Dynamic public good provision under time preference heterogeneity

Philip Trammell (Global Priorities Institute and Department of Economics, University of Oxford)

GPI Working Paper No. 9-2021

I explore the implications of time preference heterogeneity for the private funding of public goods. The assumption that players use a common discount rate is knife-edge: relaxing it yields substantially different equilibria, for two reasons. First, time preference heterogeneity motivates intertemporal polarization, analogous to the polarization seen in a static public good game. In the simplest settings, more patient players spend nothing early in time and less patient players spending nothing later. Second, and consequently, time preference heterogeneity gives less patient players a “first-mover advantage”. Departures from the common-discounting assumption are economically significant: a patient player’s payoff in equilibrium, relative to that obtained when he is constrained to act according to a higher discount rate, typically grows unboundedly as his share of the initial budget falls to zero. Finally I discuss applications of these results to the debate over legal disbursement minima.

Other working papers

In defence of fanaticism – Hayden Wilkinson (Australian National University)

Consider a decision between: 1) a certainty of a moderately good outcome, such as one additional life saved; 2) a lottery which probably gives a worse outcome, but has a tiny probability of a far better outcome (perhaps trillions of blissful lives created). Which is morally better? Expected value theory (with a plausible axiology) judges (2) as better, no matter how tiny its probability of success. But this seems fanatical. So we may be tempted to abandon expected value theory…

Economic growth under transformative AI – Philip Trammell (Global Priorities Institute, Oxford University) and Anton Korinek (University of Virginia)

Industrialized countries have long seen relatively stable growth in output per capita and a stable labor share. AI may be transformative, in the sense that it may break one or both of these stylized facts. This review outlines the ways this may happen by placing several strands of the literature on AI and growth within a common framework. We first evaluate models in which AI increases output production, for example via increases in capital’s substitutability for labor…

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. …