An Algorithmic Introduction to Savings Circles

TL;DR

This paper introduces an algorithmic framework for analyzing rotating savings and credit associations (roscas), providing welfare guarantees and empirical comparisons to explain their widespread use in communities with limited access to formal finance.

Contribution

It offers the first algorithmic analysis of roscas, including welfare approximation guarantees and experimental evaluations of their outcomes.

Findings

Welfare guarantees for roscas outcomes are established.

Experimental results show how environment features affect welfare.

The analysis rationalizes the ubiquity of roscas in informal finance.

Abstract

Rotating savings and credit associations (roscas) are informal financial organizations common in settings where communities have reduced access to formal financial institutions. In a rosca, a fixed group of participants regularly contribute sums of money to a pot. This pot is then allocated periodically using lottery, aftermarket, or auction mechanisms. Roscas are empirically well-studied in economics. They are, however, challenging to study theoretically due to their dynamic nature. Typical economic analyses of roscas stop at coarse ordinal welfare comparisons to other credit allocation mechanisms, leaving much of roscas' ubiquity unexplained. In this work, we take an algorithmic perspective on the study of roscas. Building on techniques from the price of anarchy literature, we present worst-case welfare approximation guarantees. We further experimentally compare the welfare of outcomes as key features of the environment vary. These cardinal welfare analyses further rationalize the prevalence of roscas. We conclude by discussing several other promising avenues.