Eric Recchia Econ 306 Fall 12/Prof. Antonio Bojanic 11/6/12

The Effects of Coca Cultivation and US Drug Policy on Development in Bolivia

I. Introduction

Coca (Erythroxylum coca and Erythroxylum novogranatense) is a small, shrub-like tree native to

western South America. Its leaves contain the alkaloid that forms the active ingredient in cocaine. It has

been consumed by the indigenous inhabitants of the region for thousands of years, and was

domesticated and cultivated in pre-Columbian times. Peru, Colombia, and Bolivia are the leading

producers and exporters of coca, with a combined production of nearly 160,000 hectares (as of 2009).

Bolivia is the third largest of these producers, with an estimated production of 30,900 hectares in 2009.

While some of coca produced is legally used for flavoring or medical purposes, or consumed

domestically in traditional and legal uses, the majority of it is exported to Europe and North America as

cocaine. In 2009, of the estimated 54,608 metric tons produced in Bolivia, 35,148 (64%) was produced

illegally for cocaine. [2] The majority of this illegal production occurs, 25,007 metric tons (71%), in the

Chapare Province of the department of Cochabamba in central Bolivia.

In 1971, US President Richard Nixon famously declared the US War on Drugs, and in 1973

merged pre-existing federal agencies to create the Drug Enforcement Agency (DEA). In 2012, the DEA

employed more than 10,000 employees with a budget of over $2 billion [3]. In the late 1970s and

through the 1980s, with increased cocaine use in the United States and Europe, and increased

production in Bolivia, Colombia, and Peru, cocaine interdiction and eradication became a major focus of

US anti-drug efforts. In this paper I will analyze and evaluate the relative successes and failures of these

policies, and their social and environmental impact in Bolivia.

II. Previous Research

Moreno-Sanchez, Kraybill, and Thompson (2003) found that for the period 1987 to 2001 there was a

significant positive relationship between hectares of coca in cultivation in Colombia and the farm-gate

price of coca, and also the number of hectares of coca eradicated in Colombia. They postulated that this

relationship is twofold; increased prices increase incentives for farmers to plant more coca and offsets

the costs associated with eradication, and that narcotraficos (drug traffickers) are willing to offer higher

prices to coca farmers to offset eradication costs. They also suggest that if coca farmers are expecting an

increased chance of crop loss due to eradication efforts that they would plant a larger area to begin

with. Additionally, they found a significant negative relationship between hectares of coca in cultivation

in Colombia and the number of hectares of coca under cultivation in Bolivia and Peru, and the farm-gate

price of plantains. They suggest the negative relationship between cultivation in Colombia and

cultivation Bolivia and Peru is because as factors such as cultivation, trafficking, and processing become

relatively easier in one country compared to another, cultivation shifts to the country that has the

comparative advantage in cultivation and production. This is why Peru saw a large drop in cultivation

during the 1990s while Colombia saw a similarly large increase, and why after increased eradication

efforts began in Colombia in the early 2000s with aerial fumigation during Plan Colombia there was a

drop in cultivation in Colombia and an increase in both Peru and Bolivia. Finally, the negative correlation

between plantain prices and coca cultivation is explained because plantains are a substitute crop for

coca. As the price of plantains increases, the perceived gains for growing coca illegally lessen relatively.

Additionally, the United States Agency for International Development (USAID) offers assistance for

alternative crop development, by providing seeds, plants, equipment, and technical assistance.

Rouse and Arce (2006) found a positive relationship between eradication of coca production in Peru

and Bolivia and increases in production in Colombia, what they call a balloon effect; as production is

squeezed out of one country due to increased eradication efforts, it moves to another, much like the

squeezing of an inflated balloon. This occurs for reasons explained above. They determine that the

success of eradication efforts in one country may be reduced by increased cultivation in another.

In an evaluation of the cost effectiveness of different cocaine use reduction program, Rydell and

Everingham (1994) found that reducing efforts to control the supply of cocaine and increasing treatment

of heavy users would increase the cost-effectiveness of cocaine control efforts. They found that

domestic reduction methods, such as treatment of users or domestic enforcement, could be as much as

7.3 times more cost effective than interdiction or source country control efforts.

Marcela Ibanez and Fredrik Carlsson (2009) conducted a survey based choice experiment of

Colombian coca farmers to evaluate their stated responses to increases in either the profitability of

alternative crops, increases in eradication, or both. They found that increasing the perceived risk of crop

destruction from eradication efforts decreased the proportion of farmers cultivating coca, but it did not

significantly decrease the hectares of cultivation cultivated. In fact, 10% of farmers reported an

intention to start or increase cultivation in response to eradication efforts. They also found that rural

and female farmers were more likely to cultivate coca, and that social norms and morals significantly

influenced the decision to grow coca.

McGuire (2002) critiques the results of former Bolivian President Hugo Banzers 5-year coca

eradication plan, Plan Dignidad. While the plan did manage to decrease coca cultivation levels in Bolivia

by 68% between 1997 and 2000 (from 45,800 hectares to 14,600), these efforts were widely unpopular

with the cocaleros, the peasant coca farmers, and let to much protesting and social unrest. These

decreases in cultivation were short lived, and coca cultivation levels in Bolivia increased by 90% over the

following four years, returning to 60% of their levels of eight years earlier.

One of the main failures of alternative development efforts are due to the lack of success of

crop substitution efforts. Coca substitute crops like pineapples and passion fruit take from 2 to 8 years

to mature and yield fruit, and the poor tropical soil makes many plants prohibitively difficult to grow. By

comparison, coca is a perennial bush that matures quickly, can be harvested 3 to 4 times a year, takes

little skill to grow, and does well in thin, nutrient poor soil. In their 2002 analysis of drug control efforts

in Colombia, the United States General Accounting Office (GAO) determined that while coca eradication

efforts had been successful in reducing coca cultivation in Bolivia, the fast pace of eradication efforts (as

high as 1,000 hectares per month) made the feasibility of keeping pace with alternative development

efforts difficult, as the number of project beneficiaries increased dramatically from 3,500 to 7,300. The

failures of alternative development, distrust of domestic and US government officials and programs, and

strong social, led to four different presidents in a four and a half year period between the resignation of

Hugo Banzer in 2001 and the election of cocalero union leader Evo Morales in 2005 and re-election in

2009.

III. Methodology

a) Data

Because of the illegality of coca cultivation, there is limited market data available to evaluate

illicit coca cultivation (what little market data exists is for legally approved cultivation in Bolivia, for

cultural use, and therefore doesnt necessarily relate to the study of illicit cultivation). The information

used for this analysis was obtained from the United Nations Office on Drugs and Crime (UNODC) and the

Bolivian National Direction of Development for Coca Growing Areas (Direccin Nacional de Desarrollo de

las reas Productoras de Coca, DIGPROCOCA). These organizations began collecting data in the late

1980s; some data points are missing for the earlier years and were not included in the analysis. Where

data from older reports conflicted with data from newer reports, the data from newer reports was

generally used (unless there appeared to be a typo or some similar error). Information on coca

cultivation in Peru and Colombia was also obtained from the UNODC, while information on substitute

crop prices and cultivation area was obtained from UN FAO price and cultivation data.

b) Model

Decisions made for coca cultivation are likely similar to the decisions that would be made for various

other agriculture commodities. Therefore, I hypothesize that the price of coca and the price of

substitutes is likely to positively affect the decision to cultivate coca. Additionally, being an illegal activity

with possible penalties for cultivation, the decision to cultivate coca is expected to be similar to other

criminal activities, where likelihood of being caught, in this case eradication, is expected to be

negatively correlated, and an increased payoff, in this case coca price, is positively correlated. Finally,

because of a limited market size, historical evidence, and possible comparative advantages between

countries, it is expected that coca cultivation in Peru and Colombia will be negatively correlated with

cultivation in Bolivia.

To examine the effects of alternative crop substitution on coca cultivation, a range of possible

substitution crops was examined for correlation and influence on coca harvest. Examination of

correlations and ANOVA results show that there does not appear to be any significant correlations

between prices of substitution crops and coca cultivation. However, the same analysis for a relationship

between coca cultivation and the area cultivated of these substitute crops shows a possible significant

relationship between several different crops, most notably pineapples and plantains. These two crops

were chosen to be tested in the full model to determine the influence of crop substitution on coca

cultivation. [Note that crop prices and areas were compared with the following harvest period due to

reason discussed below.]

Due to the nature of agricultural production, there is likely to be a lag time between market signals

and responses for changes in production by farmers, because of the time between planting additional

bushes and harvest, so our model will be tested with a time lag feature incorporated where all of the

predictors are regressed one period. This gives us the following model to test:

Ht=0+1E t-1+2P t-1+3CC t-1+4CP t-1+5CPi t-1+6CPl t-1+

where H is the number of hectares of coca in cultivation in Bolivia, E is the area of coca eradicated in

Bolivia, P is the price of coca in Bolivia, CC and CP are respectively the areas of coca cultivated in

Colombia and Peru, and CPi and CPl are respectively the cultivation in hectares of pineapples and

plantains in Bolivia. I will also test several variations of this model using AICc to determine which models

have the best predictive capabilities.

IV. Analysis

Based on the results of the regression analysis and correlation tables, there appears to be a

moderate negative correlation between the farm-gate price of coca and the number of hectares of coca

cultivated in Bolivia. There also appears to be a moderate negative correlation between number of

hectares cultivated and number of hectares eradicated. Coca cultivation in Bolivia is positively correlated

with cultivation in Peru and negatively related with cultivation in Colombia. For possible substitution

crops that were tested, there appears to be a strong negative relationship between cultivation of both

crops and cultivation of coca when tested separately from the other predictors. However, as this

relationship disappears in the full model, this may also mean that the price of substitution crops has no

significant effect on coca cultivation in Bolivia. This may be due to the difficulties discussed above with

crop substitution.

The model developed here for Bolivia varies from the econometric analysis of coca cultivation in

Colombia developed by Moreno-Sanchez, et al. (2003). They found similar relationships for most of the

variables examined here, except that they found a positive relationship between coca price and coca

cultivation, and a significant negative relationship between plantain price and coca cultivation.

While eradication does appear to be effective in reducing coca cultivation in Bolivia, it also causes

prices to increase. This, in turn, creates more incentives for farmers to increase coca cultivation. This is

especially important when the relationship of coca production levels between Bolivia and Colombia is

considered. As spraying and eradication continue in Colombia, coca production is likely to shift back to

Peru and Bolivia. Additionally, because there appears to be no significant relationship between the

prices of substitute crops and levels of coca cultivation, current efforts with crop substitution do not

appear to be successful. Additionally, this is well supported by the literature, as referenced above.

V. Conclusion

Long term efforts to reduce coca cultivation in the Andes appear to be at best marginally successful

and at worst ineffective and socially disruptive. Moving forward, efforts by the US government to

control cocaine supply should consider and incorporate social and cultural factors and influences within

Bolivia. Historically, coca reduction efforts have been largely ineffective, due to a focus on eradication

and not on cultural needs, development, and social mores. By recognizing the social and economic

needs of rural farmers and allowing more flexibility and support in alternative development programs

would likely increase their success. One way to accomplish this would be to allow a gradual reduction in

coca cultivation as alternative crop substitution is increased, so that farmers can maintain a steady

source of income. Additional support for meeting schooling needs and living costs would increase

support of US government programs and support coca reduction efforts. Additionally, a strong focus on

reliability is important, as unreliable and sporadic support and success has hampered efforts to increase

support about Bolivian farmers. Another large issue with the success of alternative crops is the lack of

market and transportation infrastructure to help make alternatives viable. By developing this

infrastructure, alternative development efforts would become more successful.

Finally, as evidenced by many studies, including those discussed above, focus should be increased

on reducing demand within the US and Europe through treatment and prevention programs, which are

more cost effective and put less of a burden on foreign farmers. Bolivia has seen consistent increases in

both GDP and GDP Per Capita (PPP) over the last couple of decades, even during the most recent

worldwide recession, which negatively affected several of the larger economies in the region. With

continued growth, illicit coca farming will likely become less attractive. As a final alternative,

decriminalization and/or legalization and regulation of cocaine use within the US and Europe could serve

to stabilize prices and reduce external social costs due to criminal activity related to drug trafficking,

however a discussion of the social costs and benefits of this recommendation is beyond the scope of this

paper.

Appendix I Charts and Data Sets

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Coca Price vs Eradication Levels, 1990-2010

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Eradication (in ha)

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Appendix II Linear Model Summaries, Correlation, and AICc Tables Plantains, Prices - Call: lm(formula = Coca.t.1. ~ Plantains, data = faoprices) Residuals: Min 1Q Median 3Q Max -18264 -8143 -2603 12696 15890 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 31300.41 17453.31 1.793 0.0907 . Plantains 39.49 434.54 0.091 0.9286 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 11480 on 17 degrees of freedom Multiple R-squared: 0.0004856, Adjusted R-squared: -0.05831 F-statistic: 0.008259 on 1 and 17 DF, p-value: 0.9286 Full, Prices - Call: lm(formula = Coca.t.1. ~ Tangerines + Plantains + Pineapples + Oranges + Lemons + Grapefruit + Coffee + Cocoa + Cassava + Bananas, data = faoprices) Residuals: Min 1Q Median 3Q Max -13221.5 -2704.7 200.5 3418.2 10843.0 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 11256.133 65995.079 0.171 0.8688 Tangerines -26.428 285.027 -0.093 0.9284 Plantains -6922.775 3158.729 -2.192 0.0598 . Pineapples -165.927 170.106 -0.975 0.3579 Oranges 114.453 597.819 0.191 0.8529 Lemons 492.804 1564.460 0.315 0.7608 Grapefruit 1436.975 3484.932 0.412 0.6909 Coffee 9.712 46.337 0.210 0.8392 Cocoa -91.632 89.685 -1.022 0.3368 Cassava 975.685 777.290 1.255 0.2448 Bananas 5256.441 5805.985 0.905 0.3917 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 8792 on 8 degrees of freedom Multiple R-squared: 0.7242, Adjusted R-squared: 0.3794 F-statistic: 2.101 on 10 and 8 DF, p-value: 0.1526

Full, Area - Call: lm(formula = Coca.t.1. ~ Tangerines + Citrus + Plantains + Pineapples + Oranges + Lemons + Coffee + Cocoa + Cassava + Bananas, data = faostats) Residuals: Min 1Q Median 3Q Max -3391.0 -1366.6 -30.2 1288.6 3813.2 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) -1.682e+05 5.477e+04 -3.072 0.008921 ** Tangerines -1.340e+01 1.765e+01 -0.759 0.461268 Citrus 1.971e+01 1.724e+01 1.143 0.273568 Plantains 1.351e-01 3.211e-01 0.421 0.680839 Pineapples -9.883e+00 8.522e-01 -11.597 3.14e-08 *** Oranges -3.261e+01 1.684e+01 -1.937 0.074828 . Lemons -1.749e+01 2.436e+01 -0.718 0.485493 Coffee -3.892e+00 1.082e+00 -3.595 0.003261 ** Cocoa 4.904e+01 1.161e+01 4.223 0.000997 *** Cassava 4.475e+00 1.052e+00 4.255 0.000939 *** Bananas -1.333e+00 5.225e-01 -2.551 0.024154 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 2498 on 13 degrees of freedom Multiple R-squared: 0.9751, Adjusted R-squared: 0.9559 F-statistic: 50.86 on 10 and 13 DF, p-value: 9.191e-09 PandP, Area - Call: lm(formula = Coca.t.1. ~ Plantains + Pineapples, data = faostats) Residuals: Min 1Q Median 3Q Max -11378.2 -5561.1 440.7 6473.6 12246.9 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 61175.7197 5780.8097 10.583 7.12e-10 *** Plantains -0.4902 0.1836 -2.670 0.01432 * Pineapples -3.6335 1.0432 -3.483 0.00222 ** --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 7694 on 21 degrees of freedom

Multiple R-squared: 0.6181, Adjusted R-squared: 0.5817 F-statistic: 16.99 on 2 and 21 DF, p-value: 4.08e-05 Full Model - Call: lm(formula = H ~ E + P + CC + CP + CPi + CPl, data = cocastats) Residuals: Min 1Q Median 3Q Max -3322.4 -1099.9 243.4 984.9 5359.7 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 1.477e+04 2.225e+04 0.664 0.5185 E -4.752e-01 2.622e-01 -1.812 0.0931 . P -2.268e+03 7.900e+02 -2.871 0.0131 * CC -5.760e-02 2.993e-02 -1.925 0.0764 . CP 1.998e-01 7.985e-02 2.502 0.0265 * CPi 7.213e-01 5.611e-01 1.285 0.2210 CPl 4.911e-01 3.893e-01 1.261 0.2293 --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 2439 on 13 degrees of freedom Multiple R-squared: 0.9685, Adjusted R-squared: 0.954 F-statistic: 66.68 on 6 and 13 DF, p-value: 5.197e-09 CC,CP Model - Call: lm(formula = H ~ E + P + CC + CP, data = cocastats) Residuals: Min 1Q Median 3Q Max -3830.2 -1164.7 -286.3 1000.9 5382.2 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 4.135e+04 6.972e+03 5.931 2.76e-05 *** E -3.805e-01 2.485e-01 -1.531 0.1465 P -2.410e+03 7.756e+02 -3.108 0.0072 ** CC -6.606e-02 2.710e-02 -2.438 0.0277 * CP 1.220e-01 5.126e-02 2.379 0.0311 * --- Signif. codes: 0 *** 0.001 ** 0.01 * 0.05 . 0.1 1 Residual standard error: 2452 on 15 degrees of freedom Multiple R-squared: 0.9633, Adjusted R-squared: 0.9535 F-statistic: 98.42 on 4 and 15 DF, p-value: 1.414e-10