BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF EMPLOYMENT RESEARCH

AND PROGRAM DEVELOPMENT

Supervisory Status and Upper-level Supervisory Responsibilities:

Evidence from the NLSY79

Donna S. Rothstein, U.S. Bureau of Labor Statistics

Working Paper 331

September 2000

 

The views expressed are those of the author and do not necessarily reflect the policies of the U.S. Bureau of Labor

Statistics or the views of other staff members.
Supervisory Status and Upper-level Supervisory Responsibilities:

Evidence from the NLSY79

Donna S. Rothstein, Bureau of Labor Statistics*

Revised: August 2, 2000

*I thank Erica Groshen, Michael Horrigan, Charles Pierret, James Spletzer, Jay Stewart, and Jonathan

Veum for helpful comments on an earlier version. Thanks also to Alexander Eidelman and Hector Rodriguez for

excellent research assistance. The views expressed are those of the author and do not reflect the policies of the BLS

or the views of other BLS staff members.
Abstract

This paper examines what it means to be a supervisor, in terms of what types of

responsibilities are associated with supervisory status, who is more likely to have this authority,

and what the wage consequences are from having these types of responsibilities. The results

indicate that the wage returns to being a supervisor are not associated with simply having

supervisory “status” or a supervisory title, per se, but rather from having associated upper-level

supervisory responsibilities. While women are less likely to have supervisory status, once the

status is attained, there is a small associated responsibility differential.
The attainment of supervisory status and higher-level supervisory authority represents

movement up a career ladder. Significant types of responsibilities and decision-making authority

can be involved with being in a supervisory role, such as setting subordinates’ job tasks,

evaluating their work, and having influence over their pay and promotions. Past research

(mostly theoretical) has suggested that supervisors can have an important impact on worker

productivity through (for example) monitoring, task allocation, and by acting in a mentoring

role.1 There is very little empirical work, however, that examines the likelihood of having

supervisory responsibilities, or the effects of having these responsibilities on the worker him or

herself.

This paper addresses the following questions about supervisory status and higher-level

supervisory authority: who has supervisory authority and upper-level supervisory

responsibilities; what are the wage consequences of having these supervisory roles. It also

considers whether having supervisory status or upper-level supervisory responsibilities impacts

the ability to move further up in the job hierarchy.

The empirical analysis uses a new series of questions on aspects of supervision included

in the 1996 wave of the National Longitudinal Survey of Youth 1979 (NLSY79). The 1996

interview asks whether the individual is a supervisor, the types and levels of responsibilities and

authority the individual has over his or her subordinates, and the number and gender of those

subordinates. In 1996, the individuals in the NLSY79 ranged from ages 31 through 39.

Research has shown that the early twenties are generally a period of multiple job transitions, and

the thirties appear to be a time when individuals are becoming established in their career paths.2

While the individuals in the NLSY79 may not (yet) be vying for top-most positions in their

 

1

 See, for example, Leonard (1987), Prendergast (1995), Athey, Avery, and Zemsky (forthcoming).

2

 See, for example, Gardecki and Neumark (1998).
2

firms, they are moving into supervisory positions, and a number have moved into positions with

upper-level supervisory responsibilities.

I. The Incidence of Supervision Over Time: Evidence from the General Social Surveys

Because there is little empirical literature on supervision, as a first step I turn to a data set

that shows the incidence of supervisory status among workers over time. The General Social

Surveys (GSS) data set consists of a series of cross-sections from the years 1972 through 1996.

In many of those years, respondents were questioned about whether they supervise anyone who

is directly responsible to them. The top portion of Table 1 shows the percentage of full-time,

non self-employed workers between the ages of 18 and 65 who are supervisors. The data show

that a fairly high percentage of workers consider themselves to be supervisors.3 In the 1970s,

about 42% of male and 34% of female full-time workers defined themselves as supervisors. In

the 1980s, this increased to about 46% for men and 40% for women, and then remained at about

that level in the 1990s. It appears that for full-time workers a gender gap in supervision of about

6 to 8 percentage points has remained from the 1970s through at least the mid 1990s. This

gender gap is a little bit wider (see the bottom half of Table 1), between 8 and 10 percentage

points over time, when the data are restricted to individuals in their thirties—the age group that is

the focus of the research in this paper.

The GSS data indicate that a large percentage of workers consider themselves to be

supervisors. However, there may be a hierarchy of responsibilities within that general

supervisory category, and all supervisory tasks may not be rewarded equally. That is where the

 

3

 Hill (1980) finds this also—46% of the men and 27.3% of the women in her 1976 PSID sample consider

themselves to be supervisors.
3

NLSY79 1996 data are very informative. They allow one to look beyond the supervisory “title”

to examine types and levels of responsibilities associated with being in a supervisory role and

their associated wage effects.

Why might wages vary with levels of supervisory responsibilities? In their textbook on

compensation, Milkovich and Newman (1999) posit that establishment pay structures are usually

defined by job levels, pay differentials between the levels, and criteria for determining the levels

and pay differentials. Pay structures are thus hierarchical, with higher job levels associated with

higher pay. The criteria for determining the job levels and pay differentials between levels may

be job-based (based on job tasks, responsibilities, and expected behaviors and results) or personbased (based on skills (human capital), knowledge, and competencies). Both suggest that higher

wages may be associated with jobs with higher-level supervisory responsibilities. In the first

case, this can be due to the job content involving relatively higher authority and responsibility.4

In the second case, this can be caused by more skilled/higher ability individuals filling jobs that

include increased responsibilities.

The theoretical economics literature also provides some insights into why we could

expect individuals with jobs with higher supervisory authority to earn higher wages. For

example, Calvo and Wellisz (1979) and Rosen (1982) show a potential trickle down effect of

supervisory decision-making. As individuals move up the establishment’s hierarchy, they can

have a direct or indirect influence on the productivity of the workers below them.

Establishments will thus assign more talented and productive individuals to higher-level

positions with greater responsibility and compensate them with higher wages.

 

4

 See Harvey (1990) for a description of job analysis, particularly his discussion of the measurement of dimensions

of managerial jobs.
4

Establishments may use promotions into jobs with higher-level responsibilities and

higher pay to reward workers for specific human capital accumulation (Carmichael, 1984).

Tournament theory suggests that promotion into jobs with higher pay, prestige, and

responsibility can be a mechanism to induce employee effort (Lazear and Rosen, 1981, and

Rosen, 1986). At each level, employees ‘compete’ for a prize (higher position and pay). The

more productive employee is then promoted to the next level, where he or she then competes for

the next higher position, and so on.

The above scenarios predict rewards and responsibilities will rise with job level. This

paper focuses on two different types of supervisory responsibilities: responsibility over

subordinates’ pay or promotions and responsibility over setting subordinates’ job tasks. Could

these two aspects of supervisory responsibility have wage implications beyond the effect of

supervisory status? The above literature suggests it may. Whether due to job content, the ability

and productivity of the job holder, or both, we would expect to see higher wage returns

associated with higher levels of supervisory responsibility.

The goal of this paper is to examine empirically the nature and consequences of

supervisory responsibility. There is a small empirical literature on supervisory authority and the

wage returns to having this authority. Mostly from the sociology and industrial relations

literature, and based on data from the 1970s or early 1980s, this literature shows a gender gap in

supervisory status as seen with the GSS data, particularly with respect to higher levels of

authority (Wolf and Fligstein (1979), Hill (1980), Reskin and Roos (1992)). The literature also

(mostly) suggests a significant, positive wage effect associated with supervisory responsibility

that is not statistically different for men and women (Ferber and Green (1991), Cannings (1988),

Jacobs (1992)).
5

This paper adds to the existing literature in a number of ways. First, it uses recent data

for a nationally representative sample of men and women. The NLSY79 data set also contains

more extensive labor market information than most of the other data sets. This allows the

construction of much more detailed measures that may be important predictors of supervisory

responsibility (such as tenure, training, and an aptitude measure). Second, it focuses on and

more fully explores the issue of what it means to be a supervisor, and what aspects of

supervisory status have earnings implications. Third, it investigates the effects of supervisory

status and upper-level supervisory responsibilities on individuals’ advancement potential with

their employers.

II. What Do Supervisors Do? Evidence from the NLSY79

The National Longitudinal Survey of Youth 1979 (NLSY79) is a sample of over 10,000

young men and women5 who were initially interviewed in 1979, and who had been interviewed

through 1996 at the time I began my analysis. A new series of questions on various aspects of

supervision was added in 1996. A question that asks respondents whether they “supervise the

work of other employees” is used to determine supervisory status. Respondents who are

supervisors are then asked whether they have full, some, or no responsibility for deciding the pay

or promotions of the people they supervise. Similarly, they are asked how much responsibility

they have for deciding the specific tasks or jobs to be done by their subordinates. The

respondents are also asked the number and gender of their subordinates. In 1996, respondents’

ages ranged from 31 to 39. As mentioned above, the thirties are an interesting age range to

examine with respect to supervisory responsibilities. At this age, many individuals are past the
6

job shopping stage that tends to occur in the early to mid-twenties and on a more defined career

path.

Table 2 shows the percentage of workers who supervise employees and measures of

supervisors’ authority. The subsample shown, which is that used for the analyses that follow,

consists of 2,569 men and 2,057 women who were employed at least 30 hours per week, had

hourly wages between $2 and $100, and were not self-employed or working without pay at their

main job at the time of the 1996 interview. The hours restriction should eliminate some of the

possible supervisory differential that could be due to a lesser attachment to the labor market.

The remaining male and female employees may be more homogenous with respect to their labor

market characteristics than has been seen in some of the previous investigations, which did not

impose such restrictions.

Table 2 indicates, not surprisingly given what was found in the GSS data, that a large

percentage of men and women report that they supervise the work of other employees (41.5% of

men and 35.1% of women). Thus there is a 6.4 percentage point gender gap in supervision. The

table shows differences in the extent of responsibility supervisors have over subordinates’ pay or

promotions and job tasks.6 Only 20.8% of male supervisors and 13.4% of female supervisors

report that they have full responsibility for deciding their subordinates’ pay or promotions, while

38.7% of male supervisors and 43.1% of female supervisors report having no authority in this

area of responsibility. On the other hand, most supervisors have at least some responsibility for

setting their subordinates’ job tasks; only about 6% of male and female supervisors report

having no responsibility in that area. Control over subordinates’ pay or promotions is probably a

 

5

 The NLSY79 includes oversamples of black and Hispanic individuals.

6

 Two separate questions asked about responsibility over subordinates’ pay and promotions. Because the responses

are so highly correlated (correlation coefficient of about .7) the two questions have been combined to form one

variable.
7

higher grade of responsibility than that of setting employees’ job tasks, and may be associated

with a higher rung of the job ladder. The data also show that, on average, 23% of the employees

supervised by men are female, while 73% of the employees supervised by women are female.

This indicates significant gender segregation along supervisory lines.7

III. Variables

This paper seeks to address three main questions. The first is whether there are wage

consequences associated with having different levels and types of supervisory authority. The

second concerns factors affecting the likelihood of having supervisory status and higher-level

supervisory responsibilities. The third asks whether having supervisory status or higher-level

supervisory responsibilities affects the ability to advance with the current employer.

To examine the first question, log of hourly wage equations are estimated that include

supervisory characteristics as regressors. One could make an endogeneity argument that certain

types of individuals (higher ability, for example, as discussed in the literature review in section

I.) are more likely to become supervisors. At the same time, it is very difficult to think of an

instrument that affects the probability of becoming a supervisor (or having higher-level

supervisory responsibilities) that is not correlated with the disturbance term in the wage equation.

The very detailed nature of the NLSY79 data, however, allows for extensive controls (including

an aptitude measure) that may help to mitigate this potential problem.

Probits of the probability of having supervisory authority, and conditioning on having

supervisory authority, ordered probits of the probability of having higher levels of supervisory

 

7

 Reskin and Roos (1992), in their 1982 sample of managers in Illinois, found that 77% of female managers’ highest

ranking subordinates are women, and 24% of male managers’ highest ranking subordinates are women.
8

authority (over employees’ pay or promotions) are estimated to address the second question.

Probit estimates of the probability that a future promotion is possible with the present employer

(where supervisory characteristics are also included as regressors) are used to investigate the

third question.

General background characteristics such as gender, race, ethnicity, years of education,

and years of work experience all may influence the likelihood of becoming a supervisor. Based

on previous research concerning supervision, we expect the variable female to have a negative

impact. The same direction is predicted for the variables black and Hispanic. Years of education

and years of experience and tenure are most likely positive predictors of supervisory status and

higher-level supervisory responsibilities.8 In addition, job and establishment-specific

characteristics such as union status, establishment size, and job sector (public, private, or nonprofit) may also have an impact. The structure of supervision may differ across industries and

thus ten industry dummy variables are included in some specifications.9 Having been recently

promoted or having recently received training can increase the likelihood of becoming a

supervisor and of having higher-level supervisory responsibilities.10 Because people with higher

aptitude may be more likely to have supervisory status, individuals’ scores from the Armed

Forces Qualifying Test (AFQT) are included in the analyses. The AFQT is a measure of math

and verbal aptitude. Home responsibilities may influence the likelihood of having (and perhaps

 

8

 Experience equals the number of weeks worked (divided by 52) since the individual first left school for a period of

12 months or more. For individuals who left school prior to January 1, 1978—the first date for which employment

data are available for many respondents—experience equals the number of weeks worked (divided by 52) since

January 1, 1978. The results remain very similar when a dummy variable is included for this truncation, and thus

the dummy variable is not included in the specifications shown in this paper. The majority (over 80%) of affected

respondents are from the earliest birth years of the NLSY79, 1957-59, and are ages 18-20 as of January 1, 1978.

Tenure equals the portion of experience that occurred with the current employer.

9

 Occupation controls are not included, as many occupations reflect supervisory status. For example, almost 1/3 of

supervisors in this sample are managers and administrators, as compared to less than 1/20 of non-supervisors.

However, for comparison purposes, notes throughout the empirical results sections state whether results changed

with the addition of occupation dummy variables.
9

wanting to have) supervisory status. Thus variables indicating marital status, number of

children, and presence of a child aged 6 or less are included. Indicators for urban location and

for living in the South region are included. All of these variables are also in the promotion

potential probits, and (excluding the indicators of home responsibilities) are also regressors in the

wage equations.

Descriptive statistics are shown in Table 3. On average, men earn $15.00/hour while

women earn $12.33/hour, a differential of about 18%. More men than women believe that they

could receive a promotion by their present employer during the next two years. Men and women

appear alike on a number of key background characteristics, including tenure and recent receipt

of a promotion. However, women are much more likely to work in the non-profit sector, while

men are more likely to work in the private sector. AFQT scores for men and women appear

fairly similar. The test was administered to NLSY79 respondents in 1980 and because

respondents were different ages when they took the test, the scores have been standardized by

birth year to have a mean of zero and a standard deviation of one (following Neal and Johnson,

1996).11

IV. Wage Returns to Aspects of Supervision

The wage consequences of having supervisory status, and of having different levels of

supervisory responsibilities are first examined. The findings will indicate whether supervisory

responsibilities are associated with higher wages. The findings will also be used to test whether

 

10 See Pergamit and Veum (1999).

11 This standardization was done for the entire, weighted NLSY79 sample. Thus, the mean (standard deviation) here

is not exactly zero (one), given that the data are unweighted and include oversamples of black and Hispanic

individuals.
10

the returns to supervision vary by gender. If the meaning of supervisory status or upper-level

supervisory responsibilities differs systematically for men and women, we would expect the

wage returns to differ by gender.12 However, as noted in the previous section, supervisory wage

effects should be interpreted with some caution due to the potential endogeneity of the supervisor

variables. In particular, if more talented/productive individuals are more likely to have

supervisory responsibilities and if these attributes are not fully captured by the explanatory

variables in the wage equations, then the supervisory wage effects could be overstated.

Tables 4 and 5 depict estimates from the hourly wage equations. Equations are first

pooled for men and women (estimates shown in the first two columns of each table) and then

estimated separately. The specifications in Table 4 include a measure of supervisory status, but

no measures of particular supervisory responsibilities, while the specifications in Table 5 also

control for supervisory responsibilities over subordinates’ pay or promotions, and job tasks, the

number of subordinates, and the gender composition of the group of subordinates. Specifications

in even-numbered columns of both tables include a set of industry dummy variables.

The pooled equations in Tables 4 and 5 indicate that, even with extensive controls for

human capital characteristics, being female is associated with about a -14 to -16% impact on

wages [e.g., (exp(-.178) – 1)*100].13 Being black is associated with a significant and negative

effect for men, but not for women. Higher levels of education, experience, tenure, and AFQT

scores have the expected positive impact on wages, as does off-site training. Working in the

 

12 Jacobs (1992) tests a similar hypothesis; he asks whether the rising number of women identified as managers in

Census data is “real” or rather due to women being given the title of manager without increased wages and

authority.

13 The null hypothesis that the slope coefficients are the same for men and women is rejected for all specifications.
11

private sector has a positive impact on wages for men and women, while working in the nonprofit sector has a negative effect for men only.14

Turning to the effects of supervisory status and responsibilities on wages, Table 4 shows

that supervisory status has a significant, positive effect on wages of about 10 to 11% for men and

about 6 to 9% for women; these effects are not statistically different for men and women. Table

5 shows that responsibility over pay or promotions is the aspect of supervision measured here

that has very significant wage returns. Having full responsibility over subordinates’ pay or

promotions has about a 17 to 18% impact on wages for men and about 13 to 14% for women,

depending on the specification. The wage return for having some responsibility over pay or

promotions is also significant and positive.15

Levels of responsibility for setting job tasks, which is a duty most supervisors report

having at least to some extent, have no effect on wages. The log of number of subordinates

impacts wages for men, but only when industry controls are included (specifications in evennumbered columns).16 However, when the variable was interacted with industry dummy

variables neither the interactions nor the variable itself were statistically significant. This finding

is not too surprising. On the one hand, all else equal, supervisors might be expected to be

compensated more as they supervise more employees, because they may have a larger impact on

the employer’s profitability. However, the number of subordinates might also be related to

hierarchical position. Supervisors lower down and higher up in the job hierarchy both may

directly supervise the work of fewer employees, thus confounding a potential wage effect. The

 

14 The omitted category is the public sector.

15 The coefficients for full (some) responsibility over pay or promotions are not statistically different for men and

women.

The wage impacts of levels of responsibility over subordinates’ pay or promotions remain very similar when

industry is disaggregated into 43 dummy variables.
12

results in Table 5 also indicate that there is no wage differential associated with the gender

composition of the group of subordinates.17

To obtain a somewhat different perspective on the wage returns to being a supervisor, a

look at the combined effects of supervisory variables is useful. Three cases are examined: (1)

being a supervisor with full responsibility over subordinates’ pay or promotions, and job tasks;

(2) being a supervisor with some responsibility over subordinates’ pay or promotions, and job

tasks; (3) being a supervisor with no responsibility over subordinates’ pay or promotions, and

job tasks. Coefficients and standard errors for the three cases are shown below. 18

Males Males Females Females

Case (1) .204

(.030)

.209

(.029)

.149

(.039)

.166

(.039)

Case (2) .113

(.028)

.130

(.027)

.083

(.030)

.112

(.029)

Case (3) -.010

(.050)

.009

(.049)

-.045

(.058)

-.008

(.056)

Industry dummy

vars.?

No Yes No Yes

Note: Standard errors in ()s. Estimates computed from specifications shown in columns (3) through (6) of Table 5.

The results indicate that the payoff for being a supervisor lies in the types and levels of

responsibilities, rather than in simply having supervisory ‘status.’ In addition, we cannot reject

the hypothesis that men and women receive equal wage returns for these higher authority levels.

 

16 The addition of 8 occupation dummy variables does not change this result, nor does the disaggregation of industry

into 43 dummy variables. Also, when occupation dummy variables are included without industry dummy variables,

the variable is not significant.

17 Arrow (1985) suggests that if supervisors dislike supervising a certain type of employee (say female), a wage

differential based on the proportion of female subordinates could arise. If the variable is associated with the nature

of the occupation a wage effect might be expected to occur as well. No effect is found, however.

18 The variables log of number of subordinates and proportion of subordinates who are female are evaluated at their

mean (by gender) in all three cases. The delta-method was used to compute the standard errors.
13

V. The Likelihood of Being a Supervisor, and of Having Responsibility Over Subordinates’ Pay

or Promotions

The previous section shows that supervisory responsibility over subordinates’ pay or

promotions is associated with high wage returns. To attain this high level of supervisory

responsibility, it is necessary to attain general supervisory status. The next set of estimates is

used to investigate who is more likely to attain supervisory status. It is followed by an

examination of factors that affect the likelihood of having responsibility over subordinates’ pay

or promotions, conditional on supervising the work of others.

Table 6 shows probit estimates of the probability of supervising the work of others;

marginal effects are in brackets.19 Columns (1) and (2) show that being female has a negative

and significant impact on the likelihood of being a supervisor of about -7 to -7.5%.20 This

suggests that women are less likely to attain this initial ‘rung’ of the supervisory job ladder.

Being black also is associated with a negative impact for men and women, while being Hispanic

is not associated with a significant effect. Years of education, tenure, and having been recently

promoted have the expected positive impact on the likelihood of being a supervisor. Off-site

training has a positive and significant effect for men only. Working in the non-profit sector has a

strong positive impact for women only, of about 12%. Of the variables that relate to home

responsibilities—married, number of children, and child aged 6 or less—none are significant for

women, and the number of children is significant and positive for men.

 

19 Marginal effects for dichotomous variables were calculated by taking the difference in the probability that the

individual is a supervisor defined when the dichotomous variable equals 1 and equals 0 for each individual, and then

averaging over the sample. Marginal effects for continuous variables were calculated by taking the derivative of the

probability the individual is a supervisor with respect to the particular continuous variable, and then averaging over

the sample (see Greene (1997)).

20 The null hypothesis that the slope coefficients are the same for men and women is rejected for both specifications.
14

Table 7 displays ordered probit estimates of the probability of having responsibility over

employees’ pay or promotions (conditional on supervising the work of others).21 Being female is

associated with a small negative effect (under 3%) on the probability of having the highest level

of responsibility over employees’ pay or promotions, and is only marginally significant in the

specification that includes industry dummy variables.22 This finding suggests that once women

obtain supervisory status, they do not appear to lag very much behind in attaining this higher

‘rung’ of the supervisory ladder. Being black is found not to be statistically significant, and

being Hispanic is associated with a positive effect for women. A recent promotion is a positive

predictor of the likelihood of having the highest level of responsibility over subordinates’ pay or

promotions, but recent training does not appear to be significant. In addition, indicators for

home responsibilities are not statistically significant for men or women.23

VI. Promotion Potential

Once supervisory status and higher-level supervisory responsibilities are obtained, is it

more difficult in general to advance further with the current employer? Are these upper-level

 

21 Respondents can report having full, some, or no responsibility for deciding the pay or promotions of the people

they supervise. The sign of the coefficient indicates the impact of that variable on the probability of having the

highest level (full) of responsibility over employees’ pay or promotions. –1 multiplied by the sign on the coefficient

is the direction of impact of that variable on the probability of having no responsibility over subordinates’ pay or

promotions.

Marginal effects are for the probability that the individual has the highest level of responsibility over subordinates’

pay or promotions, and are calculated in a similar manner (for continuous and dichotomous) as was described for the

probability of being a supervisor.

22 The null hypothesis that the slope coefficients are the same for men and women is not rejected for either

specification.

When occupation controls are added, the coefficient on female is statistically significant (the marginal effect is -

4.3% for the specification that excludes industry dummy variables and -3.5% for the specification that includes

industry dummy variables).

23 Results with respect to determinants of other supervisory outcomes (log of number of subordinates, proportion of

subordinates who are female, responsibility for setting subordinates’ job tasks) are available from the author upon

request.
15

supervisory responsibilities an indicator that people are reaching the top of their job hierarchy?

The variable used to address these queries is the response to a question that asks individuals

whether a promotion is possible with their current employer within the next two years. Thus

whether individuals believe that they are “stuck” at various levels in the supervisory hierarchy

can be examined. Table 3 shows that 61.5% of men and 55.2% of women believe a promotion is

possible with their present employer within the next two years. Of the individuals who believe

no further promotion is possible, about 70% of men and 64% of women choose “no further

promotion potential” as the reason, which suggests that they believe they have gone as far as

they can with their present employer.24

Tables 8 and 9 display probit estimates of the probability of responding that a promotion

is possible within the next two years. The specifications in Table 8 include a measure of

supervisory status, but no additional measures of supervisory responsibilities, while the

specifications in Table 9 include measures of supervisory responsibilities as well. The results in

both tables indicate that women are about 6% less likely to respond that a promotion is

possible.25 Table 8 shows that supervisory status is positively related to the probability a

promotion is possible within the next two years for both women and men. This positive

relationship suggests that it is not more difficult to be promoted once general supervisory status

is obtained. When levels of supervisory responsibility are included in the specifications (Table

9), it does not appear that promotion potential decreases for either women or men as they move

up the supervisory hierarchy.

 

24 Other responses for lack of promotion potential include: someone from above must leave (the next most common

response), additional education/training is needed, company reorganization, and change in company ownership.

25 The null hypothesis that the slope coefficients are the same for men and women is rejected for all specifications in

Tables 8 and 9.
16

The impact of race is also interesting: black individuals are significantly more likely to

respond that a promotion is possible with their present employer. Recent promotion also has a

significant positive impact on the probability of stating a future promotion is possible with the

current employer for both men and women. Pergamit and Veum (1999) find that a recent

promotion is a strong (positive) predictor of a current promotion. They suggest that this may

occur because certain jobs or employers tend to have more of a hierarchical structure than others,

which would generate this effect.26 This is also a possible explanation for the positive impact of

supervisory status on promotion potential. Perhaps workers employed in organizations that are

more hierarchically structured are more likely to have supervisory responsibility, and also have

more potential for advancement with their employer.

VII. Conclusion

There is little empirical literature about supervisors—who they are, what they do, and

whether there are economic rewards to having this authority. Yet supervisory status and upperlevel supervisory responsibilities are an important part of individuals’ jobs, and the attainment of

this authority can mark advancements along a career path. This paper examines what it means to

be a supervisor, in terms of what types of responsibilities are associated with supervisory status

of men and women, who is more likely to have this authority, and what are the wage and

advancement consequences from having these levels of responsibilities. A new series of

questions on aspects of supervision added to the 1996 interview of the National Longitudinal

Survey of Youth 1979 (NLSY79) makes possible the empirical investigation of these issues.

 

26 They also point out that past promotion may be an indicator for worker motivation, which would also predict this

effect.
17

The NLSY79 data show that about 42% of male and 35% of female full-time workers

consider themselves to be supervisors. This fairly high incidence of supervisory authority is also

found in other data sets, such as the General Social Surveys (GSS) (See Table 1). About 21% of

male supervisors and 13% of female supervisors report full responsibility for deciding

subordinates’ pay or promotions. While a large percentage, about 39% of male supervisors and

43% of female supervisors, report having no responsibility in this area, most supervisors have at

least some responsibility over setting subordinates’ job tasks.

Supervisory status does have a sizable wage return for both men and women, of about 9-

10%. However, the addition of supervisory responsibilities and the number and gender of

subordinates to specifications shows that responsibility over subordinates’ pay or promotions is

the aspect of supervision that has large, significant wage returns. Having supervisory status

without upper-level supervisory responsibilities is found to have no wage returns. Thus it

appears that the monetary rewards to supervision are not associated with simply having

supervisory “status” or a supervisory title, per se, but rather from having associated upper-level

supervisory responsibilities.

The empirical analyses show some interesting results with respect to gender. Men and

women are not compensated significantly differently for having supervisory status or upper-level

supervisory responsibilities. Women are, however, about 7% less likely to have supervisory

status, and, conditional on having supervisory status, marginally less likely to have upper-level

responsibility over subordinates’ pay or promotions. It appears that once women attain

supervisory status, there is a small associated responsibility differential for these mid-career

supervisors.
18

References

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19

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Managers.” Work and Occupations, Vol. 19, No. 4, pp. 342-65.
20

Rosen, Sherwin. 1982. “Authority, Control, and the Distribution of Earnings.” Bell Journal of

Economics, Vol. 13, No. 2, pp. 311-23.

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21

Table 1. Percentage of Full-Time, Non Self-Employed Workers Who Directly Supervise Employees

Workers Ages 18-65

Years

1970s 1980s 1990s

All 39.0 43.5 41.3

N 2,722 3,596 2,916

Males 41.8 46.3 44.2

N 1,762 1,987 1,546

Females 33.9 40.0 38.0

N 960 1,609 1,370

Workers Ages 31-39

Years

1970s 1980s 1990s

All 43.6 48.7 44.3

N 557 1,021 849

Males 47.0 52.9 48.1

N 362 586 466

Females 37.4 43.0 39.7

N 195 435 383

Note: The years of data included above are: 1972-74, 1976-77, 1980, 1982, 1984-85, 1987-89, 1990-91, 1993-94,

and 1996.

Source: General Social Surveys (GSS) Data, 1972-1996
22

Table 2. Percentage of Workers Who Are Supervisors and Descriptive Statistics of Supervisory Characteristics

All Males Females

Percentage with supervisory status 38.7 41.5 35.1

N full sample 4,626 2,569 2,057

Among supervisors, percentage with:

Full responsibility for subordinates’ pay or promotions 17.8 20.8 13.4

Some responsibility for subordinates’ pay or promotions 41.7 40.5 43.5

No responsibility for subordinates’ pay or promotions 40.5 38.7 43.1

Full responsibility for setting subordinates’ job tasks 54.4 57.4 50.0

Some responsibility for setting subordinates’ job tasks 39.7 36.6 44.3

No responsibility for setting subordinates’ job tasks 5.9 6.0 5.7

Additional supervisory characteristics:

Log of number of subordinates 1.654

(1.129)

1.739

(1.130)

1.528

(1.116)

Proportion of subordinates who are female .434

(.402)

.233

(.314)

.733

(.325)

N supervisors 1,789 1,067 722

Note: Means, standard deviations in ( )s. Data are restricted to those who are not self-employed or working without

pay, and who work at least 30 hours per week. Respondents are ages 31-39 in 1996.

Source: National Longitudinal Survey of Youth 1979 (NLSY79) Data, 1996
23

Table 3. Descriptive Statistics

All Males Females

Hourly wage 13.812

(7.787)

15.000

(8.496)

12.327

(6.501)

Log of hourly wage 2.498

(.496)

2.580

(.499)

2.396

(.474)

Believe a promotion is possible in next two years .587 .615 .552

Female .445 .000 1.000

Black .299 .286 .316

Hispanic .182 .180 .185

Years of education 13.299

(2.360)

13.123

(2.423)

13.520

(2.259)

AFQT score -.212

(1.016)

-.199

(1.074)

-.229

(.939)

Experience (years) 12.473

(3.572)

12.843

(3.412)

12.011

(3.712)

Experience squared/10 16.833

(8.344)

17.657

(8.207)

15.803

(8.401)

Tenure (years) 5.355

(4.715)

5.320

(4.754)

5.398

(4.666)

Tenure squared/10 5.090

(7.285)

5.090

(7.370)

5.090

(7.180)

Promotion in last two years .220 .219 .222

On-site seminar or training program in last two years .182 .169 .198

Off-site seminar or training program in last two years .068 .056 .083

Private sector .766 .823 .695

Non-profit sector .067 .045 .094

Union status .214 .224 .201

Log of establishment size 4.151

(2.026)

4.004

(2.052)

4.335

(1.979)
24

Table 3 continued

Marital status .571 .598 .539

Number of children 1.310

(1.231)

1.211

(1.271)

1.435

(1.168)

Child aged 6 or less .350 .376 .317

N 4,626 2,569 2,057

Note: Means, standard deviations in ( )s.
25

Table 4. OLS Log of Hourly Wage Equation Estimates

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Supervisory Status .076

(.012)

.098

(.012)

.093

(.017)

.106

(.016)

.057

(.017)

.085

(.017)

Female -.178

(.012)

-.154

(.012)

---- ---- ---- ----

Black -.050

(.016)

-.046

(.015)

-.107

(.022)

-.098

(.021)

.017

(.022)

.017

(.022)

Hispanic .012

(.016)

.007

(.016)

-.028

(.022)

-.028

(.022)

.063

(.023)

.054

(.023)

Years of education .061

(.003)

.059

(.003)

.057

(.004)

.057

(.004)

.064

(.005)

.061

(.005)

AFQT score .097

(.008)

.095

(.008)

.088

(.011)

.086

(.010)

.111

(.012)

.108

(.012)

Experience (years) .028

(.008)

.023

(.008)

.024

(.012)

.020

(.012)

.031

(.011)

.029

(.011)

Experience squared/10 -.002

(.004)

-.001

(.003)

-.001

(.005)

.001

(.005)

-.004

(.005)

-.004

(.005)

Tenure (years) .031

(.004)

.031

(.004)

.031

(.006)

.033

(.005)

.029

(.006)

.028

(.006)

Tenure squared/10 -.013

(.003)

-.013

(.003)

-.014

(.004)

-.014

(.003)

-.011

(.004)

-.010

(.004)

Promotion in last two

years

.026

(.014)

.027

(.014)

.042

(.019)

.041

(.019)

.007

(.020)

.010

(.019)

On-site seminar or training

program in last two years

.041

(.015)

.034

(.015)

.048

(.022)

.042

(.021)

.032

(.021)

.024

(.020)

Off-site seminar or

training program in last

two years

.087

(.023)

.080

(.022)

.088

(.035)

.084

(.034)

.099

(.029)

.086

(.029)

Private sector .077

(.017)

.086

(.020)

.094

(.025)

.116

(.032)

.060

(.023)

.059

(.026)

Non-profit sector -.051

(.026)

-.057

(.025)

-.130

(.043)

-.127

(.042)

-.010

(.032)

-.017

(.032)

Union status .150

(.015)

.137

(.015)

.180

(.021)

.160

(.020)

.109

(.023)

.106

(.023)
26

Table 4 continued

Log of establishment size .035

(.003)

.033

(.003)

.035

(.004)

.036

(.004)

.035

(.004)

.030

(.004)

Intercept 1.056

(.066)

1.117

(.072)

1.145

(.098)

1.155

(.103)

.804

(.090)

.912

(.105)

Industry dummy vars.? No Yes No Yes No Yes

N 4,626 4,626 2,569 2,569 2,057 2,057

Adjusted R-Squared .418 .453 .389 .427 .424 .453

Note: Standard errors in ( )s. Also included in equations are indicators for urban location and South region.
27

Table 5. OLS Log of Hourly Wage Equation Estimates (Includes Supervisory Responsibility Variables)

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Supervisory Status -.008

(.041)

-.021

(.039)

-.013

(.052)

-.033

(.051)

-.017

(.068)

.003

(.066)

Full responsibility for

subordinates’ pay or

promotions

.157

(.029)

.148

(.028)

.169

(.038)

.157

(.037)

.132

(.047)

.118

(.046)

Some responsibility for

subordinates’ pay or

promotions

.088

(.021)

.088

(.021)

.100

(.029)

.100

(.028)

.072

(.031)

.073

(.031)

Full responsibility for

setting subordinates’ job

tasks

.054

(.041)

.052

(.039)

.046

(.054)

.044

(.052)

.062

(.062)

.056

(.061)

Some responsibility for

setting subordinates’ job

tasks

.038

(.040)

.035

(.039)

.023

(.053)

.021

(.051)

.056

(.060)

.047

(.059)

Log of number of

subordinates

-.004 a

(.084)

.015

(.008)

.006

(.011)

.021

(.011)

-.006

(.013)

.004

(.012)

Proportion of

subordinates who are

female

-.043

(.024)

-.011

(.024)

-.034

(.040)

.019

(.039)

-.025

(.042)

-.024

(.041)

Female -.167

(.012)

-.148

(.012)

---- ---- ---- ----

Black -.049

(.015)

-.045

(.015)

-.105

(.022)

-.096

(.021)

.016

(.022)

.016

(.022)

Hispanic .011

(.016)

.005

(.016)

-.028

(.022)

-.027

(.022)

.060

(.023)

.050

(.023)

Years of education .059

(.003)

.057

(.003)

.056

(.005)

.055

(.004)

.063

(.005)

.060

(.005)

AFQT score .095

(.008)

.093

(.008)

.086

(.011)

.084

(.010)

.109

(.012)

.106

(.012)

Experience (years) .028

(.008)

.024

(.008)

.024

(.012)

.021

(.012)

.030

(.011)

.028

(.011)

Experience squared/10 -.003

(.004)

-.002

(.003)

-.001

(.005)

-.001 a

(.050)

-.004

(.005)

-.004

(.005)

Tenure (years) .030

(.004)

.030

(.004)

.029

(.006)

.031

(.005)

.029

(.006)

.028

(.006)

Tenure squared/10 -.012

(.003)

-.012

(.003)

-.013

(.004)

-.014

(.003)

-.011

(.004)

-.010

(.004)
28

Table 5 continued

Promotion in last two

years

.019

(.014)

.019

(.014)

.034

(.019)

.033

(.019)

.002

(.020)

.003

(.019)

On-site seminar or training

program in last two years

.041

(.015)

.034

(.015)

.048

(.022)

.042

(.021)

.031

(.021)

.023

(.020)

Off-site seminar or

training program in last

two years

.083

(.023)

.074

(.022)

.082

(.034)

.076

(.033)

.095

(.029)

.083

(.029)

Private sector .067

(.017)

.078

(.020)

.079

(.025)

.102

(.032)

.054

(.023)

.056

(.026)

Non-profit sector -.053

(.026)

-.061

(.025)

-.136

(.043)

-.136

(.042)

-.014

(.032)

-.021

(.032)

Union status .157

(.015)

.144

(.015)

.190

(.021)

.171

(.020)

.113

(.023)

.109

(.023)

Log of establishment size .035

(.003)

.033

(.003)

.035

(.004)

.035

(.004)

.036

(.004)

.030

(.004)

Intercept 1.087

(.067)

1.157

(.072)

1.190

(.098)

1.207

(.103)

.832

(.090)

.948

(.106)

Industry dummy vars.? No Yes No Yes No Yes

N 4,626 4,626 2,569 2,569 2,057 2,057

Adjusted R-Squared .424 .460 .396 .436 .426 .455

Note: Standard errors in ( )s. Also included in equations are indicators for urban location and South region.

a

 Coefficient and standard error have been multiplied by 10.
29

Table 6. Probit Estimates of the Probability of Supervising the Work of Others

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Female -.205

(.041)

[-.070]

-.224

(.044)

[-.076]

---- ---- ---- ----

Black -.162

(.056)

[-.055]

-.152

(.056)

[-.051]

-.148

(.075)

[-.051]

-.128

(.076)

[-.043]

-.180

(.085)

[-.060]

-.178

(.086)

[-.059]

Hispanic -.076

(.057)

[-.026]

-.055

(.058)

[-.018]

-.059

(.076)

[-.020]

-.047

(.077)

[-.016]

-.095

(.087)

[-.032]

-.063

(.088)

[-.021]

Years of education .067

(.011)

[.023]

.073

(.012)

[.025]

.076

(.015)

[.026]

.079

(.016)

[.027]

.049

(.018)

[.017]

.058

(.018)

[.019]

AFQT score .019

(.028)

[.006]

.024

(.028)

[.008]

.021

(.036)

[.007]

.032

(.037)

[.011]

.002

(.044)

[-.001]

.007

(.045)

[.002]

Experience (years) .026

(.030)

[.009]

.027

(.030)

[.009]

.013 a

(.428)

[.004]

-.004

(.043)

[-.001]

.068

(.043)

[.023]

.070

(.044)

[.023]

Experience squared/10 -.006

(.013)

[-.002]

-.006

(.013)

[-.002]

.006

(.018)

[.002]

.008

(.018)

[.003]

-.026

(.019)

[-.009]

-.024

(.019)

[-.008]

Tenure (years) .091

(.014)

[.031]

.094

(.014)

[.032]

.105

(.019)

[.036]

.109

(.019)

[.037]

.066

(.022)

[.022]

.068

(.022)

[.023]

Tenure squared/10 -.035

(.009)

[-.012]

-.036

(.009)

[-.012]

-.040

(.012)

[-.014]

-.041

(.012)

[-.001]

-.025

(.014)

[-.008]

-.026

(.014)

[-.009]

Promotion in last two

years

.680

(.047)

[.247]

.673

(.048)

[.241]

.655

(.065)

[.234]

.651

(.065)

[.229]

.708

(.070)

[.260]

.699

(.071)

[.250]

On-site seminar or training

program in last two years

.069

(.053)

[.024]

.089

(.053)

[.030]

.037

(.074)

[.013]

.064

(.074)

[.022]

.091

(.076)

[.031]

.107

(.077)

[.036]

Off-site seminar or

training program in last

two years

.235

(.078)

[.083]

.245

(.079)

[.085]

.373

(.118)

[.131]

.373

(.118)

[.129]

.154

(.106)

[.054]

.189

(.107)

[.064]
30

Table 6 continued

Private sector .114

(.061)

[.039]

.125

(.074)

[.042]

.081

(.087)

[.028]

.146

(.114)

[.049]

.152

(.087)

[.051]

.146

(.101)

[.048]

Non-profit sector .122

(.092)

[.043]

.132

(.092)

[.045]

-.170

(.150)

[-.057]

-.164

(.150)

[-.054]

.340

(.119)

[.120]

.350

(.119)

[.121]

Union status -.457

(.055)

[-.152]

-.447

(.056)

[-.146]

-.610

(.073)

[-.203]

-.607

(.074)

[-.199]

-.239

(.087)

[-.079]

-.222

(.089)

[-.072]

Log of establishment size -.066

(.010)

[-.023]

-.049

(.011)

[-.016]

-.057

(.014)

[-.020]

-.039

(.015)

[-.013]

-.077

(.016)

[-.026]

-.059

(.016)

[-.019]

Marital status .026

(.047)

[.009]

.043

(.047)

[.015]

.046

(.069)

[.016]

.072

(.069)

[.024]

-.008 a

(.652)

[-.003]

.005

(.066)

[.002]

Number of children .048

(.020)

[.017]

.043

(.020)

[.015]

.059

(.028)

[.020]

.058

(.028)

[.019]

.016

(.030)

[.005]

.007

(.031)

[.002]

Child aged 6 or less -.078

(.049)

[-.027]

-.071

(.049)

[-.024]

-.089

(.069)

[-.030]

-.094

(.070)

[-.032]

-.066

(.071)

[-.022]

-.046

(.072)

[-.015]

Intercept -1.473

(.243)

-1.913

(.274)

-1.518

(.341)

-1.981

(.377)

-1.536

(.361)

-1.920

(.432)

Industry dummy vars.? No Yes No Yes No Yes

N 4,626 4,626 2,569 2,569 2,057 2,057

log likelihood -2,793 -2,746 -1,549 1,524 -1,229 -1,200

Note: Standard errors in ( )s, marginal effects in [ ]s. Also included in equations are indicators for urban location

and South region.

a

 Coefficient, standard error, and marginal effect have been multiplied by 10.
31

Table 7. Ordered Probit Estimates of the Probability of Having Responsibility over Subordinates’ Pay or

Promotions (Conditional on Supervising the Work of Others)

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Female -.119

(.058)

[-.029]

-.102

(.061)

[-.024]

---- ---- ---- ----

Black -.046

(.080)

[-.011]

-.059

(.081)

[-.014]

-.055

(.104)

[-.015]

-.073

(.105)

[-.019]

-.036

(.128)

[-.007]

-.046

(.129)

[-.009]

Hispanic .070

(.079)

[.017]

.089

(.079)

[.022]

-.005

(.103)

[-.001]

-.005

(.103)

[-.001]

.198

(.125)

[.042]

.260

(.127)

[.055]

Years of education .088

(.016)

[.021]

.096

(.016)

[.023]

.084

(.020)

[.022]

.093

(.021)

[.024]

.091

(.026)

[.018]

.103

(.026)

[.020]

AFQT score .058

(.040)

[.014]

.058

(.040)

[.014]

.050

(.051)

[.013]

.039

(.052)

[.010]

.065

(.064)

[.013]

.073

(.065)

[.014]

Experience (years) .080

(.047)

[.019]

.081

(.047)

[.019]

.034

(.061)

[.009]

.026

(.062)

[.007]

.146

(.076)

[.029]

.147

(.076)

[.029]

Experience squared/10 -.012

(.020)

[-.003]

-.013

(.020)

[-.003]

.006

(.025)

[.001]

.008

(.025)

[.002]

-.039

(.032)

[-.008]

-.040

(.033)

[-.008]

Tenure (years) .043

(.020)

[.010]

.042

(.020)

[.010]

.053

(.026)

[.014]

.050

(.026)

[.013]

.025

(.033)

[.005]

.024

(.033)

[.005]

Tenure squared/10 -.021

(.013)

[-.005]

-.020

(.013)

[-.005]

-.025

(.016)

[-.007]

-.022

(.016)

[-.006]

-.014

(.021)

[-.003]

-.013

(.021)

[-.002]

Promotion in last two

years

.263

(.059)

[.066]

.256

(.059)

[.064]

.250

(.078)

[.069]

.252

(.078)

[.068]

.302

(.093)

[.063]

.304

(.094)

[.062]

On-site seminar or training

program in last two years

.004 a

(.695)

[.001]

.010 a

(.698)

[.002]

-.011

(.092)

[-.003]

-.017

(.093)

[-.005]

.013

(.107)

[.003]

.020

(.108)

[.004]

Off-site seminar or

training program in last

two years

.147

(.096)

[.037]

.167

(.097)

[.042]

.143

(.131)

[.040]

.124

(.132)

[.034]

.170

(.142)

[.037]

.218

(.145)

[.046]
32

Table 7 continued

Private sector .493

(.090)

[.104]

.310

(.106)

[.068]

.521

(.123)

[.119]

.389

(.152)

[.092]

.497

(.136)

[.089]

.268

(.154)

[.049]

Non-profit sector .270

(.132)

[.071]

.263

(.132)

[.069]

.285

(.219)

[.083]

.251

(.220)

[.072]

.290

(.175)

[.065]

.290

(.175)

[.063]

Union status -.445

(.090)

[-.092]

-.439

(.091)

[-.091]

-.533

(.117)

[-.120]

-.534

(.118)

[-.119]

-.304

(.145)

[-.054]

-.290

(.146)

[-.051]

Log of establishment size -.036

(.015)

[-.009]

-.029

(.015)

[-.007]

-.041

(.019)

[-.011]

-.038

(.020)

[-.010]

-.028

(.024)

[-.006]

-.011

(.025)

[-.002]

Marital status .066

(.066)

[.016]

.056

(.067)

[.013]

.054

(.094)

[.014]

.059

(.095)

[.015]

.083

(.097)

[.017]

.062

(.098)

[.012]

Number of children -.006

(.028)

[-.001]

.012 b

(2.770)

[.003]

-.021

(.037)

[-.006]

-.013

(.037)

[-.003]

.016

(.044)

[.003]

.029

(.045)

[.006]

Child aged 6 or less -.045

(.067)

[-.011]

-.032

(.067)

[-.008]

-.015

(.089)

[-.004]

-.012

(.089)

[-.003]

-.060

(.104)

[-.012]

-.025

(.106)

[-.005]

Industry dummy vars.? No Yes No Yes No Yes

N 1,789 1,789 1,067 1,067 722 722

log likelihood -1,741 -1,731 -1,062 -1,054 -672 -659

Note: Standard errors in ( )s, marginal effects for the probability of having the highest level of supervisory

responsibility are in [ ]s. Also included in equations are indicators for urban location and South region.

a

 Coefficient, standard error, and marginal effect have been multiplied by 10.

b

 Coefficient, standard error, and marginal effect have been multiplied by 100.
33

Table 8. Probit Estimates of the Probability that a Promotion Is Possible in the Next Two Years

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Supervisory status .180

(.042)

[.064]

.173

(.043)

[.061]

.211

(.057)

[.074]

.214

(.058)

[.074]

.135

(.064)

[.048]

.117

(.065)

[.041]

Female -.179

(.041)

[-.064]

-.184

(.043)

[-.065]

---- ---- ---- ----

Black .316

(.055)

[.111]

.312

(.056)

[.109]

.278

(.075)

[.096]

.270

(.075)

[.092]

.362

(.084)

[.129]

.358

(.084)

[.126]

Hispanic .117

(.056)

[.041]

.131

(.057)

[.046]

.102

(.076)

[.035]

.117

(.076)

[.040]

.136

(.085)

[.048]

.136

(.086)

[.048]

Years of education .005

(.011)

[.002]

.007

(.012)

[.002]

.032

(.015)

[.011]

.031

(.016)

[.011]

-.027

(.017)

[-.010]

-.023

(.018)

[-.008]

AFQT score -.017

(.028)

[-.006]

-.023

(.028)

[-.008]

-.064

(.036)

[-.022]

-.072

(.036)

[-.025]

.038

(.044)

[.013]

.031

(.044)

[.011]

Experience (years) .030

(.029)

[.011]

.031

(.029)

[.011]

.020

(.042)

[.007]

.024

(.042)

[.008]

.050

(.040)

[.018]

.049

(.040)

[.017]

Experience squared/10 -.013

(.012)

[-.005]

-.014

(.012)

[-.005]

-.007

(.018)

[-.002]

-.008

(.018)

[-.003]

-.024

(.018)

[-.008]

-.024

(.018)

[-.009]

Tenure (years) -.107

(.014)

[-.038]

-.106

(.014)

[-.037]

-.123

(.019)

[-.043]

-.122

(.019)

[-.042]

-.098

(.022)

[-.035]

-.093

(.022)

[-.033]

Tenure squared/10 .048

(.009)

[.017]

.047

(.009)

[.017]

.055

(.012)

[.019]

.054

(.012)

[.019]

.044

(.014)

[.016]

.041

(.014)

[.015]
34

Table 8 continued

Promotion in last two

years

.452

(.050)

[.157]

.439

(.051)

[.152]

.364

(.069)

[.125]

.358

(.069)

[.122]

.546

(.075)

[.192]

.532

(.075)

[.186]

On-site seminar or training

program in last two years

.313

(.054)

[.109]

.311

(.054)

[.108]

.440

(.078)

[.148]

.450

(.078)

[.150]

.175

(.076)

[.062]

.164

(.076)

[.058]

Off-site seminar or

training program in last

two years

-.022

(.078)

[-.008]

.0124 a

(.789)

[.004]

-.015

(.118)

[-.005]

-.020

(.119)

[-.007]

-.019

(.106)

[-.007]

.020

(.108)

[.007]

Private sector .087

(.059)

[.031]

-.044

(.072)

[-.015]

-.013

(.086)

[-.004]

-.106

(.112)

[-.036]

.155

(.084)

[.056]

.005

(.098)

[.002]

Non-profit sector -.084

(.090)

[-.030]

-.091

(.091)

[-.032]

-.190

(.146)

[-.068]

-.188

(.146)

[-.066]

-.007

(.118)

[-.002]

-.017

(.119)

[-.006]

Union status -.002

(.054)

[-.001]

.019

(.054)

[.007]

.029

(.071)

[.010]

.048

(.072)

[.016]

-.004

(.084)

[-.001]

.011

(.085)

[.004]

Log of establishment size .117

(.010)

[.041]

.119

(.011)

[.042]

.105

(.014)

[.037]

.100

(.015)

[.035]

.128

(.016)

[.046]

.139

(.017)

[.049]

Marital status .056

(.046)

[.020]

.055

(.046)

[.020]

.144

(.068)

[.050]

.152

(.069)

[.053]

-.036

(.064)

[-.013]

-.043

(.064)

[-.015]

Number of children -.052

(.020)

[-.019]

-.051

(.020)

[-.018]

-.071

(.028)

[-.025]

-.072

(.028)

[-.025]

-.060

(.029)

[-.022]

-.055

(.030)

[-.019]

Child aged 6 or less .150

(.048)

[.053]

.151

(.048)

[.053]

.202

(.069)

[.070]

.197

(.069)

[.068]

.088

(.069)

[.031]

.097

(.070)

[.034]

Intercept -.657

(.235)

-.587

(.262)

-.805

(.336)

-.845

(.365)

-.615

(.344)

-.310

(.411)

Industry dummy vars.? No Yes No Yes No Yes

N 4,626 4,626 2,569 2,569 2,057 2,057

log likelihood -2,877 -2,860 -1,571 -1,562 -1,286 -1,270

Note: Standard errors in ( )s, marginal effects in [ ]s. Also included in equations are indicators for urban location

and South region.

a

 Coefficient, standard error, and marginal effect have been multiplied by 10.
35

Table 9. Probit Estimates of the Probability that a Promotion Is Possible in the Next Two Years (Includes

Supervisory Responsibility Variables)

All All Males Males Females Females

(1) (2) (3) (4) (5) (6)

Supervisory status .178

(.143)

[.063]

.188

(.144)

[.066]

.373

(.189)

[.128]

.394

(.191)

[.134]

-.063

(.248)

[-.023]

-.105

(.250)

[-.037]

Full responsibility for

subordinates’ pay or

promotions

.156

(.102)

[.054]

.152

(.103)

[.053]

.161

(.129)

[.055]

.175

(.130)

[.059]

.146

(.173)

[.052]

.117

(.174)

[.041]

Some responsibility for

subordinates’ pay or

promotions

.178

(.075)

[.062]

.181

(.075)

[.063]

.155

(.099)

[.053]

.171

(.100)

[.058]

.200

(.115)

[.071]

.167

(.116)

[.058]

Full responsibility for

setting subordinates’ job

tasks

-.153

(.144)

[-.055]

-.147

(.144)

[-.052]

-.442

(.195)

[-.154]

-.444

(.196)

[-.154]

.237

(.226)

[.083]

.270

(.228)

[.094]

Some responsibility for

setting subordinates’ job

tasks

-.043

(.141)

[-.015]

-.035

(.142)

[-.012]

-.240

(.192)

[-.084]

-.243

(.194)

[-.085]

.229

(.220)

[.080]

.258

(.221)

[.089]

Log of number of

subordinates

.025

(.030)

[.009]

.013

(.030)

[.005]

.060

(.040)

[.021]

.050

(.041)

[.017]

-.029

(.047)

[-.010]

-.043

(.047)

[-.015]

Proportion of

subordinates who are

female

-.074

(.085)

[-.026]

-.089

(.086)

[-.031]

-.043

(.139)

[-.015]

-.082

(.142)

[-.028]

-.104

(.154)

[-.037]

.060

(.156)

[.021]

Female -.164

(.044)

[-.058]

-.170

(.045)

[-.060]

---- ---- ---- ----

Black .317

(.055)

[.111]

.313

(.056)

[.109]

.278

(.075)

[.096]

.271

(.076)

[.093]

.357

(.084)

[.127]

.352

(.085)

[.123]

Hispanic .117

(.057)

[.041]

.132

(.057)

[.046]

.114

(.076)

[.039]

.129

(.076)

[.044]

.128

(.086)

[.045]

.128

(.087)

[.045]

Years of education .003

(.011)

[.001]

.006

(.012)

[.002]

.031

(.016)

[.011]

.031

(.016)

[.011]

-.028

(.017)

[-.010]

-.023

(.018)

[-.008]

AFQT score -.018

(.028)

[-.006]

-.024

(.028)

[-.008]

-.063

(.036)

[-.022]

-.071

(.037)

[-.025]

.031

(.044)

[.011]

.024

(.044)

[.008]
36

Table 9 continued

Experience (years) .030

(.029)

[.011]

.031

(.029)

[.011]

.018

(.042)

[.006]

.020

(.042)

[.007]

.048

(.040)

[.017]

.049

(.040)

[.017]

Experience squared/10 -.013

(.012)

[-.005]

-.014

(.012)

[-.005]

-.006

(.018)

[-.002]

-.007

(.018)

[-.002]

-.023

(.018)

[-.008]

-.024

(.018)

[-.008]

Tenure (years) -.109

(.014)

[-.039]

-.108

(.014)

[-.038]

-.125

(.019)

[-.043]

-.123

(.019)

[-.042]

-.099

(.022)

[-.035]

-.094

(.022)

[-.033]

Tenure squared/10 .048

(.009)

[.017]

.048

(.009)

[.017]

.055

(.012)

[.019]

.054

(.012)

[.019]

.045

(.014)

[.016]

.042

(.014)

[.015]

Promotion in last two

years

.445

(.051)

[.155]

.434

(.051)

[.150]

.359

(.069)

[.123]

.351

(.069)

[.119]

.538

(.075)

[.189]

.527

(.076)

[.184]

On-site seminar or training

program in last two years

.314

(.054)

[.109]

.313

(.054)

[.108]

.438

(.078)

[.146]

.448

(.078)

[.148]

.174

(.076)

[.062]

.163

(.077)

[.057]

Off-site seminar or

training program in last

two years

-.022

(.079)

[-.008]

.008 a

(.791)

[.003]

-.007

(.118)

[-.002]

-.010

(.119)

[-.004]

-.028

(.107)

[-.010]

.011

(.108)

[.004]

Private sector .075

(.059)

[.027]

-.047

(.072)

[-.016]

-.023

(.087)

[-.008]

-.106

(.113)

[-.036]

.145

(.085)

[.052]

.002

(.098)

[.001]

Non-profit sector -.081

(.091)

[-.029]

-.087

(.091)

[-.031]

-.189

(.146)

[-.067]

-.185

(.146)

[-.065]

-.009

(.118)

[-.003]

-.018

(.119)

[-.006]

Union status .009

(.054)

[.003]

.029

(.054)

[.010]

.045

(.071)

[.016]

.062

(.072)

[.021]

.002

(.084)

[.001]

.015

(.085)

[.005]

Log of establishment size .116

(.011)

[.041]

.119

(.011)

[.042]

.104

(.014)

[.036]

.099

(.015)

[.034]

.130

(.016)

[.046]

.140

(.017)

[.049]

Marital status .057

(.046)

[.020]

.056

(.046)

[.020]

.135

(.069)

[.047]

.143

(.069)

[.050]

-.038

(.064)

[-.014]

-.046

(.065)

[-.016]

Number of children -.053

(.020)

[-.019]

-.052

(.020)

[-.018]

-.072

(.028)

[-.025]

-.073

(.028)

[-.025]

-.062

(.029)

[-.022]

-.057

(.030)

[-.020]
37

Table 9 continued

Child aged 6 or less .151

(.048)

[.053]

.152

(.049)

[.053]

.212

(.069)

[.073]

.207

(.070)

[.071]

.091

(.069)

[.032]

.101

(.070)

[.035]

Intercept -.622

(.236)

-.563

(.263)

-.747

(.339)

-.802

(.368)

-.581

(.348)

-.282

(.414)

Industry dummy vars.? No Yes No Yes No Yes

N 4,626 4,626 2,569 2,569 2,057 2,057

log likelihood -2,873 -2,855 -1,566 -1,557 -1,283 -1,267

Note: Standard errors in ( )s, marginal effects in [ ]s. Also included in equations are indicators for urban location

and South region.

a

 Coefficient, standard error, and marginal effect have been multiplied by 10.