# Microfinance Map of India – another go…

I gave it another go, trying to get a map that looks a bit nicer. This time, I tried to compute something like a density or intensity in a certain area. On the previous map, this was not visible very well. I used ggplot2 and a bit of R code, together with RGoogleMaps to produce the following picture:

This map displays the intensity of microfinance institution headquarter distribution across India. The data comes from the MIX Market.

The fact that many MFIs are clustered around in the south is highlighted quite strongly. What this graph does not take into account however, is their variable size. This is problematic and I agree that this needs further refinement, i.e. that the intensity takes into account how big an MFI is. However, I would conjecture that this merely makes the contrasts in such a map just stronger.

# Microfinance in India: Getting a sense of the geographic distribution

I am working on a review paper on microfinance in India and use data from the MIX market. Today, I was amazed by how quick I conjured a map of India with the headquarters of the microfinance institutions that report data to the MIX market depicted on that map. Ideally, I would have more geolocation data – but this is hard to come by. But what we can clearly see is the clustering of institutions in big cities and in the south, which was hit hardest by the recent crisis.

Microfinance Institutions across India

I dont think anybody has produced such a map before. In fact, I can do this for all institutions reporting data around the world, which may be interesting to see. Also, I already tried to make the size of the dot proportional to e.g. measures of real yield or color-coding the nearest neighborhood (say the neigbhouring districts) by the average loan sizes reported. Lots of things to do. Maybe thats something for the guys at MIX Market or for David Roodman who, I think has finished his open book.

The key difficulty was actually not in plotting the map (though it took some time), but in obtaining geo-data on where the headquarters of the microfinance institutions are located. I managed to obtain this data – though its not perfect – by making calls to the Google MAP API via a PHP script., basically using the following two functions:

# Animation from Kiva data

I just came accross this amazing animation, which depicts lending flows from Kiva lenders to Kiva borrowers in the field. I have been working on a few pieces of research with my colleague Jon de Quidt using Kiva data. However, that work has stalled a bit as prioritization moved it towards the end of the queue. However, this animization is indeed inspiring and it is somewhat awaking the urge in me, not to wait too long to continue work on Kiva.

On another note, I am going to try to do some more work with R on mapping data, trying out several packages:

I found working with ArcGIS way too clumsy…

# Back in London – really!

I just returned to London after 3 1/2 weeks in London. And what a return it was. I finally passed all the exams needed and I can now focus fully on my Phd. That was about the good news!

Of course, London greeted me with a Sunny morning but it would end up hitting me hard with rain later that day. And that was after bad news from our advisors and supervisors that our work ok Kiva is most likely in vain as we would never really get it published in a really good journal.

So we will see, the other projects are still up and running, but I need to get an idea for a joan Market paper sometime soon. I am down for 2013/2014 as the prospective year. Just two more years to go, exciting but a bit scary as well.

# Joint Liability Group Lending still very much present in Microfinance

I have been looking at data from the MIX market recently. They provide a lot of financial data from microfinance institutions around the world. This data is made accessible to donors, but especially to other financiers of microfinance. It has helped us in the understanding of the state of the microfinance world, as it has become a central gathering point for data.

In recent years, more and more institutions started to disclose data on lending methodology. There are three categories. First “Solidarity lending”. It is not 100% clear, but from the glossary of terms on the MIX market website, I assume it means classical joint liability lending groups (JL). The other categories are “Individual lending” (IL) and “Village Banking”. A lot of early theoretical work has focused on the role that joint liability group lending had in context of classical problems  of adverse selection, moral hazard and ex-post moral hazard (enforcement problems).

Though in 2002 Grameen officially abandoned explicit joint liability and other institutions, such as BancoSol seemed to follow. So one interesting question is, whether institutions are shifting away from Joint Liability groups towards more Individual lending, or whether our perceptions are biased because two popular institutions have abolished explicit joint liability.

It turns out that in the MIX data for the year 2009, we observe that joint liability lending is still very much present. The following table tells us however, that a lot of institutions seem to offer both individual liability as well as joint liability loan products.

Number of Institutions by their Lending Method

The institutions recorded as “No JL” and “No IL” are falling into the category of “Villagebanks”.

I think it is interesting to try to understand the patterns, why so many institutions use both joint liability groups and individual liability lending methods at the same time. Are there clear patterns as to when which method is predominantly used? Are for-profits more likely to use individual lending? What are the effects of competition?

Some very simple questions, which need yet to be answered.

# Margin Caps for the Microfinance Industry?

A lot of attention has been around the issue of interest rates charged by microfinance institutions. In his comment “Sacrificing Microcredit for Megaprofits” in the NYT Muhammad Yunus claims that with institutions such as SKS of India or Compartamos of Mexico going public, “microcredit would gave rise to its own breed of loan sharks.”

But what actually makes a lender a loan shark? The focus has been primarily on the interest rates charged. Yunus suggests a rule of thumb:

The maximum interest rate should not exceed the cost of the fund — meaning the cost that is incurred by the bank to procure the money to lend — plus 15 percent of the fund. That 15 percent goes to cover operational costs and contribute to profit. In the case of Grameen Bank, the cost of fund is 10 percent. So, the maximum interest rate could be 25 percent. However, we charge 20 percent to the borrowers. The ideal “spread” between the cost of the fund and the lending rate should be close to 10 percent.

This rule of thumb proves to be overly simplistic. It basically imposes a cost-target for lenders, suggesting that operational costs should be covered by a markup of 15% on the cost of raising funds. It has been noted that many institutions fall short of such a rule of thumb.

# Inequality and Growth: What Can the Data Say?

The attached PDF contains a summary of the article

Banerjee, A., Duflo, E. (2003), Journal of Economic Growth, Vol , pp. 267-299.

Abstract
This paper describes the correlations between inequality and the growth rates in cross-country data. Using non-parametric methods, we show that the growth rate is an inverted U-shaped function of net changes in inequality: changes in inequality (in any direction) are associated with reduced growth in the next period. The estimated relationship is robust to variations in control variables and estimation methods. This inverted U-curve is consistent with a simple political economy model but it could also reflect the nature of measurement errors, and, in general, efforts to interpret this evidence causally run into difficult identification problems. We show that this non-linearity is sufficient to explain why previous estimates of the relationship between the level of inequality and growth are so different from one another.

# Without loss of generality, this trivially follows and is left for the reader as exercise

Economics is not Mathematics, even though there is a lot of Math in it. Especially Calculus and Real Analysis are important for any serious student of economics and in that course you are likely going to get familiar with phrases like….

1. It is easy to see that…
2. Obviously…
3. Immediate. *
4. …and the result follows.
5. As a result, we find that…
6. One easily verifies that…
7.  The proof is left to the reader. *
8. The proof is left as an exercise.*
9. ….and the X theorem completes the proof.
10.  Clearly…
11. It can be shown that…
12. Trivially…
13. The proof is standard.
14.  …the rest of the proof follows the same line.
15. …by a trivial argument…

Found on Urch.com.

# Speed of Convergence and Capital markets

I think I somewhat understood now finally theargument, how the speed of convergence in a simple Solow model is increased by access to world capital markets, if we are in a small, open-economy setup. Capital markets can be considered to be the “grease” for economies. They link savers to investors and create in a setup with risk-aversion a value-added due to their risk pooling abilities. In a simple Solow model, we can see how capital markets increase the speed of convergence.

# History does not matter in Solow Models

Essentially, a fundamental result of Solow is that “history does not matter“; furthermore, it also suggests that “inequality does not matter“. The Solow model predicts convergence in levels of capital stock per capita and also in GDP per capita with no long-run growth (all growth is exogeneous, e.g. due to technological progress etc.).

# Inverse land-size productivity relationship: Endowments should not matter

Market frictions or market failures are seen as one of the most common reasons, why economies in developing countries suffer from a lack of growth. Some stylized facts indicate a huge disparity in the productivity especially in the primary argricultural sector. The primary sector occupies a key place in the economy of developing countries. According to the UNDP in 1996 agriculture employed 60% of the labour force while contributing 20% of GDP of less developed countries, in comparison to 2% of the labour force contributing 2 % of GDP of developed countries. This indicates that there is a huge productivity gap across countries. Continue reading Inverse land-size productivity relationship: Endowments should not matter

# The equity premium puzzle and the risk free rate puzzle

It took me some time to understand the relationship between these two puzzles in the lecture material. Essentially, it can be best seen, assuming CRRA preferences, i.e. when individuals have a utility function of the form $u(c_t) = \frac{c_t^{1-\gamma}}{1-\gamma}$. In this small note, I would like to show the relationship between these two puzzles on basis of CRRA preferences.