/I-5

Economic

Research

Service

Rural

DeveloprTîÊût

Research

Report 8i1

Work and Poverty in

Metro and Nonmetro

Areas

Elizabeth 8. Morrissey
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Work and Poverty in Metro and Nonmetro Areas. By Elizabeth S. Morrissey.

Agriculture and Rural Economy Division, Economic Research Service, U.S.

Department of Agriculture. Rural Development Research Report No. 81.

Abstract

In 1987, worker poverty rates were 10 percent in nonmetro areas compared with

5.7 percent in metro areas. Limited employment opportunities, in terms of the

number and kinds of jobs available to nonmetro workers, account for much of the

higher nonmetro worker poverty rate, while individual, family, and employment

attributes largely determine which workers will be poor.

Keywords: Worker, poverty, nonmetro, individual attributes, employment

opportunities

Acknowledgments

The author thanks Peggy Ross, Linda Ghelfi, Molly Killian, David McGranahan,

and Paul Swaim for their critical reviews of this manuscript.

Washington, DC 20005-4788 June 1991

m
Contents

Summary . v

Introduction 1

Data, Definitions, and Methods 2

Data 2

Definitions 3

Methods 3

Exploration of the Problem . 3

Worker Poverty Consistently Higher in Nonmetro Areas 3

Nonmetro Jobs Pay Less 4

Nonmetro Jobs More Likely to be Part-Time, Part-Year , 6

Who Are the Metro and Nonmetro Working Poor? 6

Profile of Metro and Nonmetro Workers 8

Multivariate Results 8

Nonmetro Residence Increases Workers' Chances of Poverty 10

Workers* Characteristics Influence Metro/Nonmetro

Poverty Chances Similarly 12

Conclusions 14

Implications 14

References . . 15

Appendix . . .,...,...,..... 17

IV
Summary

In 1987, there were 49 million family heads in the United States who worked 1 or

more weeks, about 7 percent of whom were poor. However, the likelihood of

poverty differed between metro and nonmetro areas. About 6 percent of metro

workers were poor, compared with about 10 percent for nonmetro workers. This

report examines the question: Why are nonmetro workers more likely than metro

workers to be poor? The report focuses on the role of job opportunities and

worker characteristics in explaining metro/nonmetro differences in working

poverty.

Limited job opportunities played an important role in the higher chances of

poverty for nonmetro workers. Nonmetro workers had fewer jobs from which to

choose than metro workers, and the available jobs were less desirable than metro

jobs in terms of wage levels, the likelihood of full-time, full-year employment, and

occupation.

Certain worker characteristics, such as being poorly educated, a minority, or a

single female family head, were associated with higher chances of worker poverty

in both residence areas. But, with the exception of lower levels of education,

these characteristics were not more prevalent among nonmetro than metro

workers. However, the chances of poverty associated with each characteristic were

higher for nonmetro than metro workers.

Multivariate analysis was used to clariiy the role of differences in job

opportunities and workers* individual characteristics in explaining the higher

poverty chances of nonmetro workers. The results suggest that both factors

influence workers' chances of poverty but that limited job opportunities in

nonmetro areas make a strong contribution to the higher poverty risk of

nonmetro workers. For example, in nonmetro areas, where job opportunities

were more limited, workers had significantly greater chances of poverty than

workers in metro areas. This was true even after differences in human capital

(age and education), race, family situations (marital status of head and number

and age of children), and employment characteristics-such as the number of

family workers, level of labor force participation (whether worker worked fulltime, full-year), and occupation-were taken into account.

In both residence areas, workers' characteristics significantly influenced their

poverty chances. Workers with low levels of human capital and work-inhibiting

demographic and family characteristics were at higher risk of poverty than other

workers, probably because they tended to be employed in part-time, part-year jobs

or lived in one-earner families.

Macroeconomic policies that stimulate the national economy and thus improve

job opportunities in nonmetro areas may help alleviate poverty among nonmetro

workers. Removing individual barriers to employment, by raising educational

levels (particularly important in nonmetro areas) and providing child care, will

help many poor workers in both residence areas escape poverty.
Work and Poverty in Metro and

Nonmetro Areas

Elizabeth S. Morrissey*

Introduction

A basic premise of American social policy

has been and continues to be that people

should work to live...that voluntary

employment in a full-time job ought to

be the primary way that people...meet their

needs (10).^

Work is a fundamental value that permeates the

history of American society. The notion that work is

the primary route out of poverty has been a dominant

theme in public policy for several decades. During the

1960's, a major thrust of the War on Poverty was on

job-training programs (13). Nearly 25 years later, new

anti-poverty legislation, the Family Security Act of

1988, continues to affirm the importance of work and

job training as avenues by which to escape poverty.

In reality, though, many families remain poor even

when the breadwinner works. In 1987, over 3 million

Americans who were family heads-7 percent of all

heads-worked at least to some extent, but remained

poor. Of these poor workers, 1 million held full-time,

year-round jobs (19).^

Nonmetro workers are more likely than metro workers

to be poor. In 1987, over 10 percent (1.1 million)

of rural family heads who worked at least to

some extent were poor compared with 5.7 percent of

their metro counterparts. These working poor

represented over half of all poor family heads in

nonmetro areas and two-fifths of those in metro areas.

Furthermore, the nonmetro poor work more than the

metro poor. Nearly 20 percent of nonmetro poor

*Elizabeth S. Morrissey is a social science analyst with the

Agriculture and Rural Economy Division, Economic Research

Service, U.S. Department of Agriculture.

^Underscored numbers in parentheses refer to items cited in the

References section at the end of this report.

^Throughout this report, the term worker refers to family heads

who worked 1 or more weeks in 1987. (See Data, Definitions, and

Methods.)

family heads, compared with 13 percent of metro poor

family heads, worked full-time, full-year. This report

examines poverty and conditions associated with

poverty among working family heads in nonmetro and

metro areas. Understanding the working poor and

why they are poor can help officials formulate policies

and design programs to reduce poverty among such

workers. Furthermore, the increased chances of

poverty among workers in nonmetro areas suggest that

they may have needs that require special policies.

To explain the causes of working poverty, social

scientists have derived explanations from general

theories of poverty. First, some social scientists

attribute the causes of worker poverty to economic

and social forces that limit job opportunities (the

number and kinds of jobs available) or impede equal

access to education, jobs, and livable-wage incomes (5,

8, 9, 15). For example, economic forces in local,

national, or international labor markets may increase

unemployment and displacement of industrial workers,

or fiscal and monetary forces may depress incomes or

cause high inflation that results in higher levels of

worker poverty. Furthermore, discrimination can

block educational or employment opportunities for

some demographic and social class groups. This is

particularly true of minorities and women (1, 2, 4).

Second, other social scientists trace the primary roots

of working poverty to an individual's personal

characteristics or inadequacies (5, 15, 16). This thesis

argues that workers remain poor because they lack the

human capital-education, job skills, or work

experience-or such personal attributes as initiative,

dependability, or motivation that are necessary to find

and maintain a regular job that pays a livable wage.

For example, educational attainment, a primary

indicator of human capital, is closely related to levels

of earnings. Thus, a worker with low education is

more likely to be poor than one who is better

educated. Similarly, an older worker's human capital.
because of higher earnings, more work experience, and

seniority, is higher than that of a younger worker (5).

Furthermore, workers living in certain types of

families may be more prone to worker poverty than

others. For example, workers with large families need

more income than workers with smaller families.

Also, workers who are single female family heads tend

to be more prone to poverty than other family heads.

This is attributable partly to the presence of children

in these families, increasing the need for income, to

the parent's relative unavailability for work because of

child care responsibilities, and to the limited ability of

other family members to contribute additional income

(U. 12. 15).

Third, some social scientists believe that both the job

opportunities locally open to workers and the

characteristics of the particular worker play important,

but different, roles in determining worker poverty

status (15, 17). In this view, the quantity and quality

of job opportunities are more important in

determining the general local level of worker poverty,

but individual characteristics are more important in

determining a given worker's chances of being poor.

Adapting this to a human capital perspective,

economist Lester Thurow argues that workers enter a

queue for the available jobs in a labor market, with

the best jobs going to workers at the head of the

queue (17). Workers' positions in the queue are

determined by their level of human capital, gender,

and race. Thus, workers' individual characteristics

determine their position in the job queue, but the

length of the job queue and the jobs available are

determined by prevailing economic conditions.

It is this third thesis that provides the framework for

examining metro/ nonmetro differences in workers'

chances of poverty. The central question to this study

is: why are nonmetro workers more likely than metro

workers to be poor? Is it because the job

opportunities open to nonmetro workers are scarcer

and lower paying than those open to metro workers?

Or is it that nonmetro workers tend to lack the job

skills and other personal attributes needed for success

in the workplace? Or is it perhaps both? A

secondary concern explores which personal attributes

play decisive roles in the chances of worker poverty.

But, based on previous research about the economic

and industrial structure of nonmetro areas and their

poor, the expectation is that job opportunities are an

important factor. These studies suggest that

metro/nonmetro differences in the levels of worker

poverty are largely predicted by metro/nonmetro

differences in the levels and quality of job

opportunities, with nonmetro areas having fewer and

less desirable jobs. Even so, it still remains true that

for any given worker, the chances of being poor will

be largely determined by individual characteristics that

place him or her at a disadvantage in the workplace.

Data, Definitions, and Methods

Identification of the data sources used in this paper

will be followed by definitions of key variables. The

methods of the study will then be presented.

Data

This analysis uses data from the 1988 March

Supplement of the Current Population Survey (CPS)

(19). The CPS is a stratified random sample that

collects basic demographic, income, migration, and

labor force data for persons, families, and households.

The March CPS includes a supplement that obtains

data on employment and income for the previous year,

while demographic data are for March 1988.^ "* To be

comparable with CPS published data, this study uses

family heads who worked 1 or more weeks in 1987 as

the unit of analysis. It is acknowledged that using

this definition excludes other family workers as

well as primary workers in families where the head

is not employed. Those working family heads whose

family income was below the poverty level in 1987

are referred to as poor workers (or the working

poor), while family heads with family income above

the poverty level are referred to as nonpoor workers.

In all, there were 38,137,000 working family heads

in metro areas, of which 2,166,000, or 5.7 percent,

were poor in 1987. Nonmetro areas contained

11,488,000 working family heads with 1,145,000

(10 percent) reporting family income below the

poverty level. Family heads whose metro/nonmetro

residence is unidentified for confidentiality

reasons have been included in the nonmetro

population.^

^For example, a person who was a family head in 1988, had

1987 income below the poverty level, and worked 1 or more weeks

in 1987 was considered a poor worker for the purposes of this

study.

"^The descriptive data in this report were weighted by the

March Supplemental Weight provided by CPS in order to provide

estimates that reflect the characteristics of the national

population.

^Economic Research Service (ERS) estimates show that about 70

percent of all unidentified individuals resided in nonmetro areas in

1988.
Defínitions

Definitions of many of the variables are selfexplanatory. Definitions of key variables are as

follows:

Family: Persons living together who are related by

birth, marriage, or adoption.

Family head: The person in whose name the home is

owned or rented. If the home is jointly owned or

rented by a married couple, either the husband or the

wife may be designated as the family head.

Full-time employment: Employment of 35 or more

hours per week in 1987.

Full-year employment: Employment of more than 49

weeks in 1987.

Low-wage jobs: Occupations in the three-digit

Standard Occupational Code were ranked according to

the 1980 average earnings for full-time, full-year

workers. Occupations in the bottom quintile (125

occupations) were considered low-wage.

Metropolitan resident: Person residing in a

metropolitan statistical area (MSA). MSA's, as defined

by the U.S. Office of Management and Budget, are

areas that (1) include a city of at least 50,000 persons

or (2) include a Census Bureau-defined urbanized area

of at least 50,000 with a total metropolitan population

of at least 100,000 (75,(K)0 in New England). Counties

form the major MSA units.

Nonmetropolitan resident: All residents not residing

in metro areas.

Second, several 1987 indicators of job quality in six

occupational categories were analyzed to address the

question of metro/nonmetro differences in job

opportunities. Third, the question of metro/nonmetro

differences in worker characteristics was addressed by

examining (1) metro/nonmetro variations in the

chances of being poor for workers with different

demographic and employment characteristics and (2)

the extent to which these poverty-prone characteristics

were represented among workers in both residence

areas. Finally, multivariate analysis was used to assess

the effects of metro/nonmetro residence on worker

poverty status in relation to workers' individual and

job characteristics.

Exploration of the Problem

This section documents trends in metro and nonmetro

worker poverty from 1975 to 1987 and explores some

of the causes of the higher rate of worker poverty

among nonmetro workers.

Worker Poverty Consistently Higher in Nonmetro

Areas

The extent of worker poverty in nonmetro and metro

areas is demonstrated by examining worker poverty

trends for 1975-87 (fig. 1). Over the period, the

poverty rate for all workers and for those who worked

full-time, full-year remained higher for nonmetro

family heads than for metro heads. However, the gap

between metro and nonmetro poverty rates fluctuated

over the period, largely in response to national or

cyclical shifts in the economy. For example, the

Poverty threshold: Family income cutoff used to

determine poverty status. The income cutoffs vary by

family size, number of children, and age of family

head. The poverty thresholds used in this study are

those set by the Bureau of the Census. For example,

the poverty threshold for a family of four in 1987 was

an annual cash income of $11,611.

Poor workers: Workers living in families whose 1987

income was less than the poverty threshold specified

for their family size, age of householder, and number

of children under 18 years old.

Methods

The analysis was carried out in four phases. First,

metro/nonmetro trends in levels of working poverty

were examined in relation to trends in unemployment.

Figure 1

Poverty rates for working family heads

by residence, 1975-87

Percent in poverty

12

10

8

6

4

2

All nonmetro

All metro ^_

Nonmetro full-time, full year

o..,.--'"'*"' '""' "'!:

-L JL JMetro full-time, full year

J \ \ \ I Í I

1975 80

Source: Current Population Survey Reports. Series p-60.

85
metro/nonmetro gap in poverty rates for all workers

narrowed from 4 to 2.5 percentage points during the

1975-79 economic recoveiy. In 1980, the year in which

the farm crisis began and the manufacturing and oil

industries started to decline, the metro/nonmetro

worker poverty gap began widening. It reached 5.3

percentage points by 1986 before declining to 4.3

percentage points in 1987.

The gap in the poverty rate for full-time, full-year

workers also narrowed from 1975 to 1979. It then

reached its widest point of 4.5 percentage points in

1983. Unlike the trend for all workers, the gap

narrowed after 1983 because of a slight increase in

poverty among metro full-time, full-year workers and a

corresponding decline in poverty among nonmetro

full-time, full-year workers.^

Because the unemployment rate is a common indicator

of economic performance, worker poverty was

expected to fluctuate with the unemployment rates in

both metro and nonmetro areas (fig. 2)J The data

show that, as expected, worker poverty rates did

fluctuate with unemployment rates.

Metro/nonmetro differences in unemployment rates

indicate that nonmetro areas had less advantageous

employment opportunities in terms of the number of

available jobs. Unemployment rates in nonmetro

areas were consistently higher than metro

unemployment rates from 1979 to 1987. In addition,

the nonmetro worker poverty rate was much slower

than the metro worker poverty rate to respond to the

1984-87 drop in the unemployment rate. While some

of the lag may have been due to the 1984 changes in

metro/nonmetro designations, much of it was due to

slower economic growth in nonmetro areas, and this

may have been a contributing factor to the more

limited job opportunities for nonmetro workers.

In sum, trends in metro/nonmetro worker poverty

reflected metro/nonmetro unemployment trends. The

nonmetro worker poverty rate remained consistently

higher than the metro poverty rate, and, although

^A change in CPS metro/nonmetro designation in June 1984 may

have had some effect on the poverty gap from 1984 to 1987. This

change reclassified places that grew to 50,000 or more persons or

became suburbs of metro areas, which decreased the nonmetro

population by 20.5 million people. The data show no dramatic

change in poverty rates, suggesting that reclassification had

little effect on the poverty rate among nonmetro workers (6).

^Unemployment data are from the Bureau of Labor Statistics,

U.S, Department of Labor. Unemployment rates are percentages of

those in civilian labor force who were not working but who were

available to work during the survey week. Unemployment rates

were not adjusted to take into consideration discouraged workers.

fluctuating, nonmetro unemployment rates have also

been higher than metro unemployment rates since

1979. The close correspondence between fluctuations

in the unemployment and worker poverty rates

suggests that worker poverty rates are related to job

opportunities; thus, the higher nonmetro poverty rates

reflect more limited jobs opportunities in nonmetro

than in metro areas.

Nonmetro Jobs Pay Less

As stated above, differences in job opportunities in

metro and nonmetro areas may partially explain why

nonmetro workers were more likely than metro

workers to be poor. Several studies (3, 6, 9) have

shown that employment in nonmetro areas, compared

with metro areas, tends to be dominated by part-time,

low-wage jobs in agriculture, service, and

manufacturing, with many of the better paying jobs

located in sectors with highly unstable employment,

such as mining.

The quality of jobs was lower in nonmetro than metro

areas (table 1). Nonmetro jobs yielded lower earnings.

Overall, nonmetro workers earned only about 75

percent of the earnings of their metro counterparts.

In each of six occupational categories, mean earnings

were lower in nonmetro than metro areas. The largest

earnings differences were found in the administrative

and professional; technical, sales, and administrative

support; and service occupations. Nonmetro workers

employed in these categories earned, on average, 16

percent, 79 percent, and 79 percent of metro earnings.

Poverty rates were higher for nonmetro than metro

workers in all the occupational categories. The

Figure 2

Unemployment rates and poverty rates for

working family heads, 1975-87

Percent

12

10

8

6

4

2

Nonmetro worker poverty

Js!onmetro unemployment^/*'

\ ^

" 4.^/,^ 'T'-'-'^.i

••...„„„..e,.»...-»*"** ^^^^ unemployment

Metro worker poverty

J ! \ \ L J L

1975 80

Source: Current Population Survey Reports, Series p-60.

85
Table l-Employment opportunity, by residence, 1987

Metro Nonmetro

Share of Total Share of Total

Occupational

category

Workers

1/2/

Mean

earnings

of head

total

workers

who were

poor

workers

employed

part-time,

part-year

Occupational

distributionWorkers

1/3/

Mean

earnings

of heads

total

workers

who were

poor

workers

employed

part-time,

part-year

Occupational

distributionEarnings

gap

Thousands Dollars —Percent Thousands Dollars Percent

Total 38,084 27,362 5.7 23.2 100.0 11,475 20,406 10.0 26.9 100.0 74.6

Administrative

and professional 11,538 38,514 1.7 14.8 30.3 2,235 29,426 3.4 16.0 19.5 76.4

Technical, sales.

and administrative

support 9,284 25,890 4.7 22.4 24.4 1,968 20,472 7.7 24.3 17.1 79.1

Service 3,842 14,394 18.2 39.6 10.1 1,065 11,399 26.3 43.7 9.3 79.2

Agriculture,

forestry, and

fisheries 702 14,574 16.7 37.6 1.8 1,118 13,223 18.9 32.7 9.7 90.7

Skilled crafts 6,648 25,286 3.7 22.8 17.5 2,420 21,159 7.0 25.9 21.1 83.7

Operators,

fabricators, and

laborers 6,070 20,430 7.8 29.2 15.9 2,669 18,760 9.3 29.5 23.3 91.8

Low-wage

occupation 4/ 38,084 13,157 16.5 -- 19.0 11,475 10,393 23.7 - 19.5 79.0

- = Not available.

1/ Includes all heads of family who worked 1 or more weeks in civilian jobs in 1987.

2/ Total contains 0.05 percent with 0 earnings, and 0.47 percent with negative earnings.

3/ Total contains 0.05 percent with 0 earnings, and 1.2 percent with negative earnings.

4/ Occupations in the three-digit Standard Occupational Code were ranked according to the 1980 average earnings for full-time, full-year workers. Occupations in the bottom

quintile (125 occupations) were considered low-wage. Percent part-time, part-year was not computed.
highest poverty rates were in the service and

agricultural, forestry, and fisheries occupations in

both residence areas. Furthermore, a greater share of

nonmetro workers than metro workers was employed

in these low-earnings occupations. Conversely, a

smaller share of nonmetro workers (20 percent) than

metro workers (30 percent) was employed in

administrative and professional occupations, the

category with the lowest poverty rate.

Nonmetro Jobs More Likely to be Part-Time, PartYear

Differences in the share of jobs that provide less than

full-time, full-year employment also can help explain

the lower mean earnings for nonmetro workers.

Overall, about 27 percent of nonmetro workers

worked part-time, part-year compared with 23 percent

of metro workers. Within the occupational categories,

service occupations had the largest share of part-time,

part-year workers in both areas, followed by the

agricultural and kindred jobs and the operators and

kindred jobs categories in nonmetro areas. In all but

two categories (the agricultural, forestry, and fisheries

and the operators, fabricators, and laborers

categories), the proportion of part-time, part-year

workers was higher in nonmetro than metro areas.

While the share of jobs classified as low-wage (see

section on definitions) was almost identical in metro

and nonmetro areas, nonmetro workers in low-wage

jobs earned, on average, only about 79 percent of

metro workers' earnings. However, the most striking

difference between residence areas was the

substantially higher poverty rate for nonmetro workers

in low-wage jobs. Only 17 percent of metro workers

in low-wage jobs were poor, compared with almost 24

percent of nonmetro workers.

In summary, the employment opportunities open to

nonmetro workers were more limited than for metro

workers. Compared with metro areas, nonmetro areas

had lower status jobs that were less likely to be fulltime, full-year and which resulted in lower earnings.

Who Are the Metro and Nonmetro Working Poor?

Are workers with certain characteristics more likely

than other workers to be poor? If so, are these

characteristics more prevalent among nonmetro than

metro workers? This section examines poverty

chances among different demographic and employment

groups for metro and nonmetro workers to determine

if workers' characteristics make a difference in their

poverty status. Individual (age, education, race) and

family (type of head, presence and age of children)

attributes are examined first, followed by levels of

family and individual work effort (earners per family,

weeks worked, and full-time versus part-time work).

Workers with certain demographic characteristics were

more poverty-prone than were other workers (table 2).

For example, young or poorly educated workers had a

considerably higher likelihood of poverty than their

older or better educated counterparts in both

residence areas. Also, minorities were much more

likely than whites to be poor. For example, 17

percent of blacks and 16 percent of Hispanics were

poor, compared with only 5 percent of whites. Single

female family heads had much higher chances of being

poor (21 percent) than other family heads (5 percent).

Working family heads with children were more likely

to be poor than working family heads without

children, and workers with young children (less than 6

years old) and with both young and older children

(less than 6 years old and 6-17 years old) were more

likely to be poor than workers with children who were

all in the 6-17-years-old category.

Similarly, workers' employment characteristics affected

their chances of being poor, with higher likelihoods of

poverty among workers with the least family and

individual work effort. For example, workers heading

families with fewer than two earners were much more

likely to be poor than workers heading families with

two or more earners-a 16-percent versus 3-percent

chance. Workers who worked less than 26 weeks were

particularly prone to poverty. Thirty-three percent of

these workers were poor compared with only 3 percent

of full-year workers. Finally, workers who worked

part-time were more likely than full-time workers to

be poor. Over 22 percent of part-time workers were

poor compared with 5 percent of full-time workers.

Moreover, the chances of poverty in each of the

demographic groups were consistently higher-and in

some instances substantially higher-among nonmetro

than metro workers. For example, the chances of

being poor for young nonmetro workers (29 percent)

were almost twice that of young metro workers (17

percent). Also, the chances of poverty for minority

workers were much higher in nonmetro areas. Almost

28 percent of nonmetro black workers were poor

compared with 15 percent of metro black workers. In

addition, nonmetro single female family heads were

much more likely than their metro counterparts to be

poor-31 percent compared with 19 percent. To the

extent that working female heads of family are eligible

for welfare, some of the greater chances of poverty for

nonmetro female family heads may be explained by the

lesser availability and generally lower benefit levels of

Aid to Families with Dependent Children (AFDC) in
Table 2"Worker poverty rates, by selected demographic and employment characteristics, by residence, 1987

Characteristics Metro Nonmetro All

Percent

All workers 5.7 10.0 6.7

Age:

Less than 25 years

25-45 years old

More than 45 years old

Education: 1/

Less than high school

High school only

More than high school

Race:

White, non-Hispanic 2/

Black, non-Hispanic

Hispanic

Family type:

Single female family head

Other family head

Age and presence of children:

No children

Children less than 6 years old

Children less than 6 and 6-17 years

old

Children 6-17 years old

Earners per family:

Less than two

More than two

Weeks worked:

Less than 26 weeks

26-49 weeks

50-52 weeks

Employment:

Full-time

Part-time

Occupation:

Farming, forestry, and fisheries

Service

Other

16.5

6.7

2.7

14.0

5.5

2.0

3.5

15.4

15.0

18.8

3.5

2,1

8.6

12.2

7.1

13.9

2.5

31.0

12.1

2.7

4.2

20.5

16.7

18.2

4.0

28.9 19.6

11.0 7.7

6.0 3.4

15.0 14.3

9.5 6,6

3.9 2.4

8.2 4.6

27.6 17.4

30.7 16.2

31.3 21.2

7.3 4.5

4.9 2.7

17.3 10.5

17.4 13.5

10.8 8.0

20,4 15.5

5.6 3.2

36.3 32.5

20.0 14.1

5.5 3.4

7.8 5.0

28.6 22.6

18.9 18.1

26.3 19.9

7.0 4.6

1/ Includes workers 25 years old and older.

2/ Includes other races category.
A recent Bureau of Labor Statistics (BLS)

study (7) defined the working poor as all

workers with below-poverty-level income who

worked or who looked for work 27 or more

weeks in 1987. Defining the working poor in

this way, rather than as family heads who

worked any weeks in 1987, resulted in lower

poverty chances for the most vulnerable

workers (young or poorly educated workers,

nonwhites, and female family heads).

Otherwise, the results were similar.

nonmetro areas. Finally, nonmetro poverty rates for

workers in families with fewer than two earners, for

those working 26-49 weeks, for part-time workers, and

for service workers were considerably higher than

metro worker poverty rates.

The preceding analysis showed that the chances of

poverty varied for different groups of workers, but

across all worker groups, nonmetro workers

experienced greater chances of poverty than metro

workers. Next, the distribution of at-risk

characteristics among metro and nonmetro workers

was examined to see if an overrepresentation of these

characteristics explained the higher chances of poverty

for nonmetro workers.

Profile of Metro and Nonmetro Workers

In some respects, levels of human capital were similar

for metro and nonmetro workers (table 3). Metro and

nonmetro workers were much alike in terms of age.

About two-fifths of workers in each residence area

were in the older, less poverty-prone category, and

about 5 percent were in the under-25-years-old, more

poverty-prone category. However, nonmetro workers

had less education than metro workers. About 23

percent of nonmetro workers, compared with 16

percent of metro workers, lacked a high school

education. Also, fewer workers with post-high school

education were found in nonmetro than in metro

areas.^ Only 32 percent of nonmetro workers had any

post-high school education compared with 48 percent

of metro workers.

The racial and ethnic composition of workers differed

by residence area, with whites making up a larger

share of nonmetro than metro workers. Over 90

percent of nonmetro workers were white compared

with about 81 percent of metro workers.

^^Educational attainment was limited to workers 25 years old and

older in order to capture those workers who were most likely to have

completed their education.

The family characteristics of nonmetro workers

differed somewhat from those of metro workers.

For example, female family heads made up a smaller

share of nonmetro than metro workers. Only 11

percent of nonmetro workers were female family

heads compared with 14 percent of metro workers.

The presence and age of children did not

differentiate nonmetro and metro workers. About

equal shares in both residence areas had children,

and among those family heads who had children,

there was little difference in childrens' ages,

with about 30 percent in both residence areas

having older children.

While nonmetro workers worked slightly less than

metro workers, their employment patterns were quite

similar. Just over 70 percent of workers in both

residence areas headed families with two or more

earners, and about 80 percent worked full-year.

To conclude, examination of the individual, family, and

employment characteristics showed that certain worker

attributes increased the chances of being poor. But,

the greater chances of poverty for nonmetro than

metro workers cannot be attributed to a greater

representation of poverty-prone characteristics because

such characteristics were not generally more prevalent

among nonmetro workers. In only one instance did

nonmetro workers include a larger share of workers

with a characteristic associated with poverty than did

metro workers—nonmetro workers had less education

than metro workers. Further, the chances of poverty

for nonmetro workers with each of the characteristics

associated with higher poverty chances were higher

than those for similar metro workers. Therefore,

differences in job opportunities in nonmetro and

metro areas must explain a considerable portion of the

higher chances of poverty for nonmetro workers.

Multivariate Results

Multivariate analysis was used to further determine

the extent to which residence versus individual,

family, and employment characteristics accounted for

differences in the chances of worker poverty. (See the

appendix for a full presentation of methods and

statistics.) Specifically, the multivariate analysis

results show the separate contributions (effects) of

each worker characteristic (residence, age, race,

education, and the like) to poverty chances by

indicating how much each characteristic increases or

decreases a worker's chances of being poor after the

influence of other characteristics are taken into

account, or, in a technical sense, "controlled." The

results also indicate the combined effects of the
Table 3--Work force composition, by selected demographic and employment characteristics by residence, 1987

Characteristics Metro Nonmetro All

Percent

Age:

Less than 25 years

25-45 years old

More than 45 years old

Education: 1/

Less than high school

High school only

More than high school

Race:

White, non-Hispanic 2/

Black, non-Hispanic

Hispanic

Family type:

Single female family head

Other family head

Age and presence of children:

No children

Children less than 6 years old

Children less than 6 and 6-17 years

old

Children 6-17 years old

Earners per family:

Less than two

More than two

Weeks worked:

Less than 26 weeks

26-49 weeks

50-52 weeks

Employment:

Full-time

Part-time

Occupation:

Farming, forestry, and fisheries

Service

Other

4.5

59.5

36.0

15.6

36.5

47.9

81.2

10.4

8.4

14.3

85.7

43.8

14.7

11.0

30.5

27.6

72.4

5.9

13.4

80.7

91.1

8.9

1.8

10.2

88.0

4.9 4.6

56.7 58.8

38.4 36.6

23.4 17.5

44.4 38.3

32.2 44.2

91.1 83.5

6.6 9.5

2.3 7.0

11.1 13.6

88.9 86.4

41.8 43.3

13.6 14.5

11.7 11.1

32.9 3L1

29.5 28.1

70.5 71.9

7.7 6.4

14.6 13.6

77.7 80.0

89.6 90.8

10.4 9.2

9,7 3.4

9.3 9.9

81.0 86.7

1/ Includes workers 25 years old and older.

2/ Includes other races category.
characteristics: how well these characteristics, taken as

whole, explain worker poverty chances.

Nonmetro Residence Increases Workers' Chances of

Poverty

Table 4 presents the increased or decreased differences

in poverty chances associated with each characteristic.

This table shows the increased likelihood of poverty,

first with only residence (metro or nonmetro)

considered, then with family and individual

characteristics considered, and, lastly, with employment

characteristics taken into account. Residence had an

important effect on worker poverty, all else being

equal. Before individual, family, and employment

characteristics were taken into account (see first

column), living in a nonmetro area increased a

worker's chances of poverty by 5 percentage points

over those for a metro worker. After the individual

and family characteristics were considered (second

column), the chances of poverty for nonmetro workers

were almost 8 percentage points higher than those

for metro workers. When employment characteristics

were considered as well (third column), their

poverty chances were still 8 percentage points

higher than those for metro workers. This means

that even if nonmetro workers were the same age,

had the same level of education, were the same race,

lived in the same types of families, and worked the

same number of weeks per year in the same

occupations, they would still face an 8-percentagepoint greater chance of poverty than their metro

counterparts.

The individual and family characteristics significantly

influenced worker*s chances of poverty. For example,

having children increased a workers' poverty chances

considerably, with workers who had both young and

older children (less than 6 years old and also 6-17

years old) at particularly high risk of poverty. Their

chances of poverty were about 24 percentage points

higher than for similar workers with no children.

Having young children (less than 6 years old) also

increased workers' chances of poverty, with the

likelihood of poverty for workers with children in this

age category 15 percentage points higher than that for

similar workers with no children.

Employment characteristics sometimes reduced the

poverty chances of those disadvantaged by individual

and family characteristics. The risk of poverty for

two particular groups of workers, single female family

heads and poorly educated workers, was considerably

reduced by the addition of the employment

characteristics. Single female family heads experienced

poverty chances that were over 20 percentage points

higher than those of other family heads with similar

human capital, racial, and family characteristics. But,

their likelihood of poverty was only 7 percentage

points higher than for other family heads when such

employment characteristics as work effort, number of

family earners, and occupation were also taken into

consideration. This suggests that one of the ways in

which being a single female family head contributes to

poverty is by reducing the likelihood of fufl-time, fullyear employment in an above-poverty-level-wage job.

In a like manner, workers lacking a high school

education faced a considerably greater likelihood of

poverty than workers with post-high school education.

The poverty chances for these workers were 23

percentage points higher than those for workers with

post-high school education. However, after their

employment characteristics were taken into account,

their chances of poverty were only 16 percentage

points higher than those of workers with post-high

school education. Thus, being poorly educated, like

being a female family head, increased the likelihood of

poverty by reducing the chances of successful

employment.

After taking residence and the other individual and

family characteristics into account, the poverty chances

for young workers (under 25 years old) were 9

percentage points higher than those for prime age

workers (25-45 years old). The addition of the

employment characteristics had little effect on their

poverty chances.

Minority workers were more likely to be poor than

similar white workers. The poverty chances for black

and Hispanic workers were about 8 percentage points

greater than those for whites. The effect of race was

not substantially changed by accounting for their

employment characteristics.

The employment characteristics were critical predictors

of workers' poverty chances. Working relatively few

weeks annually (less than 26 weeks) had a strong

effect on a worker's poverty chances. Workers who

worked only a few weeks per year experienced poverty

chances that were 33 percentage points higher than

those for full-year workers with the same residence,

human capital, racial, family, and employment

characteristics. Employment in agricultural, forestry,

and fisheries occupations also affected workers'

poverty chances, with the likelihood of poverty for

these workers 17 percentage points over those for

workers in other occupations after the effect of the

individual and employment characteristics were

considered. Being employed in a service occupation

did not affect the likelihood of poverty nearly so much

as working in an agricultural occupation; the increased

10
Table 4"Changes in likelihood of worker poverty for residence, family, individual, and employment

characteristics 1/

Characteristics Residence only

considered 2/

Residence,

individual, and family

characteristics considered

Residence,

individual, family,

and employment

characteristics considered

Residence:

Nonmetro

Metro 3/

0.050 0.075 0.079

Age:

Less than 25 years

24-45 years old

More than 45 years

Education:

Less than high school

High school only

More than high school

.090

-.013

.233

.073

.068

-.027

.156

.056

Race:

White, non-Hispanic

Black, non-Hispanic

Hispanic

Family type:

Single female family head

Other heads

.081

.075

.202

.083

.080

.072

Age and presence of children:

No children

Children less than 6 years old

Children less than 6 and 6-17

years old

Children 6-17 years old

.152

.239

.096

.145

.251

.134

Earners per family:

Less than two

More than two -.048

Weeks worked:

Less than 26 weeks

26-49 weeks

50-52 weeks

.327

.114

Employed:

Full-time

Part-time

-.042

Occupation:

Farming, forestry, and fisheries

Service

Other workers

.170

.073

Combined effects .010 .214 .359

- = Not applicable

1/ All variables significant at the 0.05 level.

2/ Statistics represent how much each characteristic increases or decreases chances of worker poverty over those of the reference category.

3/ Bolded characteristics are reference groups.

Source: See appendix table 1.

11
chances of poverty for service workers were only 7

percentage points greater than those for other

workers.

Even though they were statistically significant, the

other employment characteristics had less influence on

poverty chances than working a limited number of

weeks or being employed in an agricultural or kindred

occupation. Living in a multiple-earner family,

compared with living in a single-earner family,

decreased a worker's likelihood of poverty about 5

percentage points. Working full-time versus part-time

also decreased the likelihood of poverty about 4

percentage points.

Finally, the combined effect of the characteristics in

explaining worker poverty status shows that, by itself,

residence explains about 1 percent of the difference in

metro and nonmetro workers' poverty chances. The

inclusion of the human capital, race, and family

characteristics increases the amount of explained

variation in poverty chances to about 21 percent.

Adding the employment characteristics increases the

combined effect of the characteristics considerably,

explaining about 36 percent of variation in poverty

chances among workers.

Workers' Characteristics Influence Metro/Nonmetro

Poverty Chances Similarly

The preceding analysis answers the question of how

the various characteristics influenced poverty chances

for all workers. But, it raises the question of whether

the characteristics might affect workers' poverty

chances differently in metro and nonmetro areas.

Some characteristics may be important predictors of

poverty in one residential area but not in the other.

If the characteristics affect workers' chances of

poverty differently in metro and nonmetro areas, then

different policies for different areas might be

required to alleviate worker poverty. To address this

issue, the effect of the various individual, family,

and employment characteristics on poverty chances

were evaluated separately by metro/nonmetro

residence.

metro areas.^ Additionally, the combined, or overall,

effect of the characteristics in explaining worker

poverty chances was somewhat weaker in nonmetro

than metro areas, further supporting the view that

restricted job opportunities make an important

contribution to the higher chances of worker poverty

in nometro areas.

There were some other slight differences in the

characteristics' effect on poverty chances. Working

in a farming, forestry, or fisheries occupation had a

much stronger effect on the likelihood of worker

poverty in nonmetro than metro areas. The chances

of poverty for nonmetro workers in these occupations

were 22 percentage points higher than those for other

workers, while metro workers in the farming and

kindred occupations had poverty chances that were

only 14 percentage points greater than other workers.

Race affected poverty chances differently by residence.

For example, the chances of poverty for nonmetro

black workers were 9 percentage points higher tban

the chances for white workers, compared with 7

percentage points higher for metro black workers.

While both metro and nonmetro female family heads

faced a higher risk of poverty than other family

heads, the increases in their chances of poverty were

substantially higher in nonmetro areas (25 percentage

points) than metro areas (18 percentage points)

before the employment characteristics were taken into

account. Finally, being a young worker had a

somewhat stronger influence on poverty chances

for workers in nonmetro areas.

To summarize, both mullivariate analyses confirmed

that residence directly influences worker poverty. In

the first analysis, workers living in nonmetro areas had

a significantly higher likelihood of poverty than metro

workers even after differences in individual, family,

and employment characteristics were taken into

consideration. Furthermore, the results of the second

multivariate analysis showed that nonmetro workers

had higher likelihoods of poverty associated with each

of the individual, family, or employment

characteristics.

In general, individual, family, and employment

characteristics increased or decreased workers' chances

of poverty similarly in metro and nonmetro areas, and

with the exception of being an older worker in

nonmetro areas, all the characteristics were

statistically significant predictors of poverty (table 5).

However, the differences in poverty chances associated

with each of the characteristics (except working less

than 26 weeks per year) were larger in nonmetro than

But, nonmetro workers' increased chances of poverty

were not entirely explained by the quality of job

opportunities where they live. In both analyses, all

but one of the human capital characteristics, as well as

the race, family, and employment characteristics, were

^*The difference in probabilities for metro and nonmetro workers

working less than 26 weeks may not be statistically significant but

may be due to chance.

12
Table 5--Changes în likeiîhood of worker poverty for family, individual, and employment characteristics by

residence

Metro Nonmetro

Individual, Individual,

Individual family, and Individual family, and

Characteristics and family employment and family employment

characteristics characteristics characteristics characteristics

considered 1/ considered considered considered

Age:

Less than 25 years 0.075 0.057 0.127 0.101

25-45 years 2/ - ~ - -

More than 46 years -.015 -.023 -.007** -.035

Education:

Less than high school .227 .158 .246 .159

High school only .061 .046 .099 .078

More than high school ~ — — "

Race:

White, non-hispanic - - - -

Black, non-hispanic .075 .072 .089 .115

Hispanic .063 .072 .095 .078

Family type:

Single female family head .181 .059 .245 .105

Other heads -- ~ ~ ~

Age and presence of children:

No children -- ~ - ~

Children less than 6 years old .142 .130 .179 .182

Children less than 6 and 6-17

years old .234 .234 .257 .286

Children 6-17 years old .096 .125 .101 .154

Earners per family:

Less than two .. ~ -- ~

More than two - -.042 - -.061

Weeks worked:

Less than 26 weeks ~ .341 - .322

26-49 weeks -- .101 - .149

50-52 weeks -- - - -

Employment:

Full-time — -.036 -- -.060

Part-time - -- - ~

Occupation:

Farming, forestry, fisheries ~ .136 - .224

Service - .065 - .088

Other workers ~ - - -

Combined effect .231 .382 .162 .300

— = Not applicable.

* * = Not significant at 0.05 level.

1/ Statistics represent how much each characteristic increases or decreases chances of worker poverty over those of the reference category.

2/ Bolded characteristics are reference groups.

Source: See appendix table 2.

13
significant predictors of worker poverty. Also, in both

analyses, the combined effect of workers' family,

individual, and employment characteristics explained

more than 30 percent of variation in poverty chances.

Finally, the influence of workers' attributes on their

poverty chances operated much the same way in both

metro and nonmetro areas. In other words, poorly

educated workers or part-time workers have a greater

risk of poverty than well-educated workers or full-time

workers, regardless of where they live.

Conclusions

factors other than worker characteristics, such

as job opportunities, play a stronger role in nonmetro

than metro areas.

To conclude, worker poverty status was determined

by job opportunities and factors rooted in the

individual worker. The findings that nonmetro jobs

provided lower earnings and that nonmetro workers,

regardless of their characteristics, were more likely

than metro workers to be poor suggest that limited

job opportunities make a strong contribution to the

higher level of worker poverty in nonmetro areas,

while worker characteristics largely determine which

particular workers will be poor.

Despite fluctuations in the poverty gap between

residence areas, worker poverty rates remained higher

in nonmetro areas than metro areas from 1975

through 1987.

Differences in job opportunities played an important

role in the higher worker poverty rates in nonmetro

areas. Comparison of the kinds of jobs that were

available in both areas showed that job opportunities

were more limited in nonmetro areas than metro

areas. Higher unemployment rates indicated that

there were fewer jobs for nonmetro workers to choose

from, and examination of job characteristics showed

that nonmetro jobs were not as desirable as metro

jobs in terms of earnings levels, full-time,

full-year employment, or occupation. However, the

multivariate analysis showed that nonmetro workers'

poverty chances were not determined solely by job

opportunities. Residence was a significant predictor

of poverty even after differences in worker

characteristics were considered, but it did not

explain all or even most of the variation in poverty

chances.

Worker characteristics played an important role in

explaining the chances of poverty for all workers,

but they did not explain the higher chances of

poverty for nonmetro workers. While certain worker

characteristics, such as low educational attainment

or being a single female family head, are associated

with higher chances of worker poverty, these

characteristics were not generally more prevalent

among nonmetro than metro workers. For example,

although less educated workers were more prevalent

among nonmetro workers than metro workers, the

nonmetro workforce included smaller shares of

workers who were single female family heads or

minority workers than the metro workforce. Finally,

even though worker characteristics worked much the

same way in both residence areas, they accounted

for slightly less variation in poverty chances in

nonmetro than metro areas. This suggests that

Implications

The preceding analysis examined metro/nonmetro

differences in job opportunities and worker

characteristics in order to explain the higher

rates of poverty among nonmetro workers. The

findings suggest that limited job opportunities

in nonmetro areas are an important factor in their

higher worker poverty rate but that workers with

certain characteristics in both residence areas are

prone to poverty. Thus, efforts to reduce working

poverty will have to take both of these factors into

account.

Strong economic growth will improve job

opportunities, which will help many of the working

poor. Strong national economic performance

improves the number and kinds of jobs available,

improving chances for poor workers who are employed

full-time, full-year at low earnings or who are

involuntarily employed part-time, part-year to

escape poverty. Improved employment prospects are

particularly important to nonmetro areas where

job opportunities are more limited than in metro

areas.

Some workers, regardless of residence, may not

benefit much from improved job opportunities.

The findings suggest that poorly educated workers

may have difficulty finding employment that

provides above-poverty-level earnings no matter how

well the general economy is doing. Educational and

employment assistance for those who have not

acquired adequate skills could help many of

these workers achieve a higher standard of living.

Upgrading the educational levels of poor workers

would be particularly helpful to those in nonmetro

areas where more than 20 percent of workers over

25 years old had not completed high school.

Single female family heads have difficulty working

full-time, full-year regardless of the overall

14
economic situation. Assistance in meeting their

parental responsibilities would increase their

availability for work, while assurance of child

support could reduce their risk of poverty by

increasing their income.

Despite significant progress since the 1960's,

discrimination still limits the earnings of some

minorities and women. In both residence areas,

workers who are minorities or women have much

higher chances of poverty than whites or males.

A reinforced public commitment to reducing

discrimination would increase the likelihood

that all workers would be treated equally in

terms of educational and employment opportunities

and thus reduce poverty chances for those workers

who are most likely to experience discrimination.

Finally, the Earned Income Tax Credit (EITC), a

refundable Federal tax credit targeted to low-income

workers and an important source of support for many

of the working poor, might be expanded. The EITC is

extended to all workers with earnings below a

specified level, provided they have at least one

dependent child. The size of the tax credit is based

solely on the amount earned and does not take family

size into account. Adjusting the EITC to reflect the

number of children in a family would provide poor

workers with income more in line with their family

needs and would make working, even at low wages,

more competitive with welfare programs that index

benefits according to family size. In keeping with its

stated purpose of improving incentives to work, the

EITC might also be extended to all low-income

workers (20).

References

Cho, Woong K "Race Differentials In

Perceived Employment Barriers in the Rural

South," Dimensions of Poverty in the Rural

South. Eds. Jogindar S. Dhillon and

Marguerite R. Howe. Center for Community

Development and Research, Florida

Agricultural and Mechanical University,

Tallahassee, FL, 1986.

Coverhill, James E. "The Dual Economy and

Sex Differences in Earnings," Social Forces 66

(4): 970-993, 1988.

Deavers, Kenneth L., Robert A. Hoppe, and

Peggy J. Ross. "Public Policy and Rural

Poverty: View From the 1980's," Policy Studies

Journal 15 (2): 291-310, 1986.

4. Farley, Reynolds. Blacks and Whites,

Narrowing the Gap? Cambridge, MA: Harvard

University Press, 1989.

5. Gordon, David M. Theories of Poverty and

Underemployment. Washington, DC: Heath

and Co., 1972.

6. Hoppe, Robert A., and Donald L. Bellamy.

"Rural Poverty: A Continuing Problem."

Speech presented at meeting of Washington

Statistical Society, May 1989.

7. Klein, Bruce, and Philip L. Rones. "A Profile of

the Working Poor," Monthly Labor Review 112

(10): Oct. 1989.

8. Levitan, Sar A., and Isaac Shapiro. Working

But Poor, America's Contradiction. Baltimore,

MD: The Johns Hopkins Press, 1987.

9. Lichter, Daniel, and Janice A. Costanzo.

"Underemployment in Nonmetro America,

1970-1982." Unpublished paper, Dec. 1985.

10. Lynn, Lawrence E., Jr. "A Decade of Policy

Developments in the Income Maintenance

System," A Decade of Federal Antipovcrty

Programs, Achievements, Failures and Lessons.

Ed. Robert Haveman. New York: Academic

Press, 1977.

11. McLanahan, Sara, Irvin Garfinkel, and Dorothy

Watson. Family Structure. Poverty, and the

Underclass. IRP Discussion Paper #823-87.

University of Wisconsin, Institute for Research

on Poverty, Madison, WI, 1988.

12. McLaughlin, Diane K., and Carolyn Sachs.

"Poverty in Female-headed Households:

Residential Differences," Rural Sociology 53

(3): pp. 52-54, 1988.

13. Plotnick, Robert D., and Felicity Skidmore.

Progress Against Poverty: A Review of the

1964-1974 Decade. New York: Academic Press,

1975.

14. Petersen, Trond. "A Comment on Presenting

Results From Logit and Probit Models,"

American Sociological Review 50 (1): pp.

130-131, 1985.

15. Schiller, Bradley R. The Economics of Poverty

and Discrimination. Engleside Cliffs, NJ:

Prentice Hall, Inc., 1984.

15
16. Taggart, Robert. Hardship, The Welfare

Consequenœs of Labor Market Problems.

W.E. Upjohn Institute for Employment

Research, Kalamazoo, MI, 1982.

17. Thurow, Lester. Generating Equality. New

York: Basic Books, 1975.

18. U.S. Department of Commerce, Bureau of the

Census. Computer tape. March Supplement

of the Current Population Survey, Mar. 1988.

19. , Bureau of the Census. Poverty

in the United States 1987. Current Population

Reports.

1988.

Series P-60, No. 160, pp. 70-74.

20 U.S. House of Representatives, Committee on

Ways and Means. Background Material and

Data on Programs Within the Jurisdiction of the

Committee on Ways and Means. WMCP: 101-4,

pp. 834-835. 1990.

16
Appendix

Following the descriptive analysis, logistic regression

analysis was used to determine (1) the extent to which

the residential, individual and family, and employment

characteristics account for the differences in workers'

chances of poverty, and (2) whether the characteristics

differently affect poverty chances in metro and

nonmetro areas.

Logit analysis examines the relationships between a

dichotomous dependent variable and a set of

independent variables by analyzing the chances of a

dependent variable as a function of the independent

variables. In the following logit application,

poverty status (the dependent variable) is predicted

by several determinants of poverty (independent

variables).

The results of the logit analysis include beta

coefficients (B), standard errors (in parentheses),

transformed betas (P), and R2's (combined effect).

The beta coefficients reflect the effect of a unit change

in the independent variable on the log odds of being

poor, while the transformed betas reflect the increased

or decreased probability of being poor caused by a

unit change in the independent variable (14). The

R2's represent the combined effect of the variables or

the amount of variation in worker poverty explained

by the model.

Multivariate Models of Poverty Among Workers

In all, there were 10 independent variables

representing the residential, educational, racial.

family, and employment characteristics of metro

and nonmetro workers. One variable considered in

the descriptive analysis, the low-wage occupational

measure, was excluded because of its high correlation

with service occupations. The effect of each attribute

on the likelihood of poverty is the increase or decrease

in the probability of poverty associated with that

attribute compared with a reference group that has

identical characteristics. For example, the effect of

race is the additional likelihood of poverty that black

workers experience over the likelihood of poverty for

white workers of the same age who reside in the same

area and have the same educational levels, family

situations, and employment characteristics. Depending

on its sign (+ or -), the value indicates how much a

given characteristic increases or decreases the chances

(probabilities) of poverty, relative to a comparison

group. The employment characteristics are added

separately in order to determine their effect on (1) the

individual and family variables on workers' chances of

poverty and (2) the amount of explained variation in

worker poverty.

Appendix table 1 presents three hierarchical models of

worker poverty that control for a worker's residential,

individual and family, and employment traits. The

first column predicts poverty solely from the metro/

nonmetro residence variable. The second column adds

the individual and family characteristics to the

residential variable, while the third column includes

the characteristics in the preceding models and the

employment characteristics. Finally, appendix table 2

presents the family and individual factors, and then the

employment factors as these are affected by metro/

nonmetro residence.

17
Appendix table 1-Regression of poverty on residence, individual, family, and employment characteristics 1/

Characteristics

Residence only

considered

Residence,

individual, and family

characteristics considered

Residence,

individual, family,

and employment

characteristics considered

B2/ P3/ B B

Residence: 0.594 0.050

Nonmetro (.045)

Age:

Less than 25 years

More than 45 years

Education:

More than high school

High school only

Race:

Black, non-Hispanic

Hispanic

Family type:

Single female family head

Age and presence of children:

Children less than 6 years

old

Children less than 6 and 6-17

years old

Children 6-17 years old

Earners per family:

More than two

Weeks worked:

Less than 26 weeks

26-49 weeks

Employment:

Full-time

Occupation:

Farming, forestry, and fisheries

Service

Combined effects .010

0.812 0.075

(.052)

.928 .090

(.090)

-.217 -.013

(.071)

1.755 .233

(.069)

.799 .073

(.064)

.862 .081

(.069)

.811 .075

(.067)

1.605 .202

(.054)

1.338 .152

(.087)

1.785 .239

(.086)

.972 .096

(.073)

.214

0.682 0.079

(.059)

.757 .068

(.103)

-.513 -.027

(.083)

1.362 .156

(.077)

.655 .056

(.070)

.879 .083

(.078)

.859 .080

(.075)

.790 .072

(.067)

1.305 ,145

(.100)

1.842 .251

(.100)

1.227 .134

(.086)

.1,199 -.048

(.057)

2.174 .327

(.074)

1.100 .114

(.064)

-.974 -.042

(.070)

1.440 .170

(.094)

.800 .073

(.070)

.359 —

-- = Not applicable.

1/ All variables significant at the 0.05 level.

2/ Beta coefficients; standard errors in parentheses.

3/ Probability is the effect of unit change in the independent variable on the likelihood of being poor (14).

18
Appendix table 2"Logistic regression of poverty on individual, family^ and employment characteristics by

residence

Metro Nonmetro

Individual, Individual,

Individual family, and Individual family, and

and family employment and family employment

Characteristics characteristics characteristics characteristics characteristics

considered considered considered considered

B 1/ P2/ B P B P B P

Age:

Less than 25 years old 0.911 0.075 0.751 0.057 0.968 0.127 0.810 0.101

(.114) (.131) (.146) (.168)

More than 45 years old -.310

(.092)

-.015 -.528

(.108)

-.023 -.078

(.112)

-.007* -.459

(.131)

-.035

Education:

More than high school 1.865

(.087)

.227 1.500

(.097)

.158 1.556

(.116)

.246 1.139

(.127)

.158

High school only .783

(.082)

.061 .635

(.089)

.046 .801

(.105)

.099 .662

(.114)

.078

Race:

Black, non-Hispanic .915

(.081)

.075 .885

(.093)

.072 .736

(.130)

.089 .899

(.145)

.115

Hispanic .802

(.077)

.063 .888

(.086)

.072 J79

(.146)

.095 ,665

(.163)

.078

Family type:

Single female family head 1.630

(.067)

.181 .766

(.083)

.059 1.553

(.092)

.245 .834

(.117)

.105

Age and presence of children:

Children less than 6 years old 1.402

(.112)

.142 1.327

(.129)

.130 1.244

(.141)

.179 1.256

(.160)

.182

Children less than 6 and 6-17 1.903 .235 1.899 .234 1.604 .257 1.726 .286

years old (.111) (.129) (.138) (.158)

Children 6-17 years old 1.081

(.095)

.096 1.290

(.112)

.125 .813

(.116)

.101 1.114

(.134)

.154

Earners per family:

More than two ~ — -1.349

(.074)

-.042 " " -.990

(.089)

-.061

Weeks worked:

Less than 26 weeks ~ — 2.377

(.095)

.341 ~ -" 1.874

(.120)

.322

26-49 weeks " " 1.119

(.082)

.101 — " 1.089

(.101)

.149

Employment:

Full-time ~ — -.988

(.089)

-.036 " " -.965

(.115)

-.060

Occupation:

Farming, forestry, and fisheriest ~ 1.359

(.159)

.136 — ~ 1.453

(.116)

.224

Service "- — .824

(.085)

.065 ~ — .728

(.125)

.088

Combined effects .231 " .382 — .162 ~ .300 "

- = Not applicable.

* = Not significant at 0.05 level.

1/ Beta coefficients; standard errors in parentheses,

2/ Probability is the effect of unit change in independent variable on the likelihood of being poor (14).

* U.S. G.P.0.:1991~281-091:40058/ER5

19
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