It has been widely argued that the production of illegal drugs, particularly cocaine, has financed guerrilla activity in Colombia. This paper uses quarterly time-series data for Colombia 1994-2005 to examine the interaction between legal agricultural production, illegal agricultural production of drugs and the number of guerrilla attacks. The time series analysis suggests that drug production acts as a weakly exogenous stochastic trend which has a negative effect on legal agricultural production and a positive effect on guerrilla attacks; with a long-run elasticity of attacks to drug production very close to unity

Gonzalez, T.

Smith, Ron P.

English

Birkbeck Institutional Research Online

ISSN 1745-8587 Birkbeck Working Papers in Economics & Finance   School of Economics, Mathematics and Statistics     BWPEF 0906    Drugs and Violence in Colombia: a VECM Analysis     Tomás González Universidad de Los Andes, Bogota  Ron P. Smith Birkbeck, University of London     June 2009 ▪ Birkbeck, University of London ▪ Malet Street ▪ London ▪ WC1E 7HX ▪ Drugs and Violence in Colombia: a VECMAnalysis∗Toma´s Gonza´lezUniversidad de Los Andes, BogotaRon P. SmithBirkbeck, University of LondonJune 2009AbstractIt has been widely argued that the production of illegal drugs, partic-ularly cocaine, has financed guerrilla activity in Colombia. This paperuses quarterly time-series data for Colombia 1994-2005 to examine theinteraction between legal agricultural production, illegal agriculturalproduction of drugs and the number of guerrilla attacks. The time se-ries analysis suggests that drug production acts as a weakly exogenousstochastic trend which has a negative effect on legal agricultural produc-tion and a positive effect on guerrilla attacks; with a long-run elasticityof attacks to drug production very close to unity.1 IntroductionCollier and Hoeﬄer (1998) provoked an extended debate about the rolesof ’greed and grievance’, economic and political factors, in civil wars. Theirpaper is part of a large and growing economic literature on civil wars reviewedby Blattman and Miguel (2009), which underscores two economic dimensionsto conflict: each side must have both the desire and the ability to fight. Thedesire may be motivated by economic factors such as acquiring resources; theability depends on the technology of fighting and the availability of resources,∗This paper is based on Chapter 4 of Gonza´lez (2009), which contains more detailedbackground material. We are grateful to Paul Levine, Mike Spagat and Roma´n Ortiz forcomments. JEL Classification Numbers: C32, D74.1Drugs and Violence in Colombia: a VECM analysis 2which in turn depends on opportunity costs (e.g. what is needed to attractfighters).Besley and Persson (2008) provide a theoretical model that emphasisesthe elements of motive, opportunity cost and technology, and present cross-section evidence suggesting higher world market prices of exported, as wellas imported commodities, are strong and significant predictors of civil war.Using case studies, Arnson and Zartman (2005) also focus on the ways civilwars are financed, including Colombia. Their work highlights how govern-ments and insurgents have obtained revenue from oil, ’blood diamonds’, min-erals like coltan in the Democratic Republic of Congo, and the production ofillegal drugs such as poppies for opium and heroin in Afghanistan and cocafor cocaine in Colombia.We examine the case of Colombia, which has a long and complicatedhistory of conflict. Not only have combatants included left-wing guerrillas,right-wing paramilitaries, drug traffickers and various governments, but inthe last threes decades there has been a substantial production of illegaldrugs providing an alternative employment source to legitimate agriculturalproduction. So individuals not only have occupational choices between legalfarming, illegal farming of drugs or becoming guerrillas or soldiers, but rev-enues from drugs can also be used to finance guerrilla attacks. In principle,drug production has an income effect, financing attacks, and a substitutioneffect, providing an alternative occupation to guerrilla activity. In practicethe linkage may be more indirect in Colombia since coca production is notvery land or labour intensive, and it occurs in areas outside government con-trol away from the areas of legitimate agricultural production.Dube and Vargas (2008) use a very detailed Colombian panel of localitydata to examine the impact of income shocks on violence. Colombia producesoil and coffee as well as coca, and over the period they consider there weretwo large income shocks: a drop in the price of coffee and a rise in the priceof oil. By comparison of municipalities that grew coffee with those that didnot and those that had oil reserves and pipelines with those that did not,they suggest that both income shocks increased violence. The coffee shockby increasing poverty and thus reducing the opportunity cost of becominga guerrilla, the oil shock by providing resources for increased governmentcounter-insurgency activity.Unlike their detailed micro-econometric approach, we adopt a macro-econometric approach. While identification of causal influences is more diffi-cult with aggregate data, the results relate more directly to macroeconomicpolicy issues. The data we examine are quarterly measures of the produc-Drugs and Violence in Colombia: a VECM analysis 3tion of legitimate agricultural crops, the production of illegal drugs and thenumber of attacks by guerillas over the period 1994Q1-2005Q3. All three se-ries appear to be non-stationary and, a priori, there is no presumption thatany of the variables are exogenous, so we start from a vector autoregression,VAR, and develop a vector correction model, VECM. Section 2 briefly de-scribes the data and background, section 3 presents the results, and section4 contains some concluding comments. Although identification issues meanthat conclusions must be tentative, it appears that illegal drug productionis close to a random walk and acts as a weakly exogenous stochastic trendin two long-run cointegrating relationships that determine guerrilla attacksand agricultural production: illegal drug production increases attacks andreduces legal production.2 Background and dataIt is difficult to determine exactly when the Colombian conflict started. Someargue for the 1930s when a liberal won the Presidency after fifty years ofconservative rule in a system incapable of power-sharing or reform. Later,after a long wave of violence, the example and support provided by the Cubanrevolution of 1959 led to the creation of the two largest guerrilla groups inthe country’s history: the ELN (the initials for National Liberation Armyin Spanish) in 1964 and FARC (Revolutionary Armed Forces of Colombia) ayear later.In the decades that followed a series of unsuccessful peace attempts al-lowed guerrilla groups to expand and strengthen. At the same time, illegaldrug production shifted from Peru and Bolivia to Colombia thanks to techni-cal improvements in cultivation and to the country’s competitive advantagesin supplying international markets. Drug trafficking also became a morecompetitive market with the disruption of the Medell´ın and Cali Cartels inthe early 1990s. Both drug production and guerrilla bases tended to be inrelatively inaccessible jungle areas where the government had little control.A deteriorating security situation followed, with FARC’s attacks escalat-ing in the mid 1990s by taking advantage of a relatively ineffective army.Conservatives won the presidential election of 1998 on a platform of peacenegotiations, army modernisation and attacks on drug production. Althoughnegotiations failed three years later, the war on drug production gained sub-stantial international backing under the title Plan Colombia, an effort involv-ing crop eradication and social investment in cultivation areas. In 2002 theelection was won on a policy of a tougher approach to combating the guer-Drugs and Violence in Colombia: a VECM analysis 4rillas which so far has been successful in improving the country’s security.The three series are agricultural GDP, illegal drug production and guerillaattacks, which are graphed below with quarterly data on the top panel andyearly data (to remove seasonality) on the bottom. Agricultural GDP in-cludes crops, livestock, forestry and fishing, and is largely a measure of legalrural activity. Guerrillas recruit from rural areas and most attacks are lo-cated in rural areas. Legal agricultural production has a strong seasonalcomponent, is roughly flat till the late 1990s and then grows along with theeconomy.There has been considerable debate about whether the output of illegalactivities should be included in the national accounts. In the case of Colom-bia illegal activities are quite large, about 15% of GDP according to Rubio(1995), and drugs are a substantial part of exports. The IMF recommendedthat the Colombian statistical authority include some measure of the pro-duction of illicit crops (latex from poppies, marihuana and coca paste) in thenational accounts to ensure consistency between income and production inthe agricultural sector. The estimates are based on satellite surveys of theareas cultivated for the production of illicit crops; estimates of local yieldsand production losses; and estimated market prices paid to farmers for cocapaste. The value of downstream production activities, such as processing anddistribution of the coca paste, is not included, so this measure is a subsetof all illegal production that captures local incomes from illegal activities.Illegal agricultural production also has a strong seasonal component, grow-ing until the late 1990s, then declining with the large eradication campaigns,before flattening out at the end of the period. It averages about a tenth oflegal production.The series for guerrilla attacks was constructed from data in the nationalpolice database. It includes attacks by both FARC and the smaller ELN,but not by paramilitaries. Attacks show a trend increase until early 2001,when they fell sharply with the army surge against guerrillas. There is aspike 1996Q3 followed by a drop in the following quarter. The start of thesample is determined by availability of quarterly national accounts data, theend by changes in the classification methodology used in the police databasefor attacks.3 EstimatesUsing the logarithms of the three series seemed to produce better results,and we denote log drug production as dt; log of guerilla attacks as at; andDrugs and Violence in Colombia: a VECM analysis 5log agricultural production as qt. The usual univariate tests suggested thatthe three series may be I(1), but these results should be treated with cautionsince there seem to be clear structural breaks in all three series. It seems moreinformative to examine the order of integration and cointegration within amultivariate system, which could allow for any co-breaking in the series.However, the rather short time series may cause the multivariate estimatesto lack precision. Since none of the variables are, a priori, exogenous, a vectorautoregression, VAR, was estimated on the logarithms of the three variablesincluding seasonals and a deterministic trend.The AIC and SBC information criteria indicated that one lag was ade-quate (without seasonals the AIC indicated four and the SBC three). Theinverse roots of the system were all real at 0.90, 0.37 and 0.18. This isconsistent with either all three variables being stationary, or with a singlestochastic trend, root of unity, and two cointegrating vectors. The coefficientof its own lag in the illegal production equation was 0.93, with neither of thethe other variables being significant, suggesting that the logarithm of illegalproduction may be close to a random walk. Legal agricultural productionwas significantly influenced by lagged illegal production, and this was theonly apparent Granger causality. The trend was significant in agriculturalproduction though not in the other two equations, and some seasonals weresignificant in each equation.Tests for cointegration gave mixed results, with the trace and eigenvaluetests indicating between one and three cointegrating vectors depending onthe treatment of the deterministics. However, with unrestricted seasonal in-tercepts and restricted trends, both tests indicated two cointegrating vectors.This was chosen since it was also the combination selected by the AIC andis consistent with the estimated roots of the system. The system is then∆yt = γ′St + α(βyt−1 + β∗t) + εtwhere yt = (at, qt, dt)′, St is a vector of four quarterly dummy variables,α is a 3 × 2 matrix and [β, β∗] is a 2 × 4 matrix. With two cointegratingvectors, two just identifying restrictions on each vector of β are required. Thefact that drug production appears close to a random walk suggests treatingit as a forcing variable, separately determining each of the other two. Usingthese identifying restrictions the two long-run cointegrating relations areDrugs and Violence in Colombia: a VECM analysis 6qt = 16.31 −0.14 dt +0.002 t +ẑ1t(−6.64) (7.58)at = −6.89 +0.92 dt +0.0003 t +ẑ2t(3.06) (0.07)where ẑit, i = 1, 2 are the disequilibrium terms and t ratios are givenin parentheses. The long-run elasticity of attacks to drug production is notsignificantly different from one and the long run elasticity of agriculturalproduction to drugs is significantly negative. There is a significant trend inagricultural production but not in attacks.The error correction equations, which specify the adjustment to thosedisequilibrium terms are∆at = −1.90 z1t−1 −0.70 z2t−1 +γ′1St +ε̂1t(−0.95) (−4.59)∆qt = −0.75 z1t−1 +0.004 z2t−1 +γ′2St +ε̂2t(−4.81) (0.34)∆dt = −0.31 z1t−1 −0.58 z2t−1 +γ′3St +ε̂3t(−0.56) (−1.38)where St is a vector of four seasonal dummies. There was some indica-tion of heteroskedasticity in the attacks equation and of serial correlation andnon-normality in the drugs equation, otherwise the error correction equationsseemed well specified. The attack disequilibrium is significant in the attacksequation, the agricultural production disequilibrium in the agricultural out-put equation and neither in the drugs production equation. This is consistentwith drugs production being weakly exogenous. The system was restrictedby making the long run effect of dt on at equal unity, β13 = −1; the feed-back of the output disequilibrium on attacks zero α11 = 0; the feedback ofthe attacks disequilibrium on agricultural output zero, α22 = 0; and bothfeedbacks on drug production zero α31 = 0; α32 = 0. The LR test statisticfor these five restrictions is 2.97. Since this is distributed χ2(5) under thenull hypothesis, the restrictions can be easily accepted.The system can be simplified by conditioning on drugs production, drop-ping insignificant terms and estimating each equation by OLS. Since thereare no cross-equation restrictions and the contemporaneous residual correla-tions are small, all less than 0.3, the system estimates are very similar. Withthe restrictions the estimates are:Drugs and Violence in Colombia: a VECM analysis 7∆at = −5.36 +0.67 (dt−1 − at−1) +0.27 S1t +ε̂1t(−5.16)) (5.12) (2.89)R2 = 0.41 SER = 0.27 SC = 0.64 FF = 0.12 N = 0.05 H = 0.06qt = 16.32 −0.13 dt −0.03 S1t −0.12 S24t +0.002 t +ε̂2t(94.79) (−9.42) (−3.63) (−15.44) (9.63)R2 = 0.93 SER = 0.02 SC = 0.51 FF = 0.48 N = 0.93 H = 0.08∆dt = −0.08 +0.60 ∆dt−4 +0.16 S12t +ε̂3t(−2.80) (4.57) (2.92)R2 = 0.92 SER = 0.06 SC = 0.22 FF = 0.79 N = 0.00 H = 0.53Sit is a dummy variable equal to one in quarter i, Sijt = Sit + Sjt. Withthe estimates and t ratios are also included R2, standard error of regression,SER, and the p values for tests for fourth order serial correlation, SC; areset test for functional form, FF ; a skewness-kurtosis test for normality, N ;and a reset test for heteroskedasticity, H. The drugs equation shows non-normality but otherwise the equations seem well specified at the 5% level.Tests for structural change assuming unknown break points did not indicateany significant breaks.In the equation for attacks, the long-run unit elasticity restriction is stillaccepted and the final equation is an error correction process, with a sea-sonal effect. Agricultural production is a static function of drug production,seasonals and trend. Drug production is an autoregression in ∆dt with sea-sonals. The term in ∆dt−4 is needed to remove all serial correlation. Drugproduction influences agricultural output immediately, but attacks with alag.Imposing the restrictions seems to have simplified the model and improvedthe precision of the estimates, but does not seem to have changed the basicstory of a stochastic trend in drug production driving attacks and agriculturaloutput.4 ConclusionThe estimated equations, whether the unrestricted VECM or the restrictedconditional model, suggest that drug production in Colombia over this pe-riod was an exogenous stochastic trend. The permanent shocks which driveDrugs and Violence in Colombia: a VECM analysis 8this stochastic trend will include any effects not predictable from our dataset. These might include changes in the control and organisation of drug pro-duction, changes in market prices and government eradication efforts. Drugproduction had an immediate negative effect on agricultural output, withan elasticity of about −0.13 that suggests a substitution between producinglegal and illegal output due to the latter’s greater profitability. It is not clearthough that this is the full explanation because in Colombia substitution islikely to be affected by the degree of territorial control government forces haveand by the location of licit production in a country with an ample supply ofarable land.Drug production has a positive effect on guerrilla attacks with a lag. Thelong run elasticity of attacks to drug production was close to unity, suggestinga stable long-run ratio of attacks to drug production and supporting thehypothesis that illicit drugs are a critical source of funding for Colombia’sillegal armed groups. Once drug producing areas are established it seems theyacquire strategic value as resource providers and thus have to be defendedby force when attacked by government forces.Of course, any conclusions can only be tentative because the sample isshort, the data is measured with error, the results may be sensitive to speci-fication and identification assumptions and because the parameters are func-tions of underlying political and economic institutions and may not remainconstant outside the estimation sample. Unfortunately, lack of comparabledata does not allow us to judge how well the model describes events subse-quent to the end of the sample period in 2005. However, were the model iscorrect; reducing drug production, the policy the Colombian government hasfollowed, would increase agricultural production and reduce guerrilla attacks.ReferencesArnson, C., and W. Zartman (2005): Rethinking the economics of war:the intersection of need, creed, and greed. Johns Hopkins University Press,Washington.Besley, T. J., and T. Persson (2008): “The Incidence of Civil War:Theory and Evidence,” NBER Working Papers 14585, National Bureau ofEconomic Research, Inc.Blattman, C., and E. Miguel (2009): “Civil War,” Journal of EconomicLiterature, Forthcoming.Drugs and Violence in Colombia: a VECM analysis 9Collier, P., and A. Hoeffler (1998): “On economic causes of civil war,”Oxford Economic Papers, 50, 563–573.Dube, O., and J. F. Vargas (2008): “Commodity Price Shocks and CivilConflict: Evidence from Colombia,” Graduate student and postdoctoralfellow working paper no. 14, CID Harvard.Gonza´lez, T. (2009): “The Economics of Conflict: Theoretical Models withan Application to Colombia,” Ph.D. thesis, University of London.Rubio, M. (1995): “Crimen y Crecimiento en Colombia,” CoyunturaEcono´mica, 25(1).Figures2,9002,7502,6002,4502,3002,1502,000COP$billionCOP$billion199419941997199720002000200320032005200511,00010,50010,0009,5009,0008,5008,000Figure 1: Agricultural GDPDrugs and Violence in Colombia: a VECM analysis 10350300250200150100500COP$billionCOP$billion1994199419971997200020002003200320052005120010008006004002000Figure 2: Illicit CropsDrugs and Violence in Colombia: a VECM analysis 1114012010080604020019941994199719972000200020032003200520055004003002001000Figure 3: Guerrilla Attacks

Civil War,” Journal of Economic Literature, Forthcoming.Drugs and Violence in Colombia:

Commodity Price Shocks and Civil Conﬂict: Evidence from Colombia,” Graduate student and postdoctoral fellow working paper no. 14,

Rethinking the economics of war: the intersection of need, creed, and greed. Johns Hopkins

The Economics of Conﬂict: Theoretical Models with an Application to Colombia,” Ph.D. thesis,

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