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Eradication Efforts, the State, Displacement and Poverty: Explaining Coca Cultivation in Colombia during Plan Colombia*

  • MICHELLE L. DION (a1) and CATHERINE RUSSLER (a2)
Abstract
Abstract

This study models the sub-national pattern of coca cultivation in Colombia following the implementation of Plan Colombia (2001–2005). The results suggest that aerial eradication reduces coca cultivation primarily through creation of significant displacement and that coca cultivation is less intense in areas with a significant state presence. Further, coca cultivation appears to be more common in less developed, agricultural regions where access to legal markets precludes other forms of agriculture. Poverty has a significant, non-linear effect on coca cultivation; cultivation is most intense in regions of moderate poverty. Based on the findings, efforts to reduce coca cultivation should emphasise developing local public infrastructure and market access in conjunction with poverty reduction efforts and investment in alternative development.

<span class='italic'>Resumen:</span>

Este estudio establece los patrones subnacionales del cultivo de la coca en Colombia tras la implementación del Plan Colombia (2001–2005). El resultado sugiere que la erradicación aérea reduce el cultivo de la coca primeramente a través de la creación de un desplazamiento humano significativo y que el cultivo de coca es menos intenso en áreas donde hay una presencia significativa del Estado. Asimismo, el cultivo de coca parece ser más frecuente en regiones agrícolas menos desarrolladas donde el acceso a los mercados legales excluye otras formas de agricultura. La pobreza tiene un efecto significativo y no linear en el cultivo de la coca; el cultivo es más intenso en regiones de pobreza moderada. Basado en tales hallazgos, los esfuerzos para reducir el cultivo de coca deben centrarse en el desarrollo de una infraestructura y acceso al mercado en conjunto con los esfuerzos para reducir la pobreza e inversión en programas de desarrollo alternativos.

Palabras clave: Colombia, cultivo de coca, erradicación aérea, pobreza, infraestructura, acceso al mercado, Plan Colombia

<span class='italic'>Resumo:</span>

Este estudo cria um modelo do padrão sub-nacional do cultivo de coca na Colômbia seguindo a implementação do Plan Colombia (2001–2005). Os resultados sugerem que a erradicação das plantações por via aérea reduziu o cultivo de coca primeiramente por gerar deslocamentos populacionais significativos, e que o cultivo de coca é menos intenso em áreas que apresentam expressiva presença do estado. Além disso, ao que parece, o cultivo de coca é mais comum em regiões agrícolas menos desenvolvidas, onde a falta de acessibilidade aos mercados legais impede outras formas de agricultura. A pobreza tem um efeito importante e não-linear sobre o cultivo de coca; o cultivo é mais intenso em regiões de pobreza moderada. Com base nos resultados, sugere-se que esforços para reduzir o cultivo de coca deveriam concentrar-se no desenvolvimento da infra-estrutura pública local e na acessibilidade aos mercados, combinados com tentativas de redução de pobreza e investimento em desenvolvimento alternativo.

Palavras-chave: Colômbia, cultivo de coca, erradicação aérea (de cultivos), pobreza, infra-estrutura pública, acessibilidade aos mercados, Plan Colombia.

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References
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1 Rocio Moreno-Sanchez et al. , ‘An Econometric Analysis of Coca Eradication Policy in Colombia’, World Development, vol. 31, no. 2 (2003), pp. 375–83.

2 United Nations Office on Drugs and Crime (UNODC), Colombia Coca Survey (New York 2005).

3 Angel Rabasa and Peter Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability (Santa Monica 2001); Ricardo Vargas, ‘The Anti-Drug Policy, Aerial Spraying of Illicit Crops and Their Social, Environmental and Political Impacts in Colombia’, The Journal of Drug Issues, vol. 22, no. 4 (2002), pp. 11–60; María Clemencia Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’, in Coletta A. Youngers and Eileen Rosin (eds.), Drugs and Democracy in Latin America: The Impact of US Policy (London 2005), pp. 61–97.

4 Coletta A. Youngers and Eileen Rosin, ‘The US “War on Drugs”: Its Impact in Latin America and the Caribbean’, in Coletta A. Youngers and Eileen Rosin (eds.), Drugs and Democracy in Latin America: The Impact of US Policy, (London 2005), pp. 1–13.

5 ‘Battles won, a war still lost; Drugs in Latin America’, The Economist, vol. 374 (2005), pp. 35–6.

6 Washington Office on Latin America (WOLA), ‘Memorandum to Foreign Policy Aids-Appropriations. Rethinking Plan Colombia: As Drug Control Policy, Plan Colombia Doesn't Measure Up.’ (10 June 2005), http://www.wola.org/media/June%20200520FY2006%Approps%20for%20Colombia.pdf

7 Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’.

8 United Nations Office on Drugs and Crime (UNODC), Colombia Coca Survey (New York 2006).

9 Exceptions are Moreno-Sanchez, et al., ‘An Econometric Analysis of Coca Eradication Policy in Colombia’, and Ana María Díaz and Fábio Sánchez, ‘A Geography of Illicit Crops (Coca Leaf) and Armed Conflict in Colombia’, Crisis States Programme Working Paper no. 47, (London July 2004). The former is a time-series analysis of national cultivation rates, and the latter looks at diffusion of cultivation to nearby departments. Neither study includes the wide range of control variables included here. UNODC reports use bivariate analyses. See UNODC, Colombia Coca Survey, 2005 and 2006.

10 Adam Isacson, ‘The US Military in the War on Drugs’, in Coletta A. Youngers and Eileen Rosin (eds.), Drugs and Democracy in Latin America: The Impact of US Policy, (London 2005), pp. 15–60; Rosin and Youngers, ‘The US “War on Drugs”: Its Impact in Latin America and the Caribbean'; Vargas, ‘The Anti-Drug Policy, Aerial Spraying of Illicit Crops and Their Social, Environmental and Political Impacts in Colombia’; Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’.

11 Francisco E. Thoumi, Illegal Drugs, Economy, and Society in the Andes (Washington DC 2003).

12 Isacson, ‘The US Military in the War on Drugs’.

13 United States State Department, Bureau for International Narcotics Control and Law Enforcement, ‘Policy and Program Developments’, International Narcotics Control Strategy Report, 1 March 2004, http://www.state.gov/p/inl/rls/nrcrpt/2003/vol1/html/29829.htm

14 UNODC, Colombia Coca Survey (2006).

15 Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’.

16 UNODC, Colombia Coca Survey (2006).

17 Isacson, ‘The US Military in the War on Drugs’; Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’; Vargas, ‘The Anti-Drug Policy, Aerial Spraying of Illicit Crops and Their Social, Environmental and Political Impacts in Colombia.’

18 Estimates of displacement from, Ministerio de la Protección Social, ‘Consolidado de Programas.’ (2005), http://mps.minproteccionsocial.gov.co/consolidado/buscar.php. On violence of guerrilla and paramilitary groups contributing to displacement, see Ana María Ibáñez and Carlos Eduardo Velez, ‘Civil Conflict and Forced Migration: The Micro Determinates and the Welfare Losses of Displacement in Colombia’, Documento CEDE 2005-35, Universidad de los Andes (Bogotá, June 2005) and Stefanie Engel and Ana María Ibáñez, ‘Displacement Due to Violence in Colombia: A Household-Level Analysis’, Economic Development and Cultural Change, vol. 55, no. 2 (2007), 335–66. On the effects of fumigation on displacement, see Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’.

19 UNODC, Colombia Coca Survey (2004).

20 Isacson, ‘The US Military in the War on Drugs’; Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’. Jennifer S. Holmes et al. find that coca eradication, and not coca cultivation, appears to contribute to leftist guerrilla violence. Jennifer S. Holmes et al. , ‘Drugs, Violence, and Development in Colombia: A Department-Level Analysis’, Latin American Politics and Society, vol. 48, no. 3 (2006): pp. 157–84.

21 Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’; UNODC Colombia Coca Survey (2005); Thoumi, Illegal Drugs, Economy, and Society in the Andes.

22 Rabasa and Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability.

23 Kevin J. Riley, ‘Snow Job? The Efficacy of Source Country Cocaine Policies’, Graduate School Dissertation Series RGSD-102, National Defense Research Institute, 1993.

24 UNODC, Alternative Development: A Global Thematic Evaluation – Final Synthesis Report (New York 2005).

25 Thoumi, Illegal Drugs, Economy, and Society in the Andes.

26 Nancy McGuire, ‘Combating Coca in Bolivia and Colombia: A New Perspective on the Forces that Drive Peasant Coca Farming’, Report for Council For Emerging National Security Affairs, Washington, D.C.

27 UNODC, Alternative Development: A Global Thematic Evaluation – Final Synthesis Report (New York 2005).

28 Moreno-Sanchez, et al., ‘An Econometric Analysis of Coca Eradication Policy in Colombia’.

29 Ana María Díaz and Fábio Sánchez, ‘A Geography of Illicit Crops (Coca Leaf) and Armed Conflict in Colombia’; David Mansfield, ‘Alternative development: the modern thrust of supply-side policy’, Bulletin on Narcotics--Occasional Papers, vol. L1, nos. 1 & 2 (1999) pp. 19–44.

30 UNODC, Colombia Coca Survey (2005).

31 Jennifer S. Holmes et al. , ‘A Subnational Study of Insurgency: FARC Violence in the 1990s’, Studies in Conflict and Terrorism, vol. 30, no. 1 (2007), pp. 249–65.

32 Moreno-Sanchez et al., ‘An Econometric Analysis of Coca Eradication Policy in Colombia’.

33 Rabasa and Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability.

34 Moreno-Sanchez et al., ‘An Econometric Analysis of Coca Eradication Policy in Colombia’.

35 Ricardo Vargas, Drug Cultivation, Fumigation and the Conflict in Colombia (Bogota, 1999); Rabasa and Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability; Thoumi, Illegal Drugs, Economy, and Society in the Andes.

36 Rabasa and Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability; Thoumi, Illegal Drugs, Economy, and Society in the Andes.

37 UNODC, Colombia Coca Survey (2005); Díaz and Sánchez, ‘A Geography of Illicit Crops (Coca Leaf) and Armed Conflict in Colombia’.

38 Holmes et al., ‘Drugs, Violence, and Development in Colombia: A Department-Level Analysis’; Holmes, et al., ‘A Subnational Study of Insurgency: FARC Violence in the 1990s’.

39 Holmes et al., ‘A Subnational Study of Insurgency: FARC Violence in the 1990s’.

40 Holmes et al., ‘Drugs, Violence, and Development in Colombia: A Department-Level Analysis’; Holmes, et al., ‘A Subnational Study of Insurgency: FARC Violence in the 1990s’.

41 Thoumi, Illegal Drugs, Economy, and Society in the Andes.

42 Francisco E. Thoumi, ‘Why the Illegal Psychoactive Drugs Industry Grew in Colombia’, Journal of Interamerican Studies and World Affairs, vol. 34, no. 3 (1992), pp. 37–63; Thoumi, Illegal Drugs, Economy, and Society in the Andes.

43 UNODC, Alternative Development: A Global Thematic Evaluation – Final Synthesis Report; Mansfield, ‘Alternative development: the modern thrust of supply-side policy’.

44 Díaz and Sánchez, ‘A Geography of Illicit Crops (Coca Leaf) and Armed Conflict in Colombia’.

45 UNODC, Colombia Coca Survey (2005).

46 UNODC, ibid.

47 Colombia's federal district, Bogotá, is excluded from analysis. Annual data for roadways, government corruption, and poverty are not collected or published.

48 See UNODC, Colombia Coca Survey (2005) for data and a discussion of measurement issues.

49 Colombia's departments range from 24.1% to 93.7% urban. Joshua D. Angrist and Adriana D. Kugler, ‘Rural Windfall or a New Resource Curse? Coca, Income and Civil Conflict in Colombia’, National Bureau of Economics Research Working Paper No. 11219, Cambridge, MA, March 2005. Population data come from the Colombian Government's statistical database, the Departamento Administrativo Nacional de Estadistica (DANE), http://www.dane.gov.co. Unless otherwise stated, all data used in this study are retrieved from DANE.

50 Accumulated sprayed area is the sum of areas sprayed during a given time period, calculated by multiplying the length of flight lines by their width. It does not take into account effective sprayed area, which disregards the overlap between adjacent sprayed bands and areas sprayed several times in the same calendar year. UNODC, Colombia Coca Survey (2005).

51 Aerial eradication data from UNODC, Colombia Coca Survey (2006).

52 Though weak state presence may facilitate coca cultivation by enabling illegal armed groups to protect or promote cultivation, it is difficult to directly measure the magnitude of armed group presence without conflating this with violence. Typically, human rights violations or other estimates of violence are used to measure armed group presence. However, these measures should actually be associated with less cultivation due to the displacement and disruptions of cultivation associated with such violence.

54 Presidencia República de Colombia,‘Registro Único de la Población Desplazada’, http://www.red.gov.co/programas/apoyo_integral_desplazados/estadisticas.htm

55 See Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’; UNODC Colombia Coca Survey (2005); Thoumi, Illegal Drugs, Economy, and Society in the Andes; and Rabasa and Chalk, Colombian Labyrinth: The Synergy of Drugs and Insurgency and Its Implications for Regional Stability.

56 The SUR database probably underestimates the true number of displaced, in part because the government uses a more restrictive definition of displacement than that of NGOs. On the other hand, the government's SUR database has the advantage of tracking, in addition to the department in which the displaced settle, the department from which people are displaced. This allows us to disaggregate the effects of displacement in both sending and receiving departments. CODHES, a Colombian NGO, only publishes data on the departments that receive the displaced. Using the government figures reflects the more conservative approach because any effects we find would be more pronounced were more accurate data available. See Internal Displacement Monitoring Centre, ‘Colombia: government ‘peace process’ cements injustices for IDPs', Norwegian Refugee Council, Geneva, Switzerland, 30 June 2006.

57 Thoumi, ibid.

58 For department level GDP and population statistics, see DANE, http://www.dane.gov.co/

59 Ministerio de Transporte, ‘Transporte en cifras’, http://www.mintransporte.gov.co/Servicios/Estadisticas/TABLASYGRAFICOS2004.htm.

60 For a discussion of these data issues, see Holmes et al., ‘Drugs, Violence, and Development in Colombia: A Department-Level Analysis’. Data to test hypotheses that focus on factors of inequality, including the potential association between coca cultivation and land ownership, prove similarly elusive.

61 The Colombian government is in the process of adopting a new measure of poverty based on data projections. The UPN and this new measure, the System for Selecting Beneficiaries of Social Services (SISBEN), are highly correlated.

62 UNODC, Colombia Coca Survey (2005).

63 Nathaniel Beck and Jonathan N. Katz, ‘What to do (and not to do) with Time-Series Cross-Section Data’, American Political Science Review, vol. 89, no. 3 (1995), pp. 634–47. Beck and Katz's panel corrected standard errors (PCSEs) typically perform better when the number of time points exceed the number of cross-sections. In this instance, the PCSEs are nearly identical to uncorrected standard errors using OLS. Limited data and a large number of parameters preclude estimation of accurate generalized least squares models with complex corrections for assumed error structures.

64 The other modelling alternative to deal with autocorrelation would entail estimating the model using OLS and including a lagged dependent variable. In this case, the lagged dependent variable approach would be problematic or unnecessary. The lagged dependent variable approach would use up scarce degrees of freedom and convert the analysis into one of short-term change, despite our theoretical interest in cross-department variation. Further, the estimates of the autocorrelation parameter (rho) in the Prais-Winsten models suggests that autocorrelation within panels is not severe. Further, a lagged dependent variable is problematic with the inclusion of fixed effects, creating additional bias beyond that normally expected in such models.

65 Two tests were used to determine whether fixed effects were appropriate, and both indicated that estimating the model with fixed effects is necessary. First, a Chow or F test of the joint significance of the coefficients for the fixed effects generated statistically significant Chow scores (see Table 2). In addition, the modified jack-knife procedure recommended to measure the mean absolute prediction error for each department, also confirmed the utility of estimating the model with fixed effects. The jack-knife procedure estimates a separate prediction model for each department by leaving out one department at a time and using the model to predict coca cultivation in the omitted department. The mean absolute error is the difference between the predicted and observed coca cultivation for each department. The results of the test are presented in the Appendix. See Nathaniel Beck. ‘Time-Series-Cross-Section Data: What Have We Learned in the Past Few Years?’, Annual Review of Political Science, vol. 4 (2001): pp. 271–93.Fixed effects are also preferable to random effects because it is unlikely that the intercept shifts are due to random error uncorrelated with the independent variables, an assumption required of random effects models. Instead, the intercepts shifts are likely to reflect systematic differences in the average level of coca cultivation across departments rather than random error. We performed a Hausman test, which indicated that the random effects were not more efficient than fixed effects. The estimates of the random effects models were inconsistent. We accept the loss of efficiency in the fixed effects models in favour of greater consistency.

66 Studies of the effect of displacement on welfare suggest that it reduces by a third the consumption of the displaced. Ana Maria Ibáñez and Carlos Eduardo Velez, ‘Civil Conflict and Forced Migration: The Micro Determinates and the Welfare Losses of Displacement in Colombia’.

67 Though we do not model the presence of illegal groups directly for reasons discussed above, the direct effect of state presence on cultivation is consistent with studies arguing that illegal armed group presence is associated with coca cultivation.

68 We also estimated the models using the CODHES data for departments receiving the displaced. The results for all of the variables, except incoming displaced and aerial eradication, were nearly identical in terms of substantive and statistical significance to the models reported in Table 2. In the case of incoming displaced, the effect on coca cultivation remained positive but was smaller and even less statistically significant, which suggests that the displaced do not engage in significant coca cultivation. The coefficient for aerial eradication became larger but remained insignificant. That substituting the CODHES data for that of SUR only affected the coefficients of these two variables (and even then they remained statistically insignificant), suggests that the SUR figures do indeed under count those that are displaced by aerial eradication. Therefore, our results are conservative.

69 Though the linear effect of poverty in Model 1 is statistically significant, the coefficient in Model 1 is much smaller than its counterpart in the quadratic model. The quadratic term in Model 2 is statistically significant. The results are consistent with an interpretation that the coefficient of the linear parameter in Model 1 is biased downward because of omitted variable bias. The jack-knife procedure confirms that the curvilinear model of poverty better predicts actual coca cultivation than poverty as a simple, linear predictor. See the Appendix.

70 Michael Tomz, Jason Wittenberg and Gary King, ‘CLARIFY: Software for Interpreting and Presenting Statistical Results’, Version 2.0, Cambridge, MA: Harvard University, 1 June 2001.

71 See UNODC, Colombia Coca Survey (2005). UNODC was contacted for this study, and annual department-level allocation or receipt of alternative development funds have yet to be compiled.

72 Ramírez Lemus, et al., ‘Colombia: A Vicious Circle of Drugs and War’; Isacson, ‘The US Military in the War on Drugs.’

* The authors thank the JLAS editors and anonymous reviewers for constructive suggestions for improving this article.

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