Living income measurement methods – A comparative study and application to cocoa farmers in Cameroon

How should the cost of a decent life be quantified, and are living income indicators valid welfare measures for agrifood policies? This paper critically examines two methodologies for estimating living income benchmarks (LIB) and their application in rural agricultural contexts, focusing on cocoa producers in Cameroon. It highlights key differences in data sources and assumptions, finding that LIB estimates are highly sensitive to food expenditure assumptions and non-food, non-housing (NFNH) costs. Statistical tests on the living income gap (LIG) reveal that the indicators satisfy distribution sensitivity and identify vulnerable groups via stochastic dominance analysis, supporting their targeting potential. Simulations based on poverty axioms confirm robustness, leading to the proposal of a censored LIG that better captures the deprivation of vulnerable strata. The paper argues for greater methodological rigour, replicability, and harmonization to enhance the potential of living income indicators for promoting equitable agrifood system transformations.

Publication Details

Journal
AgEcon Search (University of Minnesota, USA)
Published
2025-08-18
DOI
https://doi.org/10.22004/ag.econ.365819
Primary Topic
Income, Poverty, and Inequality
Type
article
Field-Weighted Citation Impact
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article

Living income measurement methods – A comparative study and application to cocoa farmers in Cameroon

AgEcon Search (University of Minnesota, USA)
Income, Poverty, and Inequality
article

Living income measurement methods – A comparative study and application to cocoa farmers in Cameroon

article en

Abstract

How should the cost of a decent life be quantified, and are living income indicators valid welfare measures for agrifood policies? This paper critically examines two methodologies for estimating living income benchmarks (LIB) and their application in rural agricultural contexts, focusing on cocoa producers in Cameroon. It highlights key differences in data sources and assumptions, finding that LIB estimates are highly sensitive to food expenditure assumptions and non-food, non-housing (NFNH) costs. Statistical tests on the living income gap (LIG) reveal that the indicators satisfy distribution sensitivity and identify vulnerable groups via stochastic dominance analysis, supporting their targeting potential. Simulations based on poverty axioms confirm robustness, leading to the proposal of a censored LIG that better captures the deprivation of vulnerable strata. The paper argues for greater methodological rigour, replicability, and harmonization to enhance the potential of living income indicators for promoting equitable agrifood system transformations.

AgEcon Search (University of Minnesota, USA)
No poverty
Openalex Percentile: Top 29%
Income, Poverty, and Inequality
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