Financial inclusion and poverty reduction: evidence from small scale agricultural sector in Manicaland Province of Zimbabwe
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The study investigated the impact of financial inclusion on poverty reduction in Manicaland province of Zimbabwe among smallholder farmers, using household data collected through a structured household questionnaire. Further investigation was done on households that were not in farming, to compare the results. Zimbabwe is divided into ten provinces with different demographics and agricultural opportunities. The study, therefore, took Manicaland Province as a case study because of the level of farming activities in the area. The study emanated from the premise of the increasing link between financial inclusion and poverty reduction. Since many households in Zimbabwe managed to get land from the land reform programme, there was, therefore, an interest to investigate if access to finance by the newly resettled farmers can transform to prosperity and poverty reduction. The objectives of the study were two-tiered: the theoretical and empirical. The theoretical objectives were to review literature on theories of poverty and their applicability to developing countries, review measures of poverty and their applicability to the context of developing countries, specifically Zimbabwe, review literature on the measures of financial inclusion, review and analyse a theoretical framework on the determinants of financial inclusion and, finally, highlight the theoretical argument on the relationship between financial inclusion and poverty. The empirical objectives were to: profile poverty and financial inclusion among the smallholder farmers in the sampled area and develop an index to measure financial inclusion, determine the determinants of financial inclusion among smallholder farmers in Zimbabwe as well as to analyse the impact of financial inclusion on poverty in Zimbabwe among smallholder farmers and, finally, make recommendations as to how financial inclusion can be used to deal with poverty in Zimbabwe. The study employed a combination of econometric models to fulfil the objectives of the study. To get the determinants of financial inclusion, the study used the logistic regression, the multinomial logistic regression and multiple regression analysis. This study used different models so that comparisons can be made between results generated from the different models. Since the overarching aim of the study was to investigate the impact of financial inclusion on poverty reduction, the first step taken was to assess the profile of poverty and financial inclusion using the data collected. The data on financial inclusion showed that financial inclusion was low in the province. This was shown by the percentage of households who borrowed, those who saved and those with insurance, for instance, more than 70 percent of household heads indicated that they did not save with formal financial institutions. On the profile of poverty, the study used the various welfare indicators and two measures of poverty, the absolute poverty line and the income plus asset index, to assess the profile of poverty in the province. The two measures of poverty showed that poverty is generally high in the province, especially among the smallholder farmers compared to those who were not in farming. The study went on to assess the determinants of financial inclusion using various models. The factors found to influence financial inclusion from all the models were off-farm income, education level, distance, financial literacy, age of the household, distance, transaction costs and financial literacy, agricultural extension service and size of the household. Using the multiple regression to investigate the determinants of financial inclusion among the smallholder farmers, the study found out that off-farm income, education level, distance to the nearest financial institution, financial literacy and age of the household were the variables significantly influencing financial inclusion. Additionally, the determinants of financial inclusion among the non-farmers were age, the income of the household, education level, distance, transaction costs and financial literacy. The difference between farmers and non-farmers was that non-farmers were further influenced by transaction costs, the costs charged by financial institutions to perform various transactions. The study went on to use the logit model with bank account ownership as a proxy of financial inclusion to investigate further the determinants of financial inclusion so that the results obtained can be compared. However, the analysis showed that there was not much difference in terms of factors influencing financial inclusion. After estimating the logit model, the study found that age of the individual, family size, off-farm income, agricultural extension service, distance to the nearest financial institution and transaction costs were the factors influencing financial inclusion, while for households who were not in farming financial inclusion was influenced by age, household size, income, agricultural extension service, distance to the nearest financial institution and transaction costs. Closely looking at the results, we found out that, when using the index of financial inclusion and bank account ownership, there was not much difference in the determinants of financial inclusion. Agricultural extension service was the additional factor influencing financial inclusion when the logit model was used. Even among the farmers and non-farmers, there was not much difference. The study also investigated the determinants of financial inclusion in terms of the factors influencing households to use different financial services, that is, the factors that influence households to have a transaction account, to save, and to have insurance. Using the multinomial logistic regression for smallholder farmers, the study found that household size, transaction costs, age and agricultural extension service were the factors influencing demand for a transaction account. While off-farm income and age of the household were the factors influencing households to borrow. When households who were not in farming were taken into account, the factors significantly influencing access to a transaction account were household size, age of the individual and distance to the nearest financial access point, while borrowing or credit was influenced by transaction costs, age of the households and off-farm income. Looking closely there were no significant differences in the factors influencing demand for different financial services by households who were farmers and those who were not in farming. Also, the impact of financial inclusion on poverty was investigated using the developed index of financial inclusion and poverty from the two measures, the absolute poverty line and the income plus asset measure. The analysis was done separately for households who were into farming and those households who indicated that were not directly involved in farming. For both farmers and non-farmers, the results indicated that financial inclusion had an impact on poverty. A rise in the level of financial inclusion is associated with a fall in the level of poverty. An additional analysis was done to investigate the impact of various financial services like saving, borrowing, performing transactions and insurance on poverty reduction. The results indicated that having a transaction account and saving were the significant variables in influencing poverty reduction in Zimbabwe. Though other variables were not significant, the negative sign on each of the variables supported our a priori expectation that the access to financial services such as insurance and credit can reduce the level of poverty. The study concluded that policies that are intended to fight poverty should be geared towards promoting financial inclusion. There is also a requirement to create an atmosphere that enables the poor to get access to loans at reasonable interest rates and charges. Agricultural extension services, the establishment of financial access points near the households to promote financial inclusion should continue be the prime goal of the government. There is also the need for the Zimbabwe Statistics Agency to reexamine the definition and measurement of poverty so that government works with practical figures, which are not inflated and sometimes deflated poverty rates that may be reported yearly in the country.