CyberSec Research

Understanding the role of information among disadvantaged students is crucial in explaining their investment decisions in higher education. Indeed, information barriers on the returns and the gains from completing college may explain a substantial share of variation in students' degree completion. We conduct a field experiment with 7,806 university students in Italy who benefit from financial aid assistance, by providing information, either on the labor market returns of completing college or on the education returns of meeting the academic requirements attached to the financial aid. Our results suggest that only the latter information treatment has a positive effect on academic performance, increasing the number of credits obtained by around 3, and by decreasing the probability of dropout by around 4 percentage points. We also find that the results are mediated by an aspiration lift generated by our treatment.

Immigration has shaped many nations, posing the challenge of integrating immigrants into society. While economists often focus on immigrants' economic outcomes compared to natives (such as education, labor market success, and health) social interactions between immigrants and natives are equally crucial. These interactions, from everyday exchanges to teamwork, often lack enforceable contracts and require cooperation to avoid conflicts and achieve efficient outcomes. However, socioeconomic, ethnic, and cultural differences can hinder cooperation. Thus, evaluating integration should also consider its impact on fostering cooperation across diverse groups. This paper studies how priming different identity dimensions affects cooperation between immigrant and native youth. Immigrant identity includes both ethnic ties to their country of origin and connections to the host country. We test whether cooperation improves by making salient a specific identity: Common identity (shared society), Multicultural identity (ethnic group within society), or Neutral identity. In a lab in the field experiment with over 390 adolescents, participants were randomly assigned to one of these priming conditions and played a Public Good Game. Results show that immigrants are 13 percent more cooperative than natives at baseline. Natives increase cooperation by about 3 percentage points when their multicultural identity is primed, closing the initial gap with immigrant peers.

The CAP theorem asserts a trilemma between consistency, availability, and partition tolerance. This paper introduces a rigorous automata-theoretic and economically grounded framework that reframes the CAP trade-off as a constraint optimization problem. We model distributed systems as partition-aware state machines and embed economic incentive layers to stabilize consensus behavior across adversarially partitioned networks. By incorporating game-theoretic mechanisms into the global transition semantics, we define provable bounds on convergence, liveness, and correctness. Our results demonstrate that availability and consistency can be simultaneously preserved within bounded epsilon margins, effectively extending the classical CAP limits through formal economic control.

Important game-changer economic events and transformations cause uncertainties that may affect investment decisions, capital flows, international trade, and macroeconomic variables. One such major transformation is Brexit, which refers to the multiyear process through which the UK withdrew from the EU. This study develops and uses a new Brexit-Related Uncertainty Index (BRUI). In creating this index, we apply Text Mining, Context Window, Natural Language Processing (NLP), and Large Language Models (LLMs) from Deep Learning techniques to analyse the monthly country reports of the Economist Intelligence Unit from May 2012 to January 2025. Additionally, we employ a standard vector autoregression (VAR) analysis to examine the model-implied responses of various macroeconomic variables to BRUI shocks. While developing the BRUI, we also create a complementary COVID-19 Related Uncertainty Index (CRUI) to distinguish the uncertainties stemming from these distinct events. Empirical findings and comparisons of BRUI with other earlier-developed uncertainty indexes demonstrate the robustness of the new index. This new index can assist British policymakers in measuring and understanding the impacts of Brexit-related uncertainties, enabling more effective policy formulation.

Green ammonia is emerging as a strategic intermediary within green energy supply chains, serving effectively as both an industrial commodity and hydrogen carrier. This study provides a techno-economic analysis of green ammonia supply chains, comparing cost-effective pathways from global production to European consumers, and evaluates ammonia alongside alternative hydrogen carriers. Gaseous hydrogen consistently remains the most economical import option for Europe, though ammonia holds a narrowing cost advantage over liquid hydrogen (from 16 % in 2030 to 10 % by 2040). Competitive ammonia suppliers, notably Morocco, the United States, and the United Arab Emirates, benefit from low renewable energy costs, with significant price reductions expected by 2040, driven by falling costs for electricity, electrolysers, and conversion technologies. Optimal transport modes vary by consumer demand and distance: trucks are ideal for low demands at all distances, rail for medium ranges, and pipelines for high-demand scenarios. By 2040, ammonia will primarily serve direct-use applications, as hydrogen consumers increasingly shift to direct hydrogen supplies. Policymakers should prioritize pipeline infrastructure for hydrogen distribution, cautiously invest in ammonia's short- to medium-term infrastructure advantages, and limit long-term reliance on ammonia as a hydrogen carrier to mitigate stranded asset risks.

This paper introduces Natural Language Processing for identifying ``true'' green patents from official supporting documents. We start our training on about 12.4 million patents that had been classified as green from previous literature. Thus, we train a simple neural network to enlarge a baseline dictionary through vector representations of expressions related to environmental technologies. After testing, we find that ``true'' green patents represent about 20\% of the total of patents classified as green from previous literature. We show heterogeneity by technological classes, and then check that `true' green patents are about 1\% less cited by following inventions. In the second part of the paper, we test the relationship between patenting and a dashboard of firm-level financial accounts in the European Union. After controlling for reverse causality, we show that holding at least one ``true'' green patent raises sales, market shares, and productivity. If we restrict the analysis to high-novelty ``true'' green patents, we find that they also yield higher profits. Our findings underscore the importance of using text analyses to gauge finer-grained patent classifications that are useful for policymaking in different domains.

We develop a computational framework for deriving Pareto-improving and constrained optimal carbon tax rules in a stochastic overlapping generations (OLG) model with climate change. By integrating Deep Equilibrium Networks for fast policy evaluation and Gaussian process surrogate modeling with Bayesian active learning, the framework systematically locates optimal carbon tax schedules for heterogeneous agents exposed to climate risk. We apply our method to a 12-period OLG model in which exogenous shocks affect the carbon intensity of energy production, as well as the damage function. Constrained optimal carbon taxes consist of tax rates that are simple functions of observables and revenue-sharing rules that guarantee that the introduction of the taxes is Pareto improving. This reveals that a straightforward policy is highly effective: a Pareto-improving linear tax on cumulative emissions alone yields a 0.42% aggregate welfare gain in consumption-equivalent terms while adding further complexity to the tax provides only a marginal increase to 0.45%. The application demonstrates that the proposed approach produces scalable tools for macro-policy design in complex stochastic settings. Beyond climate economics, the framework offers a template for systematically analyzing welfare-improving policies in various heterogeneous-agent problems.

This paper presents a praxeological analysis of artificial intelligence and algorithmic governance, challenging assumptions about the capacity of machine systems to sustain economic and epistemic order. Drawing on Misesian a priori reasoning and Austrian theories of entrepreneurship, we argue that AI systems are incapable of performing the core functions of economic coordination: interpreting ends, discovering means, and communicating subjective value through prices. Where neoclassical and behavioural models treat decisions as optimisation under constraint, we frame them as purposive actions under uncertainty. We critique dominant ethical AI frameworks such as Fairness, Accountability, and Transparency (FAT) as extensions of constructivist rationalism, which conflict with a liberal order grounded in voluntary action and property rights. Attempts to encode moral reasoning in algorithms reflect a misunderstanding of ethics and economics. However complex, AI systems cannot originate norms, interpret institutions, or bear responsibility. They remain opaque, misaligned, and inert. Using the concept of epistemic scarcity, we explore how information abundance degrades truth discernment, enabling both entrepreneurial insight and soft totalitarianism. Our analysis ends with a civilisational claim: the debate over AI concerns the future of human autonomy, institutional evolution, and reasoned choice. The Austrian tradition, focused on action, subjectivity, and spontaneous order, offers the only coherent alternative to rising computational social control.

Using administrative data from Germany, this study provides first evidence on the wage effects of collective bargaining compliance laws. These laws require establishments receiving public contracts to pay wages set by a representative collective agreement, even if they are not formally bound by one. Leveraging variation in the timing of law implementation across federal states, and focusing on the public transport sector -- where regulation is uniform and demand is driven solely by state-level needs -- I estimate dynamic treatment effects using event-study designs. The results indicate that within five years of the law's implementation, wage increases were on average 2.9\% to 4.6\% higher in federal states with such a law compared to those without one -- but only in East Germany. These findings highlight the potential for securing collectively agreed wages in times of declining collective bargaining coverage.

We study consumption stimulus with digital coupons, which provide time-limited subsidies contingent on minimum spending. We analyze a large-scale program in China and present five main findings: (1) the program generates large short-term effects, with each $\yen$1 of government subsidy inducing $\yen$3.4 in consumer spending; (2) consumption responses vary substantially, driven by both demand-side factors (e.g., wealth) and supply-side factors (e.g., local consumption amenities); (3) The largest spending increases occur among consumers whose baseline spending already exceeds coupon thresholds and for whom coupon subsidies should be equivalent to cash, suggesting behavioral motivations; (4) high-response consumers disproportionately direct their spending toward large businesses, leading to a regressive allocation of stimulus benefits; and (5) targeting the most responsive consumers can double total stimulus effects. A hybrid design combining targeted distribution with direct support to small businesses improves both the efficiency and equity of the program.

Change orders (COs) are a common occurrence in construction projects, leading to increased costs and extended durations. Design-Bid-Build (DBB) projects, favored by state transportation agencies (STAs), often experience a higher frequency of COs compared to other project delivery methods. This study aims to identify areas of improvement to reduce CO frequency in DBB projects through a quantitative analysis. Historical bidding data from the Florida Department of Transportation (FDOT) was utilized to evaluate five factors, contracting technique, project location, type of work, project size, and duration, on specific horizontal construction projects. Two DBB contracting techniques, Unit Price (UP) and Lump Sum (LS), were evaluated using a discrete choice model. The analysis of 581 UP and 189 LS projects revealed that project size, duration, and type of work had a statistically significant influence on the frequency of change orders at a 95% confidence level. The discrete choice model showed significant improvement in identifying the appropriate contract type for a specific project compared to traditional methods used by STAs. By evaluating the contracting technique instead of project delivery methods for horizontal construction projects, the use of DBB can be enhanced, leading to reduced change orders for STAs.

Seasonal migration plays a critical role in stabilizing rural economies and sustaining the livelihoods of agricultural households. Violence and civil conflict have long been thought to disrupt these labor flows, but this hypothesis has historically been hard to test given the lack of reliable data on migration in conflict zones. Focusing on Afghanistan in the 8-year period prior to the Taliban's takeover in 2021, we first demonstrate how satellite imagery can be used to infer the timing of the opium harvest, which employs a large number of seasonal workers in relatively well-paid jobs. We then use a dataset of nationwide mobile phone records to characterize the migration response to this harvest, and examine whether and how violence and civil conflict disrupt this migration. We find that, on average, districts with high levels of poppy cultivation receive significantly more seasonal migrants than districts with no poppy cultivation. These labor flows are surprisingly resilient to idiosyncratic violent events at the source or destination, including extreme violence resulting in large numbers of fatalities. However, seasonal migration is affected by longer-term patterns of conflict, such as the extent of Taliban control in origin and destination locations.

Several noteworthy scenarios emerged in the global textile and fashion supply chains during and after the COVID-19 pandemic. The destabilizing influences of a global pandemic and a geographically localized conflict are being acutely noticed in the worldwide fashion and textile supply chains. This work examines the impact of the COVID-19 pandemic, the Russo-Ukraine conflict, Israel-Palestine conflict, and Indo-Pak conflict on supply chains within the textile and fashion industry. This research employed a content analysis method to identify relevant articles and news from sources such as Google Scholar, the Summon database of North Carolina State University, and the scholarly news portal NexisUni. The selected papers, news articles, and reports provide a comprehensive overview of the fashion, textile, and apparel supply chain disruptions caused by the pandemic and the war in Ukraine, accompanied by discussions from common supply chain perspectives. Disruptions due to COVID-19 include international brands and retailers canceling orders, closures of stores and factories in developing countries, layoffs, and furloughs of workers in both retail stores and supplier factories, the increased prominence of online and e-commerce businesses, the growing importance of automation and digitalization in the fashion supply chain, considerations of sustainability, and the need for a resilient supply chain system to facilitate post-pandemic recovery. In the case of the Russo-Ukraine war, Israel-Palestine war, and Indo-Pak war, the second-order effects of the conflict have had a more significant impact on the textile supply chain than the direct military operations themselves. In addition to these topics, the study delves into the potential strategies for restoring and strengthening the fashion supply chain

AI systems increasingly support human decision-making across domains of professional, skill-based, and personal activity. While previous work has examined how AI might affect human autonomy globally, the effects of AI on domain-specific autonomy -- the capacity for self-governed action within defined realms of skill or expertise -- remain understudied. We analyze how AI decision-support systems affect two key components of domain-specific autonomy: skilled competence (the ability to make informed judgments within one's domain) and authentic value-formation (the capacity to form genuine domain-relevant values and preferences). By engaging with prior investigations and analyzing empirical cases across medical, financial, and educational domains, we demonstrate how the absence of reliable failure indicators and the potential for unconscious value shifts can erode domain-specific autonomy both immediately and over time. We then develop a constructive framework for autonomy-preserving AI support systems. We propose specific socio-technical design patterns -- including careful role specification, implementation of defeater mechanisms, and support for reflective practice -- that can help maintain domain-specific autonomy while leveraging AI capabilities. This framework provides concrete guidance for developing AI systems that enhance rather than diminish human agency within specialized domains of action.

This paper examines the economic and environmental impacts of the European Carbon Border Adjustment Mechanism (CBAM). We develop a multi-country, multi-sector general equilibrium model with input-output linkages and characterise the general equilibrium response of trade flows, welfare and emissions. As far as we know, this is the first quantitative trade model that jointly endogenises the Emission Trading Scheme (ETS) allowances and CBAM prices. We find that the CBAM increases by 0.005\% the EU Gross National Expenditure (GNE), while trade shifts towards domestic cleaner production. Notably, emissions embodied in EU imports fall by 3\%, which is the result of a direct effect (-4.8\%) and a supply chain's upstream substitution effect (+1.8\%). The latter is a dampening effect that we can detect only by explicitly incorporating the production network. In contrast, extra-EU countries experience a slight decline in GNE (0.009\%) and emissions (0.11\%).

This paper examines how digital transformation reshapes employment structures within Chinese listed firms, focusing on occupational functions and task intensity. Drawing on recruitment data classified under ISCO-08 and the Chinese Standard Occupational Classification 2022, we categorize jobs into five functional groups: management, professional, technical, auxiliary, and manual. Using a task-based framework, we construct routine, abstract, and manual task intensity indices through keyword analysis of job descriptions. We find that digitalization is associated with increased hiring in managerial, professional, and technical roles, and reduced demand for auxiliary and manual labor. At the task level, abstract task demand rises, while routine and manual tasks decline. Moderation analyses link these shifts to improvements in managerial efficiency and executive compensation. Our findings highlight how emerging technologies, including large language models (LLMs), are reshaping skill demands and labor dynamics in Chinas corporate sector.

The proliferation of artificial intelligence (AI) in financial services has prompted growing demand for tools that can systematically detect AI-related disclosures in corporate filings. While prior approaches often rely on keyword expansion or document-level classification, they fall short in granularity, interpretability, and robustness. This study introduces FinAI-BERT, a domain-adapted transformer-based language model designed to classify AI-related content at the sentence level within financial texts. The model was fine-tuned on a manually curated and balanced dataset of 1,586 sentences drawn from 669 annual reports of U.S. banks (2015 to 2023). FinAI-BERT achieved near-perfect classification performance (accuracy of 99.37 percent, F1 score of 0.993), outperforming traditional baselines such as Logistic Regression, Naive Bayes, Random Forest, and XGBoost. Interpretability was ensured through SHAP-based token attribution, while bias analysis and robustness checks confirmed the model's stability across sentence lengths, adversarial inputs, and temporal samples. Theoretically, the study advances financial NLP by operationalizing fine-grained, theme-specific classification using transformer architectures. Practically, it offers a scalable, transparent solution for analysts, regulators, and scholars seeking to monitor the diffusion and framing of AI across financial institutions.

This Policy Comment describes how the Food Policy article entitled 'Cost and affordability of nutritious diets at retail prices: Evidence from 177 countries' (first published October 2020) and 'Retail consumer price data reveal gaps and opportunities to monitor food systems for nutrition' (first published September 2021) advanced the use of least-cost benchmark diets to monitor and improve food security. Those papers contributed to the worldwide use of least-cost diets as a new diagnostic indicator of food access, helping to distinguish among causes of poor diet quality related to high prices, low incomes, or displacement by other food options, thereby guiding intervention toward universal access to healthy diets.