The Spatial Structural Analysis of Microfinance Institutions of Cambodia

Abstract

This paper will cover two contributions on the Cambodia’s economy as banking systems located within branches and poverty and other main variables which are introduced with clustering model analysis and spatial analysis which consists of three models as OLS model regression, ML spatial Error and ML spatial Durbin model to know how the effects of branches of microfinance in districts, users of MFIs and productions of rural microfinance impacted to poverty with each provinces of Cambodia. Furthermore, with these techniques will be found the effects from neighbors to our regions and within dependence variable too. In this first part mentioned well about the locations of microfinance Institutions and poverty trend, as this results reveal that most of Cambodia ‘s microfinance institutions are located in city and some high economical provinces nearby capital city only as in clustering map at figure 10. And furthermore, total loans and deposits of Cambodia seem classifications of customers who live in remote area and city. As from bivariate analysis stated that light data is related positively with loans outstanding and poverty. This means that wherever they have had the electricity in their locations are mainly better or easier to get loan or more development than other city. This can be said that locations would be more developed than others both with better household’s income and possibility of getting the rural financial services in those locations too. As figure 8 reveals that Cambodia’s deposit are also setting the difference classifications as well as female and male, introduced in 2007, 2008 and 2011 that Cambodia’s total deposits are not spreading well while it goes to only the city and tourism provinces while some boarder provinces like Stung Treng , Rottanak Kirri , Mondul Kirri are still very low and many other provinces are not significant too. In addition of this, most of microfinance and banks are rarely located with above remote province too (see clustering analysis in figure 10).Second from this part, It will be employed the spatial model in order to deep down more with some main variables which are independent variables and dependence variable as poverty too. Yet it would be three results to compare with more appropriate models and robustness check too. So it said that Cambodia’s total deposits from MFIs is negatively related with poverty in each provinces with insignificant level too. This means that Cambodia’s less deposit in rural area and if deposit increase 1 percent ,So poverty will decrease about 1.5% too in 2007.This would have the national policy to save with microfinances or banks in order to get the poverty deduction for households too. Female deposits look very awesome in 2007 like this variable is negatively related with significant value too. This means that most of women saving in Cambodia can reduce the poverty in their provinces and themselves too where men saving are positively related. This can say that characteristic of saving deposits in Cambodia are two classifications for men and women who are tremendously different translations too. The more men deposits in the microfinances or banks , the more poorer in their provinces and themselves.