do ghosts exist? corruption in the colombian education...
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Do ghosts exist?Corruption in the Colombian education system
Leopoldo Fergusson† Arturo Harker† Carlos Molina‡
†Universidad de los Andes ‡Pearson Institute (University of Chicago)
World Bank ABCDE ConferenceJune 24-25, 2018
Ghosts exist
⇑ ⇒ ⇑ $ transfers ⇒ ⇑ Capture (corruption)
Ghosts exist
⇑ ⇒ ⇑ $ transfers ⇒ ⇑ Capture (corruption)
Ghosts exist
⇑ ⇒ ⇑ $ transfers ⇒ ⇑ Capture (corruption)
Ghosts exist
⇑ ⇒ ⇑ $ transfers ⇒ ⇑ Capture (corruption)
This project
I Political alignment (same party, RDD estimates) betweenlocal (municipality mayor) and regional (departmentalgovernor) levels:
1. Ghosts increase, and quite substantially.2. Larger effects in:
I “Autonomous municipalities”, more discretion for funds’allocation.
3. Additional findings:I More subsequent electoral fraud, improved incumbency
advantage, no increases in education quantity or quality.
I Consistent with corruption:
1. deviating resources for the reproduction of the clientelisticnetwork (more valuable if network connected in higherechelons),
2. with an efficiency cost for voters,3. and entrenching the clientelistic machine.
Related literature and contribution
1. Corruption in developing countries and efficiency costs(Olken & Pande, 2012)
2. Clientelism beyond particularism and implications forcorruption(E.g. Maiz & Requejo, 2001; Singer, 2009)
3. Political alignment beyond fiscal transfer consequences(a long list)
4. Risks of incentive and fixed-rule schemes in financing publicgoods with weak oversight.(Among others, Acemoglu, Fergusson, Robinson, Romero, andVargas (2016); Bold, Kimenyi, Mwabu, Ng’ang’a, andSandefur (2013); Behrman, Parker, Todd, and Wolpin (2015))
Contents
Context
Data
Results
Conclusion: next steps
Public education: institutional details
I Most money spent is from central government transfers(88%).
I The rest, from regional government (3%) and localgovernment (9%), is only for investment.
I Enrollment is key in central government $:
Account % totalresources
Transferred to Distribution criteria
Payroll 90% Paid directly byMinistry
# Teachers =f(enrollment)
Quality-enrollment(calidad-matrıcula)
5% Regional EducationSecretaries
performance,poverty, enrollment
Quality-access(calidad-gratuidad)
5% Schools enrollment
Public education: institutional detailsEducation Secretary and School Principals
I Regional Education Secretary (one per department, withrelevant exceptions below).
I Monitors funds managed by schools in the jurisdiction.I Hiring, promotion and firing of teachers.
I Nominally tenure track system based on open calls.I If open calls fail (which may “accidentally”), then provisional
direct hiring.I Payroll, especially direct provisional hiring, linked to local
electoral support for mayor → Classic clientelistic patronage.
I School Principals draft reports used to project enrollment.
The political landscape
Audit: census of schools
I Audit study in 2012 financed and managed by the nationalgovernment (Ministry of Education).
I Audit firms implementing the study were competitivelyselected.
I Ministry functionaries sought to protect from cooptation:no local auditors.
I Comprehensive audit that nearly reached the goal:I 8,167,051 from 8,679,035 students were audited (94.1%).
I Detailed verification:I Face to face verification.I Complementary documentary evidence.
I ResultsI 148,410 ghosts.
Ghosts and alignment in the Colombian territory
=0
(0,1%]
(1,10%]
(10,20%]
(20,24%]
Missing
=0
(0,1%]
(1,10%]
(10,20%]
(20,24%]
Missing
Descriptive StatisticsParties not aligned Parties aligned
Mean Median S.D. Min Max Mean Median S.D. Min Max
A. All SampleGhosts (%) 1.35 0.00 4.66 0.00 96.84 1.40 0.06 5.38 0.00 100.00Men 52.4 52.3 7.5 0.0 100.0 51.7 51.8 7.4 0.0 78.3Minors 90.9 96.4 12.1 1.7 100.0 90.1 95.7 12.3 32.6 100.0Secondary 26.2 28.4 25.0 0.0 100.0 29.5 33.2 23.9 0.0 100.0Rural 76.9 100.0 40.8 0.0 100.0 67.1 100.0 45.9 0.0 100.0Strata 0-2 97.0 100.0 6.8 35.3 100.0 97.1 99.7 7.0 42.0 100.0Extended school day 41.6 0.0 48.2 0.0 100.0 21.3 0.0 39.2 0.0 100.0No diploma 1.2 0.0 6.6 0.0 100.0 3.1 0.0 11.3 0.0 100.0Students 253 164 283 3 2640 320 233 318 4 2880Teachers 22 11 27 1 165 27 17 29 1 194
B. Close races (<10%)Ghosts (%) 1.31 0.08 5.05 0.00 93.12 1.61 0.13 4.80 0.00 56.19Men 52.4 52.1 6.8 6.1 84.6 51.6 51.7 8.7 0.0 78.3Minors 88.3 92.3 12.3 31.3 100.0 89.5 96.6 13.8 32.6 100.0Secondary 31.0 35.2 21.8 0.0 100.0 27.3 30.5 25.5 0.0 100.0Rural 74.7 100.0 41.3 0.0 100.0 73.3 100.0 43.2 0.0 100.0Strata 0-2 98.5 99.8 3.8 63.5 100.0 96.8 100.0 8.1 42.0 100.0Extended school day 23.9 0.0 40.7 0.0 100.0 21.2 0.0 39.8 0.0 100.0No diploma 1.9 0.0 7.9 0.0 80.0 2.2 0.0 12.0 0.0 100.0Students 320 268 261 6 1640 300 193 331 4 2880Teachers 28 21 26 1 120 23 14 27 1 194
Municipality level
Manipulation?McCrary (2008) density test
0.0
0.5
1.0
1.5
2.0D
ensity
−1.0 −0.8 −0.6 −0.4 −0.2 0.0 0.2 0.4 0.6 0.8 1.0
Vote Share Margin of Victory for Governor’s Candidate
Note: Discontinuity estimate: 0.0268 (s.e. 0.2647)
Balance on observable variablesSchool level
Men
Minors
Belonging to ethnic group
High school
With disabilities
In rural areas
In stratum 0, 1 or 2
Extended school day
Contracted service
Teachers without diploma
Coordinators
Total students
Total teachers
−2 −1 0 1 2Standardized coefficient
Note: Observations within CCT bandwidth and 95% confidence interval.
Balance on observable variablesMunicipal level
Fiscal dependency
Per capita property tax
Investment on education
SGP on education
Coca presence
Displaced rate
Births
Mortality rate
Notaries
Area
Elevation
Distance to Bogota
Car theft
Number of schools
Rural population
Urban population
Total population
−2 −1 0 1 2Standardized coefficient
Note: Observations within CCT bandwidth and 95% confidence interval.
Main results: ghosts (%)Graphical analysis
0
1
2
3
Ghosts
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Vote Share Margin of Victory for Governor’s Candidate
1−30 31−100 101−200
Observations
0
1
2
3
4
Ghosts
−0.25 −0.20 −0.15 −0.10 −0.05 0.00 0.05 0.10 0.15 0.20 0.25Vote Share Margin of Victory for Governor’s Candidate
1−30 31−100 101−200
Observations
Note: Local polynomial regression. Left: linear fit. Right: quadratic fit. Observationswithin CCT bandwidth displayed. Bin selection method: mimicking variance, evenlyspaced using spacings estimators.
Full sample
Main resultsRobustness to bandwidth choice
500
1,000
1,500
2,000
Observ
ations w
ithin
banw
idth
−1.0
0.0
1.0
2.0
3.0G
overn
or−
Mayor
alig
nm
ent
50 60 70 80 90 100 110 120 130 140 150
Bandwidth as proportion of optimal bandwidth (percentage)
Estimator CI 10% CI 5% Observations
Main results: ghosts (%)Nonparametric estimators: CCT with optimal bandwidth
(1) (2) (3) (4) (5) (6)
Panel A. No controlsAlignment 1.402** 1.272* 1.163 1.670** 1.510* 1.463*
(0.692) (0.708) (0.730) (0.748) (0.783) (0.861)
Observations 4,383 4,383 4,383 4,383 4,383 4,383Bandwidth 0.130 0.118 0.105 0.242 0.197 0.146Obs. in bandwidth 1338 1249 1091 2211 1926 1522
Kernel Triangular Epanechnikov Uniform Triangular Epanechnikov UniformLocal polynomial Order 1 1 1 2 2 2
Panel B. ControlsAlignment 1.336** 1.514** 1.660** 1.131** 1.206* 1.363**
(0.561) (0.708) (0.659) (0.514) (0.696) (0.682)
Student controls X X XSchool controls X X XTeacher controls X X XMunicipality and party controls X XObservations 3,809 3,809 3,809 3,809 3,809 3,809Bandwidth 0.101 0.140 0.164 0.127 0.0772 0.0781Obs. in bandwidth 1044 1422 1633 1323 749 755
Notes: In Panel B, regressions are weighted using a triangular kernel and assuming a linear polynomial.Bias corrected coefficients and robust standard errors clustered at municipality level (Calonico, Cattaneo,& Titiunik, 2014). Optimal bandwidth in all columns. * 10%, ** 5%, ***1%.
Extensive margin Municipal level
The impact of autonomy
“You can take advantage of the certified municipality as a channelfor good and bad things, but you can certainly take a lot ofadvantage.”
(Former Education Secretary)
Heterogeneous effectAutonomous municipality
(1) (2)
Alignment 1.301** 1.363*(0.601) (0.772)
Alignment × Autonomy 1.786*(0.935)
Observations 4,383 4,383Bandwidth 0.119 0.119Obs. in bandwidth 1338 1338
Notes: Bias-corrected coefficients and robust standard errors clus-tered at municipality level (Calonico et al., 2014). Optimal bandwidthin all columns. Default * 10%, ** 5%, *** 1%.
Other implications?
I Do these places receive better/more or worse/less?I Not better, nor more.
I Does the strategy pay off electorally?I Party electoral disadvantage in Colombia, but less for aligned
parties. (Fergusson, Querubın, Ruiz, & Vargas, 2017).
I Does it entrench clientelistic machines?I We observe increases in subsequent vote buying.
Results
Next steps
1. Mayor future career prospects.
2. Examine role of local councilors:“The city is divided by neighborhoods controlled bycouncilors.”(Secretario de Educacion, identity hidden)
I Council members play a key role in the mayor-local leader link.I Examine schools in local councilor influence areas within
municipalities, with variation in mayor-councilor alignment.
References I
Abadie, A., Athey, S., Imbens, G. W., & Wooldridge, J. (2017). When should you adjust standard errors forclustering? (Tech. Rep.). NBER Working Paper No. 24003.
Acemoglu, D., Fergusson, L., Robinson, J. A., Romero, D., & Vargas, J. F. (2016). The perils of high-poweredincentives: Evidence from colombia’s false positives (Tech. Rep.). NBER Working Paper # 22617.
Behrman, J. R., Parker, S. W., Todd, P. E., & Wolpin, K. I. (2015). Aligning learning incentives of students andteachers: results from a social experiment in mexican high schools. Journal of Political Economy, 123(2),325–364.
Bold, T., Kimenyi, M., Mwabu, G., Ng’ang’a, A., & Sandefur, J. (2013). Scaling up what works: Experimentalevidence on external validity in kenyan education.
Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust nonparametric confidence intervals forregression-discontinuity designs. Econometrica, 82(6), 2295–2326.
Fergusson, L., Querubın, P., Ruiz, A., & Vargas, J. (2017). The real winner’s curse (Documentos Cede No.2017-5). Universidad de los Andes-Cede.
Gelman, A., & Imbens, G. (2017). Why high-order polynomials should not be used in regression discontinuitydesigns. Journal of Business & Economic Statistics(just-accepted).
Maiz, R., & Requejo, R. (2001). Clientelism as a political incentive structure for corruption. In Trabajo presentadoen la conferencia de primavera del european consortium for political research, grenoble.
McCrary, J. (2008, February). Manipulation of the running variable in the regression discontinuity design: Adensity test. Journal of Econometrics, 142(2). Retrieved fromhttp://eml.berkeley.edu/~jmccrary/mccrary2006 DCdensity.pdf
Olken, B. A., & Pande, R. (2012). Corruption in developing countries. Annual Review of Economics, 4(1),479-509. Retrieved fromhttp://www.annualreviews.org/doi/abs/10.1146/annurev-economics-080511-110917 doi:10.1146/annurev-economics-080511-110917
Singer, M. (2009). Buying voters with dirty money: The relationship between clientelism and corruption.
Descriptive StatisticsMunicipality level
Mean Median S.D. Min Max
Ghosts (%) 1.266 1.266 2.346 0 23.00Fiscal dependency 0.719 0.719 0.182 0.0948 0.981Per capita property tax 25.25 25.25 37.04 0 344.3Coca presence 57.58 57.58 346.6 0 4,846Notaries 0.748 0.748 0.765 0 7Displaced rate 336.8 336.8 524.1 0 3,649Rural population 13,096 13,096 13,766 247 134,910Urban population 22,776 22,776 56,267 159 534,956Total population 35,872 35,872 61,843 1,015 535,642Births 519.4 519.4 947.7 11 7,765Mortality rate 24.71 24.71 9.964 7.770 63.16Area 6.229 6.229 1.134 2.833 11.09Elevation 754.2 754.2 1,565 2 25,221Distance to Bogota 390.4 390.4 190.8 49.91 1,271Car theft 2.280 2.280 9.497 0 103Number of schools 2.042 2.042 6.387 0 61Investment on education 6.380 6.380 19.96 0.0496 162.2SGP on education 0.155 0.155 0.154 0 0.910
Back
Extensive margine: Prob(ghosts>0)School level
(1) (2) (3) (4) (5) (6)
Alignment 0.0653 0.0700 0.0490 0.127 0.101 0.0197(0.186) (0.193) (0.193) (0.206) (0.210) (0.217)
Observations 4,383 4,383 4,383 4,383 4,383 4,383Bandwidth 0.112 0.0995 0.0928 0.159 0.155 0.144Obs. in bandwidth 1193 1041 969 1584 1560 1455
Kernel Triangular Epanechnikov Uniform Triangular Epanechnikov UniformLocal polynomial Order 1 1 1 2 2 2
Notes: Mean of dependent variable is equal to 0.444 and standard deviation equal to 0.497. Biascorrected coefficients and robust standard errors clustered at municipality level (Calonico et al., 2014).Optimal bandwidth in all columns. * 10%, ** 5%, ***1%.
Extensive margine: Prob(ghosts>0)Municipality level
(1) (2) (3) (4) (5) (6)
Alignment -0.00202 0.0228 0.0390 0.0337 0.0452 0.0146(0.0326) (0.0365) (0.0526) (0.0277) (0.0305) (0.0628)
Observations 332 332 332 332 332 332Bandwidth 0.0901 0.0786 0.0747 0.109 0.101 0.119Obs. in bandwidth 81 68 65 98 91 109
Kernel Triangular Epanechnikov Uniform Triangular Epanechnikov UniformLocal polynomial Order 1 1 1 2 2 2
Notes: Mean of dependent variable is equal to 0.886 and standard deviation equal to 0.318. Biascorrected coefficients and robust standard errors clustered at municipality level (Calonico et al., 2014).Optimal bandwidth in all columns. * 10%, ** 5%, ***1%.
Back
Main results: ghosts (%)Graphical analysis: All sample
0
1
2
3
Ghosts
−0.50 −0.40 −0.30 −0.20 −0.10 0.00 0.10 0.20 0.30 0.40Vote Share Margin of Victory for Governor’s Candidate
1−30 31−100 101−200
Observations
−1
0
1
2
3
Ghosts
−0.50 −0.40 −0.30 −0.20 −0.10 0.00 0.10 0.20 0.30 0.40Vote Share Margin of Victory for Governor’s Candidate
1−30 31−100 101−200
Observations
Note: Local polynomial regression. Left: linear fit. Right: quadratic fit. Bin selectionmethod: mimicking variance, evenly spaced using spacings estimators.
Back
Aggregating schools: ghosts (%)Municipal level regressions
(1) (2) (3) (4) (5) (6)
Alignment 3.872* 3.931* 2.232 5.270* 5.413* 3.786(2.382) (2.380) (1.919) (2.851) (2.867) (2.566)
Observations 332 332 332 332 332 332Bandwidth 0.119 0.107 0.126 0.159 0.145 0.165Obs. in bandwidth 109 96 117 143 133 153
Kernel Triangular Epanechnikov Uniform Triangular Epanechnikov UniformLocal polynomial Order 1 1 1 2 2 2
Notes: Mean of dependent variable is 1.266 and standard deviation is 2.346. Bias corrected coefficientsand robust standard errors clustered at municipality level (Calonico et al., 2014). Optimal bandwidth inall columns. * 10%, ** 5%, ***1%.
Back
A falsification testPredicting prior election results (1997, 2000, 2003 and 2007)
−0.3
−0.2
−0.1
0.0
0.1
0.2S
hare
Marg
in o
f V
icto
ry (
prior
ele
ctions)
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Share Margin of Victory
1−30 31−100 101−200
Observations
Note: Local polynomial regression. Left: linear fit. Right: quadratic fit. Observations within CCT bandwidth
displayed. Bin selection method: mimicking variance, evenly spaced using spacings estimators. By year House
Back
A falsification testPredicting prior election result
Panel A. 1997 Panel B. 2000
−0.6
−0.4
−0.2
0.0
0.2
0.4
Sh
are
Ma
rgin
of
Vic
tory
(p
rio
r e
lectio
ns)
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Share Margin of Victory
1−30 31−100 101−200
Observations
−0.5
0.0
0.5
Sh
are
Ma
rgin
of
Vic
tory
(p
rio
r e
lectio
ns)
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Share Margin of Victory
1−30 31−100 101−200
Observations
Panel C. 2003 Panel D. 2007
−0.4
−0.2
0.0
0.2
0.4
Sh
are
Ma
rgin
of
Vic
tory
(p
rio
r e
lectio
ns)
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Share Margin of Victory
1−30 31−100 101−200
Observations
−0.4
−0.2
0.0
0.2
0.4
Sh
are
Ma
rgin
of
Vic
tory
(p
rio
r e
lectio
ns)
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Share Margin of Victory
1−30 31−100 101−200
ObservationsBack
Note: Local polynomial regression. Left: linear fit. Right: quadratic fit. Observations within CCT bandwidthdisplayed. Bin selection method: mimicking variance, evenly spaced using spacings estimators.
Balance on observable variablesNo effect on vote share for key party in prior elections of lower chamber
0.10
0.15
0.20
0.25
0.30
0.35V
ote
share
low
er
cham
ber
2010
−0.15 −0.10 −0.05 0.00 0.05 0.10 0.15Vote Share Margin of Victory for Governor’s Candidate
1−30 31−100 101−200
Observations
Note: Observations within CCT bandwidth and 95% confidence interval. Back
Political alignment and tests scoresSABER 11
(1) (2) (3) (4) (5) (6)2012 2013 2014 2012− 2010 2013− 2010 2014− 2010
Panel A. Dependent variable is the score of SABER 11: Language
Alignment -0.251 -0.359* -0.132 -0.105 -0.384* -0.117(0.311) (0.216) (0.235) (0.302) (0.212) (0.229)
Observations 1,897 1,950 1,947 1,722 1,708 1,674Bandwidth 0.110 0.0663 0.0826 0.108 0.106 0.112Obs. in bandwidth 553 261 390 529 525 574Mean dependent 0 0 0 0.0632 0.0652 0.0915Std. dev. dependent 1 1 1 0.773 0.785 0.756
Panel B. Dependent variable is the score of SABER 11: Math
Alignment -0.101 0.000931 -0.0289 -0.0368 -0.222 -0.0570(0.294) (0.194) (0.231) (0.173) (0.271) (0.205)
Observations 1,897 1,950 1,947 1,722 1,708 1,674Bandwidth 0.0933 0.105 0.0926 0.125 0.0848 0.111Obs. in bandwidth 437 521 437 648 394 566Mean dependent 0 0 0 0.0520 0.0620 0.0795Std. dev. dependent 1 1 1 0.769 0.813 0.740
Coverage Rate
(1) (2) (3) (4) (5)Basic Primary Media Secondary Total
Panel A. Dependent variable is coverage rate in 2012
Alignment -0.0602 -0.0244 -0.120 -0.132 -0.0769(0.101) (0.0827) (0.0951) (0.146) (0.104)
Observations 329 329 329 329 329Bandwidth 0.144 0.151 0.127 0.128 0.141Obs. in bandwidth 131 138 118 118 128Mean dependent 0.878 0.858 0.345 0.656 0.857Std. dev. dependent 0.200 0.199 0.152 0.209 0.196
Panel B. Dependent variable is coverage rate in 2013
Alignment -0.0779 -0.0178 -0.134 -0.134 -0.0920(0.112) (0.0904) (0.101) (0.149) (0.117)
Observations 329 329 329 329 329Bandwidth 0.144 0.165 0.132 0.127 0.142Obs. in bandwidth 132 153 121 117 129Mean dependent 0.874 0.845 0.354 0.676 0.857Std. dev. dependent 0.209 0.205 0.156 0.211 0.205
Party future prospects?
(1) (2) (3) (4) (5) (6)
Dependent variable is, in next election, ...Run... Win... Vote share..
Wint=0 0.0296* 0.0198 -0.0418** -0.0429** -0.0132* -0.0193**(0.0154) (0.0159) (0.0180) (0.0179) (0.00787) (0.00805)
Alignment 0.00826 0.00329 -0.00490(0.0209) (0.0215) (0.0104)
Wint=0 × Alignment 0.0525** 0.00564 0.0322**(0.0259) (0.0280) (0.0133)
Observations 5,488 5,425 5,488 5,425 5,488 5,425R-squared 0.586 0.588 0.172 0.172 0.443 0.444
Notes: Regression include party fixed effects and year fixed effects. Bias corrected coefficientsand robust standard errors clustered at municipality level (Calonico et al., 2014). Optimalbandwidth in all columns. * 10%, ** 5%, *** 1%.
Vote buying
(1) (2) (3) (4) (5) (6)
Risk of electoral Fraud (2018)
Alignment 0.492* 0.468* 0.366 0.524** 0.464** 0.577**(0.251) (0.262) (0.228) (0.255) (0.234) (0.272)
Observations 332 332 332 332 332 332Bandwidth 0.103 0.0889 0.107 0.197 0.218 0.146Obs. in bandwidth 93 79 96 170 182 134
Kernel Triangular Epanechnikov Uniform Triangular Epanechnikov UniformLocal polynomial Order 1 1 1 2 2 2
Notes: Mean for dependent variable is 1.21 and standard deviation equals 1.15. Alignment coefficientsand robust standard errors clustered at municipality level (Calonico et al., 2014). Optimal bandwidth in allcolumns. * 10%, ** 5%, *** 1%.
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