Church Membership and Economic Recovery: Evidence from the 2005 Hurricane Season
Iftekhar Hasan, Stefano Manfredonia, Felix Noth
Economic Journal,
forthcoming
Abstract
This paper investigates the critical role of church membership in the process of economic recovery after high-impact natural disasters. We document a significant adverse treatment effect of the 2005 hurricane season in the Southeastern United States on establishment-level productivity. However, we find that establishments in counties with higher rates of church membership saw a significantly stronger recovery in terms of productivity for 2005–10. We also show that church membership is correlated with post-disaster entrepreneurship activities and population growth.
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26.09.2024 • 26/2024
Joint Economic Forecast 2/2024: German economy in transition ‒ weak momentum, low potential growth
The Joint Economic Forecast Project Group forecasts a 0.1% decline in Germany's gross domestic product in 2024. Looking further ahead, the institutes expect a weak recovery with growth of 0.8% (2025) and 1.3% (2026). Compared to the spring forecast, this represents a down-ward revision of 0.2 (2024) and 0.6 (2025) percentage points. “In addition to the economic downturn, the German economy is also being weighed down by structural change,” says Dr Geraldine Dany-Knedlik, head of Forecasting and Economic Policy at the German Institute for Economic Research (DIW Berlin). “Decarbonisation, digitalisation, and demographic change – alongside stronger competition with companies from China – have triggered structural adjustment processes that are dampening the long-term growth prospects of the German economy.”
Oliver Holtemöller
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Industry Mix, Local Labor Markets, and the Incidence of Trade Shocks
Steffen Müller, Jens Stegmaier, Moises Yi
Journal of Labor Economics,
No. 3,
2024
Abstract
We analyze how skill transferability and the local industry mix affect the adjustment costs of workers hit by a trade shock. Using German administrative data and novel measures of economic distance we construct an index of labor market absorptiveness that captures the degree to which workers from a particular industry are able to reallocate into other jobs. Among manufacturing workers, we find that the earnings loss associated with increased import exposure is much higher for those who live in the least absorptive regions. We conclude that the local industry composition plays an important role in the adjustment processes of workers.
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Regulating Zombie Mortgages
Jonathan Lee, Duc Duy Nguyen, Huyen Nguyen
IWH Discussion Papers,
No. 16,
2024
Abstract
Using the adoption of Zombie Property Law (ZL) across several US states, we show that increased lender accountability in the foreclosure process affects mortgage lending decisions and standards. Difference-in-differences estimations using a state border design show that ZL incentivizes lenders to screen mortgage applications more carefully: they deny more applications and impose higher interest rates on originated loans, especially risky loans. In turn, these loans exhibit higher ex-post performance. ZL also affects lender behavior after borrowers become distressed, causing them to strategically keep delinquent mortgages alive. Our findings inform the debate on policy responses to foreclosure crises.
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Non-Standard Errors
Albert J. Menkveld, Anna Dreber, Felix Holzmeister, Juergen Huber, Magnus Johannesson, Michael Koetter, Markus Kirchner, Sebastian Neusüss, Michael Razen, Utz Weitzel, Shuo Xia, et al.
Journal of Finance,
No. 3,
2024
Abstract
In statistics, samples are drawn from a population in a datagenerating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidencegenerating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
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Application Barriers and the Socioeconomic Gap in Child Care Enrollment
Henning Hermes, Philipp Lergetporer, Frauke Peter, Simon Wiederhold
IWH Discussion Papers,
No. 13,
2024
Abstract
Why are children with lower socioeconomic status (SES) substantially less likely to be enrolled in child care? We study whether barriers in the application process work against lower-SES children — the group known to benefit strongest from child care enrollment. In an RCT in Germany with highly subsidized child care (N = 607), we offer treated families information and personal assistance for applications. We find substantial, equity-enhancing effects of the treatment, closing half of the large SES gap in child care enrollment. Increased enrollment for lower-SES families is likely driven by altered application knowledge and behavior. We discuss scalability of our intervention and derive policy implications for the design of universal child care programs.
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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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