We provide evidence for a psychological component of inflation concerns. Higher inflation concerns relate in a positive and significant way to respondents’ reported levels of concerns about their financial situation. Results hold when controlling for income and financial constraints.

This paper examines the impact of complex yet precise language, particularly financial jargon, on information dissemination and ultimately market efficiency. As a natural laboratory, we analyze the information exchanged during earnings conference calls, where we instrument jargon with the Plain Writing Act of 2010. Our findings suggest that the Act‘s promotion of plain language usage results in a reduction in complex financial jargon for US firms. However, in contrast to the presumed benefits of accessible language, this reduction in jargon is associated with a decrease in market efficiency, implying that the Act may inadvertently hinder information flow. This finding is particularly important at the juncture where human-generated information is received by machines, which are known to be vunerable to ambiguous inputs.

Wie haben sich die Einkommen unterschiedlicher Bevölkerungsgruppen in Deutschland seit der Wiedervereinigung entwickelt? Unsere Studie untersucht die Entwicklung und Zusammensetzung des Nationaleinkommens entlang der Verteilung im Zeitraum von 1992 bis 2019. Während die untere Hälfte der Einkommensverteilung (unterhalb des Medianeinkommens) bis Mitte der 2000er Jahre reale Einkommensverluste verzeichnete, stiegen die Einkommen der oberen Mittelschicht (die obersten 10%, ohne das einkommensstärkste 1%) stetig. Die Spitzeneinkommen (oberstes 1%) blieben zwischen 1992 und 2019 relativ stabil. Arbeitseinkommen dominieren bei den unteren 99%, während das oberste 1% von Unternehmenseinkommen – insbesondere aus arbeitsintensiven Dienstleistungsunternehmen und freien Berufen – bestimmt ist. Unsere Ergebnisse sind zentral für die Debatte über Reformen der Sozialversicherungsbeiträge und der Einkommensbesteuerung.

Accurate macroeconomic forecasts are essential for effective policy decisions, yet their precision depends on the accuracy of the underlying assumptions. This paper examines the extent to which assumption errors affect forecast accuracy, introducing the average squared assumption error (ASAE) as a valid instrument to address endogeneity. Using double/debiased machine learning (DML) techniques and partial linear instrumental variable (PLIV) models, we analyze GDP growth forecasts for Germany, conditioning on key exogenous variables such as oil price, exchange rate, and world trade. We find that traditional ordinary least squares (OLS) techniques systematically underestimate the influence of assumption errors, particularly with respect to world trade, while DML effectively mitigates endogeneity, reduces multicollinearity, and captures nonlinearities in the data. However, the effect of oil price assumption errors on GDP forecast errors remains ambiguous. These results underscore the importance of advanced econometric tools to improve the evaluation of macroeconomic forecasts.

Does information leakage in a target's social networks increase its stock price prior to a merger announcement? Evidence reveals that a target with more social connections indeed experiences a higher pre-announcement price run-up. This effect does not exist during or after the merger announcement, or in windows ending two months before the announcement. It is more pronounced among targets with severe asymmetric information, and weaker when the information about the upcoming merger is publicly available prior to the announcement. It is also weaker in expedited deals such as tender offers.