Escape poverty trap with trust? An experimental study

Kenneth S. Chan, Vivian Lei*, Filip Vesely

*Corresponding author for this work

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Abstract

In this study, we introduce an experimental approach to study the causal impact of trust on economic performance. We ask if trust can serve as a coordination device to help poor economies escape a poverty trap and, if so, whether such an impact is universal regardless of their initial levels of development. We follow Lei and Noussair (2002, 2007) and design a decentralized market economy that has the structure of an optimal growth model where output is allocated between consumption and saving over a sequence of periods. As in Lei and Noussair (2007), a threshold externality is introduced to generate two equilibria where the Pareto-inferior equilibrium is considered as a poverty trap. We find that trust matters in that it is more likely for high-trust economies, generated with an endogenous matching procedure, to escape the poverty trap. But we also find that the likelihood to escape depends partially on the initial endowment condition. Trust has a much weaker impact on the economies whose initial capital and output are below the Pareto-inferior equilibrium, suggesting that formal institutions and/or policy measures may be needed to engineer a “big push” for these least developed economies. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.
Original languageEnglish
Pages (from-to)37-66
JournalSocial Choice and Welfare
Volume62
Issue number1
Online published6 Aug 2023
DOIs
Publication statusPublished - Feb 2024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 1 - No Poverty
    SDG 1 No Poverty

Research Keywords

  • Experiment
  • Growth
  • Poverty trap
  • Trust

Fingerprint

Dive into the research topics of 'Escape poverty trap with trust? An experimental study'. Together they form a unique fingerprint.

Cite this