Human Capital Mobility and Convergence. A Spatial Dynamic Panel Model of the German Regions
Alexander Kubis, Lutz Schneider
Abstract
Since the fall of the iron curtain in 1989, the migration deficit of the Eastern part of Germany has accumulated to 1.8 million people, which is over 10 percent of its ini-tial population. Depending on their human capital endowment, these migrants might either – in the case of low-skilled migration – accelerate or – in high-skilled case– impede convergence. Due to the availability of detailed data on regional human capital, migration and productivity growth, we are able to test how geographic mobil-ity affects convergence via the human capital selectivity of migration. With regard to the endogeneity of the migration flows and human capital, we apply a dynamic panel data model within the framework of β-convergence and account for spatial depend-ence. The regressions indicate a positive, robust, but modest effect of a migration surplus on regional productivity growth. After controlling for human capital, the effect of migration decreases; this decrease indicates that skill selectivity is one way that migration impacts growth.
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Human Capital Mobility and Convergence – A Spatial Dynamic Panel Model of the German Regions
Alexander Kubis, Lutz Schneider
Abstract
Since the fall of the iron curtain in 1989, the migration deficit of the Eastern part of Germany has accumulated to 1.8 million people, which is over ten percent of its initial population. Depending on their human capital endowment, these migrants might either – in the case of low-skilled migration – accelerate or – in high-skilled case – impede convergence. Due to the availability of detailed data on regional human capital, migration and productivity growth, we are able to test how geographic mobility affects convergence via the human capital selectivity of migration. With regard to the endogeneity of the migration flows and human capital, we apply a dynamic panel data model within the framework of β-convergence and account for spatial dependence. The regressions indicate a positive, robust, but modest effect of a migration surplus on regional productivity growth. After controlling for human capital, the effect of migration decreases; this decrease indicates that skill selectivity is one way that migration impacts growth.
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Determinants of Evolutionary Change Processes in Innovation Networks – Empirical Evidence from the German Laser Industry
Muhamed Kudic, Andreas Pyka, Jutta Günther
Abstract
We seek to understand the relationship between network change determinants, network change processes at the micro level and structural consequences at the overall network level. Our conceptual framework considers three groups of determinants – organizational, relational and contextual. Selected factors within these groups are assumed to cause network change processes at the micro level – tie formations and tie terminations – and to shape the structural network configuration at the overall network level. We apply a unique longitudinal event history dataset based on the full population of 233 German laser source manufacturers and 570 publicly-funded cooperation projects to answer the following research question: What kind of exogenous or endogenous determinants affect a firm’s propensity and timing to cooperate and enter the network? Estimation results from a non-parametric event history model indicate that young micro firms enter the network later than small-sized and large firms. An in-depth analysis of the size effects for medium-sized firms provides some unexpected yet quite interesting findings. The choice of cooperation type makes no significant difference for the firms’ timing to enter the network. Finally, the analysis of contextual determinants shows that cluster membership can, but do not necessarily, affect a firm’s timing to cooperate.
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Towards a Theory of Climate Innovation - A Model Framework for Analyzing Drivers and Determinants
Wilfried Ehrenfeld
Journal of Evolutionary Economics,
2013
Abstract
In this article, we describe the results of a multiple case study on the indirect corporate innovation impact of climate change in the Central German chemical industry. We investigate the demands imposed on enterprises in this context as well as the sources, outcomes and determining factors in the innovative process at the corporate level. We argue that climate change drives corporate innovations through various channels. A main finding is that rising energy prices were a key driver for incremental energy efficiency innovations in the enterprises’ production processes. For product innovation, customer requests were a main driver, though often these requests are not directly related to climate issues. The introduction or extension of environmental and energy management systems as well as the certification of these are the most common forms of organizational innovations. For marketing purposes, the topic of climate change was hardly utilized so far. As the most important determinants for corporate climate innovations, corporate structure and flexibility of the product portfolio, political asymmetry regarding environmental regulation and governmental funding were identified.
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Climate Innovation - The Case of the Central German Chemical Industry
Wilfried Ehrenfeld
IWH Discussion Papers,
No. 2,
2012
Abstract
In this article, we describe the results of a multiple case study on the indirect corporate innovation impact of climate change in the Central German chemical industry. We investigate the demands imposed on enterprises in this context as well as the sources, outcomes and determining factors in the innovative process at the corporate level. We argue that climate change drives corporate innovations through various channels. A main finding is that rising energy prices were a key driver for incremental energy efficiency innovations in the enterprises’ production processes. For product innovation, customer requests were a main driver, though often these requests are not directly related to climate issues. The introduction or extension of environmental and energy management systems as well as the certification of these are the most common forms of organizational innovations. For marketing purposes, the topic of climate change was hardly utilized so far. As the most important determinants for corporate climate innovations, corporate structure and flexibility of the product portfolio, political asymmetry regarding environmental regulation and governmental funding were identified.
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Losing Work, Moving Away? Regional Mobility After Job Loss
Daniel Fackler, Lisa Rippe
LABOUR: Review of Labour Economics and Industrial Relations,
No. 4,
2017
Abstract
Using German survey data, we investigate the relationship between involuntary job loss and regional mobility. Our results show that job loss has a strong positive effect on the propensity to relocate. We also analyse whether displaced workers who relocate to a different region after job loss are better able to catch up with non-displaced workers in terms of labour market performance than those staying in the same region. Our findings do not support this conjecture as we find substantial long-lasting earnings losses for movers and stayers and even slightly but not significantly higher losses for movers.
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Losing Work, Moving Away? Regional Mobility After Job Loss
Daniel Fackler, Lisa Rippe
Abstract
Using German survey data, we investigate the relationship between involuntary job loss and regional mobility. Our results show that job loss has a strong positive effect on the propensity to relocate. We also analyze whether the high and persistent earnings losses of displaced workers can in part be explained by limited regional mobility. Our findings do not support this conjecture as we find substantial long lasting earnings losses for both movers and stayers. In the short run, movers even face slightly higher losses, but the differences between the two groups of displaced workers are never statistically significant. This challenges whether migration is a beneficial strategy in case of involuntary job loss.
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Protest! Die Rolle kultureller Prägung im Volkswagenskandal
Felix Noth, Lena Tonzer
Wirtschaft im Wandel,
No. 3,
2020
Abstract
Die Aufdeckung manipulierter Abgaswerte bei Dieselautos des Herstellers Volkswagen (VW) durch die amerikanischen Behörden im Jahr 2015 brachte einen der größten Unternehmensskandale Deutschlands zutage. Dieser Skandal blieb nicht ohne Konsequenzen. Martin Winterkorn trat von seinem Amt als Vorstandsvorsitzender und Michael Horn als Chef von Volkswagen in den USA zurück. Viele VW-Kunden klagten gegen den Konzern, und in deutschen Großstädten wurde über Dieselfahrverbote diskutiert. Doch gab es auch eine Reaktion auf Konsumentenseite, also seitens der Autokäufer? Und wenn ja, spielen hier gesellschaftskulturelle Unterschiede wie zum Beispiel religiöse Prägung eine Rolle? Diesen Fragen geht ein im letzten Jahr erschienenes Arbeitspapier des IWH nach. Die empirische Analyse beschäftigt sich mit der Frage, ob Konsumenten nach dem VW-Skandal ihr Kaufverhalten stärker anpassen, wenn das gesellschaftliche Umfeld protestantisch geprägt ist. In der wissenschaftlichen Literatur zeigt sich, dass Protestanten mehr Wert auf eine Überwachung und Durchsetzung von Regeln legen, weshalb die Autoren von dieser Religionsgruppe eine ausgeprägtere Reaktion auf den VW-Skandal erwarten. Das Hauptergebnis der Studie legt dann genau diesen Schluss nahe: In den deutschen Regionen, in denen die Mehrheit der Bevölkerung dem protestantischen Glauben angehört, kam es zu signifikant höheren Rückgängen bei VW-Neuzulassungen infolge des VW-Skandals. Der Effekt ist umso stärker, je länger die Region durch protestantische Werte geprägt ist. Offenbar können bestimmte gesellschaftskulturelle Ausprägungen wie Religion und deren Normen ein Korrektiv für Verfehlungen von Unternehmen darstellen und somit verzögerte oder ausbleibende Maßnahmen von Politikern und Regulierern zum Teil ersetzen.
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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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Does Machine Learning Help us Predict Banking Crises?
Johannes Beutel, Sophia List, Gregor von Schweinitz
Journal of Financial Stability,
December
2019
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 metric, 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 efficiently, 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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