March 30, 2011
By Matthew Chambers, Carlos Garriga and Donald Schlagenhauf
The objective of this paper is to understand the sources of the boom in home ownership between 1940 and 1960. The increase over this period was five times larger than the recent episode 1996-2004. In the post-depression period the government opted to intervene and regulate housing finance, provide assistance programs (i.e. through the Veteran Administration), and change tax provision towards housing. The result was a change in the maturity structure of mortgage loans, downpayment requirements and increase of credit. In addition, the economy underwent important changes in the demographic structure, the income distribution. The relative importance of these different driving forces is analyzed using a quantitative general equilibrium overlapping generation model with housing. The parameterized model is consistent with key aggregate and distributional features in the U.S. in 1940. In contrast to the recent episode, income and demographics are the crucial variables in accounting for the increase in homeownership. Essentially, the level and shape of income over the life-cycle are a precondition for the government reforms in housing markets and housing finance to play an important role in generating an increase in the aggregate home ownership. The increase in life expectancy and the shift in the distribution of age cohort also had a significant effect in the demand for housing.
These three authors have already written a series of papers with a very careful modeling of homeownership. This is the latest in this series, which highlights that large increases in homeownership are not created equal. The one right after WWII happened thanks to demographic change and income increases, which one can think of a healthy evolution. The recent increase in the US was not linked to such changes, and one may consider this to be unhealthy.
March 26, 2011
By Keiichiro Kobayashi
We generalize Lagos and Wright’s (2005) framework for a monetary economy in a way that there exist two technologies, “high” and “low,” for producing the goods in a decentralized matching market. The high technology is more productive than the low technology, while the agents who use the high technology cannot commit in advance to deliver the goods. The lack of commitment makes it infeasible to produce the goods with the high technology if trade is conducted via a simple cash payment. To use the high technology, private valuable assets, e.g., residential property, should be put up as a “hostage” à la Williamson (1983) in the transaction. In this setting, a deterioration in the balance sheet due to a financial crisis leads to the disappearance of residential assets which are not yet put up as collateral, and hinder the usage of the high technology, leading to a decline in aggregate productivity. In this case, monetary injections cannot restore productivity after a financial crisis.
I believe this is the first money search model that can address a financial crisis of the type we have recently experienced, correct me if I am wrong.
March 21, 2011
By Sanjay Chugh and Christian Merkl
We characterize efficient allocations and business cycle fluctuations in a labor selection model. Due to forward-looking hiring and labor supply decisions, efficiency entails both static and intertemporal margins. We develop welfare-relevant measures of marginal rates of transformation and efficiency along each margin that nest their counterparts in frictionless labor markets. In a calibrated version of the model, efficient fluctuations feature highly volatile unemployment and job-finding rates, in line with empirical evidence. We show analytically in a simplified version of the model that volatility arises from selection effects, rather than general equilibrium effects. We also develop sufficient conditions on wages, which are independent of the wage-determination process, that decentralize efficient allocations. Unlike the Hosios condition for matching models, there is no simple restriction on Nash bargaining that guarantees that Nash wages can support efficient allocations. Cyclical fluctuations in the Nash-bargaining economy display even larger amplification of productivity shocks into labor market outcomes than in the efficient economy, without extreme assumptions about bargaining shares, inflexibility of wages, or the size of surpluses that govern labor demand. The results establish normative and positive foundations for DSGE labor selection models.
In the past few years, labor search models have made huge progress, and this paper one of those that push the research frontier. The key here is worker heterogeneity and how it influences the volatility of unemployment and vacancies. Also, efficiency here does not depend on the Hosios condition, as new margins are opened by the employee selection process.
March 1, 2011
By Pieter Gautier and Coen Teulings
We analyze a general search model with on-the-job search and sorting of heterogeneous workers into heterogeneous jobs. This model yields a simple relationship between (i) the unemployment rate, (ii) the value of non-market time, and (iii) the max-mean wage differential. The latter measure of wage dispersion is more robust than measures based on the reservation wage, due to the long left tail of the wage distribution. We estimate this wage differential using data on match quality and allow for measurement error. The estimated wage dispersion for the US is consistent with an unemployment rate of 4-6%. We find that without search frictions, output would be between 7.5% and 18.5% higher, depending on whether or not firms can ex ante commit to wage payments.
There are plenty of papers evaluating the cost of frictions, and as far as I know, this is the first one that does this convincingly for the labor search friction. It is convincing because the model can match the unemployment rate and the wage dispersion. But is it interesting to evaluate this cost? It is very unlikely that we will ever be able to get rid of such frictions unless every worker is willing to reveal everything to Big Brother.