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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.

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.

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.

This paper estimates a multi-country demand-led growth model for the EU15. A decrease in the share of wages in national income in isolation leads to lower growth in Finland, France, Germany, Greece, Italy, Luxembourg, the Netherlands, Portugal, Spain, Sweden and the United Kingdom, whereas it stimulates growth in Austria, Belgium, Denmark and Ireland. However, a simultaneous decline in the wage share leads to an overall decline in EU15 GDP; hence, the EU15 as a whole is a wage-led economy. Furthermore, Austria and Ireland also experience a decline in growth when they decrease their wage share along with their trading partners. The results indicate that the decline in the wage share had significant negative effects on growth in the EU15 and supports the case for wage coordination. We present different wage-led recovery scenarios and the effects on prices, investment and net exports.

We develop a tractable general equilibrium framework in which firms are large and have market power with respect to both products and labor, and in which a firm’s decisions are affected by its ownership structure. We characterize the Cournot–Walras equilibrium of an economy where each firm maximizes a share-weighted average of shareholder utilities—rendering the equilibrium independent of price normalization. In a one-sector economy, if returns to scale are non-increasing, then an increase in “effective” market concentration (which accounts for common ownership) leads to declines in employment, real wages, and the labor share. Yet when there are multiple sectors, due to an intersectoral pecuniary externality, an increase in common ownership could stimulate the economy when the elasticity of labor supply is high relative to the elasticity of substitution in product markets. We characterize for which ownership structures the monopolistically competitive limit or an oligopolistic one is attained as the number of sectors in the economy increases. When firms have heterogeneous constant returns to scale technologies, we find that an increase in common ownership leads to markets that are more concentrated.

We provide a new channel through which monetary policy has distributional consequences at business cycle frequencies. We show that an unexpected monetary easing increases labor income inequality between high-skilled and less- skilled workers. To rationalize these findings, we build a New Keynesian DSGE model with asymmetric search-and-matching (SAM) frictions and capital-skill complementarity (CSC) in production. We show that CSC on its own introduces a dynamic demand amplification mechanism: the increase in high-skilled employment after a monetary expansion makes complementary capital more productive, encouraging a further rise in investment demand and creating a multiplier effect. SAM asymmetries magnify this channel.

The value of the elasticity of substitution between labor and capital (σ) is a crucial assumption in understanding the secular decline in the labor share of income. This paper develops and implements a new strategy for estimating this crucial parameter by combining a low-pass filter with panel data to identify the low-frequency/long-run relations appropriate to production function estimation. Standard estimation methods, which do not filter out transitory variation, generate downwardly biased estimates of 40 percent to 70 percent relative to the benchmark value. Despite correcting for this bias, our preferred estimate of 0.40 is substantially below the Cobb-Douglas assumption of σ = 1.

A demand-driven alternative to the conventional Solow–Swan growth model is analysed. Its medium run is built around Marx–Goodwin cycles of demand and distribution. Long-run income and wealth distributions follow rules of accumulation stated by Pasinetti in combination with a technical progress function for labour productivity growth incorporating a Kaldor effect and induced innovation. An explicit steady state solution is presented along with analysis of dynamics. When wage income of capitalist households is introduced, the Samuelson–Modigliani steady state ‘dual’ to Pasinetti’s cannot be stable. Numerical simulation loosely based on US data suggests that the long-run growth rate is around 2% per year and that the capitalist share of wealth may rise from about 40 to 70% due to positive medium-term feedback of higher wealth inequality into its own growth.