DETERMINANTS OF TRAINING: AN ANALYSIS USING BOTH EMPLOYER

AND EMPLOYEE CHARACTERISTICS

Abstract. Despite the fact that employee training is now measured in a number of surveys,

important gaps remain in our knowledge of such fundamental questions as how much

training takes place, who provides it and who gets it. The goal of this paper is to fill in

some of these gaps, making use of data from the 1995 Survey of Employer-Provided

Training (SEPT95). SEPT95 collects data from employers as well as employees, enabling

a more complete analysis of the determinants of training than has been possible in the past.

Though the determinants of training show some differences across our measures of

incidence and intensity, we find strong support for the notion that those establishments

that encourage long-term relationships with their employees also provide more training.

Harley Frazis

Maury Gittleman

Mary Joyce

Bureau of Labor Statistics

2 Massachusetts Ave., NE

Washington, DC 20212

February, 1998

Maury Gittleman is currently at the Organisation for Economic Co-operation and

Development in Paris. The views expressed here are those of the authors and do not

necessarily reflect the views of the U.S. Department of Labor or the OECD. We

acknowledge comments and assistance from Mike Horrigan, Marilyn Manser, James

Spletzer, Kevin Hollenbeck, Harry Holzer, and participants at the conference “EmployerProvided Training: Who Pays? Who Benefits?” at Cornell University.
1

I. Introduction

In recent years, the issue of worker training has been pushed to the forefront of

public policy circles. Renewed interest in training has resulted in part from concerns about

the decline in real wages of less educated workers, the effect of changes in work

organization on the demand for skills, and whether U.S. workers are appropriately trained

to adapt to changes in job requirements brought about by the introduction of new

technology.1 In spite of the importance of this issue, serious gaps exist in our knowledge

of such fundamental questions as how much training takes place, who provides it and who

gets it (Lynch, 1995). Because of data limitations, little empirical work has been done

analyzing the determinants of training using both worker and firm characteristics.

The goal of this paper is to fill in some of these gaps, making use of matched

employee-employer data recently collected by the Bureau of Labor Statistics. This survey,

the 1995 Survey of Employer-Provided Training (SEPT95), has a number of unique

features that make it a valuable source of data for studying training practices. First, the

intensity (not just the incidence) of training was measured at the establishment level.

Second, training logs were used to measure training as it was occurring in order to

minimize recall problems. Third, both establishments and employees at those

establishments were surveyed, allowing the use of both employee characteristics and quite

detailed employer characteristics such as average establishment wage. Fourth, information

on both formal and informal training was collected. Accurate information on the latter is

 

1

 See Levy and Murnane (1992) for a survey of recent changes in the earnings structure, Osterman (1995)

for a discussion of the relationship between work organization and training, and Bartel and Sicherman

(1995) for an analysis of the effect of technological change on the skill acquisition of young workers.
2

especially important, given the well-known inconsistencies in the few previous attempts to

measure informal training (Lowenstein and Spletzer 1996).

We examine the effect of establishment and employee characteristics on the

incidence and intensity of training using a variety of measures. We find that the

determinants of training differ across some of the measures. However, almost all of our

measures indicate that establishments that use more innovative workplace practices and

firms that provide more fringe benefits train more.

The following section contains a detailed discussion of the features of SEPT95.

Section III presents descriptive statistics on the dependent variables used in the analysis,

namely the incidence, hours, and costs of training. This is followed by a discussion of

some of the independent variables available from the survey. In section IV, we use data

collected from employers to conduct a multivariate analysis of the relationship between

formal training and establishment characteristics. In section V, data from employees are

used to analyze the determinants of formal and informal training by examining the role of

both employee and employers characteristics. Section VI offers conclusions.

II. The 1995 Survey of Employer-Provided Training

The 1995 Survey of Employer-Provided Training (SEPT95) was conducted by the

Bureau of Labor Statistics (BLS) for the Employment Training Administration (ETA) in

order to provide nationally representative data on the current training practices of

employers. A sample of 1,433 establishments for the survey was drawn to represent the
3

universe of private establishments that had fifty or more employees.2 The survey was

limited to establishments of this size after an earlier survey had found relatively little

formal training in smaller establishments. See Frazis, Herz, and Horrigan (1995).

Establishments of fifty or more employees accounted for over 57 percent of private

employment in the first quarter of 1995.

In addition to collecting data from establishments, randomly-selected employees in

the responding establishments were interviewed.3 A primary objective of SEPT95 was to

go beyond the collection of data on training incidence and obtain solid estimates on the

intensity of training, namely the hours and costs of training. Hours and costs of formal

training were obtained from the respondent to the establishment survey,4 whereas hours

and wage and salary costs of both formal and informal training were collected from

randomly sampled employees.

The establishment survey consists of two survey instruments – a questionnaire and

a training log. The employer questionnaire collected information on a variety of

establishment characteristics and information on selected costs of formal training. The

cost items include the dollar amount spent during 1994 on: the wage and salaries of inhouse trainers, fees paid to outside training companies, and tuition reimbursement. These

items were included in the survey because field testing indicated that records were more

likely to be available on these items than on other costs of training such as materials or

 

2

 The sample frame for the survey was the list of private ownership establishments on the Bureau of

Labor Statistics’ Universe Data Base (UDB). The frame units were classified into strata based on nine

industries and five employment size classes.

3

 Two employees were randomly selected at each establishment.

4

 Experienced BLS interviewers were instructed to administer the survey to the person at the

establishment who was most familiar with the training policies and practices. At larger establishments

they were told to ask for the training or human resource director and at smaller establishments the person

who handles personnel and training issues.
4

overhead. Information on the total dollar amount spent on training during the year (i.e. a

training budget) was not asked in SEPT95.5 Usable employer questionnaires were

obtained from 1,062 of the respondents for a response rate of 74.1 percent.

In the employer log, employers reported on all the formal training events provided

or financed by the establishment during a two-week period.6 For each event, data was

obtained on the number of employees in attendance, the hours of training, the type of

training and who conducted the training. Given that recordkeeping on training is not

centralized in some establishments, a relatively short reference period was deemed

necessary to provide high quality data on the hours of formal training. Usable logs were

collected from 949 respondents, implying a response rate of 66.2 percent.

A similar design was used to collect information from randomly selected

employees. An employee questionnaire obtained information on such demographic

characteristics as age, sex, race/ethnicity, occupation, education, earnings, and tenure, in

addition to general questions on past training received. From the 2,124 potential

employees (2 employees from each of the 1,062 establishments that responded to the

employer survey), 1,074 employee questionnaires were collected for a response rate of

50.6 percent.

The employee log captured the number of hours of both formal and informal

training. Employees kept a log for 10 calendar-days.7 Employees reported on any activity

 

5

 Previous surveys that asked for total expenditures on training experienced very low response rates. The

6% response rate for the Columbia HRM survey is one such example. See Bartel (1991).

6

 Interviewers had the option of collecting the training log data for the past two weeks at the time of

interview or leaving the log with the respondent to complete over the following two weeks. This decision

was based on the availability and quality of training records.

7

 Interviewers trained the employees on the log by having them recall their activities over the prior three

days and then to keep a daily log over the next seven days.
5

in which they were ‘taught a skill or provided with new information to help them do their

job better.’ For each learning activity, the following questions were asked: who or what

helped them learn the skill or information, how they learned the skill or information, what

type of skill or information was learned, and how much time was spent learning this skill

or information. Based on answers to the first two of these questions, BLS used an

algorithm to classify each activity as formal training, informal training or self-learning.8

The response rate for the employee log was 47.7 percent.

SEPT95 was collected through personal interviews and made use of already

existing records as much as possible or information from logs that respondents kept for

the purposes of the survey. We believe this approach represents a significant improvement

in the quality of data on hours of training, particularly hours of informal training, since

employees were not assumed to have a definition of informal training in mind nor were

they asked to recall information from far back in time. Recall and definition problems

have caused estimates of informal training to vary greatly by survey (Loewenstein and

Spletzer, 1996).

III. Descriptive Statistics on Training from SEPT95

A. Dependent variables: Incidence, Hours, and Costs of Training

Numerous surveys have collected information on job training but as Jacob Mincer

put it, 'available data on job training suffer from poverty amidst plenty'.9 Comparisons

between sources are plagued by differences in definitions, reference periods and sampling

frames. As a result, basic estimates on the incidence and extent of job training vary

 

8

 Hours spent doing self-learning are not counted as training.
6

considerably across surveys. In this section, we present the findings from SEPT95 and

compare them to findings from other surveys.10

Table 1 indicates that nearly 93 percent of establishments with 50 or more

employees provided formal training to their employees in the last 12 months and almost 70

percent of employees working in those establishments received some formal training

during that time. The percent of establishments providing training in SEPT95 is

substantially higher than the estimate found in the 1992 Small Business Administration

Training survey. In that survey, only 42 percent of those with more than 100 employees

reported having formal training programs. Part of the discrepancy between the two

incidence estimates is likely due to a difference in the training concept under measure. In

SEPT95, the incidence measure is capturing the provision of any formal training during

the year which could reflect as little as one training course provided to just one employee.

The SBA survey, on the other hand, is measuring the existence of a formal training

‘program’ which is likely to involve multiple courses offered to a number of employees.

Our incidence estimate from the employees’ perspective is also on the high end of

a broad spectrum of incidence estimates obtained from a number of household surveys. In

the 1975-76 Michigan Time-Use Study (see Stafford and Duncan, 1980) 60 percent of

respondents reported having received ‘any learning that may lead to a better job or

promotion’ and 41 percent of workers in the 1991 January supplement of the CPS said

they received ‘training to improve their skills on their present job’ (See Amirault, 1992).

One reason for our relatively high estimate is that SEPT95 was limited to employees

 

9

 Mincer (1989) p. 5.

10 A fuller set of descriptive results from SEPT95 is found in Frazis, Gittleman, Horrigan, and Joyce

(1997).
7

working in establishments with 50 or more employees and we know that larger

establishments are more likely to provide formal training.11 Also, SEPT95 provided

detailed examples of formal training which may have helped respondents recall training

events.

The second panel of Table 1 goes beyond the incidence of training and gives

estimates on the intensity of training. From the employer log, it was estimated that

employers provided an average of 10.7 hours of formal training to their employees during

May-October of 1995. The similar estimate from the employee log was 13.4 hours. The

results also show that informal training is an important way in which employees acquire

skills, with 70 percent of the training for that purpose being informal. Assuming an

average work week of 40 hours, these findings suggest that workers spend roughly 4

percent of their working hours in training when both formal and informal training are

considered.

Estimates on the extent of training are considerably more limited than those on

incidence. They mainly come from household surveys and tend to measure time spent in

highly structured training. For example, Veum (1993) found that young adults in the

NLSY79 spent an average of 12 weeks in various formal training programs during the

1986-91 period, or roughly 2.5 weeks per year. Hours estimates from SEPT95 suggest

that workers spend closer to between a half and two-thirds of a week per year in formal

training.12

 

11 The 1993 Survey of Employer Provided Training included establishments of all sizes and found that the

provision of formal training increased with establishment size.

12 By doubling the six-month estimate, we are implicitly assuming that there are not seasonal patterns in

hours of training.
8

Another way of gauging the size of an employer’s investment in training is to

measure the amount of money spent on training-related activities. Given the unique

design of SEPT95, BLS was able to collect information on both direct and indirect costs

of training. From the employer survey, data were obtained on the following direct costs:

wages and salaries of in-house training personnel, fees to outside-training companies, and

tuition reimbursements. From data obtained in the employee survey, an estimate of the

wage and salary costs paid to employees while in training was constructed.

Table 2 shows that employers with 50 or more employees spent roughly $16

billion in 1994 on the selected cost items that were covered in the survey. They spent

approximately $37 billion from May-October of 1995 on the indirect wage and salary

costs of training, $13 billion for time spent in formal training and $24 billion for time spent

in informal training. By either measure of intensity--hours or expenditures-- considerable

resources are being spent by employers on training. We now turn to the factors

influencing an employer’s decision to train.

B. Covariates of Training

In the human capital model, training is viewed as an investment decision. Firms

invest in training if the costs incurred during the training period are more than offset by

future gains in productivity. Given that the returns to training are realized over time,

considerable attention has been given to the question of who should pay for the training.

The model predicts that workers pay for general training (i.e. training that is portable to

other firms) and workers and firms share in the costs of firm-specific training. With costsharing there is an incentive for both employers and employees to maintain their
9

employment relationship.13 Employers will be reluctant to lay off trained workers and

trained workers are less likely to quit before realizing a return on their shared investment.

As a result, the human capital model predicts a negative relationship between training and

turnover. With SEPT95, we are able to directly examine the relationship between training

and turnover at the establishment. 14

A further implication of the human capital model is that training will be related to

other policies that may reduce turnover. The offering of fringe benefits is one tool that

employers can use to encourage workers to stay at the company. Employer-provided

health insurance and pensions are two fringe benefits that have been shown to tie workers

to their current employers. (See Madrian, 1994; Gruber and Madrian 1994 for research

on health benefits and Lazear, 1986 for pensions). Besides pension and health, the

existence of other benefits may reduce turnover by demonstrating the employer’s

commitment to workers’ general well-being. For example, the provision of family leave or

employer-financed child care might encourage workers with children to stay at their

present firm. Another way in which firms may try to reduce labor turnover is by using

contract workers during periods of fluctuations in product demand. By relying on

contract employees, employers can protect a “core” group of workers from layoffs during

slack periods.

The firm’s decision to train may also be viewed as part of a broader business

strategy undertaken to become more competitive. Many argue that the conditions that

 

13 Recent papers by Loewenstein and Spletzer (1998) and Acemoglu and Pischke (1998) suggest that

employers are also sharing in the costs of general training.

14 To our knowledge, no study has examined the relationship between training and turnover at the

establishment level. Of course, the relationship has been studied at the individual level. See Mincer

(1988) for empirical evidence that training serves to reduce turnover.
10

made mass production techniques advantageous are no longer present and companies need

to “transform” themselves, or to adopt “high performance” workplace practices.15 The

reorganization of work at the firm is likely to be related to training policies. If a firm

adopts new workplace practices, it must, at least in theory, train workers in the skills

needed to carry out these practices. Training practices may also influence the adoption of

new workplace practices. A firm with a strong commitment to training may have a

workforce that is better equipped to successfully implement these alternative workplace

practices.16 Certain practices may also affect training through their influence on employee

turnover. For example, employee involvement programs or profit sharing may reduce

turnover by making workers more invested both emotionally and financially in the

outcomes of the firm.

Both the view of the firm embodied in traditional human capital theory and that

implicit in recent discussions surrounding so-called “high-performance” workplaces

suggest that the more important it is to maintain a long-term employee-employer

relationship, the more likely an establishment is to invest in training. Data from SEPT95 is

uniquely equipped to test hypotheses associated with these views, as they include detailed

information on a variety of establishment characteristics that are associated with an

employer’s efforts to retain workers.

A further advantage of SEPT95 is that it provides a rich set of other establishment

characteristics beyond those that have received a significant amount of study. A complete

 

15 While often described as new or innovative, many of these practices do, of course, have a long history.

See Bailey (1993) and Parks (1995) for further elaboration. For recent surveys of the literature on high

performance workplaces, see Appelbaum and Batt (1994), Kling (1995), Mavrinac and Jones (1995).
11

list of establishment characteristics used in the analysis and their means are provided in

Table 3. Variable definitions are contained in Appendix A. In addition to establishment

size, which is a variable that has received some attention as a correlate of training, we are

also able to control for the size of the larger legal entity to which the establishment

belongs. We refer to this independent variable as EIN size.17 Other establishment

characteristics available in SEPT95 include the number of part-time employees, the

presence of a labor union and employment growth at the establishment.

IV. Determinants of Training Provision-- Results from the Employer Survey

Though the main purpose of this section is to examine the relationship between

training intensity and establishment characteristics, we will first focus briefly on the

determinants of the incidence of formal training, in order to assess whether the results for

this dataset are in accord with past findings and whether these findings are robust to the

inclusion of controls for the fairly rich set of establishment characteristics contained in

SEPT95. For our multivariate analysis of the correlates of having provided formal training

in the past 12 months, we make use of the sample of 1,062 establishments that completed

questionnaires.18 Probit estimates, shown in Table 4, have been transformed to show the

effect that a one-unit change (starting from the mean) in an explanatory variable will have

 

16 See Osterman (1994, 1995) and Gittleman, Horrigan and Joyce (1995) for additional discussion of the

relationship between training and work organization.

17 From the UDB, we are able to obtain information on the total number of employees working in

establishments with the same Employer Identification Number (EIN) as the surveyed establishment.

Because EINs are used by businesses for tax purposes, the EIN size captures the size of the larger legal

entity to which the establishment belongs. The EIN size does not in all cases capture the size of the firm

as some large companies may have more than one EIN number.

18 Missing items on the employer questionnaire as well as the other three instruments were imputed using

a hotdeck procedure. The multivariate analysis makes use of observations with imputed values.
12

on the probability of providing training. The significance levels, indicated by asterisks, are

for the coefficient itself. We follow this practice for all probit regressions throughout the

paper.

We find that establishments that tend to offer more generous benefits and that use

more of the so-called innovative workplace practices are more likely to train. Note that

because the regressions also control for the average wage at the establishment, we are

measuring the generosity of benefits at a given wage level, rather than the possibility that a

larger proportion of total compensation is being offered in the form of benefits. Given the

weak effect of average establishment wages, it appears unlikely that the effect for benefits

merely reflects the association of training with compensation.

Consistent with past results, the probability of having a formal training program

increases with establishment size, even when controls for other establishment

characteristics are included. Characteristics associated with a reduced likelihood of

providing training are higher proportions of part-time workers and the presence of a labor

union. Though a number of studies have examined the relationship between labor union

status and training, a consensus has not been reached. The survey by Brown (1989) finds

examples of studies where unions have a positive, negative and no appreciable effect on

training. More recent findings have also been mixed, with Frazis, Herz and Horrigan

(1995) finding a positive effect of unions on incidence, while Lynch and Black (1995)

found no significant effect. One explanation for the results for labor unions differing from

the findings of Frazis, Herz and Horrigan (1995) and Lynch and Black (1995) is that the

universe of the present study is establishments with 50 or more employees, versus all

 

Comparisons between establishments with and without imputations did not suggest any systematic
13

establishments and those establishments with 20 or more employees in the other studies,

respectively. When the sample for Frazis, Herz, and Horrigan (1995) is restricted to

establishments with fifty or more employees, the estimated effect of unions is still positive

but not significant at conventional levels. We will return to the question of the impact of

unions on training when we discuss the determinants of training intensity.

We now turn to the relationship between the extent of formal training and

employer characteristics.19 The data from SEPT95 extends the literature in a number of

different directions. The bulk of data on the intensity of training, as measured by hours, is

derived from household surveys, so much less is known about the effect of establishment

characteristics on training hours. In addition, SEPT95 allows the intensity of training to

be measured not only by the hours but also by expenditures on training.

Many of the dependent variables on training intensity used in the analysis are

mixed continuous-discrete variables. For example, some employers reported zero training

expenditures during 1994 and many employees had no training during the log period. In

order to distinguish between the determinants of the incidence of training and the

determinants of intensity, we use the following two-part model:

ln ; * 1

* 1 0, 0 ;

;

2 2 2 2 1

1 1

1 1 1

= + =

= > =

= +

i i i i i

i i

i i i

y X u y observed if y

y if y otherwise

y X u

b

b

 

difference in training practices between the two groups.

19 For the analysis on training intensity, we are limited to the sample of 943 establishments that completed

employer logs. Actually 949 establishments completed employer logs but six of these were excluded

because they were extreme outliers either in terms of hours of training or training expenditures reported.

To test for differences between the sample of establishments that responded to questionnaires and the

subset that completed logs, we ran a probit on the incidence of formal training in the past 12 months with

a dummy variable included to indicate whether or not an establishment returned a log. The coefficient of

this variable was not statistically significant.
14

where i subscripts observations (i=1,...,n), y1 is a latent variable reflecting propensity to be

trained, y2 is hours of training, X is the matrix of regressors, u1 and u2 are normally

distributed mean zero error terms independent of X and each other, and β1 and β2 are

vectors of parameters. The assumed independence of u1 and u2 allows us to consistently

estimate β1 and β2 by simple methods; a probit regression of y1* on X estimates β1 and an

OLS regression of ln y2 on X consistently estimates β2.

20

Table 5 displays the results of the two-part analysis for the determinants of hours

of formal training. The table shows the effects of each of the independent variables on the

incidence of training and on the log of the hours trained for those establishments that did

train. The final column gives the resulting marginal impact the covariates have on hours of

formal training.21 The estimates make clear that the number of fringe benefits offered as

well the number of workplace practices in use are strong predictors not only of the

incidence of training, but of the intensity of training for those establishments that do at

least some training. The estimates shown in the marginal impact column suggest that,

ceteris paribus, the amount of training an employee receives tends to rise about 2.8 hours

 

20 When u1 and u2 are correlated, as is likely to be the case, in theory a Heckman-type correction or

maximum-likelihood procedure is required for consistent estimation. However, Monte Carlo evidence has

shown that in practice the two-part model without taking the correlation between u1 and u2 into account is

likely to perform as well or better than models that take the correlation into account. See Hay, Leu, and

Rohrer (1987) and Manning, Duan, and Rogers (1987).

21 Marginal impacts are derived as follows. The expected value of the variable of interest y2 evaluated at

sample mean X is:

),

2

( ) ( ) exp(

2

2 1 2

s

M = E y = Φ Xb Xb + where Φ is the standard normal cdf, and σ

2

 is the variance

of u2. The marginal effect of a unit increase in Xj, where j denotes variable, is:

)

2

( ( ) ( ))exp(

2

1 1 2 1 2

s

= b f Xb + b Φ Xb Xb +

dX

dM

j j

j

where β1j,β2j are elements of β1, β2 corresponding to Xj. Standard errors are estimated by the delta

method; see Goldberger (1991), Efron and Tibshirani (1993, p. 313).
15

a year with each additional benefit provided and roughly 2.3 hours per year with each

additional workplace practice. Using the sample standard deviations of 1.93 for benefits

and 2.28 for practices, establishments who are a standard deviation above the mean on

benefits provide 5.4 hours a year more training and establishments who are a standard

deviation above the mean on practices provide 5.3 hours a year more training per

employee than establishments at the mean.

The only other employer characteristics (besides industry) to have a significant

impact on training are the proportion of part-time employees and the presence of unions,

both of which have a negative impact. The result for part-time workers is consistent with

a shorter working time reducing an establishment’s incentives to invest in its employee.

We find that the presence of a union is associated with a nearly six hour per employee

annual reduction in formal training, with most, though not all, of the impact coming from a

reduced probability that the establishment trains.

One may expect unionized establishments to provide more training than their nonunion counterparts because of the requirements of collective bargaining. However, there

are a number of reasons why the presence of a labor union may reduce training. Mincer

(1983) argues that incentives for general (transferable) training are reduced for union

workers because such training is not adequately rewarded within the union firm and

because union workers are less likely to move in the first place. He predicts that the

volume of total training (general plus specific) will be smaller in union firms and finds

evidence of this using data from the NLS and PSID. Duncan and Stafford (1980) also

found less training among union workers using data from the Michigan Time-Use Study.

Another reason why unionized employees may receive less training is that they may
16

already have more job-specific skills, some of which were obtained in apprenticeship

programs or through union-sponsored training. The higher level of skills may result in

part from management efforts to recruit more skilled workers to offset higher union wage

costs and from generally higher levels of experience among union workers resulting from

the inverse relationship between layoff/quit rates and tenure in union firms. In the analysis

below using the employee data, where worker tenure is controlled for, the presence of a

labor union continues to be associated with reduced training but the impact is only

statistically significant for formal training.

As is well known, theory predicts that larger establishments provide more formal

training than their smaller counterparts, owing to such factors as the presence of

economies of scale in training, lowered required rates of return on training investments

(Barron, Black and Loewenstein 1987), greater ability to absorb losses associated with

turnover among trained employees, or a better capacity to screen potential employees

before hiring them (Holtmann and Idson 1991). In terms of hours of training, however,

neither measure of size has a significant overall impact. Larger establishments tend to be

more likely to train, but this is largely offset by a tendency to train less intensively. Entity

size has no significant effect on either the likelihood of training or its intensity.

Surprisingly, employee turnover does not have a significant negative effect on hours of

formal training. The point estimates are positive with high standard errors.

SEPT95 also allows the intensity of training to be measured by selected items of

the establishment’s training expenditures. The survey asked specifically about tuition

spent for courses taken at educational institutions, payments to outside training

companies, and wages and salaries spent on full-time and part-time training personnel.
17

Wages and salaries of in-house trainers is the largest of these items, at $139 per employee,

as shown in Table 2. These components of training expenditures were totaled and divided

by the number of employees to arrive at our second measure of intensity.

As shown in Table 6, higher numbers of benefits and practices are associated with

higher expenditures on formal training. In contrast to the case for hours, establishments

with higher average wages spend more on training per employee, owing to higher

expenditures at establishments that do train. Both the proportion of part-time employees

and the presence of unions continue to have a negative impact on training intensity,

though for expenditures the driving force is lower expenditures per employee rather than a

reduced incidence rate.

The presence of contract workers -- which was not a significant variable in

explaining hours intensity -- is associated with greater expenditures, both because of

higher incidence and greater expenditures per employee. The reason for this association

warrants further attention, as it may depend on the establishment’s motivation for relying

on contract workers.22 If establishments use contract workers to protect a “core” group

of workers from fluctuations in workload (as we speculated above), they would be

expected to provide substantial amounts of training to this low-turnover core group.

Another possibility is that establishments relying on contractors for specialized services

may need their employees to be technologically sophisticated enough to interact with the

specialized contractors.

Turnover, which was not an important correlate of hours intensity, proves to be a

significant influence on expenditures. This relationship stems both from a lower incidence

 

22 See Abraham and Taylor (1993) for a discussion of theory and evidence for contracting relationships.
18

of expenditures at high turnover establishments, as well as lower expenditures per

employee. As with hours, establishment size is not a significant overall determinant of

intensity, though it is again associated with higher incidence. Entity size, however, is

associated with greater expenditures.

A few results stand out as being robust across the two measures of intensity. On

the negative side, higher proportions of part-time workers and the presence of a union are

associated with reduced formal training. On the other side of the coin, the number of

fringe benefits and workplace practices are consistently associated with higher-intensity of

training.

To see if certain benefits or practices are more important than others in predicting

training, Table 7 provides results from two-part regressions where the benefits and

practices are entered individually. Though the hypothesis that all benefits or practices

have zero effects can be rejected in three of four cases, it is never possible to reject the

hypothesis that all the benefits or practices have equal coefficients. The only benefit or

practice to have a significant marginal impact for both measures of intensity is the

presence of an employee assistance plan.

V. Determinants of Training Receipt—Results from the Employee Survey

Given the availability of training information from a number of household surveys,

much more is known about the characteristics of workers who receive training than about

the characteristics of employers that provided it. Past research has shown consistently that

more educated workers receive more training. Race has also tended to be a predictor of

training incidence, with whites being more likely to receive training than other groups.
19

The findings on sex are less clear (Brown, 1989) but there is some evidence that men are

more likely to receive formal company training (Veum, 1993).

A limitation of much past research has been an inability to control for

establishment characteristics. As a result, it is not possible to tell whether a certain

demographic group receives more training, regardless of employer, or if the correlation

with training is primarily due to this group being more likely to find employment in

establishments that do substantial amounts of training. We can address this issue with

SEPT95 data.

Given the response rate of 51 percent mentioned in Section II, the

representativeness of the employee data is a concern. One of the sources of employee

nonresponse came about from employers denying BLS interviewers access to their

employees. To investigate this concern, we reran the two-part model on training hours

and expenditures with a dummy variable for whether the employer allowed access to their

employees. In both the probits, the access variable is positive and significant at the ten

percent level, suggesting that employers who provide training are somewhat more willing

to have their employees interviewed. However, conditional on positive training hours or

positive expenditures, the coefficients on the access variable in the OLS regressions are

negative but not significant. The estimated total marginal effect of access on hours and

expenditures is small and not significant at conventional levels in both cases.

As in the employer section, we first examine the relationship between the incidence

of formal training and employee, as well as employer, characteristics. The receipt of

formal training is measured by the following two questions from the employee

questionnaire: 1) While working for your current employer, have you ever received
20

formal training? 2) In the last 12 months, have you received any formal training from your

current employer?23

In addition to the establishment characteristics included as independent variables in

the previous analysis of the employer data, the following worker characteristics are

included: indicators for black, Hispanic, and female; six indicator variables for level of

education (with high school graduate omitted); an indicator for married; age and age

squared; tenure on the current job and tenure squared; and indicator variables for

occupation at the one-digit level. (Industry and occupation dummies are omitted from the

tables.) Sample means for the employee characteristics are shown in Table 8. The probit

results on the receipt of training are shown in Table 9.

Again, we find substantial positive effects of fringe benefits and workplace

practices on the receipt of training. At the sample mean, a standard deviation increase in

benefits increases the probability of ever having received formal training by 2.0 percent

and the probability of having been formally trained in the past 12 months by 4.4 percent.

Similarly, a standard deviation increase in workplace practices increases the probability of

ever having received training by 3.6 percent and the probability of having been trained in

the past 12 months by 5.1 percent. (These compare to probabilities of 87.7 percent and

75.8 percent for ever having been trained and being trained in the past 12 months,

respectively.)

The other establishment variables show some different patterns than those found in

the analysis of the provision of formal training from the employer perspective. Ever

 

23 For this analysis, we used the 1,073 respondents who completed employee questionnaires. We

excluded one observation from an establishment that reported only one employee. As with the employer
21

having received formal training is positively related to EIN size but establishment size per

se does not have a statistically detectable effect on either dependent variable. Turnover

has a negative effect, as predicted by the human capital theory, on both dependent

variables, significant at the 10 percent level in both cases. The presence of contract

workers has a positive effect on the receipt of formal training, while unionization appears

to have a negative effect.

The effects of individual characteristics are similar to those found in the existing

literature using household surveys. Education has a positive effect on receipt of formal

training, with bachelor’s degree holders significantly more likely (at the 5 percent level)

than high-school graduates to have ever had formal training from the current employer.

The effects of education on training in the last 12 months are not significant; the p-value

on the hypothesis that all the education coefficients are equal to zero is .112. The point

estimates tend to be larger than for ever having had formal training, however. Men are

significantly more likely to have formal training than women are, both overall and in the

last 12 months (at the 5 percent level).

While theory suggests that training should decrease with tenure, receipt of formal

training over the last 12 months shows no statistically detectable tendency to decline with

tenure at the mean level of tenure (p=.15). Age has a small effect at the sample mean but

clearly decreases the probability of being trained at the high end of the range, as an

additional year at age 55 decreases the probability of being trained by 1 percentage point.

 

data, imputed values were retained. Examination of results omitting imputed values did not show

systematic differences with the results reported here.
22

We now examine the intensity of training using data collected in the employee

logs.24 The two-part model discussed in the previous section is used to examine the extent

of training by separating out the effect of the incidence of training during the log period

and the duration of training conditional on having received training. The model is

estimated for total hours of training and separately for hours of formal and informal

training. In this analysis the log of the hourly wage rate is included as an additional

control for employee skill.25

Although employees were asked to report their training activities over a 10 day

period, in practice, respondents reported for varying lengths of time--197 of the 1,003

reported for three days or fewer. Moreover, they were asked to report for 10 calendar

days, not 10 work days. Accordingly, the dependent variable used in the intensity

regression is the log of the proportion of work time spent in training, and the probit

regression includes hours worked in the log period as an additional independent variable.

We present only the combined effects of each variable on the unconditional proportion of

work time spent in training. Unfortunately, with a 10 day log period at best and roughly a

thousand observations, our statistical power is limited, so the effects of many variables are

not precisely estimated.

Table 10 shows the marginal impacts on total training, formal training and informal

training. Looking at firm characteristics first, the estimated effects of fringe benefits are

 

24 For this analysis, we use the 1,003 respondents who completed logs for at least one day, had reported

establishment sizes greater than one, and worked positive hours during the period they completed the log.

Of 1,013 log respondents, 9 were excluded because they reported no hours worked, and 1 was excluded for

having an establishment size of 1.

25 In theory, for equally skilled workers, the pre-training wage rate should be negatively related to

training. We tend to find a positive relationship, indicating that wage is proxying for initial skill level or

previous training.
23

statistically significant at the ten percent level for proportion of time spent in training

(combined formal and informal). A standard deviation increase in benefits increases the

proportion of time spent in training by 0.7 percent. Note that both the employee wage and

the average wage in the establishment are controlled for, so this does not simply reflect the

effect of high compensation. A standard deviation increase in the number of workplace

practices is estimated to increase the proportion of time spent in training by 0.5 percent,

but this is not quite significant (t=1.48). These compare to the sample mean of 3.9

percent.

While the number of benefits and work practices was found to be positively related

to the provision and receipt of formal training, the effect of these variables on the intensity

of training as measured by the employee log works mostly through their effect on informal

training. A standard deviation increase in benefits increases the proportion of time spent

in informal training by 0.4 percent (t=1.60), as does a standard deviation increase in

workplace practices (t=2.12), while the effects on formal training are both smaller in

magnitude and with smaller t-statistics. This is relative to a sample mean of 2.6 percent

for informal training. None of the establishment variables other than number of benefits

has a significant effect at the 10 percent level on the total proportion of time spent in

training.

Turnover has a strong negative effect on formal training, consistent with results on

training expenditures, but the point estimates on its effect on informal training and total

training are both positive though insignificant. Similar to the findings from the employer

section, presence of unions appears to have a negative effect on formal training during the
24

log period; the presence of a union is predicted to reduce the proportion of time spent in

formal training by 0.6 percent (t=1.94). This compares to a sample mean of 1.3 percent.

We now turn to employee characteristics to see if they have the expected effects

even after controlling for establishment characteristics. Education has a very strong effect

on training during the log period. Those with more than a high-school degree get

substantially more training than those with high-school or lower education. (GEDs appear

to get more training than high-school graduates. This is a somewhat anomalous finding in

view of Cameron and Heckman 1993 and related literature on the small labor market

effects of GED acquisition.) Bachelor’s degree holders spend 2 percent more of their time

in training and graduate degree holders 4 percent more than do high-school graduates.

Once again, this appears mostly due to informal training, with bachelor’s degree holders

spending more than 1.3 percent and graduate degree holders 2.6 percent more time in

informal training.

The log data indicate that training decreases substantially with job tenure. A oneyear increase in job tenure at the sample mean tenure decreases the proportion of time

spent training by 0.38 percent. This is almost entirely due to its effect on informal

training; a year of tenure reduces the proportion of time spent in informal training by 0.34

percent. This finding is consistent with the typical version of the human capital model.

Note the contrast with formal training, for which the hypothesis that training declines with

tenure is not confirmed either in the employee log or in the incidence question about the

receipt of training in the last 12 months.26

 

26 Since the unit of analysis is the person and not the job, these results may not accurately reflect the timepath of training on the typical job. Since jobs with high eventual tenure are in theory more likely to be

high-training jobs, one would expect even stronger declines in informal training with tenure and perhaps
25

The specifications thus far have included both establishment and individual

characteristics. One use of the linked employee-employer data is to see whether

inferences from unlinked household or establishment data characteristics are seriously

biased. In specifications not shown here, establishment characteristics were used as

independent variables without individual characteristics, and in other specifications

individual characteristics were used without establishment characteristics. The pattern and

strength of the individual-level variables were largely unaffected by the exclusion of the

establishment variables, and vice versa.27

Similar to the analysis of the employer data, we now turn to the effects of

individual benefits and practices on training by examining the specifications with benefits

and practices entered singly rather than as sums. The results are shown in Table 11. The

strongest results are for the incidence of formal training from the employee questionnaire.

The largest coefficients in the probit for ever having been formally trained by the current

employer are for employer-financed child care and for employee wellness benefits. For

training during the past 12 months, the highest coefficients are for employee assistance

programs and pensions. The hypothesis that all benefits have the same effect is rejected at

p=.053 for ever having had formal training and p=.019 for formal training in the past 12

months. In contrast, the hypothesis that all work practices have equal effects is accepted

at any reasonable level of significance for both dependent variables.

 

declines in formal training. For evidence on “belated” formal training in the NLSY, see Loewenstein and

Spletzer (1997). They estimate the probability of ever having received training from a given employer.

They find that estimates of this probability from cross-section data are close to those from following jobs

longitudinally.

27 One exception is that the estimated effect of the log of establishment wage tends to increase when

personal characteristics are omitted.
26

As might be expected, the results for the employee log are weaker. The results

from the two-part model of training in the log period are shown in column 3 of Table 11.

The hypothesis that all benefits have an equal effect can be rejected only at the p=.156

level.

Considering both the employee and the employer results for individual benefits and

practices, no clear pattern emerges. The generally strong association of employee

assistance plans with training, especially in the employer data, confirms a result in Frazis,

Herz, and Horrigan (1995). It was argued there that the presence of employee assistance

plans may be an especially good indicator of a long-term implicit contract between

employees and employers, as employers seek to assist employees with difficulties rather

than replace them. The homogeneity of effects of alternative work practices is consistent

with the idea from the high-performance workplace literature28 that the successful

transformation of a workplace requires the adoption of several practices at the same time.

VI. Summary and conclusions

Using a new and rich database, SEPT95, we analyzed training intensity as

measured by both employer and employee surveys. On the employer side, hours of

training tended to be higher at establishments that are larger, non-union, have higher

numbers of benefits and workplace practices, and smaller proportions of part-time

workers. Turnover has the predicted negative effect when intensity is measured by

training expenditures.

 

28 See the references in footnote 15.
27

The analysis of employee data confirmed many of the findings in the literature

concerning the relationship between individual characteristics and the receipt of formal

training. Using the matched nature of SEPT95, we found that these relationships were not

significantly altered by the inclusion of establishment characteristics. Data from the survey

of employees also allowed us to compare the correlates of informal training with those of

formal training. Some variables, such as education, that affect formal training have similar

effects on informal training. However, turnover and the presence of contract employees

have opposite effects on formal and informal training such that they appear to have small

relation to total hours of training. Job tenure has evident effects on informal training but

no apparent effects on formal training.

Although the determinants of training differ somewhat across some of the

measures, in general we find that employers who show signs of promoting a long-term

relationship with their employees tend to train and to train more intensively. Our most

consistent set of results in terms of statistical significance is the effect of fringe benefits

and workplace practices. We find that establishments with many fringe benefits and

innovative workplace practices are more likely to provide formal training and to spend

more on training. Furthermore, employees working for employers who offer many fringe

benefits or who employed many innovative workplace practices received more hours of

both formal and informal training.
28

References

Abraham, Katharine G. and Susan K. Taylor. 1993. “Firms’ Use of Outside Contractors:

Theory and Evidence.” NBER Working Paper No. 4468, September.

Acemoglu, Daron and Jorn-Steffen Pischke. 1998. “Why do Firms Train? Theory and

Evidence.” Quarterly Journal of Economics, forthcoming.

Amirault, Thomas. 1992. “Training to Qualify for Jobs and Improve Skills, 1991.”

Monthly Labor Review. Vol. 115, No. 9 (September), pp. 31-36.

Appelbaum, Eileen and Rosemary Batt. 1994. The New American Workplace:

Transforming Work Systems in the United States. Ithaca, NY: ILR Press.

Bailey, Thomas. 1993. “Discretionary Effort and the Organization of Work: Employee

Participation and Work Reform Since Hawthorne.” Teachers College, Columbia

University, January.

Barron, John M., Dan A. Black and Mark A. Loewenstein. 1987. “Employer Size: The

Implications for Search, Training, Capital Investment, Starting Wages and Wage Growth.”

Journal of Labor Economics, Vol. 7, No. 1 (January), pp. 1-19.

Bartel, Ann. 1991. "Formal Employee Training Programs and Their Impact on Labor

Productivity: Evidence from a Human Resources Survey", in David Stern and Jozep

Ritzen, eds., Market Failure in Training, Springer and Verlag.

Bartel, Ann P. and Nachum Sicherman. 1995. “Technological Change and the Skill

Acquisition of Young Workers.” Unpublished, April.

Brown, Charles. 1989. “Empirical Evidence on Private Training.” In Investing in

People, vol. 1, Washington, DC: Commission on Workforce Quality and Labor Market

Efficiency, U.S. Department of Labor, pp. 301-329.

Cameron, Steven V., and James J. Heckman. 1993. “The Nonequivalence of High

School Equivalents.” Journal of Labor Economics, Vol. 11, No. 1, Part 1 (January), pp.

1-47.

Duncan, Gregg and Frank Stafford. 1980. “Do Union Members Receive Compensating

Wage Differentials?” American Economic Review, Vol. 70 (June), pp. 355-371.

Efron, Bradley and Robert J. Tibshirani. 1995. An Introduction to The Bootstrap. New

York: Chapman and Hall.
29

Frazis, Harley, Maury Gittleman, Michael Horrigan, and Mary Joyce. 1997. “Formal and

Informal Training: Evidence from a Matched Employee-Employer Survey.” In Gary D.

Libecap, ed., Advances in the Study of Entrepreneurship, Innovation, and Economic

Growth, vol. 9, Greenwich, CT: JAI Press, pp. 9-46.

Frazis, Harley J., Diane E. Herz and Michael W. Horrigan. 1995. “Employer-Provided

Training: Results from a New Survey.” Monthly Labor Review, Vol. 118, No. 5 (May),

pp. 3-17.

Gittleman, Maury, Michael Horrigan and Mary Joyce. 1995. “’Flexible’ Work

Organizations: Evidence from the Survey of Employer-Provided Training.” Unpublished,

September.

Goldberger, Arthur. 1991. A Course in Econometrics. Cambridge, MA: Harvard

University Press.

Gruber, Jonathan and Brigitte C. Madrian. 1994. “Heath Insurance and Job Mobility:

The Effects of Public Policy on Job-Lock. Industrial and Labor Relations Review, Vol.

48, No. 1 (October), pp. 86-102.

Hay, Joel W., Robert Leu, and Paul Rohrer. 1987. “Ordinary Least Squares And SampleSelection Models Of Health-Care Demand: A Monte Carlo Comparison.” Journal of

Business and Economic Statistics Vol. 5, pp. 499-506.

Holtmann, Alphonse G. and Todd L. Idson. 1991. “Employer Size and On-the-Job

Training Decisions.” Southern Economic Journal, October, pp. 33-55.

Kling, Jeffrey. 1995. “High Performance Work Systems and Firm Performance.”

Monthly Labor Review, Vol. 118, No. 5 (May), pp. 29-36.

Levy, Frank and Richard J. Murnane. 1992. “U.S. Earnings Levels and Earnings

Inequality: A Review of Recent Trends and Proposed Explanations.” Journal of

Economic Literature, Vol. 30, No. 3 (September), pp. 1333-1381.

Lazear, Edward P. 1986. “Retirement from the Labor Force.” In Orley Ashenfelter and

Richard Layard, Handbook of Labor Economics, Volume 1, Amsterdam: North-Holland.

Loewenstein, Mark and James Spletzer. 1996. “Formal and Informal Training: Evidence

from the NLSY.” Paper prepared for conference “Employer-Provided Training: Who

Pays? Who Benefits?”, Cornell University, November.

Loewenstein, Mark and James Spletzer. 1997. “ Delayed Formal On-the-Job Training.”

Industrial and Labor Relations Review, Vol. 51, No. 1 (October), pp. 82-99.
30

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

General Training.” Journal of Labor Economics, Vol. 16, No. 1 (January), pp 142-171.

Lynch, Lisa. 1995. “A Needs Analysis of Training Data: What Do We Want, What Do

We Have, Can We Ever Get It?” in Labor Statistics Measurement Issues, J. Haltiwanger,

M. Manser and R. Topel, eds. NBER/University of Chicago Press, forthcoming.

Lynch, Lisa M. and Sandra E. Black. 1995. “Beyond the Incidence of Training:

Evidence from a National Employers Survey.” NBER Working Paper No. 5231, August.

Madrian, Brigitte C. 1994. “Employment-Based Health Insurance and Job Mobility: Is

There Evidence of Job-Lock.” Quarterly Journal of Economics, Vol. 109 (February), pp.

27-51.

Manning, Willard G., Naihua Duan and W. H. Rogers. 1987. ”Monte Carlo Evidence On

The Choice Between Sample Selection And Two-Part Models”, Journal of Econometrics,

vol. 35, pp. 59-82.

Mavrinac, Sarah C. and Neil R. Jones. 1995. “The Financial and Non Financial Returns

to Innovative Workplace Practices: A Critical Review.” Report prepared for the Office

of the American Workplace, Department of Labor.

Mincer, Jacob. 1983. “Union Effects: Wages, Turnover, and Job Training.” Research in

Labor Economics, Supplement 2, JAI Press, pp. 217-252.

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

Working Paper No. 2690, August.

Mincer, Jacob. 1989. "Job Training: Costs, Returns, and Wage Profiles", NBER

Working Paper No. 3208, December.

Osterman, Paul. 1994. “How Common is Workplace Transformation and Who Adopts

It?” Industrial and Labor Relations Review, No. 2 (January), pp. 173-188.

Osterman, Paul. 1995. “Skill, Training and Work Organization in American

Establishments.” Industrial Relations, Vol. 34, No. 2 (April), pp. 125-146.

Parks, Susan. 1995. “Improving Workplace Performance: Historical and Theoretical

Contexts.” Monthly Labor Review, Vol. 118, No. 5 (May), pp. 18-28.

Stafford, Frank and Greg J. Duncan. 1980. "The Use of Time and Technology by

Households in the United States", Research in Labor Economics, Vol. 3, pp. 335-375.

Veum, Jonathan. 1993. “Training among young adults: who, what kind, and for how

long?”. Monthly Labor Review, (August), pp. 27-32.
31

Appendix A

Definitions of Variables from Employer Survey

Establishment size: The number of employees on the payroll of the establishment for the

pay period that included the 12th of the present month.

EIN Size: The number of employees in all establishments with the same Employer

Identification Number (EIN) as the surveyed establishment. This captures the size of the

larger legal entity to which the establishment belongs. It does not correspond to the firm

size because some large companies have more than one EIN number.

Establishment wage: Total payroll of the establishment for the fourth quarter of 1993

divided by the sum of employment in each month of the quarter. This information was

obtained from the BLS Universal Data Base (UDB).

Labor turnover: The ratio of the sum of hires and separations over the three months

prior to the reference period to the average employment for the three months.

Benefits: Respondents were asked to check which of the following benefits were offered

at their establishment: paid vacation; paid sick leave; health care benefits; an employee

assistance program; an employee wellness program; pension plans profit sharing; flexible

work schedules; flexible work site or telecommuting; employer-financed child care; and

paid parental/family leave.

Workplace practices: SEPT95 asked whether establishments used any of the following

practices: pay increases that are directly linked to mastering new skills; employee

involvement in the firm’s technology and equipment decisions; job redesign or

reengineering ; job rotation; just-in-time inventories; co-worker review of employee

performance; quality circles; total quality management; and self-directed work teams.

Use of contract employees: Indicates whether the establishment used any contract

workers or workers employed by temporary-help agencies during the pay period used for

reporting establishment size.

Proportion of part-time employees: The number of employees considered by the

establishment to be part-time workers divided by reported establishment size.

Presence of labor union: Indicates whether or not any of the employees are represented

by a union agreement.

Employment growth: The ratio of net change in employment over the three months

prior to the reference period to average employment for those three months.
32

Table 1

Incidence and Intensity of Training

INCIDENCE

Percent of establishments

with

50 or more employees

who provided

Percent of employees in

establishments of size 50 or

more who received

Formal training in last 12 months 92.5 69.8

INTENSITY

Hours per employee in establishments with 50 or more

employees, May-October, 1995

Employer survey Employee survey

Formal training 10.7 13.4

Informal training NA 31.1

Total NA 44.5

Table 2

Expenditures on Training among Establishments with 50 or more employees

In billions of dollars

1994

$ per

employee

Level Error range of

expenditures*

Selected costs of formal training:

 Wage and salaries of in-house trainers $139 $7.7 $7.0 - $8.5

 Payments to outside trainers 98 5.5 4.8 - 6.1

 Tuition reimbursements 51 2.8 2.6 - 3.0

May-October 1995

Wage and salary paid to trainees while in

 Formal training $224.1 $12.8 $11.0 - $14.7

 Informal training 422.8 24.2 19.9 - 28.5

 Total 646.9 37.1 32.8 - 41.4

*Plus or minus one standard deviation.
33

Table 3

Establishment Characteristics, Employer Survey

Means

Establishment Characteristic Unweighted Weighted

Employment 665.44 162.94

Establishment wage 2981.24 2177.14

EIN size 10185.41 7041.95

Turnover rate 0.240 0.353

Number of benefits 6.127 5.483

Use of contract workers 0.434 0.283

Number of workplace practices 3.238 2.785

Employment growth rate 0.010 0.001

Proportion of part-time 0.110 0.225

Presence of labor union 0.309 0.183

Sick Leave 0.863 0.806

Employee Assistance Plan 0.624 0.474

Wellness 0.408 0.266

Pension 0.831 0.725

Profit Sharing 0.469 0.374

Flexitime 0.439 0.449

Flexisite 0.172 0.135

Child Care 0.072 0.063

Family Leave 0.295 0.249

Pay for Knowledge 0.363 0.389

Employee Involvement 0.501 0.439

Job Redesign 0.442 0.313

Job Rotation 0.375 0.344

Just-in-Time Inventories 0.274 0.218

Peer Review 0.182 0.179

Quality Circles 0.310 0.267

Total Quality Management 0.511 0.437

Worker Teams 0.282 0.199

Number of observations 1062
34

Table 4

Determinants of the Incidence of Formal Training, Employer Survey

Probit Maximum-Likelihood Estimates, Unweighted

Dependent variable Any formal training in past 12 months

 (Derivatives)

Employer Characteristics Estimate (x 100) Standard Error

Ln(Employment) 0.176* 0.137

Ln(Estab. Wage) -0.180 0.170

Ln(EIN size) 0.044 0.049

% Part-time -0.904*** 0.610

Union present -0.312* 0.226

Contract workers used 0.151 0.168

Turnover rate 0.270 0.261

Employment growth rate 0.934** 0.690

Number of benefits 0.207*** 0.001

Number of workplace practices 0.188*** 0.104

Mining -0.125 0.351

Construction 0.085 0.253

Nondurable manufacturing -0.146 0.297

Durable Manufacturing -0.698*** 0.459

T.C.P.U. 0.379 0.421

Wholesale trade 0.171 0.347

Retail trade -0.400 0.341

F.I.R.E. -0.201 0.281

Constant 1.816 2.331

Log likelihood -103.569

Observations 1062

* Coefficient significant at 10 percent level

** Coefficient significant at 5 percent level

*** Coefficient significant at 1 percent level

NOTE: Statistical significance of derivatives does not correspond to significance of

coefficients.
35

Table 5

Determinants of the Intensity of Formal Training: Hours, Employer Survey

Two-Part Model, Unweighted

Dependent variable Any formal training

(Derivatives)

Ln(hours of formal

training per employee)

Marginal impact on hours

of formal training per

employee

Sample All Hours of formal training

per employee >0

Employer

Characteristics

Estimate Standard

Error

Estimate Standard

Error

Estimate Standard

Error

Ln(Employment) 0.135*** 0.018 -0.140** 0.055 0.036 0.050

Ln(Estab. Wage) -0.002 0.039 0.037 0.141 0.028 0.125

Ln(EIN size) 0.005 0.009 0.043 0.031 0.042 0.028

% Part-time -0.174* 0.103 -0.558 0.389 -0.660* 0.342

Union present -0.157*** 0.038 -0.064 0.120 -0.230** 0.109

Contract workers used 0.016 0.035 0.170 0.110 0.033 0.101

Turnover rate 0.031 0.042 0.072 0.113 0.094 0.105

Growth rate 0.124 0.084 -0.051 0.300 0.097 0.267

Number of benefits 0.042*** 0.010 0.070** 0.031 0.106*** 0.028

# of workplace practices 0.025** 0.008 0.072*** 0.026 0.089*** 0.023

Mining 0.071 0.077 0.483** 0.214 0.482** 0.199

Construction 0.029 0.067 -0.356 0.235 -0.263 0.211

Nondurable

manufacturing

 0.004 0.067 -0.145 0.223 -0.117 0.200

Durable Manufacturing 0.004 0.070 -0.049 0.208 -0.029 0.190

T.C.P.U. 0.063 0.070 0.390* 0.221 0.396** 0.202

Wholesale trade -0.029 0.070 -0.237 0.238 -0.230 0.213

Retail trade 0.034 0.067 -0.508* 0.279 -0.384 0.246

F.I.R.E. 0.057 0.074 0.238 0.202 0.262 0.187

Constant -0.845** 0.029 -1.084 1.044

Log likelihood -467.37

R

2

0.105

Observations 943 646

* Coefficient significant at 10 percent level

** Coefficient significant at 5 percent level

*** Coefficient significant at 1 percent level

NOTE: Statistical significance of derivatives in column 1 does not correspond to

significance of coefficients.
36

Table 6

Determinants of the Intensity of Formal Training: Expenditures, Employer Survey

Two-Part Model, Unweighted

Dependent variable Any expenditures on

formal training

(Derivatives)

Ln(expenditures on

formal training per

employee)

Marginal impact on

expenditures on

formal training per

employee

Sample All Hours of formal

training per employee

>0

Establishment

Characteristics

Estimate Standard

Error

Estimate Standard

Error

Estimate Standard

Error

Ln(Employment) 0.038*** 0.010 0.024 0.062 17.8 17.3

Ln(Estab. Wage) 0.008 0.017 0.652*** 0.137 182.6*** 41.4

Ln(EIN size) -0.002 0.004 0.073** 0.035 19.5* 10.0

% Part-time -0.052 0.038 -0.619** 0.315 -186.1** 88.6

Union present 0.007 0.018 -0.466** 0.129 -126.9** 36.9

Contract workers used 0.045** 0.018 0.230** 0.115 76.6** 34.1

Turnover rate -0.038** 0.017 -0.336* 0.179 -104.1** 50.0

Growth rate 0.011 0.031 -0.389 0.412 -104.2 114.9

Number of benefits 0.024*** 0.005 0.103*** 0.033 35.8*** 9.4

# of workplace practices 0.010*** 0.004 0.129*** 0.026 38.6*** 7.4

Mining -0.005 0.039 -0.118 0.233 -34.1 65.4

Construction -0.020 0.032 -0.626*** 0.244 -178.8*** 69.0

Nondurable

manufacturing

-0.047 0.031 -0.786*** 0.228 -230.9*** 64.9

Durable Manufacturing -0.015 0.035 -0.667*** 0.201 -188.9*** 58.5

T.C.P.U. -0.060* 0.033 0.129 0.222 17.9 62.2

Wholesale trade -0.044 0.030 -0.510** 0.241 -154.0** 67.6

Retail trade -0.106*** 0.030 -0.523** 0.231 -175.9*** 65.9

F.I.R.E. 0.026 0.040 0.208 0.204 64.9 57.6

Constant -0.221 0.133

Log likelihood -266.02

R

2

0.275

Observations 943 808

* Coefficient significant at 10 percent level

** Coefficient significant at 5 percent level

*** Coefficient significant at 1 percent level

NOTE: Statistical significance of derivatives does not correspond to significance of

coefficients.
37

Table 7

Marginal Impacts of Individual Benefits and Practices on

the Intensity of Formal Training, Employer Survey

Two-Part Model, Unweighted

Dependent variable Marginal impact on hours of

formal training per

employee

Marginal impact on

expenditures of formal

training per employee

Estimate Standard

Error

Estimate Standard

Error

Benefits:

Sick Leave 0.148 0.159 25.7 53.3

Employee Assistance Plan 0.369*** 0.116 66.3* 38.2

Wellness 0.015 0.143 16.3 34.0

Pension 0.001 0.146 -2.5 48.9

Profit Sharing 0.078 0.092 51.1 31.1

Flexitime 0.211* 0.103 32.3 33.2

Flexisite -0.004 0.121 36.2 41.1

Child Care -0.099 0.198 34.3 55.4

Family Leave 0.210** 0.105 29.2 34.3

Practices:

Pay for Knowledge 0.135 0.096 -36.2 31.7

Employee Involvement -0.005 0.103 19.3 33.5

Job Redesign 0.179 0.109 62.2 36.5

Job Rotation -0.034 0.106 6.6 31.7

Just-in-Time Inventories 0.111 0.115 46.0 36.6

Peer Review -0.105 0.123 52.3 36.4

Quality Circles 0.125 0.108 100.4*** 33.6

Total Quality Management 0.256** 0.111 31.7 35.0

Worker Teams -0.006 0.107 55.5 37.1

p-value, equality of benefits 0.455 0.983

p-value, joint effects of benefits 0.001 0.168

p-value, equality of work practices 0.501 0.285

p-value, joint effects of practices 0.017 0.000

* significant at 10 percent level

** significant at 5 percent level

*** significant at 1 percent level
38

Table 8

Employee Variables, Employee Survey

Means

Variable Unweighted Weighted

Dependent variables

% ever trained with current employer 0.875 0.820

% trained in last 12 months 0.757 0.669

% of employee log hours in training 0.039 0.040

% of employee log hours in formal training 0.013 0.010

% of employee log hours in informal training 0.026 0.029

Independent variables

Black 0.088 0.119

Hispanic 0.062 0.064

Male 0.518 0.500

HS Dropout 0.073 0.113

GED 0.033 0.023

Some College 0.277 0.286

Associates Degree 0.093 0.097

BA 0.195 0.134

Graduate Degree 0.060 0.068

Married 0.664 0.641

Age 38.630 38.564

Job Tenure 4.540 3.834

Part-time 0.059 0.112

Manager 0.068 0.058

Professional or Technical 0.188 0.207

Sales or Clerical 0.358 0.257

Service Occupation 0.064 0.168

Observations 1,003
39

Table 9

Determinants of the Incidence of Formal Training, Employee Survey

Probit Maximum-Likelihood Estimates, Unweighted

Dependent variable Ever formal training from

current employer

(derivatives)

Formal training from current

employer in last 12 months

(derivatives)

Employee Characteristics

Estimate Standard

Error

Estimate Standard

Error

Black -0.014 0.018 -0.006 0.018

Hispanic 0.019 0.024 -0.013 0.020

Male 0.033** 0.012 0.078** 0.012

HS Dropout -0.042 0.046 -0.029 0.021

GED 0.019 0.030 0.103 0.035

Some College. 0.022 0.020 0.060* 0.014

Assoc. Degree 0.001 0.030 -0.023 0.022

BA 0.046** 0.021 0.058 0.020

Graduate Degree 0.061 0.073 0.147* 0.043

Married 0.001 0.008 0.001 0.007

Age 0.006 0.013 0.016** 0.012

Age Squared/100 -0.009** 0.015 -0.024** 0.014

Job tenure 0.010*** 0.002 -0.008 0.003

Tenure Sq./100 -0.022* 0.007 0.019 0.009

Part-Time 0.003 0.024 -0.035 0.032

Employer Characteristics

Ln (Employment) 0.000 0.004 0.010 0.007

Ln (Estab. Wage) 0.028 0.129 0.018 0.079

Ln (EIN size) 0.016*** 0.005 0.015 0.002

% Part-Time -0.039 0.119 -0.019 0.101

Union Present -0.047** 0.022 -0.051 0.013

Contract Workers 0.040** 0.014 0.056* 0.010

Turnover Rate -0.044* 0.053 -0.097* 0.033

Growth Rate -0.042 0.059 -0.046 0.046

Number of Benefits 0.011** 0.003 0.023** 0.002

Number of Workplace Practices 0.015*** 0.003 0.022*** 0.001

Observations 1,073 1,073

* Coefficient significant at 10 percent level

** Coefficient significant at 5 percent level

*** Coefficient significant at 1 percent level

NOTE: Statistical significance of derivatives does not correspond to significance of

coefficients.
40

Table 10

Determinants of the Intensity of Training:

Hours of Formal and Informal, Employee Survey

Two-Part Model, Unweighted

Dependent

variable

Marginal impact on %

time in training in log

period

Marginal impact on %

time in formal training in

log period

Marginal impact on %

time in informal

training in log period

Employee

Characteristics

Estimate Standard

Error

Estimate Standard

Error

Estimate Standard

Error

Black 0.002 0.010 0.003 0.006 0.003 0.006

Hispanic -0.005 0.011 -0.001 0.005 -0.005 0.008

Male 0.003 0.007 -0.000 0.003 0.005 0.004

HS Dropout 0.007 0.017 -0.003 0.008 0.002 0.010

GED 0.029** 0.015 0.006 0.006 0.022** 0.010

Some College 0.014* 0.008 0.002 0.004 0.013** 0.005

Assoc. Degree 0.028*** 0.010 0.010** 0.005 0.017** 0.007

BA 0.019** 0.009 0.006 0.005 0.013** 0.006

Graduate Degree 0.038*** 0.012 0.008 0.005 0.026*** 0.007

Married 0.006 0.006 0.002 0.003 0.004 0.004

Age 0.001 0.002 0.002** 0.001 0.000 0.001

Age Squared/100 -0.002 0.002 -0.002** 0.001 -0.001 0.001

Job tenure -0.005*** 0.002 0.001 0.001 -0.005*** 0.001

Tenure Sq./100 0.000** 0.000 0.000 0.000 0.000*** 0.000

Part-Time -0.014 0.015 -0.004 0.006 -0.007 0.010

Ln Wage 0.010 0.009 0.005 0.004 0.004 0.005

Employer

Characteristics

Ln (Employment) -0.002 0.003 0.001 0.001 -0.002 0.002

Ln (Estab. Wage) -0.000 0.008 -0.001 0.004 0.000 0.005

Ln (EIN size) 0.002 0.002 -0.000 0.001 0.001 0.001

% Part-Time -0.001 0.022 -0.000 0.008 -0.002 0.013

Union Present -0.011 0.007 -0.006* 0.003 -0.005 0.005

Contract Workers -0.004 0.006 0.003 0.003 -0.006 0.004

Turnover Rate 0.006 0.011 -0.014*** 0.005 0.010 0.007

Growth Rate -0.011 0.015 -0.001 0.007 -0.009 0.010

# of benefits 0.004* 0.002 0.001 0.001 0.002 0.001

# of workplace

Practices

 0.002 0.001 0.001 0.001 0.002** 0.001

Observations 1,003 1,003 1,003

* significant at 10 percent level

** significant at 5 percent level

*** significant at 1 percent level
41

Table 11

Marginal Impacts of Individual Benefits and Practices on

the Formal Training, Employee Survey

Two-Part Model, Unweighted

Dependent variable Ever formal training

with current employer

(derivatives)

Formal training with

current employer in last

12 months (derivatives)

Marginal impact on %

time in training in log

period

Estimate Standard

Error

Estimate Standard

Error

Estimate Standard

Error

Benefits:

Sick Leave 0.018 0.038 0.049 0.023 0.019* 0.010

Employee Assistance

Plan

 0.027* 0.014 0.086*** 0.008 0.009 0.007

Wellness 0.040** 0.010 0.012 0.009 0.009 0.007

Pension 0.012 0.015 0.077* 0.013 -0.003 0.011

Profit Sharing -0.013 0.011 -0.041 0.009 -0.003 0.006

Flexitime 0.001 0.014 0.040 0.011 -0.001 0.006

Flexisite -0.028 0.024 -0.088** 0.022 0.006 0.008

Child Care 0.057 0.048 0.025 0.045 0.029** 0.012

Family Leave 0.006 0.019 0.049 0.013 -0.011* 0.006

Practices:

Pay for Knowledge 0.005 0.012 0.017 0.008 0.000 0.006

Employee Involvement 0.009 0.014 0.021 0.008 0.006 0.006

Job Redesign 0.014 0.011 -0.001 0.011 -0.006 0.007

Job Rotation 0.021 0.011 0.027 0.013 -0.003 0.006

Just-in-Time

Inventories

-0.008 0.021 0.033 0.020 0.008 0.007

Peer Review 0.027 0.022 0.023 0.018 0.009 0.008

Quality Circles 0.018 0.017 0.027 0.010 0.006 0.007

Total Quality

Management

 0.015 0.018 0.012 0.009 0.007 0.007

Worker Teams 0.034* 0.013 0.071* 0.012 0.000 0.007

p-value, equality of

benefits

 0.053 0.019 0.156

p-value, joint effect of

benefits

 0.012 0.004 0.060

p-value, equality of

work practices

 0.862 0.979 0.908

p-value, joint effect of

work practices

 0.040 0.161 0.658

* Coefficient significant at 10 percent level

** Coefficient significant at 5 percent level

*** Coefficient significant at 1 percent level

NOTE: Statistical significance of derivatives does not correspond to significance of

coefficients.