Rules Rather Than Discretion: Teacher Hiring and Rent Ex

Transcription

1 Rules Rather Than Discretion: Teacher Hiring and Rent Extraction Paris School of Economics April 2014 Improving Governance: Delivering Better Education Blavatnik School of Government, Oxford

2 Motivation This Paper Teachers Matter for Student Outcomes Ample evidence on the importance of teacher quality (Hanusheck and Rivkin, 2006). Problem: Identifying at hiring who is (can be) a good teacher. (and even after). Easy-to-observe characteristics are poor predictors of teacher quality. e.g. experience and training certicates.

3 Motivation This Paper Teacher Hiring Discretionary hiring: use current teachers' superior information about teacher quality. School principals in U.S. do well at identifying teachers in extremes of quality distribution (Jacob and Lefgren, 2008). But discretion may lead to agency problems. Hoxby (1996): Teachers' unions may shape school inputs to Use superior information to enhance student achievement or Set the school inputs that maximize own objectives. Teacher tests are modest predictors of teacher quality (Hanushek, 2011). But are dicult to manipulate.

4 Research questions Introduction Motivation This Paper Evaluate - using student outcomes - teacher hiring using: standardized test vs. discretionary process with strong involvement from teachers' union. Use reform to teacher hiring in elementary schools in Mexico.

5 Preview of Results Introduction Motivation This Paper Do DID using allocation of teachers to schools based on: Teachers' preferences over locality characteristics. NOT on past performance of schools. Allocation of test-hired teachers: Decreases student cheating in end-of-the year exam. Increases student achievement.

6 Public Schools in Mexico School System Teacher Hiring Teacher Allocation State governments operate public elementary schools. Federal Government provides the bulk of money and sets minimum standards (curricula, teaching hours, etcetera). Each state, centrally, does the hiring, promotion and allocation of teachers to schools. Key: hiring is not associated to specic vacancies in schools.

7 Telesecundaria Schools School System Teacher Hiring Teacher Allocation Junior HS (Grades 7 to 9). Small schools catered to small communities. Sample medians: 69 students, 3 classrooms in locality with 817 inhabitants. Generally: one teacher per class. Lot of IT teaching support. Widespread system: 1.26 million students in 18,000 Telesecundaria schools (2010,). 21% of enrollment in JHS.

8 Discretionary Hiring Introduction School System Teacher Hiring Teacher Allocation States enjoy wide discretion for hiring. Regulation: JHS teachers must have college-level education. Teachers' union plays key role: Disseminating information about vacancies. Directly proposing candidates. Criticisms to opacity in hiring procedure and decisions. Santibanez, 2008, Nieto, 2009 and Elizondo, 2011 Survey: One-third of elementary teachers think selling positions is frequent practice. When reform was announced: local union leaders declared it would hurt their members' right to bequest their positions.

9 Test-based Hiring Introduction School System Teacher Hiring Teacher Allocation Some recruitment based on national standardised exam introduced in Pushed by Federal Government. Test hiring expected expansion in overall stock of teachers for the period. Around 18% of brand-new teachers in One exam version for each teacher type, e.g. Primary, Math JHS. Candidates ranked by state/type according to exam results. and in some states with a weight for college GPA.

10 Allocation of Teachers to Schools School System Teacher Hiring Teacher Allocation No change with reform. Joint Committees of state ocials and union members allocate (brand-new and current) teachers to school vacancies. All vacancies subject to competition among current teachers. Evaluation criteria: certications, ability, tenure and discipline. Allocation depends hence on: teachers' preferences and committees' assessment of applicants' merit. Joint committees do not have to follow the same criteria to allocate newly-hired teachers.

11 Datasets Introduction Data Identication Strategy Results Payroll of teachers in public schools 2010 Q Q2. Includes test-hired teachers in 2008, 2009 and Discretionary-hired teachers: No information for 2008 and National standardized exam (ENLACE). Grade 9 (end of JHS): Subjects: Spanish and Math. Scores and suspected cheating. School census (Formato 911).

12 Panel of Schools Introduction Data Identication Strategy Results 6-year database with 1,148 schools in 13 states which: Received at least one brand-new teacher in Have never received a 2009 or 2008 test teacher. Did not receive both a new test teacher and a new discretionary teacher in Have at least 4 years of Enlace results.

13 Data Identication Strategy Results Probability of Receiving a Test Teacher 2010 (OLS) (1) (2) VARIABLES Coe. se Math 9 Lag (0.0198) Spanish 9 Lag e-05 (0.0187) Flagged Exams Grade 9 Lag (0.0635) Final Enrollment Grade 9 Lag (0.115) Math 9 Lag (0.0233) Spanish 9 Lag (0.0205) Flagged Exams Grade 9 Lag (0.0793) Final Enrollment Grade 9 Lag (0.0731) Class size Lag * ( ) Students Lag *** ( ) Share indigenous students Lag ** (0.0344) Principal has grad school (0.0189) Locality Poverty Rate 0.159** (0.0768) Hours to state capital *** ( ) Locality Population 4.92e-08* (2.80e-08) Share hhs electricity *** (0.117) Share hhs sewage (0.0400) Constant 0.291* (0.161) Observations 1,124 R-squared State Fixed Eects Yes F statistic Ho Var 1-8= Prob>F Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

14 Estimation model: DID Data Identication Strategy Results Do DID under parallel trend assumption: y st = β 0 + β 1 T st + ΓX st + τ t + α s + v st (1) where y st is school s outcome at time t. T st is share of 2010 test teachers among total teachers in school s at time t. X st τ t β 1 is the parameter of interest. is a vector of time-variant covariates. class size, school size, % indigenous students, principal's education and number of 2010 brand-new teachers in school s at time t. is a vector of year dummies. fully interacted with state dummies. α s is a school time-invariant component. and v st is a disturbance term. Cluster standard errors at the school level.

15 Data Identication Strategy Results DID: Pre-Treatment Trends (1) (2) (3) (4) VARIABLES Final Enrollment (%) Share Flagged Exams (%) Math Score (std) Spanish Score (std) Panel A. All Schools 2006 X Share Test Teachers (0.0360) (0.0351) (0.328) (0.273) 2007 X Share Test Teachers ** (0.0399) (0.0406) (0.320) (0.284) 2008 X Share Test Teachers (0.0441) (0.0418) (0.362) (0.336) 2009 X Share Test Teachers (0.0341) (0.0416) (0.357) (0.291) Observations 5,984 5,986 5,985 5,985 R-squared Number of id 1,231 1,231 1,231 1,231 Time-varying covariates Yes Yes Yes Yes State X Year Dummies Yes Yes Yes Yes F statistic Ho Var 1-4= Prob>F Panel B. Small Schools 2006 X Share Test Teachers (0.0380) (0.0312) (0.370) (0.294) 2007 X Share Test Teachers *** (0.0430) (0.0374) (0.331) (0.310) 2008 X Share Test Teachers * (0.0457) (0.0385) (0.389) (0.369) 2009 X Share Test Teachers (0.0381) (0.0394) (0.412) (0.337) Observations 3,256 3,258 3,257 3,257 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State X Year Dummies Yes Yes Yes Yes F statistic Ho Var 1-4= Prob>F Notes: All results are for Grade 9th outcomes. Results in Panel A include all schools in the sample and in Panel B schools with 3 groups or less. Source: Enlace and school census data and Registro Maestros Standard errors in parentheses are clustered at the school level. *** p<0.01, ** p<0.05, * p<0.1

16 Data Identication Strategy Results DID: Results (1) (2) (3) (4) VARIABLES Final Enrollment (%) Flagged Exams (%) Math Score (std) Spanish Score (std) Panel A. All Schools Share Test Teachers ** (0.0327) (0.0271) (0.263) (0.259) New Teachers ** *** *** ( ) ( ) (0.0437) (0.0474) Constant 0.944*** ** 0.168* (0.0113) (0.0143) (0.0927) (0.0917) Observations 7,210 7,212 7,211 7,211 R-squared Number of id 1,232 1,232 1,232 1,232 Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Panel B. Small Schools Share Test Teachers * 0.652** (0.0396) (0.0268) (0.286) (0.267) New Teachers *** (0.0134) (0.0104) (0.0856) (0.0887) Constant 0.951*** (0.0132) (0.0188) (0.108) (0.109) Observations 3,878 3,880 3,879 3,879 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Notes: All results are for Grade 9th outcomes. Results in Panel A include all schools in the sample and in Panel B schools with 3 groups or less. Source: Enlace and school census data and Registro Maestros Standard errors in parentheses are clustered at the school level. *** p<0.01, ** p<0.05, * p<0.1

17 Data Identication Strategy Results Exam Cheating Correlates with Test Scores (1) (2) VARIABLES Math Score (std) Spanish Score (std) Flagged Exams (%) 1.423*** 1.310*** (0.105) (0.0990) Class size * ( ) ( ) Students ( ) ( ) Share indigenous students (0.0793) (0.0785) Principal has grad school (0.0414) (0.0410) Constant 0.194** 0.167* (0.0893) (0.0892) Observations 7,211 7,211 R-squared Number of id 1,232 1,232 School Fixed Eects Yes Yes Year Dummies Yes Yes State*Year Dummies Yes Yes Grade 9th Outcomes. Robust standard errors in parentheses clustered at the school level *** p<0.01, ** p<0.05, * p<0.1

18 Data Identication Strategy Results DID No-Cheating Schools: Pre-Treatment Trends (1) (2) (3) VARIABLES Final Enrollment (%) Math Score (std) Spanish Score (std) Panel A. All Schools 2006 X Share Test Teachers (0.0478) (0.392) (0.312) 2007 X Share Test Teachers (0.0541) (0.397) (0.412) 2008 X Share Test Teachers (0.0578) (0.453) (0.453) 2009 X Share Test Teachers (0.0433) (0.461) (0.379) Observations 2,965 2,965 2,965 R-squared Number of id Time-varying covariates Yes Yes Yes State X Year Dummies Yes Yes Yes F statistic Ho Var 1-4= Prob>F Panel B. Small Schools 2006 X Share Test Teachers (0.0457) (0.415) (0.304) 2007 X Share Test Teachers (0.0543) (0.421) (0.416) 2008 X Share Test Teachers (0.0575) (0.478) (0.472) 2009 X Share Test Teachers (0.0427) (0.487) (0.406) Observations 1,992 1,992 1,992 R-squared Number of id Time-varying covariates Yes Yes Yes State X Year Dummies Yes Yes Yes F statistic Ho Var 1-4= Prob>F Notes: All results are for Grade 9th outcomes. Results in Panel A include schools in the sample with no reported agged exam in the period and in Panel B schools which in addition have 3 groups or less. Source: Enlace and school census data and Registro Maestros Standard errors in parentheses are clustered at the school level. *** p<0.01, ** p<0.05, * p<0.1

19 Data Identication Strategy Results DID No-Cheating Schools: Results (1) (2) (3) (4) VARIABLES Final Enrollment (%) Share Flagged Exams (%) Math Score (std) Spanish Score (std) Panel A. All Schools Share Test Teachers ** 0.858*** (0.0463) (0.0198) (0.307) (0.284) New Teachers *** ** (0.0123) ( ) (0.0640) (0.0600) Constant 0.955*** ** (0.0149) ( ) (0.0974) (0.111) Observations 3,572 3,572 3,572 3,572 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Panel B. Small Schools Share Test Teachers ** 1.023*** (0.0481) (0.0227) (0.316) (0.301) New Teachers ** * (0.0146) ( ) (0.113) (0.102) Constant 0.958*** (0.0156) ( ) (0.114) (0.128) Observations 2,382 2,382 2,382 2,382 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Notes: All results are for Grade 9th outcomes. Results in Panel A include schools in the sample with no reported agged exam in the period and in Panel B schools which in addition have 3 groups or less. Source: Enlace and school census data and Registro Maestros Standard errors in parentheses are clustered at the school level. *** p<0.01, ** p<0.05, * p<0.1

20 Interpretation Introduction Policy eect of interest: Relative performance of each method at identifyng and selecting teacher quality. Other mechanisms: Any response from schools and parents to the policy. Dierences in incentives after-hiring. Incomplete take-up rate. Propensity of candidates to apply through each method.

21 Conclusions Ocials hire lower-quality teachers when follow discretionary process with participation from teachers' union. Then, joint committees of ocials and union representatives allocate discretionary teachers to schools in more "desirable" localities. Results indicate agency problem with rent-extraction. Hard-to-manipulate rule reduces rent extraction.

22 Descriptive Statistics: Means (SD) (2009) (1) (2) (3) (4) All Schools All Schools Small Schools Small Schools VARIABLES Test Teachers Discretionary Teachers Test Teachers Discretionary Teachers Math Score Grade 9 (std) (1.129) (0.821) (1.082) (0.883) Spanish Score Grade 9 (std) (1.203) (0.868) (1.145) (0.977) Flagged Exams Grade (0.217) (0.169) (0.198) (0.187) Final Enrollment Grade 9th (0.0737) (0.0882) (0.0837) (0.0978) School size (61.80) (90.18) (30.72) (22.16) Class size (9.919) (7.454) (10.22) (7.366) Share indigenous students (0.397) (0.347) (0.388) (0.355) Principal has grad school (0.385) (0.440) (0.254) (0.393) Locality Population 10,476 44, ,175 (67,127) (195,285) (875.9) (51,629) Hours to state capital (1.964) (1.460) (2.324) (1.595) Locality Poverty Rate (0.148) (0.170) (0.132) (0.152) Share hhs electricity (0.237) (0.124) (0.273) (0.159) Observations 139 1, Notes: All results are for Grade 9th outcomes.

23 Past Cheating Predicts Current Cheating (1) (2) 2010 Flagged Exams (%) VARIABLES coe. se Mean Flagged Exams 0.01%-10% *** ( ) Mean Flagged Exams 10%-20% * (0.0234) Mean Flagged Exams 20%-100% *** (0.0257) Math 9 Lag ( ) Spanish 9 Lag (0.0100) Final Enrollment Grade 9 Lag (0.0491) Math 9 Lag ( ) Spanish 9 Lag ( ) Final Enrollment Grade 9 Lag (0.0403) Class size Lag ( ) Students Lag e-05 (4.75e-05) Share indigenous students Lag (0.0120) Principal has grad school Lag ( ) Locality Poverty Rate (0.0340) Hours to state capital ( ) Locality Population -5.76e-09 (1.69e-08) Share hhs electricity (0.0259) Share hhs sewage (0.0171) Constant (0.0548) Observations 1,118 R-squared State Fixed Eects Yes Robust standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1

24 DID No-Cheating Schools: other outcomes (checks) (1) (2) (3) (4) VARIABLES Share Indigenous Students Share Females Principal is normal grad Principal has grad school Panel A. All Schools Share Test Teachers (0.0506) (0.0266) (0.133) (0.0569) New Teachers (0.0144) ( ) (0.0328) (0.0221) Constant 0.152*** 0.479*** 0.455*** ** (0.0240) (0.0112) (0.0457) (0.0433) Observations 3,591 3,591 3,591 3,591 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Panel B. Small Schools Share Test Teachers (0.0490) (0.0304) (0.141) (0.0559) New Teachers (0.0273) (0.0122) (0.0594) (0.0355) Constant 0.119*** 0.495*** 0.448*** 0.108*** (0.0255) (0.0145) (0.0567) (0.0392) Observations 2,399 2,399 2,399 2,399 R-squared Number of id Time-varying covariates Yes Yes Yes Yes State*Year Dummies Yes Yes Yes Yes Mean Control in Notes: Results in Panel A include schools in the sample with no reported agged exam in the period and in Panel B schools which in addition have 3 groups or less. Source: Enlace and school census data and Registro Maestros Standard errors in parentheses are clustered at the school level. *** p<0.01, ** p<0.05, * p<0.1