Microcredit repayment in a European context: evidence from Portugal

Accepted Manuscript Title: MICROCREDIT REPAYMENT IN A EUROPEAN CONTEXT: EVIDENCE FROM PORTUGAL Authors: Jos´e Bilau, Jos´ee St-Pierre PII: DOI: Reference:

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Please cite this article as: Bilau, Jos´e., & St-Pierre, Jos´ee., MICROCREDIT REPAYMENT IN A EUROPEAN CONTEXT: EVIDENCE FROM PORTUGAL.Quarterly Review of Economics and Finance https://doi.org/10.1016/j.qref.2017.11.002 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

MICROCREDIT REPAYMENT IN A EUROPEAN CONTEXT: EVIDENCE FROM PORTUGAL

José Bilau (corresponding author) Adjunct Professor, Polytechnic Institute of Beja / ESTIG, Beja, Portugal

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Management Department R. Pedro Soares, Campus do IPB,7800-295 Beja, Portugal. Phone: +351284311540. Fax +351284361326

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E-mail: [email protected]

Josée St-Pierre

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Professeure titulaire.

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Titulaire Chaire de recherche du Canada en gestion de la performance et des risques des PME

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Institut de recherche sur les PME, Université du Québec à Trois-Rivières. 3351 boulevard des Forges,

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C.P. 500, Trois-Rivières, QC, CANADA, G9A 5H7

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The high level of microcredit repayment is confirmed in the studied context (Portugal) Microcredit appears to be a tool for disadvantaged groups in urban areas more than a solution for people of the poorest regions. Microcredit may act to facilitate integration of immigrants in the studied context Microcredit repayment can be predicted by socio-demographic and loan-related variables Economic crisis was of significant influence in microcredit repayment predictors

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HIGHLIGHTS

Abstract

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The existing empirical literature on microcredit repayment focuses primarily on explaining its determinants for developing countries. Using a binary logistic regression model, we examine microcredit repayment in Portugal, one of the EU countries hardest hit by the economic recession caused by the 2008 financial crisis. This article widens the focus by examining the determinants of microcredit repayment in a previously unstudied context, examining potential differences between expansion and recession sub-

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periods and incorporating into the model previously unstudied variables. This study also reveals clear differences between profiles of microcredit borrowers from developing countries and those from a European context.

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Keywords : microcredit; micro-enterprise; microcredit repayment; developed countries; Portugal

INTRODUCTION

Microcredit is a financial instrument that was developed three decades ago in developing countries. By

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providing small loans to low-income people who had no access to formal financing, microcredit has allowed many of the poorest people of the world to develop small businesses and helped to promote economic development in such regions of the world.

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The success of microcredit in developing countries prompted developed countries to reproduce these

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programs and use them to promote the transition from unemployment to self-employment. In recent

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years, it has played an important role in promoting social inclusion and, as such, it is of significant

both economically and socially.

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importance in rural areas and can play an important role in integrating ethnic minorities and immigrants,

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In the last decade, several initiatives were set in motion in the European Union (EU) to develop microcredit as a means of encouraging entrepreneurship through self-employment or microenterprises, especially within disadvantaged groups (unemployed or inactive citizens, welfare recipients,

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immigrants, etc.). These people, while interested in self-employment, do not have access to traditional banking services. The initiative described by the European Commission (EC) in A European initiative

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for the development of microcredit in support of growth and employment (2007) as well as the JEREMIE (Joint Action Microfinance Institutions in Europe) and JASMINE (Joint European Resources for Micro to Medium Enterprises) initiatives set forth in the European Regions framework of from 2007

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to 2013 are just a few examples of actions taken in favour of implementing microcredit in the EU. These actions are justified by the perceived positive impact of microcredit as a financial instrument that encourages not only competitiveness and entrepreneurship, but also social inclusion. The major economic and financial crisis that took place in 2008 created serious difficulties for banks in terms of liquidity which resulted in increased restrictions and tightened access to credit. The situation

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contributed to enlarging the group excluded from credit, which led EU governments to consider microcredit as “an effective financing channel for job creation and social inclusion, which can attenuate the adverse effects of financial crisis while contributing to entrepreneurship and economic growth” (COM, 2012). The Fundación Nantik Lum’s latest survey on the microcredit sector, covering activity in the first two years of the crisis (2008 and 2009), revealed that between 500 and 700 microfinance

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institutions (MFIs) offer microcredit in Europe. Despite the high number of microcredit programs in Europe, there are few studies on the efficiency and sustainability of the large number of European microfinance institutions (MFIs). In order to establish

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efficiency and sustainability of MFIs, information about borrowers’ repayment capacity in microcredit

programs and about factors influencing microcredit repayment is necessary. Repayment failure is one of the critical issues that concern all MFI stakeholders (Sharma and Zeller, 1997; Marr, 2002; Maata, 2004;

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Godquin, 2004). In fact, the high loan default rate is the primary cause of MFI failure (Yaron 1994;

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Woolcock, 1999; Marr, 2002; Maata, 2004).

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Systematic and rigorous research seems necessary to examine the repayment determinants required for microcredit programs in developed countries contexts (Bruton et al. 2013). It is critical to address the

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question of repayment determinants in these countries (Bhatt and Tang, 2002). Primarily, because the

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economic, cultural and social reality of developing countries is quite different from the reality of developed countries. Transposing the results of studies conducted in those countries to developed countries is invalid. Secondly, because by knowing the factors likely to influence microcredit

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repayment, MFIs can identify borrowers with a higher default risk in order to lend more efficiently and to increase reimbursement rates. Increasing the MFIs reimbursement rates can reduce the interest rates

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used which benefits both MFIs and borrowers. Accordingly, reducing the credit’s financial cost provides the opportunity for more borrowers to access credit (Godquin, 2004). In short, a better understanding of these issues, in particular of the factors influencing microcredit repayment in developed countries, can

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help establish strategies to improve performance and survival rates of MFIs, avoid microcredit repayment crises and contribute to consolidating microcredit. This paper’s aim is to objectively examine the relevant determinants to the microcredit repayment rates. In this regard, our study addresses three research questions: (1) How do borrower characteristics influence microcredit repayment? (2) How do loan characteristics influence microcredit repayment? (3)

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How do external factors influence microcredit repayment? We examine these issues using data from the 2006–2009 period, focusing on Portugal, one of the EU countries hardest hit by the economic recession caused by the 2008 financial crisis. The value added to the research literature is twofold. Firstly, the empirical literature on microcredit repayment is mainly concentrated on explaining determinants for developing countries paying less

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attention to developed countries. This paper fills this gap and provides substantive empirical evidence of the determinants of microcredit repayment in the context of developed countries. Secondly, the dataset utilized, obtained in regions with different levels of development, contains information on microcredit

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loans between 2006 and 2009. This timeframe includes both a GDP growth sub-period (2006–2007) and

a recession sub-period (2008–2009). This sample allows us to examine how context variables (economic cycle and level of regional development) can affect microcredit repayment. Previous studies never

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included variables related to business environment in the model even though such variables may

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influence loan repayment (Derban et al., 2005). This study is the first in the literature on microcredit

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repayment to include this type of variable.

Taken together, these contributions provide a starting point for scant scholars wishing to address the

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important topic of microcredit in developed countries, while offering practical implications for MFIs

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and policymakers.

The paper is structured as follows: Section 2 provides a brief overview of the study context. Section 3 presents a review of empirical literature and the hypotheses to be tested. The data used, the statistical

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model and measurements of the variables are detailed in Section 4. The univariate and multivariate results of our data analysis are presented and discussed in Section 5. Section 6 provides a summary and

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conclusions.

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STUDY CONTEXT

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2.1. Macroeconomic and social context Our study looked at Portugal, a country that joined the ECC (which later became a part of the EU) in 1986. Despite being classified by the OECD as a developed country, it has one of the lowest GDP per capita in the EU-27. In the period the study refers to, 2006–2009, the Portuguese GDP was ranked 18th and its GDP was about 80% of the EU-27 average. During this period, one sub-period, 2006–2007, can

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be defined where the Portuguese GDP grew by 1.1% and 2.1% annually, one of the lowest rates of growth in the EU. In sub-period 2008–2009, where the world experienced a profound and synchronized international economic recession, the Portuguese economy decelerated markedly and was left devastated. In 2008, the GDP dropped to -0.1%, and then was subject to full recession during 2009 when the GDP descended to -3.0%. The average annual unemployment rate in Portugal followed the

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evolution of the GDP in this period, and grew from 7.7% in 2006 to 9.5% in 2009. Fitch (2009) put forward the view that, in times of crisis, the microfinance sector is under pressure because MFI

borrowers have lower levels of income which reduces their microcredit service capacity and increases

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the use of restructured/reprogrammed loans.

It should also be noted that Portugal is a country with significant asymmetries of GDP between the various regions. Of the third of NUTS III which divides the country, only the Greater Lisbon and

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Madeira Island regions exceeded the EU average. The Lisbon region showed the highest standard of

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living in the country (with 163.3% of GDP national average). At the other end of the spectrum is the

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Serra da Estrela region, with a GDP per capita of only 52.6% of the national average. According to the European statistics agency (Eurostat, 2015), 25% of the Portuguese population was at

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risk of poverty in 2006–2009, a higher percentage than the EU-27 average of 23%. Immigrants were

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particularly at risk. The country’s net migration was positive between 2006 and 2009. This was not only due to immigration of citizens from countries with which Portugal had historical relations (e.g. Portuguese colonies), but also to immigration of citizens with little cultural and linguistic ties to

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Portugal. A wave of immigration from Brazil contributed to the positive net migration, but immigration also came from Eastern Europe (from Ukraine, Moldova, Romania, etc.) and Asia (mainly China).

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Financial exclusion was aggravated by the economic crisis that started in 2008 and impacted the Euro area particularly in Greece, Ireland and Portugal. The difficulties experienced by the financial systems of these countries rapidly led to tightened lending criteria and increased interest rates in the fragile

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banking sector. This situation has opened doors to the expansion of the microcredit sector. In the Western European countries, 51,027 loans were granted in 2009 totaling € 477 million. In the same year in Portugal the value of loans disbursed was € 3.8 million with average loan size of € 7,811(Jayo et al., 2010).

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2.2. Microcredit system in Portugal The first microfinancing experiences in Portugal date back to the fifteenth century. Grounded in social welfare concerns, these initiatives were implemented by Portuguese charity organizations designated Misericórdias (Alves, 2010). Another example of small scale financing that remains today is the emergence in the early twentieth century of small local banks designated Crédito Agricola Mutuo that

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played an important role in financing the activities of small farmers. More recently, with the 1974 revolution that led to democracy in Portugal, decolonization of existing Portuguese territories in Africa occurred and the majority of Portuguese residing in the former colonies returned to Portugal in the late

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’70s. Assistance was required for the social and economic integration of hundreds of thousands of

people returning without significant financial assets. Part of this assistance came from Caritas, the humanitarian organization of the Catholic Church, in the form of small loans (Alves, 2010). During the

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second half of the ’90s, following the international impetus given by Muhammad Yunus, new forms of

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microcredit emerged in Portugal. The most important was developed by the National Right to Credit

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Association (ANDC), a non-profit association established in May 1999. In 2006, another charity, Santa Casa da Misericordia de Lisboa, whose mission is to continue and develop social action and to address

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the most vulnerable citizens of the Lisbon region, also created a microcredit program in association with

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a commercial bank. Later, some banks, acting independently or in partnership with other entities, developed their own microcredit programs.

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2.3. General description of the ANDC microcredit model The ANDC microcredit system is the largest in the country and is based on a public/private partnership,

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the government represented through the Ministry of Labour (IEFP) working in conjunction with retail banking.

The role of the ANDC is to reduce transaction costs to potential beneficiaries when accessing credit and

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to help resolve other market failures that hinder access. The ANDC provides advice and technical assistance to those who wish to apply for microcredit, presents those applicants to the loan-providing banks and acts as a last resort guarantor of those loans in the event of default (Alves, 2010). The ANDC, as well as other microfinance institutions may not grant loans, accept deposits or supply other

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microfinance products directly since this is expressly prohibited by Portuguese law which reserves these functions to banks. The Government, through the IEFP, financially supports the ANDC as an entity providing services to potential beneficiaries based on the assumption that the ANDC promotes the creation of employment. This support has been crucial to its operation. The ANDC does not charge any transaction costs to

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promoters. Three banks (Millennium BCP, CGD, BES) are willing to provide microcredit to the applicants

recommended by the ANDC and their interest rates are lower than the market value for other loans. The

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interest rate used for microcredit contracts promoted by the ANDC is Euribor (6 to 9 months) plus a

spread of 2 to 3% (depending on the bank). The grace period varies among the various lenders but is usually kept under 6 months. In general, collateral is not needed but a guarantor for 20% of the capital is

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required. Repayment periods vary between 36 and 48 months maximum and payments are monthly.

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The underlying policy behind this scheme was to direct public funding to solve market failures that

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prevent potential beneficiaries from accessing credit, instead of setting a pricing policy that substantially subsidizes interest rates.

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The ANDC targets socially and economically excluded people who do not have access to traditional credit. The ANDC delivers only one type of loan, for a minimum of €1,000 and up to a maximum of

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€10,000. However, the first block of the loan cannot exceed €7,000, and the second, which cannot

(Alves, 2010).

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exceed €3,000, can only be obtained once the business has been assessed after one year of activity

The target groups for the ANDC are those who have difficulty accessing bank credit and who have

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drafted a plan for sustainable investment.

LITERATURE REVIEW AND HYPOTHESES DERIVATION

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3.

The literature on factors influencing loan repayment in microcredit is limited mainly to microcredit experience in low-income countries. Studies that have investigated the determinants of the microcredit loan repayment issue in developed countries are relatively rare (Derban et al., 2005). We identified a set of factors related to borrower characteristics and loan characteristics hypothesized in the literature that

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may affect microcredit repayment in a European context. We added some external factors such as the economic and business environment in which the borrower operates that may also influence microcredit repayment.

3.1. Borrower characteristics

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The borrower’s gender. Gender is the most tested explanatory variable in studies on microcredit repayment conducted in developing countries. Results of several studies have shown that gender

influences the repayment and that men tend to default more frequently than women (Baklouti, 2013;

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Honlonkou et al., 2006; Montalieu, 2002; Feroze et al. 2011; Dinh and Kleimeier, 2007; Roslan and Mohd Zaini, 2009; Salazar, 2008; Schreiner, 2004; Papias and Ganesan, 2009; Derban et al., 2005;

Mokhtar et al., 2012; Bennett and Goldberg, 1993). Only studies conducted by Chirwa (1997), Godquin

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(2004) and Gutiérrez-Goira and Goitisoio (2011) concluded that female and male borrowers do not

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show significantly different repayment performances. The results unambiguously point to higher

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repayment levels among women, perhaps because of the disadvantages which women face in developing countries. This makes them more sensitive to social pressure, with less tendency to mobility (migration,

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immigration) and more cohesive in terms of microcredit groups (Montalieu, 2002). Some authors

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assume that women default less frequently on loans possibly because giving women access to credit can lead to their economic empowerment, and strengthen their work ethic and financial discipline (Pitt and Khandker, 1998; Khandler et al., 1995; Bennett and Goldberg, 1993). In addition, repayment rates may

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be expected to be higher for women because they are likely to choose relatively less risky projects (Sharma and Zeller, 1997). Indeed, compared to men, women are more risk averse (Croson and Gneezy,

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2009). Other studies also indicate that women’s repayment rates are higher than men’s because females are submitted to a more thorough screening process (Brana, 2013) and because they are provided with individual monitoring (D’Espallier et al., 2011).

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In one of the few studies conducted in developed countries, Bhatt and Tang (2002) concluded that the gender influence was insignificant. The authors acknowledge two possible explanatory reasons to justify these results in the US. First, some women in the study might have been engaged in low return activities which undermined their ability to generate sufficient revenues and profits to repay their loans. Second, low-income women in the US have access to more public benefits than men of similar socio-economic

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backgrounds and this may reduce the incentives for borrowers to ensure business success and loan repayment. “Knowing that a future source of income by way of public support is available, default may be a rational choice over repayment, especially for those who have never been engaged in incomegenerating activities to begin with. Thus, unlike some developing countries where future credit is key to increasing earning ability or reducing future vulnerability, the women in the US study might not have

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been that dependent on future credit as an income source” (Bhatt and Tang, 2002). This second justification may be more acceptable in most developed countries whose economies are in the expansion phase, since often social programs are cut significantly by governments during times of

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crisis. This can be internalized by borrowers. Because that argument does not apply in the context of this study, we assume that women borrowers may have high loan repayment rates particularly in times of

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recession.

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The borrower’s age. Several studies have concluded that microcredit repayment is influenced by

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borrowers’ age. Due to lack of experience, younger borrowers have higher default risk compared to older borrowers (Arminger et al., 1997; Dunn and Kim, 1999; Mokhtar et al., 2012; Holonkou et al.,

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2006; Dorfleitner et al. 2017). Some authors assumed that older borrowers are usually wiser, more risk

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adverse, more knowledgeable and more responsible than younger borrowers and will, therefore, be less likely to default (Baklouti, 2013). Consequently, age might have positive effect on loan repayment rates,

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regardless of the economic cycle.

The borrower’s educational level. Regarding the educational level, research conducted in third-world

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countries using different methodologies shows that educated borrowers have lower risk of default. Regression analysis of the study by Arene (1992), focusing on loan repayment rates among smallholder maize farmer beneficiaries in Nigeria, shows that the level of formal education is significantly correlated

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with loan repayment rates. Salazar (2008) examined the determinants for the repayment rate in the Dominican Republic. The linear‐probability model results indicate that educational level has an effect on repayment practices. Nikhade et al. (1994) studied crop loan repayment behaviour in cotton growers in Nigeria. Analyzing behaviours and characteristics of borrowers along with the causes of nonrepayment in crop loans, the relational analysis revealed that education positively influenced the

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repayment behaviour of the borrowers. Other studies have confirmed that the number of years of formal education was an important loan repayment determinant (Eze and Ibekwe, 2007; Bhatt and Tang, 2002; Matin, 1997; Khandker et al., 1995). Only the research conducted in Benin by Honlonkou et al. (2006) found that the level of education is not a significant determinant of microcredit repayment. The study by Bhatt and Tang (2002) analyzed the loan repayment determinants for microcredit programs in the US.

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The results indicate that the educational level has a positive impact on the individuals’ likelihood of repayment. According to Bhatt et al. (1999), since the products and services are more complex in

developed countries, education is particularly relevant for microfinance borrowers. Education increases

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borrowers’ productivity, and helps borrowers better understand microfinance programs (Chaudhary et al., 2003). Hence, borrowers with higher levels of education may show higher repayment rates,

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regardless of the economic cycle.

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The borrower’s nationality. For understandable reasons that variable was not used in the studies

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conducted in low-income countries (countries of immigration). However, some arguments recommend its inclusion in a European context. First, because immigrants face financial restrictions in the start-up

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process, including financial exclusion and credit rationing. Immigrants are most likely to be excluded

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from credit because of low income and lack of appropriate documentation, as well as cultural factors (Anderloni and Carluccio, 2007; Atkinson, 2006). Second, because the number of immigrant microentrepreneurs in European countries has increased in recent years (Panaviotopoulos, 2008; Rusionovic,

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2008). Third, given that one of the objectives of microcredit in developed countries is the integration of immigrants, testing their influence on microcredit repayment makes perfect sense. Empirical literature

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on financial exclusion and racial discrimination in credit markets refers to the US, while the evidence for Europe is scarce. However, the study of Bruder et al. (2011) conducted in Germany provides evidence that entrepreneur immigrants are significantly more likely to be denied credit or to be granted

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smaller loans than native entrepreneurs. Hence, nationality might have positive effect on loan repayment rates, especially during a recession period.

The borrower’s working experience. The borrower’s working experience was not used as an explanatory variable in previous studies. However, it seems evident that the borrowers with working

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experience are more likely to have developed relationship networks with suppliers, customers, etc. This variable can serve as a proxy for the individual’s social capital formation (social networks, cooperation and trust created by human interactions within the community) (Baklouti, 2013). It is expected that borrowers with work experience are more successful and this allows them to be more regular in

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repayment of the microcredit loan, regardless of the economic cycle.

The borrower’s training. Godquin (2004) suggests that the provision of non-financial services such as training has a positive impact on repayment performance. Roslan and Mohd Zaini (2009) found that

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borrowers that did not have any training in relation to their business have a higher probability of default. Some microcredit programs seek to assist borrowers and provide some kind of training on how to create

and manage a small business. On the other hand, many potential borrowers are currently unemployed. In

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most European countries employment services provide some kind of training to the unemployed that

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helps them to create small businesses and improve their skills. In some countries attendance at such

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training courses is a condition for maintaining unemployment benefits. Thus, borrowers with business training should be better prepared to manage their business than those who have no previous training.

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Increased preparation can lead to generate better performance, results, and cash flow and, therefore,

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higher repayment rates, regardless of the economic cycle.

The borrower’s business experience. Business experience (be it successful or unsuccessful) can provide

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the borrower with valuable knowledge about running a business and achieving more stable sales and cash flows than those who are creating their first business. The borrower’s business experience was a

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variable used in some previous studies. Njoku (1997) and Arene (1992) analyzed the loan repayment performance of smallholder farmers in Nigeria. The regression analysis results in the Arene study shows that farmers with high repayment rates had more years of farming experience. Years of farming

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experience and farming as major occupation were all highly significant determinants in Njouku’s study as well. Awoke (2004), reports that most defaults arose from poor management practices. Bhatt and Tang (2002) study conducted in the US used the variable “years in business”, which they found not to be statistically significant. Even though the results of the latter did not corroborate the earlier study’s

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results, we believe those who have business experience may have higher repayment rates in both phases of the economic cycle.

3.2. Loan characteristics Loan characteristics (monthly payment and percentage of the total investment financed through

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microcredit). Hulme and Mosley (1996) argue that loan characteristics such as repayment period or loan size play an important role in determining repayment performance. Godquin (2004) claims that larger

loan sizes make it more difficult to repay the loan over a certain period of time. Roslan and Mohd Zaini

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(2009) investigate the determinants of loan repayment of microcredit loans issued by a commercial bank in Malaysia on a non‐group lending basis. The results reveal that the probability of default is negatively influenced by the amount of the loan and positively influenced by the repayment period. Feroze et al.

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(2011) conducted a study concerning a sample based in India to identify the main factors affecting

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repayment performance. Results of a Tobit regression analysis show that the loan amount has a negative

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influence on the repayment performance. Arene (1992) in her study in Nigeria also measured the effect of the amount of loan received on repayment performance. The result of the regression analysis shows

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that the size of the loan is positively linked to loan repayment rates. Hietalahti and Linden (2006) also

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found that large loan sizes increase the incidence of repayment problems. This relationship has also been confirmed in other empirical studies (Feroze et al., 2011; Arene, 1992; Hietalahti and Linden, 2006; Elloumi and Kammoun, 2013). In studies in different contexts, it was also confirmed that the larger the

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size of the loan, the less likely the loan will be repaid on time (Sharma and Zellern, 1997; Eze and Ibekwe, 2007; Guttman, 2007). A divergent result was found by Matin (1997), concluding that loan size

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has no influence on the loan repayment performance. Unlike studies conducted in developing countries, no study of microcredit conducted in developed countries used loan characteristics as an explanatory variable.

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We expect that higher monthly payments and a higher percentage of investment financed through microcredit will create additional difficulties in microcredit repayment, especially during a recession period.

3.3. External factors

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Economic and business environment (economic cycle and business location). Tedeschi (2008) notes that there are two possible reasons for default: strategic default or default due to a negative economic shock. According to Derban et al. (2005), causes of non-repayment should include systematic risk from external factors such as the economic, political and business environment in which the borrower operates. The impact that the economic crisis can have on microcredit is not unanimous. Some experts

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consider it reduced, since microfinance is considered countercyclical. However at the outset of the global financial crisis in 2007, Littlefield and Kneiding (2009) found that credit portfolio qualities were deteriorating and low-income urban clients were having difficulty repaying their loans. Bella (2011)

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examined the impact of that recent financial crisis on the microfinance sector and found that the

economic downturn negatively affected asset quality and profitability of MFIs, while raising the relatively high interest rates that MFIs already charged their low-income customers. These and other

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aspects such as the arrival of new clients of microcredit as a result of the unemployment of more

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qualified professionals can change the determinants of microcredit repayment. Khandker et al. (1995)

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raise the question of whether default is random, influenced by erratic behaviour, or systematically influenced by regional characteristics that determine local production conditions or branch-level

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efficiency. Their study on Grameen overdue loans supports the idea of partial influence of regional

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characteristics.

Although previous studies conducted in developed countries have not tested these kinds of variables, we expect that starting a business during a recession (negative annual GDP) and business location in

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disadvantaged areas where regional GDP is low can have a negative impact on business performance (particularly in times of recession) and create additional difficulties in microcredit repayment.

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Based on the above literature, we suggest the following hypotheses to explain the probability of loan repayment:

Expected sign of the variable on loan repayment

H1: Female microcredit borrowers have a higher chance of loan repayment H2: Older microcredit borrowers have a higher chance of loan repayment H3: Microcredit borrowers with higher levels of education have a higher chance of loan repayment

+

Expected sign of difference between expansion period recession period +

+

=

+

=

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Hypotheses

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+

=

+

=

+

=

+

+

+

+

+

n/a

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+

+

+

DATA AND MODEL DESCRIPTION

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4.

+

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H4: Native microcredit borrowers have a higher chance of loan repayment. H5: Microcredit borrowers with working experience have a higher chance of loan repayment H6: Microcredit borrowers with some training have a higher chance of loan repayment H7: Microcredit borrowers with business experience have a higher chance of loan repayment H8a: Microcredit borrowers with lower monthly payments have a higher chance of loan repayment H8b: Microcredit borrowers with a lower percentage of the total investment financed through microcredit have a higher chance of loan repayment H9a: Microcredit borrowers who started their business in years of expansion have a higher chance of loan repayment H9b: Microcredit borrowers who started their business in advantaged regions have a higher chance of loan repayment

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The definition of microcredit, as adopted in the International Microcredit Conference in Washington, DC,

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from 2–4 February 1997, is: “small loans given to the poor for undertaking self-employment projects that

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would generate income and enable them to provide for themselves and their families.” However, the definition of microcredit varies widely among contexts depending on the social environment, economic

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situation, and policy goals. Microcredit is defined by the European Commission as a loan or lease under €25,000 to support the development of self-employment and micro-enterprises. Data for the analysis is

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derived from Portuguese National Association for the Right to Credit (ANDC) Database. The final dataset contains information on 478 individual microloans granted between 2006 and 2009 in various regions of

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the country. On these loans, 17.4% were not repaid and were considered in default. This is a period in Portugal that includes both a sub-period of economic expansion (2006–2007) and a recession sub-period (2008–2009).

The dependent binary variable (1/0) is represented by the microcredit status (loan repayment/loan

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default). The classification of “loan repayment” was assigned in cases where microcredit loan was fully paid. They were classified as “loan default” if the microcredit loan was not repaid in full. The independent variables used in this study are qualitative or quantitative. They are divided into three sets, namely borrower characteristics, loan characteristics and external factors. The definitions of the individual level model variables are given in Table 1.

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We used the binary logistic regression model to test our hypotheses related to loan repayment given by: Ln

p = 0+ 1 p

 1 X1+  2 X2+…+  k Xk

Where p stands for the borrower’s repayment‐performance probability;

 0 is the intercept and  i is

the regression coefficient. In logistic regression the coefficients derived from the model (e.g.,

 1)

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indicate the change in the expected log odds relative to a one unit change in X1, holding all other

predictors constant. The beta coefficients are estimated through the method of maximum‐likelihood

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method. Version 23.0 of the Statistical Package for the Social Sciences (SPSS) was used to analyze the logistic regression. 5.

RESULTS

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5.1. Descriptive statistics

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Tables 2 and 3 show the characteristics of the sample. A little more than half of microcredit borrowers are female (53.3%) and the average age is relatively young (36 years old; table 3). Surprisingly, 18% of

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borrowers have higher education and the overwhelming majority had previously been employed (96.9%).

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A significant percentage of microcredit borrowers had some kind of business-related training (56.5%), and 73.2% had previous experience starting a business. As expected, immigrants were one of the groups

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that resorted to microcredit (13.4% of the sample). The sample shows that use of microcredit was uniform regardless of the economic cycle phase, i.e. both in the years of economic growth (2006–2007) and

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recession years (2008–2009).

Table 2 also shows that several independent variables that differentiate borrowers more likely to default.

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This is notably the case with education level where we see that 100% of highly educated borrowers have repaid their loans, while this percentage drops for less educated borrowers. Experience is also a significant determinant for the probability of total repayment as well as the pace of economic activity

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during a business start-up, as a stronger percentage of total repayment occurs with businesses created during recession times. However, successful repayment is not correlated to borrower gender, nationality or training. The use of microcredit was found in all regions of the country, from the poorest regions (0.50 of national GDP) to the richest regions (1.63 of national GDP). Table 3 shows that borrowers more likely to repay

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their loans are older than those who default, and as expected, the probability of loan repayment depends on the size of the monthly payments and on the percentage of the total investment financed by microcredit. 5.2. Multivariate analysis We obtained the matrix of correlations between the independent variables used in the model. Although

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some of the correlations are significant they are not high enough (none is higher than 0.5), to conduct to multicollinearity problems (Hair et al., 1998; Sharma, 1996).

The logistic regression analysis proved to be a statistically significant model (difference test/chi square =

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108.089, df = 11, p = 0.000). The result of the Hosmer-Lemeshow test (chi square = 12.709; df = 8; p = 0.122) confirmed that the model fits the data. The classification table indicates that the model correctly classifies in 83.2% of cases, which means that if a respondent matches the model’s characteristics, the

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respondent will fit in the “loan repayment” category 83.2% of the time. According to the Nagelkerke R

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Square, the dependent variable variance explained by the model is 33.7%.

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Analysis of Table 4 enables identification of the significant Wald coefficients in five variables (p<0.01): age, business experience, monthly payment, percentage of investment financed by microcredit, and

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economic cycle which are thus able to predict the microcredit repayment. The first two refer to borrowers’

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characteristics while the third and fourth refer to loan characteristics. The last significant variable is an external factor. The results obtained for borrower characteristics are as expected. However, the external factor’s role is divergent. As suggested by Derban et al. (2005), it was anticipated that enterprises created

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during prosperous periods would have a better chance of repaying their loan, having had the opportunity to capitalize on the profits made. This result will be discussed further below. The borrower’s

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characteristics: gender, education, nationality, working experience and training, and the external factor business location also used in the model, are not significant determinants of the probability of microcredit loan repayment at the usual statistical significance (p<0.05).

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5.3. Analysis of subsamples

When the sample is split according to the economic cycle, we get the subsample N1 with 235 observations for the 2006–2007 period (expansion) and N2 subsample of 243 observations for the 2008–2009 period (recession). To be noted, the level of loan repayment is significantly higher during the recession (Table 5).

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Tables 6 and 7 show the characteristics of the subsamples. The increased repayment in the recession period is registered with females, people without higher education, Portuguese natives and those who had previous employment but no previous business experience. Table 7 shows the repayment success between the two periods, N1 and N2, for continuous variables. We split the subsamples in two equal groups according to their median values. Here again some

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characteristics lead to an enhanced repayment percentage in period N2, especially for those who are under 34 years old and those with monthly payments less than €163.

Prior to performing logistic regression of each of the subsamples, we carried out the previous procedures

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to eliminate the question of multicollinearity among the variables of the model used. Although some of the correlations are significant they are not high enough (none is higher than 0.5 in N1 and 0.4 in N2),

which leads us to conclude that the question of multicollinearity does not arise among the variables of the

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model.

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The logistic regression proved to be a significant model statistically in both subsamples (N1: difference

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test/chi square = 61.232, df = 10, p = 0.000; N2: difference test/chi square = 63.773, df = 10, p = 0.000). The result of the Hosmer-Lemeshow test (N1: chi square = 7.081; df = 8, p = 0.528; N2: chi square =

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5.234, df = 8, p = 0.732) confirmed in both situations that the model fits the data. The classification tables

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indicate that the model correctly classifies in 80.3% of cases in N1 and in 87.2% of cases in N2. According to the Nagelkerke R Square, the dependent variable variance explained by the model is 34.9% and 44.6% respectively in N1 and N2.

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In the original research hypotheses, it was expected that during the recession period, borrower gender, Portuguese nationality as well as the value of the monthly payment and the percentage of the total

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investment financed by microcredit would facilitate successful repayment of the loan contracted with the microcredit firm. These assumptions would be supported if there was a positive relationship and an increase in the explanatory power of each variable for Model 2 with respect to Model 1.

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Table 8 presents the results of the logistic regression used to identify the factors that influence the probability of repayment. It is apparent that gender has a positive and significant effect (with a 10% threshold) on the probability of repayment when passing from the expansion period (N1) to the recession period (N2). Our first hypothesis (H1) is therefore partially confirmed by these results. When looking at age, the results also indicate that the variable has a positive influence during both periods examined,

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although the significance threshold drops for the recession period (from p <0.001 to p <0.012). On the other hand, the results show that the borrower’s level of education, nationality, professional experience and prior training do not affect the probability of repayment. These results suggest that the H3, H4, H5 and H6 hypotheses should be rejected. Conversely, a borrower with business experience is more likely to repay a loan during a period of expansion (p <0.001), but hypothesis H7 is not confirmed by these results

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as none were significant for the recession period. Finally, regarding loan characteristics, a lower financial burden and a more substantial personal investment also play a more significant role during recession

6.

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times, supporting hypothesis H8a, and, unexpectedly, H8b.

DISCUSSION

Empirical analysis of the data revealed that several factors may influence the probability with which a

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characteristics of the borrower and others to loan characteristics.

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borrower will repay a microcredit loan granted. Some factors are related to socio-demographic

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As expected, the results show that older borrowers are more likely to repay their loan, regardless of the economic environment. In other words, the possibility of defaulting would be higher for younger

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borrowers. These results confirm those obtained in several studies (Mokhtar et al., 2012; Holonkou et al.,

financial problems arise.

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2006) indicating that older people are more responsible and have a wider social network to turn to should

As for gender, it was noted that women are more reliable borrowers than men. Literature on

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microfinancing highlights evidence indicating that women show more discipline regarding MFI expectations (Brana, 2013; Roslan and Mohd Zaini, 2009; Montalieu, 2002). A study of

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350 microfinancing organizations also indicated that as the number of female borrowers rises, the MFI’s portfolio risk decreases (D’Espallier et al., 2011). Such results are perhaps due to MFI practices such as personalized assistance and frequent follow-ups for female borrowers. It should also be noted that women

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are often disadvantaged when attempting to access traditional financing. Therefore, since access to microcredit constitutes a means for financial emancipation, women are believed to be increasingly committed to repay their debt in an effort to retain their only source of funding (Armendariz and Morduch, 2005).

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Regarding the borrower’s nationality, results indicate that this variable has no impact on repayment performance, even in recession times. This variable has not yet been studied in research on microcredit although it has been shown that immigrants have more difficulty accessing institutional financing (Bruder et al., 2011). Our results may be interpreted in two ways. Either immigrants accessing microcredit do not present a higher defaulting risk than native Portuguese borrowers, which could serve to reassure financial

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institutions; or else immigrants might be submitted to tighter screening procedures given the higher perceived risk. Further studies are needed to achieve a better understanding and to shed light on these results.

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In addition, the borrower’s level of education, work experience and training have no effect on repayment rates. Yet, there is evidence to suggest that an educated person with working and business management skills is more likely to repay the amount borrowed as opposed to someone lacking such background

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(Roslan and Mohd Zain, 2009; Bhatt and Tang, 2002).

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By contrast, the results show that prior business experience positively influences repayment rates during

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expansion periods, however, contrary to our expectations, no significant result was observed during the recession period. Additionally, other studies indicate that borrowers with prior business experience

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demonstrate better management skills and are better suited to seize growth opportunities when difficulties

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strike (Tundui and Tundui, 2013). However, for our results to convey such a conclusion, this study would have needed to ascertain the borrower’s business experience in years. Lastly, concerning loan characteristics, the results indicate that the borrower’s inclination to repay

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increases as the percentage of the loan funded by microcredit rises. Regarding the value of monthly loan payments, it is noted that the likelihood of repayment increases the lower the monthly payments are. To

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our knowledge, these variables have not yet been studied in a microcredit perspective. Most studies focus on the effect of the value of the loans contracted and the length of the repayment period on the probability of loan repayment (Shu-Teng et al., 2015; Baklouti, 2013; Roslan and Mohd Zaini, 2009; Abafita, 2003).

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Even so, the conclusions previously drawn may apply to the present study. It could be surmised that for projects involving a substantial investment by the MFI, borrowers are more closely monitored, which would explain why they are more likely to repay their loan. Regarding monthly payment values, it could be logically assumed that lower financial charges would alleviate the debt burden. Special attention must nevertheless be given to the length of repayment periods since studies have shown that the longer the

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repayment period is, the less likely borrowers are to repay their loan. This can be explained by a tendency to spend large amounts in the months after securing a loan (Roslan and Mohd Zaini, 2009; Shu-Teng et al., 2015).

7.

CONCLUSIONS

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The results show that the use of microcredit was found both in the more developed and poorest regions of the country and there was no significant variation between the expansion and the recession periods.

However, analysis of the origin of the borrower’s microcredit in relation to the regional share of GDP

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reveals that, in Portugal (and possibly in other EU countries), microcredit appears to be a tool for

disadvantaged groups in the richest regions (e.g. urban areas) more than a solution for people of the poorest regions.

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Another important aspect to consider is the high percentage of immigrants among microcredit borrowers

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(13.4%), while their weight in the Portuguese population was only 4.3% during the period analyzed (INE,

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2012). This result seems to confirm that microcredit may act to facilitate integration of immigrants in European Union countries.

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Surprisingly, unlike what can be observed in developing countries, 18% of microcredit borrowers in this

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study had higher education. This data indicates that the target population for microcredit is wider than what is traditionally considered as its target. The high level of unemployment affecting graduates in developed countries—particularly in Portugal—is certainly a logical explanation for this. The Education

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at a Glance report (OECD, 2010; 2011) states that the average of unemployment among people with higher education reaches 42% in OECD countries, compared to 51% in Portugal. In addition, according to

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the Portuguese Ministry of Education, approximately one third of the graduates during the studied period had backgrounds with low employment opportunities such as social sciences or teaching programs. Faced with unemployment or temporary jobs, graduates sought alternative sources of income and turned to

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emigration or the creation of small businesses with the help of microcredit. Our sample showed a high percentage of microcredit borrowers having prior business training, contrary to what is observed in developing countries. In the EU, social support agencies facilitate access to training on starting a business (as for unemployment allowance beneficiaries). Although it may seem logical to

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build upon microcredit borrower skills, practices in developing countries differ from those in the EU in the sense that little or no training is offered to borrowers. The high level of loan repayment found in studies conducted in developing countries is confirmed in the studied context (a EU country). Contrary to what was expected, the level of loan repayment was higher in crisis years than in the expansion period (2006–2007). Which factors contributed to this increase? One

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explanation could be that the crisis pushed the native population and qualified young people to unemployment, and despite considerable professional experience and academic qualifications, people

were forced to resort to microcredit. In the years of the crisis, among those who had planned on careers as

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employees, some turned, of necessity, to the creation of small businesses for income. Ultimately, this

profile shift towards more qualified and experienced people may have offset the derivative impacts of the crisis (e.g. the decline in the population’s purchasing power). Another explanation could be that, in

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expansion years, an increase in job opportunities might lead a borrower to abandon the business and forgo

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the repayment of the loan. A third explanation for the higher level of loan repayment during the recession

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period could be that banks tightened their screening processes when granting microloans (higher selectivity). Such practices began when funding was scarce due to high interest rates in the sovereign debt

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bond markets.

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Our results contribute to the existing literature in various ways. First, based on literature on microcredit repayment in developing countries, we tested predictors in order to gain a better understanding of the relevant determinants of microcredit repayment in western Europe. The data used in our work covered

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sub-periods of recession and expansion and our empirical evidence showed the impact of the economic cycle on these determinants. Our study demonstrates that the economic crisis was of significant influence

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and reveals differences in microcredit repayment predictors. Our results provide some guidelines for reducing the likelihood of default, which could, according to us, ensure greater sustainability, reliability and reach for the microfinance institution. Our study innovated by including business environment

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variables in the model to verify how these variables may influence microcredit repayment. This study is the first in the literature on microcredit repayment to include this type of variable.

Limitations and future research. Although Portugal is considered to be a country with a medium level of development within the EU-27, the results presented in this paper could be transposed to other European

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countries, if care is given to account for their specific characteristics. Further empirical research in other EU countries would be desirable to consolidate the results and to allow for generalization. Furthermore, the factors identified in our study do not suffice to explain MFI performances. The fact that women are more reliable borrowers does not justify excluding men from loans. Research literature indicates that women are more likely to invest in low-return activities (Bhatt et Tang, 2002) and that they

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tend to borrow smaller amounts, which is less profitable for MFIs in terms of performance (D’Espallier et al., 2011). Further analyses are needed to understand the factors behind repayment behaviours. It may be

useful indeed to come to a clearer understanding of the influence of gender on repayment rates in order to

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assess the genuine role of support programs as well as their potential effects on the behaviour of male

borrowers. Likewise, is it the implementation of support programs or the tightened screening that offer better guarantees during recession times? Moreover, since it has been demonstrated that proximity

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facilitates borrower support and loan monitoring, what is the influence of geographic distance between

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borrower and lender?

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Future research projects should consider extending the analysis of the sub-crisis period beyond 2008 and 2009, since the consequences of the world crisis were felt beyond 2010 in Portugal. As a matter of fact,

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this crisis reached its peak in the following years (2010–2013), and a profound deterioration of all

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economic and social indicators was witnessed, forcing the Portuguese government to negotiate a financial assistance package with the European Commission (EC), European Central Bank (ECB) and International Monetary Fund (IMF). In addition, to disentangle the effects of economic crisis, it would be necessary to

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carry out similar analyses in other countries (notably Greece and Ireland) which also obtained assistance from the EC, ECB and IMF in the wake of the 2008 crisis.

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This study indicated that, in a European context, the level of microcredit use is high among immigrant populations. Beyond these observations, additional studies would be needed to verify if a high rate of microcredit use among immigrants matches their effective integration in EU countries. In this more

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specific focus, the coordination of qualitative and quantitative data would be especially valuable. In the present study it became clear that, in a European context, microcredit is not exclusively used by people traditionally considered as disadvantaged but that it also attracts new population segments. As new segments gain access to microcredit, we are led to ask ourselves which group is more likely to benefit from such measures.

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Aside from these research avenues, we would like to point out certain limitations to our study that could be addressed in the future. For example, the borrowers’ economic status was not taken into consideration in this study although it may have a significant impact on repayment ability. Incidentally, the use of dichotomous variables may have limited the understanding of the phenomenon’s complexity. Future

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research should include continuous variables in order to add depth to the results.

Implications. These results have implications for both microfinance institutions and policymakers. This study provides a number of specific variables that may be useful to MFIs in establishing strategies to

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improve their performance. As for policymakers, the relevance of our results is at least threefold. Firstly, in light of our results, EU policymakers responsible for MFIs should consider new target audiences for microcredit loans (e.g. young people with higher education) and take them into consideration when

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designing future microcredit programs. Secondly, since it has been confirmed that immigrants now have

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access to microcredit, immigration policies in European countries should make better provisions for the

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potential of microcredit in the integration of immigrants. Finally, on a more general level, continuing to rely on microcredit in an EU context can contribute to bringing about change in the economy as it is (i) a

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tool for developing microenterprises and employment for struggling populations, and (ii) a social

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Acknowledgments:

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connection tool, contributing to Europe’s social cohesion.

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Thanks to Horia El Hallam and Martin Morin for their assistance and the Canada Research Chair Programm for their funding. Thanks to ANDC for Database.

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Sharma, S. (1996) Applied multivariate techniques. NY : John Wiley & Sons, Inc. Shu-Teng, L., Zariyawati, M., Suraya-Hanim, M., and Annuar, M. (2015) Determinants of Microfinance Repayment Performance: Evidence from Small Medium Enterprises in Malaysia. International Journal of

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Economics and Finance, 7(11), 110.

Tedeschi, G. A. (2008) Overcoming selection bias in microcredit impact assessments: a case study in

CC E

Peru. Journal of Development Studies, 44(4), 504-518. Tundui, C. S., and Tundui, H. (2013) Microcredit, micro enterprising and repayment myth: the case of micro and small women business entrepreneurs in Tanzania. American Journal of Business and

A

Management, 2(1), 20-30. Woolcock, M.J.V. (1999) Learning from failures in microfinance: what unsuccessful cases tell us about how group-based programs work. American Journal of Economics and Sociology, 58, 17-42. Yaron, J. (1994). What makes rural financial markets successful? World Bank Research Observer, 9(1), 49-70.

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Table 1: Definitions of individual variables

Description

Independent variables Borrower’s characteristics

Working experience Training

Independent variables Loan characteristics Independent variables External factors

Business experience Monthly payment Percentage of microcredit Economic cycle

The percentage of microcredit loan on total investment

= Amount of microcredit loan/Total of investment

Whether or not the borrower business started in a year with GDP > 0.0% Gross Regional Product (GRP) of region where business is located

1 = if business started in year with GDP > 0.0%; 0 if otherwise

A

CC E

PT

ED

Business location

IP T

Nationality

Whether or not a borrower has higher education Whether or not a borrower is natively Portuguese The presence or absence of borrower working experience The presence or absence of some business training The presence or absence of business experience Borrower’s monthly payment

SC R

Education

=1 denoting loan repayment; 0 stands for loan default = 1 if borrower is female; 0 if borrower is male = age of borrower in years at microcredit application date = 1 if borrower has higher education; 0 if otherwise = 1 if borrower is natively Portuguese; 0 if otherwise = 1 if borrower had previous employment; 0 if otherwise = 1 if borrower had training; 0 if otherwise = 1 if borrower had start-up experience; 0 if otherwise = Amount of monthly payment (€)

U

Age

Whether or not a borrower repaid the microcredit loan Whether a borrower was female or male Age of borrower

N

Microcredit status Gender

A

Dependent variable

M

Variables

29

GRP value relative to the country average

Table 2: Descriptive statistics of categorical independent variables

Nationality

Native Portuguese Non-native Portuguese

Working experience

Previous employment No previous employment Business training No business training

Business experience

Business experience No business experience

Economic cycle

Business started in year with GDP > 0.0% Business started in year with GDP lower or equal to 0.0%

100.0 96.9

93.3

6.7

478 463

3.1

15

100.0 56.8 43.2 100.0 73.2 26.8

478 270 205 473 350 128

Chi2=18.5 p=.000 77.0 23.0

100.0 49.2

478 235

50.8

243

100.0

478

Chi2=1.2 p=.266 84.1 15.9 80.5 19.5 Chi2=1.0 p=.308 87.1 12.9 70.3 29.7

88.1

ED

Training

Chi2=0 p=.968 82.3 17.7

Frequency 223 255 478 392 86 478 414 64

IP T

No higher education Higher education

Percentage 46.7 53.3 100.0 82.0 18.0 100.0 86.6 13.4

SC R

Education

U

Male Female

Total

N

Gender

Loan Loan default repayment Percentage Percentage 79.8 20.2 851 14.9 Chi2=2.3 p=.129 78.8 21.2 100.0 0.0 Chi2=22.0 p=.000 82.6 17.4 82.8 17.2

A

Value

M

Variable

11.9

A

CC E

PT

Chi2=10.2 p=.001

30

Table 3: Descriptive statistics of continuous independent variables Loan repayment Std dev. 10.7

Loan default Std dev. 8.5

Anova-p

8.9

.003

167.4

164.9

179.1

46.9

45.6

6.4

.012

90.5

91.9

84.2

18.1

25.4

10.6

.001

1.11

1.11

1.13

.367

.370

0.3

.585

Table 4: Determinants of microcredit repayment performance

ED

A

CC E

PT

Exp(B)

p-value

2.085 1.666 8.042 .197 .140 .250 1.150 .617 .066 17.314 1.068 .000 19.783 .000 +++ .996 -.009 .000 .991 .983 -1.674 2.131 .188 .144 -.090 .103 .914 .749 1.427 19.670 4.168 .000 -.017 18.790 .983 .000 1.672 7.274 5.321 .007 -1.142 11.799 .319 .001 -.244 .406 .784 .524 large since none of the cases with a higher education had

M

Intercept Gender (female) Age (years) Education (higher) Nationality (native) Working experience (yes) Training (yes) Business experience (yes) Monthly payment (Euros) Percentage of microcredit Economic cycle (GDP > 0) Business location +++ The positive effect of Education is very defaulted.

Wald ChiSquare

N

Coefficient (B)

A

Variable

SC R

Age of borrower Monthly payment Percentage of microcredit Business location

IP T

Loan default Means 32.8

AnovaF

Means 35.9

Loan repayment Means 36.5

U

Total Value

31

Table 5: Descriptive statistics of dependent variable (Loan repayment performance) Percentage Expansion (N1) 77.0 23.0 100.0

Recession (N2) 214 29 243

Working experience

Previous employment No previous employment Business training No business training

Training

PT

Business experience No business experience

U

N1 N2 Percentage 44.7 48.6 55.3 51.4 83.8 80.2 16.2 19.8

N

88.6 84.8

N1 N2 Frequency 105 118 130 125 197 195 38 48

Chi2

p

2.6 9.2 9.2 n.a.

.108 .002 .002 n.a.

86.8 13.2

86.4 13.6

204 31

210 33

10.5 0.2

.001 .656

76.4

87.8

95.7

97.9

225

238

10.3

.001

90.0

100

4.3

2.1

10

5

0.5

.464

79.1 74.0

88.7 87.1

55.4 44.6

58.3 41.7

129 104

141 101

5.6 4.6

.018 .032

86.0 61.2

88.0 88.4

63.8 36.2

82.3 17.7

150 85

200 43

0.3 10.1

.580 .001

A

CC E

Business experience

76.5 80.6

Total

A

Nationality

Native Portuguese Non-native Portuguese

M

Education

Male Female No higher education Higher education

Loan Repayment N1 N2 Percentage 75.2 83.9 78.5 92.0 72.6 85.1 100.0 100.0

ED

Gender

Value

SC R

Table 6: Descriptive statistics of categorical independent variables

Variable

Recession (N2) 88.1 11.9 100.0

IP T

Loan repayment Loan default Total Chi2=10,2 p=.001

Frequency Expansion (N1) 181 54 235

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Table 7: Descriptive statistics of continuous independent variables

Age

< 34 >= 34 < €163 >= €163 < 100% = 100% <= national >national

Total N1 N2 Percentage 47.3 52.7 51.0 49.0 71.0 29.0 27.5 72.5 53.5 46.5 47.6 52.4 53.6 46.4 45.3 54.7

Chi2

p

109 1.4 17.9 2.6 3.1 6.3 4.6 5.5

.001 .237 .000 .110 .079 .012 .031 .019

N1 N2 Frequency 112 125 123 118 169 69 66 174 68 59 167 184 119 103 116 140

U

Value of monthly payment Percentage of microcredit Region GDP (%)

Loan Repayment N1 N2 Percentage 69.6 87.2 83.7 89.0 78.1 100 74.2 83.3 67.6 81.4 80.8 90.2 77.3 88.3 76.7 87.9

IP T

Value

SC R

Variable

Expansion (N1 :2006-2007)

Coefficient (B)

.806 .954 1.069 +++ .599 .197

.907 .897 .001 .997 .366 .163

.106 20.018

.886 5.954

-.007

2.331

1.791 -.401

A

CC E

-.121 1.784

Recession (N2 :2008-2009) Exp(B)

p-value

25.2 .829 .071 19.513 .617 -16.654

Wald ChiSquare 0.000 2.796 6.313 .000 .837 .000

+++ 2.291 1.074 +++ 1.853 .000

.999 .094 .012 .997 .360 .999

.744 .000

-.152 .309

.102 .192

.859 1.362

.750 .661

.993

.127

-.058

16.325

.944

.000

5.999

5.995

.014

2.485

2.816

12.004

.093

.618

.670

.432

-.447

.453

.640

.501

M

pvalue

-.216 -.047 .067 20.020 -.513 -1.625

Exp(B)

PT

Intercept Gender Age Education Nationality Working experience Training Business experience Monthly payment Percentage of microcredit Business location +++ The positive defaulted.

Wald ChiSquare .014 .017 10.621 .000 .817 1.947

ED

Coefficient (B)

A

Variable

N

Table 8: Determinants of microcredit repayment performance according to the economic period

effect of Education is very large since none of the cases with a higher education had

33