Eva Dan Mva Iran

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JANUARY 2012 VOL 3, NO 9

A Study of Refined Economic Value Added Explanatory Power Associated with

MVA & EPS in Tehran Stock Exchange

Dr. Abolfazl Ghadiri Moghaddam Department of Accounting, Mashhad Branch, Islamic Azad University, Mashhad, Iran.

Hossein Shoghi* (Corresponding Author)

Department of Accounting, Mashhad Branch, Islamic Azad University, Mashhad, Iran. Abstract The current study aims to examine the relationship between Refined Economic Value Added and market Value Added as the independent variable and earnings of per share as the dependent variable in companies listed in Tehran Stock Exchange during five years 2004-2008. In order to measuring the correlation of the criterion toward market Value Added in relation to earnings per share. Hence, the main question of the study is that if Refined Economic Value Added has more explainability than market Value Added in explaining the earnings per share. According to the research objective, 97 companies were selected as the sample through systematic omittance method. Then, hypotheses were stated and two estimating regression models were used to test the hypothesis; and in order to select the superior model among the other models, Voung statistics was used. The results, according to the Voung statistics and R2, indicate that in studied sample, market Value Added estimated in model has more correlation with earnings per share than Refined Economic Value Added. Keywords: Refined Economic Value Added (REVA), Market Value Added (MVA), and Earnings per share (EPS). 1. Introduction

Long term value creation is one of the most important goals of companies (Monks and Minow, 2003). Gupta (2007) stated that the main goal of each organization is to create value for its owners. Undoubtedly, the purpose of investors of investing in companies is to earn returns on their investment proportionate to their investment. If companies or organizations are successful in value creation, it will be not only benefitial for investors and internal individuals, but also in a broader level, the community will profit from it. Therefore, finding a superior identifier in evaluating the enterprise performance is a significant characteristic of current financial researches. REVA in previous research constantly considered as an important complementary and efficient evaluating criterion toward EVA criteria and variety of research has been done by researchers in order to determine the ability of these criteria in evaluating the performance of companies. This criterion seeks to close the accounting profit and economic profit and with considering the market value of capital costs in calculation of accounting profit is trying to measure create value for enterprise better than the EVA, so that it could be better evaluating basis for performance of managers and investors in enterprise. 2. Theoretical Bases and Literature review

In most cases, the power and authority of decision making in company, is often provided by managers who have a conflict of interest with outside interest groups, particularly stockholders. This conflict of interest is the result of separating ownership from management, from the past has focused the attention of many. Several studies in the field of identify the problems from the separating ownership from the management and the stockholders, managers and researchers have paid to ponder the roots reasons for conflict of interest (Alchian and Demsetz, 1972). With the formation of separation the ownership and management topics a conflict of interest between owners and managers, evaluate the performance of companies and their managers from the considered topics of creditors, landlords, government and even the managers (Jensen & Meckling, 1976). In order to mitigate conflicts of interest, criteria for evaluating managers' performance and provide a basis for determining incentive payments are based on the findings is innovation and being used of this assessment (Jensen & Murphy, 1989). For many years investors and managers are seeking to timely and reliable identifier to measure of wealth for shareholders (Worthington & West, 2000). Currently measurement techniques are more have been based on economic theories than on the accounting framework (Shinder & McDowell, 1999). 2.1 Selecting Criteria for Performance Evaluation



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Bacidore et al., (1997) state that, a scale for determining the appropriate criteria for evaluating performance, the selection criterion is the extent to which it is to create value for shareholders. A good tool to create a return commensurate with the amount of capital used in lead the company. According to many researchers, such as Dechow (1994), Lehn & Makhija (1997), Balsam & Lipka (1998), Chen & Dodd (2001) and Worthington & West (2004), profit of accounting is one of the most important criteria of performance measurment. Despite the importance and application of these criteria, there is a fundamental problem: the conflict Interests of By distorting numbers management accounting profit (Hill & Phan, 1991). Manipulation capabilities (Stewart, 1991; Bhattacharyya & Phani,1999), ignoring the time value of money and price level changes (Anand et al, 1999). The opportunity cost of investment (Chen & Dodd, 2001). Among the criteria based on profit, growth profit is counted as criterion for the status profit each company in future (Jackson, 1996) Is considered at the profit accounting with attention to price market shares at view is that shows expectations and predictions market future and profitability future. It is View Stewart (1991) growth profit also criteria misleading of operation company and the criteria to alone criteria appropriate for evaluation company performance and should not be cautious in applying it, and the amount of investment made to achieve this growth in mind. 2.2 Dividends: The dividend policy, the first time by Lintner (1956) was introduced. Lintner model by other researchers (Fama & Babiak, 1968; Correia el al., 1993; Noe & Rebello, 1996) was developed Wolmarans, 2003). Divided profit also criteria Inappropriate for evaluation operation companies most companies to two because section significant a profit more by the between shareholders own are divided. The as companies or plans profitable investment, no and or intent are the funds need own for growth and, investment, of resources foreign supply. said divided profit of lack possible investment at projects, profitable, correct is but if profit divided be and then funds necessary for growth of by increase capital supply, be policy divided profit with difficult is the increase capital for supply, financial most expensive type supply financial (Krolick, 2005; Stewart, 1991). 2.3 Free Cash Flow The concept of free cash flow was introduced first time by Michael Jensen (1986). The cash free some of the cash is that if between shareholders divided, be Effect at power Profitability Company no. According to Stewart (1991), the free cash also to alone criterion sure for measurement operation company not and only can for purposes of Short case be use. 2.4 EPS O’Hanlon & Peasnell (1996) on the believe that using EPS as the criterion for evaluating performance, encourages short-term behavior between managers and leaders and convince them that funds supply by shareholders, are free to attend. In the study of Jennings et al. (1997) also, the information content of different forms of profit accounting were evaluated that shows that the profit of each share at all forms of calculation, is useful for its users. EPS data is widely used in the evaluation of executive functions often as the only scale which is considered as the best performance method of a company. The amount of EPS, dividends per share compared to the previous period and the change in the trend, all important measures of success or failure of a company are considered. The EPS is of result divided profit (Loss), net so of fraction tax the average tunable ordinary shares of the company (Stewart, 1991). 2.5 The concept of REVA The company’s value is a function of power profitability, priorities existing investments potential and difference rate efficiency and cost capital of company (Bausch et al. 2003). The value based on performance criteria is in an effort to overcome some limitations of traditional performance measures (Erasmus, 2008). The value added at the first time by Gillchrist (1970) was used in the census of GDP in North America (Van Staden, 2000). The concept of wealth creation in the literature value added accounting for the company by its owners and employees during a financial period is defined (Mandal & Goswami, 2008). Value Added for the first time by Stern Stewart's economic institute in 1989 to title outstanding criteria evaluation operation With the highest share price performance is compared to the traditional criteria, was introduced (Stephens & Bartunck, 1997). The concept of economic value is similar to the concept of residual income (Stark & Thomas, 1998) and creates more wealth for shareholders at all times been associated with (Fernandez, 2001; Ferguson et al., 2005). 2.6 Disadvantages of using EVA



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Calculating the cost opportunity resources based on office value (Bacidore et al. 1997). Distorting the findings by swelling; therefore, in Length Courses Inflation not for Estimate Value Creation and profitability Real. Bkarbrd Company (Ferguson & Leistikow, 1998; Villiers, 1997). Inflation to the EVA: after reduction through taxation, investment and cost of capital (Warr, 2005). At conditions inflation and at form application EVA to title evaluation criteria performance operating company, it is better of version adjustment shdan for financial decisions be used for this purpose (Villiers, 1997). Deficiencies related to the economic value, EVAS another performance measurement tool in the study adjusted for the first time in 1997, was introduced by Bacidore. Economic value as a criterion for value creation for shareholders acting, but the refinded EVA for a tool to better assess the financial performance for a company in the past has had or will provide. Refinded for the total economic value of a foreign operation is used (Bacidore et al. 1997). The problem of EVA is depended on historical figures. It means that EVA uses more reliable data but this data does not necessarily relevant. In the other words to calculate the utilized resources opportunity cost REVA using market value and also EVA use office value. So, the investors expect returns on maeket value (Bausch et al. 2003).The advantage of REVA in compare with EVA is that, at any time Refind economic value. is positive for shareholder value creation is double the operating profit of the financiers at the end of the year is expressed in terms of percentage of the value of the capital market, it is more than the opportunity cost of capital. But in this situation There is no economic value because shareholders return on operating profit after subtracting the opportunity cost of capital do not get it, even when the calculated amount of economic value, is positive (Bacidore et al. 1997). 2.7 MVA Stewart in 1991 introduced a measure of density for value creation for shareholders as the value-added market, the difference between market value and book value of the company (Poulain-Rehm, 2008). Maximizing the total market value is now one of the common objectives of all companies (Gapenski, 1996). Which is generally a domestic economic performance, value-added market criteria for performance is how the market performance of foreign companies based on market values of debt and equity, with investments made in companies measure (Reilly & Brown, 2004). The results of Uyemura et al. (1996) and Milunovich & Tsuei (1996) very well represent the market value of the value created for shareholders has been introduced. A company's total market value is the sum of equity market value of the company and the market value of its liabilities. Accordingly, if the company will create value for shareholders of the company's market value Book value.( More capital is used (Medeiros, 2005). In fact, the market value equivalent to a present value of all economic value created by companies Additions (Hall & Brummer, 1999; Wet & Hall, 2004; Hawawini &Viallet, 1999; Weston et al. 1992) and as accumulative measure of value created by management in excess of capital is used in exorcism (Rama, 2005). The market value is calculated as follows (Stewart, 1991): MVA = Market Value of Company - Invested Capital.

Table 1. The brief angle of Research background Researchers & Research year Title Research result

Bacidore et al. (1997)

The Research for the Best Financial Perofmance Measure

EVA and REVA both have a positive relationship with abnormal returns. REVA in explanation and prediction of abnormal returns acts better than the EVA. It would be wise to use REVA for evaluating the performance of high levels organization and EVA for evaluating the performance of low levels organization.

Pearson (1998)

An analysis of the explanatory power of EVA and REVA for share returns

in the mining sector

EVA to some extent predicts stock returns, while the REVA of the stock return is not predictable at all.

Bausch et al. (2003)

Is market value-based residual income a superior performance

measure compared to book value-

REVA can lead to low investment in projects with positive net present value or excessive investment in projects with



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based residual income? negative net present value. Circiumaru &

Siminica (2009) REVA- An Indicator For Measuring The performances of the companies

REVA has higher explanatory power than EVA in predicting shareholder wealth.

Panahian & Mohammadi

(2011)

Relative and Incremental Information Contents of EVA and REVA used for

Prediction of the Income

Between EVA and REVA and operating cash flow or operating income with the information content did not achieve a significant relationship.

3. Research Methodology This study considering the purpose is an applied research and considering the method is a descriptive correlation research. In the present study during the 5-year period 2004 to 2008 data was performed from companies on the Tehran stock exchange (the Statistic). Tehran Stock Exchange Company were selected with the following conditions samples research method is screening (systematically removed) according to continuous activity in research period and similarity of fiscal period (except investment companies and financial intermediation). Size of the Statistical Society in this study is 316 companies and sample size was 97 active companies from Tehran Stock Exchange. 3.1 Hypothesis of the Study According to the above mentioned literature as well the objective of the study the following hypothesis is postulated in the study:

1. Refinded economic value added, better explains the earnings per share than the market value.

3.2 Variables under Study According to the research hypothesis refinded economic value and MVA as the independent variable and EPS as the dependent variables were determined. REVA is calculated as follows (Bacidore et al. 1997): REVAt = NOPATt – WACC (MCAPITALt-1) NOPATt = Net operating profit after tax in t end WACC = Weighted Average Cost of Capital MCAPITALt-1 = market value at beginning of period t (end of period t-1) (Stock market price in the first period × number of shares) – (book value of total liabilities - Interest free current liabilities) For calculation capital used and operational net profit after Fraction the taxes two approaches can be used; operational and financial supply. Using each of two approaches will lead to similar results. In this study has been used operational approach. Effective tax rate according to direct law taxes in Iran is 22/5 percent. In calculating in this study apply the capital increase and reduce storage demands and the suspect store inventory devaluation. In calculating the weighted average cost of capital rate cost debt to form the calculation the Be: Kd= The interest rate of loan (1-t) Where; Kd: the rate of cost of debt and T: tax rate Iran shares of corporate stock outstanding and no preferred shares in the costs. Cost of ordinary shares: Two methods for measuring Cost of issuing ordinary shares in the lining are: 1) Gordon Model. 2) Capital Asset Pricing Model (CAPM) (Bacidore et al. 1997). Gordon model has been used in this study. The Gordon model expectations capital investors of the profit future shares company estimate the Fibre of Price now Shares Normal to Purpose of Rate Efficiency Case Expectation Shareholders Use the: Be. At the expense of the ordinary shares is calculated by the following equation:



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Where; Ks: rate the cost of common stock, D1: The benefit is paid at the end of the first year, P0: Value of common stock at the beginning of the period and g: growth rate (in the model is assumed to be constant over time. will be). Finally, the weighted average cost of capital with the various components that comprise the company's capital such as debt and common equity is calculated (Stewart, 1991; Bacidore et al. 1997):

Where; Wd, We: the percentage of the contribution (weight) in total debt and common equity capital and

Kd, Ke, Debt and equity cost rate are normal respectively.

3.3 Testing of the Hypothesis In this study, statistical analysis methods such as correlation analysis, regression analysis and statistics were used to Vuong. Common method for selecting the best model in terms of defining the regression model, comparing the coefficients of the model is that the model is chosen that has a higher coefficient of determination. The difficulty in choosing a model, it is possible that nature has no significant results, so in this study to de termine which of the competing models is better able to explain power of one of the best and most powerful test to generalize the results of statistical models to explain the power of Vuong's statistic is used. Vuong (1989) a statistical test to determine which of these two models, the dependent variable better explains, can be provided. Difference test and other tests in the Vuong test, the likelihood ratio statistic distribution obtained with the assumption that none of the models is real. The likelihood ratio statistic Vuong, based on statistics of the null hypothesis without considering statistical evidence each model is calculated (Genius & Strazzera, 2000). Although this statistic for both models, considers power, but the shows which of the two models is closer to the actual process the data. In many studies of this test to measure a significant regression in contrast to other models by comparing the coefficient of determination is used. This statistic has a normal asymptotic distribution and less better model is that it represents. The two models are fitted:

And the order in which the residual mean square and the remaining two are related.. Is zero average residual standard deviation divided by the remnants of the standard results? K. However, the obvious difference between the standardized values of the second enlargement of the residual values of K, which indicates a difference in the two models, is explained. K. But it was the opposite of the values of K are zero: To evaluate this test for normal distribution using the mean and the statistics based on Vuong is defined as follows:

In these test method the probability ratio for the selection of competing models is presented according to if the test statistic Vuong:

1) Is positive, the second model is superior to the first model.

2) Is negative, the second model is superior to the first model.

3) Is zero, none of the two models are better than another (Dechow, 1994). The following is a

statistical hypothesis :

H0: Both models to describe the actual process of production data are similar. H1: One of the two models explained the actual production process the data further.



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According to the the hypotheses the research regression models were have been fitted to the following relationships: Hypothesis models: EPSit = α0 + β1* MVAit +εit (1) EPSit = α0 + β1* REVAit +εit (2) 4. Results of Testing the Hypotheses 4.1 Describing the values of research Table (2) shows the descriptive statistics the variables study.

Table 2. Describing the values of research Var. Min. Max. Mean Std.Dev Skewness kurtosis

REVA -25721416 797403 -617963 1992533 -6.802 64.151 MVA -889.7 4840 344.8 722.2 2.950 10.479 EPS -779 5418 816.5 895.9 2.302 7.603

4.2 Evaluation of Research Variables Normality In Table 3 with using the kolmogorov-smirnov statistical method, the normality of variables of research has been examined. As can be seen, any of the research variables are not distributed normally and this issue can cause the non-establishment normality conditions is required in the remaining regressions.

Table 3. Kolmogorov-Smirnov statistic Var. no (Year – Co) K-S p-value

REVA 485 9.894 0.000

MVA 485 6.367 0.000

EPS 485 3.788 0.000 Regarding the absence of normal research variables by Box and Cox transformation trying to normalize the dependent variable for this purpose, distribution of the variable study below conversion (Table 4) was close to normal distribution.

Table 4. Conversion for the normalize distribution of the dependent variables

Var. Box & Cox transformation K-S p-value

EPS 1.109 0.171

4.3 Estimation of Regression Models Before fitting the final models first of all research data has been converted for establishment the infrastructure conditions of regressions (Remainings independent, the normal and have constant variance) and before fitting Perth data regressions according to the standard deviation of the three out residues are eliminated.

Table 5. Regression statistics in relation with MVA & EPS Correlation coefficient R2 Adjusted R2

0.473 0.224 0.222



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Table 6. Analysis of variance in relation with MVA & EPS

changing Source Sum of squares df Mean Squared F p-value

Regression 64896.821 1 64896.821 126.215 0.000 Residual 224694.455 437 514.175

Total 289591.277 438

Table 7. Estimation of parameters in relation with MVA & EPS Parameter Std.Dev T p-value

Constant 41.376 1.271 32.564 0.000 MVA 0.671 0.060 11.235 0.000

According to the tables, 22% of the relationship between the variables is explained through the following equation:

Table 8. Statistics regressions in relation with REVA & EPS

Correlation coefficient R2 Adjusted R2 0.158 0.025 0.023

Table 9. Analysis of variance in relation with REVA & EPS

changing Source Sum of squares df Mean Squared F p-value

Regression 7250.433 1 7250.433 11.222 0.001

Residual 282340.843 437 646.089

Total 289591.277 438

Table 10. Estimation of parameters in relation with REVA & EPS

Parameter Std.Dev T p-value

Constant 50.122 1.271 39.443 0.000

REVA 0.003 0.001 3.350 0.001

According to the above tables, 2.5% of the relationship between the variables is explained through the following equation:

In each of above relationships prior relation confirmation between, appropriate models check was proved through the distribution curve. Also according to the Durbin-Watson test normality of remainded and K-s statistic; lack of remaining correlation is confirmed and was proved table (11). Also absence of trend in the distribution charts and remained stable against the estimated residual variance proved for all models.

Table 11. Durbin-Watson & Kolmogorov-Smirnov statistic Model p-value K-S D-W

(1) 0.066 1.305 2.21 (2) 0.223 1.047 1.97

4.4 Research hypotheses inferred To examine the Research hypothesis according to the fitting of two regression equation (1) & (2) is insufficient to compare the Value determination coefficient of two equations.



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That R1

2 (determination coefficient of regression equation of REVA & EPS) and R22 determination

coefficient of regression equation of MVA & EPS or in other words: H0: REVA did not explain EPS better than the MVA. REVA did explain EPS better than the MVA. H1 Results test related to hypothesis test are gathered in following table (12):

Table 12. Comparison Table of determination coefficient with Voung statistic in the hypothesis

Regression equation Correlation coefficient R2 Adjusted

R2 Voung statistic p-value

0.473 0.224 0.222

0.158 0.25 0.023 10.783 0.999

As is clear determination coefficient of MVA more than REVA and according to the p-value can be accepted that did not explain null hypothesis or this assumption that “REVA did not explain EPS better than the MVA “at the %5 level not be rejected. 5. Results and Discussion The research hypothesis states that the refind economic value added, EPS is explained better than the MVA. The results indicate that the correlation coefficient between refind economic value added and EPS of 0.158 is much weaker than correlation between MVA and EPS of 0.473. Compared with the coefficients determination and can be inferred that according to the estimated regression equations for relationships between variables refinded economic value added and MVA as a performance evaluation economic criterion for the ability to explain 0.023 and 0.222 Percent change in EPS as a have financial criterion for evaluation performance and decision making based on any of these criterion could lead to somewhat different results. Also according to the probability amount that could be admitted that null hypothesis in not rejected at 5% level thus the first research hypothesis is not confirmed. However, the positive value of the Voung statistic 10.783 and null hypothesis rejection these statistics in order to generalize difference results amount explain power of two rival models to the study population MVA in the first model than refind economic value added in the second model has more explanatory power in relation to EPS and better explain the dependent variable in the studied sample and is closer to the actual process of creating data in community than rival model the results of correlation in the selected model with the results Hall & Brummer(1999), Milunovich & Tsuei (1996), the results in the first model are consistent with Milunovich & Tsuei (1996) research. Noteworthy that the data of refind economic value added in this study was the number of 365 (Year - Co.), of 485 (Year - Co) means 75% from the Refind economic value added has been calculated was negative that this matter could be due to the following factors:

i. The high cost of capital rate in Tehran Stock Exchange market. ii. Existence of the poor market performance in the in Tehran Stock Exchange.

iii. Lack of company entrepreneurship power in expectations shareholders or absence of enjoying optimal combination of capital structure and the high cost of financing.

iv. Negative Refind economic value added can also, is due to investment and effective information affecting the company market value that the effect of investment opportunities in the present and future strategies, future will be deprive company. The effect of these factors on net operating profit after tax and the effect of this new information on future cash flows affect the operating profit after tax and have solely effect reducing during the current period. Therefore compliance to opportunity costs of future with net operating profit after current period tax may result negative Refind economic value added.



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EExxpplloorraattoorryy RReesseeaarrcchh oonn tthhee EExxppeerriieenncceess ooff DDrrooppoouutt CCuussttoommeerrss ooff MMiiccrrooffiinnaannccee BBaannkk iinn PPaakkiissttaann

Iram Rani * Dr. Amant Ali A Jalbani** Minhoon Khan Laghari ***

Abstract: The main objective of this research paper was to assess the impact of microfinance loan program on dropped-out customers and their experiences. This research is unique in the sense that for the first time the impact of microfinance programs on dropped-out customers was ascertained. Since the claim of microfinance is that they improve the lives of poor people with their products. In order to verify their claim this research study was undertaken. The survey was carried out from defected customers of Microfinance Bank in Pakistan. The instrument was adopted from SEEP/AIMS Client Exit Survey Tool. Even though there is some evidence that microfinance programs have some positive impact, but in general this research has shown inconclusive results about the positive impact of Bank program on the lives of its poor customers.

Key words: Microfinance Program, Impact, Experience, Dropped-out Customers, Pakistan.

1. INTRODUCTION

Poverty alleviation is a primary obligation of microfinance; reach out to poor is a social mission of microfinance which differentiates it from formal financial system (Balkenhol, Bernd and Churchill 2002). Over the years microfinance has provided evidence to help poor in many different ways such as decreasing their vulnerability, improving livelihoods, paying for indispensable healthcare and providing finance for children’s education of its customers (Littlefield ,David, Cracknell, Leonard, Mutesasira ,2003) . With its success microfinance has been lately used as an economic tool to alleviate poverty across the world. In Pakistan also microfinance has been used as an economic tool to alleviate poverty. Number of institutions both in private and public sector has done marvelous job in helping poor. However still need to be done more. Microfinance has dual mission that is to improve the lives of its customers and at same time achieve operational and financial sustainability ( Ozair A. Hansfi 2007). As it put forward by G. Woller (2002) the promise to alleviate poverty has a mass appeal of microfinance and its rise to global prominence (G. Woller 2002)]. However in order to have positive impact of loan program and achieve sustainability microfinance institutes should repeat its loan program to customers until they graduate from the loan program. Churchill (2000), Brand, M. and J. Gerschick (2000), Leland, Olivia. (2006), and Chuck Waterfield (2006) has highlighted the importance of customer retention in microfinance institutes (

Churchill, Craig, 2000,Brand, M. and J Gerschick 2000,Leland, Olivia. 2006, Chuck Waterfield 2006). Evidence from previous research also suggests that acquisition of new customers can cost five times more than the costs involved in satisfying and retaining existing customers (Kotler, Philip, Armstrong, Gary 1999) and five percent reduction in the customer dropouts rate increase profits by 25 percent to 85 percent, depending on the industry (Reichheld, F. and Sasser, W. 1990). Further previous research suggests that average defection ratio is approximately 30 percent in microfinance institutes of Pakistan (Research Brief (CHIP) 2005). In actuality this defection rate is understated.

* Minhoon Khan Laghari is Assistant Professor . Shah Abdul Latif University Khairpur * * Dr. Amant Ali A Jalbani , SZABIST Karachi * * * Iram Rani is Assistant Professor . Shah Abdul Latif University Khairpur



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Considering the promise of microfinance, in this research study effort has been made to verify that up to what extent microfinance in Pakistan has done enough to reduce the vulnerability, improve livelihoods, paid for healthcare, and financed children’s education of their poor customers. In general, this research is an effort to find out the dropout customers’ experiences by participating in the loan program of leading microfinance bank in Pakistan.

2 .Literature review

The impact of microfinance on poverty alleviation has recently gained a prominent position on the microfinance agenda. (Simanowitz, Anton 2000). Donors, Practitioners and Academics are realizing that microfinance institutions must concern themselves with more than their ability to reach institutional self efficiency. The ability to reach and to demonstrate a positive impact on the poorest is now becoming a core principal in poverty-focused financial institutions. A comprehensive review of the research to date looking at the case for providing appropriate, quality financial services for the poor, and outlining the principles and methods that could/should be followed to design quality Microfinance systems. (Wright, Graham A.N.2000). Economic impact of micro credit: ‘’Greeman Bank members had income about 43% higher than the target group in control villages, and about 28% higher than the target group non –participants in the project villages’’(Hossain 1988). World bank in collaboration with Bangladesh institute of development studies, showed that Greeman bank not only reduced poverty and improved welfare of participating households but also enhanced the household’s capacity to sustain their gains overtime’’( Hashemi and Morshed1997). Higher rates per capita income among Micro credit program borrowers compared to those who did not borrow’’ (Kamal 1996). Women (and men) participating in BRAC sponsored activities have more income (both in terms of amount and source),own more asset and are more often gainfully-employed than non participants’’( Chowdhury et al.1991). Members have better coping capacities in lean seasons and that these increased with length of membership and amount of credit received (Mustafa et al 1996 above the BRAC) an increases in assets of 112% for those who had been members for 48 months or more and increase in household expenditure of 28% (Mustafa et al 1996). Greeman bank members were statistically more likely to Mustafa et al 1996 be using contraceptive (59% of Greeman members as opposed to 43% of a matched control group). (Schuler and Hashemi 1994). A similar conclusion as a result of their work in Tangail (pending publication)is also concluded. Similarly ,a recent Asian Development Bank report noted that,’ contraceptive use goes up among members because they are better able to overcome the barriers to obtaining access to contraceptive services(lack of mobility,cash,information,among others).Contraceptive use goes up among non members because of the diffusion effect of changing fertility norms in the village as whole. (Rahman and de Vanzo) According to ‘’among the economically active poor of the developing word, there is string demand for small scale commercial financial services-for both credit and savings. Where available, these and other financial services help low income people improve household and enterprise management, increase productivity, smooth income flows and consumption cost, enlarge and diversify their micro business and increase their incomes.`` (Robinson Marguerite, 2001).



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Pakistan microfinance network monitor the Portfolio At Risk (PAR). apart from a few outliers this has generally remained at acceptable levels in the past

YEAR PAR LOANS OVERDUE 2005 9% More Than 90 Days 20065 .2% More Than 30 Days 2007 3.1% More Than 30 Days 2008 60% Serious Concern

In general the two major reasons behind this are the political interference and client’s dissatisfaction. A study conducted by European union commissioned to study the client drop out in microfinance I Pakistan by the help of Schreiner`s formula as follows DROP OUT RATE (DOR) =1-RETENTION RATE

RETENTION RATE =ACend/AC begin +NC ACend = number of active clients at the end of a year ACbegin = number of active clients at the beginning of a year NC = number of new clients entering during the year

Study concluded that drop out rate in Pakistan is range in between 26.5-32%.it is reported by IMF that Pakistan dropout rate was nearby to average dropout rate of 28% in Asia and below at the global level dropout rate of 48% & the major causes behind this are

• Product design 63% • Group lending 8% • Borrow again 15% • Loan size 09% • Religious reason 5%

Variables/factors for Drop out throughout the regions are same, although the strength of the variables in contribution to drop out depends on the policy /program design, business portfolios along with competitors and market status /cycle by an institution in particular region. These variables are characterized by internal and external category. (Inez Murray, April 2001)

Internal External Category High prices Illness Rigid product design Family problems Narrow range of products Death High transaction cost Seasonality Insufficient attention to customer service Natural disaster

Economic shock



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The reasons behind drop out in microfinance vary between socio-economic groups, personnel circumstances and preferences. Both poorer and wealthier clients show a propensity to drop out. They also further studied that the drop out rate become intensive when

• Clients keep taking loans regardless of enterprises needs and environmental context. • The neglect of voluntary savings and insurance products. • Lack of product flexibility. • High interest rates. •

MFIs has also pointed out that clients exit is also possible because of product and program design by institutions and saving services ,which often don’t meet client needs like

• Downturn in national economy. • Adverse climatic conditions for agriculture. •

Most MFIs had experienced at least one major shake-out when changes in agency policy or concerned about default or sustainability had led to a rapid forced exit for large number of clients. Management problems in policy, fraud on the part of staff, and when MFI had cash flow problems and could not disbursed approved loans to client on time.(Leonard Mutesasira et al for Micro Save-Africa, UNDP and DFID) Causes for client dropout are varies in both urban & rural areas. Most probable reason of client drop out in urban areas is failure of business. In urban areas same factor is also possible but up to very little extent. In urban areas major cause for client dropout is loan payment delinquency, missed loan repayments, default etc( James Copestake ). Irrespective of official policy, there is clear understanding among most field staff that they should push out loans-often with little care for whether the clients need or can use them. In the words of one BRAC Zonal Manager that ‘if we don’t disburse loans how can we cover cist?’’(Graham A, N Wright 1996) One of the key desertion determinants, often lost in the category ‘’failure to repay loan’ is the insistence by the field staff that clients take loans According to Graham A, N Wright (2001). Study of members perception of Greeman ,BRAC, Proshoka, ASA, and other Bangladeshi MFIs (using participatory rural appraisal and focus groups)found that many borrowers felt pressurized or ‘’sweet talked” in taking loans. (Prompts 1996) MFI lending technology is insensitive to variation in households conditions, Most MFI put all house holds on a treadmill of continuously increasing loan size and insist on a fixed repayment schedule.”(Matin 1998) Dropout cases in Pakistan and Bangladesh Quarterly report of Kashif Foundation in Pakistan during july-sept 2000 94% clients to be continued because they are satisfied from loan size, accessibility, although repayment schedule and central meeting are the matter of concern.

• 6% clients are wishing to leave. • 53% are dissatisfaction due to attendance and central meeting. Quarterly report of Kashif Foundation in Pakistan during April- June 2001 • 60% client expulsion is recorded due to poor performance of staff. • 40% wish to leave on account of heavy opportunity cost, loan size



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Wright in 2000 reported that BRAC, Bangladesh (1991-1992) 11%-15% lack of access to savings in times of emergency, strict rules, additional cost of borrowing. Zeller et al.2000 found that average size of borrowing groups fell overtime because dropouts exceeded new members. e.g. within the five year prior to the 1994 survey, the average size of BRAC group fell from 56 to 37. Association for social advancement (ASA) conducted a study in 1995 for client drop-out in Bangladesh as follows:- Internal factors are the cause of client exit like:-

• Program low credit ceiling • Rule against multiple loans • Joint liability frequent loan payments.

Thana Resource Development & Employment Programme (TRDEP), Bangladesh Major constrain the client felt in (TRDEP) that maximum of three in –year loan, so people switch to other MFIs. Temporary staffs are working, if they became failing in recovery they were threatened by firing. Shakti Foundation for Disadvantaged Women Bangladesh Murray 2001 found that Shakti working on the methodology of Greeman Bank.in1994 the drop-out was 14% .in 99, 09 %.the reasons for drop –out

• 33% loan amount was too small • 28% too many meeting • 25% too long meeting • 25% defaulting members • 22% loan was too expensive.

BURO Tangail Bangladesh Most MFIs in Bangladesh put their clients on a ‘’treadmill of continuously increasing loan size’’, and it is alleged that loan offers have ‘’pushed’’ larger loans to reach disbursement targets. (Sinha& Matin 1998). MFI BURO Tangail experienced a drop-out rate of only 03% in 1997 because it allowed members the possibility of borrowing when they want to and offered them attractive saving products. As a result, only about half the members actually had the loans at any one time. This suggests that not all members want loans all of the time. (Wright 2000) For lenders drop-out is undesired because it cuts into market share (Rhyne 2001, Evans et al 1999, Karim and Osada 1998). Drop –out also weakens profitability because lenders loose money on the first few small, short loans that they make to a given borrower until he/she demonstrate creditworthiness.(Brand and Gerschick 2000 and Churchill and Halpern 2001). Drop outs cost the organizations clearly. Groups from which members drop out are destabilized and must recruit new (less experienced) members, who qualify for smaller ,loans thus reducing the overall interest income for the institution. (Graham A.N.Wright 2001) MFIs typically break even on a customer only after the fourth or fifth loan (Brand and Gerschick 2000). Each drop out is a lost client who underwent lengthy, expensive training. The replacement members must either receive this training on an individual basis, or join the system with training that many MFIs regard are critical. The former option of ad hoc training is extremely in sufficient, and the latter-if indeed initial training is so important-threatens to undermine the system. In the face of frequent or multiple drop-outs, some of the groups may disintegrate entirely (Murray 2001).



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It has been pointed out that high exit rates associated with adverse effects on users may also scare away potential investors (from the private sector as well as donors) who are the jealous of their reputation (Copestake 2001). Outreach of MFIs in terms of breadth, scope, and worth to users, cost to users, depth and length. Breadth refers to the number of people with access to financial services at any moment in time and is relatively in problematic. Scope, worth to users, cost to users determines the quality of the service in terms of change they have on users wellbeing during any period. Depth takes into account the tendency for policy makers to give higher social value to such changes if they affect poorer people. The significance of length of out reach of MFIs defined in the following way:- ‘’length of outreach is the time frame in which a microfinance organization produces loans. Length matters since society cares about the welfare of the poor both now and in the future. Without length of outreach, microfinance organization may improve social welfare in the short term but Wreck its ability to do so in the long term. More length requires more profit in the short term. This means higher prices, more costs to users, and less net gain per user. (Najavas, Schreiner, Meyer, Gonzalva and Rodriguest Meza 2000) 3. OBJECTVES AND QUESTIONS OF THE STUDY The main purpose of this study is to explore the experiences of dropped-out customers with several important features of loan program of leading microfinance banks. In order to achieve said objective following questions were raised from dropped-out customers.

• How did you spend your last loan? • Did the loan program help your family? If yes how? • During the last 12 months did your income in business increase or decrease? • Do you think that you benefited from being a member of the group? • Specify the means in which being in a group helped you? • Which of the following best describes your experience in paying last loan? • Which answer best describes the impact for you of these programs? • Which best describes your experience of participating in loan program?

Above questions were asked from dropout customers of microfinance bank in rural and urban areas. 4. RESEARCH METHODOLOGY To examine the perceptions of dropout customers’ case study method was applied. The top performing microfinance bank in Pakistan was chosen for this purpose. The data for was collected from the dropout customers of a same bank, from the cities of Upper Sindh like khairpur, Ranipur, sukkur, and Rohri. . In all 110 survey questionnaire were completed. A questionnaire based on “SEEP/AIMS Client Exit Survey Tool” was used to collect the data. The questionnaire included several open and closed ended questions about the causes, experiences and perception of dropout customers, along with questions about the respondents’ demographic information, number of loan programs, and date of entry and exit from the program. Business development officers of the bank collected the data from defected customers mainly because they the contact persons with customers in microfinance setup. These officers were fully briefed and trained about the data collection process. . In all 110 survey questionnaire were completed. After review of questionnaires 93 were considered for data analysis. The data was analyzed with the help of SPSS 17 Software; the results below summarize the reasons of customer dropouts and overall experience of dropout customers with this Microfinance Bank. 5. ANALYSIS AND RESULTS



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The data summarized and interpreted here was collected from the dropout customers of leading microfinance bank. 5.1 Profile of Dropout Customers Customers’ profile has a very close relationship with the dropouts that frequently occurred in the microfinance banks. The brief profile of dropout customers of a leading microfinance bank is as follows: 5.1.1 Gender This sample of dropout customers is heavily dominated by male dropout customers. Out of 93 dropout customers in the sample as much as 92% are male while female represent only 8%. Though in this bank female customers constitute about 36% of banks active borrowers but due to cultural barriers it was quite difficult to access them for interviews. 5.1.2 Credit Products The defected customers were using different credit products prior their exit from the program. As it can be seen in table 1, most of the defected customers in sample were using working capital products (36%) prior to their dropout. Remaining were using agriculture and asset purchase loan 31% and 23% respectively.

Table 1 Type of Credit Product 5.1.3 Geographic Distribution Bank’s operations extend across whole Pakistan including northern areas of country. Majority of its customers were living in the rural and semi urban areas. As much as 66% of customers are living in rural areas, 21% dropout customers are urban based where as 13% are in semi-urban areas. Table 2, shows the detailed statistics.

Type of Loan Product

Frequency

Percent

Cumulative Percent

Asset Purchase Loan 21 23 23

Live Stock Loan 6 6 29

Working Capital Loan 33 36 65

Agriculture Loan 29 31 96

New Business Loan 4 4 100.0

Total 93 100.0



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Table 2 Geographic Location

Geographic Location

Frequency

Percent

Cumulative Percent

Urban 20 21 21

Semi-urban 12 13 34

Rural 61 66 100.0

Total 93 100.0

5.1.4 Loan Programs The positive effect of microfinance is associated with the number of times customers repeated the loan program. Unfortunately in practice it has been observed that most customers leave institutes soon after their first loan program. This sample reflect the same situation, almost 52% customer exited after first loan cycle, 29% exited after second loan cycle while 15% left after third loan cycle. Figure 1, summarizes the results.

Figure 1 loan Programs

Percent

52

29

15

3 10

10

20

30

40

50

60

LoanPrograms

1st 2nd 3rd 4th 5th

Percent

5.1.5 Poverty Pattern of Customers The poverty pattern presented here are the estimates furnished by the Business Development Officers (BDO’s), based on the data collected from defected customers at their door steps. Poverty patterns in sample are arranged in table 3, below. Table 3: Poverty Patterns

Poverty Scale

Frequency

Percent

Cumulative Percent

Very poor 2 2 2 Poor 44 47 49 Economic active poor 47 51 100.0 Total 93 100.0



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In this sample economic active poor constitute 51%, poor and very poor represented 47% and 2 % correspondingly. The poverty profile in the sample reflects the Bank’s policy. In this microfinance bank field officers undertake cash flow analysis to determine customer’s poverty level and loan repayment capability in order to safeguard the loan amount. Table 4 Poverty pattern & Geographic distribution of Customers: Cross Tabulation

For deep understanding of demographic characteristics of the defected customers, various tests were conducted. The table 4, shows the cross tabulation of poverty pattern and geographic distribution of dropout customers in sample. From table above, we can see significant pattern in terms of economic active poor and poor. Among the 47 economic active poor 37 are rural dwellers, remainders 10 customers were semi-urbanites and urbanites. Out of 44 Poor 24 are rural dwellers and remainders 15 and 5 are urban and semi-urban based locations. Other frequency test showing loan programs and geographic distribution of defected customers, expose that among the 48 first time customers, 27 were in the rural areas, and 13 from urban areas, remainders 8 were in semi-urban areas. In second loan program there were 27 customers, where 18 customers were from rural areas, remaining 7 and 2 were from urban and semi- urban locations. From 14 defected customers in third loan program 12 customers were rural residents and residual 2 from semi-urban location. The results show rural majority in these early defections. Table 5, shows the detailed statistics. Table 5 Geographic Distribution & Loan Programs: Cross Tabulation

The cross tabulation of geographic distribution and credit products it clear that most of the defected customers were from rural areas. Shown in table 6, below 61 rural based dropouts were using agriculture credit products (47.5%), while 32.8% were availing working capital loans. Second largest numbers of



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dropout customers were urban based (20). Out of the 20 urban based customers 45% were patronizing asset purchase product and 40% were using working capital credit product prior to their exit. Table 6: Geographic Location & Credit Products: Cross Tabulation

5.2 Reasons of Customer Dropout From the figure 2, below it is obvious that program policies or procedures of microfinance bank are the major cause of customer defection (39%), second and third main reasons were the group lending, and customers’ own problems (31%) and (16%) correspondingly. Personal reasons of customers were also the reason of defection in Microfinance Bank (8%). Figure 2: Showing Main Causes of Customer Dropouts

38.6

30.7

16.3

7.82.6 3.9 Problems with

Program Policiesor ProceduresGroup Lending

Client’s BusinessReasons

Personal Reasons

Environment andEconomicReasonsOther Reasons



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5.3 Exploring Experiences of Dropout customers The survey included questions which were aimed at finding out how dropout customers utilized the loan and its impact on them. In order to get insights number of multiple answer questions were asked from dropout customers of Bank, such as how did you spend your last loan, did loan help your family, experience in repaying last loan, impact of loan on business and impact of loan program , and in what ways group helped him. These questions were important to understand reasons and circumstances in which customer left the program. Following are the results presented. 5.3.1 Utilization of Last Loan The question was asked “How did you spend your last loan?” The answers from respondents analyzed and significant patterns showing how they spend their last loan are displayed in figure 3, The majority of dropout customers told that they Buy more inputs/ stock (42%), followed by Buy equipment /tools and Change type of business (13% and 11%) respectively. remainder 14% dropout customers did not answer the question. From these results it is quite evident that dropout customers utilize loan properly. This is contrary, when we did follow up interviews with field officers. Who believed that dropout customers did not utilized loan productively. Figure 3 Showing how Dropout Customers Spend loan

5.3.2 Impact of Loan The question was asked, “Did the loan program help your family? If yes how? The answers from respondents examined and the patterns are displayed in figure 4. From the patterns it is revealed that most of the customers believe that Loan did not help their family (48%), only 28% of dropout customers said that loan helps their family in different ways while remainders 24% responded that they don’t know. By analyzing the results it is quite evident that majority of dropouts thought that loan was not helpful to their family.



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Figure 4 How loan help Families of Dropout Customers

In a question, “During the last 12 months did your income in business increase or decrease?” The multiple response were presented to respondents and asked to choose one answer, the responses were then analyzed and reported in figure 5. The results show that majority of dropout customers believed that the income in their business stay the same and have no effect (54%), while 23% dropout customers reported that their income increase some. remainder 12% reported that their income business increase greatly. In totality majority dropout customers believed that their income in business has no effect after participating in program. Figure 5 During the last 12 months did your income in Business Increase or decrease



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5.3.3 Group Lending To examine the strength of group lending and its benefits for customers two interrelated question were included in the questionnaire, the first question was asked “Do you think that you benefited from being a member of the group?” and second question was asked “Please tell me the specific ways in which being in a group helped you?” the responses were explored and result patterns are shown in table 7. Table 7 Showing whether Customers were Benefited or not from Being a member of

Group

Indicators

Frequency

Percent

Cumulative Percent

Yes 82 88 88

No 11 12 100.0

Total 93 100.0

The table 8 shows that most of the dropouts believed that they were benefited from being a member of group (88%), where as, 12% dropout customers believed that they were not benefited from being a member of group. Table 8 Benefits of Group Lending to Customers

Indicators Frequency Percent Cumulative Percent Helped me to make repayments 19 23 23.2 Provided help and support 12 15 38 Gave me training and new information 7 9 47 Gave me business ideas and contacts 14 17 64 Offered me new friendship 30 36 100.0 Total 82 100.0

Among the dropouts who reported that they were benefited from being a member of group, they said that “Group helped me to make repayments” (23%), “Provided me help and support when I need” (15%), “Provided me training and new information” (9%), “Offered me new business ideas and contacts” (17%) and “Offered me friendship” (36%) table 8 above. The analysis of specific ways by which customers were benefited form group lending shows the major benefits of group lending were attained and will be quite helpful in devising future policy and intentions of customers. This analysis is valuable because group lending was found to be one of the major reasons of customer dropouts. 5.3.4 Experience in Repayment of Last Loan The inquiry made from customers: “Which of the following best describes your experience in paying last loan?” The purpose behind this question was to know the experiences of dropout customers in repayment of last loan. The resulting pattern were analyzed and presented in table 6.The majority of the dropout customers reported that the loan is difficult to repay (59%), while 29% said that loan is with in capacity to pay and 10% reported that loan is easy to pay. This fact was also proved in a focus group interview with dropout customers and field officers, who also endorsed this fact that majority of customers exit due to inability to repay loans. Figure 6 show the detailed results.



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Figure 6 Indicating Loan Repayment Experience of Dropout Customers

5.3.5 Satisfaction of Dropout Customers with Loan Program To increase the understanding of customer satisfaction level two questions were included in the questionnaire The first question was “Which answer best describes the impact for you of these programs?” The resulting responses were properly analyzed and presented in figure 7. From the figure above it is clear that two response sets Loan program helped me a little and Helped me quite a lot constitute 48%, where as in opposite two response sets Loan was a burden and Loan did not helped me at all also constitute 48%. In over all, inconclusive results show that customers were indifferent when they were asked about impact of loan program. Figure 7 display the results. Figure 7 Impact of loan Program on Dropout Customers



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In order to discover dropout customers satisfaction level with Bank, a question was asked, “Which best describes your experience of participating in loan program?” The respondents were presented five options and asked to choose one which best describes their experience in participating the loan program. The answers were examined and arranged in table 9. Table 9 Overall Experiences of Dropout Customers

Indicators

Frequency

Percent

Cumulative Percent

Very good 8 9 9 Bad 4 4 13 Very bad 4 4 17 Good 53 57 74 No effect 24 26 100.0 Total 93 100.0

Most of dropouts described their experience with the Bank positive and satisfactory. As much as 57% customers believed that their experience in participating loan program was “Good”, where as a substantial number of respondents said that participating in the program has “No effect” on them (26%). 6. CONCLUSION From the discussion above it is inferred that most of customers left the program soon after first and second loan, mainly because of the Bank’s products, group lending and customer’s own problems. This early exit is not beneficial for customers as well as for the bank in the sense that microfinance bank failed to achieve its mission, which to improve the lives of poor customers and achieve sustainability. Further this research has shown inconclusive results about the impact of loan program to its customers. However it was revealed that more and more customers are interested in patronizing the products of microfinance bank in future. Provided microfinance banks develop need based products for its customers and develop customer service and satisfaction institutional top most priority.



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