In this paper, we construct a parsimonious overlapping-generations model of human capital accumulation and study its quantitative implications for the evolution of the U.S. wage distribution from 1970 to 2000. A key feature of the model is that individuals differ in their ability to accumulate human capital, which is the main source of wage inequality in this model. We examine the response of this model to skill-biased technical change (SBTC), which is modeled as an increase in the trend growth rate of the price of human capital starting in the early 1970s. The model displays behavior that is consistent with several important trends observed in the US data, including the rise in overall wage inequality; the fall and subsequent rise in the college premium, as well as the fact that this behavior was most pronounced for younger workers; the rise in within-group inequality; the stagnation in median wage growth; and the small rise in consumption inequality despite the large rise in wage inequality. We consider different scenarios regarding how individuals’ expectations evolve during SBTC. Specifically, we study the case where individuals immediately realize the advent of SBTC (perfect foresight), and the case where they initially underestimate the future growth of the price of human capital (pessimistic priors), but learn the truth in a Bayesian fashion over time. Lack of perfect foresight appears to have little effect on the main results of the paper. Overall, the model shows promise for explaining a diverse set of wage distribution trends observed since the 1970s in a unifying human capital framework.

Search theory routinely assumes that decisions about the acceptance/rejection of job offers (and, hence, about labor market movements between jobs or across employment states) are made by individuals acting in isolation. In reality, the vast majority of workers are somewhat tied to their partners—in couples and families—and decisions are made jointly. This paper studies, from a theoretical viewpoint, the joint job-search and location problem of a household formed by a couple (e.g., husband and wife) who perfectly pools income. The objective of the exercise, very much in the spirit of standard search theory, is to characterize the reservation wage behavior of the couple and compare it to the single-agent search model in order to understand the ramifications of partnerships for individual labor market outcomes and wage dynamics. We focus on two main cases. First, when couples are risk averse and pool income, joint search yields new opportunities—similar to on-the-job search—relative to the single-agent search. Second, when the two spouses in a couple face job offers from multiple locations and a cost of living apart, joint search features new frictions and can lead to significantly worse outcomes than single-agent search.

This memo describes a revision to the mixed-frequency vector autoregression (MF-VAR) model originally constructed by Schorfheide and Song (2012) and subsequently revised by Beauchemin (2013). In this most recent version, the 14-variable model is expanded to include nonfarm payroll employment. The forecast performance of the augmented model is compared with that of its predecessor.

Does the pattern of social connections between individuals matter for macroeconomic outcomes? If so, where do these differences come from and how large are their effects? Using network analysis tools, we explore how different social network structures affect technology diffusion and thereby a country's rate of growth. The correlation between high-diffusion networks and income is strongly positive. But when we use a model to isolate the effect of a change in social networks, the effect can be positive, negative, or zero. The reason is that networks diffuse ideas and disease. Low-diffusion networks have evolved in countries where disease is prevalent because limited connectivity protects residents from epidemics. But a low-diffusion network in a low-disease environment needlessly compromises the diffusion of good ideas. In general, social networks have evolved to fit their economic and epidemiological environment. Trying to change networks in one country to mimic those in a higher-income country may well be counterproductive.

We explore the welfare consequences of different taxation schemes in an economy where agents are debt-constrained. If agents default on their debt, they are banned from future credit markets, but retain their private endowments which are subject to income taxation. A change in the tax system changes the severity of punishment from default and, hence, leads to a limitation of possible risk sharing via private contracts. The welfare consequences of a change in the tax system depend on the relative magnitudes of increased risk sharing forced by the new tax system and the reduced risk sharing in private insurance markets. We quantitatively address this issue by calibrating an artificial economy to US income and tax data. We show that for a plausible selection of the structural parameters of our model, the change to a more redistributive tax system leads to less risk sharing among individuals and lower ex-ante welfare.

This paper reviews The Defining Moment, edited by Michael D. Bordo, Claudia Goldin, and Eugene N. White. The volume studies how the Great Depression changed government policies, including changes in monetary policy, fiscal policy, banking policy, agricultural policy, social insurance, and international economic policy. I argue that a theory of policy evolution is required to answer how the Great Depression affected these policies. In the absence of this theory, the contributors provide insight into the question by showing how policies changed sharply in the 1930s with little or no historical precedent or by showing how policies were tied to political or other considerations unique to the period. While this volume doesn’t always provide answers to the questions posed, it does raise a fundamental issue in the analysis of government policy: Why during some crisis periods are bad policies adopted, whereas during other periods, they are not?

Many economists have worried about changes in the demand for money, since money demand shocks can affect output variability and have implications for monetary policy. This paper studies the theoretical implications of changes in money demand for the nonneutrality of money in the limited participation (liquidity) model and the predetermined (sticky) price model. In the liquidity model, we find that an important connection exists between the nonneutrality of money and the relative money demands of households and firms. This model predicts that the real effect of a money shock rose by 100 percent between 1952 and 1980, and subsequently declined 65 percent. In contrast, we find that the nonneutrality of money in the sticky price model is invariant to changes in money demands or other monetary factors. Several researchers have concluded from VAR analyses that the effects of money shock over time are roughly stable. This view is consistent with the predictions of the sticky price model, but is harder to reconcile with the specific pattern of time variation predicted by the liquidity model.

Antitrust regulators often attempt to prevent proposed corporate market-extension mergers or acquisitions by arguing that doing so will result in the proposer entering the market as an additional, smaller, independent competitor. In cases where this so-called doctrine of probable future competition is valid, regulators still need guidance in ranking the priority of cases to pursue. This paper modifies the approach of Dansby and Willig to compute measures of the gross benefits arising from valid regulation. Such measures relate the change in consumer plus producer surplus caused by regulation, to measures of market concentration, firm conduct assumptions, small firm profits, and market demand data.

This paper puts forward a method of policy simulation with an existing macroeconometric model under the maintained assumption that individuals form their expectations rationally. This new simulation technique grows out of Lucas’ criticism that standard econometric policy evaluation permits policy rules to change but doesn’t allow expectations mechanisms to respond as economic theory predicts they will. The technique is applied to versions of the St. Louis Federal Reserve model and the Federal Reserve-MIT-Penn (FMP) model to simulate the effects of different constant money growth policies. The results of these simulations indicate that the problem identified by Lucas may be of great quantitative importance in the econometric analysis of policy alternatives.

Real business cycles are recurrent fluctuations in an economy’s incomes, products, and factor inputs—especially labor—that are due to nonmonetary sources. These sources include changes in technology, tax rates and government spending, tastes, government regulation, terms of trade, and energy prices. Most real business cycle (RBC) models are variants or extensions of a neoclassical growth model. One such prototype is introduced. It is then shown how RBC theorists, applying the methodology of Kydland and Prescott (Econometrica 1982), use theory to make predictions about actual time series. Extensions of the prototype model, current issues, and open questions are also discussed.