Probability of Successful Implementation of Islamic Microfinance in Pakistan

This section empirically investigates the probability of successful implementation of Islamic microfinance in Pakistan. The binary dependent regression (Probit) is used with dummy independent variables to get the possible probability of success / failure. The functional form can be written down as:

𝑃

= 𝑝(𝑦

= 1) = 𝛽𝑜 + 𝛽

𝑥

+ 𝛽

𝑥

+ 𝛽

𝑥

+ … . . +𝛽

𝑥

+ 𝜀

... (5.1)

And for binary random variable

P (Y=1 / X) is equal to E (Y/ X)

The alternative approaches of Logit and Probit are preferred to avoid such estimation issues, therefore, this study uses Probit model instead of linear probability model. The functional form of Probit is written down as:

𝑃(𝑦𝑖 = 1/𝑥𝑖) = 𝜑(𝑥′𝑖𝛽) …… (5.2)

(where 𝜑is cumulative density function)

I use the questionnaire survey data which were collected to ascertain the people’s perception about conventional microfinance and Islamic microfinance in Pakistan. The data are primary in nature and the data collection was held in July to October’2014, in all districts of Karachi, Pakistan. A total of 332 survey questionnaires are used to gather the data for this study.

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The likelihood is investigated by using the variables that determine the awareness of Islamic microfinance in our respondents. For the independent variables, five categorical variables are transformed into dummy variables by assigning the values of 1 for ‘agree’ and ‘strongly agree’ Likert-scale categories, and 0 otherwise. The dependent variable ‘success’ is transformed into dummy by using the variables that gives the awareness that Islamic microfinance is a better tool for poverty alleviation.

The following model is developed for this study:

𝑃 (𝑆𝑥𝑖= 1/𝑥𝑖) = 𝜑{(𝐼𝑀𝐹𝐼𝐸𝑀𝑅. 𝛽1+ 𝐼𝑀𝐹𝐼𝐺𝑂𝑉. 𝛽2+ 𝐼𝑀𝐹𝐼𝐻𝐸𝐿𝑃. 𝛽3+ 𝐼𝑀𝐹𝑆𝐻. 𝛽4+

𝐼𝑀𝐹_𝑈𝑁. 𝛽₅+ 𝜀₁))

Where,

Sx is 1 in case of positive response that IMFI serves poorest of the poor and 0 otherwise. IMFI.EMR is 1 in case of positive response that IMFI emerges due to Islamic Banks and 0 otherwise.

IMFI.GOV is 1 in case of awareness that the government support can catalyze the poverty alleviation efforts through IMFI and 0 otherwise.

IMFI.HELP is 1 in case of awareness that it IMFI lifts poor out of the poverty and 0 otherwise.

IMFI.SH is 1 in case of awareness that IMFI works within Shariah boundaries and 0 otherwise.

IMFI.UN is 1 in case of awareness that IMFI doesn’t use unethical practice and 0 otherwise.

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The details of the variables10 are presented in summary statistics in Table-5.18.

5.4.4.1

Regression Results

Table 5.19 shows the empirical results from the Probit regression used for this study.

10 _{The limitation of the model used here is that it mainly uses variables that are based only on people’s}

perceptions, and lacks those variables that can assess the risk factors, i.e. possibility of default, general interest rates in the economy, borrower’s indebtedness, cost overrun etc.). Caution must therefore be used in generalizing the results of this regression analysis. The analysis only determines a probabilistic estimation or the feasibility of the success as it was estimated on the basis of available data. Since the actual project is not initiated yet, the collection of actual data relevant to this scheme is not possible at this stage. All the variables used in this estimation are selected from the survey data. The main idea behind the selection of those variables is that the Islamic microfinance is yet to be launched in Pakistan because the people are not well aware about its effectiveness. We therefore assume that if the people are highly aware about Islamic microfinance, then the probability of the project’s success will be high. The risk factor variables or the exogenous variables are intentionally not included to avoid the issues of heteroscedasticity and multicollinearity that are quite possible if we unnecessarily increase the explanatory variables in the binary regression model.

Table 5.18 Summary Statistics

IMFI_EMR IMFI_GOV IMFI_HELP IMFI_SH IMFI_UN SX Mean 0.7199 0.7018 0.6175 0.3946 0.4518 0.7289 Median 1.0000 1.0000 1.0000 0.0000 0.0000 1.0000 Maximum 1.0000 1.0000 1.0000 1.0000 1.0000 1.0000 Minimum 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 Std. Dev. 0.4497 0.4582 0.4867 0.4895 0.4984 0.4452 Skewness -0.9793 -0.8823 -0.4834 0.4314 0.1937 -1.0299 Kurtosis 1.9590 1.7784 1.2337 1.1861 1.0375 2.0608 Jarque-Bera 68.0560 63.7156 56.0888 55.8124 55.3528 70.8996 Probability 2E-15 1E-14 7E-13 8E-13 1E-12 4E-16

Sum 239 233 205 131 150 242

Sum Sq. Dev. 66.9488 69.4789 78.4187 79.3102 82.2289 65.6024

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Table 5.19: Regression results of LPM Binary Dependent Variable: SX Coefficients Standard Error Independent Variables Intercept -1.044* (0.18273) IMFI_SH 0.690* (0.24035) IMFI_EMR 0.541** (0.21148) IMFI_GOV 0.981* (0.22061) IMFI_HELP 0.719* (0.19739) IMFI_UN 0.355 (0.24346) Average 0.577 - McFadden R Square 0.392 - LR Statistics 152.427 (0.000) No. of observations 332 -

*, **, *** exhibit significance level at 1%, 5%, and 10%

The prediction rule is y=1 if the probability of prediction is greater than 0.5, and zero otherwise (Dollar and Svensen, 2000, p. 902). Table-5.19 shows the results of Probit regression for the assessment of probability of successful implementation of Islamic microfinance in Pakistan. It is evident from the results that the awareness about Islamic microfinance can increase the probability of successful implementation. We use five variables for the awareness and all variables are significant except IMFI_SH. The other variables are significant at 1% level of significance except IMFI_EMR which is significant at 5% level of significance. The model’s overall significance is indicated by McFadden Pseudo R-Square which is about 39 percent.

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CHAPTER 6

Conclusions and Recommendations

This study investigated the limits and prospects of microfinance for poverty alleviation in Pakistan. The dissertation is focused on four segments, and various methodological techniques are applied in different segments. The first segment evaluated the performance of microfinance institutions in Pakistan through the use of DEA and financial ratios. The second segment compared the people’s perceptions of conventional microfinance and Islamic microfinance based on survey data. The third segment proposed a new model of Islamic microfinance for poverty alleviation in Pakistan. The fourth segment tested the probability of successful implementation of Islamic microfinance in Pakistan, using regression analysis based on a linear probability model.