EMPIRICAL EVIDENCE ON THE GROWTH OF MICROFINANCE … · Maksudova (2010) examined the impact of...
Transcript of EMPIRICAL EVIDENCE ON THE GROWTH OF MICROFINANCE … · Maksudova (2010) examined the impact of...
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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
Source: STATA Output of the Research Data (2013)
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
Source: STATA Output of the Research Data (2013)
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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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
MacKinnon approximate p-value for Z(t) = 1.0000 Z(t) 3.102 -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 govrev
MacKinnon approximate p-value for Z(t) = 1.0000 Z(t) 4.226 -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 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
Dickey-Fuller test for unit root Number of obs = 11
. dfuller savings
MacKinnon approximate p-value for Z(t) = 0.1782 Z(t) -2.281 -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 invest
MacKinnon approximate p-value for Z(t) = 0.9942 Z(t) 0.993 -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 gdppc
MacKinnon approximate p-value for Z(t) = 0.9978 Z(t) 1.572 -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 gdp
MacKinnon approximate p-value for Z(t) = 1.0000 Z(t) 2.801 -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 loan
MacKinnon approximate p-value for Z(t) = 0.7655 Z(t) -0.966 -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 shgs
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Appendix 2: Unit Root Test (Natural Log of Variables)
MacKinnon approximate p-value for Z(t) = 0.9367 Z(t) -0.215 -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 loggovrev
MacKinnon approximate p-value for Z(t) = 0.9991 Z(t) 2.714 -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 logcpi
MacKinnon approximate p-value for Z(t) = 0.0642 Z(t) -2.760 -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 logsavings
MacKinnon approximate p-value for Z(t) = 0.0897 Z(t) -2.616 -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 loginvest
MacKinnon approximate p-value for Z(t) = 0.9521 Z(t) -0.073 -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 loggdppc
MacKinnon approximate p-value for Z(t) = 0.9460 Z(t) -0.133 -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 loggdp
MacKinnon approximate p-value for Z(t) = 0.0000 Z(t) -7.471 -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 logloan
MacKinnon approximate p-value for Z(t) = 0.0000 Z(t) -7.918 -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 logshgs
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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]
Total 10.7793548 11 .979941348 Root MSE = .15086 Adj R-squared = 0.9768 Residual .091029771 4 .022757443 R-squared = 0.9916 Model 10.6883251 7 1.52690358 Prob > F = 0.0006 F( 7, 4) = 67.09 Source SS df MS Number of obs = 12
. reg logshgs gdp gdppc invest savings cpi govrev cab
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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]
Total 10.7793548 11 .979941348 Root MSE = .30519 Adj R-squared = 0.9050 Residual .745137768 8 .093142221 R-squared = 0.9309 Model 10.0342171 3 3.34473902 Prob > F = 0.0001 F( 3, 8) = 35.91 Source SS df MS Number of obs = 12
. 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]
Total 10.7793548 11 .979941348 Root MSE = .2972 Adj R-squared = 0.9099 Residual .618314897 7 .0883307 R-squared = 0.9426 Model 10.1610399 4 2.54025998 Prob > F = 0.0002 F( 4, 7) = 28.76 Source SS df MS Number of obs = 12
. reg logshgs gdppc cpi govrev cab
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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
2 10 -59.831541 0.36973 1 9 -62.139599 0.92407 4.6161 3.76 0 6 -75.02926 . 30.3954 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 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
2 10 -58.512679 0.40950 1 9 -61.14662 0.97236 5.2679 3.76 0 6 -79.088256 . 41.1512 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 gdppc
2 10 -60.995176 0.39842 1 9 -63.536114 0.96653 5.0819 3.76 0 6 -80.521909 . 39.0535 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 gdp
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Appendix 6: Cointegration Tests of Explanatory Variables on Loans
2 10 -44.419968 0.47195 1 9 -47.61276 0.48736 6.3856 3.76 0 6 -50.953661 . 13.0674* 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 loan cab
2 10 -34.252504 0.34001 1 9 -36.330162 0.83853 4.1553 3.76 0 6 -45.447382 . 22.3898 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 loan govrev
2 10 -48.786944 0.04869 1 9 -49.036521 0.92593 0.4992* 3.76 0 6 -62.050199 . 26.5265 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 loan savings
2 10 -53.523458 0.00758 1 9 -53.561501 0.82536 0.0761* 3.76 0 6 -62.286602 . 17.5263 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 loan invest
2 10 -42.067511 0.00546 1 9 -42.094873 0.78206 0.0547 3.76 0 6 -49.712528 . 15.2900* 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 loan gdppc
2 10 -43.226637 0.00111 1 9 -43.232215 0.79110 0.0112* 3.76 0 6 -51.061632 . 15.6700 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 loan gdp