<abstract xmlns="http://www.w3.org/1999/xhtml">
<p>Using a cross-sectional survey data of agricultural farms, we investigate gender-based differences in farm wages among farm workers by randomly allocating farm workers into treatment (female) and control (male) groups with a simple random sampling technique. We used the Blinder–Oaxaca decomposition method to establish the gender wage gap and Propensity Score Matching to address assumptions and heterogeneity difficulties that plague the decomposition technique. Results show that female farm workers earn ₦ 9,170.83 less compared to their male counterparts, which indicates an unadjusted gender gap. This gender gap in farm wages is explained by the specific factors included in our model, so upgrading these variables could reduce gender inequalities in farm wages. Matching results indicate that the gender gaps estimated with nearest neighbour matching and kernel-based matching are 9.8% and 21.6% higher, respectively, than the gaps measured by the decomposition technique. Thus, the matching procedure was successful in identifying a sizeable proportion of gender gaps that are unexplained due to discrimination between male and female farm workers.</p>
</abstract>

Using a cross-sectional survey data of agricultural farms, we investigate gender-based differences in farm wages among farm workers by randomly allocating farm workers into treatment (female) and control (male) groups with a simple random sampling technique. We used the Blinder–Oaxaca decomposition method to establish the gender wage gap and Propensity Score Matching to address assumptions and heterogeneity difficulties that plague the decomposition technique. Results show that female farm workers earn ₦ 9,170.83 less compared to their male counterparts, which indicates an unadjusted gender gap. This gender gap in farm wages is explained by the specific factors included in our model, so upgrading these variables could reduce gender inequalities in farm wages. Matching results indicate that the gender gaps estimated with nearest neighbour matching and kernel-based matching are 9.8% and 21.6% higher, respectively, than the gaps measured by the decomposition technique. Thus, the matching procedure was successful in identifying a sizeable proportion of gender gaps that are unexplained due to discrimination between male and female farm workers.

Does Gender Wage Gap Exist among Farm Workers in Nigeria? Evidence from Decomposition-Matching Analysis

Department of Agricultural Extension and Rural Development

Department of Agricultural Economics and Extension

Acta Universitatis Sapientiae, Economics and Business

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Using a cross-sectional survey data of agricultural farms, we investigate gender-based differences in farm wages among farm workers by randomly allocating farm...

Does Gender Wage Gap Exist among Farm Workers in Nigeria? Evidence from Decomposition-Matching Analysis

Publicado en línea: 19 oct 2023

Páginas: 115 - 137

DOI: https://doi.org/10.2478/auseb-2023-0006

Palabras clave
decomposition, farm workers, gender wage inequality, labour, matching, wage gap

© 2023 Abdulrazaq K. Daudu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Does Gender Wage Gap Exist among Farm Workers in Nigeria? Evidence from Decomposition-Matching Analysis

Publicado en línea: 19 oct 2023

Páginas: 115 - 137

DOI: https://doi.org/10.2478/auseb-2023-0006

Palabras clavedecomposition, farm workers, gender wage inequality, labour, matching, wage gap

© 2023 Abdulrazaq K. Daudu et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Palabras clave
decomposition, farm workers, gender wage inequality, labour, matching, wage gap