Contract No.: 282-98-002; Task Order 34

MPR Reference No.: 8915-600

Characteristics of 

Low-Wage Workers 

and Their Labor Market 

Experiences: Evidence 

from the Mid- to 

Late 1990s 

Final Report

April 30, 2004 

Peter Schochet 

Anu Rangarajan 

Submitted to: 

Office of the Assistant Secretary for Planning 

and Evaluation 

Department of Health and Human Services 

Room 404E 

Hubert H. Humphrey Bldg. 

200 Independence Ave., SW

Washington, DC 20201 

Project Officer: 

Susan Hauan 

Submitted by: 

Mathematica Policy Research, Inc. 

P.O. Box 2393 

Princeton, NJ 08543-2393 

Telephone: (609) 799-3535 

Facsimile: (609) 799-0005 

Project Director: 

Anu Rangarajan 

Project Investigator: 

Peter Schochet 
 iii 

ACKNOWLEDGMENTS 

We would like to thank those whose efforts have made this report possible. Susan Hauan, 

from the Office of the Assistance Secretary for Planning and Evaluation (ASPE) at the U.S. 

Department of Health and Human Services, the project officer for the study, provided invaluable 

guidance throughout the course of the study, and provided very helpful comments on both the 

substance and presentation of material in this report. We also received valuable comments 

throughout the course of the study from other people at ASPE: Julia Issacs, Kelleen Kaye, and 

Don Oellerich. At Mathematica Policy Research, Jim Ohls and Rob Wood provided useful 

comments on the analysis and findings at various stages of the project. Jigar Bhatt provided 

outstanding programming assistance in constructing the large and complex data files and in 

writing the computer programs to conduct the myriad analyses that were performed for this 

study. Carol Razafindrakoto and Tim Novak also provided helpful programming assistance. 

Finally, Jennifer Chiaramonti and Bryan Gustus expertly produced the report, and Patricia 

Ciaccio provided valuable editorial assistance. We gratefully acknowledge these contributions. 
 v 

CONTENTS 

Chapter Page 

EXECUTIVE SUMMARY.......................................................................................... xv 

 I INTRODUCTION...........................................................................................................1 

A. OVERVIEW OF DATA, WAGE DEFINITIONS, ANALYSIS SAMPLES, 

AND METHODOLOGICAL APPROACH........................................................... 3 

1. Data ................................................................................................................. 3 

2. Defining Low-Wage Workers......................................................................... 3 

3. Wage Construction, Samples, and Methodological Approach ....................... 4 

B. ROADMAP OF REPORT ...................................................................................... 6 

 II LITERATURE REVIEW................................................................................................7 

A. DEFINING LOW-WAGE WORKERS.................................................................. 7 

B. CHARACTERISTICS OF LOW-WAGE WORKERS AND THEIR JOBS ......... 9 

1. What Is the Size of the Low-Wage Working Population?............................ 10 

2. Who Are Low-Wage Workers? .................................................................... 12 

3. What Are the Job and Overall Employment Characteristics of 

Low-Wage Workers? .................................................................................... 12 

C. WAGE PROGRESSION FOR LOW-WAGE WORKERS ................................. 14 

D. SUMMARY.......................................................................................................... 18 

 III CHARACTERISTICS OF LOW-WAGE WORKERS AND THEIR JOBS ...............21 

A. SIZE OF THE LOW-WAGE POPULATION...................................................... 23 

B. DEMOGRAPHIC CHARACTERISTICS OF LOW-WAGE WORKERS.......... 27 

1. Individual Characteristics ............................................................................. 28 

2. Household Characteristics ............................................................................ 34 

3. Changes Over Time ...................................................................................... 39 

4. Typologies of Low-Wage Workers............................................................... 39 
CONTENTS (continued)

Chapter Page 

 vi 

C. JOB AND OVERALL EMPLOYMENT CHARACTERISTICS OF LOWWAGE WORKERS.............................................................................................. 42 

1. Hourly Wages ............................................................................................... 43 

2. Hours Worked per Week............................................................................... 45 

3. Weekly Earnings........................................................................................... 47 

4. Availability of Health Insurance Coverage................................................... 47 

5. Self-Employment Status ............................................................................... 48 

6. Occupations, Industries, and Union Membership......................................... 49 

7. Other Employment-Related Characteristics.................................................. 51 

8. Changes Over Time ...................................................................................... 53 

 IV OVERALL EMPLOYMENT EXPERIENCES OF LOW-WAGE WORKERS......... 55 

A. DESCRIPTIVE ANALYSIS FINDINGS, BY GENDER.................................... 57 

1. Overall Employment Rates in Low-, Medium-, and High-Wage Jobs......... 57 

2. Number of Job and Employment Spells ....................................................... 59 

3. Employment Rates Over Time...................................................................... 62 

4. Time Spent in Labor Market Activities ........................................................ 64 

B. SUBGROUP FINDINGS...................................................................................... 71 

1. Findings from the Univariate Analysis ......................................................... 73 

2. Findings from the Multivariate Analysis ...................................................... 81 

 V WAGE GROWTH AND PROGRESSION AMONG LOW-WAGE WORKERS ..... 89 

A. DESCRIPTIVE ANALYSIS FINDINGS, BY GENDER.................................... 92 

1. Trends in Wages Over Time ......................................................................... 93 

2. Extent of Wage Growth Over Time.............................................................. 95 

3. Changes in Job Characteristics ..................................................................... 98 

B. SUBGROUP FINDINGS.................................................................................... 101 

1. Findings from the Univariate Analysis ....................................................... 103 

2. Findings from the Multivariate Analysis .................................................... 111 
CONTENTS (continued)

Chapter Page 

 vii 

 VI SPELL DURATION ANALYSIS ............................................................................. 121 

A. METHODOLOGICAL APPROACH................................................................. 122 

1. Defining Spells............................................................................................ 123 

2. Life Table Methods..................................................................................... 124 

3. Spell Information ........................................................................................ 127 

B. FINDINGS FROM THE LIFE TABLE ANALYSIS......................................... 130 

1. Duration of Low-Wage Job and Employment Spells and Types of Exits .. 130 

2. Duration of Alternative Job and Employment Spells ................................. 136 

3. Including Left-Censored Spells .................................................................. 137 

4. Comparing the Duration of Low-, Medium-, and High-Wage Spells ........ 137 

5. Reentry into the Low-Wage Labor Market................................................. 143 

6. Subgroup Results ........................................................................................ 147 

 VII SUMMARY AND CONCLUSIONS ........................................................................ 155 

REFERENCES........................................................................................................... 159 

APPENDIX A DATA, WAGE DEFINITIONS, ANALYSIS SAMPLES, 

AND METHODOLOGICAL APPROACH............................... A.1 

APPENDIX B SUPPLEMENTARY TABLES TO CHAPTER III.....................B.1 

APPENDIX C SUPPLEMENTARY TABLES TO CHAPTER IV ....................C.1 

APPENDIX D SUPPLEMENTARY TABLES TO CHAPTER V..................... D.1 

APPENDIX E SUPPLEMENTARY TABLES TO CHAPTER VI ....................E.1 

 
 ix 

TABLES 

 Table Page 

 III.1 PERCENTAGE OF ALL WORKERS IN MARCH 1996 WHO WERE 

LOW-WAGE WORKERS, ACCORDING TO ALTERNATIVE 

DEFINITIONS OF LOW-WAGE WORKERS.................................................... 25 

 III.2 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN KEY WORKER SUBGROUPS, BY GENDER .............. 29 

 III.3 DISTRIBUTION OF INDIVIDUAL CHARACTERISTICS OF LOWWAGE AND ALL WORKERS IN MARCH 1996, BY GENDER..................... 30 

 III.4 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN KEY HOUSEHOLD SUBGROUPS, 

BY GENDER........................................................................................................ 35 

 III.5 DISTRIBUTION OF HOUSEHOLD CHARACTERISTICS OF LOWWAGE AND ALL WORKERS IN MARCH 1996, BY GENDER..................... 36 

 III.6 DISTRIBUTION OF LOW-WAGE WORKERS, BY SUBGROUP 

AND YEAR.......................................................................................................... 40 

 III.7 DISTRIBUTION OF JOB CHARACTERISTICS OF LOW-WAGE 

AND ALL WORKERS IN MARCH 1996, BY GENDER.................................. 44 

 III.8 DISTRIBUTION OF OCCUPATIONS, INDUSTRIES, AND UNION 

MEMBERSHIP OF LOW-WAGE AND ALL WORKERS 

IN MARCH 1996, BY GENDER......................................................................... 50 

 III.9 DISTRIBUTION OF OTHER EMPLOYMENT-RELATED 

CHARACTERISTICS OF LOW-WAGE AND ALL 

WORKERS IN MARCH 1996, BY GENDER .................................................... 52 

 III.10 DISTRIBUTION OF KEY JOB CHARACTERISTICS OF LOW-WAGE 

WORKERS, BY YEAR........................................................................................ 54 

 IV.1 THE NUMBER OF NEW JOB AND EMPLOYMENT SPELLS 

DURING THE THREE AND ONE-HALF YEARS AFTER 

JOB START FOR LOW-WAGE WORKERS, 

BY WAGE TYPE AND GENDER ...................................................................... 60 

 IV.2 AVERAGE PERCENTAGE OF TIME SPENT IN LABOR MARKET 

ACTIVITIES DURING THE THREE AND ONE-HALF YEARS 

AFTER JOB START FOR LOW-WAGE WORKERS, BY GENDER............... 66 
TABLES (continued)

Table Page 

 x 

 IV.3 DISTRIBUTION OF MONTHS IN LABOR MARKET ACTIVITIES 

DURING THE THREE AND ONE-HALF YEARS AFTER JOB 

START FOR LOW-WAGE WORKERS, BY GENDER .................................... 67 

 IV.4 TIME SPENT EMPLOYED DURING THE THREE AND ONE-HALF 

YEARS AFTER JOB START FOR SUBGROUPS OF LOW-WAGE 

WORKERS DEFINED BY INDIVIDUAL AND HOUSEHOLD 

CHARACTERISTICS AT JOB START.............................................................. 74 

 IV.5 TIME SPENT EMPLOYED DURING THE THREE-AND-ONE-HALF 

YEARS AFTER JOB START FOR SUBGROUPS OF LOW-WAGE 

WORKERS DEFINED BY INITIAL JOB CHARACTERISTICS ..................... 76 

 IV.6 MULTIVARIATE ANALYSIS FINDINGS ON THE PERCENTAGE OF 

TIME LOW-WAGE WORKERS SPENT EMPLOYED IN MEDIUMOR HIGH-WAGE JOBS DURING THE 42-MONTH FOLLOW-UP 

PERIOD, BY GENDER AND MODEL .............................................................. 83 

 V.1 GROWTH IN REAL HOURLY WAGES AMONG LOW-WAGE 

WORKERS WHO REMAINED EMPLOYED THREE 

YEARS LATER.................................................................................................... 97 

 V.2 CHARACTERISITICS OF INITIAL LOW-WAGE JOB AND 

THE JOB HELD THREE YEARS LATER ......................................................... 99 

 V.3 MEASURES OF WAGE PROGRESSION AFTER JOB START 

FOR SUBGROUPS OF LOW-WAGE WORKERS DEFINED 

BY INDIVIDUAL AND HOUSEHOLD CHARACTERISTICS 

AT JOB START ................................................................................................. 105 

 V.4 MEASURES OF WAGE PROGRESSION AFTER JOB START 

FOR SUBGROUPS OF LOW-WAGE WORKERS DEFINED 

BY INITIAL JOB CHARACTERISTICS.......................................................... 109 

 V.5 MULTIVARIATE ANALYSIS FINDINGS ON THE PERCENTAGE 

OF LOW-WAGE WORKERS EARNING AT LEAST $10 THREE 

AND A HALF YEARS LATER, BY GENDER AND MODEL....................... 112 

 VI.1 JOB AND EMPLOYMENT SPELL INFORMATION FOR WORKERS 

STARTING LOW-WAGE JOBS, BY GENDER .............................................. 128 

 VI.2 CUMULATIVE EXIT RATES FOR LOW-WAGE JOB SPELLS, 

BY TYPE OF EXIT AND GENDER................................................................. 131 

 VI.3 CUMULATIVE EXIT RATES FOR LOW-WAGE EMPLOYMENT 

SPELLS, BY TYPE OF EXIT AND GENDER................................................. 135 
TABLES (continued)

Table Page 

 xi 

 VI.4 CUMULATIVE EXIT RATES FOR OVERALL EMPLOYMENT 

SPELLS OF LOW-WAGE WORKERS WITH AND WITHOUT 

LEFT-CENSORED SPELLS, BY GENDER..................................................... 138 

 VI.5 CUMULATIVE REEMPLOYMENT RATES FOR WORKERS WHO 

EXITED LOW-WAGE JOBS INTO NONEMPLOYMENT, 

BY GENDER...................................................................................................... 144 

 VI.6 CUMULATIVE EXIT RATES FROM HIGHER-WAGE JOBS 

FOR WORKERS WHO EXITED LOW-WAGE JOBS 

INTO HIGHER-WAGE JOBS, BY GENDER .................................................. 146 

 VI.7 12-MONTH CUMULATIVE EXIT RATES FROM LOW-WAGE JOB 

SPELLS FOR MALES, BY TYPE OF EXIT AND SUBGROUP .................... 149 

 VI.8 12-MONTH CUMULATIVE EXIT RATES FROM LOW-WAGE JOB 

SPELLS FOR FEMALES, BY TYPE OF EXIT AND SUBGROUP................ 151 

 
 xiii 

FIGURES 

 Figure Page 

 III.1 U.S. UNEMPLOYMENT RATE, BY YEAR...................................................... 22 

 III.2 PERCENTAGE OF WORKERS WHO WERE LOW-, MEDIUM-, AND 

HIGH-WAGE WORKERS: MARCH 1996 TO MARCH 1999......................... 23 

 III.3 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN GENDER, AGE, AND 

RACE/ETHNICITY GROUPS............................................................................. 28 

 III.4 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN EDUCATION GROUPS, BY GENDER ......................... 32 

 III.5 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN HOUSEHOLD GROUPS, BY GENDER ........................ 34 

 III.6 PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE 

WORKERS WITHIN POVERTY GROUPS, BY GENDER .............................. 38 

 III.7 SHARE OF LOW-WAGE WORKERS, BY TYPOLOGY 

AND GENDER..................................................................................................... 41 

 III.8 AVERAGE HOURLY WAGE FOR LOW-WAGE WORKERS 

IN MARCH 1996, BY AGE................................................................................. 45 

 III.9 PERCENTAGE OF LOW-WAGE AND ALL WORKERS WHO 

WORKED AT LEAST 35 HOURS PER WEEK, BY GENDER........................ 46 

 III.10 PERCENTAGE OF LOW-WAGE AND ALL WORKERS 

WITH AVAILABLE HEALTH INSURANCE 

ON THE JOB, BY GENDER ............................................................................... 48 

 IV.1 PERCENTAGE OF WORKERS STARTING LOW-WAGE JOBS 

WHO SUBSEQUENTLY HELD HIGHER-WAGE JOBS, 

BY WAGE CATEGORY AND GENDER .......................................................... 58 

 IV.2 PERCENTAGE OF LOW-WAGE WORKERS WHO HELD 

HIGHER-WAGE JOBS BUT WHO RETURNED TO THE 

LOW-WAGE LABOR MARKET, BY GENDER............................................... 59 

 IV.3 AVERAGE NUMBER OF JOBS AND EMPLOYMENT SPELLS 

OF LOW-WAGE WORKERS, BY GENDER..................................................... 61 

 IV.4 QUARTERLY EMPLOYMENT RATES OF MALE WORKERS WHO 

INITIALLY STARTED LOW-WAGE JOBS, BY WAGE TYPE ...................... 62 
FIGURES (continued)

Figure Page 

 xiv 

 IV.5 QUARTERLY EMPLOYMENT RATES OF FEMALE 

WORKERS WHO INITIALLY STARTED 

LOW-WAGE JOBS, BY WAGE TYPE .............................................................. 63 

 IV.6 AVERAGE PERCENTAGE OF MONTHS SPENT IN LABOR 

MARKET ACTIVITIES FOR LOW-WAGE WORKERS, 

BY GENDER........................................................................................................ 65 

 IV.7 AVERAGE NUMBER OF HOURS PER WEEK SPENT EMPLOYED, 

BY WAGE TYPE OF JOB AND GENDER........................................................ 68 

 IV.8 AVERAGE PERCENTAGE OF TIME SPENT IN MEDIUM- OR HIGHWAGE JOBS, FOR JOB SWITCHERS AND JOB STAYERS .......................... 70 

 V.1 TRENDS IN REAL WAGES OVER TIME AMONG THOSE WHO 

START A LOW WAGE JOB, BY GENDER...................................................... 94 

 V.2 REAL WAGES RELATIVE TO POVERTY, AT THE TIME 

OF THE FOLLOW-UP PERIOD ......................................................................... 95 

 VI.1 CUMULATIVE EXIT RATES FROM JOB AND EMPLOYMENT 

SPELLS FOR THOSE STARTING LOW-WAGE JOBS, 

BY GENDER...................................................................................................... 134 

 VI.2 CUMULATIVE EXIT RATES FROM LOW-, MEDIUM- AND 

HIGH-WAGE EMPLOYMENT SPELLS OF THE SAME 

WAGE TYPE, BY GENDER............................................................................. 140 

 VI.3 CUMULATIVE EXIT RATES FROM LOW-, MEDIUM-, AND 

HIGH-WAGE EMPLOYMENT SPELLS OF ANY WAGE 

TYPE, BY GENDER.......................................................................................... 142 
 xv

EXECUTIVE SUMMARY 

With the passage of the Personal Responsibility and Work Opportunity Reconciliation Act 

of 1996 (PRWORA), policymakers and researchers have recognized the importance of 

understanding the dynamics of the low-wage labor market and the economic opportunities in it. 

As large numbers of current and former recipients enter the low-wage labor market, it is 

important to understand issues related to job retention and mobility among low-wage workers, as 

well as their prospects for wage progression. 

While a number of researchers have examined issues related to the labor market experiences 

of workers in general, fewer studies have directly examined the labor-market experiences of lowwage workers. Moreover, these studies use data from the late 1980s and early 1990s but have 

not examined the situations of low-wage workers in more recent times. To learn how low-wage 

workers have fared in recent times, the Assistant Secretary for Planning and Evaluation (ASPE) 

at the U.S. Department of Health and Human Services (DHHS) contracted with Mathematica 

Policy Research, Inc. (MPR) to provide a comprehensive profile of the characteristics and labor 

market experiences of low-wage workers since the passage of PRWORA. This study uses data 

from the 1996 longitudinal panel of the Survey of Income and Program Participation (SIPP), 

which covers the period between late 1995 and early 2000. 

KEY RESEARCH QUESTIONS 

The study examines a broad range of research questions pertaining to the low-wage labor 

market during the mid- to late 1990s. These questions include: 

• How many workers hold low-wage jobs? 

• Who are the people in the low-wage labor market and what are the characteristics of 

the jobs they hold? 

• What are the overall employment experiences of low-wage workers over a three-andone-half-year follow-up period and what are their typical job and employment spell 

lengths? 

• What wage growth do low-wage workers experience? 

• Do labor market experiences differ across key subgroups of workers?

DATA AND METHODS 

This study was conducted using data from the 1996 longitudinal panel of the SIPP. The 

1996 SIPP is a large, multipanel, longitudinal survey that collected demographic and 

socioeconomic information on a nationally representative sample of U.S. households. The data 

cover the period from late 1995 to early 2000, and 48 months of follow-up data are available for 

each individual in the longitudinal file. 
 xvi

Our primary approach for defining low-wage workers was to use the hourly wage at which a 

full-time worker would have annual earnings below poverty for a family of four. Using federal 

poverty guidelines, and assuming a full-time worker works 2,080 hours per year, we set the lowwage cutoff at $7.50 in 1996, $7.72 in 1997, $7.91 in 1998, $8.03 in 1999, and $8.20 in 2000. 

We defined medium-wage workers as those with wage rates between one and two times the lowwage cutoff value and high-wage workers as those with wages more than twice the low-wage 

cutoff value. 

We conducted our analysis using employed SIPP sample members who were between ages 

16 and 64 and who were not enrolled in school. We excluded students and older workers, 

because their labor market experiences are likely to be very different from those of the 

population that is the focus of this study. We used both descriptive and multivariate regression 

analytic methods to address the research questions for the study. We used cross-sectional 

samples of workers to answer some analysis questions, entry cohort samples of workers starting 

low-wage jobs to answer other questions, and samples of low-wage job spells for others. We 

conducted the analyses using the full sample, as well as for key subgroups defined by worker and 

job characteristics. 

KEY FINDINGS 

Our analysis provides a complex picture of the characteristics of low-wage workers and their 

jobs, as well as their labor market dynamics. We summarize the key analysis findings here: 

How Many Workers Hold Low-Wage Jobs? 

• In March 1996, less than one-third of all workers were low-wage workers. In 

March 1996, about 28 percent of all workers were low-wage workers, with hourly 

wages below $7.50 in 1996 dollars. Most workers (43 percent) were medium-wage 

workers, with wages between $7.50 and $15 per hour. About 29 percent were highwage workers, with wages over $15 per hour. The share of low-wage workers 

decreased somewhat during the mid- to late 1990s as the unemployment rate 

declined. These estimated shares are similar to those found in previous studies 

covering earlier periods that used a similar hourly wage cutoff value to define lowwage workers. 

Who Are the People in the Low-Wage Labor Market? 

• Low-wage workers are disproportionately young, female, nonwhite, with a high 

school credential or less, and with health limitations. During the mid- to late 1990s, 

more than one-third of all employed females were in the low-wage labor market, 

compared to 22 percent of all employed males. Similarly, about 84 percent of 

employed teenagers between the ages of 16 and 19 held low-wage jobs, compared to 

less than one-quarter for those between the ages of 30 and 60. Differences by 

education level are especially large; about 56 percent of workers who did not 

complete high school were low-wage workers, compared to 36 percent of workers 
 xvii

 

with a high school diploma or GED, and only about 14 percent of workers who 

completed college. 

• Single parents with children, those who had recently received public assistance, 

and workers in households with incomes below the federal poverty level are 

disproportionately likely to be low-wage workers. More than 40 percent of employed 

single parents with children in our sample were in the low-wage labor market, 

compared to 25 percent of married couples with or without children. Similarly, 

workers who received public assistance in the past year were twice as likely as their 

counterparts to be in the low-wage labor market (58 versus 27 percent). 

• Despite these patterns, low-wage workers are a relatively diverse group. They exist 

in a wide range of subgroups defined by individual and household characteristics. 

For example, although workers in households below the federal poverty level were 

much more likely to be low-wage workers than those with incomes greater than 200 

percent of poverty (79 percent, compared to only 20 percent), nearly 60 percent of all 

low-wage workers were in higher-income households. Similarly, in March 1996, 

nearly 20 percent of all low-wage workers graduated college. 

What Are the Characteristics of Jobs That Low-Wage Workers Hold? 

• Many low-wage workers earn considerably less than the low-wage cutoff value 

used in our study. In March 1996, only 21 percent of low-wage workers earned 

between $7.00 and $7.50 (the hourly wage cutoff value used in our study). More than 

one-quarter earned less than $5.00 per hour (close to the $4.75 minimum wage). On 

average, low-wage workers earned $5.58 per hour, compared to $13.62 for all 

workers.1

 Interestingly, the wage distributions for low-wage workers are similar for 

males and females. 

• However, most work full-time, and many are covered by health insurance through 

their employers. Most low-wage workers in our sample reported working full-time 

(defined as those working at least 35 hours per week). Among male workers in 

March 1996, about 85 percent of those with low wages reported working full-time; 

the figure for higher-wage male workers is about 96 percent.2

 Similarly, about 66 

percent of low-wage female workers reported working full-time, compared to 83 

percent of other employed females. Many had health insurance coverage through 

their employers; interestingly, health insurance coverage rates for low-wage workers 

were higher for females than for males (57 percent, compared to 41 percent), perhaps 

due in part to the fact that fewer females were self-employed. 

• Low-wage workers are substantially overrepresented in service professions and 

underrepresented in professional and technical occupations. In 1996, nearly onethird of all low-wage workers were in service occupations, compared to only 10

1

 Medium-wage workers earned an average of about $11 per hour, and high-wage workers 

earned an average of about $25 per hour. 

2

 Higher-wage workers include those who were in medium-wage or high-wage jobs. 
percent of higher-wage workers. Conversely, only 14 percent of low-wage workers

were in professional and technical occupations, compared to 40 percent for other 

workers. Only about 6 percent of low-wage workers were unionized, compared to 16 

percent of medium-wage and 25 percent of high-wage workers. Finally, a larger 

share of low-wage workers than other workers are self-employed (13 percent, 

compared to 9 percent). 

What Are the Overall Employment Experiences of Low-Wage Workers? 

• Many low-wage workers were employed for most of the study’s three-and-a-halfyear follow-up period. On average, those who started low-wage jobs were employed 

about 79 percent of the time over the study period (83 percent for males and 76 

percent for females). Nearly 40 percent of the low-wage workers were employed 

every month, and only 30 percent were employed for less than half the period.

Furthermore, employment rates remained fairly constant during the follow-up period 

for both males and females (top two lines in Figure 1). These high rates of 

employment may reflect the strong economic conditions during the mid- to late 

1990s. 

• Most low-wage workers held medium-wage jobs at some point. About 69 percent of 

males held medium-wage jobs (that is, earned wages between one and two times the 

low-wage cutoff value of $7.50 in 1996 dollars) and 13 percent held high-wage jobs

 xviii

0

20

40

60

80

100

1 2 345 678 9 10 11 12 13

Quarter After Start of Low-Wage Job

Percentage Employed in Type of Job for at Least One Week in Quarter

FIGURE 1

QUARTERLY EMPLOYMENT RATES OF WORKERS WHO INITIALLY 

STARTED LOW-WAGE JOBS, BY WAGE TYPE

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

All Jobs: Males All Jobs: Females Medium-Wage Jobs: Males Medium-Wage Jobs: Females
 xix

 

during the three-and-a-half-year follow-up period; only 30 percent held low-wage 

jobs only. Employment rates in medium-wage jobs were somewhat lower for females 

than for males, suggesting that females experienced less upward mobility than males. 

However, female employment rates in these jobs were still high—about one-half of 

women workers ever held them. 

• Many low-wage workers moved in and out of the low-wage labor market. For 

example, nearly two-thirds of those who held a medium- or high-wage job 

subsequently returned to the low-wage labor market, and many of those who 

became nonemployed reentered low-wage jobs. Similarly, low-wage workers held an 

average of three jobs during the follow-up period, and 80 percent of these were lowwage jobs. Thus, there is considerable job mobility among low-wage workers. 

• Low-wage job and employment spells were typically short. During the study period, 

the median length of low-wage jobs spells was about four months for both males and 

females.3

 About 80 percent ended within a year, and more than 90 percent ended 

within two years. Low-wage workers often went directly from their low-wage jobs 

into medium- or high-wage employment, but many also left these jobs. At the same 

time, however, many low-wage workers, especially females, exited their low-wage 

jobs into another low-wage job or into nonemployment. Thus, we find additional 

evidence of substantial job mobility among low-wage workers.

• Low-wage workers in our sample experienced some upward mobility over the 

medium term. Over the entire follow-up period, sample members typically spent 

considerable more time in low-wage than higher-wage jobs (an average of 57 percent 

of months in low-wage jobs, compared to 23 percent of months in higher-wage jobs). 

However, employment rates in low-wage jobs decreased over time, whereas 

employment rates in medium-wage jobs increased over time (bottom two lines in 

Figure 1). This is especially true for males; the average male worker actually spent 

about the same amount of time in low-wage and higher-wage jobs during the second 

half of the follow-up period. These patterns, however, are weaker for female 

workers, suggesting further that females experienced less upward mobility than 

males. 

What Wage Growth Do Low-Wage Workers Experience? 

• Low-wage workers experienced considerable wage growth during the study period. 

Average wage increases for low-wage workers were about 25 percent over a threeyear period after they started their jobs, or a real wage increase of nearly 8 percent per 

year (Figure 2). Female workers had lower wages than male workers throughout the 

follow-up period, but wage growth was similar by gender. 

3

 A job spell was classified as “low-wage” on the basis of the worker’s wage rate at the start

of the job spell. A low-wage job spell ended when the worker moved to another low-wage job, 

moved to a higher-wage job (either with the same or different employer), became unemployed, 

or left the labor force. 
FIGURE 2

TRENDS IN REAL WAGES OVER TIME AMONG THOSE WHO

STARTED A LOW-WAGE JOB, BY GENDER

$7.06

$7.74

$8.31

$8.87 $8.94 $8.94

$6.49

$6.96 $7.10

$7.59 $7.91 $8.04

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

2 3 5

Average Wages Over Six-Month

Periods After Job Start

Average Wages Over Six-Month

Periods After Job Start

4

Dollars

1 6

Males Females

1 6 2 3 4 5

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of the panel period.

Note: All wages are reported in 1999 dollars.

• The majority of workers experienced some increases in wages, and some workers 

experienced fairly large gains. About 80 percent of both male and female workers

experienced an increase in real wages. Some low-wage workers also experienced

significant amounts of wage growth—for example, nearly half of males and 40 

percent of females experienced a wage growth of more than 25 percent between their 

initial job and their most recent job three years later. 

• Low-wage workers also moved to “better” jobs over time. Low-wage workers 

worked more hours over time, and a higher fraction had health insurance coverage 

through their jobs. The fraction of workers working full-time increased for both male 

and female workers. Similarly, the fraction of low-wage workers in jobs that offered

fringe benefits, such as health insurance, increased by more than 50 percent. 

• Despite the high amounts of wage growth, many workers still had low wages and

earnings. Because they started at fairly low wage levels, despite wage increases,

many workers, especially females, had low wages and annualized earnings that would 

put them below the federal poverty level for a family of four (50 percent of male 

workers and more than 60 percent of female workers). 

 xx
 xxi

Do Labor Market Experiences Differ Across Key Subgroups of Low-Wage Workers? 

• We find some differences in labor market outcomes across key demographic 

subgroups of the low-wage population, although the differences are not large. 

Males, prime-age workers (those between ages 20 and 60), educated workers, whites, 

those without health limitations, and those in higher-income households typically 

spend more time in higher-wage jobs than their respective counterparts and 

experience greater wage growth. 

• We also find that job quality matters. Those who start with better jobs (measured 

by higher initial wages, health insurance coverage, and full-time work status) 

are more likely than those in lower-quality jobs to spend time in higher-wage jobs 

and to have higher wage growth. In addition, we find some differences across 

occupations—males in professional and sales occupations and females in professional 

and clerical occupations have more positive labor market outcomes than other 

workers. 

• Among male workers, business owners were more likely than jobholders to 

experience greater wage growth. Self-employed male workers spent substantially 

more time in medium- and high-wage jobs than did male jobholders, and had higher 

wages in the last follow-up period. These differences are statistically significant in 

the multivariate regression models. 

• Time spent employed was associated with wage growth. Low-wage workers who 

were employed for most of the period (at least 75 percent of months) experienced 

greater wage growth than those who were employed for fewer months, and especially 

for males. For instance, about 33 percent of continuously-employed males earned 

more than $10 per hour at the end of the follow-up period, compared to only 17 

percent of males who were intermittently employed. 

• Among those continuously employed, job switchers experienced somewhat greater 

wage growth than job stayers, especially for females. Among continuouslyemployed workers, those who switched jobs spent more time in the medium- or highwage labor market than those who stayed in their initial jobs. The job switchers also 

experienced more wage growth during the follow-up period, and these differences are 

statistically significant. 

• In general, differences in labor market success across subgroups are smaller than 

expected. Although, in both our descriptive and multivariate analyses, we identified 

groups that are at particular risk of poor labor market outcomes, we could not fully 

account for the variation in outcomes across low-wage workers. Thus, substantial 

diversity exists in labor market success within groups. Clearly, important residual 

factors affect the wage progression of those starting low-wage jobs. 

CONCLUSIONS 

The labor market dynamics of low-wage workers—about 28 percent of all workers—are 

complex. Low-wage workers in our sample were employed for most of the three-and-one-half 

year follow-up period (about 79 percent of weeks). However, there was considerable movement 
 xxii

in and out of the low-wage labor market for these workers. While about 70 percent of male 

workers and 50 percent of female workers held medium-wage jobs at some point during the 

follow-up period, on average, males spent only about 30 percent of the time in these jobs, and the 

corresponding figure for females was about 20 percent. However, we see an upward trend in 

employment rates in these higher-paying jobs over time for both males and females. 

We find significant wage growth for low-wage workers in our sample. Overall, the average 

real wage increase was about 25 percent during the follow-up period (for those employed at the 

start and end of the period). In addition, about 80 percent of workers experienced an increase in 

real wages, with some experiencing significant amounts of wage growth. Furthermore, lowwage workers tended to move into better jobs (as measured by hours worked and available fringe 

benefits). Despite this wage growth, however, many workers still had low earnings. Because 

they started at fairly low wage levels, by the end of the follow-up period, more than one-half of 

workers had earnings that would put them below the federal poverty level for a family of four. 

We conducted subgroup analyses to try to explain the diversity in labor market outcomes 

across low-wage workers. Our analysis consistently found that, among the low-wage population, 

males, prime-age workers (those between ages 20 and 60), educated workers, whites, 

those without health limitations, and those in wealthier households typically spent more time in 

higher-wage jobs and experienced more wage growth than their respective counterparts. 

Furthermore, job quality matters—those who start with better jobs (measured by higher initial 

wages, health insurance coverage, and full-time work status) are more likely to experience wage 

growth than those in lower-quality jobs. In addition, we find some differences across 

occupations—males in professional and sales occupations and females in professional and 

clerical occupations have more positive labor market outcomes than other workers. Business 

owners were also more likely than jobholders to experience greater wage growth. 

We find also some association between the overall employment experiences of low-wage 

workers during the follow-up period and their wage growth. First, wage progression was greater 

for those who were employed for most of the period than those employed less, suggesting that 

policies promoting employment retention could improve the wage growth of low-wage workers. 

Second, among workers continuously employed during the follow-up period, those who switched 

jobs tended to have better outcomes than those who stayed with their same employer, suggesting 

that job turnover was an avenue for wage growth for some low-wage workers. 

We find also, however, that substantial diversity exists in labor market success within

worker subgroups. Thus, although we identified groups that are of particular risk of poor labor 

market outcomes, we could not fully account for the variation in labor market outcomes across 

low-wage workers. Clearly, important residual factors affect the wage progression of those 

starting low-wage jobs. 

Overall, our results clearly indicate that low-wage workers have some upward mobility over 

the medium term. At the same time, however, a segment of the low-wage population remains 

entrenched in low-wage jobs. Thus, there is considerable diversity in labor market success for 

low-wage workers. Of course, it has to be kept in mind that the economic conditions were very 

strong during the mid- to late 1990s, and our results may be different under a weaker economy. 
 1 

I. INTRODUCTION 

With passage of the Personal Responsibility and Work Opportunity Reconciliation Act of 

1996 (PRWORA), policymakers and researchers have recognized the importance of 

understanding the dynamics of the low-wage labor market and the economic opportunities in it. 

The “work first” focus and time limits established through the creation of the Temporary 

Assistance for Needy Families (TANF) program are designed to end the dependence of needy 

families by moving welfare recipients off the welfare rolls and into work. Given the low 

education and skill levels of typical welfare recipients, this work first emphasis has led many 

recipients into low-wage jobs. As large numbers of current and former recipients enter the lowwage labor market, we need to understand, in detail, job retention and mobility among low-wage 

workers, as well as their prospects for wage progression. A thorough understanding of these 

issues can provide insights into other possible policy initiatives for low-wage workers, such as 

strengthening work supports for former welfare recipients and improving job retention and career 

advancement strategies. 

This report discusses the research that Mathematica Policy Research, Inc. (MPR) has 

conducted, under contract with the Assistant Secretary for Planning and Evaluation (ASPE) at 

the U.S. Department of Health and Human Services (HHS), to provide a comprehensive profile 

of the characteristics and labor market experiences of low-wage workers since the passage of 

PRWORA. The study was conducted using data from the 1996 longitudinal panel of the Survey 

of Income and Program Participation (SIPP), which covers the period between late 1995 and 

early 2000. The economy was strong during this time period; thus, the study’s findings may be 

different under weaker economic conditions. 
 2 

The study examines a broad range of research questions pertaining to the low-wage labor 

market. We categorize these questions into the following topical areas: 

• Who are the people in the low-wage labor market? What proportion of people in the 

workforce had low-wage employment in the mid- to late 1990s? How do their 

demographic characteristics compare to those of higher-wage workers? Do the 

answers to these questions differ across key subgroups of the low-wage population? 

• What are the characteristics of the jobs that low-wage workers hold? How much do 

they earn per hour and per week? What are their usual hours worked per week? In 

which occupations and industries are they concentrated? To what extent are health 

insurance benefits available on their jobs? How do their job characteristics differ 

from those of higher-wage workers? Do the answers to these questions differ across 

key subgroups of the low-wage population? 

• What are the employment-related characteristics of low-wage workers? How long 

have they been at their jobs? What are their employment histories? How many hold 

more than one job? How many hours do they work per week in all jobs, and what are 

their total weekly earnings? 

• What are the overall employment experiences of low-wage workers over a threeand-one-half-year follow-up period? How many job and employment spells do they 

typically have? How many eventually find a higher-wage job? How many move in 

and out of the low-wage labor market? What fraction of time are they in low-wage 

jobs, higher-wage jobs, and no jobs? Do employment rates increase over time? How 

do the employment patterns of low-wage workers compare to those of higher-wage 

workers? 

• What wage growth do low-wage workers experience, and what factors are 

important for wage progression in the low-wage labor market? To what extent do 

low-wage workers experience wage growth over a three-year follow-up period? 

What circumstances are associated with wage growth in the low-wage labor market? 

Are those employed in certain occupations or industries more likely than others to 

experience wage growth? Are initial wage and earnings levels associated with wage 

growth? Do those who continue in the same job experience greater or lower wage 

growth than those who switch jobs? Do low-wage workers experience more or less 

wage growth than higher-wage workers? 

• What are typical job and employment spell lengths for low-wage workers? Are 

spell lengths related to characteristics of the worker or of the job? At what rate do 

workers move from low-wage job spells directly into higher-wage job spells? At 

what rate do they become nonemployed? How soon do those who leave a low-wage 

job become reemployed in another low-wage job or a higher-wage job? How do job 

spell lengths of low-wage workers compare to those of higher-wage workers? 
 3 

Subsequent chapters discuss our findings in detail. In the remainder of this chapter, we 

provide an overview of the data sources for the study, wage definitions, analysis samples, and 

our methodological approach. This chapter ends with a roadmap for the rest of the report. 

A. OVERVIEW OF DATA, WAGE DEFINITIONS, ANALYSIS SAMPLES, AND 

METHODOLOGICAL APPROACH 

The 1996 longitudinal panel of the Survey of Income and Program Participation (SIPP), 

collected by the U.S. Bureau of the Census, is the primary data source that we used for 

examining the low-wage labor market in our study. Because of the wide range of study 

questions, we used different samples and methodological approaches for different types of 

analyses. We discuss these issues in this section (see the Methodological Appendix A for a more 

detailed discussion of these topics). 

1. Data 

This study was conducted using data from the 1996 longitudinal panel of SIPP. The 1996 

SIPP is a large, multipanel, longitudinal survey that collected demographic and socioeconomic 

information on a nationally representative sample of U.S. households. The data cover the period 

from late 1995 to early 2000, and 48 months of follow-up data are available for each individual 

in the longitudinal file. SIPP provides detailed monthly measures on labor force participation 

(for those age 15 and older), income, participation in public programs, and household 

composition. We supplemented the SIPP data with state-level data on economic conditions and 

poverty levels. 

2. Defining Low-Wage Workers 

Our primary approach for defining low-wage workers was to use the hourly wage at which a 

full-time worker would have annual earnings below poverty for a family of four. We calculated 
 4 

separate low-wage cutoff values for each calendar year the SIPP panel covered. We then 

classified a worker as “low-wage” if the worker’s wage rate was less than the cutoff level in the 

calendar year when the wage rate was reported. Using federal poverty guidelines, and assuming 

a full-time worker works 2,080 hours per year, we set the low-wage cutoff at $7.50 in 1996, 

$7.72 in 1997, $7.91 in 1998, $8.03 in 1999, and $8.20 in 2000. We defined medium-wage 

workers as those with wage rates between one and two times the low-wage cutoff value and 

high-wage workers as those with wages more than twice the low-wage cutoff value. 

3. Wage Construction, Samples, and Methodological Approach 

We conducted our analysis using employed SIPP sample members who were between ages 

16 and 64 and who were not enrolled in school. We excluded students and older workers, 

because their labor market experiences are likely to be very different from those of the 

population that is the focus of this study. 

The main analysis sample that we used in Chapter III to examine the prevalence of lowwage jobs and the characteristics of low-wage workers and their jobs is a cross-sectional sample 

of workers in March 1996. We selected March 1996 as the reference point for several reasons, 

including the fact that it is the earliest month in the SIPP data that is covered for all sample 

members (see Appendix A). We also constructed cross-sectional samples of workers in March 

1997, March 1998, and March 1999 to examine changes in the prevalence and profiles of lowwage workers over time, due to changing economic conditions and TANF program parameters. 

The analysis of the overall employment experiences of low-wage workers (see Chapter IV) 

and the wage-growth analysis (see Chapter V) were conducted using only those who started lowwage jobs or businesses during the first six months of the panel period. We selected this timing 

to ensure a sufficient follow-up period for examining medium-term labor market experiences and 

adequate analysis sample sizes. We identified the first new job that the worker held during the 
 5 

six-month period. If the sample member had more than one job or business at the same time, we 

selected the job or business at which the sample member worked the most hours. We classified a 

sample member as a low-, medium-, or high-wage worker on the basis of the worker’s average 

hourly wage during the month of job start and the subsequent six months (for those months in 

which the worker was employed). We used this six-month period to help distinguish “true” lowwage workers from those who held low-wage jobs for only a very short time due to temporary 

changes in earnings or labor supply effort or to data errors. For similar reasons, we “smoothed” 

temporary wage fluctuations for the follow-up period using adjacent wages. 

Our analysis to examine the distribution of the length of continuous job and employment 

spells for low-wage workers and the extent to which these spells end in higher-wage jobs or in 

nonemployment focused on the low-wage spell rather than on the low-wage worker (see Chapter 

VI). The sample for this duration analysis included an entry cohort of low-wage job and 

employment spells that began at any time during the follow-up period. Spells were classified as 

low-wage (or higher-wage) on the basis of the hourly wage rate at the start of the spell. 

We used both descriptive and multivariate regression analytic methods to address the 

research questions for the study. We conducted the analysis for the full sample. In addition, 

because of differences in labor market participation decisions and experiences by gender, we 

conducted separate analyses for males and females. Within each gender group, we calculated 

statistics for the full sample, as well as for key subgroups defined by worker and job 

characteristics. We used sample weights from the SIPP files in all analyses (either the 

longitudinal or calendar year weights, depending on the analysis) to make our findings 

representative of all workers nationally. 
 6 

B. ROADMAP OF REPORT 

The rest of this report provides our findings. Chapter II reviews the literature that examines 

the low-wage labor market and discusses how our study fills in gaps in the previous research. 

Chapters III through VI present our empirical findings. In Chapter III, we discuss the 

characteristics of low-wage workers and their jobs. In Chapter IV, we discuss the overall 

employment experiences of low-wage workers during a three-year follow-up period, and Chapter 

V presents wage growth findings. Chapter VI presents results from analyses examining the 

duration of low-wage job and employment spells, the extent to which these spells end in higherwage jobs, and reentry rates into the low-wage labor market. Finally, Chapter VII presents our 

summary and conclusions. 
 7 

II. LITERATURE REVIEW 

Our literature review focuses on the small number of empirical studies that have examined 

the characteristics and labor market experiences of low-wage workers. A much larger body of 

literature exists on the labor market experiences of all adult workers, but we present results from 

these studies only when they are pertinent to low-wage workers. Similarly, there is a growing 

literature on the employment experiences of people who left welfare for work after the passage 

of PRWORA. The employed welfare population, however, is a narrow segment of the 

population of all low-wage workers. Therefore, we present findings for the employed welfare 

population to supplement our main presentation, but we do not provide a complete literature 

review for this group. Finally, a large body of literature exists on topics tangential to those that 

our study covers, such as income inequality and the demand for low-skilled workers. These 

topics are clearly related to those of our study. We do not directly address them in our empirical 

analysis, however. Thus, to keep our literature review focused, we do not discuss these topics. 

The literature review contains three sections. First, we discuss how researchers have defined 

low-wage workers. Second, we summarize the literature on the characteristics of low-wage 

workers and their jobs. Finally, we discuss what is known about job turnover and wage 

progression for those in the low-wage labor market. 

A. DEFINING LOW-WAGE WORKERS 

Researchers have used several definitions of the low-wage labor market. One approach has 

been to define low-wage workers as those whose hourly wages are below a cutoff value. Some 

researchers have defined the cutoff value as the hourly wage at which a full-time worker would 

have annual earnings below the poverty level for a family of three or four (Bernstein and 
 8 

 

Hartmann 1999; Mitnik et al. 2002; and Ryscavage 1996).1

 The wage cutoff value has also been 

defined as the minimum wage (Smith and Vavrichek 1992). 

Some researchers have defined low-wage workers as those whose annual earnings are 

below a cutoff value to account both for hourly wages that workers receive and for the amount 

that they work (that is, to adjust for the possibility that workers may not work enough hours to 

meet their families’ needs). Mishel et al. (2001) define low-wage workers as those who worked 

full- or part-time involuntarily, but whose annual earnings were not high enough to reach the 

poverty level for a family of three, which was $15,208 in 1998. Similarly, Carnevale and Rose 

(2001) use an annual earnings cutoff value of $15,000 a year, and Holzer et al. (2001) use a 

cutoff value of $12,000 a year for three consecutive years. 

Another approach used in the literature is to define low-wage workers as those whose hourly 

wages are in the bottom percentiles of the wage distribution (that is, a “relative wage” rather 

than an “absolute wage” approach). For example, Gladden and Taber (2000a) define low-wage 

workers as those whose hourly wages are below the 20th percentile of the wage distribution. 

Similarly, Long and Martini (1990) focus on those with earnings below the median value for 

full-year, full-time workers. This relative wage approach has also been used in studies that have 

examined changes over time in income inequality (see, for example, Gottschalk 1997). 

Still another approach has been to define low-wage workers as those with low education 

levels or test scores (Gladden and Taber 2000b; and Holzer and LaLonde 2000). This approach 

does not use wage or earnings information directly; instead, it relies on the fact that low-wage 

1

 For example, dividing the 2002 poverty level for a family of four ($18,100) by the number 

of full-time work hours in a year (2,080) yields a wage cutoff of $8.70 an hour for the low-wage 

sector. 
 9 

workers usually have only a high school degree or less. One problem with this approach is that a 

substantial number of higher-wage workers also have low education levels (see below). 

Finally, some studies have focused on the working poor. They define a person as a lowincome worker if the total annual income of the person’s family is below a given level and if the 

person worked a minimum number of hours during the year. For example, three papers by U.S. 

Bureau of Labor Statistics (BLS) researchers define a worker as a low-wage worker if his or her 

family’s total income was below the federal poverty level (the official U.S. Census Bureau 

definition) and if he or she worked or looked for work in at least 27 weeks over the past calendar 

year (Gardner and Herz 1992; Hale 1997; and Klein and Roens 1989). Similarly, Acs et al. 

(2001) consider a family to be poor if its total annual income was below twice the federal 

poverty level and as working if all adult family members worked an average of half-time or more 

during the year. 

B. CHARACTERISTICS OF LOW-WAGE WORKERS AND THEIR JOBS 

The literature characterizing low-wage workers and their jobs has focused on three main 

questions: (1) What is the size of the low-wage labor market? (2) Who are low-wage workers? 

and (3) What are the job and overall employment characteristics of low-wage workers? 

The fact that researchers have used several methods to define low-wage workers often 

makes it difficult to directly compare the findings across studies. In addition, researchers have 

used a number of data sets, both cross-sectional and longitudinal, to study these issues. The data 

sets often include different samples and cover different time periods, which further complicates 

direct comparisons. Despite these differences in definitions and samples, however, the key 

findings across studies are broadly consistent. This is likely due to the considerable overlap in 

samples generated using the different definitions of low-wage workers. In this literature review, 

we draw from research using each of the definitions of low-wage workers described above. 
 10 

1. What Is the Size of the Low-Wage Working Population? 

Several recent studies using a variety of data sources and definitions of low-wage workers 

(that are not based on family income levels) show that about one-quarter to one-third of all 

workers in the late 1990s and early 2000s were in low-wage jobs. Bernstein and Hartmann 

(2000) find that 29 percent of all workers in 1997 were low-wage workers, and Mitnik et al. 

(2002) find a corresponding figure of 25 percent in 2001. Both studies use cross-sectional 

Current Population Survey (CPS) data and a poverty-level wage cutoff value to define low-wage 

workers. Using a similar definition of low-wage workers, but Survey of Income and Program 

Participation (SIPP) data, Ryscavage (1996) estimates that about 25 percent of jobholders in 

1993 were in low-wage jobs. Finally, using data from the Panel Study of Income Dynamics 

(PSID), Carnevale and Rose (2001) find that, of all people who worked in 1998, 32 percent were 

low earners, whom they define as those with annual earnings below $15,000, which was just 

above the amount needed to keep a household of three out of poverty. 

Although less relevant to our study, estimates of the size of the low-income working 

population are much lower in studies that have examined workers in low-income working poor 

families. Several studies show that the poverty rate among working adults was only about six 

percent in the late 1980s, where a worker is defined as poor if his or her family’s total income 

fell below the federal poverty level and if the person worked or looked for work in at least 27 

weeks over the past calendar year (Gardner and Herz 1992; Hale 1997; and Klein and Roens 

1989). Schiller (1994) uses a stricter standard to define a worker—only those who worked fulltime and full-year—and finds that the poverty rate among them was only 2.5 percent. Kim 

(1998) uses a much less stringent standard to define workers: any adult who worked at all in the 

previous calendar year. She finds that 10 percent of workers were poor. The poverty rates for 
 11 

workers more than double if the family income threshold used to define the working poor is 

increased from 100 to 150 percent of poverty (Kim 1998; and Schwarz and Volgy 1992). 

Why are estimates of the size of the low-income working population much lower in studies 

that use total family income to define low-wage workers than in those that do not? The 

explanation is that a significant number of low-wage workers are in families with total incomes 

above the poverty level. Carnevale and Rose (2001) confirm this—they show that 57 percent of 

workers who earned less than $15,000 a year in 1998 lived in families with incomes above 

$25,000. Using SIPP data, Long and Martini (1990) find a similar result—the lower tail of the 

earnings distribution coincides only partly with the population in poverty. These results suggest 

that some low-wage workers are secondary earners and work part-time or take lower-paying 

jobs. Consequently, they fall in the low-wage group based on their own earnings (but not on 

their family income). 

Has the size of the low-wage working population changed over time? The evidence 

suggests that it has changed only slightly, although the direction of the change depends on the 

definition used to identify low-wage workers. Using cross-sectional CPS data from 1973 to 

1997, Bernstein and Hartmann (2000) find that the share of workers earning poverty-level wages 

increased slightly over time, from 24 percent in 1973, to 27 percent in 1987, to 29 percent in 

1997. Interestingly, the five percentage point increase between 1973 and 1997 was due entirely 

to an upward trend for males but not for females. Carnevale and Rose (2001), however, using 

PSID data, find that the share of the workforce with earnings below $15,000 (in 1998 dollars) 

decreased slightly over time, from 38 percent in 1979 to 36 percent in 1995 and 32 percent in 

1998. 
 12 

2. Who Are Low-Wage Workers? 

Broad consensus exists among studies that low-wage workers are disproportionately female, 

minority, young, and without a college education (Bernstein and Hartmann 1999; Carnevale and 

Rose 2001; Mishel et al. 2001; and Mitnik et al. 2002). Consistent with these findings, low-wage 

workers are also much more likely to live in households with children, that are headed by single 

females, that contain fewer adults, and that have fewer secondary workers. 

At the same time, the research indicates that low-wage workers are a relatively diverse 

group. For example, Carnevale and Rose (2001) point out that low earners are a diverse group in 

terms of their family income—among workers whose annual earnings were less than $15,000, 

more than half lived in families with total incomes above $25,000. Thus, many low-income 

workers live in families with other earners and with total family incomes above the poverty level. 

The research indicates that most changes in the composition of low-wage workers for key 

characteristics, except for gender, have mirrored those of the total workforce. For example, the 

share of workers in the low-wage labor market with a high school degree or less decreased 

substantially during the 25-year period, but the same pattern holds for all workers in the labor 

force (due to widespread educational upgrading and the long-term wage decline among noncollege graduates). Similarly, like the rest of the workforce, the low-wage sector became older 

and included more minorities. However, studies show that the low-wage workforce became 

increasingly male between 1973 and 1997, even though the female share of the entire workforce 

increased. 

3. What Are the Job and Overall Employment Characteristics of Low-Wage Workers? 

Several studies examine, in varying detail, the characteristics of jobs held by the population 

of low-wage workers and their overall employment characteristics (Acs et al. 2001; Bernstein 

and Hartmann 1999; Carnevale and Rose 2001; Mishel et al. 2001; and Mitnik et al. 2002). 
 13 

 

These studies focus on such characteristics as annual hours and weeks worked (in the low-wage 

job and in all jobs), job tenure, number of jobs held, benefits available on the job, and job 

occupations and industries. However, except for Acs et al. (2001), none of these studies examine 

the full range of job characteristics.2

The studies indicate that most low earners receive low hourly wages and are not full-time, 

full-year workers. In addition, the jobs that low-wage workers hold provide fewer benefits than 

the jobs that higher-wage workers hold, and low-wage workers have substantially less job tenure 

than higher-wage workers. Low-wage workers are represented in all occupations and all 

industries, but they are found disproportionately in retail trade industries, low-end service and 

sales occupations, and nonunion jobs (Acs [1999]; Bernstein and Hartmann [2000]; Carnevale 

and Rose [2001]; Mitnik et al. [2002]; Mishel et al. [2001]; and Osterman [2001]). 

A large literature exists demonstrating that real wages of low-skilled workers (especially 

males) declined between the early 1970s and the mid-1990s, which suggests that the economic 

circumstances of workers in the low-wage sector worsened during this period (Blank 1994; Card 

and Blank 2000; Gottschalk 1997; and Mishel et al. 2001). For example, the real wages of males 

with wages at the 20th percentile of the wage distribution declined by about 20 percent between 

1973 and 1994 (Gottschalk 1997). At the same time, real wages rose for workers in the upper 

tails of the wage distribution; thus, earnings inequality increased during the period. 

Since 1994, however, the real wages of low-skilled male and female workers increased as a 

result of the strong economy (Card and Blank 2000; and Mishel et al. 2001). For example, the 

real wage of the 10th-percentile worker rose about nine percent between 1995 and 1999. 

2

 A larger literature exists on studies that have focused on the characteristics of jobs held by 

the welfare population only (see, for example, Rangarajan et al. 1998; and Pavetti and Acs 

1997). 
 14 

Finally, some evidence exists of occupational shifts over time within the low-wage sector 

(Bernstein and Hartmann 2000). For example, low-wage workers became less likely to work in 

clerical occupations and more likely to work in low-wage sales occupations than higher-wage 

workers. Similarly, by industry, low-wage workers became less likely to work in manufacturing 

and more likely to work in low-wage services such as the retail trade. 

C. WAGE PROGRESSION FOR LOW-WAGE WORKERS 

Are low-wage jobs a stepping-stone to higher-paying jobs, or are people in low-wage jobs 

stuck in them? Despite the policy importance of this issue, little research has been conducted on 

it. Furthermore, the studies that have examined this issue have focused largely on the period 

through the early 1990s. 

The literature on this topic uses longitudinal data on the same people over time, primarily 

from the PSID, SIPP, or the National Longitudinal Survey of Youth (NLSY). The Longitudinal 

Employer Household Dynamics (LEHD) data, which combines administrative data on quarterly 

employment and earnings for individuals with data on employers, is another good source for 

examining labor market dynamics over time (see Holzer 2001). Most studies identify low-wage 

workers or low earners in a base period and examine their labor market outcomes over 

subsequent periods, ranging anywhere from 1 year to more than 15 years. For example, 

Carnevale and Rose (2001) used the PSID to identify prime-age workers with earnings less than 

$15,000 in 1988 and followed them until 1992. Similarly, Gottschalk (1997) used the PSID to 

categorize workers in 1974 into quintiles of the earnings distribution and examined their earnings 

quintiles in 1975 and 1991. As another example, Smith and Vavrichek (1992) used the SIPP 

data to examine the labor market outcomes of minimum-wage workers in 1985 one year later. 

Another approach taken in the literature to measure the extent of wage growth for low-skilled 
 15 

 

workers is to use regression methods to estimate the relationship between work experience and 

hourly wages (Gladden and Taber 2000a, and 2000b). 

The evidence in the literature about the extent of wage progression for low-wage workers 

consistently suggests that some low-wage workers experience wage growth, while others do not. 

Studies also find that movement out of the low-wage labor market into the higher-wage one 

increases with time spent in the labor market. Two patterns, however, are noteworthy. First, 

although there is some increase out of the low-wage labor market with time, the movements are 

not large. Second, a considerable number of low-wage workers drop out of the labor force over 

time, so that the group that remains is a somewhat select sample. 

Several studies examining the employment experiences of the welfare population also send a 

mixed message about the extent of wage progression for those in lower-end jobs. Using the 

NLSY, Rangarajan et al. (1998) show that job retention is a problem for most welfare recipients 

who find jobs (75 percent of the sample left their jobs within a year). However, on average, 

welfare recipients who worked steadily experienced considerable increases in earnings over time, 

primarily as a result of increases in hours and weeks worked; however, wages improved only 

modestly.3

 Studies by Bartik (1997), Burtless (1995), and Corcoran and Loeb (1999), which 

focus on the economic returns to work experience, also find modest returns to work for welfare 

recipients. 

An important policy question is the extent to which success in the labor market differs 

across key subgroups of the low-wage population. The literature on this topic is sparse. The few 

3

 Rangarajan et al. (1998) showed that a considerable number (nearly 30 percent) also 

experienced a decrease in wages over time. Recent studies that have examined wage growth 

among former welfare recipients suggest that those starting at low wages are most likely to 

experience wage growth, while those starting at relatively high wages are the ones most likely to 

experience wage reductions over time. 
 16 

studies that address this question find that wage progression is lower for females, minorities, and 

people with low education (Carnevale and Rose 2001; Smith and Vavrichek 1992; and Holtzer et 

al. 2001). Only limited information exists on the extent of wage progression for low-wage 

workers by age. The study by Smith and Vavrichek (1992), the only one we found that 

addresses this matter, finds that wage progression among minimum-wage workers was greater 

for people ages 25 to 54 than those in other age ranges. Those age 55 or older had the lowest 

wage gains, followed by teenagers. 

In addition, limited information exists on wage growth for subgroups of the low-wage 

population defined by their initial job characteristics. Rangarajan et al. (1998) examine this 

matter using NLSY data, but only for the population of welfare recipients who find jobs. They 

find that initial job characteristics are closely related to employment spell lengths and wage 

growth, even after controlling for numerous individual characteristics. In particular, wage 

growth was substantially greater for people in jobs with higher initial wages and with fringe 

benefits than for people in other jobs. Holzer and Lalonde (2000) use low-skilled youths in the 

NLSY to study job turnover rates—the extent to which workers change jobs—by initial job 

characteristics, although they do not examine wage growth directly. Their results, however, 

corroborate those of Rangarajan et al. (1998). Specifically, they find that the characteristics of 

the jobs to which less-educated workers have access, including their starting wages, occupations, 

and industries, affect their job turnover rates. For example, jobs in construction and service 

occupations have higher turnover rates than other jobs, whereas jobs in manufacturing (and to a 

lesser extent, in transportation and utility sectors) have lower turnover rates. Similarly, the 

starting wage of the job has strong negative effects on job transition rates. 

Has it become increasingly difficult for low-wage workers to move out of poverty? Duncan 

et al. (1995) suggest that the answer to this question is yes, at least during the 1970s and 1980s. 
 17 

Using the 1968-1992 waves of the PSID data, they found that, for all subgroups of 21-year-old 

men, classified by race, ethnicity, and education level, the time it took them to earn twice the 

poverty level increased throughout the 1970s and 1980s. Importantly, the worsening of mobility 

prospects has been particularly severe for workers with low levels of education. 

Another salient issue is the role of job retention in achieving wage growth for low-wage 

workers. Job turnover is common among low-skilled workers (Holzer and Lalonde 2000; Light 

and Ureta 1992; Royalty 1998; and Topel and Ward 1992). Furthermore, evidence exists that 

recent declines in employment rates among less-educated people largely reflect increasingly 

lengthy durations of nonemployment for those who leave their jobs. Consequently, an important 

policy issue is the labor market consequences of these high job turnover rates. Changing jobs, 

even with intermittent unemployment spells, might help low-wage workers progress in the labor 

market. However, it is also possible that workers progress more by staying in the same job. 

The evidence on the effect of job turnover on wage progression for low-wage workers is 

limited. However, the detailed study by Gladden and Taber (2000a) suggests that there is a 

positive return to some voluntary mobility for those with low levels of education, although the 

story is complex. Using the NLSY, they show that a voluntary job change was associated with a 

three percent increase in wage growth for low-skilled workers, although frequent job changes led 

to earnings losses. In contrast, an involuntary job change led to a five percent decrease in wages. 

They also find the intuitive result that, when workers moved directly between jobs or were 

unemployed for a short time, their wages tended to rise with turnover, but when the 

unemployment spell was longer, their wages fell. They conclude that a substantial amount of 

wage growth for low-skilled workers comes with job changes. 
 18 

D. SUMMARY 

Our review of the literature indicates that a lot is known about the characteristics of recent 

low-wage workers. About one-quarter to one-third of all workers are in the low-wage labor 

market, and their share in the full labor force has not changed much over time. Low-wage 

workers are disproportionately female, minority, young, and with low levels of education. At the 

same time, however, they are also a relatively diverse group. For example, many low-wage 

workers are poor, but many also live in families with other earners and with total family incomes 

above the poverty level. 

Consensus also exists on the characteristics of jobs that low-wage workers hold. Most 

receive low hourly wages, work part-time, and hold jobs that are markedly less stable and 

provide fewer benefits than those that higher-wage workers hold. Low-wage workers are 

represented in all occupations and industries, but they are found disproportionately in retail trade 

industries, low-end service and sales occupations, and nonunion jobs. 

Less is known about the employment dynamics and wage growth of low-wage workers, and 

the available evidence pertains to the pre-PRWORA period only. The literature has identified 

important patterns, however. First, several studies find that, although there is some movement 

out of the low-wage labor market over time, the movements are not large. Second, movement 

out of the low-wage sector increases somewhat with work experience. Third, although some 

workers escape the low-wage labor market, their wage and earnings growth is modest. Finally, 

female workers, minority workers, and those with low education levels are less likely than their 

respective counterparts to move into the higher-wage labor market. 

Our study builds on the existing literature in two ways. First, and most important, we use a 

recent cohort of low-wage workers, a unified data source, and a consistent definition of lowwage workers to address a wide range of topics covered in the literature. We provide a 
 19 

comprehensive profile of recent low-wage workers and their labor market experiences, instead of 

focusing on narrow issues typically addressed in the literature. Second, we provide a more 

complete analysis of the employment dynamics and wage progression of low-wage workers than 

is found in the literature. 

 21 

 

III. CHARACTERISTICS OF LOW-WAGE WORKERS AND THEIR JOBS 

In this chapter, we use nationally representative March cross-sectional samples of workers 

from the mid- to late 1990s to address these questions: What has the size of the low-wage 

working population been since the passage of PRWORA in 1996? Who are low-wage workers, 

and how do they compare to medium- and high-wage workers? What are the characteristics of 

jobs that low-wage workers hold? Did the characteristics of low-wage workers and their jobs 

change between 1996 and 1999? 

For most of the analysis, we use a March 1996 cross-sectional sample for several reasons, 

including the fact that it is the earliest month in the SIPP data that is covered for all sample 

members (see Appendix A). However, we also conducted some analyses using the March 1997 

to March 1999 cross-sectional samples to examine changes in the prevalence and characteristics 

of low-wage workers over time. To place our findings in perspective, we also present 

descriptive statistics for all workers and for medium- and high-wage workers.1

 Unless otherwise 

noted, all figures were calculated using our primary definition of low-wage workers: those with a 

wage below which a full-time worker would have annual earnings below poverty for a family of 

four ($7.50 in 1996, $7.72 in 1997, $7.91 in 1998, and $8.03 in 1999). All figures were 

calculated using the respective calendar year weights. Appendix B contains tables supplemental 

to those in the text of this chapter. 

1

 We refer to the combined group of medium-wage and high-wage workers as higher-wage 

workers. 
Because the mid- to late 1990s was a period of strong economic growth with low inflation, 

our findings must be interpreted carefully. The national unemployment rate decreased from 7.5 

percent in 1992 (a period of recession) to 5.4 percent in 1996, and it decreased further to 4.0 

percent in 2000, which is low by recent historical standards (see Figure III.1).2 Thus, the 

characteristics of low-wage workers during our period of investigation may be somewhat

atypical as it may include some workers who were previously unemployed or out of the labor

force. Examining trends in the characteristics of low-wage workers and their jobs using earlier 

SIPP cohorts is beyond the scope of this study. However, we did examine changes in the 

composition of the low-wage labor market between 1996 and 1999 as the economy improved. 

FIGURE III.1

U.S. UNEMPLOYMENT RATE, BY YEAR

0

2

4

6

8

10

19801981

19821983

19841985

19861987

19881989

19901991

1992199319941995

1996199719981999200020012002

Year

Source: U.S. Bureau of Labor Statistics.

Unemployment Rate (Percentage)

 22 

 2

 The unemployment rate increased to about 5.8 percent in 2002, but this is beyond the 

period our data cover. 
As discussed later, we found that the characteristics of the low-wage population did not change 

during this period, suggesting that our findings may be representative of low-wage workers in 

general. 

A. SIZE OF THE LOW-WAGE POPULATION 

The share of all workers who were low-wage workers was 28 percent in March 1996 (or 

nearly 29 million workers, Figure III.2). It decreased slightly to 27 percent in March 1997 and to 

25 percent in March 1998 and March 1999. These estimated shares are similar to those found in

previous studies (discussed in Chapter II) that used a similar cutoff value to define low-wage 

workers. For example, using March CPS data, Bernstein and Hartmann (2000) found that 29 

percent of all workers in 1997 were low-wage workers, and Mitnik et al. (2002) found a 

corresponding figure of 25 percent in 2001. 

28

43

29

27

44

30

25

44

30

25

44

31

0

10

20

30

40

50

FIGURE III.2

PERCENTAGE OF WORKERS WHO WERE LOW-, MEDIUM-, AND

HIGH-WAGE WORKERS: MARCH 1996 TO MARCH 1999

Percentage of All Workers with Wage Type

Low-Wage Medium-Wage High-Wage

Source: SIPP March cross-sectional samples.

Note: All figures were calculated using the relevant calendar year weight and the absolute poverty low-wage cutoff definition.

March 1996 March 1997 March 1998 March 1999

 23 
 24 

The slight decrease in the size of the low-wage population after 1996 may be due to declines 

in the unemployment rate during that period, suggesting that some low-wage workers may have 

been able to find higher-paying jobs because of the tight labor market. This interpretation is 

consistent with findings in the literature that the real wages of low-skilled male and female 

workers increased starting in the mid-1990s as a result of the strong economy (Card and Blank 

2000; and Mishel et al. 2001). 

Most workers in the mid- to late 1990s were medium-wage workers (Figure III.2). These 

workers are defined as those whose hourly wages were between one and two times the low-wage 

cutoff (for example, those who earned between $7.50 and $15 per hour in 1996). Roughly equal 

numbers were low-wage and high-wage workers (Figure III.2). For example, in March 1996, 43 

percent of all workers were medium-wage workers (about 44 million workers), 28 percent were 

low-wage workers (about 29 million workers), and 29 percent were high-wage workers (the 31 

million workers who earned at least $15 per hour). Interestingly, the slight decrease in the share 

of low-wage workers between 1996 and 1999 was offset by small increases in both the mediumand high-wage sectors. 

The size of the low-wage labor market differs substantially according to the definition used 

to identify low-wage workers (Table III.1). These definitions, described in greater detail in 

Chapter II, include identifying low-wage workers using the minimum wage, the 20th percentile 

of the wage distribution, annual earnings relative to the poverty level, and those with low 

education levels. The estimated fraction in the low-wage labor market according to these 

definitions range from 7 percent to 44 percent of workers. We briefly describe these findings 

below: 
 25 

TABLE III.1 

PERCENTAGE OF ALL WORKERS IN MARCH 1996 WHO WERE LOW-WAGE WORKERS, 

ACCORDING TO ALTERNATIVE DEFINITIONS OF LOW-WAGE WORKERS 

 

Wage Type 

Hourly Wage

Cutoff Used 

for the Study

($7.50) 

Hourly Wage 

Below Minimum

Wage 

($4.75) 

Hourly Wage 

Below 20th 

Percentile 

($6.57) 

Annual Earnings 

Below Poverty 

Cutoff 

($15,150)a

Low Education

Levelb

Percent of All Workers Who Are: 

Low-Wage Workers 28 7 20 32 44 

Medium-Wage Workersc 43 35 20 34 30 

Higher-Wage Workersc 30 58 60 34 26 

Sample Size 30,730 30,730 30,730 32,014 32,014 

Source: 1996 SIPP files using a March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

Annualized earnings are calculated as total monthly earnings from all jobs and businesses in March 1996 multiplied by 12. 

b

Low-wage workers are defined as those with a high school degree or less, medium-wage workers as those who had some 

college education, and higher-wage workers as those with a B.A. degree or more. 

c

Medium-wage workers are defined as those with wages/earnings that are between one and two times the level of the lowwage definition, and higher-wage workers are defined as those with wages/earnings that are greater than twice the level of 

the low-wage definition. 
 26 

 

• Only 7 percent of all workers were in the low-wage labor market if these workers 

are identified as those earning less than the minimum wage—$4.75 per hour. 

Using the minimum wage threshold, about 58 percent of those employed were highwage workers (defined as those who earned more than twice the minimum wage). 

Thus, using the minimum wage sets the bar very low for defining low-wage workers. 

• As expected, 20 percent of workers are in the low-wage labor market using the 20th 

percentile cutoff value of the wage distribution ($6.57 per hour). Thus, using our 

benchmark $7.50 cutoff value generates a larger estimate of the size of low-wage 

population than using the 20th percentile of the wage distribution as the cutoff value 

(28 percent of all workers, compared to 20 percent; Table III.1, columns 2 and 4).3

• About 32 percent were low-wage workers using an annual earnings below poverty 

cutoff. This measure defines a low-wage worker as one whose total monthly earned 

income from all jobs and businesses multiplied by 12 was below the annual poverty 

level for a family of four, and takes into account both hours worked and hourly 

wages.4

 Interestingly, while the 32 percent figure using the annual earnings measure 

is similar to our 28 percent benchmark measure, a significant number of workers are 

classified as low-wage workers using one definition but not the other. For example, 

of all those classified as low-wage workers using either definition, about 42 percent 

were classified as low-wage using one definition but not the other: 18 percent were 

classified as low-wage using only our benchmark definition, and 24 percent were 

classified as low-wage using only the annual earnings measure. These discrepancies 

suggest that there are (1) many workers with high wages who work only a limited 

number of hours, and (2) many workers with low wages who work a substantial 

number of hours. As discussed, we adopt the wage-based measure, because our study 

focuses on low-wage workers rather than low-income ones. 

• About 44 percent of workers were in the low-wage labor market if low-wage 

workers are defined as those with a high school diploma/GED or less. We believe, 

however, that the use of this education-based definition does not adequately 

characterize the low-wage population, because, according to our benchmark wagebased definition, nearly 60 percent of those with a high school credential or less were 

3

 Using the “relative” wage approach presented in column 4 of Table III.1, we defined 

medium-wage workers as those with wages between the 20th and 40th percentiles of the wage 

distribution (that is, between $6.57 and $9.25) and high-wage workers as those with wages above 

$9.25. Under our primary wage-based approach, we defined medium-wage workers as those 

who earned between $7.50 and $15 per hour, which generates a much larger estimate of the size 

of the medium-wage population than using the relative wage approach (43 percent of all 

workers, compared to 20 percent) but a much smaller estimate of the size of high-wage 

population (30 percent of all workers, compared to 60 percent). 

4

 This estimate is identical to the 1998 estimate provided by Carnevale and Rose (2001) 

using the PSID data and a similar definition of low-wage workers. 
 27 

higher-wage workers. Similarly, under our benchmark definition, about 18 percent of 

those who attended college are classified as low-wage workers. 

B. DEMOGRAPHIC CHARACTERISTICS OF LOW-WAGE WORKERS 

We examined the characteristics of low-wage workers in two interrelated ways. First, we 

examined the question: Among all workers within a particular subgroup, what percentage are 

low-wage workers? For example, we calculated the share of all male workers who are lowwage workers and the share of all workers between ages 30 and 39 who are in the low-wage 

labor market. Second, we examined the reverse question: Among all low-wage workers, what 

percentage are in a particular subgroup? For example, we calculated the percentage of all lowwage workers who are male and compared it to the corresponding shares for all workers and for 

medium- and high-wage workers. 

An example can be used to explain the difference between the two analyses and how to 

reconcile them. As discussed later, in 1996, about 84 percent of workers younger than age 20 

were low-wage workers. However, only about four percent of all low-wage workers were 

younger than age 20, because teenage workers made up only about one percent of the entire 

labor force. The two sets of findings can be reconciled by using the result that teenage workers 

were four times more likely to be low-wage than higher-wage workers. Thus, each analysis 

provides, from a different angle, important descriptive information on the characteristics of those 

in the low-wage labor market. 

We produced summary statistics for each variable one at a time. In addition, we conducted 

a cluster analysis to identify distinct groups of low-wage workers based on their full set of 

characteristics. This analysis accounts for the correlation between variables, and hence, provides 

a concise typology of groups of low-wage workers. 
Our results on the characteristics of low-wage workers corroborate findings in the literature 

that low-wage workers are disproportionately (1) young, (2) female, (3) nonwhite, (4) with a 

high school credential or less, (5) in single-adult households with children, and (6) in households

with incomes below the poverty level. At the same time, however, they are a relatively diverse 

group—they exist in a wide range of subgroups defined by individual and household 

characteristics. 

1. Individual Characteristics 

• Female workers are more likely than male workers to hold low-wage jobs. In 1996, 

more than one-third of all employed females were in the low-wage labor market, 

compared to 22 percent of employed males (Figure III.3 and Table III.2). 

Importantly, females made up about 57 percent of all low-wage workers even though 

they comprised only 46 percent of all workers (Table III.3). Thus, there were more

female than male low-wage workers, even though there were fewer females than

 28 

FIGURE III.3

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS

WITHIN GENDER, AGE, AND RACE/ETHNICITY GROUPS

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

22

35

84

41

23

30

25

37

47

0

20

40

60

80

100

Percentage of All Workers in Group Who Were Low-Wage

Male

Female

20 to 29

Black

Hispanic

White

Younger 

than 20

Older than 59

30 to 59
 29 

TABLE III.2 

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS 

WITHIN KEY WORKER SUBGROUPS, BY GENDER 

(Percentages) 

 

Individual Subgroup Malesa Femalesa Full Samplea

Percent of All Workers Who Were Low-Wage Workers 22 35 28 

Age 

Younger than 20 74 96 84 

20 to 29 37 46 41 

30 to 39 19 32 25 

40 to 49 15 27 21 

50 to 59 17 30 23 

60 or older 22 40 30 

Race/Ethnicity 

White and other non-Hispanic 18 32 25 

Black, non-Hispanic 34 39 37 

Hispanic 43 52 47 

Educational Attainment 

Less than high school/GED 46 71 56 

High school/GED 27 46 36 

Some college 22 33 27 

College graduate or more 11 18 14 

Has a Health Limitation 

No 21 33 27 

Yes 36 52 44 

Marital Status 

Married 16 32 23 

Separated, divorced, widowed 25 34 30 

Single, never married 41 43 42 

Region of Residence 

Northeast 17 28 22 

South 20 37 27 

Midwest 27 38 32 

Northwest 22 32 26 

Lives in a Metropolitan Area 

No 27 47 36 

Yes 21 31 25 

Sample Size of All Workers 16,186 14,544 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the Hispanic figures, which show that, in 1996, 43 percent of 

all male Hispanic workers and 52 percent of all female Hispanic workers were low-wage workers. 
 30 

TABLE III.3 

DISTRIBUTION OF INDIVIDUAL CHARACTERISTICS OF LOW-WAGE AND ALL WORKERS 

IN MARCH 1996, BY GENDER 

(Percentages) 

 

Male Workersa Female Workersa All Workersa

 

Individual Characteristics 

LowWage All Wage 

Levels 

LowWage All Wage 

Levels 

LowWage All Wage 

Levels

Gender 

Females 0 0 100 100 57 46 

Males 100 100 0 0 43 54 

Age 

Younger than 20 5 1 4 1 4 1 

20 to 29 34 21 27 20 30 20 

30 to 39 27 31 29 31 28 31 

40 to 49 19 27 23 29 21 28 

50 to 59 12 16 14 16 13 16 

60 or older 3 3 4 3 3 3 

Race/Ethnicity 

White and other non-Hispanic 68 82 76 81 73 81 

Black, non-Hispanic 14 9 14 12 14 11 

Hispanic 18 9 10 7 14 8 

Educational Attainment 

Less than high school/GED 22 11 17 8 19 9 

High school/GED 43 35 45 34 44 34 

Some college 17 17 18 19 17 18 

College graduate or more 18 37 21 40 20 38 

Has a Health Limitation 

No 91 95 91 94 91 94 

Yes 9 5 9 6 9 6 

Marital Status 

Married 46 66 56 61 52 63 

Separated, divorced, widowed 15 13 21 21 18 17 

Single, never married 39 21 23 18 30 20 

Region of Residence 

Northeast 15 19 16 20 16 20 

South 22 25 27 25 25 25 

Midwest 42 35 38 35 40 35 

Northwest 21 22 19 20 20 21 

Lives in a Metropolitan Area 

No 27 22 29 22 28 22 

Yes 73 78 71 78 72 78 

Sample Size 4,389 16,186 6,088 14,544 10,477 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the Hispanic figures, which show that 18 percent of all 

male low-wage workers and 10 percent of all female low-wage workers were Hispanic. 
 31 

 

males in the workforce. These findings are similar to those found in Bernstein and 

Hartmann (2000) using March 1997 CPS data. The finding that females make up a 

larger share of the low-wage population than males is critical for understanding the 

characteristics of low-wage workers, because many of the results discussed in the rest 

of this section stem from these gender differences. For example, low-wage workers 

are more likely than higher-wage ones to be in single-adult households with children 

and to be on public assistance. 

• Not surprisingly, a much higher share of younger workers than older ones earn 

low wages. In March 1996, about 84 percent of employed teenagers between ages 16 

and 19 held low-wage jobs (74 percent for male teenage workers and 96 percent for 

female teenage workers; Figure III.3 and Table III.2). The share of low-wage 

workers decreased with age from 41 percent for 20- to 29-year-old workers to 23 

percent for 50- to 59-year-old workers, but then increased slightly to 30 percent for 

those older than 60. We find a similar pattern for men and women, although lowwage shares are somewhat higher for women across all age groups. These age 

profiles are consistent with economic theory that specifies that worker productivity, 

and hence, wages, increase over time as workers accumulate work experience and 

job-specific human capital. 

• Because young workers make up only a small percentage of the full labor force, 

they constitute only a small fraction of all low-wage workers. In March 1996, only 

about 4 percent of all low-wage workers were teenagers, and 30 percent were ages 20 

to 29 (Table III.3).5

 Thus, about two-thirds of the low-wage population were primeage working adults (that is, those at least 30 years old). This occurs because only 1 

percent of the entire 1996 labor force consisted of workers who were teenagers and 

20 percent of workers who were between ages 20 and 29. Thus, although younger 

workers have a higher rate of low-wage employment than older workers, the data do 

not support the argument that low-wage workers are mainly teenagers and young 

workers without family responsibilities. 

• A higher fraction of minority workers than white workers are in the low-wage labor 

market. In March 1996, about 25 percent of white workers held low-wage jobs, 

compared to 37 percent of African American and 47 percent of Hispanic workers 

(Figure III.3 and Table III.2). Stated another way, about 28 percent of all lowwage workers were minorities, although minorities made up only 19 percent of 

the workforce (Table III.3).6

 It should be pointed out, however, that, despite the 

disproportionate share of minorities in the low-wage population, nearly three-quarters 

of all low-wage workers in March 1996 were white (Table III.3). This finding is due 

to the fact that 81 percent of workers in the entire labor force were white. 

5

 In comparison, less than one percent of medium- or high-wage workers were teenagers; 

and about 23 percent and 8 percent of medium-wage workers and high-wage workers, 

respectively, were ages 20 to 29 (Table B.1). 

6

 Bernstein and Hartmann (2000) found similar results using March 1997 CPS data. 
• Differences in the shares of low-wage workers by education level are especially 

large. For example, in 1996, about 56 percent of workers who did not complete high 

school were low-wage workers, compared to 36 percent of workers with a high 

school diploma or GED, and only about 14 percent of workers who completed college 

(Table III.2).

7

 The differences are especially large for females: nearly three-quarters

of employed females without a high school credential held low-wage jobs, compared 

to only 18 percent of those who completed college (Table III.2 and Figure III.4). 

• At the same time, however, low-wage workers are diverse in educational levels. For

example, in March 1996, nearly 20 percent of all low-wage workers graduated 

college, which is the same figure as the percentage of all low-wage workers without a

high school diploma or GED (Table III.3). Similarly, about one-quarter of all male

high-wage workers were those with a high school credential or less (Table B.1). 

Thus, there is not an exact overlap between low-wage workers and workers with low 

levels of education. 

FIGURE III.4

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS

WITHIN EDUCATION GROUPS, BY GENDER

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

46

27

22

11

71

46

33

18

0

20

40

60

80

100

Percentage of All Workers in Group Who Were Low-Wage

Males Females

Less than High School Some College

High School Credential College or More

 32 

 7

 Again, Bernstein and Hartmann (2000) found similar results. 
 33 

 

• Health status is associated with being a low-wage worker. Workers in 1996 who 

reported that they had a physical, mental, or other health condition that limited the 

kind or amount of work that could be done were much more likely to hold low-wage 

jobs than those without a health limitation (44 percent, compared to 27 percent; Table 

III.2).8

 In addition, more than half of female workers with a health limitation were in 

low-paying jobs. However, only about six percent of the workforce was made up of 

those with health problems for both males and females (Table III.3). Consequently, 

only about nine percent of all low-wage workers had health limitations. 

• Married workers tend to earn more than those not married. In 1996, only 23 percent 

of those married held low-wage jobs, compared to 30 percent of those separated, 

divorced, or widowed, and 42 percent of those single and never married (Table III.2). 

Interestingly, the differences are much larger for males than females; only 16 percent 

of married males held low-wage jobs, compared to 32 percent of married females. 

These findings by gender suggest that many married women hold secondary (parttime) jobs to supplement their spouses’ incomes.9

• Despite the overrepresentation of nonmarried workers in the low-wage population, 

more than one-half of all low-wage workers are married. For instance, in March 

1996, 52 percent of all low-wage workers were married (Table III.3). The high share 

of married workers among all workers reflects the fact that married workers are the 

predominant group of workers in the full labor force (63 percent). These findings 

further demonstrate the diversity of the low-wage population. 

• Low-wage workers are roughly proportionately dispersed across all regions of the 

country. There is some evidence that low-wage workers in 1996 were most common 

in the Midwest and least common in the Northeast, but the differences are not large 

(Tables III.2 and III.3). Interestingly, the distribution of low-wage workers across 

regions does not correlate with the magnitude of state unemployment rates across 

regions (6.5 percent for those in Northwest states, 5.6 percent for those in Northeast 

states, 5.2 percent for those in Midwest states, and 4.5 percent for those in Southern 

states; not shown). The low-wage worker findings across regions, however, are 

consistent with state poverty rates and median incomes across regions. Specifically, 

state poverty rates and median incomes were highest in the Midwest and Northwest 

regions, the regions in which workers were most likely to earn low wages (not 

shown). 

8

 Using 1996 data from the National Survey of American Families, Acs et al. (2001) also 

found a similar result that the percentage of family heads with a work-limiting health condition 

was higher in low-income working families than in higher-income working families (12 percent, 

compared to 7 percent). 

9

 To help disentangle the age findings from the marriage findings, we also computed lowwage population shares for those age 30 and older by marital status. These results are similar to 

those presented in the tables (not shown). 
• Low-wage workers are disproportionately concentrated in nonmetropolitan areas. 

This result, however, is much more pronounced for female workers than for male 

workers. For example, in 1996, about 47 percent of female workers in 

nonmetropolitan areas were low-wage workers, compared to 31 percent of female

workers in metropolitan areas (Table III.2). The corresponding figures for males are

27 nonmetropolitan and 21 percent metropolitan, respectively. Despite these

differences, however, because nearly 80 percent of workers lived in metropolitan 

areas, nearly three-quarters of low-wage workers were from them. 

2. Household Characteristics 

• Workers in households with single adults with children are more likely to hold lowwage jobs than workers in other types of households. In March 1996, about 44 

percent of female workers in single-parent households held low-wage jobs (Figure 

III.5 and Table III.4). These single parents, who account for a significant share of the 

TANF population, make up about 18 percent of all female low-wage workers 

(Table III.5). 

FIGURE III.5

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS

WITHIN HOUSEHOLD GROUPS, BY GENDER

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

36

18 20

29

44

36

30 31

0

20

40

60

80

100

Percentage of All Workers in Group Who Were Low-Wage

Males Females

Single Adults with Children Married Couples Without Children

Married Couples with Children Other Adults Without Children

• Married male workers have substantially lower rates of low-wage employment than 

unmarried male workers. For instance, under 20 percent of married male workers

were in low-wage jobs compared with over 30 percent of unmarried male workers 

(Figure III.5). Interestingly, the marriage effects for males hold for both those with 

and without children. The wage differences between married and unmarried workers 

are much smaller for female than male workers. 

 34 
 35 

TABLE III.4 

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS WITHIN 

KEY HOUSEHOLD SUBGROUPS, BY GENDER 

(Percentages) 

 

Household Subgroup Malesa Femalesa Full Samplea

Percent of All Workers Who Were Low-Wage Workers 22 35 28 

Household Type 

Single adults with children 36 44 42 

Married couples with children 18 36 26 

Married couples without children 20 30 25 

Other adults without children 29 31 30 

Household Size 

1 22 26 24 

2 20 30 25 

3 25 37 30 

4 or more 23 39 30 

Age of the Youngest Child in the Household 

(in Years for Those with Children) 

Younger than 3 22 41 30 

3 to 6 20 41 29 

6 to 12 18 37 27 

13 to 18 24 36 30 

Other Employed Adult Lives in the Household 

No 23 35 28 

Yes 22 34 28 

Has a Spouse Who Earns (for Those Married) 

No 28 45 33 

Yes 14 31 22 

Received Public Assistance in the Past Year 

No 22 34 27 

Yes 51 66 58 

In Public or Subsidized Housing 

No 22 34 27 

Yes 58 73 67 

Household Income as a Percentage of the Poverty Level 

100 percent or less 73 84 79 

101 to 200 percent 50 65 57 

More than 200 percent 15 26 20 

Sample Size of All Workers 16,186 14,544 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the household income figures, which show that, in 1996, 73 percent of 

male workers and 79 percent of female workers in households with incomes below the poverty level were low-wage workers. 
 36 

TABLE III.5 

DISTRIBUTION OF HOUSEHOLD CHARACTERISTICS OF LOW-WAGE AND 

ALL WORKERS IN MARCH 1996, BY GENDER 

(Percentages) 

 

Male Workersa Female Workersa All Workersa

Household Characteristics 

LowWage All Wage 

Levels 

LowWage All Wage 

Levels 

LowWage All Wage 

Levels 

Household Type 

Single adults with children 10 6 18 14 15 10 

Married couples with children 36 43 39 37 37 40 

Married couples without children 26 29 25 28 25 29 

Other adults without children 28 22 18 21 23 21 

Household Size 

1 10 10 7 10 8 10 

2 24 28 27 32 26 29 

3 24 22 24 23 24 22 

4 or more 41 41 41 36 41 39 

Age of the Youngest Child in the 

Household (in Years for Those 

with Children) 

Younger than 3 30 27 25 23 27 25 

3 to 6 20 21 22 20 21 21 

6 to 12 28 33 34 35 31 34 

13 to 18 22 20 20 21 21 20 

Other Employed Adult Lives in the 

Household 

No 32 31 27 27 30 29 

Yes 68 69 73 73 70 71 

Has a Spouse Who Earns (for Those 

Married) 

No 52 35 23 17 35 27 

Yes 48 65 77 83 65 73 

Received Public Assistance in the 

Past Year 

No 96 98 96 98 96 98 

Yes 4 2 4 2 4 2 

In Public or Subsidized Housing 

No 98 99 97 99 98 99 

Yes 2 1 3 1 2 1 

Household Income as a Percentage 

of the Poverty Level 

100 percent or less 14 4 12 5 13 5 

101 to 200 percent 31 14 27 14 29 14 

More than 200 percent 55 82 61 81 59 81 

Sample Size 4,389 16,186 6,088 14,544 10,477 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the household type figures, which show that 10 percent of all male 

low-wage workers and 18 percent of all female low-wage workers lived in single-adult households with children. 
 37 

 

• Workers in larger households are more likely to be in low-wage jobs, a result driven 

by females but not males. For instance, nearly 40 percent of female workers in larger 

households were in low-wage jobs compared with about 26 to 30 percent in smaller 

households (Table III.4). Interestingly, larger households have higher rates of lowwage employment than smaller households within each household type (not shown). 

For example, in 1996, single-parent female workers with at least three children were 

much more likely to hold low-wage jobs than those with fewer children (57 percent, 

compared to 40 percent). This result may be due to child care problems that make it 

more difficult for women with many children to find higher-paying jobs or to increase 

their educational levels. Similarly, low-wage workers are more prevalent in larger 

than smaller households without children, because adults living in multifamily adult 

households tend to have low incomes and low educational levels. 

• While the age of the youngest child in the household is not associated with being a 

low-wage worker for the full sample, some important differences exist by household 

type. The age of the youngest child is not associated with hourly wages for married 

workers with children (Table III.4). However, there is a strong association between 

child’s age and wage levels for females in single-parent households. Specifically, in 

1996, about 61 percent of single-parent female workers whose youngest child was 

less than three months old were low-wage workers, compared to only 37 percent for 

those whose youngest child was a teenager (not shown). Clearly, these findings are 

confounded with age effects, because as discussed, young workers tend to be in the 

low-wage labor market and are more likely than older workers to have small children. 

However, we find similar, although weaker, associations between the age of the 

youngest child and being a low-wage worker using only those single-parent females 

who were older than age 30. 

• Overall, the presence of employed adults in the household is not correlated with 

being a low-wage worker.10 However, married workers tend to earn more if their 

spouse is employed than if their spouse is nonemployed. For example, only 14 

percent of married males with a working spouse were in the low-wage labor market in 

1996, compared to 28 percent of those without a working spouse (Table III.4). These 

unexpected results are likely due to the higher education levels of workers with 

employed spouses than workers with nonemployed spouses. For example, in 1996, 

about 56 percent of workers with an employed spouse completed more than high 

school, compared to 40 percent of those with a nonworking spouse. 

10 This finding contrasts with Acs et al. (2000), who found that low-income working 

families are much less likely than higher-income working families to have secondary workers. 
• Not surprisingly, workers in households that receive public assistance or who live 

in public or subsidized housing are more than twice as likely as their counterparts 

to be low-wage workers. These findings hold equally by gender (Table III.4). 

Similarly, being a low-wage worker is highly correlated with household income for 

both male and female workers (Figure III.6 and Table III.4). For example, in 1996,

about 79 percent of those in households with incomes below the poverty level were

low-wage workers, compared to 57 percent for those in households with incomes 

between 101 to 200 percent of poverty, and only 20 percent for those in households 

with incomes more than 200 percent of poverty. 

• At the same time, however, because more than 80 percent of all workers had 

household incomes above 200 percent of poverty, nearly 60 percent of all low-wage 

workers were in these higher-income households (Table III.5). Thus, low earners

are a diverse group in terms of their household incomes. Carnevale and Rose (2001) 

found a similar result using PSID data: among workers whose annual earnings were 

less than $15,000, more than half lived in families with total incomes above $25,000. 

FIGURE III.6

PERCENTAGE OF ALL 1996 WORKERS WHO WERE LOW-WAGE WORKERS

WITHIN POVERTY GROUPS, BY GENDER

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

73

50

15

84

65

26

0

20

40

60

80

100

Percentage of All Workers in Group Who Were Low-Wage

Males Females

Below Poverty Level 100 to 200 Percent of Poverty More than 200 Percent of Poverty

 38 
 39 

3. Changes Over Time 

Did the characteristics of low-wage workers change between 1996 and 1999 as the 

unemployment rate decreased and more states implemented PRWORA provisions? The answer 

to this question appears to be no. The distribution of low-wage workers across key subgroups 

remained reasonably constant over time (Table III.6). In particular, the fraction of low-wage 

workers who were female, young, poor, and in households with single adults with children did 

not change appreciably during the mid- to late 1990s. Thus, changes in the unemployment rate 

and the implementation of new welfare rules that led many welfare recipients to leave welfare 

for work did not appear to affect the composition of the low-wage population. These results 

suggest that our empirical results about the characteristics of the low-wage population may be 

representative of the low-wage population under a weaker economy, although fully examining 

this issue is beyond the scope of this study. 

4. Typologies of Low-Wage Workers 

Thus far, we have examined worker characteristics one at a time. However, many of these 

characteristics are highly correlated with each other. Thus, we conducted a cluster analysis to 

identify typologies of low-wage workers using a combination of worker characteristics. In this 

analysis, each worker was “optimally” assigned to a cluster on the basis of the similarity of that 

worker’s characteristics to those of other workers within the cluster. A distance measure 

between two workers was constructed by calculating the sum of squared differences between 

each of the workers’ characteristics. Workers were then allocated to clusters to minimize the 

within-cluster variance and maximize the between-cluster variance. Separate analyses were 

conducted for males and females. 
 40 

TABLE III.6 

DISTRIBUTION OF LOW-WAGE WORKERS, BY SUBGROUP AND YEAR 

Subgroup March 1996a March 1997a March 1998a March 1999a

Gender 

Male 43 42 42 42 

Female 57 58 58 58 

Age 

Younger than 20 4 4 4 4 

20 to 29 30 29 28 27 

30 to 39 28 27 26 26 

40 to 49 21 22 23 24 

50 to 59 13 15 15 16 

60 or older 3 4 4 4 

Race/Ethnicity 

White and other non-Hispanic 73 72 71 72 

Black, non-Hispanic 14 14 15 15 

Hispanic 14 14 13 13 

Educational Attainment 

Less than high school/GED 19 19 19 18 

High school/GED 44 45 44 43 

Some college 17 17 17 18 

College graduate or more 20 20 20 21 

Has a Health Limitation 

Yes 9 7 6 6 

No 91 93 94 94 

Marital Status 

Married 52 52 52 52 

Separated, divorced, widowed 18 18 18 18 

Single, never married 30 29 30 30 

Household Type 

Single adults with children 15 15 15 15 

Married couples with children 37 38 39 38 

Married couples without children 25 27 26 27 

Other adults without children 23 21 20 21 

Household Income as a Percentage of the Poverty 

Level 

100 percent or less 13 14 13 12 

101 to 200 percent 29 31 31 29 

More than 200 percent 59 56 56 59 

Sample Size of All Workers 30,730 26,581 24,990 25,148 

Source: SIPP March 1996 to March 1999 cross-sectional samples. 

Note: All figures are weighted using the relevant calendar year weight. 

a

The interpretation of the statistics can be illustrated using the Hispanic figures, which show that 14 percent of all 

low-wage workers were Hispanic in 1996 and 1997, and 13 percent of all low-wage workers were Hispanic in 

1998 and 1999. 
Our cluster analysis revealed that both male and female workers could effectively be 

grouped into three clusters that captured the diversity of the low-wage population (Figure III.7 

and Table B.2). The distinguishing features of the three clusters for males can be described as 

follows: 

1. Young, Single, Educated. These workers are characterized by their high education 

levels; about 55 percent attended college (compared to only 35 percent of all male 

low-wage workers). A disproportionate number of these workers are under age 40, 

white, and unmarried, and nearly all are from well-to-do households. This cluster 

contains 39 percent of all male low-wage workers. 

2. Older, Middle-Income, Low-Education, White. In March 1996, about 84 percent

of these workers were age 30 or older, and 93 percent were white. In addition, only 

23 percent attended college. These workers are concentrated in middle-income 

households (those with incomes between one and two times the poverty level). They 

account for about 36 percent of all male low-wage workers. 

FIGURE III.7

SHARE OF LOW-WAGE WORKERS, BY TYPOLOGY AND GENDER

(Percentages)

36%

39%

25%

Source: SIPP 1996 March cross-sectional sample.

Note: All figures were calculated using the 1996 calendar year weight.

27% 56%

17%

Young, Single, Educated

Older, Middle-Income, Low-Education, White

Minority, Married, Low-Income, Low-Education

Married, Educated, White

Older, Middle-Income, Minority

Single-Parent, Low-Income

Males Females

 41 
 42 

3. Minority, Married, Low-Income, Low-Education. Nearly all of these workers are 

minorities (about 95 percent in 1996), and have low education levels (38 percent 

were high school dropouts in 1996). These workers tend to be married (nearly 80 

percent in 1996). In addition, they tend to live in poor households. These workers 

make up about 25 percent of the male low-wage worker population. 

The three clusters for females can be categorized as follows: 

1. Married, Educated, White. These workers are characterized by their high marriage 

rates and education levels. In 1996, more than 80 percent of these workers were 

married, although many did not have children. Nearly half had attended college. In 

addition, the majority had spouses who worked. Nearly all were white. Not 

surprisingly, nearly all of these workers were in households with incomes above 

twice the poverty level. Thus, many of these workers are secondary workers who 

have low-wage and part-time jobs to supplement their husbands’ incomes. These 

workers account for the largest share of female low-wage workers—56 percent in 

1996. 

2. Older, Middle-Income, Minority. These workers tend to be older than average, and 

nearly two-thirds are minorities. Most live in households with incomes between one 

and two times the poverty level. In addition, they tend to be married with children. 

Their education levels are typical of other low-wage female workers. This cluster 

contains about 27 percent of all low-wage workers. 

3. Single-Parent, Low-Income. These workers tend to be single parents and live in 

poor households. In 1996, more than three-quarters lived in single-parent 

households, and about 16 percent received public assistance in the previous year 

(compared to only 4 percent of all female low-wage workers). More than one-half of 

these workers lived in households with incomes below the poverty level. Not 

surprisingly, these workers tend to have low education levels. However, they are not

characterized by their age or race/ethnicity. In 1996, about 17 percent of all female 

low-wage workers were in this cluster. 

C. JOB AND OVERALL EMPLOYMENT CHARACTERISTICS OF LOW-WAGE 

WORKERS 

SIPP contains some information on job and business characteristics, including usual hours 

per week worked, hourly wages, monthly earnings, occupation, industry, job tenure, whether 

health insurance is available on the job, and union membership status. We followed a similar 

approach for tabulating these characteristics as for tabulating workers’ demographic 

characteristics. Our tables present distributions of job and business characteristics for low-wage 
 43 

 

workers, and all workers.11 Unlike the previous section, however, we do not present the reverse 

figures (that is, the share of low-wage workers among those with a particular job characteristic), 

because these figures have less policy relevance. We present figures separately for males and 

females and present selected statistics by age. In addition, we present selected figures for the six 

(three male and three female) low-wage worker typologies discussed above. 

We find that many low-wage workers receive hourly wages substantially below the lowwage cutoff value used in our study. In addition, low-wage workers hold jobs that are markedly 

less stable and provide fewer benefits than the jobs higher-wage workers hold. Interestingly, 

however, most report that they usually work at least 35 hours per week (that is, full-time). Lowwage workers are represented in all occupations and industries, but they are disproportionately 

found in retail trade industries, service occupations, and nonunion jobs. In combination, our 

results are similar to those found in Acs et al. (2001), Bernstein and Hartmann (1999), Carnevale 

and Rose (2001), Mishel et al. (2001), and Mitnik et al. (2002). 

1. Hourly Wages 

• Many low-wage workers earn considerably less than the low-wage cutoff value 

used in this study. As shown in Table III.7, in March 1996, only 21 percent of lowwage workers earned between $7.00 and $7.50 (the low-wage threshold value used in 

this study). More than one-quarter earned less than $5.00 per hour (which is close to 

the $4.75 minimum wage). On average, low-wage workers earned $5.58 per hour, 

compared to $13.62 for all workers.12 Interestingly, the wage distributions for lowwage workers are similar for males and females. However, males typically earned 

more than females among medium- and high-wage workers (Table B.3). 

11 A breakdown of characteristics by medium- and high-wage workers is included in 

Appendix B. 

12 Medium-wage workers earned an average of about $11.00 per hour, and high-wage 

workers earned an average of about $25 per hour (Table B.3). 
 44 

TABLE III.7 

DISTRIBUTION OF JOB CHARACTERISTICS OF LOW-WAGE AND ALL WORKERS 

IN MARCH 1996, BY GENDER 

(Percentages) 

 

Male Workersa Female Workersa All Workersa

Job Characteristics 

LowWage All Wage

Levels 

LowWage All Wage 

Levels 

LowWage All Wage

Levels 

Hourly Wages 

Less than $5.00 26 6 27 9 27 7 

$5.00 to $5.99 24 5 26 9 25 7 

$6.00 to $6.99 28 6 27 9 28 8 

$7.00 to $7.50 22 5 20 7 21 6 

$7.51 or more 0 78 0 65 0 72 

(Average hourly wage in dollars) (5.62) (15.38) (5.54) (11.52) (5.58) (13.62)

Usual Hours Worked per Week 

1 to 19 3 1 9 6 6 4 

20 to 34 13 5 25 17 20 11 

35 to 40 51 50 52 58 52 53 

More than 40 34 43 14 20 22 33 

(Average hours worked) (42.9) (44.7) (35.2) (37.7) (38.5) (41.5) 

Weekly Earnings 

Less than $150 15 4 29 12 23 7 

$150 to $299 64 16 63 27 63 21 

$300 to $600 21 40 8 42 13 41 

$600 or more 0 41 0 19 0 31 

(Average weekly earnings in dollars) (240) (702) (196) (443) (215) (584) 

Owns Business (Self-Employed) 18 12 10 7 13 10 

Covered by Health Insuranceb

41 74 57 79 50 76 

Sample Size 4,389 16,186 6,088 14,544 10,477 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the health insurance figures, which show that 41 percent 

of all male low-wage workers and 57 percent of all female low-wage workers had health insurance coverage through 

their jobs. 

b

SIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the 

time of the interview. Thus, the health insurance figures pertain to jobs held by the March 1996 cross-sectional 

sample at the time of their wave 1 interviews. These jobs sometimes differed from the jobs they held in March 

1996. 
• Low-wage workers between ages 20 and 50 typically earn more than those younger

and older (Figure III.8). However, the wage differences by age are smaller than 

expected. For example, in 1996, low-wage teenagers earned an average of $5.42 per

hour, compared to $5.60 for low-wage workers in their 30s.13

• We find some wage differences across the low-wage worker typologies. Among 

low-wage working men, hourly wages tend to be highest for the young, single, 

educated group (Table B.4). Similarly, among low-wage working women, hourly 

wages tend to be highest for the married, white, educated group, and to be lowest for

the single-parent, low-income group. 

2. Hours Worked per Week 

• Most low-wage workers report working full-time (defined as those who report 

usually working at least 35 hours per week; Table III.7 and Figure III.9). 

However, they work fewer hours than other workers. For example, among male

workers in March 1996, about 85 percent of those with low wages worked full-time,

FIGURE III.8

AVERAGE HOURLY WAGE FOR LOW-WAGE WORKERS IN MARCH 1996, BY AGE

$5.42 $5.65 $5.60 $5.55 $5.49 $5.44

0

2

4

6

8

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

Average Hourly Wage

16 to 19 20 to 29 30 to 39 40 to 49 50 to 59 60 to 64

 45 

 13 We find similar age patterns for males and females. 
85

93

66

78

74

86

0

20

40

60

80

100

FIGURE III.9

PERCENTAGE OF LOW-WAGE AND ALL WORKERS WHO WORKED

AT LEAST 35 HOURS PER WEEK, BY GENDER

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

Percentage Working Full-Time in March 1996

Males Females Total

Low-Wage All Wage Levels

compared to 93 percent of all workers (Table III.7 and Figure III.9).14 Similarly, 

about 66 percent of low-wage female workers usually worked full-time, compared to 

78 percent of all employed females. Average hours worked per week, however, did 

not vary across the low-wage worker typology groups (Table B.4). It is interesting to 

note that, in total, 86 percent of all workers worked full-time in the strong economy

of 1996. This figure is somewhat higher than the 83 percent figure per year between 

1985 and 1992 (Statistical Abstract of the United States). 

• Not surprisingly, the oldest and youngest low-wage workers work fewer hours than 

other low-wage workers (not shown). In 1996, less than two-thirds of teenage and 

elderly male low-wage workers worked full-time, compared to 85 percent of other 

low-wage workers. Similarly, only about one-half of teenage and elderly female lowwage workers were employed full-time. 

 46 

 14 For instance, 95 percent of medium-wage workers and 97 percent of high-wage workers 

worked at least 35 hours per week (Table B.3). 
 47 

 

3. Weekly Earnings 

• The weekly earnings of low-wage workers are typically much lower than for 

higher-wage workers (Table III.7). These differences are primarily due to 

differences in hourly wage rates but are also due in part to lower work effort for lowwage employees. In 1996, male low-wage workers earned an average of only $240 

per week, whereas the average U.S. male employee earned nearly three times more.15 

Low-wage females typically earn less than low-wage males (an average of $196 per 

week for females in 1996, compared to $240 for males). This is because low-wage 

females typically work fewer hours per week, although hourly wages are similar by 

gender. The weekly earnings levels of low-wage workers translate into annual 

earnings well below the poverty level for both sexes and for each of the low-wage 

worker typology groups. 

4. Availability of Health Insurance Coverage16

• Many of those in the low-wage population are covered by health insurance through 

their employers, although coverage rates are substantially lower than for higherwage workers. For instance, 50 percent of all low-wage workers had employer-based 

health insurance coverage compared with 76 percent of all workers (Table III.7 and 

Figure III.10). The comparable figures were about 90 percent for medium-wage 

workers and 96 percent for high-wage workers (Table B.3). Interestingly, health 

insurance coverage rates for low-wage workers are higher for females than males (57 

percent, compared to 41 percent), although there are no gender differences in the rates 

for medium- and high-wage workers.17

• We also find large differences in employer-based health insurance coverage rates 

across the low-wage typology groups. In particular, among males, coverage rates are 

much higher for the young, single, educated group than for the minority, married, 

low-income, low-education group (46 percent, compared to 35 percent, Table B.4). 

Differences among females are more pronounced: the coverage rate for the married, 

white, educated workers is 67 percent, compared to only 31 percent for the singleparent, low-income workers. These major differences reflect differences in the 

quality of jobs held by workers across the groups. 

15 The comparable numbers were $495 per week for male medium-wage workers and $1,217 

per week for high-wage workers (Table B.3). 

16 SIPP contains information on employer-based health insurance coverage only for jobs that 

were in progress at the time of the interview. Thus, the health insurance figures pertain to jobs 

held by the March 1996 cross-sectional sample at the time of their wave 1 interviews. These 

jobs sometimes differed from the jobs they held in March 1996. 

17 These findings may partly reflect lower rates of self-employment for low-wage female 

workers than for low-wage male workers, as discussed in the next section. 
41

74

57

79

50

76

0

20

40

60

80

100

FIGURE III.10

PERCENTAGE OF LOW-WAGE AND ALL WORKERS WITH AVAILABLE

HEALTH INSURANCE ON THE JOB, BY GENDER

Source: SIPP 1996 March cross-sectional samples.

Note: All figures were calculated using the 1996 calendar year weight.

Percentage with Available Health Insurance on the Job in March 1996

Males Females Total

Low-Wage All Wage Levels

5. Self-Employment Status 

• About 13 percent of low-wage workers in 1996 were self-employed (that is, owned 

businesses). Self-employment rates were higher for males than females (18 percent, 

compared to 10 percent, Table III.7). Furthermore, self-employment rates among 

low-wage workers were somewhat higher than for all workers, for both males and 

females. For instance, about 18 percent of low-wage male workers were selfemployed, compared to about 10 percent for other male workers. Interestingly, the

group of older, middle-income, and low-education male workers had the highest selfemployment rates among all the low-wage worker typologies (Table B.4). 

• There are some important differences between the employment characteristics of 

jobholders and business owners. Average hourly wages are significantly higher for 

those with jobs than businesses ($5.75, compared to $4.48 in 1996, Table B.5). 

Business owners also tend to work more hours than job holders (44 hours compared 

to 38 hours for all low-wage workers in 1996). Health insurance coverage rates are 

also substantially higher for those with jobs. Finally, there are some differences 

across occupations, as discussed in the next section. 

 48 
 49 

 

6. Occupations, Industries, and Union Membership 

• Low-wage workers are spread across all occupations and industries. However, they 

are substantially overrepresented in service professions and underrepresented in 

professional and technical occupations. In 1996, for example, nearly one-third of all 

low-wage workers were in service occupations, compared to only 16 percent of all 

workers (Table III.8). Conversely, only 14 percent of low-wage workers were in 

professional and technical occupations, compared to 33 percent for all workers.18 The 

share of low-wage workers in administrative support and clerical, and machine and 

construction occupations mirrored the corresponding shares for all workers. Lowwage workers are also spread across all industries (Table III.8). However, they are 

most prevalent in wholesale and retail trades. 

• There are some gender differences across occupations for low-wage workers. In 

particular, men are much more likely to be in machine and construction occupations, 

whereas women are much more likely to be in administrative support and clerical 

ones (Table III.8). We observe some similar differences by gender across occupation 

for medium- and high-wage workers (Table B.3). For instance, among medium- and 

high-wage workers, men were more likely than women to be in machine and 

construction operators (similar to the pattern for low-wage male workers). In 

contrast, however, female medium- and high-wage workers were more likely to be in 

professional and technical occupations (Table B.3). There are smaller gender 

differences, however, across industries among low-wage workers. 

• We also find differences in occupations across the low-wage worker typology 

groups that are consistent with previous findings on hourly wage rates and the 

availability of health insurance across these groups. Specifically, among low-wage 

workers, the young, single, educated male workers and the married, white, educated 

female workers are much more likely than their counterparts to be in professional and 

clerical occupations and less likely to be in service occupations (Table B.4). Thus, it 

is not surprising that these workers receive higher wages and are more likely to have 

available health insurance than their counterparts. 

• There are substantial differences in the occupations of jobholders and business 

owners, although the patterns differ by gender. For males, business owners are 

much more likely to be in professional and technical trades than jobholders, and earn 

low hourly wages because they work many hours (Table B.5). Female business 

owners, on the other hand, are overrepresented in service occupations (in 1996, onehalf of all female business owners were in service trades, compared to only one-third 

of female jobholders). 

18 For instance, 22 percent of medium-wage male workers and 51 percent of high-wage male 

workers were in professional and technical occupations (Table B.3). The comparable figures 

were 35 and 71 percent, respectively, for female workers. 
 50 

TABLE III.8 

DISTRIBUTION OF OCCUPATIONS, INDUSTRIES, AND UNION MEMBERSHIP OF LOW-WAGE 

AND ALL WORKERS IN MARCH 1996, BY GENDER 

(Percentages) 

 

Male Workersa Female Workersa All Workersa

Job Characteristics 

LowWage All Wage

Levels 

LowWage All Wage 

Levels 

LowWage All Wage

Levels 

Occupation 

Professional/technical 14 31 14 36 14 33 

Sales/Retail 11 11 16 11 14 11 

Administrative support/clerical 5 6 20 25 14 15 

Service professions/ handlers/ 

cleaners 30 14 36 18 33 16 

Machine/construction/production/ 

transportation 32 35 13 10 21 23 

Farm/agricultural/other workers 8 4 1 1 4 2 

Industry 

Agriculture/forestry/fishing/ 

hunting 11 7 8 6 9 6 

Mining/manufacturing/ 

construction 20 30 12 14 16 23 

Transportation/utilities 5 9 2 4 3 7 

Wholesale/retail trade 27 17 31 18 29 17 

Personal services 12 7 12 8 12 7 

Health services 2 3 10 15 7 8 

Other services 11 19 22 33 17 26 

Other 12 8 3 2 7 6 

Union Member 7 19 6 13 6 16 

Sample Size 4,389 16,186 6,088 14,544 10,477 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the union figures, which show that seven percent of all 

male low-wage workers and six percent of all female low-wage workers were union members. 
 51 

 

• Low-wage workers are much less likely than higher-wage ones to be union 

members. For example, in 1996, about 6 percent of low-wage workers were union 

members, compared to 16 percent of all workers (Table III.8). Among low-wage 

workers, there were no differences in union membership by gender. In comparison, 

medium- and high-wage males were more likely to be union members than their 

female counterparts. For instance, 18 percent of medium-wage and 27 percent of 

high-wage male workers were union members (Table B.3). The comparable figures 

for females were 15 and 22 percent, respectively. 

7. Other Employment-Related Characteristics 

• Many low-wage workers at a point in time have relatively long job tenure, 

but job tenure is typically shorter for low-wage workers than for all workers. In 

March 1996, for example, 41 percent of low-wage wage workers had at least three 

years of job tenure, compared to 61 percent for all workers (Table III.9). Similarly, 

average job tenure was 47 months for low-wage workers, compared to 86 months for 

all workers.19 At the same time, a substantial fraction of low-wage workers have 

short job tenure. About 35 percent of low-wage workers had started their jobs within 

a year prior to March 1996, compared to 20 percent for all workers. Interestingly, the 

distribution of months on the job is similar for low-wage males and females. 

We emphasize that these job tenure figures pertain to a cross-sectional sample, 

and not to an “entry cohort” sample of low-wage workers who started jobs. The 

cross-sectional sample contains workers with longer-than-average job spells.20 

Consequently, the job tenure figures are larger than they would be for an entry cohort 

sample. 

• Only a small percentage of low-wage workers hold more than one job or business. 

In 1996, only 8 percent of male and female low-wage workers held more than one 

job. This figure is similar to the fraction of all workers with more than one job (Table 

III.9). Because relatively few low-wage workers hold more than one job, statistics on 

their total hours worked per week and weekly earnings in all jobs are similar to those 

presented above for the primary job (Table III.9). 

19 It was 85 months for medium-wage workers and 125 months for high-wage workers (not 

shown). 

20 For example, among low-wage workers who started their jobs in March 1992, only those 

whose jobs lasted for at least four years would be in the March 1996 cross-sectional sample; 

workers with shorter spells would not be included in the cross-sectional sample. 
 52 

TABLE III.9 

DISTRIBUTION OF OTHER EMPLOYMENT-RELATED CHARACTERISTICS OF LOW-WAGE 

AND ALL WORKERS IN MARCH 1996, BY GENDER 

(Percentages) 

Male Workersa Female Workersa All Workersa

Job Characteristics 

LowWage All Wage

Levels 

LowWage All Wage 

Levels 

LowWage All Wage

Levels 

Tenure at Job or Business (Months) 

Less than 6 23 11 21 12 22 12 

6 to 12 12 7 14 9 13 8 

12 to 24 13 10 15 11 14 11 

24 to 36 9 8 11 9 10 9 

Longer than 36 43 63 39 58 41 61 

(Average tenure) (49) (93) (47) (79) (47) (86) 

Working in More than One Job or 

Business 8 7 8 7 8 7 

Total Hours Worked per Week in All 

Jobs and Businesses 

Less than 20 3 1 9 6 6 3 

20 to 34 12 5 24 16 19 10 

35 to 40 47 47 49 55 49 50 

More than 40 38 47 18 24 27 36 

(Average total hours worked) (44.8) (46.3) (36.5) (38.8) (40.1) (42.9) 

Weekly Earnings from All Jobs and 

Businesses 

Less than $150 15 4 28 11 22 7 

$150 to $299 61 15 61 26 61 20 

$300 to $600 24 39 10 43 16 41 

$600 or more 1 42 0 20 0 32 

(Average weekly earnings) (256) (717) (204) (453) (227) (596) 

Sample Size 4,389 16,186 6,088 14,544 10,477 30,730 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

The interpretation of the statistics can be illustrated using the tenure figures, which show that 23 percent of all male 

low-wage workers and 21 percent of all female low-wage workers started their jobs within six months of March 

1996. 
 53 

8. Changes Over Time 

• The quality of low-wage jobs improved slightly between 1996 and 1999 as the 

economy improved (Table III.10). Hourly wages increased from $5.58 per hour to 

$5.86 per hour, which is consistent with findings in the literature that the real wages 

of low-skilled male and female workers increased starting in the mid-1990s as a result 

of the strong economy (Card and Blank 2000; and Mishel et al. 2001). Similarly, the 

fraction with health insurance coverage from the employer increased from 51 to 54 

percent. The distributions of occupations of low-wage jobs remained fairly constant, 

although there was a slight increase in the percentage of low-wage workers in higherpaying professional and technical occupations. 
 54 

TABLE III.10 

DISTRIBUTION OF KEY JOB CHARACTERISTICS OF LOW-WAGE WORKERS, BY YEAR 

Characteristic March 1996 March 1997 March 1998 March 1999

Average Hourly Wage in 1996 Dollars 5.58 5.61 5.71 5.86 

Owns Business (Self-Employed) 13 13 13 13 

Health Insurance Available on the Job 51 53 54 54 

Occupation 

Professional/technical 14 14 14 15 

Sales/retail 14 14 14 14 

Administrative support/clerical 14 13 13 13 

Service professions/handlers/cleaners 33 33 34 34 

Machine/construction/production/ 

transportation 21 21 21 20 

Farm/agricultural/other workers 4 4 5 4 

Union Member 6 4 4 4 

Sample Size 8,530 7,091 6,258 6,150 

Source: SIPP March 1996 to March 1999 cross-sectional samples. 

Note: All figures are weighted using the relevant calendar year weight. 
 55 

 

IV. OVERALL EMPLOYMENT EXPERIENCES OF LOW-WAGE WORKERS 

What are the overall employment experiences of low-wage workers over a three-and-onehalf year follow-up period after job start? How many eventually find a higher-wage job? How 

many move in and out of the low-wage labor market? What fraction of time are they in lowwage jobs, higher-wage jobs, and no jobs? Do employment rates increase over time? How do 

the employment patterns of low-wage workers compare to those of higher-wage workers? 

Which groups of workers have the best outcomes? 

This chapter addresses these questions using a nationally representative sample of 

workers in the SIPP longitudinal panel file who started jobs during the first six months of the 

panel period (roughly in the first half of 1996). As discussed in Chapter II, to minimize 

misclassification errors, we defined a worker as a low-, medium-, or high-wage worker on the 

basis of the worker’s average wage during the month of job start and the subsequent six months. 

We then examined the labor market experiences of these workers over a 42-month (three-andone-half year) follow-up period from the month of job start. We conducted a descriptive 

(univariate) analysis by gender, as well as a multivariate analysis to efficiently summarize key 

labor market outcomes for subgroups of low-wage workers. To place our findings in context, we 

also present selected descriptive statistics for medium- and high-wage workers (a group whom 

we often refer to collectively as higher-wage workers).1

 All statistics were calculated using the 

1

 In the previous chapter, we focused our discussion on the comparison of the characteristics 

of low-wage workers to those of all workers. However, in this chapter, we focus our discussion 

on the comparison of low-wage workers to medium- and high-wage workers in order to assess 

the extent to which the labor market experiences (such as total time employed) of low-wage 

workers differ from those of higher-wage workers. 
 56 

longitudinal panel weight. Supplemental tables to those presented in the main text are found in 

Appendix B. 

The entry cohort sample used in the overall employment analysis is conceptually different 

than the March 1996 cross-sectional sample used to describe the characteristics of low-wage 

workers and their jobs in the last chapter. The entry cohort sample consists of workers who 

started a job spell during a six-month window, whereas the cross-sectional sample consists of 

workers in the middle of their job spells, and hence, contains a disproportionate share of workers 

with longer-than-average spells. The demographic and job characteristics of the two sets of 

workers reflect these differences (Table C.1). Workers in the entry cohort sample tend to be 

younger and to live in poorer households than those in the cross-sectional sample. Similarly, 

workers in the entry cohort sample typically worked fewer hours, had lower weekly earnings, 

and were much less likely to have employer-based health insurance coverage. There are few 

differences, however, between the education levels, racial and ethnic composition, hourly wages, 

and occupations of workers in the two samples. 

In the remainder of this chapter, we present descriptive findings by gender for the full set of 

outcome measures, and then present findings from the subgroup and multivariate analyses for 

selected outcomes. We caution readers again that the 1996 to 1999 follow-up period covered by 

our data was a period of strong economic growth with a high demand for labor. These strong 

economic conditions may have produced more positive labor market outcomes for our sample 

than would have been the case under a weaker economy. 
 57 

A. DESCRIPTIVE ANALYSIS FINDINGS, BY GENDER 

Our descriptive analysis reveals that there was some movement into and out of the low-wage 

labor market for low-wage workers. During a three-and-one-half-year period after job start, 

most workers held medium-wage jobs at some point. However, many also returned to the lowwage labor market. Low-wage workers were employed about 80 percent of the time. Altogether, 

low-wage workers spent about twice as much time in low-wage than higher-wage (that is, 

medium- or high-wage) jobs. However, employment rates in higher-wage jobs increased over 

time, especially for males. 

These results indicate that low-wage workers have some upward mobility over the mediumterm. At the same time, however, a segment of the low-wage population remains entrenched in 

low-wage jobs. Next, we discuss the evidence for these findings. 

1. Overall Employment Rates in Low-, Medium-, and High-Wage Jobs 

Most low-wage workers in our sample left the low-wage labor market for higher-paying 

employment—either in the same job or a different job—within three to four years after starting 

their low-wage job (Figure IV.1). About 69 percent of males held medium-wage jobs and 13 

percent held high-wage jobs during the follow-up period; only 30 percent held low-wage jobs 

only. Employment rates in higher-paying jobs were somewhat lower for females than males, 

suggesting that females experienced less upward mobility than males. However, female 

employment rates in higher-paying jobs were still high; about one half of women workers ever 

held medium-wage jobs. 
FIGURE IV.1

PERCENTAGE OF WORKERS STARTING LOW-WAGE JOBS WHO SUBSEQUENTLY

HELD HIGHER-WAGE JOBS, BY WAGE CATEGORY AND GENDER

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

12

57

1

30

4

46

1

49

0

20

40

60

80

100

Percentage of Low-Wage Workers Who Subsequently Held Jobs in Wage Category

Males Females

Had a Medium- and High-Wage Job Had a High-Wage but not a Medium-Wage Job

Had a Medium-Wage but not a High-Wage Job Had a Low-Wage Job Only

Although many low-wage workers held higher-paying jobs at some point, many returned to 

the low-wage labor market (Figure IV.2). Altogether, about 67 percent of low-wage males and 

69 percent of low-wage females who obtained higher-paying employment during the 42-month 

follow-up period subsequently returned to the low-wage labor market. 

These high mobility rates may be due in part to workers who had initial wages near the lowwage cutoff value used for this study and who periodically crossed the low-wage boundary 

because of changes in their labor supply effort or for other reasons. However, as discussed in the

previous chapter, most low-wage workers in our sample earned considerably less than the lowwage cutoff value. Hence, we believe that our findings reflect real movements of low-wage 

workers into and out of the low-wage labor market. 

 58 
FIGURE IV.2

PERCENTAGE OF LOW-WAGE WORKERS WHO HELD HIGHER-WAGE JOBS BUT

WHO RETURNED TO THE LOW-WAGE LABOR MARKET, BY GENDER

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

71

67

23

76

69

6

0

20

40

60

80

100

Percentage Who Returned to the Low-Wage Labor Market, in Category

Males Females

Had a Medium- and High-Wage Job Had a High-Wage Job but not a Medium-Wage Job

Had a Medium-Wage Job but not a High-Wage Job

There is also some movement across wage categories for medium- and high-wage workers 

(Table C.2). For example, among medium-wage workers, about 45 percent of males and females 

held low-wage jobs, and 45 percent of males and 33 percent of females held high-wage jobs. 

Similarly, nearly one-half of high-wage workers spent some time in the medium-wage labor

market sector. Thus, wage mobility is common both for low earners and higher earners. 

In sum, the low-wage population is not static. Rather, a substantial number of workers 

move between low- and medium-wage jobs. 

2. Number of Job and Employment Spells 

Consistent with the employment rate findings, low-wage workers during the mid- to late 

1990s typically held many jobs (Table IV.1 and Figure IV.3). Male low-wage workers held 

an average of 3.0 jobs during the 42-month follow-up period, and the corresponding figure is 

 59 
 60

TABLE IV.1 

THE NUMBER OF NEW JOB AND EMPLOYMENT SPELLS DURING THE THREE AND ONE-HALF 

YEARS AFTER JOB START FOR LOW-WAGE WORKERS, BY WAGE TYPE AND GENDER 

 

Males Females All Workers

Average Number of New Job and Employment 

Spells 

All Jobs 3.0 2.9 2.9 

Low-wage jobsa 2.3 2.4 2.3 

Medium-wage jobsa 0.6 0.4 0.5 

High-wage jobsa 0.1 0.0 0.1 

Employment Spells of Any Wage Typea 1.9 1.8 1.8 

Distribution of the Number of New Job and 

Employment Spells (Percentages) 

Jobs 

1 24 23 24 

2 22 26 25 

3 21 21 21 

4 or more 33 29 31 

Employment Spells 

1 48 49 49 

2 29 31 30 

3 or more 23 20 21 

Sample Size 522 817 1,339 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started jobs within six 

months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 

a

These figures pertain to the number of times a new low-, medium-, or high-wage job started during the follow-up 

period. A spell was classified as “low-wage” on the basis of the wage at the start of the job. A low-wage job spell 

ended when the worker moved to another low-wage job, moved to a higher-wage job (either with the same or 

different employer), became unemployed, or left the labor force. A low-wage employment spell ended when the 

worker moved to a higher-wage job or became unemployed. Medium- and high-wage spells were defined 

analogously. 
FIGURE IV.3

AVERAGE NUMBER OF JOBS AND EMPLOYMENT SPELLS

OF LOW-WAGE WORKERS, BY GENDER

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

3.0

1.9

2.9

1.8

0

1

2

3

4

5

Average Number of Jobs and Employment Spells

Males Females

Jobs Employment Spells

2.9 jobs for females. More than three-quarters of workers held more than one job, and nearly 

one-third experienced at least four jobs. Workers typically experienced fewer employment 

spells (2.0 spells on average), because some workers moved directly from one job to another 

(and thus, started a new job spell but continued their employment spell). These findings are 

consistent with findings from our duration analysis that low-wage job spells tend to be short 

and that nonemployment spells for those who leave low-wage jobs also tend to be short (see 

Chapter VI).

Sample members were much more likely to start low-wage jobs than higher-wage jobs 

(Table IV.1). On average, sample members started 2.3 low-wage jobs during the 42-month 

period, but only .5 medium-wage jobs and .1 high-wage jobs. Thus, nearly 80 percent of all new 

jobs were low-wage jobs. 

 61 
Interestingly, medium- and high-wage workers in the mid- to late 1990s typically 

experienced a number of job spells similar to those of low-wage workers (Table C.2). For

example, the average medium-wage worker held 2.6 jobs and the average high-wage worker held 

2.3 jobs, compared to 3.0 jobs for the average low-wage worker. Thus, job turnover is common 

among all workers, not isolated to low-wage workers. 

3. Employment Rates Over Time 

Overall quarterly employment rates after the start of the workers’ initial low-wage jobs

remained high throughout the follow-up period (Figures IV.4 and IV.5). The rates remained 

fairly constant at about 85 percent per quarter for males and 80 percent per quarter for females. 

The strong economy during the mid- to late 1990s probably had an influence on these high labor 

force participation rates. Nonetheless, the notion that low-wage workers tend to have long spells 

of unemployment is not supported by the data for either males or females. 

0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13

Quarter After Job Start

All Jobs Low-Wage Medium-Wage High-Wage

Percentage Employed in Type of Job for at Least One Week in Quarter

FIGURE IV.4

QUARTERLY EMPLOYMENT RATES OF MALE WORKERS WHO INITIALLY

STARTED LOW-WAGE JOBS, BY WAGE TYPE

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

 62 
0

20

40

60

80

100

1 2 3 4 5 6 7 8 9 10 11 12 13

Quarter After Job Start

All Jobs Low-Wage Medium-Wage High-Wage

Percentage Employed in Type of Job for at Least One Week in Quarter

FIGURE IV.5

QUARTERLY EMPLOYMENT RATES OF FEMALE WORKERS WHO INITIALLY 

STARTED LOW-WAGE JOBS, BY WAGE TYPE

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

The percentage of workers employed in low-wage jobs decreased over time, whereas

employment rates in medium-wage jobs increased, which led to quarterly employment rates in 

all jobs that remained fairly constant (Figures IV.4 and IV.5). For males, the quarterly 

employment rate in low-wage jobs decreased from 74 percent in quarter 4 after job start, to 53 

percent in quarter 8, to 45 percent in quarter 13. Conversely, the participation rate in mediumwage jobs increased from 12 percent in quarter 4, to 30 percent in quarter 8, then leveled off to 

about 40 percent for the rest of the follow-up period. By the end of the panel period, a similar 

percentage of males were employed in low-wage and medium-wage jobs. 

The same general pattern holds for females, although females experienced less successful 

outcomes than males: females experienced slower decreases in the low-wage employment rate 

 63 
 64 

 

over time and smaller increases in the medium-wage employment rate. By the end of the followup period, there were still about twice as many females in low- than medium-wage jobs. 

Employment rates in high-wage jobs were very low throughout the follow-up period for 

both sexes. Starting in quarter 10, they were about 5 percent per quarter for males and 2 percent 

per quarter for females. 

In sum, our results strongly suggest that low-wage workers have some upward mobility over 

the medium term. These workers tend to bounce in and out of the low-wage labor market, but on 

average, are more likely to hold higher-paying jobs over time; this is especially true for males. 

Not surprisingly, wage increases are not large; low-wage workers increasingly enter the mediumwage sector, but few enter the high-wage sector (as found also in Carnevale and Rose 2001; and 

Gottschalk 1997). 

4. Time Spent in Labor Market Activities 

Our findings on the percentage of time low-wage workers spend in various labor market 

activities corroborate our employment rate findings. Low-wage workers in the mid- to late 

1990s were typically employed for most months during the three and one-half years after job 

start (Figure IV.6 and Tables IV.2 and IV.3).2

 The average male worker was employed for 83 

percent of the months, and the average female worker was employed for 76 percent of the 

months (where females spent most of the rest of their time out of the labor force). About 

three-quarters of male workers and two-thirds of female workers were employed for at least 

32 months (that is, three-quarters of the time), and about 37 percent were employed every

2

 An individual was defined to have been employed in a month if he or she was employed 

for at least one week during the month. 
FIGURE IV.6

AVERAGE PERCENTAGE OF MONTHS SPENT IN LABOR MARKET ACTIVITIES

FOR LOW-WAGE WORKERS, BY GENDER

(Percentages)

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.

Low-Wage Jobs

Medium-Wage Jobs

High-Wage Jobs

Nonemployed

17%

26%

55%

24%

17%

58%

3%

1%

Males Females

month. Only 30 percent of workers were employed for less than half the period.3

 These results 

provide further evidence that low-wage workers are active participants in the labor force. 

Over the entire follow-up period, sample members typically spent considerable more time in 

low-wage than higher-wage jobs (an average of 57 percent of months in low-wage jobs, 

compared to 23 percent of months in higher-wage jobs). However, consistent with the 

employment rate results, over time, workers increasingly spent more time in medium-wage jobs. 

For example, the average male actually spent about the same amount of time in low-wage and 

higher-wage jobs during the second half of the follow-up period (42 percent of months, 

compared to 40 percent of months; Table IV.2). 

 65 

 3

 We find similar results for the percentage weeks worked (Table IV.3), because most 

individuals were employed for all weeks during the month. Thus, for simplicity, in this chapter,

we focus on the months measure. 
 66 

TABLE IV.2 

AVERAGE PERCENTAGE OF TIME SPENT IN LABOR MARKET ACTIVITIES DURING THE THREE 

AND ONE-HALF YEARS AFTER JOB START FOR LOW-WAGE WORKERS, BY GENDER 

(Percentages) 

 

Labor Market Activity Males Females All Workers

In All Monthsa

All Jobs 83 76 79 

Low-wage jobs 55 58 57 

Medium-wage jobs 26 17 21 

Higher-wage jobs 3 1 2 

Unemployment 7 5 6 

Not in the Labor Force 10 19 15 

In Months 1 to 21a

All Jobs 84 79 81 

Low-wage jobs 67 68 68 

Medium-wage jobs 16 11 13 

Higher-wage jobs 1 0 1 

In Months 22 to 42a

All Jobs 82 73 77 

Low-wage jobs 42 48 46 

Medium-wage jobs 36 24 29 

Higher-wage jobs 4 2 3 

In All Weeks

All Jobs 81 73 76 

Low-wage jobs 52 55 54 

Medium-wage jobs 25 17 20 

Higher-wage jobs 3 1 2 

Sample Size 522 817 1,339 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started jobs within six months 

after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 

a

An individual was defined to have been employed in a month if he or she was employed for at least one week 

during the month. 
 67 

TABLE IV.3 

DISTRIBUTION OF MONTHS IN LABOR MARKET ACTIVITIES DURING THE THREE AND 

ONE-HALF YEARS AFTER JOB START FOR LOW-WAGE WORKERS, BY GENDER 

(Percentages) 

 

Labor Market Activitya Males Females All Workers

All Jobs (Percent) 

0 to 25 5 10 8 

25 to 50 6 11 9 

50 to 75 13 14 13 

75 to 99 36 30 32 

100 40 35 37 

Low-Wage Jobs (Percent) 

0 to 25 20 20 20 

25 to 50 25 22 23 

50 to 75 24 21 22 

75 to 99 21 22 22 

100 10 15 13 

Medium-Wage Jobs (Percent) 

0 to 25 59 74 67 

25 to 50 19 11 15 

50 to 75 14 12 12 

75 to 99 9 3 6 

High-Wage Jobs (Percent) 

0 to 25 96 98 97 

25 to 50 3 1 2 

50 to 75 2 1 1 

75 to 99 0 0 0 

Unemployment (Percent) 

0 to 25 93 96 95 

25 to 50 6 4 4 

50 to 75 1 1 1 

75 to 99 1 0 0 

Not in the Labor Force (Percent) 

0 to 25 87 72 78 

25 to 50 8 13 11 

50 to 75 2 8 6 

75 to 99 3 8 6 

Sample Size 522 817 1,339 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started jobs within six 

months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 

a

An individual was defined to have been employed in a month if he or she was employed for at least one week 

during the month. 
We find results on the number of hours per week worked during the follow-up period similar

to those on the number of months employed (Figure IV.7). Males worked an average of 33

hours per week during the 42-month period. This high figure reflects the high percentage of time

the males were employed, as well as the fact that most worked full-time while employed (as

discussed in the previous chapter). The corresponding figure for female workers was slightly 

lower (27 hours per week). Over the whole period, workers typically worked about twice as 

many hours in low-wage jobs than in medium-wage jobs. For example, males worked an 

average of 21 hours per week in low-wage jobs during the entire follow-up period (or 3,822 

hours in total), compared to an average of 11 hours per week in medium-wage jobs (or 2,002 

hours in total).4

 However, hours worked in medium-wage jobs increased over time (not shown). 

FIGURE IV.7

AVERAGE NUMBER OF HOURS PER WEEK SPENT EMPLOYED,

BY WAGE TYPE OF JOB AND GENDER

33

21

11

1

26

20

6

0

0

10

20

30

40

50

Average Number of Hours per Week Spent in Type of Job

Males Females

All Jobs Medium-Wage Jobs

Low-Wage Jobs High-Wage Jobs

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period. Additionally, the

average number of hours per week employed by wage type of job refers to the average hours worked in that type of job over 

the entire follow-up period and includes zero hours worked in any job type.

 4

 The hours figures for medium-wage jobs include the zero hours worked by those who 

never held medium-wage jobs. 

 68 
 69 

Despite the evidence of some wage progression for the typical low-wage worker, it is 

important to realize that many low-wage workers do not experience wage gains across wage 

categories (Table IV.3). About 57 percent of workers were employed in low-wage jobs for more 

than one-half the period (55 percent of males and 58 percent of females). Similarly, about twothirds of workers spent little time (less than one-quarter of months) in medium-wage jobs. Thus, 

although there is some upward mobility for many low-wage workers, a significant portion 

remain entrenched in low-wage jobs. In the next section, we attempt to identify workers in each 

group. 

An important policy issue to consider is whether employment outcomes are better for lowwage workers who stay in their jobs or for those who change jobs. It is not clear from economic 

theory which group of workers is likely to do better. On the one hand, outcomes might be better 

for those who remain in their jobs, because these workers might experience increased 

productivity as they gain job-specific human capital. On the other hand, job search theory 

suggests that those who switch jobs might eventually find job matches that better fit their skills. 

Thus, it is an empirical question as to which effect is stronger. 

To address this issue, we used the sample of those who were employed during the entire 

follow-up period (that is, those who were continuously employed), and divided these workers 

into two groups: (1) those who held one job, and (2) those who held multiple jobs. Then, for 

each group, we tabulated the average percentage of time that these workers spent in medium- or 

high-wage jobs during the 42-month follow-up period. 

We find that those who switched jobs had somewhat better labor market outcomes than 

those who remained in their starting jobs, although the differences are larger for females than 

males (Figure IV.8). Among continuously-employed female workers, those who switched jobs 

spent an average of 28 percent of months in medium- or high-wage jobs, compared to 19 percent 
FIGURE IV.8

AVERAGE PERCENTAGE OF TIME SPENT IN MEDIUM- OR HIGH-WAGE JOBS, 

FOR JOB SWITCHERS AND JOB STAYERS

36

28

34

19

0

10

20

30

40

50

Source: 1996 SIPP longitudinal files using workers who started low-wage jobs within six months after the start of

the panel period and who were employed during the entire 42-month follow-up period.

Note: All figures were calculated using the longitudinal panel weight, and pertain to a 42 month follow-up period.

Average Percentage of Months in Medium- or High-Wage Jobs

Continuously-Employed Males Continuously-Employed Females

Job Switchers Job Stayers

of months for those who stayed in their initial jobs. The corresponding figures for male job 

switchers and job stayers are 36 and 34 percent, respectively. Thus, there is some evidence that 

job turnover can be beneficial for low-wage workers, especially for female workers. We address

this topic further in the wage growth analysis in the next chapter. 

Finally, as expected, we find that higher-wage workers spent more time employed than lowwage workers for both males and females (Table C.3). For example, medium- and high-wage 

males were employed for about 93 percent of months on average (compared to 83 percent for 

low-wage workers). Interestingly, medium-wage workers spent most of their time in mediumwage jobs, and high-wage workers spent most of their time in high-wage jobs. Thus, there was 

more movement between wage categories for workers initially in low-wage jobs than for workers

initially in higher-wage jobs, even though both groups had a similar number of jobs. 

 70 
 71 

 

B. SUBGROUP FINDINGS 

We have found that the average earnings of low-wage workers improve somewhat over 

time. At the same time, however, many low-wage workers do not experience positive labor 

market outcomes. This section addresses the important question: Which groups of low-wage 

workers experience improvements in their labor market outcomes and which groups do not? 

Examining differences in overall employment outcomes across subgroups of the low-wage 

population has important policy implications for targeting appropriate services to those who are 

at most risk of poor outcomes. 

We conducted our subgroup analysis in two interrelated ways. First, we examined key labor 

market outcomes for selected subgroups one at a time. These subgroups were defined by 

worker, area, and job characteristics at the time the workers started their low-wage jobs.5

 

Second, we conducted a multivariate analysis to examine the association between particular 

explanatory (subgroup) variables and key labor market outcomes, holding constant the effects of 

other explanatory variables. The multivariate analysis accounts for correlations among the 

subgroup variables and also allows us to efficiently examine labor market outcomes for a large 

number of subgroups. 

We examined four key labor market outcomes for the subgroup analysis: 

1. The percentage of months low-wage workers spent in low-wage jobs during the 42-

month follow-up period 

2. The percentage of months workers spent in higher-wage jobs (that is, in mediumand high-wage jobs) 

5

 We did not examine subgroup differences across the three male and three female lowworker typologies presented in the previous chapter, because the much smaller sample size used 

in the overall employment analysis yielded unstable clusters that were difficult to interpret. 
 72 

 

3. The percentage of months workers spent in all jobs 

4. Whether the worker spent less than 25 percent of months in higher-wage jobs 

We used the total time employed measure to assess the overall labor force attachment of 

subgroups of low-wage workers. We examined the average percentage of time that workers held 

higher-wage jobs to assess the extent to which subgroups of workers were able to escape the 

low-wage labor market over time. Finally, because focusing on averages can mask important 

subgroup differences in the distributions of the amount of time workers spent in various labor 

market activities, we also examined the share of workers who spent little time (less than onequarter time) in the medium- and high-wage labor market sectors. Together, these summary 

outcome measures were used to identify subgroups who had the most and least successful labor 

market experiences. 

The subgroup analysis was conducted separately by gender. Furthermore, all figures were 

calculated using the longitudinal panel weight. We estimated the multivariate models using 

ordinary least squares methods for the continuous outcome measures (the first three listed above) 

and logit maximum likelihood methods for the binary outcome measure (the fourth measure 

listed above). In the multivariate analysis, we conducted statistical tests to gauge the statistical 

significance of differences in labor market outcomes across subgroups. For some subgroups 

with small sample sizes (see Table III.1), the standard errors of the estimates are large. 

Consequently, some relatively large parameter estimates are not statistically significant.6

We included the following categories of explanatory variables in the regression models: 

6

 The standard errors of the estimates account also for design effects in the SIPP data due to 

clustering. 
 73 

 

• Individual and household characteristics measured at job start (from the longitudinal 

panel file) 

• Prepanel employment information (from the wave 1 topical module) 

• Job characteristics measured at the start of the low-wage employment spell (from the 

longitudinal panel file) 

• Area characteristics and state economic indicators measured at the start of the job, as 

well as changes in unemployment rate indicators between the start and end of the 

follow-up period (from published data sources; see the Methodological Appendix A)7

1. Findings from the Univariate Analysis 

We find some broad differences in labor market outcomes across key subgroups of the lowwage population, although the differences are smaller than expected (Tables IV.4 and IV.5). 

Males, prime-age workers, educated workers, whites, those without health limitations, and those 

in wealthier households typically spend more time in higher-wage jobs than their respective 

counterparts. Furthermore, job quality matters—those who start with better jobs (measured by 

higher initial wages, the availability of health benefits, and full-time work status) are more likely 

to spend time in medium- and high-wage jobs than those in lower-quality jobs. In addition, we 

find some differences across occupations—males in professional and sales occupations and 

females in professional and clerical occupations have more positive labor market outcomes than 

other workers. These findings are consistent with those from the few previous studies that have 

addressed wage progression across subgroups of low-wage workers (Carnevale and Rose 2001; 

Smith and Vavrichek 1992; and Holtzer et al. 2001). 

7

 We measured these indicators using information on the state in which the worker lived at 

the beginning and end of the follow-up period. 
TABLE IV.4 

TIME SPENT EMPLOYED DURING THE THREE AND ONE-HALF YEARS AFTER JOB START FOR SUBGROUPS OF LO

DEFINED BY INDIVIDUAL AND HOUSEHOLD CHARACTERISTICS AT JOB START 

(Percentages) 

Male Low-Wage Workers Female Low-W

Average Percentage of Months Average Percentage of Mon

Subgroup

In LowWage 

Jobs 

In HigherWage 

Jobs 

In All 

Jobs 

Percentage in

Higher-Wage Jobs

for Less than 25

Percent of Months 

In LowWage 

Jobs 

In HigherWage 

Jobs 

In A

Job

Overall 55 28 84 55 58 18 77 Age (in Years)

Younger than 20 59 22 82 67 56 10 67 20 to 29 56 29 86 51 54 20 76 30 to 39 53 32 86 51 59 17 77 40 to 49 51 32 84 57 64 20 84 50 to 59 56 22 79 64 66 17 83 60 or older 54 18 73 74 56 16 74 

Race/Ethnicity

White and other non-Hispanic 54 31 87 50 58 20 79 Black, non-Hispanic 50 21 72 71 57 14 72 Hispanic 64 20 85 68 63 9 73 

Educational Attainment

Less than high school/GED 58 21 80 67 58 9 6High school/GED 56 28 85 54 64 15 79 Some college 47 36 84 46 52 22 76 College graduate or more 55 33 90 48 52 28 8

Has a Health Limitation

Yes 52 17 71 73 51 11 63 No 55 30 86 53 59 19 79 

74 
TABLE IV.4 (continued) 

75 

Male Low-Wage Workers Female Low-W

Average Percentage of Months Average Percentage of Mon

Subgroup

In LowWage 

Jobs 

In HigherWage 

Jobs 

In All 

Jobs 

Percentage in

Higher-Wage Jobs

for Less than 25

Percent of Months 

In LowWage 

Jobs 

In HigherWage 

Jobs 

In A

Job

Household Type 

Single parent with children 51 28 81 56 61 16 77 Married couple with children 56 31 88 49 57 16 74 Married couple without children 54 27 81 59 60 19 79 Other adults without children 55 27 83 60 56 24 8

Household Income as a Percentage of thePoverty Level

100 percent or less 60 26 87 55 61 14 76 101 to 200 percent 56 27 84 57 56 15 72 More than 200 percent 52 31 83 54 58 22 8

Full Sample Size 522 522 522 522 817 817 81

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after th

All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 
76 

TABLE IV.5 

TIME SPENT EMPLOYED DURING THE THREE-AND-ONE-HALF YEARS AFTER JOB START FOR SUBG

OF LOW-WAGE WORKERS DEFINED BY INITIAL JOB CHARACTERISTICS 

(Percentages) 

Male Low-Wage Workers Female Low-W

Average Percentage of Months Average Percentage of Mon

Subgroup 

In LowWage 

Jobs 

In HigherWage 

Jobs 

In All 

Jobs 

Percentage in 

Higher-Wage Jobs 

for Less than 25 

Percent of Months 

In LowWage 

Jobs 

In HigherWage 

Jobs 

In A

Job

Overall 55 28 84 55 58 18 77 Hourly Wages 

Less than $5.00 63 19 83 68 59 8 68 $5.00 to $5.99 63 17 81 71 66 13 79 $6.00 to $6.99 50 37 88 42 55 26 81 $7.00 to $7.50 42 42 84 39 46 39 85 

Hours Worked per Week 

1 to 19 54 20 76 74 57 13 720 to 34 57 22 81 65 57 18 75 35 to 40 56 29 86 54 59 20 80 More than 40 49 37 88 41 58 19 79 

Weekly Earnings 

Less than $150 56 22 80 67 58 13 72 $150 to $299 57 27 85 56 59 21 80 $300 to $600 42 45 89 30 51 28 80 

Owns Business (Self-Employed) 

Yes 40 43 87 35 60 18 82 No 56 27 84 57 58 18 77 
TABLE IV.5 (continued) 

77 

Male Low-Wage Workers Female Low-W

Average Percentage of Months Average Percentage of Mon

Subgroup 

In LowWage 

Jobs 

In HigherWage 

Jobs 

In All 

Jobs 

Percentage in 

Higher-Wage Jobs 

for Less than 25 

Percent of Months 

 In LowWage 

Jobs 

In HigherWage 

Jobs 

In A

Job

Health Insurance Coveragea

Yes 50 34 86 47 56 22 79 No 57 25 84 60 60 15 76 

Occupation

Professional/technical 52 38 92 43 56 28 86 Sales/retail 53 35 90 41 56 20 77 Administrative support/clerical 59 24 84 62 51 28 80 Service professions/handlers/cleaners 57 24 82 63 62 13 76 Machine/construction/production/ 

transportation 51 32 84 49 62 10 73 

Farm/agricultural/other workers 58 23 82 65 58 18 76 

Industry

Agriculture/forestry/fishing/hunting 56 24 83 62 62 14 79 Mining/manufacturing/construction/ 

transportation/utilities 53 31 85 52 60 12 72 

Wholesale/retail trade 59 28 88 54 56 17 73 Personal/health/other services 53 26 80 61 59 22 8Other 37 44 85 33 68 30 10

Full Sample Size 522 522 522 522 817 817 81

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after th

All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 

aThese figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from oth

variable instead of the employer-based health insurance coverage variable, because data on overall health insurance coverage is avail

employer-based coverage variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health in

always be linked to the job under investigation, which led to a significant number of missing values. However, the subsets of health iconsiderably: the source of health insurance coverage was the employer for 80 percent of those with any coverage. 
 78 

At the same time, the story is complex—substantial diversity exists in labor market success 

within groups. Thus, although we identified groups that are at particular risk of poor labor 

market outcomes, we could not fully account for the variation in outcomes across low-wage 

workers. Next, we present the evidence for these findings. 

a. Findings for Subgroups Defined by Individual and Household Characteristics 

Table IV.4 presents our findings for subgroups defined by individual and household 

characteristics at the start of the low-wage job. We summarize these findings here: 

• Male low-wage workers exhibit more movement out of the low-wage labor market 

than female low-wage workers. During the mid- to late 1990s, males spent more 

time in the labor market (an average of 84 percent of the time, compared to 77 

percent of the time for females) and spent considerably more time in higher-wage 

jobs (an average of 28 percent of months, compared to 18 percent of months for 

females). Similarly, females typically spent more time in low-wage jobs. These 

gender results hold across all subgroups. 

• Low-wage workers between ages 20 and 60 have better labor market outcomes than 

those older and younger. In our sample, teenage workers and those older than 60 

had the poorest outcomes; they spent less time in the labor market and fewer months 

in higher-wage jobs than other workers. Those between ages 50 and 60 (and males, 

in particular) typically had the next poorest outcomes. Prime-age workers between 

ages 20 and 50 had the best outcomes. However, even within the 20- to-50-year-old 

age group, there was substantial diversity in labor market success; more than one-half 

of males and more than two-thirds of females in this age group spent less than onequarter of months in high- or medium-wage jobs. 

• Whites typically spend more time in higher-wage jobs than blacks and Hispanics. 

White female workers in our sample spent an average of 20 percent of months in 

higher-wage jobs, compared to about 12 percent of months for minority female 

workers. The corresponding figures for males are 31 percent for whites and 20 

percent for minorities, respectively. Similarly, whites spent less time in low-wage 

employment than Hispanics, and spent more time employed in all jobs than minority 

workers (and, in particular, than black workers). We stress again, however, that there 

is considerable variation in labor market success within each racial and ethnic group. 

• Education is strongly associated with labor market success. As expected, labor 

market outcomes for sample members were typically poorest for the high school 

dropouts and improved with education level. Among female workers, those who 

were high school dropouts at the start of their jobs spent an average of only 9 percent 

of months in higher-wage jobs, compared to 15 percent of those with a high school 
 79 

credential and about 25 percent of those who attended college. Similarly, those with 

low education levels typically spent more time in low-wage jobs than their 

counterparts and spent less time in all jobs. 

• Low-wage workers with health limitations are at particular risk of poor labor 

market outcomes. During the mid- to late 1990s, male workers with health problems 

typically spent only about 17 percent of months in higher-wage jobs, compared to 30 

percent for those healthier, and the corresponding figures for female workers are 11 

percent and 19 percent, respectively. Overall employment rates were also much 

lower for those with health problems for both males and females. Thus, those with 

health limitations tend to spend most of their time in either low-wage jobs or in 

nonemployment. 

• Married males have slightly more successful labor market experiences than other 

males. Among males in our sample, those who were married and had children were 

employed, on average, for 88 percent of all months, compared to 84 percent of 

months for all male workers. Similarly, they were employed in medium- or highwage jobs for an average of 31 percent of the follow-up period, compared to 28 

percent for all workers. These differences, however, are smaller than expected. 

• There are few differences in the labor market experiences of single-parent females 

and females living in other types of households. Figures for female low-wage 

workers on the time spent in higher-wage jobs and in all jobs are similar across 

household groups. 

• Poverty status is associated with labor market success. Not surprisingly, during the 

mid- to late 1990s, low-wage workers in wealthier households spent more time, on 

average, in higher-wage jobs than those in poorer households. For example, females 

living in households with incomes below poverty spent nearly half as much time in 

the higher-wage labor market than households with incomes more than twice the 

poverty level (14 percent, compared to 22 percent). Similarly, those in the poorest 

households spent more time in the low-wage sector than their wealthier counterparts. 

These differences are similar for females than males. Despite this, however, we again 

find considerable differences in labor market success even for those within the 

wealthiest households. For example, 67 percent of females in the wealthiest 

households spent less than one-quarter of their time in higher-wage jobs, which is not 

substantially below the 79 percent figure for males in households below poverty. 

b. Findings for Subgroups Defined by Job Characteristics 

Our findings for subgroups defined by job characteristics at the start of the low-wage job 

indicate that job quality matters—those with better jobs tend to have more positive labor market 

outcomes than those in lower-quality jobs (Table IV.5). We summarize these results here: 
 80 

 

• Those with higher initial wages are more likely than those earning lower wages to 

leave the low-wage labor market. Sample members who earned less than $5.00 per 

hour (about 27 percent of all low-wage workers) had the poorest labor market 

outcomes, and outcomes improved as wage levels increased. For example, male 

workers earning less than $5.00 per hour spent an average of 19 percent of months in 

higher-wage jobs, compared to 37 percent for those earning between $6.00 and $7.00 

and 42 percent for those earning between $7.00 and $7.50. Similarly, the lowest 

earners spent much more time in low-wage employment than higher earners. Not all 

high-earners had successful outcomes, however; about 40 percent of those earning 

more than $7.00 spent little time in higher-paying jobs. 

• Full-time workers typically have more successful outcomes than part-time workers. 

During the mid- to late 1990s, low-wage workers who reported working more than 35 

hours per week (about 26 percent of all workers) spent, on average, much more time 

in higher-wage jobs and less time in low-wage jobs than those working fewer hours. 

These results hold for both males and females. For example, males who worked less 

than 20 hours per week typically spent only about 20 percent of their time in higherwage jobs, whereas the corresponding figure is 37 percent for those working more 

than 40 hours per week. These results strongly suggest that part-time workers are at 

particular risk of poor labor market outcomes. 

• The availability of fringe benefits on the job is a strong predictor of labor market 

success. Those covered by health benefits (about 60 percent of all low-wage 

workers) spent considerably more time in higher-wage jobs than those without these 

benefits (34 percent compared to 25 percent of months for males, and 22 percent 

compared to 15 percent for females).8

 These results further confirm our findings that 

job quality matters. 

• Male business owners typically have better labor market outcomes than male 

jobholders. As discussed, business owners (about 13 percent of all low-wage 

workers), tend to work many hours in order to get their businesses off the ground and 

tend to have lower hourly wages than jobholders near the start of their employment 

spells. However, earnings growth appears to be somewhat greater for the selfemployed. Male business-owners spent an average of 43 percent of months in higherwage jobs, compared to 27 percent for male jobholders. Differences between the 

outcomes of female business owners and jobholders, however, are smaller. 

• Among male low-wage workers, those in professional and sales occupations 

experience more wage progression than other workers. The differences across 

8

 These figures pertain to health insurance coverage from all sources, including coverage 

through the employer as well as from other sources. We used this variable instead of the 

employer-based health insurance coverage variable, because data on overall health insurance 

coverage is available monthly, whereas the employer-based coverage variable pertains only to 

jobs in progress at the time of the interview. Thus, the employer-based health insurance variable 

could not always be linked to the job under investigation, which led to a significant number of 

missing values. However, the subsets of health insurance variables overlap considerably: the 

source of health insurance coverage was the employer for 80 percent of those with any coverage. 
 81 

occupations are substantial. During the mid- to late 1990s, professional and sales 

workers (14 percent of workers each) were typically employed for about 90 percent 

of the time during the 42-month follow-up period, compared to about 83 percent for 

other workers. Similarly, professional and sales workers spent about 37 percent of 

months, on average, in higher-paying jobs, compared to 24 percent for clerical 

workers, 24 percent for service workers, 32 percent for machinists and construction 

workers, and 23 percent of those in other occupations. Similarly, a relatively small 

fraction of professional and sales workers spent little time in medium- and high-wage 

jobs. 

• Female workers in professional and clerical jobs have the most labor market 

success. Female sample members in professional and clerical occupations spent more 

time employed in all jobs, and higher-wage jobs in particular, than those in other 

occupations. These workers spent about 28 percent of the follow-up period in the 

higher-wage labor market, compared to less than 20 percent for those in each of the 

other occupations. Service workers had particularly poor outcomes. 

• We find smaller differences in labor market success across industries. This result 

holds for both males and females. 

2. Findings from the Multivariate Analysis 

Thus far, we have examined subgroup results one at a time. However, many of these 

subgroups are correlated with each other. For example, we have seen that less disadvantaged 

workers and those in higher-quality jobs tend to have more successful outcomes than other 

workers. However, better-off workers are more likely than those worse off to be in high-quality 

jobs. Thus, an important question is whether labor market success is due more to worker 

characteristics or initial job characteristics. 

We isolated subgroup effects from others using multivariate regression methods. We 

estimated regression models for the four outcome measures used in the univariate subgroup 

analysis. In the main text, we present findings for the most important outcome measure: the 

percentage of months workers spent in medium- and high-wage jobs (Table IV.6). The results 

for the other three outcomes are presented in Table C.4 and are qualitatively similar to those 

presented in the text (although as discussed, in general, there was less variation in the total time 

workers spent employed than in the time workers spent in higher-wage jobs). We present 
 82 

 

“regression-adjusted” means for each subgroup level and indicate whether the difference 

between the regression-adjusted means for each subgroup and the “left-out” subgroup is 

statistically significant at the five percent significance level.9

We present estimates from three models for both males and females. The first model 

includes demographic variables only (that is, explanatory variables defined by individual, 

household, and area characteristics; model (1) in Table IV.6). The second model includes 

demographic variables as well as prepanel work experience variables from the wave 1 topical 

module [model (2)]. The third model includes demographic variables and initial job-related 

variables [model (3)]. In Table C.4, we present the model (3) results for the additional 

employment-related outcome measures only. 

a. Models Including Demographic Variables Only 

The regression-adjusted differences in labor market outcomes across subgroups defined by 

individual and household characteristics are similar to the univariate findings described above 

(Table IV.6). In particular, among our sample of low-wage workers, teenagers and older 

workers, African Americans and Hispanics, those with low levels of education, and those with 

health problems spent less time in medium- and high-wage jobs than their counterparts, and 

many of these differences are statistically significant at the ten percent level. There is also some 

evidence that those in higher-income households and males with children had better labor market 

outcomes than other workers, but these differences are not statistically significant. Thus, 

adjusting for the correlation among the demographic variables does not materially influence the 

subgroup findings. 

9

 The regression-adjusted mean for Hispanics, for example, was the average predicted value 

from the regression model, where the value of 1 was inserted for the Hispanic dummy variable 

for all individuals but where the other explanatory variables were calculated at their actual 

values. The regression-adjusted means for other explanatory variables were constructed in an 

analogous way. 
 83 

TABLE IV.6 

MULTIVARIATE ANALYSIS FINDINGS ON THE PERCENTAGE OF TIME LOW-WAGE WORKERS 

SPENT EMPLOYED IN MEDIUM- OR HIGH-WAGE JOBS DURING THE 42-MONTH 

FOLLOW-UP PERIOD, BY GENDER AND MODEL 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

Males Females

Explanatory Variable 

No Other

Variables

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Individual Characteristics 

Age 

Younger than 20a 20 27 23 12 15 12 

20 to 29 29** 30 30* 20** 20 20** 

30 to 39 33*** 32 33** 18* 17 18* 

40 to 49 33** 29 30 19* 18 19* 

50 to 59 22 17 16 18 17 18 

60 or older 18 12* 12 17 16 14 

Race/Ethnicity 

White and other non-Hispanica 31 31 31 20 19 19 

Black, non-Hispanic 21** 22** 22** 15 16 15* 

Hispanic 22** 20** 24 13** 13** 14* 

Educational Attainment 

Less than high school/GEDa 23 24 26 12 12 14 

High school/GED 28 28 29 16* 16 16 

Some college 34** 34** 30 21*** 21*** 21** 

College graduate or more 32* 33* 30 26*** 25*** 23***

Has a Health Limitation 

Noa 30 30 30 19 19 19 

Yes 17*** 17*** 19*** 10*** 10*** 13** 

Work Experience Prior to the Panel 

Period

Ever Worked for Six Straight Months 

Noa 27 19 

Yes 29 18 

Number of Years Ever Worked Six Straight 

Months 

Less than 5a 27 14 

5 to 10 31 19* 

10 to 20 26 22*** 

More than 20 32 20* 

Usually Worked at Least 35 Hours Per 

Week When Working 

Noa 20 18 

Yes 31*** 18 

Household Characteristics 

Household Type 

Single adults with childrena 30 31 30 18 18 19 

Married couples with children 32 32 30 17 17 17 

Married couples without children 26 25 27 16 16 16 

Other adults without children 25 26 26 23* 24* 23 
TABLE IV.6 (continued) 

 84 

 Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

 Males Females

Explanatory Variable 

No Other

Variables

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Household Income as a Percentage of the 

Poverty Level 

100 percent or lessa 26 24 29 17 17 20 

101 to 200 percent 27 28 28 17 17 18 

More than 200 percent 30 31 28 19 19 18 

Received Public Assistance in the Past Year 

Noa 29 29 29 18 18 18 

Yes 22 21 23 17 17 17 

Area Characteristics 

Region of Residence 

Northeasta 27 26 29 29 28 28 

South 25 26 24 18*** 18*** 18***

Midwest 30 30 30 14*** 14*** 14***

Northwest 30 30 31 18*** 18** 17***

Lives in a Metropolitan Area 

No 26 25 25 16 16 17 

Yes 30 30 30* 19 19 19 

20th Percentile of the Hourly Wage 

Distribution in State 

$250 or lessa 27 27 27 16 16 17 

$251 to $269 35* 34* 33 19 19 19 

$270 or more 27 27 28 20 20 19 

Percentage of State Population Residing in 

Metropolitan Areas 

72 or lessa 28 27 28 21 21 21 

73 to 84 31 31 31 17* 17 17** 

85 or more 27 27 27 16 15* 16* 

Poverty Rate in State 

Less than 10 percenta 29 29 27 15 15 14 

10 to 12 percent 31 30 30 19 19 20** 

More than 12 percent 26 27 28 19 19 20* 

Unemployment Rate in State 

6 percent or lessa 27 28 29 13 14 14 

More than 6 percent 29 29 28 20* 20* 20* 

Change in Unemployment Rate in State of 

Residence Between 1996 and 1999 

(Percentage Points) 

-2 percentage points or lessa 28 28 27 18 18 19 

-1 to –2 28 28 28 19 19 19 

More than –1 30 30 30 16 16 16 

Initial Job Characteristics 

Hourly Wages 

Less than $5.00a 19 11 

$5.00 to $5.99 19 14 

$6.00 to $6.99 37*** 24***

$7.00 to $7.50 40*** 34***
TABLE IV.6 (continued) 

 85 

 Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

 Males Females

Explanatory Variable 

No Other

Variables

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Pre-Panel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Usual Hours Worked per Week 

1 to 19a 24 13 

20 to 34 25 20***

35 to 40 29 19** 

More than 40 32 18 

Has More than One Job or Business 

Noa 28 18 

Yes 31 18 

Owns Business (Self-Employed) 

Noa 27 18 

Yes 44*** 24 

Health Insurance Coverageb

Noa 26 17 

Yes 34*** 20* 

Union Member 

Noa 29 18 

Yes 27 19 

Occupation 

Professional/technicala 29 22 

Sales/retail 31 21 

Administrative support/clerical 28 22 

Service professions/handlers/cleaners 26 16 

Machine/construction/production/ 

transportation 32 12** 

Farm/agricultural/other workers 24 22 

Regression R2

 Value .12 .15 .27 .14 .15 .27 

Sample Size 522 522 522 817 817 817 

Source: 1996 SIPP longitudinal and wave 1 topical module files using the entry cohort sample of workers who started lowwage jobs within six months after the start of the panel period. All workers were followed for 42 months after job 

start. 

Note: All figures are weighted using the 1996 calendar year weight, and standard errors account for design effects due to 

weighting and clustering. 

a

Denotes the omitted explanatory variable in the regression model. 

b

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from 

other sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall

health insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at 

the time of the interview. Thus, the employer-based health insurance variable could not always be linked to the job under 

investigation, which led to a significant number of missing values. However, the subsets of health insurance variables overlap

considerably: the source of health insurance coverage was the employer for 80 percent of those with any coverage. 

*Difference between the variable mean and the mean of the omitted explanatory variable is significantly different from zero at 

the .10 level, two-tailed test. 

**Difference between the variable mean and the mean of the omitted explanatory variable is significantly different from zero at

the .05 level, two-tailed test 
 86 

The explanatory variables measuring area characteristics have little predictive power in the 

regression models (Table IV.6). Those in metropolitan areas tended to have slightly better 

outcomes than those in other areas, and there is some evidence that females in the northeast 

region had more positive labor market experiences than females in other regions (although this 

result does not hold for males). However, in general, the state hourly wage and state 

unemployment measures are not statistically significant, and the parameter estimates are not in 

the expected direction. These weak results are somewhat surprising, because the area 

characteristics are intended to capture the economic conditions faced by sample members. 

Hence, we expected more positive labor market outcomes for those residing in areas with a 

higher demand for labor than those in other areas. A possible explanation for the weak findings 

is that the area characteristics are measured at the aggregated state level, so they might not 

accurately reflect demand conditions faced by the workers in their local areas. 

The regression R2

 value from model (1) is about .13 for both males and females. Thus, 

although the demographic variables explain about 13 percent of the variance in the amount of 

time workers spent in the higher-wage labor market, substantial residual factors remain that 

account for differences across workers. Stated differently, there is substantial diversity in labor 

market outcomes among members within the subgroups under investigation. 

b. Models Including Demographic and Prepanel Work Experience Measures 

Work experience matters to some extent. All else equal, sample members with more than 

five years of labor market experience typically spent slightly more time in higher-wage jobs than 

those with less work experience, and this result holds for both men and women (Table IV.6). 

Furthermore, males who typically worked full-time while employed had more wage progression, 

on average, than part-time male workers, and these differences are statistically significant. 
 87 

 

Interestingly, differences in mean outcomes across age groups diminish somewhat when the 

prepanel work experience variables are included in the models. Thus, our initial findings across 

age groups can be explained by the higher levels of work experience among older workers, 

which gave them more job-related skills and made it easier for them to find higher-paying jobs. 

c. Models Including Demographic and Initial Job-Related Variables 

In general, the inclusion of the job-related variables leads to slightly smaller differences 

across the demographic subgroups than those presented above (model (3) in Table IV.6).10 For 

example, when the initial job characteristics are included in the model, the Hispanic and 

education effects for males become statistically insignificant. The effects become slightly 

smaller due to the fact that less disadvantaged workers tend to get better jobs, even in the lowwage worker population. 

The multivariate findings support our conclusions from the univariate analysis that job 

quality matters (Table IV.6 and Table C.4). Low-wage workers who had higher starting wages, 

worked more hours, and had available health benefits spent more time, on average, in higherwage jobs than those in lower-quality jobs. Most of these differences are statistically significant 

at the 5 percent significance level. However, the regression-adjusted means across the jobrelated subgroups are slightly smaller than the univariate means because of the correlation 

between the demographic and job-related variables and the correlation among the job-related 

variables. For example, the regression results no longer suggest that males in professional and 

10 We are aware that the job variables are likely to be correlated with the error term in the 

regression models (that is, that the job variables are likely to be endogenous), which could lead 

to biased coefficient estimates on all the explanatory variables. Thus, we do not view our 

parameter estimates as “structural” relationships between the explanatory and dependent 

variables. Rather, our goal is to identify broad associations between subgroup variables and 

labor market outcomes. 
 88 

sales occupations and females in professional and clerical occupations experienced more wage 

progression than other workers. The occupational effects, however, more closely resemble those 

from the univariate analysis if the demographic variables are excluded from the models, or if the 

demographic variables are included but other job-related variables are excluded (not shown). 

Interestingly, those who had more than one job at the start of the low-wage job spell had 

slightly better outcomes than those who did not, perhaps capturing differences in the motivation 

to work and succeed across the two groups of workers (Table IV.6 and Table C.4). In addition, 

self-employed workers typically spent substantially more time than jobholders in the mediumand high-wage labor market sectors, and these differences are statistically significant for males. 

Finally, the inclusion of both the job and demographic characteristics yields a model R2

value of .27 for both males and females (Table IV.6). Thus, we find again that there remain 

substantial residual factors that account for differences in labor market success across low-wage 

workers, even after controlling for a large number of demographic and job-related factors. In 

sum, although we have identified some important differences in medium-term labor market 

outcomes across key subgroups of the low-wage worker population, there are clearly other 

important factors that we could not identify using the SIPP data. 
 89 

 

V. WAGE GROWTH AND PROGRESSION AMONG LOW-WAGE WORKERS 

What are the patterns of wage growth among low-wage workers who start a job? What is 

the amount of increase in wages for those employed three years later? Are low-wage workers 

moving into better jobs over time? What factors are associated with wage growth in the lowwage labor market? Are those employed in certain occupations or industries more likely than 

others to experience wage growth? Do initial wages matter? Do those who keep the same job 

experience greater or lower wage growth than those who switch jobs? 

This chapter addresses these and related questions using data on workers in the 1996 SIPP 

longitudinal panel file who started low-wage jobs during the first six months of the panel period 

(roughly, in the first half of 1996). We used the average wages over the initial six-month period 

after initial job start to classify individuals as low-wage workers. Low-wage workers are those 

whose average wages during this initial period were below $7.50 per hour (in 1996 dollars), 

which is the cutoff that would put them below the federal poverty level for a family of four if 

they worked full-time.1

 We then tracked their progress by examining the changes in their 

average wages over six-month intervals during the subsequent three-year period. Unless 

otherwise noted, all wages reported are real wages in 1999 dollars. 

We conducted a descriptive analysis to answer the key analysis questions and a multivariate 

analysis to better understand factors related to wage growth. To place our findings in context, 

1

 Medium-wage workers include those whose wages are between 100 and 200 percent of the 

federal poverty level, and high-wage workers are those whose wages are greater than 200 percent 

of the federal poverty level. The hourly wage cutoff for medium-wage workers is between $8.03 

and $16.06 per hour. High-wage workers are those whose hourly wages are greater than $16.06 

per hour. 
 90 

 

Appendix C presents selected descriptive statistics for workers who started medium- and highwage jobs. All statistics were calculated using the longitudinal panel weight. 

Before turning to the study findings, we discuss three important sample- and 

methodological-related issues that pertain to the analysis in this chapter. First, similar to the 

aggregate analysis described in chapter IV, the sample for this analysis includes those who 

started low-wage jobs during the first six months of the panel period.2 Among those who started 

a job in the first six months of the panel period, just under half were low-wage workers, about 38 

percent were medium-wage workers, and about 15 percent were high-wage workers. 

The second issue relates to that of the classification of job starters as low-, medium-, or 

high-wage workers. As discussed in the Methodological Appendix A, we based our initial 

classification of workers into these three groups based on their average wages during the first 

six-month period after they started their jobs. Categorizing people into low-, medium-, or highwage workers at any given point in time has two potential issues especially important for the 

wage growth analysis. First, if a worker misreports his or her wages at the time of job start, we 

may incorrectly classify an individual into a wage type that may not be their real wage type. 

Second, people sometimes obtain jobs that may not be related to their true ability levels and may 

soon move into a job that more closely matches their true human capital level. For example, if a 

worker with low productivity gets a high-wage job, he or she may not be able to sustain that job 

for long and may soon move into a low-wage one. Conversely, a high-productivity worker may 

have found a low-wage job and might soon move to a higher-wage job (defined as a medium- or 

high-wage job). Both these factors work in the direction of potentially large wage growth for 

2

 We chose to examine patterns of wage growth among those who started a job, as we 

wanted to know what wage growth welfare recipients and other low-wage workers who start a 

job might expect. 
 91 

 

low-wage workers (or lower wage growth for high-wage workers), especially in the early periods 

after job start. We were particularly concerned about minimizing the effects of any data errors, 

as these errors do not reflect true changes in wages. Thus, as described earlier, we smoothed 

wages and took the six-month average of wages after job start to classify workers into wage 

categories.3 (We call this initial period to classify workers into wage categories “period 0.”) 

While this smoothing is likely to reduce the noise due to data errors to a large extent, residual 

errors could still remain, and we may be overstating wages for low-wage workers. 

Consequently, in our analysis examining wage growth over time, we start with the average wage 

in the first six-month period after the period we used to define their initial worker type and 

examine their wage growth over the following three-year period (period 1 through period 6). For 

trends in wages over time, we present average wages of those employed in period 1, average 

wages of those employed in period 2, average wages of those employed in period 3, and so on. 

For the analysis of individual workers’ wage growth over time, we compare wages and job 

characteristics of those workers who were employed in both the first and last periods (i.e., period 

1 and period 6) regardless of their employment in other periods. We also examined the 

sensitivity of the wage growth findings to alternative definitions of low-wage workers, such as 

excluding those with very low wages and looking at longer time periods to classify low-wage 

workers, but we found that our main results were not sensitive to these alternative definitions. 

The third issue relates to sample selection. Since we observe wages only for those who are 

employed, the wage growth analysis is limited to the sample of people who were working at 

different points in time. Those who remained employed at a later time may be different from 

3

 As noted in Chapter II, the usual extent of data cleaning performed in earlier SIPP waves 

was not done for the 1996 longitudinal files. 
 92 

those who did not remain employed. As demonstrated in the previous chapter, because of the 

strong economic conditions in the mid- to late-1990s, relatively large fractions of low-wage 

workers remained employed three and a half years after job start. The high fraction of low-wage 

workers who remained employed—88 percent of male workers and 80 percent of female 

workers—suggests to us that our sample for the wage growth analysis is similar to the sample of 

those who started low-wage jobs. However, we do observe some differences between those 

working and those not working three and a half years later, which mimic the subgroup results 

from the previous chapter. For example, those with health limitations were considerably less 

likely than those with no health limitations to be employed three and a half years later. In 

addition, older men, African American males, and males working part-time in their initial jobs 

were less likely to hold a job at the end of the three-year follow-up period. Females with less 

than a high school diploma and those whose initial wages were less than $5 (in 1996 dollars) 

were also less likely to be employed at the end of the follow-up period. 

This chapter is in two sections. First, we present descriptive findings by gender for the full 

set of outcome measures; second, we present findings from the subgroup and multivariate 

analyses for selected outcomes. 

A. DESCRIPTIVE ANALYSIS FINDINGS, BY GENDER 

Our descriptive analysis shows that low-wage workers experienced considerable wage 

growth during the boom period of the mid- to late 1990s. Nearly 80 percent of low-wage 

workers experienced some wage increase over the three-year period following job start, and 

nearly one in five had jobs that paid more than $10 per hour at the end of the period. Male 

workers started at higher hourly wage levels than female workers, but both groups experienced 

similar wage growth over time (about a 25 percent increase over the three-year period). Low-
 93 

 

wage workers also moved to better jobs over time—they were more likely to work full-time, and 

a higher fraction were in jobs that offered fringe benefits. 

Although many low-wage workers experienced wage growth in their jobs and moved into 

better jobs, over half of low-wage workers remained in the low-wage labor-market three years 

later, even in this period of strong economic conditions. 

1. Trends in Wages Over Time 

Workers, as a group, who started a low-wage job experienced a steady increase in wages 

during the three-year follow-up period (Figure V.1). Real wages for male workers were just over 

$7, on average, during period 1 (which reflects the 7- to 12-month period after job start).4

 They 

increased steadily over time and were just under $9 three years later, representing about a 25 

percent increase in real wages. Increases in wages for male workers were the largest during the 

early periods after job start. Wages continued to increase at relatively high rates during the first 

couple of years after job start, then tapered off. Although the extent of wage increases is large, 

the average wage for male low-wage workers was only at about 125 percent above the federal 

poverty level for a family of four at the end of the follow-up period. Nearly half still had wages 

below the federal poverty level, and another quarter had wages between 100 and 125 percent of 

the federal poverty level (Figure V.2). 

Female workers had lower wages than male workers (about $6.50 on average for females, 

compared to $7.06 on average for males, during period 1). However, wages of female workers 

steadily increased, and their average wages were about $8 at the end of the three-year follow-up 

period (Figure V.1). Female low-wage workers also experienced about a 25 percent increase in 

4

 As described earlier, this six-month period refers to average wages during the first sixmonth period after the six-month period that was used to classify workers into low-, medium- or 

high-wage groups, which we called period 0. We do this because we are concerned about 

overstating wages which may be particularly low in period 0 for the reasons discussed earlier. 
FIGURE V.1

TRENDS IN REAL WAGES OVER TIME AMONG THOSE WHO

STARTED A LOW-WAGE JOB, BY GENDER

$7.06

$7.74

$8.31

$8.87 $8.94 $8.94

$6.49

$6.96 $7.10

$7.59 $7.91 $8.04

$0.00

$2.00

$4.00

$6.00

$8.00

$10.00

2 3 5

Average Wages Over

Six-Month Periods

Average Wages Over

Six-Month Periods

4

Dollars

1 6

Males Females

1 6 2 3 4 5

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight. Period 1 refers to the average wages during the first six-month 

interval after the period that was used to categorize workers into job type. All wages are reported in 1999 dollars.

real wages over the three-year period, and their wages at the end of the three-year period put 

their average earnings right around the federal poverty level for a family of four.5

 Sixty percent 

of female workers continued to have earnings that put them below the federal poverty level, 

and about 25 percent had incomes between 100 to 125 percent of the federal poverty level 

(Figure V.2). 

The percentage increases in real wage we observed for low-wage workers were considerably

larger than the wage increases we observed for medium- and high-wage workers. Medium-wage 

workers, as a group, experienced a real wage increase of about 10 to 12 percent over the threeyear period, and high-wage workers experienced a real wage increase of less than 5 percent over

 94 

 5

 Patterns of wage growth remain similar when we looked at alternative definitions of lowwage workers. For example, they remain similar when we use average wages across the first 

year to define low-wage workers, as well as when we exclude those with wages below $3. 
FIGURE V.2

REAL WAGES RELATIVE TO POVERTY,

AT THE END OF THE FOLLOW-UP PERIOD

4

16

27

23

17

13

4

21

35

24

9 8

0

10

20

30

40

Percentage Males Females

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of the panel period.

Note: All figures were calculated using the longitudinal panel weight.

50 to

75

76 to

100

126 to

150

Percentage of Federal Poverty Level

101 to

125

Less

than 50

More

than 150

50 to

75

76 to

100

126 to

150

Percentage of Federal Poverty Level

101 to

125

Less

than 50

More

than 150

the same period (Table D.2). The average increase in wages across all workers who started jobs, 

where we do not classify them into worker type and hence are not worried about any 

contamination, is 12 to 15 percent for the three-year period.6

 

2. Extent of Wage Growth Over Time 

While workers as a group who started low-wage jobs experienced wage increases over time, 

it is important to examine the extent to which individual workers experienced an increase in

wages. To better understand the distribution of wage growth, we examined the fraction of low-

 95 

 6

 If we examine the change including the base period (period 0) used to classify workers into

wage type, wage growth was somewhat higher (closer to 20 percent). 
 96 

wage workers who experienced wage growth, as well as the extent of wage growth during the 

three-year follow-up period. 

Most low-wage workers (nearly 80 percent) experienced an increase in real wages between 

their wages in period 1 and their wages three years later (Table V.1). The proportion 

experiencing any increase in wages was essentially the same for males and females (78 percent, 

compared to 80 percent). The amount of wage growth was also considerable for many, although 

male workers were somewhat more likely than females to experience greater amounts of growth. 

For example, nearly half of males, and just over 40 percent of females, experienced an increase 

in real wages of over 25 percent over the three-year period. In addition, more than one in five 

workers experienced an increase of over 50 percent in their wages. In contrast, few experienced 

large reductions in wages. Given the low levels of their starting wages, this is not surprising. 

Another dimension of wage growth, somewhat related to the analysis in the preceding 

chapter, is the fraction of low-wage workers who had moved into medium- or high-wage jobs 

three years later. Even though they experienced relatively large increases in wages over time, a 

significant fraction still remained in the low-wage labor market three years later (47 percent of 

males and 60 percent of females—Table V.1). Those who moved to higher-wage jobs were most 

likely to be in medium-wage jobs, and only a small fraction were in high-wage jobs. For 

example, three and a half years after they started their low-wage job, only about 2 percent of 

females and 5 percent of males had moved into high-wage jobs (with hourly wages over $16), 

and about 48 percent of males and 38 percent of females were in medium-wage jobs. 
 97 

TABLE V.1 

GROWTH IN REAL HOURLY WAGES AMONG LOW-WAGE WORKERS 

WHO REMAINED EMPLOYED THREE YEARS LATER 

 

Male Workers Female Workers 

Percentage Employed in Both Periods 82 74 

Percentage Whose Wages:a

Increased 78 80 

Decreased 22 20 

Percentage Change in Wagesa

More than 50 percent 26 20 

26 to 50 percent 21 22 

11 to 25 percent 17 21 

1 to 10 percent 14 17 

–1 to –10 percent 9 9 

Less than –10 percent 13 11 

Change in Real Wages Over Time (in Dollars)a

More than $5.00 14 9 

$2.51 to $5.00 21 15 

$1.01 to $2.50 21 27 

$0 to $1.00 21 27 

$0 to –$1.00 11 11 

Less than –$1.00 11 9 

Percentage Whose Job Three Years Later Was:a

Low wage 47 60 

Medium wage 48 38 

High wage 5 2 

Sample Size 460 636 

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start 

of the panel period. 

Note: All figures were calculated using the longitudinal panel weight. Wage changes are calculated as the 

difference between average wages in period 1 (the first six months, after initial job categorization) and 

average wages over a six-month period three years later. 

a

Figures refer to the sample that remained employed three years later.
 98 

Workers who started low-wage jobs were more likely to experience wage increases than 

those starting medium- or high-wage jobs. For example, around 70 percent of medium-wage 

workers and under 60 percent of high-wage workers experienced an increase in real wages, 

compared with 80 percent of low-wage workers (Table D.4). Because they start at higher wage 

levels, the fraction of higher-wage workers who experienced large relative increases in wages 

(over a 50 percent increase in wages) is considerably lower than the corresponding fraction of 

low-wage workers who experienced such large increases. However, higher-wage workers were 

considerably more likely than low-wage workers to have experienced an increase of $5 per hour 

over the three-year follow-up period. 

3. Changes in Job Characteristics 

Not only did low-wage workers experience wage growth, but they also worked more hours 

and moved into better jobs over time. The fraction of low-wage workers working full-time 

(defined as 35 or more hours) went up from 76 percent to 86 percent over the three-year period 

for males, and from 54 percent to 69 percent for females. Similarly, average hours worked for 

those starting low-wage jobs increased slightly over time, by about three to four hours per week 

(Table V.2). 

Low-wage workers also moved into jobs that offered fringe benefits such as health 

insurance. As Table V.2 shows, 52 percent of male workers had health coverage through their 

jobs at the end of the follow-up period, compared with only 24 percent of those in their initial 

job. Female workers were more likely than male workers to have employer-based health 

coverage at the start of their jobs (34 percent), and they continued to move into jobs with health 

insurance coverage. By the end of the follow-up period, 65 percent of females had employerbased health insurance coverage. 
 99 

TABLE V.2 

CHARACTERISITICS OF INITIAL LOW-WAGE JOB AND THE JOB HELD THREE YEARS LATER 

(Percentages, Unless Otherwise Noted) 

 

Male Workersa Female Workersa

Job Characteristics 

Initial 

Job 

Most Recent

Job 

Initial 

Job 

Most Recent

Job 

Hourly Wages 

Less than $5.00 18 7 24 7 

$5.00 to $5.99 27 12 30 16 

$6.00 to $6.99 25 12 26 17 

$7.00 to $7.99 31 13 20 19 

$8.00 to $8.99 -- 14 -- 13 

$9.00 to $9.99 -- 11 -- 11 

$10.00 to $10.99 -- 9 -- 7 

$11.00 to $11.99 -- 8 -- 3 

$12.00 or more -- 14 -- 9 

(Average hourly wage, in dollars) ($6.07) ($8.96) ($5.78) ($8.04) 

Usual Hours Worked Per Week 

1 to 19 8 5 16 10 

20 to 34 17 10 30 20 

35 to 40 54 60 46 62 

More than 40 22 26 8 8 

(Average hours worked) (38) (41) (31) (35) 

Covered by Health Insuranceb 24 52 34 65 

Occupation 

Professional/technical 8 11 10 15 

Sales/retail 11 10 17 14 

Administrative support/clerical 6 6 19 22 

Service professions/handlers/cleaners 34 31 39 34 

Machine/construction/production/ 

transportation 29 36 12 13 

Farm/agricultural/other workers 11 6 3 2 

Industry 

Agriculture/forestry/fishing/hunting 11 8 8 6 

Mining/manufacturing/construction 21 26 11 14 

Transportation/utilities 6 7 2 4 

Wholesale/retail trade 30 25 31 26 

Personal services 14 12 20 12 

Health services 2 2 8 11 

Other services 11 15 20 27 

Other 6 5 1 1 

Union Member 3 8 2 4 

Owns Business/Self-Employed 9 8 6 5 

Sample Size 491 491 693 693 
TABLE V.2 (continued) 

_____________________________________________________________________________________________ 

 100 

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of 

the panel period. 

Note: All figures are weighted using the longitudinal panel weight. 

a

The interpretation of the statistics can be illustrated using the union figures, which show that three percent of all 

male workers were union members in their initial jobs, and eight percent of all workers were union members in their 

most recent jobs. 

b

SIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the 

time of the interview. Thus, the health insurance figures in this table pertain to jobs held by sample members at the 

time of the wave 1 and the wave 12 interviews. 
 101 

We observe some small movements over time in the occupations and industries of low-wage 

workers. Compared to their initial jobs, male workers were somewhat more likely to be in 

construction and production jobs and in professional and technical jobs and were less likely to be 

in agricultural or service jobs three years later. Similarly, female workers were more likely to 

move into professional and technical and administrative support occupations and were less likely 

to be in service and sales jobs. Low-wage workers, especially male workers, were also more 

likely to move into unionized jobs. 

In contrast to low-wage workers, we did not see much change in hours worked over time for 

medium- and high-wage workers, especially among males (Table D.5). The only notable change 

we observed was for high-wage female workers, who actually experienced a slight reduction in 

hours worked. Similar to low-wage workers, medium-wage workers were considerably more 

likely to move to jobs that offer fringe benefits, such as health insurance. The majority of highwage workers already were in jobs that offered health insurance at the time of initial job start. 

We did not observe changes in industry and occupation for these higher-wage workers. 

B. SUBGROUP FINDINGS 

We found that many low-wage workers experienced some increase in wages during the midand late 1990s. At the same time, however, some low-wage workers experienced little to no 

wage growth, even in this time of strong economic conditions. This section addresses the 

important question: Which groups of low-wage workers experience significant wage increases 

over time and which groups do not? This question is important, because examining differences 

in the extent of wage growth across subgroups of the low-wage population has implications for 

targeting appropriate services to those who are at most risk of experiencing poor wage outcomes. 

We conducted our subgroup analysis in a manner similar to that done in Chapter IV. First, 

we examined key wage growth outcomes for selected subgroups one at a time. These subgroups 
 102 

were defined by worker and job characteristics at the time the workers started their low-wage 

jobs. Second, we conducted a multivariate analysis to examine the association between a 

particular explanatory (subgroup) variable and key labor market outcomes, holding constant the 

effects of other explanatory variables. The multivariate analysis accounts for correlations among 

the subgroup variables and also allows us to efficiently examine wage growth outcomes for a 

large number of subgroups. 

We examined three key outcomes for the wage growth subgroup analysis for low-wage 

workers: 

1. Whether the worker was in a medium- or high-wage job at the end of the follow-up 

period (that is, earned more than $8 per hour) 

2. Whether the worker earned $10 or more at the end of the follow-up period 

3. Whether the worker experienced more than a 50 percent increase in wages between 

period 1 and period 6 (three years later) 

While these measures are related, they capture somewhat different elements of wage growth. 

For example, the percentage of workers who were in medium- or high-wage jobs at the end of 

the follow-up period indicates the fraction that escaped the low-wage labor market. The fraction 

with hourly wages over $10 provides some indication of the fraction of individuals whose 

earnings are 20 percent higher than the $8 per hour cutoff point for low-wage workers. The 

fraction that experienced a wage increase of over 50 percent allows us to examine the extent of 

progress workers have made over the three-year period relative to their starting wage in 

period 1. 

We conducted the subgroup analysis separately by gender. Furthermore, all figures were 

calculated using the longitudinal panel weight. We estimated the multivariate models using logit 

maximum likelihood methods, as all outcomes measures are binary outcomes. In the 
 103 

 

multivariate analysis, we conducted statistical tests to gauge the statistical significance of 

differences in labor market outcomes across subgroups. For some subgroups with small sample 

sizes (see Table III.1), the standard errors of the estimates are large. Consequently, some 

relatively large parameter estimates are not statistically significant. 

Similar to the analysis in Chapter IV, we included the following categories of explanatory 

variables in the regression models: 

• Individual and household characteristics measured at the month of initial job start 

(from the longitudinal panel file) 

• Prepanel employment information (from the wave 1 topical module) 

• Job characteristics measured at the month of initial job start (from the longitudinal 

panel file) 

• Area characteristics and state economic indicators measured at the start of the job, as 

well as changes in unemployment rate indicators between the start and end of the 

follow-up period (from published data sources—Methodological Appendix A)7

 

1. Findings from the Univariate Analysis 

To a large extent, and not surprisingly, the patterns of subgroup findings for the wage 

growth analyses are fairly similar to the patterns of subgroup findings for the aggregate analysis. 

We find some broad differences in labor market outcomes across key subgroups of the low-wage 

population, although the differences are smaller than expected (Tables VI.3 and VI.4). Males, 

older workers, educated workers, whites, and those without health limitations were somewhat 

more likely to experience wage growth than their respective counterparts. Job characteristics 

also matter—those who start with better jobs (measured by higher initial wages, availability of 

health benefits, and full-time work status) were more likely to experience wage growth than 

7

 We measured these indicators using information on the state in which the worker lived at 

the beginning and end of the follow-up period. 
 104 

those in lower-quality jobs. We find few differences across occupations and industry. The 

exception is males in professional occupations and females in clerical and administrative support 

occupations—both groups were more likely to experience greater amounts of wage growth than 

workers in other occupations. 

a. Findings for Subgroups Defined by Individual and Household Characteristics 

Table V.3 presents our findings for subgroups defined by individual and household 

characteristics at the start of the low-wage job. We summarize these findings here: 

• Male low-wage workers were more likely than female low-wage workers to 

experience wage growth. Male low-wage workers were more likely than females to 

have earned at least $10 per hour at the end of the three-year follow-up period (30 

percent, compared to 18 percent for females). They were also more likely to be in 

medium- or high-wage jobs (53 percent of males, compared to 40 percent of 

females). Finally, males were somewhat more likely to have experienced a relatively 

large increase in wages over time; 26 percent of males experienced a wage growth of 

more than 50 percent during a three-year follow-up period, compared with 20 percent 

of females. These gender results hold across all subgroups. 

• Males older than age 20 experienced greater wage growth than younger males. In 

our sample, teenage male workers experienced the lowest amounts of wage growth; 

only about 19 percent had wages over $10 per hour three and a half years after job 

start, compared with between 30 and 40 percent for older males. We observe similar 

patterns for other measures of wage growth for young males. We do not observe 

much difference in patterns of wage growth by age for females, however. 

• White females were likely to have the best wage growth outcomes, and Hispanic 

female workers were likely to have the poorest wage growth outcomes. Across all 

the measures of wage growth we examined, white females were most likely to 

experience the greatest growth, followed by black females. For example, 20 percent 

of white females earned more than $10 per hour about 42 months after job start, 

compared with 15 percent of blacks and only 10 percent of Hispanics. Similarly, 

white females were also somewhat more likely than females from other race/ethnic 

groups to have experienced wage growth of over 50 percent during a three-year 

follow-up period. We do not observe differences in patterns of outcomes for males 

by race/ethnicity. 
105 

TABLE V.3 

MEASURES OF WAGE PROGRESSION AFTER JOB START FOR SUBGROUPS OF LOW-WAGE WORKER

DEFINED BY INDIVIDUAL AND HOUSEHOLD CHARACTERISTICS AT JOB START 

(Percentages) 

Male Low-Wage Workers Female Low-W

Subgroup 

Earned More

than $10 in 

Last Period 

In Medium- or 

High-Wage Jobs

in Last Period 

More than 50 

Percent Increase

in Wages 

Earned More

than $10 in 

Last Period 

In Medi

High-Wa

in Last P

Overall 30 53 26 18 4Age (in Years) 

Younger than 20 19 37 18 19 320 to 29 29 57 27 18 430 to 39 35 57 27 18 440 to 49 32 54 26 17 350 or older 38 45 30 20 3

Race/Ethnicity

White and other non-Hispanic 32 57 25 20 4Black, non-Hispanic 26 46 30 15 3Hispanic 35 42 28 10 3

Educational Attainment 

Less than high school/GED 18 40 19 9 2High school/GED 26 52 22 15 3Some college 44 66 33 22 5College graduate or more 49 61 42 33 5

Has a Health Limitation 

Yes 23 45 21 11 4No 31 54 26 18 3

Household Type 

Single parent with children 30 56 30 15 3Married couple with children 31 53 24 20 4Married couple without children 32 49 23 16 3Other adults without children 29 56 28 20 5
TABLE V.3 (continued) 

106 

Male Low-Wage Workers Female Low-W

Subgroup 

Earned More

than $10 in 

Last Period 

In Medium- or 

High-Wage Jobs

in Last Period 

More than 50 

Percent Increase

in Wages 

Earned More

than $10 in 

Last Period 

In Medi

High-Wa

in Last P

Household Income as a Percentage of the Federal Poverty Level 

100 percent or less 26 49 29 10 3101 to 200 percent 30 49 22 14 3More than 200 percent 33 57 26 23 4

Full Sample Size 491 491 491 693 69

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after the s

workers were followed for three years after job start. 

Note: All figures are weighted using the longitudinal panel weight. 
 107 

• Education is strongly associated with wage growth. As expected, wage growth 

outcomes were typically poorest for high school dropouts and improved with 

education level. Among male low-wage workers, only 18 percent of those who were 

high school dropouts at the start of their jobs had hourly wages over $10 three and a 

half years later, compared with 26 percent of those with a high school credential and 

around 45 to 50 percent among those who attended college. Similarly, males with 

lower education levels were less likely to experience substantial wage growth. We 

find similar patterns for female workers. 

• Male low-wage workers with health limitations were somewhat less likely than 

those without health problems to experience higher levels of wage growth. Around 

23 percent of low-wage male workers with health problems had wages of over $10 

per hour at the end of the follow-up period, compared with just over 30 percent of 

those without health problems. While we observe modest differences in this direction 

for all measures for males, we do not observe similar patterns for females across all 

measures of wage growth. These findings are in contrast to the findings from Chapter 

IV, where we observed better labor market outcomes for those with no health 

limitations. These findings may be explained partly by the fact that those with health 

limitation are less likely to be employed at a later time and thus are less likely to be 

part of the wage growth sample. 

• We do not observe strong patterns of wage growth by household type for either 

male or female low-wage workers. Among females in our sample, single parents 

with children and married couples without children were somewhat less likely to 

experience greater wage growth than other household types. However, the 

differences were not large. Furthermore, we did not observe any such patterns of 

wage growth by household types for male workers. 

• Poverty status is inversely associated with positive wage outcomes at followup. In 

general, low-wage workers in wealthier households were more likely than those in 

poorer households to experience greater wage growth. These findings may reflect the 

fact that those in wealthier households are also likely to be more educated, which may 

be related to the higher amounts of wage growth they experience. Interestingly, we 

find the reverse pattern for males who experienced wage growth of more than 50 

percent. Males in households with income below the federal poverty level were more 

likely than males in other households to experience large increases in their wages. 

b. Findings for Subgroups Defined by Job Characteristics

Our findings for subgroups defined by job characteristics at the start of the low-wage job 

indicate that job quality matters—those who started with better jobs tended to have jobs with 

somewhat higher hourly wages at the time of the follow-up period. However, fewer initial job 

characteristics are associated with who is most likely to experience a more than 50 percent wage 

growth. The exception is initial wages, and those with very low initial wages were most likely to 
 108 

experience the maximum increase in their wages over time (Table V.4). We summarize these 

results here: 

• In general, those with higher initial wages were more likely than those earning 

lower wages to earn more than $10 per hour at the end of the follow-up period. 

Sample members who started at less than $6 per hour were less likely to be earning 

more than $10 per hour at the end of the follow-up period or to have moved into a 

medium- or high-wage job. While they were less likely to exit the low-wage labor 

market, the lowest-wage workers (those earning less than $5 per hour) were more 

likely to experience the largest gains in their own wages over time. For example, 34 

percent of male low-wage workers who had initial hourly wages of less than $5 were 

likely to have experienced a wage increase of over 50 percent three years later, 

compared with only around 20 percent among those whose starting wage was $6 per 

hour or more. We found similar patterns for female low-wage workers. 

• Male low-wage workers working more than 40 hours per week had higher hourly 

wages at followup than those working fewer hours. During the mid- to late 1990s, 

male low-wage workers who reported working more than 40 hours per week (about 

20 percent of all workers) were more likely to be earning more than $10 per hour or 

have moved into a medium- or high-wage job three years later. For example, 42 

percent of males who worked more than 40 hours per week had earned more than $10 

per hour three and a half years after initial job start, compared with between 25 and 

30 percent for workers who had worked fewer hours. The patterns are not as strong 

for female workers or for the percentage experiencing more than 50 percent wage 

growth for either gender. 

• Those in jobs that offered fringe benefits were somewhat more likely to have 

greater hourly wages three and a half years after initial job start. Those covered by 

health benefits (about one-third to half of all low-wage workers) were more likely 

than those not covered to have earned more than $10 in the last period (38 percent, 

compared to 26 percent for males, and 23 percent, compared to 13 percent for 

females). Health insurance coverage, however, did not seem to affect the percentage 

of male and female workers experiencing 50 percent wage growth. 

• Business owners were more likely than job holders to experience greater wage 

growth. Although business owners (about 13 percent of all low-wage workers) 

tended to have lower hourly wages than job holders near the start of their employment 

spells, they were more likely than job holders to experience greater amounts of wage 

growth. For example, 46 percent of low-wage male business owners earned more 

than $10 at the last period, compared to 29 percent for male job holders. We observe 

similar patterns of outcomes for female business owners and job holders, but the 

differences are smaller. 

• Among male low-wage workers, those in professional occupations experienced 

more wage growth than other workers. During the mid- to late 1990s, male lowwage workers who worked in professional occupations (eight percent of workers) 

were most likely to be in a medium- or high-wage job at the time of the followup, and 
109 

TABLE V.4 

MEASURES OF WAGE PROGRESSION AFTER JOB START FOR SUBGROUPS OF LOW-WAGE WORKER

DEFINED BY INITIAL JOB CHARACTERISTICS 

(Percentages) 

Male Low-Wage Workers Female Low-W

Subgroup

Earned More

than $10 in 

Last Period 

In Medium- or 

High-Wage Jobs

in Last Period 

More than 50 

Percent Increase

in Wages 

Earned More 

than $10 in 

Last Period 

In Medi

High-Wa

in Last 

Overall 30 53 26 18 4Hourly Wages 

Less than $5.00 24 39 34 13 2$5.00 to $5.99 20 39 28 12 3$6.00 to $6.99 35 63 20 25 5$7.00 or more 46 76 22 30 6

Hours Worked per Week 

1 to 19 25 35 20 15 320 to 34 31 47 30 22 435 to 40 27 54 22 16 4More than 40 42 61 33 15 4

Weekly Earnings 

Less than $150 31 42 33 18 3$150 to $299 24 52 23 17 4$300 to $600 59 74 29 25 4

Owns Business (Self-Employed) 

Yes 46 69 47 24 4No 29 52 24 17 4

Health Insurance Coveragea

Yes 38 61 28 23 4No 26 49 25 13 3

Occupation

Professional/technical 48 64 34 26 4Sales/retail 38 59 25 23 4Administrative support/clerical 35 59 36 23 5
TABLE V.4 (continued) 

110 

Male Low-Wage Workers Female Low-W

Subgroup

Earned More

than $10 in 

Last Period 

In Medium- or 

High-Wage Jobs

in Last Period 

More than 50 

Percent Increase

in Wages 

Earned More 

than $10 in 

Last Period 

In Medi

High-Wa

in Last 

Service professions/handlers/cleaners 23 43 22 13 3Machine/construction/production/ 

transportation 32 61 25 15 2

Farm/agricultural/other workers 27 45 29 3 3

Industry

Agriculture/forestry/fishing/hunting/other 35 54 36 16 2Mining/manufacturing/construction/ 

transportation/utilities 29 57 24 12 3

Wholesale/retail trade 30 49 21 16 4Personal/health/other services 30 53 28 21 4

Employment Status 

Continuously employed with one job 26 52 20 10 3Continuously employed with multiple jobs 35 62 29 20 4Intermittent, employed less than 75% of time 17 27 21 14 2Intermittent, employed 75% or more of time 34 57 27 22 4

Full Sample Size 491 491 460 693 69

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after the s

workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 

aThese figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from oth

variable instead of the employer-based health insurance coverage variable, because data on overall health insurance coverage is avail

employer-based coverage variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health in

always be linked to the job under investigation, which led to a significant number of missing values. However, the subsets of health iconsiderably: the source of health insurance coverage was the employer for 80 percent of those with any coverage.
 111 

those in service professions, handlers, and cleaners were the least likely to have 

escaped the low-wage labor market. We do not see patterns quite as strong for 

females, nor do we see strong patterns by industry type. 

• Time spent employed was associated with wage growth. For instance, about 33 

percent of male workers who were employed for most of the period (at least 75 

percent of months) earned at least $10 per hour at the end of the follow-up, compared 

to only 17 percent of males who were employed for fewer months (Table V.4). The 

corresponding figures for females are 19 percent and 14 percent, respectively. Thus, 

policies that promote employment retention could improve the wage growth of lowwage workers. 

• Among those continuously employed, those who switched jobs experienced greater 

wage growth than those who remained in the same job over the entire follow-up 

period. Workers who were continuously employed, but in different jobs, were 

somewhat more likely than those who remained employed in the same job to 

experience greater wage growth. For example, 35 percent of male workers who 

switched directly from one job to another were likely to earn more than $10 per hour 

at the end of the three-year follow-up period, compared with 26 percent of those who 

remained with the same employer over time (Table V.4). We find similar patterns 

even among intermittent workers who were employed at least 75 percent of the time 

over the three-year period. We find similar patterns of findings for female workers as 

well. These findings are consistent with the findings of Gladden and Taber (2000b) 

who find positive wage growth with job turnover, when workers moved directly 

between jobs or were unemployed for a short time. 

2. Findings from the Multivariate Analysis 

Thus far, we have examined subgroup results one at a time. However, many of these 

subgroups are correlated with each other. For example, we have seen that less disadvantaged 

workers and those in higher-quality jobs tend to have more positive wage growth outcomes than 

other workers. However, better-off workers are more likely than those who are more 

disadvantaged to be in higher-quality jobs. Thus, an important question is whether labor market 

success is due more to worker characteristics or initial job characteristics. 

We isolated subgroup effects from others using multivariate regression methods. We 

estimated regression models for the three outcome measures used in the univariate subgroup 

analysis. In the main text, we present findings for the percentage who earned at least $10 at the 

last period we observed them, about 42 months after job start (Table V.5). The results for the 
 112 

TABLE V.5 

MULTIVARIATE ANALYSIS FINDINGS ON THE PERCENTAGE OF LOW-WAGE WORKERS EARNING 

AT LEAST $10 THREE AND A HALF YEARS LATER, BY GENDER AND MODEL 

 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

Males Females

Explanatory Variable 

No Other

Variables

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Individual Characteristics 

Age 

Younger than 20a 19 27 20 20 28 20 

20 to 29 30 32 30 17 17 17 

30 to 39 35** 34 36* 18 17 18 

40 to 49 32 26 32 14 14** 14 

50 or older 34 24 30 26 25 30 

Race/Ethnicity 

White and other non-Hispanica 32 32 32 19 19 19 

Black, non-Hispanic 23 25 25 17 18 17 

Hispanic 26 25 28 11* 11* 12 

Educational Attainment 

Less than high school/GEDa 19 20 22 11 12 13 

High school/GED 27 26 27 15 16 16 

Some college 39** 39** 34 21 20 19 

College graduate or more 47** 49** 44** 24** 23* 23 

Has a Health Limitation 

Noa 31 31 31 18 18 18 

Yes 24 25 27 11 11 13 

Work Experience Prior to the Panel Period

Ever Worked for Six Straight Months 

Noa 34 22 

Yes 30 17 

Number of Years Ever Worked Six Straight 

Months 

Less than 5a 27 12 

5 to 10 31 20 

10 to 20 28 23* 

More than 20 38 18 

Usually Worked at Least 35 Hours Per Week 

When Working 

Noa 20 14 

Yes 34** 20* 
TABLE V.5 (continued) 

 113 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

 Males Females

Explanatory Variable 

No Other

Variables

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Household Characteristics 

Household Type 

Single adults with childrena 33 34 32 19 19 20 

Married couples with children 36 36 34 20 20 20 

Married couples without children 28 27 31 13 13 12** 

Other adults without children 24 24 24 18 18 18 

Household Income as a Percentage of the Federal 

Poverty Level 

100 percent or lessa 29 27 30 11 12 13 

101 to 200 percent 31 32 32 14 15 15 

More than 200 percent 30 31 30 22** 22** 21 

Received Public Assistance in the Past Year 

Noa 31 32 31 18 18 17 

Yes 23 22* 28 19 19 20 

Area Characteristics 

Region of Residence 

Northeasta 27 27 29 22 21 22 

South 31 31 29 15 14 15 

Midwest 28 29 28 17 17 18 

West 33 33 36 22 22 19 

Lives in a Metropolitan Area 

No 22 22 21 13 12 13 

Yes 34** 34** 34** 20** 20** 20* 

20th Percentile of the Weekly Wage 

Distribution in State 

$250 or lessa 30 30 30 18 18 18 

$251 to $269 37 35 34 17 17 17 

$270 or more 28 29 29 18 18 18 

Percentage of State Population Residing 

in Metropolitan Areas 

72 or lessa 24 24 25 22 22 23 

73 to 84 35** 35** 35* 15* 15 15** 

85 or more 33 33 32 16 16 16 

Poverty Rate in State 

Less than 10 percenta 28 28 26 20 21 17 

10 to 12 percent 31 31 32 18 18 21 

More than 12 percent 31 32 32 15 15 15 
TABLE V.5 (continued) 

 114 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

 Males Females

Explanatory Variable 

No Other

Variables

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Unemployment Rate in State 

6 percent or lessa 31 31 30 17 17 17 

More than 6 percent 28 29 30 22 23 22 

Change in Unemployment Rate in State 

of Residence Between 1996 and 1999 

(Percentage Points) 

–2 percentage points or lessa 21 19 19 14 14 14 

–1 to –2 percentage points 30 30 31 21 21 21 

More than –1 percentage point 35 36 36 13 13 13 

Initial Job Characteristics 

Hourly Wages 

Less than $5.00a 25 14 

$5.00 to $5.99 22 13 

$6.00 to $6.99 35 25** 

$7.00 to $7.50 42** 22 

Usual Hours Worked per Week 

1 to 19a 29 14 

20 to 34 33 24** 

35 to 40 28 16 

More than 40 35 14 

Has More than One Job or Business 

Noa 31 17 

Yes 27 22 

Owns Business (Self-Employed) 

Noa 29 17 

Yes 41 29 

Health Insurance Coverageb

Noa 27 16 

Yes 35* 19 

Union Member 

Noa 30 18 

Yes 32 20 

Occupation 

Professional/technicala 34 19 

Sales/retail 30 23 

Administrative support/clerical 42 19 

Service professions/handlers/cleaners 25 13 

Machine/construction/production/ 

transportation 32 24 

Farm/agricultural/other workers 33 7* 
TABLE V.5 (continued) 

 115 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

 Males Females

Explanatory Variable 

No Other

Variables

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

No Other 

Variables 

(1) 

Prepanel 

Work History

Variables 

(2) 

Initial Job

Variables

(3) 

Industry 

Agriculture/forestry/fishing 

and huntinga 20 12 

Mining/manufacturing/construction/ 

transportation and warehousing/ 

utilities 33 13 

Wholesale/retail trade 33 16 

Services/other 29 21 

Type of Worker 

Continuous worker with only one 

employer/business 25 9 

Continuous worker with more than one 

employer/business 30 17* 

Intermittent worker, employed less than 

75% of time 22 18* 

Intermittent worker, employed 75% or 

more of time 36* 23** 

Sample Size 491 491 491 693 693 693 

Source: 1996 SIPP longitudinal and wave 1 topical module files using the entry cohort sample of workers who started low-wage 

jobs within six months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

Denotes the “omitted” explanatory variable in the regression model. 

b

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from 

other sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall

health insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at 

the time of the interview. Thus, the employer-based health insurance variable could not always be linked to the job under 

investigation, which led to a significant number of missing values. However, the subsets of health insurance variables overlap

considerably: the source of health insurance coverage was the employer for 80 percent of those with any coverage. 

*Difference between the variable mean and the mean of the “omitted” explanatory variable is significantly different from zero at 

the .10 level, two-tailed test. 

**Difference between the variable mean and the mean of the “omitted” explanatory variable is significantly different from zero at 

the .05 level, two-tailed test. 
 116 

other two outcomes are presented in Table D.6 and are qualitatively similar to those presented in 

the text (although a few differences exist). We present regression-adjusted means for each 

subgroup level and indicate whether the difference between the regression-adjusted means for 

each subgroup and the “left-out” subgroup is statistically significant at the five percent 

significance level. 

We present estimates from three models for both males and females. The first model 

includes demographic variables only—that is, explanatory variables defined by individual, 

household, and area characteristics; model (1) on Table V.5. The second model includes 

demographic variables as well as prepanel work experience variables from the wave 1 topical 

module—model (2). The third model—model (3)—includes demographic variables and initial 

job-related variables. Table D.6 presents the model (3) results for the additional employmentrelated outcome measures only. 

a. Models Including Demographic Variables Only 

The regression-adjusted differences in labor market outcomes across subgroups defined by 

individual and household characteristics are largely similar to the univariate findings described 

above, although few findings are statistically significant (Table V.5). Again, the patterns of 

findings across demographic subgroups are similar to those observed for the aggregate analyses 

in Chapter IV, although fewer differences are statistically significant in the wage growth analysis. 

Education is the strongest predictor of wage growth, especially for males, with college 

graduates more likely to experience wage growth than those with less education. Similar to 

the univariate subgroup findings, female Hispanic workers were significantly less likely than 

black non-Hispanics or white non-Hispanics to earn more than $10 per hour at the end of the 

follow-up period. 
 117 

Living in a metropolitan area is a strong predictor of wage growth for both males and 

females. Holding all else constant, 34 percent of male low-wage workers in metropolitan areas 

were likely to earn more than $10 per hour at the last period, compared with only 22 percent 

among nonmetropolitan workers. However, most other explanatory variables measuring area 

characteristics had little predictive power in the regression models. 

The regression R2

 value from model (1) is about .11 for males and .08 for females. Thus, 

demographic variables explain only about 10 percent of the variance in wage growth, and 

substantial residual factors remain that account for differences across workers. 

b. Models Including Demographic and Prepanel Work Experience Measures 

Most prepanel variables capturing prior work experience had only small effects on wage 

growth of low-wage workers. We observe some differences for female workers, with those who 

worked less than five years least likely to earn more than $10 per hour at the end of the study 

period. We also found that workers who typically worked full-time while employed prior to the 

panel period experienced better wage outcomes than part-time workers, and these differences 

were statistically significant for both males and females. The R-squared value in model (2) is 

about .14 for males and .10 for females, indicating that adding prepanel variables has only a 

small effect in explaining differences in wage growth across workers. 

c. Models Including Demographic and Initial Job-Related Variables 

The multivariate findings provide some evidence that job quality matters. Among low-wage 

male workers, those who had higher hourly wages in their initial job were more likely to be 

earning more than $10 per hour three years after job start. In addition, males in jobs with fringe 

benefits were also more likely to have higher hourly wages three years later. Among female 
 118 

 

workers, those with lower starting wages and those who worked part-time (between 20 and 34 

hours) in their initial job were more likely than those working fewer or more hours to earn $10 

per hour or more at the time of the follow-up period—model (3) on Table IV.5.8

While those self-employed seem to do better, the differences are not statistically significant. 

Nor do we observe significant differences by industry and occupation. We also find that lowwage workers who stayed continuously in the same job over time were less likely to experience 

wage growth than those who switched jobs (either continuously moved from one job to another, 

or switched jobs with a break in between jobs but were employed over most of the follow-up 

period, Table V.5). Interestingly, these findings are strongest for intermittent workers who were 

employed at least 75 percent of the time. 

In general, the inclusion of the job-related variables does not much affect the differences 

across the demographic subgroups as compared to those presented above. This is partly because 

few demographic variables were significant to begin with. However, race among females, and 

higher education for both groups, continue to remain important, although the effects of education 

are not statistically significant for females. 

The inclusion of both the job and demographic characteristics yields a model R2

 value of .18 

for males and .14 for females (not shown). Thus, while including job characteristics helps 

explain some more of the differences in wage growth across groups of workers, substantial 

residual factors remain that account for differences in wage growth outcomes across low-wage 

workers, even after controlling for a large number of demographic and job-related factors. 

8

 Because the job variables are likely to be endogenous, they could lead to biased coefficient 

estimates on all the explanatory variables. Thus, we do not view our parameter estimates as 

“structural” relationships between the explanatory and dependent variables. Rather, our goal is 

to identify broad associations between subgroup variables and labor market outcomes. 
 119 

Clearly, there are other important factors that we could not identify using the SIPP data that may 

explain differences in wage growth outcomes across groups of workers. 

 121 

VI. SPELL DURATION ANALYSIS 

Thus far, we have examined the overall employment experiences and wage growth of lowwage earners over a three-and-one-half-year period after job start. For these analyses, the worker

was the unit of analysis, and we examined aggregate measures of potentially discontinuous 

employment and nonemployment spells that workers experienced over the fixed follow-up 

period. Another interrelated way to examine the labor market experiences of low-wage workers 

is to directly examine the duration of their employment and nonemployment spells. For these 

analyses, the spell, rather than the worker, is the unit of analysis. 

These spell analyses allow us to address the following important study questions: 

• What are typical job and employment spell lengths for those who start low-wage 

jobs? How do they vary across subgroups of low-wage workers? 

• At what rate do low-wage workers exit their low-wage jobs directly into higher-wage 

jobs? At what rate do they exit into other low-wage jobs and into nonemployment? 

• How soon do those who exit the low-wage sector into nonemployment become 

reemployed in low- or higher-wage jobs? At what rate do those who exit low-wage 

jobs into higher-wage jobs return to the low-wage labor market? 

• How do job spell lengths of low-wage workers compare to those of medium- and 

high-wage workers (a group whom we refer to collectively as higher-wage workers)? 

We addressed these questions using information on the duration of job, employment, and 

nonemployment spells that started during the panel period. We used life table statistical methods 

to examine spell durations for the full sample, by gender, and for key subgroups of low-wage 

workers. 

Our spell analysis paints a complex picture of the labor market dynamics of low-wage 

workers. Most importantly, we find that the job, employment, and nonemployment spells of 

low-wage workers during the mid- to late 1990s were short, and that there was substantial 

diversity in the ways in which these spells ended. For instance, the median duration of low-wage 
 122 

 

job spells was about four months for both males and females; about 80 percent ended within a 

year, and more than 90 percent ended within two years. About 39 percent of male low-wage 

workers and 28 percent of female low-wage workers exited their low-wage jobs directly into 

higher-wage employment within three-and-one-half years after job start; at the same time, 

however, 31 percent of spells for males and 41 percent of spells for females ended in 

nonemployment (with the remainder of spells ending in another low-wage job). Similarly, more 

than one-half of those who exited their low-wage jobs into higher-wage jobs returned to the lowwage labor market within two years, and about 87 percent of males who exited their low-wage 

jobs into nonemployment became reemployed within two years (with one-quarter entering highwage jobs and the remainder entering low-wage jobs). 

These results suggest that job mobility was very common; many workers bounced in and out 

of the low-wage and higher-wage labor markets.1

 Furthermore, our results indicate that the 

pathways that led to general improvements in economic prospects over time (discussed in the 

overall employment and wage progression analyses) differed significantly across workers and 

were not smooth for most workers. Finally, and not surprisingly, we find that the same subgroups 

of workers who typically had the best overall employment experiences and wage growth also had 

the best spell-related outcomes. 

A. METHODOLOGICAL APPROACH 

We conducted multiple spell analyses to examine exit rates out of low-wage jobs and reentry 

rates into the low-wage labor market. For each spell analysis, the sample contains an entry 

1

 This job mobility, however, is not necessarily a negative result, because as discussed in the 

previous two chapters, among workers who were continuously employed during the follow-up 

period, those who switched jobs tended to have more positive labor market outcomes than those 

who remained with their initial employers. Thus, it appears that job mobility is an avenue for 

wage growth for some low-wage workers. 
 123 

cohort of job, employment, or nonemployment spells that started during the panel period. Thus, 

an individual could contribute more than one spell to an analysis file. 

Each analysis file contains one observation per month of the spell. We constructed a 

dependent variable that was set to zero in months when the spell was in progress, and to 1 when 

the spell ended (or in some analyses, to positive codes signifying the type of exit or reentry). 

The last observation for a spell corresponds to the month when the spell ended, or to the end of 

the panel period for spells that were still in progress at that time (that is, for right-censored

spells). The analysis files also contain individual and job characteristics pertaining to the month 

in which the spell started that were used for the subgroup analysis. 

Next, we discuss the various types of spells that we examined and the life table procedures 

that we used to estimate spell durations. 

1. Defining Spells 

A central, and complicated, analytic issue is how to define job, employment, and 

nonemployment spells (that is, the rules used to assign zeros and positive codes to the dependent 

variables discussed above). To facilitate this discussion, we first list the five possible states into 

which a low-wage worker could exit: 

1. Another low-wage job (or business) 

2. A higher-wage job with the same employer 

3. A higher-wage job with a different employer 

4. Unemployment 

5. Not in the labor force 

Using these possible exit states, we conducted duration analyses for four types of job and 

employment spells, each of which addresses a slightly different analytic question: 
 124 

1. Low-Wage Job Spells. The duration of these spells was measured from the start of 

the low-wage job until the worker exited into any of the five states listed above (or, 

for right-censored spells, until the end of the panel period). These spells were used 

to address the extent to which low-wage workers remain in their initial jobs and 

continue to receive low pay. 

2. Job Spells. These spells pertain to the period the worker was employed with the 

initial employer regardless of the wage level that the worker received (that is, until 

the worker exited into state 1, 3, 4, or 5). Thus, these spells provide information on 

the amount of time low-wage workers remain with their initial employer. These 

spells will produce different results than the low-wage job spells if low-wage 

workers experience wage growth within their jobs. 

3. Low-Wage Employment Spells. The duration of these spells was measured from the 

start of the low-wage job spell until the worker left all low-wage employment (that 

is, until they exited into state 2, 3, 4, or 5). This duration includes continuous 

changes from one low-wage job spell to another. Results using these spells will 

differ from those using the low-wage job spells if low-wage workers move directly 

from one low-wage job to another. 

4. Employment Spells. These spells provide information on the time between job start 

and when the worker became nonemployed (that is, until the worker exited into state 

4 or 5). Thus, these spells pertain to the number of months that the worker was 

employed in any job, regardless of the wage level. Duration results based on these 

spells will differ from those based on the other spells if low-wage workers move 

seamlessly between employers and across wage levels. 

Similar procedures were used to construct spells for those who began medium- and high-wage 

jobs during the panel period. 

We examined two types of spells for our analyses of reentry into the low-wage labor market. 

First, we examined the rate at which those who exited their low-wage jobs into nonemployment 

(that is, into exit states 4 and 5) returned to the low-wage and higher-wage labor markets. 

Second, we examined the extent to which those who exited their low-wage jobs into higher-wage 

jobs returned to the low-wage sector. 

2. Life Table Methods 

To examine the duration of job, employment, and nonemployment spells, we used “life table 

analyses.” Spells can be broken down into months; for each month, the life table displays the 
 125 

estimated hazard rate and cumulative exit rate. The hazard rate is the probability that a spell 

ended in a particular month, given that the spell lasted at least until the beginning of that month. 

The cumulative exit rate, obtained from the estimated hazard rates, is the unconditional 

probability that a spell ended within a given number of months. The cumulative exit rate enables 

policymakers to answer such questions as: Of the next 100 people who begin a low-wage job 

spell, how many will exit their low-wage jobs within one year? 

A major advantage of using life table methods is that they can effectively treat rightcensored spells (that is, spells still in progress at the end of the observation period). Rightcensored spells contribute information to the life table up to the month in which they are rightcensored (that is, up to the time we no longer have information on them). For example, if a spell 

is right-censored 12 months after the spell started, then that spell is included in the hazard rate 

calculations (that is, enters the denominator of the calculations) for months 1 to 12, but not 

afterward. 

The treatment of left-censored spells (that is, spells in progress at the start of the panel) is 

more problematic, because the duration distributions of left-censored and non-left-censored 

spells are likely to differ. For example, suppose a low-wage job spell started one year prior to 

the start of the panel period. Then, that spell would be observed in the data only if it lasted 

longer than one year (it would not be observed if it ended prior to the panel period). 

Furthermore, counting from month 1 of the panel period, the spell is likely to last longer than a 

typical non-left-censored low-wage spell because of duration dependence (that is, spell exit rates 

often decrease the longer the spell has been in progress). Thus, left-censored spells are likely to 

be longer on average and to have a different duration distribution than are typical spells. 

Left-censored spells, however, can be included in the life table analysis, because the wave 1 

core files contain information on the start dates of left-censored spells. The left-censored spells 
 126 

contribute information to the life table starting in the month in which they are left-censored. For 

example, a spell that had been in progress for 12 months would enter the life table starting in 

month 12. This procedure, however, produces unbiased estimates only if we assume a stationary

environment (that is, if spell duration distributions did not change over time). This assumption, 

however, may be unrealistic for spells that had been in progress for a long time due to changes in 

labor market structure and conditions. Furthermore, because SIPP does not contain prepanel 

information on hourly wages, left-censored spells can be included in the analysis only if we 

assume that left-censored low-wage jobs were low-wage jobs for the entire period between job 

start and month 1 of the panel period. 

For these reasons, we excluded left-censored spells in our main spell duration analyses (the 

approach that most researchers conducting event history studies use). However, left-censored 

spells were included in some analyses to examine issues pertaining to the duration of longer 

spells than could be observed in the panel period and to check the robustness of study findings. 

The life table methods described above can be extended to examine the rate at which 

workers leave the low-wage labor market, by type of exit. In this “competing risks” framework, 

the dependent variable for the analysis was set to zero in months the spell was in progress and to 

a positive code—signifying the specific exit type—in the month the spell ended. Thus, spells 

contributed information to the life table up to the month that they ended (that is, until a positive 

code appeared) or until the end of the panel period for right-censored spells. In this framework, 

the estimated monthly hazard and cumulative exit rates across the exit types sum to the 

corresponding values for the overall spell analysis where we did not distinguish between exit 

types. 

The life tables themselves contain a great deal of information and can be complicated. 

Because the cumulative exit rates efficiently and intuitively summarize the life table results, our 
 127 

 

presentation focuses on them. Furthermore, when presenting results for the subgroup analyses, 

we present summary information such as the median spell duration, as well as the percentage of 

spells that ended within a given number of months. We also conducted statistical tests to gauge 

whether the spell duration distributions differed across levels of a subgroup using the log-rank 

statistic.2 All statistics were constructed using the longitudinal panel weight. 

Finally, for several reasons, we present life table results by wave only (that is, in four-month 

intervals from 4 to 44 months after job start). First, as discussed in the Methodological 

Appendix, the constructed hourly wage for a particular job or business was constant within a 

wave. Second, sample members tended to report being employed (or unemployed) for the entire 

wave rather than for only specific months covered by the wave. Consequently, we find more 

changes in low-wage job status across waves than within waves, so that the estimated hazard 

rates spike at the “seam” points. Thus, we present the life table results in four-month intervals 

only. 

3. Spell Information 

The sample contains a large number of low-wage job spells (Table VI.1). The larger 

number of spells for females than males (10,259 spells for 5,985 female workers, compared to 

6,373 spells for 3,934 male workers) is consistent with our earlier findings that low-wage 

workers are disproportionately female. About 20 percent of spells are right-censored, and nearly 

30 percent are left-censored. Few are both right- and left-censored. Because of duration 

2

 The log-rank statistic compares the actual to expected monthly hazards, where the 

expected hazards are calculated under the null hypothesis that the monthly hazard rates are the 

same for each level of the subgroup. The log-rank statistic has a chi-squared distribution with 

the degrees of freedom equal to one less than the number of life tables being compared. 
 128 

TABLE VI.1 

JOB AND EMPLOYMENT SPELL INFORMATION FOR WORKERS STARTING 

LOW-WAGE JOBS, BY GENDER 

Spell Type for Males Spell Type for Females 

Job Employment Job Employment 

Low-Wage Spells 

Total Number of Spells 6,373 4,882 10,259 7,755 

Number of Spells per Worker (Percentages) 

1 62 75 58 73 

2 22 18 23 20 

3 or more 16 7 19 7 

(Average number) (1.7) (1.3) (1.8) (1.4) 

Percentage of Spells That Are: 

Right-censored 18 22 20 25 

Left-censored 29 38 28 36 

Right- and left-censored 4 6 3 6 

Mean Observed Spell Duration (Months)a

Non-left-censored spells 7 8 8 10 

All spells 25 31 25 32 

Percentage of Low-Wage Spells with Exit Typeb

Another low-wage job 18 NA 21 NA 

Medium- or high-wage job 32 22 

In the same job 21 15 

In a different job 11 7 

Unemployment 14 10 

Not in the labor force 13 21 

Spells of Any Wage Type 

Total Number of Spells 6,170 3,943 10,057 6,832 

Number of Spells per Worker (Percentages) 

1 61 77 58 74 

2 22 16 23 19 

3 or more 17 7 19 7 

(Average number) (1.7) (1.3) (1.8) (1.3) 

Percentage of Spells That Are: 

Right-censored 32 53 32 48 

Left-censored 28 42 27 39 

Right- and left-censored 10 27 9 21 

Mean Observed Spell Duration (Months)a

Non-left-censored spells 10 13 10 13 

All spells 29 60 28 41 

Source: 1996 SIPP longitudinal files for those in low-wage jobs. 

Note: All figures are unweighted. A job spell of any wage type pertains to the period that the worker was employed with the 

initial employer, while the employment spell of any wage type includes continuous changes from one job to another. 

Low-wage job spell pertains to the duration with the initial employer, in which the worker continues to receive low pay, 

and the low-wage employment spell includes continuous changes from one low-wage job spell to another. The 

definitions for each spell type are given in Section A.1. 

a

Figures pertain to the mean spell length observed during the panel period, including spells that are still in progress at the end of 

the period (that is, right censored spells). Thus, the figures are shorter than the ultimate mean lengths of the spells.

b

Figures pertain to exit types for non-left-censored spells only. 

NA = Not applicable 
 129 

 

dependence, mean observed spell durations are considerably longer for left-censored spells than 

for non-left-censored ones.3

The analysis file contains multiple low-wage job spells for a substantial number of workers 

(Table VI.1). On average, male and female workers each contributed about 1.8 spells to the file, 

and about 40 percent contributed at least 2 spells. These results are consistent with our findings 

from the overall employment analysis that many low-wage workers exit low-wage jobs, but 

many return to the low-wage labor market. 

Exit types vary across low-wage workers (Table VI.1). The most common exit type for both 

male and female low-wage workers in our sample was into higher-paying jobs. Among nonright-censored spells, about 32 percent of spells for males and 22 percent of spells for females 

ended in this way. Furthermore, most of these spells ended in a higher-wage job with the same

employer rather than with a different employer. At the same time, however, many workers 

exited their low-wage jobs into another low wage job (20 percent of spells for males and 

females) or into nonemployment. 

Interestingly, spell information for low-wage job and low-wage employment spells are 

similar (Table VI.1). This occurs because only a relatively small percentage of workers moved 

directly from one low-wage job to another. 

The sample contains fewer job and employment spells than low-wage job and employment 

spells (Table VI.1). This occurs because many low-wage job spells resulted in continued 

employment in higher-wage jobs. Stated differently, only a relatively small fraction of low-wage 

3

 The mean spell lengths pertain to those observed during the panel period, including the 

right-censored spells. Thus, the figures are shorter than the ultimate mean lengths of the spells. 

The spell durations for left-censored spells include the time spent in the spell during the prepanel 

period. 
 130 

spells ended in nonemployment. Thus, mean observed spell durations are somewhat longer for 

the overall job and employment spells than for the low-wage spells. Similarly, a much higher 

percentage of overall job and employment spells are right-censored. 

Finally, the analysis files contain more medium-wage than low-wage job and employment 

spells for both males and females (Tables E.1 and E.2). We expected these findings because our 

cross-sectional analysis found that the medium-wage sector is the largest labor market sector, 

and because our overall employment analysis found that many low-wage workers obtain 

medium-wage jobs. Not surprisingly, observed mean spell durations are shorter for low-wage 

than higher-wage job and employment spells. 

B. FINDINGS FROM THE LIFE TABLE ANALYSIS 

This section presents key findings from our life table analysis for various types of job, 

employment, and nonemployment spells. We present findings, by gender, for the full sample of 

spells, as well as for key subgroups of low-wage workers defined by their individual, household, 

and initial low-wage job characteristics. 

1. Duration of Low-Wage Job and Employment Spells and Types of Exits 

a. Low-Wage Job Spells 

Low-wage job spells that started during the mid- to late 1990s were typically short for both 

men and women (Table VI.2). About one-half of spells ended within four months after job start, 

about three-quarters ended within one year, and nearly 90 percent ended within two years. By 44 

months after job start (the longest period for which life table results could be obtained), about 95 

percent of low-wage job spells had ended. Thus, there is substantial wage and job mobility 

among low-wage workers. 
 131 

TABLE VI.2 

CUMULATIVE EXIT RATES FOR LOW-WAGE JOB SPELLS, BY TYPE OF EXIT AND GENDER 

(Percentages) 

 

Type of Exit 

Total 

Another LowWage Job 

Higher-Wage

with the Same

Employer 

Higher-Wage

with a Different

Employer Unemployment 

Out of the

Labor Force

Males 

Number of Months After 

Start of Low-Wage Job 

4 51 12 13 8 9 9 

8 73 17 18 11 14 12 

12 81 20 21 12 15 14 

16 87 21 23 12 16 15 

20 90 22 23 13 16 16 

24 92 22 24 13 17 16 

28 94 23 25 13 17 16 

32 95 23 25 13 17 17 

36 96 23 26 13 17 17 

40 97 23 26 13 17 17 

44 97 23 26 13 18 17 

Females 

Number of Months After 

Start of Low-Wage Job 

4 46 13 8 5 7 13 

8 65 19 12 7 10 18 

12 76 22 14 8 11 22 

16 83 24 16 8 12 23 

20 87 25 17 8 12 24 

24 90 26 18 9 13 25 

28 92 26 18 9 13 26 

32 93 27 19 9 13 26 

36 94 27 19 9 13 27 

40 95 27 19 9 13 27 

44 96 27 19 9 14 27 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 4,489 low-wage job spells for males and 

7,401 low-wage job spells for females. Left-censored spells are excluded from the sample. 

Note: All figures are weighted using the longitudinal panel weight. 
 132 

Into which labor market state did low-wage workers most often exit? The answer is that 

there is considerable diversity in exit states, although low-wage workers most often exited into 

higher-wage jobs (Table VI.2). Interestingly, most of those who entered higher-paying jobs 

stayed with the same employer. Looking at all exits that occurred within 12 months after job 

start, low-wage jobs evolved into higher-paying jobs with the same employer for 21 percent of 

males and 14 percent of females. Over the same one-year period, an additional 12 percent of 

males and 8 percent of females obtained a different higher-paying job. Thus, altogether, 33 

percent of male low-wage workers and 22 percent of female low-wage workers found higherpaying employment within one year. Thereafter, the cumulative exit rates into higher-wage 

employment leveled off to about 39 percent for males and 28 percent for females. These 

findings provide further evidence of some wage mobility for the low-wage population during the 

strong economy of the mid- to late 1990s. 

At the same time, however, many workers during the mid- to late 1990s exited their lowwage jobs directly into another low-wage job or into nonemployment (Table VI.2). For instance, 

27 percent of spells for females and 23 percent of spells for males eventually ended in another 

low-wage job. Similarly, more than one-quarter of female workers and 17 percent of male 

workers exited their jobs by leaving the labor force. Finally, spells ultimately ended in 

unemployment for about 18 percent of males and 14 percent of females. Thus, altogether, about 

41 percent of spells for females and 31 percent of spells for males ended in nonemployment. 

b. Low-Wage Employment Spells 

Thus far, we have examined the length of low-wage job spells from the start of these spells 

until the worker exited into another low-wage job, a higher-paying job, or nonemployment. As 

discussed, we also examined the duration of low-wage employment spells, which were allowed to 
 133 

continue if a worker moved continuously from one low-wage job to another. Thus, these spells 

could end only if the worker found a higher-paying job or became nonemployed. 

Low-wage employment spells tend to be slightly longer than low-wage job spells (Tables 

VI.2 and VI.3, the top two lines in Figure VI.1, and Tables E.3 and E.4). For example, among 

male low-wage workers, about 74 percent of low-wage job spells ended within one year after job 

start, compared to 81 percent of low-wage employment spells. The differences between the 

duration distributions of low-wage job and low-wage employment spells reflect the fact that 

about one-quarter of low-wage workers in our sample moved from a low-wage job directly into 

another low-wage job. 

Examining the types of exits from low-wage employment spells and low-wage job spells 

tells a somewhat similar story (Table VI.3). As expected, transition rates into higher-wage jobs 

and into nonemployment are somewhat larger for low-wage employment spells (because 

transitioning into another low-wage job is no longer a possible exit state). For instance, about 43 

percent of males eventually exited their low-wage employment spells into medium-wage jobs 

and an additional 6 percent exited into high-wage jobs. Thus, nearly one-half of males exited 

their low-wage employment spells directly into higher-paying jobs, which is somewhat larger 

than the corresponding figure of 39 percent for male low-wage job spells. Similarly, about 38 

percent of females eventually exited their low-wage employment spells because they left the 

labor force, whereas the corresponding figure is 27 percent for female low-wage job spells. 

These findings support our results from the overall employment and wage progression 

analyses that there is substantial diversity in labor market success across low-wage workers. 

They also support our previous findings that female low-wage workers typically have poorer 

labor market outcomes than male low-wage workers. 
FIGURE VI.1

CUMULATIVE EXIT RATES FROM JOB AND EMPLOYMENT SPELLS

FOR THOSE STARTING LOW-WAGE JOBS, BY GENDER

Source 1996 SIPP longitudinal files using the entry cohort sample.

Note: All figures were calculated using the longitudinal panel weight

0

20

40

60

80

100

4 8 12 16 20 24 28 32 36 40 44

Cumulative Exit Rate

Month After the Start of the Low-Wage Job

0

20

40

60

80

100

4 8 12 16 20 24 28 32 36 40 44

Cumulative Exit Rate

Month After the Start of the Low-Wage Job

Low-Wage Low-Wage Job Spells: Employment Spells:

Job Spells Employment Any Wage Any Wage

Spells Type Type

Males

Females

 134 
 135 

TABLE VI.3 

CUMULATIVE EXIT RATES FOR LOW-WAGE EMPLOYMENT SPELLS, 

BY TYPE OF EXIT AND GENDER 

(Percentages) 

 

Type of Exit 

Total 

Medium-Wage

Job 

High-Wage 

Job Unemployment 

Out of the 

Labor Force 

Males 

Number of Months After 

Start of Low-Wage Job 

4 44 20 3 11 10 

8 65 28 4 17 15 

12 74 32 5 19 18 

16 82 36 5 21 20 

20 86 38 5 22 21 

24 88 39 6 23 21 

28 90 40 6 23 21 

32 92 41 6 24 22 

36 94 42 6 24 22 

40 95 43 6 24 22 

44 96 43 6 24 23 

Females 

Number of Months After 

Start of Low-Wage Job 

4 39 13 2 8 16 

8 57 19 3 12 23 

12 68 22 3 14 28 

16 75 25 3 16 32 

20 80 27 3 16 33 

24 84 29 3 18 35 

28 87 30 4 18 36 

32 89 31 4 18 36 

36 90 31 4 18 37 

40 91 31 4 19 37 

44 93 32 4 19 38 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 3,021 low-wage employment spells for 

males and 4,926 low-wage employment spells for females. Left-censored spells are excluded from the 

sample. 

Note: All figures are weighted using the longitudinal panel weight. 
 136 

2. Duration of Alternative Job and Employment Spells 

We examined also the duration of low-wage jobs using two alternative definitions of spell 

end dates. First, we examined job spells, where a spell continued as long as the worker remained 

with their initial employer regardless of the wage received. Second, we examined employment 

spells, where a spell continued as long as the worker was employed in any job (regardless of the 

wage level received). 

Not surprisingly, job spells tend to be longer than low-wage job spells (Figure VI.1 and 

Tables E.3 and E.4). For example, nearly 80 percent of low-wage job spells in our sample ended 

within one year after job start, compared to only 67 percent of job spells. Similarly, more than 

90 percent of low-wage spells ended within 24 months, compared to only about 80 percent of job 

spells. These findings are due to the significant numbers of low-wage workers who obtained 

higher-wage jobs with the same employer. 

Despite these findings, however, job spells are not long. About two-thirds ended within a 

year after job start, and more than three-quarters ended within two years. Thus, low-wage 

workers sometimes obtain higher-wage jobs with the same employer, but many do not remain in 

these higher-wage jobs for a substantial period of time. 

The finding that job spells are not long, however, is not necessarily a negative result, 

because as discussed in the previous two chapters, among workers who were continuously 

employed during the follow-up period, those who switched jobs tended to have more positive 

labor market outcomes than those who remained with their initial employers. Thus, job turnover 

is an avenue for wage growth for some low-wage workers. 

We also find that spell durations tend to be longer for overall employment spells than for 

overall job spells (Figure VI.1 and Tables E.3 and E.4). For example, one-year cumulative exit 

rates were about 67 percent for job spells, compared to about 51 percent for employment spells. 
 137 

 

The two duration distributions differ because a sizeable fraction of low-wage workers moved 

continuously from a low-wage job to another one or to a higher-paying job with a different

employer. 

3. Including Left-Censored Spells 

The life table results that include left-censored spells (about 37 percent of all employment 

spells and 30 percent of all job spells) are similar to those that exclude these spells (Tables VI.4, 

E.3 and E.4). Cumulative exit rates from employment and job spells in months 4 to 44 are very 

similar whether or not the left-censored spells are included in the analysis (although the leftcensored spells are slightly longer than their comparable non-left-censored spells).4

 These 

results suggest that the assumptions, discussed above, that are needed to justify the use of the 

left-censored spells appear to be appropriate (at least for spells that started soon before the panel 

period). 

4. Comparing the Duration of Low-, Medium-, and High-Wage Spells 

How long are the spells of workers who start low-wage jobs compared to those of workers 

who start medium- and high-wage jobs? The answer to this question can help place in 

perspective the life table findings for low-wage workers presented above. 

To address this question, we compared two types of employment spells for those starting 

low-, medium-, and high-wage jobs. First, we examined the length of time workers were 

employed in jobs of the same wage type as their initial job. For example, we examined how long 

medium-wage workers remained in medium-wage jobs (either with the same employer or with a 

4

 We did not include left-censored spells when examining the durations of low-wage job and 

employment spells, because most of these spells ended during the panel period. Thus, the 

inclusion of the left-censored spells would not provide any new information. 
 138

TABLE VI.4 

CUMULATIVE EXIT RATES FOR OVERALL EMPLOYMENT SPELLS OF LOW-WAGE WORKERS 

WITH AND WITHOUT LEFT-CENSORED SPELLS, BY GENDER 

(Percentages) 

 

Males Females 

Without LeftCensored Spells With LeftCensored Spells Without LeftCensored Spells With LeftCensored Spells 

Number of Months After 

Start of Low-Wage Job 

4 26 24 28 25 

8 42 39 42 39 

12 51 46 52 48 

16 57 52 58 54 

20 61 56 62 58 

24 64 59 66 62 

28 66 61 68 65 

32 69 63 71 67 

36 71 65 72 69 

40 72 67 75 72 

44 74 68 78 73 

48 70 74 

52 to 104 (1 to 2 Years) 81 85 

105 to 156 (2 to 3 Years) 85 91 

157 to 208 (3 to 4 Years) 89 94 

208 to 260 (4 to 5 Years) 94 97 

Source: 1996 SIPP longitudinal files using the sample of 3,943 spells for males and 6,832 spells for females. 

Note: All figures are weighted using the longitudinal panel weight. 
 139 

different one). Second, we examined how long workers were employed at all. For example, we 

examined how long those starting high-wage jobs were continuously employed in any job and at 

any wage level. 

We find that, during the mid- to late 1990s, low-wage employment spells were typically 

shorter than medium- and high-wage employment spells, especially for males (Figure VI.2, and 

Tables E.3 and E.4). Furthermore, the differences increased somewhat over time. For example, 

among male workers, about 74 percent of low-wage employment spells ended within one year 

after job start, compared to 60 percent of high-wage spells. By 24 months, differences in the 

cumulative exit rates were larger (88 percent for low-wage employment spells, compared to 70 

percent for high-wage employment spells). This suggests that, after an initial adjustment period, 

higher-wage workers became more and more likely than low-wage workers to remain on their 

jobs. Differences in spell lengths by wage type, however, are smaller for women. The 24-month 

cumulative exit rate was 84 percent for females with low-wage employment spells and 76 

percent for females with high-wage employment spells. 

Although medium- and high-wage employment spells were somewhat longer than low-wage 

employment spells, they were shorter than expected. The reason is that a nontrivial percentage 

of medium-wage workers exited into low-wage or high-wage jobs, and a nontrivial percentage of 

high-wage workers exited into medium-wage jobs. Among medium-wage workers, about 30 

percent of males and females ultimately exited into higher-wage jobs, and 28 percent of males 

and 19 percent of females ultimately exited into low-wage jobs (not shown). Among those 

starting high-wage job spells, about 37 percent of males and females exited into medium-wage 

jobs within 44 months. 
FIGURE VI.2

CUMULATIVE EXIT RATES FROM LOW-, MEDIUM-, AND HIGH-WAGE

EMPLOYMENT SPELLS OF THE SAME WAGE TYPE, BY GENDER

44

35 37

74

62 60

88

77

70

94

83

76

0

20

40

60

80

100 Males

Source: 1996 SIPP longitudinal files using an entry cohort sample of spells.

Cumulative Exit Rate

Low-Wage Spells Medium-Wage Spells High-Wage Spells

4 Months 12 Months 24 Months 36 Months

Number of Months After Spell Start

39

33

40

68

58

64

84

72

76

90

80 82

0

20

40

60

80

100

4 Months 12 Months 24 Months 36 Months

Number of Months After Spell Start

Cumulative Exit Rate

Females

 140 
 141 

Thus, there is considerable wage movement over time for those starting medium- and highwage spells, as well as for those starting low-wage spells. These wage fluctuations could be due 

partly to reporting errors in hourly wages or in monthly earnings and hours worked per week (for 

those who could not directly report hourly wages) or to temporary changes in labor supply effort 

or earnings levels (although we “smoothed” the constructed wage measures within waves to help 

alleviate this problem). We believe, however, that our findings reflect real movements of 

medium- and high-wage workers across wage categories. This interpretation is supported by 

findings from the overall employment analysis that many workers in our sample who started 

higher-wage jobs at the start of the panel period experienced multiple job and employment spells 

over the three-and-one-half-year follow-up period. Thus, wage and job mobility is common both 

for higher earners and for low earners. 

During the mid- to late 1990s, overall employment spells lasted substantially longer for 

those starting higher-wage than lower-wage jobs (Figure VI.3, and Tables E.3 and E.4). About 

65 percent of low-wage male and female workers became nonemployed within two years after 

starting their jobs. In contrast, only about 45 percent of medium-wage and 39 percent of highwage workers became nonemployed over the same period. Thus, although wage fluctuations 

were common for all groups of workers, unemployment was less of a problem for higher-wage 

workers than for low-wage ones. Again, our overall employment analysis supports these 

findings, because over a fixed three-and-one-half-year follow-up period, low-wage workers 

spent, on average, more than twice as many weeks unemployed than higher-wage workers. 
FIGURE VI.3

CUMULATIVE EXIT RATES FROM LOW-, MEDIUM-, AND HIGH-WAGE

EMPLOYMENT SPELLS OF ANY WAGE TYPE, BY GENDER

26

16 15

57

34

30

64

46

38

71

52

44

0

20

40

60

80

100

Source: 1996 SIPP longitudinal files using an entry cohort sample of spells.

Cumulative Exit Rate

Low-Wage Spells Medium-Wage Spells High-Wage Spells

4 Months 12 Months 24 Months 36 Months

Number of Months After Spell Start

28

13 13

52

31 28

66

43 39

72

51

45

0

20

40

60

80

100

4 Months 12 Months 24 Months 36 Months

Number of Months After Spell Start

Cumulative Exit Rate

Males

Females

 142 
 143 

5. Reentry into the Low-Wage Labor Market 

What happens to low-wage workers after they leave their low-wage jobs? We have seen 

that during the mid- to late 1990s, about one-half of low-wage job spells ended in higher-wage 

employment, and about one-quarter ended in nonemployment. In this section, we examine 

reentry into the low-wage labor market for these workers. 

a. Duration of Nonemployment Spells 

During the period under investigation, about 47 percent of male low-wage workers and 57 

percent of female low-wage workers exited their low-wage employment spells into 

nonemployment (including unemployment and leaving the labor force; Table VI.3). How long 

did they stay nonemployed, and what types of jobs did they find when they became reemployed? 

Nonemployment spells for low-wage workers were typically short (Table VI.5). Among 

males in our sample, about two-thirds returned to the labor market within six months after 

becoming nonemployed, and 80 percent returned within a year. Reemployment rates were 

somewhat lower for females (51 percent found jobs within six months, and 67 percent found jobs 

within a year), in part reflecting the higher percentage of females who became nonemployed 

because they left the labor force. These relatively high reemployment rates may have been due 

to the strong economy faced by sample members. Nonetheless, they suggest that low-wage 

workers do not typically remain unemployed for a long time. 

Most nonemployed low-wage workers in our sample who became reemployed returned to 

the low-wage labor market, and fewer entered higher-paying jobs (Table VI.5). Within 24 

months after becoming nonemployed, 64 percent of males returned to low-wage jobs, compared 

to only 23 percent of males who found higher-paying jobs. Stated differently, more than 7 in 10 

males who found jobs returned to the low-wage labor market. Similarly, more than 8 in 10 

females who became reemployed returned to low-wage jobs. 
 144 

TABLE VI.5 

CUMULATIVE REEMPLOYMENT RATES FOR WORKERS WHO EXITED LOW-WAGE JOBS 

INTO NONEMPLOYMENT, BY GENDER 

 

Male Workers Female Workers 

Type of Reemployment Type of Reemployment 

Total 

Low-Wage

Job 

Higher-Wage

Job Total 

Low-Wage 

Job 

Higher-Wage

Job 

Cumulative Percentage of Spells 

Ending in Reemployment Within the 

Specified Number of Months 

1 18 14 4 13 11 2 

2 32 24 8 22 19 3 

3 42 31 10 29 25 4 

4 56 41 15 43 35 7 

5 61 45 16 47 39 8 

6 66 49 17 51 43 9 

7 69 51 17 55 46 9 

8 73 54 19 59 49 10 

9 74 55 19 61 51 10 

10 76 57 20 63 52 11 

11 78 58 20 65 54 11 

12 80 59 21 67 56 12 

13 81 60 21 69 57 12 

14 82 61 21 71 59 12 

15 83 61 21 72 60 12 

16 83 62 22 73 61 13 

17 84 62 22 74 62 13 

18 85 63 22 75 62 13 

19 85 63 22 76 63 13 

20 86 63 22 76 63 13 

21 86 64 22 77 64 13 

22 86 64 22 78 64 13 

23 86 64 23 78 65 14 

24 87 64 23 79 65 14 

Source: 1996 SIPP longitudinal files using the sample of 1,277 spells for males and 2,761 spells for females for lowwage workers who exited their low-wage job spells into nonemployment. 

Note: All figures are weighted using the longitudinal panel weight. 
 145 

b. Duration of Higher-Wage Spells 

During the mid- to late 1990s, about 49 percent of low-wage employment spells for males 

and 36 percent of low-wage employment spells for females ended in medium-wage or high-wage 

employment within a four-year period (see bottom panel for each gender group in Table VI.3). 

In this section, we examine the rate at which workers who obtained these higher-paying jobs (1) 

left these jobs, (2) returned to the low-wage labor market, and (3) became nonemployed. 

Our results on the duration of higher-wage employment spells show that the majority of 

those who obtained higher-wage jobs left these jobs within the panel period, but a significant 

number also remained in them (Table VI.6). Nearly 60 percent of males and females left the 

higher-wage labor market within one year after job start, and about 70 percent left within two 

years. Yet, nearly one-third stayed employed in these high-wage jobs for at least two years. 

Thus, we again find diversity in the labor market success of low-wage workers. 

Interestingly, nearly all those who left higher-wage jobs returned to the low-wage labor 

market, and only a small percentage exited into nonemployment (Table VI.6). For example, 

more than one-half of all workers reentered the low-wage labor market within two years, 

whereas only about 16 percent became nonemployed over the same period. Stated another way, 

nearly 80 percent of those who left higher-paying jobs reentered the low-wage labor market. 

These results are consistent with previous findings from the overall employment analysis that 

many low-wage workers experienced multiple low-wage job spells during the panel period. 

Consequently, both exits out of and reentry into the low-wage market were common for lowwage workers during the mid- to late 1990s. 
146 

TABLE VI.6 

CUMULATIVE EXIT RATES FROM HIGHER-WAGE JOBS FOR WORKERS WHO EXITED LOW-WAGE JOB

INTO HIGHER-WAGE JOBS, BY GENDER 

Male Workers Female Wo

Type of Exit Ty

Total 

Low-Wage

Job Unemployment

Left the 

Labor Force Total 

Low-Wage

Job Unem

Cumulative Percentage of Higher-Wage Employment Spells Ending Within the Specified Number of Months 

1 2 2 0 0 3 2 2 6 4 1 1 7 4 3 9 6 1 2 10 6 4 36 30 3 4 37 31 5 38 31 3 4 39 32 6 39 32 3 4 41 33 7 41 33 3 5 43 34 8 50 40 4 5 52 43 9 52 41 4 6 54 44 10 52 42 5 6 55 44 11 53 43 5 6 56 45 12 58 46 5 6 60 48 13 58 46 5 7 61 48 14 59 47 6 7 61 48 15 60 47 6 7 61 49 16 64 50 7 7 65 51 17 64 50 7 8 65 51 18 64 50 7 8 66 52 19 65 50 7 8 67 52 20 66 51 7 8 69 54 21 67 52 7 8 69 54 22 67 52 7 8 69 54 23 67 52 7 8 70 54 24 70 54 8 8 71 55 

Source: 1996 SIPP longitudinal files using the sample of 2,061 spells for males and 2,469 spells for females for low-wage workers wh

spells into medium- or higher-wage jobs. 

Note: All figures are weighted using the longitudinal panel weight. 
 147 

In sum, the labor market dynamics of low-wage workers are complex. Most low-wage 

workers find higher-paying jobs at some point. Many, however, return to the low-wage labor 

market. At the same time, however, a nontrivial share of low-wage workers exit into higherpaying employment and keep these jobs for a substantial period of time. Thus, there is 

considerable diversity in wage progression among the low-wage worker population, although, on 

average, their earnings prospects improve over time. 

6. Subgroup Results 

There is substantial diversity in job and employment spell durations among low-wage 

workers. Is it possible to identify subgroups of workers across whom spell durations differ? 

Identifying these subgroups can provide policy-relevant information as to which subgroups of 

low-wage workers fare best in the labor market. Furthermore, the analysis can be used to check 

the robustness of our previous subgroup findings from the overall employment and wage 

progression analyses. 

To keep our presentation manageable, we present subgroup findings on (1) exit rates from 

low-wage job spells within 12 months after job start by type of exit; and (2) cumulative exit rates 

from employment spells within 4, 12, and 24 months after job start. We estimated life tables, one 

at a time, for key subgroups of males and females defined by individual, household, and initial 

job characteristics. 

Because our findings strongly support those presented in previous chapters, we provide less 

detail on the results than before. In particular, we find that the same subgroups of workers who 

typically had the best overall employment experiences and wage growth also had the best spellrelated outcomes. The concurrence of the subgroup results is not surprising, because we 
 148 

expected that subgroups of low-wage workers who experienced the most wage progression over 

the medium term would also be the ones most likely to exit low-wage job spells into higher-wage 

employment and to have the longest overall employment spells. 

a. Overall Duration of Low-Wage Job Spells

Low-wage job spells are typically short across all subgroups defined by worker and initial 

job characteristics (last column in Tables VI.7 and VI.8). For example, during the mid- to late 

1990s, 12-month cumulative exit rates for males in most subgroups ranged from 78 to 85 

percent. Similarly, the cumulative exit rates for females typically ranged from 73 to 80 percent. 

Nonetheless, some patterns are evident. Low-wage spells were typically longer for older 

than for younger workers, but as discussed in the next section, this finding masks important age 

differences in the states into which workers exited. More intuitively, spell durations were likely 

to be longer for Hispanics, those who did not attend college and those with low wages than for 

their counterparts. However, exit rate differences across these subgroups are not large. 

b. Types of Exits from Low-Wage Job Spells 

We find larger subgroup differences in exit types from low-wage job spells: 

• The low-wage job spells of workers between the ages of 30 and 60 are much more 

likely to result in higher-wage employment than for those younger or older. Only 

about 20 percent of male teenagers in our sample and 8 percent of female teenagers 

obtained higher-wage jobs within 12 months after job start (either with the same 

employer or a different one; Tables VI.7 and VI.8). In contrast, the corresponding 

figures for males and females between ages 30 and 60 were about 40 percent and 25 

percent, respectively. Similarly, the younger workers were much more likely than 

those older to exit into another low-wage job and nonemployment. Thus, it is not 

surprising that in previous analyses we found that younger low-wage workers 

typically experience less wage growth than those older. 
 149 

TABLE VI.7 

12-MONTH CUMULATIVE EXIT RATES FROM LOW-WAGE JOB SPELLS 

FOR MALES, BY TYPE OF EXIT AND SUBGROUP 

(Percentages) 

 

12-Month Cumulative Exit Rate for Males, by Exit Type 

 

Subgroup 

Another 

Low-Wage 

Job 

Higher-Wage 

Job with 

the Same 

Employer 

Higher-Wage 

Job with 

a Different 

Employer 

Nonemployment Total 

Overall 20 21 12 29 81 

Individual and Household 

Characteristics 

Age (in Years) 

Younger than 20 25 13 7 43 88 

20 to 29 24 18 13 29 83 

30 to 39 15 25 14 25 79 

40 to 49 14 28 11 25 78 

50 to 59 13 27 12 22 75 

60 or older 10 19 8 25 62 

Race/Ethnicity 

White and other non-Hispanic 21 22 13 26 83 

Black, non-Hispanic 14 18 9 39 80 

Hispanic 21 17 7 32 77 

Educational Attainment 

Less than high school/GED 20 16 7 37 79 

High school/GED 20 18 11 30 80 

Some college 19 27 12 26 84 

College graduate or more 19 27 20 19 84 

Has a Health Limitation 

Yes 17 17 7 45 85 

No 20 21 12 28 81 

Household Type 

Single parent with children 19 15 8 39 81 

Married couple with children 20 23 13 27 82 

Married couple without children 20 22 10 26 78 

Other adults without children 19 18 14 32 83 

Household Income as a Percentage 

of the Poverty Level 

100 percent or less 22 14 11 34 82 

101 to 200 percent 20 19 10 31 80 

More than 200 percent 19 23 13 27 82 

Job Characteristics 

Hourly Wages 

Less than $5.00 20 18 13 30 81 

$5.00 to $5.99 23 11 9 33 75 

$6.00 to $6.99 22 18 11 30 81 

$7.00 to $7.50 14 33 15 25 87 
TABLE VI.7 (continued) 

 150 

12-Month Cumulative Exit Rate for Males, by Exit Type 

 

Subgroup 

Another 

Low-Wage 

Job 

Higher-Wage 

Job with 

the Same 

Employer 

Higher-Wage 

Job with 

a Different 

Employer 

Nonemployment Total 

Hours Worked per Week 

1 to 19 26 9 15 35 86 

20 to 34 24 13 10 33 81 

35 to 40 18 20 11 30 79 

More than 40 17 33 15 20 86 

Weekly Earnings 

Less than $150 27 11 13 32 83 

$150 to $299 20 18 11 30 79 

$300 to $600 13 38 14 24 88 

Owns Business 

Yes 14 40 25 12 90 

No 20 20 11 30 81 

Health Insurance Coveragea

Yes 18 26 14 24 82 

No 21 17 10 33 81 

Occupation 

Professional/technical 15 35 18 17 86 

Sales/retail 23 27 10 20 81 

Administrative support/clerical 17 20 11 30 79 

Service professions/ 

handlers/cleaners 22 14 10 35 80 

Machine/construction/ 

production/transportation 17 25 13 27 83 

Farm/agricultural/other workers 22 15 10 35 82 

Industry 

Agriculture/forestry/ 

fishing/hunting 20 20 12 31 83 

Mining/manufacturing/ 

construction/ 

transportation/utilities 18 25 13 28 83 

Wholesale/retail trade 22 17 10 29 79 

Personal/health/other services 19 18 11 32 81 

Other 14 40 22 12 89 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 4,489 low-wage job spells for males. Leftcensored spells were excluded from the sample. 

Note: All figures are weighted using the longitudinal panel weight. 

a

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well 

as from other sources. We used this variable instead of the employer-based health insurance coverage variable, 

because data on overall health insurance coverage is available monthly, whereas the employer-based coverage 

variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health insurance 

variable could not always be linked to the job under investigation, which led to a significant number of missing 

values. However, the subsets of health insurance variables overlap considerably: the source of health insurance 

coverage was the employer for 80 percent of those with any coverage. 
TABLE VI.8 

12-MONTH CUMULATIVE EXIT RATES FROM LOW-WAGE JOB SPELLS 

FOR FEMALES, BY TYPE OF EXIT AND SUBGROUP 

(Percentages) 

12-Month Cumulative Exit Rate for Females, by Exit Type 

Subgroup 

Another 

Low-Wage

Job 

Higher-Wage 

Job with

the Same

Employer 

Higher-Wage 

Job with

a Different 

Employer 

Nonemployment Total

Overall 22 14 8 33 76

Individual and Household 

Characteristics 

Age (in Years)

Younger than 20 29 4 4 44 82 

20 to 29 25 13 8 35 81 

30 to 39 21 15 7 31 75 

40 to 49 19 18 8 24 70 

50 to 59 15 17 9 26 67 

60 or older 14 14 1 33 62 

Race/Ethnicity

White and other non-Hispanic 23 15 8 30 76 

Black, non-Hispanic 20 12 5 39 76 

Hispanic 20 12 6 37 74

Educational Attainment

Less than high school/GED 23 6 3 43 76 

High school/GED 22 13 6 33 73 

Some college 25 17 9 30 80 

College graduate or more 19 22 14 24 80 

Has a Health Limitation 

Yes 23 8 5 46 81

No 22 15 8 31 76

Household Type 

Single parent with children 24 11 6 37 78 

Married couple with children 19 15 6 34 76 

Married couple without children 23 15 8 27 73 

Other adults without children 26 15 11 27 79 

Household Income as a Percentage 

of the Poverty Level

100 percent or less 25 7 5 40 77 

101 to 200 percent 22 11 6 36 76 

More than 200 percent 21 18 9 28 76 

Job Characteristics 

Hourly Wages

Less than $5.00 24 10 7 37 78 

$5.00 to $5.99 26 6 5 37 74 

$6.00 to $6.99 22 13 7 31 73 

$7.00 to $7.50 15 31 11 24 82 

 151 
TABLE VI.8 (continued) 

 152 

12-Month Cumulative Exit Rate for Females, by Exit Type 

Subgroup 

Another 

Low-Wage 

Job 

Higher-Wage 

Job with 

the Same 

Employer 

Higher-Wage 

Job with 

a Different 

Employer 

Nonemployment Total 

Hours Worked per Week 

1 to 19 24 10 7 38 80 

20 to 34 26 9 7 35 77 

35 to 40 19 17 7 30 74 

More than 40 21 21 11 27 80 

Weekly Earnings 

Less than $150 26 9 7 38 79 

$150 to $299 22 14 7 31 73 

$300 to $600 12 40 13 22 87 

Owns Business 

Yes 15 27 20 19 81 

No 22 14 7 33 76 

Health Insurance Coveragea

Yes 18 19 10 28 74 

No 26 10 5 37 78 

Occupation 

Professional/technical 17 29 11 24 80 

Sales/retail 25 10 7 35 78 

Administrative support/clerical 19 23 10 26 77 

Service professions/ 

handlers/cleaners 25 9 6 34 74 

Machine/construction/ 

production/transportation 17 12 5 38 73 

Farm/agricultural/other workers 21 7 5 49 82 

Industry 

Agriculture/forestry/ 

fishing/hunting 15 17 13 35 81 

Mining/manufacturing/ 

construction/ 

transportation/utilities 17 16 5 36 75 

Wholesale/retail trade 26 9 6 35 77 

Personal/health/other services 21 17 8 29 75 

Other 18 31 17 10 77 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 7,401 low-wage job spells for females. Leftcensored spells were excluded from the sample. 

Note: All figures are weighted using the longitudinal panel weight. 

a

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as 

from other sources. We used this variable instead of the employer-based health insurance coverage variable, because data 

on overall health insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to 

jobs in progress at the time of the interview. Thus, the employer-based health insurance variable could not always be linked 

to the job under investigation, which led to a significant number of missing values. However, the subsets of health 

insurance variables overlap considerably: the source of health insurance coverage was the employer for 80 percent of those 

with any coverage. 
 153 

• White workers are more likely to obtain higher-paying jobs than minority workers. 

During the mid- to late 1990s, the 12-month cumulative exit rate into higher-wage 

jobs was 39 percent for white males, compared to 27 percent for African American 

males, and 24 percent for Hispanic males. A similar pattern holds for females. In 

addition, more minorities exited into nonemployment, which we have seen is a state 

from which many return to the low-wage labor market. 

• Education level is strongly associated with entry into the higher-wage labor market 

for both men and women. Nearly one-half of low-wage job spells for males who 

completed some college ended in a higher-paying job, compared to 29 percent for 

those with a high school credential only, and 23 percent for those who did not 

complete high school. Differences in cumulative exit rates by education level are 

even larger for females (ranging downward from 36 percent for college graduates to 9 

percent for high school dropouts). Correspondingly, rates of exit into 

nonemployment substantially decreased with education level. 

• Those with health limitations tend to have poor spell-related outcomes. Workers 

with health problems are likely to exit their low-wage jobs into nonemployment, and 

only a small percentage exit directly into higher-wage jobs. Thus, it is not surprising 

that our previous subgroup analyses found that those with health limitations are at 

particular risk of poor labor market outcomes. 

• Entry into higher-paying jobs is less prevalent for lower-income households than 

for wealthier ones. Within a year after job start, about 27 percent of female sample 

members in households with incomes more than twice the poverty level experienced 

exits into high-wage employment, compared to only 12 percent for females in 

households with incomes below the poverty level. Consistent with these results, we 

find poorer spell outcomes for females in single-parent households than for females in 

other types of households. However, as has been the case throughout our study, there 

is considerable diversity in spell outcomes within household income groups; for 

example, nearly 30 percent of females in the wealthiest households exited their lowwage job spells into nonemployment, and 21 percent exited their jobs into another 

low-wage job. 

• Job quality matters: those with better jobs tend to have more positive spell outcomes 

than those in lower-quality jobs. Those whose initial jobs offer higher hourly wages, 

more work hours, and health benefits are more likely to move into higher-paying jobs 

than those in lower-quality jobs. For example, during the mid- to late 1990s, 29 

percent of female workers with available health insurance coverage entered highwage employment, compared to only 15 percent of female workers without this fringe 

benefit. The corresponding figures for males are 40 percent and 27 percent, 

respectively. 

• Entry rates into higher-paying jobs are much higher for the self-employed than for 

jobholders. For example, nearly two-thirds of male business owners in our sample 

became higher-wage workers within one year, compared to only 31 percent of 

jobholders. These findings are consistent with earlier results that the wages of selfemployed workers grow substantially faster than those of other workers, even though 

they start their jobs with lower wages. 
 154 

• Those in professional or technical occupations experience the most movement into 

higher-wage employment. Among males, those in sales occupations experience the 

next best spell-related outcomes, and those in service occupations experience the 

worst ones. Among females, those in clerical positions perform nearly as well as 

those in professional positions, although there are few differences in performance 

across those in other occupations. These results are identical to those found in our 

previous subgroup analyses. 

c. Duration of Employment Spells 

The ordering of subgroups for those with the longest to shortest employment spells (of any 

wage type) are similar to the ordering of subgroups discussed above. This occurs because 

subgroups most likely to exit into higher-wage employment were also those least likely to exit 

into nonemployment. Consequently, subgroups that tended to obtain higher-paying jobs also 

tended to have the longest employment spells. The life table results for employment spells are 

presented in Tables E.5 and E.6, which also show log-rank statistics to test differences in hazard 

rate distributions across subgroup levels. Many of the subgroup differences are statistically 

significant. 
 155 

VII. SUMMARY AND CONCLUSIONS 

Our analysis provides a complex picture of the characteristics of low-wage workers and their 

jobs, as well as their labor market dynamics. During the mid- to late 1990s, the share of all 

workers at a point in time who were low-wage workers—defined as those earning less than the 

hourly wage at which a full-time worker would have annual earnings below the poverty level for 

a family of four—was about 28 percent. Low-wage workers were disproportionately young, 

female, nonwhite, with a high school credential or less, in single-adult households with children, 

and in households with incomes below the poverty level. At the same time, however, they are a 

relatively diverse group—they exist in a wide range of subgroups defined by individual and 

household characteristics. 

We find that many low-wage workers receive hourly wages substantially below the lowwage cutoff value used in this study, and hold jobs that are markedly less stable and that provide 

fewer benefits than jobs held by higher-wage workers. Interestingly, however, most report that 

they usually work full-time. Low-wage workers are represented in all occupations and 

industries, but they are disproportionately found in retail trade industries, service occupations, 

and nonunion jobs. 

Low-wage workers in our sample were employed for most of the three-and-one-half year 

follow-up period, and the majority held higher-paying jobs at some point. Low-wage workers 

were employed about 79 percent of weeks, which may reflect the strong economic conditions 

during the mid- to late 1990s. About 70 percent of male and 50 percent of female workers held 

higher-wage jobs at some point during the follow-up period. Overall, males spent an average of 

about 30 percent of the time in higher paying jobs, and the corresponding figure for females was 
 156 

about 20 percent. While these figures are less than the total time spent in low-wage jobs (55 

percent of months for males and 58 percent of months for females), employment rates in higherwage jobs increased over time for both males and females. For instance, during the second half 

of the follow-up period, males spent roughly equal amounts of time in low-wage and higherwage jobs. 

We find also, that during the mid- to late 1990s, low-wage workers moved frequently into 

and out of the low-wage labor market. Most held multiple jobs (an average of 3 jobs during the 

three-and-one-half-year follow-up period), and low-wage job spells were typically short—about 

three-quarters ended within a year. Low-wage workers often exited their low-wage jobs directly 

into higher-wage jobs, although many also exited into other low-wage jobs or into 

nonemployment. Many exiters, however, also returned to the low-wage labor market. 

We find significant wage growth for low-wage workers in our sample. Overall, the average 

real wage increase was about 25 percent during the follow-up period (for those employed at the 

start and end of the period). In addition, about three-quarters of workers experienced an increase 

in real wages, with some experiencing significant amounts of wage growth. Furthermore, lowwage workers tended to move into better jobs (as measured by hours worked and available fringe 

benefits). Despite this wage growth, however, many workers still had low earnings. Because 

they started at fairly low wage levels, by the end of the follow-up period, more than one-half of 

workers had earnings that would put them below the federal poverty level for a family of four. 

We conducted subgroup analyses to try to explain the diversity in labor market outcomes 

across low-wage workers. Our analysis consistently found that, among the low-wage population, 

males, prime-age workers (those between ages 20 and 60), educated workers, whites, those 

without health limitations, and those in wealthier households typically spent more time in higherwage jobs and experienced more wage growth than their respective counterparts. Furthermore, 
 157 

job quality matters—those who start with better jobs (measured by higher initial wages, health 

insurance coverage, and full-time work status) are more likely to experience wage growth than 

those in lower-quality jobs. In addition, we find some differences across occupations—males in 

professional and sales occupations and females in professional and clerical occupations have 

more positive labor market outcomes than other workers. Business owners were also more likely 

than jobholders to experience greater wage growth. 

We find several interesting results about the association between overall employment 

experiences during the follow-up period and wage growth. First, wage progression was greater 

for those who were employed for most of the period than those employed less, suggesting that 

policies promoting employment retention could improve the wage growth of low-wage workers. 

Second, among workers continuously employed during the follow-up period, those who switched 

jobs tended to have better outcomes than those who stayed with their same employer, suggesting 

that job turnover was an avenue for wage growth for some low-wage workers. 

At the same time, however, substantial diversity exists in labor market success within

worker subgroups. Thus, although we identified groups that are of particular risk of poor labor 

market outcomes, we could not fully account for the variation in labor market outcomes across 

low-wage workers. Clearly, important residual factors affect the wage progression of those 

starting low-wage jobs. 

In sum, our results clearly indicate that low-wage workers have some upward mobility over 

the medium term. At the same time, however, a segment of the low-wage population remains 

entrenched in low-wage jobs. Thus, there is considerable diversity in labor market success for 

low-wage workers. These findings are inevitable in a study such as this, and the extent to which 

the findings are interpreted as positive or negative depends on whether one views the glass as 
 158 

half empty or half full. Of course, it has to be kept in mind that the economic conditions were 

very strong during the mid- to late 1990s, and our results for the employment prospects of lowwage workers may be different under a weaker economy. 
 159 

REFERENCES 

Acs, Gregory. A Profile of Low-Wage Workers. Washington DC: The Urban Institute, 1999. 

Acs, Gregory, Katherin Ross, and Daniel McKenzie. “Chapter 2—Playing by the Rules, but 

Losing the Game: Americans in Low-Income Working Families.” in Low-Wage Workers in 

the New Economy. Edited by Richard Kazis and Marc S. Miller, Washington, DC: The 

Urban Institute Press, 2001, pp. 21-44. 

Bartik, Timothy. “Short-Term Employment Persistence for Welfare Recipients: The Effects of 

Wages, Industry, Occupation, and Firm Size.” W.E. Upjohn Institute for Employment 

Research Mimeo, June 1997. 

Bernstein, Jared, and Heidi Hartmann. “Chapter 1—Defining and Characterizing the Low-Wage 

Labor Market.” in The Low-Wage Labor Market. Edited by Kelleen Kaye and Demetra 

Smith Nightingale, Washington, DC: The Urban Institute Press, 2000, pp. 15-40. 

Blank, Rebecca. “Outlook for the U.S. Labor Market and Prospects for Low-Wage Entry Jobs.” 

in The Work Alternative: Welfare Reform and the Realities in the Job Market. Edited by 

Demetra Nightingale and Robert Haveman. Washington, DC: The Urban Institute Press, 

1995. 

Burtless, Gary. “Employment Prospects of Welfare Recipients.” in The Work Alternative: 

Welfare Reform and the Realties of the Job Market. Edited by Demetra Nightingale and 

Robert Haveman. Washington, DC: The Urban Institute Press, 1995. 

Card, David, and Rebecca Blank. “Introduction—Job Change and Job Stability Among Less 

Skilled Young Workers.” in Finding Jobs: Work and Welfare Reform. Edited by David 

Card and Rebecca M. Blank, New York: The Russell Sage Foundation, 2000, pp. 1-19. 

Carnevale, Anthony P., and Stephen J. Rose. “Chapter 3—Low Earners: Who Are They? Do 

They Have a Way Out?” in Low-Wage Workers in the New Economy. Edited by Richard 

Kazis and Marc S. Miller, Washington, DC: The Urban Institute Press, 2001, pp.45-66. 

Corcoran, Mary and Susanna Loeb. “Welfare, Work Experience, and Economic SelfSufficiency.” U. of Michigan Mimeo, 1999. 

Duncan, Greg J., Johanne Boisjoly, and Timothy M. Smeeding. “Slow Motion: Economic 

Mobility of Young Workers in the 1970s and 1980s.” Income Security Policy Series, Paper 

No. 11, Syracuse, NY: Center for Policy Research, Maxwell School of Citizenship and 

Public Affairs, Syracuse University, September 1995. 

Gardner, Jennifer and Diane Herz. “Working and Poor in 1990.” in Monthly Labor Review. 

December 1992, pp 20-28. 

Gladden, Tricia, and Christopher Taber. “The Relationship Between Wage Growth and Wage 

Levels.” Paper prepared for the Joint Center for Poverty Research, October 26, 2000a. 
 160 

Gladden, Tricia, and Christopher Taber. “Chapter 4—Wage Progression Among Less Skilled 

Workers.” in Finding Jobs: Work and Welfare Reform. Edited by David Card and Rebecca 

M. Blank, New York: The Russell Sage Foundation, 2000b, pp. 160-192. 

Gottschalk, Peter. “Inequality, Income Growth, and Mobility: The Basic Facts.” in The Journal 

of Economic Perspectives. Vol. 11, No. 2, Spring 1997, pp. 21-40. 

Gottschalk, Peter. “Chapter 8—Work as a Stepping-Stone for Welfare Recipients: What Is the 

Evidence?” in The Low-Wage Labor Market. Edited by Kelleen Kaye and Demetra Smith 

Nightingale, Washington, DC: The Urban Institute Press, 2000, pp. 171-184. 

Hale, Thomas. “The Working Poor.” in Monthly Labor Review. September 1997, pp 47-48. 

Holzer, Harry J., and Robert J. Lalonde. “Chapter 3—Job Change and Job Stability Among Less 

Skilled Young Workers.” in Finding Jobs: Work and Welfare Reform. Edited by David 

Card and Rebecca M. Blank, New York: The Russell Sage Foundation, 2000, pp. 125-159. 

Holzer, Harry, Julia I. Lane, Lars Vilhuber, Henry Jackson, and George Putnam. Escaping 

Poverty for Low-Wage Workers: The Role of Employer Characteristics and Changes. 

Report prepared for the U.S. Census Bureau’s Longitudinal Employer-Household Dynamics 

Program, June 2001. 

Kim, Marlene. “Are the Working Poor Lazy?” in Challenges. Vol. 41, no. 3, 1998, pp 85-99. 

Klein, Bruce and Philip Roens. “A Profile of the Working Poor.” in Monthly Labor Review. 

October 1989, pp 3-13. 

Light, Audrey and Manuelita Ureta. “Panel Estimates of Male and Female Job Turnover 

Behavior: Can Female Nonquitters Be Identified?” in Journal of Labor Economics. Vol. 

10, no. 2, 1992, pp. 156-181. 

Long, Sharon K., and Alberto Martini. “Wages and Employment Among the Working Poor: 

New Evidence from SIPP.” Paper No. 9014, Paper prepared for the Bureau of the Census, 

U.S. Department of Commerce, November 1990. 

Mishel, Lawrence, Jared Bernstein, and John Schmitt. The State of Working America 2000-

2001. Ithaca, NY: ILR Press, Cornell University Press, 2001. 

Mitnik, Pablo A., Matthew Zeidenberg, and Laura Dresser. “Can Career Ladders Really Be a 

Way Out of Dead-End Jobs? A Look at Job Structure and Upward Mobility in the Service 

Industries.” Madison, WI: Center on Wisconsin Strategy, 2002. 

Osterman, Paul. “Chapter 4—Employers in the Low-Wage/Low-Skill Labor Market” in LowWage Workers in the New Economy. Edited by Richard Kazis and Marc S. Miller, 

Washington, DC: The Urban Institute Press, 2001, pp.45-66. 

Pavetti, LaDonna, and Gregory Acs. “Moving Up, Moving Out or Going Nowhere? A Study of 

the Employment Patterns of Young Women.” Washington, DC: The Urban Institute, 1997. 
 161 

Rangarajan, Anu, Peter Schochet, and Dexter Chu. “Employment Experiences of Welfare 

Recipients Who Find Jobs: Is Targeting Possible?” Princeton, NJ: Mathematica Policy 

Research, Inc., August 1998. 

Royalty, Anne Beeson. “Job-to-Job and Job-to-Nonemployment Turnover by Gender and 

Education Level.” in Journal of Labor Economics, Vol. 16, No. 2, April 1998, pp. 392-443. 

Ryscavage, Paul. “A Perspective on Low-Wage Workers.” in Current Population Reports, No. 

P70-57, Newsletter from the U.S. Census Bureau, August 1996. 

Schiller, Bradley. “Who Are the Working Poor?” in The Public Interest. Spring 1994, 

pp 61-71. 

Schwarz, John and Thomas Volgy. The Forgotten Americans. New York: W.W. Norton and 

Company, 1992 

Smith, Ralph E., and Bruce Vavrichek. “The Wage Mobility of Minimum Wage Workers.” in 

Industrial & Labor Relations Review, Vol. 46, No. 1, October 1992, pp. 82-88. 

Topel, Robert H., and Michael P. Ward. “Job Mobility and the Careers of Young Men.” in The 

Quarterly Journal of Economics, Vol. CVII, Issue 2, May 1992, pp. 439-479. 

U.S. Census Bureau. Statistical Abstract of the United States. Washington, DC: 1985 to 2001. 

 
APPENDIX A 

DATA, WAGE DEFINITIONS, ANALYSIS SAMPLES, 

AND METHODOLOGICAL APPROACH

 A.3 

The 1996 longitudinal panel of the Survey of Income and Program Participation (SIPP), 

collected by the U.S. Bureau of the Census, is the primary data source that we used for 

examining the low-wage labor market in our study. Because of the wide range of study 

questions, we used different samples and methodological approaches for different types of 

analyses. We discuss these issues in this methodological appendix. 

A. DATA 

The 1996 SIPP is a large, multipanel, longitudinal survey that collected demographic and 

socioeconomic information on a nationally representative sample of U.S. households. The data 

cover the period from late 1995 to early 2000. SIPP provides detailed monthly measures on 

labor force participation (for those age 15 and older), income, participation in public programs, 

and household composition. Our study also used data from several SIPP topical modules that 

contain information on supplemental topics and on sample members’ experiences before the 

beginning of the panel period. Finally, the SIPP data were supplemented with state-level data on 

the economic conditions and poverty levels in the states. 

1. Advantages of the SIPP Data for the Study 

The 1996 SIPP panel is particularly well suited for the study, for several reasons. First, 

because it covers a period between late 1995 and early 2000, we can examine the dynamics of 

the low-wage labor market during the post-PRWORA period. Second, because it contains 

detailed monthly information on jobs each sample member held during the panel period, we can 

conduct individual-level longitudinal analyses of employment spells and wage progression. 

The SIPP data also have several advantages over other national data sets. Cross-sectional 

data sets, such as the March Current Population Survey (CPS), can provide point-in-time 

information on low-wage workers, but they do not allow analyses of individual-level 
 A.4 

employment and earnings experiences over time. The Panel Study of Income Dynamics (PSID), 

begun in 1968, is a longitudinal study of a representative sample of people in the United States 

that contains information through 1999. Thus, the PSID covers the post-PRWORA period and, 

because it is a long panel, has more information than SIPP on employment histories. However, 

because PSID data have been collected annually (and recently every other year), compared to 

every four months for SIPP, recall error is likely to be larger in the PSID. This is a particularly 

important problem for this study, because the job spells of many low-wage workers are likely to 

be short. Furthermore, sample sizes are much larger in SIPP (more than 40,000 households were 

sampled for the 1996 SIPP, whereas the 1999 PSID contains information on only about 7,000 

families). The National Longitudinal Survey of Youth (NLSY) is limited to people who were 

ages 14 to 21 in 1978, so data from the NLSY are not well suited for examining the experiences 

of low-wage workers of all ages. 

2. Description of the 1996 SIPP Panels 

Adults followed in the SIPP panel come from a nationally representative sample of 

households in the civilian, noninstitutionalized population of the United States. Sample members 

were interviewed once every four months during the 48-month panel period. If original 

(primary) sample members older than age 14 moved from their original residences, they were 

interviewed at their new addresses. Secondary sample members—those who were not part of the 

original sample but who lived with primary sample members after the first interview—were 

interviewed if they were in the same household as primary sample members. 

The Census Bureau used multistage sampling techniques to select a representative set of 

households for the 1996 SIPP panels. The first interviews for the panel began in April 1996 with 

a sample of 40,188 households and 95,402 primary sample members, where households in the 
 A.5 

 

low-income stratum were sampled at 1.66 times the rate of the higher-income stratum.1

 Sample 

households were divided into four “rotation groups” of roughly equal size, and one rotation 

group was interviewed each month. Thus, each household was interviewed in four-month 

intervals, called “waves.” The 1996 SIPP contains 12 waves, which provide 48 months of data 

for each person in the sample.2

At each interview, sample members provided information about their experiences during the 

preceding four-month period, called the “reference period.” For example, people in rotation 

group 1 whose wave 1 interviews were conducted in April 1996 (the earliest interviews) were 

asked about their experiences between December 1995 and March 1996. Similarly, people in 

rotation group 4 whose wave 12 interviews were conducted in March 2000 (the latest interviews) 

were asked about their experiences between November 1999 and February 2000. Thus, the 12 

reference periods for the 1996 SIPP panel cover December 1995 through February 2000. 

The 1996 SIPP interviews were administered using computer-assisted interviewing (CAI) to 

increase data quality. CAI, used for the first time in the 1996 SIPP, permitted automatic 

consistency checks of reported data during the interview and allowed for the use of prior-wave 

data for editing missing data. 

The SIPP questionnaire is made up of the core questions and the topical modules. The core 

questions provide information on (1) demographic characteristics; and (2) work behavior, 

income, and program participation for each of the four months preceding the interview date. The 

core questions were asked in every wave interview. Sample members were asked the topical 

1

 The sample size for the 1996 SIPP was larger than for previous panels. For example, the 

1990 panel contains 21,900 sampled households and 43,799 sampled people. 

2

 The 1996 SIPP redesign called for 12 panels, rather than the 8 used in previous SIPP 

panels. 
 A.6 

 

module questions after the core questions. The content of the topical module changed from wave 

to wave. For our purposes, the topical modules administered in wave 1 are of special interest, 

because they contain information on respondents’ prepanel experiences (see Section 4 below). 

3. The 1996 SIPP Longitudinal Research File 

The Census Bureau constructed a full-panel, longitudinal research file by linking the data 

collected for each sample person over the life of the panel. Unlike the individual core wave files 

that contain one record per person-month, the longitudinal file contains one record per person. 

The longitudinal sample that this research file represents consists of all primary sample members 

who have complete data (either reported or imputed) for every month of the panel (excluding 

months of ineligibility). This longitudinal sample contains 55,484 people and is the main sample 

that was used for the analysis. 

The 1996 longitudinal file contains a smaller percentage of all primary sample members 

than in previous SIPP panels, for several reasons. First, sample attrition was higher in the 1996 

panel than in earlier panels because the 1996 panel was longer (12 waves, compared to 8 waves 

in previous panels). For example, the sample loss rate was 35.5 percent by the end of wave 12 in 

the 1996 panel, but it was 26.9 percent by the end of wave 8 in the 1993 panel.3

 Second, in 

creating the final data files, the Census Bureau typically performs imputations for missing 

responses to individual questions or to entire wave interviews (see U.S. Census Bureau 2003, 

SIPP Data Editing and Imputation), thereby increasing the sample size in the analysis files. In 

3

 The sample loss rate at the end of wave 8 in the 1996 panel was 32.8 percent. 
 A.7 

 

creating the 1996 SIPP data files currently available, however, the Census Bureau has performed 

fewer imputations than in previous panels.4

The longitudinal research file is available online using the FERRET system. As the Census 

Bureau specifies, however, this system is efficient (practical) only for downloading a small

number of variables, because variable requests must be performed separately for each variable 

using a series of menus and because downloading even a few variables takes considerable time. 

Our study employs a large number of variables, so we did not use the FERRET system to obtain 

the longitudinal data needed for the analysis. 

Instead, we downloaded (from the SIPP Web page) the entire ASCII database for each of 

the 12 individual core wave files and constructed our own longitudinal file following the same 

procedures the Census Bureau used to construct its longitudinal file. Specifically, we “flattened” 

each core file to obtain one record per person (rather than per person-month) and merged these 

12 flattened files using the unique person identification code (LGTKEY). We compared key 

selected variables (such as earnings and hourly wage rates) in our constructed longitudinal file to 

those in the longitudinal file on the FERRET system and found the variables to be identical in 

both data files. 

Finally, to take into account nonresponse, sample attrition, and the complex sample design 

of the 1996 SIPP (including the oversampling of poor households), the longitudinal research file 

contains panel weights (which we downloaded using the FERRET system). These weights make 

the SIPP longitudinal sample representative of the noninstitutionalized, resident population of the 

4

 The Census Bureau has performed imputations for “Type Z” noninterviews, which 

occurred when an interview was conducted with at least one household member but not with one 

or more sample people in the household. The Census Bureau, however, has indicated that it will 

not perform additional imputations for the 1996 SIPP panel. 
 A.8 

 

United States as of March 1996 (the only month common to all four rotation groups in wave 1).5

 

We used weights throughout the statistical analyses and adjusted the standard errors of our 

estimates to account for design effects due to weighting and clustering. 

4. Topical Modules 

The topical modules contain more detailed information on particular topics than are 

contained in the core files. We used data from the topical modules to construct explanatory 

variables for the multivariate analysis. 

The wave 1 topical module contains retrospective information on sample members’ prepanel 

activities and experiences. The most important such information for this study concerns prepanel 

employment experiences (including the number of years the respondent worked at least six 

months, breaks from the labor force, the date last worked, and whether the respondent generally 

worked 35 or more hours per week since he or she first started working at least six months per 

year). Unlike previous SIPP panels, information on the starting dates for those in the middle of 

job spells at the start of the SIPP panel period (that is, who have left-censored job spells) are in 

the wave 1 core file and not in the topical module. 

Several topical modules contain information on work schedules and health status. Data on 

work schedules are contained in the wave 4 and wave 10 topical modules. However, as 

discussed later, most of our analysis was conducted using samples of workers who began lowwage jobs at the start of the panel period (that is, in waves 1 and 2), and our analysis described 

the characteristics of low-wage workers and their jobs at the start of these jobs. Thus, the data 

on work schedules was collected too late to be useful for our study, so we did not use them in the 

5

 The longitudinal data file also contains calendar year weights. The 1997, 1998, and 1999 

calendar year weights pertain to the January population in those years. The 1996 weight, 

however, pertains to the March population. 
 A.9 

 

analysis. For a similar reason, we did not use the detailed information on functional limitations 

and disabilities contained in topical modules 5 and 11.6

 However, we did use in the analysis the 

health status variable contained in each core data file concerning whether the respondent had a 

physical, mental, or other health condition that limited the kind or amount of work that could be 

done. 

5. State-Level Data 

The state-level data for our analysis included information on states’ economic conditions. 

We merged this state-level information by month or year (depending on data availability) to the 

SIPP data file using monthly (annual) information on the state in which each sample member 

lived.7

 We used this information to explore the relationship between state characteristics and the 

dynamics of the low-wage labor market in the multivariate analysis. 

We used variables from the following categories of state economic indicator variables that 

are intended to proxy for the labor market situation faced by SIPP sample members: 

• Unemployment rate and the change in the unemployment rate during the follow-up 

period (Source: U.S. Department of Labor’s Bureau of Labor Statistics [BLS]) 

• Employment growth per capita (Source: BLS) 

• Poverty rate (source: Statistical Abstract of the United States) 

• Household median income (source: Statistical Abstract of the United States) 

• 20th percentile of monthly wages of employed people age 18 and older 

6

 Topical modules 3, 6, 9, and 12 contain detailed information on medical expenses and the 

utilization of health care, but these data cover topics that are beyond the scope of the analysis. 

7

 For nine states with relatively few SIPP respondents, the data do not identify the state 

individually, but rather in three groups: (1) Maine and Vermont; (2) Iowa, North Dakota, and 

South Dakota; and (3) Alaska, Idaho, Montana, and Wyoming. For these groups, we inserted 

mean characteristics across all states in the group. 
 A.10 

• Per-capita income (source: Bureau of Economic Analysis) 

• Real minimum wage (source: Statistical Abstract of the United States) 

• Mean wage in the manufacturing industry 

• Rural population share 

Although we initially included all these measures as explanatory variables in our 

multivariate models, we ultimately narrowed the list because of the high correlation among 

many of the state-level measures. This high degree of multicollinearity increased the standard 

errors of all parameter estimates and made it difficult to isolate the separate effects of each of the 

state-level measures. The final list of explanatory variables included (1) the unemployment rate 

measures, (2) the poverty rate measure, (3) the 20th percentile of monthly wages, and (4) the 

rural population share. 

B. DEFINING LOW-WAGE WORKERS 

A central analysis issue for the study is how to define low-wage workers. As discussed in 

detail in Chapter II, researchers have used a variety of definitions of the low-wage labor market, 

and each definition has advantages and disadvantages. Because of project budget constraints, it 

was not feasible to conduct analyses using each of these measures. Therefore, we needed to 

select among the alternative measures. 

Our primary approach for defining low-wage workers was to use the hourly wage at which a 

full-time worker would have annual earnings below poverty for a family of four. We calculated 

separate low-wage cutoff values for each calendar year the SIPP panel covered. We then 

classified a worker as “low-wage” if the worker’s wage rate was less than the cutoff level in the 

calendar year when the wage rate was reported. Using U.S. Department of Health and Human 

Services poverty guidelines and assuming a full-time worker works 2,080 hours per year, we set 

the low-wage cutoff at $7.50 in 1996, $7.72 in 1997, $7.91 in 1998, $8.03 in 1999, and $8.20 in 
 A.11 

2000. We also defined medium-wage workers as those with wage rates between one and two 

times the low-wage cutoff value and higher-wage workers as those with wages more than twice 

the low-wage cutoff value. 

We adopted the absolute low-wage cutoff approach so that the analysis could focus on lowwage workers and their jobs based on a well-defined cutoff value. We did not use the minimum 

wage as the absolute wage cutoff value, because it sets the bar too low for defining the low-wage 

labor market. We rejected using definitions based on family income levels, because that 

approach would be appropriate for examining working poor households rather than low-wage 

workers. 

We used the absolute wage cutoff rather than a relative wage cutoff, because the relative 

wage cutoff allows for no change over time in the fraction of the labor force that is defined as 

low wage, even if living standards of low-income workers change. For example, under the 

relative wage approach, a worker earning a wage rate at the 20th percentile of the wage 

distribution at two time points would be classified as a low-wage worker at each point, even if 

the wage distribution for low-wage workers shifted over time (that is, even if the worker’s wage 

rate changed). Thus, the relative wage approach would provide less information than the 

absolute wage cutoff approach on the extent to which low-wage workers enter and exit the lowwage labor market over time. Furthermore, we rejected using a definition based on the skill 

levels of workers, because not all workers in the low-wage labor market have low skills. 

We did, however, construct samples of low-wage workers using alternative definitions when 

we estimated the size of the low-wage labor market as part of the descriptive analysis presented 

in Chapter III. The rest of the analysis, however, was conducted using only the absolute lowwage cutoff measure. 
 A.12 

Finally, one implication with the absolute low-wage cutoff measure is that the low-wage 

threshold was constructed for a household of average size and, thus, may be too low for largerthan-average households and too high for smaller-than-average ones (although it is correct on 

average). One approach for addressing this issue would be to define wage cutoff levels by 

household size so that the cutoff values would be higher in larger households than in smaller 

ones. We rejected this option, however, for two main reasons. First, the unit of analysis is the 

low-wage worker, rather than the low-income working household; thus, it is preferable to use a 

uniform definition for all workers. Second, household size often changes over time, so people’s 

cutoff values would often change over time, which would lead to analytic complications. For 

example, suppose a worker held the same job and received the same wage rate in two successive 

months. If the worker’s household size decreased in the second month, then the worker could be 

classified as a low-wage worker in the first month but not in the subsequent one. 

C. WAGE CONSTRUCTION, SAMPLES, AND METHODOLOGICAL APPROACH 

Our study seeks to address a broad range of research questions related to the low-wage labor 

market, including questions that require the analysis of employment-related data at a point in 

time and over specific intervals. Furthermore, to address some questions, the individual is the 

unit of analysis; to address others, the low-wage job or employment spell is the unit of analysis. 

Thus, we employed various analysis samples and statistical methods for the study. 

In this section, we first discuss general issues about which workers were included in the 

empirical analysis and the construction of hourly wage rates. Then, we provide an overview of 

specific analytic issues separately by type of analysis. We provide a more complete discussion 

of these issues in each of the relevant topical chapters presented in this report. 
 A.13 

1. Sample Inclusion Criteria 

Our analysis was conducted using employed SIPP sample members who were between ages 

16 and 64 and who were not enrolled in school at the start of their jobs. We excluded students 

and older workers, because their labor market experiences are likely to be very different from 

those of the population that is the focus of this study. 

Our analysis included information on those who worked for employers (that is, those who 

held jobs) and on those who owned businesses. At each wave, SIPP contains information on up 

to two jobs held by sample members and two businesses owned by sample members during the 

reference (four-month) period. Although the studies of low-wage workers reviewed in Chapter 

II typically examined those in jobs only, we included both jobs and businesses in our analysis, 

because a significant percentage of those with businesses were low-wage workers. For example, 

in March 1996, about 12 percent of all low-wage workers in our sample owned businesses, and 

an equal share of those with jobs and businesses were low-wage workers. Thus, we did not want 

to exclude from the analysis self-employed workers who constitute an important segment of the 

low-wage labor market. 

2. Construction of Hourly Wages 

For each month of the panel period, we constructed hourly wages for each job and business 

using detailed employment information in SIPP. SIPP contains direct information on hourly 

wage rates for the 60 percent of jobholders who could provide wage data in this way. Hourly 

wage rate information, however, is not available for the remaining 40 percent of jobholders and 

for all those with businesses. For these workers, we constructed hourly wages by combining 

information on monthly earnings (which are reported for each month of the panel period) and 

usual hours worked per week at each job or business during the reference period (topcoded at 84 
 A.14 

 

hours), and assuming that the worker was employed for the entire month.8

 The “earnings-based” 

hourly wage measure was then constructed for each month by dividing monthly earnings by the 

number of hours worked in the month.9

Our preliminary analysis of the SIPP data showed that hourly wage rates fluctuated 

considerably over time, and especially for the constructed earnings-based measures. These 

fluctuations are often due to sudden large changes in wage rates that appear to be due more to 

reporting errors or SIPP data errors than to real wage changes. Furthermore, they yield more 

worker transitions into and out of the low-wage labor market than we deem plausible. 

Consequently, we used several methods to “smooth” the hourly wage rates to identify those who 

were truly in low-wage jobs: 

• We set outliers to missing. Wages below $1 and above $150 were treated as missing, 

which affected 2.7 percent of workers. Furthermore, the SIPP user notes report a data 

imputation problem for some jobholders whose earnings information was missing. 

Earnings are reported as zero for these workers rather than as a positive imputed 

value. SIPP reports that this problem may have affected around 1.5 percent of the 

observations in the monthly earnings distributions. However, it is not possible to 

identify these individuals from those who truly reported zero wages. Thus, we set 

zero wage values to missing. Finally, SIPP topcoded monthly employment income at 

$12,500. Due to our focus on low-wage workers, however, this constraint does not 

materially affect the analysis. 

• We smoothed the earnings-based hourly wage rates by averaging positive wage 

values across the four months within a wave. We smoothed in this way because the 

8

 Initially, we used job and business start and end dates to calculate the exact number of 

weeks that the worker was employed in the month. However, we found in the data that workers 

who started jobs in the middle of the month tended to report monthly earnings for the full month. 

For example, we found many instances where workers reported the same monthly earnings in 

months in which they worked only part of the month (that is, in months when they started their 

jobs) and in subsequent months. Thus, in order to avoid inflating the constructed wage rates, we 

assumed that workers were employed for the full month. 

9

 We did not use the earnings-based measure for those who directly reported an hourly 

wage, because we believe that the direct measure is more accurate. This approach has typically 

been used in the literature discussed in Chapter II. 
 A.15 

earnings-based measure varies by month (because sample members were asked to 

report their earnings for each month of the reference period), whereas the direct 

hourly wage measure pertains to the entire wave and not to specific months within the 

wave. Thus, there is considerably more fluctuation in the earnings-based hourly wage 

measure than in the direct hourly wage measure, which generates more frequent and 

shorter spells of low-wage employment using the earnings-based measure. 

• We smoothed unusual changes in hourly wage rates in the same job across waves. 

If wages within a job suddenly increased by 25 percent and then rapidly decreased by 

25 percent or vice versa, then we smoothed (imputed) wages at the “spike” points as 

the average of the surrounding wages on that job. We set a conservative 25 percent 

threshold value to avoid over-smoothing the data. 

Finally, for those with multiple jobs and businesses in a particular month, we selected the 

hourly wage from the job or business in which the sample member worked the most hours. In 

March 1996, about 11 percent of workers held multiple jobs and businesses. Thus, we defined 

whether a worker was a low- medium-, or high-wage worker using the wage on the selected 

“main” job or business in that month. 

3. Overview of Samples and Methodological Approach by Topical Area 

Our analysis addresses questions in four topical areas: (1) the characteristics of low-wage 

workers and their jobs, (2) the employment experiences of low-wage workers over a three-year 

follow-up period, (3) the wage growth of low-wage workers over a three-year period, and (4) the 

duration of low-wage job and employment spells and types of exits from the low-wage labor 

market. Next, we briefly provide an overview, by topical area, of the analysis samples and 

methodological approaches used in the study, as well as the subgroups for which separate 

estimates were obtained. We provide additional details in the report chapters that present the 

study findings. We begin here, however, with a brief discussion of general analytic issues that 

pertain to all analyses. 
 A.16 

a. General Analytic Issues 

Our descriptive and multivariate analyses were conducted separately for males and females, 

because of differences in labor market participation decisions and experiences by gender. Within 

each gender group, we calculated statistics for the full sample, as well as for key subgroups 

defined by worker and job characteristics. We used sample weights in all analyses (either the 

longitudinal or calendar year weights, depending on the analysis) to make our findings 

representative of all workers nationally. 

An important component of our analysis was to compare the characteristics and labor market 

experiences of low-wage workers to those of medium- and high-wage workers (labeled hereafter 

as “higher-wage workers”). We conducted these analyses to provide a context from which to 

understand the findings for those in the low-wage labor market. Thus, in selected analyses, we 

computed statistics for workers in each of the three wage categories. For example, to help 

interpret findings on the percentage of time that low-wage workers were employed during the 

follow-up period, we also computed these employment measures for medium- and high-wage 

workers. 

b. Describing Low-Wage Workers and Their Jobs 

The main analysis sample that we used in our descriptive analysis to examine the prevalence 

of low-wage jobs and the characteristics of low-wage workers and their jobs is a cross-sectional 

sample of workers in March 1996. We selected March 1996 as the reference point because 

(1) it is the earliest month in the SIPP data that is covered for all sample members; (2) the 1996 

calendar year weight is constructed to make the sample representative of the U.S. population in 

March 1, 1996; (3) previous cross-sectional studies examining the low-wage sector have used the 

March CPS data, so we can compare our results to those from previous studies; and (4) the 

sample used to examine the overall employment experiences and wage growth of low-wage 
 A.17 

workers was based on those who started low-wage jobs early in the panel period. We also 

constructed cross-section samples of workers in March 1997, March 1998, and March 1999 to 

examine changes in the prevalence and profiles of low-wage workers over time, due to changing 

economic conditions and TANF program parameters. 

We described three main aspects of low-wage workers and the types of jobs they hold 

separately for men and women. First, we examined the fraction of all workers who are in the 

low-wage labor market. Second, we examined their demographic characteristics and compared 

them with those of medium- and high-wage workers. Finally, we described the job and other 

employment-related characteristics of low-wage workers. 

A worker was defined as a low-, medium, or high-wage worker on the basis of the worker’s 

hourly wage measure (on the main job or business) at the time the worker entered the sample (for 

example, March 1996). Similarly, worker and job characteristics were defined at the sampling 

point. 

c. Examining Overall Employment Experiences 

The analysis of the overall employment experiences of low-wage workers was conducted 

using only those who started jobs or businesses during the first six months of the panel period, to 

ensure a sufficient follow-up period for examining overall employment patterns and adequate 

sample sizes. We identified the first job that the worker held during the six-month period, and if 

the sample member held multiple jobs or businesses at the same time, we selected the job or 

business at which the sample member worked the most hours. We classified a sample member 

as a low-, medium-, or high-wage worker on the basis of the worker’s average hourly wage 

during the month of job start and the subsequent six months (for those months in which the 

worker was employed). We used this six-month period to help identify “true” low-wage workers 

from those who held low-wage jobs for only a very short time due to temporary changes in 
 A.18 

 

earnings or labor supply effort, or to data errors. The follow-up period was measured at the start 

of the initial job, and was 42 months for all sample members (the longest period that could be 

examined for those who started jobs in panel month 6). Thus, the follow-up period was not 

measured in calendar time, but in the number of months since job start. 

We constructed the following categories of outcome measures for the analysis: 

• Movements into and out of the low-wage and higher-wage labor markets, including 

the percentage of low-wage workers who (1) found higher-wage jobs, (2) found other 

low-wage jobs, and (3) cycled between low-wage and higher-wage jobs. 

• Time spent in various labor market activities, including the percentage of all 

months the worker was (1) employed in all jobs, (2) employed in low-wage jobs, 

(3) employed in medium-wage jobs, (4) employed in higher-wage jobs, and 

(5) unemployed or out of the labor force.10

• The number of job and employment spells, including the number of low-wage jobs, 

higher-wage jobs, and nonemployment spells. For this analysis, we defined a lowwage job spell as ending when a worker moved to another low-wage job, moved to a 

higher-wage job (either with the same or different employer), became unemployed, or 

left the labor force. Medium- and high-wage job spells were defined in a similar way. 

• Changes in employment patterns over time, including employment rates in low-wage 

and higher-wage jobs by quarter after job start. 

We calculated summary statistics for each outcome measure for the whole sample and for 

selected subgroups. In addition, we conducted selected analyses using medium- and high-wage 

workers to place the findings for low-wage workers in perspective. All estimates were 

constructed using the longitudinal panel weights. We also estimated multivariate regression 

models to examine factors associated with positive overall employment outcomes during the 

follow-up period. This analysis allowed us to more efficiently examine a larger set of factors 

than could be examined in the descriptive analysis. The analysis also allowed us to isolate the 

10 These categories are not mutually exclusive. 
 A.19 

contribution of each factor from others. In Chapter IV, we discuss the specific dependent and 

explanatory variables included in the models and the statistical techniques used to estimate the 

models. 

d. Examining Wage Progression 

For the analysis of wage progression, we examined the extent to which the wages of lowwage workers grow over time and what factors are associated with wage growth over a threeyear follow-up period. Similar to the overall employment analysis, the wage progression 

analysis was conducted using only those who started jobs or businesses during the first six 

months of the panel period. The key difference between the wage progression analysis and the 

overall employment analysis is that the wage progression analysis focused on continuous 

measures of wage growth, whereas the overall employment analysis focused on employment 

patterns over the follow-up period. 

As described earlier, to classify job starters as low-, medium-, or high-wage workers, we 

based our initial classification of workers into these three groups based on their average wages 

during the first six-month period after they started their jobs. Categorizing people into low-, 

medium-, or high-wage workers at any given point in time has two potential issues especially 

important for the wage growth analysis. First, if a worker misreports his or her wages at the time 

of job start, we may incorrectly classify an individual into a wage type that may not be their real 

wage type. Second, people sometimes obtain jobs that may not be related to their true ability 

levels and may soon move into a job that more closely matches their true human capital level. 

For example, if a worker with low productivity gets a high-wage job, he or she may not be able 

to sustain that job for long and may soon move into a low-wage one. Conversely, a highproductivity worker may have found a low-wage job and might soon move to a higher-wage job 

(defined as a medium- or high-wage job). Both these factors work in the direction of potentially 
 A.20 

 

large wage growth for low-wage workers (or lower wage growth for high-wage workers), 

especially in the early periods after job start. We were particularly concerned about minimizing 

the effects of any data errors, as these errors do not reflect true changes in wages. Thus, as 

described earlier, we smoothed wages and took the six-month average of wages after job start to 

classify workers into wage categories.11 (We call this initial period to classify workers into wage 

categories “period 0.”) While this smoothing is likely to reduce the noise due to data errors to a 

large extent, residual errors could still remain, and we may be overstating wages for low-wage 

workers. Consequently, in our analysis examining wage growth over time, we start with the 

average wage in the first six-month period after the period we used to define their initial worker 

type and examine their wage growth over the following three-year period (period 1 through 

period 6). For trends in wages over time, we present average wages of those employed in period 

1, average wages of those employed in period 2, average wages of those employed in period 3, 

and so on. For the analysis of individual workers’ wage growth over time, we compare wages 

and job characteristics of those workers who were employed in both the first and last periods 

(i.e., period 1 and period 6) regardless of their employment in other periods. 

Additionally, the wage progression analysis was conducted using only those who were 

employed at various follow-up points. This is because hourly wages are observed only for those 

who were employed—they are not observed for nonworkers (they are missing, not zero).12 Thus, 

11 As noted in Chapter II, the usual extent of data cleaning performed in earlier SIPP waves 

was not done for the 1996 longitudinal files. 

12 According to economic theory, an individual chooses not to work if the person’s market 

(offered) wage is lower than the person’s reservation wage (the minimum wage for which the 

person would be willing to work). Otherwise, the individual chooses to work, and hours worked 

are adjusted to equate the reservation and market wages. Thus, for nonworkers, the reservation 

wage is missing, and one cannot assign a zero wage rate to these individuals. 
 A.21 

 

the sample to examine wage progression was restricted to those who reported being employed in 

various follow-up intervals (six-month intervals), so that initial hourly wages could be compared 

to hourly wages reported later. This is the usual approach used in the literature to address wage 

growth issues.13

We measured wage progression as the difference in (real) hourly wage rates at various fixed 

time points after the start of the low-wage job. We also measured wage growth as the percentage 

increase in real wages (relative to the starting wage) at the same follow-up points. In addition, 

we constructed indicator variables signifying whether the worker’s wage increased, decreased, or 

stayed the same (and by how much). 

We conducted descriptive and multivariate analyses to examine wage progression. To help 

interpret the wage growth results, we also compared the distribution of key job characteristics for 

the initial low-wage job and the most recent job held in the last year of the panel period. This 

analysis provides information on whether potential increases in wage growth between years 1 

and 4 are associated with improvements in other job characteristics, such as the availability of 

fringe benefits, hours worked, and occupations. As discussed in more detail in Chapter V, we 

also conducted multivariate analyses to examine factors associated with wage progression. 

e. Examining Spell Durations and Types of Exits 

An important component of our analysis was to examine the distribution of the length of 

continuous job and employment spells for low-wage workers and the extent to which these spells 

end in higher-wage jobs or in nonemployment. This duration analysis differed from (but 

13 As discussed in Chapter VI, we examined the extent of potential sample selection biases 

in our estimates by comparing the characteristics of workers who were included and excluded 

from the analysis sample. The results from this wage growth analysis may represent a best-case 

scenario, because the sample is likely to overrepresent those who had positive employment 

outcomes at the various points. 
 A.22 

complements) the overall employment analysis in several respects. First, the duration analysis 

focused on the low-wage spell rather than the low-wage worker. Thus, the analysis file for the 

duration analysis contains one record per spell month rather than one record per person. Second, 

the duration analysis focused on the length of continuous low-wage job and employment spells, 

whereas the overall analysis described patterns of potentially discontinuous employment and 

nonemployment spells that workers experienced over a fixed follow-up period. 

A central, and complicated, analytic issue is how to define job, employment, and 

nonemployment spells. To facilitate this discussion, we first list the five possible states into 

which a low-wage worker could exit: 

1. Another low-wage job (or business) 

2. A higher wage job with the same employer 

3. A higher-wage job with a different employer 

4. Unemployment 

5. Not in the labor force 

Using these possible exit states, we conducted duration analyses for four types of job and 

employment spells, each of which addresses a slightly different analytic question: 

1. Low-Wage Job Spells. The duration of these spells was measured from the start of 

the low-wage job until the worker exited into any of the five states listed above (or, 

for right-censored spells, until the end of the panel period). These spells were used 

to address the extent to which low-wage workers remain in their initial jobs and 

continue to receive low pay. 

2. Job Spells. These spells pertain to the period the worker was employed with the 

initial employer regardless of the wage level that the worker received (that is, until 

the worker exited into state 1, 3, 4, or 5). Thus, these spells provide information on 

the amount of time low-wage workers remain with their initial employer. These 

spells will produce different results than the low-wage job spells if low-wage 

workers experience wage growth within their jobs. 

3. Low-Wage Employment Spells. The duration of these spells was measured from the 

start of the low-wage job spell until the worker left all low-wage employment (that 
 A.23 

is, until they exited into state 2, 3, 4, or 5). This duration includes continuous 

changes from one low-wage job spell to another. Results using these spells will 

differ from those using the low-wage job spells if low-wage workers move directly 

from one low-wage job to another. 

4. Employment Spells. These spells provide information on the time between job start 

and when the worker became nonemployed (that is, until the worker exited into state 

4 or 5). Thus, these spells pertain to the number of months that the worker was 

employed in any job, regardless of the wage level. Duration results based on these 

spells will differ from those based on the other spells if low-wage workers move 

seamlessly between employers and across wage levels. 

Similar procedures were used to construct spells for those who began medium- and high-wage 

jobs during the panel period. 

We examined also two types of spells for our analyses of reentry into the low-wage labor 

market. First, we examined the rate at which those who exited their low-wage jobs into 

nonemployment (that is, into exit states 4 and 5) returned to the low-wage and higher-wage labor 

markets. Second, we examined the extent to which those who exited their low-wage jobs into 

higher-wage jobs returned to the low-wage sector. 

In sum, the samples for the duration analysis included entry cohorts of job, employment, and 

nonemployment spells that began during the panel period. Job and employment spells were 

classified as low-wage (or higher-wage) on the basis of the hourly wage rate at the start of the 

spell, and a spell ended if the worker exited into one of the various exit states described above. 

These samples allow us to answer such hypothetical questions as (1) Of those who begin a lowwage job, what percentage will still be working at that job one year later? and (2) Of those who 

begin a low-wage job, how many will leave that job and go directly into a higher-paying job? 

Similarly, the sample for the analysis of nonemployment spells allows us to answer such 

questions as: Of those who exit a low-wage job into nonemployment, how many will become 

reemployed in low-wage or higher-wage jobs within eight months? 
 A.24 

 

We used standard life table statistical methods to estimate the proportion of spells that ended 

within a given number of months after the start of the spell (that is, cumulative exit rates). As 

discussed in Chapter VI, these methods adjust for right-censored spells (that is, spells still in 

progress at the end of the panel period) and left-censored spells (spells in progress at the start of 

the panel period). We conducted analyses for the full sample of males and females, as well as 

for key population subgroups defined by worker characteristics at the start of the spell. We also 

compared findings for low-wage workers to those of medium- and higher-wage ones. 

f. Subgroup Analysis and Sample Sizes 

As discussed, we conducted all analyses separately for male and female workers because 

of differences in labor market participation decisions by gender. In addition, within each 

gender group, we conducted selected analyses for key subgroups of low-wage workers defined 

by their demographic and job characteristics at the start of their low-wage jobs (for the overall 

employment, wage progression, and duration analyses). The subgroup analysis provides 

information on whether labor market experiences differ for different groups of low-wage 

workers. We selected the following policy-relevant categories of subgroups across whom we 

hypothesized study findings might differ: 

• Individual and Household Characteristics at Job Start: (1) age; (2) race/ethnicity; 

(3) educational attainment; (4) whether has a physical, mental, or other health 

condition that limited the kind or amount of work that could be done; (5) household 

income as a percentage of the poverty level; and (6) household type 

• Job Characteristics at Job Start: (1) hourly wage rate; (2) hours worked per week; 

(3) weekly earnings; (4) occupation; and (5) whether has health insurance available 

on the job14

14 For the overall employment and wage progression analyses, the hourly wage rate and 

weekly earnings subgroups were formed using the average wage during the month of job start 

and the subsequent six months. 
 A.25 

Table A.1 displays subgroup definitions and sample sizes by type of analysis. In addition to 

these subgroups, we examined the relationship between a broader set of characteristics and key 

labor market outcomes in our multivariate analysis (as discussed further in the main report). 
A.26 

TABLE A.1 

SUBGROUP DEFINITIONS AND SAMPLE SIZES OF LOW-WAGE WORKERS AND LOW-WAGE JOB SPEL

BY GENDER AND TYPE OF ANALYSIS 

Describing 

Demographic and 

Job Characteristicsa

Overall Employment 

Analysisb

Wage Progression 

Analysisc

Subgroup Males Females Males Females Males FemalesTotal 3,466 5,044 522 817 491 693 Individual and Household Characteristics Age (in Years) 

Younger than 20 172 177 67 56 61 47 20 to 29 1,106 1,256 198 262 189 225 30 to 39 941 1,476 122 240 127 197 40 to 49 687 1,227 71 157 69 145 50 to 59 462 709 39 81 45 79 60 or older 118 199 25 21 

Race/Ethnicity

White and other non-Hispanic 2,401 3,777 379 614 357 523 Black, non-Hispanic 441 726 62 103 51 86 Hispanic 644 541 81 100 83 84 

Educational Attainment

Less than high school/GED 811 890 147 155 131 118 High school/GED 1,460 2,254 212 344 198 300 Some college 586 871 84 138 112 204 College graduate or more 629 1,029 79 180 50 71 

Has a Physical, Mental, or Other Health Condition That Limited the Kind or Amount of Work That Could Be Done 

Yes 300 454 61 83 50 60 No 3,186 4,590 461 734 441 633 

Household Income as a Percentage of the Poverty Level 

100 percent or less 514 665 120 179 121 153 101 to 200 percent 1,137 1,380 150 247 137 199 More than 200 percent 1,835 2,999 252 391 233 341 
TABLE A.1 (continued) 

A.27 

Describing 

Demographic and 

Job Characteristicsa

Overall Employment 

Analysisb

Wage Progression 

Analysisc

Subgroup Males Females Males Females Males Females Household Type

Single parent with children 331 958 60 203 58 173 Married couple with children 1,333 1,931 204 313 203 268 Married couple without children 864 1,220 129 165 112 139 Other adults without children 958 935 129 136 118 113 

Job Characteristics Hourly Wages

Less than $5.00 886 1,383 130 257 114 187 $5.00 to $6.00 816 1,299 130 250 136 223 $6.00 to $7.00 984 1,378 158 208 122 153 $7.00 to $7.50 800 984 104 102 119 130 

Hours Worked per Week 

1 to 19 99 459 40 128 40 113 20 to 34 435 1,252 100 252 82 209 35 to 40 1,750 2,633 280 371 263 318 More than 40 1,202 700 102 66 106 53 

Weekly Earnings

Less than $150 532 1,470 110 332 108 280 

$150 to $299 2,216 3,183 342 454 291 356 $300 to $600 738 391 70 31 92 57 

Occupation

Professional/technical 487 719 36 70 39 72 Sales/retail 396 784 57 136 55 119 Administrative support/clerical 174 999 33 159 31 132 Service professions/handlers/cleaners 1,008 1,805 187 324 167 267 Machinists/construction/production/ 

transportation 1,131 681 151 104 143 83 

Farm/agriculture/other workers 290 56 58 24 56 20 

Health Insurance Coveragee

Yes 1,823 3,350 196 403 179 343 No 1,663 1,694 326 414 312 350 

Source: 1996 SIPP files. Note: All samples exclude those in school and workers younger than age 16 and older than age 64 at the start of their jobs. 
TABLE A.1 (continued) 

A.28 

aThis sample includes low-wage workers in March 1996 with a positive 1996 calendar year weight. bThis sample includes workers who (1) started low-wage jobs during the first six months of the panel period, (2) who have a positive longitu

had at least 38 months of follow-up data. 

cThis sample includes workers who (1) started low-wage jobs during the first six months of the panel period, (2) were employed at some po

later, and (3) had a positive longitudinal panel weight. 

dThis sample includes low-wage employment spells that started during the panel period or were in progress at the start of the panel period (ab

left-censored). The sample includes the spells of only those with positive longitudinal panel weights. A worker can contribute more than one 

eThese figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from oth

variable instead of the employer-based health insurance coverage variable, because data on overall health insurance coverage is avail

employer-based coverage variable pertains only to jobs in progress at the time of the interview. Thus, the employer-based health in

always be linked to the job under investigation, which led to a significant number of missing values. However, the subsets of health iconsiderably: the source of health insurance coverage was the employer for 80 percent of those with any coverage.
APPENDIX B 

SUPPLEMENTARY TABLES TO CHAPTER III

 B.3 

TABLE B.1 

DISTRIBUTION OF INDIVIDUAL AND HOUSEHOLD CHARACTERISTICS OF LOW-, MEDIUM-, 

AND HIGH-WAGE WORKERS IN MARCH 1996, BY GENDER 

(Percentages) 

Male Workersa Female Workersa

 

Characteristics 

LowWage MediumWage HighWage 

LowWage Medium 

Wage 

HighWage 

Individual Characteristics 

Age 

Younger than 20 5 1 0 4 0 0 

20 to 29 34 25 8 27 20 9 

30 to 39 27 33 31 29 31 34 

40 to 49 19 24 37 23 29 37 

50 to 59 12 14 21 14 17 18 

60 or older 3 3 3 4 3 2 

Race/Ethnicity 

White and other non-Hispanic 68 82 90 76 82 86 

Black, non-Hispanic 14 10 6 14 12 10 

Hispanic 18 9 4 10 6 4 

Educational Attainment 

Less than high school/GED 22 11 3 17 5 1 

High school/GED 43 41 22 45 34 14 

Some college 17 19 16 18 22 13 

College graduate or more 18 29 59 21 39 73 

Has a Health Limitation 

No 91 95 96 91 95 97 

Yes 9 5 4 9 5 3 

Marital Status 

Married 46 64 79 56 62 65 

Separated, divorced, widowed 15 14 11 21 21 21 

Single, never married 39 22 10 23 16 14 

Region of Residence 

Northeast 15 19 22 16 21 26 

South 22 26 25 27 25 23 

Midwest 42 36 28 38 34 29 

Northwest 21 19 24 19 20 23 

Lives in a Metropolitan Area 

No 27 26 15 29 20 13 

Yes 73 74 85 71 80 87 
TABLE B.1 (continued) 

 B.4 

 Male Workersa Female Workersa

 

Characteristics 

LowWage MediumWage HighWage 

LowWage Medium 

Wage 

HighWage 

Household Characteristics 

Household Type 

Single adults with children 10 6 3 18 13 9 

Married couples with children 36 42 49 39 36 37 

Married couples without children 26 28 32 25 30 29 

Other adults without children 28 23 16 18 21 24 

Household Size 

1 10 11 9 7 10 13 

2 24 28 29 27 33 35 

3 24 22 20 24 23 21 

4 or more 41 39 42 41 34 31 

Age of the Youngest Child in the 

Household (in Years for Those with 

Children) 

Younger than 3 30 29 24 25 22 23 

3 to 6 20 22 20 22 20 19 

6 to 12 28 32 36 34 36 36 

13 to 18 22 17 20 20 22 22 

Other Employed Adult Lives in the 

Household 

No 32 30 32 27 26 27 

Yes 68 70 68 73 74 73 

Has a Spouse Who Earns (for Those 

Married) 

No 52 32 30 23 15 10 

Yes 48 68 70 77 85 90 

Received Public Assistance in the Past 

Year 

No 96 98 99 96 99 100 

Yes 4 2 1 4 1 0 

In Public or Subsidized Housing 

No 98 99 100 97 99 100 

Yes 2 1 0 3 1 0 

Household Income as a Percentage of the 

Poverty Level 

100 percent or less 14 2 0 12 2 1 

101 to 200 percent 31 15 2 27 10 2 

More than 200 percent 55 83 97 61 88 98 

Sample Size 4,389 7,890 6,841 6,088 7,434 3,495 

Source: SIPP March 1996 cross-sectional sample. 

 

Note: All figures are weighted using the 1996 calendar year weight. 
B.5 

TABLE B.2 

DISTRIBUTION OF THE CHARACTERISTICS OF LOW-WAGE WORKERS BY CLUSTER/TYPOLOGY AND 

(Percentages) 

Male Low-Wage Workersa Female Low-Wa

Characteristic 

Young, 

Single, 

Educated 

Older, MiddleIncome, LowEducation 

Minority, 

Married, LowIncome, LowEducation Total Married, Educated, White Older, Middle- Income, Minority 

Age

Younger than 20 6 3 5 5 3 3 20 to 29 48 14 42 34 33 11 30 to 39 18 36 27 27 23 38 40 to 49 15 25 16 19 23 27 50 to 59 10 17 8 12 15 15 60 or older 3 5 1 3 3 6 

Race/Ethnicity

White and other non-Hispanic 86 93 5 68 96 37 Black, non-Hispanic 8 4 38 14 2 35 Hispanic 6 3 56 18 2 28 

Educational Attainment 

Less than high school/GED 11 22 38 22 9 26 High school/GED 33 55 41 43 43 43 Some college 25 11 13 17 20 16 College graduate or more 30 12 7 18 28 15 

Has a Health Limitation 9 10 7 9 8 9 Lives in a Metropolitan Area 77 65 79 73 71 73 Household Type 

Single adults with children 4 13 15 10 2 15 Married couples with children 20 40 55 36 45 44 Married couples without 

children 27 26 24 26 36 16 

Other adults without children 49 22 6 28 17 25 
TABLE B.2 (continued) 

B.6 

 Male Low-Wage Workersa Female Low-WaCharacteristic 

Young, 

Single, 

Educated 

Older, MiddleIncome, LowEducation 

Minority, 

Married, LowIncome, LowEducation Total Married, Educated, White Older, Middle- Income, Minority 

Has a Spouse Who Earns 24 36 29 29 65 43 Received Public Assistance in the Past Year 1 6 8 4 1 3 Household Income as a Percentage of the Poverty Level 

100 percent or less 4 15 28 14 2 7 101 to 200 percent 3 61 33 31 6 69 More than 200 percent 93 25 39 55 92 24 

Sample Size 1,305 1,299 882 3,486 2,723 1,437 

Source: SIPP March 1996 cross-sectional sample. Note: All figures are weighted using the 1996 calendar year weight. 
 B.7 

TABLE B.3 

DISTRIBUTION OF JOB CHARACTERISTICS OF LOW-, MEDIUM, AND HIGH-WAGE WORKERS 

IN MARCH 1996, BY GENDER 

(Percentages) 

 

Male Workers Female Workers 

Job Characteristics 

LowWage MediumWage HighWage LowWage MediumWage HighWage 

Average Hourly Wage in Dollars 5.62 11.05 26.22 5.54 10.71 22.95 

Usual Hours Worked per Week 

1 to 19 3 1 1 9 4 5 

20 to 34 13 4 2 25 12 12 

35 to 40 51 52 47 52 63 56 

More than 40 34 43 50 14 21 27 

(Average hours worked) 42.9 44.8 45.6 35.2 38.9 39.3 

Average Weekly Earnings in Dollars 240 495 1,217 196 417 898 

Owns Business (Self-Employed) 18 9 12 10 5 7 

Covered by Health Insurancea 41 77 89 57 87 92 

Occupation 

Professional/technical 14 22 51 14 35 71 

Sales/retail 11 10 10 16 9 7 

Administrative support/clerical 5 7 4 20 35 14 

Service professions/handlers/cleaners 30 14 5 36 11 4 

Machinists/construction/production/ 

transportation 32 44 26 13 10 3 

Farm/agricultural/other workers 8 3 2 1 0 0 

Industry 

Agriculture, forestry, fishing, and 

hunting 11 5 7 8 3 6 

Mining/manufacturing/ construction 20 35 31 12 16 12 

Transportation/utilities 5 9 11 2 5 7 

Wholesale/retail trade 27 18 10 31 13 6 

Personal services 12 7 5 12 6 4 

Health services 2 3 3 10 16 22 

Other services 11 18 26 22 38 42 

Other 12 7 7 3 2 2 

Union Member 7 18 27 6 15 22 

Sample Size 4,389 7,890 6,841 6,088 7,434 3,495 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

SIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the 

time of the interview. Thus, the health insurance figures pertain to jobs held by the March 1996 cross-sectional 

sample at the time of their wave 1 interviews. These jobs sometimes differed from the jobs they held in March 1996.
 B.8 

TABLE B.4 

DISTRIBUTION OF JOB CHARACTERISTICS OF LOW-WAGE WORKERS IN MARCH 1996, 

BY TYPOLOGY AND GENDER 

(Percentages) 

Male Low-Wage Workers Female Low-Wage Workers 

Job Characteristics 

Young, 

Single, 

Educated 

Older, MiddleIncome, LowEducation Minority Married, 

Low-Income, 

Low-Education 

Married, 

White 

Educated 

Older, MiddleIncome, 

Minority 

SingleParent, 

Low-Income

Average Hourly Wage in Dollars 5.76 5.49 5.58 5.64 5.48 5.30 

Usual Hours Worked per Week 

1 to 19 3 3 3 10 8 8 

20 to 34 14 12 11 25 22 29 

35 to 40 47 47 62 49 60 52 

More than 40 36 38 24 16 11 10 

(Average hours worked) 43.0 43.8 41.3 35.2 35.7 34.2 

Average Weekly Earnings in Dollars 249 237 230 200 196 183 

Owns Business (Self-Employed) 14 26 11 12 8 7 

Covered by Health Insurancea 46 39 35 67 53 31 

Occupation 

Professional/technical 19 14 6 19 10 7 

Sales/retail 15 12 6 17 13 19 

Administrative support/clerical 6 4 5 23 17 14 

Service professions/handlers/ 

cleaners 29 26 36 30 41 44 

Machinists/construction/ 

production/transportation 27 37 34 10 17 16 

Farm/agricultural/other workers 5 8 13 1 2 1 

Industry 

Agriculture/ forestry/fishing/ 

hunting 10 10 14 10 7 6 

Mining/manufacturing/ 

construction 18 21 22 10 16 12 

Transportation/utilities 5 5 5 2 1 1 

Wholesale/retail trade 32 23 24 31 26 38 

Personal services 12 10 14 11 13 16 

Health services 3 2 2 9 13 10 

Other services 12 10 11 24 21 14 

Other 8 20 8 4 2 2 

Union Member 7 6 7 5 7 5 

Sample Size 1,305 1,299 882 2,723 1,437 884 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

SIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the time of the 

interview. Thus, the health insurance figures pertain to jobs held by the March 1996 cross-sectional sample at the time of their 

wave 1 interviews. These jobs sometimes differed from the jobs they held in March 1996.
 B.9 

TABLE B.5 

DISTRIBUTION OF JOB CHARACTERISTICS OF LOW-WAGE WORKERS IN MARCH 1996 

FOR THOSE IN JOBS AND BUSINESSES, BY GENDER 

(Percentages) 

Male Workersa Female Workersa All Workersa

Characteristics 

Has 

Job 

Owns 

Business 

Has 

Job 

Owns 

Business 

Has 

Job 

Owns 

Business 

Average Hourly Wage in Dollars 5.82 4.73 5.70 4.14 5.75 4.48 

Usual Hours Worked per Week 

1 to 19 3 3 9 15 6 8 

20 to 34 13 13 25 25 20 18 

35 to 40 57 23 55 26 56 24 

More than 40 28 61 11 35 18 50 

(Average hours worked) 41.3 50.0 35.0 37.0 37.6 44.4 

Average Weekly Earnings in Dollars 241 235 201 153 217 200 

Owns Business (Self-Employed) -- 100 -- 100 -- 100 

Covered by Health Insurancea 44 20 59 27 53 23 

Occupation 

Professional/technical 10 34 14 22 12 28 

Sales/retail 10 17 16 15 14 16 

Administrative support/clerical 6 1 22 5 15 3 

Service professions/ 

handlers/cleaners 35 7 34 50 34 25 

Machinists/construction/production/ 

transportation 32 33 14 6 21 22 

Farm/agricultural/other workers 8 9 1 2 4 6 

Sample Size 2,858 628 4,540 504 7,398 1,132 

Source: SIPP March 1996 cross-sectional sample. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

SIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the time of 

the interview. Thus, the health insurance figures pertain to jobs held by the March 1996 cross-sectional sample at the time 

of their wave 1 interviews. These jobs sometimes differed from the jobs they held in March 1996.
APPENDIX C 

SUPPLEMENTARY TABLES TO CHAPTER IV

 C.3 

TABLE C.1 

DISTRIBUTION OF CHARACTERISTICS OF LOW-WAGE WORKERS IN THE ENTRY COHORT 

AND MARCH 1996 CROSS-SECTIONAL SAMPLES, BY GENDER 

(Percentages) 

Male Low-Wage 

Workers

Female Low-Wage 

Workers

All Low-Wage 

Workers

Characteristics 

CrossSection Entry 

Cohort 

CrossSection Entry 

Cohort 

CrossSection Entry 

Cohort

Individual and Household 

Characteristics 

Gender 

Females 0 0 100 100 57 59 

Males 100 100 0 0 43 41 

Age 

Younger than 20 5 13 4 8 4 10 

20 to 29 34 43 27 38 30 40 

30 to 39 27 22 29 26 28 25 

40 to 49 19 13 23 17 21 15 

50 to 59 12 6 14 9 13 8 

60 or older 3 4 4 2 3 3 

Race/Ethnicity 

White and other non-Hispanic 68 72 76 74 73 73 

Black, non-Hispanic 14 14 14 14 14 14 

Hispanic 18 14 10 12 14 12 

Educational Attainment 

Less than high school/GED 22 27 17 18 19 22 

High school/GED 43 43 45 43 44 43 

Some college 17 16 18 17 17 17 

College graduate or more 18 14 21 21 20 19 

Has a Health Limitation 9 12 9 10 9 11 

Household Type 

Single adults with children 10 12 18 25 15 20 

Married couples with children 36 37 39 37 37 37 

Married couples without children 26 24 25 20 25 22 

Other adults without children 28 26 18 18 23 21 

Household Income as a Percentage of 

the Poverty Level 

100 percent or less 14 22 12 21 13 21 

101 to 200 percent 31 29 27 30 29 30 

More than 200 percent 55 49 61 49 59 49 
TABLE C.1 (continued) 

 C.4 

 Male Low-Wage 

Workers

Female Low-Wage 

Workers

 All Low-Wage 

Workers

Characteristics 

CrossSection Entry 

Cohort 

CrossSection Entry 

Cohort 

 CrossSection Entry 

Cohort

Job Characteristics 

Hourly Wages 

Less than $5.00 26 25 27 30 27 28 

$5.00 to $5.99 24 25 26 31 25 28 

$6.00 to $6.99 28 30 27 26 28 28 

$7.00 to $7.50 22 20 20 12 21 15 

(Average hourly wage in dollars) 5.62 5.73 5.54 5.40 5.58 5.53 

Usual Hours Worked per Week 

1 to 19 3 8 9 15 6 12 

20 to 34 13 20 25 30 20 26 

35 to 40 51 54 52 47 52 50 

More than 40 34 19 14 8 22 12 

(Average hours worked) 42.9 37.5 35.6 31.4 38.5 33.9 

Weekly Earnings 

Less than $150 15 22 29 39 23 32 

$150 to $299 64 66 63 57 63 60 

$300 to $600 21 12 8 4 13 8 

(Average weekly earnings in dollars) 240 217 196 172 215 191 

Covered by Health Insurance 41 24 57 34 50 29 

Occupation 

Professional/technical 14 7 14 8 14 7 

Sales/retail 11 11 16 17 14 15 

Administrative support/clerical 5 8 20 20 14 15 

Service professions/ 

handlers/cleaners 30 36 36 39 33 38 

Machinists/construction/production/ 

transportation 32 29 13 13 21 20 

Farm/agricultural/other workers 8 11 1 3 4 6 

Sample Size 3,486 522 5,044 817 8,530 1,339 

Source: SIPP March 1996 cross-sectional sample, and an entry cohort sample of those in the longitudinal panel file who 

started low-wage jobs during the first six months of the panel period. 

Note: Cross-sectional figures are weighted using the 1996 calendar year weight, and entry cohort figures are weighted 

using the longitudinal panel weight. 
 C.5 

TABLE C.2 

EMPLOYMENT RATES AND THE NUMBER OF JOB AND EMPLOYMENT SPELLS DURING 

THE THREE AND ONE-HALF YEARS AFTER JOB START FOR LOW-, MEDIUM-, 

AND HIGH-WAGE WORKERS, BY WAGE TYPE AND GENDER 

Starting Wage Type of the First Job Held in Panel Months 1 to 6 

Male Workers Female Workers 

LowWageMediumWageHighWage

LowWage

MediumWageHighWage

Employment Rates (Percentages) 

Type of Job Ever Held 

High-wage job 14 45 100 5 33 100 

Medium-wage job 72 100 49 54 99 45 

Low-wage job 100 46 14 100 45 21 

Combinations of Jobs Ever Held 

Low-, medium-, and high-wage 12 19 14 4 11 17 

Low- and medium-wage 57 27 0 46 32 0 

Low- and high-wage 1 0 2 1 1 8 

Medium- and high-wage 0 23 36 0 18 28 

Low-wage only 30 0 0 49 0 0 

Medium-wage only 0 31 0 0 39 0 

High-wage only 0 0 49 0 0 47 

Average Number of Job and 

Employment Spells 

Job Spells 3.0 2.6 2.3 2.9 2.3 2.2 

Employment Spells 1.9 1.5 1.4 1.8 1.4 1.4 

Distribution of the Number of Job and 

Employment Spells (Percentages) 

Jobs 

1 24 31 45 23 38 30 

2 22 24 24 26 25 41 

3 21 21 14 21 20 16 

4 or more 33 25 17 29 18 13 

Employment Spells 

1 48 65 79 49 68 68 

2 29 23 10 31 22 26 

3 or more 23 13 11 20 9 6 

Sample Size 521 545 258 814 464 125 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started jobs within six 

months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 
 C.6 

TABLE C.3 

TIME SPENT IN LABOR ACTIVITIES DURING THE THREE AND ONE-HALF YEARS AFTER JOB START 

FOR LOW-, MEDIUM-, AND HIGH-WAGE WORKERS, BY WAGE TYPE AND GENDER 

(Percentages) 

Starting Wage Type of the First Job Held in Panel Months 1 to 6 

Male Workers Female Workers 

 LowWage

MediumWageHighWageLowWageMediumWageHighWage

Average Percentage of Months Spent 

in Labor Market Activities 

All Jobs 83 92 93 76 88 89 

Low-wage jobs 55 11 4 58 11 5 

Medium-wage jobs 26 69 15 17 68 15 

Higher-wage jobs 3 12 74 1 10 70 

Unemployment 7 4 2 5 2 3 

Not in the Labor Force 10 5 5 19 10 8 

Distribution of the Percentage of Time 

Spent in Labor Market Activities 

All Jobs 

0 to 25 5 2 3 10 4 5 

25 to 50 6 2 2 11 5 2 

50 to 75 13 6 3 14 6 6 

75 to 99 36 31 18 30 30 28 

100 40 59 75 35 56 58 

Low-Wage Jobs 

0 to 25 20 84 94 20 85 94 

25 to 50 25 10 4 22 9 1 

50 to 75 24 5 2 21 5 5 

75 to 99 21 2 0 22 2 0 

100 10 0 0 15 0 0 

Medium-Wage Jobs 

0 to 25 59 10 80 74 14 79 

25 to 50 19 18 9 11 16 9 

50 to 75 14 20 6 12 18 6 

75 to 99 9 32 5 3 28 7 

100 0 20 0 0 24 0 

High-Wage Jobs 

0 to 25 96 83 13 98 83 17 

25 to 50 3 10 12 1 8 11 

50 to 75 2 6 8 1 7 15 

75 to 99 0 2 29 0 1 24 

100 0 0 37 0 0 33 
TABLE C.3 (continued) 

 C.7 

Starting Wage Type of the First Job Held in Panel Months 1 to 6 

Male Workers Female Workers 

 LowWage

MediumWageHighWage

LowWage

MediumWageHighWage

Unemployment 

0 to 25 93 97 98 96 98 99 

25 to 50 6 2 2 4 2 1 

50 to 75 1 0 0 1 0 0 

75 to 99 1 0 0 0 0 0 

Not in the Labor Force 

0 to 25 87 94 95 72 87 90 

25 to 50 8 4 2 13 5 3 

50 to 75 2 2 1 8 4 3 

75 to 99 3 1 2 8 4 4 

Average Number of Hours Per Week 

Worked 

All Jobs 33 39 41 26 33 33 

Low-Wage Jobs 21 5 2 20 4 1 

Medium-Wage Jobs 11 29 7 6 25 6 

High-Wage Jobs 1 5 33 0 4 26 

Sample Size 521 545 258 814 464 125 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started jobs within six 

months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the longitudinal panel weight. 
 C.8 

TABLE C.4 

MULTIVARIATE ANALYSIS FINDINGS FOR ADDITIONAL OVERALL EMPLOYMENT MEASURES 

DURING THE 42-MONTH PERIOD, BY GENDER 

Regression-Adjusted Means for the Denoted Dependent Variable 

Males Females

Explanatory Variable 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Individual Characteristics 

Age 

Younger than 20+ 81 57 65 65 52 80 

20 to 29 86 54 49* 75** 54 72 

30 to 39 87 53 51 77*** 58 70 

40 to 49 83 52 60 85*** 66*** 71 

50 to 59 79 62 71 85*** 66*** 74 

60 or older 71 58 78 74 58 74 

Race/Ethnicity 

White and other non-Hispanic+ 87 55 51 78 58 71 

Black, non-Hispanic 72*** 49 70** 73* 57 73 

Hispanic 84 60 63 75 60 81 

Educational Attainment 

Less than high school/GED+ 81 54 61 71 56 80 

High school/GED 86* 56 53 80*** 63** 76 

Some college 83 52 54 76 54 68* 

College graduate or more 88* 57 53 78* 54 66* 

Has a Health Limitation 

No+ 86 55 53 79 59 72 

Yes 72*** 52 71** 62*** 48*** 81 

Household Characteristics 

Household Type 

Single adults with children+ 82 51 52 80 61 72 

Married couples with children 87 55 49 75* 58 76 

Married couples without children 82 54 59 75 58 73 

Other adults without children 84 57 62 79 55 63 

Household Income as a Percentage of 

the Poverty Level 

100 percent or less+ 88 57 51 79 58 71 

101 to 200 percent 85 55 55 72* 53 72 

More than 200 percent 83* 53 58 79 61 73 

Received Public Assistance in the Past 

Year 

No+ 85 55 55 78 59 72 

Yes 80 56 64 71** 53 73 
TABLE C.4 (continued) 

 C.9 

 Regression-Adjusted Means for the Denoted Dependent Variable 

Males Females

Explanatory Variable 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Area Characteristics 

Region of Residence 

Northeast+ 87 57 55 83 54 62 

South 79* 54 61 79 60 72 

Midwest 86 56 56 77 62 77* 

Northwest 83 51 49 71*** 53 73 

Lives in a Metropolitan Area 

No 80 54 64 80 63 76 

Yes 86** 55 52* 76* 56** 71 

20th Percentile of the Hourly Wage 

Distribution in State 

$250 or less+ 83 54 58 77 59 74 

$251 to $269 83 49 51 79 59 68 

$270 or more 87 57 54 77 57 73 

Percentage of State Population 

Residing in Metropolitan Areas 

72 or less+ 86 58 55 79 57 67 

73 to 84 86 54 51 76 58 76* 

85 or more 80 52 60 76 59 76 

Poverty Rate in State 

Less than 10 percent+

 85 56 56 74 59 76 

10 to 12 percent 83 52 53 78 57 71 

More than 12 percent 85 56 57 79 59 71 

Unemployment Rate in State 

6 percent or less+

 86 56 52 74 59 79 

More than 6 percent 84 54 56 78 58 70 

Change in Unemployment Rate in 

State of Residence Between 1996 and 

1999 (Percentage Points) 

-2 percentage points or less+

 86 59 68 89 69 73 

-1 to –2 83 54 54 76** 56** 71 

More than –1 85 54 51 75** 58 76 

Initial Job Characteristics 

Hourly Wages 

Less than $5.00+ 83 62 66 69 57 84 

$5.00 to $5.99 82 62 69 80*** 65*** 80 

$6.00 to $6.99 88 50*** 44*** 81*** 56 64*** 

$7.00 to $7.50 83 42*** 42*** 83*** 48* 51*** 

Usual Hours Worked per Week 

1 to 19+ 79 54 71 70 56 79 

20 to 34 82 55 60 78** 57 71 

35 to 40 86* 56 54 80*** 60 71 

More than 40 84 50 49* 73 54 73 
TABLE C.4 (continued) 

 C.10 

 Regression-Adjusted Means for the Denoted Dependent Variable 

Males Females

Explanatory Variable 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Percentage 

of Months 

in All Jobs 

Percentage 

of Months in 

Low-Wage 

Jobs 

Percentage in 

Higher-Wage 

Jobs for Less 

than 25 Percent 

of Months 

Has More than One Job or Business 

No+ 83 54 56 76 57 72 

Yes 92*** 60 52* 84*** 65** 73 

Owns Business (Self-Employed) 

No+ 84 56 57 77 58 73 

Yes 88 41*** 30** 89** 61 62 

Covered by Health Insurancea

No+ 83 56 60 79 61 74 

Yes 87* 52 46** 76 55** 71 

Union Member 

No+ 85 55 55 77 58 72 

Yes 72** 45 63 73 54 72 

Occupation 

Professional/technical+ 91 61 56 81 58 66 

Sales/retail 86 54 48 77 55 69 

Administrative support/ clerical 85 56 55 75 53 67 

Service professions/ 

handlers/cleaners 83 56 60 79 62 75 

Machinists/construction/ 

production/transportation 85 51 48 72* 59 85*** 

Farm/agricultural/other workers 80* 55 66 81 59 74 

Regression R2

 Value .21 .17 NA .18 .15 NA 

Sample Size 522 522 522 817 817 817 

Source: 1996 SIPP longitudinal files using the entry cohort sample of workers who started low-wage jobs within six months after the start 

of the panel period. All workers were followed for 42 months after job start. 

 

Note: All figures are weighted using the 1996 calendar year weight, and standard errors account for design effects due to weighting and 

clustering. 

a

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from other 

sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall health insurance 

coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at the time of the interview. 

Thus, the employer-based health insurance variable could not always be linked to the job under investigation, which led to a significant 

number of missing values. However, the subsets of health insurance variables overlap considerably: the source of health insurance 

coverage was the employer for 80 percent of those with any coverage.

+ Denotes the “left-out” explanatory variable in the regression model. 

 *Difference between the variable mean and the mean of the “left-out” explanatory variable is significantly different from zero at the .10 

level, two-tailed test. 

**Difference between the variable mean and the mean of the “left-out” explanatory variable is significantly different from zero at the .05 

level, two-tailed test. 
APPENDIX D 

SUPPLEMENTARY TABLES TO CHAPTER V

 D.3 

TABLE D.1 

DISTRIBUTION OF INITIAL DEMOGRAPHIC AND JOB CHARACTERISTICS OF LOW-WAGE WORKERS 

EMPLOYED THREE YEARS LATER COMPARED WITH THOSE 

NOT EMPLOYED THREE YEARS LATER 

(Percentages) 

 

Male Low-Wage Workers Female Low-Wage Workers

Characteristics 

Employed 

at Both 

Periods 

Not Employed 

Three Years 

Later 

Employed 

at Both 

Periods 

Not Employed 

Three Years 

Later 

Individual and Household Characteristics 

Gender 

Females 0 0 80 20 

Males 88 12 0 0 

Age 

Younger than 20 13 8 8 7 

20 to 29 43 33 39 37 

30 to 39 24 15 25 29 

40 to 49 13 15 18 13 

50 or older 8 28 9 15 

(Average age) (31.0) (37.7) (33.3) (34.1) 

Race/Ethnicity 

White and other non-Hispanic 72 68 75 74 

Black, non-Hispanic 13 25 14 13 

Hispanic 15 7 11 13 

Educational Attainment 

Less than high school/GED 26 27 16 25 

High school/GED 43 42 45 35 

Some college 16 22 17 21 

College graduate or more 16 9 22 20 

Has a Health Limitation 10 31 8 19 

Household Type 

Single adults with children 13 11 25 24 

Married couples with children 39 20 38 38 

Married couples without children 23 39 20 22 

Other adults without children 26 30 17 16 

Household Income as a Percentage of the Federal 

Poverty Level 

100 percent or less 23 14 22 18 

101 to 200 percent 28 38 28 38 

More than 200 percent 49 48 51 45 
TABLE D.1 (continued) 

 D.4 

 Male Low-Wage Workers Female Low-Wage Workers

Characteristics 

Employed 

at Both 

Periods 

Not Employed 

Three Years 

Later 

Employed 

at Both 

Periods 

Not Employed 

Three Years 

Later 

Job Characteristics 

Hourly Wages 

Less than $5.00 26 29 30 44 

$5.00 to $5.99 25 26 31 25 

$6.00 to $6.99 31 21 26 23 

$7.00 to $7.50 18 25 13 9 

(Average hourly wage in dollars) ($5.72) ($5.47) ($5.48) ($4.95) 

Usual Hours Worked per Week 

1 to 19 8 12 16 19 

20 to 34 17 33 29 32 

35 to 40 54 38 47 40 

More than 40 21 18 8 10 

(Average hours worked) (38.2) (35.0) (31.5) (38.5) 

Weekly Earnings 

Less than $150 21 33 39 50 

$150 to $299 65 54 56 48 

$300 or more 13 12 4 2 

(Average weekly earnings in dollars) ($220) ($194) ($176) ($154) 

Occupation 

Professional/technical 8 6 10 6 

Sales/retail 11 9 18 14 

Administrative support/clerical 8 5 19 17 

Service professions/ handlers/cleaners 34 38 38 45 

Machinists/construction/production/ 

transportation 28 34 12 16 

Farm/agricultural/other workers 11 9 3 3 

Sample Size 491 67 693 170 

Source: SIPP March 1996 cross-sectional sample, and an entry cohort sample of those in the longitudinal panel file who 

started low-wage jobs during the first six months of the panel period. 

Note: Figures are weighted using the longitudinal panel weight. 
D.5 

TABLE D.2 

AVERAGE REAL WAGES OVER TIME AMONG ALL JOB STARTERS, BY WAGE TYPE 

(In Dollars) 

Low Wage Medium Wage High Wage 

Males Females Males Females Males Females 

Six-Month Period from Time of Job Start 

1 7.06 6.49 11.58 11.13 22.79 22.69 2 7.74 6.96 11.94 11.68 22.43 21.57 3 8.31 7.10 12.19 11.97 22.91 21.48 4 8.87 7.59 12.45 12.22 23.20 22.06 5 8.94 7.91 12.86 12.40 22.58 23.14 6 8.94 8.04 13.30 12.56 23.11 22.23 

Sample Sizes 491 to 558 687 to 863 541 to 571 420 to 481 270 to 286 122 to 138 130

Source: 1996 SIPP longitudinal files. 

aSample sizes are usually highest in period 0 and usually decrease as time from job start increases. 
D.6 

TABLE D.3 

REAL WAGES RELATIVE TO POVERTY, AT THE TIME OF THE FOLLOW-UP PERIOD, 

BY WAGE TYPE AND GENDER 

(Percentages) 

Low Wage Medium Wage 

Males Females Males Females M

Full-Time Earnings as a Percentage of Federal Poverty Levela

Less than 50 percent 4 4 2 2 50 to 100 percent 43 55 13 12 101 to 150 percent 40 33 33 41 151 to 200 percent 8 5 29 28 201 to 250 percent 3 1 14 10 More than 250 percent 2 1 9 7 

Sample Sizes 491 693 541 420 

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of the panel period. 

Note: All figures were calculated using the longitudinal panel weight. 

aRefers to federal poverty level for a family of three. 
D.7 

TABLE D.4 

GROWTH IN REAL HOURLY WAGES OVER THREE YEARS, BY WORKER TYPE 

Low-Wage Workers Medium-Wage Workers H

Male Female Male Female M

Percentage Employed in Both Periodsa 82 74 92 85 93 Percentage Whose Wages:

Increased 78 81 68 73 59 Decreased 22 20 32 28 41 

Percentage Change in Wages

More than 50 percent 26 20 17 13 9 26 to 50 percent 21 22 18 15 9 11 to 25 percent 17 21 18 23 18 1 to 10 percent 14 17 15 22 23 –1 to –10 percent 9 9 12 11 16 –11 to –25 percent 6 6 9 8 11 –26 to –50 percent 3 2 8 6 6 Less than –50 percent 4 2 3 3 8 

Change in Real Wages Over Time (in Dollars) 

More than $5.00 14 9 16 12 17 $2.51 to $5.00 21 15 22 19 14 $1.01 to $2.50 21 29 16 21 13 $0 to $1.00 21 27 15 21 14 $0 to –$1.00 11 11 11 10 7 –$1.01 to –$2.50 6 6 7 8 8 –$2.51 to –$5.00 3 2 9 6 11 Less than –$5.00 3 2 5 4 16 

Percentage Whose Job Was: 

Low wage 47 60 14 13 5 Medium wage 48 38 62 69 20 High wage 5 2 23 18 75 

Sample Size 460 to 481 636 to 693 529 to 641 409 to 420 256 t

Source: 1996 SIPP longitudinal file using workers who started low-wage jobs within six months after the start of the panel period. 

Note: All figures were calculated using the longitudinal panel weight. 

aRefers to the average wages during period 1, the first six-month wage average after the base period used to categorize workers into wage type

wage three years later. 
D.8 

TABLE D.5 

DISTRIBUTION OF JOB CHARACTERISTICS ACROSS INITIAL JOB AND MOST RECENT JOB THREE AND HALF YEARS LATE

LOW-, MEDIUM, AND HIGH-WAGE WORKERS, BY GENDER 

(Percentages) 

Male Workers Female Wor

Low Wage Medium Wage High Wage Low Wage Medium W

Job Characteristics 

Initial

Job 

Most 

Recent Job 

Initial

Job 

Most 

Recent Job 

Initial

Job 

Most 

Recent Job 

Initial

Job 

Most 

Recent Job 

Initial

Job 

M

Rece

Usual Hours Worked per Week 

1 to 19 8 5 4 2 4 2 16 10 9 20 to 34 17 10 10 5 3 4 30 20 20 135 to 40 54 60 49 58 50 49 46 62 55 6More than 40 22 26 37 36 43 46 8 8 16 1(Average hours worked) (38) (41) (42) (43) (44) (44) (31) (35) (36) (3

Owns Business (Self-Employed) 9 8 7 7 12 12 6 5 5 Covered by Health Insurancea 24 52 46 74 77 89 34 65 64 8

Occupation

Professional/technical 8 11 18 20 48 52 10 15 34 3Sales/retail 11 10 11 12 11 12 17 14 8 Administrative support/clerical 6 6 9 7 5 4 19 22 35 3Service professions/handlers/ 

cleaners 34 31 17 16 8 6 39 34 15 1

Machinists/construction/production/ 

transportation 29 36 41 42 26 26 12 13 8 

Farm/agricultural/other workers 11 6 4 4 2 0 3 2 1 

Industry

Agriculture, forestry, fishing, and hunting 11 8 6 5 6 6 8 6 4 Mining/manufacturing/ construction 21 26 37 35 34 32 11 14 13 1Transportation/utilities 6 7 7 7 9 8 2 4 5 Wholesale/retail trade 30 25 16 19 10 11 31 26 13 1Personal services 14 12 12 9 10 9 20 12 15 1Health services 2 2 2 2 3 3 8 11 16 1Other services 11 15 16 17 21 25 20 27 32 3Other 6 5 5 6 6 6 1 1 1 

Union Member 3 8 8 12 24 26 2 4 6 

Sample Size 491 491 541 541 270 270 693 693 420 42

Source: 1996 SIPP longitudinal files using workers who started jobs within six months after the start of the panel period. Note: All figures are weighted using longitudinal panel weight. Sample includes individuals who started jobs at the start of the panel period and who held jobs threaSIPP contains information on employer-based health insurance coverage only for jobs that were in progress at the time of the interview. Thus, the health insurance fi

held by sample members at the time of the wave 1 and the wave 12 interviews.
 D.9 

TABLE D.6 

MULTIVARIATE ANALYSIS FINDINGS ON THE PERCENTAGE OF LOW-WAGE WORKERS SWITCHING 

TO A MEDIUM- OR HIGH-WAGE JOB AND THE PERCENTAGE OF LOW-WAGE WORKERS 

EXPERIENCING AT LEAST A 50 PERCENT INCREASE IN WAGES BY THE 

END OF THE FOLLOWUP PERIOD, BY GENDER 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

Switched to Medium or 

High-Wage Job 

Experienced 50 Percent 

Increase in Wages 

Explanatory Variable Males Females Males Females 

Individual Characteristics 

Age 

Younger than 20a 39 39 19 23 

20 to 29 58** 43 27 19 

30 to 39 58** 43 25 20 

40 to 49 52 32 25 18 

50 or older 34 38 32 32 

Race/Ethnicity 

White and other non-Hispanica 55 41 25 20 

Black, non-Hispanic 43* 38 29 25 

Hispanic 49 39 27 17 

Educational Attainment 

Less than high school/GEDa 47 34 18 15 

High school/GED 51 36 23 17 

Some college 61* 48** 32* 27* 

College graduate or more 58 47* 38** 26 

Has a Health Limitation 

Noa 53 40 26 20 

Yes 51 41 21 28 

Household Characteristics 

Household Type 

Single adults with childrena 61 41 27 23 

Married couples with children 55 41 28 24 

Married couples without children 49 30* 21 14* 

Other adults without children 50 50 26 19 

Household Income as a Percentage of the Federal 

Poverty Level 

100 percent or lessa 55 43 34 17 

101 to 200 percent 49 36 24* 18 

More than 200 percent 54 42 24 23 

Received Public Assistance in the Past Year 

Noa 54 41 27 20 

Yes 45 39 18* 21 
TABLE D.6 (continued) 

 D.10 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

Switched to Medium or 

High-Wage Job 

Experienced 50 Percent 

Increase in Wages 

Explanatory Variable Males Females Males Females 

Area Characteristics 

Region of Residence 

Northeasta 56 37 29 21 

South 53 43 26 27 

Midwest 50 39 16** 20 

West 53 42 33 13 

Lives in a Metropolitan Area 

No 46 35 16 17 

Yes 56* 43* 30** 22 

20th Percentile of the Weekly Wage Distribution 

in State 

$250 or lessa 51 41 22 21 

$251 to $269 54 44 31 23 

$270 or more 55 38 27 19 

Percentage of State Population Residing in 

Metropolitan Areas 

72 percent or lessa 52 41 29 20 

73 to 84 percent 60 32** 29 16 

85 percent or more 47 49 20 25 

Poverty Rate in State 

Less than 10 percenta 52 47 20 23 

10 to 12 percent 59 45 31 20 

More than 12 percent 49 30** 26 19 

Unemployment Rate in State 

6 percent or lessa 50 42 25 22 

More than 6 percent 61 37 27 15 

Change in Unemployment Rate in State of 

Residence Between 1996 and 1999 (Percentage 

Points) 

–2 percentage points or lessa 46 41 14 31 

–1 to –2 percentage points 51 39 28 20 

More than –1 percentage point 59 44 28 19 

Initial Job Characteristics 

Hourly Wages 

Less than $5.00a 40 30 34 28 

$5.00 to $5.99 39 35 29 18** 

$6.00 to $6.99 62** 51** 20** 21 

$7.00 to $7.50 72** 52** 21** 12** 

Usual Hours Worked per Week 

1 to 19a 39 33 19 14 

20 to 34 53 45** 32 24* 

35 to 40 55* 40 24 20 

More than 40 54* 40 27 28 

Has More than One Job or Business 

Noa 52 39 24 19 

Yes 59 47 34 30* 
TABLE D.6 (continued) 

 D.11 

Regression-Adjusted Means for Models with Demographic 

and Other Denoted Explanatory Variables 

Switched to Medium or 

High-Wage Job 

Experienced 50 Percent 

Increase in Wages 

Explanatory Variable Males Females Males Females 

Owns Business (Self-Employed) 

Noa 51 39 25 20 

Yes 71** 72** 39 33 

Covered by Health Insuranceb

Noa 50 38 26 21 

Yes 59* 43 26 20 

Union Member 

Noa 53 40 26 20 

Yes 62 57 17 33 

Occupation 

Professional/technicala 49 38 21 21 

Sales/retail 54 47 29 24 

Administrative support/clerical 64 49 36 19 

Service professions/handlers/cleaners 46 34 23 17 

Machine/construction/production/ 

transportation 60 36 27 27 

Farm/agricultural/other workers 49 56 27 29 

Industry 

Agriculture/forestry/fishing 

and huntinga 48 15 21 8 

Mining/manufacturing/construction/ 

transportation and warehousing/utilities 54 40** 29 16 

Wholesale/retail trade 54 43** 22 25* 

Services/other 52 44** 28 21 

Type of Worker 

Continuous worker with only one employer/ 

business 51 35 20 11 

Continuous worker with more than one 

employer/business 55 43 24 18 

Intermittent worker, employed less than 75% 

of time 36/** 37 27 23* 

Intermittent worker, employed 75% or more 

of time 59 43* 29 26** 

Regression R2

 Value 

Sample Size 491 693 491 693 

Source: 1996 SIPP longitudinal and wave 1 topical module files using the entry cohort sample of workers who started low-wage jobs 

within six months after the start of the panel period. All workers were followed for 42 months after job start. 

Note: All figures are weighted using the 1996 calendar year weight. 

a

Denotes the “left-out” explanatory variable in the regression model. 

b

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well as from other 

sources. We used this variable instead of the employer-based health insurance coverage variable, because data on overall health 

insurance coverage is available monthly, whereas the employer-based coverage variable pertains only to jobs in progress at the time of 

the interview. Thus, the employer-based health insurance variable could not always be linked to the job under investigation, which led to 

a significant number of missing values. However, the subsets of health insurance variables overlap considerably: the source of health 

insurance coverage was the employer for 80 percent of those with any coverage. 

 *Difference between the variable mean and the mean of the “left-out” explanatory variable is significantly different from zero at the .10 

level, two-tailed test. 

**Difference between the variable mean and the mean of the “left-out” explanatory variable is significantly different from zero at the .05 

level, two-tailed test. 
APPENDIX E 

SUPPLEMENTARY TABLES TO CHAPTER VI

 E.3 

TABLE E.1 

JOB SPELL INFORMATION 

Male Workers Female Workers 

Wage Type at Start of Spell Wage Type at Start of Spell 

LowWage 

MediumWage HighWage 

LowWage 

MediumWage HighWage 

Job Spells of the Same Wage Type 

Total Number of Spells 6,373 9,211 6,182 10,259 8,697 3,234 

Number of Spells per Worker 

(Percentages) 

1 62 69 77 58 73 79 

2 22 20 16 23 19 15 

3 9 7 4 10 6 4 

4 or more 7 4 3 8 3 2 

(Average number) (1.7) (1.5) (1.4) (1.8) (1.4) (1.3) 

Percentage of Spells That Are: 

Right-censored 18 26 37 20 30 35 

Left-censored 29 46 58 28 48 57 

Right- and left-censored 4 10 20 3 11 18 

Mean Observed Spell Duration 

(Months)a

Non-left-censored spells 7 10 11 8 10 10 

All spells 25 55 98 25 59 89 

Job Spells of Any Wage Type 

Total Number of Spells 6,170 8,871 5,895 10,057 8,369 3,073 

Number of Spells per Worker 

(Percentages) 

1 61 69 77 58 72 79 

2 22 20 16 23 19 15 

3 10 7 4 10 6 4 

4 or more 7 4 3 9 3 2 

(Average number) (1.7) (1.5) (1.4) (1.8) (1.4) (1.3) 

Percentage of Spells That Are: 

Right-censored 32 49 60 32 51 58 

Left-censored 28 45 58 27 47 56 

Right- and left-censored 10 24 36 9 24 34 

Mean Observed Spell Duration 

(Months)a

Non-left-censored spells 10 13 14 10 13 14 

All spells 29 62 106 28 65 96 
TABLE E.1 (continued) 

_________________________________________________________________________________________________ 

E.4 

Source: 1996 SIPP longitudinal files using the entry cohort sample. 

Note: All figures are unweighted. 

a

Figures pertain to the mean spell length observed during the panel period, including spells that are still in progress at the 

end of the period (that is, right censored spells). Thus, the figures are shorter than the ultimate mean lengths of the spells. 
 E.5 

TABLE E.2 

EMPLOYMENT SPELL INFORMATION 

Male Workers Female Workers 

Wage Type at Start of Spell Wage Type at Start of Spell 

Low-Wage 

MediumWage HighWage 

LowWage 

MediumWage HighWage 

Continuous Employment Spells of the 

Same Wage Type 

Total Number of Spells 4,882 7,285 4,545 7,755 7,130 2,714 

Number of Spells per Worker 

(Percentages) 

1 75 83 89 73 84 89 

2 18 14 9 20 13 9 

3 5 3 1 5 2 1 

4 or more 2 1 1 2 1 0 

(Average number) (1.3) (1.2) (1.1) (1.4) (1.2) (1.1) 

Percentage of Spells That Are: 

Right-censored 22 30 42 25 33 37 

Left-censored 38 58 70 36 58 68 

Right-and left-censored 6 15 29 6 16 24 

Mean Observed Spell Duration 

(Months)a

Non-left-censored spells 8 11 12 10 11 10 

All spells 31 69 118 32 71 105 

Continuous Employment Spells of 

Any Wage Type 

Total Number of Spells 3,943 5,635 4,048 6,832 5,679 2,119 

Number of Spells per Worker 

(Percentages) 

1 77 88 93 74 88 92 

2 16 10 5.9 19 10 7 

3 5 2 1 5 1 1 

4 or more 2 0 0 2 0 0 

(Average number) (1.3) (1.1) (1.1) (1.3) (1.1) (1.1) 

Percentage of Spells That Are: 

Right-censored 53 71 79 48 68 75 

Left-censored 42 69 81 39 67 80 

Right-and left-censored 27 52 65 21 47 61 

Mean Observed Spell Duration 

(Months)a

Non-left-censored spells 13 17 17 13 16 17 

All spells 60 95 150 41 94 137 

Source: 1996 SIPP longitudinal files using the entry cohort sample. 

Note: All figures are unweighted. 

a

Figures pertain to the mean spell length observed during the panel period, including spells that are still in progress at the end of the 

period (that is, right censored spells). Thus, the figures are shorter than the ultimate mean lengths of the spells. 
 E.6 

TABLE E.3 

CUMULATIVE EXIT RATES FROM JOB SPELLS, BY WAGE LEVEL AND GENDER 

(Percentages) 

Male Workers Female Workers 

Wage Type at Start of Spell Wage Type at Start of Spell 

LowWage

MediumWageHighWageLowWageMediumWageHighWage

Job Spells of the Same Wage Type 

Number of Months After Job Start 

4 51 39 37 46 36 37 

8 73 57 52 65 52 53 

12 81 68 61 76 63 63 

16 87 74 66 83 69 68 

20 90 79 69 87 75 73 

24 92 82 73 90 78 78 

28 94 85 76 92 82 79 

32 95 88 79 93 84 81 

36 96 90 81 94 86 83 

40 97 91 82 95 89 85 

44 97 92 83 96 90 85 

Job Spells of Any Wage Type 

Number of Months After Job Start 

4 39 25 21 39 22 21 

8 59 40 33 56 36 34 

12 68 51 41 66 46 43 

16 74 58 47 72 52 48 

20 78 63 50 77 58 55 

24 81 67 55 80 62 59 

28 83 69 59 82 66 62 

32 85 73 62 84 70 66 

36 86 75 64 86 72 69 

40 88 77 64 87 75 71 

44 89 77 68 89 80 73 

Including Left-Censored Spells 

4 38 24 20 37 21 21 

8 57 39 32 54 35 33 

12 65 49 40 64 45 42 

16 72 56 45 71 51 47 

20 77 60 49 75 56 54 

24 80 64 54 79 60 59 

28 82 67 57 82 65 63 

32 84 70 60 83 67 66 

36 86 73 62 85 71 69 

40 88 75 64 87 73 71 

44 89 77 67 88 75 73 

48 90 78 68 89 76 74 

52 to 104 97 90 83 97 89 88 

105 to 156 98 94 90 98 94 93 

157 to 208 99 96 93 99 96 95 

208 to 260 100 97 95 100 98 96 

Source: 1996 SIPP longitudinal files using the entry cohort sample. All figures are weighted using the longitudinal panel 

weight. 
 E.7 

TABLE E.4 

CUMULATIVE EXIT RATES FROM EMPLOYMENT SPELLS, BY WAGE LEVEL AND GENDER 

(Percentages) 

 

Male Workers Female Workers 

Wage Type at Start of Spell Wage Type at Start of Spell 

LowWageMediumWageHighWageLowWageMediumWageHighWage

Continuous Employment Spells of 

the Same Type 

Number of Months After Start of Spell 

4 44 35 37 39 33 40 

8 65 52 52 57 49 54 

12 74 62 60 68 58 64 

16 82 68 64 75 63 68 

20 86 73 68 80 69 73 

24 88 77 70 84 72 76 

28 90 79 72 87 76 78 

32 92 82 74 89 78 79 

36 94 83 76 90 80 82 

40 95 85 78 91 83 83 

44 96 86 80 93 85 83 

Continuous Employment Spells of 

Any Wage Type 

Number of Months After Start of Spell 

4 26 16 15 28 13 13 

8 42 27 25 42 23 22 

12 51 34 30 52 31 28 

16 57 39 34 58 35 33 

20 61 42 36 62 40 36 

24 64 46 38 66 43 39 

28 66 48 40 68 46 40 

32 69 51 41 71 50 43 

36 71 52 44 72 51 45 

40 72 53 45 75 54 48 

44 74 54 46 78 57 48 

Including Left-Censored Spells 

4 24 15 14 25 11 12 

8 39 24 23 39 20 20 

12 46 30 27 48 29 27 

16 52 34 30 54 33 30 

20 56 37 33 58 37 33 

24 59 40 35 62 40 36 

28 61 42 37 65 43 40 

32 63 44 37 67 45 42 

36 65 46 39 69 48 45 

40 67 48 40 72 50 47 

44 68 49 41 73 51 48 

48 70 50 43 74 53 50 

52 to 104 81 65 54 85 69 64 

105 to 156 85 72 63 91 77 71 

157 to 208 89 77 69 94 82 76 

208 to 260 94 82 75 97 87 81 

Source: 1996 SIPP longitudinal files using the entry cohort sample. All figures are weighted using the longitudinal panel 

weight. 
 E.8 

TABLE E.5 

CUMULATIVE EXIT RATES FROM EMPLOYMENT SPELLS AMONG MALE LOW-WAGE WORKERS, 

BY SUBGROUP 

 

 

Cumulative Exit Rates for Males 

Subgroup 

4 Months 

or Less 

(Percentages) 

12 Months 

or Less 

(Percentages) 

24 Months 

or Less 

(Percentages) 

Log-Rank Statistic

to Test Differences

Across Subgroups 

Overall 26 51 64 

Individual and Household 

Characteristics 

Age (in Years) 21*** 

Younger than 20 34 64 79 

20 to 29 26 51 66 

30 to 39 22 48 57 

40 to 49 25 44 55 

50 to 59 18 40 56 

60 or older 23 43 63 

Race/Ethnicity 6** 

White and other non-Hispanic 25 48 61 

Black, non-Hispanic 31 59 72 

Hispanic 22 51 66 

Educational Attainment 14** 

Less than high school/GED 29 58 74 

High school/GED 28 50 64 

Some college 21 44 57 

College graduate or more 18 43 53 

Has a Health Limitation 13*** 

Yes 40 65 77 

No 24 49 62 

Household Type 8** 

Single parent with children 35 59 70 

Married couple with children 24 49 64 

Married couple without children 22 44 59 

Other adults without children 28 55 66 

Household Income as a Percentage 

of the Poverty Level 3 

100 percent or less 28 54 68 

101 to 200 percent 26 54 66 

More than 200 percent 25 47 61 
TABLE E.5 (continued) 

 E.9 

Cumulative Exit Rates for Males 

Subgroup 

4 Months 

or Less 

(Percentages) 

12 Months 

or Less 

(Percentages) 

24 Months 

or Less 

(Percentages) 

Log-Rank Statistic

to Test Differences

Across Subgroups 

Job Characteristics 

Hourly Wages 2 

Less than $5.00 24 53 69 

$5.00 to $5.99 27 50 67 

$6.00 to $6.99 25 52 62 

$7.00 to $7.50 27 50 59 

Hours Worked per Week 6 

1 to 19 30 60 73 

20 to 34 29 55 69 

35 to 40 25 49 61 

More than 40 24 45 63 

Weekly Earnings 5* 

Less than $150 28 57 72 

$150 to $299 26 49 62 

$300 to $600 24 48 62 

Owns Business 3* 

Yes 12 31 45 

No 26 51 65 

Covered by Health Insurancea 4* 

Yes 24 45 60 

No 27 54 66 

Occupation 11* 

Professional/technical 18 40 48 

Sales/retail 14 39 55 

Administrative support/clerical 24 48 61 

Service professions/ 

handlers/cleaners 29 54 67 

Machine/construction/ 

production/transportation 26 50 66 

Farm/agricultural/other workers 28 59 69 

Industry 3 

Agriculture/forestry/ 

fishing/hunting 27 59 69 

Mining/manufacturing/ 

construction/ 

transportation/utilities 28 50 62 

Wholesale/retail trade 23 48 65 

Personal/health/other services 27 53 65 

Other 14 33 48 
TABLE E.5 (continued) 

 E.10 

_______________________________________________________________________________________________ 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 2,239 employment spells for male low-wage 

workers. 

Note: All figures are weighted using the longitudinal panel weight. 

a

 These figures pertain to health insurance coverage from all sources, including coverage through the employer as well 

as from other sources. We used this variable instead of the employer-based health insurance coverage variable, 

because data on overall health insurance coverage is available monthly, whereas the employer-based coverage variable 

pertains only to jobs in progress at the time of the interview. Thus, the employer-based health insurance variable could 

not always be linked to the job under investigation, which led to a significant number of missing values. However, the 

subsets of health insurance variables overlap considerably: the source of health insurance coverage was the employer 

for 80 percent of those with any coverage.

 *Significantly different from zero at the .10 level, two-tailed test 

 **Significantly different from zero at the .05 level, two-tailed test. 

***Significantly different from zero at the .01 level, two-tailed test. 
 E.11 

TABLE E.6 

CUMULATIVE EXIT RATES FROM EMPLOYMENT SPELLS AMONG FEMALE 

LOW-WAGE WORKERS, BY SUBGROUP 

 

 

Cumulative Exit Rates for Females 

Subgroup 

4 Months 

or Less 

(Percentages) 

12 Months 

or Less 

(Percentages) 

24 Months 

or Less 

(Percentages) 

Log-Rank Statistic

to Test Differences

Across Subgroups 

Overall 28 52 66 

Individual and Household 

Characteristics 

Age (in Years) 39*** 

Younger than 20 37 65 80 

20 to 29 30 56 71 

30 to 39 26 49 62 

40 to 49 22 42 58 

50 to 59 25 42 53 

60 or older 25 45 61 

Race/Ethnicity 5* 

White and other non-Hispanic 26 50 64 

Black, non-Hispanic 32 56 70 

Hispanic 27 53 70 

Educational Attainment 18*** 

Less than high school/GED 34 61 74 

High school/GED 26 49 66 

Some college 30 50 63 

College graduate or more 21 47 59 

Has a Health Limitation 22*** 

Yes 49 68 77 

No 25 50 65 

Household Type 2 

Single parent with children 27 55 69 

Married couple with children 28 51 66 

Married couple without children 26 48 64 

Other adults without children 28 50 63 

Household Income as a Percentage 

of the Poverty Level 10*** 

100 percent or less 33 58 69 

101 to 200 percent 29 53 69 

More than 200 percent 24 48 62 
TABLE E.6 (continued) 

 E.12 

Cumulative Exit Rates for Females 

Subgroup 

4 Months 

or Less 

(Percentages) 

12 Months 

or Less 

(Percentages) 

24 Months 

or Less 

(Percentages) 

Log-Rank Statistic

to Test Differences

Across Subgroups 

Job Characteristics 

Hourly Wages 19*** 

Less than $5.00 33 59 72 

$5.00 to $5.99 29 54 70 

$6.00 to $6.99 24 48 63 

$7.00 to $7.50 24 44 57 

Hours Worked per Week 9* 

1 to 19 35 56 68 

20 to 34 28 54 69 

35 to 40 25 48 63 

More than 40 24 52 65 

Weekly Earnings 10*** 

Less than $150 32 57 69 

$150 to $299 25 49 65 

$300 to $600 24 44 55 

Owns Business 2 

Yes 19 43 48 

No 28 52 66 

Covered by Health Insurancea 16*** 

Yes 24 46 60 

No 30 56 71 

Occupation 27*** 

Professional/technical 21 45 61 

Sales/retail 31 57 70 

Administrative support/clerical 24 43 56 

Service professions/ 

handlers/cleaners 26 51 66 

Machine/construction/ 

production/transportation 30 56 68 

Farm/agricultural/other workers 53 70 84 

Industry 9* 

Agriculture/forestry/ 

fishing/hunting 35 56 68 

Mining/manufacturing/ 

construction/ 

transportation/utilities 30 56 69 

Wholesale/retail trade 29 54 68 

Personal/health/other services 25 47 63 

Other 20 29 29 
TABLE E.6 (continued) 

 E.13 

_______________________________________________________________________________________________ 

Source: 1996 SIPP longitudinal files using the entry cohort sample of 4,099 employment spells for female low-wage 

workers. 

Note: All figures are weighted using the longitudinal panel weight. 

a

These figures pertain to health insurance coverage from all sources, including coverage through the employer as well 

as from other sources. We used this variable instead of the employer-based health insurance coverage variable, 

because data on overall health insurance coverage is available monthly, whereas the employer-based coverage variable 

pertains only to jobs in progress at the time of the interview. Thus, the employer-based health insurance variable could 

not always be linked to the job under investigation, which led to a significant number of missing values. However, the 

subsets of health insurance variables overlap considerably: the source of health insurance coverage was the employer 

for 80 percent of those with any coverage.

 *Significantly different from zero at the .10 level, two-tailed test 

 **Significantly different from zero at the .05 level, two-tailed test. 

***Significantly different from zero at the .01 level, two-tailed test.