Economic Anthropology最新文献

In the early 2010s, the passing of US legislation mandating that health care manufacturers publicly disclose their financial relationships with physicians gave rise to the field of aggregate spend. Within corporate compliance offices in the life sciences, aggregate spend professionals track updates to transparency legislation, develop protocols for reporting data on their company's interactions with and payments to health care providers, and monitor data to assess the company's compliance with relevant laws. Drawing on observations of discourse at professional events, I argue that the labor of producing compliant transparency data defies rationalization even as it seeks to abide by laws and regulations. The article extends anthropological theorizing of compliance by showing how aggregate spend professionals carve out certain agencies to comply in strategic ways. It further contributes to the anthropological understanding of labor in the data economy by showing how the work of these data professionals, while undervalued in the organization, invisibilized by the logics of the transparency program, and often presumed to involve routinized labor, in fact involves considerable care and discernment.

This essay introduces a collection of essays on illiberal economies, arenas for paradoxical practices that simultaneously critique but rely upon liberal institutions and norms. Looking across the cases that the essays present, we argue that three themes stand out as especially salient for helping us gain greater analytical purchase on the challenges that contemporary forms of illiberalism pose, both analytically and politically: exclusion and marginalization, affective sociability, and the navigation of contradictions. Taken together, we argue, these three thematics offer avenues for exploring more fully the affordances and limits of contemporary illiberalism, both in its economic guise and more broadly.

Vertical farming, a form of indoor controlled-environment agriculture, attracted substantial amounts of private and public capital and media praise especially during the five-year period from 2018 to 2023. Both funding and accolades rest on a number of contradictory value propositions concerning land, labor, and capital. These contradictions emerge, in turn, from the sector's marketing as a technologically revolutionary, data-driven, postscarcity, postmaterial form of farming with the capability to spark commensurate revolutions in farm labor. To its public funders, whose support is typically reduced taxation and regulation, vertical farming promises high-tech green-collar employment in economically distressed areas. To its private funders, the prospect of returns on intellectual property embedded in proprietary data analysis platforms implicitly promises reduced labor spending through automation. Recent bankruptcies among vertical farms in the United States that attracted the largest capital investments have provided new material for retrospective study, including public court dockets and asset auctions. In combination with multimodal media analysis and anonymous interviews, this article proposes that vertical farming's failures reflect its inherent dependence on site-specific legacies of disinvestment and devaluation. These failures highlight structural tensions and contradictions between mobile, speculative green capital and the constraints of fixed assets, local labor markets, and the demands of an elite consumer base whose expectations are shaped by narratives of sustainability, convenience, and social distinction.

The 2024 joint meeting of the Society for Economic Anthropology and the Society for the Anthropology of Work took the theme of “Work and the Data Economy.” The articles in this special issue, first presented at the meeting, explore the datafication of ethnographic settings not conventionally associated with high-tech or knowledge work. In this introduction, we argue that datafication is a key technology of abstraction that aggregates and transforms the contextual particularities of the world into countable, computationally tractable representations. These transformations are often explicitly or implicitly contemptuous of the work practices they abstract from. Reading the issue through the themes of abstraction and contempt, we highlight the emerging shifts in the organization, valuation, and control of labor that the articles collectively reveal.

The mental health care profession is undergoing significant shifts as it increasingly moves online and more people engage in psychotherapy through mental health platforms. This article examines how US-based therapists experience the transformation of their work as they are recruited into the economic, technological, and labor arrangements of the digital economy, in a new type of therapeutic exchange. Engaging new forms of therapeutic labor, therapists become (a) teletherapists managing the affordances of virtual care, (b) platform workers subject to algorithmic management that renders their work fungible, and (c) managers facilitating the collection of data upon which platforms rely. The reshaping of therapeutic labor in platform therapy is affecting how therapeutic work is done: the psychotherapeutic profession, the relationships that can be established with clients, and the outcomes and expectations placed on the therapeutic process itself. Drawing on interviews and group sessions with mental health providers who work on therapy platforms, I examine how therapists contend with this triple transformation, showing how platform therapy is embedded in processes of platformization, datafication, and financialization to deliver the promise of “on-demand” mental health care.

The nonprofit human service sector in the United States is much slower than the private sector in adopting new data technologies to track and improve services, evaluate outcomes, and communicate successes. While for-profit companies sell data warehouses and analytic services to human service organizations, many organizations lack the resources or administrative commitment to develop data cultures and systems required to foment knowledge production and meaningful data use. Furthermore, documented tensions between key stakeholders, such as funders, managers, frontline staff, and service users, highlight important differences between industry and other sectors in the adoption of data systems. This article draws from interviews and focus groups with many stakeholders and human service organizations to highlight multiple, sometimes conflicting folk theories of data labor in human service organizations. The results demonstrate numerous competing theories for the uses of data and the work of laboring with data in human services. Drawing on these results, we propose a novel competing data values framework for reading data laborers' theories of data and data work, with a horizontal axis spanning from that categorization of poverty of data to information abundance. Our findings indicate that folk theories cluster in specific quadrants of the model, in particular, poverty and extractivism.

This article analyzes the data processes that render US agricultural landscapes knowable at scale as objects of anthropological inquiry. We focus our inquiry on the US Department of Agriculture's Cropland Data Layer (CDL), a widely used, moderate-resolution raster data set classifying national agricultural land use annually. The CDL's crop categories are based upon—but depart significantly from—those of another federal agricultural office, the Farm Service Agency (FSA). We visualize several transformations from the FSA's data categories to the CDL's to identify which crop varieties are preserved during this process and which are coarsened into higher-level categories. These patterns illustrate the logics underlying the CDL's data categorization schema. Constrained by the technical limits of remote sensing technology, these data most often obscure the presence of specialty, native, and food crops, rendering them unknowable at a national scale and entrenching long-standing productivist values into the country's agri-data infrastructure. Bounded by the same path dependencies shaping the very agricultural landscapes they codify, these data themselves become barriers to recognizing where agricultural transformations may already be under way.