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.

Recently, several economies with interest rates close to zero have received large capital inflows while their central banks accumulated large foreign reserves. Concurrently, significant deviations from covered interest parity have appeared. We show that, with limited international arbitrage, a central bank's pursuit of an exchange rate policy at the ZLB can explain these facts. We provide a measure of the costs associated with this policy and show they can be sizable. Changes in external conditions that increase capital inflows are detrimental, even when they are beneficial away from the ZLB. Negative nominal rates and capital controls can reduce the costs.

Recent studies say prices change about every four months. Economists have interpreted this high frequency as evidence against the importance of sticky prices for the real effects of monetary policy. Theory implies that this interpretation is correct if most price changes are regular, but not if most are temporary, as in the data. Temporary changes have a striking feature: after such a change, the nominal price tends to return exactly to its preexisting level. We study versions of Calvo and menu cost models that replicate this feature. Both models predict that the degree of aggregate price stickiness is determined mostly by the frequency of regular price changes, not by the combined frequency of temporary and regular price changes. Since regular prices are sticky in the data, the models predict a substantial degree of aggregate price stickiness even though micro prices change frequently. In particular, the aggregate price level in our models is as sticky as in standard models in which micro prices change about once a year. In this sense, prices are sticky after all.

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.

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.

This paper describes recent modifications to the mixed-frequency model vector autoregression (MF-VAR) constructed by Schorfheide and Song (2012). The changes to the model are restricted solely to the set of variables included in the model; all other aspects of the model remain unchanged. Forecast evaluations are conducted to gauge the accuracy of the revised model to standard benchmarks and the original model.

Three of the arguments made by Temin (2008) in his review of Great Depressions of the Twentieth Century are demonstrably wrong: that the treatment of the data in the volume is cursory; that the definition of great depressions is too general and, in particular, groups slow growth experiences in Latin America in the 1980s with far more severe great depressions in Europe in the 1930s; and that the book is an advertisement for the real business cycle methodology. Without these three arguments — which are the results of obvious conceptual and arithmetical errors, including copying the wrong column of data from a source — his review says little more than that he does not think it appropriate to apply our dynamic general equilibrium methodology to the study of great depressions, and he does not like the conclusion that we draw: that a successful model of a great depression needs to be able to account for the effects of government policy on productivity.

In 2008, Peter Temin wrote a review of the book that appeared in the Journal of Economic Literature. This staff report and accompanying data file are in response to the review.

Citation for review: Temin, Peter. 2008. "Real Business Cycle Views of the Great Depression and Recent Events: A Review of Timothy J. Kehoe and Edward C. Prescott's Great Depressions of the Twentieth Century." Journal of Economic Literature, 46 (3): 669-84. DOI: https://doi.org/10.1257/jel.46.3.669

We examine the quantitative impact of policy-induced changes in innovative investment by firms on growth in aggregate productivity and output in a model that nests several of the canonical models in the literature. We isolate two statistics, the impact elasticity of aggregate productivity growth with respect to an increase in aggregate innovative investment and the degree of intertemporal knowledge spillovers in research, that play a key role in shaping the model’s predicted dynamic response of aggregate productivity, output, and welfare to a policy-induced change in the innovation intensity of the economy. Given estimates of these statistics, we find that there is only modest scope for increasing aggregate productivity and output over a 20-year horizon with uniform subsidies to firms’ investments in innovation of a reasonable magnitude, but the welfare gains from such a subsidy may be substantial.

The U.S. Great Recession featured a large decline in output and labor, tighter financial conditions, and a large increase in firm growth dispersion. We build a model in which increased volatility at the firm level generates a downturn and worsened credit conditions. The key idea is that hiring inputs is risky because financial frictions limit firms' ability to insure against shocks. An increase in volatility induces firms to reduce their inputs to reduce such risk. Out model can generate most of the decline in output and labor in the Great Recession and the observed increase in firms' interest rate spreads.