Spatial analysis of the effect of microfinance on poverty and inequality in Ghana

Abstract

Although microfinance is usually delivered with a spatial outlook, the literature is so far silent on the potential spatial effect of microfinance delivery. The aim of this study was, therefore, to examine the effect of microfinance intensity on spatial inequality and poverty in Ghana. Using the 6th (2012/2013) and 7th (2016/2017) rounds of data from a national survey on living standards in Ghana, the study first examined the pattern of district-level poverty and inequality in Ghana and then adopted spatial econometric techniques to explore the spatial correlation between microfinance, inequality, and poverty. The results revealed that microfinance has a significant negative impact on spatial inequality and poverty in Ghana. The spatial effect of microfinance intensity on poverty and inequality is characterized by both direct and spillover effects on neighbours. It was identified that the outreach of microfinance drives within-district disparity, whereas the disparity in microfinance credit distribution powers between-district disparity. Additionally, while there is evidence of an indirect effect, the indirect effect diffuses monotonically as the number of neighbours increases. The study's findings advocate for a complementary approach to microfinance delivery, as well as the elimination of institutional barriers that limit access, availability, and operational delivery of microfinance services in order to achieve spatially optimal microfinance delivery. Supplementary Information The online version contains supplementary material available at 10.1007/s40847-022-00210-3.

Financial inclusion, vulnerability coping strategies and multidimensional poverty: Does conceptualisation of financial inclusion matter?

“…The first consists of those that have investigated the role of informal financial services in reducing poverty in Ghana (Annim, 2018; Nukpezah & Blankson, 2017; Oteng‐Abayie et al, 2022) and the second relates to those that have the same subject in other developing economies (Abdallah Ali et al, 2022; Binaté Fofana et al, 2015; Brannen & Sheehan‐Connor, 2016; Chagwiza et al, 2016; Imai et al, 2010; Pagura & Kirsten, 2006; Wossen et al, 2017). Across‐the‐board, evidence suggests that informal financial services such as microfinance (Abdallah Ali et al, 2022; Annim, 2018; Binaté Fofana et al, 2015; Imai et al, 2010; Khandker, 2005; Oteng‐Abayie et al, 2022), cooperative unions (Chagwiza et al, 2016; Getnet & Anullo, 2012; Wossen et al, 2017), and village savings and credit unions (Brannen & Sheehan‐Connor, 2016; Ksoll et al, 2016) significantly reduce individuals and households poverty at varying degrees. However, we know of no study that builds a composite index incorporating individuals' or households' consumption of all these informal financial services and their implications on poverty.…”

Nonparametric prediction for univariate spatial data: Methods and applications

“…There is clear evidence that many socio‐economic, geographic and demographic data used in the empirical analysis of regional science are characterised by the presence of spatial heterogeneity and spatial clustering. This can be found in some studies of the real estate market (e.g., Baumont, 2009; an de Meulen & Mitze, 2014; Cellmer et al, 2020; Wang et al, 2019), the location and/or economic performance of firms (e.g., Nilsson & Smirnov, 2017; Nilsson et al, 2019), the spatial distribution of poverty (e.g., Curtis et al, 2019; Oteng‐Abayie et al, 2022), local employment (e.g., Bradley et al, 2020), international migration (e.g., Hierro et al, 2013) and the regional distribution of per capita GDP, (e.g., Le Gallo & Ertur, 2003), among others. The inclusion of such information about the natural clustering of the data is captured through the inclusion in the weights of similarities or dissimilarities within neighbourhoods and between neighbourhoods in the predictor using either the local Moran's index or similarity measures between neighbourhoods via medians.…”