Prepared by Jonathan Hoechst
Rebecca McAtee
Jonathan McBride
Ulrike Nischan
Overview
 Cost-Benefit Analysis Process
 Theoretical Framework
 Costs
 Benefits
 Monte Carlo
 Conclusion and Limitations
Theoretical Framework
 ALAC Effect on Corruption
 Societal approach
 Negative Impacts of Corruption
 Social welfare
 Political
 Economic
Costs
 Costs of Running an ALAC:
 Initial start-up costs (€95,600)
 Total administration costs, which include salary,
equipment, rent, etc. (€131,300)
 Volunteer opportunity cost (€3,600)
 Cost to Clients:
 Opportunity cost (€3,360)
 Travel costs (€795)
Benefits
 Societal Benefit
 Economic gain from reduced corruption


Pervasive corruption decreases actual economic activity
below level of full potential
Any decrease in corruption will yield economic gains
 Alternative Benefit Calculation: Client Level
 Legal advice provided by the ALACs
 Cannot Use Both Measures
 Double counting
Calculating Economic Gains derived
from ALACs, Part 1
 In order to calculate the economic gains from
ALACs, one must first calculate an ALAC’s impact on
corruption
 Using an adjusted Corruption Perceptions Index to
measure corruption and worldwide economic data
from 2003 through 2008 we were able to estimate that
a single ALAC per million people reduces corruption
by 0.74 on the 0-10 scale
Controlling for CPI Concerns
 Change in Methodology
 2006
 2007
 Controlled for with statistical model
 Surveys
 21 different surveys used
 3 required to be included in index
 Controlled for using survey variables
The Model
 Change in adjusted CPI indicator was measured by
holding all differences among countries and over
time constant
 This allowed us to look only at the change within time
and within countries attributable to ALAC presence
Model Results
 One ALAC per one million inhabitants has a 0.88
reduction effect on the measure of corruption
 There is a diminishing return of 0.14 for each ALAC
per one million inhabitants
Calculating Economic Gains derived
from ALACs, Part 2
 Estimates indicate that for every one-point change in
corruption GDP increases 0.38 percent (Pellegrini
and Gerlagh, 2004)
 Economic benefits of ALACs are calculated using the
estimate of an ALAC’s impact on corruption and the
impact of corruption on GDP
How to Calculate Benefits
 Example Country
 Syriana
 Population
 2,500,000
 Number of ALACs
 2 currently in operation
 GDP
 €34,000,000,000
How to Calculate Benefits
 Change in corruption attributed to ALACs in Syriana:
 ∆Corrupt
#ALAC /mil×#
ALAC/mil+ returns|#ALAC /mil×
# ALAC2 /mil
 (i) -0.88  (2/2.5) + 0.14  (2/2.5)2 = -0.614
 Percent change in GDP attributed to ALACs in Syriana:
 Multiply previous equation by %∆GDP| ∆Corrupt /mil, 0.38%
 (ii) -0.614  -0.0038 = 0.0023
 The change in GDP attributed to ALACs
 %∆GDP| ∆Corrupt /mil × GDPSyriana
 (iii) 0.0023  €34 bil. = $78.2 mil.
Standing
 As ALACs operate at the national level, we give
national standing
 Criminals are not given standing
Cost-Benefit Analysis
 Timeframe – 3.5 Years
 Cost associated with Year 0 is include the initial six month
startup period

No benefits accrue during Year 0
 Years 1 through 3 include administration costs, client costs,
volunteer costs, and economic benefits
 All costs and benefits are discounted to Year 0
 Three countries are used: Turkey, Hungary, and Cameroon
to show the range of net benefits in a low GDP, moderate
GDP, and high GDP country
 The calculated net benefit is an estimate of the net benefit
over the 3.5 years that the country in question receives if an
ALAC is founded this year
The Monte Carlo Process
 Varying Uncertain Parameters
 Running Simulations
 Evaluating Results
Monte Carlo Data Ranges Part 1
Min Value
Max Value
Distribution
Client Hours
0.5
4.0
Uniform
Yearly Caseload
154
158
Uniform
Operating Costs
€43,500
€48,625
Uniform
Rural Travel
Costs
€3.00
€12.00
Uniform
Urban Travel
Costs
€0.81
€1.81
Uniform
Monte Carlo Data Ranges Part 2
Median
Standard Dev.
Distribution
Marginal ALAC
Impact
-0.88
0.45
Bivariate
Normal
Marginal ALAC
Diminishing
Returns
0.14
0.08
Bivariate
Normal
Corruption’s Effect
on GDP
0.38
0.13
Normal
Monte Carlo Results
(Millions of Euros)
Country
Average Net
Benefits
Low
High
Below Zero
Turkey
63.0
-251.0
518.0
8%
Hungary
97.8
-275.0
653.0
8%
Cameroon
6.9
-28.9
51.9
9%
Monte Carlo – Turkey
•High GDP – High
population
•Average Net
Benefit: €63.0 mil
•Below Zero: 8%
Monte Carlo – Hungary
•High GDP – Low
Population
•Average Net
Benefit:
•€97.8 mil
•Below Zero: 8%
Monte Carlo – Cameroon
•Low GDP – Low
Population
•Average Net
Benefit: €6.9 mil
•Below Zero: 9%
Conclusions and Limitations
 Significant ALAC benefits
 Data limitations
 Availability of other research
 Relatively new strategy