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“First Meeting of the Network of Mining Regions”

Mining social conflicts and IBAs in Perú. An empirical

model

Carlos Casas Tragodara Universidad del Pacífico - Perú

Mining is important in Perú

• Mining and energy has 14.1% of GDP share. In some regions like Pasco is 44%.

• 50%-60% of total exports. Copper and gold are the main exports.

• 5.1% of labor force works in mining.

• Government take represented almost 20% of government revenues last decade.

• Environmental issues.

• Conflicts around areas of mining activity

Approaches Factors

Hershel I. Grossman

Ernesto Dal Bó & Pedro Dal Bó

Besley & Persson

Sharing benefits

Positive realtionship

between government take

and conflict incidence

Economies with capital intensive

sectors often shows more conflicts.

Positive relationship

between commercial volatlity and

conflict incidence .

Theoretical Framework

Approaches

Factors

Fearon Chassang & Padro -

i- Miquel’s Aumann & Schelling

Information assymetry and

incomplete contracts

Agreements can be Pareto effiicient

but there are reasons that limit

that outcome

Transitory economic shocks

increase incentives in the present to control resources

in the future

Dynamic Inconsistency

Theoretical Framework

Conflicts has increased

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Conflictos mineros Conflictos totales % conflictos asociados a minería

Source: Ombudsman office - Perú

7

Social conflicts 2005-2013

Conflictivity Map: Perú

Source: National Statistics Institute and Ombudsman Office

High correlation between mininig regions and conflicts

Gráfico N° 4: VAB Minero e incidencia de conflictos por departamento

VAB Minería* Incidencia de Conflictos**

* VAB Minero del año 2011 (Fuente: INEI)

** Incidencia de conflictos desde enero del 2005 a junio del 2013

Fuente: Defensoría del Pueblo

Elaboración propia

Conflicts consequences

• Direct economic losses: Less taxes, employment and economic activity.

• Delay or cancellation of mining projects. Tia María, Conga are examples.

• Social and economic damage in mining zones: Death of people and loss of infraestructure.

Methodology

Logit model

Probability of conflcit ocurrence in a district during 2004-2016 period.

Poisson regression

Explain numbers of conflicts in a district during 2004-2016 period.

We estimate 4 models: • Dependent variable: Total conflicts

Dependent variable: Minig conflicts Observations: 1836 districts.

• This number is the total districts that exist in 2004 (They increase each year)

VARIABLES DESCRIPCIÓN

Staff profesional Staff profesional / Total de población IDH Índice de Desarrollo Humano Tasa de mortalidad Tasa de mortalidad Pob. Agrícola (%) Porcentaje de la población dedicada a la agricultura Pob. Minera (%) Porcentaje de la población dedicada a la minería Por. Canon en IT Importancia del canon en el total de ingresos de la

municipalidad promedio (2012-2015) Por. RDR en IT Importancia de los recursos directamente recaudados en el

total de ingresos de la municipalidad promedio (2012-2015) Fn. Naturales Incidencia de fenómenos naturales promedio (2012-2015) Pro. Social Incidencia de programas sociales promedio (2012-2015) Tierra cultivada Hectáreas de tierra cultivada per cápita Canon Promedio Canon minero promedio entregado a las municipalidades

(2004-2016)

Explanatory variables

LOGIT

POISSON

VARIABLES CONFLICTOS

SOCIALES

CONFLICTOS

MINEROS

CONFLICTOS

SOCIALES

CONFLICTOS

MINEROS

Staffprofesional 11.37 31.57 5.380** 10.09***

(20.32) (24.59) (2.284) (2.920)

IDH 0.0825 -4.001** -5.668*** -12.29***

(1.297) (1.589) (0.359) (0.513)

Tasademortalidad 0.0163** 0.0146 0.0107*** 0.00814**

(0.00808) (0.00958) (0.00228) (0.00338)

Pob.Agrícola(%) -2.449*** -2.850*** -2.930*** -5.263***

(0.651) (0.834) (0.211) (0.322)

Pob.Minera(%) 6.732*** 10.61*** 4.254*** 5.799***

(1.630) (1.726) (0.179) (0.211)

Por.CanonenIT 0.400 0.785** 0.666*** 0.811***

(0.314) (0.392) (0.0918) (0.130)

Por.RDRenIT 2.155*** 3.298*** 2.907*** 2.834***

(0.829) (0.971) (0.205) (0.286)

Fn.Naturales 0.111*** 0.157*** 0.0919*** 0.113***

(0.0312) (0.0363) (0.00778) (0.0109)

Pro.Social 0.000129*** 6.25e-05*** 6.83e-05*** 7.39e-05***

(1.91e-05) (1.68e-05) (2.50e-06) (3.63e-06)

Tierracultivada 0.0927** 0.101*** 0.0239** 0.0451***

(0.0372) (0.0370) (0.0111) (0.0148)

Canonpromedio 5.90e-08** 5.73e-08*** 4.29e-08*** 4.60e-08***

(2.31e-08) (1.99e-08) (8.04e-10) (9.73e-10)

Constante -1.972** -0.959 2.773*** 6.172*** (0.892) (1.082) (0.249) (0.351) Observaciones 1,710 1,710 1,710 1,710

Standarderrorsinparentheses***p<0.01,**p<0.05,*p<0.1

Then we choose those districts with a higher predicted probability of conflict

Ubigeo Departamento Provincia Distrito

020105 Ancash Huaraz Independencia

021014 Ancash Huari SanMarcos

021801 Ancash Santa Chimbote

030406 Apurimac Aymaraes Cotaruse

030412 Apurimac Aymaraes Sañayca

030503 Apurimac Cotabambas Coyllurqui

030506 Apurimac Cotabambas Challhuahuacho

040603 Arequipa Condesuyos Cayarani

040608 Arequipa Condesuyos Yanaquihua

050705 Ayacucho Parinacochas Pullo

060105 Cajamarca Cajamarca Encañada

060701 Cajamarca Hualgayoc Bambamarca

080601 Cusco Canchis Sicuani

081304 Cusco Urubamba Machupicchu

090117 Huancavelica Huancavelica Yauli

090301 Huancavelica Angaraes Lircay

090411 Huancavelica Castrovirreyna SantaAna

We found the following causes

Factors related to environmental and economic risk perception

Envirinmental damage by mining firms (67) Districts in the followind regions: Ayacucho (1), Cajamarca (1), Huancavelica (5), Ancash (1), Apurímac (2), Puno (3), Moquegua (1) y Pasco (1).

Potential envirinmental and economic damage (13) Ayacucho (5), Cajamarca (2), Ancash (1), Apurimac (1), Lima (1), Moquegua (2) y Tacna (1).

Factores realted to sharing benefits

Unfulfillment of IBAs (28) Apurímac (9), Ayacucho (1), Lima (2), Cajamarca (2), Huancavelica (3), Ancash (3), Puno (3) y Pasco (2).

Communities require IBAs (23) Apurímac (6), Cajamarca (5), Arequipa (4), Pasco (2) y La libertad (2).

Requirement de beneficios laborales y preferencias comerciales (12)

Áncash (3), Apurímac (3), Cajamarca (2), Lima (2), La Libertad (1) y Pasco (1).

Requirement of impovement in benefit sharing by communities (23)

Apurímac (4), Cajamarca (4), Pasco (4), Áncash (3), La libertad (3), Cuzco (2), Moquegua (2) y Puno (1)

Other factors Territorial factors and property rights(17)

Assymetryc Information (12)

Conclusions

Cuantitative

Analysis

Main factor behind conflict ocurrence are:

Institutional factors

Socioeconomic factors

Factors related to productive structure of districts.

Intergovermental transfers and municipality revenue structure.

Cualitative

Analysis

There is no framework to negotiation and signature of IBAs. Too much variety

There is no monitoring system of IBAs

Correlation between conflicts and IBAs

Some Recommendations

• Need for early warning system of conflicts. “Social intelligence could be smart.

• With the estimated models government and firms know the main factors that impact on conflicts

• For each new mining project the governmente can create a task force in order to increase probability of a better outcome for everybody.

• Neccesity of a legal and regulatory framework.

• Follow up system of IBAs

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