Dictionary of Occupational Titles (DOT).1 The DOT figures are also problematic as required ged is not provided in years. Occupations are given a score ranging ...
Applied Economics Letters, 1994, 1, 142 –144
Measurement error and the returns to excess schooling JO H N RO B S T Department of Economics, State University of New York, Binghamton, NY 13902-6000, USA Received: 17 June 1994
This paper considers the degree of measurement error in estimates of required schooling for jobs and the bias created by errors when estimating the returns to overeducation. Using the Panel Study of Income Dynamics, it finds substantial differences between estimates of required schooling. Thus errors in measurement may seriously bias previous results examining the wage effects of overeducation. This paper uses instrumental variable techniques to correct for measurement error and finds no significant returns for surplus schooling.
I. INTRODUCTION The returns to education are the subject of a vast number of studies. A segment of this research looks at the wage effects of being over- or undereducated. A person is considered overeducated if he/she has received more education than required for the job. Alternatively, a person is considered undereducated if he/she has received less education than is required for the job. Previous studies have consistently found that overeducated workers are compensated for their excess schooling, but at a lower rate of return than for required schooling. Undereducated workers have been found to earn less than adequately educated workers with similar required schooling (for example Duncan and Hoffman, 1981; Rumberger, 1987; Hartog and Oosterbeek, 1988; Sicherman, 1991; and AlbaRamirez, 1993). A majority of previous research uses one of two different methods for estimating required schooling. The first method is to survey workers about requirements for their job. However, worker estimates of job requirements have been criticized for their subjective nature. The second measure is based on estimates of required general educational development (ged) provided in the Dictionary of Occupational Titles (DOT).1 The DOT figures are also problematic as required ged is not provided in years. Occupations are given a score ranging from 1 to 6, with several
translations into years of schooling being used over time.2 In addition, the ged estimate treats all jobs within most occupations as having the same required schooling. As a result, while the ged estimate may be appropriate for the typical job within an occupation, it may not be accurate for every job within an occupation. Thus both estimates of required schooling may be subject to measurement error. Researchers have generally used one of the above estimates of required schooling to classify workers as over-, adequately, or undereducated. This paper investigates how an individual is classified using both measures. I find the two measures often place the same worker in different classifications; thus one or both of these measures of required schooling may be subject to substantial error. Numerous studies discuss how measurement error biases ordinary least square coefficients (for example Freeman, 1984; Chowdhury and Nickell, 1985). Thus errors in measurement may be a significant problem in prior studies estimating the wage effects of overeducation. I use instrumental variable techniques in an attempt to reduce the bias created by measurement error. Results indicate that there are no significant returns to surplus schooling when using instrumental variable techniques. These results are in contrast to previous studies which do not correct for measurement error and find significant positive returns to excess schooling.
1
See Fine (1968) or Cain and Treiman (1981) for a more thorough description of the DOT estimates of job requirements. For example, Eckaus (1964) converts the ratings into the following brackets: 0–4, 6–8, 9–11, 12, 13–16, and 17+ years. The United States Department of Labor (1971) uses: 0–3, 4–6, 7–8, 9–12, 13–14, 15–16 years. 2
1350–5851 © 1994 Chapman & Hall
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Returns to excess schooling II. DATA
roles, and uses the PSID estimates as instruments for the DOT measures.
The primary data used in this study are the Panel Study of Income Dynamics (PSID). The PSID began in 1968 and attempts to follow over 5000 families across time. In the 1985 survey, respondents are asked to estimate the required schooling for their job. Specifically, workers are asked the question ‘How much formal education is required these days to get a job like yours?’ The PSID reports answers in seven brackets: 0–5, 6–8, 9–11, 12, 13–15, 16 and 17+ years. Only male heads of household between the ages of 18 and 64 are looked at in this study. The sample has a total of 2648 observations. The 1985 wave also contains the three-digit Census codes for occupations, allowing the ged estimate of required education to be added for each worker. Ged is converted into years using the translation suggested by Eckaus. In order to convert the brackets into single years of required schooling, I use the mode of actual schooling within each bracket. Thus for the PSID brackets 5, 8, 11, 12, 15, 16 and 17 years are assigned. For the ged estimates, 4, 7, 10, 12, 16 and 17 years are assigned. Using the ged estimates of required schooling to classify workers leads to a substantial number of overeducated workers, with 54% of all workers having more education than needed. The PSID estimates classify 32% of workers as overeducated.
I I . MO D E L A standard wage equation is assumed in this paper: W = b iX* + e i
(1)
where i = 1,2 … N, W is the natural logarithm of hourly wages, X is a vector of independent variables, and e i is a normally distributed mean zero error term. If X* is the true measure of X, then the coefficients are unbiased. However, if X* is measured with error, Xi = Xi* + ui
(2)
the estimated coefficients will be biased: W = b iX + (e i – b u)
(3)
Since we have two independent measurements of required schooling, an instrumental variables approach can be used to provide consistent estimates of b and remove the bias created by measurement error. Two sets of results are found using twostage least squares. The first uses the estimates of required, excess, and shortage of schooling from the DOT estimates as instruments for the PSID measures. The second reverses the 3
I V . RE S U L T S Table 1 indicates how workers are classified using both measures of required schooling. 3 Substantial differences exist, with only 53% of individuals placed in the same classification by both methods. Over 200 observations are classified as overeducated by one measure and undereducated by the other measure. Thus previous estimates of the wage effects of overeducation may be seriously biased by measurement error. Table 1. Extent of over-, adequate and undereducation by measure of required schooling Utilizing worker estimates of required schooling OverAdequately UnderTotal educated educated educated Utilizing DOT estimates of required schooling Overeducated
663 0.25
591 0.22
164 0.06
1418 0.54
Adequately educated
151 0.06
609 0.23
221 0.08
981 0.37
Undereducated
41 0.02
66 0.02
142 0.05
249 0.09
Total
855 0.32
1266 0.48
527 0.20
2648 1.0
Note: The data are the 1985 wave of the SPID. The top number represents the number of people in the classification. The bottom number is the percentage of the sample in the classification.
As reported in Table 2, the returns to required schooling are larger when using an instrumental variables approach, but the difference is only significant when comparing the PSID and PSID(IV) estimated returns to required schooling. The returns to excess and underschooling become insignificantly different from zero in both cases. Thus excess schooling may not carry the economic value found in previous studies. In addition, individuals in jobs for which they are undereducated do not receive significantly lower wages. However, the results for undereducated workers should be treated with caution as the magnitudes of the coefficients are not significantly different from the OLS results. Consistent results are found when controlling for completed schooling instead of required education. When comparing individuals with similar levels of completed schooling, workers earn 11.5% less for each year of excess schooling when using the DOT measures as instruments for the PSID measures.
A worker’s educational attainment must be greater (less) than the highest (lowest) value in the relevant bracket for the worker to be classified as over- (under-) educated.
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J. Robst
Table 2. Regression results
V. CONCLUSION
Dependent variable: log of hourly wage
Previous studies examining overeducation have found surplus schooling carries some economic value. This paper provides evidence that the returns to surplus schooling previously found may be biased due to errors in measuring required schooling. When employing instrumental variable techniques to correct for measurement error, the returns to surplus schooling are not significantly different from zero. Future work may wish to concentrate on other econometric issues. Selectivity and heterogeneity biases may be important in estimating the returns to excess schooling. In addition, work is needed on developing an objective and accurate measure of required schooling.
Variable
PSID
PSID(IV)
DOT
DOT(IV)
Required 0.0697** education (0.004) Years 0.0467** overeducated (0.005) Years –0.0257** undereducated (0.006) Adj R2 0.5238 N 2648
0.0898** (0.009) –0.0273 (0.021) –0.0157 (0.020) 0.4441
0.0843** 0.0881** (0.006) (0.007) 0.0489** –0.0176 (0.005) (0.018) –0.0417** –0.0317 (0.006) (0.022) 0.5151 0.4654
Education
0.0808** (0.007) –0.1150** (0.021) 0.0582** (0.023) 0.4510
0.0810** 0.0859** (0.006) (0.007) –0.0390** –0.1075** (0.006) (0.018) 0.0334** 0.0570** (0.008) (0.025) 0.5170 0.4687
0.0675** (0.004) Years –0.0238** overeducated (0.003) Years 0.0408** undereducated (0.007) Adj R2 0.5242 N 2648
Notes: The data are the 1985 wave of the PSID. PSID uses the survey response as the measure of required schooling. PSID(IV) uses the DOT estimates for years of required, excess, and under schooling as instruments. DOT uses the DOT estimate as the measure of required schooling. DOT(IV) uses the PSID estimates for years of required, excess, and under schooling as instruments. Other variables include an intercept, experience, experience squared, tenure, tenure squared, required training, SMSA size, kids, and dummy variables for race, union, marital status, and occupation (1-digit). ** indicates the coefficient is significant at the 5% level. Standard errors are in parentheses.
Overeducated workers earn 10.8% less when using the PSID measures as instruments for the DOT measures. In both cases the wage loss from a year of excess schooling is not significantly different from the returns to a year of completed education. In other words, it appears wages are determined by the requirements of the job and not the educational attainment of the worker. An additional area of interest concerns potential errors in completed education. Several studies address this issue and find measurement error to be a serious problem when estimating the returns to education (for example Ashenfelter and Krueger, 1993). In an effort to reduce the impact of errors in completed schooling, I utilize the panel nature of the PSID and create a subsample of 1603 individuals who report the same level of schooling across time. Again, no significant economic benefits are found for surplus schooling. Results for this sample are available upon request.
R E F E RE N C E S Alba-Ramirez, A. (1993) Mismatch in the Spanish labor market, Journal of Human Resources, 28, 259–78. Ashenfelter, O. and Krueger, A. (1993) Estimates of the economic return to schooling from a new sample of twins, paper presented at the 1993 Allied Social Science meetings. Cain, P. S. and T rei man, D. J. (19 81 ) T he Dict iona ry of Occupational Titles as a source of occupational data, American Sociological Review, 46, 253–78. Chowdhury, G. and Nickell, S. (1985) Individual earnings in the US: another look at unionization, schooling, sickness, and unem ployment using Panel Data, Journal of Labor Economics, 3, 38–69. Duncan, G. J. and Hoffman, S. (1981) The incidence and wage effects of overeducation, Economics of Education Review, 1, 75–86. Eckaus, R. S. (1964) Economic criteria for education and training, Review of Economics and Statistics, 46, 181–90. Fine, S. A. (1968) The use of the Dictionary of Occupational Titles as a source of estimates of educational and training requirements, Journal of Human Resources, 3, 363–75. Freeman, R. B. (1984) Longitudinal analysis of the effects of trade unions, Journal of Labor Economics, 2, 1–26. Hartog, J. and Oosterbeek, H. (1988) Education, allocation and earnings i n t he Net herland s: ov erscho ol ing? Econo mi cs of Education Review, 7, 185–94. Griliches, Z. (1977) Estimating the returns to schooling: some econometric problems, Econometrica, 45, 1–22. Griliches, Z. and Hausman, J. A. (1986) Errors in variables in Panel Data, Journal of Econometrics, 32, 93–118. Rumberger, R. W. (1987) The impact of surplus schooling on productivity and earnings, Journal of Human Resources, 22, 21–50. Sicherman, N. (1991) Overeducation in the labor market, Journal of Labor Economics, 9, 101–22. United States Department of Labor (1971) Relating General Educational Developm ent to Ca reer Planni ng , USG PO, Washington, DC.
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