Is microcredit procyclical? A focus on microcredit and GDP, from 2004 to 2010.

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1

1. Motivation

The microcredit phenomenon has raised much interest all over the world,

especially since the creation of the Grameen Bank. It existed for centuries

under very different forms and was ruled by very diverse subjects: institutional

and non-institutional, private and public, formal or informal. However, it only

became a popular issue in the latest decades, with Muhammad Yunus.

That is why I decided to stick to Yunus' definition of microcredit: "it is a system

of loan granting with no guarantees, that is able to burst activities which can

generate a sufficient income to free the poor from misery"

1

.

Therefore, when speaking about microcredit I will speak about:

• Very small loans.

• Loans accorded to someone with no guarantees in return.

• Loans generally used for investment projects, or for the acquisition of

durable goods, especially in developing countries.

• Loans that are usually put in place by non profit organizations, social

business companies or ethic banks, with the aim of providing better living

standards and social inclusion to the poorest.

We can therefore exclude a very common kind of credit that we can often see in

developed countries as well: the lending of small amounts of money for

consumer goods, at very high interest rates.

Literature on microcredit is huge. All kinds of aspects and effects of microcredit

and microfinance have been analyzed by economists and social scientists.

Results are very controversial: some are strongly optimistic and some are

terribly pessimistic.

There is however a general agreement on the human and social advantages

that microcredit holds in comparison to charity or international aid.

Niccoli and Presbitero identify some forces that increase solidarity and

interdependence among society members, leading to a boost in trust and social

1 "Un sistema di concessione di prestiti senza garanzia capace di far decollare attività che generino reddito sufficiente a liberare i poveri dalla povertà". M. YUNUS, Un mondo senza povertà, Milano, Feltrinelli, 1998, p. 80.

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2 capital.

The channels through which credit enhances social capital are:

• "The essence of the relationship between the borrower and the lender"

2

:

such relation is based on interdependence, it is not an impersonal

exchange where parts only negotiate the price; both parts know each

other personally, and are connected for a long period of time.

• The promotion of "new ways of implementation of labour division"

3

: credit

relations lead to the most important form of specialization, which is

important for economic development.

• "The role of time"

4

: credit relations last for a long period on time. Long

run evaluations play a predominant role.

The authors also establish several specific channels through which microcredit

can lead to a social capital improvement, in addition to those of simple credit.

For the sake of simplicity I decided to summarize them in a table.

2 "La natura dei rapporti tra debitore e creditore" A. NICCOLI, A. F. PRESBITERO, Microcredito e macrosperanze- Opportunità, limiti e responsabilità, Milano, Egea, 2010.

3 "Nuove modalità di attuazione della divisione del lavoro" Ibidem.

4 "Il ruolo del tempo" A. NICCOLI, A. F. PRESBITERO, Microcredito e macrosperanze- Opportunità, limiti e responsabilità, Milano, Egea, 2010.

Single individuals Groups

1. Amount. Very small credit: the loan is just a s mall part of the project. Suc-ces s depends on the abilities of the borrower and the behaviour of family and com munity mem bers .

1. Group dimensions. Sm all groups, direct knowledge of individuals, solidarity, interdependence of members .

2. Progress on time. The amount of the loan increas es on time as it is reimboursed. Im portance of long period undertakings.

2. Frequency of meetings. Very frequent m eetings, m eetings not only for economic matters but als o for comm unity government.

3. Repayment terms. Small tranches, no big projects. Attention to local matters.

3. Group guarantees. Trust among members , peer control mechanisms . 4. Typology of eventual guarantees.

Guarantees with em otional value. Im portance of non economic aspects of life.

5. Gender effect. Many loans given to women, les s conflictive and m ore generous

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3 Leaving social considerations aside, I wanted to focus on the economic effects

of microcredit.

Measuring the impact of microcredit, however, is not an easy task. It is not

always possible to find useful data in developing nations, and local or

microcredit institutions may try to manipulate them.

Furthermore, research in the economic field faces a serious bias. To produce

valid comparisons, experiments require a "treatment group"

5

that possesses the

characteristics one wants to analyze, and a "control group"

6

, that does not.

When trying to spot the outcome of an economic policy it is hard to identify such

control group: in the case of microcredit it would be difficult to deduce the

consequences of the exclusion from a program for an individual's life.

Considering all these reasons, I decided to focus on a possible macroeconomic

side effect of microcredit: does it stop or slow down during crisis and

recessions? Or does it not? Is microcredit pro-cyclical?

If it turned out to be counter-cyclical it would have the further advantaged of

stabilizing the business cycle

.

If it turned out to be pro-cyclical some measures

would have to be put in place by institutions or governments to avoid worsening

the economical situation in recession periods.

Previous literature has diverging conclusions on this field again: traditionally,

microfinance assets were believed to be detached from International market

trends, but two cases studies proved to have very different implications.

That is why I chose to develop an analysis on microcredit and GDP trends that

would include not just one country but a wide range of states. I decided to

enclose all of the developing countries whose data appears in the yearly reports

of the Microcredit Summit Campaign.

The data collected in this dissertation and the focus that is used may be useful

to extend the analysis to other countries and to build up far more specific and

consistent models, to find out causality links between microcredit and GDP

trends on a global basis.

5 A. NICCOLI, A. F. PRESBITERO, Microcredito e macrosperanze- Opportunità, limiti e responsabilità, Milano, Egea, 2010.

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4

2. Literature Review

The ongoing economic crisis determined a turning point in the analysis of the

microcredit phenomenon in relation to the broader economic fundamentals.

The first productions were two case studies that analyzed the pro-cyclicality of

microcredit institutions and assets in Indonesia (Patten, Rosengard, Johnston

2001)

7

and Bolivia (Marconi, Mosley 2005)

8

. Results are antithetical: while in the

first case microcredit institutions experienced a growth in their clientele base

and assets, in the second one most of them experienced losses and some of

them had to quit the market, suggesting that differences in institutional

characteristics and their management strategies may matter.

In more recent years, but still before the economic crisis spread, Gonzalez

(2007)

9

found no substantial evidence of a link between microcredit portfolios

performances and economic shocks, while Krauss and Walter (2006)

10

determined a weak link among microcredit assets’ conduct and international

market trends, and a relatively low correlation with local markets.

As argued by Di Bella (2011)

11

their results may be biased as: “they were based

on samples that [...] comprised a relatively short period of time. The period

analyzed largely coincided with a period of expansion of the world economy and

[...] with what seems to represent a period of “diffusion” of the microfinance

industry”.

12

The results in Di Bella’s model highlight a link between real GDP growth and the

lending rates of microfinance institutions.

An empirical evidence of the impact of microcredit on business cycles can be

7

PATTEN R. ROSENGARD J.K, JOHNSTON D. E. JR, 2001, Microfinance Success Amidst

Macroeconomic Failure: the Experience of Bank Rakyat Indonesia During the East Asian Crisis, World Development, Vol. 29, Nr. 6, pp. 1057-1069.

8

REYNALDO M., MOSLEY P., 2005, Bolivia during the global crisis 1998-2004: towards a “macroeconomics of micro finance”. Sheffield Economic Research Paper No 2005007.

9

GONZALEZ A., 2007, Resilience of Microfinance Institutions to National Macroeconomic Events: an Econometric Analysis of MFI Asset Quality, MIX Discussion Paper No. 1.

10

KRAUSS N., WALTER I., 2006, Can Microfinance Reduce Portfolio Volatility?, SSRN id 943786, 2008 Version.

11

DI BELLA G., 2011, The Impact of the Global Financial Crisis on Microfinance and Policy Implications, IMF Working Paper WP/11/175.

12

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5 also found in Maksudova’s analysis (Maksudova 2010)

13

, that underlines the

indirect effects of microfinance on economy through its interaction with the

monetary base and commercial banks.

I will show the data, methodology and main results of the works mentioned

above.

4.1 Patten, Rosengard, Johnston (2001)

Their article Microfinance Success Amidst Macroeconomic Failure: the

Experience of Bank Rakyat Indonesia During the East Asian Crisis

14

examines

the reasons of the flawless performance of the country’s most important

microfinance institution.

As reported by the authors “in December 1997 the BRI’S total assets were

$

16,7 billion […] and it had 324 branches covering the entire country.”

15

In 1997 the Indonesian Central Bank could not manage to support the

exchange rate to the US dollar, thus giving origin to the monetary crisis.

The national currency had depreciated in the previous year of 5%, and the open

account policy pursued by the Government led to an increase in hot money

flowing into the country. Furthermore many large corporations had indebted in

dollars and this foreign private debt was more than twice the central bank’s

reserves. To worsen the situation a drought wasted a whole crop of rice in

1997/1998.

In the same year the Bank Rakyat split up into four business units: SBU Micro

banking, SBU Retail Banking, SBU Corporate and SBU Treasury and

Investment. The former provided financial services to micro enterprises and

households while the Corporate SBU operated with bigger firms, and the

Treasury and Investment one was in charge of BRI’s treasury functions.

13

MAKSUDOVA N., 2010, Macroeconomics of Microfinance: how do the cannels work?, CERGE-EI Working Paper Series ISSN 1211-3298, 423.

14

Macroeconomic Failure: the Experience of Bank Rakyat Indonesia During the East Asian Crisis, World Development, Vol. 29, Nr. 6, pp. 1057-1069.

15

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6 The three business units had different repayment paths during the crisis. The

worst performances came from the Corporate Unit, since loans could be

accorded in dollars as well. With the sharp devaluation of the Rupiah the

repayment had become almost impossible. In addition, many of the investments

had been made in the hotels sector, which suffered a drastic decline.

The Retail and Micro banking Loans had instead great repayment rates. There

was no restriction of lending, nor any restructuring action during the years of the

crisis, but still 97% of total loans were repaid.

The authors ascribe the success of Bank Rakyat to four main factors:

• “The microenterprise loans are all instalment loans adjusted to the

borrower’s cash flows”

16

. When borrowers pay back their loans they

reinvest part of the profit in their own business and thus become less

vulnerable to financial shocks.

• “The microenterprises are more likely to be engaged in the purchase and

sale of domestically-produced essentials”

17

, which have a less cyclical

demand and do not depend on imports, in opposition to more

sophisticated, largely imported goods demanded by bigger companies.

• “The rural sector may be less affected by the monetary crisis than the

urban areas but more affected by a severe drought”

18

. However two

following rice crops kept agricultural microenterprises alive and lead to

an export boom due to the significant devaluation of the national

currency.

• “The microenterprise borrowers appear to value their access to credit

and savings services very highly”

19

. They prefer reducing their

consumption in order to repay their instalments on time, rather than

breaking bond with the Bank Rakyat. The BRI in return encouraged new

borrowing in clients who were in line with repayments and did not restrict

its lending activity. This translated into a growing clientele base and

16

Macroeconomic Failure: the Experience of Bank Rakyat Indonesia During the East Asian Crisis, World Development, Vol. 29, Nr. 6, p. 1065. 17 Ibidem 18 Ibidem, p. 1066. 19 Ibidem.

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7 excellent repayment terms.

The article presents a valid example of the achievement of counter-cyclical

effects of microcredit on business cycles through an adequate institutional

management strategy.

4.2 Marconi, Mosely 2004

The opposite conclusions are inferred by Marconi and Mosley in their analysis

of the Bolivian crisis.

With the 1985 hyperinflation consumers lost their trust in the formal financial

sector and started to be interested in the services offered by microfinance

NGOs. The sector grew rapidly and became very profitable, attracting a new

kind of entities into the market: “consumer-credit houses (FFPs de

Consumo)”

20

. These new entrants did not offer any collateral services: only

credit for larger loans, with higher interest rates and generally for consumer

purchases rather than business durables. In their profit-oriented perspective

they often undervalued the risk connected to the loan granting activity and the

repayment capability of borrowers.

In 1998 Hugo Blazer got the power and implemented confused and conflicting

economic policies.

He promoted microfinance as a determinant tool to alleviate poverty but at the

same time he destroyed a big part of the microenterprises market through the

reduction of custom duties on many low-cost consumer goods. He also

rescheduled and cancelled various debts of parastatal agencies, thus

undermining “the repayment ethic for all-including microfinance-borrowers”.

21

Illegal practices in FFPs de Consumo were condoned in a deregulation scheme

of the financial sector.

The crisis spread in Bolivia in 2002, after Argentina’s collapse.

20

REYNALDO M., MOSLEY P., 2005, Bolivia during the global crisis 1998-2004: towards a “macroeconomics of micro finance”. Sheffield Economic Research Paper No 2005007, p. 3

21

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8 The performance of the different microfinance entities differs strongly.

Consumer FFPs shrank soon with high default rates.

Commercial banks sharply decreased their lending and continued on this trend

during the whole crisis.

Microfinance FFPs had steady even if very low growth rates, while NGOs had

antithetical trends: two of them continued to grow (Crecer and ProMujer), while

the others experienced increasing default rates.

Their trends can be clearly seen in Fig.1

22

The authors attribute the achievements of the NGOs ProMujer and Crecer to

22

REYNALDO M., MOSLEY P., 2005, Bolivia during the global crisis 1998-2004: towards a

“macroeconomics of micro finance”. Sheffield Economic Research Paper No 2005007, p. 3, Fig. 1. Fig. 1 Portfolios, arrears rates and estimated investment rates.

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9 the fact that they provided smaller credit tranches, mostly to women (that

traditionally have higher repayment rates), in village banking methodology

schemes with close relationships with the clients.

The reasons beyond good performances of FIE and Caja Los Andes, instead,

have to be found in their consumer type, generally higher income level

individuals, and in their ability to track and monitor their clients.

All four institutions have an “integrated lending model” that does not only give

access to credit, but provides a whole range of collateral services, including

training, technical assistance, insurance-type models etc.

The authors believe this strategy to be auspicious in recession times, when

clients can choose the institution while institutions cannot choose the client

anymore. Integrated-model institutions, with social and human aims that differed

from a pure profit-seeking approach gained the loyalty of their borrowers and

increased their portfolio values even during the crisis.

This intuition is converted into a model that seeks for bonds between

microfinance and macroeconomy.

Their model is summarized in fig. 2.

23

The macroeconomic policy is treated as exogenous in the model (link n.1).

Consumption and imports vary with the income level (link n. 2). The behaviour

of the microfinance institutions depends on the demand of the production sector

to which the borrowing microenterprises belong; on the external financing and

the institution’s attitude to it; on the country’s economic policy and on the default

rates, which can be influenced by the economic policy as in the case of Bolivia

(links 3a, 3b, 3c). The formal sector financial offer and demand is left

exogenous, while the investment in the informal sector depends again on the

demand for products of a specific sector, and on the financing options available

and future expectations (link n. 4). Microfinance is considered a reduction

poverty tool (link n.5) and its impact is both direct, through the income multiplier,

and indirect, through its effects on market labours, human capital, training etc

(link n.6).

23

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10

Fig. 2 Macro-micro linkages: the basic model structure.

Mosley and Marconi focus on the linkages 3 and 4 and estimate total

investment by the microfinance sector. Results show a positive and statistically

significant correlation between the total investment and demand growth. The

presence of insurance schemes does not have relevant effects on total

investments but it has a significant positive effect on the rate of investment

growth.

Using such estimates the authors calculate the contribution of the microfinance

sector investment on GNP and evaluate that it is around 4% per year.

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11 or economic policy. Results are reported in Table 1.

24

Table 1: Estimated effects of ‘design changes’ and other macroeconomic influences on Bolivian GNP

As pointed out by the authors the microenterprise sector’s demand on

investment is cyclical by definition, but microfinance may have either a

pro-cyclical or a counter-pro-cyclical effect on macroeconomy if “countervailing factors

bearing on its investment level”

25

are put in place.

Marconi and Mosley conclude their study with a comparative analysis of Bolivia

and Indonesia and underline the importance of “conducting a detailed

examination of the forces which determine the macroeconomic role of

microfinance, rather than expect the relationship between demand and

investments to be conventional.”

26

24

“macroeconomics of micro finance”. Sheffield Economic Research Paper No 2005007, p. 18, fig. 3.

25

Ibidem p. 23.

26

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12 4.3 Gonzalez 2007

In his MIX discussion paper

27

Gonzalez investigates looks for correlation in

between economic growth and four different measures of microfinance portfolio

risk:

• Portfolios at risk over 30 days (PAR-30)

• Portfolios at risk over 90 days (PAR-90)

• Loan Loss Rate (LLR)

• Write-Off Ratio

The first two indicators are strongly correlated and the same applies to the latter

ones.

The author uses data from 639 institutions pooled into 88 countries that

reported to the Microfinance Information Exchange (MIX) in the years

1999-2005. The dataset is unbalanced as some of the institutions did not report every

year and some started only from 1996.

I will only report tables with fixed effects regression estimates that are related to

growth. As we can see in Table 2

28

only the PAR-30 has significant correlation

with growth, while PAR-90 does not. Furthermore the coefficient decreases with

the lags.

Table 2: Dependent Variables PAR-30 and PAR-90. Fixed effects.

27

GONZALEZ A., 2007, Resilience of Microfinance Institutions to National Macroeconomic Events: an Econometric Analysis of MFI Asset Quality, MIX Discussion Paper No. 1

28

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13 No significant correlation can be found in the case of LLR and WOR, as shown

in Table 3

29

.

Table 3: Dependent Variables LLR and WRO. Fixed effects.

The authors conclude that non-positive economic cycles do have a weak impact

on the short term repayment rate of microloans, but microfinance institutions still

manage to collect their credits in the right terms: that is why there is no increase

in the Loan Loss Ratio, nor in the Write Off Ratio. They affirm that negative

growth periods may affect microfinance institutions only through higher costs

that have to be “compensated with lower profits or higher interest rates”

30

.

4.4 Krauss, Walter 2008

Krauss and Walter point out that Gonzalez’ estimates reported above are only

based on domestic market benchmarks, but do not include any global market

correlation measure.

The aim in their paper Can Microfinance Reduce Portfolio Volatility?

31

is to find

out whether there is any correlation between microfinance institutions portfolios

and both global capital markets and domestic GDP.

29

GONZALEZ A., 2007, Resilience of Microfinance Institutions to National Macroeconomic Events: an Econometric Analysis of MFI Asset Quality, MIX Discussion Paper No. 1, p. 21, Table 4.

30

Ibidem, p. 6

31

KRAUSS N., WALTER I., 2006, Can Microfinance Reduce Portfolio Volatility?, SSRN id 943786, 2008 Version

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14 The authors check for correlation with the main global stock indexes (S&P 500,

MSCI World and MSCI Emerging Markets) of six key variables for microfinance

institutions:

• Net Operating Income (NOI)

• Return On Equity (ROE)

• Profit Margin (PM)

• Change in Total Assets (TA%)

• Change in Gross Loan Portfolio (GLP%)

• Loan Portfolio at Risk (PAR)

To examine correlation with domestic markets they use GDP instead of local

stock indexes. Regression is run with fixed effects and repeated for traditional

emerging markets investments (EMIs) and for emerging markets commercial

banks (EMCBs) in order to see if microfinance institutions’ portfolios follow

general trends of other emerging markets assets.

Results show no significant correlation to global market indexes (Tables 4, 5,

6)

32

and significant correlation to domestic markets (Table 7)

33

Table 4: regressions with S&P500 Table 5: regressions with MSCI World

32

KRAUSS N., WALTER I., 2006, Can Microfinance Reduce Portfolio Volatility?, SSRN id 943786, 2008 Version, p. 29-30, Tables 2, 3, 4.

33

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15

Table 6: regressions with MSCI Emerging Markets Table 7: regressions with domestic GDP

Results for EMIs and EMCBs show much higher correlation with international

markets. The reasons lying beyond this evidence has to be found in the nature

of microfinance institutions, which are generally privately owned by

shareholders with long term objectives, while other emerging markets

investments tend to have a short term, market-oriented perspective that causes

higher volatility and correlation to global markets’ trends. Other causes of low

systemic risk of MFIs come from the social environment in which they operate,

as they provide credit to the “unbanked” that have less choice and often no

second chance of getting credit, and mainly to women, who traditionally have

very high repayment rates.

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16 4.5 Maksudova 2010

Maksudova verifies the impact of microfinance on growth. The expected

causality links are summarized in Fig. 3

34

Fig. 3 Microfinance channels

Microfinance affects growth directly through an increase in production, human

capital and the reduction of poverty (A). It also provides access to financial

credits to the unbanked, which raises money circulation in the economy (B).

Another indirect effect is the possible rival or complementary role of

microfinance institution with traditional banks (C).

To control for this possible connections the author makes three hypotheses and

verifies them:

• “H1: Microfinance causes economic growth”

35

.

Microfinance indicators are the growth rate of gross loan portfolio and the

growth in the number of clients, while growth is captured by real GDP

34

MAKSUDOVA N., 2010, Macroeconomics of Microfinance: how do the cannels work?, CERGE-EI Working Paper Series ISSN 1211-3298, 423, p. 5 Figure 2.

35

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17 growth rate.

• “H2: Microfinance causes increase in money supply”

36

.

The authors monitor the trends of M2 and M3 aggregates.

• “H3: Microfinance positively/negatively affects commercial banks”.

The presence of commercial banks can be measured by the growth rate

of the private credit to GDP ratio (PCredit). The sign of the coefficients

can be used to find out whether this relation is complementary o

substitutive.

Regression is run with an Arellano-Bond model using a panel data for 102

countries, collected from the MIX database for the years 1995-2009. Results

are shown in Table 8

37

Table 8: Microfinance direct and indirect channels.

36

MAKSUDOVA N., 2010, Macroeconomics of Microfinance: how do the cannels work?, CERGE-EI Working Paper Series ISSN 1211-3298, 423, p. 8.

37

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18 As can be noticed, both the direct effect on growth and the effect on the

monetary base prove to be positive and statistically significant. However the

nature of the relation with commercial banks is not clear, as the interaction

variable is negative and significant (suggesting a rival relation) while the total

effect of microfinance is positive and robust (suggesting collateral links).

To further investigate the indirect effects of microfinance, the author implements

a Granger-causality test from macroeconomic fundamentals to microfinance

and vice versa.

Results are clear, as shown in Table 9.

38

Table 9: Granger causality running in both directions.

The test running from macroeconomic fundamentals to microfinance (left side of

the table) is invalidated by the Sargan test (high p-values), while microfinance

does have positive and significant impact on macroeconomic variables, as seen

in the right side of the table.

The author concludes that the real world case studies of Indonesia and Bolivia

demonstrate that indirect effects of microfinance on real economy could have

opposite outcomes and need to be further investigated.

38

MAKSUDOVA N., 2010, Macroeconomics of Microfinance: how do the cannels work?, CERGE-EI Working Paper Series ISSN 1211-3298, 423, p. 27 table 6.

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19 4.6 Di Bella, 2011

Di Bella

39

argues that previous estimates on microfinance portfolios’ systemic

risk were based on insufficient time observations, and biased by the fact that

they were all collected during expansion periods, with general high growth rates

of both domestic markets and microfinance assets.

Table 10: Regression on selected sample

39_{DI BELLA G., 2011, The Impact of the Global Financial Crisis on Microfinance and Policy} Implications, IMF Working Paper WP/11/175.

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20 The author uses Krauss and Walter’s model

40

with larger time series, collecting

MIX data from institutions reporting at least 7 years observations in the period

1998-2009. Another requirement is that institutions should report data for 2007,

2008 and 2009, in order to see the impact of the crisis.

MFIs are divided into different categories that take into consideration the macro

area of origin, their nature, whether they are regulated or not, profit or non profit

and their maturity.

Results can be seen in Table 10 in the previous page.

41

MFIs do show systemic risk not only in relation to domestic markets but also to

international markets indexes. Systemic risk is higher for countries in the East

European and Central America and Caribbean areas, while it is lower for Asia

and the Pacific and South America. As it could be expected systemic risk is not

very high for NGOs, but it is higher for banks. Non profit institutions are

relatively less risky, as well as mature ones.

This paper proves the evidence that microfinance assets, in opposition to what

was believed before, do show vulnerability in relation to global markets trends.

40

KRAUSS N., WALTER I., 2006, Can Microfinance Reduce Portfolio Volatility?, SSRN id 943786, 2008 Version

41

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21

3. Data

To use publicly-avaliable, high standard information I decided to dispose

of the

yearly reports of the Microcredit Summit Campaign, an organization which

collects data from individual institutions and verifies it before

divulgation.

All of

the reports are

accessible

online at the web page:

www.microcreditsummit.org.

The MSC data collection process starts with the submission of the "Institutional

Action Plans" by the microcredit institutions that are willing to appear in the

reports. The information required in such IA

PS concerns:

"total number of active

clients (clients with a current loan); total number of active clients who were

among the poorest when they received their first loan; what poverty

measurement tool was used, if any, to determine the number of poorest

clients;percentage of poorest clients who are women; average size of first loan;

total number of active savers; average savings per saver; percentage of poorest

clients who have crossed the poverty line; what impact measurement tool was

used, if any, to determine the number of clients who were very poor when they

took their first loan and have now crossed the poverty line; financial or business

development services offered, if any; and percent financial self-sufficiency an

institution has reached."

42

The

dossiers submitted

are then verified by the Micro

credit Summit Campaign

staff, which ask for clarification when needed: if there is no feedback, data is not

included.

Every year new institutions undergo the verification procedure.

The repo

rts are only available for the years: 2001, 2004, 2005, 2006, 2007,

2009, 2010. I decided to use the 2004-2010 reports as 2001 is quite distant in

time.

At the end of each report you can find a list including the name of each

institution, its country, the total number of clients, the total number of poorest

clients, the percentage of poorest clients that are women and the name of the

person who verified the details.

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22 "The Microcredit Summit Campaign defines “poorest” as those who are in the

bottom half of those living below their nation’s poverty line, or any of the 1.2

billion who live on less than US$1 a day adjusted for purchasing power parity

(PPP), when they started with a program".

43

I chose to consider the number of clients and poorest clients, regardless of their

gender.

As it very often happens, the data shown in the reports certainly underestimates

the impact of microcredit. The MSC itself recognizes this underestimation and

lists the potential clients that are not being counted

44

:

• Clients who graduated successfully from programs.

• Clients of large government programs in China and Thailand, which

never submitted any IAPS.

• Programs not reporting in a certain year: programs are only included

when reported, so you may not find any trace of them in a certain year

even if they still exist.

• Institutions that have not been identified: thousands of small institutions

are still not being counted.

The reports only show individual data for each institutions, or aggregate data for

macroareas that keep changing their composition. For example in the 2004

report all African countries ar

e grouped

together, while in the 2005 report

Subsaharian Africa is split from North African countries. This makes aggregate

data non comparable over time.

I decided t

o divide

all of the institutions by country for each year, and calculate

country totals. For every year I will show country data and keep the macroarea

division that is used in the reports.

After obtaining all country data per year, I gathered global data for each country

for the period 2004-2010, excluding 2008. Since observations over time were so

few I decided to leave out the countries that did

not have

complete time series.

43 Ibidem, pg. 1.

(23)

23 In the global-data tables I separated countries in three macroareas: Africa, Asia

and Latin America. Unfortunately, I could not acquire enough data on Middle

East countries.

I only used a geographical criterion when allocatin

g countries to

a certain

macroarea. In the reports Tunisa and Egypt are often put in different

cathegories such as "Others" or "Middle East/North Africa." I chose to associate

them to the other African countries, to leave aside any cultural, religious or

politic

al benchmark.

For monitoring the trend of business cycles I downloaded per capita GDP data

from the World Bank's web page. Myanmar information was missing and I

decided to exclude it from the analy

sis. I prefered not use alternative sources,

not to incur in statistical incongruity.

In the final global-data tables I calculated logarithms of the three main variables,

in order to have comparable data. The regression is run on such logarithms.

Data on single countries for each year will be shown first, presented with the

same macroarea division as used in the reports.

(24)

24 3.1 2004 Data

3.1.a Africa

NIGERIA

Institution name Number of clients reported

Number of poorest clients reported Grassroots Health Organization of Nigeria 12152 1240 Nigerian Agricultural Cooperative and Rural

Development Bank 853323 682658

Country Women Association of Nigeria 158000 155000

Life Above Poverty Organization 29812 18675

Justice, Development and Peace Commission 12784 12784 Self Reliance Economic Advancement Programme 4206 4206

Partners for Development 5000 4000

Dass Women Multi Purpose Co-Operative 2760 2760

Total Nigeria 1078037 881323

ETHIOPIA

Number of poorest clients reported Debit Credit and Saving Share Company 326764 326764 Amhara Credit and Savings Institution 371163 344134 Oromia Credit & Savings Loan 115000 115000

Omo Microfinance Institution 78836 43369

Addis Credit and Saving Institution 31182 24945

Wasasa Microfinance Institution 8949 5369

Africa Village Financial Services 4867 4667

Total Ethiopia 936761 864248

NIGER

Number of poorest clients reported

CARE Niger 85484 55656

Programme Mata Matu Dubar 19375 19375

(25)

25

MALAWI

Number of poorest clients reported Malawi Rural Finance Company Ltd. 202633 131711 Malawi Union of Savings and Credit Cooperatives 52000 17950

FINCA Malawi 22062 20642

Total Malawi 276695 170303

BURKINA FASO

Number of poorest clients reported Federation des Caisses Populaires du Burkina

Faso 103102 57124

Fonds d' Appui Aux Activités Rémuneratrices des

Femmes (FAARF) 154920 154920

Mutualité Femme et Développment 17206 15000

Total Burkina Faso 275228 227044

TANZANIA

Institution name Number of clients reported Number of poorest clients reported PRIDE Tanzania 54272 42332 FINCA Tanzania 40495 24297 Total Tanzania 94767 66629 SENEGAL

Number of poorest clients reported Reseau des Caisses d' Epargne et Crédit des

Femmes de Dakar 27458 27458

Alliance de Crédit et d' Epargne Pour la

Production 95437 83030

Féderation des ONG du Sénégal 39328 39328

Femme Développement Enterprise en Afrique 24947 20955

Horizon Verts 4025 4025

MECARUL 3507 2805

UMEC de Sedhiou 3051 2440

(26)

26

UGANDA

PRIDE Uganda 56135 42620

FINCA Uganda 45243 28956

Microcredit Development Trust 7650 7650

Micro Entreprise Development Network 16750 7538

Total Uganda 125778 86764

GHANA

Number of poorest clients reported Freedom from Hunger Ghana Development Action

Association 20002 18500

Nsoatreman Rural Bank 40200 38500

Kraban Support Foundation 6517 6503

Grameen Ghana 4102 4102

Total Ghana 70821 67605

BENIN

Number of poorest clients reported Association pour la promotion et l' Appui au

Développement des Micro-Enterprises 37661 37661

Total Benin 37661 37661

THE GAMBIA

Number of poorest clients reported Gambia Women's Finance Association 22750 19702 The Gambia Social Development Fund 51395 41116 Rural Finance and Community Initiatives Project 39000 35000

Total Gambia 113145 95818

MALI

Institution name Number of clients reported Number of poorest clients reported Kafo Jiginew 186088 176102 Total Mali 186088 176102

(27)

27

GUINEE

Number of poorest clients reported Credit Rural De Guinee Societé Anonyme 120674 92500

Total Guinee 120674 92500

MOROCCO

Zakoura Foundation 174480 88949

Fondation pour le Développement Local et le

Partenariat 24845 14000

Total Morocco 199325 102949

KENYA

Kenya Women Finance Trust 89674 44837

Total Kenya 89674 44837

TOGO

Number of poorest clients reported Association pour la Promotion des Groupements

Agricoles 26450 22000

Total Togo 26450 22000

ZIMBABWE

Institution name Number of clients reported Number of poorest clients reported Zambuko Trust 10252 4000 Total Zimbabwe 10252 4000 SOUTH AFRICA

The Small Enterprise Foundation 24593 11695

(28)

28

MALI

Number of poorest clients reported Centre d' Appui Nutritionnel et économique aux

femmes 16571 15432

Nyesigiso 20816 14571

Total Mali 37387 30003

TUNISIA

Institution name Number of clients reported Number of poorest clients reported ENDA Inter-Arabe 15946 11162 Total Tunisia 15946 11162 GUINEA

Institution name Number of clients reported Number of poorest clients reported Pride/Finance 12276 11136 Total Guinea 12276 11136 COTE D'IVOIRE

Réseau CMEC-CI 11418 7000

Caisse d' Epargne Financière pour le

Développement de la Petite Enterprise 6521 2700

Total Cote d'Ivoire 17939 9700

ERITREA

Number of poorest clients reported Agency for Cooperation and Research in

Development 22465 4493

(29)

29

ZAMBIA

Micro Bankers Trust (MBT) 3647 3647

Christian Enterprise Trust of Zambia 4677 3538

Pulse Holdings, Ltd. 2806 1550

Total Zambia 11130 8735

3.1.b Asia and the Pacific

BANGLADESH

Institution Number of clients reported

Graamen Bank 4060000 4060000

BRAC 3990000 3630000

Association for Social Advancement 2770000 2490000 PROSHIKA, A Center for Human Development 1545130 1236104 Palli Daridro Bimochon Foundation 327604 138939

Rangpur Dinajpur Rural Service 228199 175713

BURO, Tangail 221366 221366

Thengamara Mohila Sabuj Sangha 278516 250664

Shakti Foundation for Disadvantaged Women 102600 102600 United Development Initiatives for Programmed

Actions 52259 50259

HEED Bangladesh 49343 42300

Integrated Development Foundation 45294 45294

Jagorani Chakra 99387 82582

PADAKHEP Manabik Unnayan Kendra 108000 38500

Manabik Shahajya Sangstha 59920 29500

Bangladesh Rural Development Board (BRDB) 3713728 3528041

Sonali Bank 3800000 500000

Caritas Bangladesh 284947 251273

Swanirvar Bangladesh 860815 160300

Islami Bank Bangladesh Limited 131102 131102

Bangladesh Extension Education Services 115000 115000

Society for Social Service 106998 106998

Community Development Center (CODEC) 68997 68728

Resource Integration Center 50000 50000

ASHRAI 44507 44507

Welfare Association of Village Environment 40498 33698

Small Farmers Development Project 59304 31940

Noakhali Rural Development Society (NRDS) 31285 31285 Institute of Integrated Rural Development 28967 28967

(30)

30

South Asia Partnership-Bangladesh 27000 27000

Centre for Development Innovation and Practices 29773 26595 Association for Realisation of Basic Needs 31662 25330 Coastal Association for Social Transformation 27943 22354 People's Oriented Program Implementation 65370 22320

Rural Reconstruction Centre 42297 21148

Dushtha Shasthya Kendra 47935 20588

Assistance for Social Organization and

Desha Sechsashebi Artho Samajik Unnayan O

Manobik Kallyan 29058 19000

Uttara Development Program Society 27634 18500

Juba Jiban Advancement Committee 17622 17622

Christian Service Society 27821 16700

Srizony Bangladesh 28012 16490

Bangladesh Rural Integrated Development for

Grub-Street Economy (BRIDGE) 16274 16274

Eco Social Development Organisation 20251 16200

Eskander Welfare Foundation 13574 13574

Annesha Foundation 25073 13255

Village Education Resource Center 17355 13114

The Institute of Rural Development (IRD) 13002 13002 Bangladesh Association for Social Advancement 24510 13000 Centre for Mass Education in Science 31919 12448

PAGE Development Centre 18321 12214

Nowabenki Gonomukhi Samabay Samity 54239 11121

Social Upliftment Society 23314 10540

Palli Mongol Karmosuchi 42041 10510

Unnayan 10793 10139

Gram Unnayan Karma 12563 10050

Jatiyo Kallyan Sangstha-Jakas 9735 9735

Gono Kallayan Trust 12000 9200

Sabalamby Unnayan Samity 13093 9165

Voluntary Association for Rural Development 8849 8406

Dudumari Gram Unnayan Shangstha 7922 7922

ATMABISWAS 7500 7500

BWDA Finance Limited (BFL) 7222 7149

Centre for Action Research Barind 10986 7146

Development Association for Basic Improvement 17927 7099 Sheva Nari O, Shishu Kallyan Kendra 11779 7067

Pally Bikash Kendra 19094 6700

NIJPATH 12560 6280

Joypurhat Rural Development Movement 6234 6234

Samannita Unnayan Seba Sangathan 6850 5982

(31)

31

Samaj Kallyan Sanstha 15696 5625

MAMATA 13652 5313

Voluntary Rural Development Society 5249 5197 Association for Community Development 5548 5000 BASTOB-Initiative for People's Self-Development 5394 5000 Development Organisation of the Rural Poor 9026 4964 Society for Development Initiatives 16327 4896 Hilful Fuzul Samaj Kallyan Sangstha 5623 4800

Koinonia 10588 4788

Centre for Advanced Research and Social Action 4723 4723

Alternative Develpoment Initiative 7746 4580

Association for Renovation of Community Health

Education Services 4443 4443

Concern for Environmental Development and

Research 5554 4165

Naria Unnaayan Samity 4080 4080

Somaj O Jati Gathan 5045 4036

Dak Diye Jai 12959 3629

Centre for Rehabilitation Education & Earning

Social Upliftment Foundation 3500 3500

GHASHFUL 9795 3500

Sangkalpa Trust 10991 3297

Life Association 3218 3218

PRISM Bangladesh 3093 3695

Mukti Cox's Bazar 3283 2970

Society Development Committee 16538 2655

Prodipan 11916 2433

Palashipara Samaj Kallayan Samity 8399 2099

Self-Help and Rehabilitation Programme 2527 1997 PROGRESS- Akti Samaj Unnayan Mulak

Sangstha 4637 1854

Shariatpur Development Society 8865 1790

Bedo 3098 1487

Shaplaful 4500 1350

Bandhu Kallayan Sanstha 15227 15227

Total Bangladesh 24427509 18365666

THAILAND

Number of poorest clients reported Association of Asian Confederation of Credit

Unions 3137398 3137398

(32)

32

INDIA

Number of poorest clients reported National Bank for Agriculture and Rural

Development 24277140 19421070

Society for Empowerment of Rural Poor (formerly Comissionerate of Women Empowerment and Self Employment)

6941228 5552982

Working Women's Forum 378033 378033

Share Microfin Limited 328846 328846

Development of Humane Action Foundation 250827 250827

Spandana 241214 174673

Friends of Women's World Banking 193715 174343

U.P. Bhumi Sudhur Nigam 181761 136321

CARE India 185270 129689

All India Association for Micro-Enterprise

Cauvery Grameena Bank 136900 116300

Professional Assistance for Development Action 86240 81350

Swayam Krishi Sangam 68648 65778

Mahila Arthik Vikas Mahamandal Ltd. 55677 55677

Asmitha Microfin Limited 69415 54938

CASHPOR Financial &Techincal Services 54467 54467 Activists for Social Alternatives, The 68187 53876 Karnataka Regional Organisation for Social

Service 80000 52000

Sreema Mahila Samity 46265 43951

People's Rural Education Movement 42995 42995

League for Education and Development 44843 40807 People's Multipurpose Development Society 50000 40000

Grama Siri 45303 39161

North Malabar Gramin Bank 40739 36600

Bandhan-Konnagar 33574 33574

Peermade Development Society 34450 33160

Rashtriya Gramin Vikas Nidhi 31498 31498

Bharati Integrated Rural Development Society 28900 28900 BAIF Institute for Rural Development-Karnataka 30800 23100

Holy Cross Social Service Centre 23700 18000

OUTREACH, Association of Volunteers for Rural

The Bridge Foundation/Opportunity Microfinance

India Ltd. 15174 15174

Mahasemam Trust 26017 14395

All India Women's Conference 31210 13871

Village Welfare Society 18279 13784

(33)

33

Development Promotion Group 17450 12450

Star Youth Association 13886 10355

Bharatha Swamukti Samsthe 11131 10000

Palli Unnayan Samiti Baruipur 9500 9500

Ramakrishna Mission Lokasiksha Parishad 9000 9000 Bullock-cart Workers Development Association 8983 8983 Development Support Team (Community Aid

Abroad) 14797 8758

Centre for Overall Development 7786 7500

Liberation Movement for Women 20000 7500

New Life 12290 6400

Rashtriya Seva Samithi 8581 6110

Maharshi Sambamurthy Institute of Social and

Development Studies 5144 4644

GRAM UTTHAN 6876 4194

Association of Development for Economic and

Social Help 4645 3299

SNEHA MACS, Ltd. 2573 2573

South Asia Research Society 6893 2560

Guidance Society for Labor, Orhpans & Women 4050 1202 Bharat Integrated Social Welfare Agency 1019 1019

Deshabandhu Club 2825 724

Total India 34489091 27817258

P.R. OF CHINA

Number of poorest clients reported China Association for Microfinance 85021 40000 Heifer Project International China 46328 35000 Funding for the Poor Cooperative- Chinese

Academy of Social Sciences 15735 7800

Total China 147084 82800

VIETNAM

Central People's Credit Fund 1120000 505000

Capital Aid Fund for Employment of the Poor 49330 16607 Vietnam Bank for Social Policies (Vietnam Bank

for the Poor) 3740179 1100000

(34)

34

INDONESIA

Number of poorest clients reported P4k-III/Rural Income Generation Project 640000 640000

Bank Rakyat Indonesia 3210678 321625

Yayasan Bina Swadaya 34621 27281

Christian Children's Fund, Inc. 10447 9961

Yayasan Wahana Kria Putri-Foundation 3618 3527 National Family Planning Coordinating Board 5210675 5210675

Total Indonesia 3899364 1002394

NEPAL

Agricultural Development Bank 160000 128000

Paschimanchal Grameen Bikas Bank 39293 39293

Grameen Bank Biratnagar Nepal 50737 38052

Madhyamanchal Grameen Bikas Bank

(Mid-Region Rural Development Bank) 36500 36500

Nirdhan Utthan Bank Limited 32678 32678

Centre for Micro-Finance, Nepal 29769 20838

Swabalamban Bikas Bank 30359 20680

Sudur Paschimanchal Grameen Bikas Bank 11132 11132

Deprosc Development Bank Limited 10964 10964

Development Project Service Centre, Nepal 10183 10183 Canadian Centre for International Studies and

Cooperation, Nepal 37142 9285

Rural Reconstruction Nepal 12700 9120

Nepal Rural Development Society Centre 9010 9010

Nari Bikash Sangh 8000 6500

Centre for Self-Help Development 4514 4288

Rastriya Banijya Bank 63227 47400

Total Nepal 546208 433923

MYANMAR

Pact Myanmar 51169 51169

Microfinance Delta Project 39668 39668

(35)

35

SRI LANKA

Samurdhi Authority of Sri Lanka 467565 467565

Sarvodaya Economic Enterprises Development

Services 435181 261108

Samastha Lanka Praja Sanwardana Mandalaya 43212 32468

Agro Micro Finance 23562 9750

Christian Children's Fund Sri Lanka, Inc. 3000 3000

Total Sri Lanka 972520 773891

PHILIPPINES

Number of poorest clients reported Negros Women for Tomorrow Foundation 54863 52120 Center for Agriculture and Rural Development 109580 109580

TSPI Development Corporation 97295 97021

WOCCU/CUES Philippines 38761 38761

Norfil Foundation, Inc. 14002 1120

Producers Rural Banking Corporation 39963 29570 Alalay Sa Kaunlaran Sa Gitnang Luzon, Inc. 22392 21272

Community Economic Ventures 15767 15767

Ahon Sa Hirap Inc. 12065 12065

Talete King Panyulung Kampampangan 11588 10800

Rural Bank of Montevista 10665 10665

Kazama-Grameen 15709 10367

Hagdan Sa Pag-uswag Foundation 14115 9800

Milamdec Foundation, Inc. 9525 9525

Serviamus Foundation Incorporated Project: Small

Enterprise Development Program 8863 8863

Peoples Bank of Caraga 11690 7599

Rural Bank of Pres. M.A. Roxas 2990 2840

Total Philippines 489833 447735

CAMBODIA

Number of poorest clients reported Angkor Mikroheranhvatho Kampuchea Co., Ltd. 20502 19066

National Bank of Cambodia 322056 322056

ACLEDA Bank, Ltd. 122173 91566

(36)

36

MALAYSIA

Number of poorest clients reported Credit Union Promotion Centre/ Koperasi Kredit

Rakyat 42708 42708

Amanah Ikthiar Malaysia (AIM) 123289 92974

Total Malaysia 165997 135682

PAKISTAN

Sudar Paschimandal G Bank 79489 63590

Kashf Foundation 67552 15530

Orix Leasing Pakistan Limited 3000 2700

Development Action for Mobilization and

Emancipation 6980 2620

Total Pakistan 157021 84440

MYANMAR

Pact Myanmar 51169 51169

Microfinance Delta Project 39668 39668

Total Myanmar 90837 90837

3.1.c Latin America and The Caribbean

ECUADOR

Number of poorest clients reported Corporacion Viviendas Hogar de Cristo 15748 15748 Fondo Ecuatoriano Populorum Progresivo 32000 30000

FINCA Ecuador 42676 12376

Fundacion para el Desarrollo Integral Espoir 11251 8440

(37)

37

COLOMBIA

Number of poorest clients reported Caja de Compensacion Familiar de Antioquia 106888 79097 Oportunidad Latinoamericana Colombia 10951 10951

Corporacion Mundial de la Mujer 21468 3023

Bancolombia 5500 2750

Fundacion Mando Mujer 86816 30000

Total Colombia 231623 125821

BOLIVIA

Number of poorest clients reported Fundacion Boliviana para el Desarrollo 7853 3926

Pro Mujer- Bolivia 48496 38796

Fundacion para Alternativas de Desarrollo 21405 21405 Credito con Educacion Rural (CRECER)/ Freedom

from Hunger 51471 19867

Total Bolivia 129225 83994

DOMINICAN REPUBLIC

Number of poorest clients reported Asociacon Dominicana para el Desarrollo de la

Mujer 39933 20039

Total Dominican Republic 39933 20039

NICARAGUA

Number of poorest clients reported Asociacion de Oportunidad y Desarrollo de

Nicaragua 31884 25250

Fondo de Desarrollo Local 33676 16446

Financiera Nicaraguense de Desarrollo 25621 10000

Fundacion PRODESA 9923 6946

Fundacion Jose Nieborowski 14873 4462

(38)

38

HONDURAS

World Relief Honduras 14762 9389

Organizacion de Desarrollo Empresarial Femenino 13800 8280 Fundacion para el Desarrollo de Honduras 11253 7500

Asociacion PILAHR 5500 3575

Fundacion Jose Maria Covelo 14407 2962

Total Honduras 59722 31706

GUATEMALA

Number of poorest clients reported Fundacion para el Desarrollo Integral de

Programas Socioeconomicos 20946 16757

GENESIS Empresarial 42491 9860

Fundacion de Asesoria Financiera a Instituciones

de Desarrollo y Servicio Social 8439 6329

Catholic Relief Services 10508 6305

Instituto para la Superacion de la Miseria Urbana 20000 5100 FUNDEA- Fundacion de Desarrollo Empresarial y

Agricola 9013 2703

Total Guatemala 111397 47054

PERU

Number of poorest clients reported Consorcio PROMUC, Promocion de la Mujer y la

Comunidad 27851 17611

Edpyme Edyficar 47122 14750

Pro Mujer Perù 22871 7204

Equipo de Educacion y Autogestion Social 6005 4500 Consorcio de Organizaciones no

Gubernamentales de Promocion y Desarrollo de la Libertad

2968 1484

(39)

39

HAITI

Number of poorest clients reported Fonkoze Shoulder to Shoulder Foundation 28183 20000

Total Haiti 28183 20000

MEXICO

Number of poorest clients reported Fondo para el Desarrollo Social de la Ciudad de

Mexico 115089 103580

Centro de Apoyo al Microempresario 59037 56085 Instituto para el Desarrollo de la Mixteca A.C. 19020 15900

Asociacion PROMUJER de México 9386 5585

Total Mexico 202532 181150

BRAZIL

Number of poorest clients reported CREDIAMIGO- Programa de Microcredito do

Banco do Nordeste 162868 146644

Total Brazil 162868 146644

VENEZUELA

Institution Number of clients reported Number of poorest clients reported BANGENTE 13500 6750 Total Venezuela 13500 6750

3.1.d Others

YEMEN

Social Fund for Development-Yemen 18245 16420

(40)

40

JORDAN

Jordan Micro Credit Company 8352 6246

Total Jordan 8352 6246

UZBEKISTAN

DAULET (NGO Daulet) 3249 3054

Total Uzbekistan 3249 3054

EGYPT

Alexandria Business Association 38446 17853

Dakahlya Businessmen's Association for

Community Development 32571 14385

Al Tadamun Microfinance Program 9232 9232