Trends In Real Wages Over Time Among Those Who Start A Low Wage Job, By Gender
Note: All figures were calculated using the longitudinal panel weight and pertain to a 42-month follow-up period.
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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 low-wage 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.(37) 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, Appendix C presents selected descriptive statistics for workers who started medium- and high-wage 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.(38) 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 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.(39) (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 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.
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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-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.
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).(40) 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).
|
| 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. |
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 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. (41) 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 three-year period, and high-wage workers experienced a real wage increase of less than 5 percent over 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 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.(42)
 |
| 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. |
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-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.
| Male Workers | Female Workers | |
|---|---|---|
| Percentage Employed in Both Periods | 82 | 74 |
| Percentage Whose Wages:(a) | ||
| Increased | 78 | 80 |
| Decreased | 22 | 20 |
| Percentage Change in Wages(a) | ||
| 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. |
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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.
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 employer-based health insurance coverage.
| Job Characteristics | Male Workers(a) | Female Workers(a) | ||
|---|---|---|---|---|
| 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 Insurance(b) | 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 |
| 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. |
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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 high-wage 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.
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We found that many low-wage workers experienced some increase in wages during the mid- and 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 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:
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 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:
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 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:
| Subgroup | Male Low-Wage Workers | Female Low-Wage Workers | ||||
|---|---|---|---|---|---|---|
| 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 Medium- or High-Wage Jobs in Last Period | More than 50 Percent Increase in Wages | |
| Overall | 30 | 53 | 26 | 18 | 40 | 20 |
| Age (in Years) | ||||||
| Younger than 20 | 19 | 37 | 18 | 19 | 35 | 26 |
| 20 to 29 | 29 | 57 | 27 | 18 | 45 | 21 |
| 30 to 39 | 35 | 57 | 27 | 18 | 42 | 20 |
| 40 to 49 | 32 | 54 | 26 | 17 | 33 | 18 |
| 50 or older | 38 | 45 | 30 | 20 | 35 | 21 |
| Race/Ethnicity | ||||||
| White and other non-Hispanic | 32 | 57 | 25 | 20 | 43 | 21 |
| Black, non-Hispanic | 26 | 46 | 30 | 15 | 36 | 19 |
| Hispanic | 35 | 42 | 28 | 10 | 30 | 17 |
| Educational Attainment | ||||||
| Less than high school/GED | 18 | 40 | 19 | 9 | 23 | 14 |
| High school/GED | 26 | 52 | 22 | 15 | 35 | 18 |
| Some college | 44 | 66 | 33 | 22 | 53 | 24 |
| College graduate or more | 49 | 61 | 42 | 33 | 56 | 33 |
| Has a Health Limitation | ||||||
| Yes | 23 | 45 | 21 | 11 | 41 | 26 |
| No | 31 | 54 | 26 | 18 | 35 | 20 |
| Household Type | ||||||
| Single parent with children | 30 | 56 | 30 | 15 | 35 | 19 |
| Married couple with children | 31 | 53 | 24 | 20 | 41 | 23 |
| Married couple without children | 32 | 49 | 23 | 16 | 38 | 18 |
| Other adults without children | 29 | 56 | 28 | 20 | 51 | 21 |
| Household Income as a Percentage of the Federal Poverty Level | ||||||
| 100 percent or less | 26 | 49 | 29 | 10 | 32 | 17 |
| 101 to 200 percent | 30 | 49 | 22 | 14 | 32 | 17 |
| More than 200 percent | 33 | 57 | 26 | 23 | 48 | 23 |
| Full Sample Size | 491 | 491 | 491 | 693 | 693 | 636 |
| 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 three years after job start. Note: All figures are weighted using the longitudinal panel weight. |
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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 experience the maximum increase in their wages over time (Table V.4). We summarize these results here:
| Subgroup | Male Low-Wage Workers | Female Low-Wage Workers | ||||
|---|---|---|---|---|---|---|
| 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 Medium- or High-Wage Jobs in Last Period | More than 50 Percent Increase in Wages | |
| Overall | 30 | 53 | 26 | 18 | 40 | 20 |
| Hourly Wages | ||||||
| Less than $5.00 | 24 | 39 | 34 | 13 | 25 | 27 |
| $5.00 to $5.99 | 20 | 39 | 28 | 12 | 33 | 17 |
| $6.00 to $6.99 | 35 | 63 | 20 | 25 | 54 | 20 |
| $7.00 or more | 46 | 76 | 22 | 30 | 66 | 15 |
| Hours Worked per Week | ||||||
| 1 to 19 | 25 | 35 | 20 | 15 | 31 | 18 |
| 20 to 34 | 31 | 47 | 30 | 22 | 42 | 24 |
| 35 to 40 | 27 | 54 | 22 | 16 | 42 | 18 |
| More than 40 | 42 | 61 | 33 | 15 | 44 | 27 |
| Weekly Earnings | ||||||
| Less than $150 | 31 | 42 | 33 | 18 | 34 | 24 |
| $150 to $299 | 24 | 52 | 23 | 17 | 44 | 19 |
| $300 to $600 | 59 | 74 | 29 | 25 | 47 | 15 |
| Owns Business (Self-Employed) | ||||||
| Yes | 46 | 69 | 47 | 24 | 40 | 27 |
| No | 29 | 52 | 24 | 17 | 41 | 20 |
| Health Insurance Coverage(a) | ||||||
| Yes | 38 | 61 | 28 | 23 | 47 | 21 |
| No | 26 | 49 | 25 | 13 | 35 | 20 |
| Occupation | ||||||
| Professional/technical | 48 | 64 | 34 | 26 | 46 | 25 |
| Sales/retail | 38 | 59 | 25 | 23 | 49 | 28 |
| Administrative support/clerical | 35 | 59 | 36 | 23 | 58 | 17 |
| Service professions/handlers/cleaners | 23 | 43 | 22 | 13 | 30 | 18 |
| Machine/construction/production/ transportation |
32 | 61 | 25 | 15 | 29 | 20 |
| Farm/agricultural/other workers | 27 | 45 | 29 | 3 | 30 | 16 |
| Industry | ||||||
| Agriculture/forestry/fishing/hunting/other | 35 | 54 | 36 | 16 | 28 | 19 |
| Mining/manufacturing/construction/ transportation/utilities | 29 | 57 | 24 | 12 | 30 | 16 |
| Wholesale/retail trade | 30 | 49 | 21 | 16 | 41 | 25 |
| Personal/health/other services | 30 | 53 | 28 | 21 | 45 | 19 |
| Employment Status | ||||||
| Continuously employed with one job | 26 | 52 | 20 | 10 | 37 | 11 |
| Continuously employed with multiple jobs | 35 | 62 | 29 | 20 | 48 | 21 |
| Intermittent, employed less than 75% of time | 17 | 27 | 21 | 14 | 29 | 21 |
| Intermittent, employed 75% or more of time | 34 | 57 | 27 | 22 | 44 | 25 |
| Full Sample Size | 491 | 491 | 460 | 693 | 693 | 636 |
| 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. 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. |
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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 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.
| Explanatory Variable | Regression-Adjusted Means for Models with Demographic and Other Denoted Explanatory Variables |
|||||
|---|---|---|---|---|---|---|
| Male Workers | Female Workers | |||||
| 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 20(a) | 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-Hispanic(a) | 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/GED(a) | 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 | ||||||
| No(a) | 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 | ||||||
| No(a) | 34 | 22 | ||||
| Yes | 30 | 17 | ||||
| Number of Years Ever Worked Six Straight Months | ||||||
| Less than 5(a) | 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 | ||||||
| No(a) | 20 | 14 | ||||
| Yes | 34** | 20* | ||||
| Household Characteristics | ||||||
| Household Type | ||||||
| Single adults with children(a) | 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 less(a) | 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 | ||||||
| No(a) | 31 | 32 | 31 | 18 | 18 | 17 |
| Yes | 23 | 22* | 28 | 19 | 19 | 20 |
| Area Characteristics | ||||||
| Region of Residence | ||||||
| Northeast(a) | 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 less(a) | 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 less(a) | 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 percent(a) | 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 |
| Unemployment Rate in State | ||||||
| 6 percent or less(a) | 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 less(a) | 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 $5.00(a) | 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 19(a) | 29 | 14 | ||||
| 20 to 34 | 33 | 24** | ||||
| 35 to 40 | 28 | 16 | ||||
| More than 40 | 35 | 14 | ||||
| Has More than One Job or Business | ||||||
| No(a) | 31 | 17 | ||||
| Yes | 27 | 22 | ||||
| Owns Business (Self-Employed) | ||||||
| No(a) | 29 | 17 | ||||
| Yes | 41 | 29 | ||||
| Health Insurance Coverageb | ||||||
| No(a) | 27 | 16 | ||||
| Yes | 35* | 19 | ||||
| Union Member | ||||||
| No(a) | 30 | 18 | ||||
| Yes | 32 | 20 | ||||
| Occupation | ||||||
| Professional/technical(a) | 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* | ||||
| Industry | ||||||
| Agriculture/forestry/fishing and hunting(a) | 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. |
||||||
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 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 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.
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 R(2) 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
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.(44)
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 low-wage 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. 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.
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(37) 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.
(38) 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.
(39) As noted in Chapter II, the usual extent of data cleaning performed in earlier SIPP waves was not done for the 1996 longitudinal files.
(40) As described earlier, this six-month period refers to average wages during the first six-month 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.
(41) Patterns of wage growth remain similar when we looked at alternative definitions of low-wage 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.
(42) 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).
(43) We measured these indicators using information on the state in which the worker lived at the beginning and end of the follow-up period.
(44) 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.
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