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Federally subsidized occupational training and the employment and earnings of male trainees
Journal
of Econometrics
8 (1978) 111-125.
0 North-Holland
Publishing
Company
FEDERALLY SUBSIDIZED OCCUPATIONAL TRAINING AND EMPLOYMENT AND EARNINGS OF MALE TRAINEES Nicholas
THE
M. KIEFER*
University of Chicago, Chicago, IL 60637, USA
Received
The impact are studied techniques It is found late sixties interpreted program.
April 1977, final version
received October
1977
of MDTA training on the earnings and employment probabilities of male trainees on the basis of a longitudinal data set on trainees and non-trainees. Econometric which eliminate many of the ambiguities in interpreting previous estimates are used. that the program had little or no effect on employment or earnings of trainees in the (relative to continued normal employment). It is suggested that the program be as a pure income-maintenance program rather than a subsidized skill-acquisition
1. Introduction Government-sponsored training programs are introduced as a means of helping the unemployed and underemployed, particularly the disadvantaged, to develop job skills. These new skills are intended to bring more employment and higher earnings to trainees, resulting in a reduction in poverty and a reduction in income inequality. More than a decade of continuous experience with training has been accumulated, but little is known about the actual effects of training on program participants. In view of the fact that there are alternative methods of achieving the program’s stated goals, for example increasing the level of direct redistribution through transfer payto ask how effective ments and the tax system, it seems reasonable government training programs are in increasing the earnings of trainees. The training studied in this paper is classroom training under the Manpower Development and Training Act (MDTA) of 1962. Training under MDTA took place beginning in 1963. Originally the programs were retraining programs, designed for workers whose jobs had become obsolete because of automation. However, amendments to the MDTA passed in 1966 (as part *I thank James Heckman, Ed Lazear and George Neumann for helpful comments during the preparation of this paper. Useful comments on initial drafts were received at workshops at the University of Chicago, Cornell University, and The University of Rochester. Any remaining errors are mine. Ralph Shnelvar did the computations. This work was partially supported by the U.S. Department of Labor under contract J-q-m-7-0035.
112
NM. Kiefer,
Training,
employment
and earnings
of the War on Poverty) shifted the emphasis of the programs to training the disadvantaged. MDTA institutional training continued in this form until 1973, when the Comprehensive Employment and Training Act came into effect. (On-the-job training under MDTA was superseded by Job Opportunities in the Business Sector in 1968.) Programs similar to MDTA are now operated under Title I of CETA. MDTA training has been widely studied, but few firm results are available.’ The literature is reviewed in Goldstein (1972), Perry et al. (1975), and Stromsdorfer (1972). Most previous studies suffer from two major problems in addition to small sample sizes: the first is that not all individuals have jobs, so that the series on earnings (or wage rates) used for calculating the effectiveness of the program contain a number of zeros (or missing observations). Typically the problem is ignored, observations are either included with zero values or deleted as missing-either procedure can be expected to introduce bias into the estimated effects of training.2 The second major problem with previous studies arises because the data are analyzed as though they came from a carefully designed experiment. The fact that the MDTA training was not operated as an experiment is usually noted and ignored. When the non-experimental nature of the data is discussed, it is treated as a hindrance to research, if not as a defect of the training program. The critical issue is that of the selection of an appropriate control group (now usually called a comparison group but treated as a control group in analysis). In the simplest kind of experimental design, individuals would be drawn from the same population, and some of them would be chosen at random and assigned to training. A post-training comparison of earnings would then reflect the impact of training on earnings. In fact, trainees and members of available comparison groups are not necessarily alike (before training), and selection into training is not necessarily entirely random. Though analyses of the effects of training on earnings have become increasingly sophisticated, the problem of selection into training has not been directly addressed. Typically arguments are made that trainees and controls are as similar as ‘possible’-i.e., all available variables are included in the regressions, and post-training earning differences are due to training. In this study I take an alternative approach which realizes and exploits the facts that trainees and controls are not identical, that assignment to training may not be effectively random, and that some individuals are not employed (have zero earnings). The effect of training is broken down into an effect on ‘The effect of training on the earnings of the 1964 cohort of trainees has been carefully studied by Ashenfelter (1978) on the basis of Social Security Administration annual earnings records. A small and persistent effect of training was found. As mentioned in the text, the 1964 trainees and programs were substantially different from the post-1966 trainees and programs. ‘Kiefer (1977) uses tobit analysis in an attempt to handle this problem.
NM.
Kiefer,
Trclirpiinob.employment
cmd earnings
113
potential earnings and an effect on employment. In section 2 the model is described and the estimating equations are developed. In section 3 the estimation procedure is outlined and the data, from the OEO-DOL longitudinal survey, are described. ktimates are then presented. Section 4 contains a discussion of the results.
2. The model The effects of training will be measured in this study as a function of the number of weeks an individual stays in the program. This represents a generalization of the usual practice of using a dummy variable indicating only whether or not an individual participated in a program, without regard to the amount of time actually spent receiving training.3 The impact of training on an individual’s potential earnings, that is, the weekly earnings an individual could expect if he were employed, is assumed to be adequately approximated by a quadratic function of the number of weeks the individual spends in training. The quadratic is constrained to have a zero intercept. Possible interaction between the parameters and a trainee’s demographic characteristics is ruled out-though the number of weeks an individual stays in the program may depend on various household and personal characteristics.4 Post-training earnings _v~for a potential trainee is given by expected earnings without training plus a quadratic in the number of weeks an individual will spend in training. Expected earnings without training is specified as a linear function of pre-training expected earnings yiL. So
The variable _riL is constructed on the basis of data on pre-training earnings and employment for trainees and non-trainees. It represents the earnings an individual could have expected on the basis of his measurable characteristics without adjustment for the probability he is employed. The construction of the series is described below. Note that eq. (1) is estimated only over those men who are working. If earnings and the probability of employment are not uncorrelated, then the expectation of pii conditional on the ith individual’s being employed is generally non-zero; in fact E(pl, 1employment of individual i)=~(a*)“~,$ where p is the correlation between the error in the earnings relation and the employment relation, CJ* is the variance of the error in the
‘Other problems remain, e.g., the effect of a week of training may vary across program sites or instructors. 4A more detailed analysis, using more and better data, could investigate the adequacy of this approximation. Since the sample consists only of males, training is measured in weeks, and only one particular program is being considered the approximation may not be so restrictive. Different effects by race are permitted.
114
earnings
N.M.
relation,
Kiefer,
Training,
employment
und earnings
and
ni=f(4i)/(1-F(4i))7
(2)
wheref is the standard normal density, F is the standard normal distribution and pi is discussed below. Heckman ‘(1976) has studied this estimation problem and has suggested a simple and computationally efficient estimator. Simply do a probit of employment on independent variables X, then use predicted values -Xiy, where y is the normalized coefftcient vector from the probit, for di; then construct ,Ii using (2) and include it as a regressor in (5). Ordinary least squares then provides consistent estimates of the ~3~. Generalized least squares provides more efficient estimates. The probit employment equation specified here is Si=yo+y1yiL+yZWEEKSi+‘y3WEEKS2+y4MARi.
(3)
The variable S is not observed, but its sign is known. S is sometimes interpreted as the difference between a wage offer and a reservation wage, though that interpretation is not exploited or necessary here. Given the function determining returns to training, each individual considers the costs to him associated with various levels of training and chooses an appropriate number of weeks to spend in the program. The equation determining the number of weeks in the program is specified as
+/l,AGE+/?,AGE*,
(4)
in which & captures the benefit considerations common across individuals, lagged earnings represents opportunity costs of time, marital status and number of children are included to pick up their effects on costs, and a quadratic in age adjusts for the different number of periods benefits from training will be received by people of different ages. Tobit is a natural method of estimation since there is no threshold which can be expected to vary across individuals (like the reservation wage in the earnings relation). The predicted values of WEEKS may then be used as instruments for actual values in the employment and earnings equations. Although (4) has been referred to as reflecting an individual’s decision to take weeks in training (facing an elastic supply), it could also represent a reduced form equation reflecting selection by the program manager as well as the individual’s demand for training. In this case the coefficients would reflect supply effects as well as demand effects. The included variables could be expected to influence the program manager’s estimate of the probability the individual will benefit from the program or at least find employment
N.M. Kiefer,
Training,
employment
and earnings
115
after the programthis probability will certainly affect the selection decision. Yet another interpretation is that the programs serve as a means of income maintenance (there is a stipend associated with the program) while individuals sample wage offers from some distribution. Lagged income may then affect time in training through its association with the wage offer distribution. The interpretation of the coefficients of this equation plainly depends on the underlying economic process, however, the interpretation of the final estimated training effects does not depend on the interpretation of this equation. The series on lagged earnings (_v~) is constructed from observations on earnings and employment three quarters before training. The choice of period is important since the purpose of this variable is to standardize for pre-training stocks of human capital; typically trainees experience a spell of unemployment just before entering the training program and their observed earnings are unusually low as a result. Consequently standardizing on the basis of earnings immediately before training would overstate the effect of training if the pre-training decline in trainee earnings was transitory, or if part of it were transitory. To put this another way, suppose all individuals had the same expected life-cycle of earnings but earnings fluctuated randomly from period to period. Then if individuals were selected on the basis of having unusually low earnings in period t and were not trained but just followed into some later period, we would expect their earnings to have risen.’ Early studies, consisting primarily of before-and-after earnings comparisons, failed to recognize the drop in earnings immediately prior to training. These studies tended to show substantial ‘effects’ of training on earnings, under the implicit assumption that the pre-training fall in earnings was permanent rather than transitory. Ashenfelter (1974) illustrates the problem using Social Security Administration earnings records for the 1964 MDTA trainees and provides a full discussion of the issues involved. The assumption I make is that the decline in trainee earnings one quarter prior to training was transitory and that earnings data from three quarters before training do not pick up this transitory fall in earnings. If the fall in earnings was actually permanent rather than transitory, then the estimates developed here will be downward biased measures of the effects of training. If the fall in earnings was already taking place three quarters before training, then the constructed series on lagged income may contain biases, and this may introduce bias into the final estimate. 6 The series on lagged earnings is ‘The best studies used some kind of control group, in which case the problem disappears if individuals are assigned to their group, trainees or controls, randomly or on the basis of a selection rule which is compensated for in the statistical estimation procedures. 6Mean annual earnings *for the trainees (calculated from SSA records) are: for whites 1968, $1899: 1969. S1978: 1970. $1467: for blacks 1968. $1830: 1969. $1867: 1970. $1499. These numbers give a rough indication that the earnings decline was not going on until the year of training (1970).
116
comprised
N.M. Ki&fes
of predicted
Training, employment
values from the following
+G,EDUC,AGE,
+&MAR,
and earnings
equation:
+ G,CHIZlXEN,,
(51
in which the dependent variable is observed earnings and the sample consists of those who were employed during the third quarter before training. In estimating these coefficients the employment selection problem described above arises. A correction is made for the non-zero expectation of the error term in (5) on the basis of estimates of the pre-training employment probit,
where Sf is the latent employed.7
variable
which must be positive
for an individual
to be
3. Data and estimation The data are from a two-and-a-half year longitudinal study of four federally-sponsored training programs including MDTA which was undertaken in 1969 under contract to the Office of Economic Opportunity and the Department of Labor. Trainees in ten major Standard Metropolitan Statistical Areas were sampled. s Members of the subsample of the trainee group were matched, on age, race, and sex, to individuals from the pool of people in the particular SMSA who were eligible for training but did not participate in a program. These individuals were interviewed at the same time as their ‘running mates’ in the trainee group. Interviews took place in four waves; the first as soon as possible after the trainee entered the program, the second when the trainee left the program, the third four months after the second, and the last eight months later. Retrospective data on employment, earnings, and hours of work were collected along with demographic information. Although the survey was usually successful, in terms of response rates, the transcription of the data from questionnaires to a machine-readable form was not reliably done. Researchers at the OEO managed to correct the data for a number of observations (about one-third ‘Eqs. (5) and (6) are estimated only to provide an instrument for y. Consequently, changes in their specification will change the asymptotic efficiency of the final parameter estimates (in unknown directions), but will not affect the property of consistency or the form of the limiting distribution. The correction for the non-zero error expectation is made only for estimation of the parameters of (5), not for the construction of the instrument for pre-training earnings. ‘The SMSAs were Chicago, New York, Atlanta, Cincinnati, DallassFort Worth, Detroit, Norfolk-Portsmouth, Los AngelessLong Beach, Philadelphia, and Seattle.
N.M. Kiefer,
Training,
emplo.vment and earnings
117
of the cases had to be corrected during the data cleaning). Despite concerns about possible biases arising from missing data and lost observations, the OEO/DOL data set is the best available collection of detailed information on trainees. The cost of this widely-discussed data set ran to six million dollars. The questionnaires have been destroyed.’ The first estimates to be obtained are those of eqs. (5) and (6), the equations necessary to generate the instrument for lagged earnings.” Throughout separate equations are run for blacks and whites.” Estimates are presented in table 1. After some experimentation, marital status variables were omitted from the earnings equation for blacks- the effect of marital status on pre-training earnings seemed to be only through its effect on employment probabilities. All the equations are significant though the coefficients are generally imprecisely determined. The instrument for lagged earnings is constructed by taking predicted earnings from the earnings series is not adjusted for regressions, excluding J.. Thus, the earnings employment probabilities. The next equation to be estimated is (3) giving the number of weeks an individual spends in training. Tobit estimates are presented in table2. The single most significant variable is the number of children the potential trainee has; individuals with children are significantly less likely to enter training, or ‘For details on the data collection and reduction, see the U.S. Department of Labor (1978). Pre-training data were collected in the first interview; the post-training data used here in the third and fourth interviews. “Before turning to the empirical results, it is useful to consider the possible effects of unobserved ability on the final estimates of the effects of the programs. The usual treatment of unobserved ability is to assume that it is uncorrelated with anything. In this uninteresting case the above estimation techniques give consistent estimates. However, in several cases in which ability is correlated with observables, our estimation techniques yield consistent estimates of x2, First, suppose that ability c+, y2 and y3, the effects of training on earnings and employment. determines some initial level of earnings, but subsequent earnings are determined by a difference equation such as (1). In this case the only part of ability entering the post-training earnings eq. (1) is that part not predictable on the basis of the variables in the initial earnings eq. (5). Plainly that error is uncorrelated with the instrument for lagged earnings; it is also uncorrelated with the instrument for WEEKS which is based on a subset of the variables included in (5). Therefore the coefficients a of (I) are estimated consistently. Similarly, if ability affects employment only through its effect on lagged earnings, then the y in (4) are estimated consistently. Now, suppose ability enters the equation determining the number of weeks an individual spends in training. Then, although the coefficients of the variables in that equation may be inconsistent, the predicted values of WEEKS can be used as instruments in the employment and earnings equations as long as ability affects employment and earnings only through its effect on WEEKS and lagged earnings. Finally, suppose ability enters the employment and earnings equations directly but does not enter the WEEKS equation except through its effect on lagged earnings. In this case the instrument for WEEKS constructed from (3) is again uncorrelated with the errors in (1) and (4) and consistent estimates are obtained. Observed WEEKS, however, need not be uncorrelated with the errors in these equations; if there is correlation our tests will pick it up and the instruments will be used. Obviously this list does not include all possible ways in which ability may enter the estimating equations, however, the problem may not be as serious as it seems. “For ‘white’ in this sample, read ‘other’.
__-.
0.184 (1.68)
Black
10.92 (4.17)
Black
)
-0.166 (4.50)
- 0.0026 (0.411)
- 0.002 (1.30)
-0.001 (1.02
Age’
employment
3.34 (1.22)
- 2.34 (0.469)
0.201 (1.78)
0.071 (0.98)
-0.0059 (0.637)
0.127 (0.646)
-0.0057 (1.40)
- 0.0027 (1.10)
Table
-
20.51 (0.749)
838 (1.11)
0.283 (0.690)
Marital status
regressions
Education* age
and earnings
Education
probits
1
3.32 (2.93)
1.47 (0.920)
0.556 (0.909)
0.013 (0.330)
Number of children
5.73 (0.005) - 5.46 (0.323)
i
(t-statistics
is the value of the likelihood least squares.
- 0.003 (0.122)
0.007 (0.425)
Marital status* age
for data construction
“The chi-squared statistic reported in the last column for the probit equations probit against the binomial model. The regressions were esttmated by generalized
1.27 (0.280)
White
Earnings/week
___~
0.071 (1.12)
White
Employmetlt
Age
Pre-training
ratio statistic
-88.1 (1.68)
57.3 (0.404)
-3.14 (1.82)
- 0.880 (0.847)
Constant
750
N
0.127
0.160
R2
44.57
x1(7) 25.87
for testing
733
507
1051
in parentheses).”
F
the litted
17.6
13.5
Blacks
Weeks in training Whites
_______
0.755 (0.426)
(0.044)
(0.442)
-0.171 (0.946)
0.333
Marital status
0.0794
Pre-training earnings
Tobit estimates
-2.05 (2.57)
(4.01)
- 2.63
Number children
of
2.94 (1.35)
(1.05)
- 0.272
Age
(r-statistics
(1.44)
- 0.052
(0.272)
- 0.066
Age’
equation
Table 2 of the weeks-in-training
- 20.63 (1.31)
(0.736)
7.55
Constant
in parentheses).
1049
750
N
510
294
Number of zeros
18.9
18.7
D
S
f =a
-!! z ‘rl 3 :
8. g. ?Q
3
120
NM.
Kiefer,
Training,
employment
and
earnings
to continue in training having entered, than comparable individuals without children or with fewer children. Pre-training earnings, which is included as a measure of the opportunity costs of the time spent in training, has the anticipated negative sign in the equation for blacks, but has a positive sign in the equation for whites (though it is not significantly different from zero). This is consistent with the interpretation of (3) as a reduced form equation reflecting both the program manager’s desire to choose trainees who will have high post-training earnings (using pre-training earnings as an indicator of this) and the potential trainee’s demand for weeks of training. Age has the anticipated negative effect in the equation for whites; in the equation for blacks age has an increasing effect until about age 28, then a decreasing effect. Again, this may reflect program managers’ selection of people who will be easier to place.12 The next equation which can be estimated is the employment probit Si=y,+y,yi,+y2WEEKS+y,WEEKS2+y&lAR.
(7)
At this point some preliminary hypothesis-testing may be done, in hopes of obtaining more efftcient estimates of the effect of training. Using instruments for WEEKS and WEEKS2 provides consistent estimates of y2 and y3 whether or not the error in observed WEEKS is correlated with the error in eq. (7); however, if these errors are uncorrelated, then using observed values of WEEKS provides more efficient estimates.13 A test may be made by estimating (7) using the observed values of WEEKS and WEEKS2 instead of the instruments, and testing that speciticiation against a specification which includes both the observed values of WEEKS and WEEKS2 and the instruments for WEEKS and WEEKS’. This is a test of the joint significance of the instruments given the observed values. Plainly, if the errors are uncorrelated, then adding the instruments will add nothing to the explanatory power of the equation, and they will not be significant.14 Our procedure will be to perform this test, and on the basis of its outcome to use (7) with either observed WEEKS or the instrument for WEEKS obtained by fitting (3). Minus twice the log of the likelihood ratio for testing the significance of the instruments given the observations is 2.96 for whites, 4.3 for blacks. These values are to be compared with x2(2), which has a 0.05 critical value of 5.99. Therefore the null hypothesis of no correlation between the error in observed WEEKS and the error in the employment equation is 12The age-specific relative black -white unemployment rates tend to fall with age. “Note that for consistency in the instrumental variable specification predicted values of weeks should be used for trainees and non-trainees. 141n the linear regression framework. this is the Wu (1973) test, written in Chow-test form by Farebrother (1976). The statistical foundation of this test is the theorem that an efficient estimator is uncorrelated with the difference between it and an inefficient estimator. These results are extended and discussed in an econometric context by Hausman (1976).
121
N.M. Kiefer, Training, employment and earnings
Table 3 Probit
estimates
Lagged earnings Emplo_vment of Whites
of the post-training
Marital status
employment
WEEKS
equation.”
WEEK Sz _____.__
Constant
X2(4)
- 0.0089 (1.49)
0.757 (3.7)
- 0.005 (0.486)
- 0.0002 (0.806)
0.935 (2.01)
43.02
Blacks
0.0067 (1.88)
0.232 (2.30)
0.0084 (1.01)
- 0.0004 (1.76)
- 0.465 (1.48)
21.24
“The chi-squared
test is a test of the probit
model against
the binomial
model.
not rejected and the observed values of the WEEKS variable are used in (7) to increase the efficiency of the estimates of the parameters. Estimated coefficients are given in table 3. Both probit equations are significantly different from the binomial model according to the x2(4) statistic. Marital status has a significant positive effect on employment probabilities, Training seems to have a negative effect on the employment of whites, a positive but decreasing effect on the employment probabilities of blacks. The effect of training in both cases is very small. The significance of the training effects at the sample means of the number of weeks in training can be easily tested. For whites the total effect is -0.141, with a standard error of 0.108; for blacks the effect is 0.035, with a standard error of 0.086.” Neither of these effects is significantly different from zero, but that in itself is not particularly informative about how large the effects might be. To examine this loosely, add twice the standard errors to the estimated effect and note that even the largest effect, that of 0.2 for blacks, translates into an increase in employment probability of at most 0.07 and probably much less for most individuals (the probability depends on independent variables -for example, someone who had pre-training earnings of $lOO/week and was married has an increase in employment probability of only abcut 0.03). Employment effects, if any, are therefore very small. The next step is to form the series on i using the probit coefficients from the employment equation and formula (2). This is then included as a regressor in the post-training earnings eq. (1). Again, a test is made to determine whether or not the instrument for weeks contributes anything to the equation, given the observed weeks. The appropriate test is an F-test, and the values are: for whites F(2,475)=0.45, for blacks F(2,620)=0.33. The null hypothesis of no correlation between the error in the earnings equation and the observed values of weeks is not rejected. Coefftcients of the regression using observed values of WEEKS are reported in table 4. Both “The -0.2487
covariances E-5, blacks
between the coefficients of WEEKS and those of WEEKS2 are: whites -0.1551 E-5. The mean numbers of weeks in training are 16.8 and 15.3.
122
N.M. Kiefer, Training, employment und earnings
regressions are significant. The single most significant variable explaining weekly earnings is lagged weekly earnings.” Both samples give a negative coefficient to the linear term in WEEKS and a positive coefhcient to the quadratic term. Again, a confidence interval for the total training effects at the mean values of WEEKS can be calculated using the estimated covariances between the coefficients on WEEKS and on WEEKS’.” The estimated total effects of training on weekly earnings are - 12.38 for whites, -6.83 for blacks with standard errors 11.2 and 2.29. For whites the effect is not significantly different from zero and the estimated effect plus twice its standard error is about $10. Thus training probably had no effect on earnings and it is unlikely that any effect on weekly earnings is greater than $10. For blacks the effect is more precisely estimated. The estimated effect plus twice its standard error is -2.25. It is therefore unlikely that training had any positive effect on the earnings of blacks. Indeed, trainees who stayed in the programs the mean length of time seem to have experienced a relative decline in their weekly earnings.
4. Conclusions A systematic method of estimating the effect of training separately on employment and weekly earnings has been applied to a sample of MDTA trainees. The approach taken here deals directly with two problems commonly ignored in studies of training: the fact that earnings are not observed for individuals who are not employed, and the potential correlation between selection into training and the error in the earnings equation. The first problem is handled using Heckman’s (1976) framework, the second by testing for correlation between the number of weeks an individual spends in training and the error in the earnings equation. Nonrandom selection into training did not seem to present a problem in measuring either the earnings effects of the employment effects of training, given that training was measured by the number of weeks an individual stayed in the program. However, this finding should be treated somewhat cautiously; the power of the test is not known. Results using the instrument for WEEKS instead of the observed values are qualitatively the same. The results imply that the effect of training on employment was small. The effect on employment, evaluated at the mean number of weeks trainees participated in the program, is not significantly different from zero. This in itself is not particularly informative, however the training effect plus twice its ‘“Although (I) is not intended to be a time-series model of earnings dynamics it is reassuring to note that the estimated coefficients have reasonable implications in terms of a dilference equation generating earnings. The profiles have the characteristic concave shape and the implied equilibrium earnings are sensible. “The covariances are: whites - 1.780 E-3. blacks -4.605 E-3.
124
N.M. Kiefer,
Trcrining, employment
and earnings
standard error (roughly a 95 percent confidence upper bound) has a maximum of about a 0.07 increase in the probability of being employed (for blacks with the most favorable combination of independent variables). The effect of training on earnings was more precisely determined for blacks than for whites. The estimated effects, evaluated at the mean number of weeks in training, were negative for both samples. The rough 95 percent confidence interval for the effect on whites had a maximum effect of $10 a week, that for blacks-$2 a week. Trainees who stayed in the program longer had larger effects on earnings. The estimates reported in this paper, which imply that training is not as beneficial as continued normal employment, lead naturally to the question: Why do people enter training programs? The answer, I think is that the programs provide income maintenance during a spell of unemployment. An individual who is ineligible for or has exhausted other forms of income maintenance may enter a training program and receive the stipend.‘* This interpretation raises new questions of evaluation. If the training programs are regarded by participants as income maintenance programs, then perhaps the appropriate comparisons to make are between training programs and other transfer programs which are explicitly intended to provide income maintenance such as unemployment insurance, the negative income tax and (not as explicitly) public sector employment programs. Plainly the costs of these programs differ substantially; so probably do the effects on labor supply. These comparisons are topics of further research. “This was found eligible for training Act of 1962.
by Neumann (1977) to be the case for a sample of individuals who became under the Trade Adjustment Assistance program of the Trade Expansion
References Ashenfelter, Orley, 1974, The effect of manpower training on earnings: Preliminary results, Proceedings of the 27th Annual Meeting of the IRRA, 252-260. Ashenfelter, Orley, 1978, Estimating the effect of training programs on earnings, Review of Economics and Statistics 60, 47-57. Farebrother, R.W., 1976, A remark on the Wu test, Econometrica 44, 475477. Goldstein, Jon, 1972, The effectiveness of manpower training programs: A review of research on the impact on the poor (US. Government Printing Office, Washington, DC). Unpublished paper, Aug. (M.I.T., Hausman, J.A., 1976, Specification tests in econometrics, Cambridge, MA). Heckman, James, 1976, The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models, Annals of Economic and Social Measurement 5, 475-492. Johnson, George, 1975, Evaluating the impact of CETA programs on participants’ earnings: Office of Evaluation and Research,. ASPER, Methodological issues and problems, Framework for Evaluation Paper No. 4, Jan. Kiefer, Nicholas M., 1977, The economic benefits from manpower training, Research in Labor Economics, Suppl. 1.
N.M. Kiefer,
Training,
emplownent
and earnings
125
Neumann, George, 1977, The direct labor market effects of the trade adjustment assistance program: The evidence from the TAA survey, in: William Dewald, ed., The impact of foreign trade and investment on domestic employment, forthcoming. Perry et al., 1975, The impact of government manpower programs (Industrial Relations Unit, The Wharton School, Philadelphia, PA). Stromsdorfer, Ernst, 1972, Review and synthesis of cost-effectiveness studies of vocational and technical education (ERIC, Columbus, OH). U.S. Department of Labor and U.S. Department of Health, Education and Welfare, 1978, Cost eNective analysis of four categorical employment and training programs: MDTA, JOBS, Job Corps, and NYC-OS, in: G. Goodfellow and E.W. Stromsdorfer, eds., A report to the General Accounting Oflice, 4 ~01s. (U.S. Department of Labor, Washington, DC). Wu, D., 1973, Alternative tests of independence between stochastic regressors and disturbances, Econometrica 41, 733-750.
of Econometrics
8 (1978) 111-125.
0 North-Holland
Publishing
Company
FEDERALLY SUBSIDIZED OCCUPATIONAL TRAINING AND EMPLOYMENT AND EARNINGS OF MALE TRAINEES Nicholas
THE
M. KIEFER*
University of Chicago, Chicago, IL 60637, USA
Received
The impact are studied techniques It is found late sixties interpreted program.
April 1977, final version
received October
1977
of MDTA training on the earnings and employment probabilities of male trainees on the basis of a longitudinal data set on trainees and non-trainees. Econometric which eliminate many of the ambiguities in interpreting previous estimates are used. that the program had little or no effect on employment or earnings of trainees in the (relative to continued normal employment). It is suggested that the program be as a pure income-maintenance program rather than a subsidized skill-acquisition
1. Introduction Government-sponsored training programs are introduced as a means of helping the unemployed and underemployed, particularly the disadvantaged, to develop job skills. These new skills are intended to bring more employment and higher earnings to trainees, resulting in a reduction in poverty and a reduction in income inequality. More than a decade of continuous experience with training has been accumulated, but little is known about the actual effects of training on program participants. In view of the fact that there are alternative methods of achieving the program’s stated goals, for example increasing the level of direct redistribution through transfer payto ask how effective ments and the tax system, it seems reasonable government training programs are in increasing the earnings of trainees. The training studied in this paper is classroom training under the Manpower Development and Training Act (MDTA) of 1962. Training under MDTA took place beginning in 1963. Originally the programs were retraining programs, designed for workers whose jobs had become obsolete because of automation. However, amendments to the MDTA passed in 1966 (as part *I thank James Heckman, Ed Lazear and George Neumann for helpful comments during the preparation of this paper. Useful comments on initial drafts were received at workshops at the University of Chicago, Cornell University, and The University of Rochester. Any remaining errors are mine. Ralph Shnelvar did the computations. This work was partially supported by the U.S. Department of Labor under contract J-q-m-7-0035.
112
NM. Kiefer,
Training,
employment
and earnings
of the War on Poverty) shifted the emphasis of the programs to training the disadvantaged. MDTA institutional training continued in this form until 1973, when the Comprehensive Employment and Training Act came into effect. (On-the-job training under MDTA was superseded by Job Opportunities in the Business Sector in 1968.) Programs similar to MDTA are now operated under Title I of CETA. MDTA training has been widely studied, but few firm results are available.’ The literature is reviewed in Goldstein (1972), Perry et al. (1975), and Stromsdorfer (1972). Most previous studies suffer from two major problems in addition to small sample sizes: the first is that not all individuals have jobs, so that the series on earnings (or wage rates) used for calculating the effectiveness of the program contain a number of zeros (or missing observations). Typically the problem is ignored, observations are either included with zero values or deleted as missing-either procedure can be expected to introduce bias into the estimated effects of training.2 The second major problem with previous studies arises because the data are analyzed as though they came from a carefully designed experiment. The fact that the MDTA training was not operated as an experiment is usually noted and ignored. When the non-experimental nature of the data is discussed, it is treated as a hindrance to research, if not as a defect of the training program. The critical issue is that of the selection of an appropriate control group (now usually called a comparison group but treated as a control group in analysis). In the simplest kind of experimental design, individuals would be drawn from the same population, and some of them would be chosen at random and assigned to training. A post-training comparison of earnings would then reflect the impact of training on earnings. In fact, trainees and members of available comparison groups are not necessarily alike (before training), and selection into training is not necessarily entirely random. Though analyses of the effects of training on earnings have become increasingly sophisticated, the problem of selection into training has not been directly addressed. Typically arguments are made that trainees and controls are as similar as ‘possible’-i.e., all available variables are included in the regressions, and post-training earning differences are due to training. In this study I take an alternative approach which realizes and exploits the facts that trainees and controls are not identical, that assignment to training may not be effectively random, and that some individuals are not employed (have zero earnings). The effect of training is broken down into an effect on ‘The effect of training on the earnings of the 1964 cohort of trainees has been carefully studied by Ashenfelter (1978) on the basis of Social Security Administration annual earnings records. A small and persistent effect of training was found. As mentioned in the text, the 1964 trainees and programs were substantially different from the post-1966 trainees and programs. ‘Kiefer (1977) uses tobit analysis in an attempt to handle this problem.
NM.
Kiefer,
Trclirpiinob.employment
cmd earnings
113
potential earnings and an effect on employment. In section 2 the model is described and the estimating equations are developed. In section 3 the estimation procedure is outlined and the data, from the OEO-DOL longitudinal survey, are described. ktimates are then presented. Section 4 contains a discussion of the results.
2. The model The effects of training will be measured in this study as a function of the number of weeks an individual stays in the program. This represents a generalization of the usual practice of using a dummy variable indicating only whether or not an individual participated in a program, without regard to the amount of time actually spent receiving training.3 The impact of training on an individual’s potential earnings, that is, the weekly earnings an individual could expect if he were employed, is assumed to be adequately approximated by a quadratic function of the number of weeks the individual spends in training. The quadratic is constrained to have a zero intercept. Possible interaction between the parameters and a trainee’s demographic characteristics is ruled out-though the number of weeks an individual stays in the program may depend on various household and personal characteristics.4 Post-training earnings _v~for a potential trainee is given by expected earnings without training plus a quadratic in the number of weeks an individual will spend in training. Expected earnings without training is specified as a linear function of pre-training expected earnings yiL. So
The variable _riL is constructed on the basis of data on pre-training earnings and employment for trainees and non-trainees. It represents the earnings an individual could have expected on the basis of his measurable characteristics without adjustment for the probability he is employed. The construction of the series is described below. Note that eq. (1) is estimated only over those men who are working. If earnings and the probability of employment are not uncorrelated, then the expectation of pii conditional on the ith individual’s being employed is generally non-zero; in fact E(pl, 1employment of individual i)=~(a*)“~,$ where p is the correlation between the error in the earnings relation and the employment relation, CJ* is the variance of the error in the
‘Other problems remain, e.g., the effect of a week of training may vary across program sites or instructors. 4A more detailed analysis, using more and better data, could investigate the adequacy of this approximation. Since the sample consists only of males, training is measured in weeks, and only one particular program is being considered the approximation may not be so restrictive. Different effects by race are permitted.
114
earnings
N.M.
relation,
Kiefer,
Training,
employment
und earnings
and
ni=f(4i)/(1-F(4i))7
(2)
wheref is the standard normal density, F is the standard normal distribution and pi is discussed below. Heckman ‘(1976) has studied this estimation problem and has suggested a simple and computationally efficient estimator. Simply do a probit of employment on independent variables X, then use predicted values -Xiy, where y is the normalized coefftcient vector from the probit, for di; then construct ,Ii using (2) and include it as a regressor in (5). Ordinary least squares then provides consistent estimates of the ~3~. Generalized least squares provides more efficient estimates. The probit employment equation specified here is Si=yo+y1yiL+yZWEEKSi+‘y3WEEKS2+y4MARi.
(3)
The variable S is not observed, but its sign is known. S is sometimes interpreted as the difference between a wage offer and a reservation wage, though that interpretation is not exploited or necessary here. Given the function determining returns to training, each individual considers the costs to him associated with various levels of training and chooses an appropriate number of weeks to spend in the program. The equation determining the number of weeks in the program is specified as
+/l,AGE+/?,AGE*,
(4)
in which & captures the benefit considerations common across individuals, lagged earnings represents opportunity costs of time, marital status and number of children are included to pick up their effects on costs, and a quadratic in age adjusts for the different number of periods benefits from training will be received by people of different ages. Tobit is a natural method of estimation since there is no threshold which can be expected to vary across individuals (like the reservation wage in the earnings relation). The predicted values of WEEKS may then be used as instruments for actual values in the employment and earnings equations. Although (4) has been referred to as reflecting an individual’s decision to take weeks in training (facing an elastic supply), it could also represent a reduced form equation reflecting selection by the program manager as well as the individual’s demand for training. In this case the coefficients would reflect supply effects as well as demand effects. The included variables could be expected to influence the program manager’s estimate of the probability the individual will benefit from the program or at least find employment
N.M. Kiefer,
Training,
employment
and earnings
115
after the programthis probability will certainly affect the selection decision. Yet another interpretation is that the programs serve as a means of income maintenance (there is a stipend associated with the program) while individuals sample wage offers from some distribution. Lagged income may then affect time in training through its association with the wage offer distribution. The interpretation of the coefficients of this equation plainly depends on the underlying economic process, however, the interpretation of the final estimated training effects does not depend on the interpretation of this equation. The series on lagged earnings (_v~) is constructed from observations on earnings and employment three quarters before training. The choice of period is important since the purpose of this variable is to standardize for pre-training stocks of human capital; typically trainees experience a spell of unemployment just before entering the training program and their observed earnings are unusually low as a result. Consequently standardizing on the basis of earnings immediately before training would overstate the effect of training if the pre-training decline in trainee earnings was transitory, or if part of it were transitory. To put this another way, suppose all individuals had the same expected life-cycle of earnings but earnings fluctuated randomly from period to period. Then if individuals were selected on the basis of having unusually low earnings in period t and were not trained but just followed into some later period, we would expect their earnings to have risen.’ Early studies, consisting primarily of before-and-after earnings comparisons, failed to recognize the drop in earnings immediately prior to training. These studies tended to show substantial ‘effects’ of training on earnings, under the implicit assumption that the pre-training fall in earnings was permanent rather than transitory. Ashenfelter (1974) illustrates the problem using Social Security Administration earnings records for the 1964 MDTA trainees and provides a full discussion of the issues involved. The assumption I make is that the decline in trainee earnings one quarter prior to training was transitory and that earnings data from three quarters before training do not pick up this transitory fall in earnings. If the fall in earnings was actually permanent rather than transitory, then the estimates developed here will be downward biased measures of the effects of training. If the fall in earnings was already taking place three quarters before training, then the constructed series on lagged income may contain biases, and this may introduce bias into the final estimate. 6 The series on lagged earnings is ‘The best studies used some kind of control group, in which case the problem disappears if individuals are assigned to their group, trainees or controls, randomly or on the basis of a selection rule which is compensated for in the statistical estimation procedures. 6Mean annual earnings *for the trainees (calculated from SSA records) are: for whites 1968, $1899: 1969. S1978: 1970. $1467: for blacks 1968. $1830: 1969. $1867: 1970. $1499. These numbers give a rough indication that the earnings decline was not going on until the year of training (1970).
116
comprised
N.M. Ki&fes
of predicted
Training, employment
values from the following
+G,EDUC,AGE,
+&MAR,
and earnings
equation:
+ G,CHIZlXEN,,
(51
in which the dependent variable is observed earnings and the sample consists of those who were employed during the third quarter before training. In estimating these coefficients the employment selection problem described above arises. A correction is made for the non-zero expectation of the error term in (5) on the basis of estimates of the pre-training employment probit,
where Sf is the latent employed.7
variable
which must be positive
for an individual
to be
3. Data and estimation The data are from a two-and-a-half year longitudinal study of four federally-sponsored training programs including MDTA which was undertaken in 1969 under contract to the Office of Economic Opportunity and the Department of Labor. Trainees in ten major Standard Metropolitan Statistical Areas were sampled. s Members of the subsample of the trainee group were matched, on age, race, and sex, to individuals from the pool of people in the particular SMSA who were eligible for training but did not participate in a program. These individuals were interviewed at the same time as their ‘running mates’ in the trainee group. Interviews took place in four waves; the first as soon as possible after the trainee entered the program, the second when the trainee left the program, the third four months after the second, and the last eight months later. Retrospective data on employment, earnings, and hours of work were collected along with demographic information. Although the survey was usually successful, in terms of response rates, the transcription of the data from questionnaires to a machine-readable form was not reliably done. Researchers at the OEO managed to correct the data for a number of observations (about one-third ‘Eqs. (5) and (6) are estimated only to provide an instrument for y. Consequently, changes in their specification will change the asymptotic efficiency of the final parameter estimates (in unknown directions), but will not affect the property of consistency or the form of the limiting distribution. The correction for the non-zero error expectation is made only for estimation of the parameters of (5), not for the construction of the instrument for pre-training earnings. ‘The SMSAs were Chicago, New York, Atlanta, Cincinnati, DallassFort Worth, Detroit, Norfolk-Portsmouth, Los AngelessLong Beach, Philadelphia, and Seattle.
N.M. Kiefer,
Training,
emplo.vment and earnings
117
of the cases had to be corrected during the data cleaning). Despite concerns about possible biases arising from missing data and lost observations, the OEO/DOL data set is the best available collection of detailed information on trainees. The cost of this widely-discussed data set ran to six million dollars. The questionnaires have been destroyed.’ The first estimates to be obtained are those of eqs. (5) and (6), the equations necessary to generate the instrument for lagged earnings.” Throughout separate equations are run for blacks and whites.” Estimates are presented in table 1. After some experimentation, marital status variables were omitted from the earnings equation for blacks- the effect of marital status on pre-training earnings seemed to be only through its effect on employment probabilities. All the equations are significant though the coefficients are generally imprecisely determined. The instrument for lagged earnings is constructed by taking predicted earnings from the earnings series is not adjusted for regressions, excluding J.. Thus, the earnings employment probabilities. The next equation to be estimated is (3) giving the number of weeks an individual spends in training. Tobit estimates are presented in table2. The single most significant variable is the number of children the potential trainee has; individuals with children are significantly less likely to enter training, or ‘For details on the data collection and reduction, see the U.S. Department of Labor (1978). Pre-training data were collected in the first interview; the post-training data used here in the third and fourth interviews. “Before turning to the empirical results, it is useful to consider the possible effects of unobserved ability on the final estimates of the effects of the programs. The usual treatment of unobserved ability is to assume that it is uncorrelated with anything. In this uninteresting case the above estimation techniques give consistent estimates. However, in several cases in which ability is correlated with observables, our estimation techniques yield consistent estimates of x2, First, suppose that ability c+, y2 and y3, the effects of training on earnings and employment. determines some initial level of earnings, but subsequent earnings are determined by a difference equation such as (1). In this case the only part of ability entering the post-training earnings eq. (1) is that part not predictable on the basis of the variables in the initial earnings eq. (5). Plainly that error is uncorrelated with the instrument for lagged earnings; it is also uncorrelated with the instrument for WEEKS which is based on a subset of the variables included in (5). Therefore the coefficients a of (I) are estimated consistently. Similarly, if ability affects employment only through its effect on lagged earnings, then the y in (4) are estimated consistently. Now, suppose ability enters the equation determining the number of weeks an individual spends in training. Then, although the coefficients of the variables in that equation may be inconsistent, the predicted values of WEEKS can be used as instruments in the employment and earnings equations as long as ability affects employment and earnings only through its effect on WEEKS and lagged earnings. Finally, suppose ability enters the employment and earnings equations directly but does not enter the WEEKS equation except through its effect on lagged earnings. In this case the instrument for WEEKS constructed from (3) is again uncorrelated with the errors in (1) and (4) and consistent estimates are obtained. Observed WEEKS, however, need not be uncorrelated with the errors in these equations; if there is correlation our tests will pick it up and the instruments will be used. Obviously this list does not include all possible ways in which ability may enter the estimating equations, however, the problem may not be as serious as it seems. “For ‘white’ in this sample, read ‘other’.
__-.
0.184 (1.68)
Black
10.92 (4.17)
Black
)
-0.166 (4.50)
- 0.0026 (0.411)
- 0.002 (1.30)
-0.001 (1.02
Age’
employment
3.34 (1.22)
- 2.34 (0.469)
0.201 (1.78)
0.071 (0.98)
-0.0059 (0.637)
0.127 (0.646)
-0.0057 (1.40)
- 0.0027 (1.10)
Table
-
20.51 (0.749)
838 (1.11)
0.283 (0.690)
Marital status
regressions
Education* age
and earnings
Education
probits
1
3.32 (2.93)
1.47 (0.920)
0.556 (0.909)
0.013 (0.330)
Number of children
5.73 (0.005) - 5.46 (0.323)
i
(t-statistics
is the value of the likelihood least squares.
- 0.003 (0.122)
0.007 (0.425)
Marital status* age
for data construction
“The chi-squared statistic reported in the last column for the probit equations probit against the binomial model. The regressions were esttmated by generalized
1.27 (0.280)
White
Earnings/week
___~
0.071 (1.12)
White
Employmetlt
Age
Pre-training
ratio statistic
-88.1 (1.68)
57.3 (0.404)
-3.14 (1.82)
- 0.880 (0.847)
Constant
750
N
0.127
0.160
R2
44.57
x1(7) 25.87
for testing
733
507
1051
in parentheses).”
F
the litted
17.6
13.5
Blacks
Weeks in training Whites
_______
0.755 (0.426)
(0.044)
(0.442)
-0.171 (0.946)
0.333
Marital status
0.0794
Pre-training earnings
Tobit estimates
-2.05 (2.57)
(4.01)
- 2.63
Number children
of
2.94 (1.35)
(1.05)
- 0.272
Age
(r-statistics
(1.44)
- 0.052
(0.272)
- 0.066
Age’
equation
Table 2 of the weeks-in-training
- 20.63 (1.31)
(0.736)
7.55
Constant
in parentheses).
1049
750
N
510
294
Number of zeros
18.9
18.7
D
S
f =a
-!! z ‘rl 3 :
8. g. ?Q
3
120
NM.
Kiefer,
Training,
employment
and
earnings
to continue in training having entered, than comparable individuals without children or with fewer children. Pre-training earnings, which is included as a measure of the opportunity costs of the time spent in training, has the anticipated negative sign in the equation for blacks, but has a positive sign in the equation for whites (though it is not significantly different from zero). This is consistent with the interpretation of (3) as a reduced form equation reflecting both the program manager’s desire to choose trainees who will have high post-training earnings (using pre-training earnings as an indicator of this) and the potential trainee’s demand for weeks of training. Age has the anticipated negative effect in the equation for whites; in the equation for blacks age has an increasing effect until about age 28, then a decreasing effect. Again, this may reflect program managers’ selection of people who will be easier to place.12 The next equation which can be estimated is the employment probit Si=y,+y,yi,+y2WEEKS+y,WEEKS2+y&lAR.
(7)
At this point some preliminary hypothesis-testing may be done, in hopes of obtaining more efftcient estimates of the effect of training. Using instruments for WEEKS and WEEKS2 provides consistent estimates of y2 and y3 whether or not the error in observed WEEKS is correlated with the error in eq. (7); however, if these errors are uncorrelated, then using observed values of WEEKS provides more efficient estimates.13 A test may be made by estimating (7) using the observed values of WEEKS and WEEKS2 instead of the instruments, and testing that speciticiation against a specification which includes both the observed values of WEEKS and WEEKS2 and the instruments for WEEKS and WEEKS’. This is a test of the joint significance of the instruments given the observed values. Plainly, if the errors are uncorrelated, then adding the instruments will add nothing to the explanatory power of the equation, and they will not be significant.14 Our procedure will be to perform this test, and on the basis of its outcome to use (7) with either observed WEEKS or the instrument for WEEKS obtained by fitting (3). Minus twice the log of the likelihood ratio for testing the significance of the instruments given the observations is 2.96 for whites, 4.3 for blacks. These values are to be compared with x2(2), which has a 0.05 critical value of 5.99. Therefore the null hypothesis of no correlation between the error in observed WEEKS and the error in the employment equation is 12The age-specific relative black -white unemployment rates tend to fall with age. “Note that for consistency in the instrumental variable specification predicted values of weeks should be used for trainees and non-trainees. 141n the linear regression framework. this is the Wu (1973) test, written in Chow-test form by Farebrother (1976). The statistical foundation of this test is the theorem that an efficient estimator is uncorrelated with the difference between it and an inefficient estimator. These results are extended and discussed in an econometric context by Hausman (1976).
121
N.M. Kiefer, Training, employment and earnings
Table 3 Probit
estimates
Lagged earnings Emplo_vment of Whites
of the post-training
Marital status
employment
WEEKS
equation.”
WEEK Sz _____.__
Constant
X2(4)
- 0.0089 (1.49)
0.757 (3.7)
- 0.005 (0.486)
- 0.0002 (0.806)
0.935 (2.01)
43.02
Blacks
0.0067 (1.88)
0.232 (2.30)
0.0084 (1.01)
- 0.0004 (1.76)
- 0.465 (1.48)
21.24
“The chi-squared
test is a test of the probit
model against
the binomial
model.
not rejected and the observed values of the WEEKS variable are used in (7) to increase the efficiency of the estimates of the parameters. Estimated coefficients are given in table 3. Both probit equations are significantly different from the binomial model according to the x2(4) statistic. Marital status has a significant positive effect on employment probabilities, Training seems to have a negative effect on the employment of whites, a positive but decreasing effect on the employment probabilities of blacks. The effect of training in both cases is very small. The significance of the training effects at the sample means of the number of weeks in training can be easily tested. For whites the total effect is -0.141, with a standard error of 0.108; for blacks the effect is 0.035, with a standard error of 0.086.” Neither of these effects is significantly different from zero, but that in itself is not particularly informative about how large the effects might be. To examine this loosely, add twice the standard errors to the estimated effect and note that even the largest effect, that of 0.2 for blacks, translates into an increase in employment probability of at most 0.07 and probably much less for most individuals (the probability depends on independent variables -for example, someone who had pre-training earnings of $lOO/week and was married has an increase in employment probability of only abcut 0.03). Employment effects, if any, are therefore very small. The next step is to form the series on i using the probit coefficients from the employment equation and formula (2). This is then included as a regressor in the post-training earnings eq. (1). Again, a test is made to determine whether or not the instrument for weeks contributes anything to the equation, given the observed weeks. The appropriate test is an F-test, and the values are: for whites F(2,475)=0.45, for blacks F(2,620)=0.33. The null hypothesis of no correlation between the error in the earnings equation and the observed values of weeks is not rejected. Coefftcients of the regression using observed values of WEEKS are reported in table 4. Both “The -0.2487
covariances E-5, blacks
between the coefficients of WEEKS and those of WEEKS2 are: whites -0.1551 E-5. The mean numbers of weeks in training are 16.8 and 15.3.
122
N.M. Kiefer, Training, employment und earnings
regressions are significant. The single most significant variable explaining weekly earnings is lagged weekly earnings.” Both samples give a negative coefficient to the linear term in WEEKS and a positive coefhcient to the quadratic term. Again, a confidence interval for the total training effects at the mean values of WEEKS can be calculated using the estimated covariances between the coefficients on WEEKS and on WEEKS’.” The estimated total effects of training on weekly earnings are - 12.38 for whites, -6.83 for blacks with standard errors 11.2 and 2.29. For whites the effect is not significantly different from zero and the estimated effect plus twice its standard error is about $10. Thus training probably had no effect on earnings and it is unlikely that any effect on weekly earnings is greater than $10. For blacks the effect is more precisely estimated. The estimated effect plus twice its standard error is -2.25. It is therefore unlikely that training had any positive effect on the earnings of blacks. Indeed, trainees who stayed in the programs the mean length of time seem to have experienced a relative decline in their weekly earnings.
4. Conclusions A systematic method of estimating the effect of training separately on employment and weekly earnings has been applied to a sample of MDTA trainees. The approach taken here deals directly with two problems commonly ignored in studies of training: the fact that earnings are not observed for individuals who are not employed, and the potential correlation between selection into training and the error in the earnings equation. The first problem is handled using Heckman’s (1976) framework, the second by testing for correlation between the number of weeks an individual spends in training and the error in the earnings equation. Nonrandom selection into training did not seem to present a problem in measuring either the earnings effects of the employment effects of training, given that training was measured by the number of weeks an individual stayed in the program. However, this finding should be treated somewhat cautiously; the power of the test is not known. Results using the instrument for WEEKS instead of the observed values are qualitatively the same. The results imply that the effect of training on employment was small. The effect on employment, evaluated at the mean number of weeks trainees participated in the program, is not significantly different from zero. This in itself is not particularly informative, however the training effect plus twice its ‘“Although (I) is not intended to be a time-series model of earnings dynamics it is reassuring to note that the estimated coefficients have reasonable implications in terms of a dilference equation generating earnings. The profiles have the characteristic concave shape and the implied equilibrium earnings are sensible. “The covariances are: whites - 1.780 E-3. blacks -4.605 E-3.
124
N.M. Kiefer,
Trcrining, employment
and earnings
standard error (roughly a 95 percent confidence upper bound) has a maximum of about a 0.07 increase in the probability of being employed (for blacks with the most favorable combination of independent variables). The effect of training on earnings was more precisely determined for blacks than for whites. The estimated effects, evaluated at the mean number of weeks in training, were negative for both samples. The rough 95 percent confidence interval for the effect on whites had a maximum effect of $10 a week, that for blacks-$2 a week. Trainees who stayed in the program longer had larger effects on earnings. The estimates reported in this paper, which imply that training is not as beneficial as continued normal employment, lead naturally to the question: Why do people enter training programs? The answer, I think is that the programs provide income maintenance during a spell of unemployment. An individual who is ineligible for or has exhausted other forms of income maintenance may enter a training program and receive the stipend.‘* This interpretation raises new questions of evaluation. If the training programs are regarded by participants as income maintenance programs, then perhaps the appropriate comparisons to make are between training programs and other transfer programs which are explicitly intended to provide income maintenance such as unemployment insurance, the negative income tax and (not as explicitly) public sector employment programs. Plainly the costs of these programs differ substantially; so probably do the effects on labor supply. These comparisons are topics of further research. “This was found eligible for training Act of 1962.
by Neumann (1977) to be the case for a sample of individuals who became under the Trade Adjustment Assistance program of the Trade Expansion
References Ashenfelter, Orley, 1974, The effect of manpower training on earnings: Preliminary results, Proceedings of the 27th Annual Meeting of the IRRA, 252-260. Ashenfelter, Orley, 1978, Estimating the effect of training programs on earnings, Review of Economics and Statistics 60, 47-57. Farebrother, R.W., 1976, A remark on the Wu test, Econometrica 44, 475477. Goldstein, Jon, 1972, The effectiveness of manpower training programs: A review of research on the impact on the poor (US. Government Printing Office, Washington, DC). Unpublished paper, Aug. (M.I.T., Hausman, J.A., 1976, Specification tests in econometrics, Cambridge, MA). Heckman, James, 1976, The common structure of statistical models of truncation, sample selection and limited dependent variables and a simple estimator for such models, Annals of Economic and Social Measurement 5, 475-492. Johnson, George, 1975, Evaluating the impact of CETA programs on participants’ earnings: Office of Evaluation and Research,. ASPER, Methodological issues and problems, Framework for Evaluation Paper No. 4, Jan. Kiefer, Nicholas M., 1977, The economic benefits from manpower training, Research in Labor Economics, Suppl. 1.
N.M. Kiefer,
Training,
emplownent
and earnings
125
Neumann, George, 1977, The direct labor market effects of the trade adjustment assistance program: The evidence from the TAA survey, in: William Dewald, ed., The impact of foreign trade and investment on domestic employment, forthcoming. Perry et al., 1975, The impact of government manpower programs (Industrial Relations Unit, The Wharton School, Philadelphia, PA). Stromsdorfer, Ernst, 1972, Review and synthesis of cost-effectiveness studies of vocational and technical education (ERIC, Columbus, OH). U.S. Department of Labor and U.S. Department of Health, Education and Welfare, 1978, Cost eNective analysis of four categorical employment and training programs: MDTA, JOBS, Job Corps, and NYC-OS, in: G. Goodfellow and E.W. Stromsdorfer, eds., A report to the General Accounting Oflice, 4 ~01s. (U.S. Department of Labor, Washington, DC). Wu, D., 1973, Alternative tests of independence between stochastic regressors and disturbances, Econometrica 41, 733-750.