Multilevel latent class modeling to segment the microfinance market

Purpose Microfinance has become an important way to alleviate poverty. Though four decades have passed since its introduction, its impact is still not entirely clear. What makes it difficult to ascertain its efficacy is the existence of diverse types of microfinance organizations and client profiles. Microfinance institutions must primarily pay more attention to the client, and to the mechanism through which financial services are delivered. The purpose of this paper is to identify the profiles of microfinance customers and the features of their operations. Design/methodology/approach In this paper, multilevel latent class models were estimated to reveal clusters of operations and classes of clients. Findings The results show that there are six clusters of operations and four classes of clients in the market, each with distinct profiles and needs. Different strategies are recommended for each cluster and class. Originality/value Numerous studies have focused on the importance of getting to know the clients of microfinance programs, but none as yet have used market segmentation as a way to do so. The goal is to generate better strategies to help clients improve their business results. Applying market segmentation to the microfinance market may point to different products for different groups of clients, taking the real needs of each of them into account.

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Farmers' preferences and willingness to pay for microcredit in Benin: results from in-the-field choice experiments in Benin

Agricultural Finance Review ◽

10.1108/afr-01-2020-0004 ◽

2020 ◽

Vol 80 (5) ◽

pp. 665-692 ◽

Cited By ~ 1

Author(s):

Tchekpo Fortune Ogouvide ◽

Ygue Patrice Adegbola ◽

Roch Cedrique Zossou ◽

Afio Zannou ◽

Gauthier Biaou

Keyword(s):

Willingness To Pay ◽

Interest Rates ◽

Rural Areas ◽

Financial Services ◽

Poverty Reduction ◽

Latent Class ◽

Microfinance Institutions ◽

Choice Modelling ◽

Attribute Importance ◽

Content Type

PurposeThis document analyses farmers' preferences and willingness to pay (CAP) for microcredit, in order to facilitate their access in rural areas.Design/methodology/approachData are based on a discrete choice experiment with 400 randomly selected farmers from 20 villages of the 7 Benin agricultural development hubs (ADHs). The preference choice modelling was performed using mixed logit (MXL) and latent class logit (LCL) models. Farmers' willingness to pay for each preferred attribute was estimated. The endogenous attribute attendance (EAA) model was also used to capture attribute non-attendance (ANA) phenomenon.FindingsThe results indicate that, on average, farmers prefer individual loans, low interest rates, in kind + cash loans, cash loans, disbursement before planting and loans with at least 10-month duration. These preferences vary according to farmers' classes. Farmers are willing to pay higher or lower interest rates depending on attribute importance. The estimate of the EAA model indicates that, when taking the ANA phenomenon into consideration, people will show stronger attitudes regarding WTP for important factors.Research limitations/implicationsBased on these results from Benin, microfinance institutions (MFIs) in developing countries can, based on the interest rates currently charged, attract more farmers as customers, reviewing the combination of the levels of the attributes associated with the nature of the loan, the type of loan (individual or collective), the disbursement period of funds, the waiting period of the loan and the loan duration. However, the study only considered production credit, ignoring equipment or investment credit.Practical implicationsThe document provides information on the key factors that can facilitate producers' access to MFI products and services.Social implicationsFacilitating small farmers' access to financial service will contribute to poverty reduction.Originality/valueThis research contributes to the knowledge of the attributes and attribute levels favoured by farmers when choosing financial products and the amounts they agree to pay for these attributes. The implementation of the results would facilitate small producers' access to financial services; thus contributing to poverty reduction.

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Smallholder preferences and willingness-to-pay measures for microcredit

China Agricultural Economic Review ◽

10.1108/caer-02-2017-0022 ◽

2018 ◽

Vol 10 (3) ◽

pp. 462-481 ◽

Cited By ~ 3

Author(s):

Zhao Ding ◽

Awudu Abdulai

Keyword(s):

Willingness To Pay ◽

Interest Rates ◽

Latent Class ◽

Smallholder Farmers ◽

Mixed Logit ◽

Latent Class Models ◽

Preference Heterogeneity ◽

Content Type ◽

Lower Interest Rate ◽

Class Models

Purpose The purpose of this paper is to examine smallholders’ preferences and willingness to pay for microcredit products with varying attribute combinations, in order to contribute to the debate on the optimal design of rural microcredit. Design/methodology/approach Data used in this study are based on a discrete choice experiment from 552 randomly selected respondents. Mixed logit and latent class models are estimated to examine the choice probability and sources of preference heterogeneity. Endogenous attribute attendance models are applied to account for attribute non-attendance (ANA) phenomenon, focusing on separate non-attendance probability as well as joint non-attendance probability. Findings The results demonstrate that preference heterogeneity and ANA exist in the smallholder farmers’ microcredit choices. Averagely, smallholder farmers prefer longer credit period, smaller credit size, lower transaction costs and lower interest rate. Guarantor collateral method and installment repayment positively affect their preferences as well. Moreover, respondents are found to be willing to pay more for the attributes they consider important. The microcredit providers are able to attract new customers under the current interest rates, if the combination of attributes is appropriately adjusted. Originality/value This study contributes to the debate by assessing the preference trade-off of different microcredit attributes more comprehensively than in previous analyses, by taking preference heterogeneity and ANA into account.

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