Identifying Municipal Risk Factors for Leftist Guerrilla Violence in Colombia
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Abstract
This study examines determinants of leftist violence at the municipal level in Colombia from 2000 through 2010. A multilevel GLMM model with a negative binomial distribution is used to take advantage of the information available at the municipal and department level. Surprisingly, inequality was not a significant covariate of violence, and agricultural GDP tended to reduce, instead of increase, guerrilla violence. The main risk factors identified include physical characteristics such as rugged topography and prior violence, but also factors that are candidates for policy action, such as unemployment, incorporation of the poor into public services, repression, and the energy and mining sector. These findings suggest interventions to decrease risks of guerrilla violence beyond merely strengthening the state. While repression tends to escalate violence, targeted policies to provide health benefits to those currently underserved, and securing mining and oil operations can effectively reduce the risk of violence.
Funding source: Division of Civil, Mechanical and Manufacturing Innovation
Award Identifier / Grant number: 1541199
Funding statement: Division of Civil, Mechanical and Manufacturing Innovation, Funder Id: 10.13039/100000147, Grant Number: 1541199.
Appendix A
Appendix B
Granger causality tests.
| Granger causality resources/greed | Granger causality test | |||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Lag | Log–deter | Chi-square | p-Value | AIC | BIC | HQ | F-statistic | p-Value | ||||
| Guerrilla and coca cultivation | 1 | 22.06 | 0.00 | 0.00 | 23.78 | 23.73 | 23.21 | Coca | → | Guerrilla | 7.10 | 0.04 |
| 2 | 20.72 | 2.69 | 0.61 | 23.58 | 23.50 | 22.62 | Guerrilla | → | Coca | 0.01 | 0.91 | |
| 3 | −93.43 | 0.00 | 1.00 | −89.43 | −89.54 | −90.76 | ||||||
| Guerrilla and GDP agriculture | 1 | 21.20 | 0.00 | 0.00 | 22.70 | 22.76 | 22.30 | GDP agric. | → | Guerrilla | 3.53 | 0.11 |
| 2 | 20.45 | 2.24 | 0.69 | 22.95 | 23.05 | 22.28 | Guerrilla | → | GDP agric. | 0.89 | 0.38 | |
| Guerrilla and mining | 1 | 15.94 | 6.63 | 0.16 | 18.80 | 18.72 | 17.85 | GDP min. | → | Guerrilla | 5.12 | 0.11 |
| 2 | 19.26 | 0.00 | 0.00 | 20.98 | 20.93 | 20.40 | Guerrilla | → | GDP min. | 0.61 | 0.60 | |
| 3 | −107.50 | 0.00 | 1.00 | −103.50 | −103.61 | −104.84 | ||||||
| Guerrilla and coffee | 1 | 9.92 | 0.00 | 0.00 | 11.64 | 11.59 | 11.06 | Coffee | → | Guerrilla | 23.20 | 0.00 |
| 2 | 9.14 | 1.57 | 0.81 | 12.00 | 11.92 | 11.04 | Guerrilla | → | Coffee | 0.19 | 0.68 | |
| 3 | −93.56 | 0.00 | 1.00 | −89.56 | −89.67 | −90.90 | ||||||
| Guerrilla and oil | 1 | 21.86 | 6.37 | 0.17 | 24.71 | 24.64 | 23.76 | Oil | → | Guerrilla | 53.40 | 0.00 |
| 2 | 25.04 | 0.00 | 0.00 | 26.76 | 26.71 | 26.18 | Guerrilla | → | Oil | 2.33 | 0.24 | |
| 3 | −87.63 | 0.00 | 1.00 | −83.63 | −83.74 | −84.96 | ||||||
| Guerrilla and gold | 1 | 38.30 | 0.00 | 0.00 | 40.01 | 39.97 | 39.44 | Gold | → | Guerrilla | 0.29 | 0.77 |
| 2 | 35.96 | 4.68 | 0.32 | 38.82 | 38.74 | 37.86 | Guerrilla | → | Gold | 1.09 | 0.44 | |
| 3 | −90.45 | 0.00 | 1.00 | −86.45 | −86.56 | −87.79 | ||||||
Appendix C
Granger causality human rights violations (guerrilla, paramilitary and government).
| Variable lags specification | ||||||
|---|---|---|---|---|---|---|
| Lags (months) | Log–determinant | Chi-square | p-Value | AIC | BIC | HQ |
| 1 | 19.5 | 0 | 0 | 19.7 | 20.1 | 19.9 |
| 2 | 19.4 | 9.9 | 0.4 | 19.8 | 20.4 | 20 |
| 3 | 19.2 | 13.4 | 0.1 | 19.9 | 20.7 | 20.2 |
| 4 | 19.2 | 5.5 | 0.8 | 20 | 21 | 20.4 |
| 5 | 19 | 15.1 | 0.1 | 20 | 21.3 | 20.5 |
| 6 | 18.8 | 11.7 | 0.2 | 20 | 21.5 | 20.6 |
| 7 | 18.5 | 26.4 | 0 | 19.8 | 21.6 | 20.6 |
| 8 | 18.3 | 8.9 | 0.5 | 19.9 | 21.9 | 20.7 |
| 9 | 18.2 | 12.6 | 0.2 | 19.9 | 22.2 | 20.8 |
| 10 | 18.1 | 5 | 0.8 | 20 | 22.5 | 21 |
| 11 | 17.9 | 10.4 | 0.3 | 20 | 22.8 | 21.1 |
| 12 | 17.7 | 13.3 | 0.2 | 20 | 23 | 21.2 |
| 13 | 17.5 | 10 | 0.4 | 20 | 23.2 | 21.3 |
| 14 | 17.3 | 9.6 | 0.4 | 20 | 23.5 | 21.4 |
| 15 | 17.2 | 4.3 | 0.9 | 20.1 | 23.8 | 21.6 |
| 16 | 17.1 | 5.3 | 0.8 | 20.2 | 24.1 | 21.8 |
| 17 | 16.7 | 20.6 | 0 | 19.9 | 24.1 | 21.6 |
| 18 | 16.4 | 12.6 | 0.2 | 19.8 | 24.2 | 21.6 |
| 19 | 16.1 | 10.5 | 0.3 | 19.7 | 24.3 | 21.6 |
| 20 | 15.7 | 13.4 | 0.1 | 19.5 | 24.4 | 21.5 |
| 21 | 15.5 | 6.8 | 0.7 | 19.5 | 24.6 | 21.6 |
| 22 | 15.3 | 6.1 | 0.7 | 19.5 | 24.8 | 21.6 |
| 23 | 15.1 | 4.1 | 0.9 | 19.5 | 25.1 | 21.8 |
| 24 | 14.1 | 23.3 | 0 | 18.7 | 24.5 | 21 |
| Granger causality test (lag selected=1) | ||
|---|---|---|
| Causal directions | F-statistic | p-Value |
| Paramilitary→guerrilla | 0.082 | 0.776 |
| Goverment→guerrilla | 5.288 | 0.023 |
| Guerrilla→paramilitary | 3.627 | 0.059 |
| Goverment→paramilitary | 6.347 | 0.013 |
| Guerrilla→goverment | 9.165 | 0.003 |
| Paramilitary→goverment | 4.914 | 0.029 |
Appendix D
OLS fixed effects balanced panel.
| Oneway (individual) effect within model | ||||
|---|---|---|---|---|
| Municipalities | Years | Number of observations | ||
| 1118 | 10 | 11,180 | ||
| Residuals | ||||
| Min. | 1st Qu. | Median | 3rd Qu. | Max. |
| −6.064391 | −0.264771 | −0.034855 | 0.138515 | 15.15151 |
| Coefficients | ||||
| Social service spending | −0.00015576 (0.00077991) | |||
| % Poor affiliated with public services | −1.0412 (0.08531) | *** | ||
| Coca cultivation | 0.0047722 (0.0024169) | * | ||
| Agriculture, fishing and ranching GDP | −0.89082 (0.11873) | *** | ||
| Mining, energy and quarries GDP | −0.019055 (0.016666) | |||
| Aerial erradication | 0.0076375 (0.0020391) | *** | ||
| Government HR violations | 0.15969 (0.022072) | *** | ||
| GDP (per capita) | −0.030119 (0.045779) | |||
| Population | −13.494 (0.94526) | *** | ||
| Total sum of squares: | 10,784 |
| Residual sum of squares: | 9931.8 |
| R-Squared: | 0.079005 |
| Adj. R-Squared: | −0.024152 |
| F-statistic: | 95.8194 on 9 and 10053 df, p-value: < 2.22e-16 |
| Residual tests | |
| Breusch-Pagan LM test for cross-sectional dependence in panels | |
| p-Value: | 2.2E-16 |
| Alternative hypothesis: cross-sectional dependence | |
| Pesaran CD test for cross-sectional dependence in panels | |
| p-Value: | 2.2E-16 |
| Alternative hypothesis: cross-sectional dependence | |
| Breusch-Godfrey/Wooldridge test for serial correlation in panel models | |
| P-Value: | 2.2E-16 |
| Alternative hypothesis: serial correlation in idiosyncratic errors | |
p ≤ 0.001*** p ≤ 0.01** p ≤ 0.05*.
Appendix E
Moran’s I test for Models’ residuals spatial autocorrelation.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| With sectoral GDPs (agriculture and mining) | With coffee production and mining GDP | No sectoral GDPs. Coffee, mining and energy commodities | Model 1 + GINI and unemployment (24 depts) | Model 1 with warlike actions as dependent variable | |
| Observed | −0.00034 | 0.00012 | −0.00004 | −0.00021 | −0.00009 |
| Expected | −0.00009 | −0.00009 | −0.00009 | −0.00010 | −0.00009 |
| SD | 0.00029 | 0.00029 | 0.00028 | 0.00029 | 0.00028 |
| p-Value | 0.38 | 0.48 | 0.87 | 0.70 | 0.99 |
Research hypothesis: spatial autocorrelation.
Appendix F
The GLMM-ADBM with a negative binomial distribution and variance specification = ϕμ is the model with the lowest AIC.
Model comparison.
| Model | Akaike information criterion (AIC) | dAIC | Degrees of freedom |
|---|---|---|---|
| Negative binomial two levels (variance = ϕμ) CINEP violence/agriculture GDP | 11082 | 0 | 20 |
| Negative binomial two levels (variance = ϕμ) CINEP violence/coffee cultivation | 11093.1 | 11.1 | 20 |
| Negative binomial two levels (variance = μ (1 + μ/k)) coffee cultivation | 11892.9 | 810.9 | 21 |
| Negative binomial two levels (variance = μ (1 + μ/k)) agriculture GDP | 12064.1 | 982.1 | 21 |
| Negative binomail zero-inflated two levels GDP agriculture | 12287.1 | 1205.1 | 19 |
| Negative binomial zero-inflated one level coffee cultivation | 12354.5 | 1272.5 | 18 |
| Negative binomial zero-inflated one level agriculture GDP | 12436.3 | 1354.3 | 18 |
The model pre-selection also showed that the variance of the municipal random intercepts is greater than the variance of the departmental random intercepts (σ2municipal > σ2departmental) with a considerable range of variation (σmunicipal = 0.9, σdepartmental = 0.75). Such level of variation at the two hierarchical levels indicates that multilevel modelling with a non-normal response function is suitable for municipal and departmental factor estimation to explain leftist guerrilla violence as the dispersion parameter for the negative binomial distribution is significant.
Appendix G
Bayesian INLA model considering time, municipality, department variables as random effects.
| Mean | SD | 0.025 quant | 0.5 quant | 0.975 quant | Mode | |
|---|---|---|---|---|---|---|
| Fixed effects | ||||||
| (Intercept) | −3.435 | 0.301 | −4.031 | −3.434 | −2.848 | −3.431 |
| SocServicespc_b08 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| pctj_hthcov | −0.416 | 0.256 | −0.918 | −0.416 | 0.086 | −0.416 |
| CC_Hect_T2 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| GDP_Agr_T1 | −0.001 | 0.000 | −0.001 | −0.001 | 0.000 | −0.001 |
| GDP_Min_T1 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| AE_Hect | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 | 0.000 |
| Govt_fitted | 0.114 | 0.042 | 0.034 | 0.114 | 0.199 | 0.112 |
| Forest_pctg | 1.737 | 0.366 | 1.021 | 1.736 | 2.456 | 1.735 |
| Slope | 0.034 | 0.012 | 0.009 | 0.034 | 0.058 | 0.034 |
| GDPpcConst | 0.039 | 0.026 | −0.012 | 0.039 | 0.089 | 0.040 |
| HRV_pre_Viol | 0.409 | 0.040 | 0.332 | 0.409 | 0.487 | 0.408 |
| Pop | 0.115 | 0.143 | −0.165 | 0.114 | 0.399 | 0.113 |
| Random effects | ||||||
| Size for the nbinomial observations (1/overdispersion) | 0.615 | 0.044 | 0.538 | 0.611 | 0.709 | 0.603 |
| Precision for ID_Mun_c | 0.152 | 0.015 | 0.125 | 0.151 | 0.185 | 0.148 |
| Precision for Year | 2.367 | 1.338 | 0.690 | 2.075 | 5.751 | 1.560 |
| Precision for ID_Dept_c | 17,310 | 17,880 | 950 | 11,880 | 64,650 | 2424 |
Expected number of effective parameters (SD): 509.34 (15.13)
Number of equivalent replicates: 21.95
Marginal log-Likelihood: −6024.31
Mean variation of Violence from 2000 to 2009
Appendix H
GLMM-ADMB negative binomial on HRV reported by CINEP including time as a fixed effect.
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | |
|---|---|---|---|---|---|
| With sectoral GDPs (agriculture and mining) | With coffee production and mining GDP | No sectoral GDPs. Coffee, mining and energy commodities | Model 1 + GINI and unemployment (24 depts) | Model 1 with warlike actions as dependent variable | |
| Intercept | 678.38374 (69.352)*** | 761.37776 (66.902)*** | 687.97122 (71.53)*** | 672.00 (76.3)*** | 356.000 (33.800)*** |
| State presence | |||||
| – Social Service Spending | 0.00242 (0.00238) | 0.00243 (0.00243) | 0.00233 (0.002) | 0.00249 (0.002410) | 0.000335 (0.001230) |
| – % poor affiliated w/public services | −0.46294 (0.3437) | −0.3343 (0.33996) | −0.3211 (0.341) | −0.434 (0.368000) | 0.27400 (0.184000) |
| Resources/greed | |||||
| – Coca cultivation | 0.01211 (0.00994) | 0.01243 (0.01034) | 0.01246 (0.01) | 0.0383 (0.017900)* | −0.00471 (0.003470) |
| – Mining, Energy and Quarries GDP | 0.02299 (0.0748) | 0.04552 (0.07669) | −0.0253 (0.144000) | 0.0436 (0.037800) | |
| – Oil production | 0.13429 (0.054)* | ||||
| Grievances | |||||
| – Coca aerial eradication | 0.00809 (0.00768) | 0.00793 (0.00783) | 0.00803 (0.008) | −0.000911 (0.008460) | 0.010200 (0.003320)** |
| – Unemployment rate | 0.092200 (0.023400)*** | ||||
| – GINI Coefficient | −3.540000 (2.310000) | ||||
| – Coffee cultivation | 0.37765 (0.29525) | 0.38677 (0.288) | |||
| – Agriculture, fishing and ranching GDP | −2.9694 (0.59359)*** | −2.9000 (0.559000)*** | −0.609 (0.221000)** | ||
| Repression | |||||
| – Government HR violations | 0.19051 (0.0917)* | 0.19283 (0.09306)* | 0.1967 (0.093)* | 0.19800 (0.095600)* | 0.0478 (0.037200) |
| Geography/prior violence | |||||
| – % of forest land | 2.38821 (0.48132)*** | 2.63415 (0.45588)*** | 2.57198 (0.457)*** | 2.840000 (0.50000)*** | 2.13000 (0.298000)*** |
| – Land slope | 0.05524 (0.01452)*** | 0.04597 (0.01392)*** | 0.04722 (0.014)*** | 0.051500 (0.014900)*** | 0.067400 (0.009040)*** |
| – Prior HR/WL violence | 0.33935 (0.03646)*** | 0.33015 (0.03587)*** | 0.3311 (0.036)*** | 0.327000 (0.036700)*** | 0.39000 (0.025400)*** |
| – Year | −0.33941 (0.03475)*** | −0.38161 (0.03351)*** | −0.34484 (0.036)*** | −0.336000 (0.038300)*** | −0.179 (0.0169000)*** |
| Controls | |||||
| – GDP per capita | 0.53886 (0.30538) | 0.13714 (0.29591) | −0.34675 (0.343) | 0.706000 (0.45000) | −0.473 (0.14900)** |
| – Population | −0.04304 (0.38452) | 0.21971 (0.34931) | 0.28197 (0.345) | −0.165000 (0.358000) | 0.1730 (0.22000) |
| # Obs, # municipalities, # departments | 11,180, 1118, 32 | 11,180, 1118, 32 | 11,180, 1118, 32 | 10,450, 1045, 24 | 11,180, 1118, 32 |
| Negative binomal dispersion parameter | 0.13434 (0.0063041) | 0.13034 (0.0061173) | 0.13064 (0.0061261) | 0.13661 (0.006645) | 0.72522 (0.032076) |
| Random effects municipality level (intercepts) | Variance: 2.894 | Variance: 2.799 | Variance: 2.814 | Variance: 2.99 | Variance: 1.598 |
| SD: 1.701 | SD: 1.673 | SD: 1.677 | SD:1.729 | SD: 1.264 | |
| Random effects department level (intercepts) | Variance: 3.986 | Variance: 2.307 | Variance: 2.129 | Variance: 2.135 | Variance: 1.063 |
| SD: 1.997 | SD: 1.519 | SD: 1.459 | SD: 1.461 | SD: 1.031 | |
| AIC | 15283.6 | 15319.9 | 15313.7 | 14153.5 | 20593.8 |
p ≤ 0.001*** p ≤ 0.01** p ≤ 0.05*.
Appendix I
GLMM-ADMB Negative Binomial on terrorist violence reported by the Ministry of Interior.
| Model 1 | Model 2 | Model 3 | Model 4 | |
|---|---|---|---|---|
| With sectoral GDPs (agriculture and mining) | With coffee production and mining GDP | No sectoral GDPs. Coffee, mining and energy commodities | Model 1 + GINI and unemployment (24 depts) | |
| Intercept | −3.5934 (0.33514)*** | −4.11724 (0.38536)*** | −4.07768 (0.3835)*** | −2.26277 (0.82471)** |
| State presence | ||||
| – Social service spending | 0.00283 (0.00172) | 0.00283 (0.00172) | 0.0028 (0.000172) | 0.00328 (0.00183) |
| – % poor affiliated w/public services | −0.08543 (0.21195) | −0.13004 (0.21298) | −0.07668 (0.21919) | −0.14831 (0.23256) |
| Resources/greed | ||||
| – Coca cultivation | 0.00228 (0.00457) | 0.00196 (0.00454) | 0.002 (0.00454) | 0.01136 (0.00907) |
| – Mining, energy and quarries GDP | 0.04489 (0.04564) | 0.05949 (0.04595) | 0.09448 (0.07863) | |
| – Oil production | 0.04032 (0.02673) | |||
| Grievances | ||||
| – Coca aerial eradication | 0.00456 (0.00422) | 0.00422 (0.000419) | 0.00433 (0.00419) | 0.00474 (0.00589) |
| – Unemployment rate | −0.02254 (0.01548) | |||
| – GINI coefficient | −1.67956 (1.4572) | |||
| – Coffee cultivation | 0.93003 (0.29549)** | 0.91846 (0.29404)** | ||
| – Agriculture, fishing and ranching GDP | 0.11976 (0.3157) | −0.10339 (0.29438) | ||
| Repression | ||||
| – Government HR violations | 0.05703 (0.04165) | 0.05638 (0.04152) | 0.05609 (0.04142) | 0.025 (0.04274) |
| Geography/prior violence | ||||
| – % of forest land | 1.57061 (0.36412)*** | 1.74947 (0.36516)*** | 1.73751 (0.36544)*** | 1.83475 (0.38459)*** |
| – Land slope | 0.02946 (0.01122)** | 0.02332 (0.01132)* | 0.02316 (0.01132)* | 0.0319 (0.01165)** |
| – Prior HR/WL violence | 0.19201 (0.02186)*** | 0.18701 (0.02181)*** | 0.18736 (0.02183)*** | 0.17893 (0.02174)*** |
| Controls | ||||
| – GDP per capita | 0.06606 (0.1355) | 0.03885 (0.12295) | −0.00394 (0.13365) | 0.00327 (0.19368) |
| – Population | 0.91917 (0.26957)*** | 1.03491 (0.28257)*** | 1.04417 (0.28246)*** | 0.85504 (0.24548)*** |
| # Obs, # Municipalities, # departments | 11,180, 1118, 32 | 11,180, 1118, 32 | 11,180, 1118, 32 | 10,450, 1045, 24 |
| Negative binomal dispersion parameter | 0.5486 (0.032483) | 0.55458 (0.032913) | 0.5548 (0.03293) | 0.52862 (0.03382) |
| Random effects municipality level (intercepts) | Variance: 2.099 | Variance: 2.094 | Variance: 2.097 | Variance: 2.081 |
| SD:1.449 | SD: 1.447 | SD: 1.448 | SD:1.443 | |
| Random effects department level (intercepts) | Variance: 1.567 | Variance: 2.092 | Variance: 2.061 | Variance: 0.6954 |
| SD: 1.252 | SD: 1.446 | SD: 1.436 | SD: 0.8339 | |
| AIC | 12026.6 | 12013.8 | 12013.2 | 10718.5 |
p ≤ 0.001*** p ≤ 0.01** p ≤ 0.05*.
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Artikel in diesem Heft
- Research Articles
- Identifying Municipal Risk Factors for Leftist Guerrilla Violence in Colombia
- Controlling for Import Price Effects in Civil War Regressions
- Food Insecurity and Conflict Events in Africa
- Illusory Gains from Privatizing Social Security when Reform is Politically Unstable
- Letters and Proceedings
- A Note on Borders, Dyads and the Distribution of Democracy in Sub-Saharan Africa
Artikel in diesem Heft
- Research Articles
- Identifying Municipal Risk Factors for Leftist Guerrilla Violence in Colombia
- Controlling for Import Price Effects in Civil War Regressions
- Food Insecurity and Conflict Events in Africa
- Illusory Gains from Privatizing Social Security when Reform is Politically Unstable
- Letters and Proceedings
- A Note on Borders, Dyads and the Distribution of Democracy in Sub-Saharan Africa
