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| − | < | + | <font size = 6; color="#2B3856"><span style="font-family:Garamond;">Perceiving Evil: The Study of the Corruption Perception Index |
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| + | =Conclusion and Future Works= | ||
| + | CPI has been relatively consistent for most countries over the last 5 years, implying that countries that are perceived as corrupt stay corrupt and those that are perceived as clean stay clean. To change the public’s perception of a country’s propensity for corruption takes a long period of time. | ||
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| + | In our R app, we can discover corruption’s correlations with gender equality, education and economy across countries, but these are exploratory in nature and take only a limited dataset from carefully selected WDIs. | ||
| − | + | Future efforts can extend such data exploration to other factors such as environment, rural / urban divide and even tourism numbers. All these data are available in the World Bank Open Data Base. Validation of these findings can potentially lead to public policies that can influence the future CPI rating for each country. | |
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| + | Another possible expansion will be creating a prediction model with respect to bribery statistics, to predict future CPI of a country. | ||
| + | ==Acknowledgements== | ||
| + | The authors wish to thank Prof Kam Tin Seong for his patience, mentorship and guidance on analytical techniques that was applied for our project, which benefitted us in our individual learning journey. | ||
Back to Project Group Page | Back to Project Group Page | ||
[[File:Go back.png|40px|frameless|left|link=Project_Groups]] | [[File:Go back.png|40px|frameless|left|link=Project_Groups]] | ||
Latest revision as of 00:00, 13 August 2018
Perceiving Evil: The Study of the Corruption Perception Index
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Conclusion and Future Works
CPI has been relatively consistent for most countries over the last 5 years, implying that countries that are perceived as corrupt stay corrupt and those that are perceived as clean stay clean. To change the public’s perception of a country’s propensity for corruption takes a long period of time.
In our R app, we can discover corruption’s correlations with gender equality, education and economy across countries, but these are exploratory in nature and take only a limited dataset from carefully selected WDIs.
Future efforts can extend such data exploration to other factors such as environment, rural / urban divide and even tourism numbers. All these data are available in the World Bank Open Data Base. Validation of these findings can potentially lead to public policies that can influence the future CPI rating for each country.
Another possible expansion will be creating a prediction model with respect to bribery statistics, to predict future CPI of a country.
Acknowledgements
The authors wish to thank Prof Kam Tin Seong for his patience, mentorship and guidance on analytical techniques that was applied for our project, which benefitted us in our individual learning journey.
Back to Project Group Page
