ARC-LEAP User Instructions 

Appalachian Regional Commission 

Local Economic Assessment Package 

prepared for the 

Appalachian Regional Commission 

prepared by 

Economic Development Research Group, Inc. 

Glen Weisbrod 

Teresa Lynch 

Margaret Collins

January, 2004 
ARC-LEAP User Instructions 

Appalachian Regional Commission 

Local Economic Assessment Package 

prepared for the 

Appalachian Regional Commission 

prepared by 

Economic Development Research Group, Inc. 

2 Oliver Street, 9th Floor, Boston, MA 02109 

Telephone 617.338.6775 

Fax 617.338.1174 

e-mail info@edrgroup.com

Website www.edrgroup.com 

January, 2004
ARC-LEAP User Instructions 

Economic Development Research Group i 

PREFACE 

LEAP is a software tool that was designed and developed by Economic Development 

Research Group, Inc. (www.edrgroup.com) to assist practitioners in evaluating local 

economic development needs and opportunities. ARC-LEAP is a version of this tool 

developed specifically for the Appalachian Regional Commission (ARC) and it’s Local 

Development Districts (LDDs). Development of this user guide was funded by ARC as a 

companion to the ARC-LEAP analysis system. 

This document presents user instructions and technical documentation for ARC-LEAP. It is 

organized into three parts: 

I. overview of the ARC-LEAP tool 

II. instructions for users to obtain input information and run the analysis model 

III. interpretation of output tables 

. 

A separate Handbook document provides more detailed discussion of the economic 

development assessment process, including analysis of local economic performance, 

diagnosis of local strengths and weaknesses, and application of business opportunity 

information for developing an economic development strategy. 
ARC-LEAP User Instructions 

Economic Development Research Group 1 

I. OVERVIEW 

The ARC-LEAP model serves to three related purposes, each aimed at helping practitioners identify 

target industries for economic development. The first is to provide a tool for local practitioners to 

assess current economic conditions and likely future trends. The second is to provide a diagnostic tool 

to aid practitioners in targeting industries that can provide the basis for economic development. The 

third is to provide an analysis tool for assessing the effects of policy (e.g., tax) changes and new 

investments (e.g., transportation improvements) on the attractiveness of an area for different 

industries. 

The assessment portion of the model provides baseline growth projections for 71 industries. 

(Industries are defined on a Standard Industrial Classification (SIC) basis.) In addition, each industry 

is classified according to whether there is a potential for business attraction in the local area and the 

magnitude of the business attraction potential for each of these industries. (The magnitude for 

business attraction is measured in number of jobs.) An area is classified as having a potential for 

business attraction if employment in an industry is lower than in a comparable area (i.e., the 

Comparison Area in the model) or if employment in an industry has grown more slowly than in other 

areas of the country. 

The magnitude of the business attraction potential for each industry in the local area is assessed 

through a process of adding or subtracting “weights” associated with local area’s relative advantages 

or disadvantages. Advantages and disadvantages are defined on the basis of: (1) costs of labor, 

materials, utilities, transportation and taxes and the sensitivity of each industry to those various cost 

factors; (2) size and characteristics of the local area’s workforce and the sensitivity of each industry to 

these factors; and (3) the availability and cost of different modes of transportation (i.e., highway, air, 

rail, and marine) and the sensitivity of each industry to these factors. In other words, the ARC-LEAP 

model identifies sets of industries that are good targets for economic development by matching an 

area’s labor and infrastructure characteristics (e.g., wage rates, education levels, airport access) with 

operating requirements of each industry. 

The diagnostic portion of the model includes a complete set of area diagnostics, based on an 

assessment of the local area’s competitiveness (relative to a comparison area chosen by the user) for 

each industry. In addition, more detailed diagnostics are presented for each industry for which there is 

a potential business attraction, as identified in the assessment portion of the model. This set of 

diagnostics identifies “critical” and “important” weaknesses that need to be addressed if the area is to 

fulfill some of the growth potential identified in the local area assessment. The diagnostics presented 

in ARC-LEAP are developed by looking at each industry’s sensitivity to different factors and for the 

factors most important to an industry, the strength of the local area relative to the comparison area. 

Factors assessed in the diagnostic portion of the model include total production costs; labor costs; 

energy costs; tax burdens; availability of labor (i.e., “work base”); availability of skilled workers; 

water transportation; air transportation; rail transportation; highway transportation; and availability of 

Broadband Internet access. 

The policy portion of the model allows users to analyze the effects of policies and investments on the 

business attraction potential of a local area. Users can estimate the likely business attraction impacts 

of changes in availability or quality of key inputs (labor and economic infrastructure), including labor 

force size and skill levels; Broadband access; tax policy; availability of commercial land, industrial 

parks, office sites; access to airports, sea ports, and rail; and improvements to highways. Estimates of 

these presented as estimated new jobs associated with improved business attraction potential. 
ARC-LEAP User Instructions 

Economic Development Research Group 2 

II. INSTRUCTIONS FOR ARC-LEAP USERS 

ARC-LEAP is a large spreadsheet workbook application. It requires a computer with Microsoft Excel 

software, that has sufficient internal memory to load and operate a 3.7 MB spreadsheet workbook. It 

contains visual basic macros that have been checked and are virus free. You must have your security 

level set to medium so that Excel prompts you about whether it is OK to enable the macros (you must 

answer “yes” to allow ARC-LEAP to work). If your security level is set to “high” then the macros 

will not be enabled. In that case, go to Excel’s top line menu and choose “Tools,” then “Options,” 

then select the “Security” tab and click on the “Macro Security” button to set it to “medium.” 

ARC-LEAP is comprised of four input and four output tables. Users who wish to perform only an 

assessment of an economic area are only required to complete Input Forms 1 and 2. Users who wish 

to perform a diagnostic of an area’s strengths and weaknesses must complete Input Forms 1, 2, and 3. 

Users wishing to perform analysis of the effects of policy changes and new investments must complete 

Input Forms 1, 2, 3, and 4. The locations of the required input forms and relevant output tables are 

presented in Table 1. (Instructions on how to interpret the output tables are presented in Section III.) 

INPUT FORM 1

Input Form 1 must be completed in order to perform an area assessment, area diagnostics, or policy 

analysis. The form, which is shown in Figure 1, requires the user to input the following information: 

1) scenario title; 2) date the assessment is being performed; 3) geographic definition of study area; 4) 

geographic definition of comparison area; 5) latest year for the economic data; and 6) earliest year for 

the economic data. These inputs are referred to as Inputs (1), (2), (3a), (3b), (4a), (4b), (5a), (5b), (6a), 

and (6b); their placement on Input Form 1 is illustrated in Figure 1. (Throughout the manual, the user 

can refer to the relevant Figures to match the input (or “variable”) name, number, and placement in the 

relevant input form.

Table 1. Basic Structure of the ARC-LEAP Model 

Function Required Input Forms Output Tables

Area Assessment Input-1 ASSESS (columns A-E) 

Input-2 SUMMARY (columns C, E) 

Area Diagnostics Input-1 Table A1 

Input-2 Table B1 

Input-3 SUMMARY (columns D, F) 

 

Policy Analysis Input-1 POLICY OUT 

Input-2 

Input-3 

Input-4 
ARC-LEAP User Instructions 

Economic Development Research Group 3 

Figure 1. Input Form 1 

Scenario Title (Input 1): The user has the option of naming each area assessment/policy analysis performed. 

This option is particularly useful when the user wants to compare the Study Area to multiple Comparison 

Areas or wants to run multiple policy simulations. The scenario title provided by the user 

Will be printed on each of the other input forms, as well as each output table. This will allow the user to save 

and/or print multiple sets of results, each identified by its Scenario Title. 

Date (Input 2): This is automatically filled in by the model. (The user can fill in a date, but the model’s 

ability to automatically enter the date will be lost. If this happens, the user can enter the text “=NOW()”, 

which instructs the model to automatically fill in the current date.) 

Defining Study and Comparison Areas (Inputs 3a, 3b, 4a, and 4b): The user can input both the state (Input 

3a) and county or counties (Input 3b) that define the Study Area; and the state (Input 4a) and counties (Input 

4b) that define the Comparator Area. Input 3b can consist of a single county, a list of multiple counties, or a 

designation, e.g., “Boston Metro Area” that describes the counties included in the Study or Comparison area. 

Because the information from Inputs 3a, 3b, 4a, and 4b will be reproduced in other tables in the model, the 

user should use as few characters as possible to describe the state and counties of the study area. 

TIP 1Æ Choosing a Comparison Area

The choice of comparison area will greatly affect the results of assessment, diagnostic, and policy analysis 

portions of the model. As such, the user should choose the comparison area carefully to ensure that the results 

are valid. An area will be a good comparison area if at least one of the following conditions is met: 1) the 

study and comparison areas share many of the same basic characteristics; or 2) recent transportation 

improvements have “linked” the areas by reducing travel time between the two areas. In case of shared 

characteristics, a user should choose a comparison area with: 1) similar basic characteristics, especially the mix 

of agriculture, services, and manufacturing, e.g., farming communities should not be compared to serviceintensive suburban or urban areas; 2) roughly the same population levels and population density, i.e., the user 
ARC-LEAP User Instructions 

Economic Development Research Group 4 

should compare rural areas with other rural areas and urban areas with other urban areas;1 and 3) no major 

differences in structural features, such as availability of natural resources (e.g., forests, oceans, metal and ore 

deposits). Choosing the right comparison area is important because the existence of a potential for business 

attraction in the Study Area is determined by identifying industries that are under-represented in the Study 

Area relative to the Comparison Area. An industry should be considered “under-represented” only if it is 

feasible that the industry could locate in that area: thus, the choice of Comparison Area should eliminate 

differences based on features that are unlikely or impossible to change even in the long-term, such as presence 

of a university or proximity to forest and fishing resources. A second condition under which an area will be a 

good Comparison Area is if it has been recently “linked” to the Study Area because of improvements in 

transportation infrastructure, usually a highway project. New and improved highways can expand job 

opportunities (for workers) and customer markets (for businesses) by reducing travel times to/from the Study 

Area. This is referred to as an “accessibility improvement”. Improved access to a Comparison Area can allow 

the Study Area to gain employment in the industries now over-represented in the Comparison Area (a kind of 

“spillover” effect) or achieve growth rates being achieved by those same industries elsewhere because of better 

and more competitive infrastructure. 

Years for Economic Data (Inputs 5 and 6): Input 5 asks the user for the earliest and latest dates for the 

Study and Comparison Area employment data to be entered in Input Form 2. The default values for the 

earliest and latest years of data are 1990 and 2000. (The user should consult TIP 2Æ Choosing the Analysis 

Period below for guidance on choosing the most appropriate analysis period.) The ARC-LEAP model also 

utilizes US employment data in its business attraction estimates. Input 6 asks the user to enter the earliest and 

latest dates for the US data to be used. The default US data included in the model cover the period 1990-2000. 

However, if a time period other than 1990-2000 is used for Study and Comparison Areas, the US data must 

also be changed so that the three sets of employment data (Study Area Comparison Area, and US) cover the 

same period. 

TIP 2Æ Choosing the Analysis Period: Except under special circumstances, the analysis period 

should cover the 1990-2000 period. (2000 is the last year for which SIC-based employment data are 

available.) This period is long enough to ensure that the results are not skewed by short-term changes 

in the economy and short enough to ensure that the data accurately reflect current economic conditions 

and trends. However, in some cases, the user might prefer a shorter time period, e.g., 1995-2000. 

Circumstances that might require a different analysis period than 1990-2000 include: 1) an area lost or 

added a large employer after 1990, e.g., 1994. In this case, the analysis period should begin after the 

opening or closing of the large employer, i.e., 1995-2000; 2) Economic conditions in the local area 

were much different in the second half of the 1990s than the first half in ways not mirrored in national 

trends. For example, an area might have experienced a change in local infrastructure—e.g., the 

closing of a port, the opening of an airport, the expansion of a university—that drastically changed the 

number and types of businesses that operate in the area. In these cases, the analysis period should be 

shortened to capture only the period after the changes took place. The user should note, however, that 

in almost all cases, the 1990-2000 period is appropriate and should be changed only there was a 

dramatic change in infrastructure after 1990 and before 2000; a very large local plant or firm that 

existed in 1990 closed; or a large plant or firm arrived after 1990. The user can also do multiple runs 

with different time periods (i.e., 1990-2000, as well as a shorter period that aligns with changes in 

local infrastructure) to assess whether changing the time period greatly affects results. 

 

1 cite the USDA rurality measures
ARC-LEAP User Instructions 

Economic Development Research Group 5 

INPUT FORM 2

Input Form 2 must be completed in order to perform an area assessment, area diagnostics, or policy 

analysis. The form, which is shown in Figure 2, requires the user to input employment, by SIC, for the 

Study and Comparison Areas. In addition, the user can change the default US employment 

information, which is based on the years 1990 and 2000. 

Figure 2. Input Form 2 

Employment Data for the Study Area (Inputs 7a1, 7a2, 7b1, 7b2, etc.): Employment data for each 

SIC must be entered for the Study Area for two years. (The years the data pertain to were identified in 

Input Form 1.) The user should choose the analysis period, i.e., the period defined by the earliest and 

latest years of the data based on the guidance in Tip 2, Choosing the Analysis Period. 

There are a limited number of national sources that report employment data by SIC. One source is the 

US Department of Census’ County Business Patterns, which can be found on-line at a number of sites, 

including one maintained by the Department of Census2

 and one by the University of Virginia.3 Both 

sites include SIC employment data for each county and state in the US, as well as national totals. Data 

on the Department of Census site goes back to 1988; data on the University of Virginia site go back to 

1977. On both sites, the last year for which SIC-based employment data is available is 1997. (After 

1997, County Business Pattern data are reported by NAICS code.) Unfortunately, some of the 

individual data components, i.e., employment in a specific sector and county are suppressed in order to 

maintain the confidentiality of firms. In cases where data are suppressed, CBP reports a letter that 

specifies the range of employment. If the user is using CBP data and encounters suppressed data, the 

user should replace the letter with the midpoint of the range, as follows: A = 10; B = 60; C = 175; E = 

375; F = 750; G = 1,750; H = 3,750; I= 7,500; J = 17,500; K = 37,500; L = 75,000; M = 100,000. 

 

2 http://www.census.gov/epcd/cbp/view/cbpview.html

3 http://fisher.lib.virginia.edu/collections/stats/cbp/county.html
ARC-LEAP User Instructions 

Economic Development Research Group 6 

The Minnesota IMPLAN Group, Inc (MIG, Inc) collects employment data for 528 sectors, most of 

which correspond directly to an SIC code, for each county and state in the US. These data are 

available for years as late as 2000. The Appalachian Regional Commission (ARC) has licensed the 

use of employment data for each county in the ARC region for use with ARC-LEAP. ARC also has 

available a spreadsheet that will automatically convert IMPLAN data into SIC data. For these reasons, 

IMPLAN is the recommended source of employment data. 

Some individual state and local agencies also compile employment data by county and SIC. State and 

local data should only be used if the available data sets include data for both Study and Comparison 

Areas. Under no circumstances should different sources be used for employment data for the Study 

and Comparison Areas. Mixing of data sources creates unreliable results, as the comparisons between 

areas will be skewed by the techniques used to gather and report data in each of the sources. 

Employment Data for the Comparison Area (Inputs 8a1, 8a2, 8b1, 8b2, etc): The guidelines for 

“Input (7)—Employment data for the Study Area” also pertain to data for Input 8. 

Employment data for the US (Inputs 9a1, 9a2, 9b1, 9b2, etc): The guidelines for “Input (7)—

Employment data for the Study Area” also pertain to data for Input 8. It is important that, when 

possible, the US data be taken from the same source (e.g., IMPLAN, County Business Patterns) as 

used for the Study and Comparison Area data. 

US Growth Forecast (Inputs 10a, 10b, 10c, 10d, etc.): National growth forecasts for each SIC are 

automatically calculated by extrapolating the data entered for the US in Input 9. The user, however, 

may enter other employment forecasts for particular sectors or for all sectors. Employment forecasts 

should be entered in annual percent change and should accord to the same time period as the US data. 

Examples of national employment forecasts include 10-year employment forecasts developed by the 

US Bureau of Labor Statistics.4

 The growth forecasts must be employment forecasts rather than output 

or value added forecasts, which are not always good predictors of likely future employment trends in 

an industry. 

INPUT FORM 3

Input Form 3 must be completed in order to perform area diagnostics or policy analysis. The form, 

which is shown in Figure 3, requires the user to input information on a variety of factors in the Study 

and Comparison Areas, including labor costs, electricity cost, taxes, housing costs, population, skilled 

workers, proximity to different transportation modes, highway congestion, and Broadband access. (In 

some cases, users will not be able to find all the required information. In these cases, users should 

consult TIP 3Æ Dealing with Missing Information.)

Labor Cost Data (Inputs 11a and 12a): Labor costs per hour (Inputs 11a and 12a) should be 

calculated based on average hourly or average annual wages of manufacturing workers in the Study 

and Comparison Areas. Although the wage data can come from a different source than the 

employment data entered in Input Form 2, the wage data entered for Inputs 11a and 12a must come 

from the same source, must be entered in consistent units, i.e., in wage costs per hour or wage costs 

per year, and must be for the same group of workers, i.e., all manufacturing workers or if that 

information is not available because of data suppression issues, all workers. 

 

4 These forecasts can be found at http://stats.bls.gov/news.release/ooh.t03.htm. 
ARC-LEAP User Instructions 

Economic Development Research Group 7 

Figure 3. Input Form 3 

There are two primary national sources of wage data: the US Bureau of Labor Statistics (BLS) and US 

Department of Census’ County Business Patterns (CBP). The BLS data provide information on 

average annual wages and average hourly wages by sector. The CBP data provide information on 

number of workers and total annual payroll; from these data, an average annual wage can be 

calculated. (Instructions are provided below.) Because they control for differences in hours worked in 

different areas, average hourly wages from BLS provide a more accurate measure of labor costs than 

the annual wages reported in CBP. However, BLS data are available only for states and metropolitan 

areas, while CBP data are available for states and counties. Users whose Study Area consists of a 

lone county or a set of counties outside the metropolitan areas covered by BLS should attempt to use 

the CBP data. For some areas, the CBP manufacturing data may be suppressed for confidentiality 

reasons. In these cases, the user should: 1) determine whether BLS metropolitan data are available 

and representative and if so, use BLS data; 2) choose a county close to the Study Area whose wages 

are likely to be similar to those in the Study Area and use CBP data; or 3) estimate county 

employment based on the letter used to indicate the range of employment in industrial sectors were 

data is suppressed. (In such cases, the user should replace the letter with the midpoint of the range, as 

follows: A = 10; B = 60; C = 175; E = 375; F = 750; G = 1,750; H = 3,750; I= 7,500; J = 17,500; K = 

37,500; L = 75,000; M = 100,000.) 

To retrieve BLS average hourly labor cost data from the Internet: 

1. Go to http://www.bls.gov/data/

2. Choose “Discontinued BLS Databases”, the fourth option from the top 

3. Choose “Create Customized Tables (one screen)” 

4. in the “Employment, Hours, and Earnings from the Current Employment Statistics survey 

(State and Metro Area, SIC basis)” row 

5. For Input 1, select the relevant state; for Input 2, select “A 1-Digit Industry (Industry 

Division)”; for Input 3, select the relevant metropolitan area or “statewide” if the (Study or 

Comparison) Area covers an entire state; “ State; for Input 4, select “Manufacturing”; for 
ARC-LEAP User Instructions 

Economic Development Research Group 8 

Input 5, select “Average Hourly Earnings, in Dollars”; for Input 6, select “Not Seasonally 

Adjusted”; for Input 7, select “Get Data” 

6. From the table shown, choose the annual average hourly wage from the last full year reported 

To retrieve CBP data on annual wages from the US Department of Commerce Website: 

1. Go to http://www.census.gov/epcd/cbp/view/cbpview.html 

2. Choose “View County, State, U.S., ZIP, or MSA Database on a NAICS Basis (1998 - 2001)” 

3. Select the relevant state and click on “Go” button 

4. From the pull down menu, choose the relevant county/counties or the entire state and click on 

“Submit” button 

5. From the then table shown, record the “Number of Employees for week including March 12” 

and “Annual Payroll” for “Manufacturing” (Industry Code 31). The data should refer to the 

last year available; if not, select most recent year from pull down menu in the top middle part 

of the page 

6. Divide annual payroll by number of employees to get average annual wage 

To retrieve CBP data on annual wages from the University of Virginia Website: 

1. Go to http://fisher.lib.virginia.edu/collections/stats/cbp/

2. Choose “COUNTY LEVEL DATA” under 1998-2001 data (NAICS code) 

3. Choose the relevant state; click on “Submit Query” button 

4. In the middle of the page, the user must select a number of variables: choose relevant 

county/counties under the sate name; for “Industry Selection” choose “Manufacturing 

Division”, leave second “Industry Selection” option blank; for “Variable Selection”, choose 

“Number of Employees (Week including March 12)” and “Payroll() 

5. Divide annual payroll by number of employees to get average annual wage 

Energy/Electricity Cost Data (Inputs 11b and 12b): There are two types of energy cost data 

available nationally. The first are state data on total energy costs for industrial and commercial users 

from the US Department of Energy’s Energy Information Agency (EIA). The second are data on 

electricity costs in local (i.e., sub-state) areas available from the Energy User News (EUN). The state 

data can be used if the Comparison and Study Areas are in different states and the user believes the 

state data are representative of costs on the Study and Comparison Areas. The local data, which are 

available only for electricity costs, should be used if the Study and Comparison Areas are in the same 

state or if the available state data do not reflect costs in the Study and/or Comparison Areas. Some 

state and local agencies also collect energy cost data—users can use local data as long as the data 

cover both the Study and Comparison Areas. In other words, as with information entered in Input 

Form 3, it is important that the same data source be used for the Study and Comparison Areas. 

To retrieve energy cost data from the EIA: 

1. Go to http://www.eia.doe.gov/emeu/states/_states.html

2. Click on the relevant state, then choose “Industrial” under “”Prices and Expenditures” in/of 

the “Total Energy” menu 

3. Record the most recent year’s cost in Nominal Dollars per Million Btu, the last row in the last 

column of Table 4 (This is the average cost of energy for industrial users.) 

4. Return to relevant state’s menu and choose “Commercial” under ”Prices and Expenditures” 

in/of the “Total Energy” menu (This is the average cost of energy for commercial users.) 

Take the average of the two costs. (This represents the average energy cost for the state.) 
ARC-LEAP User Instructions 

Economic Development Research Group 9 

To retrieve local electricity cost data from the EUN: 

1. Go to www.energyusernews.com

2. Select Statistics, Trends and Energy Data 

3. Open the reports from the last two months reported—one will have industrial electricity prices 

for each utility; the other will have commercial electricity prices for each utility. 

4. Take the average of industrial and commercial electricity costs for the major utility in the 

county. (This represents the average electricity cost for the county.) 

Note: Users can identify the major utilities that serve a particular county by accessing available state 

data as well as EIA’s Inventory of Electric Utility Power Plants in the United States, which lists all the 

utilities in the U.S. and the county location of their home office, and EIA’s “Form EIA-412 Database”, 

which lists all the transmission lines and their starting and ending location for each utility in the US. 

These reports are available at www.eia.doe.gov/cneaf/electricity/page/pubs.html and 

http://www.eia.doe.gov/cneaf/electricity/page/eia412.html, respectively. 

Overall Tax per Person (Inputs 11c and 12c): Users can calculate the average local, county and 

state tax burden per capita. To do that, the user must obtain tax revenues for the applicable city/town, 

county and state and then divide it by the population of the applicable area. Local and state economic 

development agencies, as well as local and county governments sources for this type of information. 

Instructions for obtaining information on county population can be found below under “Population 

Data (Inputs 11g, 12g, 11h, and 12h)”. 

Housing Cost Data (Inputs 11d and 12d): 

1. Go to http://factfinder.census.gov

2. Click on “Housing” 

3. Choose the relevant state and fill in the name of the relevant county 

4. Choose “Value and Mortgage Status” under “Financial Characteristics” 

5. Record the Median (dollars) value under “Specified Owner-Occupied Housing Units” (This is 

the median housing value of the county.) 

6. If there is more than one county in the Comparison or Study Area, repeat for each county and 

record the total number of Owner-Occupied Housing Units. Take a weighted average of the 

total number of Owner-Occupied Housing Units and the median value in each county. 

Rental Cost Data (Inputs 11fe and 12e): 

1. Go to http://factfinder.census.gov

2. Click on “Housing” 

3. Choose the relevant state and fill in the name of the relevant county 

4. Choose “Rental Costs” under “Financial Characteristics”. Scroll to the end of the table to get 

the median monthly rent. 

5. Record the Median (dollars) Contract Rent under “Specified Renter-Occupied Housing units”. 

(This is the median rental cost for the county.) 

6. If there is more than one county in the Comparison or Study Area, repeat for each county and 

record the total number of Renter-Occupied Housing Units. Take a weighted average of the 

total number of Renter-Occupied Housing Units and the median value in each county. 

Population Data (Inputs 11f, 12f, 11g, and 12g): 

1. Go to http://factfinder.census.gov

2. Click on “Data Sets” 
ARC-LEAP User Instructions 

Economic Development Research Group 10 

3. Choose “Census 2000 Summary File 1 (SF 1) 100-Percent Data”, then click on “Geographic 

Comparison Tables.” 

4. Choose “Nation” (the default) for “Geographic Type” and “United States—County by State, 

and for Puerto Rico” under “Select a table format”. Click “Next”. 

5. Select Table “GCT-PH1. Population, Housing Units, Area, and Density” , then click “Show 

Results”. 

6. Record “Population” and “Population Per Sq Mile of Land Area” for the relevant counties. 

(These are the population and population density figures for each county.) 

7. If there is more than one county in the Comparison/Study Area, add the population figures for 

each to obtain an area-wide population number; and take a weighted average of population 

density based on the population in each county for an area-wide population density number. 

Skilled Workers Data (Inputs 11h and 12h): 

1. Go to http://factfinder.census.gov

2. Click on “People” 

3. Choose relevant state and county 

4. Choose “Educational Attainment” under “Education” 

5. Under “Percent of Population 25 years and Over”, record “Percent Bachelor's Degree or 

Higher” under “Percent of Population 25 years and Over”. Use the number in the in the first 

column, which refers to both male and female populations. (This is a proxy for the percent of 

skilled workers in a county.) Note that the percent should not be added in decimal format but 

as the number of over-25 persons per 100 person with at least a bachelor’s degree. Thus, if 

24.8% of the area’s over-16 population is in the labor force, the user should enter 24.8. 

6. If there is more than one county in the Comparison/Study Area, use the population for each 

county to calculate a weighted average of skilled workers in the area. 

Labor Market Participation Data (Inputs 11h and 12h): 

1. Go to http://factfinder.census.gov

2. Click on “People” 

3. Choose relevant state and county 

4. Choose “Employment Status by Sex” under “Income and Employment” 

5. Record percent “In Labor Force, Both Sexes” under “Population 16 years and over”. Note 

that the percent should not be added in decimal format but as the number of participants per 

100 person in the population. Thus, if 63.5% of the area’s over-16 population is in the labor 

force, the user should enter 63.5. 

Travel Time Data (Inputs 11j, 12j, 11k, 12k, 11l, and 12l): 

The user must calculate the average time to key transportation modes: commercial airports, marine 

(river or sea) ports, and freight rail (truck/rail transfer) facilities. To identify the closest facilities, you 

can consult the list of intermodal connectors that is maintained by the US Department of 

Transportation. Go to http://www.fhwa.dot.gov/hep10/nhs/intermodalconnectors . 

To estimate travel times to the closest airport, marine port and rail intermodal facilities, assume travel 

to those facilities from the middle of your study area. You can have these travel times calculated by 

regional or state transportation planning agencies through use of their own transportation databases or 

highway network models. Alternatively, you can obtain estimates of the travel times through the free 

online resource MapQuest or by purchasing mapping software programs provided by Rand McNally 

or Microsoft (“Streets and Trips”). To use MapQuest, go to http://www.mapquest.com and select 

“driving directions.” Enter the trip origin as a community name in the middle of your study area, and 
ARC-LEAP User Instructions 

Economic Development Research Group 11 

enter the trip destination as the name of the community (or actual address) in which the transportation 

facility is located. The mileage and travel time appear at the bottom of the driving directions.) 

Relative Road Speed: MPH or Congestion Rating (Inputs 11m and 12m): 

The user must estimate the average travel speed within the county or ideally, within a 60-minute 

radius of the center of the county. Engineers and planners at State transportation departments often 

collect information on speeds for individual highways and might also be able to provide an estimate of 

average highway travel speeds within the county. If such information is not available, the user should 

provide an estimate that rates relative travel speeds in the Comparison and Study Areas. The rating 

should be between 1 and 10, with “1” signifying very slow speeds reflecting high levels of congestion 

(e.g., in congested urban areas such as New York or Los Angeles) and a “10” signifying relatively 

higher and speeds with essentially no congestion. 

For these inputs, the critical consideration is not the absolute levels of congestion or the exact average 

travel speed, but the percentage difference in travel times between the Study and Comparison Areas. 

Thus, if the user is unsure of exact speeds or congestion levels but believes that highway travel is 

slower in the Study Area than in the Comparison Area, the inputs should reflect that. For example, if 

the user believes that travel speeds in the Study Area are 10% slower than in the Comparison Area, 

then he or she might enter speeds such as a “36” (mph) for the Study Area and a “40” (mph) for the 

Comparison Area. Alternatively, the model would recognize the exact same 10% differential if 

figures are reported using a 1-10 rating scale, with “4.5” for the Study Area and a “5.0” for the 

Comparison Area. 

Broadband Access (Inputs 11n and 12n): 

Whether or not an area has access to high-speed and (relatively) low-cost Broadband Internet services 

will affect the number and types of jobs that an area will attract. Inputs 11n and 12n ask the user to 

assess the quality and cost of Broadband available in the Study and Comparison Areas. Unfortunately, 

because of rapid and on-going changes in the speed and cost of Broadband services nationally, there is 

no set rule for assessing the quality and cost in any one area. Instead, the user should get an estimate 

of the speed and cost of Broadband services in major metropolitan markets (e.g., New York, Los 

Angeles, Atlanta, Boston San Francisco, etc.), as well as in the Study and Comparison Areas. The 

user should using the speed and cost profiles for one of the major metropolitan areas as the baseline 

and assign it a value of “10”. (Note that these calculations should done outside the model and are 

simply a method for assessing Broadband in the Study and Comparison Areas: the model does not 

require an assessment of Broadband for any other markets or for the US as a whole.) The user should 

then deduct three points for each technology generation that the Study and Comparison Areas lag; and 

one point for each 10% cost penalty paid in the Study and Comparison Areas. 

Thus, in the example shown in Table 2, New York would be given a Broadband access rating of “10”, 

the Study Area a rating of “5” and the Comparison Area a rating of “8”. The Study Area rating of “5” 

is derived as follows: starting with a baseline rating of “10”, the Study Area loses 3 points because it is 

one generation behind New York, i.e., T1 lines are available in New York but not the Study Area; and 

loses an additional 2 points because the cost of the latest technology shared with New York, i.e., Cable 

and DSL, is 20% higher. The Comparison Area, though, gets a rating of “8”: starting with a baseline 

rating of “10”, the Study Area loses no points in the technology generation criterion but 2 points in 

the cost criterion, because the cost of the latest technology shared with New York, i.e., T1 dedicated 

lines, is 20% higher. The user should note that the relevant cost and technology comparison is for 

computer networks (5 or more computers), which better capture the cost to commercial and industrial 

users than residential prices. 
ARC-LEAP User Instructions 

Economic Development Research Group 12 

Table 2. Assessing Broadband Access (Prices for Computer Network, i.e., 5 or more computers) 

Study Area Comparison Area New York 

Type Service Speed

Monthly 

Cost Speed

Monthly 

Cost Speed

Monthly 

Cost

Dialup 20K - 53K $33 20K - 53K $36 20K - 53K $30 

Wireless 128K $77 128K $77 128K $70 

Satellite 900K $120 900K $120 900K $120 

DSL 256K download $84 256K download $85 256K download $70 

Cable 256K up $72 256K up $60 256K up $60 

56K dedicated line 

(frame relay) 56K constant $198 56K constant $198 56K constant $180 

T1 dedicated line 

(frame relay) 1.5 M burst Not avail. 1.5 M burst $720 1.5 M burst $600 

TIP 3Æ Dealing with Missing Information Users might have problems filling out all the required 

fields for Input Form 3. In particular, because of incomplete coverage of local areas in the US and/or 

data suppression problems, there is the potential that local information will not be available on 

energy/electricity costs, labor costs, tax per person, travel times to transportation modes, highway 

travel conditions, and Broadband Access. (For the other variables, data are available for each county 

in the US and county values are never suppressed.) In these case, the user has two options. If the user 

has no information on or idea about the relative values of a variable in the Study and Comparison 

Area, he or she should simply enter “1”s for both the Study and Comparison Areas. If, however, the 

user does not have precise data but does have a sense of the relative difference between the Study and 

Comparison Area, he or she can enter values that reflect the relative difference. This can be done by 

setting the value for the Study Area to “1” and setting a value for the Comparison Area that reflects the 

relative difference. For example, if labor cost data are not available, but the user knows that labor 

costs in the Comparison Area are about 20% higher than in the Study Area, the user should enter “1” 

for the Study Area and “1.2” for the Comparison Area. If, on the other hand, the user knows that labor 

costs in the Comparison Area are about 20% lower than in the Study Area, the user should enter “1” 

for the Study Area and “0.83” for the Comparison Area. (The value of 0.83 is calculated by dividing 1 

by 1.2.) 

INPUT FORM 4

Input Form 4 must be completed in order to perform policy analyses. The form, which is shown in 

Figure 4, requires the user to assess the effects of likely future changes in the Study Area in a number 

of categories, including basic skills and labor market participation of local workers; Broadband access; 

the availability of industrial and commercial development land and sites; access to transportation 

modes (air, rail, port); and changes in highway conditions. In most cases, changes to these variables 

will be the result of policy initiatives. However, for some variables—especially Broadband access and 

availability of commercial and industrial development sites—private sector initiatives can be the 

driving force behind expected future changes. 
ARC-LEAP User Instructions 

Economic Development Research Group 13 

Figure 4. Input Form 4 

In the policy assessment, two of the sets of variables—“Technology and Education” and 

“Development Constraints and Initiatives”—use 1-10 ranking systems to compare pre- and post-policy 

conditions in the Study Area. For the third set of variables (“Transportation Initiatives”), actual 

numbers are used. (There is one exception to this: inputs for the variable “Minor Improvements to 

Highway Flow” can be entered in either highway speed in average miles per hour (MPH) or using a 

(1-10) rating of highway congestion.) In order to help the user determine the correct rating for postpolicy Technology and Education conditions, the model automatically assigns a rating to the Study 

and Comparison Areas based on information from Input Form 3. These variable inputs are shaded in 

the model to alert the user that the model will automatically calculate the values. For all variables that 

use a ranking system, higher numbers denote conditions more conducive to business attraction, i.e., 

more skilled workers, higher participation rates, better access to Broadband, and higher availability of 

land, industrial parks, and industrial and commercial development sites. 

Labor Force Skills and Labor Market Participation (Inputs 14a, 14b, and 14c):

These variables capture improvements in labor market skills in the Study Area. “Advanced Skills 

Training” (Input 14a) refers to the availability of programs to offer higher-level industrial and service 

sector skills. Examples of such programs include targeted industrial training, certification programs, 

and expansion of educational opportunities. Such changes might be triggered by expansion of 

community college, vocational school, or university programs or even the opening of new educational 

facilities in the local area. (These programs and schools can be outside of the Study Area boundaries 

but must be accessible to people who live and/or work in the Study Area.) “Labor Market 

Participation” (Input 14b) refers to the growth in the number of active participants in the labor market. 

Such an expansion could be the result of due improvements in child care availability or access to 

public transportation, which can increase the number of person able to work, or greater availability of 

basic skills training (e.g., literacy programs, GED programs, etc.), which can increase the number of 

quality of labor market participants in an area. 

Broadband Access (Input 14c): 

The user should follow the instructions given for this variable in the Input 3 Form instructions. 
ARC-LEAP User Instructions 

Economic Development Research Group 14 

Availability of Highway-Related Commercial Land, Industrial Park Sites with Full 

Infrastructure, and Office/Commercial Development Sites (Inputs 13d, 14d, 13e, 14e, 13f, and 

14f): 

An area might have an abundance of industrial parks sitting empty, just waiting for a business to walk 

in and instantly find vacant buildings and plots that are already hooked up with ample sewer, water, 

electricity, broadband communications, rail spurs and access roads. However, it is quite common, 

particularly in areas that have historically been economically depressed, to find that there is a 

significant lack of readily available industrial parks, buildings or sites that are located near the new 

highway and are available with full infrastructure support. Sometimes there are constraints on the 

availability of desirable business location sites that cannot be overcome. This may occur if the most 

desirable sites along the highway or nearby and accessibility to it are all already taken, or if the most 

desirable locations cannot be built upon because of topography or designation as wetlands, parklands 

or Native American reservations. 

Variables 13d, 14d, 13e, 14e, 13f, and 14f ask the user to assess whether the Study Area currently has 

particularly advantageous or particularly limited opportunities for highway-related commercial 

business, industrial plants or office development (Inputs 13d, 13e, and 13f) and to assess likely future 

opportunities (Inputs 14d, 14e, and 14f). The current and future values for most areas are likely to be 

in the range of 3 to 8. A value of 1 would indicate that there are no attractive sites available; a value 

of 10 would indicate virtually unlimited availability of parks, land, and/or sites. 

Access to Airports, River/ or Sea Ports, and Rail Intermodal (Inputs 14g, 14h, and 14i): 

Variables 11h, 12h, 11i, 12i, 11j, and 12j capture changes in access (travel time) to airports, river or 

sea ports, and rail intermodal facilities. Changes in travel times are usually the result of one of two 

factors: 1) the opening or closing of transportation facilities including highways, airports, rail or 

marine transport facilities; and/or 2) changes in access time because of improvements or deterioration 

in highways and roads that directly access transportation facilities. The measurement of these 

variables is straightforward: each of the inputs should reflect the time in minutes from the center of the 

Study Area to the closest facility. (Methods for measuring travel times are presented in detail in the 

description of “Travel Time Data (Inputs 11j, 12j, 11k, 12k, 11l, and 12l)” in Input Form 3 

Instructions). 

Minor Improvements to Highway Flow: Region-wide (Input 14j):

Variable 14j captures minor but broad-based changes in highway flow throughout the Study Area. 

Such changes can be the result of region-wide programs for addressing congestion through 

transportation systems management techniques, traffic light management systems, introduction of 

automated toll systems, or other measures that permanently improve the flow of traffic within an area. 

These changes should be estimated as improvements in either average highway speed (MPH) or in the 

10-point rating scale, as previously defined under Input Form 3. The model evaluates this change in 

terms of the percentage shift in values between the pre-policy and the post-policy value. 

Note that this variable, “Minor Improvements to Highway Flow,”is not meant to capture major 

improvements in highway travel in the Study Area—such as those resulting from substantial highway 

investments--or improvements in highway flows between the Study Area and other economic areas. 

Those changes should be modeled using the “Major Improvements in Highway Flow” variables 

discussed below. 
ARC-LEAP User Instructions 

Economic Development Research Group 15 

Major Improvements to Highway Flow (Input 14k, 13l, and 14l):

One of the major economic benefits associated with significant new or expanded highway investments 

is their effect on the level and predictability of economic activities. That is, better highway systems 

make it possible for businesses to expand their customer and labor force bases, allow workers to 

search for jobs over greater distances, and reduce the uncertainties associated with high (and 

unpredictable) levels of congestion. In some cases, highway investments are substantial enough to 

effectively increase the market areas for workers and firms. Such changes should be modeled using 

two variables: “Major Improvements to Highway Flow (1)”, which captures changes in the population 

accessible within one hour of the Study Area, and “Major Improvements to Highway Flow (2)”, which 

captures changes in the population or employment accessible within three hours of the Study Area. 

One-Hour Access. LEAP evaluates changes in the population within a one-hour travel time as 

reflective of the change in area labor market base. It automatically estimates the current (pre-policy) 

population market within 60 minutes travel time of the Study Area center, based on previouslyreported values of regional population density. This value can be over-written with a more accurate 

estimate, if desired. In any case, the user must estimate a value of the post-policy population 

accessible within 60 minutes. 

The most precise measures of population accessible within a one-hour travel time (with and without 

roadway changes) would be obtained by using a Geographic Information System, combined with road 

network traffic model. Some county, regional and state planning agencies have this type of 

information and analysis system. However, such precision is not required. Since the model merely 

compares pre-policy and post-policy values to establish a percentage change, the specific values are of 

these variables are not critical. What is really necessary is to establish an appropriate differential that 

reflects the expected percentage growth in size of the population base for regional commuters and 

deliveries. 

Three-Hour Access. LEAP evaluates changes in the population or employment within a three-hour 

travel time as reflective of the change in base for same-day delivery truck trips. This can encompass a 

very broad area that can span many communities and counties, and also cross state lines. That breadth 

is a barrier preventing use of most single county, single region or single state Geographic Information 

Systems. The recommended approach is therefore to utilize a four step process, described below. 

This process provides a method for calculating the change in population or employment base that is 

accessible within three hours, both before and after the proposed project is implemented (referred to as 

“pre-policy” and “post-policy” scenarios). 

The simplest approach is to focus on changes in the total population of metropolitan areas accessible 

within three hours (before or after the proposed policy), and that is assumed in the description of the 

four steps shown here. However, a more refined analysis approach would be actually count total 

population or employment of all counties or all individual communities accessible within three hours 

(before or after the proposed policy change), and those options are noted in parentheses in these four 

steps. 

1. Consult a map to identify metropolitan areas (or else all counties and communities) located 

within roughly 200 miles of the Study Area center. This can be done by applying map scale 

information with a map wheel (a manual device) or a planimeter (an electronic device) to trace 

distances along major highways, or else by just using a ruler (with a rough assumption that there 

tends to be roughly 120 roadway miles for every 100 straight air-line miles on a map). 

2. Refine the list by using online mapping resources (such as www.mapquest.com ) or 

trip/mapping software to calculate the actual highway travel times to those metropolitan areas 
ARC-LEAP User Instructions 

Economic Development Research Group 16 

(or counties or communities) that appear to be within the outside fringe, e.g.., in the 130-200 

mile range. (See further discussion on how to use trip/mapping online resources and software, 

provided earlier for Input Form 3.) This will subdivide the outside fringe group into those that 

currently do and do not fall within the three-hour access range. 

3. For those metropolitan areas (or counties or communities) that are now within 3 hours travel 

time, look up their metropolitan area populations (or employment) using any of the sources 

listed in Table 3. Then sum those values to obtain the pre-policy value for metropolitan 

population (or total employment) within 3 hours travel time. 

4. To calculate the post-policy value, first determine the direction(s) in which proposed new road 

improvements will lead to faster travel times, and then estimate the percentage increase in 

effective speeds for access in those directions. If, for example, there is a proposal for new or 

improved highway facilities that will increase speeds 30% for access to points north and south 

of the Study Area, then the new three hour access limit may extend to areas that are now 3.9 

hours away (3 hours * 1.3). Finally, repeat steps #1, 2 and 3 with a 30% longer distance and 

travel time limit to identify additional metropolitan areas (or counties or communities) that 

would now be included. Add their populations (or employment) to get the post-policy value. 

Table 3. Sources of Residential and Business Data for Areas 

Data Series Spatial Detail Update Source of Data 

Sources of Population Data 

Population Estimates 

Program, Bureau of the 

Census (population) 

States, counties, 

metro areas, places, 

Every 

year 

http://www.census.gov/population/www 

estimates/popest.html 

Decennial Census 

(population, labor 

force) 

Census tracts, 

towns, zip codes, 

counties, states, 

metro areas 

Every 

10 

years 

http://www.census.gov 

Sources of Employment Data 

Local Area 

Unemployment 

Statistics 

States, counties, 

large cities 

Every 

month 

Bureau of Labor Statistics (www.bls.gov ) 

or State Labor Market Info. (LMI) 

agencies 

Covered Employment 

and Wages (ES-202) 

States, metro areas, 

labor market areas, 

counties, small cities 

and towns 

Every 

quarter 

www.bls.gov and State LMI agencies 

County Business 

Patterns 

Counties, metro 

areas, zip codes 

Every 

year 

www.census.gov/epcd/view/cbpview.html 

IMPLAN data Counties Every 

year 

ARC has purchased IMPLAN data for 

each county in the ARC region 

Regional Economic 

Information System 

(employment, earnings) 

States, metro areas, 

counties 

Every 

year 

http://fisher.lib.Virginia.edu/reis 
ARC-LEAP User Instructions 

Economic Development Research Group 17 

III. INTERPETING ARC-LEAP OUTPUT TABLES 

 

In this section, the ARC-LEAP output tables are discussed and sample outputs from a comparison of 

two areas are shown. As was summarized in Table 1, there are five output sheets in the LEAP model--

“ASSESSMENT”, “TABLE A1”, “TABLE B1”, “SUMMARY”, and “POLICY OUT”. The 

assessment (“ASSESS”) and summary (“SUMMARY”) tables provide the information necessary to 

assess the state of the local economy, including employment growth trends and opportunities for 

further growth in each sector. Tables A1, B1, and “Summary” provide diagnostic information for the 

local economy, including factors impeding business attraction in sectors with growth potential and an 

assessment of the extent to which shortcomings in different economic factors (e.g., skill levels, access 

to airports) affect the area’s ability to attract certain industries. The policy output table (“POLICY 

OUT”) provides an analysis of the effects of policies and investments on the business attraction 

potential of a local area. In the next sections, each of the output tables will be discussed in detail. 

OUTPUT FORM “ASSESSMENT”

The “Assessment” output form, which is shown in Figure 6, provides six metrics to help the user 

assess the state of the local economy. The first two, found in the third and fourth columns of the table, 

provide information on the likely ten-year growth trends for each sector. The first of these, “10-Year 

Baseline Growth Range,” provides high and low estimates of the next decade’s growth. These 

estimates are based on Study Area and US growth trends in the analysis period. The second growth 

measure, “Average 10-Year Growth Estimate”, is a simple average of the low and high growth 

estimates. 

The next two metrics provide the user with information about the growth potential in each sector. The 

first of this metrics, “Additional Growth Potential”, is a simple binary measure that identifies sectors 

with growth potential (“Yes”) and those without growth potential (“No”). These determinations are 

based on the existing economic bases in the Study and Comparison Areas and growth trends in the 

Study Area and the US as a whole. The second metric, “Potential Add’l Growth (# of Jobs)” uses the 

same information to estimate the size of the maximum expected employment growth above baseline 

growth trends for each sector. These estimates are presented in number of jobs. 

Figure 6. Output Form “ASSESSMENT” 
ARC-LEAP User Instructions 

Economic Development Research Group 18 

The last three metrics are aimed at local development personnel interested in crafting employment 

growth policies. The first of these metrics, “Industrial Trend Rating”, is based on a comparison of the 

growth “trend” of industries in the Study Area with their national performance. This provides users 

with a means to identify the types of businesses that are particularly thriving or faltering in the local 

area (compared to their performance elsewhere). Specifically the model compares employment 

growth in the Study Area over the analysis period with employment growth in the entire US. Based 

on these comparisons, each sector is assigned a category from 1 to 7, where:

1 = Industry growing “faster” locally than nationally* 

2 = Industry declining locally while growing nationally 

3 = Industry growing locally while declining nationally 

4 = Industry declining locally “slower” than nationally* 

5 = Industry growing locally “slower” than nationally * 

6 = Industry declining locally “faster” than nationally* 

7 = Industry growing or declining locally at a rate “similar” to national trend* 

 (Or industry not present locally) 

* Note: “Faster” denotes local growth or decline trend that is more than 20% greater than 

the national trend. “Slower” denotes local growth or decline trend that is more than 20% 

less than the national trend. “Similar rate” denotes trends that are less than 20% different. 

Economic development personnel might want to focus their attention on industries where the Study 

Area is lagging and the prospects for future employment are strong. Thus, the second of the economic 

development metrics, “Potential Candidates for Growth” identifies those sectors that meet two criteria: 

there is positive additional growth potential in the Study Area and national employment in the sector is 

forecast to grow over the next decade. Sectors that meet both these criteria are identified with the term 

“STRONG”; for those sectors that do not meet one or both of these criteria, the row is left blank. 

Finally, economic development personnel can’t forget to maintain reasonable support for those 

industries that are already doing well in the Study Area. The final metric is labeled as “Potential for 

Building on Existing Growth,” and it reflects those sectors that are already growing locally and 

outperforming the national average performance. Unlike the prior metric, the performance of these 

sectors may not be held back by local deficiencies, but they still need to be supported to continue their 

strong performance into the future. 

OUTPUT FORM “TABLE A1”

The “Table A1” output form, which is shown in Figure 7, provides six metrics to help the user 

diagnose the factors that are impeding growth in sectors with employment growth potential. Two 

types of disadvantages are identified: “critical” and “important” disadvantages. A factor is categorized 

as causing a “critical” disadvantage if that factor is very important to the competitiveness of a specific 

industry (e.g., labor costs in labor-intensive industries) and the Study Area’s disadvantage is very large 

relative to the Comparison Area. A factor is categorized as causing a “important” disadvantage if: 1) 

that factor is very important to the competitiveness of a specific industry (e.g., labor costs in laborintensive industries) and 2) the Study Area’s disadvantage is significant but not huge relative to the 

Comparison Area; or if: 1) that factor is relatively important to the competitiveness of a specific 

industry (e.g., labor costs in labor-intensive industries) and 2) the Study Area’s disadvantage is 

significant or even large relative to the Comparison Area. As shown in Figure 7, Study Area factors 

that are a critical disadvantage in an industry are marked with a “1”, while factors causing a significant 

disadvantage are marked with a “2”. For Study Area factors that are a strength or only a minor 

disadvantage—or for factors that do not strongly affect an industry’s competitiveness--the relevant 
ARC-LEAP User Instructions 

Economic Development Research Group 19 

row is left blank. The contribution of the following factors to each industry is diagnosed in Table A1: 

total production costs; labor costs; land/office costs; energy costs; taxes; worker base; skilled workers; 

Broadband Internet access; and availability, and/or proximity of water, air, rail, and highway transport. 

 

Figure 7. Output Form “TABLE A1” 

OUTPUT FORM “TABLE B1”

The “Table B1” output form, which is shown in Figure 8, provides a different way of showing the 

information presented in Table A1. In Table A1, (critical and important) disadvantages in economic 

infrastructure impeding the employment growth in each sector with growth potential are identified. In 

this table, area diagnostics are organized by production factor, e.g., labor costs, access to airports. For 

each production factor, industries that are critically or importantly disadvantaged by the current quality 

or cost of infrastructure are identified. In addition, the number of jobs that could be created if the 

impediment were removed, i.e., if the quality and cost of the factor were the same in the Study Area as 

in the Comparison Area, is estimated. (As can be seen in Figure 8, the estimated effect on 

employment is shown in parentheses beneath each industry identified.) 

Figure 8. Output Form “TABLE B1” 
ARC-LEAP User Instructions 

Economic Development Research Group 20 

OUTPUT FORM “SUMMARY”

The “SUMMARY” output form, which is shown in Figure 9, summarizes key information from the 

earlier output firms. The first two columns identify the SIC code and name of each sector with growth 

potential. The third and fourth columns report the size of the employment deficiency or gap in the 

sector (third column), as well as the amount of the gap that could be reduced if the Study and 

Comparison Area shared the same economic infrastructure (fourth column). The fifth column reports 

the 10-year baseline employment growth forecast for the sector. The last column reports the total 

potential growth associated with each sector, which is equal to the sum of the baseline growth and the 

potentially achievable employment gap reduction. 

Figure 9. Output Form “SUMMARY” 
ARC-LEAP User Instructions 

Economic Development Research Group 21 

OUTPUT FORM “POLICY OUT”

The “POLICY OUT” output form, which is shown in Figure 10, estimates the employment effects of 

policy changes defined by the user in Input Form 4. As is shown in Figure 10, the pre- and post-policy 

values are imported directly from Input Form 4. The model then calculates the estimated employment 

effects associated with each policy change. Thus, as with the other output forms, the user is not 

required to enter any new information. 

With POLICY OUT, the user can see the estimated employment effects of changes in the following 

variables: advanced skills training; labor market participation; Broadband access; 

availability of highway-related commercial land, industrial park sites, and office/commercial 

development sites; and access to airports, river or sea ports, and rail stations; and major and minor 

improvements in highway flow. Each of these variables was described in the section on Input Form 4. 

Figure 10. Output Form “POLICY OUT” 
 

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