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Methodology: Quantile regression panel data for Bolsa Familia

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Dario Debowicz explains the methodology behind his analysis of the impact of Bolsa Familia transfers within different municipalities in Brazil. Read the full research at: http://bit.ly/1plN4yI

Published in: Education
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Methodology: Quantile regression panel data for Bolsa Familia

  1. 1. METHODOLOGICAL PRESENTATION QUANTILE REGRESSION PANEL DATA FOR BOLSA FAMILIA SOCIAL TRANSFERS Armando Barrientos & Dario Debowicz BWPI, University of Manchester
  2. 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. 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. 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. 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. 6. RESULTS: FEMALE SCHOOL ATTENDANCE (%) .1 .2 .3 0 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles OLS QRPD
  7. 7. RESULTS: MALE SCHOOL ATTENDANCE (%) .1 .2 -.1 0 OLS 0 .1 .2 .3 .4 .5 .6 .7 .8 .9 1 Quantiles QRPD
  8. 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. 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. 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. 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. 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/

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