BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF COMPENSATION

AND WORKING CONDITIONS

Compensation Inequality

Brooks Pierce

Working Paper 323

June 1999

 

I am grateful to Tony Barkume, Will Carrington, Daniel Hamermesh, Larry Katz, Maury Gittleman, Mike Lettau, Mark

Loewenstein, Kristin McCue, John Ruser, and seminar participants at the Bureau of the Census and the University of

Maryland for helpful comments. The views expressed are those of the author and do not necessarily reflect the views or

policies of the BLS or any other agency of the US. Department of Labor.
Compensation Inequality

Brooks Pierce

This paper uses Employment Cost Index (ECI) micro data to investigate inequality in

compensation rates. The results help fill a gap in our knowledge on this issue, in that

currently available data are not as comprehensive as those in the ECI. For example, most

public use data lack benefit cost measures.

In the cross-section wage inequality understates compensation inequality. This is largely

due to differences in the lower half of the wage distribution. The fraction of

compensation taken in the form of wages is much higher at the 10th percentile of the

compensation distribution than at the median, implying larger compensation than wage

differentials across different distributional points in the lower half of the wage

distribution. On the other hand, the compensation and wage differentials between

workers at the median and the 90th percentile of the wage distribution are roughly equal

to each other. The findings differ substantially depending on what benefits are included

in the measure of compensation.

The data also allow one to investigate recent changes in wage and compensation

inequality. Compensation inequality growth slightly exceeds wage inequality growth

over the 1982-96 period. As with inequality at a point in time, some of the more

interesting phenomena occur in jobs with below-median wages. The differences between

compensation and wage inequality growth are largely due to declining health insurance

coverage in the lower half of the compensation distribution.

The fact that compensation fell more than wages at points lower in the distribution

suggests very large percentage drops in benefit costs in that range. For instance, the real

costs associated with paid leave at the 10th percentile of the compensation distribution fell

by about 50 percent over the 1982-96 period. Costs associated with pensions and health

insurance also fell dramatically for low-compensation jobs. The data suggest income

effects as a potential contributory factor in the relative decline of fringe benefits at lower

points in the compensation distribution.
Compensation Inequality

Brooks Pierce

pierce_b@bls.gov

Bureau of Labor Statistics

March 1999

I am grateful to Tony Barkume, Will Carrington, Daniel Hamermesh, Larry Katz, Maury

Gittleman, Mike Lettau, Mark Loewenstein, Kristin McCue, John Ruser, and seminar

participants at the Bureau of the Census and the University of Maryland for helpful

comments. The views expressed are those of the author and do not necessarily reflect the

views or policies of the BLS or any other agency of the U.S. Department of Labor.
1

I. Introduction

Although there has been a great deal of recent empirical research on wage

inequality, there has been relatively little empirical work on compensation inequality.

Compensation is arguably a more relevant concept than wages if one is concerned about

gauging incentives to work or incentives to hire, or if one is interested in broader aspects

of well-being. Workers choose jobs partly on the basis of the job’s wage-benefits mix, and

higher productivity workers are likely to choose different mixes than lower productivity

workers, for a number of reasons. Individuals’ demands for pension coverage, health

insurance coverage, etc., may vary with demographic characteristics such as age, gender,

and family situation. Some forms of compensation are tax-advantaged so that incentives

vary with individuals’ marginal tax rates. Furthermore, the ability to take time off from

work, health care, and so forth may be highly normal goods. Accordingly, it is natural to

think of heterogeneous individuals choosing benefits packages in part through sorting

across establishments. One might therefore expect compensation inequality to differ from

wage inequality in systematic ways.

The lack of information about compensation inequality, despite intense interest in

the provision of particular benefits such as health care and pension plans, can surely be

traced to data availability. Currently available data often do not have sufficiently large and

representative microdata samples, or they lack information on benefit costs and multiple

benefits categories. In this paper I utilize the establishment survey microdata collected to

produce the Employment Cost Index (ECI). These data cover a large part of the

workforce and have cost information on many benefits components.
2

The main findings with respect to inequality at a point in time are as follows.

Wage inequality tends to understate compensation inequality. This is largely due to

differences in the lower half of the wage distribution. The fraction of compensation taken

in the form of wages is much higher at the 10th percentile of the compensation distribution

than at the median, implying larger compensation than wage differentials across different

distributional points in the lower half of the wage distribution. On the other hand, the

compensation and wage differentials between workers at the median and the 90th

percentile of the wage distribution are roughly equal to each other. The findings differ

substantially depending on what benefits are included in the measure of compensation.

For example, wage inequality understates compensation inequality most when

compensation is defined to exclude legally required costs such as unemployment and

workers’ compensation insurance, which often have fixed cost attributes. Among

voluntary (non-legally required) benefits there are also qualitatively different results.

The data also allow one to investigate recent changes in wage and compensation

inequality. Compensation inequality growth slightly exceeds wage inequality growth over

the 1982-96 period. For example, estimated growth in the relative compensation at the

90th and 10th percentiles in the compensation distribution is about 2-3 percentage points

(or about 15 percent) larger than the analogous figure for growth in relative wages. As

with inequality at a point in time, some of the more interesting phenomena occur in jobs

with below-median wages. Over this period compensation growth is larger than wage

growth at the median, while the reverse is true at the 10th or 25th percentiles. The

differences are primarily due to declining health insurance coverage in the lower half of the

compensation distribution.
3

The fact that compensation fell more than wages at points lower in the distribution

suggests very large percentage drops in benefit costs in that range. To take an example,

the real costs associated with paid leave at the 10th percentile of the compensation

distribution fell by about 50 percent over the 1982-96 period. Even larger percentage

declines are observed for measured health insurance and pension costs. Leave and pension

benefit costs decline less dramatically, and health insurance benefit costs increase,

elsewhere in the distribution. This paper only speculates on the root causes of these

observed patterns. It is quite likely that substitution effects are at work, especially in the

case of health care costs. A further possibility involves income effects. The fringe benefits

studied here are likely to be highly income elastic goods, perhaps especially at lower

income levels. In that case wage declines would be associated with outsized benefit

declines in the lower half of the wage distribution. The suppositions regarding income

elasticities are at least consistent with the cross-sectional patterns of benefits described

above. In fact, the observed patterns in fringe benefit cost changes parallel those one

would predict based on the observed time series changes in compensation in conjunction

with the cross-sectional relationship between compensation and fringe benefits. This

suggests income effects as a potential contributory factor in the relative decline of fringe

benefits at lower points in the compensation distribution.

II. The Employment Cost Index (ECI) Microdata

The ECI is a quarterly index measuring changes over time in the cost of wages and

various nonwage compensation costs. This section is designed to give a broad overview

of the survey design and data elements. With apologies to the reader, some important
4

details about the data collection and processing are left to an appendix. Two overall

provisos are given here. The first is that cost data refer to employer costs, which may not

always reflect employee valuations. The second is that data such as these are inevitably

subject to some degree of measurement error. Nonetheless, I claim that these data are the

best available for this particular application, as they span a substantial time period, include

cost measures for several important fringe benefits, and are derived from employer and

administrative records.

The survey scope of the ECI is the civilian workforce, excluding agricultural,

federal government, self-employed, and private household workers. Establishments are

the primary sampling units. Within a sampled establishment, 1 to 8 jobs are selected. The

unit of observation in the microdata is therefore a “job”, as defined by the sampled

establishment.1 Information is collected on the wages, other compensation costs, and

work schedules of the individual incumbents in the sampled jobs. Various categories of

non-wage compensation are collected, including health and life insurance, several forms of

leave, pension and savings plans, bonuses, and legally required expenditures on Social

Security, workers’ compensation, and unemployment insurance. This data is converted to

a cost per hour worked, and averaged over the incumbents within a job. Data elements

describing job or establishment characteristics include the establishment’s number of

employees, detailed SIC code, and location; an occupational classification for the job;

whether the job is covered by a union contract; and whether the employment is full-time or

 

11 In typical practice individuals are sampled and data is collected for all workers with the same company

job title as the sampled individual. The appendix discusses the interpretation of inequality statistics when

the unit of observation is a job rather than an individual (essentially one misses variation across

individuals in the same job).
5

part-time. Data are collected quarterly; samples in recent quarters have about 19,000

observations from 4500 establishments. The paper uses data from 1981 to 1997.

Table 1 gives sample statistics on non-wage compensation costs for the most

recent quarter, December 1997. These statistics give an idea for magnitudes, and also

provide a vehicle for discussing some interpretation and data collection issues. The table

gives costs per hour worked, the benefits share of total compensation, and an incidence

rate.2

 The various benefits are grouped roughly in order of average importance (although

not in order of importance for distributional considerations). About nine percent of

compensation costs come in the form of legally required compensation, the bulk of which

is attributable to Social Security, Medicare and worker’s compensation. The other major

categories are leave, insurance, and retirement plans. For a basis of comparison, the

average wage rate is $14.36 per hour in this quarter.3

Paid leave of various sorts, which is valued at the hourly wage, is the largest of the

voluntary categories and accounts for costs of $1.32 an hour on average. On average,

leave’s compensation share is 5.6 percent and most sampled jobs, 86 percent, have some

associated leave costs. Some discussion about the interpretation placed on leave benefits

is warranted given that it has not been studied as extensively as some other benefits.

Leave is an aggregate of paid vacation time, holidays, sick leave and an “other” category.

Of these, the vacation and holidays components are the most important. Vacation and

 

2

 The incidence rates are calculated as the (worker-weighted) percent of jobs with positive costs. Because

cost data are averaged over job incumbents the benefit coverage rates in table 1 can only proxy coverage

rates which would be derived from individual data. For example, some individuals within a job may

decline receipt of a particular benefit, or be excluded on the basis of length of service restrictions.

Nonetheless, I present statistics using this incidence measure as it is likely to be informative for

comparisons to other data, especially with respect to changes through time.

3

 The hourly wage rate is a straight-time hourly earnings figure adjusted to include overtime premium pay

and shift differentials. Throughout the paper cost figures are deflated to 1997 dollars using the CPI.
6

holiday benefits are each typically collected in time units at some accrual rate (4 hours per

pay period, 8 days per year, etc.), converted to an hours accrued per hour worked basis,

and then valued at the job’s hourly wage. The conceptual interpretation of leave is not

entirely obvious. It may simply represent one margin of labor supply. Or leave plans may

reflect firms’ attempts to monitor and coordinate time off. I interpret leave to also

represent some flexibility to the worker in scheduling hours or coordinating time for nonwork purposes. Regardless, it is useful to present statistics on leave rather than subsume

it into the wage measure as is sometimes done. These benefits are likely excluded from

wage calculations using household survey data such as the Current Population Survey, as

those calculations include earnings while on leave but probably do not adjust the hours

worked to reflect leave time (see appendix). The best reason for analyzing leave

separately, however, is that there are interesting facts that would otherwise be obscured.

Insurance consists primarily of health insurance, and accounts for 5.8 percent of

compensation costs. The health insurance component itself accounts for $1.15 per hour in

compensation costs, roughly comparable to average social security costs. The health

insurance benefits coverage figure is 73.1 percent in these data. This figure is somewhat

larger than similar statistics based on CPS data (see, e.g., Farber and Levy (1998) and

Currie and Yelowitz (1998)).

Pension and savings plans tend to be less prevalent than leave or health insurance.

Defined benefits plans are present 35.7 percent of the time while defined contribution

plans (with positive employer payments) are present 40.9 percent of the time. Defined

contribution plans include 401(k) plans as well as some other plans, such as deferred profit

sharing. The ECI data reflect current pension costs, which in the case of defined benefit
7

plans can vary with pension asset returns and firms’ chosen liability accounting methods.

Presumably these costs can only approximate the long run actuarial obligation associated

with a pension plan. The cost figure is fairly substantial at $0.78 per hour, and costs

conditional on positive costs are quite large. Retirement compensation accounts for 2.8

percent of compensation, with defined benefit plans accounting for the majority of this

category. There is some overlap in this category, with some jobs having both defined

benefit and defined contribution plan contributions. The compensation share of this

category conditional on receipt is about 4.5 percent.

III. Benefits’ Effects on Inequality

The purpose of this section is to document benefits’ effects on measured inequality

at a point in time. In order to quantify compensation inequality and compare it to wage

inequality, define total compensation per hour, c, as wages per hour, w, plus various

benefits per hour, bj, and rearrange terms,

1 s

w

w sc

c w b w s c

j j

j j

− =

= +

= + ∑ ∑ = +

where s b c j = j is benefit j’s share in total compensation and s is the sum of these shares

over j. Therefore compensation differentials in logarithms can be approximated as

analogous wage differentials plus a term reflecting differences in benefit cost shares. For

example, the log compensation differential between the 90th percentile and median can be

written as
8

{ }

1 s

(s s ) ln(w ) ln(w )

ln(c ) ln(c ) ln(w ) ln(w ) ln(1 s ) ln(1 s )

90 50

90 50

90 50 90 50 90 50

−

− ≈ − +

− = − − − − −

(1)

where superscripts refer to location in the compensation distribution, and where s is the

average of the 90th percentile and median benefits shares.4 This representation argues for

treating the benefit cost shares as a function of compensation in order to describe the

effect of benefits on inequality. These effects can further be split into component

( ) sj parts. Here the decomposition in equation (1) is applied to pooled 1995-97 crosssectional data.

Figure 1 gives the flavor of many results that follow. It graphs the share of

compensation costs taken in the form of benefits against the percentile of the

compensation distribution.5 Voluntary (not legally required) nonwage compensation is

graphed, along with all nonwage compensation. Except for the distributional extremes,

the benefit shares are relatively smooth increasing functions of the percentile. The

immediate implication is that benefits tend to increase measured compensation dispersion.

Most of the observed increases in both series occur in the lower half of the compensation

 

4

 One would like to compare compensation dispersion with data sorted on compensation to wage

dispersion with data sorted on wages. Equation (1) does not do that because it uses the same data

ordering for both the compensation and wage differentials. Therefore moving from wage dispersion to

compensation dispersion requires an additional term that quantifies the effects of resorting when moving

from a wage to a compensation distribution.

5

 For series graphed by percentile I adopt the convention of averaging the statistic within percentile.

Percentiles are defined to be the one percent of the (weighted) data centered on the relevant number. In

tables I smooth these series by taking averages over the five percent of the data nearest the indicated

percentile; e.g., the 25-10 percentile differential is the difference between averages over percentiles 23-27

and percentiles 8-12.
9

distribution. The difference between the two series is attributable to legally required

compensation costs. This difference is somewhat larger at lower percentiles, reflecting the

fact that some of these costs have fixed cost attributes. For example, unemployment

insurance costs are often a percentage of earnings up to some relatively low earnings

cutoff. Notice also that the non-required benefits are virtually zero in the lowest decile.

The series in the graph imply that the 50-10 wage differential understates the 50-10

voluntary compensation differential by about 15 percentage points, or about 20 percent of

the 50-10 log wage differential.

The results in figure 1 obscure some interesting phenomena related to benefits

components and extensive and intensive margins. Here I focus on compensation

attributable to three main benefits subcomponents: leave, pensions and savings plans, and

health insurance.6 In the graphs that follow, series based on position in the compensation

distribution are overlaid on series based on position in the wage distribution. If one

believes that benefits are measured with little error, then one would prefer series based on

position in the compensation distribution, as those series are likely to give a more accurate

accounting of any benefits-wealth relationships. If benefits are noisy, however, one might

prefer series based on position in the wage distribution, as those series are not

contaminated with spurious positive benefits-compensation correlation. As any spurious

correlation between benefit shares and wages would likely be negative, one can reasonably

view the series based on wage percentile as lower bounds on true benefits-compensation

relationships.

 

6

 Aggregating pensions and savings plans is necessary due to changes through time in how the ECI

categorized defined contribution plans. One can disaggregate the leave component consistently through

time, but such detail seems more appropriate for a separate study on leave.
10

Figure 2 graphs leave’s benefit share against percentile in the wage and

compensation distributions. As leave is valued at the wage rate, dividing leave costs by

compensation returns it to units somewhat like accrual rates. The share of compensation

taken as leave increases with compensation, and generically looks similar to the series in

figure 1. It would be interesting to know how much of the cross-sectional covariation of

benefits shares and compensation (or other explanatory variables) operates through

coverage and how much operates through intensities among the covered. To give some

idea about the extensive margin, figure 3 graphs the leave coverage rate against percentile.

There is clearly increasing incidence with compensation, and by the 40th percentile nearly

all the sampled jobs have some leave costs associated with them. Since the relationships

shown in figure 2 combine the extensive and intensive margins, it must be the case that the

increasing benefit share beyond the 40th percentile is attributable to an intensive margin.

Although retirement compensation represents on average only about 2-3 percent

of compensation, it substantially affects inequality calculations. Figure 4 shows why: jobs

in the bottom quartile of the compensation distribution have very little in the way of

retirement benefits while jobs in the top decile have 5-6 percent of compensation in this

form. Unlike the analogous graph for leave (and unlike, as shown below, the analogous

graph for health insurance), the compensation share for retirement benefits continues to

increase in the upper half of the distribution. This implies of course that retirement

compensation tends to increase compensation inequality in the upper as well as the lower

tail of the distribution.

Figure 5 shows the incidence of retirement benefits costs, where incidence is

defined as employer contributions to either a defined benefit or a defined contribution
11

plan. Incidence increases with percentile throughout the distributions, but there is a

somewhat greater increase in the lower half of the data. This in conjunction with figure 4

suggests some role for the intensive margin for this benefit. Figure 6 shows the incidence

of pension benefits costs separately for defined benefit and defined contribution plans,

graphed against percentile of the compensation distribution. Jobs with compensation near

the median have about a 40 percent chance each of having defined benefit and defined

contribution plan compensation. Both series increase with percentile, but the gradient is

slightly steeper for defined benefit plans.

The patterns that hold for health insurance are somewhat different from those for

leave and pensions. From figure 1 we know that benefits increase inequality in the lower

half of the compensation distribution, but hardly do so in the upper half. The data for the

leave and pension components suggested less of a dichotomy between the upper and lower

halves of the distribution. It follows then that the distinction between the upper and lower

halves of the distribution would likely be quite stark for the remaining major component,

health insurance. Figure 7 makes this distinction plain by graphing the health insurance

benefit share against percentile of the wage and compensation distributions. The share is

quite low at the 10th percentile, increases rapidly through about the 40th percentile, doesn’t

change noticeably in the middle of the distribution, and tails off noticeably above the 60th

percentile. Health benefit costs per hour are rising over this range, but not proportionately

with total compensation. Depending on where one makes comparisons, the share falls by

1-2 percentage points (on a base of 7-8 percentage points) in this range. Health insurance

is the benefit component where sorting by compensation instead of wages seems to matter

the most. The increase in the health insurance compensation share is less pronounced over
12

the first half of the distribution when the sorting is done by wages rather than

compensation. The fact that the health cost share declines beyond the 60th percentile in

both series indicates it is not a spurious phenomenon.

Figure 8 graphs the incidence of health care coverage. It is clear in comparing

figures 7 and 8 that much of the observed differences in the bottom half of the

compensation distribution is attributable to the extensive margin. The coverage rate in the

bottom decile is on the order of 10 percent; by the 30th percentile it is about 60 percent. It

is equally obvious that the equalizing influence of health insurance in the top half of the

compensation distribution is attributable to an intensive margin, as coverage is roughly

constant or slightly increasing over that range.

Table 2 brings together the results from these figures. The table gives wage and

compensation dispersion across various points of the distribution, and indicates each

group of benefits’ contribution to compensation inequality using the approximation in

equation (1). For example, the first row indicates that the log wage differential between

the 25th and 10th percentile of the wage distribution is 0.299. The log compensation

differential between these two points (again, in the wage distribution) is 0.375. Therefore

about 0.076 log points in compensation dispersion can be attributed to various benefits.

The number .026 for leave is derived from the different leave benefit shares at the 25th and

10th wage percentile as in equation (1). Since we are looking at various points in the wage

distribution, we are in effect following the wage-sorted series in figure 2. The analogous

calculations for pensions and health insurance follow the wage-sorted series in figures 4

and 7, respectively. Table 2 in addition gives “other voluntary” and “legally required”

benefits categories. The wage dispersion column and the first four benefits columns add
13

up to the “voluntary dispersion” column; adding in the last benefit component (“legally

required”) gives the wage-sorted compensation dispersion. The final column of table 2

gives compensation dispersion based on the compensation-sorted distribution of the data.

That is, reordering the data by compensation per hour rather than by the wage rate must

increase overall compensation dispersion measures.

Consider first the broadest range, the 90th-10th differential. The leave and pensions

components each add about 6 percent to measured compensation dispersion. Health

insurance adds slightly less, 5.1 percent. At least over the whole distribution, leave and

pension benefits are more important in determining compensation dispersion than are

health insurance benefits. The “other voluntary” category adds 1.3 percent. The sum of

these non-legally required benefits gives 0.187 in log points. Legally required

compensation costs tend to equalize the compensation distribution, and in fact they reduce

dispersion over this range almost as much as health insurance increases dispersion.

There are also interesting differences among benefits components across the

various parts of the 90th-10th percentile range. These differences are apparent in

previously referenced figures: leave effects occur mostly in the bottom half of the wage

distribution, pension effects operate throughout the distribution, and health insurance is

tremendously important in adding to dispersion in the lower half of the distribution but is

equalizing in the upper half. The contrasting effects of health insurance in the 25th-10th

and 90th-75th percentile ranges are especially stark. In the 25th-10th wage percentile range,

voluntary compensation dispersion is about 30 percent larger than wage dispersion.

Nearly half of this is attributable solely to the effects of health insurance benefits. And

this, as we have seen, is largely an issue of coverage.
14

IV. Characteristics of Establishments and Jobs Affecting Fringe Benefits Choices

The analysis thus far has focused on deriving some factual relationships between

wage and compensation distributions, and identifying which benefits are important in

explaining these relationships. It has not identified explanations for why workers and

firms arrange the compensation packages they do. There is no shortage of possibilities

here. High ability workers may choose a relatively benefit-intensive compensation

package because benefits are highly normal goods or because of tax issues. Larger

establishments may offer compensation packages relatively skewed toward benefits like

pensions in the hopes of reducing turnover, or because there are fixed costs of setting up

plans. Firms may not offer health benefits to part-time workers because the costs of the

plans have fixed cost attributes with respect to hours worked, and in fact part-time work

may be a way for some firms and workers to transact so as to choose a relatively wageskewed compensation package without being constrained by the plan coverage choices of

other workers. Union bargaining may reflect median member preferences, which may be

skewed toward benefits (see Freeman (1981)). Indeed, some observed effects of firm or

job characteristics may simply reflect something about worker preferences: for example,

workers in large establishments or public sector employment may be older or more risk

averse than the typical person.

Table 3 presents (log) wage, compensation, and voluntary compensation

regressions on controls for full-time status, union status, establishment size (employment),

the presence of incentive pay, a set of public sector indicators, regional indicators, a

metropolitan statistical area indicator and industry and occupation indicators. These
15

regressions are executed on a sample from the fourth quarter of 1994.7 Given the

unmeasured worker characteristics, it is not surprising that in these data there are fairly

substantial wage and compensation premia associated with most of the covariates.

Comparing the three regressions gives some feel for benefits choices. The effects of fulltime status, union coverage, and establishment size are all larger in the compensation

equation than in the wage equation, and larger still in the voluntary compensation

equation. Furthermore, the coefficients in the compensation equation are all 30-35

percent larger than the analogous wage regression coefficients for these variables. The

state and local government effects tend to be much more pronounced in the voluntary

compensation equation than in the other two. One sees different patterns for the incentive

pay indicator, with generally larger effects in the wage than the compensation equations.8

Generally, though, compensation premia tend to be larger than wage premia here.

Tables 4 and 5 give regressions directly describing benefits components choices.

The first table describes benefit incidence, and the second describes benefit levels

conditional on positive costs for the relevant benefit. The extensive margins are estimated

with probit models, and the intensive margins are regressions with log benefit costs for the

fringe benefit in question as the dependent variables. So as to capture the possibility of

income effects, controls for compensation are included, in the form of percentile indicators

for the data sorted on compensation less the benefit costs for the benefit in question.

 

7

 Some covariates are unavailable to me in more recent quarters. 8

 There are several possible explanations for the incentive pay wage premium, including selection on

ability, higher induced effort, and compensating differentials to risk averse workers. The fact that the

compensation premium is smaller than the wage premium is consistent with the view of incentive pay as a

substitute for other incentive mechanisms such as deferred compensation. See, among others, Barkume

(1999), Brown (1990), Goldin (1986), and Lazear (1986).
16

Sorting in this manner minimizes any spurious correlation between dependent and

independent variables due to benefit cost measurement error.9

The first column of table 4 gives the extensive margin for leave benefits (the

dependent variable is 1 if there are any positive costs associated with vacation, holidays,

sick leave, or the “other” leave category). The second column gives analogous results for

pensions, treated here as an aggregate category including both defined benefit and defined

contribution plans. The final column in the table gives results for health insurance

incidence. The coefficients (which have been transformed to show effects on the

probability in question) generally accord with priors formed from the results in table 3.

The consistently largest and most precisely estimated effects in these regressions are

associated with the full-time and union status indicators, and the establishment size

variable.10 There are some contrasts across equations, in particular the very large effect of

the full-time status indicator in the health insurance equation and the rather large

establishment size effects for pensions and health insurance incidence. It seems quite

likely that one way employees choose to take compensation in the form of wages rather

than benefits is to opt into part-time positions (see Farber and Levy (1998)).

Table 5 repeats these exercises for intensive margins. The dependent variable in

each regression is the natural logarithm of the benefit costs in question; the covariates are

as in table 4. The pattern of results here looks somewhat like that in table 4, with some

differences in emphasis. For example, the government employment and incentive pay

indicators tend to have larger effects, relative to other variables, here than in the extensive

 

9

 Instrumenting compensation is not feasible given the desire to maintain a very flexible functional form;

the treatment in the text is the lesser of two (minor) evils.
17

margin equations above. One interesting result involves the negative (though imprecisely

estimated) sign on full-time status in the health insurance cost regression. This is

consistent with health care benefits having fixed cost attributes with respect to hours

worked – in comparing two jobs with the same compensation per hour and the same

health care plan, the job with the shorter workweek will have higher health care benefit

costs on a per-hour basis. The coefficient on full-time status indicates some incomplete

prorating of benefits to offset this effect (see Cutler and Madrian (1996) and Lettau and

Buchmueller (1998)). Since pensions and leave are benefits that are much less likely to

have these fixed cost attributes, they do not exhibit this sort of negative partial correlation

between benefits and full-time status.

The fact that the intensive and extensive margin coefficients in tables 4 and 5 are in

different units makes comparison more difficult. Table 6 recombines the extensive and

intensive margin estimates above to give a total effect, in log points.11 The table also gives

the fraction of the estimated total attributed to the extensive margin. Most of the effects

are fairly substantial, with a large fraction of the total effect attributed to the intensive

margin for many of these covariates. This is a rather important point for this paper

because it suggests that ECI based cost data is useful above and beyond the more typical

incidence data.12 Although not reported in the table, distributional effects are present after

controlling for establishment and job attributes, with substantial variation along the

intensive margin.

 

10 A one standard deviation change in the establishment size variable is roughly 2.2 and implies

approximately a 7 percent wage differential and 9 percent compensation differentials.

11 That is, c b = pb where b is the benefit cost, p is the probability of receiving benefits, and c b is the

benefit cost conditional on receipt, and therefore c d ln b = (dp / p) + d ln b .
18

V. Changing Wage and Compensation Inequality

Over the past 30 years there have been large and well-documented changes in

wage inequality. A substantial portion of this shift occurred during the 1980s and early- to

mid-1990s, the period covered by the ECI. One consequence of this trend has been

shifting patterns of labor force participation, with those groups experiencing relative wage

declines also tending to reduce labor force participation the most (Juhn (1992), Welch

(1997)); the interpretation is that of a substitution effect. Changing fringe benefits choices

would be a natural example of changes driven by income effects (Hamermesh (1997),

Hamermesh (1998), Bloom and Freeman (1992)). This gives rise to the question: are low

compensation jobs, which have experienced the largest declines in real wages, also

experiencing relative declines in the share of fringe benefits in compensation?

In order to answer questions like this I pool data for four subperiods, 1981-83,

1985-87, 1990-92, and 1995-97. These subperiods are chosen to be about 5 years apart

so as to produce independent samples (establishments stay in sample approximately 41⁄2

years), while still covering the bulk of the time that the ECI data are available. I focus on

changes over the whole time period, but also present information on changes by

subperiods.

Table 7 gives average wages, benefit costs, benefit shares, and incidence rates for

these four subperiods. The table also presents these statistics calculated at the median of

 

12 It is helpful to keep in mind that table 6 gives effects on log benefit costs, not on cost shares. Intensive

margins are obviously less important when estimating the effect of compensation on cost shares.
19

the wage distribution.13 One interesting fact is that cost levels for the different benefit

components changed quite a lot, and in offsetting directions. Real leave compensation

costs fell over the entire 1982-96 period, by approximately 15 percent.14 Some of this

decline was induced directly through real wage changes, as leave is valued at the wage

rate to determine leave costs. However, some of the leave decline reflects changes in the

amount of time earned for leave per hour worked, since leave as a share of total

compensation fell over the period. Average real pension costs fell by about 30 percent

over this 14 year time frame, with most of the decline occurring by the 1990-92 period;

the share and incidence statistics mirror this pattern.15 It is worth noting that average

pension costs, conditional on positive costs, also fell quite a bit over the period. The

ultimate causes of this trend are not entirely clear. The shift over this period toward

defined contribution plans and away from defined benefit plans is well-known. Those

changes may provide some basis for an accounting, but probably not a true explanation.

Demographic changes in the labor market are likely to have had relatively small effects.16

Changes in the tax treatment of various alternative savings vehicles, or changes in

marginal tax rates could potentially induce substitution away from pensions for some

workers. There is also some evidence that, within defined benefit plans, regulatory

 

13 “At median” refers to statistics averaged over the five percent of the data centered on the median wage.

Statistics are first calculated by quarter, and then pooled across quarters by taking simple averages.

14 The paper references 3 year averages by middle year in referring to changes; e.g., the change from

1981-83 to 1995-97 is a “1982-96” change.

15 Pension coverage trends reported elsewhere vary with the data source, sample, and time period analyzed

(Gale (1994), PWBA (1994), Currie and Yelowitz (1998)). Bloom and Freeman (1992) report coverage

declines using 1979 and 1988 May CPS data.

16 Consider changes in the gender composition of the labor force. Currie and Yelowitz (1998) report

gender differences in pension and health insurance coverage rates within schooling group of roughly 10-

15 percentage points. The gender makeup of the non-agricultural civilian labor force, aged 20 or older,

changed from 43.1 percent female in 1982 to 46.0 percent female in 1996.
20

changes during the mid- to late-1980s resulted in different pension funding levels (Gale

(1994), Ippolito (1998)).

Average real health insurance costs rose quite rapidly over most of this period,

before falling somewhat over the past few years. As is well known, health insurance

coverage rates fell over this period. Note also that, in these data, health insurance costs

conditional on positive costs rose much more rapidly in the 1980s than they have since the

early- to mid-1990s (see Krueger and Levy (1997)). The observed trends may of course

be due to falling coverage in response to rising prices for health care goods and services.

For example, Gruber and Poterba (1994), using variation generated by changes in the tax

code, find a fairly elastic response of coverage to insurance cost changes. Even if the

rising prices reflect quality improvements (Cutler, McClellan, Newhouse, and Remler

(1998)) there may still be an inducement to drop coverage absent some ability to restrict

quality of care once injury or illness occurs. There is also evidence that changes in

Medicaid eligibility acted to crowd out private health insurance over a relatively short

period in the late 1980s and early 1990s (Cutler and Gruber (1996), Shore-Sheppard

(1996)). Finally, it is worth noting the possibility of cross-price effects for any of these

benefit categories, although there appears to be less evidence on this point. Of course,

these factors might operate differentially at different points in the compensation

distribution.

The differences in numbers between the average and “at median” costs in table 7

hint at distributional differences. For all three benefit categories, the incidence and share

statistics tend to be larger at the median than at the mean. This follows from the patterns

exhibited in the figures above: many of the distributional differences in incidence rates and
21

shares occur in the lower half of the distribution. In fact the difference in the incidence

rates on average and at the median increase through time for all three benefit categories,

suggesting some interesting changes in benefits dispersion.

Substantial changes in wage and compensation distributions took place coincident

with these changes in average benefits levels. Figure 9 compares changes in compensation

inequality to changes in wage inequality. Each panel overlays a plot of real compensation

growth by compensation percentile with a plot of real wage growth by wage percentile.

Panel A gives changes over the whole time period and the remaining panels each give

changes over shorter subperiods. The generally upward sloping graphs indicate increasing

compensation and wage inequality: growth rates are higher at higher percentiles. Panel A

shows that wage inequality in these data increased mainly over the top half of the

distribution. The series for compensation growth suggests a slightly more broad-based

inequality increase. If anything, benefit differentials between the 90th and 50th percentiles

changed in such a way that compensation inequality grew somewhat less than did wage

inequality. If there is an area where there are substantial differences, it would be at the

lower end. For example, comparing the median with the 25th percentile, compensation

inequality rose by .031 log points whereas wage inequality fell by .005 log points, meaning

that adding in benefits increased inequality growth in that range by 3-4 percentage points.

The other panels in figure 9 show some interesting differences by subperiod. One

distinguishing feature of the 1982-86 changes is that the wage and compensation declines

at the very low end of the distribution were much larger than those at the median. That is,

over that period the increased dispersion occurred throughout the entire distribution. The

differences between the two series appear fairly small, however. Therefore many of the
22

main results of the paper hold when the earlier years of the survey are excluded. The

changes since 1986 shown in panels C and D differ from those shown in panel B in that

inequality decreased somewhat over the later periods at low percentiles. The figures in the

last two panels also indicate that one’s conclusions about dispersion in the lower half of

the distribution are dependent on the measures of compensation used; dispersion increases

are more apparent when including costs associated with leave, pensions, health insurance,

and other benefits.

Table 8 gives wage and compensation differentials across various parts of the

distribution, for each of the four periods. This table puts numbers to the series in figure 9.

The final two columns are repeated from table 2, and the others give analogous statistics

from earlier periods. At any point in time, including nonwage compensation increases

measured inequality, especially in the lower half of the distribution. In terms of changes

through time, including nonwage compensation tends to result in modestly larger

inequality increases as measured by 90-10 dispersion, mostly accounted for by modestly

smaller (in absolute value) inequality decreases over the bottom half of the distribution.

For instance, the changes in the 90-10 log differentials over the 1982-96 period are .180

for wages and .202 for compensation; the relevant figures for the 50-10 differentials are

.005 and .034. Thus if one measures inequality by a 90-10 differential, including benefits

boosts measured inequality growth by 12 percent.

Table 9 shows the ultimate source of the differences between wage and

compensation inequality changes, and gives a sense for whether the differences are

statistically significant. The first column gives the change in wage dispersion over the

1982-96 period for the relevant percentile range, derived by differencing appropriately
23

from table 8. The next three columns give the contribution of individual benefits to

compensation inequality, as operationalized by equation (1). The last two columns in the

table give changing dispersion in voluntary compensation and in total compensation,

where the distributional range continues to be defined based on points in the wage

distribution. The difference between the voluntary compensation and wage dispersion

columns gives the sum total of the effects on measured inequality growth of including

voluntary benefits; the difference between the first and last columns also includes effects of

legally required compensation costs.

Generally speaking, the point estimates for the benefit contributions columns are

small and not statistically different from zero. The main exception is for health insurance

in the bottom half of the distribution; health insurance costs as a share of compensation fall

enough at the 10th and 25th percentiles relative to the median to induce higher measured

changes in compensation inequality. For example, the change in 90-10 wage dispersion of

.181 would be approximately 18 percent higher (.033/.181) were health insurance costs

included. There is also some slight evidence for increased dispersion due to leave over the

middle of the wage distribution, and decreased dispersion due to pensions over the lower

half of the wage distribution.17

VI. Income Effects and Time Series Changes in Fringe Benefit Costs

Comparing wage and compensation inequality growth can partly obscure the

magnitude of fringe benefit cost changes, as modest differences between wage and

 

17 The reader should be aware, however, that this last result for pensions is one qualitative result that is

not robust. Under reasonable alternative sample exclusions as outlined in the appendix, pensions increase

dispersion growth slightly.
24

compensation changes can imply rather large percentage changes in benefits. If

compensation per hour falls 2-3 percentage points more than wages at, say, the 25th

percentile, it must be the case that benefits fall by quite a lot in percentage terms, simply

because benefits are such a small fraction of total compensation for low compensation

jobs. And absent any large declines in required nonwage compensation costs, this means

that voluntary benefit costs must fall by an even greater amount in percentage terms.

Similar reasoning leads to the conclusion that percentage changes in voluntary benefit

costs are likely to be more moderate near the median and in the upper tail of the

distribution.

This section details what happened to benefit costs and makes an admittedly

speculative case for income effects as contributory to those changes. Benefit cost declines

were quite large in percentage terms in those parts of the compensation distribution where

compensation differences predict benefit differences well. This is at least consistent with

the notion of important income effects, and leads one to a corollary question: do observed

time series changes in fringe benefits at a particular point in the compensation distribution

resemble what one would predict based on time series changes in compensation at that

same point, in conjunction with a reasonable estimate of the behavioral relationship

between compensation and fringes?

Answering this question requires an assessment of how fringe benefits vary with

incomes. Lacking an accurate estimate of this behavioral relationship, I utilize the crosssectional relationship observed between fringes and compensation. This proxy is most

accurate when the underlying variation behind the cross-sectional relationship is roughly

similar to the variation driving the observed time series changes. For example, the cross-
25

sectional patterns apparent in figures 4 through 8 may reflect substitution effects induced

by progressive taxation and the tax treatment of pensions and health insurance. Applying

those cross-sectional relationships to the time series changes in compensation is

appropriate if the time series changes in compensation would also induce similar

substitution patterns. This despite the fact that the cross-sectional relationship between

fringes and compensation is not the same as that which would obtain if price and taste

differences could be held fixed (a true income effect). On the other hand, suppose the

cross-sectional relationship between, say, pension benefits and compensation was driven

entirely by preferences (more forward looking people tend to be high earners). In that

situation applying the cross-sectional relationship between pension choices and

compensation to time series changes in compensation is inappropriate, as changing

compensation in the time series is not likely to be due to changing preferences. The same

comments are applicable to fringe benefits-compensation relationships due to e.g., age

differences among workers in the cross-section.

Predicted values of percentage changes in benefit costs b j , at different

compensation percentiles θ , are defined as

{ } { }time series d ln c( )

cross sectional d ln c( )

d ln b ( )

predicted d ln b ( ) j

j ⋅ θ













θ

θ

θ =

The time series changes for compensation (the last term above) are those graphed in figure

9. The cross-sectional relationships dlnbj and dlnc are constructed from the relationships

observed in the pooled 1981-97 data, by differencing the relevant variable in the
26

neighborhood of the percentile.18 Applying time series compensation changes to the

constructed cross-sectional benefits expenditure elasticities gives the predicted percentage

cost changes. The resultant series can be compared to actual percentage changes in

benefits.

Table 10 gives benefit cost elasticities with respect to compensation, based on

pooled 1981-97 data, to quantify the cross-sectional relationships. Elasticities at any

given percentile are noisy measures, so the table gives averages over ranges of the

distribution; the ranges are centered on the 25th, 50th and 75th percentiles. The elasticities

for pensions tend to be higher than those for leave and health insurance. The elasticities

are much larger in the lower parts of the distribution for all three benefits components. If

one were looking for the effects of changing inequality on fringe benefit choices, one

would look first to the bottom third of the compensation distribution.

Figures 10 through 12 plot actual and predicted changes, in percentage terms, for

the 3 main voluntary benefit components. (Table 11 gives precise numbers at various

points in the compensation distribution, and is presented without additional comment).

Although no single story explains all the patterns for these figures, one commonality is the

tendency for benefit costs to fall more in percentage terms in the lower part of the

distribution. This is roughly what one would expect based on the distributional differences

in changing compensation, and in fact the predicted series do tend to track the actual

changes.

 

18 The cross-sectional dlnb terms are constructed as ) j (b ( 2) b ( 2)) / 4*b ( j θ + − j θ − j θ and the actual time

series changes from date t to date tau are constructed as, suppressing the θ notation, j jt b jt (b − b ) / τ .
27

Figure 10 shows the patterns for leave changes; the time periods in the various

panels mirror those in figure 9. Over the whole 1982-96 period, actual leave costs fall

throughout the entire distribution. The declines are 25-30 percent near the median, and as

much as 50 percent near the 25th percentile. Throughout the entire range, actual costs fall

by more than would be predicted by compensation changes alone. However, the

distributional aspects of the changes are captured quite well by the counterfactual. The

correlation between the two series in panel A is 0.91. Unlike the case with pensions and

health insurance, there are relatively few stories to tell regarding changes in tax policy,

regulations, and other public policies affecting leave choices that might provide alternative

explanations to these patterns.19 And, although the series are inevitably more noisy, a

similar story emerges when looking at changes in the subperiods of the data. In the earlyto mid-1980s (panel B) there was a relatively small gradient with respect to percentile.

The trends in the late 1980s and early 1990s are more apparent. In both cases (although

especially in the last subperiod) the actual tends to lie below the predicted and there are

clear gradients with respect to percentile.

Figure 11 shows the patterns for pensions. This figure looks more noisy than that

for leave, but there are some similarities. Pension costs fell on average by more than the

compensation changes would have predicted. The distributional differences in the

predicted series roughly mirror those in the actual series, with pension costs falling most in

percentage terms in the area between the 10th and 30th percentiles. Figure 11 also shows

 

19 Leave-compensation relationships reflect hours worked choices as well as income-elastic demands for

amenities like “flexibility”. There is some ambiguity about the hours worked – wage relationship in crosssectional microdata because of various measurement issues. Welch (1997), using CPS data, finds a

positive covariance even for samples of workers, once wage measures are instrumented. Although beyond

the scope of this paper, leave considerations might affect such calculations.
28

the series over different subperiods. The pension declines occurred in all three subperiods,

although the declines were more modest in the last period. If anything, the most recent

change (1991-96) shows a slightly smaller pension decline than would have been predicted

based on the observed compensation changes. The actual and counterfactual series tend

to exhibit roughly the same pattern with respect to percentile. The two series in panel A

have a correlation coefficient of 0.80; furthermore, the series tend to track each other in

each of the individual subperiods.

Some of the policy changes affecting pensions alluded to above – changing

marginal tax rates, regulatory changes, etc. – might be expected to lower employer

contributions to pension plans. These policy changes therefore might help to explain why

the actual series in figure 11 lie so far below the counterfactuals. It is likely that these

factors differentially affected pension contributions at different percentiles, but the extent

or even direction of these differential effects is not obvious. For example, one might

expect marginal tax rate changes to have a smaller effect in the lower than upper half of

the compensation distribution. Or consider the hypothesis advanced by Ippolito (1999),

that regulatory changes made it profitable to reduce contributions in overfunded defined

benefit plans: it is not obvious that low compensation per hour workers are

disproportionately in overfunded defined benefit plans (intuition and figure 6 might

suggest otherwise to the reader). A very real possibility is that changes in individual

retirement account deductibility with the Tax Reform Act of 1986 induced substitution

away from IRAs and toward close substitutes such as pensions, and that this

disproportionately affected high compensation individuals. This probably wouldn’t go

very far toward explaining the patterns for the first and last subperiods of the data. Given
29

the uncertainty surrounding these issues, the most reasonable statement is necessarily

weak: the observed changes are consistent with income effects, but there are likely to be

other factors at work as well.

Figure 12 shows the same graphs for health insurance cost changes, and represents

a cautionary tale. Over the 1982-96 period jobs with above-median wages experienced

large increases in health insurance costs, while the experience below the median and

especially in the range of the 10th-30th percentiles, was somewhat different. One might be

tempted to declare that the actual and counterfactual series in these figures track each

other, except for the fact that one expects some price effects in this market. The different

time periods in figure 12 are interesting in this regard. For the 1982-86 changes health

insurance costs rose everywhere except at the very low end of the distribution. The actual

series lies about 30 percentage points above the predicted series at all percentiles; if there

is any substitution at work here it is apparently at work only at very low compensation

levels (income effects would predict the same pattern). The 1986-91 changes look quite

different. Here one sees high benefit cost growth rates in the upper half of the distribution

and much lower growth rates elsewhere. The differential responses at, say, the 10th and

40th percentiles are much larger than would be predicted solely from the income effects

counterfactual series. The final period, 1991-96, is one where health insurance premium

increases moderated substantially (see Krueger and Levy (1997)). The changes over that

period look much more like the pension and leave pictures in that costs are generally

falling, with some distributional detail explainable by the fact that compensation is falling

most in the lower half of the distribution. Nonetheless, it seems clear that benefit cost
30

declines at the very low end of the compensation distribution exceeded what an income

effects counterfactual would predict.

In the case of health insurance, at least, a very likely explanation is that higher

health insurance costs resulted in a substitution away from coverage in low compensation

jobs and cost increases for those jobs that kept coverage.20 It is easy to imagine models

where it is difficult to buy “a little” health insurance. With such indivisibilities a price

increase causes higher expenditures for those that continue coverage (assuming price

inelastic demand), and lower coverage rates as some who were once just indifferent to

obtaining health insurance find it advantageous to drop coverage. Presumably these will

tend to be lower wealth people in the subset initially with coverage. Another cause for

more elastic demand at lower incomes is the presence of publicly provided insurance as a

substitute. If these interpretations are accurate, then one would want to model health

insurance price elasticities as a function of income. It is a truism that if demand is more

elastic at lower incomes, then income elasticities are larger at higher prices. Therefore if

one believes the trends shown in figure 12 are primarily due to rising costs of private

health insurance, then one might also want to entertain the possibility of larger income

elasticities for health insurance coverage when anticipating future trends. This may be

especially relevant if compensation growth continues to increase for those with low

compensation levels.

VII. Summary

 

20 Also, the public insurance crowdout argument would apply for the 1986-91 period. Cutler and Gruber

estimate that 17 percent of the 1987-92 decline in private health insurance coverage was due to Medicaid

expansions during that period. This would be a substantial minority of below-median changes.
31

The intent of this paper is to present facts on the level and distribution of fringe

benefits, on the relationship between wages and fringe benefits, and on how these

relationships have changed through time. Research into this issue has been to some extent

limited by the absence of microdata on fringe benefits receipt and the costs associated with

benefits when present. The data utilized here have the advantage of giving wage and

fringe benefits costs for large samples of jobs, over a period of time with relatively

substantial changes in wage distributions.

As a general proposition, voluntary fringe benefits like leave, pensions, and health

insurance increase compensation dispersion in the lower half of the distribution. Of these

benefits, only the pensions component substantially increases dispersion in the upper half

of the distribution; health insurance in fact reduces dispersion in that range. Legally

required compensation costs such as worker’s compensation insurance and social security

tend to reduce dispersion throughout the entire distribution. The equalizing effects of

required compensation over the entire range of the distribution (as measured by a 90-10

differential) roughly offset the effects of health insurance, and are slightly smaller in

absolute magnitude than are the effects of leave or pensions.

One may also use these data to look at changes in inequality through time.

Compensation inequality rose over the past 10-15 years by a greater amount than did

wage inequality. The differences are apparent mainly in the bottom half of the respective

distributions, and are largely due to declines in health insurance coverage rates.

On average, employers’ real leave and pension costs per hour worked fell over this

period, while health insurance costs per hour worked rose. The fact that compensation

inequality rose relative to wage inequality implies that benefit costs declined more in
32

percentage terms in the lower half of the compensation distribution. These trends are

consistent with many different possible explanations, including changes in health insurance

premium rates and changes in public policies affecting savings behavior. The distributional

changes are also consistent with income effects, where low wage workers, facing declining

real wages, choose to take a disproportionately large fraction of the compensation

decreases in the form of lower fringe benefits. Distinguishing this last possibility from

alternatives would be especially useful if inequality continues to change substantially.
33

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35

Appendix: ECI Microdata

A. Panel Aspects

The ECI measures changing wages and compensation costs over a sample of fixed

jobs. To do so it follows sampled establishments and jobs over multiple quarters. Sample

replenishment takes the form of drawing a new establishment sample for a given industry

(as defined by 2 digit SIC code) and dropping the old sample from that industry. The new

industry sample, except for subsequent attriters, remains in the ECI sample for

approximately 41⁄2 years. Each quarter different industries are replenished. Sample

weights are constructed at the time of initiation into the sample, and reflect aggregate

employment in the industry.

The panel aspect of the data raises some issues relevant to treating the data as

annual cross-sections. To correct for attrition within an industry sample and changing

industrial distributions in the economy, sample weights are adjusted quarter by quarter so

that the cross section maintains a proper industry distribution. This reweighting does not

correct for nonrandom attrition within industry or the fact that the within-industry

distribution of sampled jobs (say, with respect to occupations) is static until the industry is

resampled. This treatment is very similar to what the BLS undertakes in producing its

annual Employer Costs for Employee Compensation release.

The paper looks at periods approximately five years apart in an attempt to maintain

comparable industry samples, by capturing a given industry’s panels at similar points in

each panel’s lifetime. Also, cross sections are not independent at high frequencies, but are

at the frequency corresponding to the length of a panel’s life.

B. Leave Costs and Scheduled Hours versus Hours Worked

An example demonstrates how leave is treated in the ECI. Consider a job where

incumbents are paid $400 per week for a 40 hour scheduled workweek. Assume workers

receive 2 hours per week in paid vacation, and that there are no other benefits. The wage

rate is calculated as $10 per hour. Leave costs are calculated as the hourly wage times the

ratio of leave hours to hours worked (which is scheduled hours minus leave hours). Here

the computation is $10 times 2/(40-2) = $0.53 per hour worked. Note that the figure is

on a per hour worked (versus per scheduled hour) basis. Total compensation is $10.53

per hour, also on an hours worked basis. One could arrive at the same $10.53 figure by

dividing weekly earnings by weekly hours worked ($400/38). Had there been other

benefits in this example, they would have been converted to a cost per hour worked and

added to the $10.53 figure to arrive at total compensation.

As this example should make clear, whether leave is incorporated into wage

measures in CPS and similar data depends on how “hours worked” is defined, and how

respondents answer in practice. March CPS retrospective data has response heaping at 52

weeks and 40 hours per week, suggesting that annual hours worked do not typically net

out leave. Analyses using CPS Outgoing Rotation Group data that generate hourly wages

as the ratio of usual weekly earnings to usual hours worked per week would not net out

leave if usual hours per week is interpreted as scheduled hours or modal hours.
36

C. Benefits Data Quality

Benefits data in the ECI microdata are often imputed or estimated. Cases where

establishments do not provide information on work schedules or a benefit are termed

“refusals”. The BLS imputes benefit cost data for refusals, as follows. If there are valid

past benefits data for the job in question, those figures are brought forward using average

growth rates observed for other jobs in the same industry/occupation group (2 digit SIC

by major occupational group). If the imputation must be done at the initiation quarter,

which would for instance be the case where work schedule information cannot be

obtained, benefit levels are imputed based on the average benefit levels observed for other

jobs in the industry/occupation group. Imputations take into account whether the

respondent indicates there are positive costs associated with the particular benefit and job.

A more frequent outcome than outright refusals is the estimation, by the

respondent, of benefit costs for workers in a particular sampled job using data relevant for

a broader set of workers. The BLS has preferred and fallback data collection methods for

benefits. The preferred method for the main voluntary benefits highlighted in the paper is

to identify distinct plans at initiation into the sample and categorize the job’s incumbent

workers at that point in time into the distinct plans. Costs are calculated at initiation by

applying the different plan premiums or cost rates to the incumbent distribution across

plans. Costs are updated by applying changing plan cost rates to the fixed incumbent

distribution across plans. A common fallback collection method is to use expenditure

data. Typically costs collected using the preferred method refer to job incumbents while

expenditure data refer to a larger group of workers than the job incumbents. For example,

an establishment self-insuring health care costs would report expenditure data for broad

sets of workers. The use of expenditure data is the most important measurement issue for

this paper, because it leads to within-establishment smearing of benefits costs across jobs.

That is, high wage workers tend to chose higher benefits than lower wage workers in the

same establishment, and those differences may not be well measured for many

establishments.

Appendix table 1 gives fractions of the data satisfying varying levels of quality.

These statistics are for the last quarter of 1997. Results are given separately for aboveand below- median compensation jobs, as well as for the whole distribution. For the

benefits listed, imputations tend to be for 10-15 percent of the data. Of the imputed data,

most indicate positive costs. There are differences by place in distribution, with a greater

fraction of data imputed in higher deciles. Observations with “No Plan” obviously have

zero costs (establishments with plans do report zero costs for some jobs). Among the

non-imputed positive cost data for leave, most of the data refers to job incumbents. For

the pensions and health insurance components, the non-imputed data is more evenly split

between referring to job incumbents only and referring to broader groups of workers.

Clearly the “No Plan” observations and the observations with non-imputed data for

job incumbents have more accurate cost data than the non-imputed data based on the

broader group, and the imputed data (which have very little benefit cost information).

Over time, the fraction of the sample with the most accurate data – zero costs or nonimputed positive cost data for job incumbents – has changed relatively little. Within the

subsample of positive cost data there has been a slow shift over time toward observations

with imputed data or data based on broader groups of workers than the job incumbents.
37

There is of course a tension between excluding data that might be less accurate and

including that data so as to produce more precise estimates and maintain sample

representation. In these data there is also an issue of when to exclude observations with

actual data for some benefit costs and imputed data for others. Here are my general rules

for handling the various contingencies. For the purpose of computing compensation and

wage percentiles, all data are used, including the imputed benefits for refusals. The idea

here is that the wage data contain a lot of information about overall compensation, and

that information is useful in assigning distributional places to all the observations. For

tables that report average statistics, like table 1, all data are used in the calculations. For

graphs and tables dealing with distributional differences in a particular benefit’s costs,

imputed data for that benefit are excluded (or more precisely, as indicated above, imputed

data are used in assigning percentiles, but only nonimputed data within an assigned

percentile are used in the calculations). For tables dealing with the distribution of

compensation more generally, such as table 2, observations with imputed data for any of

the voluntary benefits listed in appendix table 1 are excluded. The idea here is to compute

statistics relevant to different benefits using the same data sample.

D. Robustness of Results to Sample Exclusions

For the most part, the qualitative results presented in the paper are insensitive to

the chosen exclusions. Excluding the imputed benefits data matters little. The main

question involves the treatment of expenditure data that refer to broader groups of

workers. As discussed above, those data can be expected to obscure within-establishment

differences in benefit costs, and there has been a slight increase through time in the

prevalence of those data.

For a robustness check I recalculate parts of table 2 using different samples. I

focus on cross-sectional statistics in the last period because that is where the differences

are likely to be (and actually are) greatest. As before, all data are used in defining

percentiles. Within any percentile, average costs equal the fraction of the data with

positive costs times average benefit costs conditional on positive costs. For the fraction

with positive costs I use incidence data from the subsample where plan existence is

known. For average costs conditional on positive costs I use the subsample of

observations where the data refer to the jobs’ incumbent workers only. The presumption

is that reported zero cost data are accurate, and that the most accurate data where costs

are positive come from observations reporting cost figures for the job incumbents only.

The results are given in appendix table 2. The first column under each benefit

category is reprinted from table 2, the second gives the statistics as calculated using the

alternative samples. The results accord with priors in that the dispersion statistics are

generally larger using the more restricted sample. This is most true for pensions, a benefit

that is more directly related to wages and tends to have a substantial fraction of data

reported for broader groups of workers than the job. It is least true for leave costs, which

are also directly related to wages, but are based mainly on more accurate data.

It is a simple matter to reproduce appendix table 2 for different subperiods, and I

will summarize those results briefly here (calculations available on request). If results

differ greatly in earlier periods, then the sample exclusions will have some effect on

inequality change calculations (recall equation (1)). The differences between calculations
38

for columns (1) and (2) for leave in the 1981-83 period are roughly similar to what is

shown in appendix table 2. However for pensions and health insurance there are relatively

small differences between columns (1) and (2) in the 1981-83 period. This suggests that

compensation inequality may have grown slightly more than indicated in table 9, especially

as relates to the contribution of pensions. The one qualitative result of the paper that is

not robust to the sample exclusions is the result in table 9 of decreased inequality due to

pensions. Therefore the sample restrictions as used in the paper should be viewed as

producing conservative estimates of the effects of health insurance and pension benefits on

compensation inequality and on changes in compensation inequality.

E. Within-job Compensation Variation

One way in which ECI-based inequality statistics differ from those based on

household survey data is that the ECI microdata unit of observation is the job rather than

the individual. The inequality statistics presented in the paper are therefore interpretable

as what one would observe using individual microdata, except that individuals’ wages and

benefit costs are proxied by their job averages. That is, one misses within-job wage and

benefit cost dispersion. From a firm’s perspective this may not be very relevant – the

within-job dispersion in, say, health insurance takeup rates may reflect ex post outcomes

rather than ex ante expected costs – but it would be relevant from the perspective of the

individual workers.

For wage rates, evidence from other establishment survey data suggests that

relatively little of the total log wage variation is within-job (Groshen (1991)). More recent

evidence from another establishment survey, the National Compensation Survey, indicates

that within-job log wage variance is on the order of 3 to 4 percent of total log wage

variance (calculations by author). The sampling design and data collection for these

surveys are similar, suggesting that wage dispersion measures as presented in the paper are

quite like what would obtain were individual wage rates observed.

Unfortunately, there is little evidence on within-job differences in benefit costs.

Within-job dispersion in legally required benefit costs should approximately equal the

within-job wage dispersion, since those costs tend to be direct functions of earnings. And

obviously there is no within-job variance where costs are zero, which is a substantial

portion of the data for some benefits. For observations with positive voluntary benefits

costs, one can conceptually attribute within-job cost differences to differences in employer

offers or in employees’ take-up. One would expect within-job differences in employer

benefit offers to be small because of non-discrimination rules and the desire to be

perceived as treating similar workers in a similar fashion. Note in this regard that ECI

sampling treats full-time and part-time workers as occupying different jobs, even if they

have the same job title (the same treatment holds for differences in union status and

incentive pay status). Therefore any dispersion due to full-time/part-time differentials in

health insurance (etc.) offers will be reflected in the ECI data as dispersion across jobs,

and so will be incorporated in the paper’s inequality calculations. The main offer rate

differences within-job probably relate to tenure or age service requirements, which are

relevant mainly for retirement plans and vacation leave. Within-job differences in

individuals’ take-up of benefit offers are most likely to occur for benefits where there is
39

some copayment or immediate cost to the worker, e.g., health insurance or matching

contributions to defined contribution plans.

Therefore most of the within-job compensation variation is likely to be due to

health insurance and pension plan costs. I have no choice but to presume this dispersion is

small relative to the across-job dispersion, and that it largely differences out over time, so

that the dispersion estimates presented in the paper can be viewed as being fairly good

proxies for what would obtain in individual microdata.
Table 1: Nonwage Compensation Costs, Fourth Quarter 1997

Average

Compensation Costs

($/hour)

Average

Compensation

Share

Percent of Jobs

With Positive

Costs

Legally Required

Social Security/Medicare 1.13 .062 99.7

Worker’s Compensation 0.38 .022 97.8

State UI 0.10 .007 88.5

Federal UI 0.03 .002 78.8

All Legally Required 1.64 .094 100.0

Leave

Vacation 0.61 .026 74.3

Holidays 0.45 .020 75.2

Sick Leave 0.19 .008 54.5

Other Leave 0.06 .003 49.9

All Leave 1.32 .056 86.0

Insurance

Health Insurance 1.15 .054 73.1

Life Insurance 0.05 .002 61.3

Sickness/Accident Insurance 0.03 .002 33.4

All Insurance 1.23 .058 77.4

Retirement and Savings

Defined Benefit Plans 0.51 .017 35.7

Defined Contribution Plans 0.27 .011 40.9

All Retirement and Savings 0.78 .028 62.6

Other

Nonproduction Bonuses 0.25 .008 35.2

Severance Pay 0.02 .001 10.2

Supplemental UI 0.01 .000 1.1

Voluntary Nonwage Compensation 3.61 .152 91.9

Nonwage Compensation 5.26 .246 100.0

Notes: The source is microdata from the fourth quarter 1997 Employment Cost Index. The average wage rate

in this quarter’s sample is $14.36. All cost figures are expressed in real (CPI-deflated) 1997 dollars.
Table 2: Benefits’ Contribution to Compensation Dispersion, 1995-97

Benefit’s Contribution Compensation Dispersion

Distributional

Range

Wage

Dispersion Leave Pensions

Health

Insurance

Other

Voluntary

Legally

Required

Voluntary

Compensation

Compensation

(wage sort) Compensation

25-10 .299

(.008)

.026

(.004)

.014

(.001)

.039

(.005)

.007

(.003)

-.013

(.004)

.385

(.014)

.375

(.014)

.368

(.011)

50-25 .406

(.008)

.030

(.003)

.014

(.003)

.022

(.006)

.001

(.003)

-.016

(.003)

.473

(.012)

.456

(.013)

.471

(.008)

75-50 .460

(.007)

.016

(.003)

.017

(.004)

-.003

(.004)

.002

(.002)

-.006

(.003)

.492

(.012)

.486

(.011)

.482

(.008)

90-75 .385

(.007)

-.011

(.003)

.017

(.004)

-.007

(.003)

.003

(.002)

-.014

(.003)

.387

(.011)

.373

(.010)

.391

(.007)

50-10 .705

(.011)

.056

(.003)

.028

(.002)

.061

(.004)

.008

(.002)

-.029

(.003)

.858

(.013)

.831

(.012)

.839

(.012)

90-50 .845

(.010)

.005

(.003)

.034

(.004)

-.010

(.005)

.005

(.002)

-.020

(.003)

.879

(.014)

.859

(.014)

.873

(.011)

75-25 .866

(.010)

.046

(.003)

.031

(.003)

.019

(.005)

.003

(.003)

-.022

(.003)

.965

(.013)

.942

(.013)

.953

(.010)

90-10 1.550

(.012)

.061

(.003)

.062

(.003)

.051

(.004)

.013

(.002)

-.049

(.004)

1.737

(.016)

1.690

(.016)

1.712

(.014)

Notes: ECI quarterly data from the first quarter 1995 to the fourth quarter 1997 are pooled and equally weighted to obtain these statistics. The“Distributional Range” column indicates the percentile range over which comparisons are made. Statistics are based on averages over the fivepercentiles of the data centered on the relevant point; for example, the row “25-10” refers to differences between the 23rd-27th and the 8th-12thpercentile ranges. In all columns except the last the percentiles reference place in the wage distribution; for the last column percentiles referenceplace in the compensation distribution. Wage and compensation dispersion columns are log wage and log compensation differentials. Standarderrors are in parentheses and take into account the clustering of observations within establishments as well as the non-independence of observationsdue to pooling.
Table 3: Wage and Compensation Determinants

log hourly

wage

log hourly

compensation

log hourly

voluntary

compensation

Full Time .233

(16.3)

.311

(20.5)

.329

(20.9)

Union Coverage .185

(13.0)

.243

(15.9)

.251

(15.9)

ln(establishment size) .032

(11.6)

.040

(13.8)

.042

(14.3)

Incentive Pay Indicator .180

(6.8)

.152

(6.1)

.154

(5.9)

Ownership (Private Omitted)

 State Government -.026

(1.1)

.040

(1.7)

.058

(2.6)

 Local Government .044

(2.5)

.024

(4.2)

.095

(5.2)

R-Squared .711 .737 .740

N 22128 22128 22128

Notes: Data are from the fourth quarter 1994. Regressions include controls for region,

presence in a metropolitan statistical area (MSA), and detailed industry and occupation. Tstatistics are in parentheses; standard errors are robust and account for the clustering of

observations within establishments.
Table 4: Benefit Incidence

Leave Pensions Health Insurance

Full Time .259

(18.4)

.179

(7.2)

.526

(23.6)

Union Coverage .028

(4.6)

.138

(4.7)

.137

(9.3)

ln(establishment size) .005

(3.7)

.098

(16.0)

.051

(14.0)

Incentive Pay Indicator -.126

(8.3)

-.113

(3.1)

-.036

(1.3)

Ownership (Private Omitted)

 State Government .033

(4.3)

.084

(1.6)

.069

(3.0)

 Local Government .028

(4.7)

.123

(2.7)

.008

(0.4)

Percentile Indicators

 (sorted on)

(non-leave

compensation)

(non-pension

compensation)

(non-health

insurance

compensation)

R-Squared .436 .355 .547

N 20750 20612 20751

Notes: Data are from the fourth quarter 1994. The dependent variables are indicators for presence

of positive compensation costs for the benefit in question. Estimates are from maximum-likelihood

probit models; coefficients are transformed to give changes in probability. All equations include

region, MSA, and major industry and major occupation controls. T-statistics are in parentheses;

standard errors are robust and account for the clustering of observations within establishments.
Table 5: Benefit Intensive Margin

Leave Pensions Health Insurance

Full Time .288

(6.5)

.191

(2.2)

-.157

(1.9)

Union Coverage .076

(2.9)

.220

(4.5)

.390

(10.1)

ln(establishment size) .055

(10.3)

.009

(0.7)

.007

(0.9)

Incentive Pay Indicator -.296

(6.9)

-.308

(2.2)

-.329

(5.3)

Ownership (Private Omitted)

 State Government .328

(6.2)

.387

(6.1)

.124

(2.0)

 Local Government .117

(2.9)

.305

(4.7)

.129

(2.9)

Percentile Indicators

 (sorted on)

(non-leave

compensation)

(non-pension

compensation)

(non-health

insurance

compensation)

R-Squared .721 .605 .467

N 15237 6538 7398

Notes: Data are from the fourth quarter 1994. The dependent variables are the natural logarithms

of the benefits in question. All regressions include region, MSA, and major industry and major

occupation controls. T-statistics are in parentheses; standard errors are robust and account for the

clustering of observations within establishments.
Table 6: Combined Extensive and Intensive Margins

Leave Pensions Health Insurance

Combined

Effect

Fraction

Extensive

Combined

Effect

Fraction

Extensive

Combined

Effect

Fraction

Extensive

Full Time .582 .504 .482 .604 .537 1.293Union Coverage .108 .295 .445 .506 .573 .319ln(establishment size) .062 .099 .169 .946 .074 .903Incentive Pay Indicator -.440 .327 -.492 .375 -.377 .127State Government .365 .103 .524 .262 .216 .427Local Government .149 .214 .504 .396 .139 .072Notes: Columns labelled “Combined Effect” give the marginal effect in percentage terms of the given covariate on benefit costs.These statistics are constructed by combining estimates from tables 4 and 5 (see text for description). Columns labelled “FractionExtensive” show what fraction of the combined effect is attributable to the extensive margin.
Table 7: Wages, Benefit Costs, and Compensation Costs, 1981-97

1981-83 1985-87 1990-92 1995-97

Average At Median Average At Median Average At Median Average At Median

Hourly Wage 15.18 12.86 15.20 12.69 14.43 11.78 14.15 11.20Leave Costs 1.53 1.35 1.50 1.25 1.41 1.15 1.31 1.03 Share .066 .075 .064 .070 .061 .068 .056 .065 Incidence (%) 92.2 96.2 90.3 95.2 89.9 96.6 86.1 94.4Pensions Costs 1.12 0.84 0.98 0.70 0.79 0.52 0.78 0.48 Share .042 .045 .036 .038 .029 .030 .029 .029 Incidence (%) 70.5 76.5 65.7 72.0 61.4 69.0 62.0 69.5Health Insurance Costs 0.82 0.82 0.99 0.97 1.25 1.25 1.19 1.15 Share .039 .045 .047 .054 .059 .073 .056 .070 Incidence (%) 84.7 93.5 82.2 90.9 77.7 88.1 73.5 83.7Voluntary Benefits 3.85 3.23 3.76 3.13 3.76 3.13 3.64 2.86Total Benefits 5.38 4.64 5.35 4.59 5.43 4.64 5.29 4.25All Compensation 20.56 17.50 20.55 17.29 19.86 16.42 19.44 15.43Notes: Columns labeled “Average” refer to averages of the given statistics over the entire distribution, for the relevant pooled subperiod. Columnslabeled “At Median” refer to averages of the given statistics over the 48th-52nd percentiles in the wage distribution. All cost figures are deflated to

1997 dollars using the Consumer Price Index.
Table 8: Wage and Compensation Dispersion, 1981-97

1981-83 1985-87 1990-92 1995-97

Distributional Range dlnw dlnc dlnw dlnc dlnw dlnc dlnw dlnc

25-10 .288 .364 .339 .420 .322 .398 .299 .36850-25 .411 .440 .432 .458 .420 .471 .406 .47175-50 .407 .422 .427 .438 .435 .444 .460 .48290-75 .263 .284 .322 .327 .350 .364 .385 .39150-10 .699 .804 .771 .878 .742 .869 .705 .83890-50 .670 .706 .749 .765 .785 .808 .845 .87390-10 1.369 1.510 1.521 1.643 1.526 1.677 1.550 1.712Quarterly Average: Observations 11710 16711 24839 19504 Establishments 2431 3188 5318 4606Notes: ECI quarterly data for the relevant periods are pooled and equally weighted to obtain these statistics. The “Distributional Range”column indicates the percentile range over which comparisons are made. Statistics are based on averages over the five percentiles of thedata centered on the relevant point; for example, the row “25-10” refers to differences between the 23rd-27th and the 8th-12th percentile

ranges. Columns labeled “dlnw” are log wage differentials across the stated percentiles of the wage distribution. Columns labeled “dlnc”are log compensation differentials across stated percentiles in the compensation distribution.
Table 9: Changing Wage and Compensation Dispersion, 1982-96

Change In:

Benefit’s Contribution Compensation Dispersion

Distributional

Range

Wage

Dispersion Leave Pensions

Health

Insurance

Voluntary

Compensation

All

Compensation

25-10 .011

(.015)

-.005

(.007)

-.011

(.005)

.016

(.008)

.010

(.024)

.025

(.023)

50-25 -.005

(.013)

.004

(.007)

-.005

(.007)

.017

(.008)

.013

(.022)

.003

(.022)

75-50 .053

(.013)

.008

(.007)

.001

(.009)

.005

(.006)

.064

(.024)

.066

(.023)

90-75 .122

(.014)

.002

(.008)

.002

(.010)

-.004

(.005)

.122

(.024)

.114

(.023)

50-10 .006

(.019)

-.001

(.006)

-.016

(.006)

.032

(.006)

.023

(.024)

.027

(.024)

90-50 .175

(.018)

.010

(.007)

.003

(.009)

.001

(.006)

.186

(.028)

.179

(.027)

75-25 .049

(.015)

.012

(.006)

-.004

(.008)

.021

(.007)

.077

(.024)

.068

(.023)

90-10 .181

(.020)

.009

(.006)

-.013

(.007)

.033

(.006)

.210

(.027)

.207

(.028)

Notes: ECI quarterly data from the first quarter 1982 to the fourth quarter 1996 are pooled and equally

weighted to obtain these statistics. The “Distributional Range” column indicates the percentile range over

which comparisons are made. Statistics are based on averages over the five percentiles of the data centered

on the relevant point; for example, the row “25-10” refers to differences between the 23rd-27th and the 8th-12th

percentile ranges. In all columns percentiles reference place in the wage distribution. Wage and

compensation dispersion columns are log wage and log compensation differentials. Standard errors, in

parentheses, take into account the clustering of observations within establishments as well as the nonindependence of observations due to pooling.
Table 10: Benefit Cost Elasticities with Respect to Compensation,

By Compensation Percentile

Compensation Percentiles Leave Pensions

Health

Insurance

 Percentiles 10-40 2.29 3.42 2.83

 Percentiles 40-60 1.47 1.83 1.09

 Percentiles 60-90 1.16 1.93 0.97

Notes: Elasticities are based on pooled 1981-97 cross-sectional data, and are simple averages of

compensation percentile-specific estimates over the ranges indicated.
Table 11: Actual and Predicted Benefit Cost Changes

Change In:

Leave Costs Pension Costs

Health Insurance

Costs

Compensation

Percentile Actual Predicted Actual Predicted Actual Predicted

A. 1982 to 1996

 10th -.541 -.557 -.675 -.871 -.755 -.935

 25th -.489 -.317 -.710 -.571 -.090 -.344

 50th -.276 -.185 -.413 -.246 .397 -.153

 75th -.189 -.061 -.233 -.085 .766 -.033

 90th -.135 .045 -.149 .081 .452 .005

B. 1982-1986

 10th -.115 -.298 -.328 -.467 -.172 -.489

 25th -.117 -.053 -.369 -.095 .192 -.058

 50th -.056 -.010 -.163 -.014 .214 -.009

 75th -.038 .009 -.010 .016 .381 .006

 90th -.087 .051 -.053 .090 .111 .012

C. 1986-1991

 10th -.293 -.150 .180 -.236 -.395 -.266

 25th -.242 -.137 -.406 -.248 .075 -.149

 50th -.101 -.080 -.278 -.107 .217 -.067

 75th -.044 -.047 -.245 -.068 .294 -.026

 90th .032 -.009 -.074 -.016 .355 -.004

D. 1991-1996

 10th -.252 -.109 -.453 -.169 -.508 -.179

 25th -.235 -.127 -.222 -.229 -.286 -.138

 50th -.145 -.094 -.025 -.125 -.050 -.078

 75th -.117 -.023 .031 -.033 -.010 -.013

 90th -.083 .004 -.030 .006 -.037 -.002
Appendix Table 1: Imputed Data, Fourth Quarter 1997

Whole

Distribution

Below

Median

Above

Median

Vacation Leave

 Imputed Data – No Information .042 .036 .049

 Imputed Data – Known Positive Costs .082 .063 .101

 No Plan .196 .257 .137

 Non-Imputed Cost Data – Job Incumbents .596 .571 .619

 Non-Imputed Cost Data – Broader Group .084 .074 .095

Holiday Leave

 Imputed Data – No Information .042 .036 .049

 Imputed Data – Known Positive Costs .026 .025 .027

 No Plan .162 .220 .105

 Non-Imputed Cost Data – Job Incumbents .714 .656 .769

 Non-Imputed Cost Data – Broader Group .056 .064 .050

Defined Benefit Plans

 Imputed Data – No Information .045 .038 .051

 Imputed Data – Known Positive Costs .053 .030 .075

 No Plan .596 .778 .414

 Non-Imputed Cost Data – Job Incumbents .173 .085 .261

 Non-Imputed Cost Data – Broader Group .134 .070 .199

Defined Contribution Plans

 Imputed Data – No Information .043 .036 .050

 Imputed Data – Known Positive Costs .074 .054 .095

 No Plan .553 .649 .458

 Non-Imputed Cost Data – Job Incumbents .136 .107 .164

 Non-Imputed Cost Data – Broader Group .194 .154 .232

Health Insurance

 Imputed Data – No Information .045 .039 .051

 Imputed Data – Known Positive Costs .100 .069 .131

 No Plan .237 .418 .058

 Non-Imputed Cost Data – Job Incumbents .327 .228 .375

 Non-Imputed Cost Data – Broader Group .291 .196 .385

Notes: Statistics presented are the fraction of the sample with benefit status flags as indicated. Numbers sum

to one within benefit and column.
Appendix Table 2: Benefits Contribution to Compensation Dispersion, 1995-97,

Under Alternative Sample Restrictions

Leave Pensions Health Insurance

Distributional Range (1) (2) (1) (2) (1) (2)25-10 .026 .027 .014 .020 .039 .04750-25 .030 .027 .014 .026 .022 .02275-50 .016 .014 .017 .027 -.003 -.00290-75 -.011 .001 .017 .015 -.007 -.00450-10 .056 .054 .028 .046 .061 .06990-50 .005 .015 .034 .042 -.010 -.00690-10 .061 .069 .062 .088 .051 .063Notes: Constructs are as in table 2, except for the samples used in the calculations. Columns labeled (1) exclude imputeddata, and are from table 2. Columns labeled (2) are constructed as follows. Incidence of the benefit in question iscalculated from the subsample of the data with non-imputed incidence. Benefit shares conditional on receiving the benefitare calculated from the subsample of the data where all major benefits data refer to the sampled job (meaning the data arenot imputed and refer to the job’s incumbents only). Within percentile, a benefit’s share of compensation is estimated asthe incidence times the benefit share conditional on receipt. Statistics in columns labeled (2) are obtained by applyingequation (1) to these estimated series.
0.0

0.1

0.2

0.3

0 10 20 30 40 50 60 70 80 90 100

Percentile of Compensation Distribution

Fraction of Total Compensation

All benefits

Non-legally required benefits

Figure 1. Benefits' Share of Compensation, 1995-1997
0.00

0.02

0.04

0.06

0.08

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction of Total Compensation

Sorted on compensation

Sorted on wages

Figure 2. Leave's Share of Compensation, 1995-1997
0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction with Positive Costs

Sorted on compensation

Sorted on wages

Figure 3. Leave Cost Incidence, 1995-1997
0.00

0.02

0.04

0.06

0.08

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction of Total Compensation

Sorted on compensation

Sorted on wages

Figure 4. Pensions' Share of Compensation, 1995-1997
0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction with Positive Costs

Sorted on compensation

Sorted on wages

Figure 5. Pension Cost Incidence, 1995-1997
0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Percentile of Compensation Distribution

Fraction with Positive Costs

Any retirement

Defined Contribution

Defined Benefit

Figure 6. Pension Incidence by Type, 1995-1997
0.00

0.02

0.04

0.06

0.08

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction of Total Compensation

Sorted on compensation

Sorted on wages

Figure 7. Health Insurance's Share of Compensation,

1995-1997
0.0

0.2

0.4

0.6

0.8

1.0

0 10 20 30 40 50 60 70 80 90 100

Percentile

Fraction with Positive Costs

Sorted on compensation

Sorted on wages

Figure 8. Health Insurance Cost Incidence, 1995-1997
A. 1982-1996

-0.10-0.20

0.100.00

0 10 20 30 40 50 60 70 80 90 100

Percentile

Log Change

Compensation growthWage growth

B. 1982-1986

-0.05-0.10

0.050.00

0 10 20 30 40 50 60 70 80 90 100

Percentile

Log Change

C. 1986-1991

-0.05-0.10

0.050.00

0 10 20 30 40 50 60 70 80 90 100

Percentile

Log Change

D. 1991-1996

-0.05-0.10

0.050.00

0 10 20 30 40 50 60 70 80 90 100

Percentile

Log Change

Figure 9. Wage and Compensation Growth by Percentile
A. 1982-1996

-0.25-0.50-0.75

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate Predicted

Actual

B. 1982-1986

-0.25-0.50

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

C. 1986-1991

-0.25-0.50

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

D. 1991-1996

-0.25-0.50

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

Figure 10. Leave Cost Changes by Percentile
A. 1982-1996

-0.25-0.50-0.75-1.00

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate Predicted

Actual

B. 1982-1986

-0.25-0.50-0.75

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

C. 1986-1991

-0.25-0.50-0.75

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

D. 1991-1996

-0.25-0.50-0.75

0.250.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

Figure 11. Pension Cost Changes by Percentile
A. 1982-1996

-0.50-1.00

1.000.500.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate Predicted

Actual

B. 1982-1986

-0.30-0.60

0.600.300.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

C. 1986-1991

-0.30-0.60

0.600.300.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

D. 1991-1996

-0.30-0.60

0.600.300.00

0 10 20 30 40 50 60 70 80 90 100

Compensation Percentile

Growth Rate

Figure 12. Health Insurance Cost Changes by Percentile