A User Guide to the

A User Guide to the

SEBAS Socio-Economic Benefits Assessment System A Rural Development Assessment Tool for Economic Development Dennis Robinson Thomas G. Johnson

Community Policy Analysis Center University of Missouri-Columbia Columbia, MO

January 2005

Executive Summary The mission of the United States Department of Agriculture Rural Development (RD) is to promote a dynamic business environment in rural America. RD uses a variety of business guaranteed loan and grant programs to help facilitate projects that create or preserve quality jobs and enhance the quality of life in rural communities across the nation. RD works in partnership with the private sector and community-based organizations to provide financial assistance to meet business and credit needs in under-served areas. The USDA Economic Research Service has entered into a cooperative agreement with the Community Policy Analysis Center (CPAC) and the Rural Policy Research Institute (RUPRI) to develop an information system and economic models to assess the effectiveness of the rural business loan and grant programs. The resulting information system, the RD Socio-Economic Benefits Assessment System (SEBAS), assesses the effectiveness of RD programs in increasing 5 quantitative measures of local and regional economic performance as well as more qualitative changes in quality of life in rural area. SEBAS (developed specifically for RD by faculty and students at the CPAC) has to date been developed for Vermont, New Hampshire, North Carolina, Montana and California. Expanding RD’ Criteria for measuring Performance Currently, RD reports jobs either created or retained by its loan and grant recipients. “Direct” jobs increases are used by many federal and state agencies as a measure of their performance. However, the number of direct jobs created or retained is, in isolation, a poor indicator of economic change and performance. For example, reporting direct jobs treats part-time and full-time jobs equally despite the fact that full-time jobs generate more income, more security and support families better. Job estimates should be adjusted to reflect full-time equivalency (FTE). This provides a performance measure that doesn’t ignore seasonal and part-time work but would give greater weight to projects that produce full-time jobs compared to part-time jobs. But even full-time employment is too narrow a measure of economic performance. With SEBAS, RD is able to track and report contributions to gross domestic product (GDP) from its projects. GDP is the broadest available measure of income and the most widely used measure of macro-economic performance. It is the sum of four impact variables estimated by SEBAS: employee compensation (wages and salaries plus employee benefits), proprietors’ income, other property-type income (profits, dividends, interest, rents, etc.), and indirect business taxes.

i

Direct changes in jobs, employment and contributions to GDP are perhaps the best measures of change at a national accounting level. But at the local and regional level, linkages between sectors are critical determinants of change in the economic well-being. Total economic effects (direct and indirect) reflect the impact of programs on the activities of existing firms in a local economy. Finally, the “quality” of the jobs created by RD loans and grants should be a key indicator of its performance. Those employers that pay higher wages, more benefits, and contribute to the tax base, and those that indirectly stimulate existing firms that pay higher wages, more benefits and contribute to the tax base, will contribute more to the local, rural community welfare. This factor can be measured by the ratio of GDP to FTE or the “GDP per worker” ratio. What SEBAS Does and Does Not Do? SEBAS offers an opportunity to consider a much wider and richer array of assessment criteria. The array is “wider” because SEBAS tracks a greater number of possible assessment criteria than just the number of jobs created or retained. This list includes: 1. 2. 3. 4. 5.

Direct jobs Direct full-time equivalent employment Total full-time equivalent employment Total gross domestic product Total gross domestic product per full-time worker

The array is “richer” for two important reasons. First, SEBAS not only considers the direct effects of RD’ activity, but it also addresses the indirect effects of the loan and grant programs. Second, SEBAS provides an evaluation of the geographic dispersion of RD’ social and economic effects by measuring the impacts at the county, region and state levels. But SEBAS is more than these key indicators. In addition to the indicators listed above, SEBAS also generates a variety of economic impact measures that may be useful when more detailed and inclusive accounts of particular projects are desired. These measures include such evaluation information as business sales, personal income, indirect business taxes, an implicit wage for the overall impact, and federal, state, and local taxes. SEBAS also generates estimates of how RD’ loans and grants affect the distribution of household income, the occupational distribution of employment impacts, and the generation of various types of tax revenues. SEBAS focuses on the mission of the Rural Business and Cooperative Services, which is to improve the quality of life in rural areas. It is not intended to measure the impacts of RD programs on the National economy. This is why the impacts are limited to the county, region and state. Beyond the region or state,

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economic impacts are less certain, and more likely to involve shifts from place to place rather than new economic activities. SEBAS’ benefits assessment is predicated on the accuracy of the job and economic activity estimates entered into the program. These jobs should represent either new jobs or activity for the region or the jobs and economic activity that would otherwise leave the area without the loan or grant. SEBAS, does not itself, assure the accuracy of this information. It does introduce a new process for monitoring the performance of economic activity associated with RD loans and grants and for measure the impacts of these reported activities.

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Table of Contents Executive Summary Table of Contents List of Tables List of Figures

i iv v vi

Chapter 1: Introduction What is the Socio-Economic Benefits Assessment System? History of the System What Does SEBAS Do? What SEBAS Does Not Do?

1 1 2 3 4

Chapter 2: Using the Socio-Economic Benefits Assessment System What You Need to Know to Begin? Running SEBAS: An Intermediary Relending Loan in Butte, Montana Two Case Studies Other Uses of SEBAS: Two Commercial Developments in Ashe County, North Carolina

6 6

Chapter 3: Recommendations Proposal to Improve RD’ Reporting Criteria An Opportunity for Evaluating Rural Economic Development Expand SEBAS Geographically Format and User Access to SEBAS

7 19 25 31 31 37 39 39

Appendices A. USDA RD Loan Program Benefit Assessment Questionnaire 41 B. Rural Economic Grant Program Benefit Assessment Questionnaire C. RD Counties and Their Neighboring Counties: California, Montana, North Carolina, New Hampshire, and Vermont D. Wages and Contribution to Gross Domestic Product per Worker: California, Montana, North Carolina, New Hampshire, and Vermont E. Additional Empirical Considerations References

46 50

57 63 75

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List of Tables

2.1

Retained Direct Jobs and Weekly Salaries of Two IRP Loan Case Study Recipients—Dairy Farm and Flower Grower

20

Retained Sales Revenues of Two IRP Loan Case Study Recipients—Dairy Farm and Flower Grower

20

Retained Business Expenses of Two IRP Loan Case Study Recipients—Dairy Farmer and Flower Grower

21

Jobs and Average Weekly Wages by Occupation Created Due to Alternative Development Strategies in Ashe County, North Carolina

26

Business Expense Expenditures Due to Alternative Development Strategies in Ashe County, North Carolina

27

Direct Full-Time Equivalent Jobs Created by the RD Loan to the Government Contracting Firm Located in Butte, Montana

33

2001 Average Weekly Hours Worked for the SEBAS Models Used as Test Cases

34

Five Proposed Performance Measures for the RD Loan Issued to the Government Contracting Firm Located in Butte, Montana

35

3.4

Five Proposed Performance Measures for Case Studies

37

3.5

Contributions to Gross Domestic Product per Worker and Wage Impacts for Five Test Cases and Comparisons with Base Levels

38

2.2

2.3

2.4

2.5

3.1

3.2

3.3

v

List of Figures 2.1

SEBAS Welcome Message

7

2.2

Selection of Geographic Region

8

2.3

Worker Survey Table

9

2.4

Sales Revenue Survey

10

2.5

Business Expense Survey

11

2.6

Spatial Distribution of Firm Expenses

12

2.7

Impact Estimates

14

2.8

Wage and Salaries per Worker: Existing vs Impact of RD Loan to the Government Contracting Firm in Butte, Montana

15

Gross Domestic Product per Worker: Existing vs Impact of RD Loan to the Government Contracting Firm in Butte, Montana

16

2.10

Geographic Pie Charts of Impact Estimates

17

2.11

Bar Charts for the Distributional Impact Estimates

18

2.12

Impacts Due to the IRP Loan Issued to the Dairy Farmer

23

2.13

Impacts Due to the IRP Loan Issued to the Flower Grower

24

2.14

Impact Estimates for the Hypothetical Industrial Park

28

2.15

Impact Estimates for the Hypothetical Professional/Office Complex

29

Comparison of Employment Impact Estimates: Hypothetical Industrial Park vs Professional/Office Complex

30

Gross Domestic Product per Full-Time Worker: Existing vs Impact of RD Loan

36

2.9

2.16

3.1

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CHAPTER 1: INTRODUCTION

The United States Department of Agriculture (USDA) Rural Development (RD) promotes a dynamic business environment in rural America. RD helps fund projects that create or preserve quality jobs and enhance the quality of life in rural communities across the nation. RD works in partnership with the private sector and community-based organizations to provide financial assistance to meet business and credit needs in under-served areas.

Responding to increasing requirements for program performance measures and changing conditions in rural areas, the Rural Development is reevaluating the efficacy of its loan and grant programs. The USDA Economic Research Service has entered into a cooperative agreement with the Community Policy Analysis Center (CPAC) at the University of Missouri-Columbia to develop a research program to assess the effectiveness of the RD programs. This research will assess the need that the RD programs fulfill, the effectiveness of the programs in meetings those needs, and the impacts of economic, demographic, and policy changes on RD operations.

WHAT IS THE SOCIO-ECONOMIC BENEFITS ASSESSMENT SYSTEM?

The RD Socio-Economic Benefits Assessment System (SEBAS) is a locally-based decision support system developed by the Community Policy Analysis Center of the University of Missouri-Columbia. SEBAS evaluates the performance and effectiveness of RD’ loan and grant programs across the nation by measuring the economic and social impacts that these loans and grants have on the affected rural community environments.

The purpose of this report is to introduce SEBAS. Included here is brief history of the system’s development and implementation. Instructions for the use

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of SEBAS are provided by way several example applications of SEBAS. Three such case studies are presented in this report. One case study is based on an actual RD Intermediary Relending Loan given to a small firm in Butte, Montana. The other two case studies involve two hypothetical rural commercial developments in Ashe County, North Carolina. The purpose of the two hypothetical commercial development case studies is to illustrate and compare the impact results provided by SEBAS in the same geographical area but two different users of RD’ loan program. A number of alternative suggestions are given for the current RD program evaluation criterion based on the regional impact results provided by SEBAS. Finally, recommendations are made regarding possible enhancements to SEBAS.

HISTORY OF THE SYSTEM Researchers at CPAC have assembled SEBAS to address RD’ evaluation needs. SEBAS is be able to evaluate not only the number of jobs created or retained, but it will also be able to estimate the types and quality of jobs affected directly and indirectly due to your firm’s activities (among other benefit measures). Researchers at CPAC have implemented an innovative regional economic impact methodology that not only addresses the local social and economic effects of RD’ loans and grants, it also provides estimates of the effects as they spread to surrounding counties and beyond, within the state where the loans or grants are issued.

A multi-regional social accounting matrix (SAM) modeling methodology was implemented for the RD benefit evaluations. The basic idea underlying the SAM framework is to provide a simple and convenient method of keeping track of the flows of payments (incomes or receipts) and expenditures (payments or purchases) within an economy. Actually, a SAM framework consists of a series of interrelated accounts where “what is ‘incoming’ into one account must be ‘outgoing’ from another account” (King, 1985). The information within a SAM

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reveals much about the economic and social structure of the area for which it is constructed.

Researchers at CPAC used the SAM databases compiled by IMPLAN for the year 2001 to construct multi-regional SAM models for each all counties within the five states of California, Montana, North Carolina, New Hampshire, and Vermont.1 In all, there are 238 counties in these five states. Each SEBAS multiregional SAM impact model consists of three geographic areas. One area is the county where a loan or grant is given. A second area consists of the surrounding, adjacent counties. And, a third area is an aggregation of the remaining counties within the same state.

WHAT DOES SEBAS DO?

Currently, RD evaluates the effectiveness of its loan and grant programs using the number of jobs created or retained due to its programs. SEBAS offers an opportunity to consider a much wider and richer array of possible assessment criteria. The array is “wider” because there are a greater number of possible assessment variables than just the number of jobs created or retained. Also, the array is “richer” for two important reasons. First, SEBAS not only considers the direct effects of RD’ activities (like the current evaluation procedure) but it also addresses the indirect effects of the loan and grant programs. Second, SEBAS provides an evaluation of the geographic dispersion of RD’ social and economic effects.

SEBAS generates the standard economic impact measures that are provided by most regional economic impact models. These measures include such evaluation information as business sales (called output in SEBAS), income (measured either by employee compensation or proprietor’s income or both),

1

The States of New Hampshire and Vermont were treated as if they were one state for modeling purposes.

3

other property-type income, indirect business taxes, employment, an implicit wage for the overall impact, household income, and public revenues (federal, state, and local taxes). Not often provided in impact assessments, SEBAS also generates estimates how RD’ loans and grants affect the distribution of household income by size, the occupational distribution of employment impacts, and the generation of various types of tax revenues.

Using SEBAS, one may choose form a great number of possible evaluation criteria. However, only a few will be addressed here. First, RD may only want to consider a “single-valued” assessment criterion. Jobs (whether they only include direct jobs created or retained or they also include the indirect employment effects) provide a simply and relatively easily understood evaluation measure. However, concerns have arisen in the press surrounding recent “jobless” recovery cycles. There have been suggestions that employers are becoming more productive, meaning that business activities can expand without increasing employment. An alternative suggestion, that is also a “single-valued” assessment criterion, is to use a measure of created “wealth” or contributions to the gross domestic product (GDP). GDP would be able to address the benefits of RD’ loan and grant programs, even during “jobless” recoveries. Similarly, other indicators present themselves as “single-valued” assessment criteria. For example, total household income, the implicit impact wage rate, or tax revenues. However, economic analysts frequently consider contribution gross domestic product to provide the “best” alternative as a single-valued assessment criterion.

WHAT SEBAS DOES NOT DO? SEBAS’ benefits assessment is predicated on the user providing key information concerning a RD loan or grant. A basic assumption underlying the SEBAS methodology is that the job and economic activity information entered into the program either represents new jobs and activity for the regions being impacted due to the RD loan or grant (i.e., the county, its neighbors, and the

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state) or the jobs and economic activity would otherwise leave the area without the loan or grant. It is difficult to know for certain if a RD loan or grant is critical for the decision to undertake an activity or not. However, there are few alternatives to trusting the judgment of a loan or grant applicant in determining if a loan or grant is critical for a particular activity to actually occur.

Fortunately, geographically limiting the SEBAS models to only include the social and economic effects mitigates this problem to a large degree. At the national level it is frequently assumed that an economic activity will occur whether or not a particular loan or grant is acquired. There are many sources of investment funds and financial assistance. Sometimes higher interest rates are required to obtain the necessary financing, but the extra cost of is little reason to halt a good project. A project may not take place at a given location, it can occur elsewhere. When the geographic scale is lowered (for example, by only considering a specific state or sub-state area), a decision to undertake a project at a different location still means a loss for the region losing the investment.

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CHAPTER 2: USING THE SOCIO-ECONOMIC BENEFITS ASSESSMENT SYSTEM

WHAT YOU NEED TO KNOW TO BEGIN?

A SEBAS benefits assessment is predicated on the user providing key information concerning a RD loan or grant. The accuracy of a SEBAS benefit assessment largely rests on the accuracy of the data provided by the firms in these survey tables. Ultimately, if a SEBAS benefit assessment is made mandatory for the RD loan and grant program then it will serve an applicant well to provide requested information. The location of the firm needs to be determined. The firm’s “location” is the county in which it operates. There may be occasions when a firm’s owner has an administrative reporting office that is not located with its operation activities As an example, a firm’s owner may perform administrative activities at a residence while the its operational activities are located elsewhere. The locations of the two functions can very well be in two different counties. It is important to determine the location of the firm’s operational activities.

In addition, a SEBAS user is expected to have collected information describing the firm’s workforce characteristics and its business operations related directly to the RD loan or grant. Appendices A and B provide copies of questionnaires that have been designed to be used to collect these data for firms and institutions that are receive RD loans and grants. The information requirements for SEBAS may seem overly burdensome, however, it is assumed that a RD loan or grant applicant is knowledgeable about its operational activities and can estimate the values that are requested. However, the information requested in the survey forms is similar to the information that is require by the U.S. Census Bureau for its Economic Censuses every five years.

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RUNNING SEBAS: AN INTERMEDIARY RELENDING LOAN IN BUTTE, MONTANA A detailed case study is show here to demonstrate the operation and features of SEBAS using an Intermediate Relending Loan example. A firm in Butte, Montana used the funds of a RD IRP loan to help position itself to accept and perform major governmental contracts. Currently, the firm currently employs 23 workers with an annual payroll of just over $2 million. The firm asserted that the loan was vital for its current operations and capabilities to perform the contracts that it gets. The firm expects that it will experience a major business expansion in the near future and will employ an additional 50 workers. The firm provided the workforce, revenue, and expense information necessary to perform a SEBAS benefit assessment.

Figure 2.1: SEBAS Welcome Message

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The current format and user access to SEBAS is through a series of spreadsheet programs—one for each state. This provides an easily understood and user-friendly format. The actual operation of SEBAS is quite convenient and simple. “Clicking on” the appropriate file in the location where SEBAS resides on your computer is all that is required to start a SEBAS evaluation session. After invoking SEBAS a user is placed in a “Welcome” sheet (Figure 2.1). One should start their SEBAS session from the Welcome sheet. If the Welcome sheet is not seen then it can be easily located.

Note: Please follow the steps below

Step 1

The State you choose here is Montana

Montana Step 2

Click the Red cell below to choose a county

Silver Bow

Step 3

Click to fill in the survey table

Figure 2.2: Selection of Geographic Region

The second step in operating SEBAS is to enter the “State&County” sheet (Figure 2.2) to select the appropriate state and county where the firm’s Rural Business-Cooperative Business Services (RD) loan or grant is being evaluated. Actually, only the appropriate county needs to be selected. Each county and its economic and social relationships with its surrounding neighboring counties and

8

state are the same. Consequently, no two counties will provide the same benefit assessment the same loan or grant information. Because the information is SEBAS is specific to the location where the firm is located, it is very important that the correct county be selected. The third step in a SEBAS evaluation is to enter the “Survey” sheet and fill-in the RD loan or grant survey information. This is the most difficult step in the benefits assessment process. It is important to provide in the requested information as accurately as possible. Three survey questionnaires need to be addressed by the user: (1) a Worker Survey, (2) a Sales Revenue Survey, and (3) a Business Expense Survey. The data entered into the survey forms should be relevant and related to the loan or grant received by the recipient.

LET THE PROGRAM ESTIMATE THE COMMUTING PERCENTAGES

Workers' Survey Table

I KNOW THE COMMUTING PERCENTAGES AND WANT TO FILL IN THEM

# Jobs Created Executive, Business and Finance Professional and Technical Service Sales Office and Administrative Support Farming, Fishing and Forestry Construction and Extraction Installation, Maintance and Repair Production Workers Transportation & Material Moving Total Jobs Created Direct Full-Time Equivalent Jobs Created

Average Hours Worked per Week 5 40 10 40 4 3

40 17

1

15

Average # Weeks Employed per Year

Average Average % Commuters My Estimation Weekly Weekly from Outside County Wages Benefits 52 $1,964 $608 0.00% 0.00% 52 $1,337 $415 10.00% 10.00% 0.00% 52 $1,248 $387 25.00% 25.00% 52 $373 $116 0.00% 0.00% 0.00% 0.00% 52 $250 $78 0.00% 0.00% 0.00% 0.00%

23 20.7

Figure 2.3: Worker Survey Table

The Worker Survey asks for data concerning the firm’s workforce (Figure 2.3). A SEBAS user will need information on the following data items by occupational category: (1) the number of jobs created or retained because of the RD loan or grant, (2) the average hours worked per week, (3) the average number of weeks employed during the year under consideration, (4) the average weekly wages, (5) the average weekly benefits, and (6) an estimate of what

9

percentage of the workers who commute to their jobs from outside the county where the job is located. If this commuting percentage is not known or difficult to estimate, SEBAS can provide default values based on the county-specific commuting data from the 2000 Census of Population and Housing.

Percent Sales to Buyers in Total Sales Revenue

County(%)

Adjacent Counties (%)

Rest of State Elsewhere (%) (%)

$2,885,783

100.00%

Figure 2.4: Sales Revenue Survey

The Sales Revenue Survey asks for the level of sales revenues and an approximate geographic distribution of where it sold its products (Figure 2.4). The firm’s expense items and estimates of the geographic distribution of where purchased items are produced are entered into the Business Expense Survey (Figures 2.5 and 2.6). Again, if the geographic distribution information entered into Figure 2.6 is not available or is difficult to acquire, SEBAS can provide default values based on estimates of commodity trade between the county and other areas.

10

Figure 2.5: Business Expense Survey

11

Please fill in the percentage information here. Click here when you done, then fill in other information

Business Expense Survey

Purchase Requirements Worksheet for Firms Receiving Rural Development (USDA) Loans or Grants Percent from

Total Cost

# 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38

Sector Crops Livestock Forestry and logging Fishing, hunting and trapping Petroleum and natural gas Mined ores Construction Food, beverages and tobacco products Textile products Apparel Leather and allied products Wood products Paper products Refined petroleum and coal products Chemical products Plastics and rubber products Mineral products Metal products Nonelectrical machinery and equipment Computers and electronic components Electircal appliances and equipment Transportation equipment Furniture and related products Other manufactured goods Wholesale and retail trade Transportation Finance Insurance Real estate Utilities Agriculture and forestry services Mining services Printing and publishing services Internet and data process services Motion picture and sound recording Broadcasting Rental and leasing services Scientific and technical consulting services

39 Administrative and management support services 40 Waste management and remediation services 41 Educational services 42 Health care services 43 Recreation services 44 Hotels and other accomodations 45 Dining and drinking places 46 Repair and maintenance services 47 Personal and laundry services 48 Religious, grantmaking and similar organizations

(including transportation)

County(%)

$0

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$42,400 $0

100.00% 100.00%

$0

100.00%

$51,500

100.00%

$124,000

100.00%

$110,000

100.00%

$23,500 $0

100.00% 100.00%

$0

100.00%

$12,000

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$100,267

0.00%

$36,645

100.00%

$9,000

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

$0

100.00%

$0

100.00%

$0 $0

100.00% 100.00%

$0

100.00%

49 Private households

$0

100.00%

50 Social assistance services 51 Post office

$0 $0

100.00% 100.00%

Adjacent Counties(%)

Rest of State(%)

25.00%

Click here when you done, then fill in other information

Figure 2.6: Spatial Distribution of Firm Expenses

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Elsewhere(%)

75.00%

Finally, the fourth step is to examine the benefits assessment results. The benefits assessment results are provided in two sheets, the “Impact Analysis” sheet and the “Chart” sheet. The Impact Analysis sheet provides the empirical estimates of a SEBAS benefits assessment and the Chart sheet shows a variety of graphical presentations (bar and pie charts) of the respective benefit assessment results. A SEBAS benefits assessment results are based on the user-provided information in the survey tables, which specifies the relevant characteristics of a RD loan or grant, and the county- and sector-specific impact parameters that are embedded in the SEBAS spreadsheet program. It is recommended that the results of a SEBAS benefits assessment be saved—copy the empirical results and paste them to a new spreadsheet “by value” or paste them as a “picture” in a document. The charts should be copied and pasted as a picture in a document.

Figure 2.7 presents the values of the impact estimates by impact variable. Impact estimates are provided for the county in which the loan or grant recipient is located, the surrounding adjacent counties, and the remaining counties within the states. Summing across each row of Figure 2.7, SEBAS calculates the state’s total impacts. There is much useful information contained in this table: for example, information that summarizes the total effects that the loan grantee contributes to its community, to the surrounding area, and to the state. These overall effects can be measured in a variety of ways such as by business sales, income of workers, proprietors, and households, employment, and tax revenues. In addition, the distribution of household income and employment by occupational category are shown. Tax revenues are provided by major component.

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Community Development Summary Impact Analysis Impact

Income Size

Employment Impact by Occupation Income Impact by Household

Fedral Tax Impact

State & Local Tax Impact

Table 1: Impact by the Change of Final Demand in County Silver Bow

Back to survey table

Impact for State Montana

Rest of the State

Business Sales (Output) Employee Compensation Proprietors' Income Other Property-Type Income Indirect Business taxes Employment Sum of Household Income Sum of Federal Taxes Sum of State&Local Taxes Wages GDP conribution per worker Ratio of Wage to base Ratio of GDP conribution to base Households LT10K Households 10-15K Households 15-25K Households 25-35K Households 35-50K Households 50-75K Households 75-100K Households 100-150K Households 150K+ Executive, Business and Finance Professional and Technical Service Sales Office and Administrative Support Farming, Fishing and Forestry Construction and Extraction Installation, Maintance and Repair Production Workers Transportation & Material Moving Income Tax (Federal Tax) Other Federal Taxes Social Ins Tax (Federal Tax) Property Taxes (State&Local) Sales Taxes (State&Local) Income Taxes (State&Local) Other State & Local Taxes Social Ins Tax (State&Local)

County Silver Bow

Counties Adjacent to Silver Bow

Whole State

$3,395,291 $2,355,414 $64,828 $258,042 $69,729 41.06 $1,678,285 $101,881 $70,097

$85,660 $20,413 $4,515 $16,329 $4,613 1.52 $151,598 $6,285 $3,637

$392,737 $98,005 $23,581 $56,513 $17,573 4.99 $186,313 $28,420 $18,247

$3,873,689 $2,473,833 $92,924 $330,885 $91,915 47.57 $2,016,196 $136,586 $91,982

$57,370

$13,436

$19,643

$52,009

$66,932

$30,192

$39,217

$62,851

2.88

1.02

1.18

3.13

1.59

0.85

1.18

1.87

$3,122 $7,140 $18,954 $24,056 $34,579 $60,816 $1,494,763 $20,120 $14,736

$943 $1,918 $5,688 $6,524 $11,331 $17,822 $98,142 $5,617 $3,612

$1,920 $3,798 $11,750 $15,802 $26,524 $41,157 $58,673 $15,376 $11,314

$5,984 $12,856 $36,393 $46,383 $72,435 $119,794 $1,651,577 $41,114 $29,662

6.96 11.26 3.29 5.42 7.23 0.06 0.27 1.81 1.51 1.21

0.13 0.93 0.48 0.82 0.24 0.01 0.02 0.06 0.04 0.08

0.51 1.09 0.97 0.87 0.91 0.17 0.17 0.25 0.23 0.36

7.60 13.28 4.74 7.11 8.39 0.25 0.47 2.12 1.77 1.65

$27,975 $26,714 $47,192

$1,713 $1,726 $2,846

$8,356 $6,306 $13,758

$38,044 $34,746 $63,796

$38,501 $0 $8,458 $22,016 $1,122

$2,546 $0 $518 $507 $67

$9,716 $0 $2,526 $5,684 $321

$50,762 $0 $11,503 $28,207 $1,510

Figure 2.7: Impact Estimates

Several measures of community develop are tabulated and presented, such as, wages and contributions to gross domestic product (GDP) per worker. These two values are also compared with corresponding current local values for the areas considered in the form of ratios. These last two values are useful for determining whether the loan or grant applicant is likely to improve the local economic conditions. The example shown indicates that the contracting firm is

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likely to provide substantial contributions. According to the SEBAS benefits evaluation (Figure 2.7) the average annual wages and salaries (employee compensation) per worker affected (directly and indirectly) by the RD loan issued to the government contracting firm within the firm’s home county is $57,370. For all firms located in Silver Bow County, the average annual employee compensation per worker is $19,899. In other words, the RD loan to the government contracting firm directly and indirectly affects jobs in Silver Bow County that provide wages and salaries that are 2.88 times higher that other sources of employment in the county. Figure 2.8 graphically illustrates this relationship.

Wages and Salaries per Worker: Existing vs Impact of RD Loan (without FTE adjustment) $60,000 $45,000 Existing Impact

$30,000 $15,000 $0

county

adjacent counties

rest of state

entire state

Figure 2.8

Computations are also shown for the analogous effects on the average annual wages and salaries per worker impacted by the RD loan on those jobs located in the counties surrounding Silver Bow County and the remaining parts of Montana (approximately 1.02 and 1.18, respectively). For the State of Montana as a whole, the jobs created directly and indirectly by the RD loan provide

15

average annual employee compensation per worker that is more than three times higher than other firms.

In addition, the associated contributions to GDP per worker are almost 60 percent higher that the local average (see Figure 2.9).

Gross Domestic Product per Worker: Existing vs Impact of RBS Loan (w ithout F T E adjustm ent)

$75,000 $60,000 E xisting Im pact

$45,000 $30,000 $15,000 $0

county

adjacent counties

rest of state

entire state

Figure 2.9

In addition to the tabular information, SEBAS has the capability to present its impact estimates graphically (i.e., using pie and bar charts). The pie charts (Figure 2.10) show the geographic dispersion if the impact estimates for the county, the adjacent counties, and the rest of the state. Figure 2.11 provides two examples of distributional bar charts; one for the employment impacts by occupation and the other for tax revenues, Figure 2.11.

16

Impact Chart for State: Montana

Please click the blue cell blew to choose the Category you want to view

County: Silver Bow

Employment

Click to view some Column Charts for:

adjacent counties 3%

Income Impact by Household Income Size Employment Impact by Occupations Federal and Local&State Tax Impact

rest of the state 10%

Wages and Salaries per Worker: Existing vs Impact of RBS Load (without FTE adjustment) Gross Domestic Product per Workers:Existing vs Impact of RBS Load (without FTE adjustment) Wages and Salaries per Worker: Existing vs Impact of RBS Load (with FTE adjustment) Gross Domestic Product per Workers:Existing vs Impact of RBS Load (with FTE adjustment)

county itself 87%


Back to the Impact Table Back to choose another State & County Back to the Survey Table


County: Silver Bow

Employee Compensation

Click to view some Column Charts for: Income Impact by Household Income Size Employment Impact by Occupations Federal and Local&State Tax Impact

adjacent rest counties of the state 1% 4%


county itself 95%




County: Silver Bow

Sum of Household Income

Click to view some Column Charts for:

adjacent counties 8%

Income Impact by Household Income Size Employment Impact by Occupations Federal and Local&State Tax Impact

rest of the state 9%


county itself 83%


Figure 2.10: Geographic Pie Charts of Impact Estimates

17

county adjacent counties rest of the state

18 Social Ins Tax (State&Local)

Other State & Local Taxes

Income Taxes (State&Local)

Sales Taxes (State&Local)

Property Taxes (State&Local)

Social Ins Tax (Federal Tax)

rest of the state

Other Federal Taxes

adjacent counties

Income Tax (Federal Tax)

county

Transportation & Material Moving

Production Workers

Installation, Maintance and Repair

Construction and Extraction

Farming, Fishing and Forestry

Office and Administrative Support

Sales

Service

Professional and Technical

Executive, Business and Finance

BACK

Employment Impact by Occupation

12.00 10.00 8.00 6.00 4.00

2.00

0.00

adjacent counties

rest of the state

county

Federal and State&Local Tax Impact

$50,000 $45,000 $40,000 $35,000 $30,000 $25,000 $20,000 $15,000 $10,000 $5,000 $0

adjacent counties rest of the state

county

Figure 2.11: Bar Charts for the Distributional Impact Estimates

A user can begin another SEBAS benefits assessment by starting with the survey entry process (step three above) assuming that the next evaluation is for a firm located in the same county and state. If the next evaluation is for a firm located in a different county but the same state then the user should return to the county selection process (step two above). If the next evaluation is for a firm located in another state then the user should exit the current SEBAS spreadsheet and launch the appropriate state-specific SEBAS program.

TWO CASE STUDIES

Two recent RD IRP loans provide additional illustrative examples of the efficacy of use of SEBAS. The two cases are from California during 2001. One is of a dairy farm located in Monterey County and the other is of a cut flower grower situated in San Benito County. Both recipients attested to the importance of the IRP loan for their continued business operations and the jobs that are supported by their activities. The firms’ owners did not indicate where their workers resided. The commuting patterns of the two counties from the 2000 U.S. Census of Population were used to spatially distribute the firms’ workers.

The dairy farmer currently employs 3 full-time and 1 part-time farm workers and the cut flower grower employs 2 full-time and 1 part-time farm workers. In each case these are sole proprietor business. As a consequence, it is assumed that the owner is the executive agent of the respective business. Table 2.1 shows the direct jobs and the average weekly salaries of the direct employees. Further it is assumed that full-time employees are paid for 52 40hour weeks a year.2 The weekly salaries include employee benefits.

2

Part-time workers are paid for 26 40-hour weeks per year.

19

Table 2.1: Retained Direct Jobs and Weekly Salaries of Two IRP Loan Case Study Recipients—Dairy Farm and Flower Grower

Occupational Category Executive, Business & Finance

FT

Monterey

Benito

Dairy Farm

Flower Grower

PT

1

Weekly Wages*

FT

$1,567

1

$560

2

1

3

1

PT

Weekly Wages* $470

Professional & Technical Service Sales Office and Administrative Support Occupations Farming, Fishing, and Forestry Occupations

3

1

4

1

$176

Construction and Extraction Occupations Installation, Maintenance, and Repair Occupations Production Occupations Transportation and Material Moving Occupations Total

Table 2.2 shows retained sales revenues of the two firms. The two recipients indicated that their continued business depended on receiving the IRP loans. Table 2.3 provides the retained business expense data for the two loan recipients. The products of the two firms were assumed to be sold outside the state. In both cases the spatial distribution of the business expenses were not known and was approximated by the sector trade flow parameters for the SEBAS SAM models for the respective counties. Figures 2.12 and 2.13 show the SEBAS benefit assessment results for the dairy farmer and flower grower.

Table 2.2: Retained Sales Revenues of Two IRP Loan Case Study Recipients—Dairy Farmer and Flower Grower

Retained Business Sales

Monterey

San Benito

Dairy Farm

Flower Grower

$513,319

20

$97,500

Table 2.3: Retained Business Expenses of Two IRP Loan Case Study Recipients—Dairy Farm and Flower Grower Business Expense Category 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33

Monterey

San Benito

Dairy Farm

Flower Grower $3,250

Crops Livestock Forestry and logging Fishing, hunting and trapping Petroleum and natural gas Mined ores Construction Food, beverages and tobacco products Textile products Apparel Leather and allied products Wood products Paper products Refined petroleum and coal products Chemical products Plastics and rubber products Mineral products Metal products Nonelectrical machinery and equipment Computers and electronic components Electircal appliances and equipment Transportation equipment Furniture and related products Other manufactured goods Wholesale and retail trade Transportation Finance Insurance Real estate Utilities Agriculture and forestry services Mining services Printing and publishing services

$186,624

$24,000 $10,800

21

$3,900 $4,300

$23,400

$1,950

$3,600

$2,028

$19,200

$5,200

Table 2.3: (continued) Business Expense Category 34 35 36 37 38

Internet and data process services Motion picture and sound recording Broadcasting Rental and leasing services Scientific and technical consulting services

39 40 41 42 43 44 45 46 47 48 49 50 51

Administrative and management support services Waste management and remediation services Educational services Health care services Recreation services Hotels and other accomodations Dining and drinking places Repair and maintenance services Personal and laundry services Religious, grantmaking and similar organizations Private households Social assistance services Post office

Employee Compensation Proprietor's Income Other Property Income Indirect Business Taxes Total

22

Monterey

San Benito

Dairy Farm

Flower Grower

$12,000

$1,895 $3,900

$6,000

$17,988

$84,000 $99,344 $32,111 $12,240

$8,060 $39,280 $4,379 $1,370

$513,319

$97,500


Income Size


Fedral Tax Impact


Table 1: Impact by the Change of Final Demand in County Monterey


Impact for State California

Rest of the State

Business Sales (Output) Employee Compensation Proprietors' Income Other Property-Type Income Indirect Business taxes Employment Sum of Household Income Sum of Federal Taxes Sum of State&Local Taxes

County Monterey

Counties Adjacent to Monterey

Whole State

$419,468 $247,425 $8,340 $35,189 $8,422 6.55 $75,985 $16,516 $10,023

$40,678 $9,251 $1,578 $5,663 $1,536 0.36 $17,384 $3,033 $1,804

$226,427 $59,978 $9,735 $36,747 $10,518 1.64 $75,090 $19,658 $12,395

$686,573 $316,654 $19,654 $77,599 $20,476 8.56 $168,458 $39,207 $24,223

Wages GDP conribution per worker Ratio of Wage to base Ratio of GDP conribution to base

$37,767

$25,354

$36,555

$37,006

$45,697

$49,407

$71,296

$50,764

1.54

1.19

1.10

1.14

0.92

1.23

1.14

0.83

Households LT10K Households 10-15K Households 15-25K Households 25-35K Households 35-50K Households 50-75K Households 75-100K Households 100-150K Households 150K+ Executive, Business and Finance Professional and Technical Service Sales Office and Administrative Support Farming, Fishing and Forestry Construction and Extraction Installation, Maintance and Repair Production Workers Transportation & Material Moving Income Tax (Federal Tax) Other Federal Taxes Social Ins Tax (Federal Tax) Property Taxes (State&Local) Sales Taxes (State&Local) Income Taxes (State&Local) Other State & Local Taxes Social Ins Tax (State&Local)

$75 $148 $536 $860 $63,574 $3,452 $2,491 $2,768 $2,082

$148 $251 $810 $1,118 $5,367 $3,437 $2,268 $2,316 $1,671

$495 $828 $2,692 $4,041 $9,186 $15,837 $12,253 $15,342 $14,416

$718 $1,226 $4,038 $6,019 $78,126 $22,725 $17,012 $20,425 $18,169

1.06 0.13 0.19 0.19 0.25 3.73 0.02 0.10 0.26 0.21

0.09 0.04 0.06 0.04 0.06 0.24 0.01 0.02 0.04 0.04

0.19 0.21 0.27 0.20 0.30 0.13 0.03 0.08 0.20 0.15

1.33 0.38 0.52 0.43 0.61 4.09 0.06 0.21 0.50 0.40

$7,059 $3,302 $6,155

$1,323 $558 $1,152

$8,514 $3,699 $7,445

$16,896 $7,559 $14,752

$2,581 $3,775 $2,037 $1,531 $99

$471 $688 $382 $245 $19

$3,223 $4,715 $2,457 $1,881 $120

$6,275 $9,178 $4,875 $3,657 $238

Figure 2.12: Impacts Due to the IRP Loan Issued to the Dairy Farmer

23


Income Size


Fedral Tax Impact


Table 1: Impact by the Change of Final Demand in County San Benito


Impact for State California

Rest of the State

County San Benito

Counties Adjacent to San Benito

Whole State

Business Sales (Output) Employee Compensation Proprietors' Income Other Property-Type Income Indirect Business taxes Employment Sum of Household Income Sum of Federal Taxes Sum of State&Local Taxes

$83,301 $58,663 $2,097 $5,797 $1,066 4.28 $6,357 $2,470 $1,341

$8,099 $2,278 $495 $1,603 $377 0.07 $5,583 $778 $441

$29,147 $8,121 $1,647 $5,274 $1,604 0.23 $11,141 $2,761 $1,849

$120,547 $69,063 $4,239 $12,675 $3,047 4.58 $23,081 $6,009 $3,630

Wages GDP conribution per worker Ratio of Wage to base Ratio of GDP conribution to base

$13,713

$32,934

$34,598

$15,073

$15,807

$68,720

$70,918

$19,429

0.62

0.75

1.11

0.46

0.35

0.90

1.20

0.32

Households LT10K Households 10-15K Households 15-25K Households 25-35K Households 35-50K Households 50-75K Households 75-100K Households 100-150K Households 150K+ Executive, Business and Finance Professional and Technical Service Sales Office and Administrative Support Farming, Fishing and Forestry Construction and Extraction Installation, Maintance and Repair Production Workers Transportation & Material Moving Income Tax (Federal Tax) Other Federal Taxes Social Ins Tax (Federal Tax) Property Taxes (State&Local) Sales Taxes (State&Local) Income Taxes (State&Local) Other State & Local Taxes Social Ins Tax (State&Local)

$5 $4,975 $39 $61 $123 $307 $298 $352 $197

$19 $1,364 $107 $167 $334 $785 $703 $1,036 $1,069

$78 $278 $423 $631 $1,179 $2,420 $1,840 $2,242 $2,050

$102 $6,616 $569 $860 $1,636 $3,512 $2,841 $3,630 $3,315

0.80 0.03 0.01 0.02 0.05 2.32 0.00 0.06 0.03 0.03

0.21 0.01 0.01 0.01 0.01 0.63 0.00 0.00 0.00 0.01

0.05 0.03 0.04 0.03 0.04 0.07 0.01 0.02 0.02 0.02

1.06 0.07 0.07 0.06 0.10 3.03 0.01 0.09 0.05 0.06

$1,083 $498 $889

$339 $150 $289

$1,193 $544 $1,024

$2,614 $1,192 $2,202

$327 $478 $312 $210 $14

$116 $169 $98 $54 $5

$491 $719 $344 $278 $16

$934 $1,366 $754 $541 $34

Figure 2.13: Impacts Due to the IRP Loan Issued to the Cut Flower Grower

24

OTHER USES OF SEBAS: TWO COMMERCIAL DEVELOPMENTS IN ASHE COUNTY, NORTH CAROLINA In addition to evaluating the benefits of RD local and grant applications, SEBAS is also capable of being used to strategically assessment the relative benefits of more than one option at a time. For example, to compare the relative merits of two loan applications. To illustrate this, of two different hypothetical commercial developments located in Ashe County, North Carolina are used. The hypothetical developments involve the reuse of two abandoned industrial plants and their grounds.

Each abandoned industrial site (buildings and grounds) covers about 500 acres with approximately one million square feet of floor space available for the development of various commercial activities. The two commercial developments include: (1) an industrial park and (2) a professional/office complex. The development alternatives consist of the following industrial activities:

Commercial Developments

Industrial Activities

(1) Industrial park

Household furniture & musical instruments Motor freight & warehousing Cable TV station

(2) Professional/office complex

Computer & data processing services Management consulting services Cable TV station Recreational club Restaurant

Table 2.4 provides the occupational distribution for the created jobs at the development activities. Business activity levels and expense data are given in

25

Table 2.5.3 Although employment levels are higher in the case of the professional/office complex, industrial production levels (revenues and expenditures) are significantly higher for the industrial park development alternative.

Table 2.4: Jobs and Average Weekly Wages by Occupation Created Due to Alternative Development Strategies in Ashe County, North Carolina Industrial Park Weekly Wages* Occupational Category Jobs Executive, Business & Finance 39.4 $1,109 Professional & Technical 19.2 $1,006 Service 8.8 $543 Sales 19.5 $510 Office and Administrative Support Occupations 111.2 $480 Farming, Fishing, and Forestry Occupations 6.1 $346 Construction and Extraction Occupations 25.4 $625 Installation, Maintenance, and Repair Occupations 32.7 $617 Production Occupations 218.1 $487 Transportation and Material Moving Occupations 218.6 $469 Total 699.0 *Average weekly wages include benefits working full time for 52 weeks a year

3

Office/Professional Complex Jobs 73.1 242.4 169.4 36.1 185.2 0.8 4.1 19.4 11.7 19.7 762.0

Weekly Wages* $930 $844 $456 $428 $403 $290 $525 $517 $408 $393

Conversions of the jobs created estimates into business activity levels and expenditure distributions are based on local “output-per-worker” and “input-output” relationships for each sector in Ashe County. Data for these computations are available at the county-level in the IMPLAN SAM data bases.

26

Table 2.5: Business Expense Expenditures Due to Alternative Development Strategies in Ashe County, North Carolina Industrial Park 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51

Crops Livestock Forestry & logging Fishing, hunting & trapping Petroleum & natural gas Mined ores Construction Food, beverages & tobacco products Textile products Apparel Leather & allied products Wood products Paper products Refined petroleum & coal products Chemical products Plastics & rubber products Mineral products Metal products Nonelectrical machinery & equipment Computers & electronic components Electrical appliances & equipment Transportation equipment Furniture & related products Other manufactured goods Wholesale & retail trade Transportation Finance Insurance Real estate Utilities Agriculture & forestry services Mining services Printing & publishing services Internet & data processing services Motion picture & sound recording Broadcasting Rental & leasing services Scientific & technical consulting services Admin & management support services Waste management & remediation services Educational services Health care services Recreational services Hotels & other accomodations Dining & drinking places Repair & maintenance services Personal & laundry services Religious, grantmaking & similar orgs Private households Social assistance services Post office Employee Comp/Proprietor's Income Profits & Dividends Business Taxes Total expenses

27

$3,983,735 $4,828 $4,103,808 $5,223,926 $0 $87 $1,162,152 $14 $153,521 $2,550 $2,851 $29,423,284 $8,110,820 $0 $0 $7,473 $171,322 $315,793 $711,623 $597,470 $578,928 $127,392 $484,373 $510,092 $2,175,486 $13,807,049 $6,067,296 $1,115,771 $734,682 $731,087 $783,488 $0 $59,875 $589,650 $199,002 $555,093 $1,765,771 $1,148,063 $1,663,624 $1,308,415 $209,746 $48,773 $78,123 $312,959 $433,314 $1,680,665 $1,646,838 $74,487 $35,820 $0 $186,252 $19,654,149 $5,498,744 $1,869,095 $120,109,357

Professional/Office Complex $739,085 $44,440 $13,410 $12 $3,597 $50 $196,915 $26,268 $347,253 $20,699 $18,580 $23,977 $23,169 $0 $0 $421 $66,615 $27,045 $99,526 $232,821 $960,393 $37,995 $35,063 $183,503 $299,674 $662,556 $641,934 $698,018 $452,220 $717,266 $134,561 $0 $166,540 $777,689 $189,706 $815,876 $2,145,153 $1,462,967 $1,553,699 $1,297,974 $55,555 $43,009 $1,235,546 $972,068 $338,282 $226,597 $237,192 $93,548 $18,444 $10 $45,361 $24,164,073 $5,950,808 $1,341,627 $49,838,791

Figures 2.14 and 2.15 present comparisons of the regional economic impacts for the two alternative development scenarios using SEBAS. Consistent with the expected higher production levels with industrial park option, the economic impact estimates are substantially higher for the industrial park than for the professional/office complex. An interesting difference between the two development alternatives is shown in the comparison between the occupational distributions of employment, Figure 2.16.

Table 1: Impact by the Change of Final Demand in County Ashe


Fedral Tax Impact

Income Size



Impact for State NorthCarolina


County Ashe


Counties Adjacent to Ashe

Rest of the State

Whole State

$76,607,277 $29,957,697 $1,534,493 $7,267,363 $1,752,642 1240.45 $15,298,740 $3,116,251 $1,826,080

$1,557,574 $378,218 $91,316 $380,938 $75,120 18.06 $2,071,920 $127,936 $72,656

$40,557,707 $11,082,489 $1,725,828 $6,805,600 $2,142,216 457.02 $12,965,686 $3,353,731 $2,173,348

$118,722,559 $41,418,404 $3,351,637 $14,453,900 $3,969,978 1715.52 $30,336,346 $6,597,918 $4,072,084

$24,151

$20,947

$24,250

$24,143

$32,659

$51,262

$47,605

$36,837

1.49

1.03

0.95

0.95

0.91

1.40

1.01

0.78

$28,226 $47,347 $137,765 $14,004,038 $283,684 $414,853 $143,161 $109,786 $129,880

$19,938 $27,775 $86,276 $1,053,522 $213,272 $319,721 $150,071 $103,878 $97,469

$129,178 $197,905 $684,994 $1,219,845 $1,870,749 $3,397,127 $2,020,140 $1,921,269 $1,524,480

$177,342 $273,026 $909,036 $16,277,404 $2,367,704 $4,131,701 $2,313,372 $2,134,933 $1,751,829

84.59 66.86 72.67 70.12 191.99 32.28 42.74 55.89 270.11 268.28

4.04 3.34 4.17 3.60 9.65 1.84 1.78 2.79 13.99 14.38

42.49 46.90 74.26 43.39 76.51 53.85 10.42 21.59 34.84 59.81

131.12 117.09 151.10 117.11 278.15 87.96 54.94 80.27 318.94 342.46

$1,024,821 $784,154 $1,307,276

$40,656 $37,577 $49,704

$1,109,034 $834,620 $1,410,077

$2,174,511 $1,656,351 $2,767,056

$475,380 $731,708 $346,711 $257,991 $14,290

$20,363 $31,362 $13,754 $6,653 $525

$580,504 $894,351 $375,201 $307,918 $15,374

$1,076,247 $1,657,421 $735,666 $572,561 $30,188

Figure 2.14: Impact Estimates for the Hypothetical Industrial Park 28


Income Size


Fedral Tax Impact


Table 1: Impact by the Change of Final Demand in County Ashe


Impact for State NorthCarolina

Rest of the State


County Ashe

Counties Adjacent to Ashe

Whole State

$37,751,050 $27,699,073 $754,215 $2,627,649 $631,641 982.58 $17,758,861 $1,112,560 $658,377

$926,997 $254,783 $43,045 $193,962 $53,248 12.05 $1,674,994 $80,571 $48,078

$12,115,621 $3,429,562 $468,174 $2,177,564 $674,228 120.21 $4,146,484 $1,035,248 $679,413

$50,793,668 $31,383,418 $1,265,435 $4,999,174 $1,359,118 1114.84 $23,580,339 $2,228,379 $1,385,868

$28,190

$21,145

$28,529

$28,150

$32,275

$45,235

$56,147

$34,989

1.74

1.04

1.12

1.11

0.90

1.23

1.19

0.75

$14,563 $24,413 $70,951 $17,092,686 $145,972 $213,423 $73,539 $56,446 $66,869

$9,264 $12,928 $40,088 $1,204,100 $98,835 $147,953 $69,134 $47,726 $44,966

$39,872 $61,060 $211,113 $538,589 $575,818 $1,044,146 $619,855 $588,652 $467,379

$63,698 $98,401 $322,153 $18,835,374 $820,624 $1,405,522 $762,527 $692,825 $579,214

86.63 248.60 201.63 57.73 202.55 8.00 7.43 27.39 19.53 30.64

5.37 16.10 12.98 4.03 13.09 0.43 0.39 1.72 1.23 1.97

12.15 17.20 26.18 15.30 24.50 1.87 3.15 6.81 10.22 11.10

104.14 281.90 240.79 77.06 240.14 10.30 10.97 35.92 30.98 43.71

$370,691 $283,124 $458,746

$25,788 $22,201 $32,582

$337,493 $264,610 $433,145

$733,972 $569,935 $924,472

$171,329 $263,703 $125,410 $93,042 $4,893

$14,423 $22,231 $8,724 $2,347 $353

$182,660 $281,483 $114,179 $96,334 $4,758

$368,413 $567,416 $248,313 $191,723 $10,004

Figure 2.15: Impact Estimates for the Hypothetical Professional/Office Complex

29

0.00

county

adjacent counties

rest of the state

30 rest of the state

adjacent counties

county


Production Workers





Sales

rest of the state

Service

adjacent counties


county

rest of the state

adjacent counties

county


Production Workers





Sales

Service



0.00


BACK

300.00


250.00

200.00

150.00

100.00 50.00

Industrial Park

BACK

250.00


200.00

150.00

100.00

50.00

Professional/Office Complex

Figure 2.16: Comparison of Employment Impact Estimates: Hypothetical Industrial Park vs Professional/Office Complex

CHAPTER 3: RECOMMENDATIONS PROPOSAL TO IMPROVE RD’s REPORTING CRITERIA

Currently, RD reports jobs either created or retained by its loan and grant recipients. “Direct” jobs increases are used by many federal and state agencies as a measure of their performance. However, the number of direct jobs created or retained is, in isolation, a poor indicator of economic change and performance. For example, reporting direct jobs treats part-time and full-time jobs equally despite the fact that full-time jobs generate more income. Since many rural residents prefer part time employment as an off-farm income supplement and in certain locales seasonal work provides the best opportunities, it is important that our indicators not discriminate against part-time and seasonal jobs. By adjusting part-time and seasonal jobs to reflect full-time equivalency (FTE) we generate a performance measure that doesn’t ignore seasonal and part-time work but treats part-time, seasonal, and full-time jobs more comparably.

The number of jobs is too narrow a measure of economic performance. With the SEBAS, RD also will be able to track, in both the short run and long term, economic contributions to gross domestic product (GDP) from each project. GDP is the broadest available measure of income, commonly understood, and the most widely used measure of macro-economic performance. It is the sum of four impact indicators computed by SEBAS: employee compensation (wages and salaries plus employee benefits), proprietors’ income, other property-type income (profits, dividends, interest, rents, etc.), and indirect business taxes.

Direct changes in jobs, employment and GDP are agreed by economist to be the best measures of change at a national accounting level. But unique to the local and regional level, linkages between sectors are critical determinants of change in the economic well-being. Total economic effects (direct and indirect) reflect the impact of programs on the activities of existing firms in a local

31

economy. For this reason we recommend that RD track and report total as well as direct economic changes. Finally, the “quality” of the jobs created by RD loans and grants should be a key indicator of performance. Those employers that pay higher wages, more benefits, and contribute more to the tax base, and those that indirectly stimulate existing firms that pay higher wages, more benefits and contribute more to the tax base, will make the greatest contribution to the local, rural community welfare. As an indicator of job quality, we calculate the ratio of GDP to FTE or the “GDP per worker” ratio. Similarly, those entities that fully engage with the local economy, and “buy local” will reflect a higher GDP impact.

Consequently, it is proposed that RD also report a range of other values in addition to the currently reported direct jobs. This list includes:

6. Direct jobs 7. Direct full-time equivalent employment 8. Total full-time equivalent employment 9. Total gross domestic product 10. Total gross domestic product per full-time worker

The government contracting firm located in Butte, Montana that received a RD loan is used here to illustrate the derivation of the five proposed RD performance indicators. The direct jobs estimate (23 jobs) comes from the information provided by the RD loan recipient as shown in the Workers’ Survey Table (Figure 2.3). This is the total number of jobs (both full-time and part-time jobs created by the firms due to the loan. The direct full-time equivalent (FTE) employment is the direct jobs that have been adjusted to reflect the number of equivalent fulltime jobs created or retained. The full-time equivalent employment is computed by adjusting the jobs created in Figure 2.3 for each type of occupation to

32

represent how many employees worked 40 hours for 52 weeks per year.4 The total number of full-time equivalent jobs created or retained is obtained by simply summing across the occupational estimates. These computations are shown in Table 3.1.

Table 3.1: Direct Full-Time Equivalent Jobs Created by the RD Loan to the Government Contracting Firm Located in Butte, Montana

Executive, Business and Finance Professional and Technical Service Sales Office and Administrative Support Farming, Fishing and Forestry Construciton and Extraction Installation, Maintenance and Repair Production Workers Transportation and Material Moving Total Jobs Created

# Jobs Created 5 10

Average # of Average Weeks Full-Time Hours Worker Employed per Equivalent per Week Year Jobs Created 40 52 5.0 40 52 10.0

4 3

40 17

52 52

4.0 1.3

1

15

52

0.4

23

20.7

Total full-time equivalent employment is the number of directly and indirectly affected by a RD loan or grant that has been adjusted to reflect full-time work. The direct full-time equivalent employment is shown above. The indirect employment impact values generated by the SEBAS spread-sheet (like the IMPLAN models it is based on) reflect both full-time and part-time work. Consequently, then also require adjustment for reflect full-time equivalency. However, there is no good source for geographically-based full-time equivalency factors.

4

That is, one full-time worker is a person working 40 hours per week for 52 weeks per year for a total of 2,080 hours per year. The full-time equivalent jobs created or retained by occupation is calculated for each category by multiplying the number of jobs by the average hours worked per week and the average number of weeks worker per year and then dividing by 2,080.

33

Full-time equivalency (FTE) factors for the indirect employment impacts generated by the SEBAS spreadsheet are based on 2001 U.S. Bureau of Labor Statistics national average industry-specific average weekly hours worked. The national average weekly hours worked were then weighted by local employment estimates for each of the geographic areas used in the SEBAS model (county, adjacent counties, and the remaining portions of the state). FTE factors are computed by dividing the local average weekly hours worked by 40.5 Table 3.2 provides the average weekly hours worked for the Silver Bow County SEBAS model. The estimated FTE factors derived from these average weekly hours worked estimates were used to adjust the indirect employment impact values generated by the SEBAS benefit assessment for the RD loan issued to the government contracting firm. The resulting FTE adjustments of the indirect employment impacts indicate that the RD loan to the government contracting firm will generate 15.1 full-time indirect jobs located in Silver Bow County, Montana. The total FTE employment impact on Silver Bow County due to the RD loan to the contracting firm is 35.8 (20.7 direct FTE jobs plus 15.1 indirect FTE jobs).

Table 3.2: 2001 Average Weekly Hours Worked for the SEBAS Models Used as Test Cases 2001 Average Weekly Hours Worked SEBAS Model In County In Adjacent Counties In Rest of State Monterey, CA 34.6 34.8 34.2 San Benito, CA 35.4 34.8 34.2 Silver Bow, MT 33.5 34.5 34.1 Ashe, NC 35.7 34.6 34.7

Contribution to total gross domestic product (GDP) is the broadest available measure of income the economists use. It is defined as the sum of employee compensation (wages and salaries), proprietors’ income, other

5

This assumes that the national values reflect local labor market conditions in terms of the average hours worked per week and the average weeks worked per year.

34

property-type income (profits, dividends, interest, rents, etc.), and indirect business taxes. These impacts are estimated in the SEBAS spreadsheet program and can be located in the top section of SEBAS’ Table 1. Following the government contracting firm example (see Figure 2.7), the firm’s contribution to GDP in Silver Bow County is $2,748,014, or employee compensation ($2,355,291) plus proprietors’ income ($64,828) plus other property-type income ($258,042) plus indirect business taxes ($69,729). Contribution to total gross domestic product per full-time worker (GDP/FTE) is the ratio of total gross domestic product ($2,748,014) to total full-time equivalent employment (35.8) or $76,849. Table 3.3 summarizes the five proposed performance measures for the government contracting firm.

Table 3.3: Five Proposed Performance Measures for the RD Loan Issued to the Government Contracting Firm Located in Butte, Montana Rual Business Service Performance Measures County Direct jobs Direct FTE employment Total FTE employment Total GDP GDP/FTE

Adjacent Counties

23 20.7 35.8 $2,748,014 $76,849

1.3 $45,870 $35,015

Remainder of State

State Total

4.3 $195,673 $45,955

41.3 $2,989,557 $72,473

The contribution to gross domestic product per worker made by the RD loan to the government contracting firm is one measure of the loan’s performance. But a comparison of the estimates to the average GDP per FTE job in these areas provide a sense as to whether the loan is improving local living standards. In 2001 the average GDP per FTE job in Silver Bow County, its adjacent counties, the remaining portion of Montana, and the State of Montana are $50,411, $40,986, $39,073, and $39,494 (respectively). This means that the RD loan to the government contracting firm has an impact on GDP per FTE job that is 52 percent higher than the average for Silver Bow County, Montana. Similar comparisons can be made for the other affected areas (i.e., the adjacent

35

counties, remaining part of Montana, for the state as a whole). Graphically, the comparison of the RD loan impact on GDP per FTE job to the existing local average GDP per FTE worker is shown in Figure 3.1.

Gross Domestic Product per Worker: Existing vs Impact of RBS Loan (w ith F T E adjustm ent) $80,000 $70,000 $60,000

E xisting Im pact

$50,000 $40,000 $30,000 $20,000 $10,000 $0

county

adjacent counties

rest of state

entire state

Figure 3.1

Table 3.4 compares these five proposed RD performance indicators for the case studies illustrated in this report.

36

Table 3.4: Five Proposed Performance Measures for Case Studies County

Adjacent Counties

Remainder of State

State Total

Direct jobs Gov't Contractor Direct FTE employment Total FTE employment Total GDP GDP/FTE

23 20.7 35.8 $2,748,014 $76,849

1.3 $45,870 $35,015

4.3 $195,673 $45,955

41.3 $2,989,557 $72,473

Dairy Farm Direct jobs Direct FTE employment Total FTE employment Total GDP GDP/FTE

5 4.5 5.8 $299,376 $51,235

0.3 $18,028 $56,867

1.4 $116,979 $83,294

7.6 $434,383 $57,423

Cut Flower Direct jobs Grower Direct FTE employment Total FTE employment Total GDP GDP/FTE

4 3.5 3.7 $67,623 $18,172

0.1 $4,754 $78,937

0.2 $16,647 $82,949

4.0 $89,024 $22,355

Direct jobs Direct FTE employment Total FTE employment Total GDP GDP/FTE

699 699.0 1,182.2 $40,512,195 $34,269

15.6 $925,592 $59,321

396.3 $21,756,133 $54,894

1,594.1 $63,193,919 $39,642

Direct jobs Direct FTE employment Total FTE employment Total GDP GDP/FTE

762 762.0 958.8 $31,712,578 $33,074

10.4 $545,039 $52,346

104.2 $6,749,528 $64,744

1,073.5 $39,007,145 $36,336

Industrial Park

Office Complex

AN OPPORTUNITY FOR EVALUATING RURAL ECONOMIC DEVELOPMENT

The five performance indicators provide a broad picture of how RD loan and grant recipients are affected their rural communities and regions. In addition, it is recommended that RD consider measures of how their loans and grants are improving the affect rural communities. Two possible measure of economic social improvement are the implicit impact wage and GDP per worker ratios relative to the “base level” values of these two indicators for the respective

37

counties, adjacent counties, rest of the state, and the entire state. Table 3.5 shows these values for the five test case studies.

Table 3.5: Contributions to Gross Domestic Product per Worker and Wage Impacts for Five Test Cases and Comparisons with Base Levels County

Adjacent Counties

Remainder of State

State Total

Wages Gov't Contractor GDP per worker Ratio of wage to base Ratio of GDP/worker to base

$57,370 $66,932 2.88 1.59

$13,436 $30,192 1.02 0.85

$19,643 $39,217 1.18 1.18

$52,009 $62,851 3.13 1.87

Wages Dairy Farmer GDP per worker Ratio of wage to base Ratio of GDP/worker to base

$37,767 $45,697 1.54 0.92

$25,354 $49,407 1.19 1.23

$36,555 $71,296 1.10 1.14

$37,006 $50,764 1.14 0.83

Cut Flower Wages Grower GDP per worker Ratio of wage to base Ratio of GDP/worker to base

$13,713 $15,807 0.62 0.35

$32,934 $68,720 0.75 0.90

$34,598 $70,918 1.11 1.20

$15,073 $19,429 0.46 0.32

Industrial Park

Wages GDP per worker Ratio of wage to base Ratio of GDP/worker to base

$24,151 $32,659 1.49 0.91

$20,947 $51,262 1.03 1.40

$24,250 $47,605 0.95 1.01

$24,143 $36,837 0.95 0.78

Office Complex

Wages GDP per worker Ratio of wage to base Ratio of GDP/worker to base

$28,190 $32,275 1.74 0.90

$21,145 $45,235 1.04 1.23

$28,529 $56,147 1.12 1.19

$28,150 $34,989 1.11 0.75

The impact on wages and contribution to GDP relative to their base values is far greater for the government contracting firm in Butte, Montana is far greater than that for either of the two commercial developments in Ashe County, North Carolina. To a large extent, this result occurred because of the “high wage” and technical nature of the contracting firm’s employees. The two commercial developments provide interesting contrasts. For example, the impact on the contribution to GDP per worker for the industrial park was lower than for all 38

respective base level values. However, the impact on the contribution to GDP due to the professional/office complex was higher than the base level value for the rest of the state area. The wage impact for the industrial park is greater than the base level for the county and adjacent counties but lower for the rest of the state and the state as a whole. On the other hand, the wage impact for the professional/office complex is higher than the base levels for all geographic levels.

EXPAND SEBAS GEOGRAPHICALLY

Currently SEBAS is configured to provide impact estimates for five states (California, Montana, North Carolina, New Hampshire, and Vermont). This includes an area containing 238 counties. All fifty (50) states should be included to make SEBAS complete.

However, expanding SEBAS to encompass the entire nation raises several thorny issues. First, the current version of SEBAS stops at a state’s boundary. That is, the counties which are located on the edge of do not have neighboring counties or remaining portions of the state completely surrounding them. This causes a downward bias, to some extent, in the resulting SAM multipliers relative to those counties that are surrounded by neighboring counties and remaining portions of the state. To some extent the regional relationships are incomplete, consequently, the economic interrelationships to be “shortcircuited”. This can be corrected by considering variations of “nodal” regional patterns for the boundary counties. A general discussion of defining regions can be found in Appendix E.

FORMAT AND USER ACCESS TO SEBAS

Current format and user access to SEBAS is through a spreadsheet program. This provides an easily understood and user-friendly format. The actual

39

operation of SEBAS is quite convenient and simple. However, loading the program is a bit slow and computer space requirements are somewhat voluminous (currently, almost 150 Mbytes). Expanding SEBAS to include the entire nation will make these issues more critical. Possible options for SEBAS’ format and user access include: 1. Current format and user access. Don’t change either the format or user access. 2. “RD’s own” impact database. Currently SEBAS is based on the IMPLAN input-output modeling system. The IMPLAN database is available commercially at a cost of $30,000 for each year it is updated. This cost only includes the databases, not the cost of processing to access and use the data. Alternatively, RD could use the RIMS system available through the U.S. Bureau of Economic Analysis. Or, a custom-made database system could be constructed for RD. These cost issues need to be addressed every time the SEBAS databases are updated. 3. “Models on the fly”. Currently, SEBAS contains impact models that have already been compiled and included in the SEBAS program. This is one of the reasons that the SEBAS program is so large. An alternative is to store the models in a separate database file and access only that model requested by the user.

4. Web access. SEBAS uses a spreadsheet for a personal computer. Alternatively, access to SEBAS could be made available through a website.

40

APPENDIX A USDA RD LOAN PROGRAM BENEFIT ASSESSMENT QUESTIONNAIRE

41

USDA RD Loan Program Benefit Assessment Questionnaire The following information is needed from each borrower participating in a USDA Rural Development loan program. This information will only be used to evaluate the efficacy of the Rural Development programs. This research will produce information on the operation, performance, and effectiveness of the RD programs toward the goal of improving the economic well being of rural people and communities.

Notes and explanations: 

The information provided below should refer to only one type of RD loan (either a Business and Industry Loan or an Intermediary Relending Loan). If you receive more than one type of RD loan, please copy this questionnaire to respond for the other type of loan that you receive.



Please provide the following information for the preceding fiscal year starting October 1 and ending September 30.



Report the employment, sales and expenses of the part of your business that is attributable to the USDA funds. If this is a start-up activity, then report all employment, sales and expenses.



If you do not have exact figures, please provide your best estimates.



If the geographic distribution information can not reasonably be gathered, please leave it blank.



If the information asked does not apply to you, please leave it blank.



Do not enter information in shaded areas of the table.



The percentages should add up to 100% across each row of the table.



Be sure that “Total sales revenues” are equal to “Total expenses”.

Definitions: 

“Total Costs” of expense items should include any transportation charges to your business.



“County” refers to the county where the borrower is located.



“Adjacent Counties” refers to the counties that are adjacent to the county where the borrower is located.



“Rest of State” refers to areas of the state where the borrower is located, other than the home county and the adjacent counties.



“Elsewhere” refers to all places outside the state where the borrower is located.

42

Name of Business: ___________________________________________________________________

Address: ________________________________________________________________________ ________________________________________________________________________ ________________________________________________________________________

Telephone Number:

______________________________________ E-mail: ______________________________________

1. Which RD loan program does the information in this questionnaire refer to: Business and Industry Loan Program: Intermediary Relending Loan Program:

_________ _________

2. What is the likelihood that this project would have occurred without USDA Funds? ___ ___ ___ ___

Not possible without USDA funds. Possible, but fewer jobs would have been created. Possible, but the time frame would have been delayed. Possible, but not at this location.

3. Briefly how the RD loan program has impacted your business.

43

4. Employment and compensation

Occupational Groups

# Jobs Created

Average Average # Hours Weeks Worked Per Employed Week* per Year

Average Weekly Wages

Average Weekly Benefits

% Commuters from Outside County


%


%

Service

%

Sales

%


%


%


%

Installation, Maintenance and Repair

%

Production Workers

%


%

*If seasonal or periodic, report average hours worked while employed.

5. Sales Total Sales Revenue

Percent Sales to Buyers in Adjacent Rest of County Elsewhere Counties State

%

44

%

%

%

6. Expenses (can be continued on another page) Percent from

Total Cost Expense Items

(including transportation)

Labor compensation Profits and dividends Business taxes Finance Insurance Real estate Utilities Construction Raw materials*: specify

County

Adjacent Counties

Rest of State

Elsewhere

% % % % %

% % % % %

% % % % %

% % % % %

% % % %

% % % %

% % % %

% % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

% % % % % % % % %

Manufactured goods*: specify

Services*: specify

Total expenses

45

APPENDIX B Rural Economic Grant Program Benefit Assessment Questionnaire

46

Rural Economic Grant Program Benefit Assessment Questionnaire The following information is needed from each recipient participating in a USDA Rural Development (RD) Grant program. The intention of this questionnaire is to produce information on the operation, performance, and effectiveness of the RD programs toward the goal of improving the economic well being of rural people and communities. Please take a few minutes to complete the following four questions.

Notes and explanations: 

The information provided below should refer to only one type of RD grant. If you receive more than one type of RD grant, please copy this questionnaire to respond for the other types of grants that you receive.



Please provide the above information for the preceding fiscal year starting October 1 and ending September 30.



If you do not have exact figures, please provide your best estimates.



If the information asked does not apply to you, please leave it blank.



“Jobs created” are new jobs that are created by new activities or expansions of existing activities.

Name of Grant Recipient: ________________________ ________________________ __

47

Address: ______________________ ________________________ _______________ ______________________ ______________________ _________________ ______________________ ________________________ _______________

48

Telephone Number: ______________________ ________________ E-mail: ________________________ _____________ 1. What type of USDA RD Grant do the responses in this questionnaire refer to: Rural Economic Development (ED) Grant: Rural Business Enterprise (RBEG) Grant: Rural Business Opportunity (RBOG) Grant:

49

_________ _________ _________

2. Briefly describe what the impact the Rural Economic Development Grant has been on your project. _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________

3. Briefly describe what the impact of your project has been on your community and/or region. _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________

4. Does your project tie into a regional/larger plan? If so, then how? _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________ _________________________________________________________________

____________________________________________________________

50

5. Please complete the following table of measurements to document the impact of the Rural Economic Development Grant on your community and/or region. Complete only those measurements that apply to your project.

Measurement

Results

Number of grants Number of new businesses created Number of existing businesses expanded Number of businesses assisted Private dollars leveraged Number of full-time jobs created Number of full-time jobs retained Cost reductions Product line expansion(s) Increase in energy efficiency Increase in local purchasing

Thank you!

51

APPENDIX C RD COUNTIES AND THEIR NEIGHBORING COUNTIES: CALIFORNIA, MONTANA, NORTH CAROLINA, NEW HAMPSHIRE, AND VERMONT

52

Table C: RD Counties and Their Neighbors in California, Montana, New Hampshire, North Carolina, and Vermont County

alpine ca

contra costa ca el dorado ca

san joaquin ca amador ca

amador ca

el dorado ca

alpine ca

calaveras ca

butte ca

tehama ca

plumas ca

yuba ca

calaveras ca

amador ca

alpine ca

tuolumne ca

colusa ca

lake ca

contra costa ca

solano ca

butte ca san joaquin ca

del norte ca

siskiyou ca

el dorado ca

placer ca

glenn ca sacramento ca humboldt ca sacramento ca

Neighboring Counties san francisco ca tuolumne ca mono ca san joaquin sacramento ca ca sutter ca colusa ca glenn ca san joaquin stanislaus ca ca sutter ca yolo ca san francisco alameda ca ca

amador ca

alpine ca

fresno ca

monterey ca

san benito ca merced ca

madera ca

mono ca

glenn ca

tehama ca

butte ca

colusa ca

lake ca

mendocino ca

humboldt ca

del norte ca

siskiyou ca

trinity ca

mendocino ca

imperial ca

riverside ca

san diego ca

inyo ca

mono ca

fresno ca

tulare ca

kern ca

kern ca

kings ca

tulare ca

inyo ca

san los angeles ca ventura ca bernardino ca

kings ca

monterey ca

fresno ca

tulare ca

kern ca

san luis obispo ca

lake ca

mendocino ca glenn ca

colusa ca

yolo ca

napa ca

lassen ca

modoc ca

shasta ca

plumas ca

sierra ca

los angeles ca

ventura ca

kern ca

san orange ca bernardino ca

madera ca

mariposa ca

marin ca

sonoma ca

tuolumne ca mono ca fresno ca san francisco san mateo ca ca

mariposa ca

tuolumne ca

madera ca

merced ca

stanislaus ca

mendocino ca

humboldt ca

trinity ca

tehama ca

glenn ca

lake ca

sonoma ca

merced ca

stanislaus ca tuolumne ca

mariposa ca

madera ca

fresno ca

san benito ca santa clara ca

modoc ca mono ca

siskiyou ca alpine ca

lassen ca madera ca

fresno ca

inyo ca

monterey ca

santa cruz ca san benito ca fresno ca

kings ca

kern ca

napa ca

lake ca

yolo ca

solano ca

san francisco sonoma ca ca

nevada ca

sierra ca

yuba ca

placer ca

orange ca

san los angeles ca riverside ca bernardino ca

placer ca

nevada ca

yuba ca

sutter ca

plumas ca

sierra ca

yuba ca

butte ca

riverside ca

san los angeles ca orange ca bernardino ca

san diego ca

imperial ca

sacramento ca

sutter ca

amador ca

san joaquin ca

san benito ca

santa cruz ca santa clara ca merced ca

fresno ca

monterey ca

san bernardino ca

inyo ca

kern ca

los angeles ca orange ca

riverside ca

san diego ca

imperial ca

riverside ca

orange ca

san francisco ca

marin ca

sonoma ca

napa ca

solano ca

alameda ca

contra costa ca

sacramento ca

alameda ca

san joaquin ca san luis obispo ca

monterey ca

shasta ca tuolumne ca

placer ca

kings ca

stanislaus ca santa clara ca san mateo ca calaveras ca

el dorado ca

kern ca

inyo ca

tulare ca

kings ca

san bernardino ca santa barbara san luis ca obispo ca

sonoma ca

merced ca

san luis obispo ca

san diego ca sacramento ca tehama ca

el dorado ca shasta ca

lassen ca

contra costa ca

solano ca

contra costa ca

alameda ca

san mateo ca

amador ca

calaveras ca

stanislaus ca santa clara ca

ventura ca

santa barbara ca

53

yolo ca

monterey ca

Table C: (continued) County

Neighboring Counties

san mateo ca

marin ca

san francisco alameda ca ca

santa barbara ca

san luis obispo ca

kern ca

santa clara ca

santa cruz ca san mateo ca alameda ca

santa cruz ca

san mateo ca santa clara ca san benito ca monterey ca

shasta ca sierra ca siskiyou ca

siskiyou ca lassen ca del norte ca

modoc ca plumas ca humboldt ca

solano ca

napa ca

yolo ca

sonoma ca stanislaus ca

santa clara ca santa cruz ca

ventura ca san joaquin ca

stanislaus ca merced ca

lassen ca yuba ca trinity ca sacramento ca

plumas ca nevada ca shasta ca contra costa ca

mendocino ca lake ca

napa ca

marin ca

san joaquin ca

tuolumne ca

mariposa ca

merced ca

santa clara ca alameda ca

yolo ca

colusa ca

glenn ca

mendocino ca

calaveras ca

tehama ca

sutter ca

butte ca

yuba ca

placer ca

tehama ca

trinity ca

shasta ca

plumas ca

butte ca

trinity ca

siskiyou ca

shasta ca

tehama ca

mendocino ca humboldt ca

tulare ca

kings ca

fresno ca

inyo ca

kern ca

tuolumne ca

alpine ca

mono ca

madera ca

mariposa ca

ventura ca

santa barbara kern ca ca

yolo ca

lake ca

colusa ca

yuba ca

butte ca

plumas ca

beaverhead mt

madison mt

silver bow mt deer lodge mt granite mt

ravalli mt

big horn mt

carbon mt

powder river mt

blaine mt

hill mt

yellowstone mt chouteau mt

broadwater mt

meagher mt

gallatin mt

jefferson mt

carbon mt

park mt

stillwater mt

yellowstone mt

carter mt

powder river mt

custer mt

fallon mt

cascade mt

chouteau mt

judith basin mt

meagher mt

chouteau mt

hill mt

blaine mt

fergus mt

san luis los angeles ca obispo ca sacramento sutter ca ca sierra ca nevada ca

treasure mt

rosebud mt

fergus mt

phillips mt lewis and clark mt

trinity ca

modoc ca san francisco sonoma ca ca

sacramento ca

merced ca

stanislaus ca calaveras ca

solano ca

napa ca

placer ca

sutter ca

big horn mt

lewis and clark mt judith basin mt

custer mt

fallon mt

prairie mt

garfield mt

rosebud mt

daniels mt dawson mt

valley mt richland mt

roosevelt mt mccone mt

sheridan mt prairie mt

wibaux mt

deer lodge mt

ravalli mt

granite mt

powell mt

jefferson mt

fallon mt

wibaux mt

prairie mt

custer mt

fergus mt

blaine mt

phillips mt

petroleum mt

carter mt musselshell mt

flathead mt

lincoln mt

sanders mt

lake mt

powell mt meagher mt

teton mt cascade mt

teton mt

powder river mt

carter mt

silver bow mt

beaverhead mt

gallatin mt

madison mt

jefferson mt

garfield mt

valley mt

phillips mt

petroleum mt rosebud mt

prairie mt

mccone mt

glacier mt

flathead mt

golden valley mt

fergus mt

pondera mt musselshell mt

toole mt yellowstone mt

sweet grass mt

wheatland mt

stillwater mt

granite mt

missoula mt

powell mt

hill mt

liberty mt broadwater mt

chouteau mt

beaverhead deer lodge mt mt blaine mt

gallatin mt

madison mt

judith basin mt

chouteau mt

fergus mt

wheatland mt meagher mt

lake mt

flathead mt

missoula mt

sanders mt

park mt custer mt

ravalli mt

silver bow mt deer lodge mt powell mt

54

pondera mt

golden valley judith basin wheatland mt mt mt lewis and missoula mt teton mt clark mt

broadwater mt

jefferson mt

san benito ca

cascade mt

lewis and clark mt

toole mt

liberty mt

chouteau mt pondera mt

glacier mt

Table C: (continued) County lewis and clark mt

teton mt

cascade mt

meagher mt

liberty mt lincoln mt

pondera mt flathead mt

chouteau mt

mccone mt

toole mt sanders mt beaverhead mt garfield mt

meagher mt

cascade mt

mineral mt missoula mt

madison mt

Neighboring Counties broadwater jefferson mt powell mt mt hill mt

flathead mt

silver bow mt jefferson mt

gallatin mt richland mt sweet grass mt

dawson mt

prairie mt

park mt

gallatin mt

sanders mt mineral mt

valley mt judith basin mt missoula mt sanders mt

flathead mt

musselshell mt

fergus mt

petroleum mt rosebud mt

powell mt yellowstone mt

granite mt ravalli mt golden valley mt

park mt

gallatin mt

meagher mt

petroleum mt

garfield mt

rosebud mt

phillips mt

blaine mt

fergus mt

petroleum mt garfield mt

valley mt

pondera mt powder river mt

toole mt rosebud mt

liberty mt custer mt

teton mt

flathead mt

jefferson mt

deer lodge mt granite mt

missoula mt

flathead mt

prairie mt

glacier mt big horn mt lewis and clark mt wibaux mt

dawson mt

mccone mt

rosebud mt

custer mt

fallon mt

ravalli mt

missoula mt

richland mt roosevelt mt

roosevelt mt sheridan mt

rosebud mt

garfield mt

powder river mt

custer mt

sanders mt sheridan mt

lincoln mt daniels mt beaverhead mt

rosebud mt

treasure mt

essex vt

caledonia vt

powell mt

silver bow mt

roosevelt mt wheatland mt lake mt

sweet grass mt musselshell mt

treasure mt stillwater mt

carbon mt

fergus mt

phillips mt

chouteau mt carter mt

garfield mt beaverhead granite mt deer lodge mt mt mccone mt dawson mt wibaux mt daniels mt valley mt mccone mt musselshell petroleum mt treasure mt mt flathead mt lake mt missoula mt roosevelt mt madison mt

jefferson mt

golden valley yellowstone mt mt golden valley stillwater mt wheatland mt mt lewis and chouteau mt cascade mt clark mt pondera mt liberty mt yellowstone musselshell big horn mt mt mt garfield mt mccone mt roosevelt mt golden valley sweet grass meagher mt mt mt dawson mt prairie mt fallon mt golden valley carbon mt stillwater mt mt

park mt

sweet grass mt

park mt

teton mt

pondera mt

toole mt

glacier mt

treasure mt

rosebud mt

valley mt

phillips mt

wheatland mt

fergus mt

wibaux mt

richland mt

yellowstone mt

big horn mt

belknap nh

carroll nh

grafton nh

merrimack nh strafford nh

carroll nh

grafton nh

belknap nh

sullivan nh

windham vt

coos nh

coos nh hillsborough nh grafton nh

carroll nh

essex vt

grafton nh

coos nh

carroll nh

belknap nh

hillsborough nh merrimack nh

rockingham nh rockingham nh

big horn mt

strafford nh

belknap nh

grafton nh

daniels mt judith basin mt musselshell mt

windsor vt

windham vt

guilford nc

randolph nc

chatham nc

orange nc

catawba nc surry nc

iredell nc

strafford nh

carroll nh

belknap nh

merrimack nh

sullivan nh

cheshire nh

alamance nc

caswell nc

alexander nc alleghany nc

wilkes nc ashe nc

windsor vt

flathead mt

merrimack nh grafton nh

hillsborough nh

orange vt

meagher mt

hillsborough nh

merrimack nh

yellowstone mt

carbon mt

sullivan nh

strafford nh

prairie mt

mineral mt

merrimack nh sullivan nh cheshire nh

hillsborough nh rockingham nc caldwell nc wilkes nc

richland mt

strafford nh

merrimack nh sullivan nh

rockingham nh

lewis and clark mt

deer lodge mt

sweet grass mt

stillwater mt

cheshire nh

broadwater mt

rockingham nh

55


Neighboring Counties

anson nc

union nc

stanly nc

ashe nc avery nc

watauga nc mitchell nc

alleghany nc mcdowell nc

montgomery nc wilkes nc burke nc

caldwell nc

watauga nc

beaufort nc

martin nc

pitt nc

craven nc

pamlico nc

hyde nc

washington nc

bertie nc

hertford nc

northampton nc

halifax nc

martin nc

washington nc

chowan nc

bladen nc

cumberland nc

robeson nc

columbus nc pender nc

sampson nc

richmond nc

gates nc

brunswick nc

columbus nc pender nc

buncombe nc

madison nc

haywood nc

burke nc

avery nc

mcdowell nc

cabarrus nc

rowan nc

iredell nc

caldwell nc

watauga nc

avery nc

camden nc

currituck nc

gates nc

carteret nc

jones nc guilford nc

catawba nc chatham nc cherokee nc

onslow nc rockingham nc lincoln nc randolph nc graham nc

new hanover nc transylvania nc rutherford nc mecklenburg nc burke nc pasquotank nc craven nc

cleveland nc alamance nc macon nc

chowan nc

gates nc

hertford nc

bertie nc

perquimans nc

clay nc cleveland nc

cherokee nc macon nc rutherford nc burke nc

catawba nc

lincoln nc

columbus nc

robeson nc

bladen nc

pender nc

brunswick nc

craven nc cumberland nc currituck nc dare nc

jones nc harnett nc camden nc currituck nc

lenoir nc moore nc dare nc tyrrell nc

pitt nc hoke nc

beaufort nc robeson nc

davidson nc

forsyth nc

davie nc

rowan nc

davie nc duplin nc durham nc edgecombe nc

yadkin nc wayne nc person nc nash nc

iredell nc sampson nc orange nc wilson nc

rowan nc pender nc chatham nc pitt nc

montgomery nc davidson nc onslow nc granville nc martin nc

forsyth nc

stokes nc

surry nc

yadkin nc

davie nc

davidson nc

guilford nc

franklin nc

warren nc

vance nc

wake nc

johnston nc

nash nc

gaston nc

cleveland nc

lincoln nc

gates nc

hertford nc

chowan nc

pasquotank nc

camden nc

graham nc granville nc greene nc

cherokee nc person nc wilson nc rockingham nc

macon nc durham nc wayne nc

granville nc mecklenburg nc perquimans nc swain nc wake nc lenoir nc

rockingham nc halifax nc

franklin nc pitt nc

vance nc

stokes nc

forsyth nc

davidson nc

randolph nc

alamance nc

caswell nc

martin nc

bertie nc

northampton nc

cumberland nc

sampson nc

johnston nc

caswell nc

guilford nc

henderson nc rutherford nc mcdowell nc

yancey nc

cleveland nc

lincoln nc

catawba nc

caldwell nc

union nc

stanly nc

davidson nc

catawba nc

alexander nc

wilkes nc

pamlico nc

hyde nc

alamance nc

orange nc

person nc

burke nc orange nc clay nc

caldwell nc durham nc

alexander nc wake nc

iredell nc lee nc

pamlico nc bladen nc

carteret nc sampson nc

randolph nc

guilford nc

hyde nc

forsyth nc jones nc wake nc halifax nc

warren nc

franklin nc

nash nc

harnett nc

wake nc

chatham nc

lee nc

moore nc

jackson nc

transylvania nc

henderson nc buncombe nc madison nc

haywood nc

buncombe nc rutherford nc polk nc

bertie nc

chowan nc

gates nc

richmond nc

scotland nc

hoke nc

cumberland nc

harnett nc

beaufort nc

washington nc

tyrrell nc

dare nc

lincoln nc

mecklenburg cabarrus nc nc

henderson nc hertford nc hoke nc hyde nc iredell nc

transylvania nc northampton nc moore nc carteret nc wilkes nc

pamlico nc alexander nc

catawba nc

56

stanly nc

lenoir nc

halifax nc

swain nc

moore nc

gaston nc

edgecombe nc

haywood nc

harnett nc

rowan nc

davie nc

yadkin nc


Neighboring Counties transylvania nc wayne nc wilson nc nash nc craven nc carteret nc

jackson nc

macon nc

swain nc

haywood nc

johnston nc jones nc lee nc lenoir nc

wake nc onslow nc chatham nc wayne nc

harnett nc duplin nc moore nc duplin nc

sampson nc lenoir nc harnett nc jones nc

lincoln nc

catawba nc

burke nc

cleveland nc

gaston nc

macon nc

clay nc

cherokee nc

graham nc

swain nc

pitt nc greene nc mecklenburg iredell nc nc jackson nc

madison nc

haywood nc

buncombe nc yancey nc

martin nc

bertie nc

halifax nc

edgecombe nc

pitt nc

beaufort nc

mcdowell nc

mitchell nc

yancey nc

buncombe nc rutherford nc burke nc

avery nc

mecklenburg nc mitchell nc montgomery nc

gaston nc yancey nc randolph nc

lincoln nc mcdowell nc davidson nc

iredell nc avery nc rowan nc

moore nc

lee nc

chatham nc

randolph nc

nash nc

warren nc

franklin nc

new hanover nc

brunswick nc pender nc

northampton nc onslow nc orange nc pamlico nc

warren nc pender nc person nc beaufort nc

pasquotank nc

gates nc

pender nc

new hanover nc

halifax nc duplin nc caswell nc craven nc perquimans nc

craven nc

washington nc

cabarrus nc

union nc

stanly nc montgomery nc

anson nc

richmond nc

moore nc

richmond nc

scotland nc

hoke nc

wilson nc

edgecombe nc

halifax nc

sampson nc

duplin nc

onslow nc

edgecombe nc

wake nc

johnston nc

bertie nc jones nc alamance nc carteret nc

hertford nc carteret nc chatham nc hyde nc

durham nc

camden nc

brunswick nc columbus nc bladen nc

perquimans nc

gates nc

chowan nc

person nc

orange nc

durham nc

pasquotank nc granville nc

pitt nc

wilson nc

greene nc

lenoir nc

craven nc

beaufort nc

martin nc

polk nc

henderson nc rutherford nc

randolph nc

montgomery nc

guilford nc

alamance nc

chatham nc

moore nc

moore nc

hoke nc

scotland nc

bladen nc

columbus nc

alamance nc

caswell nc montgomery nc

davidson nc

caswell nc

richmond nc

anson nc

stanly nc

robeson nc

scotland nc

hoke nc

rockingham nc

stokes nc

forsyth nc

montgomery nc cumberland nc guilford nc

rowan nc

davie nc

iredell nc

cabarrus nc

rutherford nc

polk nc

henderson nc buncombe nc mcdowell nc

sampson nc

harnett nc

scotland nc

robeson nc

cumberland nc hoke nc

stanly nc

rowan nc

stokes nc surry nc swain nc transylvania nc

stanly nc

bladen nc

pender nc

moore nc

richmond nc

cabarrus nc

union nc

richmond nc

surry nc

yadkin nc

forsyth nc

guilford nc

alleghany nc graham nc

wilkes nc macon nc

yadkin nc jackson nc

forsyth nc haywood nc

jackson nc

haywood nc

buncombe nc henderson nc

hyde nc

dare nc

cabarrus nc

stanly nc

anson nc

franklin nc chatham nc

warren nc harnett nc

johnston nc

vance nc wake nc

washington nc mecklenburg nc granville nc durham nc

warren nc

vance nc

franklin nc

nash nc

halifax nc

washington nc watauga nc

bertie nc avery nc

martin nc caldwell nc

beaufort nc wilkes nc

hyde nc ashe nc

tyrrell nc union nc

franklin nc

57

davidson nc

burke nc

cleveland nc

duplin nc

wayne nc

montgomery nc rockingham nc stokes nc

nash nc northampton nc tyrrell nc

johnston nc

davidson nc

franklin nc

granville nc

cumberland nc

harnett nc


Neighboring Counties duplin nc lenoir nc greene nc alexander nc iredell nc yadkin nc edgecombe greene nc pitt nc nc davie nc forsyth nc stokes nc

wayne nc wilkes nc

wilson nc ashe nc

johnston nc watauga nc

sampson nc caldwell nc

wilson nc

nash nc

johnston nc

wayne nc

yadkin nc

surry nc

wilkes nc

iredell nc

yancey nc

madison nc

buncombe nc mcdowell nc

mitchell nc

addison vt

rutland vt

windsor vt

orange vt

washington vt chittenden vt

bennington vt

windham vt

windsor vt

rutland vt

caledonia vt

grafton nh

orange vt

washington vt lamoille vt

chittenden vt

addison vt

washington vt lamoille vt

essex vt franklin vt grandisle vt

coos nh grafton nh caledonia vt grandisle vt chittenden vt lamoille vt chittenden vt franklin vt

orleans vt orleans vt

lamoille vt

chittenden vt franklin vt

orleans vt

caledonia vt

orange vt

grafton nh

windsor vt

addison vt

washington vt caledonia vt

orleans vt

franklin vt

lamoille vt

caledonia vt

essex vt

rutland vt

bennington vt windsor vt

washington vt

addison vt

chittenden vt lamoille vt

windham vt

cheshire nh

sullivan nh

bennington vt windsor vt

windsor vt

grafton nh

sullivan nh

windham vt

franklin vt

orleans vt

surry nc

essex vt

grandisle vt

washington vt

addison vt caledonia vt

orange vt

bennington vt rutland vt

58

addison vt

orange vt

alleghany nc

APPENDIX D WAGES AND CONTRIBUTION TO GROSS DOMESTIC PRODUCT PER WORKER: CALIFORNIA, MONTANA, NORTH CAROLINA, NEW HAMPSHIRE, AND VERMONT

59

Table D: Wage and Contribution to GDP per Worker (2001 prices) FIPS 6001 6003 6005 6007 6009 6011 6013 6015 6017 6019 6021 6023 6025 6027 6029 6031 6033 6035 6037 6039 6041 6043 6045 6047 6049 6051 6053 6055 6057 6059 6061 6063 6065 6067 6069 6071 6073 6075 6077 6079 6081 6083 6085 6087 6089 6091 6093 6095

County alameda ca alpine ca amador ca butte ca calaveras ca colusa ca contra costa ca del norte ca el dorado ca fresno ca glenn ca humboldt ca imperial ca inyo ca kern ca kings ca lake ca lassen ca los angeles ca madera ca marin ca mariposa ca mendocino ca merced ca modoc ca mono ca monterey ca napa ca nevada ca orange ca placer ca plumas ca riverside ca sacramento ca san benito ca san bernardino ca san diego ca san francisco ca san joaquin ca san luis obispo ca san mateo ca santa barbara ca santa clara ca santa cruz ca shasta ca sierra ca siskiyou ca solano ca

Employee Compensation per Worker Cnty Next RUS State $37,613 $47,533 $28,644 $32,427 $13,739 $19,340 $32,524 $32,427 $15,688 $24,015 $32,938 $32,427 $18,232 $17,072 $32,598 $32,427 $15,633 $22,907 $32,697 $32,427 $18,583 $19,717 $32,622 $32,427 $35,443 $35,281 $31,849 $32,427 $12,218 $16,739 $32,510 $32,427 $22,409 $24,827 $32,840 $32,427 $20,548 $20,351 $33,095 $32,427 $15,135 $17,766 $32,592 $32,427 $17,509 $15,599 $32,553 $32,427 $16,031 $26,141 $33,380 $32,427 $14,720 $21,063 $33,487 $32,427 $21,039 $30,791 $33,751 $32,427 $15,706 $20,994 $33,267 $32,427 $16,458 $25,454 $32,640 $32,427 $11,544 $19,538 $32,510 $32,427 $33,527 $29,467 $32,793 $32,427 $18,721 $20,072 $32,813 $32,427 $32,597 $46,541 $31,217 $32,427 $16,855 $21,320 $32,647 $32,427 $17,446 $24,537 $32,630 $32,427 $19,736 $44,620 $31,085 $32,427 $9,367 $18,601 $32,519 $32,427 $14,805 $20,012 $32,759 $32,427 $24,462 $21,332 $33,185 $32,427 $27,053 $39,215 $31,964 $32,427 $21,880 $25,861 $32,517 $32,427 $33,882 $30,458 $33,976 $32,427 $27,671 $23,635 $32,909 $32,427 $15,203 $18,369 $32,619 $32,427 $21,079 $31,674 $34,098 $32,427 $24,443 $28,025 $33,087 $32,427 $22,159 $43,697 $31,049 $32,427 $22,233 $32,081 $33,415 $32,427 $28,117 $30,138 $33,318 $32,427 $49,715 $37,429 $30,837 $32,427 $23,823 $40,821 $30,620 $32,427 $20,884 $24,552 $33,052 $32,427 $51,470 $47,605 $28,671 $32,427 $24,095 $24,632 $32,901 $32,427 $57,908 $36,246 $29,890 $32,427 $26,457 $52,312 $30,139 $32,427 $20,819 $14,244 $32,553 $32,427 $9,594 $18,225 $32,503 $32,427 $14,317 $18,397 $32,573 $32,427 $23,408 $34,829 $32,172 $32,427

60

Contribution to GDP per Worker Cnty Next RUS State $68,967 $84,511 $55,074 $60,966 $32,034 $37,538 $61,140 $60,966 $30,285 $45,000 $61,933 $60,966 $34,620 $33,538 $61,276 $60,966 $33,752 $42,980 $61,472 $60,966 $44,115 $37,520 $61,320 $60,966 $71,036 $64,877 $60,000 $60,966 $25,050 $32,113 $61,117 $60,966 $42,160 $46,082 $61,772 $60,966 $38,149 $39,877 $62,178 $60,966 $30,792 $34,602 $61,261 $60,966 $33,170 $31,287 $61,194 $60,966 $33,946 $50,898 $62,495 $60,966 $29,064 $39,740 $62,944 $60,966 $39,776 $59,020 $62,746 $60,966 $30,720 $40,039 $62,502 $60,966 $33,068 $48,243 $61,353 $60,966 $22,630 $37,205 $61,118 $60,966 $64,227 $57,074 $60,541 $60,966 $35,000 $37,575 $61,695 $60,966 $65,403 $83,096 $59,025 $60,966 $30,792 $39,958 $61,382 $60,966 $34,622 $45,971 $61,345 $60,966 $37,500 $77,638 $59,160 $60,966 $20,731 $35,333 $61,135 $60,966 $33,328 $37,319 $61,595 $60,966 $49,441 $40,211 $62,342 $60,966 $53,611 $71,873 $60,215 $60,966 $41,190 $46,588 $61,154 $60,966 $66,326 $58,544 $62,135 $60,966 $49,984 $44,214 $61,900 $60,966 $32,199 $34,411 $61,327 $60,966 $41,463 $61,063 $62,329 $60,966 $45,627 $54,050 $62,119 $60,966 $44,545 $76,713 $59,043 $60,966 $42,169 $61,846 $61,593 $60,966 $54,584 $59,106 $62,021 $60,966 $90,777 $69,180 $58,289 $60,966 $44,807 $73,417 $58,342 $60,966 $39,625 $47,450 $62,049 $60,966 $88,328 $84,799 $55,150 $60,966 $46,911 $46,814 $61,812 $60,966 $99,273 $65,402 $57,325 $60,966 $50,232 $90,579 $57,574 $60,966 $39,034 $28,173 $61,196 $60,966 $21,585 $34,205 $61,108 $60,966 $28,835 $34,935 $61,234 $60,966 $43,759 $65,325 $60,511 $60,966

Table D: (continued) FIPS 6097 6099 6101 6103 6105 6107 6109 6111 6113 6115 30001 30003 30005 30007 30009 30011 30013 30015 30017 30019 30021 30023 30025 30027 30029 30031 30033 30035 30037 30039 30041 30043 30045 30047 30049 30051 30053 30055 30057 30059 30061 30063 30065 30067 30069 30071 30073 30075

County sonoma ca stanislaus ca sutter ca tehama ca trinity ca tulare ca tuolumne ca ventura ca yolo ca yuba ca beaverhead mt big horn mt blaine mt broadwater mt carbon mt carter mt cascade mt chouteau mt custer mt daniels mt dawson mt deer lodge mt fallon mt fergus mt flathead mt gallatin mt garfield mt glacier mt golden valley mt granite mt hill mt jefferson mt judith basin mt lake mt lewis and clark mt liberty mt lincoln mt madison mt mccone mt meagher mt mineral mt missoula mt musselshell mt park mt petroleum mt phillips mt pondera mt powder river mt


61



County powell mt prairie mt ravalli mt richland mt roosevelt mt rosebud mt sanders mt sheridan mt silver bow mt stillwater mt sweet grass mt teton mt toole mt treasure mt valley mt wheatland mt wibaux mt yellowstone mt belknap nh carroll nh cheshire nh coos nh grafton nh hillsborough nh merrimack nh rockingham nh strafford nh sullivan nh alamance nc alexander nc alleghany nc anson nc ashe nc avery nc beaufort nc bertie nc bladen nc brunswick nc buncombe nc burke nc cabarrus nc caldwell nc camden nc carteret nc caswell nc catawba nc chatham nc cherokee nc


62



County chowan nc clay nc cleveland nc columbus nc craven nc cumberland nc currituck nc dare nc davidson nc davie nc duplin nc durham nc edgecombe nc forsyth nc franklin nc gaston nc gates nc graham nc granville nc greene nc guilford nc halifax nc harnett nc haywood nc henderson nc hertford nc hoke nc hyde nc iredell nc jackson nc johnston nc jones nc lee nc lenoir nc lincoln nc macon nc madison nc martin nc mcdowell nc mecklenburg nc mitchell nc montgomery nc moore nc nash nc new hanover nc northampton nc onslow nc orange nc


63


Table D: (continued) FIPS 37137 37139 37141 37143 37145 37147 37149 37151 37153 37155 37157 37159 37161 37163 37165 37167 37169 37171 37173 37175 37177 37179 37181 37183 37185 37187 37189 37191 37193 37195 37197 37199 50001 50003 50005 50007 50009 50011 50013 50015 50017 50019 50021 50023 50025 50027

County pamlico nc pasquotank nc pender nc perquimans nc person nc pitt nc polk nc randolph nc richmond nc robeson nc rockingham nc rowan nc rutherford nc sampson nc scotland nc stanly nc stokes nc surry nc swain nc transylvania nc tyrrell nc union nc vance nc wake nc warren nc washington nc watauga nc wayne nc wilkes nc wilson nc yadkin nc yancey nc addison vt bennington vt caledonia vt chittenden vt essex vt franklin vt grandisle vt lamoille vt orange vt orleans vt rutland vt washington vt windham vt windsor vt

Employee Compensation per Worker Cnty Next RUS State $12,683 $17,658 $25,605 $25,402 $13,275 $11,767 $25,473 $25,402 $14,357 $17,174 $26,041 $25,402 $11,357 $14,550 $25,477 $25,402 $19,825 $31,968 $24,976 $25,402 $19,451 $20,234 $25,747 $25,402 $18,176 $22,071 $25,464 $25,402 $22,634 $26,742 $25,265 $25,402 $19,615 $20,190 $25,561 $25,402 $18,290 $16,064 $26,005 $25,402 $21,787 $29,472 $24,810 $25,402 $29,344 $21,862 $25,569 $25,402 $22,035 $22,295 $25,627 $25,402 $16,722 $16,504 $26,224 $25,402 $21,565 $19,333 $25,574 $25,402 $21,027 $23,386 $25,558 $25,402 $14,245 $29,052 $24,877 $25,402 $19,662 $28,751 $25,248 $25,402 $12,776 $17,336 $25,526 $25,402 $23,298 $21,638 $25,591 $25,402 $9,939 $15,049 $25,478 $25,402 $24,748 $35,581 $23,584 $25,402 $19,859 $16,220 $25,517 $25,402 $29,941 $30,563 $24,337 $25,402 $12,649 $20,911 $25,529 $25,402 $11,823 $18,271 $25,488 $25,402 $16,566 $21,023 $25,547 $25,402 $17,028 $19,964 $25,737 $25,402 $24,066 $20,421 $25,663 $25,402 $24,678 $20,359 $25,717 $25,402 $16,790 $27,107 $25,280 $25,402 $14,495 $22,019 $25,542 $25,402 $20,707 $24,260 $27,960 $27,039 $21,021 $21,668 $27,712 $27,039 $17,737 $22,211 $27,906 $27,039 $28,293 $19,454 $27,692 $27,039 $18,704 $23,400 $27,441 $27,039 $18,022 $26,140 $27,374 $27,039 $12,579 $26,743 $27,109 $27,039 $18,002 $24,200 $27,828 $27,039 $15,667 $22,598 $27,992 $27,039 $17,283 $17,960 $27,614 $27,039 $21,537 $20,845 $27,704 $27,039 $20,350 $24,826 $27,782 $27,039 $22,695 $22,313 $27,736 $27,039 $20,803 $22,910 $28,210 $27,039

64

Contribution to GDP per Worker Cnty Next RUS State $28,525 $31,143 $47,364 $46,957 $26,328 $30,754 $47,065 $46,957 $30,840 $33,804 $47,973 $46,957 $29,263 $29,462 $47,074 $46,957 $36,717 $50,851 $46,726 $46,957 $35,029 $35,403 $47,703 $46,957 $32,119 $39,554 $47,093 $46,957 $41,109 $48,154 $46,884 $46,957 $37,503 $36,467 $47,267 $46,957 $33,447 $29,446 $48,090 $46,957 $42,915 $61,602 $44,738 $46,957 $47,538 $47,919 $46,893 $46,957 $37,033 $38,662 $47,562 $46,957 $37,043 $30,979 $48,401 $46,957 $32,641 $36,080 $47,296 $46,957 $37,268 $49,459 $46,849 $46,957 $28,172 $61,139 $44,837 $46,957 $34,319 $71,252 $45,555 $46,957 $22,083 $33,188 $47,173 $46,957 $47,014 $38,350 $47,372 $46,957 $32,473 $33,402 $47,055 $46,957 $43,170 $66,464 $43,511 $46,957 $33,583 $34,897 $47,135 $46,957 $53,904 $51,514 $45,665 $46,957 $29,055 $41,597 $47,111 $46,957 $25,841 $33,229 $47,116 $46,957 $31,484 $37,405 $47,249 $46,957 $29,916 $38,273 $47,540 $46,957 $41,724 $35,767 $47,560 $46,957 $42,415 $38,931 $47,492 $46,957 $30,492 $62,320 $45,653 $46,957 $30,984 $38,274 $47,298 $46,957 $35,176 $44,877 $49,310 $48,104 $38,259 $40,238 $49,114 $48,104 $33,918 $38,619 $49,740 $48,104 $53,005 $35,529 $48,821 $48,104 $34,962 $40,944 $48,889 $48,104 $36,409 $49,053 $48,215 $48,104 $28,998 $50,500 $47,805 $48,104 $33,943 $45,255 $48,971 $48,104 $29,680 $39,211 $49,953 $48,104 $32,261 $34,960 $48,958 $48,104 $39,650 $37,255 $49,226 $48,104 $36,102 $46,185 $49,026 $48,104 $43,306 $39,952 $49,222 $48,104 $37,906 $40,667 $50,176 $48,104

APPENDIX E ADDITIONAL EMPIRICAL CONSIDERATIONS

65

ADDITIONAL EMPIRICAL CONSIDERATIONS Beyond the theory of a SAM impact analysis or the mechanical aspects of running the SEBAS software, a person performing a socio-economic impact analysis of a RD loan or grant is frequently faced with a myriad of technical issues that have to be solved or addressed.

ADJUSTING FOR INFLATION

The purpose of most impact analyses is to estimate the likely economic and social consequences of projects or actions. In most economic impact models, this estimation is done with a series of equations whose parametric values are computed with reference to a base year (e.g., 2001 or some other convenient year). As a result, the technical relationships only reflect the existing economic conditions for that base year. Among the changes that occur over time is the rate of inflation. Normally, inflation is handled in economic models by deflating current monetary values of model inputs in terms of a model’s reference year and then model’s output values are inflated to the desired impact year.

In its simplest form, a monetary value is the product of price and quantity. Therefore, the task of price deflation is to separate the prices from the quantities within the monetary values. The importance of consistency in time/price/quantity relationships becomes apparent in the context of economic models. Consequently, it is very important that the input information provided by a model’s user be as consistent with the technical relationships of the model as possible.

Inflation has two effects on measuring monetary evaluations of quantities that are important for properly using economic impact models. First, inflation reduces the overall purchasing power of expenditures. Second, inflation alters the mix of commodities purchased by the expenditures. That is, although

66

inflation generally affects the prices for all goods and services, some commodities are more affected than others. Thus, the relationship between the prices of commodity changes due to the differential effects of inflation (or as economists like to say, “the relative prices of goods and services change”). As this occurs, consumers and producers purchase more of some things and less of others, especially when some “substitutability” exists among commodities. This happens because consumers and producers attempt to reduce the deleterious effects of inflation has on their general welfare or profit situation.

A price index is a number that indicates a relative change in the price of a commodity over time or that shows the relative change in an average of the prices of several goods over time. Price indexes are compiled with reference to a base year (e.g., 1987) and computed in relation to a standard value (e.g., 19878=100). A price index can be restated for another base year by dividing its current value be the price index value for the desired base year. The resultant price index can be stated in terms of a standard value (e.g., new base year = 100) by multiplying it by the standard value.

Arithmetically, deflating monetary values is simple: just multiply the monetary value by the ratio of the standard value to the appropriate price index. If the standard value is equal to one, then deflating a monetary value is computed by dividing the monetary value by the price index. This does not mean that actual physical quantities have been determined (e.g., bushels of wheat). Instead, the monetary values have been made consistent with the prices that existed during the reference period. That is, the effects of price changes since the base period have been removed, revealing the changes in the physical quantities since the base year (expressed in terms of the prices for the base period).

There are two types of price indexes: commodity price indexes and composite price indexes. A commodity price index is a price index for a specific

67

good or service (such as cotton) or for a narrowly defined group of commodities (e.g., household appliances). Deflating the changes in expenditures due to a project or action by type of product or by industrial sector permits an analyst to accurately estimate the relevant changes in expenditures, because the differential effects of inflation on the relative prices of goods and services are taken into account. Detailed commodity price indexes are published monthly in terms of prices paid by producers and consumers. An analyst should check with the U.S. Bureau of Labor Statistics (BLS) for copies of the reports, Producer Prices and Price Indexes and CPI Detailed Report. In addition, detailed industrial price indexes are available on an annual basis from BLS through their publication, Time Series Data on Output, Prices, and Employment. These reports contain the latest available commodity price indexes.

Whereas a commodity price index reflects the relative price change for a specific commodity, a composite price index is the average relative change in prices for a broad range of commodities over time. Composite price indexes have been compiled for many groups of commodities (e.g., consumer expenditures, construction costs, government purchases, and investment expenditures). A good source for many of the published composite price indexes is BLS (again) and a current issue of the Survey of Current Business, published by the U.S. Bureau of Economic Analysis (BEA).

Because composite price indexes are weighted averages of relative price changes for groups of specific commodities, their proper use depends on whether the quantity weights used in their calculation are relevant to the situation to which they are being applied. They can be useful when applied appropriately, especially to deflate expenditures for which the pattern of commodities purchased is not known; however, they can present problems of impact analysis when they are used improperly. For example, probably the most widely used composite price index for measuring the overall rate of inflation is the Consumer

68

Price Index (CPI) from BLS.6 But there seems to be little understanding of or little attention paid to the procedures used to compile the CPI. Specifically, the CPI is computed using commodity prices paid by urban residents and weighted by a specific expenditure pattern. Thus, it seems inappropriate to deflate the consumer expenditures by residents of a rural area using a CPI, because the expenditure pattern for urban residents is not likely to be the same as for rural residents.

An analyst should also be aware of the time period that the quantity weights for the composite commodities are chosen. Composite price indexes that are computed using a fixed set of quantity weights are called “fixedweighted” price indexes. Because the quantity weights are held constant over time, the changes observed in the price index result from price changes. Composite price indexes computed by permitting the quantity weights to vary from one period to the next are called “implicit” price indexes. Both the weights and prices fluctuate, which makes comparing price indexes for two different years difficult. The selection of the most appropriate composite price index will depend on its use. An implicit price index is good for determining the current rate of inflation, because the most recent set of quantity weights is used; thus, price changes implied by an implicit price index reflect the average relative price change for the actual set of goods and services most recently purchased. On the other hand, for computing relative price changes over a period of time (e.g., for deflating expenditures), fixed-weighted price indexes would seem most appropriate when they are available. Table E.1 provides several types of composite price indexes that a acceptable for use in economic impact analysis.

6

Evidence for this statement is that the CPI is used to determine the change in benefits paid to recipients of programs such as Social Security, Federal Retirement, and many state retirement programs, and even some wage contracts negotiated by unions.

69

Table E.1: Examples of Composite Price Indexes 1997

Producer Price Indexes

Consumer Price Index, All Urban Consumers - (CPI-U) Finished goods Capital equipment Finished goods less food and energy Finished energy goods Intermediate materials, supplies & components Materials & components for construction Crude materials All commodities Industrial commodities Gross domestic product Personal consumption expenditures Gross domestic purchases Gross national product Manufacturing and trade industries Manufacturing industries Durable goods manufacturing industries Nondurable goods manufacturing industries Merchant wholesale industries Durable goods merchant wholesale industries Nondurable goods merchant wholesale industries Retail trade industries

1998 1999 2000 2001 2002 2003 Bureau of Labor Statistics [Index numbers, 1982-1984=100] 160.50 163.00 166.60 172.20 177.10 179.90 184.00 131.8 130.7 133 138 140.7 138.9 143.3 138.2 137.6 137.6 138.8 139.7 139.1 139.5 142.4 143.7 146.1 148 150 150.2 150.5 83.4 75.1 78.8 94.1 96.7 88.8 102 125.6 123 123.2 129.2 129.7 127.8 133.7 146.5 146.8 148.9 150.7 150.6 151.3 153.6 111.1 96.8 98.2 120.6 121 108.1 135.3 127.6 124.4 125.5 132.7 134.2 131.1 138.1 127.7 124.8 126.5 134.8 135.7 132.4 139.1 Bureau of Economic Analysis [Index numbers, 2000=100] 96.8 97.0 98.0 100.0 102.4 104.1 106.1 96.4 96.5 97.7 100.0 102.1 103.6 105.7 96.9 96.6 97.7 100.0 102.0 103.5 105.7 96.8 97.0 98.0 100.0 102.4 104.1 106.1 99.37 97.10 97.25 100.00 99.43 98.46 100.07 98.63 96.82 97.04 100.00 100.02 99.11 101.46 102.34 100.85 99.98 100.00 99.46 98.85 98.61 93.87 91.69 93.27 100.00 100.72 99.45 104.97 102.25 97.56 96.70 100.00 98.59 97.85 100.90 108.84 103.88 100.58 100.00 97.09 95.77 95.84 95.34 90.93 92.57 100.00 100.17 100.03 106.04 99.32 97.85 98.25 100.00 99.77 98.31 97.85

SPATIALLY DISTRIBUTING FIRM EXPENDITURES

Using a multiregional model, such as the RD SEBAS, provides a great degree of flexibility in carrying out almost any type of regional economic impact analysis and the information content of the of the results allows much to said about an activity and its influence on local areas, as well as, broader geographic spaces. It combines the industry-specific nature of the firm causing an impact with the added spatial dimension to produce impact estimates for the industrial sectors that are affected by a project and their locations. But all this analytical capacity does not come without a “price”. As usual, a user of SEBAS must interpret a firm’s activities and derive a set of geographically specific expense expenditures reflecting those interpretations. This means that a firm’s expenses must not only be specified by commodity but also according to where the purchases are made. For example, 70

we must specify both the quantity of leather goods purchased (in monetary terms) and the amounts purchased from producers in the county where the firm is located, in the adjacent counties, in the remainder of the state, and from places elsewhere. If the loan or grant recipient knows and provides this data then it is appropriate to use this information. On the other hand, it is common for a firm to have a reasonably good idea of the commodity distribution of its expenses, but not where the goods and services come from (especially since the billing address may not be the actual location where the purchased goods and services are produced).

There has been some concern expressed in the past over the accuracy of regionally distributing expenditures, especially in the industrial detail required by multi-regional models like SEBAS. Probably the most desirable method of determining the regional distribution is to perform a survey. This ensures the quality of the estimates, as long as the survey is conducted properly. However, surveying is not only time consuming but also expensive. Under conditions of limited timeframes and tight budgets (which is characteristic of most impact analyses) surveying is normally out of the question.

Several alternative options to surveying for distributing expenditures have been developed by regional analysts, for example, shares and gravity indexes. and multi-regional trade coefficients. However, the sector-specific multi-regional trade coefficients in SEBAS provide ideal measures for distributing business expenses (Miller and Blair, 1985, pp. 69-85). Suppose one has a vector of industry-specific expenditures, say ER, that defines spending that originates in region R but not where they occur. For example, a firm in a Montana county might require $2,000,000 worth of office supplies but not know where they are purchased from or produced. To distribute the firm’s expenditures to other region S, one pre-multiplies ER by the matrix of regional trade coefficients, TSR, that indicate the proportion of goods and services purchased from region S by businesses and residents of region R.

71

Rest of State

Adjacent Counties

County

Table E.2: Business Expenses of a Hypothetical Firm

Agriculture Mining & Construction Manufacturing Trade & Transportation Services Agriculture Mining & Construction Manufacturing Trade & Transportation Services Agriculture Mining & Construction Manufacturing Trade & Transportation Services Rest of World Total

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

Expenses at Origin $1,000 $300 $10,000 $20,000 $5,000 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $0 $36,300

Suppose that a firm located in the “county” region requires the purchase of $36.3 million worth of goods and services (Table E.2). The spending represents a firm’s purchases of its requirements in order to carry on its business. Next, suppose we have a multi-region trade coefficients matrix containing a 5-sector (agriculture, mining and construction, manufacturing, trade and transportation, and services), 3-region (county, adjacent counties, and rest of state) set of regional trade coefficients (Table E.3). The geographically distributed business expenses are calculated by pre-multiplying the business expense table by the multi-region trade coefficients table (TSR  ER in matrix notation); see Table E.4.

72

Table E.3: Multi-Regional Trade Coefficients for Hypothetical Regions County 1

2

3

Adjacent Counties 4

5

1

2

3

4

Rest of State 5

1

2

3

4

5

Agriculture

1

0.427 0.000 0.000 0.000 0.000 0.037 0.000 0.000 0.000 0.000 0.015 0.000 0.000 0.000 0.000

Mining & Construction

2

0.000 0.347 0.000 0.000 0.000 0.000 0.039 0.000 0.000 0.000 0.000 0.015 0.000 0.000 0.000

Manufacturing

3

0.000 0.000 0.405 0.000 0.000 0.000 0.000 0.091 0.000 0.000 0.000 0.000 0.076 0.000 0.000

Trade & Transportation

4

0.000 0.000 0.000 0.483 0.000 0.000 0.000 0.000 0.095 0.000 0.000 0.000 0.000 0.053 0.000

Services

5

0.000 0.000 0.000 0.000 0.375 0.000 0.000 0.000 0.000 0.100 0.000 0.000 0.000 0.000 0.100

Agriculture

1

0.237 0.000 0.000 0.000 0.000 0.582 0.000 0.000 0.000 0.000 0.160 0.000 0.000 0.000 0.000


2

0.000 0.222 0.000 0.000 0.000 0.000 0.524 0.000 0.000 0.000 0.000 0.172 0.000 0.000 0.000

Manufacturing

3

0.000 0.000 0.131 0.000 0.000 0.000 0.000 0.468 0.000 0.000 0.000 0.000 0.129 0.000 0.000


4

0.000 0.000 0.000 0.108 0.000 0.000 0.000 0.000 0.555 0.000 0.000 0.000 0.000 0.088 0.000

Services

5

0.000 0.000 0.000 0.000 0.134 0.000 0.000 0.000 0.000 0.480 0.000 0.000 0.000 0.000 0.162

Agriculture

1

0.023 0.000 0.000 0.000 0.000 0.054 0.000 0.000 0.000 0.000 0.634 0.000 0.000 0.000 0.000


2

0.000 0.023 0.000 0.000 0.000 0.000 0.053 0.000 0.000 0.000 0.000 0.480 0.000 0.000 0.000

Manufacturing

3

0.000 0.000 0.013 0.000 0.000 0.000 0.000 0.023 0.000 0.000 0.000 0.000 0.522 0.000 0.000


4

0.000 0.000 0.000 0.153 0.000 0.000 0.000 0.000 0.022 0.000 0.000 0.000 0.000 0.633 0.000

Services

5

0.000 0.000 0.000 0.000 0.011 0.000 0.000 0.000 0.000 0.025 0.000 0.000 0.000 0.000 0.533

Rest of World

0.313 0.407 0.451 0.257 0.480 0.328 0.384 0.419 0.328 0.396 0.190 0.333 0.273 0.225 0.205

Total

1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000 1.000

Table E.4: Regionally Distributed Business Expenses of a Hypothetical Firm

Adjacent Counties

County

Regionally Distributed Expenses

Rest of State

Rest of State

Adjacent Counties

County

Producing Region/Industry

Agriculture Mining & Construction Manufacturing Trade & Transportation Services Agriculture Mining & Construction Manufacturing Trade & Transportation Services Agriculture Mining & Construction Manufacturing Trade & Transportation Services Rest of World Total

1 2 3 4 5 1 2 3 4 5 1 2 3 4 5

73

$427 $104 $4,055 $9,651 $1,876 $237 $67 $1,313 $2,156 $669 $23 $7 $126 $3,063 $57 $12,470 $36,300

DEFINING REGIONS

How should one define a region or set of regions for a socio-economic impact analysis? For people not accustomed to carrying out regional analysis, justifying a particular study area may not be easy. Even among experienced regional analysts, delineating a study region is a thorny, but important issue. The justifications of most study areas often are ignored—perhaps because the region is predefined (e.g., for an analysis of the fiscal impact of a tax cut within Alabama) or maybe because the region was the only available unit of observation for a “cross-section” study. Unfortunately, few universally accepted rules are available to help an analyst choose a study area. Thus, the regional setting for an impact analysis is usually somewhat subjective or arbitrary. Careful thought and judgment should always be exercised when delineating regions.

Other than a geographic aggregate, what is a region? There are as many answers to this question as there are people who use geographic settings for their analyses. Such diversity of opinion is due mostly to the different uses of spatial aggregates.7 Most regional and urban analysts performing socioeconomic impact analysis prefer the functional economic area concept for defining study regions.8 Regions defined in this way explicitly consider the economic linkages and spatial dimensions between and among the residential

7

Two common methods of defining regions are frequently used. First, regions are sometimes delineated along administrative or political boundaries (e.g., the State of Alabama). It is often claimed that since the institutional framework within which economic and social policies are designed and implemented is of overriding importance, then the geographic unit of analysis should coincide with the same administrative or political boundaries. Second, homogeneity of one form or another can be used to justify some regions. For example, one can envision coal mining regions, river-basin regions, air pollution regions, or even German-speaking areas. What binds these areas is usually some common physical, economic, social, or statistical characteristic. 8 The concept of a functional economic area (FEA) appears attributable to Karl Fox: see K.A. Fox and T.K. Kuman, “The Functional Economic Area: Delineation and Implications for Economic Analysis and Policy.” Papers and Proceedings, Regional Science Association, Vol. 15 (1965), pp. 57-85.

74

population and businesses located in the geographic area. In other words, commuting and trading patterns are of prime concern. This type of region is often called “nodal” because: …the region is perceived as being composed of heterogeneous nodes of different size (cities, towns, villages, and sparsely populated rural areas) that are linked together functionally. These functional links can be identified through observation of flows of people, factors, goods, and communications (Richardson, 1979, p. 21). An examination of a map shows that population and businesses are not spread evenly over space, but are concentrated at specific locations called “agglomerations.” The factors that generate these agglomerations are varied; e.g., transportation advantages (such as the confluences of several rivers), resource deposits, factor endowments, local infrastructure (such as goods schools and public transportation facilities), climate, and even proximity to firms that supply needed production requirements or provide ready markets.

Beyond the general conceptual guidelines for region types (above), there is little formal advice about defining regions. However, when an analyst decides to delineate a study area, the decision should based on his/her considered judgment—possibly form past experience and specific knowledge of the area under consideration. At a practical level, another important issue is determining the smallest geographic unit for which relevant data are available. For the most part, counties provide these data.9 With respect to economic impact analysis, it is probably obvious that a region should be the geographic area in which the significant economic and social consequences of a RD grant or loan occur.

The definition of the affected region must include all the ingredients of selfsustaining region/local businesses, local governments, and individuals. The 9

Although some data are available at the census tract level (e.g., population and income) which possible could be used to delineate regions, the data needed to analyze economic impacts are most readily available only at the county level, unless one is willing to conduct expensive and time-consuming surveys.

75

region must reflect the limits of the economic activity associated with the affected population. This is not an easy definition to obtain and numerous “simplistic” attempts at a standard methodology have failed. Through experience, however, it has become obvious that the following considerations must be included in the definition. 

The residence patterns of the affected personnel determine where they are likely to spend their salaries. There are records of addresses of personnel which can serve as a means to document this consideration.



The availability of local retail shopping is also a factor in the regional definition. The location of new malls or other popular shopping opportunities can dictate an expansion of a region if no comparable opportunities exist in the immediate vicinity.



The “journey-to-work” time for employees often is a large part of the regional definition. On average, a journey-to-work time of one hour is considered a maximum criteria, however, some regions in the country are characterized by longer travel times for a typical commute. It is affected significantly by the quality of the transportation network, the availability of mass transit, and what impacts are felt during “rush hour” peaks.



Local customs and culture often affect the boundaries of a region. Long versus short commute patterns, willingness to approach the “inner city”, the sense of local community, and other factors often lead to seeming inconsistencies in the regional limits. These are unfortunately, hard to address factors, but are nonetheless a fact of life which must enter into the analysis process and the definition of the region. None of the considerations above can be used exclusively to define

regions for all socio-economic studies. It is necessary that all these considerations enter into the decision process. This often requires input from local planning officials in addition to analysis of primary and secondary data sources (interviews, map, etc.).

76

REFERENCES Holland, David and Wyeth, Peter. 1993. “SAM Multipliers: Their Interpretation and Relationship to Input-Output Multipliers.” Edited by Daniel M. Otto and Thomas G. Johnson in Microcomputer-Based Input-Output Modeling: Applications to Economic Development. Boulder, CO: Westview Press, 181-197. King, Benjamin B. 1985. “What is a SAM?” Edited by Graham Pyatt and Jefferey I. Round in Social Accounting Matrices: A Basis for Planning. Washington, DC: The World Bank, 17-51. Miller, Ronald E. and Blair, Peter D. 1985. Input-Output Analysis: Foundations and Extensions. Englewood Cliffs, NJ: Prentice-Hall, Inc. Minnesota IMPLAN Group, Inc. (MIG). 1998. Elements of the Social Accounting Matrix, MIG IMPLAN Technical Report TR-98002. Stillwater, MN: Minnesota IMPLAN Group, Inc. Minnesota IMPLAN Group, Inc. (MIG). 2000. IMPLAN Professional, Version 2.0: Social Accounting & Impact Analysis Software, 2nd Edition. Stillwater, MN: Minnesota IMPLAN Group, Inc. (June). Pyatt, Graham and Round, Jefferey I. 1985. “Regional Accounts in a SAM Framework.” Edited by Graham Pyatt and Jefferey I. Round in Social Accounting Matrices: A Basis for Planning. Washington, DC: The World Bank, 84-95. Round, Jeffery. 2003. “Social Accounting Matrices and SAM-Based Multiplier Analysis. Edited by Francois Bourguignon and Luiz A. Pereira da Silva in Evaluating the Poverty and Distributional Impact of Economic Policies (Techniques and Tools). Washington, DC: The World Bank, in draft (March).

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