Impact of agricultural credit on farm income under the Savanna and Transitional zones of Ghana

Abstract

PurposeThe purpose of this paper is twofold: to determine the factors contributing to farm income in the Transitional and Savanna zones of Ghana and to ascertain variations between in the same and across the two locations; and to determine the impact of credit on farm income in each of the two zones and to ascertain the variation in impact of credit across the two locations.Design/methodology/approachIn order to address endogeneity and sample selection bias, the authors draw from the theory of impact evaluation in nonrandom experiment, employing the endogenous switching regression (ESR) while using the propensity score matching (PSM) to check for robustness of the results.FindingsThe results show significant mean differences between some characteristics of households that have access to credit and those that did not have access. Further, the results revealed farm size, labor; gender, age, literacy, wealth and group membership as the significant determinants of both credit access and income in the two zones. With the ESR, credit access increases households farm income by GH¢206.56/ha and GH¢39.74/ha in the Transitional and Savanna zones, respectively, but with the PSM, credit increases farm income by GH¢201.50 and GH¢45.69 and in the Transitional and Savanna, respectively.Research limitations/implicationsThe mean differences in characteristics of the households revealed the presence of selection bias in the distribution of household’s covariates in the two zones. The results further indicate the importance of productive resources, information and household characteristics in improved access to credit and farm income. Also, the results from both methods indicate that credit access leads to significant gains in farm income for households in both zones. However, differences exist in the results of PSM and that of the ESR results.Practical implicationsThe presence of selection bias in the samples suggests that the use of ESR and PSM techniques is appropriate. Further, the results suggesting that enhanced credit access and farm income could be attained through improved access to household resources and information. The results also suggest the need for establishing and expanding credit programs to cover more households in both zones. The differential impact of credit between the two methods employed in each zone revealed the weakness of each model. The low values from PSM could indicate the presence of selection bias resulting from unobservable factors whiles the high values from the ESR could stem from the restrictive assumption of the model. This reinforces the importance of combining mixed methods to check robustness of results and to explore the weakness of each method employed.Originality/valueThe novelty of this study lies in the use of a very extensive and unique data set to decompose the determinants of credit access and farm income and as well as the impacts of credit into zones.