Dynamics of Financial Inclusion and Welfare in Kenya

Abstract

Financial inclusion (FI) is not an objective in itself, but only to the extent that it helps improve welfare. Evidence linking FI and welfare is however less conclusive despite growing interest. Understanding this link is often hampered first by, lack of a substantive and universally acceptable measure of FI comparable across time and geography and secondly by lack of empirical evidence. To address this gap, an empirical examination on Kenya using GMM was preceded by the modeling of FI to establish its determinants. The inter-temporal variation in household consumption to generate vulnerability as expected poverty was also analyzed to inform on the impact of FI on household vulnerability to poverty. Financial Access surveys (2006, 2009, 2013 and 2016) organized into 126 cohorts provided a solid empirical basis for tracking FI and its impact on welfare. Per capita income was found to be one of the main drivers of FI pointing to operation of the demand following hypothesis in Kenya. In terms of welfare impacts, transactionary, credit, insurance and portfolio usage of financial services significantly raise consumption expenditure per adult equivalent by 74.3, 81.6, 39.8 and 3.473 percent respectively all things held constant. This welfare impact is also extended towards poverty reduction. Safe for vulnerability to poverty in rural areas, FI was found to significantly lower vulnerability to poverty among urban households as well as headcount poverty in both rural and urban areas. The study recommends a reduction in transactionary costs by financial service providers to consolidate gains from financial inclusion, increased investment in human capital development by the government to supplement financial inclusion, employment creation and increased provision of basic services by government to enable households release part of their income towards improving household welfare. Key words: Pseudo panel estimation, financial inclusion, welfare, vulnerability, transition matrix, dynamic regression