PhD on (multidisciplinary) Economics for the project ‘Bridging the Gaps in Evidence, Regulation and Impact of Anti-Corruption Policies’ (1.0 FTE)

We are looking for two PhD candidates who will both work within the same EU-financed research project on related but different research. Find below the research proposals for the two PhD positions followed by a short abstract of the EU-financed project in which you will operate. The PhD supervisors for both positions will be prof. dr. Marcel Boumans , dr. Joras Ferwerda , and prof. dr. Brigitte Unger .
The Use of Artificial Intelligence (AI) tools in the Fight Against Corruption

The advancement of digital technologies, in particular artificial intelligence, provides new hope to fight corruption more effectively. The field of AI tools is developing fast (see e.g. Koebis, Starke, Rahwan 2021 for the possibility of using AI in top-down and bottom-up approaches). To apply AI tools to fight corruption, three main questions have to be resolved: 1. Are these tools effective? 2. Are they fair? and 3. Do they make sense? Effectiveness, fairness, and explainability are the three criteria by which AI tools will be analysed.

Explaining Cross-Border Corruption: A Gravity Model Application

Corruption doesn’t stop at borders. The Luanda Leaks of 2020, to give an example, published hundreds of leaked documents that focused on the financial affairs of Ms. Isabel dos Santos, the daughter of a former president of Angola, and various companies and individuals linked to her. It revealed that corruption and money laundering are closely linked and use a worldwide network of international financial expertise.
This research wants to expand the gravity model, used for measuring and analysing money laundering flows, to cross-border corruption.
The gravity model has been applied successfully for some decades to measure and predict all types of flows, like commuting, patient flows to hospitals, migration, and interregional and international trade. It was just a matter of time until the model was also applied to illegal flows, like heroin trade (Berlusconi et al., 2017), money flows to tax havens (Cassetta et al., 2014), and money laundering (eg. Ferwerda et al., 2020, Walker and Unger, 2009). The gravity model is inspired by Newton's universal law of gravity: the attraction between two objects depends on the mass of these objects and (the inverse of) their squared mutual distance and the gravity constant. Tinbergen (1962) laid the foundation for gravity models within economics. The gravity model applied outside of physics stipulates that a flow is determined by stimulating or restraining forces relating to the specific flow and by supply conditions at the origin and demand conditions at the destination. (Ferwerda et al., 2020) Gravity models have been applied to understand the importance of domestic corruption for flows like FDI (Bellos and Subasat, 2012) and migration (Poprawe, 2015).
This research proposes a direct application of the gravity model to cross-border corruption based on 3 deliverables:

Short abstract of the EU-financed project BRIDGEGAP in which you will operate:

BRIDGEGAP is a multidisciplinary research project reuniting former members of the ANTICORRP consortium (Transparency International, ERCAS/SAR, CSD, University of PISA, University of Perugia). They have continued to invest in the development of data commons allowing corruption understanding and monitoring on the basis of objective data (e.g. Integrity Watch, Index for Public Integrity, T-Index, Russian Economic Footprint), with new academic partners who published novel methods to measure money laundering (Utrecht University) anthropologists and criminologists who pioneered corruption studies in liberal democracies (IFFS), and new IT groups like the Ukrainian organization YouControl, the first to interconnect data to enable searches of the assets of sanctioned individuals through its algorithm Follow the Money. BRIDGEGAP fills the knowledge gaps regarding both the extent to and the mechanisms by which corruption infiltrates open societies even across borders and it produces measurements of corruption across countries and time by its innovative models, as well as social network maps. It also assesses and offers solutions to the digital transparency gaps, ranging from the tools of transparency, the use and abuse of technology in corruption and anticorruption to the state of it. Finally, it assesses public accountability and anticorruption regulation across EUMS and candidate states to identify regulatory and impact gaps. Thereby it addresses the academia–policy gap in corruption studies. The research will result in academic publications as well as in interactive analytical and research commons like comparative law repositories EU Compass, European Transparency Index, Follow the Money search engines across newly interconnected databases. All its pooled data will be displayed transparently on the website as a Data Hub and will offer end users the same investigation and analytical tools as the project researchers, inviting crowd-sourcing and offering online tutorials.
References

Bellos, S., & Subasat, T. (2012). Corruption and foreign direct investment: A panel gravity model approach. Bulletin of Economic Research, 64(4), 565-574.

Berlusconi, G., Aziani, A., & Giommoni, L. (2017). The determinants of heroin flows in Europe: A latent space approach. Social Networks, 51, 104-117.

Cassetta, A., Pauselli, C., Rizzica, L. & Tonello, M. Exploring flows to tax havens through means of a gravity model: Evidence from Italy. Bank Italy Occas. Pap. (2014).

Ferwerda, J., van Saase, A., Unger, B., & Getzner, M. (2020). Estimating money laundering flows with a gravity model-based simulation. Scientific reports, 10(1), 1-11.

Jensen, R. I. T., & Iosifidis, A. (2023). Qualifying and raising anti-money laundering alarms with deep learning. Expert Systems with Applications, 214, 119037.

Koebis, N., Starke, Ch. & Rahwan I (2021). Artificial Intelligence as an Anti-Corruption Tool. Potentials and Pitfalls for Top Down and Bottom Up Approaches, Center for Humans and Machines, Max Planck Institute for Human Development, Berlin.

Larson, J., Mattu, S., Kirchner, L., & Angwin, J. (2016). How we analysed the COMPAS recidivism algorithm. ProPublica, 5(2016), 9.

Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392.

Obermeyer, Z., Powers, B., Vogeli, C., & Mullainathan, S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447–453.

McDermid, J. A., Jia, Y., Porter, Z., & Habli, I. (2021). Artificial intelligence explainability: the technical and ethical dimensions. Philosophical Transactions of the Royal Society A, 379(2207), 20200363.

Poprawe, M. (2015). On the relationship between corruption and migration: empirical evidence from a gravity model of migration. Public Choice, 163(3), 337-354.

Tinbergen, J. (1962). An analysis of world trade flows. Shaping the world economy, 3, 1-117.

Walker, J., & Unger, B. (2009). Measuring global money laundering:" The walker gravity model". Review of Law & Economics, 5(2), 821-853.

Candidates are expected to have completed an MSc degree or to be in the last year of their MSc (finishing in the summer of 2024). This is a three-year PhD position, which means an (almost) completed 2-year research master is preferred. The position is part of a multidisciplinary research project, so an interest in multiple disciplines is expected. The main discipline used for this position will be economics, in combination with other disciplines. Those without an MSc in Economics are encouraged to apply as well, explaining in the motivation letter their relationship with the field of economics.

We offer:

• a position (1.0 FTE) for one year, with an extension to a total of three years upon successful assessment;
• a full-time gross salary between €2,770 in the first year and €3,539 in the third year of employment in scale P of the Collective Labour Agreement Dutch Universities (CAO);
• 8% holiday bonus and 8.3% end-of-year bonus;
• a pension scheme, partially paid parental leave, and flexible employment conditions based on the CAO.

In addition to the employment conditions from the CAO for Dutch Universities, Utrecht University has a number of its own arrangements. These include agreements on professional development , leave arrangements, sports and cultural schemes and you get discounts on software and other IT products. We also give you the opportunity to expand your terms of employment through the Employment Conditions Selection Model. This is how we encourage you to grow. For more information, please visit working at Utrecht University .

For more information about this position, please contact dr. Joras Ferwerda (Associate Professor of Applied Economics) at [email protected] .