Conflict and Female Leaders: Evidence from Colombia2011) I Still, di cult question to answer as con ict does not arise randomly & rulers don’t just \come" to power. I Dube & Harish

$Page 1: Conflict and Female Leaders: Evidence from Colombia2011) I Still, di cult question to answer as con ict does not arise randomly & rulers don’t just \come" to power. I Dube & Harish$
Conflict and Female Leaders:Evidence from Colombia

Francisco Eslava SaenzUBC

CEPR/LEAP Workshop in Development Economics 2021

May 20th, 2021

Do female leaders behave different when in a conflict?

I It is commonly believed that women are less violent than men(Goldstein, 2003).I Different preferences as policymakers (Chattopadhyay & Duflo,

2004)I Increased negotiation skills (Niederle & Vesterlund, 2007;

Exley et al., 2020)

I Female leaders may be perceived as weakerI ↑ “supply” of conflict in their communities.I Trigger strategic reactions on their behalf (Koch & Fulton,

2011)

I Still, difficult question to answer as conflict does not ariserandomly & rulers don’t just “come” to power.I Dube & Harish (2020) study Queens in medieval Europe.

Setting & empirical strategyExploiting conflict and close local elections for mayor in Colombia

I Colombia has experienced an uninterrupted conflict since1963.I Two main illegal actors: leftist guerrillas & right-wing

paramilitary armies.

I Political power historically concentrated in a narrow & maledominated economic elite → local elections since 1988.

I Peace agreements were reached in 2006 and 2016 withparamilitary and guerrilla groups respectively.

I I exploit close local elections for mayor’s office decidedbetween a woman and a man.

I 20% of total races → 1,045 (municipality × electoral cycle).

Data

I Daily count of violent actions coded from local news sources(updated version of Restrepo et al. (2003))

I Actor (Guerrilla, Paramilitaries, government forces)I Type (e.g., attack, clash).I Motive (political or not) and scope (municipalities affected).

I 6 rounds of local elections held between 1997 and 2015 fromnational electoral authority.

I Other outcomes & characteristics (e.g., central governmenttransfers) from Acevedo & Bornacelly (2014).

Descriptive statistics

Female electoral success and guerrilla attacksVictory of a woman led to a 60% decrease in guerrilla attacks

attacksgi ,t = α + β1Femaleit + β2f (Xit) + β3Femaleit × f (Xit) + εit

Table Bandwidths Polynomial

The effect is larger when there is a woman on both sides ofthe negotiation

Figure: FARC’s blocks & frontsI FARC was a centralized

hierarchical organization.I Leverage on

geographical variationand administrativedivision.

I Rural & peasant guerrillaled almost entirely by men.

I Identify the most salientfemale figures and theunits they belonged to.

I Establish the gender ofthe fronts commanders.

Fronts

Female influence within the guerrillasFemale influence = ↓ violence

Dep. var: yearly avg. # of guerrilla attacks (per 100,000)

(1) (2)

Panel A: FARC Blocks with female leaders

Woman mayor -2.698** -6.658***(1.345) (2.574)

Observations 257 161Bandwidth 0.119 0.0939

Panel B: FARC Blocks without female leaders (conditional)

Woman mayor -0.848 -1.371(0.631) (1.741)

Observations 788 198Bandwidth 0.120 0.132

Data source: VA F&RRobust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear localpolynomials and optimal CCT robust and bias-corrected estimators and bandwidths usedin all regressions. Each coefficient reports a different regression. Running variable is theshare of votes out of the two highest votings for female candidate. Panel A includes onlymunicipalities in the jurisdiction of guerrilla structures (blocks or fronts) with a female figureof authority. Female authority defined as the presence of Lobo, Montero or Sandino. PanelB includes only municipalities with an active FARC structure different from those in panelA. Data for column 1 is drawn from VerdadAbierta (2021) (VA), data for column 2 fromMedina-Gallego (2011) (F&R).

Unconditional effect Male mayors Block & Front FE’s

Evidence in favor of the negotiation skills interpretation

I Female mayors are also more successful at dealing with other“strategic” actors.I ↑ private donations to local governments

I Citizens have higher regard for their mayors whenever theseare women.I Individual attitudes from survey data

I Guerrillas are willing to compromise whenever this doesn’tmean giving up strategic territories.I Intersection with drug routes – (Dell, 2015)

Alternative explanations that are not consistent with theresults

I Neither partisan affiliation nor ideology can fully explain theresults Political Right-wing Traditional

I The central government is not altering local conflict dynamicsvia transfers or army units Transfers Army

I Paramilitaries are not fighting guerrillas out of these areas

I Females are not using their police forces more frequently(Koch & Fulton, 2011)

I Experience not playing any role Experience Timing Previous mayors

I Female mayors are not acting different in other dimensions(Chattopadhyay & Duflo, 2004; Iyer et al., 2012)I Public goods & policy Policy Land

I Outside options for combatants

Identification & robustness

I Validation of identification assumptions:I (No) sorting the threshold (McCrary test)I Balance on observable characteristics between groupsI No preexisting differences in incidence of violenceI Guerrillas behavior in campaign year does not differ

I Robustness:I Intensive margin of conflict Indicator Casualties

I Different measures of violence Raw IHS Normalizations

I Extending & restricting the sample periodI Including additional municipality-level controlsI Removing the most violent municipalities

Summing-up

I Municipalities that elected a woman as a mayor experienced adecrease in conflict victimization:

I Driven by guerrilla attacks, no change on right-wing violence.

I Robust to measurement and specification concerns.

I 3 key findings that help explain the drop in violence:

1. There is suggestive evidence that women show betternegotiation skills.

2. Guerrillas are willing to cede control of less strategic areas.

3. Female influence within the guerrillas reinforces the effect.

Appendix

Noche y Niebla

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Descriptive Statistics

Mean Std. Dev Min Max

Panel A: Electoral outcomesFemale candidates:% of victories 0.443 0.497 0 1Vote share -0.019 0.120 -0.5 0.5

Panel B: Violence# of... per mayor periodAttacks 3.7 14.5 0 320Clashes 0.7 2.5 0 46

Yearly average # of... by guerrillas per mayor periodAttacks 1.1 3.9 0 73Clashes 0.7 2.4 0 42

Notes: All variables in all panels have 1,045 observations. Voteshare in panel A is out of the 2 highest votings. Figures in panel Bcome from CERAC.

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No sorting - McCrary

01

23

45

Den

sity

-.5 -.4 -.3 -.2 -.1 0 .1 .2 .3 .4 .5Relative vote share female candidate

Each point represents a bin. Bin size is .007Discontinuity estimate (standard error): .009 (.122)

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Balance on observablesNo significant differences between places where women won or lost

Means Difference p-value

Time varying characteristics:

Tax Income 3,400.6 0.17Total Expenditure 7,741.9 0.36Public servants investigated 31.246 0.45

Time invariant characteristics:

Area (km2) 61.09 0.66Distance to capital -5.36 0.10*Historical violence -0.021 0.32Historical land conflict -0.011 0.45

Baseline:

Population 12,708 0.23Rural index 0.000 0.98GINI -0.007 0.25Poberty index -0.017 0.12

1,045 and 797 observations in and out of sample respectively. Base-line characteristics measured in 1993. Difference is races wherewomen (lost - won).

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Balance in races with and without women

Means Difference p-value

Time varying characteristics:

Tax Income 2,954.6 0.21Total Expenditure 3,505.0 0.55Public servants investigated -7.785 0.75

Time invariant characteristics:

Area (km2) 98.00 0.21Distance to capital 4.26 0.02**Historical violence 0.003 0.77Historical land conflict -0.004 0.62

Baseline:

Population 2,708 0.67Rural index 0.010 0.24GINI 0.000 0.94Poberty index 0.006 0.34

1,045 and 797 observations in and out of sample respectively. Base-line characteristics measured in 1993. Difference is races (without- with) female participation.

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Effects on the # of attacks in last year of previouselectoral cycle

Dep. var: avg. # of attacks in last year of previous cycle

Perpetrator: Total Guerrillas Paramilitaries

(1) (2) (3)

Linear polynomial:

Woman mayor -1.816 -0.953 -0.262(1.318) (0.742) (0.543)

Bandwidth 0.126 0.119 0.156

Mean of dep. var 4.244 2.124 0.795Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observa-tions in all regressions. Optimal CCT bandwidth used in all regressions. Each coefficientreports a different regression. Running variable is the share of votes out of the two highestvotings for female candidate.

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Effects on political violence in campaign year

Dep. var: avg. # of politically motivated armed actions

in last year of previous cycle


(1) (2) (3)

Linear polynomial:

Woman mayor -0.038 -0.238 -0.658(0.684) (1.288) (1.851)

Bandwidth 0.167 0.137 0.139


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Main resultsFemale victory led to a 60% decrease in violence, but only from guerrillas

Dep. var: yearly avg. # of attacks per 100,000 inhabitants


(1) (2) (3)

Linear polynomial:

Woman mayor -1.353 -1.200** -0.267(1.295) (0.571) (0.705)

Bandwidth 0.201 0.132 0.179


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Robustness to bandwidth selectionLinear polynomials

(a) Linear polynomials (b) Quadratic polynomials

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Robustness to polynomial degree

Dep. var: yearly avg. # of guerrilla attacks per 100,000 inhabitants

Polynomial degree: 1 2 3 4

Woman mayor -1.200** -1.636** -1.947** -1.860*(0.571) (0.738) (0.871) (0.952)

Bandwidth 0.132 0.147 0.173 0.211Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.Observation is the municipality per electoral period. 1,045 observations inall regressions. Optimal Calonico et al. (2014) robust bandwidth and bias-corrected estimators used in all regressions.

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Robustness: Intensive marginDependent variable is 1 if municipality suffered any attack

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Robustness: Effect on civilian casualties

Dep. var: indicator of civilian casualties during electoral cycle


(1) (2) (3)

Linear polynomial:

Woman mayor -0.146 -0.189** -0.071(0.106) (0.089) (0.065)

Bandwidth 0.135 0.112 0.199

Mean of dep. var 0.395 0.224 0.185Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observa-tions in all regressions. Optimal CCT bandwidth used in all regressions. Each coefficientreports a different regression.

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Robustness: raw # of attacks as dependent variable

Dep. var: # of attacks


(1) (2) (3)

Linear polynomial:

Woman mayor -1.761 -0.710* 0.010(1.663) (0.390) (0.826)

Bandwidth 0.130 0.136 0.167

Mean of dep. var 3.744 1.108 0.989Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 ob-servations in all regressions. Optimal CCT bandwidth used in all regressions. Eachcoefficient reports a different regression. Running variable is the share of votes out ofthe two highest votings for female candidate. All regressions control for duration ofmayor period.

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Robustness - Inverse Hyperbolic Sine

Dep. var: Inverse Hyperbolic Sine of the yearly avg. # of

attacks per 100.000 inhabitants


(1) (2) (3)

Linear polynomial:

Woman mayor -0.838* -0.639* -0.284(0.495) (0.359) (0.293)

Bandwidth 0.164 0.126 0.150

Mean of dep. var 1.855 0.992 0.537Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 ob-servations in all regressions. Optimal CCT bandwidth used in all regressions. Eachcoefficient reports a different regression. Running variable is the share of votes out ofthe two highest votings for female candidate. All regressions control for duration ofmayor period.

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Robustness: other measures of guerrilla attacks

Dep. var: # of Guerrilla attacks in

Per 100,000 inhabitants

Mayor period Mayor period First year First 2 years

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

Woman mayorWoman mayor -0.728* -3.032* -0.959 -2.403*

(0.393) (1.688) (0.676) (1.302)Bandwidth 0.134 0.132 0.142 0.129

Mean of dep. var 1.108 6.251 2.158 4.189Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observations in all regressions.Optimal CCT bandwidth used in all regressions. Each coefficient reports a different regression. Running variableis the share of votes out of the two highest votings for female candidate.

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Robustness: different sample periods

(a) Extended until 2018 (b) Reduced up to 2014

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Robustness: controlling for institutional characteristics


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

Woman mayor -1.307** -1.331** -1.090** -1.219**(0.577) (0.582) (0.556) (0.576)

Observations 1045 1045 946 1045

Controls: Colonial Poverty State presence FinancialRobust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials and optimal CCTbandwidth used in all regressions. Each coefficient reports a different regression. Running variable is the share of votesout of the two highest votings for female candidate. Colonial controls are {indigenous settlement, Spanish settlement,historical violence and land conflict}. Poverty controls are {GINI, UBNI, rurality and poverty indexes, and % of urbanpopulation}, all measured in 1993. State presence controls are {# of public and municipal employees; % of unpavedprincipal roads and % of dirt roads}, all drawn from Acemoglu et al. (2015) and measured in 2015. Financial controlsare tax revenue, transfers from national government and total expenditure, all measured in 1987.

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Robustness: removing outlier observationsOutliers defined as municipalities in the top 5% of attacks



(1) (2) (3)

Linear polynomial:

Woman mayor -2.535*** -0.817* 0.037(0.858) (0.452) (0.167)

Bandwidth 0.144 0.119 0.174

Observations 993 993 993Mean of dep. var 2.469 0.794 0.346Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Optimal CCTbandwidth used in all regressions. Each coefficient reports a different regression. Runningvariable is the share of votes out of the two highest votings for female candidate.

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Evolution of the presence of fronts in the central AndesStructures belonging to the Central Joint Command (CCC)

(a) 2002(b) 2011

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Female influence within the guerrillas: unconditionalsample

Dep. var: yearly avg. # of guerrilla attacks (per 100,000 inhabitants)

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

Panel A: FARC structure with female influence

Block Front

Woman mayor -2.698** -6.658*** -2.807* -3.031*(1.345) (2.574) (1.489) (1.615)

Observations 257 161 80 81Bandwidth 0.119 0.0939 0.145 0.109

Panel B: FARC structure without female influence (unconditional)

Block Front

Woman mayor -0.848 -0.551 -0.902 -0.892(0.631) (0.580) (0.597) (0.595)

Observations 884 965 964Bandwidth 0.132 0.135 0.136

Data source: VA F&R F&R FullRobust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials and optimalCCT robust and bias-corrected estimators and bandwidths used in all regressions. Each coefficient reports adifferent regression. Running variable is the share of votes out of the two highest votings for female candidate.Panel A includes only municipalities in the jurisdiction of guerrilla structures (blocks or fronts) with a femalefigure of authority. Columns 1 and 2 define as female of authority the presence of Lobo, Montero or Sandino;columns 3 and 4 add those women who were front commanders. Panel B includes all municipalities not includedin panel A. Columns 1 and 2 use blocks as FARC structures, columns 3 and 4 use fronts. Data for column 1 isdrawn from VerdadAbierta (2021) (VA), data for columns 2 and 3 from Medina-Gallego (2011) (F&R). Column4 uses both data sources.

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Block and Front FE’sNo significative change in the main results

Dep. var: yearly avg. # of guerrilla attacks per 100,000

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

Linear polynomial:

Woman mayor -1.155** -2.488** -2.399** -1.976** -1.791**(0.564) (1.072) (1.029) (0.868) (0.794)

Bandwidth 0.135 0.0779 0.0767 0.0746 0.0743Observations 1044 301 321 301 321

Quadratic polynomial:

Woman mayor -1.583** -3.282** -3.259*** -2.521** -2.532***(0.720) (1.298) (1.243) (1.013) (0.950)


Fixed Effects:Block X X X X XF&R Fronts X X X X XVA Fronts X X X X X

Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear and quadratic local polynomials used inpanels A and B respectively. Optimal CCT robust and bias-corrected estimators and bandwidths used in all regressions.Each coefficient reports a different regression. Running variable is the share of votes out of the two highest votings forfemale candidate. Column 1 includes fixed effects of fronts as defined in VerdadAbierta (2021). Column 2 only includesmunicipalities where a FARC front was active according to Medina-Gallego (2011) (F&R) and includes a full set of Frontfixed effects. Column 3 adds those fronts identified by VerdadAbierta (2021) (VA). Column 4 includes both Front and Blockfixed effects using the same fronts as in column 2. Column 5 does the corresponding with column 3 fronts.

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Block and Front FE’sNo effect on paramilitary attacks as expected

Dep. var: yearly avg. # of paramilitary attacks per 100,000

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

Linear polynomial:

Woman mayor -0.365 -0.170 -0.251 -0.166 0.250(0.697) (1.026) (0.930) (1.012) (0.964)


Quadratic polynomial:

Woman mayor -0.811 -1.433 -1.387 -1.294 -1.329(0.784) (1.035) (0.991) (1.040) (0.988)


Fixed Effects:Block X X X X XF&R Fronts X X X X XVA Fronts X X X X X

Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear and quadratic local polynomialsused in panels A and B respectively. Optimal CCT robust and bias-corrected estimators and bandwidths used inall regressions. Each coefficient reports a different regression. Running variable is the share of votes out of the twohighest votings for female candidate. Column 1 includes fixed effects of fronts as defined in VerdadAbierta (2021).Column 2 only includes municipalities where a FARC front was active according to Medina-Gallego (2011) (F&R)and includes a full set of Front fixed effects. Column 3 adds those fronts identified by VerdadAbierta (2021)(VA). Column 4 includes both Front and Block fixed effects using the same fronts as in column 2. Column 5does the corresponding with column 3 fronts.

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“Female guerrillas” & “male mayors”Simple correlation

Dep. var: yearly avg. # of guerrilla attacks (per 100,000 inhabitants)

(1) (2) (3)

FARC structure with female influence

Block Front

Female FARC commander -1.626* -1.411** -1.457**(0.920) (0.676) (0.636)

Observations 2,175 1,936 2,044R-squared 0.183 0.182 0.183

Data source: F&R F&R FullStandard errors clustered by state-year in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. OLS estimationsusing the full sample of Colombian municipalities in all columns. All regressions include year and statefixed effects. Columns 1 and 2 define as female of authority the presence of Lobo, Montero or Sandino;columns 3 and 4 add those women who were front commanders. Columns 1 and 2 use blocks as FARCstructures, columns 3 and 4 use fronts. Data for column 1 is drawn from VerdadAbierta (2021) (VA),data for columns 2 and 3 from Medina-Gallego (2011) (F&R). Column 4 uses both data sources.

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→ Women seem to be better at negotiating with the private sector in order to

attract funds to their governments.

Figure: Effect on private transfers received by local government

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Experimental evidence pointing in the same directionIndividuals tend to trust more the performance of female mayors

Relative

transparency

Budget

meetingattendance

Resources

execution

Trusts in

mayorDep. var:

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

Woman mayor 0.036 0.065** 0.263* 0.459*(0.028) (0.028) (0.135) (0.226)

Observations 1,490 1,767 1,192 3,260Mean of dep. var 0.072 0.073 0.369 4.007Standard errors clustered at the municipality × electoral cycle level in parenthesis. *** p<0.01, ** p<0.05,* p<0.1. All outcomes drawn from LAPOP (2018) survey. Years included are 2004-2014, 2016 and 2018.Observations weighted by number of years under treatment.

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Smuggling routes

“ICG” and “FIP” correspond to contemporaneous drug traffic routes. ICG information drawn from ICG (2017).FIP information drawn from Cajiao et al. (2018). Historical routes drawn from Laurent (2008). Green and red(dashed) lines overlap when in the same region.

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Heterogeneous effects by the presence of smuggling routes

Dep. var: yearly avg. # of ... attacks (per 100,000)

Guerrilla Paramilitary

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

Panel A: Municipalities that intersect with smuggling route

Current Historic Current Historic

Woman mayor 0.325 -0.114 -0.340 -0.070(0.964) (0.125) (0.977) (0.046)


Panel B: Municipalities that do not intersect with smuggling route

Current Historic Current Historic

Woman mayor -1.392** -1.175** -0.314 -0.168(0.650) (0.583) (0.819) (0.731)


Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials andoptimal Calonico et al. (2014) robust and bias-corrected estimators and bandwidths used in all regressions.Each coefficient reports a different regression. Running variable is the share of votes out of the two highestvotings for female candidate. Odd columns break the sample between municipalities that lie on a currentsmuggling route as defined by Cajiao et al. (2018) and ICG (2017). Even columns do the analogous usingXIX-th century smuggling routes as defined by Laurent (2008).

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Central government’s transfers

Dep. var: log of ... per 100,000 inhabitants in last year of mayor period

Gov’t transfers K transfers Gov’t credit

(1) (2) (3)

Women mayor 0.142 0.0460 0.00826(0.233) (0.142) (0.307)

Bandwidth 0.171 0.136 0.151

Observations 754 1030 752Mean of dep. var 8.476 10.597 9.187Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Optimal CCT bandwidthand linear polynomials used in all regressions. Each coefficient reports a different regression. Runningvariable is the share of votes out of the two highest votings for female candidate.

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National army engagements

Dep. var: yearly avg. # of ... per 100,000 inhabitants

Army Guerrilla

Actions Clashes

(1) (2) (3)

Woman mayor 0.907 0.583 0.554(1.023) (0.919) (0.911)

Bandwidth 0.133 0.133 0.134

Mean of dep. var 1.635 1.269 1.249Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045observations in all regressions. Optimal CCT bandwidth and linear polynomials usedin all regressions. Each coefficient reports a different regression. Running variable isthe share of votes out of the two highest votings for female candidate. Total armyclashes include clashes with Paramilitary groups.

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No effect on right-wing paramilitary attacks

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“Demand” less violence through increased police activity?

Dep. var: yearly avg. # of police ... per 100,000 inhabitants

Actions Clashes

Total Guerrillas

(1) (2) (3)

Linear polynomial:

Woman mayor -0.271 -0.123 -0.107(0.214) (0.134) (0.131)

Bandwidth 0.142 0.108 0.110

Mean of dep. var 0.555 0.185 0.179Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045observations in all regressions. Optimal CCT bandwidth used in all regressions. Eachcoefficient reports a different regression. Running variable is the share of votes out ofthe two highest votings for female candidate. Total police clashes include clashes withparamilitary groups.

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Taking previous experience into account85% of candidates without previous experience (balanced across gender)


(1) (2)

Woman had previous: Success Experience

Woman mayor -0.530* -0.478*(0.290) (0.279)

Observations 158 178

Woman didn’t have previous: Success Experience

Woman mayor -1.146* -1.272*(0.645) (0.676)

Observations 887 867Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linearlocal polynomials and optimal CCT bandwidth used in all regressions. Each coefficientreports a different regression. Running variable is the share of votes out of the twohighest votings for female candidate. Success is an indicator of victory in a previouselectoral race (for city council). Experience is only participation in the electoral race.

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Exploiting the timing of attacks

Dep. var: indicator of guerrilla attacks in the ... year of electoral cycle

First Second Third

(1) (2) (3)

Woman mayor -0.211*** -0.029 -0.124**(0.071) (0.068) (0.053)

Bandwidth 0.118 0.131 0.115

Mean of dep. var 0.144 0.160 0.116Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Observation is themunicipality per electoral period. 1,045 observations in all regressions. Optimal Calonico et al.(2014) robust bandwidth and bias-corrected estimators used in all regressions. Columns 1 and 4use as outcome the violence in first year of electoral period, columns 2 and 5 use violence in thesecond year of electoral period, columns 3 and 6 use violence in last year of electoral period.

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Effect is stronger when controlling for gender of previousmayor80% of women came to power for first time in municipality’s history



(1) (2) (3)

Woman mayor -1.430 -1.188** -0.250(1.296) (0.570) (0.705)

Bandwidth 0.200 0.132 0.179

Mean of dep. var 4.615 1.979 1.069Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observationsin all regressions. Optimal CCT bandwidth used in all regressions. Each coefficient reportsa different regression. Running variable is the share of votes out of the two highest votingsfor female candidate. All regressions control for the gender of previous mayors. Onlymunicipalities where a woman won for the first time an election for the mayor’s officeconsidered “treated”.

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It doesn’t seem a story about ability or preferences

Dep. var: change in the number of ... during mayor period

Servants prosecuted Students enroled CMI

(Corruption) (Education) (Health)

(1) (2) (3)

Woman mayor -20.361 -3.950 -3.320(126.972) (2.937) (2.889)

Bandwidth 0.130 0.122 0.159

Observations 786 801 577Mean of dep. var 266.203 1.862 -2.539Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observations in all re-gressions. Optimal CCT bandwidth and linear polynomials used in all regressions. Each coefficient reportsa different regression. Running variable is the share of votes out of the two highest votings for femalecandidate.

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Land policy

Dep. var: Share of land reallocated in municipality

Raw > mean > median Potential

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

Linear polynomial:

Woman mayor -0.001 0.010 -0.074 -1.015(0.001) (0.087) (0.073) (0.847)

Bandwidth 0.133 0.174 0.188 0.145

Mean of dep. var 0.002 0.526 0.719 1.018Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Observation is the municipalityper electoral period. 589 observations in columns 1 and 4, 1,045 in columns 2 and 3. Optimal Calonicoet al. (2014) robust bandwidth and bias-corrected estimators used in all regressions. Dependent variablesdrawn from Faguet et al. (2020).

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Providing combatants with an outside option

Dep. var: indicator of demobilizations in municipality


(1) (2) (3)

Linear polynomial:

Woman mayor -0.049 -0.056 -0.036(0.125) (0.120) (0.068)

Bandwidth 0.155 0.155 0.146

Mean of dep. var 0.314 0.268 0.126Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Observationis the municipality per electoral period. 1,045 observations in all regressions. OptimalCalonico et al. (2014) robust bandwidth and bias-corrected estimators used in allregressions. Dependent variable equals 1 whenever a member from an armed group(guerrilla in column 2, paramilitary in column 3) surrenders her arms to state forcesduring mayor period.

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Zooming into politically motivated violenceLargest decrease in politically motivated violence.

Dep. var: yearly avg. # of politically motivated... (per 100,000 inhabitants)

Guerrilla Paramilitaries GovernmentActions Attacks Actions Attacks Actions Attacks

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

Woman mayor -0.897*** -0.929*** -0.152 -0.155 0.202* 0.122(0.326) (0.328) (0.683) (0.683) (0.119) (0.113)

Bandwidth 0.118 0.116 0.169 0.170 0.154 0.135

Mean of dep. var 0.755 0.751 0.966 0.963 0.238 0.232Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials and optimal CCT bandwidth used in all regressions.Each coefficient reports a different regression. Running variable is the share of votes out of the two highest votings for female candidate. Only eventsclassified as “politically motivated” included

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Is the effect being driven by females who beat right-wingcandidates?Doesn’t seem to be → CAUTION, sample size is small


Heterogeneous Effects Ideology Fixed Effects

(1) (2) (3)

Panel A: Municipalities where right wing candidate: Linear Polynomial

Participated Won

Woman mayor -0.781** -1.112 -1.022*(0.370) (0.730) (0.552)


Panel B: Municipalities where right wing candidate did not: Quadratic Polynomial

Participate Win

Woman mayor -1.019 -1.102* -1.684**(0.730) (0.632) (0.731)

Observations 772 902 1045Bandwidth 0.141 0.133 0.152Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials and optimal CCT bandwidthused in all regressions. Each coefficient reports a different regression. Running variable is the share of votes out of the two highestvotings for female candidate. Ideology classification drawn from Fergusson et al. (2019).

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On the attackers side: Guerrillas favored political outsidersHeterogeneous effects by partisan affiliation


(1) (2) (3)

Panel A: Municipalities where traditional parties:

Won Lost Were incumbent

Woman mayor -0.667 -3.189*** -1.665**(0.912) (1.117) (0.779)


Panel B: Municipalities where traditional parties did/were not:

Win Lose Incumbent

Woman mayor -1.231** 0.335 -0.382(0.572) (0.578) (0.802)


Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. Linear local polynomials andoptimal Calonico et al. (2014) robust and bias-corrected estimators and bandwidths used in all regressions.Traditional parties are Liberal and Conservative. Column 1 breaks the sample between places where acandidate of a traditional party won (panel A) or did not win (panel B). Columns 2 and 3 do correspondingbetween places where traditional parties lost (2) or where incumbents (3), and not.

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But it’s not only about being political outsidersClose race between two “traditional” candidates



(1) (2) (3)

Linear polynomial:

Woman mayor -7.008 -3.182* -1.811(4.383) (1.872) (2.058)

Bandwidth 0.105 0.136 0.145

Mean of dep. var 8.658 4.405 2.035Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 176 observa-tions in all regressions. Optimal CCT bandwidth used in all regressions. Each coefficientreports a different regression. Running variable is the share of votes out of the two highestvotings for female candidate.

→ Bottom line is that guerrillas favor candidates who beat thosefrom “traditional parties”, but only when the former are women.

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Effect of having a “traditional” mayor

Dep. var: yearly avg. # of attacks per 100,000 in following period


(1) (2) (3)

Linear polynomial:

Mayor from traditional party -0.286 -0.086 -0.304(0.888) (0.553) (0.332)

Bandwidth 0.186 0.123 0.130

Mean of dep. var 4.663 2.000 0.968Robust standard errors in parenthesis. *** p<0.01, ** p<0.05, * p<0.1. 1,045 observations inall regressions. Optimal CCT bandwidth used in all regressions. Each coefficient reports a differentregression. Running variable is the share of votes out of the two highest votings for female candidate.

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References I

Acemoglu, D., Garcıa-Jimeno, C., & Robinson, J. A. (2015). State capacity and economic development: A networkapproach. American Economic Review, 105(8), 2364–2409.

Acevedo, K. M., & Bornacelly, I. D. (2014). Panel municipal del cede (Tech. Rep.). Universidad de losAndes-CEDE.

Cajiao, A., Gonzalez, P., Pardo, D., & Zapata, O. (2018). Una aproximacion al crimen transnacional organizado:redes de narcotrafico colombia-espana. Documento de trabajo, 5(15), 9.

Calonico, S., Cattaneo, M. D., & Titiunik, R. (2014). Robust nonparametric confidence intervals forregression-discontinuity designs. Econometrica, 82(6), 2295–2326.

Chattopadhyay, R., & Duflo, E. (2004). Women as policy makers: Evidence from a randomized policy experimentin india. Econometrica, 72(5), 1409–1443.

Dell, M. (2015). Trafficking networks and the mexican drug war. American Economic Review, 105(6), 1738–79.

Dube, O., & Harish, S. P. (2020). Queens. Journal of Political Economy, 128(7), 2579-2652. Retrieved fromhttps://doi.org/10.1086/707011 doi: 10.1086/707011

Exley, C. L., Niederle, M., & Vesterlund, L. (2020). Knowing when to ask: The cost of leaning in. Journal ofPolitical Economy, 128(3), 816–854.

Faguet, J.-P., Sanchez, F., & Villaveces, M.-J. (2020). The perversion of public land distribution by landed elites:Power, inequality and development in colombia. World Development, 136, 105036.

Fergusson, L., Querubin, P., Ruiz Guarin, N., & Vargas, J. (2019). The real winner’s curse. American Journal ofPolitical Science.

Goldstein, J. S. (2003). War and gender: How gender shapes the war system and vice versa. Cambridge UniversityPress.

ICG, I. C. G. (2017). Colombia’s armed groups battle for the spoils of peace. International Crisis Group LatinAmerica Report, no. 63.

Iyer, L., Mani, A., Mishra, P., & Topalova, P. (2012). The power of political voice: women’s politicalrepresentation and crime in india. American Economic Journal: Applied Economics, 4(4), 165–93.

Koch, M. T., & Fulton, S. A. (2011). In the defense of women: Gender, office holding, and national security policyin established democracies. The Journal of politics, 73(1), 1–16.

https://doi.org/10.1086/707011

References II

LAPOP. (2018). Latin American Public Opinion Project - Colombia. Harvard Dataverse. Retrieved fromhttps://doi.org/10.7910/DVN/GG5QRW doi: 10.7910/DVN/GG5QRW

Laurent, M. (2008). Contrabando en colombia en el siglo xix: practicas y discursos de resistencia y reproduccion.Universidad de los Andes Facultad de Ciencias Sociales-Ceso.

Medina-Gallego, C. (2011). Farc-ep: Flujos y reflujos. Universidad Nacional de Colombia.

Niederle, M., & Vesterlund, L. (2007). Do women shy away from competition? do men compete too much? Thequarterly journal of economics, 122(3), 1067–1101.

Restrepo, J. A., Spagat, M., & Vargas, J. F. (2003). The dynamics of the colombian civil conflict: A new data set.

Verdadabierta. (2021). https://verdadabierta.com. (Accessed: 2021-01-30)

https://doi.org/10.7910/DVN/GG5QRW

https://verdadabierta.com

Documents

Conflict and Female Leaders: Evidence from Colombia2011) I Still, di cult question to answer as con ict does not arise randomly & rulers don’t just \come" to power. I Dube & Harish