)6'3

United States

Department

of Agriculture

Economic

Research

Service

Rural Development

Research Report

Number 64

November 1986

How Well Can Alternative

Policies Reduce Rural

Substandard Housing?

Donald L. Lerman* ^

r->

cc

i»

C33

Over 2 million of the Nation's 5.1 million substandard

housing units were located in rural areas in 1981,

with over half of them in the South. This report examines where substandard housing is concentrated

and compares tivo approaches to reduce substandard housing in rural areas: building new housing or

subsidizing existing housing. Programs to subsidize

existing housing would reduce rural substandard

housing more per dollar spent than would programs

to construct new housing. A combined program that

targets money toward housing construction in lowvacancy areas and toward subsidies of existing

housing in high-vacancy areas would be the most

cost-effective of the programs considered to reduce

rural substandard conditions.

Substandard housing has been more concentrated

and more severe in rural areas than in the Nation

as a whole. Since 1970, at least a third of the Nation's substandard housing has been in rural areas,

although rural areas have only a quarter of total occupied housing. Substandard housing units in rural

areas also generally have greater and more severe

structural defects than do those in urban areas. In

1979, maintenance problems were the major reason

for classifying urban dwellings as physically unsound, while plumbing problems were the most likely

cause for rural dwellings. Rural areas fare even

worse if one considers only lack of complete plumbing. Since 1950, over half of occupied units lacking

complete plumbing were in rural areas, although

rural areas never contained more than a third of

total occupied housing. Units with more than one

flaw were also much more common in rural areas

(44 percent compared with 26 percent in urban

areas)(13).^

*The author is an economist with the Agriculture and Rural

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

of Agriculture. Jim Mikesell, Dan Milkove, Carol Meeks, Mary

Maher, and Debra Haugan assisted in the preparation of this

report.

italicized numbers in parentheses refer to literature cited in

the References section.

"Substandard"?

C3

For this report, housing is substandard if it has

more people than rooms or if it lacks complete

plumbing (hot and cold piped water, a flush toilet,

and a bathtub or shower) for the exclusive use of

the occupying household. Under this definition. 5.2

percent of all occupied units (rural and urban) were

classified substandard in 1983.

More comprehensive definitions of substandard or

inadequate are sometimes used. By a measure of

housing inadequacy used at the U.S. Department of

Housing and Urban Development, 6.1 percent of all

housing in 1983 was "seriously inadequate," or

possessed a major defect in plumbing, heating, electricity, or maintenance. A stricter measure developed

by the Office of Management and Budget classified

15.3 percent of all units as inadequate in 1983. By

this criterion, a unit is inadequate if it contains one

or more of seven major housing defects (including

plumbing, heating, kitchen, and sewage defects) or

two or more of eight maintenance problems (including leaking roof and exposed wiring). Both of these

measures classify a higher proportion of the

Nation's housing as substandard than does the

definition used in this report.

Rural Substandard Housing is

Concentrated in the South

Texas leads all States, with nearly 143,000 rural occupied substandard units (7.1 percent of the national

total), Wlowed by North Carolina with 121,000 (6

percentrknd Kentucky with 103,000 (5.1 percent)—

but these States have very high rural populations.

Texas, North Carolina, and Kentucky rank as numbers 2, 3, and 15, respectively, in the number of

rural households (table 1, column 3). The percentage of rural households in substandard housing

(table 1, column 5) measures the concentration, not
Table 1—Rural occupied substandard units and rural households, ranked by State

Rank

and

State

Rural occupied substandard

housing units

Total Share of U.S.

total

Rural

households

Total Share of U.S.

total

Percentage of

rural households

living in substandard units

Number Percent Number Percent

U.S. total 2,023,166

1 Texas 142,952

2 North Carolina 121.257

3 Kentucky 102,792

4 Georgia 96,999

5 Virginia 91,063

6 California 83,598

7 Tennessee 80,488

8 Mississippi 78,574

9 Alabama 78,122

10 Ohio 71,385

11 Pennsylvania 70,175

12 South Carolina 69,168

13 Louisiana 67,103

14 Arkansas 54,838

15 West Virginia 53,476

16 Michigan 52,155

17 Florida 51,608

18 Missouri 50.924

19 Indiana 43,633

20 Illinois 43,180

21 New York 40,385

22 Wisconsin 37,638

23 Arizona 36,948

24 Oklahoma 36,309

25 Minnesota 35,288

26 New Mexico 30,984

27 Washington 28,008

28 Maryland 23.755

29 Maine 22,638

30 Oregon 22,570

31 Iowa 22,086

32 Alaska 17,984

33 Kansas 16,388

34 Colorado 15,170

35 Hawaii 13,137

36 Idaho 13.072

37 South Dakota 11.378

38 Montana 10,944

39 Massachusetts 10,348

40 Nebraska 9,793

41 Utah 9.423

42 New Hampshire 8.969

43 New Jersey 8,246

44 North Dakota 8,105

45 Vermont 7,488

46 Wyoming 6,476

47 Connecticut 5.579

48 Delaware 4.796

49 Nevada 4,099

50 Rhode Island 1,672

10.0 27,171,066

7.1 1,464,421

6.0 1,263,981

5.1 744.041

4.8 906.396

4.5 732.685

4.1 1,054.062

4.0 790,810

3.9 542,537

3.9 692,245

3.5 1,257,741

3.5 1,551,921

3.4 609.385

3.3 614,059

2.7 545,797

2.6 493,351

2.6 1,098,355

2.6 772,988

2.5 769,541

2.2 876,258

2.1 1,002,984

2.0 1.155,774

1.9 754,582

1.8 230,809

1.8 519,308

1.7 642.197

1.5 189,880

1.4 519,630

1.2 327,966

1.1 297.275

1.1 457,584

1.1 609,733

.9 63,393

.8 467,423

.7 278,178

.6 73,745

.6 204,249

.6 182,039

.5 175.363

.5 436,892

.5 293,695

.5 109,020

.4 201,668

.4 336,998

.4 151,386

.4 152,689

.3 114,590

.3 255,265

.2 70,179

.2 63,875

.1 52,123

100.0

5.4

4.7

2.7

3.3

2.7

3.9

2.9

2.0

2.5

4.6

5.7

2.2

2.3

2.0

1.8

4.0

2.8

2.8

3.2

3.7

4.3

2.8

.8

1.9

2.4

.7

1.9

1.2

1.1

1.7

2.2

.2

1.7

1.0

.3

.7

.6

1.6

1.1

.4

.7

1.2

.6

.6

.4

.9

.3

.2

.2

7.4

9.8

9.6

13.8

10.7

12.4

7.9

10.2

14.5

11.3

5.7

4.5

11.4

10.9

10.0

10.8

4.7

6.7

6.6

5.0

4.3

3.5

5.0

16.0

7.0

5.5

16.3

5.4

7.2

7.6

4.9

3.6

28.4

3.5

5.5

17.8

6.4

6.3

6.2

2.4

3.3

8.6

4.4

2.4

5.4

4.9

5.7

2.2

6.8

6.4

3.2
the absolute number, of substandard units, and thus

does not depend on the size of the rural population.

Texas, North Carolina, and Kentucky, which rank

highest in the number of rural substandard units,

rank as numbers 15, 16, and 6, respectively, in the

proportion of rural households in substandard units.

Rhode Island, Nevada, and Delav^are, v^hich had the

least substandard units, rank as numbers 47, 25,

and 22, respectively, in the proportion of rural

households living in substandard units.

States with the highest concentration of occupied

rural substandard units are Alaska (28.4 percent),

Hawaii (17.8 percent). New Mexico (16.3 percent),

Arizona (16 percent), and Mississippi (14.5 percent).

Figure 1 displays the concentration of rural substandard housing in contiguous U.S. counties. Rural

substandard housing is heavily concentrated in the

Southeast coastal region, southern Texas, Kentucky,

West Virginia, Virginia, and parts of New Mexico,

Arizona, and South Dakota. Large pockets of these

areas are occupied by blacks (in the Southeast),

Hispanics (in southern Texas and parts of New

Mexico and Arizona), American Indians (in South

Dakota and parts of New Mexico and Arizona), and

whites (in the Appalachian regions of Kentucky,

West Virginia, and parts of Virginia).

The South has 57 percent of the Nation's occupied

rural substandard units but only 41 percent of rural

households (table 2). More than 10 percent of rural

southern households live in substandard units. The

South Atlantic region has the largest share of the

Nation's rural substandard units (25.3 percent) of

the nine census regions (fig. 2). However, the East

South Central region has the highest concentration

(over 12 percent) of rural substandard housing.

Population and Area Factors Affect

Housing Quality

What influences the amount of substandard housing

in rural parts of U.S. counties?

• The proportion of rural households in poverty—

The greater the proportion of poverty households,

the greater the expected proportion of substandard housing (table 3, row 1, shows a positive

coefficient for this variable). Many low-income

households can afford only low-quality housing.

Figure 1

Rural Substandard Housing in U.S. Counties

Concentration of rural substandard housing

n Under 10 percent

11 - 20 percent

Over 20 percent
Figure 2

Regional shares of rural substandard

housing, 1980

Region Percent share of total U.S. rural

p^çjljçi substandard units

Mountain

Share of rural substandard

units within region

West North Central

The Data

The Economic Research Service (ERS). U.S.

Department of Agriculture, used 1980 census

data to derive over 40 variables that profile

the rural population at the county, State, and

national levels for the Farmers Home Administration (FmHA) in 1983. FmHA uses ERS data

when making program decisions, such as when

allocating funds for housing loans to the States

for the Section 502 and 515 programs of the

National Housing Act of 1976. The information

in the census that is classified by rural/urban

status is limited to general summary statistics

and basic housing characterisitcs. FmHA needed

more specific rural data, such as the number

of rural occupied substandard units, rural

population 62 years old and older, and rural

families with incomes below the poverty level.

The census definition of rural as open country

and places with less than 2,500 people was too

restrictive for FmHA use. For FmHA rural

housing programs, a rural area includes open

country and towns or places with 10,000 or

fewer people and that are not part of or closely

associated with urban areas. Under certain

conditions, a place in a nonmetropolitan

(nonmetro) county may be considered rural even

if it contains up to 20,000 people.

The data in Summary Tape File 3C (STF3C] of

the 1980 census summarizes several hundred

population and housing variables for urbanized

areas within States, counties, and places with

10,000 or more people. I transformed each

variable into a rural variable, which is the

value of the variable minus its urban portion,

where the urban portion consists of urbanized

areas plus places of 10,000 or more people

(20,000 or more for nonmetro counties)(4).

The U.S. total of approximately 2.02 million

rural occupied substandard units is 21 percent

larger than the census measure of 1.67 million

(table 1). This difference is expected since the

rural definition used in this report is broader

than the census defintion.
Table 2—Rural occupied substandard units and rural households, by region

Rural occupied substandard Rural

housing units households

Region^ —

U.S. total

West

North Central

Northeast

South

Total Share of U.S.

total

Number

2,023,166

292,413

401,953

175,500

1,153,300

Percent

100.0

14.5

19.9

8.7

57.0

Total

Number

27,171,066

3,534,378

8,105,934

4,440,605

11.090,149

^Regions are:

West = Pacific and Mountain regions.

North Central = West North Central and East North Central regions.

Northeast = Middle Atlantic and New England regions.

South = West South Central, East South Central, and South Atlantic regions.

Source: Tabulations by Economic Research Service from 1980 census.

Share of U.S.

total

Percentage of

rural households

living in substandard units

^^J^

100.0

Lit;LU

7.4

13.0 8.3

29.8 5.0

16.3 4.0

40.8 10.4

The proportion of nonwhite rural households—

The greater the proportion of rural nonwhite

households, the greater the expected proportion

of rural substandard housing (table 3, row 2).

Studies of discrimination in housing show that

nonwhites face higher housing prices than do

whites. For example, blacks pay about 15 percent

more than whites pay for comparable housing

within any given neighborhood (15). Therefore,

nonwhites may purchase slightly lower quality

housing than would whites with equal incomes.

The percentage of the rural aged—The higher

the percentage of the rural population 62 years

old and older, the lower the percentage of substandard housing (table 3, row 3, shows a negative coefficient). Many elderly already own their

homes and may be more likely to live in better

housing than would other households.

The rural vacancy raie—The lower a county's

unoccupied units as a proportion of available

units, the greater the tendency for residents to

occupy lower quality housing (table 3, row 4).

With less housing on the market, people have less

of a selection of units.

The population growth rate from 1970 to 1980—

The greater the population growth rate, the

greater the percentage of people hving in substandard housing (table 3, row 5). Housing supply

may not respond quickly enough to rapid population growth, leading to greater use of a county's

lower quality housing.

The average household s/ze—Areas with larger

rural households may have a greater proportion

of crowded rural units, thus a higher rate of

rural substandard housing (table 3, row 6).

Build More Units or Subsidize

Existing Housing?

Many analysts (including the President's Commission

on Housing) argue that programs to subsidize existing housing (called demand-side programs) are more

sensible than the traditional construction-oriented

programs (14). This position may be more vahd in

urban than in rural areas. Most programs that subsidize existing housing do not serve ov^mer-occupants

or the lowest income-eligible households, two groups

especially concentrated in rural areas. Also, construction and operating costs per unit of public

housing are significantly lower in rural areas than

in urban areas (3).

How effectively would these alternative policies

reduce substandard housing in rural areas? This

report estimates the reduction in substandard units

from increased spending on a program that subsidizes existing housing, a program to construct new

housing, and one that combines both features.

Subsidize Existing Housing

Suppose that current annual subsidies of existing

housing programs such as the Department of Housing and Urban Development's rent subsidy program,

the Section 8 existing housing program, were ex-
panded by 10 percent, about a $150-million yearly

increase in current programs. Such an increase

would allow an additional 97,000 households (based

on current average annual subsidies) to participate

in the program. By doing so, the program raises

about 26,000 of these additional participants out of

poverty, based on an estimate that 27 percent of

Section 8 existing housing program participants are

raised out of poverty by the program (6). This constitutes a 0.1-percentage-point reduction in the rural

poor, from 8.9 percent to 8.8 percent.

Because of the decrease in poverty, the number of

rural households living in substandard units would

decrease by 0.07 percentage point, from 7.81 to 7.74

percent—an 18,000-unit reduction in rural occupied

substandard housing. Also, about 4,000 of the 71,000

participating households remaining poor would

move out of substandard housing after the subsidy

(see box outlining the demand-side simulation).

Thus, the number of rural substandard units would

decUne by 22,000 units if funding to existing housing were increased $150 million.

Construct New Housing

About 83,000 new units would be built if annual

subsidies for construction-oriented programs in

rural areas rose by $150 million.^ The influx of new

units would initially increase the rural vacancy rate

by 0.3 percentage point, from 3.5 to 3.8 percent.

The number of substandard units would then fall by

0.05 percentage point, from 7.81 to 7.76 percent—a

12,000-unit reduction in rural substandard housing

(see box on Using the Regression Findings in the

Simulations).

Combine Both Programs

If funding were instead increased by $75 million for

both types of programs, targeting increases to localities where each program may be most effective,

rural substandard housing would be reduced by

31,000 units (see box on Using the Regression Findings in the Simulations). Increased new construction

would be targeted to low-vacancy areas, which may

need to expand the housing stock. Increased sub-

^This assumes a $1,800 annual average subsidy per participating household in 1980, a figure obtained using the formula (gross

rent plus other costs, minus tenant payments) in the box outlining

the demand-side simulation. The estimate of annual gross rent

($2,664) comes from (1). Annual other costs ($660) and annual

tenant payments ($1,524) are based on 1979 monthly cost data on

the Section 8 new construction program (13) updated to 1980

dollars.

How the Demand-Side

Simulation Worlds

The current average annual subsidy per participating household in the Section 8 existing

housing program is $1,548. This figure is estimated from gross rent plus other costs, minus

tenant payments. The 1980 annual gross rent

($2,712) came from a study of the Section 8

existing housing program in rural areas (1).

Annual other costs ($360) and annual tenant

payments ($1,524) v^ere derived from 1979

monthly cost data on the Section 8 existing

housing program (14) and were updated to

1980 using an average ratio of 1980 to 1979

price levels for mortgage rates, fuel and

utilities, maintenance and repair, and construction. All simulation findings were rounded

to the nearest thousand.

Dividing the number of rural substandard units

(2.15 million) by the number of rural households (27.5 million) yields the percentage substandard (7.81 percent). The $1,548 annual

subsidy (monthly subsidy of $129) would increase average monthly housing expenditures

by $28 for participating households, based on

Reeder's finding that approximately 22 percent

of the average Section 8 subsidy was spent on

housing (6). Some participating households

remaining poor will move from substandard to

standard housing because of this $28 increase

in monthly housing expenditures. Following the

subsidy, about 6 percent of the rural poor living in substandard units would spend more on

housing than $290, the mean monthly rent for

standard units (12). Therefore, about 4,000

households (about 6 percent) of the 71,000

households in poverty receiving the subsidy

would move out of substandard housing into

standard units. This estimate is probably conservative because many participating households may find standard units renting for less

than the average rent of all standard units.
Combining the features of programs that maintain/improve the existing housing stocit with those that

construct new units—especially when money is targeted according to the area's vacancy

rate—results in the largest reduction in rural substandard housing for the money spent (table 5).

Under the combined approach, money is targeted so that:

Existing housing is maintained in areas with high

vacancy rates, and

New housing is constructed in areas with low

vacancy rates.
sidies for existing housing would be targeted to

areas with high vacancy rates.

Adding $75 million in subsidies to existing housing

in high-vacancy areas would reduce rural substandard housing by 11,000 units. The 0.05-percentagepoint drop in the percentage poor produces most

of this reduction (9,000 units], and the remainder

(2,000 units] is produced by the effect of additional

subsidy dollars on households remaining in poverty.-

'Identical figures as in the demand-side simulation were used

for the average annual subsidy, the proportion raised out of

poverty, the proportion of the subsidy spent on housing, and the

proportion of rural poverty households who would move out of

substandard housing after the subsidy.

An additional $75 million in funding toward new

housing construction programs would increase the

rural vacancy rate by 0.14 percentage point and

eventually reduce the number of rural substandard

units by 20,000. There is a much larger per-dollar

reduction in substandard housing under this combined program than was found in the other simulations where funds were not targeted (see box on

Using Regression Findings in the Simulations].

Of the three programs, the combined approach is

the most cost-effective. For a $150-million expenditure, the combined program would reduce substandard housing in rural areas by 31,000 units. This

Using the Regression Findings in the Simulations

Subsidizing existing or new housing can cause

people to move out of substandard conditions

into housing that is at least standard. I used

the regression findings to estimate the reduction in substandard units from increased

spending on a program that subsidizes existing

housing, a construction-oriented program, and

a program that combines both features.

To simulate which program produces the

largest reduction, I analyzed what happens to

the percentage of rural substandard housing

(the dependent variable) when the factors

related to substandard housing (the independent variables] change (table 3], In particular,

increased subsidies to existing housing can increase incomes of participating households,

reducing the percentage of rural households in

poverty. Increased funding of new construction

can increase the rural vacancy rate by increasing the housing stock.

• In the existing Ijousing simulation (regression 1], the 0.1-percentage-point decrease in

the proportion of poor households is multiphed

by the regression 1 coefficient for percentage in poverty of 0.69, yielding a 0.07-

percentage-point decrease in the proportion

of rural households living in substandard

units. The number of rural substandard

units would ultimately decline by 22,000

units if funding of existing housing programs

were increased $150 million.

• In the new construction simulation (regression 1], the 0.3-percentage-point increase in

the vacancy rate is applied to the regression

1 coefficient for vacancy rate of -0.16,

reducing the proportion of rural households

living in substandard units by 0.05 percentTable 3—Regression 1: Factors affecting rural

substandard housing

Dependent variable: Percentage of rural substandard housing

Independent variable Coefficient t-value*

Percentage of rural

households in poverty

0.69 40.9

Percentage of nonwhite

rural households

.13 22.5

Percentage of elderly

rural households

-.11 -5.7

Rural vacancy rate -.16 -3.8

Total 1970-80 population

growth rate

.03 9.2

Rural household size .03 9.1

Constant -.07 -5.6

Number of observations = 3,088.

*R2 = 0.67.

age point. The number of rural substandard

units would ultimately decline by 12,000

units if funding toward new construction

programs were increased $150 million.

Under the combined program simulation,

regression 1 should be revised to include

how low- or high-vacancy rates affect the

percentage of substandard housing, both

directly and through the poverty and vacancy

rate variables. Regression 2 includes those

effects on the percentage of rural substandard units (table 4):

* The 0,05-percentage-point reduction in

the percentage in poverty is multiplied by

the regression 2 coefficient for the percentage in poverty in high-vacancy areas
compares with reductions of 22,000 and 12,000

units generated by the simulations subsidizing existing housing and new construction, respectively

(table 5).

Table 5—Simulation results

Housing program Reduction in rural substandard units

Subsidize existing housing*

Construct new housing*

Combine both programs^

Number

22,000

12,000

31,000

'$150-million increase in funding.

^$150-million increase, spht into $75 million on demand-side

and $75 million on construction-oriented programs.

References

(1) Combs, J. Paul, and others. Evaluation of the

Section 8 Existing Housing Program in fturaJ

Areas. Appalachian State Univ. Boone, NC.

1982.

(2) Evaluation of the Section 8 New

Construction Program in Rural Areas. Appalachian State Univ. Boone, NC. 1982.

(3) Ebels, Daniel M., and Harriet Newburger.

"Housing Vouchers for Rural America," flural

Policy Problems: Changing Dimensions. William

Table 4—Regression 2: Effect of poverty and vacancy

rate in low- versus high-vacancy rate counties

Dependent variable: Percentage of rural substandard housing

Independent variable Coefficient t-value*

Percentage of rural

households in poverty

0.66 25.4

Percentage in poverty times

low-vacancy rate dummy variable

.05 1.9

Rural vacancy rate .01 .2

Low rural vacancy rate

dummy variable^

.02 2.6

Vacancy rate times lowvacancy rate dummy variable

-.53 -3.5

Percentage of nonwhite

rural households

.13 22.4

Percentage of elderly

rural households

-.11 -5.4

Growth rate in total

population, 1970-80

.03 8.8

Rural household size .03 9.1

Constant -.08 -6.0

Number of observations = 3,088.

IR2 = 0.67.

^Low-vacancy rate dimimy variable

= 1 if vacancy rate < 0.037,

= 0 otherwise,

where 0.037 is the mean rural vacancy rate.

of 0.66, reducing the percentage living in

substandard housing by 0.03 percentage

point—a decrease of 9,000 substandard

units. When combined with the 2,000

poor households estimated to move out of

substandard housing after receiving the

subsidy, substandard housing is reduced

11,000 units.

* The 0.14-percentage-point increase in

vacancy rate is multiplied by the regression 2 coefficient for vacancy rate in lowvacancy areas of -0.52, reducing the percentage living in substandard housing by

0.07 percentage point—20,000 fewer units.

Therefore, $150 million in funding, split

into $75 million spent to maintain existing

housing and $75 million spent on constructing new housing, reduces rural

substandard housing by 31,000 units.

This effect is larger than that of the new

construction simulation because increased

vacancy rates have a much stronger effect on the percentage of substandard

housing in low-vacancy areas (where the

coefficient for regression 2 - -0.52) than

in all areas as in the new construction

simulation (where the coefficient for

regression 1 = -0.16).

A word of caution, however. These one-equation

models do not completely describe the relationships between the variables: several of the

independent variables may be related to each

other, and the dependent variable may affect

some of the independent variables. These factors may bias some of the estimated coefficients. Also, the poverty and vacancy rate

variables are imperfect indicators of housing

policy actions. For example, a vacancy rate

variable may not appropriately simulate increases in a housing program because the

characteristics of vacant units may be different after newly constructed, still vacant,

public housing units are introduced. Nevertheless, the simulations provide insights on

how alternative government housing programs

can affect housing quality.
p. Browne and Don F. Hadwiger, eds.

Lexington, MA: Lexington Books. 1982.

(4) Lerman, Donald L. *'Documentation of FmHA

Data Project," unpublished paper. U.S. Dept.

Agr., Econ. Res. Serv. 1984.

(5) Mayo, Stephen K., and others. Housing

AJJoivances and Other fientaJ Housing Assistance Programs—A Comparison Based on the

Housing AJJoivance Demand Experiment, Part 2:

Costs and Efficiency. Cambridge, MA: Abt

Associates, Inc. 1980.

(6) Reeder, WiUiam J. 'The Benefits and Costs of

the Nationwide Section 8 Existing Housing Program," Journal of Public Economics. Vol. 26,

1985. pp. 349-77.

(7] U.S. Department of Commerce, Bureau of the

Census. U.S. Census of Housing: 1950.

(10)

(11)

(12)

. 1980 Census of Housing.

. Census of Population and Housing, 1980: Summary Tape File 3C.

data tape.

1983 Annual Housing Survey,

(8)

(9)

. U.S. Census of Housing: 1960.

. 1970 Census of Housing.

(13) U.S. Department of Housing and Urban Development. How Well Are We Housed? Vol. 5.

1979.

(14) Wallace, James E., and others. "Participation

and Benefits in the Urban Section 8 Program:

New Construction and Existing Housing," The

Report of the President's Commission on Housing. Cambridge, MA: Abt Associates, Inc. 1981.

pp. 12-14.

(15) Yinger, John. "Prejudice and Discrimination in

the Urban Housing Market," Current issues in

Urban Economics. Peter Mieszkowski and Mahlon

Straszheim, eds. Baltimore, MD: Johns Hopkins

University Press, 1979.

10
Economic Growth Is Good For Everyone. Right?

Not necessarily, according to recent findings from USDA's Economic Research Service. Find

out from these related reports just what can happen when rapid economic growth comes to

a rural area.

Will Employment Growth Benefit All Households?

A Case Study in Nine Nonmetro Kentucky Counties, by Donald K. Larson and Claudia K. White.

SN: 001-019-00425-8.

Overall economic growth in a rural area will probably

not benefit all households or residents in that area.

In a nine-county area of south central Kentucky,

rapid employment growth between 1974 and 1979

did create new job opportunities. However, only 18

percent of the households had members who took

advantage of the new jobs. The employment growth

also did not reduce the area's overall poverty level.

About as many households fell into poverty as left

the poverty ranks during the study period. Some

population groups, such as households headed by

women, remained economically disadvantaged

despite the area's growth. Other groups, such as the

elderly, maintained their income status by relying on

public and private income transfer programs.

Distribution of Employment Growth in Nine Kentucky Counties: A Case Study, by Stan G.

Daberkow, Donald K. Larson, Robert Coltrane, and

Thomas A. Carlin.

SN: 001-019-00337-5.

Rapid employment growth between 1974 and 1979

in a nine-county study area of south central Kentucky provided job opportunities both for local

residents and for persons with limited labor force experience. But, recent inmigrants held a disproportionate share of better paying executive jobs. This

case study, which examines the distributional effects

of rapid employment growth in a nonmetro area,

shows that inmigrants also held a disproportionate

share of jobs in growing business establishments.

Although manufacturing was the major economic

force in the study area in January 1980, jobs in the

private service sector increased more than in other

sectors.

Distribution of Employment Growth in 10 Georgia

Counties: A Case Study, by James D.

Schaub and Victor J. Oliveira.

SN: 001-019-00412-6.

Rapid economic growth in a 10-county rural area in

south Georgia during 1976-81 favored employment

of whites, men, and inmigrants. They earned higher

average weekly salaries than blacks, women, and

long-term residents. This study of growth in a mixed

manufacturing- and agricultural-based economy flows

from a research project on the impacts of economic

expansion in nonmetro economies with different industrial bases. The Georgia area's job growth was

greatest in the trades and services sectors. Few

businesses used public sector funds to start or expand their operations. Government employed 25 percent of the area's wage and salary workers.

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