The Impact of Personal Financial Literacy on the Choice of Lending Channel by Vietnamese Householders Who Just Escaped Poverty

Purpose: The Newly-Escaped-Poverty-List householders (NEPLH) in Vietnam served as the survey subjects for this study, which sought to determine impact of "Personal Financial Literacy" on "The Probability Using Lending Services Bank Social Policies (P_VBSP)" and Informal Private (P_ILC)". Design/methodology/approach: Survey data were collected from 186 respondents, by structured online questionnaire with convenient non-probability sampling technique. Aspects personal financial literacy (FLi) such knowledge (FL1), skills (FL2), attitude (FL3), behavior (FL4) are measured. When respondents divided into groups based subgroup criteria (CVj), gender, age, marital status, role family, career, education level, religion, number years since being removed list poor households, ANOVA techniques used examine differences various aspects literacy. FLi variables (CVj) P_VBSP P_ILC was assessed using binary logistic regression analysis Findings: Research results show that there is a statistical difference average score FL1, FL2, FL3 FL4. research findings also FL4, "Career Status – CAR" have positive P_VBSP, while "Aged group - AGE", "Education level EDU", "Number households YEAR" negative P_VBSP. Meanwhile, influenced AGE factor moves opposite direction EDU, YEAR. Research, Practical & implications: provide basis governance implications well policy recommendations promote among NEPLH Vietnam, encouraging them access lending services Policies, at same time help stay away informal private services, thereby helping reduce poverty achieve sustainable economic development family. Originality/value: A theoretical framework finance literacy, including (i) knowledge, (ii) attitude, (iii) skills, (iv) behavior, has been developed authors after reviewing earlier studies. At time, it more significant measure objective assessment than relying subjective self-assessment each respondent context actual study Vietnam. In model analyzing influence decision choosing or Credit Channels, took account additional factors age group, households.