Subsidy Allocations in the Presence of Income Shocks

Abstract

Poverty and economic hardship are understood to be highly complex and dynamic phenomena. Due to the multi-faceted nature of welfare, assistance programs targeted at alleviating hardship can face challenges, as they often rely on simpler welfare measurements, such as income or wealth, that fail to capture to full complexity of each family's state. Here, we explore one important dimension – susceptibility to income shocks. We introduce a model of welfare that incorporates income, wealth, and income shocks and analyze this model to show that it can vary, at times substantially, from measures of welfare that only use income or wealth. We then study the algorithmic problem of optimally allocating subsidies in the presence of income shocks. We consider two well-studied objectives: the first aims to minimize the expected number of agents that fall below a given welfare threshold (a min-sum objective) and the second aims to minimize the likelihood that the most vulnerable agent falls below this threshold (a min-max objective). We present optimal and near-optimal algorithms for various general settings. We close with a discussion on future directions on allocating societal resources and ethical implications of related approaches.

Roles for computing in social change

“…In another context, Abebe et al's work [4] on income shocks demonstrates that alternative objective functions can result in very different allocations, even when the broad policy goal is the same. When allocating subsidies to households to prevent them from experiencing adverse financial outcomes due to income shocks, we might reasonably want to minimize the expected number of people that will experience a certain negative financial state (a minsum objective function).…”

“…Several cities in the United States have issued bans against police use of facial recognition, with further bans under consideration across various states, at the federal level, and outside the United States. 4 A second risk of the rebuttal approach is that focusing on what is not possible may encourage policymakers to throw up their hands at a problem and unduly write off computational approaches altogether, when they may still have a positive role to play. Analysis of social media posts for hate speech, for example, is impossible to execute with complete fidelity; but it does not necessarily follow that platforms have no responsibility to try to filter such content.…”

Precarity: Modeling the Long Term Effects of Compounded Decisions on Individual Instability

“…We use population-level economic data to initialize the system, and allow the agents to make either locally reasonable decisions (in a bounded rationality-like framework) or allow them to maximize expected utility within epochs. Using a simulation framework with realistic input parameters and controls allows us to observe the evolution of the system in a way that would be difficult to do formally (like for example, [11] is able to do for the more specific problem of income shocks), and allows us to experiment with different kinds of interventions.…”

“…Aneja et al [10] study the effect of incarceration on access to credit -arguing that incarceration reduces the access to credit, which in turn increases recidivism. An important recent work that has greatly influenced our thinking is by Abebe et al [11]. In it, they build a theoretical model to capture the effect of income shocks on one's chance of going bankrupt and propose efficient allocations of limited stimulus to maximize the expected number of individuals saved from bankruptcy.…”

“…The decision making process: The IFP model presents challenges for the income shock process that we described in Section 4.1. In the macroeconomic literature on income fluctuation (and indeed also in the work by [11]), shocks are assumed to present in a stochastic form. Thus, while there is randomness in the shock generation process, it is a predictable kind that can be optimized for (in an expected sense).…”