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.
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/15060534 |
| Date | 19 February 2022 |
| Creators | Seyedali Ghahari (11182092) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/Detecting_and_Measuring_Corruption_and_Inefficiency_in_Infrastructure_Projects_Using_Machine_Learning_and_Data_Analytics/15060534 |
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