Percentage of Workers Starting Low-Wage Jobs Who Subsequently
Held Higher-Wage Jobs, By Wage Category and Gender
Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.
[ Main page of Report | Contents of Report ]
What are the overall employment experiences of low-wage workers over a three-and-one-half 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 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? 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-and-one-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).(27) All statistics were calculated using the 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.
[ Go to Contents ]
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 low-wage 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 medium-term. 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.
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.
|
| 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. |
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 low-wage 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 low-wage cutoff value. Hence, we believe that our findings reflect real movements of low-wage workers into and out of the low-wage labor market.
 |
| 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. |
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.
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 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).
| Males | Females | All Workers | |
|---|---|---|---|
| Average Number of New Job and Employment Spells | |||
| All Jobs | 3.0 | 2.9 | 2.9 |
| Low-wage jobs(a) | 2.3 | 2.4 | 2.3 |
| Medium-wage jobs(a) | 0.6 | 0.4 | 0.5 |
| High-wage jobs(a) | 0.1 | 0.0 | 0.1 |
| Employment Spells of Any Wage Type(a) | 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. |
|||
 |
| 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. |
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.
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.
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.
 |
| 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. |
 |
| 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 medium-wage 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 over time and smaller increases in the medium-wage employment rate. By the end of the follow-up 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 medium-wage sector, but few enter the high-wage sector (as found also in Carnevale and Rose 2001; and Gottschalk 1997).
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).(28) 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 month. Only 30 percent of workers were employed for less than half the period. (29) These results provide further evidence that low-wage workers are active participants in the labor force.
 |
| 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. |
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).
| Labor Market Activity | Males | Females | All Workers |
|---|---|---|---|
| In All Months(a) | |||
| 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 21(a) | |||
| 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 42(a) | |||
| 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. |
|||
| Labor Market Activity(a) | 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).(30) However, hours worked in medium-wage jobs increased over time (not shown).
 |
| 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. |
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 two-thirds 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 low-wage 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 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.
 |
| 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. |
Finally, as expected, we find that higher-wage workers spent more time employed than low-wage 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 medium-wage 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.
[ Go to Contents ]
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. (31) 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:
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 one-quarter 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. (32)
We included the following categories of explanatory variables in the regression models:
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 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).
| Subgroup | Male Low-Wage Workers | Female Low-Wage Workers | ||||||
|---|---|---|---|---|---|---|---|---|
| Average Percentage of Months | Percentage in Higher-Wage Jobs for Less than 25 Percent of Months | Average Percentage of Months | Percentage in Higher-Wage Jobs for Less than 25 Percent of Months | |||||
| In Low-Wage Jobs | In Higher- Wage Jobs | In All Jobs | In Low- Wage Jobs | In Higher- Wage Jobs | In All Jobs | |||
| Overall | 55 | 28 | 84 | 55 | 58 | 18 | 77 | 73 |
| Age (in Years) | ||||||||
| Younger than 20 | 59 | 22 | 82 | 67 | 56 | 10 | 67 | 84 |
| 20 to 29 | 56 | 29 | 86 | 51 | 54 | 20 | 76 | 71 |
| 30 to 39 | 53 | 32 | 86 | 51 | 59 | 17 | 77 | 73 |
| 40 to 49 | 51 | 32 | 84 | 57 | 64 | 20 | 84 | 69 |
| 50 to 59 | 56 | 22 | 79 | 64 | 66 | 17 | 83 | 75 |
| 60 or older | 54 | 18 | 73 | 74 | 56 | 16 | 74 | 70 |
| Race/Ethnicity | ||||||||
| White and other non-Hispanic | 54 | 31 | 87 | 50 | 58 | 20 | 79 | 69 |
| Black, non-Hispanic | 50 | 21 | 72 | 71 | 57 | 14 | 72 | 75 |
| Hispanic | 64 | 20 | 85 | 68 | 63 | 9 | 73 | 89 |
| Educational Attainment | ||||||||
| Less than high school/GED | 58 | 21 | 80 | 67 | 58 | 9 | 68 | 88 |
| High school/GED | 56 | 28 | 85 | 54 | 64 | 15 | 79 | 78 |
| Some college | 47 | 36 | 84 | 46 | 52 | 22 | 76 | 64 |
| College graduate or more | 55 | 33 | 90 | 48 | 52 | 28 | 81 | 56 |
| Has a Health Limitation | ||||||||
| Yes | 52 | 17 | 71 | 73 | 51 | 11 | 63 | 83 |
| No | 55 | 30 | 86 | 53 | 59 | 19 | 79 | 71 |
| Household Type | ||||||||
| Single parent with children | 51 | 28 | 81 | 56 | 61 | 16 | 77 | 76 |
| Married couple with children | 56 | 31 | 88 | 49 | 57 | 16 | 74 | 77 |
| Married couple without children | 54 | 27 | 81 | 59 | 60 | 19 | 79 | 69 |
| Other adults without children | 55 | 27 | 83 | 60 | 56 | 24 | 81 | 62 |
| Household Income as a Percentage of the Poverty Level | ||||||||
| 100 percent or less | 60 | 26 | 87 | 55 | 61 | 14 | 76 | 79 |
| 101 to 200 percent | 56 | 27 | 84 | 57 | 56 | 15 | 72 | 77 |
| More than 200 percent | 52 | 31 | 83 | 54 | 58 | 22 | 81 | 67 |
| Full Sample Size | 522 | 522 | 522 | 522 | 817 | 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 longitudinal panel weight. |
||||||||
| Subgroup | Male Low-Wage Workers | Female Low-Wage Workers | ||||||
|---|---|---|---|---|---|---|---|---|
| Average Percentage of Months | Percentage in Higher-Wage Jobs for Less than 25 Percent of Months | Average Percentage of Months | Percentage in Higher-Wage Jobs for Less than 25 Percent of Months | |||||
| In Low-Wage Jobs | In Higher- Wage Jobs | In All Jobs | In Low- Wage Jobs | In Higher- Wage Jobs | In All Jobs | |||
| Overall | 55 | 28 | 84 | 55 | 58 | 18 | 77 | 73 |
| Hourly Wages | ||||||||
| Less than $5.00 | 63 | 19 | 83 | 68 | 59 | 8 | 68 | 88 |
| $5.00 to $5.99 | 63 | 17 | 81 | 71 | 66 | 13 | 79 | 81 |
| $6.00 to $6.99 | 50 | 37 | 88 | 42 | 55 | 26 | 81 | 59 |
| $7.00 to $7.50 | 42 | 42 | 84 | 39 | 46 | 39 | 85 | 41 |
| Hours Worked per Week | ||||||||
| 1 to 19 | 54 | 20 | 76 | 74 | 57 | 13 | 71 | 80 |
| 20 to 34 | 57 | 22 | 81 | 65 | 57 | 18 | 75 | 75 |
| 35 to 40 | 56 | 29 | 86 | 54 | 59 | 20 | 80 | 69 |
| More than 40 | 49 | 37 | 88 | 41 | 58 | 19 | 79 | 71 |
| Weekly Earnings | ||||||||
| Less than $150 | 56 | 22 | 80 | 67 | 58 | 13 | 72 | 81 |
| $150 to $299 | 57 | 27 | 85 | 56 | 59 | 21 | 80 | 68 |
| $300 to $600 | 42 | 45 | 89 | 30 | 51 | 28 | 80 | 47 |
| Owns Business (Self-Employed) | ||||||||
| Yes | 40 | 43 | 87 | 35 | 60 | 18 | 82 | 73 |
| No | 56 | 27 | 84 | 57 | 58 | 18 | 77 | 73 |
| Health Insurance Coverage(a) | ||||||||
| Yes | 50 | 34 | 86 | 47 | 56 | 22 | 79 | 67 |
| No | 57 | 25 | 84 | 60 | 60 | 15 | 76 | 77 |
| Occupation | ||||||||
| Professional/technical | 52 | 38 | 92 | 43 | 56 | 28 | 86 | 55 |
| Sales/retail | 53 | 35 | 90 | 41 | 56 | 20 | 77 | 71 |
| Administrative support/clerical | 59 | 24 | 84 | 62 | 51 | 28 | 80 | 55 |
| Service professions/handlers/cleaners | 57 | 24 | 82 | 63 | 62 | 13 | 76 | 80 |
| Machine/construction/production/ transportation |
51 | 32 | 84 | 49 | 62 | 10 | 73 | 88 |
| Farm/agricultural/other workers | 58 | 23 | 82 | 65 | 58 | 18 | 76 | 80 |
| Industry | ||||||||
| Agriculture/forestry/fishing/hunting | 56 | 24 | 83 | 62 | 62 | 14 | 79 | 80 |
| Mining/manufacturing/construction/ transportation/utilities | 53 | 31 | 85 | 52 | 60 | 12 | 72 | 84 |
| Wholesale/retail trade | 59 | 28 | 88 | 54 | 56 | 17 | 73 | 74 |
| Personal/health/other services | 53 | 26 | 80 | 61 | 59 | 22 | 81 | 67 |
| Other | 37 | 44 | 85 | 33 | 68 | 30 | 102 | 58 |
| Full Sample Size | 522 | 522 | 522 | 522 | 817 | 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. 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. |
||||||||
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:
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:
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 "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.(35)
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.
| Explanatory Variables | Regression-Adjusted Means for Models with Demographic and Other Denoted Explanatory Variables | |||||
|---|---|---|---|---|---|---|
| Males | Females | |||||
| 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 20(a) | 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-Hispanic(a) | 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/GED(a) | 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 | ||||||
| No(a) | 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 | ||||||
| No(a) | 27 | 19 | ||||
| Yes | 29 | 18 | ||||
| Number of Years Ever Worked Six Straight Months | ||||||
| Less than 5(a) | 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 | ||||||
| No(a) | 20 | 18 | ||||
| Yes | 31*** | 18 | ||||
| Household Characteristics | ||||||
| Household Type | ||||||
| Single adults with children(a) | 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 |
| Household Income as a Percentage of the Poverty Level | ||||||
| 100 percent or less(a) | 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 | ||||||
| No(a) | 29 | 29 | 29 | 18 | 18 | 18 |
| Yes | 22 | 21 | 23 | 17 | 17 | 17 |
| Area Characteristics | ||||||
| Region of Residence | ||||||
| Northeast(a) | 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 less(a) | 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 less(a) | 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 percent(a) | 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 less(a) | 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 less(a) | 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 $5.00(a) | 19 | 11 | ||||
| $5.00 to $5.99 | 19 | 14 | ||||
| $6.00 to $6.99 | 37*** | 24*** | ||||
| $7.00 to $7.50 | 40*** | 34*** | ||||
| Usual Hours Worked per Week | ||||||
| 1 to 19(a) | 24 | 13 | ||||
| 20 to 34 | 25 | 20*** | ||||
| 35 to 40 | 29 | 19** | ||||
| More than 40 | 32 | 18 | ||||
| Has More than One Job or Business | ||||||
| No(a) | 28 | 18 | ||||
| Yes | 31 | 18 | ||||
| Owns Business (Self-Employed) | ||||||
| No(a) | 27 | 18 | ||||
| Yes | 44*** | 24 | ||||
| Health Insurance Coverage(b) | ||||||
| No(a) | 26 | 17 | ||||
| Yes | 34*** | 20* | ||||
| Union Member | ||||||
| No(a) | 29 | 18 | ||||
| Yes | 27 | 19 | ||||
| Occupation | ||||||
| Professional/technical(a) | 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 R(2) 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 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. 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. *** Difference between the variable mean and the mean of the omitted explanatory variable is significantly different from zero at the .01 level, two-tailed test |
||||||
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 R(2) 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.
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). (36) 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 low-wage 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 higher-wage 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 job-related 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 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 medium- and 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 R(2) 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.
[ Go to Contents ]
(27) 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.
(28) 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.
(29) 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.
(30) The hours figures for medium-wage jobs include the zero hours worked by those who never held medium-wage jobs.
(31) We did not examine subgroup differences across the three male and three female low-worker 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.
(32) The standard errors of the estimates account also for design effects in the SIPP data due to clustering.
(33) We measured these indicators using information on the state in which the worker lived at the beginning and end of the follow-up period.
(34) 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.
(35) 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.
(36) 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.
Main Page of Report | Contents of Report
Home Pages:
Human Services Policy
(HSP)
Assistant Secretary for Planning and Evaluation
(ASPE)
U.S. Department of Health and Human Services
(HHS)