Responsible Finance Forum

Factors Influencing Poverty Outreach Among Microfinance Institutions: Guatemala

guatemala-case-study

Since 1996, the Guatemala’s economy has stabilized and liberalized, experiencing an average growth rate of 3.5% and a doubling of exports between 2002 and 2010.

 

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Background

Guatemala is a multi-ethnic country in which 40% of the population identify as Indigenous. The median age is 17, and it boasts the highest GDP growth rate (3.3%) in Central America (The World Factbook, 2014). A 36-year civil war that ended in 1996 has left a legacy that persists through distinct regional inequalities in poverty incidences. The Commission of Historical Clarification found that 83% of fully identified victims of human rights violations were Mayan (Chamarbagwala and Morán, 2011). The Western highlands bore the brunt of the conflict and includes the two departments with the highest poverty incidences, as depicted in Figures 1 and 2, and coincidentally the highest proportion of indigenous inhabitants. The microeconomic impact of war can have persistent and profound repercussions on productive capacities of future generations. Damages include injury and death, loss of property, displacement from homes, and reductions in human capital through inability to attend school. A recent paper found that the final period of civil war (1985–1996) resulted in 23% less schooling among men and 30% less schooling among women (Chamarbagwala and Morán, 2011).

Since 1996, the economy has stabilized and liberalized, experiencing an average growth rate of 3.5% and a doubling of exports between 2002 and 2010 (MINECO, 2014). Guatemala has continued to improve. The 2013 figures are evidence of this; GDP grew at a rate of 3.7%, inflation in November 2014 was only 3.5%, and interest rates were approximately 4% (BANGUAT, 2014). Despite these improvements in economic performance, Guatemala faces substantial challenges in terms of low human capital and high inequality. The size of the government and its investment capacity is constrained by a national budget, which equals a mere 15.5% of GDP, the lowest in Central America and nearly ten percentage points below the average. Among other things, these metrics have led to Fitch Rating downgrading the national debt from BB+ to BB in 2014. Three out of four people live in poverty in 44% of rural municipalities (World Bank & INE, 2014). In addition, Guatemala has suffered natural disasters that have disproportionately affected the poor. According to the Economic Commission for Latin America and the Caribbean’s impact evaluation, Hurricane Stan in 2005 caused damage and loss amounting to 3.4% of 2004 GDP.

In 2014, the Superintendencia de Bancos (SIB) published data on 12,802 points-of-service (ATM, branch or banking agent), nearly 1,400 more than the year before (The Center for the Study of Financial Innovation 2014). This indicates increased financial inclusion in the country. However, the IMF has expressed concern over growing levels of indebtedness.

Conclusions

  • MFI G reaches a poverty concentration of 40% across its area of operation, equal to the national poverty incidence, and 20% of its clients live below the $2.50-per-day international purchasing power parity line.
  • Although at the time of data collection, the mission statement of Guatemala did not specify a target poverty level, their choice of which areas to enter was based upon poverty maps and they believe that their product design of low loan sizes and compulsory educational training sessions leads to self-selection of the poorest into the services. For example, their second largest operation and highest concentration (68%) are in Quiche, the area with the highest poverty incidence (72%).
  • The area of largest operations is in Escuintla. This is emblematic of the need for portfolio diversification of MFIs who seek to reach the poorest segments of the population. In their interview the MFI discussed how the choice to enter this area with the lowest poverty incidences (32%) was made to mitigate the risk and cost of operating in the most vulnerable areas.
  • Once again, the characteristics of this MFI are of an institution seeking to balance financial sustainability with outreach to the poorest.
  • The regression output reveals a number of characteristics of client profiles. The loan amount increases with age, and each additional loan to a given individual is associated with a GTQ 286 increase in the size of the loan. Married individuals receive larger loans than all other situations. The largest loan sizes are in some of the poorest departments such as Sololá and Totonicapán, whereas some of the least poor departments receive relatively smaller loans on average such as Escuintla, Guatemala and Sacatepéquez. Wealthier people are more likely to receive larger loans; however, wealthier clients are not more likely to receive multiple loans. This is the only instance of that in our report.

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