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This study aims to examine the influence of training and development (TAD), including performance appraisal (PFA), on the performance of microfinance institutions in a developing economy.

A random sampling drew 100 microfinance institutions in Ghana's Greater Accra Region. Then, a purposive sampling approach selected a cross-section of employees in these institutions. Finally, the data were collected from a sample of 367 respondents, such as managers, utilizing a survey questionnaire. Structural equation modeling (SEM) was used to test hypothesized relationships.

The study results indicate that PFA has a statistically significant positive relationship with organizational performance, and this relationship is partially mediated by job satisfaction (JBS). Interestingly, the TAD process does not have a statistically significant positive relationship with organizational performance when JBS is present in the model. In fact, JBS fully mediates the relationship between TAD and organizational performance.

The study is limited to microfinance in tiers two and three in Greater Accra city of Ghana and did not include the entire country. Although the city of Accra provides a generalized representation of the research, which can be replicated, some variables and results may be impacted if other tiers of microfinance organizations are incorporated.

TAD, as well as PFA, enhance the performance of microfinance and can be utilized as tools for competitive advantage in small and medium-sized enterprises (SMEs) (e.g. microfinance institutions). The study accentuates the value of TAD, PFA and JBS in microfinance in a developing country like Ghana.

This is an original study investigating the effect of TAD and PFA practices on the performance of SMEs in a developing country like Ghana. Also, the study analyses JBS as a mediation variable to performance using SEM, which advances the research methodology in this research field.

The 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.

In 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.

The 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.

The 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.

The 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.

The 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.