The Segmentation of Micro-Credit Program

The Segmentation of Micro-Credit Program

in Bosnia andHerzegovinaand its Impact

By; Mohammad HAMAD

Introduction

Microfinance is often used as a tool for fighting poverty and as a tool for post-conflict reconciliation and economic development. Since the 2000s microfinance has increasingly been employed as a means for poverty-reduction. Its international profile as a tool for poverty alleviation was secured in 2006, when Muhammad Yunus and Grameen Bank were awarded the Nobel Peace Prize.

In Bosnia and Herzegovina (BH), both approaches of microfinance were used after the 1992–1995 war that left the country in ruin. The BH microfinance sector developed rapidly, transforming microfinance institutions (MFIs) from donor-funded institutions into financially sustainable microcredit organizations (MCOs). The sector gradually became institutionalized. Many commentators have therefore proclaimed a case of the BH microfinance sector as successful. However, there are also voices raised against such evaluation, due to the country’s stagnation in developing the small and medium enterprise (SME) sector (Bateman various years).

Microfinance is extensively used as a development tool, and is largely supported by existing financial, technical and political resources. However, less is known about microfinance in post-conflict situations. For instance, Woodworth (2006) argues that more research is needed on the complexities of managing MFIs in times of conflict and post-conflict, while Nagarajan and McNulty (2004) find that implementing effective microfinance in specific post-conflict contexts is not yet well understood.

BH represents a key test case for this type of inquiry because it mirrors a broader debate about the impact of microfinance, a divided opinion among scholars and practitioners about the impact and contribution of microfinance to the country’s overall development.

Therefore, the purpose of this study is to segment the MC market into Borrowers groups based on their perspectives of the impact of MC on their daily life. The primary objective is to identify segments with the most impact. The segments with the less impact are also identified. All segments are described in terms of impact content and their demographic characteristics are presented.

The questions motivating this paper are; first, what are the groups most benefit, and less benefit from the microcredit program in Bosnia and Herzegovina, and to which extent the program has impact on them? And second, does the segmentation of the clients of the program contribute in building more effective marketing strategies?

Literature Review

Microcredit (MC) is a development tool designed to address issues of poverty, under-development and marginalization. It is based on a simple idea: to provide poor individuals with access to microloans. These microloans allow a client to start a micro-business. In the best-case scenario, such micro-businesses develop into a small-medium enterprise. Such activities provided bymicrocredit institutions (MCIs) are often supported by government or international donor funding. Over time, these institutions aim at commercializing their operations and achieving their own financial self-sustainability. As a development tool, microfinance (MF) has been used both to reduce the poverty and to support countries recovering from a conflict or a major disaster. MF is seen as an important factor in reaching the Millennium Development Goals (Littlefield, Murduch and Hashemi 2003). Donor funding provided to MCIs usually includes poverty reduction in their mission.

Based on a review of impact studies that took place in the period 1994–2002, Littlefield et al. (2003) find that microfinance goes beyond just business loans, it affects investments in health and education, management of household requirements, and other cash needs. For example, since 1989 ‘Freedom from Hunger’ has worked with local partners to develop and distribute a cost-effective strategy to improve the nutritional status and food security of poor households in rural areas of Africa, Latin America and Asia. MkNelly and Dunford conducted an impact evaluation study of these programs in Ghana in 1998 and Bolivia (Credit with Education Impact review No.3) in 1999. In Ghana, clients’ economic, social and health status were shown to improve due to microfinance(MkNelly and Dunford 1998). In Bolivia, MkNelly and Dunford (1999) documented that microfinance led to improve nutritional and health status of the clients’ families, as well as higher involvement in local government.

These studies point to what Boudreaux and Cowen (2008) named ‘the micromagic of microcredit.’ Boudreaux and Cowen (2008, 31) explain: ‘With microcredit, life becomes more bearable and easier to manage. According to Boudreaux and Cowen (2008), microcredit is an alternative to money lending, which is a traditional way of borrowing and lending money to the poor part of the population. As such, microcredit is a more humane way of providing access to credit for the poor.

In short, microfinance initiatives reduce poverty, promote education of children, improve health and empower women. However, because poverty and war are sometimes linked, microfinance is often used in post-conflict contexts. Many post-conflict environments suffer from a lack of financial and social capital, infrastructure and functioning relationships (Nagarajan 1999). Business activities are negatively affected by such instable macro-economic frameworks. Humanitarian aid projects are suitable for the immediate post-conflict period, helping the population overcome starvation and diseases. Microfinance can be a means for managing the transition from humanitarian relief to economic reconstruction and sustainable development (Seibel 2006; Hudon and Seibel 2007).

Microfinance may also have a broader impact in terms of peace and reconciliation. For example Nagarajan (1999) emphasizes the possibility of MFIs playing a role in the restoration of social capital when they provide long-term viable services. Doyle (1998) shows how microfinance initiatives in Rwanda helped local Hutu and Tutsi populations to overcome their differences following the civil war in the 1990s, and to find common ground in business development through microfinance.

A strong critique asks whether microfinance can actually underminemedium-term economic development because it supports inefficient activities.This relates specifically to commercial microfinance. Bateman(2007a) criticized ‘new wave commercial microfinance’ institutions, he argues, has two negative consequences: first, a high rate of microenterprise exit caused by the saturation phenomenon within the informal sector; and second, high opportunity costs for the countries, since a standard commercial microfinance business model does not make it possible for microenterprise to deploy advanced technologies, skills and product and process innovations. Bateman found that there is little solid evidence to confirm that commercial microfinance facilitates sustainable economic and social development.

A broader debate concerns the issue of whether microfinance fits a country’s strategy for economic growth. Calling for ‘jobs, not microcredit,’ Karnani (2007a) reviewed macroeconomic data and found that although microcredit yields some non-economic benefits, it does not significantly eradicate poverty. Even thoughmicrofinance can make lifebetter at the ‘bottom of the pyramid,’ creating jobs and increasing worker productivity is a better way to get rid of poverty. Unless microfinancedirectly affects the jobless, it is merely a way of transforming employeesinto micro-entrepreneurs – simply by replacing old businesses withmicrocredit-funded micro-businesses. Such crowding-out results neitherin net job nor in income gains (Storey 1994). Therefore,Karnani(2007b) suggested that romanticizing the poor as ‘resilient and creativeentrepreneurs’ harms the same poor individuals in two ways: it underemphasizesmodern enterprises as well as legal, regulatory and socialmechanisms to protect the poor; and it overemphasizes the impact ofmicrocredit.Therefore, Karnani (2007a) suggested that governments, businessesand civil society should work together to reallocate their resourcesaway from microfinance and instead support larger enterprises in labour intensiveindustries. This formula, he claims, worked well in China, Korea, and Taiwan.

Methodology and Data Collection

Research Design

A direct personal survey will be conducted in most of the cantons in the Federation of Bosnia and Herzegovina (FBH) and Republic of Serbia (RS). The respondents will be analytically grouped into distinct segments based on their perspectives of the impact of MC on their live. The pattern of their responses on impact variables will be used as the basis for the grouping process. Thus, respondents with a similar expression on the variables will be grouped together. Respondents with different expressions on those variables willbe assigned to different groups. After the grouping completed, the identified segments will be described in terms of their impact content and will be profiled with demographic characteristics.

Sample and distribution procedure

For the study population lists of borrowers will be obtained from up to five existing MCFs in BH, the sample will be randomly selected to cover most of cantons in FBH and RS. A total of 1.000 surveys will be distributed. Surveys will be distributed by the loan officers of the selected MCFs, and few follow-up telephone calls reminders will be done one to two weeks later. The non-response issue will be addressed by comparing early (first 50 responses) to late respondents (last 50 responses) on several demographic characteristics in order to test whether significant differences will be found or not. Assuming that the late respondents are similar to the non-respondents, the class of non-respondents will be identified and reasons behind not being interesting to the study object could be determined.

According to their answers, the study respondents could be classified according to their; living areas (urban, suburban, rural), the average household income, educational level, gender, average, marital status, the average household size, racial/ethnic background, religion background, preference of using loans (housing, business, social, education,….).

Measurement

A questionnaire will be developed for the data collection. The questions will consist of four main components: An impact measurement component, a set of demographic variables, a question about the respondent's intention of getting loan and a question about the respondent's preference on type of loans. For pretesting purposes, the questionnaire will be administered to a Master and PHD students (n=40) at IBU university in Sarajevo in order to provide feedback on readability. This procedure might suggest some changes in the instructions and could estimate the time needed to complete the questionnaire.

The impact measurement component will consist up to20 selected impact items.The respondents will be asked to state their extent of agreement to these impact items on a five point Likert type scale (1 = strongly disagree, 5 = strongly agree). To help ensure the content validity of the impact items, these items will be generated in a staged process. The initial pool will be composed of items used in other impact studies reported in the literature and then adapted to the study. The items will then be discussed with MC and marketing professionals in the study area. At each stage, items were added, reworded, or deleted. At the final stage, the impact measurement component expected to consist of 20 variables.

These items will be factor-analyzed; this approach expected to result in the extraction of few factors. Impact items with loadings of approximately 0.5 and above (substantialloadings) will be used to represent the factors. The factor solution along with the means and standard deviations of the variables will be presented in a table as below;

Impact dimension / Mean / Stand. Dev. / Factor loading / Explained variance
Financial Impact
….
….
Social Impact
….
….
Other Impacts
….
….

Overall, the factors will be readily interpretable and the individual impact items will show the correlations with the corresponding factor. The factors expected to explain not less than 60% percent of the variance of the original variables. Each factor will show the loadings of impact items. For further analysis, factor scores will be computed for the all impact dimensions.

Cluster Analysis

The respondents will be clustered into mutually exclusive groups based on similarity of their factor scores. Owing to the marketing segmentation approach taken in this study, which is to arrive at a few meaningful groups, the number of clusters will be restricted to a maximum of ten. Among those ten, the best number will be selected after inspecting the error function. The behaviour of the semi-partial R2 will be inspected over the ten cluster solutions. After disregarding the outliers and examining those who expressed an overall extremely unfavourable impact, the remaining clusters, along with the corresponding cluster means, will be presented in table as below. The cluster means reflect the cluster members' perceptions on the impacts of each factor or dimension. Generally, the higher the score, the more favourable the impact of that dimension will be.

Impact
Dimension / Cluster mean
Cluster-1 / Cluster-2 / Cluster-3 / Cluster-……

The cluster analysis also will show the intention of borrowers in each cluster of getting new loans as shown in the below table.

Clusters / Percentage of respondents
Will take / May or may not take / Will not take
Cluster-1
Cluster-2
Cluster-….

The cluster analysis will also show the borrower’s preference of types of loans in each cluster as shown in the below table.

Clusters / Respondent’s Preference
Business loan / Housing loan / Education loan / Social loan / Other types of loans
Cluster-1
Cluster-2
Cluster-….

Demographic profile of image segments

Frequency and percentage tables will be established to demonstrate the distribution ofthe groups within the demographic profile. To examine if significant demographic differences exist between the members of the clusters, the below tables will be computed.

Demographic Segments / Percentage of respondents
Custer-1 / Cluster-2 / Cluster-… / Total Sample
Annual household income
Gender
Residential area
Education
Household size
Religion/ethnic background
Marital status

The study will identify segments from which target markets can be selected and marketing strategies can be developed. It provides insight into the most and the least influencing impact aspects by market segment. The results provide guidelines for the allocation of promotional resources to the segments and the development of promotional messages. The study will identify segments from which target markets can be selected.

As a short-term strategy, the groups with the more impressed impact are clearly the most attractive segments. These segments form the most responsive target markets, because they not only have a good impression but also have the greatest intention of taking more loans. By directing a promotional campaign at those markets, MC marketers can further enhance their impressions.As a long-term strategy, a MC marketer can focus on redirecting the perception of the segments that expressed less impact.

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