"Promotion Incentives in the Public Sector: Evidence from Chinese Schools" - Naureen Karachiwalla and Albert Park SIG 2015 Workshop "Integrating Multi-level Governance into the Post-2015 Development Agenda: Opportunities, Trade-offs, and Implications", Nov 9-10, 2015

1. Promotion Incentives in the Public Sector: Evidence
from Chinese Schools
Naureen Karachiwalla and Albert Park
IFPRI (PHND) and HKUST
N.Karachiwalla@cgiar.org
November 10, 2015
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 1 / 17

2. Motivations
Low public service delivery in low-income countries, including for
teachers.
Much interest in incentivizing teachers.
The public sector faces greater incentive problems.
Thus far, focus on bonuses.
China is one of few countries with promotion incentives, very
sophisticated.
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 2 / 17

3. Overview
1 Motivations
2 Related Literature
3 Promotions in China and Data
4 Predictions from Theoretical Model
5 Empirical Specification
6 Results
7 Implications
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4. Related Literature
Theory: Promotions as tournaments
Lazear and Rosen (1981) - promotions also induce effort
Macleod and Malcolmson (1988) - employees that differ by ability
Gibbs (1989) - multi-person tournaments with heterogeneous employees
Empirics:
Wage determination in firms - Baker, Gibbs and Holmstrom, 1994;
Medoff and Abraham, 1980; Medoff and Abraham, 1981
Little direct evidence on effort - Gibbs, 1995; Campbell, 2008; Kwon,
2006; Haeck and Verboven, 2011
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5. Promotions in China
Four ranks at each of Primary and Middle school.
To apply for a promotion, need:
To wait a certain number of years (depending on education); and,
Favourable evaluation scores (one ‘excellent’ or two ‘good’) in the last
5 years.
Promotion depends on the number of spaces available in a township.
Wages are higher at higher rank levels, and increase every five years.
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 5 / 17

6. Teacher Evaluations
Annual evaluations on a four point scale: excellent, good, pass, fail.
Based on four criteria: student test scores (34%), attendance (13%),
preparation (30%), and ‘attitude’ (23%).
Township committee chooses weights and conducts evaluations
(classroom observation, questionnaires, principal reports).
Top 10-15% (by rank and township) get ‘excellent’, next 30-40% get
‘good’ scores. Rest get a ‘pass’.
Results announced at annual teacher meetings.
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7. Data
Gansu Survey of Children and Families (GSCF), focussed on rural
schools.
3 waves, use 2007 and construct teacher panel for 2003-2006.
Sampled 100 villages in 42 townships in 20 counties.
Sampled the main primary and middle school in each village.
Sample of 2,350 teachers.
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8. Theoretical Model Basics
School offers promotions, teachers hired in lowest rank, n teachers
compete for k promotion slots (p∗ = k/n).
School offers an increase in wages if promoted (∆EV ).
p∗ and ∆EV are fixed - heirarchy is in a steady state.
There is a (known) distribution of skill across teachers. Teachers know
their skill but not that of others. Beliefs on skill updated over time.
Output: qi = si + ei + πi where πi = i + µ. ’Luck’ is distributed
symmetrically around a mean of zero.
Effort has a cost, which is increasing at an increasing rate
(C (e) > 0, C (e) > 0)
Teacher chooses effort so that the marginal cost of effort is equal to
the gain from promotion (change in wages) weighted by how
responsive promotion is to effort - C (e) = ∆EV ∗ dp/de
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9. Theoretical Model Predictions
Incentives are highest for the marginal skilled teacher (s = 1 − p∗).
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 9 / 17

10. Theoretical Model Predictions
Incentives are highest for the marginal skilled teacher (s = 1 − p∗).
When n increases but p∗ stays the same, incentives increase for those
close with skill percentile close to 1 − p∗ (and decrease for those with
very high or very low skill).
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 9 / 17

11. Theoretical Model Predictions
Incentives are highest for the marginal skilled teacher (s = 1 − p∗).
When n increases but p∗ stays the same, incentives increase for those
close with skill percentile close to 1 − p∗ (and decrease for those with
very high or very low skill).
Teacher effort is expected to be zero if it is 5 or more years before the
teacher is eligible for promotion.
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 9 / 17

12. Theoretical Model Predictions
Incentives are highest for the marginal skilled teacher (s = 1 − p∗).
When n increases but p∗ stays the same, incentives increase for those
close with skill percentile close to 1 − p∗ (and decrease for those with
very high or very low skill).
Teacher effort is expected to be zero if it is 5 or more years before the
teacher is eligible for promotion.
Teacher effort is expected to increase in the years leading up to
promotion eligibility.
Higher discount rate.
More years in which current evaluation score counts.
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 9 / 17

13. Theoretical Model Predictions
Incentives are highest for the marginal skilled teacher (s = 1 − p∗).
When n increases but p∗ stays the same, incentives increase for those
close with skill percentile close to 1 − p∗ (and decrease for those with
very high or very low skill).
Teacher effort is expected to be zero if it is 5 or more years before the
teacher is eligible for promotion.
Teacher effort is expected to increase in the years leading up to
promotion eligibility.
Higher discount rate.
More years in which current evaluation score counts.
Teacher effort is expected to decrease if the teacher is passed over for
promotion several times:
Retirement becomes closer.
Update beliefs on skill.
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 9 / 17

14. Empirical Specification
evit = δ1Dit +δ2a10,it +δ3a90,it +δ4nit +δ5nit ∗a90,it +δ6nit ∗a90,it +ϕi +vit
(1)
evit - evaluation scores for 2003, 2004, 2005, 2006
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 10 / 17

15. Empirical Specification
evit = δ1Dit +δ2a10,it +δ3a90,it +δ4nit +δ5nit ∗a90,it +δ6nit ∗a90,it +ϕi +vit
(1)
evit - evaluation scores for 2003, 2004, 2005, 2006
a - ability index, dummies for top and bottom 10% of ability
distribution
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 10 / 17

16. Empirical Specification
evit = δ1Dit +δ2a10,it +δ3a90,it +δ4nit +δ5nit ∗a90,it +δ6nit ∗a90,it +ϕi +vit
(1)
evit - evaluation scores for 2003, 2004, 2005, 2006
a - ability index, dummies for top and bottom 10% of ability
distribution
n - number of teachers in rank in township (log), and interacted with
ability dummies
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 10 / 17

17. Empirical Specification
evit = δ1Dit +δ2a10,it +δ3a90,it +δ4nit +δ5nit ∗a90,it +δ6nit ∗a90,it +ϕi +vit
(1)
evit - evaluation scores for 2003, 2004, 2005, 2006
a - ability index, dummies for top and bottom 10% of ability
distribution
n - number of teachers in rank in township (log), and interacted with
ability dummies
ϕ - individual teacher fixed effect
Naureen Karachiwalla and Albert Park Promotion Incentives in the Public Sector November 10, 2015 10 / 17

18. Empirical Specification
evit = δ1Dit +δ2a10,it +δ3a90,it +δ4nit +δ5nit ∗a90,it +δ6nit ∗a90,it +ϕi +vit
(1)
evit - evaluation scores for 2003, 2004, 2005, 2006
a - ability index, dummies for top and bottom 10% of ability
distribution
n - number of teachers in rank in township (log), and interacted with
ability dummies
ϕ - individual teacher fixed effect
D - dummies for:
t = X − 4, t = X − 3, t = X − 2, t = X − 1, t = X, t = X + 1,
t = X + 2, ..., X = X + 15 or greater
t ≤ 5 - omitted category
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19. Results - time dummies
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20. Results
Variable Coefficient SE
Number of teachers in same rank in township (log) 0.013 0.082
Number of teachers * ability bottom 10 per-cent -0.091* 0.052
Number of teachers * ability top 10 per-cent -0.043 0.062
Ability in bottom 10 per-cent 0.320* 0.193
Ability in top 10 per-cent 0.038 0.200
Number of observations 3,683
R2 0.022
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21. What if evaluations are not a good proxy for effort?
Evaluation scores may capture both ability and effort.
Fixed effect and ability index mitigate this problem.
However, evaluation scores are related to measures of teacher time
use and test scores. Time Use Test scores
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22. What if principals are manipulating evaluation scores or
teachers are learning?
Teachers could be learning and that would also produce an upward
trend pre-eligibility.
The teachers in the sample have an average of 12 years of experience.
What if principals are just awarding high scores to teachers who are
nearing eligibility for promotion?
Again, evaluation scores are related to time use and test scores.
Ranks strongly predict test scores (Park and Hannum, 2001; Ding and
Lehrer, 2001)
Principals are also evaluated, results announced every year, done by an
entire committee at the township.
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23. What about selection?
What if the downward trend is just bad teachers?
Fixed effect - within teacher regression.
Older teachers are just slowing down.
Similar regression for Primary high rank, there is a strong downward
trend. Primary High
Primary high teachers also spend much less time working.
Primary High time
The average age of Primary high teachers is 47 years, for those 10-15
years into the rank it’s 50.
The average age of the teachers in the downwards sloping part is 44
years.
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24. Do higher evaluatin scores increase promotion probability?
Yes! One unit increase in evaluation score increases the probability of
promotion by almost 15% (significant at the 5% level).
Regression of promotion on evaluation scores (instrumented by
change in log wages), with many controls, and county fixed effects.
Controls include: education, experience, gender, promotion rate,
ability, number of teachers, county variables (education, experience,
gender), time, pre-teaching test scores.
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25. Implications
Effort responds to promotion incentives
Implications for design of incentives in the public sector
Optimal contest size and promotion rate
Timing of incentives
Incentivizing teachers falling behind
Combining pay for performance (within-rank incentives) with
promotion incentives (happening in China!)
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26. Evaluation Scores and Time Use
Back
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27. Evaluation Scores and Student Test Scores
Back
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28. Primary High Level Regression
Back
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29. Primary High Time Use
Back
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