BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF RESEARCH

AND EVALUATION

Compensation in Part-Time Jobs versus Full-Time Jobs:

What if the Job is the Same?

Michael K. Lettau

Working Paper 260

December 1994

 

My thanks for comments on earlier drafts go to Tom Buchmueller, Larry Buron, the members of the BLS Office of Economic

Research, the BLS Office of Compensation and Working Conditions, and participants of the Hamilton-Colgate seminar series. I also

thank Wayne Shelly and William Wiatrowski for help with obtaining the data. The views expressed are those of the author and do not

necessarily reflect the policies of the BLS or the views of other BLS staff members.
Compensation in Part-time Jobs versus Full-Time Jobs:

What if the Job is the Same?

Abstract

The paper uses data from the BLS' Employment Cost Index (ECI) Program to

document differences in compensation between part-time and full-time jobs.

The design of the ECI survey allows me to compare wage and nonwage

compensation in jobs from the same establishment and occupation, but where one

job is part-time and the other job is full-time.

I find that compensation per hour is substantially lower in part-time

jobs than in full-time jobs, even when the jobs are from the same

establishment and occupation. Supplementary results using data from the

Current Population Survey suggest that human capital differences between parttime and full-time workers explain at most a minority of the large difference

in expected compensation. Therefore, the results suggest that an individual

can expect a lower wage rate if he or she decides to work part-time rather

than full-time, and much lower benefits per hour.
I. Introduction and Previous Research

For September 1994, the U.S. Department of Labor estimates that about 23

million U.S. workers work part-time. That is, 23 million U.S. workers usually

work between one and 35 hours per week, which represents 18.6% of all U.S.

workers. About 11.4% of U.S. male workers work part-time, while 27.0% of U.S.

female workers usually work part-time. Moreover, another 4.5 million U.S.

workers work at a secondary part-time job in addition to a primary full-time

job, and about 1.7 million U.S. workers work at a secondary part-time job in

addition to a primary part-time job.1 Certainly, part-time employment

represents a nontrivial segment of the U.S. labor market. And given that

part-time employment is such an important segment of the labor market, an

important question is whether a worker who decides to work part time can

expect to receive the same compensation per hour as the worker would have

received had he or she decided to work full time. If not, why not?

Basic models of labor supply assume that an individual can choose any

number of hours per week to work at a fixed market wage. However, previous

empirical research suggests that average wage rates for part-time workers are

significantly lower than average wage rates for full-time workers, even for

workers with similar human capital characteristics. The difference in average

wage rates between part-time and full-time workers is usually estimated by

including a part-time dummy variable in a log wage regression similar to

equation (1).

 

1Statistics are from Employment and Earnings, October 1994. They refer to

U.S. workers 16 years of age or older. Statistics other than for multiple job

holdings are seasonally adjusted.
2

(1) wi = xi'β + δPTi + εi

where: wi = log wage rate for individual i

 xi = characteristics of individual i

 PTi = 1 if individual i works part time, 0 if individual i works

 full time

In general, previous regressions like equation (1) suggest that average wage

rates for part-time workers are 10% to 30% lower than average wage rates for

similar full-time workers. The magnitude of the difference depends on the

particular demographic, industry, or occupation group sampled, but the

difference is nearly always negative.

Whether this implies that a worker can expect a lower wage rate if he or

she decides to work part time rather than full time depends on why average

wage rates among part-time workers are lower than average wage rates among

full-time workers with similar characteristics. Previous studies offer three

explanations.

1. Endogeneity of the choice to work part-time:

Labor supply models suggest that individuals are not assigned randomly

to part-time or full-time work. Individuals choose their hours per week by

comparing their market wage to their reservation wage for working part-time

and their reservation wage for working full-time. The coefficient estimate

for the part-time dummy variable from a regression like equation (1) may be

biased downward because of the way in which individuals sort themselves

between part-time and full-time work.

Previous studies account for correlation between the part-time dummy

variable and εi in equation (1) by also modeling the individual's decision to

work part-time. They generally include a correction term in equation (1) for
3

the selection of the individual into part-time work. The effect of correcting

for selection is not uniform across studies, however. Simpson (1986, using

Canadian data) and Main (1988, using British data for women) find that

correcting for selection slightly reduces the difference in average wage rates

between part-time and full-time workers. Hotchkiss (1991) finds that

correcting for selection slightly increases the difference in average wage

rates between part-time and full-time workers. In contrast, Blank (1990)

finds that accounting for selection sharply changes the difference in average

wage rates between part-time and full-time workers from significantly negative

to significantly positive for female workers.

2. Dual labor markets

Dual labor markets are often suggested as a possible explanation for the

difference in average wage rates between part-time and full-time workers that

is not explained by human capital variables or by selection-correction terms

(for example, the last sentence of Hotchkiss, 1991). In dual labor market

models, the primary labor market contains good jobs that pay high wage rates

and offer generous fringe benefits, while the secondary labor market contains

bad jobs that pay low wage rates and offer little or no fringe benefits.

Average wage rates among part-time workers are lower than average wage rates

among full-time workers because part-time workers are disproportionately in

jobs from the secondary labor market.

Although often cited, the dual labor market explanation has not received

much direct empirical attention. Dual labor market models emphasize

characteristics of the job rather than characteristics of the individual, so

household data sets do not suit testing dual labor markets nearly as well as

they suit testing the selection model. Indirect evidence supports the dual

labor market explanation, however. Both Rebitzer and Taylor (1991) and Tilly
4

(1992) hypothesize that part-time jobs in the primary labor market pay at

least the same wage rate as full-time jobs, but that most part-time jobs are

low-wage jobs in the secondary labor market. As support for their hypotheses,

they cite Blank (1990). Blank notes that her finding that average wage rates

for part-time workers exceed average wage rates for similar full-time workers,

once she accounts for the endogeneity of the choice to work part-time, is

particularly strong for female workers in professional and managerial

occupations.

 Rebitzer and Robinson (1991) also provide indirect support for the dual

labor markets explanation. Using the switching regression methodology

suggested by Dickens and Lang (1985), they estimate that only 5% of workers

with a high probability of being in the primary labor market are part-time,

while 31% of other workers are part-time. Moreover, Rebitzer and Taylor

(1991) and Rebitzer (1993) give theoretic justification for why part-time

workers are disproportionately in the secondary labor market. They apply the

two-sector model from Bulow and Summers (1986) to show that workers who prefer

shorter hours will disproportionately work in the secondary labor market. In

Bulow and Summers' model, workers in the secondary workers perform relatively

simple tasks, so their production is monitored costlessly. Workers in the

primary market perform more complex tasks, so their performance is relatively

costly to monitor. Therefore, workers in the primary labor market receive a

higher wage to insure that they will not shirk. Rebitzer and Taylor show that

firms in the primary labor market have an incentive to hire workers with

preferences for longer hours, so part-time workers will be stuck

disproportionately in the secondary labor market.

3. Technology
5

Selection models emphasize that individuals who choose full-time work

tend to be inherently more productive than individuals who choose part-time

work. They generally assume that the same worker is equally productive

regardless of whether he or she works a part-time or a full-time schedule. In

contrast, the technology model conjectures that a worker's output over the

course of a day is an S-shaped function of hours worked. Productivity rises

over low levels of hours due to setup costs, while productivity eventually

declines at higher levels of hours due to boredom and/or fatigue. Full-time

workers receive a higher wage rate per hour than part-time workers, not

because full-time workers are inherently more able than part-time workers, but

because a worker's output per hour is higher when he or she works a full-time

rather than a part-time schedule.

Barzel (1973) and Rosen (1978) derive equilibrium hours and wage rates

under the assumption that productivity per hour varies with the number of

hours worked per day. However, to my knowledge, no study tests the technology

explanation empirically.

To summarize, most empirical studies suggest that average wage rates are

lower for part-time workers than for full-time workers with the same

observable characteristics, even after accounting for the endogeneity of the

individual's choice to work part-time. Blank's (1990) result for female

workers is a major exception, however. Indirect empirical evidence supports

the dual labor markets explanation. For example, Blank (1987,1990) finds that

average wage rates for part-time workers exceed average wage rates for similar

full-time workers for females in professional and managerial occupations,

which others interpret as evidence that part-time workers earn at least the

same wage rate as full-time workers in the primary labor market. Finally,
6

Barzel (1973) and Rosen (1978) conjecture that a worker's productivity per

hour is tied to the number of hours per day worked, which may explain why

part-time workers receive lower wage rates than similar full-time workers.

However, their conjecture has not been tested.

II. Overview of the paper

Previous studies address the question of whether a worker can expect a

lower wage rate if he or she works part time rather than full time by

comparing average wage rates for part-time workers with average wage rates for

full-time workers with the same human capital characteristics, through a

regression like equation (1). In other words, average wage rates among fulltime workers with the same age, education, etc. estimate the wage rate a parttime worker would receive if he or she works full-time. When the study also

corrects for selection, average wage rates among full-time workers with the

same human capital characteristics and the same characteristics that affect

the individual's reservation wage (number of children, other income in family,

etc.) estimate the wage rate the part-time worker would receive as a full-time

worker.

In contrast, this paper addresses whether a worker can expect a lower

wage rate if he or she works part-time by comparing compensation in part-time

jobs with compensation in full-time jobs from the same establishment and

occupation. I use data from the BLS' Employment Cost Index (ECI) program.

The design of the ECI survey allows me to compare both wage and nonwage

compensation per hour in jobs from the same establishment and the same

occupation, but where one job is part-time while the other job is full-time.

In other words, compensation per hour in a full-time job from the same

establishment and occupation estimates the compensation per hour a part-time

worker would receive as a full-time worker. Certainly, a full-time job from
7

the same establishment and occupation provides a plausible estimate of the

type of job the part-time worker would hold if he or she worked full-time.

Therefore, it should provide a plausible estimate of the compensation per hour

the part-time worker would receive as a full-time worker. Moreover, comparing

compensation in part-time jobs with full-time jobs from the same establishment

and occupation allows me to consider previous explanations for lower average

wage rates among part-time workers more broadly.

I find that compensation per hour is substantially lower for part-time

jobs than for full-time jobs, even for jobs from the same establishment and

occupation. Supplementary results using data from the Current Population

Survey (CPS) suggest that human capital differences between part-time and

full-time workers explain at most a minority of the part-time/full-time

difference in expected compensation. Therefore, the ECI results suggest that

an individual can expect a lower wage rate as a part-time worker than as a

full-time worker, and much lower benefits per hour.

This paper is organized as follows. Section III describes the ECI micro

data used in this paper. Section IV presents the ECI results, some extensions

to the ECI results, and supplementary results using CPS data. Section V

discusses what the ECI and CPS results imply regarding previous explanations

for lower average wage rates among part-time workers.

III. Data

I use March 1994 micro data from the BLS' Employment Cost Index program.

The Employment Cost Index measures quarterly movement in wage and nonwage

compensation among private and state and local government workers in the

United States. (The ECI excludes federal government workers and the self

employed.) The ECI micro data contain detailed information on employers'

expenditures for nonwage as well as wage compensation. Moreover, the design
8

of the ECI survey makes it particularly well-suited to comparing compensation

in part-time versus full-time jobs.

The ECI is an survey of jobs within establishments.2 For each

establishment in the survey, the ECI collects data for four, six, or eight

jobs. The number depends on the establishment's size. A job refers to the

most detailed level of job recognized by the establishment. Within each

establishment, the jobs sampled are chosen randomly with the probability of

selection proportional to the job's employment.

Because the unit of observation for the ECI is a job within an

establishment, the ECI micro data are average data for the group of employees

who hold the sampled job. Note, however, that when the ECI matches employees

to a job in an establishment, employees with a different part-time/full-time

status, union/nonunion status, or time-paid/incentive-paid status are never

matched to the same job. For example, the ECI considers full-time, nonunion,

and time-paid cashiers in Establishment X to be a separate job from part-time,

nonunion, and time-paid cashiers in Establishment X. Therefore, the ECI micro

data provides multiple observations from the same establishment and 3-digit

Census occupation but with a different part-time/full-time status.

The ECI collects average hourly earnings and expenditures for the 22

benefits listed in Table A1. The ECI converts all benefit expenditures to the

average cost per hour worked among employees in the job. The ECI micro data

also reports the job's 3-digit Census occupation code and the job's work

 

2Note that the BLS' Employee Benefits Survey program uses the same sample as

the BLS' Employee Cost Index program. (See BLS Handbook of Methods, 1992.)

Also, what I refer to as a job within an establishment, BLS documentation

sometimes refers to as an occupation within an establishment or a quote within

an establishment. I use the term job rather than occupation to avoid

confusion with occupations as defined by the 3-digit Census codes, which do

not necessarily correspond to how the ECI defines a detailed job within an

establishment.
9

schedule: scheduled hours per day, scheduled hours per week, and scheduled

weeks per year. Also available are characteristics of the establishment: its

size, industry, and location. No demographic characteristics of individual

workers are collected, however.

The ECI regressions in this paper use the following specification, where

j indexes for jobs.

(2) cj = x1j'β1 + δPTj + εj; ωj

where: cj = log compensation per hour in job j

 x1j = characteristics of job j

 PTj = 1 if the job is part-time, 0 if the job is full-time

 ωj = sample weight for job j

I define a job as part time if scheduled hours per week is less than 35, and

full time if scheduled hours per week is 35 or greater. The ECI sample

weights account for the ECI's sampling scheme.3

I estimate equation (2) using weighted least-squares. Unfortunately,

the ECI micro data file does not have the number of employees within each job,

so any correction to equation (2) for the number of employees in each job is

not feasible.4 Therefore, because the variance of εj may depend on the number

of employees in job j, I use the procedure suggested by White (1980) to

calculate asymptotic standard errors for the weighted least-squares estimates.

White's procedure allows for general forms of heteroskedasticity in εj. I use

a block form of White's procedure to allow also for general forms of

 

3The ECI sample weights used in this paper are the same sample weights used to

calculate the Employer Cost for Employee Compensation estimates reported each

March by the BLS.

4Section AI of the appendix reports some information regarding the number of

employees in jobs from the March 1994 ECI sample.
10

covariance between jobs from the same establishment. Section IV.C discusses

the CPS data used in supplementary regressions.

IV. Empirical Results

I use two samples from the March 1994 ECI micro data. The top left of

Table 1A shows the size of the first sample, which I refer to as the full

sample. The full sample contains all jobs for which scheduled hours per week

is available and for which the job's average hourly earnings is not imputed.5

Of 21,642 total jobs, 18,287 are full-time and 3,355 are part-time. Much of

the analysis compares compensation within occupations, so note that the full

sample contains at least one observation from 465 occupations and two or more

observations from 443 occupations. Table 1B lists the 20 most frequent

occupations in the full sample.

The bottom left of Table 1A shows the distribution of part-time and

full-time jobs among nine occupation groups for the full sample. (All

statistics in this paper other than sample-size counts are calculated using

the ECI sample weights.) About 22% of all jobs are part-time. Part-time jobs

are concentrated in professional and technical, sales, administrative support,

and service occupations. There are few part-time jobs in the executive and

managerial, precision, craft, and machine operator occupations.

The second sample from the ECI micro data is a subset of the full

sample. I refer to the second as the same-job sample. Observations are

included in the same-job sample if there are corresponding part-time and full-

 

5For the March 1994 ECI, 3,738 jobs had a positive sample weight but no

information on scheduled hours. In addition, 468 jobs have a positive sample

weight and information on scheduled hours but the job's average hourly

earnings is imputed.

I do not delete observations for which benefit costs are in part or

fully imputed. Section AII of the appendix discusses the frequency with which

benefit costs are imputed for the two samples.
11

time jobs from the same establishment and 3-digit occupation. For example, a

part-time cashier from establishment X is included if there is at least one

full-time cashier from establishment X in the sample. The corresponding parttime and full-time observations make up the same-job sample. The same-job

sample allows regressions like equation (2) to include a dummy variable for

each establishment/occupation combination.

The top right of Table 1A shows the size of the same-job sample. Of the

21,642 observations in the full-sample, 1,138 qualify for the same-job sample.

The same-job sample has 567 part-time jobs and 571 full-time jobs from 495

distinct occupation/establishment combinations. The bottom right of Table 1A

shows the distribution of part-time jobs among occupation groups for the samejob sample. The occupation distributions of full-time and part-time jobs are

relatively similar, as both a full-time and part-time job must coexist in the

establishment and occupation for the jobs to be included in the same-job

sample. Table 1B lists the 20 most frequent occupations in the same-job

sample.

A. ECI results

Tables 2A and 2B use the full sample to show gross differences in

compensation between part-time and full-time jobs. Table 2A shows average

hourly earnings and the average cost per hour for five benefit categories.6

Table A1 of the appendix lists the benefits in each category. Average hourly

earnings is much lower in part-time jobs than in full-time jobs: $9.09 to

 

6The estimates in Table 2A differ from the Employer Cost for Employee

Compensation estimates reported for March 1994 (see News Release, USDL: 94-

290, June 16, 1994) because I delete jobs for which scheduled hours per week

is not available and jobs for which average hourly earnings is imputed. Also,

I define jobs as part time or full time based on scheduled hours per week,

whereas the Employer Cost for Employee Compensation defines jobs as part time

or full time based on how the establishment defines the job.
12

$13.97. Moreover, the average expenditure per hour for all five benefit

categories is lower for part-time jobs than for full-time jobs. And, except

for legally-required benefits, the average expenditure per hour for each

benefit category is a smaller percentage of total compensation per hour for

part-time jobs than for full-time jobs.

Table 2B shows results for the first set of regressions in the form of

equation (2). Four dependent variables: log average hourly earnings, log

benefits per hours, log total compensation per hour, and the log of average

hourly earnings divided by total compensation per hour are regressed on a

constant and a part-time dummy variable. Benefits per hour equals the sum of

the costs per hour for the 22 benefits listed in Table A1. Total compensation

per hour equals average hourly earnings plus benefits per hour.

The part-time coefficient estimate in Table 2B equals -0.531 when log

average hourly earnings is the dependent variable, which is larger than most

comparable estimates reported previously. However, the part-time coefficient

estimate is twice the magnitude at -1.083 when log of benefits per hour is the

dependent variable. Thus, the difference in average hourly earnings between

part-time and full-time jobs substantially understates the difference in total

compensation per hour between part-time and full-time jobs.

Table 3A shows coefficient estimates for equation (2) with other

explanatory variables included in the regressions. In Table 3A, there are the

four dependent variables and three sets of explanatory variables. Besides the

explanatory variables listed, all 12 regressions include the job's log

establishment size and dummy variables for the job's 3-digit Census

occupation, major industry,7 msa status, and census region. Therefore, the

 

7The 13 major industries follow the CPS' Major Industry Recode.
13

estimates for the part-time coefficient represent differences in expected

compensation within occupations and adjusted for characteristics of the

establishment. Union is a dummy variable for whether the job is unionized,

and gov is a dummy variable for whether the job is in state or local

government. Because 22 occupations have only one observation in the full

sample, observations from these occupations are dropped.

In regression [1], the coefficient estimates for the part-time dummy

variable are smaller in magnitude than the corresponding estimates in Table

2B, so differences in expected compensation between part-time and full-time

jobs are smaller within occupations than the gross differences in Table 2B.

However, the part-time coefficient estimates remain statistically significant

and are still quite large in magnitude. The part-time coefficient estimate is

-0.144 when log average hourly earnings is the dependent variable and -0.509

when log benefits per hour is the dependent variable.

Regressions [2] and [3] show results when the part-time dummy variable

is interacted with the union dummy variable and both the union and government

dummy variables. For example, Simpson (1986, Canadian data) finds that the

union/nonunion wage differential is greater for part-time workers than for

full-time workers: 42 percent for part-time workers, 19 percent for full-time

workers, which implies that the part-time/full-time wage differential is

smaller for union workers than for nonunion workers. Also note as background

that, for the full sample, 12.9% of part-time jobs are unionized, while 21.4%

of full-time jobs are unionized. Of the part-time jobs that are unionized,

58.8% are in state and local government. The three most frequent occupations

among part-time and unionized jobs are elementary school teachers, secondary

school teachers, and teacher's aids.
14

To make comparisons easier, Table 3B shows part-time/full-time

compensation differences calculated using the coefficient estimates from

regressions [1], [2], and [3] in Table 3A. For example, the union

differential for regression [2] equals the coefficient estimate for the parttime dummy variable plus the coefficient estimate for the part-time/union

interaction. The results from regression [2] suggest that compensation is

significantly lower in part-time jobs than in comparable full-time jobs for

nonunion jobs. However, the same is not true for union jobs. For all four

dependent variables, the part-time/full-time difference is not significantly

different from zero. Regression [3] allows part-time/full-time differences in

expected compensation to vary by union and government status. For jobs that

are both unionized and in state or local government, the part-time/full-time

difference is actually positive and statistically significant when log average

hourly earnings, log benefits per hour, and log total compensation per hour

are the dependent variables.

The results in Tables 3A and 3B suggest that, at least in jobs that are

not both unionized and in state and local government, compensation per hour is

significantly lower in part-time jobs than in full-time jobs, even within

occupations and adjusted for observable characteristics of the establishment.

However, unobserved differences between part-time and full-time jobs may still

cause the lower compensation in part-time jobs. For example, even within 3-

digit occupations, part-time jobs may still be disproportionately low-wage

jobs from the secondary labor market. Therefore, to control for unobserved

differences among jobs, Table 4A shows regressions results for the same-job

sample. The regressions in Table 4A include an dummy variable for each

establishment/occupation combination, so there is a separate intercept for

cashiers who work in establishment X, etc. Therefore, the regressions compare
15

compensation between part-time and full-time jobs from the same establishment

and the same occupation.

Remarkably, however, the coefficient estimates for the part-time dummy

variable in Table 4A are quite similar to the coefficient estimates for the

part-time dummy variable in Table 3A. They remain statistically significant

and quite large in magnitude.8 Within the same establishment and occupation,

the estimated part-time/full-time difference is -0.164 when log average hourly

earnings is the dependent variable and -0.475 when log benefits per hour is

the dependent variable.

The major change in the estimates between Tables 3B and 4B is for union

jobs. The part-time/full-time difference in expected compensation becomes

statistically significant and even larger in magnitude for union jobs than for

nonunion jobs once the establishment/occupation intercepts are included.

Analogous results hold for regression [3]. For all four categories of jobs,

compensation in part-time jobs is significantly lower than in full-time jobs

when jobs from the same establishment and occupation are compared.

The following chart reinforces the results in column 2 of Table 4B. It

shows just how different the provision of benefits is to part-time versus

full-time jobs, even when the jobs are from the same establishment and

occupation. Each of the 495 establishment/occupation combinations in the

same-job sample is categorized as to whether it offers insurance, paid leave,

and retirement and savings to its part-time job and/or its full-time job.9

 

8Because I use the procedure suggested by White (1980), the standard error

estimates are not adjusted for the number of parameters estimated, which is

large relative to size of the same-job sample. However, I reestimated the

standard errors assuming that the residuals in equation (2) are uncorrelated

and homoskedastic. Adjusted for the number of parameters estimated, the

standard error estimates are similar in magnitude to the ones reported.

9I count the establishment as offering the benefit to the job even if the job

has a benefit plan but a zero cost per hour. For example, the job may have a
16

The chart shows the total number of establishment/occupation combinations in

each category. (If the establishment/occupation has more than one part-time

or full-time job, the job is coded as receiving the benefit if at least one of

the jobs receives the benefit.)

________________________________________________

insurance

paid

leave

retirement

and savings

________________________________________________

both jobs 194 412 209

full-time job only 247 78 115

part-time job only 1 1 1

neither job 53 4 170

________________________________________________

It is virtually never true that the part-time job receives the benefit while

the full-time job does not. However, it is often true that the full-time job

receives the benefit while the part-time job does not.10

B. Extensions

In this section, I extend the regressions to test their robustness and

implications. Except as noted otherwise, the regressions using the full

sample follow the specification from regression [1] in Table 3A, and the

regressions using the same-job sample follow the specification from regression

[1] in Table 4A. That is, the full sample results show part-time/full-time

differences within occupations and adjusted for characteristics of the

establishment, and the same-job sample results show part-time/full-time

differences within the same establishment and occupation. I emphasize results

for the same-job sample, though I also report the full sample results for

comparison.

 

plan for paid leave, but none of the workers in the job qualify, so the job as

a zero cost per hour. I still count the job as being offered paid leave.

10Nollen and Martin (1978, p. 31) report similar discrepancies in the

provision of benefits to part-time versus full-time jobs, although they report

higher rates of benefit provisions to both part-time and full-time jobs.

Their results are based on a survey of managers who employ both part-time and

full-time workers.
17

1. Hours per day versus hours per week

The ECI micro data not only report average scheduled hours per week

among employees for each job, but also average scheduled hours per day. To my

knowledge, previous research defines part-time and full-time work based

exclusively on hours per week.11 The top of Table 5A shows results for the

full sample when log hours per day, the square of log hours per day, log hours

per week, and the square of log hours per week replace the part-time dummy

variable. The bottom of Table 5A shows results for the full sample when dummy

variables for different categories of hours per day and hours per week replace

the part-time dummy variable. Table 5B shows analogous results for the samejob sample, although I combine the category [50+) hours per week with the

omitted category [35,50) hours per week because the same-job sample has only

one job with 50 or more hours per week. Below each set of coefficient

estimates, I report estimated differences relative to an 8 hrs/day, 40

hrs/week job for three common combinations of hour per day and hours per week,

based on the coefficient estimates.

I report the two sets of coefficient estimates because the results are

somewhat sensitive to whether continuous functions of hours or hours category

variables replace the part-time dummy variable. (Results using linear,

squared, and cubed logs of hours and results using linear, squared, and cubed

levels of hours are similar to the results using linear and squared logs of

hours. Results using the category variables are robust to adding categories

for very short hours per day and hours per week, but sensitive to how hours

per day and hours per week are grouped into categories, particularly the

estimates for 6 hrs/day, 30 hrs/week jobs.) Regardless of whether the log

 

11Hamermesh (1994) presents some statistics for the joint distribution of

usual hours per week worked and days per week worked for the U.S. and Germany.
18

variables or the category variables are used, however, the hours per day

coefficient estimates are jointly significant at 5% in all four regressions

for the full sample, and when log average hourly earnings and log total

compensation per hours are the dependent variable for the same-job sample.

The hours per week coefficient estimates are jointly significant at 5% in all

four regressions for both the full and same-job samples.

Focusing on the results for the same-job sample in Table 5B, the

estimates suggest that the length of the workday is important, even among jobs

usually defined as part-time based on hours per week. Relative to an 8

hrs/day, 40 hrs/week job, the estimated difference in expected log average

hourly earnings for a 4 hrs/day, 20 hrs/week job is two to three times the

magnitude of the estimated difference for an 8 hrs/day, 24 hrs/week job. The

length of the workday appears to be less important when log benefits per hour

is the dependent variable, however. Relative to an 8 hrs/day, 40 hrs/week

job, the estimated difference in expected log benefits per hours for a 4

hrs/day, 20 hrs/week job is only one and one-fourth to one and two-thirds the

magnitude of the estimated difference for an 8 hrs/day, 24 hrs/week job.

2. Differences by occupation group

Tables 6A and 6B show results when I allow part-time/full-time

compensation differences to vary across major occupation groups. Log hours

per day, the square of log hours per day, log hours per week, and the square

of log hours per week replace the part-time dummy variable. The log hours

variables are interacted with dummy variables for the major occupation groups

in Table 1A. To increase the sample size of some groups, I combine

professional, technical with executive, managerial, and I combine precision,

craft, machine operators, transportation occupations, and handlers, laborers
19

into blue-collar occupations. I list only estimated differences relative to

an 8 hrs/day, 40 hrs/week job for brevity.

Table 6A show results for the full sample. As mentioned in Section I,

previous studies emphasize Blank's (1990) result that average wage rates for

part-time female workers exceed average wage rates for similar full-time

female workers in professional and managerial occupations. The results for

professional, executive occupations in Table 6A are generally consistent. For

all three combinations of hours, the estimated difference in expected log

average hourly earnings is smaller in magnitude for professional, executive

occupations than for the other occupations groups, and not statistically

significant. However, the estimated difference in expected log benefits per

hour is quite large in magnitude for all occupation groups, including

professional, executive occupations, particularly when 8 hrs/day, 40 hrs/week

jobs are compared to 4 hrs/day, 20 hrs/week jobs.

In contrast, however, the results for the same-job sample in Table 6B

contradict Blank's findings. In Table 6B, estimated differences for service

occupations stand out from the other occupation groups as smaller in

magnitude, particularly for 6 hrs/day, 30 hrs/week and 4 hrs/day, 20 hrs/week

jobs. Estimated differences for professional, executive occupations are very

much in line with the other occupations groups outside of service occupations.

For example, the estimated difference in expected log total compensation per

hour between an 8 hrs/day, 40 hrs/week job and a 4 hrs/day, 20 hrs/week job

equals -0.312 for professional, executive occupations, but only -0.106 for

service occupations.

When dummy variables for categories of hours per day and hours per week

are interacted with dummy variables for the major occupation groups (not

shown), the results are fairly similar. For the same-job sample, estimated
20

differences for service occupations again stand out from the other occupation

groups as smaller in magnitude for 6 hrs/day, 30 hrs/week and 4 hrs/day, 20

hrs/week jobs, although less so for 8 hrs/day, 24 hours/week jobs.

3. Establishments with extended hours

Using data from surveys of firms, Nollen and Martin (1978) and

Zeytinoglu (1992) find that the most common advantage to firms of hiring parttime workers is that part-time workers provide flexibility in scheduling

workers to match peak workload hours and/or extended hours of operation.

Table 7 shows results for the full and same-job samples with the hours

variables interacted with a dummy variable for whether the job is from a

retail trade, personal service, or entertainment industry.12 I assume that

establishments from these industries are more likely to have extended hours of

operation and therefore more likely to benefit from part-time workers.

However, estimated differences for jobs from retail trade, personal

service, or entertainment industries are generally larger in magnitude than

estimated differences for other industries. Thus, although establishments in

these industries may be more likely to benefit from hiring part-time workers,

the results in Table 7 provide no evidence that this translates into a smaller

part-time/full-time difference in expected compensation per hour. (The same

is true when dummy variables for categories of hours are used rather than logs

of hours.)

 

12The results in Table 7 relate to the results in Tables 6A and 6B in that,

for the full sample, observations from a retail trade, personal service, or

entertainment industry are 36.4% from service occupations, 30.4% from sales

occupations, and 10.0% from handlers, laborers occupations. Remaining

observations are from other major occupations groups. For the same-job

sample, 55.8% are from sales occupations, 26.4% from service occupations,

14.1% are from handlers, laborers occupations, with the remaining observations

from other major occupation groups.
21

C. CPS results

The ECI same-job sample results suggest quite strongly that a worker can

expect much lower compensation per hour if he or she decides to work part-time

rather than full-time. However, this assumes that compensation per hour among

full-time workers from the same establishment and occupation accurately

reflects what the part-time worker would receive as a full-time worker. The

obvious weakness of the ECI micro data is that no demographic information

about individual workers is available. Human capital differences between the

part-time and full-time workers may still explain the large difference in

compensation between part-time and full-time jobs. Therefore, I supplement

the ECI results with results using household data from the CPS.13 The CPS

results suggest that human capital differences between part-time and full-time

workers account at most for a minority of the large part-time/full-time

differences in expected compensation suggested by the ECI results.

The CPS regressions use the following specification; i indexes for

individuals.

(3) wi = x1i'β1 + x2i'β2 + δPTi + εi; ωi

where: wi = log average hourly earnings for individual i

 x1i = characteristics of individual i's job

 x2i = demographic characteristics of individual i

 PTi = 1 if individual i works part-time, 0 if individual i works

 full-time

 ωi = CPS earnings weight for individual i

 

13Groshen (1991) performs a somewhat analogous exercise using both

establishment and CPS data.
22

The vector x1i contains characteristics of the individual i's job that are

available in both the ECI and the CPS: dummy variables for 3-digit

occupations, major industries, census regions, union status, government

status, and a union/government interaction. (Establishment size is not

available in the CPS.) The vector x2i contains demographic characteristics of

individual i that are known to affect expected compensation but are not

available in the ECI data: age, age squared, dummy variables for whether the

individual is male, married, and white, and dummy variables for the

individual's level of education: less than high school degree, high school

degree, some college, college degree, and post-college degree.

 The strategy is the following. I first estimate equation (3) with x1i

as the only explanatory variables. I then estimate equation (3) with x1i and

x2i as the explanatory variables. The change in the part-time coefficient

estimate guides how much not including demographic characteristics potentially

affects the ECI results. As mentioned in Section I, previous studies include

a correction term in regressions like equation (3) to account for the

selection of individuals into part-time work. However, because I include

dummy variables for 3-digit Census occupation to make the results comparable

to the ECI results, the number of explanatory variables in equation (3) is

prohibitively large to allow for standard selection correction procedures.

Instead, to account for the endogeneity of the part-time dummy variable, I

also estimate the parameters in equation (3) by two-stage least-squares. The

first-stage regression follows equation (4).

(4) PTi = x1i'γ1 + x2i'γ2 + zi'γ3 + ηi; ωi

where: zi = instruments for whether individual i works part time
23

The vector zi contains instruments for an individual's decision to work parttime: number of children in the family less than five years of age, number of

children in the family at least five and under 18 years of age, dummy

variables for whether the individual is enrolled in high school, enrolled in

college part-time, and enrolled in college full-time,14 a dummy variable for

whether the individual is at least 65 years of age, and a dummy variable for

whether there are other family members 16 years of age or older who have

positive usual weekly earnings. The dummy variable for other earners in the

family is also interacted with the log of the sum of usual weekly earnings

among other family members 16 years of age and older. Moreover, all variables

in the vector zi are interacted with the male dummy variable and with a dummy

variable for whether the individual is less than 21 years old, so the

parameters in γ3 are allowed to differ between male and female workers and

between very young workers and other workers.

The data are for CPS outgoing rotation groups from all months of 1993

and all months of 1994. The samples includes individuals from age 16 through

age 70 with a positive usual hours per week worked. The samples exclude selfemployed workers and workers in agricultural industries. Average hourly

earnings equals usual weekly earnings divided by usual hour per week worked.

Observations with average hourly earnings below one or above 250 are dropped.

Part-time equals 1 if usual hours per week worked is less than 35, and zero if

usual hours per week worked is greater than or equal to 35. I report results

for all workers and separately for male and female workers.

 

14The CPS asks only individuals 24 years of age and younger the enrollment

questions. I assume workers older than 24 years of age are not enrolled in

high school or college.
24

Besides using the CPS earnings weights to account for the sample scheme

of the CPS, I create two other sets of sample weights. They are calculated

according to equations (5) and (6).

(5) ωi

full = ωi

*(fo

full/foCPS)

(6) ωi

same-job = ωi

*(fo

same-job/foCPS)

where: fo

full = proportion of ECI full sample in occupation o

 fo

same-job = proportion of ECI same-job sample in occupation o

 fo

CPS = proportion of CPS sample in occupation o

The weights from equation (5) force the CPS sample to have the same

distribution of 3-digit occupations as the ECI full sample. The weights from

equation (6) force the CPS sample to have the same distribution of 3-digit

occupations as the same-job sample. When I use the adjusted weights in

equation (5) or (6), I exclude federal government workers and workers in

agricultural or private household occupations from the sample, as these

workers are outside the ECI's sample universe. Moreover, when using the

weights from equation (6), I also report results separately for service and

nonservice occupations, because the estimated differences in compensation

between part-time and full-time jobs for service occupations stand out from

the estimates for other occupation groups in Table 6B as being smaller in

magnitude.

Table 8A shows coefficient estimates for the part-time dummy variable in

equation (3) using data pooled from all months of 1993. Table 8B shows

results using data pooled from all months of 1994.15 The results in each

 

15All regressions also include dummy variables for the survey month. In

addition, equation (3) includes a dummy variable for whether usual weekly

earnings is topcoded, both by itself and interacted with the part-time dummy

variable. Equation (4) also includes a dummy variable for whether usual

weekly earnings for any other workers in the family is topcoded.
25

table are broken into three blocks: results for all workers, results for male

workers, and results for female workers. The weights vary across the columns.

The first column uses the CPS earnings weights. The second column uses the

CPS earnings weights adjusted to the occupation distribution of the ECI full

sample. The third column uses the CPS earnings weights adjusted to the

occupation distribution of the ECI same-job sample. The fourth and fifth

columns also use the CPS earnings weights adjusted to the occupation

distribution of the ECI same-job sample. Column 4 shows results using workers

from service occupations only. Column 5 shows results using workers from

nonservice occupations only. Within each block, the set of independent

variables varies down the rows. In the first row, job characteristics are

included in equation (3). In the second row, job and demographic

characteristics are included in equation (3). In the third row, job and

demographic characteristics are included in equation (3) and the part-time

dummy variable is estimated according to equation (4). Table A3 shows sample

statistics for the variables used in the 1993 regressions for all workers.

Sample statistics for 1994 (not shown) are similar.

Note three results from Tables 8A and 8B. First, adding demographic

variables from x2i to equation (3) reduces the magnitude of the part-time

coefficient estimate for all workers by about one-third for 1993, regardless

of whether or not the CPS weights are adjusted to the ECI samples. Results

(not shown) are similar for 1992. However, the demographic variables reduce

the magnitude of the part-time coefficient estimate for all workers by over

40% for 1994. Second, the demographic variable generally reduce the magnitude

of the part-time coefficient estimates more for male workers than for female

workers. For 1993, for example, the part-time coefficient estimates are 40%
26

to 50% smaller for male workers when demographic variables are added to

equation (3), but only about 20% to 30% smaller for female workers.

Finally, focus on the two-stage least-squares results for all workers in

the third columns of Table 8A and 8B, where the CPS sample weights are

adjusted to the ECI same-job sample's distribution of occupations. For both

1993 and 1994, the two-stage least-squares estimate is statistically

significant and slightly larger in magnitude than the corresponding leastsquares estimates for all workers.16

I report the CPS results to give some sense of whether the results from

the ECI regressions would be significantly different if demographic variables

were included as explanatory variables. In general, they suggest that

including the human capital and demographic variable in x2i from equation (2)

might explain some of the large difference in expected compensation between

part-time and full-time jobs suggested by the ECI results, but the majority of

the difference would remain. Moreover, the 35% to 40% reduction in the parttime coefficient estimate when x2i is added to equation (3) probably

overstates the degree to which human capital and demographic differences

between part-time and full-time workers explain the ECI results, particularly

the same-job sample results. The CPS regressions compare part-time and fulltime workers from the same occupation, adjusted for characteristics of the

worker's establishment that are observable in the CPS. In contrast, the ECI

same-job sample regressions compare part-time and full-time jobs from both the

same occupation and the same establishment. Workers within the same

establishment and occupation are probably much more homogeneous than workers

 

16I experimented with subsamples of the CPS data from 1992, 1993, and 1994.

The two-stage least-squares estimates proved much more erratic than the

corresponding least-squares estimates.
27

in the occupation across all establishments with the same observable

characteristics. For example, cashiers who work in the same store are

probably much more alike in terms of their human capital (as measured by their

age, education, etc.) than are all cashiers who work in retail trade and live

in the same census region.17

V. Discussion

With the ECI and supplementary CPS results as background, I return to

the three previous explanations for lower average wage rates among part-time

workers than among full-time workers, and to the question of whether a worker

can expect lower compensation per hour if he or she decides to work part-time

rather than full-time.

Of the three previous explanations, the ECI results contradict dual

labor markets most strongly, at least as applied in previous studies. Dual

labor market models are often descriptive rather than theoretic, so they tend

not to lead to clear hypotheses that can be tested empirically. Nonetheless,

the striking result from the ECI samples is the remarkable robustness of the

difference in compensation between part-time and full-time jobs, even between

jobs that are otherwise similar; that is, from the same establishment and

occupation. The common theme of dual labor market models is that jobs can be

divided meaningfully between the high-wage primary labor market and low-wage

 

17There is also a more technical reason to believe that the CPS results

overstate the degree to which including demographic information about workers

would affect the ECI same-job sample results. Previous research suggests that

error in coding 3-digit occupations in the CPS is not negligible (see Polivka

and Rothgeb, 1993). Some of the explanatory power of the demographic

variables in equation (3) may account for the miscoding of workers'

occupations. By the design of the ECI, the same BLS field economist who

initially collects earnings and benefit information from the establishment

also codes the occupation for all of the establishment's sampled jobs.

Therefore, the ECI same-job sample has no error caused by different coders

coding 3-digit occupations differently.
28

secondary labor market. For example, in Rebitzer and Taylor's (1992) model,

the primary and secondary labor markets coexist because the performance in a

primary-sector job is difficult to monitor while the performance in a

secondary-sector job is monitored costlessly. Yet it is hard to imagine that

the difficulty in monitoring a part-time job differs significantly from the

difficulty in monitoring a full-time job when the two jobs are from the same

establishment and the same occupation.

Moreover, the ECI same-job sample results contradict the conclusion used

to support dual labor markets that part-time wage rates are at least as large

as full-time wage rates for workers who work in the primary labor market.

Dual labor market models universally classify professional, executive jobs and

union jobs as from the primary sector. However, and again quite strikingly,

the ECI same-job sample results suggest that within job differences in

compensation between part-time and full-time jobs are significant and at least

as large in magnitude for professional, executive jobs and for union jobs as

they are for other job categories.

The other two explanations for lower average wage rates among part-time

workers are selection and technology. Distinguishing between the two is quite

difficult, as it comes down to whether average compensation per hour differs

between part-time and full-time workers because workers who more productive

(regardless of whether they work part-time or full-time) tend to work full

time or because workers are more productive when they work a full-time rather

than a part-time schedule. Nonetheless, the technology explanation is at

least consistent with expected compensation being lower among part-time jobs

than among full-time jobs from the same occupation and establishment,

particularly because the supplementary CPS results suggest the including

information on workers' age, education, gender, and race in the ECI same-job
29

regressions would explain at most a minority of the difference. Moreover,

Barzel (1973) and Rosen (1978) focus on hours worked per day as important in

determining a worker's equilibrium wage rate. Consistent with their

formulation, the ECI same-job sample suggests that the difference in the

expected wage rate between a full-day, but short-week job and a similar fullday, full-week job is much smaller than the difference between a half-day,

full-week job and a similar full-day, full-week job. (This appears to be less

true for expected benefits per hour, however).

Thus, although the results may not distinguish between the selection and

technology explanations definitively and certainly other explanations are also

consistent with them,18 the results do provide insight into the question of

whether a worker can expect to receive the same compensation per hour if he or

she decides to work part-time rather than full-time. Previous studies use the

average wage rate among full-time workers with the same age, education, etc.

to estimate the part-time worker's wage rate as a full-time worker. The

results are somewhat mixed, however, at least when the average wage rates also

incorporate information about the worker's reservation wage through selectioncorrelation procedures. This paper uses an alternative comparison group:

full-time workers from the same establishment and occupation. Comparing

workers who work "side-by-side", the results suggest clearly that an

individual can expect a lower wage rate as a part-time worker than as a fulltime worker, and much lower benefits per hour.

 

18In Nollen and Martin (1978), for example, managers report that part-time

workers lead to higher administrative and training costs. Full-time workers

may be able to capture the savings on these costs in terms of higher

compensation.
30

Appendix

AI. Number of employees per job

As mentioned in Section III, the ECI micro data do not report the number

of employees in each job. However, the number of employees for some jobs in

the ECI sample is available from the BLS' Employee Benefits Survey (EBS)

program, which shares the same sample as the ECI.

Table A4 shows descriptive statistics for the number of employees in

each job I could match to EBS data. I could not match the majority of jobs,

so the descriptive statistics may not represent all jobs from the full sample

particularly well. Nonetheless, they give some idea of the number of workers

represented by each observation in the ECI.

AII. Imputation of benefit costs

Information on an establishment's cost per hour for one or more of a

job's benefits is sometimes not available. When the cost is not available,

the ECI imputes the cost per hour as equal to the average cost per hour among

jobs from the same industry and major occupation. Industry refers to one of

72 industry groups, which correspond approximately to 2-digit SIC industries.

Table 1 lists the nine major occupations.

Table A5 lists descriptive statistics for the proportion of benefit

costs per hour imputed in the full and same-job samples. For each job, I sum

benefit costs per hour that are imputed, and then divide the sum by the job's

total benefit costs per hour. Table A2 lists descriptive statistics for this

ratio. About half of the jobs have no benefit costs imputed, although both

samples have some jobs for which the majority of benefit costs per hour are

imputed.

Note that when the ECI imputes a cost for a job, it accounts only for

the job's industry and major occupation. A part-time job and a full-time job
31

from the same establishment and occupation will get the same imputed value for

the benefit if the benefit cost is missing. Therefore, the ECI's imputation

scheme probably leads to the same-job sample results understating the

difference in benefit costs per hour between part-time and full-time jobs,

rather than overstating them.
References

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Table 1A

Description of full and same-job samples from March 1994 ECI data

full sample same-job sample

no. of obs. 21,642 1,138

no. of part-time obs. 3,355 567

no. of full-time obs. 18,287 571

no. of occupations 465 107

no. of occup. with >1 obs. 443 107

no. of establishments 4,974 453

no. of establ./occup. combinations --- 495

% of total % of total

% part-time part-time full-time % part-time part-time full-time

all jobs 22.0% 100.0% 100.0% 50.3% 100.0% 100.0%

professional, technical 20.6% 15.0% 16.2% 46.2% 12.9% 15.2%

executive, managerial 3.3% 1.2% 10.4% 45.9% 1.0% 1.2%

sales occupations 41.6% 17.9% 7.1% 55.8% 28.6% 22.9%

administrative support 16.8% 14.0% 19.6% 52.1% 12.1% 11.3%

precision, craft 2.1% 0.9% 11.5% 50.0% 1.0% 1.0%

machine operators 1.8% 0.6% 9.6% 52.5% 0.8% 0.7%

transportation occs. 13.0% 2.8% 5.2% 34.7% 1.6% 3.1%

handlers, laborers 23.4% 8.1% 7.5% 52.1% 12.1% 11.3%

service occupations 46.4% 39.6% 12.9% 47.5% 29.8% 33.3%

Note: Statistics other than sample-size counts are calculated using the March 1994 ECI sample weights.
Table 1B

Most frequent occupations for full and same-job samples from March 1994 ECI data

1990 weighted

census no. of percent

occupation major occupation group code obs. of total

full sample

1) secretaries administrative support 313 771 3.1%

2) managers, n.e.c. executive, managerial 22 735 2.8%

3) janitors and cleaners handlers, laborers 453 568 2.7%

4) elementary school teachers professional, technical 156 545 1.8%

5) bookkeepers administrative support 337 498 2.0%

6) general office clerks administrative support 379 479 2.1%

7) nursing aids, orderlies, attendants service occupations 447 454 1.8%

8) registered nurses professional, technical 95 452 1.8%

9) cashiers sales occupations 276 446 3.2%

10) secondary school teachers professional, technical 157 399 1.3%

11) truck drivers transportation occs. 804 274 2.1%

12) sales workers, other commodities sales occupations 274 268 1.6%

13) education administrators executive, managerial 14 261 0.6%

14) stock handlers and baggers handlers, laborers 877 249 1.3%

15) supervisors, sales occupations sales occupations 243 248 1.2%

16) laborers, n.e.c. handlers, laborers 889 245 1.6%

17) admin. support occs., n.e.c. administrative support 389 228 0.9%

18) machine operators, n.e.c. machine operators 777 220 1.3%

19) financial managers executive, managerial 7 220 0.7%

20) assemblers machine operators 785 199 1.0%

same-job sample

1) nursing aids, orderlies, attendants service occupations 447 132 6.2%

2) cashiers sales occupations 276 127 12.7%

3) sales workers, other commodities sales occupations 274 105 7.5%

4) registered nurses professional, technical 95 99 7.2%

5) stock handlers and baggers handlers, laborers 877 62 4.7%

6) bank tellers administrative support 383 38 2.1%

7) janitors and cleaners service occupations 453 35 4.8%

8) elementary school teachers professional, technical 156 30 1.0%

9) secretaries administrative support 313 28 0.9%

10) sales workers, apparel sales occupations 264 23 4.1%

11) food preparation occs, n.e.c. service occupations 444 23 3.0%

12) guards, except public service service occupations 426 20 3.1%

13) general office clerks administrative support 379 20 1.8%

14) secondary school teachers professional, technical 157 20 1.0%

15) investigators, except insurance administrative support 376 18 0.8%

16) food counter and related occs. service occupations 438 14 2.6%

17) material handlers, n.e.c. handlers, laborers 883 14 2.3%

18) bus drivers transportation occs. 808 12 1.0%

19) waiters and waitresses service occupations 435 10 1.8%

20) truck drivers transportation occs. 804 10 1.4%

Note: Statistics other than sample-size counts are calculated using the March 1994 ECI sample weights.
Table 2A

Gross differences in average hourly earnings and benefit costs per hour between part-time and full-time jobs,

using full sample from March 1994 ECI data

 part-time jobs full-time jobs

average % of total average % of total

average hourly earnings $9.09 76.7% $13.97 69.7%

legally-required benefits $1.13 9.5% $1.71 8.5%

insurance $0.66 5.6% $1.57 7.8%

supplemental pay $0.14 1.2% $0.52 2.6%

retirement and savings $0.34 2.9% $0.85 4.2%

paid leave $0.49 4.1% $1.43 7.1%

total compensation per hour $11.84 100.0% $20.06 100.0%

no. of observations 3,355 18,287

Note: Statistics calculated using the March 1994 ECI sample weights.

Table 2B

Weighted least-squares coefficient estimates using full sample from March 1994 ECI data

log total log avg. hourly

log average log benefits compensation earnings/total comp.

hourly earnings per hour per hour per hour

intercept 2.480 ** 1.558 ** 2.833 ** -0.353 **

( 0.010 ) ( 0.015 ) ( 0.011 ) ( 0.002 )

part-time -0.531 ** -1.083 ** -0.653 ** 0.122 **

( 0.025 ) ( 0.042 ) ( 0.027 ) ( 0.005 )

no. of observations 21,642 21,641 21,642 21,642

Note: Asymptotic standard errors in parentheses, * indicates significance at 10%, ** indicates significance at 5%.
Table 3A

Weighted least-squares coefficient estimates using full sample from March 1994 ECI data;

All regressions also include log establishment size and dummy variables for occupations,

major industries, msa status, and census regions.

log total log avg. hourly

log average log benefits compensation earnings/total comp.

hourly earnings per hour per hour per hour

[1] part-time -0.144 ** -0.509 ** -0.219 ** 0.074 **

( 0.012 ) ( 0.024 ) ( 0.013 ) ( 0.004 )

union 0.253 ** 0.571 ** 0.350 ** -0.097 **

( 0.016 ) ( 0.028 ) ( 0.018 ) ( 0.006 )

gov 0.014 0.167 ** 0.058 ** -0.043 **

( 0.016 ) ( 0.028 ) ( 0.017 ) ( 0.006 )

union*gov -0.073 ** -0.238 ** -0.127 ** 0.054 **

( 0.020 ) ( 0.039 ) ( 0.024 ) ( 0.008 )

[2] part-time -0.170 ** -0.593 ** -0.257 ** 0.087 **

( 0.013 ) ( 0.027 ) ( 0.015 ) ( 0.005 )

union 0.232 ** 0.504 ** 0.319 ** -0.087 **

( 0.016 ) ( 0.027 ) ( 0.019 ) ( 0.006 )

gov 0.014 0.167 ** 0.058 ** -0.043 **

( 0.016 ) ( 0.028 ) ( 0.017 ) ( 0.006 )

union*gov -0.085 ** -0.277 ** -0.145 ** 0.060 **

( 0.020 ) ( 0.040 ) ( 0.024 ) ( 0.008 )

part-time*union 0.171 ** 0.553 ** 0.252 ** -0.081 **

( 0.027 ) ( 0.059 ) ( 0.032 ) ( 0.011 )

[3] part-time -0.186 ** -0.598 ** -0.270 ** 0.084 **

( 0.014 ) ( 0.027 ) ( 0.015 ) ( 0.005 )

union 0.246 ** 0.520 ** 0.334 ** -0.088 **

( 0.016 ) ( 0.027 ) ( 0.019 ) ( 0.006 )

gov -0.007 0.164 ** 0.041 ** -0.048 **

( 0.017 ) ( 0.027 ) ( 0.018 ) ( 0.006 )

union*gov -0.100 ** -0.312 ** -0.166 ** 0.066 **

( 0.021 ) ( 0.035 ) ( 0.023 ) ( 0.008 )

part-time*union 0.039 0.404 ** 0.110 ** -0.071 **

( 0.041 ) ( 0.106 ) ( 0.054 ) ( 0.019 )

part-time*gov 0.136 ** 0.027 0.109 ** 0.027 *

( 0.035 ) ( 0.091 ) ( 0.042 ) ( 0.015 )

part-time*union*gov 0.123 ** 0.243 * 0.163 ** -0.039

( 0.056 ) ( 0.140 ) ( 0.070 ) ( 0.025 )

no. of observations 21,620 21,619 21,620 21,620

Note: Asymptotic standard errors in parentheses, * indicates significance at 10%, ** indicates significance at 5%.
Table 3B

Part-time/full-time differences among job categories, calculated using

weighted least-squares coefficient estimates from Table 3A.

log total log avg. hourly

log average log benefits compensation earnings/total comp. no. of observations

hourly earnings per hour per hour per hour for job category

[1] all jobs -0.144 ** -0.509 ** -0.219 ** 0.074 ** 21,620

( 0.012 ) ( 0.024 ) ( 0.013 ) ( 0.004 )

[2] nonunion -0.170 ** -0.593 ** -0.257 ** 0.087 ** 16,247

( 0.013 ) ( 0.027 ) ( 0.015 ) ( 0.005 )

union 0.001 -0.039 -0.005 0.006 5,373

( 0.024 ) ( 0.052 ) ( 0.029 ) ( 0.010 )

[3] nonunion/nongovernment -0.186 ** -0.598 ** -0.270 ** 0.084 ** 13,519

( 0.014 ) ( 0.027 ) ( 0.015 ) ( 0.005 )

union/nongovernment -0.148 ** -0.194 * -0.161 ** 0.013 2,890

( 0.040 ) ( 0.103 ) ( 0.053 ) ( 0.019 )

nonunion/government -0.051 -0.572 ** -0.162 ** 0.111 ** 2,728

( 0.033 ) ( 0.089 ) ( 0.040 ) ( 0.015 )

union/government 0.111 ** 0.075 * 0.111 ** 0.001 2,483

( 0.022 ) ( 0.043 ) ( 0.024 ) ( 0.010 )

no. of observations 21,620 21,619 21,620 21,620

Notes: Asymptotic standard errors in parentheses, * indicates significance at 10%, ** indicates significance at 5%.

The observation with zero benefits per hour is nonunion and nongovernment.
Table 4A

Weighted least-squares coefficient estimates using same-job sample from March 1994 ECI data;

dummy variables for establishment/occupation combinations also included in all regressions.

log total log avg. hourly

log average log benefits compensation earnings/total comp.

hourly earnings per hour per hour per hour

[1] part-time -0.164 ** -0.475 ** -0.227 ** 0.063 **

( 0.017 ) ( 0.035 ) ( 0.017 ) ( 0.007 )

union 0.186 * 0.467 0.275 -0.089

( 0.112 ) ( 0.351 ) ( 0.176 ) ( 0.080 )

union*gov 0.229 0.372 0.234 -0.005

( 0.167 ) ( 0.576 ) ( 0.240 ) ( 0.120 )

[2] part-time -0.155 ** -0.475 ** -0.220 ** 0.065 **

( 0.019 ) ( 0.038 ) ( 0.019 ) ( 0.008 )

union 0.223 ** 0.466 0.303 * -0.080

( 0.107 ) ( 0.359 ) ( 0.175 ) ( 0.083 )

union*gov 0.213 0.372 0.222 -0.009

( 0.163 ) ( 0.578 ) ( 0.236 ) ( 0.123 )

part-time*union -0.084 ** 0.001 -0.064 -0.021

( 0.035 ) ( 0.101 ) ( 0.040 ) ( 0.023 )

[3] part-time -0.155 ** -0.462 ** -0.217 ** 0.062 **

( 0.020 ) ( 0.038 ) ( 0.019 ) ( 0.008 )

union 0.230 ** 0.383 0.289 -0.060

( 0.107 ) ( 0.381 ) ( 0.181 ) ( 0.088 )

union*gov 0.199 0.319 0.204 -0.005

( 0.173 ) ( 0.557 ) ( 0.243 ) ( 0.119 )

part-time*union -0.099 ** 0.207 ** -0.029 -0.070 **

( 0.048 ) ( 0.082 ) ( 0.049 ) ( 0.022 )

part-time*gov -0.005 -0.300 -0.063 0.058

( 0.051 ) ( 0.191 ) ( 0.073 ) ( 0.037 )

part-time*union*gov 0.042 -0.237 -0.028 0.070

( 0.075 ) ( 0.258 ) ( 0.100 ) ( 0.052 )

no. of observations 1,138 1,138 1,138 1,138

Note: Asymptotic standard errors in parentheses, * indicates significance at 10%, ** indicates significance at 5%.
Table 4B

Part-time/full-time differences among job categories, calculated using

weighted least-squares coefficient estimates from Table 4A.

log total log avg. hourly

log average log benefits compensation earnings/total comp. no. of obs.

hourly earnings per hour per hour per hour for job cat.

[1] all jobs -0.164 ** -0.475 ** -0.227 ** 0.063 ** 1,138

( 0.017 ) ( 0.035 ) ( 0.017 ) ( 0.007 )

[2] nonunion/nongovernment -0.155 ** -0.475 ** -0.220 ** 0.065 ** 959

( 0.019 ) ( 0.038 ) ( 0.019 ) ( 0.008 )

union -0.239 ** -0.475 ** -0.284 ** 0.044 ** 179

( 0.029 ) ( 0.094 ) ( 0.036 ) ( 0.022 )

[3] nonunion/nongovernment -0.155 ** -0.462 ** -0.217 ** 0.062 ** 885

( 0.020 ) ( 0.038 ) ( 0.019 ) ( 0.008 )

union/nongovernment -0.254 ** -0.255 ** -0.246 ** -0.008 102

( 0.043 ) ( 0.072 ) ( 0.045 ) ( 0.020 )

nonunion/government -0.160 ** -0.763 ** -0.280 ** 0.120 ** 74

( 0.047 ) ( 0.188 ) ( 0.071 ) ( 0.036 )

union/government -0.217 ** -0.792 ** -0.337 ** 0.120 ** 77

( 0.034 ) ( 0.151 ) ( 0.049 ) ( 0.030 )

no. of observations 1,138 1,138 1,138 1,138

Notes: Asymptotic standard errors in parentheses, * indicates significance at 10%, ** indicates significance at 5%.
Table 5

Weighted least-squares coefficient estimates using same-job sample from March 1994 ECI data;

Part-time dummy variable replaced with functions of daily hours and weekly hours.

log total log avg. hourly

log average log benefits compensation earnings/total comp.

logs of hours included hourly earnings per hour per hour per hour

log hours per day -0.211 0.619 -0.114 -0.097

( 0.172 ) ( 0.726 ) ( 0.213 ) ( 0.145 )

square of log hours per day 0.105 ** -0.128 0.080 0.025

( 0.049 ) ( 0.211 ) ( 0.063 ) ( 0.042 )

log hours per week 0.029 -0.687 -0.063 0.092

( 0.311 ) ( 0.576 ) ( 0.302 ) ( 0.129 )

square of log hours per week 0.015 0.186 ** 0.041 -0.026

( 0.051 ) ( 0.088 ) ( 0.051 ) ( 0.019 )

no. of observations 1,138 1,138 1,138 1,138

Relative to 8 hrs/day, 40 hrs/week:

8 hrs/day, 24 hrs/week -0.068 ** -0.301 ** -0.111 ** 0.043 **

( 0.029 ) ( 0.065 ) ( 0.031 ) ( 0.013 )

6 hrs/day, 30 hrs/week -0.096 ** -0.217 ** -0.121 ** 0.026 **

( 0.014 ) ( 0.048 ) ( 0.019 ) ( 0.008 )

4 hrs/day, 20 hrs/week -0.197 ** -0.507 ** -0.259 ** 0.062 **

( 0.022 ) ( 0.064 ) ( 0.028 ) ( 0.011 )

hours categories included

hours per day: (0,5) -0.094 ** -0.111 -0.095 * 0.002

( 0.047 ) ( 0.109 ) ( 0.051 ) ( 0.022 )

hours per day: [5,7) -0.079 * -0.043 -0.072 -0.007

( 0.045 ) ( 0.096 ) ( 0.045 ) ( 0.020 )

hours per day: [10+) 0.146 ** -0.013 0.137 0.009

( 0.059 ) ( 0.261 ) ( 0.101 ) ( 0.054 )

hours per week: (0,25) -0.106 ** -0.442 ** -0.174 ** 0.068 **

( 0.039 ) ( 0.095 ) ( 0.042 ) ( 0.019 )

hours per week: [25,35) -0.090 ** -0.356 ** -0.145 ** 0.055 **

( 0.046 ) ( 0.092 ) ( 0.048 ) ( 0.017 )

no. of observations 1,138 1,138 1,138 1,138

Relative to 8 hrs/day, 40 hrs/week:

8 hrs/day, 24 hrs/week -0.106 ** -0.442 ** -0.174 ** 0.068 **

( 0.039 ) ( 0.095 ) ( 0.042 ) ( 0.019 )

6 hrs/day, 30 hrs/week -0.170 ** -0.399 ** -0.218 ** 0.048 **

( 0.027 ) ( 0.051 ) ( 0.027 ) ( 0.010 )

4 hrs/day, 20 hrs/week -0.199 ** -0.552 ** -0.269 ** 0.070 **

( 0.026 ) ( 0.051 ) ( 0.028 ) ( 0.010 )

Notes: Asymptotic standard errors in parentheses; * indicates significance at 10%; ** indicates significance at 5%.

Regressions also include dummy variables for establishment/occupation, union status, and union/gov.

Omitted hours categories are hours per day: [7,10) and hours per week: [35+).
Table 6

Estimated compensation differences, calculated from weighted least-squares coefficient estimates using same-job

sample from March 1994 ECI data; estimated differences allowed to vary by major occupation groups.

log total log avg. hourly

log average log benefits compensation earnings/total comp. no. of obs.

hourly earnings per hour per hour per hour for job cat.

8 hrs/day, 24 hrs/week: relative to 8 hrs/day, 40 hrs/week

professional, executive -0.021 -0.317 ** -0.073 * 0.052 ** 241

( 0.036 ) ( 0.079 ) ( 0.038 ) ( 0.017 )

sales occupations -0.174 ** -0.294 -0.185 ** 0.011 274

( 0.042 ) ( 0.208 ) ( 0.047 ) ( 0.049 )

administrative support -0.029 -0.450 ** -0.110 ** 0.080 ** 176

( 0.037 ) ( 0.218 ) ( 0.055 ) ( 0.040 )

service occupations -0.085 -0.266 * -0.120 0.035 * 305

( 0.079 ) ( 0.143 ) ( 0.086 ) ( 0.020 )

blue-collar occupations -0.151 ** -0.527 ** -0.246 ** 0.095 ** 142

( 0.046 ) ( 0.110 ) ( 0.051 ) ( 0.021 )

6 hrs/day, 30 hrs/week: relative to 8 hrs/day, 40 hrs/week

professional, executive -0.124 ** -0.301 ** -0.164 ** 0.040 ** 241

( 0.034 ) ( 0.064 ) ( 0.035 ) ( 0.012 )

sales occupations -0.110 ** -0.350 ** -0.155 ** 0.045 ** 274

( 0.017 ) ( 0.031 ) ( 0.017 ) ( 0.006 )

administrative support -0.148 ** -0.266 ** -0.175 ** 0.027 ** 176

( 0.025 ) ( 0.063 ) ( 0.027 ) ( 0.013 )

service occupations -0.035 ** -0.038 -0.038 ** 0.003 305

( 0.013 ) ( 0.041 ) ( 0.017 ) ( 0.007 )

blue-collar occupations -0.199 ** -0.290 ** -0.217 ** 0.018 142

( 0.035 ) ( 0.063 ) ( 0.036 ) ( 0.014 )

4 hrs/day, 20 hrs/week: relative to 8 hrs/day, 40 hrs/week

professional, executive -0.230 ** -0.638 ** -0.312 ** 0.082 ** 241

( 0.057 ) ( 0.117 ) ( 0.060 ) ( 0.021 )

sales occupations -0.229 ** -0.700 ** -0.319 ** 0.090 ** 274

( 0.041 ) ( 0.057 ) ( 0.040 ) ( 0.011 )

administrative support -0.254 ** -0.691 ** -0.346 ** 0.093 ** 176

( 0.039 ) ( 0.106 ) ( 0.045 ) ( 0.021 )

service occupations -0.052 * -0.319 ** -0.110 ** 0.058 ** 305

( 0.028 ) ( 0.088 ) ( 0.034 ) ( 0.018 )

blue-collar occupations -0.325 ** -0.458 ** -0.351 ** 0.025 142

( 0.054 ) ( 0.092 ) ( 0.056 ) ( 0.021 )

no. of observations 1,138 1,138 1,138 1,138

Notes: Asymptotic standard errors in parentheses; * indicates significance at 10%; ** indicates significance at 5%.

All regressions also include dummy variables for establishment/occupation combinations, union status, and union/government

 interaction.
Table 7

Estimated compensation differences, calculated from weighted least-squares coefficient estimates using full sample

from March 1994 ECI data; estimated differences allowed to differ for two industry groups.

log total log avg. hourly

log average log benefits compensation earnings/total comp. no. of obs.

full sample hourly earnings per hour per hour per hour for job cat.

8 hrs/day, 24 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.098 ** -0.274 ** -0.133 ** 0.035 ** 2,773

( 0.038 ) ( 0.060 ) ( 0.036 ) ( 0.012 )

other industries -0.043 ** -0.288 ** -0.091 ** 0.048 ** 18,847

( 0.019 ) ( 0.034 ) ( 0.020 ) ( 0.006 )

6 hrs/day, 30 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.130 ** -0.284 ** -0.161 ** 0.031 ** 2,773

( 0.017 ) ( 0.029 ) ( 0.018 ) ( 0.005 )

other industries -0.041 ** -0.228 ** -0.080 ** 0.039 ** 18,847

( 0.009 ) ( 0.016 ) ( 0.010 ) ( 0.003 )

4 hrs/day, 20 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.248 ** -0.563 ** -0.313 ** 0.065 ** 2,773

( 0.025 ) ( 0.045 ) ( 0.026 ) ( 0.008 )

other industries -0.104 ** -0.534 ** -0.193 ** 0.089 ** 18,847

( 0.019 ) ( 0.033 ) ( 0.020 ) ( 0.006 )

no. of observations 21,620 21,619 21,620 21,620

same-job sample

8 hrs/day, 24 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.194 ** -0.428 ** -0.229 ** 0.035 405

( 0.056 ) ( 0.147 ) ( 0.060 ) ( 0.029 )

other industries -0.008 -0.254 ** -0.058 ** 0.049 ** 733

( 0.024 ) ( 0.078 ) ( 0.028 ) ( 0.017 )

6 hrs/day, 30 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.125 ** -0.302 ** -0.158 ** 0.033 ** 405

( 0.021 ) ( 0.057 ) ( 0.026 ) ( 0.010 )

other industries -0.072 ** -0.181 ** -0.097 ** 0.025 ** 733

( 0.018 ) ( 0.066 ) ( 0.025 ) ( 0.012 )

4 hrs/day, 20 hrs/week: relative to 8 hrs/day, 40 hrs/week

retail trade, pers. serv.,enter. -0.223 ** -0.546 ** -0.287 ** 0.063 ** 405

( 0.028 ) ( 0.065 ) ( 0.032 ) ( 0.012 )

other industries -0.158 ** -0.449 ** -0.219 ** 0.061 ** 733

( 0.030 ) ( 0.097 ) ( 0.040 ) ( 0.018 )

no. of observations 1,138 1,138 1,138 1,138

Notes: Asymptotic standard errors in parentheses; * indicates significance at 10%; ** indicates significance at 5%.

Full sample regressions also include dummy variables for 3-digit occupations, major industries, msa status, census region,

 union status, government status, and union/government interaction.

Same-job sample regressions also include dummy variables for establishment/occupation, union status, and union/government

 interaction.

The observation with zero benefits per hour is from other industries.
Table 8

Least-Squares and Two-Stage Least-Squares Coefficient Estimates for Part-Time Dummy Variable,

using pooled CPS data from all months of 1993 and 1994.

1993 1994

CPS ECI same-job sample CPS ECI same-job sample

all workers occupation dist'n occupation dist'n occupation dist'n occupation dist'n

job characteristics -0.178 ** -0.149 ** -0.178 ** -0.158 **

( 0.004 ) ( 0.006 ) ( 0.004 ) ( 0.007 )

job and demographic char. -0.116 ** -0.097 ** -0.101 ** -0.094 **

( 0.004 ) ( 0.006 ) ( 0.004 ) ( 0.007 )

job and demographic char.; -0.099 ** -0.109 ** -0.097 ** -0.103 **

 PT endogenous (2SLS) ( 0.012 ) ( 0.020 ) ( 0.013 ) ( 0.024 )

no. of observations 129,755 71,236 141,300 76,330

no. of occupations matched --- 103 --- 104

no. of occupations not matched --- 4 --- 3

weighted prop. of unmatched occs. --- 0.017 --- 0.016

male workers

job characteristics -0.263 ** -0.224 ** -0.266 ** -0.259 **

( 0.007 ) ( 0.016 ) ( 0.007 ) ( 0.017 )

job and demographic char. -0.138 ** -0.119 ** -0.133 ** -0.147 **

( 0.007 ) ( 0.017 ) ( 0.007 ) ( 0.018 )

job and demographic char.; -0.096 ** -0.075 -0.093 ** -0.049

 PT endogenous (2SLS) ( 0.018 ) ( 0.050 ) ( 0.019 ) ( 0.050 )

no. of observations 65,333 27,463 72,116 29,679

no. of occupations matched --- 103 --- 103

no. of occupations not matched --- 4 --- 4

weighted prop. of unmatched occs. --- 0.017 --- 0.017

female workers

job characteristics -0.121 ** -0.114 ** -0.115 ** -0.115 **

( 0.004 ) ( 0.009 ) ( 0.005 ) ( 0.009 )

job and demographic char. -0.095 ** -0.082 ** -0.079 ** -0.076 **

( 0.004 ) ( 0.008 ) ( 0.004 ) ( 0.009 )

job and demographic char.; -0.085 ** -0.089 ** -0.100 ** -0.095 **

 PT endogenous (2SLS) ( 0.015 ) ( 0.032 ) ( 0.018 ) ( 0.036 )

no. of observations 64,377 43,773 69,147 46,651

no. of occupations matched --- 102 --- 103

no. of occupations not matched --- 5 --- 4

weighted prop. of unmatched occs. --- 0.021 --- 0.021

Note: Asymptotic standard errors in parentheses; * indicates significance at 10%; ** indicates significance at 5%.
Table A1

Benefits collected by ECI

benefit benefit category

1) social security legally-required benefits

2) railroad retirement legally-required benefits

3) railroad supplemental retirement legally-required benefits

4) railroad unemployment insurance legally-required benefits

5) federal unemployment insurance legally-required benefits

6) state unemployment insurance legally-required benefits

7) workers' compensation legally-required benefits

8) other legally-required benefits legally-required benefits

9) life insurance insurance

10) health insurance insurance

11) sickness and accident insurance insurance

12) overtime supplemental pay

13) shift differential supplemental pay

14) nonproduction bonus supplemental pay

15) severance pay supplemental pay

16) supplemental unemployment supplemental pay

17) pension and retirements retirement and savings

18) savings and thrift retirement and savings

19) vacations paid leave

20) holidays paid leave

21) sick leave paid leave

22) other paid leave paid leave
Table A2

Sample means and standard deviations for full and same-job samples from March 1994 ECI data.

full sample same-job sample

mean std mean std

log avg. hourly earnings 2.36 0.61 2.02 0.53

log benefits per hour 1.32 0.90 0.72 0.86

log total comp. per hour 2.69 0.65 2.28 0.58

log avg. hourly earn./total comp. per hour -0.33 0.13 -0.26 0.12

part-time 0.22 0.41 0.50 0.50

union 0.20 0.40 0.11 0.31

government 0.16 0.37 0.09 0.28

union*government 0.08 0.27 0.05 0.21

part-time*union 0.03 0.17 0.05 0.21

part-time*government 0.03 0.17 0.04 0.19

part-time*union*government 0.02 0.13 0.02 0.13

log establ. size 4.99 2.17 4.69 1.75

msa status 0.83 0.37 0.87 0.34

northeast 0.23 0.42 0.26 0.44

south 0.32 0.47 0.28 0.45

midwest 0.25 0.43 0.23 0.42

west 0.20 0.40 0.23 0.42

mining 0.005 0.07 0.000 0.00

construction 0.044 0.21 0.017 0.13

durable manufacturing 0.093 0.29 0.005 0.07

nondurable manufacturing 0.073 0.26 0.014 0.12

trans, comm, and pub util 0.056 0.23 0.044 0.20

wholesale trade 0.059 0.24 0.021 0.14

retail trade 0.178 0.38 0.408 0.49

finance, insur, real estate 0.057 0.23 0.048 0.21

business, repair services 0.068 0.25 0.096 0.29

personal services 0.025 0.16 0.029 0.17

entertainment, recreation 0.017 0.13 0.005 0.07

professional services 0.278 0.45 0.295 0.46

public administration 0.044 0.21 0.019 0.14

log hours per day 1.97 0.27 1.85 0.33

square of log hours per day 3.96 0.88 3.55 1.12

log hours per week 3.53 0.36 3.31 0.47

square of log hours per week 12.57 2.16 11.19 2.83

hours per day: (0,5) 0.09 0.28 0.20 0.40

hours per day: [5,7) 0.10 0.31 0.21 0.41

hours per day: [7,10) 0.78 0.41 0.57 0.50

hours per day: [10+) 0.03 0.16 0.02 0.14

hours per week: (0,25) 0.15 0.35 0.36 0.48

hours per week: [25,35) 0.07 0.26 0.15 0.35

hours per week: [35+) 0.78 0.41 0.50 0.50

no. of observations 21,620 21,620 1,138 1,138

Note: Statistics calculated using the March 1994 ECI sample weights.
Table A3 (pg. 1)

Sample means and standard deviations for CPS, data pooled from all months of 1993.

CPS ECI same-job sample

occupation dist'n occupation dist'n

mean std mean std

log avg. hourly earnings 2.31 0.57 2.01 0.53

part-time 0.19 0.39 0.41 0.49

union 0.19 0.39 0.15 0.36

government 0.18 0.38 0.14 0.34

union*government 0.08 0.28 0.06 0.24

msa status 0.77 0.42 0.76 0.43

northeast 0.27 0.44 0.27 0.44

south 0.29 0.46 0.29 0.45

midwest 0.28 0.45 0.28 0.45

west 0.16 0.36 0.16 0.37

mining 0.005 0.072 0.001 0.037

construction 0.049 0.215 0.021 0.144

durable manufacturing 0.114 0.318 0.023 0.149

nondurable manufacturing 0.084 0.277 0.026 0.158

trans, comm, and pub util 0.074 0.262 0.040 0.196

wholesale trade 0.038 0.192 0.018 0.134

retail trade 0.174 0.379 0.395 0.489

finance, insur, real estate 0.066 0.248 0.051 0.220

business, repair services 0.049 0.216 0.045 0.207

personal services 0.032 0.176 0.040 0.195

entertainment, recreation 0.005 0.070 0.007 0.083

professional services 0.258 0.437 0.315 0.464

public administration 0.052 0.223 0.019 0.136

age 37.45 12.05 34.93 13.64

age squared/100 15.48 9.71 14.06 10.70

less than high school 0.12 0.33 0.19 0.40

high school degree 0.35 0.48 0.36 0.48

some college 0.03 0.18 0.03 0.18

college degree 0.17 0.37 0.11 0.31

post-college degree 0.08 0.27 0.04 0.20

male 0.51 0.50 0.35 0.48

white 0.88 0.33 0.85 0.36

married 0.60 0.49 0.48 0.50

no. of observations 129,755 129,755 71,236 71,236
Table A3 (pg. 2)

Sample means and standard deviations for CPS, data pooled from all months of 1993.

CPS ECI same-job sample

occupation dist'n occupation dist'n

mean std mean std

enrolled in high school 0.018 0.134 0.059 0.236

enrolled in college full time 0.024 0.152 0.057 0.232

enrolled in college part time 0.007 0.085 0.013 0.113

kids less than five years old in family 0.22 0.52 0.19 0.49

kids between five and 17 years old in family 0.62 0.95 0.67 0.99

other earners in family 0.56 0.50 0.57 0.50

other earners in family*log other family earnings 3.39 3.08 3.51 3.10

age greater than 65 0.02 0.12 0.02 0.15

male*enrolled in high school 0.009 0.097 0.026 0.160

male*enrolled in college full time 0.010 0.101 0.023 0.149

male*enrolled in college part time 0.003 0.055 0.005 0.070

male*kids less than five years old in family 0.12 0.41 0.06 0.30

male*kids between five and 17 years old in family 0.31 0.75 0.22 0.65

male*other earners in family 0.28 0.45 0.20 0.40

male*other earners in family*log other family earnings 1.65 2.69 1.20 2.44

male*age greater than 65 0.01 0.08 0.01 0.09

age<21*enrolled in high school 0.018 0.134 0.059 0.235

age<21*enrolled in college full time 0.012 0.108 0.033 0.177

age<21*enrolled in college part time 0.002 0.049 0.005 0.071

age<21*kids less than five years old in family 0.01 0.10 0.02 0.15

age<21*kids between five and 17 years old in family 0.06 0.38 0.18 0.61

age<21*other earners in family 0.05 0.22 0.13 0.34

age<21*other earners in family*log other family earnings 0.32 1.40 0.85 2.19

no. of observations 129,755 129,755 71,236 71,236
Table A4

Descriptive statistics for the number of employees in each job, based on

1993 EBS data matched to the full sample from March 1994 ECI data.

full sample, full sample, full sample,

all jobs full-time jobs part-time jobs

mean 54.9 48.0 87.0

standard deviation 174.0 162.8 216.2

minimum 1 1 1

P10 1 1 1

P25 3 3 5

median 9 8 19

P75 36 30 55

P90 113 95 190

maximum 3,130 3,130 2,858

no. of observations matched 6,628 5,779 849

no. of observations not matched 15,014 12,508 2,506

wgt. proportion of obs. matched 25.2% 26.7% 20.1%

Note: Statistics calculated using the March 1994 ECI sample weights.

Table A5

Descriptive statistics for ratio of imputed benefit costs to total benefit costs,

for full and same-job samples from March 1994 ECI data.

full sample same-job sample

mean 0.085 0.094

standard deviation 0.152 0.151

minimum 0.000 0.000

P10 0.000 0.000

P25 0.000 0.000

median 0.000 0.007

P75 0.102 0.146

P90 0.307 0.324

maximum 0.994 0.812

proportion equal to 0 0.500 0.456

no. of observations 21,642 1,138

Note: Statistics calculated using the March 1994 ECI sample weights.