Local electoral effects of conditional cash transfers in brazil ross van horn
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Local electoral effects of conditional cash transfers in brazil ross van horn

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The presentation analyses the impact of Bolsa Família programme expansion on strengthening the political positions of incumbent politicians at the municipal level.

The presentation analyses the impact of Bolsa Família programme expansion on strengthening the political positions of incumbent politicians at the municipal level.

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Local electoral effects of conditional cash transfers in brazil ross van horn Local electoral effects of conditional cash transfers in brazil ross van horn Presentation Transcript

  • LOCAL ELECTORAL EFFECTS OF CONDITIONAL CASH TRANSFERS IN BRAZIL Ross Van Horn INSTITUTE OF LATIN AMERICAN STUDIES THE LYNDON B. JOHNSON SCHOOL OF PUBLIC AFFAIRS UNIVERISTY OF TEXAS, AUSTIN
  • THEORY & BACKGROUND Bolsa Família Brazil Impact Evaluations for Bolsa Familia  Inequality (responsible for 25% drop in Gini)*  Poverty ( reduces poverty gap and extreme poverty)*  Schooling  Work  Fertility  Citizenship/social isolation  Electoral Effect
  • PROGRAM EXPANSION FOLLOWING CONSOLIDATION (2004)
  • PRIOR RESEARCH ON BOLSA FAMÍLIA ELECTORAL EFFECTS  Relationship between Bolsa recipients and Voting behavior (survey data, spatial statistical analysis, OLS etc)  (+) Hunter and Power (2007) “BF single most plausible explanation” for Lula’s reelection/largely concerned with shift in traditional voter base for the workers Party  (+) Zucco (2011&2012): Expansion of program beneficiaries increased incumbent vote share  (+)Others: Marques et al. (2009); Nicolau and Peixoto (2007); Soares and Terron (2008); Bohn (2011)  (-) “It’s the economy companheiro!” Shikida et al.  National Focus: Lula, Lula e Lula
  • PROGRAM EXPANSION FOLLOWING CONSOLIDATION (2004)
  • Municipal Mayor Policy Variables GDP Log transformation B>0 Age Age in Years B>0 Program Expansion Program Expansion 2004-2008 (Measured as percent change of beneficiary target set for each Municipality) B>0 GDP per capita Log transformation B>0 Political Party Political Party of Candidate B≠0 Size of Program Beneficiary Families as percent of All Families in Municipality B>0 Federal Transfers Log transformation B>0 Vote Share Share of Valid Votes in 2004 B>0 Program Expansion X Size of Program Interaction of two Policy Variables B>0 Regions South Southeast Northeast Dummy South B≠0 Southeast B≠0 Northeast B>0 Education Dummy 3 Levels : Primary, Secondary and College Area ( sq. km) Area of Municipality B>0 Density Population / Area sq Km B<0 Human Development Index B>0 IDF (vulnerability index) B<0 Variable Definitions and Measurement
  • LOGISTIC FUNCTION; ELECTION OUTCOMES AS DEPENDENT VARIABLE  P(Re-election|X)= (Municipal Characteristics)  b1 + b2 GDP (log) + b3 GDP per capita (log) + b4 Federal Transfers (log) + b5 Southeast (dummy) + b6 Northeast (dummy) + b7 Area (sq. Km) + b8 Density + b9 HDI + b10 Population + b11 IDF +(Mayor)  b12 i.Political Party (dummy) + b13 Edu medium (dummy) + b14 Edu High (dummy) + b15 Age + b16 Vote Share (2004) +(Policy variables)  b17 Program Expansion + b18 Size of Program + b19 Program Expansion*Size of Program + u
  • LIMITATIONS  Aggregated indicators (ecological fallacy, at best)  Electoral Effect: Political Perception (politician) vs. Voting (citizens).  Inertia: retrospective voting and the problem of time  Economic Performance: Municipal GDP, a second best proxy  Omitted Variables: Characteristics of the Electorate, campaign expenses, performance in other social programs etc.
  • exp_szfam 1.196966 .0942477 2.28 0.022 1.025792 1.396704 bffamtotal~m .1626049 .1896481 -1.56 0.119 .0165336 1.599191 abs0408 .9256862 .031741 -2.25 0.024 .8655194 .9900354 votshar 1.017376 .0063333 2.77 0.006 1.005039 1.029865 age .985457 .005953 -2.43 0.015 .9738582 .9971939 edu_high 1.042893 .1745579 0.25 0.802 .7512199 1.447814 edu_med 1.153074 .2018277 0.81 0.416 .8182152 1.624975 70 .0805633 .1618678 -1.25 0.210 .00157 4.134019 65 .5840059 .7274309 -0.43 0.666 .0508359 6.709094 45 .5474608 .4160588 -0.79 0.428 .1234405 2.427998 44 .5031131 .697619 -0.50 0.620 .0332189 7.619836 43 .7404935 .7521364 -0.30 0.767 .1011431 5.421337 40 .3428128 .2769108 -1.33 0.185 .0703866 1.669646 36 (empty) 33 .2363716 .3178845 -1.07 0.283 .0169378 3.298634 31 (empty) 28 (empty) 27 (empty) 25 .3211535 .2461758 -1.48 0.138 .0714885 1.442744 23 .2636301 .2156493 -1.63 0.103 .0530533 1.310018 22 .4098039 .3181435 -1.15 0.251 .0894874 1.876681 20 .109494 .1020998 -2.37 0.018 .0176064 .6809412 19 (empty) 17 .4517115 .5946711 -0.60 0.546 .034219 5.962861 15 .3183329 .2396896 -1.52 0.128 .0727725 1.392502 14 .3702832 .2864181 -1.28 0.199 .0813056 1.68635 13 .5281267 .4075911 -0.83 0.408 .1163612 2.396999 12 .4046828 .3202066 -1.14 0.253 .085822 1.90823 11 .4319633 .3314029 -1.09 0.274 .0960293 1.943077 party idf 2.510818 6.339796 0.36 0.715 .0178049 354.0726 logpop_07 (omitted) hdi_mun .1259456 .2898129 -0.90 0.368 .0013851 11.45197 density .9993502 .0003111 -2.09 0.037 .9987407 .9999602 area_tot .9999103 .000067 -1.34 0.181 .9997789 1.000042 r3 1.075653 .3484073 0.23 0.822 .5701186 2.029452 r2 1.023761 .1607611 0.15 0.881 .7525484 1.392716 logtransfers 2.042127 .6683006 2.18 0.029 1.075279 3.878328 loggdp_pc 1.241298 .29253 0.92 0.359 .7821285 1.970036 loggdp .6888662 .1416914 -1.81 0.070 .4603129 1.0309 elect Odds Ratio Std. Err. z P>|z| [95% Conf. Interval] Robust Log pseudolikelihood = -864.19984 Pseudo R2 = 0.0328 Prob > chi2 = 0.0093 Wald chi2(33) = 55.07 Logistic regression Number of obs = 1467 Explanatory Variables Vote Share 2004 -Odds Ratio 1.01 -Z Statistic: 2.77 Program Expansion & Program Size (interaction) -Odds Ratio : 1.19 -Z Statistic: 2.28 Program Expansion -Odds Ration .92 -Z Statistic -2.25 Goodness of fit -Hosmer and Lemeshow chi2 = 0.7153
  • PROGRAM EXPANSION & PROGRAM SIZE (CONTINUOUS BY CONTINUOUS INTERACTION 10 .0227073 .0112094 2.03 0.043 .0007373 .0446773 9 .0193125 .0090796 2.13 0.033 .0015169 .0371082 8 .0154817 .0071701 2.16 0.031 .0014286 .0295348 7 .0113654 .0053619 2.12 0.034 .0008563 .0218744 6 .0071328 .0036332 1.96 0.050 .0000117 .0142538 5 .0029571 .0021595 1.37 0.171 -.0012754 .0071896 4 -.0010005 .0016654 -0.60 0.548 -.0042646 .0022637 3 -.0045941 .0025044 -1.83 0.067 -.0095026 .0003144 2 -.0077118 .0036706 -2.10 0.036 -.0149061 -.0005175 1 -.0102893 .004804 -2.14 0.032 -.0197049 -.0008737 _at abs0408 dy/dx Std. Err. z P>|z| [95% Conf. Interval] Delta-method -.02 0 .02.04 EffectsonPr(Elect) .1 .2 .3 .4 .5 .6 .7 .8 .9 1 bffamtotalfam Average Marginal Effects of abs0408 with 95% CIs -* n/a +* Program Size= 10-30 percent negative statistical influence Program Size= 40-60 percent no significance Program Size=70-100 percent Strong and positive influence At 80 Percent coverage, a one percent increase in coverage increases probability by almost 2 Percent
  • MODEL COMPARISON: ROC CURVES 0.000.250.500.751.00 0.00 0.25 0.50 0.75 1.00 False Positive Rate Municipal Model area: 0.6092 Bolsa Familia Model area: 0.6276 Reference . chi2(1) = 5.53 Prob>chi2 = 0.0187 Ho: area(xb30) = area(xb40) xb40 1468 0.6276 0.0160 0.59627 0.65895 xb30 1468 0.6092 0.0160 0.57785 0.64059 Obs Area Std. Err. [95% Conf. Interval] ROC Asymptotic Normal . roccomp elect xb30 xb40, graph summary
  • CONCLUSIONS  The electoral effect associated with program expansion in 2006 for national elections is mirrored at the local level.  Ecological Fallacy: Mayors expand the program had a higher probability of being re-elected. CANNOT link to individual voters or beneficiaries of Bolsa Familia. Municipal-level Correlation  Mayors were rewarded for good governance. The electoral effect can come from beneficiaries or from non-beneficiaries who see expansion as beneficial in their municipality. Zucco (2011): “Solidarity effect” Non beneficiaries who knew a program participant 18 percent more likely to vote for Lula Higer program coverage at municipal level increases non-beneficiary vote for Lula by 30 percent
  • THANK YOU