(0.288) (0.688) (0.702) 4\. Instit. Quality Index 0.258** 0.121 –0.037 0.232*
0.320* 0.128 –0.026 0.276 0.134 (0.128) (0.117) (0.124) (0.121) (0.172)
(0.110) (0.092) (0.177) (0.163)

$Notes$: Considers only years from 2009 and before 2020. Defines the post
period as 2015. All panels use indices created following Anderson (2008). The
first panel uses the index based on the different violence variables. The
second panel uses the index based on weighted nighttime light intensity, GDP
per capita (from DANE), share of urban population, agricultural productivity,
formal employment and firm entry measures. The third panel uses the index
based on financial measures of local institutions, while the last panel is
based on measures of the overall performance of local institutions. The
columns show the total effect of treatment, the columns show the spillovers on
control municipalities within a distance $d$ of the closest treated
municipality, with $d\in${40, 60, 80}kms, and columns 3-5 disaggregate the
spillover on the control within certain rings for the preferred specification
with $d=60$. Standard errors are estimated following Conley (1999), allowing
for spatial correlation up to $d$kms away from a municipality’s centroid.

## Online Appendix

###  A Data Sources

First, to create the measures of historical presence of insurgent groups, I
use detailed data from the National Police and the Administrative Department
of Security. These data record, for different types of crimes, how many of
these crimes were committed by a given insurgent group in each municipality-
year pair. The criminal acts used for the creation of the two measures are:
attacks and assaults against the population, incursions to populated center,
incendiary terrorist acts, explosive terrorist acts, offensive actions,
kidnappings of politicians, members of armed forces and civilians, attacks
against transport infrastructure, illegal road checkpoints, armed harassment,
attacks against official entities, illegal roadblocks, assaults against
private property, political attacks, ambushes, clashes, armed contacts,
homicides, armed forces wounded and killed, and killings of members of the
insurgent group. The creation of the two measures is described in detail in
Section 2.

Population estimates come from the latest projections (January 2022) from the
Colombian National Administrative Department of Statistics (DANE), available
here, not from CEDE.

In Table LABEL:data_sources_tab, I summarize the data sources of the different
variables used throughout the paper. A * denotes those that come from CEDE.
For the Victims’ Unit measures, “events” means the number of occurrences of
such an act. Therefore, a person can suffer several “events” of the same type
over time.

Table A1: Data Sources Variable Name | Description | Source
---|---|---
Panel A. Violence Measures
Fights |  Number of terrorist acts, fights, combats, clashes and attacks (events) |  Victims’ Unit
Threats |  Number of threats |  Victims’ Unit
(Forced) Disappearances |  Number of forced disappearances (events) |  Victims’ Unit
(Forced) Displaced |  Number of forced displaced individuals (events) |  Victims’ Unit
Homicides |  Number of homicides (events), from two different sources |  Victims’ Unit and Ministry of Defence*
Kidnaps |  Number of kidnappings (events), from two different sources |  Victims’ Unit and Ministry of Defence*
Torture |  Number of tortures (events) |  Victims’ Unit
Property loss |  Number of losses of real estate property (events) |  Victims’ Unit
Terrorist acts |  Number of terrorist acts |  Ministry of Defence*
Mines |  Number of mine-related events |  Ministry of Defence*
Theft |  Total number of thefts |  Ministry of Defence*
Clashes/Attacks |  These data were kindly provided by Juan Vargas and have been used in a multitude of papers (see, e.g. De Roux and Martínez, 2021; Dube and Vargas, 2013; Guerra-Cújar et al., 2021). The data were originally collected by Restrepo, Spagat, and Vargas (2003) and have been updated by Universidad del Rosario. It covers the period from 1996 to 2018. De Roux and Martínez (2021) say the following about these data “records conflict events (i.e. clashes, attacks) involving the different agents in the conflict (left-wing guerrillas, right-wing paramilitaries, government forces). For each event, the dataset records of the location and the date of occurrence. The data is based on news reports from over 20 major newspapers, complemented with additional reports from NGOs and the Catholic church”. Also see Dube and Vargas (2013) for more information. I simply add all the clashes and attacks between the three different groups to come to a single, aggregate measure. |  Juan Vargas
Forced migration |  I received data from the Victims’ Unit on the amount of forced displaced coming into a municipality and the mount of individuals forced to leave a municipality. Based on these in- and out-migration flows, I create this measure of total forced migration as ((total forced migrated into municipality - total forced to outmigrate from municipality) / the municipality’s population) * 1000, as a measure of total forced migration. |  Victims’ Unit
Panel B. Economic Indicators
Nighttime light intensity |  Nighttime light intensity from two sources (the Defense Meteorological Satellite Program (DMSP), and the Visible Infrared Imaging Radiometer Suite (VIIRS)) are combined, cleaned, and harmonized to create a single panel dataset by Li et al. (2020). |  Li et al. (2020)
GDP per capita |  One measure of GDP per capita at the municipal level comes from DANE, here, available only from 2011 to 2020. The other measure is created following the procedure proposed by Sánchez Torres and España Eljaiek (2012). This is done in the following way: first, sum the total tax intake (property plus industry and commerce) for a municipality $m$ in department $d$ in year $t$. Then, calculate the total tax intake in $d$ in year $t$. Calculate the share of $m$’s tax intake relative to the $d$’s intake. This gives $m$’s relative importance. Then assign this share of $d$’s GDP to municipality $m$. Department GDP comes from DANE, tax revenue from the National Planning Department (DNP). |  DANE and DNP*
Urban built-up |  This is generated using the Band Ratio for Built-Up Area (BRBA) index developed by Waqar et al. (2012). It is measured as $BRBA=TM3/TM5$, where TM are different (spectral) bands from Landsat TM satellite images. This measure has been shown to perform very well in identifying urban built-up areas in settings like Colombia (Valdiviezo-Navarro et al., 2018). I estimate the index using Google Earth Engine. I first mask clouds and water bodies (identified using the MNDWI index), as water bodies affect the performance of the BRBA index. To classify a pixel as urban built-up, I use a threshold of 0.48 based on visual inspection of different threshold values. Values above 0.48 are classified as urban built-up. Then, I calculate the share of pixels within each municipality classified as urban. |  Landsat satellite images via Google Earth Engine
Agricultural productivity |  Defined as total tonnes produced of 271 agricultural crops in a given municipality divided by total area cultivated in hectares in the municipality. |  Based on data from the Ministry of Agriculture*
Agricultural production |  Defined as total tonnes produced of 271 agricultural crops per capita. |  Based on data from the Ministry of Agriculture*
Share of urban population |  Estimates of urban population based on population projections. |  DANE*
Firm entry |  Number of (new) businesses that requested a mercantile license from a Chamber of Commerce in a given year, and listed their commercial activities as taking place in a given municipality |  Confecámaras
Formal employment |  Average number of individuals (wage earners and self-employed) making contributions to healthcare (mandatory), pension funds and/or workers’ compensations in a given municipality-year. Note that for some of the earlier years, data are available for only some months |  Ministry of Health and Social Protection, PILA
Panel C. State Capacity Measures
Total revenue |  Sum of current revenue and capital income. |  DNP*
Tax revenue |  Total tax revenue, including property tax, industry and commerce tax, and gasoline tax, among others. |  DNP*
Government transfers |  Transfers to the municipality from other government levels. |  DNP*
Total expenditures |  Sum of current expenditures and capital expenditures. |  DNP*
Operational expenditures |  Expenditures on the operation of the municipality administration. |  DNP*
Total deficit |  Difference between current income and current expenditures. |  DNP*
Credit |  Net income from internal and external credits (received - paid). |  DNP*
Savings capacity |  Current savings over current income. |  DNP*
% of Expenditures invested (also called “Investment” in some tables) |  Share of expenditures used for investments. |  DNP*
Fiscal performance |  Index created by the DNP to measure municipalities’ fiscal performance. Goes from 0 (lowest) to 100 (highest). It is composed of six different indicators: share of current income spent on the operation of the local government, total debt, share of income coming from transfers from other levels of the government, capacity to generate own income, savings capacity and size of investments. |  DNP*
Overall performance |  From 2016, based on score on municipal performance from DNP. Before 2016, based on the Index of Integral Performance, also by the DNP. Both try to capture the effectiveness, efficiency and administrative capacity of the municipal administration. |  DNP*
Information openness |  Index of Open Government created by the Office of the Inspector General of Colombia, available only since 2010. Created using measures of internal control, organization of information and document management and others. For a precise definition of this variable, see the CEDE data catalogue. |  Office of the Inspector General of Colombia*
Aqueduct coverage |  Total aqueduct coverage. Data from CEDE covers only from 2010 to 2016. Data from the 2018 Census is used to complement this, from DANE. |  Sistema Único de Información de Servicios Públicos* and DANE
Garbage collection |  Total coverage of garbage collection system. Data from CEDE covers only from 2010 to 2016. Data from the 2018 Census is used to complement this, from DANE. |  Sistema Único de Información de Servicios Públicos* and DANE
Sewage coverage |  Total coverage of sewage system. Data from CEDE covers only from 2010 to 2016. Data from the 2018 Census is used to complement this, from DANE. |  Sistema Único de Información de Servicios Públicos* and DANE
Share voting |  Voting data for elections since 2006 (both national, department, and municipal) come from CEDE. |  CEDE
Panel D. Other Variables
Area |  |  DANE*
Dist. Capital |  Distance to department’s capital |  DANE*
Dist. Bogotá |  Distance to Bogotá |  DANE*
Small credit |  Value of credit to small producers |  Agronet*
Total credit |  Total value of credit to producers |  Agronet*
Conf. 1901/30 |  Dummy for whether the municipality experienced a land-related conflict between 1901 and 1931 |  CEDE
Spanish occup. |  Dummy for whether the municipality was occupied by the Spanish between 1510 and 1561 |  CEDE
GINI |  |  CEDE based on census data from DANE
MDP |  Municipal Poverty Incidence |  CEDE based on census data from DANE
NBI |  Unsatisfied Basic Needs |  DANE*
Social leaders killed |  Number of social leaders killed. |  Somos Defensores
Extortion |  Number of total extortion acts. |  Ministry of Defense*
Harbers15 |  Tax revenue divided by nighttime light intensity (standardized). |  Based on Harbers (2015)
Distance to border to Venezuela |  Distance to border to Venezuela in kms. |  From Martinez (2017)
Venezuelan Migrants with PEP |  Numbers of Venezuelan migrants that have received a Permiso Especial de Permanencia and registered at a given municipality |  From Colombia’s Migration Office, can be accessed here
Coca production |  Area cultivated with coca crops (in HAs). |  Ministry of Justice
Coca eradication areas |  Total area of coca crops eradicated (manually and by plane). |  Ministry of Justice
PNIS program |  Number of municipalities and beneficiaries from the PNIS coca cultivation program. |  UNODC, from here
Credit to agricultural producers (Banco Agrario) |  Total value of credits to different types of agricultural producers given by the Banco Agrario |  Agronet*
Credit to agricultural producers (FINAGRO) |  Total value of credits to different types of agricultural producers given by FINAGRO |  FINAGRO
First-year students |  Number of students enrolled in the first year of higher education programs (technical, university or post-graduate degrees) |  Ministry of Education
Number of higher education institutions |  Number of institutions providing higher educations programs (technical, university or post-graduate degrees) |  Ministry of Education
Demobilized individuals |  Number of demobilized members of insurgent groups (FARC/ELN) that have registered as living at a given municipality. |  Agency for Reincorporation and Normalization
Variables related to land restitution processes |  Number of land restitution claims presented to UAEGRTD (including the number of claimants and plots involved), number of requests handled and denied by land restitution courts, and the number of beneficiaries and plots returned by courts to claimants |  Land Restitution Unit (URT)
Survey data |  Data come from the AmericasBarometer surveys (since 2011) and other occasional surveys (“Muestra Especial Zonas Conflicto”, for 2013, 2015 and 2017) conducted by the Observatorio de la Democracia. Can be found here |  Observatorio de la Democracia

####  A.1 Creation of Summary Indices

In several of the sections of this study, I analyze the effects of the start
of the ceasefire on a wide-array of outcome variables for one particular
indicator (e.g. violence, economic activity or state capacity). For brevity
and compactness, I use two different approaches to summarize all the different
outcome variables into single measures that capture the relevant information.
One is based on Kling, Liebman, and Katz (2007) and the other one is based on
Anderson (2008). Here I explain how these are created:

  1. 1.

Kling, Liebman, and Katz (2007): results using the index suggested by Kling,
Liebman, and Katz (2007) are called “KLK index”. KLK argue that “the
aggregation improves statistical power to detect effects that go in the same
direction within a domain”. To create this index, the following steps are
followed:

    1. (a)

First, each of the measures is standardized by the pre-intervention values of
the variable in the control group (note: in the original paper, there is no
time dimension, so they just standardize by the control group. However, for
the purposes here, only pre-intervention periods are used to avoid treatment
contamination).

    2. (b)

Some variables have missing values for some years. For variables with data
missing for a year before (after) the treatment, I follow their imputation
method, and assign to the control/treatment group the mean (standardized)
value of the control/treatment group in the pre-(post-)treatment periods.

    3. (c)

All variables are aligned in the same direction, such that higher values
indicate “better” outcomes.

    4. (d)

The final index is the equally-weighted average of $z$-scores of the index’s
individual component variables, created in the first two steps.

    5. (e)

The final measure is then standardized for easiness of interpretation.

  2. This index has been used, for example, by Blumenstock et al. (2022) and Casey, Glennerster, and Miguel (2012).

  3. 2.

Anderson (2008): the second index has been used by Egger et al. (2019);
Gilligan, Pasquale, and Samii (2014); Haushofer and Shapiro (2016) and Karlan
and Zinman (2010), for example. It is implemented using the “swindex” command
in Stata created by Schwab et al. (2020). This one is very similar to the KLK
index, but assigns more weight to component variables that are less correlated
to other component variables, as these might carry more relevant information
not captured by the other component variables. Intuitively, uncorrelated
indicators represent “new” information and therefore receive more weight. It
also assigns less value to indicators with missing observations. The
estimation procedure, detailed in Egger et al. (2019)’s PAP, is the following:

    1. (a)

For each outcome variable $y_{jk}$, where $j$ indexes the outcome group and
$k$ indexes variables within outcome groups, variables are recoded so that
higher values denote “better” outcomes.

    2. (b)

Then the covariance matrix $\hat{\Sigma}_{j}$ for outcomes in group $j$ is
estimated, which consists of elements
$\hat{\Sigma}_{jmn}=\sum_{i=1}^{N_{jmn}}\dfrac{y_{ijm}-\bar{y}_{jm}}{\sigma_{jm}^{y}}\dfrac{y_{ijn}-\bar{y}_{jn}}{\sigma_{jn}^{y}}$,
where $N_{jmn}$ is the number of non-missing observations for outcomes $m$ and
$n$ in outcome group $j$, $\bar{y}_{jm}$ and $\bar{y}_{jn}$ are the means for
outcomes $m$ and $n$ in outcome group $j$, and the sigmas are the standard
deviations in the pure control group for the same outcomes for the entire
analyzed period.

    3. (c)

Then the covariance matrix is inverted, and they define the weight $w_{jk}$
for each outcome $k$ in outcome group $j$ by summing the entries in the row of
the inverted covariance matrix corresponding to that outcome.

    4. (d)

Each outcome variable is transformed by subtracting its mean and dividing by
the control group SD, and then weighting it with the weights obtained above.
Formally, $\hat{y}_{ij}=(\sum_{k\in K_{j}}w_{jk})^{-1}\sum_{k\in
K_{j}}w_{jk}\dfrac{y_{ijk}-\bar{y}_{jk}}{\sigma_{jk}^{y}}$.

###  B Difference-in-Discontinuities – ZOMAC

In this Section, I explain in more detail the ZOMAC program, the
identification strategy and the underlying assumptions that need to be
satisfied. I present evidence in support of the different assumptions and
robustness checks that confirm the baseline results.

As a way to incentivize business and employment creation in areas that have
been affected by the conflict (ZOMAC municipalities), the government started a
tax incentive program for firms in 2017. The main incentive is a progressive
business tax tariff for a period of 10 years starting in 2017, which varies
depending on the size of the firm, as shown below in Table 4. For firms to
benefit from the tax reduction they must i) have been created after December
29, 2016, ii) have their main address in a ZOMAC municipality, iii) perform
their whole productive processes in ZOMAC municipalities, and iv) satisfy some
investment and job-creation requirements. These investment and job-creation
requirements vary depending on the sector and the size of the firm. Informal
firms that formalize and meet these criteria can also benefit from these
incentives. Firms in the mining and oil sectors are excluded from the
benefits.

These are the conditions the government took into account to designate a
municipality as a ZOMAC municipality:

  1. 1.

Municipalities that have a Multidimensional Poverty Index (IPM in Spanish)
above 0,49, or an Index of Fiscal Performance (DF in Spanish) below 70.

  2. 2.

Not be part of an agglomeration.

  3. 3.

Municipalities most affected by the conflict, denoted by having a score above
0,0191 in an index of incidence of the armed conflict (IICA in Spanish). The
index is calculated as the average over 2002 and 2013 of six violence-related
variables: armed actions, homicides, kidnappings, antipersonal-mine victims,
forced displacement, and coca crops.222222The program’s decree does not
mention where these variables come from and how exactly the are aggregated
into a single indicator.

  4. 4.

Municipalities that belong have a Territorially Focused Development Programme
(PDET in Spanish).232323However, it has been reported that the implementation
of these PDET has been the slowest of any of the components of the peace
agreement, has been plagued by problems, and has been considerably underfunded
(García-Giraldo, 2020; Washington Office on Latin America, 2021; Valencia,
2022).

  5. 5.

Municipalities below 450.000 inhabitants and more than 60 minutes away from
the department’s capital.

More specifically, a municipality is denoted as ZOMAC if one of the following
two criteria hold:

  1. 1.

It either satisfies 4) and 5), i.e. it is part of PDET and satisfies the
demographic requirements, or

  2. 2.

It satisfies 1), 2), 3) and 5), i.e. it is poor or badly managed, affected by
conflict, it is not part of an agglomeration, not a PDET, and it satisfies the
demographic requirements.

The selection criteria thus depends on clear thresholds on some variables,
that I exploit to create a sample of treated municipalities just above the
thresholds and a sample of control municipalities just below the thresholds,
in a regression discontinuity (RD) approach. I will focus on the second
criterium, as being a PDET municipality or not is not defined by clear
thresholds (it is simply a categorical variable). Even for the second
criterium, there are several ways in which a municipality can be categorized
as ZOMAC or not based on strict thresholds. In practice, among the possible
selection combinations based on the second criterium, the only one that has
enough municipalities near the threshold is the one based on the IICA score.
More precisely, I focus on municipalities that are just above and just below
the IICA score necessary to be classified as a ZOMAC municipality, and meet
all the other requirements, meaning municipalities just below or above IICA
$=$ 0,0191, with IPM $>=$ 0,49 or DF $<=$ 70, not part of an agglomeration,
below 450.000 population and over 60 minutes drive from department’s capital,
not PDET. For these municipalities, their IICA score was the sole determinant
of ZOMAC classification or not.

The RD based on the strict IICA score among these municipalities creates a
sample of municipalities that are supposed to be equal in all respects other
than being a ZOMAC municipality or not, if they are sufficiently close to the
IICA cutoff. Given that I am interested in understanding the dynamic effects
of the peace agreement, I evaluate the evolution of these municipalities just
above and just below the IICA threshold over time, basically embedding the RD
design in a difference-in-difference setting. Such an approach was first
formalized by Grembi, Nannicini, and Troiano (2016), and called the
“difference-in-discontinuity” estimator. As this approach is new and has not
been frequently used (exceptions are Bazzi, Koehler-Derrick, and Marx, 2020;
Bergolo and Cruces, 2021; Grembi, Nannicini, and Troiano, 2016; Hansen,
Miller, and Weber, 2020), there is no clear set of empirical guidelines or
practices for using it. However, I follow the different robustness checks and
assumption tests that have been used in other papers to argue that the
assumptions needed for the validity of the difference-in-discontinuity
estimator seem to be satisfied in this context.

To recover the difference-in-disconuities estimator, three assumptions need to
be satisfied. First, all the potential outcomes must be continuous at the
discontinuity. Second, if there is a different, confounding policy that
affects municipalities above and below the discontinuity differently, then the
effect of the confounding policy at the discontinuity in the case of no
treatment must be constant over time (so that it can be netted out using the
“difference” part of the estimator). This is the equivalent of the parallel
trends assumption in traditional difference-in-difference settings. Third, the
effect of the treatment at the discontinuity does not depend on the
confounding policy. In this Appendix I present evidence in support of these
three assumptions.

Following Grembi, Nannicini, and Troiano (2016), I estimate the regression:

$\displaystyle y_{it}=$
$\displaystyle\delta_{0}+\delta_{1}IICA_{m}^{*}+\delta_{2}\textit{IICA
Treatment}_{m}+\delta_{3}IICA_{m}^{*}\times\textit{IICA Treatment}_{m}$ (4)
$\displaystyle+\delta_{4}Post_{t}+\delta_{5}Post_{t}\times
IICA_{m}^{*}+\delta_{6}Post_{t}\times\textit{IICA Treatment}_{m}$
$\displaystyle+\delta_{7}Post_{t}\times\textit{IICA Treatment}_{m}\times
IICA_{m}^{*}+u_{mt}$

where $IICA_{m}^{*}$ is the normalized IICA score
($IICA_{m}^{*}=IICA_{m}-0.0191$) of municipality $m$ in year $t$, IICA
Treatmentm is a dummy for municipalities with an $IICA$ score above 0.0191
(i.e. ZOMAC municipalities), and $Post_{t}$ is an indicator for the post-
treatment period. As the ZOMAC program started in 2017, in these regressions I
denote the post-treatment years as those from 2017. Standard errors are still
clustered at the municipality level. The difference-in-discontinuity estimator
of interest is the coefficient $\delta_{6}$ and identifies the treatment
effect of receiving the fiscal incentives for firms.

While Table 5 shows the baseline results, I now assess the robustness of the
results to using different functional forms, controls, definition of the post-
treatment period and bandwidths. First, in Figure B1, I plot the coefficients
for each of the outcome variables using 20 (evenly-spaced) different
bandwidths, up to one standard deviation of the running variable. The red line
indicates the optimal bandwidth selected to minimize the MSE. The results are
very similar regardless of the bandwidth selected. Only for the share of urban
population are small bandwidths associated with significant coefficients.
Second, Panel A in Table B1 shows the robustness of the results to different
methods of inference. I first estimate Conley SEs (Conley, 1999) that allow
for correlated unobservables across municipalities within certain distances of
the given municipality’s centroid. For distances, I use the 25, 50 and 75
percentiles of municipalities’ distance to their department’s capital. I also
show $p$-values using the wild cluster bootstrap suggested by Cameron,
Gelbach, and Miller (2008) for difference-in-difference settings. The SEs and
results remain unchanged. In Panel B, I estimate Equation (3) controlling for
municipality and year fixed effects, with the coefficients basically unchanged
from the baseline scenario. Then in Panels C and D, instead of using a linear
function for the running variable, I use a quadratic and cubic polynomial on
either side of the cutoff. The results remain qualitatively the same and
similar in magnitude, although the SEs increase. Finally, although the ZOMAC
program started in 2017, to rule out any anticipatory effects, in Panel E, I
run a regression defining the post period as starting in 2016 rather than
2017, which does not affect the results.

The results using the Anderson Index are not driven by any single component
variable. Figure B2 shows in black the results of estimating Equation (3)
including 95% confidence intervals and in blue p-values from a test of joint
significance of all pre-treatment coefficients from estimates of Equation (2)
following Freyaldenhoven et al. (2021), dropping each component variable
individually. Regardless of the variable dropped, the results are basically
identical to the baseline results, suggesting the no individual component
variable is driving the results.

Finally, I present evidence in support of the validity of the assumptions
needed to recover the difference-in-discontinuities estimator. The first
assumption requires no manipulation of the running variable, as is usual in RD
designs. Figure B3 shows the results of a manipulation test using local
polynomial density estimators as suggested by Cattaneo, Jansson, and Ma
(2020). Regardless of whether using bias correction or not, the test fails to
reject the null, supporting the hypothesis of no manipulation of the running
variable. This makes intuitive sense, given that the IICA score is the average
between 2002 and 2013 of different violence indicators, long before the ZOMAC
program was even conceived, making manipulation very unlikely. Assumptions 2
and 3 ask for municipalities above and below the cutoff to be on a (local)
parallel trend in the absence of the new policy and that the effect of the
treatment does not depend on other confounding policies. I provide several
pieces of evidence in support of these assumptions. First, Figure B4 shows the
results of estimating regressions akin to Equation 2 but for the difference-
in-discontinuity setting. There seem to be no pre-trends for any of the
variables. Only for three, the joint test of pre-intervention coefficients are
marginally rejected, but the sup-t confidence bands cover the 0 for the entire
path. For the Anderson Index, the significance of the joint test comes
entirely from the agricultural productivity measure, although the main
coefficient remains unchanged when excluding this variable from the index (see
Figure B2). These graphs also show that there is no improvement in these
economic indicators over time, as could have been expected. Second, results of
running regressions of several different municipalities’ characteristics pre-
intervention (in 2008) on the IICA treatment dummy are presented in Appendix
Table B2. Out of the 20 different characteristics, only one is marginally
significantly different between municipalities just below and just above the
IICA threshold, suggesting that these municipalities were very similar to
being with. Finally, Appendix Figure B5 shows the results of running placebo
versions of Equation (3). These placebos only use data between 2009 and 2016
(before the start of the ZOMAC policy), and assign the post-intervention
period sequentially as years between 2011 and 2015. Reassuringly, the results
are insignificant for all variables and placebo years, only for some years and
the share of urban population are some of the results marginally significant.

Figure B1: Robustness Difference-in-Discontinuities Results to Bandwidth
Selection

(a) Nighttime Light Intensity (Weighted)

(b) GDP Per Capita (DANE)

(c) Share Urban

(d) Urban Built-Up

(e) Formal Employment

(f) Firm Entry

(g) Agricultural Productivity

(h) Anderson Index

Notes: Estimates of Equation (3), including 95% confidence intervals (based on
standard errors clustered at the municipality level). The red line is the
optimal bandwidth calculated following Calonico, Cattaneo, and Titiunik
(2014), selected to minimize the Mean Squared Error. The blue estimates and
confidence intervals are the results from this same exercise but using 20
different (evenly-spaced) bandwidths.

Figure B2: Robustness of Anderson Index to Individual Component Variables

Notes: Figure shows in black estimates of Equation (3) including 95%
confidence intervals (based on standard errors clustered at the municipality
level) and in blue p-values from a test of joint significance of all pre-
treatment coefficients from estimates of Equation (2) following Freyaldenhoven
et al. (2021). The Figure uses the index summarizing the different economic
indicator variables (nighttime light intensity, GDP per capita (from DANE),
share of urban population, agricultural productivity, firm entry and formal
employment measures) created following Anderson (2008). The names in the
$x$-axis correspond to the variable dropped from the corresponding index when
estimating the results.

Figure B3: Density Test Running Variable – IICA Score

Notes: Density test of the running variable, the IICA score, using local
polynomial density estimators as suggested by Cattaneo, Jansson, and Ma
(2020). At the bottom of the Figure are the p-values of this test using bias
correction or not.

Figure B4: Dynamic Difference-in-Discontinuities Specifications

(a) Nighttime Light Intensity (Weighted, Std.)

(b) GDP Per Capita (DANE)

(c) Share Urban Population

(d) Urban Built-Up

(e) Formal Employment

(f) Firm Entry

(g) Agricultural Productivity

(h) Anderson Index

Notes: Event study plots from estimating Equation (2), including including 95%
confidence intervals (based on standard errors clustered at the municipality
level). The index is created following Anderson (2008) and is based on
weighted nighttime light intensity, GDP per capita (from DANE), share of urban
population and agricultural productivity measures.

Figure B5: Difference-in-Discontinuities: Placebo Estimates

Notes: Estimates of Equation (3), including 95% confidence intervals (based on
standard errors clustered at the municipality level), using data only from the
pre-treatment period (2009-2017, as the ZOMAC program started in 2017). The
number in the $y$-axis corresponds to the “placebo” treatment year. Each
figure uses a different economic outcome.

Table B1: Robustness RDD Analysis Inference, Functional Form & Controls

Nighttime GDP Per Capita Urban Agricultural Population Firm Formal Anderson
Light DANE S&E (2012) Built Up Productivity Production Share Urban Entry Empl.
Index (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Panel A. Baseline IICA Treat.
$\times$ Post 0.031 0.692 –1.218 –0.004 –0.341 –1.567 –0.027** –1.064 –0.001
–0.022 (0.047) (1.269) (1.576) (0.004) (1.606) (1.577) (0.014) (0.966) (0.011)
(0.109) Conley Perc. 25 (0.037) (1.313) (1.609) (0.004) (1.770) (1.526)
(0.014) (0.918) (0.010) (0.095) Conley Perc. 50 (0.039) (1.267) (1.527)
(0.005) (1.612) (1.543) (0.014) (0.616) (0.011) (0.103) Conley Perc. 75
(0.047) (1.187) (2.014) (0.005) (1.612) (1.576) (0.015) (0.846) (0.010)
(0.134) Wild Bootstrap p-value [0.513] [0.596] [0.550] [0.384] [0.845] [0.422]
[0.045] [0.290] [0.919] [0.844] Observations 1,859 936 1,516 2,244 1,100 737
1,067 1,584 1,166 1,353 R Squared 0.010 0.044 0.019 0.015 0.024 0.119 0.054
0.059 0.029 0.073 Panel B. TWFE IICA Treat. $\times$ Post 0.031 0.692 –1.252
–0.004 –0.341 –1.567 –0.027* –1.064 –0.001 –0.022 (0.049) (1.349) (1.659)
(0.005) (1.690) (1.661) (0.014) (1.015) (0.011) (0.114) Observations 1,859 936
1,516 2,244 1,100 737 1,067 1,584 1,166 1,353 R Squared 0.941 0.867 0.840
0.357 0.970 0.862 0.989 0.771 0.962 0.935 Panel C. Quadratic Polynomial IICA
Treat. $\times$ Post –0.063 2.667 –3.993* –0.010 0.904 –1.199 –0.047** 1.890
–0.027* 0.157 (0.081) (2.009) (2.341) (0.008) (2.573) (0.914) (0.023) (1.416)
(0.015) (0.176) Observations 1,859 936 1,516 2,244 1,100 737 1,067 1,584 1,166
1,353 R Squared 0.036 0.062 0.025 0.017 0.048 0.130 0.095 0.094 0.040 0.075
Panel D. Cubic Polynomial IICA Treat. $\times$ Post –0.066 3.920 –2.490 –0.008
0.008 0.771 –0.039 0.366 –0.020 0.140 (0.117) (2.407) (2.246) (0.011) (3.322)
(1.534) (0.028) (1.753) (0.015) (0.228) Observations 1,859 936 1,516 2,244
1,100 737 1,067 1,584 1,166 1,353 R Squared 0.054 0.079 0.031 0.020 0.060
0.189 0.129 0.104 0.040 0.113 Panel E. Alt. Post Definition IICA Treat.
$\times$ Post –0.046 2.469 –3.071 –0.005 –1.869 –2.647 –0.023 –0.123 –0.018
–0.027 (0.082) (2.094) (2.186) (0.005) (3.104) (1.601) (0.016) (1.196) (0.014)
(0.147) Observations 913 639 1,165 2,134 627 451 451 825 385 968 R Squared
0.043 0.044 0.015 0.022 0.089 0.078 0.084 0.073 0.093 0.045

$Notes$: Results from estimating Equation (3). Standard errors clustered at
the municipality level. Considers only years from 2009 and before 2020.
Defines the post period as 2017. Bandwidth optimally estimated using Calonico
et al. (2014) approach. Conley SEs at different distance bandwidths (25th,
50th and 75th percentile of municipalities’ distance to department’s capital).
Wild bootstrap p-value in squared brackets. Panel E defines the post-treatment
period as the year 2016, after the peace agreement was approved. Nighttime
light intensity defined so that grid cells in the border of multiple
municipalities are assigned in proportion to the share of the grid cell in
each municipality (weighted). GDP per capita comes from two sources: From DANE
(National Department of Statistics) or based on calculations following Sanchez
& Espana (2012), both in millions of COP. Agricultural productivity is defined
as total tonnes produced of 271 agricultural crops divided by total area
cultivated in hectares. Agricultural production is defined as total tonnes
produced of 271 agricultural crops per capita. Urban built up is estimated
using the Band Ratio for Built-Up Area (BRBA) index developed by Waqar et al.
(2012), and measures the average amount of pixels within a municipality that
are classified as urban built up based on the BRBA index. Firm entry comes
from RUES and is measured per 1000 inhabitants. Formal employment is measured
as the average number of individuals paying contributions to healthcare,
pension funds and workers’ compensations across the year in the municipality
per 18-60 years old, from PILA. Anderson Index is a summary measure created
following Anderson (2008) that summarizes the weighted nighttime light
intensity, GDP per capita (from DANE), share of urban population, agricultural
productivity, formal employment and firm entry measures. It is the weighted
average of the standardized outcomes, weighted by their inverted covariance
matrix.

Table B2: Difference-in-Discontinuities Analysis: Pre-Treatment Balance Check

All Within BW Bandwidth Treatment Control Difference Variable (1) (2) (3) (4)
(5) (6) Population 38.44 25.38 0.008 23.08 26.64 0.528 Area 1017.6 568.49
0.003 521.4 605.31 0.61 Dist. Capital 81.46 75.24 0.008 73.13 76.35 0.65 Dist.
Bogota 321.55 272.2 0.005 265.06 276.69 0.648 Small Credit 0.05 0.05 0.005
0.06 0.05 0.682 Total Credit 0.31 0.32 0.004 0.25 0.4 0.641 Gov. Transfers
0.07 0.05 0.006 0.06 0.05 0.873 Savings Capac. 32.91 30.46 0.009 30.02 30.66
0.777 Fiscal Perf. 62.1 61.56 0.008 62.19 61.28 0.422 Overall Perf. 58.85
58.41 0.009 57.04 59.05 0.272 Mun. Develop. 66.67 68.56 0.008 69.63 68.04
0.332 Conf. 1901/30 0.05 0.07 0.007 0.08 0.07 0.809 Spanish Occup. 0.37 0.25
0.007 0.31 0.21 0.122 Aqueduct 59.55 62.08 0.007 58.6 64.17 0.238 Garbage
Coll. 45.77 51.8 0.006 52.58 51.27 0.8 Sewage 42.37 51.57 0.005 50.96 52.01
0.853 PC Expenditure 0.26 0.25 0.005 0.26 0.25 0.095 GINI 0.45 0.46 0.008 0.46
0.46 0.633 MDP 69.46 68.53 0.009 67.78 68.87 0.536 NBI 45.4 41.47 0.009 40.41
41.96 0.527

$Notes$: All the variables are measured in 2008 (the last period before the
panel used in the main analysis) but for the last four variables that are only
available for 2005. Conf. 1901/1930 denotes whether the municipality
experienced social conflict between 1901-1930. Distance variables are measured
in kilometers. MDP is a multidimensional poverty index. Population in 1000s,
value of credit in 1.000.000s COP per capita, expenditure per capita in
1.000.000s COP.

###  C Alternative Mechanisms

The results in Section 5 suggest that a lack of state capacity from both
before the peace agreement and a lack of state entry post-ceasefire are the
reasons why the large reduction in violence did not translate into economic
improvements in FARC municipalities. However, there might be alternative
mechanisms that could potentially explain this set of results as well. In this
Section, I present evidence on some of these alternative mechanisms that
suggest that they are unlikely to be driving the results.

####  C.1 Migration of Venezuelans

The worsening humanitarian situation in Venezuela due to the authoritarian
regimes of Hugo Chávez and Nicolás Maduro has led to large numbers of
Venezuelans migrating to Colombia since 2014. The UN estimates that about 1.8
million Venezuelans have migrated to Colombia until 2020. These large inflows
from forced migrants could have negative impacts on local labor markets, with
Bahar, Ibáñez, and Rozo (2021) showing that larger inflows of Venezuelan
migrants reduced the employment of Colombian workers. Thus, if Venezuelan
migrants disproportionately located in FARC municipalities, as these were more
attractive due to the reduction in violence, then this might explain the lack
of economic improvements after the ceasefire.

Looking at Table C1, this does not seem to be the case. The Table shows the
number of Venezuelan migrants that obtained a Permiso Especial de Permanencia
(PEP, a formal temporary migratory status that gives migrants the legal right
to work and to access basic public services) and registered in a FARC/ELN
municipality in a given year between 2017 and 2019 (first three columns), and
the cumulative number of migrants that have registered in a FARC/ELN
municipality since 2017 (last three columns). Unfortunately, data on the
universe of Venezuelan migrants is unavailable, thus the numbers underestimate
the true number of Venezuelan migrants in these municipalities.

The Table shows that, if anything, Venezuelan migrants were significantly more
likely to locate in ELN rather than FARC municipalities. This result is
unsurprising, as ELN municipalities are much closer to the border with
Venezuela than FARC municipalities: ELN municipalities are on average 213kms
away from the Venezuelan border, while FARC municipalities are 472kms away,
with the difference being significant.

####  C.2 Coca Production and Eradication

In 2014, during the peace negotiations with the FARC and before the start of
the ceasefire, the government announced that, post-agreement, the government
would provide farmers with material incentives to switch from growing coca to
other crops. Prem, Vargas, and Mejía (2021) show that this policy announcement
led to a sharp increase in coca farming in areas suitable for coca production,
as farmers expected to benefit from the government’s announced substitution
program. Thus, an alternative explanation to the baseline results could be
that the start of the ceasefire led to a shift of economic activity from the
legal sector towards coca production in FARC municipalities, which would be
largely missed by the set of economic indicators used in Section 4.2.
Moreover, it could also be that the government shifted its coca eradication
programs towards FARC municipalities, as these were not controlled by an
insurgent group anymore, shifting labor from productive activities towards
eradication programs.

However, Table C2 suggests that this does not seem to be the case. The first
three columns shows results using indicator variables for whether the
municipalities were producing coca or whether the government had implemented a
coca eradication program in a given municipality, while the following three
columns show coca production and eradication per 10.000 HAs of municipality’s
size. FARC municipalities were not more likely to be growing coca or to have
ongoing eradication programs after the start of the ceasefire relative to ELN
municipalities. There is also no increase in either of these measures when
measured in 10.000 HAs. Thus, it seems like a shift towards coca production
and eradication is also not the case of the results.

The actual coca-substitution program (PNIS) that the government announced in
2014 was officially signed into decree in 2017. However, the program has been
plagued by logistical and administrative problems, formally starting in
2018/2019 in a smaller magnitude than originally intended. Thus, this program
started too late to explain the results. The last two columns of the Table
show that i) FARC municipalities (despite not having increased their coca
production relative to ELN municipalities) were more likely to be selected for
the program in 2019, and ii) also had significantly more beneficiaries of the
economic incentives per 10.000 inhabitants than ELN municipalities.

####  C.3 Credit Constraints

FARC municipalities tend to be mostly rural, distant municipalities, where
agriculture plays a big role. Thus, an alternative explanation could be that
FARC municipalities could not reap the economic benefits because, despite the
large reduction in violence, agricultural farmers were credit constrained and
thus could not finance their projects.

However, this seems unlikely to be the case. First, De Roux and Martínez
(2021) show using detailed data from the largest public bank serving rural
producers in Colombia (the Banco Agrario) and a similar identification
strategy as here, that the number of business loans increases in FARC
municipalities after the peace agreement (but not after the start of the
ceasefire). Table C3 shows a similar pattern. It shows the value of credit
given by two large credit-providing institutions focused on rural agricultural
projects, the Banco Agrario and FINAGRO, to agricultural producers of
different sizes. As is the case in De Roux and Martínez (2021), credit to
small agricultural producers (those most likely to be credit constrained)
increased significantly after the signing of the peace agreement (but not
after the start of the ceasefire). Thus, it seems like credit constraints were
eased for those more likely suffering from them in FARC municipalities after
the peace agreement was ratified.

####  C.4 Shift Towards Education

An alternative mechanism could be that the end of the FARC allowed young
individuals to go back to school and pursue an education now that they did not
need to worry about the conflict or work for the FARC. This would mean that
these young individuals shifted from working in productive activities to
getting an education in the short-run, potentially explaining the observed
lack of economic improvements.

The results in Table C4 suggest that this does not seem to be the case. It
shows that the number of first-year students in higher education programs (a
measure of entry to higher education) in FARC municipalities did not increase
significantly more than in ELN municipalities after the start of the
ceasefire, regardless of the type of educational program considered (technical
education programs, bachelors programs or post-graduate programs). There is
also no significant change in the number of higher education institutions in
FARC municipalities, so it does not seem like a shift from production toward
education is driving the results.

####  C.5 Productive Land Tied-Up in Land Restitution Processes

Disputes over land ownership have been at the center of the armed conflict in
Colombia. For example, López-Uribe and Torres (2017) show that the historical
dispossession of peasants’ lands by landlords between 1914 and 1946 is
associated with FARC presence in the early stages of the conflict (1974-1985).
Recognizing this, and as a first signal of its willingness to achieve peace,
the government signed in 2011 the “Victims and Land Restitution Law”, which
provided the legal framework for conflict victims to obtain assistance and
reparations from the government, and a mechanism for victims to recover the
lands they had lost during the conflict.

Yet another reason for the results could be that FARC municipalities could not
benefit economically because the productive land in those municipalities was
either stolen by the FARC and inaccessible to farmers, or tied up in the
courts set up to handle land restitution processes. If this were the case, it
would be expected to see large increases in land restitution claims and cases
in court in FARC municipalities after the start of the ceasefire. The land
restitution process proceeds in three stages: first, a person or group of
people presents a land restitution claim to the government unit in charge of
the land restitution process (UAEGRTD). The UAEGRTD then decides whether to
bring the case to the land restitution courts or not, in the second stage.
Lastly, the court decides whether to restitute the land or not. The process
has been criticized for its slowness: until 2019, out of the 120.000+
submitted land restitution requests, less than 10% had been resolved (see
Deutsche Welle, 2019).

However, these hypotheses do not seem to be supported by the data. Table C5
shows the results of estimating Equation (1) on measures related to the land
restitution process. The first three columns correspond to the first step in
the restitute process, and show the number of requests, people and plots
involved in claims brought forward to the UAEGRTD. Columns 4 and 5 show the
number of requests solved and denied by the land restitution courts. The last
three columns show the number of beneficiaries, plots and the total plot size
of the claims approved by the courts. If anything, it seems like, relative to
ELN municipalities, there have been less land restitution claims presented and
resolved in FARC municipalities after the start of the ceasefire (columns
1-4), regardless of whether one measures these in per capita terms (Panel A)
or not (Panel B), although the coefficients are insignificant. When looking at
the requests that have been approved by the courts, the coefficients on the
number of beneficiaries, plots returned, or total area restituted are mostly
negative and insignificant (columns 6-8). Thus, this alternative explanation
seems unlikely to be behind the results.

####  C.6 Beliefs About the Peace Agreement

The Democracy Observatory (Observatorio de la Democracia) at the Universidad
de los Andes has been conducting surveys in Colombia as part of the broader
AmericasBarometer initiative since 2004. Broadly speaking, these surveys aim
to study the attitudes, experiences, values and beliefs of people across the
Americas about their political institutions. While the survey contains a set
of questions that are asked in every country and across time, it also includes
country-specific questions. Most of the questions specific to Colombia are
related to the armed conflict and the peace agreement with the FARC. On top of
the AmericasBarometer surveys, the Democracy Observatory has conducted three
surveys focused on areas affected by the armed conflict, in 2013, 2015 and
2017. The data as well as the survey instruments can be found here.

I combine the data from all the AmericasBarometer and the special conflict-
areas surveys since 2011 to shed light on the opinions and beliefs of citizens
of FARC municipalities towards the peace agreement. One concern could be that
these areas did not experience economic improvements because their residents
did not believe in the peace agreement or they thought the situation was not
going to change and therefore avoided making any economic investments. While
these data can be useful for these purposes, it is worth emphasizing that they
have clear limitations. For each survey wave, only between 35 to 103
municipalities are surveyed, with on average (median) 58 (36) individuals
surveyed per municipality-year pair. Among FARC (ELN) municipalities, 70 out
of 216 (10 out of 41) are surveyed at least once between 2011 and 2019, with
35 (9) being surveyed at most twice. While the data are meant to be
representative at the municipality level, it is clear that there are
limitations in terms of geographical coverage. On the other hand, these are
the only surveys representative at the municipality level that are 1)
conducted regularly and 2) outside the main cities of the country, and thus
can provide a useful snapshot of the opinions and beliefs of people in FARC
municipalities. Given the restricted sample of surveyed ELN municipalities, I
will focus mostly on FARC municipalities and the rest of the country.

##### Did residents of FARC municipalities believe that the peace agreement
was going to be beneficial?

First, in 2017, 2018 and 2019, people were asked whether the implementation of
the peace agreement was going to improve the economic situation of their
municipality, on a scale from 1 (“strongly disagree”) to 7 (“strongly agree”).
The share agreeing with this statement (score of 5 or above) in FARC
municipalities has been constantly rising over time, from 44% in 2017 to 47%
in 2018 to 51% in 2019. The same pattern holds when asked whether it would
improve the security situation in their municipality (going from 46% in 2017
to 56% in 2019) and access to land for farmers (57% in 2017 to 64% in 2019).
Second, even in 2013, residents of FARC municipalities were 12% more likely
than people in the rest of the country to say that they believed that the end
of the FARC would bring economic benefits to their municipalities. Third, when
asked in 2019 if they agreed with statements saying that two key parts of the
peace agreement (the PDET program and the additional seats in congress for
conflict-affected areas) would benefit people like them, 59% and 66% of
respondents agreed, higher shares than in the rest of the country. Thus, this
suggests that residents of FARC municipalities believed that the peace
agreement would be beneficial for them, becoming more optimistic over time
about the value of the agreement.

##### Were residents of FARC municipalities in favor of the agreement?

The answer is yes. First, residents of FARC municipalities have always been
more likely to believe that the best way to end the armed conflict with
guerrilla groups was through negotiations rather than military action. Before
(after) the start of the ceasefire, 65% (78%) of citizens in FARC
municipalities believed this to be the best option, compared with 57% (72%) in
the rest of the country. This also holds for the year 2011, before it was
leaked that the government was negotiating with the FARC. Second, the 2016
plebiscite on the final peace agreement received 50,3% of votes in favor in
FARC municipalities (i.e. would have passed), while in the rest of the country
it was rejected, receiving only 49,5% of votes in favor. Third, since 2013 the
surveys have included a question asking citizens how supportive they are of
a/the peace agreement (both before and after the actual agreement was
completed), and the majority of citizens in FARC municipalities have agreed
with the idea of an agreement or the actual agreement in all years but one,
with their support above that in the rest of the country in 5 out of 7 years
for which data exist.242424Across all years, 56% of citizens in FARC
municipalities and 51% in the rest of the country have been supportive of the
peace agreement. Before (after) the start of the ceasefire, these numbers were
59% and 55% (56% and 50%), respectively. Moreover, the share in support of the
agreement in FARC municipalities has been steadily increasing since 2016. The
majority of people in FARC municipalities have believed (both before and after
ceasefire) that it is possible for people to forgive and reconcile with former
FARC fighters, higher than in the rest of the country.

There was also broad support for different components of the peace agreement.
In 2019, people were asked what percentage of people in their municipality
they believed supported the implementation of the peace agreement and the
Rural Reform, a key part of the agreement. In FARC municipalities, 66% of
respondents stated that they believed that at least half of their municipality
supported the implementation of the Rural Reform, while 73% believed the same
to be true for the implementation of the full peace agreement, both higher
than in the rest of the country. Panel A of Table C6 shows that, in general,
citizens of FARC municipalities were more likely to agree with key elements of
the peace agreement relative to people in the rest of the country. The
majority of citizens in FARC municipalities agree with most of these
components, and unpopular components fare significantly better in FARC
municipalities relative to the rest of the country. Interestingly, row 12
shows that, compared to citizens in other municipalities, the proposal to make
changes to the original agreement after the plebiscite was rejected was less
popular in FARC municipalities. Moreover, between 2013 and 2015, when asked
for their level of support to hypothetical policies similar to the ones in the
actual agreement (such as penalty reductions, political participation, and so
on), people in FARC municipalities were significantly more likely to support
these than people in the rest of the country, although the overall level of
support was low.

##### Did residents of FARC municipalities understand the agreement?

One concern with the evidence so far is that it could be that people in FARC
municipalities did not know the details of the peace agreement and thus these
answers reflect a lack of understanding of what was agreed. However, this
seems unlikely to be the case. Panel B of Table C6 shows the proportion of
citizens in FARC/rest of the country municipalities that correctly answered
three different questions regarding the peace agreement: whether their
municipality has been designated as a PDET municipality, whether creating 16
seats for conflict-affected areas was part of the peace agreement, and what
the maximum amount of years a FARC member could be sentenced to jail under the
peace agreement is (8 years). Citizens in FARC municipalities are
significantly more likely to get these questions right compared to citizens in
the rest of the country, and the overall rate of correct answers is high.
Moreover, Table C7 shows that, while surveyed and non-surveyed FARC
municipalities are not identical (non-surveyed ones tend to be smaller, more
rural and slightly poorer), they are fairly similar.

Overall, the results in this Section show that citizens in FARC municipalities
1) supported the peace agreement and its key components, 2) believed that the
peace agreement was going to benefit them and their municipalities (also
economically), and 3) were knowledgeable regarding the content of the peace
agreement, in general more so than citizens in the rest of the country. Thus,
uncertainty around, opposition against, or lack of trust in the peace
agreement seem unlikely to be reasons why FARC municipalities did not benefit
economically from the peace agreement.

Table C1: Venezuelan Migrants with Permits – Extensive Margin, Events in Over
60% of Years

Flow Stock FARC ELN p-value Diff. FARC ELN p-value Diff. Year (1) (2) (3) (4)
(5) (6) 2017 1.41 2.02 1.41 2.02 2018 17.51 29.29 18.9 31.27 2019 1.78 1.92
20.41 32.17 All Years 6.9 11.08 0.088 13.57 21.82 0.014

$Notes$: Venezuelan migrants per 10.000 inhabitants. The first three columns
show the number of Venezuelan migrants who received a special permit to live
and work in Colombia (PEP) and registered in a given municipality and year
(flow), while the last three columns show the total number of Venezuelan
migrants with the permit that have registered in a given municipality over
time.

Table C2: DiD Analysis – Alternative Mechanisms – Coca Production &
Eradication: Extensive Margin, Events in Over 60% of Years

Indicator Variables Per 10.000 HAs PNIS Program Coca Production Manual Erad.
Total Erad. Coca Production Manual Erad. Total Erad. Participating
Beneficiaries (1) (2) (3) (4) (5) (6) (7) (8) Ceasefire $\times$ FARC –0.042
–0.009 0.009 0.721 0.197 –0.000 (0.039) (0.044) (0.037) (0.550) (0.337)
(0.336) FARC 0.152*** 118.918*** (0.036) (26.961) Treated Munic. 216 216 216
216 216 216 216 216 Control Munic. 41 41 41 41 41 41 41 41 Mean Dep. Var.
0.323 0.320 0.364 0.559 0.474 1.135 Observations 2,827 2,827 2,827 2,827 2,827
2,827 257 257 R Squared 0.857 0.628 0.696 0.639 0.264 0.382 0.024 0.019

$Notes$: Standard errors clustered at the municipality level. For the first
six columns, includes municipality and year fixed effects. Considers only
years from 2009 and before 2020. The last two columns consider only 2019,
after PNIS started. Mean Dep. Var.: Mean of the dependent variable in the pre-
intervention period for municipalities in the sample (FARC and ELN). Defines
the post period as 2015. Coca production, manual and total eradication are per
10.000 hectares. Beneficiaries is the number of PNIS beneficiaries previously
working harvesting coca per 10.000 inhabitants.

Table C3: DiD Analysis – Alternative Mechanisms – Credit to Agricultural
Producers: Extensive Margin, Events in Over 60% of Years

FINAGRO Value of Credit Banco Agrario Value of Credit Small Medium Large Total
Credit Small Medium Large Total Credit (1) (2) (3) (4) (5) (6) (7) (8)
Ceasefire $\times$ FARC 0.000 0.008 0.027 0.035 0.007 0.008 0.028 0.043
(0.008) (0.008) (0.036) (0.040) (0.009) (0.007) (0.037) (0.040) Agreement
$\times$ FARC 0.032** 0.007 0.002 0.041 0.029* 0.001 –0.014 0.016 (0.015)
(0.011) (0.032) (0.045) (0.017) (0.012) (0.032) (0.045) Treated Munic. 216 216
216 216 216 216 216 216 Control Munic. 41 41 41 41 41 41 41 41 Mean Dep. Var.
0.127 0.095 0.056 0.278 0.110 0.088 0.045 0.244 Observations 2,313 2,313 2,313
2,313 2,827 2,827 2,827 2,827 R Squared 0.876 0.823 0.671 0.721 0.775 0.749
0.545 0.603

$Notes$: Standard errors clustered at the municipality level. Includes
municipality and year fixed effects. Considers only years from 2009 and before
2020. Mean Dep. Var.: Mean of the dependent variable in the pre-intervention
period for municipalities in the sample (FARC and ELN). Defines the post-
ceasefire period as 2015 and 2016, and the post-agreement period as the years
from 2017. Value of credit to small, medium and large agricultural producers,
in COP million per capita, from two different credit-giving organizations,
FINAGRO and the Banco Agrario.

Table C4: DiD Analysis – Alternative Mechanisms – Higher Education: Extensive
Margin, Events in Over 60% of Years

Number of First-Year Students Learning Inst. Technical University Post Grad
(1) (2) (3) (4) Ceasefire $\times$ FARC 0.151 –0.061 1.766 –0.027 (0.129)
(1.485) (1.650) (0.105) Treated Munic. 216 216 216 216 Control Munic. 41 41 41
41 Mean Dep. Var. 0.867 6.638 6.939 0.084 Observations 2,827 2,827 2,827 2,827
R Squared 0.435 0.472 0.803 0.385

$Notes$: Standard errors clustered at the municipality level. Includes
municipality and year fixed effects. Considers only years from 2009 and before
2020. Mean Dep. Var.: Mean of the dependent variable in the pre-intervention
period for municipalities in the sample (FARC and ELN). Defines the post
period as 2015. Educational variables are per 10.000 inhabitants, and come
from the Ministry of Education. Technical is the sum of students in technical
and technological educational programs. University is the sum of students in
undergraduate programs. Post grad is the sum of students enrolled in masters’,
specializations or PhD programs. First column shows the number of higher
learning institutions in the municipality.

Table C5: DiD Analysis – Alternative Mechanisms – Land Restitution: Extensive
Margin, Events in Over 60% of Years

Land Restitution Requests Court Decision Restitutions Requests Benefic. Plots
Solved Denied Benefic. Plots Size Plots (1) (2) (3) (4) (5) (6) (7) (8) Panel
A. Per Capita Ceasefire $\times$ FARC –3.378 –1.665 –2.397 –0.113 –0.056 0.247
–0.082 –0.235 (2.451) (1.782) (2.211) (0.428) (0.040) (1.016) (0.268) (0.179)
Treated Munic. 216 216 216 216 216 216 216 216 Control Munic. 41 41 41 41 41
41 41 41 Mean Dep. Var. 21.309 16.004 19.136 0.500 0.020 1.033 0.344 0.206
Observations 2,313 2,313 2,313 2,056 2,056 2,056 2,056 2,056 R Squared 0.566
0.618 0.585 0.472 0.273 0.475 0.478 0.472 Panel B. Total Ceasefire $\times$
FARC –5.707 –3.623 –4.190 –0.925 –0.170 –1.123 –0.471 –14.032 (4.622) (3.524)
(4.027) (0.964) (0.108) (2.538) (0.631) (11.959) Treated Munic. 216 216 216
216 216 216 216 216 Control Munic. 41 41 41 41 41 41 41 41 Mean Dep. Var.
38.137 29.154 34.364 1.157 0.032 2.920 0.860 30.783 Observations 2,313 2,313
2,313 2,056 2,056 2,056 2,056 2,056 R Squared 0.528 0.520 0.541 0.521 0.412
0.516 0.549 0.590

$Notes$: Standard errors clustered at the municipality level. Includes
municipality and year fixed effects. Considers only years from 2011 (for first
three columns) or 2012 (remaining columns) and before 2020. Mean Dep. Var.:
Mean of the dependent variable in the pre-intervention period for
municipalities in the sample (FARC and ELN). Outcome variables in Panel A are
in either 10.000 inhabitants (first seven columns) or in 10.000 of HAs (last
column), while those in Panel B are in totals. Data come from the Unit of Land
Restitution.

Table C6: Support and Knowledge of Peace Agreement

FARC Rest Country p-value N Obs. (1) (2) (3) (4) Panel A. Support of
Agreement’s Components 1\. Former FARC members form a political party 3.654
3.431 0 7240 2\. Former FARC members run in elections 3.203 3.124 0.56 2927
3\. Would accept results if FARC member wins 61.6 45.2 0 3880 4\. 16 add.
seats for most-affected municipalities 69.1 66 0.03 5116 5\. Redistribution of
land to poor farmers 81.4 81.6 0.94 2778 6\. Concentration of FARC members in
certain areas 34.7 30.9 0.27 1394 7\. Crop substitution programs in
municipality 83.4 85.2 0.39 1341 8\. No jail for demobilized foot soldiers
24.8 19.8 0 2739 9\. 5–8 years in jail for confessed atrocious crimes 57.6
55.4 0.53 1416 10\. Over 8 years in jail for not confessed crimes 71.7 72.2
0.86 1411 11\. PDET in most affected regions 76 72.8 0.05 3187 12\. Changes to
original agreement 45.9 46.7 0.64 4631 Panel B. Knowledge of Agreement 1\. Is
this municipality a PDET? 68.4 56.8 0 1751 2\. Is creation of 16 seats part of
agreement? 71.7 67 0.02 2092 3\. What is the max. jail penalty for FARC
member? 27.4 18.3 0 1646

$Notes$: Each row corresponds to a different component of the peace agreement.
Columns 1 and 2 show the number of respondents in FARC and rest of the country
municipalities that in general agree with a given component (indicated by a
score higher than 5 in a 1-7 Likert-scaled question) or on a 1-10 scale in
Panel A, and the number of respondents that correctly answered general
questions regarding the peace agreement in Panel B. The third column shows the
p-value of the difference between columns 1 and 2 (using robust standard
errors). The fourth column shows the number respondents.

Table C7: Comparison Surveyed vs. Non-Surveyed Municipalities (Population
Weighted): Extensive Margin, Events in Over 60% of Years

FARC Municipalities p-value All Not Surveyed Surveyed Difference Variable (1)
(2) (3) (4) Population 25.39 19.13 38.47 0 Urban Population 0.41 0.38 0.47 0
Area 1842.32 1498 2560.5 0.04 Distance Capital 77.75 76.26 79.30 0.70 Distance
Bogota 334.58 272.10 399.42 0 Gov. Transfers 0.02 0.03 0.01 0 Savings Capac.
35.90 36.34 35.41 0.61 %Exp. in Investment 86.75 86.87 86.62 0.74 Fiscal Perf.
60.11 60.38 59.84 0.48 Overall Perf. 58.11 62.45 53.59 0 Mun. Develop. 50.36
51.04 49.66 0.35 Conflict 1901/30 0.20 0.17 0.25 0.13 Spanish Occup. 0.30 0.25
0.34 0.15 PC Expenditure 0.25 0.25 0.23 0.19 GINI 0.46 0.46 0.46 0.04 MDP
67.75 66.52 69.01 0.27 NBI 44.02 40.72 47.45 0.01 # of Municipalities 216 146
70

$Notes$: All the variables are measured in 2008 (the last period before the
panel used in the main analysis) but for the last five variables that are only
available for 2005 and 2016. Conf. 1901/1930 denotes whether the municipality
experienced social conflict between 1901-1930. Distance variables are measured
in kilometers. MDP is a multidimensional poverty index. Population in 1000s,
expenditure per capita in 1.000.000s COP. The fourth column shows the p-value
of the difference between surveyed and non-surveyed FARC municipalities.

###  D Additional Robustness Checks

In this Section, I present results of additional robustness checks to the
baseline results. For brevity, I focus only on the results for the main
(Anderson) index for violence, economic activity and the two indices based on
the state capacity measures.

Figure D1 shows the results for the four main (Anderson) indices of a placebo
exercise. In the exercise, I use data only from the pre-ceasefire period,
2009-2014, and run the usual diff-in-diff regression assigning the treatment
to be 2011, 2012, or 2013 (so that there are always at least two pre-treatment
and post-treatment periods). For all the placebo treatments (but one), the
coefficients are insignificant, ruling out anticipation problems.

Figure D1: Placebo Treatment – Extensive Margin

Notes: Estimates of Equation (1), including 95% confidence intervals (based on
standard errors clustered at the municipality level), using data only from the
pre-treatment period (2009-2014). The number in the $y$-axis corresponds to
the “placebo” treatment year. All figures use indices created following
Anderson (2008). The top left Figure uses the index based on the different
violence variables. The top right Figure uses the index based on weighted
nighttime light intensity, GDP per capita (from DANE), share of urban
population and agricultural productivity measures. The bottom left Figure uses
the index based on financial measures of local institutions, while the bottom
right Figure is based on measures of the overall performance of local
institutions.

In Figure D2, I assess how likely it is to observe the significant and large
negative violence results by chance, in a type of permutation test. 252525I
focus only on the violence results because the other ones are insignificant.
More specifically, I randomly assign all municipalities in Colombia (but for
those I classified as both FARC and ELN, which I excluded from my original
analysis) to a treatment and control group, each containing the same number of
municipalities as the original treatment and control groups. I then estimate
the usual diff-in-diff regression on the Anderson violence index. The Figure
displays the distribution of coefficients from this exercise, with 1000
repetitions. Only in 4 out of the 1000 repetitions do I get a coefficient
larger in magnitude than the original one, suggesting that this is not simply
a chance event.

Figure D2: Permutation Test – Extensive Margin

Notes: The Figure shows the distribution of estimates of Equation (1) for the
violence (Anderson) index, using 1000 randomly-created treatment and control
groups (in the same proportion as in the original groups). The index is
created following Anderson (2008) and is based on the violence measures in
Table 2. The red line corresponds to the baseline results.

Table D1 shows results of performing different robustness checks to the
baseline results, focusing on the four summary indices for brevity. Overall,
the results are in line with the baseline ones: regardless of the
specification, the effect on violence is significant, large, and negative,
while the effect on economic activity is insignificant and precisely-
estimated. The state capacity index composed of financial performance measures
is statistically significant twice, but in opposite directions, and the state
capacity index composed of measures of institutional quality is statistically
significant once, but only marginally so. Thus, it is unlikely that this shows
a problem with those results.

Table D1: Robustness Checks, Baseline Results

State Capacity Violence Economic Fin. Performance Inst. Quality (1) (2) (3)
(4) Panel A. Alt. Inference Ceasefire $\times$ FARC –0.283 –0.004 0.008 0.149
Baseline (0.108)*** (0.040) (0.193) (0.113) Conley Perc. 25 (0.076)*** (0.023)
(0.166) (0.086)* Conley Perc. 50 (0.082)*** (0.023) (0.169) (0.085)* Conley
Perc. 75 (0.085)*** (0.022) (0.175) (0.087)* Wild Bootstrap p-value [0.008]***
[0.919] [0.965] [0.179] Observations 2,827 2,827 2,822 2,827 Panel B. Alt.
(KLK) Index Ceasefire $\times$ FARC –0.487*** –0.011 –0.037 0.251** (0.086)
(0.036) (0.048) (0.109) Observations 2,827 2,827 2,822 2,827 Panel C.
Collapsing Ceasefire $\times$ FARC –0.283* –0.004 0.005 0.149 (0.146) (0.053)
(0.257) (0.152) Observations 514 514 514 514 Panel D. Adding Controls
Ceasefire $\times$ FARC –0.240** –0.012 0.021 0.122 (0.112) (0.037) (0.231)
(0.112) Observations 2,827 2,827 2,822 2,827 Panel E. Munic. Trends Ceasefire
$\times$ FARC –0.331*** –0.057 0.442** –0.050 (0.062) (0.035) (0.206) (0.066)
Observations 2,827 2,827 2,822 2,827 Panel F. Alt. Definition Ceasefire
$\times$ FARC –0.517*** 0.021 0.105 0.097 (0.117) (0.038) (0.252) (0.093)
Observations 2,266 2,266 2,263 2,266

$Notes$: Results from estimating Equation (1). For Panels B to G, standard
errors clustered at the municipality level. Considers only years from 2009 and
before 2020. Defines the post period as 2015. The first column uses summary
measures based on the different violence variables. The second column uses
summary measures based on weighted nighttime light intensity, GDP per capita
(from DANE), share of urban population, agricultural productivity, formal
employment and firm entry measures. The third column uses summary measures
based on financial measures of local institutions, while the last column is
based on measures of the overall performance of local institutions. Panel A
uses alternative inference approaches, standard errors estimated following
Conley (1999), allowing for spatial autocorrelation up to the 25th/50th/75th
percentile of the distance to the department’s capital, and the wild cluster
bootstrap p-values (in squared brackets). Panel B creates the summary indices
following the approach suggested by Kling, Liebman & Katz (2008). Panel C
follows Bertrand, Duflo & Mullainathan (2004) and collapses all pre- and post-
intervention periods together. Panel D adds pre-treatment variables (distance
to department’s capital, size of municipality, measures of poverty and basic
needs, and the log of the municipality’s population) interacted with the
ceasefire dummy. Panel E includes municipality-specific time trends rather
than year fixed effects. Panel F defines a municipality as FARC (ELN) if it is
classified as FARC (ELN) using both the baseline intensive and extensive
margins of presence.

In Panel A, I estimate standard errors that take into account spatial
correlation following Conley (1999)262626Up to the 25, 50 and 75 percentile of
the distance of municipalities to their department’s capital. and the wild
cluster bootstrap t-statistic method suggested by Cameron, Gelbach, and Miller
(2008), with the same results. In Panel B, I use the KLK index proposed by
Kling, Liebman, and Katz (2007) rather than the Anderson index. In Panel C, I
follow Bertrand, Duflo, and Mullainathan (2004) and collapse all the pre- and
post-ceasefire periods together to deal with serial correlation.
Unsurprisingly, given that my panel is balanced for most of the variables, the
coefficients are identical to the baseline ones, and while the SEs increase
marginally, the significance of the coefficients remains unchanged. In Panel
D, I add several pre-treatment variables (distance to department’s capital,
size of municipality, measures of poverty and basic needs, and the log
population) interacted with the ceasefire dummy, and the results remain
basically unchanged. In Panel E, I add municipality-specific time trends
rather than year FEs. One of the state capacity indices is significant when
doing so. In Panel F, I classify municipalities as FARC/ELN municipalities if
the FARC/ELN operated there for a long time and were very violent. More
specifically, I classify a municipality as FARC (ELN) if it is classified as
FARC (ELN) using both the [baseline] intensive and extensive margin measures
of presence (ignoring those that are classified as both FARC and ELN), finding
qualitatively similar results, if anything an even larger decrease in
violence. In Panel G, I use data on the historic presence of insurgent groups
created by a Colombian NGO, the Pares Foundation (PARES, 2015). If anything,
using this definition suggests that state capacity worsened in FARC
municipalities relative to ELN ones after the ceasefire, while the remaining
results stay in line with the baseline results.

An additional concern could be that the results for the main indices are
driven by the selection of variables that compose the indices. To allay these
concerns, in Figure D3, I show the results of estimating the baseline set of
results for each of the four main indices, eliminating each of the component
variables individually. I present results for the estimates from Equation (1)
in black with 95% CIs, as well as the p-values of the test of joint
significance of pre-treatment coefficients i) suggested by Freyaldenhoven et
al. (2021) in blue, and ii) suggested by Borusyak, Jaravel, and Spiess (2021)
in red. The Figure also shows the baseline set of results for comparison.

For the violence results in Panel A, the diff-in-diff estimates are always
significant at the 95% significance level, and very close in magnitude to the
baseline results, but when excluding terrorist attacks (slightly larger
estimate in magnitude) and threats (marginally insignificant at this level).
All the p-values for pre-trends are insignificant. The results for the
economic index in Panel B are similar: the coefficients are very close in
magnitude to the baseline results. While the p-values are below 0.05, this is
entirely driven by the firm entry variable, as shown in the last column. The
state capacity indices show a similar pattern: the one composed of financial
performance outcomes (Panel C) shows similar results to the baseline index,
except when excluding operational costs, where the p-values of the pre-trends
are marginally significant. The one composed of measures of administrative
quality (Panel D) shows that the marginally significant p-values of the pre-
trends are driven entirely by the savings capacity measure. Overall, this
shows that the baseline results remain consistent when altering the
composition of the index measures.

Figure D3: Robustness of Anderson Indices to Individual Component Variables

(a) Violence Outcomes

(b) Economic Outcomes

(c) SC Financial Performance Outcomes

(d) SC Institutional Quality Outcomes

Notes: Figures show in black estimates of Equation (1) including 95%
confidence intervals (based on standard errors clustered at the municipality
level), in blue p-values from a test of joint significance of all pre-
treatment coefficients from estimates of Equation (2) following Freyaldenhoven
et al. (2021), and in red p-values from a test of joint significance of all
pre-treatment coefficients following Borusyak, Jaravel, and Spiess (2021). All
figures use indices created following Anderson (2008). Panel A uses the index
based on the different violence variables. Panel B uses the index based on
nighttime light intensity, GDP per capita (from DANE), share of urban
population, agricultural productivity, firm entry and formal employment
measures. Panel C uses the index based on financial measures of local
institutions, while Panel D is based on measures of the overall performance of
local institutions. The names in the $x$-axis correspond to the variable
dropped from the corresponding index when estimating the results.

Finally, another reason for the lack of significant results in terms of
economic improvements in FARC municipalities could be that both FARC and ELN
municipalities were growing faster than the rest of the country. Rather than
FARC municipalities not experiencing economic improvements, these could be
masked simply by virtue of the control group growing at a similar pace.
However, Figure D4 shows that this is not the case. Panel A plots the
evolution of GDP per capita (estimated following Sánchez Torres and España
Eljaiek, 2012), Panel B that of nighttime light intensity, Panel C of formal
employment (using PILA data) and Panel D for the Anderson Index of the
different economic indicators for FARC (red), ELN (blue) and the remaining
municipalities (black).272727The latter is standardised each year for
comparison purposes. The time series show that 1) FARC and ELN municipalities
are much poorer than the rest of the country using all measures, and 2) they
have not caught up with the rest of the country since 2008. Thus, the diff-in-
diff estimates are not masking economic improvements in FARC and ELN
municipalities relative to the rest of the country.

Figure D4: Time Series Economic Indicators

(a) GDP per Capita (S&E, 2012)

(b) Nighttime Light Intensity

(c) Formal Employment

(d) Anderson Index

Notes: Panel A shows the evolution of GDP per capita (following Sánchez Torres
and España Eljaiek, 2012), Panel B of nighttime light intensity, Panel C of
formal employment (using PILA data) and Panel D for the Anderson Index of the
different economic indicators for FARC (red), ELN (blue) and the remaining
municipalities (black), excluding those municipalities classified as both FARC
and ELN.

###  E Additional Figures

####  E.1 Violence Event Studies

The following Figures show the event-study estimates for each of the different
violence measures presented in Section 4.1. Figure E1 shows the results for
the measures from the Victims’ Unit, while Figure E2 shows the results for the
other measures of conflict from different sources. For most of the measures,
the pre-treatment coefficients are jointly and individually significant. For
those with significant joint tests, these are driven by a single significant
year (expected due to the number of coefficients estimated), no trend is
apparent, and the sup-t confidence bands cover the zero.

Figure E1: Dynamic Estimation, Extensive Margin, Events in Over 60% of Years,
Victims’ Unit Measures

(a) Terrorist Acts/Armed Fights

(b) Threats

(c) Sex Crimes

(d) Forced Disappearences

(e) Torture

(f) Child Recruitment

Notes: Event study plots from estimating Equation (2) for different violence
measures from the Victims’ Unit, including including 95% confidence intervals
(based on standard errors clustered at the municipality level).

Figure E2: Dynamic Estimation, Extensive Margin, Events in Over 60% of Years,
Different Sources

(a) Property Losses (Victims’ Unit)

(b) Forced Migration (Victims’ Unit)

(c) Homicides (Ministry of Defense)

(d) Kidnappings (Ministry of Defense)

(e) Mines (DAIMA)

(f) Theft (Ministry of Defense)

Notes: Event study plots from estimating Equation (2) for different violence
measures from several sources, including including 95% confidence intervals
(based on standard errors clustered at the municipality level).

####  E.2 Alternative Measures of Insurgent Groups’ Presence

Figure E3: Violence in FARC Municipalities vs. ELN Municipalities – Intensive
Margin, Top 20% Most Violent

Notes: Event study plots from estimating Equation (2), including including 95%
confidence intervals (based on standard errors clustered at the municipality
level). The index is created following Anderson (2008) and is based on the
violence measures in Table 2.

Figure E4: Economic Activity in FARC vs. ELN Municipalities – Intensive
Margin, Top 20% Most Violent Figure E5: State Capacity Outcomes in FARC vs.
ELN Municipalities – Intensive Margin, Top 20% Most Violent

(a) Tax Revenue per Capita

(b) Government Transfers per Capita

(c) Anderson Financial Perf. Index

(d) Anderson Inst. Quality Index

Notes: Event study plots from estimating Equation (2) for the different state
capacity measures, including including 95% confidence intervals (based on
standard errors clustered at the municipality level). The financial
performance index uses financial measures of local institutions (e.g. tax
revenue, government transfers, expenditures, etc), while the institutional
quality index uses measures of the overall performance of local institutions.
Both are created following Anderson (2008).

Figure E6: Robustness to Alt. Thresholds of Presence Measures – Intensive
Margin

(a) Violence Measures

(b) Economic Indicators

(c) State Capacity Measures

Notes: In Panel A, all variables are in 1000s of inhabitants, except for the
migration ones (forced displaced and forced migration) which are measured in
per capita terms. In Panel B, the measures of GDP per capita have been
standardized for comparability.

####  E.3 Synthetic Difference-in-Difference Results

Figure E7: Synthetic Difference-in-Difference – Violence Indices

Extensive Margin, Events in Over 60% of Years

(a) Extensive Margin – KLK Index

(b) Extensive Margin – Anderson Index

Intensive Margin, Top 20% Most Violent

(c) KLK Index

(d) Anderson Index

Notes: Event study plots from the synthetic difference-in-difference estimator
developed by Arkhangelsky et al. (2021). KLK is a summary index created
following Kling, Liebman, and Katz (2007), while Anderson is a summary index
created following Anderson (2008). The index is based on the violence measures
in Table 2.

Figure E8: Synthetic Difference-in-Difference – Economic Indicators

Extensive Margin, Events in Over 60% of Years

(a) Extensive Margin – KLK Index

(b) Extensive Margin – Anderson Index

Intensive Margin, Top 20% Most Violent

(c) Intensive Margin – KLK Index

(d) Intensive Margin – Anderson Index

Notes: Event study plots from the synthetic difference-in-difference estimator
developed by Arkhangelsky et al. (2021). KLK is a summary index created
following Kling, Liebman, and Katz (2007), while Anderson is a summary index
created following Anderson (2008). The index is based on weighted nighttime
light intensity, GDP per capita (from DANE), share of urban population and
agricultural productivity measures.

Figure E9: Synthetic Difference-in-Difference – State Capacity Indices

Extensive Margin, Events in Over 60% of Years

(a) KLK Financial Perf. Index

(b) Anderson Financial Perf.Index

(c) KLK Inst. Quality Index

(d) Anderson Inst. Quality Index

Intensive Margin, Top 20% Most Violent

(e) KLK Financial Perf. Index

(f) Anderson Financial Perf. Index

(g) KLK Inst. Quality Index

(h) Anderson Inst. Quality Index

Notes: Event study plots from the synthetic difference-in-difference estimator
developed by Arkhangelsky et al. (2021). KLK is a summary index created
following Kling, Liebman, and Katz (2007), while Anderson is a summary index
created following Anderson (2008). The financial performance index uses
financial measures of local institutions (e.g. tax revenue, government
transfers, expenditures, etc), while the administrative quality index uses
measures of the overall performance of local institutions.

####  E.4 Violations of Parallel Trend Assumption

Figure E10: Linear Violation of Parallel Trends Assumption at 80% Power

(a) Extensive Margin, Events in Over 60% of Years

(b) Intensive Margin, Top 20% Most Violent

Notes: Event study plots from estimating Equation (2) for the violence
(Anderson) index, including including 95% confidence intervals (based on
standard errors clustered at the municipality level). The red line is the
hypothetical linear violation of the parallel trends assumption that would be
detected with 80% power, estimated following Roth (Forthcoming).

####  E.5 Spatial Spillovers

Figure E11: Event Studies – All Treated Municipalities vs. Control
Municipalities Over 60kms From Closest Treated Municipality

(a) Violence Index

(b) Economic Index

(c) Financial Perf. Index

(d) Inst. Quality Index

Notes: Event study plots from estimates of Equation (2), including 95%
confidence intervals (based on standard errors clustered at the municipality
level). All figures use indices created following Anderson (2008). Panel A
uses the index based on the different violence variables. Panel B uses the
index based on weighted nighttime light intensity, GDP per capita (from DANE),
share of urban population and agricultural productivity measures. Panel C uses
the index based on financial measures of local institutions, while Panel D is
based on measures of the overall performance of local institutions. The
treatment group is composed of all treated (FARC) municipalities, and the
control group is composed of the control (ELN) municipalities located over
60kms away from the closest treated municipality.

###  F Additional Tables

####  F.1 Location of Demobilized FARC/ELN Members

Table F1: Demobilizations by Criminal Organization Over Time – Extensive
Margin, Events in Over 60% of Years

Demobilizations FARC Members Demobilizations ELN Members Rest Country
Treatment Control Treat-Control Rest Country Treatment Control Treat-Control
Year (1) (2) (3) (4) (5) (6) (7) (8) 2005 0.12 0.3 0.03 0.02 0.04 0.05 2006
0.18 0.61 0.22 0.04 0.06 0.15 2007 0.2 0.73 0.13 0.06 0.06 0.12 2008 0.26 1.12
0.12 0.02 0.04 0.13 2009 0.24 0.95 0.28 0.04 0.06 0.04 2010 0.23 0.87 0.24
0.04 0.06 0.07 2011 0.16 0.56 0.05 0.02 0.04 0.04 2012 0.09 0.41 0.24 0.01
0.01 0.03 2013 0.11 0.5 0.1 0.02 0.05 0.07 2014 0.12 0.56 0.13 0.03 0.03 0.07
2015 0.09 0.45 0.08 0.03 0.05 0.07 2016 0.05 0.4 0.05 0.04 0.04 0.2 2017 0.07
0.45 0 0.05 0.07 0.25 2018 0.01 0.08 0 0.05 0.05 0.21 2019 0 0.01 0 0.03 0.04
0.06 Pre-Cease 0.17 0.66 0.15 0 0.03 0.05 0.08 0.017 Post-Cease 0.05 0.28 0.03
0 0.04 0.05 0.16 0.011

Demobilization numbers from President’s Office. All measures per 10.000
inhabitants. Columns 4 and 8 show p-values between the treatment and control
means.

####  F.2 Alternative Measures of Insurgent Groups’ Presence

Table F2: DiD Violence Analysis: Intensive Margin, Top 20% Most Violent

(1) (2) (3) (4) (5) (6) (7) Panel A. UARIV Terror. Act Threats Disapp. Sex
Crimes Child Recruit. Torture Prop. Loss Ceasefire $\times$ FARC –0.361*
–0.940*** –0.053*** –0.012 –0.034*** –0.006** –0.338 (0.209) (0.201) (0.016)
(0.008) (0.006) (0.003) (0.242) Treated Munic. 153 153 153 153 153 153 153
Control Munic. 153 153 153 153 153 153 153 Mean Dep. Var. 0.555 2.124 0.087
0.059 0.031 0.012 0.669 Observations 3,366 3,366 3,366 3,366 3,366 3,366 3,366
R Squared 0.273 0.549 0.277 0.547 0.453 0.196 0.362 Panel B. Other Kidnap.
Homicides Theft Mines Forced Mig. Clash/Att. And. Index Ceasefire $\times$
FARC –0.002 –0.133*** –0.295** –0.383*** –12.996*** –0.021*** –0.428***
(0.003) (0.031) (0.143) (0.066) (2.221) (0.006) (0.096) Treated Munic. 153 153
153 153 153 153 153 Control Munic. 153 153 153 153 153 153 153 Mean Dep. Var.
0.013 0.376 0.817 0.370 17.196 0.031 0.134 Observations 3,366 3,366 3,366
3,366 3,366 3,060 3,366 R Squared 0.156 0.537 0.722 0.625 0.482 0.329 0.497

$Notes$: Results from estimating Equation (1). Standard errors clustered at
the municipality level. Includes municipality and year fixed effects.
Considers only years from 2009 and before 2020. Mean Dep. Var.: Mean of the
dependent variable in the pre-intervention period for municipalities in the
sample (FARC and ELN). Defines the post period as 2015. All variables are
measures in 1000’s of inhabitants. Prop. Loss: Property Loss. Clash/Att.:
Number of clashes and attacks between government and paramilitary and
guerrilla groups. Panel B shows variables from other data sources, with the
first three columns coming from the Ministry of Defense, the fourth from the
agency against anti-personnel mines (DAIMA), the fifth from the Victims’ Unit,
and the sixth from Juan Vargas. And. Index is a summary measure created
following Anderson (2008) that summarizes all the different outcomes
variables. It is the weighted average of the standardized outcomes, weighted
by their inverted covariance matrix.

Table F3: DiD Economics Analysis: Intensive Margin, Top 20% Most Violent

Nighttime GDP Per Capita Urban Agricultural Population Firm Formal Anderson
Light DANE S&E (2012) Built Up Productivity Production Share Urban Entry Empl.
Index (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) Ceasefire $\times$ FARC –0.032*
–2.498 –0.310 –0.006* 0.062 0.243* 0.006 0.695** 0.002 0.036 (0.017) (1.739)
(0.529) (0.003) (0.285) (0.138) (0.004) (0.276) (0.003) (0.040) Treated Munic.
153 153 153 153 153 153 153 153 153 153 Control Munic. 153 153 153 153 153 153
153 153 153 153 Mean Dep. Var. –0.365 11.673 6.416 0.024 4.736 1.480 0.395
5.431 0.082 –0.071 Observations 3,366 2,754 3,362 3,365 3,365 3,366 3,366
3,366 3,366 3,366 R Squared 0.917 0.859 0.859 0.314 0.904 0.883 0.974 0.696
0.956 0.871

$Notes$: Results from estimating Equation (1). Standard errors clustered at
the municipality level. Includes municipality and year fixed effects.
Considers only years from 2009 and before 2020. Mean Dep. Var.: Mean of the
dependent variable in the pre-intervention period for municipalities in the
sample (FARC and ELN). Defines the post period as 2015. Nighttime light
intensity defined so that grid cells in the border of multiple municipalities
are assigned in proportion to the share of the grid cell in each municipality
(weighted). GDP per capita comes from two sources: From DANE (National
Department of Statistics) or based on calculations following Sanchez & Espana
(2012), both in millions of COP. Agricultural productivity is defined as total
tonnes produced of 271 agricultural crops divided by total area cultivated in
hectares. Agricultural production is defined as total tonnes produced of 271
agricultural crops per capita. Urban built up is estimated using the Band
Ratio for Built-Up Area (BRBA) index developed by Waqar et al. (2012), and
measures the average amount of pixels within a municipality that are
classified as urban built up based on the BRBA index. Firm entry comes from
RUES and is measured per 1000 inhabitants. Formal employment is measured as
the average number of individuals paying contributions to healthcare, pension
funds and workers’ compensations across the year in the municipality per 18-60
years old, from PILA. Anderson Index is a summary measure created following
Anderson (2008) that summarizes the weighted nighttime light intensity, GDP
per capita (from DANE), share of urban population, agricultural productivity,
formal employment and firm entry measures. It is the weighted average of the
standardized outcomes, weighted by their inverted covariance matrix.

Table F4: Baseline State Capacity Summary Statistics (Weighted by Population):
Intensive Margin, Top 20% Most Violent

Mean p-value of Difference Rest Country Treatment Control Rest-Treat Rest-
Control Treat-Control Variable (1) (2) (3) (4) (5) (6) 1\. Total Revenue
787.36 672.75 727.97 0.01 0.18 0.27 2\. Tax Revenue 261.61 76.16 122.61 0 0 0
3\. Gov. Transfers 18.81 53.56 43.70 0 0 0.05 4\. Total Expenditures 792.76
722.03 768.35 0.16 0.59 0.37 5\. Operational Exp. 125.13 87.09 92.23 0 0 0.44
6\. Total Deficit –5.38 –49.27 –40.38 0.01 0.03 0.62 7\. Credit 5.01 5.53
–1.55 0.93 0.22 0.06 8\. Savings Capacity 0.97 –0.15 0.18 0 0 0 9\. %Exp.
Invested –0.34 0.30 0.31 0 0 0.89 10\. Fiscal Perf. 1.04 –0.03 0.23 0 0 0.02
11\. Overall Perf. 0.5 –0.25 –0.10 0 0 0.13 12\. Aqueduct Cov. 76.51 50.84
69.69 0 0.06 0 13\. Garbage Collec. 68.5 45.58 58.20 0 0.01 0 14\. Sewage Cov.
68.13 39.38 54.02 0 0 0 15\. Info. Openness 0.64 –0.17 0.28 0 0 0

$Notes$: All the variables are measured in 2008 (the last period before the
panel used in the main analysis) but for the last variable that is only
available from 2010. The financial measures are in per capita terms (in
thousand COP), and the performance measures are standardised (by year).

Table F5: DiD State Capacity Analysis: Intensive Margin, Top 20% Most Violent

(1) (2) (3) (4) (5) (6) (7) Panel A. Financial Revenue Tax Revenue Gov.
Transfers Debt Oper. Costs Deficit And. Fin. Perf. Ceasefire $\times$ FARC
0.013 –0.008 0.003 0.041 –0.001 –0.029 0.085 (0.044) (0.009) (0.006) (0.047)
(0.012) (0.029) (0.138) Treated Municip. 153 153 153 153 153 153 153 Control
Municip. 153 153 153 153 153 153 153 Mean Dependent Var. 1.059 0.106 0.110
1.089 0.138 –0.030 –0.259 Observations 3,362 3,362 3,362 3,362 3,362 3,362
3,362 R Squared 0.720 0.817 0.828 0.707 0.278 0.136 0.177 Panel B. Inst.
Quality Investment Savings Cap. Fiscal Perf. Overall Perf. Info. Openness And.
Inst. Qual. Ceasefire $\times$ FARC 0.035 –0.017 –0.016 –0.089 0.050 –0.003
(0.059) (0.076) (0.059) (0.078) (0.072) (0.072) Treated Municip. 153 153 153
153 153 153 Control Municip. 153 153 153 153 153 153 Mean Dependent Var. 0.103
–0.139 –0.187 –0.061 –0.069 –0.025 Observations 3,359 3,358 3,358 3,366 3,055
3,366 R Squared 0.429 0.497 0.547 0.412 0.404 0.454

$Notes$: Results from estimating Equation (1). Standard errors clustered at
the municipality level. Includes municipality and year fixed effects.
Considers only years from 2009 and before 2020. Mean Dep. Var.: Mean of the
dependent variable in the pre-intervention period for municipalities in the
sample (FARC and ELN). Defines the post period as 2015. All the financial
measures are in million COP per capita terms, and all performance measures are
standardised. Transfers are transfers from the central government. Oper. Costs
are the municipality’s operational costs. Investment measures the share of a
municipality’s income that is spent on investment. Fiscal Perf. is an index
created by the Department of Planning and measures how well the municipality
spent its resources. Overall Perf. is also created by the Department of
Planning and measures overall performance. Info. Openness is created by the
Attorney General and measures how well municipalities report information and
implement basic management rules. And. Index is a summary measure created
following Anderson (2008) that summarizes all the different outcomes
variables. It is the weighted average of the standardized outcomes, weighted
by their inverted covariance matrix.

####  F.3 Synthetic Difference-in-Difference

Table F6: Synthetic DiD Analysis: Extensive Margin, Events in Over 60% of
Years

(1) (2) (3) (4) (5) (6) (7) (8) (9) Panel A. Violence KLK Index Anderson Index
Ceasefire $\times$ FARC –0.5143*** –0.3072*** (0.0904) (0.0695) Treated
Municipalities 216 216 Potential Controls 727 727 Control Municipalities 572
633 Panel B. Economic Light Int. GDP pc (DANE) Built Up Agr. Productivity
Share Urban Firm Entry Formal Empl. KLK Index Anderson Index Ceasefire
$\times$ FARC –0.0071 –1.0999 –0.0020 0.1048 0.0002 –0.2330 –0.0111***
–0.0546*** –0.0258 (0.0084) (1.0281) (0.0014) (0.1853) (0.0021) (0.1818)
(0.0018) (0.0185) (0.0202) Treated Municipalities 216 216 216 216 216 216 216
216 216 Potential Controls 711 711 711 711 711 711 711 727 727 Control
Municipalities 610 633 576 632 638 629 625 631 624 Panel C. State Capacity
Revenue Tax Revenue Gov. Transfers Fiscal Perf. Overall Perf. KLK Fin. Perf.
KLK Inst. Qual. And. Fin. Perf. And. Inst. Qual. Ceasefire $\times$ FARC
–0.1511*** –0.0325*** –0.0143*** –0.0832** –0.0679 –0.0139 –0.0347 –0.0372
–0.0403 (0.0423) (0.0074) (0.0050) (0.0380) (0.0526) (0.0448) (0.0877)
(0.0415) (0.1002) Treated Municipalities 208 208 208 208 208 216 216 216 216
Potential Controls 707 707 707 707 707 727 727 727 727 Control Municipalities
623 618 590 604 594 586 540 584 504

$Notes$: Estimated using Arkhangelsky et al. (2021) Synthetic Difference-in-
Difference command, synthdid. Standard errors calculated using Jackknife.
Considers only years from 2009 and before 2020. Defines the post period as
2015. KLK Index is a measure created following Kling, Liebman & Katz (2007)
that summarizes all the different outcomes variables. It is the (standardised)
average of the z-scores of all dependent variables. Anderson index is based on
Anderson (2008), and follows a similar procedure but attaching less weight to
highly correlated variables (which bring relatively less new information to
the index).

Table F7: Synthetic DiD Analysis: Intensive Margin, Top 20% Most Violent

(1) (2) (3) (4) (5) (6) (7) (8) (9) Panel A. Violence KLK Index Anderson Index
Ceasefire $\times$ FARC –0.5963*** –0.3998*** (0.0813) (0.0744) Treated
Municipalities 153 153 Potential Controls 774 774 Control Municipalities 604
665 Panel B. Economic Light Int. GDP pc (DANE) Built Up Agr. Productivity
Share Urban Firm Entry Formal Empl. KLK Index Anderson Index Ceasefire
$\times$ FARC –0.0056 –1.7944 –0.0004 –0.0825 0.0067** 0.3894* –0.0078***
–0.0283 –0.0033 (0.0093) (1.3941) (0.0011) (0.1617) (0.0028) (0.2013) (0.0022)
(0.0227) (0.0236) Treated Municipalities 151 151 151 151 151 151 151 153 153
Potential Controls 759 759 759 759 759 759 759 774 774 Control Municipalities
639 671 625 675 673 669 665 672 655 Panel C. State Capacity Revenue Tax
Revenue Gov. Transfers Fiscal Perf. Overall Perf. KLK Fin. Perf. KLK Inst.
Qual. And. Fin. Perf. And. Inst. Qual. Ceasefire $\times$ FARC –0.1104*
–0.0312*** –0.0139 –0.0238 –0.2529*** –0.1027** 0.0624 –0.0911* 0.0814
(0.0573) (0.0098) (0.0146) (0.0440) (0.0582) (0.0522) (0.1082) (0.0469)
(0.1514) Treated Municipalities 146 146 146 146 146 153 153 153 153 Potential
Controls 755 755 755 755 755 774 774 774 774 Control Municipalities 618 667
666 647 631 587 556 588 472

$Notes$: Estimated using Arkhangelsky et al. (2021) Synthetic Difference-in-
Difference command, synthdid. Standard errors calculated using Jackknife.
Considers only years from 2009 and before 2020. Defines the post period as
2015. KLK Index is a measure created following Kling, Liebman & Katz (2007)
that summarizes all the different outcomes variables. It is the (standardised)
average of the z-scores of all dependent variables. Anderson index is based on
Anderson (2008), and follows a similar procedure but attaching less weight to
highly correlated variables (which bring relatively less new information to
the index).

####  F.4 Violations of Parallel Trend Assumption

Table F8: Slope and Bias of Pre-Trends – Roth (2022)

Panel A. Extensive Margin, 60% Panel B. Intensive Margin, 20% Slope Uncond.
Bias Cond. Bias Mean Post $\hat{\beta}s$ Slope Uncond. Bias Cond. Bias Mean
Post $\hat{\beta}s$ (1) (2) (3) (4) (5) (6) (7) (8) Forced Disappearances
0.021 0.064 0.071 –0.011 0.019 0.056 0.068 –0.012 Forced Displacements 1.722
5.165 5.253 –10.258 1.351 4.054 7.047 –7.86 Property Losses 0.103 0.309 0.306
–0.466 0.174 0.523 0.611 –0.272 Homicides 0.04 0.12 0.197 –0.045 0.029 0.086
0.117 –0.094 Mines 0.045 0.134 0.211 –0.221 0.056 0.167 0.235 –0.306 And.
Viol. Measures 0.079 0.238 0.268 –0.32 0.081 0.243 0.417 –0.231 Nighttime
Light Int. 0.021 0.063 0.072 –0.018 0.015 0.046 0.056 0.009 GDP per Capita
(DANE) 0.443 1.328 1.792 –0.831 0.6 1.8 2.062 –1.362 Urban Built-Up 0.002
0.006 0.007 –0.001 0.002 0.007 0.007 0.001 Agr. Productivity 0.289 0.867 0.792
0.001 0.204 0.612 0.648 –0.173 Agr. Production 0.082 0.246 0.245 0.095 0.087
0.262 0.267 0.148 Share Urban Pop. 0.001 0.004 0.008 –0.001 0.001 0.003 0.006
0.007 Firm Entry 0.295 0.884 1.123 0.21 0.267 0.8 0.947 0.568 Formal
Employment 0.003 0.009 0.009 –0.003 0.002 0.007 0.007 –0.004 And. Econ.
Measures 0.027 0.08 0.083 –0.004 0.033 0.098 0.102 0.027 Tax Revenue 0.007
0.021 0.023 –0.019 0.009 0.027 0.031 –0.018 And. Fin. Performance 0.186 0.558
0.92 0.052 0.097 0.291 0.376 –0.057 And. Instit. Quality 0.12 0.359 0.388
–0.062 0.091 0.274 0.28 –0.244

Estimated following Roth (2022). Considers only years from 2009 and before
2020. Defines the post period as 2015. Mean Post $\hat{\beta}s$ is the mean of
all the post-treatment coefficients. Anderson Index is a summary measure
created following Anderson (2008) that summarizes all the different outcomes
variables. It is the weighted average of the standardized outcomes, weighted
by their inverted covariance matrix. Nighttime light intensity defined so that
grid cells in the border of multiple municipalities are assigned in proportion
to the share of the grid cell in each municipality (weighted). GDP per capita
comes from two sources: From DANE (National Department of Statistics) or based
on calculations following Sanchez & Espana (2012), both in millions of COP.
Agricultural productivity is defined as total tonnes produced of 271
agricultural crops divided by total area cultivated in hectares. Agricultural
production is defined as total tonnes produced of 271 agricultural crops per
capita. Urban built up is estimated using the Band Ratio for Built-Up Area
(BRBA) index developed by Waqar et al. (2012), and measures the average amount
of pixels within a municipality that are classified as urban built up based on
the BRBA index. Firm entry comes from RUES and is measured per 1000
inhabitants. Formal employment is measured as the average number of
individuals paying contributions to healthcare, pension funds and workers’
compensations across the year in the municipality per 18-60 years old, from
PILA. And. Fin. Measures and And. Perf. Measures are created using the
financial and performance measures of state capacity, as in the main analysis.