How Different Has Microfinance Been from Traditional Banking - Example

Methodology

This research was conducted to identify the differences existing between microfinance as well as traditional banking system, and what roles financial crises play. To do so, the researcher sought to analyze the developments within the microfinance sector prior as well as after the Lehman Brothersin 2008 (United States) by comparing them with developments within the traditional banking sector for rising market economies as well as developing countries. The study was designed to come up with evidence from earlier crises within the developing market as well as the ways in which microfinance emerged to become profitable within the international credit cycle. The study was divided into three main parts. The first two parts focused on microfinance as well as international financial crises. The studies were different in terms of methodology as well as scope. It took into consideration the main outcomes of the traditional banking segment (for instance, Mendoza and Terrones 2008), who analyzed if, as well as how far such international financial crisis affected microfinance.

The first part

Part one “From Bum to Bust: How microfinance has been different from traditional banking,” comprised of a deep pre-Lehman review on microfinance assess aspects which lead microfinance resilience in the past, and analyzed if as well as how they could have changed later in the 2000s. This was succeeded by some descriptive evaluation of microfinance within the international financial crisis, comprising of some comparison with the old fashioned banking system.

Different data was collected in different parts of the study. In some parts off the study, qualitative data was used. According to Bazeley (2013), qualitative data could at times reveal things that quantitative data cannot tell. For instance, it can show why some approaches are working while others are just not working. It may as well show if part of what is being done conflicts with what is perceived as important by participants. Again, qualitative data is important as it may reveal patterns in the physical, behavioral, as well as the social environment s or even other aspects. It may even at times identify variables, which a researcher was not aware of (Bazeley 2013).

The last analysis, however, comprised of descriptive statistics as well as simple correlations. It was based on quantitative data collected from the international financial statistics of IMF as well as the mix market. The data sample for microfinance included information regarding 813 microfinance institutions in 47EMEs as well as DCs from year 2003 to 2009 engaged at country level.

The second part

The second part, referred to as “the microfinance vulnerability to financial chaos – evidence from the international financial crises,” did address similar questions; however, it used econometric evidence on the international financial crisis effect of international financial crisis on the patterns of credit growth for micro financial institutions. During the beginning of 2000s through running panel as well as cross-section regressions.

The part used econometrics approaches by putting in place panel as well as cross-section regressions to help put the outcomes ion some stronger footing. Econometrics involves mathematical, statically approaches, as well as computer science to economic data. Econometrics is an economics branch, which focuses on giving empirical data to economic relations (Bruce 2016, 1). The analysis focusing on the sector of microfinance was grounded on credit development panel as well as cross-section data for 722 (437) microfinance institutions operatingwithin74 (49) developing market economies (EMEs) as well as developing countries (DCs). The researchers utilized data on the institutional level (that is market mix) as well as country level (for instance, world development indicators) for the 2000 to2009 period. The researchers also acquired information on the institutional form for each among the microfinance institutions.

Tests were carried out to see if financial crisis had effects on MFI’s real credit growth management for macroeconomic, institutional, as well as factors influencing the performance of MFI in non-crisis periods. Other important tests carried out were: to see if global finance crisis impact differ across the legal status of microfinance institutions as well as the area within which they operate; possible channels of transmission through which such financial crisis affected the growth of the MFI credit. A cross-sectional regression test was finally run for the bum bust cycle relationship within microfinance (to see if the decline depth within MFI credit growth across the years of crisis positively relates to the development of microfinance within the pre-crisis time), which is a major feature of traditional banking within the rising market crisis for the past.

The Third part

The third part of the study “Microfinance Clients; growth patterns- Sub-Saharan Africa evidence” was encouraged by the literature of the impact crises. It used econometric evidence for the growth patterns of the microfinance clients with regard to income, size of loans, as well as employment. This was achieved through the utilization of some linear puled regression approach founded on huge borrowers’ samples within sub-Saharan Africa. This portion linked the microfinance clients’ development discussions to evidence as well as the methodology for the literature of empirical business growth (Coad 2009, Nichter and Goldmark 2009). In addition, this third part of the study looked at the contribution towards the empirical literature towards the growth of small businesses, in spite of the fact that the part was triggered by the microfinance impact discussion.

Analysis in this part was founded on loan level data of nearly 40,000 microfinance clients who had received at least 2 loans in between a 10-years duration Oct. 2000 to Oct. 2011. These were from 3 MFIs within the Sub-Saharan Arica. The experiment here was hugely founded on anecdotal evidence. Anecdotal evidence is usually evidence from narratives or what people say about something (Manjunath 2016). Though not very reliable, this kind of evidence is important as people use it every day in decision making. In order to eliminate the weaknesses of the approach, the researcher used a large sample over a long duration to understand the growth of microfinance over a prolonged period unlike in RTCs where only a small period microfinance period is studied (Betterevaluation.org 2016). The sample collected included business characteristics’ information for the personal characteristics of clients as well as loan characteristics. The key dependent variable for all three countries was achieved by computing sales growth rate between 2 consecutive loans for a single client. In addition, sales variable was divided by the GDP per capita for a respective country with the specific period. As a result, any growth rate huge than zero was taken to suggest that the sales growth of the client is huge compared to the country’s GDP per capita. This allowed for comparison of sales growth rates between these 3 countries. The sales variables were then divided by the duration in terms of months in between 2 successive loans. The client growth was then measured by loan size growth rate as well as the number of staff. Some linear pooled regression approach assessment was then run to test if there is some connection between client growth and the initial business size, lending relationship length, as well as other business-borrower & loan features. Information was presented in way of graphs. Graphs view can present any data type. Another thing is that graphs present data in an easily readable manner (Rasel 2013).