PROCEDIMIENTO CONTABILIZACIÓN PROVEEDORES NACIONALES

Objective 3 of the study sought to determine the socio-economic factors that influence the demand for credit by smallholder farmers in South Africa. Accordingly, the following hypothetical structural equation model (Figure 7.9) was derived and the covariances among the explanatory variables thereof estimated. Both the dependent and explanatory variables are defined in Table 7.29 below.

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Variable Definition

Q39 Purpose of credit demanded Q40 Factors limiting credit demand Q35 Collateral offered to the lender Q36 Interest rate charged by the lender Q1 Age of the farmer in years

Q2 Marital status of the farmer

Q3 Highest level of education of the farmer Q30 Family culture towards borrowing

Q29 Number of loans received by the farmer in the previous farming season (Source: Author construction)

Figure 7.9: Model 2: Determinants of demand for credit (Source: AMOS 21)

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When disaggregated data series for the variables purpose of credit demanded (Q39) and factors limiting the demand for credit (Q40) were included in the analysis, missing values were observed and the data could therefore not be analysed. To overcome this problem, both Q39 and Q40 were collapsed and included in the analysis, as depicted in Figure 7.9 above, showing the estimated SEM.

The results show that the combined effect of the variable Q39 and Q40 yields a negative impact on the demand for credit. This is in contrast with results from previous studies, in which it was observed that the sub-questions/variables for Q39 and Q40 were positive and significant. The model was re-estimated and subjected to goodness-of-model-fit tests. The model failed the chi-square test, implying that the model does not explain the data. The chi-square test results for Model 2 are presented in Table 7.23. The chi-square test statistic shows a lack of good model fit (p ˂ 0.05). In this case the researcher failed to reject the null that the explanatory variables do not predict the dependent variable. These results conform to the recommendations of Tomer and Pugesek (2003), who posit that a non-significant chi-square test statistic indicates that the observed matrix and the reproduced matrix are not statistically different, thus indicating a good fit of the model to the data. Therefore, Figure 7.10 depicts the final model for the demand for credit by smallholder farmers in South Africa. All model variables are defined in Table 7.30.

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Figure 7.10: Model 2a: Determinants of demand for credit (Source: AMOS 21)

Table 7.30: Definition of variables

Variable Definition of variable

IE Denotes the farming inputs and capital equipment to be purchased FC Family culture is not to borrow

Collateral Collateral offered by the farmer to the lender

EF Denotes economic factors that influence the demand for credit ACValue Denotes the credit accessed by the farmer in the previous season e1 Denotes the error term

(Source: Author construction)

7.12.1 Maximum likelihood estimates

Regression weights for the model variables were computed and are presented in Table 7.31 below. Farming inputs and capital equipment, family culture and collateral were observed to have a significant relationship with the demand for credit (p ˂ 0.05). However, the coefficients were negative, indicating a negative influence on the demand for credit (collateral = -0.151; inputs and capital equipment = -0.375; family culture = -0.120). An interesting and otherwise unique finding in this analysis is that

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family culture has a negative and significant influence on the demand for credit (p < 0.05). Farming inputs such as fertiliser, seed and pesticides, wages for workers and capital equipment were found to have a negative and significant influence on the demand for credit. This suggests that smallholder farmers mainly rely on equity finance, as family culture is seen to negatively influence borrowing. Collateral, which in empirical literature is observed to impede access to credit by smallholder farmers, is confirmed to have a negative influence on the demand for credit. In other words, as credit providers emphasise on borrowers providing collateral, this tends to diminish the demand for credit, because most smallholder farmers have no assets suitable for assigning as collateral.

Table 7.31: Regression weights (group number 1 – default model)

Estimate S.E. C.R. P Access to credit (Q29) <--- Collateral (Q35) -0.151 0.047 -3.233 0.001 Access to credit (Q29) <--- Economic factors (Q40) 0.127 0.065 1.953 0.051 Access to credit (Q29) <--- Inputs and equipment (Q39) -0.375 0.156 -2.405 0.016 Access to credit (Q29) <--- Family culture (Q30) -0.120 0.058 -2.072 0.038 Estimate = estimated path coefficient (prediction) for arrows in the model (Garson, 2010)

SE = standard error

CR = critical ratio (estimate divided by its standard error [Garson, 2010:4]) (˃ 1.96 = significant at 0.05 level (Garson 2009:22; 2010:4)

P = probability value (˂ 0.05 = significant on the 0.001 level *** [Garson 2009]) (Source: AMOS 21)

Table 7.32 below shows the bi-directional correlations between dimensions. The relationship between family culture towards borrowing and economic factors such as interest rates is observed to be positive and significant with a p-value below 0.05 at the 0.001 (two-tailed) level. The relationship between family culture towards borrowing and economic factors (interest rates) was also found to be strongly significant with a p-value below 0.05, also at the 0.001 (two-tailed) level. Family culture towards borrowing and inputs and capital equipment were found to be weakly significant at 0.1 with a p-value greater than 0.05. The causal relationships between collateral and family culture towards borrowing, economic factors and inputs and capital equipment were all observed to be insignificant with p-values greater than 0.05. Furthermore, the relationship between collateral and family culture towards borrowing was found to be negative.

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Table 7.32: Covariances (group number 1 – default model)

Estimate S.E. C.R. P Inputs and equipment (Q39) <--> Family culture (Q30) 0.056 0.032 1.759 0.079 Family culture (Q30) <--> Economic factors (Q40) 0.299 0.079 3.793 *** Family culture (Q30) <--> Collateral (Q35) -0.088 0.088 -1.000 0.317 Economic factors (Q40) <--> Collateral (Q35) 0.081 0.094 0.866 0.387 Inputs and equipment (Q39) <--> Collateral (Q35) 0.017 0.039 0.435 0.664 Inputs and equipment (Q39) <--> Economic factors (Q40) 0.365 0.039 9.359 *** (Source: AMOS 21)

Finally, Table 7.33 below shows that approximately 5.1% of the demand for credit model is explained by the predictor variables in the model shown as Figure 7.10 above.

Table 7.33: Squared multiple correlations (group number 1 – default model)

Estimate

Access to credit (Q29) 0.051

(Source: AMOS 21)

The chi-square test results discussed above (Table 7.23) have rejected the null hypothesis of a good fit for Model 2. In keeping with Schreiber et al. (2006), more robust tests were applied using goodness-of-fit indices. For Model 2, the demand for credit was proxied by 0 for the respondents who did not apply for credit and 1 for those who applied. Table 7.34 presents the indices used to analyse the SEM fit (CMIN = 0.00, CFI = 1.00, PCFI = 0.00, NFI = 1.00 and PCLOSE = 0.00). Those values indicate a good fit between the hypothesised model and the observed data. Only RMSEA = 0.215 showed a poor model fit; however, as the majority of indices confirmed a good model fit, the RMSEA index was discarded. Figure 7.11 below shows the final model for Hypothesis 2.

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Index Recommended value Output Remark

CMIN ˂ 0.05 0.000 Very good

GFI ≥ 0.95 (not generally recommended)

1.000 Very good

TLI values close to 1

indicate a very good fit)

0.000 Good

CFI (values close to 1 indicate a very good fit)

1.000 Very good

PCFI Sensitive to model size 0.000 Very good

RMSEA ˂ 0.06 to 0.08 with confidence interval

0.215 Insignificant,

therefore no model fit NFI (values close to 1

indicate a very good fit); indices less than 0.9 can be improved substantially

1.000 Very good

PCLOSE ˂ 0.05 0.000 Very good

(Source; AMOS 21)