Parijat Chakrabarti

Many of our most pressing challenges such as adapting food systems to climate change, protecting biodiversity, and tempering entrenched inequality and polarization are characterized by complex interdependencies which standard management approaches do not well equip us to navigate. 

For example, open and critical questions include: How can we design market and innovation systems to balance a multiplicity of goals and values and to respond to challenges as they arise? How can mission-driven funders and organizations build, sustain, and coordinate relationships across institutional boundaries in effective service of broader goals? How can organizations direct systems change while embracing the fundamental uncertainties that often accompany complex problems?

In addressing such questions, I develop new organizational theory and practical strategies to inform scholars and practitioners working to build a more sustainable and humane economy.

A Paradox of Scale: Moving from Marginal Gains to Systems Analysis in the Theory and Practice of Development.

• Winner of the 2024 OMT Louis Pondy Best Dissertation Paper Award
• Nominated for AOM’s William H.Newman Award for best paper from a dissertation in the past three years
• Short version published in the AOM Best Paper Proceedings 2024 (top 10% of conference papers)
• Description: Established development practice seeks to first identify effective interventions and then scale them up. Significant scholarly attention (and not insignificant funding) is devoted to identifying effective interventions; yet comparatively little attention is given to the question of scale. Taking the case of platform-driven development interventions along Nairobi’s food system and leveraging a multi-study full-cycle research design, this article uncovers a paradox at the core of dominant strategies to scale impact for small farmers and traders. First, Study 1 uses interview and archival data to trace how market platforms, as they scale, increasingly rely on institutional investors that prioritize growth over impact. I show how these shifting investor interests structure the institutional field in which platforms operate, percolate into the organizational design of platforms, and shape relationships between platform agents and retail entrepreneurs on the ground. Building on this multi-level analysis, Study 2 leverages an original survey with matched interview data to show that, while food retail platforms can and do benefit individual small-scale (and often women) traders consistent with their original development aims, as platforms pursue growth, they tend to prioritize large-scale (and predominantly male) traders. As a result, benefits become distributed in ways that entrench existing organizational and gender inequalities in the food system. In attending to this paradox, this article develops a systems-level analysis of development interventions as they scale over time.

Making Markets Machine-Readable: Digital Transformations of Social-Ecological Systems in East Africa.

• Winner of the 2024 ONE Best Dissertation Paper Award
• Nominated for AOM’s William H.Newman Award for best paper from a dissertation in the past three years
• Short version published in the AOM Best Paper Proceedings 2024 (top 10% of conference papers)
• Description: Sociological studies typically analyze how the use of data produces the social order. Often overlooked is how social and ecological systems are ordered so as to be “seen” and governed with data. Drawing on 24 months of fieldwork in Kenya's "Silicon Savannah," this study analyzes how digital platforms intervene in agricultural markets and remake social-ecological systems. Contrary to popular narratives of the data economy, I show that platforms do not simply layer on top of existing market relations; instead, they constrain, structure, and transform market networks as they work to render market exchange machine-readable. This process, I show, reconstitutes not only the nature of exchange in Nairobi’s agricultural markets but also the actors involved, sometimes displacing the same farmers and traders whom platforms initially set out to support while pulling others into new relationships with multinational distributors and financial institutions. The study underscores implications for inequality, inclusion, and human-environment interaction in data-driven markets.

Control or Adapt: Strategies for Managing Social-Ecological Uncertainty and Implications for Market Ecosystems.

• Description: Digital platforms and wholesalers in open markets across the Global South serve similar functions; they anchor market ecosystems by linking buyers, sellers, and other actors involved in the production and distribution of food. Leveraging interview and ethnographic data with platform managers alongside data from a deep ethnography of Nairobi’s open markets, I describe the shared social and ecological uncertainties they both face in moving produce from farm to table; I compare and contrast the different tactics they each take in managing those uncertainties; and I show how those practices structure the ecosystems that they anchor. Using abductive analysis (Tavory and Timmermans 2014), I identify two heuristic strategies for managing uncertainty in social-ecological systems— one based on a logic of simplification and control and another based on a logic of diversification and adaptation. While strategies of simplification have been well-described and well-analyzed by organizational scholars, I argue that strategies rooted in a logic of diversification and adaptation present a challenge for dominant management theory and practice. I conclude by outlining a research agenda for systems strategy, relevant for an emerging class of organizations that aim to structure ecosystems and drive systems change.

Epistemic Cultures and the Partisan Divide in Attitudes Toward Climate Change: Re-evaluating Evidence from a Natural Experiment.

• Description: A large literature beginning with Egan and Mullin's canonical paper (2012) demonstrates that belief in climate change can shift on the basis of direct experience of extreme weather. This was a surprising finding showing that political beliefs may not be as rigidly attached to partisan identity and more linked to personal experience than previously believed. Using variation in local temperature as a natural experiment, I re-analyze the data in Egan and Mullin (2012) alongside newly available data to show that aggregate change in belief due to experience of extreme weather is driven largely by individuals on the right of the political spectrum. I interpret this finding in light of an emerging sociological literature that identifies partisan differences in epistemic cultures, i.e. group differences in evaluating what counts as legitimate knowledge. While left-leaning individuals are more likely to trust institutionally-vetted claims, right-leaning individuals are more likely to trust claims of direct experience. I discuss implications of this finding for strategies around climate change communication.

Chakrabarti, Parijat. 2024. “How Inclusion Scales: From Marginal Effects to Systems Analysis in the Theory and Practice of Development.” In Sonia Taneja (Ed.), Proceedings of the 84th Annual Meeting of the Academy of Management.

Chakrabarti, Parijat. 2024. “Making Markets Machine-Readable: Digital Transformations of Social-Ecological Systems in East Africa.” In Sonia Taneja (Ed.), Proceedings of the 84th Annual Meeting of the Academy of Management.

Goldstein, Adam, Charlie Eaton, Amber Villalobos, Parijat Chakrabarti, Jeremy Cohen, and Katie Donnelly. 2023. “Administrative Burden in Federal Student Loan Repayment, and Socially Stratified Access to Income-Driven Repayment Plans.” RSF: The Russell Sage Foundation Journal of the Social Sciences 9(4):86–111.

Wherry, Frederick F. and Parijat Chakrabarti. 2022. “Accounting for Credit.” Annual Review of Sociology 48(1):131-47.

Chakrabarti, Parijat and Margaret Frye. 2017. “A Mixed Methods Framework for Analyzing Text Data: Integrating Computational Techniques with Qualitative Methods.” Demographic Research 37(42):1351-1382.

• One of the earliest papers to propose a framework to integrate machine-learning with qualitative approaches to textual data. Cited by now-canonical papers in the field.