Promotion Incentives in the Public Sector: Evidence from Chinese Schools

1. Naureen Karachiwalla, University of Oxford Albert Park, HKUST Promotion Incentives in the Public Sector: Evidence from Chinese Schools

2. Motivations • Teachers are central to the learning process • Often undermotivated in developing countries • Exclusive focus on incentive pay (bonuses) • China ideal case to study use of promotions to provide incentives—sophisticated system, good performance • Incentives for civil servants, puzzle of governance and rapid growth in China? • Empirical evidence on promotion incentives • Previous evidence mostly on use of incentives (by studying wage patterns) in US companies • Little direct evidence on effort/performance (Gibbs, 1995; Campbell 2008, Kwon 2006)

3. Outline • Motivations • Promotion of teachers in China • Data • Model of Promotions as Incentives • Empirical model • Results • Conclusion

4. Promotion of Teachers in China • Four ranks in both primary and middle school • To apply for a promotion, need: • To wait a certain number of years (depending on education) • Favourable annual 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

5. Promotion eligibility rules

6. Mean wages by rank

7. Wage regressions

8. Teacher evaluations • Annual evaluations on a four point scale: excellent, good, pass, fail. Set proportions. • Based on four criteria: student test scores, attendance, preparation and ‘attitude’. Committee chooses weights. • Classroom observation, questionnaires to teachers and students, principal reports. Points for each component. • Points added, teachers are ranked. Top 10% get ‘excellent’, next 10% get ‘good’ scores. Rest get a ‘pass’. • Results of ‘excellent’ and ‘good’ evaluation scores announced at annual meetings

9. Teacher evaluation criteria

10. Data • Gansu Survey of Children and Families (GSCF), focussed on rural schools • 3 waves, we use 2007. Child, teacher, principal etc. • Sampled 100 villages in 42 townships in 20 counties • Sampled the main primary and middle school in each village • Sample of 2,350 teachers

11. Data

12. Data

13. Literature – Theory • Promotions as tournaments, Lazear and Rosen (1981). Wage gap that can induce first best effort exists. • Macleod and Malcolmson (1988) model of skill and effort as private information. Employees sort into ranks according to ability. • Fairburn and Malcolmson (1994) sorting into different jobs. Promotions can be made incentive compatible. • Gibbs (1989) multi-person tournaments with heterogeneous competitors. Predictions on ability, number of competitors, time after promotion, beliefs on ability etc.

14. Model of Promotions as Incentives • School offers promotions, teachers hired in lowest rank, n teachers compete for k promotion slots at each rank level • School offers ΔEU  (W2 - W1)*tenure after promotion • Teachers have different skill, s with B(s) and b(s), E(s)=0 • Cost of effort (e) is C(e) where C’ , C’’ >0 • p(e, s, e) is probability of promotion

15. Model of Promotions as Incentives • Teacher solves: • First order condition: • dp/de is marginal probability of promotion (MPE)

16. Model of Promotions as Incentives • qi = si + ei + πi where πi = εi + μ, CDF R(q) PDF r(q) • E(πi)=E(εi)=E(μ)=0, CDF F(ε), PDF f(ε) • Probability teacher i beats teacher g: pr(qi > qg) = pr(ei + si + εi + μ > eg + sg + εg + μ) = pr(eg + sg + εg + < ei + si + εi) = R(ei + si + εi) • Probability of promotion:

17. Model of Promotions as Incentives

18. Model of Promotions as Incentives

19. Model of Promotions as Incentives

20. Predictions • Incentives higher with higher wage increases when promoted • Incentives decline with age • Incentive highest when skill percentile = 1 – p*, and declines with distance from 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)

21. Multiperiod Model • Teachers have careers of T periods, eligible for promotion in year t = X • Probability of promotion, pt is based on performance in past 5 years • Normalize per period utility before promotion to zero, define Uh > 0 utility from wages after promotion • In year j, lifetime expected discounted utility is: • Prior belief on skill, s1, 1/N ≤ s1 ≤ 1. True relative rank s. • Teachers update beliefs on skill rank st , adjust st downward when passed over for promotion

22. More Predictions • Predictions on teacher performance over time • If t ≤ X – 5 effort is zero • Effort is increasing from t=X – 4 to X • Teachers update beliefs on s based on whether or not they are promoted. When teachers are not promoted, s is revised downwards, effort is decreasing for every year of non-promotion

23. Empirical Specification • From the one-period model’s FOC: • Estimate as: • We will estimate with fixed effects so w and p will drop out. We will also add in the time dimension.

24. Empirical specification • ev = evaluation scores for t = 2003, 2004, 2005, 2006 • a = ability index, dummies for top and bottom 10% • n = number of teachers, also interacted with ability in top and bottom 10% • w = fixed effect • D – dummies for: • t = X – 5 or greater • t = X – 4, t = X – 3, t = X – 2, t = X – 1 , t=X • t > after half the other teachers are promoted (dummies from one to ten years after half of colleagues are promoted)

25. Empirical results • Evaluation scores increase with higher expected wage increases • Evaluation scores increase in the years preceding promotion eligibility and decrease after not being promoted (inverted U) or reaching the highest rank • Evaluation scores increase with competition (number of teachers) for those in the middle of the skill distribution but do not for those in the tails of the skill distribution • Promotion probability positively affected by high evaluation scores

26. Results – X-5 to X X-5 X-4 X-3 X-2 X-1 X

27. Results – post-eligibility

28. Primary High Theory predicts no effort incentive after achieving highest rank, decline suggests older teachers slowing down (rising cost of effort?)

29. Other results

30. Evaluations as a proxy for effort • One could argue that the evaluation scores capture both ability and effort • However, the use of the fixed effect and the ability index mitigate this problem • A regression was also run of the probability of obtaining an ‘excellent’ or ‘good’ evaluation score on measures of teacher time use • This was done for 2006 only since that is what we have data on • Coefficient on number of hours (spent with students, preparing lesson plans, marking homework etc.) is positive and significant

31. Robustness • What if principals are just awarding high scores to teachers who are nearing eligibility for promotion? • Again, evaluation scores are related to time use • Restricted the sample to counties that have high correlations between time use and evaluation scores and the effect remains • Ranks strongly predict test scores (other studies) • Or, teachers could be learning and that would also produce an upward trend pre-eligibility • The teachers in the sample have already been teaching for many years (average experience is 12 years)

32. Evaluation scores and time spent on teaching

33. Do higher evaluation scores increase promotion probability?

34. Conclusion • Effort responds to promotion incentives • Implications for design • Optimal contest size and promotion rate? • Incentivizing teachers falling behind • Combining pay for performance (within-rank incentives) with promotion incentives (happening in China!)