Group03 Proposal

Perceiving Evil: The Study of the Corruption Perception Index

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KEY MOTIVATION

First launched in 1995, the Corruption Perceptions Index (CPI) has been widely credited with putting the issue of corruption on the forefront of the international policy agenda. Transparency International (TI), is an international non-governmental organization based in Berlin, Germany which acts to combat global corruption and prevent criminal activities arising from corruption. TI publishes the CPI, annually ranking countries "by their perceived levels of corruption, as determined by expert assessments and opinion surveys. The CPI generally defines corruption as "the misuse of public power for private benefit". The CPI currently ranks 176 countries on a scale from 100 (very clean) to 0 (highly corrupt). Denmark is the least corrupt country in the world, ranking consistently high among international financial transparency, while the most corrupt country in the world is North Korea, remaining on 8 out of 100 since 2012. In our project, we married the data set from Transparency International on their CPI records for specifically 2016 versus the World Bank data set through the years, which contains economical, agricultural, social, environmental data of the same countries. We will seek to find out if there is indeed any correlations between the perceived corruption level of a country, and its internal conditions.

OBJECTIVES (QUESTIONS WE LIKE TO ANSWER)

It has been a challenge to validate whether CPI is an accurate index to represent corruption. A study in 2002 found a “strong and significant correlation” between CPI and 2 proxies: black market activity and overabundance of regulation. But it is hard to find any clear indicators of black market activities and regulations. There were some claims by other studies as well: Researchers found a correlation between higher CPI and higher long-term economic growth There is an increase of 1.7% in GDP for every unit increase in a country GPI’s score There is a “power-law” dependence linking higher CPI score with higher rates of foreign investment in a country There is also criticism in the usage of CPI’s methodology, some flaws pointed are: Corruption is too complex to be captured by a single score. The nature of corruption in rural Kansas will, for instance, be different than in the city administration of New York, yet the Index measures them in the same way By measuring perceptions of corruption, as opposed to corruption itself, the Index may simply be reinforcing stereotypes and clichés The Index only measures public-sector corruption, leaving out private actors The objective of our study is to find out if: There is a correlation between CPI and economic growth (through GDP) There is a correlation between CPI and the rate / amount of foreign investment There are correlations between CPI and the following factors Urban / Rural mix (e.g. % of agricultural land) Environmental conditions (e.g. CO2 emissions level) Education level (e.g. education expenditure for primary, secondary, tertiary, educational attainment) Literacy Rates (e.g. between adults and youths) Debt Level (e.g. amount used to service debts) Tourism (e.g. international tourism arrivals and departures, expenditures and receipts) Mortality rates (e.g. male / female / neo-natal) Populations numbers Employment Details (e.g. unemployment rates, male / female rates) Attempt to debunk any stereotypes and myths we may have for individual countries

DATA SOURCES

The data came from two sources. The first one came from: https://www.kaggle.com/transparencyint/corruption-index The data set contains the following important columns: CPI 2016 Rank Country Country Code Region Corruption Perceptions Index The second data set from the World Bank came from: https://datacatalog.worldbank.org/dataset/world-development-indicators This data set is a collection of development indicators, compiled from officially-recognized international sources. It presents the most current and accurate global development data available, and includes national, regional and global estimates However, due to the huge amount of data, we only kept the data for countries which appeared in the CPI data set and only indices from 2006 to 2016. The filtered dataset for the World Bank data was 259,750 rows across 171 countries.

METHODOLOGY

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TOOLS AND PACKAGES

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REFERENCES TO RELATED DATAVIZ

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