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Working Papers

  • [1901]

    David Martinez-Miera, Rafael Repullo

    Monetary Policy, Macroprudential Policy, and Financial Stability


    This paper reexamines from a theoretical perspective the role of monetary and macroprudential policies in addressing the build-up of risks in the financial system. We construct a stylized general equilibrium model in which the key friction comes from a moral hazard problem in firms' financing that banks' equity capital serves to ameliorate. Tight monetary policy is introduced by open market sales of government debt, and tight macroprudential policy by an increase in capital requirements. We show that both policies are useful, but macroprudential policy is more effective in terms of financial stability and leads to higher social welfare.

  • [1902]

    Samuel Bentolila, Juan J. Dolado, Juan F. Jimeno

    Dual Labour Markets Revisited


    This paper provides an overview of recent research on dual labour markets. Theoretical and empirical contributions on the labour-market effects of dual employment protection legislation are revisited, as well as factors behind its resilience and policies geared towards correcting its negative economic and social consequences. The topics covered include the stepping-stone or dead-end nature of temporary contracts, their effects on employment, unemployment, churn, training, productivity growth, wages, and labour market inflows and outflows. The paper reviews both theoretical advances and relevant policy discussions on a very relevant topic in many European countries, in particular in several that had a very poor employment performance during the recent global economic and financial crisis.

  • [1903]

    Dmitry Arkhangelsky

    Dealing with a Technological Bias: The Difference-in-Difference Approach


    I construct a nonlinear model for causal inference in the empirical settings where researchers observe individual-level data for few large clusters over at least two time periods. It allows for identification (sometimes partial) of the counterfactual distribution, in particular, identifying average treatment effects and quantile treatment effects. The model is exible enough to handle multiple outcome variables, multidimensional heterogeneity, and multiple clusters. It applies to the settings where the new policy is introduced in some of the clusters, and a researcher additionally has information about the pretreatment periods. I argue that in such environments we need to deal with two different sources of bias: selection and technological. In my model, I employ standard methods of causal inference to address the selection problem and use pretreatment information to eliminate the technological bias. In case of one-dimensional heterogeneity, identification is achieved under natural monotonicity assumptions. The situation is considerably more complicated in case of multidimensional heterogeneity where I propose three di erent approaches to identification using results from transportation theory.

  • [1904]

    Esteban García-Miralles, Nezih Guner, Roberto Ramos

    The Spanish Personal Income Tax: Facts and Parametric Estimates


    In this paper, we use administrative data on tax returns to characterize the distributions of before and after-tax income, tax liabilities, and tax credits in Spain for individuals and households. We use the most recent available data, 2015 for individuals and 2013 for households, but also discuss how the income distribution and taxes have changed since 2002. We also estimate effective tax functions that capture the underlying heterogeneity of the data in a parsimonious way. These parametric functions can be used to calculate after-tax incomes in surveys where this information is not directly available, and can also be used in quantitative work in macroeconomics and public finance.

  • [1905]

    Dmitry Arkhangelsky, Guido W. Imbens

    The Role of the Propensity Score in Fixed Effect Models


    We develop a new approach for estimating average treatment effects in the observational studies with unobserved cluster-level heterogeneity. The previous approach relied heavily on linear fixed effect specifications that severely limit the heterogeneity between clusters. These methods imply that linearly adjusting for differences between clusters in average covariate values addresses all concerns with cross-cluster comparisons. Instead, we consider an exponential family structure on the within-cluster distribution of covariates and treatments that implies that a low-dimensional sufficient statistic can summarize the empirical distribution, where this sufficient statistic may include functions of the data beyond average covariate values. Then we use modern causal inference methods to construct flexible and robust estimators.

  • [1906]

    Paula Bustos, Juan Manuel Castro Vincenzi, Joan Monras, Jacopo Ponticelli

    Structural Transformation, Industrial Specialization, and Endogenous Growth


    The introduction of new technologies in agriculture can foster structural transformation by freeing workers who find occupation in other sectors. The traditional view is that this increase in labor supply in manufacturing can lead to industrial development. However, when workers moving to manufacturing are mostly unskilled, this process reinforces a country's comparative advantage in low-skill intensive industries. To the extent that these industries undertake less R&D, this change in industrial composition can lead to lower long-run growth. We provide empirical evidence of this mechanism using a large and exogenous increase in agricultural productivity due to the legalization of genetically engineered soy in Brazil. Our results indicate that improvements in agricultural productivity, while positive in the short-run, can generate specialization in less-innovative industries and have negative effects on productivity in the long-run.


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