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. 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 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 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 low-wage 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 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 low-wage workers grow over time and what factors are associated with wage growth over a three-year 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 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 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, 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 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:
- Another low-wage job (or business)
- A higher wage job with the same employer
- A higher-wage job with a different employer
- 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:
- 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.
- 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.
- 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.
- 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 low-wage 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?
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 job(14)
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).
|Subgroup||Describing Demographic and Job Characteristics(a)||Overall Employment Analysis(b)||Wage Progression Analysis(c)||Employment Spell Duration Analysis(d)|
|Males||Females||Males||Females||Males||Females||Male Spells||Female Spells|
|Individual and Household Characteristics|
|Age (in Years)|
|Younger than 20||172||177||67||56||61||47||613||538|
|20 to 29||1,106||1,256||198||262||189||225||2,246||2,721|
|30 to 39||941||1,476||122||240||127||197||2,174||3,118|
|40 to 49||687||1,227||71||157||69||145||1,791||2,811|
|50 to 59||462||709||39||81||45||79||1,147||1,726|
|60 or older||118||199||25||21||303||419|
|White and other non-Hispanic||2,401||3,777||379||614||357||523||6,047||8,418|
|Less than high school/GED||811||890||147||155||131||118||1,754||1,844|
|College graduate or more||629||1,029||79||180||50||71||1,785||2,713|
|Has a Physical, Mental, or Other Health Condition That Limited the Kind or Amount of Work That Could Be Done|
|Household Income as a Percentage of the Poverty Level|
|100 percent or less||514||665||120||179||121||153||1,161||1,564|
|101 to 200 percent||1,137||1,380||150||247||137||199||2,340||2,905|
|More than 200 percent||1,835||2,999||252||391||233||341||4,773||6,864|
|Single parent with children||331||958||60||203||58||173||706||2,160|
|Married couple with children||1,333||1,931||204||313||203||268||3,412||4,541|
|Married couple without children||864||1,220||129||165||112||139||2,288||2,695|
|Other adults without children||958||935||129||136||118||113||1,868||1,937|
|Less than $5.00||886||1,383||130||257||114||187||1,410||1,967|
|$5.00 to $6.00||816||1,299||130||250||136||223||1,790||2,917|
|$6.00 to $7.00||984||1,378||158||208||122||153||2,222||2,971|
|$7.00 to $7.50||800||984||104||102||119||130||2,852||3,478|
|Hours Worked per Week|
|1 to 19||99||459||40||128||40||113||339||1,314|
|20 to 34||435||1,252||100||252||82||209||1,005||2,913|
|35 to 40||1,750||2,633||280||371||263||318||4,149||5,767|
|More than 40||1,202||700||102||66||106||53||2,781||1,339|
|Less than $150||532||1,470||110||332||108||280||1,154||3,199|
|$150 to $299||2,216||3,183||342||454||291||356||4,626||6,566|
|$300 to $600||738||391||70||31||92||57||2,494||1,568|
|Health Insurance Coverage(e)|
|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.
a. This sample includes low-wage workers in March 1996 with a positive 1996 calendar year weight.
b. This sample includes workers who (1) started low-wage jobs during the first six months of the panel period, (2) who have a positive longitudinal panel weight, and (3) had at least 38 months of follow-up data.
c. This sample includes workers who (1) started low-wage jobs during the first six months of the panel period, (2) were employed at some point between 2.5 and 3 years later, and (3) had a positive longitudinal panel weight.
d. This sample includes low-wage employment spells that started during the panel period or were in progress at the start of the panel period (about 20 percent of spells are left-censored). The sample includes the spells of only those with positive longitudinal panel weights. A worker can contribute more than one spell to the sample.
e. 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.
(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.
(3) The sample loss rate at the end of wave 8 in the 1996 panel was 32.8 percent.
(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.
(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.
(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.
(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.
(10) These categories are not mutually exclusive.
(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.
(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.
(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.