Working Papers
2019
December 27, 2019
Immigration and Wage Dynamics in Germany
Description: German wages have not increased very rapidly in the last decade despite strong employment growth and a 5 percentage point decline in the unemployment rate. Our analysis shows that a large part of the decline in unemployment was structural. Micro-founded Phillips curves fit the German data rather well and suggest that relatively low wage growth can be largely attributed to low inflation expectations and low productivity growth. There is no evidence – from either aggregate or micro-level administrative data – that large immigration flows since 2012 have had dampening effects on aggregate wage growth, as complementarity effects offset composition and competition effects.
December 27, 2019
Global Value Chains and External Adjustment: Do Exchange Rates Still Matter?
Description: The paper explores how international integration through global value chains shapes the working of exchange rates to induce external adjustment both in the short and medium run. The analysis indicates that greater integration into international value chains reduces the exchange rate elasticity of gross trade volumes. This result holds both in the short and medium term, pointing to the rigidity of value chains. At the same time, greater value chain integration is associated with larger gross trade flows, relative to GDP, which tends to amplify the effect of exchange rate movements. Overall, combining these two results suggests that, for most countries, integration into global value chains does not materially alter the working of exchange rates and the benefits of exchange rate flexibility in facilitating external adjustment remain.
December 27, 2019
Cross-Border Currency Exposures
Description: This paper provides a dataset on the currency composition of the international investment position for a group of 50 countries for the period 1990-2017. It improves available data based on estimates by incorporating actual data reported by statistical authorities and refining estimation methods. The paper illustrates current and new uses of these data, with particular focus on the evolution of currency exposures of cross-border positions.
December 27, 2019
Political Costs of Tax-Based Consolidations
Description: This paper studies the impact of tax-based consolidations on reelection outcomes. Using a granular database of tax-based consolidations for a panel of 10 OECD countries over the last 40 years, we find that tax reforms are politically costly but some reforms are costlier than others. Measures aimed primarily at reducing existing deficits and debt are costlier than tax consolidation policies for improving long-term growth prospects. Electoral costs are particularly high for broad-based indirect tax and corporate tax reforms. Voters tend to penalize governments less if tax consolidations are announced early in the government’s term or if the government has a strong political mandate. Favorable economic conditions increase public support for tax-based consolidations. Personal income tax reforms are electorally salient if the reforms are frontloaded, announced during recessions, and in less progressive tax systems.
December 27, 2019
Inflation and Public Debt Reversals in Advanced Economies
Description: This paper quantitatively assesses the effects of inflation shocks on the public debt-to-GDP ratio in 19 advanced economies using simulation and estimation approaches. The simulations based on the debt dynamics equation and estimations of impulse responses by local projections both suggest that a 1 percentage point shock to inflation rate reduces the debt-to-GDP ratio by about 0.5 to 1 percentage points. The results also suggest that the impact is larger and more persistent when the debt maturity is longer, but the difference from the benchmark case is not significant. These results imply that modestly higher inflation, even if accompanied by some financial repression, could reduce public debt burden only marginally in many advanced economies.
December 27, 2019
Autonomous Factor Forecast Quality: The Case of the Eurosystem
Description: The publication of liquidity forecasts can be understood as part of central banks’ push toward greater transparency regarding monetary policy implementation. However, the advantages of transparency can only be realized if the information provided is accurate and reliable. This paper (1) provides an overview of the international practice of publishing the forecasts; (2) proposes and implements a framework to evaluate the accuracy and reliability of forecasts using the long history of Eurosystem forecasts as a case study; and (3) analyzes the Eurosystem forecast errors to determine the factors influencing forecast quality. A supporting factor for a high-quality forecast is the contemporaneousness of the information used, whereas money market segmentation can weigh on forecast quality.
December 27, 2019
Post-Crisis Changes in Global Bank Business Models: A New Taxonomy
Description: The Global Financial Crisis unleashed changes in the operating and regulatory environments for large international banks. This paper proposes a novel taxonomy to identify and track business model evolution for the 30 Global Systemically Important Banks (G-SIBs). Drawing from banks’ reporting, it identifies strategies along four dimensions –consolidated lines of business and geographic orientation, and the funding models and legal entity structures of international operations. G-SIBs have adjusted their business models, especially by reducing market intensity. While G-SIBs have maintained international orientation, pressures on funding models and entity structures could affect the efficiency of capital flows through the bank channel.
December 27, 2019
Innovate to Lead or Innovate to Prevail: When do Monopolistic Rents Induce Growth?
Description: This paper extends the Schumpeterian model of creative destruction by allowing followers’ cost of innovation to increase in their technological distance from the leader. This assumption is motivated by the observation the more technologically ad- vanced the leader is, the harder it is for a follower to leapfrog without incurring extra cost for using leader’s patented knowledge. Under this R&D cost structure, leaders innovate to increase their technological advantage so that followers will eventually stop innovating, allowing leadership to prevail. A new steady state then emerges featuring both leaders and followers innovating in few industries with low aggregate growth.
December 27, 2019
The Role of Board Oversight in Central Bank Governance: Key Legal Design Issues
Description: This paper discusses key legal issues in the design of Board Oversight in central banks. Central banks are complex and sophisticated organizations that are challenging to manage. While most economic literature focuses on decision-making in the context of monetary policy formulation, this paper focuses on the Board oversight of central banks—a central feature of sound governance. This form of oversight is the decision-making responsibility through which an internal body of the central bank—the Oversight Board—ensures that the central bank is well-managed. First, the paper will contextualize the role of Board oversight into the broader legal structure for central bank governance by considering this form of oversight as one of the core decision-making responsibilities of central banks. Secondly, the paper will focus on a number of important legal design issues for Board Oversight, by contrasting the current practices of the IMF membership’s 174 central banks with staff’s advisory practice developed over the past 50 years.
December 27, 2019
Completing the Market: Generating Shadow CDS Spreads by Machine Learning
Description: We compared the predictive performance of a series of machine learning and traditional methods for monthly CDS spreads, using firms’ accounting-based, market-based and macroeconomics variables for a time period of 2006 to 2016. We find that ensemble machine learning methods (Bagging, Gradient Boosting and Random Forest) strongly outperform other estimators, and Bagging particularly stands out in terms of accuracy. Traditional credit risk models using OLS techniques have the lowest out-of-sample prediction accuracy. The results suggest that the non-linear machine learning methods, especially the ensemble methods, add considerable value to existent credit risk prediction accuracy and enable CDS shadow pricing for companies missing those securities.