Factors Associated with Poverty Movement of Negros Women for Tomorrow Foundation’s Clients

Asuncion M. Sebastian

Factors Associated withPoverty Movement of Negros Women

for Tomorrow Foundation’s Clients

Published by the Ateneo de Manila University Graduate School of BusinessAteneo Professional Schools BuildingRockwell Drive, Rockwell Center, Makati City, Philippines 1200Tel.: (632) 899-7691 to 96 or (632) 729-2001 to 2003Fax: (632) 899-5548Website: http://gsb.ateneo.edu/

Limited copies may be requested from the Research Unit, Ateneo Graduate School of Business Telefax: (632) 898-5007Email: [email protected]

Copyright © 2013Ateneo Graduate School of Business

All rights reserved.No part of this publication may be reproduced in any form or by any means without the written permission from the copyright owner.

ISBN 978-971-94909-4-4

Printed in the Philippines

he Ateneo Graduate School of Business (AGSB) is guided by its motto, “Our country is our business,” in its delivery of quality and relevant education. It may be a short message, but it goes beyond transforming students through education. The school breaks the barrier that separates the academe and industry and between theory and practice:

it thus links the academe to industry, and with the rest of society. Indeed, education institutions have taken an active role, more deeply involved in such topics as green economy and social business enterprises that affect the lives of everybody.

That the AGSB has published the study by Prof. Asuncion M. Sebastian, MBA, is another concrete expression of the school’s commitment to advance the fruits of education to benefit the poor. The general theme of the paper is alleviating the lives of the poor; the discourse is a vehicle toward a common responsibility of the government and business. Needless to say that poverty alleviation is the concern of every sector and every Filipino.

The “mass market” or low-income group covered by the study is not one big, homogenous market. Understanding their needs and behaviors is crucial, both to developmental organizations and the businesses that cater to them. The study found out that principles and techniques of market segmentation traditionally used in business can be applied in the analysis and effectiveness of development programs such as microfinance. The study also underscored that market research, goal congruence, and performance management are as important in running poverty alleviation programs as they are in managing for-profit institutions.

The publication of research studies are mutually beneficial to business and development. It is hoped that the AGSB, as an academic institution, would continue to bridge these two sectors and contribute to building of the nation.

Alberto L. Buenviaje, MBADeanAteneo de Manila University Graduate School of Business

Foreword

T

v

Acknowledgment

The author wishes to acknowledge the invaluable support and contribution of the following in completing this study:

The Negros Women for Tomorrow Foundation for giving access to their database and providing manpower support, especially Dr. Cecile Del Castillo, Ms. Suzzette Gaston, and Ms. Corazon Henares; Mr. Gilbert Maramba, head of the Research Group; Mr. Raymond Serios, head of the Special Projects Team; Jubert Maquiling of the Management Information System; and Edrich Dadivas and Zyrene Marapo;

Professor Jonathan Y. Cagas of the University of the Philippines for contributing in the statistical analysis;

Professor Ronald T. Chua of the Asian Institute of Management for providing insights on the issues presented in the paper;

Dr. Mark Schreiner of the Microfinance Risk Management, LLC, for his guidance in understanding and using the Progress out of Poverty Index; and

Dr. Emmanuel Esguerra, deputy director general of the National Economic and Development Authority; and Dr. Aniceto Orbeta, Jr., senior research fellow at the Philippine Institute for Development Studies, for their comprehensive reviews.

i

vi FACTORS ASSOCIATED WITH POVERTY MOVEMENT OF NEGROS WOMEN FOR TOMORROW FOUNDATION’S CLIENTS

vii

List of Tables and Figure......................................................................... ivAbstract................................................................................................... viiIntroduction............................................................................................. 1Review of Related Literature.................................................................. 5 Impact of Microfinance ...................................................................... 5 Microfinance Impact Studies in the Philippines................................ 13 Factors Associated with Poverty Reduction of Microfinance Clients......................................................................... 18Methodology.......................................................................................... 21 Limitations and Scope of the Study................................................... 21 Specification of Variables................................................................... 22 Sampling Design............................................................................... 27 Statistical Tests.................................................................................. 27Analysis of Data..................................................................................... 28 Profile of Clients................................................................................ 28 Testing of Hypotheses........................................................................ 36Psychographics of the Poor and Positive Poverty Movement............ 46 Psychographics of the Poor................................................................ 46 Small Group Discussion.................................................................... 47 Results............................................................................................... 48Summary and Conclusion..................................................................... 52Discussion of Other Related Issues...................................................... 54Direction for Future Research............................................................... 57References............................................................................................... 58Annexes................................................................................................... 61 Annex 1. NWTF Client Profile Form................................................ 61 Annex 2. A. Progress Out of Poverty Questionnaire....................... 62 B. Philippine Poverty Likelihoods Look Up Table............. 63

Annex 3. SGD Questionnaire........................................................... 64Annex 4. SGD Participants’ Profiling Questionnaire........................ 65

Contents

iii

List of Tables and Figure

TABLES

Table 1. Microfinance Impact Studies.................................................. 7

Table 2. Microfinance Impact Studies in the Philippines................. 15

Table 3. Classification of Geographic Location of Cities and Municipalities Covered by the Study........................... 23

Table 4. Poverty Possibilities Used in the Study................................ 25

Table 5. Number of Continuing Clients Who Survived the Program in 2011 and the Number of Their Businesses in 2008................................................................. 26

Table 6. Baseline Profile of NWTF Clients Covered by the Study Based on Geographic Location.................................. 28

Table 7. Baseline Profile of NWTF Clients Based on the Initial Index of Likelihood of Living Above the Poverty Line..................................................................... 29

Table 8. Baseline Profile of NWTF Clients Based on Number of Business............................................................... 30

Table 9. Likelihood of Clients Living Above Poverty in Geographic Income Class..................................................... 31

Table 10. Likelihood of Clients Living Above Poverty According to Number of Business Owned.......................... 31

Table 11. Profile of NWTF Clients Who Dropped Out of the Program by Geographical Location and Income Class.......................................................................... 33

Table 12. Profile of NWTF Clients Who Dropped Out of the Program by Poverty Probability Index........................... 34

Table 13. Profile of NWTF Clients Who Dropped Out of the Program by Number of Business............................... 35

iv

Table 14. Change in Likelihood of Clients Using Above Poverty by Geographic Income Class................................... 36

Table 15. Change in Likelihood of Clients Living Above Poverty and Dropouts by Geographic Income Class........................ 38

Table 16. Change in Likelihood of Clients Living Above Poverty by Probability Index.................................................. 39

Table 17. Change in Likelihood of Clients Living Above Poverty and Dropouts by Probability Index......................... 41

Table 18. Change in Likelihood of Clients Living Above Poverty According to Number of Business Owned, 2008........................................................................... 42

Table 19. Change in Likelihood of Clients Living Above Poverty and Dropouts According to Number of Business Owned, 2008........................................................... 43

Table 20. Change in Likelihood of Clients Living Above Poverty by Change in the Number of Business Control.................................................................................... 44

Table 21. Summary of Answers to the Psychographic Elements by Group................................................................ 49

Table 22. Change in Likelihood of Clients Living Above Poverty Among the SGD Participants................................... 51

FIGURE

Figure 1. Change in the Likelihood of Continuing Clients Living Above Poverty............................................................ 35

v

espite microfinance’s goal of moving the marginalized out of poverty, most studies neither categorize microfinance clients by poverty status nor measure their poverty movement (Maes & Vekaria, 2008). Available studies have shown evidence on microfinance improving the many indicators of quality of life such

as income and asset, but these items do not necessarily translate to the poor’s positive poverty movement.

This study identifies the poverty status and poverty movements of clients of Negros Women for Tomorrow Foundation (NWTF) using the Progress out of Poverty Index (PPI). The 10,650 samples were randomly selected from among the clients with 2008 PPI baseline data and were based in the six provinces where NWTF had operated the longest. The study also identifies the factors associated with their positive poverty movement. The variables tested for association were income of clients’ business location, the clients’ initial poverty index, and entrepreneurial capacity of clients, as measured by the number of their business in the base year and the change in the number of their business over a three-year period.

Results show that continuing clients tend to experience positive change in higher-income areas, specifically in the cities. The impact of microfinance on poverty is also greater for those starting in extreme poverty than those in higher levels. Those with two or more businesses in the base year had greater tendency to achieve positive movement compared to those with only one business. Finally, sample clients that increased the number of their business became less poor.

The study also explores the possible association of psychographics of the microfinance clients on their poverty movement.

Key words/phrases: microfinance, poverty movement, poverty profile, dropout profile, psychographics of microfinance borrowers

Abstract

D

vii

xii FACTORS ASSOCIATED WITH POVERTY MOVEMENT OF NEGROS WOMEN FOR TOMORROW FOUNDATION’S CLIENTS

1I N T R O D U C T I O N

Introduction

imited, or lack of, access to financial services is one of the reasons explaining the inability of poor households to acquire funds to improve their livelihoods. Banks and other formal financial institutions are reluctant to offer services

to the poor who lack collateral to secure their loans. As a result, poor households tend to be excluded from formal financial services, preventing them to invest in profitable projects, improve their consumption spending, and cope with unexpected shocks (Okten & Osili, 2004; Conning & Udry, 2007 as cited in Kono and Takahashi, 2009).

In the absence of financial services, the poor generally rely on loans from informal moneylenders at high interest rates (Weiss & Montgomery, 2004; Conning & Udry, 2007 as cited in Kono & Takahashi, 2009). They also resort to friends and family but amounts borrowed may not be interest-free or guarantee repeat loans.

To supply cheaper credit to marginalized groups, governments of many developing countries implemented large-scale, subsidized credit programs between the 1950s and 1970s. These credit schemes

originally targeted rural farmers, with subsidized credit at below-market interest rates. A twin objective was directed at the programs: not only to increase the poor’s income but also to promote investment in agricultural modernization. An example of investments was the diffusion of fertilizer and high-yielding crop varieties to increase food production and, in turn, to stimulate overall economic growth (Yaron & Benjamin, 2002 as cited in Kono & Takahashi, 2009). Later, however, the subsidized credit programs became politicized, resulting in rent-seeking activities, insufficient outreach to the rural poor farmers,

L

Banks and other formal financial

institutions are reluctant to offer services to

the poor who lack collateral to secure

their loans.

2 FACTORS ASSOCIATED WITH POVERTY MOVEMENT OF NEGROS WOMEN FOR TOMORROW FOUNDATION’S CLIENTS

very low loan repayment rates, and inefficient financial markets with excess demand (Adams, Graham, & Pischke, 1984; Robinson, 2001; Zeller & Meyer, 2002 as cited in Kono & Takahashi, 2009).

Beginning the 1980s, microfinance has attracted growing attention of the government and private sector as an alternative means of overcoming barriers faced by the poor to acquire funding (Morduch, 1999 as cited in Kono & Takahashi, 2009). Microfinance institutions (MFIs), in general, provide low-income, “unbankable” households with collateral-free small loans, to be used for self-employment and income- generating activities (Kono & Takahashi, 2009). Microfinance refers to a range of financial services for low-income people, including credit, savings, insurance, and money transfers.

The question commonly asked is whether microfinance works. In response, the answer is not straightforward for the following reasons: (1) microfinance is not a single tool but a collection of tools; (2) MFIs serve different types of clients; and (3) MFIs operate in diverse environments worldwide. These reasons demonstrate the heterogeneity of MFIs and, hence, the impact studies done on them should be taken in context and viewed “as a small piece of a growing body of knowledge about how microfinance, in all its forms, functions in the world and how it affects the lives of the poor” (Odell, 2010, 12).

While the ultimate goal of microfinance is to move the marginalized out of poverty, most studies do not either categorize microfinance clients by poverty status or measure the poverty movement of clients (Maes & Vekaria, 2008). Available studies have shown both anecdotal and empirical evidence on microfinance improving the many indicators of quality of life. However, many of the indicators do not show the poor’s upward movement out of the poverty ladder and the factors associated with their improvement.

This study aims to

(1) present a profile of microfinance clients, both the dropouts and the continuing clients;

FACTORS ASSOCIATED WITH POVERTY MOVEMENT OF NEGROS WOMEN FOR TOMORROW FOUNDATION’S CLIENTS

3I N T R O D U C T I O N

(2) determine factors associated with the continuing clients’ positive movement on the poverty ladder, that is, their becoming less poor over time;(3) segment the continuing clients, accordingly; and (4) explore potential psychographical differences between those who have become less poor over time and those who have remained in the same poverty ladder or even become poorer.

The Negros Women for Tomorrow Foundation (NWTF), an MFI based in Negros Occidental, is the first in the Philippines to adopt the poverty measure tool called the Progress out of Poverty Index (PPI). To date, only the NWTF has baseline data gathered from actual poverty index in 2008 (as opposed to asking the borrowers in 2011 to recall of their state back in 2008). For these reasons, the NWTF was selected for the study. A limitation of the study on the poverty movement of clients in only one MFI is that it rules out the possible effects of varying product offerings and institutional capacities on the borrowers’ poverty movement. If other MFIs in the country would have poverty data of their clients, a comparative study would be informative and useful.

The scope of the study was determined by the nature of operations of NWTF. Hence, the study has the following limitations:

(1) The microfinance product extended to clients includes micro loans with group liability (or the Grameen methodology), compulsory and voluntary savings, and micro insurance.(2) The product is targetted to women who have business or at least the inclination to start a business. (3) The MFI deliberately targets the “poor,” which it defined for the study as the lower 50% of the population living below the poverty line set by the National Statistical Coordination Board.


The study seeks to contribute to the field of microfinance in these two areas:

(1) factors associated with positive poverty movement of microfinance clients operating under specified conditions; and (2) initial indicators of psychographic differences between microfinance clients who experienced positive movement out of poverty and those who did not, but lived under the same conditions.

Following are the hypotheses to be tested:

Hypothesis 1: A change in poverty probability of clients is associated with the income levels of the geographic location of their business. The premise is that microfinance works among clients who operate their business in areas characterized by low growth and high informal economy. Otherwise, high economic growth areas would have encouraged employment instead of (micro) entrepreneurship.

Hypothesis 2: A change in poverty probability is associated with the initial poverty probability. It is premised on the condition that microfinance makes more impact on those who live in extreme poverty than those who are less poor.

The following hypotheses are the attempts of the study to contribute new insights by linking poverty movement with the entrepreneurial capabilities of the microfinance clients. Entrepreneurial skill or spirit was mentioned in literature as a determinant of differences in the performance of clients.

Hypothesis 3: A change in poverty probability is associated with the clients’ having more than one business, which is used as proxy indicator of entrepreneurial capacity.

Hypothesis 4: A change in poverty probability is associated with the clients’ increasing number of business over time, which is also used as proxy indicator of entrepreneurial capacity.

5

Impact of Microfinance

arious studies have shown the impact of microfinance, especially when offered to women, in terms of increase in income and savings, and household expenditures; growth in asset; consumption smoothing in the household;

improvement of nutrition and health; and empowerment. At the community level, among the impact of microfinance are employment and higher number of informal business ownership.

Hossain (1988 as cited in Goldberg, 2005) found that the average household income of Grameen Bank members in Bangladesh was 43% higher than target non-participants in comparison villages. The increase in income among the Grameen members was highest for the landless, followed by marginal landowners. However, the result could be overstated because Grameen members were found to be younger and better educated than nonmembers who were more likely to be

landless. This difference between participants and comparison households was prevalent in microfinance impact evaluations, but limited the conclusions that could be drawn from many of the studies.

In 1995, the United States Agency for International Development (USAID) launched the Assessing the Impacts of Microenterprise Services (AIMS) Project. It

developed five tools (two quantitative and three qualitative) that recommended comparing existing clients to incoming clients, and using their differences to estimate program impact. “The idea behind

Review of Related Literature

V

At the community level, among the impact of

microfinance are employment and higher

number of informal business ownership.

REVIEW OF RELATED LITERATURE


the methodology was that since both the clients and the comparison households had chosen to join the program, there should be no difference in their entrepreneurial spirit. Otherwise, higher incomes among participants might simply be driven by superior business acumen” (Goldberg, 2005 as cited in Odell, 2010, 35). Similar to the results of Hossain’s study (1998 as cited in Goldberg, 2005), differences between program clients and comparison groups could be driven by the age and better education of the microfinance members.

Karlan (2001 as cited in Goldberg, 2005) questioned the validity of this type of comparison, as its design could yield biased estimates of impact. The MFIs may have originally started to work with different types of clients (e.g., those living in better-off communities) than they currently serve (e.g., branching out to poorer areas). Further, clients who chose to enroll earlier may differ from those who chose to wait and see before joining the program.

According to Khandker and Pitt (1998 as cited in Odell, 2010), every additional taka lent to a Bangladeshi woman adds an additional 0.18 taka to annual household expenditures or an 18% return to income from borrowing. With the benefit of more data, Khandker (2005 as cited in Goldberg, 2005) was able to improve their model, which showed that each additional 100 taka of credit to women increased total annual household expenditures by more than 20 taka. There were no returns to male borrowing at all.

Weiss, Montgomery, and Kurmanalieva (2003) summarized the various impact studies of microfinance in Asia from 1998 to 2003 as shown in Table 1.

7

Table 1. Microfinance Impact Studies

Study Coverage (in Asia only) Methodology Results

Hulme and Mosley (1996)

Bangladesh

(BRAC, Grameen Bank, TRDEP)

India (regional

rural banks)

Indonesia (BKK, BRI, KURK)

Sri Lanka (PTCCS)

Borrowers and control samples, before and after

Growth of incomes of borowers always exceeds that of control group.

Increase in borrowers’ income was higher for better-off borrowers

MkNelly et al. (1996)

Bangladesh

(BRAC, Grameen Bank)

Non-participants in non-program villages used as controls

Benefits were positive, but statistical tests for differences were not reported

Khandler (1998)

Bangladesh

(BRAC, Grameen Bank)

Double difference comparison between eligible and ineligible households and between program and non-program villages

Of participant households, 5% moved out of poverty annually.

Additional consumption of 18 taka for every 100 taka of loan was taken out by women

Pitt and Khandler (1998)

Bangladesh

(BRAC, BRDB,

Grameen Bank)

Double difference estimation between eligible and non-eligible households and programs with and without microfinance programs

Estimations conducted separately for male and female borrowing

Positive impact of program participation was reported on total weekly expenditure per capita, and women’s nonland assets and labor supply.

Female participation in Grameen Bank had strong effect on schooling of girls.

Credit programs can change village attitudes and other village characteristics.




Coleman (2004)

Thailand

(village banks)

Double difference comparison between participant and non-participant households and between villages, with program introduced and in villages not yet introduced

There was no evidence of program impact. Village bank membership did impact asset or income variables.

Chen and Snodgrass (2001)

India

(SEWA Bank)

Control group from the same geographic area

Average income of bank’s clients increased in comparison with control group.

Overall change in incidence of poverty was low, but movement above and below poverty line was substantial

Coleman (2004)

Thailand

(village banks)

Double difference estimation between participants and non-participants, and villages with and without microfinance program

In NGO and mixed programs, the very rich even if eligible clients (for mixed programs) were excluded from participation. In government programs, the rich clients were both eligible and more likely to participate. Impact estimation found evidence of positive impact of micro credit on income

Duong and Izumida (2002)

Viet Nam

(VBA: 84% of total lending), VBP, PCFs, commerical banks, public funds

Tobit estimation of

(i) participation in rural credit market,

(ii) behavior of lender toward credit-constraind household, and

The poor had difficulties in accessing credit facilities: livestock and farming land, as determinants of household participation; and reputation and amount of credit applied,

(. . . Table 1 continuation)

9


(iii)weighted least square estimation for impact on output supply

as determinants of credit rationing by lenders. Impact estimation showed positive correlation between credit and output.

Kaboski and Townsend (2002)

Thailand

(production credit groups, rice banks, buffalo banks, women groups)

Two-staged least squares (LS) and maximum likelihood estimator (MLE) test of microfinance impact on asset growth, probability of reduction in consumption in bad years, probability of becoming moneylender, probability of starting business, and probability of changing job. Separate estimation according to type of MFI, and policies of MFI.

Production credit groups and women groups, combined with training and savings, had positive impact on asset growth, although rice banks and buffalo banks had negative impacts. Emergency services, training, and savings helped to smooth responses to income shock. Women groups helped to reduce reliance on moneylenders

Amin et al. (2003)

Bangladesh

(ASA, BRAC, Grameen Bank)

(1) Nonparametric test of stochastic dominance of average monthly consumption of members and nonmembers; and

(2) Maximum likelihood test of micro credit membership on vulnerability, consumption, and household characteristics

In NGO and mixed programs, the very rich even if eligible clients (for mixed programs) were excluded from participation. In government programs, the rich clients were both eligible and more likely to participate. Impact estimation found evidence of positive impact of micro credit on income





Gertler et al. (2003)

Indonesia

(Bank Rakyat Indonesia, Bank Kredit Desa, commercial banks

(1) Basic consumption-smoothing test on household’s ability to perform daily living activities (ADL Index),

(2) State dependence tests of basic regression (relative man-woman earning, physical job, savings), and

(3) Test of geographical proximity to financial institutions on consumption smoothing

Positive correlation between household’s consumption and measure of health was significant. Wealthier households were better insured against illness. Households that lived far from financial institutions suffered more from sudden reductions in consumption.

Khandher (2003)

Bangladesh

(BRAC, BRDB, Grameen Bank)

(1) Fixed effect Tobit estimation of borrowing dependent on land education endowments of households; and

(2) Panel data fixed effects IV estimation to definite long-term impact of microfinance borrowing on expenditure, non-land assets, and poverty (moderate and extreme).

Households who were poor in landholding and formal education tended to participate more.

Microfinance helped to reduce extreme poverty much more than moderate poverty (18 percentage points compared with 8.5 percentage points over 7 years).

Welfare impact was also positive for all households, including non-participants, as there were spillover effects.


11

ASA = Association for Social AdvancementBRAC = Bangladesh Rural Advancement CommitteeBRDB = Bangladesh Rural Development BoardMFI = microfinance institutionNGO = nongovernment organizationPCF = People’s Credit FundsPTCCS =Primary Thrift and Credit Cooperative SocietiesVBA = Vietnam Bank for Agriculture

VBP = Vietnam Board Poor.

Source: Weiss, Montgomery, and Kurmanalieva (2003), pp. 7−10.


Pitt et al. (2003)

Bangladesh (BRAC, BRDB, Grameen Bank)

Maximum likelihood estimation controlling for endogeneity of individual participation, and of the placement of microfinance programs.

Impact variables: health of boys and girls (arm circumference, body mass index, and height-for-age)

Significantly positive effect of female credit on height-for-age and arm circumference of both boys and girls was significantly positive. Borrowing by men had either negative or non-significant impact on health of children.




In other parts of the world, in Zimbabwe in particular, Barnes (2001 as cited in Goldberg, 2005) found that the clients’ income was significantly higher than the incomes of other groups in 1997. By 1999, the difference was no longer statistically significant, although continuing clients still earned the most. In Peru, Dunn and Arbuckle (2001 as cited in Goldberg, 2005) found that clients earned $266 more per household member annually than the non-participants.

In Mexico, Bruhn and Love (2009 as cited in Odell, 2010) found that microfinance program led to an increase in informal business ownership, employment, and income of residents in municipalities where a branch was opened. In Thailand, Kaboski and Townsend (2005 as cited in Odell, 2010) showed that MFIs, especially those targeted at women, promoted asset growth, consumption smoothing, and occupational mobility; and reduced reliance on moneylenders. Another study by the same authors found that the Thailand government’s microlending program relieved credit constraints in participating villages, and led to increases in consumption and income.

Some studies suggested that microcredit does not adequately alleviate poverty (Banerjee, Duflo, Glennerster, & Kinnan, 2009; Bennett, 2009; Karlan & Zinman, 2010 as cited in Odell, 2010). However, this criticism failed to consider that microfinance is not limited to credit only but it also involves other financial products such as savings and insurance.

Three studies—Banerjee, Duflo, Glennerster, and Kinnan (2009), Dupas and Robinson (2009), and Karlan and Zinman (2010 as cited in Odell, 2010)—suggested that over relatively short time periods, microfinance had positive impacts on business investment and outcomes. Microfinance did not have positive or negative impacts on broader measures of poverty and social wellbeing though. On the other hand, Banerjee, Duflo, and Karlan (2009 as cited in Odell, 2010) defended that “microcredit seems to have delivered exactly what a successful new financial product is supposed deliver—allowing people to make large purchases that they would not have been able to otherwise” (9) .

Many studies seek to determine the impact of microfinance in terms of the clients’ poverty reduction. However, it is not easy and quick to

13

measure both the effect of microfinance and poverty movement. Tracking the change in the income of clients due to microfinance may prove difficult since a household may have more than one source of income. In addition, a client, who has a reliable way of tracking household income, may not divulge their information for various reasons. These factors, thus, suggest that change in income is not an accurate measure of poverty movement; and that the use proxy indicators for household income level may prove more effective. Poverty tools, such as the PPI, use indicators other than income that are more easily verifiable and perceived to be less incriminating.

Microfinance Impact studies in the Philippines

A few studies in the Philippines have been written to establish evidence of impact of microfinance on poverty alleviation.

In 2008, the John J. Carroll Institute on Church and Social Issues and the International Network for Alternative Financial Solutions-Philippines conducted a study among 10 MFIs in the country, including NWTF. The study aimed to prepare an indicative assessment of the achievement of microfinance in terms of poverty alleviation and empowerment, especially of women. The instruments employed to gather the data included: organizational documents (e.g., list of reports, papers, and documents requested), survey questionnaire for clients, and interview guide and one-page questionnaire for key informants.

The interviews involved a total of 47 officers and staff from the MFIs and 54 microfinance clients, including dropouts. The respondents of the client survey, which totaled 317, were asked to rate whether the MFIs achieved the their two-pronged objectives of poverty alleviation and empowerment. A rating scale was used: 1 as “not achieved;” 2, “somewhat achieved;” 3, “achieved;” and 4, “very much achieved.” They were also asked to answer open-ended questions about other benefits of microfinance services that they availed of (Marin, Mercado, Palomo, & Fernandez, 2008).



The findings of the indicative assessment showed that the key informants perceived the twin objectives as achieved. The scores given were: poverty alleviation at 2.97 and empowerment at 2.83 (Marin et al., 2008).

Innovations for Poverty Action (2011) also conducted an experimental study with First Macro Bank (FMB), a for-profit lender. It operated in the outskirts of Manila offering small, short-term, uncollateralized credit with fixed repayment schedules to micro entrepreneurs. The borrowers sampled in this study were representative of most microlending clients: they lack credit history or collateral; most clients (85%) are female; and nearly all are well educated and having finished high school (93%), but are wealthy as compared to local and national averages of people living below the poverty threshold. FMB used credit-scoring software to identify marginally creditworthy applicants, placing roughly equal emphasis on business capacity, personal financial resources, outside financial resources, and personal and business stability. Those with median score comprised the sample for the study, totaling 1,601 applicants; and were given between Php5,000 and Php25,000 pesos in loan with individual liability (as opposed to NWTF’s group liability).

The results, however, focused on the impact of borrowing and business outcomes. Further, the study revealed that treatment effects were stronger for groups not typically targeted by microfinance: male and relatively high-income borrowers had the most benefit. The findings suggested that low-income women, the typical target groups of microcredit programs’ poverty alleviation thrust, might not necessarily benefit from these programs as originally thought (Innovations for Poverty Action, 2011). In addition, the differences in the nature of loans of FMB and of that offered by NWTF, as well the difference in the two organizations’ target markets make impact studies of the two MFIs incomparable.

Others studies on the impact of microfinance in the country are presented in Table 2.

15

Table 2. Microfinance Impact Studies in the Philippines

Study Methodology Results

Chua et. al (1999)

Risk, Vulnerability, Assets and the Role of Financial Services in Reducing Vulnerability: A Study of Women Clients of Bay, Laguna

Focus group discussions Access to financial services

(1) improved asset and cash money management,

(2) allowed for diversification and accumulation of physical assets, and

(3) resulted in increased savings and labor mobilization.

In addition, participation in the programs resulted in improvements in their own personality, self-confidence, and ability to deal with others.

Hossain and Diaz (1999)

Reaching the Poor with Effective Microcredit

Microcredit provided by CARD had a positive impact on income of the borrowing households.

Todd (2000)

Poverty Reduced Through Microfinance: The Impact of ASHI in the Philippines

Survey of 152 ASHI clients in the fourth loan cycle or above (average of 4.5 years with ASHI) and to a comparison group of 90 non-clients, who lived in similar communities to the ASHI clients, but had no access to microfinance services

Based on ownership of productive assets, most ASHI clients had moved from a position of extreme poverty to being moderately poor, but only 22 percent had moved right out of the poverty group.

McKelly (2001)

CARD/FFH Client Assessment Approaches for Progress Workshop Report

The results showed that the treatment group was aware of the “correct” health information better. They also tended to advise others about good business practices; and keep their business and personal money separate. The respondents also reported changes in the last 6 months in their businesses to reduce or prevent money or stock losses.



Study Methodology Results

Dingcong, Zerrudo, and Ledesma (2002)

Impact Assessment of the TSPI Kabuhayan Program

Impact areas included

(1) increase in income, sources of income, and asset base;

(2) better housing facilities;

(3) ability to send children to school;

(4) more savings; and

(5) better enterprise management of TSPI clients.

Plopenio (2002)

Program Impact on CARD Bay Clients

AIMS/SEEP Main Survey tool, comparative survey

Results showed increased income, capital sufficiency, and business reinvestment; and better entrepreneurial skills among the CARD clients.

Agabin and Gusto (2006)

Assessing the Impact of Microfinance Loans on the Clients’ Enterprises in the Philippines: Report on the Third Survey of Borrowers of MABS Participating Rual Banks

Survey of clients Access to credit is more likely to have a positive influence in the following:

(1) sales in the farming/fishing business and in the production/manufacturing enterprise;

(2) diversification of business, as indicated by the constant rise in the proportion of borrowers with multiple business; and

(3) expansion of micro business, thereby creating employment

Ashraf, Karlan, and Yin (2006)

Female Empowerment: Impact of a Commitment Savings Products in the Philippines

Individuals in the treatment group increased savings by 81% relative to the control group. After two-and-a-half years, decision-making power for women increased and household saving did not decrease.

AIMS = Assessing the Impacts of Microenterprise Services, CARD = Center for Agriculture and Rural Development, FFH = Freedom From Hunger, MABS = Microenterprise Access to Banking Services, SEEP = Small Enterprise Education and Promotion, TSPI = Tulay sa Pag-unlad Development Corporation,

Source: Microfinance Council of the Philippines & SEEP Network (2008), pp79-90.


17

In 2003, the NWTF conducted an impact study using the AIMS tools. The study covered 146 mature clients (defined as those who have been in the program for at least 5 years) from its three branches: Victorias, Hinoba-an, and Escalante. The poverty level of clients covered by the study was determined using four indicators, namely:

(1) Sources of income as substitute value for normal household income; (2) Household dependency burden, computed as the number of household members divided by the number of earning members; the ratio was coded according to predetermined ranges: a ratio of 3 or more = 1; between 2 and 2.9 = 2; and less than 2 = 3;(3) Productive assets as a measurement of sustainability of poverty reduction/elimination, coded according to their value: Php8,000 and below = 1; Php8,001 to Php35,000 = 2; and Php35,001 and up = 3; and (4) Housing quality as an indicator of household wealth, coded according to predetermined ranges on the House Index: 4 or less = 1; 5 to 8 = 2; and 9 or more = 3.

The scores on the four indicators were averaged and classified into three groups: 1−1.49 is considered Very Poor; 1.5−2.49, Moderately Poor; and 2.5−3, Non-Poor.

To gather the profile of these mature clients upon their entry into the program for comparison with their current situation, a survey using recall method on Client Profile Index (CPI) was used. This method was adopted since all of the NWTF branches at that time did not keep client records of more than 3 years. The AIMS’s Inventory of Current Household Assets mainly focused on the Inventory of Business Assets. However, this was not consistent with the CPI form, which used the Inventory of Previous Household Asset. Thus, there was a discrepancy in the results of the two indices, which required some adjustments to make the data sets comparable (Negros Women for Tomorrow Foundation, 2003).



NWTF’s hypothesis was that mature clients had experienced significant reduction in poverty condition. Results showed that 57% of the mature clients experienced positive poverty movement (became less poor). Of the other clients, 41% had no change while 2% had downward movement (became poorer). Out of the 57% with upward movement, 32% moved from being Very Poor to Moderately Poor, 5% shifted from being Very Poor to Non-Poor, and 20% moved from being Moderately Poor to Non-Poor (Negros Women for Tomorrow Foundation, 2003).

These results, however, may not be quite accurate due to the quality of data from recall method applied and instrument inconsistencies. Further, examining only the mature clients without considering the program dropouts does not exclude survival bias. Mature clients survived the program in the first place because of certain factors unique to them while the dropouts failed to benefit from these factors.

Factors Associated with Poverty Reduction of Microfinance Clients

Some studies that analyzed the impact of microfinance on poverty reduction showed that impact was greater among the poorest than among the less poor group. The study of Khandker (2005 as cited in Goldberg, 2005) entitled, Microfinance and Poverty: Evidence Using Panel Data from Bangladesh, supported the findings of the impact. The EDA Rural Systems (2004 as cited in Goldberg, 2005) shared a similar conclusion: non-poor clients most often reported an increase in household income but did not perform much better than non-clients. Compared to non-clients, the very poor benefited most from program participation.

The study of Khandker (2005 as cited in Goldberg, 2005) also revealed that between 1991/92 and 1998/99, moderate poverty in all participating Bangladeshi villages declined by an average of 17 percentage points: 18 percentage points in program areas and 13 percentage points in non-program areas. Among program participants who had been

19

members since 1991/92, poverty rates declined by more than 20 percentage points or about 3 percentage points annually. Khandker estimated that more than half of the reduction was directly attributable to microfinance. The impact was greater for extreme poverty, which was reduced by 2.2 percentage points annually, than for moderate poverty, which was reduced by 1.6 percentage points per year.

Dunford (2006 as cited in Maes & Vekaria, 2008) supported this finding, one of the most credible large-scale microfinance impact assessments until 2008. The study showed that the impact of microfinance on poverty was greater for those starting in extreme poverty than those in moderate poverty.

On the other hand, Hulme and Mosley (1996 as cited in Maes & Vekaria, 2008) suggested the exact opposite: microfinance tends to generate positive outcomes only for people above the poverty line. The Second Impact Assessment Study of the Bangladesh Rural Advancement Committee (BRAC as cited in Goldberg, 2005), also found that landless clients (the poorest clients) benefited least from the program while those with 1−50 decimals of land (“the poor”) benefited most. In another study, Copestake, Dawson, Fanning, McKay, and Wright-Revolledo (2005 as cited in Goldberg, 2005) found that the impact for the wealthier half of Promuc clients was 80% higher than the impact for the poorer half.

Kondo (2007 as cited in Odell, 2010) concluded that participation in the lending program led to increases in per capita incomes, expenditures, food expenditures, and formal saving. However, these positive effects occurred only among the relatively wealthy borrowers; the poorest borrowers actually saw negative effects. Kondo’s study could also be due either to the strategic choice of market by the MFIs or to the weakness of their targeting strategy.

Coleman (2006 as cited in Odell, 2010) found that for two microlending programs in northeast Thailand, the services were more likely to reach relatively wealthy borrowers than the target poor. Using Coleman’s methodology, Kondo (2007 as cited in Odell, 2010) conducted a study



of microfinance operation aimed at the poorest 30% of the rural population in the Philippines. The results found that borrowers in the said program were actually relatively wealthy.

Given these two opposing views, a statistical, empirical study would be helpful to determine in which poverty group would microfinance work better.

Wittwer (2010) cited the work of Ahlin, arguing that microfinance may thrive under two conditions. One is a “vibrant informal economy, a situation that tends to grow rarer as an economy and its institutions develop. If growth occurs and the informal work force is diminished, there is no longer an urgent need for microfinance.” Another is when “a deceleration of growth may also raise demand for products made by micro-enterprises as consumers substitute away from imports or high- quality goods.” Wittwer added that the “growing wage-earning opportunities, which come[s] with growth, guarantee[s] a steady income that lures potential entrepreneurs away from microfinance since entrepreneurship usually entails risk and variability in income” .

Given this premise, one may hypothesize that microfinance is more effective in the less developed areas. There is more informal economy but less established companies in less developed areas that could provide employment options to the micro entrepreneurs. This hypothesis could be supported by Khandker (2005 as cited in Goldberg, 2005), which further calculated that microfinance accounted for 40% of the entire reduction of moderate poverty in rural Bangladesh.

21

Limitations and scope of the study

his study looks into the profile of the 10,650 clients of NWTF, randomly selected from a total of 30,309 clients with baseline data in 2008. The rest of its total client base of 80,000 clients either do not have baseline data in 2008 (i.e., they joined the

program after 2008) or have data that have not yet been migrated into NWTF’s information system. The study does not use census of the 30,309 clients due to technical diffculties in data migration and processing.

The clients covered by the study are based in six major provinces in the Visayas where NWTF has the longest period of operation: Bohol, Cebu, Leyte, Negros Occidental, Negros Oriental, and Samar. This selection criterion ruled out any possible difference in the operational efficiency of the NWTF branches that may have affected client selection and recruitment, monitoring, collection, and client relations.

However, the operational efficiency of mature branches may have influenced client survival and performance.

The variables tested in this study were limited by the data sets being collected by NWTF since it adopted PPI (Annex 1). The PPI is a scorecard constructed based on the Philippines’ 2004 Annual Poverty Indicators Survey conducted by the National Statistics Office. The PPI estimates the likelihood that a household has income below the country’s poverty line; and, inversely, the likelihood that it is living above the poverty line (Annex 2). The PPI is based on predictive-modeling and designed to (i) estimate the likelihood that a household

Methodology

T

The study does not use census of the

30,309 clients due to technical diffculties

in data migration and processing.

METHODOLOGY


has income below a given poverty line, (ii) monitor the groups’ poverty rates at a particular period, (iii) track changes in the groups’ poverty rates between two periods, and (iv) target services to households. Employing an indirect approach via poverty scoring, PPI uses 10 verifiable indicators to derive a score that is highly correlated with poverty status, as measured by the exhaustive survey (Schreiner, 2009). Of the eight poverty likelihood tables provided by the PPI, NWTF used the national poverty threshhold in predicting poverty levels of its clients.

This study does not intend to examine the validity and reliability of the PPI instrument. Details of the instrument may be accessed at the Progress Out of Poverty website (http://progressoutofpoverty.org/philippines).

specification of Variables

• Geographic location. For the purpose of the study, it refers to the location of the client’s business (as opposed to the location of the NWTF branch). It is classified into nine categories based on the Philippine Standard Geographic Code as of January 2012 (Table 3).

23

Cities Covered by the Study

Class Average Annual Income City

1st Php 400 million or more Bogo, Calbayog, Catbalogan, Bogo, Escalante, Catbalogan, Guihulngan, and Kabankalan

2nd Php 320 million or more but less than Php 400 million

Bago, Bayawan, Bago, Cadiz, and San Carlos

3rd Php 240 million or more but less than Php 320 million

Bais, Dumaguete, Silay, and Toledo

4th Php 160 million or more but less than Php 240 million

Tanjay and Victorias

Municipalities Covered by the Study

Class Average Annual Income Municipality

1st Php 55 million or more Bantayan, Binalbagan, Catarman, Umay, Bantayan, Dalaguete, Binalbagan, Cauayan, Dalaguete, Dumagnas, Hinigaran, Hinobaan, La Castellana, Minglanilla, and Umay

2nd Php 45 million or more but less than Php 55 million

None

3rd Php 35 million or more but less than Php 45 million

Banga, Compostela, Banga, Guindulman, and Pan-ay

4th Php 25 million or more but less than Php 35 million

Ivisan, Numancia, Santander, and Valladolid

Unclassified Bato and Maasin (in Leyte) and Cordoba (in Cebu)

Table 3. Classification of Geographic Location of Cities and Municipalities Covered by the Study

Sources: Department of Finance. Department Order No. 23-08, dated July 29, 2008 (http://wwww.nscb.gov.ph/activestats/psgc/articles/DepOrderReclass.pdf) and National Statistical Coordination Board Philippine Standard Geographic Code as of January 2012 (http://www.nscb.gov.ph/activestats/psgc/default.asp)

METHODOLOGY


According to literature, microfinance is more effective in the less developed areas, particularly in the rural areas. However, this classification cannot be applied in the study, as the criteria for the classification of areas in the Philippines into rural and urban can be highly affected by the presence of a single industrial or commercial entity. For instance, an agricultural area may be classified as urban in the presence of a milling facility. Thus, the annual income of the cities and municipalities is used instead as proxy indicator of the location’s level of growth and economic activities.

• Poverty probability. It is derived from the NWTF clients’ PPI raw scores translated into poverty probabilities using the likelihood table based on the national poverty line (Table 4). For ease of computing and interpreting the direction of poverty movement for this study, the probability table used is the “Total Above the National Poverty Line,” which means the probability that the client is above the poverty line.

25

Table 4. Poverty Probabilities Used in the Study

National Poverty Line

PPI ScoreTotal Below the

National Poverty LineTotal Above the


0-4 96.6% 3.4%

5-9 93.7% 6.3%

10-14 91.5% 8.5%

15-19 87.8% 12.2%

20-24 80.9% 19.1%

25-29 68.5% 31.5%

30-34 59.6% 40.4%

35-39 48.9% 51.1%

40-44 36.8% 63.2%

45-49 21.1% 78.9%

50-54 14.8% 85.2%

55-59 7.2% 92.8%

60-64 5.0% 95.0%

65-69 3.2% 96.8%

70-74 1.4% 98.6%

75-79 1.4% 98.6%

80-84 0.0% 100.0%

85-89 0.0% 100.0%

90-94 1.5% 98.5%

95-100 0.0% 100.0%

Source: Microfinance Risk Management, L.L.C. Based on the 2004 APIS

METHODOLOGY


Of the 17 categories, 3.4 indicates the lowest probability of borrowers living above the poverty line or the poorest while 100 is the highest probability of their living above the poverty line or the richest.

• Poverty movement. It means the poverty probability, showing an

- Increase or positive change in poverty likelihood, when clients become less poor; - No change (remained the same); or - Decrease or negative change in poverty likelihood, when clients become poorer.

• Number of business. It refers to the number of business owned by the clients, and categorized either with one business or with two or more businesses. Only these two categories were used because few samples had three or more businesses. Table 5 shows the number of businesses owned by the clients.

Table 5. Number of Continuing Clients Who Survived the Program in 2011 and the Number of Their Businesses in 2008

Number of businesses Number of clients Percentage

1 6616 77.2%

2 1659 19.3%

3 248 2.9%

4 42 0.5%

5 8 0.1%

7 1 0.0%

Total 8574

Note: 25 data entries from the 8,599 continuing clients are invalid

Source: NWTF Database

27

• Change in the number of business. It refers to the change in the number of business owned by the clients, which may be any of the following:

- Increase in the number of business, - No change in the number of business (remained the same), or - Decrease in the number of business.

sampling Design

Using Cochran’s formula for sample size n, n = Z2 [p(1-p)] MOE2

where Z value at 99% confidence level = 2.58 p(1-p) or degree of variability is maximum at p = 0.50 MOE or margin of error = 5% n = 10,650

Of the 30,309 available data sets, 10,650 samples were chosen using the random selection function in the Excel software.

statistical Tests

Pearson’s Chi-Square is used to test for significance of association of nonparametric or non-interval data.

The study presents descriptive statistics and association of variables.

METHODOLOGY


Profile of clients

The baseline profile of the clients based on the geographic location of business, initial index of likelihood of living above the poverty line, and number of business are shown in Tables 6, 7, and 8.

Table 6. Baseline Profile of NWTF Clients Based on Geographical Location

Analysis of Data

Sample of Clients Covered by the Study

Geographic Location and Income Class

Average Annual IncomeNo. of Clients,

2008Distribution

Others/Unclassified 1,290 12.1%

4th class municipalityPhp 25 million or more but less than Php 35 million

470 4.4%

3rd class municipalityPhp 35 million or more but less than Php 45 million

920 8.6%

1st class municipality Php 55 million or more 2,668 25.1%

4th class cityPhp 160 million or more but less than Php 240 million

767 7.2%

3rd class cityPhp 240 million or more but less than Php 320 million

1,744 16.4%

2nd class cityPhp 320 million or more but less than Php 400 million

1,226 11.5%

1st class city Php 400 million or more 1,565 14.7%

Total 10,650 100.0%


29

Likelihood of Clients Living Above the Poverty Line

Probability of Index No. of Clients, 2008 Distribution

3.40 87 0.8%

6.30 343 3.2%

8.50 549 5.2%

12.20 702 6.6%

19.10 1103 10.4%

31.50 1348 12.7%

40.40 1162 10.9%

51.10 1284 12.1%

63.20 984 9.2%

78.90 842 7.9%

85.20 594 5.6%

92.50 540 5.1%

95.00 338 3.2%

96.80 316 3.0%

98.50 13 0.1%

98.60 345 3.2%

100.00 100 0.9%

Total 10,650 100.0%

ANALYSIS OF DATA

Table 7. Baseline Profile of NWTF Clients Based on the Initial Index of Likelihood of Living Above the Poverty Line



The biggest share of microfinance clients (25%) was from 1st class municipalities. Of the total sample population, 38% were from municipalities while 50% from the cities. The remaining samples (12%) were unclassified in terms of geographic location and average annual income. These figures may be indicative of the presence of the entrepreneurial poor in the more economically active cities than in the municipalities. However, in terms of NWTF operation, 22 of its branches1 included in the study were in municipalities while only 16 branches were based in cities. Considering the distribution of the NTWF branches, the bigger share of city-based clients than the municipality-based ones cannot be interpreted as NWTF’s bias for city operation.

The sample population has mean likelihood of clients living above the poverty line of 49.67; median, 45.75; and mode, 31.50. Data in Table 9 show that the likelihood of clients living above the poverty line is not associated with the annual income of the geographic location of their business. It does not hold true that the richer the area, the higher is the likelihood that clients who conduct business in that area are non-poor. To illustrate the case, the average likelihood of clients in 4th class municipalities to be non-poor was high at 61.8. In contrast, the average likelihood of clients in 1st class cities was lower at 48.2.

1 This number included Bato and Maasin, which are classified as municipalities by the National Statistical Coordination Board albeit without average income data.

Business Owned by Clients

No. of Business No. of Clients, 2008 Distirbution

With 1 business 8,268 77.6%

With 2 or more businesses 2,382 22.4%

Total 10,650 100.0%

Table 8. Baseline Profile of NWTF Clients Based on Number of Business


31

As of base year 2008, 78% of the sample NWTF clients had only one business while the rest (22%) had at least two businesses. The clients, with only one business in 2008, had an average poverty likelihood of 43.26 (Table 10). This average is slightly lower than the sample population mean of 49.62, which means that those clients with one business were poorer than the sample as a whole. On the other hand, the average likelihood of those clients with more than one business stood at 54.69, which is higher than the mean population of 49.62. Those with more than one business, therefore, had higher likelihood of being non-poor.

Table 10. Likelihood of Clients Living Above Poverty According to Number of Business Owned

Table 9. Likelihood of Clients Living Above Poverty by Geographic Income Class

Income class 3.40 6.30 8.50 12.20 19.10 31.50 40.40 51.10 63.20 78.90 85.30 92.50 95.00 96.80 98.50 98.60 100.00 Total Average Likelihood

Others/unclassified 9 64 99 118 152 194 161 124 88 85 52 44 27 33 - 34 6 1,290 42.7

4th class municipalities 25M 2 3 11 9 28 38 61 52 66 51 40 41 29 14 2 17 6 470 61.8

3rd class municipalities 35M 6 37 67 96 103 118 108 109 80 63 46 29 17 22 - 16 3 920 43.3

1st class municipalities 55M 11 73 117 167 295 366 280 404 249 232 141 119 88 50 3 61 12 2,668 48.9

4th class cities 160M 6 18 33 38 79 88 93 89 73 62 45 46 27 26 1 34 9 767 52.8

3rd class cities 240M 17 39 52 83 127 185 199 222 182 131 112 117 75 81 3 83 36 1,744 56.6

2nd class cities 320M 11 43 79 70 155 156 121 109 121 101 67 63 38 35 2 42 13 1,226 48.9

1st class cities 400M 25 66 91 121 164 203 139 175 125 117 91 81 37 55 2 58 15 1,565 48.2

No. of business3.40 6.30 8.50 12.20 19.10 31.50 40.40 51.10 63.20 78.90 85.30 92.50 95.00 96.80 98.50 98.60 100.00 Total Average

LikelihoodNumber of Clients

With 1 business 76 291 461 610 935 1,105 900 992 749 608 430 363 237 213 6 220 72 8,268 43.26

With 2 or more businesses 11 52 88 92 168 243 262 292 235 234 164 177 101 103 7 125 28 2,382 54.69

Total 87 343 549 702 1,103 1,348 1,162 1,284 984 842 594 540 338 316 13 345 100 10,650

ANALYSIS OF DATA

Source: The author’s computation



By 2011, 2,051 clients had dropped out or 19% of the total clients in 2008 were no longer part of the NWTF program (Table 11).

Many of the dropout clients came from the 1st class municipalities (33%), 1st class cities (19%), and 2nd class cities (15%). There were no data, however, explaining the dropouts—it can only be surmised that the clients left because they no longer needed credit as they had become non-poor, they had tapped other sources of capital, they could no longer repay their loan, or their businesses had totally shut down.

The mean likelihood of dropouts living above the poverty line is 44.92, lower than the sample mean of 49.67. The figures imply that the dropouts were generally poorer than the sample population—more than half (56%) of the dropouts belonged to the lower strata between 3.40 and 40.40.

Finally, almost 80% of those who dropped out of the program had only one business.

33

Table 11. Profile of NWTF Clients Who Dropped Out of the Program by Geographical Location and Income Class

Sample of Clients Covered by the Study

Geographic Location and Income Class

Average Annual IncomeNo. of Dropouts,

2011Distribution

Others/Unclassified 189 9.2%

4th class municipalityPhp 25 million or more but less than Php 35 million

161 7.8%

3rd class municipalityPhp 35 million or more but less than Php 45 million

66 3.2%

1st class municipality Php 55 million or more 678 33.1%

4th class cityPhp 160 million or more but less than Php 240 million

115 5.6%

3rd class cityPhp 240 million or more but less than Php 320 million

143 7.0%

2nd class cityPhp 320 million or more but less than Php 400 million

312 15.2%

1st class city Php 400 million or more 387 18.9%

Total 2,051 100.0%

ANALYSIS OF DATA



Likelihood of Clients Living Above the Poverty Line

Probability IndexNo. of Drpouts,

2011Distribution

3.40 25 1.2%

6.30 66 3.2%

8.50 128 5.2%

12.20 152 7.4%

19.10 256 12.5%

31.50 295 14.4%

40.40 219 10.7%

51.10 259 12.6%

63.20 189 9.2%

78.90 159 7.8%

85.20 91 4.4%

92.50 80 3.9%

95.00 48 2.3%

96.80 37 1.8%

98.50 2 0.1%

98.60 35 1.7%

100.00 10 0.5%

Total 2,051 100.0%

Table 12. Profile of NWTF Clients Who Dropped Out of the Program by Poverty Probability Index


35

Decrease: 30%

Increase: 28%

No change: 42%

ANALYSIS OF DATA

Number of Business Owned by Clients

Category No. of Dropouts, 2011 Distirbution

With 1 business 1,631 79.5%

With 2 or more businesses 420 20.5%

Total 2,051 100.0%

Table 13. Profile of NWTF Clients Who Dropped Out of the Program by Number of Business

On the poverty movement of clients who continued to the program, 42% did not change in their likelihood of living above poverty line between 2008 and 2011 (Figure 1). On the other hand, 28% became less poor; and a slightly higher proportion of clients at 30%, became poorer.

Figure 1. Change in the Likelihood of Continuing Clients Living Above Poverty




Testing of Hypotheses

Hypothesis 1: A change in poverty probability of continuing clients is associated with the income level of the geographic location of their business.

The highest proportion of clients experiencing a positive change or becoming less poor was in the 4th class cities (38%). The other clients in these same cities remained at status quo (29%) or became poorer (33%) (Table 14).

Table 14. Change in Likelihood of Clients Using Above Poverty by Geographic Income Class

Geographic Location

and Income Class

AverageAnnual Income

Increase (Less Poor) No Change Decrease

(Poorer) Total

No. Of Client Distribution No. of

Clients Dirstribution No. of Clients Dirstribution No. of

Clients Dirstribution

Others/Unclassified 190 17.3% 699 63.5% 212 19.3% 1,101 100.0%

4th Class Municipality Php 25 million or more but less than Php 35 million 72 23.3% 152 49.2% 85 27.5% 309 100.0%

3rd Class Municipality Php 35 million or more but less than Php 45 million 195 22.8% 389 45.6% 270 31.6% 854 100.0%

1st Class Municipality Php 55 million or more 589 29.6% 805 40.5% 596 29.9% 1,990 100.0%

4th Class City Php 160 million or more but less than Php 240 million 249 38.2% 188 28.8% 215 33.0% 652 100.0%

3rd Class City Php 240 million or more but less than Php 320 million 521 32.5% 565 35.3% 515 32.2% 1,601 100.0%

2nd Class City Php 320 million or more but less than Php 400 million 229 25.1% 395 43.2% 290 31.7% 914 100.0%

1st Class City Php 400 million 394 33.4% 426 36.2% 358 30.4% 1,178 100.0%

Total/Distribution 2,439 28.4% 3,169 42.1% 2,541 0.30 8,599 100.0%


37

The geographic income class of the clients seems to have affected the change in their poverty likelihood. As the geographic income class moved up, the percentage of clients who became less poor was higher, and also exceeded the share of those who became poorer.

Statistical null hypothesis: A change in poverty probability of continuing clients is not associated with the income level of the geographic location of their business.

With the resulting χ2=338.675, ρ=0.000, the null hypothesis is rejected. A change in poverty probability of continuing clients is associated with the income levels of the geographic locations of their businesses. Continuing clients in higher income class areas, specifically in cities, tend to experience a positive change in their poverty likelihoods. The result, thus, negates Wittwer’s (2010) observation that microfinance thrives in low-growth areas where informal economy is vibrant.

The dropouts, shown in Table 15, do not exhibit consistent patterns across the various geographic income classes. In the 1st and 4th class municipalities and 1st and 2nd class cities, the dropout shares were high, and even higher than the share of clients becoming poorer. The distribution of clients shows rates for dropouts that are higher than the share of clients becoming poorer in the 1st and 4th class municipalities and in the 1st and 2nd class cities. Except for the 1st class cities, in the income classes where the same trend is observed, the share of clients becoming poorer is higher than the share of those becoming richer.

In the 3rd and 4th class cities, the percentage of clients becoming less poor is higher than that of those becoming poorer. In these locations, the dropout rates of clients are relatively low (and lower than the mean dropout rate of the whole sample population). The association is more apparent between clients becoming poorer and dropping out

As the geographic income class moved up, the percentage of clients who became less poor was higher, and also exceeded the share of those who became poorer.

ANALYSIS OF DATA


Hypothesis 2: A change in poverty probability of continuing clients is associated with the initial poverty probability.

Microfinance makes more impact on the users of facilities who live in extreme poverty than those who are less poor. Table 16 shows on the average 29% of the continuing clients had lower probability of living above the poverty line (or became poorer), 43% had no probability to change in index, and 28% experienced higher probability of living above the poverty line (or became less poor). It is interesting to note, however, that there are certain patterns in certain segments.

The “no change” scenario dominated the probability indexes between 6.30 and 78.90, accounting for almost 92% of the samples falling under

than between geographic income class and becoming a dropout—the proportion of clients becoming poorer and dropout rate are moderately negatively correlated with coefficient of −0.65279.

Table 15. Change in Likelihood of Clients Living Above Poverty and Dropouts by Geographic Income Class

Income ClassIncrease

(less poor)No change

Decrease(poorer)

Dropout Total

Others/Unclassified 190 14.7% 699 54.2 212 16.4 189 14.7% 1,290

4th Class Municipalities

25M 72 15.3% 152 32.3% 85 18.1% 161 34.3% 470

3rd Class Municipalities

35M 195 21.2% 389 42.3% 270 29.3% 66 7.2% 920

1st Class Municipalities

55M 59589 22.1% 805 30.2% 596 22.3% 678 25.4% 2,668

4th Class Cities 160M 249 32.5% 188 24.5% 215 28.0% 115 15.0% 767

3rd Class Cities 240M 521 29.9% 565 32.4% 515 29.5% 143 8.2% 1,744

2nd Class Cities 320M 229 18.7% 395 32.2% 290 23.7% 312 25.4% 1,226

1st Class Cities 400M 394 25.2% 426 27.2% 358 22.9% 387 24.7% 1,,565

2,439 22.9% 3,619 34.0% 2,541 23.9% 2,051 19.3% 10,650


39

Probability Index

Increase (Less Poor) No Change Decrease (Poorer) Total

No. of Clients

DistributionNo. of Clients



Distribution

3.40 41 66.1% 21 33.9% 0 0.0% 62 100.0%

6.30 113 40.8% 162 58.5% 2 0.7% 277 100.0%

8.50 182 43.2% 233 55.3% 6 1.4% 421 100.0%

12.20 258 46.9% 277 50.4% 15 2.7% 550 100.0%

19.10 329 38.8% 460 54.3% 58 6.8% 847 100.0%

31.50 389 36.9% 545 51.8% 119 11.3% 1,053 100.0%

40.40 309 32.8% 466 49.4% 206 20.1% 1,025 100.0%

51.10 314 30.6% 505 49.3% 218 27.4% 795 100.0%

63.20 184 23.1% 393 49.4% 218 27.4% 795 100.0%

78.90 130 17.3% 360 47.9% 261 34.8% 751 100.0%

85.20 78 17.9% 65 14.9% 292 67.1% 435 100.0%

92.50 57 12.4% 72 15.7% 331 72.0% 460 100.0%

95.00 20 6.9% 92 31.7% 178 61.4% 290 100.0%

96.80 26 9.3% 12 4.3% 241 86.4% 279 100.0%

98.50 1 9.1% 0 0.0% 10 90.9% 11 100.0%

98.60 8 2.6% 22 7.1 2800000 90.3% 310 100.0%

100.00 0 0.0% 2 2.2% 88 97.8% 90 100.0%

Total/Distribution 2,439 28.4% 3,687 42.9% 2,473 28.8% 8,599 100.0%

ANALYSIS OF DATA

these indexes. At 3.40, the lowest possible likelihood so clients in this index could not possibly get worse, 66% of them became less poor, and 34% had no change. When the probability index reached 85.20, however, the number of clients with no change dropped from 48% to a range of zero to 32% percent. Similarly, at this index, the proportion of clients becoming poorer expanded to 67%, further rising to 98% at 100 level. Out of the total samples falling under the indexes from 85.20 to 100.00, 76% would most likely be poorer.

Table 16. Change in Likelihood of Clients Living Above Poverty by Probability Index



It can be observed that the probability of becoming less poor occurs more common at the lower indexes, from 51.10 and below. On the other hand, the probability of becoming poorer is more frequent in likelihood indexes from 85.20 and above.

Statistical null hypothesis: A change in poverty probability is not associated with the initial poverty probability index of the clients.

With the resulting χ2=3269.158, ρ=0.000, the null hypothesis is rejected. A change in poverty probability is associated with the initial poverty probability index of the clients. This conclusion supports the findings of previous studies that the impact of microfinance on poverty is greater for those starting in extreme poverty than those in moderate poverty. Findings further show that those who have higher probability of living above the poverty threshold in the base year have greater chances of becoming poorer over time.

If the dropouts were included in the analysis, it is ironic to note that dropout rates are high in the probability indexes—from 3.40 to 51.10—where the positive change is also highest (Table 13). Within the same indexes, the dropout rates are higher than the rates of clients having decreased in their probabilities of living above poverty (or has become poorer). An interpretation of the data suggests that the poorer clients seemed to drop out of the program rather than risk staying on in the program and becoming even poorer later on. On the other hand, those clients with higher likelihood of living above the poverty line tend to be risk takers. Either they opt to continue with the program even if they may be worse off or they would have the capacity to absorb bigger risks or losses. Thus, similar to the observation under hypothesis 1 but at a greater degree, the association between the proportion of clients becoming poorer and the dropout rate is apparent—they are inversely correlated, with coefficient of −0.90167.

41

Table 17. Change in Likelihood of Clients Living Above Poverty and Dropouts by Probability Index

Probability Index

Increase (Less Poor) No Change Decrease (Poorer) Dropout Total

No. of Clients





Distribution

3.40 41 47.1% 21 24.1% 0 0.0% 25 28.7% 87 100.0%

6.30 113 32.9% 162 47.2% 2 0.6% 66 19.2% 343 100.0%

8.50 182 33.2% 233 42.4% 6 1.1% 128 23.3% 549 100.0%

12.20 258 36.8% 277 39.5% 15 2.1% 152 21.7% 702 100.0%

19.10 329 29.8% 460 41.7% 58 5.3% 256 23.2% 1,103 100.0%

31.50 389 28.9% 545 40.4% 119 8.8% 295 21.9% 1,348 100.0%

40.40 309 26.6% 466 40.1% 168 14.5% 219 18.8% 1,162 100.0%

51.10 314 24.5% 505 39.3% 206 16.0% 259 20.2% 1,284 100.0%

63.20 184 18.7% 393 39.9% 218 22.2% 189 19.2% 984 100.0%

78.90 130 14.3% 360 39.6% 261 28.7% 159 17.5% 910 100.0%

85.20 78 14.8% 65 12.4% 292 55.5% 91 17.3% 526 100.0%

92.50 57 10.6% 72 13.3% 331 61.3% 80 14.8% 540 100.0%

95.00 20 5.9% 92 27.2% 178 52.7% 48 14.2% 338 100.0%

96.80 26 8.2% 12 3.8% 241 76.3% 37 11.7% 316 100.0%

98.50 1 7.7% 0 0.0% 10 76.9% 2 15.4% 13 100.0%

98.60 8 2.3% 22 6.4% 280 81.2% 35 10.1% 345 100.0%

100.00 0 0.0% 2 2.0% 88 88.0% 10 10.0% 100 100.0%

Total/Distribution 2,439 22.9% 3,687 34.6% 2,473 23.2% 2,051 19.3% 10,650 100.0%

ANALYSIS OF DATA



Hypothesis 3: A change in the poverty probability is associated with the clients’ having more than one business.

The number of business of the clients is used as a proxy indicator of their entrepreneurial capability.

Table 18 shows that 44% of clients with only one business do not experience a change in their poverty likelihood while those who own two or more businesses are likely to become poorer (37%) or remain the same (34%). However, between the two groups, those who have two or more businesses have slightly better chances of becoming less poor (29% versus 28% of those who have single enterprise). The resulting figures may be indicative of the inherent high-risk-high-return of entrepreneurial ventures.

Table 18. Change in Likelihood of Clients Living Above Poverty According to Number of Business Owned, 2008

No. of Business In 2008

Increase(Less Poor)

No ChangeDecrease(Poorer)

Total Distribution

No. of Clients



Distribution

With 1 Business 1,863 28.1% 2,954 44.5% 1,820 27.4% 6,637 77.2%

With 2 or more businesses

576 29.4% 665 33.9% 721 36.7% 1,962 22.8%

Total/Distribution 2,439 28.4% 3,619 42.1% 2,541 29.5% 8,599 100..0%


43

Statistical null hypothesis: A change in poverty probability is not associated with the number of business owned by the clients.

With the resulting χ2=85.995, ρ=0.000, the null hypothesis is rejected. A change in poverty probability is associated with the number of clients’ businesses. Out of the clients with two or more businesses, 29% had become less poor in 2011 than they were in 2008. Of those with only one business owned by the clients, 28% become less poor.

Table 19 shows that those with two or more businesses were also less likely to drop out of the program (18%) than those with single business (20%).

Table 19. Change in Likelihood of Clients Living Above Poverty and Dropouts According to Number of Business Owned, 2008


Increase (Less Poor)

No ChangeDecrease (Poorer)

Dropout

Total Distribution

No. of Clients




Distribution

With 1 Business

1,863 22.5% 2,954 35.73% 1,820 22.0% 1,631 19.7% 8,268 77.6%

With 2 or more

businesses576 24.2% 665 27.9% 721 30.3% 420 17.6% 2,382 22.4%

Total/Distribution

2,439 3,619 34.0% 2,541 23.9% 2,051 19.3% 10,650 10,650 100.0%

ANALYSIS OF DATA



Hypothesis 4: A change in poverty probability is associated with the clients’ increasing number of business over time.

The clients’ ability to expand their businesses over time is also used as a proxy indicator of the clients’ entrepreneurial capability. For the study, the number of business owned remained the only criterion, and it did not consider possible expansion in terms of asset growth.

Table 20 shows that the direction of change in business is similar to the direction in the change of the clients’ poverty likelihood:

• 50% of the sample clients that did not change the number of their business had no change in their poverty likelihood, • 53% of sample clients that increased the number of their business also increased their poverty status by becoming less poor, and • 46% of sample clients that decreased the number of their business also experienced decrease in their poverty status and became poorer.

Table 20. Change in Likelihood of Clients Living Above Poverty by Change in the Number of Business Owned

Note: There are 25 missing cases or clients with incomplete business data, resulting to a total of 8,574 clients.



Increase(Less Poor)

No ChangeDecrease(Poorer)

Total Distribution

No. of Clients



Distribution

Increase inBusiness

450 52.8% 116 13.6% 287 33.6% 853 9.9%

No change in business

1,604 23.4% 3,406 49.7% 1,845 26.9% 6,855 80.0%

Decrease in business 372 43.0% 94 10.9% 400 46.2% 866 10.1%

Total/Distribution 2,426 28.4% 3,616 74.1% 2,532 29.5% 8,574 100..0%

45

Statistical null hypothesis: A change in poverty likelihood is not associated with the change in the number of business owned by the clients.

The resulting χ2=864.685, ρ=0.000, rejects the null hypothesis. A change in poverty likelihood is associated with a change in the number of businesses. Within the categories and among those clients in positive movement (less poor), 53% had an increase in the number of businesses, 23% had no change in business, and 43% decreased their number of businesses.

Therefore, the conclusions in testing hypotheses 3 and 4 have established the association between positive movement and entrepreneurial capacity of the clients.

ANALYSIS OF DATA


Psychographics of the Poor

he variables tested in the previous section include geographic location, initial poverty likelihood, number of business owned, and change in the number of business over time. However, there are other factors that may be associated with positive poverty

movement or becoming less poor such as psychographics, attitudinal, and behavioral attributes of clients.

ACCION International offered services to other MFIs that include market segmentation. Believing that the labels ‘women’ and ‘informal microentrepreneurs’ do not provide enough focus, ACCION helped MFIs in market segmentation by using a systematic process involving advanced qualitative and quantitative techniques. To construct groups of segments, the techniques used consumer-belief dynamics and cluster and discriminant analysis (ACCION International, 2007). This methodology referred to psychographic segmentation; however, ACCION lacked documents on market segmentation available to the public, and its operations in Asia were also limited to India.

Several studies in the past identified psychographics segments with varying results: Retail Coach, 66 segments (The Retail Coach, 2009); Claritas, 64 clusters (Wedeen, 2005); Leapfrog Interactive, 12 groups (The Leap Frog Interactive, 2009); and the more popular Strategic Business Insights’ VALS (or values and lifestyles) which suggested eight psychographic categories. However, the studies may not be applicable in the microfinance sector for the following reasons:

(1) The subjects were American consumers, being different from the Philippines consumers in terms of culture and context.(2) The subjects were analyzed in terms of their behaviors and attitudes toward consumer goods, not financial services.

T

Psychographics of the Poor and Positive Poverty Movement

47

(3) The poor population was treated under one category, described as “needs-driven,” “struggling,” or “constrained.”

Nielsen conducted a study that segmented the Philippine poor market according to psychographics. However, the sample of respondents was taken only from Bacolod, Bohol, Cebu, Iloilo, Dumaguete, and Tacloban.

small Group Discussion

An instrument suitable for small group discussion (SGD) was developed based on existing studies, to identify the psychographic profiles of clients who experienced positive poverty movement. The elements incorporated in the questionnaire are

• family life cycle, race or ethnic origin, lifestyle/vacation/form of entertainment, eating patterns/food preference, shopping behavior/spending pattern, and entrepreneurial inclinations (Wedeen, 2005);• sociability, aspirations, level of self-esteem, community participation, spirituality/religiosity, and shopping behavior/ spending pattern (The North Crossing, n.d.);• education level, home ownership, race and ethnicity, family composition/number of children by age group, and lifestyle behaviors (The Retail Coach, 2009); and• familiarity with/awareness of modern technology, interest in news/ national/international affairs, spirituality, sociability, self-image and personal appearance, forms of leisure of activities/entertainment, and media habits (The Nielsen Company, 2010).

Four small groups composed of 13 participants were conducted. The participants were selected in Bago, Negros Occidental, a 2nd class city, and with existing businesses. Two groups (Group 1 and Group 2) were for single business, and another two groups (Group 3 and Group 4) were for multiple businesses. The researcher was not informed at

PSYCHOGRAPHICS OF THE POOR AND POSITIVE POVERTY MOVEMENT


the start of the discussion on the characteristics of the group and the poverty movement of the individual members.

The questionnaire used for the small group discussions consisted of 21 questions (Annex 3) for five psychographic elements: business acumen or entrepreneurial inclination, lifestyle, motivation or aspiration, financial discipline or spending behavior, and self-image or self-esteem. Other elements were incorporated in the participant’s profiling questionnaire (Annex 4). The questionnaires were translated into the local language, and the briefing of participants and discussion were conducted in the vernacular.

Results

The average age of the participants is 45, with educational attainment in high school level or high school graduate. Most of them are married with husbands running their own microenterprises. The average household size is 4.4, and their children are in different age brackets. Almost all of the respondents are Ilonggos; and they have at least one cellular phone in the household, live in their own house, and aspire for leadership position in one form or another (e.g., in their microfinance groups and centers, and in barangay council). Many are engaged in sari-sari stores, retailing, trading, and agriculture. They have been in their current business from less than a year to as long as 20 years, or 8 years on the average. The study samples are almost split in participating in community activities, with seven of them not interested in these activities.

Their answers do not differ in terms of spirituality, sociability, having interest in news/issues, self-image, hope, and happiness level in the family. However, most Group 1 answers on business acumen and lifestyle, and on a number items under financial discipline and aspiration were different from the replies of the three other groups (Table 21).

49

Table 21. Summary of Answers to the Psychographic Elements by Group

Element Group 1 Groups 2, 3, and 4

Business acumen • In choosing a business, they scan the community for opportunities and or follow their own interests.

• In choosing a business, they seek their husbands’ opinions. Others are influenced by the line of business of the husbands, families, or friends; and cash flow of business.

• They hardly get cash/profit from the business, except perhaps during Christmas. In general, profits are plowed back to the business.

• They get money from the business as frequent as daily or as needed by the family.

• Profits are used to build household savings.

• Profits are used for utilities, loan repayment, household needs, and business capital.

• The keys to business success are experience, thriftiness, and friendliness to customers.

• The keys to business success are hard work and motivation to earn.

• They are happiest in their business when they are able to dispose of the inventories, have many customers, and enjoy selling.

• They are happiest in their business when they make (big) money.

Lifestyle • They only spend 30 minutes on the average daily watching television.

• They spend 2 to 4 hours every day watching television.

• They have no time for socialization but meetings in NWTF are their form for socials.

• They watch basketball in the village and chat with their friends, neighbors, or customers.

• Their “luxurious spending” includes cell phone load, internet rental, hair rebonding, and manicure/pedicure.

• Their “luxurious spending” includes clothes and celebrations for Christmas and New Year.

Financial Discipline • They teach their children to save their allowance and monitor their piggybank. They also save money for their children.

• They encourage their children to work hard and not to depend on their parents, and refrain from buying things or going out.

Aspiration • They want to have better understanding among members of the family, be able to help extended family, and have more time for leisure/relaxation.

• Group 3: They want to be able to buy clothes, feed their children three times a day, have better life, and achieve spiritual growth.

• Group 2 and 4: No response


Source: The author’s SGD results


Group 1 members tended to put their money in business and savings. For “luxurious spending,” they spent much less (ranging from Php10 amount of cellphone load to Php300 for hair rebonding) than those of Groups 2, 3, and 4 (from Php30 for a shirt to as much as about Php1,000 for a celebration). Respondents in Group 1 also knew more concrete ways of teaching their children how to save.

Group 1 clients also appeared to enjoy what they were doing more than simply earning money. They were also able to find opportunities for themselves compared with the other groups relying on others’ opinions and experiences. Those in the first group also spent less time on television and socials than the other groups.

For their aspirations, Groups 2 and 4 did not articulate any while the Group 3 respondents ranged from basic needs (food and clothing) to intangibles (spiritual growth). The replies of those in Group 1 were more concrete, being focused on the family and time to enjoy leisure/relaxation.

After gathering the replies of the respondents in the small group discussion, the poverty movement of each member of the group was determined. Group 1 as a whole had a positive movement while the other groups had average negative change (Table 22). For further research, an instrument could be developed to determine the psychographic profiles of the clients and correlate the variables to poverty movement.

51

Table 22. Change in Likelihood of Clients Living Above the Poverty Line Among the SGD Participants

In Group 1, the positive poverty movement of clients ran counter with the tested hypotheses. Their business was located in a 2nd class city where clients were more likely to experience a no change in poverty movement (43%) or become poorer (32%). Since all members of Group 1 had only one business, their chance of having no change in poverty likelihood (44%) was also higher.

Given, too, that Group 1 members could have either reduced their businesses or continued to have only one business over the time hence they had one business as of the time of the SGD, they had higher chances of becoming poor (46%) or staying the same (50%).

The only variable consistent with the hypotheses tested is their coming from the poorest segment with probability index of being non-poor at 3.40. Their chance of becoming less poor was 66%. This result warrants further research on the psychographic variables associated to a positive poverty movement of the poor.


Group PPI 2008 Likelihood PPI 2010 LikelihoodChange in

Poverty Likelihood

1 0 3.4 23 19.1 15.7

1 0 3.4 30 30.4 27

1 0 3.4 31 30.4 27

1 0 3.4 33 30.4 27

1 0 3.4 34 30.4 27

2 48 78.9 44 63.2 -15.7

2 47 78.9 43 63.2 -15.7

2 46 78.9 51 85.2 6.3

3 52 85.2 44 63.2 -22

3 68 96.8 68 96.8 0

3 59 92.8 60 95 2.2

4 34 40.4 11 8.5 -31.9

4 21 19.1 32 40.4 21.3



he associations of the variables show that positive poverty movement is related to the economic activities of location of the clients’ business, as indicated by the annual income of the municipality or city. The higher the income class of the

area, the higher is the chance of the clients to have positive poverty movement.

A positive change in poverty likelihood is used as metric for social performance of MFIs. Considering the findings of the study, the NTWF then has to bear in mind that its deliberate choice of targeting rural and low-income areas would not necessarily generate positive poverty movement.

The pattern of correlation between changes in poverty likelihood and the initial poverty index of clients was established: the poorer clients tended to become less poor than those with higher index of likelihood of living above the poverty line. The reverse is also true: the richer clients or those in segments of higher likelihood to live above the poverty line tended to become poorer than those who are in lower poverty segments. Thus, this result supports NWTF’s thrust to cater to those with likelihood of living above the poverty line at 40.4 or below, as they would most likely benefit the most from microfinance.

Data also show a strong correlation between positive poverty movement and entrepreneurial capacity. The clients’ number of business in the base year and the change in their number of business over time were used to gauge the clients’ entrepreneurial capacity. The microfinance clients who started with two or more businesses as well as those who had increased their number of business over time tended to increase their chances of living above the poverty line. They are better than those who only have a single business in the base year, did not expand their business, or even reduced their businesses over time.

T

Summary and Conclusion

53

These results present serious implications for NWTF in helping its clients to achieve positive poverty movement. It is noted that the number of business operated by 77% of the continuing clients did not change. However, the number reflects neither any possible asset expansion of the same business nor the possibility of change of nature of business with the number remaining the same. For example, a client may have shifted business from food vending to retailing. Still, the results showing a high proportion of samples that did not have changes in the number of business could be due to inadequate economic or business development at the client level. Thus, providing more business development-related assistance in addition to access to capital could facilitate the clients’ positive poverty movement and hence, their chances of becoming less poor over time.

The study further analyzed the qualitative attributes of the microfinance clients in terms of business acumen, lifestyle, financial discipline, and aspirations. The results show that these factors spelled out differences in poverty movement.

Determining the variables associated with the likelihood of clients to have positive poverty movement, more so the causal relationship, may, however, inevitably pose moral issues. On one hand, the information could help the MFIs to develop interventions that are more effective in serving the poor. On the other hand, if the social performance of MFIs is evaluated on the basis of their clients’ positive poverty movement, the MFIs may not be inclined to serve the group less likely to move out of poverty. By concentrating only on the entrepreneurs with two or more businesses or by operating only in cities where economic activities are high, their clients would have more chances of becoming better off. Such decision is more strategic and practical in that it is easier to exclude certain segments than to develop, test, and implement new interventions to make the microfinance program more effective as well as more inclusive. However, if the MFIs take this stance, the problem of the excluded, unbanked poor would remain.

SUMMARY AND CONCLUSION


he following discourse was based on the panel discussion held on November 28, 2012 at the Ateneo Graduate School of Business.

The descriptive study presents only the profiles of the NWTF clients and the variables associated with movements in poverty likelihood. It did not include the direction of causality; thus, the longstanding issues in the microfinance sector remain unresolved—what makes microfinance work and under what circumstances.

To summarize the movements in poverty likelihood of the clients, the proportions show that (i) only 23% of the 2008 clients became less poor in 2011, (ii) 34% experienced no change in their poverty status, (iii) 24% became poorer, and (iv) 19% dropped out of the program. These proportions thus raise the question whether microfinance is worth pursuing as a mechanism for poverty alleviation.

The role of financial institutions is to provide financial services; for the MFIs, their objective is to provide these services to clients who are either unbanked or inaccessible to the traditional providers. If the MFIs were able to promote financial inclusion of the normally excluded class, then they would have responded to the needs of their clients. Further, access to financial services of the low-income group does not necessarily translate to alleviating poverty. Other intervening mechanisms beyond the scope of microfinance may have contributed to the improvements in the poverty status of clients.

In addition, there are other attributes that determine effectiveness of microfinance programs. First, using microfinance as a poverty-alleviating tool assumes that the entire low-income population is

T

Discussion of Other Related Issues

2 Panelists included Dr. Emmanuel Esguerra, deputy director general of the National Economic and Development Authority; Dr. Aniceto Orbeta, Jr., senior research fellow at the Philippine Institute for Development Studies; and Dr. Carol S. Guina, research adviser to the dean, and Prof. Rick Palo, both from the Ateneo Graduate School of Business.

55

entrepreneurial. Second, the design and type of microfinance products, and the strict, bordering on being coercive, credit discipline required by most programs may not suit well the risk-taking nature of the entrepreneurs. Third, a weekly repayment scheme could work only in businesses with daily cash inflows, such as running corner (sari-sari) store and food vending. Finally, it is assumed that the non-poor would not participate in microlending. On the contrary, they participate in the process and, thus, the leakage.

Another impact of microfinance is women empowerment, as the program provides women the opportunity to be productive and to earn outside their households. Similarly, empowerment does not necessarily lead to poverty alleviation.

Many, if not most, MFIs now offer non-financial services that aim to address the intervening mechanisms between financial access and poverty alleviation.

These services include financial education, livelihood training, basic business skills, and leadership, among others. Some MFIs are also providing health services; education, water, and sanitation facilities; and marketing or distribution services. However, that a family now enjoying a regular supply of clean drinking water, thanks to a program sponsored by an MFI, does not mean the household is no longer poor. On the flip side, if the said family remains poor, it does not follow that the MFI is not effective in providing the services. After more than 20 years of microfinance operations in the country, the call is high time to define what the MFIs could and should do, and to use appropriate metrics for measuring their performance.

For their part, MFIs further need to address some organizational concerns. For many MFIs, donors, and support agencies, poverty alleviation has been the overarching reason for their programs. The

Another impact of microfinance is women

empowerment, as the program provides

women the opporunity to be productive and to earn outside their

households.

DISCUSSION OF OTHER RELATED ISSUES


old mindset that microfinance is for poverty alleviation still works well for them, and continues as a key intervention for the poor. It is therefore unsurprising for many of these MFIs to challenge research findings that they are reaching the non-poor (even more than the poor segments in some cases). The NWTF had made a brave statement in accepting the research results without questioning the validity of the PPI tool. They had been resolute as well to align their operations with an articulated mission to reach the poorest of the poor.

Another concern is managing the performance of the MFI loan officers. For example, the MFI performance would be measured on the basis of the poverty movement of its clients. This performance metric may not be consistent with the incentives of loan officers if they were based on loan repayment rate and client recruitment. Clearly, there is incongruence between institutional thrust of the MFI and the individual goal of the staff. Hence, operationalizing the poverty alleviation mechanism would be problematic without an appropriate performance management system.

57

he study focused on descriptive statistics using the four variables—location of business, initial poverty index, number of business, and change in the number of business. The objective of the analysis is to determine the variables associated with the

changes in the poverty index of the clients who started in 2008 and continued in 2011. In future research, the data gathered by the study could be used to develop predictive models linking these variables to poverty movement.

Another research could similarly correlate the longitudinal impact of MFI programs but using other variables. For this future study, the information or variables needed should be identified and included

in the baseline data at the outset. The variables included in this study, for example, were largely determined by information collected by NWTF. If variables not previously included in NWTF’s database were to be collected, the research method would require using recall. However, this method would tend to generate unreliable data.

Another possible research is the use of available instruments to measure the entrepreneurial skills or business potential of the microfinance clients.

The results could similarly be used in predicting the clients’ positive poverty movement.

In the small group discussions conducted for this study, the group with an average positive poverty movement exhibited attitudes and behaviors that were not observed in the groups that had negative poverty movement. Testing the efficacy of the psychographic profile of the clients in predicting their likelihood to get out of poverty may also be a subject for future research.

T

Direction for Future Research

In future research, the data gathered

by the study could be used to develop predictive models

linking these variables to

poverty movement.

DIRECTION FOR FUTURE RESEARCH


References

ACCION International. (2007). ACCION Products and Services. Retrieved May 23, 2010, from ACCION website: http://www.accion.org/Page.aspx?pid=809

Goldberg, N. (2005). Measuring the Impact of Microfinance: Taking Stock of What We Know. Washington, D.C.: Grameen Foundation USA.

Innovations for Poverty Action. (2011). Project Evaluation. Retrieved May 7, 2011 from Innovations for Poverty Action website: http://www.poverty-action.org/project/0035

Kono, H., & Takahashi, K. (2009). Microfinance Revolution: Its Effects, Innovations, and Challenges. Retrieved May 23, 2010, from http://www.microfinancegateway.org/gm/document-1.1.9506/microfinance%20revolution.pdf

Maes, J., & Vekaria, K. (2008). Moving the World’s Poorest Families out of Poverty: How Will Microfinance and Microenterprise Develoment Meet the Challenge? The USAID and the SEEP Network. Washington, DC: The SEEP Network Poverty Outreach Working Group.

Marin, G., Mercado, R., Palomo, M., & Fernandez, I. (2008). Heeding Poverty: An Assessment of Microfinance in the Philippines. John J. Carroll Institute on Church and Social Issues. Quezon City: John J. Carroll Institute on Church and Social Issues and International Network of Alternative Financial Solutions-Philippines.

59

Microfinance Council of the Philippines and SEEP Network. (2008). Microfinance Industry Assessment: A Report on the Philippines. Pasig: Microfinance Council of the Philippines and SEEP Network .

Negros Women for Tomorrow Foundation. (2003). Main Survey: Impact on Poverty (Unpublished study). Bacolod City, Philippines.

Odell, K. (2010). Measuring the Impact of Microfinance Taking Another Look. Measuring the Impact of Microfinance Taking Another Look. Washington DC, USA: Grameen Foundation.

Schreiner, M. (2009). A Simple Poverty Scorecard for the Philippines. Washington, USA: Grameen Foundation.

Strategic Business Insights. (n.d.). US Framework and VALSTM Types. Retrieved July 18, 2013, from Strategic Business Insights: http://www.strategicbusinessinsights.com/vals/ustypes.shtml

The Leap Frog Interactive. (2009). Industry Research: Psychographics. Retrieved May 19, 2010, from Chief Marketer: http://whitepapers.chiefmarketer.com/whitepaper4304

The Nielsen Company. (2010). Nielsen Media Index Quarter 2 Report (Unpublished document). Pasig City, Philippines.

The North Crossing. (n.d.). A quick how-to on targeting your message to a specific market segment. Retrieved May 19, 2010, from Psychographics: Marketing, Advertsising and Sales catered to different personality types: http://www.northcrossing.com/PsychographicMarketing.html

REFERENCES


The Retail Coach. (2009). Psychographic Profile. Magee, Mississippi – Retail Trade Area. Retrieved May 19, 2010, from from http://www.simpsoncounty.biz/images/uploads/Magee_RTA_

Psychographics.pdf

Vaugh Wedeen Creative Inc. (2005). Vaughn Wedeen Psychographics. Retrieved May 23, 2010, from Vaugh Wedeen Psychographics: http://www.downtownlascrucespartnership.org/

CommunityProfile/Vaughn_Wedeen_Psychographics_Study.pdf

Weiss, J., Montgomery, H., & Kurmanalieva, E. (2003). Micro Finance and Poverty Reduction in Asia: What is the Evidence? ADB Institute Research Paper Series No. 53. Tokyo, JP: Asian Development Bank Institute.

Wittwer, B. (2010, July). Microfinance: Back to the Drawing Board. Retrieved October 19, 2010, from Miller-McCune: http://www.miller-mccune.com/business-economics/microfinance-

back-to-the-drawing-board-19144/

ANNEXES

61

Annex 1

NWTF Client Profile FormNegros Women For Tommorw Foundation, Inc.Project DungganonPOVERTY SCORECARD

Branch : ______________________ Center #: _________

Name : ______________________ # of Years in the Program: _________

Age : ____________ Cycle: _________

Type of Area : ____ Urban ____ Rural

I.

1. a. How many business/es do you have?

1 2 3 4 5 6 & Above

b. How many of these belong to the following?

Start-up Existing (present already)

2. What type/s of business do you have?

Sari-sari Store

Retailing (Food vending, clothes veding, etc.)

Agi-based (any crop)

Fishing / aqua - based

Manufacturing/production (candy-making, etc.)

Processing (Food processing i.e. tocino, dried fish)

Trading / Buy & Sell (Wholesaling; in bulk sold to retailers)

Service (barber shops, parlors, etc.)

ANNEXES


A. Progress Out of Poverty Questionnaire

Indicator Answer Points Value

1. How many people in the family are aged 0 to 14? A. Five or more

B. Four

C. Three

D. Two

E. One

F. None

0

4

9

15

20

26

2. Do all children in the family of ages 6 to 14 go to school? A. No

B. Yes

C. No children ages 6 to 14

0

2

4

3. What is the education level of the female head/spouse? A. Graduate primary or less

B. First- to fourth-year secondary

C. Graduate secondary

D. First year college or higher,or no female head/spouse

0

3

6

11

4. Do you family members have salaried employment? A. No

B. Yes

0

5

5. What are the house’s outer walls made of? A. Light materials (cogon, nipa or sawali, bamboo, anahaw)

B. Strong materials (iron, aluminum, tile, concrete, brick, stone, wood, asbestos)

0

4

6. What is the house’s roof made of? A. Light materials (Salvaged, makeshift, cogon, nipa, or anahaw)

B. Strong materials (Galvanized iron, aluminum tile, concrete, brick, stone, or asbestos)

0

2

7. What kind of toilet facility does the family have? A. None, open pit, closed pit, or other

B. Water sealed

0

7

8. Does the family own a refrigerator? A. No

B. Yes

0

10

9. How many television sets does the family own? A. None

B. One

C. Two or more

0

6

10

10. Does the family own a washing machine? A. No

B. Yes

0

10

Total

Annex 2

63


PPI ScoreTotal Below the

National Poverty LineTotal Above the


0.4 96.6% 3.4%

5-9 93.7% 6.3%

10-14 91.5% 8.5%

15-19 87.8% 12.2%

20-24 80.9% 19.1%

25-29 68.5% 31.5%

30-34 59.6% 40.4%

35-39 48.9% 51.1%

40-44 36.8% 63.2%

45-49 21.1% 78.9%

50-54 14.8% 85.2%

55-59 7.2% 92.8%

60-64 5.0% 95.0%

65-69 3.2% 96.8%

70-74 1.4% 98.6%

75-79 1.4% 98.6%

80-84 0.0% 100.0%

85-89 0.0% 100.0%

90-94 1.5% 98.5%

95-100 0.0% 100.0%

B. Philippines Poverty Likelihoods Look Up Table

ANNEXES


Business Acumen 1. How one chooses business?

2. When is business profit used/spent? For what?

3. What makes a business successful?

4. Are you happy doing business? What is the most fun part of doing business?

Lifestyle 1. What is your usual meal like?

2. How often does one go to shop for personal/family needs? Where?

3. What is your common form of entertainment?

4. How many hours do you normally spend watching TV per day?

5. What does one/the family do on weekends?

6. What is your common form of socialization?

7. What would you consider “Luxurious” spending yet spend for it still?

Motivation/ aspiration

1. What do you aspire for in your family?

2. What do you aspire for in your business?

3. What do you aspire for in your personal life?

Financial discipline1. Do you encourage your children to save? How?

2. Do you encourage your children to be resourceful/industrious/hard working?

3. If you were to win lotto today, what would you do with the money?

Self-image/ self-esteem

1. What makes you unique/special?

2. What do you think you can do well that others cannot?

3. If there were something you want to change in yourself, what would it be?

4. What else do you want to do/achieve in life?

SGD Questionnaire

ANNEXES

Annex 3

65

SGD Participant’s Profile

Name: ____________________

Age: ______________________

Eductional attainment: (please check)

Grade school level

High school level

High school graduate

College level

College graduate or higher

Location/city/municipality of

residence:__________________

Renting

Living with relatives

Own house

Others __________

Number of loan cycle completed:________

Current loan amount: Php ________

Sari-sari store/tiangge

Retailing/RTW

Agriculture/Any crop

Aquaculture/fishing

Manufacturing/production

Food processing

Trading

Service

Number of years in doing business/

micro enterprise: ________

Household size (including self): _________

Age of children:

All below 7 years old

All between 7 and 15 years old

All between 16 and 25 years old

Children in different age brackets mentioned above

Size of family of origin (incl. self): ________

Status:

Single

Widowed/separated

Married/common law; work of spouse/partner

Employed

Own business/self-employed

Helping the spouse/partner

None

Ethnic origin

Ilonggo/Negros/Panay Island/

Western Visayas

Cebuano/Cebuano-speaking provinces/

Eastern Visayas

Tagalog-speaking provinces, including Palawan/Central-Southern Luzon

Ilocano/Northern Luzon/

Mountain Province

Mindanao-Central/cultural melting pot

Mindanao-Coastal/Island communities/

Islam-influenced

For NWTF’S use onlyPPI 2008 ________________PPI 2010 ________________Branch ________________

City _______________Municipality _______________

SGD Participants’ Profiling Questionnaire

Annex 4

ANNEXES


What cellphone line do you use?

Smart Sun

Globe No Cellular phone

Are you involved in community activities?

Yes No

Do you enjoy/aspire for leadership

position? (e.g. center, group, barangay

leadership, etc.)

Yes No

Do you spend on beauty products, cosmetics?

(e.g. pressed powder, lipstick, hair gel/spray,

cologne/perfume, etc.)

Yes No

Do you consider yourself spiritual/prayerful/

with strong faith?

Yes No

Do you consider yourself friendly/sociable/

with many friends?

Yes No

Do you keep yourself updated with the latest

news (national, local, showbiz)?

Yes No

Do you consider your family life happy/healthy

Yes No

Do you feel you cannot do anything else to

improve your life?

Yes No

Factors Associated with Poverty Movement of Negros Women for Tomorrow Foundation’s Clients

Documents

Transcript of Factors Associated with Poverty Movement of Negros Women for Tomorrow Foundation’s Clients