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![Page 1: Presentación de PowerPoint · S O C IA L E S C O N F L IC T O S M IN E R O S C O N F L IC T O S S O C IA L E S C O N F L IC T O S M IN E R O S S t a ff p r o fe s io n a l 1 1 .3](https://reader034.vdocuments.co/reader034/viewer/2022051606/6029698f9fb312566a2d9bc1/html5/thumbnails/1.jpg)
C O N V E N E S C O O R D I N A T E S P R E S E N T S
O R G A N I Z E S M E D I A P A R T N E R C O L L A B O R A T E S
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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
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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
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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
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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
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Conflicts has increased
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Po
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Conflictos mineros Conflictos totales % conflictos asociados a minería
Source: Ombudsman office - Perú
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7
Social conflicts 2005-2013
Conflictivity Map: Perú
Source: National Statistics Institute and Ombudsman Office
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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
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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.
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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
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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)
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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
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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
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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)
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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
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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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C O N V E N E S C O O R D I N A T E S P R E S E N T S
O R G A N I Z E S M E D I A P A R T N E R C O L L A B O R A T E S