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Germany’s economy is so bad even sausage factories are closingIWHThe Economist, January 15, 2026
This study examines how antitrust law adoptions affect horizontal merger and acquisition (M&A) outcomes. Using the staggered introduction of competition laws in 20 countries, we find antitrust regulation decreases acquirers’ five-day cumulative abnormal returns surrounding horizontal merger announcements. A decrease in deal value, target book assets, and industry peers' announcement returns are consistent with the market power hypothesis. Exploiting antitrust law adoptions addresses a downward bias to an estimated effect of antitrust enforcement (Baker (2003)). The potential bias from heterogeneous treatment effects does not nullify our results. Overall, antitrust policies seem to deter post-merger monopolistic gains, potentially improving customer welfare.
This paper provides a comprehensive analysis of the forecastability of the real price of natural gas in the United States at the monthly frequency considering a universe of models that differ in their complexity and economic content. Our key finding is that considerable reductions in mean-squared prediction error relative to a random walk benchmark can be achieved in real time for forecast horizons of up to two years. A particularly promising model is a six-variable Bayesian vector autoregressive model that includes the fundamental determinants of the supply and demand for natural gas. To capture real-time data constraints of these and other predictor variables, we assemble a rich database of historical vintages from multiple sources. We also compare our model-based forecasts to readily available model-free forecasts provided by experts and futures markets. Given that no single forecasting method dominates all others, we explore the usefulness of pooling forecasts and find that combining forecasts from individual models selected in real time based on their most recent performance delivers the most accurate forecasts.
We review an empirical literature that studies how political polarization affects financial decisions. We first discuss the degree of partisan segregation in finance and corporate America, the mechanisms through which partisanship may influence financial decisions, and the available data sources used to infer individuals’ partisan leanings. We then describe and discuss the empirical evidence. Our review suggests an economically large and often growing partisan gap in the financial decisions of households, corporate executives, and financial intermediaries. Partisan alignment between individuals explains team and financial relationship formation, with initial evidence suggesting that high levels of partisan homogeneity may be associated with economic costs. We conclude by proposing several promising directions for future research.
We analyse the impact of robot adoption on employment composition using novel micro data on robot use in German manufacturing plants linked with social security records and data on job tasks. Our task-based model predicts more favourable employment effects for the least routine-task intensive occupations and for young workers, with the latter being better at adapting to change. An event-study analysis of robot adoption confirms both predictions. We do not find adverse employment effects for any occupational or age group, but churning among low-skilled workers rises sharply. We conclude that the displacement effect of robots is occupation biased but age neutral, whereas the reinstatement effect is age biased and benefits young workers most.
Auf die Erhöhung des Mindestlohns auf 12 Euro im Oktober 2022 haben rund 30 Prozent der Betriebe in Deutschland mit Lohnerhöhungen reagiert. Eine weitere Anhebung des Mindestlohns auf 14 Euro könnte mehr als jeden zweiten Betrieb betreffen. Etwa ein Drittel der Betriebe, die direkt davon betroffen wären, geht davon aus, innerhalb der kommenden zwölf Monate Beschäftigung abbauen zu müssen.
Kinder mit Migrationshintergrund besuchen im Vergleich zu Kindern ohne Migrationshintergrund deutlich seltener Kindertagesstätten (Kitas), obwohl gerade sie von einem Kitabesuch besonders profitieren würden. Ist sich die Bevölkerung dieser Diskrepanz bewusst? Und wie steht sie zu verschiedenen politischen Maßnahmen, die dieses Problem angehen? Wir untersuchen, ob Informationen über Ungleichheiten und Diskriminierung in der frühkindlichen Betreuung die Zustimmung der Bevölkerung zu gleichheitsfördernden Politikmaßnahmen beeinflussen. Dafür befragen wir 4 800 repräsentativ ausgewählte Personen aus der deutschen Bevölkerung. Es zeigt sich, dass die Befragten oft erhebliche Fehleinschätzungen über das Ausmaß der Ungleichheiten und Diskriminierung beim Kitazugang zwischen Familien mit und ohne Migrationshintergrund haben. Zufällig bereitgestellte Informationen über das tatsächliche Ausmaß dieser Diskrepanzen verringern die politische Polarisierung, d.h. Unterschiede in der Zustimmung zu Unterstützungsmaßnahmen für Familien mit Migrationshintergrund. Unsere Ergebnisse legen nahe, dass verzerrte Wahrnehmungen sozialer Ungleichheiten zu Meinungsunterschieden in der Bevölkerung beitragen. Ein besserer Informationsstand in der Bevölkerung könnte diese Polarisierung in der Zustimmung zu gleichheitsfördernden Politikmaßnahmen beim Zugang zu Kitas verringern.
The IAB Job Vacancy Survey is a quarterly and representative establishment survey on labor demand and recruitment processes in Germany. The survey identifies the overall stock of vacancies in the German labor market, including those vacancies that are not reported to the Federal Employment Agency (FEA). The first module of the questionnaire collects information about the number and structure of vacancies, future personnel requirements, about the current economic situation and the expected development of participating establishments. The second module enquires about employer attitudes and firm use of current labor market instruments as well as the employer handling of people disadvantaged in the labor market. The third module asks for information about the last new hire and the last case of a failed recruitment effort. The Research Data Centre of the Federal Employment Agency offers the data sets of the survey waves from 2000 onwards.
The European Commission’s growth forecasts play a crucial role in shaping policies and provide a benchmark for many (national) forecasters. The annual forecasts are built on quarterly estimates, which do not receive much attention and are hardly known. Therefore, this paper provides a comprehensive analysis of multi-period ahead quarterly GDP growth forecasts for the European Union (EU), euro area, and several EU member states with respect to first-release and current-release data. Forecast revisions and forecast errors are analyzed, and the results show that the forecasts are not systematically biased. However, GDP forecasts for several member states tend to be overestimated at short-time horizons. Furthermore, the final forecast revision in the current quarter is generally downward biased for almost all countries. Overall, the differences in mean forecast errors are minor when using real-time data or pseudo-real-time data and these differences do not significantly impact the overall assessment of the forecasts’ quality. Additionally, the forecast performance varies across countries, with smaller countries and Central and Eastern European countries (CEECs) experiencing larger forecast errors. The paper provides evidence that there is still potential for improvement in forecasting techniques both for nowcasts but also forecasts up to eight quarters ahead. In the latter case, the performance of the mean forecast tends to be superior for many countries.
This paper investigates forecast aggregation via the random subspace regressions method (RSM) and explores the potential link between RSM and the Shapley value decomposition (SVD) using the US GDP growth rates. This technique combination enables handling high-dimensional data and reveals the relative importance of each individual forecast. First, it is possible to enhance forecasting performance in certain practical instances by randomly selecting smaller subsets of individual forecasts and obtaining a new set of predictions based on a regression-based weighting scheme. The optimal value of selected individual forecasts is also empirically studied. Then, a connection between RSM and SVD is proposed, enabling the examination of each individual forecast’s contribution to the final prediction, even when there is a large number of forecasts. This approach is model-agnostic (can be applied to any set of predictions) and facilitates understanding of how the aggregated prediction is obtained based on individual forecasts, which is crucial for decision-makers.
