SRI International | page 1

SRI International

November 30, 2017

Innovative Metrics for Economic 

Development: Supplemental Report

Infrastructure impact analysis using the REMI model

This report was developed through a cooperative agreement between 

EDA and SRI International
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Impacts of Infrastructure Grants 

In addition to the work on a logic model and econometric model to assess the impacts of EDA’s Non-Infrastructure 

Grants portfolio, SRI conducted a national-level assessment of the impact of EDA infrastructure grants using REMI’s 

PI+ model. REMI, or Regional Economic Models Inc., is an economic modeling organization that produces a variety 

of modeling products and services that can assess the impact of public policy actions. Their PI+ model generates 

year-by-year estimates of the effects of a given policy initiative, and is used broadly by governments, consulting 

firms, nonprofits, and universities. The model has been thoroughly vetted and verified and is considered a reliable 

tool for estimating public policy impacts.1

Before explaining the results of the impact model, it is important to note how EDA funding supports infrastructure 

projects. EDA makes a substantial number of infrastructure grants directly to organizations and communities across 

the nation, to support projects to improve roads, highways, water supplies, and other critical infrastructure 

components. While these grants provide a substantial amount of funding to such projects, they generally do not 

fully fund the project costs, and as such recipient organizations find additional funds to cover the remaining project 

costs. These additional funds come from a variety of sources – state and local governments, other federal grants, 

economic development organizations, and private sources of funding – and combined with the EDA grants, directly 

fund the infrastructure projects. 

In addition to these direct funds, EDA-supported projects also mobilize additional private investment. This includes 

incremental private sector investment above and beyond the funds used for project construction, and could be 

investment for site improvements, new plants and equipment, and other such improvement or expansion activities. 

EDA grant recipients are required to report on the impact of the grant, including the impact on private investment 

leveraged, on job creation and on job retention, at 3-, 6-, and 9-year intervals after grant award. 

In many situations, EDA funds act as the primary catalyst for other funding – that is, without the support and funding 

of EDA, many projects would struggle to attract the necessary capital. EDA grants act as a signaling mechanism to 

validate the project and mobilize outside investment. 

For this analysis, therefore, we wanted to not only analyze the impact of EDA funds themselves, but to also include 

the additional funds that EDA grants helped mobilize. Table 1 below presents the data on these funds, showing 

three variables from 2000 to 2010: total EDA spend, which is the aggregated amount of EDA infrastructure grants 

per fiscal year; total additional project funds, which is the amount of additional funding obtained to directly fund 

project costs; and total estimated private investment reported by the grantees at 6 years, reflecting the estimated 

amount of additional private investment generated by EDA-supported projects.2

 

1 For more information on the REMI model, see: http://www.remi.com/wp-content/uploads/2017/03/2013-REMI-Brochure.pdf. 2 Data was provided to SRI by EDA staff, and represents the total spending on infrastructure grants across the United States. 
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 Table 1: EDA and private investment spending, 2000 to 2010

Year Total EDA Spend Total Additional 

Project Funds

Total Estimated Private 

Investment, after 6 years

2000 $297,638,299 $347,211,663 $4,214,309,847

2001 $379,316,525 $361,100,378 $5,523,613,041

2002 $259,721,490 $328,513,723 $3,755,346,896

2003 $236,800,503 $260,859,615 $7,618,677,163

2004 $230,041,408 $293,002,127 $5,428,796,687

2005 $205,397,477 $269,313,918 $5,345,175,060

2006 $183,680,447 $305,482,535 $3,308,866,484

2007 $188,493,643 $285,578,593 $3,282,106,311

2008 $194,914,891 $258,884,699 $3,121,414,183

2009 $433,698,301 $367,577,306 $5,624,706,609

2010 $465,113,000 $386,361,265 $6,673,953,517

Yearly Avg. $279,528,726 $314,898,711 $4,899,724,163

As the table shows, there was considerable variation in the amount of funding for infrastructure projects by both 

EDA and other sources across the 10-year time period. (This variation is visualized in Figures 1 and 2 below). 

However, on average, EDA provided approximately $279 million in funding for infrastructure projects and those 

projects secured an additional $314 million in direct funding, and mobilized $4.9 billion in additional private 

investment, each year. Thus, on average, we estimate that every dollar EDA invested in infrastructure projects 

mobilized $1.12 in direct project funding and $17.5 in additional private investment; combined, this represents 

$18.6 in additional funds catalyzed each year for each dollar invested by EDA. 
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Figure 1: Total EDA spend and additional project funding, 2000-2010

Figure 2: Total estimated private investment mobilized, 2000-2010

The funding varies from year to year, at times due to disaster supplemental or stimulus funding, such as the 

American Recovery and Reinvestment Act in 2009. 

The SRI team used these data on investments in infrastructure projects and, leveraging the REMI model, estimated 

the impact of these investments across the US on three outcome variables: employment, wages and salaries, and 

personal income.3 As was explained earlier, SRI wanted to assess the full impact of EDA infrastructure grants, 

meaning both their direct funding and their role as a signal and catalyst for the generation of additional funding and 

private investment. As such, the team input both the value of the total project cost and that of the estimated private 

investment at 6 years into the REMI model to generate the impact estimates. Following the advice of REMI staff, 

the project cost and estimated private investment data were input as an increase in “Exogenous Final Demand –

 3 Information on how SRI input this data to run the REMI model can be found in the Appendix.

$0

$50

$100

$150

$200

$250

$300

$350

$400

$450

$500

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

USD, Millions

Total EDA Spend and Additional Project Funding

Total EDA Spend Additional Project Funding

$0

$1,000

$2,000

$3,000

$4,000

$5,000

$6,000

$7,000

$8,000

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Millions

Total Estimated Private Investment Mobilized
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Construction Industry” – representing the fact that the funding flowed through the recipient organizations and 

ultimately stimulated the construction industry. Additionally, also following the advice of REMI staff, SRI included a 

negative adjustment for “Government Spending” in the model, set to the amount of EDA funding each year, to 

account for any budgetary offset of the grant funding. 

SRI ran the model to estimate three economic outcomes: employment, wages and salaries, and personal income. 

The REMI model uses data from the Bureau of Economic Analysis to forecast all three of these variables. The 

definitions of the variables are presented below. 4

Employment: Employment comprises estimates of the number of jobs, full-time plus part-time, by place of 

work. Full-time and part-time jobs are counted at equal weight. Employees, sole proprietors, and active 

partners are included, but unpaid family workers and volunteers are not included.

Wages and Salaries: The sum of wages and salaries for private non-farm industries, state and local 

government, federal civilian, federal military, and farm sectors.

Personal Income: Income received by persons from all sources. It includes income received from 

participation in production as well as from government and business transfer payments. It is the sum of 

compensation of employees (received), supplements to wages and salaries, proprietors' income with 

inventory valuation adjustment (IVA) and capital consumption adjustment (CCAdj), rental income of 

persons with CCAdj, personal income receipts on assets, and personal current transfer receipts, less 

contributions for government social insurance.

SRI ran the model twice. The first time, the team input just the amount of EDA funding spent on infrastructure 

grants. These results are shown in Table 2. The team ran the model a second time using the combined value of the 

total project cost and the estimated private investment, to assess the full impact of the EDA grants. (Note: total 

project cost includes the EDA funding for infrastructure grants). These results are shown in Table 3. 

 4 Definitions taken from the REMI model software. 
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Table 2: Increase in outcome variable per year, from EDA spend only

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total 

Employment

4,558 5,931 4,026 3,220 2,574 1,832 1,260 1,106 1,083 3,451 3,712 

Total Wages 

and Salaries

(millions)

$193 $270 $207 $181 $158 $124 $96 $86 $84 $217 $246 

Total Personal 

Income

(millions)

$250 $347 $262 $229 $197 $155 $119 $107 $105 $279 $315 

Table 3: Increase in outcome variables per year, from total project cost plus private investment

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Total 

Employment

71,075 93,472 64,229 108,426 70,990 59,776 27,453 20,266 14,878 40,698 50,828 

Total Wages 

and Salaries

(millions)

$2,972 $4,199 $3,253 $5,483 $4,085 $3,686 $2,148 $1,720 $1,367 $2,729 $3,422

Total Personal 

Income

(millions)

$3,866 $5,416 $4,143 $7,007 $5,159 $4,639 $2,656 $2,125 $1,691 $3,493 $4,388
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The REMI model estimates impact by running a simulation using the policy data and comparing it against a national 

control, and presents impacts as the difference between the simulation and the control. As such, the first cell in 

Table 2 indicates that in the year 2000, there was an additional 4,558 jobs in the economy due to increased industry 

demand from EDA grants. Inevitably, the results from the combined project cost and private investment are much

larger than the results from the EDA spend by itself. As was discussed earlier, EDA grants are intended to catalyze 

funding by third parties, as well as directly fund projects; thus, it is important to acknowledge the full scope of 

impact of the grants. Considering only the results from the direct EDA funding would neglect to capture a critical 

role played by EDA grants in regional economies. 

Across the 11 years of analysis, using the combined impact of project costs and private investment generated from 

the REMI model analysis, EDA-supported projects and the investment they attracted generated an average of an 

additional 56,554 jobs, $3.187 billion in wages and salaries, and $4.053 billion in personal income each year.5 From 

these results, SRI calculated that EDA spending per job created ranged from $2,184 to $13,101, averaging $6,368 

across the 11 years (see Table 4 below).

SRI compared the estimates of jobs created generated by the REMI model to the reported actuals by EDA 

grantees in their Government Performance and Results Act (GPRA)6 reports at 6 years in order to assess the 

differences between the two sources. Across the 11-year period, GPRA reports jobs created ranges from 12,486 

to 49,806, averaging 27,822 jobs per year. The fact that GPRA reported figures are lower than the REMI estimates 

can be accounted for by two primary distinctions between the two reporting methods. The first is that the REMI 

estimates are national level estimates, and include indirect and induced job creation; GPRA reported figures, 

however, capture the direct jobs created in the regions that received grants, and therefore do not account for the 

indirect and national effects of the EDA grant funding. 

The second distinction contributing to the difference in estimates is that the REMI model estimates both full- and 

part-time jobs, whereas EDA grantees report on full-time jobs in accordance with GPRA requirements. However, 

in reviewing full- and part-time jobs data for the years during the analysis, SRI determined that approximately 20% 

of jobs in the economy were part-time in any given year. 7

Discounting the REMI results by 20% to only reflect full-time jobs generates a new average of approximately

45,243 additional jobs per year, a figure that is closer to the GPRA reported number of additional jobs, that still 

reflects the expanded scope of the REMI model. Ultimately, this comparison supports both methods as valid, as 

they do not present wholly different estimates of the impact of EDA infrastructure grants. The fact that GPRA 

estimates require supporting documentation and are tractable through 6 years after completion of the project, 

implies that GPRA estimates represent a ‘floor’ for jobs for any EDA project. It’s probable that these numbers 

represent the most conservative reflections of the ultimate impact of the grant funding.

 

5 As the REMI model generates impacts on a year-by-year basis, it is incorrect to add up the impacts from each year a generate a total 

number across the years. Some of the impacts in year 2 overlap in year 1, etc., and since it’s not a net change from the previous year, but 

from the control model, the only way to interpret the model results is on a year by year basis. 

6 EDA collects and reports on performance measures in compliance with Government Performance and Results Act of 1993 that, as 

amended by the GPRA Modernization Act of 2010, establishes requirements for all agencies to collect, analyze and report on their 

performance. For more information, please visit eda.gov.

7 U.S. Bureau of Labor Statistics, Labor Force Statistics from the Current Population Survey. Data reviewed was from 2002-2015. See 

https://www.bls.gov/cps/lfcharacteristics.htm#fullpart.
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Table 4: EDA spend per job

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Jobs 71,075 93,472 64,229 108,426 70,990 59,776 27,453 20,266 14,878 40,698 50,828 

EDA Spend

(thousands) $297,638 $379,316 $259,721 $236,800 $230,041 $205,397 $183,680 $188,493 $194,914 $433,698 $465,113

EDA 

Spend/Job $4,188 $4,058 $4,044 $2,184 $3,240 $3,436 $6,691 $9,301 $13,101 $10,657 $9,151 

Average EDA 

Spend/Job 

$6,368
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Limitations

While the REMI model is widely validated and a helpful tool for estimating impacts, it is important to note a few 

limitations of this analysis. The first limitation is that while the model treats the project costs and private investment 

amounts as discrete injections that occur fully in the year of award, the reality is that this funding tends to be 

dispersed over time. Infrastructure projects typically span several years. There is no standard or typical schedule 

that models the timing of the disbursement of EDA and project funding into the construction industry; therefore, 

there is no clear way to address this discrepancy. However, given that the analysis is considering a range of 11 years, 

the impact of this limitation is largely mitigated due to year-over-year overlaps; that is, funding that is attributed to 

the year 2001 but may be spread out over the years 2002-2004 is likely balanced out by funding that is attributed 

to the year 2002, but should have similarly been spread out across years. Therefore, while the estimates for each 

year may not be exact, across the full range of the analysis, the model provides a good estimate of the impacts of 

EDA-funded infrastructure projects. 

A second key limitation to note is that the model used for this analysis was a national-level model, and EDA grants 

were aggregated across the country when entered into the model. As such, the results from this analysis may be 

muted since EDA grants are not distributed uniformly across the country. Figure 3 below presents a visualization of 

the geographic distribution of EDA infrastructure grants in FY 2010. The visualization shows that some regions 

receive more EDA grants than other regions, while there are plenty of regions that do not receive any EDA grants 

at all. 

Figure 3: Geographic distribution of EDA grants

However, the REMI model used for this analysis was not able to tailor the impact according to geographic 

distribution of grants; thus, the impacts of EDA-grant heavy regions were tempered by the results in regions that 

did not receive EDA grants. 
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A third limitation is the counterfactual question regarding the other investments mobilized by EDA activity. In some 

cases, it may be that these investments would have been made anyway. The degree to which this is the case could 

be discovered by a large-scale survey of grantees; however, there was not sufficient time or resources for this kind 

of investigation under the scope of this project. However, if we imagine that private sources are the first recourse 

for those looking for investment, we may also suppose that seeking EDA investment only occurs when private 

support is not forthcoming. In short, the fact that a grantee turns to the EDA suggests that nothing would happen 

absent EDA intervention. 
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Appendix

REMI Model Specifications

SRI consulted with REMI staff on the best way to model EDA grants and private sector investments using the REMI 

model to generate estimated impacts. Per their recommendations, SRI did the following:

• Using an East/West model, SRI input EDA spending data, project costs, and private investment, year by year, 

into the model and used REMI’s spreader tool to allocate the spending across the Eastern and Western 

regions of the United States by population.

• SRI input the spending, project cost, and investment data as “Exogenous Final Demand” in the Construction 

Industry. REMI staff explained that government spending as an input variable in the REMI model largely 

refers to service spending, i.e. health, education, etc. They said it was a better modeling approach to use 

the data on the demand side, as the funds are given to organizations who then increase the demand of the 

Construction Industry.

• SRI input the amount of EDA spending in as a negative government spending variable, to account for any 

budgetary outset.