The Productive Postdoc: Do Working Conditions Affect Outcomes?

The Productive Postdoc:Do Working Conditions Affect Outcomes?

Geoff Davis

Visiting Scholar and Survey Principal Investigator

Sigma Xi, The Scientific Research Society

[email protected]

Improving the Postdoctoral Experience

• Many calls for changes to the postdoc– National Academies, AAU, NPA, etc

• Big question: What, if anything, works?

What Works? Changes have costs (money, time)

Do benefits justify investments? What should priorities be?

What gives the biggest bang for the buck?

These are empirical questions

Our “Experiment” Postdoc administration takes place largely at the

level of the PI Tremendous variability in conditions from lab to lab Recent, limited introduction of new practices

Natural experiment Ask postdocs about their working conditions Ask about how well they are doing Find conditions associated with positive outcomes

Sigma Xi Postdoc Survey Ran a big web survey

Contacted 22,400 postdocs at 47 institutions ~40% of all postdocs in US

Overall response rate: 38%* (*See tech report for details)

Our Sponsor

The Alfred P. Sloan Foundation

Alfred P. Sloan Michael Teitelbaum

Additional Support

Werthheim Fellowship, Harvard University

Partner Organizations

National Postdoc Association

Science’s Next Wave

NBER/Sloan Scientific Workforce Group

Sketch of Our Analysis

• Create measures of inputs (working conditions, demographics, etc) and outcomes

• Build linear models to test hypothesis that inputs have an impact, gauge magnitude of impact (if any)

How Do We Determine Success?• Ideal: track people down in 10 years, see what

they are doing / have done• Problems:

– Very expensive– Takes 10 years to learn anything

• Driving via the rear view mirror

• Instead, look at immediate proxies for longitudinal data

Outcomes• What makes for a “good” experience?

• No single “best” measure– Different people want different things

• Create collection of outcome measures– Look at impact of inputs on each

Subjective Outcome Measures• Subjective success measure

– Overall satisfaction, preparation for independent research, quality of training in research / teaching / management

• Advisor relations measure– How is your advisor doing? Is s/he a mentor? How

would s/he say you are doing?

• Generate numerical scores by summing Likert scored answers

Objective Outcome Measures• Absence of Conflict/Misconduct

– Has postdoc had a conflict with advisor? Has s/he seen misconduct in the lab?

• Productivity– Rate at which papers submitted to peer

reviewed journals

Outcome Measure Distributions

Outcome Measure Details• Correlations all fairly low

– Subjective success and advisor relations ~0.45– Other pairwise correlations all < 0.2

Our Explanatory Variables• Model outcomes as function of explanatory

variables– Field of research– Institution– Basic demographic variables

• Sex• Citizenship• Minority/Majority Status• Type of degree (MD vs PhD)

– Total time as a postdoc– “Working Conditions”

“Working Conditions”• How do we measure working conditions?

• Inspiration comes from various calls for changes– Look at rate of implementation

Recommended Changes• 5 broad classes of recommended changes

– Pay people more– Fellowships rather than assistantships– Better benefits– More structured oversight– Transferable skills training

Measures of Working Conditions

• Salary measure– log(annual salary), full-time people only

• Independent Funding measure– Dummy variable, 1 if fellowship, 0 otherwise

• Benefits measure– Count of different benefits received (health

insurance, retirement plan, etc)

Structured Oversight• Structured Oversight measure

– Count of administrative measures in place• Individual development plans

• Formal reviews

• Policies (authorship / misconduct / IP / etc)

• Letters of appointment

– High values = lots of structure, low = little

Training• Transferable Skills Training measure

– Count of areas in which postdoc reports receiving training

– Grant writing, project/lab management, exposure to non-academic careers, negotiation, conflict resolution, English language, etc

– High values = training in lots of areas– Low values = no training in lots of areas

Working Conditions Distributions

Working Conditions Details• Again, correlations all fairly low

– Structured oversight and skills training ~0.30– Other pairwise correlations all < 0.15

What Has Biggest Impact?

• Who is most satisfied, most productive, etc?

• People with– Independent funding?– High salaries?– Lots of benefits?– Lots of structured oversight?– Lots of types of transferable training?

Simple Analysis• Crude analysis: compare satisfaction,

productivity, etc for people in appointments with – Fellowships / other funding– High / low salaries – High / low benefits– High / low structure– High / low training

Independent FundingFellowship Other

% satisfied 74% 70%

Advisor grade (0=F, 4=A)

3.0 3.1

% reporting conflicts

14% 14%

Papers submitted / year

1.1 1.2

Salary

Highest 25% Lowest 25%

% satisfied 71% 68%


3.0 3.1


16% 13%


1.2 1.2

Benefits

Highest 25% Lowest 25%

% satisfied 76% 62%


3.2 2.9


11% 18%


1.3 1.2

Structured OversightHigh structure Low structure

% satisfied 80% 60%


3.4 2.7


9% 21%


1.4 1.0

Transferable Skills TrainingHigh training Low training

% satisfied 83% 56%


3.4 2.7


10% 17%


1.3 1.1

Regression Coefficients Subjective

Success Advisor

Relations Absence of

Conflict Productivity

total structure 0.158 *** 0.159 *** 0.283 *** 0.045 *** total training 0.455 *** 0.247 *** 0.120 *** 0.050 *** total benefits 0.094 *** -0.000 0.125 *** -0.033 * log(salary) 0.024 0.112 *** -0.036 0.031 . funding 0.178 *** 0.048 0.131 0.015 male 0.089 ** 0.015 0.138 0.081 ** citizen 0.081 ** 0.035 0.077 -0.058 * underrepresented 0.051 0.013 0.017 -0.019 medical degree -0.178 *** -0.107 * -0.452 *** -0.032 months total -0.004 *** -0.003 *** 0.018 *** 0.001

Take Home Message #1

• Structured oversight and transferable skills training

make a big difference

Causality?• We have correlation. Is there causation?

– Psych literature gives reasons to believe in causation

• Alternative explanations1. Structure and training attract people who are

intrinsically more satisfied / productive / successful

2. Structure / training correlate with some other unobserved factor– Advisors are effective managers / have more resources– Postdocs take more initiative / are better organized / etc

Causality?• 2 classes of explanation

1. Structure/training attract intrinsically more productive people

2. Structure/training directly cause productivity or are indicators for some causal mechanism

(Some combination of 1 & 2 also possible)

• Should be able to differentiate between 1 & 2 by looking at people with multiple appointments

Intrinsic vs. Time-Localized

Causality?• Add in terms that allow for change in slope of

papers(t) curve starting at beginning of most recent postdoc

• Equivalent to adding interactions with ratio (months in current postdoc / total months as postdoc) to regression model

• Training appears to have a time-localized effect• Other inputs ambiguous

Don’t Pay Postdocs?• Not saying postdocs shouldn’t be paid!

– Hard to attract US students to science if you don’t pay them

• Maslow’s hierarchy of needs– Must meet basic physical security needs first– Living wage, basic benefits

• More nuanced interpretation of data: beyond a certain threshold, structure and training matter more than compensation

• Institutional “postdoc tax” to support service provision?

More Details

• Look at individual components of structure and training measure

• What specific measures have the greatest impact?

Impact

• One measure appears to have significant impact all 4 outcomes:– Research / career plans

• Written plans

• Plans that spell out what both postdoc and PI will do

• Advocated by FASEB, National Academies

Plans• Compare those with such a plan to those

without:– Much less likely (~40%) to be dissatisfied– Much less likely (~30%) to have conflicts

• After controlling for field, institution, demographics:– Submitted ~14% more papers for publication

Why?• Plans:

– Expectation setting device• Postdocs without plans were much more likely to report PI had

not lived up to expectations– Contract

• Research shows that people are more likely to live up to explicit (esp. written) commitments

– Forces postdocs to take responsibility for their careers early

• More time to take advantage of training opportunities– Time management device

• Mechanism for focusing effort

Take Home Message #2

• Individual development plans make a big difference

Additional Measures Several other measures show concrete

benefits: Teaching experience Exposure to non-academic careers Training in proposal writing Training in project management Training in ethics

Policy Implications For postdocs, more effective to invest

additional dollars in management than in salaries

Management at all levels: Infrastructure for institutional oversight /

training Management training for PIs Management training for postdocs

Further information More information at

http://postdoc.sigmaxi.org

Workshop (with NPA) in January 2006

Contacts Geoff Davis, PI, [email protected] Jenny Zilaro, Project Manager, [email protected]

mailto:[email protected]


Extra Material

End Products Sigma Xi:

Highlights in May/June issue of American Scientist Tech reports (2 out now, more to come) Scholarly paper this fall

NPA: Analyses of various topics

NBER SEWP

Workshop in January 2006

Aside: Postdoc Definition• Half a dozen different definitions

– AAMC, AAU, FASEB, NAS, NSF

• BUT if you read and compare them, they all say the same thing– Only substantive difference is that FASEB includes

narrow subset of clinical fellows

– (We excluded them from this analysis)

• Most people don’t fully satisfy definition anyway

Postdoc Definition• The appointee has a PhD or equivalent degree,• the degree was received recently,• the appointment is temporary,• the purpose of the appointment is training for a research

career,• the appointment involves substantially full-time research

or scholarship,• the appointee is expected to publish the results of his or

her research, and• the appointee works under the supervision of a senior

scholar or a department in a university or research institution.

Survey Non-Response 30-second summary of non-response

analysis: Non-citizens and African Americans appear to

be slightly under-represented No evidence of bias based on level of

satisfaction (respondents not overly disgruntled)

Survey Non-Response• Survey respondents atypical in one

important way– Participating institutions all had PDO, PDA, or

administrator interested in postdoc affairs

• Participating institutions probably better off than average

Salaries

• Median salary: $38,000

• Up from $28,000 in 1995

Inflation

• A 10% increase above inflation since 1995– ($28,000 in 1995 = $34,700 in 2004)

• NIH budget doubled over the same period(in inflation-adjusted dollars)

Experience

• Salaries increase at about 2.9% per year of experience

Field• Overall average = $39,300

• Average salary in most common fields ranges from $37,500 to $40,000

• Higher: – Electrical engineering ($45,000)– Physics ($42,600)– Oncology ($41,400)– Materials science ($41,200)

• Lower:– Ecology ($35,600)

Institution Type

• Govt labs pay 20% more than average

• Public universities pay 9% less than average

Taxes• Tax loophole: some postdocs don’t have to pay FICA

(7.65% of income)– 23% benefit– New IRS rules affect this

• Tax penalty: some postdocs pay extra self-employment tax (also 7.65% of income)– 12% pay– Independent contractor status carries hidden tax penalty!

• Potential $6,000 impact on salary

Part-time

• 3% report part-time status

• Average hours worked previous week: 45

Hours

• 51 hours/week median

• Postdoc hourly wage ~ $14.90

Hours

• 51 hours/week median

• Postdoc hourly wages = $14.90/hour

• Harvard janitors = $14.00/hour

Foreign Postdocs

• International Men and Womenof Mystery

Basic Demographics Citizenship:

Citizens: 40% Permanent residents: 6% Temporary visa holders: 54%

PhD: US PhD: 53% Non-US: 47%

Non-US PhDs Where PhD earned:

Almost 80% of postdocs on temporary visas earned their PhDs outside the US

Non-US PhDs invisible in NSF stats

All US citizens (41%)

Permanent residents (6%)

Temporary (53%)

US 53% 97% 51% 21%

Elsewhere 47% 3% 49% 79%

Non-US PhDs Where non-US PhDs were earned:

Country of citizenship 86% Different country, same continent 7% Different continent 7%

Temporary Visa HoldersCitizenship

China 24%

India 11%

Germany 6%

South Korea 6%

Japan 6%

Canada 5%

France 5%

United Kingdom 4%

Spain 3%

Italy 3%

Top 10 73%

Source of PhD

China 18%

India 10%

Japan 8%

UK 8%

Germany 8%

France 6%

Canada 5%

South Korea 4%

Israel 3%

Spain 3%

Top 10 73%

Non-US Postdocs and PhDs China and India dominate

Market share of postdocs comparable to share of doctorates (China = 23%, India = 10%)

Next largest LDC is Argentina, #16 for both citizenship and PhDs, with 1% of each

Temporary Visa Holders by FieldElectrical engineering 72%

Physics 67%

Chemistry 61%

Molecular biology 58%

Biochemistry 57%

Cell biology 57%

Earth sciences 52%

Ecology 36%

Psychology 21%

Broad Field

Temporary visas Non-US PhDs

Life/health sciences

52% 47%

Physical sciences / engineering

63% 44%

Social sciences 23% 18%

Other Characteristics

US postdocs: 49% men/51% women

69% married 33% have children Median age: 33

International postdocs: 65% men/35% women

69% married 35% have children Median age: 33

Other Characteristics One notable difference for married postdocs

US postdocs: 15% have non-working spouse Non-citizen postdocs: 44% have non-working spouse

Some visas (e.g. H) don’t have provision for spouse to work

Domestic vs International: Papers

International postdocs publish more Average peer-reviewed publications as a postdoc

Citizens/PR 2.6 Temporary 3.3 (27% more)

Difference is smaller (.1 papers/year) after we control for time as a postdoc, field, institution, sex, but statistically significant

Domestic vs International: Hours Non-citizens work longer hours Average weekly hours worked

Citizens/PR 50 Temporary 52 (4% more)

Difference is smaller (1.3 hours/week) after we control for time as a postdoc, field, institution, sex, but still statistically significant

Domestic vs International: Salary BUT non-citizens are paid substantially less Median annual salary

Domestic $40,000 International $37,000 (8% less)

Domestic postdocs earn $2,200/year more than international postdocs after controlling for field, institution, sex, time as a postdoc, and funding mechanism

Domestic vs International: Grants

Citizens write more grant proposals (results suggest mostly fellowship applications)

Grant proposals written while a postdoc Citizens 1.6 Non-citizens 1.1 (31% fewer)

International postdocs write fewer grant proposals even after controlling for field, institution, sex

Domestic vs International: Satisfaction Non-citizens report slightly lower levels of

satisfaction with the postdoc experience Average satisfaction

(-2 = dissatisfied / 2 = satisfied) Citizens/PR 0.8 Temporary 0.6

Difference disappears when one controls for salary, discipline, institution, sex, and time as a postdoc

Security Problems To what extent have US national security regulations

affected your ability to do the following:(% responding “Some” or “A lot”)

Conduct your research in the US: 30% Travel outside the US to conduct your research: 40% Visit your country of citizenship: 55% Re-enter the US after leaving the country: 57% Bring your immediate family members to the US: 36%

Free-text comments express considerable frustration

More information More information at

http://postdoc.sigmaxi.org

Contacts Geoff Davis, PI, [email protected] Jenny Zilaro, Project Manager,

[email protected]

http://postdoc.sigmaxi.org/

http://postdoc.sigmaxi.org/



Survey Responders Difficult to obtain ground truth for

assessing results Plan: compare results of pilot survey to known

values for one institution with good records Reality: survey revealed that the institution in

question was missing lots of postdocs (~10% of the local population)

Survey Responders Fortunately we found an alternative with better

records Differences in response rates consistent with

levels of variation in a random sample for Sex Citizenship Minority status

No strong evidence of non-response bias

Further Non-response Analysis Survey literature: propensity to respond is a

continuous variable Early responders: high propensity Late responders: lower Non-responders: lowest

Idea is that non-responders are more similar to late responders than early responders

Compare early and late responders. Differences suggest potential non-response bias.

Non-response Bias? Who are missing 66% of postdocs? No significant difference between early and late

responders by Sex Overall satisfaction

Significant but small difference by citizenship (p ~0.04) Early responders: ~49% citizens Late responders: ~45% citizens

Non-citizen postdocs are probably slightly underrepresented

Domestic vs International: Satisfaction Non-citizens report slightly lower levels of

satisfaction with the postdoc experience Average satisfaction

(-2 = dissatisfied / 2 = satisfied) Citizens/PR 0.8 Temporary 0.6

Difference disappears when one controls for salary, discipline, institution, sex, and time as a postdoc

Settlement Interests

Level of interest (0=None, 2=High) in settling in various regions (ignoring visa issues)*

US Europe Asia

US citizens 2.0 0.8 0.2

European citizens

1.4 1.8 0.3

Asian citizens 1.6 1.2 1.3

Settlement Interests Level of interest (0=None, 2=High) in settling in

various regions (ignoring visa issues)*

US Europe Asia

US citizen, US PhD 1.97 0.75 0.20

US citizen, non-US PhD 1.67 1.50 0.25

European citizen, US PhD 1.64 1.43 0.21

European citizen, non-US PhD 1.35 1.86 0.28

Asian citizen, US PhD 1.73 1.04 1.33

Asian citizen, non-US PhD 1.58 1.20 1.26

Documents

The Productive Postdoc: Do Working Conditions Affect Outcomes?