Total Factor Productivity and the Terms of Trade
Jan Teresinski
IWH-CompNet Discussion Papers,
Nr. 6,
2019
Abstract
In this paper we analyse how the terms of trade (TOT) – the ratio of export prices to import prices – affect total factor productivity (TFP). We provide empirical macroeconomic evidence for the European Union countries based on the times series SVAR analysis and microeconomic evidence based on industry level data from the Competitiveness Research Network (CompNet) database which shows that the terms of trade improvements are associated with a slowdown in the total factor productivity growth. Next, we build a theoretical model which combines open economy framework with the endogenous growth theory. In the model the terms of trade improvements increase demand for labour employed in exportable goods production at the expense of technology production (research and development – R&D) which leads to a shift of resources from knowledge development towards physical exportable goods. This reallocation has a negative impact on the TFP growth. Under a plausible calibration the model is able to replicate the observed empirical pattern.
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Interactions between Bank Levies and Corporate Taxes: How is the Bank Leverage Affected?
Franziska Bremus, Kirsten Schmidt, Lena Tonzer
Abstract
Regulatory bank levies set incentives for banks to reduce leverage. At the same time, corporate income taxation makes funding through debt more attractive. In this paper, we explore how regulatory levies affect bank capital structure, depending on corporate income taxation. Based on bank balance sheet data from 2006 to 2014 for a panel of EU-banks, our analysis yields three main results: The introduction of bank levies leads to lower leverage as liabilities become more expensive. This effect is weaker the more elevated corporate income taxes are. In countries charging very high corporate income taxes, the incentives of bank levies to reduce leverage turn ineffective. Thus, bank levies can counteract the debt bias of taxation only partially.
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Do Start-ups Provide Employment Opportunities for Disadvantaged Workers?
Daniel Fackler, Michaela Fuchs, Lisa Hölscher, Claus Schnabel
ILR Review,
Nr. 5,
2019
Abstract
This article compares the hiring patterns of start-ups and incumbent firms to analyze whether start-ups offer relatively more job opportunities to disadvantaged workers. Using administrative linked employer–employee data for Germany that provide the complete employment biographies of newly hired workers, the authors show that young firms are more likely than incumbents to hire applicants who are older, foreign, or unemployed, or who have unstable employment histories, arrive from outside the labor force, or were affected by a plant closure. Analysis of entry wages shows that penalties for these disadvantaged workers, however, are higher in start-ups than in incumbent firms. Therefore, even if start-ups provide employment opportunities for certain groups of disadvantaged workers, the quality of these jobs in terms of initial remuneration appears to be low.
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College Choice, Selection, and Allocation Mechanisms: A Structural Empirical Analysis
J.-R. Carvalho, T. Magnac, Qizhou Xiong
Quantitative Economics,
Nr. 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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HIP, RIP, and the Robustness of Empirical Earnings Processes
Florian Hoffmann
Quantitative Economics,
Nr. 3,
2019
Abstract
The dispersion of individual returns to experience, often referred to as heterogeneity of income profiles (HIP), is a key parameter in empirical human capital models, in studies of life‐cycle income inequality, and in heterogeneous agent models of life‐cycle labor market dynamics. It is commonly estimated from age variation in the covariance structure of earnings. In this study, I show that this approach is invalid and tends to deliver estimates of HIP that are biased upward. The reason is that any age variation in covariance structures can be rationalized by age‐dependent heteroscedasticity in the distribution of earnings shocks. Once one models such age effects flexibly the remaining identifying variation for HIP is the shape of the tails of lag profiles. Credible estimation of HIP thus imposes strong demands on the data since one requires many earnings observations per individual and a low rate of sample attrition. To investigate empirically whether the bias in estimates of HIP from omitting age effects is quantitatively important, I thus rely on administrative data from Germany on quarterly earnings that follow workers from labor market entry until 27 years into their career. To strengthen external validity, I focus my analysis on an education group that displays a covariance structure with qualitatively similar properties like its North American counterpart. I find that a HIP model with age effects in transitory, persistent and permanent shocks fits the covariance structure almost perfectly and delivers small and insignificant estimates for the HIP component. In sharp contrast, once I estimate a standard HIP model without age‐effects the estimated slope heterogeneity increases by a factor of thirteen and becomes highly significant, with a dramatic deterioration of model fit. I reach the same conclusions from estimating the two models on a different covariance structure and from conducting a Monte Carlo analysis, suggesting that my quantitative results are not an artifact of one particular sample.
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Predicting Free-riding in a Public Goods Game – Analysis of Content and Dynamic Facial Expressions in Face-to-Face Communication
Dmitri Bershadskyy, Ehsan Othman, Frerk Saxen
IWH Discussion Papers,
Nr. 9,
2019
Abstract
This paper illustrates how audio-visual data from pre-play face-to-face communication can be used to identify groups which contain free-riders in a public goods experiment. It focuses on two channels over which face-to-face communication influences contributions to a public good. Firstly, the contents of the face-to-face communication are investigated by categorising specific strategic information and using simple meta-data. Secondly, a machine-learning approach to analyse facial expressions of the subjects during their communications is implemented. These approaches constitute the first of their kind, analysing content and facial expressions in face-to-face communication aiming to predict the behaviour of the subjects in a public goods game. The analysis shows that verbally mentioning to fully contribute to the public good until the very end and communicating through facial clues reduce the commonly observed end-game behaviour. The length of the face-to-face communication quantified in number of words is further a good measure to predict cooperation behaviour towards the end of the game. The obtained findings provide first insights how a priori available information can be utilised to predict free-riding behaviour in public goods games.
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flexpaneldid: A Stata Command for Causal Analysis with Varying Treatment Time and Duration
Eva Dettmann, Alexander Giebler, Antje Weyh
Abstract
>>A completely revised version of this paper has been published as: Dettmann, Eva; Giebler, Alexander; Weyh, Antje: flexpaneldid. A Stata Toolbox for Causal Analysis with Varying Treatment Time and Duration. IWH Discussion Paper 3/2020. Halle (Saale) 2020.<<
The paper presents a modification of the matching and difference-in-differences approach of Heckman et al. (1998) and its Stata implementation, the command flexpaneldid. The approach is particularly useful for causal analysis of treatments with varying start dates and varying treatment durations (like investment grants or other subsidy schemes). Introducing more flexibility enables the user to consider individual treatment and outcome periods for the treated observations. The flexpaneldid command for panel data implements the developed flexible difference-in-differences approach and commonly used alternatives like CEM Matching and difference-in-differences models. The novelty of this tool is an extensive data preprocessing to include time information into the matching approach and the treatment effect estimation. The core of the paper gives two comprehensive examples to explain the use of flexpaneldid and its options on the basis of a publicly accessible data set.
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The Effect of the Single Currency on Exports: Comparative Firm-level Evidence
Tibor Lalinsky, Jaanika Meriküll
IWH-CompNet Discussion Papers,
Nr. 1,
2019
Abstract
We investigate how adopting the euro affects exports using firm-level data from Slovakia and Estonia. In contrast to previous studies, we focus on countries that adopted the euro individually and had different exchange rate regimes prior to doing so. Following the New Trade Theory we consider three types of adjustment: firm selection, changes in product varieties and changes in the average value of the exports that compose the exports of individual firms. The euro effect is identified by a difference in differences analysis comparing exports by firms to the euro area countries with exports to the EU countries that are not members of the euro area. The results highlight the importance of the transaction costs channel related to exchange rate volatility. We find the euro has a strong pro-trade effect in Slovakia, which switched to the euro from a floating exchange rate, while it has almost no effect in Estonia, which had a fixed exchange rate to the euro prior to the euro changeover. Our findings indicate that the euro effect manifested itself mainly through the intensive margin and that the gains from trade were heterogeneous across firm characteristics.
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Do Diasporas Affect Regional Knowledge Transfer within Host Countries? A Panel Analysis of German R&D Collaborations
Lutz Schneider, Alexander Kubis, Mirko Titze
Regional Studies,
Nr. 1,
2019
Abstract
Interactive regional learning involving various actors is considered a precondition for successful innovations and, hence, for regional development. Diasporas as non-native ethnic groups are regarded as beneficial since they enrich the creative class by broadening the cultural base and introducing new routines. Using data on research and development (R&D) collaboration projects, the analysis provides tentative evidence that the size of diasporas positively affects the region’s share of outward R&D linkages enabling the exchange of knowledge. The empirical analysis further confirms that these interactions mainly occur between regions hosting the same diasporas, pointing to a positive effect of ethnic proximity rather than ethnic diversity.
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Identifying Cooperation for Innovation – A Comparison of Data Sources
Michael Fritsch, Matthias Piontek, Mirko Titze
Abstract
The value of social network analysis is critically dependent on the comprehensive and reliable identification of actors and their relationships. We compare regional knowledge networks based on different types of data sources, namely, co-patents, co-publications, and publicly subsidised collaborative Research and Development projects. Moreover, by combining these three data sources, we construct a multilayer network that provides a comprehensive picture of intraregional interactions. By comparing the networks based on the data sources, we address the problems of coverage and selection bias. We observe that using only one data source leads to a severe underestimation of regional knowledge interactions, especially those of private sector firms and independent researchers. The key role of universities that connect many regional actors is identified in all three types of data.
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