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We use the prolonged Greek crisis as a case study to understand how a lasting economic shock affects the innovation strategies of firms in economies with moderate innovation activities. Adopting the 3-stage CDM model, we explore the link between R&D, innovation, and productivity for different size groups of Greek manufacturing firms during the prolonged crisis. At the first stage, we find that the continuation of the crisis is harmful for the R&D engagement of smaller firms while it increased the willingness for R&D activities among the larger ones. At the second stage, among smaller firms the knowledge production remains unaffected by R&D investments, while among larger firms the R&D decision is positively correlated with the probability of producing innovation, albeit the relationship is weakened as the crisis continues. At the third stage, innovation output benefits only larger firms in terms of labor productivity, while the innovation-productivity nexus is insignificant for smaller firms during the lasting crisis.
Do internships pay off?
(2022)
We study the causal effect of student internship experience in firms on earnings later in life. We use mandatory firm internships at German universities as an instrument for doing a firm internship while attending university. Employing longitudinal data from graduate surveys, we find positive and significant earnings returns of about 6 percent in both ordinary least squares (OLS) and instrumental variables (IV) regressions. The positive returns are particularly pronounced for individuals and areas of study that are characterized by a weak labor market orientation. The empirical findings show that graduates who completed a firm internship face a lower risk of unemployment during the first year of their careers, suggesting a smoother transition to the labor market.
The self-employed faced strong income losses during the Covid-19 pandemic. Many governments introduced programs to financially support the self-employed during the pandemic, including Germany. The German Ministry for Economic Affairs announced a €50bn emergency-aid program in March 2020, offering one-off lump-sum payments of up to €15,000 to those facing substantial revenue declines. By reassuring the self- employed that the government ‘would not let them down’ during the crisis, the program had also the important aim of motivating the self-employed to get through the crisis. We investigate whether the program affected the confidence of the self-employed to survive the crisis using real-time online-survey data comprising more than 20,000 observations. We employ propensity score matching, making use of a rich set of variables that influence the subjective survival probability as main outcome measure. We observe that this program had significant effects, with the subjective survival probability of the self- employed being moderately increased. We reveal important effect heterogeneities with respect to education, industries, and speed of payment. Notably, positive effects only occur among those self-employed whose application was processed quickly. This suggests stress-induced waiting costs due to the uncertainty associated with the administrative processing and the overall pandemic situation. Our findings have policy implications for the design of support programs, while also contributing to the literature on the instruments and effects of entrepreneurship policy interventions in crisis situations.
We demonstrate how the incentives of firms that partially own their suppliers or customers to foreclose rivals depend on how the partial owner can extract profits from the target (tunneling). Compared to a fully vertically integrated firm, a partial owner may obtain only a share of the target’s profit but influence the target’s strategy significantly. We show that the incentives for customer and input foreclosure can be higher, equal, or even lower with partial ownership than with a vertical merger, depending on how the protection of minority shareholders and transfer price regulations affect the scope for profit extraction.
Gruppierung von Daten
(2022)
Dieser Beitrag beinhaltet einen Vergleich zwischen den Methoden der Topologischen Datenanalyse (TDA) und statistischen Clusterverfahren bei der Gruppierung von Daten. Es werden Gemeinsamkeiten und Unterschiede bei der Bildung der Cluster und Zuordnung der statistischen Einheiten identifiziert. Hierzu werden zwei empirische Datensätze aus der Biologie und Medizin herangezogen.
Zusammengefasst haben sich die Verfahren der TDA als ein praktikables Werkzeug bei der Gruppierung von Objekten erwiesen. Vor allem mit dem Mapper-Algorithmus konnten adäquate Cluster erkannt werden. Beim Iris Flower-Datensatz hat die TDA ähnliche Ergebnisse wie die Clusteranalyse erzielt. Der Heart Disease-Datensatz war schwieriger zu behandeln. Die genutzten clusteranalytischen Verfahren waren nicht geeignet, die beiden Gruppen von Patienten korrekt zu identifizieren. Im Vergleich zu den Standardverfahren der Clusteranalyse zeigte sich eine leichte Überlegenheit der topologischen Verfahren.