Are There Fast Tracks in Economics Departments?

Valerie Smeets (UC3M & CCP, ASB)

Madrid, October 2007

Motivation Careers in organizations Specific aspect – timing of promotions

Studies using personnel records have found the existence of fast tracks: workers promoted quickly at one level of the hierarchy are promoted more quickly at the next level

But the question of why this is observed remains open Baker et al. (1994) interpret this finding as evidence of

learning Ariga et al. (1999) suggest more elaborate models

incorporating firm specific human capital acquisition to explain the existence of fast tracks

Theory Building blocks model of careers (Gibbons-Waldman

(1999)) Learning, job assignment and on the job human capital

acquisition Individuals are assigned to the job where they are the

most productive on the basis of their expected effective ability

Firms learn about the effective ability of individuals by observing their performance

Effective ability is a combination of innate ability (natural talent) and accumulated human capital (experience)

Workers climb job ladder as experience increases Serial correlation in promotions (fast tracks) Other predictions on careers, wage, etc.

Theory Literature on biased contests

Organizations might choose to favor (or handicap) the winner of the first round in the successive rounds of a multi-period promotion contest (Meyer (1991), (1992) and Prendergast (1992))

INCENTIVES: favor the winner Improve the incentives of identical agents in the first

period Even if the bias leads to a loss of incentives in the

second period, it is outweighed by the gain of incentives in the first period.

Theory Literature on biased contests

SORTING: if someone has to be promoted, favor the winner if not, handicap the winner

INCENTIVES & SORTING: Trade-off optimal to handicap the winner for incentives (to

offset the likely edge in ability) while optimal to favor the winner for learning

This paper Test for the existence of fast tracks in academia Similar to previous studies in other industries (Baker et

al. (1994), Ariga et al. (1999), Belzil and Bognanno (2004), Acosta (2006))

Drawback of previous papers: lack performance measure, makes it difficult to test why fast tracks exist (except Chiappori et al. (1999))

Advantage of our approach: Data on individual performance allow us to test the

reasons behind fast tracks

Data Panel on career and performance pattern of top 1000

academic economists (created by CSW, 2006, JLEO) Econlit for individual and university performances (publications) Top 1000 economists over 1987-1998 CV’s downloaded from internet Economists with balanced panel of entire career – 652

individuals Survey on tenure decision (60%+ response rate)

To have data suited to fast tracks analysis Individuals must have become professor in 1998 Individuals must have followed the hierarchical ladder Individuals must have spent their entire career in universities

323 individuals

Data323 economists over 1987-1998

Career path over 1969-1998Job title, promotion

& University changes

Individual performance(research) over 1969-1998

Controls: age, experience, education, nationality

Universities performance(research) over 1970-1998

Survey on tenure, teaching & consulting only tenure data used

Job level 1: assistantJob level 2: associateJob level 3: professor

Summary Statistics Number of years spent in lower ranks

Time as assistant Number of Time as associate Number of Time as assistant Number of professor individuals professor individuals and associate individuals (years) (years) professor (years)

1 9 1 21 2 1

2 21 2 39 3 2

3 59 3 76 4 3

4 65 4 70 5 24

5 71 5 59 6 37

6 53 6 31 7 38

7 32 7 14 8 49

8 9 8 7 9 55

9 1 9 2 10 52

10+ 3 10+ 4 11+ 62

323 323 323

Summary Statistics

Number and proportion of fast tracks Number of years as Associate

≤ 3 years > 3 years #

≤ 3 years 39 (12.1%) 50 (15.5%) 89 Number of Years as Assistant > 3 years 97 (30.0%) 137 (42.4%) 234

# 136 187 323

Baker, Gibbs and Holmström

(1994)

Time to promotion in level 1 versus time to promotion in level 2

Years at level 1 Years at level 2 (associate) before promotion (assistant)

Statistics 1 2 3 4 5 6 7 8 9 10+

1 Promotion rate (%) 11 0 0 25 33 50 50 0 100 - N 1 0 0 2 2 2 1 0 1 0 2 Promotion rate (%) 10 11 24 46 43 25 67 100 - - N 2 2 4 6 3 1 2 1 0 0 3 Promotion rate (%) 2 21 37 24 64 38 20 50 0 100 N 1 12 17 7 14 3 1 2 0 2 4 Promotion rate (%) 9 8 28 33 54 75 0 67 0 100 N 6 5 15 13 14 9 0 2 0 1 5 Promotion rate (%) 10 16 30 42 50 55 80 0 0 100 N 7 10 16 16 11 6 4 0 0 1 6 Promotion rate (%) 2 6 31 50 53 38 40 67 100 - N 1 3 15 17 9 3 2 2 1 0 7 Promotion rate (%) 3 13 33 33 33 63 100 - - - N 1 4 9 6 4 5 3 0 0 0 8 Promotion rate (%) 11 25 0 33 25 67 100 - - - N 1 2 0 2 1 2 1 0 0 0 9 Promotion rate (%) 100 - - - - - - - - - N 1 0 0 0 0 0 0 0 0 0

10+ Promotion rate (%) 0 33 0 50 100 - - - - - N 0 1 0 1 1 0 0 0 0 0

Summary Statistics

Correlation between the time spent at each level

# years as associate

Stay associate more than 4 years

# years Stay assistant as assistant

-0.046 less than 4 years

0.021

Empirical Analysis Whether there exists a positive relationship between

the time spent at the 1st hierarchical level (assistant professor) and the time spent at the 2nd hierarchical level (associate professor)

Controlling for individual performance Test alternative explanations for our results Productivity pattern of individuals with different

career profiles

DO SYSTEMATI C FAST TRACKS EXI ST?

Dep.var: # years as associate professor

# years as assistant and

associate professor

-0.54*** 0.46*** # years as assistant professor (-14.36) (12.11)

-0.67*** -0.67*** Perf ormance since last promotion

(PSLP) (-20.37) (-20.37)

0.09*** 0.09*** PSLP*EXP (27.74) (27.74)

5.92*** 5.92*** Constant (24.95) (24.95)

Adj . R² 0.73 0.85

Nr. Obs. 323

Results Having been promoted quickly in the past decreases the

chances of being quickly promoted in the future, evidence of a handicapping policy

The handicap is relative: the ”winner” is handicapped in the 2nd stage by 54% of the time he gained in the 1st stage

The handicap can be beaten if the performance during the 2nd stage is high enough. Moreover, the effect of performance decreases with experience, in line with learning theory

The handicap does not survive the whole career: individuals who have experienced quick past promotions are the ones who have the fastest careers

Results These findings can be explained by the need to

balance incentives and sorting issues as a negative bias set against the faster improves

incentives and reestablishes a balanced contest [Meyer (1992) & Prendergast (1992)]

but a strong handicap can be detrimental for sorting issues, as it may lead to the selection of inadequate individuals [Meyer (1991)]

Incentives and sorting matter in academia: using relative handicaps may help to balance these concerns

Alternative Explanations Human capital accumulation

Individuals receiving a fast promotion at the beginning have accumulated less human capital and have to wait longer for their next promotion

Or administrative rules Tenure

Tenured and untenured associates exhibit different behaviors

Endogeneity Number of years as assistant is determined by the

productivity of the individual during this period and individual productivity may be correlated across time

Human Capital Accumulation Add quadratic in experience in previous

estimations colinearity?

Test: if human capital accumulation is all that matters for career paths, then individuals who were slow at the beginning should have better careers than those who were fast, due to a higher level of human capital rejected

Tenure TENURED ASSOCI ATE ONLY

Dep.var: # years as associate professor

# years as assistant and

associate professor

-0.58*** 0.42*** # years as assistant professor (-8.72) (6.36)

-0.72*** -0.72*** Performance since last promotion

(PSLP) (-13.05) (-13.91)

0.08*** 0.08*** PSLP*EXP (17.22) (17.22)

6.70*** 6.70*** Constant (15.91) (15.91)

Adj . R² 0.76 0.86

Nr. Obs. 101

Endogeneity Two steps strategy:

First, regress # years as assistant over the performance when assistant and performance interacted with experience

Then replace # years as assistant by its estimated value, NASTPE, in the estimation of # years as associate

May use the residual: as the residual represents the unexplained part of the time spent as assistant, a negative value could be interpreted as a promotion that occurred earlier than it should have been

Endogeneity2SLS ESTI MATI ON

(1) (2)

Dep.var: NASSP NFULL

NASTPE -0.69*** - 0.43*** - (-9.70) (5.84) φ - -0.53*** - 0.55*** (-9.19) (10.37) PSLP -0.54*** -0.57*** -0.81*** -0.73*** (-15.97) (-15.90) (-23.65) (-22.18) PSLP*EXP 0.07*** 0.08*** 0.09*** 0.09*** (23.13) (22.70) (30.37) (29.42) Constant 6.37*** 3.29*** 6.31*** 8.08*** (17.08) (22.68) (16.62) (60.79) Nr. Obs. 323 Adj . R² 0.65 0.64 0.8 0.83

Productivity Patterns and Careers Are fast individuals more productive after being

promoted to full professor. Is the early selection effective?

Compare the average productivity of four groups of individuals: Fast tracks: spent less than 4 years in each layer Early fast: spent less than 4 years at the first layer

and more than 3 years at the second layer Late fast: spent more than 3 years at the first layer

and less than 4 years at the second layer Not fast: spent more than 4 years at each layer

Productivity Patterns and CareersAverage productivity over a period of three years after promotion to professor

Number of years as associate ≤ 3 years > 3 years

5.97 2.75 ≤ 3 years

(8.8) (2.9) 3.41 2.38

Number of years as assistant

> 3 years (4.7) (2.0)

Number of indidivuals: 272 Average productivity: 3.21

Productivity Patterns and CareersAverage productivity over a period of five years after promotion to professor

Number of years as associate ≤ 3 years > 3 years

9.98 4.60 ≤ 3 years

(10.6) (3.5) 5.42 4.80

Number of years as assistant

> 3 years (3.8) (3.27)

Number of indidivuals: 201 Average productivity: 5.61

Productivity Patterns and Careers

Productivity Patterns and Careers

Productivity Patterns and Careers This handicapping policy seems to lead to an

effective selection process as the fastest individuals are the best performers ex post: effective cherry picking

Fast tracks are also the most productive along the whole career

Effective selection as fast tracks always exhibit a higher productivity // handicaps used for sorting issues

Conclusions Test alternative theories of fast tracks in economics

departments, using performance data A priori no relation btw time spent at each layer But when controlling for performance, evidence of a

“partial” handicap (>< systematic fast tracks effect) Individuals can beat this handicap if they achieve a

given level of productivity Seem in line with the incentives and sorting story

(having a “partial” handicap) Among different career patterns, fast individuals are

always the most productive