JOB MOBILITY AND EARNINGS OVER THE LIFE CYCLE: The Earnings of Older Men A. The Sample 2

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In order to pool the samples a simple method Is used throughout. All Individuals are assumed to have a current job. Define FIRST as the first job after completion of schooling, if different from the current job; RESID1 as the residual following the first job; LONGEST as the longest job ever, if different from both the first and current jobs; RESID2 as the residual following the longest job; and CURRENT as the current Job. If a job does not exist for a given Individual, a zero Is coded as his experience for that particular job.


The sample was restricted to white, salaried men who were working In 1966 and who had valid data for wages, working life histories and the other key variables In the analysis. These restrictions reduced the sample size to 1976 observations of which about 90 percent are In mobility patterns 2 or 4.

TABLE 1 List of Variables

RATE – Wage rate In 1966
ANNUAL – Annuel earnings In 1965
EDUC “ Completed years of education
EXPER – Experience since completion of schooling
FIRST – Duration of first job after completion of school—If different from the current job
RESID1 – Residual experience following FIRST
LONGEST – Duration of longest job ever—If different from first and current jobs
RESID2 – Residual experience following LONGEST
CURRENT – Current job experience
FIRST2 – FIRST squared, etc.Interaction term pertaining to the 1th job; experience In 1 job times experience prior to the lfc^ job
INTER(1) –
HLTH – 1 If health Is good or excellent; 0 otherwise
TRAIN – Number of years of formal post-school training
MAR – 1 If married spouse present; 0 otherwise

Table 1 gives the list of variables used In the study. Table 2 gives eunmary statistics for each of the nobility patterns and for the pooled sample. It shows systematic variations In the characteristics of these Individuals across mobility patterns. The least mobile men (Pattern 1) have wagarlng the two largest mobility patterns, differences In personal characteristics such as education, health, etc., are too small to explain the sizable wage differential.

TABLE 2 Summary Statistics

Variable Pattern1 Pattern2 Pattern3 Pattern4 PooledSample
EDUC 12.38 10.48 9.95 10.22 10.48
AGE 50.39 51.15 51.80 51.13 51.14
ANNUAL 9997.6 8286.4 6103.2 6863.7 7814.1
RATE A.38 3.71 2.77 3.19 3.53
FIRST 3.20 17.91 2.98 3.79
RESID1 12.16 7.36 10.00 10.53
LONGEST 12.16 3.79
RESID2 5.21 1.62
CURRENT 25.36 18.21 5.75 3.99 13.46
HLTH .86 .81 .80 .82 .81
TRAIN .96 .82 .85 .83 .83
MAR .93 .93 .89 .89 .92
Number of Observations 111 1136 113 616 1976