Monetary and Multidimensional Poverty in Cameroon: Measurements, Determinants, and Policy Implications

Abstract

Cameroon has witnessed substantial economic growth in the new millennium, while poverty reduction has been limited and inequality has worsened. In this context, this chapter investigates the different facets of poverty in Cameroon, factors affecting them and policy options to tackle poverty and achieve inclusive and sustainable development. We apply two prominent poverty measurement methods (Alkire-Foster and Foster-Greer-Thorbecke) to a series of household consumption and living standards (ECAM) surveys and Demographic and Health Surveys (DHS) collected between 2001 and 2018, and perform various empirical analyses to elucidate poverty dynamics and features. Our results indicate that both monetary and multidimensional poverty have decreased in Cameroon between 2001 and 2018, albeit slowly and to varying degrees across the different demographic, socioeconomic and spatial groups of the population. We find that the proportion of multidimensional poor people is always higher than the proportion of the monetary poor. At the same time, multidimensional poverty has reduced much faster than monetary poverty at the national level. Lastly, we find that higher levels of poverty in Cameroon are strongly associated with rural livelihoods, large family size, less education, employment in agriculture and the Northern regions of the country. Our micro-economic analysis is complemented with a review of structural factors affecting poverty at the macro level. We point out the need to accelerate the structural transformation of the Cameroonian economy, to reduce inequalities across the different regions and sub-groups of the population and expand economic opportunities for the youth to achieve the demographic dividend.

Can monetary poverty measurement detect multidimensionally deprived? Evidence from Egypt

“…This paper follows suit with the findings of Andrianarison et al (2022), Lekobane (2022), Ozdamar and Giovanis (2021), Salecker et al (2020), Burchi et al (2018) and Tran et al (2015) that HH education attainment plays a significant role in reducing poverty. With higher level of HH education, the probability of a household member being poor falls more and the reduction of being multidimensionally deprived is higher than being monetary poor.…”

“…The interpretation of the results is limited to only statistically significant variables across models. Similar to Andrianarison et al (2022) and Tran et al (2015)’s results, Female Headed Households (FHH) have 33% higher chance of falling into multidimensional poverty than Male Headed Households (MHH) but they have 54.9% lower probability being monetary poor. These opposite effects are reflected in the poverty rates within subgroups of HH gender.…”

“…The Alkire and Foster (AF) method is a widely used tool for computing MPI for research and official national estimates purposes (Andrianarison et al, 2022;Salecker et al, 2020;Alkire and Fang, 2019;Alkire et al, 2017aAlkire et al, , 2017bAlkire et al, 2016). This method is particularly appealing due to four features:…”

“…The Alkire and Foster (AF) method is a widely used tool for computing MPI for research and official national estimates purposes (Andrianarison et al , 2022; Salecker et al , 2020; Alkire and Fang, 2019; Alkire et al , 2017a, 2017b; Alkire et al , 2016). This method is particularly appealing due to four features: its sensitivity to the number of experienced deprivations by the multidimensionally poor; its ability to be disaggregated by dimension and by indicator, providing policymakers with valuable insights about the primary domains of poverty and the poor segments of the population in each domain; its satisfaction of dimensional monotonicity, which means that if the poor become deprived in an additional indicator, the adjusted headcount ratio will increase; and its adaptability to specific conditions by incorporating different dimensions and indicators (Alkire and Santos, 2014; Alkire and Foster, 2011).…”

“…Günther and Klasen (2009) corroborates this result for Vietnam, using the same nonmonetary indicators, adding that there is high heterogeneity in intra-household nonmonetary deprivation overtime, which would be ignored if only income poverty measures were used. Later, using Global MPI, Tran et al (2015) (Vietnam), Alkire and Fang (2019) (China) and Andrianarison et al (2022) (Cameroon) find a robust faster decrease in monetary poverty than multidimensional poverty overtime. Also, both measures experience different levels of mobility across population sub-groups and overtime, with monetary poverty showing higher mobility among the poor, whereas multidimensional poverty showing greater persistence.…”