Micro Data on Robots from the IAB Establishment Panel
Verena Plümpe, Jens Stegmaier
Jahrbücher für Nationalökonomie und Statistik,
No. 3,
2023
Abstract
Micro-data on robots have been very sparse in Germany so far. Consequently, a dedicated section has been introduced in the IAB Establishment Panel 2019 that includes questions on the number and type of robots used. This article describes the background and development of the survey questions, provides information on the quality of the data, possible checks and steps of data preparation. The resulting data is aggregated on industry level and compared with the frequently used robot data by the International Federation of Robotics (IFR) which contains robot supplier information on aggregate robot stocks and deliveries.
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Imputation Rules for the Implementation of the Pre-unification Education Variable in the BASiD Data Set
André Diegmann
Journal for Labour Market Research,
2017
Abstract
Using combined data from the German Pension Insurance and the Federal Employment Agency (BASiD), this study proposes different procedures for imputing the pre-unification education variable in the BASiD data. To do so, we exploit information on education-related periods that are creditable for the Pension Insurance. Combining these periods with information on the educational system in the former GDR, we propose three different imputation procedures, which we validate using external GDR census data for selected age groups. A common result from all procedures is that they tend to underpredict (overpredict) the share of high-skilled (low-skilled) for the oldest age groups. Comparing our imputed education variable with information on educational attainment from the Integrated Employment Biographies (IEB) reveals that the best match is obtained for the vocational training degree. Although regressions show that misclassification with respect to IEB information is clearly related to observables, we do not find any systematic pattern across skill groups.
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The Importance of Estimation Uncertainty in a Multi-Rating Class Loan Portfolio
Henry Dannenberg
IWH Discussion Papers,
No. 11,
2011
Abstract
This article seeks to make an assessment of estimation uncertainty in a multi-rating class loan portfolio. Relationships are established between estimation uncertainty and parameters such as probability of default, intra- and inter-rating class correlation, degree of inhomogeneity, number of rating classes used, number of debtors and number of historical periods used for parameter estimations. In addition, by using an exemplary portfolio based on Moody’s ratings, it becomes clear that estimation uncertainty does indeed have an effect on interest rates.
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A Novel Approach to Incubator Evaluations: The PROMETHEE Outranking Procedures
Michael Schwartz, Maximilian Göthner
IWH Discussion Papers,
No. 1,
2009
Abstract
Considerable public resources are devoted to the establishment and operation of business incubators (BIs), which are seen as catalysts for the promotion of entrepreneurship, innovation activities and regional development. Despite the vast amount of research that has focused on the outcomes or effectiveness of incubator initiatives and how to measure incubator performance, there is still little understanding of how to determine incubators that are more effective than others. Based on data from 410 graduate firms, this paper applies the multi-criteria outranking technique PROMETHEE (Preference Ranking Organization Method for Enrichment Evaluation) and compares the long-term effectiveness of five technology-oriented BIs in Germany. This is the first time that outranking procedures are used in incubator evaluations. In particular, we investigate whether PROMETHEE is a well-suited methodological approach for the evaluation and comparisons in the specific context of business incubation.
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Estimation Uncertainty in Credit Risk Assessment: Comparison of Credit Risk Using Bootstrapping and an Asymptotic Approach
Henry Dannenberg
IWH Discussion Papers,
No. 3,
2009
Abstract
For credit risk assessment, probability of default and correlation have to be estimated simultaneously. However, these estimates are uncertain. To assess this uncertainty the literature has discussed the use of asymptotic confidence regions. This kind of region though needs a long credit history for exact assessment. An alternative method to generate a confidence region for a short credit history is bootstrapping. Hence, it could be more appropriate to assess estimation uncertainty with bootstrapping than with asymptotic methods if only a short credit history is available. Based on a simulation study, it is analyzed how many periods should be available for assessing credit risk – taking account of estimation uncertainty – if bootstrapping and a Wald confidence region shall achieve similar results. This article shows that more than 100 cycles have to be available for similar results.
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Capital Stock Approximation using Firm Level Panel Data: A Modified Perpetual Inventory Approach
Steffen Müller
Jahrbücher für Nationalökonomie und Statistik,
No. 4,
2008
Abstract
Many recent studies exploring conditional factor demand or factor substitution issues use firm level panel data. A considerable number of establishment panels contains no direct information on the capital input, necessary for production or cost function estimation. Incorrect measurement of capital leads to biased estimates and casts doubt on any inference on output elasticities or input substitution properties. The perpetual inventory approach, commonly used for long panels, is a method that attenuates these problems. In this paper a modified perpetual inventory approach is proposed. This method provides more reliable measures for capital input when short firm panels are used and no direct information on capital input is available. The empirical results based on a replication study of Addison et al. (2006) support the conclusion that modified perpetual inventory is superior to previous attempts in particular when fixed effects estimation techniques are used. The method thus makes a considerable number of recently established firm panels accessible to more sophisticated production function or factor demand analyses.
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Schätzunsicherheit oder Korrelation, Welche Risikokomponente sollten Unternehmen bei der Bewertung von Kreditportfoliorisiken wann berücksichtigen?
Henry Dannenberg
IWH Discussion Papers,
No. 5,
2007
Abstract
The use of probability of default estimates to assess the risks of a credit portfolio should not ignore estimation uncertainty. The latter can be quantified by confidence intervals. But assumptions about dependencies of these intervals are inconsistent with assumptions of conventional credit portfolio models. Based on simulation studies this paper shows, that a model which include estimation uncertainty but ignore default correlation might estimate the real credit risk more correctly than a model that implicates default correlation but ignore estimation uncertainty. The latter is a trait of conventional credit portfolio models. In this paper quantifying of estimation uncertainty based on the idea of confidence intervals and the underlying probability distributions of these intervals.
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