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U.S. data reveal three facts: (1) the share of goods in total expenditure declines at a constant rate over time, (2) the price of goods relative to services declines at a constant rate over time, and (3) poor households spend a larger fraction of their budget on goods than do rich households. I provide a macroeconomic model with non-Gorman preferences that rationalizes these facts, along with the aggregate Kaldor facts. The model is parsimonious and admits an analytical solution. Its functional form allows a decomposition of U.S. structural change into an income and substitution effect. Estimates from micro data show each of these effects to be of roughly equal importance.

In the postwar era, developed economies have experienced two substantial trends in the net capital share of aggregate income: a rise during the last several decades, which is well known, and a fall of comparable magnitude that continued until the 1970s, which is less well known. Overall, the net capital share has increased since 1948, but once disaggregated this increase turns out to come entirely from the housing sector: the contribution to net capital income from all other sectors has been zero or slightly negative, as the fall and rise have offset each other. Several influential accounts of the recent rise emphasize the role of increased capital accumulation, but this view is at odds with theory and evidence: it requires empirically improbable elasticities of substitution, and it presumes a correlation between the capital-income ratio and capital share that is not visible in the data. A more limited narrative that stresses scarcity and the increased cost of housing better fits the data.These results are clarified using a new, multisector model of factor shares.

We develop new evidence on the cumulative earnings losses associated with job displacement, drawing on longitudinal Social Security records from 1974 to 2008. In present-value terms, men lose an average of 1.4 years of predisplacement earnings if displaced in mass-layoff events that occur when the national unemployment rate is below 6 percent. They lose a staggering 2.8 years of predisplacement earnings if displaced when the unemployment rate exceeds 8 percent. These results reflect discounting at a 5 percent annual rate over 20 years after displacement. We also document large cyclical movements in the incidence of job loss and job displacement and present evidence on how worker anxieties about job loss, wage cuts, and job opportunities respond to contemporaneous economic conditions. Finally, we confront leading models of unemployment fluctuations with evidence on the present-value earnings losses associated with job displacement. The 1994 model of Dale Mortensen and Christopher Pissarides, extended to include search on the job, generates present-value losses that are only one-fourth as large as observed losses. Moreover, present-value losses in the model vary little with aggregate conditions at the time of displacement, unlike the pattern in the data.

Many technological innovations replace workers with machines. But this capital–labor substitution need not reduce aggregate labor demand, because it simultaneously induces four countervailing responses: own-industry output effects; cross-industry input–output effects; between-industry shifts; and final demand effects. We quantify these channels using four decades of harmonized cross-country and industry data, whereby we measure automation as industry-level movements in total factor productivity that are common across countries. We find that automation displaces employment and reduces labor’s share of value added in the industries where it originates (a direct effect). In the case of employment, these own-industry losses are reversed by indirect gains in customer industries and induced increases in aggregate demand. By contrast, own-industry labor share losses are not recouped elsewhere. Our framework can account for a substantial fraction of the reallocation of employment across industries and the aggregate fall in the labor share over the last three decades. It does not, however, explain why the labor share fell more rapidly during the 2000s.

The supply and price of skilled labor relative to unskilled labor have changed dramatically over the postwar period. The relative quantity of skilled labor has increased substantially, and the skill premium, which is the wage of skilled labor relative to that of unskilled labor, has grown significantly since 1980. Many studies have found that accounting for the increase in the skill premium on the basis of observable variables is difficult and have concluded implicitly that latent skill-biased technological change must be the main factor responsible. This paper examines that view systematically. We develop a framework that provides a simple, explicit economic mechanism for understanding skill-biased technological change in terms of observable variables, and we use the framework to evaluate the fraction of variation in the skill premium that can be accounted for by changes in observed factor quantities. We find that with capital-skill complementarity, changes in observed inputs alone can account for most of the variations in the skill premium over the last 30 years.

Technology is widely considered the main source of economic progress, but it has also generated cultural anxiety throughout history. The developed world is now suffering from another bout of such angst. Anxieties over technology can take on several forms, and we focus on three of the most prominent concerns. First, there is the concern that technological progress will cause widespread substitution of machines for labor, which in turn could lead to technological unemployment and a further increase in inequality in the short run, even if the long-run effects are beneficial. Second, there has been anxiety over the moral implications of technological process for human welfare, broadly defined. While, during the Industrial Revolution, the worry was about the dehumanizing effects of work, in modern times, perhaps the greater fear is a world where the elimination of work itself is the source of dehumanization. A third concern cuts in the opposite direction, suggesting that the epoch of major technological progress is behind us. Understanding the history of technological anxiety provides perspective on whether this time is truly different. We consider the role of these three anxieties among economists, primarily focusing on the historical period from the late 18th to the early 20th century, and then compare the historical and current manifestations of these three concerns.

Rising profitability and market valuations of US businesses, sluggish wage growth and a declining labor share of income, and reduced unemployment and inflation have defined the macroeconomic environment of the last generation. This paper offers a unified explanation for these phenomena based on reduced worker power. Using individual, industry, and state-level data, we demonstrate that measures of reduced worker power are associated with lower wage levels, higher profit shares, and reductions in measures of the non-accelerating inflation rate of unemployment (NAIRU). We argue that the declining worker power hypothesis is more compelling as an explanation for observed changes than increases in firms’ market power, both because it can simultaneously explain a falling labor share and a reduced NAIRU and because it is more directly supported by the data.

We present a task-based model in which high- and low-skill workers compete against machines in the production of tasks. Low-skill (high-skill) automation corresponds to tasks performed by low-skill (high-skill) labor being taken over by capital. Automation displaces the type of labor it directly affects, depressing its wage. Through ripple effects, automation also affects the real wage of other workers. Counteracting these forces, automation creates a positive productivity effect, pushing up the price of all factors. Because capital adjusts to keep the interest rate constant, the productivity effect dominates in the long run. Finally, low-skill (high-skill) automation increases (reduces) wage inequality.

We show that labor market frictions are first-order for understanding credit markets. Wage growth and labor share forecast aggregate credit spreads and debt growth as well as or better than alternative predictors. They also predict credit risk and debt growth in a cross section of international firms. Finally, high labor share firms choose lower financial leverage. A model with labor market frictions and risky long-term debt can explain these findings, and produce large credit spreads despite realistically low default probabilities. This is because precommitted payments to labor make other committed payments (i.e., interest) riskier.

We study the cyclical comovement nominal wage growth (either monthly earnings or hourly wage rate) and labor market flows. We use microdata from the Survey of Income and Program Participation over 1996-2013 to purge composition effects in worker and job characteristics and to isolate the reallocative effect of Employer-to-Employer (EE) transitions. We find an “EE wage Phillips curve”: wage inflation comoves positively with EE as strongly as with the employment rate. This correlation holds for job stayers; we interpret the EE rate as a measure of labor demand. We find no analogous evidence for the job-finding rate from unemployment.