- —
Table 3, ‘Age and education differences in mobility within the overall earings distribution in 1990-91, using matched CPs data

Positive sample Full-time, year-round sample
Sex, ose, Percent in Percent that stay Percent in Percent that stay
_ in quintile— jintile— quini
uintile— qu
education a
1 5 1 5 1 8 1 5
Men
18 14 53 74 22 12 58 7
10 28 49 79 13 27 61 7
6 41 46 79 8 38 50 79
9 40 57 71 9 38 64 69
23 10 64 50 31 8 65 61
ab 20 47 67 14 19 59 62
7 34 50 75 8 0 51 75
5 56 37 86 5 52 49 83
32 6 73 55 5 74 46
30 8 73 ry 3t 7 7 66
28 10 69 66 24 Ww 74 66
31 8 74 64 KJ 75 63
55 2 B81 10 67 1 85 33
36 3 71 54 40 4 75 55
25 7 71 59 25 6 66 58
15 20 63 69 10 19 57 68

have the necessary skills to reach the top quintile and re-
main there. If a less educated worker, on the other hand,
reaches the top quintile, then it is more likely to be due to a
favorable transitory shock that will dissipate with time.

Levels of mobility within various subdistributions. With re-
gard to the second type of mobility examined in this article—
movement within the earnings distribution of a particular de-
mographic group—transition matrices are also calculated, ex-
cept that in this case an individual is assigned to a quintile for
a pair of years in terms of his or her position in the earnings
distribution for a given demographic group, not for the entire
population. In addition to the proportions that remain in the
top and bottom quintiles, two further measures are calculated.
The first reflects the percentage of people that stay in the same
quintile for both years or, in other words, stay on the diagonal
of the transition matrix. To calculate this measure, it is neces-
sary to add up the percentages in the diagonal and then divide
by 5 (because each of the percentages is calculated with a base
that represents one-fifth of the population).

If there is perfect immobility—that is, if every individual
stays in the same quintile—then the measure will equal 1.0,

because all the diagonal elements will be 1.0 (and all the other
elements 0,0). If, on the other hand, there is perfect mobility—
that is, if an individual’s position in the beginning year has no
impact on his or her position in the ending year—then the
measure will equal 0.2, because all the diagonal elements—
and, in fact, all elements-——will equal 0.2. Making the relevant
calculations for the transition matrix in table | results in a
value of 0.38 ([0.4 + 0.3 + 0.3 + 0.4 + 0.5)/5) for this measure
of mobility.

An additional measure calculates the percentage of indi-
viduals who either stay in the same quintile or move into an
adjacent one—in other words, those who stay on or near the
diagonal of the transition matrix. Under perfect immobility,
this measure will also be 1.0, as everyone stays on the diag-
onal. With perfect mobility, it will be 0.52 because there are
13 elements on or adjacent to the diagonal, each of which
would equal 0.2 ([13 x 0.2/5 = 0.52). As applied to table 1,
the measure equals 0.68.'*

The final measure for assessing the extent of mobility
within a given distribution is the correlation coefficient,
which gives a guide to the extent to which individuals main-
tain their positions within the earnings distribution. The

Monthly Labor Review September 1995 7



Earnings Mobility

> For a survey of this literature, see Frank Levy and Richard J. Murnane,
“U.S. Earnings Levels and Earnings Inequality: A Review of Recent Trends
and Proposed Explanations,” Journal of Economic Literature, September
1992, pp. 1333-81.

* See Lynn Karoly, “The Trend in Inequality among Families, Individuals,
and Workers in the United States: A Twenty-Five Year Perspective,” in Sheldon
Danziger and Peter Gottschalk, eds., Uneven Tides: Rising Inequality in
America (New York, Russell Sage Foundation, 1993), for an illuminating dis-
cussion of similar issues in studies of earnings inequality.

$ Fortwo recent studies of mobility based on family income, see Thomas L.
Hungerford, “U.S. Income Mobility in the Seventies and Eighties,” Review of
Income and Wealth, December 1993, pp. 403-17; and Isabel V. Sawhill and

Mark Condon, “Is U.S. Income Inequality Really Growing?” Policy Bites, The,

Urban Institute, June 1992, pp. 1-4.

* Ofcourse, the two may be connected, as the pace of economic growth may
have implications for earnings mobility.

7 The crs is a monthly survey of approximately 60,000 households con-
ducted by the Bureau of the Census for the Bureau of Labor Statistics. The
March survey contains a special supplement that asks about income earned in
the year prior to the interview.

® See the appendix for more information on matching crs’s over time, in-
cluding a discussion of biases that may arise in using the matched cps’s for
analysis

Tn lieu of the cps, the Panel Study of Income Dynamics might have been
used; however, while the “split-offs” from the original members enable this
survey to maintain representation across all groups, the impact of attrition on
the representativeness of the sample is an issue of concern,

"See the appendix for further information.

"" As an alternative to selecting those who are full-time, year-round workers
as a way to control for differences in hours worked, calculations were done
with samples for which the measure of economic status was the hourly wage.
‘These results, which were broadly similar to the findings in this article, were
not reported for two reasons: the data necessary to calculate hourly wages from
the March crs—weeks worked in the previous year and usual hours worked
per week—are available only beginning with the 1976 ces; and there is likely
to be substantial measurement error in calculating hourly wages by dividing
annual wage and salary income by number of weeks worked multiplied by
usual number of hours worked per week, making the results less reliable.

” While the 99th percentile was used as a cutoff, the bunching of incomes,
in some cases at the top codes, caused those that were trimmed to constitute a
somewhat larger portion of the distribution for some years. See Karoly, “In-
equality among Families, Individuals, and Workers,” for a discussion of alter-
native treatments of the top code and their impact on measures of inequality.

»sResults are not reported separately for the racial group defined as “other,”
because of its small size and heterogeneity.

'4See A. B. Atkinson, F. Bourguignon, and C. Morrisson, Empirical Stud-
ies of Earnings Mobility (Chur, Switzerland, Harwood Publishers, 1992), for
a fuller discussion of ways to measure mobility.

'S For a discussion of this trend and potential explanations of it, see June

O'Neill and Solomon Polachek, “Why the Gender Gap in Wages Narrowed in
the 1980s,” Journal of Labor Economics, January 1993, pp. 205-28.

'S See Bradley R. Schiller, “Relative Eamings Mobility in the U.S..” Ameri-
can Economic Review, December 1977, pp. 926~41; Greg Duncan and Saul
Hoffman, “Dynamics of Wage Change,” in Martha Hill, Daniel Hill, and James
N. Morgan, eds., Five Thousand American Families—Patterns of Economic
Progress, vol. IX (Ann Arbor, a, Institute for Social Research, 1981); and Linda
Datcher-Loury, “Racial Differences in the Stability of High Eamings among
‘Young Men,” Journal of Labor Economics, July 1986, pp. 301-17.

See Jacob Mincer, Schooling, Experience and Earnings (New York,
Columbia University Press, 1974), for an elaboration of this view.

"* Note that no summary measures were used to assess mobility within the
overall distribution, as such measures are potentially misleading. By defini-
tion, in assessing mobility within a demographic group, 20 percent of the popu-
lation will be in each quintile. This is not the case when one examines the
mobility of a demographic group within the overall earnings distribution, be-
cause a group is not likely to be evenly spread across the overall distribution.
Asaresult, in calculating summary measures, differences across demographic
groups in the degree of movement in and out of quintiles will get confounded
with differences across these groups in their initial distribution over the quintiles.

" See Donald Parsons, “The Autocorrelation of Earnings, Human Wealth
Inequality and Income Contingent Loans,” Quarterly Journal of Economics,
November 1978, pp. 551-69. The National Longitudinal Survey cohort of
young men is a nationally representative group of 5,225 men aged 14 to 24
years in 1966 who were surveyed periodically beginning that year. The cohort
of older men, with whom interviews also began in 1966, is a nationally repre-
sentative group of men aged 45 to 59 years in 1966.

® See Jacob Mincer and Boyan Jovanovic, “Labor Mobility and Wages,” in
Sherwin Rosen, ed., Studies in Labor Markets (Chicago, University of Chi-
cago Press, 1981), fora discussion of variation in job mobility by age.

| See Atkinson, Bourguignon, and Morrisson, Empirical Studies of Earn-
ings Mobility; and Bjéra Gustaffson, “The Degree and Pattem of Income Im-
mobility in Sweden,” Review of Income and Wealth, March 1994, pp. 67-86.

Parsons, “Earnings, Inequality and Loans.”

® See Greg Duncan, “An Empirical Model of Wage Growth,” in Greg
Duncan and James Morgan, eds., Five Thousand American Families—Pat-
terns of Economic Progress, vol. VIt (Ann Arbor, mi, Institute for Social Re-
search, 1979).

> See Blau and Beller, “Black-White Earnings.”

% See Lee A. Lillard, “Inequality: Earnings Versus Human Wealth,” Ameri-
can Economic Review, March 1977, pp. 42-53.

% For a more detailed discussion of the connections between mobility and
inequality in the context of the recent rise in earnings dispersion in the United
States, see Paul R, Krugman, The American Prospect, Fall 1992, pp. 19-31.

® For a detailed elaboration of this view, see Chinhui Juhn, Kevin M.
Murphy, and Brooks Pierce, “Wage Inequality and the Rise in Returns to Skill,"
Journal of Political Economy, June 1993, pp. 410-42.

7*Robert Moffitt and Peter Gottschalk, “Trends in the Covariance Structure
of Eamings in the U.S.: 1969-87,” mimeograph, Boston College, March 1993,

APPENDIX: Construction and evaluation of matched samples from the cps

‘The data used in this article are from March-March matched files from the
Annual Demographic Files of the Current Population Survey (crs). At the time
of the analysis, the crs was available for the period 1968-92, containing eam-
ings data for the year prior to the interview. While that implies the existence of
24 adjacent-year pairs of records (1968-69 through 1991-92), changes in
household identifiers across adjacent years make it impossible to perform
matches for 1971-72, 1972-73, 1976-77, and 1985-86. ‘Thus, we were able
to construct matched files for 20 pairs of years between 1968 and 1992.
Under the sample design of thecrs, half of any March sample can be matched

12 Monthly Labor Review September 1995

with the March sample of an adjacent year. A household will be in the sample
for 4 months, out for 8 months, and then back in for an additional 4, Thus,
households that are in their first through fourth months in the sample in March
of year? will be in their fifth through eighth months in the sample in yeart+ 1

In practice, it is not possible to match fully half of the sample, given that i
viduals leave it for various reasons. The match rates used in this article result
froma fairly conservative algorithm and tend to fall in the range of 60 percent
to 70 percent of individuals who are eligible to be matched. This attrition rate
raises the concer as to whether matched samples can be considered rep-



| Table 5. immobility measures by demographic group in 1990-91, using matched crs positive earnings sample. |

Sex, age, Percent Percent Percent Percent
education, Correlation that stay on that stay on that stay In that stay in
ond race coefficient diagonal ‘of Near dlagonail — first quintile fifth quintie
Men

o77 59 88 62 70

73 583 8 62 70

7 59 89 64 74

75 59 88 64 70

74 57 87 65 72

Fewer than 1 66 53 83 58 69

70 584 85 80 67

72 57 87 69 69

74 6 88 68 72

7 59 88 65 74

65 51 83 55 70

8 60 89 70 a

1 59 89 70 72

79 at 8 a az)

80 59 89 68 76

76 59 88 66 73

68 53 85 61 62

74 57 87 68 ral

1S 58 86 72 69

5 58 88 er 68

5 61 89 638 73

52 86 68 67

cating that the differences are largely due to blacks’ greater
instability in pay rates, rather than greater fluctuations in
hours worked. Evidence of a higher degree of earnings mo-
bility or instability among blacks was also found by Duncan,
who used hourly earnings of males from the Panel Study of
Income Dynamics.” The differences across races in short-
term earnings mobility appear larger for men than for
women. This is consistent with the fact that the earnings
differential between blacks and whites is much smaller for
women than for men.*

Trends

This section examines the trends in three measures of earn-
ings immobility over the 1967-91 period: the percentage of
individuals that stay on the diagonal in the transition ma-
trix, the proportion that stay at or near the diagonal, and the
correlation coefficient. The trends in earnings mobility are
particularly interesting in light of the increase in cross-sec-
tional earnings inequality observed during the 1980's, because

these trends affect patterns in long-run inequality. To give a
simple example, suppose an economy has just two people. In
1994, person A earns $100,000 and person B eams nothing.
Clearly, a good deal of inequality is present in this economy,
and from an equity standpoint, it may be a matter of concern.
But suppose now that in 1995, the fortunes of A and B are
reversed, so that A earns nothing and B earns $100,000. Then,
when earnings are summed up over the 2-year span, both indi-
viduals have earned $100,000, so no inequality is present.
Thus, in this example, mobility is such that, even though there
is a great deal of inequality in 1 year, over a longer span the
distribution of earnings is exactly equal.

Certainly, in the U.S. economy, the degree of mobility is
not high enough so that an individual’s position in the earn-
ings distribution in any year is not relevant to his or her
position as earnings are summed up over a lifetime. Even so,
there is enough mobility that the degree of inequality over
longer spans is less than that over | year. For example, Lee
A. Lillard estimated that inequality in a single year was 50
percent greater than over a lifetime.”

Monthly Labor Review September 1995 9

——


Earnings Mobility

measure ranges from ~1.0 to 1.0, with 1.0 indicating perfect
immobility, 0.0 perfect mobility, and negative values (not
observed in the calculations carried out) some reversal of
positions.

In this section, mobility patterns are examined for 1967-91,
and both the levels and trends in various relative immobility
indexes are documented. As noted earlier, what is of interest is
mobility within the earnings distributions defined by the four
main subsamples and mobility within various distributions for
particular demographic groups. Table 4 reports average im-
mobility measures for the 1967-91 period for the four main
subsamples, As expected, the measures are slightly higher for
the full-time, year-round samples than for the positive earn-
ings samples, because, for the former, fluctuations in hours of
work are largely eliminated,

ow do mobility indexes differ across sex, age, education,

and racial groups? Table 5 gives the 1990-91 immobil-
ity indexes for both the positive earnings and full-time, year-
round samples. The 1990-91 immobility measures for the posi-
tive earnings sample are slightly higher for women than for
men, with differences in mobility being more pronounced at
the extremes of the earnings distributions. The table shows
that 62 percent of men remain in the bottom quintile of their
earnings distribution, compared with 70 percent of women.
Similarly, the proportion of men who stay at the top of their
distribution is 5 percentage points lower than the correspond-
ing proportion of women. However, among full-time, year-
round workers, the differences in mobility between the sexes
are smaller.

Table 5 also suggests that short-term immobility is typically
lower among young workers, both male and female. This find-
ing is in accord with that of Donald Parsons, who compares
the National Longitudinal Survey cohorts of young men and
older men.'? Given the wider range of ages covered in the crs,
the current study is able to examine more closely the relation-
ship between short-term mobility and age. Table 5 indicates
that short-term earnings immobility initially increases with age

- and then levels off. In other words, those in their twenties
have higher mobility rates than other workers, but there is little

difference across other age groups, except within the positive
earnings sample, where workers in their fifties have signifi-
cantly higher mobility rates than do workers in their forties.
This difference in regard to older workers does not exist in the
full-time, year-round sample, which implicitly controls for
variations in hours, and thus may be the result of a change in
the degree of labor force attachment as workers approach re-
tirement age. The difference in mobility rates for the young is
greater for the positive earnings sample than for the full-time,
year-round sample, indicating that the high mobility rates for
the young are also partly the result of greater fluctuations in
hours. In addition, greater job mobility among the young prob-
ably is an important contributor.” The findings presented in
this article differ from the strictly positive relationship found
between 1-year earnings correlation coefficients and age in the
United Kingdom, but are broadly consistent with recent find-
ings in regard to Sweden.”

Table 5 also shows a positive relationship between educa-
tion and earnings stability or immobility. Within the men’s
positive earnings samiple, the 1990-91 correlation coefficient
was 12 percent higher for college graduates than for high
school dropouts. Short-term earnings mobility or instability
levels were highest for those who did not complete high
school, particularly high school dropouts in the positive earn-
ings sample. In both the positive earnings and full-time,
year-round samples, college graduates had significantly
lower earnings instability than those in the other education
groups. Parsons also found a positive relationship between
schooling and 1-year earnings correlation coefficients for the
National Longitudinal Survey cohort of older men, but not
for that of young men, among whom he found mobility lev-
els to be highest for college graduates.” This suggests that
the relationship between education and mobility might dif-
fer across age groups.

Perhaps the most striking difference in short-term mobil-
ity levels recorded in table 5 occurs between blacks and
whites. Over the 1990-91 period, the correlation coeffi-
cient for black men was 16 percent lower than for their white
counterparts. These racial differences—particularly with re-
gard to men—persist across both earnings samples, indi-

Average immobility measures, by earings sample, 1967-91
Percent Percent Percent Percent
Sample Correlation that stay on | that stay on that stay in that stay in
diagonal | or near diagonal) first quintile fifth quintile
Men:
Positive sample 0.76 87 88 65 ta}
Full-time, year-round sampl 77 59 89 69 72
Women:
Positive sample...... rAd 58 89 64 7
Fulltime, year- round sample . 78 569 89 67 74

8 Monthly Labor Review September 1995



The preceding example of a two-person economy demon-
Strates how, with annual inequality staying constant, move-
ment in the earnings distribution can still work to reduce
inequality when earnings are summed over a longer period.
Just as mobility may help allay concerns about a degree of
inequality in one particular year, it can also help alleviate
worries about a rise in annual inequality. If annual inequal-
ity rises, as it did in the 1980’s in the United States, then
this will automatically translate into higher inequality over
a longer period if there is no change in the extent to which
individuals exchange positions in the earnings distribution.
If the degree of mobility increases, however, it will reduce
the extent to which increases in annual inequality are trans-
lated into increases in long-run inequality. On the other
hand, a reduction in mobility would reinforce the inequal-
ity-increasing effects of rises in annual inequality.

Ww". is the pattern for recent trends in earnings mobil-
ity? Chart 1 graphs the trends in 1-year correlation
coefficients and two transition matrix measures for the men’s
and women’s positive earnings and full-time, year-round
samples for the period 1967-91. As mentioned earlier, four
Pairs of years are missing from the time series. The missing
pairs make it difficult to distinguish much of a trend over the
early portion of the series. After this, however, short-term
immobility indexes appear to follow a stable trend. For the
men’s positive eamings sample, immobility, as measured by
the correlation coefficient, declined from 0.78 in 1977 to 0.71
in 1982 and increased moderately thereafter. This U-shaped
pattern applies as well to the men’s full-time, year-round
sample. For the women’s positive earnings and full-time,
year-round samples, {-year correlation coefficients began to
decline sometime in the early 1970’s and rose gradually after
1978. Note, however, that the fluctuations in the correlation
coefficient graphed in chart 1 take place over a fairly limited
range. On the whole, then, the findings suggest that mobility
patterns have not been that different in the 1980's from what
they were in the 1970s,

‘What are the implications of these findings for the extent
to which increased annual inequality is being translated into
increases in long-run inequality? Clearly, additional research
is needed here, but the results presented in this article do not
suggest that mobility patterns have changed in such a way as
to offset the recent rise in earnings inequality.

More speculatively, these same results can also be used to

Footnotes

shed additional light on the causes of the recent rise in earn-
ings inequality. While a detailed review of the literature on in-
equality is beyond the scope of the article, one view holds that a
key factor behind the rise in earnings inequality is that the de-
mand for skilled workers has increased, leading to a widening
of the earnings gap between those who are skilled and those
who are not.” Given that such a shift in favor of the skilled
would be likely to persist over time, this has an important impli-
cation for patterns of mobility: if the distance in earnings across
skill levels has widened, it becomes more difficult for individu-
als to pass each other on the earnings ladder, implying that mo-
bility will decline over time.

It is also possible that the increase in inequality in a given
year has been caused by increased randomness in the economy.
As Robert Moffitt and Peter Gottschalk maintain, the amount of
turbulence in the economy may have increased because of grow-
ing international competition, a reduction in regulations, the
waning influence of labor unions, and a variety of other fac-
tors.”* This increased influence of transitory factors would im-
ply that mobility would increase, as it is more likely that, with
regard to the economic ladder, someone who has the good for-
tune of benefiting from the increased turbulence will surpass
someone who has not, Because we do not see strong trends in
mobility—either a rise or a fall— the results suggest that both
the permanent factors associated with a rise in returns to skill
and the transitory factors associated with growing turbulence in
the economy may be important in the recent rise in earnings
inequality.

THIS ARTICLE HAS UNCOVERED several interesting differences
in short-term earnings mobility across demographic groups.
First, men have higher short-term earnings mobility levels than
women do, Second, workers in their twenties have high levels
of earnings mobility or instability relative to their older
counterparts. Aside from this, however, mobility levels do not
show any clear pattern with age. Third, higher education levels
generally mean higher 1-year correlations—in other words,
more stability—in short-term earnings. Fourth, black men have
more instability in their earnings than their white counterparts
have, and this racial difference in mobility levels is present, but
less pronounced, for women. Last, mobility measures followed
a general U-shaped pattern during the 1967-91 period, although
the magnitude of the shifts that occurred indicates that short-
term mobility in the 1980’s was not profoundly different from
that in the 1970's. a

' Fora more detailed discussion of the importance of data on mobility, see
A. B. Atkinson, F. Bourguignon, and C. Morrisson, “Earnings Mobility,” Eu-
ropean Economic Review, vol. 32 (1988), pp. 619-32.

? See Lawrence F. Katz and Kevin M. Murphy, “Changes in Relative Wages,
1963-87: Supply and Demand Factors,” Quarterly Journal of Economics,

February 1992, pp. 35-78, for a recent study of changes in the pattem of pay by
‘age (experience), education, and sex; and Francine D. Blau and Andrea H. Beller,
“Black-White Earnings over the 1970s and 1980s; Gender Differences in
Trends,” Review of Economics and Statistics, May 1992, pp. 276-86, for an
examination of earnings differentials by race.

u

Monthly Labor Review September 1995 11

—- a


ings Mobility

Maury Gittleman
and
Mary Joyce

Maury Gittleman and
Mary Joyce are econo-
mists in the Office of
Publications and Spe-
cial Studies, Bureau of
Labor Statistics.

Earnings mobility

in the United States, 1967-91

The young, the less educated,
and blacks have more instability
in their earnings than do those

who are older, more educated, or white

ers and low earners in the United States has

widened. Information about this phenom-
enon is generally reported in relation to a par-
ticular point in time. The Census Bureau, for
example, reports on the percentage of families
whose income is below the poverty line during a
particular year and releases annual data on the
share of household income by quintile. While
such statistics reveal important insights into how
individuals are faring economically, they paint
an incomplete picture.

To gain a fuller appreciation of the impact of
poverty, one must understand not only trends in
poverty rates, but also the extent to which a fam-
ily that is in poverty in a given year will remain
there in a particular specified period that follows.
In a similar way, those concerned about equity
will want to know not only whether the share of
income going to the top fifth of the income distri-
bution is growing or declining, but also whether
there are patterns in the degree to which house-
holds move in and out of a given portion of the
income distribution.

To move from the static view of the economy
inherent in most economic data on the income
distribution to a more dynamic perspective, it is
necessary to have information on the mobility of
individuals, families, and households over time—
that is, the extent to which these economic units
change positions in the income distribution over a
given period. What proportion of families in
poverty this year will escape poverty next year?
Are those in the middle class now likely to be there
5 years from now? Do the rich in one year tend to
be the rich in the next, or do individuals from other
income classes move into the top tiers? A study of

I n recent years, the gap between high earn-

mobility can provide insights relevant to an-
swering important questions such as these. In
addition, the degree of earnings mobility is im-
portant not only for developing a more com-
prehensive view of the workings of the economy,
but also in such areas as designing pension
schemes or income-contingent student loan
programs, where benefits or repayment respon-
sibilities depend on a person’s earnings over his
or her working life and not during a particular
year, Further, mobility patterns contribute to an
understanding of labor markets, as certain patterns
will be consistent with some labor market theories
but not with others.!

This article addresses two important questions
concerning earnings mobility in the United
States. First, how do pattems of earnings mobil-
ity differ by sex, age, race, and education? While
many recent studies examine trends in earnings
across demographic groups,’ much less atten-
tion has been devoted to the extent to which
those of a given group are able to maintain or
improve their relative economic status from one
year to the next. And, second, how have mobil-
ity patterns changed over time? A vast literature
hhas developed that seeks to document and ex-
plain the large increase in earnings inequality
in the United States,’ but little is known about
whether—as the earnings distribution became
more pulled apart—it got harder or easier for
individuals to work their way up the economic
ladder. Trends in mobility have implications
both for the causes of the rise in earnings in-
equality and for the extent to which inequities
in earnings in a given year even out over time.

A number of important findings emerge from
this study. First, important differences appear

Monthly Labor Review September 1995 3

—


resentative. Franco Peracchi and Finis Welch recently subjected matched
March samples to a rigorous testing and concluded that, while the matched
and unmatched populations are different in important dimensions, “no
major biases appear in the estimates of transitions between labor force
states after controlling for sex, age and labor force status at the time of
the first survey.”! While the research focus of the current article is differ-
ent from theirs, Peracchi and Welch's results provide some support for
using matchedcrs data in analyzing labor force dynamics. One caveat they
mention is that attrition rates are highest among the very young. Similar
conclusions were reached in an earlier analysis by Francis W. Horvath?

Accordingly, to minimize attrition problems in the present research, very
young workers were omitted from the samples and analyses were performed

separately by age group. One of the sensitivity tests that was carried outinvolved
the calculation of inequality statistics for various samples from the matched data.
The results indicated that both the levels and trends obtained are comparable to
‘those calculated from the full Marchcps,

Footnotes to the appendix

" See Franco Peracchi and Finis Welch, "How Representative Are Matched
Cross-Sections: Evidence from the Current Population Survey.” unpublished
manuscript, October 1992,

? See Francis W. Horvath, “Tracking Individual Earnings Mobility with the
Current Population Survey,” Monthly Labor Review, May 1980, pp. 43-46.

Where are you publishing your research?
The Monthly Labor Review will consider for publication studies of the labor force,
labor-management relations, business conditions, industry productivity,
compensation, occupational safety and health, demographic trends, and other
economic developments. Papers should be factual and analytical, not polemical in
tone. Potential articles should be submitted to: Editor-in-Chief, Monthly Labor
Review, Bureau of Labor Statistics, Washington, DC 20212-0001.

Monthly Labor Review September 1995 13



Earnings Mobility

One-year correlation coefficients and immobility Indexes, 1968-70, 1973-75, 1977-84, and 1986-91

Positive sample, men Positive sample, women
1 1
Indext —Index2 ce Indext —Index2 oe
eS -_ = 2 -_ +
09 Fe 09 +
08 08

0.7 |

4 Ny ee

PF in ln Ss tie

05 F

0.4 UL 1 1 L 1 n

~

0.7

“lel? oo le oon?

0.5 |-

1968 = 1972, 1976) 1980 «19841988.

Full-time, year-round sample, men

o4 Lt 1 1 4 n L
1968 «= 1972, 1976 «= «1980 1984S 1988

Full-time, year-round sample, women

Law
os A a NA

ae Var eo

oh oN SA AN
wi \

1968 «= «19721976 = 198019841988

1968 = 1972, 1976 1980-1984 1988

10 Monthly Labor Review September 1995



Earnings Mobility

USK Sox and race differences in mobility within the
overall earnings distribution in 1990-91, using
matched crs data

Percent in Percent that stay

$ex and race
quintie— in quintie—
_

1 6 1 8
20 20 66 74
10 Ki St WT
wn 8 72 63
8 33 48 78
23 7 S7 59
29 9 2 65
31 B 44
20 20 68 74
12 % 58 76
30 8 73 64
10 ot 56 7
6 7 8 84
29 9 73 66
38 5 73 42

that group will stay in the highest quintile, and the greater is
the likelihood that they will stay in the bottom quintile. For
example, women are more likely to stay at the bottom than
men: some 72 percent of women who were in the bottom
quintile of the earnings distribution of the positive sample in
1990 stayed there in 1991, compared with only 51 percent of
men. By contrast, 77 percent of men at the top in 1990 re-
mained there in 1991, compared with only 63 percent of
women. Low-earning women appear to be stuck at the bot-
tom, even when the labor supply is controlled for by restrict-
ing the sample to those who work full time, year round in
both. years, which suggests that persistently low hours of
work are not the sole source of these women’s lack of up-
ward mobility, It may be that women in the bottom quintiles
are more likely to work in occupations that consistently pay
low wages and have limited promotion potential.

A caveat must be mentioned before continuing with the
findings: even within quintiles, groups will have different
earnings distributions. For example, among those in the bot-
tom quintile, men are closer than women, on average, to the
boundary between the first and second quintiles. Thus, even
if men and women have the same increase in earnings from
one year to the next, men will be more likely than women to
move out of the bottom quintile, boosting the meas-ure of
mobility presented for men. Experimentation with other

6 Monthly Labor Review September 1995

measures, however, suggests that the results would be quali-
tatively similar even if these intraquintile differences were
taken into account when measuring mobility.

The ability to maintain one’s position at the top of the
overall earnings distribution appears to be more elusive for
blacks than for whites—even for black men relative to white
women. About 65 percent of white women who were in the
top quintile in 1990 were there in 1991, compared with 59
percent of black men and 44 percent of black women. Simi-
lar racial differences in the ability to maintain the top eco-
nomic status were also found by Bradley R. Schiller, Greg
Duncan and Saul Hoffman, and Linda Datcher-Loury.'*
Datcher-Loury found that high-earning black men and high-
earning white men differ in their distribution across occupa-
tions, which may contribute to their differences in earnings
mobility. High-earning white men were more likely to work
in managerial or professional occupations, in which earn-
ings are more stable, whereas high-earning black men were
more likely to be employed in sales and clerical jobs, in which
earnings tend to fluctuate more. Significant differences
across races in movements out of the bottom quintile exist
only for men, with 52 percent of white men leaving the bot-
tom quintile, compared with 43 percent of black men. These
general patterns hold for both earnings samples.

able 3 reports differences in mobility within the overall

earnings distributions across age and education groups.
Notsurprisingly, younger, less educated workers are more likely
than older, more educated workers to be in the bottom quintile,
and less likely to be in the top quintile, of both earnings distri-
butions. The percentage of each age group that remains in the
bottom quintile decreases with age, except for the oldest group,
whose percentage is higher than that of the youngest group.
Similarly, the percentage of each age group that remains in the
top quintile increases with age, also except for the oldest group,
whose percentage is lower than that of the youngest group.
These patterns are consistent with the human capital view of
the pattern of earnings over the life cycle, which suggests that
as a worker ages, earnings rise rapidly at first, then flatten out,
and ultimately begin to fall.”

The percentage of each education group that stays in the
bottom quintile decreases consistently with years of school-
ing, and the percentage that stays in the top quintile increases
consistently with years of schooling, indicating that it is
easier for more educated workers to move out of the bottom
and to remain at the top than it is for workers with less edu-
cation. These mobility patterns are similar for men and
women within both earnings distributions. The education
mobility patterns are not surprising if one believes that edu-
cation represents a permanent improvement in an in-
dividual’s human capital and thus earnings capacity. In that
case, the highly educated workers would be more likely to



samples.” Each of these was divided into the following four
main subsamples, using annual wage and salary income as the
measure of economic status in a given year: men with positive
wage and salary income in both years; men working full time,
year round (at least 50 weeks’ work, usually working at least
35 hours per week) in both years, women with positive earnings
in both years; and women working full time, year round in
both years." For all samples, the following criteria had to be
met for both years: age between 25 and 59 years; not self-
employed; and not in the top percentile of the earnings
distribution of the appropriate subsample. The trimming of the
top | percent of earners is done both because some of the
measures of mobility used in this article are sensitive to outliers
and because it is desirable to eliminate from the sample those
for whom data on earnings have been censored or “top coded.”
For the latter individuals, it is known that their earnings are
above a certain threshold, but it is not known by how much.!?
To be included in the group of those with positive wage and
salary income in a given 2-year sample (either men or women;
referred to later as the positive samples), annual earnings
merely had to be nonzero in both years. To be included in the
group of those working full time, year round in both years of
the sample (again, either men or women; referred to later as
the full-time, year-round samples), which implicitly controls
for differences across individuals in hours worked, annual
earnings had to exceed 1,750 (50 weeks times 35 hours) times
one-half the applicable minimum nonfarm hourly wage rate in
both years.

Results are presented for both samples because they repre-
sent different aspects of mobility. For the full-time, year-round
samples, the movement within the distributions is due mainly
to relative changes in the rate of pay, while in the positive
samples, changes in hours worked also play a role. In part
because not all changes in hours worked are voluntary, it is
important to assess mobility for both samples.

In addition to these four subsamples, the following samples,
divided along three demographic dimensions, were used:
age—intervals of 25-29, 30-39, 40-49, and 50-59 years; years
of schooling completed—fewer than 12 years, 12 years, 13-15
years, and 16 or more years; and race—white and black."

Mobility patterns, 1967-91

Mobility within the overall earnings distribution. To measure
both kinds of mobility defined earlier, appropriate yardsticks
are required.'* For the first type of mobility—movement in
the overall earnings distribution—consider a device known as
a transition matrix. If the overall earnings distribution is
divided into quintiles in year r— 1 and year t, a5 x 5 matrix can
be calculated wherein each cell (i, j) shows the proportion of
those in quintile i in year ¢ — 1 that are in quintile j in year 1.
Table 1 presents a hypothetical example of such a matrix. The

matrix shows that, of those who are in the second quintile
in year 1, 0.3, or 30 percent, will fall to the bottom quintile
in year 2. The percentages in each row must sum to 1,
because all of the individuals who were in a given quintile
in year 1 must be in some quintile in year 2. By similar
reasoning, the columns must sum to 1 as well. While every
cell is of potential interest, for purposes of discussing
movements within the overall distribution, consider cells
(1, 1) and (5, 5}that is, the percentage of those who start
off in the bottom quintile of the overall earnings distribution
and remain there, and the same measure for the top quintile.

H. do demographic groups differ in terms of their po-
sitions and movements within the overall earnings dis-
tribution? To answer this question, let us examine the pat-
terns of the two sexes and then, separately by sex, of the 10
demographic groups defined by age, years of schooling, and
race. The first two columns of table 2 report the percentage

+ of each demographic group that was in the first (bottom)

and in the fifth (top) quintile of the overall earnings distri-
bution during 1990, and the second two columns show the
percentage of these that remained in those quintiles during
1991. The percentages are given for the positive and the full-
time, year-round samples. While the results shown are for
1990-91 only, the basic patterns hold for any pair of years
during the 1967-91 period.

Although differences in mean earnings between men and
women have been declining,"* striking differences remain at
the extremes of the distribution, with women being much
more likely than men to be in the bottom quintile and much
less likely to be in the top quintile. In fact, about the same
percentage of women were in the bottom quintile (30 per-
cent) as men were in the top quintile (31 percent) of the
earnings distribution for the positive sample during 1990.
As regards each of the sexes, blacks were much more likely
to be in the lowest quintile, and much less likely to be in the
highest quintile, than whites were. White men were the least
likely to be at the bottom and the most likely to be at the top,
whereas the tendency for black women was just the opposite.

Mobility patterns within the overall distribution also dif-
fer by sex and race. In general, the lower a group’s average
earnings, the lower is the likelihood that individuals from

Hypothetical transition matrix
‘Quintile in yeas f
‘Quintiie In year f-1
1 2 3 4 5
a ce ae
1 04 02 02 01 | 0.4
2 3 3 2 a 1
3 2 2 3 2 4
4 a a 2 4 2
5 0 2 4 2 5
Monthly Labor Review September 1995 5



Earnings Mobility

across demographic groups in regard to their mobility within
the overall earnings distribution: women are more likely to
remain in the bottom quintile and less likely to remain in the
top quintile of the overall earnings distribution than are men;
and blacks are more likely than whites to slip out of the top
quintile and to remain in the bottom quintile of the overall
distribution. Second, differences also appear in relative mobility
within various earnings distributions for groups defined by their
demographic characteristics: the young, the less educated, and
blacks have more instability in their earnings than those who
are older, more highly educated, or white. Third, short-term
mobility levels have not undergone major changes over the
time span 1967-91.

Measuring mobility

Before mobility can be measured, a number of methodologi-
cal issues must be addressed. First is the choice of the unit
of analysis—that is, whether it is to be families or individu-
als.‘ Because this article examines the way in which the
labor market distributes rewards and how the process
changes over time, the focus is on individuals. For the same
reason, earnings are emphasized rather than income, as the
latter may include income from property, government pro-
grams, and other sources outside of the labor market. If the
goal were to assess changes in the distribution of economic
well-being, the family would probably be the appropriate
choice, because one’s welfare is determined not only by one’s
own income, but also by the income of other household mem-
bers.> In addition, in that instance, it would be advisable to
include as broad a measure of a family’s economic resources
as possible, not just its labor-market earnings.

The article focuses on two different concepts of earnings
mobility. The first is concerned with the positions and move-
ments of various demographic groups within the earnings dis-
tribution of the entire population. Measures of this type of
mobility seek to provide answers to questions such as the fol-
lowing: What proportion of the blacks that are in the top
quintile (top fifth) of the overall earnings distribution in a given
year maintain that position over time? Or, what proportion of
white males in the bottom quintile in a particular year will
have moved to a higher quintile the following year? Such a
concept of mobility highlights differences in various demo-
graphic groups’ ability to change or maintain their relative
positions within the overall earnings distribution.

The second type of mobility examines relative earnings
movements within subdistributions defined by demographic
characteristics. For example, it is well known that those with
less education will have lower earnings, on average, than
the more educated. But focusing, say, on high school drop-
outs, do the better off within this group tend to be the same
year after year, or is there a substantial reshuffling of eco-

4 Monthly Labor Review September 1995

nomic positions? And how does this “churning” in the earn-
ings distribution for high school dropouts compare with that
for other groups?

Issues in interpreting findings. A number of important is-
sues must be kept in mind in interpreting the results to be pre-
sented. Suppose one of the findings is that individuals experi-
enced substantial changes in their relative positions within the
overall earnings distribution or within that of a subpopulation.
This can be thought of as evidence of either a high degree of
short-term earnings mobility or a high level of short-term earn-
ings instability, depending on one’s perspective. To most ears,
“earnings mobility” sounds like something to be favored on
equity grounds, as it connotes the opportunity to change one’s
relative economic position. The term “earnings instability,” on
the other hand, suggests a negative flip side to this, hinting at
potential difficulties involved in attempting to maintain one’s
economic status. Thus, the normative aspects of the findings
are a matter of interpretation, open to debate about whether the
glass is “half empty” or “half full.”

It is also important to keep in mind the distinction be-
tween earnings mobility and earnings growth. The measures
presented in this article of earnings mobility over a given
period are concerned solely with the degree to which indi-
viduals shift relative positions within the earnings distribu-
tion, not with absolute growth in real earnings levels over
time.* Thus, by definition, mobility implies that one person’s
upward movement within the earnings distribution is ac-
companied by another person’s downward shift.

Data

The analysis to be presented uses March-March matched files
from the Annual Demographic Files of the Current Population
Survey (cps)’ from 1968 to 1992. The CPs is designed so that
potentially half of the individuals surveyed in a given March
will also be present in the sample in the following March.’ By
linking surveys, one can follow an individual for 2 years and
see how his or her position in the earnings distribution changes
over that period. While earnings mobility is best studied over
as long a time span as possible, there are several important
advantages to using the sequence of 2-year panels made avail-
able by linking cps data. First, the cps is a nationally repre-
sentative data set, so one can follow all age groups over time.?
Second, the samples obtainable from the matched cPs's are
generally larger than those from the longitudinal data sets, al-
lowing more precise estimates of mobility for various subpopu-
lations than is possible using smaller panel data sets. Third, 2-
year panels can be constructed to cover a lengthy period—
nearly 25 years.

Construction of samples. From the 25 March cps's from
1968 to 1992, it was possible to construct 20 matched