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Research Report

Number 65

Federal Outlays by

Type of Nonmetro

County DISPLAY COPY

Bernai L Green

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Federal Outlays by Type of Nonmetro County, by Bernai L. Green, Agriculture

and Rural Economics Division, Economic Research Service, U.S. Department of

Agriculture. Rural Development Research Report Number 65.

Abstract

This report uses a new method of grouping Federal payments into six categories

to examine how the payments were distributed among eight types of nonmetro

counties in fiscal year 1980. Federal payments to nonmetro areas were highest for

the *'income transfers" category (which includes Social Security) and lowest for

the ^^agriculture'' category (which includes commodity programs and other farm

programs). Per capita income transfers were highest in "retirement" counties

(counties that attract retirees), suggesting that the economic base of these counties

may be more stable than that of counties that depend mainly on farming,

manufacturingj or mining.

Keywords: Federal outlays. Federal spending, grant equivalents, economic base,

nonmetropolitan, economic specialization, county typology, rural development

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1301 New York Ave., NW.

Washington, DC 20005-4788 January 1987
Contents

Page

Summary iv

Introduction 1

Federal Outlays Data 2

Federal Outlays Categorized by Function and Type of Assistance 2

Net Grant Equivalent Adjustments 3

County Classifications 3

Results 4

Total Federal Outlays 4

Targeted Economic Development Programs 6

Income Transfer Programs 6

Human Capital Programs 6

Infrastructure Programs 8

General Government Programs , 8

Commodity Agriculture Programs 8

Implications. . , 9

Income Transfers and Retirement Counties 9

Defense Spending and Research in Government Counties 10

Relationships Between Lower Incomes and Defense Outlays 10

References 11

Appendix; Explanation of Discounting Procedures 13

Appendix Tables 15

Acknowledgments

Several colleagues provided important assistance to the author and deserve special

thanks: Norman Reíd and Eleanor Whitehead provided briefings and materials

related to Federal outlays; Peggy Ross, Robert Coltrane, Patrick Sullivan, Robert

Hoppe, and James Schaub provided useful insights into the subject matter; and

Pat Saunders and Brenda Heggie typed the report.
Summary

Nonmetro counties received nearly $1,500 per capita in

Federal payments in 1980, the most recent year for

which detailed data are available. The largest component of that, $844 per capita, was for income transfers,

which includes Social Security and government and

military retirements. The lowest payments ($56 per

capita) were for agriculture.

This report uses a new method of condensing about

1,900 Federal budget elements into six broad groupings—targeted economic development, income transfers,

human capital (levels of education, job skills, and

health), infrastructure (roads, housing, industrial parks,

and water systems), general government, and agriculture (commodity programs and disaster relief)'—to examine how Federal outlays were distributed among eight

types of nonmetro counties as categorized by their principal source of income (for example, farming or

manufacturing).

Per capita income transfers were highest in retirement

counties ($914), suggesting that the economic base of

these counties is more stable than that of counties

dependent on farming, manufacturing, and mining,

where income transfers were much lower. The stability

of the retirement counties, however, depends on Social

Security payments and on the continuation of the current trend of older Americans' moving to those

counties.

Other major fmdings of this study are:

• Federal outlays allocated to all counties amounted to

$527 billion in 1980, which included $61 billion in loan

guarantees and $8 billion in direct loans. Loans and

loan guarantees were directed more toward nonmetro

farming-dependent counties which received, along with

other outlays, over $2,300 per capita. However, when

adjustments were made to loans and loan guarantees to

convert them to net grant equivalents, this figure was

reduced by 38 percent to $1,454.

• Loans and loan guarantees in nonmetro America, as

contrasted to direct grants and other spending, were

directed chiefly to agriculture and infrastructure (roads,

housing, industrial parks, and water systems). When

these loans and loan guarantees were converted to net

grant equivalents, the face value was reduced by 68 percent for agriculture and by 57 percent for infrastructure.

• The poverty counties (those ranked in the lowest income quintile for the past three decades) are concentrated in the South, as are large military installations

such as Fort Benning, GA; four of the seven counties

adjacent to Fort Benning are in the poverty group. This

pattern is typical of large military bases. Thus, the

economic future of such counties may depend on

Federal spending for civilian employment on military

bases.

• Government counties (25 percent or more of income

from government sources) received the largest per capita

outlays {$2,269); important sources were in the general

government (especially military) and income transfer

functions.

• Manufacturing counties (30 percent or mor^ of income from manufacturing) ranked last in per capita

Federal outlays ($1,279). If the amount of tax concessions by the Federal Government were known and included, however, this ranking might have changed.

The eight nonmetro county groups identified in a companion study. The Diverse Social and Economic Structure of

Nonmetropolitan America, are: farming-dependent, manufacturing-dependent, mining-dependent, specialized

government, persistent poverty. Federal lands, retirement, and unclassified or ungrouped.
Federal Outlays by Type

of Nonmetro County

By Bernai L. Green

Introduction

As the economic base of rural areas changes in the

eighties, altering traditional employment patterns, the

Federal Government will face increased requests for

help. For example, employment in farming, manufacturing, and mining is stable or declining, while employment in services is increasing at a rapid pace, thereby

dramatically altering the economies of most rural counties. Changes in residence as denoted by rapid population increases in some areas, especially in retirement

communities, coupled with employment changes in rural

America, further strain Government resources (2, 3,

21).^ Federal outlays are powerful stimuli helping to

shape State and local economies. Anyone who reviews

the 1,900 elements in the Federal budget will discern

both efforts to respond to past economic stresses and attempts to expand the flow of goods and services {18).

The budget and county groupings developed here give

decisionmakers a powerful tool for formulating programs

suitable for the late eighties.

In this study, I explore the linkages between types of

counties and types of Federal outlays. An earlier report,

The Diverse Social and Economic Structure of Nonmetropolitan

America (to which this study is a companion), identified

eight distinct types of rural counties and investigated the

policy relevance of economic diversity in rural America

(^).2 The eight nonmetro county types are: farmingdependent, manufacturing-dependent, miningdependent, specialized government, persistent poverty,

Federal lands, retirement, and unclassified or

ungrouped. Because of the varying characteristics of

these county groups, Government policies and expenditures will affect them in quite different ways {19, 20).

The author is an economist in the Agricuhure and Rural Economics

Division, Economic Research Service, U.S. Department of

Agricuhure.

^Itahcized numbers in parentheses refer to items in the References.

^The leader of the Policy Impacts Project, begun in late 1982, was

Lloyd Bender. Others on the research team were Bemal Green,

Thomas Hady, John Kuehn, Marlys Nelson, Leon Perkinson, and

Peggy Ross.

My focus here is on the financial role that the Federal

Government plays in rural development. The benefits of

this inquiry depend on the reader's ability to select

county types and groupings of Federal monies so as to

tell whether or not the pattern of Federal outlays is appropriate to the needs of specific nonmetro counties. I

have categorized the outlays into six major groups:

Targeted

economic

development:

Income

transfers:

Human

capital:

Infrastructure:

General

government:

Commodity

agriculture:

Government has discretion in selection of areas that receive these

funds. Funds usually go to

economically lagging areas for a

broad range of goods and services.

Any income that people receive for

which no service is currently being

rendered. For example, Social

Security payments received by

retirees are based on work done in

the past.

Represents results from investments

in education, job training, health,

and esthetic experiences.

Consists of facilities used to supply

public services for communities,

such as roads and streets, industrial

parks, water systems, housing, and

police and fire protection.

Dominated by military expenditures, but other functions are also

included, such as civilian pay and

operation of prisons.

Consists mainly of commodity loans

to farmers by the Commodity

Credit Corporation (CCC), commodity inventory operations by the

CCC, and broad categories of

loans by the Farmers Home

Administration.
I further categorized these six broad functions into 16

subgroups. For example, I divided income transfers into

retirement, health and disability, and income

maintenance.

Two examples illustrate the usefulness of this crossclassification, showing the connection between Federal

payments and the dominant economic resources (such as

human or capital) of the different types of nonmetro

counties. First, the current agricultural crisis will cause

people to move out of agriculture to more profitable

alternatives {8, 9, 27), This adjustment process may

center on efforts to bolster nonfarm employment opportunities as well as on new types of job training and

retraining. Thus, categories of Federal assistance to

farm areas in the form of targeted economic development, human capital, and infrastructure (roads, schools,

industrial buildings and parks, and water and utilities)

receive more emphasis. Declining land values and tax

revenues in many areas make it increasingly difficult to

maintain public services.

Second, insights about resources in poverty counties

(chronic underinvestment in education, job skills, and

health services) may suggest that more emphasis be

placed on income transfers coupled with human capital

expenditures, such as education and job training. We

clearly need to know more about the levek and categories of assistance to counties at the bottom of the

economic ladder since the effectiveness of past programs

is under increased scrutiny in this current period of

Federal budget stress. For example, programs eliminated may adversely affect people in poverty counties

more than those elsewhere.

from the Community Services Administration.^'* The

data were classified by type of assistance: transfer

payments, loans, loan guarantees, insurance, grants-inaid, contracts, salaries, and other administrative expenditures. Reid and Whitehead note that the data on

outlays are ^^highly detailed, with separate entries for

over 1,900 program categories in 1980. . .(and) are the

only source that presents figures on total Federal funding to each of the Nation's counties and its cities over

25,000 population" {18, p. 1).

Reid and Whitehead concluded that 85 percent ($532.4

billion of 1629.5 billion) of the outlays embodied in

1,683 budget elements were reliable for county-level

analysis.^ They cautioned against assuming that areas

receiving the highest level of funds also receive the most

benefits: "Actually many programs have benefits that

accrue to areas broader than those represented in the

data. Some programs buy useful services or facilities for

a community, while others may only contribute to local

payrolls or services'* {18, p. iii). Reid and Whitehead

reported that nonmetro counties rely more heavily on

loans and transfer payments, whereas metro areas obtain more of their funds from contracts (especially

military) and from salaries and administrative expenses

{18, p. iv).

Federal Outlays Categorized by Function

and Type of Assistance

For this study, I categorized the data on Federal outlays

(1,683 budget elements) into 6 major functions (table 1)

with 16 subfunctions. The following major functions are

described in more detail later:

By contrast, Federal payments in the form of income

transfers are highest in the retirement counties. Such

transfers are any income received for which no service is

currently being rendered. For example, Social Security

payments received by retirees are based on work done

in the past. An aging population is raising the percentage of elderly people, thereby changing both Federal

budget elements (such as Social Security, government

and military retirement income, and medicare) and the

economies of local areas receiving these retirees. In

1980, the last year for which we have detailed county

data for Federal outlays, the largest budgetary component for nonmetro counties went to income transfers.

The stability of retirement counties, however, depends

on these payments and on the continuation of the current trend of older Americans' moving to these counties.

Federal Outlays Data

One of the two data sets I used was county4evel

estimates of Federal outlays, fiscal year 1980, obtained

• Targeted econamic development,

• Income transfers,

• Human capital,

• Infrastructure,

• General government, and

• Commodity agriculture.

I collapsed type of assistance, which Reid and

Whitehead had classified into 10 types that involved

1,683 budget elements, into three categories:

^The Federal funds considered do not include tax concessions by the

Federal Govem^ment. These excused taxes are due to proyisions of the

tax code providing tax rehef to various groups. For 1984, tax concessions in behalf of the business sector were substantial; the largest concession was Investment Tax Credits at $27.5 billion {11^ 23, 26).

*The Community Services Administration was abolished in 1981; its

report on ñseal year 1980 funds was the final report in the annual

series, formerly known as tht Federal Outlays reports (18). However, the

Bureau of the Census prepared a modified Federal funds tape for fiscal

year 1983.

^Examples of the 280 budget elements excluded ($97.1 billion) are

interest on the public debt^ disaster relief assistance, civil service retirement and disability fund, and foreign assistance programs.
Table 1—Total Federal funds, by function and type of assistance, 1980

Type of assistance

Function Grants/direct

spending Loans Loanguarantees

Total

Targeted economic development

Income transfers

Human capital

Infrastructure

General government

Commodity agriculture

Total

2L4

217.7

41.2

26.3

146.6

5.1

458.4

Billion dollars

1.8 4.9

0 1,2

.04 .7

1.4 47.7

0 .3

4.4 6.4

7.6 61.1

28.1

218.9

41.9

75.4

146.9

15.9

527.1

grants/direct spending, loans, and loan guarantees. I

reduced the number of counties from 3,134 to 3,069,

mainly by deleting data for Alaska and Hawaii^ and by

combining Virginia's independent cities into county

equivalents.

Thus, the outlay data are nov^ comparable v^ith a

socioeconomic data file prepared for more general

analysis.^

Net Grant Equivalent Adjustments

The $527.1 billion in Federal outlays (face value) are

distributed among the following types of assistance

categories:

Grants/direct spending

Loans

Loan guarantees

Total

$458.4 billion

$ 7.6 billion

$ 61.1 billion

$527.1 billion

Table 1 shows how these funds are allocated.

Appropriate adjustment procedures were needed to convert the subsidy components of loans and loan

guarantees into grant equivalents {Î, 16, 26), Such adjustments require information about interest rates, loan

durations, and payment/repayment schedules used to

amortize loans. I selected the following three decision

guides to adjust data:

• List of selected Federal loan programs, 1979, with

interest rates, time periods, and loan amounts, as

prepared in 1981 by the State and Local Government

^For the companion study, The Diverse Social and Economic Structure of

Nonmetropolitan America, the research team decided to delete data for

Alaska and Hawaii since some needed data were not available for

these two States. Thus, for this study, I deleted Alaska and Hawaii to

make the data tapes compatible. This adjustment resulted in total

oudays of $527.1 billion allocable to the remaining counties rather

than the $532.4 billion reported by Reid and Whitehead.

^The other data file, prepared by Peggy Ross and Bernai Green,

was the Policy Impacts Project {19). It was completed prior to this

study and contains information about county prototypes.

Section, Economic Development Division, Economic

Research Service, U.S. Department of Agriculture;

• Descending array of Federal outlay budget amounts,

fiscal year 1980, grouped into grants/direct payments,

loans, and loan guarantees to determine the dominance

of the largest ones and to gain insights into repayment

schedules and terms from the title and stated purpose of

each segment of the legislation; and

• Economic Indicators, April 1984, published by the

Council of Economic Advisors (5), which contained

several interest rate series for 1978-83.

I used these decision guides, along with assumptions

about interest rates, to make needed adjustments in the

three Federal outlay categories: grants and direct

payments, direct loans, and loan guarantees. Details

and examples appear in the appendix at the end of this

report.

County Classifications

The policy impacts team completed a policy-oriented

classification of nonmetro counties (one of our three objectives in the Policy Impacts project) during the summer of 1984 {4, 19, 20). Our analysis was based on 12

national data sources, such as unpublished income

estimates for 1975-79 from the Survey of Income,

Bureau of Economic Analysis, U.S. Department of

Commerce. These data constitute a broad array of

social, economic, and demographic information about

U.S. counties.

The classification of nonmetro counties into eight types

involved a systematic process of data selection, reduction, and evaluation. The following types were selected:

Agriculture: 20 percent or more of total labor and

proprietor income from agriculture

(excludes agricultural services,
Federal lands:

Government:

Manufacturing:

Mining;

Poverty:

Retirement:^

Unclassified:

fisheries, and forestry), annual average

1975-79.

33 percent or mare of total land area

in Federal ownership (including

military), 1977.

25 percent or more of total labor and

proprietor income from government,

1979.

30 percent or more of total labor and

proprietor income from manufacturing, 1979.

20 percent or more of total labor and

proprietor income from mining, 1979.

Per capita personal income in bottom

quintile of the 2,443 nonmetro courities in 1950, 1959, 1969, and 1979.

15 percent or more of 1970-80 net inmigration rate for persons age 60 and

over.

Did not meet any of above criteria.

These counties tend to have diversified

economies.

Table 2 shows the distribution of the 2,443 nonmetro

counties among the eight county types; 2,073 (85 per-

^These counties are sometimes referred to as ''retirement inmigration" counties. Special calculations and initial operational definitions

were made by Calvin L. Beale, Agriculture and Rural Economies

Division, Economic Research Service, U.S. Department of

Agriculture. The threshold of 15 percent is based on the net number

of persons age 50 and over in 1970 who migrated to a county during

1970-80, expressed as a percentage of the 1970 resident population

projected to survive to age 60 or more in 1980.

cent) of the 2,443 nonmetro counties are accounted for

by one or more of the socioeconomic types. The remaining 15 percent fall into the unclassified category.

Agriculture is the largest group with 702 counties,

whereas mining is the smallest with 200 counties. A

detailed discussion of the construction of this classification scheme is contained in Ross and Green (19).

Results

Table 3 shows the cross-classification between Federal

outlays and county type. Per capita expenditures are

grouped into six broad functions (see rows, table 3) and

are expressed at face value (see numbers in parentheses)

as well as adjusted to net grant equivalents. Percentage

reductions vary directly with the percentage of loan and

loan guarantee amounts in the six functions (see bottom

of table 3).

Total Federal Outlays

Total per capita outlays for the functions (grant equivalents) show the nonmetro sector lagging behind the

metro by 23 percent ($1,490 compared with $1,936).

Among the nonmetro county types, the governrnent

group had the highest outlays with $2,269, followed by

Federal lands with $1,925. At or near the bottom were

manufacturing with $1,279 and poverty counties with

$1,319. Special tax benefits could lift the manufacturing

group off the bottom if excused tax benefits were known

and included in these Federal outlays. However, the low

per capita outlays to people in poverty counties despite

their needs agrees with findings by other analysts (M,

25). Poverty counties do not appear to be effective competitors for Federal assistance. The agricultural group,

although having relatively high nominal expenditures

($2,335), also had the highest downward adjustment

(-38 percent) from face value, compared with -23 percent at the national level.

Table 2—Distribution of nonmetro counties among eight soeioeconomic types

Type Defijiition Counties Share

(N = 2,443)

Number^ Percent

Agriculture

Federal lands

Government

Manufacturing

Mining

Poverty

Retirement

Unclassified

^ 20 percent labor and proprietor income from agriculture

^33 percent land federally ov/ned

> 25 percent labor and proprietor income from government

^ 30 percent labor and proprietor income from manufacturing

> 20 percent labor and proprietor income from mining

Persistent low-income county^

Retirement inmigration county^

Did not meet any of above criteria

702 29

247 10

315 13

678 28

200 8

242 10

515 21

370 15

^About 27 percent of counties overlapped on two or more types.

^Per capita income in bottom quintile in four time periods (1950, 1959, 1969, and 1979).

^^ 15 percent 1970-80 net inmigration of persons age 60 and over.
Table 3—Per capita Federal funds, net grant equivalent adjusted and face value amounts, by county type, fiscal year 1980

Function Agri[Culture Manuiacturing Mining Federal lands

(702 icounties) (678 1counties) (200 counties) (247 counties)

Dollars per capita net grant equivalent and face value

Targeted economic development 72 (135) 71 (113) 133 (202) 104 (161)

Income transfers 860 (1,001) 823 (958) 843 (901) 769 (897)

Human capital 54 (65) 92 (108) 72 (85) 188 (219)

Infrastructure 146 (388) 96 (225) 177 (460) 374 (738)

General government 129 (149) 169 (197) 145 (168) 449 (521)

Commodity agriculture 193 (597) 28 (94) 35 (106) 41 (116)

Total 1,454 (2,335) 1,279 (1,695) 1,405 (2,002) 1,925 (2,652)

Percentage reduction due to present worth factors

Targeted economic development -47 -37 -34 -35

Income transfers -14 -14 -14 -14

Human capital -17 -15 -15 -14

Infrastructure -62 -57 -62 -49

General government -13 -14 -14 -14

Commodity agriculture -68 -70 -67 -65

Overall -38 -25 -30 -27

Government Poverty Retirement Unclassified

(315 .counties) (242 counties) (515 counties) (370 counties)

Dollars per capita net grant equivalent and face value

Targeted economic development 142 (194) 97 (161) 87 (136) 88 (147)

Income transfers 785 (914) 858 (998) 914 (1,064) 861 (1,002)

Human capital 254 (296) 64 (75) 97 (113) 108 (127)

Infrastructure 198 (422) 141 (360) 156 (350) 118 (303)

General government 856 (993) 114 (133) 295 (342) 184 (214)

Commodity agriculture 34 (110) 45 (204) 23 (87) 66 (203)

Total 2,269 (2,929) 1,319 (1,931) 1,572 (2,092) 1,425 (1,996)

Percentage reduction due to present worth factors

Targeted economic development -27 -40 -36 -40

Income transfers -14 -14 -14 -14

Human capital -14 -15 -14 -15

Infrastructure -53 -61 -55 -61

General government -14 -14 -14 -14

Commodity agriculture -69 -78 -73 -67

Overall -12 -32 -25 -29

Nonmetro Metro United States

(2,443 counties) (626 counties) (3,069 counties)

Dollars per capita net grant equivalent and face value

Targeted economic development 87 (139) 88 (120) 87 (125)

Income transfers 844 (983) 831 (968) 835 (972)

Human capital 102 (120) 180 (212) 159 (186)

Infrastructure 141 (331) 142 (336) 142 (335)

General government 260 (302) 678 (787) 562 (653)

Commodity agriculture 56 (176) 17 (30) 28 (70)

Total 1,490 (2,051) 1,936 (2,453) 1,812 (2,341)

Percentage? reduction due to present worth factors

Targeted economic development -37 -27 -30

Income transfers -14 -14 -14

Human capital -15 -15 -15

Infrastructure -57 -58 -58

General government -14 -14 -14

Commodity agriculture -68 -43 -60

Overall -27 -21 -23

Note: About 27 percent of counties overlapped on two or more types.

Numbers in parentheses represent outlays with loans and loan guarantees at face valuee

guarantees are adjusted to grant equivalents and are summed to direct spending oudays.

the numbers to the left represent amounts after loans and loan
Targeted Economic Development Programs

Targeted economic development accounted for $19.7

billion (not shown in a table) in the United States after

being adjusted downward 30 percent (table 3, last column) from the nominal amount of $28.1 billion (table

1). As the function title suggests, the Federal Government maintains substantial discretion as to how these

programs are used (6). They are generally targeted

either to economically depressed communities to improve and develop business and industry or to community facilities. Some of the major budget elements

(face value) for 1980 were:

Tennessee Valley Authority

(TVA)

Construction grants for

wastewater treatment/

drinking water

Revenue sharing

Community development block

grants

American Indian programs

Water and sewer grants/loans,

Farmers Home Administration

(FmHA)

Urban development

$4.80 billion

$4.60 billion

$4.52 billion

$3.54 billion

$1.41 billion

$1.00 billion

$ ,61 billion

The nominal per capita amount to nonmetro counties

for Targeted Economic Development was $139, compared with $120 for metro counties (table 3, first row,

last three columns). After adjustment to grant equivalents, the amounts were almost the same for both

county types (about $88). The largest per capita

amounts went to the government county group ($142),

followed by the mining group ($133). The lowest

amounts went to the manufacturing ($71) and agriculture groups ($72). The downward adjustment for the

agriculture group was highest at 47 percent. The reason

for the low amount to agricultural counties is that a

high proportion of Federal outlays to these areas is in

the form of loans and loan guarantees. The reason for

the low amount to manufacturing counties is that one of

three major subfunctions of targeted economic development (funds for American Indians) was negligible in

these counties; the manufacturing counties are concentrated in the Southeast where there are few American

Indians (table 4).

Income Transfer Programs

Income transfers were $187.9 billion after being adjusted downward 14 percent from the nominal amount

of $218.9 billion. The Federal Government has little

discretion over the geographic distribution of these

funds. They are earmarked to eligible persons, such as

Social Security recipients, regardless of location. Major

budget elements (face value) include:

Social Security retirement

insurance $73.34 billion

Social Security survivors

insurance $27.17 billion

Social Security disability

insurance $14.64 billion

Medicare—hospital and supplementary medical $23.85 billion

Medical assistance programs $14.38 billion

Food stamps $ 7.83 billion

The nonmetro areas received slightly larger ai^usted per

capita income transfers than did the metro areas (|844

versus $831). The low reductions (14 percent) due to

present worth factors connote few loans or loan

guarantees. Highest per capita amounts went to retirement ($914) and unclassified ($861) counties, whereas

lowest amounts went to Federal lands ($769) and

government ($785) counties. The income transfers

category was generally the largest, representing 46 percent of total adjusted per capita outlays at the national

level ($835 of $1,812).^ Because age is the factor most

associated with eligibility for Social Security and

Medicare payments and because elderly people are concentrated in the 515 destination retirement counties,

these areas received the highest per capita income

transfer amounts. The lowest amounts to Federal lands

and government counties connote the lowest proportions

of elderly people.

Human Capital Programs

Human capital spending amounted to $35.7 billion as

adjusted downward 15 percent from the nominal

amount of $41.9 billion (table 3, last column). The

Federal Government has some discretion in locating

these programs, but much less than in programs for

targeted economic development and infrastructure.

Skills, learning, work attóudes, and health are the outcomes of these programs rather than buildings, roads,

and waste treatment (5). Major budget elements for

1980 included:

Energy research, technology,

application

Research and development (space

science and development)

National Institutes of Health

$8.04 billion

$4.41 biUion

$3.25 billion

^Hoppe and Saupc (10) emphasize the importance of transfer

payments to nonmetro America. Using 1977 data, they report that per

capita transfer payments were 16 percent of nometro per capita personal income ($5,742). The highest percentage (18) was in the

nonmetro portion of the Northeast.
Table 4—Per capita Federal funds, by budgetary function, by county type, fiscal year 1980

Agriculture Manufacturing Mining Federal Government Poverty Retirement Unclassified Nonmetro Metro United

Function and (702 (678 (200 lands (247 (315 (242 (515 (370 (2,443 (626 States

subfunction counties) counties) counties) counties) counties) counties) counties) counties) counties) counties) (3,069 counties)

Dollars per capita I

Targeted economic development 72 71 133 104 142 97 87 88 87 88 87

Business/TVA^ 43 52 89 57 87 60 54 56 57 65 63

Revenue sharing/urban

development 19 17 16 18 17 21 16 18 18 20 19

American Indians 10 2 27 29 .38 17 17 14 12 2 5

Income transfers 860 823 843 769 785 858 914 861 844 831 835

Retirement 468 441 392 446 431 349 527 462 456 449 451

Nonmilitary 450 419 376 398 370 330 476 437 426 405 411

Military 18 22 16 48 61 19 50 25 30 43 40

Health/disability 311 294 354 254 273 341 305 315 303 298 300

Income maintenance 81 88 97 69 82 168 82 84 85 84 84

Human capital 54 92 72 188 254 64 97 108 102 180 159

Highly skilled 25 68 34 147 187 22 62 78 69 124 109

Skilled 2 1 2 7 11 2 4 1 3 3 3

Basic/rehabilitation 27 22 36 34 56 40 31 29 30 53 47

Infrastructure 146 96 177 374 198 141 156 118 141 142 142

Transportation 47 33 58 101 86 32 56 46 51 71 66

Housing 28 25 37 52 43 30 35 33 31 51 46

Natural resources 41 29 51 193 54 51 52 22 42 18 24

Electric/telephone utilities 31 9 32 28 14 27 13 17 17 2 6

General government 129 169 145 449 856 114 295 ,184 260 678 562

Administrative/maintenance 70 55 68 85 86 55 65 72 66 142 121

Regulatory 0 0 1 1 2 0 1 1 1 6 5

Defense 58 113 76 363 768 59 229 111 193 529 436

Military contracts 22 87 50 185 231 24 109 48 88 361 285

Military payrolls 37 27 27 178 537 35 121 63 105 168 151

Commodity agriculture 193 28 35 41 34 45 23 m 56 17 28

Total 1,454 1,279 1,405 1,925 2,269 1,319 1,572 1,425 1,490 1,936 1,813

Note: About 27 percent of counties overlapped on two or more types.

* Numbers may not sum to totals because of rounding. Net grant equivalent adjusted.

^Tennessee Valley Authority.
Education of deprived children |2,37 billion

Veterans' readjustment training $2%03 billion

Basic education opportunity

grants $1.94 billion

Nonmetro sector spending ($102) lagged its metro

counterpart ($180) by a large amount ($78). The likely

reason is that Federal expenditures for human capital

are concentrated in the highly skilled category (illustrated partly by the emphasis oa research); metro areas

have the resources required to support research activities. Federal expenditures were highest for government counties ($254), followed by Federal lands ($188).

The likely reason is that much research is conducted in

government-owned facilities and involves many government employees. Federal expenditures were lowest for

agricultural counties ($54); poverty counties were

slightly higher ($64). The low expenditures for human

capital in agricultural and poverty counties jnay prevent

their populations from reaching their economic

potential.

Infrastructure Programs

Infrastructure outlays were $31.9 billion as adjusted

downward 58 percent from th^ nominal amount of

$75.4 billion. Of the major functions, infrastructure

received next to the highest downward adjustment (-58

percent) nationwide from face value because large proportions of total funds were loans and loan guarantees.

These monies go to communities as well as to individuals, and the Federal Government has considerable

discretion in selecting recipients. The typical project

adds to a community's capital stock. Major budget

elements (face value) for 1980 included:

Veterans' guaranteed and insured

(home) loans $15.06 billion

Mortgage insurance for homes $9.71 billion

Highway planning and

construction $8.81 billion

Rural electric loans and loan

guarantees, Rural Electrification Administration $6.56 billion

Mortgage insurance, graduated

payments $5.76 billion

Low- to moderate-income

housing loans, FmHA $2.69 billion

Capital improvement grants,

Urban Mass Transportation

Administration $2.34 billion

Metro and nonmetro areas received almost equal

amounts ($142). Federal lands counties received the

most per capita ($374), while government counties were

a distant second ($198). The relatively large amount to

Federal lands counties is due to capital improvements

and natural resource uses in the context of a sparse

population (table 4), Manufacturing counties wnere last

($96). This situationmay be due to higher population

density and more recent funding of programs in

economically lagging regions.

General Government Programs

General government outlays were $126.4 billion as adjusted downward 14 percent from the nominal amount

of $ 146.9 billion. This function includes a broad array of

components, such as the Bureau of Prisons($481 million)

and taxpayers' service and returns processing ($797

million). However, it is dominated by military responsibilities as shown by the following sample of major

budget elements (face value):

Military prime supply contracts

Military active duty pay

Civilian pay, military

Postal service

Military prime service contracts

Military prime research, development, test, and evaluation

contracts

$43.13 billion

$20.29 billion

$17.71 billion

$17.05 billion

$12.37 billion

$ 9.34 billion

The nonmetro sector was far behind ($260) its metro

counterpart ($678) in receipts from this function.

Government counties had a high level of tbis type of

assistance ($856), whereas poverty counties were last

($114). Metro and government areas were highest principally because of more Federal outlays for defense per

capita (table 4).

Commodity Agriculture Programs

Commodity agriculture outlays at $6.3 billion were adjusted downward 60 percent from the nominal amount

of $15.9 billion. These outlays represent efforts to ensure adequate supplies of food and fiber with large

volumes moving as exports, thereby provixiing major

assistance in the balance of trade. This category includes all farm programs, the most important of wbich

are:

Commodity loans^ Commodity

Credit Corporaáon (CGC) $4.00 billion

Commodity inventory operations

(CGC) $2.56 billion

Emergency disaster loans, FmHA $2.26 billion

Economic emergency loans,

FmHA $2.17 bilHon

Farm ownership loans, FmHA $ .93 billion

Farm operating loans, FmHA $ .87 billion
Although per capita amounts are relatively small,

nonmetro areas had more receipts ($56) than did metro

areas ($17). The farming-dependent counties received

the largest amount ($193 per capita) of such outlays,

and retirement counties received the smallest ($23).

Crops eligible for Federal farm payments are relatively

scarce in retirement counties. The high proportion of

loans and loan guarantees is reflected in the large

downward adjustment (-68 percent).

Implications

The approach used in the study—namely, employing

Federal expenditure categories and county prototypes—

constitutes a flexible, useful way to analyze the impacts

of large economic events or forces, including Federal

spending. Consider the situation of the farmingdependent counties. Considerable national debate in

1985 focused on proposed reductions in Federal Government support for farming to allow free market forces to

determine farm product prices and income. Had there

been large Federal reductions, the most intense impact

would have been felt in farming-dependent counties

most specialized in farming and receiving the highest

Federal farm payments per capita. These counties and

their major characteristics were identified by Green and

Carlin {8),

The data in tables 3 and 4, based on cross-classification

of Federal Government spending functions and nonmetro county types, permit numerous insights into the

current and potential roles of the Federal Government

in shaping rural economies. Two examples illustrate

these linkages in greater depth: (1) income transfers in

retirement counties and (2) defense spending and

research in government counties. I selected the retirement counties because concentrations of older, mobile

Americans with income and wealth offer an important

option for regional economic growth. Analysts who tend

to concentrate their attention on measures to increase

the manufacturing sector may underemphasize this

option. I selected the government counties, despite their

dispersion throughout rural America, because of the

geographical overlap in the South between major

defense outlays to government counties and a concentration of poverty counties. I tried to clarify some of the

economic relationships between defense spending and

poverty.

Income Transfers and Retirement Counties

The data in table 4 extend those in table 3; that is, per

capita Federal outlays are arranged by subfunction. Expenditures for the income transfer function are highest

at the national level among the functional categories

($835 per capita), with the nonmetro sector ($844)

receiving slightly more per capita than the metro sector

($831) (table 4, row 5, last three columns).

Retirement counties ($914) rank first because retirement

income sources are much higher. This flow of Federal

funds is one of several reasons suggesting that the

economic future of retirement counties may be relatively

stable. In his presidential address to the Population

Association of America, Preston emphasized the recent

substantial increase in the amount of elderly-oriented

Federal funds; these funds increased fivefold in 1971-83

to $217 billion, or $7,700 per elderly American {15,

p. 9). Preston noted that the rapid increase in the

elderly population (28 percent in 1971-81), combined

with considerable political activity, will create even more

pressure that will shift Federal funds in favor of the

elderly {15, pp. 2, 4, 22-23).

In considering the varied economic effects of Federal

funds, Reid and Fox emphasized that adding more

direct spending to resident incomes boosts the local

multiplier, thus creating a greater economic stimulus

{17, p. 97). Such direct spending contrasts with Federal

purchases of goods and services because larger portions

go for items produced outside the receiving areas. Thus,

income transfers are a powerful part of the economic

base in recipient counties. Business opportunities,

especially services-producing ones, can help those interested in forming new businesses. But, what are the

characteristics of the fast-growing retirement areas, and

where are such areas located?

Retirement counties are concentrated in a band from

southwestern Texas northeast through the Ozarks region

of Arkansas-Missouri-Oklahoma; in Florida; in concentrated county groups in the upper Great Lakes States,

especially northern Michigan; along a narrow band of

the Appalachian mountains from northern Georgia

through Delaware; and throughout scattered locations in

Arizona, New Mexico, northern California, and western

Oregon {4). After considering over 100 descriptor

variables as applied to county types, the Policy Impacts

team reported that retirement counties differ from the

other groups in the following ways {4);

• Very high population growth rates during the sixties

and seventies,

• Remote rural locations,

• Large proportions of income from transfer payments,

and

• Large services-producing sectors.

Retirement concentrations in specific areas offer opportunities for rural growth, especially in activities serving
the wants and needs of older people. One example of

the prottems attending such growth is the need for

technical-iinancial-management assistance for emerging

small businesses. Providing adequate health services

may be another challenge because rules for medicare

reimbursements are urban-oriented (higher for procedures done in urban areas) and because cost containment efforts will adversely affect rural hospitals {^3),

The aging of the population, increased population

mobility, and the growing attractiveness of certain

places suggest that destination retirement areas will continue to grow despite adjustment strains.

Defense Spending and Research

in Government Counties*^

Government counties rank first in per capita Federal

outlays (12^269), receiving 52 percent more than the

nonmetro average ($1,490). This outcome is due to

more funds to government counties for general government (|856) and human capital ($254) expenditures

(table 4). Defense, especially military payrolls ($537)

and military contracts ($231), plays a dominant role in

the economies of government counties. The **highly

skilled'^ component of the human capital function is also

relatively high at $187 and may reflect high-technology

research efforts in the areas of energy, health, and the

military; Specialized government counties are scattered

throughout the United States; their location is determined more by historical precedent than by market

forces. This county group has the following characteristics relative to other county groups (4):

• Somewhat more urbanized,

• Higher rates of population increase during the sixties

and seventies,

• Lower average per capita incomes, and

• A low-wage mix of economic activities.

Average per capita income in the government counties

in 1979 \väs $6,195; only the poverty counties, with

$4,914 in 1979, were lower. However, above-average

proportions of government county populations were

under age 65 and were at least high school graduates.

These tíaits generally boost income. This paradox may

be largely explained by: (1) a low-wage mix of economic

activities employing younger workers, (2) a relatively

large professional staff providing services for large

classroom or needy populations, and (3) a high military

presence characterized by relatively low military pay

scales and young families.

Relationships Between Lower Incomes

and Defense Outlays

Because government counties have relatively low per

capita incomes and a high proportion of persons in

poverty (18.5 percent in 1980 compared with 28.4 percent in poverty counties) despite large defense outlays, a

better understanding of the relationship between government and poverty county groups is important. Geography represents a partial, but revealing, overlap. The

government group is widely dispersed, whereas the

poverty group is concentrated in the South and includes

higher percentages of racial minorities (4). Poverty

counties tend to have the following distinctive

characteristics:

• A sparse and nonmetro population settlement

pattern,

• Low income levels that have persisted for decades,

and

• Disproportionate numbers of people with disadvantages affecting their productive labor force participation.

However, large military installations are concentrated in

the South (app. table 6). These installations include major training bases, which are often the permanent locations of military units. For example, Fort Bragg, NG, a

vast base covering nearly all of Hoke Gounty and part

of adjacent Gumberland Gounty (app. table 6), is the

permanent location of the 82nd Airborne Division.

Hoke Gounty is in the poverty groupe^ as is Bladen, one

of six adjacent counties. Onslow Gounty, about 100

miles to the east, contains Gamp Lejeune, home of the

2nd Marine Division. It is in the government county

group, whereas Pender, one of four adjacent counties, is

in the poverty group.

Appendix table 6, which lists another dozen large

military or quasi-military installations in 10 Southern

States, shows the pattern between military outlays and

the location of persistent poverty counties. The pattern

is one in which at least one of the counties adjacent to

the one(s) containing the military installation is a

member of the poverty group. The counties with military bases are generally isolated and classified as

^^These 315 counties are scattered throughout th:e United States instead of being clustered. The combination of locat, State, and Federal

government labor and proprietor income used to identify the counties

leads to a deeentralized spending pattern—for example^ umversitiesj

prisons, and military bases.

^^Hoke Gounty is also in the government and Federal lands groups

by virtue of having 25 percent or more of its labor and proprietor income in 1979 from government sources (including military) and over a

third ofits land area in government ownership.

10
government; few have population centers large enough

to be classified as metro. The adjacent counties, with

few exceptions, are remote and isolated.

One can see the following implications: (1) these bases

are located in areas where artillery, small arms, and

tank training and related activities do not pose undue

danger to population centers; (2) poverty in the vicinity

of military bases appears to be related more to remote,

rugged, less developed land areas with fewer economic

options to relatively low military pay; and (3) there are

major linkages, chiefly civilian employment, between

military functions and economic support activities in

counties within and around the bases. The economic

future of an important subset of poverty counties seems

connected more to the emphasis on military spending

for conventional military forces and associated civilian

employment (table 5) than, for example, to manufacturing. Civilian defense personnel (360,000) in the eight

States shown in table 5 amount to more than a third of

total defense personnel in these States (28), Without

such employment, their economic condition could be

much worse.

The approach used here varies from related ones (14,

18) in three important ways. First, loans ($7.6 billion)

and loan guarantees ($61.1 billion) were converted into

their estimated grant equivalents. This conversion

revealed large downward adjustments in outlays going

both to farm counties and for infrastructure expenditures. Thus, one can measure a major force. Federal

Government spending and its expected impact on a

number of receiving areas, more precisely. Second, I

classified the hundreds of Federal budget elements into

a small number of policy-relevant categories, such as

Table 5—States with highest numbers of Defense

personnel, 1982

Defense personnel

State Military Civilian Total

Thousands

California 198 128 326

Texas 135 60 195

North Carolina 91 15 106

Florida 71 29 100

Georgia 64 36 100

Virginia^ 54 53 107

South Carolina 48 19 67

Hawaii^ 43 20 63

Subtotal 704 360 1,064

United States 1,388 899 2,287

^Excludes Virginia portion of Washington, DG, metro area. The

Washington, DG, metro area contains 60,573 military and 81,507

civilian personnel for a total of 140,080.

^The next three States in declining order are tied with about 41,000

military personnel: Golorado, Kentucky, and Washington.

Source: {28).

targeted economic development and human capital. This

classification enables one to compare outcomes desired

by policymakers with inputs by the Federal Government. Third, I classified the geographical areas where

Federal outlays are received into a few prototypes based

on socioeconomic criteria {4). This county classification

scheme helps to clarify the environment within which

public expenditures exert their impacts. An example is

the relationship between destination retirement counties

and income transfers. The income transfer function was

the largest of those classified, and the largest per capita

amount went to the retirement counties. This allocation

suggests that the economic future of such counties may

be relatively stable compared with farming, manufacturing, and mining counties.

References

(1)

(2)

(3)

(4)

Allen, Kevin, Chris Hull, and Douglas Yuill. Options in Regional Incentive Policy. Center for the

Study of Public Policy, University of Strathclyde,

Glasgow; and International Institute of Management, Science Center, Berlin. Paper prepared for

the Atlantic Conference on Balanced National

Growth, Racine, WI, Jan. 4-6, 1978.

Beale, Calvin L. /'People on the Land," Rural

U.S.A.: Persistence and Change (ed. Thomas R.

Ford). Ames: Iowa State University Press, 1978.

. Rural and Small Town Population

Change, 1970-80. ESS-5. U.S. Dept. of Agr.,

Econ. Stat. Serv., Feb. 1981.

Bender, Lloyd D., and others. The Diverse Social

and Economic Structure of Nonmetropolitan America.

RDRR-49. U.S. Dept. of Agr., Econ. Res. Serv.,

Sept. 1985.

(5) Council of Economic Advisors. Economic Indicators.

Prepared for the Joint Economic Committee, 98th

Congress, Second Session, U.S. Congress, Apr.

1984.

(6) Deavers, Kenneth L., and David L. Brown. Social

and Economic Trends in Rural America. White House

Rurcd Development Baekground Paper (unnumbered). U.S. Dept. of Agr., Econ. Res.

Serv., Oct. 1979.

(7) Estes, Jack C. Interest Amortization Tables. New

York: McGraw-Hill, 1976.

(8) Green, Bernai L., and Thomas A. Carlin.

Agricultural Policy, Rural Counties, and Political

11
Geography. Staff Report No. AGES850429. U.S.

Dept. of Agr., Eeon. Res. Serv., May 1985.

(9) Hoppe,^ Robert A. Agricultural Counties: Their LocaiiouyFomns, ana Economies, Staff Report No.

AGESS81G213. U.S. Dept. of Agr., Eeon. Res.

Serv., 1981.

(10) , and William E. Saupe. Transfer

Payments in Nonmetropolitan Areas. Staff Report No.

AaES820827. U.S. Dept. of Agr., Eeon. Res.

Serv., Sept. 1982.

(11) MeHis, C.L., and P.W. Richardson. *'Value of

Investnaent Incentives for Manufacturing Industry, 1946 to 1974,'' The Economics of Industrial

Subsidies (ed. Alan Whiting). Papers and Proceedings of the Conference on the Economics of

Industrial Subsidies, Sunningdale, England, Feb.

1?75, pp. 23-43.

(12) Moody, T., and K.G.D. Smith. ''Some Problems

in the Evaluation of Subsidies to British Manufacturing Industry/' Oxford Economic Papers, Vol. 27,

1975, pp. 274-94.

(13) Moseovice, Ira, and Roger A. Rosenblatt. "A

Prognosis for the Rural Hospital: Part XI: Are

Rural Hospitals Econoniically Viable?'' The Journal cf Mural Health, Yen. 1, No. 2, Jul^t985, pp.

11-31.

(14) Nelson, Marlys (Knutson). "Impacts of Federal

Outlays on County Development." Unpublished

Ph.D. dissertation, Oklahoma State University,

Department of Agricultural Economics, July 1979,

(15) Preston, Samuel H. "Children and the Elderly:

Divergent Paths for America's Dependents."

(Preston quoted Statistical Abstract of the United

States, 1984, U.S. Bureau of the Census.)

Presidential address to the Population Association

of America, Minneapolis, MN, May 4, 1984.

(16) Rasmussen, David W., Marc Bendick^yr., and

Larry C. Ledebur. "A Methodology for Selecting

Economic Developnient Incentives,'' Öröit;^ ató

Change, Yol. 15, No. 1, Jan. 1984, pp. 18-25. (A

fuller treatment by the authors is erititled 7%^

Cost-Effectiveness of Economic Deveiopment incentives.

Project Report, The Urban Institute^ 2100 M St.,

NW, Washington, DC 20Ö27, Jan. B82v)

(17) Reid, J. Norman, and William F. Fox: "Interregional Benefits from Federal Spending: A New

Look at an Old Issue," Policy Studies Journal, Vol.

9, Ño. 1., Autumn 1980, pp. 95-102.

(18) Reid, J. Norman, and Eleanor Whitehead. Federal

Funds in 1980: Geographic Distribution and Recent

Trends. Staff Report No. AGES820927. U.S.

Dept. of Agr., Eeon. Res. Serv., Nov. 1982.

(19)

(2Û)

(21)

Ross, Peggy J., and Bernai L. Green. Procedures

for Developing a Policy Oriented Classification of

Nonmetropolitan Counties. Staff Report No.

AGES850308. U.S. Dept. of Agr., Eeon. Res.

Serv., Aug. 1985.

and Robert A. Hoppe. *'Classifying Major Socioeconomic Patterns Among U.S.

Nonmetropolitan Counties." Paper presented at

annual meeting of Rural Sociological Society, College Station, TX, Aug. 1984.

Schultz, T.W. The Economic Organization of

Agriculture. New York: McGraw-Hill, 1958.

(22) Steiner, Henry M. Public and Private Investments,

Socioeconomic Analysis. New York: John Wiley and

Sons, 1980, pp. 372-401.

(23) Sumner, M.T. ''Some Further Problems in the

Evaluation of Subsidies to British Manufacturm%r Oxford Economic Papers, Vol. 29, 1977, pp.

152-55.

(24) Systan, Inc. Analysis of Alternative Subsidy Programs:

Impact on Regional Development. Washington, DC,

Aia^. 1973.

(25) Tweeten, Luther, and Daryll Ray. "Impact of

Public Compensation Policies," Externalities in the

Transformation ofAgriculture (ed. Earl O. Heady).

Ames: Iowa State University Press, 1975. pp.

98-120.

(26) U.S. Congress, Congressional Budget Office.

Federal Support of U.S. Business. Jan. 1984.

(27) U.S. Department of Agriculture, Economic

Research Service Study Group (James P. Cavin,

Chair). Agriculture and Economic Growth. AER-28.

Mar. 1963.

(28) U.S. Department of Defense, American Forces

Information Service. Defense 63 Almanac. Sept.

1983; pp. 47-49.

12
Appendix: Explanation of Discounting

Procedures

This appendix explains the three types of Federal

outlays: grants/direct payments, direct loans, and loan

guarantees.

Grants/Direct Payments

These payments, amounting to almost $460 billion, include formula and block grants as well as salaries,

direct payments, and purchases of goods and services.

Payments are received annually, quarterly, or monthly,

depending on the type of program. Highway planning

and construction ($9 billion) is an example of a major

direct formula grant, whereas Social Security ($115

billion) is an example of a major payment program to

individuals. A conservative approach to discounting

grants and direct payments was followed. The present

value of the outlays at the beginning of fiscal year 1980

is assumed to be the face value of a single payment discounted at 15 percent for 1 year with interest compounded monthly. The monthly compounding yields an

effective interest rate of 16.08 percent. The 1.08-percent

difference represents a small allowance for Federal taxes

in some components of grants and direct payments to

individuals.*

Example: If the Federal outlay (F) of $1,000 is received

the last day of the year and discounted at 16.08 percent, what is its present value (P)?

P = $1,000 X 1/L1608

P = $861.51**

Direct Loans

This category of assistance was exemplified by the

Commodity Credit Corporation ($4 billion) and by

Housing for Elderly or Handicapped ($0.9 billion). The

market rate of interest was 15 percent, and the Government rate was 11 percent, leaving a subsidy of 4 percent. Loan duration was assumed to be 10 years.

• Step 1. Find annual amount needed to amortize

loan at 15 percent over a 10-year period.

• Step 2. Find annual amount needed to amortize

loan at 11 percent over a 10-year period.

• Step 3. Subtract result of Step 2 from Step 1.

• Step 4. Result of Step 3 is subject to formula

yielding present worth factor for a uniform series (7,

22).

Thus, the asset value of assistance is the difference between the net present value of the subsidized interest

and principal repayment costs and the comparable value

at commercial interest rates (12^ 16, 24).

Example: Find asset value or grant equivalent of a

direct Federal loan of $1,000 discounted at 11-percent

interest and 15-percent interest for 10 years.

• Step 1. Annual payment to amortize at 0.15 =

$199.26 (7).

• Step 2. Annual payment to amortize at 0.11 =

$169.81.

• Step 3. $199.26 - $169.81 = $29.45, a uniform

series for 10 years.

• Step 4. P = $29.45 x (Í + Q-15)^o - 1

0.15 (1 +0.15)10

P = $29.45 (5.019)—present worth factor is 5.019

{22), P = $147.81.

Loan Guarantees

Veterans' Administration Housing ($15 billion). Watershed Protection ($9.9 billion), and Rural Electrification

Administration ($6.6 billion) exemplify this type of

assistance. The market rate of interest is increased by 3

percent because of perceived higher credit risks. The

loan guarantee permits the market value of 15 percent

to be available. The subsidy is 0.18 - 0.15 = 0.03,

and loan duration is assumed to be 30 years.

Example: Find grant equivalent of a Federal loan

guarantee of $1,000 discounted at 15 percent for 30

years.

• Step 1. Annual payment to amortize at 0.18 =

$181.27.

*Another option is to assume that the $1,000 is received in 12 equal

monthly payments (a uniform series). Thus, P = $923.24, instead of

$861.51.

**I used $861.90 because of rounding error.

• Step 2. Annual payment to amortize at 0.15 =

$152.31.

• Step 3. $181.27 - $152,31 = $28.96, a uniform

series for 30 years.

13
• Step 4. P = $28.96 x (6.566)—present worth factor is 6.566 (22), P = $19ÜJ5.

Others looking at the same införniatian might inake

somewhat different assumptions and decisions, but their

results will likely be not too dissimilar. I caution the

reader that: (1) I "was unaMe to acljust ibr the various

interest rates faced by individuals and firms; (2) I had

no weighted indexes with respect to loan durations or

interest rates; and (3)^ the interest rates I used only

crudely embody an unknown pattern of defaults and the

loan terms within which th^se defaults occurred.

14
Appendix table 1—Targeted economic development: Per capita Federal funds, net grant equivalent adjusted,

by county type and nonmetro/metro area, fiscal year 1980^

Function and

subfunction

Agriculture Manufacturing

(702 (678

Mining

(200

counties) counties)

Federal

lands (247

counties) counties)

Government

(315

counties)

Poverty

(242

counties)

Retirement

(515

counties)

Unclassified

(370

counties)

Nonmetro

(2,443

counties)

Metro

(626

counties)

United

States

(3,069 counties)

Dollars per capita

Targeted economic development 72 71 133 104 142 97 87 88 87 88 87

Grants/direct spending 61 64 122 95 135 86 79 78 78 84 82

Loans 3 1 2 1 1 1 1 1 1 1 1

Loan guarantees 8 6 9 8 6 10 7 9 8 3 4

Business/TVA2 43 52 89 57 87 60 54 56 57 65 63

Grants/direct spending 31 45 78 48 80 49 46 45 48 61 58

Loans 3 1 2 1 1 1 1 2 1 1 1

Loan guarantees 9 6 9 8 6 10 7 9 8 3 4

American Indians 11 2 27 29 38 17 17 14 12 2 5

Grants/direct spending 11 2 27 29 38 17 17 14 12 2 5

Loans 0 0 0 0 0 0 0 0 0 0 0

Loan guarantees 0 0 0 0 0 0 0 0 0 0 0

Revenue sharing/urban

development 19 17 16 18 17 21 16 18 18 20 19

Grants/direct spending 19 17 16 18 17 21 16 18 18 20 19

Loans 0 0 0 0 0 0 0 0 0 0 0

Loan guarantees 0 0 0 0 0 0 0 0 0 0 0

Note: About 27 percent of counties overlapped on two or more types.

^Numbers may not sum to subtotals and totals because of rounding. ^Tennessee Valley Authority.

Appendix table 2—Income transfers; Per capita Federal funds, net grant equivalent adjusted, by county type and nonmetro/metro area, fiscal year 1980^

Agriculture Manufacturing Mining Federal Government Poverty Retirement Unclassified Nonmetro Metro United

Function and (702 (678 (200 lands (247 (315 (242 (515 (370 (2,443 (626 States

subfunction counties) counties) eoiinties) counties) counties) counties) counties) counties) counties) counties) (3,069 counties)

Dollars per capita

Inconie transfers^ 860 823 843 769 785 858 914 861 844 831 835

Grants/direct spending 859 821 842 768 784 857 913 860 843 830 834

Loans 0 0 0 0 0 0 0 0 0 0 0

Loan guarantees 1 1 1 1 1 1 1 1 1 1 1

Retirement 468 441 392 446 431 349 527 462 456 449 451

Nonmilitary 450 419 376 398 370 330 476 437 426 406 411

Military 18 22 16 48 61 19 50 25 30 43 40

Health/disability 311 294 354 254 272 341 305 315 303 298 300

Income maintenance 81 88 97 69 82 168 82 84 85 84 84

Note: About 27 percent of counties overlapped on two or more types.

'Numbers may not sum to subtotals and totals because of rounding. Loans and loan guarantees are almost nonexistent.
Appendix table 3—Human capital: Per capita Federal funds, net grant equivalent adjusted, by county type and nonmetro/metro area, fiscal year 1980*

Function and

subfunction

Agriculture

(702

counties)

Manufacturing

(678

counties)

Mining

(200

counties)

Federal

lands (247

counties)

Government

(315

counties)

Poverty

(242

counties)

Retirement

(515

counties)

Unclassified

(370

counties)

Nonmetro

(2,443

counties)

Metro

(626

counties)

United

States

(3,069 counties)

Human capital^

Grants/direct spending

Loans

Loan guarantees

54

54

0

0

92

92

0

0

72

72

0

0

188

188

0

0

Dollars pet capita

254 64

254 64

0 0

0 0

97

97

0

0

108

107

0

1

102

102

0

0

180

179

0

1

159

158

0

1

Highly skilled 25 68 34 147 187 22 62 78 69 124 109

Skilled 2 1 2 , 1 11 2 4 1 3 3 3

Basic/rehabilitation 27 23 36 34 56 40 31 29 30 53 47

Note: About 27 percent of counties overlapped on two or more types.

^Numbers rnay not sum to totals and subtotals because of rounding. Loans and loan guarantees are almost nonexistent.

Appendix table 4^Infrastructure: Per capita Federal funds, net grant equivalent adjusted, by county type and nonmetro/metro area, fiscal year 1980*

Functic^n and

subfunction

Agriculture

counties)

Manufacturing

counties)

Mining

(200

counties)

Federal

lands (247

counties)

GoVernnlent

(315

counties)

Poverty

(242

counties)

Retirement

(515

cpunties)

Unclassified

(370

counties)

Nonmetro

(2,443

counties)

Metro

(626

counties)

United

States

(3,069 counties)

Infrastructure

Grants/direct spending

Loans

Loan guarantees

146

93

1

' 52 „

96

68

1

,, ,;27

177

115

1

61;, „ „

374

301

1

72

Dollars per capita

198 141

151 94

2 1

45 ,, 47

156

115

1

,„40„,:„ „

118

78

1

39

141

100

1

40

142

101

1

41 „ ,

142

101

1

40

Transportation

Grants/direct spending

Loans

Loan guarantees

47

47

0

0, .

33

33

0

o,,„.

58

58

0

', 0

101

101

0

0

87

87

0

0

32

32

0

0

56

56

0

0

46

46

0

0

51

51

0

, ; „:0

71

71

0

0

66

66

0

0

Housing

Grants/direct spending

Loans

Loan guarantees

6

0

22

25

6

1

18

36

7

0

29

52

7

0

45

43

11

1

31

30

10

1

19

35

7

1

27

33

10

1

22

31

7

1

23

51

il

1

39

46

11

1

34

Natural resources

Grants/direct spending

Loans

Loan guarantees

40

40

0

0

29

29

0

0

51

51

0

0

193

193

0

0

54

54

0

0

51

51

0

0

52

52

0

0

22

22

0

0

42

42

0

0

18

18

0

0

24

24

0

0

Electric/telephone utilities

Grahts/dii'ect spending

" ' ,Lö,ans

Loan guarantees

31

31

9

0

0

9

32

0

0

32

28

27

14

14

28

0

0

28

13

0

1

13

17

0

0

17

17

0

0

17

2

0

0

2

6

0

6

Note: About 27 percent

^Numbers may not sum

of counties overlapped

to totals and subtotals

on two or more types,

because of rounding-
Appendix table 5- -General government and commodity agriculture: Per capita Federal funds, net grant equivalent adjusted,

by county type and nonmetro/metro area, fiscal year 1980^

Agriculture Manufacturing Mining Federal Government Poverty Retirement Unclassified Nonmetro Metro United

Function and (702 (678 (200 lands (247 (315 (242 (515 (370 (2,443 (626 States

subfunction counties) counties) counties) counties) counties) counties) counties) counties) counties) counties) (3,069 counties)

Dollars per capita

General government 129 169 145 449 856 114 295 184 260 678 562

Grants/direct payments 129 169 145 '449 856 114 295 184 260 677 562

Loans 0 0 0 0 0 0 0 0 0 0 0

Loan guarantees 0 0 0 0 0 0 0 0 0 0 0

Administrative/maintenance 70 55 68 85 86 55 65 72 66 142 121

Regulatory 0 0 1 1 2 0 1 1 1 6 5

Defense 58 113 76 363 768 59 230 111 193 529 436

Military contracts 21 86 50 185 231 24 109 48 88 361 285

Military payrolls 37 27 27 178 537 35 121 63 105 168 151

Commodity agriculture 193 28 35 41 34r 45 23 66 56 17 28

Grants/direct spending 114 15 21 25 18 10 10 39 31 15 20

Loans 32 3 4 3^ 4 S- 2 9^ 8 1 3

Loan guarantees 47 11 11 13 12 30 11 18 17 1 5

Note: About 27 percent of counties overlapped on two or more types.

'Numbers may not sum to subtotals and totals because of rounding.
Appendix table 6—Location of major military bases in the South

State Military bases^ Counties containing

military bases Counties adjacent to military bases

North Carolina

South Carolina

Georgia

Florida

Alabama

Louisiana

Arkansas

Tennessee

Kentucky

Texas

Oklahoma

Fort Bragg

Camp Lejeune

Savannah River Plant^

Fort Stewart

Fort Benning

Fort Gordon

Eglin Air Force Base

Fort MeClellan

Fort Rucker

Fort Polk

Fort Chaffee

Fort Campbell

Fort Campbell

Fort Hood

Fort Sill

'^Hoke, Cumberland

Onslow

Barnwell, Aiken

Bryan, Liberty, Long,

Evans, *Tattnall

Chattahoochee

Richmond

Qkaloosa, Santa Rosa,

•^Walton

Calhoun

Dale, Cofïee

Vernon

Sebastian

Stewart, Montgomery

Trigg, Christian

Coryell, Bell

Comanche

*Bladen, Moore, Harnett, Scotland,

Robeson, Sampson

*Peiider, Carteret, Jones, Duplin

Edgéfield, Lexington, Orangeburg,

*Bamberg, *Allendale

GA: Richmond, * Burke

Bulloch, Effmgham, Chatham

*McIritosh^ Wkyne

* Stewart, *Webster, Marion, *Talbot,

*Harris, Muscogee

AL: Russell

Columbia, McDulïle, Jefferson, *Burke

SC: Aiken

^Washingtons Bay, *Holmes

AL: Covin^ton, Geneva

Etowah, Cherokee, Ckburne, Talladega,

St. Clair

Pike, *Barbour, *Henry, Houston, Geneva,

Covington, *Crenshaw

* Sabine, Natchitoches, Rapides, Allen,

: Beauregard

TX-*Newton

* Scott, Logan, Franklin, Crawïord

OK: Le Flore

Henry, Houstoîi, Dickson, Cheatham,

Robertson

Calloway, Marshall, Lyon, Caldwell,

Hopkins, Muhlenberg,Todd

Bosqu«, McLennan, Falls^ Milam,

Williamson, Burnet, Làmpasas, Mîlls,

Hamilton

Caddo, Grady, Stephens, Cotton, Tillman,

Kiowa

Note- Asterisks denote poverty county status as classified by researchers working on related Policy Impacts Project. In some instances, adjacent

counties are in a neighboring State,

^Smaller military installations are not listed,

^This installation's purposes are not generaUy known, but they are likely linked to the military.

«U.S. Government Printing Ofriec : 1987 -l«0'917/60032 18