The sample used to study the effects of nobility on earnings at younger ages Is the National Longitudinal Survey of Young Men (aged 14-24 during 1966, the original survey year). Due to the young age range of the Individuals being analyzed and the short duration of the jobs, the analysis Is conducted with two segments of post-school experience: duration of all previous jobs and current job experience. The non-mobile Individuals are defined as those men who have always been In the current job. The data shows that the non-mobile Individuals are younger. This Is because of a selectivity bias inherent In the data: younger men have had less labor force experience, therefore they have had less opportunity to leave the current job , and are thus classified as non-mobile.

The average (1969) wage of the non-mobile Is $3,207; while that of the mobile men Is $3,372. Thus the more mobile have wage rates 5.7 percent higher than the non-mobile. This can, of course, be due to the fact that the mobile have had, on the average, more labor force experience. The estimated earnings function for the two mobility patterns Is given In Table 8. Using the experience coefficients In the regressions, the Investment ratios, kQl, can be estimated.

Young Men Earnings Functions Dependent – Ln (Rate )*

Non-MobileSample Mobile Sample
Variable b t b t
ะก -.093 -.082
EDUC .076 (8.9) .078 (8.8)
EXPER .110 (4.9)
EXPER2 -.0078 (-2.6)
PREVIOUS .071 (3.9)
CURRENT .085 (2.8)
PREVI0US2 -.0021 (-1.2)
CURRENT2 -.0031 (-.7)
INTER -.0057 (-1.3)
R .258 .189

Given a rate of return of 10 percent, the non-mobile Invested .162 of their time In their only job. The mobile men had an Investment ratio of .12 In their previous job and .29 In the current job. Thus the volume of Investment for the non-mobile Is higher than Investment for the previous job of the mobile but lower than Investment In the mobile’s current job. However, It Is Important to realize that due to the selectivity bias Inherent In the non-mobile sample (younger men are more likely not to have changed jobs since they have not sampled the labor market very long) this group is likely to include individuals who will soon be mobile. Thus the average investment ratio is under-estimated for this group.

A more conclusive finding that the volume of investment is positively correlated with Job duration is given by using equation (11), where the completed duration of the current Job is known. The dependent variable in this case is the log of the wage rate in 1966. The equation was estimated for a sample of individuals who left the 1966 Job before 1969. The estimated equation was:
Since the coefficient of REM is negative, and the interaction term is positive (and both are statistically significant) we find that there is a strong positive correlation between job duration and on-the-job training.

In order to study the wage differential between the mobile and the non-mobile groups, a dummy variable set equal to one if the individual has not been in the current job since the beginning of labor force experience was included in the equation. Its effect on earnings was insignificantly different from zero. Thus we find that there is no wage differential by mobility patterns in this age group. Therefore even though the calculated investment ratios suggested more on-the-job Investment for the non-mobile, the gains from job mobility are partly compensating the mobile at young ages. As the individuals age, and less mobility is undertaken (both in absolute terms, and in terms of the proportion that Is voluntary) the accumulation of on-the-job training begins to outweigh the gains from job mobility. This process results In significant wage differentials by the time the men reach age 50 as shown earlier.