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					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
                    €3.00      €12.00       Uniform
   Costs
 Urban Travel
                    €0.81       €1.81       Uniform
    Costs
Monte Carlo Data Ranges Part 2
                      Median   Standard Dev.   Distribution

Marginal ALAC                                  Bivariate
Impact
                      -0.88        0.45
                                               Normal
Marginal ALAC
                                               Bivariate
Diminishing           0.14         0.08
Returns                                        Normal

Corruption’s Effect
on GDP
                      0.38         0.13         Normal
            Monte Carlo Results
            (Millions of Euros)
           Average Net
Country                  Low      High    Below Zero
            Benefits

 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