This article synthesizes the insights gained through presentations and discussions at the 2023 IEEE Workshop on Norbert Wiener in the 21st Century (21CW2023), which focused on “The Future of Work in the Age of Automation.” Hosted at Purdue University, this interdisciplinary convening of technologists, social scientists, and humanists explored the impacts of automation on labor, drawing on Wiener’s legacy of insights as a backdrop to examine the technologically mediated future we face in coming decades. The workshop presented a rare opportunity to reflect critically on these issues at a pivotal moment in human and technological history, and to elicit underappreciated dimensions. Areas of focus include: the qualitative and quantitative losses associated with automation and AI, the impacts automation has for questions about the meaningfulness of work, the challenges we face related to uncertainty and lack of predictability in technological advancement, and the opportunities that exist for centering human values and agency in these conversations. While acknowledging many items for concern in the context of automation in the future of work, such as the domination of economic narratives, a potential loss of qualitative texture, and the neglect of certain issues key to human identity, the authors conclude by offering optimistic visions—or calls—for redefining value and labor, preserving human agency, and embracing creative problem-solving.

The article examines stock index price responses in Brazil, Chile, and Mexico to those in the US, Spain, and four European countries during three sub-periods surrounding the neoliberal reforms of the 1990s: 1988 to 1994, 1995 to 1998, and 1999 to 2004, using VAR modeling. It finds that equity markets became more interconnected as countries opened to international trade and capital flows, and that there was an increasing impact of Spain on Latin American equity markets. Stronger economic linkages (more trade and foreign direct investment) between Spain and these countries, especially in Brazil, seem to explain increased equity markets’ interconnectedness.

In a linear economy, manufacturing is less costly and more profitable than remanufacturing because of reduced private costs of utilization and production. However, manufacturing also involves higher resource extraction and waste as externalized costs than remanufacturing. We use a vintage capital framework to assess technological innovations in remanufacturing and their potential benefits to society and human occupations. Our study shows that replacing manufacturing with remanufacturing technologies creates positive static and dynamic circular economy externalities. These externalities can be quantified to assess improvements in social outcomes. A smartphone remanufacturing innovation case study is presented as an illustration of the article’s main ideas. Future research should investigate additional specific cases to develop a comprehensive methodology for assessing the impact of remanufacturing innovations on social outcomes. This will provide valuable insights into the broader implications of remanufacturing practices.

This paper presents an asymptotically optimal time interval selection criterion for the long-run correlation block estimator (Bartlett kernel estimator) based on the Newey–West and Andrews–Monahan approaches. An alignment criterion that enhances finite-sample performance is also proposed. The procedure offers an optimal alternative to the customary practice in finance and economics of heuristically or arbitrarily choosing time intervals or lags in correlation studies. A Monte Carlo experiment using parameters derived from Dow Jones returns data confirms that the procedure can be MSE-superior to alternatives such as aggregation over arbitrary time intervals, parametric VAR, and Newey–West covariance matrix estimation with automatic lag selection.