Tobit Regression Model

Table 1 showed the empirical results of microfinance and poverty reduction through the Tobit regression method of analysis. For this study to evaluate the influence of microfinance on the poverty reduction, Tobit regression model was regressed on the poverty reduction, on the key variables in this study. These include micro-credit, age, household size, qualification, nature of business, duration of membership and village type. In this model, poverty reduction is a dummy and is considered as the dependent variable, while micro-credit, age, household size, qualification, business type, duration of membership and village type as independent or explanatory variables.
Generally, four out of the seven explanatory or independent variables were found to be statistically significant, while the remaining three were not statistically significant. Therefore, the study found that the explanatory power of Tobit model can competently be used to predict the likelihood of evaluating the influence of microfinance in poverty reduction in the area of the study. The model made up of all the explanatory variables (predictors) was statistically significant at 0.000 with a chi2 of 331.68 with 7 degree of freedom.
It is important to know that the model with poverty reduction as a dependent variable has the main aim of
Tobit regression model was adopted to analyze the data. Seven explanatory variables (e.g. micro-credit, age, qualification, duration of membership, household size, nature of business and village type) were utilized to predict poverty reduction. Four explanatory variables out of the seven (micro-credit, duration of membership, qualification and nature of business) were discovered to be statistically significant in evaluating poverty reduction of the beneficiaries of micro-credit in Nigeria. However, rest were discovered to be insignificant in evaluating poverty