Trade Liberalization and Labor Market Dynamics

This paper estimates a structural dynamic equilibrium model of the Brazilian labor market in order to study trade‐induced transitional dynamics. The model features a multi‐sector economy with overlapping generations, heterogeneous workers, endogenous accumulation of sector‐specific experience, and costly switching of sectors. The model's estimates yield median costs of mobility ranging from 1.4 to 2.7 times annual average wages, but a high dispersion of these costs across the population. In addition, sector‐specific experience is imperfectly transferable across sectors, leading to additional barriers to mobility. Using the estimated model for counterfactual trade liberalization experiments, the main findings are: (1) there is a large labor market response following trade liberalization but the transition may take several years; (2) potential aggregate welfare gains are significantly reduced due to the delayed adjustment; (3) trade‐induced welfare effects depend on initial sector of employment and on worker demographics such as age and education. The experiments also highlight the sensitivity of the transitional dynamics with respect to assumptions regarding the mobility of capital.     

From reform to crisis: Argentina's pension system

Argentina underwent economic and social collapse in December 2001. The crisis brought the worst socioeconomic indicators in its history, and the pension system was not immune from this disaster, which was unparalleled in any middle-income developing country. In 1994 the pension system had been restructured, and was regarded as a viable model for other reforms elsewhere. This article discusses in general terms the features of the current pension scheme, the structural problems that were not resolved in the reform of 1994, the relation between that reform and the government's financial crisis, and the impact of the economic collapse on the pension system. Finally, it discusses some aspects of the challenges faced in building a system that is financially viable and has the potential to close the major gaps in coverage affecting both the working population and older persons.     

Future life expectancy in Australia,Europe, Japan and North America

Human life expectancy has risen in most developed countries over the last century, causing the observed demographic shifts. Babel, Bomsdorf and Schmidt (forthcoming) introduce a stochastic mortality model using panel data procedures which distinguishes between a common time effect and a common age effect of mortality evolvement. Using this mortality model, the present paper provides forecasts of future life expectancy for 17 countries divided into 12 regions: Australia, Alps, Bene, Canada, England and Wales, France, Germany, Italy, Japan, Spain, Scandinavia and the United States of America. We consider (traditional) period life expectancies as well as cohort life expectancies, the latter being a more realistic approach but less common. It turns out that a continuing increase of life expectancy is expected in all considered countries. Further, we show that the probabilistic uncertainty of forecast life expectancies is different if either period life expectancies or cohort life expectancies are considered and, moreover, the uncertainty increases substantially if the error of parameter estimation is included.     

Bayesian inference for the Birnbaum–Saunders nonlinear regression model

We develop a Bayesian analysis for the class of Birnbaum–Saunders nonlinear regression models introduced by Lemonte and Cordeiro (Comput Stat Data Anal 53:4441–4452, 2009). This regression model, which is based on the Birnbaum–Saunders distribution (Birnbaum and Saunders in J Appl Probab 6:319–327, 1969a), has been used successfully to model fatigue failure times. We have considered a Bayesian analysis under a normal-gamma prior. Due to the complexity of the model, Markov chain Monte Carlo methods are used to develop a Bayesian procedure for the considered model. We describe tools for model determination, which include the conditional predictive ordinate, the logarithm of the pseudo-marginal likelihood and the pseudo-Bayes factor. Additionally, case deletion influence diagnostics is developed for the joint posterior distribution based on the Kullback–Leibler divergence. Two empirical applications are considered in order to illustrate the developed procedures.     

Consistency of the kernel density estimator: a survey

Various consistency proofs for the kernel density estimator have been developed over the last few decades. Important milestones are the pointwise consistency and almost sure uniform convergence with a fixed bandwidth on the one hand and the rate of convergence with a fixed or even a variable bandwidth on the other hand. While considering global properties of the empirical distribution functions is sufficient for strong consistency, proofs of exact convergence rates use deeper information about the underlying empirical processes. A unifying character, however, is that earlier and more recent proofs use bounds on the probability that a sum of random variables deviates from its mean.     

HUMAN CAPITAL IN GROWTH REGRESSIONS: HOW MUCH DIFFERENCE DOES DATA QUALITY MAKE?

We construct estimates of educational attainment for a sample of 21 OECD countries. Our series incorporate previously unexploited information and remove sharp breaks in the data that can only reflect changes in classification criteria. We then construct indicators of the information content of our estimates and a number of previously available data sets and examine their performance in several growth specifications. We find a clear positive correlation between data quality and the size and significance of human capital coefficients in growth regressions. Using an extension of the classical errors in variables model to correct for measurement error bias, we construct a set of meta‐estimates of the coefficient of years of schooling in an aggregate Cobb‐Douglas production function. Our results suggest that the value of this parameter is likely to be above 0.60. (JEL: O40, I20, O30, C19)