Tercer Eje: Aumento y Mejora del Capital Humano

Table 5.19 shows the estimated results of factors that influence rural households to become clients of Islamic MFIs. In general, the logistic model successfully predicted the possibility of rural

households’ access to Islamic MFI at 70.63% (see Appendix B). The likelihood ratio test, which is the

Chi-square statistic, is 65.74, significant at the 1 percent level with 10 degrees of freedom, which means rejecting the null hypothesis and that the logistic model can be used to explain the probability of rural households accessing Islamic MFIs. Based on the results, four of the ten variables have a significant influence on rural households becoming Islamic MFI clients. These are: Age, Age squared, Gender, and Income. Most variables have signs as hypothesised (Table 5.19).

The positive and significant sign of Age indicates that rural households with higher ages have a higher probability of accessing finance from Islamic MFIs. The possible reason is that older rural households have more responsibility when they obtain financing from Islamic MFIs. Another reason is mature rural households usually have settled jobs compared with the youth. Conversely, the Age Squared is negative and significant which implies that rural households have certain or maximum ages to access Islamic MFIs. After the rural households have reached the maximum age set by Islamic MFIs, the

probability of accessing finance is low. The reason is that older clients have various risks such as retirement, getting diseases and being less productive.

Gender shows a positive and significant sign, which indicates that male rural household members have a higher probability of accessing finance from Islamic MFIs. The reason is because most household heads in Indonesia are men and most household decisions including financial ones are influenced by men. This is why men have a higher probability of obtaining finance from Islamic MFIs

than women. The rural households’ annual income exhibits a positive and significant sign which

implies the higher income will lead to a higher probability of obtaining finance from Islamic MFIs. The possible reason is that rural households with high income have more capacity to repay the loans.

Table 5.19 Factors influencing rural household to become client of Islamic MFI (logistic regression). Independent Variables Estimated Coefficients Standard Error Wald Statistic Average Marginal Effect Age 2.060103*** 0.4749611 4.34 0.3850034 Age Square -0.2758062*** 0.068518 -4.03 -0.0515442 Gender 0.9167494*** 0.2304793 3.98 0.1713272 Household Size 0.083492 0.0887255 0.94 0.0156035 Education Level 0.1865408 0.2678583 0.70 0.0348618 Official Status -0.2518213 0.3979556 -0.63 -0.0470618 Additional Income 0.0680126 0.2271891 0.30 0.0127106 Income 0.1708825* 0.0961412 1.78 0.0319355 Expenditure 0.1194949 0.0878702 1.36 0.0223319 Married 0.2706212 0.4381109 0.62 0.0505752 Constant -5.057964*** 0.9238645 -5.47 McFadden R-squared 0.1213 Log likelihood -238.06987 LR Statistics 65.74*** Degrees of Freedom 10 Total Observation 429

*, *** represent the 10%, and 1% significance levels and means it is appropriate to use;

Source: Author’s calculations based on the survey questionnaire.

Table 5.19 shows the average marginal effect for each variable of the logit model. The marginal

effect provides an interpretation of the influence of variables on rural households’ access to Islamic

MFIs (Phan, 2012). The marginal effect measures the change in probability of a certain choice made with respect to a unit change in an explanatory variable (Gao, 2011). For instance, the marginal effect

of becoming a client by 5.1%. The probability of a rural household becoming an Islamic MFI client increases by 17.1% if the person is male. An increase in income of a rural household increases its probability by 3.1% of becoming an Islamic MFI client.

In conclusion, the empirical results from the logistic regression show that rural households’ age,

gender and income are three factors that influence a rural household to become an Islamic MFI client. This implies that Islamic MFIs consider age, gender and income of the rural household before disbursing finance.

5.6

Chapter summary

This chapter discussed and described the characteristics of respondents, which include clients and non-clients of Islamic MFI. In this study, clients were categorised into three types: clients who received PLS financing; clients who received non-PLS financing; and clients who received both financing (PLS and non-PLS). The chapter also provided the welfare impact estimation with a

standard DID method between clients and non-clients of Islamic MFIs. Following the DID estimation, the chapter provided the results of fixed effect regression estimation and fixed effect robustness tests for clients of Islamic MFIs. The standard DID, fixed effect regression, and fixed effect robustness test estimations show that financing from Islamic MFIs have positive and significant impacts on rural

households’ incomes. This implies that rural households that received financing from Islamic MFIs will experience more increases in their incomes compared to non-clients.

The Islamic values and shari’a compliance evaluation were also discussed in this chapter, the results show that financing from Islamic MFIs in this study parallels the standards set by the National Shari’ah Board of Indonesia. The Islamic values’ evaluation indicated that the financial activities of

clients of Islamic MFIs in this study are influenced by their religious beliefs and they believe Islamic MFIs always give proper service to their clients according to Islamic values. The investigation of the two financing mechanisms in Islamic MFIs (PLS and non-PLS) show that PLS financing has more positive impacts on rural household incomes compared to non-PLS. Finally, Age, Age Squared, Gender, and Income are factors that influence rural households to become clients of Islamic MFI.