Recent SRS Research

Unemployment among doctoral scientists and engineers - Carolyn Shettle

Congruence between field of doctorate and occupation - Mark Regets

Correlates of professional society memberships among scientists and engineers - Carlos Kruytbosch

Release of 1994 data on science and engineering doctorate awards - Mary Golladay

Unemployment among doctoral scientists and engineers - Carolyn Shettle

SHETTLE: Thank you. I would like to share with you some work I've been doing for a report on unemployment among doctoral scientists and engineers. The report is based primarily on our 1993 Survey of Doctorate Recipients (SDR) -- one of the surveys we discussed at our last workshop. As many of you know, the SDR is a large national survey of individuals under age 76 with doctorates from U.S. institutions. The survey is conducted for SRS by the National Research Council at the National Academy of Sciences.

Today, I will talk about trend data and then will talk about differences in the unemployment rate among subgroups, based on field of degree, length of time since completion of degree, age at time of degree, work history, and demographic subgroups. Finally, I'll talk briefly about where I'm trying to go in developing a multivariate model in order to sort out the effects of the different factors that affect the unemployment rate.

This graph is still preliminary. I Xeroxed a graph prepared by the Department of Labor and added onto it information for doctorates in science and engineering. We're very cautious about using the 1991 and 1993 SDR in trend analysis. As we talked about last time, we made some major changes and improvements in our methodology in those years that makes the data not strictly comparable to prior years. However, if we're cautious, I think we can indeed tell something about the trends. I have adjusted some of the past years to make them as comparable as I could, though it's not totally comparable.

Looking at this graph, certain things about the trends become fairly obvious. First of all, the higher the educational level, generally speaking, the lower the unemployment rate. That is a very basic fact that needs to be thought about when we interpret unemployment in the doctoral science and engineer population, because everything in life is relative. When we talk about problems here in the doctoral population, we're talking from a much lower base range. Some people--and I think this came out in the COSEPUP report -- look at the unemployment rate of 1.6 percent that we observed in 1993, and they say, "gee, that's nothing," because their reference is the general population figure.

Another thing that you can tell by looking at this graph is that the volatility in the unemployment rate tends to decline as the education level increases. Note, however, that we only do this survey every other year, whereas DOL has annual figures. That tends to make the doctoral graph look smoother.

Another thing that emerges from examining the graph is that, while the shapes of the curves are not identical, they're quite similar. Therefore, what's happening in the overall population is certainly important for understanding what's happening in the doctoral population.

While the 1.6 percent unemployment rate for doctoral scientists and engineers in 1993 is low relative to the unemployment rate for less highly educated populations, as far as I can determine, it is the highest point that we've ever recorded for doctoral scientists and engineers. However, it's not very much higher than the 1.4 percent we saw in 1991 or the 1987 1.3 percent. So, we're not way out of the range when we talk about disturbing unemployment trends. It's high, but not extraordinarily high.

Another way of looking at the unemployment trends for doctoral scientists and engineers is to take the ratio of the national unemployment rate to the doctoral unemployment rate. When I do that, I get a ratio in 1993 that shows the national unemployment rate to be 4.3 times the doctoral. Over the time period examined, it ranged from 4.1 to 8.7. So, again, the data indicate that, in a relativistic sense, unemployment in the doctoral S&E population was high, but not outside the range of what has been experienced in the past. This does not lend a great deal of support to the view of some that something terribly new and different is happening here.

In examining this data, it is important to remember that in 1993 there was already a great deal of concern about the doctoral S&E market. Ken Brown chaired a task force in 1993 for FCCSET (now NSTC), on this topic because everyone was concerned about it.

In order to understand the differences in unemployment among the subgroups within the S&E population, I have been doing a lot of cross-tabulations -- because that's always a good place to start.

As I'm sure is not news to anyone in this room, unemployment rates vary by field. In the 1993 SDR they varied from around 1 percent to 2.5 percent. While this is a statistically significant relationship, the effect of field of degree on unemployment can be described as only moderate.

Looking next at the length of time since receiving the doctorate, there was a slight curvilinear relationship observed. For the most recent cohort -- which in our survey were people interviewed 10 or 11 months after their degree -- the unemployment rate was 3 percent. At the other end of the continuum, we had a 2.2 percent unemployment rate for peoplewho got their degree 25 or more years ago. In between, the unemployment rate was pretty flat.

Another variable that is interesting to look at is the age at the time the degree was received. We see here a fairly strong relationship -- and this relationship is not one that I've seen frequently discussed in the literature. People who get their degree at a young age are relatively unlikely to be unemployed. We can speculate that these are people with a lot of get-up and go, and that employers are impressed by that -- rightly or wrongly.

Work history is an important factor in unemployment. Obviously, employers consider work history as a key ingredient in the hiring process. In 1993 we didn't get a complete work history in the SDR, but we will get more in 1995. However, in 1993 we did get some information about what individuals were doing in 1988. For those people who were not employed in 1988, that is, they were either unemployed or not in the labor force in 1988, the unemployment rate was 4.1 percent, which is quite high, obviously. However, some of those individuals who were not employed in 1988 were still in graduate school getting their degree. If we exclude from the analysis individuals who had not completed their doctorate by 1988, the unemployment rate was 9.6 percent -- the highest rate I've seen in going through the data. The old adage of the best predictor of current behavior is past behavior is perhaps at play here. There may also be certain underlying factors that lead people to be unemployed at different points in time.

In looking at those people who were employed in 1988, occupation in 1988 is fairly strongly associated with the 1993 unemployment rate in both the total population and in the subgroup who had completed their doctoral degree by 1988. Note that the post-secondary teachers have unemployment rates that were considerably lower than their colleagues in the same field who were outside of academia. So if you want tostay away from being unemployed...

BROWN: Tell us again what that right-hand column was.

SHETTLE: Okay. The total SDR population is in the left-hand column. For the right hand column, I said, let's exclude the people who didn't get their doctorate until after 1988, since they may have been in graduate school and only had a temporary job.

SPAR: When you talk about people not being employed, that doesn't mean they were unemployed. They may not have been in the labor force.

SHETTLE: That's absolutely right. I have not broken that out here, because it's not in the data, unfortunately.

LEDERMAN: Do you have an age distribution in that column so you can leave off everybody over 60, let's say?

SHETTLE: We could. There are lots of ways to slice it. Right now we have people under the age of 76. For all the analyses that I'm talking about, I only looked at people who were in the labor force in 1993. They were either unemployed or they were employed. If they were out of the labor force in 1993, they're not in there. So that pulls down the age distribution some.

This is a chart similar to the last one that examines the type of employer individuals had in 1988. As is not surprising, given what we saw in looking at postsecondary teachers, academia generally has a lower unemployment rate than the private sector. The private sector has the highest unemployment rate of the sectors -- 2.6 percent. State and federal government do pretty well also. And it doesn't make much difference in terms of type of employment whether we look at all individuals in the labor force in 1993 or just individuals who had their degrees prior to 1988.

This next chart is for years of full-time work experience. As we might expect based on what we saw for the number of years since the doctorate was received, there was a curvilinear relationship between time since the doctorate and unemployment.

The relationship between part-time work experience and unemployment is also of interest. Having no part-time work experience is associated with a relatively low unemployment rate. Individuals having part-time experience have higher unemployment rates, regardless of the amount of part-time work experience. I could speculate that some of what's happening is that part-time work experience may indicate to the employer some lack of commitment to the labor market. Alternately, it may be that factors, like having a disability or having unusual family problems, that lead to part-time work experience at one point in one's career may also lead to having problems in finding employment later.

The final set of variables I want to show you are cross-tabulations between unemployment and some of the demographic variables. We see gender has an effect on unemployment, but it's quite mild. Race/ ethnicity was not very important except for Native Americans -- but their sample size is small.

Disability status is related to the unemployment rate. People who had vision difficulties have a slightly higher unemployment rate than individuals without disabilities. For hearing, walking, and lifting, we have unemployment rates in the 3 percent and higher range.

ELLIS: Do you have any controls for those figures? I would assume that all disabled people are more likely to be unemployed if they had trouble lifting.

SHETTLE: Right. Controlling is always a big issue. What I'm working towards in this report, instead of doing a series of three-way cross-tabulations, as we often do where we control for age or gender or degree field and so on, is to control using a logistic regression approach. The things that I'm looking at in the model at this point are listed here.

I won't read through all of them, but what this is going to let us do is come up with a way of saying what is the impact of each of these variables on the unemployment rate, when we control for all the other variables. Let me refer here to the demographics since that's what you were asking about -- though I want to caution you that these results are still very tentative, since I'm still tweaking the model.

What I'm doing at this very preliminary point is saying, okay, let me take as a starting point a "straw person," who is a rather average-looking scientist, i.e., a non-Hispanic white male without disabilities who has average values on the continuous variables. I've selected characteristics, so that this straw person as an expected unemployment rate of 1.6 percent. Then I ask what would happen if I changed one characteristic at a time. For example, suppose someone had all the same characteristics as the straw person except for having a vision disability. Based on the model, having a vision does not appear to affect the unemployment rate, i.e., there's no difference between the expected unemployment rate for the person with a vision disability and the straw person. However, even with all these controls, we find a higher unemployment rate for people with hearing, walking, or lifting disability, though the difference is lower than it was originally.

I think the race/ethnicity differences, based on these very preliminary findings, are also quite interesting. In particular, blacks have a quite low unemployment rate -- lower than for whites when we do this control. This is consistent with some of what we are reporting for salary in our forthcoming Women and Minorities report. For blacks, it looks as if for those who jump over the major hurdles they face in order to get a doctorate degree are not at a disadvantage in the labor market.

The high unemployment rate for Native American is statistically significant -- even though the sample size was smaller than I would like to see.

I have one other chart that I was going to show you on the standardized values -- the age of getting the degree. There actually seems to be a stronger relationship when we control for all the other variables in the model than there was in the unstandardized relationship

By the way of a summary, what I'm finding today is: (1) the 1993 unemployment situation for doctoral scientists and engineers was, from a historical perspective, not good, but it was not extraordinarily different from past times when the labor market for doctoral scientists and engineers was weak. (2) in terms of differences among subgroups, we've seen there are a lot of variables that affect unemployment. That's important in terms of assessing the overall picture -- it means that looking at the total figure just doesn't give you the overall picture. Even when unemployment is low within the total population, there are going to be pockets of unemployment that are higher. (3) what I'm working on at the moment is trying to sort out the effects of the individual variables in order to help us understand even better what's happening in terms of unemployment in the doctoralpopulation.

SPAR: If you look at individuals who obtain their degree at age 40 or older by type of degree that they got, do you have evidence that they're going for the wrong degree? Or is it that they're in the same malaise as just about anybody in the 40-plus age group who is having a problem getting a job?

SHETTLE: I've only controlled by type of degree in the sense that when I did the multivariate analysis, degree type is being controlled for. So the analysis is saying that even controlling for the type of degree they have a high unemployment rate. It would be interesting to look separately at individuals who got their degrees later in life. There are a lot of things, as I look at the multivariate analysis, that I would like to go back and do some graphs on. This is really a fun project, and like a lot of fun projects, the number of things that I'd like to do mushrooms faster than my ability to do them.

Any other questions?

LEDERMAN: How have you dealt with the collinearity, let's say, of age and disability?

SHETTLE: I did standard multicollinearity tests and dropped some variables that were giving me problems. I had to drop age, because I couldn't keep in age and years of work experience and age at the time they got their degree.

FRAUMENI: I assume your unemployment statistics are unemployment in general. Do you ever look at if people are in jobs that they think are relevant or appropriate for their degree?

SHETTLE: Mark Regets who will be doing the next presentation has been doing some work on that.

REGETS: Well, actually, I'm not talking that much about that today.

BOYLAN: Following that idea up, have you thought of examining the distribution of earnings among people who are employed -- looking at demographic issues and trends? Some of the things you read about in the popular press suggest that part of the surprise is not just unemployment but under-employment or employment in low-paying jobs.

SHETTLE: Mark will be talking a little bit about some of these things related to what we call under-employment. As far as salary goes, we did an analysis that will be coming out in our Women and Minorities report soon on salaries and demographic groups. This was based on the 1993 SDR, using regression analysis techniques.

In terms of unemployment I've also been looking at a report that was done by NRC using the 1973 and 1975 SDR surveys. One of the most interesting things in the demographic work was gender. In 1973, the unemployment rate was, I think, 3.9 percent for women and 0.9 percent for men, which is dramatically different than today; and it was so large that all of their analyses were done separately for men and women. And today we're seeing gender having no effect in the multivariate analysis.

The Department of Labor shows a similar convergence for unemployment rates by gender over time, though I don't think it was as extreme for the general population.

Congruence between field of doctorate and occupation - Mark Regets

REGETS: And we have an analysis of people being involuntarily out of their field of degree. That is going to be in the latest Science Indicators. That's going to address a lot of what you're talking about.

BROWN: Thank you, Carolyn.

This was a talk that was based on our Survey of Doctoral Recipients and gives you an idea of the types of analysis that are possible with our data. Our next two speakers are going to use additional data sets as well. And I wonder, Mark, if either you or Carlos would have a few minutes in your presentation to cover our SESTAT system and the potential for its use for analysis by people outside SRS.

REGETS: I see Carlos gesturing that it's part of his presentation.

KRUYTBOSCH: I'll say a few words about that. For example, somebody did a dissertation on one of the surveys, right?

REGETS: Yes. We actually had a dissertation done from the 1993 National Survey of College Graduates already.

For those of you who don't know already, we have three work force surveys where we get the information directly from the individuals themselves: the Survey of Doctoral Recipients, the National Survey of College Graduates, and the National Survey of Recent College Graduates.

The first one is the SDR, which deals with Ph.D.'s from U.S. institutions. The National Survey of College Graduates, NSCG, deals with anyone who had a college degree as of April 1990, including graduates of foreign institutions. The third is the Survey of Recent College Graduates, which deals with recent science and engineering master's and bachelor's graduates one to two years after they receive their degree.

Prior to 1993, we didn't collect occupational information in very much detail on individuals. Starting with the 1993 surveys, we're able to explore in much greater detail than before the relationship between education and occupation in science and engineering fields. After the collection and editing of our 1995 surveys that are still in the field, we'll be able to do even more with this, because we're asking people to give us some information on their occupational history -- what occupations they've held over the course of their careers. For now, let me show you a little bit of what is still very much an analysis in progress for the 1993 files.

This first slide deals with information for people with bachelor's degrees from the National Survey of College Graduates on whether the person believes he or she is working in an occupation directly related to the field of their degree -- a person with an economics degree working as an economist, for instance.

One thing that probably won't surprise you is that most people with a bachelor's degree don't work in occupations in the same field as their degree, with the notable exception of engineering. However, a majority work in fields that are closely or somewhat related to their undergraduate education, i.e., to their bachelor's degree.

Personally, I was a little bit surprised by this since the National Survey of College Graduates is quizzing not just recent graduates, but people who have been out of school 20 and 30 years -- and they still seem to find that there's some relevance of the field of their bachelor's degree to their work. But even in a field like economics where a very low percent of those with bachelor's degrees claim to be economists, you see fairly high numbers saying that their job is closely related to their degree.

Another thing worth noting here is that the physical sciences are nearly evenly distributed among the three categories -- closely related to their degree field, somewhat related, and not related.

The second column indicates whether those not employed in their educational field are in another science and engineering occupation. For those in a non-science and engineering occupation, I have broken it out by whether the person says that his job is closely related, somewhat related, or unrelated to the field of the highest degree. I do it this way, because it can be very hard to tell whether or not a job is relevant to the degree. For example, when a person says he's a top manager, we don't know whether he is a top manager of a national lab or shift manager at McDonald's.

In the physical sciences, more than in the others, you see an ability to work in other science and engineering fields even with a bachelor's degree.

Looking next at data from the Survey of Doctoral Recipients on people with doctorates, it's not surprising to find that most people work in jobs in the same field as their Ph.D.'s. What might be surprising here, however, is that around 40 percent do not work in their degree field --and I'm defining field of degree rather broadly here. So someone with a degree in electrical engineering who works as another type of engineer would have still been listed as being in the same field.

Again, you see the physical scientists, in particular, finding jobs in other science and engineering fields. And one thing that's notable is that about a fifth of people with Ph.D.'s in life sciences are in non-science and engineering jobs that they say are unrelated to their doctorates. I need to do some more exploration of that. However, it's not just that many people in the life sciences are working in medical fields, because most of the people who say their occupation is medicine also say that their job is at least somewhat related to their Ph.D.

BROWN: Could you comment maybe on how this is related to the finding of the COSEPUP report a few months back where the authors were worried that the research degree is making people too specialized once they have their Ph.D. and that we should therefore consider new, more flexible approaches to graduate study? Do you think you could interpret this as saying Ph.D.'s are already pretty versatile in the job market or--

REGETS: Well, I think you've said it well. One thing to note, though, is that people do tend to spread out from their field as they progress in their careers, which might not be that much of a surprise. If I were to present this chart just for recent Ph.D.'s, you would see much greater percentages saying that they're working in the same field as their degree.

There do seem to be some blips in there for cohort size that might be worth exploring. It's not a very clean line when you trace the same field and percentages. I think graduating with a lot of other people may affect your willingness to be mobile.

BROWN: So a new Ph.D. in one of these fields may not have that great a chance of getting an unrelated non-S&E job. He stays pretty much with what he knows -- the field of his Ph.D. It is in learning by doing, I guess that you become broader in your ability to get other jobs.

REGETS: That's one of the things we hope to explore a little bit more, both by torturing the 1993 data and in even more depth when we get into career histories in the 1995 surveys.

SUTER: Are all these categories self-reported, like the closely related, unrelated? Or is that a judgment that you made based on their reporting of the--

REGETS: The S&E/non-S&E distinction is based upon our classification of their occupational titles. For those who are in what we consider to be non-S&E occupations we rely on the individuals report of whether the job is closely related. And it's true that different people interpret the question "closely related" differently. There is at least one person with a Ph.D. in physics and an occupation of physicist, who says his job is unrelated to the degree. And there's at least one physicist whose occupation is artist who says it's highly related to his degree. And maybe it is.

ELLIS: That's what I was wondering about -- the first cut you make is the field cut.

REGETS: That's right -- the first cut is by field.

ELLIS: Then the question on how related the occupation doesn't come into play unless somebody is out of S&E.

REGETS: That's right.

KRUYTBOSCH: But you could do the other two.

REGETS: Yes. In some of my other analyses, I look directly at whether or not they say the job is relevant and ignore the occupation information.

This next slide is by no means comprehensive -- it's based on some of the larger categories of occupations. If Ph.D.'s in physics aren't working as physicists, what are they doing? For Ph.D.'s in every field, top manager is a very common category. Here, I have broken out top managers by whether or not they say their jobs relate to their degree.

Beyond top manager, there is a great deal of variation by field. In chemistry, you see a fairly large number of chemistry Ph.D.'s in biological occupations. Another 4.5 percent are in engineering and physics.

You'll remember from the previous chart that a very large number of persons with doctorates in mathematics work in their own field. Not surprisingly, you get about 10 percent that are in computer occupations, either teaching computer science in colleges or working in computers in industry.

Physics is interesting. If you remember, we saw in the physical sciences at both the bachelor's and Ph.D. level a great deal of occupational spread. I wouldn't have predicted that nearly 10 percent of Ph.D.'s in physics from U.S. institutions would tell us their occupation was engineer. And we have another 5 percent that are in biology or computers, and another 5 percent that are college teachers in fields other than physics or astronomy.

Sociology is another field that's very spread out -- only here what is common is other social sciences either outside of academia or in academia. One in four Ph.D.'s in sociology is a college teacher in some field other than sociology or anthropology.

CZUJKO: What's your best guess as to what people mean when they say they are a top manager? Do you think top manager includes somebody who's managing 23 Ph.D. scientists?

REGETS: We asked people to define themselves as a top manager only if they manage other managers. People on these surveys sometimes make other judgments.

NEUSCHATZ: In this table, it says percent of total in Ph.D. field. Am I right that these percents include both S&E and non-S&E? In other words, those percentages would be anyone who is not in the same field as their field of degree?

REGETS: Right. If, for example, they have a physics degree and they're in computer science or biology or engineering, they would have been listed in the other S&E column in the earlier table we looked at. Also, in the first table, I considered the chemist who worked as a physicist as being in the same broad field, because I grouped all the physical sciences together. In this table I ask about the occupational distribution of those with degrees in physics who are not employed as physicists or postsecondary teachers of physics.

O.K. Does it pay to do work related to your degree? I could cross-tab this to death. But being an economist, I stuck it into a wage equation and regressed the log of salaries for people employed full-time on a number of control variables: their years since degree, years since degree squared, a number of dummy variables for sector of employment, whether or not their degree was from a Carnegie R1 school, and a flag for whether or not they were paid on an academic-year basis. Adding into that, dummy variables for whether or not they said their degree was closely related or somewhat related to their degree field. I excluded from the regression the dummy variable for people that said that their job was not related to their degree -- so that becomes the comparison group.

We do find that, in general, there is an economic return to doing work that's related to your field of degree. But there is not necessarily, in the aggregate anyway, that much difference between the job being closely related or somewhat related to your degree.

Now, there's two things to note on this. One may surprise no one, and that's the negative return for people with Ph.D.'s in humanities. Before everyone feels very smug and laughs -- note that you have a very low return for physics.

This is really something that I should do more empirical work on; however, I can speculate somewhat at this point. I think we all realize that a person with a Ph.D. in humanities who wants to work as a historian may make less than if he goes into some other field. But physics generally is a fairly highly paid field. So why are we finding such a low return for a physics Ph.D. doing work relevant to his degree? My best explanation is because the training in physics facilitates transferring of skills to other highly paid fields, making it fairly irrelevant as to whether or not you are working in the same area as your degree.

CZUJKO: Mark, I think you might also have an employment sector phenomenon. If you work in academe, you probably work in your degree field. If you work in industry, you probably don't and you make a lot more money doing it.

REGETS: Well, that was why I tried to control--

CZUJKO: You did control on that?

REGETS: And these are from separate regressions for each field, so this would have been the regression of all physics Ph.D.'s with the control for government, industry, education.

NEUSCHATZ: I don't know if this would make any difference, but is this percent difference of the natural log of salary or of raw salary?

REGETS: The dependent variable is log salary. If, for example, a coefficient was 0.079, the interpretation would be that the salary for a category was 7.9 percent greater than for the excluded category, i.e., the salary for those in occupations not related to their degree field.

NEUSCHATZ: But is it 7.9 percent of the log of salary greater or of the raw salary?

REGETS: Of the raw salary.

HEINIG: The greatest advantage to working in your field seems to be in fields like the life sciences where there are the highest number of people working in unrelated fields.

REGETS: This is another reason why I suspect that there's a transferability argument going on. The life sciences are lower paid than the physical sciences in general, and you see at every degree level higher percentages going outside of the field. It's something we have to do more exploration on.

BOYLAN: In the regression, did you include post-doctoral positions?

REGETS: No, actually, I didn't. And I should.

BOYLAN: It wasn't a criticism. It's just that's my interpretation of some of those parameters.

REGETS: That would be worth doing. To some extent, the propensity to be a post-doc in some of the fields would have fed into the years since degree and years since degree squared. So it might be controlled for it, just not as well if I had explicitly included it. That's worth doing.

BOYLAN: A second question. I'm on dangerous ground now, but did you employ gender as an explanatory variable in any of the work that you've done here, like the percent outside of field, for example, or the analysis that you did?

REGETS: Not yet. Well, gender certainly has been part of some other pay analyses.

BOYLAN: Right. I wasn't being original in bringing it up.

LEDERMAN: What about the actual job title? Does this include, for example, top managers? Do you exclude them if they're not working in their field?

REGETS: This would include top managers. If a top manager said that his work was not related to his degree. Occupation was not directly included in this regression at all. We just separated people by their field of Ph.D. and controlled for how they described the relatedness of their occupation.

LEDERMAN: You didn't try occupation as an explanatory variable?

REGETS: Not yet -- but this work is still exploratory.

BROWN: Do you want to say a few words about the SESTAT system -- or will Carlos?

REGETS: Well, I think Carlos is going to tell you about some work we have under way on an easy way to access our three data sets and to access something we call the SESTAT file, which is an integrated file of all three SDR, NSCG, and NSRCG data. We hope to have this system available to you shortly.

BROWN: Thank you, Mark. Our next speaker is Carlos Kruytbosch.

Correlates of professional society memberships among scientists and engineers - Carlos Kruytbosch

KRUYTBOSCH: I like Mark's comment that instead of cross-tabbing us to death, he was going to do a regression analysis. Well, I'm a sociologist, and I'll probably cross-tab you to death.

My project here really stems from the testing and the development of our integrated database that Mark mentioned -- the SESTAT system. SESTAT stands for Scientists and Engineers Statistics. Our system is in the mode of Statistics Canada. We have developed a hardware system, a database system, and have developed a point-and-click access system for extracting data, doing cross-tabs, and doing some calculations. This system makes analysis very easy. You don't have to know the complex database access programs and so on and so forth.

We have been testing the SESTAT system for about three months, and we have had people interested in different areas of the science and engineering workforce engaged in doing analyses in their area of interest. We've had people working on secondary school teachers in mathematics and science. We've had people working on minorities and women and we've had people who are beginning to work on an analysis of engineers. They've been using the data to explore the world of the particular trends of scientists and engineers in different areas, thereby really testing the way in which the whole database is put together. While we've encountered a number of problems over the last three months, we think we're at the end of the process and we hope that we can complete the process by the end of the year.

I thought that as part of this process of getting the professional societies concerned with developing data on the S&E workforce together with SRS staff, it might be interesting to look at our data in terms of what it shows about individuals' propensity to join professional societies, so that we have a better idea of what kinds of people join professional societies and what kinds of people don't. Thus, when there are surveys of a particular professional society, we have a better idea of the coverage of the total number of scientists and engineers eligible in that area.

This analysis is basically an exploration. I don't have a great theory and I don't have any intent to make a global statement about this at the end.

These are the basic areas I have looked at so far. First, are indicators of professionalism. There is a literature and sociology about professionalism. In fact, a lot of dissertations and work have been done in this area. In that literature, membership in a professional association is generally taken as being the hallmark of professionalism. So, it was natural to look at variables that would be related to professionalism. This is the key phenomenon. These variables have very strong relationships with the propensity to join professional societies

Secondly, there are demographic factors, and these generally -- kind of surprisingly -- have relatively small associations with membership in professional societies. The third category of variables are what I call behavioral factors. There is very little variation among fields of degree in terms of propensity to join professional societies. However, the level of degree is an important factor in whether individuals join professional societies.

Now, part of the point of saying that I'll cross-tab you to death is the form of this presentation. I haven't yet mastered the ability to download a table and import it into an Excel file, like Mark is so good at doing. So, I ask your patience with the way these overheads look. I have, however, highlighted in yellow some of the key findings.

In the first table I have level of degree by labor force status, i.e., employed, unemployed, or not in the labor force. At the bachelor's level, you see a fairly strong relationship, though I haven't done statistical tests of significance. Those who are employed are more likely to report one or more memberships in professional societies than are those who are unemployed, and, in turn, than those who are not in the labor force because they are retired or for other reasons not looking for work.

Among employed individuals, about two-fifths of bachelor's level scientists and engineers belong to a professional society compared to about 60 percent at the master's level, and nearly 90 percent at the doctorate level. This is a very strong relationship.

I don't know how this observation correlates with the information you have. As I understand it, in the American Chemical Society I think about 60 percent of the membership are doctorates. I don't know what it is with the other societies.

JORDAN: It's that dynamic.

KRUYTBOSCH: This can serve as kind of a market survey, you might say, to look at where the potential for membership is in a particular area.

I might say that this measure was, "Do you belong to a professional association? How many do you belong to?" I simply collapsed it into one or more and none. So, if you're looking at chemists or biologists, you don't know whether they belong to the Chemical Society or to the Association of Professional Rose Growers or what have you.

The employment sector is also important. This is the percent who are members of a professional association by the employment sector and by the level of degree. For all levels of degree, those in the educational sector are most interested in joining the professional societies. Those in government are significantly less interested. In business, they are doing more important things than attending professional society meetings or whatever, or perhaps their professional identity is less crystallized and their work is less dependent upon a professional identity.

SPAR: Often, it's a function of whether the employer will pay for you to belong to the association.

KRUYTBOSCH: That's a good point. That's a very good point. And you see this is probably strongest at the bachelor's level and weakest at the doctoral level.

This next table looks at the effect of gender on professional association membership within level of degree and also within type of employment and major field of highest degree. Every pair that is marked in yellow represents a case where the males are more likely to be members of professional associations than are females.

If you look at the bachelor's level, out of the 15 possible comparisons, in 11 males are more likely to report belonging to a professional association. There is one cell here, in math and computer science in the educational sector, where females are more likely to report being members of a professional association.

The asterisks indicate cells where the sample size was too small to provide reliable estimates.

At the master's level, you find a similar pattern. At the doctoral level, you're bedeviled by the small number of women in many fields. But you see the differences are not very large. However, they're consistent.

The next table looks at the year of the cohort, using three-year cohorts of bachelor's degrees, beginning in 1992. The youngest ones got their degrees in 1989 to 1992, 1986 to 1989 and so on, going back to 1929. This shows the percentage of all people whose highest degree is a bachelor's degree who reported membership in an association. It was 40 percent. And this percent stays just about the same all the way up to people who are 65 years old where retirement becomes a factor. So, age doesn't seem to make any difference. You start off born into a professional identity, and somehow you continue it.

You find the same thing for the doctorates, except at a higher professional society participation level. It starts off with 84% as members of a professional association. It fluctuates a little as age changes. However, looking at people at retirement age, the doctorates stay members of a society. In fact, even in the oldest group, 90 percent report membership in a professional association. So the doctorates' professional identity is very robust.

Next, we see that in terms of attendance at professional society meetings, the professional identity of the doctorates also remains. A total of 75 percent say they attended a professional society meeting in the last year. Among the younger ones, they're gung-ho. They've got to go and show off their work and meet with potential employers and establish and firm up their professional identity -- they're all there attending these meetings. Then gradually their attendance begins to tail off. However, even in the oldest age group, half are still attending. I guess that's partly the nature of science, too. We have our "grand old people" in science, and they are very highly valued at meetings.

If you cross-tab the membership by attendance, you find that 80 percent of people who are members have attended a professional society meeting in the last year. However, you also find that 30 percent of the people who are not members have also attended. For a lot of people it has to do with the payment and the costs of membership, I think. But you have a fairly large chunk of people attending meetings who are not members. You can do that by field and look at it in greater detail if you want.

The next table shows membership in professional societies by the field of degree for individuals with a bachelor's degree as their highest degree. The average is 40% for bachelor's, you'll remember. Computer science is probably statistically significantly below the average and math scientists a little below. Also, remember that a lot of people are working outside their field. A lot of the computer scientists are employed in industry. I think what amazed me most about this table is how little variation there is among these enormously various fields.

Now, let's look at a few of the demographic variables. First, citizenship -- this is an interesting research field right now. At the bachelor's and master's levels, the naturalized citizens and the non-U.S. citizens who are permanent residents, i.e., the immigrants, are significantly less likely to join a professional society than are U.S. citizens. However, these differences sort of disappear at the doctorate level. Hopefully, Mark who is a recognized expert on immigration, will explore this further.

The race/ethnicity table has one interesting finding in it. There is little difference between whites, Hispanics, and blacks at the bachelor's level. However, Asians and Pacific Islanders are relatively unlikely to be members of professional societies. Maybe they're working more often in industry or maybe it's related to their immigration status or maybe it's due to cultural differences.

Now here's the last table -- on whether your occupation is related to your degree? There is a robust relationship between professional society membership and whether individuals feel subjectively that their work is related to their degree. This holds at all degree levels. Even at the doctoral level, it's strong. It doesn't wash out like many of the other characteristics at the doctoral level. Those doctorates who feel their work is not related to their degree are much less likely to belong to a professional society.

Also, I've looked at salary and there's a slight salary advantage to belonging to a professional society. At any rate, I'll be continuing to work on this. If you have any suggestions as to what you'd like to see, I'd be happy to follow up.

Thank you.

BROWN: Thank you, Carlos. Well, you see we're not cross-tabbed to death. We actually like cross-tabs in this room.

KRUYTBOSCH: It's a simpler way of thinking.

BROWN: Any questions on any of the three presentations or analysis? Ed?

SPAR: Are these degrees gained in the United States or could they be abroad?

KRUYTBOSCH: These could be anywhere.

SPAR: That could explain why the Asians are not more frequently professional society members, because there could be a language problem.

KRUYTBOSCH: That would be easy to check to see whether their degrees were gotten here or not. But this is the SESTAT database which includes foreign-trained doctorates, foreign-trained bachelor's. I think the foreign-trained doctorates are about 10 percent of all doctorates in science and engineering. It's a significant chunk.

SPAR: It might pay to break those out.


BOYLAN: I was curious. Is there any way to determine whether these professional associations charge lower membership rates for people who are unemployed?

KRUYTBOSCH: I don't know. You've got them all here. I do believe that the costs are an important thing. I'm not quite sure how to get a handle on that.

WINDUS: I could give you an example of a 90-year-old man who is still active in a professional society, because my father is 90 and is a member of the ACS. Part of the reason he is a member is that once you've been a member for 50 years, you are a member--

KRUYTBOSCH: For a lifetime.

WINDUS: Like forever, for free, and he still gets the magazine and so on. So that's not to say he wouldn't be a member if he had to pay, because it's one of his favorite publications, but you need to take into account the fact you do get a life membership without cost.

MARASCO: I was just going to add that ACS does have deferred dues for unemployed members.

KRUYTBOSCH: It does? I should run the cross-tab for the chemists and see whether unemployed chemists are more likely than other unemployed scientists to be members of a professional society.

MARASCO: You also find among the chemists that the industrial chemists are less likely to belong to ACS. And the relevance of their degree to their field of work may also be a factor. Another hot topic of discussion at ACS right now is the boutique society.


MARASCO: The boutique societies -- the smaller, more specialized, groups that are springing up. I don't want to say they're threatening our membership, but they're attracting people who might otherwise belong to ACS.

KRUYTBOSCH: You could look at that a little bit with the data by looking at those who belong to more than one to see whether they are different from those with only one membership.

VOYTUK: Carlos, you said that this data included those that earned Ph.D.'s outside of this country.


VOYTUK: What survey does that come from?

KRUYTBOSCH: This is from the National Survey of College Graduates that's based on everybody who had a bachelor's degree or higher who was in the U.S. in 1990. We surveyed them in 1993. So for the first time in a decade, we have a sample of people with doctorates in science and engineering who received them from abroad. That group is not captured in the SDR. We also, of course, had people who got other degrees, bachelor's or master's degrees, abroad. So it's a rich database. We're looking at the issues of immigration which, as you know, are rather pressing right now.

VOYTUK: You've weighted the data, I assume, because that was only a sample, right?

KRUYTBOSCH: Yes -- it's all weighted, though I'm not the best person to answer your questions on weighting techniques.

SPAR: It's almost a perverse logic. The unemployed need the professional societies more than the employed do, because they're the ones looking for the jobs. In a sense, it's a critique of the associations that their employment program is not stronger than it is.

KRUYTBOSCH: I think that's an interesting point.

LEDERMAN: I don't think that's correct. Most people with a job are working in a local market area -- and there are local professional associations that are not necessarily part of national associations.

KRUYTBOSCH: I think it does hold at the doctorate level. In fact, in sociology, we always refer to the National Association meetings as the slave market.

LEDERMAN: Yes, but that's normally for academics who are jumping from one academic institution to another.


LEDERMAN: It's not for Ph.D. unemployed aerospace workers in Southern California.

REGETS: We'd be interested in breaking it out by age and years since degree.

BROWN: We have one more presentation before we take a break, and that is Mary Golladay, who will tell us about the Survey of Earned Doctorates.

Release of 1994 data on science and engineering doctorate awards - Mary Golladay

GOLLADAY: This is actually more an announcement than a presentation. I'm sorry that Susan Hill who was going to give this presentation couldn't be here. She is home with the flu. I do not have slides, but I will try, during the break, to get advance release copies of the most recent Survey of Earned Doctorates report. I think this is a major data set that probably needs no introduction at all to this group. It was released just last week by the federal sponsors and the National Research Council simultaneously. We had hoped to get copies of this yesterday from the warehouse, but it wasn't here yet earlier this morning.

While we call these advance copies, they are totally final numbers -- these are the same tables that will be in the final version, but they are not as pretty. While I know you'll be interested primarily in the detail for your respective fields, let me indicate a few of the highlights.

First of all, Table 1 gives you the overall totals -- you will see that all of the numbers are up. They're continuing to rise. Science and engineering doctorate totals for the first time are above 26,000. Not only are science and engineering degrees up, but the overall totals are up as well -- to 41,000. This is the first time we've passed the 40,000 mark.

This report breaks out non-science and engineering fields a little bit. We used to just give one line to all non-science and engineering. This time we break out health, humanities, education, and all other. They're all up. So the numbers are continuing to rise.

Table 2 gives the awards to women and gives percentage of awards to women for the 10 years, 1985-94. And you'll notice here, again, the increases are continuing, but there are no major changes in those fields that are a majority women. Tables 1 and 2 provide a great detail by field -- so, you should be able to find the specialties in which you're most interested.

Table 5 contains one of the most interesting blips in this particular data set for 1994 which is the awards by visa status of recipient. Here you will see a substantial increase in those persons with permanent visas, including Ph.D.'s who are from the People's Republic of China. This is the first year that we have seen a major effect of the Chinese Student Protection Act of 1992. You'll see a very strong -- four- to five-fold increase -- in those numbers.

Table 9 gets a lot of attention, too, because that has the post-graduation plans of doctorate recipients. The interest is especially in the status of the plans for staying in the U.S. among non-U.S. persons receiving degrees. The percent hasn't changed a great deal for those on temporary visas. It's been over 50 percent for a number of years -- it's 55 percent in 1994. So this is a little bit of an increase, but not a major increase. For those with permanent visas, it's about 90 percent, very little difference in those holding permanent visas than for U.S. citizens, where it's about 95 percent.

So there are lots of interesting facts in this very important data set. We encourage you to look at them and ask us as many questions about them as you can.

Thank you.

TUPEK: The publication is also available on the Web on our SRS home page in its entirety.

GOLLADAY: Yes, Susan?

MITCHELL: I would like to say, too, that the NRC summary report which contains these data on other fields will be available tomorrow. You can get a copy by calling our budget office or, since most of you are probably on the mailing list, you can just wait.

GOLLADAY: You may also be interested in the story that was in the Chronicle of Higher Education, dated December 8th that Carolyn is handing out.

BROWN: It's time now to turn to some of the professional association presentations, starting with Dick Ellis who will tell us about new developments in the production and employment of engineers.

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