Análisis Crítico del Árbol de Objetivos

The variables of microfinance performance and human development related variables in cross-correlation were examined in tens of papers. Many studies analyze impact of the performance of MFIs on macroeconomic growth measures such as GDP, GNI, capital markets volatility, unemployment or inflation. Macroeconomic influence was considered by a number of scholars such as McGuire and Conroy in 1998, Jansson in 2001, Marconi and Moseley in 2005, Gonzalez in 2007, Wooley in 2008, Ahlin, Lin and Maio in 2010, Tchrouassi in 2011, Kai in 2011 and di Bella in 2011. Vanroose inspects outreach and performance of MFIs in terms of the number of borrowers as well as of loan portfolio, operational sustainability, Return On Assets and ROE influenced by GDP growth.

Despite numerous studies performed, the creativity of attempts expressing the true nature of aggregate microfinance is limited. Most studies on the one hand use simple microfinance performance indicators such as microfinance outreach expressed by simple number of clients determining the microfinance influence, and seek for their influence on economically expressed values such as income as dependent variables. An extensive literature on microcredit shows that microcredit has a significant impact on income increase, with focuse on quantitative money-centric measurement (Baker and Schuler, 2004). Yet money- centric measures do not capture many other dimensions of quality of life, since quality of life is a multidimensional notion (Baker and Schuler, 2004). As argued by mesoeconomists, important structures exist beyond the large economic indicators. Human development

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concept transcends income growth as the human development framework views income as the means of development with many other aspects of life, including political, economic and cultural ones, leading to improvement of quality of human life.

Outcome of these studies that prove correlation between mentioned macroeconomic indicators and intensity, outreach or performance of MFIs also lead to general statements which confirm that MFIs thrive or languish in their environment, impinged by the selected factors. Yet despite undoubted contribution of these studies, should not the question be more oriented towards the social and economic development of the clientele, seen from the

macroeconomic perspective? At the end, the success of MFIs, often private companies with contestable benefits on the clientele, can wrongly insinuate the progression of greater

development good. Instead, the research should at least complementarily target the state of the end clientele as the real and only purpose of microfinance.

It is time to follow the evolution of welfare indices in order to understand that protuberance of measures such as the GDP growth when used to measure microfinance, a domain of small scale entrepreneurs who are excluded from the formal financial system, should be considered only in combination of other indices capturing the stark reality of microfinance clientele, on local as well as on global level. Therefore, indices including social information should be involved in the Sisyphus evaluation quest for comprehension of effects of microfinance within the global development sector, as they encapsulate multiple factors and represent agregate information.

As to the present time, an analysis targeting a relationship between HDI and other major development indices and microfinance has not been exercised. Planet Rating in a recent isolated initiative accomplished by Javoy and Rozas in 2013 established a new Microfinance Index of Market Outreach and Saturation (MIMOSA), which uses HDI as one of three

components within the index methodology, finding a correlation between credit provision and HDI from the point of view of financial access, albeit including not only microfinance, but also mainstream financial services within the regressions. Similarly, analysis of influence of microfinance on MDGPI nor GDI had not been executed up to now. Besides, the fatal flaw of these popular findings is a use of measures which by default do not measure output of a significant part of microfinance clientele operating largely in shadow economics and therefore not providing data to statistical departments of states.

The proposal of this work is therefore to target different types of independent variables than the common macroeconomic ones, searching for correlation with human development indices, with the advantage that the mentioned indices entail already processed data on

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holistic human development and are accord to most recent evolution of development measurement strategies.

General human development, recorded by methodology of HDI including non-

economic values of quality of human life is the target and justification of social microfinance development vision. Besides, microfinance is recognized at present as a tool for achieving several key MDGs, as it has a positive impact on education, gender equality, reduction of infant and maternal mortality and reproductive health services, while perhaps the most important of the microfinance impacts are effects on gender empowerment as women comprise the majority of borrowers (Coleman, 2005).

In consequence, metainformation captured by modern development indices51, which are going through steady evolution could thus become an important source of complementary information seen within the panorama of previously executed cases of analysis. The chosen indices deemed representative for microfinance focusing on gender problematic as MFIs target in women for their products, as well as HDI, providing a different insight into the microfinance impact mechanics.

5.2. Hypothesis

In reaction to the above mentioned reasoning, the paper studies the impact of microfinance illustrated on newly introduced Microfinance Penetration Ratio (MPI) described in 5.3.2.1. on social and economic development of societies, expressed by two global aggregate indices. The assumption that microfinance is positively contributing to the development of world’s societies from different points of view leads to a formulation of the following hypothesis:

H1: General human development can be positively influenced by microfinance as per

correlation between microfinance and Human Development Index.

H2: Millenium Development Goals Index perceives correlation with microfinance

penetration index.

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An index number is a data figure reflecting quantity compared with a standard or base value. Index numbers generally are time seriessummarising movements in a group of related variables.

53 5.3. Variables

The statistical model is using cross-sectional observations of 55 countries recorded in 2010, as stated in the descriptive statistical table below.

Tab.1 Descriptive statistics on variables used for testing of H1 and H2

5.3.1. Dependent variables

5.3.1.1. TheHuman Development Index (HDI)

The Human Development Index (HDI) is a composite measure, a geometric mean of

normalized indices of life longevity, education, and income that can be used to rank countries into tiered human development evaluation. It combines life expectancy at birth, mean years of schooling52 and expected years of schooling53, as well as GNI per capita at purchasing power per capita in US$54. Created by the Mahbub ul Haq and Sen in 1990, HDI is currently used by the UNDP as a breakthrough single statistic serving introduced in 2010 Human Development Report for both not only economic but also social development. Its introduction was considered a milestone for new era of measurement of human existence related

52 _{Years that a 25-year-old person or older has spent in schools} 53

Years that a 5-year-old child will spend with his education in his whole life 54

HDI3LEI*EI*II

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indicators, and became an important argument for mainstreaming of welfare economics, reducing the importance of income to general understanding of quality of human life. The HDI sets a maximum and a minimum for every dimension55 and shows standing of every country stands in relation to these set dimensions, expressed in values between 0 and 1. HDI values in the global Human Development Report are calculated using the most recent international data from international data sources. The 2012 HDI covered 187 countries.

5.3.1.2. MDG Progress Index (MDGPI)

The index, developed by Ben Leo, compares observed progress on 8 MDG56 indicators by every observed country based on linear, annualized rates of improvement for each

respective MDG indicator, having allocated scores on each target, leading to a total score57. The methodology has been customized to address annual data observation gaps for most indicators capturing absolute and relative progress on MDG indicators and accounting for the alleged not very realistic nature of some MDGs. The methodology compares country’s performance against required achievement trajectories for each of the examined MDG58 indicators, based on linear, annualized rates of betterment for each respective MDG indicator. The Index is calculated in the way of aggregating performances of country performance across the eight core MDG targets targeting poverty, hunger, education, gender, child

mortality, maternal mortality, HIV/AIDS prevalence rates, and safe drinking water (Leo et al., 2010).

55 _{Defined as “goalposts” by UNDP}

56 _{The Millenium Development Goals (MDGs) are 8 goals (eradication of extreme poverty, achievement of} universal primary education, promotion of gender equality, reduction of child mortality, improvement of maternal health, disease combat, environmental sustainability, development of global partnership) comprising of 21 targets and 60 indicators intended to halve extreme poverty by 2015. The strategy was agreed on to by the world's countries and the leading multilateral institutions in September 2000 in UN Millenium Summit.

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The total of 8.0 means a country is on track with all the MDG targets.

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The MDG Progress Index focuses on solely 8 out of 60 progress indicators reported by the UN, due to their accuracy in adressing the MDGs, data availability and usage in the development mainstream. The Index excludes MDG nr. 8 (Global Partnership for Development) since the progress indicators relate to donor countries, excluding 16 of the progress indicators. Also, seven environmental indicators and five malaria indicators are excluded due to lack of data availability.

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5.3.2. Independent variables

In our statistical model we explain the dependent variable through a set of independent socio-economic variables and one microfinance variable, provided by World Development Indicators database, which are expected to have an influence on HDI and MDGPI, focusing primarily on the microfinance regressor. There are three important dimensions captured in HDI, which represent gender related issues such as reproductive health (lifetime risk of

maternal death in %)59, empowerment represented by female secondary school enrollment (%, gross), and labor market participation of women (% of total labor force).

We keep the same data sample as variables for HDI as well as for MDGPI, as microfinance serves predominantly women and it is to be expected that changes in these variables are therefore significantly influencing MPI performance. As we are trying to find relationship to microfinance, with majority of women involved, we involve in our model modeled ratio of maternal mortality ratio per 100,000 inhabitants, secondary female school enrollment, and female labor force participation rate, in combination with Microfinance Penetration Ratio, described in 5.2.1.3. .

5.3.2.1. Microfinance Penetration Indicator

Correction of a misleading use of independent variables is one of the targets of this work, but also elements of dependent variables are at stake. We focus in particular on

Microfinance Intensity or Microfinance Outreach, indicators with little expressive value, used by many economists in the past. The conceptualization of outreach of microfinance is not well developed (Meyer et al., 2000). Navajas indicated in 2000 that there should be several

aspects of measuring outreach such as depth, cost to users and scope too. Despite Yarons suggestion in 1992 to measure outreach by more variables than loan portfolio value, average loan size, amount of savings such as by a variety of financial products offered, number of branches, percentage of target population served, and number of women served, or as

Christen, Rhyne, and Vogel proposed in 1995 to categorize measures of outreach by defining it along quality of service, scale and depth of outreach to the poor, it is difficult to find

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Life time risk of maternal death: the probability that a 15-year-old female will die from a maternal cause assuming that present levels of fertility / mortality do not change in the future (World Bank, 2013).

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

employment of these in most correlation studies of the past decade. Most investigation use simple outreach model such as the argued microfinance intensity or outreach of microfinance assessing the impact of microfinance by employing the number of MFIs and the number of borrowers as a measure of its aggregate performance and impact on the totality of a market.

We study the impact with the help of a newly introduced indicator, which is the Microfinance Penetration Indicator (MPI). The ration is calculated in the following way:

Fig. 6: MPI calculation

This ratio can be seen as a further evolutionary stage graduation of frequently Microfinance Intensity indicator. Microfinance Intensity indicator was used in many studies, as mentioned in the literature review. Obvious flaw of these indicators is the fact that they use microfinance outreach employing the number of clients with active loans and loan portfolio as main pillars not taking into account the volume of savings nor number of savers or other microfinancial products. The Microfinance Intensity or Microfinance Outreach are thus misleading terms and should therefore better be called Microcredit Intensity or Microcredit Outreach indicators. This fact is further strengthened by the source of data, Mixmarket (MIX), a database based on voluntary contributions of MFIs. As per author’s experience with microfinance market in Mexico in a period of 2002-2012, it is especially leverage based, technologically mature MFIs that register in the database in the quest for external international funding, rather than savings based informal players that are not in the need of public presentation. Considering that general estimates speak of volume of savings as a multiple of volume of loans, not taking savings into explicit consideration of microfinance outreach is a serious econometric fault. These

allusions in no way diminish the importance of state of the art of used indicators during this evolutionary stage of research, crucial role of MIX for global microfinance research being the only present major global database of microfinance data pioneering an ambitious

standardizing approach in the midst of plethora of different and unorthodox reporting techniques of infant financial intermediaries, nor generalize the varied nature of MIX

57 contributors.

In response to these shortcomings, the proposed MPI involves within the intensity – outreach concept also number of savers and savings volumes. We expect that developing countries with higher microfinance penetration experience more people participating in microfinance programs, having as a consequence a greater share of the poor having access to financial services WITH higher social and economic development expressed by HDI and confirmation of positive MDGs track evolution, expressed by MDGPI.

Existing correlation analyzed through OLS simple regression analysis confirms the beneficial nature of the sector and thus confirms a potential for a creation of an international framework protecting microfinance as an area of opportunity.

5.4. Estimation method

Our descriptive analysis model consists of the following variables:

yi = α + β1 Xi + β2 Hi + β3 Ei + β4 Li + ᶓi

yi be the dependent variable HDI / MDGPI

α be the intercept of the regression line and the Y axis β be a coefficient of MPI

Xi be an MPI in microfinance market i

Hi be a maternal health (HEA) related variable describing market i Ei be an female education (EDU) related variable describing market i Li be a labour participation related (LAB) variable describing market i ᶓit e a residual value

58 RESEARCH OUTLINE

Empirical method

Goal 1 Goal 2

Relationship between microfinance development and MDGPI

Relationship between microfinance development and HDI

Regression type Least squares, cross-sectional data, multiple regression model.

Regression function HDI = f(HEA, LAB, EDU, MPI) MGDPI = f(HEA, LAB, EDU, MPI)

Dependent Variable HDI MGDPI

Independent Variable MPI, HEA, LAB, EDU,

Expected Results MPI influences both development indices HDI and MGDPI, as dependent variables

Table 2. Summary of Objectives and Methodology of H1 – H2

5.5. Robustness of results.

The data was tested for heteroskedasticity with Breusch-Pagan-Godfrey test and with White test with satisfactory results, which permits us to accept the present statistic model as the data are not heteroscedastic. We found negative multicollinearity of -0.85 between maternal heath variable and female education, explaining that the two variables are strongly correlated, however as MPI is of our only main interest, we do not treat this issue.

5.6. Interpretation of results

Our statistic model show strong coefficients of determination R2 in case of H1 test, explaining

92% of the variability observed of HDI. In case of H2 test we can demonstrate 31 % of the

variability observed in MDGPI. We can accept both hypothesis H1 and H2, on the influence of

microfinance on HDI and MGDPI as p-values of MPI variable are significant on the level of confidence of 95%.

59 Table 3. Results of H1 – H2