Honors Presentation 4 10[1]
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Honors Presentation 4 10[1]

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This was an honors presentation I gave to the public during the my senior year at Hood College.

This was an honors presentation I gave to the public during the my senior year at Hood College.

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Honors Presentation 4 10[1] Honors Presentation 4 10[1] Presentation Transcript

  • The Effect of Income on Corruption Brittni Smith Department of Economics and Management Hood College April 16, 2010
  • Corruption
    • Misuse of public power for personal or private gain
    • Examples
        • Former Governor of Illinois Rod Blagojevich: auctioning off President Obama’s Senate seat
        • In 2009, China has convicted 106,000 officials for corruption
          • Senior official accused of taking $500,000 dollars in bribes from businesses seeking approval of projects.
          • Former vice president of China’s highest court was jailed for life for bribes totaling $600,000
  • Corruption
    • Seminal Work by Mauro (1995)
      • Consequence of corruption is lower investment which decreases development
    • President of the World Bank Jim Wolfensohn,
      • “ Let’s not mince words, we need to deal with the causes of corruption.” (1996)
    • Today considered to be one of the biggest obstacles to economic development.
  • Countries in Corruption Index New Zealand 0.6 Somalia 8.9 Least Corrupt Most Corrupt Spain 2.5 USA China Mexico Russia Haiti Afghanistan 7.8 6.7 6.4 8.2 8.7 S. Korea 4.5 4
  • Corruption Index
    • Source: Transparency International World Bank
    • Perceived level of corruption in a country
    • Based on poll-of-polls data from:
      • Experts and business persons in the country and abroad
      • Independent, reputable institutions
        • Ex) World Bank
    • Available from 1995-present for 178 countries.
      • Panel data: for each country there are 14 observations, total of 2,492 observations.
  • Previous Studies on the Effect of Income on Corruption Frechette (2006) Braun-Di Tella (2004) Treisman (2000) Brown (2005), Kunicova-R. Ackerman (2005), Lederman (2005), Chang-Golden (2004), Damania (2004), Dreher (2004), Alt-Lassen (2003), Brunett-Weder (2003), Graeff-Mehlkop (2003), Herzfeld-Weiss (2003), Knack-Azfar (2003) Person (2003), Tavares (2003), Fisman-Gatti (2002), Paldam (2001), Bonanglia(2001), Swamy (2001), Abed-Davoodi (2000), Rauch-Evan (2000), Wei (2000), Goldsmith (1999), Ades-Di Tella (1997) Income Positive-Significant Negative-Significant Variable
  • Fixed-Effects
    • Focus: time-varying factors of corruption
      • Ex) Income, education, etc.
    • Error term of model accounts for:
      • Time-invariant factors that could effect corruption
        • Ex) Colonization, religion, geography and could affect corruption
      • Country unobservables
        • Could be correlated and potentially fostering corruption
        • Accounting for the unaccountable
  • Endogeneity
    • Income is endogenous with corruption
    • Causal relationship
    • Direction of causation is not clear
      • Do low income countries generate more corruption?
      • Does corruption makes countries poorer?
  • Frechette (2006)
    • ICRG index
    • Fixed-effects specification
    • Accounts for this endogenous relationship
    • Main findings:
      • Income increases corruption
      • Education increases corruption
  • Explanatory Variables
    • (-) Income
          • Real GDP per capita
    • (-) Education
          • Number of pupils in primary school
    • (-) Share of Imports in GDP
          • Merchandise trades as % of GDP
    • (+) Fuel, Ore, and Mineral Exports
          • % of Merchandise exports
    • (-) Internet
          • Number of Users
  • Instrument Variable
    • Instrument should be correlated with income but should not directly effect corruption
    Income OECD Trading Partner's Income Haiti U.S. Corruption
    • Isolate endogenous variable
    • Instrument to be statistically significant
    • F statistic >10
    First Stage Regression
  • Non-Linear Relationship Between Corruption and Income Bangladesh Luxembourg
  • Model
    • Specifies non-linear relationship between income and corruption
    • Two-Stage Least Squares with:
      • Panel Data
      • Fixed-Effects
      • Instrument Variable
  • Sub-Sample Income Levels
  • Various Income Levels ***, **, * indicate statistical significance at the 1%, 5%, and 10% level respectively Standard errors in parenthesis
  • Conclusions
    • Non-linear relationship between income and corruption
    • Subsample reveals as income increases, corruption decreases at a decreasing rate
    • Internet reduces corruption for countries with income above $18,000.
  • Summary
    • Clearly shows as income increases corruption decreases
      • Opposite of Frechette (2006).
    • Same technique as Frechette panel data and fixed-effects method
    • Corrected for problems in past empirical research
        • Endogeneity of income
        • Non-linear relationship between income and corruption
    • Proved using this model the results is income does decrease corruption but differently with countries of different income levels.