People
People Doctoral Students PhD Representatives Alumni Supervisors Lecturers Coordinators Doctoral Students Afroza Alam (Supervisor: Reint Gropp ) Julian Andres Diaz Acosta…
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Macroeconomic Reports
Macroeconomic Reports Local and global: IWH regularly provides current economic data - be it about the state of the East German economy, the macroeconomic development in Germany…
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IWH FDI Micro Database
IWH FDI Micro Database The IWH FDI Micro Database (FDI = Foreign Direct Investment) comprises a total population of affiliates of multinational enterprises (MNEs) in selected…
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Who Benefits from Place-based Policies? Evidence from Matched Employer-Employee Data
Philipp Grunau, Florian Hoffmann, Thomas Lemieux, Mirko Titze
IWH Discussion Papers,
No. 11,
2024
Abstract
We study the granular wage and employment effects of a German place-based policy using a research design that leverages conditionally exogenous EU-wide rules governing program parameters at the regional level. The place-based program subsidizes investments to create jobs with a subsidy rate that varies across labor market regions. The analysis uses matched data on the universe of establishments and their employees, establishment-level panel data on program participation, and regional scores that generate spatial discontinuities in program eligibility and generosity. Spatial spillovers of the program linked to changing commuting patterns can be assessed using information on place of work and place of residence, a unique feature of the data. These rich data enable us to study the incidence of the place-based program on different groups of individuals. We find that the program helps establishments create jobs that disproportionately benefit younger and less-educated workers. Funded establishments increase their wages but, unlike employment, wage gains do not persist in the long run. Employment effects estimated at the local area level are slightly larger than establishment- level estimates, suggesting limited economic spillover effects. On the other hand, spatial spillovers are large as over half of the employment increase comes from commuters. Using subsidy rates as an instrumental variable for actual subsidies indicates that it costs approximately EUR 25,000 to create a new job in the economically disadvantaged areas targeted by the program.
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27.03.2024 • 10/2024
Joint Economic Forecast 1/2024: Headwinds from Germany and abroad: institutes revise forecast significantly downwards
According to Germany’s five leading economic research institutes, the country’s economy shows cyclical and structural weaknesses. In their spring report, they revised their GDP forecast for the current year significantly downward to 0.1%. In the recent fall report, the figure was still 1.3%. Expectations for the coming year are almost unchanged at 1.4% (previously 1.5%). However, the level of economic activity will then be over 30 billion euros lower due to the current weak phase.
Oliver Holtemöller
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Alumni
IWH Alumni The IWH maintains contact with its former employees worldwide. We involve our alumni in our work and keep them informed, for example, with a newsletter. We also plan…
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Gender Equality & Anti-Discrimination
Equal Opportunities at IWH IWH commits to actively promoting equal opportunities for men and women, going beyond already existing guidelines. In 2013, 2016, 2019, and again in…
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Forecasting Economic Activity Using a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to the
German GDP
Oliver Holtemöller, Boris Kozyrev
IWH Discussion Papers,
No. 6,
2024
Abstract
In this study, we analyzed the forecasting and nowcasting performance of a generalized regression neural network (GRNN). We provide evidence from Monte Carlo simulations for the relative forecast performance of GRNN depending on the data-generating process. We show that GRNN outperforms an autoregressive benchmark model in many practically relevant cases. Then, we applied GRNN to forecast quarterly German GDP growth by extending univariate GRNN to multivariate and mixed-frequency settings. We could distinguish between “normal” times and situations where the time-series behavior is very different from “normal” times such as during the COVID-19 recession and recovery. GRNN was superior in terms of root mean forecast errors compared to an autoregressive model and to more sophisticated approaches such as dynamic factor models if applied appropriately.
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Teaching
Teaching Within the framework of its cooperations with both German and foreign universities IWH researchers are actively committed to teaching by offering academic courses. These…
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Tracking Weekly State-Level Economic Conditions
Christiane Baumeister, Danilo Leiva-León, Eric Sims
Review of Economics and Statistics,
No. 2,
2024
Abstract
This paper develops a novel dataset of weekly economic conditions indices for the 50 U.S. states going back to 1987 based on mixed-frequency dynamic factor models with weekly, monthly, and quarterly variables that cover multiple dimensions of state economies. We find considerable cross-state heterogeneity in the length, depth, and timing of business cycles. We illustrate the usefulness of these state-level indices for quantifying the main contributors to the economic collapse caused by the COVID-19 pandemic and for evaluating the effectiveness of the Paycheck Protection Program. We also propose an aggregate indicator that gauges the overall weakness of the U.S. economy.
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