions. Two years 

of the multi-year NYSERDA study were analyzed using JEDI: 2013, the first year of estimated 

new capacity arising from PV policy incentives, and 2025, the last year of the study, specifically 

focused on jobs impacts from systems installed in 2025. Figure 4 shows NYSERDA employment 

estimates from 2013 to 2025. 

Job values are listed in the NYSERDA study as “job years,” which are defined as one job for the 

duration of one year. This is assumed equivalent to JEDI’s use of full time equivalents (FTEs), 

meaning one full-time, 40-hour/week job for one person for the duration of one year. The 

NYSERDA study evaluated a base PV scenario, as well as low-cost and high-cost PV cases; only 

the base PV scenario was used for this comparison.

 

16All models rely on assumptions, although these assumptions vary between models and techniques. The 

comparisons made in this report are to illustrate similarities and differences between JEDI and other models and 

studies, not to portray specific analyses as fact. 
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Source: NYSERDA 2012, Figure 36 (used by permission)

Figure 4. Phase-specific annual direct job impacts calculated in the base PV scenario case

Results

Table 4 details the results of the JEDI analysis using the inputs from the NYSERDA study. The 

results for the four system types are shown here as a point of information; the NYSERDA study 

did not break down results for the individual system types. 
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Table 4. Detailed Solar PV JEDI Results Using the REMI PI+ Inputs

Direct, 

Onsite 

FTEs

Construction Phase (FTEs)

Residential 192

Small Commercial 280

Large Commercial 562

Utility 173

Total for 2013 1,207

Residential 395

Small Commercial 586

Large Commercial 1,175

Utility 421

Total for 2025 2,577

O&M Phase (FTEs per year for 20 

years)

Residential 3

Small Commercial 4

Large Commercial 11

Utility 5

Total for 2013 24

Residential 15

Small Commercial 18

Large Commercial 52

Utility 25

Total for 2025 110

Sum of Construction Phase and O&M 

Phase FTEs

Residential 195

Small Commercial 284

Large Commercial 573

Utility 178

Total for 2013 1,231

Residential 410

Small Commercial 604

Large Commercial 1,227

Utility 446

Total for 2025 2,687

Source: Inputs from NYSERDA 2012; NREL JEDI results
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Table 5 compares the results from the NYSERDA study using REMI PI+ to the JEDI results. 

JEDI results for direct jobs, summed over construction and O&M phases, were 10%-12% lower

than comparable REMI PI+ estimates. 

Table 5. Comparison of Jobs Impacts for REMI PI+ and JEDI 

(Direct Only, Construction plus O&M Phases) 

Year Capacity Built 

in This Year 

(MW)

REMI PI+ 

Direct FTEs

JEDI Direct, 

Onsite FTEs

Difference 

(Level)

Difference 

(Percent)

2013 122 1,400 1,231 -169 -12%

2025 568 3,000 2,687 -313 -10%

Source: NYSERDA 2012, Figure 36 and private correspondence; NREL

Comparison 2: JEDI vs. an Econometric Analysis of Actual Wind

Power Plant Additions

Economists at the U.S. Department of Agriculture (USDA), Economic Research Service (ERS) 

collaborated with researchers at Lawrence Berkeley National Laboratory and the National 

Renewable Energy Laboratory (NREL) to conduct an ex post (after-the-fact) econometric 

analysis to estimate the county-level (local) economic development impacts of wind power 

installations. This is the first study to empirically test for the economic development impacts of 

wind power installations using such a methodology. This study included the set of counties that 

had installed wind farms in the period 2000–2008 in 12 states in the region that includes the 

Great Plains and eastern Rocky Mountains (Brown et al. 2012). The analysis examined actual 

changes in employment and annual personal income in these counties,17 and the extent that the 

changes could be attributed to the additional construction and operation of wind farms. In the 

course of this analysis, the author also examined and compared his results to published projectlevel case studies of some of these counties. One of these studies was conducted by Texas 

Christian University (TCU) and NREL using the JEDI Wind model (Slattery et al. 2011). 

Methodology

Table 6 compares results for two types of metrics: employment in terms of FTEs/MW of wind 

capacity installed, and annual personal income in dollars of earnings per MW of wind capacity 

installed. Table 6 includes results from three sources (Brown et al. 2012): 

• Results as calculated in the econometric study 

• The range of results from several published project-level studies using I/O models other 

than JEDI 

• Results as calculated by JEDI in Slattery et al., which was also one of the project-level 

studies in the range of results. 

 

17 While the econometric analysis was applied to ex ante, observed county-level employment data, it is presented in 

the “Comparison to Modeled Estimates” section of this report. The reason is that it derived an outcome based on a 

modeling technique and did not employ the empirical “job counting” approach as a basis for comparisons 

documented later in the report.
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The econometric analysis calculated average annual FTEs, normalized per MW of capacity 

installed, for wind capacity additions over the period 2000–2008. The published studies, cited by 

Brown et al. 2012, provided results that could also be normalized per MW of capacity and 

compared to the analysis within this set of counties and this timeframe. The Slattery et al. study 

examined two specific wind farms in two specific counties in Texas that were constructed in 

2008 and are within the timeframe and geographic set of studies by Brown et al.; the total 

installed capacity is 1,398 MW. 

The econometric study distinguished between analyses of the impacts of wind plants that were 

locally owned from those that were absentee owned. Only the data for the absentee-owned wind 

farms (the larger data set) are shown in Table 6 because Slattery et al. only analyzed absenteeowned wind farms. 

Table 6 shows employment and income impacts econometrically estimated by Brown et al. 

alongside I/O impacts referenced by Brown et al., and JEDI employment and income impacts 

estimated by Slattery et al. All three analyses reported similar metrics arising from similar wind 

projects that are within a similar general time frame.

There are differences between the studies. The time period of analysis varies between studies–

Brown et al. focused on 2000 through 2008 while Slattery et al. focused on projects constructed 

in 2008 and operating between 2009 and 2028. The phase of projects also varies. I/O studies split 

construction and operating periods, whereas the econometric estimates in Brown et al. combined 

the two. 

Results

Table 6. Comparison of Jobs and Earnings Impacts for an Econometric Study and JEDI

FTEs/MW Annual Earnings $/MW (2010$)

Other I/O Studies Other I/O Studies

Year or Time PeriodProject Phase ConsideredCapacity Built in this Time Period (MW)Econometric Results (Brown 2012)Results Range (Brown 2012)Number of Studies Consulted (Brown 2012)JEDI (Slattery 2011)Econometric Results (Brown 2012)Results Range (Brown 2012)Number of Studies Consulted (Brown 2012)

JEDI (Slattery 2011)

2000-2008 Both 15,750 0.50 $11,000

2000-2008 Construction 0.1-2.6 5 N/A N/A

2000-2008 

and beyond

O&M 0.1-0.6 7 $5,000-

$18,000

6 

2008 Construction 1,398 0.7 $8,000

2009-2028 O&M 1,398 0.3 $13,000

Source: Brown et al. 2012; Slattery 2011

Table 6 indicates that the JEDI construction jobs estimate of 0.7 FTEs/MW was in the lower 

third of the range of I/O studies (0.1 to 2.6 FTEs/MW). The JEDI O&M jobs estimate was just 

below the middle of the range of the I/O studies (0.3 FTEs/MW compared to 0.35 FTEs/MW). 

Compared to the econometric study, the JEDI jobs estimates were similar. The JEDI results (0.7 

FTEs/MW for construction and 0.3 FTEs/MW for O&M) bracket the Brown et al. econometric 
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calculation (0.5 FTEs/MW reflecting both construction and O&M phases) by +/- 40%.18 Similar

trends are seen with annual earnings as well. 

Brown et al. suggest that, while a strict comparison between the results of the econometric 

analysis and other jobs studies they examine is not possible, the I/O approach used as the basis of 

these other studies does not appear to over-state the economic impacts of wind development: 

Interesting, despite a number of known limitations to the standard application of 

I/O models to estimating economic development impacts, our results are of a 

similar general magnitude to I/O derived estimated impacts ...Though the two 

results are not strictly comparable, this suggests that I/O models that are used to 

assess the economic impacts of wind energy (at least at the county or local level) 

may not be unduly impacted by the generic limitations to those models discussed 

earlier in this paper and do not appear to be overstating the impacts of wind 

development. (Brown et al. 2012) 

Comparison 3: JEDI vs. Published Sources of Modeled Jobs 

Estimates for Corn Ethanol Plants

NREL recently prepared a report on the JEDI corn and cellulosic ethanol models (Zhang 2012). 

Part of that report compared JEDI results for O&M jobs to published results in the recent 

literature and available databases. This report included a comparison of JEDI results to published 

results in the recent literature of modeled estimates for corn ethanol plants. 

Methodology

The NREL study focused on comparing jobs and/or jobs multipliers for the O&M phase 

produced by the JEDI model with various published results. A jobs multiplier is defined as the 

total jobs (direct, indirect, and induced19) divided by the direct jobs. The total jobs describes the

total effects within the state where the project is located resulting from a single expenditure, so 

the jobs multiplier describes the expectation that the direct investment will have a ripple effect 

that will create additional jobs within the state.

Results

Table 7 describes the comparison of published values and JEDI values, where sufficient detailed 

information was available to calculate comparable JEDI estimates. In this table, “jobs” means 

 

18 Econometric estimates are also necessarily net changes, incorporating job losses as well as gains. Because the 

Brown et al. analysis focuses on county employment, the difference between net and gross changes is somewhat 

mitigated. Job gains tend to be geographically concentrated (i.e. around the wind site) while losses tend to be difuse 

(i.e. around the state). 

19 Direct impacts refer to changes in jobs, economic activity, and earnings associated with the on-site or immediate 

impacts created by the investment. Indirect impacts refer to changes in jobs, economic activity, and earnings 

associated with upstream linked sectors in the economy, such as suppliers of hardware or equipment. Induced 

impacts refer to further changes in economic activity and earnings created by changes in household, business, or 

government spending patterns. These impacts occur when the earnings generated from the direct and indirect 

impacts are re-spent in the local economy. An example would be increased spending at local restaurants or grocery 

stores as a result of direct and indirect impacts.
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FTEs.20 Because these are O&M jobs, these jobs would be expected to continue over the 

estimated 30-year life of a corn ethanol plant.

Table 7. Comparison of JEDI and Published Results (Modeled) 

for Annual Corn Ethanol Plant O&M Jobs 

Study Description of Published 

Study

Metric for 

Approximate 

Comparison 

Published 

Value

NREL 

JEDI 

Value

Difference

(% of 

Published 

Value)

Swenson 

(2008)

O&M jobs for a 50 MGY

(million gallon per year) 

dry mill in Iowa in 2005 

Direct jobs:

Indirect jobs:

Direct + Indirect:

35

75

92

48

50

98

37%

-33%

6.5% 

O&M jobs for a 100 MGY 

dry mill in Iowa in 2005 

Direct jobs:

Indirect jobs:

Direct + Indirect:

46

95

141

52

85

137

13%

-11%

-2.8% 

Low and 

Isserman 

(2009)

O&M jobs for a 100 MGY 

plant in Hamilton, IL in 

2006

Direct jobs:

Indirect jobs:

Direct + Indirect:

39

97

136

52

122

174

33%

26%

28% 

O&M jobs for a 100 MGY 

plant in Kankakee, IL in 

2006

Direct jobs: 

Indirect jobs: 

Direct + Indirect:

39

152

191

52

101

153

33%

-34%

-20% 

O&M jobs for a 60 MGY 

plant in Coles, IL in 2006

Direct jobs: 

Indirect jobs: 

Direct + Indirect:

35

83

118

51

60

111

46%

-28%

-5.9% 

O&M jobs for a 55 MGY 

plant in Harlan, NE in 2006

Direct jobs: 

Indirect jobs: 

Direct + Indirect:

35

50

85

50

51

101

43%

2%

19% 

Source: Swenson 2008; Low and Isserman 2009

As can be seen in Table 7, the JEDI results for direct plus indirect compared to several other 

modeled estimates range from 20% lower to 28% higher than the published results for the sum of 

direct and indirect jobs. Comparing the sum was considered more valid than the individual direct 

and indirect estimates due to differences in methodologies of JEDI versus the published modeled 

results.

21 Further, the sum shows lower variability.22

 

20 An FTE means one full-time (40 hours per week) job for one person for one year. 21 These differences could largely be due to how impacts were modeled. For example, within the JEDI framework, 

contracted service providers are considered to be indirect. Other researchers may assume that these service providers 

would be directly hired rather than contracted and thus classify them as direct. 22 Additional statistical analysis was not undertaken due to the small sample size of six cases.
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JEDI Validity: Comparison with Observed Direct 

Employment

A comparison of JEDI model results with observed (also referred to in this report as empirical) 

job estimates presents several challenges. I/O models relate what is observed about the U.S. 

economy–that is, economic linkages between sectors at a single point in time – to a discrete, 

explicitly specified change, such as the construction and operation of a wind farm. I/O models do 

not factor in outside impacts that occur over time (such as business cycles) or price changes 

(such as changes in utility electricity rates).

23 Nor are other economic changes considered, such 

as factory closures/openings, changes in resource use, or changes in productivity. 

A scenario modeled in JEDI (such as building a wind farm) may have positive employment 

impacts. A subsequent analysis of labor data may not show this due to job losses elsewhere in the 

economy. Job growth due to renewable energy development may cause overall employment to 

remain unchanged or slow any decline in employment. While this characteristic of I/O models is 

useful for isolating impacts related to a specific change, it makes comparing JEDI estimates with 

actual labor data difficult. 

Incomplete or uncertain labor data, especially as it relates to clean energy industries, further 

contributes to the problem of comparing actual job figures with JEDI estimates. Companies or 

project owners may or may not collect labor data that relate to a particular project. Even if data 

are available, problems arise with the classification of companies and definitions of employment. 

These problems limit any comparisons of JEDI results to real world direct or on-site employment 

impacts. Detailed studies of actual projects, job sites, and suppliers would be necessary to collect 

useful employment data throughout the supply chain, so supply chain impacts cannot be counted 

or compared at this time. When available, direct or on-site employment data can be isolated and 

associated with an identifiable project along with specific parameters, such as the project’s 

nameplate capacity. Entering capacity and holding all other parameters to their default values 

allows comparison of JEDI calculations with real-world scenarios. 

Comparison 4: JEDI vs. Empirical Land-based Wind Farm Data

Methodology

This analysis incorporates observed jobs associated with the O&M phase of nine operating wind 

farms that became operational between 2007 and 2010 (Table 8). These facilities were selected 

because the operator provided NREL with employment information. For each wind farm, 

nameplate capacity and turbine size were used as JEDI inputs. JEDI default values were used for 

all other parameters, including technology cost.

 

23 Some models that utilize input-output as a part of the analysis are dynamic. In this case, they utilize other 

processes, such as econometric forecasts to account for changes. These models may or may not produce net (as 

opposed to gross) estimates. 
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Table 8. Analysis of Land-based Wind Facilities for Direct, On-Site O&M Jobs

Facility Name Location Nameplate Capacity

(MW)

Turbine Size (MW)

Hull Wind II Hull, MA 1.8 1.8 

Bluegrass Ridge Wind 

Farm 

Gentry County, MO 56.7 2.1 

Peetz Table Wind 

Energy Center

Peetz, CO 400.5 1.5 

Northern Colorado Wind 

Energy Center

Logan County, CO 174.3 1.5–2.3 

Streator Cayuga Ridge 

South Wind Power 

Project

Livingston County, IL 300 2.0 

Rail Splitter Wind Farm Logan and Tazewell, IL 100.5 1.5 

Ruby Wind Power 

Project

Pierce County, ND 149.1 2.1 

Twin Buttes Wind Farm Bent County, CO 75 1.5 

Source: NREL

Results

Employment at each individual wind facility cannot be disclosed due to confidentiality 

agreements, but aggregate comparisons of JEDI results with actual information can be presented. 

Of the wind farms in Table 8, JEDI results in three cases were identical to the number of 

employees reported by wind farm operators and lower in all the other cases. 

Normalized to nameplate capacity, JEDI calculated an average of 0.05 on-site FTEs per MW of 

capacity installed for O&M. Observed data showed employment closer to 0.07 jobs per MW of 

capacity installed for O&M. For example, for a 250 MW project, JEDI would, on average, 

project 12.5 FTEs for O&M while the number of actual jobs (according to this sample) was 

found to be closer to 17.5. The calculated differences for each wind farm (Table 9) shows that 

JEDI results ranged from zero difference to seven FTEs less than the observed employment. 
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Table 9. Differences Between Observed Wind Employment and JEDI FTE Estimates for Direct O&M 

Phase (Observed Minus JEDI On Site)

Facility 

Number24

Difference Between JEDI FTEs 

and Observed Employment

1 0 

2 -1 

3 -2 

4 0 

5 -4 

6 -1 

7 0 

8 -7 

One possible explanation for JEDI results being lower than observed employment is that JEDI 

estimates jobs as FTEs, whereas companies generally consider the number employed as the total 

number of employees.25 For example, two employees who each work 20 hours per week in 

combination count as one FTE. An employer, however, may consider them to be two employees. 

The difference between what organizations consider to be their number of workers and FTEs as 

reported by JEDI is most significant during the construction period. Many wind farms report the 

peak number of construction workers. This metric does not make a distinction between full- and 

part-time workers, nor does it indicate the duration of the construction project and related 

employment. For this reason this analysis does not compare reported peak construction jobs and 

JEDI estimates. 

Comparison 5: JEDI vs. Empirical Solar Photovoltaic Installation Data

Solar PV facilities differ from wind farms in several ways that affect quantifying the number of 

employees. A wind farm is typically a large construction project that takes place over a single 

time period in a concentrated geographic area. Distributed solar PV is installed on residences or 

commercial buildings and can be distributed across a large geographic area. Rather than a single 

large construction project, as is the case with a wind farm, solar PV installers can have enough 

small projects to maintain staffing levels that are consistent over a longer period of time. 

It is more straightforward to obtain staffing levels of solar PV installers than for solar PV system 

maintenance services. Solar PV installers are typically specialized and know both their installed 

nameplate capacity over the course of a year and the number of staff required to complete these 

installations. Depending on what element of a PV system is involved, maintenance can be 

 

24 The “Facility Number” is arbitrarily assigned and observed employment levels are not listed to preserve data 

anonymity. 25 The reason for this situation has to do with state unemployment insurance (UI) filings. State UI forms require the 

employer to submit the number of individuals working for a company each month as well as wages paid. States do 

not request the number of hours worked by employees or whether an employee works full or part time. These state 

filings are a significant source of employment information utilized by the Bureau of Labor Statistics, the Bureau of 

Economic Analysis, and many other organizations that track or estimate employment. 
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performed by electricians, roofers, or any number of firms and/or occupations that do not 

specialize in solar PV. For these reasons, this analysis compares JEDI estimates of direct/on-site 

jobs from construction (referred to as “installation” for solar PV), rather than O&M, to observed 

employment numbers provided by installation firms. 

Methodology

As part of a 2011 study, Friedman, Jordan, and Carrese surveyed thousands of known solar PV 

installers across the United States, as well as potential installers such as construction companies. 

Over 1,400 establishments responded, providing information about employment and sales

(Friedman 2011). Sales data include what portion of a firm’s revenue involves solar PV 

installations, total installed solar PV capacity, and the average installed capacity. Employment 

data include full-time employees, part-time employees, and seasonal workers. 

Some assumptions about the survey data must be made in order to compare it to the FTEs 

reported by JEDI. This analysis assumes that part-time employees work an average of 20 hours 

per week and seasonal workers are employed for four months. 

Employment data from the survey also need to be scaled to reflect the portion of an employee’s 

time that was spent installing solar systems. This was accomplished by multiplying reported 

employment by the percentage of a firm’s revenue that came from solar PV installations.

Respondents provided ranges when asked what percentage of their revenue came from solar PV 

installations. In order to scale employment, a specific percentage needed to be applied. Table 10 

shows the ranges and corresponding single values that were used in this analysis. The top range 

was set at 100%; the survey was sent to many known solar installers, and it is likely that many 

exclusively install solar systems. Conversely, the survey was sent to many potential installers 

and it is likely that some do not install solar PV systems at all. For this reason, in order to be 

conservative, the bottom of the range was assigned zero. The other two ranges, spanning 25% to 

75% of firm revenue, were assigned values in the middle of the range. 
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Table 10. Values Assigned to Survey Ranges for Percent of Revenue from Solar PV Installations

Response Assigned Value

Most to all of it (76% to 100%) 100%

Half to three-quarters (50% to 75%) 63%

A quarter to almost half of it (25% to 49%) 37%

Less than a quarter (1% to 24%) 0 

Source: Friedman 2011

Respondents also provided ranges when asked how much capacity they installed. This analysis 

assumes values close to range midpoints, as show in Table 11. Installers that indicated 

installations greater than 2 MW were assigned a value of 5 MW. This value is in line with 

observed utility-scale solar PV installations that are greater than 2 MW (NREL 2009, 

unpublished). 

Table 11. Values Assigned to Survey Ranges for How Much Solar PV Capacity Was Installed

Response Assigned Value (kW)

1 to 100 kilowatts 50

101 to 500 kilowatts 300

501 kilowatts to 1 megawatt 750

1.1 megawatts to 2 megawatts 1,500

More than 2 megawatts 5,000

Source: Friedman 2011

Several assumptions must be made to construct JEDI estimates. All JEDI analyses used model 

default values and California as the project location. Only direct, on-site construction/installation 

jobs or construction/installation-related services were reported in this JEDI analysis.

Table 12 shows results from the survey and JEDI in terms of FTEs/MW. JEDI estimates differ 

the most from survey results in the residential category, the largest category in terms of installed 

capacity. The JEDI model includes two categories of residential: new construction and retrofits 

(existing construction). The survey did not distinguish between the two, so both JEDI estimates 

are shown. In this case, JEDI estimates range from 8.1 FTEs/MW to 11.4 FTEs/MW lower than

the survey. For all other categories, JEDI estimates are slightly higher than the survey, ranging 

from 0.9 FTEs/MW higher in medium-to-large commercial systems to 3.1 FTEs/MW higher in 

utility-scale or large commercial installations. 
This report is available at no cost from the 22 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 12. Comparison of Direct, On-Site Solar PV Installation Jobs Reported by Companies vs.

JEDI Estimates

Survey Results JEDI Results

Average 

Installation 

Type

Size 

Range 

(kW)

FTEs Installed 

Capacity 

(MW)

FTEs/MW FTEs/MW Difference 

FTEs/MW)

Residential <6 5,401 161.9 33.4 22.0 (Retrofit)

25.3 (New 

Construction)

-11.4 

(Retrofit)

-8.1 (New)

Small 

commercial 

6-50 456 21.8 20.9 23.2 2.3

Medium to 

large 

commercial/ 

industrial 

51-200 283 14.9 19.0 19.9 0.9

Large 

commercial/ 

industrial or 

utility 

>201 230 19.5 11.8 14.9 3.1

Source: Friedman, Jordan, and Carrese 2011; NREL (JEDI estimates)

One possible source of variance between JEDI estimates and solar PV installation data is the 

difference between a company’s anticipated workload and actual demand for its services. If a 

company overestimated demand it may have hired too many workers, resulting in a relatively 

high number of employees per MW installed. If the company underestimated, it may have asked

employees to work additional hours beyond the 40-hour work week. In that case, it would have a 

relatively low number of employees per MW installed. 

Comparison 6: JEDI U.S.-Based vs. Bloomberg Global Wind Industry 

Study

Bloomberg New Energy Finance (BNEF) (BNEF 2012) developed global average estimates of 

direct employment in the wind energy sector, including both O&M positions and construction 

positions. The most comparable estimates from a JEDI wind model are at the national level. 

The technology involved in construction and maintenance of wind farms is fairly standard 

throughout the developed world. Components are bought and sold in an international market. 

Therefore, this comparison assumes that neither material prices nor labor intensiveness of 

projects varies significantly across the developed world, and that the Bloomberg global results 

and JEDI U.S. national results can be reasonably compared. JEDI’s direct, on-site jobs results are 

compared in this analysis solely with the elements of the Bloomberg analysis that are also direct, 

on-site jobs. Bloomberg jobs data that appear to be related to supply chain or indirect jobs are not 

included in this comparison. Job impacts are reported as FTEs by both Bloomberg and JEDI. 

JEDI wind employment estimates for on-site construction and development are lower than those 

estimated by Bloomberg’s global study, and higher than those for O&M (Table 13). BNEF noted 
This report is available at no cost from the 23 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

that increasing global productivity is reducing the labor needs of the land-based wind industry. A 

possible explanation for the lower JEDI results for construction is a productivity difference 

between the average U.S. and global wind farms. 

Table 13. Comparison of Bloomberg Global Wind Study vs. JEDI U.S.-based Direct Impact 

Estimates for Both Construction Phase and O&M Phase

Bloomberg FTEs/MW JEDI (On Site) FTEs/MW

Wind farm construction and 

development 

1.7 0.7

Wind farm operation 0.1 0.5

Source: BNEF 2012; NREL

Comparison 7: JEDI U.S.-Based vs. Bloomberg Global Solar PV 

Industry Study

Methodology

Bloomberg New Energy Finance (BNEF 2012) also developed global estimates of employment 

in the solar PV energy sector. The JEDI PV model is for the United States only. Yet as noted in 

the BNEF wind energy analysis discussion above, PV markets are global and technology is fairly 

standard across the developed world. Bloomberg estimates can be reasonably compared with 

JEDI at the national level. 

 The BNEF study isolated solely the installers (i.e., construction workers), excluding sales and 

engineering labor, for two categories of PV installations: small scale and utility scale. The JEDI 

model includes installers, as well as the sales, engineering, and other workers. JEDI also 

produces results for five PV categories, as opposed to two, as shown in Table 14. JEDI assumes 

all labor to be available within the United States. All jobs are FTEs, both those reported by 

Bloomberg and JEDI estimates.

Results

As shown in Table 14, JEDI estimates are close to, though in all cases lower than, Bloomberg’s 

results for smaller installations, ranging from 6.8 FTEs/MW to 10.6 FTEs/MW compared to 11.0 

FTEs/MW. For utility-scale installations, JEDI’s results are significantly higher—6.8 FTEs/MW 

compared to 3.5 FTEs/MW. The JEDI estimates include engineering and sales labor, in addition 

to the installers. Engineering and sales labor could be significantly higher for companies that are 

selling and designing much larger systems with greater impacts on communities, compared to 

small companies dealing with single home or building owners, possibly explaining the higher 

JEDI results for utility scale and lower JEDI results for residential and commercial installations. 
This report is available at no cost from the 24 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 14. Comparison of Bloomberg Global Solar PV Study and JEDI U.S.-Based Direct Impact 

Estimates for Installation Labor

Solar PV Installation Size 

(Installation/Construction Phase Only)

Bloomberg 

FTEs/MW

JEDI FTEs/MW

New Residential Installation (3.5 kW, Fixed Mount)

11.0

10.6

Residential Retrofit Installation (5 kW, Fixed Mount) 8.5

Small Commercial Installation (20 kW, Fixed 

Mount)

9.0

Large Commercial Installation (150 kW, Fixed 

Mount)

8.3

Utility-Scale Construction (1,000 kW, Single Axis) 3.5 6.8

Source: BNEF 2012; NREL

Comparison 8: JEDI vs. Published Engineering Estimates and Actual 

Counts of Corn Ethanol Jobs

NREL recently prepared a report on the JEDI corn and cellulosic ethanol models (Zhang 2012). 

NREL included a comparison of JEDI results for direct jobs for the O&M of corn ethanol plants 

to published results in the recent literature for empirical estimates for three actual corn ethanol 

plants and one state-wide industry estimate. 

Table 15 indicates that, for four estimates of empirical employee counts, JEDI results for similar 

corn ethanol plants ranged from 9% higher to 21% lower than the empirical values for direct, 

O&M jobs. 
This report is available at no cost from the 25 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Table 15. Comparison of JEDI vs. Empirical Measures of Direct, O&M Corn Ethanol Jobs

Study Description 

of Study

Type of 

Study

Metric for 

Approximate 

Comparison 

Empirical

Value

NREL JEDI 

Value

Difference 

From 

Empirical 

Value (%) 

Simonson and 

Liska (2009)

Wisconsin 

state-wide 

corn ethanol 

industry in 

2008 (nine 

ethanol 

plants 

produced 

498 million 

gallons of 

ethanol and 

hired 420 

full-time and 

nine parttime 

employees)

Actual count Direct 

employees

425 450 

(assuming 

nine plants 

at an 

average 

production 

capacity of 

55.3 MGY)

6%

John Kneiss 

(Hart Energy, 

Personal 

Communication 

March 2012)

O&M jobs 

for a 100 

MGY plant 

in 2012

Discussion 

with design/

engineering 

firms and inplace 

operations

Direct jobs 45-55 52 4%

O&M jobs 

for a 50 

MGY plant 

in 2012

Discussion 

with design/

engineering 

firms and inplace 

operations 

Direct jobs 41-47 48 9%

Ethanol Across 

America (2006)

O&M jobs 

for a 25 

MGY plant 

in Nebraska 

Actual count Direct jobs 33 26 -21%
This report is available at no cost from the 26 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Summary, Conclusions, and Possible Improvements

Summary

As of August 2012, nine JEDI models for varying technologies are available to the public, and 

seven additional models are in development. JEDI models can be accessed at 

http://www.nrel.gov/analysis/jedi. The website provides some assistance in interpretation of 

results, a summary of inherent limitations of the I/O methodology, current JEDI citations, and 

other information for appropriate application of the model. 

JEDI models have been developed using technology and cost information derived from 

interviews with project developers and other experts, and are reviewed by additional industry 

experts before being released. The technology cost data, as well as the I/O coefficients relating 

outputs of one sector to inputs from a different sector, are periodically updated for all models 

based on new industry information. 26

The value and impact of the JEDI models has been demonstrated in these terms:

• JEDI has been used and cited in more than 70 public studies from 2004 to August 2012, 

including 12 studies in five different peer-reviewed journals. 

• Three economic modeling experts have reviewed the JEDI model and its methodology. In 

general, these reviewers commented positively on various aspects of the JEDI model and 

provided helpful recommendations for improvements. 

• Studies have been published regarding six additional countries where JEDI has been 

modified for international use. 

• The annual number of unique downloads of JEDI models (one model by one user one 

time during one year) for fiscal years 2010 through 2012 (estimated) range from 2,700 to 

3,300. Currently about 1,700 unique users download models each year. 

The validity of JEDI estimates was assessed by comparing both published, modeled estimates 

and empirical observations of jobs associated with specific projects. For published, modeled 

estimates, the comparisons yielded the following findings: 

• Using the same inputs and assumptions, JEDI direct FTE estimates for the sum of 

construction and O&M phases were 10% to 12% lower than REMI PI+ modeled 

estimates when modeling an increase in solar PV capacity in New York to 5,000 MW. 

• Estimates using statistical (econometric) analyses of empirical county-level economic 

data for counties that experienced wind development from 2000-2008 were similar to 

JEDI estimates. The econometric analysis estimated 0.5 FTEs/MW for all jobs, 

construction and O&M, and JEDI results were 0.7 FTEs/MW for construction-period 

jobs and 0.3 FTEs/MW for O&M-period jobs. 

• Two peer-reviewed journal articles modeled O&M jobs at corn ethanol plants for six 

scenarios of varying sizes and locations. In the comparison of the sum of direct plus 

 

26 Additional data can be found in the Department of Energy Transparent Cost Database at 

http://en.openei.org/wiki/Transparent_Cost_Database. 
This report is available at no cost from the 27 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

indirect jobs, the JEDI results ranged from 20% lower to 28% higher than the modeled 

results. 

Comparisons to empirical (observed) jobs is problematic for several reasons, including 

incomplete collection of jobs data across an entire project, differences in classifications of 

workers or contractors, and counting of full-time and part-time workers. Detailed, documented 

studies of actual projects and job sites are rare. For available empirical observations, the 

comparisons yielded the following findings:

• Analysis of O&M employment data from eight wind farms indicated that JEDI estimated 

an average of 0.05 FTEs/MW, whereas actual employment estimates averaged almost 

0.07 jobs/MW. One explanation for JEDI results being lower is the common practice of 

companies counting workers rather than labor hours (FTEs). 

• Comparison to solar installation jobs survey data indicated that JEDI results were lower 

than the surveyed results for residential systems (8 FTEs/MW for new and 11 FTEs/MW 

for retrofit for JEDI, compared to 33 jobs/MW for all residential installations surveyed). 

For larger PV systems, JEDI results were very close to and slightly higher than those 

from the installer survey (15 FTEs/MW to 23 FTEs/MW for JEDI, compared to 12 

jobs/MW to 21 jobs/MW for the installer survey).

• A comparison for wind employment between JEDI’s U.S.-based results and a global 

study from Bloomberg New Energy Finance indicated that the JEDI U.S. result for onsite construction and development was significantly lower than the Bloomberg estimate 

(0.7 FTEs/MW compared to 1.7 FTEs/MW). For O&M, the opposite was true—JEDI 

results were higher (0.5 FTEs/MW compared to 0.1 FTEs/MW). 

• A comparison of solar PV employment between JEDI’s U.S.-based results and 

Bloomberg’s global study indicated that JEDI’s estimates are close to, but lower than, 

Bloomberg’s results for smaller, distributed installations, ranging from 7 FTEs/MW to 11 

FTEs/MW for JEDI compared to 11 FTEs/MW. For utility-scale installations, JEDI’s 

results are higher—6.8 FTEs/MW compared to 3.5 FTEs/MW. A possible explanation for 

the higher JEDI utility-scale estimate is that JEDI includes the soft construction labor 

(e.g., design engineers and sales labor), whereas Bloomberg includes only actual 

installers. 

• A comparison of data for four estimates of empirical employee counts in corn ethanol 

plants showed that JEDI O&M results for similar corn ethanol plants ranged from 9% 

higher to 21% lower than the empirical O&M values.

Conclusion

Based on the assessment of expert review, citations, and user download data, the JEDI suite of 

models is a credible and well-used estimation and screening tool for gross job estimates for the 

construction and operation of renewable energy power and fuel plants in the United States. Jobs 

are an important metric because they are widely analyzed and reported, and are a consideration 

in decisions made about energy. 
This report is available at no cost from the 28 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

Further, based on the above comparisons, subject to the limitations and challenges inherent to 

any comparisons of jobs estimates, JEDI results were reasonably comparable to these other 

modeled results and empirical observations. 

Comparing and validating job estimates between modeled and empirical employment data 

continues to be problematic due to the many differences between how actual employment data 

are collected and reported in the United States collectively or by individual companies, and the 

type of data required by economic impact models in general. Within the limitations and 

challenges inherent to any comparisons of jobs estimates, whether between one modeled result 

and another or between a modeled result and empirical observations, the comparisons 

documented in this report demonstrate that JEDI results were reasonably comparable to these 

other sources.

Possible Improvements

Opportunities for improving the JEDI capability were identified during the course of this study 

from sources such as feedback from model users and JEDI technical assistance. The following 

ideas are offered as possibilities that could improve both estimates derived from, and 

interpretation of results for, today’s JEDI models: 

• Improvements in validation and documentation of default data sources 

• Continuing to compare JEDI estimates to modeled and empirical employment data 

• Improving consistency in terminology and the interfaces across models 

• More consistent availability of model-specific information to help user interpretation and 

understanding 

• Improvements to the employment data collected by various federal and state agencies in 

terms of adding renewable energy-related economic sectors and targeted questions on 

employment that would benefit jobs estimating models in general.

Several improvements could help DOE and NREL maximize the value received for the 

investment already made in this suite of tools, and add value to the ongoing discussions of 

energy-related jobs in the United States and the global economy. The following ideas are offered 

as possibilities: 

• Expand the suite of models to include additional power generation technologies, such as 

combined heat and power, fuel cells, nuclear, and other natural gas and coal technologies. 

• Expand the suite of models to include fuel technologies not currently in development, 

such as jet fuel and other advanced biofuels beyond cellulosic ethanol. 

• Establish an online JEDI user group to help improve and broaden the appropriate 

application of the tool, provide a forum for exchange of information, case studies, and 

ideas. 

• Post on the JEDI website periodic summaries of newly published JEDI-based studies, 

periodic short articles on tips for use or interpretation, and other timely information. 
This report is available at no cost from the 29 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

• Transition the tools to web-based applications from the current spreadsheet-based 

approach, to encourage a wider user base. 

• Assist individuals in other countries interested in adapting the JEDI tool for their 

economies. 

• With select industry partners or via carefully constructed industry interviews or surveys, 

commission more controlled studies to better document actual jobs in all renewable 

energy technology areas, for direct, on-site jobs.
This report is available at no cost from the 30 

National Renewable Energy Laboratory (NREL) 

at www.nrel.gov/publications.

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