Rural Conditions and Trends, Vol. 11, No. 2 December 2000

Douglas E. Bowers

Executive Editor

For this issue:

Peggy Cook

Robert Gibbs

Issue Editors

Linda Hatcher

Managing Editor

Shashunga Clayton

J.B. McCraw

Editing Assistance

Alma Young

Graphic Assistance

Tina Terry-Eley

Table Preparation

Anne E. Pearl

Text Layout 

Victor B.Phillips, Jr.

Cover Design

Cover Photo

Leslie Whitener 



Special Note

ERS will discontinue publication of Rural Conditions and Trends with this issue; however,

much of the information now in RCaT will be published through other ERS outlets. These

include Rural America, which will incorporate some of the information and analyses formerly published in RCaT, and the ERS website (http://www.ers.usda.gov). Both will provide regular updates of social and economic conditions, timely research, and data on a

wide variety of related issues.

Questions? Contact Doug Bowers at 202-694-5398 or dbowers@ers.usda.gov



Contents of this journal may be reprinted without permission, but the editors would appreciate acknowledgment of such use and an advance copy of the material to be reprinted.

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Rural Conditions and Trends2000, Volume 11, Number 24 Overview

Favorable Rural Socioeconomic Conditions Persist, but Not in All Areas

9 Low-Wage, Low-Skill Employment

Rural Low-Wage Employment Rises Among Men

Low-Wage Counties Face Locational Disadvantages

27 Population and Employment

Nonmetro Population Growth Rate Recedes in a Time of Unprecedented

National Prosperity

Nonmetro Migration Drops in the West and Among College Graduates

Nonmetro Employment and Unemployment Trends Remain Favorable

Almost Half of Hired Farmworkers 25 Years and Older Earn Poverty-Level Wages 

51 Earnings

Rural Nonfarm Earnings Increase in 1997, but Lag Urban Earnings Growth

Skills Training and Manufacturing Innovations Are Key to Raising Rural Workers’

Wages 

62 Poverty and Income

Rural Poverty Rate Declines, While Family Income Grows

Food Stamp and Family Assistance Benefits Sharply Decline in the Post-WelfareReform Era

75 Rural Well-Being

Unique Housing Challenges Face Rural America and Its Low-Income Workers

Prevalence of Hunger Declines in Rural Households

87 Appendix: Data Sources and Definitions94 Appendix Tables
The socioeconomic climate of rural areas

remained favorable in the

late 1990’s, according to

the most current economic and population

indicators.

Unemployment rates

continued to fall, and

population, employment,

and income remained on

the rise, albeit more

slowly than earlier in the

decade. At the same

time, favorable economic

performance did not benefit all rural people and

areas equally. About 27

percent of rural workers,

mostly women or minorities, held low-wage jobs

in 1999. Furthermore,

low-wage employment

was clustered in counties

in the Great Plains and

South. These counties

tended to have small

populations, locations

remote from urban centers, and less diversified

economies.

Overview

4 • Rural Conditions and Trends, Vol. 11, No. 2

This issue of Rural Conditions and Trends (RCaT) provides an assessment of the current conditions and trends in socioeconomic well-being for rural people and places

during the late 1990’s. The core articles update analyses reported annually by focusing

on such topics as population, migration, employment, unemployment, poverty, earnings,

and transfer payments. Articles that were new to last year’s issue on housing and household food security and hunger are featured again this year. Also returning to the issue is

an article based on data from ERS’s Rural Manufacturing Survey that compares rural and

urban wage differentials and examines how the business practices of manufacturing firms

shape wage levels.

This year’s socioeconomic issue highlights low-wage workers and geographic patterns of

low-wage employment, topics of special concern in rural America. Despite the strength of

the current economic expansion, over a quarter of rural wage and salary workers ages 25

and older earned full-time-equivalent wages below the poverty threshold for a family of

four in 1999 ($17,028). Earnings among the lowest paid rural workers have grown more

slowly than for the rest of the labor force, often less than the inflation rate, even as their

education levels have risen. The changing location of economic activities within the United

States and across international borders, technological innovations, and declining unionization and real minimum wage rates all play a part in explaining low-wage trends.

Many rural areas where low-wage workers are clustered have been hard hit by these larger economic forces, and have not shared fully in the benefits of national economic growth.

Recent rural development policy initiatives, such as the New Markets program, promise to

jumpstart growth in many of these distressed areas. At the same time, rural areas face

new challenges as the Personal Responsibility and Work Opportunity Reconciliation Act

of 1996 (PRWORA) gradually moves a new set of workers into the low-wage labor force.

The analyses of conditions and trends reported in this issue help us to understand the

economic and social context in which these policy developments take place, to identify

the people and places that most need assistance, and to appreciate both the possibilities

and limitations of our efforts to improve well-being in all of rural America.

The first of two thematic articles examines the prevalence and characteristics of low-wage

workers in rural areas. A second article presents a new classification of rural low-wage

counties containing high proportions of jobs in low-wage industries and analyzes the geographic and economic characteristics of these counties. Other articles focus on various

aspects of socioeconomic well-being among either low-wage workers or low-wage counties.

Rural Conditions and Trends last reported on rural socioeconomic conditions and trends

in its February 1999 issue (Vol. 9, No. 2), which looked at the socioeconomic status of

rural minorities also. That issue painted a mixed picture for economic performance in rural

areas for 1996-97. Like their urban counterparts, rural areas saw unemployment decline,

per capita incomes grow, and weekly earnings rise because of strong national economic

expansion. At the same time, the rural advantage in economic and population growth

observed during the early to mid-1990’s eroded. Furthermore, significant rural-urban gaps

persisted and even widened. This issue shows a continuation and sharpening of the conditions and trends reported last year. Several main themes emerging from the issue’s

analyses are highlighted on the following pages.

Favorable Rural Socioeconomic Conditions

Persist, but Not in All Areas
Rural Conditions and Trends, Vol. 11, No. 2 • 5

Overview

Overall Rural Economic Climate Remains Favorable . . .

Rural areas, as a whole, enjoyed relatively good economic times in the late 1990’s,

according to the most recent population and economic indicators available (table 1). The

rural unemployment rate, which fell to its lowest levels in over 20 years in 1998, dropped

even more to 5.1 percent in 1999. Nonmetro employment expanded further in 1999, and

nonmetro employment growth even outpaced metro growth during the last 2 quarters of

1998. The population rebound from declines observed in the 1980’s continued (at a diminishing rate). Net inmigration from metro to nonmetro areas resulted in an average gain of

281,000 people per year between 1997 and 1999. Rural per capita income rose 2 percent

in 1997, while rural real earnings per nonfarm job rose by 1.3 percent. Reflecting the

strong national economy, annual growth in nonmetro and metro per capita transfer payments to individuals steadily decreased from over 6 percent per year in the early 1990’s

to around 2 percent per year between 1994 and 1997.

. . . But Pace of Growth Slows

Current trends, however, also show a marked slowing of economic and population growth,

compared with trends earlier in the decade. Despite quickening during late 1998, the

pace of employment growth slowed to 1.5 percent during 1999. In addition, the nonmetro

rate of population growth has steadily dropped since 1994-95, when it momentarily

exceeded the metro rate. By 1997-99, the nonmetro rate of population growth was little

more than half of the metro rate. Despite the net inflow of people from metro areas, the

rate of net migration, which steadily increased during the early and mid-1990’s, dropped

to about one-half of 1 percent during 1997-99. Reversing earlier trends of record growth,

the West was the center of the 1997-99 slowdown in rural migration. Furthermore, much

of the recent decrease in rural net migration occurred among college graduates, although

the tightened linkages between rural and urban economies make a return to the severe

rural “brain drain” of earlier decades seem unlikely.

In response to the policy and program changes brought about by the enactment of

PRWORA, as well as to the strong national economy, per capita transfers for the major

public assistance programs—Temporary Assistance for Needy Families (TANF), food

stamps, and Supplemental Security Income (SSI)—continued to decline sharply. During

1996-97, per capita benefits for TANF declined more rapidly in rural than in urban areas,

while per capita benefits for food stamps declined more rapidly in urban than in rural

areas.

Rural Areas Lag Urban Areas on Many Indicators

Even in the face of favorable economic conditions, rural areas lagged urban areas on

many indicators. Following a longstanding trend, poverty rates were 2 percentage points

higher in rural than in urban areas. In 1997, rural areas lagged urban areas by at least

$9,000 in real per capita income and by well over $7,000 in real earnings per job. These

gaps have widened since the late 1980’s. Compared with urban economies, rural

economies rely more heavily on transfer payments as a source of income. In 1997, per

capita transfer payments made up 21 percent of rural personal income, compared with

approximately 15 percent of urban personal income.

Furthermore, metro population and employment growth exceeds nonmetro growth. The

pattern was reversed in the mid-1990’s when rural rates of population and employment

growth were higher than urban rates.

Benefits of Favorable Economic Conditions Are Spread 

Unevenly Across Rural Areas

The current conditions and patterns of growth just reported did not necessarily affect all

rural areas equally. For example, not all rural areas in the country have benefited from

increased population growth; populations of many nonmetro counties once again declined

in the late 1990’s. Despite the overall decline in rural unemployment, it increased in large
Overview

6 • Rural Conditions and Trends, Vol. 11, No. 2

Table 1

Indicators of nonmetro economic performance

Socioeconomic conditions in the mid-1990’s show signs of continued improvement, although rural-urban gaps persist

Indicator Performance Indicator Performance

Percent Percent

Annual population change: Annual employment change:

1995-99 0.62 1995-99 1.0

1990-95 .96 1990-95 1.6

Annual net migration rate: Annual unemployment rate:

1995-99 .35 1999 5.1

1980-90 .72 1995 6.3

1991 7.7

Poverty rate: Annual change in real per capita income:

1998 14.3 1996-97 2.06

1994 16.4 1994-97 2.00

1989 15.7 1991-94 1.57

1997 dollars

Per capita income: Annual change in real transfer payments:1

1997 19,090 1996-97 1.74

1994 17,993 1994-97 2.59

1991 17,170 1991-94 3.45

Per capita transfer payments:1 Annual change in earnings per nonfarm job:

1997 4,055 1996-97 1.3

1994 3,756 1991-97 .4

1991 3,395 1989-91 -1.3

1997 dollars 1997 dollars

Per capita earnings: Rural-urban gap in per capita income:

1997 11,630 1997 7,771

1994 11,139 1991 6,897

1991 10,492 1989 7,134

Earnings per nonfarm job: Rural-urban gap in earnings per nonfarm job:

1997 22,985 1997 9,840

1991 22,473 1991 8,482

1989 23,059 1988 8,171

1999 dollars 1999 dollars

Average weekly wage and

salary earnings: Rural-urban gap in average weekly earnings:

1999 485 1999 125

1990 438 1990 130

1Transfer payments to individuals account for 96 percent of all transfers.

Source: Other articles and appendix tables in this issue of Rural Conditions and Trends, Economic Research Service.
Rural Conditions and Trends, Vol. 11, No. 2 • 7

Overview

clusters of counties in the Great Plains and South Central States. As the next section discusses, rural counties with high rates of low-wage employment are primarily clustered in

parts of the Great Plains and South.

Low-Wage Employment Higher in Rural Than in Urban Areas

About 27 percent of the adult rural wage and salary workforce in 1999 earned less than

the poverty threshold for a family of four (adjusted for full-time equivalency) and were thus

classified as low-wage workers. Low-wage employment rates were higher in rural areas

than in urban areas. Rural-urban differences in the kinds of jobs available and in education levels partly explain the employment rate differences. Urban jobs, for instance, are

still more likely to require a college degree or highly specialized technical skills than are

rural jobs.

In addition, current rural low-wage employment rates remain higher than in the late

1970’s, despite a better educated workforce today with very low unemployment rates.

Rural low-wage employment has also changed since the late 1970’s. Although most lowwage workers are women, men’s share of low-wage work has risen over the past two

decades. Similarly, Black rural workers comprise a smaller share of the total low-wage

workforce today than in the past, with their numbers replaced by the rising share of

Hispanic workers.

Low-Wage Counties Are Small and Often Far From Metro Areas

ERS identified a set of rural counties that had high rates of employment in low-wage

industries in 1995. (For an explanation of what a low-wage county is, see the box in “LowWage Counties Face Locational Disadvantages,” p. 18) Although located in all regions of

the United States, most low-wage counties were clustered in the Great Plains and the

South. A typical low-wage county had a small population, was distant from large urban

centers, and lacked economic diversity. The kinds of industries found disproportionately in

low-wage counties tended to pay lower wages on average. More importantly, however,

nearly all industries in low-wage counties paid lower wages than the same industries in

other counties, implicating location as the primary reason for low earnings rather than

industry mix.

Low-wage counties made a surprisingly strong showing by some measures, however.

Unemployment rates were only slightly higher, and net inmigration slightly lower, than in

other rural counties. In fact, outside the farm areas of the Great Plains, low-wage counties

enjoyed above-average inmigration and population growth. The reasons are unclear, but

low-wage counties may attract low-wage workers because of lower living costs and the

relative ease of finding work that requires low skills or education.

Characteristics of Low-Wage Counties Vary by Region

While low-wage counties shared some characteristics, much depended on the region in

which they were located (fig. 1). Not surprisingly, low-wage counties in the Great Plains

largely depended on farming and had slower population growth than other low-wage

areas. As is true for the Plains overall, education levels were fairly high, even higher than

non-low-wage counties in the South. But low-wage counties in the Great Plains were also

among the smallest, most remote, and least economically diverse in the Nation. In comparison, low-wage counties in the South reflected the region’s mix of low education levels

and a greater range of economic activity. Despite the large number of persistent-poverty

areas in the South, however, poverty rates in most southern low-wage counties were not

unusually high. The article on the geography of low-wage employment further explores

the relationship between persistent poverty and low earnings (see “Low-Wage Counties

Face Locational Disadvantages,” p. 18).

The extent to which the conditions and trends of the late 1990’s continue in the 21st century depends largely on national macroeconomic and demographic changes. How States
Overview

8 • Rural Conditions and Trends, Vol. 11, No. 2

and local communities deal with the challenges of building and sustaining strong rural

economies will affect future trends and conditions.

A Special Note About RCaT

ERS will discontinue publication of Rural Conditions and Trends with this issue. Since

1990, RCaT has provided information and understanding about the effects of demographic and economic trends and policies on rural people, economies, and communities. In

building a database of reliable indicators for publication in RCaT, ERS has relied not only

on national data bases from other government agencies, but the agency has also developed its own research tools to communicate with its customers in the policy and research

arenas. In its efforts to continue to use the most effective means of information dissemination, ERS will begin in 2001 to incorporate some information and analyses formerly

published in RCaT in its publication, Rural America, combining feature articles with regular updates of social and economic conditions. Readers may also find much of the information now in RCaT at the ERS rural development briefing room website:

http://www.ers.usda.gov/rural. In addition to articles on current socioeconomic conditions,

the briefing room will contain timely research articles and data on a wide variety of related issues. [Peggy J. Cook, 202-694-5419, pcook@ers.usda.gov; and Robert M. Gibbs,

202-694-5423, rgibbs@ers.usda.gov]

 Growing low-wage

 Declining low-wage

 Other nonmetro

 Metro

Source: Calculated by ERS using data from the Bureau of Labor Statistics.

Figure 1

Population change in low-wage counties, 1990-99

Most low-wage counties in the Great Plains lost population in the 1990's
In 1999, nearly onefourth of the rural wage

and salary workforce

over 25 earned low

wages. A large share of

these workers are the

sole or main wage earner

in the household. Rural

low-wage workers are

more likely to be

employed in service and

retail trade industries. But

within a given industry,

low-wage workers tend to

be employed in lessskilled occupations

requiring less education.

Although low-wage rural

workers continue to be

overwhelmingly women

and minorities, the share

of White men in lowwage jobs has grown

since 1979.

Rural Conditions and Trends, Vol. 11, No. 2 • 9

	




According to 1999 data from the Current Population Survey, nearly 5 million rural wageand salary workers ages 25 and older (27.2 percent) received wages that, if earned

full-time, full-year, would not lift a family of four above the official poverty level. Brisk

growth in the U.S. economy since mid-decade has pushed up real wages and improved

living standards for most workers. In 1999, average weekly earnings for rural wage and

salary workers stood at $485, a 10.7-percent increase since 1990 after adjusting for inflation. Nonetheless, the share of rural workers receiving low pay at present remained higher than the 24.6 percent rural rate in 1979 or the current urban rate of 19.3 percent. Rural

low-wage workers earned a median hourly wage of $6.50, slightly less than the $6.65

earned by their urban counterparts and much less than the $11.25 rate earned by rural

wage and salary workers overall. (See box “How Low-Wage Workers Are Defined.”)

The pervasiveness of rural low-wage employment in the face of significant technological

change and increased policy emphasis on workforce development signals a fundamental

challenge to the Nation’s ability to improve the well-being of working citizens. Global economic competition and innovation continue to dampen wage pressures at the low-wage

end of the rural labor market, even as these forces present new opportunities for many

workers. The Personal Responsibility and Work Opportunity Reconciliation Act of 1996,

designed to reform the Nation’s welfare system, will have its largest wage and employment impact on the low-wage workforce, as recipients and ex-recipients of public assistance join the ranks of low-wage earners. Understanding both the current characteristics

and recent trends among low-wage workers helps us to assess the prospects for change

and to tailor policies that truly meet the needs of this segment of the rural labor force.

Most rural adult low-wage workers hold jobs that require limited education or training and

that offer limited paths for advancement. In places where low-wage employment is concentrated, workers’ prospects are constrained even more by low wage scales and by long

distances to good jobs in larger labor markets (see “Low Wage Counties Face Locational

Disadvantages,” p. 18). In addition to schooling and geography, stagnant or declining

wages in rural labor markets since the 1970’s has spread low-wage work to jobs other

than the low-skilled. Today, the likelihood of holding a low-wage job varies widely by the

kind of job held and by the personal characteristics of workers. Even so, low-wage

employment can be found among all demographic groups, and in all major industry and

occupation groups. Men were historically less likely to work in low-wage jobs than

women, but are becoming a larger share of the low-wage labor force.

Rural Low-Wage Employment Rises 

Among Men 

How Low-Wage Workers Are Defined

In this analysis, low-wage workers are defined as persons ages 25 and older employed in the

wage and salary workforce whose earnings, adjusted to a full-time, full-year equivalent, would

fall below the weighted poverty threshold for a family of four ($17,028 in 1999). Workers

younger than 25 are excluded to omit recent labor force entrants who are more likely to have

unstable work histories or weak labor force attachment.

Social scientists have used numerous definitions of low-wage work. A common alternative to

the one provided here is to compare full-time full-year equivalent earnings with a three-person

family poverty threshold, in keeping with actual average family size in the United States. Others

argue that the official poverty standards are inappropriately low for families with working adults,

and, thus, that the low-wage threshold should be significantly higher than the four-person family

threshold. We use the four-person measure both because it has substantial precedence in the

labor literature and because it appears to represent a middle ground between more and less

restrictive definitions.
Low-Wage, Low-Skill Employment

10 • Rural Conditions and Trends, Vol. 11, No. 2

Low-Wage Workers Are Not Necessarily Poor

We base the measure of low wages in this article on the weighted average poverty

threshold for a family of four ($17,028). Most rural low-wage workers, however, do not live

in poor families, often because the family has other wage earners or because these families receive cash income from other sources. In addition, family size or composition may

indicate a poverty threshold different from the adjusted family-of-four threshold used here.

Similarly, not all adult low-wage workers experience severe economic hardship. In particular, the presence of other, often well-paid workers in the household mitigates the impact

of a worker’s low pay on his or her well-being. In 1999, a significant share of rural lowwage workers, 43 percent, were either the sole wage earner in their households, or the

household member with the highest weekly earnings, the primary earner (table 1). Rural

low-wage men were more likely to be sole or primary earners in a household than were

women, while rural workers with a high school education or less were more likely to be

sole or primary earners than were workers with at least some college.

Other low-wage workers are secondary earners in the sense that they bring home less

pay than the primary earner. Secondary low-wage workers are the least likely to experience severe hardship because by definition they live in two-or-more-earner households.

Some of these workers may supplement comfortable household incomes with low-paying

jobs that have other desirable attributes, such as flexible work hours.

In many households with secondary low-wage workers, however, both primary and secondary workers hold low-wage jobs. For these households, the lack of nonwage benefits

and greater instability that are commonly associated with low-wage employment may

affect their long-term financial security in a way not fully captured by total income.

In addition to the worker’s earnings role within a household, the Current Population

Survey allows us to identify the relationship of each adult member to the “respondent”

who answered the survey. Most rural low-wage workers—over 80 percent—were either

the respondent or the spouse. The definition of low-wage worker used in this article

excludes young adults under 25, so that few workers in this analysis could be classified

as “children” within the household. This distinction is important because children are typically secondary wage earners, and are not a mainstay of a household’s financial support.

Table 1

Rural low-wage workers by earnings role in the household, 1999

Slightly less than half of all rural adult low-wage workers are the sole or primary wage earner in the household

Item Sole earner Primary earner Secondary earner

Low-wage Other

household household Total

Percent

All 32 11 7 50 100

Men 33 19 8 40 100

Women 31 7 7 55 100

Black 39 13 10 38 100

Hispanic1 29 14 11 46 100

White 30 11 6 53 100

1Hispanics may be of any race. All other categories exclude Hispanics.

Source: Calculated by ERS from the 1999 Current Population Survey microdata earnings file.
Rural Conditions and Trends, Vol. 11, No. 2 • 11

Low-Wage, Low-Skill Employment

Rural Low-Wage Shares Remain Above 1979 Rate

The rural low-wage rate of 27.2 percent in 1999 far exceeded the urban rate of 19.3 percent. The rural-urban gap in low-wage employment has remained remarkably stable, with

rural and urban changes in the share of low pay tracking each other closely in most

years. The higher rural rate may be due in part to cost-of-living differences that are

reflected in lower rural take-home pay, although no definitive studies document lower rural

living costs. Basic differences in the industrial and occupational structure of the rural and

urban economies likely play a more decisive role.

In rural and urban labor markets, changes in the share of workers earning low wages

have been significant. Since the 1970’s, the share of rural workers 25 and older earning

low wages rose substantially, up from 24.6 percent in 1979 (fig. 1). The rise reflects

national economic trends, especially the relatively slow productivity gains over much of

the period, shifts in labor demand toward more highly skilled and educated workers, and

the declining influence of labor unions. The urban low-wage share, meanwhile, rose from

a 1979 low of 14.9 percent.

Despite the difficulty of measuring rural wage trends due to periodic changes in the official definition of metro since 1979, it is evident that the rise in low-wage employment has

not been constant. In many ways, the 1980’s and 1990’s trends are mirror images.

Average earnings rose only slightly in the 1980’s, while the income distribution widened.

As the demand for workers with few skills or limited education fell, their wages dropped,

1979 81 83 85 87 89 91 93 95 97 99

0

5

10

15

20

25

30

35

Rural Urban U.S.

Source: Calculated by ERS using data from the Current Population Survey earnings files, 1979-99.

Percent

Note: The dotted lines indicate data are not available.

Figure 1

Share of experienced wage and salary workers earning low wages, 1979-99

The percentage of rural wage and salary low-wage workers has fallen since 1996
Low-Wage, Low-Skill Employment

12 • Rural Conditions and Trends, Vol. 11, No. 2

while those with college degrees rose. Rural areas were hit harder than urban areas,

because the rural industry and occupational mix required a lower share of highly educated and highly skilled workers.

In the 1990’s, the share of rural workers earning low wages fell as real wages began to

increase once more. The size of the urban low-wage workforce declined at the same time,

at a slightly smaller rate. Changes in both workers’ skills and job quality were partly

responsible for the recent rural low-wage trends, as workers became better educated and

as high-tech jobs requiring a more skilled workforce filtered into rural areas. The decline

in the low-wage share of workers between 1989 and 1999 was shared by all demographic

groups.

Most Low-Wage Workers Are Women and Minorities

Perhaps the most salient feature of the rural low-wage workforce is the preponderance of

women and racial and ethnic minorities, despite the slow decline of long standing discrimination in education and labor markets. Women made up 67.3 percent of rural low-wage

employment in 1999, although their overall employment share was just 48.7 percent.

Relative to men, rural women are more likely to engage in part-time work, more likely to

transition in and out of the labor force to meet household obligations, and more likely to

work in jobs with limited bargaining power with employers.

Women’s participation in low-wage work, however, depends critically on race and ethnicity. Black women were the most likely group to be employed in low-wage work (57 percent), followed by Hispanic and white women, Hispanic and black men, and finally white

men, just 14.9 percent of whom receive low wages (fig. 2). White men made up 44 percent of the overall rural workforce, but only 24 percent of the low-wage workforce in rural

areas.

The large low-wage share among rural blacks in general (46 percent), and black women

in particular, reflects the historical role of the South as a predominantly low-wage region

Figure 2

Source: Calculated by ERS using data from the 1999 Current Population Survey earnings files.

Share of rural wage and salary workers in low-wage employment by

gender and race/ethnicity, 1999

Percent

Woman and minorities are much more likely to be engaged in low-wage employment

70

60

50

40

30

20

10

0

Men Women 

White Black Hispanic

14.9

32.5 33.3 34.8

57

54
Rural Conditions and Trends, Vol. 11, No. 2 • 13

Low-Wage, Low-Skill Employment

and as the home of a majority of rural black workers. In effect, they have long faced a

low-wage “double jeopardy,” both through the effects of discrimination and by participation

in a labor market characterized by relatively strong demand for workers with limited skills.

A similar phenomenon may be observed in the West, where the fast-growing rural

Hispanic population is changing the face of low-wage work in that region. Due to a combination of their increased numbers in the labor force and an upward trend in low-wage

employment rates, Hispanics now comprise a majority of the low-wage workforce in the

rural West, just as blacks once did in the South.

Racial and regional profiles are often intertwined. The South maintained its lead as the

region with the highest share of rural low-wage employment (fig. 3). Yet regional differences have narrowed dramatically since 1979. Low-wage employment has grown more

slowly in the rural South than elsewhere. Meanwhile the rural West, where low-wage

employment was once relatively uncommon, now has a more prominent low-wage workforce (25.1 percent) than either the Northeast or the Midwest.

Service Sector, Blue-Collar Occupations Dominate Rural Low-Wage Employment

The kinds of services performed or goods produced are key determinants of workers’

earnings. Industry is the best measure of this dimension of a worker’s employment. Lowwage workers are present in every major industry group, but tend to be concentrated in a

few major groups (table 2). More than half of all low-wage workers were employed in the

retail trade (25 percent) and service industries (37 percent) in 1999, which comprised well

under half the total employment for other rural workers. Many jobs in these industries

involve entry-level work requiring few pre-existing skills, thereby offering valuable experience even for older labor force entrants. But these jobs often provide limited opportunities

for career advancement and long-term wage increases.

0

5

10

15

20

25

30

35

40

Source: Calculated by ERS using data from the 1979 and 1999 Current Population Survey earnings files.

Percent

Share of rural wage and salary workers in low-wage employment by region, 1979

and 1999

Although rural Southerners are the most likely to work in low-wage jobs, low-wage employment grew

fastest in the rural West

Figure 3

1979 1999

Northeast Midwest South West

18

21.8 21.9

23.8

29.7

32

19.2

25.1
Low-Wage, Low-Skill Employment

14 • Rural Conditions and Trends, Vol. 11, No. 2

The industry mix of low-wage employment in rural and urban areas shows a striking

resemblance. About 66 percent of urban low-wage workers were engaged in retail trade

or service industry employment, only slightly more than for their rural counterparts (62

percent). Low-wage work in both urban and rural areas was relatively infrequent in industries that tend to provide more skills training or job security, including the government and

manufacturing industries. The long, steady decline in manufacturing employment, coupled

with a growing service sector, has contributed significantly to the rise in low-wage work

nationwide.

But while pay levels appear to be rooted largely in the similarity or divergence of their

industry employment patterns, rural low-wage workers also perform very different kinds of

jobs in industries, compared with other rural workers and with urban low-wage workers.

Overall, rural low-wage employment is concentrated in a few major occupational groups,

much as with key industries (table 3). Most low-wage workers are employed in less skilled

white- and blue-collar occupations, especially service occupations. A much lower percentage are managers or professionals, holding jobs that often require moderate to high levels of education. Workers in these “atypical” low-paying jobs, usually women, are more

likely to be employed part-time.

Within industries, occupational divisions sharply demarcate low-wage work from other

employment. In manufacturing, an industry employing relatively few low-wage workers,

two-thirds of these workers were engaged in blue-collar occupations, versus half of all

rural manufacturing workers (table 3). The contrast in services was even starker, with

nearly 60 percent of low-wage workers in blue-collar and service occupations, twice the

overall rate. Occupational concentration, therefore, seems to be key in understanding the

industrial make up of low-wage work. In turn, occupational employment is, to a large

degree, a product of a worker’s level of education and training.

Low-Wage Work Tied to Education

Because the likelihood of entering particular occupations and industries strongly depends

on a worker’s skills and knowledge, social scientists usually point to low levels of these

attributes as a reason for low pay. The amount of schooling a worker has completed, for

example, typically indicates potential productiveness, and therefore, the amount an

employer will pay to hire and retain a worker.

Table 2

Low-wage employment by major industry, 1999

Low-wage workers are concentrated in the agriculture, retail trade, and service industries

Industry Rural Urban

All Low-wage All Low-wage

Percent

Agriculture 2.5 4.4 1.1 2.8

Construction 6.3 3.8 5.6 4.0

Mining 1.4 .5 .3 .1

Manufacturing 23.2 16.6 16.4 12.1

Transportation 7.1 3.4 8.4 4.8

Wholesale trade 3.2 2.8 4.2 3.5

Retail trade 13.7 24.9 13.1 25.8

Finance, real estate,

insurance 4.1 3.9 7.5 4.2

Services 32.7 36.8 37.8 40.6

Government 5.8 3.0 5.7 2.1

Total 100.0 100.0 100.0 100.0

Source: Calculated by ERS from the 1999 Current Population Survey earnings file.
Rural Conditions and Trends, Vol. 11, No. 2 • 15

Low-Wage, Low-Skill Employment

Not surprisingly, rural low-wage workers average fewer years of schooling than do other

workers, and the likelihood of earning low wages falls sharply as educational attainment

increases (fig. 4). In 1999, about half of rural workers without a high school degree

earned low wages, compared with only 10 percent of college graduates, and 23 percent

of those with at least 1 year of college. In most cases, urban workers were less likely to

hold low-wage jobs than were rural workers at each level of educational attainment.

Urban high school dropouts were an exception, however, and were about equally likely to 

Table 3

Rural low-wage employment by major occupation, 1999

A majority of low-wage workers are employed in service and less skilled white collar occupations

All Manufacturing Service industry

Occupation Low-wage Other Low-wage Other Low-wage Other

Percent

Managers,

professionals,

technical 11.3 28.8 3.5 15.3 19.4 56.7

Sales, clerical,

administrative 28.7 21.5 10.9 9.2 21.9 16.8

Service 27.6 11.6 3.5 1.3 49.2 17.4

Farm 4.6 2.2 .9 .8 1.1 .9

Craft and repair 6.0 14.9 13.1 23.9 2.1 4.7

Other blue-collar 21.8 21.1 68.1 49.5 6.3 3.5

Total 100.0 100.0 100.0 100.0 100.0 100.0

Source: Calculated by ERS from the 1999 Current Population Survey earnings file.

Figure 4

Urban workers at most education levels are less likely to hold a low-wage job than are 

rural workers

Share of rural and urban wage and salary workers in low-wage jobs by 

education, 1999

Percent

Source: Calculated by ERS using data from the 1979 and 1999 Current Population Survey earnings files.

70

60

50

40

30

20

10

0

50.2 51.9

31.9

25.1 23.1

16.7

9.6

6.3

Less than High school Some college College graduate

high school graduate

Rural Urban
Low-Wage, Low-Skill Employment

16 • Rural Conditions and Trends, Vol. 11, No. 2

be low-wage employees. Previous issues of Rural Conditions and Trends have reported

near-convergence in the wages of rural and urban workers without a high school diploma,

a finding consistent with similar rates of low-wage employment.

Men’s Share of Low-Wage Work Has Risen

Although the 20-year rise in low-wage employment affected workers in all demographic

categories, the face of the low-wage workforce has changed significantly since the late

1970’s. Despite the influx of women into the labor force, men made up a larger proportion

of the low-wage rural workforce in 1999 than in 1979, increasing their share from 29 percent to 33 percent, while their share of the total labor force slipped from 58 percent to 51

percent (table 4). Through an increase in educational attainment and occupational mobility, women were able to stay ahead of wage stagnation, and the share of women workers

earning low wages remained constant. In fact, the rise in low-wage employment in both

rural and urban areas since the late 1970’s is entirely attributable to stagnant or falling

wages among men.

A similar shift has occurred in the minority composition of rural low-wage employment.

Since 1979, Black workers have held a constant share of the low-wage workforce, while

the share of Hispanics has risen. The increase in Hispanic representation among rural

low-wage workers is not surprising, given the large increase in the rural Hispanic population during the period. The shift from Blacks to other workers, however, runs contrary to

the generally increasing proportion of Blacks in the rural workforce. As is true for women,

the share of Blacks engaged in low-wage work has remained fairly stable (47 percent in

1979 to 46 percent in 1999). Meanwhile, White men and Hispanics overall were more likely to work for low pay in 1999 than was true in 1979. The increase in the Hispanic lowwage rate, coupled with population growth, largely explains the rise of the West as a new

center of low-wage employment.

For both women and Blacks, the stability in low-wage employment trends is welcome

news, representing gains in education and skills, in labor force attachment, and in career

mobility. Yet, it is also a manifestation of the spread of low-wage work beyond its traditional bounds to affect jobs historically held largely by Whites and men. In spite of the continTable 4

Distribution of rural low-wage workers by gender and race/ethnicity

The rural low-wage workforce had a greater proportion of Hispanics and men in 1999 than in 1979

Item Low-wage All workers

1979 1999 1979 1999

Percent

Men:

Black 5.4 4.5 4.2 3.7

Hispanic1 1.8 3.4 2.4 2.8

White 21.0 23.9 50.9 43.8

Other .8 .9 1.0 1.1

Total 29.0 32.7 58.5 51.4

Women:

Black 8.4 9.4 4.2 4.5

Hispanic 1.9 3.7 1.5 1.9

White 59.6 52.6 34.8 41.2

Other 1.1 1.6 1.0 1.1

Total 71.0 67.3 41.5 48.7

1Hispanics may be of any race. All other categories exclude Hispanics.

Source: Calculated by ERS from the 1979 and 1999 Current Population Survey microdata earnings files.
Rural Conditions and Trends, Vol. 11, No. 2 • 17

Low-Wage, Low-Skill Employment

ued predominance of less educated workers among the low-wage workforce, the share of

workers with at least some college who hold low-wage jobs has increased significantly

between 1979 and 1998, especially for college graduates. The increases in low-wage

work are also apparent in higher skilled occupations, especially administrative, managerial, professional, and skilled craft occupations. It remains to be seen whether recent

declines in rural low-wage employment will continue, or whether longer term 

trends will re-establish themselves in the wake of inevitable economic downturns.

[Robert Gibbs, 202-694-5423, rgibbs@ers.usda.gov, and Timothy Parker, 202-694-5435,

tparker@ers.usda.gov]
Rural counties with the

largest share of jobs in

low-wage industries are

typically less populated

and more remote from

urban centers. These

locational attributes coincide with fewer job

opportunities in industries, such as manufacturing, that typically pay

high wages. Yet most of

the difference between

low-wage and other rural

counties is rooted in

lower wage scales

across all industries.

Although adults in lowwage counties have less

education and labor force

participation overall, the

role played by these

forces varies by region.

Low-Wage, Low-Skill Employment

18 • Rural Conditions and Trends, Vol. 11, No. 2

Low-wage employment exists in every corner of rural America and is a significant shareof all rural jobs. Yet, the distribution of low-wage employment reflects strong geographic patterns. In some rural counties, low-wage work accounts for half or more of the available jobs, essentially creating a low-wage local economy. This article describes the demographic and economic attributes of 465 rural counties with the highest proportion of workers employed in low-paying industries (see box, “What Is a Low-Wage County?”).

The low-wage counties identified here do not follow the more familiar geographic patterns

of local economic distress, such as persistent poverty. Although earnings in low-wage

counties were 8 percent lower than in other rural counties in 1997 (the most recent year

for which data are available), poverty, unemployment, and population growth rates were

not substantially different from those of other rural counties. However, low-wage counties

offer a different mix of jobs. Industries that pay well as a rule are less common in these

counties. In addition, jobs pay less, on average, than similar jobs elsewhere, reflecting

both lower productivity and a relative lack of competition among employers.

Low-wage counties typically have a small number of workers and are located outside the

commuting range of metro labor markets. Young, educated workers in these areas, facing

a set of local jobs that offer limited room for advancement, often choose to move to larger, more diverse, and more lucrative job markets. With a long history of outmigration and,

consequently, a larger share of older workers with limited formal education, low-wage

counties find it difficult to attract more technology-dependent, “new economy” industries

that offer skills development and wage progression.

Low-Wage Counties Tend To Be Small, Less Urban, Remote

Low-wage counties are present in three of the four Census regions (the Northeast has no

low-wage counties), primarily in a few clusters (fig. 1). The northern Great Plains, a region

of low population density and few cities, has the largest cluster of low-wage counties.

Nearly half are located in just three States: North and South Dakota and Nebraska.

Smaller concentrations are evident in the Ozarks of southern Missouri and northern

Arkansas; along the Rio Grande River in southern Texas; in parts of Appalachia and the

coastal plains of the Southeast (including the southernmost portion of the Mississippi

Delta); near the northern Great Lakes; and in scattered areas across the intermountain

Low-Wage Counties Face Locational

Disadvantages

What Is a Low-Wage County?

A county is identified as low-wage if it falls into the top 20 percent (quintile) of rural counties

ranked by the share of wage and salary workers in low-wage industries. At least 41 percent of

all workers in these 465 counties are employed in industries paying average wages that would

not lift a full-time, full-year worker above the weighted-average poverty threshold for a family of

four ($15,569 in 1995). This study is unique in that average wages are calculated for each

three-digit SIC industry in each county, rather than assuming a single average for each industry.

The data source for this analysis is the 1995 Covered Wages and Employment Data collected

by the Bureau of Labor Statistics of the U.S. Department of Labor. Because only total payroll

and total employment by industry are available, a simple measure of earnings-per-worker would

understate the actual wage rate for part-time workers. We used industry-specific shares of parttime workers from the Current Population Survey to adjust average wages. The exact composition of the top quintile would change slightly if we used a different year or a different poverty

threshold. However, most of the identified counties would be categorized as “low-wage” and the

geographic distribution of low-wage counties would remain stable under a fairly wide range of

alternative assumptions.
Rural Conditions and Trends, Vol. 11, No. 2 • 19

Low-Wage, Low-Skill Employment

West. Low-wage counties are absent altogether in rural New England and through the

northern manufacturing belt from New York through Ohio. In the Piedmont South, also a

manufacturing region, no rural counties that surround the wide band of metro areas

stretching from eastern North Carolina to northern Alabama were typed as low-wage.

Only three low-wage counties are found in the States along the Pacific Coast, where most

rural economies have been growing and diversifying in recent years. Hawaii and rapidly

growing Arizona and Nevada have no low-wage counties.

The areas in which rural low-wage counties are clustered tend to have smaller than average populations, are less urban, and are remote from metro areas. Over 90 percent of

low-wage counties have fewer than 20,000 people, compared with fewer than half of other

counties (fig. 2). Only six low-wage counties out of 465 have more than 40,000 people.

Their smaller populations also tend to be more dispersed, with fewer large-town dwellers

and more people living in villages or open countryside. Two-thirds have no urban population, meaning no towns with at least 2,500 people, compared with one-quarter of non-lowwage counties. Low-wage counties are likely to be some distance away from metro areas

as well–over 70 percent are not adjacent to a metro area, compared with 53 percent of

other rural counties (table 1).

Smaller populations that are remote from urban areas have both advantages and disadvantages for low-wage counties. During the early 1990’s, these attributes often attracted

new residents looking for an alternative to troubled, congested urban areas. A substantial

number of small, remote counties enjoyed renewed employment and population growth,

 Low-wage nonmetro LWCNTY

 Other nonmetro

 Metro

Figure 1

Nonmetro low-wage counties, 1995

Source: Calculated by ERS using data from the Bureau of Labor Statistics.

Most nonmetro low-wage counties are located in the Great Plains and the South
Low-Wage, Low-Skill Employment

20 • Rural Conditions and Trends, Vol. 11, No. 2

or had smaller declines, a welcome change to the economic and demographic declines of

the preceding decade. Yet in the long run, the same qualities that appeal to many

migrants also deter employers who need ready access to suppliers and customers and

larger pools of workers with more diverse skills than small, remote low-wage counties can

usually provide. More recent data show that the population resurgence of the early 1990’s

Table 1

Distribution of rural low-wage counties by rurality

Most low-wage counties are remote from cities and have no urban population

Rural-urban continuum Low-wage counties Other rural counties

Number Percent Number Percent

Adjacent, highly urban 1 0.2 137 7.4

Nonadjacent, highly urban 3 .7 111 6.0

Adjacent, less urban 63 13.6 551 29.9

Nonadjacent, less urban 87 18.7 570 31.0

Adjacent, nonurban 64 13.8 184 10.0

Nonadjacent, nonurban 247 53.1 288 15.6

Total rural 465 100 1,841 100

Total nonadjacent 337 72.4 969 52.7

Total nonurban 311 66.9 472 25.6

Source: Calculated by ERS using 1995 Bureau of Labor Statistics Covered Wage and Employment Data.

Low wage Other nonmetro

0

10

20

30

40

50

60

70

Less than 10,000

10,000-19,999

20,000-29,999

30,000-39,999

40,000-49,999

50,000 or more

Population size

Percent

Source: Calculated by ERS using data from the U.S. Census Bureau.

Over 90 percent of low-wage counties have fewer than 20,000 people, compared with 50 percent of other counties

Figure 2

Share of low-wage and other nonmetro counties by population size, 1999
Rural Conditions and Trends, Vol. 11, No. 2 • 21

Low-Wage, Low-Skill Employment

has subsided (see box, “The Nonmetro Population Growth Rate Recedes in a Time of

Unprecedented National Prosperity,” p. 27).

Low-Wage Counties Often Depend on Farm Income

Nearly half of all low-wage counties are also classified as farming-dependent in the

Economic Research Service’s county typology, which means that a relatively large share

of county income derives from farm operations (fig. 3). Most of these counties are located

in the northern Great Plains, a region largely bypassed by the economic growth occurring

in the rest of rural America.

Farm employment, however, is unlikely to be the key source of low wages in many of

these counties. The large number of self-employed farm jobs in Midwestern low-wage

counties was not counted in the data used to identify low-wage employment. The remaining farm jobs, held mostly by hired farmworkers, typically made up less than 10 percent of

all jobs in the county. Rather, farming-dependent counties are often low-wage because

they are small and remote–attributes that have discouraged the location and expansion of

high-wage industries. Slow-growing or declining employment, coupled with a lack of

industrial diversity, left wage and salary workers in these counties with limited opportunities to move out of entry-level jobs requiring relatively little education or training.

Conversely, few low-wage counties depend on mining or manufacturing, which usually

pay above-average wages in rural counties. Rural manufacturing has historically relied on

a low-cost labor force, and its advantages to employers have been strengthened by

improved national transportation and communication networks over the past half century.

For the average rural worker, manufacturing offers better paying, steadier employment

with fringe benefits such as health insurance and paid leave, compared with many service

and trade jobs. Like farming-dependent low-wage counties, those reliant on manufacturing

and mining exhibit strong regional concentration. Nearly all manufacturing-dependent lowwage counties are located in the rural South, where manufacturing continues to employ a

Low wage Other nonmetro

0

10

20

30

40

50

Farming

Mining

Manufacturing

Services

Nonspecialized

Percent

Figure 3

Share of low-wage and other nonmetro counties in ERS county typologies

Low-wage counties are more likely to be farming-dependent, less likely to be manufacturing-dependent than other

nonmetro counties

ERS county types

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Low-Wage, Low-Skill Employment

22 • Rural Conditions and Trends, Vol. 11, No. 2

significant share of the workforce. Similarly, all mining-dependent low-wage counties are

in the West.

Low Wages Within Industries Define Low-Wage Counties 

More Than Industry Composition

Broad differences by economic type suggest that low-wage counties have relatively fewer

jobs in industries that typically pay good wages, such as higher education or motor vehicle manufacturing, and more jobs in industries that have mostly low-wage work.

Employment shares within detailed industry classifications differ between low-wage and

other rural counties (table 2). Six of the 10 industries with the largest employment in lowwage counties qualify as low-wage industries because the average wage was below the

poverty threshold for a family of four ($15,569 in 1995, the year for which low-wage counties are identified). In other rural counties, 2 of these 6 were not in the top 10 industries.

Similarly, the 10 largest low-wage industries all exhibit greater employment shares in lowwage counties than in other rural counties. Thus, broad differences in economic type

noted above accrue from smaller, cumulative differences within detailed industries.

A county also may have lower wages within each industry, so that even with the same

jobs, wages would be lower. Without exception among the top 25 industries, average

wages are lower in low-wage counties. Medical doctors and other health care workers in

clinics in low-wage counties earn 28 percent less than rural workers in medical clinics

elsewhere ($30,364 and $42,290). The pay gap is similar for public safety employees and

government workers. The gap is smaller for other key industries, with a gap of only 3 percent for banking and 1 percent for home health care.

The industry mix and wage results combined suggest that low-wage counties are unique

both because the jobs available are somewhat different and because general wage

scales are lower. The larger effect comes from lower wages. A rough measure of the relative contributions of each to the overall low-wage condition can be calculated using different scenarios. If the industry mix remained the same but low-wage counties had the same

industry-specific wages as other rural counties, then overall earnings per job would

increase 18 percent, from $16,538 to $19,575, which explains most of the gap between

low-wage and other rural counties, where overall earnings per job is $20,691. If wages

remained constant and industry mix shifted to that of other rural counties, the overall

earnings per job in low-wage counties would rise by 4.9 percent, to $17,189. Thus, industry mix is less a problem for these counties than the lower wages paid by any given

industry. Lack of competition in smaller, more isolated labor markets may serve to drive

down wages across the board.

Education Levels in Low-Wage Counties Are Lower in South, Not in West

Industry differences are reflected in the labor force characteristics of low-wage counties.

Labor force participation rates are lower than average, partly a result of lower wage

incentives to employment, but probably also due to slightly older populations and fewer

job opportunities for dual-earner households. Similarly, unemployment rates in these

counties are slightly above average, although regional differences play an important role

here, with rates much lower in the agricultural Great Plains counties than in the South

(see “Nonmetro Employment and Unemployment Trends Remain Favorable,” p. 39).

Rural areas overall have a larger share of jobs requiring low or medium levels of education and training, compared with urban areas. Corporate headquarters and research and

development facilities are less likely to locate in rural areas, and thus skill requirements

differ. Lower wage scales and dependence on agriculture in many low-wage counties

reinforce the rural-urban education gap. Low-wage counties overall have only slightly

lower education levels than other rural counties, with a higher proportion of adults without

a high school diploma (30.7 versus 35.4 percent) and a lower proportion of college graduates (10.8 versus 12.9 percent) (table 3).
Rural Conditions and Trends, Vol. 11, No. 2 • 23

Low-Wage, Low-Skill Employment

Table 2

Top 25 industries in rural counties, 1995

Rural low-wage counties have lower wages across all industries compared with other rural counties; they also have a higher share of

workers in the top low-wage industries, such as eating and drinking establishments, grocery stores, hotels and motels, and gas stations

Low wage-counties Other rural counties

Share Anual Share Annual

Standard of earnings of earnings

Rank Industrial Classification Jobs jobs per job Rank jobs per job

Number Percent Dollars Percent Dollars

1 Elementary and 

secondary schools (821) 128,976 10.6 20,230 1 7.5 22,487

2 Eating/drinking places (581) 88,514 7.3 6,997 2 6.6 7,788

3 Grocery stores (541) 50,255 4.1 10,671 4 3.4 12,047

4 Nursing/personal care (805) 47,286 3.9 12,015 5 2.4 13,981

5 Government offices (913) 42,777 3.5 14,062 7 2.0 18,572

6 Hospitals (806) 41,006 3.4 19,917 3 3.9 24,161

7 Hotels and motels (701) 26,569 2.2 9,878 9 1.6 12,584

8 Mens/boys clothing (232) 24,872 2.1 12,714 25 .7 14,705

9 Banks (602) 23,868 2.0 22,291 12 1.3 23,091

10 Amusement/recreation (799) 18,335 1.5 12,611 14 1.1 13,498

11 Gas stations (554) 17,635 1.5 10,674 17 1.0 11,907

12 Trucking and courier (421) 17,464 1.4 21,067 10 1.6 24,714

13 Meatpacking (201) 17,141 1.4 15,817 11 1.4 19,986

14 Department stores (531) 15,545 1.3 11,352 6 2.0 12,216

15 Public safety (922) 11,930 1.0 20,289 13 1.3 27,359

16 Solid waste management (951) 11,345 .9 24,682 44 .5 28,274

17 Sawmills (242) 11,325 .9 18,725 22 .7 24,311

18 U.S. Postal Service (431) 11,257 .9 26,783 28 .6 30,625

19 Medical offices/clinics (801) 10,995 .9 30,364 15 1.1 42,290

20 Farm wholesaling (515) 10,978 .9 15,044 64 .3 18,758

21 Car dealers (551) 10,863 .9 23,171 18 .9 27,269

22 Family services (832) 10,807 .9 13,499 24 .7 15,386

23 Home health care (808) 10,049 .8 16,458 40 .5 16,678

24 Nondurable wholesaling (519) 9,988 .8 19,581 31 .6 21,533

25 Highway construction (161) 9,385 .8 20,963 29 .6 27,147

Note: Industries with average earnings per job in low-wage counties below the four-person poverty threshold are in bold.

Source: Prepared by ERS using data from the Bureau of Labor Statistics.
Low-Wage, Low-Skill Employment

24 • Rural Conditions and Trends, Vol. 11, No. 2

Both absolute and relative education levels in low-wage counties depend largely on location. Southern rural counties have the highest rate of adults without high school degrees.

The very high rates in southern low-wage counties—43 percent in 1990—are only 5 percentage points above the rest of the rural South. In the West, low-wage and other rural

counties exhibit no differences in education levels; the high school dropout rate for both

types of counties in the rural West (29 percent) is lower than that for non-low-wage counties in the rural South (38 percent).

Most Low-Wage Counties Are Not Persistently Poor, Except in the South

The distinctive geographic patterns of low-wage counties suggest that regional concentrations of poverty and low earnings are related, but not synonymous. While low-wage areas

overlap substantially with areas of persistent rural poverty, key differences are also evident. Only a third of all low-wage counties are also persistent poverty counties, as defined

by the Economic Research Service, and slightly less than a third of persistent poverty

counties are low-wage (fig. 4).

The relationship between low wages and persistent poverty varies sharply by region. In

both the Midwest, which includes the Great Plains, and the West, only 14 percent of the

low-wage counties are persistently poor, compared with well over half (61 percent) of lowwage counties in the South. These regional differences are not surprising, given the

South’s high incidence of persistent poverty overall. Nonetheless, most southern persistent poverty counties (three out of four) are not among the low-wage group, which suggests that the economic and social conditions associated with poverty and low pay differ.

Workers in the low-wage counties of the Midwest/Great Plains and the West may largely

avoid poverty through strategies such as two-earner families or multiple jobs (combining

farm and off-farm work, for example). Other residents with few alternatives to low-paying

jobs often migrate elsewhere. By contrast, southern workers in low-wage counties may be

less likely to employ such strategies, boosting county poverty rates relative to those in

other regions. In addition, high poverty rates in the South are often closely associated

Table 3

Educational attainment in low-wage and other rural counties, 1990

Low-wage counties have larger shares of adults without a high school diploma than other rural

counties, with the largest differences in the rural South

Less than Some College

Location high school High school college degree Total

Percent

All rural:

Low-wage 35.4 34.4 19.2 10.8 100

Other 30.7 34.9 21.3 12.9 100

Midwest:

Low-wage 29.2 38.3 21.0 11.3 100

Other 26.0 39.6 22.0 12.2 100

South:

Low-wage 43.2 31.5 15.9 9.1 100

Other 38.3 31.7 18.5 11.3 100

West:

Low-wage 22.9 32.2 28.0 16.7 100

Other 22.9 31.1 29.5 16.3 100

Source: Calculated by ERS using data from the 1990 Census of Population.
Rural Conditions and Trends, Vol. 11, No. 2 • 25

Low-Wage, Low-Skill Employment

with family structure and nonparticipation in the labor force. These latter characteristics

only partly depend on the wage structure of local labor markets.

Human Capital Needs of Low-Wage Counties Differ by Region

Economic activity in rural America has historically been rooted in an industrial and agricultural base generating large numbers of low-wage jobs. Although the 20th century witnessed a dramatic transformation of rural employment away from farming and resource

extraction to manufacturing and services, a significant share of rural workers still receive

low wages. The geographic concentration of low-wage work in specific regions and types

of counties reflects the economic diversity of the Nation’s rural counties. In rural areas in

which people commute to nearby urban centers, the workforce has taken on many characteristics of relatively high-wage urban economies. In remote counties with smaller populations, low-wage work often comprises a majority of available jobs.

Small populations and remoteness remain the most salient features of low-wage counties

at the end of the century. Yet the character of low-wage counties also depends critically

 Low-wage and persistently poor

 Low-wage only

 Persistently poor only

 Other nonmetro

 Metro

Source: Calculated by ERS using data from the Bureau of Labor Statistics.

Figure 4

Low-wage and persistently poor nonmetro counties

Despite some overlap, low-wage and persistently poor nonmetro counties form largely distinct groups
Low-Wage, Low-Skill Employment

26 • Rural Conditions and Trends, Vol. 11, No. 2

on broader regional forces. The South, West, and Midwest (including the northern Great

Plains) present contrasting pictures of low-wage areas.

The Midwest/Great Plains presents the dominant picture of the low-wage county–a county

that depends largely on capital-intensive farming. Workers in low-wage counties in the

Midwest are generally well-educated, though with slightly lower high school completion

rates than workers in non-low-wage counties. Small populations and remoteness are

especially acute and constrain the number and kind of employers willing to locate or

expand in these counties.

Low-wage counties in the South share low rates of high school completion and relatively

low labor force participation with other counties in the region. Human capital development

is a more pressing issue here than elsewhere. Although the economies of southern lowwage counties are more diversified than in other regions, they tend to appeal to employers seeking unskilled, low-cost, but relatively plentiful labor.

Conditions in low-wage counties in the West suggest a different set of forces at work. A

minority of western low-wage counties, particularly those with large Hispanic or Native

American populations, resemble the South with their low human capital levels. Instead,

school completion rates in most western low-wage counties are as high as in the rest of

the region; moreover, workers in these counties have even higher labor force participation

rates and lower unemployment than their non-low-wage counterparts. Lower returns to

education, rather than the lack of education, may play a key role in the West. In addition,

many low-wage counties in the region are high-amenity counties, often characterized by a

large number of seasonal, low-paying jobs in recreation-related industries, and by a relatively large number of residents willing to work for less in order to take advantage of the

region’s natural attractions.

The economic and social environments that give rise to low-wage areas will require closer

scrutiny in the coming years, as Federal and State assistance policies shift from maintaining households to encouraging employment. The experiences of these counties may provide clues to help people find self-sustaining work where good-paying jobs, especially for

less-educated workers, are difficult to find. At the same time, the nature of low-wage

counties will inevitably change as the adult population in persistently poor (but not lowwage) counties moves toward greater labor force participation. [Robert Gibbs, 202-694-

5423, rgibbs@ers.usda.gov, and John B. Cromartie, 202-694-5421, jbc@ers.usda.gov]
Despite very favorable

national trends in income

and employment, the

nonmetro population

growth rate has steadily

dropped since it momentarily exceeded the metro

level in 1994-95. By

1998-99, the rate of population growth in nonmetro areas was less

than half of that elsewhere, as the net inflow

of newcomers from metro

places dwindled.

Rural Conditions and Trends, Vol. 11, No. 2 • 27

Population and Employment

Last year’s review of nonmetro population trends (RCaT, Vol. 9, No. 2), was titled“Nonmetro Population Rebound: Still Real but Diminishing.” The trend since then

might well be called “Still Real, but Diminishing Further.” Two post-1990 trends are equally

important to note: (1) nonmetro America as a whole had some net inmovement of people

from metro areas throughout the decade in contrast with the 1980’s, but (2) the amount of

such gain in the second half of the decade was much reduced from its peak in 1994-95,

with a number of nonmetro counties reverting to outmigration and population loss.

All told, the nonmetro population grew by 3.9 million, or 7.6 percent, from April 1990 to

July 1999, compared with an increase of just 1.3 million, or 2.7 percent, during the entire

1980’s. From the decade’s point of view, a rebound in growth clearly occurred. All of the

upward change in trend is the product of migration, for the annual rate of natural

increase—the margin of births over deaths—slumped by a third in nonmetro counties during the 1990’s. Net migration, however, shifted from an average annual outmovement of

269,000 in the 1980’s to an average inmovement of 242,000 in the 1990’s.

The demographic rebound affected most rural and small town sections of the country and

almost every type of county. In some counties, it took the form of dramatic reversals from

earlier loss to substantial gain; in others, it simply occurred as a reduced degree of loss.

Its causes are not fully understood in every instance, but several factors are evident.

The first half of the 1990’s saw an improved nonmetro economic picture compared with

that in metro places, as measured by both employment growth and unemployment 

levels.

Further sprawl of population out from metro centers to adjacent nonmetro 

counties is visible on the ground and also reflected in the statistics, in a process 

of incipient suburbanization.

Numerous more distant areas reported growth from the arrival of people moving

to smaller-scale places for noneconomic, quality-of-life reasons. Some of these

newcomers are conventionally retired, but more seem to be of working-age with

families or are people who have retired early from a career but are still economically

active.

The growth of recreation activity and second homes has also played a role, along with

the rejection of large-scale urban life, which for many resulted in “urban flight.”

The rebound of the 1990’s was less pronounced than that of the 1970’s. Metro areas continued to have a somewhat higher rate of population increase than did nonmetro counties,

with the exception of 1994-95. The higher metro rate of natural increase and disproportionate receipt of foreign immigrants produced the faster growth despite some net outmovement to nonmetro counties each year.

Nonmetro Growth Turned Downward After 1995

But, as noted earlier, the pace of rural and small-town rebound lessened steadily after its

peak from July 1994 to July 1995 (fig.1). During that time, the nonmetro population grew

by 1.0 percent. In steady annual dropoffs thereafter, it fell to 0.5 percent in 1998-99. Metro

growth in the same time frame rose somewhat from 0.9 percent to 1.0 percent. The nonmetro downturn corresponded with a drop in nonmetro employment growth and a boom in

the metro economy.

All types of nonmetro counties were affected by the reduction in population growth except

for commuter counties—that is, those in which 40 percent or more of resident workers

commuted to another county for work in 1990. Counties dependent on the two traditional

Nonmetro Population Growth Rate Recedes in

a Time of Unprecedented National Prosperity
Population and Employment

28 • Rural Conditions and Trends, Vol. 11, No. 2

0

2

4

6

8

1.0

1.2

1.4

Figure 1

Annual population growth rates for metro counties, nonmetro counties,

and the Nation, 1990-99

The pace of nonmetro population growth in 1998-99 continues the slowdown that began after 1994-95

Percent

Source: Calculated by ERS, using data from the Bureau of the Census.

1990-91 91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99

Nonmetro

Metro

U.S. total

Table 1

Regional population change, 1990-99

The South had the largest regional nonmetro population gain; the West had the highest rate of change

Population Population change Net migration Net migration rate

Region 1990 1995 1999 1990-95 1995-99 1990-95 1995-99 1990-95 1995-99

Thousands Percent Thousands Percent

United States 248,791 262,803 272,691 5.6 3.8 4,441 3,573 1.8 1.4

Nonmetro 50,906 53,419 54,780 4.9 2.5 1,480 758 2.9 1.4

Metro 197,885 209,385 217,911 5.8 4.1 2,961 2,815 1.5 1.3

Northeast 50,828 51,444 51,830 1.2 .8 -827 -452 -1.6 -.9

Nonmetro 5,267 5,377 5,399 2.1 .4 23 -5 .4 -.1

Metro 45,561 46,067 46,431 1.1 .8 -850 -447 -1.9 -1.0

Midwest 59,669 61,992 63,242 3.9 2.0 408 25 .7 0

Nonmetro 15,978 16,450 16,654 3.0 1.2 247 84 1.5 .5

Metro 43,691 45,542 46,588 4.2 2.3 162 -59 .4 -.1

South 85,456 91,778 96,468 7.4 5.1 3,220 2,521 3.8 2.7

Nonmetro 22,362 23,441 24,178 4.8 3.1 653 474 2.9 2.0

Metro 63,094 68,336 72,291 8.3 5.8 2,567 2,046 4.1 3.0

West 52,837 57,590 61,150 9.0 6.2 1,639 1,480 3.1 2.6

Nonmetro 7,299 8,150 8,549 11.7 4.9 557 205 7.6 2.5

Metro 45,539 49,440 52,601 8.6 6.4 1,082 1,275 2.4 2.6

Note: See appendix for definitions of regions.

Source: Calculated by ERS using data from the Bureau of the Census.
Rural Conditions and Trends, Vol. 11, No. 2 • 29

Population and Employment

rural industries of mining and farming had the greatest relative fall off in their pace of

growth. (Note that the decline in farming counties predates the crisis period of oversupply,

low commodity prices, and regional weather disasters that has prevailed since July 1999,

the date of our last population estimates). The number of nonmetro counties with

decreasing population rose from 600 in 1990-95 to 855 in 1995-99.

A curious feature of the 1995-99 period was that the diminishing pace of growth was also

heavy in counties with high dependence on recreational activity. This occurred despite the

unprecedented nonfarm prosperity and high discretionary spending power of the period

and the attraction of recreation districts for people having the means and desire to relocate away from metro areas. Recreation counties still had an above average rate of population increase during 1995-99, with net inmigration, but had a one-third reduction in

annual growth rate, compared with 1990-95.

A map of growth trends reveals that 375 counties went from population gain to loss

between 1990-95 and 1995-99 (fig. 2). Few regions were immune to such reversals. They

were common, for example, in the Appalachian coal fields and in many counties of the

Corn Belt and the Great Plains—areas still experiencing job losses in mining and farming.

But, there were also areas of decline in western States, such as California, Idaho,

 Loss in both periods (489 counties)

 Growth in 1990-95, loss in 1995-99 (375 counties)

 Loss in 1990-95, growth in 1995-99 (111 counties)

 Growth in both periods (1,330 counties)

 Metro (836 counties)

Source: Prepared by Economic Research Service, USDA, using data from the Bureau of the Census.

Figure 2

Some 375 widely distributed nonmetro counties reverted from growth to decline in 1995-99

Patterns in nonmetro population change, 1990-95 versus 1995-99
Population and Employment

30 • Rural Conditions and Trends, Vol. 11, No. 2

Montana, and Oregon, whose early 1990’s nonmetro growth had been the source of popular attention. Florida is represented as well. Beyond the association of the recent period

of diminishing nonmetro population growth with reduced nonmetro employment growth

and improved metro conditions, a more complete explanation of the onset of the recent

trend of reduced growth or new loss has not been deduced.

During the same 1995-99 period, 111 other counties had the opposite trend—a growing

population after loss in the first half of the decade. These, too, are rather scattered, but

with some frequency in the southern Corn Belt and central Texas. Individual events caused

some of these recoveries, such as the opening of a prison after 1995, or recovery from an

earlier military base closing. The limitations of making population estimates for very small

counties may produce the results in some other counties, but the predominant picture of

an overall sizable increase in the number of declining areas after 1995 seems reliable.

Nonmetro Counties Have Low Entry of Persons into Age Group 65 Years and Over 

The number of nonmetro counties with declining numbers of people 65 years of age or

older continued to rise in 1998-99. Except as affected by immigration over the years, the

population reaching age 65 was at its modern low in 1998, stemming from the low number of births in the early 1930’s, the worst period of the Great Depression. Only 2.3 million

births occurred in 1933, the lowest number in the entire 20th century. (In contrast, there

were 3 million in 1921, the earlier high, and over 4 million in each baby-boom year from

1954 to 1964). The effect of the small birth cohorts of the early 1930’s, coupled with

extensive outmigration of many of their members from farming areas as young adults in

the 1950’s or as older adults more recently, contributed to over half (1,190) of all nonmetro counties having a decline in older population in 1998-99. Some 259 have seen the

older population fall by 10 percent or more since 1990, a trend that is rare in metro counties. The popular impression that nonmetro counties have a higher than average proportion of older people is correct, but the absolute numbers of nonmetro elderly are now as

likely to be falling as rising.

Low-Wage Counties Have Mixed Population Trends

Population growth in low wage counties was modestly lower from 1990 to 1999 than in

other nonmetro counties (6.8 percent vs. 7.7 percent). This stemmed from the very low

natural increase of the low-wage group (just 1.7 percent compared with 3.4 percent in

other counties). The low-wage counties actually had somewhat higher net inmigration

than all other counties (5.1 percent vs. 4.3 percent). Thus, prevalence of low-wage work

has not been a prohibitive deterrent in itself to inmovement.

Low-wage counties proved to have less than 9 percent of the total nonmetro population,

although by definition they accounted for 20 percent of all nonmetro counties. This relative

sparsity of people is largely determined by the fact that nearly half of the low-wage counties are farming-dependent areas, many of which are thinly settled (see “Low-Wage

Counties Face Locational Disadvantages,” p. 18). Two-fifths of all farm-dependent counties

are low wage areas, and had a distinctly lower rate of 1990’s population growth (just 1.3

percent) than did the medium and higher wage majority (5.7 percent).

Because the low-wage farm counties are so very thinly settled (averaging just a little over

6,000 people each), they lack urbanization and the greater variety of work and frequency

of well-paid jobs that urban settlement brings. Over three-fifths of these counties declined

in population in the 1990’s, often after decades of earlier decline as labor requirements in

agriculture fell and offsetting sources of new nonfarm work failed to develop.

Excluding the farming-dependent areas, low-wage counties as a group exhibited a higher

rate of population growth during the 1990’s (8.6 percent) than did other nonmetro counties (7.3 percent), despite their low-wage status (derived from appendix table 1). All of the

higher margin of growth is from net inmovement of people. Some of the growth occurs in

low-wage counties that are adjacent to metro areas. In such cases, local low wages are

no deterrent to people moving in who can commute to better metro work. Government-
Rural Conditions and Trends, Vol. 11, No. 2 • 31

Population and Employment

dependent counties and transfer-payment counties are other types where the low wage

counties have the higher growth.

The government-dependent counties have various functions. Some are college counties,

some have military bases, prisons, or international border crossings, and many have

national forests and parks. The difference in population change between the low-wage

and other government counties can be accounted for by the high growth of several lowwage Mexican border counties and the negligible growth or outright decline of 10 average- to high-wage counties that had military base cutbacks or closings.

The largest types of transfer income by far are Social Security and other retirement payments. Counties with at least 25 percent of their personal income derived from transfer

payments were classified as transfer-dependent. A number of these counties are comparatively poor, with some very poor. But if low wages are associated with relatively low costs

of living, the transfer income goes further than it might elsewhere, and high dependence

on such income has not precluded population inmovement. Many of the counties that rely

heavily on this income are in the North Woods country of the Upper Great Lakes and the

Ozark and Ouachita Mountains areas of the South, which are attractive to retirees.

Although low wages in rural and small town areas have for years been thought of partly in

connection with transfer of routinized manufacturing operations from cities to nonmetro

places, only 26 nonmetro manufacturing counties proved to be in the lowest wage quintile, or just 5 percent of the manufacturing group. With one exception, they were scattered

around the South. They had a fast 10.6-percent population growth from 1990 to 1999, but

were too few to have much influence on the overall change of the critical manufacturing

group that contains 31 percent of the entire nonmetro population.

An interesting feature of nonmetro population change in low wage counties relates to

diminishing nonmetro growth during 1995-99. Although a net of 264 more nonmetro counties slipped into decline in 1995-99 than there were in 1990-95, only 10 percent of them

were low-wage counties. Population loss was still more common in the low-wage areas

than elsewhere (47 percent incidence vs. 35 percent). But, their susceptibility to decline

was only moderately greater during the downturn years after 1995 than before then. The

middle- and high-wage counties that depended on manufacturing, government, or trade

and services work were the most likely to have shifts in their economy or attraction to

migrants that led to decline in the last half of the decade.

Conclusion

It would be idle to think that low wages have no meaningful effect on the propensity of

people to move to other places where work is better rewarded or where rewarding work is

more available. But overall population change in nonmetro low-wage areas appears not to

be fully determined by a conventional migration response to the economic problems of

agriculture or other businesses. It is also clearly shaped in part by changes introduced by

worker commuting and by the influx of people motivated by nonpecuniary concerns rather

than by a desire to maximize income.

The overall trend of nonmetro population in the near future is conjectural at this point.

Agriculture continues to undergo consolidation and productivity gains that lead to fewer

workers and population loss in farming-dependent areas. The downward drift in nonmetro

growth rate since 1995 cannot continue much longer without entailing an end to net

inmovement from metro areas. The ever-widening perimeter of metro America, however,

steadily brings more rural and small town areas into the outskirts of urban labor markets,

changing their demographic future. And before the end of the new decade, the first

cohorts of the post-World War II baby boom will reach early retirement age, with a probable significant impact on many rural communities. [Calvin L. Beale, 202-694-5416,

cbeale@ers.usda.gov]
The nonmetro population

continued to increase

from net migration but at

a much lower rate than in

previous years. After

leading other regions in

the first half of the

1990’s, the nonmetro

West experienced a substantial drop in net migration during 1996-99.

Metro-to-nonmetro migration among college graduates also dropped substantially, though not to

the level of the rural brain

drain of earlier decades.

Net migration rates were

higher for low-wage

workers despite lower

rates of in- and outmigration combined.

Population and Employment

32 • Rural Conditions and Trends, Vol. 11, No. 2

During the 2-year period ending March 1999, 3.9 million people moved to nonmetroareas from metro locations, while 3.3 million moved out. The average annual gain of

281,000 people per year reflects continuing strength in the rural economy and in people’s

preferences for small-town living. The gain, however, is significantly lower than the

415,000 annual gain reported last year (RCaT, Vol. 9, No. 2) for the 2-year period ending

March 1997. Annual population growth from net migration, including immigration from

abroad, increased steadily during the early and mid-1990’s, but dropped to half of 1 percent during 1997-99, according to the latest data from the Current Population Survey (see

box, “About the Data”).

Much of the recent decline in nonmetro net migration occurred among college graduates,

who moved out in numbers almost equal to those moving in for the first time since the

early 1990’s. Regional changes accompanied the drop in migration among the well-educated, who contributed disproportionately to the high population growth in the West during the early 1990’s. The nonmetro South and Midwest have become more popular

migration destinations. Although high-income migrants had substantially higher rates of

in- and outmigration, the two streams were close to equal in size, so that nonmetro areas

gained more population among low-wage workers. Hispanics also had high nonmetro

migration gains.

Net Migration Losses Were Among Labor Force Entrants and Retirees

Over 14 percent of the nonmetro population changed homes each year in a variety of

moves, ranging from strictly local to cross-country and even international relocations

(table 1). Over half were within-county moves, many of which coincided with milestone life

events, such as entering the labor market, getting married, and having children. Others

moved between nonmetro counties, typically also a local move but often linked with a

change in employment or educational pursuits. These moves begin and end in nonmetro

Nonmetro Migration Drops in the West and

Among College Graduates

About the Data

These migration statistics are from the Current Population Survey (CPS), conducted monthly by

the U.S. Census Bureau for the U.S. Department of Labor. CPS derives estimates based on a

national sample of about 60,000 households that are representative of the U.S. civilian, noninstitutional population. The sample is large enough to provide information on the demographic

and economic characteristics of the nonmetro population at the national and regional level, but

not generally at State or local levels. The March CPS contains a supplemental question asking

respondents where they were living a year prior to the survey. Metro and nonmetro migration

statistics are derived by comparing past to current residence.

This article uses 4 years of March CPS data, 1996-99, the only years with consistent, up-todate metro and nonmetro residence classifications available. Prior to 1996, the CPS used a

metro-nonmetro definition based on 1980 rather than 1990 census data. In this article, data are

reported separately for each year for broad national and regional statistics and large subpopulations (figs. 1-2). For smaller groups (figs. 3-5), the latest two annual surveys were combined,

providing data on migration during 1997-99, because combining surveys increases the reliability

of the migration estimates.

Net migration is the small difference between two much larger migration streams—inmigration

and outmigration—that are known to fluctuate year to year. In addition, estimates from the CPS

can fluctuate even when actual net migration is stable. Therefore, readers should interpret nonmetro migration statistics with caution.
Rural Conditions and Trends, Vol. 11, No. 2 • 33

Population and Employment

areas and therefore do not affect overall nonmetro population numbers, but they contribute greatly to changing settlement patterns, which can shape local economic growth

and contribute to fiscal problems.

Change to the nonmetro population came from those who moved each year between

metro and nonmetro counties. Close to 2 million people moved into nonmetro areas each

year during 1997-99, while the number of outmigrants jumped from roughly 1.6 to 1.8 million. Among those moving in, about 100,000 were immigrants, moving directly to nonmetro from foreign countries. New immigrants are a relatively small group in any given

year, representing just 0.2 percent of the nonmetro population. They are regionally concentrated, however, in a few States such as Florida, Texas, and Arizona, and in specific

counties in other States, and, thus, have significantly altered their local economies. (The

Current Population Survey does not provide an estimate of annual emigration to countries

outside the United States.)

Mobility is concentrated among young adults, who often require several moves to reach

educational goals and gain work experience. Nearly 30 percent of 18-24 year olds living

in nonmetro areas moved in the previous year, including 6.6 percent moving in from

metro areas. But a larger number moved away, resulting in a 1.6-percent population loss

overall for this age group. Leaving rural areas after high school for colleges and jobs in

the big city is a well-established pattern, but a large proportion return home after a few

years. Although not measurable with the data used here, return migration no doubt contributes to the large nonmetro net migration gains among 25-29 year olds; similarly high

gains among children ages 1-17 indicate that a large share of younger working-age

adults moving to nonmetro areas have already started families. Compared with the 31-

percent mobility rate among 18-24 year olds, less than 4 percent of retirees moved in a

given year during 1997-99. Like those entering the labor force, slightly more of them

moved out of nonmetro areas, contributing to a marked decrease in overall population

growth among nonmetro retirees during the 1990’s.

Recent Slowdown in Nonmetro Migration Centered in the West

New metro and nonmetro classifications based on 1990 data were fully incorporated into

the Current Population Survey in 1996, so that 4 years of consistent data showing the

Table 1

Average annual percentage of nonmetro residents who moved, by age, 1997-99

Nonmetro net migration loss during the early-adult years(18-24) and among retirees (65+) is offset by migration gains during

early-career and family-formation ages (25-29)

Age group

Mobility/migration status 1-17 18-24 25-29 30-39 40-64 65+ All ages

Percent

Total mobility of nonmetro residents1 17.2 29.7 26.1 15.9 8.7 3.6 14.4

Moved within same county 10.6 17.4 14.9 9.2 4.5 2.2 8.4

Moved between nonmetro counties 2.4 5.2 3.5 2.4 1.5 .6 2.2

Moved from metro to nonmetro 4.0 6.6 7.2 4.2 2.6 .8 3.6

Moved from abroad .2 .6 .5 .1 .2 0 .2

Moved from nonmetro to metro 3.1 8.2 6.2 3.6 2.1 1.0 3.3

Net migration from metro to nonmetro .9 -1.6 1.0 .6 .5 -.2 .3

1Total mobility is the percentage of current residents who moved during the previous year, whether within the same county, between nonmetro counties, or in from a metro area or abroad. Movement out of nonmetro areas is also expressed here as a percentage of current residents in order to calculate a consistent net migration rate.

Source: Prepared by ERS using data from the March 1997 and March 1998 Current Population Surveys.
Population and Employment

34 • Rural Conditions and Trends, Vol. 11, No. 2

flows into and out of nonmetro areas are now available. The trends indicate a slowdown

in nonmetro migration gains from 458,000 in 1995-96 to 170,000 in 1998-99 (fig. 1). Both

the number of inmigrants and outmigrants increased over the 3-year period, but the

increase was higher among outmigrants, reflecting a booming metro economy with

increasing employment opportunities for labor force entrants.

Unlike the 1980’s when the rural economy faced a major recession with setbacks in agriculture and mining, net migration continued to be positive through 1999. This growth both

reflects and enhances the economic advantages found in many rural locations that attract

both people and jobs. These advantages were particularly attractive during the early

1990’s when metro areas were harder hit and slower to recover from the economic recession. As large cities continue to prosper, we may expect continued increases in outmigration from nonmetro areas; however, prosperity also tends to increase nonmetro inmigration, as more people have the discretionary income to act on preferences for a rural

lifestyle.

The downturn in metro economies and the preference for high-amenity rural settings

spurred growth to record levels in the nonmetro West through the mid-1990’s. As late as

1995-96, the West led other regions in net migration gains by a large margin (fig. 2).

Migration dropped dramatically in the following 3 years, at a time when metro areas

throughout the West, especially in southern California (a major point of origin for migrants

to other western States), were several years into a strong economic recovery. The emergence of net outmigration from the nonmetro West during 1997-99 is surprising given the

continuing allure of the West’s natural amenities. Other data (see “Nonmetro Population

Growth Rate Recedes in a Time of Unprecedented National Prosperity,” p. 27) indicate

that the region is still receiving a small though rapidly diminishing surplus of migrants. The

small population base in the nonmetro West, compared with other regions, lowers the

precision of the population estimates derived from the Current Population Survey. We can

safely say that the nonmetro population boom that drew much media attention, and

prompted the description of a “new economy” emerging in the nonmetro West, has ended

for now.

1,901

1,443

458

2,096

1,706

390

1,964

1,571

393

1,954

1,784

170

1995-96 1996-97 1997-98 1998-99

0

500

1,000

1,500

2,000

2,500

In Out Net

Figure 1

Nonmetro in-, out-, and net migration, 1995-99

Nonmetro outmigration rose faster than inmigration during 1995-99, lowering net migration

Source: Calculated by ERS using March 1996, 1997, 1998, and 1999 Current Population Surveys.

Thousands
Rural Conditions and Trends, Vol. 11, No. 2 • 35

Population and Employment

Net migration continued to rise in the South and Midwest during 1997-99, but consistently

declined in the Northeast. The mirror image of positive migration in the South and negative in the Northeast partly reflects the continued attractiveness of sunbelt locations, a

defining feature of U.S. migration since the 1950’s.

Net Inmigration of College Graduates Slows

One of the striking features of the rural recovery of the 1990’s was the high educational

composition of inmigrants relative to outmigrants. In 1992, more college-educated people

migrated into than out of nonmetro areas, ending a brain drain that characterized migration patterns in the 1980’s and contributed to a large rural-urban education gap (see

“Rural-Urban Migration Patterns Shift,” RCaT, Vol. 6, No. 1, p. 11). The trend deepened

through 1995-96, when net inmigration of college graduates reached 1.4 percent, twice

the rate for high school graduates. Since then, outmigration among the college educated

declined, while high rates of inmigration continued among those with less education (fig.

3). Net migration rates are now highest among people without a high school degree,

reflecting a narrower range of options available to them in technology-driven urban job

markets and, perhaps, the higher availability of low-skill work in nonmetro areas.

Net migration among the college educated dropped to near zero during 1997-99, but not

below as it was during the 1980’s, when net outmigration among this group reached 2

percent a year. Advances in transportation and telecommunications strengthened the linkages between rural and urban economies during the 1990’s, making it easier for internetbased entrepreneurs and other high-tech firms to conduct business far from the urban

customers they mostly serve. These and other economic restructuring trends, especially

in rural manufacturing, have increased rural opportunities for the well educated and

diminished the chances that the rural brain drain will resume.

Although migration is notoriously difficult to predict, it would not be surprising to see

migration to the nonmetro West and among the college educated rebound in the coming

years. There is considerable overlap in the recent diminished growth in the nonmetro

West and among those with college degrees, who can better afford high-amenity destinaFigure 2

-1.6

0.05

0.66

2.76

-0.73 -0.65

1.62

0.48

-0.77

0.46

1.01

-0.77 -0.93

0.83

0.26

-1.24

1995-96 1996-97 1997-98 1998-99

-2

0

2

4

Northeast

Midwest

South

West

Nonmetro net migration by region, 1995-99

Consistent net migration gains in the South mirrored losses in the Northeast; migration to the nonmetro West declined

Percent

Source: Calculated by ERS using March 1996, 1997, 1998, and 1999 Current Population Surveys.
Population and Employment

36 • Rural Conditions and Trends, Vol. 11, No. 2

tions found in the West than those with fewer educational credentials. Even migrants filling

relatively low-skill jobs, such as in the booming retail sector, had much higher educational

levels in the nonmetro West than elsewhere. According to a number of surveys, many

migrants give up higher paying jobs in the city to live in high-amenity areas. Despite the

drop-off in the past 2 years, this trend is likely to continue, shaping the course of rural

economies in the coming years.

Nonmetro Minorities Moving Mostly to High-Minority Areas

The presence of minorities in nonmetro areas is increasing incrementally due to positive

rates of net migration (fig. 4). The data used here do not allow us to show migration origins and destinations at the county level, which differ considerably by race and ethnicity.

Current migration continues to reinforce high minority nonmetro populations, in the southern Coastal Plains for Blacks and the Rio Grande Valley and other southwestern locations for Hispanics (see “Minority Counties are Geographically Clustered,” RCaT, Vol. 9,

No. 2, p. 14).

However, minority presence in other regions is increasing. Seventy percent of Blacks

moving from metro to nonmetro areas in 1997-99 moved to the nonmetro South, compared with 85 percent just 2 years earlier, indicating some deconcentration for Blacks into

other nonmetro regions. According to the Current Population Survey estimates during the

last 2 years, as many Blacks moved from the metro South to the nonmetro South as in

the opposite direction. If continued, this pattern would shift a historic trend, because for

decades, Blacks, on balance, moved from the countryside to the South’s urban centers.

Almost a third of the 6.1-percent inmigration among nonmetro Hispanics represents immigration from abroad. The outmigration stream does not include emigration to other countries, which the Current Population Survey does not record. Net migration among

Hispanics, and to a lesser degree among non-Hispanic Whites and Blacks, is therefore

somewhat overstated. Without the contribution of immigrants, nonmetro net migration

2.38

1.67

0.71

3.2

2.5

0.73

4.4

4.2

0.2

3.31

2.7

0.62

Less than high High school College graduate Combined

In Out Net

Figure 3

school graduate

Average annual migration rates to nonmetro areas, by education, 1997-99

Source: Calculated by ERS using March 1998 and 1999 Current Population Surveys.

Percent

0

1

2

3

4

5

Nonmetro college graduates were highly mobile, but population gains were low
Rural Conditions and Trends, Vol. 11, No. 2 • 37

Population and Employment

gains among Hispanics would still be positive, but closer in magnitude to those of Blacks

and non-Hispanic Whites.

Net Migration Gains Higher Among Low-Wage Workers Despite Lower Mobility

Net migration among low-wage workers (defined here as persons ages 25-64 earning fulltime equivalent wages at or below the poverty line for a family of four) was close to 1 percent per year during 1997-99. It decreased steadily as income increased, approaching

0.25 percent for workers earning 300 percent or more above the poverty line (fig. 5).

Nonmetro Hispanics earn less, on average, than Whites, so the strong correlation

between low wages and high net migration during 1997-99 corresponds with minority

migration patterns. However, the pattern held for White workers as well.

Migration is an important means of adjustment when economic restructuring, such as the

loss of manufacturing jobs, or changing government policy, such as welfare reform, shift

supply and demand in local labor markets. However, it is difficult to pinpoint why nonmetro

areas have recently attracted low-wage workers disproportionately. At the very least, the

higher migration suggests that competition for low-wage work in nonmetro areas did not

increase rapidly during the mid-1990’s, as some had predicted could happen with declining welfare caseloads. Competition is likely much higher in metro areas, where immigration from abroad has been higher.

Higher inmigration among low-wage workers also indicates that booming metro job markets, driven by growth in high-tech industries, are friendlier to migrants with education and

experience. In addition, income often enhances a person’s ability to migrate, so it is not

surprising that, combined, in- and outmigration flows to and from nonmetro areas were

much higher for workers in the highest income group. More high-wage workers had the

resources to respond to opportunities and preferences in both nonmetro and metro locations. Even though nonmetro growth was small for this group as a whole, the process

served to draw income out of some nonmetro areas and add it to others. [John B.

Cromartie, 202-694-5421, jbc@ers.usda.gov]

Figure 4

3.66

3.09

0.57

4.2

3.9

0.32

6.1

4.2

1.9

3.8

3.3

0.55

Non-Hispanic White Black Hispanic Combined

0

1

2

3

4

5

6

7

In Out Net

Average annual migration rates to nonmetro areas, by race and ethnicity, 1997-99

Source: Calculated by ERS using March 1998 and 1999 Current Population Surveys.

Percent

Higher net migration rates for nonmetro Hispanics were mostly due to higher inmigration
Population and Employment

38 • Rural Conditions and Trends, Vol. 11, No. 2

3.65

2.69

0.97

3.8

3.07

0.73

3.91

3.21

0.7

5.05

4.76

0.29

3.85

3.08

0.77

0-100 100-200 200-300 300+ Combined

In Out Net

Figure 5

Percent

Average annual migration rates to nonmetro areas, by wage level, 1997-99

0

1

2

3

4

5

6

Net migration rates were higher for low-wage workers despite lower overall mobility

Source: Calculated by ERS using March 1998 and 1999 Current Population Surveys.

Wages as a percent of poverty line
Nonmetro employment

continued to expand in

1999, although the nonmetro employment

growth rate lagged

behind the metro rate as

it had since 1995.

Unemployment rates

continued to fall in both

nonmetro and metro

areas. These trends held

consistently across the

different regions of the

country over the past

several years.

Employment growth in

low-wage nonmetro

counties was generally

lower than in other nonmetro counties, although

this trend was reversed

in the early 1990’s.

Unemployment rates in

low-wage counties have

remained modestly

above the nonmetro

average.

Rural Conditions and Trends, Vol. 11, No. 2 • 39

Population and Employment

After 13 straight quarters where metro employment growth exceeded nonmetro growth,nonmetro growth edged ahead during the third and fourth quarters of 1998 (fig. 1).

But metro growth again outpaced nonmetro growth throughout 1999. This is in marked

contrast to the early 1990’s, when nonmetro employment growth rates exceeded metro

rates.

Employment grew more slowly in nonmetro areas for most Census divisions in 1998-1999

(fig. 2), continuing trends observed for 1995-98 (fig. 3). The slowest nonmetro employment growth rates over the past year were in the Middle Atlantic and East South Central

States. Mountain and East South Central States showed the greatest lag of nonmetro

growth behind metro growth.

Employment Growth Rates Rose Across Nonmetro County Types in 1999

Between 1995 and 1998, annual nonmetro employment growth rates by county type

ranged from 0.3 percent to 2.0 percent. Growth rates were minimal for agriculture, mining,

manufacturing, persistent-poverty, and transfer counties (0.3-0.6 percent annually) and

moderate for government, services, retirement destination, Federal land, and commuting

counties (1.3-2.0 percent annually) (table 1).

In 1999, employment growth rates rose for most nonmetro county types. Employment

growth remained minimal in mining counties (0.1 percent) and was more rapid in retirement counties (2.6 percent) than in other county types. Growth rates in all other county

types ranged from 1.2 to 2.0 percent.

Unemployment Fell in All Regions of the Country Between 1995 and 1999

Unemployment rates, at their lowest levels in more than 20 years in both metro and nonmetro areas in 1998, fell even further in 1999. Overall, unemployment fell by more than a

percentage point in both metro and nonmetro areas since 1995, when the Nation’s economic expansion was already several years old (table 2).

This decline in unemployment has been widely distributed geographically. Metro New

England and the metro Pacific States saw the sharpest declines in unemployment (-2.3

percent and -2.1 percent, respectively), perhaps reflecting the recent prosperity of the

high-tech sector. The Mountain West (-0.8 percent metro, -0.9 percent nonmetro) and the

nonmetro West South Central States (-0.8 percent) had the smallest declines. Other

regions saw unemployment rates decline between 1.0 and 1.8 percentage points.

Despite these declines in unemployment, geographic differences in unemployment are

largely intact. The nonmetro unemployment rate remains about a percentage point above

the metro rate, a differential which varies considerably by region. In both 1995 and 1999,

the nonmetro unemployment rate was lowest in the West North Central States, where it

fell from 4.4 percent to 3.3 percent; in both years, the rate was highest in the Pacific

States, where it fell from 9.5 percent to 7.9 percent. Overall, the only changes in the relative ranking of nonmetro employment rates across the nine regions were those occasioned by the smaller declines in the Mountain and West South Central States.

The distribution of counties with stable or increasing unemployment rates is also an indicator of breadth of the unemployment rate decline. Unemployment fell in more than 60

percent of nonmetro counties in each of the six county economic types and five county

policy types between 1995 and 1999. It also fell in two-thirds or more of the nonmetro

counties in each Census region, except the West South Central (table 3).

Nonmetro Employment and Unemployment

Trends Remain Favorable
Population and Employment

40 • Rural Conditions and Trends, Vol. 11, No. 2

Percent

1992 93 94 95 96 97 98 99

Nonmetro

Metro

-1

0

1

2

3

4

Metro employment growth continued to outpace nonmetro growth

Source: Calculated by ERS from Bureau of Labor Statistics' Local Area Unemployment Statistics.

Note: Rate shown is quarterly, seasonally adjusted annualized percentage employment growth, from first quarter 1992 through fourth quarter 1999.

Figure 1

Employment growth, 1992-99

Percent

Figure 2

1.1

2.9

1.2

0.6

2.7 2.7

2.3

0.7

2.5

1.8 1.8

1.2

2.5

1.0

3.9

1.7

2.0

1.2

Metro Nonmetro

Employment growth, by Census division,1998-99

0

1

2

3

4

5

Percent metro employment growth has been faster in most regions

 New Middle South East West East West Mountain Pacific

England Atlantic Atlantic South South North North 

 Central Central Central Central 

Source: Calculated by ERS from Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Rural Conditions and Trends, Vol. 11, No. 2 • 41

Population and Employment

Table 1

Employment growth in nonmetro areas, by county type, 1995-99

Employment growth for nonmetro county types rose in 1998-99

Annual growth rates Change,

Item 1995-98 1998-99 1995-99

Percent Percentage

point

Metro 1.9 2.1 0.2

Nonmetro .8 1.5 .7

County types:

Agricultural .3 1.2 .9

Mining .6 .1 -.5

Manufacturing .5 1.4 .9

Government 1.3 1.9 .6

Services 1.3 1.9 .6

Nonspecialized .9 1.6 .8

Retirement 2.0 2.6 .6

Federal lands 1.5 1.7 .2

Commuting 1.4 2.0 .6

Persistent poverty .5 1.5 1.0

Transfers .6 1.4 .8

Source: Calculated by ERS from Bureau of Labor Statistics’ Local Area Unemployment Statistics.

Percent

1.4

0.9

1.4

0.6

1.9

1.4

2.1

0.2

2.3

0.3

1.4

0.6

1.3

0.6

2.5

1.8

2.7

1.4

Metro

Nonmetro

Figure 3

Between 1995 and 1998, nonmetro growth lagged metro growth in all regions

Employment growth by Census division,1995-98

Source: Calculated by ERS from Bureau of Labor Statistics’ Local Area Unemployment Statistics.

0

1

2

3

 New Middle South East West East West Mountain Pacific

England Atlantic Atlantic South South North North 

 Central Central Central Central 
Population and Employment

42 • Rural Conditions and Trends, Vol. 11, No. 2

Table 2

Unemployment by Census divisions, metro and nonmetro, 1995 and 1999

Unemployment declined in both metro and nonmetro areas in recent years

1999 1995 Change, 1995-99

Area Metro Nonmetro Metro Nonmetro Metro Nonmetro

Percent Percentage point

U.S. total 3.9 5.1 5.4 6.3 -1.5 -1.3

Census divisions:

New England 3.1 3.4 5.4 5.2 -2.3 -1.8

Middle Atlantic 4.7 5.7 6.1 7.0 -1.5 -1.3

South Atlantic 3.4 5.2 4.8 6.5 -1.4 -1.4

East South Central 3.3 5.6 4.7 7.0 -1.4 -1.4

West South Central 4.2 5.8 5.7 6.7 -1.5 -.8

East North Central 3.6 4.6 4.6 5.9 -1.0 -1.3

West North Central 2.5 3.3 3.6 4.4 -1.1 -1.2

Mountain 3.5 5.6 4.3 6.5 -.8 -.9

Pacific 5.0 7.9 7.2 9.5 -2.1 -1.6

Source: Calculated by ERS from Bureau of Labor Statistics’ Local Area Unemployment Statistics.

Table 3

Nonmetro counties with declining unemployment rates, 1995-99

Declining unemployment rates were the norm in all groups of nonmetro counties

Counties Declining rate Steady or rising rate

Percent

Metro 94.9 5.1

Nonmetro 79.1 20.9

Nonmetro by Census division:

New England 97.3 2.7

Middle Atlantic 91.4 8.6

South Atlantic 82.2 17.8

East South Central 83.0 17.0

West South Central 63.6 36.4

East North Central 85.8 14.2

West North Central 81.0 19.0

Mountain 77.6 22.4

Pacific 73.4 26.6

County type:

Agricultural 72.1 27.9

Mining 60.5 39.5

Manufacturing 81.0 19.0

Government 84.7 15.3

Services 85.1 14.9

Nonspecialized 83.4 16.6

Retirement 85.3 14.7

Federal lands 78.9 21.1

Commuting 86.3 13.7

Persistent poverty 74.6 25.4

Transfers 80.6 19.4

Nonmetro by low-wage:

Low-wage 75.9 24.1

Other 79.9 20.1

Source: Calculated by ERS from Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Rural Conditions and Trends, Vol. 11, No. 2 • 43

Population and Employment

Unemployment rates did not decline in some areas, however. Clusters of counties with

stable or rising nonmetro unemployment rates appeared in the coastal plains of Georgia,

south-central Kentucky, Mississippi, Arkansas, Illinois, the northern Great Plains, western

Texas, New Mexico, Oregon, and Alaska (fig. 4).

In the northern Plains, the unemployment rate increases were mostly small, the base

level of unemployment was relatively low, and the areas affected were mostly sparsely

populated, limiting the importance of these increases. The numbers affected by unemployment increases were somewhat greater in the rural South and Southwest and in

Oregon.

These clusters were not dominated by any particular county economic type. While unemployment was more likely to increase in farming, mining, persistent-poverty, and low-wage

counties than in other counties, it also rose in about 15 percent of the nonmetro counties

outside all these categories. Many observed increases in unemployment rates may reflect

local factors, such as plant relocations.

 Nonmetro, stable or increasing

 Nonmetro, decreasing

 Metro

Figure 4

Change in nonmetro unemployment, 1995-99

Unemployment increased in many nonmetro counties in the upper Great Plains and 

South Central States

Source: Calculated by ERS from the Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Population and Employment

44 • Rural Conditions and Trends, Vol. 11, No. 2

Low-Wage Counties’ Employment Growth Outpaced

Nonmetro Average in Early 1990’s

Employment growth trends in nonmetro low-wage counties have generally moved with

employment growth trends in nonmetro areas as a whole (fig. 5). Compared with all nonmetro areas, employment growth was slower in these low-wage counties in the latter half

of the 1980’s and again in the latter half of the 1990’s.

However, employment growth in these counties equaled or outpaced growth in all nonmetro counties during the early 1990’s. This faster growth partly reflected relatively strong

growth in farming-dependent counties during the early 1990’s—these counties account for

one-third of all low-wage county employment. It also reflected relatively strong growth in

low-wage counties relative to higher wage counties in the government-dependent, service-dependent, and retirement-destination categories.

During this period, overall nonmetro employment growth outpaced metro employment

growth, suggesting that factors favoring nonmetro areas in the early 1990’s operated with

particular force in low-wage counties. One factor might be that many firms downsized

their white-collar workforces, which tended to be in metro areas and in higher wage counties in nonmetro areas.

The lag in employment growth in low-wage counties during the late 1990’s can be seen

across several county economic types (fig. 6). The lag is most pronounced in farming- and

manufacturing-dependent counties, but is also observed in service-dependent and nonspecialized counties. Employment in government- and mining-dependent counties grew at

a faster pace when these were also low-wage counties, but these county economic types

account for less than one-fifth of nonmetro employment.

Percent

1985-86 86-87 87-88 88-89 89-90 90-91 91-92 92-93 93-94 94-95 95-96 96-97 97-98 98-99

-1

0

1

2

3

4

Metro

Nonmetro

Low-wage

6

Figure 5

Employment growth, by metro, nonmetro, and low-wage county status, 1985-99

Employment growth in low-wage counties has been below the nonmetro average since 1994-95

Source: Calculated by ERS from Bureau of Labor Statistics' Local Area Unemployment Statistics.
Rural Conditions and Trends, Vol. 11, No. 2 • 45

Population and Employment

Unemployment Rates Are Slightly Higher in Low-Wage Counties

Unemployment in low-wage counties has been somewhat higher than unemployment in

all nonmetro counties since at least the mid-1980’s. The difference is not large—between

0.6 and 1.0 percentage point in most years (fig. 7). The gap was smallest in the early

1990’s, consistent with relatively strong employment growth for low-wage counties in that

period. [Lorin Kusmin, 202-694-5429, lkusmin@ers.usda.gov]

-0.3

0.8

0.7

0.5

-0.4

0.8

1.6

1.4

1.1

1.5

0.7

1.3

Farming Mining Manufacturing Government Services Nonspecialized

-1

0

1

2

Low-wage counties Other nonmetro counties

Figure 6

Annual average nonmetro employment growth, by county economic type and low-wage status, 1995-99

Taking county economic types into account, employment growth rates are mostly lower for low-wage counties

Source: Calculated by ERS from Bureau of Labor Statistics' Local Area Unemployment Statistics.

Percent
Population and Employment

46 • Rural Conditions and Trends, Vol. 11, No. 2

Percent

1985 86 87 88 89 90 91 92 93 94 95 96 97 98 99

1

2

3

4

5

6

7

8

9

10

11

12

Metro

All nonmetro

Low-wage

Figure 7

Unemployment rates in low-wage counties are somewhat above the nonmetro average

Note: Values are annual averages.

Metro, all nonmetro, and low-wage unemployment rates, 1985-99

Source: Calculated by ERS from Bureau of Labor Statistics' Local Area Unemployment Statistics.
The demographic characteristics of hired farmworkers have changed

little during the 1990’s.

These workers continue

to earn about 58 percent

as much as all wage and

salary workers. About 45

percent of all hired farmworkers 25 years and

older are low-wage earners who earn less than

the poverty threshold for

a family of four. Over

one-third have annual

family incomes of less

than $15,000.

Rural Conditions and Trends, Vol. 11, No. 2 • 47

Population and Employment

Hired farmworkers accounted for less than 1 percent (840,000) of all wage and salaryworkers in 1999. They made up one-third of the farm workforce, yet this proportion

understates their contribution to the total farm production process. Hired farmworkers provide the labor at critical production times when operators and family members are unable

to supply the necessary labor. Hired farmwork is often seasonal, is usually performed outdoors, involves lifting and carrying heavy objects, and pays substantially less than most

other occupations. The overwhelming share of hired farmworkers (82 percent) work at

planting, cultivating, and harvesting crops or tending to livestock. Smaller numbers are

employed as farm managers (9 percent), supervisors of farmworkers (5 percent), and

nursery workers (4 percent).

Demographic Characteristics of Hired Farmworkers Change Little

The size of the hired farm labor force in 1999 was 40,000 (or 4 percent) fewer than for the

previous year (app. table 5). Demographic groups with larger than average decreases

included men (8 percent), white non-Hispanic workers (7 percent), workers ages 25-34

(13 percent), workers with fewer than 9 years of education (20 percent), and workers

employed in the Northeast. Women had the largest increase between 1998 and 1999.

Compared with all wage and salary workers, hired farmworkers are predominately male,

Hispanic, young, never married, less educated, noncitizens, and located in the South and

West census regions (table 1). Many of these differences persisted throughout the 1990’s,

although some changes, such as the increase in Hispanic farmworkers, most likely resulted from CPS survey design changes in 1994. Since 1994, neither the number nor percentage of hired farmworkers who reported they are not U.S. citizens changed significantly. The percentage of hired farmworkers has decreased in the South, increased significantly in the West since 1994, and stayed the same in the Northeast and Midwest (app.

tables 5 and 6).

Hired Farmworkers’ Family Incomes, Earnings Lag Most Other Occupations

Hired farmworkers have slightly lower family incomes than all wage and salary workers.

About 64 percent of hired farmworkers had family incomes of less than $30,000 in 1999,

compared with 36 percent of all wage and salary workers (table 2). Even more striking,

almost 68 percent (451,000 of 668,000 workers) of full-time hired farmworkers, those who

usually worked 35 or more hours per week, had family incomes of less than $30,000 in

1999.

The median weekly earnings of all hired farmworkers ($280) were about 58 percent of the

median weekly earnings of all wage and salary workers ($479) (table 1). The median

weekly earnings for full-time hired farmworkers ($320) were also about 58 percent of

those for all full-time wage and salary workers ($550) (app. tables 5 and 6). Full-time hired

farmworkers earned less than those in most occupations, except for private household

and other nonprotective services (fig. 1).

Most Hired Farmworkers Are Low-Wage Workers

Low-wage workers are defined here as those 25 years of age and over whose earnings,

on a full-time full-year equivalent basis, fall below the official U.S. weighted average

poverty threshold for a family of four ($17,028). In 1999, there were 585,000 hired farmworkers 25 years and older, about 47 percent of whom earned low wages. The percentage of full-time hired farmworkers with low wages was higher than in other occupations,

except for private household and other nonprotective services (fig. 2).

Almost Half of Hired Farmworkers 25 Years

and Older Earn Poverty-Level Wages
Population and Employment

48 • Rural Conditions and Trends, Vol. 11, No. 2

Low-wage hired farmworkers tend to possess many of the same characteristics associated with low-wage employment in other occupations. More than half of the low-wage hired

farmworkers were Hispanic, and over 64 percent completed less than 12 years of education, a much higher proportion than either the general labor force or the low-wage workforce as a whole. In addition, low-wage hired farmworkers were more likely to work in

crop production (61 percent) and more likely to be U.S. citizens (56 percent).

Table 1

Demographic and earnings characteristics of hired farmworkers and all wage and salary workers, 1999

Almost all demographic characteristics of the hired farm workforce differ from those of all wage and salary workers

Hired All wage and

Characteristics farmworkers salary workers

Thousands Percent Thousands Percent

All 840 100 119,130 100

Gender:

Male 678 80.7* 61,986 52.0

Female 162 19.3* 57,144 48.0

Race:

White 425 50.6* 87,100 73.1

Hispanic 361 43.0* 12,852 10.8

Black and other 54 6.4* 19,178 16.1

Education completed:

0-4 years 95 11.3* 831 .7

5-8 years 189 22.6* 3,246 2.7

9-11 years 174 20.7* 11,817 9.9

12 years 228 27.1* 37,575 31.6

13 years or more 154 18.3* 65,661 55.1

Age (years):

Less than 20 130 15.5* 7,488 6.3

20-24 125 14.9* 12,522 10.5

25-34 196 23.3 28,599 24.0

35-44 174 20.7* 32,354 27.2

45-54 122 14.5* 24,598 20.6

55 and over 93 11.1 13,569 11.4

Citizenship status:

U.S. citizen 556 66.2* 110,423 92.7

Not U.S. citizen 284 33.8* 8,707 7.3

Census region:

Northeast 57 6.8* 22,422 18.8

South 277 32.9* 28,970 24.3

Midwest 164 19.6* 41,636 35.0

West 342 40.7* 26,082 21.9

Years

Median age 33* 38

Dollars

Median weekly

earnings $280* $479 

*Significantly different from all wage and salary workers at the 95-percent confidence level.

Source: Calculated by ERS using data from the Current Population Survey earnings microdata file.
Rural Conditions and Trends, Vol. 11, No. 2 • 49

Population and Employment

Table 2

Family income of hired farmworkers and all wage and salary workers, 19991

Hired farmworkers have significantly lower family incomes than all wage and salary workers

Income Hired farmworkers All wage and salary workers

Thousands Percent Thousands Percent

Total 840 100 119,130 100

Less than $10,000 198 23.5* 17,619 14.8

$10,000-$14,999 192 22.8* 10,508 8.8

$15,000-$29,999 146 17.4* 14,484 12.2

$30,000-$39,000 112 13.3 14,859 12.5

$40,000-$49,999 48 5.8* 12,500 10.5

$50,000 and over 144 17.2* 49,160 41.2

*Significantly different from all wage and salary workers at the 95-percent confidence level.

1Combined income of all family members during the last 12 months. Includes money from jobs; net income from businesses, farms, or rents; pensions,

dividends, interest, social security payments; and any other money income received by family members who are 15 years of age and older.

Source: Calculated by ERS using data from the Current Population Survey earnings microdata file.

*

* *

* *

*

*

*

*

*

*

*Significantly different from hired farmworkers at the 95-percent confidence level.

Occupation

Hired farmworkers

240

315

320

350

360

421

446

504

520

596

600

615

800

Executive, administrative, and managerial 800

Professional specialties

Technical and related support

Precision production, craft, and repair

Protective service

Sales

Transportation and material moving

Administrative support, including clerical

Operators, assemblers, and inspectors

Handlers, equipment cleaners, helpers, and laborers

Other farming, fishery, and forestry

Services, except protective and household

Private household

0 200 400 600 800

*

*

*

Median weekly earnings

Dollars

Source: Calculated by ERS using data from the 1999 Current Population Survey earnings microdata file.

Figure 1

Median weekly earnings of full-time workers, by occupation, 1999

Hired farmworkers rank near the bottom of major occupational groups
Population and Employment

50 • Rural Conditions and Trends, Vol. 11, No. 2

These characteristics showed little change when the analysis was limited to the 519,000

hired farmworkers 25 years of age and older working full time. About 231,000 of these

workers (44 percent of those 25 years of age and older) earned low wages. More than

half these workers were Hispanic (65 percent), nearly half (49 percent) had less than a

9th-grade education, over 60 percent worked in crop production, and over half (51 percent) were U.S. citizens. Also, over 60 percent of them had family incomes less than

$15,000 in the last 12 months. [Jack L. Runyan, 202-694-5438, jrunyan@ers.usda.gov]

*

*

*

*

*

*

*

*

*

*Significantly different from hired farmworkers at the 95-percent confidence level.

Occupation

*

Hired farmworkers

Percent

64

48

44.4

37

32.8

26.3

18.1

17.5

14.5

12

9.1

6.3

5.9

Executive, administrative, and managerial 4.4

Professional specialties

Technical and related support

Precision production, craft, and repair

Protective service

Transportation and material moving

Administrative support, including clerical

Sales

Operators, assemblers, and inspectors

Handlers, equipment cleaners, helpers, and laborers

Other farming, fishery, and forestry

Services, except protective and household

Private household

0 20 40 60 80

*

*

*

Source: Calculated by ERS using data from the1999 Current Population Survey earnings microdata file.

Figure 2

Full-time workers who are low-wage workers, by occupation, 1999

Hired farmworkers rank near the top of low-wage earners
During 1997, real earnings per nonfarm job

grew more slowly in rural

than in urban areas.

Earnings per job grew

slightly faster in lowwage rural counties than

in other rural counties,

but low-wage counties

still have jobs that average far lower earnings in

every major industry

group.

Rural Conditions and Trends, Vol. 11, No. 2 • 51

Earnings

Rural real earnings per nonfarm job rose by 1.3 percent during 1997, from $22,473 in1996 to $22,985 in 1997. Urban real earnings per nonfarm job increased at a faster

pace (2.1 percent), rising from $30,955 in 1996 to $32,825 in 1997. Since 1990, earnings

per nonfarm job have fallen less or increased more in rural than in urban areas in only 2

years, 1993 and 1994 (fig. 1 and app. table 7). The rural-urban earnings gap persisted

and widened during the 1990’s. In 1989, rural earnings per nonfarm job were 73.8 percent of urban earnings. By 1997, that ratio had fallen to 70 percent.

Rural Earnings Lag Urban in All Nonfarm Industries

The rural-urban gap in earnings per nonfarm job exists in all industry sectors (table 1).

During the 1990’s, the gap widened sharply in four industry groups—agricultural services,

forestry, and fishing; mining; transportation and public utilities; and finance, insurance, and

real estate. The gap remained largest in the finance, insurance, and real estate industry.

Rural earnings were only 54.3 percent of urban earnings in this industry in 1989 and fell

to 45.8 percent of urban earnings by 1997. Rural jobs in this industry are more often part

time and in lower paying administrative support and clerical occupations, while urban jobs

in this industry are more often full time and in higher paying executive and technical occupations.

Earnings Per Nonfarm Job Increased More in Low-Wage 

Than in Other Rural Counties

During 1997, real earnings per nonfarm job grew more in low-wage rural counties (1.5

percent) than in other nonmetro counties (1.2 percent). (For an explanation of what a lowRural Nonfarm Earnings Increase in 1997, but

Lag Urban Earnings Growth

Nonmetro

Metro

Figure 1

Annual change in real earnings per nonfarm job, 1989-97

Percent change from previous year

1990 91 92 93 94 95 96 97

Nonmetro earnings per job grew faster than inflation in 1997, only the third annual increase in real nonmetro earnings so far in 

the 1990’s

 Note: Prevous years’ earnings converted to 1997 dollars using the chain-type personal consumption expenditures price index.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.

-2

-1

0

1

2

3
Earnings

52 • Rural Conditions and Trends, Vol. 11, No. 2

wage county is, see the box on page 18). But this 1 year of slightly faster growth follows

a decade of slower growth in low-wage counties. From 1989, the last year of growth

before the 1990-91 recession, to 1991, earnings per job fell at an annual rate of 1.7 percent in low-wage counties, a faster rate of decline than in other rural counties (table 2).

From 1991 to 1997, earnings per nonfarm job increased by 0.3 percent annually in lowwage counties, slightly slower than the 0.4 percent rate of increase in other nonmetro

counties. The gap between real earnings per nonfarm job in low-wage and other rural

counties grew from $4,734 in 1989 to $4,995 in 1997.

Earnings grew somewhat more in low-wage counties during 1997 because earnings in

most industries, especially mining, manufacturing, and services, grew more in those

counties than in other rural counties (table 3). The average earnings in every industry,

however, are far lower than in other rural counties. The gap ranges from a high of over

$15,000 per mining job to a low of $888 per agricultural services, forestry, and fishing job.

Also, manufacturing jobs in low-wage counties average $10,000 per job lower earnings

than manufacturing jobs in other nonmetro counties, and low-wage county jobs are much

less concentrated in manufacturing. While manufacturing accounts for about 17 percent of

jobs in other rural counties, manufacturing accounts for 11 percent of jobs in low-wage

counties.

The low-wage counties rely more on government and government-sponsored enterprises

(the largest of which is the U.S. Postal Service) for jobs, but not because government jobs

are concentrated in low-wage counties. In fact, there is one government job for every 12

residents in other rural counties while there is one government job for every 13 residents

in low-wage counties. The greater dependence of low-wage counties on government jobs

reflects lower numbers of jobs in other industries relative to the normal need for government services, such as law enforcement, public education, and mail delivery.

Table 1

Nonmetro real earnings per nonfarm job by industry, 1989 and 1997

Nonmetro earnings trail metro earnings in all nonfarm industries, and most gaps widened during the 1990’s

1989 1997

Earnings Ratio to Earnings Ratio to

per metro per metro

Industry job earnings job earnings

1997

Dollars Percent Dollars Percent

Nonmetro nonfarm 23,059 73.8 22,985 70.0

Agricultural services, forestry, fishing, and other1 15,831 86.0 12,399 75.2

Mining 37,070 92.4 41,020 70.2

Construction 26,908 73.8 25,532 73.9

Manufacturing 30,767 70.3 32,204 67.6

Transportation and public utilities 36,030 82.6 33,305 73.1

Wholesale trade 27,272 66.2 28,877 64.9

Retail trade 14,505 81.2 13,758 79.5

Finance, insurance, and real estate 15,052 54.3 17,063 45.8

Services 18,452 64.1 18,954 63.2

Government 25,031 77.9 26,411 76.6

Note: Earnings and jobs in any industries other than government are suppressed in counties with few jobs in that industry or where a dominant

employer accounts for a high share of the jobs in the industry. This suppression affects the calculation of earnings per job in both metro and nonmetro

areas, causing the estimates shown here to vary somewhat from the true estimates that would be calculated if no county information were suppressed. 1Other is employees of foreign embassies working in the United States.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Rural Conditions and Trends, Vol. 11, No. 2 • 53

Earnings

Low-Wage Counties’ Economies Tend To Be Small and Remote

According to ERS’ typology of nonmetro counties, 48 percent of low-wage counties’

economies depend on farming for a large share of earnings. According to ERS’ urban

influence codes, 52 percent of low-wage counties are completely rural (they lack a town

of even 2,500 residents) and not adjacent to metro areas. With so many remote, small

county economies, it is not surprising that their nonfarm earnings are lower than in other

rural or urban counties. Looking at the number of establishments in each private industry

group in the low-wage counties shows the small number of local employers. In all nine private industries, low-wage counties average fewer employers and fewer jobs per employer

(table 4). The low-wage counties have much smaller populations on average than other

nonmetro counties, and the numbers of establishments are in line with the size of county

populations. Regardless, few small employers tend to create less competition for workers

than many large employers, and less competition decreases pressure to raise wages.

Most Low-Wage Counties Also Have Low Income

Many people work outside their counties of residence, bringing home earnings to their

counties. ERS’ county typology indicates that 21 percent of the low-wage counties have

40 percent or more of their workers employed outside their counties of residence. Farm

incomes and income from sources other than earnings, such as interest, dividends, rents,

Table 2

Real earnings per nonfarm job, by place of work, selected years

Earnings per job in low-wage nonmetro counties did not improve relative to earnings in other nonmetro areas during the 1990’s, and both types of nonmetro counties fell farther behind metro areas

Place of work 1989 1991 1997

1997 dollars

Nonmetro 23,059 22,473 22,985

Low-wage 18,654 18,022 18,345

Other 23,388 22,809 23,341

Metro 31,230 30,955 32,825

United States 29,875 29,529 31,144

Average annual change

1989-91 1991-97 1996-97

Percent

Nonmetro -1.3 0.4 1.3

Low-wage 1.7 .3 1.5

Other -1.2 .4 1.2

Metro -.4 1.0 2.1

United States -.6 .9 2.0

Ratio of earnings to metro earnings

1989 1991 1997

Percent

United States -0.6 0.9 2.0

Nonmetro 73.8 72.6 70.0

Low-wage 59.7 58.2 55.9

Other 74.9 73.7 71.1

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Earnings

54 • Rural Conditions and Trends, Vol. 11, No. 2

and government-transfer payments, also contribute to people’s incomes, but are not considered in the low-wage county definition or in the analysis of nonfarm earnings per job.

These sources of income could raise county economic status above that indicated by lowwage status.

Investigation of the Bureau of Economic Analysis’ personal income data series, however,

indicates that low-wage counties, and the commuting counties among them, tend to be

low-income counties as well. When all U.S. counties are ranked by 1997 per capita

Table 3

Real earnings per nonfarm job in low-wage counties by major industry group, 1997

Low-wage counties’ earnings trail other nonmetro counties’ earnings in all nonfarm industries 

Low-wage counties Other nonmetro counties

Earnings Change, Earnings Change,

Industry group per job 1996-97 per job 1996-97

Dollars Percent Dollars Percent

Nonfarm 18,345 1.5 23,341 1.2

Agricultural services, forestry, fishing, and other1 11,628 2.2 12,516 .5

Mining 26,394 5.4 41,852 1.4

Construction 20,895 .7 25,912 .1

Manufacturing 22,646 3.9 32,670 2.6

Transportation and public utilities 28,740 -.8 33,654 -.6

Wholesale trade 24,283 2.2 29,219 2.8

Retail trade 12,508 1.3 13,860 1.4

Finance, insurance, and real estate 14,345 -.7 17,290 .1

Services 15,415 2.2 19,234 1.4

Government 23,077 .7 26,707 1.0

1Other is employees of foreign embassies working in the United States.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.

Table 4

Average establishments per county and wage and salary workers per establishment,

by major industry group, 1996

Compared with other nonmetro counties, low-wage counties average fewer business establishments and fewer workers per business

in all nine major private industry groups

Low-wage nonmetro counties Other nonmetro counties

Wage and salary Wage and salary

Establishments workers per Establishments workers per

Industry group per county establishment per county establishment

Number

Agricultural services, forestry, and fishing 4.3 4.4 11.6 4.9

Mining 2.2 9.5 5.6 20.3

Construction 24.4 4.1 71.8 5.5

Manufacturing 13.8 29.1 41.7 50.6

Transportation and public utilities 15.6 6.3 37.2 10.3

Wholesale trade 15.2 7.5 40.7 9.4

Retail trade 65.4 8.1 172.5 10.6

Finance, insurance, and real estate 18.7 5.6 52.1 6.3

Services 70.4 8.6 208.0 10.7

Source: Calculated by ERS using data from the Bureau of the Census’ 1996 County Business Patterns file as enhanced by Claritas, Inc., to estimate

suppressed data items.
Rural Conditions and Trends, Vol. 11, No. 2 • 55

Earnings

income and that distribution is divided into quintiles, 43 percent of the low-wage counties

fall into the lowest income quintile (fig. 2), as do 50 percent of the low-wage counties with

large numbers of commuters. Most remaining low-wage counties are in the next two higher income quintiles, with few making it into the top two income quintiles. Farm income as

a share of total county income rises from 2 to 6 percent of low-wage county income as

the income quintile rises. Dividends, interest, and rent are more strongly related to lowwage counties making it into higher income quintiles. That source of income rises from 14

percent of income in low-wage counties in the lowest income quintile to 29 percent of

income in the low-wage counties in the highest income quintile.

Although the low-wage counties’ earnings improved in the last year, these earnings numbers are subject to revision when the Bureau of Economic Analysis releases its 1998 estimates. It would be premature to characterize the 1996-97 improvement as the start of a

trend. [Linda M. Ghelfi, 202-694-5437, lghelfi@ers.usda.gov]

43

21.4

3.9

22.9

25

17.7

22.3

16.1

12.5

20.2

23.8

3.9

11.1

49

Lowest 2nd 3rd 4th Highest

0

10

20

30

40

50

60

Low-wage

Other nonmetro

Metro

Figure 2

Distribution of counties across per capita income quintiles, 1997

Low-wage nonmetro counties are concentrated in the lowest fifth of per capita incomes nationwide

Source: Caculated by ERS using data from the Bureau of Economic Analysis.

Percent

7.2

Per capita income quintiles
Manufacturing innovations, such as new technologies and work organization practices, have

generally been linked 

to higher wages in both

rural and urban areas.

These innovations have

also been linked to

increased training and

higher skill levels and

needs, particularly in the

areas of interpersonal,

problem-solving, and

computer skills. Lowwage workers, who tend

to be more concentrated

in rural areas, are less

likely to receive training

than are higher wage

workers. Thus, an

emphasis on and encouragement of training and

skills enhancement

among more vulnerable

low-wage workers is

important and may

enhance these workers’

future earning capabilities.

Earnings

56 • Rural Conditions and Trends, Vol. 11, No. 2

Rural manufacturing wages lag behind urban wages, according to data from the ERSRural Manufacturing Survey (RMS) (see “Data Sources” appendix). Rural manufacturers reported paying their workers an average of $8.90 an hour in 1995, $1.60 (18 percent) less than in urban areas. In addition, rural firms were far more likely to be low-wage

firms, where low-wage is defined as paying average wages of $7.50 (the hourly rate

equivalent of the 1995 poverty line) or less. About 36 percent of rural firms were lowwage employers, compared with 20 percent of urban firms. Furthermore, the more rural

the location, the more likely a firm was to pay average wages at or below $7.50.

A number of other characteristics identify low-wage firms. They tend to adopt fewer technologies and are less likely to introduce new work organization techniques (see box,

“RMS Measures of Technology Use and Work Organization Practices”). Firms with fewer

employees are more likely to pay lower wages as are those hiring larger shares of women

and minority workers. Low-wage firms also tend to hire less educated workers. In addition, low-wage employers are lumped into particular industries. Well over 50 percent of

firms in the apparel and leather industries are low-wage. In rural areas, 50 percent or

more of all producers of food and kindred products, textiles, lumber, and electronics are

also low-wage employers. Finally, low-wage firms are less likely to provide training for

workers, suggesting that workers in low-wage plants, who are already likely to be less

educated than other workers, are also less likely to obtain further training through their

employer, thus perpetuating their low-wage, low-skill status.

According to the RMS survey, not only are wages in metro areas higher than in nonmetro

areas, but they increased more in metro areas, between 1992 and 1995. In real terms

nonmetro wages rose an average of $0.42, compared with $0.52 in metro areas, about a

20-percent greater increase. Thus, the gap between metro and nonmetro manufacturing

wages may be growing.

Nonmetro Wages Rise With New Technology and Work Organization Use

The greater the number of new technologies a firm adopted, the higher the workers’ average wages were, for both metro and nonmetro areas. The gap neither narrowed nor

widened with the introduction of new technologies (fig. 1). Differences in technology adoption, then, cannot explain the metro/nonmetro wage gap, although they can shed some

light on wage differences among workers in metro and nonmetro areas.

Skills Training and Manufacturing Innovations

Are Key to Raising Rural Workers’ Wages

RMS Measures of Technology Use and 

Work Organization Practices

In the Rural Manufacturing Survey, interviewees were asked to identify whether they had adopted one or more of five manufacturing technologies, including: Computer Aided Design (CAD),

CAD/Computer Aided Manufacturing (CAD/CAM), Local Area Networks (LAN), numerical

machines and/or programmable controllers. In addition, they were asked if they used the following work organization strategies: self-directed teams, quality circles (employee problem-solving),

statistical control processing, total quality management (TQM), and/or job rotation. Interviewees

were also asked about the average hourly wage for manufacturing workers in their plant at the

time of the survey (1995) and in 1992. Finally, interviewees were asked how much they thought

that various skills required of production workers, including reading, math, problem-solving,

interpersonal, computer and other technical skills, had increased.
Rural Conditions and Trends, Vol. 11, No. 2 • 57

Earnings

The use of new technologies did not change the rural/urban gap, but the adoption of new

organizational practices did narrow the gap a little. The positive relationship between

wages and work organization techniques was ambiguous in metro areas, while among

nonmetro firms a positive association between the number of work organization techniques a firm adopted and the wages they paid was more evident (fig. 2).

Urban wages grew more than rural wages, no matter how many new technologies were

introduced, suggesting that something other than manufacturing innovations was driving

the wage gains (fig. 3). Similarly, urban workers experienced greater wage gains no matter how many work organization techniques the firm had adopted (fig. 4). Although rural

workers gained less than urban workers generally, the largest gains for rural workers

were in firms using more new technologies and work organization practices.

Nontraditional Skills May Prepare Workers for Better-Paying Jobs

New technology and work organization adoption practices can explain differences in

wages between workers, although they cannot shed much light on the rural/urban gap.

Both rural and urban workers benefit from working in innovative firms. A firm’s ability to

adopt new manufacturing techniques though may be contingent on workers’ skills. One of

the reasons businesses reported for not adopting new technologies or management practices was inadequacy of worker skills. Earlier analysis also showed that nonmetro firms

lagged behind metro firms in technology adoption, primarily because more low technology industries were located in rural areas (see F. Gale, Agricultural Information Bulletin

736-01, Aug. 1997). Firms using newer technologies may be reluctant to locate in rural

areas and rural firms may not adopt new technologies, because of a perceived or actual

lack of skills among rural workers.

The RMS survey asked employers about changes in the production job requirements for

six types of worker skills, including math, reading, computer, problem-solving, teamwork,

and other technical skills. Firms reporting the use of new technologies and/or work organization techniques were more likely to report increases in their skill requirements in all

012345

0

2

4

6

8

10

12

14

Nonmetro

Metro

Number of new technologies

Figure 1

Average hourly wages, by number of new technologies and 

residence, 1995

Workers earned more in plants that used more new technologies

Dollars

Source: Calculated by ERS using data from the Rural Manufacturing Survey.
Earnings

58 • Rural Conditions and Trends, Vol. 11, No. 2

012345

0

2

4

6

8

10

12

14

Nonmetro

Metro

Figure 2

Average hourly wages, by number of work organization practices and

residence, 1995

Dollars

Source: Calculated by ERS using data from the Rural Manufacturing Survey.

Number of work organization practices

Nonmetro workers earned more in plants that used more work organization practices

012345

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Nonmetro

Metro

Figure 3

Number of new technologies

Change in real wages, by number of new technologies and residence, 1992-95

Real wage gains were lower for nonmetro workers, except in firms with no new technologies

Note: 1995 wages were converted to 1992 dollars using the chain-type personal consumption expenditures

price index.

Source: Calculated by ERS using data from the Rural Manufacturing Survey.

Dollars
Rural Conditions and Trends, Vol. 11, No. 2 • 59

Earnings

six areas. In particular, nontraditional skills (skills other than reading and math) appear

increasingly important in today’s labor market. Employers, particularly those who have

introduced new technologies and work organization practices, emphasized the importance of computer and problem-solving skills, as well as the ability to work in teams.

These skills, which are not traditionally emphasized in formal education, may be key to

preparing workers for higher wage jobs.

Firms often address the lack of skills among workers by implementing their own training

programs. Employers who have adopted innovations such as new technologies and work

organization practices are far more likely to report providing training for their workers. The

more new technologies a firm used, the more likely employers are to provide training, in

both metro and nonmetro locations. Among firms using two to five new technologies, rural

training rates actually exceeded urban rates (fig. 5). Similarly, the more work organization

techniques a firm introduced, the more likely employers were to provide training (fig. 6).

While rural workers in firms that have introduced new technologies and work organization

practices were likely to receive on-the-job training, workers in firms with fewer innovations

generally received less training. As such, workers already at the bottom end of the wage

scale—including rural, female, and minority workers—were less likely to obtain additional

skills through their employers.

Worker training and skills enhancement are important both to ensure that industries have

the workers they need and to increase workers’ access to better paying jobs. While some

firms may take the initiative to provide training and introduce innovative work practices,

others may not. Firms may be hesitant to introduce innovations, precisely because of a

012345

0.0

0.1

0.2

0.3

0.4

0.5

0.6

Nonmetro Metro

Dollars

Number of work organization practices

Note: 1995 wages were converted to 1992 dollars using the chain-type personal consumption expenditures

price index.

Source: Calculated by ERS using data from the Rural Manufacturing Survey. 

0

Figure 4

Change in real wages, by number of work organization practices and

residence, 1992-95

Real wage gains were lower for nonmetro workers, particularly in firms with fewer than two work

organization practices
Earnings

60 • Rural Conditions and Trends, Vol. 11, No. 2

012345

0

10

20

30

40

50

60

70

80

90

100

Nonmetro

Metro

Figure 5

Number of new technologies

Source: Calculated by ERS using data from the Rural Manufacturing Survey. 

Percent 

Firms providing training, by number of new technologies and residence, 1995

Training increased with the introduction of new technologies

012345

0

10

20

30

40

50

60

70

80

90

100

Nonmetro

Metro

Percent 

Figure 6

Firms providing training, by number of work organization practices and

residence, 1995

Training inceased with the introduction of work organization practices

Source: Calculated by ERS using data from the Rural Manufacturing Survey.

Number of work organization practices
Rural Conditions and Trends, Vol. 11, No. 2 • 61

Earnings

perceived or real scarcity of skilled workers. As such, an increased emphasis on policies

that focus on skill enhancement may be warranted, although the focus should be on skills

not traditionally associated with formal schooling. In addition to computer and technical

skills, the ability to solve problems and work in teams were skills that manufacturing

employers identified as increasingly important in the workplace, particularly among firms

adopting new technologies and innovative work organization practices. Targeting groups

likely to be left behind because they are employed in low-wage firms, in particular workers

in rural areas, as well as women and minorities, may help ensure higher wages for these

groups.

Skills can be improved by targeting individual workers and employers and by providing

additional incentives for training programs. For instance, targeting industries with low

wages and a lack of training is one possibility. Such programs may be important because

low-wage employers are the least likely to initiate training programs. By targeting those

employed in low-wage manufacturing firms, or the low-wage plants themselves, policies

can both enhance workers’ earning potential and the pool of workers available to firms.

This, in turn, may stimulate new innovations in manufacturing that are linked to higher

productivity and wages. [Jennifer C. Olmsted, jolmsted@oxy.edu; or Peggy Cook, 202-

694-5419, pcook@ers.usda.gov]
The poverty rate for rural

persons declined from

1997 to1998, and rural

median family income

rose in 1998. The family

income of persons in

poor families declined

considerably, while it

grew or changed little for

persons in the higher

income groups. A sizable

share of the rural poor

families had at least one

worker. Poor rural workers often worked parttime, tended to live in

female-headed families,

and seldom had more

than a high school education. Rural working

poor families relied more

on benefits from assistance programs and less

on family earnings

income than working

nonpoor families.

Poverty and Income

62 • Rural Conditions and Trends, Vol. 11, No. 2

I

n 1998, the rural poverty rate was 14.3 percent, down 1.5 percentage points from the

1997 level. The rural poverty rate exceeded the urban poverty rate by 2 percentage

points (see box, ”How Is Poverty Determined?”). In all, 7,480,000 rural persons lived in

poverty. In addition to having a larger share of persons living below the poverty line, rural

areas had a larger share of persons living close to the poverty line—11 percent of rural

residents fell between 100-150 percent of the poverty line, compared with only 8 percent

of urban residents. People in this income category risk falling into poverty should a family

crisis or economic downturn occur. At the high end of the income distribution, only 16 percent of rural residents’ incomes exceeded 500 percent of the poverty threshold, compared

with 28 percent of urban dwellers (fig. 1; app. table 8).

The rural and urban poor populations differ demographically. Compared with the urban

poor, the rural poor are more likely to be non-Hispanic Whites and somewhat more likely

to live in intact families. In addition, poverty was more prevalent in the rural than the urban

South. Over half the rural poor live in the South, while the urban poor are more evenly

distributed throughout the Nation.

The share of poor living in families with at least one full-time, full-year worker changed

dramatically. In 1998, 29 percent of the rural poor lived in families with one or more fulltime, full-year workers, a 9-percentage-point increase since 1996. The share of urban

poor living in families with full-time, full-year workers also increased, climbing from 21

percent in 1996 to 25 percent in 1998, but it trailed the rural value by 4 percentage

points. Welfare reform’s emphasis on employment for cash assistance recipients may

have contributed to increased family labor force effort among both rural and urban poor

(app. table 9).

Rural Poverty Rate Declines, While Family

Income Grows

14.3

12.3

43.5

33.9

26.3 26.1

15.9

27.7

Less than 100 percent 100-299 percent 300-499 percent 500+ percent

0

10

20

30

40

50

Nonmetro

Metro

Poverty/income ratio

Source: Calculated by ERS using data from the March Supplement of the Current Population Survey, 1999. 

Percent 

Figure 1

Distribution of persons, by poverty/income ratio and residence, 1998

More than half of rural residents lived in families with income less than 300 percent of the 

poverty level
Rural Conditions and Trends, Vol. 11, No. 2 • 63

Poverty and Income

Poverty Rates Declined in 1998 . . .

During the 1990’s, rural poverty rates remained consistently higher than urban poverty

rates, but the rural/urban poverty gap narrowed slightly, dropping from 3.6 percentage

points in 1990 to 2 percentage points in 1998. During the recession and early recovery

years of the 1990’s, the rural poverty rate rose steadily from 16.3 percent in 1990 to 17.2

percent in 1993. Along with the strengthening recovery, it dropped to 14.3 percent in 1998

(fig. 2; app. table 10).

. . .While Rural Family Income Increased 

Growth in rural family income accompanied the decline in the rural poverty rate. After

adjustments for inflation, median family income in rural areas grew 4.9 percent between

How Is Poverty Determined?

The poverty line is the minimum income level needed by a family or individual to meet basic

needs such as food, shelter, clothing and other essential goods and services. The Office of

Management and Budget (OMB) sets the official poverty lines, adjusted for family size and

composition. In 1998, the poverty line for a family of four, including two children, was set at

$16,530. Cash income for each family or individual (including pretax income and cash welfare

assistance, but excluding in-kind welfare assistance, such as food stamps and Medicare) is

compared with the poverty line for families of similar composition. The poverty rate for an area

or for a category of people is the percentage of persons living alone or in families with income

less than the poverty line. The nonmetro population includes those persons whose metro/nonmetro residency is not identified for purposes of confidentiality.

1990 91 92 93 94 95 96 97 98

0

5

10

15

20

Metro

Nonmetro

Source: Calculated by ERS using data from the March Supplement of the Current Population Survey, 1990-98.

Note: Change in the metro/nonmetro status of some counties caused a discontinuity in the 1994 data.

Figure 2

Poverty rates, by residence, 1990-98

Percent

Since 1996, the nonmetro poverty rate has declined slightly but remains higher than the metro

poverty rate
Poverty and Income

64 • Rural Conditions and Trends, Vol. 11, No. 2

1997 and 1998, outpacing urban growth of 2.3 percent. Rural family income growth, however, was not evenly distributed among all income categories. The median family income

of families below the poverty line declined 4.6 percent between 1997 and 1998, while

incomes grew for families with incomes between 100 and 299 percent of the poverty line

and families with incomes between 300 and 499 percent of the poverty line. For those

families with income over 500 percent of the poverty line, median income declined slightly. This finding may be due to slight upward shifts of family income resulting in a redistribution of families among the various income categories. For example, the share of families

with incomes below the poverty line decreased 1.3 percentage points between 1997 and

1998, while the share of rural families with income over 500 percent of the poverty level

increased by 2.2 percentage points. It is likely that families that entered the highest

income category had somewhat lower incomes that depressed the median income and

resulted in the slight income decline (fig. 3; fig. 4; app. table 10).

Rural Family Poverty Follows a Familiar Pattern 

The traditional patterns of rural poverty continued in 1998, with poverty rates varying substantially by race/ethnicity and other demographic characteristics (app. table 11). Even

though their poverty rates declined, non-Hispanic Black, Hispanic, and non-Hispanic

Native American families’ chances of being poor were more than twice that of nonHispanic White families. These minority groups also had much lower median family

incomes than that of non-Hispanic White families. Non-Hispanic Asian families had the

highest median family income ($49,687) among racial/ethnic groups, even though 15 percent of Asian families were poor. The disparity between a high median family income and

a poverty rate of 15 percent suggests a high level of income inequality among rural Asian

families.

Metro

Nonmetro

1990 91 92 93 94 95 96 97

0

10

20

30

40

50

Figure 3

9

Median family income, by residence, 1990-98

Median family income in metro and nonmetro areas was largely stagnant in the early 1990’s, 

but increased between1996 and 1998

Source: Calculated by ERS using data from the March Supplement of the Current Population Survey, 

1990-99

Note: In 1998 dollars. Change in the metro\nonmetro status of some counties caused a discontinuity in the

1994 data.

$1,000

98
Rural Conditions and Trends, Vol. 11, No. 2 • 65

Poverty and Income

Heads of families who lacked a high school education had more than twice the likelihood

of poverty and much lower median family income than family heads with a better education. Incomes grew modestly in families headed by a person who had at least a high

school education, compared with families headed by a person who had not completed

high school.

Family structure continues to strongly influence poverty status. Families headed by a single female had a high poverty rate and the lowest median family income of any group.

Thirty-five percent of these families were poor, more than four times the share of marriedcouple families. Having working adults in the family also strongly influences family poverty

rates and family income, with poverty rates declining and income rising as the number of

workers per family increased. In general, larger families had higher poverty rates than

smaller families. Two-children families were the exception, reflecting the tendency of better-off families to have two children (app. table 11).

Rural Working Poor Families Rely Less on Earnings, More on Income Assistance

Than Working Nonpoor Families

Most rural poor families contain one or more workers. More than two-thirds of rural poor

families have at least one worker, while 16 percent have two or more workers (fig. 5). The

structure of working poor families differs a great deal from working nonpoor families. Rural

working poor families were much more likely than rural working nonpoor families to be

headed by a single female (app. table 12; see “Who Is a Worker?”). Forty-six percent of

working poor families were headed by a single female, compared with only 12 percent of

working nonpoor families.

By definition, rural working poor families earned less than their nonpoor counterparts.

These families had median family earnings of $7,000, while working nonpoor families had

median family earnings of $40,000. Median earnings for working poor families headed by

a single female ($5,732) were even lower than for other working poor families. Working

-4.6

1.1

1.5

-0.6

Less than 100 percent 100-299 percent 300-499 percent 500+ percent

-6

-4

-2

0

2

4

Income/poverty ratio

Note: Family income in 1998 dollars.

Source: Calculated by ERS using data from the March Supplement of the Current Population Survey,

1998-99.

Percent change

Figure 4

Changes in nonmetro median family income, by income group, 1997-98

The income of poor families dropped almost 5 percent in 1998
Poverty and Income

66 • Rural Conditions and Trends, Vol. 11, No. 2

poor families relied less on family earnings than working nonpoor families. For example,

only 64 percent of working poor families received 80 percent or more of their family

income from family earnings, while 76 percent of working nonpoor families received 80

percent or more of their family income from family earnings.

In addition to earnings income, many rural working poor families relied on benefits from

assistance programs such as Temporary Assistance for Needy Families, Supplemental

Security Income, and food stamps. This reliance is partially explained by the fact that

working poor families are eligible for assistance to a greater extent than nonpoor families.

About 39 percent of working poor families received some assistance benefits, compared

with about 4 percent of working nonpoor families. Among working poor families, families

headed by a single female had the highest median assistance income, at $3,120, while

other working poor families received median income assistance benefits of $2,561 (app.

table 12).

Rural Working Poor Work Less, and Are Less Educated Than Nonpoor Workers

A tendency to work less than full-time, full-year contributes to the poverty of rural poor

workers (app. table 13). Only 36 percent of poor workers worked full-time, full-year, compared with 71 percent of nonpoor workers. Poor workers living in female-headed families

had particularly low levels of employment. Only 29 percent of these workers worked fulltime, full-year, while 69 percent of their nonpoor counterparts worked full-time, full-year.

Who Is a Worker?

A worker is a person 21 years old or older who worked any time in 1998. Family heads of any

age who worked at all in 1998 are also defined as workers. A poor worker is defined as above

but with family income below the poverty level. A working poor family is defined as a family with

one or more workers and whose family income fell below the poverty level.

2 or mo rkers 2 or more workers

2 or mo

2 or more workers2 or more workers

Source: Calculated by ERS using data from the March Supplement of the Current Population Survey, 1999. 

Figure 5

More than two-thirds of nonmetro poor families had at least one worker

Number of workers per nonmetro family, by poverty status, 1998

Poor families Nonpoor families

Two or more workers

 55.0%

No worker

 14.2%

One worker

 30.8%

Two or more workers

 15.9%

No worker

 32.7%

One worker

 51.4%
Rural Conditions and Trends, Vol. 11, No. 2 • 67

Poverty and Income

As expected, given that they work fewer hours, individual median annual earnings for the

rural working poor ($4,800) were much lower than for nonpoor workers ($22,500). All rural

workers relied heavily on wage and salary earnings, although the working poor were

more likely than nonpoor workers to have earnings from self-employment.

In addition to working less than nonpoor workers, the rural working poor are less educated than nonpoor workers, which limits their opportunities to find higher-wage employment

when they do work. Not only was the share of high school dropouts larger among the

working poor (28 percent), but the share of workers in this group with a post-high school

education was much smaller. In all, 28 percent of poor workers had education beyond

high school, compared with 48 percent of nonpoor workers (app. table 13). [Elizabeth M.

Dagata, 202-694-5422, edagata@ers.usda.gov] 
Influenced by a robust

economy, growth rates in

overall per capita transfers slowed from about 5

percent per year in the

early 1990’s to 2-3 percent annually in metro

and nonmetro areas

between 1994 and 1997.

The patterns of growth

and decline differed

across program categories and individual programs, especially the

income maintenance category. Per capita transfers for family (cash)

assistance and food

stamp benefits sharply

declined in both metro

and nonmetro areas.

Food stamp benefits

declined more rapidly in

metro than nonmetro

areas, while benefits for

family assistance

declined more rapidly in

nonmetro than metro

areas. In 1997, government transfer programs

accounted for 21 percent

of nonmetro personal

income, compared with

14.7 percent of metro

personal income.

Poverty and Income

68 • Rural Conditions and Trends, Vol. 11, No. 2

I

n 1997, Federal, State, and local governments transferred $1.1 trillion to individuals,

organizations, businesses, and administrative and service costs for various social welfare programs. Of the $1 trillion distributed in 1997 to individuals who received cash benefits through government programs, $218 billion, or $4,055 per capita, went to nonmetro

residents. In comparison, metro residents received $846 billion in government transfers,

or $3,950 per capita (app. table 14; app. table 15).

The proportional share of transfer payments for various programs was essentially the

same in nonmetro and metro areas. About half of transfer dollars for individuals went to

retirees and the disabled as payments for Social Security and government pensions.

Approximately 35 percent was distributed for medical payments to suppliers of Medicare

and Medicaid care. About 9 percent of transfer dollars was cash income benefits paid to

qualifying families and persons through income maintenance programs, such as family

assistance (see box, “TANF Replaces AFDC”), Supplemental Security Income (SSI), food

stamps, and other income maintenance programs, including the Earned Income Tax

Credit (EITC). Unemployment insurance, veterans’ benefits, and employment, education,

and training programs accounted for the remaining 6 percent (app. table 15).

Nonmetro Areas Rely Heavily on Government Transfer Payments

Nonmetro areas rely more heavily on transfer payments than do metro areas. Per capita

transfers accounted for 21.2 percent of rural personal income, compared with 14.7 percent of urban personal income in 1997. The levels of rural per capita transfer payments

surpassed urban per capita payments all years between 1989 and 1997. In contrast, rural

per capita personal income consistently lagged urban per capita income in all years,

remaining about 70 percent of urban income (app. table 14; app. table 15).

Annual Rates of Transfer Growth Continue To Slow 

Annual rates of change in total per capita transfer payments generally follow changes in

the economy, growing during recessions and falling during periods of economic recovery.

Nonmetro and metro areas exhibited similar patterns of change during the 1990’s. During

1989-97, nonmetro per capita transfer payments grew at an average annual rate of about

4 percent, about the same as for metro per capita transfer payments. During the recessionary years in the early part of the decade, per capita transfer payments grew at rates

slightly above 5 percent in both areas. As economic recovery set in, the growth rates

dropped to around 3.5 percent between 1992 and 1994 (app. table 15). Reflective of the

strong national economy since 1994, annual growth rates in per capita transfer payments

slowed consistently to well under 2 percent per year in nonmetro and metro areas (fig. 1;

app. table 15).

. . . But Trends Vary Across Programs 

In comparison to the trends observed above for total transfers, the patterns of growth (or

decline) vary considerably across the major program categories. Over the decade, nonmetro and metro per capita payments for retirement and disability benefits grew more

slowly (around 2 percent per year), while benefits for medical programs increased more

rapidly (around 6 or 7 percent per year) (app. table 15). For income maintenance programs, annual growth rates began to slow during the 1992-94 period and shrank to 0.4

percent in nonmetro areas and -1.6 percent in metro areas during 1994 and 1997 (app.

table 15).

The patterns differ markedly among individual programs in the various program categories, especially Medicaid, and the main income maintenance programs, Supplemental

Food Stamp and Family Assistance Benefits

Sharply Decline in the Post-Welfare-Reform Era 
Rural Conditions and Trends, Vol. 11, No. 2 • 69

Poverty and Income

Security Income (SSI), family assistance, and food stamps. Since 1994, benefits for the

Medicaid program, which grew rapidly during 1989-91 at average annual rates of 18 percent and 22 percent in metro and nonmetro areas, steadily slowed. SSI benefits grew at

relatively slow rates in the first 2 years of the period, but during 1996-97, declined 1.4

percent in nonmetro and 1.8 percent in metro areas (fig. 2).

Changes were most striking in the food stamp and family assistance programs.

Continuing the 1995-96 trends, nonmetro per capita benefits for family assistance

declined in 1996-97 by around 15 percent, while per capita food stamp benefits declined

by more than 15 percent. Family assistance benefits, however, declined more sharply in

nonmetro than metro areas, while food stamp benefits declined more sharply in metro

than nonmetro areas (fig. 2).

Unlike the per capita trends for SSI, family assistance, and food stamps, “other income

maintenance programs”—Earned Income Tax Credit, general assistance, emergency

assistance and others—grew more rapidly than any of the other programs, although the

TANF Replaces AFDC 

Family assistance refers to cash (welfare) payments made to eligible low-income families with

children under Aid to Families with Dependent Children (AFDC). AFDC was replaced by

Temporary Assistance for Needy Families (TANF) with the passage of the Personal

Responsibility and Work Opportunity Act (PRWORA) of 1996. PRWORA transferred Federal

welfare dollars to States in the form of block grants along with the responsibility to tailor their

own State welfare plans to local conditions and needs. Many States, especially those that had

implemented alternative welfare systems under the State waiver system, set up their own

uniquely named welfare programs. In this article, family assistance and TANF are used interchangeably to refer to cash welfare assistance.

Annual change in real per capita transfer payments, by residence, 1989-97

Percent change

Source: Calculated by ERS using data from the Bureau of the Census.

4.26

3.69

6.88 6.73 6.52

6.91

2.66 2.55

1.17 1.07

3.22

2.89 2.8

2.07

1.74

1.08

1989-90 90-91 91-92 92-93 93-94 94-95 95-96 96-97

Nonmetro

Metro

Figure 1

Growth in government transfer payments to individuals continued to slow in both nonmetro and metro areas

0

2

4

6

8
Poverty and Income

70 • Rural Conditions and Trends, Vol. 11, No. 2

6.55 5.87

3.79 3.57 1.81 1.06 2.53 3.61

-10.23

-6.77 -6.02 -4.46

19.17

15.55

-16

-12

-8

-4

0

4

8

12

16

20

24

Nonmetro

Metro

Medicaid Food stamps Other income

maintenance

programs

Supplemental

Security Income

All income

maintenance

All medical

with Dependent

Children

Source: Calculated by ERS using data from the Bureau of Economic Analysis.

Percent

4.47 4.01 2.73 2.43 2.7

-0.35

0.81 1.35

-11.64 -9.68

-3.68 -5.3

18.97

13.35

-16

-12

-8

-4

0

4

8

12

16

20

24

Medicaid Supplemental

Security Income

Other income

maintenance

programs

All income

maintenance

All medical Aid to Families Food stamps

with Dependent

Children

2.83 2.09 1.83 0.17

-3.29

-5.63

-1.38 -1.72

-14.59

-10.85

-15.71-17.38

8.87

4.8

-24

-20

-16

-12

-8

-4

0

4

8

12

16

20

Medicaid Supplemental

Security Income

Other income

maintenance

programs

All income

maintenance

All medical Aid to Families Food stamps

with Dependent

Children/TAN F 

1995-96

1994-95

1996-97

Percent

Percent

1

1

PRWORA's provisions replaced Aid to Families with Dependent Children (AFDC) with Temporary Assistance for Needy families (TANF) in August 1996.

Figure 2

Average annual change in transfer payments for selected programs, by residence,

1994-95, 1995-96, and 1996-97

TANF and food stamp benefits declined sharply during 1996-97 in both metro and nonmetro areas

Aid to Families
Rural Conditions and Trends, Vol. 11, No. 2 • 71

Poverty and Income

growth slowed considerably in 1996-97 from the previous years (fig. 2). Not surprisingly,

these results correspond with dramatic declines in the size of the TANF and food stamp

caseloads. In the years immediately before and following the passage of PRWORA, the

number of families on welfare have dramatically declined, and participation in the Food

Stamp program declined by about one-third, mostly after 1996.

While the reasons for these current trends are not fully understood, they have been attributed to a strong economy and the effects of welfare reform legislation on the operation of

programs by States and local areas. Favorable economic conditions opened up new jobs

in local labor markets and reduced unemployment and poverty rates, thereby diminishing

the need for public cash assistance.

Passage of PRWORA in August 1996 not only altered the scope and structure of the system of cash assistance for needy families, but also enacted changes in other programs,

including food stamps, SSI, and Medicaid. Even before the legislation became law, many

States had begun to reform their welfare systems under Federal waivers. PRWORA tightened the eligibility requirements for the programs, limited eligibility of most legal immigrants and able-bodied unemployed adults without children for family assistance and food

stamps, and instituted time limits and work requirements for family assistance. In

response to PRWORA, many States also created programs to divert families seeking

assistance to other forms of temporary help. Furthermore, by allowing States to plan and

operate their own State welfare plans, PRWORA shifted the national emphasis on assistance from welfare to work.

The relative importance of the economy versus welfare reform in explaining the declines

remains a matter of debate among researchers. Recent ERS research using State data

indicates that declines in unemployment rates accounted for more than a third of State

differences in food stamp participation, while waivers and political factors, such as a governor’s political party, explained an additional 10 percent of the differences. In this study

as well as other similar studies, a large proportion of the differences remained unexplained.

The faster declines in family assistance benefits in nonmetro than metro areas are consistent with published statistics showing that States with disproportionately large rural

and/or minority populations traditionally have paid low welfare benefits, which may affect

the amount of TANF Federal block grants available to predominantly rural States to run

their own State programs (see Rural Conditions and Trends, Vol. 8, No. 1, 1997, pp. 38-

47). The faster declines in food stamp benefits in metro than nonmetro areas partly reflect

the concentration of disproportionate numbers of immigrants in metro areas who became

ineligible for TANF under PRWORA.

Counties With Large TANF and Food Stamp Declines

Concentrated in Certain States

Counties with TANF and food stamp declines greater than the national average (20 percent) during 1996-97 tend to be concentrated within the boundaries of certain States. In

the case of TANF, nearly all counties in 3 States (Tennessee, Wisconsin, and Wyoming)

and a sizable number of counties in about 20 States had higher than average rates of

decline in benefits. In the case of food stamps, most counties in 5 States (Wisconsin,

Ohio, Florida, Nevada and Kansas) as well as a substantial share of the counties in about

11 other States had high rates of declining benefits. Both family assistance and food

stamp benefits declined rapidly in nearly all counties in Wisconsin, one of the earliest

States to implement a waiver program (fig. 3 and fig. 4).

Economic Reliance on Transfers Varies by Nonmetro County Type

Per capita transfers and the reliance on transfer payment income varied among county

types. Counties in the Midwest, West, and those that are more highly urbanized had

somewhat lower levels of per capita transfers and economic reliance on income from

transfers than all nonmetro counties. In comparison, per capita transfer payments were
Poverty and Income

72 • Rural Conditions and Trends, Vol. 11, No. 2

higher in the South, with its higher concentrations of poor populations, including minorities

(app. table 16).

Nonmetro counties with concentrations of elderly, poor, and low-income populations tend

to have higher per capita transfer payments and greater economic reliance on transfer

income. For example, retirement-destination counties had the highest per capita payments

($4,525), which came disproportionately from programs benefiting people age 65 years or

older, such as Social Security, government pensions, and Medicare (app. table 16).

Similarly, persistent-poverty and low-wage counties (see p. 18 for definition) depended

more heavily on transfer payments. With poverty rates exceeding 20 percent for several

decades, persistent-poverty counties derived over 27 percent of total personal income

from transfer payments, with disproportionate shares coming from medical payments (primarily Medicaid) and income maintenance benefits for programs traditionally serving poor

groups. In low-wage counties, transfer payments accounted for over 25 percent of total

personal income. Compared with all nonmetro counties, these counties had slightly higher

shares of transfers represented by income maintenance programs and slightly lower

shares coming from retirement/disability payments (app. table 16).

Compared with low-wage counties, persistent-poverty counties had higher per capita benefits for all of the programs traditionally aimed at poor groups: Medicaid, family assistance, food stamps, SSI, and other income maintenance programs. With higher concen-

 Nonmetro

 Metro

Figure 3

Note: Rapid decline is defined as greater than the national average decline of 20 percent.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.

Counties with rapid decline in per capita family assistance benefits,

by residence, 1996-97

Declines in family assistance benefits were greater than the decline in the national average 

in about one-third of counties
Rural Conditions and Trends, Vol. 11, No. 2 • 73

Poverty and Income

trations of the elderly population, the low-wage counties had higher per capita benefits for

Social Security and Medicare than those received by the persistent-poverty counties. This

finding suggests that working low-wage families in these counties may not qualify, may be

unaware of their eligibility, or may choose not to seek assistance from Medicaid and the

income maintenance programs (fig. 5; app. table 17).

It is difficult to predict whether or not current trends of reliance on government transfer

payments will continue should the National economy enter another recessionary period.

For a number of years, transfer payments have consistently accounted for around 21 percent of rural total personal income. The mix of transfer payments from different programs,

however, has shifted toward slightly higher shares of transfers from retirement/disability

programs, including Social Security, and slightly lower shares from various income maintenance programs. The outcomes of new changes in welfare laws resulting from re-authorization legislation for PRWORA may change the balance even more. [Peggy J. Cook,

202-694-5419, pcook@ers.usda.gov]

 Nonmetro

 Metro

Figure 4

Counties with rapid decline in per capita food stamp benefits, by residence, 1996-97

Counties with rapid declines in food stamp benefits were concentrated in about a third of the States

Note: Rapid decline is defined as greater than the national average decline of 20 percent.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Poverty and Income

74 • Rural Conditions and Trends, Vol. 11, No. 2

666

819

131

216

37 56 89

136

177

277

Medicaid SSI Family assistance Food stamps Other income

0

200

400

600

800

1,000

Low-wage counties

Persistent poverty counties

maintenance

1997 Dollars

Source: Calculated by ERS using data from the Bureau of Economic Analysis.

Figure 5

Per capita transfer payments for selected programs in nonmetro low-wage

and persistent-poverty counties, 1997

Per capita benefits for low-income programs were higher in persistent-poverty than

low-wage counties
Compared with typical

urban housing, housing

in rural America is inferior in physical quality and

size. While rural households spend a smaller

share of their income on

housing, they less often

live in crowded conditions, and are more satisfied with their home and

neighborhood. Lowincome rural households

that depend on employment earnings for most

of their income are more

likely to have housing difficulties.

Rural Conditions and Trends, Vol. 11, No. 2 • 75

Rural Well-Being

Access to adequate and appropriate housing is a basic need for all U.S. residents.These housing needs and our abilities to satisfy them vary across the Nation. Rural

communities, particularly those sparsely populated and in remote locations, are widely

thought to be disadvantaged in their housing and housing finance markets. Similarly, in

urban areas, the housing situation of central-city residents is typically inferior to that of

suburbanites.

A basic tenet of U.S. housing programs, which has received more emphasis in recent

years, is that promoting homeownership is an appropriate role for government. Quarterly

Census Bureau surveys show that the homeownership share, which has been increasing

in both rural and urban areas, reached a historic high of 67.7 percent in September 2000.

This is up roughly 2 percentage points since 1997, when the American Housing Survey

that is the basis for much of this report’s material was conducted. Recent Federal initiatives to promote both rural and urban homeownership include home mortgage targets for

Fannie Mae and Freddie Mac, increased flexibility given to housing authorities to support

home purchase as an alternative to rental assistance, and greater Federal agency support for activities of nonprofit organizations, and State and local governments. While

homeownership may be a positive step for many who do not own a home, access to

affordable housing of acceptable quality is a more basic need for those whose current

homes fail to meet minimum standards.

We used data from the 1997 American Housing Survey to investigate differences

between nonmetro and metro housing. The definition of metro and nonmetro areas has

been modified to reflect the reality of effective housing markets (see box, “Definitions”).

While this rural/urban delineation is meaningful for investigating housing differences, keep

in mind the great diversity of communities within these aggregations. We specifically

focus on low-income households, the population most likely to have housing difficulties.

We give special attention to those low-income households that largely depend on wage

and salary income because, for most, increased earnings will make possible a better

housing situation.

Rural and Urban Homes and Neighborhoods Often Differ

In 1997, 22 percent of the Nation’s nearly 100 million households lived outside metro

housing markets. While in some ways the housing situation of these rural households is

Unique Housing Challenges Face Rural

America and Its Low-Income Workers

Definitions

Metro/nonmetro: For all material based on the 1997 American Housing Survey, we necessarily

use the associated metro definition, which is from the official list of metro areas published June

27, 1983, by the Office of Management and Budget. All other material uses more recent OMB

designations of metro areas.

Low-income: Household income of $24,600 or less, which was 150 percent of the poverty

threshold for a four-person family in 1997. This income threshold is roughly equivalent to that for

participants in numerous government housing subsidy programs, including USDA’s SingleFamily Housing Program.

Wage-dependent: Wage or salary earnings account for at least half of annual household

income.
Rural Well-Being

76 • Rural Conditions and Trends, Vol. 11, No. 2

much like that of urban households, in other ways it is quite different (see box, “Indicators

of Housing Quality”).

Typical rural homes are smaller and less costly than urban homes. Rural households had

median monthly housing costs of $362 and a median of 1,500 square feet of living space

(app. table 18). Corresponding urban medians were higher, $599 and 1,750 square feet.

Rural households were three times more likely than urban households to live in a mobile

home, a less expensive housing alternative (fig. 1).

Urban homes often were more crowded, with nearly 8 percent deficient by this measure,

compared with 6 percent for nonmetro homes. Rural homes were more likely to have

Indicators of Housing Quality

Physical quality: This index is widely used by the Bureau of the Census to identify housing

units with significant physical problems. Severely inadequate homes were those with a severe

physical problem in at least one of five categories: plumbing, heating, electric, upkeep, and

hallways. Moderately inadequate homes had no severe problems, but had at least one moderate problem (such as no kitchen sink) in one of five categories: plumbing, heating, kitchen,

upkeep, and hallways.

Expensive: Situations of high cost burden, where monthly housing costs were more than 30

percent of a household’s monthly income. Housing costs include all expenditures for mortgage

payments (including contract or installment loans), rent, utilities, insurance, and taxes.

Overall quality: Householders rated both their residence and their neighborhood on a 10-point

scale, with 1 the worst and 10 the best. These responses are reported here in three categories: 9 -10 are “good,” 5 - 8 are “moderate,” and 1 - 4 are “poor.”

Crowded: Household members outnumber rooms in the housing unit.

Owner Renter

Single-family

Mobile home

Town house/

Apartment

0

10

20

30

40

50

60

70

80

Metro

Nonmetro

Nonmetro wage-dependent

Percent

Most homes are single-family detached, and mobile homes are a larger share of nonmetro homes, particularly

for the wage-dependent

Source: Calculated by ERS from the 1997 American Housing Survey, HUD and Census Bureau.

detached row house

Figure 1

Tenure and type of residence for metro, nonmetro, and nonmetro low-income wage-dependent

households, 1997
Rural Conditions and Trends, Vol. 11, No. 2 • 77

Rural Well-Being

either moderate or severe physical problems. By this measure, rural homes more often

came up short, with over 8 percent deemed less than fully adequate, compared with

under 7 percent of urban homes.

Housing costs as a percentage of income is used as a measure of affordability, with any

amount over 30 percent considered problematic. The 30-percent limit, long used in

Federal housing programs, is most often exceeded by urban households. The housing of

31 percent of urban households and 25 percent of rural households was deemed too 

expensive. When the affordability issue was addressed with higher thresholds, affordability problems continued to be more frequent in urban areas. Substituting a 50-percent

threshold reduced the urban “expensive” share to 14 percent, and the rural share to 11

percent.

Rural residents were more positive about their homes and neighborhoods. When asked

for their overall opinion on a 10-point scale, urban householders gave their homes and

their neighborhoods lower marks than did rural householders. About 41 percent of urban

and 45 percent of rural respondents gave “good” marks (either 9 or 10) to their home.

The rural/urban gap in neighborhood satisfaction was much greater. Forty-nine of every

100 rural respondents gave their neighborhoods a good mark, 10 more than for urban

respondents.

Nearly three-quarters of all rural households owned their homes, well above the 63 percent homeownership rate for urban households. This higher homeownership rate may be

seen as a plus, but also reflects underlying causes that are less positive for rural communities. On average, rural households change residences less often than their urban counterparts—a characteristic favoring ownership. But, underlying factors may include lower

rural job mobility, or greater difficulties selling rural residences. The rural homeownership

rate may also be elevated by the rural population’s aging demographic profile, or a shortage of rental housing.

Nearly three of every four rural residences are conventionally built single-family homes.

Detached homes of all types, including both conventional and mobile homes, total 87 percent of rural and 64 percent of urban homes. Three of 10 urban households live in an

apartment, compared with 1 of 10 rural households.

Between 5 and 6 percent of both rural and urban householders reported receiving government housing assistance. These figures exclude the mostly middle-income homeowners with market-rate FHA- and VA-insured home mortgages, since they involve little or no

subsidy. Recipients of government housing assistance often get substantial subsidies

from Federal, State, or local sources, through rental assistance or reduced-interest-rate

home mortgages.

Rural Low-Income Housing Problems Are Greater for the Wage-Dependent 

Housing is a basic need, with low-income households more likely to have difficulty finding

acceptable housing that is also affordable. Of the Nation’s 22 million nonmetro households, nearly 10 million, or 45 percent were “low-income” by our 150 percent of poverty

definition (see box, “Definitions”). Nearly 4.3 million of these low-income nonmetro households received at least half of their income from wage and salary earnings. Most of these

“wage-dependent” households had little or no additional income.

Wage-dependent rural householders were much younger than other low-income householders. Nearly two-thirds were younger than 40, compared with only 13 percent of other

low-income householders. And, two-thirds of other low-income householders were elderly,

10 times the elderly share of wage-dependent householders. Since wage-dependent

householders were much younger, many more had young children. Single parents and

married couples with children totaled over 47 percent of all wage-dependent rural households, more than three times their share of other low-income households.

Compared with other low-income rural households, the wage-dependent were more likely

to have housing difficulties. Excessive housing costs, crowding, moderate physical inade-
Rural Well-Being

78 • Rural Conditions and Trends, Vol. 11, No. 2

quacies, and lower satisfaction with home or neighborhood were all more frequent for

these wage-dependent households. However, the incidence of severe physical inadequacies is similar for wage-dependent and other low-income households.

At first, the poorer housing of these wage-dependent households may be surprising,

given their typically higher housing expenditures and incomes near the upper end of the

low-income range. Adding to this phenomenon of spending more and receiving less, the

homes of low-income wage-dependent households were also typically smaller and lower

valued.

Major factors behind this conundrum are wage-dependent households’ greater propensity

to live in mobile homes (see subsequent discussion in this article), and to be either

renters or relatively new homeowners. With a few exceptions, such as the small proportion of renters that receive government subsidies, renters’ housing costs reflect recent

market prices.

On the other hand, the housing expenses of most homeowners are partially determined

by the length of time they have owned a home. Mortgage payments of long-time homeowners were likely well below those of recent purchasers, and the current values of their

homes were often determined by their income, inflation (or deflation) in home prices, and

housing prices at some earlier date. Most often, youthful wage-dependent households

lack the advantage of accumulated home equity, which is enjoyed by a substantial share

of other low-income rural households. In consideration of these factors, housing statistics

are calculated separately for renters, all homeowners, and homeowners that have a mortgage on their home (app. table 19).

While half of all wage-dependent homeowners had a mortgage on their home, only 20

percent of other low-income rural owners did. Homeowners with a substantial mortgage

payment relative to their income can afford less house than can those with low or no

mortgage payments. The result is that they live in a less expensive home, face higher

housing expenses, or have a combination of less house and higher expenses.

By all of the indicators, rural low-income homeowners had better housing than did

renters, whether or not they were wage-dependent. Although the incomes of renters were

lower than those of owners, their housing expenses were typically higher. Half of wagedependent households and three-fourths of other low-income households were homeowners. Thus, some of the higher incidence of housing difficulties among wage-dependent households can be attributed to their greater propensity to rent. But there is more to

this story. Comparing owners with owners and renters with renters, wage-dependent rural

households still had worse housing than did other low-income households by most measures (fig. 2). Low-income wage-dependent households were less likely to spend an

excessive share of their income on housing, only because the incomes of other lowincome households were typically much lower.

Whether they owned or rented, wage-dependent households were nearly twice as likely

as other low-income rural households to live in a mobile home. Over 31 percent of the

residences owned by low-income wage-dependent households were mobile homes.

Compared with conventionally constructed homes, mobile homes are likely to combine

lower home values with higher monthly housing costs. Monthly costs of mobile homes

can be higher despite a lower purchase price because they are typically financed at higher interest rates over a shorter repayment period. Additionally, mobile homes are often

located on rented sites, adding another component to monthly housing costs.

Wage-dependent households received government rental housing assistance much less

often than did other low-income households. Only 8 percent of wage-dependent renters

got such assistance, compared with nearly 20 percent of other low-income renters. This

relationship was reversed for homeowners, where wage-dependent homeowners, in total

and for only those with mortgages, were the most likely to have received government

housing assistance. About one of every six wage-dependent households with a mortgage,

and one in nine of their other low-income counterparts, received such a subsidy on their

home. Most housing assistance to low-income homeowners is provided by preferential
Rural Conditions and Trends, Vol. 11, No. 2 • 79

Rural Well-Being

conditions on home mortgages, meaning that those without a home mortgage would have

little opportunity for such assistance.

Housing Issues for Wage-Dependent Households Require Different Solutions

Assessment of how rural housing compares with housing in the rest of the Nation

depends on the indicators chosen. The housing problems of low-income rural Americans,

however, are often more severe for those who depend mainly on wage and salary earnings. Compared with other low-income rural households, wage-dependent households

lived in residences that had more physical problems, were more often too small for their

family size, and provided an overall quality of home and neighborhood with which they

were less satisfied. Fewer wage-dependent households owned their home, and when

they were owners, their residences were more often mobile homes, were typically smaller

with lower values, and entailed higher monthly expenditures.

An initiative to promote mobile home loans with terms more comparable to those on other

home purchase loans could help the housing situation of many low-income wage-dependent rural households. Programs to assist these households should also recognize important demographic differences from those of other low-income households. Wage-dependent householders tended to be younger and belong to a minority group. Their households more frequently included children and had at least two members. Finding ways to

better meet the housing needs of these households is important to the present and future

of rural America. [James Mikesell, 202-694-5432, mikesell@ers.usda.gov, and George

Wallace, 202-694-5369, gwallace@ers.usda.gov]

Percent

Wage-dependent Not wage-dependent Wage-dependent Not wage-dependent

0

2

4

6

8

10

12

14

16

18

20

All homeowners

Homeowners with mortgage

Renters

Source: Calculated by ERS from the 1997 American Housing Survey, HUD and Census Bureau.

Figure 2

Physically inadequate and crowded homes of low-income nonmetro households, by tenure and

wage-dependency

Housing conditions of renters are worse than those of owners, while mortgaged homes are in better condition but often

more crowded

Physically inadequate Crowded
The proportion of rural

households in which people were hungry at times

because there was not

enough money for food

declined somewhat from

1995 to 1998. However,

the proportion that were

food insecure—that is,

they were not consistently and dependably able

to get enough food for an

active and healthy life—

was about the same in

1998 as in 1995. Singleparent families and racial

and ethnic minorities had

rates of food insecurity

and hunger higher than

the national average.

Rural Well-Being

80 • Rural Conditions and Trends, Vol. 11, No. 2

The long-running expansion of the U.S. economy and the continuing strength of theNation’s nutrition safety net have helped a large majority of rural American households

achieve or maintain food security. During the year ending in August 1998, 88 percent of

rural households were food secure (fig. 1), while 12 percent of rural households—about

2.4 million—were food insecure. Among the food insecure rural households were 0.7 million (3.4 percent of all rural households) in which food insecurity reached levels of severity great enough that one or more household members were hungry at times during the

year due to inadequate resources for food.

Households are food secure when they have assured access at all times to enough food

for an active healthy life, with no need for recourse to emergency food sources or other

extraordinary coping behaviors to meet their basic food needs. They experience food

insecurity when they do not have this assured access to enough food to fully meet basic

needs at all times. As food insecurity increases in severity, the quality and variety of

meals are reduced and food intake may become irregular. At still more severe levels,

insufficient or irregular food intake results in periods of hunger for at least some family

members. In households with children, adults usually restrict their own food intake first to

provide enough food for the children. Thus, children usually do not go hungry except in

households with more severe levels of adult hunger.

Prevalence of Hunger Declined, Food Insecurity Unchanged, 1995-98

Last year, Rural Conditions and Trends first reported on new survey questions developed

by USDA and the Department of Health and Human Services to monitor food insecurity

and hunger in the United States (Rural Conditions and Trends, Vol. 9, No. 2, February

1999, pp. 91-96; see “Food Security Data,” appendix p. 88). Statistics on food security,

food insecurity, and hunger from this annual survey are now available for each year durPrevalence of Hunger Declines in Rural

Households

Figure 1

Food security, food insecurity, and hunger in nonmetro households, 1998

A large majority of rural households were food secure, but nearly 12 percent did not always

have access to enough food for active healthy lives, and 3.4 percent had household members

who were hungry at times due to a lack of money

Food insecure without hunger,

 8.4%

Food insecure with hunger,

 3.4%

Food secure,

 88.2%

Source: Prepared by ERS using data from the Current Population Survey Food Security Supplement, 

August 1998.
Rural Conditions and Trends, Vol. 11, No. 2 • 81

Rural Well-Being

ing 1995-98. The 1998 prevalence rates reported in this article, however, are not directly

comparable with those for 1995 reported in last year’s issue. Because of refinements in

the questionnaire design and changes in the screening of households to reduce the burden on the people who respond to the survey, the data for each year must be adjusted to

be comparable across years (see box, “Monitoring Trends in the Prevalence of Food

Insecurity and Hunger”).

When these adjustments are taken into account, the prevalence rate of hunger in rural

areas declined by about half of 1 percentage point from 1995 to 1998, while the prevalence of food insecurity remained unchanged (fig. 2). Trends in rural and urban areas

were virtually identical. For example, the lower prevalence of food insecurity and, to a

lesser extent, of hunger, in 1997, was similar in both rural and urban areas. This trend

was also consistent across regions, racial/ethnic groups, household types, and income

categories. Reasons for the lower prevalence of food insecurity in 1997 as well as other

year-to-year fluctuations are not yet known.

Food Insecurity Rates Similar in Rural and Urban Areas

The prevalence of food insecurity during the year ending in August 1998 was the same

(12 percent) for rural and urban households (table 1). To be classified as food insecure, a

household must report at least three indicators of food insecurity, most commonly that (1)

they worried that their food would run out before they got money to buy more, (2) the food

they bought did not last and they did not have money to get more, and (3) they could not

afford to eat balanced meals. More serious indicators, including indicators of hunger, were

also reported by many food insecure households. In figure 2, food insecurity appears

slightly more prevalent in rural than in urban areas, but this is due to the adjustment of

the data for cross-year comparability. The statistics reported in table 1, based on the complete data as collected in 1998, reflect more accurately the food security situations in rural

and urban areas.

Note: Prevalences are adjusted for screening differences across years.

1995 96 97 98

0

2

4

6

8

10

12

Food insecurity nonmetro

Food insecurity metro

Hunger nonmetro

Hunger metro

Source: Calculated by ERS based on Current Population Survey Food Security Supplement data, 1995, 1996,

1997, and 1998.

Percent (households)

Figure 2

Prevalence rates of food insecurity and hunger, by residence, 1995-98

In both metro and nonmetro areas, the prevalence of food insecurity was about the same in 1998

as in 1995, while prevalence of hunger declined somewhat
Rural Well-Being

82 • Rural Conditions and Trends, Vol. 11, No. 2

Monitoring Trends in the Prevalence of 

Food Insecurity and Hunger

An important purpose of the Food Security Survey, fielded annually as a supplement to the

Current Population Survey, is to monitor year-to-year changes in the prevalence of food insecurity and hunger. Information on these trends is important to assess the need for, and effects of,

USDA’s food assistance programs and to target those programs more effectively. Changes in

the “screening” of questions in the first few years of the Food Security Survey—the years

reported in this article—make the task of monitoring trends more difficult. These changes were

made to improve the quality of the data and to reduce the burden placed on respondents, but

they make it necessary to adjust the data to avoid biasing comparisons across years.

Screening procedures are used in the survey to reduce respondent burden and embarrassment. Households that give no indication of even slight food stress on a few initial questions

skip over the remaining questions and are classified as food secure. However, the screening

rules changed somewhat in each of the first 4 years of the survey. Consequently, some households were screened out in one year while, in other years, households with the same responses

to the initial questions were asked the full battery of items. Some of these households affirmed

enough items to be classified as food insecure. Thus, differences in screening affected the measured prevalence of food insecurity differently in each year.

The trends presented in figure 2 (and reported in Household Food Security in the United

States, 1995-1998) are adjusted to a “common screen” for 1995, 1996, 1997, and 1998. Each

year’s data are recoded so that households that would have been screened out in any of the 4

years are classified as food secure without reference to their actual responses. This assures

maximum comparability across years, although at some cost in sensitivity.

All other statistics in this article are based on the full data as collected in the 1998 survey and

are, therefore, somewhat higher than those presented in figure 2. Food security surveys in

future years will follow the 1998 screening methods, making them directly comparable to the

statistics reported in this article.

USDA Reports on Food Security and Hunger

The following reports on the Food Security Measurement Project are available from USDA:

Household Food Security in the United States in 1995: Summary Report of the Food Security

Measurement Project

Household Food Security in the United States in 1995: Technical Report

Household Food Security in the United States, 1995-1998

Prevalence of Food Insecurity and Hunger, by State, 1996-1998

Guide to Measuring Household Food Security, Revised 2000

Household Food Security in the United States, 1999

Links to these reports and other information on the Federal Food Security Measurement Project

are available from the ERS Domestic Food Security Briefing Room on the World Wide Web at:

<http://www.ers.usda.gov/briefing/foodsecurity>.
Rural Conditions and Trends, Vol. 11, No. 2 • 83

Rural Well-Being

Food insecurity was highest in the rural West and South (14 percent) and lowest in the

rural Midwest (8 percent). In 1998, 14 percent of the entire rural population lived in food

insecure households. This proportion was somewhat higher than the proportion of households that were food insecure because larger families are more likely to be food insecure

than are smaller families and persons living alone.

Food Insecurity Rates Higher for Families with Children

One out of five rural children lived in food insecure households, reflecting the greater economic difficulties faced by many families with children (table 1). Food insecurity was much

higher in single-parent families with children than in any other household type. Nationally,

one-third of such households experienced food insecurity sometime during the year ending in August 1998, and the proportion was somewhat higher in rural areas (35.4 percent). Even in two-parent families with children, the incidence of food insecurity (12.8 percent) was more than double that in multi-adult households with no children, although

much lower than that of single-parent families.

The lowest rate of food insecurity was in multiple-adult households with no children present (5.4 percent) in both rural and urban areas. Food insecurity was more prevalent

Table 1

Households with food insecurity, 1998

Levels of food insecurity were very similar in rural and urban households; food insecurity was most prevalent in single-parent families

with children and among minorities

Category Nonmetro Metro U.S. total

Percent (households)

All households 11.8 11.8 11.8

Census region:

Northeast 9.7 10.7 10.6

Midwest 8.3 9.6 9.3

South 14.1 12.3 12.8

West 14.4 13.9 14.0

Race and ethnicity (of household head):

White non-Hispanic 9.6 7.9 8.3

Black 27.9 23.7 24.3

Hispanic 21.2 25.4 25.0

Household structure:

Two-parent families with children 12.8 11.1 11.5

Single-parent families with children 35.4 33.1 33.6

Multiple-adult households—no children 5.4 5.4 5.4

Single men living alone 12.8 12.2 12.3

Single women living alone 9.8 10.9 10.7

Percent (persons)1

Age:

All ages 13.7 13.4 13.5

0-17 20.4 19.5 19.7

18-64 12.8 12.2 12.3

65 and over 5.0 5.9 5.7

1Food security is determined at the household level. In the age breakdown, the numbers represent the percentage of people in each age category living in households classified as food insecure.

Source: Prepared by ERS using data from the Current Population Survey Food Security Supplement, August 1998.
Rural Well-Being

84 • Rural Conditions and Trends, Vol. 11, No. 2

among men living alone than among women living alone, even though the poverty rate

for women living alone was substantially higher than that for men living alone.

The elderly are less than half as likely as working-age adults to live in food-insecure

households, and this was true in both rural and urban areas. However, the questions in

this survey may not adequately identify and measure food insecurity among the elderly.

Problems not measured by the food insecurity scale, such as mobility limitations and

restricted capacity and facilities for food preparation, pose additional challenges for some

elderly people.

Food Insecurity Higher for Minorities

Food insecurity was almost three times as prevalent among rural Blacks as among rural

Whites. For rural Hispanics, the rate was about twice that of Whites. These differences

reflect the higher poverty rates of racial and ethnic minorities (see “Rural Poverty Rate

Declines, While Family Income Grows,” p. 62). For Blacks and Whites, food insecurity was

more prevalent in rural than in urban areas, while for Hispanics, the reverse was true. The

lower level of food insecurity among rural Hispanics is unexpected because they had a

substantially higher poverty rate than did urban Hispanics. The reasons for this difference

are not known, but the data were consistent with the pattern observed in 1995.

Hunger Due to Lack of Money Reported in 4 Percent of Rural Households 

In about one-third of food insecure households—those in which food shortages were

more serious or prolonged—food intake was curtailed to the extent that household members were repeatedly hungry. These households report experiences and behaviors associated with more severe levels of food insecurity. Adults reported eating less than they felt

they should and cutting and skipping meals repeatedly due to lack of money for food.

Households with children reported inability to feed the children balanced meals and

reliance on only a few kinds of low-cost food to feed the children because they were running out of money to buy food. At least some household members, mainly adults, in 3.4

percent of rural households experienced such hunger during the year prior to the survey;

this proportion was not significantly different in urban areas (table 2).

The pattern of the incidence of hunger across regions, racial-ethnic groups, household

types, and age groups followed closely that of food insecurity. In both rural and urban

areas, just over 10 percent of single-parent families had episodes of hunger during the

year.

One Percent of Rural Households Report Indicators of Hunger among Children

Although 4.5 percent of rural children lived in households classified as food insecure with

hunger (table 2), the children themselves in most of these households were not hungry. In

most U.S. households, children—especially younger children—are protected from reductions in food intake unless the level of adults’ deprivation is quite severe. Nevertheless, an

estimated 1.1 percent of rural households had levels of food insecurity so severe that children were also hungry at times (table 3). Households classified as having hunger among

children responded “yes” to at least five of the eight items in the food security survey that

asked specifically about children’s experiences of food stress. These households typically

reported all of the following: they relied on a few kinds of low-cost food to feed the children because they were running out of money to buy food; they couldn’t afford to feed the

children balanced meals; the children were not eating enough because the family could

not afford enough food; they cut the size of the children’s meals because there was not

enough money for food; and the children were hungry, but the family could not afford

more food.

Children’s hunger was much more prevalent in single-parent families than in two-parent

families. Rates of hunger among children were about the same for rural Blacks and nonHispanic Whites, but were higher for rural Hispanics. [Mark Nord, 202-694-5433, marknord@ers.usda.gov and F. Joshua Winicki, 202-694-5448, jwinicki@ers.usda.gov] 
Rural Conditions and Trends, Vol. 11, No. 2 • 85

Rural Well-Being

Table 2

Households with poverty-related hunger, 1998

One or more household members experienced poverty-related hunger in 3.4 percent of rural households

Category Nonmetro Metro U.S. total

Percent (households)

All households 3.4 3.8 3.7

Census region:

Northeast 2.0 3.5 3.4

Midwest 2.3 3.0 2.8

South 4.1 4.1 4.1

West 5.1 4.3 4.4

Race and ethnicity (of household head):

White non-Hispanic 2.8 2.6 2.6

Black 7.2 8.7 8.5

Hispanic 6.5 6.8 6.8

Household structure:

Two-parent families with children 2.3 2.1 2.1

Single-parent families with children 10.1 10.5 10.4

Multiple-adult households—no children 1.8 1.9 1.9

Single men living alone 5.6 5.5 5.5

Single women living alone 3.7 4.4 4.3

Percent (persons)1

Age:

All ages 3.4 3.7 3.7

0-17 4.52 4.82 4.72

18-64 3.5 3.7 3.6

65 and over 1.4 1.7 1.6

1Hunger is measured at the household level. In the age breakdown, the numbers represent the percentage of people in each age category living in

households that registered hunger.

2Children are not usually hungry except in households in which adults have more severe and prolonged hunger (see table 3). Thus, the prevalence

rates for children shown in this table should be interpreted as the proportion of children living in households with hunger among adults. Most of these

children had diets of reduced quality and variety.

Source: Prepared by ERS using data from the Current Population Survey Food Security Supplement, August 1998.
Rural Well-Being

86 • Rural Conditions and Trends, Vol. 11, No. 2

Table 3

Households with poverty-related hunger among children, 1998

Slightly more than 1 percent of rural households with children reported hunger among the children

Category Nonmetro Metro U.S. total

Percent (households)1

All households with children 1.1 0.8 0.9

Race and ethnicity (of household head):

White non-Hispanic 1.0 .4 .6

Black 1.1 1.9 1.7

Hispanic 2.8 1.4 1.6

Household structure:

Two-parent families with children .3 .4 .4 

Single-parent families with children 2.9 1.9 2.1

Percent (children)2

Children 1.0 1.0 1.0

1Households classified as having hunger among children reported multiple indicators of reduced food intake among children, including cutting the size

of children’s meals, children not eating enough, and children being hungry because they couldn’t afford more food. Households with no children were

excluded from the denominator.

2Children’s hunger is measured at the household level. In the bottom row, the numbers represent the percentage of children living in households in

which any children were hungry.

Source: Prepared by ERS using data from the Current Population Survey Food Security Supplement, August 1998.
Data Sources

Rural Conditions and Trends, Vol. 11, No. 2 • 87

Appendix: Data Sources and Definitions

Employment data: Data on metro and nonmetro employment and unemployment reported in this issue come from two sources. The monthly Current Population Survey (CPS),

conducted by the Bureau of the Census for the Bureau of Labor Statistics (BLS), provides

detailed information on the labor force, employment, unemployment, and demographic

characteristics of the metro and nonmetro population. The CPS derives estimates based

on interviews of a national sample of about 47,000 households that are representative of

the U.S. civilian noninstitutional population 15 years of age and over. Labor force information is based on respondents’ activity during 1 week each month. Among the data products of the CPS are the monthly files, the earnings microdata files, and the March Annual

Demographic Supplement (known as the March CPS). BLS county-level employment

data, the Local Area Unemployment Statistics (LAUS), are taken from unemployment

insurance claims and State surveys of establishment payrolls, which are then benchmarked to State totals from the CPS. The BLS data series provides monthly estimates of

labor force, employment, and unemployment for individual counties.

Each of these data sets has its advantages and disadvantages. The CPS furnishes

detailed employment, unemployment, and demographic data for metro and nonmetro portions of the Nation. The LAUS provides less detailed employment data than the CPS, but

it offers very current employment and unemployment information at the county level and

is less subject to short-term fluctuations due to sample variability. While these data

sources are likely to provide different estimates of employment conditions at any point in

time, they generally indicate similar trends.

Earnings data: Data on metro and nonmetro earnings reported in this issue come from

two sources. The data for average and median weekly earnings worked are drawn from

the outgoing rotation of respondents in the monthly CPS, about one-quarter of the total

sample. These respondents are asked about the usual earnings on their sole or primary

job. The CPS earnings microdata file, referred to as the earnings file, consists of all

records from the monthly quarter-samples of CPS households that were subject to having

these questions on hours worked and earnings asked during the year. The 1999 data file

contained earnings information on almost 160,000 persons. Data are available for all

wage and salary workers in both the public and private sectors. The CPS collects information from people at their residences. They may work in other areas, such as nonmetro

residents who work in metro areas.

The Bureau of Economic Analysis’ (BEA) Regional Economic Information System is the

source of the county-level earnings and jobs data used in this issue to analyze nonfarm

earnings per job. These BEA data are based primarily on administrative records of the

unemployment insurance program. While the CPS analysis is of the earnings of metro and

nonmetro residents, the BEA earnings per nonfarm jobs analysis covers the jobs located

in metro and nonmetro areas. The analyses also differ in that the CPS earnings are based

on full-time workers while the BEA earnings are the average of all jobs in an area, including both full- and part-time jobs. The CPS earnings are an indicator of worker well-being

while the BEA earnings are an indicator of the strength of the local labor market.

Establishment data: Data on establishments is drawn from County Business Patterns,

an annual series published by the U.S. Census Bureau that provides estimates of employment, establishments, and payroll by industry for each county. These data are the most

comprehensive source of information on geographic patterns of employment for detailed

industries. The Census Bureau does not publish data that could disclose information

about the operations of individual companies or establishments. To account for the suppression of these confidential data, ERS uses an enhanced County Business Patterns file

produced by Claritas Incorporated that contains imputed values for the suppressed data.

Employees totally exempt from the Federal Insurance Contribution Act (farm operators,

other self-employed persons, hired farm workers, most government employees, railroad

workers, and domestic service workers) are not counted by County Business Patterns.
Appendix: Data Sources and Definitions

88 • Rural Conditions and Trends, Vol. 11, No. 2

Farm labor data: Information on the characteristics and earnings of hired farmworkers

are from the CPS earnings microdata file. The data for average and median weekly earnings and usual weekly hours worked are drawn from the outgoing rotation of respondents

in the monthly CPS, as were the overall metro and nonmetro earnings. The 1999 data file

is based on information from 1,454 hired farmworkers, which is used to estimate the hired

farmworker population.

Food security data: USDA sponsors the Food Security Survey, which is conducted by

the Census Bureau as a supplement to the Current Population Survey once a year, alternating between spring and fall. A nationally representative sample of about 43,700 households responded to the August 1998 survey. The survey includes questions about household food expenditures, sources of food assistance, food security, and hunger. The food

insecurity and hunger-related questions ask about a wide range of perceptions and

behaviors reported by households known to have difficulty meeting their food needs.

Household food security status ranges from food secure at one extreme to severe hunger

at the other. Based on a thorough statistical analysis of the data from the Food Security

Survey, 18 questions have been identified that form a valid, reliable scale measuring the

severity of food insecurity and hunger across this range. All questions referred to the 12

months prior to the survey and included a qualifying phrase reminding respondents to

report only those occurrences due to limited financial resources. Restrictions to food

intake due to dieting or busy schedules were excluded. The full questionnaire is available

from ERS. Examples of questions across the range include:

[Light end of scale] “The food we bought just didn’t last, and we didn’t have

money to get more.” Was that often, sometimes, or never true for you in the last 12

months?

[Middle of scale] In the last 12 months did you ever cut the size of your meals or

skip meals because there wasn’t enough money for food?

[Severe end of scale] In the last 12 months did you ever not eat for a whole day

because there wasn’t enough money for food?

Based on responses to these 18 questions, each household is assigned a scale score

measuring the severity of food insecurity experienced over the previous year. For analytic

and policy purposes, each household is then classified into one of three categories based

on their food security scale score: (1) food secure; (2) food insecure with no hunger evident; and (3) food insecure with hunger. The fourth category reported last year, food insecure with severe hunger, was dropped in this year’s report in favor of a separately scaled

measure of children’s hunger. This scale is based only on questions that ask specifically

about the experiences of children in the household and provides a more precise measure

of children’s hunger.

Housing data: Housing data are from the 1997 American Housing Survey, conducted by

the Bureau of the Census for the Department of Housing and Urban Development. The

American Housing Survey is a longitudinal survey designed to provide detailed information on housing structure, use and plumbing characteristics, equipment and fuel use,

housing and neighborhood quality, financial characteristics, and household attributes of

current occupants. The 1997 national survey interviewed about 46,000 households.

Results are weighted to reflect the U.S. population. Data were collected annually 1973-

1981 as the Annual Housing Survey, and every other year since 1981 as the American

Housing Survey.

Income, poverty and transfers data: The household income and poverty data reported

in this issue were calculated from the March Annual Demographic Supplement of the

Current Population Survey, known as the March CPS. Every year, the March CPS

includes supplemental questions on sources and amounts of money received during the

previous calendar year. Consequently, income information in the March CPS refers to the

previous year. The Census Bureau publishes estimates from the March CPS in the

Consumer Income P-60 series. Information on family size and income is used to estimate
Rural Conditions and Trends, Vol. 11, No. 2 • 89

Appendix: Data Sources and Definitions

the number of families and individuals in poverty based on official guidelines issued by

the Office of Management and Budget. Demographic data are available to examine the

distribution of income and the characteristics of the poverty populations in metro and nonmetro areas.

Information on personal income and transfers payments derives from the Bureau of

Economic Analysis (BEA) employment and income data. BEA estimates annual earnings,

proprietor’s income, transfer payments, and other personal income at the county level

based primarily on administrative records. BEA’s estimates of personal income includes

in-kind sources, such as Medicare and food stamps.

The CPS household income estimates exclude in-kind income, so the two sources differ

in both the unit of analysis (local area income per person versus income of households)

and the income definition (cash and in-kind versus cash only). The CPS incomes are an

indicator of household well-being while the BEA income and transfers are indicators of

local area well-being and program dependence.

Low-wage worker and county data: The analysis of low-wage workers uses the outgoing rotation of respondents from the 1979 and 1999 Current Population Survey (see

“Earnings data” in this appendix).

The county earnings data required to identify low-wage counties comes from the 1995

Covered Wages and Employment Data collected by the Bureau of Labor Statistics (BLS)

of the U.S. Department of Labor. These data are collected under a cooperative program

(also known as the ES-202 program) involving the Bureau of Labor Statistics and the

State Employment Security Agencies, which produces a comprehensive tabulation of

employment and wage information for workers covered by State unemployment insurance

laws and Federal workers covered by the Unemployment Compensation for Federal

Employees program. Excluded from the tabulation are members of the armed forces, the

self-employed, proprietors, domestic workers, unpaid family workers, and railroad workers

covered by the railroad unemployment insurance system. Partial information on agricultural industries and employees in private households is available.

In addition to standard wage and salary cash earnings, wages include vacation and leave

pay, bonuses, stock options, tips, the cash value of meals and lodging, and in some

States, contributions to deferred compensation plans (such as 401(k) plans). For more

complete information about the ES-202 program, visit the Covered Employment and

Wages page on the Bureau of Labor Statistics’ website at:

<http://stats.bls.gov/cewhome.htm>.

Population and migration data: Estimates of population change, net migration, and natural increase are from the Bureau of the Census county population estimates issued

annually. Population estimates are based on various data sources. Births and deaths are

based on vital statistics records. Migration estimates are derived as a residual by subtracting natural population increase from actual increases. Estimates include net gain

from other counties as well as the institutional population. Data on the characteristics of

migrants are from the March 1999 Current Population Survey.

Civilian labor force: Noninstitutional civilians age 16 or older who are either employed

or unemployed. Individuals who are neither employed nor unemployed are out of the

labor force.

Family: Family is defined as two or more people residing together who are related by

birth, marriage, or adoption.

Hired farmworkers: Persons age 15 and older who do farm work for cash wages or

salary, including persons who manage farms for employers on a paid basis, supervisors

of farmworkers, and general farm and nursery workers.

Household: Households consist of all persons living in a housing unit. A house, an apartment, or a single room is considered a housing unit if it is occupied as separate living

Definitions
Appendix: Data Sources and Definitions

90 • Rural Conditions and Trends, Vol. 11, No. 2

quarters. To be classified as separate living quarters, the occupants of the housing unit

must not live and eat with any other people in the structure.

Household income: The sum of the amounts of money received from wages and

salaries; nonfarm self-employment income; farm self-employment income; Social Security

or railroad retirement; Supplement Security Income; cash public assistance or welfare

payments; dividends, interest, or net rental income; veterans payments; unemployment or

workers’ compensation; private or government employee pensions; alimony or child support; and other periodic payments for all household members.

Inflation rate: The percentage change in a measure of the average price level. The two

measures of the average price level used in this issue are the Consumer Price Index for

All Urban Consumers (CPI-U) and the chain-type price index for Personal Consumption

Expenditures.

Inmigration and inmovement are used interchangeably.

Low-wage counties: A county is identified as low-wage if it falls into the top 20 percent

(quintile) of rural counties ranked by the share of wage and salary workers in low-wage

industries. At least 41 percent of all workers in these 465 counties are employed in industries paying average wages that would not lift a full-time, full-year worker above the poverty threshold for a family of four. Average wages are calculated for each 3-digit SIC industry in each county, rather than assuming a single average for each industry.

Low-wage workers: Persons ages 25 and older employed in the wage and salary workforce whose earnings, adjusted to a full-time, full-year equivalent, would fall below the

weighted average poverty threshold for a family of four ($17,028 in 1999). Workers

younger than 25 are excluded to omit recent labor force entrants who are more likely to

have unstable work histories or weak labor force attachment.

Median household income: The median household income is the income of the household at the center of the income ranking; that is, where half of all households have higher

incomes and half have lower incomes. The median has the advantage of not being influenced by the very high incomes of a small minority of households or persons.

Metro areas: Metropolitan Statistical Areas (MSA’s), as defined by the Office of

Management and Budget, include core counties containing a city of 50,000 or more people or have an urbanized area of 50,000 or more and total area population of at least

100,000. Additional contiguous counties are included in the MSA if they are economically

integrated with the core county or counties. For most data sources, these designations

are based on population and commuting data from the 1990 Census of Population. The

Current Population Survey data beginning in 1995 categorizes counties as metro and

nonmetro based on population and commuting data from the 1990 census. Throughout

this publication, “urban” and “metro” have been used interchangeably to refer to people

and places within MSA’s.

Natural amenities index: Natural amenities are measured using an index created at the

Economic Research Service, combining measures of climate, topography, and the presence of bodies of water. The index of climate attractiveness is defined using January temperature, number of days with sun in January, July temperature (expressed as a residual

when regressed against January temperature), and July humidity. Topography is defined

as the difference between an index of mountainous or rugged terrain and average elevation. The presence of bodies of water is measured using the percentage of land area covered by water.

Nonfarm earnings: The sum of wage and salary income, other labor income, such as

privately administered pension and profit-sharing plans, and current production income of

nonfarm sole proprietorships, partnerships, and tax-exempt cooperatives.

Nonmetro areas: Counties outside metro area boundaries. Throughout this publication,

“rural” and “nonmetro” are used interchangeably to refer to people and places outside of

MSA’s.
Rural Conditions and Trends, Vol. 11, No. 2 • 91

Appendix: Data Sources and Definitions

Outmigration and outmovement are used interchangeably.

Personal income: The sum of money income to a person from all sources, from which

money income is regularly received, reported as having been received in the previous

calendar year. The sources of money income are wages and salary; net income from the

operation of a business or farm; dividends, interest, royalties, and net rental income;

alimony and child support payments received from outside the household; pensions; and

transfer payments. Specifically excluded under this definition are windfalls, such as a

lump sum payment of an inheritance even though in money; capital gains or losses;

income in kind; and all within-household gifts or transfers whether in cash or kind.

Poverty: A person is in poverty if his or her family’s money income is below the official

poverty threshold appropriate for that size and type of family. Different thresholds exist for

elderly and nonelderly persons living alone, for two-person families with and without

elderly heads, and for different family sizes by number of children. The thresholds are

adjusted for inflation annually using the Consumer Price Index.

Region: For analytical purposes, the United States is divided into four Census regions,

which are further divided into nine divisions.

Northeast region:

New England—Connecticut, Maine, Massachusetts, New Hampshire, Rhode Island, and

Vermont.

Middle Atlantic—New Jersey, New York, and Pennsylvania.

Midwest region:

East North Central (or Great Lakes)—Illinois, Indiana, Michigan, Ohio, and Wisconsin.

West North Central—Iowa, Kansas, Minnesota, Missouri, Nebraska, North Dakota, and

South Dakota.

South region:

South Atlantic—Delaware, District of Columbia, Florida, Georgia, Maryland, North

Carolina, South Carolina, Virginia, and West Virginia.

East South Central—Alabama, Kentucky, Mississippi, and Tennessee.

West South Central—Arkansas, Louisiana, Oklahoma, and Texas.

West region:

Mountain—Arizona, Colorado, Idaho, Montana, Nevada, New Mexico, Utah, and

Wyoming.

Pacific—Alaska, California, Hawaii, Oregon, and Washington.

Rural-urban continuum codes: Classification system developed by ERS to group counties by the size of their urban population and their adjacency to larger areas. (See

Margaret A. Butler and Calvin L. Beale, Rural-Urban Continuum Codes for Metro and

Nonmetro Counties, 1993, AGES 9425, U.S. Department of Agriculture, Economic

Research Service, Sept. 1994).

Metro counties—

Central counties of metro areas of 1 million population or more

Fringe counties of metro areas of 1 million population or more

Counties in metro areas of 250,000 to 1 million population

Counties in metro areas of fewer than 250,000 population

Nonmetro counties—

Urban population of 20,000 or more, adjacent to a metro area

Urban population of 20,000 or more, not adjacent to a metro area
Appendix: Data Sources and Definitions

92 • Rural Conditions and Trends, Vol. 11, No. 2

Urban population of 2,500 to 19,999, adjacent to a metro area

Urban population of 2,500 to 19,999, not adjacent to a metro area

Completely rural or less than 2,500 urban population, adjacent to a metro area

Completely rural or less than 2,500 urban population, not adjacent to a metro area

Nonmetro adjacent counties—

Nonmetro counties are classified as adjacent if they are physically adjacent to one or

more metro areas and have at least 2 percent of the employment labor force in the county commuting to the central metro county for work.

Transfer payments: Cash or goods that people and nonprofit institutions receive from

government and some businesses (for example, liability payments) for which no work is

currently performed. Receipt of transfer payments, however, may reflect work performed

in the past. For example, elderly people receive Social Security now because they worked

earlier in their lives and paid taxes to fund the program. Government transfers to individuals are grouped into the following categories: retirement and disability programs, medical

programs, income maintenance programs, unemployment insurance, veterans’ programs,

and other. Further classification combines Medicaid benefits with income maintenance

benefits to form a pubic assistance category comparable with the classification used by

the Social Security Administration.

Note that payments from farm commodity programs are received as part of farmers’ gross

cash income from current farming activities. They are not transfer payments.

Typology codes: Classification system developed and periodically revised by ERS to

group counties by economic and policy-relevant characteristics. The typology codes used

in this issue are those described in Peggy J. Cook and Karen L. Mizer, The Revised ERS

County Typology: An Overview, RDRR-89, U.S. Department of Agriculture, Economic

Research Service, Dec. 1994.

Economic types (mutually exclusive, a county may fall into only one economic type):

Farming dependent—Farming contributed a weighted annual average of 20 percent or

more of total labor and proprietors’ income over the 3 years from 1987 to 1989.

Mining dependent—Mining contributed a weighted annual average of 15 percent or more

of total labor and proprietors’ income over the 3 years from 1987 to 1989.

Manufacturing dependent—Manufacturing contributed a weighted annual average of 30

percent or more of total labor and proprietors’ income over the 3 years from 1987 to

1989.

Government dependent—Federal, State, and local government activities contributed a

weighted annual average of 25 percent or more of total labor and proprietors’ income over

the 3 years from 1987 to 1989.

Services dependent—Service activities (private and personal services, agricultural services, wholesale and retail trade, finance, insurance, real estate, transportation, and public utilities) contributed a weighted annual average of 50 percent or more of total labor

and proprietors’ income over the 3 years from 1987 to 1989.

Nonspecialized—Counties not classified as a specialized economic type over the 3 years

from 1987 to 1989.

Policy types (overlapping, a county may fall into any number of these types and one economic type):

Retirement-destination—The population age 60 years and over in 1990 increased by 15

percent or more during 1980-90 through inmovement of people.

Federal lands—Federally owned lands made up 30 percent or more of a county’s land

area in 1987.
Rural Conditions and Trends, Vol. 11, No. 2 • 93

Appendix: Data Sources and Definitions

Commuting—Workers age 16 years and over commuting to jobs outside their county of

residence were 40 percent or more of all the county’s workers in 1990.

Persistent-poverty—Persons with poverty-level income in the preceding year were 20 percent or more of total population in each of 4 years: 1960, 1970, 1980, 1990.

Transfers-dependent—Income from transfer payments contributed a weighted annual

average of 25 percent or more of total personal income over the 3 years from 1987 to

1989.

Unemployment rate: The number of unemployed people 16 years and older as a percentage of the civilian labor force age 16 years and older.

Urban influence codes: Classification system developed by ERS to group nonmetro

counties by the size of their largest city and their adjacency to either large or small metro

areas. (See the “Measuring Rurality” briefing room at the ERS website:

<http://www.ers.usda.gov> for more information.)

Metro 

Large—Central and fringe counties of metro areas of 1 million population or more

Small—Counties in metro areas of fewer than 1 million population

Nonmetro

Adjacent to large metro with own city—adjacent to a large metro area and county

contains its own city of 10,000-49,999 residents

Adjacent to large metro without city—adjacent to a large metro area and the

county contains no city of at least 10,000 residents

Adjacent to small metro with own city—adjacent to a small metro area and county

contains its own city of 10,000-49,999 residents

Adjacent to small metro without city—adjacent to a small metro area and the

county contains no city of at least 10,000 residents

Not adjacent with city–not adjacent to a metro area and county contains its own

city of 10,000-49,999 residents

Not adjacent with town–not adjacent to a metro area and contains its own town of

2,500-9,999 residents

Not adjacent totally rural–not adjacent to a metro area and contains no town of

even 2,500 residents

Note: For a nonmetro county to be classified as adjacent, it must physically abut a metro

area and have at least 2 percent of its employed labor force commuting to central metro

counties for work. A nonmetro county that is adjacent to metro areas of both sizes is classified as adjacent to the metro area to which the largest percent of its employed labor

force commutes to work. The metro-nonmetro definition is based on the Office of

Management and Budget definition as of June 1, 1993.
Appendix Tables

94 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 1 Nonmetro population trends by county status and wage level, 1990-95 and 1995-99

Item Counties 1990-95 1995-99 1990 1995 1999 1990-95 1995-99 1990-95 1995-99 1990-95 1995-99

Number

All nonmetro 2,290 25.9 37.3 50,906 53,419 54,780 4.9 2.5 1,480 758 2.9 1.4

Not low wage 1,827 21.9 34.9 46,497 48,818 50,071 5.0 2.6 1,339 676 2.9 1.4

Low wage 463 41.5 47.1 4,409 4,601 4,709 4.4 2.3 141 82 3.2 1.8

By adjacency status:

Nonadjacent 1,301 33.5 48.3 22,835 23,796 24,152 4.2 1.5 490 88 2.1 .4

Not low wage 964 28.2 46.0 20,125 20,998 21,312 4.3 1.5 424 54 2.1 .3

Low wage 337 48.7 54.9 2,710 2,799 2,839 3.3 1.5 66 34 2.4 1.2 

All adjacent 989 15.8 23.0 28,070 29,622 30,628 5.5 3.4 990 670 3.5 2.3

Not low wage 863 14.8 22.5 26,372 27,820 28,759 5.5 3.4 915 621 3.5 2.2

Low wage 126 22.2 26.2 1,698 1,802 1,870 6.1 3.7 75 48 4.4 2.7 

Adjacent to large metro 184 10.3 12.5 6,008 6,433 6,744 7.1 4.8 285 221 4.7 3.4

Not low wage 167 10.2 12.0 5,828 6,242 6,542 7.1 4.8 275 210 4.7 3.4

Low wage 17 11.8 17.6 179 191 202 6.4 5.8 11 11 5.9 5.7 

Adjacent to small metro 805 17.0 25.3 22,063 23,189 23,884 5.1 3.0 705 449 3.2 1.9

Not low wage 696 15.9 25.0 20,544 21,578 22,217 5.0 3.0 640 411 3.1 1.9

Low wage 109 23.9 27.5 1,519 1,611 1,667 6.1 3.5 64 38 4.2 2.3

By county type:

Farming 556 50.9 58.6 4,650 4,795 4,854 3.1 1.2 71 15 1.5 .3

 Not low wage 334 44.3 53.0 3,297 3,425 3,484 3.9 1.7 61 16 1.9 .5

 Low wage 222 60.8 67.1 1,353 1,370 1,370 1.2 .0 10 -1 .7 -.1 

Mining 146 37.0 52.7 2,847 2,914 2,917 2.4 .1 3 -35 .1 -1.2

 Not low wage 131 35.1 52.7 2,753 2,817 2,819 2.3 .0 0 -36 .0 -1.3

 Low wage 15 53.3 53.3 94 97 98 3.4 1.3 3 1 3.0 1.4 

Manufacturing 506 12.3 25.1 15,771 16,457 16,870 4.4 2.5 376 225 2.4 1.4

 Not low wage 480 12.3 25.4 15,449 16,116 16,515 4.3 2.5 362 213 2.3 1.3

 Low wage 26 11.5 19.2 322 341 356 6.1 4.3 15 12 4.5 3.4 

Government 253 15.0 31.6 6,573 6,917 7,083 5.2 2.4 102 16 1.6 .2

Not low wage 200 14.5 33.0 5,849 6,135 6,277 4.9 2.3 70 9 1.2 .1

Low wage 53 17.0 26.4 723 782 806 8.1 3.1 32 7 4.5 .9 

Services 323 18.0 32.2 9,594 10,284 10,654 7.2 3.6 522 281 5.4 2.7

Not low wage 263 16.3 32.3 8,731 9,368 9,702 7.3 3.6 477 249 5.5 2.7

Low wage 60 25.0 31.7 863 916 952 6.2 4.0 44 32 5.2 3.4 

Unspecialized 485 19.6 26.8 11,106 11,671 12,018 5.1 3.0 396 254 3.6 2.2

Not low wage 399 18.3 26.8 10,064 10,589 10,906 5.2 3.0 361 224 3.6 2.1

Low wage 86 25.6 26.7 1,042 1,081 1,111 3.8 2.8 36 30 3.4 2.8 

Retirement 190 .5 3.7 5,206 5,919 6,386 13.7 7.9 631 425 12.1 7.2

Not low wage 150 .0 4.0 4,678 5,327 5,747 13.9 7.9 571 381 12.2 7.1

Low wage 40 2.5 2.5 528 592 639 12.3 7.8 60 44 11.4 7.5 

Persistent poverty 539 27.1 37.3 9,568 9,956 10,158 4.1 2.0 129 40 1.4 .4

Not low wage 384 24.2 37.2 7,935 8,251 8,413 4.0 2.0 91 22 1.2 .3

Low wage 155 34.2 37.4 1,633 1,705 1,745 4.4 2.3 38 19 2.3 1.1 

Commuting 381 11.5 11.5 6,049 6,460 6,796 6.8 5.2 299 268 4.9 4.1

Not low wage 282 11.0 11.0 4,992 5,336 5,618 6.9 5.3 246 221 4.9 4.1

Low wage 99 13.1 13.1 1,057 1,124 1,178 6.3 4.8 54 47 5.1 4.2 

Transfers dependent 385 25.7 36.4 6,660 6,971 7,106 4.7 1.9 226 97 3.4 1.4

Not low wage 260 23.8 38.5 5,276 5,510 5,603 4.4 1.7 167 64 3.2 1.2

Low wage 125 29.6 32.0 1,384 1,462 1,503 5.6 2.8 59 33 4.3 2.2 

Recreation 282 7.1 20.2 7,722 8,457 8,859 9.5 4.7 559 310 7.2 3.7

Not low wage 213 5.6 21.1 6,969 7,634 7,990 9.5 4.7 498 270 7.1 3.5

Low wage 69 11.6 17.4 752 824 868 9.5 5.4 61 40 8.1 4.9

Source: Calculated by ERS using data from the Bureau of the Census.

Population Change Net migration

Notes: Adjacency is defined by the urban influence codes, Ghelfi and Parker, A County Level Measure of Urban Influence, ERS staff paper AGES-9702, Feb. 1997. County types are not mutually 

exclusive, except that farming, mining, manufacturing, government, services, and unspecialized county types are exclusive of one another. Recreation counties are defined by Johnson and Beale in

Rural Conditions and Trends, Vol. 5, No. 1, Spring 1994. All other types are defined in Cook and Mizer, The Revised Economic Research Service County Typology: An Overview, RDRR-89, Economic

Research Service, 1994.

Net migration rate

Percent Thousands Percent Thousands Percent

Declining
Rural Conditions and Trends, Vol. 11, No. 2 • 95

Appendix Tables

Appendix table 2—Metro labor force and employment, seasonally adjusted, first quarter 1990 through fourth

quarter 1999

Labor Labor force Employment

Year/quarter force Employed growth growth

Thousands Percent

1999 4th 114,122 109,799 1.0 1.5

3rd 113,847 109,391 1.1 1.4

2nd 113,535 109,024 1.2 1.5

1st 113,195 108,624 2.2 2.6

1998 4th 112,570 107,941 2.4 3.0

3rd 111,916 107,151 2.1 2.2

2nd 111,341 106,576 1.3 1.9

1st 110,976 106,080 .7 1.2

1997 4th 110,777 105,765 .9 1.5

3rd 110,521 105,383 1.7 2.1

2nd 110,064 104,845 1.8 2.4

1st 109,581 104,225 2.1 2.5

1996 4th 109,024 103,594 2.3 2.7

3rd 108,405 102,913 1.7 2.3

2nd 107,945 102,320 1.5 2.1

1st 107,549 101,798 .6 .8

1995 4th 107,382 101,594 1.4 1.6

3rd 107.005 101,203 1.3 1.4

2nd 106,650 100,845 1.3 1.1

1st 106,311 100,559 1.3 1.9

1994 4th 105,974 100,086 .5 1.9

3rd 105,830 99,627 1.2 2.2

2nd 105,504 99,096 1.4 2.5

1st 104,131 98,481 1.2 2.3

1993 4th 104,822 97,924 1.5 2.1

3rd 104,426 97,406 1.2 1.8

2nd 104,106 96,962 1.2 1.5

1st 103,798 96,607 -.1 1.4

1992 4th 103,835 96,279 .4 1.2

3rd 103,729 95,987 1.1 .8

2nd 103,458 95,787 1.6 .8

1st 103,055 95,609 2.9 1.1

1991 4th 102,310 95,357 1.3 .2

3rd 101,977 95,300 .1 -.1

2nd 101,964 95,332 .3 -1.0

1st 101,884 95,565 .1 -1.8

1990 4th 101,853 96,008 .0 1.7

3rd 101,863 96,427 .6 -6

2nd 101,714 96,568 .4 .0

1st 101,618 96,565 NA NA

NA = Not available.

Source: Calculated by ERS using data from Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Appendix Tables

96 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 3—Nonmetro labor force and employment, seasonally adjusted, first quarter 1990 through fourth

quarter 1999

Labor Labor force Employment

Year/quarter force Employed growth growth

Thousands Percent

1999 4th 26,387 25,108 .3 1.1 

3rd 26,365 25,040 0 .5

2nd 26,365 25,006 -.5 -.1

1st 26,400 25,012 1.1 1.7

1998 4th 26,331 24,909 2.8 3.3

3rd 26,147 24,705 2.2 2.3

2nd 26,006 24,564 0.7 1.4

1st 25,959 24,477 -1.2 -.9

1997 4th 26,038 24,531 -.7 -.3

3rd 26,087 24,549 .3 .6

2nd 26,066 24,515 .5 1.5

1st 26,031 24,427 .7 1.3

1996 4th 25,984 24,349 .7 1.2

3rd 25,936 24,278 .5 1.0

2nd 25,906 24,219 1.2 1.3

1st 25,830 24,138 .8 .5

1995 4th 25,778 24,110 .9 .7

3rd 25,720 24,066 1.0 .4

2nd 25,659 24,039 1.1 .5

1st 25,588 24,010 1.3 1.9

1994 4th 25,504 23,896 1.6 2.7

3rd 25,405 23,740 2.1 2.9

2nd 25,276 23,570 1.9 3.1

1st 25,155 23,392 1.7 2.8

1993 4th 25,050 23,228 1.7 2.6

3rd 24,944 23,078 1.9 2.7

2nd 24,827 22,923 2.3 1.9

1st 24,689 22,814 .3 1.8

1992 4th 24,673 22,711 0 1.3

3rd 24,676 22,639 1.9 1.9

2nd 24,559 22,530 2.3 1.9

1st 24,419 22,424 4.3 2.7

1991 4th 24,164 22,276 1.4 .5

3rd 24,080 22,246 1.0 -1.0

2nd 24,022 22,173 1.4 -.4

1st 23,941 22,143 .9 -.5

1990 4th 23,886 22,198 1.7 -1.0

3rd 23,783 22,221 .8 -.4

2nd 23,738 22,247 .3 .3 

1st 23,719 22,233 NA NA

NA = Not available.

Source: Calculated by ERS using data from Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Rural Conditions and Trends, Vol. 11, No. 2 • 97

Appendix Tables

Appendix table 4—Nonmetro employment and unemployment, low-wage counties and 

other nonmetro counties, 1985-99

Employment Unemployment

Low-wage Other nonmetro Nonmetro Low-wage Other nonmetro Nonmetro 

Year counties counties total counties counties total

Thousands

1985 1,771.5 19,057.0 20,828.5 194.0 1,926.1 2,120.1

1986 1,764.3 19,204.5 20,968.7 198.5 1,916.7 2,115.2

1987 1,783.0 19,524.2 21,307.2 176.7 1,668.5 1,845.2

1988 1,810.0 19,977.7 21,787.7 156.3 1,469.4 1,625.7

1989 1,838.3 20,404.3 22,242.5 145.0 1,392.0 1,537.0

1990 1,831.5 20,393.3 22,224.9 139.9 1,412.8 1,552.7

1991 1,835.7 20,373.7 22,209.4 159.3 1,681.6 1,841.0

1992 1,867.5 20,709.2 22,576.7 171.9 1,834.7 2,006.6

1993 1,898.7 21,113.1 23,011.8 166.9 1,701.4 1,868.3

1994 1,961.5 21,689.6 23,651.2 154.7 1,535.9 1,690.6

1995 1,996.5 22,060.4 24,056.8 153.3 1,474.9 1,628.3

1996 2,003.3 22,240.1 24,243.4 159.7 1,509.2 1,668.9

1997 2,012.8 22,493.3 24,506.1 148.1 1,404.5 1,552.6

1998 2,016.1 22,648.3 24,664.5 140.4 1,308.8 1,449.1

1999 2,041.1 23,000.4 25,041.5 125.6 1,214.9 1,340.6

Source: Calculated by ERS using data from Bureau of Labor Statistics’ Local Area Unemployment Statistics.
Appendix Tables

98 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 5—Demographic and earnings characteristics of hired farmworkers, annual averages, 1990-99

Characteristics 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Thousands

Number of workers 886 884 848 803 793 849 906 889 875 840

Percent

Total 100 100 100 100 100 100 100 100 100 100

Sex:

Male 82.9 82.4 83.8 84.7 83.7 84.5 84.2 83.3 83.8 80.7

Female 17.1 17.6 16.2 15.3 16.3 15.5 15.8 16.7 16.2 19.3

Racial/ethnic group:

White 61.0 60.3 59.7 57.5 51.3 53.5 58.9 52.4 52.4 50.6

Hispanic 29.4 28.3 30.7 33.6 41.3 41.1 36.0 41.0 41.8 43.0

Black and other 9.6 11.4 9.6 8.9 7.4 5.3 5.1 6.6 5.8 6.4

Age (years):

Less than 25 31.5 25.0 24.7 27.2 28.0 30.1 27.9 30.7 28.4 30.4

25-44 47.6 51.6 52.6 51.1 48.8 44.2 46.0 45.6 46.7 44.0

45-59 14.4 15.1 16.3 16.2 17.2 18.2 19.1 17.1 17.8 18.8

60 and older 6.5 8.3 6.4 5.5 6.0 7.5 7.0 6.6 7.1 6.8

Marital status:

Married 53.3 53.4 53.5 51.8 58.5 58.5 56.3 52.1 51.9 55.5

Widowed, divorced,

or separated 8.9 11.2 10.1 9.5 8.7 7.5 8.1 8.4 9.3 6.9

Never married 37.8 35.4 36.4 38.6 32.8 34.0 35.6 39.5 38.8 37.6

Schooling completed:1

0-4 years 11.1 11.5 14.1 16.4 13.4 14.2 13.1 12.2 10.9 11.3

5-8 years 21.6 21.2 16.0 17.4 22.9 22.5 19.9 22.1 21.1 22.6

9-11 years 22.8 22.6 27.0 21.8 22.7 22.7 24.2 24.8 24.9 20.7

12 years 31.4 31.0 26.9 27.0 25.9 25.9 25.4 22.3 26.5 27.1

13 years or more 13.1 13.7 16.0 17.4 15.6 14.7 17.4 18.6 16.6 18.3

Census region:

Northeast 6.9 6.1 6.1 6.1 6.0 7.1 7.2 6.4 7.4 6.8

South 35.6 37.1 37.8 37.5 39.4 32.3 30.9 32.1 31.4 32.9

Midwest 24.1 23.3 23.7 21.4 18.4 20.0 23.9 19.8 19.1 19.6

West 33.4 33.5 32.4 35.0 36.2 40.6 38.0 41.7 42.2 40.7

Dollars

Median weekly

earnings:2

Full-time workers3 306 296 285 288 281 284 297 286 294 320

All workers 255 257 242 254 268 262 265 260 266 280

Note: Data for 1994 and later years are not directly comparable with data for 1993 and earlier years.

1Educational attainment levels, beginning January 1992, were revised to reflect degrees or diplomas received rather than years of school completed. 2Median earnings are in 1999 dollars.

3Full-time workers usually work 35 or more hours per week.

Source: Calculated by ERS using data from the Current Population Survey earnings microdata file.
Rural Conditions and Trends, Vol. 11, No. 2 • 99

Appendix Tables

Appendix table 6—Demographic and earnings characteristics of all wage and salary workers,

annual averages, 1990-99

Characteristics 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

Thousands

Number of workers 104,351 103,166 104,054 105,407 108,166 110,220 112,142 114,697 116,882 119,130

Percent

Total 100 100 100 100 100 100 100 100 100 100

Sex:

Male 52.7 52.5 52.2 52.1 52.4 52.4 52.2 52.2 52.2 52.0

Female 47.3 47.5 47.8 47.9 47.6 47.6 47.8 47.8 47.8 48.0

Racial/ethnic group:

White 78.3 78.1 77.9 77.7 76.3 76.2 75.0 74.0 73.4 73.1

Hispanic 7.9 8.0 8.0 8.2 9.3 9.5 9.7 10.4 10.6 10.8

Black and other 13.8 13.9 14.1 14.1 14.4 14.3 15.3 15.6 16.0 16.1

Age (years):

Less than 25 15.8 17.2 16.7 16.6 17.1 16.8 16.2 16.4 16.7 16.8

25-44 56.5 55.4 55.2 54.7 54.3 53.9 53.8 53.0 52.1 51.2

45-59 21.8 21.7 22.5 23.2 23.4 24.0 24.7 25.4 25.9 26.6

60 and older 5.9 5.7 5.6 5.5 5.2 5.3 5.3 5.2 5.3 5.4

Marital status:

Married 58.2 58.5 58.3 58.2 57.9 58.0 58.0 57.0 56.4 56.2

Widowed, divorced,

or separated 14.3 14.3 15.4 14.6 14.5 14.4 14.5 14.6 14.7 14.6

Never married 27.5 27.2 27.2 27.1 27.6 27.6 27.5 28.4 28.9 29.2

Schooling completed:1

0-4 years 1.0 .9 .9 .8 .8 .8 .7 .8 .8 .7

5-8 years 4.0 3.7 3.0 2.8 2.8 2.7 2.7 2.8 2.7 2.7

9-11 years 10.8 10.2 10.1 9.8 9.5 9.5 9.7 10.0 10.2 9.9

12 years 39.4 39.2 35.0 34.4 33.3 32.7 32.4 32.4 31.8 31.6

13 years or more 44.8 46.0 51.0 52.2 53.6 54.3 54.4 54.0 54.5 55.1

1999 dollars

Median weekly earnings:

Full-time workers2 515 522 522 523 540 525 511 519 531 550

All workers 459 450 449 461 450 437 440 448 466 479

Note: Data for 1994 and later years are not directly comparable with data for 1993 and earlier years.

1Educational attainment levels, beginning January 1992, were revised to reflect degrees or diplomas received rather than years of school completed. 2Full-time workers usually work 35 or more hours per week.

Source: Calculated by ERS using data from the Current Population Survey earnings microdata file.
Appendix Tables

100 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 7—Real earnings per nonfarm job, by place of work, 1990-97

Place of work 1990 1991 1992 1993 1994 1995 1996 1997

1997 dollars

Nonmetro 22,732 22,473 22,860 22,922 22,903 22,739 22,701 22,985

Low-wage 18,187 18,022 18,289 18,376 18,291 18,056 18,082 18,345

Farming 17,372 17,218 17,490 17,628 17,430 17,231 17,169 17,470

Commuting 17,738 17,498 17,761 17,781 17,784 17,517 17,517 17,848

Rural/remote 17,106 16,925 17,213 17,370 17,229 16,967 16,977 17,262

Other 23,073 22,809 23,205 23,268 23,256 23,098 23,055 23,341

Farming 20,136 19,959 20,282 20,437 20,406 20,296 20,271 20,564

Commuting 22,032 21,636 22,005 22,144 22,179 21,991 21,993 22,284

Rural/remote 21,559 21,201 21,540 21,565 21,486 21,237 21,172 21,387

Metro 31,230 30,955 31,872 31,866 31,785 31,946 32,142 32,825

United States 29,814 29,529 30,341 30,338 30,255 30,359 30,521 31,144

Percent

Change in earnings from

previous year:

Nonmetro -1.4 -1.1 1.7 0.3 -0.1 -0.7 -0.2 1.3

Low-wage -2.5 -.9 1.5 .5 -.5 -1.3 .1 1.5

Farming -2.7 -.9 1.6 .8 -1.1 -1.1 -.4 1.8

Commuting -2.7 -1.4 1.5 .1 .0 -1.5 .0 1.9

Rural/remote -2.8 -1.1 1.7 .9 -.8 -1.5 .1 1.7

Other -1.3 -1.1 1.7 .3 -.0 -.7 -.2 1.2

Farming -1.1 -.9 1.6 .8 -.2 -.5 -.1 1.4

Commuting -2.3 -1.8 1.7 .6 .2 -.8 .0 1.3

Rural/remote -2.2 -1.7 1.6 .1 -.4 -1.2 -.3 1.0

Metro -.0 -.9 3.0 -.0 -.3 .5 .6 2.1

United States -.2 -1.0 2.7 -.0 -.3 .3 .5 2.0

1997 dollars

Amount by which earnings

lag metro earnings:

Nonmetro 8,497 8,482 9,012 8,944 8,882 9,207 9,442 9,841

Low-wage 13,043 12,933 13,583 13,489 13,494 13,890 14,060 14,480

Farming 13,858 13,737 14,382 14,237 14,355 14,715 14,973 15,355

Commuting 13,492 13,457 14,112 14,085 14,001 14,430 14,625 14,977

Rural/remote 14,124 14,031 14,659 14,496 14,556 14,979 15,165 15,563

Other 8,156 8,146 8,667 8,598 8,529 8,848 9,088 9,485

Farming 11,094 10,997 11,590 11,428 11,378 11,651 11,872 12,262

Commuting 9,198 9,319 9,867 9,722 9,606 9,955 10,149 10,542

Rural/remote 9,671 9,754 10,332 10,301 10,299 10,709 10,971 11,438

Percent

Ratio of earnings to

metro earnings:

Nonmetro 72.8 72.6 71.7 71.9 72.1 71.2 70.6 70.0

Low-wage 58.2 58.2 57.4 57.7 57.5 56.5 56.3 55.9

Farming 55.6 55.6 54.9 55.3 54.8 53.9 53.4 53.2

Commuting 56.8 56.5 55.7 55.8 56.0 54.8 54.5 54.4

Rural/remote 54.8 54.7 54.0 54.5 54.2 53.1 52.8 52.6

Other 73.9 73.7 72.8 73.0 73.2 72.3 71.7 71.1

Farming 64.5 64.5 63.6 64.1 64.2 63.5 63.1 62.6

Commuting 70.5 69.9 69.0 69.5 69.8 68.8 68.4 67.9

Rural/remote 69.0 68.5 67.6 67.7 67.6 66.5 65.9 65.2

0 and -.0 = Positive and negative change of less than 0.05 percent.

Note: Previous years’ earnings converted to 1997 dollars using the chained-type personal consumption expenditures price index.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Rural Conditions and Trends, Vol. 11, No. 2 • 101

Appendix Tables

Appendix table 8—Persons by income/poverty ratio, by residence, 1998

Income/poverty ratio Nonmetro Metro United States

Percent

Less than 50 5.0 5.1 5.1

50-99 9.3 7.2 7.7

100-149 10.9 8.3 8.8

150-199 11.6 8.7 9.2

200-299 21.0 16.9 17.7

300-399 15.9 14.6 14.8

400-499 10.4 11.5 11.3

500 + 15.9 27.7 25.4

Source: Calculated by ERS using data from the March 1999 Current Population Survey.
Appendix Tables

102 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 9—Poverty rates by residence, region, and selected characteristics, 1998

Poverty rate Share of poor

Item Nonmetro Metro Nonmetro Metro

Percent

Total 14.3 12.3 100 100

By region:

Northwest 9.5 12.6 7.2 21.6

Midwest 10.9 10.1 23.3 17.6

South 17.6 12.5 53.5 33.3

West 15.5 13.8 16.0 27.5

By race/ethnicity:

White, non-Hispanic 11.5 7.2 66.7 40.0

Black, non-Hispanic 29.8 25.2 19.3 27.3

Hispanic 27.4 25.5 9.1 26.9

Native American, non-Hispanic 30.1 18.4 4.0 10.0

By family type:

Husband-wife headed families 7.8 5.8 37.4 30.3

Female-headed families 36.5 32.7 34.1 40.9

Women living alone 27.6 21.6 14.5 14.5

Men living alone 21.2 16.1 9.7 1.0

By age:

0-17 20.4 18.5 36.8 39.7

18-64 12.2 10.2 50.9 51.2

65+ 12.5 9.1 12.3 9.1

By family employment:

One or more full-time, full-year workers 6.2 4.3 29.4 25.2

Part-time or part-year worker(s) only 33.9 32.6 34.0 35.4

No family members employed 52.6 60.9 26.0 31.7

No working-age person in family 14.0 11.2 10.6 7.7

By educational attainment

(persons age 25 and older only):

Less than high school graduation 25.6 22.8 44.9 39.1

High school diploma or GED 10.4 10.0 35.3 34.9

Some college or associate degree 7.1 6.3 14.9 17.3

Bachelor’s degree or more 3.6 2.9 4.9 8.7 

Note: Shares of poor by race/ethnicity and family type do not add to 100 percent because not all categories are included. Work status refers to employment during the entire year.

Source: Calculated by ERS using data from the March 1999 Current Population Survey.
Rural Conditions and Trends, Vol. 11, No. 2 • 103

Appendix Tables

Appendix table 10—Poverty rates and family income by residence, 1990-98

Nonmetro Metro

Change Median Real change Change Median Real change 

Poverty from family from Poverty from family from

Year rate previous year income previous year rate previous year income previous year

Percent Percentage 1998 Percent Percentage 1998

point dollars point dollars

1990 16.3 NA 34,920 NA 12.7 NA 46,830 NA

1991 16.1 -.2 34,703 -217 13.7 1.0 45,477 -1,353

1992 16.9 .8 34,357 -346 14.2 .5 45,322 -155

1993 17.2 .3 35,588 1,231 14.6 .4 46,389 1,067

1994 15.8 NA NA NA 14.4 NA NA NA

1995 15.6 NA 35,041 NA 13.4 NA 45,734 NA

1996 15.9 .3 34,687 -354 13.2 -.2 46,386 652

1997 15.8 -.1 36,238 1,551 12.6 -.6 47,732 1,346

1998 14.3 -1.5 38,006 1,768 12.3 -.3 49,016 1,284

NA = Not applicable.

Notes: Change in the metro status of some counties caused a discontinuity in the 1994 data.

Source: Calculated by ERS using data from the March Current Population Survey, 1991-99.
Appendix Tables

104 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 11—Nonmetro family poverty and family income, by family characteristics, 1998

Percentage

point change, Dollar change,

Item Poverty rate 1997-98 Median income 1997-981 

Percent 1998 dollars

All nonmetro 11.5 -1.3 38,005 1,767

Race of family householder:

Non-Hispanic White 9.3 -1.0 40,000 1,644

Non-Hispanic Black 26.0 -2.4 25,347 309

Hispanic 24.5 -4.9 24,995 673

Non-Hispanic Asian 15.4 -3.1 49,687 12,731

Non-Hispanic Native American 29.2 5.3 26,678 290

Age of family householder:

Less than 25 32.9 3.1 20,020 -1,166

25-44 14.6 -1.1 39,170 1,086

45-64 7.2 -2.4 47,049 3,421

65+ 7.6 -.2 27,135 582

Education of householder:

Less than high school 23.3 -1.7 22,602 1,389

High school 11.5 -.6 36,135 795

More than high school 6.0 -.6 48,642 462

Family structure:

Married couple 7.3 -.7 42,200 1,577

Female single parent 34.6 -2.8 18,448 2,246

Number of working adults:

0 23.1 -4.3 19,062 1,978

1 17.8 -2.4 28,533 2,073

2 3.8 .6 50,929 1,978

3+ 1.3 .2 69,469 -2,129

Number of children under 18:

None 6.1 .1 38,711 2,151

1 14.6 -1.6 36,000 430

2 13.8 -1.9 39,535 1,884

3 21.5 -5.5 36,920 3,333

4+ 40.6 .6 29,610 -12

Region of residence:

Northwest 7.8 -2.2 44,688 4,701

Midwest 9.0 -.4 42,240 2,608

South 15.3 -.2 34,935 1,730

West 12.2 -2.3 36,200 858

1Change in real 1998 dollars.

Source: Calculated by ERS using data from the March Current Population Survey, 1998 and 1999.
Rural Conditions and Trends, Vol. 11, No. 2 • 105

Appendix Tables

Appendix table 12—Work experience of nonmetro working families, by family type and poverty status, 1998

Working poor families Working nonpoor families

Female- Femaleheaded Other All headed Other All

Item families families families families families families

Percent

Share of families 45.9 54.1 100 11.8 88.2 100

Share of family income

from earnings:

Less than 20 9.0 9.5 9.2 3.0 3.2 3.2

20-39 10.9 4.7 7.6 4.3 3.4 3.5

40-59 11.1 7.1 9.0 10.1 5.4 5.9

60-79 14.9 6.5 10.4 19.8 10.3 11.4

More than 80 54.1 72.2 63.8 62.8 77.7 76.0

Dollars

Median family earnings 5,732 8,000 7,000 22,069 43,482 40,000

Median family income 8,245 9,792 8,815 28,316 49,000 46,200

Percent

Share of families 61.1 38.9 100 35.6 64.4 100

Share of family income

from assistance:

Less than 20 27.9 46.8 35.3 77.9 74.6 75.8

20-39 22.3 19.3 21.1 9.2 15.9 13.5

40-59 13.0 10.5 12.0 8.6 9.2 9.0

60-79 4.8 9.4 6.6 1.8 .3 .9

80+ 32.0 14.0 25.0 2.5 NA .8

Dollars

Median family assistance 3,120 2,561 2,760 1,800 2,800 2,514

NA = Insufficient number of cases.

Includes only families who received Temporary Assistance for Needy Families, Supplementary Security Income, or food stamps.

Source: Calculated by ERS using data from the March 1999 Current Population Survey.
Appendix Tables

106 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 13—Employment, earnings and education of nonmetro workers, by family type and poverty 

status, 1998

Poor workers Nonpoor workers

Female- Femaleheaded Other All headed Other All

Item families families families families families families

Percent

Share of workers 40 60 100 14.3 85.7 100

Worked:

Full-time, full year 28.9 40.5 35.9 69.0 70.9 70.6

Part-time, full year 15.4 10.4 12.4 9.8 8.6 8.8

Full-time, part year 32.9 32.6 32.7 14.2 13.9 13.9

Part-time, part year 22.8 16.5 19.0 7.0 6.6 6.7

Dollars

Median annual earnings 4,048 5,000 4,800 18,000 24,000 22,500

Percent

Earnings sources:

Wage and salary 92.3 67.3 77.4 93.8 86.6 87.8

Self-employment 4.2 20.3 13.8 3.5 6.7 6.3

Farm income .8 8.1 5.2 .2 1.9 1.6

Combinations 2.7 4.3 3.6 2.5 4.8 4.3

Education:

Less than high school 29.5 27.6 28.3 11.4 11.0 11.1

High school 44.3 43.3 43.7 37.2 41.5 40.9

More than high school 26.2 29.1 28.0 51.4 47.5 48.0

Source: Calculated by ERS using data from the March 1999 Current Population Survey.
Rural Conditions and Trends, Vol. 11, No. 2 • 107

Appendix Tables

Appendix table 14—Real per capita income and transfer payments, by residence, 1989-97

Per capita income Per capita transfers

Nonmetro/ Nonmetro/

Item Nonmetro Metro metro ratio Nonmetro Metro metro ratio

1997 dollars Percent 1997 dollars Percent

1989 17,284 24,418 70.7 3,047 3,035 100.4

1990 17,383 24,509 70.9 3,177 3,147 101.0

1991 17,170 24,067 71.3 3,395 3,358 101.1

1992 17,518 24,366 71.9 3,617 3,591 101.7

1993 17,665 24,501 72.1 3,713 3,682 100.8

1994 17,993 24,845 72.4 3,756 3,721 100.9

1995 18,141 25,457 71.3 3,877 3,828 101.3

1996 18,704 26,124 71.6 3,986 3,908 102.0

1997 19,090 26,861 71.1 4,055 3,950 102.6

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Appendix Tables

108 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 15—Per capita income and transfer payments by residence, 1997, and average annual changes

in transfer payments,1 1989-97

1997 Average annual change2

Share of

Item Income transfers 1989-97 1989-91 1991-94 1994-97

Dollars Percent3 Percent

Nonmetro:

Earnings 11,630 NA 0.86 -1.15 2.02 1.46

Personal income 19,090 NA 1.17 -.32 1.57 2.00

Transfer payments 4,055 100 4.01 5.57 3.45 2.59 

Retirement/disability 2,034 50.1 2.03 2.29 1.81 1.92

Social security 1,514 37.3 1.92 2.16 1.84 1.66

Medical 1,421 35.0 7.89 11.79 7.41 4.62

Medicare 788 19.4 6.46 5.04 7.53 6.33

Medicaid 625 15.4 10.18 21.69 7.73 2.78

Income maintenance 364 9.0 4.15 6.74 5.54 .40

programs:

Supplemental Security Income 114 2.8 4.97 5.25 7.83 .65

Family assistance4 44 1.1 -4.20 4.57 -2.26 -12.15 

Food stamps 74 1.8 .25 11.53 .12 -8.47

Other income maintenance 130 3.2 13.80 4.13 20.27 15.67

Unemployment insurance 79 1.9 7.87 25.54 -4.62 -4.40

Veterans’ benefits 104 2.6 -.50 -3.27 -.89 1.17

Other transfer programs 53 1.3 2.72 -5.06 -1.99 8.76

Metro:

Earnings 18,095 NA 1.13 -1.31 1.26 2.59

Personal income 26,861 NA 1.18 -.72 1.07 2.63

Transfer payments 3,950 100 3.70 5.21 3.51 2.01 

Retirement/disability 1,969 49.8 1.95 1.99 1.85 1.86

Social security 1,286 32.6 1.71 1.87 1.75 1.42

Medical 1,421 36.0 6.79 9.51 7.11 3.99

Medicare 780 19.7 5.20 3.70 6.47 5.87

Medicaid 633 16.0 9.08 17.93 8.38 2.06

Income maintenance 361 9.1 3.37 6.95 5.80 -1.64

programs:

Supplemental Security Income 108 2.7 5.02 5.47 7.68 1.08

Family assistance4 81 2.1 -2.44 4.58 -.19 -9.10

Food stamps 69 1.7 2.90 16.32 4.96 -9.05

Other income maintenance 103 2.6 9.94 1.89 15.86 11.23

Unemployment insurance 75 1.9 8.62 30.49 -2.47 -9.01

Veterans’ benefits 77 2.0 -.68 -2.82 -.89 .73

Other transfer programs 47 1.1 2.80 -3.03 -1.12 6.89

NA = Not applicable.

1Government transfer payments to individuals (96 percent of all transfer payments). See p. 92 for definition of government transfer programs. 2Change in real 1997 dollars.

3Percentages shown for the major categories sum to 100. Percents for the subcategories may not sum to the category value because only selected

programs are included.

4Formerly Aid to Families with Dependent Children, replaced by Temporary Assistance for Needy Families (TANF) in 1996.

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Rural Conditions and Trends, Vol. 11, No. 2 • 109

Appendix Tables

Appendix table 16—Nonmetro per capita income and transfer payments, by region and selected county 

types, 1997

Share of transfers from—

Average

Transfers Income annual

County Per capita Per capita as a share Retirement Medical maintenance change,

types income transfers of income disability programs programs 1989-971

Dollars Percent

All nonmetro 19,090 4,055 21.2 50.1 35.0 9.0 4.0 

Region:

Northeast 21,231 4,315 20.3 50.1 37.2 7.2 4.4

Midwest 19,816 3,789 19.1 53.5 34.4 6.6 3.2

South 18,101 4,248 23.5 47.4 36.7 10.7 4.7

West 19,096 3,866 20.2 52.5 29.5 9.5 3.4

Rural-urban continuum:

Highly urbanized 20,461 3,977 19.4 51.8 33.4 8.5 4.0

Urbanized 18,694 4,086 21.9 49.6 35.9 9.0 4.0

Totally rural 17,353 4,117 23.7 49.0 35.2 10.1 4.0

Metro adjacency:

Adjacent 19,297 4,015 20.8 50.7 35.4 8.5 4.0

Nonadjacent 18,830 4,104 21.8 49.6 34.6 9.6 4.0 

Other types:

Retirement-destination 20,199 4,525 22.4 55.2 31.9 7.3 3.6

Persistent-poverty 15,968 4,349 27.2 40.7 38.8 14.6 4.8

Low wage 16,795 4,253 25.3 48.1 36.3 10.2 4.0

Commuting 18,192 3,725 20.5 50.4 35.1 9.2 3.9

Note: See p. 91 and p. 92 for definition of region and ERS county types (typology codes).

1Change in real 1997 dollars.

Source: Calculated by ERS using data from the Bureau of Economic Analysis and revised ERS topology codes.
Appendix Tables

110 • Rural Conditions and Trends, Vol. 11, No. 2

Appendix table 17—Per capita transfer benefits for selected programs, by nonmetro county type, 1997;

change in benefits, 1996-97

Per capita benefits Change in benefits, 1996-97

Program All Low-wage Persistent All Low-wage Persistent

poverty poverty

1997 dollars Percent

Social security 1,514 1,572 1,344 1.80 1.66 1.91

Medicare 788 872 864 3.71 3.66 3.38

Medicaid 625 666 819 1.83 2.14 3.18

Supplemental

Security Income 114 131 216 -1.38 -1.81 -1.97

Family assistance 44 37 56 -14.59 -16.76 -12.33

Food stamps 74 89 136 -15.71 -16.12 -14.99

Other income

maintenance 130 177 227 8.87 9.99 10.58

Source: Calculated by ERS using data from the Bureau of Economic Analysis.
Rural Conditions and Trends, Vol. 11, No. 2 • 111

Appendix Tables

Appendix table 18 Housing of nonmetro and metro households, 1997

Item

 Wage- Not wage-

 All Metro All dependent dependent

Number of households 99,487 77,417 22,070 4,265 5,704

Physical quality of housing unit:

 Moderately inadequate 5.2 4.9 6.5 10.1 7.6

 Severely inadequate 1.8 1.8 1.7 2.7 2.9

 Inadequate Total 7.0 6.7 8.2 12.8 10.5

Expensive 29.5 30.8 24.8 47.1 48.0

Crowded 7.5 7.9 5.9 10.4 2.6

Overall quality of house:

 Good 41.8 40.9 45.1 35.4 52.2

 Moderate 55.0 55.9 51.7 58.9 45.2

 Poor 3.2 3.2 3.2 5.7 2.6

Overall quality of neighborhood:

 Good 40.9 38.5 49.2 42.1 55.0

 Moderate 54.0 55.9 47.4 52.0 42.3

 Poor 5.1 5.6 3.4 5.9 2.7

Government housing assistance 5.6 5.7 5.1 7.7 6.4

House structure type:

 Single-family detached 62.4 59.6 72.4 52.4 68.9

 Mobile home 6.6 4.3 14.6 23.0 14.5

 Town house or row house 5.9 7.0 2.0 3.0 2.0

 Apartment 25.1 29.1 11.1 21.6 14.6

Tenure:

 Owner-occupied 65.8 63.3 74.7 51.5 73.8

 Rented 34.2 36.7 25.3 48.5 26.2

Race/ethnicity - householder:

 White 75.8 72.7 86.7 75.5 85.5

 Black 11.9 13.1 7.7 13.3 10.0

 Hispanic 8.6 9.9 4.0 7.7 3.1

 Other 3.8 4.3 1.7 3.5 1.4

Age of householder:

 Less than 40 47.6 49.2 42.1 64.9 12.5

 40 to 64 31.4 31.2 32.0 28.7 21.3

 65 or older 21.0 19.6 25.9 6.5 66.2

Married couples:

 Total 53.2 52.2 56.4 36.7 36.0

 With children under 18 30.4 30.9 28.8 22.7 5.2

One-person household 25.4 25.5 25.1 29.0 50.2

Single parent 13.2 13.6 11.9 24.5 9.5

Median housing costs

 as share of income 19.8 20.6 16.9 28.5 26.7

Dollars

Median annual household income 34,500 36,000 27,200 15,000 9,552

Median monthly housing costs 543 599 362 334 230

Median home value 96,000 105,000 70,000 41,000 60,000

 Square feet

Median living space 1,685 1,750 1,500 1,200 1,312

Source: Calculated by ERS using data from the 1997 American Housing Survey, HUD and Census Bureau.

Nonmetro

Low-income

 Thousands

Percent
Appendix Tables

112 • Rural Conditions and Trends, Vol. 11, No. 2

Table 19 Housing of low-income nonmetro households by tenure and wage-dependency, 1997

Item

Wage- Wage- Wagedependent Other dependent Other dependent Other

Number of households 2,197 4,211 998 669 2,068 1,493

Physical quality of housing unit:

 Moderately inadequate 7.9 6.3 6.4 5.3 12.5 11.5

 Severely inadequate 2.5 2.6 1.5 1.0 2.9 3.8

 Inadequate total 10.4 8.8 8.0 6.3 15.4 15.3

Expensive 40.3 42.8 64.6 78.9 54.3 62.6

Crowded 7.3 1.4 8.0 2.1 13.8 5.9

Overall quality of house:

 Good 43.1 55.2 42.6 55.3 27.4 43.6

 Moderate 53.6 42.7 53.9 42.6 64.5 52.2

 Poor 3.3 2.0 3.5 2.1 8.1 4.2

Overall quality of neighborhood:

 Good 49.4 56.6 43.2 52.9 34.5 50.6

 Moderate 46.0 41.5 47.3 44.2 58.3 44.6

 Poor 4.6 2.0 9.5 2.9 7.2 4.8

Government housing assistance 7.8 1.8 17.8 11.2 7.8 19.9

House structure type:

 Single-family detached 66.9 80.4 67.3 77.6 37.0 36.4

 Mobile home 31.4 16.8 30.4 20.7 14.0 8.1

 Town house or row house 0.6 1.0 0.9 0.3 5.5 4.8

 Apartment 1.0 1.8 1.4 1.4 43.5 50.7

Race/ethnicity of householder:

 White 78.3 88.4 81.3 82.8 72.6 77.4

 Black 12.3 7.9 12.1 13.0 14.4 15.8

 Hispanic 6.8 2.6 4.4 3.8 8.6 4.5

 Other 2.6 1.1 2.1 0.4 4.4 2.4

Age of householder:

 Less than 40 49.6 6.6 62.2 15.5 81.1 29.3

 40 to 64 39.7 20.6 34.1 39.0 16.9 23.2

 65 or older 10.7 72.8 3.7 45.5 2.0 47.5

Married couples:

 Total 44.9 43.5 43.6 54.4 28.1 14.7

 With children under 18 26.4 5.3 30.0 11.5 18.7 4.9

One-person household 26.5 46.2 25.9 29.5 31.6 61.3

Single parent 22.6 6.0 24.7 11.0 26.5 19.4

Median housing costs

 as share of income 25.8 23.4 37.4 46.6 31.8 35.7

Dollars

Median annual household income 16,000 10,600 16,500 11,064 13,500 7,100

Median monthly housing costs 300 225 506 473 349 247

Median home value 41,000 60,000 50,000 56,500 NA NA

Square Feet

Median living space 1,248 1,362 1,280 1,400 1,000 1,024

NA = not applicable

Source: Calculated by ERS using data from the 1997 American Housing Survey, HUD and Census Bureau.

Percent

 Homeowners Homeowners with mortgages Renters 

Thousands