BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF PRODUCTIVITY AND TECHNOLOGY

On the Estimation of Classical Human Capital Wage Equations with Two Independent

Sources of Data on Actual Work Experience

Linda L. Moeller, U.S. Bureau of Labor Statistics

Working Paper 362

June 2002

 

This research was conducted under an inter-agency agreement between the Bureau of Labor Statistics and the

Bureau of the Census. The research results and conclusions discussed here are those of the author and do not reflect

the views of the Department of Labor, the Bureau of Labor Statistics, the Department of Commerce or the Bureau of the

Census.
On the Estimation of Classical Human Capital Wage Equations

With Two Independent Sources of Data on Actual Work Experience

Linda L. Moeller

Division of Productivity Research

Office of Productivity and Technology

Bureau of Labor Statistics

Revised May 28, 2002

Original June 4, 1998

This research was conducted under an inter-agency agreement between the Bureau of

Labor Statistics and the Bureau of the Census. The research results and conclusions

discussed here are those of the author and do not reflect the views of the Department of

Labor, the Bureau of Labor Statistics, the Department of Commerce or the Bureau of the

Census.
2

Abstract: The BLS multifactor productivity series decomposes labor productivity growth into

components associated with increased capital intensity of production and shifts in the skillcomposition of the work force attributable to changes in the level and distribution of human

capital. The use of administrative record data on actual accumulated work experience as an

indicator of workers’ current productivity is a distinguishing features of this series. The current

procedure relies on a one-time match of Social Security Administration (SSA) data to records

from the March 1973 CPS to develop an experience proxy that is entered in a human capital wage

equation. Since the parametric relationship between accumulated work experience and the

demographic characteristics of the work force is unlikely to remain stable over time, the BLS has

undertaken a long-run research project to update the work experience data at regular intervals.

Two independent sources of information on accumulated work experience were examined: SSA

administrative record data on quarters of covered employment, and data from the 1984 Survey of

Income and Program Participation (SIPP) on the number of years in which the respondent was

employed for 6 months or longer. Direct comparison of the SIPP and SSA data show that the

SSA data generate biased estimates of total actual work experience due to incomplete program

coverage at the beginning of the Social Security program, in 1937, and subsequent changes in

SSA coverage rates. Contemporaneous experience profiles estimated with SSA data are flatter

than their SIPP counterparts, while the 1973 CPS-SSA experience profiles are flatter than their

1984 counterparts. Wage profiles estimated with the 1973 CPS-SSA experience proxy are too

high for younger and older workers. Consequently weights assigned to the hours growth rates for

younger and older workers are too large, while those assigned to hours growth rates for primeaged workers are too small. These results tell a cautionary tale with respect the assumption that

the coefficients of the experience equation are stable over time.
3

On the Estimation of Classical Human Capital Wage Equations

With Two Independent Sources of Data on Actual Work Experience

Linda L. Moeller

Table of Contents

I. Introduction

II. Description of the Labor Composition Index

III. Measurement of Hours Worked

A. March Current Population Survey Question on Hours Worked

B. Prior Research on Respondent Recall Error

IV. Measurement of Actual Work Experience

A. Detailed Description of SIPP Work Experience 

B. Detailed Description of SSA Work Experience 

C. Comparison of SIPP and SSA Work Experience Measures

D. Simulated Experience Profiles

V. Estimation of Wage Equations

A. Selection Bias Correction

B. Simulated Wage Rate Profiles

VI. Conclusion

VII. Bibliography

VIII. Tables

IX. Appendix A: Correction for Selection Bias in Wage Equation

X. Appendix B: SIPP Sample Estimates of Earnings and Wage Rates in 1984

XI. Charts
4

Introduction

This paper discusses results from a research project in which the ultimate objective is the

construction and analysis of a new BLS index of the skill-composition of the work force with

annual data on the accumulated employment experience of U.S. workers. 1 Within the BLS

multifactor productivity series, the labor composition index and the capital services index

respectively serve as measures of the contributions of human and physical capital services to U.S.

productivity growth.2

 The labor composition index is motivated by the widely-accepted view that

workers become more efficient as they accumulate education and market work experience, and

that economy-wide increases in average levels of education and work experience contribute to

observed growth in productivity. Within the conceptual framework of models of long-run

economic growth the labor composition index converts growth in hours worked into growth in

labor efficiency units.3

 

1

 I would like to thank Judy Eargle, Enrique Lamas, and the entire staff of the Housing and Household

Economic Statistics Division of the Census Bureau (HHES) for their generous hospitality during this

project. This research has also benefited from the advice and encouragement of Phoebus J. Dhrymes,

Howard M. Iams, David B. McMillen, Larry Rosenblum and Harold W. Watts. Any errors that remain are

my responsibility. The results reported here pertain exclusively to the 1984 panel of the SIPP. Results from

the period 1984-1993 are discussed in Linda Moeller (1999), “A Second Decade of Productivity Growth:

Prototype Labor Composition Indexes Based on the SIPP,” presented at the 1999 NBER Summer Institute

Workshop on Price Output and Quality Measurement.

I am particularly indebted to Ms. Stacy Masano of the HHES staff, who assembled several SAS data sets

containing information from selected SIPP topical modules and a subset of the variables available in the

SIPP-SSA matched file for this project. Her careful work greatly facilitated my initital progress.

2

 The procedures currently used by BLS, as described in BLS Bulletin 2426, Labor Composition and

Productivity Growth, 1948-90, U. S. Department of Labor, Bureau of Labor Statistics, December 1993.

This paper describes possible improvements to the current approach, focusing on survey and administrative

record data from 1984.

3 Many prominent studies of the sources of productivity growth, such as those by Jorgenson, Gollop and

Fraumeni, also contain estimates of the contribution of changes in the composition of the workforce. For

example, see Dale W. Jorgenson, Frank M. Gollop and Barbara M. Fraumeni (1987), Productivity and U.S.

Economic Growth, Cambridge, Mass.: Harvard University Press.
5

The BLS labor composition index is a hedonic index in which education and experience levels

are assumed to be fundamental structural determinants of workers’ productivity and market wage

rates.

 4

 Regression coefficients from a standard human capital wage equation are used to estimate

within-cell conditional mean wage rates for workers with different levels of experience and

education. These conditional mean wage rates are used to estimate the labor cost shares

attributable to workers with different sets of skill characteristics. The labor cost shares are then

used to aggregate rates of growth of hours worked across cells. These calculations are described

in more detail below.

The use of administrative record data on actual acccumulated work experience is one of the

distinguishing characteristics of the current BLS labor composition index. Actual work

experience is a particularly important explanatory variable in the determination of female wage

rates because the labor force participation of women can be intermittent. Labor economists have

appreciated the importance of long-run increases in the labor force participation rates of women

to growth in market-oriented production, perhaps one of the most dramatic reorganizations of

productive activity observed during this century, at least since the seminal work of Mincer,

Becker, and Gronau. A substantial portion of this “first generation” labor supply literature was

devoted to the estimation of labor supply and earnings equations with relatively large, nationally

representative cross-section sets of microdata. The standard data base for many of these efforts

 

4

 This general approach is discussed extensively in Zvi Griliches (1970), "Notes on the Role of Education

in Production Functions and Growth Accounting," in Education, Income and Capital, W. Lee Hanson, Ed.,

New York: NBER and Columbia University Press, pp. 71-115; and in Griliches (1971), "Introduction:

Hedonic Price Indexes Revisited," in Price Indexes and Quality Change: Studies in New Methods of

Measurement, Griliches, Ed., Cambridge: Harvard University Press, pp. 3-15. For a contemporary critical

discussion of this approach see Ariel Pakes (2002), “A Reconsideration of Hedonic Price Indices with an

Application to PC’s,” NBER Working Paper No. 8715. Some of Pakes’ points also arise implicitly in this

paper and in Moeller (1999), cited above, to the extent that I discuss the problem of selection bias as it

arises in the estimation of wage equations and the roles of both supply and demand factors in the

determination of market wage rates. But for the most part this paper follows a conventional hedonic

approach.
6

has been the Current Population Survey (CPS), at least in part because its careful and consistent

design have supported regular efforts to replicate significant findings. Subsequently several

influential nationally representative surveys, notably the National Longitudinal Surveys (NLS)

and the Panel Study of Income Dynamics (PSID), spawned increasingly sophisticated

methodological work to deal with problems of heterogeneity and change over time that could not

be addressed with the CPS.

From time to time, important complementary research has been conducted with large sets of

confidential administrative record data. 5 Administrative record data from the Social Security

Administration or the Internal Revenue Service have been matched to CPS microdata on a

number of occasions in order to construct data sets that could be used for analytical purposes. In

particular, experience equation coefficients estimated with microdata from the March 1973 CPS

matched to Social Security Administration (SSA) data are used to construct a proxy for actual

work experience in wage equations that are estimated annually by the BLS Office of Productivity

and Technology (OPT). But in view of the recognized secular increase in women’s labor force

participation rates mentioned above, it seems unlikely that the true parametric relationship

between actual work experience and the explanatory variables of the experience equation has

been stable over time. Consequently BLS has undertaken a long-run project to update its labor

composition measures.

This paper focuses on results obtained during the first stage of this project, in which actual work

experience data from the 1984 panel of the Survey of Income and Program Participation (SIPP)

were compared with actual work experience data from SSA administrative records, and wage

 

5

 Insightful prior research with survey data linked to SSA administrative records is illustrated in Nancy

Ruggles and Richard Ruggles (1977), "The Anatomy of Earnings Behavior," The Distribution of

Economic Well-Being, New York: NBER, pp. 115-158.
7

equations that are intermediate products in the composition of BLS’s labor composition index

were estimated with work experience data from each of these two independent data sources. Two

shortcomings of the data with which BLS currently measures changes in the efficiency of labor

services, and two shortcomings in the estimation procedures with which the data are aggregated

into an index of shifts in the skill-composition of hours worked are discussed in this paper. These

shortcomings have been recognized by BLS for a number of years; this research reflects a

substantial effort to address them.6

The first shortcoming of current data sources is that estimates of annual earnings and annual

hours worked are based on responses to questions from the annual March supplement to the CPS

that pertain to the longest job held in the prior year. Many survey respondents hold more than

one job during the course of a year, and many hold two jobs simultaneously. In a sense, the

current estimates of the total number of earnings and hours worked during the course of the prior

year are somewhat imprecise. The wage equation parameter estimates reported below have been

obtained with microdata from the SIPP, an important and relatively new source of nationally

representative panel data on earnings and hours worked that does not have these limitations.

The second shortcoming of current data sources is that information on actual accumulated work

experience, which is an important determinant of average wage rates from the perspective of

human capital theory, is not available on an annual basis. The current procedure relies on the

estimated coefficients of a single set of work experience equations that have been estimated with

a data set constructed by matching individual earnings records from the SSA to microdata from

the March 1973 CPS, as noted above. Results from a direct comparison of 1984 SIPP estimates

 

6

 In particular, see the BLS Handbook of Methods (1997), BLS Bulletin 2490, pp. 90-92, and Labor

Composition and Productivity Growth, 1948-90 (1993), BLS Bulletin 2426, Appendix G.
8

of total work experience to similarly linked SSA administrative records reveals that the latter are

biased downward due to incomplete program coverage at the beginning of the Social Security

program, in 1937, and subsequent changes in SSA coverage rates over time.7

 That is, the work

experience recorded in the administrative records is known to be an underestimate of true work

experience.8

The first methodological shortcoming discussed below is that the current procedure involves the

estimation of a conventional wage equation, but does not incorporate a selection bias correction

factor. Although it was argued in the early labor supply literature that the estimation of wage

equations without accounting for the probability of employment resulted in coefficient estimates

that were consistent “for the population of interest,” over time the incorporation of an additive

selection bias correction factor into the procedures with which wage equations are estimated has

become more standard practice. Incorporation of a conventional selection bias correction factor

that follows an early specification of Heckman is found to have small, but economically

meaningful effects on the estimated earnings profiles of women, especially among highlyeducated women.

 

7

 The SIPP was designed to permit matching with administrative records, in order to facilitate the analysis

of issues related to the administration of income transfer and other Federal programs that cannot be

accomplished with administrative record data alone. The files analyzed in the research reported here were

originally linked for an SSA research project that is described in Howard M. Iams (1991), “Child Care

Effects on Social Security Benefits,” 1991 Annual Research Conference Proceedings, Bureau of the

Census, pp. 255-271.

8

 This bias is worthy of some note, in light of a recent upsurge of interest in the cost savings that would

result from increased use of administrative record data to construct Federal statistics. Several sessions of

the Census Bureau's 1995 Annual Research Conference were devoted to this topic. In particular, see the

following papers in the forthcoming Conference Proceedings: Ib Thompson, "Use of Administrative

Records in the Norwegian Censuses Since 1970;" Pekka Myrskyla, Joe Knott and Cynthia Taeuber, "Uses

of Administrative Records in Censuses and Health Research: Finland and the United States," and Ann

Brown and Richard Veevers, "The Use and Evaluation of Administrative Records in the Self-Sufficiency

Project." Some of the limitations of administrative record data for analytical purposes are emphasized in

Janet L. Norwood (1985), “Administrative Statistics: A BLS Perspective,” Journal of Business and

Economic Statistics, 3(4), pp. 398-400.
9

The second shortcoming of the current methodology is that it implicitly imposes a number of

structural restrictions on the estimated parameters of the wage equation.9

 It is shown below that

these implicit restrictions distort the estimated wage profiles of older workers, presumably

because the true structural relationship among the explanatory variables that are common to both

the experience equation and the wage has not remained stable over time. The coefficient of the

squared work experience proxy, which would otherwise cause estimated wage profiles to increase

at a decreasing rate, becomes very small. Consequently estimated wage profiles become almost

linear. Since the labor composition index cost-share weights are constructed with predicted wage

rate values, the result is that the weights associated with growth in hours worked by younger and

older workers are too large, and the weights associated with growth in hours worked by primeaged workers too small under the current approach. This problem does not arise when

contemporaneous work experience data are employed to estimate the experience equation.

Interestingly, earnings profiles generated with the estimated parameters of an equation in which

potential experience is a proxy for actual work experience are similar to those obtained with

current-year SIPP- and SSA-based experience estimates. Since potential experience is a function

of age and years of schooling completed, and schooling dummies also enter the earnings equation

directly, this result may indicate that the potential experience variable is capturing cohort effects

or other compositional effects that influence the estimated parameters of the earnings equation.10

 

9

 These restrictions can be shown by explicitly substituting the right-hand-side of the experience equation

into the wage equation, and comparing the result with the wage equation that is actually estimated.

10 These compositional, or aggregation effects are discussed, for example, in Ray C. Fair and Kathryn

Dominguez (1991), "Effects of the Changing U.S. Age Distribution on Macroeconomic Equations,"

American Economic Review, pp. 1276-1294; and in Thomas M. Stoker (1986), "Simple Tests of

Distributional Effects on Macroeconomic Equations," Journal of Political Economy, pp. 763-795.
10

Description of the Labor Composition Index

Consider the secular increases in stocks of assets relative to the number of employed persons, and

in the labor force participation rates of males and females, that are portrayed in Charts 1 and 2.

The rising trends in capital intensity and female labor force participation rates suggest that

increasing fractions of successive cohorts of women have found their marginal productivity in

market production to be higher than their marginal productivity in household or other non-market

production.11 The concurrent long-run decline in labor force participation rates among men is

largely attributable to increasingly early retirement.12 The trend toward earlier retirement among

men may in turn be due to declines in the relative marginal productivity of older male workers, as

labor force participation rates of prime-aged females increase. As noted above, this reallocation

of labor between the market and non-market sectors and the growing capital intensity of

production appear to be two of the fundamental economic forces that have contributed to long-run

growth in the productivity of labor.13

 

11 A formal discussion of the determinants of the family labor supply decision is provided in Ruben

Gronau (1986), "Home Production - A Survey." In Handbook of Labor Economics, Vol. I, Ashenfelter

and Layard, Eds., Amsterdam: North-Holland, pp. 274-304. Strong empirical evidence on the increased

labor force participation of women is provided in Goldin’s study of labor force participation rates and

average years of work experience among women from the last decade of the 1800’s to the present. Labor

force participation rates among married women increased with age in every cohort examined and labor

force participation rates among new entrants increased with each successive cohort, while average levels of

work experience among employed women increased slowly. See especially Figure 1 in Claudia Goldin

(1989), "Life-Cycle Labor Force Participation of Married Women: Historical Evidence and Implications,"

Journal of Labor Economics, 7(1), pp. 20-47. Also see Mark R. Killingsworth and James J. Heckman

(1986), "Female Labor Supply: A Survey," in Handbook of Labor Economics, Volume I, O. Ashenfelter

and R. Layard, Eds., pp. 103-204.

12 John Penceval (1986), "Labor Supply of Men: A Survey," in Handbook of Labor Economics, Volume

I, O. Ashenfelter and R. Layard, Eds., pp. 3-102.

13 For example, Jorgenson and Fraumeni assert that "The contributions of capital and labor inputs are the

predominant sources of U. S. economic growth for the period as a whole [1948-1986]," in Dale W.

Jorgenson and Barbara Fraumeni (1992), "Investment in Education and U. S. Economic Growth,"

Scandinavian Journal of Economics, 94(0), Supplement, pp. 51-70. Also see William J. Baumol, Sue
11

A familiar abstract representation of the employment decision associated with this process is

provided in Chart 3.14 The distribution of wage offers for a given category of labor at time t is

represented by the left-most distribution, drawn with a dashed line. The acceptance wage of a

person with a particular vector of demographic and household characteristics is represented by

the vertical line. A person’s acceptance wage is assumed to be determined by the relative

marginal productivities of all household members in market and non-market production, their

time constraints, and non-labor income. Persons who receive wage offers to the right of the

vertical line accept them, and persons who receive wage offers to the left of the vertical line

undertake non-market production. The probability that the person will be employed in period t is

represented by the total area under the dashed line and to the right of the vertical line.

If increased capital intensity raises the marginal productivity of labor at time t+1, all else equal,

the wage offer distribution for this category of labor will shift to the right. For t+1, the wage

offer distribution becomes the distribution drawn with a solid line. If the composition of the

population is unchanged between t and t+1, the probability increases that a person with the same

observable characteristics and the same reservation wage will receive a wage offer above his or

her reservation wage. In Chart 3, the probability of employment for a person within this category

of labor becomes the larger area under the solid line, and to the right of the vertical line. Labor

force participation rates will increase for this category of labor, holding other effects constant.

 

Anne Batey Blackman, and Edward N. Wolff (1989), Productivity and American Leadership: The Long

View, Cambridge: MIT Press.

14 The characterization of the labor force participation decision that follows is a simplified version of one

used in many characterizations of the labor supply decision. See, for example, Mark R. Killingsworth

(1983), "Second-generation Studies of Static Labor Supply Models: Methodology and Empirical Results,"

Chapter 4 in Labor Supply, Cambridge: Cambridge University Press.
12

Notice that the observed mean wage rate depends on the labor force participation rate, because

market wage rates are not distributed symmetrically about the mean wage offer.15

Of course, increased capital intensity of production is not the sole determinant of observed trends

in real wage rates. Education is widely believed to be a determinant of productivity growth

because it increases skill levels observed during subsequent employment, and/or because it

refines native abilities. Chart 4 shows that hours-weighted averages of years of school completed

for men and women have increased, and converged, during the post-war period. The wage

premiums associated with increased levels of schooling have been well documented.16 And wage

premiums associated with seniority and total work experience have been attributed to increased

productivity due to formal and informal on-the-job training, increases in efficiency that come

with experience at performing a particular set of work tasks, and with improved knowledge of the

organizational or institutional structures through which work is organized.17

Average levels of work experience are believed to have declined during the 1970’s as unusually

large cohorts of new entrants joined the labor force and female labor force participation rates

continued to increase. This demographic shift may have contributed to the decline in productivity

growth rates that was observed during the 1970’s. Similarly during the 1980’s, cohorts of new

 

15 The use of a single category of labor to illustrate the problem of selection bias follows the lucid

discussion in Penceval (1986), "Labor Supply of Men," cited above.

16 For evidence on the wage premiums associated with schooling see, for example, Marvin H. Kosters

(1991), "Wages and Demographics," and the following commentary by Gary Burtless, in Workers and

Their Wages, Marvin H. Kosters, Ed., Washington: AEI, pp. 1-38. Theodore W. Schultz provides an

excellent early discussion of the view that education contributes to productivity in his 1963 essay, The

Economic Value of Education, New York: Columbia University Press.

17 For example, Topel estimates that a 10-year increase in job seniority is associated with a 25 percent

increase in the wage rate. See Robert Topel (1991), "Specific Capital, Mobility, and Wages: Wages Rise

with Job Seniority," Journal of Political Economy, pp. 145-176. Jacob Mincer provides a strong

conceptual and empirical discussion of the relationship between earnings and work experience in his

seminal work of 1974, Schooling, Experience and Earnings, New York: NBER and Columbia University

Press; see especially Chapter 4 of that volume.
13

labor market entrants declined in size and productivity growth rates increased.18 The labor

composition index is an attempt to quantify the contribution of these changes in the composition

of the work force on multifactor productivity growth rates.

As noted by Medoff and Abraham, "earnings which are associated with more labor market

experience at a point in time" are not necessarily "explained by higher productivity at the same

point in time."19 In addition to prior work experience, the current allocation of time between

training and other productive activities that may be undertaken on the job is widely assumed to be

a primary determinant of current earnings.20 If some fraction of workers’ time is allocated to onthe-job training and the costs of that training are shared between employees and employers, then

the earnings of workers who are investing in training may be lower than the earnings of those

who are not, all else equal. However, the finiteness of human life constrains the frequency and

duration of these investments as work experience increases.21 Consequently, over the long run

 

18 Discussions of these demographic trends are provided in Kosters (1991), "Wages and Demographics,"

cited above; John Bound and George Johnson (1991), "Wages in the United States During the 1980s and

Beyond," in Workers and their Wages: Changing Patterns in the United States, M. H. Kosters, Ed., pp. 77-

103, and in the comment on Bound and Johnson from Lawrence Katz , pp. 104-106. Also see Jacob

Mincer (1991), "Human Capital, Technology, and the Wage Structure," cited above; and BLS Bulletin

2426, Labor Composition, cited above.

19 James L. Medoff and Katherine G. Abraham (1981), "Are Those Paid More Really More Productive?

The Case of Experience," Journal of Human Resources, 16, pp. 186-216. Also see Judith K. Hellerstein

and David Neumark (1995), "Are Earnings Profiles Steeper than Productivity Profiles? Evidence from

Israeli Firm-Level Data," Journal of Human Resources, pp. 89-112.

20 For example, in their analysis of data on entry-level positions Barron, Black and Loewenstein find that a

10% increase in training is associated with a 1.5% increase in earnings, but a 3% increase in productivity.

See John M. Barron, Dan A. Black, and Mark A. Loewenstein (1989), "Job Matching and On-the-Job

Training," Journal of Labor Economics, 7, pp. 1-19. For early estimates of time-investment profiles when

the share of time devoted to on-the-job training is an unobserved explanatory variable, see James J.

Heckman (1975), "Estimates of a Human Capital Production Function Embedded in a Life-Cycle Model of

Labor Supply, " in Household Production and Consumption, Nestor E. Terleckyj, Ed., Studies in Income

and Wealth by the Conference for Research in Income and Wealth, volume 40, New York: NBER and

Columbia University, pp. 227-264.

21 A strong theoretical discussion of this point is provided in Alan S. Blinder and Yoram Weis (1976),

“Human Capital and Labor Supply: A Synthesis,” Journal of Political Economy, 84 (3), pp. 449-472.
14

both education and work experience remain important explanatory variables, for an analysis of

the contribution of changes in the composition of hours worked to productivity within a national

growth-accounting framework.

The labor composition index is the component of the BLS multifactor productivity series that is

intended to reflect changes in the skill composition of the work force. The overall approach is to

calculate multifactor productivity growth as the difference between the growth of output and a

weighted sum of inputs, assuming that the inputs are paid the value of their marginal products, on

average. Assuming that an aggregate production of the form ( ) Qt AtF Kt Lt = , exists and that

capital and labor services are paid the value of their marginal products, logarithmic derivatives of

the production function can be rearranged to obtain the following expression for multifactor

productivity growth, t

t

A

A .

22

,

t

t L

t

t K

t

t

t

t

L

L

s

K

K

s

Q

Q

A

A    = − −

where K L s ,s denote the shares of capital and labor respectively in national income.

The labor composition index decomposes growth in labor services, or t

t

L

L , into growth in hours

worked and changes in the skill-composition of the work force. Labor composition index weights

ω l are defined to be two-year averages of the labor cost shares of workers with characteristics l ,

as discussed below. Letting Hl,t denote hours worked by workers with characteristics l at time t ,

omitting subscripts from aggregated values, and omitting time subcripts for simplicity:

 

22 This exposition is generally well known. See, for example, Charles R. Hulten (2001), “Total Factor

Productivity: A Short Biography,” in New Developments in Productivity Analysis, edited by Charles R.

Hulten, Edwin R. Dean and Michael J. Harper, NBER and CRIW Studies in Income and Wealth, Vol. 63,

Chicago: University of Chicago Press, pp. 1-53.
15

H

H

C

C

H

H

H

H

H

H

H

H

H

H

H

H

H

H

L

L

n

n

n

n n

          + ≡ + 













 + + − 













 = + + = − 1

1

1

1

1

1 ω1 ω ω ω .

Thus 

H

H

L

L

C

C   ≡ − . Just as multifactor productivity growth rates are an estimate of the degree to

which labor productivity growth is attributable to shifts in the composition of physical assets

employed in production, the labor composition index is an estimate of the degree to which labor

productivity and multifactor productivity growth rates are attributable to shifts in the skillcomposition of total hours worked.

In practice the labor composition index is obtained by taking the exponent of the expression on

the right-hand side of the following equation.

ln ln [ (ln ln ) (ln ln )] [ ] ln ln . Ct − Ct−1 = ω1 H1,t − H1,t−1 ++ωn Hn,t − Hn,t−1 − Ht − Ht−1

A sequence of values for the “quality” of labor services employed in the market is calculated by

setting C0 = 1and “chaining” the following expression:

exp{[ (ln ln ) (ln ln )] [ ] ln ln .}. Ct = Ct−1 ω1 H1,t − H1,t−1 ++ω n Hn,t − Hn,t−1 − Ht − Ht−1

BLS uses a conventional human capital wage equation to estimate conditional mean wage rates,

ˆ , wl,t which are used in turn to calculate labor cost share weights for persons with characteristics

l at time t :
16





































+





















=

∑ ∑∀ − − −

− −

∀ , 1 , 1 , 1

, 1 , 1

, , ,

, , , ˆ

ˆ

ˆ

ˆ

2

1

j t j t j t

l t l t

j t j t j t

l t l t l t w H

w H

w H

w H

ω .

The conditional mean wage rates wl,t ˆ are calculated as follows:

l t s t l t e t l t d t l t r t l t w s e d r , 0 , , . , , , , , ˆ ˆ ˆ ˆ ˆ ˆ = β + β + β + β + β .

In this last equation an overbar denotes a within-cell weighted sample mean. Categorical

variables (dummy variables) for the number of years of school completed are represented as l t s , ,

and l t e , represents years of actual work experience for persons with characteristics l . The

demographic variables in the vector l t d , include ever-married status, Black or ethnicity, and fulltime/part-time status. Categorical variables in the vector l t r, include region and a city size

variable.

Because the distinction between actual experience and potential experience may be an important

one, especially in the case of women, BLS enters a proxy for actual experience as an explanatory

variable in the wage equation. In light of the cohort-specific increases in the labor force

participation and actual work experience of women that have been well-documented by Goldin,

actual work experience data from successive panels of nationally representative surveys are to be

preferred to the current proxy, which is based on a single CPS cross-section matched to Social

Security administrative records.23

 

23 Econometric procedures that might be implemented with the SSA data, to compensate for selection bias

due to relatively low Social Security coverage rates at the inception of the program and time-varying

ceilings on recorded earnings, are discussed in Honig and Hanoch (1985), cited above. However the

simplifying assumptions that would be required to adjust for both of these limitations simultaneously are

non-trivial. Also see Goldin (1989), cited above.
17

Measurement of Hours Worked

With respect to the measurement of growth in hours worked, the SIPP has two noteworthy

strengths. First, SIPP calendar year estimates are based on information collected during three or

four separate interviews. Questions on the number of hours usually worked per week, and on the

number of weeks worked in each month of the 4-month reference period, are asked for each of

two jobs, and for each of two types of self-employment. In contrast, the generality of the

questions on hours worked in the previous calendar year in the annual supplement to the March

CPS suggests that hours worked on intermittent jobs and second jobs may be excluded from the

associated CPS estimates of total hours worked. Thus the coverage and accuracy of SIPP

estimates of the number of hours worked in a calendar year dominate may estimates based on the

annual March supplement to the CPS.24

Second, results from a PSID validation study, originally conducted to gauge respondent recall

error, suggest that respondent recall errors in periodic questions on the number of weekly hours

usually worked may tend to be serially independent. Consequently the measurement error

contained in calendar year estimates from several SIPP interviews repeated during the course of a

single calendar year may be relatively small. These points are developed in more detail below.

 

24 The sample size of the CPS is substantially larger than that of the SIPP, but the discrepancy between the

two has been diminishing in recent years.
18

March Current Population Survey (CPS) Questions on Hours Worked

The hours growth rates currently used to construct the labor composition index are estimated

from answers to questions included in the annual March supplement to the CPS. Specifically, the

total number of hours that the respondent worked during the preceding calendar year is estimated

as A× B, where A and B are responses to the following questions.

A: During 19.. in how many weeks did ... work even for a few hours? Include paid

vacations and sick leave as work.

B: In the weeks that ... worked how many hours did ... usually work per week?

These questions are clearly designed to characterize the general labor force behavior of the work

force; they are less than ideal as a source of information about the number of hours worked in an

accounting sense.25

A separate question on the total number of jobs held has been asked of one-fourth of CPS

respondents since 1994.26 Analysis of averages for calendar year 1995, based on these data,

reveals that roughly 6% of the employed held a second, as well as a primary job. Given the very

 

25 During her tenure as a BLS Research Fellow Nancy Mathiowetz, a faculty member in the Joint Program

in Survey Methodology, worked on a project to improve the reliability of March CPS-based estimates of

the number of hours worked during the previous calendar year. She focused on the development of a

separate set of survey questions for respondents whose employment is intermittent or otherwise nonstandard.

26 A similar question was asked periodically in the May survey, over the period 1970-1992. See John F.

Stintson, Jr. (1997), “New data on multiple jobholding available from the CPS,” Monthly Labor Review,

March issue, p. 7.
19

general nature of the questions listed above, it seems seem likely that information on the number

of hours worked on intermittent second jobs will be omitted from the product of A and B.

Similarly the industry and occupation codes assigned to respondents who were employed during

the previous calendar year are based on the longest-held job. Especially in the case of persons

who are employed part-time it seems clear that hours worked on the longest-held job do not

necessarily correspond to the industry and occupation in which the largest number of hours were

worked.27

The CPS data indicate that the probability of holding a secondary job is largely constant across

quintiles of weekly earnings on the primary job: 6.4 percent of respondents in the lowest quintile

of weekly earnings (($0-210) and 5.9 percent of respondents in the highest quintile ($731 and

over) were multiple jobholders.28 However, multiple jobholding rates increase with education,

from approximately 3.3 percent among persons with less than a high school diploma to 9.4

percent among persons with a PhD.

The occupational categories that accounted for the largest numbers of multiple jobholders, and for

the highest multiple jobholding rates, are reported in the following Table. Industries with high

multiple jobholding rates include services (7.6%), especially educational services (10.2%), and

public administrators (9.2%), especially justice, public order and safety workers (11.9%).

 

27 For evidence that women’s career paths incorporate a high degree of occupational mobility, see Mary

Corcoran, Greg J. Duncan, and Michael Ponze (1983), "Work Experience and Wage Growth of Women

Workers," in Five Thousand American Families - Patterns of Economic Progress: Analyses of the First

Thirteen Years of the Panel Study of Income Dynamics, Greg J. Duncan and James N. Morgan, Eds., Ann

Arbor: Institute for Social Research, University of Michigan, pp. 249-323.

28 Thomas Amirault (1997), “Characteristics of multiple jobholders,” Monthly Labor Review, March issue,

pp. 9-15.
20

Occupations of Multiple Jobholders

Largest numbers of multiple jobholders Rate Largest rates of multiple jobholding Rate

Salesworkers, retail & personal services 6.1 Firefighting occupations 28.1

Supervisors & proprietors, sales occup’s 5.1 Physicians assistants 23.4

Mechanics & repairers 5.1 Announcers 19.3

Motor vehicle operators 5.4 Artists, performers, & related n.e.c. 16.0

Construction trades 4.2 Psychologists 15.6

Secretaries 6.0 Therapists 14.5

Writers, artists, entertainers, & athletes 9.3 Dental hygenists 14.4

Registered nurses 9.6 Teachers, college & university 14.1

Teachers, secondary school 13.3 Teachers, secondary school 13.3

Teachers, elementary school 8.8 Musicians & composers 13.0

Source: Amirault (1997), “Characteristics,” Monthly Labor Review, Tables 4 and 5.

Cross-tabulations of occupational and industry categories of the primary and secondary job show

that secondary jobs are distributed across all categories, although a secondary job in the same

industry or occupation as the primary job is observed most frequently. In particular, 53% of

persons whose primary job is in the professional specialty occupations also hold secondary jobs

in professional specialty occupations. Similarly 45 percent of secondary jobholders whose

primary job was in services except private household services also held secondary jobs in

services. Among workers in sales who also held secondary jobs, 23% held secondary jobs in

sales.

Among secondary jobholders whose primary job was in occupations that may be characterized by

cyclical employment variability, a substantial fraction of secondary jobs were also in occupations

that include a significant cyclical component. In particular, the table below shows that roughly

half of workers whose primary jobs were in sales, administrative support, or blue-collar work also

held secondary jobs one of in the occupational categories also listed in the table. This pattern

suggests that a significant percentage of secondary jobs may be demand-driven, with employment

and hours worked on both the primary job and the secondary job increasing during cyclical

upswings, and decreasing in downturns. To the extent that this is so, and to the extent that hours
21

worked on these secondary jobs are omitted from the survey questions listed above, cyclical

variability in hours worked may be underestimated in the CPS.

Percentage of secondary jobs in an occupation listed

below, by occupation in primary job.

Occupation in the primary job: %

Sales 49.4

Administrative support 58.2

Precision production, craft and repair 51.5

Machine operators, assemblers, and inspectors 43.7

Transportation and material moving 48.8

Handlers, equipment handlers, and laborers 49.5

Source: Amirault, “Characteristics,” Table 7.

Prior Research on Respondent Recall Error

Results from validation studies, in which respondents’ answers to questions about their earnings

and hours worked are compared with establishment payroll records or Social Security

Administration unemployment insurance records have been summarized by Rodgers, Brown and

Duncan, and by Bound and Kreuger.29 Particularly noteworthy is a comparison of household

survey responses matched to payroll data from a large manufacturing establishment in which

Rodgers, Brown and Duncan found that respondent recall errors in periodic questions on the

number of weekly hours worked were serially uncorrelated. This result suggests that overall

estimates of hours worked obtained by aggregating responses to these periodic questions were

unbiased and, by extension, that respondent recall errors in successive SIPP interviews may also

be uncorrelated, and largely offsetting, in calendar-year estimates of total hours worked.30

 

29 Willard L. Rodgers, Charles Brown and Greg J. Duncan (1993), “Errors in Survey Reports of Earnings,

Hours Worked, and Hourly Wages,” Journal of the American Statistical Association, 88(424), December,

pp. 1208-1218; and John Bound and Alan B. Kreuger (1991), “The Extent of Measurement Error in

Longitudinal Earnings Data: Do Two Wrongs Make a Right?” Journal of Labor Economics, 9 (1), pp. 1-

24.

30 However, Rodgers, Brown and Duncan found that contemporaneous respondent recall errors for

questions on earnings and hours worked, asked within a single interview, were significantly positively
22

Measurement of Actual of Work Experience

The data examined during the initial stage of the project were extracted from longitudinal and

topical module files of the 1984 Panel of the SIPP, and matched to administrative record data

from the Social Security Administration (SSA). First, the frequency with which discrepancies

arise between work experience estimates obtained with the two independent data sources

examined (SIPP and SSA), the magnitudes of these discrepancies, and institutional reasons for

them were assessed. These comparisons help to establish the integrity of the SIPP and the SSA

data, and to establish a quantitative relationship between SIPP- and SSA-based work experience

measures that can be used to evaluate differences in magnitudes in the associated experience and

wage equation parameter estimates.

Some discrepancies between the SIPP and the SSA work experience variables are expected

because the SIPP identifies spells of employment whose duration is six months or longer, while

the SSA records "quarters of coverage," i.e., quarters in which the individual has been paid at

least $50 in covered wages.31 Years of employment reported in the SIPP would be a subset of

years of employment recorded in the SSA records if the SSA covered all employment. But both

 correlated. Like Bound and Kreuger, they found that respondent recall errors on earnings questions were

significantly positively correlated in successive interviews.

31 The full definition of the term "quarters of coverage" prior to 1978 was as follows.

Calendar quarters in which a worker is paid $50 or more in wages for employment covered 

under the law (except wages for agricultural labor) or calendar quarters deemed to be quarters of 

coverage because the worker (1) was paid the maximum annual taxable earnings in a calendar 

year, (2) had self-employment net earnings of at least $400 in a taxable year, or (3) met the 

condition, defined by law, for acquiring a quarter of coverage through agricultural labor.

Social Security Bulletin, Annual Statistical Supplement, 1974, p. 12. Beginning in 1978, a quarter of

coverage was credited for each $250 of annual earnings, with fractions truncated and earnings measured in

1978 dollars. Thus the SSA and SIPP data for the period 1978-1984 are less directly comparable than they

are for earlier years. I thank Howard Iams for clarifying this point.
23

compulsory and elective coverage of employment by SSA has expanded significantly since SSA

legislation was first enacted.

Neither the SSA nor the SIPP record industry of prior employment, so it is not possible to

identify specific records of persons whose prior work history includes spells of employment not

covered by SSA. Judging from population coverage rates, however, it is expected that roughly

two-fifths of the early experience reported in the 1984 SIPP panel by persons who began working

during or before the 1940s is not included in the SSA administrative record data, although

virtually all of the employment in calendar year 1984 should be confirmed in SSA records.32

This institutional shift in SSA coverage imparts a systematic downward bias to SSA-based

estimates of work experience among mature workers. In fact SSA estimates of total work

experience are smaller than SIPP estimates of total work experience in some cases, even though

the SIPP collects little information on short, discrete spells of employment that occurred during

the early stages of respondents’ careers.

The SIPP work experience variables are subject to error because the recall period is the

respondent's entire working life to date.33 SSA data cannot be used to evaluate the degree of

 

32 For example, in 1940 roughly 58% of all employed persons were covered by SSA, but by 1974 the

coverage rate had increased to 90%. In 1980 the SSA coverage rate was 97%, and by 1985 it was 99%.

Social Security Bulletin, Annual Statistical Supplement, 1974, Table 33, and Social Security Bulletin,

Annual Statistical Supplement, 1991, Table 3.B.

33 The likelihood that retrospective recall data on work experience may suffer from substantial

measurement error was stressed by Greg J. Duncan during a helpful phone conversation in the beginning

stages of this project. See Greg J. Duncan and Daniel H. Hill (1985), "An Investigation of the Extent and

Consequences of Measurement Error," cited above; Nancy Mathiowetz and Greg Duncan (1988). "Out of

Work, Out of Mind: Response Errors in Retrospective Reports of Unemployment." Journal of Business

and Economic Statistics, pp. 221-229; John Bound, Charles Brown, Greg J. Duncan and Willard Rodgers

(1989), "Measurement Error in Cross-Sectional and Longitudinal Labor Market Surveys: Results From

Two Validation Studies," NBER Working Paper No. 2884; Robert Topel (1991), "Specific Capital,

Mobility, and Wages: Wages Rise with Job Seniority," Journal of Political Economy, 99, pp. 145-176; and

James N. Brown and Audrey Light (1992), "Interpreting Panel Data on Job Tenure," cited above. Note,

however, that the design of the SIPP and the design of the PSID are not directly comparable with respect to
24

respondent recall error in the SIPP with very much precision, because non-coverage of some

industries and occupations, and shifts in coverage over time, mean that the incidence and duration

of some true employment spells is not recorded in SSA records. Nonetheless the sensitivity of

experience and wage equation parameter estimates to the use of alternative work experience

measures can be examined, and the published time series of coverage rates provide a rough gauge

of the incidence of annual discrepancies attributable to changes in SSA coverage for the

population as a whole. Therefore a "broad brush" evaluation of the two variables is possible.

Internal discrepancies between alternative work experience measures within each data source

were calculated, to provide a quantitative frame of reference within which to evaluate

discrepancies that arise between the two independent data sources.34 In the case of the SIPP,

responses to a direct question on the total number of years worked since the first job lasting 6

months or longer were compared to estimates that have been constructed by summing the

durations of all distinct employment spells reported.35 In the case of the SSA, counts of years in

which individuals worked 2 or more quarters were compared to counts of years in which the level

of real annual earnings suggests that the respondent worked at least six months during the year.36

 

the collection of work experience data, and that Brown and Light find substantially less inconsistency in

National Longitudinal Survey (NLS) work experience data than they do in the PSID.

34 These comparisons of alternative measures from a single source also assist in debugging computer

programs.

35 A maximum of 6 discontinuous employment spells could be reported in responses to work history

questions in the 1984 Panel of the SIPP .

36 The focus on employment spells of 2 quarters or longer increases the comparability of SIPP and SSA

measures of work experience. In prior work by OPT with a file in which microdata from the 1973 March

CPS are matched to SSA administrative records, work experience is measured as the sum of all recorded

quarters of employment.
25

Detailed Description of SIPP Work Experience Measures

Detailed questions on the duration of employment spells within respondents’ recent and early

work experience and a question on the total number of years worked are asked in a single SIPP

interview.37 Consequently checks on the internal consistency of the SIPP work experience data

are limited to comparisons between the reported total number of years worked, on the one hand,

and the total number of years in employment that may be inferred from responses to detailed

early and recent work history questions, on the other hand.38 The full set of detailed early and

recent work history questions is asked only of currently or recently employed persons aged 21-65

who have not worked continuously since the first year in which they were employed for 6 months

or longer. Persons with no spells out of employment lasting 6 months or longer are only asked

the year in which they first worked 6 months or longer.

In the case of respondents between the ages of 21 and 65 who have not been employed

continuously since their first job, questions are asked about beginning and ending dates for the

first four employment spells lasting 6 months or longer.39 In the case of respondents aged 16 to

20 older who are currently employed, or who have been employed during the past two-year

period, information is only collected on the most recent job held. In the case of currently or

recently employed persons aged 21 or older who held their current job for less than 10 years,

information is also collected on the duration of a prior job.

 

37 Questions about the respondents’ current labor force status are also asked in each of 8 successive

interviews, but since these interviews span a relatively brief period of 32 months they cannot be used to

measure total work experience.

38 Presumably internal inconsistencies in the SIPP will be reduced significantly with the implementation of

computer-assisted interviewing procedures.
26

Distributions of years worked as reported directly by all respondents to the third topical module

of the 1984 SIPP, in which the work history data are collected, are shown in Table 1 below.

These distributions are reported separately by gender.40 The distributions on the left of each

table characterize all respondents, including persons who have been unemployed or out of the

labor force for more than a year. The distributions on the right are for persons who are currently

employed.41 The availability of work experience variables for persons aged 16-21 is reported

separately because, as noted above, the summary question on the total number of years worked

and the detailed early work history questions are not asked of persons in this age group. Young

adults are asked only about the duration of a current and prior job.

As expected, Table 1 shows that a larger fraction of the female population has fewer than 10

years of work experience, compared with men. A larger fraction of the male population has

worked for longer than twenty years. It is interesting to note that roughly a fifth of the population

of each gender reported 10 to 20 years of work experience in 1984. But the essential point for

current purposes is that work experience data are available for SIPP sample observations that

represent all but 1-2% of the employed population.

Detailed work history data are pertinent for the estimation of earnings and experience equations

in which tenure on the current job is a separate explanatory variable from previously accumulated

 39 These questions do not distinguish among jobs that may have been held sequentially or concurrently

within a single spell of employment.

40 Total work experience has been calculated for persons who report having been employed continuously,

because a separate question on the total number of years worked is not asked of these respondents.

41 To maintain comparability with frequency distributions from the screened SSA series, 6050 observations

(unweighted) on persons who report a total work experience of less than 6 months in the SIPP are counted

as having 0 work experience. The vast majority (93%) of these observations are from persons who are not

currently employed; 74% are from women over the age of 65. Among the employed, 58% of the 405

observations on persons who worked less than one year are from women.
27

work experience.42 The availability of detailed data on the duration of employment spells is

shown in Table 2, where total work experience estimates have been obtained by summing the

employment spells of persons who have not been employed continuously since the first job that

lasted 6 months or longer. Table 2 shows that SIPP work history data permit a distinction

between tenure on the current job (or the last job held) and prior work experience for

approximately 83% of the total population, and for 89% of the currently employed.43

 

42 Mincer and Jovanovic find tenure on the current job to be a significant separate explanatory variable in

wage equations for men, and indicate that this variable may capture returns to job-specific human capital.

In work with the PSID, Abraham and Farber find evidence of small but significant returns to seniority

among white collar males, and returns to seniority that are insignificantly different from zero in the case of

blue collar males. Topel also finds positive returns to seniority, as noted above.

Marshall and Zarkin note that the duration of tenure is a function of the difference between the observed

offered wage growth rate and the unobserved reservation wage growth rate of the employee, and estimate a

two-step switching model to control for selectivity bias attributable to under-representation of persons for

whom the offer wage after a trial period of employment is lower than the reservation wage of the employee

after the trial period of employment. They find that tenure on the current job is not a significant

explanatory variable in the context of their switching model, although it is significant when they follow the

estimation procedures employed by Jovanovic and Mincer. However, Marshall and Zarkin work

exclusively with data from young men, for whom accumulated stocks of specific human capital may be

small. I am unaware of other research in which these results have been sustained in applications with

microdata from older men. See Jacob Mincer and Boyan Jovanovic (1981), "Labor Mobility and Wages,"

in Studies in Labor Markets, S. Rosen, Ed., Chicago: University of Chicago Press; Katherine G. Abraham

and Henry S. Farber (1987), "Job Duration, Seniority, and Earnings," American Economic Review, pp.

278-297; Marshall and Zarkin (1987)," The Effect of Job Tenure on Wage Offers," cited above; and

Robert Topel (1991), "Specific Capital, Mobility, and Wages: Wages Rise with Job Seniority," cited

above.

43 More specifically, detailed work history data are available for observations representing about 28% of

the overall population, and about 27% of the currently employed. Persons representing an additional 44%

of the total population, and 61% of the currently employed, report that they were employed continuously

since the first year that they held a job that lasted 6 months or more. In addition 11% of the observations

from the overall population, and 1% of the observations from the employed, are from persons aged 65 or

older. Information on the current job, or on the job held most recently, is available for the latter two

categories of observations (the continuously employed and persons over the age of 65), although a detailed

work history is not. Among the remaining observations, those from persons aged 16-21 account for about

10% of the population, and 9% of the employed. Information on the duration of the current or most recent

job held by these younger persons is available, but the total number of years worked is not.
28

Distributions of discrepancies of more than three years between reported and counted spells of

work experience, both recorded in the SIPP, are reported separately by gender in Table 3.44 Both

measures of total work experience are available in the SIPP only for observations represented by

the first three rows of each panel. Detailed work history questions are not asked of persons who

report having been employed continuously since their first job, nor are they asked of persons aged

65 or older. As noted above, early work history questions are not asked of young adults.

However the findings reported in Table 3 indicate that discrepancies between the two work

experience measures are small for the vast majority of observations on prime-aged employed

persons (persons between the ages of 16 and 65) who report some discontinuous employment. As

expected given the higher incidence of spells out of the labor force among women, discrepancies

occur among a larger fraction of records for women than they do among records for men.45

Cell means for total reported work experience, for which distributions are given in Table 1, have

been compared with cell means for total calculated work experience, for which distributions are

given in Table 2. The latter variable is obtained by summing the number of years spanned by the

separate employment spells that are recorded in respondents’ detailed work histories. Genderspecific averages within 5-year age intervals, not reported here, are consistently larger for

calculated total work experience than for total reported work experience, and these discrepancies

increase in absolute value with age. Among respondents whose entire work history is not covered

in the detailed work history questions, the average duration of the period during which detailed

information is not available also increases with age. These patterns suggest that some spells out

 

44 Discrepancies of three years are less may be attributable to the fact that specific months in which

employment spells began or ended are not recorded. For example, an employment spell that began in

January of 1970 and ended in December of 1971 is likely to be counted as two years in the reported

number of years worked, but would only contribute one year to the counted number of years worked.

45 Cases in which the counted number of years worked is not available are generally ones in which the

sequence of employment spells contains inconsistencies. The specific problem that arises is usually an
29

of employment of significant duration are omitted from the detailed work history questions of

some respondents, and that the measurement error associated with these omissions is correlated

with age and potential experience. Therefore the direct response to the total work history

question is taken to be the preferred measure of total work experience in the SIPP.

Detailed Description of SSA Work Experience Measures

The SSA records examined consist of all sample observations from the SIPP work history topical

module that could be matched to SSA administrative record data; a match was achieved for 92%

of the SIPP observations. The SSA data consist of individual annual time series data on

earnings, number of quarters of covered employment, number of quarters of covered selfemployment, and number of quarters of covered agricultural employment.46

The shares of SIPP observations from a given age group that could be matched to SSA

administrative record data are given in Table 4, along with the share of total observations

represented by each group. For example, records on persons aged 16-21 accounted for 14% of all

records in the SIPP 1984 Wave 3 file. Within that age group, 13% of the records were not

matched to SSA data, and 87% were matched.47 The shifts in the distributions of matched and

 inconsistent sequence of dates for the beginning and ending of a spell of employment. There were 39 such

cases among observations on women, and 6 among observations on men (unweighted).

46 Data on quarters in of employment extend back to 1937, when the Social Security program was begun.

Topcoded earnings data are avilable beginning in 1951. Self-employment data are available beginning in

1953, and agricultural employment data begin in 1955. To increase the comparability of these series with

the SIPP information on number of years worked 6 months or longer, the SSA employment series were

screened to include only observations in which two or more quarters of employment were reported. The

agricultural and self-employment fields flag whether or not this type of employment was reported during

the year but do not record the number of quarters worked beginning in 1978, when there was a shift in the

coding scheme. See footnote 31, above, for more detail on the 1978 coding changes.

47 A non-match does not necessarily mean that the SIPP respondent does not have a record at the Social

Security Administration. In some instances survey respondents refuse to provide the necessary matching

information in order to safeguard their privacy. In other instances the information provided by survey
30

non-matched records by cohort, shown in the remaining rows of Table 4, indicate that eventually

most people work in an industry that is covered by SSA. However, the true duration of total

employment cannot be determined from the SSA records for reasons given above, and illustrated

in the tables that follow.

The SSA data show a relatively smooth increase in the incidence of covered (i.e., recorded)

employment over time. In an attempt to increase the comparability between SSA data on the

number of quarters when a person earned at least $50 in wages, on the one hand, and SIPP data

on spells of employment lasting 6 months or longer on the other hand, information on the median

weekly earnings of part-time workers was used to calculate the annual earnings of respondents

who worked part-time for 25 weeks, and earned one-fourth of the median weekly earnings for

part-time workers of the respondent’s race and gender.48 Earnings below this level were

interpreted as evidence that the persons’ employment spell had been shorter than six months.

After implementation of this screen for short employment spells roughly 17% of all 1984 SIPP

respondents were found to have worked in jobs covered by SSA in 1955, while 42 of all 1984

SIPP respondents worked in jobs covered by SSA in 1984.49 This gradual increase reflects both

the current age composition of the 1984 SIPP panel and the change in SSA coverage rates noted

above. Many SIPP respondents had not yet reached working age by 1955; younger SIPP

respondents were not yet born. But the true percentage of 1984 SIPP respondents who were

 

respondents is insufficient to achieve a match. The second case often arises in the case of proxy

respondents, i.e., when one family member responds to the survey on behalf of another family member who

is absent.

48 This information is published in recent issues of Employment and Earnings ; topcoded earnings

information is available in the SSA records from 1951 on.

49 This sentence describes unweighted sample counts. Weighted and unweighted distributions of sample

observations are usually quite similar, because the sample is designed to be "self-weighting." I am grateful

to Raj Singh for a helpful discussion of the sample design of the 1984 SIPP panel. Recall that many young

and elderly SIPP respondents were not employed in 1984.
31

employed in 1955 is likely to be strictly greater than 17%, because the SSA coverage rate in 1955

was only 82%.50

The internal consistency checks undertaken with the SSA administrative data consist of

comparisons of the earnings and employment series on an annual basis. Overall the earnings and

employment series appear to be quite consistent. In no single year does the share of observations

with possible inconsistencies exceed 2%, and some of these discrepancies may be due to the

differential effects of the screens used to eliminate very short employment spells.51

Estimated distributions of SSA-based measures of total work experience after screening out

isolated employment spells of less than 2 quarters are shown in Table 5. Comparison with Table

1 reveals that the SSA distributions are shifted left on the experience axis, showing a greater

number of persons with lower levels of work experience and a smaller number of persons with

higher levels of work experience than the distribution obtained with the reported work experience

variable in the SIPP. Despite the fact that 8.3% of SIPP records are unmatched by SSA records

for the population as a whole, the SIPP work experience variable takes values between 1 and 40

years for approximately 128,694 thousand persons, while for the SSA variable the corresponding

estimate is 140,741 thousand persons. In contrast, the SIPP experience variable takes values

higher than 40 among an estimated 9,334 thousand persons, while for the SSA variable the

corresponding estimate is 3,783 thousand persons.52 Discrepancies between the SIPP and SSA

 

50 SSA coverage rates are provided in footnote 54 below.

51 These comparisons are made after the data are screened to eliminate very short spells of employment, as

described above: small earnings values are set to zero, and salaried employment of one quarter is set to

zero. Some of the discrepancies between SSA earnings and employment variables may be due to the

differential effects of the screens. The timing of observed discrepancies suggests that employment may be

recorded when an employer files an employee’s social security number with the SSA, while earnings may

be recorded only after a quarter of covered employment has been logged.

52 Notice that the SIPP estimate of the total number of persons with some work experience is 138,028

thousand persons, which is significantly lower than the corresponding SSA estimate of 144,524 thousand
32

work experience distributions are less pronounced when limited to employed persons, but

truncation of the upper tail of the SSA experience distribution remains evident.

Comparison of SIPP and SSA Work Experience Measures

The SIPP-SSA matched files allow for direct comparisons between survey data and

administrative record data with respect to the duration of total work experience. Summary data

on discrepancies between SIPP reported experience and SSA counted experience variables are

provided in Table 6, and graphed in Chart 5.

The relative frequency with which the SSA measure exceeds the SIPP measure is greater among

all observations than it is among the currently employed. This result appears to be attributable in

part to the fact that young persons, who account for a larger share of the population than they do

among the employed, are more likely to have worked in industries covered by SSA during their

entire working lives, because SSA coverage rates were relatively high during their entire working

lifetimes. Younger persons are also more likely to have held jobs whose duration was 6 months

or less during summer vacations from school, and these short-lived summer jobs would be

recorded in SSA administrative records, but not in the SIPP. Therefore young persons are likely

to have SSA total work experience values that are significantly greater than SIPP total work

experience values.

Decomposition of these results for cohorts of the employed, reported in Table 7, shows a

distinctly smaller share of observations in which the SIPP reported value exceed the SSA counted

 

persons. Presumably this result is due to the fact that the SSA records include a large number of short

employment spells that are not incorporated in the SIPP work history questions. See the discussion directly

below.
33

value among respondents younger than 50, and a distinctly increasing share in which the SIPP

reported value exceed the SSA counted value among respondents older than 50. This shift in the

sign of the discrepancy between work experience estimates corresponds to a substantial increase

in coverage, and especially voluntary coverage, that was instituted in 1955.53

Taken together these results suggest that the incidence of measurement error in the SIPP data is

higher than it is in the SSA records for observations on young persons, while for observations on

older persons the incidence of measurement error is higher in the SSA records than it is in the

SIPP data. However on-the-job training is likely to have been minimal for young persons aged

16-21 who were employed less than 6 months, and the duration of the current or most recent job

lasting 6 months or longer may be a reasonable proxy for total work experience at these young

ages. The magnitudes of measurement errors in the SSA records are likely to be greater than

they are in the SIPP, especially among mature respondents, because job tenure is positively

correlated with age and because SSA coverage was quite incomplete for 20 of the 50 years

spanned by Social Security. Therefore, on balance, these results indicate that the SIPP measures

of work experience dominate the measures obtained with administrative record data.

 

53 The share of civilian wage and salary disbursements covered by unemployment insurance increased to

81.5 percent in 1955, compared with 76.2 percent in 1954 and the previous all-time high of 77.1 percent in

1948. The large percentage increase in 1955 was caused by an expansion of coverage to include Federal,

State, and local employees except for those in the Armed Forces. See the Social Security Bulletin Annual

Statistical Supplement, 1974, Table 6, p. 42.

Persons who were 21 years old in 1955, at the time of this expansion in SSA coverage, were roughly 50

years old in 1984 when the SIPP survey data were collected. Therefore the cohort aged 50-55 in 1984 was

the first to be significantly affected, roughly from the beginning of their adult working life, by the 1955

expansion in SSA coverage.
34

Simulated Experience Profiles

The quantitative implications of these differences in sources of actual work experience data are

illustrated in simulated experience profiles for married women and men, generated with

alternative sets of experience equation parameter estimates.54 These experience profiles are

presented in Charts 6-17, where the mnemonic potexp denotes potential experience, pexpsipp

identifies simulated values based on an experience equation whose dependent variable is total

work experience reported in the 1984 SIPP, and pexpssa denotes simulated values based on the

1984 SSA work experience variable.55

Among persons with sixteen or more years of schooling, simulated profiles based on the 1984

SSA experience equation are flatter than those based on the 1984 SIPP experience equation.

There is clearly a systematic relationship between the downward bias of the SSA experience

estimates among older cohorts, shown in Table 7, and the flatness of the SSA experience profiles

shown in Charts 6-17.

Intuitively, the experience profiles shown in Charts 6-17 are cross-section "snapshot"

characterizations of the relationship between age and total actual work experience at a single

point in time; they do not describe the "representative individual" over the course of her or his

lifetime. Rather, these profiles show how rapidly average levels of work experience increase as

 

54 The specification of the experience equation follows that given in BLS Bulletin 2426, cited above.

55 Experience profiles were also generated with coefficient estimates based on the entire SIPP sample,

including persons who are currently employed in industries that are dominated by non-profit corporations,

and persons who are currently unemployed or out of the labor force. These sample selection criteria are

more comparable to those employed by Heckman. The resulting profiles are not reported here because the

object of the exercise was to benchmark against results from OPT’s current methodology. See Heckman

(1980), "Sample Selection Bias," cited above, especially the notes to Tables 5.1 and 5.2.
35

one moves along the age distribution of persons who were employed in 1984.56 Actual SSA work

experience values recorded for older persons are below their true values because SSA coverage

rates were relatively low during the early years of the program, and the experience profiles

estimated from these data are correspondingly flatter.

Overally, experience profiles based on the 1984 SIPP and the 1984 SIPP-SSA experience

equation parameter estimates are similar, but profiles based on the 1984 SSA data appear to be

slightly flatter than their SIPP counterparts in most cases.57 Experience profiles based on lessrestrictive sample selection criteria and the SIPP work history variable, not displayed here,

exhibit greater curvature; this result reflects lower employment rates among younger and older

respondents in the population as a whole. Discrepancies between SIPP- and SSA-based profiles

are greatest among highly-educated workers. Especially among women, it seems possible that in

the 1930’s and 1940’s highly-educated employees were employed by non-profit organizations

that were not covered by SSA legislation.

Predicted work experience values for older females based on the less-restrictive sample selection

criteria mentioned above, not reported here, are substantially lower than those displayed there,

where the sample is restricted to persons currently employed in for-profit industries. This result

reflects the relatively weak lifetime labor force attachment of many older women. Declines in

 

56 Helpful illustrations of the distinction between cross-sectional and longitudinal profiles are provided in

Figures 1, 1A, 2 and 2A, and in the text that discusses these Figures, found in Ruggles and Ruggles (1977),

"Anatomy of Earnings," cited above. The cross-sectional age-earnings profiles in their Figures 1 and 1A

display much greater curvature than the average longitudinal age-cohort earnings profiles in their Figures 2

and 2A.

57 Note that discrepancies between SIPP- and SSA-based profiles are greatest among highly-educated

workers. Especially among women, it seems possible that highly-educated employees were likely to have

been employed by non-profit organizations that were not covered by the early SSA legislation.
36

predicted work experience based on the less-restrictive sample are much smaller for older men, as

is consistent with their stronger labor force attachment.

Estimation of Wage Rates

Recall Chart 3, in which it is shown that average wage rates and the probability of employment

will vary with shifts in the wage offer distribution. Sample selection bias may arise when the

probability of employment is a function of the individual’s characteristics, and wage equations are

estimated without taking account of the probability that an individual with a given set of

characteristics will be employed.58 Wage equations that do not take account of the probability of

employment generate predicted wage rates reasonably well in the neighborhood of sample means,

but may be relatively inaccurate for observations that are far from observed mean values.59

Accuracy across the full distribution of observed wage rates is particularly desirable for the

construction of the labor composition index, because it requires predicted wage rates for cells that

span a broad range of values of potential experience and schooling. The implications of the

coefficients obtained for predicted wage rate values across the entire age distribution of workers,

when wage equations have been estimated with work experience proxies based on different

sources of work experience data, and after incorporating a selection bias correction factor, is

illustrated with a number of charts of wage rate profiles.

 

58 Early research in this area includes that of Cain and Watts, Gronau, Heckman, and Lewis. Heckman’s

work has been especially influential. See Glen C. Cain and Harold W. Watts (1973), "Toward a Summary

and Synthesis of the Evidence," in Income Maintenance and Labor Supply, Cain and Watts, Eds., New

York: Academic Press, pp. 328-367; Ruben Gronau (1973), "The Intrafamily Allocation of Time: The

Value of Housewives’ Time," American Economic Review, 63(4), pp. 634-651; and (1974), "Wage

Comparisons - A Selectivity Bias," Journal of Political Economy, 82(6), pp. 1119-1145; H. Greg Lewis

(1974), "Comments on Selectivity Biases in Wage Comparisons," Journal of Political Economy, 82(6), pp.

1145-1155; and James J. Heckman (1974), "Shadow Prices, Market Wages, and Labor Supply,"

Econometrica, 42 (4), pp. 679-694.

59 See, for example, Smith (1980), "Introduction," cited above, and Marshall and Zarkin (1987), "Effect of

Job Tenure," cited above.
37

Selection Bias Correction

Work with the SIPP to incorporate a selection bias correction factor into the specification of the

OPT wage equation, in order to take account of the probability of employment, follows the twostage procedure of Heckman. In the first stage an additive selection-bias correction factor, λ , is

derived from the predicted values of a probit equation whose dependent variable is the probability

of being unemployed or out of the labor force. The probability of not being employed is modeled

as a function of the determinants of offered and reservation wage rates. In the second stage of

the procedure λ is entered as an additional explanatory variable in a wage equation.60 Data on all

sample observations are brought to bear in the estimation of λ , while the wage equation is

estimated only with observations on persons who were employed in 1984, for whom 1984 wage

data are available.61

The specification of the labor supply equation estimated for this project follows one developed by

Heckman for the analysis of data on married women. In Heckman’s model the probability of

employment is represented as a function of the number of children less than 6 years old, the value

of assets, the hourly wage rate of the husband, the number of years of labor market experience of

 

60 For details on the estimation procedure see James Heckman (1980), "Sample Selection Bias as a

Specification Error," in Female Labor Supply: Theory and Estimation, James P. Smith, Editor, Princeton,

Princeton University Press, pp. 206-248; and the description of "Procedure V" in Killingsworth’s 1983

taxonomy, "Second-generation studies of static models," cited above; and the attached Appendix.

61 In earlier empirical work it was argued that estimation of a wage equation with data on employed

persons alone would yield parameter estimates that were representative for employed persons, though not

for the population as a whole. However this view no longer appears to be sustained in the literature. For

example, in discussing predicted values of wage rates that have been estimated with an equation that

incorporates a selection bias correction factor, Killingsworth notes that a predicted value obtained by

setting the coefficient for λ equal to zero "is an unbiased estimate of the wage that someone with

characteristics Xi in the population can earn, on average," while a predicted value that incorporates an

estimated value for λ and its coefficient "is an unbiased estimate of the wage that a worker with

characteristics Xi earns." Killingsworth (1983), "Second-generation studies," cited above.
38

the wife, and the wife’s education level.62 The specification examined here is essentially similar,

except that the ratio of income from assets to the level of income below which a family is

officially in poverty is substituted for the asset variable to adjust for variations in household size,

a dummy variable is entered for persons who report a disability, a spline function in age is

incorporated to allow the relationship to change for observations on elderly people, the wage of

the spouse is given a zero value for persons living in single-headed households, and actual work

experience 5 years prior to the survey is entered in place of actual or estimated current work

experience.63 

Table 14 presents results from initial probit equations whose dependent variable is the probability

that the respondent is not employed. These equations are estimated by separately by gender. All

explanatory variables are clearly significant, for both men and women. Notice that the signs of

the estimated coefficients of these equations are opposite for males and females in the case of the

intercept, the number of children younger than 6, and the wage of the spouse. And the magnitude

of the coefficient associated with the normalized asset income variable is larger for females than

for males. All other estimated coefficients have the same sign, and comparable magnitudes, for

both genders.

 

62 Heckman reports probit estimates for two versions of this specification. In the first, actual work

experience is treated as an exogenous variable. In the second, a predicted experience is entered as a proxy

for actual work experience. Heckman reports in Table 5.1 of his paper that the following variables were

used to predict total work experience: "linear and squared terms for children less than six, 1967 assets,

husband’s age, husband’s education, husband’s hourly wage, wife’s education, and interactions of all linear

terms." OPT’s current experience equation is similar to Heckman’s specification, except that variables that

characterize the productivity of the spouse are omitted, potential experience is included, and the functional

form is a quadratic in potential experience, rather than a quadratic in number of children.

63 The use of lagged work experience as an explanatory variable in order to avoid simultaneous equations

bias is motivated by the distinction between within-sample and pre-sample experience that is made in

Heckman’s 1981 paper, "Heterogeneity and State Dependence," in Studies in Labor Markets, Sherwin

Rosen, Ed., Chicago: University of Chicago Press, pp. 91-139. I am indebted to Jeff Zabel for this

reference. The econometric motivation for this approach is discussed, for the more general case, in

Phoebus J. Dhrymes (1981), Distributed Lags: Problems of Estimation and Formulation. New York:

North-Holland.
39

Predicted values of λ , generated with estimated coefficients of the probit equations reported in

Table 14, were used to generate a selection bias correction factor that is entered in the current

OPT wage equation. Columns 3 and 5 of Table 15 present the resulting wage equation parameter

estimates. In both the male and female wage equations, the t-statistic reported for the coefficient

of λ is significant at the 5% level, providing an initial indication that the adjustment factor should

be incorporated into the specification of the wage equation.64 For both genders estimated

standard errors of wage equation residuals are slightly smaller when the selection bias correction

factor is included.65

One plausible explanation of the improved fit obtained with the selection-bias-corrected

procedure is that it takes account of systematic determinants of the likelihood that a young

woman is specializing in household production. The probit equation parameter estimates

reported in Table 14 show that the presence of young children and the wage rate of the spouse are

both negatively related to the converse of the probability of employment among men, and

therefore positively related to the probability of employment. In contrast, the presence of young

children and the wage rate of the spouse are positively related to the converse of the probability

of employment among women, and thus negatively related to the probability of employment.

 

64 As noted by Heckman, this t-statistic is biased upward unless the residuals of the labor supply and

wage equations are uncorrelated. The formulas to be implemented in the calculation of unbiased t-statistics

are given in Appendix A of Heckman (1980), "Estimation of Labor Supply Functions," cited above.

65 Specifically, the estimated standard deviations of residual values for the wage equation for females,

with and without the adjustment, are 36.8720217 and 37.1452819 respectively. For males, the estimated

standard deviations of adjusted and unadjusted equation residuals are 40.3670767 and 40.7318284

respectively. These differences may be too small to be statistically significant. Formal tests of the null

hypothesis that the true coefficient for lambda is equal to zero are described in Phoebus J.Dhrymes (1986),

"Limited Dependent Variables," Handbook of Econometrics, Vol. III, Z. Griliches and M. Intrilligator,

Eds., Amsterdam: North-Holland.
40

There is a positive relationship between λ and the coefficients of the probit labor supply

equation, and λ enters both wage equations with a positive coefficient. Therefore the presence of

young children and the wage rate of the spouse are both negatively related to wage rates observed

among men, but positively related to wage rates observed among women. This result is

consistent with theoretical models in which the reservation wage of young women who specialize

in household production is higher than the reservation wage of young women who do not, all else

equal, and positively correlated with the wage rate of the spouse.66 It is also consistent with

models in which employers do not invest in training for women whose labor force attachment is

expected to be weak.

Allowing the point at which the schooling/wage rate profile intersects the wage rate axis to vary

with the characteristics that are included in the labor force participation equation, through the

introduction of λ , appears to permit a slightly slower increase in wage rates with increased

schooling. That is, the schooling coefficients reported in Table 15 for the equations that include

λ in their specification imply slightly smaller returns to schooling than those implied by

coefficients of the equation estimated without λ .

67 However, under the assumptions of the

former model the coefficients in the selection-bias-corrected equation are normalized by the

variance of the labor supply equation, and therefore the two sets of parameter estimates are not

directly comparable.

 

66 These models are presented in the articles by Gronau and Becker, cited above.

67 The differences between the coefficients of the categorical schooling variables in equations that include

or exclude the selection bias correction factor are almost always less than two estimated standard errors

apart. Conventional F tests of the joint null that the schooling coefficients of the adjusted and unadjusted

wage equations are equal to one another fail to reject the null. As noted in footnote 68, standard errors

estimated with OLS may be biased downward when the selection correction is appropriate for the true

model, and conventional F statistics are subject to the same limitation.
41

Simulated Wage Rate Profiles

Wage rate profiles based on the estimated parameters of wage equations, in which the predicted

values described above serve as proxies for actual work experience, have also been generated

with microdata from the SIPP. Illustrative profiles based on these wage equation parameter

estimates are provided in Charts 18-29. In these charts the mnemonic wagepot refers to profiles

based on wage equations in which potential experience is used as a proxy for actual work

experience. The mnemonic wagesip identifies profiles estimated with coefficients from an

equation in which predicted SIPP work experience in 1984 is used as a proxy for actual work

experience, wagessa identifies the equation in which predicted SSA work experience is a proxy

for actual work experience, and wageold identifies the equation in which predicted experience is

estimated with experience equation coefficients previously estimated with 1973 CPS-SSA linked

microdata.

The wage rate profiles in which the work experience proxy is based on coefficient estimates from

the 1973 CPS-SSA matched files exhibit fairly dramatic differences from the wage rate profiles

estimated with contemporaneous work experience data, for all cases examined. These results may

be telling something of a cautionary tale with respect to the unwarranted imposition of

simplifying structural restrictions and/or the assumption that the experience equation coefficients

are stable over time, since these wage rate profiles are clearly too steep at younger and older age

ranges. Assuming that this problem also arises in other years, as it seems likely to do, these

results indicate that the current labor composition index may assign weights that are too large to

hours growth rates associated with younger and older workers, and weights that are too small for

hours growth rates that are associated with prime-aged workers.
42

The incorporation of λ into the wage equation has a quantitatively smaller, but economically

interesting effect on the estimated wage rate profiles (not shown here). It raises the wage rate

profiles for men very slightly, and results in a distinct downward shift of the wage rate profiles of

women, particularly among highly educated women. Viewed independently from the adjusted

wage profiles for men, the fact that this downward shift is observed in profiles of both married

and unmarried women, with and without children, might indicate that it reflects respondents’

decisions to forego earnings during the early stages of their working life in order to invest in

education, since persons who complete sixteen or more years of schooling are generally enrolled

in school at ages 16-22.

However, highly-educated young men are also still in school during the early stages of their

working lives, and in this case incorporation of λ has a much smaller effect. Since the signs of all

other coefficients in the female and male labor force participation equations are identical, except

for the coefficients associated with the intercept, the number of young children and the wage rate

of the spouse, and the magnitudes of the same-signed coefficients are usually comparable, the

differential effects of young children and the wage rate of the spouse on the gap between the

market-oriented human capital accumulated by young men and young women appear to be the

forces that underly the differential effects of λ in these wage rate profiles.

While the results that pertain to the specification and estimation of the labor supply equations that

are used to estimate λ lend themselves to straightforward economic interpretation, further

research is indicated to evaluate their robustness and statistical significance. In particular, an

alternative specification of the labor supply equation that takes more detailed account of

household composition and household stability might yield a better-fitting wage equation. Some

of this work has been undertaken during the second stage of the project, which focuses on the

estimation of extended specifications of the labor supply and wage equations with successive
43

overlapping panels of the SIPP that span the 10-year period 1984-1993. In view of the new and

challenging structure of the SIPP, it is particularly encouraging to report that the achievement and

duplication of sensible empirical results has been straightforward in both stages of this research.

Conclusion

The SIPP is the only large, nationally-representative longitudinal survey for the U.S. that collects

work history data on all household members of working age or older on an annual basis. Direct

comparison of SIPP work experience data with administrative record data from the SSA reveals

that the effect of incomplete SSA coverage at the inception of the Social Security program in

1937 is a leftward shift of the distribution of total work experience as measured in 1984, and a

truncation of the distribution of the1984 SSA work experience measure at higher values.

Consequently, 1984 experience profiles estimated with SSA data are flatter than experience

profiles estimated with data from the 1984 SIPP. Work experience parameter estimates based on

the 1984 SSA work experience measure are, nonetheless, remarkably comparable to those

obtained with the 1984 SIPP reported experience variable. It is expected that discrepancies

between the SIPP and SSA work experience variables are smaller for more recent years, because

SSA coverage rates have been higher throughout most current workers’ lifetimes.

Under the assumption that the discrepancies between wage profiles generated with the 1973 CPSSSA matched files and those generated with contemporaneous work experience data are sustained

in other years, the results reported in this paper indicate that the labor composition index obtained

with the 1973 one-time match to administrative record data may overstate the rate of growth of

hours worked by older workers, and understate the rate of growth of hours worked by younger
44

workers.68 However the surprisingly strong performance of simulated wage rate profiles in which

potential experience replaces actual work experience as an explanatory variable, while schooling

variables are entered separately, may indicate that cohort effects also play an important role in the

determination of observed wage rates.69

For all cases examined here, female wage rate profiles that incorporate a selection bias correction

for the probability of not working are lower than profiles that omit this adjustment. One

plausible reason for the latter result is the greater likelihood that young women, rather than young

men, will specialize in household production when there are young children in the household and

the wage of the male spouse is relatively high. Within the framework of a behavioral model

similar to those of Heckman, these variables appear to be important determinants of differences

between the acceptance wages of, and the wage offers received by, young women and young

men.

In summary, these results indicate that, subject to further research into the stability of selected

experience and wage equations over time, the use of micro data from the SIPP could strengthen

BLS labor composition indexes that span the recent past. Additional, related research on the

stability of these structural relationships over time might also may help to evaluate the likelihood

that CPS-SSA and SIPP-SSA matched files could be brought to bear systematically to revise

labor composition indexes prior to 1984.

 

68 Further work with the SIPP that will help evaluate this possibility is currently ongoing.

69 Willis notes his conviction that "...an important and promising area of future research lies in the further

exploration of the general equilibrium interaction of the supply and demand for human capital which has

begun with the recent studies of cohort size effects," in Robert J. Willis (1986), "Wage Determinants: A

Survey and Reinterpretation of Human Capital Earnings Functions," Chapter 10 in the Handbook of Labor

Economics, Volume I, Ashenfelter and Layard, Eds, Amsterdam: North-Holland, pp. 525-602.
45

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Tables
55

 TABLE 1

 SIPP Reported Years Worked

 Weighted Estimates, In Thousands

 Population Employed

Years of Experience Females

1 to 5 13,598 14.4 7,402 14.8

> 5 to 10 15,535 16.5 10,955 21.9

> 10 to 20 18,125 19.2 14,221 28.4

> 20 to 30 7,650 8.1 6,135 12.3

> 30 to 40 4,379 4.6 3,343 6.7

> 40 to 50 1,939 2.1 1,543 3.1

> 50 529 0.5 413 0.8

age < 16 1,426 1.5 6 0.0

age 16-21, no wrk 3,181 3.4 6 0.0

age 16-21, w wrk 6,174 6.6 4,864 9.7

never worked 5,546 5.9 0 0.0

zero, niu 16,150 17.1 1,104 2.2

total 94,233 100.0 49,996 100.0

Years of Experience Males

1 to 5 7,070 8.3 6,011 9.5

> 5 to 10 9,987 11.7 9,168 14.5

> 10 to 20 18,510 21.6 17,375 27.6

> 20 to 30 11,116 13.0 10,097 16.0

> 30 to 40 9,815 11.5 8,559 13.6

> 40 to 50 5,395 6.3 4,384 7.0

> 50 1,471 1.7 1,070 1.7

age < 16 1,576 1.8 11 0.0

age 16-21, no wrk 2,592 3.0 5 0.0

age 16-21, w wrk 6,735 7.9 5,522 8.8

never worked 599 0.7 0 0.0

zero, niu 10,810 12.6 846 1.3

total 85,677 100.0 63,049 100.0

Individual entries may not sum to totals, due to rounding.
56

 TABLE 2

 SIPP Calculated Years Worked

 Weighted Estimates, In Thousands

 All Observations Employed

Years of Experience Females

1 to 5 8,486 9.0 2,844 5.7

> 5 to 10 8,973 9.5 4,815 9.6

> 10 to 20 10,645 11.3 7,200 14.4

> 20 to 30 4,666 5.0 3,389 6.8

> 30 to 40 2,388 2.5 1,573 3.1

> 40 to 50 453 0.5 322 0.6

> 50 44 0.0 18 0.0

age < 16 1,426 1.5 6 0.0

age 16-21, no wrk 3,181 3.4 6 0.0

age 16-21, w wrk 6,141 6.5 4,853 9.7

age ge 65 13,494 14.3 745 1.5

continuous emp. 26,854 28.5 23,312 46.6

never worked 3,307 3.5 0 0.0

zero, niu 4,029 4.3 894 1.8

dk, other 131 0.1 2 0.0

total 94,233 100.0 49,996 100.0

Years of Experience Males

1 to 5 2,433 2.8 1,706 2.7

> 5 to 10 2,499 2.9 1,938 3.1

> 10 to 20 3,958 4.6 3,198 5.1

> 20 to 30 2,232 2.6 1,509 2.4

> 30 to 40 2,360 2.8 1,459 2.3

> 40 to 50 859 1.0 474 0.8

> 50 142 0.2 6 0.0

age < 16 1,576 1.8 11 0.0

age 16-21, no wrk 2,592 3.0 5 0.0

age 16-21, w wrk 6,718 7.8 5,522 8.8

age ge 65 6,489 7.6 510 0.8

continuous emp. 51,430 60.0 46,036 73.0

never worked 554 0.6 8 0.0

zero, niu 1,820 2.1 660 1.0

dk, other 7 0.0 7 0.0

total 85,677 100.0 63,049 100.0

A person who is currently employed, but who has never worked on a job for a

spell of 6 months or longer, is coded as "never worked." In the preceding table a

single observation on an employed male who "never worked" has been recoded

to the category "zero, niu." Individual entries may not sum to totals, due to

rounding.
57

 TABLE 3

 SIPP Reported minus Calculated SIPP Employment Duration

Weighted Estimates, In Thousands

 All Observations Employed

Discrepancy Females

reported 4+ lt counted 3,731 3.97 2,020 4.04

reported = counted +/- 3 33,890 36.03 18,110 36.24

reported 4+ gt counted 1,962 2.09 805 1.61

continuous employment 26,850 28.55 23,310 46.65

age ge 65 11,110 11.87 729 1.46

age gt 16, lt 21 6,536 6.95 4,859 9.72

never worked 9,758 10.37 6 0.01

na 162 0.17 131 0.26

total 94,060 100.00 49,980 100.00

Discrepancy Males

reported 4+ lt counted 935 1.09 558 0.89

reported = counted +/- 3 14,680 17.13 9,978 15.83

reported 4+ gt counted 553 0.65 247 0.39

continuous employment 51,430 60.04 46,040 73.03

age ge 65 6,405 7.48 505 0.80

age gt 16, lt 21 7,173 8.37 5,527 8.77

never worked 4,312 5.03 11 0.02

na 177 0.21 173 0.27

total 85,660 100.00 63,030 100.00

Individual items may not sum to totals, due to rounding.
58

 TABLE 4

 Unmatched and Matched SIPP-SSA Records

 Distributions By Cohort, Unweighted

 Percentage by Cohort Cohort Share

 Age Unmatched Matched of Total

16-21 13.14 86.86 13.58

21-25 13.24 86.76 9.14

26-30 8.89 91.11 11.48

31-35 6.92 93.08 10.21

36-40 6.93 93.07 8.71

41-45 7.89 92.11 7.35

46-50 7.78 92.22 6.80

51-55 8.39 91.61 6.31

56-60 6.65 93.35 6.29

61-65 6.35 93.65 6.03

66-70 4.99 95.01 5.09

71+ 3.34 96.66 9.00
59

 TABLE 5

 SSA Counted Years Worked

Weighted Estimates, In Thousands

 All Observations Employed

Years of Experience Females

1 to 5 14,910 15.8 4,889 9.8

> 5 to 10 19,071 20.2 11,598 23.2

> 10 to 20 25,309 26.9 17,680 35.4

> 20 to 30 10,382 11.0 6,593 13.2

> 30 to 40 3,402 3.6 2,052 4.1

> 40 to 50 387 0.4 262 0.5

> 50 0 0.0 0 0.0

age < 16 1,361 1.4 6 0.0

age < 16, nonmtch 64 0.1 0 0.3

age 16-21, w wrk 8,986 9.5 4,724 9.4

age 16-21, nonmtch 369 0.4 147 0.3

age > 21, nonmtch 2,308 2.4 1,301 2.6

niu 7,382 8.2 743 1.5

total 94,233 100.0 49,996 100.0

Years of Experience Males

1 to 5 4,626 5.0 2,677 4.2

> 5 to 10 11,531 13.5 9,596 15.2

> 10 to 20 21,538 25.1 18,519 29.4

> 20 to 30 16,214 18.9 12,477 19.8

> 30 to 40 13,758 16.1 9,440 15.0

> 40 to 50 3,396 4.0 2,129 3.4

> 50 0 0.0 0 0.0

age < 16 1,500 1.8 11 0.0

age < 16, nonmtch 76 0.1 0 0.0

age 16-21, w wrk 8,817 10.3 5,264 8.3

age 16-21, nonmtch 509 0.6 263 0.4

age > 21, nonmtch 2,682 3.1 2,156 3.4

niu 1,389 1.6 516 0.8

total 85,677 100.0 63,049 100.0

These calculations are based on SSA administrative record data, and include only

employment spells of 2 quarters or longer. Individual entries may not sum to totals,

due to rounding.
60

 TABLE 6

 SIPP and SSA Work Experience Measures

 Weighted Discrepancies Between Estimates, In Thousands

 All Observations Employed

Discrepancy Females

SSA 4+ > SIPP 22,425 23.8 10,859 21.7

Difference < 4 37,982 40.3 22,774 45.6

SIPP 4+ > SSA 15,367 16.3 10,185 20.4

Non-match 2,741 2.9 1,448 2.9

SIPP Vbl Na 15,718 16.7 4,730 9.5

Total 94,233 100.0 49,996 100.0

Discrepancy Males

SSA 4+ > SIPP 25,303 29.5 15,498 24.6

Difference < 4 31,881 37.2 28,304 44.9

SIPP 4+ > SSA 14,333 16.7 11,552 18.3

Non-match 3,267 3.8 2,419 3.8

SIPP Vbl Na 10,892 12.7 5,275 8.4

Total 85,677 100.0 63,049 100.0

The category "SIPP Vbl Na" includes all observations on persons younger

than 21, for whom work history data are either unavailable, or potentially

incomplete, and all observations on persons who have never worked. For

all observations, these two categories account for 6,431 observations, of

which 728 were not matched to SSA records. Among the employed these

categories account for 2,409 observations, including 333 observations that

were not matched to SSA records. Individual entries may not sum to totals,

due to rounding.
61

 TABLE 7

 SIPP and SSA Work Experience Estimates, Employed Persons

 Discrepancies by Cohort, Weighted Sample Observations

 Percentage by Cohort Cohort

 Age SIPP <

SSA

SIPP =

SSA

SIPP >

SSA

No Match Share of

Sample

 Females

 16-21 0.00 0.00 0.00 3.00 9.75

 21-25 26.67 66.31 3.50 3.52 15.13

 26-30 28.55 61.70 7.37 2.38 14.80

 31-35 28.37 55.86 13.28 2.49 13.09

 36-40 24.78 52.90 19.57 2.75 11.07

 41-45 23.76 48.11 24.62 3.52 9.63

 46-50 26.02 35.68 35.22 3.07 7.40

 51-55 18.70 35.86 42.42 3.01 6.80

 56-60 15.43 36.69 45.37 2.51 6.10

 61-65 11.02 26.00 60.01 2.96 3.80

 66-60 6.56 13.79 77.47 2.17 1.43

 71+ 6.49 8.07 83.46 1.98 0.99

 Age Males

 16-21 0.00 0.00 0.00 4.75 8.78

 21-25 33.06 58.42 3.61 4.91 13.68

 26-30 33.68 56.28 6.57 3.47 15.14

 31-35 33.50 52.34 11.45 2.71 13.18

 36-40 27.96 51.66 16.80 3.57 11.77

 41-45 29.87 49.68 16.14 4.40 9.15

 46-50 28.35 45.84 21.41 4.39 7.88

 51-55 13.00 47.63 35.97 3.41 7.12

 56-60 13.71 35.05 47.58 3.66 6.46

 61-65 9.22 29.79 57.59 3.41 4.14

 66-60 6.92 18.38 69.94 4.76 1.45

 71+ 0.00 7.07 92.30 0.63 1.25

Unweighted percentages of SIPP records that can be matched to SSA

administrative records for the age group 16-21 are 87.52% among women,

and 85.03% among men. Information on the duration of the current job, or

last job held, is available, but the SIPP total work experience variable is not

consistently available for persons in this age group. Therefore no comparison

is made in this Table. Shares may not sum to 100%, due to rounding.
62

 TABLE 8

 Distributions of Alternative Work Experience Measures, In Thousands

 Weighted Observations used to Estimate Experience Equation

 Proxy SIPP SSA

Experience Females

0 to 5 2,235 36.1 1,393 24.2 1,131 20.2

6 to 10 1,493 24.1 1,514 26.3 1,501 26.8

11 to 15 942 15.1 997 17.4 1,164 20.8

16 to 20 1,247 20.1 660 11.5 802 14.3

21 to 25 228 3.7 433 7.5 418 7.5

26 to 30 32 0.5 248 4.3 242 4.3

31 to 35 14 0.2 208 3.6 192 3.4

36 to 40 139 2.4 80 1.4

41 to 45 93 1.6 35 0.6

46 to 50 38 0.7 12 0.2

> 50 23 0.3 14 0.3

 total 6,191 5,746 5,591

missing 445 600

Experience Males

0 to 5 2,481 31.4 1,255 17.1 1,051 14.9

6 to 10 1,378 17.4 1,223 16.7 1,225 17.4

11 to 15 1,066 13.5 1,185 16.1 1,141 16.2

16 to 20 849 10.7 933 12.7 980 13.9

21 to 25 794 10.1 637 8.7 690 9.8

26 to 30 785 9.9 554 7.5 714 10.1

31 to 35 469 5.9 563 7.7 622 8.8

36 to 40 70 0.9 463 6.3 256 3.6

41 to 45 6 0.1 335 4.5 206 2.9

46 to 50 119 1.6 78 1.1

> 50 77 1.0 86 1.2

 total 7,898 7,344 7,049

missing 554 849

Individual entries may not sum to totals, due to rounding.
63

Tables 9A-9C: Mnemonics for Equations Estimated

Table 9A

Mnemonic Experience Equation, Variable Definitions

SIPPYR dependent variable, years of total work experience reported in the SIPP

SSAYR dependent variable, number of years with 2 or more quarters employment in SSA data

SSAQTR dependent variable, number of quarters of employment recorded in SSA data

potexp potential experience: age-years of schooling completed-6.

potexpsq squared potential experience

married 1 if ever married, 0 otherwise

pomar potexp*married

posqmar pomar squared

potkid1 1 if 1 child present in family, 0 otherwise

potkid23 1 if 2 or 3 children present in family, 0 otherwise

potkid4 1 if 4 or more children present in family, 0 otherwise

s0to4 1 if less than 4 years of schooling completed, 0 otherwise

s5to8 1 if 5 to 8 years of schooling completed, 0 otherwise

s12 1 if 12 years of schooling completed, 0 otherwise

s13to15 1 if 13 to 15 years of schooling completed, 0 otherwise

s16 1 if 16 years of schooling completed, 0 otherwise

s17up 1 if 17 or more years of schooling completed, 0 otherwise

pos0to4 s0to4*potexp

pos5to8 s5to8*potexp

pos12 s12*potexp

pos13to15 s13to15*potexp

pos16 s16*potexp

pos17up s17up*potexp

potexpnp potexp if currently employed in an industry dominated by nonprofits, 0 otherwise

prev 1 if year of first job was prior to 1937

black 1 if respondent is Black, 0 otherwise

hisp 1 if respondent is Hispanic, 0 otherwise
64

Table 9B

Mnemonic Wage Rate Equation, Variable Definitions

whhr dependent variable, annual earnings divided by annual hours worked

SIPPYR indicates experience proxy estimated with SIPP data on years worked

SSAYR indicates experience proxy estimated with SSA data on years worked 2+ quarters

SSAQTR indicates experience proxy estimated with SSA data on total quarters worked

OLDSSA indicates experience proxy estimated with data from 1973 CPS-SSA matched file

POTEXP indicates experienc proxy is estimated as age-years of school completed-6

s0to4 1 if less than 4 years of schooling completed, 0 otherwise

s9to11 1 if 5 to 8 years of schooling completed, 0 otherwise

s12 1 if 12 years of schooling completed, 0 otherwise

s13to15 1 if 13 to 15 years of schooling completed, 0 otherwise

s16 1 if 16 years of schooling completed, 0 otherwise

s17up 1 if 17 or more years of schooling completed, 0 otherwise

experience proxy for actual work experience

expsq squared proxy for actual work experience

black 1 if respondent is Black, 0 otherwise

hisp 1 if respondent is Hispanic, 0 otherwise

ptime4 1 if usual hours worked when employed is less than 35, 0 otherwise

vet 1 if served in armed forces, 0 otherwise

city 1 if a resident of a central city within an SMSA

neweng 1 if a resident of the New England region, 0 otherwise

midatl 1 if a resident of the Mid Atlantic region, 0 otherwise

encent 1 if a resident of the East North Central region, 0 otherwise

wncent 1 if a resident of the West North Central region, 0 otherwise

satl 1 if a resident of the South Atlantic region, 0 otherwise

escent 1 if a resident of the East South Central region, 0 otherwise

wscent 1 if a resident of the West South Central region, 0 otherwise

mountain 1 if a resident of the Mountain region, 0 otherwise

λ selection bias correction factor

Table 9C

 Labor Supply Equation, Variable Definitions

working dependent variable, 0 if wage rate can be calculated for observation, 1 otherwise**

young number of children 6 years old or younger present in family

assypov income from financial assets during 1984, divided by family’s poverty cutoff level

othwage wage rate of spouse if present, 0 otherwise

last5 years of work experience accumulated 5 years prior to interview

retire years of work experience - 20 if difference is positive, 0 otherwise

schoolyr years of schooling completed

splold age - 65 if difference is positive, 0 otherwise

notwell 1 if a physical, mental or other health condition limits work, 0 otherwise

** SAS estimates the probability that the event with the lowest coded value occurs
65

 TABLE 9

 1984 Experience Equation Parameter Estimates

 Specific Source of Experience Data Shown in Column Headings

 Women Men

variable SIPPYR SSAYR SSAQTR SIPPYR SSAYR SSAQTR

R2=.58 R 2=.52 R 2=0.61 R 2=.88 R 2=.74 R 2=.82

F=301.90 F=231.24 F=363.73 F=2763.6 F=1022.1 F=1918.5

intercept -1.151** 0.832* -2.8509* -1.274** 0.553 -1.4073

potexp 0.977** 0.949** 3.6722** 1.071** 1.124** 4.1425**

potexpsq -0.002 -0.004** -0.0126** -0.004** -0.007** -0.0209**

married 0.47700 0.703* 2.7757**

pomar -0.050 -0.058 -0.4507**

posqmar -0.005** -0.004** -0.0006

potkid1 -0.089** -0.060** -0.1771**

potkid23 -0.143** -0.111** -0.4639**

potkid4 -0.198** -0.201** -0.8435**

s0to4 -5.200* -2.240 -8.0759 -1.229 -1.976 -1.8783

s5to8 -4.072** -2.157* -6.7611 -2.187** -3.789** -12.5838**

s12 1.783** 1.864** 9.0017** 1.309** 2.238** 6.9897**

s13to15 3.151** 4.087** 16.7200** 2.533** 3.523** 11.2252**

s16 3.884** 5.432** 21.2730** 3.142** 5.048** 15.8537**

s17up 4.292** 6.074** 25.5769** 4.014** 7.302** 20.6890**

pos0to4 0.139 -0.047 -0.1723 -0.096** -0.144** -0.6162**

pos5to8 0.104** 0.025 -0.0965 0.020** 0.024 0.0641

pos12 -0.011 -0.007 -0.1505** 0.011 -0.055** -0.0767

pos13to15 -0.040 -0.108** -0.4464** 0.026* -0.057** -0.0468

pos16 -0.067** -0.193** -0.8095** 0.011 -0.064** -0.1002

pos17up -0.011 -0.236** -1.0269** -0.018 -0.280** -0.5227**

potexpnp -0.032** -0.073** -0.2560** 0.005 -0.134** -0.4637**

prev 0.995** 0.179* -0.2639 1.519** -0.107* -0.5313**

black -0.084 -0.260 -0.9638 -1.874** -1.700** -4.4481**

hisp -0.834** -0.962** -4.5128** -1.162** -2.422** -9.8445**

** denotes significance at the 5% level,* denotes significance at the 10% level.

 Mnemonics are defined in Appendix A. Specification and sample selection criteria are

comparable to current OPT procedures.
66

 TABLE 10

 1984 Wage Equation Parameter Estimates

 Specific Source of Experience Data Shown in Column Headings

 Women

variable SIPPYR SSAYR SSAQTR OLDSSA POTEXP

R2=0.26 R2=0.27 R2=0.24 R2=0.26 R2=0.27

F=88.83 F=89.47 F=93.87 F=86.80 F=92.40

intercept 1.2564** 1.1024** 1.1386** 1.2561** 1.3121**

s0to4 -0.1353** -0.1100 0.0543 -0.1155* -0.1434**

s9to11 0.1120** 0.1180** 0.1130** 0.1081** 0.0715**

s12 0.2341** 0.2325** 0.2423** 0.2398** 0.2039**

s13to15 0.4056** 0.3964** 0.3914** 0.4352** 0.3950**

s16 0.5681** 0.5538** 0.5528** 0.5880** 0.5709**

s17up 0.6485** 0.6414** 0.6424** 0.6810** 0.6555**

exp 0.0362** 0.0521** 0.0135** 0.0278** 0.0278**

expsq -0.0008** -0.0012** -0.000090** -0.0000010** -0.0005**

black -0.0783** -0.0759** -0.0776** -0.0765** -0.0889**

hisp -0.0090 -0.0041 -0.0121 -0.0178 -0.0360**

ptime4 -0.2401** -0.2368** -0.1827** -0.2419** -0.2400**

city 0.0410** 0.0414** 0.0473** 0.0395** 0.0473**

neweng 0.0325 0.0313* 0.0532** 0.0337 0.0358

midatl 0.0318* 0.0318* 0.0461** 0.0319* 0.0366**

encent -0.0363** -0.0356** -0.0207 -0.0361** -0.0342**

wncent -0.0654** -0.0638** -0.0434* -0.0637** -0.0585**

satl -0.0687** -0.0688** -0.0460** -0.0671** -0.0631**

escent -0.1330** -0.1316** -0.1282** -0.1293** -0.1292**

wscent -0.0476** -0.0495** -0.0281 -0.0462 -0.0469**

mountain -0.0513 -0.0491 -0.05980* -0.0508 -0.0454

 ** denotes significance at the 5% level,* denotes significance at the 10% level.

 Mnemonics are defined in Appendix A. Specification and sample selection criteria are

comparable to current OPT procedures. Selection bias corrected results are in Table 15.
67

 TABLE 10

 Continued

 1984 Wage Equation Parameter Estimates

 Specific Source of Experience Data Shown in Column Headings

 Men

variable SIPPYR SSAYR SSAQTR OLDSSA POTEXP

R2=0.40 R2=0.40 R2=0.39 R2=0.40 R2=0.40

F=209.13 F=208.69 F=231.56 F=213.35 F=215.31

intercept 1.3108** 1.0821** 1.1687** 1.2514** 1.2973**

s0to4 -0.0682 -0.0776 -0.1387** -0.0713 -0.0717

s9to11 0.1917** 0.1906** 0.1501** 0.1775** 0.1651**

s12 0.3127** 0.2878** 0.2904** 0.2977** 0.3104**

s13to15 0.4220** 0.3938** 0.3824** 0.4299** 0.4508**

s16 0.6843** 0.6289** 0.6446** 0.6514** 0.7222**

s17up 0.7524** 0.7030** 0.7321** 0.7370** 0.8000**

exp 0.0477** 0.0698** 0.0183** 0.0446** 0.0517**

expsq -0.0007** -0.0012** -0.00009** -0.0000006** -0.0008**

black -0.2047** -0.2075** -0.1651** -0.2072** -0.2031**

hisp -0.0610** -0.0586** -0.0073** -0.0616** -0.0792**

ptime4 -0.3277** -0.3109** -0.1540** -0.3164** -0.3140**

vet 0.0065 0.0249* 0.0251** 0.0127 0.0073

city -0.0110 -0.0132 -0.0204* -0.0094 -0.0076

neweng 0.0162 0.0131 0.0016 0.0191 0.0166

midatl 0.0140 0.0173 -0.0069 0.0184 0.0216

encent -0.0218 -0.0171 -0.0373** -0.0196 -0.0177

wncent -0.0641** -0.0639** -0.0970** -0.0610** -0.0574**

satl -0.1065** -0.1076** -0.1198** -0.1112** -0.1088**

escent -0.1340** -0.1348** -0.1648** -0.1325** -0.1344**

wscent -0.0046 -0.0079 -0.0031 -0.0052 -0.0063

mountain -0.0043 -0.0071 -0.0556* -0.0068 -0.0064

prev -0.0120**

 ** denotes significance at the 5% level, * denotes significance at the 10% level.

 Mnemonics are defined in Appendix A. Specification and sample selection criteria are

comparable to current OPT procedures. Selection bias corrected results are in Table 15.
68

 TABLE 11

 F Statistics for the Null that 1984 Experience Equation Coefficients

Are Equal to those Estimated for 1973

 Specific Source of Experience Data Shown in Column Headings

 

 Women Men

variable SIPPYR SSAYR SSAQTR SIPPYR SSAYR SSAQTR

intercept 9.57** 0.34 8.47** 48.61** 0.07 15.07**

potexp 2.18 4.92** 11.20** 30.94** 40.63** 15.27**

potexpsq 24.85** 13.62** 147.78** 22.80** 9.58** 1011.52**

married 0.00 0.24 0.30

pomar 9.78** 11.47** 56.23**

posqmar 35.03** 35.56** 15.31**

potkid1 1.43 1.90 40.01**

potkid23 23.95** 68.99** 353.70**

potkid4 11.91** 14.24** 292.51**

s0to4 0.04 0.70 9.12** 8.50** 3.02* 14.60**

s5to8 0.85 0.25 15.12** 2.71* 0.56 100.20**

s12 1.97 3.05* 8.60** 0.14 4.31** 2.79*

s13to15 2.10 11.74** 18.38** 0.02 5.02** 0.99

s16 2.23 18.04** 22.13** 2.33 7.70** 0.95

s17up 0.05 6.83** 12.41** 0.02 30.70** 7.04**

pos0to4 0.96 1.29 1.87 4.80** 7.38** 18.18**

pos5to8 2.89* 0.09 3.25* 0.35 0.08 1.03

pos12 0.03 0.15 10.23** 2.54 7.48** 0.67

pos13to15 7.93** 0.05 128.34** 8.48** 4.23** 2.50

pos16 6.36** 1.88 189.68** 9.11** 0.74 12.97**

pos17up 28.99** 0.57 239.66** 21.17** 30.93** 122.03**

potexpnp 9.00** 0.13 52.48** 47.22** 8.77** 84.40**

prev 79.88** 3.29* 0.75 1063.39** 11.98** 10.13**

black 0.07 0.82 0.99 86.48** 40.94** 27.90**

hisp 3.88** 6.57** 11.44** 27.15** 67.89** 103.85**

all 27.10** 36.00** 8377.63** 270.70** 48.53** 4101.14**

** denotes rejection at the 5% significance level or lower,

 * denotes rejection at the 10% significance level or lower.

 Mnemonics are defined in Appendix A. Specification and sample selection criteria are

comparable to current OPT procedures. Selection bias corrected coefficients are reported in

Table 15.
69

 TABLE 12

 F Statistics for the Null that 1984 Wage Equation Coefficients

Estimated with 1984 SIPP Data Are Equal to those Estimated

 with 1973 SIPP-SSA Proxy and 1984 CPS Data

 Specific Source of Experience Data Shown in Column Headings

Females

 SIPPYR SSAYR SSAQTR 1973 Proxy

intercept 29.32** 1.17 4.70** 28.35**

s0to4 1.68 2.76* 17.10** 2.48

s9to11 0.66 1.00 0.77 0.49

s12 0.18 0.22 0.02 0.05

s13to15 0.18 0.02 0.00 1.84

s16 0.80 0.23 0.22 2.15

s17up 2.23 2.82* 1.43 0.43

exp 91.89** 1.66 3150.57** 308.76**

expsq 145.78** 9.86** 53993.4** 242185843**

ptime4 19.52** 17.30** 0.00 20.70**

city 31.09** 30.75** 29.89** 32.12**

neweng 3.36* 3.21* 7.88** 3.50**

midatl 18.87** 18.92** 29.60** 18.81**

encent 0.35 0.31 0.07 0.33

wncent 0.02 0.04 1.05 0.04

satl 3.28* 3.27* 9.95** 3.56**

escent 1.26 1.15 1.09 0.98

wscent 2.78* 2.51 7.14** 2.96**

mountain 0.02 0.01 0.15 0.02

vet

all 23.74** 10.61** 36140.6** 36860267**

 * denotes rejection at the 10% significance level or lower,

** denotes rejection at the 5% significance level or lower.

Mnemonics are defined in Appendix A. Specification and sample

selection criteria are comparable to current OPT procedures. Selection

bias corrected coefficients are reported in Table 15.
70

 TABLE 12

 Continued

 F Statistics for the Null that 1984 Wage Equation Coefficients

 Estimated with 1984 SIPP Data Are Equal to those Estimated

 with 1973 SIPP-SSA Proxy and 1984 CPS Data

 Specific Source of Experience Data Shown in Column Headings

Males

 SIPPYR SSAYR SSAQTR 1973 Proxy

intercept 63.82** 0.43 12.63** 35.98**

s0to4 2.13 1.64 0.02 1.98

s9to11 6.53** 6.39** 1.30 4.23**

s12 3.45** 0.75 1.11 1.61

s13to15 7.94** 3.11* 2.09 9.79**

s16 20.65** 6.93** 12.24** 11.75**

s17up 0.29 1.06 0.01 0.00

exp 545.05** 25.26** 14451.2** 849.32**

expsq 1054.6** 63.91** 2300736** 3.40947E9**

ptime4 52.78** 38.48** 11.91** 43.70**

city 31.67** 33.64** 48.22** 30.52**

neweng 6.68** 6.09** 5.18** 7.31**

midatl 14.13** 15.46** 8.46** 16.05**

encent 0.17 0.02 1.96 0.08

wncent 0.21 0.22 0.82 0.35

satl 0.06 0.10 1.05 0.25

escent 1.26 1.33 5.93 1.15

wscent 1.96 1.56 2.49 1.90

mountain 0.84 0.71 0.24 0.73

vet 5.22** 0.93 1.12 3.41*

all 92.48** 16.74** 170328.7** 929368400**

 * denotes rejection at the 10% significance level or lower

** denotes rejection at the 5% significance level or lower

 Mnemonics are defined in Appendix A. Specification and sample

selection criteria are comparable to current OPT procedures. Selection

bias corrected coefficients are reported in Table 15.
71

 TABLE 13

F Statistics for the Null that 1984 Experience and Wage Equation Coefficients

 Estimated with 1984 SIPP and1984 SSA Data are Equal

 Experience Equation Wage Equation

 Females Males Females Males

intercept 4.34** 11.72** intercept 10.75** 17.31**

potexp 0.12 3.53* s0to4 0.09 0.14

potexpsq 0.74 38.35** s9to11 0.52 0.03

married 0.02 s12 0.22 0.44

pomar 0.01 s13to15 0.04 0.54

posqmar 0.02 s16 0.01 1.78

potkid1 0.72 s17up 0.01 1.30

potkid23 3.43** exp 19.50** 45.81**

potkid4 0.32 expsq 15.63** 45.37**

s0to4 0.03 0.15 ptime4 0.02 0.45

s5to8 3.02** 2.99* city 0.00 0.01

s12 0.11 0.83 neweng 0.00 0.01

s13to15 1.01 1.04 midatl 0.01 0.01

s16 2.19 6.03** encent 0.00 0.02

s17up 3.20** 16.12** wncent 0.01 0.00

pos0to4 0.43 2.50 satl 0.00 0.01

pos5to8 5.00** 0.54 escent 0.00 0.00

pos12 0.05 2.97* wscent 0.00 0.01

pos13to15 2.15** 5.31* mountain 0.01 0.01

pos16 7.02** 4.14** vet 1.08

pos17up 16.48** 44.69** all 2.06** 5.00**

potexpnp 8.14** 19.74**

prev 38.66** 495.18**

black 0.31 1.21

hisp 0.05 14.74**

all 13.55 7.63**

** denotes significance at the 5% confidence level,

 * denotes significance at the 10% confidence level.

 Mnemonics are defined in Appendix A. Specification and sample selection criteria are comparable to

current OPT procedures. Selection bias corrected coefficients are reported in Table 15.
72

 TABLE 14

Preliminary Probit Equation Estimates of Probability of Not Working

 For Construction of Heckman-Style Correction Factor for Sample Truncation

Variable Women Men

log likelihood -47,357,733 -38,305,933

intercept 0.0934** -0.5293**

young 0.1571** -0.1620**

assypov 0.2253** 0.0854**

othwage 0.0162** -0.0117**

last5 -0.0531** -0.0235**

retire 0.0610** 0.0311**

schoolyr -0.0375** -0.0145**

splold 0.1885** 0.1815**

notwell 0.9404** 0.8848**

**denotes significance at the 5% level or lower.

 Mnemonics are defined in Appendix A.
73

 TABLE 15

 1984 SIPP Wage Equation with Selection Bias Correction

 Women Men

Variable No Adj. With Adj. No Adj. With Adj.

R2=0.27 R2=0.28 R2=0.31 R2=0.32

F=164.27 F=165.05 F=206.06 F=209.14

intercept 1.2523** 1.0676** 1.4368** 1.0758**

s0to4 0.0135 0.0234 -0.0486 -0.0205

s9to11 0.1419** 0.1142** 0.1833** 0.1425**

s12 0.2840** 0.2424** 0.3298** 0.2782**

s13to15 0.4185** 0.3217** 0.4279** 0.3456**

s16 0.5783** 0.4716** 0.6601** 0.5670**

s17up 0.6725** 0.5566** 0.7056** 0.6003**

experience 0.0332** 0.0315** 0.0307** 0.0275**

expsq -0.0009** -0.0010** -0.0004** -0.0003**

black -0.0701** -0.0713** -0.1097** -0.1102**

hisp -0.0168 -0.0191 0.0065 -0.0122

ptime4 -0.3471** -0.3184** -0.4085** -0.3637**

vet 0.0371** 0.0395**

city 0.0528** 0.0530** -0.0093 -0.0054

neweng 0.0642** 0.0537** -0.0013 0.0044

midatl 0.0644** 0.0591** 0.0175 0.0143

encent -0.0261* -0.0302* -0.0305* -0.0310*

wncent -0.0437** -0.0468** -0.0478** -0.0496**

satl -0.0456** -0.0478** -0.0743** -0.0707**

escent -0.1283** -0.1288** -0.1266** -0.1312**

wscent -0.0388* -0.0343* -0.0255 -0.0242

mountain -0.0308 -0.0303 -0.0434 -0.0360

λ 0.2233** 0.2964**

** denotes significance at the 5% confidence level,

* denotes significance at the 10% confidence level.

 Mnemonics are defined in Appendix A.

 The coefficients reported here differ somewhat from those reported in Table 10.

For the estimates reported in Table 10, actual experience is predicted with a

sample that is restricted to persons who were employed during the interview in

which work history data were collected, and this same sample selection criterion

was applied to the sample with which the wage equation is estimated. Persons

employed in industries that were not covered by Social Security legislation are

omitted from the estimating sample with which coefficients in Table 10 were

estimated, for comparability with OPT’s current procedures. For the coefficient

estimates reported in Table 14, the sample with which actual experience is

predicted includes all adults, the wage equation selection criterion is that the

person worked at least one hour during calendar year 1984, and persons

employed in industries dominated by non-profit corporations and domestic

services are not deleted from the estimating sample. The latter sample selection

criteria are consistent with the sample selection criteria used to estimate the total

number of hours worked during calendar year 1984.
74

Appendix A: Correction for Selection Bias in Wage Equation

A lucid explanation of the problem of selection bias is provided by Penceval; his discussion may be

summarized as follows.70 Consider a sub-population of N persons whose observed values for all

explanatory variables entered in a wage equation, represented by the matrix X, are identical. Let the

reservation wage be a linear function of X and a stochastic term, denoted e. Then the true reservation wage

rates for this sub-population is:

w* = a + X b + e.

Here w* is (Nx1), X is (NxG), and e is a (Nx1) random vector whose elements are assumed to be

distributed normally, with mean zero. Notice that e is the sole source of differences among the reservation

wages of the persons in this sub-population by assumption, and that the shape of the distribution of the

elements of w* around the mean reservation wage for this group is identical to the shape of the distribution

of e.

In general, all of the persons in this sub-population are not employed. Let the subscript i denotes an

individual observation. Assuming a zero unemployment rate for simplicity, those who do not participate in

the labor force are persons whose reservation wage wi

* is above the market wage rate, denoted w, that is

available to persons with characteristics X. Then if all observations in this group are from persons with the

same observable characteristics, so that Xi = X for all i, there is some value e* such that w is greater than

wi

* if and only if ei

 is less than e*. That is, person i is employed if and only if ei is less than e*

.

In this context OLS procedures violate standard assumptions under which OLS yields best linear unbiased

coefficient estimates. Since ei has a mean value of zero in the population as a whole, the mean value of ei

for persons who are employed is less than zero. It is not possible to remedy the situation by simply shifting

the intercept, since the residuals for the employed are not distributed symmetrically around their mean

value, as shown in Chart 3.

Furthermore, the vector X is not orthogonal to the residuals associated with observations on the employed.

To see why the orthogonality assumption is violated, assume that n of the N persons in the population are

employed.71 Let ee be the sub-vector of e that corresponds to these n persons, and let Xe be the

corresponding submatrix of X. Each (nx1)-element column vector xe of Xe consists of observations on a

single explanatory variable. For each column xe in Xe:

E(xe’ee) = E(xe’e1 + ... + xe’en)

 = Exe’(e1 + ... + en)

 = xe’nem,

where em=(1/n)(e1 + ... + en). The mean value of the elements of e among the employed, em, is strictly less

than zero. In general, therefore, E(Xe’ee) is unequal to zero.

These violations of the standard assumptions under which OLS coefficient estimates are best linear

unbiased estimates mean that wage equation parameter estimates based exclusively on data from the

 

70 John Penceval (1986), “Labor Supply of Men,” cited above.

71 Recall that orthogonality means E(X'e)=0.
75

employed will generally be biased and inconsistent. Of the two properties inconsistency is the most

important, because it means that the estimated parameters will not converge to the true parameters no

matter how large the sample size.

One approach to this problem that has been adopted in the literature involves modeling the labor supply

decision explicitly as a separate equation with its own stochastic process. In this formulation an equation

for the reservation wage and a labor supply equation may be determined simultaneously, or they may form

a recursive system, depending on whether or not the stochastic processes associated with the two equations

are correlated, and on whether or not both endogenous variables appear on the right-hand-sides of both

equations.

In the case in which the true model is recursive, which is to say that the two stochastic processes are

uncorrelated and one of the two endogenous variables does not appear on the right-hand-side of either

equation, estimation of the wage equation through OLS procedures yields unbiased and consistent,

although inefficient, parameter estimates. But in the case in which the true system is determined

simultaneously OLS procedures result in parameter estimates that are biased and inconsistent. Maximum

likelihood procedures are indicated in the second case.72

Although the exact estimation strategies differ in accordance with the model specified, the following

prototype, which follows the work of Heckman, is frequently adopted when the hypothesis of a recursive

structure is to be tested. To review Heckman’s approach, retain the simplifying assumption that the

individuals within the population examined are identical with respect to their observable characteristics.73

Let the wage rate received by employed persons be a function of the variables Z, and let the wage offer be

modeled as:

wi = a’ + Z d + ui.

Assume again that the distribution of wi is identical to the distribution of ui by virtue of the simplifying

assumption that Zi=Z for all i.

Assume that X and Z have some elements in common, and let the matrix of explanatory variables that enter

both equations be denoted C. Then X = (S,C) Z=(D,C), and the wage offer and reservation wage equations

respectively may be written:

wi = a’ + D dd + C dc + ui,

wi

* = a + S bs + C bc + ei

.

In this revised context person i will be employed and wi will be observed if and only if:

wi - wi

* = a’ - a + D dd - S bs + C (dc - bc) + vi ≥ 0,

where vi = ui - ei. Notice that the explanatory variables in the equation for wi are a subset of the

explanatory variables in the equation for wi - w*

i

. If the correlation between ui and vi is zero, then the

equations for wi and w*

i

 - wi form a simply recursive system.

 

72 A test of the null hypothesis that the system is recursive is described in Dhrymes (1986), "Limited

Dependent Variables," cited above.

73 This description of Heckman’s general approach follows Heckman (1974), "Shadow Prices, Market

Wages," cited above, and Killingsworth (1983), "Second-generation studies of static models," cited above.
76

In general, however, ui and vi will be correlated since, when ui and ei are distributed independently with

zero means,

Cov(ui,vi) = E(uivi) = E[ui(ui - ei)] = Var(ui)≠0.

Only if Var(ui) = Cov(eiui) will Cov(ui,vi) = E[ui(ui - ei)] = 0. If persons with higher-than-average values

of wi have higher-than-average values of w*

i

, for example, and the correlation between ui and ei

approaches the variance of ei, it may be the case that Cov(ui,vi) = 0. However one generally expects that

Var(ui) > Cov(ui,vi), in which case the possibility of selection bias should be addressed in estimation.

To appreciate the motivation for the estimation procedures that are employed to compensate for this form

of selection bias, notice that when wi - wi

* = 0,

 vi = -[ a’ - a + D dd - S bs + C (dc - bc)].

Let F represent the cumulative distribution function for vi. By substitution, the probability that person i

will be employed is:

pi = PR (wi > wi

*)

 = PR (vi < v*)

 = F[a - a’ - D dd + S bs - C (dc - bc)].

The corresponding probability distribution function for vi is f(v) = g(u,e)/ge(u), where g is the joint

probability distribution for u-e and ge is the marginal distribution of u, given e.

This last equation might be estimated by setting pi=1 for all employed persons and pi=0 for non

participants, and regressing pi on C, D and S. But OLS procedures will not necessarily generate an

estimate for pi that lies within the (0,1) interval, as is required for a probability distribution function. Some

econometricians adopt weighted least squares procedures to minimize this problem.74 Theil seems to find

weighted least-squares procedures to be acceptable when the cells for which weights are defined span

relatively small intervals.75 However other econometricians, including Pyndyck and Rotenberg, stress that

greater explanatory power is obtained through the use of maximum likelihood procedures.76

Maximum likelihood, i.e., probit and logit procedures, involve estimating an equation of the form:

F-1 (pi ) = a’ - a + D dd - S bs + C (dc - bc),

 

74 Goldberger, for example, has recommended an approach in which weights are constructed from expected

values of pi, originally calculated with OLS; this approach is less costly than maximum likelihood

estimation. However the costs of implementing maximum likelihood procedures have been reduced

significantly since 1964. See Arthur Goldberger (1964), Econometric Theory, New York: Wiley, p. 250

75 Henri Theil (1971), Principles of Econometrics, New York: Wiley, p. 633, and 1967, Sections 3.7-3.8

76 Robert S. Pindyck and Daniel L. Rubinfeld (1981), Econometric Models and Economic Forecasts,

Second Edition, New York: McGraw Hill, p. 310.
77

with nonlinear, iterative techniques.77 The resulting parameter estimates are used to calculate f(vi), the

conditional mean of ui for the truncated sample of observations on persons who are employed. Notice that

when the simplifying assumption that Z=Zi and X=Xi is abandoned the conditional expected values vary

across individuals, because vi is a function of Zi and Xi.

Consistent parameter estimates for the wage equation are subsequently obtained from the following

augmented equation:

wi = Zi d +g λ i ,

where the estimated value λ i

, a function of the predicted value for F-1(pi), is entered as an additional

explanatory variable in the augmented wage equation.

In the case of empirical models of male labor supply, procedures to control for selection bias often do not

result in parameter estimates that are very different from those obtained by OLS procedures, according to

Penceval.78 But some procedure to test for selection bias is usually adopted for applications involving the

labor supply of women, since the labor force participation rates of women remain much lower than those of

men.

 

77 This review of probit and logit estimation procedures follows Pindyck and Rubinfeld (1981), cited

above, and Killingsworth (1983), "Second-generation studies of static models," cited above.

78 John Penceval (1986), "Labor Supply of Men," cited above. A noteworthy exception to this statement is

provided in the more recent work of Marshall and Zarkin (1987), cited above, which was published after

Penceval’s essay.
78

Appendix B: SIPP Estimates of Earnings and Wage Rates in 1984

 Mean and Median Weekly Earnings

 1984 SIPP Sample Estimates

 median weekly earnings

full-time part-time

age males females males females

16to19 186.29 151.89 81.65 67.00

20to24 255.21 210.38 157.35 122.98

25to34 374.62 274.65 269.38 159.80

35to44 480.72 283.75 350.00 141.36

45to54 476.41 278.10 367.06 145.69

55to64 446.94 262.31 318.88 132.31

65+ 312.57 201.81 95.72 75.83

mean weekly earnings

full-time part-time

age males females males females

16to19 213.91 199.93 97.09 77.01

20to24 302.07 243.11 159.44 122.07

25to34 417.77 316.22 219.39 154.12

35to44 555.05 327.20 282.10 133.37

45to54 545.30 305.14 278.94 134.64

55to64 516.49 283.26 265.44 143.40

65+ 392.70 223.83 149.70 87.47
79

 Mean Hourly Wage Rates

 1984 SIPP Sample Estimate

mean wage rates

years of whites blacks

schooling age males females males females

11 16to19 3.42 3.01 3.14 3.27

20to24 5.05 3.84 3.43 3.56

25to34 6.94 4.47 4.38 4.49

35to44 8.95 4.81 6.94 4.03

45to54 8.55 5.27 7.20 4.04

55to64 9.50 5.07 7.61 4.83

65+ 5.87 4.28 ** **

12 16to19 3.88 3.58 4.41 3.43

20to24 5.94 4.53 4.22 4.21

25to34 8.07 5.74 6.25 5.19

35to44 10.51 6.36 8.21 6.23

45to54 10.80 6.17 8.32 5.61

55to64 10.46 6.18 7.04 5.74

65+ 8.04 4.61  

14 16to19 4.59 3.44 4.36 ** 

20to24 5.38 4.72 5.06 5.44

25to34 8.67 6.78 7.18 5.82

35to44 11.56 7.10 9.75 8.71

45to54 12.56 7.50 9.97 7.04

55to64 11.61 7.53 10.79 7.05

65+ 10.38 6.65 ** **

16 20to24 6.73 6.09 ** 5.87

25to34 10.35 8.60 9.68 7.00

35to44 14.25 8.10 11.69 8.26

45to54 17.76 9.16 ** 10.63

55to64 18.03 8.92  

65+ 9.57 5.88 **  **

17+ 20to24 7.15 5.39  

25to34 10.87 8.95 10.37 6.76

35to44 15.59 10.50 12.99 9.89

45to54 16.97 9.40 11.04 12.13

55to64 16.26 8.13 **  **

65+ 13.70 6.85  

**  identifies cells with fewer than 10 observations.

Note that SIPP sample sizes can be increased by pooling observations

from adjacent panels that span the same calendar year. Work with pooled

panels is planned for the next stage of the project.
80

Charts
81

U. S. Assets Per Employee

Private Business Assets, Billions of 1987 Dollars

All Employed Civilians Ages 16 and Over

0

0.02

0.04

0.06

0.08

0.1

53

56

59

62

65

68

71

74

77

80

83

86

89

92

year

capital labor ratio

Source: Divison of Productivty Research, BLS

Hours-Weighted Average Levels of Educational Attainment

Employed Worker s in the Private Business Sector

0

2

4

6

8

10

12

14

53

55

57

59

61

63

65

67

69

71

73

75

77

79

81

83

85

87

89

91

93

year

years of school completed

men

women

Source: Division of Productivity Research, BLS

Distributions of Real Wage Offers 

For a Given Catogory of Labor at Times t and t+1

Range of Offered Real Wage Rates

time t

time t+1

reservation wage
82

E stim ated E x perience P ro files

S ingle W o m e n, N o C hild ren, Sixteen Y ears o f Scho o ing

-20

-10

0

1 0

2 0

3 0

4 0

5 0

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp

Comparison of SIPP and SSA Measures of Total Work Experience 

0

5

10

15

20

25

30

35

40

45

50

SSA>SIPP Dif < 4 SIPP>SSA No Match SIPP NA

Chart 5

Estimated Percent

Males, All Persons 16+

Males, Employed Persons 16+
83

Estim ated E xperience Profiles

Single W om en, N o Children, N ine Y ears of Schooing

-10

0

1 0

2 0

3 0

4 0

5 0

6 0

1619222528313437404346495255586164

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp

E stim ated E xperience P rofiles

S ingle W om en, N o C hild ren, T w elve Y ears of Schoo ing

-10

0

1 0

2 0

3 0

4 0

5 0

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp

E stim ated W ork E xperience

S in g le W o m en , N o C hild ren , S ev en teen + Y ears o f S cho o lin g

-2 0

-1 0

0

1 0

2 0

3 0

4 0

5 0

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp
84

Estimated Experience Profiles

Single Women, Three Children, Nine Years of Schooing

-10

0

10

20

30

40

50

60

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp

Estimated Experience Profiles

Single Women, Three Children, Twelve Years of Schooing

-10

0

10

20

30

40

50

16182022242628303234363840424446485052545658606264

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp
85

Estimated Experience Profiles

Single Women, Three Children, Sixteen Years of Schooing

-20

-10

0

10

20

30

40

50

16182022242628303234363840424446485052545658606264

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp

Estimated Work Experience

Single Women, Three Children, Seventeen+ Years of Schooling

-20

-10

0

10

20

30

40

50

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

age

estimated number of years worked

potexp pexpsipp

pexpssa oldexp
86

E stim ated E xperience P rofiles

M en, N in e Y ears o f S cho o ling

-10

0

1 0

2 0

3 0

4 0

5 0

6 0

16

18

20

22

24

26

28

30

32

34

36

38

40

42

44

46

48

50

52

54

56

58

60

62

64

age

estimated number of years worked

potexp

pexpsipp

pexpssa

oldexp

E stim ated E xp erience P rofiles

M en, T w elve Y ears of S chooling

-10

0

1 0

2 0

3 0

4 0

5 0

16182022242628303234363840424446485052545658606264

age

estimated number of years worked

p otexp

pexpsipp

pexpssa

o ldexp
87

E stim ated E xp erien ce P ro files

M en , S ix teen Y ears o f Sch oo ling

-20

-10

0

1 0

2 0

3 0

4 0

5 0

16182022242628303234363840424446485052545658606264

age

estimated number of years worked

potexp

p exp sip p

pexpssa

o ld exp

Estimated Experience Profiles

Men, Seventeen+ Years of Schooling

-20

-10

0

10

20

30

40

50

16

19

22

25

28

31

34

37

40

43

46

49

52

55

58

61

64

age

estimated number of years worked

potexp

pexpsipp

pexpssa

oldexp
88

F em a le W ag e P ro files

SIPP and SSA D ata Sources

N e ver M arried , 9 Y ea rs of S ch o o lin g , 1 C h ild

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

16182022242628303234363840424446485052545658606264

age

wage rate

wagepot

w agesip

w agessa

w ageold

wagepot

Fem ale W age P rofile

SIP P and S SA D ata

N ev er M arried , H igh S cho o l D egree, 1 C hile

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

5 0

16182022242628303234363840424446485052545658606264

age

wage rate

wagepot

w agesip

w agessa

w ageold
89

Female Wage Rate Profile

SIPP and SSA Data

Never Married, 17+ Years of Schooling, No Children

0

10

20

30

40

50

60

16182022242628303234363840424446485052545658606264

age

wage rate

wagepot

wagesip

wagessa

wageold

F em ale W age R ate P rofile

S IP P and S S A D ata

E ver M arried , 1 7 + Y ears o f S cho o lin g, N o C h ild ren

0

5

1 0

1 5

2 0

2 5

3 0

3 5

4 0

4 5

5 0

16

18

20

22

24

26

28

30

3234

36

38

40

42

44

46

4850

52

54

56

58

60

6264

age

wage rate

wagepot

w agesip

wagessa

w ageold
90

Male Wage Rate Profile

SIPP and SSA Data

Never Married, 17+ Years of Schooling, No Children

0

10

20

30

40

50

60

70

80

90

100

16182022242628303234363840424446485052545658606264

age

wage rate

wagepot

wagesip

wagessa

wageold

Male Wage Rate Profile

SIPP and SSA Data

Ever Married, 17+ Years of Schooling, No Children

0

10

20

30

40

50

60

70

80

90

100

16182022242628303234363840424446485052545658606264

age

wage rate

wagepot

wagesip

wagessa

wageold