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Abstract
Objective:To estimate a model incorporating the variables poverty, extreme poverty, and agricultural income using generalized least squares, in order to demonstrate the effect of agricultural income on poverty and extreme poverty. This analysis aims to inform the design of agricultural public policies that effectively contribute to poverty reduction in the sector.
Methodology: The generalized least squares (GLS) method was employed. The primary variables analyzed were poverty and extreme poverty, while agricultural income, planted area, number of workdays, and daily wages were included as control variables.
Results: Between 2018 and 2022, agricultural income was found to significantly reduce extreme poverty. In prior years, no statistically significant relationship was identified. The number of workdays and daily wages were positively associated with poverty suggesting that increases in planted area, workdays, and wages may, paradoxically, be linked to higher poverty levels.
Limitations: The limited availability of multidimensional poverty and extreme poverty data at the state level poses a constraint on conducting robust time-series analyses.
Conclusions: The findings underscore the need to reassess public policies directed at the agricultural sector, with the objective of enhancing their effectiveness in poverty alleviation.