How Laws Affect Contracts: Evidence from Yankee Bond Covenants

We examine how country-level legal and institutional differences in creditor and shareholder rights shape the use of bond covenants. Using comprehensive debt covenant information for a sample of Yankee bonds issued by firms from more than 50 countries, we find that bond contracts for firms incorporated in countries with stronger creditor rights use fewer restrictive covenants. This finding suggests that creditor rights laws substitute for debt covenants in reducing the agency cost of debt. On the other hand, bond contracts for firms incorporated in legal regimes with stronger shareholder rights include more covenants, suggesting that greater shareholder rights may actually increase the shareholder-bondholder agency conflict. These results are robust to alternative measures of creditor rights and shareholder rights. We also document that stronger firm-level corporate governance is positively related to the use of restrictive covenants even after controlling for country institutions.Covenants, contracts, creditor rights, shareholder rights, corporate governance

Income shocks and suicides : causal evidence from Indonesia

We examine how income shocks affect the suicide rate in Indonesia. We use a difference-in-differences approach, exploiting the cash transfer's nationwide roll-out, and corroborate the findings using a randomized experiment. Our estimates show that the cash transfers reduce the yearly suicide rate by 0.36 per 100,000 people, corresponding to an 18 percent decrease. Moreover, a different type of income shock, variability in agricultural productivity, also affects the suicide rate. The cash transfer program reduces the causal impact of the agricultural productivity shocks, suggesting an important role for policy interventions. Finally, we provide evidence for depression as a psychological mechanism

AI-Recycling : Objektklassifizierung im Recyclinghof Hagenholz

Durch den kontinuierlich wachsenden Rohstoffverbrauch weltweit, ist die effiziente Nutzung und Wiederverwendung von Werkstoffen von zentraler Bedeutung. Im Recyclinghof Hagenholz (RH) werden in 25 Containern Materialien getrennt gesammelt. Die Zuordnung ist, durch die Vielfallt an Sammelkategorien, für viele Kunden schwierig und immer wieder landen Objekte in den falschen Containern. Diese Arbeit zeigt eine Lösung auf, wie mittels eines Machine Learning Models ein Objektklassifikator erstellt werden kann, welcher anhand eines Fotos des Recyclingobjektes, dessen Klassifizierung der Sammelkategorie liefern kann. Das Model kommt in einer Mobile-App zum Einsatz und soll dem Kunden des RH die Unterstützung bei der korrekten Zuordnung des mitgebrachten Materials bieten. Das methodische Vorgehen besteht aus drei Teilen: der Datenerhebung für das Image Dataset, der Modellentwicklung und der Entwicklung des Prototyps. Die Datenerhebung umfasst das Sammeln eigener Fotos aus dem RH, die Evaluierung öffentlicher Datasets sowie die Methode des Image-Scraping. Bei der Modellentwicklung wurde ein Modell von Grund auf, sowie zwei Modelle mittels der Retraining-Methode, auf Basis der vortrainierten Modelle mit MobileNet bzw. EfficientNet entwickelt. Validiert wurden die Modelle mittels Out-Of-Sample (OOS) und K-Fold Cross Validation (KFCV). Das Retrained MobileNetV3 Model (RMM) wurde in die entwickelte Android App integriert und abschliessend im RH einem Feldtest unterzogen. Das Dataset besteht aus 24 Klassen, aus jeweils ca. 600 Bildern. Die darauf trainierten Modelle haben wie folgt abgeschnitten: Das einfache Modell konnte im OOS 46.8, bzw. 46.2 Prozent Genauigkeit in der KFCV erreichen. Das RMM konnte eine Genauigkeit von 85.9 (OOS), bzw. 88.34 Prozent in der KFCV und mit dem Retrained EfficientNet-B0 Model (REM) liegt die Genauigkeit bei 86.7 in OOS, bzw. 87.6 Prozent in der KFCV. Die Confusion Matrix (CM) zeigte Defizite des RMM in den Klassen Elektro, Mischmaterialen, Metall, sowie Buntmetall. Im Feldtest konnte der Prototyp in 32 von 40 Tests den richtigen Container erkennen, wobei sichtbar wurde, dass in der Klasse Sperrgut die Erkennung sehr schlecht ist und die Kategorien Elektro, Mischmaterialien und Metall, im Vergleich zur CM gut abschnitten. Diese Arbeit zeigt, dass mittels vortrainierter Modelle und eines verhältnismässig kleinen Datasets, aus vielen Klassen, bereits eine gute Erkennung erreicht werden kann. Es zeigt sich jedoch, dass es schwierig ist, Klassen vollständig abzubilden, welche aus unendlicher Anzahl Objekte bestehen, wie Sperrgut oder Metall. Objekte, welche sich nur durch ihr Material unterscheiden, nicht aber durch Form und Farbe, können kaum durch Image-Classification unterschieden werden. Durch die zeitliche Limitierung der Arbeit konnte nur ein begrenzter Aufwand in die Entwicklung des Modells, sowie des Datasets gesteckt werden. Durch eine umfangreichere Datenerhebung im RH, könnte eine praxisnähere Datengrundlage geschaffen werden, auf welcher durch weitere Optimierung der Hyperparameter und eines besseren Fine-Tunings im Modell, durchaus noch bessere Resultate möglich wären

Motor Skill Improvement in Preschoolers: How Effective Are Activity Cards?

Strategies to early develop and implement motor skill promotion in preschoolers are lacking. Thus, we examined the effects of a card-based exercise promotion program in a kindergarten setting. 214 preschool children (5.5 ± 0.6 y, range 4.2–6.7 y) were examined in the present intervention study. Body mass index (BMI) and waist circumference were measured. Children were randomly assigned to the KIDZ-Box® physical activity intervention program (INT: n = 107) or the control group (CON: n = 107). Children were trained daily for 15 min over 7 month at the preschool for agility, balance, endurance and jump performance, employing the card-based KIDZ-Box® media package. At pre- and post-testing, dynamic balance, jump and agility performance were tested. Cross-sectionally, agility testing differed between sexes (p = 0.01) and BMI (p = 0.02). Trends towards a significant association were found between BMI and side-to-side jumping (p = 0.1) and beam balancing (p = 0.05). Relevant interventional effects favoring the intervention group were slightly found for agility (p = 0.04, ηp2 = 0.02) and moderately for side-to-side jumping (p < 0.001, ηp2 = 0.08). Balance performance did not relevantly improve. As jumping cards have been used frequently by the teachers, jumping improvements are plausible. The activity cards are feasibly applicable but should be employed with more structure during longer training sessions

Feminist Politics of Connectedness in the Americas

Roth J. Feminist Politics of Connectedness in the Americas. In: Rehm L, Kemner J, Kaltmeier O, eds. Politics of Entanglement in the Americas. Connecting transnational flows and local perspectives. Inter-American Studies. Vol 19. Trier: WVT; 2017: 73-96

Group Size and Protest Mobilization across Movements and Countermovements

Many social movements face fierce resistance in the form of a countermovement. Therefore, when deciding to become politically active, a movement supporter has to consider both her own movement’s activity and that of the opponent. This paper studies the decision of a movement supporter to attend a protest when faced with a counterprotest. We implement two field experiments among supporters of a right- and left-leaning movement ahead of two protest–counterprotest interactions in Germany. Supporters were exposed to low or high official estimates about their own and the opposing group’s turnout. We find that the size of the opposing group has no effect on supporters’ protest intentions. However, as the own protest gets larger, supporters of the right-leaning movement become less while supporters of the left-leaning movement become more willing to protest. We argue that the difference is best explained by stronger social motives on the political left.Peer Reviewe

Does party competition affect political activism?

This paper studies the decision of party supporters to join political campaigns. We present a framework that incorporates supporters’ instrumental and expressive motives and illustrates that party competition can either increase or decrease party activism. To distinguish between these competing predictions, we implemented a field experiment with a European party during a national election. In a seemingly unrelated party survey, we randomly assigned 1,417 party supporters to true information that the canvassing activity of the main competitor party was exceptionally high. Using unobtrusive, real-time data on party supporters’ canvassing behavior, we find that treated respondents are 30 percent less likely to go canvassing. To investigate the causal mechanism, we leverage additional survey evidence collected two months after the campaign. Consistent with affective accounts of political activism, we show that increased competition lowered party supporters’ political self-efficacy, which plausibly led them to remain inactive