U.S. production has shifted from goods-producing to service-producing industries. We assess whether this shift contributed to the decline in U.S. output volatility over the period 1949–2005 and provide an estimate of its relative importance. Growth rates of GDP by industry are analyzed in a seemingly unrelated multivariate autoregression framework with time-varying innovation covariance matrices. These changing unobserved covariance matrices are modeled as a Wishart autoregressive process of order one, which results in a nonlinear state-space system. The particle filter is used to obtain estimates of the innovation covariance matrix at each point in time. Several counterfactual experiments make it possible to apportion the decline in output volatility between the shift in the sectoral composition and changes in innovations. Our main finding is that the shift into the service sector can explain about 30% of the decline in GDP's volatility, despite the fact that some sectors became even more volatile. This result is robust across a wide variety of alternative specifications.

This paper explores the main determinants of productivity growth. The analysis is performed using Spanish firm-level data. We define a framework where the relative magnitudes of alternative, but not exclusive, sources of technical change are simultaneously estimated. Our main finding is that the average total factor productivity (TFP) growth is fully explained by embodied technical progress (i.e., either new capital goods or human capital). Our results contradict the existence of a positive contribution of economywide neutral technological progress in determining average TFP growth. They also leave little room for large, unpriced effects external to the firm, both at the aggregate and at the industry level. We find evidence of firm-specific learning by doing, short-lived and due to adoption of new processes.

This paper presents an overlapping generations model with technology choice and imperfect financial markets, and examines the evolution of the income distribution in economic development. The model shows that improvements in financial infrastructure facilitate economic development both by raising the aggregate capital–labor ratio and by causing a technological shift to more capital-intensive technologies. Although a higher capital–labor ratio under a given technology reduces inequality, a technological shift can lead to a concentration of economic rents among a smaller number of agents. We derive the condition under which an improvement in financial infrastructure actually decreases the average utility of agents.

This paper analyzes the role and importance of “inside” money, made out of commercial banks' liabilities, for a New Keynesian model of the type commonly used for monetary policy analysis. The active role of inside money stems from its unique role in allowing payment for at least some consumption goods; shocks to the production of inside money may therefore have real effects. A calibrated version of the model is shown to generate small, but nonnegligible effects of inside money shocks on output and inflation. Moreover, the presence of inside money in the model leads to a slight attenuation of the effect of technology and monetary policy shocks. Finally, it is found that it is optimal for monetary policy to react to inside money shocks, but reacting to inflation alone does not result in a significant loss of household welfare.

The Friedman rule states that steady-state welfare is maximized when there is deflation at the real rate of interest. Recent work by Khan, King, and Wolman [Review of Economic Studies 10 (4), 825–860] uses a richer model but still finds deflation optimal. In an otherwise standard New Keynesian model we show that, if households have hyperbolic discounting, small positive rates of inflation can be optimal. In our baseline calibration, the optimal rate of inflation is 2.1% and remains positive across a wide range of calibrations.

I add intangible capital to a variant of the neoclassical growth model that already features physical and human capital, and study the implications for international income differences. I calibrate the parameters associated with intangible capital by using new estimates of investment in intangibles by Corrado et al. [Review of Income and Wealth 55, 661–685 (2009)] and depreciation rates by Corrado and Hulten [American Economic Review 100, 99–104 (2010)]. I find that for a given efficiency difference between rich and poor countries, the model with intangible capital can explain more than double the income differences of the model without. Put another way, in the benchmark case, differences in intangible capital account for 14.3% of the observed income differences. I also examine the role played by intangible capital in versions of the model with barriers to accumulation. In all the variants that I consider, differences in intangible capital account for 10% to 22% of the observed income differences.

Since the 1970s, the characteristics of international business cycles have changed, and deeper economic integration has modified the features of cross-country comovement. We formally test for correlation shifts in measures of real economic activity and economic/financial integration. In Europe we find some statistically significant evidence of higher correlations for several subgroups of countries following the creation of the EMU in 1999. We detect significantly more pronounced correlations between Mexico and the United States and between Mexico and Canada in North America after the enforcement of the NAFTA in 1994. Results are derived from an econometric framework based on nonparametric iterated stationary bootstrap methods, whose statistical reliability and performance we assess through Monte Carlo simulations.

This study examines the expectational stability of rational expectations equilibria (REE) under alternative Taylor rules when trend inflation is nonzero. We find that when trend inflation is high, the REE is likely to be expectationally unstable. This result holds true regardless of the nature of the data (such as contemporaneous, forecast, or lagged data) introduced in the Taylor rule. Our results suggest that high macroeconomic volatility during the period of high trend inflation can be explained well by introducing the concept of expectational stability.