Presentation is loading. Please wait.

Presentation is loading. Please wait.

Naureen Karachiwalla, University of Oxford Albert Park, HKUST.

Similar presentations

Presentation on theme: "Naureen Karachiwalla, University of Oxford Albert Park, HKUST."— Presentation transcript:

1 Naureen Karachiwalla, University of Oxford Albert Park, HKUST

2 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 incentivessophisticated 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 Motivations Promotion of teachers in China Data Model of Promotions as Incentives Empirical model Results Conclusion

4 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


6 Average salary Standard deviationIncrease Primary Primary % Primary high % Middle Middle % Middle % Middle high %


8 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 Criteria Mean percentage weightStandard deviation Attitude23.22 %10.57 Preparation29.45 %11.39 Attendance13.16 %5.82 Tests Scores34.17 %15.59

10 Gansu Survey of Children and Families (GSCF), focussed on rural schools 3 waves, we use 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 Primary 2Primary 1 Primary highMiddle 3Middle 2Middle 1 Middle high Number of teachers Total Female 58%45%25%49%37%17%15% Basic characteristics Average Age Average Years teaching Years of education Number of teachers competing Number of teachers (in the township for Primary school, in the school for Middle school)


13 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 School offers promotions, teachers hired in lowest rank, n teachers compete for k promotion slots at each rank level School offers ΔEU (W 2 - W 1 )*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 Teacher solves: First order condition: dp/de is marginal probability of promotion (MPE)

16 q i = s i + e i + π i where π i = ε i + μ, CDF R(q) PDF r(q) E(π i )=E(ε i )=E(μ)=0, CDF F(ε), PDF f(ε) Probability teacher i beats teacher g: pr(q i > q g ) = pr(e i + s i + ε i + μ > e g + s g + ε g + μ) = pr(e g + s g + ε g + < e i + s i + ε i ) = R(e i + s i + ε i ) Probability of promotion:




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

22 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 From the one-period models 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 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 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 X-5 X-4 X-3 X-2 X-1 X


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

29 VariableCoefficientStandard error Number of teachers0.001**0.000 Number of teachers * ability bottom 10% ***0.001 Number of teachers * ability top 10% **0.000 Ability bottom 10%0.192***0.067 Ability top 10%

30 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 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)



34 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!)

Download ppt "Naureen Karachiwalla, University of Oxford Albert Park, HKUST."

Similar presentations

Ads by Google