Calibration dilemmas in the ethics of distribution

Jacob M. Nebel (University of Southern California) and H. Orri Stefánsson (Stockholm University and Swedish Collegium for Advanced Study)

GPI Working Paper No. 10-2021, published in Economics & Philosophy

PDF Download
This paper was the basis for the Parfit Memorial Lecture 2021.
The recording of the Parfit Memorial Lecture is now available to view here.
This paper presents a new kind of problem in the ethics of distribution. The problem takes the form of several “calibration dilemmas,” in which intuitively reasonable aversion to small-stakes inequalities requires leading theories of distribution to recommend intuitively unreasonable aversion to large-stakes inequalities—e.g., inequalities in which half the population would gain an arbitrarily large quantity of well-being or resources. We first lay out a series of such dilemmas for a family of broadly prioritarian theories. We then consider a widely endorsed family of egalitarian views and show that, despite avoiding the dilemmas for prioritarianism, they are subject to even more forceful calibration dilemmas. We then show how our results challenge common utilitarian accounts of the badness of inequalities in resources (e.g., wealth inequality). These dilemmas leave us with a few options, all of which we find unpalatable. We conclude by laying out these options and suggesting avenues for further research.

Other working papers

Maximal cluelessness – Andreas Mogensen (Global Priorities Institute, Oxford University)

I argue that many of the priority rankings that have been proposed by effective altruists seem to be in tension with apparently reasonable assumptions about the rational pursuit of our aims in the face of uncertainty. The particular issue on which I focus arises from recognition of the overwhelming importance…

Measuring AI-Driven Risk with Stock Prices – Susana Campos-Martins (Global Priorities Institute, University of Oxford)

We propose an empirical approach to identify and measure AI-driven shocks based on the co-movements of relevant financial asset prices. For that purpose, we first calculate the common volatility of the share prices of major US AI-relevant companies. Then we isolate the events that shake this industry only from those that shake all sectors of economic activity at the same time. For the sample analysed, AI shocks are identified when there are announcements about (mergers and) acquisitions in the AI industry, launching of…

Estimating long-term treatment effects without long-term outcome data – David Rhys Bernard (Rethink Priorities), Jojo Lee and Victor Yaneng Wang (Global Priorities Institute, University of Oxford)

The surrogate index method allows policymakers to estimate long-run treatment effects before long-run outcomes are observable. We meta-analyse this approach over nine long-run RCTs in development economics, comparing surrogate estimates to estimates from actual long-run RCT outcomes. We introduce the M-lasso algorithm for constructing the surrogate approach’s first-stage predictive model and compare its performance with other surrogate estimation methods. …