JOB MOBILITY AND EARNINGS OVER THE LIFE CYCLE: The Earnings of Older Men B. Estimates of the Human Capital Earnings Function

Table 3 gives the unsegmented earnings function derived In equation (3) for the pooled sample and across mobility patterns using the natural logarithm of the wage rate as the dependent variable. The explanatory power of the equation Is small. The estimated Investment ratios are larger and more significant for the less mobile, Patterns 1 and 2. For the most mobile men In Pattern 4, the estimate of the Investment ratio Is negative. This result might be caused by two factors: this sample might have an average earnings profile that has already peaked and/or mobile men Invested significantly less than the non-mobile men In on-the-job training, and once the depreciation rate Is taken Into account net Investment becomes zero or negative. Thus even at this level, the basic hypothesis of this paper, namely that job mobility al’fects the rate of growth of earnings adversely, Is confirmed.

The Individuals In Pattern 1 have only had one job. Therefore the analysis of their earnings profile does not require any further segmentation of the Investment path. The coefficient of experience can be used to calculate an estimate of the Investment ratio. If the rate of return Is assumed to be 10 percent, the Initial Investment ratio Is . Electronic Payday Loans Online

TABLE 3

Unsegmented Earnings Functions* All Samples Dependent = Ln(RATE)

Variable Pattern . 1 Pattern

2

Pattern

3

Pattern

4

Pooled

Sample

С .541 .171 -.0006 .716 .247
EDUC .038 .069 .072 .059 .067
(2.8) (15.3) (5.1) (8.1) (18.6)
EXPER .030 .016 -.0009 -.017 .010
(1.6) (1.0) (-.04) (-.7) (1.3)
EXPER2 -.0006 -.0002 .0002 .0002 -.0001
(-1.4) (-.9) (.4) (.6) (-1.1)
R2 .120 .208 .211 .140 .181