Do household characteristics affect per capita household income? An empirical analysis of Zambia’s living condition monitoring survey data

Demographic shifts have brought many socio-economic challenges and opportunities. The changes in household size and composition have in many cases been found to alter the path of socio-economic progress. In this paper, we analyse how changes in household characteristics affect per capita household income using data from the 2022 Living Condition Monitoring Survey (LCMS). We estimate the effect size of each of the covariates and estimate the age primer. Using OLS regression and omega squared effect size measurement, we found that household size had significant negative effect of 9 percent. We further found that the size of the working-age members had an increasing effect on per capita income, though the measured effect size was small at 0.3 percent.  On education, we found that education level had an increasing effect on per capita income with a measured effect size of about 14 percent. The age of the household head was also found to have an increasing effect on per capita income of 0.2 percent, though this effect was small. On gender, region and access to credit, we found male headed households to have about 22 percent higher per capita household income compared to female headed households and that rural household had 25 percent higher capita income than urban households. Lastly, we found that access to credit through village banking had an increasing effect on per capita income though the effect size was found to be small at 0.1 percent. We estimated the age-primer and found that per capita household income keeps increasing with age till at the age of 44 years, beyond which it starts falling. 
We concluded that at household level, income based socio-economic development can be enhanced through increased number of working-age members, investing in education, having access to credit and reducing on the household size especially the proportion of members falling outside the working-age. We recommend developing targeted policy interventions to abate structural inequalities inherent in gender roles to equilibrate earnings and job opportunities between males and females, and investment in programmes and trainings for the population aged 15-44 years.