Empowering Parents to Improve Education

Paul Gertler*Harry Patrinos**

Marta Rubio-Codina***

*UC Berkeley, **The World Bank, ***UCL and IFS

20 June 2009

School Based Management (SBM)

Shift responsibility and decision-making powers to end users: parents, teachers, school committees (World Bank, 2007)

SBM reforms can take many different forms:who they devolve decision making powerlevel of decision making they devolve

SBM long popular in the US, UK, Australia and Canada

Developing countries (El Salvador, Nicaragua, Kenya, Nepal, Paraguay, Mexico, Indonesia, etc.)

AID agencies (e.g. WDR 2004)

DebateIncreased participation (voice) of local agents

may result in more effective policies:Allocations better meet local needs and preferences

Better performance through monitoring & accountability

SBM may not improve school quality if:parents lack the ability to make their voices heard,local elites can capture public resources,end users are less technically able to administer

schools. Galiani, Gertler and Schargrodshy (2008)

SBM Evidence in Developing Countries

Most evidence has technical problems

Two exceptions (Mexico and Kenya):

Shapiro and Skoufias (2005) & Murnane et al. (2006) – DID estimates of Mexican PEC (School Quality Program)

Duflo et al (2007) – RCT in Kenya that combines empowering local school committees with contract teacher hiring

Both SBM programs involve many endline users with control over lots of school functions

This Paper: Role of ParentsExamine policy to increase participation of parentsin school matters in rural Mexico: the AGE

1. Does this result in a better and more conducive learning environment for students?

create pressure to influence school management & form of decision making to favor students parent focus groups and school principal interviews

1. Does this improve learning outcomes?impacts on intermediate school quality indicators - grade failure, repetition and intra-year dropout

Outline of the TalkThe AGE Intervention

Mechanisms

Qualitative Evidence on Increased Parental Participation

Quantitative Evidence on Improved Learning Estimation and IdentificationResults

Threats to Identification

Conclusions

7

AGE: Apoyo a la Gestión Escolar

Monetary Grants to Parents Associations US $500-$700 a year depending on school sizeCannot spend money on teacher compensation

or hire new teachers; cannot design curriculumMostly spent on infrastructure and small civil

works

Parents trained in management of the funds transferredparticipatory skills (in school activities)information on measuring student achievements ways parents can help improve learning

AGE role outAGE started in 1996 in rural Mexico

Part of 1992 Compensatory Education Program:Series of interventions designed to strengthen

disadvantaged schools to reduce school inequalitiesSupplies, equipment, teacher training & incentivesExpanded enrollment over time starting in most

disadvantaged areas Broader reform to decentralize educational services

from the federal to the state level

Currently covering 46% of primary schoolsNot a small efficacy trial – at scale

PathwaysPTAs exist by law but are rather dysfunctionalAGE creates a need and a right for parents to:

access schools decide on the allocation of the funds and manage themdirectly participate in the infrastructure works

Parents spend more time in schoolmonitor school activities/performance (e.g. teacher absent,

quality of teaching, children’s attention levels, etc)

better able to voice opinions on school policy and the allocation of resources

AGE may improve communication channel between parents and teachers, and overall school climate

Empowers parents by improving parental “identity”

(Akerlof and Kranton 2000, 2005)

Increased Parental Participation?

Parents focus groups in 10 benef & non-benef schools:improve school maintenance and suppliesfacilitate dialogue amongst parents and with teachers and

directors & promote community participation

motivate teachers & parents to follow students more closelygreater teacher effort: longer hours to help low performing

students

Survey of 100 Principals report that parents:Are more interested in their children’s academic performance

(40%); and interact more with teachers (30%) main change

complain if teachers are absent (82%)accept their children’s poor performance (78%) and

take responsibility (53%)

Better learning environment improved learning outcomes?

Impact on Increased LearningQuasi-Experimental Approach:

Exploit phased rollout of AGE for identification

Treatment Schools: schools intervened between 1998 - 2001

Comparison Schools: schools not (yet) intervened by 2002

AGE Treatment 2,194 (41%)

AGE Comparison 3,158 (59%)

Total Number of Schools 5,352

• Sample of Non-Indigenous Rural Primary Schools

Identification StrategyCompare early to late adopter (benef)

schools

Dif-in-Dif estimation: school FE (s)changes in outcomes as a function of changes in

treatment statusRemove time invariant characteristics that may

vary between early and late beneficiary schools (wealthier, more motivated staff, etc.)

Learning outcomes:Grade failure, grade repetition intra-year drop out rates

Empirical SpecificationEvaluation Period: t = 1998 -2001; Baseline: t=1997

AGE Treatment:Dummy =1 if intervention yearHeterogeneous effects:

by number of periods school has received AGE by grade

School-grade-year observations

Ygst s g t lt ttt EverAGEst

+ 1AGEs ,t1 kXskt gstk2

K

Estimation and IdentificationGrade, Year and School Fixed Effects

control for time invariant unobserved heterogeneity

State * Year Dummies: st demographic trends, changes in govt preferences, etc.

Treatment-Specific Time Trends different evolutions b/w treatment & control schools over time

Time Varying School Characteristics: student-teacher ratio, average class crowding index, other

programs

Robust SE Clustered at the School allow heteroskedasticity and serial correlation within a school

and over time

Treatment Effects: Grade Failure

Model A Model B Model C Model D

AGE =1-0.005*

(0.002)

1 Year of AGE =1-0.005*

(0.002)

> 1 Year of AGE =1-0.006*

(0.003)

AGE * Grade 1 & 2 & 3 =1-0.010*

(0.002)

AGE * Grade 4 & 5 =10.002

(0.002)

AGE 1 Year*Grade 1&2&3=1-0.010*

(0.002)

AGE 1 Year*Grade 4&5=10.001

(0.002)

AGE > 1 Year*Grade 1&2&3=1

-0.011*

(0.003)

AGE 1 > Year*Grade 4&5=10.002

(0.003)

Number of Obs. 133,800 133,800 133,800 133,800

Number of Schools 5,352 5,352 5,352 5,352

Mean Dep. Var. 0.10 0.10 0.10 0.10LS regressions with school FE and robust SE clustered at the school level in parenthesis. Grade, year, state*year dummies, treatment specific trend and time varying school characteristics included; *significant at the 5 percent

Treatment Effects: Grade Repetition

Model A Model B Model C Model D

AGE =1-0.004*

(0.002)

1 Year of AGE =1-0.004*

(0.002)

> 1 Year of AGE =1-0.003

(0.003)

AGE * Grade 1 & 2 & 3 =1-0.008*

(0.002)

AGE * Grade 4 & 5 =10.003

(0.002)

AGE 1 Year*Grade 1&2&3=1-0.007*

(0.002)

AGE 1 Year*Grade 4&5=10.002

(0.002)

AGE > 1 Year*Grade 1&2&3=1

-0.007*

(0.003)

AGE 1 > Year*Grade 4&5=10.004

(0.003)

Number of Obs. 133,800 133,800 133,800 133,800

Number of Schools 5,352 5,352 5,352 5,352

Mean Dep. Var. 0.10 0.10 0.10 0.10LS regressions with school FE and robust SE clustered at the school level in parenthesis. Grade, year, state*year dummies, treatment specific trend and time varying school characteristics included; *significant at the 5 percent; **significant at the 1 percent

Intra-Year Drop Out

Model A Model B Model C Model D

AGE =10.002

(0.002)

1 Year of AGE =10.002

(0.002)

> 1 Year of AGE =10.002

(0.002)

AGE * Grade 1 & 2 & 3 =1-0.003

(0.002)

AGE * Grade 4 & 5 =10.003

(0.002)

AGE 1 Year*Grade 1&2&3=10.001

(0.002)

AGE 1 Year*Grade 4&5=10.003

(0.002)

AGE > 1 Year*Grade 1&2&3=1

0.001

(0.002)

AGE 1 > Year*Grade 4&5=10.003

(0.002)

Number of Obs. 133,800 133,800 133,800 133,800

Number of Schools 5,352 5,352 5,352 5,352

Mean Dep. Var. 0.04 0.04 0.04 0.04

LS regressions with school FE and robust SE clustered at the school level in parenthesis. Grade, year, state*year dummies, treatment specific trend and time varying school characteristics included

Threats to IdentificationEndogenous Program Placement Bias

AGE are non-randomly allocated - balanced pre-intervention trends &

FE: school, grade, state * year, Treatment specific trend

Other Education Interventions Coexisting in School or AreaTime * State dummies – local policy & economic changesexplicit controls: Oportunidades, other Compensatory Education

interventions, Carrera Magisterial scheme results are not affected

Student Learning vs. Parental Pressure< 3% reported parents demanded failed students be allowed to

pass.83% reported parents accepted result and 53% take

responsibility

Student – teacher sorting to/from AGE schools: no evidence

ConclusionsAGE associated with reduction in grade

repetition and grade failure rates in earlier grades (7 to 8 percent decrease)Measures associated with poorer test performance

& higher drop out in later years

Same effect sizes as OPORTUNIDADESVery cheap

Channels seem to be that AGE… …increases parental participation …improves communication b/w parents and

teachers …increases attention to student performance …increased teacher effort

Evaluation of a public program implemented at scale (external validity?)

Limitations and next stepsLimited school outcomesDespite a scaled program

Non-indigenous schoolsRural communitiesPrimary schoolsLATE

New evaluation just coming out of fieldSecondary schoolsCross-over design with demand-side

(OPORTUNIDADES)Randomized design

Thank you!

Extra Slides

Data SourcesData Sources

CONAFE admin data on AGE coverage (1996 - 2002)

School Census data, Censo Escolar 911 (1995 – 2003)

2000 Mexican Census & 1995 ConteoAdmin data on coexisting educational

interventions

Two Types of Analysis

Dif in Dif estimates of impact on school outcomes: failure, repetition and dropout

Qualitative analysisParent focus groups in 10 beneficiary and non-

beneficiary schoolsSchool director surveys in 115 beneficiary

schools

0.2

.4.6

De

nsity

4 6 8 10 12Targeting Index

CONAFE Treatment Schools CONAFE Control Schools

CONAFE Treatment and CONAFE Control SchoolsFigure 1: Distribution of the 2000 Targeting Index

Increased Parental Participation –most important change

The most important change induced by increased parental participation

30

40

30

0

5

10

15

20

25

30

35

40

45

Better interactin with teachers More interested in the school More interested in children's academicprogress

Parental Attitude Towards Teacher Absenteeism

Parents' attitudes about teacher absenteeism

4

14

82

0

10

20

30

40

50

60

70

80

90

Other Not interested in this issue Complain if teachers are absent

Do parents pressure teachers to pass their children?

Parental attitudes about prospects their child will repeat a year or receive very poor grades

23

7

10

25

53

0

10

20

30

40

50

60

Don't know Do not accept andpressure teacher

Other Accept it but blameteacher

Accept it Accept it and takeresponsibility

DID Identifying Assumption Pre-intervention trends are equal between treatment

and control schools: t’ = 1995 -1997

EverAGEs*Yeart’=1 if s is a potential treatment school (i.e. receives AGE for all or some of the treatment years)

t’ =0 is a test of equality of pre-intervention trendsbetween treatment and comparison schools

K

kstsktk

tts

tttt

ltgsgst

uXYREverAGEYR

Y

2''

''

''''

''

*

Balanced Pre-Intervention Trends

Failure Rate

Failure Rate

Repetition Rate

Repetition Rate

Intra Drop

Out Rate

Intra Drop

Out Rate

Ever AGE * Year 1996 =10.005*

(0.002)

0.002

(0.002)

0.001

(0.002)

Ever AGE * Year 1997 =1-0.001

(0.003)

-0.001

(0.003)

0.001

(0.002)

Ever AGE * Year 1996 *

Grade 1 & 2 & 3 =1

0.003

(0.003)

0.000

(0.003)

-0.001

(0.002)

Ever AGE * Year 1996 *

Grade 4 & 5 =1

0.008*

(0.003)

0.005

(0.003)

0.003

(0.002)

Ever AGE * Year 1997 *

Grade 1 & 2 & 3 =1

-0.004

(0.003)

-0.004

(0.003)

-0.001

(0.002)

Ever AGE * Year 1997 *

Grade 4 & 5 =1

0.005

(0.003)

0.003

(0.003)

0.004

(0.002)

Number of Observations 80,280 80,280 80,280 80,280 80,280 80,280

Number of Schools 5,352 5,352 5,352 5,352 5,352 5,352

LS regressions with school FE and robust SE clustered at the school level in parenthesis. Grade, year, state*year dummies and time varying school characteristics included; *significant at the 5 percent

Student/Teacher Sorting to Better Schools

Total Student

Enrolment

Total Student

Enrolment

Student Teacher

Ratio

Student Teacher

Ratio

Prop High Edu

Teachers

Prop High Edu

Teachers

AGE =10.045

(0.071)

0.151

(0.162)

0.000

(0.009)

AGE 1 Year =10.050

(0.073)

0.128

(0.163)

-0.000

(0.009)

AGE > 1 Year =10.176

(0.144)

-0.462

(0.263)

-0.013

(0.014)

Number of Observations 113,800 113,800 113,800 113,800 113,800 113,800

Mean Dep Var 23.65 23.65 26.05 26.05 0.53 0.53

Changes in the denominator: better/worse students/teachers attracted to treatment schools

No changes on total enrolment nor the prop of higher qualified teachers in AGE schools over evaluation period