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In current discussions on large language models (LLMs) such as GPT, understanding their ability to emulate facets of human intelligence stands central. Using behavioral economic paradigms and structural models, we investigate GPT’s cooperativeness in human interactions and assess its rational goal-oriented behavior. We discover that GPT cooperates more than humans and has overly optimistic expectations about human cooperation. Intriguingly, additional analyses reveal that GPT’s behavior isn’t random; it displays a level of goal-oriented rationality surpassing human counterparts. Our findings suggest that GPT hyper-rationally aims to maximize social welfare, coupled with a strive of self-preservation. Methodologically, our esearch highlights how structural models, typically employed to decipher human behavior, can illuminate the rationality and goal-orientation of LLMs. This opens a compelling path for future research into the intricate rationality of sophisticated, yet enigmatic artificial agents.
We study the redistributive effects of inflation combining administrative bank data with an information provision experiment during an episode of historic inflation. On average, households are well-informed about prevailing inflation and are concerned about its impact on their wealth; yet, while many households know about inflation eroding nominal assets, most are unaware of nominal-debt erosion. Once they receive information on the debt-erosion channel, households update upwards their beliefs about nominal debt and their own real net wealth. These changes in beliefs causally affect actual consumption and hypothetical debt decisions. Our findings suggest that real wealth mediates the sensitivity of consumption to inflation once households are aware of the wealth effects of inflation.
Dynamics of life course family transitions in Germany: exploring patterns, process and relationships
(2023)
This paper explores dynamics of family life events in Germany using discrete time event history analysis based on SOEP data. We find that higher educational attainment, better income level, and marriage emerge as salient protective factors mitigating the risk of mortality; better education also reduces the likelihood of first marriage whereas, lower educational attainment, protracted period, and presence of children act as protective factors against divorce. Our key finding shows that disparity in mean life expectancies between individuals from low- and high-income brackets is observed to be 9 years among males and 6 years among females, thereby illustrating the mortality inequality attributed to income disparities. Our estimates show that West Germans have low risk of death, less likelihood of first marriage, and they have a high risk of divorce and remarriage compared to East Germans.
Die Erklärung von Intelligenz fasziniert Menschen seit Jahrtausenden, scheint sich doch mit ihr die menschliche Singularität gegenüber Natur und Tier zu manifestieren. Zugleich betonen nicht nur philosophische Strömungen, sondern auch die Mathematik, die Neuro- und die Computerwissenschaften die Abhängigkeit menschlicher Intelligenz von mechanistischen Prozessen. Ob damit eine Verwandtschaft beider Formen der Informationsverarbeitung verbunden ist oder genau umgekehrt fundamentale Unterschiede bestehen, ist seit knapp hundert Jahren Gegenstand wissenschaftlicher Kontroversen. Fest steht allerdings, dass Maschinen jedenfalls in manchen Bereichen die menschliche Leistungsfähigkeit in Schnelligkeit und Präzision übertreffen können. Nähert man sich dieser Vorstellung, drängt sich die Frage auf, ob es sich empfiehlt, bestimmte Entscheidungen besser von Maschinen treffen, jedenfalls aber unterstützen zu lassen. Neben Ärzten, Rechtsanwälten und Börsenhändlern betrifft das auch Leitungsentscheidungen von Unternehmensführern.
Vor diesem Hintergrund wird im Folgenden ein Überblick über Formen künstlicher Intelligenz (KI) gegeben. Im Anschluss fokussiert der Beitrag auf die Rolle von KI im Kontext von Vorstandsentscheidungen. Dazu zählen allgemeine Sorgfaltspflichten, wenn über den Einsatz von KI im Unternehmen zu entscheiden ist. Geht es um die Unterstützung gerade von Vorstandsentscheidungen stellen sich zusätzlich Fragen der Kooperation von Mensch und Maschine, der Delegation des Kernbestands von Leitungsentscheidungen und der Einstandspflicht für KI.
In this study, we introduce a novel entity matching (EM) framework. It com-bines state-of-the-art EM approaches based on Artificial Neural Networks (ANN) with a new similarity encoding derived from matching techniques that are preva-lent in finance and economics. Our framework is on-par or outperforms alternative end-to-end frameworks in standard benchmark cases. Because similarity encod-ing is constructed using (edit) distances instead of semantic similarities, it avoids out-of-vocabulary problems when matching dirty data. We highlight this property by applying an EM application to dirty financial firm-level data extracted from historical archives.
Biodiversity loss poses a significant threat to the global economy and affects ecosystem services on which most large companies rely heavily. The severe financial implications of such a reduced species diversity have attracted the attention of companies and stakeholders, with numerous calls to increase corporate transparency. Using textual analysis, this study thus investigates the current state of voluntary biodiversity reporting of 359 European blue-chip companies and assesses the extent to which it aligns with the upcoming disclosure framework of the Task Force on Nature-related Financial Disclosures (TNFD). The descriptive results suggest a substantial gap between current reporting practices and the proposed TNFD framework, with disclosures largely lacking quantification, details and clear targets. In addition, the disclosures appear to be relatively unstandardized. Companies in sectors or regions exposed to higher nature-related risks as well as larger companies are more likely to report on aspects of biodiversity. This study contributes to the emerging literature on nature-related risks and provides detailed insights on the extent of the reporting gap in light of the upcoming standards.
This paper analyzes the current implementation status of sustainability and taxonomy-aligned disclosure under the Sustainable Finance Disclosure Regulation (SFDR) as well as the development of the SFDR categorization of funds offered via banks in Germany. Examining data provided by WM Group, which consists of more than 10,000 investment funds and 2,000 index funds between September 2022 and March 2023, we have observed a significant proportion of Article 9 (dark green) funds transitioning to Article 8 (light green) funds, particularly among index funds. As a consequence of this process, the profile of the SFDR classes has sharpened, which reflects an increased share of sustainable investments in the group of Article 9 funds. When differentiating between environmental and social investments, the share of environmental investments increased, but the share of social investments decreased in the group of Article 9 funds at the beginning of 2023. The share of taxonomy-aligned investments is very low, but slightly increasing for Article 9 funds. However, by March 2023 only around 1,000 funds have reported their sustainability proportions and this picture might change due to legal changes which require all funds in the scope of the SFDR to report these proportions in their annual reports being published after 1 January 2023.
Industry classification groups firms into finer partitions to help investments and empirical analysis. To overcome the well-documented limitations of existing industry definitions, like their stale nature and coarse categories for firms with multiple operations, we employ a clustering approach on 69 firm characteristics and allocate companies to novel economic sectors maximizing the within-group explained variation. Such sectors are dynamic yet stable, and represent a superior investment set compared to standard classification schemes for portfolio optimization and for trading strategies based on within-industry mean-reversion, which give rise to a latent risk factor significantly priced in the cross-section. We provide a new metric to quantify feature importance for clustering methods, finding that size drives differences across classical industries while book-to-market and financial liquidity variables matter for clustering-based sectors.
Um eine grüne Transformation der Volkswirtschaft zu erreichen, werden Finanzmärkte und die mit ihnen verbundenen Banken eine wichtige Rolle einnehmen müssen. Aber allein vermögen Banken und Kapitalmärkte wenig, wenn sie nicht im Kontext einer klugen, politischen Rahmensetzung und einer transparenten Erfassung der verursachten Schäden auf Unternehmensebene gesehen werden. Diese drei Pfeiler stellen bildlich den tragenden Unterbau für eine Brücke hin zu einer klimaneutralen Wirt-schaftsverfassung dar. Ihr Zusammenwirken ist eine Voraussetzung dafür, dass die Finanzwirtschaft die benötigten Finanzmittel für die grüne Transformation bereitstellen kann.
Homeownership rates differ widely across European countries. We document that part of this variation is driven by differences in the fraction of adults co-residing with their par-ents. Comparing Germany and Italy, we show that in contrast to homeownership rates per household, homeownership rates per individual are very similar during the first part of the life cycle. To understand these patterns, we build an overlapping-generations model where individuals face uninsurable income risk and make consumption-saving and housing tenure decisions. We embed an explicit intergenerational link between children and parents to cap-ture the three-way trade-off between owning, renting, and co-residing. Calibrating the model to Germany we explore the role of income profiles, housing policies, and the taste for inde-pendence and show that a combination of these factors goes a long way in explaining the differential life-cycle patterns of living arrangements between the two countries.