Does Autonomous Spending Work? Evidence from Brazil´s

Direct Cash to School Program.

Joana Costa

PUC-Rio/IPEA

Claudio Ferraz

PUC-Rio

Introduction

Pure resource policies have disappointing effects on student achievement. Changing

incentives and system distortions might be key to effectiveness of increasing

resources. (Hanushek, 2006) (Kremer and Holla, 2009)

Decentralization of decision making to local levels is indicated as a possible way to

improve school efficiency.

School-based management is a strong form of decentralization and a promising route

to enhance the use of (additional) school inputs.

(Bruns, Filmer & Patrinos, 2011)

Objective

The aim of this study is to assess if an increase of monetary resources locally

managed by schools results in better educational outcomes.

We investigate the use of the resources (on materials and physical infrastructure) and

its impact on student test scores.

We also explore how the results vary with different local institutional settings, such

as more parent engagement.

OUTLINE 1. Literature Review

2. Brazilian framework: PDDE Description

3. Data and Identification strategy

4. Preliminary findings

PDDE´s effect on aggregated infrastructure indicators

PDDE´s effect on disaggregated equipment indicators

PDDE´s effect on student performance

Heterogeneous effects according to parent’s engagement

5. Conclusions

6. Next steps

1. Literature Review

Evidence on resource policies:

- General evidence is that the pure increase in school resources does not

improve school quality. (Hanushek, 2006)

- Examples of learning materials interventions with no effect on student score:

Glewwe et al (2009): book provision, Kenya.

Glewwe et al (2004): flip charts, Kenya.

Leuven et al (2007): computers & software, Netherlands.

Angrist & Lavy (2005): computer use in classrooms, Israel.

- Examples of learning materials interventions with effect on student score:

Banerjee et al (2007): computer, India.

Das el at (2011): grant for materials used by students ($3 per pupil)

resulted in a 0.10 sd increase of test scores in rural India.

Evidence on school based management and increased funding:

- Clark (2009): improvement of 0.25 sd in pass rates on standardized exams

in UK.

- Gertler, Patrinos & Rubio-Codina (2006): Monetary support ($500-$700) to parent associations´ management (AGE) reduced grade failure by 7.4% in Mexico.

- Bruns et al (2011): Gertler, Patrinos & Rodríguez-Oreggia (2010) conduct

evaluation of AGE 125- a RCT that doubled resources in AGE schools.

Preliminary findings suggest that double funding increased Spanish scores

in 5-5.6% and Math scores in 6.3-8%.

Evidence that local characteristics matters for local decision making:

- Galiani et al (2008): poor municipalities did not benefited from school

decentralization in Argentina

- Duflo, Dupas & Kremer (2012): The Extra Teacher Program in Kenya was

more effective if parents received a SBM training (teachers were less absent

and less relatives were hired)

2. Brazilian framework: PDDE Description

It provides supplementary funding for public schools to improve its physical and

pedagogical infrastructure.

The use of funds is restricted to school maintenance, to equipment´s expenses, to

pedagogical project´s implementation or to school activities´ development. It is

expressly forbidden to pay wages or taxes.

In order to receive the PDDE´s monetary support, the school must establish an

institution termed Unidade Executora (Implementing Unit). All school community

members should be represented in this association.

This organization is responsible for deciding the PDDE´s resource allocation and for

annually preparing reports for the local government level.

PDDE coverage: approximately 92% among urban primary schools and 80% for rural

primary schools, in 2010.

PDDE grant varies according to the number of students and the school region. In

2010, the median value transferred to primary urban schools was R$3,975,

approximately U$2209. For primary rural school this amount was R$646.20, nearly

U$358.

Primary urban schools which achieved its IDEB target receive an increase of 50%.

IDEB´s formula combines pass rates and test scores and it ranges from zero to ten.

For 2007 on, there are IDEB targets set for each school by the Brazilian Government.

The targets were planned with the aim of enhancing the national IDEB from 3.8 in

2005 to 6.0 in 2022.

3. Data and Identification Strategy

DATA:

PDDE´s administrative record, FNDE

- Info on PDDE amount received by each school

School census, INEP

- School characteristics variables: teacher-student ration, % of non-white

students, % of teachers with post-graduate degree,…

“Prova Brasil” microdata, INEP

- Outcome variables: Student test grades, aggregated indices constructed

for literature, materials and physical infrastructure and availability of

resources.

- Infrastructure indices were built on perceptions variables from the

principal, teachers, and survey taker about the school.

Descriptive Statistics

Table 1: 2008 PDDE characteristics for primary urban schools

median mean sd N

all:

PDDE R$ 4976.10 R$ 5847.34 2772.03 14127

PDDE/student R$ 14.17 R$ 15.10 5.12 14127

Only bonus schools:

bonus R$ 1962.60 R$ 2136.60 946.17 10278

bonus/student R$ 5.18 R$ 5.51 1.61 10278

Table 2: School´s indicators for infrastructure

2007 2009

Principal´s view: Availability of monetary resources .66 .68

Physical Infrastructure .21 .21

Equipment Infrastructure .57 .65

Teachers´view: Availability of pedagogical resources .90 .91

Availability of monetary resources .91 .91

Equipment Infrastructure .53 .69

Literature Infrastructure .97 .95

Interviewer´s view: Physical Infrastructure .81 .81

Equipment Infrastructure .64 .69

Literature Infrastructure .65 .68

Table 3: School´s indicators for equipment

2007 2009

television .92 .92

parabolic antenna .43 .39

VHS .74 .67

copy machine .41 .62

mimeo .77 .74

video projector .18 .35

slide projector .58 .62

printing machine .80 .89

sound machine .84 .86

computer .82 .89

IDENTIFICATION STRATEGY:

- Regression Discontinuity Design

- Our strategy is to compare schools that barely accomplished its IDEB target

with those that almost achieved it in order to evaluate how the extra

monetary support was allocated and if it promoted education quality.

- There is no other national/regional program that considers this same rule.

- Fuzzy regression discontinuity model for schools´ outcomes:

b b e09 08 07

i 0 1 i i iY = + PDDE + f(z ) +

09

iY : school i´s outcome in 2009

08

iPDDE : school i´s PDDE income per student received in 2008 07

iz : forcing variable (the 2007´s IDEB score minus the 2007´s IDEB target)

- TSLS estimator where the dummy variable [ 0]iD I zi is used as an

instrument for the continuous variable 08

iPDDE .

- 1st stage: 07

0 1 ( )i i iD g za a m08

iPDDE

- The key hypothesis to our identification strategy is that these two groups are

comparable since schools would not be able to precisely control their IDEB

results.

Figure 1: Density of Forcing Variable – Primary urban schools

0.2

.4.6

.8

Density

-4 -2 0 2 4 6Forcing Variable-1st Primary Cycle

- It is also key that there is a discontinuity in the distribution of PDDE income at

the cutoff value of the forcing variable.

Figure 2: PDDE value per student in 2008

10

12

14

16

18

2008 P

DD

E/s

tudent

(R$)

-2 -1 0 1 2Forcing Variable, 2007

4. Preliminary findings

PDDE´s effect on aggregated infrastructure indicators

Table 4: Principal´s indicators

OLS RDD

Linear Quadratic Cubic Linear

Principal´s view: Availability of monetary resources PDDE/student (2008) 0.418***

0.535*** 0.505** 0.560** 0.581***

(0.0712)

(0.190) (0.233) (0.268) (0.184)

11404

11404 11404 11404 11404

Physical Infrastructure PDDE/student (2008) 0.0276

0.268** 0.228* 0.0658 0.173**

(0.0337)

(0.106) (0.130) (0.149) (0.0851)

10785

10785 10785 10785 10785

Equipment Infrastructure PDDE/student (2008) 0.206***

0.450*** 0.345** 0.218 0.412***

(0.0414)

(0.120) (0.147) (0.169) (0.105)

9296

9296 9296 9296 9296

Controls: regions & number of students Y Y Y Y Y

other school characteristics controls Y N N N Y

Table 5: Teacher´s indicators

OLS RDD

Linear Quadratic Cubic Linear

Teachers´view:

Availability of pedagogical resources PDDE/student (2008) 0.374***

0.638*** 0.543** 0.492* 0.610***

(0.0750)

(0.185) (0.223) (0.257) (0.183)

8614

8614 8614 8614 8614

Availability of monetary resources PDDE/student (2008) 0.395***

0.750*** 0.774*** 0.742*** 0.728***

(0.0735)

(0.181) (0.218) (0.252) (0.179)

8614

8614 8614 8614 8614

Equipment Infrastructure PDDE/student (2008) 0.138*

0.728*** 0.785*** 0.762*** 0.544***

(0.0760)

(0.210) (0.253) (0.292) (0.185)

8614

8614 8614 8614 8614

Literature Infrastructure PDDE/student (2008) 0.0653**

0.0683 -0.0172 0.0421 0.0485

(0.0306)

(0.0762) (0.0916) (0.106) (0.0747)

8614

8614 8614 8614 8614

Controls: regions & number of students Y Y Y Y Y

other school characteristics controls Y N N N Y

Table 6: Interviewer´s indicators

OLS RDD

Linear Quadratic Cubic Linear

Interviewer´s view: Physical Infrastructure PDDE/student (2008) 0.0529

0.0206 -0.0892 -0.204 -0.00549

(0.0427)

(0.115) (0.141) (0.162) (0.108)

9352

9352 9352 9352 9352

Equipment Infrastructure PDDE/student (2008) 0.232***

0.374*** 0.287** 0.195 0.366***

(0.0431)

(0.115) (0.141) (0.163) (0.105)

8909

8909 8909 8909 8909

Literature Infrastructure PDDE/student (2008) 0.0492

0.118 0.00675 0.0858 0.123

(0.0696)

(0.175) (0.214) (0.248) (0.168)

8194

8194 8194 8194 8194

Controls: regions & number of students Y Y Y Y Y

other school characteristics controls Y N N N Y

PDDE´s effect on disaggregated equipment indicators

Table 7: Disaggregated interviewer´s indicator OLS RDD Linear Quadratic Cubic Linear

television 0.121**

0.0653 0.0586 0.130 0.0719

(0.0473)

(0.116) (0.141) (0.164) (0.115)

8771

8771 8771 8771 8771

parabolic antenna 0.362***

0.0887 -0.0323 -0.462 0.201

(0.107)

(0.291) (0.355) (0.412) (0.261)

8771

8771 8771 8771 8771

VHS 0.225**

0.586** 0.436 0.384 0.549**

(0.109)

(0.272) (0.331) (0.385) (0.265)

8771

8771 8771 8771 8771

copy machine 0.0988

0.664** 0.494 0.0985 0.442

(0.115)

(0.297) (0.362) (0.420) (0.281)

8771

8771 8771 8771 8771

mimeo 0.329***

0.270 0.296 0.422 0.365*

(0.0871)

(0.218) (0.266) (0.309) (0.212)

8771

8771 8771 8771 8771

video projector 0.108

0.377 0.301 0.541 0.321

(0.127)

(0.313) (0.381) (0.443) (0.308)

8771

8771 8771 8771 8771

slide projector 0.185*

0.337 0.114 -0.258 0.218

(0.103)

(0.304) (0.370) (0.430) (0.251)

8771

8771 8771 8771 8771

printing machine 0.141**

0.570*** 0.507** 0.459* 0.590***

(0.0670)

(0.167) (0.204) (0.237) (0.164)

8771

8771 8771 8771 8771

sound machine 0.285***

0.168 0.0300 0.0607 0.130

(0.0733)

(0.181) (0.220) (0.256) (0.178)

8771

8771 8771 8771 8771

computer 0.152**

0.455*** 0.502*** 0.427* 0.441***

(0.0625)

(0.155) (0.189) (0.220) (0.152)

8771 8771 8771 8771 8771

PDDE´s effect on student performance

Table 8: PDDE´s effect on academic achievement

OLS RDD

Linear Quadratic Cubic Linear

Math score (2009) PDDE/student (2008) -0.00144**

0.000945 0.00285 0.00363 -0.000119

(0.000656)

(0.00179) (0.00219) (0.00253) (0.00166)

14127

14127 14127 14127 14127

Portuguese score (2009) PDDE/student (2008) -0.00115*

0.00127 0.00211 0.00260 0.000411

(0.000624)

(0.00170) (0.00208) (0.00241) (0.00158)

14127

14127 14127 14127 14127

Pass rate (2009) PDDE/student (2008) 0.110***

0.0841* 0.0304 0.0187 0.0684*

(0.0161)

(0.0430) (0.0525) (0.0607) (0.0408)

14117

14117 14117 14117 14117

Controls: regions & number of students Y Y Y Y Y

other school characteristics controls Y N N N Y

Heterogeneous effect according to parent’s engagement (through

Parent Teacher Association)

Less active PTA (less than 3 meetings/year or inexistent)

X

More active PTA (3 or more meetings/year)

Table 9: Effect on infrastructure indicators Less Active PTA More Active PTA

RDD Specification Linear Quadratic Cubic Linear Linear Quadratic Cubic Linear

Principal´s view: Availability of monetary resources

PDDE/student (2008) 0.0173 0.155 -0.0593 0.110

0.863*** 0.759** 0.808** 0.855***

(0.312) (0.386) (0.464) (0.304)

(0.248) (0.304) (0.347) (0.238)

4,140 4,140 4,140 4,140

6,915 6,915 6,915 6,915 Equipment Infrastructure

PDDE/student (2008) 0.476** 0.399* 0.0534 0.397**

0.410*** 0.276 0.184 0.429***

(0.195) (0.240) (0.291) (0.172)

(0.155) (0.190) (0.218) (0.137)

3,406 3,406 3,406 3,406

5,627 5,627 5,627 5,627

Teachers´view: Availability of monetary resources

PDDE/student (2008) 0.607* 0.514 0.544 0.609*

0.811*** 0.821*** 0.726** 0.775***

(0.317) (0.402) (0.475) (0.315)

(0.225) (0.273) (0.316) (0.222)

3,070 3,070 3,070 3,070

5,097 5,097 5,097 5,097 Equipment Infrastructure

PDDE/student (2008) 0.935** 0.947** 0.771 0.709**

0.565** 0.581* 0.579 0.406*

(0.366) (0.464) (0.547) (0.324)

(0.262) (0.317) (0.367) (0.231)

3,070 3,070 3,070 3,070

5,097 5,097 5,097 5,097

Interviewer´s view: Equipment Infrastructure

PDDE/student (2008) 0.328 -0.0540 -0.313 0.275

0.324** 0.325* 0.371* 0.342***

(0.205) (0.262) (0.317) (0.183)

(0.144) (0.175) (0.205) (0.132)

2,995 2,995 2,995 2,995

5,451 5,451 5,451 5,451 Literature Infrastructure

PDDE/student (2008) -0.0338 -0.0392 0.300 0.0542

0.116 -0.0388 0.0119 0.0871

(0.291) (0.374) (0.469) (0.278)

(0.226) (0.276) (0.316) (0.219)

2,619 2,619 2,619 2,619

5,168 5,168 5,168 5,168 Controls: regions & number of students Y Y Y Y Y Y Y Y

other school characteristics controls N N N Y N N N Y

Table 10: Effect on disaggregated equipment indicators (interviewer´s view)

Less Active PTA More Active PTA

RDD Specification Linear Quadratic Cubic Linear Linear Quadratic Cubic Linear

television 0.227 0.161 0.109 0.222

-0.024 -0.018 0.112 -0.001

(0.202) (0.260) (0.314) (0.201)

(0.144) (0.176) (0.205) (0.143)

2995 2995 2995 2995

5451 5451 5451 5451

copy machine 0.986* 0.193 0.288 0.745

0.356 0.345 0.0155 0.163

(0.516) (0.660) (0.798) (0.489)

(0.370) (0.451) (0.526) (0.351)

2995 2995 2995 2995

5451 5451 5451 5451

mimeo -0.337 -0.285 -0.405 -0.241

0.533* 0.631* 0.825** 0.661**

(0.353) (0.452) (0.547) (0.346)

(0.285) (0.348) (0.406) (0.276)

2995 2995 2995 2995

5451 5451 5451 5451

video projector 0.710 0.785 0.296 0.684

0.071 -0.131 0.409 0.004

(0.518) (0.664) (0.802) (0.512)

(0.405) (0.493) (0.577) (0.398)

2995 2995 2995 2995

5451 5451 5451 5451

printing machine 0.989*** 0.351 0.006 1.027***

0.292 0.366 0.580** 0.322

(0.311) (0.396) (0.478) (0.303)

(0.200) (0.244) (0.286) (0.197)

2995 2995 2995 2995

5451 5451 5451 5451

sound machine -0.168 -0.151 -0.807 -0.172

0.304 0.274 0.424 0.296

(0.332) (0.425) (0.516) (0.325)

(0.218) (0.266) (0.311) (0.215)

2995 2995 2995 2995

5451 5451 5451 5451

computer 0.492* 0.139 -0.028 0.428

0.432** 0.584** 0.594** 0.447**

(0.285) (0.365) (0.441) (0.279)

(0.187) (0.229) (0.267) (0.185)

2995 2995 2995 2995 5451 5451 5451 5451

5. Conclusions

- PDDE´s extra cash is used for improving school materials, such as

computer and printing machine

- No impact on academic performance

- More parent´s participation leads to more visible cash spending

6. Next steps

- Explore other heterogeneous effects for different local institution

features and other school characteristics.

- Explore audit information about irregularities of PDDE for some

random chosen municipalities.