how not to build a state: evidence from colombia's false positives · 2015-03-24 · how not...

How Not to Build a State:Evidence from Colombia’s False Positives

D. AcemogluMIT

L. FergussonU. Andes

J. RobinsonHarvard

D. RomeroIADB

J. VargasU. Rosario

36th Meeting of the Brazilian Econometric SocietyDecember 10, 2014, Natal, Brazil

Brazilian Econometric Society (12-10-2014) How Not to Build a State Colombia’s ‘False Positives’ 1 / 68

Contents

1 Motivation

2 Context

3 A simple model

4 Data and Empirical StrategyDataEmpirical StrategyDescriptive Statistics

5 Main results: Incentives and false positives

6 The impact on true positives and institutionsTrue positivesImpact on institutions

7 Conclusions


Motivation

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Motivation

State capacityMany countries lack state capacity, despite large payoffs

Weak states: argued to be the root of civil wars(Fearon and Laitin, 2003).

Difference between economically successful and unsuccessful countries(Evans, 1995, Herbst, 2000, Besley and Persson, 2011, Acemoglu andRobinson, 2012... many others).

Variation in state capacity related to many different factors:

historical path dependence (Evans),ecology population density (Herbst),political economy (Besley and Persson, Acemoglu and Robinson).


Motivation

But how does a state with low capacity build it?Paying for performance?

Capacity is multi-dimensional:

fiscal, bureaucratic, legal......and key: monopoly of violence.How should a state which lacks monopoly of violence acquire it?

Imagine a political majority in favor of building capacity.

Alvaro Uribe’s presidential election in May 2002.

To attain monopoly of violence could expand on:

extensive margin (hire more soldiers), orintensive margin (make existing soldiers work harder).

How to manage the intensive margin?

One way is high-powered incentives.


Motivation

This researchPaying for performance in Colombia and its consequences

We investigate high-powered incentives in the Colombian army.

We show these incentives:1 Are systematically related to murder of civilians portrayed as guerrillas

killed, “false positives”, especially:

1 In units of military officers with pressing career concerns.2 Where the judiciary is weak.

2 Created an incentive to corrupt the judiciary, hence eroding otherdimensions of institutions.

High-powered incentives can have very perverse effects.

Innocents killed.Stronger military, but not consensually strong.Other institutions hurt.

→ Hard to build state in one dimension.→ Complementary efforts in several dimensions are needed.


Context

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


201020082006

2003

20022000

19991997

19701960

Government

FARC and ELN

“Autodefensas”

“Zona de Distension”Major peace process between

FARC and President Pastrana

FARChijacked a plane

(Left-wing guerrillas)

(Right-wing paramilitaries)

AUCUmbrella

Organization

Splinter paramilitary groups

Dismantling of AUCDisarm and peace deals

1995

Uribe GovernmentAnti-FARC platform, “Democratic Security Policy”

reelected after amending Constitution

Context

Introducing incentivesTrends and regulation

FP had long existed in Colombia, but more common in 2000s.

Figure

Increase coincided with incentives to fight insurgents:

Law 782 of 2002: fund for intelligence operations and rewards todemobilized rebels.2003: Democratic Security document announces system of rewards forinformation (regulated by decrees 128 of 2003 and 2767 of 2004).Other directives and decrees:

Directive 029 of 2005 (and 015 and 016 de 2007): incentive scheme forinformants leading to captures or killings. (‘Secret’ documentsnot so secret).Decree 1400 of 2006 (Boina or Beret).


Context

Introducing incentivesFeatures of incentives: formal and informal

Formally set a reward schedule for killings and capturing insurgents,seizing weapons and sharing information:

1 Military personnel was not explicitly excluded(also not explicitly included, except in Boina: up to one year salary),

2 No authorization ex ante by a superior officer required for operation,3 Posterior intelligence could be used to justify the killings.

Informal and unregulated incentives (see, e.g. UN SpecialRapporteur):

Days off when holidays approached, send to platoon on Sinai(foot soldiers).Medals, and promotions(commanders).


Context

Removing incentives

FP fall substantially in late 2008: media scandal after killing ofseveral men from Soacha, near Bogota.

Government claimed victims were guerrillas killed in combat.Judicial investigations revealed this was not the case, and FP werewidespread.

Government issued new directives changing incentive structure:1 Explicitly exclude rewards to military personnel.2 Prioritize rewards to successful operations that did not involve killings

(demobilizations, captures).3 Require first investigation of combat-related deaths by judiciary.4 Require prior intelligence for operations.

Also ousted high-ranked officials involved in FPs and created specialunit for FPs at Office of Attorney General.


Context

False positives and career concernsThe case of colonels

Colombian army nearly tripled during 2000s→ some brigades commanded by colonels, not generals.

Career concerns attached to new incentives more likely to affectcolonels, who still can go up the military ladder.

Captain Rozo Valbuena testimony against other officer:His only objective was to gather enough “statistics” to be able to bepromoted to General.

27 soldiers expelled by platoon commander (a colonel) for not killingtwo people (dressed as civilians). Soldier description:

“When my colonel came in he started insulting us and scoldingus, and told us that we were good for nothing, that we did notunderstand that a guerrilla member alive was useless for him,and that what mattered were killings because he was going to bepromoted to general and that is how his performance wasmeasured. He told us he was going to have us all expelled.”


Context

False positives and institutional weaknessThe case of weak judicial institutions

The difficulty in controlling abuses reflects, and promotes,institutional weakness.

Of the 1,056 cases of killings by armed forces that were assigned to theFiscalıa (Attorney General) through April 2009, only 16 resulted inconvictions (Alston, 2010, p. 13).

Example: testimony from witnesses in case against Colonel Mejıa

“Mejıa had no trouble doing it because the local director of theAttorney General Office helped him with the setup”“When a person disappeared, his family members went todenounce it to the Police or the Ombudsman or any otherinstitution in charge and, after this, the next victims where thosedenouncing.”Yesterday news’ example


A simple model

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


A simple model

Incentive scheme and agent utility

A principal sets a linear incentive scheme.

Focus on implications for agent, who exerts:

good effort: aT → qT (true positives), orbad effort: aF → qF (false positives).

Output linear in effort and noise εJ , independent and N(0, σ2J):

qJ = aJ + εJ , for J ∈ {T ,F},

CARA preferences over wage w net of effort costs Ψ(aT , aF ),

E[−e−η(w−Ψ(aT ,aF ))

].


A simple model

Technological complementarity or substitutability

Effort cost:

Ψ(aT , aF ) =1

2(cTa2

T + cFa2F ) + δaTaF for |δ| ≤

√cT cA

Effort substitution, δ = Ψ′′aT aF > 0:

Extreme with specialization:

δ =√

cT cA → Ψ(aT , aF ) =1

2(√

cTaT +√

cFaF )2.

Technological complements, δ = Ψ′′aT aF < 0

Extreme with constant ratio:

δ = −√

cT cA → Ψ(aT , aF ) =1

2(√

cTaT −√

cFaF )2.


A simple model

Reported killings and wageIntroducing misreporting and differential incentives

Misreporting: α false positives can be portrayed by true positives.

α: poor quality of local institutions.Reported true positives qT :

qT = qT + αqF .

Incentives: colonels’ payoff depends more on output.

π: relative importance of output in the agent’s wage.Flat salary t plus linear incentive s based on reported killings:

w = (1− π)t + πsqT

= (1− π)t + πs(qT + αqF ).


A simple model

Payoff and equilibrium effort

Agent’s utility:

u(aT , aF ) = (1− π)t + πs(aT + αaF )︸︷︷︸expected wage

−(

1

2(cTa2

T + cFa2F ) + δaTaF

)︸︷︷︸

effort costs

− ηπ2s2

2(σ2

T + α2σ2F )︸︷︷︸

risk premium

.

Interior solution (assume αcT − δ ≥ 0 and cF − δα ≥ 0),

a∗F = πsαcT − δ

cT cF − δ2, (1)

a∗T = πscF − δα

cT cF − δ2. (2)


A simple model

Proposition 1: Testable implications of ↑ sTechnological complements, δ ∈ (−√cT cF , 0]

1 ↑ false and true positives:

∂a∗F∂s

> 0 ,∂a∗T∂s

> 0.

2 Larger ↑ in false and true positives where misrepresentation is morelikely (high α):

∂2a∗F∂s∂α

> 0 ,∂2a∗T∂s∂α

> 0.

3 Larger ↑ in false and true positives where output is more important incompensation (high π):

∂2a∗F∂s∂π

> 0 ,∂2a∗T∂s∂π

> 0.


A simple model

Proposition 2: Testable implications of ↑ sTechnological substitutes (interior solution), δ ∈ (0,

√cT cF )

1 ↑ false and true positives:

∂a∗F∂s

> 0,∂a∗T∂s

> 0.

2 Larger ↑ in false positives, smaller in true, with high α:

∂2a∗F∂s∂α

> 0 ,∂2a∗T∂s∂α

<0.

3 Larger ↑ in false and true positives with high π:

∂2a∗F∂s∂π

> 0,∂2a∗T∂s∂π

> 0.


A simple model

Proposition 3: Impact on quality of institutions

Consider the agents equilibrium payoff u(a∗T , a∗F ). Provided risk aversion

and output volatility are not too large (bound on ησ2F ):

∂u(a∗T , a∗F )

∂α> 0

∂2u(a∗T , a∗F )

∂α∂π> 0.

Agent’s are especially interested in reducing the quality of institutionswhen facing stronger incentives (high π).


A simple model

SummaryTestable predictions

1 Increase in incentives:1 Increase average qT and qF ,2 Especially with colonels (high π),3 Especially with weak local institutions (high α),

except if effort substitution → high α attenuate effect on qT .

2 Impact on institutions:1 Agents interested in decreasing quality of local institutions.2 Especially where π is larger (colonels).

3 Remarks:

Many other things changed when incentives were introduced.Impact on true positives indirectly test technological relation.


Data and Empirical Strategy

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Data and Empirical Strategy Data

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions



Measuring FP

Source: Colombian Human Rights NGO CINEP.

Compiles list of events of arbitrary executions of alleged rebels.

Information on:

date and place of recruitment and execution; victim presented asguerrilla or paramilitary; perpetrators from Army, Police, or Navy;battalion or brigade responsible.

Alternative datasets are likely to be less accurate:

Official counts based on investigations: underreporting or geographicbias related to state capacity.Counts from victims’ associations: criticized as overstating FP.

Our data: 925 cases of FP involving 1,513 victims from 1988 to 2011.



Identifying army ranks

Reconstructed historical organizational structure of the army:

Current structure (number, position, jurisdiction and commanders ofDivisions, Battalions and Brigades) available from the army website.For previous:

Expired versions of the website (available since 2000 from the “WayBack Machine”).Other online sources (notably news stories in media archives, especiallyEl Tiempo, Colombia’s main newspaper)

Match with CINEP data on brigade involved in FP cases to identifyrank of commanders of alleged criminals.



Measuring judicial inefficiency

Inspector General (Procuradurıa): disciplinary oversight of publicservants.

Event-based dataset with all processes, by municipality, 1995-2008.

Jud. Inefficiencym,0 =

∑1999t=1995 Judicial functionaries casesm,t∑1999

t=1995 All casesm,t

Jud. Inefficiencym,t =Judicial functionaries casesm,t

All casesm,t, t∈{2000,...,2008}


201020082006

2003

20022000

19991997

19701960

“Zona de Distension”

FARChijacked a plane

AUCUmbrella

Organization

Dismantling of AUC

1995

Uribe Government

Baseline Judicial Ine�ciency

Yearly Judicial Ineficiency


Other data

Large set of municipal-specific characteristics to control fordifferential trends associated with the following sets of variables:

1 Geographical characteristics (7 variables)2 Socioeconomic characteristics (42 variables).

1 Population2 Security conditions in terms of conflict and crime (4 variables)3 Educational outcomes (4 variables)4 Municipal income and rents (6 variables)5 Presence of natural resources (5 variables)6 State presence and institutional capacity (22 variables)


Data and Empirical Strategy Empirical Strategy

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions



False positives, judicial inefficiency, and army ranks

F. Positivem,t = α + δm + δt + β0Colonelm,t

+ β1 (Colonelm,t × Posty )

+ β2

(Judicial Inefficiencym,0 × Posty

)+∑x∈Xm

Φxx × Posty + εm,t , t∈{2000−2008}.

F. positivem,t = {Dummy, Count, Killings}.

Posty =

1, if t ≥ 2003,

1, if t ≥ 2004,

1, if t ≥ 2005,

1, if t ≥ 2005 Excl. 2003, 2004.






+ β2


)+∑x∈Xm

Φxx × Posty + εm,t , t∈{2000−2008}.


Posty =

1, if t ≥ 2003,

1, if t ≥ 2004,

1, if t ≥ 2005,

1, if t ≥ 2005 Excl. 2003, 2004.


Data and Empirical Strategy Descriptive Statistics

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Data and Empirical Strategy Descriptive Statistics

Table 1 : Descriptive Statistics for Variables, 2000-2008

VARIABLES Mean Std. Dev. Min Max

False positives dummy 0.0498 0.2175 0.00 1.00False positives (cases) 0.0782 0.4716 0.00 15.00False positives (killed) 0.1229 0.7747 0.00 20.00True positives dummy 0.1838 0.3873 0.00 1.00True positives (cases) 0.3608 1.0813 0.00 24.00True positives (killed) 0.8385 3.7624 0.00 260.00Colonel 0.2042 0.3924 0.00 1.00Judicial Inefficiency 0.0594 0.1202 0.00 1.00


Main results: Incentives and false positives

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Table 2 : False Positives, Judicial Inefficiency, and Colonels

Dependent variable: Dummy Number (killings)

(1) (2) (3) (4) (5) (6)

Panel A: Post ≥ 2003Judicial Inefficiency x Post 0.1622∗∗∗ 0.1868∗∗∗ 0.0916 0.4807∗∗ 0.6087∗∗ 0.3981

(0.0608) (0.0647) (0.0617) (0.2141) (0.2537) (0.2612)Colonel x Post 0.0510∗∗∗ 0.0317∗∗ 0.0261∗ 0.1845∗∗∗ 0.1516∗∗∗ 0.1617∗∗∗

(0.0139) (0.0145) (0.0143) (0.0515) (0.0575) (0.0609)

R-squared 0.048 0.066 0.087 0.028 0.038 0.052

Panel D: Post ≥ 2005 Excl. 2003-4Judicial Inefficiency x Post 0.1697∗∗ 0.2128∗∗ 0.1002 0.6713∗∗ 0.8831∗∗ 0.5257

(0.0761) (0.0826) (0.0860) (0.3044) (0.3612) (0.3809)Colonel x Post 0.0751∗∗∗ 0.0502∗∗∗ 0.0477∗∗∗ 0.2465∗∗∗ 0.1930∗∗∗ 0.2032∗∗∗

(0.0177) (0.0179) (0.0179) (0.0650) (0.0713) (0.0769)R-squared 0.056 0.084 0.119 0.032 0.050 0.071

Geography (7) X X X XSocioeconomic (42) X X

Observations 7524 6282 5698 7524 6282 5698Number of municipalities 1078 898 814 1078 898 814

Table 3 : False Positives, Judicial Inefficiency, and Colonels

Dependent variable: Dummy Number (killings)

(1) (2) (3) (4) (5) (6)

Panel B: Post ≥ 2004Judicial Inefficiency x Post 0.1593∗∗ 0.2181∗∗∗ 0.1216 0.5865∗∗ 0.7906∗∗ 0.5546∗

(0.0674) (0.0773) (0.0802) (0.2577) (0.3070) (0.3244)Colonel x Post 0.0644∗∗∗ 0.0451∗∗∗ 0.0378∗∗ 0.2195∗∗∗ 0.1810∗∗∗ 0.1829∗∗∗

(0.0164) (0.0169) (0.0170) (0.0598) (0.0657) (0.0684)R-squared 0.049 0.071 0.097 0.029 0.043 0.061

Panel C: Post ≥ 2005Judicial Inefficiency x Post 0.1035 0.1540∗ 0.0661 0.6116∗∗ 0.8480∗∗ 0.4598

(0.0713) (0.0800) (0.0890) (0.3084) (0.3702) (0.3987)Colonel x Post 0.0675∗∗∗ 0.0469∗∗∗ 0.0473∗∗∗ 0.2276∗∗∗ 0.1779∗∗∗ 0.1846∗∗

(0.0170) (0.0173) (0.0177) (0.0615) (0.0669) (0.0745)R-squared 0.049 0.073 0.099 0.029 0.047 0.065



Table 4 : Pretrends. False Positives, Judicial Inefficiency, and Colonels, 1992-2002

Dependent variable: Post ≥ 1994 Post ≥ 1996 Post ≥ 1998 Post ≥ 2000(1) (2) (3) (4)

Panel A: DummyJudicial Inefficiency x Post 0.0591 0.0680 0.0502 0.0072

(0.0550) (0.0593) (0.0429) (0.0531)Colonel x Post 0.0138∗∗ 0.0052 0.0042 0.0000

(0.0055) (0.0040) (0.0033) (0.0039)R-squared 0.004 0.004 0.004 0.004

Panel B: Number (cases)Judicial Inefficiency x Post 0.0282 0.0375 0.0287 0.0200

(0.0359) (0.0330) (0.0237) (0.0254)Colonel x Post 0.0106∗∗ 0.0040 0.0039 0.0013

(0.0045) (0.0034) (0.0027) (0.0029)R-squared 0.004 0.004 0.004 0.004

Panel C: Number (killed)Judicial Inefficiency x Post −0.0172 0.0550 0.0217 −0.0173

(0.1413) (0.1034) (0.0722) (0.1008)Colonel x Post 0.0217 0.0078 0.0042 −0.0022

(0.0183) (0.0102) (0.0080) (0.0088)R-squared 0.005 0.004 0.004 0.004

Observations 11636 11636 11636 11636Number of municipalities 1078 1078 1078 1078

The impact on true positives and institutions

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


The impact on true positives and institutions True positives

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Table 5 : True Positives, Judicial Inefficiency, and Colonels

Dependent variable: Dummy Number (killed)

(1) (2) (3) (4) (5) (6)

Panel A: Post ≥ 2003Judicial Inefficiency x Post −0.0356 −0.0393 −0.0392 −0.3063 −0.3725 0.7434

(0.0985) (0.1104) (0.1267) (1.2366) (1.4918) (1.9117)Colonel x Post 0.0504∗ 0.0514∗ 0.0206 0.4885∗∗∗ 0.6217∗∗∗ 0.5870∗∗

(0.0264) (0.0300) (0.0334) (0.1878) (0.2297) (0.2959)R-squared 0.007 0.011 0.019 0.009 0.013 0.023

Panel D: Post ≥ 2005 Excl. 2003-4Judicial Inefficiency x Post −0.0528 −0.0391 −0.1260 −0.2675 −0.0831 0.6264

(0.1069) (0.1186) (0.1309) (1.2756) (1.5490) (1.9801)Colonel x Post 0.0597∗∗ 0.0639∗∗ 0.0290 0.5971∗∗∗ 0.7847∗∗∗ 0.6818∗∗

(0.0283) (0.0325) (0.0360) (0.2004) (0.2538) (0.3301)R-squared 0.007 0.012 0.025 0.009 0.016 0.028



Table 6 : True Positives, Judicial Inefficiency, and Colonels

Dependent variable: Dummy Number (killed)

(1) (2) (3) (4) (5) (6)

Panel B: Post ≥ 2004Judicial Inefficiency x Post −0.0215 −0.0175 −0.0829 −0.1274 −0.0599 0.3275

(0.0944) (0.0995) (0.1144) (1.0266) (1.2053) (1.4960)Colonel x Post 0.0671∗∗ 0.0760∗∗ 0.0418 0.5942∗∗∗ 0.7551∗∗∗ 0.5582∗∗

(0.0264) (0.0310) (0.0336) (0.1733) (0.2222) (0.2672)R-squared 0.007 0.012 0.022 0.009 0.015 0.027

Panel C: Post ≥ 2005Judicial Inefficiency x Post −0.0540 −0.0248 −0.1772 −0.1175 0.2723 0.2245

(0.0892) (0.1010) (0.1094) (0.8888) (1.0875) (1.3508)Colonel x Post 0.0683∗∗∗ 0.0744∗∗ 0.0536 0.5817∗∗∗ 0.7711∗∗∗ 0.6684∗∗∗

(0.0264) (0.0309) (0.0336) (0.1692) (0.2147) (0.2545)R-squared 0.007 0.011 0.021 0.009 0.015 0.025



The impact on true positives and institutions Impact on institutions

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Table 7 : Impact on institutions: relative frequency of judicial disciplinary cases:Baseline results

Dependent variable: percent judicial casesPost ≥ 2003 Post ≥ 2004 Post ≥ 2005 Post ≥ 2005 Excl. 2003-4

(1) (2) (3) (4)

Panel A: Percent judicial casesColonel x Post 0.0227∗∗ 0.0172∗ 0.0145 0.0221∗∗

(0.0098) (0.0093) (0.0091) (0.0103)

R-squared 0.011 0.011 0.010 0.014Observations 7326 7326 7326 5698Number of municipalities 814 814 814 814

Geography (7) X X X XSocio-economic (42) X X X X

Table 8 : Impact on institutions: relative frequency of judicial disciplinary casesVerifying robustness to zero cases

Dependent variable: Post ≥ 2003 Post ≥ 2004 Post ≥ 2005 Post ≥ 2005 Excl. 2003-4(1) (2) (3) (4)

Panel B: Percent judicial cases Without 0/0Colonel x Post 0.0243∗∗ 0.0180∗ 0.0160∗ 0.0251∗∗

(0.0103) (0.0097) (0.0095) (0.0108)



Table 9 : Impact on institutions: relative frequency of judicial disciplinary casesVerifying not driven by fall in general-led brigades


Panel C: With Post DummyColonel x Post 0.0205∗∗ 0.0154∗ 0.0119 0.0208∗∗

(0.0093) (0.0086) (0.0086) (0.0100)Post 0.2282 0.4190∗∗ 0.2020 0.2626

(0.1990) (0.1847) (0.1849) (0.2179)



Table 10 : Impact on institutions: relative frequency of judicial disciplinary casesVerifying not driven by trends in total cases


Panel D: Constant DenominatorColonel x Post 0.0594∗∗∗ 0.0484∗∗ 0.0451∗∗ 0.0569∗∗

(0.0210) (0.0212) (0.0201) (0.0225)



The impact on true positives and institutions Impact on institutions

Size of the effectsTaking 2003 as the post year

A one-standard deviation increase in Judicial Inefficiency implies anincrease, relative to the pre-period,...

about 1-2 percentage points higher probability of a FP occurring(FP dummy: average increase was from 0.7% to 7%)...about 0.05-0.07 more false guerrilas killed(FP killed: average increase was from 0.02 to 0.13).

A colonel rather than a general implies an increase, relative to thepre-period,...

about 3 to 5 percentage points higher FP dummy...about 0.16 more FP killed...about 0.02 higher judicial inefficiency(Judicial inefficiency constant on average at 0.06).


Conclusions

Contents

1 Motivation

2 Context

3 A simple model




7 Conclusions


Conclusions

Conclusion: How Not to Build a StateHow does a State lacking the monopoly of violence acquire it?

High-powered incentives to army members in the fight against theinsurgency in Colombia:

Are systematically related to ‘false positives’.Specially for military officers with career concerns & where statejudicial institutions are weak.Created an incentive to corrupt other institutions.

What do we learn from this?

→ Building state capacity in one dimension is difficult, evencounterproductive, when state is generally weak.

→ High-powered incentives in this context can have very perverse effects.→ Complementary efforts in several dimensions at the same time are

required.


Conclusions

Thank you!


Cuadro 8: Execution by Year

Year Cases Executions

1988 (0 %) 6 171989 (0 %) 6 111990 (1 %) 10 291991 (1 %) 11 291992 (1 %) 11 181993 (2 %) 27 691994 (1 %) 11 161995 (0 %) 6 81996 (0 %) 7 111997 (1 %) 13 281998 (0 %) 5 91999 (0 %) 3 72000 (0 %) 4 112001 (0 %) 8 132002 (2 %) 19 422003 (3 %) 33 502004 (8 %) 75 1112005 (10 %) 101 1502006 (17 %) 162 2562007 (26 %) 246 3822008 (15 %) 140 2242009 (0 %) 9 92010 (0 %) 7 92011 (0 %) 5 4Total (100 %) 925 1,513

Notas:

6Back

SECRETO

REPUBLICA DE COLOMBIA

MINISTERIO DE DEFENS.A NACIONAL

COPIA No [2- DE I j, COPIP,s MINISTERIO DE DEFENSA NACIONAL BOGOTA, D.C. 1 7 NOV . .2005

DIRECTIVA MINISTERI.AL PERMANENTE

ASUNTO : Politica ministerial que desarrolla critErios para el pago de recompensas por la captura 0 abatimiento en combale de cabecillas de las organizaciones armadas al margen de la ley, material de guerra, intendencia 0 comunicaciones e informacion sobre actividades relacionadas con el narcotrafico y pago de informacion que sirva de fundamento para la

de labores de. inteligencia y el posterior planeamiento de operaciones.

AL

1. OBJETO Y ALCANCE.-

a. Finalidad

Oefinir una politica ministerial que desarrolle criterios claros y definidos para el pago de recompensas por la captura 0 abatimiento en combate de cabecillas de las organizaciolles armadas al margen de la ley, material de guerra, in!endencia 0 comunicaciones e informacion sobre actividades relacionadas con el narcotrafico y pago informacion que sirva de fundamento para la continuacion de labores de inteligenCla y el posterior planeamiento de operaciones.

b. Objetivos Especificos

i) Definir pago por informacion Y'·pago por recompensas.

ii) Fijar critErios de valoraci6n para cancelar recompensas por los principales cabecillas de las OA.ML y los cabecillas de narcotrafico, de

SEep.ETO

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Back

020

040

060

080

0Ki

lls

1990q1 1995q1 2000q1 2005q1 2010q1Quarter

Guerrilla Kills 1988 - 2009True Positives by Quarter

Cuadro 8: Execution by Year

Year Cases Executions

1988 (0 %) 6 171989 (0 %) 6 111990 (1 %) 10 291991 (1 %) 11 291992 (1 %) 11 181993 (2 %) 27 691994 (1 %) 11 161995 (0 %) 6 81996 (0 %) 7 111997 (1 %) 13 281998 (0 %) 5 91999 (0 %) 3 72000 (0 %) 4 112001 (0 %) 8 132002 (2 %) 19 422003 (3 %) 33 502004 (8 %) 75 1112005 (10 %) 101 1502006 (17 %) 162 2562007 (26 %) 246 3822008 (15 %) 140 2242009 (0 %) 9 92010 (0 %) 7 92011 (0 %) 5 4Total (100 %) 925 1,513

Notas:

6Back

Conclusions

Table 11 : False Positives, 1988-2011.Alleged group of the victim and organization of the perpetrator

Cases Executions

Panel A: Alleged group of the victimGuerrilla 693 (74.9%) 1,162 (76.8%)Paramilitary 36 (4.9%) 67 (4.4%)Other 196 (21.2%) 284 (18.8%)

Panel B: Organization of the perpetratorArmy 853 (92.2%) 1,422 (94%)Police 37 (4%) 37 (2.4%)Other 35 (3.8%) 54 (3.6%)

Total 925 1,513


Table 12 : Descriptive Statistics for Variables, 2000-2008

VARIABLES Mean Std. Dev. Min Max

Panel A: 2000-2003False positives dummy 0.0498 0.2175 0.00 1.00False positives kills 0.1229 0.7747 0.00 20.00True positives dummy 0.1838 0.3873 0.00 1.00True positives kills 0.8385 3.7624 0.00 260.00Colonel 0.2042 0.3924 0.00 1.00Judicial Inefficiency 0.0594 0.1202 0.00 1.00Judicial Inefficiency 1995 1999 0.0779 0.0805 0.00 0.53

Panel B: Pre 2003False positives dummy 0.0072 0.0844 0.00 1.00False positives kills 0.0197 0.2725 0.00 7.00True positives dummy 0.1920 0.3939 0.00 1.00True positives kills 1.0493 5.4375 0.00 260.00Colonel 0.1013 0.2999 0.00 1.00Judicial Inefficiency 0.0616 0.1231 0.00 1.00

Panel C: Post 2003False positives dummy 0.0711 0.2569 0.00 1.00False positives kills 0.1744 0.9245 0.00 20.00True positives dummy 0.1797 0.3839 0.00 1.00True positives kills 0.7334 2.5373 0.00 51.00Colonel 0.2557 0.4220 0.00 1.00Judicial Inefficiency 0.0582 0.1187 0.00 1.00

Table 13 : False positives by commander rank, 2000-2008

Full Sample General CoronelMean Std. Dev. Min Max N Mean Std. Dev. N Mean Std. Dev. N Diff

Panel A: Dummy

All years0.0498 0.2175 0 1 10062 0.0386 0.1926 7622 0.0923 0.2894 2168 0.0537∗∗∗

Before year...... 2003

0.0072 0.0844 0 1 3349 0.0075 0.0864 2929 0.0030 0.0544 338 −0.0046After year...

... 20030.0711 0.2569 0 1 6713 0.0580 0.2337 4693 0.1087 0.3114 1830 0.0508∗∗∗

Panel B: Number (cases)

All years0.0782 0.4716 0 15 10062 0.0559 0.3505 7622 0.1628 0.7660 2168 0.1069∗∗∗


0.0093 0.1324 0 5 3349 0.0099 0.1392 2929 0.0030 0.0544 338 −0.0069∗

After year...... 2003

0.1126 0.5666 0 15 6713 0.0846 0.4305 4693 0.1923 0.8301 1830 0.1078∗∗∗

Panel C: Number (Killed)

All years0.1229 0.7747 0 20 10062 0.0896 0.6039 7622 0.2500 1.2111 2168 0.1604∗∗∗


0.0197 0.2725 0 7 3349 0.0205 0.2758 2929 0.0030 0.0544 338 −0.0175∗∗∗


0.1744 0.9245 0 20 6713 0.1328 0.7349 4693 0.2956 1.3130 1830 0.1629∗∗∗

Table 14 : False positives by Judicial Inefficiency, 2000-2008

Full Sample Low Inefficiency High InefficiencyMean Std. Dev. Min Max N Mean Std. Dev. N Mean Std. Dev. N Diff

Panel A: Dummy

All years0.0246 0.1549 0 1 25076 0.0203 0.1410 12670 0.0292 0.1684 12255 0.0089∗∗∗


0.0077 0.0874 0 1 16119 0.0065 0.0805 8126 0.0089 0.0940 7959 0.0024∗


0.0550 0.2281 0 1 8957 0.0449 0.2071 4544 0.0668 0.2497 4296 0.0219∗∗∗


All years0.0366 0.3119 0 15 25076 0.0298 0.2934 12670 0.0440 0.3313 12255 0.0141∗∗∗


0.0091 0.1157 0 5 16119 0.0078 0.1090 8126 0.0106 0.1223 7959 0.0028After year...

... 20030.0862 0.4944 0 15 8957 0.0693 0.4652 4544 0.1059 0.5288 4296 0.0366∗∗∗

Panel C: Number (killed)

All years0.0601 0.5378 0 20 25076 0.0476 0.4683 12670 0.0735 0.6036 12255 0.0259∗∗∗


0.0197 0.2911 0 13 16119 0.0162 0.2681 8126 0.0234 0.3134 7959 0.0071After year...

... 20030.1327 0.8057 0 20 8957 0.1037 0.6915 4544 0.1664 0.9188 4296 0.0628∗∗∗

Table 15 : True Positives by Commander Rank, 2000-2008

Full Sample General CoronelMean Std. Dev. Min Max N Mean Std. Dev. N Mean Std. Dev. N Diff

Panel A: Dummy

All years0.1838 0.3873 0 1 10062 0.1807 0.3848 7622 0.1970 0.3978 2168 0.0163∗


0.1920 0.3939 0 1 3349 0.1919 0.3938 2929 0.2160 0.4121 338 0.0241After year...

... 20030.1797 0.3839 0 1 6713 0.1737 0.3789 4693 0.1934 0.3951 1830 0.0198∗


All years0.3608 1.0813 0 24 10062 0.3474 1.0643 7622 0.4004 1.1134 2168 0.0530∗∗


0.3398 0.9414 0 15 3349 0.3404 0.9488 2929 0.3698 0.9289 338 0.0294After year...

... 20030.3712 1.1447 0 24 6713 0.3518 1.1305 4693 0.4060 1.1444 1830 0.0542∗


All years0.8385 3.7624 0 260 10062 0.8502 4.0621 7622 0.7869 2.4532 2168 −0.0633


1.0493 5.4375 0 260 3349 1.0775 5.7416 2929 0.9231 2.4507 338 −0.1544After year...

... 20030.7334 2.5373 0 51 6713 0.7083 2.4852 4693 0.7617 2.4535 1830 0.0535

Table 16 : True Positives by Judicial Inefficiency, 2000-2008

Full Sample Low Inefficiency High InefficiencyMean Std. Dev. Min Max N Mean Std. Dev. N Mean Std. Dev. N Diff

Panel A: Dummy

All years0.1514 0.3585 0 1 25076 0.1491 0.3562 12670 0.1551 0.3620 12255 0.0060


0.1562 0.3631 0 1 16119 0.1506 0.3577 8126 0.1623 0.3688 7959 0.0117∗∗


0.1428 0.3499 0 1 8957 0.1463 0.3535 4544 0.1418 0.3488 4296 −0.0046


All years0.2757 0.9133 0 24 25076 0.2649 0.8617 12670 0.2890 0.9669 12255 0.0241∗∗


0.2676 0.8535 0 17 16119 0.2458 0.7554 8126 0.2905 0.9432 7959 0.0447∗∗∗


0.2902 1.0119 0 24 8957 0.2991 1.0239 4544 0.2863 1.0094 4296 −0.0128


All years0.6656 2.9667 0 260 25076 0.6588 3.3481 12670 0.6789 2.5317 12255 0.0201


0.7090 3.2590 0 260 16119 0.6878 3.7821 8126 0.7325 2.6270 7959 0.0447After year...

... 20030.5875 2.3489 0 51 8957 0.6070 2.3822 4544 0.5796 2.3419 4296 −0.0273

Table 17 : Judicial Inefficiency by Colonel, 2000-2008

Full Sample General ColonelMean Std. Dev. Min Max N Mean Std. Dev. N Mean Std. Dev. N Diff

All years0.0594 0.1202 0 1 10062 0.0603 0.1199 7622 0.0592 0.1255 2168 −0.0011


0.0616 0.1231 0 1 3349 0.0637 0.1259 2929 0.0487 0.1036 338 −0.0151∗∗


0.0582 0.1187 0 1 6713 0.0581 0.1160 4693 0.0611 0.1291 1830 0.0030

Figure 1 : False positives (dummy) by judicial inefficiency

0.0

5.1

.15

.2.2

5FP

2000 2001 2002 2003 2004 2005 2006 2007 2008Year

Low Judicial Inefficiency High Judicial Inefficiency

Figure 2 : False positives (executions) by judicial inefficiency

0.1

.2.3

.4Ex

ecut

ions

2000 2001 2002 2003 2004 2005 2006 2007 2008Year

Low Judicial Inefficiency High Judicial Inefficiency

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