Transferability of Skills across Sectors and Heterogeneous Displacement Costs
Moises Yi, Steffen Müller, Jens Stegmaier
American Economic Review: Papers and Proceedings,
No. 5,
2017
Abstract
We use rich German administrative data to estimate new measures of skill transferability between manufacturing and other sectors. These measures capture the value of workers' human capital when applied in different sectors and are directly related to workers' displacement costs. We estimate these transferability measures using a selection correction model, which addresses workers' endogenous mobility, and a novel selection instrument based on the social network of workers. Our results indicate substantial heterogeneity in how workers can transfer their skills when they move across sectors, which implies heterogeneous displacement costs that depend on the sector to which workers reallocate.
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Do Conventional Monetary Policy Instruments Matter in Unconventional Times?
Manuel Buchholz, Kirsten Schmidt, Lena Tonzer
Abstract
This paper investigates how declines in the deposit facility rate set by the European Central Bank (ECB) affect bank behavior. The ECB aims to reduce banks’ incentives to hold reserves at the central bank and thus to encourage loan supply. However, given depressed margins in a low interest environment, banks might reallocate their liquidity toward more profitable liquid assets other than traditional loans. Our analysis is based on a sample of euro area banks for the period from 2009 to 2014. Three key findings arise. First, banks reduce their reserve holdings following declines in the deposit facility rate. Second, this effect is heterogeneous across banks depending on their business model. Banks with a more interest-sensitive business model are more responsive to changes in the deposit facility rate. Third, there is evidence of a reallocation of liquidity toward loans but not toward other liquid assets. This result is most pronounced for non-GIIPS countries of the euro area.
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Tail-risk Protection Trading Strategies
Natalie Packham, Jochen Papenbrock, Peter Schwendner, Fabian Wöbbeking
Quantitative Finance,
No. 5,
2017
Abstract
Starting from well-known empirical stylized facts of financial time series, we develop dynamic portfolio protection trading strategies based on econometric methods. As a criterion for riskiness, we consider the evolution of the value-at-risk spread from a GARCH model with normal innovations relative to a GARCH model with generalized innovations. These generalized innovations may for example follow a Student t, a generalized hyperbolic, an alpha-stable or a Generalized Pareto distribution (GPD). Our results indicate that the GPD distribution provides the strongest signals for avoiding tail risks. This is not surprising as the GPD distribution arises as a limit of tail behaviour in extreme value theory and therefore is especially suited to deal with tail risks. Out-of-sample backtests on 11 years of DAX futures data, indicate that the dynamic tail-risk protection strategy effectively reduces the tail risk while outperforming traditional portfolio protection strategies. The results are further validated by calculating the statistical significance of the results obtained using bootstrap methods. A number of robustness tests including application to other assets further underline the effectiveness of the strategy. Finally, by empirically testing for second-order stochastic dominance, we find that risk averse investors would be willing to pay a positive premium to move from a static buy-and-hold investment in the DAX future to the tail-risk protection strategy.
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Inflation Dynamics During the Financial Crisis in Europe: Cross-sectional Identification of Long-run Inflation Expectations
Geraldine Dany-Knedlik, Oliver Holtemöller
IWH Discussion Papers,
No. 10,
2017
Abstract
We investigate drivers of Euro area inflation dynamics using a panel of regional Phillips curves and identify long-run inflation expectations by exploiting the crosssectional dimension of the data. Our approach simultaneously allows for the inclusion of country-specific inflation and unemployment-gaps, as well as time-varying parameters. Our preferred panel specification outperforms various aggregate, uni- and multivariate unobserved component models in terms of forecast accuracy. We find that declining long-run trend inflation expectations and rising inflation persistence indicate an altered risk of inflation expectations de-anchoring. Lower trend inflation, and persistently negative unemployment-gaps, a slightly increasing Phillips curve slope and the downward pressure of low oil prices mainly explain the low inflation rate during the recent years.
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Skills, Earnings, and Employment: Exploring Causality in the Estimation of Returns to Skills
Franziska Hampf, Simon Wiederhold, Ludger Woessmann
Large-scale Assessments in Education,
No. 12,
2017
Abstract
Ample evidence indicates that a person’s human capital is important for success on the labor market in terms of both wages and employment prospects. However, unlike the efforts to identify the impact of school attainment on labor-market outcomes, the literature on returns to cognitive skills has not yet provided convincing evidence that the estimated returns can be causally interpreted. Using the PIAAC Survey of Adult Skills, this paper explores several approaches that aim to address potential threats to causal identification of returns to skills, in terms of both higher wages and better employment chances. We address measurement error by exploiting the fact that PIAAC measures skills in several domains. Furthermore, we estimate instrumental-variable models that use skill variation stemming from school attainment and parental education to circumvent reverse causation. Results show a strikingly similar pattern across the diverse set of countries in our sample. In fact, the instrumental-variable estimates are consistently larger than those found in standard least-squares estimations. The same is true in two “natural experiments,” one of which exploits variation in skills from changes in compulsory-schooling laws across U.S. states. The other one identifies technologically induced variation in broadband Internet availability that gives rise to variation in ICT skills across German municipalities. Together, the results suggest that least-squares estimates may provide a lower bound of the true returns to skills in the labor market.
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Complex-task Biased Technological Change and the Labor Market
Colin Caines, Florian Hoffmann, Gueorgui Kambourov
Review of Economic Dynamics,
April
2017
Abstract
In this paper we study the relationship between task complexity and the occupational wage- and employment structure. Complex tasks are defined as those requiring higher-order skills, such as the ability to abstract, solve problems, make decisions, or communicate effectively. We measure the task complexity of an occupation by performing Principal Component Analysis on a broad set of occupational descriptors in the Occupational Information Network (O*NET) data. We establish four main empirical facts for the U.S. over the 1980–2005 time period that are robust to the inclusion of a detailed set of controls, subsamples, and levels of aggregation: (1) There is a positive relationship across occupations between task complexity and wages and wage growth; (2) Conditional on task complexity, routine-intensity of an occupation is not a significant predictor of wage growth and wage levels; (3) Labor has reallocated from less complex to more complex occupations over time; (4) Within groups of occupations with similar task complexity labor has reallocated to non-routine occupations over time. We then formulate a model of Complex-Task Biased Technological Change with heterogeneous skills and show analytically that it can rationalize these facts. We conclude that workers in non-routine occupations with low ability of solving complex tasks are not shielded from the labor market effects of automatization.
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Optimizing Policymakers' Loss Functions in Crisis Prediction: Before, Within or After?
Peter Sarlin, Gregor von Schweinitz
Abstract
Early-warning models most commonly optimize signaling thresholds on crisis probabilities. The expost threshold optimization is based upon a loss function accounting for preferences between forecast errors, but comes with two crucial drawbacks: unstable thresholds in recursive estimations and an in-sample overfit at the expense of out-of-sample performance. We propose two alternatives for threshold setting: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide simulated and real-world evidence that this simplification results in stable thresholds and improves out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.
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Should Forecasters Use Real-time Data to Evaluate Leading Indicator Models for GDP Prediction? German Evidence
Katja Heinisch, Rolf Scheufele
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 survey 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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Banks Credit and Productivity Growth
Fadi Hassan, Filippo di Mauro, Gianmarco Ottaviano
ECB Working Paper,
No. 2008,
2017
Abstract
Financial institutions are key to allocate capital to its most productive uses. In order to examine the relationship between productivity and bank credit in the context of different financial market set-ups, we introduce a model of overlapping generations of entrepreneurs under complete and incomplete credit markets. Then, we exploit firm-level data for France, Germany and Italy to explore the relation between bank credit and productivity following the main derivations of the model. We estimate an extended set of elasticities of bank credit with respect to a series of productivity measures of firms. We focus not only on the elasticity between bank credit and productivity during the same year, but also on the elasticity between credit and future realised productivity. Our estimates show a clear Eurozone core-periphery divide, the elasticities between credit and productivity estimated in France and Germany are consistent with complete markets, whereas in Italy they are consistent with incomplete markets. The implication is that in Italy firms turn to be constrained in their long-term investments and bank credit is allocated less efficiently than in France and Germany. Hence capital misallocation by banks can be a key driver of the long-standing slow productivity growth that characterises Italy and other periphery countries.
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Creative Destruction and Subjective Well-being
Philippe Aghion, Ufuk Akcigit, Angus Deaton, Alexandra Roulet
American Economic Review,
No. 12,
2016
Abstract
In this paper we analyze the relationship between turnover-driven growth and subjective well-being. Our model of innovation-led growth and unemployment predicts that: (i) the effect of creative destruction on expected individual welfare should be unambiguously positive if we control for unemployment, less so if we do not; (ii) job creation has a positive and job destruction has a negative impact on well-being; (iii) job destruction has a less negative impact in areas with more generous unemployment insurance policies; and (iv) job creation has a more positive effect on individuals that are more forward-looking. The empirical analysis using cross sectional MSA (metropolitan statistical area)-level and individual-level data provide empirical support to these predictions.
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