02.10.2019 • 20/2019
Joint Economic Forecast Autumn 2019: Economy Cools Further – Industry in Recession
Berlin, October 2, 2019 – Germany’s leading economics research institutes have revised their economic forecast for Germany significantly downward. Whereas in the spring they still expected gross domestic product (GDP) to grow by 0.8% in 2019, they now expect GDP growth to be only 0.5%. Reasons for the poor performance are the falling worldwide demand for capital goods – in the exporting of which the Germany economy is specialised – as well as political uncertainty and structural changes in the automotive industry. By contrast, monetary policy is shoring up macroeconomic expansion. For the coming year, the economic researchers have also reduced their forecast of GDP growth to 1.1%, having predicted 1.8% in the spring.
Oliver Holtemöller
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The Value of Smarter Teachers: International Evidence on Teacher Cognitive Skills and Student Performance
Eric A. Hanushek, Marc Piopiunik, Simon Wiederhold
Journal of Human Resources,
No. 4,
2019
Abstract
We construct country-level measures of teacher cognitive skills using unique assessment data for 31 countries. We find substantial differences in teacher cognitive skills across countries that are strongly related to student performance. Results are supported by fixed-effects estimation exploiting within-country between-subject variation in teacher skills. A series of robustness and placebo tests indicate a systematic influence of teacher skills as distinct from overall differences among countries in the level of cognitive skills. Moreover, observed country variations in teacher cognitive skills are significantly related to differences in women’s access to high-skill occupations outside teaching and to salary premiums for teachers.
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Gender Stereotypes still in MIND: Information on Relative Performance and Competition Entry
Sabrina Jeworrek
Journal of Behavioral and Experimental Economics,
October
2019
Abstract
By conducting a laboratory experiment, I test whether the gender tournament gap diminishes in its size after providing information on the relative performance of the two genders. Indeed, the gap shrinks sizeably, it even becomes statistically insignificant. Hence, individuals’ entry decisions seem to be driven not only by incorrect self-assessments in general but also by incorrect stereotypical beliefs about the genders’ average abilities. Overconfident men opt less often for the tournament and, thereby, increase their expected payoff. Overall efficiency, however, is not affected by the intervention.
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CEO Investment of Deferred Compensation Plans and Firm Performance
Domenico Rocco Cambrea, Stefano Colonnello, Giuliano Curatola, Giulia Fantini
Journal of Business Finance and Accounting,
No. 7,
2019
Abstract
We study how US chief executive officers (CEOs) invest their deferred compensation plans depending on the firm's profitability. By looking at the correlation between the CEO's return on these plans and the firm's stock return, we show that deferred compensation is to a large extent invested in the company equity in good times and divested from it in bad times. The divestment from company equity in bad times arguably reflects CEOs' incentive to abandon the firm and to invest in alternative instruments to preserve the value of their deferred compensation plans. This result suggests that the incentive alignment effects of deferred compensation crucially depend on the firm's health status.
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College Choice, Selection, and Allocation Mechanisms: A Structural Empirical Analysis
J.-R. Carvalho, T. Magnac, Qizhou Xiong
Quantitative Economics,
No. 3,
2019
Abstract
We use rich microeconomic data on performance and choices of students at college entry to analyze interactions between the selection mechanism, eliciting college preferences through exams, and the allocation mechanism. We set up a framework in which success probabilities and student preferences are shown to be identified from data on their choices and their exam grades under exclusion restrictions and support conditions. The counterfactuals we consider balance the severity of congestion and the quality of the match between schools and students. Moving to deferred acceptance or inverting the timing of choices and exams are shown to increase welfare. Redistribution among students and among schools is also sizeable in all counterfactual experiments.
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Mission, Motivation, and the Active Decision to Work for a Social Cause
Sabrina Jeworrek, Vanessa Mertins
Abstract
The mission of a job does not only affect the type of worker attracted to an organisation, but may also provide incentives to an existing workforce. We conducted a natural field experiment with 267 short-time workers and randomly allocated them to either a prosocial or a commercial job. Our data suggest that the mission of a job itself has a performance enhancing motivational impact on particular individuals only, i.e., workers with a prosocial attitude. However, the mission is very important if it has been actively selected. Those workers who have chosen to contribute to a social cause outperform the ones randomly assigned to the same job by about 15 percent. This effect seems to be a universal phenomenon which is not driven by information about the alternative job, the choice itself or a particular subgroup.
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Expectation Formation, Financial Frictions, and Forecasting Performance of Dynamic Stochastic General Equilibrium Models
Oliver Holtemöller, Christoph Schult
Historical Social Research,
Special Issue: Governing by Numbers
2019
Abstract
In this paper, we document the forecasting performance of estimated basic dynamic stochastic general equilibrium (DSGE) models and compare this to extended versions which consider alternative expectation formation assumptions and financial frictions. We also show how standard model features, such as price and wage rigidities, contribute to forecasting performance. It turns out that neither alternative expectation formation behaviour nor financial frictions can systematically increase the forecasting performance of basic DSGE models. Financial frictions improve forecasts only during periods of financial crises. However, traditional price and wage rigidities systematically help to increase the forecasting performance.
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Badly Hurt? Natural Disasters and Direct Firm Effects
Felix Noth, Oliver Rehbein
Finance Research Letters,
2019
Abstract
We investigate firm outcomes after a major flood in Germany in 2013. We robustly find that firms located in the disaster regions have significantly higher turnover, lower leverage, and higher cash in the period after 2013. We provide evidence that the effects stem from firms that already experienced a similar major disaster in 2002. Overall, our results document a positive net effect on firm performance in the direct aftermath of a natural disaster.
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Benign Neglect of Covenant Violations: Blissful Banking or Ignorant Monitoring?
Stefano Colonnello, Michael Koetter, Moritz Stieglitz
Abstract
Theoretically, bank‘s loan monitoring activity hinges critically on its capitalisation. To proxy for monitoring intensity, we use changes in borrowers‘ investment following loan covenant violations, when creditors can intervene in the governance of the firm. Exploiting granular bank-firm relationships observed in the syndicated loan market, we document substantial heterogeneity in monitoring across banks and through time. Better capitalised banks are more lenient monitors that intervene less with covenant violators. Importantly, this hands-off approach is associated with improved borrowers‘ performance. Beyond enhancing financial resilience, regulation that requires banks to hold more capital may thus also mitigate the tightening of credit terms when firms experience shocks.
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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Abstract
This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.
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