BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics 

OFFICE OF EMPLOYMENT RESEARCH

AND PROGRAM DEVELOPMENT

Tracking the Returns to Education in the Nineties:

Bridging the Gap Between the New and Old CPS Education

Items

Harley Frazis

Jay Stewart

Working Paper 288

September 1996

 

We acknowledge suggestions and comments from Marilyn Manser and David Jaeger, and the research assistance from

Mariya Charney. The opinions expressed here are those of the authors and do not reflect the views of the U.S. Bureau of

Labor Statistics or those of its staff. Any remaining errors are the responsibility of the authors.
Tracking the Returns to Education in the Nineties: Bridging the Gap

Between the New and Old CPS Education Items

Harley Frazis

ph. (202) 606-7394

frazis_h@bls.gov

Jay Stewart

ph. (202) 606-7376

stewart_j@bls.gov

Bureau of Labor Statistics

Office of Employment Research and

 Program Development

2 Massachusetts Avenue, Room 4945

Washington, D.C. 20212-0001

We acknowledge suggestions and comments from Marilyn Manser and David Jaeger, and

research assistance from Mariya Charny. The opinions expressed here are those of the

authors and do not reflect the views of the Bureau of Labor Statistics or those of its staff.

Any remaining errors are the responsibility of the authors.
Abstract

The Current Population Survey (CPS) is used for many studies examining trends in the

returns to education. The CPS changed its education item in 1992. This paper develops

adjustment factors for earnings at different education levels to make pre- and post-1992

earnings comparable. To accomplish this, contradictory results from 1991 and 1992 data

are analyzed and reconciled. Adjusted 1989-93 earnings show a much more moderate

increase in the returns to college graduation over the period than unadjusted earnings.

JEL Classifications: J31, C81
I. Introduction

Much of what economists know about the returns to education has been learned

through studies that use data from the Current Population Survey (CPS). Many of these

studies take advantage of the fact that it is possible to construct series on earnings and

education extending back to 1964 to look at long term trends in earnings by educational

attainment (for example, Murphy and Welch [1992], Bound and Johnson [1992,1995]).

Time series studies such as these will be sensitive to changes in questions that pertain to

earnings or educational attainment.

In 1992, the CPS changed its educational attainment questions. The pre-1992

questions asked “What is the highest grade or year of regular school that ... has ever

attended?” and “Did ... complete the grade?” In 1992, the CPS began asking a single

question “What is the highest level of school ... has completed or the highest degree ... has

received?”1

The new question represents an improvement over the pre-1992 item because it

provides more detailed information about post-secondary educational attainment, which

has become more prevalent in recent years. It has also been shown to be more accurate as

a predictor of earnings than the pre-1992 item (Siegel 1991, Jaeger 1993, Park 1994,

Ureta and Welch 1995). Unfortunately, its introduction caused a break in series.

As with any break in series, researchers need to know how it affects time series

comparisons that span the break, and what adjustments must be made to compare pre1992 data with post-1991 data. Already, researchers have begun to publish comparisons
2

based on unadjusted series (see Bound and Johnson 1995 and Gordon 1996). The

purpose of this paper is to derive adjustment factors for earnings for the traditional four

education groups: High School Dropout, High School Graduate, Some College, and

College Graduate. We find that applying these adjustment factors to earnings for the

1989-1993 period substantially reduces the increase in the earnings advantage of college

graduates over this period.

Another approach to bridging the break in series has been taken in papers by

Jaeger (1996) and Park (1996). These papers propose ways of categorizing the data to

improve comparability between the items. However, we show that re-categorization is

limited in its ability to reconcile median and mean log earnings between the two

measures.2

Although the main purpose of Jaeger (1996) is to suggest methods of recategorization, the paper also contains a brief comparison of earnings under the two

measures. He uses matched 1991-92 data from the March Income Supplement (which

span the break in series). He finds that the ratio of college-graduate to high-school

graduate earnings are the same under both measures when using earnings from March

1991 and are 2.4 percent higher under the new measure when using earnings from March

1992, and takes these estimates as upper and lower bounds for the effect of the new

measure.

 

1 For more information on the current item and comparisons with the old item, see Kominski and Siegel

(1993) and Frazis, Ports, and Stewart (1995).

2 Since the results for the medians were very similar to those for log means, we report the median results

in Appendix 2.
3

In this paper, we analyze and reconcile the contradictory results for earnings from

the 1991 and 1992 samples. Our analysis reveals that the contradiction is due to the way

the education question was updated in 1992. We estimate, and correct for, the effects of

this updating procedure. Our analysis also differs from Jaeger’s in that we use data from

the CPS Outgoing Rotation Groups (ORG) rather than the March Income Supplement.

We find that data from the first half of the year do not fully reflect the impact of the

change in question due to a shakedown period, when interviewers became familiar with

the question. Consequently, we restrict our sample to the last half of the year. We

estimate that the college/high-school earnings ratio is 3.7 percentage points higher under

the new measure, substantially higher than Jaeger’s upper bound.3

The rest of the paper proceeds as follows. Section II describes the dataset we use

in our primary analysis, and discusses some of the data problems we encountered. In

Section III we show how estimates of earnings by education group have been affected by

the introduction of the new question, and discuss and model the difference between

estimates of the effect using 1991 earnings and estimates using 1992 earnings. In Section

IV we give adjustment factors and examine how the measured trend in earnings by

education level has been affected by the new item in the period 1989-93. Readers who are

not interested in our derivation of the adjustment factors can skip Section III.

II. Data

 

3 Jaeger (1996) also develops a numeric recoding scheme to allow comparison of linear returns to
4

To analyze the effect of the change in the education item, we need a CPS dataset

that contains both the old and new questions. In the CPS, households are interviewed

each month for 4 consecutive months, out of the survey for 8 months, and interviewed

again for 4 months. This 4-8-4 rotation scheme makes it possible to match the responses

for up to one-half of the sample to their responses in the same month of the previous year.

In any one month, there are 8 rotation groups that are denoted by their Month In Sample

(MIS). Respondents in MIS 5-8 can be matched to their responses in the previous year.

Respondents in the outgoing rotation groups (MIS 4 and MIS 8) are asked about usual

weekly earnings and usual weekly hours on their main job last week. Hence, we use

matched4 CPS Outgoing Rotation Group (ORG) data from 1991-1992. Not all members

of the sample in MIS 4 can be matched, because they have moved between years or for

other reasons. However, this does not appear to affect our results. To verify this, we

matched December 1991 data to ORG data from January through March 1992. These

data have a higher match rate than the year-to-year-match, but show no major differences

with that match.

Before proceeding, we should note that there are two other CPS data sets with

earnings information and education measures for both the 1991 and 1992 measure: the

February 1990 test of the new item (used in Park 1996), and matched March Income

Supplements in 1991 and 1992. (The March Income Supplements ask about annual

earnings and labor market experience during the previous calendar year.) The February

1990 test asked both questions during the same interview, which has the advantage of

 

schooling. This appears to be unaffected by the difference between the March and the ORG data.
5

eliminating any true changes in educational attainment. However, Frazis, Ports and

Stewart [1995] have presented evidence that responses to the new question (which was

asked at the end of the interview) were contaminated by the responses to the old questions

(which were at the beginning of the interview). As a result, the correspondence between

educational attainment in the old and new items differs from that in matched data from

1991-1992. The March Income Supplement data contain independent responses to the

two questions. However, as mentioned in the introduction, the data early in the year

appear to have been contaminated by a “shakedown” period that is not reflective of later

experience with the item.

We present evidence for a shakedown period in Appendix 1. Because of this

contamination, we use ORG data and restrict our sample to July-December 1991-92. To

mitigate biases from actual changes in educational attainment, we also restrict our analysis

to individuals who are ages 25-64.

In this paper, we use the traditional breakdown of: High School Dropouts, High

School Graduates, Some College, and College Graduate. Using the old measure, the four

categories are defined in the usual way by years of schooling completed: 0-11, 12, 13-15,

and 16+. Under the new measure, High School Dropouts include only those who do not

report having at least a high school diploma, High School Graduates includes only those

who report High School Diploma, Some College includes people who have some college

(no degree) and people with Associate’s degrees, and College Graduate includes people

who have a Bachelor’s degree or higher.

 

4 We match by household ID number, line number, sex, and race. In addition, matches are disallowed if

reported age increases by more than two years or decreases.
6

As in Jaeger (1996) and Park (1996), for our main analysis we assign respondents

who reported attending but not completing the 13th year of school--hereafter referred to

as “13As”--to the Some College category.5 This makes the distribution of attainment

more comparable between the two items. In most of the remainder of this paper, we

compare the new question with the 13A-adjusted old question (hereafter “old (13A)”).

Analysts with access to CPS microdata can perform the 13A adjustment on their own.

For analysts seeking to compare published numbers without using microdata, we analyze

the effect of making the 13A adjustment, and provide adjustment factors that can be used

with published (non-13A-adjusted) data.

Our earnings sample is composed of all wage and salary workers ages 25-64

whose primary activity was working. We excluded those working in agriculture, forestry,

and fisheries, and private household workers, those with major activity student in either

year, as well as those making less than $1 or more than $100 in 1979 dollars as measured

by the CPI-U. The CPS topcodes weekly earnings at $1923. We multiply this amount by

1.45 as in Katz and Murphy (1992). Finally, all data in this paper are weighted.

 

5 Jaeger (1996) also reassigns respondents who report “12th grade--no diploma” to the new question to

the High School Graduate category on the basis of 55 percent of such respondents reporting 12th grade

completion to the old question. We do not make such a reassignment, since our purpose is to adjust the

old question to the new categories rather than adjusting both to maximize comparability. See footnote 8

below.
7

III. Analysis of Earnings

Table 1 shows geometric mean hourly earnings6 for all wage and salary workers

and for workers by sex.7 We draw three main conclusions from these tables. First, the

High School Dropout, High School Graduate, and Some College categories show lower

earnings under the new question than under the old (13A) question, while the College

Graduate category shows higher earnings. Consequently, the college/high school wage

premium is higher under the new measure. For both sexes, these differences are

statistically significant in all categories in 1992 and in all categories except for High

School Dropouts and High School Graduate in 1991.8

Second, there appears to be no consistent pattern of differences between men and

women in the effects of the new measure. None of the differences between men and

women in the effect of the new question relative to the old (13A) categories are

statistically significant.

Third, the effects of the new question compared to the old (13A) are larger when

using 1992 earnings than when using 1991 earnings. This difference is statistically

significant for the college graduate category (p<.003), and also for the college/high school

ratio. In what follows, we analyze possible reasons for this discrepancy. We then develop

 

6 Table A4 in Appendix 2 contains the analogous results for medians.

7 The standard errors in Table 1 and in all other tables are computed using the method of random groups

(see Wolter, 1985). Standard errors were computed by dividing the sample into 20 random groups,

estimating the standard deviation of each estimate across the groups, and dividing by 20 .

8 Assigning “12th grade, no diploma” to the High School Graduate category as in footnote 4 increases the

magnitude of the difference in mean earnings between the two measures. In the first panel of Table 1

below, the difference in geometric mean earnings between the new and old (13A) categories would fall

between 1 and 1.4 percentage points for high-school drop-outs, to −1.8 - −2.8 percentage points. It would

fall between 0.1 and 0.2 percentage points for high-school graduates, to −0.6 - −0.7 percentage points.
8

and apply a method for reconciling the discrepancy in order to get a single estimate of the

effect of the new question.

One explanation for the difference between 1991 and 1992 results is that it reflects

true differences in earnings growth among education categories. For a given education

category, the difference in mean earnings between the old and new measures will

predominantly reflect differences in “off-diagonal cells”--cells in which respondents do not

report the same level of education for both questions. Differences in rates of growth of

earnings among the off-diagonal cells would imply changes between years in the difference

between the measures.

However, since the size of these off-diagonal cells is small relative to the main

diagonal, only large year-to-year differences in mean earnings in these cells will generate

non-trivial year-to-year differences in differences between the measures. Table 2a shows

the percent change in the geometric mean of hourly earnings between 1991 and 1992 for

each (j,k) cell for respondents who are in the earnings sample in both years,9 as well as the

percent of the sample that falls into each cell. We focus on cells that are one off the main

diagonal, because cells further from the main diagonal are too small to analyze.

All else equal, one would expect year-to-year changes in earnings to be fairly

similar across neighboring cells. However, the (High School Dropout, High School

Graduate) and the (College Graduate, Some College) cells exhibit changes in earnings that

are quite different from neighboring cells. In the former cell, earnings increased by 4.8

percent between 1991 and 1992, compared with 1.8 percent for the neighboring cells on
9

the main diagonal and 2.5 percent for the (High School Graduate, High School Dropout)

cell. In the (College Graduate, Some College) cell, hourly earnings fell by nearly 5 percent

compared to a less than 1 percent increase in the (Some College, Some College) cell, a 3.7

percent increase in the (College Graduate, College Graduate) cell, and a 2.8 percent

increase for the (Some College, College Graduate) cell.

Do off-diagonal cells exhibit different earnings growth even in years when there

was no change in the education measure? Table 2b shows the analogous numbers for

1990-91. Here there are three cells that stand out. The (Some College, High School

Graduate) and the (College Graduate, Some College) cells both exhibit large decreases in

earnings, while the (Some College, College Graduate) cell shows a large increase in

earnings.

These results suggest that, conditional on the first year’s response to the education

questions, the response in the second year is correlated with earnings (or with

measurement error in earnings), and that the correlation seems to be stronger at higher

education levels. Corroborating this finding, the corresponding results for the March

Income Supplement (not shown here) show an even stronger correlation between changes

in income and changes in reported education, and larger differences in differences between

measures between 1991 and 1992.

At first blush, it appears that this problem is less severe in the 1991-92 data than in

the 1990-91 data because fewer cells are affected and the magnitudes of the changes are

smaller. However, due to the change in the education item, a larger fraction of

 

9 Year-to-year comparisons will also be affected by changes in the composition of the earnings sample.

Since our explanation of year-to-year differences is based on changes in responses, we ignore this factor
10

respondents change categories in the 1991-92 data than in the 1990-91 data. Because

changes in reported education due to the change in question should not be closely related

to changes in earnings, one would expect to see attenuation of the differences in earnings

growth between off-diagonal cells and the main diagonal.

One cell that appears to be driving much of the year-to-year difference in

differences between the measures is the (College Graduate, Some College) cell. Although

the percent decrease in earnings is smaller in the 1991-92 data than in the 1990-91 data,

this combination occurs over 6 times as often in the 1991-92 data. The continued

differences in earnings growth between this cell and the neighboring main diagonal cells

despite the increase in the probability of moving to this cell indicates a strengthening of the

relationship between changes in earnings and changes in reported education category. To

demonstrate this, for both 1990-91 and 1991-92, we ran a linear probability model

regression of the probability of remaining in the College Graduate category in year 2

conditional on being in the category in year 1 on change in log earnings (for respondents

in the earnings sample in both years). The coefficient of change in log earnings is 0.006 in

1990-91, but increases to 0.025 in 1991-92.

We believe that this contrast between 1991-92 and other year-to-year matches is

due to a difference in the method of updating the education question. Normally, when the

education question is updated, interviewers have the previous response in front of them

and can confront the respondent about any decreases in education (increases in education

are not questioned). But when the new education question was introduced, interviewers

were not given the previous responses. As a result, there were more changes in education

 

here.
11

category than would have been the case if the questions could have been updated

normally. More importantly, there was a stronger relationship between changes in

education and changes in earnings, which generated the discrepancy between the results

using 1991 and 1992 earnings.

Correcting for Independent Updating

Here, we develop a method for correcting for the independent updating. We begin

by assuming that earnings in each period are described by:

(1)

Y e

Y fe

ij i ij

ij i j ij

1 1

2 2

= +

= ++

μ

μ ,

where Yijt is earnings of individual i in first-year education category j in year t, μi is a

person-specific fixed effect, eijt is a mean-zero transitory component of year t’s earnings,

and fj is a year-specific effect for year 2 common to all persons with year 1 education

category j. The variables μi and eijt are assumed mutually independent, though eij1 and eij2

are not assumed identically distributed (nor are eijt and eij’t for j≠j’).

Let Zijk = 1 if person i in schooling category j in year 1 is in schooling category k

in year 2, and 0 otherwise. The probability of being in k in year 2 given schooling

category j in year 1 is modeled as:

(2)

P S kS j e e EZ e e D D ijk i ij ij ijk i ij ij jk

T

jk

T

i jk

T

i jk

T = = = = =+ + + Pr( | , , , ) ( | , , ) 2 1 12 12 i2 μ μ α βμ γ δ ,

where Di =eij2−eij1 and T = 01,12, with the superscripts referring to the calendar years
12

1990-91 and 1991-92.10 In words, we use a linear probability model to estimate the

probability of reporting a given education level in the second year of a year-to-year match,

with coefficients varying both by initial education level and by calendar year. The

explanatory variables are permanent earnings, the change in transitory earnings and its

square. This model is the simplest model that allows changes in the effect of transitory

earnings to affect transition probabilities. It is straightforward to show that all values of

γ jk

T yield the same aggregate transition probabilities, so a quadratic term is necessary.

Our goal is to estimate what the probability of reporting education level k would

have been in 1992 for the 1991-92 matched sample if the normal dependent updating of

the education item had occurred. Given that Table 1 indicates significant differences only

for College Graduates, it is natural to estimate (2) for k=College Graduate. Since we are

particularly interested in the college/high school earnings ratio, we also estimate (2) for

k=High School Graduate. We estimate these “counter-factual” probabilities from the

1991-92 matched data using coefficients on permanent earnings from 1991-92, and the

intercept and coefficients on changes in earnings from 1990-91. That is, we aim to

construct an estimated probability approximating:

(3) P D D ijk

C =+ + + α βμ γ δ jk jk i jk i jk i

12 12 01 01 2 ,

where Pijk

C is the counter-factual probability that respondent i in education category j in

1991 would have responded education category k in 1992 had there been the normal

relationship between change in earnings and change in reported education. We then use

 

10 We experimented with using 1992-93 matched data instead of the 1990-91 data and got similar results.

We report the 1990-91 results because we can use a larger sample, as data from January-June 1992-93

have the problems mentioned above.
13

Pijk

C to estimate E Y

P Y

P kt

C ihk

C

it j ih j

ihk

C

j ih j

( ) |

|

= =

=

∑∑

∑∑ , the average period t earnings of those who

would have reported education level k in period 2 had the normal updating procedures

been used in 1992.

Since neither μ nor D are directly observable, we must use estimated values. But

since D and D2

 are independent of μ, regressing Z on D and D2 yields consistent estimates

of γ and δ. For individual i who has initial education level j, D is estimated as:

 ( )( )( )( ) D Y Y Y Y Y Y Y Y D ffD  ij ij j ij j ij ij j j ij j j ij = − − − = − − − = +−≅ 2 2 11 2 1 21 ,

where Yj1 and Yj2 denote the mean earnings in year 1 and year 2 for year 1 education

category j. The equality is inexact because of error in estimating fj (we ignore this

hereafter). Omitting the subscript i for clarity, we use the regression equation

(4) Z a c D d D u jk jk jk j jk j j =+ + +   2 ,

where the regression is estimated over the population who report schooling level j in year

1. Estimating cjk and djk from (4) by least squares will consistently estimate γjk and δjk.

In determining the effect of the new question, it is not clear whether we should

compare the counter-factual mean to the predicted mean from the regression model, Ykt ,

or the actual mean, Ykt . Naturally, it would simplify our analysis if Ykt = Ykt . Direct use

of equation (4) does not guarantee this equality, but the following device does.

Adding first period earnings to the equation results in X Y D D = [   1 ] 1

2 ,

where 1 is a vector of ones. The best linear prediction of Z given X is the least squares

predictor Z XXX XZ = ( ) ′ ′ −1 . Similarly, for the counter-factual, 

Z XXX XZ ( ) C C = ′ ′ −1 ,
14

where ZC

 is the value of Z generated by (3). Note that for 1991-92 data,

(5) XZ X D D u ′ = ′( ) α βμ γ δ ++ + + 12 12 12 12 2 and

(6) XZ X D D u ′ = ′ ++ + + C C ( ) α βμ γ δ 12 12 01 01 2 .

Noting that X′u=X′u

C

=0, subtraction of (6) from (5), and premultiplication by X X X ( ) ′ −1

yields:

(7)   Z Z XXX X D D ( ) ' (( ) ( ) ) C = + ′ − +− −1 01 12 01 12 2 γγ δδ

after arranging terms. Since we do not observe the γ's and the δ's, we estimate Z C using

our values of γ and δ estimated from equation (4) using 1991-92 and 1990-1991 data.

Counter-factual predicted mean of earnings for those with education level k in the

second year is  



|

|

Y

Y Z

Z kt

C it ihk

C

j ih j

ihk

C

j ih j

= =

=

∑∑

∑∑ , whereas the predicted mean from the actual

process is  



|

|

Y

Y Z

Z kt

it ihk j ih j

ihk j ih j

= =

=

∑∑

∑∑ . Note that the linear probability model assures us that:

(8) 

Y Y kt kt = ,

the predicted mean earnings equals actual mean earnings, as desired.11

To summarize, our strategy is as follows: (a) estimate the probability that an

individual with education level j in the first year reports education level k in the second

year [equation (4)] for both the 1990-91 and 1991-92 matched samples. (b) Compute

counter-factual probabilities for the 1991-92 sample using the coefficients on the earnings
15

variables from the 1990-91 matched sample [using equation (7)].12 (c) Reweight each

observation by multiplying its sample weight by its counter-factual probability, and

recompute mean earnings.

We used the formulation above to generate counter-factual estimates for both

geometric means and medians. The median results are presented in Appendix 2.

Another problem of interpretation arises because the preceding method assumes a

constant population. It does not allow for changes in the composition of the sample as

people gain and lose employment, enter or leave the relevant age range, etc. We assess

the effect of this problem on our results below.

Results from the Counter-factuals

Table 3a shows actual and counter-factual mean hourly earnings for High School

Graduates and College Graduates. Note that the means for the old (13A) and new

measures are slightly different from those presented earlier in the paper as the sample

includes only respondents who meet our sample inclusion criteria in both years. This

change in the sample eliminates the already small difference between using 1991 and 1992

earnings when comparing the measures for high school earnings, and consequently

narrows the between-year gap in the difference for the college/high-school ratio. Applying

the counter-factual correction completely eliminates the difference between using 1991

 

11 Note that, from the normal equations for least squares, for any linear combination L of the elements of

X, L Z Z ' (  − = ) 0 . Earnings Y1 and Y2 are both linear combinations of the elements of X, so

∑ ∑ YZ YZ t t =  . Also, Z Z ∑ ∑=  , so Y

Y Z

Z

Y Z

Z t Y t t === t

∑

∑

∑

∑



  .

12 These counter-factual probabilities show slightly lower probabilities of changing education category

between years, as desired.
16

and 1992 earnings for the between measure comparison of college earnings, as well as for

the college/high school ratio. The difference is eliminated because the adjustment

increases 1991 College Graduate earnings to the 1992 level; the adjustment leaves 1992

earnings essentially unchanged.

This suggests the following simple method for generating a preferred adjustment to

the pre-1992 geometric mean earnings: average the 1991 and 1992 percentage differences

between the measures for the high school drop-out, high school graduates, and some

college categories, and use the 1992 percentage change for college graduates. Results are

shown in the first column of Table 4. Adjustment factors for medians are also presented in

Table 4, although the discussion is in Appendix 2. The largest (in absolute value)

adjustment factor is for College Graduates. This reflects the new question’s superiority in

measuring attainment of Bachelors Degrees.13

IV. Adjustment Factors and Adjusted Trends in Earnings

The above analysis yields adjustment factors for broad education categories for

pre-1992 data that have been adjusted for the 13As. These are shown in the first and third

columns of Table 4. Since analysts working from published tables may not have access to

the CPS microdata necessary to make the 13A adjustment, we derive adjustment factors

for non-13A-adjusted data. To do this, we derive 13A adjustment factors and add them to

our preferred adjustments described above. The 13A adjustment factors were constructed

 

13 As further evidence along this line, Jaeger and Page (1996) find that using the degree measure from the

new question generates larger diploma effects in the returns to education than attribution of a Bachelor’s

degree to 16-year completers using the old question.
17

by averaging the observed effect of the adjustment for unmatched ORG data across the

years 1989-91. While the effect of the 13A adjustment for the High School category does

not differ between the sexes, the adjustment to the Some College category is substantially

higher for women. The adjustment factors for non-13A-adjusted data are shown in the

second and fourth columns of Table 4.

What effect do these adjustments have on estimated trends in earnings by

education level and returns to a college degree? Table 5 shows the trend in (geometric)

mean hourly earnings14 by educational attainment for ORG data for 1989-1993, by sex.15

The first rows of the table show the series without adjusting for the break in series. The

second group of rows shows the effect of making the 13A adjustment to the data.16 The

third group of rows shows the effect of our suggested adjustments to the old (13A) item.

The unadjusted series in general show small declines or increases for college

graduates, and substantial declines in real earnings for everyone else. The contrast

between college graduates and other education levels is fairly dramatic for both sexes.

Between 1989 and 1993, the increase in the college/high school premium is 6.0 percent

for women and 8.6 percent for men . The 13A adjustment decreases the estimated

increase in the ratio by between a half and a full percentage point. Our adjustments

substantially reduce the estimated increase in the college/high-school premium by around

half to two-thirds. After these adjustments, changes in the college/high school premium

 

14 Analogous tables for medians are in Appendix 2.

15 The CPS processing system changed between 1988 and 1989, which would complicate comparisons to

1988 and earlier years. In 1994 the survey was revised substantially. Between 1989 and 1993 there was

no change in the processing system, which allows a clear examination of the effect of the new education

question.
18

now is 1.8 percent for women and 4.7 percent for men. Moreover, for both sexes, the

college/high-school ratio is no higher in 1993 than it was in 1990.

We also computed adjustments for weekly earnings for full-time workers.

Suggested adjustment factors and 1989-93 trends are shown in tables in Appendix 2.17

Adjusting March CPS Data

Given that the adjustments described above were derived using CPS ORG data, it

is natural to ask whether they can be used to adjust data from the March CPS Income

Supplements. Differences in the surveys make it preferable to derive adjustment factors

from the March data directly.18 However, as our discussion of the shakedown effect

points out, data from the first part of 1992 are not representative of post-1991 data.

To determine which adjustment factors should be used with March data, we

replicated Tables A2a-A2d in Appendix 1 (which are analogous to Table 1) using 1991-92

matched March CPS data. The pattern of differences between the new and old measures

were similar to those using the first 6 months of ORG data, and quite different from those

using the second six months. This is consistent with the hypothesis that the shakedown

effect affected the March data to a large degree. Hence, we recommend using the factors

in Table 5 for the March data as well.

V. Summary and Conclusion

The adjustments derived in this paper provide a general guide for researchers who

seek to describe trends in wages by education level in the CPS across the 1992 break in

 

16 That is, recategorizing the micro-data, not using the average adjustments suggested in Table 5.

17 Tables for weekly earnings duplicating the analysis of hourly earnings are available from the authors on

request.
19

series. Adjustment is clearly necessary, since unadjusted series substantially exaggerate

the increase in the earnings advantage of college graduates in the early 1990s. We did not

find substantial variation in the effect of the break in series by sex or (in results not shown

here) by age. We believe that in most cases the adjustments suggested here provide a

reasonable method for bridging the gap.

The CPS processing system was revised in 1989. The CPS survey itself was

substantially revised in 1994, including significant changes to the earnings questions. The

effect of these changes on measurement of trends in earnings by education level is a topic

for further research.

 

18 The main differences are that the ORG questions ask about earnings and hours on the main job last

week, whereas the March questions ask about earnings, hours, and weeks worked at all jobs in the

previous year.
20

Tables

Table 1: Geometric Mean of Hourly Earnings by Education Category for July-December, 1991-1992

Matched Outgoing Rotation Groups (1991 dollars) Both sexes

(Standard Errors in Parentheses)

{p-values in Brackets}

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 7.89 7.89 7.83 -0.8 -0.8

(0.6) (0.6)

{0.170} {0.170}

High School Graduate 9.47 9.42 9.38 -0.9 -0.4 -0.5

(0.3) (0.3) (0.2)

{0.002} {0.172} {0.001}

Some College 11.63 11.46 11.34 -2.5 -1.2 -1.4

(0.4) (0.4) (0.2)

{0.000} {0.014} {0.000}

College Graduate 15.46 15.46 15.60 0.9 0.9

(0.3) (0.3)

{0.001} {0.001}

College Grad/HS Grad 1.632 1.641 1.663 0.031 0.022 0.009

(0.011) (0.011) (0.011) (0.007) (0.007) (0.003)

{0.000} {0.001} {0.001}

n=25,667

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 7.91 7.91 7.80 -1.4 -1.4

(0.4) (0.4)

{0.001} {0.001}

High School Graduate 9.57 9.49 9.43 -1.4 -0.6 -0.8

(0.2) (0.2) (0.2)

{0.000} {0.013} {0.000}

Some College 11.44 11.35 11.17 -2.3 -1.6 -0.8

(0.5) (0.4) (0.3)

{0.000} {0.000} {0.006}

College Graduate 15.52 15.52 15.80 1.8 1.8

(0.3) (0.3)

{0.000} {0.000}

College Grad/HS Grad 1.623 1.636 1.675 0.052 0.040 0.013

(0.009) (0.010) (0.012) (0.007) (0.007) (0.003)

{0.000} {0.000} {0.000}

n=25,673
21

Table 1 (continued) Men

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 8.96 8.96 8.88 -0.7 -0.7

(0.7) (0.7)

{0.308} {0.308}

High School Graduate 11.10 11.02 10.97 -1.5 -0.7 -0.7

(0.3) (0.3) (0.2)

{0.000} {0.025} {0.000}

Some College 13.16 13.07 12.75 -1.3 -0.7 -0.7

(0.7) (0.6) (0.3)

{0.040} {0.255} {0.038}

College Graduate 17.39 17.39 17.64 0.8 0.8

(0.4) (0.4)

{0.049} {0.049}

College Grad/HS Grad 1.567 1.579 1.607 0.036 0.025 0.011

(0.018) (0.018) (0.015) (0.010) (0.009) (0.003)

{0.000} {0.005} {0.000}

n=13,305

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 9.03 9.03 8.88 -1.6 -1.6

(0.7) (0.7)

{0.021} {0.021}

High School Graduate 11.14 11.03 10.97 -1.5 -0.5 -1.0

(0.3) (0.3) (0.2)

{0.000} {0.143} {0.000}

Some College 12.95 12.95 12.75 -1.6 -1.5 -0.1

(0.6) (0.5) (0.3)

{0.007} {0.001} {0.853}

College Graduate 17.36 17.36 17.64 1.6 1.6

(0.4) (0.4)

{0.000} {0.000}

College Grad/HS Grad 1.559 1.574 1.607 0.049 0.033 0.015

(0.015) (0.016) (0.015) (0.008) (0.009) (0.003)

{0.000} {0.000} {0.000}

n=13,232
22

Table 1 (continued) Women

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 6.49 6.49 6.49 0.0 0.0

(1.1) (1.1)

{0.996} {0.996}

High School Graduate 8.11 8.07 8.04 -0.9 -0.4 -0.4

(0.4) (0.4) (0.2)

{0.029} {0.260} {0.0052}

Some College 10.18 9.98 9.85 -3.2 -1.3 -2.0

(0.5) (0.6) (0.2)

{0.000} {0.029} {0.000}

College Graduate 13.32 13.32 13.46 1.0 1.0

(0.4) (0.4)

{0.011} {0.011}

College Grad/HS Grad 1.643 1.650 1.674 0.031 0.024 0.007

(0.018) (0.018) (0.017) (0.009) (0.008) (0.003)

{0.000} {0.004} {0.006}

n=12,362

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 6.44 6.44 6.41 -0.5 -0.5

(1.0) (1.0)

{0.595} {0.595}

High School Graduate 8.23 8.18 8.08 -1.8 -1.1 -0.7

(0.3) (0.3) (0.2)

{0.000} {0.000} {0.001}

Some College 10.05 9.93 9.79 -2.6 -1.4 -1.2

(0.6) (0.6) (0.4)

{0.000} {0.015} {0.001}

College Graduate 13.51 13.51 13.79 2.0 2.0

(0.5) (0.5)

{0.000} {0.000}

College Grad/HS Grad 1.641 1.653 1.706 0.065 0.053 0.012

(0.022) (0.022) (0.023) (0.011) (0.010) (0.003)

{0.000} {0.000} {0.001}

n=12,441
23

Table 2a: Cell by Cell Percentage Change in the Geometric Mean of Hourly Earnings. 1991-92.

Workers in earnings sample in both years

New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate Total

High School Dropout 1.8 4.8 6.2 37.0 2.5

Cell % 8.4 1.4 0.2 0.1 10.1

High School Graduate 2.5 1.8 -0.1 9.2 1.7

Cell % 1.1 32.3 3.3 0.3 37.0

Some College -14.7 0.3 0.7 2.8 0.7

Cell % 0.1 2.0 21.4 1.5 25.1

College Graduate 15.4 -4.3 -5.0 3.7 3.0

Cell % 0.0 0.3 1.8 25.8 27.9

Total 1.7 1.7 0.2 3.8 1.9

Cell % 9.6 36.0 26.8 27.7 100.0

n=22,577

Table 2b: Cell by Cell Percentage Change in the Geometric Mean of Hourly Earnings, 1990-91

Workers in earnings sample in both years

Old (13A) Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate Total

High School Dropout 0.2 1.8 2.6 23.9 0.3

Cell % 10.4 0.4 0.1 0.0 10.9

High School Graduate -0.6 1.1 1.0 5.2 1.1

Cell % 0.3 36.2 0.9 0.2 37.6

Some College -17.7 -7.6 1.1 6.1 1.0

Cell % 0.1 0.3 23.1 0.4 23.9

College Graduate -27.4 -7.8 -8.5 2.1 2.0

Cell % 0.0 0.1 0.3 27.3 27.7

Total 0.0 1.0 1.0 2.2 1.2

Cell % 10.8 37.0 24.3 27.9 100.0

n=45,825
24

Table 3a: Counter-factual (geometric) means of earnings(Standard Errors in Parentheses){p-values in Brackets}

Old (13A) New Counter-factual

1991 1992 1991 1992 1991 1992

High School Graduate 9.58 9.74 9.53 9.70 9.53 9.70College Graduate 15.50 15.97 15.66 16.25 15.75 16.24College/High School ratio 1.618 1.639 1.643 1.6760 1.652 1.674

(0.012) (0.010) (0.012) (0.013) (0.012) (0.014){0.000} {0.000} {0.000} {0.000} {0.000} {0.000}

n=22,577Note: Mean earnings will differ from those in reported in earlier tables, because these meansare computed using only individuals in earnings sample in both 1991 and 1992.Table 3b: Percent Difference Between Counter-factual, Actual, and Estimated (geometric) means of earnings(Standard Errors in Parentheses){p-values in Brackets}

Counter-factual and Old (13A) Counter-factual and New New and Old (13A)

1991 1992 Difference 1991 1992 Difference 1991 1992 Difference

High School Graduate -0.5 -0.5 0.1 0.0 0.0 0.0 -0.5 -0.5 0.1

(0.3) (0.4) (0.3) (0.2) (0.2) (0.3) (0.3) (0.3) (0.2)

{0.053} {0.214} {0.812} {0.982} {0.906} {0.961} {0.118} {0.072} {0.753}

College Graduate 1.6 1.7 0.1 0.6 -0.1 -0.6 1.0 1.8 0.7

(0.4) (0.3) (0.2) (0.2) (0.1) (0.3) (0.3) (0.3) (0.2)

{0.000} {0.000} {0.784} {0.013} {0.550} {0.041} {0.000} {0.000} {0.003}

College/High School ratio 0.035 0.035 0.000 0.009 -0.002 -0.011 0.026 0.037 0.011

(0.007) (0.007) (0.007) (0.004) (0.004) (0.006) (0.007) (0.007) (0.007){0.000} {0.000} {0.989} {0.021} {0.650} {0.065} {0.000} {0.000} {0.089}

n=22,577
25

Table 4: Suggested Adjustment Factors for Hourly Earnings at New Education Categories (%)

Geometric Means Medians

Education Category Old (13A) Old Old (13A) Old

Drop-outs -1.1 -1.1 -1.1 -1.1

High School Grad. -0.5 -1.1 -0.5 -1.1

Some College -1.3 Men:-2.2

Women:-2.7

-1.2 Men:-1.6

Women:-1.9

College Grad. 1.8 1.8 1.8 1.8

College/High School

ratio (computed as the

average adjustment

factor for 1989-1991)1

3.7 4.7 3.9 4.9

1 Applying adjustment factors for each education level will cause adjustment to College/High School ratio

to vary with the ratio.
26

Table 5. Geometric mean of hourly earnings by educational attainment for workers age 25-64 (in $1991),

by sex. Both sexes

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Hourly Earnings

 High School Dropout 7.97 7.75 7.63 7.56 7.44 -6.6%

 High School Graduate 9.54 9.38 9.31 9.19 9.13 -4.3%

 Some College 11.39 11.29 11.24 10.82 10.73 -5.8%

 College Graduate 15.17 15.18 15.02 15.11 15.06 -0.7%

 College/High School ratio 1.590 1.619 1.613 1.644 1.649 5.9%

Making 13A adjustment to old item

 High School Graduate 9.50 9.31 9.25 -3.9%

 Some College 11.25 11.17 11.10 -4.6%

 College/High School ratio 1.598 1.632 1.623 5.2%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 7.88 7.66 7.55 -5.6%

 High School Graduate 9.45 9.26 9.20 -3.4%

 Some College 11.10 11.02 10.96 -3.3%

 College Graduate 15.44 15.45 15.29 -2.5%

 College/High School ratio 1.634 1.668 1.662 1.5%
27

Table 5 (continued) Men

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Hourly Earnings

 High School Dropout 9.18 8.87 8.61 8.51 8.33 -9.2%

 High School Graduate 11.25 10.96 10.77 10.57 10.45 -7.1%

 Some College 12.95 12.90 12.70 12.12 11.99 -7.4%

 College Graduate 17.04 17.07 16.88 16.86 16.72 -1.9%

 College/High School ratio 1.515 1.557 1.568 1.595 1.601 8.6%

Making 13A adjustment to old item

 High School Graduate 11.20 10.89 10.70 -6.7%

 Some College 12.84 12.79 12.57 -6.6%

 College/High School ratio 1.522 1.567 1.577 7.9%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 9.08 8.77 8.51 -8.2%

 High School Graduate 11.14 10.84 10.65 -6.2%

 Some College 12.67 12.63 12.41 -5.4%

 College Graduate 17.31 17.35 17.16 -3.4%

 College/High School ratio 1.554 1.601 1.611 4.7%

Table 5 (continued) Women

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Hourly Earnings

 High School Dropout 6.34 6.25 6.31 6.30 6.22 -1.9%

 High School Graduate 8.03 7.96 8.01 7.94 7.91 -1.4%

 Some College 9.90 9.81 9.85 9.62 9.59 -3.2%

 College Graduate 12.98 13.01 12.95 13.20 13.27 2.2%

 College/High School ratio 1.617 1.634 1.616 1.664 1.677 6.0%

Making 13A adjustment to old item

 High School Graduate 7.99 7.90 7.96 -1.0%

 Some College 9.74 9.69 9.71 -1.6%

 College/High School ratio 1.624 1.647 1.627 5.3%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 6.27 6.18 6.24 -0.8%

 High School Graduate 7.95 7.86 7.92 -0.5%

 Some College 9.62 9.56 9.59 -0.3%

 College Graduate 13.19 13.22 13.16 0.6%

 College/High School ratio 1.659 1.682 1.662 1.8%
28

Appendix 1: Evidence on the Shakedown Effect

This appendix presents evidence that responses to the 1992 question changed over

the course of the year, implying that the first few months’ responses cannot be taken as a

guide to the final effect of the change in the education item.

Let pjk be the probability of being in education category k under the 1992 measure

conditional on being in education category j in 1991. Tables A1a and A1b show the 4×4

transition matrix of the pjk as well as the unconditional probabilities of being in each

category under each measure for all matched individuals 25-64 (not just wage earners) in

the first half of the year (January-June) and the last half of the year (July-December). Cellby-cell differences in probabilities are shown in Table A1c. If the administration of the

question had remained constant through the year, these differences in probabilities should

be indistinguishable from zero.

While differences in the pjk appear small, in some cells they are a large portion--

over ten percent--of the original pjk and are statistically significant at the five percent level

in several cases. In the (j=High School Graduate, k=Some College) cell, the difference of

.9 represents nearly a 13 percent increase in transition probability. The probability of

making the transition from High School Dropout to High School Graduate is about 12

percent higher in the last half of the year, while the probability of the reverse transition is

about 12 percent lower.

We also compared the distribution of educational attainment between Jan. 1992

and Dec. 1992. During that time, the percentage of the population that reported Some

College increased by 1.1 percentage points, while the percentage of the population that
29

reported High School Dropout decreased by the same amount. In other years, changes in

the distribution are usually on the order of a half a percentage point. We now examine

whether this change in the distribution affects earnings comparisons.

Tables A2a and A2b compare the geometric mean of hourly earnings by education

measure for ORGs in January-June 1991-92 to those from July-December 1991-92.

Comparing the 13A-adjusted measure to the new measure, the July-December period

shows substantially greater differences between the two measures for the High School

Dropout, Some College, and College Graduate categories for both 1991 and 1992

earnings (we discuss the differences between 1991 and 1992 in the text). Earnings data

from Jan.-June show only small differences between the two measures. However, for

July-Dec. using the new measure results in earnings estimates that are 0.8-1.6 percent

lower for high school dropouts and people with some college, and estimates for college

graduates that are 0.9-1.8 percent higher. The college/high school earnings ratio is not

much affected by the change in questions in Jan.-June. The new measure indicates that the

ratio is 0.3 percentage points higher when using 1991 earnings, and 1.7 percentage points

higher when using 1992 earnings. For the last half of the year, this ratio is 2.2 and 4.0

percentage points higher than under the old measure using 1991 and 1992 earnings.

Tables A3a and A3b show mean log hourly earnings for each (j,k) combination for

January-June and July-December. Cell by cell differences of mean log wage between the

first half and last half of the year are shown in Table A3c. In 10 of the cells, the differences

are greater than one percentage point, but most of these differences do not exceed their

standard errors. In only one cell (j=College Graduate, k=Some College), where it equals

16.0, is the difference statistically significant. The difference in means for this cell
30

accounts for most of the difference in the between-measure difference in the Some College

and College Graduate categories between the two halves of the year.

While the magnitude of the difference between the two half-years for this cell

could conceivably be due to seasonal or labor market factors, we do not think this is the

case. First, we would expect other cells to exhibit comparable differences between halfyears, but none do. Second, the pattern of decreasing wages for this cell with time is

striking. The correlation of the monthly mean log hourly wage for this cell with month

last asked the education item is -0.81 (p=.002). Using a non-parametric test, Kendall’s tau

(a measure of rank correlation) equals -0.64 (p=.004).
31

Tables for Appendix 1

Table A1a: Percentage Probabilities of Reporting 1992 Education Categories Conditional On 13A

Adjusted 1991 Categories, Matched Outgoing Rotation Groups, January-June.

New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate

Unconditional

Probability

High School Dropout 88.1 10.5 1.2 0.3 15.1

High School Graduate 4.2 88.0 7.0 0.8 37.5

Some College 0.6 9.0 85.1 5.3 23.1

College Graduate 0.2 1.4 6.7 91.8 24.4

Unconditional Probability 15.1 37.0 24.0 23.9 100.0

n=32,889

Table A1b: Percentage Probabilities of Reporting 1992 Education Categories Conditional On 13A

Adjusted 1991 Categories, Matched Outgoing Rotation Groups, July-December.

New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate

Unconditional

Probability

High School Dropout 86.2 11.7 1.6 0.4 15.3

High School Graduate 3.7 87.6 7.9 0.8 37.2

Some College 0.6 8.3 85.3 5.8 23.2

College Graduate 0.2 0.9 7.3 91.6 24.3

Unconditional Probability 14.8 36.5 24.8 24.0

n=33,655

Table A1c: Second-Half First-Half Year Differences in Probabilities of Reporting 1992 Educational

Categories Conditional on 13A Adjusted 1991 Categories, Matched Outgoing Rotation Groups.

(Standard Errors in Parentheses)

{p-values in Brackets}

New Measure

Old (13A) Measure

(1)

< HS Grad

(2)

HS Grad

(3)

Some Col.

(4)

Coll. Grad

(5) Unconditional

Probability

(1) High School Dropout -1.8 1.3 0.5 0.1 0.2

(0.6) (0.6) (0.2) (0.1) (0.2)

. {0.003} {0.030} {0.036} {0.281} {0.234}

(2) High School Graduate -0.5 -0.3 0.9 -0.1 -0.3

(0.3) (0.5) (0.4) (0.1) (0.2)

{0.071} {0.548} {0.036} {0.415} {0.130}

(3) Some College 0.0 -0.7 0.2 0.5 0.1

(0.2) (0.4) (0.6) (0.5) (0.3)

{0.986} {0.074} {0.704} {0.264} {0.593}

(4) College Graduate 0.1 -0.5 0.6 -0.2 0.0

(0.1) (0.2) (0.4) (0.5) (0.3)

{0.437} {0.001} {0.149} {0.726} {0.889}

(5) Unconditional Probability -0.3 -0.5 0.7 0.0

(0.3) (0.2) (0.3) (0.3)

{0.272} {0.035} {0.008} {0.897}

(6) Difference Between Old 0.5 0.1 -0.6 -0.1

(13A) and New Measures (0.1) (0.2) (0.2) (0.1)

Unconditional Probabilities {0.000} {0.466} {0.001} {0.549}

Between Half Years (Column 5 −

Row 5)
32

Table A2a: Geometric Mean of Hourly Earnings by Education Category for January-June, 1991-1992

Matched Outgoing Rotation Groups (1991 dollars) Both sexes

(Standard Errors in Parentheses)

{p-values in Brackets}

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 7.85 7.85 7.87 0.3 0.3

(0.4) (0.4)

{0.512} {0.512}

High School Graduate 9.52 9.45 9.45 -0.7 -0.1

(0.4) (0.3)

{0.044} {0.817}

Some College 11.48 11.37 11.35 -1.2 -0.1

(0.6) (0.5)

{0.043} {0.763}

College Graduate 15.22 15.22 15.24 0.1 0.1

(0.3) (0.3)

{0.617} {0.617}

College Grad/HS Grad 1.600 1.610 1.613 0.014 0.003

(0.018) (0.018) (0.018) (0.006) (0.006)

{0.027} {0.577}

n=25,673

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 7.84 7.84 7.83 -0.1 -0.1

(0.5) (0.5)

{0.816} {0.816}

High School Graduate 9.55 9.46 9.41 -1.4 -0.5

(0.3) (0.3)

{0.000} {0.072}

Some College 11.31 11.26 11.27 -0.4 0.1

(0.5) (0.4)

{0.454} {0.822}

College Graduate 15.49 15.49 15.58 0.6 0.6

(0.2) (0.2)

{0.010} {0.010}

College Grad/HS Grad 1.622 1.637 1.654 0.032 0.017

(0.017) (0.017) (0.017) (0.006) (0.005)

{0.000} {0.000}

n=25,558
33

Table A2b: Geometric Mean of Hourly Earnings by Education Category for July-December, 1991-1992

Matched Outgoing Rotation Groups (1991 dollars) Both sexes

(Standard Errors in Parentheses)

{p-values in Brackets}

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 7.89 7.89 7.83 -0.8 -0.8

(0.6) (0.6)

{0.170} {0.170}

High School Graduate 9.47 9.42 9.38 -0.9 -0.4 -0.5

(0.3) (0.3) (0.2)

{0.002} {0.172} {0.001}

Some College 11.63 11.46 11.34 -2.5 -1.2 -1.4

(0.4) (0.4) (0.2)

{0.000} {0.014} {0.000}

College Graduate 15.46 15.46 15.60 0.9 0.9

(0.3) (0.3)

{0.001} {0.001}

College Grad/HS Grad 1.632 1.641 1.663 0.031 0.022 0.009

(0.011) (0.011) (0.011) (0.007) (0.007) (0.003)

{0.000} {0.001} {0.001}

n=25,667

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

% Difference

Between Old

(13A) and Old

High School Dropout 7.91 7.91 7.80 -1.4 -1.4

(0.4) (0.4)

{0.001} {0.001}

High School Graduate 9.57 9.49 9.43 -1.4 -0.6 -0.8

(0.2) (0.2) (0.2)

{0.000} {0.013} {0.000}

Some College 11.44 11.35 11.17 -2.3 -1.6 -0.8

(0.5) (0.4) (0.3)

{0.000} {0.000} {0.006}

College Graduate 15.52 15.52 15.80 1.8 1.8

(0.3) (0.3)

{0.000} {0.000}

College Grad/HS Grad 1.623 1.636 1.675 0.052 0.040 0.013

(0.009) (0.010) (0.012) (0.007) (0.007) (0.003)

{0.000} {0.000} {0.000}

n=25,673
34

Table A3a: Geometric Mean of 1992 Hourly Wages by Old (13A) and New Education Measures

January-June, 1991-1992 Matched Outgoing Rotation Groups (in 1979 dollars)

New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate

Overall

Geo. Mean

High School Dropout 7.66 8.86 9.53 12.42 7.84

High School Graduate 8.93 9.37 10.56 12.39 9.46

Some College 9.92 10.38 11.24 13.54 11.26

College Graduate 10.38 10.57 13.24 15.73 15.49

Overall Geo. Mean 7.83 9.41 11.27 15.58

n (1991)=25,673

n (1992)=25,558

Table A3b: Geometric Mean of 1992 Hourly Wages by Old (13A) and New Education Measures

July-December, 1991-1992 Matched Outgoing Rotation Groups (in 1979 dollars)

New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate

Overall

Geo. Mean

High School Dropout 7.69 8.73 9.83 14.21 7.91

High School Graduate 8.60 9.41 10.39 12.95 9.49

Some College 8.65 10.35 11.31 13.92 11.35

College Graduate 9.09 10.25 11.28 15.96 15.52

Overall Geo. Mean 7.80 9.43 11.17 15.80

n (1991)=25,667

n (1992)=25,673

Table A3c: Second-Half First-Half Year Differences In Mean Log Wages (x 100) by Old (13A) and New

Education Categories, 1991-1992 Matched Outgoing Rotation Groups

(Standard Errors in Parentheses)

{p-values in Brackets}

 New Measure

Old (13A) Measure

High School

Dropout

High School

Graduate

Some

College

College

Graduate

Overall

Difference

High School Dropout 0.5 -1.5 3.1 13.5 0.9

(1.4) (3.2) -- -- (0.9)

{0.748} {0.646} {0.337}

High School Graduate -3.8 0.4 -1.6 4.4 0.3

(4.3) (0.8) (4.1) (10.0) (0.6)

{0.372} {0.604} {0.693} {0.657} {0.573}

Some College -13.6 -0.3 0.6 2.7 0.8

-- (3.1) (1.0) (4.7) (0.7)

{0.912} {0.559} {0.559} {0.2891}

College Graduate -13.4 -3.1 -16.0 1.5 0.2

-- (9.9) (4.5) (1.2) (0.8)

{0.755} {0.000} {0.218} {0.778}

Overall Difference -0.4 0.2 -0.9 1.4

(1.2) (0.6) (0.7) (0.8)

{0.743} {0.740} {0.244} {0.069}

Difference Between 1.3 0.1 1.7 -1.2

Old (13A) and New (0.5) (0.3) (0.4) (0.2)

Measures Changes in {0.014} {0.594} {0.000} {0.000}

Mean Log Wages

Between Half of Years

(Column 5 − Row 5)

Dashes indicate insufficient sample to calculate standard error.
35

Appendix 2: Adjustment Factors and Earnings Trends, Median Hourly

Earnings of All Workers and Geometric Mean and Median Weekly

Earnings of Full-Time Workers

This

Appendix discusses the adjustment of median earnings. We also present adjustment factors

and earnings trends for geometric mean and median weekly earnings for full-time workers,

derived using the methods presented in this paper.

Throughout the section, median earnings were calculated using the same interval

procedure used by the Bureau of Labor Statistics for official statistics. 19 The counterfactual medians were computed as described in footnote 21, but sample weights were

multiplied by the counter-factual probabilities as was done with geometric means. To

prevent negative weights, in calculating medians we assign values of zero to observations

where 

ZC <0 and one where ZC >1.20

Estimation of the counter-factual medians poses a problem because the condition

in equation (8) no longer holds. Hence, it is not clear whether one should compare the

 

19 We calculate medians using the same procedure that the Bureau of Labor Statistics uses for official

statistics. The first step is to divide the range of possible earnings values into intervals (the interval width

is $.50 for hourly earnings and $50 for weekly earnings). Next, determine which interval contains the

median observation. Let N be the total number of observations, Wi denote the weight attached to each

observation, and DL

 and DU denote the lower and upper earnings levels of the interval that contains the

median. The median equals

Median

W

W

W W

D

W

N W

W W

D

i

i

N

i

Ii DL

i

Ii DU

i

Ii DL

L

i

Ii DU

i

i

N

i

Ii DU

i

Ii DL

U =

−

























−

















× +

−

























−

















×

=

<

< <

<

=

< <

∑

∑

∑ ∑

∑

∑

∑ ∑

1 1

2 2

.

20 There were few values that were outside the unit interval.
36

counter-factual medians to the predicted medians or to the actual medians. Accordingly,

we report both actual and predicted (non-counter-factual) medians.

Results are shown in Tables A4 and A5. A comparison of Table A4 with Table A5

shows that the change in the sample eliminates the difference between using 1991 earnings

and 1992 earnings when comparing the two measures for college graduates (the difference

in differences falls from .0045 to .0002) and reduces it by more than half for high school

graduates (from .0026 to .0013)and more than half for the college/high school ratio (from

.0119 to .0037), leaving very little for the counter-factual adjustment to do. Using the

difference between the counter-factual with the estimated medians as the counter-factual

adjustment, we find that this adjustment exhibits roughly the same pattern as it does for

the geometric means; the difference between the measures for 1991 median college

graduate earnings is 0.0076 greater under the counter-factual than the estimated.

However, if we add this adjustment to the difference in measures between the new and the

old (13A) it over-corrects in the sense that the correction is larger that the initial

difference between 1991 and 1992 earnings. As a result, the college graduate earnings

and the college/high-school ratio between the years are greater in 1991 than in 1992 after

the adjustment.

Since our analysis gives us no clear guidance on how to adjust the medians and the

counter-factual estimates show the same basic pattern as the geometric means, we use the

method described above to generate our preferred adjustment factors for the medians:

average the 1991 and 1992 differences between measures for all categories except college

graduate, where we use the 1992 difference. Results are shown in the third column of

Table 4.

Table A6, which is analogous to Table 5, shows the effect of applying the

adjustment factors to earnings data from 1989-1993. Note that the growth of the

college/high school earnings ratio falls in half once the adjustment has been made.

Results for weekly earnings for full-time workers are shown in Tables A7-A9.
37
38

Table A4: Median Hourly Earnings by Education Category for July-December, 1991-1992

Matched Outgoing Rotation Groups (1991 dollars) Both sexes

(Standard Errors in Parentheses)

{p-values in Brackets}

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 7.73 7.73 7.69 -0.5 -0.5

(0.6) (0.6)

{0.409} {0.409}

High School Graduate 9.43 9.35 9.32 -1.2 -0.4

(0.4) (0.4)

{0.001} {0.386}

Some College 11.89 11.73 11.59 -2.5 -1.2

(0.6) (0.5)

{0.000} {0.030}

College Graduate 15.46 15.46 15.71 1.6 1.6

(0.4) (0.4)

{0.000} {0.000}

College Grad/HS Grad 1.639 1.653 1.686 0.047 0.032

(.0165) (.0174) (.0187) (.010) (.010)

{0.000} {0.000} {0.000} {0.000} {0.001}

n=25,667

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 7.65 7.65 7.52 -1.6 -1.6

(0.7) (0.7)

{0.018} {0.018}

High School Graduate 9.55 9.51 9.45 -1.1 -0.6

(0.3) (0.4)

{0.000} {0.080}

Some College 11.59 11.49 11.36 -2.1 -1.2

(0.7) (0.6)

{0.002} {0.056}

College Graduate 15.72 15.72 16.05 2.1 2.1

(0.5) (0.5)

{0.000} {0.000}

College Grad/HS Grad 1.646 1.654 1.698 0.052 0.044

(.011) (.012) (.016) (.009) (.009)

{0.000} {0.000} {0.000} {0.000} {0.000}

n=25,673
39

Table A4 (continued) Men

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 9.23 9.23 8.95 -3.0 -3.0

(0.6) (0.6)

{0.000} {0.000}

High School Graduate 11.47 11.37 11.32 -1.3 -0.5

(0.4) (0.3)

{0.000} {0.140}

Some College 13.49 13.46 13.23 -1.9 -1.7

(0.6) (0.6)

{0.001} {0.003}

College Graduate 17.94 17.94 18.34 2.2 2.2

(0.5) (0.5)

{0.000} {0.000}

College Grad/HS Grad 1.565 1.578 1.620 0.055 0.042

(0.016) (0.016) (0.020) (0.012) (0.011)

{0.000} {0.000} {0.000} {0.000} {0.000}

n=13,305

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 8.94 8.94 8.86 -0.8 -0.8

(1.2) (1.2)

{0.438} {0.438}

High School Graduate 11.35 11.25 11.17 -1.6 -0.7

(0.4) (0.4)

{0.000} {0.089}

Some College 13.67 13.54 13.49 -1.3 -0.4

(0.7) (0.5)

{0.061} {0.407}

College Graduate 17.72 17.72 17.87 0.8 0.8

(0.5) (0.5)

{0.076} {0.076}

College Grad/HS Grad 1.561 1.576 1.510 0.039 0.024

(0.018) (0.020) (0.021) (0.010) (0.011)

{0.000} {0.000} {0.000} {0.000} {0.027}

n=13,232
40

Table A4 (continued) Women

Panel 1 -- 1991 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 6.20 6.20 6.20 0.0 0.0

(1.1) (1.1)

{0.996} {0.996}

High School Graduate 7.97 7.93 7.91 -0.6 -0.2

(0.4) (0.3)

{0.091} {0.476}

Some College 10.07 9.94 9.84 -2.3 -1.0

(0.9) (1.0)

{0.017} {0.317}

College Graduate 13.80 13.80 13.95 1.1 1.1

(0.6) (0.6)

{0.058} {0.058}

College Grad/HS Grad 1.732 1.739 1.762 0.030 0.023

(0.023) (0.023) (0.021) (0.012) (0.011)

{0.000} {0.000} {0.000} {0.014} {0.042}

n=12,362

Panel 2 -- 1992 Earnings Old Old (13A) New

% Difference

Between New

and Old

% Difference

Between New

and Old (13A)

High School Dropout 6.12 6.12 6.12 -0.0 -0.0

(1.1) (1.1)

{0.969} {0.969}

High School Graduate 8.10 8.02 7.91 -2.3 -1.4

(0.5) (0.4)

{0.000} {0.000}

Some College 9.81 9.71 9.66 -1.5 -0.5

(0.7) (0.6)

{0.029} {0.401}

College Graduate 14.04 14.04 14.31 1.9 1.9

(0.7) (0.7)

{0.006} {0.006}

College Grad/HS Grad 1.733 1.750 1.809 0.075 0.059

(0.028) (0.028) (0.027) (0.016) (0.015)

{0.000} {0.000} {0.000} {0.000} {0.000}

n=12,441
41

Table A5a: Counter-factual medians of earnings(Standard Errors in Parentheses){p-values in Brackets}

Old (13A) New Counter-factual Estimated

1991 1992 1991 1992 1991 1992 1991 1992

High School Graduate 9.58 9.98 9.54 9.92 9.52 9.95 9.52 9.94College Graduate 15.56 16.67 15.85 16.99 15.78 16.88 15.66 16.83College/High School ratio 1.625 1.671 1.662 1.713 1.658 1.696 1.646 1.693

(0.018) (0.014) (0.019) (0.017) (0.016) (0.015) (0.017) (0.015){0.000} {0.000} {0.000} {0.000} {0.000} {0.000} {0.000} {0.000}

n=22,577Note: Median earnings will differ from those in reported in earlier tables, because these meansare computed using only individuals in earnings sample in both 1991 and 1992.Table A5b: Percent Difference Between Counter-factual, Actual, and Estimated medians of earnings(Standard Errors in Parentheses){p-values in Brackets}

New and Old (13A) Counter-factual and Old (13A) Counter-factual and Estimated

1991 1992 Difference 1991 1992 Difference 1991 1992 Difference

High School Graduate -0.4 -0.5 -0.1 -0.6 -0.2 0.4 0.0 0.1 0.1

(0.4) (0.2) (0.4) (0.4) (0.2) (0.3) (0.3) (0.1) (0.2)

{0.356} {0.009} {0.714} {0.128} {0.233} {0.239} {0.950} {0.327} {0.653}

College Graduate 1.9 1.9 0.0 1.4 1.2 -0.2 0.8 0.3 -0.5

(0.4) (0.4) (0.3) (0.3) (0.3) (0.2) (0.1) (0.2) (0.2)

{0.000} {0.000} {0.944} {0.000} {0.000} {0.175} {0.000} {0.228} {0.042}

College/High School ratio 0.038 0.041 0.004 0.033 0.025 -0.0087 0.012 0.003 -0.010

(0.011) (0.007) (0.009) (0.008) (0.005) (0.006) (0.005) (0.003) (0.006){0.001} {0.000} {0.668} {0.000} {0.000} {0.158} {0.008} {0.415} {0.095}

n=22,577
42

Table A6. Median earnings by educational attainment for workers age 25-64 (in $1991), by sex

Both sexes

Educational Attainment 1989 1990 1991 1992 1993 %Change

, 1989-93

Hourly Earnings

 High School Dropout 7.78 7.57 7.37 7.27 7.17 -7.8%

 High School Graduate 9.60 9.37 9.24 9.18 9.20 -4.2%

 Some College 11.29 11.38 11.37 10.86 10.82 -4.2%

 College Graduate 15.47 15.47 15.14 15.29 15.34 -0.8%

 College/High School ratio 1.612 1.651 1.639 1.666 1.668 5.6%

Making 13A adjustment to old item

 High School Graduate 9.54 9.31 9.18 -3.6%

 Some College 11.17 11.23 11.20 -3.1%

 College/High School ratio 1.623 1.662 1.648 4.5%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 7.69 7.49 7.30 -6.8%

 High School Graduate 9.49 9.26 9.13 -3.1%

 Some College 11.04 11.10 11.07 -2.0%

 College Graduate 15.75 15.75 15.41 -2.6%

 College/High School ratio 1.660 1.701 1.688 0.8%
43

Table A6 (continued) Men

Educational Attainment 1989 1990 1991 1992 1993 %Change

, 1989-93

Hourly Earnings

 High School Dropout 9.15 8.82 8.59 8.43 8.28 -9.6%

 High School Graduate 11.37 11.10 10.88 10.75 10.59 -6.9%

 Some College 13.25 13.11 13.00 12.33 12.14 -8.3%

 College Graduate 17.43 17.44 17.36 17.31 17.23 -1.2%

 College/High School ratio 1.533 1.572 1.596 1.611 1.627 9.4%

Making 13A adjustment to old item

 High School Graduate 11.29 10.98 10.80 -6.2%

 Some College 13.15 13.04 12.85 -7.6%

 College/High School ratio 1.544 1.589 1.608 8.3%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 9.05 8.72 8.50 -8.6%

 High School Graduate 11.23 10.92 10.74 -5.7%

 Some College 12.99 12.88 12.70 -6.5%

 College Graduate 17.74 17.75 17.67 -2.9%

 College/High School ratio 1.580 1.625 1.645 4.7%

Table A6 (continued) Women

Educational Attainment 1989 1990 1991 1992 1993 %Change

, 1989-93

Hourly Earnings

 High School Dropout 6.16 6.07 6.09 6.00 5.96 -3.1%

 High School Graduate 7.98 7.86 7.86 7.78 7.71 -3.3%

 Some College 9.93 9.90 9.86 9.58 9.49 -4.5%

 College Graduate 13.33 13.14 13.06 13.52 13.60 2.1%

 College/High School ratio 1.670 1.673 1.662 1.738 1.763 9.3%

Making 13A adjustment to old item

 High School Graduate 7.94 7.80 7.80 -2.8%

 Some College 9.77 9.69 9.76 -2.9%

 College/High School ratio 1.679 1.686 1.674 8.5%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 6.09 6.00 6.02 -2.1%

 High School Graduate 7.90 7.76 7.76 -2.3%

 Some College 9.66 9.57 9.65 -1.7%

 College Graduate 13.57 13.38 13.30 0.2%

 College/High School ratio 1.718 1.724 1.714 4.5%
44

Table A7. Suggested Adjustment Factors for Weekly Earnings at New Education Categories (%)

Geometric Means Medians

Education Category Old (13A) Old Old (13A) Old

Drop-outs -1.5 -1.5 Men: -1.7

Women: -0.5

-1.7

-0.5

High School Grad. -0.4 -1.1 -0.4 -1.0

Some College -1.2 Men:-2.1

Women:-2.5

-1.0 Men: -1.4

Women: -2.0

College Grad. 1.6 1.6 1.5 1.5
45

Table A8 Geometric mean of weekly earnings by educational attainment for workers age 25-64 (in $1991), by sex.

Men

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Weekly Earnings

 High School Dropout 392 380 367 362 355 -9.5%

 High School Graduate 485 472 463 456 453 -6.6%

 Some College 567 563 556 530 529 -6.8%

 College Graduate 769 768 760 763 756 -1.6%

 College/High School ratio 1.585 1.628 1.642 1.674 1.669 8.4%

Making 13A adjustment to old item

 High School Graduate 482 468 460 -6.0%

 Some College 562 559 550 -6.0%

 College/High School ratio 1.594 1.641 1.652 7.5%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 387 374 362 -8.1%

 High School Graduate 480 466 458 -5.6%

 Some College 556 552 544 -4.9%

 College Graduate 781 781 772 -3.2%

 College/High School ratio 1.627 1.674 1.686 4.2%

Women

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Weekly Earnings

 High School Dropout 265 261 263 262 261 -1.8%

 High School Graduate 337 335 338 334 335 -0.6%

 Some College 415 412 412 403 403 -3.0%

 College Graduate 556 559 557 571 575 3.4%

 College/High School ratio 1.647 1.668 1.648 1.708 1.714 6.7%

Making 13A adjustment to old item

 High School Graduate 336 332 336 -0.1%

 Some College 409 407 407 -1.5%

 College/High School ratio 1.655 1.682 1.659 5.9%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 261 257 259 -0.3%

 High School Graduate 334 331 335 0.3%

 Some College 404 402 402 -0.3%

 College Graduate 565 568 566 1.8%

 College/High School ratio 1.689 1.716 1.693 2.5%
46

Table A9 Median of weekly earnings by educational attainment for workers age 25-64 (in $1991), by sex.

Men

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Weekly Earnings

 High School Dropout 397 380 363 358 353 -11.1%

 High School Graduate 500 485 476 469 463 -7.4%

 Some College 569 569 569 543 545 -4.3%

 College Graduate 779 777 768 771 762 -2.1%

 College/High School ratio 1.558 1.602 1.615 1.645 1.647 8.9%

Making 13A adjustment to old item

 High School Graduate 496 480 472 -6.7%

 Some College 566 565 562 -3.8%

 College/High School ratio 1.569 1.619 1.628 7.8%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 390 374 357 -9.5%

 High School Graduate 494 478 470 -6.3%

 Some College 560 559 556 -2.7%

 College Graduate 791 789 780 -3.7%

 College/High School ratio 1.601 1.651 1.660 4.6%

Women

Educational Attainment 1989 1990 1991 1992 1993 %Change,

1989-93

Weekly Earnings

 High School Dropout 261 256 256 254 253 -3.2%

 High School Graduate 337 330 332 330 330 -1.9%

 Some College 419 414 411 399 401 -4.4%

 College Graduate 560 561 566 579 577 3.1%

 College/High School ratio 1.661 1.700 1.706 1.755 1.746 8.6%

Making 13A adjustment to old item

 High School Graduate 335 328 329 -1.5%

 Some College 412 409 406 -2.8%

 College/High School ratio 1.669 1.712 1.720 7.8%

Making Suggested Adjustment to old

 (13A) item

 High School Dropout 260 255 255 -2.7%

 High School Graduate 334 327 328 -1.2%

 Some College 408 405 402 -1.7%

 College Graduate 568 569 574 1.6%

 College/High School ratio 1.701 1.740 1.750 4.5%
47

References

Bound, John, and George Johnson [1992] “Changes in the Structure of Wages in the

1980s: An Evaluation of Alternative Explanations”. American Economic Review

82(3) (June), pp. 371-392.

Bound, John, and George Johnson [1995] “What Are The Causes of Rising Wage

Inequality in the United States?” Federal Reserve Bank of New York Policy

Review 1(1) (January), pp. 9-17.

Frazis, Harley, Michelle Harrison Ports, and Jay Stewart [1995] “Comparing Measures of

Educational Attainment in the CPS,” Monthly Labor Review 118(9), (September

1995), pp. 40-44.

Gordon, David M. [1996] Fat and Mean: The Corporate Squeeze of Working Americans

and the Myth of Managerial “Downsizing”. New York: Martin Kessler Books.

Jaeger, David A. [1993] "The New Current Population Survey Education Variable: A

Recommendation," University of Michigan, Population Studies Center Research

Report No. 93-289, September.

Jaeger, David A. [1995] “Reconciling the Old and New Census Bureau Education

Questions: A Recommendation for Researchers,” Bureau of Labor Statistics

Working Paper #270, forthcoming Journal of Business and Economic Statistics.

____________ and Marianne Page [1996] “Degrees Matter: New Evidence on Sheepskin

Effects in the Returns to Education,” forthcoming Review of Economics and

Statistics.

Katz, Lawrence F. and Kevin M. Murphy. [1992] “Changes in Relative Wages, 1963-

1987: Supply and Demand Factors.” Quarterly Journal of Economics 107(1)

(February), pp. 35-78.

Kominski, Robert and Paul Siegel [1993] “Measuring Education in the Current

Population Survey,” Monthly Labor Review 116(9), (September), pp. 34-38.

Murphy, Kevin M. and Finis Welch [1992] “The Structure of Wages”. Quarterly Journal

of Economics 107(1) (February) pp. 285-326.

Park, Jin Heum [1994] “Estimation of Sheepskin Effects and Returns to Schooling Using

the Old and New CPS Measures of Educational Attainment”. Industrial Relations

Section Working Paper, Princeton University.
48

Park, Jin Heum [1996] “Measuring Education Over Time: A Comparison Of Old and

New Measures of Education from The Current Population Survey.” Economics

Letters 50(3) (March), pp. 425-8.

Siegel, Paul [1991] “Note on the Proposed Change in the Measurement of Educational

Attainment in the Current Population Survey,” mimeo, US Bureau of the Census.

Ureta, Manuelita, and Finis Welch. [1995] “Measuring Educational Attainment: The Old

and the New Census Taxonomies of Educational Attainment”. Mimeo, Texas

A&M.

Wolter, Kirk M. [1985] Introduction to Variance Estimation. New York: SpringerVerlag.