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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 
푄휏 푦푚푡 푥푚푡 
= 푥푚푡 
′ 훽휏 + 휓휏 
푡 + 푠푒푡 표푓 푑푖푠푡푟푖푏푢푡푒푑 푙푎푔푠 
푦푚푡 = 퐿퐴푚,푡 , 푆퐹푚,푡 , 푆푀푚,푡 
푥푚,푡 = 퐵퐹푚,푡 , 푋푂푚,0 
푋푂푚,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 (%) 
.1 .2 .3 
0 
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 
Quantiles 
OLS 
QRPD
RESULTS: MALE SCHOOL ATTENDANCE (%) 
.1 .2 
-.1 
0 
OLS 
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 
Quantiles 
QRPD
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.
FOLLOWING STEPS AND FUTURE 
RESEARCH 
 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.
RESULTS: LABOR SUPPLY (%) 
.1 .2 
-.1 
0 
QRPD 
0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 
Quantiles 
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.
WORKING PAPER: 
 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/

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Methodology: Quantile regression panel data for Bolsa Familia

  • 1. METHODOLOGICAL PRESENTATION QUANTILE REGRESSION PANEL DATA FOR BOLSA FAMILIA SOCIAL TRANSFERS Armando Barrientos & Dario Debowicz BWPI, University of Manchester
  • 2. 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.
  • 3. 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).
  • 4. EMPIRICAL MODEL 푄휏 푦푚푡 푥푚푡 = 푥푚푡 ′ 훽휏 + 휓휏 푡 + 푠푒푡 표푓 푑푖푠푡푟푖푏푢푡푒푑 푙푎푔푠 푦푚푡 = 퐿퐴푚,푡 , 푆퐹푚,푡 , 푆푀푚,푡 푥푚,푡 = 퐵퐹푚,푡 , 푋푂푚,0 푋푂푚,0: the other covariates.
  • 5. 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.
  • 6. RESULTS: FEMALE SCHOOL ATTENDANCE (%) .1 .2 .3 0 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles OLS QRPD
  • 7. RESULTS: MALE SCHOOL ATTENDANCE (%) .1 .2 -.1 0 OLS 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles QRPD
  • 8. 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.
  • 9. 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.
  • 10. FOLLOWING STEPS AND FUTURE RESEARCH  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.
  • 11. RESULTS: LABOR SUPPLY (%) .1 .2 -.1 0 QRPD 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles 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.
  • 12. WORKING PAPER:  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/

Editor's Notes

  1. PNAD=Pesquisa Nacional por Amostra de Domicilios, National Survey of Residences
  2. 𝑄 𝜏 𝑦 𝑚𝑡 𝑥 𝑚 are the conditional quantiles of the response variable 𝑦 𝑚𝑡 , 𝑥 𝑚𝑡 ′ is a row vector of covariates of municipalities 𝑚 at time 𝑡. 𝜓 𝜏 𝑡 , 𝑦𝑒𝑎𝑟 𝑠𝑝𝑒𝑐𝑖𝑓𝑖𝑐 𝑖𝑛𝑡𝑒𝑟𝑐𝑒𝑝𝑡𝑠. Linear function. 𝛽 𝜏 denotes a time-invariant effect column vector by which the covariates effect the conditional quantiles of the observables above and beyond the effects that work through the unobservables, the 𝛽 𝜏 corresponding to 𝐵𝐹 being our main focus of interest. 𝜓 𝜏 𝑡 is a location shift in the conditional quantiles and the last generic term 𝑥 𝑚𝑡 ′ λ 𝜏 𝑡 captures the effects of the unobservables into the conditional quantiles at the different times 𝑡, with the unobservables being a linear projection onto the observables. 𝐿𝐴 stands for adult labour participation rate in the working-age population (18-64 years olds); 𝐿𝐴𝐼 𝑚,𝑡 denotes the share of informal employment in the population of working age, where informality includes all those reporting having worked in the week previous to the survey but no in formal employment (contribution to social security).
  3. We report on the estimated effects of municipality coverage of Bolsa Familia on the distribution of labour outcome variables for the working-age population, the 𝛽 𝜏 associated with Bolsa Familia coverage. Figure shows the estimates for the effects of Bolsa Família on adult labour force participation rates. To facilitate comparison of our results with those in Ribas and Soares (2011), we can look at the median in the distribution, both are measures of central tendency. In contrast to the non-statistically significant effects of Bolsa Família on adult labour supply found by Ribas and Soares, we observe a positive and statistically significant effect that for the municipality in the median of adult labour supply participation rate. The estimate suggests that adult labour supply increases by 0.09 percentage point for each percentage point increase in programme coverage. We also observe that above-median municipalities have lower effects. Overall, we find that Bolsa Familia tends to reduce the spread of labour participation rates among municipalities We plan to test this hypothesis formally.
  4. We report on the estimated effects of municipality coverage of Bolsa Familia on the distribution of labour outcome variables for the working-age population, the 𝛽 𝜏 associated with Bolsa Familia coverage. Figure shows the estimates for the effects of Bolsa Família on adult labour force participation rates. To facilitate comparison of our results with those in Ribas and Soares (2011), we can look at the median in the distribution, both are measures of central tendency. In contrast to the non-statistically significant effects of Bolsa Família on adult labour supply found by Ribas and Soares, we observe a positive and statistically significant effect that for the municipality in the median of adult labour supply participation rate. The estimate suggests that adult labour supply increases by 0.09 percentage point for each percentage point increase in programme coverage. We also observe that above-median municipalities have lower effects. Overall, we find that Bolsa Familia tends to reduce the spread of labour participation rates among municipalities We plan to test this hypothesis formally.
  5. We report on the estimated effects of municipality coverage of Bolsa Familia on the distribution of labour outcome variables for the working-age population, the 𝛽 𝜏 associated with Bolsa Familia coverage. Figure shows the estimates for the effects of Bolsa Família on adult labour force participation rates. To facilitate comparison of our results with those in Ribas and Soares (2011), we can look at the median in the distribution, both are measures of central tendency. In contrast to the non-statistically significant effects of Bolsa Família on adult labour supply found by Ribas and Soares, we observe a positive and statistically significant effect that for the municipality in the median of adult labour supply participation rate. The estimate suggests that adult labour supply increases by 0.09 percentage point for each percentage point increase in programme coverage. We also observe that above-median municipalities have lower effects. Overall, we find that Bolsa Familia tends to reduce the spread of labour participation rates among municipalities We plan to test this hypothesis formally.