Jobs and Matches: Quits, Replacement Hiring, and Vacancy Chains
Yusuf Mercan, Benjamin Schoefer
American Economic Review: Insights,
Nr. 1,
2020
Abstract
In the canonical DMP model of job openings, all job openings stem from new job creation. Jobs denote worker-firm matches, which are destroyed following worker quits. Yet, employers classify 56 percent of vacancies as quit-driven replacement hiring into old jobs, which evidently outlived their previous matches. Accordingly, aggregate and firm-level hiring tightly track quits. We augment the DMP model with longer-lived jobs arising from sunk job creation costs and replacement hiring. Quits trigger vacancies, which beget vacancies through replacement hiring. This vacancy chain can raise total job openings and net employment. The procyclicality of quits can thereby amplify business cycles.
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Financial Linkages and Sectoral Business Cycle Synchronisation: Evidence from Europe
Hannes Böhm, Julia Schaumburg, Lena Tonzer
Abstract
We analyse whether financial integration between countries leads to converging or diverging business cycles using a dynamic spatial model. Our model allows for contemporaneous spillovers of shocks to GDP growth between countries that are financially integrated and delivers a scalar measure of the spillover intensity at each point in time. For a financial network of ten European countries from 1996-2017, we find that the spillover effects are positive on average but much larger during periods of financial stress, pointing towards stronger business cycle synchronisation. Dismantling GDP growth into value added growth of ten major industries, we observe that some sectors are strongly affected by positive spillovers (wholesale & retail trade, industrial production), others only to a weaker degree (agriculture, construction, finance), while more nationally influenced industries show no evidence for significant spillover effects (public administration, arts & entertainment, real estate).
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Empirical Economics,
Nr. 1,
2020
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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Why is Unemployment so Countercyclical?
Lawrence J. Christiano, Martin S. Eichenbaum, Mathias Trabandt
Abstract
We argue that wage inertia plays a pivotal role in allowing empirically plausible variants of the standard search and matching model to account for the large countercyclical response of unemployment to shocks.
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Nowcasting East German GDP Growth: a MIDAS Approach
João Carlos Claudio, Katja Heinisch, Oliver Holtemöller
Abstract
Economic forecasts are an important element of rational economic policy both on the federal and on the local or regional level. Solid budgetary plans for government expenditures and revenues rely on efficient macroeconomic projections. However, official data on quarterly regional GDP in Germany are not available, and hence, regional GDP forecasts do not play an important role in public budget planning. We provide a new quarterly time series for East German GDP and develop a forecasting approach for East German GDP that takes data availability in real time and regional economic indicators into account. Overall, we find that mixed-data sampling model forecasts for East German GDP in combination with model averaging outperform regional forecast models that only rely on aggregate national information.
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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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Trade, Misallocation, and Capital Market Integration
Laszlo Tetenyi
IWH-CompNet Discussion Papers,
Nr. 8,
2019
Abstract
I study how cross-country capital market integration affects the gains from trade in a model with financial frictions and heterogeneous, forward-looking firms. The model predicts that misallocation among exporters increases as trade barriers fall, even as misallocation decreases in the aggregate. The reason is that financially constrained productive exporters increase their production only marginally, while unproductive exporters survive for longer and increase their size. Allowing capital inflows magnifies misallocation, because unproductive firms expand even more, leading to a decline in aggregate productivity. Nevertheless, under integrated capital markets, access to cheaper capital dominates the adverse effect on productivity, leading to higher output, consumption and welfare than under closed capital markets. Applied to the period of European integration between 1992 and 2008, I find that underdeveloped sectors experiencing higher export exposure had more misallocation of capital and a higher share of unproductive firms, thus the data is consistent with the model’s predictions. A key implication of the model is that TFP is a poor proxy for consumption growth after trade liberalisation.
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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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Thou Shalt not Bear False Witness Against Your Customers: Cultural Norms and the Volkswagen Scandal
Iftekhar Hasan, Felix Noth, Lena Tonzer
Abstract
This paper investigates whether cultural norms shaped by religion drive consumer decisions after a corporate scandal. We exploit the unexpected notice of violation by the US Environmental Protection Agency in September 2015, accusing the car producer Volkswagen (VW) to have used software to manipulate car emission values during test phases. Using a difference-in-difference model, we show that new registrations of VW (diesel) cars decline significantly in German counties with a high share of Protestants following the VW scandal. Our results suggest that the enforcement culture rooted in Protestantism affects consumer decisions and penalises corporate fraud.
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Should Forecasters Use Real‐time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
German Economic Review,
Nr. 4,
2019
Abstract
In this paper, we investigate whether differences exist among forecasts using real‐time or latest‐available data to predict gross domestic product (GDP). We employ mixed‐frequency models and real‐time data to reassess the role of surveys and financial data relative to industrial production and orders in Germany. Although we find evidence that forecast characteristics based on real‐time and final data releases differ, we also observe minimal impacts on the relative forecasting performance of indicator models. However, when obtaining the optimal combination of soft and hard data, the use of final release data may understate the role of survey information.
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