Appeared in Research in Labor Economics, Volume 18, 1999, pp. 243-79.

POSTAL SERVICE COMPENSATION AND THE COMPARABILITY STANDARD

Barry T. Hirsch

Department of Economics Trinity University

San Antonio, TX 78212-7200 phone: (210)999-8112

fax: (210)999-7255 e-mail: bhirsch@trinity.edu

Michael L. Wachter

Institute for Law and Economics University of Pennsylvania

3400 Chestnut Street Philadelphia, PA 19104-6204

phone: (215)898-7852 fax: (215)573-2025

e-mail: mwachter@law.upenn.edu

James W. Gillula Standard and Poor's DRI

Suite 1000 1200 G Street NW

Washington, DC 20005-3814 phone: (202)383-3525

fax: (202)383-2005 e-mail: james_gillula@mcgraw-hill.com

ABSTRACT

The directive of the Postal Reorganization Act is to maintain compensation similar to that awarded for comparable levels of work in the private sector. This paper examines a wide array of recent evidence to assess the comparability of postal and private sector compensation. The evidence points to a substantial postal premium. Cross-sectional analysis controlling for worker characteristics indicates that bargaining unit postal employees receive wages 28% higher than similar private sector workers. A premium estimate of 34% is obtained following an accounting for occupational skill requirements and working conditions, while inclusion of fringe benefits increases further the size of the premium. Longitudinal evidence from the Postal New Hire Survey, matched CPS panels, and Displaced Worker Surveys indicate wage gains of 30%-40% among postal entrants. Data on quit rates and applicant queues reinforce the conclusion that postal workers receive substantial rents. We explore in depth methodological issues, with particular attention given to the choice of the comparison group with whom postal workers are most directly compared. Many of the issues analyzed here have general applicability to studies of wage comparability. The authors are, respectively, E.M. Stevens Distinguished Professor of Economics, Trinity University, San Antonio, TX; William B. Johnson Professor of Law and Economics and Director of the Institute of Law and Economics, University of Pennsylvania, Philadelphia, PA; and Economist and Principal, Standard and Poor's DRI, Washington, DC. We acknowledge computational and data assistance from Timothy Gill and David Macpherson. We have benefitted from the suggestions of two anonymous referees and seminar participants at Florida State University and Princeton University, and discussions with Ted Clark, Andrew German, Donald Develin, D. Richard Froelke, and David Macpherson. The authors have been employed as economic consultants by the U.S. Postal Service during labor negotiations. The views in the paper reflect those of the authors and not necessarily those of the U.S. Postal Service.

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I. INTRODUCTION

A standard directive of federal, state, and local laws is that compensation of public employees be comparable to

that in the private sector. The Postal Reorganization Act (PRA) of 1970 requires the following:

It shall be the policy of the Postal Service to maintain compensation and benefits for all officers

and employees on a standard of comparability to the compensation and benefits paid for

comparable levels of work in the private sector of the economy. (39 U.S.C. 1003(a))

Compensation for most postal employees is determined through the collective bargaining process. Absent

agreement between postal unions and management, compensation is determined by mandatory interest arbitration.1

Central to the arbitration process is the interpretation of the PRA standard and of econometric evidence on

comparability. This paper provides an analysis of these issues and presents empirical evidence on comparability in

the U.S. Postal Service. We emphasize methodological issues and the need to bring a variety of evidence to bear on

the issue of comparability. Although our emphasis is on compensation among postal employees, the issues analyzed

here have general applicability to studies of wage comparability.

In what follows, we interpret and operationalize the PRA comparability standard as follows: Public sector

workers should receive compensation equivalent to that received by similar workers employed in jobs with similar

characteristics in the private sector. This interpretation follows from the economic theory of equalizing

differentials, and is consistent with both efficiency and equity (for a clear statement of this view, see Venti 1986).

Legal and political interpretations of the PRA standard tend to focus on its language and intent, each of which can

engender disagreement. We would argue that interpretation should be evaluated based on the statute's explicit

language, as long as that language is reasonably clear. In what follows, we argue that our measures of postal

comparability represent both a reasonable interpretation of the PRA's language, and are compatible with economic

theory regarding wage differentials. Ultimately, public sector wages will be determined by some combination of

market forces and the interpetation of comparability statutes through the political process or collective bargaining.

Comparability analyses typically compare wages differences controlling either for worker characteristics or job

characteristics. Labor economists frequently utilize regression analysis comparing the wages of public and private

sector workers after accounting for measurable individual characteristics, absent direct measures of job

characteristics. Job analysts typically rely on direct job comparisons, whereby compensation in public sector jobs is

compared to selected private sector jobs deemed equivalent, with no explicit accounting for worker characteristics.2

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These alternative approaches should be viewed as complementary. Economic theory from Adam Smith forward

posits that labor markets provide compensating differentials for worker skills and job characteristics. Studies that

compare workers attempt to control for individual characteristics that proxy worker or job skills and labor market

conditions. Studies comparing jobs may be viewed as an attempt to account for compensating differentials by

measuring wages in jobs requiring workers with similar skills and working conditions. In short, human capital

theory provides a foundation for micro wage equations and job-based comparisons. Nor is accounting for job

characteristics inconsistent with human capital theory. The crucial issue is whether the method of analysis and

choice of a comparison group provides a reliable measure of comparability.

The analysis that follows provides measures of postal wage comparability based on the use of CPS-based

individual wage equations, supplemented by measures of occupational skills and working conditions. The Postal

Service is unique in that its employees can be identified in the CPS owing to an industry code for the U.S. Postal

Service, as well as separate occupational codes for USPS clerks and carriers. Our regression approach provides a

measure of comparability that we argue is superior to that available from direct job matches, while at the same time

satisfying the legal requirements of the PRA. Direct job comparisons can be useful. But to provide an appropriate

measure of comparability, it is essential not only that a close occupational match is found, but also that the match be

with firms where workers as well as job descriptions are similar. For example, one could compare compensation

among postal workers with that received by workers at United Parcel Service (UPS) and Federal Express (FedEx).

But such jobs provide an inappropriate comparison for a number of reasons. First, job duties, required skills,

working conditions, and work effort are not equivalent for postal clerks or carriers and for workers at UPS or FedEx.

At the same time, one is excluding from comparison other private sector jobs that may be similar to postal jobs.

Second, UPS and FedEx operate in product markets in which the Postal Service workforce has a limited presence.

In 1995, Express Mail and Parcel Post -- the USPS services competing most directly with FedEx and UPS --

accounted for less than 1% of total Postal Service volume (but a higher percentage of revenue). Third, a jobs

comparison among workers in one or two firms may not be appropriate since their wages may reflect industry rents

or non-competitive wage determination and not be indicative of the opportunity cost wages that would be available

to postal workers were they employed in the private sector. Job comparisons comport most closely with the

economic and legal standards of comparability where jobs require comparable skills and working conditions,

workers are similar, and there exist a relatively large number of private sector employers.3

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In the sections below, we first offer evidence on wage differences between postal workers and those of

comparable workers and jobs in the private sector.4 We provide estimates based on wage level regressions using

standard CPS variables, estimates from expanded specifications including measures of job skill requirements and

working conditions, and evidence from longitudinal data on postal entrants. Following estimation of postal wage

differences, we turn to methodological and specification issues raised in the literature and in postal arbitration

hearings, most relating to the issue of the appropriate comparison group for postal workers. In addition to our wage

analysis, additional evidence is provided on nonwage benefits, the length of postal queues, and quit rates. A

concluding section discusses links between postal contract settlements and the regulatory and product market

environment in which the Postal Service operates.

II. SPECIFICATION AND MEASUREMENT OF THE POSTAL PREMIUM

Below we describe the method used to calculate postal wage differentials. Key methodological issues are

subsequently addressed in section VI. The postal wage premium is calculated from micro log wage equations

estimated separately for each of four race/gender groups. All full-time wage and salary workers are included,

allowing the wage structure (coefficients) to be determined on an economy-wide basis. The regression includes

numerous measures of individual, labor market, and job characteristics. Industry dummies are included, interacted

with union status. Unionized Postal Service workers, referred to as the bargaining unit, form the omitted or

reference industry. The postal premium is first calculated within each race/gender group based on the weighted

average of the postal-private log wage differential across non-agricultural private sector union-by-industry goups.

Following calculation of the premium by race/gender group, the postal premium is calculated by taking the weighted

average across the four groups.

More formally, let:

where lnWi is the natural log of hourly earnings for individual i; j represents race/gender group; Xl is a vector of

person-specific characteristics (indexed by l) included in our base specification, with ∃1 the corresponding

coefficient vector; Zm is a vector of job-related skill and working condition variables (indexed by m) included in our

expanded specification and ∋m is the corresponding coefficient vector; UNΑINDk is a vector of industry dummies

(1),ijjkijkINDNUjkijkINDUNjmijmZjlijlXijWln εφθΓβ +⋅+⋅++=

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(indexed by k) interacted with a binary union membership variable and 2k is the coefficient vector; NUΑINDk is a

vector of industry dummies interacted with a binary nonunion variable (it includes the same industries as UNΑIND

plus a dummy for nonunion postal workers) and k is the coefficient vector; and ,i is a random error term with mean

zero and a constant variance. The reference group is bargaining unit Postal Service workers.

The postal premium, D, is calculated from the union and nonunion industry coefficients, 2 and Ν, which measure

the log wage differential between bargaining unit postal workers and each union-by-industry sector. We first

calculate the differential for each race/gender group:

where wjk are the employment shares of non-managerial and non-professional private sector workers across the

union-by-industry groups (Εwjk = 1.0, with weights of zero attached to public sector industry dummies). After

obtaining a premium estimate for each race/gender group j, we take the weighted average of the premiums, using

Postal Service employment as weights. That is,

D = Dj pj (3)

where pj represents the proportions of postal employment among the four race/gender groups.5 D, which reflects the

average premium across postal workers is then presented following two downward adjustments, one to account for

differences in night shift work between postal and non-postal workers and a second to adjust for differences in

tenure. These adjustments are described in detail in the Appendix.

Our method for calculating the postal premium has considerable appeal. Wage coefficients are determined based

on full-time wage and salary workers economy-wide.6 Calculation of the postal premium, however, is based on a

comparison to private sector workers, as indicated by the PRA comparability standard. The inclusion of separate

industry dummies interacted with union status is less restrictive than simpler specifications, and allows one to

extract separate estimates of the postal premium by industry and industry-by-union status.7 Finally, estimates of D

are presented with and without inclusion of Z, a vector of occupational skill and working condition variables. In

section VI, we discuss further how specification and the choice of variables to include in X and Z determine the

standard of comparison. We emphasize the importance of distinguishing between variables that reflect skills or

compensating differentials and those that reflect non-transferable rents (Linneman and Wachter, 1990).

(2),wwD njkjkujkjkj Φθ +=

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III. DATA

The primary data source for the analysis is the 1994 CPS Outgoing Rotation Group (ORG) Earnings File, which

includes the quarter sample from each month's CPS who are administered the earnings supplement including

questions on, among other things, usual weekly earnings, usual hours worked per week, and union status. Our

estimation sample includes 127,435 non-agricultural full-time wage and salary workers, ages 16 and over. We

deleted those for whom: the Census had allocated industry, occupation, or union membership; hourly earnings could

not be calculated; the industry designation was manufacturing, n.e.c. (not elsewhere classified); there was missing

information for any variables used in the wage equation (a small number of observations); implied hourly earnings

were less than $1; and usual hours worked per week exceed 60. USPS workers are identified by their Census

industry code. Our principal analysis focuses on "bargaining unit" postal employees, defined as the 78.5% of our

postal sample coded as union members. Excluded from the bargaining unit are nonunion postal employees, a group

including a mix of management, white-collar workers, and nonbargaining casual (temporary) workers.8

The vector X from equation (1) includes standard variables from the CPS defined at the individual level.

Included are a set of dummy variables for educational degree (13), marital status (2), hours worked (4), region (8),

MSA/CMSA status and size (7), and occupation (14). Continuous variables included are potential experience (the

minimum of age minus schooling minus 6, or age minus 16) and its square, and the local area unemployment rate

(Employment and Earnings, May 1995). We supplement the CPS data set with variables from the Dictionary of

Occupational Titles (DOT) -- vector Z in equation (1). The DOT provides a broad range of information on the

content and characteristics of detailed occupations based on evaluations by job analysts. We use the DOT data set

and matching process developed by England and Kilbourne (1988), who provide a weighted mapping of 1977

Fourth Edition DOT variables to 1980 Census occupation codes. England and Kilbourne provide means of DOT

variables for approximately 500 1980 Census occupational categories, calculated as weighted averages across

roughly 12,000 DOT occupations (special Census projects mapped CPS workers to DOT occupations, and 1980

Census of Population respondents to both 1970 and 1980 Census occupation codes). We match the England-

Kilbourne data with 497 time-consistent 1980/1990 Census occupational codes and reassign codes for a few small

occupations for which England and Kilbourne have missing data. We explicitly assign more recent DOT values to

postal carriers and clerks.9

DOT variables are grouped into the following categories measuring occupational skills and working conditions:

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Training Variables. Variables measure a) general educational development required in reasoning, math, and

language, and b) training required for specific vocational preparation, measured by time required to learn

techniques, acquire the information, and develop the facility needed for average performance in a specific job-

worker situation.

Aptitude Factors. The set of variables measure aptitude levels required to perform adequately in an occupation.

Measures include required verbal, numerical, and spatial aptitudes, form and clerical perceptions, and physical

coordination and dexterity.

Worker Function Scales. These measure information, knowledge, and concepts related to data, people, or things,

obtained by observation, investigation, interpretation, visualization, and mental creation.

Environmental Conditions. Variables measuring whether jobs are performed indoors, outdoors, or both; and the

presence of non-weather related environmental conditions such as extreme cold, heat, wetness, noise, atmospheric

conditions (fumes, noxious odors, etc.), and hazards.

Physical Demands. A strength index measure, plus variables measuring whether an occupation demands substantial

climbing and/or balancing; stooping, kneeling, crouching, and/or crawling; reaching, handling, fingering, and/or

feeling; seeing; and talking and/or hearing.

Expanding the standard CPS specification to include the DOT addresses the legitimate concern that in CPS-only

wage studies schooling, broad occupation, and other available variables do not account fully for skills or

compensating differentials associated with working conditions.10 The CPS-DOT analysis also may be advantageous

as compared to the alternative of an explicit but typically narrow job comparison analysis, since the CPS-DOT

regression model provides a broad comparison while at the same time differentiating among workers and jobs based

on individual characteristics, occupational skills, and working conditions. Moreover, the DOT allows one to control

directly for job skills, rather than including rent-related variables (e.g., union status and firm size) that are shaky

proxies for skill.

Although it is important to control for job working conditions, neither the magnitude nor signs of these variables

can be determined a priori since they depend on the marginal valuations of job attributes based on worker demand

and employer supply. If tastes are sufficiently heterogeneous and workers sort on the basis of job attributes,

compensation differentials will be small. Coefficients on individual DOT variables are not likely to provide precise

estimates of compensating differentials since there is a high degree of collinearity among the variables and working

condition variables may in part reflect unmeasured worker skills and income effects (Hwang, Reed, and Hubbard,

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1992). That being said, descriptive evidence on the means of the DOT variables provides information on how job

analysts rate postal occupations as compared to private sector occuptions. And control for a large number of

occupational skill and working condition variables in the CPS-DOT regressions should, at a minimum, provide

evidence as to whether CPS-only postal premium estimates overstate or understate the true differential. Such

analysis is subsequently complemented by evidence on wage changes among postal entrants, quit rates, and

applicant queues.

TABLE 1 ABOUT HERE

Table 1 presents means for selected variables for the postal and non-postal samples of workers -- the postal

bargaining unit sample, clerks, carriers, non-postal private clerical occupations (the category for which carriers and

clerks are coded one in the wage regressions), and all private sector workers. Evident from the CPS data is that

postal as compared to private sector workers are older, less likely to be single, less likely to be female, and more

likely to be nonwhite. An examination of the means of the DOT variables indicates that postal occupations require

lower levels of training, skills, and aptitudes than do non-postal jobs, on average. This is evident in lower postal

mean levels of required general training (GED), fewer months training required for occupational proficiency (SVP),

and the lower levels of verbal, numerical, and spatial aptitudes required for postal than for non-postal jobs. On the

other hand, postal carrier jobs have some working conditions more demanding than in the average private sector job,

as measured by the DOT. In particular, carrier jobs require greater strength, involve higher hazard risk, and require

more work outdoors than do non-carrier postal occupations or private sector occupations (but see footnote 9).

IV. POSTAL PREMIUM ESTIMATES

Below we first report our preferred estimates of the postal premium using the CPS-only and CPS-DOT

regression analyses. A summary of postal premium estimates is provided in Table 2. The estimate of the postal

wage premium from the CPS-only model for 1994 is .247 log points, a 28.0% wage advantage for postal relative to

private sector non-postal workers with similar characteristics.11 The CPS premium estimate includes the adjustment

for tenure and shift work (see the Appendix), which reduced the differential from .292 to .247. The postal wage

advantage understates the postal compensation advantage, which is examined briefly in section VII.

TABLE 2 ABOUT HERE

The CPS-DOT analysis addresses shortcomings in the CPS-only approach by controlling for occupational

differences in required skills and working conditions. Following control for ccupational characteristics, the postal

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premium increases from .247 to .293 log points, or from 28.0% to 34.0%.12 Why is there an increase in the premium

following control for job characteristics? In short, skill requirements tend to be lower for postal than for non-postal

workers, increasing premium estimates substantially, while working conditions tend to be less favorable for postal

workers, moderately decreasing premium estimates but not fully offsetting increases owing to skill variables.13 Our

conclusion that the CPS-DOT premium is higher than standard CPS estimates is consistent with longitudinal

evidence below that reveals large wage gains for workers switching from non-postal to postal jobs.

Appendix A-1 presents regression coefficients and standard errors for the DOT variables by race/gender group

and, for purposes of exposition, the employment weighted average of coefficients across groups. The CPS variables

and CPS-only analysis are standard in the literature and neither require discussion nor warrant the space necessary

for presentation. The DOT variables appear to provide reasonable controls for otherwise unmeasured worker/job

skills and working conditions, with the signs of the DOT coefficients generally corresponding to expectations.14

Positively related to wages are variables reflecting required general education (GED) and job training (SVP

converted to months training, and its square). Highly significant are aptitude variables measuring required verbal,

numerical, and spatial aptitudes.15 Unimportant are the DOT variables measuring complexity with respect to Data

or Things. This is not surprising given the non-hierarchical structure of these variables and the presence of other

variables more precisely measuring required job skills. DOT variables measuring physical demands and

environmental job conditions tend to be less important than DOT skill variables, either because they are less

precisely measured, because their true wage effects are small, or because they are negatively correlated with

unmeasured worker and job skills. Among the variables that do have a substantial impact are the presence of

significant hazards (Hazard) and the requirement of working outdoors (In & Out and Outdoors). Required strength

has a positive impact on wages for women, among whom strength is more scarce and a clear disamenity, but is

negatively related to male wages, possibly due to a negative correlation with skill.

We also examined differentials separately for the largest bargaining units -- postal carriers and clerks. As seen in

Table 2, premium estimates from the CPS-DOT analysis for carriers and clerks turn out to be remarkably similar.

We obtain log premium estimates of .297 for carriers and .288 for postal clerks, as opposed to .293 for the full

bargaining unit. The similarity between the carrier and clerk estimates results in part because the crafts have similar

wage schedules and largely similar worker and job characteristics. But they also result because differences in job

characteristics happened to just offset each other in the CPS-DOT analysis. On the one hand, the carrier premium is

decreased by its being rated as a hazardous occupation and, to a lesser extent, as requiring greater strength. On the

9

other hand, the DOT evaluates the mail clerks as requiring greater clerical aptitude and clerks work substantial hours

of evening and night work.

V. LONGITUDINAL ESTIMATES OF POSTAL PREMIUMS

In this section, we provide longitudinal postal premium estimates based on wage changes among postal entrants

and leavers. This approach controls for individual fixed effects, in particular unmeasured worker skills. Such an

approach creates a natural comparison group, each postal worker's wage being compared to that same worker's wage

on the prior or subsequent non-postal job. Longitudinal analysis, which can provide a good approximation of

opportunity cost employment, has been recommended as a method for measuring wage premiums for public sector

workers (see, for example, Krueger, 1988; Venti, 1986). Examining simple mean wage changes rather than use of

regression analysis is reasonable, given that most worker and location characteristics do not change.16 Wage change

for postal entrants can be compared to wage change among a control group of private sector workers voluntarily

changing industry and occupation as they move from full-time private sector jobs to alternative non-postal jobs.

What does the evidence show? We use three alternative data sets, the Postal Service New Hire Survey (NHS),

matched panels of the CPS for the pairs of years 1983/84 through 1993/94, and the six biennial CPS Displaced

Worker Surveys between 1984 and 1994.17 As discussed below, the NHS is the preferred data set. The CPS

matched panel is not ideal since sample sizes of postal entrants and leavers are small, even after pooling across

years. The CPS panels also exclude individuals who switch households or whose household moves during a year,

making the sample not fully representative or new hires and leavers. And because figures are based on separate

surveys one year apart, with different interviewers, coders, and possibly interviewees, measurement error is a

concern.18 But the CPS panels do provide the opportunity to construct a large comparison group of job changers not

employed by the Postal Service in either period. The NHS has several advantages. Annual sample sizes are large

and available separately by postal craft, the current postal wage is based on administrative records rather than

worker responses, and new workers are likely to report with reasonable accuracy the wage on their immediate (full-

time) job prior to gaining a postal position. The NHS data do not allow one to hold constant changes in other wage

determinants, but this is of little consequence when one is measuring wage change between full-time jobs over a

short time period (see footnote 16). The DWS is discussed subsequently.

We turn first to the NHS evidence, which is based on information from employment applications (USPS Form

2591) and personnel action forms (USPS Form 50). The sample includes all new hires into the clerk, city carrier,

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and mail handler crafts during the final quarter of FY-1994 who were 25 years or older, were previously employed

full-time in the private sector, and were working at their previous job within the 12 months prior to joining the

Postal Service. To reduce measurement error, observations reporting earnings of less than $3.60 (the minimum

wage for those with tip income) or more than $25.00 per hour are deleted from the sample. The NHS final sample

includes 733 postal employees (165 clerks, 403 carriers, and 165 mail handlers).

As seen in Table 2, evidence from the NHS is clear-cut, indicating large wage gains for postal entrants. The

mean log wage change for new hires is .329, or 39.0% in the unweighted sample and .362 or 43.6% when we

reweight based on July 1994 postal employment by craft rather than 1994 new hire proportions. Wage gains for

clerks, carriers, and mail handlers are .403, .328, and .259, respectively.19 The NHS wage premium estimates of

.329 and .362 are considerably larger than the CPS-only wage level estimate of .247, consistent with the hypothesis

that postal workers’ unmeasured skills are low relative to their private sector counterparts with similar measured

skills. Greater similarity exists between the CPS-DOT wage level and NHS longitudinal estimates. Both methods

attempt to control for unmeasured skills, albeit using very different methods. Although we place less weight on CPS

panel estimates than the NHS, for reasons stated above, estimated wage gains for CPS postal entrants are similar to

those among leavers while being moderately lower than the gains measured in the NHS. In order to provide

comparability with the NHS results, the CPS sample is restricted to workers ages 25 to 64 who are full-time

bargaining unit postal workers in year one or two, and full-time private sector worker in the other year. We omit

workers for whom industry or occupation has been allocated by the Census in either year (and, in years possible,

earnings). Log wage gains among the 90 postal joiners during 1983/4-1993/4 are .304 (35.5%), somewhat less than

the .329 NHS measure, while wage losses among the 50 leavers are .273 (-23.9%, or 31.4% using the private sector

base).

Because a real wage gain can be expected from job mobility and industry/occupation change, the new hire and

CPS postal wage changes may overstate the premium associated with postal employment. In order to control for

this we use the CPS panels to measure year-to-year wage change among workers ages 25 to 64 switching between

full-time private sector jobs who change occupation and industry (all other sample restrictions are the same as

above). Over the entire 1983/84-1993/94 period, mean real wage change among private sector job changers is .016.

Real wage change is a larger (.026) among young workers ages 25-34 who switch industry and occupation. If we

focus just on 1993/94 in order to construct a comparison group for the New Hire Survey, we find real wage gains of

.018 and .031 for the full and young samples, respectively. If we subtract the private sector returns from job

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switching from the wage gains of postal entrants, the longitudinal premium estimates are reduced by about 1 to 3

percentage points, depending on the choice of reference group.20

Although CPS estimates for postal entrants and leavers are similar, evidence for entrants is the preferable

measure. An important concern in longitudinal analysis is the issue of selection. The ideal experiment would be

one in which potential postal workers were randomly assigned to move between the Postal Service and the private

sector. Instead, we observe workers who switch for a variety of reasons, some of which are correlated with the

change in compensation and working conditions. The sample of postal leavers from the matched CPS panel is least

likely to be representative, containing individuals who obtain unusually high wage offers elsewhere, workers

dismissed from the Postal Service, and postal retirees (as it turns out, we find mean wage losses for leavers similar

to wage gains for joiners). Selection bias is less of a concern among the postal entrant sample. As noted by Krueger

(1988, p. 225) in his discussion of federal workers:

Consideration of the selection forces that affect job changers suggests that the relative wage gains

for workers who join the federal government are more representative of the ‘true’ average

difference in wages between the federal government and private sector. ... Focusing on workers

who join the federal government obviates many of the selectivity problems.

In particular, Krueger stresses that in the presence of a wage premium, observing the wage gain of joiners is likely

to correspond more closely to the ideal of a lottery or exogenous job switch. This point applies with far greater

force to the Postal Service than to federal workers since the wage advantage for the former group is considerably

higher. In short, in the presence of a substantial premium (evinced by, among other things, long queues for postal

jobs), selection issues are less critical. Wage changes for new hires are likely to provide accurate measures of postal

premiums, with a natural control for worker-specific skills and a measure of alternative employment opportunities.21

Calculations from the Displaced Worker Surveys (DWS) for 1984-94 are provided for full-time postal clerks and

carriers (analysis is restricted to this group to approximate the bargaining unit, since selection based on union status

cannot be readily done with the DWS). Use of the DWS should account for selection bias potentially present among

the NHS and CPS samples based primarily on voluntary leavers from private sector employment (for a related

argument, see Gibbons and Katz, 1992). As seen in Table 2, we find an average log real wage gain of .256 among

the relatively small sample (n=32) of displaced workers subsequently obtaining postal employment. If the postal

premium is estimated by comparing the wage gain for these workers relative to what we find is a .104 log wage loss

for the large sample of displaced workers not obtaining postal employment (for a summary of similar evidence, see

12

Fallick, 1996), the postal premium estimate is substantially higher, at .360. Despite the small size of the DWS, the

broad consistency between results from the DWS, NHS, and CPS suggests that job change endogeneity does not

bias severely longitudinal postal wage premium estimates.

Taken as a whole, the longitudinal evidence corroborates the previous CPS-DOT wage level analysis. The

analysis indicates that the postal premium is not only large, but larger than standard CPS-only estimates. The postal

premium cannot be accounted for by unusually high unmeasured skill among postal workers. To the contrary, postal

jobs and postal workers appear to have low levels of unmeasured skill, as compared to their private sector

counterparts.

VI. SPECIFICATION AND ISSUES OF COMPARABILITY: WITH WHOM SHOULD POSTAL

WORKERS BE COMPARED?

A critical issue in the measurement of postal wage comparability has been the choice of the comparison group

with whom postal workers should be compared. Principal areas of disagreement (see footnote 4) have centered on

specification issues regarding union status, employer size, race and gender, and industry coding. For example,

Asher and Popkin (1984) propose a standard whereby postal wages are compared implicitly to wages for private

sector workers who are white, male, unionized, in large firms, in transportation, communication, and utility

industries (TCU). Calculations from the April 1993 CPS benefits supplement indicate that white male union

workers in firms with 1,000 or more workers account for only 5.6% of the private sector labor force; the figure is

reduced further to 1.4% if it is restricted to TCU.

As described previously, we adopt a comparison group of full-time private sector workers with individual and

job characteristics similar to those among postal workers. Bargaining unit postal employees are compared to both

union and nonunion workers, to workers in large and small firms, and to workers in large and small establishments.

The implicit and sometimes explicit weighting given each group corresponds to their distribution among the private

sector comparison group of workers. Below we analyze in some detail the issues involved in the treatment of union

status, employer size, race and gender, and industry and occupation dummies.

A. Union Status

CPS estimates of the postal premium by Asher and Popkin (1984) and Belman and Voos (1997), among others,

are based on specifications including a control for union status, wherein unionized postal and non-postal workers are

coded as one for union status. This has the effect of comparing bargaining unit postal workers to the small minority

13

of private sector workers who are unionized, and a small number of nonunion postal workers to workers in the large

nonunion private sector. In this respect, union status is treated just like schooling and other transferable skill

variables (Linneman and Wachter, 1990). To the extent that union membership (or schooling, etc.) is associated

with a wage advantage in the private sector, postal workers are credited with possessing that attribute; none of the

wage advantage associated with that attribute is included in the wage premium. In short, a union standard of

comparison is adopted in that bargaining unit postal workers are implicitly compared to unionized private sector

workers. Our analysis includes unionization interacted with industry dummies and the postal premium is calculated

by measuring the wage of bargaining unit postal workers relative to a weighted average of all private sector union

and nonunion industry groups of workers. This approach is similar to omitting a union variable from the regression

equation, which would implicitly compare postal workers to a group of nonunion and union workers (see footnote

7).

Under what conditions might a union standard of comparison, effected by inclusion of a union dummy coded as

one for postal and non-postal union members, provide an appropriate approach? One such case would be if the

union variable is a proxy for transferable skill, largely analogous to the standard treatment of the schooling variable.

A second case would be if all postal workers would have been or could be union members were they instead

employed in the private sector. We turn to each of these arguments below.

Union Status as a Proxy for Skill

Inclusion of the union variable has been defended on the grounds that it is a proxy for skill, reflecting a

compensating differential for worker quality. Much of the union wage advantage, however, is due to union

bargaining power and the ability to obtain economic rents. Even those sympathetic to the view that union workers

are more productive do not argue that the union wage advantage is due entirely to higher skills.22

Some have argued that the existence of a union wage premium both allows and provides incentive for employers

to upgrade the skill level of their workers. That is, high wages are expected to increase the quantity and quality of

workers in the job queue and decrease turnover through a reduction in quits. Employers can select and retain higher

quality workers offsetting part (but not all) of the higher hourly wage. Even this conclusion need not follow.

Wessels (1994) has shown that it is incorrect to assume that union wage premiums lead to skill upgrading. Although

skill upgrading follows in a non-repetitive bargaining situation, the union-firm relationship is typically one of

repeated bargaining. If firms upgrade in response to higher union wages, the union can then bargain in a future

contract for an even higher wage to restore its premium. Firms that upgrade will face higher future wage demands

14

and will have distorted their factor mix. Wessels shows that skill levels can either increase or decrease in response

to a union wage increase. His review of empirical studies leads him to conclude that evidence is not consistent with

skill upgrading.

What evidence can be brought to bear on the relationship between union status and skill? Although it is useful to

examine economy-wide evidence, one must also evaluate evidence for the Postal Service since the correlation

between union status and unmeasured skill in the private sector need not generalize to the Postal Service. The

economics literature has attempted to measure the relationship between union status and skills using both a

production function approach and a wage equation approach. The production function approach attempts to see if

unionized firms or more unionized industries have higher technical efficiency, or measured output for given

combinations of inputs (for surveys, see Addison and Hirsch, 1989, and Booth, 1995; a more positive view of union

effects is provided by Belman, 1992). Studies attempt to measure whether union establishments are more

productive owing to unionization, holding constant the capital-labor ratio and labor quality. They do not try to

measure union effects transferable to other firms. Nonetheless, if union status is a close proxy for unmeasured

worker skills, it is likely that those same skills, unmeasured in a production function study, would show up as a

positive union productivity effect. The most reliable economy-wide evidence, however, based on data at the line-of-

business or firm level (e.g., Clark, 1984; Hirsch, 1991) does not indicate positive union productivity effects, on

average.23 The production function literature should not be used in support of the argument that union status among

postal workers represents a proxy for otherwise unmeasured skills.

The relationship between unmeasured skill and unionization can be examined within a wage equation framework

in (at least) three distinct ways. The most direct approach is to include in the wage equation additional variables that

measure more directly worker and/or job skills. This is exactly what we have done in our analysis using the DOT,

which includes 15 skill-related occupational variables in the wage equation. A second approach is to estimate a

Heckit-type selection model, which attempts to account for unmeasured differences between union and nonunion

workers. The reliability of such methods has been questioned by prominent labor economists (Lewis, 1986;

Freeman and Medoff, 1984) owing to the enormous variance of estimates resulting from the use of such methods.24

A third approach to control or account for otherwise unmeasured skills, used widely in recent literature, is to

estimate longitudinal wage change or fixed effects equations (e.g., Freeman, 1984; Jakubson, 1991). Longitudinal

analysis often obtains lower union premium estimates than those implied by wage level OLS equations, and some

authors conclude that the lower estimates support the proposition that union workers have high unmeasured skills

15

(for such a conclusion regarding truck drivers, see Hirsch, 1993). Longitudinal studies by Card (1996) and Hirsch

and Schumacher (1998), however, show that unionism is primarily associated with lower dispersion in skill, with

modest and uncertain effects on average skill levels following explicit control for measurement error in the union

change variable. Relative to nonunion workers, union workers with low measured skills have high unmeasured

ability, while those with high measured skills have low unmeasured ability. Longitudinal analysis for postal

workers using the NHS, CPS, and DWS clearly shows large wage gains among postal entrants, exceeding those

from standard cross-sectional analysis. These findings point toward the conclusion that unmeasured skills among

postal workers are relatively low and that standard wage level estimates understate the postal premium. In short,

there is not a clear consensus as to the correlation between union status and unmeasured skills in the private sector,

but whatever that relationship, union status need not convey a high level of unmeasured skill in the Postal Service.

Evidence from the DOT and on postal entrants indicates otherwise.

Union Status and Private Sector Employment Opportunities

We have rejected the assertion that the private sector union wage advantage is attributable primarily to skill, and

the assumption that the union-skills relationship found for the private sector implies a similar relationship in the

Postal Service. But that leaves unanswered the question of how best to treat union status in the calculation of the

postal wage premium. One approach (e.g., Smith, 1976), which we reject, is to compare postal wages to those for

nonunion private sector workers. Such an approach might be appropriate if all of the union premium reflected a rent

and public policy dictated that the Postal Service should pay a nonunion or competitive wage. Even if the former

were correct, the latter is not. The PRA dictates that postal workers be compared to private sector workers

performing comparable levels of work. If rents exist in the private sector, those rents should not be ignored when

evaluating postal wage comparability. A method that weights union and nonunion wages in the private sector by

their employment shares is a natural way to calculate the standard indicated by the PRA. This is precisely what is

done in our analysis.

An alternative approach, and one that we believe also has merit, is to assign union and nonunion weights based

on the jobs postal workers would have were they not postal employees.25 The unobservable counterfactual must be

estimated, however, so acceptance of this approach is predicated on there being confidence in the prediction model

used. We have rejected the assumption, implicit in studies adopting a union comparison, that all bargaining unit

postal workers would have been employed in a private sector union job. Postal workers would have to have an

unusual set of personal characteristics if their predicted union membership density is to differ substantially from

16

13% (i.e., the 1994 union share among non-managerial and non-professional full-time workers), let alone be close to

100%. Our prediction of private sector union density for workers with postal characteristics suggest that between

11% and 17% of postal workers would be union members were they employed in the private sector.26 Hence, an

analysis that compares postal wages to the weighted average of union and nonunion private sector wages appears to

be consistent not only with law, but also a reasonable approximation of opportunity cost wages for postal workers.

B. Employer Size

The treatment of employer size is another issue to be considered in evaluating postal comparability. We have

adopted an "agnostic" approach to the treatment of employer size (Linneman and Wachter, 1990). Because the full-

year CPS ORG files do not contain measures of employer size, no such measures are included. This has the effect

of comparing postal workers to private sector workers across all firm and establishment size categories, with an

implicit weighting equal to that in the private sector. Our treatment of employer size is similar in principle to our

approach for union status, although in the latter case weighting is explicit rather than implicit.

Some postal studies have included firm size (Asher and Popkin, 1984) or firm and establishment size (Belman

and Voos, 1997). If one is going to control for employer size, it is generally appropriate to control for both since

they may measure distinct wage determinants and each has an independent effect (Brown and Medoff, 1989). When

one includes both size measures in the wage regression, the postal premium is little different than when size

measures are excluded.27 While it is true that all postal workers are in the largest firm size category, their

establishment size is not particularly large. Even when size is omitted from the equation, postal workers are

compared to private sector workers for whom 42% are employed in the largest firm size category (1,000+ workers)

and another 5% in the next largest size category. That is, private sector wages with which postal wages are

compared are heavily influenced by employer size regardless of whether or not size is controlled for explicitly in the

premium calculation.

Arguments regarding the appropriate treatment of employer size are similar to those for union status. If

employer size were entirely a proxy for transferable worker skills in the private sector and the Postal Service, then it

would be appropriate to include firm and establishment size as controls. There exists evidence that some of the

wage advantage associated with large employers is skill related. Brown and Medoff conclude that roughly half of

the gross wage-size relationship is accounted for by measurable skills (i.e., characteristics for which we already

control), but conclude that a large premium remains that is largely unexplained. Indeed, they use longitudinal wage

change analysis to account for worker-specific unmeasured skills, and find that a size premium remains. So the size

17

premium is more than just a proxy for skills. And evidence suggesting lower quits and higher application rates for

large employers (Brown and Medoff, 1989) suggests that workers for large employers realize rents.

Even if the private sector employer size wage advantage is in part skill related, it does not follow that private

sector evidence on skill applies to the Postal Service. Our analysis explicitly accounts for otherwise unmeasured

skills by use of the DOT and through longitudinal analysis for postal entrants and leavers. Both pieces of evidence

unambiguously suggest that unmeasured skill levels for postal employees are low as compared to their private sector

counterparts. We accept the proposition that part of the economy-wide wage-size relationship is skill related. Our

method of random assignment compares postal workers to private sector employees across all firm and

establishment sizes. Because 42% of private sector workers are in the largest firms, this means that we effectively

do a large firm comparison for a sizable share of the sample. Only a small share of the sample is in the smallest

firms. In short, our comparison is with the employer size of the average private sector worker, and this is a

relatively large employer.28

C. Race and Gender

An argument that has surfaced in the debate over postal comparability is that postal wages ought to be compared

to those of private sector white males.29 The contention is that a white male standard is appropriate because lower

wages in the private sector for women and minorities result from discrimination and, absent discrimination, wages

for all workers would rise to the level of white males.

The argument for a white male standard is not convincing for several reasons. It is predicated on the assumption

that wage differentials by gender and race in the private sector, conditional on years of schooling, potential

experience, and other readily measured attributes, are due entirely to labor market discrimination, and that the

current white male wage rate represents the nondiscriminatory wage.30 Such assumptions are false. Numerous

studies show that some portion of the very large gender wage differential is due to differences in work experience,

accumulated human capital, choices stemming from the division of labor within the family, and job characteristics

(e.g., O'Neill and Polachek, 1993). Likewise, differences in wages between black and white workers, conditional on

schooling and potential experience, is not entirely the result of labor market discrimination. The research literature

indicates that much of the current racial wage gap is due to differences in premarket factors (in particular, skills) that

black and white workers bring to the labor market (O'Neill, 1990; Neal and Johnson, 1996), and not so much from

current labor market discrimination. In their survey of labor economists, Fuchs, Krueger, and Poterba ask the

question: "What is your best estimate of the percentage of the male-female wage gap attributable to employer

18

discrimination?" The median response was 17.5% and mean response 21.4% (Fuchs, Krueger, and Poterba, 1998,

pp. 1392, 1418). No question was asked about the racial wage gap.

The white male benchmark also assumes that in the absence of discrimination, all private sector wages would

rise to the level of white males. Even if all private sector race/gender differences in wages were due to

discrimination, the nondiscriminatory wage would be less than the current white male wage. That is, wage rates

would decrease for white males if we moved from a high to low discrimination environment, since discriminatory

preferences of employers would lead to a wage for white males in excess of the marginal products and a wage lower

than marginal products for groups facing such discrimination. If economy-wide output and labor's share of income

remained roughly constant, average wages would change little.31

D. Occupation and Industry Comparisons

A final issue concerning the choice of a comparison group is how to account for industry and occupation. As

outlined previously, we have argued for adoption of an all-industry comparison, with weights across industries based

on private sector employment. An alternative view is that postal workers should be compared to workers in

transportation, communication, and utility (TCU) industries (e.g., Asher and Popkin, 1984; Belman and Voos,

1997). This is effected by inclusion of broad industry dummies, with postal workers coded as one for TCU.

We reject the TCU industry comparison group for several reasons. First, it is less consistent with the charge of

the PRA than is a comparison with comparable workers and jobs throughout the private sector economy. Second,

use of a narrow industry comparison runs the risk of capturing worker rents or skills that are non-representative of

the larger labor market. This is particularly true in the TCU sector, which is heavily populated by workers in

regulated or previously regulated sectors of the economy -- railroads, trucking, airlines, utilities, and communication

industries. A substantial number of workers in these industries have received rents and/or possess high levels of

measured and unmeasured skills (Hirsch, 1993; Hirsch and Macpherson, 2000; Hendricks, 1994). Although high-

skill jobs in regulated industries should not be disregarded, they should not form the primary comparison group. A

case for the TCU comparison for postal workers should be made only if the industry of employment provides a

superior measure of worker skills and job attributes and if TCU industries uniformly require workers highly similar

to bargaining unit workers in the Postal Service. We do not believe such a case can been made. Results from the

NHS indicate that only 9% of new hires come from the TCU sector. And in the empirical literature, industry wage

differentials have been attributed to the presence of rents (Dickens and Katz, 1987; Krueger and Summers, 1987;

19

Gibbons and Katz, 1992) or have been shown to be a reflection of occupational differences across industries

(Helwege, 1992).

The earnings equations also include dummies corresponding to broad occupational (i.e., skill) groups, with postal

carriers and clerks being included among a large group of workers in administrative support and clerical

occupations. There has been little dispute over this categorization, which follows the Census designation.32 Control

for broad occupation is readily justified on the grounds that it provides a control for worker skills not otherwise

reflected in measured characteristics. The case for inclusion is particularly strong in the analysis without DOT skill

and working condition measures. The comparison standard, therefore, is one of workers with identical personal and

job characteristics in similar occupational categories across all U.S. industries. This approach comports well with

economic theory and the charge of the PRA that postal compensation should reflect compensation paid for

comparable levels of work in the private sector of the economy.

Apart from the specifics of postal comparability, the principal point in this section is that the treatment of

industry and occupation, union status, employer size, and race/gender, among other things, must be considered

carefully in postal and non-postal comparability studies. Choices regarding such treatment define the standard of

comparison and affect the magnitude of estimated wage differentials.

VII. ADDITIONAL EVIDENCE ON POSTAL COMPENSATION: FRINGES, JOB LOSS, QUITS, AND

APPLICANT QUEUES

Evidence on nonwage benefits, job loss risk, quits, and applicant queues reinforces the conclusion that postal

workers realize a substantial compensation premium. Economic theory and requirements of the PRA dictate that

total compensation and not just wages be examined. We do not have a data set that measures the dollar value of

individual postal and non-postal worker fringe benefits and worker characteristics. Hence, we are unable to estimate

a compensation differential in a fashion analogous to estimation of the wage premium. We can make adjustments to

the CPS wage differential based on average benefits paid to private sector workers (excluding professional,

technical, and managerial workers) as measured in the BLS Employment Cost Index (ECI), and to bargaining unit

postal workers based USPS administrative records in which benefits are measured in analogous fashion (Wachter,

Hirsch, and Gillula, 1995, pp. 30-34). Benefits included in this calculation are health, life and accident insurance,

retirement plans, and the value of paid leave. Total annual cost for these benefits among postal workers averages

$15,410 in 1994, as compared to $8,328 for private sector workers. Inclusion of fringes yields a postal total

20

compensation premium that is about 8 percentage points higher than the wage premium. Although a total

compensation premium for "equivalent levels of work" cannot be estimated precisely, it is clear that our measures of

the postal wage premium understate what is an even more substantial compensation premium.

An additional way in which wage premium estimates understate the compensation premium is that they fail to

take into account the high degree of job security in the Postal ervice.33 We provide information on differences in the

risk of permanent job loss between the Postal Service and the private sector, based on calculations from the 1984-94

DWS. Using the methodology outlined in Farber (1993), we present in Table 3 the probability of permanent job loss

among full-time workers over the two-year periods from 1982-83 through 1992-93.34 There have been no layoffs

among bargaining unit postal workers (a handful of postal workers report such a loss in the DWS, either in error or

because they are not bargaining unit workers). By contrast, job loss risk in the private sector can be substantial.

Private sector rates of displacement over a two-year period averaged 5% to 9% over the 1982-93 period, with the

highest displacement rates during recessions (the manufacturing rate reached 12.9% during 1982-83 but was 5.9%

during 1988-89). Displacement rates vary substantially across sectors, being highest in mining and construction and

lowest in communication and utilities. Although we have not attached a value to job loss risk, failure to do so

causes us to understate the compensation premium.

TABLE 3 ABOUT HERE

If there exists a compensation premium for Postal Service workers, we should observe low levels of quits among

incumbent workers and high rates of applications (i.e., long queues). We find evidence for both. Table 4 presents

quit rates per 100 full-time employees, by postal craft, for the years 1981-94. Quit rates are very low, being .7 per

100 workers among clerks and .6 among city carriers. Moreover, quits during 1993-94 are at the lowest rate

recorded since at least 1981. To our knowledge, comparable quit rate information is not readily available for the

private sector. In 1981, the last year data were collected for private sector manufacturing, the quit rate was over

15%. Rates were higher during the 1970s, and presumably higher in other sectors of the economy. By any measure,

quit rates are exceedingly low in the Postal Service, corroborating evidence for a large compensation premium.35

TABLE 4 ABOUT HERE

It's hard to imagine that the cause of such low quit rates could be anything other than a sizable compensation

premium. Quit rates, however, are not an ideal indicator of rents because they may be affected as well by the

sequencing of pay and the specificity of training. Jobs that backload compensation in the form of a steep wage-age

profile relative to productivity and defined-benefit pensions will have low quit rates among experienced workers,

21

even absent career rents. Likewise, jobs involving substantial firm-specific training may have low quit rates even in

the absence of rents.36 For these reasons, the length of the job queue may be a preferable indicator of a

compensation premium, since applicant queues are determined by the expected present value of career compensation

at the beginning of a job (Krueger, 1988; and Holzer, Katz, and Krueger, 1991).

There is no regular source of data on private sector queues or application rates. Survey data from the

Employment Opportunities Pilot Project (EOPP) in 1982, as reported in Krueger (1988, p. 233), indicates that there

was a weighted average of 8 applicants per new hire in the private sector. Data for 1992 from a University of

Kentucky Survey Research Center study (sponsored by the Small Business Administration) found an unweighted

mean of 14.1 applicants per offer and 16.1 applicants per hire (Barron, Berger, and Black, 1997).37 The Postal

Service collects applicant data owing to the fact that postal jobs are filled off of employment registers. The postal

registers include those who have applied for a postal job and taken and passed the placement exam (a single exam is

used for seven different postal crafts). Postal registers are closed most of the time because of such a large number of

applicants. The application rate can be measured by the number of persons on postal registers, divided by the total

number hired. In FY-1994, the application rate per new postal hire in 28 selected cities was 111 (Wachter, Hirsch,

and Gillula, Table 4, p. 53). Although postal-nonpostal wage differentials are smaller in larger urban area (Belman

and Voos, 1997), applicant rates are high in large as well as in smaller cities. Nationwide, there are about 1.5

million workers on the postal clerk register and 1.1 million on the carrier register.38 Although application rates can

be affected by many factors, rates of this magnitude provide further corroboration of the wage equation results

indicating that compensation for postal workers is substantially higher than for comparable levels of work in the

private sector.

VIII. INTERPRETATION AND CONCLUSION

Congress has mandated that there be comparability in compensation between Postal Service and private sector

employees performing comparable levels of work. We conclude that postal workers realize a substantial

compensation premium relative to the private sector. Our base analysis using standard CPS control variables

indicates that bargaining unit postal workers earned 28% more in 1994 than did private sector workers with the same

characteristics. The CPS-DOT analysis, which accounts for differences among jobs in skill requirements and

working conditions, indicates an even larger premium, of about 34%. An alternative approach to account for

unmeasured worker-specific skills is to examine wage changes among given workers entering or exiting postal

22

employment. The New Hire Survey reveals wage gains of new postal employees in 1994 of 39%. Using matched

panels of CPS workers for 1983-93, we find wage gains of 32% among postal entrants and losses of 25% among

postal leavers (or 33% using the private sector wage base). Using the six Displaced Worker Surveys for 1984-94,

which provide a measure of exogenous switching, we find real wage gains of 29% among postal entrants displaced

from their previous job; the gain is 43% when measured relative to what are wage losses among displaced workers

not taking postal jobs. A clear implication of the CPS-DOT and longitudinal evidence is that the CPS-only

regression estimate understates the magnitude of the postal wage premium. A premium of such magnitude is

inconsistent with economic efficiency and the comparability mandate in the PRA.

Perhaps the most important issue in determining comparability is the choice of the appropriate private sector

comparison group. An approach producing smaller premium estimates, proffered by Asher and Popkin (1984),

promulgates a comparison group of white, male, union workers in large firms employed in transportation,

communication, and utility industries. We have argued in the paper that this standard of comparison is difficult to

justify on economic or legal grounds. Our CPS-DOT regression estimates have been based on a weighted average

across union and nonunion workers employed by large and small employers across all private sector industries,

following the use of detailed control variables reflecting skills and working conditions, measured at both the

individual and occupational levels. Subsequent longitudinal analysis provides a natural control group or method of

comparison -- the wage received by individual workers prior to their postal job. The magnitude of the longitudinal

premium estimates makes it difficult to defend the position of using a narrow high-wage private sector reference

group. Additional evidence on postal fringe benefits, quit rates, and application rates reinforces the conclusion that

there exists a substantial postal premium relative to comparable levels of work in the private sector.

The arbitration proceedings involving the Postal Service and its unions have been notable for the visible role

played by economic and statistical analysis. Beginning with the 1984 hearings before Arbitrator Clark Kerr, in what

has been referred to as "the battle of the economists" (Walsh and Mangum, 1992, p. 193), economic analysis of a

type normally seen in economics journals and not in collective bargaining proceedings has become the standard.

Arbitrator Kerr explicitly recognized the existence of a compensation premium, and proposed a method for

achieving "moderate restraint" that over time would gradually reduce the premium. Closing of the postal pay gap

between 1984 and 1994 was modest, owing primarily to slower private sector wage growth than anticipated by

arbitrators (there has been closing since 1994). A goal of the Postal Service has been to lock-in moderate restraint

through an explicit linkage of postal compensation to changes in the Employment Cost Index (a fixed-weight index

23

of private sector compensation), minus an adjustment factor to gradually reduce the postal premium (Froelke and

Clark, 1997).39

The determination of compensation in the Postal Service is a matter of some importance. A large compensation

premium is inconsistent with the public interest goals of economic regulation, which include the pursuit of

productive and allocative efficiency (including advantages from scale economies) and competitive costs. Even

small differences in contract terms translate into large costs to businesses and households.40 Labor costs, which

account for roughly 81% of total postal costs (Wachter, Hirsch, and Gillula, p. 11), are quickly reflected in postal

rates and have a substantial impact on USPS competitiveness. Product demand elasticities are far from zero.

Estimates by the Postal Service place own-price elasticities of demand in 1994 at -.32 for first class, -.80 for priority,

-1.59 for express, -.27 for second class, -.55 for third class, and -1.16 for fourth class mail. In 1994, first class mail

accounted for 52.2% and third class mail 38.8% of postal revenues, as compared to shares of 59.1% and 23.5%,

respectively, in 1970 (Wachter, Hirsch, and Gillula, 1995, pp. 74, 76). The Postal Service has found it difficult to

compete in those services with the highest price elasticities. Even at current levels of product market competition,

postal compensation has a nontrivial impact on rates, volume, and employment.

Changes in the product market for message and package service are likely to impact future postal employment

and compensation. Warnings of eminent doom have occurred in the past and not materialized. But given the

diverse set of competitive threats currently faced, coupled with the possibility of some forms of postal deregulation,

it would be surprising were we not to witness substantial challenges to important Postal Service lines of business.

The Postal Service has fought losing battles with UPS over parcel post and with Federal Express, UPS, and others

over expedited mail. Two-day mail service from UPS and Federal Express competes directly with priority mail.

Second class mail faces competition from newspapers' own local delivery systems and the use of alternative

distribution systems by several large national publications. The continued growth of third class mail depends greatly

on a continuation of the "mailbox monopoly." Bulk mailers have pushed for the right to develop alternative delivery

systems. Were restrictions to mailbox access relaxed, it is likely that private presort firms, which already process a

substantial share of third class mail, would find delivery profitable in major markets. Limited forms of deregulation,

moreover, are likely to fuel demand for further deregulation. Finally, electronic mail, on-line bill payment, and

other financial transactions via the Internet pose a long-run threat to the Postal Service's flagship first class mail

product, most of which is already generated on a computer. For the Postal Service to compete successfully in such

an environment, they will need a productive work force and competitive labor costs.

24

APPENDIX

Adjustments For Tenure And Work Shift

Adjustments to equation (3) are made to account for tenure and shift work. Our final measure of the postal log

wage premium D is

where Djpj is the weighted average of the log postal differentials among the race/gender groups j. The 1994 CPS

premium estimate absent the tenure and shift adjustments is .2916. Adjusting downward by .0353 for tenure and

.0091 for work shift leads to an estimate of D = .2472, as reported in the text. Details on the tenure and shift

adjustments are provided below.

Estimates of the postal wage premium are made based primarily on the full-year 1994 CPS Outgoing Rotation

Group (ORG) Earnings File. Because ORG files do not include a measure of job tenure, the tenure adjustment is

made based on estimates from the April 1993 CPS Benefits Supplement, which includes years with the current

employer. Using methods identical to that shown in equations (1) and (2), we estimated race/gender weighted

postal premiums, Djpj, from the April 1993 CPS using both the CPS-only and CPS-DOT specifications (i.e., with

and without Z). Both of these models are estimated with and without job tenure variables. Because postal workers

have relatively high tenure, estimates of the CPS postal premium that included tenure were .035 and .027 log points

lower in specifications excluding and including Z, respectively. These log point differences are then subtracted from

Djpj to obtain D. The advantage of this approach, rather than drawing postal premium estimates directly from the

April 1993 CPS, is that the 1994 CPS ORG is both more current and has a sample size approximately six times as

large as the April supplement (twelve quarter samples versus a single half sample). The assumption implicit in

accounting for tenure is that it reflects a productivity difference. To the extent that tenure is high as a result of high

wages, rather than a cause of high productivity and wages, adjusting for tenure causes the postal premium to be

understated. The bias is nontrivial given the low quit rates and concomitant lengthy tenure among postal employees.

Earnings reported in the CPS include payment for compensating shift differentials associated with working

evening or night hours. If postal and non-postal employees worked the same proportion of evening hours, then there

would be no bias in premium estimates that fail to take shift hours into account. Evening and night shifts in the

Postal Service are more prevalent than in the private sector, however, and shift work differs substantially across

)(3',AdjShiftjAdjTenurejpjDD −−=

25

postal crafts, being rare among mail carriers and common among postal clerks and mail handlers. An adjustment for

shift work is made on the basis of information provided by the Postal Service and available in the May 1985 and

May 1991 CPS Dual Job Supplements. Our usual weekly earnings measure from the CPS includes shift pay.

Information from the Postal Service indicates that they award an approximate 10% shift premium and that 2.2% of

postal pay is for shift work. The Dual Job Supplements indicate that 12.9% of hours in the private sector for wage

and salary workers are evening and night work. The 10% shift differential paid by the Postal Service is assumed to

represent the compensating differential for shift work in both postal employment and the private sector. These

figures imply that weekly earnings in the CPS are "inflated" by shift pay for private sector workers by .0129 (12.9%

times a 10% shift premium), versus .0220 for postal workers. Hence, our shift adjustment decreases the postal

premium by .0091, the difference between .0129 and .0220. Whereas our shift adjustment to the bargaining unit

postal premium is roughly 1%, premium estimates for letter carriers and postal clerks include very different shift

adjustments. Using the methodology outlined above, the letter carrier premium is adjusted upward by about 1%

(.0114 log points), while the premium for postal clerks is adjusted downward by about 3% (.0345 log points). An

alternative shift adjustment approach that we do not follow is to utilize the private sector shift premium estimated

from a wage regression using the CPS supplements. Estimated shift differentials are only 1% for evening and 2%

for night work, implying only a trivial adjustment to the postal premium. The 1%-2% measured shift differentials

clearly understate the true compensating differential. Those working less desirable shifts are, on average, more

likely to have lower (measured and unmeasured) skills or be low tenure workers willing to work evening/night shift

on the expectation of eventually transferring to day shift (for evidence supporting these propositions, see

Hamermesh, 1995). Each of these factors would bias toward zero estimates of the shift differential. Kostiuk (1990)

uses a selection model to estimate an approximate 10% compensating shift differential in manufacturing. Hence,

use of the contractual Postal Service shift differential is a reasonable approximation.

26

Appendix Table A-1: Wage Regression Coefficients on DOT Variables by Race/Gender Group, 1994 ________________________________________________________________________________________ Weighted White males White females Nonwhite males Nonwhite females Average coeff t coef t coeff t coeff t coeff ________________________________________________________________________________________ Required Training: GED Scale (1 to 6) .0131 1.116 .0427 3.336 -.0204 -0.676 .1132 3.666 .0258 SVP (months)/100 .4487 6.173 -.1123 -1.314 .2459 1.255 -1.1049 -5.382 .0820 SVP-squared/100 -.0050 -7.285 -.0008 -0.971 -.0033 -1.754 .0084 4.388 -.0020 Worker Functions: Data (0 to 6) .0085 1.628 -.0034 -0.629 .0180 1.324 -.0266 -2.140 .0033 People (0 to 8) .0277 8.500 .0388 12.836 .0151 1.804 .0309 4.254 .0278 Things (0 to 7) .0031 1.048 -.0070 -2.105 -.0011 -0.144 -.0094 -1.268 -.0015 Required Aptitudes: Ratings 1-high to 5-low): Verbal Aptitude -.0687 -5.348 -.1718 -13.324 -.1485 -4.246 -.0826 -2.459 -.1083 Numerical Aptitude -.0719 -8.057 -.0367 -3.714 -.0259 -1.047 -.0513 -2.082 -.0525 Spatial Aptitude -.0818 -9.746 -.0864 -1.670 -.1017 -4.594 -.0408 -2.114 -.0814 Form Perception .0090 0.860 .0118 1.169 .0091 0.330 -.0291 -1.208 .0045 Clerical Perception -.0311 -3.605 .0032 0.433 -.0870 -4.005 -.0121 -0.756 -.0330 Motor Coordination .0898 6.480 .0586 4.880 .0513 1.580 -.0225 -0.796 .0604 Finger Dexterity -.0414 -3.355 -.0209 -1.571 -.0061 -0.201 .0201 0.680 -.0217 Manual Dexterity .0026 0.194 -.0249 -1.771 -.0137 -0.444 .0672 2.141 .0023 Eye-Hand-Foot Coord. -.0483 -6.400 -.0408 -4.374 -.0221 -1.152 -.0007 -0.035 -.0350 Color Discrimination .0177 2.689 .0567 8.193 .0020 0.115 .0854 4.822 .0317 Physical Demands: Strength Index (1 to 5) -.0225 -2.688 .0891 11.149 -.0669 -3.195 .1281 6.787 .0116 Climbing/100 -.1479 -7.897 .1417 4.939 -.1457 -3.182 .1922 3.499 -.0420 Stooping/100 -.0608 -3.524 -.2327 -13.341 .1070 2.637 -.1964 -5.369 -.0800 Reaching/100 .0394 2.229 -.0412 -2.456 .0322 0.688 -.0028 -0.068 .0157 Seeing/100 -.0450 -3.139 -.0065 -0.468 -.0353 -0.996 -.0167 -0.480 -.0313 Talking/100 .0244 1.706 -.0107 -0.792 .0592 1.707 .0906 3.038 .0333 Environmental Conditions: Indoors & Outdoors/100 .0627 4.897 -.0102 -0.615 .0969 3.123 .0547 1.473 .0537 Outdoors/100 .1219 5.674 .0813 1.823 .1679 3.095 -.0879 -0.791 .0948 Hazards/100 .1368 8.852 .1273 6.315 .1421 3.759 .0797 1.740 .1284 Cold/100 .1187 3.333 -.0679 -0.961 .1718 2.024 -.2064 -1.866 .0474 Heat/100 .005` 0.172 -.1660 -2.987 .1144 1.661 -.0924 -0.914 -.0208 Wet/100 -.0179 -0.666 .1070 2.346 -.1135 -1.966 .1390 1.772 .0093 Noise/100 -.0320 -2.611 -.2348 -1.496 -.0349 -1.085 -.1351 -2.833 -.0883 Atmosphere/100 .0809 4.243 .1117 4.012 -.0113 -0.228 .0502 0.809 .0642 ________________________________________________________________________________________ See note to Table 1 for definition of the DOT variables. The coefficients are from separate CPS-DOT race/gender log wage equations. The "All Group" coefficients shown for the bargaining unit represent the weighted average of coefficients across the four groups, where employment shares are: white males = .454, white females = .206, nonwhite males = .206, and nonwhite females = .135.

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NOTES 1. Mail clerks and city carriers, who comprise 84% of bargaining unit employees, reached negotiated settlements in 1981 and 1987, while relying on arbitration in 1984, 1991, and 1995. City carriers are represented by the National Association of Letter Carriers (NALC) and mail clerks by the American Postal Workers Union (APWU). Smaller bargaining units include mail handlers, rural carriers, nurses, and police. Included in the bargaining units are so-called "part-time flexibles" -- new workers who typically work full-time but have not yet received a permanent position. "Casuals" or transitional employees receive lower compensation and are not part of a bargaining unit. 2. These approaches often yield different results. Comparability studies relying on the federal survey of private sector professional, administrative, technical, and clerical workers (PATC surveys) typically conclude that relative pay for federal workers is lower than do regression based wage studies controlling for individual characteristics. For discussion and attempts at reconciliation, see Krueger (1988), the U.S. GAO (1994), and the U.S. CBO (1997). 3. The Postal Service has argued that a more appropriate match for postal clerks and, to a lesser extent, carriers, are employees of postal presort businesses. In a similar vein, it has argued that its nurses perform jobs more similar to non-postal nurses employed outside of hospitals than to nurses employed in hospitals. 4. Postal compensation is the primary focus of studies by Smith (1976), Adie (1977), Quinn (1979), Perloff and Wachter (1984), Asher and Popkin (1984), Wachter, Hirsch, and Gillula (1997), and Belman and Voos (1997). Several empirical analyses of public sector compensation contain postal premium estimates; see, for example, Freeman (1986), Krueger (1988), Linneman and Wachter (1990), and Moore and Raisian (1991). Geddes (1998) examines aggregate time-series data on wages, employment, and productivity, concluding that there was a one-time wage break at the time of reorganization and maintenance of a postal wage advantage over time. 5. Postal Service bargaining unit employment shares pj (in 1994) are .454 for white males, .206 for white females, .206 for nonwhite males, and .135 for nonwhite females. The CPS-DOT bargaining unit differential (i.e., D with Z included) is based on a specification with dummies distinguishing carriers, clerks, and other bargaining unit postal workers to permit presentation of craft-specific premiums. The overall CPS-DOT premium is the employment-weighted average of the separate craft premiums. 6. Use of the full sample to determine the earnings structure permits one to measure more accurately the returns to schooling and other wage determinants. For example, if one excluded managerial/professional and non-postal public sector workers, the coefficient on schooling would not reflect the returns from increased access to such jobs. Likewise, differentials associated with job characteristics might best be estimated when including a full range of jobs. 7. Simple but more restrictive specifications produce roughly similar results. We estimate a pooled log wage equation (rather than separate equations by race and gender), exclude all private industry-by-union status dummies, include a bargaining unit postal dummy (with the omitted private sector as the reference group) and dummies for other public sector employee groups, and include all other CPS control variables. The coefficient on the postal dummy is .316 in an equation without race/gender dummies and .272 with race/gender controls, as compared to .292 obtained from our preferred method shown in the text (all estimates are without the tenure and shift adjustments, described in the appendix). 8. Postal premium estimates are virtually identical when the bargaining unit is expanded to include nonunion postal workers who indicate they are covered by a collective bargaining agreement. Earnings allocation flags are not provided in the 1994 CPS ORG file. 9. For a detailed description and analysis of the DOT, see Miller, et al. (1980). Explicit assignment of the DOT variables for postal workers allows us to account for 1986 changes in city carrier ratings for hazards (from not hazardous to hazardous) and required strength (from light to medium). These changes led to substantially lower postal premium estimates than were obtained absent such changes. 10. The DOT has been used most frequently in the literature on comparable worth and wage differences between heavily female and male occupations. For a recent example, see Macpherson and Hirsch (1995).

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11. Log differentials are converted to approximate percentage differentials by 100[exp(D)-1], where D is the log differential. "Premium" estimates presented to postal arbitration panels have been stated using the postal rather than private wage base. The CPS-only premium using the postal base is 21.9% (i.e., -.247 converted to a percentage), representing how much less private sector workers are paid than comparable postal workers or, alternatively, the percentage decrease in postal wages that would eliminate the differential. 12. Exact standard errors cannot readily be calculated for the wage premium estimates since independent adjustments are made to account for shift work and tenure (see appendix). Standard errors are calculated absent these adjustments from "stacked" wage equations wherein all variables are fully interacted with race/gender dummies. Coefficients are equivalent to those obtained with separate race/gender equations. The difference between the CPS-only and CPS-DOT premiums is statistically significant at standard levels. 13. In a specification that includes DOT skill but not working condition variables, the log differential is .347. In a specification that includes working conditions but excludes job skill variables, the log differential is .186. We do not report results including two additional sets of skill-related variables in the DOT, one measuring worker interests and the other temperaments required within occupations. When these sets of variables are included, the estimated postal premium is substantially higher. 14. We chose to include a comprehensive set of DOT variables rather than select a more limited set of variables. Alternative specifications with the DOT generally produced higher estimates of the postal premium (see, also, footnotes 9 and 13). As noted by Moulton (1990), the use of grouped (e.g., occupational) variables in a regression with individual data need not bias coefficient estimates, but biases downward standard errors on the grouped variables. The full set of coefficient estimates from the CPS and CPS-DOT specifications are available on request. 15. For an analysis showing the increasing importance of numerical aptitude on earnings determination, see Murnane and Levy (1995). 16. Mean wage changes in longitudinal samples are typically similar to wage changes from regression analysis holding other changes constant. Regression analyses most frequently hold constant changes in part-time status, union status, and experience squared. But in the NHS analysis, we include only workers who hold full-time postal and non-postal jobs, union status should not be controlled for reasons developed below, and there is virtually no change in experience since wage change is measured from their most recent prior job. 17. For a description of the CPS matched panel data set, see the description in Hirsch (1993, Appendix). For a description of the DWS, see, among others, Farber (1993) and Fallick (1996). 18. One loses a disproportionate number of young workers (Peracchi and Welch, 1995) in the CPS panels. We suspect that CPS measurement error in the postal change variable is not severe, since postal employment is well defined and we omit workers with industry or occupation allocated by the Census. 19. Entrant wage gains measured in the NHS are similarly large for years prior to 1994. The lower value for mail handlers than for clerks or carriers is likely to reflect the lower entry wage that was included in their 1991 contract. 20. The younger comparison group is not necessarily preferred, since many postal new hires are over age 34. Absent an age restriction, a representative private sector panel would heavily weight young workers since job, occupation, and industry changes decline sharply with age. CPS panels, however, underrepresent young workers owing to the requirement that household location not change (see footnote 18). A minor issue is the use of nominal wage change in the New Hire Survey, as opposed to the more appropriate real wage change in the CPS panel. However, most postal new hires switch directly from another full-time job and all changes in our sample are within a year, so real and nominal wage changes are highly similar, particularly during a period of low inflation such as 1994. 21. A caveat is in order. If wage growth differs across jobs, wage gains for entrants will differ from the present value of career gains. A very flat wage-experience profile would imply that entry gains overstate the career premium, while low entry wages (e.g., two-tier wage schedules, apprenticeships) would lead to an understatement.

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22. In a survey of economic analyses of unionism, Booth (1995, pp. 157, 168) states:

The union-nonunion wage differential has often been used as a measure of union power. ... We have argued earlier that the ability of a union to achieve a wage rate higher than the nonunion level depends on the existence of economic rents or surplus in the product market, and on the power of the union to act as a monopolist in the supply of labour.

23. Booth (1995, p. 197) summarizes the literature as follows: Rather than survey the findings of a number of other studies of the union productivity differential, we summarise the main 'stylised facts' or empirical regularities to emerge from these studies. First, unions in the USA do not appear to increase productivity on average. Secondly, it seems that significant positive union productivity differentials are typically found in the private sector, in particular where there is a degree of product market competition

A recent survey has measured the views of labor economists at top universities. In response to the question: "What is your best estimate of the percentage impact of unions on the productivity of unionized companies" the median response was zero and mean response 3.1% (Fuchs, Krueger, and Poterba, 1998, pp. 1392, 1418). 24. A typical problem in applications of the Heckit approach is that it is difficult to find variables that meaningfully identify the model (the model is estimable owing to nonlinearity of the probit equation). Meaningful estimates require at least one instrument that determines participation (postal employment) but not the wage. Calculations provided by David Macpherson at Florida State University show a higher postal premium using a selection model, with veteran status being a variable that affects the probability of postal employment while not directly affecting the wage. 25. For development of this idea, with estimates applied to employer size among federal workers, see Belman and Heywood (1993). 26. These estimates, based on the 1994 CPS ORG, are obtained using coefficients from a private sector union membership probit equation, combined with mean characteristics among postal clerks and carriers. The lower number is obtained in a specification with occupational dummies, and the higher number from a specification without occupational controls. 27. Using the April 1993 Pension Supplement we obtain estimates of the overall Postal premium (without the DOT variables) of .252 without employer size variables included and .240 with size variables included. Employer size has little impact on the premium after controlling for tenure. Absent control for tenure, addition of employer size has a large impact. The postal premium is .038 log points lower when firm but not establishment size dummies are included in the analysis. 28. Because CPS firm size categories are top-coded at 1,000 or more employees, we know little about wage differences between large and very large firms. Since the regression analysis is employee weighted, however, results in the 1,000 plus category are heavily influenced by the largest firms. 29. For a presentation of this view, see Asher and Popkin (1984) and the response by Perloff and Wachter (1984). The white male standard has received little support within the research community. A GAO report (U.S. GAO, 1994) examining federal-private pay differentials provides a relatively uncritical presentation of this view, but stops short of endorsing it as an appropriate methodology. Our base specification results in a postal premium estimate of .247 -- the weighted average of .185, .277, .324, and .294 for white males, nonwhite males, white females, and nonwhite females, respectively. Comparing postal wages to only white male non-postal workers results in a .143 overall differential, the weighted average of .185, .160, .108, and .028 for the four groups, respectively. 30. BLS Commissioner Katherine Abraham, in her letter commenting on the GAO study, also states that a white male benchmark is defensible only if all private sector differences in earnings between race/gender groups result from discrimination (U.S. GAO, 1994, pp. 77-78). Use of a white male standard assumes that none of the wage difference associated with race and gender is due to unmeasured skill differences. Yet at the same time, adoption of a union and large employer standard assumes that all differences associated with union status and employer size are due to unmeasured skills.

30

31. There would exist a small deadweight loss. The presumption that the nondiscriminatory wage structure can be approximated by the average wage structure across race/gender groups is modeled explicitly in Neumark (1988). 32. Belman and Voos (1997) provide an explicit comparison of carriers and clerks to other detailed occupations within the administrtive support and clerical occupational category. Although we agree with the spirit of such an analysis, we take issue with their control for union status and industry (few workers in the comparison occupations are union members or employed in TCU), and their emphasis on statistical significance between specific occupations given small sample sizes within most individual occupations. 33. We include a local area unemployment rate variable in our wage regression. As shown in Blanchflower and Oswald (1994), area unemployment rates primarily reflect labor market tightness and are negatively correlated with wage levels (i.e., the "wage curve"). A direct measure of employment risk, which is not included in our analysis, should be positively correlated with the wage. 34. Our calculations differ from figures in Farber (1993) in that we include (besides postal carriers and clerks) only full-time private sector-workers displaced owing to a plant closing, slack work, or worker's position or shift being abolished. Farber includes part-time workers, public-sector workers, and counts additional forms of job loss. 35. The Bureau of National Affairs publishes a quarterly report entitled Job Absence and Turnover, presenting results from a survey of private and public employers. Their turnover rate measures permanent separations including quits, retirements, and firings, but excludes all forms of layoffs and the departure of temporary workers. A simple downward adjustment to their figures based on estimated retirement rates from the March CPS produces an economy-wide "quit" rate in 1994 at least 10 times the equivalent figure for the Postal Service. 36. Quit rates also tend to be lower in large firms, in part because mobility can occur within firms (see Brown and Medoff, 1989). In the case of the Postal Service, transfers are possible across most areas of the country. 37. Krueger reports 8.4 applications for the most recently filled position and 7.6 applications for each accepted job offer. The EOPP survey, conducted by Gallup, covers private sector establishments and figures are weighted to reflect the general population of employers. Barron, Berger, and Black (1997) also provide figures from a smaller and what they believe is a less reliable 1993 Upjohn survey, and unweighted figures from the EOPP. Data from the EOPP, Kentucky, and Upjohn surveys indicate that application rates increase with firm size. The Kentucky survey, which included the largest sample of large firms, indicated an applicant per offer (hire) rate of 19.9 (22.7). 38. These figures are for August and July 1995, respectively. Applicants can and typically do include their name on several of the postal craft registers. 39. Collective bargaining contracts beginning in 1994 with postal nurses and police dictate wage increases tied to the ECI and ECI-1, respectively. 40. In 1994, bargaining unit workers accounted for 1.464 billion hours of pay (including holiday, sick, and other pay while absent from work). A change in compensation of, say, $2 an hour thus translates into an annual labor cost change of $2.9 billion.

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Table 1: Means of Selected CPS and DOT Variables Among Postal and Private Sector Workers _______________________________________________________________________________ Bargaining Postal Postal Private All Unit Clerks Carriers Clerical Private _______________________________________________________________________________ CPS Variables: Wage (S) 16.29 15.73 16.14 11.10 13.47 1n (Wage) 2.747 2.715 2.745 2.308 2.427 Experience (yrs.) 24.556 24.463 23.705 18.178 18.874 Some College & Above .556 .554 .554 .531 .523 Nonwhite .243 .317 .160 .158 .140 Female .342 .409 .290 .664 .432 Married, w/Spouse .680 .609 .725 .546 .604 Sep., Div., Widowed .189 .229 .145 .178 .159 DOT Occupational Variables: Required Training: GED Scale (1 to 6) 3.10 3.00 3.00 3.58 3.76 SVP (months) 7.32 4.50 3.83 12.00 25.64 Worker Functions: Data (0 to 6) 3.19 3.46 3.00 3.24 3.01 People (0 to 8) 6.20 6.31 6.00 6.24 6.02 Things (0 to 7) 6.29 7.00 6.33 4.75 4.48 Required Aptitudes (ratings 1-high to 5-low): Verbal Aptitude 3.05 3.15 3.00 2.87 2.86 Numerical Aptitude 3.47 3.15 3.83 3.11 3.13 Spatial Aptitude 3.84 3.85 4.00 3.68 3.42 Form Perception 3.55 3.15 4.00 3.39 3.29 Clerical Perception 2.72 2.00 3.00 2.64 3.30 Motor Coordination 3.13 2.85 3.17 3.46 3.43 Finger Dexterity 3.61 3.15 4.00 3.45 3.46 Manual Dexterity 3.16 3.00 3.17 3.59 3.33 Eye-Hand-Foot Coord. 4.84 5.00 4.83 4.88 4.56 Color Discrimination 4.48 5.00 4.00 4.65 4.37 Physical Demands: Strength (1 to 5) 2.51 2.00 3.00 1.85 2.22 Climbing 3.65 0.00 0.00 2.21 11.81 Stooping 6.45 0.00 0.00 6.41 24.97 Reaching 94.10 100.00 100.00 73.36 73.92 Seeing 88.75 100.00 100.00 62.46 58.06 Talking 42.58 84.55 0.00 57.24 58.50 Environmental Cond. & Hazards: Indoors & Outdoors 46.71 0.00 100.00 9.86 16.88 Outdoors 0.60 0.00 0.00 1.91 4.42 Hazards 37.54 0.00 83.23 1.06 14.25 Cold 1.38 0.00 0.00 0.28 1.07 Heat 1.45 0.00 0.00 0.30 2.68 Wet 1.84 0.00 0.00 1.39 4.40 Noise 4.08 0.00 0.00 2.12 16.67 Atmosphere 2.40 0.00 0.00 0.75 7.98 N 974 350 407 7,627 103,467 _______________________________________________________________________________

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Notes to Table 1: Unless stated otherwise, CPS variables are in proportions and DOT variables are percentages. DOT variables for non-postal workers are assigned based on workers' CPS occupation and values provided in England and Kilbourne (1988). DOT variables for postal clerks and carriers are assigned directly from the DOT. Clerks (CPS code 354) are assigned values based on the weighted average of DOT categories Post-Office Clerk, Mail Handler/Distribution Clerk, and Mail-Distribution-Scheme Examiner; postal carrier (CPS code 355) are assigned values based on the weighted average of DOT categories of Mail Carrier and Rural-Mail Carrier. All other bargaining unit postal workers are assigned CPS occupational values from England and Kilbourne. Definitions of DOT variables are as follows (see Miller et al., 1980): Required Training: GED (1-6 point scale) = General educational development includes formal and informal education required for occupation in areas of reasoning, math, and language. GED scores reported in England-Kilbourne are the highest of the three measures; SVP (in months) = Specific vocational preparation or training time required for occupational proficiency. It includes most forms of training, except for schooling without specific vocational content. Worker Functions: Measure information, knowledge, and conceptions related to data, people, or things, obtained by observation, investigation, interpretation, visualization, and mental creation. A low value signifies greater complexity. Scores based on the greatest level of complexity in each category. Data (0-6 scale) is intangible and include numbers, words, symbols, ideas, concepts, and oral verbalization, with 0-Synthesizing, 1-Coordinating, 2-Analyzing, 3-Compiling, 4-Computing, 5-Copying, 6-Comparing. People (0-8 scale) Dealing with others through: 0-Mentoring, 1-Negotiating, 2-Instructing, 3-Supervising, 4-Diverting, 5-Persuading, 6-Speaking/Signaling, 7-Serving, 8-Taking Instructions or Helping. Things (0-7 scale) Inanimate objects such as machines, tools, equipment and products, dealing with by 0-Setting-Up, 1-Precision Working, 2-Operating Controlling, 3-Driving/Operating, 4-Manipulating, 5-Tending, 6-Feeding/Offbearing, 7-Handling. Aptitudes: Aptitude required for occupational proficiency. Aptitude factors are scored 1 to 5, with 1 signifying 90+ percentile (high degree of aptitude) 2 the 67-90 percentile (above average), 3 the 33-67 percentile (average), 4 the 10-33 percentile (below average), and 5 the 0-10 percentile (negligible aptitude). Measured are Verbal Aptitude, Numerical Aptitude, Spatial Aptitude, Form Perception, Clerical Perception, Motor Coordination, Finger Dexterity, Manual Dexterity, Eye-Hand-Foot Coordination, and Color Discrimination. Physical Demands: Strength (1-5) = Index representing the required strength, with 1-Sedentary, 2-Light, 3-Medium, 4-Heavy, 5-Very Heavy. Climb = % of workers whose occupation demands climbing and/or balancing; Stoop = % of workers whose occupation demands stooping, kneeling, crouching, and/or crawling; Reach = % of workers whose occupation demands reaching, handling, fingering, and/or feeling; See = % of workers whose occupation demands seeing; and Talk = % of workers whose occupation demands talking and/or hearing. Environmental Conditions: The following variables reflect environmental conditions, measured by the percentage of workers in occupations whose job involves these conditions. Only the first two are weather related. Indoors & Outdoors = % whose job is performed indoors and outdoors; Outdoors = % whose job is performed primarily (75%+) outdoors; Hazards = % whose job involves significant hazards (exposure to bodily injury, exposure to electrical shock, working in high exposed places, exposure to radiation, working with explosives, exposure to toxic caustic chemicals, and other exposure to dangerous conditions); Cold = % whose job involves extreme cold with or without temperature change; Heat = % whose job involves extreme heat with or without temperature change; Wet = % whose job involves wet and/or humid conditions; Noise = % whose job involves noise and/or vibrations; and Atmosphere = % whose job involves extreme atmospheric conditions (fumes, noxious odors, dusts, mists, gases, poor ventilation).

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Table 2: Postal Log Wage Premium Estimates _______________________________________________________________________________ Postal Non-postal Log Standard Sample Sample Differential Error Size Size _______________________________________________________________________________ Wage Level Regression Estimates (1994): CPS-only: All Bargaining Unit .247 .014 974 126,461 CPS-DOT: All Bargaining Unit .293 .026 974 126,461 Postal Clerks .288 .028 350 126,461 Postal Carriers .297 .028 407 126,461 Longitudinal Mean Log Wage Changes: New Hire Survey (1994): All Bargaining Unit (weighted) .362 .011 733 -- All Bargaining Unit (unweighted) .329 .011 733 -- Postal Clerks .403 .021 165 -- Postal Carriers .328 .015 403 -- Mail Handlers .259 .023 165 -- CPS Panel (1993/94): Private Sector Ind/Occ Changers .018 .006 -- 4,887 Private Ind/Occ changes Ages 25-34 .031 .010 -- 1,792 CPS Panel (1983/84-1993/94): Postal Bargaining Unit: Postal Entrants .304 .050 90 -- Postal Leavers -.273 .067 50 -- Private Sector Ind/Occ changers .016 .002 -- 39,721 Private Ind/Occ Changers Ages 25-34 .026 .003 -- 16,730 CPS Displaced Worker Surveys (1984-94) Full-time Clerks & Carriers: Entrant Wage Gain .256 .087 32 -- Entrant Relative Wage Gain .360 .085 32 10,807 _______________________________________________________________________________ All figures in column 1 represent log wage differentials between postal and private sector full-time employment, with the exception of the CPS panel private industry/occupation change comparison groups. Estimation method, specification, and data sources are described in the text. CPS wage level estimates are based on regression analysis (equations 1, 2, and 3) and a total sample of 127,435, while longitudinal estimates are mean log wage changes. The weighted New Hire Survey differential is based on postal craft employment in July 1994. Standard errors presented for the CPS-only and CPS-DOT results are approximate since they do not account for the tenure and shift adjustment (see appendix).

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Table 3: Percentage of Postal and Private Sector Full-Time Workers with Permanent Job Loss in Previous Two Years, 1984-1994 _______________________________________________________________________________ Job Loss Periods 1982-83 1984-85 1986-87 1988-89 1990-91 1992-93 ______________________________________________________________________________ Private Sector 8.9 6.9 6.1 5.1 8.3 7.9 Mining 24.2 19.1 19.3 10.0 16.1 11.8 Construction 15.2 10.3 14.1 10.6 22.5 18.6 Manufacturing 12.9 9.9 6.8 5.9 9.6 9.5 Transportation 10.1 8.3 6.3 4.5 7.8 6.2 Communication & Utilities 3.4 3.6 2.5 2.1 3.0 4.4 Other Private Sector 5.7 4.6 4.8 4.2 6.5 6.6 Postal Carriers & Clerks 0.0 0.0 0.4 0.4 0.7 1.2 _______________________________________________________________________________ Calculations by the authors from the CPS Displaced Worker Surveys for January 1984, 1986, 1988, 1990, 1992, and February 1994. Displacement rates are for full-time workers ages 20-64. The postal sample includes both bargaining unit (union) and non-bargaining unit (nonunion) employees (there have been no layoffs of bargaining unit workers). The rates represent only those job losses due to a plant or company closing or move, slack work, or a worker's position or shift being abolished. The rates represent the probability of losing a job at least once over a two-year time period for one of the above reasons. The methodology for measuring the probability of job loss is outlined in Farber (1993).

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Table 4: Postal Service Quit Rates by Bargaining Unit, 1981-1994 _______________________________________________________________________________ APWU NALC Rural Mail Year Clerks Carriers Carriers Handlers _______________________________________________________________________________ 1994 0.7 0.6 0.4 0.9 1993 0.7 0.6 0.5 0.8 1992 0.9 0.8 0.4 1.0 1991 1.0 0.9 0.5 1.2 1990 1.5 1.4 0.7 1.7 1989 1.8 1.6 0.9 1.9 1988 1.7 1.6 1.4 2.1 1987 1.6 1.6 1.6 2.0 1986 1.5 1.5 1.3 1.7 1985 1.4 1.5 1.0 1.6 1984 1.4 1.3 0.9 1.4 1983 1.1 1.0 0.5 1.1 1982 1.4 1.2 0.6 1.4 1981 1.6 1.3 0.5 1.6 _______________________________________________________________________________ Figures are quit rates per 100 workers, based on full-time bargaining unit workers. Source is U.S. Postal Service, Labor Relations Department.