As a new general-purpose technology, robots have the potential to radically transform employment and organizations. In contrast to prior studies that predict dramatic employment declines, we find that investments in robotics are associated with increases in total firm employment but decreases in the total number of managers. Similarly, we find that robots are associated with an increase in the span of control for supervisors remaining within the organization. We also provide evidence that robot adoption is not motivated by the desire to reduce labor costs but is instead related to improving product and service quality. Our findings are consistent with the notion that robots reduce variance in production processes, diminishing the need for managers to monitor worker activities to ensure production quality. As additional evidence, we also find that robot investments predict improved performance measurement and increased adoption of incentive pay based on individual employee performance. With respect to changes in skill composition within the organization, robots predict decreases in employment for middle-skilled workers but increases in employment for low- and high-skilled workers. We also find that robots predict not only changes in employment but also corresponding adaptations in organizational structure. Robot investments are associated with both centralization and decentralization of decision-making authority depending on the task, but decision rights in either case are reassigned away from the managerial level of the hierarchy. Overall, our results suggest that robots have distinct and profound effects on employment and organizations that require fundamental changes in firm practices and organizational design.

International relative prices across industrialized countries show large and systematic deviations from relative purchasing power parity. We embed a model of imperfect competition and variable markups in a quantitative model of international trade. We find that when our model is parameterized to match salient features of the data on international trade and market structure in the United States, it can reproduce deviations from relative purchasing power parity similar to those observed in the data because firms choose to price-to-market. We then examine how pricing-to-market depends on the presence of international trade costs and various features of market structure. (JEL F12, F14, F31)

This paper examines the frequency, pervasiveness, and determinants of product switching by US manufacturing firms. We find that one-half of firms alter their mix of five-digit SIC products every five years, that product switching is correlated with both firm- and firm-product attributes, and that product adding and dropping induce large changes in firm scope. The behavior we observe is consistent with a natural generalization of existing theories of industry dynamics that incorporates endogenous product selection within firms. Our findings suggest that product switching contributes to a reallocation of resources within firms toward their most efficient use. (JEL L11, L21, L25, L60)

We study the microeconomic implications of robot adoption using a rich panel data set of Spanish manufacturing firms over a 27-year period (1990–2016). We provide causal evidence on two central questions: (1) Which firm characteristics prompt firms to adopt robots? (2) What is the impact of robots on adopting firms relative to non-adopting firms? To address these questions, we look at our data through the lens of recent attempts in the literature to formalise the implications of robot technology. As for the first question, we establish robust evidence for positive selection, i.e., ex ante better performing firms (measured through output and labour productivity) are more likely to adopt robots. On the other hand, conditional on size, ex ante more skill-intensive firms are less likely to do so. As for the second question, we find that robot adoption generates substantial output gains in the vicinity of 20–25% within four years, reduces the labour cost share by 5–7% points, and leads to net job creation at a rate of 10%. These results are robust to controlling for non-random selection into robot adoption through a difference-in-differences approach combined with a propensity score reweighting estimator. To further validate these results, we also offer structural estimates of total factor productivity (TFP) where robot technology enters the (endogenous) productivity process of firms. The results demonstrate a positive causal effect of robots on productivity as well as a complementarity between robots and exporting in boosting productivity.

In this paper, we develop a method to estimate markups using plant-level production data. Our approach relies on cost-minimizing producers and the existence of at least one variable input of production. The suggested empirical framework relies on the estimation of a production function and provides estimates of plant-level mark-ups without specifying how firms compete in the product market. We rely on our method to explore the relationship between markups and export behavior. We find that markups are estimated significantly higher when controlling for unobserved productivity; that exporters charge, on average, higher markups and that markups increase upon export entry. (JEL D22, D24, F14, L11, L60)

We examine the concerns that new technologies will render labor redundant in a framework in which tasks previously performed by labor can be automated and new versions of existing tasks, in which labor has a comparative advantage, can be created. In a static version where capital is fixed and technology is exogenous, automation reduces employment and the labor share, and may even reduce wages, while the creation of new tasks has the opposite effects. Our full model endogenizes capital accumulation and the direction of research toward automation and the creation of new tasks. If the long-run rental rate of capital relative to the wage is sufficiently low, the long-run equilibrium involves automation of all tasks. Otherwise, there exists a stable balanced growth path in which the two types of innovations go hand-in-hand. Stability is a consequence of the fact that automation reduces the cost of producing using labor, and thus discourages further automation and encourages the creation of new tasks. In an extension with heterogeneous skills, we show that inequality increases during transitions driven both by faster automation and the introduction of new tasks, and characterize the conditions under which inequality stabilizes in the long run. (JEL D63, E22, E23, E24, J24, O33, O41)

基于价值共创和要素集聚视角，采用文本挖掘方法，以2011-2019年沪深A股上市公司中的传统制造企业为样本，验证智能化转型是否提升了传统制造企业绩效，同时探讨成本粘性在智能化转型与传统制造企业绩效之间的中介效应。研究发现，智能化转型对传统制造企业的绩效水平具有显著的正向作用，且智能化转型能够通过降低成本粘性提升传统制造企业的绩效水平。当考虑企业异质性特征时，与资本密集型企业相比，劳动密集型企业的智能化转型更易降低成本粘性、实现企业绩效提升；与非国有企业相比，国有企业进行智能化转型对降低成本粘性、提升企业绩效的影响更明显。研究结论为推动我国传统制造企业智能化转型、改善企业成本管理提供理论借鉴。

Based on the perspective of value co-creation and element aggregation, this paper selects the A-share traditional manufacturing listed enterprises from 2011 to 2019 as empirical samples to verify whether intelligent transformation improves the performance of traditional manufacturing enterprises, and also discuss the moderating effect of cost stickiness through using the text mining methods. The empirical results show that, intelligent transformation has a significant positive impact on the performance of traditional manufacturing enterprises. That is, intelligent transformation of traditional manufacturing enterprises can achieve "intelligence" to win, and intelligent transformation can improve enterprise performance by reducing cost stickiness. Also, considering the heterogeneous characteristics of enterprises, compared with non-state-owned enterprises, intelligent transformation of State Owned Enterprises（SOEs）has a more obvious impact on reducing cost stickiness and improving enterprise performance; compared with capital-intensive manufacturing enterprises, intelligent transformation of labor-intensive manufacturing enterprises is also easier to reduce cost stickiness and achieve the improvement of enterprise performance. This paper provides the useful reference for promoting intelligent transformation of traditional manufacturing enterprises and improving enterprise cost management.

The fall of labor’s share of GDP in the United States and many other countries in recent decades is well documented but its causes remain uncertain. Existing empirical assessments typically rely on industry or macro data, obscuring heterogeneity among firms. In this article, we analyze micro panel data from the U.S. Economic Census since 1982 and document empirical patterns to assess a new interpretation of the fall in the labor share based on the rise of “superstar firms.” If globalization or technological changes push sales toward the most productive firms in each industry, product market concentration will rise as industries become increasingly dominated by superstar firms, which have high markups and a low labor share of value added. We empirically assess seven predictions of this hypothesis: (i) industry sales will increasingly concentrate in a small number of firms; (ii) industries where concentration rises most will have the largest declines in the labor share; (iii) the fall in the labor share will be driven largely by reallocation rather than a fall in the unweighted mean labor share across all firms; (iv) the between-firm reallocation component of the fall in the labor share will be greatest in the sectors with the largest increases in market concentration; (v) the industries that are becoming more concentrated will exhibit faster growth of productivity; (vi) the aggregate markup will rise more than the typical firm’s markup; and (vii) these patterns should be observed not only in U.S. firms but also internationally. We find support for all of these predictions.