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We consider a model with on-the-job search where current wages depend only on current aggregate labor market conditions and idiosyncratic match-specific productivities. We show theoretically that the model replicates the findings in Bils (1985) and Beaudry and DiNardo (1991) on the history dependence in wages. We develop a method to measure match qualities in the data and show empirically that various variables summarizing past aggregate labor market conditions have explanatory power for current wages only because they are correlated with match qualities. They lose any predictive power once match qualities are accounted for. (JEL E3, E24, J3)

Recently, a number of authors have argued that the standard search model cannot generate the observed business-cycle-frequency fluctuations in unemployment and job vacancies, given shocks of a plausible magnitude. We propose a new calibration strategy of the standard model that uses data on the cost of vacancy creation and cyclicality of wages to identify the two key parameters –- the value of nonmarket activity and the bargaining weights. Our calibration implies that the model is consistent with the data. (JEL E24, E32, J31, J63, J64)

AbstractThe literature on the returns to training has pointed out that, immediately following a training episode, wages of participants in employer‐sponsored training increase substantially while wages of participants in government‐sponsored training hardly change. We argue that there is a potential selection issue—most of the government‐sponsored trainees are occupation switchers while most participants in employer‐sponsored training are occupation stayers. An occupational switch involves a substantial destruction of human capital, and once we account for the associated decline in wages, we find a large positive impact of both employer‐ and government‐sponsored training on workers' human capital.

We measure the size of the fiscal multiplier using a heterogeneous agents model with incomplete markets, capital and rigid prices and wages. This environment captures all elements that are considered essential for a quantitative analysis. First, output is (partially) demand determined due to pricing frictions in product and labor markets, so that a fiscal stimulus increases aggregate demand. Second, incomplete markets deliver a realistic distribution of the marginal propensity to consume across the population, whereas all households counterfactually behave according to the permanent income hypothesis if markets are complete. Here, poor households feature high MPCs and thus tend to spend a large fraction of the additional income that arises as a result of a fiscal stimulus, assigning a quantitatively important role to the standard textbook Keynesian cross logic. Interestingly, and unlike conventional wisdom would suggest, our dynamic forward looking model reinforces this channel significantly. Third, the model features a realistic wealth to income ratio since we allow two assets, government bonds and capital. We find that market incompleteness plays the key role in determining the size of the fiscal multiplier, which is about 1.5 if deficit financed and about 0.6 if tax financed. Surprisingly, the size of fiscal multiplier remains similar in the Great recession where the economy was in a liquidity trap. Finally, we elucidate the differences between our heterogeneous-agent incomplete-markets model to those featuring complete markets or hand-to-mouth consumers. ; The ADEMU Working Paper Series is being supported by the European Commission Horizon 2020 European Union funding for Research & Innovation, grant agreement No 649396.

We identify a key role of factor supply, driven by demographic changes, in shaping several empirical regularities that are a focus of active research in macro and labor economics. In particular, demographic changes alone can account for the large movements of the return to experience over the last four decades, for the differential dynamics of the age premium across education groups emphasized by Katz and Murphy (1992), for the differential dynamics of the college premium across age groups emphasized by Card and Lemieux (2001), and for the changes in cross-sectional and cohort-based life-cycle profiles emphasized by Kambourov and Manovskii (2005). (JEL D91, E24, I23, J11, J24, J31)