We study how connections to German federal parliamentarians affect firm dynamics by constructing a novel dataset linking politicians and election candidates to the universe of firms. To identify the causal effect of access to political power, we exploit (i) new appointments to the company leadership team and (ii) discontinuities around the marginal seat of party election lists. Our results reveal that connections lead to reductions in firm exits, gradual increases in employment growth without improvements in productivity. Adding information on credit ratings, subsidies and procurement contracts allows us to distinguish between mechanisms driving the effects over the politician’s career.

Die deutsche Wirtschaft stagniert nun schon seit zwei Jahren. Auch zu Beginn des Jahres 2024 gab es mit einem leichten Plus von 0,2% keinen konjunkturellen Neustart. Vor allem war die Kauflaune der privaten Haushalte gedrückt, aber auch die Industrie kämpft weiter mit ungünstigen Rahmenbedingungen und einer schwachen Nachfrage. Hohe Zinsen und Energiepreise, aber auch strukturelle Probleme wie bürokratische Restriktionen fordern dabei ihren Tribut, wie nicht zuletzt an den steigenden Insolvenzzahlen zu sehen ist. Im internationalen Umfeld werden die Krisenherde und Spannungen nicht weniger, dennoch ist die Weltkonjunktur weiter aufwärtsgerichtet. Damit steigt bei den deutschen Unternehmen die Hoffnung, im Kielwasser mitziehen zu können. Alles in allem dürfte das Bruttoinlandsprodukt (BIP) laut IWH-Flash-Indikator im zweiten Quartal 2024 um 0,3% und im dritten Quartal 2024 um 0,1% steigen (vgl. Abbildung 1).

How do firms invest when financial constraints are relaxed? We document that firms affected by a large positive credit supply shock predominantly increase borrowing for transaction-based purposes. These treated firms have larger asset and employment growth rates; however, growth entirely stems from the increased takeover activity. Announcement returns indicate a low quality of the credit-supply-induced takeover activity. These results offer the possibility that credit-driven growth can simply reflect redistribution, rather than net gains in assets or employment.

In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.