The vanishing procyclicality of labor productivity

We document three changes in postwar US macroeconomic dynamics: (i) theprocyclicality of labor productivity has vanished, (ii) the relative volatility of employment has risen, and (iii) the relative (and absolute) volatility of the real wagehas risen. We propose an explanation for all three changes that is based on a common source: a decline in labor market frictions. We develop a simple model withlabor market frictions, variable effort, and endogenous wage rigidities to illustratethe mechanisms underlying our explanation. We show that the reduction in frictionsmay also have contributed to the observed decline in output volatility.

Speed of convergence of recursive least squares learning with ARMA perceptions

This paper fills a gap in the existing literature on least squareslearning in linear rational expectations models by studying a setup inwhich agents learn by fitting ARMA models to a subset of the statevariables. This is a natural specification in models with privateinformation because in the presence of hidden state variables, agentshave an incentive to condition forecasts on the infinite past recordsof observables. We study a particular setting in which it sufficesfor agents to fit a first order ARMA process, which preserves thetractability of a finite dimensional parameterization, while permittingconditioning on the infinite past record. We describe how previousresults (Marcet and Sargent [1989a, 1989b] can be adapted to handlethe convergence of estimators of an ARMA process in our self--referentialenvironment. We also study ``rates'' of convergence analytically and viacomputer simulation.

Shared knowledge

Agents use their knowledge on the history of the economy in orderto choose what is the optimal action to take at any given moment of time,but each individual observes history with some noise. This paper showsthat the amount of information available on the past evolution of theeconomy is an endogenous variable, and that this leads to overconcentrationof the investment, which can be interpreted as underinvestment in research.It presents a model in which agents have to invest at each period in one of$K$ sectors, each of them paying an exogenous return that follows a welldefined stochastic path. At any moment of time each agent receives an unbiasednoisy signal on the payoff of each sector. The signals differ across agents,but all of them have the same variance, which depends on the aggregate investmentin that particular sector (so that if almost everybody invests in it theperceptions of everybody will be very accurate, but if almost nobody doesthe perceptions of everybody will be very noisy). The degree of hetereogeneityacross agents is then an endogenous variable, evolving across time determining,and being determined by, the amount of information disclosed.As long as both the level of social interaction and the underlying precisionof the observations are relatively large agents behave in a very preciseway. This behavior is unmodified for a huge range of informational parameters,and it is characterized by an excessive concentration of the investment ina few sectors. Additionally the model shows that generalized improvements in thequality of the information that each agent gets may lead to a worse outcomefor all the agents due to the overconcentration of the investment that thisproduces.

Regional growth in Japan

I study the role of internal migration in income convergence acrossregions in Japan. Neoclassical theory predicts that migration should have beenan important source of convergence. Regression results, however, suggest thatmigration did not contribute to convergence. I investigate the possibilitythat this discrepancy is explained by taking into account the effects ofmigration on population composition, especially on educational attainment.I propose an empirical approach to quantify this ``educational compositioneffect''. It is shown that, although this effect did slow down convergence,its magnitude was too small to account for the discrepancy between theoryand empirics.

I just ran four million regressions

In this paper I try to move away from the Extreme Bounds method ofidentifying ``robust'' empirical relations in the economic growth literature.Instead of analyzing the extreme bounds of the estimates of the coefficientof a particular variable, I analyze the entire distribution. My claimin this paper is that, if we do this, the picture emerging from theempirical growth literature is not the pessimistic ``Nothing is Robust''that we get with the extreme bound analysis. Instead, we find that asubstantial number of variables can be found to be strongly relatedto growth.