Determinants on households’ partial credit rationing - An analysis from VARHS 2008

Abstract

This study aim to identify key factors affected the partial credit ration’s probability and its degree in rural area of 12 provinces in Vietnam including Ha Tay, Nghe An, Khanh Hoa, Lam Dong, Phu Tho, Quang Nam, Long An, Dac Lac, Dac Nong, Lao Cai, Dien Bien, Lai Chau period 2006-2008. Based on VARHS 2008 data set, the research has employed Heckman sample selection bias model to investigate the determinants of partial ration’s degree, and bivariate probit with sample selection model to examine the determinants of partial ration’s probability. Besides that, the impact of credit accessibility’s determinants, as a supplement outcome from the two regression models, were also revealed. The result showed that households who have following characteristics - Kinh ethnicity, large household size, high land value, suffering shock at household level (economic shock, illness, unemployment, etc.), holding social position (at least one member working for government, local authority unit) tend to have higher chance of credit access, while those who have high dependency ratio, and older household, tend to have negative correlation with credit accessibility. Formal credit institutions appeared to have higher rate of partial credit rationed than the informal sector, and those who requested a large size of loan were likely to be partial rationed as well. In contrast, households who own larger house, borrowed for investment purposes (build/buying house, land and other assets) or holding social position had a lower chance of being partial rationed. The finding also uncovered the negative correlation between the degree of partial credit ration and following factors - Household head age, dependency ratio, house size and collateral value. On the contrary, household size, loan size applied, loan for consumption purposes negatively affect the degree of partial credit ration. The regression result also shown that unless treatments such as bivariate probit with sample selection bias or Heckman two stages regression are applied, the regression result might be bias due to inherent sample selection problem in the data set.