Empirical Analysis of Career Transitionsof Sciences and Engineering Doctorates in the US

Natalia Mishagina∗,Queen’s University †

August 10, 2009

Abstract

This paper studies the career choices and mobility of white male doctorates in sci-ences and engineering (S&E). Relevance of employment to R&D and S&E is evaluatedusing an alternative approach that uses activities on the job rather than occupations.Transitions between tasks were assessed using a duration model with competing risksestimated on the Survey of Doctorate Recipients (1973-2001). It was found R&D tasksemploy only 57% of S&E doctorates, and this number varied over time. Most transi-tions out of R&D happen when doctorates are young. Decisions to switch tasks areaffected by demographics, performance on the job, and general economic conditions.

Keywords: Occupational transitions; duration analysis; competing risks; scienceand technology workforce; high-skilled labor.

JEL Classification Codes: C41, J24, J44.

∗I am grateful to Christopher Ferrall for his guidance in working on this paper. I appreciate commentsand suggestions from Charles Beach, Paula Stephan, Sharon Levin, Stephane Robin, Mark Regets, SusanHill, the participants of the CEA (Montreal, 2006) and SRS/SEWP workshop (Arlington,VA, 2006). Iwould also like to thank SRS, National Science Foundation, for providing the data and valuable assistance.I acknowledge Research Fellowship from the SRS/American Statistical Association.

†Queen’s University, Dunning Hall, Room 209 Kingston, ON, K7L 3N6 Canada Tel: (312) 533-2250.Email: mishagin@econ.queensu.ca

1 Introduction

This paper studies the career choices and mobility patterns of white male PhDs in natural

sciences and engineering (S&E). While a PhD career is typically associated with research and

development (R&D), there is anecdotal evidence that a large fraction of S&E doctorates find

employment opportunities in non-traditional for scientists areas such as financial, accounting,

and other business services. This fact has never been acknowledged or assessed in the relevant

literature before. In fact, little is known about the employment choices of S&E doctorates

and especially their career outcomes once they leave S&E. This raises the following questions:

What do we know about the career choices of S&E doctorates and their relevance to S&E

in general and R&D in particular? How does outward mobility from S&E compare to that

within S&E? And finally, what factors affect the decisions of scientists and engineers to

switch careers and the timing of the change?

The consequences of career transitions depend on their nature and the factors that affect

them. Whether these transitions are of concern for public policy depends on who makes

the transitions, when, and why. For example, if those who leave R&D are the leading

researchers, then one should be concerned with losing highly trained specialists. If, however,

switchers are the relatively “worse” scientists, then it may be worrisome that this sorting did

not happen earlier, say during their training. The reasons for switching also shed light on

how marketable the skills of doctorates are outside their professions. One may suspect that

switchers are lured away from research by better monetary rewards and career advancement

prospects in non-S&E areas. Then the question is: What do non-S&E employers value

in doctorates? Perhaps, employers believe that the ability to generate interesting ideas and

conduct independent research is positively correlated with the ability to perform well in non-

S&E. Perhaps, some aspects of the research skill is transferable to non-S&E; for example,

mathematical and computing skills, modeling and analytical abilities. Alternatively, a PhD

degree is just a signal of exceptional intellectual abilities and suggests a high possibility to

easily re-train a doctorate to meet the demands of a new career.

The first objective of this paper is to assess the relevance of PhD employment to S&E

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in general, and R&D in particular. The second goal is to compare the employment rates

and mobility patterns of doctorates in different types of careers. The third objective is

to evaluate how various personal and job characteristics, research productivity, and labor

market conditions affect mobility rates and timing within and out of S&E. To do so, an

econometric model of transitions with competing risks is specified and estimated. To meet

these objectives, this paper uses the Survey of Doctorate Recipients (SDR) collected by the

National Science Foundation (NSF) from 1973 to 2001. This longitudinal data set is unique

because of its large representative sample of professionals whose share in the labor force is

small. As a data source on careers, the SDR is rich in employment characteristics specific

to S&E.

To achieve the first goal, I propose a new approach that classifies employment choices

based on primary activities first and only then on occupational titles and employment sectors.

According to this method all individuals in the survey were categorized as employed in one

of three types of employment or “tasks”: 1) creation of new knowledge (“R&D task”), 2) ap-

plication of existing knowledge (“applied tasks”), and 3) tasks unrelated to S&E (“non-S&E

tasks”). This approach is an alternative to a more traditional method that would involve us-

ing occupational titles or sectors of employment alone. According to this traditional method,

only “scientists” or even “academic scientists” would be qualified as employed in R&D. In

this case, a recent decrease in available tenured-track positions in academia would be con-

sidered as a threat to R&D because it reduces employment opportunities. The approach

proposed in this paper allows one to see jobs in R&D from a very different angle. It uncovers

the ever-evolving nature of R&D that has spilled out of academia into other sectors. More-

over, it helps evaluate retention rates in R&D correctly because by construction it is not

sensitive to individuals changing firms or sectors as long as R&D activities remain primary

activities on the job. Finally, it eliminates confusion related to interpretation of occupational

titles that often occur in surveys when occupations are self-reported.

I found that in the data only 57 percent of doctorates worked in tasks directly oriented

towards R&D, including its supervision. Another 35 percent worked in applied tasks, such as

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software development by non-computer science majors, teaching, or professional services. Fi-

nally, 8 percent of doctorates worked in non-S&E tasks in such sectors as financial consulting

or accounting.

The second finding is that task choices vary within a PhD career. Doctorates tended

to begin their careers in R&D tasks (72%), but only roughly half of them remained there

thirty years later. Some of those who left R&D went to applied tasks (80%), and others

left S&E for good (20%). The transitions within and out of S&E differ in their timing and

patterns. The only common feature was their high frequency within the first 16 years of

task-specific tenure. Decisions to switch tasks did not differ significantly by cohort with

minor exceptions. For example, those who graduated in the 1980s were the most likely to

leave R&D for application in all specifications. At the same time, mobility from R&D to

non-S&E was the lowest for this cohort (1980s). The transitions differed by field of study,

current activity, sector of employment, and academic rank conditional on being in academia.

Analysis of the employment histories showed that mostly academic researchers on tenure-

track or with tenure switched to applied tasks. This finding agrees with the predictions of

the human capital theory that scientists invest into their experience or reputation in order

to cash in on it later on, Cater and Lew (2005). It was also found that individuals who leave

S&E are more likely to be employed in non-academic sectors and have non-S&E degrees

prior to their PhD. Several measures of research productivity and recent research activity

prior to transitions had a significant and positive effect only on the transitions from applied

tasks back to R&D tasks, although low response rates to all productivity questions make

interpretation of the results difficult. Another result is that the decisions to switch tasks are

sensitive to the labor market conditions measured by the overall unemployment rate, share

of the R&D expenditures in the GDP, and enrollment rates in S&E postsecondary programs.

The final interesting result, which is a by-product of the main analysis of the paper,

concerns the nature of postdoctoral appointments and their influence on a doctoral career.

It was found that individuals who held several postdoctoral appointments were more likely

to leave S&E for good, whereas they did not exhibit higher transition rates between tasks

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within S&E. This supports the hypothesis that postdoctoral appointments are probationary

positions or “waiting lists” of a sort rather than extensions to doctoral education that provide

additional training. This new result contributes to understanding of the market for scientists.

This study contributes to several strands of literature. Mostly, it contributes to the

growing literature on employment choices of high skilled labor and S&E as industry. In

particular, this study proposes a method of distinguishing the jobs of PhDs with respect to

their relevance to R&D from other types of employment. A detailed analysis of employment

choices of S&E PhDs and their dynamics throughout a career is conducted. This study

is also the first to document the employment choices of PhDs outside the scope of S&E.

Results on behavior of postdoctorates described above are also new and deserving further

investigation. Finally, the effect on mobility of such factors as labor market conditions and

research productivity has never been assessed before.

2 Literature

The literature on science and technology as an industry together with its labor force is

well summarized in Stephan (1996). In particular, the author discusses relevant issues for

this paper such as the training of doctoral scientists and engineers, markets for their skills,

incentives, and earnings profiles, the importance of the non-pecuniary benefit of research

(i.e., “ the joy of puzzle solving”), and research funding and its influence on scientists’

productivity.

There is also literature that focuses on the specific aspects of the traditional career paths

for doctorates; for example, R&D or teaching in academia, government or the private sector.

Levin and Stephan (1991) study a very similar problem to the one raised in this paper. The

authors develop and estimate a dynamic model to study how academic scientists allocate time

to research versus teaching. Research in their model has an investment motive: Discoveries

improve the scientists’ stock of knowledge, which in turn enhances scientists’ future research,

and helps in teaching. Teaching is the only activity that provides consumption benefits.

In this study, I analyze mobility between research and teaching activities, which assumes

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specialization in one of the two rather than the optimal allocation of time between them.

The mobility out of R&D and the complete career changes of doctorates considered in this

paper have not been assessed in the S&E literature before. The studies of traditional career

paths can be grouped into two major strands. The first strand is concerned with employment

and mobility within a specific sector, such as managerial versus technical jobs in R&D firms

(Ferrall (1997), Biddle and Roberts (1994)), promotions within academia (Robin (2002),

Grimes and Register (1997)), or mobility between firms (e.g Moen (2001), Fallick et al.

(2005), Almeida and Kogut (1999)). The second strand studies mobility between R&D

sectors (e.g., Zucker and Darby (2006), and Audretsch and Stephan (1999)).

Mobility within R&D sectors serves as a vehicle of transferring new ideas through their

inventors who most often hold patents on these inventions rather than publish their findings

freely in scientific journals. In these cases employing the inventors becomes the only way

to access new ideas. On one hand, this type of mobility is a concern since it reduces firms’

incentives to invest in human capital. On the other hand, it is beneficial for the recipient

firms. Almeida and Kogut (1999) analyze the mobility of engineers who are patent-holders

and the patterns of their patent citations in the semiconductors industry. The authors find

notable regional differences in knowledge localization, especially in such regions as Silicon

Valley and the New York Triangle. They explain this phenomenon by the existence of a large

local market for scientists and engineers in these regions. Their finding about high mobility

is supported by Fallick et al. (2005) who analyze interfirm transitions in high technology

clusters using the CPS data. They have discovered very high transition rates within computer

clusters in California, such as Silicon Valley. The authors explain this phenomenon with the

features of the state law such as restriction on non-compete agreements. Firms with superior

innovations benefit from such mobility because it causes the reallocation of human resources

towards these firms. The authors show that under certain conditions there is a net benefit

of this kind of mobility, but for technological reasons it is specific to the computer industry

only. Moen (2001) considers this issue using the human capital framework. He shows that in

R&D-intensive firms workers are paid much lower wages early in their career. He considers

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this a payment for acquiring training, for which they are compensated later in their careers.

He suggests that all possible externalities of labor mobility are internalized by the market.

The second strand of the literature considers mobility between different sectors such as

academia or government laboratories and industry (e.g., Zucker et al. (1997), Zucker and

Darby (2006), Audretsch and Stephan (1999)). This type of mobility is interesting because it

causes knowledge diffusion across sectors through the reallocation of researchers. Zucker et al.

(1997) study behavior of academic bioscientists moving to industry. Later Zucker and Darby

(2006) extend the scope of analysis to all scientific fields. Both studies estimate a duration

model to show that it is the “star” scientists that make the move. The quality of a scientist

is determined by the number of primary accession numbers (similar to citations) obtained

from the GenBank. Audretsch and Stephan (1999) study mobility of doctorate biologists

who left academia to found bioscience firms. They also found that the “switchers” are the

“star” scientists, who start their own company rather than join a bigger one. This interesting

observation proves the prediction of the human capital theory that early in their careers,

academic scientists would heavily invest in their knowledge in order to build reputation, and

cash it out later, by selling their services to a private firm or by starting their own business.

A slightly different aspect of mobility was considered in an earlier paper, by Biddle and

Roberts (1994), where the authors model and empirically test one of the career paths of

industrial engineers that begins in a technical occupation and then can be continued in a

managerial occupation. A self-selection and job matching framework was used to determine

who decides and why to switch to the managerial track and whether to stay on it or not.

The key assumption in the paper is that the skills to perform technical and managerial

tasks are positively but imperfectly correlated. Therefore, the model predicts that the most

productive technical workers would also be more productive as managers, and thus they

choose to switch to management later in their career. Another prediction of the model

was that those who switched tracks and turned out to be unproductive as managers would

backtrack. The authors found empirical support for the first prediction of the model but not

the second.

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The next strand of the relevant literature is concerned with retention in other professions

and career changes. The following studies are especially closely related to the present paper

because they answer similar questions about the careers of high school teachers. For example,

Stinebrickner (1998) and Stinebrickner (2002) evaluate reasons for teachers attrition from

the profession. Both studies use duration analysis techniques to evaluate the factors that

affect the timing and reasons for exiting the profession. They find that teachers who exit

are more likely to be women who leave the labor force altogether for raising newly-born

children. Scafidi et al. (2007) assesses how attrition is affected by the availability of higher

paid occupations outside teaching. Similarly to doctoral scientists and engineers, teachers’

skills are marketable outside the teaching sector. Non-teaching sectors offer higher salaries

which facilitates the teachers’ mobility out of the profession. However, the authors find that

attrition to higher paid occupations is not common, at least not among the female teachers

they studied.

Two more studies should be mentioned as relevant to the current investigation. The first

is a study by Van den Berg et al. (2002) who compare various paths from medical student to

junior medical specialist (or “trainee”) based on the data from the Netherlands. The authors

use a duration model with competing risks to evaluate the time to become a trainee from

two different origins: unemployment versus medical assistant. They find that it is easier to

become a trainee if an individual chose to be a medical assistant first. Therefore, the job of

a medical assistant may be considered a stepping-stone job. Although the model does not

explain whether employment as a trainee provides additional skill or simply scans for the

most talented or eager to learn students, this finding is interesting and very relevant.

The second study by Sauer (1998) studies early job choices of lawyers. The author de-

velops and estimates a dynamic discrete choice model where employment options differ in

terms of their monetary and non-pecuniary compensations, retention and dismissal proba-

bilities, and their effect on future options. The results show that motives to choose jobs

in the private sector, which are highly demanding and highly paid, differ depending on the

ability of an attorney. High-ability attorneys face high promotion probabilities and high

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earnings in these jobs so it is natural for them to choose them. Low ability attorneys these

jobs even if their survival probability in these jobs is low. This finding would be puzzling

if the effects of current jobs on future employment was ignored. The author suggests that

employment in private firms early in the career improves the chances of low-ability lawyers

to secure well-paid jobs in other sectors.

3 Empirical evidence on career choices and transitions

This section describes the data and outlines main facts regarding the career choices of PhDs,

the relevance of these choices to S&E and R&D, and the transition patterns between the

different types of careers.

3.1 Data

The stylized facts in this section are derived from the rarely used Survey of Doctorate Recip-

ients (SDR), collected biennially by the NSF since 1973. This longitudinal survey includes

only individuals who graduated from US PhD programs and resided in the US at the time

of the survey. In the original 1973 survey, the target population included graduates between

1930 and 1973. With every survey a fraction of new graduates was added. Information on

these individuals is obtained from the Doctorate Records File (DRF) maintained by the NSF.

The primary information for the DRF is the Survey of Earned Doctorates, a one-time census

type survey on all doctorates from the US institutions in S&E or health fields collected at

the time of their graduation. Since the latter started in 1957, the information source on the

graduates prior to 1957 are taken from a registry on highly qualified scientists and engineers.

The latter is assembled by the National Academy of Sciences from university catalogues,

federal laboratories, and other sources. This way, in 2001 the survey had a sample of 40,000

individuals representing 650,000 doctorates under the age of 76 residing in the US. Due to

the information collection techniques (multiple follow-ups and phone interviews), the survey

has very high response rates (80 percent). This potential source of error is adjusted in the

survey using the weights. This study bases its analysis on the weighted data. Non-response

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rates for the employment and personal information used in this study are low (0-1.2 percent

and 0-2.7 percent respectively).

The survey is unique, first of all, because it provides information on a group of profession-

als which is small relative to the population. For this reason other data sets (e.g., CPS) would

have a very small number of individuals with PhD degrees. Second, the data in the SDR

is longitudinal and allows one to follow individuals as their careers unfold. This is crucial

for the analysis of transitions and their duration. Finally, the survey asks about academic

achievements and other profession-specific information usually unavailable in other surveys.

Several studies of doctorates used other data sets, primarily collected by the authors, for

example, Mangematin (2000), Gaughan and Robin (2004), Oyer (2005), Diamond (2001),

Grimes and Register (1997). Their data sets were compiled in most cases from individuals’

CVs posted on their web sites. The advantage of this approach is that it allows one to

construct complete employment histories and have full productivity information unavailable

in the SDR. Unfortunately, I could not use a similar approach or use the data sets from the

studies described above because of the small number of observations which also come from

the academic sector only.

Survey questions related to employment were used in this study to analyze the types of

employment held by doctoral scientists and engineers. They contain information on employ-

ers (e.g., region, sector, size, detailed type of industry), occupations and their relevance to

the degree major according to the NSF taxonomy, primary and secondary activities on the

job including specific activities (e.g., basic and applied research, design, and development),

information on professional activity (e.g., membership in scientific societies and participation

in conferences), and scientific productivity variables (e.g., publications and patents). Ques-

tions about research productivity were collected only in selected years (1983, 1991-2001)

and did not include publications or patents obtained prior to graduation. These questions

had very low response rates. The only variable suitable to serve as a proxy for the quality

of a researcher that was consistent throughout the entire period was the information on

the degree-granting institution (e.g., institution code, its public or private status, and its

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Carnegie classification).

The employment information collected by the survey was problematic because it was

not retrospective. That is, the individuals were asked only about their last two places of

employment. The response rates regarding the earliest of these two places were low, and

these responses were not consistent. For example, some individuals stated that they changed

jobs, whereas their responses about their previous occupations and employers indicated

no changes. For this reason, only responses regarding the most recent employment were

considered in this study. The longitudinal nature of the survey allowed construction of

the employment histories and task-to-task transitions for the period during which a person

responded to the survey. However, this approach had several problems. First, some histories

had gaps due to non-responses in some years. Second, there were career histories that had

missing information at the beginning because some individuals were included in the survey

years after they had graduated.

The sample was limited to white men with PhDs in natural sciences1 and engineering, who

were employed full-time, had no secondary employment, and responded to all employment-

related questions in at least three consecutive surveys. Due to these conditions, the sample

size was reduced to 15,000 individuals, whose characteristics are described in the last column

of Table 3. Individuals in the estimation sample were on average 45 years of age, US native

citizens (82%), unmarried (53%), with a life science or physical science degree (44% and

28% respectively), and had graduated from public universities (66%) that were most likely

to be a Research I/II institution in the Carnegie classification (88%). They were more likely

to work in the academic sector (49%). Among those in academia, majority was tenured.

Finally, the sample consisted mostly of the cohorts which graduated between 1970 and 1975.

3.2 Types of careers and assignment principles

In this section, data on occupations, primary activities on the job, and employer sector are

analyzed to determine the extent to which the careers of scientists are related to R&D.

1Natural sciences include mathematics and computer sciences, life and health sciences, and physicalsciences.

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First, I evaluated occupations and their relevance to R&D. The survey respondents were

given a list of occupations and asked to select one that most closely resembled their primary

occupation at the time of the survey (see the Appendix for the list of major occupational

groups). I grouped all occupations into several categories: a) scientists, b) engineers, c) post-

secondary teachers, d)top- and mid-level managers, and e) non-S&E others. A comparison of

the occupational choice by scientific field showed that individuals were concentrated mostly

in occupations defined as “scientist” (“engineer”) or “post-secondary teacher” in the same

discipline as the major of their degree (Table 1), with some minor exceptions. For example,

some mathematicians or physicists by training worked as teachers in engineering or life

sciences programs. For the purposes of my analysis, I chose these aggregate occupational

groups rather than detailed occupations.

To have a better understanding of the relevance of these occupations to R&D, I ana-

lyzed primary activities in each of these occupations. The respondents were asked to report

activities that occupied most of their time on a typical week at their primary job. They

were offered a list of activities containing 14 choices as a reference. These activities could be

grouped into a) R&D activities (e.g., basic and applied research, development and design);

b) non-R&D activities (e.g., teaching, professional services, software design); c) managerial

and administrative activities (e.g., marketing, sales, quality control, general management),

and d) non-S&E activities (e.g., finance and accounting). The activities in the first group can

be thought of as creation of new knowledge, whereas the activities in the second group are

oriented towards application of accumulated knowledge and technologies. The third group

is not as easy to interpret, because it is not stated whether the person directed a research

laboratory, production facility, or a financial firm. The fourth set of activities is clearly

unrelated to S&E activities.

Cross-tabulation of these activities by occupation shows that occupations include a mix-

ture of activities which have varying relevance to R&D (Table 2). In addition, since an

occupation was self-reported, there were cases where major activities did not agree with the

reported occupation. For example, some individuals classified themselves as physicists, but

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their primary activities were reported as accounting and finance. This could be a physicist

by training employed in an occupation unrelated to S&E (e.g., financial analyst). Another

example is when individuals are recorded as “teachers” but report R&D-type activities and

no teaching at all. It is possible that these individuals were academic scientists and thus

were categorized as “post-secondary teachers” (probably recoded by the NSF) even if they

did primarily research. Therefore, judging the relevance of employment to R&D from occu-

pations alone can be misleading. Moreover, after studying the industry of the employer, it

became evident that some individuals reported themselves as scientists even if they worked

in non-S&E industries, such as financial and other business services. The analysis of the

activities of these scientists showed that they were mostly involved in accounting, finance, or

professional services. The conclusion was to study employment tasks corrected by industry

to infer the relevance of employment to S&E in general and R&D in particular.

Using information on occupations, industry of employment, and primary activities, I

divided all jobs into three types according to the main task they involve. Because information

on primary activities played a crucial role in assigning a job to a particular type, they are

referred to as “tasks” throughout the remainder of the paper. Two of the tasks belong

to the S&E sector. The third task includes jobs unrelated to S&E and is further referred

to as a non-S&E task. I distinguished between S&E and non-S&E tasks to pin down a

“career change” type of mobility as opposed to other types considered in previous studies.

Within S&E, jobs differed in required skills, prices, depreciation rates, and other features.

To reflect this, I further divided them into two groups: R&D and applied tasks. I assigned

individuals to be in the R&D task if their occupation was a scientist, an engineer, or a “post-

secondary teacher” if they indicated R&D as their primary activity and they were employed

in sectors other than financial and business services. I also included individuals in certain

managerial occupations, which involve R&D activities, supervision of R&D activities, or

imply employment in R&D related sectors.

A job was classified as “applied” if individuals reported a) their occupations as “post-

secondary teachers”, “technicians and technologists”, “surveyors”, “sales and marketing spe-

12

cialists”, and “management and administration”, b) indicated applied activities as their pri-

mary activities, and c) their employment sectors belonged to S&E. Individuals in applied

jobs were expected to be involved in mostly teaching S&E subjects, performing professional

services in S&E (e.g., technical consulting, project evaluations, and surveying), or manage-

rial and sales activities in these areas. Note that according to the NSF taxonomy most of

these individuals were recorded as working in non-S&E jobs.

The last category, non-S&E tasks, included tasks unrelated to S&E. In order to distin-

guish the relevance of a job to S&E, I used a taxonomy different from that adopted by the

NSF. For example, the NSF taxonomy considers all managerial occupations as unrelated

to S&E. This way, a person becoming the head of a university department or a director

of a research laboratory would be considered as changing his career. For the purposes of

my analysis, such a classification would give misleading results because mobility from S&E

to non-S&E would include both career advancements (exits due to promotions) and career

changes. Distinguishing between the two is possible only when managerial occupations are

separated by their relevance to S&E. One example of a non-S&E task would be teaching

non-S&E subjects (e.g. in humanities, business2, law, or arts). Another example would be

employment in the areas of legislation, business services such as finance, accounting, non-

technical consulting, or marketing and sales of products and services in non-S&E industries

(e.g., tourism and hospitality, entertainment, and media). While business consulting or

legislation in high-tech industries or manufacturing requires technical knowledge, technical

education for these professions does not require to be at the level of a research doctorate.

3.3 Career choices and empirical transitions

Overall, 56.7 percent of all doctorates in the sample were employed in R&D-related tasks.

Applied tasks accounted for 35.4 percent of all employment. The remaining 7.8 percent

worked in non-S&E tasks. These facts contradict a common perception that the S&E skills

of doctorates are narrowly specialized and find no utilization outside their profession.

2For certain social science majors, especially for economists, business would not be an unrelated area.However, social science doctorates were excluded from the estimation sample.

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The first three columns in Table 3 report the summary statistics by task. The main

observation from the summary statistics is that R&D tasks are performed by the youngest

(44 years old) and non-S&E tasks by the oldest PhDs (49 years old). Non-S&E tasks were

predominantly found in industry, followed by R&D, and then by applied tasks (75%, 48%,

and 25% respectively). Finally, doctorates employed in R&D tasks were the least likely to

report having a non-S&E degree (8%) compared to 23% in non-S&E tasks. This may sug-

gest that individuals employed in non-S&E tasks obtained non-S&E skills or demonstrated

interest in non-S&E before joining their doctoral training. Doctorates in academic R&D

were less likely to be tenured than the average (44% vs. 51%).

Task choices varied throughout a career. As shown on Figure 1, early in an individual’s

career participation in R&D tasks was high (75 percent), while employment outside S&E was

very low (3 percent). However, only 45 percent of doctorates were still in R&D thirty years

after graduation. Employment in applied tasks grew from 25 percent right after graduation to

42 percent at the end of a career. Non-S&E tasks accounted for 10 percent of all employment

twenty years after graduation and remained stable thereafter. The next section describes

the transition patterns in more detail to better understand their nature.

3.3.1 Mobility patterns

Transitions between different types of tasks can be divided into transitions within S&E

versus transitions out of S&E. For 15,000 individuals in the sample, there were on average

1.73 task-specific spells. Unfortunately, this means that repeated spells of the same type were

not observed, which made it impossible to estimate a duration model with dependent risks

using the joint distribution of unobserved heterogeneity3. The longest observed spells were

28 years long. However, the number of individuals in these spells was fewer than 100 people

in total. For this reason only the spells with more than 20 observations were considered,

which shortened the longest spell to 18 years. The seven most common career paths in the

sample were a) uninterrupted employment in R&D (28%), b) uninterrupted employment in

3See D’Addio and Rosholm (2002) for a discussion of identification problems in this type of models

14

applied tasks (22%), c) careers starting in R&D and eventually ending in application (12%),

d) careers starting in application and eventually ending in R&D (6%), e) employment in

R&D with a short intermediate spell in an applied task (5.35%), f) employment in applied

tasks with a short intermediate spell in R&D (3.28%), g) careers starting in either R&D

or application and eventually ending in non-S&E (2.8 percent and 2 percent respectively).

The remaining 18 percent of career histories had other patterns. Table 4 contains average

transition rates. That is, each number is computed as a fraction of individuals who changed

task i for task j between periods t− 1 and t over all individuals in task i at time t− 1. One

can notice that R&D-related tasks have slightly higher retention rates compared to those in

applied tasks: 0.61 versus 0.58 respectively. In addition, average transition rates to non-S&E

were similar: 0.07 out of R&D compared to 0.08 out of application. The next subsection

considers the transition dynamics in more detail.

Reallocations within S&E

Figures 2 and 3 demonstrate transition rates between S&E tasks by discipline over time.

The horizontal axis depicts task-specific tenure at the time of the transition. The empirical

exit rate eij(t) on the vertical axis is the fraction of individuals who left task i for task j

after being employed in task i for t years out of the total number of the employed in task i

for t years. There are several interesting observations that are worth noting. First of all, the

transition rates from R&D to application are non-monotonic: They are stable within the first

10 years of employment, then they fall by half by year 14, and increase thereafter. This type

of transition occurs in two “waves”: the first one within the first 8 to 10 years in occupation

and the second after 14 to 18 years. Notably, the first wave occurs during the time believed

to be crucial for building the foundation for reputation and recognition in R&D. The second

wave could include established scientists who leave R&D for consulting and similar tasks

(Zucker et al. (1997), Cater and Lew (2005)). This is consistent with the predictions of

the human capital theory. The transitions are notably high for mathematicians (twice of

that in other disciplines), especially within the first 14 years. This might suggest higher

competition for R&D positions in mathematics, especially considering that most of these

15

people are employed in academic institutions with tenure requirements, or higher return on

experience in R&D for mathematicians in applied tasks.

Mobility out of applied occupations back to R&D monotonically decreases within 12-

14 years of employment, which might suggest that R&D related skills depreciate quickly.

Different disciplines experience different transition rates although very similar in pattern and

timing. They peak at 4 years of task-specific experience from as high as 0.14 for engineers to

as low as 0.1 for mathematicians. The transition rates fall to 0.06 and 0.02 respectively by

14 years of experience. The observed difference in transition rates by discipline can suggest

different depreciation rates of R&D skills or a different return in R&D on experience in

applied tasks. But overall, the timing of the transition rates to R&D is consistent with a

belief that returns or late entries to R&D are possible only within a short period of time.

Transitions out of S&E

Figures 4 and 5 show transition rates from R&D and application to non-S&E. The first

observation is that in both cases the rates are much lower than those within S&E. The

second is that the transition rates are non-monotonic: The rates out of R&D are mostly

single-peaked and similar in shape for all disciplines, while those out of applied occupations

differ substantially by discipline in shape and magnitude. Transitions out of applied tasks

are lower than those out of R&D and do not exhibit a simple pattern. The third observation

is that the transitions out of R&D die out after 16 years of R&D-specific tenure and are

at their highest between 8 and 14 years in R&D, which corresponds to the period believed

to be crucial for building a foundation for the development of scientific reputation. The

peaks at 8, 10, or 12 years may correspond to several postdoctoral appointments, which

are on average 2-3 years long, or a combination of postdoctoral appointments and a tenure-

track appointment, which is on average 6 years long. Some studies find that postdoctoral

appointments became more common and much longer in the last decade or so, and in some

fields they became a prerequisite for a tenure-track position (Stephan and Ma (2004), and

Robin and Cahuzac (2003)). It is possible that individuals who leave R&D for non-S&E

occupations are those unable to land a tenure-track position. To test this hypothesis more

16

explicitly, I controlled for the type of employment and number of postdoctoral appointments

when estimating a transition model described in more detail in the next subsection.

4 Model

In order to understand the reasons and the timing of the transitions, I developed an econo-

metric duration model of transitions and estimated it for four types of transitions: 1) from

R&D to applied tasks, 2) from applied to R&D tasks, 3) from R&D to non-S&E tasks, and

4) from applied to non-S&E tasks.

4.1 Specification of the transition model

In every period individuals can be in one of three states denoted as s(t), corresponding to

“R&D”, “application”, and “non-S&E”, denoted respectively as 1, 2, and 3. Let the rate of

each transition be denoted by:

µij(t∣X) = lim∂t→0

P [s(t+ ∂t) = j∣s(t) = i]

∂t, i, j = 1, 2, 3, i ∕= j

where X is a set of observable characteristics. Once state i is chosen, the duration of the stay

in i is determined by µi =∑

i µij, i ∕=j. Regardless of the initial state, an individual can exit

into one of the two remaining states. Suppose, there exist 3 latent duration variables, {Tj}3j=1,

with Tj corresponding to the length of stay in the initial state i before exiting to state j. In

terms of the terminology of the duration analysis, they correspond to two competing risks

that affect the length of stay in the initial state. In empirical duration analysis literature,

it is often assumed that competing risks are independent which is much easier to deal with

computationally. In most economic problems, this assumption is often very difficult to justify

because individual characteristics (e.g., skills or preferences for different sectors) are more

likely to be correlated. This dependency can be captured through the joint distribution

of some unobservable characteristic " over the three states, that is, transition rates are

independent only conditional on ". For computational reasons, "’s are often assumed to be

discrete with a finite support and a joint probability mass function G("). The identification

17

of these types of models requires data sets with multiple spells of the same type. However,

the data set used in this study contains at most one spell of each type, and, therefore, does

not contain enough information to include unobserved heterogeneity. Therefore, I follow the

traditional approach and assume that the risks are independent conditional on the choice of

the observable characteristics.

The set of observable characteristics X includes four groups: 1) demographic and educa-

tional characteristicsDEM , 2) job and activity characteristics JOB, 3) research productivity

variables PROD, and 4) a set of macroeconomic variables MACRO. The first group DEM

includes:

DEM = {AGE,CLASS,MARIND,CITIZEN,MAJOR, TOPSCHOOL,NSE}

The first two variables, AGE and CLASS, are two sets of indicators for different age groups

and graduation years. They control for life cycle and vintage effects on the duration of

employment. Grouping into different age and vintage categories is done to preserve the

proportionality of the hazard ratio over time. Next, MARIND indicates that a person is

married or in a common-law relationship. CITIZEN is an indicator for native or naturalized

US citizens. MAJOR is a set of indicators for the field of PhD. TOPSCHOOL is an

indicator for graduates from both Carnegie Research I/II and highly-ranked schools4. Finally,

NSE is an indicator that individuals had a BA or MA in non-S&E fields5 prior to their PhD.

This indicator is expected to control for non-S&E-specific skills or preference to non-S&E

tasks.

The next set of characteristics, JOB, describes major activities and features of the task,

from which the transition originated:

JOB = {SECTOR, TENURE,POSTDOC, 2PDOCS,CARNRES,ACT}

where SECTOR contains an indicator for the employment sector (academia, government,

or industry), TENURE contains indicators for tenure status if individuals were employed

4These include CalTech, UC Berkeley, Stanford, MIT, Harvard, Princeton and Yale.5Social sciences, arts, humanities, business or law

18

in academia, POSTDOC is an indicator for a postdoctoral appointment, 2PDOCS indi-

cates that a person completed more than two postdoctoral fellowships, CARNRES indicates

that if the employer is an academic institution it belongs to a Carnegie Research I/II cat-

egory, and finally ACT indicates primary activities on the job and serves as a proxy for

occupation-specific skills. Two variables are also used to control for the effect of postdoc-

toral appointments: an indicator that a current position is a postdoc (POSTDOC) and

an indicator that a person held two or more postdoctoral appointments (2PDOCS). This

last indicator is used instead of the number of appointments in order to preserve hazard

proportionality.

The third set of characteristics includes several possible measures of research success:

PROD = {ARTGRAD, ART, PAPERS, PATENTS}

where ARTGRAD and ART contain the number of publications (at graduation and in total,

respectively), PAPERS contains the number of working papers in two years preceding the

survey, PATENTS includes the total number of granted patents. This set of variables is

expected to capture the effect of research productivity on transitions.

The final group, MACRO, contains the following variables:

MACRO = {RD,UE,ENROLL}

where RD is the share of R&D expenditures in the GDP, UE is the unemployment rate,

ENROLL contains the enrollment rates in undergraduate programs in S&E. All other vari-

ables in MACRO correspond to the year preceding the survey. The R&D rates are a proxy

for the demand for research skill, ENROLL is a proxy for the demand for teachers, which

represents a part of those employed in applied occupations, and, finally, UE represents over-

all economic conditions. The descriptive statistics for the variables can be found in Table

3.

19

4.2 Functional forms

Each transition probability µij is assumed to have the following functional form:

µij(ti∣X) = µ0ij(X) ⋅ µij(ti),

where µij(ti) is a baseline hazard and µ0ij(X) is a systematic hazard. To derive the likelihood

function, we need to consider contribution of spells of different types. For a single spell

k which does not end in an exit to either of the states (i.e., a right-censored spell), the

likelihood contribution is simply the survival function, Sk(t) = 1−Fk(t) = exp[−∑ti=1 µk(i)].

Contribution of the spells ending in either of the states would be equal to:

Lk = S1−∑

j ±kj(t)

k

∏j

µkj(t)±kj(t)

where ±kj(t) is an indicator that an exit from k to j occurs at time t. Since each individual

can have more than one spell, denote R the number of spells that an individual has in R&D,

A in applied occupations, and N in non-S&E. For this individual the likelihood contribution

is:

Li(°;X) =R∏j

Lrj

A∏

k

Lak

N∏

l

Lnl, (1)

where X is the matrix of observable characteristics, and ° is the vector of parameter values.

Only the spells observed from their start were included, that is, no left-censored spells were

used. Therefore, no correction for initial conditions were used.

Finally, the loglikelihood function over all individuals is:

l(°;X) =∑i

ln(Li(°;X)) (2)

The final loglikelihood function (2) is maximized with respect to the parameters on the

observable characteristics ¯ij and baseline hazard parameters µmij .

4.3 Baseline hazard

I assume an exponential distribution of the survival times, which in turn implies an expo-

nential, or constant, baseline hazard. This assumption can be applicable to labor market

20

models if one slightly modifies it by allowing baseline hazards to be constant only on certain

intervals, that is, piecewise constant. The time is divided into 15 periods of 2 years, with

the 15th period being in (28,∞) interval. For the mapping principles between time and the

intervals and modifications of the hazards functions to accommodate that, see D’Addio and

Rosholm (2002). The baseline hazard becomes:

µik(t) = exp(µj(t)ik ), j(t) ∈ (1, 15)

Analysis of hazard proportionality suggested the proportionality assumption was violated for

postdocs. One way to solve this problem is to stratify the model for postdocs by introducing

both a postdoc-specific intercept and a postdoc-specific baseline hazard.

5 Estimation results

Several specifications of the model were estimated. The first specification includes only

personal and employment characteristics, DEM and JOB. The next four specifications

sequentially introduce four different research productivity variables from the PROD set of

characteristics. The sixth specification includes an indicator if the person had more than one

postdoctoral appointment. The final specification includes labor market variables, MACRO.

All seven specifications were estimated on the pooled sample.6 The base categories are

“engineering” for MAJOR, “government” for SECTOR, “under 30” for AGE, and “post-

1990” for CLASS. Base categories for activities are a) design and development in R&D and

b) professional services in application.

Mobility within S&E

Tables 5 and 10 present estimation results for the transitions between R&D and applied

tasks. We are mainly interested in answering the following questions: Is the observed mobility

age- and cohort specific? Does it depend on research productivity? How is mobility affected

by the labor market conditions?

First, specifications without productivity variables are analyzed. The first result is that

6Estimation by discipline produces similar results.

21

there is no significant cohort effect on transitions within S&E except for the class of 1980-

1989. The age effects are significant only for the transitions from application to R&D. The

estimates suggest that older doctorates are less likely to leave applied tasks to return to R&D.

Next, scientists are more likely to leave R&D for applied tasks compared to engineers with

an exception of computer scientists. This suggests that either the competition in scientific

R&D is more intense, or that the skills and knowledge accumulated in scientific research are

more applicable in applied tasks than those in engineering. Mobility from applied tasks back

to R&D exhibits no significant differences by discipline. The estimates show that individuals

engaged in research (both applied and theoretical) and managerial activities were less likely

to leave R&D for application compared to those in design and development. The next result

is that substantial differences exist in transition rates from R&D to application by sector after

one controls for demographic characteristics. The most likely to leave R&D are the doctorates

employed in the academic sector, followed by those in governmental jobs. Notably, the more

advanced the tenure status of academics, the likely they are to leave R&D for applied tasks.

This finding may suggest that academic scientists invest in R&D early in their careers to

enhance their total scientific knowledge, which they later “sell” to their students or clients

depending on the types of applied task they choose, as was suggested by Cater and Lew

(2005). At the same time, transitions to R&D from applied tasks are made mostly out of

non-academic sectors. Among academics those who are tenured are the least likely to switch

from applied tasks to R&D. Finally, individuals employed in Carnegie Research universities

have lower probability of leaving R&D for applied tasks, and higher probability to leave

applied tasks for R&D. In the first specification, the “top school” indicator is the only

measure for quality of the individual’s ability or skills. The estimates show that top school

graduates are more likely to switch to R&D after employment in applied tasks. However,

their probability of leaving R&D for applied tasks is insignificant although also positive. The

results of the benchmark model do not change when the set of variables is augmented with

an indicator of postdoctoral appointments or variables of economic conditions, which are

described later on.

22

Up to this point, only the individual characteristics and job features were considered.

It would be interesting to see, however, whether the transitions within S&E are affected

by general economic conditions. Three variables were considered: 1) unemployment rate

for the entire labor force, 2) the share of the total R&D expenditures in the GDP, and 3)

enrollment in S&E programs in universities and colleges. The estimates suggest that labor

market conditions at the time of a transition matter for mobility within S&E. High R&D

expenditures decrease mobility both out of and into R&D, while higher unemployment rates

increase mobility. Enrollment rates, which represent the demand for labor in teaching, have

a negative but insignificant effect on mobility from R&D to application, and positive and

significant effect on mobility out of the applied tasks to R&D. It is possible that when the

demand for teachers is high, it creates demand for researchers as well since in academia

these two activities are combined. This way, some scientists performing professional services

switch to academic jobs when demand in academia is high although they spend more time

doing research than teaching, therefore, switching tasks. This is one of the examples when

switching tasks is accompanied by changing jobs.

Mobility out of S&E

Tables 7 and 8 present the estimation results for the transitions to non-S&E from R&D

and application respectively. The first finding is that in the basic model there are no cohort

or age effects on mobility, unlike the case with the mobility within S&E. Second, out of all

disciplines only life scientists are more likely to leave R&D for non-S&E. One reason for

that may be a higher competition in R&D positions for life scientists due to the increased

number of students in the field or decreased number of positions (Tilghman et al. (1998)).

Alternatively, the R&D skills of life scientists are more valued outside S&E than those of

other majors. As for the mobility from applied tasks to non-S&E, there is no significant

difference between scientists and engineers. The only exception are computer scientists who

are the least likely to make the switch. Next, individuals with non-S&E degrees are more

likely to switch to non-S&E tasks regardless of the origin of the transition suggesting that

these individuals possess the skills required in non-S&E tasks or have preferences towards

23

non-S&E. Academics are the least likely to leave S&E. This might suggest that employment

in the industry or government makes the individuals more exposed to non-S&E employers,

or that these sectors provide skills valued outside S&E.

Finally, labor market conditions also seem to have significant effects on the mobility out of

S&E. Just as the case with the mobility within S&E, the transitions out of S&E seem to slow

down during the periods of high R&D expenditures, with the effect being more significant

for exits out of R&D. Enrollment rates which were proxies for demand for post-secondary

teachers and maybe academic researchers with teaching duties have the same significant

negative effect. Finally, mobility is higher during the periods with higher unemployment

rates. This can be explained by budget cuts for the academic sector which accounts for half

of the researchers. The budget cuts can affect hiring and dismissal policies, structure of the

department with emphasis on temporary faculty, and decrease in funding opportunities and

acquisition of new laboratory equipment and materials.

Analysis of the baseline hazards shows that most of the time dummies have significant

coefficients, thus exhibiting effects not picked up by the chosen explanatory variables. This

is also true for the time dummies interacted with the postdoctoral status. If we add labor

market conditions as controls, then some of the time dummies become insignificant, but

others remain significant. Estimation on the limited sub-sample with non-missing responses

to the productivity variables produces less significant effects of the baseline hazard, which

may be a result of the effects of the productivity variable or problems with the sample

selection described earlier. It remains a task to explain the variation in transition rates in

most periods.

Research productivity and mobility

Research productivity is an important indicator of success in R&D. Four measures of

productivity were considered in this study, but only the estimates for the total number of

published articles are shown as representative of the remaining three. Response rates for all

questions regarding productivity were very low. The subsample with non-missing responses

to the productivity questions is not only smaller but it also differs from the main sample

24

for each set of transitions. For example, the failure rates in the main sample are much

higher than those in the subsample with productivity variables, which indicates a highly

selective group of doctorates less prone to leave S&E. Therefore, the estimation results for

this subsample should be interpreted with great caution.

According to the estimates, individuals with a higher number of publications do exhibit

lower transition rates from R&D to application, but this effect is insignificant. However,

individuals who published more were significantly more likely to switch from applied tasks

to R&D suggesting that returns or late entries to R&D are conditional on active publishing

or patenting activity even if R&D is not a primary activity. Even though this is commonly

perceived to be true, so far no-one has empirically evaluated it. In this specification the

magnitude, sign, and significance of some variables changes compared to the baseline model,

most likely due to a smaller and non-random subsample. For example, the cohort effects

become significant at 1 percent, meaning that the probability to leave R&D is higher for

younger cohorts after controlling for the age effects. In this specification significance at

tenure track status disappears for non-tenured academics together with the cohort effects.

For transitions from S&E to non-S&E tasks, the results suggest that individuals with a

higher number of publications are less likely to leave both S&E tasks for non-S&E, but in

both cases the coefficients are insignificant. As was the case with the mobility within S&E,

the indicators of tenure status in the academic sector lose their significance in this subsample

except for the postdocs in R&D and tenured academics in application with both groups being

less likely to leave S&E. At the same time, the indicators of graduation period, or vintage,

gain significance for transitions from application to R&D. Finally, other coefficients change

their magnitude but not their signs in this specification.

Postdoctoral appointments

An analysis of the transitions in and out of R&D (and S&E in general) allows to better

understand the nature of postdoctoral appointments. There are several competing expla-

nations about the nature of and reasons for postdoctoral appointments. One explanation

is that research methods become more sophisticated or equipment required for research be-

25

comes more expensive. Therefore, researchers need to go through additional training before

they can handle that equipment independently and this training is provided during post-

doctoral appointments. In this case the probability of surviving in R&D should increase

with the number of postdoctoral appointments. Alternatively, postdoctoral appointment is

a probationary position required to observe potential candidates for a job and eliminate un-

suitable ones, for example, to reduce the risk of damaging expensive laboratory equipment.

In this case, the more postdoctoral appointments individuals have, the more likely they are

to eventually leave S&E altogether. Another possibility is that postdoctoral appointments

are results of the oversupply of scientists, and a perfectly competitive market reacts by re-

ducing wages and, thus, creating postdoctoral positions. However, this explanation does not

have a direct implication for mobility between tasks and cannot be tested with the approach

used in this paper.

The results show that having more than one postdoctoral appointment in the past had

a negative but insignificant effect on transitions from R&D to application. However, having

more than two postdoctoral appointments significantly reduced the chances of going from

application back to R&D. An interesting result is obtained for the transition from R&D to

non-S&E tasks. The effect of multiple postdoctoral appointments is positive and significant

for these transitions. This suggests that the longer it takes a person to secure a position

in R&D, the more likely the person is to leave S&E altogether, which can resemble the

“discouraged worker” effect for the exits from unemployment spells. This makes postdoctoral

appointments “waiting lists” or probations rather than skill-enhancing training. If the latter

was the case, then it would manifest itself in a negative significant effect on transitions out of

R&D as it would make individuals who had more training to be more suitable and successful

in R&D and prevent the transitions.

Baseline hazard

Baseline hazard in duration analysis is the hazard when all used covariates are equal to

zero. Therefore, it picks up the part of transitions unexplained by the used covariates. In

this case, the baseline hazard was split into several periods, and therefore the coefficients

26

at baseline hazard in each period show how much mobility remained unexplained. From

Tables 9, 10, 11, and 12, we can see that in all specifications for all types of transitions

most coefficients at the piece-wise constant baseline hazards remain highly significant. This

means that more work needs to be done to explain the dynamic pattern of task transitions.

A potentially fruitful extension could be to introduce unobserved heterogeneity that would

generate correlation between the risks, which was not feasible in the data used in this study.

6 Conclusion

This paper proposes a new approach to study careers of doctorates in natural sciences and

engineering. It conducts a careful empirical analysis of the dynamics of doctoral careers

in S&E in general and R&D in particular. In this study career dynamics were described

as transitions between different tasks and their relevance to R&D, application of scientific

knowledge, and non-S&E. Finally, it was empirically evaluated how research productivity,

labor market conditions, and other covariates affect these career dynamics.

The first finding is that only 57 percent of all PhDs worked in R&D tasks. Another

35 percent worked in applied tasks such as teaching, software development, or professional

services. I also found that, contrary to common belief, S&E doctorates find employment

outside S&E. In the data 8 percent of all S&E doctorates held positions outside S&E fields

in sectors such as finance or business services. The distribution of PhDs in all three types of

tasks changed as their careers developed. The majority of doctorates started their careers in

R&D (72 percent), but only 45 percent were still in R&D 30 years later. About 80 percent

of those who left, switched to applied tasks, while the rest moved out of S&E completely.

Most transitions occurred during the time believed to be critical for researchers to build

a reputation in their field. An analysis shows that it was mostly academic researchers on

tenure-track or tenured who switched to the applied tasks, which agrees with the predictions

of the human capital theory. Returns from applied to R&D tasks were possible with the

evidence of high research productivity. It was also found that individuals who leave S&E

are more likely to be employed in non-academic sectors and have non-S&E degrees prior

27

to their PhD. The probability of leaving R&D increases with the number of postdoctoral

appointments, which was interpreted as evidence that postdoctoral appointments serve as

waiting lists for permanent academic appointments rather than provide additional training.

Analysis of the labor market conditions at the time of the transition shows that mobility

both within and out of S&E is sensitive to both general economic conditions measured

by unemployment rates, and conditions specific to S&E measured by the share of R&D

expenditures in GDP and enrollment rates in S&E programs in universities and colleges.

Analysis of the baseline hazard shows in all specifications it remains a non-constant function

of time periods since graduation because the covariates were not able to completely explain

the career dynamics.

The results are new and interesting, but it remains important to understand and model

the mechanisms that generate these outcomes in order to create effective policies to attract

and retain the best doctoral scientists in R&D.

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Occupation Field of the PhDComp and Math Life sci Phys sci Engineering

Comp and Math Scientists 0.2603 0.0033 0.0259 0.0501Comp and Math Teach. 0.4941 0.0093 0.0075 0.0144Life Scientists 0.0029 0.3704 0.0422 0.0070Life Teachers 0.0000 0.2167 0.0126 0.0033Physicists and astronomers 0.0068 0.0184 0.3971 0.0271Physics Teachers 0.0010 0.0124 0.2034 0.0041Engineers 0.0254 0.0082 0.0572 0.4465Engineering Teachers 0.0078 0.0020 0.0112 0.1851Social scientists 0.0000 0.0038 0.0009 0.0004Social sci Teachers 0.0049 0.0024 0.0006 0.0012Managers 0.1360 0.1823 0.1876 0.2089Non-S&E Teachers 0.0157 0.0606 0.0092 0.0123Other Non-S&E 0.0450 0.1099 0.0445 0.0398

Table 1: Occupational choice in S&E by discipline

Occupation Primary activityR&D Teaching, consulting, prof.svcs. Management and finance

Scientists 0.8412 0.0536 0.0778Engineers 0.7079 0.1207 0.1350S&E teachers 0.2938 0.6663 0.0319Managers 0.2057 0.0908 0.6642Non-S&E others 0.1791 0.1537 0.5427

Table 2: Primary activities of PhDs by occupation

TaskR&D applied non-S&E Overall

Age 44 47 49 45Married 0.471 0.452 0.670 0.470

Citizen, native 0.81 0.84 0.82 0.82Citizen, naturalized 0.09 0.09 0.13 0.10Permanent resident 0.07 0.05 0.04 0.06Temporary resident 0.02 0.01 0.01 0.02

Comp.sciences 0.02 0.03 0.01 0.02Mathematics 0.05 0.12 0.05 0.07

Health/Life sciences 0.45 0.44 0.44 0.44Physics 0.30 0.25 0.28 0.28

Engineering 0.19 0.17 0.22 0.18Has a non-S&E degree 0.08 0.15 0.23 0.12

Class 1990-2000 0.17 0.13 0.13 0.15Class 1980-1990 0.26 0.22 0.24 0.24Class 1970-1975 0.49 0.52 0.51 0.51Class 1960-1965 0.07 0.12 0.13 0.10

Public university graduate 0.65 0.68 0.68 0.66Graduate of Research I and II 0.88 0.87 0.87 0.88

Academic sector 0.38 0.70 0.17 0.49Government sector 0.14 0.05 0.11 0.10

Industry 0.48 0.25 0.72 0.41Acad. x ten.track 0.062 0.075 0.019 0.067Acad. x Tenured 0.168 0.513 0.091 0.250

Acad. x non-ten.track 0.140 0.127 0.078 0.173Has a postdoc 0.116 0.020 0.006 0.074

Unemployment rate 0.064a

R&D in GDP 0.025b

Enrollment, thousands 12,957c

Number spells 9,978 7,574 2,363Number of individuals 14,988

aSource: US Bureau of Labor Statistics.bSource: National Science Foundation/Division of Science Resources Statistics.cSource: U.S. Department of Education, National Center for Education Statistics, IPEDS

Table 3: Summary statistics

DestinationOrigin R&D Application Non-S&ER&D 0.6130 0.3129 0.0741Application 0.3398 0.5801 0.0801Non-S&E 0.2248 0.2461 0.5292

Table 4: Average transition rates.

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29

yrs since graduation

fraction

research application non-S&E

Figure 1: Participation rates in three type of tasks, by years since graduation

0

0.04

0.08

0.12

0.16

0.2

2 4 6 8 10 12 14 16 18 20

yrs on task

hazard

math sciences life sciences physical sciences engineering

Figure 2: Transitions from R&D to application, by discipline

0

0.04

0.08

0.12

0.16

2 4 6 8 10 12 14

yrs on task

hazard

math sciences life sciences physical sciences engineering

Figure 3: Transitions from application to R&D, by discipline

0

0.01

0.02

0.03

0.04

0.05

0.06

2 4 6 8 10 12 14 16yrs on task

hazard

math sciences life sciences physical sciences engineering

Figure 4: Transitions from R&D to non-S&E, by discipline

0

0.02

0.04

2 4 6 8 10 12 14 16

yrs on task

hazard

math sciences life sciences physical sciences engineering

Figure 5: Transitions from application to non-S&E, by discipline

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

married

0.302

0.047

***

0.049

0.107

0.300

0.047

***

0.046

0.058

computersci

0.058

0.107

0.277

0.239

0.061

0.107

0.186

0.106

*mathsci

0.487

0.074

***

0.644

0.182

***

0.486

0.074

***

0.505

0.074

***

life

sci

0.287

0.056

***

0.498

0.145

***

0.280

0.056

***

0.311

0.055

***

physicalsci

0.161

0.057

***

0.245

0.151

0.155

0.057

***

0.186

0.056

***

30-40y

rsold

-0.083

0.072

-0.215

0.155

-0.082

0.072

-0.059

0.073

40-50yrs

old

0.065

0.080

-0.137

0.174

0.069

0.080

0.111

0.083

50-65yrs

old

0.123

0.095

-0.233

0.234

0.127

0.095

0.214

0.099

**class

pre-1950

-0.112

0.142

-0.971

0.262

***

-0.114

0.143

-0.026

0.143

class1950-1969

0.076

0.149

-1.001

0.301

***

0.073

0.149

0.308

0.156

**class

1970-1979

-0.096

0.155

-0.822

0.323

**-0.099

0.156

0.238

0.168

class

1980-1989

0.302

0.174

*-0.761

0.360

**0.294

0.174

*0.649

0.195

***

activity-applied

research

-3.522

0.145

***

-3.470

0.248

***

-3.520

0.145

***

-3.614

0.145

***

activity-basicresearch

-3.975

0.146

***

-4.652

0.335

***

-3.978

0.146

***

-4.086

0.146

***

activity-managem

ent

-0.247

0.039

***

-0.976

0.103

***

-0.247

0.039

***

-0.335

0.041

***

topschool

0.011

0.045

0.146

0.111

0.009

0.045

0.009

0.044

non

-S&E

degree

0.069

0.077

-0.105

0.171

0.070

0.077

0.019

0.077

citizen

0.003

0.099

-0.048

0.252

0.001

0.099

-0.020

0.098

industry

-0.146

0.066

**-0.405

0.162

**-0.147

0.066

**

-0.174

0.066

***

acadxten.track

1.234

0.080

***

1.131

0.175

***

1.226

0.080

***

1.300

0.080

***

acad

xtenured

1.267

0.073

***

1.235

0.170

***

1.265

0.073

***

1.271

0.072

***

acad

xnon

-tenure

track

0.940

0.074

***

0.689

0.165

***

0.937

0.074

***

0.944

0.074

***

acad

xpostdoc

-0.666

0.250

***

-1.544

0.899

*-0.676

0.250

***

-0.524

0.247

**acadxCarnegie

ResearchI/II

-0.373

0.048

***

-0.562

0.114

***

-0.373

0.048

***

-0.374

0.048

***

#articles

-0.003

0.004

>2postdocs

-0.031

0.039

UE

rate

0.160

0.019

***

R&D

inGDP

-1.193

0.134

***

enrollment

-0.004

0.009

constant

-2.803

0.217

***

-0.927

0.462

**-2.768

0.222

***

-2.467

0.435

***

Nindividuals

7488

3390

7488

7488

Nfailures

2497

393

2497

2497

Loglikelihood

-4163.216

-440.939

-4163.010

-4095.249

***-sign

ificantat

1%,**-sign

ificantat5%

,*-sign

ificantat

10%

Tab

le5:

Proportion

alhazardmodel

estimationresults,R&D

toap

plication

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

married

0.211

0.048

***

0.191

0.139

0.194

0.049

***

0.007

0.063

computersci

-0.031

0.097

0.210

0.252

-0.025

0.097

0.045

0.099

math

sci

-0.090

0.073

-0.124

0.229

-0.094

0.073

-0.079

0.074

life

sci

-0.083

0.055

-0.085

0.160

-0.102

0.056

*-0.064

0.056

physicalsci

0.002

0.055

-0.065

0.168

-0.020

0.056

0.033

0.056

30-40yrs

old

-0.384

0.113

***

-0.115

0.286

-0.388

0.114

***

-0.408

0.111

***

40-50y

rsold

-0.631

0.117

***

-0.213

0.304

-0.631

0.118

***

-0.657

0.117

***

50-65yrs

old

-0.794

0.126

***

-0.293

0.325

-0.787

0.127

***

-0.767

0.129

***

classpre-1950

-0.042

0.154

-0.633

0.471

-0.055

0.154

0.110

0.157

class1950-1969

-0.122

0.159

-0.536

0.478

-0.132

0.159

0.148

0.168

class1970-1979

-0.104

0.166

-0.701

0.503

-0.116

0.166

0.171

0.181

class1980-1989

-0.089

0.188

-0.349

0.528

-0.108

0.188

0.168

0.207

activity-teaching

-5.196

0.278

***

-4.107

0.405

***

-5.192

0.278

***

-5.238

0.278

***

activity-managem

ent

0.361

0.051

***

-0.270

0.161

*0.360

0.051

***

0.301

0.052

***

topschoolgrad

0.122

0.046

***

0.187

0.139

0.117

0.046

**0.115

0.046

**

non

-S&E

degree

-0.360

0.091

***

-0.332

0.210

-0.350

0.090

***

-0.380

0.090

***

citizen

-0.093

0.107

-0.318

0.314

-0.100

0.107

-0.144

0.107

sector-industry

-0.147

0.062

**-0.303

0.194

-0.151

0.062

**-0.153

0.062

**

acadxtenure

track

-0.317

0.094

***

-0.347

0.249

-0.339

0.094

***

-0.198

0.095

**

acadxtenured

-0.355

0.073

***

-0.445

0.195

**-0.364

0.073

***

-0.332

0.073

***

acadxnon

tenure

track

-0.340

0.084

***

-0.314

0.230

-0.353

0.084

***

-0.337

0.084

***

acadxCarnegie

ResearchI/II

0.705

0.053

***

0.710

0.137

***

0.704

0.053

***

0.687

0.053

***

#totalarticles

0.008

0.004

*>2postdocs

-0.129

0.044

***

UE

rate

0.098

0.018

***

R&D

inGDP

-1.035

0.129

***

enrollmentlevel

0.032

0.009

***

Nindividuals

5937

1962

5937

5937

Nfailures

2214

263

2214

2214

Log

likelihood

-3683.063

-376.486

-3680.237

-3638.120

Tab

le6:

Proportion

alhazardmodel

estimationresults,ap

plication

toR&D.

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

married

1.076

0.114

***

-0.274

0.352

1.089

0.115

***

0.111

0.128

computersci

-0.143

0.291

0.263

0.766

-0.158

0.290

-0.108

0.289

mathsci

0.247

0.215

0.653

0.832

0.247

0.215

0.183

0.218

life

sci

0.433

0.112

***

0.617

0.424

0.499

0.115

***

0.421

0.113

***

physicalsci

0.136

0.110

0.252

0.424

0.182

0.112

0.121

0.110

30-40yrs

old

-0.135

0.146

-0.027

0.581

-0.140

0.147

-0.170

0.145

40-50y

rsold

-0.083

0.164

-0.067

0.660

-0.115

0.165

-0.190

0.167

50-65yrs

old

0.090

0.205

1.274

0.745

*0.055

0.206

-0.018

0.209

classpre-1950

-0.337

0.347

-2.261

1.050

**-0.329

0.347

-0.644

0.355

*class1950-1969

-0.274

0.360

-1.075

1.201

-0.257

0.360

-0.789

0.381

**

class1970-1979

-0.331

0.378

-0.461

1.214

-0.316

0.378

-0.901

0.406

**

class1980-1989

-0.504

0.427

-0.292

1.362

-0.453

0.426

-1.133

0.470

**

activity-applied

research

-1.770

0.131

***

-2.795

0.486

***

-1.770

0.131

***

-1.875

0.130

***

activity-basicresearch

-2.761

0.245

***

-2.769

0.573

***

-2.727

0.245

***

-2.872

0.249

***

activity-managem

ent

0.089

0.082

-0.235

0.304

0.087

0.082

0.032

0.082

topschool

0.105

0.099

-0.522

0.483

0.123

0.099

0.109

0.099

non

-S&E

degree

0.549

0.130

***

0.599

0.374

0.540

0.130

***

0.444

0.123

***

citizen

0.332

0.346

16.017

0.299

***

0.346

0.346

0.095

0.349

sector-industry

0.174

0.108

0.057

0.375

0.183

0.109

*0.141

0.108

acadxtenure

track

-1.205

0.494

**-0.804

0.826

-1.180

0.494

**-1.057

0.495

**

acadxtenured

-0.696

0.233

***

-0.695

0.671

-0.691

0.233

***

-0.629

0.235

***

acadxnon

tenure

track

-0.589

0.230

**-0.765

0.708

-0.569

0.231

**-0.537

0.230

**

acadxpostdoc

-1.370

1.017

-16.471

0.453

***

-1.300

1.016

-1.233

1.022

acadxCarnegie

ResearchI/II

0.125

0.205

0.440

0.482

0.137

0.206

0.069

0.209

#totalarticles

-0.005

0.013

>2postdocs

0.255

0.093

***

UE

rate

0.301

0.046

***

R&D

inGDP

-0.837

0.357

**

enrollmentlevel

-0.116

0.025

***

Nindividuals

7134

3187

7134

7134

Nfailures

677

58677

677

Log

likelihood

-1813.027

-170.596

-1809.425

-1714.484

Tab

le7:

Proportion

alhazardmodel

estimationresults,R&D

tonon

-S&E

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

married

0.504

0.097

***

0.444

0.428

0.517

0.098

***

0.007

0.119

computersci

-1.101

0.366

***

-15.222

0.457

***

-1.104

0.366

***

-1.118

0.361

***

mathsci

-0.287

0.177

-0.991

0.762

-0.285

0.177

-0.352

0.178

**

life

sci

-0.030

0.119

0.034

0.396

-0.014

0.120

-0.088

0.118

30-40y

rsold

0.241

0.325

-0.114

1.341

0.254

0.326

0.146

0.328

40-50yrs

old

0.421

0.326

0.001

1.341

0.430

0.327

0.280

0.332

50-65

yrs

old

0.386

0.335

0.367

1.370

0.390

0.335

0.315

0.342

classpre-1950

0.040

0.338

14.364

3.701

***

0.047

0.338

-0.073

0.345

class1950-1969

0.081

0.341

15.124

2.765

***

0.088

0.342

-0.091

0.357

class1970-1979

0.159

0.353

15.822

4.226

***

0.164

0.354

-0.024

0.374

class1980-1989

0.049

0.406

15.873

3.506

***

0.054

0.407

-0.061

0.438

activity-teaching

-1.741

0.172

***

-1.463

0.550

***

-1.741

0.172

***

-1.716

0.170

***

activity-prof.servics

-1.851

0.210

***

-1.923

0.636

***

-1.851

0.210

***

-1.884

0.210

***

activity-managem

ent

0.521

0.096

***

0.145

0.361

0.520

0.096

***

0.452

0.093

***

topschoolgrad

0.093

0.103

0.246

0.444

0.098

0.103

0.107

0.101

non

-S&E

degree

0.205

0.124

*0.924

0.391

**0.196

0.124

0.174

0.119

citizen

0.520

0.306

*-1.039

1.034

0.527

0.306

*0.465

0.312

sector-industry

0.436

0.123

***

1.184

0.600

**0.442

0.123

***

0.402

0.121

***

acadxtenure

track

-0.837

0.265

***

-0.586

0.890

-0.819

0.266

***

-0.788

0.267

***

acadxtenured

-1.183

0.175

***

-1.475

0.682

**-1.177

0.176

***

-1.125

0.176

***

acadxnon

tenure

track

-0.572

0.189

***

-0.873

0.734

-0.563

0.189

***

-0.506

0.186

***

acadxCarnegie

ResearchI/II

0.629

0.140

***

1.504

0.380

***

0.635

0.140

***

0.597

0.140

***

#totalarticles

-0.008

0.025

>2postdocs

0.104

0.101

UE

rate

0.161

0.042

***

R&D

inGDP

-0.510

0.300

*enrollmentlevel

-0.115

0.025

***

Nindividuals

5477

1797

5477

5477

Nfailures

689

45689

689

Log

likelihood

-1847.037

-130.472

-1846.531

-1803.537

Tab

le8:

Proportion

alhazardmodel

estimationresults,ap

plication

tonon

-S&E.

Variab

leCoeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

t2-t4

-0.250

0.059

***

0.106

0.155

-0.251

0.059

***

-0.073

0.064

t4-t6

-0.332

0.063

***

-0.015

0.199

-0.333

0.063

***

-0.073

0.068

t6-t8

-0.141

0.063

**-0.317

0.142

**-0.143

0.063

**0.032

0.069

t8-t10

-0.218

0.068

***

-0.527

0.163

***

-0.220

0.068

***

-0.123

0.080

t10-t12

-0.541

0.080

***

-0.803

0.287

***

-0.543

0.080

***

-0.294

0.089

***

t12-t14

-0.831

0.095

***

-0.568

0.220

***

-0.834

0.095

***

-0.549

0.105

***

t14-t16

-0.332

0.085

***

-0.434

0.224

*-0.336

0.085

***

-0.107

0.101

t16-t18

-0.387

0.096

***

-0.378

0.246

-0.391

0.097

***

-0.211

0.113

*t18-t20

-0.418

0.127

***

-0.344

0.223

-0.424

0.127

***

-0.237

0.131

*t20-t22

-0.636

0.155

***

-0.471

0.288

-0.640

0.155

***

-0.351

0.164

**

t22-t24

-0.250

0.138

*-0.155

0.358

-0.253

0.138

*0.138

0.151

t24-t26

-0.327

0.187

*-0.307

0.418

-0.329

0.187

*0.279

0.200

t26-t28

-0.627

0.301

**-0.310

0.298

-0.630

0.300

**0.009

0.310

postdocxt2-t4

-0.216

0.491

-10.902

0.959

***

-0.211

0.491

-0.252

0.487

postdocxt4-t6

-0.428

0.941

-0.427

0.940

-0.524

0.941

postdocxt6-t8

1.002

0.356

***

2.133

1.040

**1.009

0.356

***

0.658

0.353

*postdocxt8-t10

-14.466

0.460

***

-12.069

1.294

***

-12.325

0.460

***

-13.672

0.480

***

postdocxt10-t12

-14.865

0.572

***

-10.002

1.375

***

-12.724

0.574

***

-14.139

0.543

***

postdocxt12-t14

-14.607

0.591

***

-12.476

0.589

***

-13.754

0.572

***

postdocxt14-t16

0.715

0.600

-14.326

1.374

***

0.722

0.606

0.584

0.659

postdocxt16-

t18

1.353

0.977

1.364

0.971

1.080

0.964

postdocxt18-t20

-13.151

0.684

***

-11.016

0.683

***

-12.305

0.689

***

postdocxt20-t22

-13.130

0.671

***

-11.345

1.307

***

-10.979

0.672

***

-12.329

0.667

***

postdocxt22-t24

-17.661

1.049

***

-15.513

1.050

***

-16.955

1.048

***

Tab

le9:

Baselinehazard,R&D

toap

plication

.

41

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

t2-t4

0.147

0.048

***

0.199

0.149

0.144

0.048

***

0.251

0.052

***

t4-t6

-0.111

0.057

*-0.080

0.165

-0.112

0.057

*0.017

0.060

t6-t8

-0.211

0.065

***

-0.471

0.194

**-0.212

0.065

***

-0.128

0.068

*t8

-t10

-0.385

0.079

***

-0.320

0.182

*-0.386

0.078

***

-0.277

0.084

***

t10-t12

-0.642

0.093

***

-0.601

0.317

*-0.640

0.093

***

-0.460

0.098

***

t12-t14

-1.211

0.140

***

-0.784

0.643

-1.210

0.140

***

-1.015

0.142

***

t14-t16

-0.552

0.130

***

-0.502

0.503

-0.554

0.130

***

-0.492

0.137

***

t16-t18

-0.577

0.136

***

-1.142

0.356

***

-0.576

0.136

***

-0.561

0.143

***

t18-t20

-0.283

0.172

0.205

0.525

-0.290

0.171

*-0.245

0.180

t20-t22

-0.695

0.257

***

-0.364

0.340

-0.679

0.257

***

-0.589

0.265

**t22-t24

-0.527

0.281

*-0.581

0.973

-0.511

0.281

*-0.349

0.280

t24-t26

-0.154

0.327

-0.011

0.449

-0.161

0.323

0.123

0.331

t26-t28

-0.238

0.553

0.237

0.575

-0.256

0.551

0.045

0.559

constan

t-1.495

0.234

***

-0.877

0.663

-1.344

0.240

***

-1.173

0.402

***

Tab

le10:Baselinehazard,ap

plication

toR&D

42

Variab

leCoeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

t2-t4

-0.568

0.168

***

-16.857

0.323

***

-0.560

0.168

***

-0.492

0.171

***

t4-t6

-0.467

0.172

***

0.068

0.552

-0.457

0.172

***

-0.268

0.182

t6-t8

0.095

0.144

-0.322

0.425

0.115

0.144

-0.057

0.152

t8-t10

0.137

0.148

-0.205

0.601

0.154

0.148

-0.281

0.161

*t10-t12

0.247

0.139

*-0.728

0.712

0.271

0.139

*-0.154

0.157

t12-t14

-0.162

0.160

0.327

0.590

-0.139

0.160

-0.453

0.184

**

t14-t16

-0.680

0.214

***

0.095

0.585

-0.656

0.214

***

-0.847

0.237

***

t16-t18

0.108

0.161

-0.850

0.877

0.133

0.161

-0.303

0.183

*t18-t20

0.126

0.195

-0.204

0.620

0.165

0.196

-0.263

0.206

t20-t22

-0.666

0.293

**-0.216

0.765

-0.623

0.294

**-0.956

0.314

***

t22-t24

-1.056

0.413

**-16.361

0.764

***

-1.025

0.412

**-0.944

0.438

**

t24-t26

-1.509

0.558

***

-16.565

0.493

***

-1.484

0.556

***

-0.869

0.585

t26-t28

-1.832

1.039

*-17.194

0.878

***

-1.815

1.041

*-1.220

1.038

postdocxt2

-t4

-11.919

1.027

***

17.502

0.515

***

-11.949

1.027

***

-11.913

1.026

***

postdocxt4

-t6

3.207

1.271

**3.213

1.268

**3.302

1.278

***

postdocxt6

-t8

-12.688

1.117

***

0.340

0.804

-12.752

1.111

***

-12.954

1.136

***

postdocxt8

-t10

-12.917

1.086

***

0.794

0.748

-12.980

1.087

***

-13.256

1.094

***

postdocxt10-t12

-12.943

1.129

***

0.642

1.258

-12.953

1.124

***

-13.131

1.114

***

postdocxt12-t14

-12.300

1.112

***

-12.333

1.109

***

-12.404

1.105

***

postdocxt14-t16

-12.499

1.236

***

-0.819

1.387

-12.535

1.224

***

-12.702

1.274

***

postdocxt16-

t18

1.639

1.109

1.687

1.122

1.134

1.115

postdocxt18-t20

3.370

1.050

***

3.203

1.050

***

2.687

1.044

***

postdocxt20-t22

-11.355

1.284

***

1.976

1.198

*-11.561

1.283

***

-11.886

1.310

***

postdocxt22-t24

-13.814

1.508

***

-13.982

1.507

***

-14.752

1.518

***

constant

-4.434

0.532

***

-18.843

0.400

***

-4.721

0.543

***

-7.089

1.182

***

Tab

le11:Baselinehazard,R&D

tonon

-S&E

43

Variable

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

Coeff

St.Err

t2-t4

-0.023

0.110

-0.087

0.375

-0.023

0.110

-0.019

0.110

t4-t6

-0.138

0.126

-0.725

0.499

-0.138

0.126

-0.038

0.125

t6-t8

-0.124

0.133

-1.923

1.041

*-0.126

0.133

-0.176

0.134

t8-t10

0.018

0.129

-0.020

0.465

0.013

0.129

-0.169

0.132

t10-t12

-0.102

0.151

-0.776

0.709

-0.108

0.152

-0.191

0.154

t12-t14

-0.353

0.179

**

-14.515

0.517

***

-0.358

0.179

**

-0.379

0.182

**

t14-t16

-0.288

0.198

0.482

0.779

-0.299

0.197

-0.320

0.209

t16-t18

0.390

0.171

**

-0.589

1.105

0.383

0.172

**

0.165

0.184

t18-t20

0.614

0.230

***

-14.544

0.511

***

0.604

0.232

***

0.284

0.238

t20-t22

-0.564

0.465

-14.541

0.579

***

-0.569

0.468

-0.641

0.452

t22-t24

-1.498

1.004

-15.176

0.917

***

-1.511

1.006

-1.279

0.979

t24-t26

0.789

0.315

**

0.534

0.573

0.777

0.313

**

1.333

0.344

***

t26-t28

-10.812

0.322

***

-13.619

1.030

***

-12.427

0.319

***

-11.504

0.358

***

constant

-4.588

0.572

***

-18.423

4.021

***

-4.720

0.586

***

-5.463

1.030

***

Tab

le12:Baselinehazard,ap

plication

tonon

-S&E.

44

Appendix

Occupational Categories in Sciences and EngineeringMajor category Minor categories of occupation

Sciences and EngineeringComputer Computer & Information Scientists& Mathematical Mathematical ScientistsScientists Postsecondary Teachers - Computer & Mathematical SciencesLife Scientists Agricultural & Food Scientists

Biological ScientistsEnvironmental Life ScientistsPostsecondary Teachers - Life & Health Sciences

Physical Scientists Chemists, except BiochemistsEarth Scientists, Geologists & OceanographersPhysicists & AstronomersOther Physical ScientistsPostsecondary Teachers - Physical Sciences

Engineers Aerospace & Related EngineersChemical EngineersCivil & Architectural EngineersElectrical & Related EngineersIndustrial EngineersMechanical EngineersPostsecondary Teachers - EngineeringNon-Sciences and Engineering

Non-S&E occupations Managers & AdministratorsNon-S&E Postsecondary TeachersSocial Services & Related occupationsTechnologists & TechniciansSales & Marketing occupationsArt, Humanities & related occupations

SOURCE: SESTAT: A Tool for Studying Scientists and Engineers in the United States.Division of Science Resources Studies. NSF.

45