SeyedAli Ghahari PhD Dissertation - 07282021.pdf (9.49 MB)
Download fileDetecting and Measuring Corruption and Inefficiency in Infrastructure Projects Using Machine Learning and Data Analytics
Corruption is a social evil that resonates far and deep in societies,
eroding trust in governance, weakening the rule of law, impairing economic
development, and exacerbating poverty, social tension, and inequality. It is
a multidimensional and complex societal malady that occurs in various forms and
contexts. As such, any effort to combat corruption must be accompanied by a
thorough examination of the attributes that might play a key role in
exacerbating or mitigating corrupt environments. This dissertation identifies a number of attributes that
influence corruption, using machine learning techniques, neural network
analysis, and time series causal relationship analysis and aggregated data from
113 countries from 2007 to 2017. The results suggest that improvements in
technological readiness, human development index, and e-governance index have
the most profound impacts on corruption reduction. This dissertation discusses
corruption at each phase of infrastructure systems development and engineering
ethics that serve as a foundation for corruption mitigation. The dissertation then applies novel analytical
efficiency measurement methods to measure infrastructure inefficiencies, and to rank
infrastructure administrative jurisdictions at the state level. An efficiency frontier is
developed using optimization and the highest performing jurisdictions are
identified. The dissertation’s framework could serve as a
starting point for governmental and non-governmental oversight agencies to
study forms and contexts of corruption and inefficiencies, and to propose
influential methods for reducing the instances. Moreover, the framework can help
oversight agencies to promote the overall accountability of infrastructure
agencies by establishing a clearer connection between infrastructure investment
and performance, and by carrying out comparative assessments of infrastructure
performance across the jurisdictions under their oversight or supervision.
History
Degree Type
- Doctor of Philosophy
Department
- Civil Engineering
Campus location
- West Lafayette
Advisor/Supervisor/Committee Chair
Samuel LabiAdditional Committee Member 2
Philip DunstonAdditional Committee Member 3
John HaddockAdditional Committee Member 4
Sue McNeilAdditional Committee Member 5
Cesar QueirozAdditional Committee Member 6
Andrew TarkoUsage metrics
Keywords
PolicyCorruptionInefficiencyEconomicsEconometricsData analyticsTime series analysisMachine learningRandom forest modelsArtificial Neural Networks methodsNonlinear autoregressive exogenous models (NARX)Principal component analysis (PCA)Cluster analysisPanel vector autoregression (PVAR)Impulse-response functionInfrastructure Engineering and Asset Management