114
Views
0
CrossRef citations to date
0
Altmetric
Note

Loss aversion or hand-to-mouth behaviour in private consumption models

ORCID Icon, , ORCID Icon & ORCID Icon
Pages 247-259 | Received 17 Jun 2022, Accepted 18 Jan 2023, Published online: 15 Feb 2023
 

Abstract

This study empirically tests whether the loss aversion or hand-to-mouth theories of consumption behaviour is present in Fiji. The loss aversion hypothesis implies that consumers would maintain their consumption when income falls. To estimate this model, we apply the nonlinear autoregressive distributed lag model with annual data from 1981 to 2019. Our findings are in contrast to the predictions of the loss aversion hypothesis and support the hand-to-mouth hypothesis in Fiji. The results are robust to alternative measures of liquidity, and a sample that includes the COVID-19 pandemic. We contribute to the literature by providing evidence of nonlinearity’s in the consumption-income association. The findings are useful for policymakers in developing countries for policies on economic growth and stabilization.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 In Leeper and Gordon (Citation1992), this is referred to as the Friedman-Cagan analysis.

2 We thank associate editor of New Zealand Economic Papers, Dr Asha Sundaram, for highlighting this point.

3 We thank an anonymous reviewer for suggesting to include the COVID-19 period and M2 in the specification.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 53.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 178.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.