NISTIR 6499

National Institute of Standards and Technology

Technology Administration, U.S. Department of Commerce

Office of Applied Economics

Building and Fire Research Laboratory

Gaithersburg, Maryland 20899

Guide for Estimating and

Reporting Macroeconomic

Impacts of MEP Centers

Mark A. Ehlen and M. Hayden Brown
Census

Survey

Client firm Client firm Client firm

Activity Data Logs

Chapter 3:

Compile Project and

Industry Impacts

Chapter 2:

Determine whether to

estimate impacts

Chapter 5:

Report Results

Chapter 4:

Estimate State

Macroeconomic

Impacts

Checklist for Estimating and Reporting Impacts

Obtain REMI model (p. 7)

Ken Voytek (MEP): (301) 975-4338

George Treyz (REMI): (413) 549-1169



Perform REMI macroeconomic analysis (p. 19)

Ken Voytek (MEP): (301) 975-4338

Hayden Brown (NIST/OAE): (301) 975-6857



Submit Census Survey Data File to NIST (p. 7)  Hayden Brown (NIST/OAE): (301) 975-6857

Prepare data for summary report (p. 29)  Hayden Brown (NIST/OAE): (301) 975-6857

Write summary report (p. 29)  Hayden Brown (NIST/OAE): (301) 975-6857

Determine whether to perform analysis (p. 5)  Hayden Brown (NIST/OAE): (301) 975-6857

Request Survey Data Summary Page (p. 5)  Ken Voytek (MEP): (301) 975-4338

Perform the survey T

 Ken Voytek (MEP): (301) 975-4338

If no: stop.

If yes:

Optional: compile supporting information (p. 36)  Hayden Brown (NIST/OAE): (301) 975-6857

Survey results
NISTIR 6499

U.S. Department of Commerce Office of Applied Economics U.S. Department of Commerce Office of Applied Economics

Technology Administration Building and Fire Research Laboratory Technology Administration Building and Fire Research Laboratory

National Institute of Standards and Technology Gaithersburg, Maryland 20899

A Guide for Estimating and Reporting the

Macroeconomic Impacts of MEP Centers

Mark A. Ehlen and M. Hayden Brown

May 2000

Prepared for:

Manufacturing Extension Partnership

National Institute of Standards and Technology

Gaithersburg, MD 20899

U.S. Department of Commerce

William M. Daley, Secretary

Technology Administration

Dr. Cheryl L. Shavers, Under Secretary for Technology

National Institute of Standards and Technology

Raymond G. Kammer, NIST Director

Table of Contents

Chapter 1: About this Guide Chapter 1: About this Guide 1

1.1 Purpose 1

1.2 Topics covered 3

1.3 Who should read this guide 4

Chapter 2: Determine Whether To Estimate Impacts Chapter 2: Determine Whether To Estimate Impacts 5

Chapter 3: Compile Project and Industry Impacts Chapter 3: Compile Project and Industry Impacts 7

3.1 The Census Follow-up Survey Data File 7

3.2 The NIST Data Processor 8

3.3 Identify and select impacts 8

3.4 Perform inflation and annualizing adjustments 10

3.5 Adjust labor, material, and inventory impacts 11

3.6 Adjust sales impacts 13

3.7 Adjust jobs (created and retained) impacts 15

3.8 Adjust investment impacts 17

3.9 Conclusions 18

Chapter 4: Estimate State Macroeconomic Impacts Chapter 4: Estimate State Macroeconomic Impacts 19

4.1 The REMI model 19

4.2 Run a REMI simulation of state impacts 20

Chapter 5: Report Results Chapter 5: Report Results 25

5.1 Determine the best format for reporting results 25

5.2 Make the two-page summary report 29

5.3 Explain the two-page summary report 32

5.4 Further explain the results 36

Appendix A: Supporting Information Appendix A: Supporting Information 39

Glossary 39

Federal, state, and local tax revenues as a percent of personal income 42

The REMI 14-sector classification, with two-digit-SIC manufacturing detail 43

BLS and REMI Data 44

Depreciation schedule for capital investment 45

Appendix B: NIST REMI Baseline-forecast Data File Appendix B: NIST REMI Baseline-forecast Data File 47

References 51 References 51
Disclaimer:

Certain trade names and company products are mentioned in the text or identified in an

illustration in order to adequately specify the experimental procedure and equipment used.

In no case does such identification imply recommendation or endorsement by the National

Institute of Standards and Technology, nor does it imply that the products are necessarily

the best available for the purpose.
Chapter 1: About this Guide 1

1. About this Guide

1.1 Purpose

Manufacturing extension centers must often report on the impacts they have had on their client firms. 

The main data source of these impacts is the survey of Manufacturing Extension Partnership (MEP)

clients, which interviews MEP client firms to see how their performance has changed as a direct result

of center services. The 1999 MEP publication A Guide for Communicating Impact[10] suggests

numerous ways to report these direct, client-reported impacts to center stakeholders.

Another impact that the Guide suggests reporting is the macroeconomic impact an MEP center has on

the state economy. By “macroeconomic impact” we mean the total effect on the entire state economy,

such as on state aggregate output, employment, personal income, and tax revenues. The total effect is

the sum of the direct effect of the client-reported impact—appropriately adjusted—plus its indirect

effect on the rest of the state economy. For example, if a client firm increases its sales and this

increases production in the state, the new production creates demand for other firms’

production—similar to how new machine-tool production creates new demand for raw steel—and the

combined new production creates additional jobs, income, and spending. This estimate of total impact,

of course, depends on the client-reported impacts: if these reported impacts are a complete measure of

the direct economic impact of a center, then the estimates of state macroeconomic impact are also

complete.

Clearly, however, there are limitations to this method. First, the client survey is by no means a measure

of all direct impacts; many are not captured in the survey. Second, the tools available for modeling

firm-level impacts and generating estimates of state-wide impacts are imperfect. Both measures are, at

best, only estimates. Still, these quantitative estimates of macroeconomic impact are very useful for

center evaluation.

At least three tools are available to centers for modeling and estimating the state macroeconomic

impact of client-reported impacts, ranging from the simplistic to the more valid and reliable:

Multipliers

Multipliers are single numbers, such as “2.5,” which estimate the total impact on the

state economy of a change in one of its sectors. If sales in one manufacturing industry

increase by $1 million and the industry’s sales multiplier is 2.5, then applying the

multiplier yields an estimated increase in total state sales of $2.5 million ($1 million x

2.5). Multipliers for national and regional economies can be obtained from a number

of sources, most notably the Bureau of Economic Analysis[12]. Multipliers are easy to

use, but simplistic. They do not estimate inter-industry effects, such as the effect of a

change in manufacturing output on non-manufacturing sectors. They also do not

estimate how impacts vary over time; that is, they are static measures of impact. For

these reasons, some center stakeholders may require a more comprehensive and

defensible modeling approach.
2 Chapter 1: About this Guide

Input-Output Models

Input-output (I/O) models expand upon the concept of multipliers. Not only can I/O

models compute the total effect on the state economy of a change to one or more

manufacturing sectors, but they can also estimate the effect on other, nonmanufacturing sectors and on consumers. To do this, they use a matrix of relationships

between industrial and consumer sectors. IMPLAN[7] and RIMS II[12] are two widely

used I/O models available at reasonable prices. While more defensible than multipliers,

I/O models are still static, that is, they measure the one-time effect of a one-time

economic change.

Dynamic Macroeconomic Models

Dynamic macroeconomic models combine I/O models, economic theory, and

econometrically-estimated, time-dependent equations to model year-to-year changes in

the economy. If client-reported sales in one industry increase from $250,000 to $1

million over a four-year period, dynamic macroeconomic models can model the yearto-year inter-industry and total state impacts of these client sales over the four-year

period and beyond. Dynamic models can be used to estimate multipliers and the

industry and consumer impacts computed in I/O models. REMI[9], DRI-McGraw[1],

and WEFA[15] are dynamic models available in various forms, ranging from

inexpensive simulations executed by the model’s developers to consulting services and

outright purchases of the models. Analyses based on dynamic models are considered

to be among the most comprehensive and defensible approaches available.

As one moves from simplistic multipliers to complex dynamic models, the richer and more precise the

analysis is and the more defensible the results are. Models of increasing sophistication can, however,

complicate the process of estimating impacts and interpreting and reporting results and can increase the

time spent by a center for the analysis.

The NIST Office of Applied Economics (OAE), in support of MEP, has developed a process for using

one of the dynamic macroeconomic models available, REMI, to estimate and report the state

macroeconomic impacts of the client impacts reported in the client survey. This step-by-step process is

designed to be time-efficient and affordable. Centers in Alabama, California, Illinois, Kentucky, New

York, Ohio, Pennsylvania, Tennessee, and Texas have worked with the NIST OAE to test and refine

this process. This Guide for Estimating and Reporting Macroeconomic Impacts of MEP Centers

describes the NIST-developed process in detail.

The Guide is designed to use client survey data derived from the Census Follow-up Survey[8],

employed by MEP from 1995 to 2000. A new survey instrument was started in 2000, replacing the

Census Survey. The Guide can be applied to data from the new survey, with appropriate modifications

to the methodology described in Chapter 3. For clarity, however, the Guide only describes using the

Census Survey data.

The NIST-developed process generates industry-impact data that, in general, could be used with

multipliers, input-output models, and dynamic macroeconomic models other than REMI. For clarity

and best practice, however, this guide is tailored to help centers specifically use REMI to estimate and

report state impacts in a defensible, efficient, and cost-effective manner.
Chapter 1: About this Guide 3

This guide provides

! a set of criteria for assessing whether your center has sufficient Census Follow-up Survey data

to estimate state macroeconomic impacts;

! a step-by-step process for compiling client-reported impacts in an automated fashion and

estimating state macroeconomic impacts;

! a sample two-page summary report that describes the Census Survey results and your

estimates of state macroeconomic impacts; and 

! supporting information supporting information to strengthen your presentation of the results in the summary report.

1.2 Topics Covered

This guide is organized to make estimation and reporting both efficient and accurate. The step-by-step

process will help you:

! Determine whether to proceed with estimation (Chapter 2). Determine whether to proceed with estimation (Chapter 2). Before investing time, money,

and other center resources on extensive analysis, a center should determine whether it has

sufficient data (1) to lessen the effect of errors in how clients report impacts; and (2) to

generate large impacts.

! Compute client-reported impacts and industry impacts required for modeling

macroeconomic impacts in REMI (Chapter 3). macroeconomic impacts in REMI (Chapter 3). The results of the Census Follow-up Survey

are returned to a center in a form that is not amenable to either computation of client impact

totals or use in macroeconomic modeling. An automated process developed at NIST quickly

and correctly computes project-impact totals and adjusts them so they can be used in REMI.

! Run an analysis using the REMI model (Chapter 4). Run an analysis using the REMI model (Chapter 4). This guide assumes that a center does

not own a macroeconomic model in-house and will use a third-party—such as an outside

contractor, an agency in its state, or the model’s developer—to perform the analysis. So that

you remain informed about each step, the guide explains how a REMI simulation is performed,

from input of the data to simulation and viewing of the results. Still, sufficient detail is given so

that centers that have REMI in-house can perform their own simulations.

! Report the analysis results in a two-page summary (Chapter 5). Report the analysis results in a two-page summary (Chapter 5). Once macroeconomicimpact estimation is complete, the results need to be reported in a way that is easy for center

personnel to understand and, most importantly, deliver and explain to their stakeholders. This

guide suggests a two-page summary report and provides supporting information for informed

and defensible reporting.

Use the checklist on the inside cover as a guide in completing these steps.
4 Chapter 1: About this Guide

1.3 Who Should Read This Guide

This guide is specifically written for center personnel who need to estimate and report the state

macroeconomic impacts of their client-reported impacts. These personnel include:

! Center directors Center directors who need to understand and describe to their stakeholders the state impacts of

their center;

! Evaluation staff Evaluation staff who need to estimate and report the state impacts of center projects; and

! Center strategic planners Center strategic planners who need to understand the state economy so that the center can

better address the needs of the manufacturing industry in the state.

Because the macroeconomic impact simulations can be performed by REMI (the company), by another

in-state agency that has the REMI model, or by you (as a REMI licensee), this guide explains estimation

and reporting in the context of each of these scenarios. If you are only overseeing one or more of the

steps, the guide provides enough detail so that you can both monitor analysis and have sufficient

information about it to support the presentation of your results. If, on the other hand, you are

performing most of the analysis yourself, the guide provides explicit mathematical formulas and other

modeling details so that you can perform the analysis correctly.
Chapter 2: Determine Whether to Estimate Impacts 5

2. Determine Whether to Estimate Impacts

Estimating the state macroeconomic impacts of client-reported sales and other impacts is a multi-step

process. Because dynamic macroeconomic models such as REMI estimate the effect of an industry on

all other parts of the economy, one must first convert the Census Follow-up Survey client-level impacts

to industry-level impacts. Once industry impacts are computed, REMI can estimate total state impacts.

Converting client-reported impacts to industry impacts and then to state impacts takes expertise, time,

and center resources. Before starting estimation, then, one should be relatively certain that the analysis

will be successful, that is, the results will be defensible and of sufficient magnitude to be worth

reporting. Two criteria are offered here as indicators of potential success:

! To be a defensible analysis, there should be a sufficient number of projects so that errors in

reporting by client firms tend to average out. The larger the number of projects in the analysis,

the smaller the effect of this reporting error will be.

! To yield macroeconomic impacts that suggest a center is at least fiscally neutral, there should

be sufficient client-reported impacts such that, after macroeconomic analysis, the increase in

state and local tax revenues is worth reporting.

To determine whether you have projects and impacts that satisfy these criteria, first request from MEP

the Survey Data Summary Page for your center, a sample of which is shown in Figure 1. Next,

compare the total number of projects, sales impacts, jobs impacts (created plus retained), and capital

investment impacts listed in this summary to their suggested-minimum counterparts in Table 1. If the

values from your Summary Page are all larger than the minimums listed in Table 1, reporting errors

will be reduced and analysis of your project data will likely generate tax revenue estimates worth

reporting. 

It is possible, however, that your data will not meet one or more of the criteria in Table 1. In this case,

since the criteria are an indicator of success rather than the basis of a rigid “go or no-go” decision, a

judgement call can be made, based on which of your data satisfy the criteria and by how much. Since

there are trade-offs between the impact types, an impact type that is less that the required minimum can

be offset by another impact type that exceeds its minimum requirement. Here are some suggestions: 

If your data have fewer than the suggested minimum number of projects or fewer than the

minimum sales, you should probably postpone analysis until you have additional surveyed

projects and impacts. 

If your data have fewer than the minimum jobs or capital investment listed but your sales

impacts are $15 million or more (per $1 million in annual center funding), macroeconomic

analysis should generate impacts of sufficient magnitude to be worth reporting. 
Sales impacts

Number of

projects with

completed

surveys

Capital

investment

impacts

Jobs impacts

(created plus

retained)

NIST/MEP Short Term Follow-up Survey Data

6 Chapter 2: Determine Whether to Estimate Impacts

Figure 1. Survey Data Summary Page Figure 1. Survey Data Summary Page

 Table 1. Criteria for Conducting Analysis

Minimum

 Item Value

 Number of projects with completed surveys 100

 Client-reported economic impacts

Sales Impacts $10 million

(per $1 million in center funding for period covered)

Jobs Impacts 100

(created plus retained, per $1 million in center funding for period covered)

Capital Investment Impacts $6 million

(per $1 million in annual center funding for period covered)
Chapter 3: Compile Project and Industry Impacts 7

3. Compile Project and Industry Impacts

Now that you have decided to proceed with the macroeconomic analysis, you need to prepare for it.

The checklist in Table 2 describes the process of getting ready to estimate macroeconomic impacts

using your Census Follow-up Survey data and the REMI model.

Table 2. Preparation Checklist

 Obtain access to the REMI model of your state. Either have REMI (the company) run the impact

simulation for you, have another agency in the your state run the simulation, or lease the REMI model.

 Provide NIST with the Census Follow-up Survey Data File for your state center(s). This is the file that

the Census Bureau provides to each center.

 Provide NIST with select REMI baseline-forecast data. Provide NIST with select REMI baseline-forecast data. If you are having REMI (the company) run your

impact simulation, they will provide this data to NIST. If you own the REMI model, you will need to send the data

to NIST, using the method described in Appendix B.

After you have completed these three steps, NIST is ready to use the NIST Data Processor to compile

project and industry impacts for the REMI model. The NIST Data Processor is a software program

written and maintained by NIST that generates two analysis files:

! The NIST Data Report. This is a text file that lists summary statistics for use in the two-page

summary report (see Section 5.2).

! The NIST REMI Input File. This file contains all the project impacts for your state,

appropriately adjusted by the NIST Data Processor and ready for input into REMI. The impacts

are in the form of REMI policy input variables. If you are having REMI (the company) or

another agency run your macroeconomic simulations, they will use this file (you will also

receive a copy of it). If you own the REMI model yourself, you will import the NIST REMI

Input File in the REMI model (see Section 4.2).

The following sections describe how the Census Follow-Up Survey Data are converted by the NIST

Data Processor into industry level impacts, and the form in which they are output.

3.1 The Census Follow-up Survey Data File

Each center maintains an activity data log (ADL) of the projects they have had with client firms. MEP

collects these data and submits them to Census, which then interviews each firm by telephone. The

combined ADL and survey responses—the Census Follow-up Survey data—are then sent to MEP.

The following sections describe how the NIST Data Processor takes data from a Census Follow-up

Survey data file and converts them to REMI inputs. These inputs are used in the macroeconomic

impact simulation of client-reported impacts.
8 Chapter 3: Compile Project and Industry Impacts

3.2 The NIST Data Processor

The Office of Applied Economics at NIST has developed a computer algorithm called the “NIST Data

Processor” which compiles data from the Census Follow-up Survey and prepares it for use in the REMI

model. Table 3 lists the steps performed by the NIST Data Processor. All of these steps are applied to

quantified project impacts except jobs impacts (created and retained), which do not require the

adjustment for inflation (in step 2).

Table 3. Steps the NIST Data Processor Performs

1. Identify and select impacts.

2. Adjust the impact for inflation and annualize the impact.

3. Perform any adjustments unique to the impact type.

4. For each year of the project impact, add the previous two years’ project impacts.

5. Sum all impacts by two-digit-SIC manufacturing industry.

6. Generate the NIST Data Report and the NIST REMI Input File.

The following sections explain these and other specific adjustments that the NIST Data Processor

performs. Each section addresses two questions:

! What steps need to be done?

! Why do these steps need to be done?

All operations are done on each individual project record in the Census Follow-up Survey Data File.

The NIST Data Processor then gathers and consolidates these adjusted impacts, at the two-digit level of

the Standard Industrial Classification (SIC) Manual.

3.3 Identify and Select Impacts

The NIST Data Processor first identifies projects that have incomplete surveys and drops them.

Incomplete surveys result when firms cannot be reached, and also when they cannot provide impact

quantities.

Next, the NIST Data Processor identifies which projects report impacts. A project may have any

combination of the following impacts: change in sales, change in jobs, capital investment, materials cost

savings, labor cost savings, and inventory reduction. Due to the set of questions in the Census Followup Survey data, it is possible for these impacts to exist without a corresponding quantity. For example,

a firm may respond that jobs increased but not specify by how much. Therefore, the NIST Data

Processor must also confirm that there is a quantity for every reported impact. Only impacts with a

reported, nonzero quantity are used in analysis.

The NIST Data Report lists how many projects it dropped due to an incomplete survey. It also tells you

how many projects had positive impacts that could not be included in the analysis because the firm did
Chapter 3: Compile Project and Industry Impacts 9

not indicate a quantity.

Why All Impacts are Not Used

The NIST Data Processor uses an impact preference hierarchy to select the impacts that will be

adjusted and analyzed. Three of the impacts are easy to deal with since they are generally not related in

an economic manner: materials cost savings, labor cost savings, and inventory reduction. For example,

if a firm reduces the amount of product in inventory, the same firm will not necessarily reduce labor or

materials cost. Therefore, the change in materials cost, labor cost, and inventory cost may all be used

together when they occur in the same project. Quantified materials cost savings, labor cost savings, and

inventory reduction are always used when they are present in a project.

The other three types of impacts—sales, jobs, and investment—are, however, related in an economic

manner. For example, a firm that has a sales increase will often need more employees. Or perhaps a

firm re-tools a production line (investment) or hires more employees with the intent of increasing sales.

Said another way, a firm is likely to see correlated changes in sales, jobs, and investment. In order to

avoid double- or triple-counting of client-reported impacts, the NIST Data Processor uses an impact

preference hierarchy to select which impact—sales, jobs, or investment—should be included in the

analysis and to what extent.

Select Preferred Impact

Figure 2 illustrates the impact preference hierarchy used by the NIST Data Processor to select the

preferred impact of a project that has any combination of sales, jobs, and investment impacts.
Sales impact?

Jobs impact

(created, retained

or both)?

Sales quantity?

Jobs quantity

(created, retained,

or both)?

Capital

investment

impact?

Capital

investment

quantity?

Sales Project

Use jobs impact

(created plus retained) No usable impacts Use capital investment

impact

Yes Yes

No

Yes Yes

Yes

Yes

No No

No No

No

Use sales impact

Jobs Project Investment Project Jobs Project Investment Project (discard the project)

10 Chapter 3: Compile Project and Industry Impacts

Figure 2. Impact Modeling Decision Tree: Sales, Jobs, and Figure 2. Impact Modeling Decision Tree: Sales, Jobs, and

Capital Investment Impacts

If a project reports a nonzero sales impact, then the project is classified as a “sales project” and only the

sales impact is used. A sales impact is likely to be the initial change that a typical firm sees as a result of

MEP assistance. The NIST Data Processor analyzes sales first and if a project has a usable sales

quantity, any reported change in jobs or investment in the same project is ignored. If a project reports

zero sales impact but nonzero jobs impact, then the project is classified as a “jobs project;” the jobs

impact is used and any investment impact is ignored. If the project reports zero sales and jobs impacts

but nonzero investment impact, then the project is classified as an “investment project” and investment

impact is used. Investment has an indirect, long-term effect on production; care has been taken in

modeling this change in REMI.

3.4 Perform Inflation and Annualizing Adjustments

Dollar-denominated project impacts are assumed to be reported in nominal dollars, that is, in values

based on the prices in the year the impacts occurred. Because of inflation—the change in prices from

year to year—nominal-dollar amounts from different years should not be compared or combined. To

correct for inflation, the NIST Data Processor uses a price index to convert nominal dollar amounts to

constant dollars. For each project, the NIST Data Processor identifies the year the project was

surveyed. Then it adjusts dollar-denominated reported impacts using that year’s price index, which is

obtained from the REMI baseline forecast.
time

Project end

date

Project survey

date

1995 1996 1997 1995

2 12 4

18-month 

reporting period

Chapter 3: Compile Project and Industry Impacts 11

The NIST Data Processor calculates the reporting period, the time between project end date and project

survey date, and assumes that project impacts occur uniformly over the period. It next calculates the

fraction of the reported impact occurring in each calendar year. In this annualizing adjustment, each

year’s share of the impact is determined by the number of days the impact lasted in that year. Figure 3

illustrates how the NIST Data Processor performs the annualizing adjustment on a hypothetical impact

that has an 18-month reporting period. 

Figure 3. Annualizing Adjustment of a Client-Reported Impact Figure 3. Annualizing Adjustment of a Client-Reported Impact

The first 2 months of the impact occur in 1995, the next 12 months occur in 1996, and the last 4

months occur in 1997. Therefore, 11% of the reported impact is assumed to occur in 1995, 67% in

1996, and 22% in 1997. The NIST Data Processor performs this annualizing adjustment on every

impact.

There are several benefits to this approach. First, impacts are precisely allocated to the year in which

they are assumed to occur. Second, impacts that last more than one year do not cause overestimation of

macroeconomic impacts. Third, the macroeconomic analysis is not restricted to 12 months of data. The

NIST Data Processor takes advantage of REMI’s ability to model impacts over time by allocating

impacts to the year(s) in which they occurred.

3.5 Adjust Labor, Material, and Inventory Impacts

All projects with usable labor cost or material cost values have a positive or negative dollar amount of

cost change in one or both categories. Usually these values are negative, indicating that costs decreased.

All projects with a usable inventory change have a positive or negative dollar amount of change in the

total value of product in inventory. Generally, this value is negative, indicating that inventory decreased.

The material cost change, the labor cost change, and one-tenth of the change in inventory value are

combined by the NIST Data Processor into one variable, the change in production cost. Client-reported

inventory changes are modeled as changes in the annual cost of holding inventory, which is assumed to

be 10% of the reported inventory change. These holding costs include charges for financing, insurance,

and warehousing.
time

Original impact

Project survey

date

1995 1996 1997

18-month 

reporting period 2 years

1998 1999

Project end

date

First year of persistance

Second year of persistance

12 Chapter 3: Compile Project and Industry Impacts

The NIST Data Processor adjusts the change in production cost for inflation and then annualizes it. 

It then takes each year’s annualized amount and adds the annualized amounts from the previous two

years. This is done to let the original client-reported impact persist for two years. Impacts from 1993

and 1994 are assumed to be zero.

Figure 4 illustrates the two-year impact persistence model, using the hypothetical impact presented in

Figure 3. Each inflation-adjusted, annualized amount from the overall reported impact (the 2-month,

12-month, and 4-month portions) is allowed to persist for two years.

Figure 4. Persistence of a Client-Reported Impact Figure 4. Persistence of a Client-Reported Impact

To avoid overlapping, the first year of persistence is drawn stacked on top of the initial impact.

Likewise, the second year of persistence is shown another year forward on the time line, and is drawn

above the first year of persistence.

Figure 5 shows the resulting impact in each year, after the impact is allowed to persist for two years.
Impact Persistance

0

2

4

6

8

10

12

14

16

18

20

1995 1996 1997 1998 1999 2000 2001

Years

Impact Months

impact

Chapter 3: Compile Project and Industry Impacts 13

Figure 5. Impact Persistence Graph Figure 5. Impact Persistence Graph

Figure 5 shows the impact months—proportional to the impact amount—for the years 1995 through

2001. In this case, the NIST Data Processor would generate production cost impacts of zero in the

years 2000 and 2001. Therefore, all seven years (1995-2001) are accounted for.

Now, all impacts are summed by two-digit-SIC manufacturing industry. The NIST Data Processor

outputs seven cost impacts (1995 to 2001) to the NIST REMI Input File, in 1992 base year dollars and

by two-digit-SIC manufacturing industry.

3.6 Adjust Sales Impacts

All projects which have been classified as “sales projects” (Figure 2) have a positive or negative dollar

amount by which sales increased or decreased. The NIST Data Processor has already adjusted the sales

numbers for inflation, and has performed the annualizing adjustment.

Some fraction of the sales reported in the Census Follow-up Survey are new sales and represent an

increase in total state output. But, the remaining fraction displace the sales of an in-state firm not

surveyed in the Census Follow-up Survey, a problem sometimes referred to as “beggar thy neighbor”

(see Glossary in Appendix A). The latter fraction of sales do not represent a net increase in total state

output.

Therefore, the NIST Data Processor makes a special adjustment to sales. It estimates the amount of

sales which represent an increase in total state output—sales which do not displace other in-state firms. 

The NIST Data Processor uses location-of-sales data from the Census Follow-up Survey and divides

the sales impact (after the inflation and annualizing adjustments) into sales made in-state and sales

made out-of-state. Out-of-state sales include both sales made in other U.S. states and sales made in
Sales made out-of-state

Sales made in-state

Sales made in-state that

displace in-state production,

thereby not increasing total state

production

Sales made in-state that

displace imports, thereby

increasing total state production

Total clientreported salesFraction of sales

made in-state

Increase in total state production

A

B

Increase in state production = A + B

sales impact S

instate

S

instate imports

demand 1995 95 95

95

95

100

100 100

= ×  −



 



 +× ×





 





14 Chapter 3: Compile Project and Industry Impacts

other countries (exports). If a firm did not report the location of sales, the NIST Data Processor

assumes all sales occurred in-state. Sales made in-state are assumed to be satisfying local state demand,

which itself is assumed to be fixed and unaffected by the MEP program. Local state demand is satisfied

by either locally produced products sold in-state, or imports sold in-state. This breakdown is depicted

in Figure 6.

Figure 6. Location of Sales Figure 6. Location of Sales

All sales made out-of-state are not assumed to displace the existing sales of other in-state firms, and are

thus counted in full (area A). The NIST Data Processor only counts in-state sales that displace imports

(area B); sales made in-state that displaced local producers are not counted. The NIST Data Processor

estimates this import displacement by multiplying sales made in-state by the fraction of imports sold instate over local state demand for the same SIC manufacturing industry, and for the same year, as the

sales being adjusted. The data needed from the REMI baseline forecast are imports sold in-state and

local state demand, by SIC manufacturing industry and year.

The net change in-state production is the sum of the sales made out of state (area A) and the countable

sales made in-state (area B). As an example, equation (1) illustrates the adjustment made by the NIST

Data Processor for sales made in 1995.

(1)

The variable S95 represents the total sales impact for 1995 and is constructed from the annualizing

adjustment of the Census Follow-up Survey. The variable instate is the percent of the sales impact

made in-state and is obtained from the location-of-sales data in the Census Follow-up Survey. The

imports and demand variables are obtained from the REMI baseline forecast. The NIST Data Processor
jobs impact J output

employee

imports

demand 1995 95

95

95

95

95

+ + =× ×

J%

95

Chapter 3: Compile Project and Industry Impacts 15

generates sales impacts in the same way for the years 1996 to 2001. If the sales impact in a particular

year is zero, the NIST Data Processor still reports that year, but with a zero impact. Therefore, all seven

years (1995-2001) are accounted for.

The resulting sales impact for each year is then adjusted by the NIST Data Processor per Table 3. The

NIST Data Processor adds the sales impacts from the previous two years to the current year’s sales

impact. This adjustment reflects the assumption that reported sales persist for an additional two years.

Then all impacts are summed by two-digit SIC manufacturing industry. The NIST Data Processor

outputs seven sales impacts (1995 to 2001) to the NIST REMI Input File, in base year dollars and by

two-digit-SIC manufacturing industry.

3.7 Adjust Jobs (Created and Retained) Impacts

All projects which have been classified by the NIST Data Processor as “jobs projects” (Figure 2) have

usable quantities for one or both of the following: the net number of jobs created or lost (positive or

negative), and the number of jobs retained which would otherwise have been eliminated (positive).

Jobs created (or lost) and jobs retained are added together by the NIST Data Processor. The sum may

be a negative number if the number of jobs lost is greater than the number of jobs retained. This

produces the net number of jobs created or retained in each project. The NIST Data Processor does not

net out increases and decreases in jobs at any level other than the individual project level. This is

because firms that report jobs increases are assumed to be undergoing a fundamentally different change

than firms that report decreases.

At this point, the NIST Data Processor has already performed the annualizing adjustment, and there is

no need to adjust jobs for inflation. Further adjustments that the NIST Data Processor makes to jobs

created or retained depend upon whether the net jobs amount is positive or negative.

Net Increase in Jobs

An increase in jobs created or retained is assumed to directly increase client-firm production above the

level it would otherwise have been without assistance. The NIST Data Processor converts the increase

in jobs to an increase in output by multiplying the increase in jobs by the ratio of local state output over

local state employment for the same industry and year as the jobs being adjusted. The REMI baselineforecast data needed for this calculation are state employment and output, by two-digit SIC code and by

year.

Some fraction of the resulting output change will displace the sales of other in-state firms. Therefore, as

is done for sales, the output resulting from an increase in jobs is adjusted by the NIST Data Processor.

As an example, equation (2) illustrates the adjustment made by the NIST Data Processor for jobs

created or retained in 1995.

(2)

In equation 2, represents the total jobs added in 1995 (obtained from the annualizing adjustment).
increase in RPC Jobs impact

demand 1995

1995

95

=

+

percent employment decrease ( ) J

employment =− ×

−

100 95

95

productivity increase percent employment decrease

percent employment decrease 1995

1995

1995

100

100 = ×−

J&

95

16 Chapter 3: Compile Project and Industry Impacts

The output, employee, imports, and demand data are obtained from the REMI baseline forecast. The

resulting jobs impact for 1995 then continues to be adjusted by the NIST Data Processor per Table 3.

The NIST Data Processor adds the jobs impacts from the previous two years to the current year,

reflecting the assumption that reported jobs persist for an additional two years. Then all impacts are

summed by two-digit-SIC manufacturing industry.

Now the NIST Data Processor makes a special adjustment to the jobs impact so that it may be input

into the REMI model. A jobs impact is converted into a percent change in the two-digit-SIC industry’s

regional purchase coefficient (RPC), defined as the fraction of local state demand satisfied by goods

produced in-state. When an increase in jobs causes more in-state production, the RPC increases, as

shown in equation (3).

(3)

Equation (3) is performed for each year in the period 1995 to 2001, using the jobs impacts from

equation (2) and demand numbers from the REMI baseline forecast. The NIST Data Processor outputs

seven jobs impacts (1995 to 2001) to the NIST REMI Input File as percent productivity increases by

two-digit-SIC manufacturing industry.

Net Decrease in Jobs

A decrease in jobs is modeled as a productivity increase—the firm produces the same output with

fewer workers. The NIST Data Processor converts the decrease in jobs to a percent employment

decrease, using the total industry employment in the state (obtained from REMI baseline-forecast data).

The NIST Data Processor then translates the percent employment decrease into a percent productivity

increase for input into REMI.

As an example, equations (4) and (5) illustrate the adjustment made by the NIST Data Processor for a

net decrease in jobs created or retained in 1995.

(4)

(5)

In equation 4, represents the decrease in jobs in 1995 (obtained from the annualizing adjustment).

The employment data are obtained from the REMI baseline forecast. The percent employment decrease

generated by equation (4) is then used in both the numerator and denominator of equation (5). The

resulting productivity increase continues to be adjusted by the NIST Data Processor per Table 3. The

NIST Data Processor performs the two-year persistence adjustment, adding the jobs impacts from the

previous two years to the current year. All impacts are summed by two-digit-SIC manufacturing

industry. Then the NIST Data Processor outputs the jobs impacts to the NIST REMI Input File as
investment impact I capital life

output

capital used

imports

demand 1995 95

95

95

1

=× × × $1

I95

increase in RPC investment impact

demand 1995

1995

95

=

Chapter 3: Compile Project and Industry Impacts 17

percent productivity increases, by two-digit-SIC manufacturing industry and year.

3.8 Adjust Investment Impacts

All projects which have been classified as “investment projects” (Figure 2) have a positive dollar

amount of new capital investment. The NIST Data Processor has already adjusted the capital

investment numbers for inflation and performed the annualizing adjustment.

An increase in capital investment is assumed to increase output. The NIST Data Processor multiplies

the adjusted capital investment by the yearly depreciation rate for capital in that industry. This yields a

conservative estimate of the new capital used each year in production. The product is then multiplied

by the output per capital used. The result is an estimate of the output associated with the capital

investment impact. The NIST Data Processor assumes the output associated with this capital

investment change is all sold in-state. Some fraction of the resulting output change will displace other

in-state firms not in the Census Follow-up Survey Data. Therefore, as is done for sales, the output

resulting from an increase in capital investment is adjusted by the NIST Data Processor. As an

example, equation (6) illustrates the adjustment to capital investment in 1995.

(6)

In equation (6), represents the capital investment in 1995 (after the inflation and annualizing

adjustments). The imports and demand variables are obtained from REMI Baseline-Forecast data. The

NIST Data Processor acquires industry capital life data from the Bureau of Labor Statistics[13] (see

Appendix A) and industry “output per capital used” data from REMI (the company). Investment

impacts for the years 1996 to 2001 are done in a similar manner.

Returning to the process outlined in Table 3, the NIST Data Processor adds the investment impacts

from the previous two years to the current year’s investment impact. This adjustment reflects the

modeling assumption that reported investment persists for an additional two years.

The investment impact is input in REMI as an increase in the regional purchasing coefficient (RPC).

The NIST Data Processor converts each year’s investment impact into a percent increase in the industry

RPC. As an example, equation (7) illustrates the adjustment made by the NIST Data Processor for the

1995 capital investment impact.

(7)

Equation (7) is performed for each year 1995 to 2001, using the investment impact calculated in

equation (6) and the demand figures from the REMI baseline forecast for the appropriate year. If the

investment impact in a particular year is zero, the NIST Data Processor still reports that year, but with a

zero impact. Therefore, all seven years (1995-2001) are accounted for.
18 Chapter 3: Compile Project and Industry Impacts

All of the RPC increases are then summed by two-digit-SIC manufacturing industry. The NIST Data

Processor outputs seven capital investment impacts (1995 to 2001) to the NIST REMI Input File, as

percent increases in the RPC, by two-digit-SIC manufacturing industry and year.

3.9 Conclusions

When the NIST Data Processor finishes adjusting the data, it outputs two files. The first, the NIST Data

Report, lists the summary statistics needed for the text and Table 1 of the two-page summary report

(see Chapter 5). The second, the NIST REMI Input File, contains the industry impacts ready for input

into REMI. If you own the REMI model, you will input the NIST REMI Input File yourself. If you are

having REMI (the company) or another agency perform the simulations for you, NIST will send this

file directly to the them.
Output

Population &

Labor Supply

Labor &

Capital

Demand

Market

Shares

Wages,

Prices, &

Profits

Chapter 4: Estimate State Macroeconomic Impacts 19

 For a comprehensive description of the REMI model see Treyz[11]. 1

4. Estimate State Macroeconomic Impacts

Once the client-reported impacts have been converted to industry-level impacts using the NIST Data

Processor, the next step is to estimate state macroeconomic impacts. This chapter provides guidelines

for using the NIST REMI Input File and the REMI model to estimate the impact on the entire state

economy. It first describes the REMI model itself, so that you understand the scope and purpose of the

model. It then describes how to perform an impact simulation in REMI.

4.1 The REMI Model1

The REMI model was developed for regional economic planners and analysts who need to estimate the

impacts of changes to their economy. In addition to several recent NIST studies[2,3,4], the model has

been used extensively for economic development, forecasting and planning, transportation projects,

environmental-policy analysis, energy and natural resource analysis, and taxation, budget, welfare, and

federal economic policies. In general, the model computes the total effect over time on a regional

economy, such as a state, of a change to a component of the economy. In the case of your center’s

impacts on the state economy, if your center has client-reported impacts over, say, the four-year 1997-

2000 period, REMI can model the multi-year client-reported impact in those years and report the state

impact as a time series over, say, the 1997-2005 period. 

The model is based on economic theory, input-output (I/O) accounting, and econometrically estimated,

time-dependent relationships between components of the economy. Figure 7 illustrates the structure of

the variables and relationships in the REMI model.

Figure 7. Economic Variables and Causal

Relationships in the REMI Model

Each block in Figure 7 contains economic variables, each of which serve to model changes and
Year

State

output

1999 2001 2003 2000 2001

Baseline

forecast

Alternative

forcast

Impact

20 Chapter 4: Estimate State Macroeconomic Impacts

measure changes in components of the economy. For example, the Output block contains measures of

industry output, by two-digit SIC code and the type of demand (consumption, investment, government,

and net exports), along with the input-output structure of industrial production. When modeling an

increase in sales or output in, say, SIC 35, you can both adjust the level of SIC 35 output and see how it

responds over time. Economic equations dictate the relationships between variables in different blocks;

for example, the level of industry output determines the labor needs for that industry, as illustrated in

Figure 7 by the arrow pointing from Output to Labor & Capital Demand.

The model computes state impacts as the difference between a baseline forecast baseline forecast of the state economy

and an alternative forecast alternative forecast that includes the modeled changes, i.e., the industry changes that result

from the client-reported impacts. Since both forecasts are time series of economic data, the state

macroeconomic impacts—the difference between the two forecasts—is also a time series, as illustrated

in Figure 8. Viewing data on the changes to output, employment, and personal income allows one to

characterize the fundamentally dynamic response of the entire state economy to changes in individual

industries.

Figure 8. An Example of How to Measure Figure 8. An Example of How to Measure

Impacts in REMI

4.2 Run a REMI Simulation of State Impacts

Since the NIST Data Processor generates an impact file that REMI imports directly, the process of

running a REMI simulation of state impacts is greatly simplified. The following sections explain this

process.

There are five tab panels in the REMI software, representing five components of a REMI simulation:

! Forecast Selection - setting the baseline forecast parameters

! Policy Variable Selection - selecting the parts of the economy on which to model impacts.

! Policy Variable Values - setting the values of the impact-related adjustments

! Run Options - setting run parameters and running the simulation

! Results - viewing the results of the simulation
Chapter 4: Estimate State Macroeconomic Impacts 21

Given that the NIST REMI Input File is used to import the impacts directly into the REMI model, you

will not need to use the Policy Variable Selection panel, but rather can proceed directly to importing the

Input file in the Policy Variable Value tab panel.

Forecast Selection

REMI presents macroeconomic impacts as the difference between the baseline forecast and the

alternative forecast. Therefore, the first step is to select a baseline forecast of the state economy, i.e., a

forecast where there are no changes caused by the client-reported impacts in the Census Follow-up

Survey. This forecast is has already been run by REMI (the company). The Forecast Selection tab panel

is shown below in Figure 9.

Figure 9. Forecast Selection tab

Forecast simulations can only be carried out in years that occur after the last year of REMI historical

data. In the REMI Forecast Selection shown in Figure 9, the last year of historical data is 1996. Thus

the earliest forecast year is 1997, and the forecast is being run from 1997 to 2003 inclusive. The NIST

Data Processor shifts all impacts forward to the earliest forecast year. For example, impacts that

occurred in 1995 are entered in REMI as 1997 impacts, and the macroeconomic impacts reported by

REMI for the modeling years 1997 to 2003 are estimates of the effects in reporting years 1995-2001.

Policy Variable Values

The next step is to input the economic changes caused by the client-reported impacts; these data are

contained in the NIST REMI Input File. To import the NIST REMI Input File, select the Policy

Variable Values tab panel (skip the Policy Variable Selection tab panel). From the menu, choose File,

then Import. Find the NIST REMI Input File on your computer (the file yrstate.txt in Figure 10) and

then click on “Open.”
22 Chapter 4: Estimate State Macroeconomic Impacts

Figure 10. Importing the NIST REMI

Input File

The adjusted impacts for your state are now in REMI; they can be viewed in the right-hand-side

spreadsheet of input variables.

Run Options

The next step is to run the alternative forecast in the Run Options tab panel (Figure 11). If you own the

REMI model, run the alternative forecast out to the year 2003 or later. If you are having REMI (the

company) perform the simulations for you, this panel will be disabled.

Figure 11. Run Options Tab Panel

Results

Figure 12 shows the Results tab panel. Because the NIST Data Processor shifted the years of the clientreported impacts forward two years so that they would occur during the REMI forecast period, the

results must be shifted back two years (this is done automatically in the NIST ExcelTM spreadsheet

described below). For example, all the impacts that REMI calculated for 1997 should be thought of as

occurring in 1995. 
Chapter 4: Estimate State Macroeconomic Impacts 23

Figure 12. Results Tab Panel

REMI measures impacts as the difference between the baseline forecast and the alternative forecast.

Make sure that “Differences” is chosen in the Type box. A select set of these differences is used in the

two–page summary report described in the next chapter.
24 Chapter 4: Estimate State Macroeconomic Impacts
Chapter 5: Report Results 25

5. Report Results

5.1 Determine the Best Format for Reporting Results

Once your macroeconomic analysis is completed, you need to report the results. The impact measures

you ultimately choose to report depend largely on the needs of your audience, be they center directors,

marketing staff, or legislative liaisons. The REMI model supports your need for flexibility by offering

data on hundreds of variables, including industry output, exports, imports, employment, wages, prices,

production costs, and more. Still, your presentation should be concise. Therefore, this section

describes a two-page summary report, shown in Figures 10 and 11, that will serve as an excellent

template and starting point for your own report. Depending on your audience, you can optionally add

other results and information to support your presentation.

Table 2 in this summary report (see Figure 11) presents four common macroeconomic-impact

measures: one measure of economic growth (the change in state value-added output), two measures

of employment (the change in employment and personal income), and, optionally, measures of fiscal

impact (the changes in local, state, and federal tax revenues). These four give a concise picture of state

macroeconomic impacts.

The two-page summary report is designed to

! Summarize the Census Follow-up Survey process. Summarize the Census Follow-up Survey process. It identifies the centers for which

macroeconomic impacts are being measured, describes the survey process, and lists the number

of projects that were surveyed.

! Tabulate the client-reported impacts. Tabulate the client-reported impacts. It lists in table form the aggregate client-reported

impacts, by type of impact (Table 1 in the report).

! Describe how state macroeconomic impacts are estimated. Describe how state macroeconomic impacts are estimated. It describes the REMI model

and some important modeling assumptions.

! Summarize the macroeconomic impacts. Summarize the macroeconomic impacts. It lists the estimated macroeconomic impacts over

a four-year forecast period, including the total impact (Table 2 in the report).

! Optionally compare the estimated change in-state tax revenues with center funding. Optionally compare the estimated change in-state tax revenues with center funding. If

reporting the change in tax revenues, the report tabulates the tax revenue changes that result

from the client-reported impacts (Table 2 in the report).
Summary of work

Summary of

project impacts

Macroeconomic

model used and

modeling

assumptions

Macroeconomicimpact results

26 Chapter 5: Report Results

Figure 13. Page One of Two-Page Summary Report
Total impact

Caveats

Optional: tax

revenues

Four-year forecast period

State

macroeconomic

impact results

Chapter 5: Report Results 27

Figure 14. Page Two of the Two-Page Summary Report
28 Chapter 5: Report Results

Figure 15. Appendix for the Two-Page Summary Report
Chapter 5: Report Results 29

5.2 Make the Two-Page Summary Report

The data required for the report come from three sources: the NIST Data Report, the REMI alternative

forecast, and the NIST ExcelTM spreadsheet. The NIST Data Report, which you will receive from

NIST, provides you with eight statistics for the text of the report:

! the total number of projects

! the number of projects with incomplete surveys

! the earliest and latest project end dates

! the earliest and latest project survey dates

! the average length of reporting period (the time from the project end to survey date)

! the number of projects with a reporting period longer than one year

! the number of projects in which the reporting period cannot be calculated

! the number of projects that reported an impact which cannot be quantified, and for

which there is no substitute impact lower in the preference hierarchy

Beginning with the third set of statistics (earliest and latest project end dates), only completed projects

are used to compute these data. The NIST Data Report also provides you the data for use in Table 1 of

the report:

! sales changes, across all industries, all years

! jobs created or retained, across all industries and all years

! capital investment, across all industries and all years

! materials and labor cost (summed), across all industries and all years

! value of inventory reduction, across all industries and all years.

The NIST Data Report produces a snapshot of the client impacts in the Census Follow-up Survey Data

File. Table 1 helps the reader of the two-page summary understand the project impacts that firms are

reporting. (In contrast, the NIST REMI Input File contains data which has been adjusted for input into

the REMI model; see Chapter 3.)

Data in Table 2

The simulation results and the NIST ExcelTM spreadsheet together provide you with the state

macroeconomic impact data needed for Table 2 of the summary report. If you had REMI (the

company) perform the impact simulation or if you ran the simulation yourself, then you will need to

locate and extract the impact data listed in the Results tab panel in the REMI model. If a third-party

agency ran the simulation, then they will need to perform the extraction.

NIST has a standard method of extracting all the relevant information, copying it into an ExcelTM

spreadsheet created by NIST, and pasting information from ExcelTM to the summary report. The

mechanics of this process are simple and repeated for each type of results data needed: go to the

appropriate category in the REMI Results tab panel, copy the line of data, and paste into the

appropriate line in the NIST ExcelTM spreadsheet, shown in Figures 16 and 17.
30 Chapter 5: Report Results

Figure 16. Top Half of the NIST ExcelTM Spreadsheet

Enter or paste values into all the named rows: taxes, price index, GRP, employment, and personal

income. First enter information on your state and local tax rate, found in Appendix A. (Federal taxes

are estimated to be 13% of personal income and have been already entered into the template.) Then,

paste data from REMI into the remaining areas of the spreadsheet. Table 4 shows you where in REMI

you will find the data. The spreadsheet only uses the price index for 2000 in its calculations, so you

have a choice of pasting the whole price index line or just entering the 2000 price index.
Chapter 5: Report Results 31

Table 4. Location of Impact Results in the REMI Model

Macroeconomic Impact Location in REMI Results Menu Macroeconomic Impact Location in REMI Results Menu

Price Index

Gross State Product

Total Employment

Personal Income

Figure 17 shows the lower half of the NIST ExcelTM spreadsheet before your data has been input (as

indicated by the zeroes). Even though the table looks empty, the cells contain formulas.
32 Chapter 5: Report Results

Figure 17. Lower Half of NIST ExcelTM Spreadsheet

When you have completed pasting the data, the spreadsheet will calculate the data for the Table 2 of

the summary report. Copy the data in Figure 17 to your report.

5.3 Explain the Two-Page Summary Report

Center personnel may need to verbally convey the results in the two-page summary or present the

results in other publications. This section provides guidance on how to discuss the state impact results,

including some caveats and supporting background information. Following the format in the Guide for

Communicating Impact[10], this discussion material is presented according to:

! what you will be able to say;

! how the economic impact is calculated;

! how to explain the impact; and

! what to watch out for.
Chapter 5: Report Results 33

Gross State Product

What you will be “As a direct result of center services, gross state product increased by an estimated

able to say: $117.2 million over the illion over the 1996-1999 period.”

How it is calculated: Gross state product, also known as state value-added output, is the difference between the

sales value of all in-state firms’ output minus the cost of the inputs these firms purchased

from other firms. The REMI model estimates how in-state firms’ output and costs are

affected by center projects. The change in the difference between these two variables is the

change in gross state product.

Here’s an example of how REMI models the state impact of center projects, in particular a

change in sales. New client sales generate new production in the client firm’s industry and

in industries that supply inputs to it. This new production creates new jobs and income, the

latter of which is then spent on goods and services, creating more sales and production. 

The change in gross state product is the new level of in-state firm output minus the new

level of costs of inputs from other firms.

How to explain it: Explain that gross state product, or value-added output, is the best measure of the real

value produced in the state by labor and capital, since it nets out the value of the materials

embodied in product. Contrast value-added output with output measured by sales, that is,

the dollar value of the goods when they are sold. Here’s an example: consider an in-state

firm that imports 1000 televisions at a cost of $100 per television and 1000 stereos at a cost

of $100 per stereo, packages one of each together, and then exports 1000 sets of

“multimedia centers” at a price of $220 each. What is the change in value-added output in

the state? It is the value added in-state, that is, ($220 - $100 - $100) = $20 per multimedia

center, or $20,000. Output as measured by sales, on the other hand, increases by $220 x

1000 = $220,000. 

Explain that this value-added output approximately equals what is paid to workers and to

owners of capital, an example of the latter being owners of in-state firms. Gross state

product is retained within the state in the form of wages, salaries, and firm profits (which

are ultimately income). In the above example, the payments to labor and capital, both

factors of production, is $20,000.

What to watch out Value-added output is often mistaken for output as measured by sales. Make clear to your

for: audience that gross state product is much lower in value than sales-based output but is a

better measure of what was both produced within the state and returned to its residents as

income.
34 Chapter 5: Report Results

State Employment

What you will be “As a direct result of center services, state employment increased by an average of 250

able to say: workers over a four-year period.”

or

“The $117 million in new value-added production created an average of illion in new value-added production created an average of 250 new jobs

over the four-year period.”

How it is calculated: When the level and distribution of state production changes, either due to new levels of

production or due to shifts in production between sectors, REMI estimates the level and

distribution of labor needed to produce this output. For example, if output in SIC 35

increases by one percent, then employment in SIC 35 will increase by a similar amount.

Similarly, percent shifts in output between sectors will cause similar percent shifts in

employment. The distribution of employment can also be affected by changes in sectoral

wages—workers may move to a higher paying job, but the strongest factor affecting

employment in a sector is the level of production in the sector.

How to explain it: Explain that jobs are created whenever industry production increases, whether it be an

expansion of output or a switching of production from one sector to another. 

What to watch out The employment figures listed in 1996-1999 columns in Table 2 of the report cannot be

for: added up, since they are not new jobs created each year, but rather are simply the difference

between the REMI baseline and alternative forecasts. 

Not all of the new jobs are filled by in-state workers; some are filled by economic migrants,

or workers who commute daily from out-of-state. This means that while it is true that “250

jobs were created,” it is not always true that “250 state residents got new jobs.” Of the instate workers who fill the new jobs, some are permanent immigrants who move to the state

for the job, thereby increasing state population.
Chapter 5: Report Results 35

Personal Income

What you will be “As a direct result of center services, state personal income increased by an estimated

able to say: $94 million.”

How it is calculated: When output increases, new jobs are created and these new jobs generate new personal

income. At the same time, shifts of production from one industry to another cause the

distribution of employment to change, changing aggregate personal income. To compute the

change in personal income, REMI first computes the change in output, computes then

change in employment that results from the output change, and then computes the change in

personal income that results from the employment change.

How to explain it: Personal income will likely increase for at least two reasons. First, the expansion of state

output will expand the employment base—more production requires more workers. 

Second, personal income will rise if workers move to higher paying jobs. Emphasize that

even if aggregate employment does not change, the number of workers who switch to

higher-wage jobs can still be significant.

What to watch out Be sure to not confuse personal income with returns to factors; they are slightly different

for: terms. Personal income is the sum of specific national income accounting concepts,

including wages, proceeds from the sale of assets, and transfer payments, but does not

include social security payments. Returns to factors, on the other hand, is the sum of

payments made to the factors of production, labor and capital. Payments to factors of

production at the state level are, by definition, equal to state value added output.

Optional: State and Local (and Federal) Tax Revenues

What you will be “As a direct result of center services, state and local tax revenues increased by an

able to say: estimated $9.4 million.”

How it is calculated: REMI does not directly compute local, state, and federal tax revenues; these must be

computed after simulation. The tax revenues shown in Table 2 of Figure 11 are computed

as a fraction of the estimated personal income increases, using values reported by the

Federation of Tax Administrators[5,6] and listed in Appendix A.

These taxes can also be computed as a fraction of gross state product, as is done in the A

Guide for Communicating Impact[10]. Both methods, along with other methods your own

state may estimate revenues, should produce similar tax revenue estimates.

How to explain it: Explain that the client-reported impacts increase sales, income, and other concepts against

which your federal, state, and local governments levy taxes. 

What to watch out Before reporting changes in state tax revenues, decide whether reporting these tax revenues

for: will raise false expectations of fiscal neutrality fiscal neutrality that may not be realized every year. If you

define your metric for evaluating center performance as fiscal neutrality, then you will have

to explain benefit if you don’t achieve this neutrality. Mention that there are other center

benefits to the state besides tax revenues, such as the observed (surveyed) increases in state

output, employment, and income, and the unobserved increases in firm productivity and

competitiveness. 
36 Chapter 5: Report Results

5.4 Further Explain the Results

The two-page summary report is designed to be concise but sufficient. Sometimes you may, however,

have a need—or opportunity—to present more economic data, whether to support your

macroeconomic analysis or to further describe the client impacts. Results from the Census Follow-up

Survey and the REMI alternative forecast provide numerous additional data which can increase the

clarity and effectiveness of your presentation.

Additional Uses of the REMI Simulation

There are several natural extensions to the macroeconomic impacts reported in Table 2 of the report. 

Each of the following can be retrieved from the fifth, “Results” tab panel in REMI (Figure 12).

! Impact on gross state product, by two-digit-SIC manufacturing industry. Impact on gross state product, by two-digit-SIC manufacturing industry. This is a further

breakdown of the change in durables and non-durables manufacturing gross state product listed

in Table 2 of the summary report. These data are retrieved from the Results tab panel in REMI.

When presenting these impacts, mention that this is the sum of direct and indirect impacts of

the client-reported impacts, and that it is not the same as output as measured by sales; it is the

value added in each industry.

! Impact on state employment, by two-digit SIC industry. Impact on state employment, by two-digit SIC industry. This is a further breakdown of the

change in employment shown in Table 2 of the report. The data are retrieved from the REMI

“Results” panel. When presenting, mention that many of the employment increases have

occurred in high-wage manufacturing sectors.

In addition to the simulation results, use the following factual information about REMI to support your

analysis and results:

! The REMI model is used by over 40% of the state governments in the U.S. for analysis of their

tax, energy, and economic development policies.

! REMI is one of the most highly regarded macroeconomic models. The model and analyses

based on it have been critiqued in numerous refereed journal articles.

! The model is open-source, that is, the internal programming code is publicly documented.

! The model was developed by Dr. George I. Treyz, President of Regional Economic Modeling,

Incorporated and former Professor of Economics at the University of Massachusetts at

Amherst.

! A REMI model can be made of any set of county-level regions of the United States, such as a

four-county statistical metropolitan area (SMA) or of a multi-state region of the United States. 

Additional Uses of The Census Follow-Up Survey

The MEP publication A Guide for Communicating Impact[10] suggests numerous ways to report the
Chapter 5: Report Results 37

direct impacts of the Census Survey data. Some of the data generated by the NIST Data Processor are

good supporters of the client impacts reported in Table 1 of the summary report.

! Sales impacts by manufacturing industry. Sales impacts by manufacturing industry. If your clients have reported large sales increases

and these sales have occurred in manufacturing sectors important to the state economy, you can

add a table or graph displaying the client-reported impacts by two-digit-SIC manufacturing

industry. Use the data from the NIST Data Processor that lists sales impacts by two-digit SIC

code and by destination of sales.

! Jobs impacts by manufacturing industry. Jobs impacts by manufacturing industry. Similarly, an additional table or graph can be added

listing the client-reported jobs impacts (both created and retained) by industry. Use the table

from the NIST automated process that lists employment impacts by two-digit SIC code.

The following information about or contained in the Census Survey data can be used to support your

general presentation.

! The Census Follow-up Survey is an independent survey of MEP clients, in which these clients

attest how MEP affected their business performance as a direct result of center services.

! On average, only 70% of a center’s clients complete the survey. The impacts reported by

clients, however, are not extrapolated to the clients not surveyed.

! The Census Survey captures at best only a fraction of the true impacts of the center services. 

Many other economic benefits, such as disseminating information about new technologies or

increasing compliance of centers to industry codes, will not show up in the Survey results but

do have potentially large impacts.
38 Chapter 5: Report Results
Appendix A: Supporting Information 39

Appendix A: Supporting Information

Table A1. Glossary

Term Explanation Term Explanation

alternative forecast The REMI forecast of the state economy in which there are simulated, impactrelated changes to the economy. Economic impacts are measured by REMI as

the difference between the baseline forecast baseline forecast and alternative forecast. See

baseline forecast.

baseline forecast The REMI forecast of the state economy in which there are no simulated,

impact-related changes to the economy. Economic impacts are measured by

REMI as the difference between the baseline forecast baseline forecast and alternative

forecast. See also alternative forecast.

beggar thy neighbor To beggar thy neighbor is to, in the course of improving one’s own condition,

make a “neighbor” worse off. Relevant to state impacts, the sales increases

that MEP clients report may only be displacing the existing sales of other instate firms; they “beggar” their neighbor. If so, there is no net increase in sales

in the state. A client-reported sales impact does not beggar thy neighbor if it

displaces imports from out-of-state forms, or if the sale is made to an out-ofstate customer who does not currently buy from an in-state firm. The NIST

Data Processor takes full account of the beggar-thy-neighbor problem in

reported sales by only including displaced imports (new sales that displace the

in-state sales of out-of-state firms) and exports (new out-of-state sales).

constant dollars An amount valued at the prices in a particular base or reference year, say,

1999. Consider an example: a client firm reports sales impacts in 1995 and

1999. These values should not be combined or compared since prices have

changed from 1995 to 1999. To correct for these price changes, the two

amounts are converted to a reference year using a price index such as the

Consumer Price Index (CPI) or the GDP Deflator. The NIST Data Processor

converts dollar-valued client impacts to 1992 constant-dollar amounts before

modeling in REMI. REMI then reports the macroeconomic impacts in

constant 1992 dollars, which are then converted to constant dollars in a more

recent year, say 1999, using the CPI. Compare with nominal dollars.

double counting of The act of counting the effect of a single MEP project twice. When modeling a

impacts project with sales and employment impacts in REMI, either the sales impact

or the employment impact should be modeled but not both, since a sales

impact generally implies that employment will increase, and vice-versa. To

model both simultaneously would be double counting of impacts.

dynamic macroeconomic Economic models that analyze an economy over time, using time series data

models and intertemporal relationships between components of the economy. Unlike

input-output models and multipliers, dynamic models allow for excess labor,

excess capital, or excess supply or demand in any given market. REMI is a

dynamic macroeconomic model. See Section 1.1 for more discussion.
40 Appendix A: Supporting Information

Table A1. Glossary

Term Explanation Term Explanation

economic growth The increased productive capabilities of an economy. Economic growth can

be achieved from an increase in the resources available for production—land,

labor, and capital, and productive efficiency—or an increase in the efficient

use of these resources. MEP assistance helps achieve economic growth, for

example, by increasing sales which command additional labor and other

resources, and by improving the labor productivity and total factor

productivity of in-state firms.

factors of production Re-usable resources used in production, generally labor and capital. To make

a manufactured product, a firm purchases products from other firms and

utilizes its factors of production. The value added to the economy is the value

created by the factors of production.

fiscal neutrality When the tax revenues generated by a state program (e.g., the in-state MEP

centers) equal program funding. 

gross state product See value-added output.

input-output (I/O) models Economic models that model an economy as set of linear production functions

describing the interrelationships between sectors. I/O models are general

static representations of an economy, where markets are always cleared. See

Section 1.1 for more discussion.

market failure Occurs when a market-based system does not provide certain goods at optimal

levels, or at all. The inability of small manufacturers to access the latest in

public information on process improvement, marketing, and information

technology is a market failure that MEP strives to prevent.

multiplier The ratio of a change in output or income to a change in an expenditure that

brought it about. For example, the state output multiplier is the ratio of the

change in state output to a change in output in a component of the state

economy. If SIC 35 output were increased by $1 million (say, through project

assistance) and this increase—through the additional intermediate goods

product, additional income, and additional spending—caused total state output

to increase by $2.5 million, then the SIC 35 output multiplier is 2.5. See

Section 1.1 for more discussion.

nominal dollars An amount valued at the prices in the current year. A sales impact of $1

million that occurred in 1999 is valued at $1 million in 1999 nominal dollars,

but would have a different value in other years, owing to inflation (a change in

prices). Client firms express dollar impacts in nominal dollars. Compare with

constant dollars.

personal income The amount of income that individuals actually receive. The values of personal

income reported by REMI are based the standard definition: personal income

= national income - (undistributed corporate profits) - (social insurance taxes)

- (corporate profits taxes) + transfer payments.
Appendix A: Supporting Information 41

Table A1. Glossary

Term Explanation Term Explanation

regional purchase A number representing the fraction of state demand for a good or service that

coefficient is produced by in-state firms.

value-added output A measure of the value created by the factors of production (labor and capital)

It is measured as the sales value of output minus the cost of materials

purchased from other firms. 
42 Appendix A: Supporting Information

Table A2. Federal, State, and Local Tax Revenues as a Percent of Personal Income

State Total State Total Federal Federal Local State Local

State

and

All States 24.3 All States 24.3 13 11.3 7.0 4.3

ALABAMA 22.4 13 9.4 6.4 3

ALASKA 28.9 13 15.9 7.8 8.1

ARIZONA 24.8 13 11.8 6.9 4.9

ARKANSAS 23.8 13 10.8 8.2 2.6

CALIFORNIA 24.3 13 11.3 8.0 3.3

COLORADO 23.3 13 10.3 5.6 4.7

CONNECTICUT 25.1 13 12.1 8.0 4.1

DELAWARE 23.9 13 10.9 9.5 1.4

FLORIDA 23.3 13 10.3 6.2 4.1

GEORGIA 24.1 13 11.1 6.5 4.6

HAWAII 26.2 13 13.2 10.4 2.8

IDAHO 24.6 13 11.6 8.3 3.3

ILLINOIS 23.9 13 10.9 6.0 4.9

INDIANA 23.4 13 10.4 7.2 3.2

IOWA 24.7 13 11.7 7.3 4.4

KANSAS 24.4 13 11.4 7.5 3.9

KENTUCKY 24.6 13 11.6 8.8 2.8

LOUISIANA 23.3 13 10.3 6.8 3.5

MAINE 25.9 13 12.9 8.7 4.2

MARYLAND 23.6 13 10.6 6.3 4.3

MASSACHUSETTS 24.2 13 11.2 7.6 3.6

MICHIGAN 23.9 13 10.9 8.9 2

MINNESOTA 26.2 13 13.2 9.4 3.8

MISSISSIPPI 24.4 13 11.4 8.8 2.6

MISSOURI 23.1 13 10.1 6.4 3.7

MONTANA 24.1 13 11.1 7.7 3.4

NEBRASKA 24.9 13 11.9 6.7 5.2

NEVADA 24.4 13 11.4 7.3 4.1

NEW HAMPSHIRE 21.9 13 8.9 3.1 5.8

NEW JERSEY 24.6 13 11.6 6.0 5.6

NEW MEXICO 25.6 13 12.6 10.7 1.9

NEW YORK 27.4 13 14.4 6.6 7.8

NORTH CAROLINA 23.9 13 10.9 8.1 2.8

NORTH DAKOTA 25.1 13 12.1 8.4 3.7

OHIO 24.1 13 11.1 6.5 4.6

OKLAHOMA 23.8 13 10.8 7.9 2.9

OREGON 23.7 13 10.7 6.4 4.3

PENNSYLVANIA 23.6 13 10.6 6.7 3.9

RHODE ISLAND 24.5 13 11.5 7.0 4.5

SOUTH CAROLINA 23.5 13 10.5 7.3 3.2

SOUTH DAKOTA 23.1 13 10.1 5.4 4.7

TENNESSEE 22 13 9 5.7 3.3

TEXAS 23.3 13 10.3 5.4 4.9

UTAH 25.1 13 12.1 8.3 3.8

VERMONT 25.2 13 12.2 7.1 5.1

VIRGINIA 22.8 13 9.8 6.0 3.8

WASHINGTON 25 13 12 8.0 4

WEST VIRGINIA 24.3 13 11.3 8.9 2.4

WISCONSIN 26.3 13 13.3 8.9 4.4

WYOMING 24.7 13 11.7 7.9 3.8

Source: U.S. Bureau of the Census and Bureau of Economic Analysis.
Appendix A: Supporting Information 43

Table A3. The REMI 14-sector classification, with two-digit-SIC manufacturing detail

No. REMI Sector No. REMI Sector SIC(s)

1 Durables Manufacturing 24, 25, 32-39

Lumber and wood products 24

Furniture and fixtures 25

Stone, clay, and glass products 32

Primary metals industries 33

Fabricated metal products 34

Machinery and computer equipment 35

Electronic equipment, except computer equipment 36

Motor vehicles and equipment 371

Transportation equipment excluding motor vehicles Rest of 37

Instruments and related products 38

Miscellaneous manufacturing industries 39

2 Nondurables Manufacturing 20-23, 26-31

Food and kindred products 20

Tobacco products 21

Textile mill products 22

Apparel and other textile products 23

Paper and allied products 26

Printing and publishing 27

Chemicals and allied products 28

Petroleum and coal products 29

Rubber and miscellaneous plastics products 30

Leather and leather products 31

3 Mining 10, 12-14

4 Construction 15-17

5 Transportation and Public Utilities 40-42, 44-49

6 Finance, Insurance, and Real Estate 60-65

7 Retail Trade 52-59

8 Wholesale Trade 50, 51

9 Services 70, 72, 73, 75, 76, 78, 84, 86, 87, 89

10 Agriculture, Forestry, and Fishing 07,08,09

11 State and Local Government *

12 Federal Government, Civilian *

13 Federal Government, Military *

14 Farm 01, 02

* There is no SIC for this REMI sector
44 Appendix A: Supporting Information

BLS and REMI Data

The NIST Data Processor calculates return to capital—the “payment” to capital for every $1 of

production (sales) for each industry. A REMI input/output table shows composition of $1 of

production in terms of physical inputs (including fuel) and wages. The REMI input/output table is

based on Bureau of Labor Statistics data, and may be provided to you in spreadsheet form directly

from REMI.

Return to capital is the difference between $1 and the cost of all the enumerated physical inputs and

wages. The inverse of the amount of capital used per dollar of output is output per capital used. Output

per capital used is the amount of output produced given the use of $1 of capital. Note that this measure

is not the same as the total value of the capital equipment—only the capital used.

A conservative estimate of yearly capital use may come from applying the depreciation rate for the

industry to the total value of capital in the industry. Yearly depreciation can be thought of as how much

of the capital asset was “used up” each year. A conservative assumption is that a machine must

contribute to production (use) at least as much as the amount by which the machine depreciated.

The NIST Data Processor calculates depreciation, that is, the yearly decrease in value of the capital

asset, using the estimated capital life for a manufacturing industry; these data are shown in Table A5

and are available from the Bureau of Labor Statistics[13]. Capital life is the number of years the capital

is expected to last. A uniform yearly depreciation rate is assumed. Depreciation is the fraction (of the

original value) by which the capital asset deceased in value. For example, a $1 million capital asset

which lasts for 20 years (a 5% depreciation rate) is assumed to contribute at least $50 thousand of

capital input towards production (use) each year.
Appendix A: Supporting Information 45

Table A5. Depreciation Schedule for Capital Investment

Industry Machinery Industry Machinery Machinery Machinery Handling Handling

Metal Special Metal Special Including Including

Working Industry Working Industry Materials Materials 

General 

Industrial

Equipment, 

Lumber and Wood Products (24) 12 15 14

Furniture and Fixtures (25) 15 17 17

Stone, Clay, and Glass Products (32) 20 24 23

Primary Metal Industries (33) 28 33 32

Fabricated Metal Products (34) 25 30 29

Industrial Machinery and Computer Equipment (35) 26 31 30

Electronics; Other Electric Equipment (36) 15 17 17

Motor Vehicles and Equipment (371) 15 17 17

Transportation Equipment (37) 18 21 20

Instruments and Related Products (38) 15 17 17

Misc. Manufacturing Industries (39) 18 21 20

Food and Kindred Products (20) 21 25 24

Tobacco Products (21) 22 26 25

Textile Mill Products (22) 17 20 19

Apparel; Other Textile Products (23) 16 19 18

Paper and Allied Products (26) 17 20 19

Printing and Publishing (27) 17 19 18

Chemicals and Allied Products (28) 17 20 19

Petroleum and Coal Products (29) 23 27 26

Rubber and Misc. Plastic Products (30) 15 17 17

Leather and Leather Products (31) 16 19 18

Source: Bureau of Labor Statistics: http://www.bls.gov/mprcaptl.htm

The NIST Data Processor uses the average capital life for each industry.
46 Appendix A: Supporting Information
Appendix B: NIST REMI Baseline-forecast Data File 47

Appendix B. NIST REMI Baseline-forecast Data File

To retrieve REMI baseline-forecast data, simply go to the Results tab panel (Figure 12), select “Levels”

from the Type box, and display the appropriate data.

Figure B1. Figure B1. REMI Baseline Forecast REMI Baseline Forecast 

Use the Years button in the upper right corner to select the years for which you need data (unless

otherwise indicated, provide data for the years 1995 through 2005). The following data should be

provided to NIST, from the above screen in REMI:

! Price Index. The price indices are used by the NIST Data Processor to adjust all dollar

denominated impacts in the Census Follow-up Data.

! Imports and demand, by two-digit-SIC manufacturing industry. The fraction of imports over

demand in each industry and year are used to adjust client-reported changes in-state sales, jobs,

and capital investment.

! Output and employment, by two-digit-SIC manufacturing industry. Dividing output by

employment yields output per employee in each industry and year. This is used by the NIST

Data Processor to determine the increase in production from new jobs.

How to Provide the REMI Baseline-forecast Data to NIST

To facilitate the quick use of the NIST Data Processor, NIST needs the REMI baseline forecast data in
48 Appendix B: NIST REMI Baseline-forecast Data File

electronic-file format. Table B1 assists you in making these files. The first column tells you what data

you are retrieving. The second column tells you where the data is located in the Results tab panel; you

will need to select this exact label in REMI. The third column tells you what to name the data files.

Table B1. Data Needed From REMI Baseline Forecast

Measure Location in Figure B1 Measure Location in Figure B1 File Name File Name

PCE-Price Index 92$ price.txt

Employment (Thous), Durables employ1.txt

Employment (Thous), Non-Durables employ2.txt

Demand (Bil $), Durables demand1.txt

Demand (Bil $), Non-Durables demand2.txt

Output (Bil $), Durables output1.txt

Output (Bil $), Non-Durables output2.txt

Imports (Bil $), Durables imports1.txt

Imports (Bil $), Non-Durables imports2.txt

The method of making each file is simple and consistent. Once you have expanded the data in the

Results Tab to the appropriate level (shown in column 2), go to the task bar at the top of REMI, choose

File, and then Print Preview. On the Print Preview window, there is a button labeled “Export.” This is
Appendix B: NIST REMI Baseline-forecast Data File 49

depicted in Figure B2.

Figure B2. Print Preview Screen

Export the data to a tab-delimited text file; Figure B3 illustrates this step.

Figure B3. Saving the File

You will end up with nine files, one for each data type required. You may send them to NIST as an

attachment to an email. The contact for this process is Hayden Brown, email address

“hayden.brown@nist.gov.” 
50 Appendix B: NIST REMI Baseline-forecast Data File
References 51

References

1. DRI McGraw-Hill (Macroeconomic Group), 24 Hartwell Avenue, Lexington, MA 02421.

2. Ehlen, Mark A. “The Economic Impact of Manufacturing Extension Centers,” Economic

Development Quarterly, forthcoming.

3. Ehlen, Mark A. Estimated Economic Impacts of the Advanced Technology Program's Flow-Control

Machining Project: Early Applications in the Automobile Industry. NISTIR 6373. Gaithersburg, MD:

National Institute of Standards and Technology, 1999.

4. Ehlen, Mark A, and Weber, Stephen W. Estimating Economic Impacts of Government Technology

Programs: Manufacturing Studies Using the REMI Model. NISTIR 6107. Gaithersburg, MD: National

Institute of Standards and Technology, 1997.

5. Federation of Tax Administrators. “1996 State & Local Revenues as a Percentage of Personal

Income,” Washington, D.C., 1998.

6. Federation of Tax Administrators. “1998 State Tax Revenue,” Washington, D.C., 1998.

7. Minnesota IMPLAN Group, Inc. 1725 Tower Drive West Suite 140, Stillwater, MN 55082.

8. Manufacturing Extension Partnership. Census Follow-up Survey, 1995-1999.

9. Regional Economic Modeling, Incorporated. REMI Policy Insight, Version 1.1 (software). Amherst,

MA, 1999.

10. Thompson, Chris. A Guide for Communicating Impact, Version 2.0, Manufacturing Extension

Partnership, National Institute of Standards and Technology, July 1999.

11. Treyz, G.I. Regional Economic Modeling (Norwell, Massachusetts: Kluwer Academic Publishers),

1993, p. 91.

12. U.S. Bureau of Economic Analysis. Regional Input-Output Multipliers RIMS II,

http:/www.bea.doc.gov/bea/regional/rims/, 1999.

13. U.S. Bureau of Labor Statistics. Revisions to Capital Inputs for the BLS Multifactor Productivity

Measures, http://www.bls.gov/mprcaptl.htm, 1999.

14. WEFA. 800 Baldwin Tower, Eddystone, PA 19022.