BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR

Bureau of Labor Statistics

 

OFFICE OF RESEARCH

AND EVALUATION

Training, Wages, and the Human Capital Model

Jonathan R. Veum

Working Paper 262

January 1995

 

This paper is circulated for the information of members of the staff of the Bureau of Labor Statistics (BLS) and other

professionals interested in the work of the Bureau. It has not been cleared for publication and should not be quoted without the

permission of the author. The views expressed are those of the author and do not reflect the policies of BLS or the views of other BLS

staff members.
Training, Wages, and the Human Capital Model

Jonathan R. Veum

Running Head: Training and the Human Capital Model

U.S. Bureau of Labor Statistics

2 Massachusetts Ave., NE, Suite 4945

Washington, DC 20212

E-mail: veum_j@bls.gov

JEL codes: J24, J31

The views expressed in this paper are those of the author and do not reflect the policies or

views of the Bureau of Labor Statistics. The author thanks two anonymous referees for

helpful comments and Alexander Eidelman for excellent research assistance.
Abstract

Training, Wages, and the Human Capital Model

Using recent data from the National Longitudinal Survey of Youth, this study

examines the predictions of the human capital model concerning the relationship between

training, starting wages, and wage growth. As implied by the model, training, particularly

employer financed training, is positively related to wage growth. Company financed

training also appears to be portable across jobs, or to have a general component. In

addition, there is some evidence that workers pay for initial training through a reduced

starting wage. The results provide partial support for the human capital model.
1

1. Introduction

Enhancing the skills of American workers through increased job training is often

deemed necessary for the United States to compete in the global market. Yet primarily

due to a lack of data, there is little research into the role training plays in increasing the

productivity and wages of workers. While there are a number of theories as to why wages

increase over an individual’s work life, a commonly accepted interpretation of this

relationship is that wages increase over time due to investments in human capital,

particularly investments in job training.

The human capital model (Becker 1962; Mincer 1962) suggests that an individual's

decision to invest in training is based upon an examination of the net present value of the

costs and benefits of such an investment. Individuals are assumed to invest in training

during an initial period and receive returns to the investment in subsequent periods.

Workers pay for training by receiving a wage which is lower than what could be received

elsewhere while being trained. Since training is thought to make workers more

productive, workers collect the returns from their investment in later periods through

higher marginal products and higher wages.

Human capital models usually decompose training into specific training, which

increases productivity in only one firm, and general training, which increases productivity

in more than one firm. Purely general training is financed by workers, and the workers

receive all of the returns to this training. In contrast, employees and employers will share

in the costs and returns of specific training. Despite these differences between general and

specific training, the model predicts that both forms of training lower the starting wage

and increase wage growth.

Recent improvements in the available data on training have produced a growing

body of literature which analyzes the different aspects of the human capital model and

documents the consequences of training. In particular, most studies find that training

received from the current employer is associated with increased wage growth (Duncan and
2

Hoffman 1979; Mincer 1988; Barron, Black, and Loewenstein 1989, 1993; Brown 1989;

Altonji and Spletzer 1991; Bartel 1995). However, there have been only limited tests of

other aspects of the human capital model. For instance, Barron, Black, and Loewenstein

(1989) and Parsons (1989) both find no statistically significant relationship between

training and the starting wage. Also, although Barron, Berger, and Black (1993) find that

training has a negative effect on the starting wage, the estimated effect is small relative to

the impact of training on productivity.

In addition, there is mixed evidence as to whether training is specific or general.

Lynch (1992), using data from the early years of the National Longitudinal Survey of

Youth (NLSY), concludes that company training is primarily firm-specific. In contrast, a

recent study using the NLSY data by Loewenstein and Spletzer (1998) indicates that firms

often pay the direct costs of training that takes place outside the workplace. They find

that these employer financed forms of training have a lasting impact on wages for those

who switched employers after training, suggesting that these forms of training are general.

They hypothesize that firms and workers enter into wage contracts that allow firms and

workers to share in the costs and returns to general training.

Similarly, others have suggested that alternatives to the traditional human capital

model should be considered. For instance, Bishop (1996) offers a number of possible

explanations as to why employers might finance general training, such as uncertainties

about workers’ skills, liquidity constraints, and the presence of federal regulations.

Similarly, Acemoglu and Pischke (1996) attempt to explain why German firms pay for

apprenticeship training, a form of training that offers a number of skills that are not firmspecific. The authors hypothesize that firms pay for general training because the current

employer has more information about a worker’s ability than potential employers. The

existence of this asymmetric information provides the firm with some monopsony power

and allows the firm to extract rents from the worker. Since the firm obtains part of the

worker’s marginal product, it has an incentive to provide training and increase the
3

worker’s marginal product. The worker may be reluctant to pay for the training, however,

since the worker receives only part of the return from the training.

Despite these recent analyses of the human capital model, no study to date has

directly tested the predictions of the traditional human capital model relating to starting

wages, wage growth, and the specificity of training. For instance, Loewenstein and

Spletzer (1998) examine the relationship between training and wage levels in a particular

year, but do not examine the relationship between training and starting wages or wage

growth. They also use data from a relatively short time period (1988 to 1991), so that

their results only reflect the short-term effects of training on wages.

In this paper, recent data from the NLSY over a relatively long time horizon (1986

to 1996) are used to directly test the implications of the standard human capital model.

Measures of time spent in training programs are the key variables of interest. To preview

the results, there is evidence that some forms of initial training are inversely related to the

starting wage. Employer-financed training appears to be portable across employers, or to

have a general component. Training that is financed by employers is also particularly

effective in enhancing wage growth. Taken together, the results provide partial support

for the traditional human capital model.

The paper proceeds as follows. In the next section, a description of the data used

here is provided. Section 3 presents results from estimating the impact of training on

starting wages, while Section 4 provides estimates from wage growth equations. Section

5 offers some concluding remarks.

2. The Data

In this analysis, data from the National Longitudinal Survey of Youth (NLSY) are

used to examine the impact of prior and current training on starting wages and wage

growth. A number of previous studies using the NLSY, such as that by Lynch (1992) and

Parsons (1989) have used information from the 1979-86 surveys, where time spent in
4

private sector training is only available for programs that last over a month. In subsequent

years, the training questions in the survey were changed so that respondents were asked

about all types of training (up to four programs) since the last interview, regardless of

duration.1 Consequently, this past research using the pre-1986 data from NLSY captures

the effects of participation in relatively formal training programs. Lynch (1992) reports a

company training incidence of 4.2 percent, while the more recent NLSY data indicate that

the incidence is about 20 percent (U.S. Bureau of Labor Statistics 1993), suggesting that

early NLSY data miss the majority of training events.

The NLSY is a sample of approximately 10,000 young men and women who were

between the ages of 14 and 22 in 1979 and who have been interviewed annually from

1979 to 1994.2 After 1994, the survey moved to a biennial interview cycle. It is possible

to create a measure of hours spent in training programs taken after the 1986 interview

date by taking the product of answers to separate questions about the number of weeks of

training and hours per weeks of training. The training programs exclude any training

received through formal schooling.

Also, while the measures of training are more comprehensive than those available

from the 1979-86 surveys, it is important to mention that they do not capture the extent of

informal training. Methods of informal training such as observing coworkers, learning by

doing, and speaking with supervisors, which are notoriously difficult to measure and

quantify, are not included in these training variables. Hence, while the NLSY contains the

most complete data currently available on training, the training measures used here may

not fully capture the effects of all forms of training on wages.

A key feature of the NLSY is that it garners information in an event history format,

in which dates are collected for the beginning and ending of important life events. In

particular, the starting dates and ending dates of all jobs are recorded, as well as are the

timing of training programs. Based upon the timing of these events it is possible to create
5

measures of training received on the current job along with measures of training received

prior to the current job.

While the earlier years of the NLSY data primarily provide information on where

the training took place, the more recent data include information both on training location

and on who pays the direct costs of this training. Incorporating data on the payer of the

direct costs of training is particularly important when estimating the effects of training on

the starting wage. Presumably, even though some employers pay for the explicit costs of

training, employees indirectly pay for "company paid" training through a lower starting

wage.

The issue of who pays for the training is also important since many company

training programs take place "off-the-job." For instance, classes which offer training in the

latest developments in the field, such as changes in accounting laws, advancements in

computer technology, or new medical techniques may not take place at the work site, but

instead may be directly financed by the employer. Yet there may also exist some forms of

training that take place "on-the-job," but are financed by the employee. In particular,

seminars or classes which provide more general skills, such as those in management,

leadership, public speaking, or a foreign language may occur at the work site but be paid

for by the worker.

Consequently in this analysis, training is separated into categories based upon

location and payer.3 Since the focus here is primarily on the effect of company or "on-thejob" training on wages, location is divided into categories of "on-site" and "off-site," and

payer is broken into "company paid" and "other paid," where "other paid" includes training

paid for by the individual, family, government, or other external sources.4 The resulting

four categories are: on-site, company paid; on-site, other paid; off-site, company paid; and

off-site, other paid. When estimating the impact of training on starting wages and wage

growth, these categories are also broken into training received at the current job and

training received prior to the current job.
6

The primary sample used here is restricted to those who were working for pay and

not enrolled in school in 1996, who started the 1996 job after the 1986 interview date, and

with nonmissing information on other variables used in the analysis (details of sample

creation are provided in the Appendix). The employment restriction does not imply that

the respondent was working at the 1996 interview date, but he or she had to be working at

some time over the interview year. The resulting sample is a group of 5,459 men and

women who were age 31 to 39 in 1996, and it is important to note that the results are

specific to this age cohort.

Since the sample is limited to those who began the 1996 job after the 1986

interview date, complete data on training received while working with the current

employer are available for all sample members. While information on training received

prior to the 1996 job is incomplete, the impact of previous training may also be partially

captured by the previous experience variables. If training, particularly specific training, is

associated with greater job attachment, restricting the sample to those with less than ten

years of tenure may result in a sample that is less apt to receive training than if the sample

included those with more than ten years of tenure (approximately 16 percent of workers in

1996). If so, the full impact of training on wages may not be completely captured in this

analysis due to this sample restriction.5

Table 1 provides information on the receipt of training and time spent in training

by sample members. Over one-half of the sample participated in some form of training

over the ten year period. Approximately 31 percent of the sample received on-site,

company paid training, while nearly 20 percent received company paid training which took

place outside the work place. About 17 percent participated in off-site training which was

not employer financed, while close to four percent received training at the work site which

was not directly financed by the firm. The percentage of individuals receiving company

training is slightly higher than that suggested by previous research, which indicates that

between 4 and 25 percent of workers receive company training (Bishop 1996), although
7

the samples, time frame, and measures of training vary substantially across studies.6 In

particular, most prior studies examine training received from the current employer,

whereas in this study training received from multiple employers over an extended time

frame is analyzed.

Individuals spent on average about 132 hours, or about 13 hours per year, in

training over the time period. Training recipients (excluding those with zero hours of

training) spent about 256 total hours in training. Recipients of off-site, other paid training

spent on average over 330 hours in these programs, which is more than any other source.

This category includes training received from vocational/technical schools, business

schools, and correspondence courses, and these programs are probably more formal than

some of the on-the-job training programs. It should also be noted that the standard

deviations for each of the forms of training are relatively large, implying a wide dispersion

in the hours of training received.

3. Training and Starting Wages

The NLSY collects information on the current wage rate of all jobs held. In

addition, in the year in which a job begins, respondents are asked about their starting wage

at the job (specifically, the question reads, “How much did you earn when you first started

working for (EMPLOYER)?”). Respondents can report earnings over any time frame

(hour, day, month, etc.). For those who do not report an hourly wage, one is constructed

using usual hours worked over the time frame. Hence, the NLSY is one of the few data

sets that allows for an examination of the relationship between initial training and starting

wages.7

The impact of training on starting wages is estimated by specifying the following

wage equation:

ln ws = αoTo + αpTp + βX + ε (1)
8

where ln ws is the log starting wage rate of the job held in 1996, To is training received

within the first six months of employment at the current job, Tp is training received prior

to the current job, X is a vector of worker and firm characteristics, and ε is a standard

error term.8 The X vector includes variables such as a quartic in prior work experience,

sex, race/ethnicity, education, firm size, urban residence, local unemployment rate, union

status, and marital status. In addition, an individual's score on the Armed Forces

Qualifying Test (AFQT) is included and taken to be a measure of ability.9

As mentioned, except for a few prior studies, the impact of prior training on wages

has been ignored, since creating a measure of past training (Tp) requires the use of

longitudinal or quality retrospective data. The human capital model predicts that initial

training received at the current job is negatively related to the starting wage (αo < 0).

Prior training has a positive impact on the wage if training is general (αp > 0), but has no

impact if training is firm-specific (αp = 0).

Estimating the impact of training on wages is complicated by the fact that

individuals may be nonrandomly selected into training based on unmeasured factors. The

individual and firm characteristics that are available in the NLSY, including a measure of

ability, are used here to control for individual heterogeneity between training recipients

and non-recipients. In order to provide additional controls for job type, the wage

equations are estimated with and without industry and occupation controls in the vector of

explanatory variables. These broad industry and occupation categorical variables should

provide a crude measure of the nature of a worker’s job. In addition, these variables may

be a proxy for the extent of informal training that is received on the job. Still, unobserved

characteristics of workers and firms that are positively correlated with both initial training

and starting wages may affect the estimates of training on wages.10

Table 2 presents estimates from log starting wage equations. For ease of

presentation, only the estimates of the training, education, and ability coefficients are

presented. The estimates in Model 1 for the full sample indicate that training received in
9

the first six months of employment is negatively related to the starting wage, although the

estimate is not statistically significant.

Training received while employed at previous jobs is positively related to the

starting wage, indicating that training is portable across jobs, or is general. The results

also indicate that education and ability (as measured by AFQT percentile) are positively

related to starting wages, as might be expected. Although the estimated education

coefficient is somewhat smaller than that often found in the returns to education literature,

it is similar to such studies that use the cohort-based NLSY (Cawley, Heckman, and

Vytlacil 1998). Evaluated at the sample means, the implied elasticities suggest that a ten

percent increase in previous training increases starting wages by less than .05 percent,

while similar increases in education and ability do so by approximately 7.5 percent and 1.5

percent, respectively. Hence, the impact of training on starting wages relative to

education and ability is small.

Model 2 presents estimates when the different types of training are used as

independent variables as opposed to the aggregate training measures. The estimates

indicate that three of the four forms of initial training are negatively related to starting

wages. In particular, training in the “off-site, other paid” category has a negative and

significant association with starting wages at the ten percent significance level. For this

type of training, there is an implicit cost of reduced worker productivity during the

training period even though the employer does not pay the explicit cost of this training.

This result suggests that employees pay the implicit costs of this initial training through a

reduced starting wage.

The results in Model 2 also indicate that previous company financed training, both

on-site and off-site, is positively related to the starting wage. Hence these forms of prior

training are valued by subsequent employers and appear to have a general component.

These results suggest that firms are particularly effective in providing skill enhancements

that are useful to other employers. It is somewhat surprising, however, that both forms of
10

previous “other paid” training are unrelated to wages. These results may suggest that

these forms of training that are not financed by the employer are not particularly effective

in enhancing productivity. It may also be true that these forms of training are taken for

consumption purposes.

Model 3 presents estimates when industry and occupation variables are included as

additional covariates in the starting wage regression. The estimates for the training

variables are only slightly changed with the inclusion of the industry/occupation dummies.

When the starting wage regressions are estimated separately by gender, there is some

indication of a negative correlation between certain forms of initial training and starting

wages for males. In particular, on-site, company paid training and off-site, other paid

training are marginally significant at the .13 and .15 levels, respectively. Also, both forms

of company paid training are portable across employers for males, whereas only off-site,

company paid training is portable for females.

These results indicate that training has a general component, and there is some

evidence that workers pay for training through a lower starting wage, although the

presence of a negative relationship between training and starting wages depends on the

training measure used. It is important to reemphasize, however, that due to the limitations

of the training measures and restrictions in the nature of the sample imposed by the data,

the inverse relationship between training and starting wages may be understated by these

estimates. Also, unobserved characteristics of workers and firms may prevent the negative

relationship between initial training and starting wages from being more evident. Despite

these limitations, the regression estimates provide some indications that there is an inverse

relationship between initial training and starting wages, as predicted by the human capital

model.

4. Training and Wage Growth

The impact of training on wage growth is estimated using the specification:
11

ln (wc/ws) = γcTc + γpTp + θ Y + ν (2)

where wc is the current wage at the 1996 job, Tc is training received at the current job, Tp

is training received at prior jobs, Y is similar to the previously defined X vector but also

includes a quartic in tenure at the current job, and ν is the error term. The human capital

model predicts that current training is positively related to wage growth (γc > 0), while

previous training should have no impact on wage growth (γp = 0). Similar to the starting

wage equations, specifications that include and exclude industry and occupation controls

as additional covariates are estimated.

The results from estimating equation (2) are presented in Table 3. The results for

the full sample in Model 1 indicate that training received at the current job is positively

related to wage growth, which is similar to the findings from most prior studies (Duncan

and Hoffman 1979; Mincer 1988; Barron, Black, and Loewenstein 1989, 1993; Brown

1989; Altonji and Spletzer 1991; Bartel 1995). Also, as predicted by the human capital

model, prior training is unrelated to wage growth. In addition, the results suggest that

education and ability are positively related to wage growth. The implied elasticities

indicate that a ten percent increase in training at the current job increases wage growth by

.03 percent, while a ten percent increase in education and ability does so by .8 percent and

.4 percent, respectively. Hence the estimates suggest that the impact of training on wage

growth relative to education and ability is fairly small.

When the disaggregated training measures are included in Model 2, the results

imply that company financed training at the current job, both on-site and off-site, is

positively related to wage growth. When the industry and occupation dummy variables

are included as additional regressors in Model 3, there is little change in the results. Also,

when the wage growth equations are estimated separately by gender, both forms of

current company paid training are positively related to wage growth for males, while only

on-site, company paid training is significantly related to wage growth among females.
12

The results for company paid training are of interest given that the starting wage

estimates indicate that this form of training has a general component. Hence, on-site and

off-site company sponsored training, which are the most common forms of training,

appear to enhance productivity, both at the current firm and at other firms. It is

surprising, however, that the other forms of training received at the current job have no

estimated impact on wage growth. Similar to the results in the prior section, the wage

growth results suggest that training that is not financed by the employer, or is in the “other

paid” category, is either ineffective in enhancing productivity or is taken largely for

consumption purposes.

5. Conclusions

This study uses recent data from the National Longitudinal Survey of Youth to

examine the predictions of the human capital model concerning the relationship between

training and wages. The results indicate that training received at the current employer is

positively related to wage growth, as predicted by the human capital model. Training,

particularly training that is financed by employers, has a general component, or is portable

across employers. While finding strong empirical support for the predicted negative

relationship between initial training and starting wages is somewhat elusive, the data

provide some indications of an inverse relationship. Given that the typical worker spends

about a day and a half in training per year, the time costs of training are relatively low.

Consequently, it is likely that firms can recoup the costs of training not only through a

lower starting wage, but also through small changes in non-pecuniary aspects of the job,

such as reduced perquisites or fringe benefits. Taken together, these results provide

partial support for the traditional version of the human capital model.

The finding that firms often pay the explicit costs of training that is portable across

employers is consistent with variants of the human capital model that introduce factors

such as implicit contracts, uncertainties about workers’ skills, or transactions costs into
13

the human capital framework. Yet since the results also suggest that workers implicitly

pay for some forms of training through a reduced starting wage, the conventional human

capital model may require relatively minor modifications in order for its predictions to be

consistent with the observed relationship between training and wages. In addition, it is

again important to mention that estimating the relationship between training and wages is

largely a function of the quality of data on training, as training is in many ways a difficult

concept to measure and quantify. For instance, the training measures used in this analysis

do not capture time spent in informal training. If those workers who do not participate in

formal training instead receive informal training in lieu of formal training, the estimated

impact of training on wages will be biased towards zero. Improved data on training

should allow for additional tests of the traditional human capital model versus possible

alternatives.
14

References

Acemoglu, Daron, and Jorn-Steffan Pischke. 1996. Why do Firms Train? Theory and

Evidence. NBER Working Paper 5605.

Altonji, Joseph G., and James R. Spletzer. 1991. Worker Characteristics, Job

Characteristics, and the Receipt of On-the-Job Training. Industrial and Labor Relations

Review 45: 58-79.

Barron, John M., Mark C. Berger, and Dan A. Black. 1993. Do Workers Pay For On-theJob Training? Unpublished paper, University of Kentucky.

Barron, John M., Dan A. Black, and Mark A. Loewenstein. 1989. Job Matching and Onthe-Job Training. Journal of Labor Economics 7: 1-19.

Barron, John M., Dan A. Black, and Mark A. Lowenstein. 1993. Gender Differences in

Training Capital and Wages. Journal of Human Resources 28: 343-64.

Bartel, Ann P. 1995. Training, Wage Growth, and Job Performance: Evidence from a

Company Database. Journal of Labor Economics 12: 401-25.

Becker, Gary S. 1962. Investment in Human Capital: A Theoretical Analysis. Journal of

Political Economy 70: 9-49.

Bishop, John H. 1996. What We Know About Employer-Provided Training: A Review of

the Literature. Working Paper No. 96-09, Center for Advanced Human Resource Studies,

Cornell University.

Brown, James N. 1989. Why Do Wages Increase with Tenure? On-the-Job Training and

Life-Cycle Wage Growth Observed Within Firms. American Economic Review 79: 971-

91.
15

Cawley, John, James Heckman, and Edward Vytlacil. 1998. Cognitive Ability and the

Rising Return to Education. NBER Working Paper 6388.

Duncan, Greg J., and Saul Hoffman. 1979. "On-the-Job Training and Earnings Differences

by Race and Sex. Review of Economics and Statistics 61: 594-603.

Loewenstein, Mark A., and James R. Spletzer. 1998. Dividing the Costs and Returns to

General Training. Journal of Labor Economics 16: 142-71.

Lynch, Lisa M. 1992. Private-Sector Training and the Earnings of Young Workers.

American Economic Review 82: 299-312.

Mincer, Jacob. 1962. On-the-Job Training: Costs, Returns, and Some Implications.

Journal of Political Economy 70: 50-79.

Mincer, Jacob. 1988. Job Training, Wage Growth, and Labor Turnover. NBER Working

Paper No. 2690.

Parsons, Donald O. 1989. On-the-Job Learning and Wage Growth. Unpublished paper,

Ohio State University.

U.S. Bureau of Labor Statistics. 1993. Work and Family: Employer-Provided Training

Among Young Adults. Report No. 838.
16

Notes

1. Although no training questions were included in the 1987 survey, the training

questions in the 1988 survey refer to all training programs dating back to the 1986

interview. Respondents were asked about training in each survey after 1988.

2. The Youth survey includes oversamples of blacks and Hispanics.

3. The only type of training in which categorization is somewhat ambiguous is

apprenticeships. Apprenticeships often involve both on-the-job training along with

course work which may take place off-site. In this analysis, apprenticeships are

included in the "on-site" category, although the results for the most part are unaffected

if apprenticeships are considered "off-site" (96 sample members participated in

apprenticeships).

4. Another reason that the components of the "other paid" category are grouped together

is because cell sizes within each of these components are relatively small, particularly

when subdivided into previous and current training. The primary component of "other

paid" training is self or family (59.9 percent), followed by government (17.6 percent).

5. Results from a probit estimation describing those with ten or more years of tenure are

provided in the Appendix. For the most part, those with long tenures are more likely

to be male, white, married, have high AFQT scores, work in large firms, and to live in

areas with low unemployment rates. When a selectivity correction term based upon

this probit is included in the wage regressions, there is little impact on the estimated

coefficients.
17

6. Since individuals can participate in more than one form of training, the participation in

any forms of training is less than the sum of the percentages for the different types of

training.

7. The CPI-U is used to convert all wages to 1996 dollars. The average starting wage is

$11.72.

8. The results for initial training and starting wages are fairly similar if the definition of

“initial training” is made more restrictive to include only training received in the first

three months of employment, or made more expansive to include training in the first

year of employment.

9. The AFQT was administered to all respondents in 1980. The score used in the

estimations is the percentile ranking of the score based upon the respondent’s age

when the test was taken.

10. Attempts were made to control for unobserved heterogeneity through the inclusion of

individual fixed effects in the wage equations. The use of a fixed-effects specification,

however, requires restricting the sample to those individuals who changed jobs at least

twice between 1986 and 1996. The results from a fixed-effect starting wage

regression for this restricted sample indicates that the estimate for initial training

becomes larger (less negative) when using fixed effects, suggesting that the

unobservables are actually negatively correlated with training receipt. Hence, any gain

from using a fixed-effects specification appears to be mitigated by the sample

restrictions necessary to perform the estimation.
Table 1. The Incidence and Duration of Training from 1986 to 1996

Mean Hours of

Percent Who Mean Hours of Training Among

Received Training Training Training Recipients

Any form of training 51.46 131.67 255.90

(371.80) (486.72)

Type of training

On-site, company paid 31.15 48.87 152.00

(216.56) (360.91)

On-site, other paid 3.96 5.19 131.10

(86.79) (417.91)

Off-site, company paid 19.51 20.48 105.02

(137.48) (296.77)

Off-site, other paid 17.31 57.13 330.04

(249.79) (520.18)

Notes: The number of observations is 5459. Standard deviations are in parentheses.
Table 2. Log Starting Wage Regressions

All

Variable Model 1 Model 2 Model 3 Males Females

Training in first six months

at current job

Any form -.075

(1.15)

On-site, company paid -.068 -.058 -.167 .026

(.79) (.65) (1.51) (.16)

On-site, other paid -.132 -.271 -3.37 -.064

(.19) (.40) (1.18) (.09)

Off-site, company paid .263 .261 .186 .654

(1.37) (1.41) (.93) (1.06)

Off-site, other paid -.177*-.175*-.188 .132

(1.70) (1.75) (1.44) (.78)

Training at previous jobs

Any form .054**

(2.78)

On-site, company paid .078** .080** .106** -.044

(2.23) (2.35) (2.81) (.45)

On-site, other paid .002 -.016 .019 -.601

(.02) (.21) (.24) (.97)

Off-site, company paid .182** .168** .171** .482*

(3.31) (3.17) (3.07) (1.82)

Off-site, other paid .104 .017 -.013 .015

(.37) (.62) (.30) (.43)

Education .058** .058** .041** .047** .069**

(16.32) (16.23) (10.79) (9.55) (13.30)

AFQT percentile .004** .004** .003** .004** .004**

(10.53) (10.39) (8.93) (8.28) (6.84)

Industry/occupation dummy No No Yes No No

variables included?

_

R2 .28 .28 .34 .27 .25

Number of observations 5459 5459 5459 2805 2654
Table 2 (Cont.)

Notes: Numbers in parentheses are t-statistics. Additional covariates include controls for

female, black, Hispanic, a quartic in experience, firm size, union, urban, and married (see

Appendix for means of all variables).

* Statistically significant at the 10-percent level.

** Statistically significant at the 5-percent level.
Table 3. Wage Growth Regressions

All

Variable Model 1 Model 2 Model 3 Males Females

Training at current job

Any form .154**

(5.12)

On-site, company paid .233** .222** .257** .198**

(5.41) (5.10) (4.20) (3.24)

On-site, other paid .016 -.040 .457 -.288

(.06) (.15) (1.06) (.89)

Off-site, company paid .161** .153** .203** .115

(2.37) (2.25) (1.98) (1.26)

Off-site, other paid .081 .027 .034 .003

(.33) (.48) (.41) (.05)

Training at previous jobs

Any form -.015

(1.09)

On-site, company paid -.017 .023 -.027 .034

(.70) (.94) (.99) (.53)

On-site, other paid .021 .021 -.003 .110

(.37) (.37) (.05) (.27)

Off-site, company paid -.041 -.041 -.048 -.078

(1.08) (1.07) (1.19) (.45)

Off-site, other paid -.010 -.006 -.047 .013

(.55) (.32) (1.46) (.54)

Education .008** .008** .006** .006*.008**

(3.15) (3.09) (2.08) (1.73) (2.19)

AFQT percentile .001** .001** .001 .001 .001**

(2.36) (2.35) (1.45) (1.38) (2.11)

Industry/occupation dummy No No Yes No No

variables included?

_

R2 .05 .05 .05 .06 .04

Number of observations 5459 5459 5459 2805 2654
Table 3 (Cont.)

Notes: Numbers in parentheses are t-statistics. Additional covariates include those

mentioned in the note in Table 2 as well as a quartic in tenure.

* Statistically significant at the 10-percent level.

** Statistically significant at the 5-percent level.
Appendix

Sample Creation

NLSY total sample 9964

Respondents in 1996 8636

Deletions: Remaining sample:

Enrolled in school at 1996 331 8305

interview date

Missing AFQT 380 7925

Missing urban locality variable 139 7786

Missing firm size 160 7626

Missing industry 115 7511

Missing occupation 35 7476

Missing/invalid (nonpositive) wage 160 7316

Nonworker 816 6500

Started job prior to 1986 interview 1041 5459

date
Appendix (Cont.)

Sample Means/Probit for Tenure > 10 years

Variable Sample Means Tenure > 10 years Probit

Female .486 -.325**

(5.25)

Black .306 -.346**

(4.57)

Hispanic .185 -.128

(1.53)

Education 13.08 -.003

(.20)

Armed Forces Qualifying Test percentile 38.74 .007**

(5.25)

Prior experience (in weeks/1000) .489 -10.62**

(5.40)

Experience2.284 40.66**

(3.35)

Experience3.181 -113.04**

(4.27)

Experience4.123 83.61**

(4.95)

Firm > 1000 employees .366 .127**

(2.17)

Urban locality .801 .038

(.54)

Local unemployment rate 6.79 -.025**

(2.48)

Married .560 .233**

(4.06)

Professional and Technical .172 .258*

(1.71)

Manager .131 .452**

(3.09)
Appendix (Cont.)

Variable Sample Means Tenure > 10 years Probit

Sales .046 .183

(.95)

Clerical .163 .184

(1.27)

Operative .141 -.053

(.36)

Craft Worker .115 .470*

(3.24)

Service and Private Household .162 .025

(.17)

Agriculture and Mining .030 .100

(.60)

Construction .081 -.158

(1.12)

Transportation .073 -.049

(.42)

Wholesale and Retail Trade .171 -.410**

(3.98)

Finance .055 .090

(.66)

Business .085 -.185

(1.44)

Personal Services and Entertainment .066 -.500**

(3.28)

Professional Services .212 -.156

(1.56)

Public Administration .058 .016

(.14)

Constant .824**

(3.42)

Log-likelihood -1424.8

Number of observations 5459 6500
Appendix (Cont.)

Notes: Absolute value of t-statistics are in parentheses. The omitted occupational

category is laborers and farmers and the omitted industrial category is manufacturing.

*Statistically significant at the 10-percent level.

**Statistically significant at the 5-percent level.