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1. Empirically Measuring Data Localization in the EU

   https://doi.org/10.56553/popets-2025-0107  

   Gamero-Garrido, Alexander; Yu, Kicho; Vasisht_Shankar, Sumukh; Singh, Sachin Kumar; Balasubramanian, Sindhya; Wilcox, Alexander; Choffnes, David (July 2025, Proceedings on Privacy Enhancing Technologies)

   EU data localization regulations limit data transfers to non-EU countries with the GDPR. However, BGP, DNS and other Internet protocols were not designed to enforce jurisdictional constraints, so implementing data localization is challenging. Despite initial research on the topic, little is known about if or how companies currently operate their server infrastructure to comply with the regulations. We close this knowledge gap by empirically measuring the extent to which servers and routers that process EU requests are located outside of the EU (and a handful of 'adequate' non-EU countries). The key challenge is that both browser measurements (to infer relevant endpoints) and data-plane measurements (to infer relevant IP addresses) are needed, but no large-scale public infrastructure allows both. We build a novel methodology that combines BrightData (browser) and RIPE Atlas (data-plane) probes, with joint measurements from over 1,000 networks in 20 EU countries. We find that, on average, 2.2% of servers serving users in each EU country are located in non-adequate destination countries (1.4% of known trackers). Our findings suggest that data localization policies are largely being followed by content providers, though there are exceptions. 

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2. The Impact of Brexit on Foreign Investment and Production

   https://doi.org/10.1257/mac.20170399  

   McGrattan, Ellen R.; Waddle, Andrea (January 2020, American Economic Journal: Macroeconomics)

   Using simulations from a multi-country neoclassical growth model, we analyze several post-Brexit scenarios. First, the United Kingdom unilaterally imposes tighter restrictions on FDI and trade from other EU nations. Second, the European Uunion retaliates and imposes the same restrictions on the United Kingdom. Finally, the United Kingdom reduces restrictions on other nations during the post-Brexit transition. Model predictions depend crucially on the policy response of multinationals’ investment in technology capital, accumulated know-how from investments in R&D, brands, and organizations used simultaneously in their domestic and foreign operations. (JEL D25, F13, F15, F23, G31, O32) 

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3. Turkish Judge: A Peer Evaluation Framework for Crowd Work Appeals

   Cohen, Edward; Venkateswaran, Mukund; Sankar, Nivedita; Callison-Burch, Chris (July 2020, Proceedings the AAAI Conference on Human Computation and Crowdsourcing)

   We present a crowd-driven adjudication system for rejected work on Amazon Mechanical Turk. The Mechanical Turk crowdsourcing platform allows Requesters to approve or reject assignments submitted by Workers. If the work is rejected, then Workers aren’t paid, and their reputation suffers. Currently, there is no built-in mechanism for Workers to appeal rejections, other than contacting Requesters directly. The time it takes Requesters to review potentially incorrectly rejected tasks means that their costs are substantially higher than the payment amount that is in dispute. As a solution to this issue, we present an automated appeals system called Turkish Judge which employs crowd workers as judges to adjudicate whether work was fairly rejected when their peers initiate an appeal. We describe our system, analyze the added cost to Requesters, and discuss the advantages of such a system to the Mechanical Turk marketplace and other similar microtasking platforms. 

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4. The statistical and dynamic modeling of the first part of the 2013-2014 Euromaidan protests in Ukraine: The Revolution of Dignity and preceding times

   https://doi.org/10.1371/journal.pone.0301639  

   Bahid, Yassin; Kutsenko, Olga; Rodríguez, Nancy; White, David (May 2024, PLOS ONE)

   Topaz, Chad M (Ed.)

   Ukraine’s tug-of-war between Russia and the West has had significant and lasting consequences for the country. In 2013, Viktor Yanukovych, the Ukrainian president aligned with Russia, opted against signing an association agreement with the European Union. This agreement aimed to facilitate trade and travel between the EU and Ukraine. This decision sparked widespread protests that coalesced in Kyiv’s Maidan Square, eventually becoming known as the Euromaidan protests. In this study, we analyze the protest data from 2013, sourced from Ukraine’s Center for Social and Labor Research. Despite the dataset’s limitations and occasional inconsistencies, we demonstrate the extraction of valuable insights and the construction of a descriptive model from such data. Our investigation reveals a pre-existing state of self-excitation within the system even before the onset of the Euromaidan protests. This self-excitation intensified during the Euromaidan protests. A statistical analysis indicates that the government’s utilization of force correlates with increased future protests, exacerbating rather than quelling the protest movement. Furthermore, we introduce the implementation of Hawkes process models to comprehend the spatiotemporal dynamics of the protest activity. Our findings highlight that, while protest activities spread across the entire country, the driving force behind the dynamics of these protests was the level of activity in Kyiv. Furthermore, in contrast to prior research that emphasized geographical proximity as a key predictor of event propagation, our study illustrates that the political alignment among oblasts, which are the distinct municipalities comprising Ukraine, had a more profound impact than mere geographic distance. This underscores the significance of social and cultural factors in molding the trajectory of political movements. 

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5. International Economic Sanctions and Third-Country Effects

   https://doi.org/10.1057/s41308-023-00232-9  

   Ghironi, Fabio; Kim, Daisoon; Ozhan, Galip Kemal (January 2024, IMF Economic Review)

   Levchenko, Andrei (Ed.)

   This paper studies international trade and macroeconomic dynamics triggered by economic sanctions, and the associated welfare losses, in a calibrated, asymmetric, three-country model of the world economy. We assume that there are two production sectors in each country, and the sanctioned country has a comparative advantage in production of a commodity (for convenience, gas) needed to produce final, differentiated consumption goods. We consider three types of sanctions: sanctions on trade in final goods, financial sanctions, and gas trade sanctions. We calibrate the model to an aggregate of countries that are currently imposing sanctions on Russia (the European Union, the UK, and the USA), Russia, and an aggregate of third countries (China, India, and Turkey). We show that, instead of reflecting the success of sanctions, exchange rate movements reflect the type of sanctions and the direction of the resulting within-country sectoral reallocations. Our welfare analysis demonstrates that the sanctioned country’s welfare losses are significantly mitigated, and the sanctioning country’s losses are amplified, if the third country does not join the sanctions, but the third country benefits from not joining. These findings highlight the necessity, but also the challenge, of coordinating sanctions internationally. 

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