Economic inequality and the long-term future

Andreas T. Schmidt (University of Groningen) and Daan Juijn (CE Delft)

GPI Working Paper No. 4-2021, published in Politics, Philosophy & Economics

Why, if at all, should we object to economic inequality? Some central arguments – the argument from decreasing marginal utility for example – invoke instrumental reasons and object to inequality because of its effects. Such instrumental arguments, however, often concern only the static effects of inequality and neglect its intertemporal consequences. In this article, we address this striking gap and investigate income inequality’s intertemporal consequences, including its potential effects on humanity’s (very) long-term future. Following recent arguments around future generations and so-called longtermism, those effects might arguably matter more than inequality’s short-term consequences. We assess whether we have instrumental reason to reduce economic inequality based on its intertemporal effects in the short, medium and the very long term. We find a good short and medium-term instrumental case for lower economic inequality. We then argue, somewhat speculatively, that we have instrumental reasons for inequality reduction from a longtermist perspective too, because greater inequality could increase existential risk. We thus have instrumental reasons for reducing inequality, regardless of which time-horizon we take. We then argue that from most consequentialist perspectives, this pro tanto reason also gives us all-things-considered reason. And even across most non-consequentialist views in philosophy, this argument gives us either an all-things-considered or at least weighty pro tanto reason against inequality.

Other working papers

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Some worry that advanced artificial agents may resist being shut down. The Incomplete Preferences Proposal (IPP) is an idea for ensuring that does not happen. A key part of the IPP is using a novel ‘Discounted Reward for Same-Length Trajectories (DReST)’ reward function to train agents to (1) pursue goals effectively conditional on each trajectory-length (be ‘USEFUL’), and (2) choose stochastically between different trajectory-lengths (be ‘NEUTRAL’ about trajectory-lengths). In this paper, we propose…

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