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With Big Data, decisions made by machine learning algorithms depend on training data generated by many individuals. In an experiment, we identify the effect of varying individual responsibility for the moral choices of an artificially intelligent algorithm. Across treatments, we manipulated the sources of training data and thus the impact of each individual’s decisions on the algorithm. Diffusing such individual pivotality for algorithmic choices increased the share of selfish decisions and weakened revealed prosocial preferences. This does not result from a change in the structure of incentives. Rather, our results show that Big Data offers an excuse for selfish behavior through lower responsibility for one’s and others’ fate.
In more and more situations, artificially intelligent algorithms have to model humans’ (social) preferences on whose behalf they increasingly make decisions. They can learn these preferences through the repeated observation of human behavior in social encounters. In such a context, do individuals adjust the selfishness or prosociality of their behavior when it is common knowledge that their actions produce various externalities through the training of an algorithm? In an online experiment, we let participants’ choices in dictator games train an algorithm. Thereby, they create an externality on future decision making of an intelligent system that affects future participants. We show that individuals who are aware of the consequences of their training on the pay- offs of a future generation behave more prosocially, but only when they bear the risk of being harmed themselves by future algorithmic choices. In that case, the externality of artificially intelligence training induces a significantly higher share of egalitarian decisions in the present.
Since the 2008 financial crisis, European largest banks’ size and business models have largely remained unchallenged. Is that because of banks’ continued structural power over States? This paper challenges the view that States are sheer hostages of banks’ capacity to provide credit to the real economy – which is the conventional definition of structural power. Instead, it sheds light on the geo-economic dimension of banks’ power: key public officials conceive the position of “their own” market-based banks in global financial markets as a crucial dimension of State power. State priority towards banking thus result from political choices over what structurally matters the most for the State. Based on a discourse analysis of parliamentary debates in France, Germany and Spain between 2010 and 2020 as well as on a comparative analysis of the implementation of a special tax on banks in the early 2010s, this paper shows that State’s Finance ministries tend to prioritize geo-economic considerations over credit to firms. By contrast, Parliaments tend to prioritize investment. Power dynamics within the State thus largely shape political priorities towards banking at the domestic and international levels.
Biased auctioneers
(2022)
We construct a neural network algorithm that generates price predictions for art at auction, relying on both visual and non-visual object characteristics. We find that higher automated valuations relative to auction house pre-sale estimates are associated with substantially higher price-to-estimate ratios and lower buy-in rates, pointing to estimates’ informational inefficiency. The relative contribution of machine learning is higher for artists with less dispersed and lower average prices. Furthermore, we show that auctioneers’ prediction errors are persistent both at the artist and at the auction house level, and hence directly predictable themselves using information on past errors.
Lack of privacy due to surveillance of personal data, which is becoming ubiquitous around the world, induces persistent conformity to the norms prevalent under the surveillance regime. We document this channel in a unique laboratory---the widespread surveillance of private citizens in East Germany. Exploiting localized variation in the intensity of surveillance before the fall of the Berlin Wall, we show that, at the present day, individuals who lived in high-surveillance counties are more likely to recall they were spied upon, display more conformist beliefs about society and individual interactions, and are hesitant about institutional and social change. Social conformity is accompanied by conformist economic choices: individuals in high-surveillance counties save more and are less likely to take out credit, consistent with norms of frugality. The lack of differences in risk aversion and binding financial constraints by exposure to surveillance helps to support a beliefs channel.
Large technology firms («BigTechs») increasingly extend their influence in finance, primarily taking over market shares in payment services. A further expansion of their businesses into the territory of cryptocurrencies could entail new and unprecedented risks for the future, namely for financial stability, competition in the private sector and monetary policy. When creating a regulatory toolbox to address these risks, financial regulatory, antitrust, and platform-specific solutions should be closely intertwined in order to fully absorb all the potential threats and to take account of the complex risks these platform companies bear. This policy letter evaluates the solutions lately proposed by the European Commission, with specific focus on the upcoming regulation of Markets in crypto-assets (MiCA), but also the Digital Markets Act (DMA) and Digital services act (DSA), against the background of cryptocurrencies issued by BigTechs and sheds light on financial regulatory, competition and monetary law issues coming along with the possible designs of these cryptocurrencies.
Colocation services offered by stock exchanges enable market participants to achieve execution costs for large orders that are substantially lower and less sensitive to transacting against high-frequency traders. However, these benefits manifest only for orders executed on the colocated brokers' own behalf, whereas customers' order execution costs are substantially higher. Analyses of individual order executions indicate that customer orders originating from colocated brokers are less actively monitored and achieve inferior execution quality. This suggests that brokers do not make effective use of their technology, possibly due to agency frictions or poor algorithm selection and parameter choice by customers.
We employ a proprietary transaction-level dataset in Germany to examine how capital requirements affect the liquidity of corporate bonds. Using the 2011 European Banking Authority capital exercise that mandated certain banks to increase regulatory capital, we find that affected banks reduce their inventory holdings, pre-arrange more trades, and have smaller average trade size. While non-bank affiliated dealers increase their market-making activity, they are unable to bridge this gap - aggregate liquidity declines. Our results are stronger for banks with a higher capital shortfall, for non-investment grade bonds, and for bonds where the affected banks were the dominant market-maker.
We develop a two-sector incomplete markets integrated assessment model to analyze the effectiveness of green quantitative easing (QE) in complementing fiscal policies for climate change mitigation. We model green QE through an outstanding stock of private assets held by a monetary authority and its portfolio allocation between a clean and a dirty sector of production. Green QE leads to a partial crowding out of private capital in the green sector and to a modest reduction of the global temperature by 0.04 degrees of Celsius until 2100. A moderate global carbon tax of 50 USD per tonne of carbon is 4 times more effective.
The European Central Bank (ECB) recently proclaimed a more active role for itself in the fight against climate change. Did the European Parliament (EP) play a part in this regard, and if so what was it? To answer this question, this paper builds on a multi-method text analysis of original datasets compiling communications between the ECB and the EP across three accountability forums between 2014 and 2021. The paper shows that there has been discursive convergence between central bankers and parliamentarians concerning the role of the ECB in combatting climate change. It argues that this convergence has resulted from a pragmatic (yet precarious) adoption of a common repertoire1 between ‘green’ central bankers and parliamentarians who have favored a more active role for the ECB in the fight against climate change. The adoption of a common repertoire is pragmatic, in that it results from the strategic use of specific discursive elements that are ambitious enough to address their respective opponents and trigger political change, yet vague enough to allow both sets of actors to converge on them momentarily. It is also precarious in the sense that it involves discarding fundamental political tensions, which is hardly tenable in the long term. The paper shows that both organizational and politicization dynamics have been at work in the emergence of this pragmatic yet precarious bedfellowship between ‘green’ central bankers and parliamentarians.
Common ownership and the (non-)transparency of institutional shareholdings: an EU-US comparison
(2022)
This paper compares the extent of common ownership in the US and the EU stock markets, with a particular focus on differences in the ap- plicable ownership transparency requirements. Most empirical research on common ownership to date has focused on US issuers, largely relying on ownership data obtained from institutional investors’ 13F filings. This type of data is generally not available for EU issuers. Absent 13F filings, researchers have to use ownership records sourced from mutual funds’ periodic reports and blockholder disclosures. Constructing a “reduced dataset” that seeks to capture only ownership information available for both EU and US issuers, I demonstrate that the “extra” ownership information introduced by 13F filings is substantial. However, even when taking differences in the transparency situation into due account, common ownership among listed EU firms is much less pronounced than among listed US firms by any measure. This is true even if the analysis is limited to non-controlled firms.
The great financial crisis and the euro area crisis led to a substantial reform of financial safety nets across Europe and – critically – to the introduction of supranational elements. Specifically, a supranational supervisor was established for the euro area, with discrete arrangements for supervisory competences and tasks depending on the systemic relevance of supervised credit institutions. A resolution mechanism was created to allow the frictionless resolution of large financial institutions. This resolution mechanism has been now complemented with a funding instrument.
While much more progress has been achieved than most observers could imagine 12 years ago, the banking union remains unfinished with important gaps and deficiencies. The experience over the past years, especially in the area of crisis management and resolution, has provided impetus for reform discussions, as reflected most lately in the Eurogroup statement of 16 June 2022.
This Policy Insight looks primarily at the current and the desired state of the banking union project. The key underlying question, and the focus here, is the level of ambition and how it is matched with effective legal and regulatory tools. Specifically, two questions will structure the discussions:
What would be a reasonable definition and rationale for a ‘complete’ banking union? And what legal reforms would be required to achieve it?
Banking union is a case of a new remit of EU-level policy that so far has been established on the basis of long pre-existing treaty stipulations, namely, Article 127(6) TFEU (for banking supervision) and Article 114 TFEU (for crisis management and deposit insurance). Could its completion be similarly carried out through secondary law? Or would a more comprehensive overhaul of the legal architecture be required to ensure legal certainty and legitimacy?
This paper studies the interactions between corporate law and VC exits by acquisitions, an increasingly common source of VC-related litigation. We find that transactions by VC funds under liquidity pressure are characterized by (i) a substantially lower sale price; (ii) a greater probability of industry outsiders as acquirers; (iii) a positive abnormal return for acquirers. These features indicate the existence of fire sales, which satisfy VCs' liquidation preferences but hurt common shareholders, leaving board members with conflicting fiduciary duties and litigation risks. Exploiting an important court ruling that establishes the board’s fiduciary duties to common shareholders as a priority, we find that after the ruling maturing VCs become less likely to exit by fire sales and they distribute cash to their investors less timely. However, VCs experience more difficult fundraising ex-ante, highlighting the potential cost of a common-favoring regime. Overall the evidence has important implications for optimal fiduciary duty design in VC-backed start-ups.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Search costs for lenders when evaluating potential borrowers are driven by the quality of the underwriting model and by access to data. Both have undergone radical change over the last years, due to the advent of big data and machine learning. For some, this holds the promise of inclusion and better access to finance. Invisible prime applicants perform better under AI than under traditional metrics. Broader data and more refined models help to detect them without triggering prohibitive costs. However, not all applicants profit to the same extent. Historic training data shape algorithms, biases distort results, and data as well as model quality are not always assured. Against this background, an intense debate over algorithmic discrimination has developed. This paper takes a first step towards developing principles of fair lending in the age of AI. It submits that there are fundamental difficulties in fitting algorithmic discrimination into the traditional regime of anti-discrimination laws. Received doctrine with its focus on causation is in many cases ill-equipped to deal with algorithmic decision-making under both, disparate treatment, and disparate impact doctrine. The paper concludes with a suggestion to reorient the discussion and with the attempt to outline contours of fair lending law in the age of AI.
Large companies are increasingly on trial. Over the last decade, many of the world’s biggest firms have been embroiled in legal disputes over corruption charges, financial fraud, environmental damage, taxation issues or sanction violations, ending in convictions or settlements of record-breaking fines, well above the billion-dollar mark. For critics of globalization, this turn towards corporate accountability is a welcome sea-change showing that multinational companies are no longer above the law. For legal experts, the trend is noteworthy because of the extraterritorial dimensions of law enforcement, as companies are increasingly held accountable for activities independent of their nationality or the place of the activities. Indeed, the global trend required understanding the evolution of corporate criminal law enforcement in the United States in particular, where authorities have skillfully expanded its effective jurisdiction beyond its territory. This paper traces the evolution of corporate prosecutions in the United States. Analyzing federal prosecution data, it then shows that foreign firms are more likely to pay a fine, which is on average 6,6 times larger.
Energy efficiency represents one of the key planned actions aiming at reducing greenhouse emissions and the consumption of fossil fuel to mitigate the impact of climate change. In this paper, we investigate the relationship between energy efficiency and the borrower’s solvency risk in the Italian market. Specifically, we analyze a residential mortgage portfolio of four financial institutions which includes about 70,000 loans matched with the energy performance certificate of the associated buildings. Our findings show that there is a negative relationship between a building’s energy efficiency and the owner’s probability of default. Findings survive after we account for dwelling, household, mortgage, market control variables, and regional and year fixed effect. Additionally, a ROC analysis shows that there is an improvement in the estimation of the mortgage default probability when the energy efficiency characteristic is included as a risk predictor in the model.
In den letzten zwei Jahrzehnten sind Anmeldungen von Rechten des geistigen Eigentums (intellectual property, IP) bzw. Verletzungsklagen wiederholt an der unzureichenden Bestimmtheit des exklusiv beanspruchten Gegenstands gescheitert. Ihren Ausgang nahm diese Entwicklung im Markenrecht mit Entscheidungen des EuGH (Stichworte: Sieckmann, Heidelberger Bauchemie, Dyson, IP Translator, Oy Hartwall) und später auch des BGH (Stichwort: UHU). Die markenrechtlichen Grundsätze strahlten auf das Designrecht (Stichworte: Sporthelm, Mast-Jägermeister) und zuletzt auch auf das Urheberrecht (Stichwort: Levola) aus. Im Folgenden werden die maßgeblichen Urteile zur Schutzfähigkeit von Zeichen, Designs und Werken zusammengetragen und systematisiert. Dabei treten zwei Aspekte eines Bestimmtheitsgebots zu Tage, die, wie abschließend zu zeigen sein wird, auch im Patentrecht gelten
Der Beitrag bietet einen Überblick über den entstehungsgeschichtlichen Hintergrund sowie den Inhalt des ursprünglichen Netzwerkdurchsetzungsgesetzes (NetzDG) 2017, seine Wirkungen in der Praxis und die Änderungen durch die NetzDG-Reform 2021. Es wird gezeigt, dass aus einem Regelwerk mit engem Fokus auf die Durchsetzung des Strafrechts in OnlineNetzwerken ein Plattformregulierungsgesetz wurde, das sowohl Löschgebote (Strafrecht) als auch Löschverbote (Meinungsfreiheit) prozeduralisiert. Während das NetzDG 2017 keinen nennenswerten Niederschlag in gerichtlichen oder behördlichen Entscheidungen fand und inzwischen auch kaum noch eine Rolle in der Löschpraxis der Netzwerke spielt, dürfte es dazu beigetragen haben, dass die Netzwerkbetreiber ihre privaten Kommunikationsregeln verschärft haben. Hintergrund für diese „Flucht in die AGB“ ist, dass die großen Netzwerkbetreiber und der Gesetzgeber dasselbe Nahziel verfolgen: Einer Verrohung der Debattenkultur soll aus ökonomischen bzw. gesellschaftspolitischen Gründen entgegengewirkt werden. Der Beitrag schließt mit einem Ausblick auf den Digital Services Act (DSA), mit dem der Compliance-Ansatz des NetzDG europäisiert würde.
This policy note summarizes our assessment of financial sanctions against Russia. We see an increase in sanctions severity starting from (1) the widely discussed SWIFT exclusions, followed by (2) blocking of correspondent banking relationships with Russian banks, including the Central Bank, alongside secondary sanctions, and (3) a full blacklisting of the ‘real’ export-import flows underlying the financial transactions. We assess option (1) as being less impactful than often believed yet sending a strong signal of EU unity; option (2) as an effective way to isolate the Russian banking system, particularly if secondary sanctions are in place, to avoid workarounds. Option (3) represents possibly the most effective way to apply economic and financial pressure, interrupting trade relationships.