1. Identifying critical factors that
influence community
acceptance of mining projects
Sisi Que
Dr. Kwame Awuah-offei
1

2. Introduction
The US consumes over 6.5 billion
tons/year of mineral & energy resources
----20 tons/person/year
Peru
• 245 social conflicts in June 2012
• >2,400 injured, >200 killed
(2006—2012)
2

3. Objectives
Characteristics of
mine
Accept or
Reject
Demographic
factors
3

4. Independent and
transparent information
Mine life
Management Environmental
and other
impact
Mine buffer
social
Infrastructure
improve
Population
change
Cultural impact
Traffic and crime
increase
Social
impact
Economical
impact
Environmental
Decision making
mechanism
Economical
Management
Characteristics of mine
Water pollution
Land pollution
Noise pollution
Air pollution
Job opportunities
Income increase
Cost of housing or
housing shortage
Labor shortage for
4
other business

5. Demographic
factors
Age
# of children
Gender
Job field
Income
Education
5

6. Online survey
Mining communities
6

7. Importance of the Factors
Median (95% confidence,
distribution free)
Mining factors
Labor shortage for other business
Population change
Infrastructure improve
Noise pollution
Cultural impact
Cost of housing or shortage
Independent and transparent information
Decision making mechanism
Traffic and crime increase
Income increase
Mine buffer
Mine life
Job opportunities
Water pollution
Air pollution
Land pollution
Land pollution and
subsidence
4
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
5
5
5
5
6
6
6
6
6
6
6
6
7
7
7
7
1
Not at all Important
2
Very Unimportant
3
Somewhat Unimportant
4
Neither Important nor
Unimportant
5
Somewhat Important
6
Very Important
7
Extremely Important
7

8. Correlative analysis of
mining communities
• Traffic and crime
increase
• Water pollution
• Air pollution
• Land pollution
• Population change
• Cultural impact
• Mine buffer
• Job opportunity
• Income increase
•
•
•
•
•
•
Cultural impact
Traffic and crime increase
Cost of housing or shortage
Land pollution
Decision making mechanism
Independent and transparent
information
• Mine buffer
8
• Mine life

9. Demographics of Respondents
Gender
Age
70%
60%
35%
Mining
30%
Control
50%
40%
15%
20%
10%
10%
Control
20%
30%
Mining
25%
5%
0%
0%
Male
18—25
Female
Education
26—34
35—54
55—64
>65
Annual income
40%
40%
Mining
Control
Mining
30%
30%
20%
20%
10%
10%
0%
Control
0%
9

10. Correlative analysis of
non-mining communities
• Job opportunity
• Labor shortage for
other businesses
• Job opportunity
• Noise pollution
• Cost of housing
or shortage
• Infrastructure
improvement
• Labor shortage for
other businesses
• Information
available
10

11. Mining and non-mining groups
1
2
Somewhat Unimportant
4
Neither Important nor
Unimportant
5
Somewhat Important
6
Very Important
7
N—Median of ranking from non-mining communities
Very Unimportant
3
M—Median of ranking from mining communities
Not at all Important
Extremely Important
11

12. Discrete Choice Model
Mixed logit
model (ML)
Daniel McFadden
2000 Nobel
Memorial Prize
Multinomial
probit model
(MNP)
Multinomial logit
model (MNL)
Random utility
maximization
(1959)
Pni
Prob U ni
U nj ,
i
j, i and j
J
(1)
12

13. 13

14. Discrete Choice Experiment
14

15. Literature
Review
Mining
group
Control
group
Final
variables list
Proposed framework for
future research
Burden
study
Discrete
choice
experiments
Discrete
choice
model
15

16. 16