METHODOLOGICAL

PRESENTATION

QUANTILE REGRESSION PANEL

DATA FOR BOLSA FAMILIA SOCIAL

TRANSFERS

Armando Barrientos & Dario Debowicz

BWPI, University of Manchester

CURRENT RESEARCH

The effects of Bolsa Familia social transfers in Brazil on labour supply and school attendance (DFID IRIBA Phase I).

Goal is to analyse the distribution of program outcomes across municipalities in Brazil using quantile regressions, which have not been used in this context...

taking into account the indirect effects of the program on beneficiaries and non-beneficiaries.

controlling for time-invariant conditions at the municipality level.

(partially) addressing the issue of endogeneity in program assignment.

Bolsa Familia is Brazil’s flagship social protection program. Its national budget (0.5% GDP) is allocated among municipalities, mainly as a function of the distribution of pre-program poverty, based on household data. Municipalities then allocate the transfers at the household level. The transfers are conditional on children’s school attendance and health check-ups.

METHODOLOGY AND DATA

Quantile regressions panel data for short period following Abrevaya & Dahl (2008), on municipality-level data.

Our database is constructed using an annual cross-section of a nationally representative household-level survey(PNAD), focusing on the municipalities for which PNAD is representative (273).

We use seven waves to build our panel, from the start of the program (2003) to the last year when the set of surveyed municipalities remains unchanged (2009).

Among the covariates, we include pre-program poverty data used by the Brazilian government to allocate the program quotas among municipalities (PNAD 2001).

Households benefiting from the program are identified via a specific question in the survey (when possible) or via typical transfer values (otherwise).

EMPIRICAL MODEL

𝑄𝜏 𝑦𝑚𝑡 𝑥𝑚𝑡

= 𝑥𝑚𝑡′ 𝛽𝜏 + 𝜓𝜏

𝑡 + 𝑠𝑒𝑡 𝑜𝑓 𝑑𝑖𝑠𝑡𝑟𝑖𝑏𝑢𝑡𝑒𝑑 𝑙𝑎𝑔𝑠

𝑦𝑚𝑡 = 𝐿𝐴𝑚,𝑡 , 𝑆𝐹𝑚,𝑡 , 𝑆𝑀𝑚,𝑡

𝑥𝑚,𝑡 = 𝐵𝐹𝑚,𝑡 , 𝑋𝑂𝑚,0

𝑋𝑂𝑚,0: the other covariates.

PRECISION OF PARAMETER ESTIMATES

Usual bootstrap procedures to calculate standard

errors imbedded in Stata are not consistent for this

method (Abrevaya and Dahl, 2008).

Starting with Abrevaya and Dahl’s bootstrap

estimation procedure, we extend it to include more

than two years and time-invariant regressors, such

as pre-program poverty at municipality level, using

Stata.

RESULTS: FEMALE SCHOOL ATTENDANCE (%) 0

.1.2

.3

Ch

an

ge

(p

.p.)

fo

r 1

p.p

. in

crea

se in

pro

gra

m in

cide

nce

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles

OLS

QRPD

RESULTS: MALE SCHOOL ATTENDANCE (%) -.

10

.1.2

Ch

an

ge

(p

.p.)

fo

r 1

p.p

. in

crea

se in

pro

gra

m inci

de

nce

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles

QRPD

OLS

FINDINGS (1 OF 2) The estimated effect of the municipal coverage of Bolsa Família on the

distribution of female school attendance among conditional quantiles of

outcome indicates significant heterogeneity, in contrast to OLS results.

While point estimates are positive for all conditional quantiles analysed, the

effect is statistically significant (with a ten percent significance level) from

quantile .10 to quantile .40 of the outcome distribution. The maximum is

achieved in quantile .10, where a less than 8 (0.1268-1) percentage point

(p.p.) increase in program incidence is needed to “buy” a p.p. of female

school attendance. This contrasts sharply with the median municipality,

where more than 28 (0.0349-1) p.p. of program incidence are needed for the

same target, or with municipalities in the top of the distribution, where the

program leads to practically no school attendance increases.

FINDINGS (2 OF 2) A formal test of the uniformity of Bolsa Família effects on female school

attendance among quantiles, which we conduct by extending the analysis of

AD, rejects the null of uniformity even at one percent level of significance.

At the same significance level, a test of significance of the correlated random

effects rejects the null of insignificance. This suggests that unobservables

affecting female school attendance may be captured, in part, by repeated

observations on the program incidence, and that results not accounting for

them could lead to a significant bias in the estimation of program effects.

A test of overall significance of the regression does not reject the null of lack

of significance, suggesting significant variation of school attendance was left

unexplained.

With the notable exception of the bottom quantile analysed, the program has

worked to reduce differential outcomes across the conditional schooling

distribution of municipalities. This adds to the evidence in support of Bolsa

Família’s contribution to inclusive growth.

Try to include 2000 census poverty, yearly Bolsa Familia benefits, and

other municipal-level information in our database, for which we are trying

to get the names of the municipalities in PNAD from the Brazilian statistical

office (IBGE).

Future research could look into other program outcomes of interest.

FOLLOWING STEPS AND FUTURE

RESEARCH

RESULTS: LABOR SUPPLY (%)

-.1

0.1

.2

Ch

an

ge

(p

.p.)

fo

r 1

p.p

. in

cre

ase in p

rogra

m incid

ence

0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1Quantiles

OLS

Source: Authors’ quantile regression panel data and OLS regressions. For

quantile regressions, point estimates and 90% and 95% confidence

intervals are included. Municipal cluster standard errors are reported in

parenthesis. All specifications include all regressors as listed in Table A1 in

the Appendix. The sample size is 1,911 municipal-level cases.

QRPD

Antipoverty transfers and inclusive growth in Brazil:

http://bit.ly/1plN4yI

Other working papers from the International Research Initiative on Brazil and

Africa (IRIBA) are available at:

http://www.brazil4africa.org/publications/

WORKING PAPER: