Provision of Public Goods and Violent Conflict: Evidence from
Colombia∗
Darwin Cortés†
Daniel Montolio
‡
October 20, 2013
Abstract:
The Colombian conflict has lasted for around 50 years. It has been fueled by the financial opportunities coming from production and traffic of illegal drugs, and predation of other natural resources.
In such a context it is not clear what policies are more effective to reduce conflict. Two public policies
that are frequently mentioned as effective to reduce conflict are investments in roads and education.
However, a priori, both investments in roads and education may either increase or reduce conflict.
After controlling for possible problems of endogeneity, we show that increases in roads provision reduces conflict while education does not. Because this is robust to controlling for measures of state
capacity and governance, and the opportunity cost of conflict, our results are likely to be explained
by the relative mobility of education and roads. Policies that increase roads provision might help to
fight against the intensity of conflict.
JEL codes: D74, H41
Keywords: Conflict, Road Density, Education.
∗
We thank two anonymous referees and the editor, Juan Fernando Vargas, for very detailed comments
and insights to previous versions of this paper. We are also grateful to Dario Maldonado for his comments.
Usual disclaimer applies. We also thank Daniel Martı́nez and Alejandro Montoya for their research assistance.
Daniel Montolio acknowledges support from grant 2009SGR102 from the Catalan Autonomous Government.
†
Corresponding author, Department of Economics and CeiBA-Complejidad, Universidad del Rosario,
Colombia, Calle 12C 4-69, Piso 3, 111711, Bogota, Colombia - darwin.cortes[at]urosario.edu.co
‡
IEB, Universitat de Barcelona, Spain. montolio[at]ub.edu
1
1
Introduction
The economic causes of conflicts have received considerable attention in the economic literature. Part of the literature is dedicated to the analysis of income shocks (e.g. Miguel et al.,
2004; Dube and Vargas, 2013), with special attention to natural resources (Frankel, 2010).
In the Colombian case, it is well known that irregular armies (paramilitaries and guerrilla)
seek rents by predating on coca production (Angrist and Kugler, 2008), oil revenues (Dube
and Vargas, 2013), and other similar resources. Although the role of the provision of public
goods in conflicts has been studied both theoretically (Scoones, 2013) and in other contexts
(e.g. Berman et al., 2011, 2013), whether public good provision mitigates or exacerbates the
Colombian conflict is largely unknown.
Among policymakers, investments in roads and education are often claimed to be effective
to mitigate conflict. It is argued that building roads reduces transportation cost. This in
turn reduces cost of raising legal crops increasing opportunity cost of raising coca crops.
It may also facilitate the arrival of other institutions and the construction of other public
goods, which might improve state capacity and governance and increase the opportunity cost
of making war. However, road construction might increase conflict for at least two reasons.
First, irregular armies are likely to attack more intensely those regions in which the state
intends to make more presence as a way to have more control on those regions or to fight
against the enemy. Second, irregular armies may take advantage of roads to make their
activities (controlling areas, transporting coca, weapons, fighters, etc.) more easily.
Similarly, and depending on its nature, content and delivery, education can either fuel
or deter conflict. On the one hand, as Davies (2004) highlights, conflict can be fueled by
education through malicious educational policies that, for instance, exclude or humiliate
minorities, perform cultural repression, exacerbate class and gender differences or indoctrinate
students into hate and revenge. Uneven educational policies across the country, affecting the
educational opportunities, can be also a catalyst of violent conflict (Bush and Saltarelli, 2000).
On the other hand, there is also long standing evidence regarding education as a mechanism
that promotes peace, human rights and the defence and protection of democracy and, hence,
preventing conflict (McGlynn, 2009). Moreover, apart from the social component (or public
good component) of education, individuals with better levels of education are likely to have
access to jobs that are better paid. This increases the opportunity cost of making war.
The aim of this paper is, hence, to study the effect of the provision of roads and education
on conflict. The main results suggest that an increase in road density is likely to reduce
conflict, while an increase in quality of education is not. The result on education shows
that effective policies to prevent conflict might be not effective when the conflict is already
in place. The finding is in line with the fact that households, regardless of their human
2
capital accumulation, might move to other municipalities, leaving regions where conflict is
more intense. Migration of households might make education policies less effective as a tool
to reduce conflict.
The main empirical problem in identifying the effect of the provision of roads and education on conflict is endogeneity. Not only may the provision of public goods affect conflict
but also conflict may affect the provision of public goods. To tackle this issue we adopt an
instrumental variables (IV) approach. We use historical instruments related to the provision
of public goods. To instrument road density we use road density in 1949, a measure that
is previous to the foundation of the oldest irregular army that is involved in the contemporaneous Colombian conflict. Finally, to instrument current quality of education we use the
average educational attainment of household heads in 1993. As is well known in the literature
of economics of education, in every society people’s educational achievement is positively correlated with their parents’ education (Björklund and Salvanes, 2011). The idea is to exploit
the variation across municipalities of educational attaintment of household heads in 1993 to
instrument the average education quality in the period 2000-2005.
There are several theories that might be behind our results. Broadly speaking, those
theories relate to governance and state capacity, opportunity cost and gratitude (Berman et
al. 2012). The provision of roads and education might improve state capacity and governance
by facilitating the construction of other public goods or institutions. It might also increase
the opportunity cost of making war or make people more thankful to the government. Since
our data is a municipality cross-section, some of these explanations are likely to raise concerns
on the credibility of our empirical strategy. For this reason, we introduce as controls three
(exogenous) variables that are related to these explanations. We control for presence of
institutions and political elections, which are measures of governance and state capacity. We
also control for poverty, which can be interpreted as a measure of opportunity cost of conflict
(and to some extent of gratitude).1 Our results are robust to these controls.
In the Colombian case, the bulk of the studies on the relationship between public goods
and conflict concentrate on the consequences of conflict for the provision of public goods. The
literature has studied the effect of conflict on social development (Sánchez and Diaz, 2005),
education (Barrera and Ibáñez, 2004; Dueñas and Sánchez, 2007; Rodrı́guez and Sánchez,
2009, 2010) and infrastructure (Villegas and Duque, 2009). To the best of our knowledge no
study tackles the causal effect of the provision of public goods on conflict in the Colombian
case.
1. These variables are not affected by the 2000-2005 conflict since they are measured in 1993 (poverty), 1995
(institutions) and 1997 (election results). In this sense these controls are exogenous to the contemporary
conflict.
3
In other contexts, the relationship between public goods and conflict has been studied
more extensively. The literature shows that this relationship is complex and evidence is
mixed. Some studies show that increases in certain kind of government services reduce
violence (Berman et al. 2011). Moreover, reductions of conflict might be associated to
improvements in state capacity and governance (Besley and Persson, 2009, 2011). Other
papers show that increased economic activity in areas with low presence of the government
might increase rent-seeking and predatory behavior (Collier and Hoeffler, 2004 and Berman
et al. 2013).
The rest of the paper is organized as follows. Section 2 presents a brief overview of the
Colombian conflict. Section 3 specifies the empirical strategy, paying special attention to the
identification of causal relations between the variables of interest. Section 4 describes the
data. Section 5 presents the main empirical results. Finally, section 6 concludes and offers
some policy implications of the main results.
2
Background on the Colombian Conflict
The Colombian conflict has lasted for over fifty years. One of the fighting groups is the
Revolutionary Armed Forces of Colombia (FARC), the oldest active guerrilla in the world,
founded in 1964. This guerrilla has political roots that can be at least traced back to the
period known as La Violencia, which was triggered by the 1948 assassination of populist
political leader Jorge Eliécer Gaitán. La Violencia is a period of civil conflict in the whole
country between supporters of the Liberal and Conservative parties. It covers the period
1948-1958. During La Violencia, several members of the Liberal Party organized self-defence
groups and guerrilla units in the countryside, which fought against the police and other
groups under the control of the Conservative Party.
In 1957 both parties signed the pact of Sitges, an agreement between the two parties to
alternate the presidential power for 16 years, the so-called Frente Nacional (National Front).
In the meantime, during the 50s, there were several attempts to sign peace agreements with
the liberal guerrillas. Some of them signed these agreements, other did not. The remaining
militants of liberal guerrillas together with some communist militants reorganized themselves
into the FARC. This guerrilla movement claims to be fighting for the rights of the poor in
Colombia, to protect them from government violence and to redistribute land to the poor.
Colombia is a country very rich in oil, gold and coal. The presence of these resources has
fueled the conflict for decades.2 In the 1960s Colombia became a producer and exporter of
marihuana. Its production and exports rapidly fell down, mainly because of the increased
2. See Dube and Vargas (2013) for empirical evidence on oil and conflict.
4
supply of Californian marihuana. By the end of the 1970s the traffickers began to import
coca leaf from Bolivia and Peru. This coca leaf was processed in Colombia into cocaine, that
was re-exported to the USA (Diaz ad Sanchez, 2004).
The first paramilitary groups were organized in the 1970s, but it was during the 1980s
that they scaled-up and became better organized, mainly because large rural landowners and
drug traffickers used paramilitaries to defend themselves from guerrilla extortions.
However it is only in the 1990s that both phenomena, paramilitaries and illegal crops
production, acquired national importance. The paramilitaries scaled up during the 1980s
but only developed a national organization in the second part of the 1990s. Coca crops
harvested in Colombia experienced a huge increase after the US government eliminated the
Amazon air bridge in 1993. Indeed, according to Colombian and international authorities,
during the nineties both the guerrilla and the paramilitaries increased their involvement in the
drug industry (protection of crop fields and clandestine labs) as reported, for instance, by the
Ministry of National Defence of Colombia (2000) and the United States General Accounting
Office (1994).
For these reasons the Colombian conflict became more complex during the nineties. It
has three conflicting groups: the guerrilla trying to overthrow the government and control
the state, the government struggling to retain power, and the paramilitary groups fighting
against guerrillas, trying to seize the territory under the guerrilla control. Moreover, even if
formally the Colombian conflict has three actors, there is evidence showing that the paramilitaries have acted in coordination with the regular army to protect the interests of powerful
elites, including multinational companies, large landowners and drug traffickers. Acemoglu
et al. (2009) present evidence supporting the fact that paramilitary groups have significant
effects on elections. The authors claim that this supports the idea that paramilitaries have
a symbiotic relationship with politicians: the irregular army provides votes to politicians
with similar preferences concerning the provision of public goods, and politicians implement
policies that are close to those preferred by the paramilitaries.
3
Empirical Strategy
In order to assess the impact of the provision of roads and education on the Colombian conflict
we model both the probability for an irregular army to commit an attack (onset of conflict)
and the average number of attacks (intensity of conflict) in the Colombian municipalities.
The naive specification for the onset of conflict is:
1l [Ai > 0] = α + θ1 Ri + θ2 Ei + Xi0 δ + ui ,
5
(1)
.
Similarly, for the intensity of conflict we have:
Ai = β + θ3 Ri + θ4 Ei + Xi0 γ + νi ,
(2)
In equation (1) the dependent variable, 1l [Ai > 0], is a dummy variable taking value one
if municipality i has experienced at least one attack by an irregular army in the period 20002005; and zero otherwise. We use two outcome dummies, one for guerrilla attacks and the
other for paramilitary attacks. In Equation (2) the dependent variable, Ai is a continuous
(latent) variable with a lower limit.3 We use two outcome variables, yearly average of guerrilla
attacks and paramilitary attacks in the period 2000-2005. We use OLS in order to estimate
the best linear approximation to the conditional expectation function. Given the nature of
our dependent variables, Equation (1) is also estimated using a probit model and Equation
(2) is also estimated using a tobit model.4 The results using the probit and tobit models are
reported in Appendix A.
For both equations variable Ri measures the density of primary and secondary roads
(Km of roads/Area) in municipality i, Ei is the average quality of education in municipality
i measured by a standardized national test. Vector Xi is a vector of control variables of
municipality i, including department fixed effects, size of municipality (average population),
geographical controls (longitude, latitude, temperature and rainfall), and welfare and institutional controls (institutions, political elections and poverty). Variables ui and νi are the
error terms. The coefficients θj , j = 1, ..., 4, are the coefficients of interest. They capture
correlations between our variables of interest and conflict.
Estimates of Equations (1) and (2) are very likely to be biased due to endogeneity issues.
There might be a reverse causality between the provision of public goods and conflict. Not
only may the provision of public goods affect conflict, but conflict may also affect the provision
of public goods. For instance, Villegas and Duque (2009) report that part of the violent
activity of irregular armies in Colombia is against public infrastructures. Barrera and Ibáñez
(2004) provide evidence to show that conflict lowers education enrolment rates for all age
groups. The effect is greater for more vulnerable groups like women and youngsters, especially
among indigenous people. In addition, there exist various ways, as presented in Novelli (2010)
that education (opportunities, actors and institutions) can be negatively affected by violent
3. Since all continuous variables are standardized, the lower limit is not zero anymore. For the outcome A, it
is equal to 0−E[A]
, where E[·] denotes the mean and V [·] denotes the variance.
V [A]
4. The tobit model allows us to estimate the effect for municipalities that already suffer conflict. We do not
use a poisson or negative binomial functions which are indicated for count data, because our outcome is the
standardized (mean equals to zero and variance equals to one) yearly average of a count variable (attacks of
irregular armies), which is a continuous variable.
6
conflict. Aspects such as direct attacks to schools, students or teachers; sexual violence; forced
recruitment or occupation of school buildings and the psychological damage of exposure to
conflict of the main actors of the educational process.
To deal with the endogeneity issues we use an IV approach. We instrument the provision
of each of the two public goods mentioned above. To estimate the model we use Two-Stage
Least Squares. Formally,
Ri = λ + ηIViR + ζIViE + Xi0 ϑ + i
Ei = κ + ιIViR + ξIViE + Xi0 ρ + µi
(3)
b i + θ6 E
bi +
Outcomei = ϕ + θ5 R
Xi0 ϕ
+ εi ,
where Outcomei is either 1l [Ai > 0] for the onset of conflict or Ai for the intensity of conflict.
Ri is the road density in municipality i, Ei is the quality of education in municipality i,
IViR is the instrument for the road density in municipality i and IViE is the instrument for
the quality of education in municipality i. The other variables are the same as described
previously. Our coefficients of interest are θ5 and θ6 . This captures the causal effect of
quality of education and road density on conflict. The first two equations in Equation (3) are
bi and E
bi , that come from
estimated in the first stage. The estimated values of R and E, R
the first stage are used in the second stage as regressors.
The instrument for road density is the road density in 1949 by municipality. This instrument is a proxy for the historical level of public good provision in each municipality. The
provision of public goods is more likely to be improved in those municipalities that have
already invested on them. One key advantage of our instrument is that it is previous to the
year in which the oldest Colombian guerrilla, the FARC, was founded (1964).
The instrument for quality of education is the average years of education of household
heads in 1993 by municipality. This instrument is a proxy for the average educational level of
the previous generation in each municipality. The literature on economics of education has
shown that there exists a positive correlation between the level of parents education and the
educational achievement of their children.
Since both instruments are historical they are likely to not fulfill the exclusion restriction.
Not only might road density in 1949 affect conflict through contemporary road density but
also through other variables. For instance, enjoying more road density in 1949 makes more
likely for a municipality to have better institutions or less poverty. Similarly, a municipality
whose households heads are more (less) educated is more likely to be relatively richer (poorer)
7
than the others. This in turn affects where the irregular armies attack. In order to tackle
this issue we directly include a set of variables that might be related to both the provision
of public goods and conflict, including some of institutions and poverty. These controls are
explained in the next section.
Some other caveats to our approach are in order. First, there might be problems of
omitted variables. This might occur if, for instance, roads were build in municipalities with
a specific (low or high) level of poverty. In this case, the instrument would be related to
the outcome through an omitted variable (poverty). To control for this, and the other main
explanations provided by the literature, namely, state capacity and governance of conflict,
we include controls of political elections, presence of institutions and poverty. A caveat is
important in this point. Governance is a concept that is not easy to measure. It has to
do with the political process and how politicians are accountable to citizens. However the
relationship between governance and elections is complex. On the one hand, the literature on
public economics has stressed the relationship between elections and politician performance.
Particularly important for us, it has shown that a larger margin of victory in elections might
be related to larger taxes because of the large citizens approval of politicians performance
(Solé-Ollé, 2003). On the other hand, it might reflect that citizens have been forced to vote for
some candidates (Acemoglu, et. al. 2013). Even if it is hard to interpret this relationship it
seems that governance is related to election results. We use the proportion of votes obtained
by the winner in mayoral elections as a measure of election results.5
Second, we might have problems of weak instruments. To check this we look at the
t-statistic of the coefficients of the instruments included in the two first-stage equations in
Equation (3). We also look at the F-statistic of the first stage. These regressions are estimated
using 2SLS.6
Since our instruments are cross-section data, we collapse the other variables into averages
using annual data for the period 2000 - 2005. Using panel data together with cross-section IV
could artificially reduce the standard errors of the estimates. The price of using cross-section
data is that we cannot introduce municipality fixed effects to sweep out the effect of unobservables at the municipality level. Instead we introduce a complete battery of controls. As
mentioned above we include department fixed effects, size controls (population), geographical
and climate controls (longitude, latitude, temperature and rainfall), welfare and institutional
5. One of the most common measures of market concentration in the literature of industrial organization
is
P
the concentration ratio. This is the sum of the m-largest firms’ shares in the market, CRm = m
s
.
The
i
i=1
proportion of votes obtained by the winner can be interpreted as the one-candidate concentration ratio, CR1 .
6. In the Appendix we report results for the probit and tobit models. We use the ML estimator. Since this
estimator is very likely not to converge when there are more than one endogenous variable, we only report
the results introducing each endogenous variable one by one. Instead we control for the instrument of the
endogenous variable that we are not instrumenting.
8
controls (institutions, political elections and poverty) and the level of coca production in
1994. Population size helps to control for the scale of conflict. Geographical controls are
fully exogenous and may help to explain some likelihood of conflict, related to strategic
zones or strategic corridors for irregular armies. Finally, we standardize7 all the continuous
(explained and explanatory) variables to facilitate comparison between the coefficients of
interest.
4
Data and Descriptive Statistics
As mentioned in the previous section, we build up a unique cross-section database that comes
from collapsed panel data for the period 2000-2005, gathered from different sources. Data on
the Colombian conflict comes from CERAC, an event-based database at municipality level
(1,002 Colombian municipalities). Data on roads and geographic coordinates come from the
National Geography Office (IGAC). The measure of the quality of education is a language
test score in a national standardized test administrated by the National Office for Education Quality (ICFES). Population size, educational attainment and poverty data (proportion
of households in the municipality with unmet basic needs) are from the National Statistics
Office (DANE). Population sizes are annual estimations based on population censuses. Educational attainment and poverty variables come from the 1993 population census. Data
on temperature and rainfall are from the National Weather Office (IDEAM). Data on the
number of institutions per 1,000 inhabitants in 1995 (including security, law enforcement,
financial, social and bureaucratic institutions) come from Fundacion Social, a Jesuit NGO
in Colombia. Data on the proportion of votes obtained by the winner in mayoral elections
in 1997 come from the National Election Office (Registradurı́a Nacional). The variable that
measures the level of coca production in 1994 is taken from Diaz and Sanchez (2004). The
variable is a categorical variable that takes the following values: 0 (no hectares of coca harvested); 1 (1-100 hectares of coca harvested); 2 (101-1,000 hectares of coca harvested) and 3
(more than 1,000 hectares of coca harvested).
In Table 1 we report the main descriptive statistics of the variables used in the empirical
estimation. Regarding conflict, a given municipality has a 36% probability of being attacked
by the paramilitary and 59% of being attacked by the guerrilla, on average; while the average
intensity of conflict, measured by the number of attacks, is higher for the guerrilla (0.63) than
for the paramilitary (0.17). In Figure 1 we report the spatial distribution of the onset and
intensity of the Colombian conflict for both paramilitaries and the guerrilla. Both groups
carry out attacks in practically all of Colombia except for the south, which is covered by the
northern part of the Amazon jungle.
7. Mean equals to zero and variance equals to one.
9
Table 1: Descriptive statistics
Variable
Obs
Mean
Std. Dev.
Min
Max
Onset of conflict (guerrilla)
Onset of conflict (paramilitary)
Intensity of conflict (guerrilla attacks)
Intensity of conflict (paramilitary attacks)
1,002
1,002
1,002
1,002
0.59
0.36
0.63
0.17
0.49
0.48
1.30
0.41
0
0
0
0
1
1
14.71
4.71
Road density (kms/km2)
Education (test score)
1,002
1,002
0.27
46.17
0.54
2.02
0
38.23
8.74
53.76
Population (thousand inhabitants)
Temperature
Rainfall
Area (km2)
Longitude
Latitude
Institutions in 1995
Poverty in 1993
Winner voting in 1997 (%)
Coca crops in 1994
1,002
1,002
1,002
1,002
1,002
1,002
948
1,002
934
1,002
41.02
21.35
1,879.01
756.66
-74.71
5.59
1.19
52.04
51.37
0.09
240.23
4.97
1,030.98
1,821.72
1.53
2.46
0.85
18.82
12.69
0.45
1.09
3.90
160.00
17.53
-78.83
-4.19
0
9
12
0
6,725.95
28.90
7,750.00
18,381.05
-67.54
11.74
12.49
100.00
94.17
3
1,002
1,002
0.08
4.67
0.13
1.15
0
0
1.28
8.50
Outcomes
Endogenous regressors
Controls
Instruments
Road density in 1949 (kms/km2)
Education Attainment in 1993 (years)
Regarding road density, Colombian municipalities had an average of 0.27 kilometres of
roads per square kilometre in 2001. Again, road density varies widely across municipalities,
from zero to 8.74 kilometres of roads per square kilometre. The quality of education (language
score) is measured on a scale from 0 to 100. Language score averages 46.17 points across
Colombian municipalities. In Figure 2 we depict the distribution of these variables on the
map of Colombia. Both road density and the quality of education are worse on the periphery
of the country. Road density is also bad in Magdalena Medio, and the poorest quality of
education is concentrated on both the Pacific and Caribbean coasts.
Regarding controls, the population size of Colombian municipalities ranges from one thousand to almost seven million, with an average of 41 thousand inhabitants. The average temperature is 21 Celsius degrees and average rainfall is 1,879 millimeters per year. There was an
average of one institution per 1,000 inhabitants in 1995, with a maximum of 12 institutions.
Around 52.04% of the population was poor in 1993. The average winner voting proportion in
mayoral elections in 1997 was of 51.37% with a range that include mayors elected with only
12.0% of the votes up to 94.17% of the votes.
10
Figure 1: Onset and Intensity of conflict
The instruments used to tackle the endogeneity problem are depicted in Figure 2. First,
in 1949, the municipalities with the smallest quantity of primary and secondary roads were
evenly distributed across Colombian Departments. Municipalities with no roads are very
often far from their respective department capitals. Second, the educational attainment of
heads of households in 1993 was 4.67 years, on average.
5
Main Results
In this section we focus on the results obtained for the determinants of both the average
number of attacks (intensity of conflict) and the probability of suffering an attack (onset
of conflict).8 Recall that all continuous variables have been standardized for the regression
analysis in order to facilitate comparison of coefficients. As a baseline set of results in Table 2
we report the estimations of the naive specifications presented in Equation (1) and Equation
8. The tables for both intensity and onset report the results for paramilitaries an guerrilla. We have done
all the estimations for the total number of attacks and the total onset of conflict (sum of paramilitaries an
guerrilla) obtaining consistent estimates with those reported. These results are available upon request.
11
Figure 2: Main explanatory variables and historical instruments
(2). For each measure of conflict (onset and intensity) we report coefficients with the full set
of controls and Department fixed effects. The correlation between road density and conflict
seems to be negative. It is significant for all measures of conflict except for the onset of
paramilitary attacks. Finally, the naive estimations for education show that the correlation
between the quality of education and conflict is less robust. In this case the estimated
coefficients are more erratic both in magnitude and significance.
Estimates of Equation (3) for road density and education quality are reported in Table 3
(intensity of conflict) and Table 4 (onset of conflict).
Each of these tables has three panels and six columns. In panel A we report the results
for paramilitary attacks. In panel B we report the results for guerrilla attacks. In panel C we
report the first stage results. In column (1) we report results with no controls. We progressively add controls in the subsequent columns. In column (2) we report results controlling
for the instruments of the other endogenous variable, size and geographical controls (average
municipality population, longitude, latitude, temperature and rainfall), coca crops in 1994,
and department fixed effects. In column (3)-(5) we separately control for presence of institutions, poverty incidence and vote proportion of winner in mayoral elections, respectively. In
12
Table 2: Naive estimations
VARIABLES
Paramilitaries
Onset
Intensity
Guerrilla
Onset
Intensity
Road Density
-0.0188
(0.0234)
-0.189***
(0.0600)
-0.124***
(0.0286)
-0.142***
(0.0530)
Education Quality
0.0269
(0.0220)
0.0351
(0.0322)
0.00557
(0.0235)
0.0801*
(0.0360)
X
X
885
X
X
885
X
X
885
X
X
885
Other Controls
Department FE
Observations
Note: OLS results for both onset and intensity of conflict. Robust standard
errors in parenthesis. Included controls are: average municipality population,
longitude, latitude, temperature, rainfall, number of institutions per 1,000
inhabitants in 1995, average education attainment of household heads in 1993,
poverty in 1993, winner voting proportion in mayoral elections in 1997 and
coca crops in 1994. *** is significant at the 1% level. ** is significant at the
5% level. * is significant at the 10% level.
column (6) we report results with all controls.The estimates of θ5 and θ6 are reported in the
first two rows of panels A and B. The estimates of η and ξ are reported in the first row and
fifth row of panel C, respectively.
In the estimations with the full set of control variables (column 6 in Table 3) the results for
road density show that a one standard deviation increase (departing from its mean value) of
road density causes a 0.56 standard deviation decrease in the number of paramilitary attacks
and a 0.22 standard deviation decrease in the number of guerrilla attacks. The effect on
paramilitaries attacks is statistically significant. The effect on guerrilla attacks is not. The
presence of institutions seems to reduce the number of paramilitary and guerrilla attacks,
which support the state capacity story. Other explanations seem to play no role. The results
are far less conclusive for quality of education. The estimates of the coefficient of interest
for paramilitary attacks are stable in magnitude and sign but are not significant (Panel A,
Table 3). The estimates of the effect on guerrilla attacks are not stable in magnitude, sign
nor significance (Panel B, same table). Panel C of Table 3 shows that the instrument for
roads (road density in 1949) and education quality (education attaintment in 1993) are very
significant to explain the endogenous variables. An increase of one standard deviation of road
density in 1949 causes an increase of 0.08 standard deviations of road density in the 2000s.
An increase of one standard deviation of education attaintment in 1993 causes an increase of
0.17 standard deviations of education quality in the 2000s. All F-stats except one are larger
than 10.
The results for the onset of conflict are more robust (see Table 4). The results for road
13
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table 3: Intensity of Conflict - Road Density - Education Quality
Road Density
Education Quality
(2)
(3)
(4)
(5)
(6)
-0.881***
(0.179)
0.481***
(0.121)
-0.481**
(0.211)
0.151
(0.153)
-0.390*
(0.206)
0.210
(0.159)
-0.0690***
(0.0243)
-0.633**
(0.271)
0.254
(0.313)
-0.593**
(0.237)
0.167
(0.165)
-0.0455
(0.0303)
-0.564**
(0.283)
0.439
(0.340)
-0.0619**
(0.0300)
0.0210
(0.117)
-0.0538*
(0.0304)
Institutions in 1995
Poverty in 1993
-0.0867
(0.107)
Winner voting in 1997
Road Density
Education Quality
(1)
(2)
(3)
(4)
(5)
(6)
-0.808***
(0.178)
0.198**
(0.0945)
-0.298*
(0.179)
-0.0582
(0.0887)
-0.239
(0.190)
-0.00852
(0.0978)
-0.0883**
(0.0385)
-0.378
(0.241)
0.0941
(0.212)
-0.329
(0.200)
-0.0272
(0.0941)
0.0174
(0.0477)
-0.224
(0.267)
0.255
(0.245)
-0.109***
(0.0330)
0.0948
(0.115)
0.0160
(0.0497)
Institutions in 1995
Poverty in 1993
0.00367
(0.108)
Winner voting in 1997
Road Density in 1949
Panel C:
First Stages
(1)
F-test
R2 1st stage
Education Attainment in 1993
F-test
R2 1st stage
Observations
Other Controls
Department FE
0.089***
(0.0288)
41.493***
0.2246
0.3875***
(0.0309)
82.447***
0.1678
0.084***
(0.0242)
26.427***
0.4719
0.3701***
(0.0295)
81.799***
0.5632
0.091***
(0.0240)
21.953***
0.5368
0.3586***
(0.0301)
74.468***
0.5733
0.082***
(0.0275)
17.036***
0.472
0.179***
(0.0395)
10.365***
0.5877
0.077***
(0.0273)
25.284***
0.4773
0.3622***
(0.0307)
72.723***
0.5596
0.0844***
(0.0243)
13.428***
0.5506
0.172***
(0.0427)
8.207***
0.5905
1,002
1002
X
X
948
X
X
1,002
X
X
934
X
X
885
X
X
Note: OLS regressions. Robust standard errors in parenthesis. Institutions in 1995 are the number of institution per 1000 inhabitants
including security, law enforcement, financial, social and bureaucratic institutions. Poverty in 1993 is the proportion of households in
the municipality with unmet basic needs. Winner voting in 1997 is the proportion of votes obtained by the winner in mayoral elections
in 1997. Other controls are: average municipality population, longitude, latitude, temperature, rainfall and coca crops in 1994. *** is
significant at the 1% level. ** is significant at the 5% level. * is significant at the 10% level.
density show that a one standard deviation increase of road density causes a reduction of 0.28
in the probability of being attacked by paramilitaries and a reduction of 0.41 in the probability of being attacked by the guerrilla. Both effects are significant and robust to different
specifications. The presence of institutions seems to reduce the onset of both paramilitary
and guerrilla attacks, which support the state capacity story. Notice as well that poverty
14
seems to increase the onset of guerrilla attacks. This is compatible with the opportunitycost-of-conflict explanation. Other explanations seem to play no role. The results are a bit
less conclusive for quality of education. The estimates of the coefficient of interest for the
onset of both paramilitary and guerrilla attacks are stable in magnitude and (positive) sign
but are not always significant (Panels A and B, Table 4). Panel C of Table 4 replicates Panel
C of Table 3.9
Furthermore we look at whether the usual explanations in the conflict literature might
be behind these results. This has two purposes. On the one hand, to check whether some
omitted variables might be behind our results. This also serves to check whether the exclusion restriction holds. On the other hand, to provide some evidence that supports these
explanations. We look at state capacity and governance, and opportunity cost of conflict explanations. As variables of state capacity and governance we use the number of institutions
per one thousand inhabitants in 1995 and the proportion of votes obtained by the winner
in mayoral elections in 1997. The first variable measures institutional presence in different
dimensions like security, law enforcement, financial sector, social sector and bureaucracy. The
intuition is that the larger the institutional presence in a given municipality, the larger the
state capacity and governance there. Interestingly, both paramilitary and guerrilla attacks
decrease with the presence of institutions (see column (3) of Tables 3 and 4). The result on
paramilitaries is in line with findings of Acemoglu et al. (2013) who show that paramilitaries
somewhat replace the state in those municipalities where the presence of state is scarce. The
results for the proportion of votes are reported in column (5) of Panels A and B in Tables
3 and 4. They show that election results has no significant effect on both paramilitary and
guerrilla attacks. In column (6) we report the results including all controls. Significance and
signs of the previous coefficients are preserved.
As a variable of opportunity cost of conflict we use the proportion of households with
unmet basic needs in 1993. The idea is that the opportunity cost of making war is smaller for
poor households. Of course, the variable is also related to opportunities to predate. Irregular
armies might attack richer municipalities to seek rents. Results show that poverty has no
effect on the intensity of conflict (see column (4)of Panels A and B in Table 3). Poverty has
a positive effect on the onset of guerrilla attacks and no effect on the onset of paramilitary
attacks (see column (4) of Panel B in Table 4). Findings for the onset of guerrilla attacks are
compatible with the opportunity cost story.
Our results are robust to controlling for measures of opportunity cost of conflict and
state capacity and governance. They suggest that public policies that are useful to prevent
conflict are not necessarily the same as those that are useful to mitigate conflict. Mobility
9. The tobit results in the Appendix show that the negative effect of road density on conflict is larger for
those municipalities that are already suffering paramilitary and guerrilla attacks.
15
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table 4: Onset of Conflict - Road Density - Education Quality
Road Density
Education Quality
(2)
(3)
(4)
(5)
(6)
-0.564***
(0.0935)
0.212***
(0.0381)
-0.364***
(0.0899)
0.129***
(0.0451)
-0.282***
(0.0918)
0.111**
(0.0452)
-0.0462***
(0.0144)
-0.362***
(0.0900)
0.0779
(0.0585)
-0.375***
(0.0936)
0.145***
(0.0464)
-0.0156
(0.0143)
-0.285***
(0.0953)
0.0704
(0.0598)
-0.0507***
(0.0141)
-0.0384
(0.0281)
-0.00954
(0.0145)
Institutions in 1995
Poverty in 1993
-0.0324
(0.0261)
Winner voting in 1997
Road Density
Education Quality
(1)
(2)
(3)
(4)
(5)
(6)
-0.706***
(0.134)
0.0657*
(0.0390)
-0.455***
(0.132)
0.0527
(0.0449)
-0.433***
(0.138)
0.0525
(0.0487)
-0.0266
(0.0213)
-0.460***
(0.134)
0.180***
(0.0610)
-0.455***
(0.135)
0.0942**
(0.0464)
0.00989
(0.0141)
-0.410***
(0.143)
0.242***
(0.0681)
-0.0444**
(0.0183)
0.0943***
(0.0293)
0.00667
(0.0141)
Institutions in 1995
Poverty in 1993
0.0814***
(0.0259)
Winner voting in 1997
Road Density in 1949
Panel C:
First Stages
(1)
F-test
R2 1st stage
Education Attainment in 1993
F-test
R2 1st stage
Observations
Other Controls
Department FE
0.089***
(0.0288)
41.493***
0.2246
0.3875***
(0.0309)
82.447***
0.1678
0.084***
(0.0242)
26.427***
0.4719
0.3701***
(0.0295)
81.799***
0.5632
0.091***
(0.0240)
21.953***
0.5368
0.3586***
(0.0301)
74.468***
0.5733
0.082***
(0.0275)
17.036***
0.472
0.179***
(0.0395)
10.365***
0.5877
0.077***
(0.0273)
25.284***
0.4773
0.3622***
(0.0307)
72.723***
0.5596
0.0844***
(0.0243)
13.428***
0.5506
0.172***
(0.0427)
8.207***
0.5905
1,002
1,002
X
X
948
X
X
1,002
X
X
934
X
X
885
X
X
Note: OLS regressions. Robust standard errors in parenthesis. Institutions in 1995 are the number of institution per 1000 inhabitants
including security, law enforcement, financial, social and bureaucratic institutions. Poverty in 1993 is the proportion of households in
the municipality with unmet basic needs. Winner voting in 1997 is the proportion of votes obtained by the winner in mayoral elections
in 1997. Other controls are: average municipality population, longitude, latitude, temperature, rainfall and coca crops in 1994. *** is
significant at the 1% level. ** is significant at the 5% level. * is significant at the 10% level.
of public investments might pay a role. Investments in roads are not movable and might
generate new economic opportunities by reducing cost of transportation or making possible
other investments. In contrast, investments in education are not. They help to accumulate
human capital of people that might migrate. The relationship between migration and conflict
is complex. There is evidence showing that violence has a non-linear effect on migration
16
(Bohra-Mishra et al., 2011). The impact of education policies will depend on who migrates,
particularly important if migrants are relatively poor or relatively rich. If investments in
education increases migration of more educated people to, say, the big cities, we may end
up in a situation with more inequality across municipalities, which can in turn exacerbate
conflict. This analysis requires data at the household level and goes beyond the scope of this
paper .
6
Final Remarks
Is there any empirical evidence regarding the impact of the provision of public goods on
the onset and intensity of conflict? Which are the most effective public policies to reduce
conflict? To answer these questions, this paper has addressed an important and yet to be
unveiled issue regarding the determinants of the violent conflict in Colombia.
We analyse whether two public policies that are frequently mentioned as effective to
reduce conflict are effective, that is, whether public investments in roads and education
reduce conflict. Our main findings show that conflict goes down with the provision of public
road infrastructures; however, results for investments in education seems not to have a clear
cut impact on conflict. These findings are robust to controlling for measures of state capacity,
governance, opportunity cost of conflict, population size, coca crops, geographical and welfare
controls. This opens the possibility for alternative explanations: it might have to do with
the immobility/mobility of roads and education. There is already some literature that points
out to the role of public goods in conflict and rent-seeking behavior (see Esteban and Ray,
2001 and Katz et al. 1990).
Our results, framed in the existent literature on the relationship between public goods and
conflict, seem to point out that the finding on roads is compatible with the idea that having
more access to roads might reduce opportunity cost of conflict and increase state capacity and
governance. Any land reform intended to improve living conditions of the rural population
should consider policies that increase access to roads as a key complementary policy. The
finding on education is in line with the fact that more educated people might emigrate to
big cities going away from regions in conflict. Policy recommendations are straightforward.
Most policy efforts in Colombia have been put into the war against drugs, including coca
eradication. Those efforts must be complemented with investments in public infrastructures.
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from Colombia. Journal of the European Economic Association, European Economic
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Association, 11 (s1), pages 5-44.
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[15] Dube, O., Vargas, J.F., 2013. Commodity Price Shocks and Civil Conflict: Evidence
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19
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Polı́tico, ISBN: 958-44-5928-2.
20
A
Probit (onset) and Tobit (intensity) results
Table A.1: Naive estimations
VARIABLES
Paramilitaries
Onset
Intensity
Guerrilla
Onset
Intensity
Road Density
-0.0241
(0.0453)
-0.319***
(0.119)
-0.253***
(0.0436)
-0.376***
(0.0858)
Education Quality
0.0441
(0.0285)
0.231**
(0.107)
0.00205
(0.0277)
0.125*
(0.0702)
X
X
873
X
X
885
X
X
859
X
X
885
Other Controls
Department FE
Observations
Note: Marginal effects of Probit regressions for onset and of Tobit regressions
for intensity. Robust standard errors in parenthesis. Included controls are: average municipality population, longitude, latitude, temperature, rainfall, coca
crops in 1994, number of institutions per 1,000 inhabitants in 1995, average
education attainment of household heads in 1993, poverty in 1993 and winner
voting proportion in mayoral elections in 1997. *** is significant at the 1%
level. ** is significant at the 5% level. * is significant at the 10% level.
21
Panel C:
First Stage
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table A.2: Intensity of Conflict - Road Density
Road Density
(1)
(2)
(3)
(4)
(5)
(6)
-2.771***
(1.056)
-3.492**
(1.543)
-2.252**
(1.053)
-0.530***
(0.133)
-3.677**
(1.660)
-4.118**
(1.932)
-0.0615
(0.115)
-2.811**
(1.309)
-0.531***
(0.158)
-0.236
(0.175)
-0.115
(0.0990)
Institutions in 1995
Poverty in 1993
-0.202
(0.191)
Winner voting in 1997
Road Density
(1)
(2)
(3)
(4)
(5)
(6)
-1.963***
(0.571)
-1.540***
(0.572)
-1.335**
(0.534)
-0.156
(0.0969)
-1.456**
(0.589)
-1.700**
(0.689)
0.0501
(0.0706)
-1.001*
(0.592)
-0.275***
(0.0729)
0.152*
(0.0831)
0.0237
(0.0675)
Institutions in 1995
Poverty in 1993
0.127
(0.0800)
Winner voting in 1997
Road Density in 1949
0.128***
(0.0320)
0.084***
(0.0260)
0.0920***
(0.0236)
0.0829***
(0.0270)
0.0772***
(0.0267)
0.0844***
(0.0237)
F-test
R2 1st stage
16.04***
0.022
14.37***
0.4214
14.60***
0.5368
9.05***
0.472
7.99***
0.4773
12.04***
0.5506
1,002
1002
X
X
948
X
X
1,002
X
X
934
X
X
885
X
X
Observations
Other Controls
Department FE
Note: Coefficients are marginal effects of Tobit regressions. Robust standard errors in parenthesis. Institutions in 1995
are the number of institution per 1000 inhabitants including security, law enforcement, financial, social and bureaucratic
institutions. Poverty in 1993 is the proportion of households in the municipality with unmet basic needs. Winner voting
in 1997 is the proportion of votes obtained by the winner in mayoral elections in 1997. Other controls are: average
municipality population, longitude, latitude, temperature, rainfall and coca crops in 1994. *** is significant at the 1%
level. ** is significant at the 5% level. * is significant at the 10% level.
22
Panel C:
First Stage
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table A.3: Onset of Conflict - Road Density
Road Density
(1)
(2)
(3)
(4)
(5)
(6)
-0.802***
(0.117)
-1.176***
(0.175)
-1.162***
(0.322)
-0.188**
(0.0791)
-1.202***
(0.170)
-1.206***
(0.171)
-1.294***
(0.299)
-0.151*
(0.0852)
-0.126
(0.0896)
-0.0469
(0.0446)
873
Institutions in 1995
Poverty in 1993
-0.0977
(0.0641)
Winner voting in 1997
Observations
Road Density
1,002
990
935
990
-0.0279
(0.0390)
922
(1)
(2)
(3)
(4)
(5)
(6)
-0.923***
(0.101)
-1.365***
(0.179)
-1.435***
(0.302)
-0.0181
(0.0561)
-1.304***
(0.227)
-1.400***
(0.176)
0.0199
(0.0423)
-1.301***
(0.448)
-0.0463
(0.0695)
0.299**
(0.119)
-0.00511
(0.0509)
Institutions in 1995
Poverty in 1993
0.197*
(0.108)
Winner voting in 1997
Road Density in 1949
0.128***
(0.0320)
0.084***
(0.0260)
0.0917***
(0.0236)
0.0826***
(0.0270)
0.0772***
(0.0267)
0.0844***
(0.0238)
F-test
R2 1st stage
16.04***
0.022
14.37***
0.4214
14.60***
0.5368
9.05***
0.472
7.99***
0.4773
12.04***
0.5506
1,002
974
X
X
919
X
X
974
X
X
908
X
X
859
X
X
Observations
Other Controls
Department FE
Note: Coefficients are marginal effects of Probit regressions. Robust standard errors in parenthesis. Institutions
in 1995 are the number of institution per 1000 inhabitants including security, law enforcement, financial, social
and bureaucratic institutions. Poverty in 1993 is the proportion of households in the municipality with unmet
basic needs. Winner voting in 1997 is the proportion of votes obtained by the winner in mayoral elections in 1997.
Other controls are: average municipality population, longitude, latitude, temperature, rainfall and coca crops in
1994. *** is significant at the 1% level. ** is significant at the 5% level. * is significant at the 10% level.
23
Panel C:
First Stage
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table A.4: Intensity of Conflict - Education
Education Quality
(1)
(2)
(3)
(4)
(5)
(6)
1.281***
(0.298)
0.788***
(0.296)
0.859***
(0.307)
-0.464***
(0.119)
1.191
(0.740)
0.906***
(0.328)
-0.0692
(0.0783)
1.223
(0.813)
-0.437***
(0.140)
0.161
(0.343)
-0.0771
(0.0793)
Institutions in 1995
Poverty in 1993
0.250
(0.319)
Winner voting in 1997
Education Quality
(1)
(2)
(3)
(4)
(5)
(6)
0.322**
(0.140)
0.0504
(0.129)
0.115
(0.145)
-0.137
(0.0896)
0.665*
(0.361)
0.148
(0.140)
0.0456
(0.0632)
1.077**
(0.481)
-0.191**
(0.0781)
0.499**
(0.212)
0.0331
(0.0671)
Institutions in 1995
Poverty in 1993
0.374**
(0.164)
Winner voting in 1997
Education attainment in 1993
0.388***
(0.0309)
0.370***
(0.0290)
0.359***
(0.0295)
0.180***
(0.0388)
0.362***
(0.0301)
0.172***
(0.0417)
F-test
R2 1st stage
156,67***
0,1678
157.05***
0,5632
141.57***
0,5735
20.66***
0,5877
138.67***
0,5596
16.28***
0,5905
1,002
1,002
X
X
948
X
X
1,002
X
X
934
X
X
885
X
X
Observations
Other Controls
Department FE
Note: Coefficients are marginal effects of Tobit regressions. Robust standard errors in parenthesis. Institutions in 1995 are the number
of institution per 1000 inhabitants including security, law enforcement, financial, social and bureaucratic institutions. Poverty in 1993
is the proportion of households in the municipality with unmet basic needs. Winner voting in 1997 is the proportion of votes obtained
by the winner in mayoral elections in 1997. Other controls are: average municipality population, longitude, latitude, temperature,
rainfall and coca crops in 1994. *** is significant at the 1% level. ** is significant at the 5% level. * is significant at the 10% level.
24
Panel C:
First Stage
Panel B: Guerrilla Attacks
Panel A: Paramilitary Attacks
Table A.5: Onset of Conflict - Education
Education Quality
(1)
(2)
(3)
(4)
(5)
(6)
0.486***
(0.0780)
0.103
(0.179)
0.212
(0.197)
-0.232***
(0.0822)
-0.274
(0.448)
0.193
(0.193)
-0.0191
(0.544)
-0.227**
(0.0925)
-0.181
(0.239)
-0.0361
(0.0534)
873
Institutions in 1995
Poverty in 1993
-0.222
(0.201)
Winner voting in 1997
Observations
Education Quality
1,002
990
935
990
-0.0586
(0.0522)
922
(1)
(2)
(3)
(4)
(5)
(6)
0.177*
(0.100)
-0.102
(0.179)
-0.220
(0.195)
-0.0219
(0.0553)
0.678*
(0.378)
0.0611
(0.188)
0.0363
(0.0473)
0.861**
(0.355)
0.0165
(0.0668)
0.536***
(0.138)
0.0300
(0.0435)
Institutions in 1995
Poverty in 1993
0.468***
(0.156)
Winner voting in 1997
Educational attainment in 1993
0.388***
(0.0309)
0.371***
(0.0291)
0.359***
(0.0297)
0.182***
(0.0390)
0.362***
(0.0303)
0.174***
(0.0420)
F-test
R2 1st stage
156,67***
0,1678
157.05***
0,5632
141.57***
0,5735
20.66***
0,5877
138.67***
0,5596
16.28***
0,5905
1,002
1,002
X
X
948
X
X
1,002
X
X
934
X
X
885
X
X
Observations
Other Controls
Department FE
Note: Coefficients are marginal effects of Probit regressions. Robust standard errors in parenthesis. Institutions in 1995 are the number
of institution per 1000 inhabitants including security, law enforcement, financial, social and bureaucratic institutions. Poverty in 1993
is the proportion of households in the municipality with unmet basic needs. Winner voting in 1997 is the proportion of votes obtained
by the winner in mayoral elections in 1997. Other controls are: average municipality population, longitude, latitude, temperature
rainfall and coca crops in 1994. *** is significant at the 1% level. ** is significant at the 5% level. * is significant at the 10% level.
25