empirical evidence on the growth of microfinance … · maksudova (2010) examined the impact of...

Vol-1, Issue-I Amity Journal of Strategic Management 2017

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EMPIRICAL EVIDENCE ON THE GROWTH OF MICROFINANCE SECTOR AND ITS IMPACT TO

INDIAN ECONOMY

Mosses Mwizarubi, Strategic Expert, Bank of Tanzania, Tanzania

In the past three decades Microfinance Institutions (MFIs) have been striving on financial inclusion agenda, especially to the destitute people, so as to improve their standard of living. It has been put in theory that if MFIs are really successfully in reaching large numbers of poor households,

then we would expect to see some kind of changes at the macro level. This paper therefore aimed at exploring the relationship between

microfinance growth and the associated impact in Indian macro-economy. The macroeconomic variables taken into consideration were GDP, GDP per capita, total investment, gross national savings, consumer price index (CPI), government revenue and current account balance. The

econometrics tests applied include the Augmented Dickey-Fuller unit root test, Variance Inflation Factor (VIF) test for multicollinearity and test

of correlation by using Ordinary Least Squares (OLS) method. The research reveals the importance of microfinance to macroeconomic growth and stability, something that has not been discussed my most researchers.

Keywords: Microfinance, Macro-economy, Financial inclusion, Quantitative, India

JEL Codes: G21, E01

Introduction

Financial services are the focal point to economic growth and development. Through financial services such as

banking, savings and investment, debt and equity financing, and insurance citizens are able to save money, guard

against uncertainty, and build credit, something that enables them to startup businesses, expand and increase

efficiency in their current businesses, thus being able to compete in local and international markets. For the poor,

financial services enable them to reduce vulnerability and manage their assets in ways that generate more income,

eventually creating paths out of poverty. (Sutton and Jenkins, 2007)

Despite the importance of financial services in the economic growth, financial exclusion is among the most

discussed challenged in the world. According to World Bank (2012), there are sharp disparities in the usage of

financial services when comparison is made between high-income and developing economies as well as across

demographic groups. This is when looking at the percentage of people having accounts in formal financial

institutions. While about half of all adults in the world have an account, the share in high-income economies is 89

percent while that of developing economies is 41 percent. The report further points out that more than 2.5 billion

adults in the world have no formal account, most of them living in developing economies. Demographically, the

gaps in account use are particularly large in developing economies, whereby 46 percent of men have an account

while only 37 percent of women do. Moreover, the individuals from the highest income quintile are on average more

than twice likely to have a formal account compared to those in the lowest quintile.

Seeing the challenge of financial exclusion, microfinance industry has emerged to be among the key players in the

global financial inclusion agenda. In India, Microfinance Institutions (MFIs), being among the key stakeholders to

financial inclusion agenda (Chakrabarty, 2012), are faced with a challenge of making sure that financial services

reach majority of the citizens, especially in rural areas. Although there are some limitations in the extent of their

outreach to those who are financially excluded, we cannot deny the fact that MFIs do break down many barriers to

financial inclusion as well (Shankar, 2013). Now, if microfinance institutions are really successfully in reaching

large numbers of poor households hence improving the financial inclusion status and increasing the incomes of these

people by giving them access to credit to fund private enterprises, then we would expect to see some kind of change

at the macro level (Mitchell, 2003). According to Sobhan (1997), we should expect transformation effects of micro-

credit in the macro-economy. This assumption is not only referring to the village economy, a macro-entity in itself,

but also to the national economy. In short, micro-credit interventions should have transformation effect on poverty

alleviation at the macro level as well as social transformation.

Basing on the above background, this study aimed at exploring the relationship between microfinance growth and

trends observed in the macroeconomic variables in India economy. The basic assumption here is that once the

relationship is well known, it will assist the India policy makers in adjusting the focus in the vision for India

financial sector development.

Literature Review

This motivation to conduct this study was conceived from the core motive behind provision of microfinance

services. Since its conception, microfinance has been growing rapidly with the major aim of lifting people out of


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poverty, and promoting economic growth and development. If this happens, then the impact of microfinance will be

revealed through macro-economic indicators. In laying a foundation for this study, a broader framework on finance

and growth is considered. In this framework microfinance is seen as a new pillar capturing informal intermediation

and directly contributing to financial sector development. Borrowed from Maksudova (2010) and modified, Figure 1

below is the best illustration of theoretical base, showing how microfinance links with the macroeconomic and

financial environment.

Figure 1: Microfinance Channels

Source: Maksudova (2010)

For the time being, it has empirically been proved that financial sector development has a positive contribution to

economic growth and development. Financial sector has been facilitating economic growth through mobilizing

savings, providing investment information, risk management, monitoring/governance, and facilitation of exchange

of goods and services (Levine, 2004), while at the same time maintaining its role of reducing information,

enforcement and transaction costs. Now, microfinance contributes towards this through three channels A, B and C as

shown in Figure 1 above. Below is a short discussion of the facts.

In Channel A, microfinance directly impact the macro-economy by adding value to small entrepreneurs and

businesses, positive spill-overs, reduction of poverty and income inequality, and improvements in human

development indicators such as health, nutrition, and education (Ravallion 2001). In Channel B, microfinance

contributes to economic growth indirectly through financial sector development i.e. it leads to improved access to

finance by integrating households’ financial needs and formalization of informal intermediation. This is particularly

more experienced in less developed economies. This is supported by Barr (2005) who suggested financial

development should be viewed through the lens of microfinance because: (i) financially sustainable MFIs foster

market deepening and therefore advancing financial development, (ii) microfinance can be a powerful tool in

countries that have poor governance, which leads to improper functioning of development programs, and (iii)

microfinance breaks down constraints thus supporting domestic financial reforms.

Within the financial sector, the nature of interaction between banks and MFIs is of particular importance in the

financial sector development particularly for low-income countries. Therefore, microfinance uses Channel C to

indirectly impact the macro-economy through linkage with banks and stock markets. Here, the focus is on the

interaction of commercial banks and MFIs, which is caused by the forces from both the banks and the MFIs. For

commercial banks, increasing competition pushes them to look for new markets and clients; hence engaging in

microfinance, which recently has shown to be profitable. For commercial banks, this is seen as a promising

opportunity to serve a large demand for credit that MFIs are unable to meet fully on their own. Delfiner and Peron

(2007) evidenced this by showing that there is a downscaling of commercial banks through their ventures into

microfinance. From a banker’s point of view, micro-lenders are seen as “specialists” in delivering micro-loans. On

the other hand, although MFIs are seen as specialists in delivering micro-loans, they lack enough resources to meet

the credit demand of all micro and small enterprises. At the same time these enterprises may not be able to pay a

higher interest rate to the MFIs as their business expands (Beck et al 2008). Therefore, the solution is to link the

microfinance sector with the banking sector so as to meet these needs.

FINANCIAL SECTOR DEVELOPMENT

FORMAL

Bank Sector

Stock Markets

INFORMAL

Microfinanc

e

IMPACT IN THE

MACROECONOMY

C

A

B

Indirect

Indirect

Direct


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There is a broad empirical literature relating financial inclusion and economic growth, as well as on the impact of

microfinance on social economic lives of people at microeconomic level (that is at individual level or firm level

especially for micro and small enterprises), but there is limited literature on the impact of microfinance on macro-

economy. The following paragraphs present a review of the empirical evidence that is either close or fitting in the

subject matter of this paper, thus identifying the research gap to be filled by this study.

Woolley (2008) looked at the impact of both the financial and the outreach performance of MFIs on domestic GDP

growth. Using panel data from the Mix Market and applying fixed effects regressions, the findings showed no

significant correlation between domestic GDP growth and microfinance performance, suggesting that microfinance

may not necessarily be an effective means of addressing poverty even in environments of low GDP growth. On the

other hand, Kai and Hamori (2009), using cross-country data of 61 developing countries, showed that microfinance

plays an important role in creating a financial system that has equalizing effect. They found that microfinance can

lower inequality and suggested that poorer countries need to focus more on these equalizing effects of microfinance.

Leegwater and Shaw (2008) looked at the impact of microfinance by analysing the role of micro, small, and medium

enterprises to the growth of per capita income. They found that there is a causal relationship between economic

growth and the prevalence these enterprises, although there was limited causal relationship between growth and the

prevalence of such firms. According to the authors of this paper, these findings can be replicated to microfinance as

it the key player in financing such kind of enterprises.

Maksudova (2010) examined the impact of microfinance on economic growth by using panel data of 103 countries

for the period from 1995 to 2008 and applying Arellano-Bond (1991) instrumental technique. The findings came

with the evidence that microfinance has Granger-causality on economic growth, and this relationship is positive only

in less developed countries where formal financial intermediation is immature thus leaving significant opportunity

for alternative means such as microfinance. Further, he pointed out that there is a possibility of this contribution to

be negative as the country experience further economic development as middle-income countries already face it

through current values. Buera, Kaboski and Shin (2012) propounded that when a typical microfinance program is

made widely available in an economy, it can have significant aggregate and distributional impacts to the economy,

more significantly through wages and interest rates. Nevertheless, it was pointed out that the vast majority of the

population are positively affected by microfinance through the increase in equilibrium wages.

A general picture drawn from the empirical findings is that microfinance is more important for economic

development of less developing economies, and therefore it is more pronounced there. From the above theoretical

framework and empirical evidences, this paper focuses on looking at the impact of microfinance to the macro-

economy in India – which is an emerging market and middle-income country. The study of this kind has not yet

been conducted in India so far.

Research Objective and Hypotheses

The objective of this research was to explore the relationship between microfinance growth and the associated

impact in Indian macro-economy. The macroeconomic variables taken into consideration in this study were Gross

Domestic Product (GDP), GDP per capita, total investment, Gross National Savings, end of period consumer price

index (CPI), government revenue and current account balance (CAB). In line with this objective, the research had

the following null and alternative hypotheses:

H0: There is no significant relationship between microfinance growth and changes in the macroeconomic

variables.

H1: There is a significant relationship between microfinance growth and changes in the macroeconomic

variable.

4.0 Research Methodology

This study adopted quantitative research techniques for data analysis purposes, and employed secondary data only.

The growth of microfinance sector was measures by using two criteria: (i) the increase in number of Self-Help

Groups (SHGs) – which is one of the two major microfinance models in India – that are provided with bank loan

from year 2001 to 2012, and (ii) the monetary amount of bank loan that is disbursed to SHGs from year 2001 to

2012. This kind of data was obtained from Microfinance in India: State of Sector Reports for seven consecutive


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years from 2006 to 20121. Data for the India macroeconomic indicators for the same periods were obtained from the

World Economic Outlook (WEO) database of the International Monetary Fund (IMF). STATA software (version

11) was used for data analysis and Ordinary Least Squares (OLS) method and Johansen’s cointegration tests were

used to establish the relationship between microfinance growth and changes in macroeconomic variables. Prior to

conducting the above mentioned tests, as pointed out by Gujarat (2003), the Augmented Dickey-Fuller unit root test

was done to check for stationarity of all the variables, and Variance Inflation Factor (VIF) test was used to test for

multicollinearity (if any) in the explanatory variables. The following functional forms summarize the study:

SHGs = f (GDP, GDPPC, Invest, Savings, CPI, GovRev, CAB)………………… (1)

Loans = f (GDP, GDPPC, Invest, Savings, CPI, GovRev, CAB)………………… (2)

Where: SHGs is the number of SHGs provided with bank loans, Loans represent the amount of bank loans disbursed

to SHGs, GDPPC is GDP per capita, Invest represents Total Investments, Savings stands for Gross National

Savings, CPI stands for end of period consumer price index, GovRev stands for government revenue and CAB

stands for current account balance.

5.0 Data Set and Analyses

The researchers succeeded to collect annual time series data for the variables intended for this study for twelve years

from 2001 to 2012. Table 1 below gives a summary of the raw data. The headings are in short form; the long form

(interpretation) of each variable is as shown in Section 4.0 above. This is the best that the researchers could collect

although their intention was to obtain monthly or quarterly data.

Table 1: Time Series Data on the Variables from 2001 to 2012

Year

SHGs Loans GDP GDPPC Invest Savings CPI GovRev CAB

Number in

Thousands

Billion

Rupees

Trillion

Rupees

Thousand

Rupees

Percent

of GDP

Percent

of GDP Index

Trillion

Rupees

Percent

of GDP

2001 263.825 4.81 26.315358 25.206281 22.607 22.896 101.296 3.939736 0.289

2002 461.478 10.26 27.514859 25.957414 23.933 25.317 104.536 4.371095 1.384

2003 717.360 20.49 29.400096 27.317162 26.113 27.597 108.423 5.001857 1.485

2004 1079.091 39.04 31.652693 28.95284 31.246 31.360 112.527 5.887634 0.113

2005 1618.456 68.96 34.517105 31.096491 34.222 32.947 118.790 6.812043 -1.275

2006 2238.565 113.98 37.759178 33.533906 35.263 34.241 127.000 8.310989 -1.022

2007 2924.973 123.66 41.564447 36.396188 37.692 36.996 134.000 10.398024 -0.696

2008 3625.941 169.99 44.135995 38.113985 34.643 32.215 147.000 11.059868 -2.428

2009 4224.338 226.76 46.359317 39.488345 36.995 34.937 169.000 11.770659 -2.058

2010 4587.178 272.66 51.564426 43.312371 36.921 33.687 185.000 13.987046 -3.234

2011 4813.864 306.19 55.558301 46.033224 35.332 31.918 197.000 16.312959 -3.414

2012 4354.567 363.41 57.77271 47.23197 34.915 29.802 219.000 18.711946 -5.113

Sources: Microfinance State of Sector Reports (2006-2012) and World Economic Outlook (2013)

The first thing that was done on analyzing the data was to test the stationarity of each variable by using Augmented

Dickey-Fuller test of unit root. As it can be seen in Appendix 1, all the variables except CPI were found to have the

unit root, thus they were not stationary and could not be suitable for regression analysis. The researchers decided

create other variables by take a natural logarithm of each variable (except CAB because it had some negative values

whose logarithm would be undefined thus useless) and subject them into the unit root test as well. As it can be seen

in Appendix 2, only two variables here – logarithm of SHGs and logarithm of loans – were found to be stationary.

This implies that the data that was suitable for OLS analysis was that of logarithm of SHGs, logarithm of loans and

CPI. Table 2 below fives a summary statistics for all the variables included in the study

1 Downloaded through the link http://www.microfinanceindia.org/1018-publications on October 19, 2013


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Table 2: Summary Statistics of the Raw and Created Variables

Source: STATA Output of the Research Data (2013)

The second step was to check for multicollinearity of the explanatory variables, by using variance inflation factor

(VIF) approach, so as to make the estimation exercise precise. As it can be seen in Appendix 3 at the first stage

Invest and Savings variables were found to have the highest VIF and hence were dropped; and in the second stage

GDP was found to have the highest VIF compared to the remaining variables, hence it was dropped. Appendix 4

gives a summary of the last three stages of the multicollinearity tests were by in the third stage GovRev variable was

dropped, in the fourth stage GDPPC had to be dropped while in the firth and last stage CAB and CPI were found to

have a VIF of less than 10 and hence suitable for regression analysis. However, it should be remembered that from

the unit root tests CPI was stationary while CAB was not, making again CAB not to be suitable for regression

analysis and leaving only CPI, which was then the only explanatory variable to be regressed against stationary

dependent variables – which are logarithm of SHGs and logarithm of Loans. After this step regression analysis and

cointergration tests were conducted, the finding of which are discussed in Section 6 below.

Findings and Discussion

From the regression analysis, it was found that microfinance growth has a significant positive relationship with the

CPI trend. Looking at Table 3 below we see that when CPI was regressed against logarithm of SHGs, it had a

positive coefficient of correlation, its t-score was greater than the critical values of t on both one and two tailed tests

at 5% significance level (1.796 and 2.201 respectively), and its F value was greater than the critical value read from

the F distribution (4.96). This makes us to reject the null hypothesis that there is no significant relationship between

SHGs growth and CPI and conclude that there is a significant positive relationship between the two variables. Table

3 below gives more details.

Table 3: Results of Regression of CPI on Log of SHGs


Again, looking at Table 4 below we see that when CPI was regressed against logarithm of Loans, it had a positive

coefficient of correlation, its t-score was greater than the critical values of t on both one and two tailed tests at 5%

significance level (1.796 and 2.201 respectively), and its F value was greater than the critical value read from the F

loggovrev 12 2.152905 .5229937 1.371114 2.929162 logcpi 12 4.934097 .2651204 4.618047 5.389072 logsavings 12 3.430471 .1402246 3.130962 3.61081 loginvest 12 3.467113 .1791245 3.11826 3.629448 loggdppc 12 3.539192 .2217386 3.227093 3.855071 loggdp 12 3.663426 .2734789 3.270153 4.056517 logloan 12 4.346513 1.416229 1.570697 5.895532 logshgs 12 7.515565 .9899199 5.575286 8.479256 cab 12 -1.33075 2.011517 -5.113 1.485 govrev 12 9.713655 4.846754 3.939736 18.71195 cpi 12 143.631 39.78262 101.296 219 savings 12 31.15942 4.113142 22.896 36.996 invest 12 32.49017 5.303357 22.607 37.692 gdppc 12 35.22001 7.759601 25.20628 47.23197 gdp 12 40.34287 10.91503 26.31536 57.77271 loan 12 143.3508 123.949 4.81 363.41 shgs 12 2575.803 1724.179 263.825 4813.864 Variable Obs Mean Std. Dev. Min Max

_cons 4.55425 .6546523 6.96 0.000 3.095594 6.012906 cpi .0206175 .0044056 4.68 0.001 .0108012 .0304338 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 10.7793548 11 .979941348 Root MSE = .58129 Adj R-squared = 0.6552 Residual 3.37900818 10 .337900818 R-squared = 0.6865 Model 7.40034665 1 7.40034665 Prob > F = 0.0009 F( 1, 10) = 21.90 Source SS df MS Number of obs = 12

. reg logshgs cpi


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distribution (4.96). This makes us to reject the null hypothesis that there is no significant relationship between Loans

growth and CPI and conclude that there is a significant positive relationship between the two variables. The

interpretation of these results is that the growth of microfinance sector has not been able to low the inflation in the

country; prices of goods and services have kept on rising despite the expansion of microfinance sector. Hence

although the incomes of poor people might be increasing as a result of support from microfinance institutions, there

is a great chance that their purchasing power will not improve significantly due to inflation. A proper solution is

needed here for poor people to enjoy fully the benefits of the microfinance industry as it grows.

Table 4: Results of Regression of CPI on Log of Loans


After using OLS method, keeping in mind that most of the variables were found to have a unit root, the researchers

also conducted Johansen’s cointegration test to establish the relationship between the variables. Appendix 5 shows

the results of the cointegration test between SHGs and all explanatory variables except CPI; and it was found that

GDP, GDPPC, Invest, GovRev and CAB had two cointegration relationships while Savings had only one

cointegration equation in a bivariate model with SHGs. On the other hand, looking at Appendix 6 that shows the

cointegration results between Loans and all explanatory variables except CPI, we find that only GovRev had two

cointegration relationships in a bivariate model with Loan, while GDP, Invest and Savings had only one

cointegration equation and GDPPC and CAB had no cointegration with Loan at all. Combining the two sets of

results together, we see that there is a stronger cointegration between microfinance growth and the increase in

government revenue than compared to other explanatory variables. A two way cointegration is found, whereby the

growth of microfinance seems to increase government revenues while at the same time the increase in government

revenues is a vital factor in the growth of microfinance. This suggests that the government should put more efforts

to facilitate the growth of microfinance industry, either directly or indirectly, because this will make the low income

people, micro and small enterprises more productive, and eventually the revenue is going to rise on the government

side. However, as pointed out by Hargreaves (1994), Johansen’s method is the best if the sample size is fairly large

(about 100 observations or more); the number of observations in this study small and hence limiting the study to

some extent. The results of this study would be more robust if the researchers succeeded to obtain monthly data, or

if that is not possible then at least quarterly data, for the chosen period.

Conclusion and Way Forward

This study aimed at looking at the relationship between the growth in microfinance industry and its implication to

the macro-economy. It we have seen, the growth of microfinance has been found to have connection mostly with the

inflation and government revenue. Inflation could not be brought down by the growth on microfinance sector; this is

probably because the amount of money supplied to MFIs is still very small compared to the national income, hence

its impact on the inflation will take long to be felt. On the other side, putting money to micro producers and micro-

enterprises by using MFIs has been seen to have impact in increasing government revenue, which implies that the

money supplied to them has a positive impact towards their productivity. Therefore, the government and other

stakeholders are encouraged to keep on putting efforts towards microfinance sector growth, and increase the flow of

funds as well as providing financial education to the target group. Although there are challenges in providing

financial services to the poor, there is still the hope that one day something good will come out of them; thus we

should not give up serving them.

_cons -.0144868 .8733312 -0.02 0.987 -1.96039 1.931416 cpi .0303625 .0058772 5.17 0.000 .0172672 .0434578 logloan Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 22.0627424 11 2.00570386 Root MSE = .77547 Adj R-squared = 0.7002 Residual 6.01347891 10 .601347891 R-squared = 0.7274 Model 16.0492635 1 16.0492635 Prob > F = 0.0004 F( 1, 10) = 26.69 Source SS df MS Number of obs = 12

. reg logloan cpi


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References

1. Barr, M. (2005), Microfinance and Financial Development, Michigan Journal of International Law, Vol. 26, p.

271, 2005.

2. Beck T, Demirgüç-Kunt A. and Honohan P. (2008), Finance for all? Policies and Pitfalls in Expanding Access,

World Bank Policy Research Report, World Bank.

3. Buera, F.J, Kaboski, J.P and Shin, Y. (2012), The Macroeconomics of Microfinance, National Bureau of

Economic Research, 1050 Massachusetts Avenue, Cambridge MA 02138, cited from

http://www.nber.org/papers/w17905 on October 31, 2013.

4. Chakrabarty K.C (2012), Financial Inclusion – Issues in Measurement and Analysis, Keynote Address by Dr.

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(Ed.), Nonstationary Time Series Analysis and Cointegration. Oxford University Press Inc., New York.

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1948-5433, Vol.1, No. 1: E14.

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October 2013, through the link

http://www.imf.org/external/pubs/ft/weo/2013/01/weodata/index.aspx

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A Cross-Country Regression Analysis, IRIS Center, University of Maryland, Department of Economics, 3106

Morrill Hall, College Park, MD 20742, USA.

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Bureau of Economic Research, Cambridge, MA.

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Center for Economic Research and Graduate Education, Academy of Sciences of the Czech Republic,

Economics Institute.

13. Microfinance India (2013), State of Sector Reports from 2006 to 2012, cited on 19th October 2013, through the

link http://www.microfinanceindia.org/1018-publications

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Appendix 1: Unit Root Test (Variables as they are)

MacKinnon approximate p-value for Z(t) = 0.9747 Z(t) 0.245 -3.750 -3.000 -2.630 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller

Dickey-Fuller test for unit root Number of obs = 11

. dfuller cab



. dfuller govrev



. dfuller cpi

MacKinnon approximate p-value for Z(t) = 0.1368 Z(t) -2.418 -3.750 -3.000 -2.630 Statistic Value Value Value Test 1% Critical 5% Critical 10% Critical Interpolated Dickey-Fuller


. dfuller savings



. dfuller invest



. dfuller gdppc



. dfuller gdp



. dfuller loan



. dfuller shgs


20

Appendix 2: Unit Root Test (Natural Log of Variables)



. dfuller loggovrev



. dfuller logcpi



. dfuller logsavings



. dfuller loginvest



. dfuller loggdppc



. dfuller loggdp



. dfuller logloan



. dfuller logshgs


21

Appendix 3: Multicollinearity Test (First Two Stages)

Mean VIF 6471.19 cab 10.27 0.097416 cpi 146.67 0.006818 govrev 159.44 0.006272 gdppc 13839.09 0.000072 gdp 18200.50 0.000055 Variable VIF 1/VIF

. vif

_cons 13.64265 6.432788 2.12 0.078 -2.097814 29.38312 cab .0122698 .1139467 0.11 0.918 -.2665478 .2910874 govrev -.4665884 .1863729 -2.50 0.046 -.9226264 -.0105503 cpi -.0659321 .021778 -3.03 0.023 -.119221 -.0126431 gdppc -2.036916 1.084564 -1.88 0.109 -4.690749 .6169175 gdp 1.973871 .8842143 2.23 0.067 -.1897238 4.137465 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 10.7793548 11 .979941348 Root MSE = .23727 Adj R-squared = 0.9426 Residual .337773613 6 .056295602 R-squared = 0.9687 Model 10.4415812 5 2.08831624 Prob > F = 0.0002 F( 5, 6) = 37.10 Source SS df MS Number of obs = 12

. reg logshgs gdp gdppc cpi govrev cab

Mean VIF 4.85e+07 govrev 198.86 0.005029 cpi 234.18 0.004270 gdppc 15214.28 0.000066 gdp 21063.14 0.000047 cab 2.79e+07 0.000000 savings 1.17e+08 0.000000 invest 1.94e+08 0.000000 Variable VIF 1/VIF

. vif

_cons 9.416344 4.34336 2.17 0.096 -2.642756 21.47544 cab 99.10282 119.5293 0.83 0.454 -232.7638 430.9695 govrev -.2763556 .1323395 -2.09 0.105 -.643789 .0910778 cpi -.0319393 .0174962 -1.83 0.142 -.0805166 .016638 savings -99.06234 119.5537 -0.83 0.454 -430.9966 232.8719 invest 99.1445 119.5598 0.83 0.454 -232.8067 431.0957 gdppc -1.39834 .7230223 -1.93 0.125 -3.405771 .6090921 gdp 1.28908 .6047862 2.13 0.100 -.3900757 2.968236 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg logshgs gdp gdppc invest savings cpi govrev cab


22

Appendix 4: Multicollinearity Test (Last Three Stages)

Mean VIF 8.76 cpi 8.76 0.114121 cab 8.76 0.114121 Variable VIF 1/VIF

. vif

_cons 5.901504 1.572076 3.75 0.005 2.345222 9.457787 cab -.244747 .2593473 -0.94 0.370 -.8314314 .3419374 cpi .00897 .0131133 0.68 0.511 -.0206944 .0386343 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 10.7793548 11 .979941348 Root MSE = .5845 Adj R-squared = 0.6514 Residual 3.07475233 9 .341639148 R-squared = 0.7148 Model 7.7046025 2 3.85230125 Prob > F = 0.0035 F( 2, 9) = 11.28 Source SS df MS Number of obs = 12

. reg logshgs cpi cab

Mean VIF 18.91 cab 10.10 0.098990 cpi 22.49 0.044458 gdppc 24.14 0.041421 Variable VIF 1/VIF

. vif

_cons 2.147222 1.112349 1.93 0.090 -.4178599 4.712305 cab .020024 .1453976 0.14 0.894 -.3152636 .3553115 cpi -.0338941 .01097 -3.09 0.015 -.059191 -.0085972 gdppc .2914035 .0582675 5.00 0.001 .1570385 .4257685 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg logshgs gdppc cpi cab

Mean VIF 54.64 cab 10.24 0.097640 cpi 36.49 0.027403 gdppc 63.86 0.015659 govrev 107.96 0.009263 Variable VIF 1/VIF

. vif

_cons -.1652826 2.213143 -0.07 0.943 -5.398534 5.067969 cab .0000766 .1425676 0.00 1.000 -.3370423 .3371955 govrev -.2301902 .1921073 -1.20 0.270 -.6844518 .2240714 cpi -.0237957 .0136071 -1.75 0.124 -.0559712 .0083799 gdppc .3786124 .0922864 4.10 0.005 .1603897 .5968351 logshgs Coef. Std. Err. t P>|t| [95% Conf. Interval]


. reg logshgs gdppc cpi govrev cab


23

Appendix 5: Cointegration Tests of Explanatory Variables on SHGs

2 10 -68.176331 0.38041 1 9 -70.569838 0.85991 4.7870 3.76 0 6 -80.397215 . 24.4418 15.41 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 2003 - 2012 Lags = 2Trend: constant Number of obs = 10 Johansen tests for cointegration

. vecrank shgs cab


. vecrank shgs govrev

2 10 -78.041859 0.19481 1 9 -79.125228 0.80479 2.1667* 3.76 0 6 -87.293625 . 18.5035 15.41 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 2003 - 2012 Lags = 2Trend: constant Number of obs = 10 Johansen tests for cointegration

. vecrank shgs savings

2 10 -78.353537 0.05533 1 9 -78.638142 0.77135 0.5692 3.76 0 6 -86.015941 . 15.3248* 15.41 rank parms LL eigenvalue statistic valuemaximum trace critical 5% Sample: 2003 - 2012 Lags = 2Trend: constant Number of obs = 10 Johansen tests for cointegration

. vecrank shgs invest


. vecrank shgs gdppc


. vecrank shgs gdp


24

Appendix 6: Cointegration Tests of Explanatory Variables on Loans


. vecrank loan cab


. vecrank loan govrev


. vecrank loan savings


. vecrank loan invest


. vecrank loan gdppc


. vecrank loan gdp

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