5 - Lavy Lecture Notes

5 - Lavy Lecture Notes - Lavy “Performance Pay and...

Info icon This preview shows pages 1–8. Sign up to view the full content.

View Full Document Right Arrow Icon
Image of page 1

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 2
Image of page 3

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 4
Image of page 5

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 6
Image of page 7

Info iconThis preview has intentionally blurred sections. Sign up to view the full version.

View Full Document Right Arrow Icon
Image of page 8
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: Lavy: “Performance Pay and Teachers’ Productivity” Policy guestion: should we use teacher bonuses to increase student performance? mg: creates greater incentives for teachers to work hard bonuses for individuals will reduce “free riding” (a problem with group bonuses) con: makes pay riskier, which employees don’t like, so pay rate will have to go up employees will emphasize only the behaviors that generate bonuses (e.g., "teaching to the test”) much work is complex, requires a lot of teamwork/cooperation, group can monitor behavior of individuals,(so use group bonuses?) Use tournaments (based on rank order of finish, not absolute amount)? announce prizes in advance (easy to stay within budget) each employee has incentive to work incredibly hard (if employees are basically identical, huge incentive to exert greater effort) problem: sabotage? Empirical guestion: will bonuses to teacherincrease student performance? Background: high schools assigned to an individual-performance bonus experiment (bonus to a teacher based on ranking of his/her students’ test scores) assignment of school based on the school’s “matriculation rate” (“matriculation rate” = percent passing national exams) school assigned to the experiment if its matriculation rate was below 45% BUT: “official” matriculation rate S was based on incomplete data “true” matriculation rate 3* only became known after schools were selected for the experiment S = S* + a, where a is measurement error key insight: conditional on S*, a is random! thus, conditional on 8*, selection of schools and students into the experiment is random thus, control for S* and pick schools with S < 45 _r_19_t in the experiment these schools form a “control group” that can be compared with schools with the same 8* that were in the experiment (“experimentals”) effectively, we have random selection of experimentals and controls (conditional on S*, the “true” matriculation score) basic model (data for two years: pre-experiment in 2000, experiment in 2001) Yijt=a+xijtl3+zjtV+5Tjt+¢j+ rI Dt+8ijt where Yijt = score of student i in school j at time t Xijt = characteristics of student I in school j at time t th = characteristics of school j at time t Tjt = interaction: dummy variable for “treated school” dummy variable for “year is 2001” (equals 1 if a treated school in 2001, equals 0 otherwise) 4)th = set of dummy variables for school (j = 1, 2., ...) Dt = dummy variable for “year is 2001” (= year of experiment) -)The estimated coefficient on T, namely 5, is the “difference-in-difference” estimate of the effect of the experiment on student scores. First consider the difference for the experimental schools between 2000 (no experiment) and 2001 (with experiment): improvement could have been due to unmeasured things that changed between 2000 and 2001, rather than due to the experiment itself. (Beware the “post hoc” fallacy — “post hoc, ergo propter hoc.”) Likewise, consider the difference for the control schools between 2000 and 2001: again, any change could have been due to unmeasured things that changed between 2000 and 2001, rather than due to the experiment itself. But why is 6 an estimate of the effect of the experiment? To simplify, assume that there is only one experimental school and one control school. (Argument doesn’t depend on this — allowing for multiple schools just makes the math more messy.) Likewise, ignore differences in X, Z, etc. (Again, argument doesn’t depend on this.) So the equation is now Yijt = a + 6 Tjt + (b,- + n D, + 8m where Tjt = 1 for the experimental school in 2001, and 0 otherwise; <|>j = 1 for the experimental school, 0 for the control Dt = 1 for the year 2001, and 0 for 2000 For the experimental school, the difference between 2001 and 2000 is Y'11=a+5"1+¢1+nx1+3i11 Yi10=a+6x0+¢1+nx0+8i10 Y'11:: _Yi10 = 5 + n "' 3m ' 3i1o Likewise, for the control school, the 2001-2000 difference is Yi11=a"'5"0""l>o"'r|"1"":io1 Yi1o=a+5x0+¢o+rlx0+8ioo Yi01x " i00 = '1 + 3m ' £i00 So, finally, we have: Yi11 -- m, = 6 + n + am - cm, experimental difference Yi01 "" Yioo = n + Sim " Sioo COI’ItI’OI difference Provided both types of schools have the same trends over time, the n‘s will cancel. Provided the differences in the 52’s are the same, the terms involving the s’s will also cancel. In this case, the difference between the two differences gives the effect of the experiment — and this is equal to 6! ”H1 — Yi1o] - [Yi01 - Yioo] = 5 ( = experimental — control difference) Thus, the difference between the two differences provides an estimate of the effect of the experiment. (Note that this estimate avoids the “post hoc” fallacy.) Lavy’s main results for the treatment effectI 6 (Table 4, p. 1992) math (all guartiles) limited full outcome control control testing rate 0.046 0.041 (0.027) (0.021) control group mean 0.802 pass rate 0.110 0.087 (0.036) (0.028) control group mean 0.637 average score 5.469 5.307 (2.292) (1.950) control group mean 55.046 Standard errors are in parentheses, underneath the coefficient estimates En lish all uartiles limited full control control 0.040 0.033 (0.017) (0.013) 0.865 0.047 0.039 (0.022) (0.020) 0.795 3.240 2.527 (1.666) (1.452) 59.496 Table 5: Ignoring dummy variables for “school” produces estimates that are less statistically significant Further looks at Table 4: * The program helped students atlbelow median the most (if high-level student, might pass anyway?) * Check to see how the program worked: improved scores of students who took the exam got more students to take the exam Tables 6-7: regression-discontinuity estimates (probability of being treated drops sharply if matriculation rate goes above 45%) — results similar to the basic results Table 8: effect of experiment on teaching methods, teaching effort effects evident mostly for English teaching, mostly via extra teaching time Table 9: did the program cause teachers to inflate their grades? (part of the overall score was based on local test results) answer — apparently not (sanctions for discrepancies between local and national test scores!) ...
View Full Document

{[ snackBarMessage ]}

What students are saying

  • Left Quote Icon

    As a current student on this bumpy collegiate pathway, I stumbled upon Course Hero, where I can find study resources for nearly all my courses, get online help from tutors 24/7, and even share my old projects, papers, and lecture notes with other students.

    Student Picture

    Kiran Temple University Fox School of Business ‘17, Course Hero Intern

  • Left Quote Icon

    I cannot even describe how much Course Hero helped me this summer. It’s truly become something I can always rely on and help me. In the end, I was not only able to survive summer classes, but I was able to thrive thanks to Course Hero.

    Student Picture

    Dana University of Pennsylvania ‘17, Course Hero Intern

  • Left Quote Icon

    The ability to access any university’s resources through Course Hero proved invaluable in my case. I was behind on Tulane coursework and actually used UCLA’s materials to help me move forward and get everything together on time.

    Student Picture

    Jill Tulane University ‘16, Course Hero Intern