Click for next page ( 74


The National Academies | 500 Fifth St. N.W. | Washington, D.C. 20001
Copyright © National Academy of Sciences. All rights reserved.
Terms of Use and Privacy Statement



Below are the first 10 and last 10 pages of uncorrected machine-read text (when available) of this chapter, followed by the top 30 algorithmically extracted key phrases from the chapter as a whole.
Intended to provide our own search engines and external engines with highly rich, chapter-representative searchable text on the opening pages of each chapter. Because it is UNCORRECTED material, please consider the following text as a useful but insufficient proxy for the authoritative book pages.

Do not use for reproduction, copying, pasting, or reading; exclusively for search engines.

OCR for page 73
EVIDENCE OF ADAPTABILITY IN THE LABOR MARKET FOR ENGINEERS: A REVIEW OF RECENT STUDIES Robert C. Dauffenbach Oklahoma State Un~versi~ and Michael G. Finn National Research Council Introduction Understanding the degree of adaptability in the labor force is important for analysis of many important issues that need to be addressed today in planning for the needs of tome. In the occupational domain of scientists and engineers, that need for understanding is paramount. It is arguable that no other occupational domain is more crucial to the present and future compeduveness of the U.S. economy. Adaptability can be measured in a variety of ways, such as by the ability of individuals trained in given disciplines to attain career success in alternative occupations; by the rates of mobility among firms, occupations, and regions of the country; and by the time and resource costs associated with retraining. The willingness and ability of individuals trained in one field of study but working in alternative occupations would seem to be a primary measure of flexibility in the economy. In this sense, anyway, the extent of flexibility among science- and engineering-educated personnel would seem quite large. The Swey of Income and Program Participation (SIPP) provides national labor force evidence on education/occupation correspondence. While this evidence is highly aggregated and is, given the relative small sample base for the SIPP sweys, subject to large sample variation, it is practically the only evidence available. The education and occupation correspondence shares for bachelor's and higher degree holders who are employed in scientific and eng~neenng (S&E) occupations are as follows: 73

OCR for page 73
S&E Major Degree Field Engineenng/compu~ang Math/sta~astics Agoculture/fores~y Biology PhysicaVearth science Psychology Economics Social sciences Yield to S&E Employment 46.3% 24.9% 15.4% 19.7% 34.7% 17.1% 15.7% _ ~5.9% The largest of these numbers, 46.3 percent for engineers, is surprisingly small. Note that this number reflects all S&E employment, not just simply engineering employment (which, of course, dominates this category). The next highest value is for physical and earth sciences, at slightly more than one-third. Social science discipline yields are particularly low. Evidently, most individuals trained as scientists and engineers work in jobs outside science and engineering. Unfortunately, Deere is no knowledge of dhe extent to which such individuals are willing and able to undertake S&E jobs that is, the rate of back flows. It is apparent, however, that among S&E graduates the yield to S&E employment is quite low. ~ a study by Dauffenbach (1989), an analysis of venous adaptability issues in science and engineering labor markets is presented. Analysis of the correspondence between occupation and education is seen in his study as a primary vehicle for appraisal of flexibility in labor markets. Dauffenbach provides several cross-tabulations of detailed occupation by field of study for science and engineering occupations using NSF's 1982 postcensal survey data. A major finding is that while detailed field of study is a good predictor of occupational pursuit, the amount of variance is surprisingly high. Such prevalence of non-exact correspondence between occupation and education can be taken as evidence of flexibility in the S&E labor market. Dauffenbach hypothesizes, however, that such flexibility is not without attendant costs in the form of diminished productivity. Presumably, if there are productivity differences between workers who are appropriately credentialed and others doing the same job, these differences should show up, systematically, in salanes. Thus, he undertakes an extensive analysis of salary 74

OCR for page 73
differentials by applying the statistical methodology of multiple regression analysis to the aforementioned NSF survey data. These differentials, in general, support the notion that quality differentials result from non-alignment of degree field and occupational pursuit. In addition, because of the variety of other factors that need to be held constant in order to have an unbiased assessment of the impact of education/occupation correspondence on salaries, the regression results provide a general assessment of the various factors on salary differentials, such as race, sex, primary work activity, industry, occupation, and professional work expenence. Also, because in the 1982 postcensal survey, certain questions pertaining to mobility were asked, it is possible to investigate the impact of inter- f~n and ~nter-occupa~onal changes on earnings. Degree and Employment Fields Dauffenbach provides separate regression results for each of the major domains of S&E employment: engineering, biological sciences, math/computer science, physical sciences, and social sciences. He concentrates on detailed categories of field of study and occupation in his analysis and explores the correspondence between field of study and occupation on three levels. First, there is the exact match level in which an individual is working In an occupation that corresponds exactly to his or her field of study, such as a mechanical engineer holding a highest degree from the mechanical engineering field. Second, there is the associated-f~eld level of correspondence, such as a mechanical engineer working as an aeronautical engineer. Third, there is the non-associated level, such as an individual with a highest degree in education working as an engineer. Of course, there could well be differences among major degree fields. Physical science graduates would be expected to be more readily interchanged for engineers ~an, say, business school graduates. Consequently, non-associated fields were divided into several major fields of study (including health, education, business, and "all other") in addition to the major science fields. After substantial investigation it was decided to use 35 distinct degree fields, which were mapped into 40 S&E occupations. These categories represent the numerically significant fields and occupations in the NSF postcensal survey. In the five sets of regressions provided by Dauffenbach, a basic point of interest is the extent to which individuals appear to be working in fields not directly associated with their detailed major field of study (Table 1 ). These results are interpreted in the following 75

OCR for page 73
Table 1. Degree Field Shares, by Field of Employment (in percent) Occupational Field of Employment Field of Mad & PhysicalBiological Social Study Engineering Computer ScienceScience Science Exact Match 54.9 21.6 71.161.3 68.4 Engineering 25.1 11.0 4.80.8 1.0 Maw & Computer 3.0 23.0 1.90.3 1.2 Physical Science 5.0 5.2 8.24.2 0.4 Biological Science 1.2 3.4 7.426.1 1.3 Social Science 1.3 8.4 1.91.5 10.5 Education 1.5 5.6 1.52.4 3.2 Health 0.1 0.4 0.60.5 0.3 Business 4.6 1 3.0 0.90.5 4.9 AD Other 3.3 8.4 I.72.4 8.8 manner. Among all of the nearly 20,000 observations of employed engineers, about 55 percent had an exact match between their detailed employment field and the detailed field of Weir highest degree earned Another 25.1 percent of the employed engineers had an engineering degree, but their degree field did not match their employment field. This leaves a residual of about 20 percent with a degree in a non-associated field. For engineenng, the most prominent of these non-associated fields was physical science, followed closely by business. Other results are read in a similar mariner. Note also that individuals with their highest degree in engineering represent sizable proportions of both the math and computer science and the physical science employment fields. Earnings Differentials Of primary interest is the impact on earnings differentials associated with not having an exact match or having a highest degree in a non-associated field of study if, in fact, there are real productivity differentials associated with individuals who do not match in 76

OCR for page 73
Table 2. Regression Estimates of Earnings Differentials (in percent) Occupational Field of Employment Field of Math & Physical Study Engineering Computer Science Biological Social Science Science . . Exact Match 10.04*7.27* 16.42* 0.679.19* Engineering 9.87*7.76* 18.58* 15.72*16.84* Math&Computer 9.93*8.51* 21.61* 6.3510.62 Physical Science 5.64*5.97* 12.04* 8.9318.24 Biological Science 0.81-0.21 5.79 -1.50-12.46 Social Science 1.83-0.19 0.78 1.481.74 Education -5.03*-2.67 4.90 -4.26-8.91 Health 3.913.81 12.25 9.40-59.86* Business 3.~*2.83 23.20* 16.516.66 All Other * The coefficient is statistically significant at conventional levels. tempts of degree field and employment field (Table 21. The coefficients are read relative to the salary associated with the "all other" fields of study, which is the excluded group in the regression analysis. Thus, we see that an exact match in the engineering employment field pays about a 10 percent differential above those who have a degree in the "all other" field. However, having a non-exact match but still having an engineering degree pays almost as much, a 9.87 percent differential. Note also that having a degree in math/computer science and working as an engineer also pays a handsome differential of 9.93 percent. Yet, having a highest degree in biology, physical science, social science, education, health, or business pays somewhat less. ~ the math/computer domain, the coefficient for the associated field (same general field, but not an exact match) is actually higher than the exact-match coefficient. Engineering graduates earn about the same; physical science graduates, slightly less. Other fields of study are somewhat lower. Business graduates are a large contributor to this 77

OCR for page 73
employment field. They earn 4-5 percent less than engineers working as math/computer . specialists. A total of 75 percent of those working in the physical sciences have physical science degrees. In this employment domain, individuals with biological science degrees are the most frequent other contributor. They earn significantly less, about 7-1 1 percent. Engineers, another significant contributor, earn about the same amount, if not a little higher Can the exact-match. Also of interest in these results is the finding that in all of the major S&E employment fields, those who have engineering degrees earn as much or more than those who have exact matches with their employment field. This result seems especially significant in regard to flexibility of engineers. However, as noted previously, engineers represent a sizable proportion of only the math/computer and physical science employment categories. Still, the biological and social sciences are large employment fields and even a percent composition of engineers is not an insignificant number. Mobility Dauffenbach's results also provide ~nfonnation on the extent of mobility of venous types: change in employer, in occupation, and in responsibilities. Table 3 provides these gross mobility rates for the major S&E employment fields, 1976-1982. Change in responsibilities appears to be rather frequent among the S&E categories and about 1 in 4 or Table 3. Estimates of Mobility Rates, 1976-1982 (in percent) Occupational Field of Employment Mad1 & Physical Biological Social Science Type of Change En~neer~ne Computer Science Science Employer 21.99 27.50 23.22 19.34 C OCR for page 73
5 changed employer within the six years. Occupational mobility is substantially lower. Those working in the math/computer employment field were the most likely to be mobile occupationally, about 17 percent as compared to the more common 10-12 percent. These mobility figures are derived from retrospective questions-that is, in the 1982 survey, respondents were asked how their jobs had changed since 1976. Mobility results tend to be substantially higher when tabulated from longitudinal data, a result that is most likely a consequence of coding error. These findings from Dauffenbach's study allow one to place some bounds on the extent of mobility, its character, and earnings consequences. The results imply that there is a fairly high degree of adaptability among engineers, math/computer specialists, and physical scientists. This finding is validated by (1) the magnitudes of individuals having their highest degree in one of these fields, but working in one of these other fields, and (2) the essentially nil pay differentials among these degree fields within each respective employment field. Other fields, especially business disciplines, contribute significantly at time, but in general have substantially lower pay differentials. When these results are coupled with the evidence that the majority of S&E degree holders do not work anywhere in science or engineering, the extent of flexibility is large, indeed. A major gap in our knowledge is the extent to which such individuals are both willing and able to return to S&E career pursuits. This is a knowledge gap in great need of being closed. Other Findings Other recent studies find similar results to those of Dauffenbach and also examine explicitly the value of an engineering degree for persons in management jobs. Korb (1987) studied the employment of 1983-84 baccalaureate degree recipients in 1985, using a Department of Education survey of recent graduates that included all degree fields. Not surprisingly she found that engineering graduates reported the highest salaries and that those with nonengineering jobs 1-2 years after graduation earned somewhat less than those with engineering jobs. However, the engineering degree seems to be valued more highly for the principal nonengineering jobs entered by recent engineering graduates when compared with the earnings of other college majors in the same jobs. For example, she found that engineers were employed as technicians after graduation much less frequently than were biological sciences graduates (9 percent versus 40 percent), but that they reported much higher earnings than the biological sciences graduates when they did work as 79

OCR for page 73
technicians ($22,000 versus $15,000). Only 5 percent of engineering graduates took jobs as managers 1-2 years after graduation. They earned less than the engineering graduates who took engineering jobs but more than business/management graduates who took jobs as managers. In short, the Department of Education survey indicates that new engineers are valued most highly for engineering jobs, but they are valued more highly than other majors in all of the occupations they enter. This suggests a great deal of adaptability. The strong labor market for recent engineering graduates is widely known' but are persons trained as engineers as highly valued later in their careers? Evidence from another Department of Education survey suggests that the answer to this question is "yes." James et al. (1989) examined data from the National Longitudinal Study of the High School Class of 1972, and used data they reported about their jobs in 1986, about 9-10 years after the typical person In the sample had completed a bachelor's degree. Their study was innovative in that it controlled for differences in ability as measured by SAT scores, for differences in college grades, and for the number of math courses taken in college, all factors that enhance earnings. Still, engineering graduates were found to have the highest earnings in 1986, about 20 percent higher than the second highest-paid bachelor's degree field, business. Relevant to die adaptability question is the fact that engineering graduates who held noneng~neer~ng jobs earned salaries Hat were just as high as those with engineering jobs. On the other hand, business majors seem less versatile: they reported high wages only when they held jobs as managers. We could cite other studies which document that engineers earn more when they devote a relatively high percentage of their time to management activities (e.g., Finn, 19851. However, this is probably widely accepted and needs little discussion. Summary The studies cited here indicate that persons with engineering degrees do frequently take jobs outside engineering-not only in He physical, mathematical, and computer sciences, but also as managers and in other capacities such as sales. Salary is a simple and imperfect summary measure of the adequacy with which they perform these jobs. Yet findings of these studies of earnings are unambiguous. They indicate that, no matter what the occupation studied to date, persons trained as engineers do at least as well as persons trained in any other degree field. This might be the result not merely of the adaptability provided by an engineering education, but also of the superior ability or willingness to 80

OCR for page 73
work hard that characterizes the students who complete engineering degrees. However, we note that one study that was able to control for some measures correlated with native ability and willingness to work hard (SAT scores, college grades) found evidence of the same high degree of adaptability as did the other studies without such measures. It would be incorrect, however, to infer that degree field doesn't matter, that all college graduates are adaptable. The Dauffenbach study indicates clearly that persons without engineering degrees earn less than those with engineering degrees when the job is engineering. The one exception seems to be the math and computer science degree- recipients. They seem to show as much adaptability as engineering degree-recipients, at least for jobs in the broad groupings of engineering, mathematical sciences, computer science, and physical science. References Dauffenbach, Robert C. 1989. The Issue of Quality in the Market for Scientists and Engineers - A Report to the National Science Foundai'on. Shllwater, OHa.: Oklahoma State University. Finn, Michael G. 1985. Foreign National Scientists and Engineers in the U.S. Labor Force, 1972-1982 (ORAU-244). Oak Ridge, Tenn.: Oak Ridge Associated Universities. James, Estelle, N. Alsalam, J. C. Conaty, and D. To. 1989. College quality and future earnings: Where should you send your children to college? American Economic Review 79(May):247-252. Korb, Roslyn A. 1987. Occupational and Educational Consequences of a Baccalaureate Degree. Washington, D.C.: U.S. Government Printing Office. ~1

OCR for page 73