Fig 1.
Source: Author’s survey.
Fig 2.
AHP model of factors influencing credit access and net crop income.
An age, G gender, FE farming experience, EL education level, FN farm income, OJ of farm Income, FA farm assets, FI finance interest rates, LR loan repayments, CR collateral, TA technology adoption, CL climatic issues, AC credit access from various sources.
Table 1.
Saaty’s (1980) valuation scale of various elements of credit access.
Table 2.
Random matrix variable prioritization.
Table 3.
Descriptive statistics on mean comparison differences between households with credit access and non-users.
Table 4.
Microcredit sources of maize farmers in Uasin Gishu.
Fig 3.
Self-reported impacts on causes of low adoption of agricultural technologies on maize yields.
Source: Author’s processing.
Table 5.
Estimation of the endogenous switching regression model using FIML.
Fig 4.
Balancing of covariates with 14% observations off support.
Source: Authors’ computations.
Fig 5.
Bias reduction after matching-comparable observable characteristics.
Table 6.
Impacts of microcredit participation on household income from the ESR model.
Table 7.
Robustness checks; propensity score matching (PSM) estimates impacts on income.
Table 8.
Quality of matching.
Table 9.
Rosenbaum bounding sensitivity analysis results.