I. CONTEXT AND QUESTIONS

Greed or Grievance?Coca, Income and Civil Conflict in Rural Colombia Joshua Angrist Massachusetts Institute of Technology and NBERandAdriana KuglerUniversity of Houston, Universitat Pompeu Fabra,NBER and CEPRNOVEMBER 2004

I. CONTEXT AND QUESTIONS • Cocaine comes by various routes from Bolivia, Peru, and Colombia, where it is a major industry. • The US, working alone and with local gov’ts, tries hard to stem the flow. Colombia is the 3rd largest recipient of US aid. • In the mid-1990s, U.S. policy saw a remarkable success: drastic reduction in Coca cultivation in Bolivia and Peru. • Colombian Coca farmers who took up the slack were the most immediate “beneficiaries”. • In this paper, we use this shift to ask: What are the economic and social consequences of Coca production (or interdiction) for rural producers?

WHY STUDY THE COCA ECONOMY? Narrow issues • Critics of US drug policy emphasize the role Coca plays in supporting rural economies (e.g., National Geographic, July 2004). How much do peasant farmers benefit from Coca cultivation? • At the same time, the Colombian cocaine trade is clearly linked to organized crime. Homicide peaked in the struggle against extradition in the early 1990s. Effects on crime and violence are therefore of interest. • But until recently, Colombian involvement in the cocaine business has been through processing and distribution, mainly an urban phenomenon (most famously, in Medellin and Cali). • The recent increase in Colombian Coca cultivation raises new questions about the impact of Coca on rural violence and living standards.

BROADER IMPLICATIONS • The link between economic conditions and civil conflict or violence is of general interest. [The Colombian countryside is scarred by bloody civil conflicts that long predate the cocaine trade (beginning with “La Violencia” in 1948-57)]. • In particular, increased demand for Coca leaf raises the question of how (presumably) improved economic conditions in growing areas affects those already affected by civil war. Two views: • The Coca boom was a positive economic shock. We might expect conflict reduction if poverty causes civil war, as for the African economic shocks studied by Miguel, Satyanath, and Sergenti (2004). • Natural resources generate easy financing for rebel groups. Collier and Hoeffler (2004) and Fearon and Laitin (2003) argue that civil conflict is fueled more by greed mixed w/ (economic) opportunity than by (economic and/or political) grievance.

More on Greed vs. Grievance. . . • (Economic) “Grievance” scenario: Coca  reduces poverty, increases returns to work instead of war,  civil conflict/violence moderates. (Assuming, not implausibly, that law enforcement is lax; Coca is just another cash crop, perhaps riskier). • “Greed” scenario: Coca finances combatants, provides targets to further tax/extort  civil conflict/violence worsens. Analogous to the story of blood diamonds in Africa. Anecdotal evidence points to the “Greed” story: “If it weren’t for the armed groups, I think we could reach a consensus on what the region needs to progress. But all the armed groups want is to control the economic question, and all are willing to massacre or murder or force people from their homes to win. -- small town mayor, Gloria Cuartas (quoted in Kirk, 2003).

II. THE END OF THE AIR BRIDGE • Cocaine production function • Farmers harvest and dry coca leaves (volume=1) • Entrepreneurs or farmers make dry leaves into paste (volume=1/100) • Paste is made into cocaine base (volume=1/200) • Cocaine hydrochloride refined from base (typically in cities or towns) • Colombia has long been the distribution center for refined cocaine, with middlemen importing paste (or base) from Bolivia and Peru. • Drug war militarization: In 4/92, Peruvian air force began aggressively targeting jungle air strips & small planes, part of US-backed shift in interdiction policy made official in PDD 14, 11/93. Colombia adopted similar shoot-down policy in 94 . . . •  • In response to disruption of air bridge ferrying paste, Colombian Coca cultivation increased 50% from 93-94, increasing steadily thereafter: Figure 1a. This shift is what we are studying.

Figure 1a.Production of Coca Leaf in Colombia, Peru and Bolivia1990 - 2000

THEORETICAL FRAMEWORK • Farmers maximize U(income) by allocating time to wage labor, or time and land to two crops, Coca leaf (y1) and an alternative (flowers or coffee; y0). • Production: yj = fj(hj, Lj); j=0,1. • Net Revenue: (1-τ)[ p0y0 + p1(1−κ)y1 ] − ψL0 − w(h0+ h1) • Constraints: h0 + h1 ≤ T; L0 + L1 = L. • FARC/AUC types impose taxes, either on income (0<τ<1), Coca leaf sales (0<κ<1), and/or acreage not devoted to Coca (ψ, an intimidation factor). • The end of the air bridge increases demand for Coca leaf in Colombia and raises p1/p0. • Living standards, proxied by the profit function (optimal choices denoted *), (1-τ){ p0f0(h0*,L0*) + p1(1−κ)f1(h1*,L1*)} − ψL0* − w(h0*+ h1*) increase if tax rates and p0 are fixed. In practice, tax rates may increase with p1 since revenues fund collection. Coca supply should nevertheless increase in p1[1- κ]/p0, in ψ, and probably in τ (allowing for income effects).

Cross-country comparisons According to both the “greed” and “grievance” stories, the change in economic environ. causes the change in violence and conflict intensity. We begin with a brief cross-country analysis of per-capita GDP and homicide trends in Figures 1b and 1c. (GDP clearly not quite the right outcome). GDP growth on downward trend in Co.; Peru GDP growth is declining more steeply but highly volatile. Could be evidence of a “Coca boom, ” but confounded w/other things. A coup in Peru, and much of Latin America hit by recession during 2nd half of the 1990s. Homicide in Co. began to decline in 1992, and fell steeply in 93-94 and 94-95, when Coca production was skyrocketing. Rates also fell (more gradually) in Peru and Bolivia. Did the “Coca boom” really reduce crime/violence? Difficult to tell with cross-country data. The early 1990s saw unusually high homicide in Colombia but rates fell in 1993. Strong country-specific trends suggest a within-country/cross-region (and rural-specific) analysis may be more fruitful.

Figure 1b.Per-Capita GDP Growth Rate for Colombia, Peru and Bolivia1990 - 2002

Figure 1c.Homicide Rate in Selected South American Countries1990 - 2001

III. CLASSIFICATION OF REGIONS • We exploit the likelihood that the shift in Coca production had a disproportionate effect on (rural parts of) departments hospitable to Coca. • Research question and design similar to Black, et al (2002a,b; 2005) who use regional controls to look at economic and social effects of coal boom. • We identified potential growing depts in two ways: • 9-department growing region; departments with at least 1,000 hectares under cultivation according to international observer reports for 1994. • 14-department growing region; adds to this 5 other departments identified as growing. • Note that the 9-dept region includes two (the DMZ) ceded to the FARC in 1998. We allow separate DMZ effects in the empirical work. • The 9 are mostly South and East, but excludes some in these areas like Amazonas. The other 5 are mostly in the North. See map.

Map of Colombia showing Regions by Type

First-stage and descriptive statistics by region type Figure 2 shows a strong correlation between 1994-99 coca growth and base-period growing status. (estimates in Table 1; e.g., 7554 hectares more growth in 9-dept region). Omission of DMZ increases first-stage, as does growth through 2000 instead of 1999. The 14 depts also saw substantially more growth in production, with none (intercept of zero) in the non-growing region. We mainly focus on the 14-department coding scheme, but also show results based on the 9-department coding scheme. Table 2 compares regions. Growing regions more rural; to improve comparability we drop 3 big-city departments from the mortality study. Similar primary but lower secondary enrollment in growing. More homicide (per 100,000 men aged 15-59) in non-growing, but w/o the 3 big-cities, reasonably similar across types.

COMPLICATIONS Previous insurgent activity • Growing regions include areas with a previous guerilla presence. This raises the possibility that we are uncovering a “pre-existing guerrilla trend,” not driven by Coca. • But overlap is not perfect, especially earlier. We also look separately at the DMZ, where FARC is strongest, and include region-specific trends. Rural Displacement and migration • Economic migrants move to work in coca fields, among other reasons. Refugees flee conflict. • As a partial check, we drop migrants from some economic analyses. Also, we examine effects on migration in growing regions after the disruption of the air bridge and find no evidence of any effects. • The bulk of refugee movements appear to be within departments, though much is rural-to-urban. Displacement also predates drugs and includes growing and non-growing depts.

IV. ECONOMIC CONSEQUENCES • Is there an economic payoff to Coca production in rural areas? Do peasant farmers benefit from Coca cultivation? • Or are benefits “taxed away”? Samples and framework • We studied this using the rural component of Colombia’s annual household survey for 1992-2000. • The survey was conducted in 23 of Colombia’s 33 depts, including 7 growing departments plus the DMZ (i.e., 9 out of the 14). (Results are robust to using 9-department classification). • Different samples for different outcomes; descriptive statistics are shown in Table 5.

Estimates for adults Table 6a reports interaction term estimates for adults, from the following: yjit = Xi’μ + βj + δt + α0tgjt + α1tdjt + εjit. (1) yjit is the dependent variable; Xi includes individual covariates including age, marital status, and number of household members; βj, δt are dept and year effects; gjt indicates non-DMZ growing departments in year t; djt indicates DMZ departments in year t; α0t and α1t (t=1994, . . ., 2000) are the corresponding year/region-type interaction terms. Logit marginal effects for self-employment and any-employment. Regression effects on log(SE income), log(hours), log(WS income). Some evidence of increase in SE (not sig), and log SE income (sig.) in 96-98, consistent w/coca story (note: selection bias is probably neg).

To improve precision, we pooled yearly interactions using yijt = Xi’μ + βj + δt + α0,95-97gj,95-97 + α0,98-00gj,98-00 + α1,95-97dj,95-98 + α1,98-00dj,98-00 + εijt, (2) where α0,95-97 and α0,98-00 are pooled terms for non-DMZ growing, and α1,95-97 and α1,98-00 are pooled for the DMZ. We use this model to try specifications with region-specific trends. In particular, some models include trends for each department type, replacing βj with β0j + β1jt, where β1j takes on 3 values, one for each department type. Results from pooled models appear in Table 6b. W/o trends these show strong effects on pos SE and log SE for 95-97 in non-DMZ growing. With trends: Pos SE results disappear, log SE becomes insignificant. However, trends themselves are insignificant, leaving the choice between models with and w/o trends unresolved. Little evidence of employment effects. Possibly something for hours, but only in the later period and again not sig. w/trends.

Children and youth Table 7a reports yearly interaction term estimates for school enrollment of boys and girls and (boys) teen labor. Table 7b reports coefficients for children and youth from pooled models. Some evidence of an increase in hours worked for boys in the growing region.

Rural/urban stack -- Rationale Increased in coca cultivation should have affected rural areas more. We stack our rural data with urban data to explore. Specification details Estimates are from a pooled urban/rural model, that allows for region-type main effects, and urban- and rural-specific growing*year interaction terms. Results are from models with region-specific trends. We drop the one DMZ dept common to urban and rural data; estimates are for growing-non-DMZ only. Other variations Drop medium coca producers (i.e., those with less than 1,000 hectares of cultivation pre-treatment) from the sample, since our first stage results suggest greater effects on departments with larger pre-existing coca production.

Results for adults Results using the rural/urban data appear in Table 6c. Again, some evidence of effects on log SE; also, in this case, adult hours. Discarding medium coca producers makes this more precise and sharpens the rural/urban contrast: Significant effects on log SE and hours in rural areas; rural effects are significantly larger than urban. Possible spillovers on log SE and adult hours in urban areas. No effect on SE participation, employment, or wages (puzzling neg. effect on wages w/o medium producers). Youth Labor Market Youth results using rural/urban stack are reported in Table 7c. Results with and w/o the middle producers show (marginally) significant increase in hours worked for boys, w/sig. larger effects in rural than urban. (Note: in these tables, employment and participation effects not yet converted to marginal effects)

V. COCA AND VIOLENCE • Coca appears to have a moderate impact on economic conditions in rural areas, with some evidence of a positive impact on self-employment income and hours of work. • Little evidence or region-wide spillovers as in Black, et al studies of coal. A moderate impact may indicate that the coca cultivation is not as profitable as many believe. If so, effects on violence may also be small. • But modest effects may also reflect the fact that benefits are largely “taxed away” by guerrilla and paramilitary groups. In this case, we may expect a positive effect of coca on violence. • We used vital statistics micro data to calculate and estimates effects on: violent death rate = homicide + suicide + military/insurgent + other external causes, divided by intercensal population estimates, for men aged 15-59.

Graphical Analysis • Figure 3a plots log death rates by region, after removing region means. • Similar 90-93 patterns, up-then-down, but rates continued to fall in non-growing region after 93, before turning up in 1996. • Implied DD: growing region up in 94-96 period. • Figure 3b shows trend improvement in deaths from disease (the region definition here and in Fig. 3 uses 14 departments). • In contrast with region differences in the evolution of violent death rates, disease shows similar trend in all three regions, or perhaps even a relative improvement in non-growing (non-DMZ). • Figures 4a and 4b repeat these plots using the 9-dept definition of the growing region (sharper break in growing in 94).

Figure 3a. Death rates by violence for men, aged 15-59, split by 14 provinces growing. Log rates, relative to average by province type; Non – growing omits Antioquia, Valle and Bogota DC. Figure 3b. Death rates by disease for men, aged 15-59, split by 14 provinces growing. Log rates, relative to average by province type; Non – growing omits Antioquia, Valle and Bogota DC.

Other Checks A possible concern is population base for rates, especially given migration. To obviate the need for a base, Figures 5a and 5b show the (log) ratio of violent deaths over total deaths by region type. Results again show clear (perhaps clearer) breaks in growing regions. Figures 6a, b pools entire 14-dept growing (including the DMZ). This provides good pre-treatment control, and a fairly clear break in 94-96. Figures 7a, b repeat the pooled analysis using 9-dept growing. Pattern is similar, the break is sharper, but pre-treatment match is not quite as good.

Figure 5a. Death rates by violence for men, aged 15-59, split by 14 provinces growing. Logits, relative to average by province type; Non – growing omits Antioquia, Valle and Bogota DC. Figure 5b. Death rates by violence for men, aged 15-59, split by 9 provinces growing. Logits, relative to average by province type; Non – growing omits Antioquia, Valle and Bogota DC.

Pooled estimates and estimates by urban-rural status Table 3 reports estimates paralleling the DD implicit in the figures, from an equation similar to equation (1): ln(vajt/pajt) = μa + βj + δt + α0tgjt + α1tdjt + εajt. ln(vajt/pajt) = log death rate by 10-year age group, department, and year. μa, βj, δt are age, dept, year effects gjt indicates non-DMZ growing departments in year t; djt indicates DMZ departments in year t; α0t and α1t (t=1993, . . ., 2000) are the corresponding year/region-type interaction terms. Some models also include trends for each department type, replacing βj with β0j + β1jt, where β1j takes on 3 values, one for each department type. Table 4 repeats the analysis separately by urban/rural residence α0t and α1t mostly larger in rural areas.

VI. SUMMARY AND CONCLUSIONS • Coca is sometimes said to fuel a rural economic boom. Disruption of the Andean air bridge to Colombia generated moderate evidence of this, beyond possible effects on SE income and teen boys’ hours worked. • Hard to distinguish economic effects from region-specific trends; this motivates an implicit triple difference using urban controls. • Results using an urban/rural show that in growing regions, rural SE income increased by 15% and rural youth hours increased by 12% for those already employed in this sector. This probably did little to boost income region-wide. • On the other hand, there is fairly clear evidence of increased violence in Colombian growing areas (not only or even especially in the DMZ). • Urban/rural contrast is consistent with accelerated civil conflict in areas with increased coca cultivation. • Results appear roughly consistent with “primary resources/insurgent extraction story.” Greed and economic opportunity more than political/economic grievance as an engine of conflict. • Where do we go from here? Peruvian Data. In the mean time, US interdiction in Colombia has accelerated, while Bolivian production is up 17%.

I. CONTEXT AND QUESTIONS