Employment Effects of Introducing a Minimum Wage: The Case of Germany
Oliver Holtemöller, Felix Pohle
Abstract
This paper contributes to the empirical literature on the employment effects of minimum wages. We analysed the introduction of a statutory minimum wage in Germany in 2015 exploiting cross-sectional variation of the minimum wage affectedness. We construct two variables that measure the affectedness for approximately 300 state-industry combinations based on aggregate monthly income data. The estimation strategy consists of two steps. We test for (unidentified) structural breaks in a model with cross-section specific trends to control for state-industry specific developments prior to 2015. In a second step, we test whether the trend deviations are correlated with the minimum wage affectedness. To identify the minimum wage effect on employment, we assume that the minimum wage introduction is exogenous. Our results point towards a negative effect on marginal employment and a positive effect on socially insured employment. Furthermore, we analyse if the increase in socially insured employment is systematically related to the reduction of marginal employment but do not detect evidence.
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The Joint Dynamics of Sovereign Ratings and Government Bond Yields
Makram El-Shagi, Gregor von Schweinitz
Abstract
Can a negative shock to sovereign ratings invoke a vicious cycle of increasing government bond yields and further downgrades, ultimately pushing a country toward default? The narratives of public and political discussions, as well as of some widely cited papers, suggest this possibility. In this paper, we will investigate the possible existence of such a vicious cycle. We find no evidence of a bad long-run equilibrium and cannot confirm a negative feedback loop leading into default as a transitory state for all but the very worst ratings.
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Nested Models and Model Uncertainty
Alexander Kriwoluzky, Christian A. Stoltenberg
Scandinavian Journal of Economics,
Nr. 2,
2016
Abstract
Uncertainty about the appropriate choice among nested models is a concern for optimal policy when policy prescriptions from those models differ. The standard procedure is to specify a prior over the parameter space, ignoring the special status of submodels (e.g., those resulting from zero restrictions). Following Sims (2008, Journal of Economic Dynamics and Control 32, 2460–2475), we treat nested submodels as probability models, and we formalize a procedure that ensures that submodels are not discarded too easily and do matter for optimal policy. For the United States, we find that optimal policy based on our procedure leads to substantial welfare gains compared to the standard procedure.
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The Quantity Theory Revisited: A New Structural Approach
Makram El-Shagi, Sebastian Giesen
Macroeconomic Dynamics,
Nr. 1,
2015
Abstract
We propose a unified identification scheme to identify monetary shocks and track their propagation through the economy. We combine three approaches dealing with the consequences of monetary shocks. First, we adjust a state space version of the P-star type model employing money overhang as the driving force of inflation. Second, we identify the contemporaneous impact of monetary policy shocks by applying a sign restriction identification scheme to the reduced form given by the state space signal equations. Third, to ensure that our results are not distorted by the measurement error exhibited by the official monetary data, we employ the Divisia M4 monetary aggregate provided by the Center for Financial Stability. Our approach overcomes one of the major difficulties of previous models by using a data-driven identification of equilibrium velocity. Thus, we are able to show that a P-star model can fit U.S. data and money did indeed matter in the United States.
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Financial Constraints of Private Firms and Bank Lending Behavior
Patrick Behr, L. Norden, Felix Noth
Journal of Banking and Finance,
Nr. 9,
2013
Abstract
We investigate whether and how financial constraints of private firms depend on bank lending behavior. Bank lending behavior, especially its scale, scope and timing, is largely driven by bank business models which differ between privately owned and state-owned banks. Using a unique dataset on private small and medium-sized enterprises (SMEs) we find that an increase in relative borrowings from local state-owned banks significantly reduces firms’ financial constraints, while there is no such effect for privately owned banks. Improved credit availability and private information production are the main channels that explain our result. We also show that the lending behavior of local state-owned banks can be sustainable because it is less cyclical and neither leads to more risk taking nor underperformance.
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Money and Inflation: Consequences of the Recent Monetary Policy
Makram El-Shagi, Sebastian Giesen
Journal of Policy Modeling,
Nr. 4,
2013
Abstract
We use a multivariate state space framework to analyze the short run impact of money on prices in the United States. The key contribution of this approach is that it allows to identify the impact of money growth on inflation without having to model money demand explicitly.
Using our results, that provide evidence for a substantial impact of money on prices in the US, we analyze the consequences of the Fed's response to the financial crisis. Our results indicate a raise of US inflation above 5% for more than a decade. Alternative exit strategies that we simulate cannot fully compensate for the monetary pressure without risking serious repercussions on the real economy. Further simulations of a double dip in the United States indicate that a repetition of the unusually expansive monetary policy – in addition to increased inflation – might cause growth losses exceeding the contemporary easing of the crisis.
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Bottom-up or Direct? Forecasting German GDP in a Data-rich Environment
Katja Drechsel, Rolf Scheufele
Abstract
This paper presents a method to conduct early estimates of GDP growth in Germany. We employ MIDAS regressions to circumvent the mixed frequency problem and use pooling techniques to summarize efficiently the information content of the various indicators. More specifically, we investigate whether it is better to disaggregate GDP (either via total value added of each sector or by the expenditure side) or whether a direct approach is more appropriate when it comes to forecasting GDP growth. Our approach combines a large set of monthly and quarterly coincident and leading indicators and takes into account the respective publication delay. In a simulated out-of-sample experiment we evaluate the different modelling strategies conditional on the given state of information and depending on the model averaging technique. The proposed approach is computationally simple and can be easily implemented as a nowcasting tool. Finally, this method also allows retracing the driving forces of the forecast and hence enables the interpretability of the forecast outcome.
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Testing for Structural Breaks at Unknown Time: A Steeplechase
Makram El-Shagi, Sebastian Giesen
Computational Economics,
Nr. 1,
2013
Abstract
This paper analyzes the role of common data problems when identifying structural breaks in small samples. Most notably, we survey small sample properties of the most commonly applied endogenous break tests developed by Brown et al. (J R Stat Soc B 37:149–163, 1975) and Zeileis (Stat Pap 45(1):123–131, 2004), Nyblom (J Am Stat Assoc 84(405):223–230, 1989) and Hansen (J Policy Model 14(4):517–533, 1992), and Andrews et al. (J Econ 70(1):9–38, 1996). Power and size properties are derived using Monte Carlo simulations. We find that the Nyblom test is on par with the commonly used F type tests in a small sample in terms of power. While the Nyblom test’s power decreases if the structural break occurs close to the margin of the sample, it proves far more robust to nonnormal distributions of the error term that are found to matter strongly in small samples although being irrelevant asymptotically for all tests that are analyzed in this paper.
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Protect and Survive? Did Capital Controls Help Shield Emerging Markets from the Crisis?
Makram El-Shagi
Economics Bulletin,
Nr. 1,
2012
Abstract
Using a new dataset on capital market regulation, we analyze whether capital controls helped protect emerging markets from the real economic consequences of the 2009 financial and economic crisis. The impact of the crisis is measured by the 2009 forecast error of a panel state space model, which analyzes the business cycle dynamics of 63 middle-income countries. We find that neither capital controls in general nor controls that were specifically targeted to derivatives (that played a crucial role during the crisis) helped shield economies. However, banking regulation that limits the exposure of banks to global risks has been highly successful.
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Towards Unrestricted Public Use Business Microdata: The Synthetic Longitudinal Business Database
John M. Abowd, Ron S. Jarmin, Satkartar K. Kinney, Javier Miranda, Jerome P. Reiter, Arnold P. Reznek
International Statistical Review,
Nr. 3,
2011
Abstract
In most countries, national statistical agencies do not release establishment-level business microdata, because doing so represents too large a risk to establishments’ confidentiality. One approach with the potential for overcoming these risks is to release synthetic data; that is, the released establishment data are simulated from statistical models designed to mimic the distributions of the underlying real microdata. In this article, we describe an application of this strategy to create a public use file for the Longitudinal Business Database, an annual economic census of establishments in the United States comprising more than 20 million records dating back to 1976. The U.S. Bureau of the Census and the Internal Revenue Service recently approved the release of these synthetic microdata for public use, making the synthetic Longitudinal Business Database the first-ever business microdata set publicly released in the United States. We describe how we created the synthetic data, evaluated analytical validity, and assessed disclosure risk.
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