International Journal of Advanced Research in ISSN: 2278-6236 Management and Social Sciences Impact Factor: 7.065 ACCESS TO CREDIT AND ITS DETERMINANTS IN GUBALAFTO DISTRICT MR. SISAY GENANU-Department of Economics Woldia University, Woldia Ethiopia ABSTRACT Access to credit is an indispensable prerequisite for agricultural development where agriculture is found with a shortage of agricultural technologies and recurrent drought. This paper examines access to credit and its determinants in Gubalafto district. Primary data from 250 households were collected. To select a sample household, a multistage sampling technique was exercised. Questionnaire and interview were utilized to collect primary data. Both descriptive and econometric methods of analysis were employed. For econometric analysis with necessary statistical diagnosis tests, a logistic regression procedure was developed on farmers’ socio-economic cross-sectional data collected in 2018. The descriptive statistics revealed that the amount of credit is very low and the interest rate is high. The Econometrics model showed that sex, education level, family size, land size of the household, access to extension service and annual earned income were found to be significant in determining access to credit. These results have important policy implications to reduce interest rates and increase loan size and minimizing the distance between credit institutions and farm households. KEYWORDS: 1.ACSI, 2.Agriculture, 3.Households, 4.Logistic Regression, 5.ROC Curve. DATABASE AND METHODOLOGY This study employed both primary and secondary data. Primary data was collected through administering a structured questionnaire and interview to rural households in Gubalafto District. The questionnaire was designed to gather qualitative and quantitative data. In addition, secondary sources were used to substantiate the analysis and get support for primary data. Three stage sampling was used to acquire the required primary data. Firstly, Gubalafto district was selected purposely. Gubalafto district consists of thirty one sub-districts. Secondly, among Gubalafto’s sub-districts, seven sub-districts were selected purposively. In Vol. 9 | No. 12 | December 2020 www.garph.co.uk IJARMSS | 1 International Journal of Advanced Research in ISSN: 2278-6236 Management and Social Sciences Impact Factor: 7.065 the third-stage simple random sampling was employed. Finally, a probability proportional to sample size sampling procedure was employed to select 250 sample households. Two hundred fifty sample households were taken based on (Glenn, 1992) sampling method. According to him, for the population greater than 100,000 (139,825 = total population of Gubalafto District), it is possible to take 250 samples at 95% confidence level, degree of variability = 0.07 and level of precision=7%. In addition, according to 2007 census the number of households in Gubalafto District is 40569. It is once more possible to use 250 samples because the maximum sample size is 204, but 250 is greater than 204. This would help us to increase the precision of the result; in statistics to the degree that there is more sample size precision of the result would increase. The study employed the logit model in line with (Copas, 1998). 1 PFZii( ) ....................(1) 1 e( |X |) Where Pi = the probability that an individual is being food secure given Xi Xi= a vector of explanatory variables And β = regression parameters to be estimated. e= the base of the natural logarithm For ease of interpretation of the coefficients, a logistic model could be written in terms of the odds and log of odd. The odds ratio is the ratios of the probability that a household would be food secure (Pi) to the probability of a household not being food secure (1- Pi). That is: P i eZi ................................................(2) 1 Pi Taking the natural logarithm of the equation yeilds Pi Ln( ) Zi 1 X 1 2 X 2 ....... m X m ...............................(3) 1 Pi If the error term taken into account the equation becomes Pi Ln( ) Zi 1 X 1 2 X 2 ....... m X m e t ...............................(4) 1 Pi Vol. 9 | No. 12 | December 2020 www.garph.co.uk IJARMSS | 2 International Journal of Advanced Research in ISSN: 2278-6236 Management and Social Sciences Impact Factor: 7.065 The dependent variable in this study is Access to credit. It is dichotomous taking: 1 when having access to credit and 0 when a farmer is having no access to credit. The independent variables contain both dichotomous and continuous variables. Access to credit was estimated using computer software STATA 14 program for descriptive and econometric analysis. The Model is expressed as follow with the dependent and all explanatory variables which are included in the model. Credit = α - 1Agehh - 2Sexhh - 3Eduhh + 4Famsize + 5Landize + 6Dpratio - 7Amntfert + 8TLU + 9Extension - 10Dist + 11Nonfarminc + 12Yincome + …………………………………………………………….……….(5) Table 1: Measurements and hypothesis of independent variables Variable Type Definition Measurement Expecte d sign Agehh Continuous Age of the household head Year - Sexhh Dummy Sex of Household Head 1=male; 0=female - Edu Continuous Education level of the household Year of schooling - Famsize Continuous Total number of family size Number of family + Landsize Continuous Amount of land owned by Hectare + hectare Dpratio Continuous Dependency ratio Ratio of number of dependent - family to active labour force of the family Amntfert Continuous Amount of fertilizer used in farm Kilogram + Tlu Continuous Number Livestock TLU + Extension Dummy Access to extension service 1 if a household has access to + extension service, 0 if not Dist Continuous Distance from credit institution Kilometer + Nonfarminc Dummy participation in non-farm job 1 if participated in non-farm + job, 0 if not Yincome Continuous The yearly income earned Birr (Ethiopian Currency) + Source: own definition Vol. 9 | No. 12 | December 2020 www.garph.co.uk IJARMSS | 3 International Journal of Advanced Research in ISSN: 2278-6236 Management and Social Sciences Impact Factor: 7.065 STUDY AREA The survey was conducted from February 2018 to April 2018 in Gubalafto districts of Amhara Region, Ethiopia. Gubalafto district is one of eleven districts in North Wollo of Amhara Regional State. The district is located between 39012‘9’ and 39045‘58”East and 11034‘54‘‘and 11058‘59” North. It is bounded on the North by Gidan; to the North West by Meket; to the northeast by Kobo; to the east Afar regional state; to the south North Wollo zone; to the south east by Habru district and to the west Dawunt and Delanta. The district has 31 rural sub-districts. Gubalafto district has a total population of 139,825 according to 2007 census. Of which, 50.6% are men and 49.4% women; and only 3.49% are urban inhabitants. With an area of 900.49 square kilometers, Gubalafto has a population density of 155.28 which is greater than the zone average of 123.25 persons per square kilometer. About 32,824 housing units and 33,676 households were counted in this Woreda. The average persons to a household were 4.15. Gubalafto district have been grouped amongst the 48 districts identified as the most drought prone and food insecure districts in the Amhara Region State (Belay and Eyasu, 2019). INTRODUCTION In Ethiopia, different economic policies have been applied to improve the living standards of the people, to achieve economic growth and to renovate agriculture by industrial economy. Back to fifteen years, even if there was economic growth, Ethiopia remains one of the poorest countries in the world, ranking 169th out of 177 countries on the Human Development Index and 105th out of 108 countries on the Human Poverty Index (IFAD, 2011). According to Mulat et al., (2004) the Ethiopian economy is among the most vulnerable economies in Africa and its performance has been less than satisfactory. The economy is also characterized by the persistence of low technology, intensive less productivity agriculture dominated by smallholder farm households (Tilahun, 2015). The country still depends highly on agriculture. The sector is dominated by over 15 million smallholders producing about 95 percent of the national agricultural production. This shows that the overall economy of the country and the food security of the majority of the population depend on small holder agriculture. The Vol. 9 | No. 12 | December 2020 www.garph.co.uk IJARMSS | 4 International Journal of Advanced Research in ISSN: 2278-6236 Management and Social Sciences Impact Factor: 7.065 growth of agricultural sector is taken as an engine and the last resort to take-off the national economy (CSA, 2017/18). Although we deemed the task of agriculture as described above, the dominance of agriculture has not changed over time mainly because of its poor performance in terms of generating surplus that could be invested in other sectors of the economy (Mulat et al., 2004). At this time, farmers face credit constraints in financing, and modern agricultural inputs are the most important barriers for the development of small holder agriculture (Komicha, 2007). Credit is a crucial instrument for rural development (Komicha, 2007: Kumar, 2020).This credit has countless advantages for rural households. According to Abi (2003) access to credit pledges the availability of financial resources which can be used to buy inputs, finance business start ups and hence reduce poverty. In addition, better access to credit is able to open up new economic activities and significantly improve