This study investigates the impact of inaccurate assumptions on economic forecast precision. We construct a new dataset comprising an unbalanced panel of annual German GDP forecasts from various institutions, taking into account their underlying assumptions. We explicitly control for different forecast horizons to reflect the information available at the time of release. Our analysis reveals that approximately 75% of the variation in squared forecast errors can be attributed to the variation in squared errors of the initial assumptions. This finding emphasizes the importance of accurate assumptions in economic forecasting and suggests that forecasters should transparently disclose their assumptions to enhance the usefulness of their forecasts in shaping effective policy recommendations.

Der vorliegende Report beschreibt fünf Datenprodukte zu den Fördermaßnahmen im Rahmen des Investitionsgesetzes Kohleregionen (InvKG). Über dieses Programm stellt die Bundesregierung bis zum Jahr 2038 Fördermittel im Umfang von bis zu 41,09 Mrd. Euro zur Verfügung, um die wirtschaftlichen und sozialen Folgen der thermischen Verwertung von Kohle abzufedern. Das InvKG besteht aus einer breiten Palette verschiedener Programme bzw. Programmfamilien. Nach einer Einordnung in den rechtlichen Rahmen geht der Datenreport detailliert ein auf die einzelnen Schritte der Aufbereitung sowie die in den Datenprodukten enthaltenen Informationen. Da das Fördergeschehen einer hohen Dynamik unterliegt, werden die Datenprodukte fortlaufend aktualisiert und erweitert.

We exploit an information shock related to the German Supply Chain Due Diligence Act and use detailed customs data to analyze how smaller, non-listed firms respond when expecting accountability for externalities beyond their organizational boundaries. Product-level regressions reveal a substantial reduction in imports from high ESG-risk production sectors. Adjustments occur mainly at the extensive margin, indicating that firms cut ties with high-risk suppliers. The product-level results translate into meaningful changes in overall international procurement for firms with Big Four auditors. Our findings suggest potential limits to mandates requiring firms to integrate broad sustainability considerations into operational decisions.

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.

We estimate the effects of worker voice on productivity, job quality, and separations. We study the 1991 introduction of a right to worker representation on boards or advisory councils in Finnish firms with at least 150 employees, designed primarily to facilitate workforce-management communication. Consistent with information sharing theories, our difference-in-differences design reveals that worker voice slightly raised labor productivity, firm survival, and capital intensity. In contrast to the exit-voice theory, we find no effects on voluntary job separations, and at most small positive effects on other measures of job quality. A 2008 introduction of shop-floor representation had similarly limited effects.

In this study, establishment-level employment effects of investment grants in Germany are estimated. In addition to the quantitative effects, I provide empirical evidence of funding effects on different aspects of employment quality (earnings, qualifications, and job security) for the period 2004 to 2020. The database combines project-level treatment data, establishment-level information on firm characteristics and employee structure, and regional information at the district-level. For the estimations, I combine the difference-in-differences approach of Callaway and Sant’Anna (2021) with ties matching at the cohort level. The estimations yield positive effects on the number of employees, but point to contradicting effects of investment grants on different aspects of employment quality.

Using the universe of high school and college admissions data in Croatia, we geocoded nearly half a million students’ residential addresses to investigate how their college and major choices are influenced by older neighbors and peers. Using an RDD to exploit time and program variation in admission cutoffs, we find that having an older neighbor who was admitted to and enrolled in a program increases a student’s probability of applying to the program by about 20%. We find that this effect consistently holds only for the closest neighbors, both in terms of distance and age difference. Female students are more likely to be influenced by older neighbors’ choices, and male older neighbors’ admission has a larger impact on both male and female students compared to female older neighbors. The effect is stronger if the student-neighbor pair lives in a region that does not have its own university, implying that the value of information in rural areas is higher. We find evidence that students don’t follow their older neighbors to less competitive programs; instead, they are more likely to apply for the same programs their older neighbors were admitted to when the program is more prestigious. Next, we utilize the variation in weight scheme of Croatia’s college study programs to show evidence, beyond college choices, of how older neighbors affect the human capital formation of their younger peers. The main channel through which we observe this effect is during high school, through specialization in the subjects needed to gain admittance to older neighbors’ college programs. These findings shed light on the intricate dynamics shaping educational decisions and underscores the significant role older neighbors play in guiding younger peers toward specific academic pathways.

How do beliefs on admission probability influence application choices? In this study, we empirically investigate whether and how admission probability is reflected in application choices in a centralized admission system. We exploit a novel setting of a dynamic deferred acceptance mechanism as employed in Croatia with hourly information updates and simultaneous application choices. This setting allows us to explore within-applicant strategic adjustments as a reaction to changing signals on admission probability. We show in an RDD analysis that applicants react to negative signals on admission probability with an increased propensity to adjust their application choices by 11-23%. Additionally, we show how application strategies evolve over time, while applicants learn about their admission probability. The group most-at-risk to remain unmatched improves their application choices by applying to programs with a higher admission probability towards the application deadline. Yet, we also identify a popular and potentially harmful strategy of applying to safer programs before applying to more risky “reach” programs. About a quarter of applicants have the potential to improve their application choices by resorting their application choices.