Knowledge that Transforms

I examine the impact of competition on wages and productivity using a panel data set of U.K. manufacturing industries over 1954–1973. The introduction of cartel law in the United Kingdom in the late 1950s caused an intensification of price competition in previously cartelized manufacturing industries, but it did not affect those industries that were not cartelized. The econometric results from a comparison of the two groups of industries before and after the introduction of cartel law provide strong evidence of a negative effect of collusion on labor productivity growth. There is no evidence of any effect of collusion on wages. These results are robust to controlling for the potential endogeneity of collusion and are further strengthened by a comparison with U.S. data.

We compare the empirical performance of unitary and collective labor supply models, using representative data from the Dutch DNB Household Survey. We conduct a nonparametric analysis that avoids the distortive impact of an erroneously specified functional form for the preferences and/or the intrahousehold bargaining process. Our analysis focuses on the goodness of fit of the two behavioral models. To guarantee a fair comparison, we complement this goodness-of-fit analysis with a power analysis. Our results strongly favor the collective approach to modeling the behavior of multiperson households.

This paper demonstrates a method for estimating treatment effects in spatial tests, utilizing a second control group to measure unexplained spatial phenomena. The technique is implemented on two innovations in Ugandan microfinance, and we measure the ways in which concurrent shocks such as an Ebola outbreak and a contentious presidential election altered outcomes differentially across regions. By correcting for this spatial heterogeneity, we measure the impact of the policies; a program that increased borrowers' control over the terms of their loans improved outcomes, while the results of a program that bundled health insurance into the lending contract were more mixed. Copyright by the President and Fellows of Harvard College and the Massachusetts Institute of Technology.

Economists have devoted substantial attention to firms' supply of variety, but little to consumers' demand for variety. Employing the framework of home production, we trace differences in demand to differences in the opportunity costs of activities, associated with investments in human capital. Schooling alters time costs and changes the variety of activities household members choose. Time budgets from Australia, Israel, and West Germany show that higher own and spouses' incomes raise variety (suggesting positive income effects). Education increases variety independent of income and earnings; part of its impact goes beyond a correlation of educational attainment with preferences for variety.

Estimates of the effect of education on GDP (the social return) have been hard to reconcile with micro evidence on the private return to schooling. We present a simple explanation combining two ideas: imperfect substitution and endogenous skill-biased technological progress and use cross-country panel data on inequality and GDP to test these ideas. A one-year increase in the level of education reduces the private return by 2 percentage points, consistent with Katz-Murphy's (1992) elasticity of substitution. We find no evidence for reversal of this initial effect as in Acemoglu (2002). In the short run, the social return equals the private return.

This paper establishes the existence of a previously overlooked relationship between agglomeration and hours worked. Among nonprofessionals, hours worked decrease with the density of workers in the same occupation. Among professionals, the relationship is positive. This relationship is stronger for the young than for the middle-aged. Moreover, young professional hours worked are especially sensitive to the presence of rivals. The paper shows that these patterns are consistent with the selection of hard workers into cities and with the high productivity of agglomerated labor. The behavior of young professionals is also consistent with the presence of keen rivalry in larger markets, a kind of urban rat race.

A long-standing assumption among economists is that nursing home quality is common across Medicaid and private-pay patients within a shared facility. However, there has been only limited empirical work addressing this issue. Using a unique individual level panel of residents of nursing homes from seven states, we exploit both within-facility and within-person variation in payer source and quality to examine this issue. We also test the robustness of these results across states with different Medicaid and private-pay rate differentials. Across various identification strategies, our results are consistent with the assumption of common quality across Medicaid and private-paying patients within facilities.

This note revisits the proof of the steady-state growth theorem, first given by Uzawa in 1961.We provide a clear statement of the theorem, discuss intuition for why it holds, and present a new, elegant proof due to Schlicht (2006).

Identification of potential wage distributions by education is important to the study of the causal links between education, inequality, and uncertainty. Potential wage inequality within an educational group (that is, the variance in wages if all workers had the same education) exceeds the observed statistics because self-selected education truncates wage distributions. Decomposing potential wage inequality into heterogeneity (known to the agent making the educational choice) and uncertainty (unknown to the agent) suggests that wage uncertainty does not necessarily rise with education. It is unobserved heterogeneity, not uncertainty, that explains the observed relationship between college graduation and higher wage inequality.

Methods are described for the appropriate use of data obtained and analysed in real time to represent the output gap. The methods employ cointegrating VAR techniques to model real-time measures and realizations of output series jointly. The model is used to mitigate the impact of data revisions; to generate appropriate forecasts that can deliver economically meaningful output trends and that can take into account the end-of-sample problems encountered in measuring these trends; and to calculate probability forecasts that convey in a clear way the uncertainties associated with the gap measures. The methods are applied to data for the United States 1965q4–2004q4, and the improvements over standard methods are illustrated.