The effect interruptions in work experience have on wages.

• Author: Stratton, Leslie S.

1. Introduction

   Individuals who interrupt their employment are generally expected to pay a price in the workplace [15]. Most researchers acknowledge that wages rise more rapidly with time spent in paid employment than with time spent in other noneducational activities. Thus, the wages received by individuals reentering employment are expected to be below those obtained by similar individuals with continuous work records. How far below, is still a matter of debate.(1) One important line of research has focused upon the difference between pre-interruption and post-interruption wages. Specifically, do real wages rise, fall, or remain unchanged during periods of nonemployment?

   Two findings stand out from past research. First, a wide variety of studies employing a wide variety of data sets have found that wages fall during periods of nonemployment. The estimated decline ranges from 0.6% to over 5% annually, but averages around 2%. Second, this estimated depreciation rate is higher when the sample under investigation contains a greater proportion of recent reentrants. Thus, there is a pattern to the estimates.

   The purpose of this study is to investigate the observed sensitivity to sample composition. In the past such sample sensitivity has been attributed to a rapid depreciation followed by a rebound in wages that over time partially offsets the initial decline. If true, the depreciation rate will be underestimated, especially if the data include few recent reentrants. Two alternative hypotheses are tested in this study.

   The first relies upon certain assumptions regarding part-time employment patterns and wages. If part-time workers are paid less than full-time workers, and if part-time employment is more common following reentry because it is used as a bridge to full-time employment, then estimates of the depreciation rate will be biased upward particularly when obtained from samples of recent reentrants, if information on hours worked is ignored. There may, in fact, be no depreciation at all. Results indicate that while part-time workers are paid less than full-time workers, estimates of the depreciation rate remain substantially unchanged when wages in full-time and part-time jobs are allowed to differ.

   Alternatively, the sensitivity of the estimated depreciation rate to the sample composition may represent a specific sort of sample selection bias. If individuals who receive lower post-reentry wages are more likely to exit again than those who receive higher post-reentry wages, then samples containing fewer recent reentrants will naturally contain fewer individuals whose post-reentry wages were relatively low and vice versa. This hypothesis is tested by allowing the depreciation rate to be a simple function of post-reentry experience. The results confirm that the longer the spell of post-reentry experience, the lower the estimated depreciation rate.

   This paper is organized as follows. A brief review of the literature is presented in section II. In section III the data are introduced. The empirical specification and the sampling technique are discussed and applied in section IV. The alternative hypotheses for the sensitivity of the estimated depreciation rate to the sample composition are developed in section V and tested in section VI. Section VII concludes.

2. Literature Review

   Numerous studies have attempted to identify the impact interruptions in work experience have upon wages. One of the first was a study of married women age 30 to 44 by Mincer and Polachek [11]. Using the 1966 cross-section from the National Longitudinal Survey (NLS) of Mature Women, they estimated wage equations including both years of employment and years of non-employment as explanatory variables. Their results indicate that a period of nonemployment not only carries a penalty of forgone experience, but also a significant negative return of about 1.5% per year.

   Corcoran [2] attempted to replicate these results using a cross-section of women from the Panel Study of Income Dynamics (PSID). She found a significant 1.2% net depreciation rate when restricting the sample to a comparable group of 30 to 44 year olds, but a much lower 0.6% rate when women of all ages were included. She attributes this differential to sample composition: those in the 30 to 44 year old age group are more likely to be observed shortly after reentry than are those in the more inclusive sample. Thus, the 1.2% depreciation rate is portrayed as the short run effect and the 0.6% depreciation rate is portrayed as the long run effect of a withdrawal upon wages. Mincer and Ofek [10] obtained similar results using data from the NLS of Mature Women, and became the first to suggest that wages may initially decline following an interruption but then rebound and make up in part for the initial decline.

   Such a wage-experience profile can be justified in several ways. For example, if job skills or job market information deteriorates during periods of nonemployment, reentry wages will be lower than wages received just prior to the interruption. If these skills or this information is regained more rapidly than it was first learned, then reentry wages will rise more rapidly than the wages of others with similar experience.(2) These explanations are based upon human capital theory. Other explanations relying upon signaling theory, fixed training costs, or differential quit rates could be constructed.

   Since 1982, many additional studies have provided evidence of a significant net depreciation rate for time spent not employed. These studies have employed a wide variety of different data sets as well as several alternative specifications. The data sets employed include the 1973 Current Population Survey/Social Security Match File [4], the Canadian Survey of Social Change [13], MBA graduates from the University of Pittsburgh [12], the NLS of Young Women and Men [9], home economics majors from the University of Illinois [8], and SIPP data [7]. The finding of a significant depreciation rate appears robust to such variation, as does the finding of different long run and short run effects, when permitted by the specification. While point estimates range from 0.6% to over 5% per year, most are around 2%.(3) This is true even when an individual specific fixed effects model is used in order to eliminate any bias caused by unobservables such as ability or motivation, or by imperfectly recalled prior work experience [10; 3].

3. The Data

   The data used for this study are drawn from the National Longitudinal Survey of Young Women (NLSYW). This survey follows 5159 women from 1968, when they were between the ages of 14 and 24, until either they are lost through attrition or until 1982, the last year of data employed here. As none of the women had reached age 40 by the conclusion of this period, the results reported below may not be applicable to women who have longer interruptions and return to the work force after age 39. This sample is also restricted to include only white women in recognition of apparent racial differences in labor supply patterns [2], and the results should be interpreted accordingly.

   For each woman, a time path of employment activity is recorded, beginning with her last date of full-time school enrollment and ending with her last interview date. This activity log is constructed using information obtained at every interview regarding when the current job (if applicable) began, when the last job ended, when the last job began, . . . etc. When this information is unavailable or incomplete for some period of time, it is so noted in the activity log. Such recording gaps are treated as missing data.

   For each period of employment, information on both actual hours worked and the hourly wage (in constant 1982 dollars(4)) is gathered. In those rare cases (1.5% of all reported wages) in which real wages fall below $1.50 per hour - approximately the minimum wage for employees who receive tips - or above $27.50 per hour, wages are recoded as missing. Since the estimation technique entails wage differencing to control for unobserved, individual-specific fixed effects, only individuals who report wages at two or more points in time and for whom no intervening activity data are missing are included in the final sample. Women who exit employment to return to full-time school are also excluded from the analysis as their wage patterns are expected to be quite different. These criteria are satisfied by 2612 individuals.(5)

   While the data sets used in earlier studies were constructed in much the same way, there are important differences in these data that could affect the estimates. These data contain far more precise measures of employment and hours worked than do most of the earlier studies. Earlier studies [2] were often restricted by data availability to code employment spells one year at a time using information on hours worked during the year to classify that year as one of full-time, part-time, or no employment. While full-time, full-year work can certainly be identified using such information, part-time employment is indistinguishable from part-year or intermittent employment. Much more precise definitions are applied here. Part-time employment, for example, is defined as work involving thirty or fewer hours per week and non-employment means no paid employment. Interruptions of virtually any length are observable. Since job skills are not expected to become obsolete overnight, however, and unemployment or job search is not exactly a non-market activity, spells of non-employment lasting less than three months are ignored. These data differences should generate more accurate estimates of the return to part-time and full-time employment. They may also result in different estimates of the depreciation rate, particularly if the duration of nonemployment is an important factor influencing depreciation.

4. The Empirical Specification and Sampling Technique

   In order to test for such data...