Download physical savings - Editorial Express

The Determinants of Household Savings in India
Pradyumna Dash
Abstract
This paper examines the determinants of household total saving, financial saving,
and bank fixed deposits in India during 1970 to 2011 using ARDL procedure developed
by Pesaran and Shin (1999) and Pesaran and et al (2001). It finds that an increase in per
capita income and a decrease in population per bank branch lead to an increase in all
types of savings. An increase in foreign saving leads to a decrease in household total and
financial savings. It also finds that an increase in rate of inflation has negative effect on
financial saving and it changes the composition of household total saving. Similarly, an
increase in real rate of interest has positive effect on bank fixed deposit and it affects the
composition of financial assets.

Pradyumna Dash is Associate Professor of Economics at Indian Institute of Management Raipur,GEC
Campus, Sejbahar, Raipur, India. Email: [email protected] and [email protected] .
1
The Determinants of Household Savings in India
I. Introduction
The gross domestic saving rate in India increased sharply from 18.95 percent
during 1980-1990 to 23 percent during 1990-2000. It has increased even further and has
exceeded 30 percent of GDP since 2004-05. Investment is also very high in India. But as
investment exceeds saving, India has been running a net saving deficit, which, in turn,
results in a current account deficit. The deficit has been growing sharply. For instance,
the current account balance has deteriorated from 2.32 percent of GDP in 2003-04 to 1.06 percent of GDP in 2006-07 and to -2.88 percent of GDP in 2009-2010. India has
become a net importer of capital from abroad.
More recently, the gross domestic saving rate has declined to 34.0 percent in
2010-2011 and 30.8 percent in 2011-2012 after reaching a peak of 36.8 percent in 200708. Besides this, the household saving, a major component of gross domestic saving, as
percentage of GDP has stagnated during 2005-2010 and declined to 23.5 percent in 201011 and 22.3 percent in 2011-12 after touching a record high of 25.2 percent in 2009-10.
Within the household saving, the share of financial saving has increased and the share of
physical saving has declined. It is argued that ‘…the reduction in the financial savings
rate of households could be partly attributable to inflationary tendencies in the economy
during the period that resulted in higher growth of private final consumption expenditure
than of personal disposable income and partly to a reduction in real interest rate’ (MOF,
2011-12, p.5). It is also argued that ‘…with real interest rates on bank deposits and
instruments such as small savings remaining relatively low on account of the persistent
high inflation, and the stock market adversely impacted by global developments,
households seemed to have favoured investment in valuables, such as gold’ (RBI, 2012,
p.20). The fall in financial saving and the increase in physical saving rates of households
have received a lot of media attention in the recent past (Santosh Kumar, 2012;
Narasimhan, 2012; TOI, 2012; BS, 2012). Therefore, it is important to understand the
determinants of household saving rates in India. Specifically, we ask the questions: why
the household saving rate has stagnated since 2005-06. What has caused the composition
of household saving rates to change? How does the household financial saving respond to
the rate of inflation? How do changes in the real rate of interest affect the household
financial saving? To answer these questions, we estimate the determinants of household
2
total saving, financial saving, and bank fixed deposits in India during 1970 to 2011 using
ARDL procedure developed by Pesaran and Shin (1999) and Pesaran and et al (2001).
There are many studies that have studied the determinants of saving abroad
(Edwards, 1996; Athukorala and Tsai, 2003; Shrestha and Chowdhury, 2007;
Jongwanich, 2010). There are also a few latest studies in Indian context (Athukorala,
1998; Loayza and et al, 2000; Athukorala and Sen, 2004; Agrawal and et al, 2010). This
study improves upon these earlier Indian studies in four ways: (i) we use much newer
data, (ii) we use household total saving, household financial saving, and bank fixed
deposits as dependent variables which few previous studies have undertaken; (iii) we
include variables not included by previous studies such as foreign saving and bank credit;
(iv) we use advanced econometric techniques.
The rest of the paper is structured as follows: Section II provides an overview of
the saving performance in India. Section III presents the analytical framework. Section
IV discusses the data and econometric procedures. The results are presented and analyzed
in Section V. The conclusions are presented in Section VI.
II. Saving in India
In this section, we present overview of the saving performance in India. Total
domestic saving is the sum of household, private corporate, and public sector savings. As
it can be seen from the figure 1, the household, private corporate, public sector saving
rates are very different from each other with respect to their levels. For example,
household, private corporate, and public saving rates fluctuated in the 9.45-24.53 percent,
1.3-9.7 percent, and -2.0-5.4 percent ranges and averaged 16.45 percent, 3.5 percent, and
2.6 percent, respectively, during 1970-2012. The household sector saving is the major
component of the domestic saving in India.
Let us turn to the trends of various saving rates. There is a gradual increase in all
types of saving rates in India except the public saving (see figure 1). The domestic saving
rate in India increased consistently during 1970-1971 to 1999-2000. The total domestic
saving rate increased from 14.21 percent in 1970-1971 to 24.81 percent in 1999-2000. It
was due to increase in household and private corporate savings rates. The domestic
saving rate increased significantly during 2002-2003 to 2007-08. It increased from 26.34
percent in 2002-03 to the peak of 36 percent in 2007-08. It was mainly on account of the
increase in private corporate and public sector saving rates. The domestic saving rate
declined from 33.05 in 2009-2010 to 31.62 percent in 2010-2011. It was due to decrease
in household and private corporate savings rates. However, India is still one of the top
savers in the world (Athukorala and Sen, 2004).
3
The household saving rate has also increased consistently during 1970-1971 to
2003-04. It increased from 9.45 percent in 1970-1971 to 24.11 percent in 2003-2004. But
it has stagnated in recent years. For example, the household total saving rate remained at
22.83 percent during 2005-1010 and declined to 22.03 percent in 2010-2011 after
increasing from 17.70 percent during 1990-2000 to 22.71 percent during 2000-2005. The
composition of household saving has also undergone a significant change since 20002001. The share of household financial saving in total household saving has declined
from 56 percent during 1990-2000 to 48 percent during 2000-2010. It has further
declined to 45 and 36 percent in 2010-2011 and 2011-2012, respectively (see figure 2).
The declining trend in savings of financial assets partly reflects the huge diversion of
funds to purchases of gold and silver (Yamini and Deokar, 2012, p.76). The next section
develops an analytical framework to study the reasons for the stagnation and the changes
in the compositions of the household saving rates in India.
4
III.
The Analytical Framework
a. The Base Model
Let us first assume a closed economy. As per the Keynesian theory, the aggregate
consumption behavior of household depends on the level of national income. Assuming
there is a linear relationship between the consumption spending and level of national
income, the consumption function can be expressed as
C=C0+cY -------------------------------------(1)
Where, C0= autonomous spending, c =marginal propensity to consume, and Y=the level
of national income.
But in an open economy with free capital mobility, consumption may be
influenced by foreign saving. Griffin (1970) argues that “foreign capital represents a
transfer of resources or purchasing power from one nation to another and how these
additional resources will be used cannot be determined a priori. There certainly is no
presumption that none will be used to increase consumption” (p.103). As both
consumption and investment are ‘normal goods’, it is expected that an increase in
purchasing power through increase in foreign saving will lead to an increase in both
consumption and investment. Therefore, consumption behavior of the household is
expected to depend on total available resources, that is, national income plus foreign
saving. Accordingly, the household consumption function can be stated as follows:
C=C0+c(Y+FS)-------------------------------(2)
5
Where, FS is foreign saving. As the household saving is the difference between total
income and total consumption expenditure, the household saving can be written as
S=Y-C ---------------------------------------(3)
If we substitute equation 2 in equation 3, we find
S= - C0 +(1-c)Y-cFS---------------------------(4)
This implies that given the level of national income, an increase in foreign saving
leads to a decrease in household saving in an open economy. In a flexible exchange rate
system, it may affect the saving through real exchange rate appreciation. For instance, an
increase in capital inflow leads to an appreciation of domestic currency vis-à-vis the
foreign currency and hence, makes foreign goods relatively cheaper. It may encourage
domestic consumption expenditure and thereby imports. In a fixed or a managed floating
exchange rate system, the time it takes to affect the domestic saving depends on whether
the monetary authority sterilizes (partly or fully) or does not sterilize the intervention. If
the intervention is not sterilized at all or sterilized partly, the increase in net capital inflow
leads to an increase in high powered money and thereby an increase in money supply,
which in turn, may lead to an increase (a decrease) in consumption (saving) spending and
imports. If the monetary authority sterilizes the intervention fully, it may not affect the
saving in the short-run. But as full sterilization cannot be sustained in the long-run, an
increase in foreign saving is expected to reduce domestic saving in the long run. It was
found that the increase in foreign saving reduced domestic saving in 32 underdeveloped
countries during 1962-64 (Griffin, 1970), Mexico (Sachs and et al, 1995), and India
(Agarwal,et al 2010).
In India, the exchange rate was pegged to a basket of currency of its major trading
partners from 1971 to 1992. Following the balance of payment crisis, India embarked on
the era of a managed floating exchange rate system in 1993. As a part of exchange rate
policy, the Reserve Bank of India has been managing exchange rate from time to time to
reduce excess volatility, prevent the emergence of destabilizing speculative activities,
maintain adequate level of reserves, and develop an orderly foreign exchange market
(Mohan, 2008, p.2059). One of the instruments of exchange rate management is
intervention with partial sterilization. Hence, it is expected that an increase in capital
inflow reduces domestic savings and increases consumption spending in India.
We expect that there is a positive relationship between the level of national
income and savings. This is as per the well-known Keynesian absolute income
hypothesis.
6
b. Modifications and Extensions
Apart from the national income and foreign saving, the household saving behavior
in India is expected to depend on some other relevant variables. The first variable is the
real interest rate. What is the effect of real interest rate (IR) on savings? The effect of an
increase in real interest rate on saving could be either positive or negative. An increase in
real interest rate increases the relative price of current consumption. Hence, it encourages
people to substitute saving (future consumption) for current consumption. Ceteris
paribus, the effect of an increase in relative price of current consumption due to increase
in real interest rate on saving is called ‘Substitution Effect’. So an increase in real interest
rate leads to an increase in savings through Substitution effect. On the contrary, an
increase in real interest rate also increases the purchasing power of the savers. It may
encourage the savers to consume more now and save less for the future. The effect of an
increase in purchasing power due to rise in real interest rate on saving is called the
‘Income Effect’. It results in a decrease in saving. If the substitution effect (does not)
outweighs the income effect, there is a net increase (decrease) in savings. Mackinnon
(1973) and Shaw (1973) argue that the net effect of an increase in real interest rate would
be positive in a typical developing country. The reasons are as follows: Firstly, in a
developing country, a large chunk of savings is kept in the form of bank deposits in the
absence of other financial instruments. Secondly, investors may have to accumulate some
savings in the form of deposits before they make investment. Therefore, an increase in
interest rate encourages saving, that is, the substitution effect outweighs the income
effect. On the contrary, if income effect outweighs the substitution effect, an increase in
interest rate discourages saving. Athukorala and Sen (2004) found that a 1 percent
increase in real interest rate is associated with a 0.20 percentage point increase in the
private saving rate in India.
Another factor that is expected to affect the household saving is inflation (INF).
Inflation is expected to affect saving either positively or negatively. An increase in
inflation erodes the real value of household assets. Hence, if an increase in inflation is
anticipated, it is usually associated with an increase in nominal rate of interest to
compensate for this loss. Household is expected to save those higher interest rate receipts
to maintain the real value of their wealth. Therefore, an increase in rate of inflation leads
to an increase in saving. If the inflation was unanticipated, it would not be reflected in
nominal rate of interest and hence interest receipts would not be sufficient to compensate
the households for the loss in their real value of assets. As a result, households will be
forced to reduce their current consumption to increase savings to maintain the real value
of their assets. Many previous studies have found inflation as significant determinants of
saving (Deaton, 1989; Athukorala, 1998; Loayza, and et al, 2000; Athukorala and Sen,
2004; Jongwanich, 2010).
7
However, the above arguments are based on either of the two assumptions: (i)
people spend smaller percentage of their income on necessities, (ii) Interest rates move in
tandem with rate of inflation. But in a developing country like India neither of these
assumptions is true. In India, people spend a large fraction of their income on essential
commodities such as oil, food, clothing and so on. Further, there is no one to one
correspondence between inflation and nominal interest rate. As a result, an increase in
inflation (whether anticipated or anticipated) forces people to spend more on essential
commodities as the demand for these goods and services are relatively inelastic. So a
smaller percentage income is available for saving. Hence, inflation is expected to affect
saving negatively . An increase in inflation is also expected to encourage physical saving
and discourage financial saving. It is so because it makes physical assets such as gold,
silver, and other precious metals more attractive as these are considered to be better
hedges against inflation than bank deposits. Therefore, an increase in inflation is expected
to lead to a fall in financial saving and an increase in physical saving.
Population per bank branch or bank density (BDN) is expected to have negative
effect on saving. After the nationalization of banks in 1969, the population per bank
branch has fallen significantly. For instance, the population per bank branch was 63
thousand in 1968-69 but it declined to 13 thousand in 2010-11. It might have contributed
to an increase in savings by increasing the accessibility to banks and reducing the cost of
availing banking services. Hence, saving might have responded negatively to the bank
density. It was found that improved access to banking facilities increased savings in India
(Athukorala, 1998; Athukoral and Sen, 2004; Agrawal and et al, 2010).
As per the life cycle theory of consumption function, the saving rate depends on
the growth rate of per capita income not the level of per capita income. An increase in
rate of growth of per capita income (GR) is expected to have either positive or negative
effect on saving. An increase in the rate of growth of per capita income leads to an
increase in lifetime income of younger-age groups relative to older-age groups. As a
result, the young would be saving more than what the old would be dissaving. So the net
effect is an increase in saving. However, according to Carroll and Weil (1994), as the
increase growth rate of per capita income leads to an increase in life time income of
younger-age group people, it may encourage them to consume more and save less. As a
result, savings would fall due to increase in the growth rate. Athukorala and Sen (2004)
found that an increase in growth rate of income by 1 percentage point leads to an increase
in private saving rate by 0.15 percentage point in India. Therefore, apart from the level of
the income, growth rate of income is also expected to determine the saving behavior in
India.
8
Liquidity constraint is expected to influence saving. Liquidity constraint refers to
a situation in which people borrow less than what they are willing to borrow to fulfill
their purchasing decisions. In a typical developing country with repressed financial
system, it may not be possible for people with low level of income to borrow against
future income, as they may be believed to be less creditworthy. Even if they have access
to credit market, they may face a much higher cost of borrowing. It is argued that the
liquidity constraint can arise on account of (i) regulation, (ii) the cost of enforcing loan
contracts, and (iii) the information on borrower’s creditworthiness available to lenders
(Jappelli and Pagano, 1994). Together these may lead to rationing and to differences
between borrowing and lending rates (Jaffee and Stiglitz, 1990). Therefore, consumers
are forced to save now to make lump-sum expenditure in the future. Jongwanich (2010)
found that an increase in bank credit of 1 percent results in a decrease in household
saving rate by 0.04 percent in the long-run in Thailand. As the total bank credit to the
private saector as percentage of GDP has increased from 2.79 percent during 1999-2000
to 9.09 percent during 2005-2010 in India, it is expected to have the negative impact on
the household saving. Therefore, availability of credit (BC) may be treated as potential
determinant of household saving in India.
Another factor that is expected to affect household saving in India is the changes
in the age structure of population. People in terms of age can be broadly divided into
three types: (1) people younger than 14, (2) people between ages 15 and 64, (3) people
older than 64. The working population mostly belongs to the second category and
dependent population mostly belongs to the first and third category. Working population
save and non-working population (or dependent) dissave. A decrease in dependent
population as a percentage of working population is expected to increase savings rate and
vice versa. It was found that a 1 percent increase in young dependency (i.e., ratio of the
population aged 14 and younger to the working population) leads to a reduction
household savings of 0.88 percent in the long run in Thailand. In India, the share of
dependent population (people younger than 14 and older than 64) in working population
(those ages 15 to 64) has declined from 78 in 1960 to 55 in 2011. This decrease in
dependent population as percentage of working population must have increased savings.
Therefore, the decrease in dependency ratio (DR), that is, percentage of dependent
population to the working population in the population is expected to increase household
saving.
Public saving (PS) is expected to affect household saving negatively. It is so
because a decrease in public saving (an increase in supply of government bonds) makes
the household sector to increase their saving in anticipation of future increase in taxes to
service the bonds. So the household sector care for the total saving, as they view the
public saving and household saving as perfect substitutes. At the extreme case, a decrease
9
in government saving would be offset by an increase in household saving so that the
national saving is unaffected. This is as per the Ricardian Equivalence proposition (Barro,
1974). It was found that public saving leads to a fall private saving in India (Athukorala
and Sen, 2004) and household and private savings in Thailand (Jongwanich, 2010).
Apart from public saving, an increase in corporate saving (CS) is also expected to
have negative impact on household saving. It is so because households own business
firms and hence take into account the saving decisions of business firms while making
their own saving decisions. Therefore, an increase in corporate saving is expected to
decrease household saving if the latter ‘pierce the corporate veil’. If this is the case,
private saving becomes that variable of interest and it is not required to study household
and corporate savings separately. However, it is argued that household savings may not
offset the corporate savings on account of the imperfect information to the shareholders
about the savings of firms, tax policies, liquidity constraints and so on (Jongwanich,
2012). Moreover, large fractions of household saving in India come from the people who
do not have any access to capital markets. Therefore, the CS is chosen to be included as
an explanatory variable in our household saving function.
c. The Empirical Model
On the basis of the above discussions, we specify the empirical saving function as
follows:
S =  (IR, INF, BDN, PCY, GR, BC, FS, DR, PS, CS)
Where, S is the saving rate. The independent variables with their expected signs in
brackets are given below:
IR = the real interest rate (?)
INF = the rate of inflation (?)
BDN = the bank density ()
PCY = the per capita income (+)
GR = the growth rate of per capita income (?)
BC = the availability of bank credit ()
FS = the foreign saving ()
DR = the dependency ratio ()
PS = the public saving ()
CS = the corporate saving ()
We have estimated saving function separately for household total savings (HTS),
household financial saving (HFS), and bank fixed deposit (BFD). We have estimated
10
financial saving because household total saving (financial saving plus physical saving)
may not respond to an increase in rate of inflation. It is so because an increase in rate of
inflation may encourage people to hold more of physical assets and less of financial
assets. As a result, an increase in physical saving in response to an increase in rate of
inflation may get offset due to a fall in financial saving. Hence, it is possible that the
effect of a change in the rate of inflation on household total saving may found to be
nonexistent. We have also estimated the term deposit function separately. It is so because
it would enable us to know whether changes in real interest rate cause substitutability
between term deposits and other forms of financial assets. Secondly, it would also enable
us to know the link between the real deposit rate and the financial saving through the
term deposits.
IV.
Data and Econometric Procedure
We use annual data from 1969-1970 to 2010-2011. Data on household total
saving, household financial saving, corporate saving, public saving, bank term deposits,
bank density, availability of credit, foreign saving, per capita GDP, and growth rate of
GDP are obtained from the Handbook of Statistics on Indian Economy, Bombay, India.
Dependency ratio is obtained from World Development Indicators, World Bank.
Variables related to saving such as household total saving, household financial
saving, time deposits, corporate saving, public saving are expressed as ratio of GDP at
market price. Inflation is measured as the percentage change in GDP deflator. Real
interest rate is calculated as r=ln[(1+i)/(1+inf), where i is the 1 to 3 years nominal bank
deposit rate and inf is the current rate of inflation measured by GDP deflator. Bank
density is measured as population (in thousand) per bank branch. Per capita income is
measured as per capita real GDP ( in rupees) in terms of 1999-2000 prices. Growth rate is
measured as the growth rate of real per capita GDP in terms of 1999-2000 prices.
Availability of credit is measured as a ratio of change in total bank credit to the private
sector to GDP. Capital import is proxied by the negative of current account deficit
(CAD), which is measured as a ratio of CAD to GDP. Dependency ratio is measured as
ratio of dependent population (people younger than 14 and older than 64) to working
population (those ages 15 to 64). CS and PS are measured as ratio of corporate and public
saving to GDP, respectively. All variables are expressed in logarithmic form.
The assumptions of the classical linear regression model require that both the
dependent and independent variables are stationary. In the presence of nonstationary
variables, there might be what Granger and Newbold (1974) call a spurious regression. A
spurious regression has a high R2, significant t-statistics, but the results are without any
economic meaning. Therefore, the first econometric step we have taken is to test if the
11
series are non-stationary by using Augmented Dickey-Fuller tests. It involves running
regression for each considered series with first difference as the dependent variable and
the first lagged level and the lagged first differences as independent variables. The
Akaike Information Criterion (AIC) determines the appropriate number of first
differences for ADF test. The null hypothesis of the test is that that the undifferenced
form of the series is non-stationary or contains a unit root. In order to reject the null
hypothesis, the t-statistic of the coefficient of the lagged level term must be larger in
absolute terms than the critical values reported in Fuller in Table 8.5.2 (1976, p.373).
Table 1 presents the ADF unit root test results for all variables. All variables do
not have the same order of integration. Variables such as household financial saving,
bank deposits, bank density, bank credit, percapita GDP, corporate saving are
nonstionary, whereas remaining variables are stationary I(0). But all non-stationary
variables are found to be stationary in their first difference I(1) (See table 1).
Table 1: ADF test for unit root
Variables t-statistics for level
without time trend a
HTS c
-1.86 (0)
HFS
-3.53 (1)**
BFD
-1.90 (0)
IR
-4.49 (1)***
INF
-6.58(0) ***
BDN
-2.16 (1)
PCY
4.27 (1) ***
GR
-6.48 (0)***
BC
-1.17 (1)
FS
-4.42 (0)***
DR
-0.36(1)
CS
-0.49(0)
PS
-3.19(0)**
t-statistics for level with
time trend b
-3.59(0)**
-3.49 (1)
-3.35 (0)
-5.15 (1) ***
-6.60 (0) ***
-2.03 (1)
-0.024 (0)
-6.92 (0)***
-2.42 (1)
-4.36 (0)***
-8.64(1)***
-2.43 (0)
-3.50(0)
t-statistics for without
time trend a
-7.71 (0)***
-11.02 (0)***
-7.13***
----- 3.11 (0) **
-2.96 (1) **
---7.31 (1)***
-----7.11(0)***
-7.50(0)***
Notes:
a
The critical values are -3.60, and -2.93 at 1%, and 5% level of significance, respectively.
b
The critical values are -4.19, and -3.52 at 1%, and 5% level of significance, respectively.
***, and ** denote rejection of null hypothesis at 1%, and 5% level of significance, respectively.
Figures in brackets indicate the lag length of the lagged dependent variable selected on the basis of Akaike
Information Criterion(AIC) criterion.
c
The ADF test statistics in household saving functions are -1.19, -1.28, and -1.24(without trend) and -2.76,
-3.05, and -3.01 (with trend) for the order 1, 2, and 3 respectively.
If the variables are found to be non-stationary, the next step is to test the
possibility of one or more co-integrating relationships between variables. There are
several methods available to carry out the cointegration test. The most commonly used
methods are two-step residual-based procedure (Engle and Granger, 1987) and the
12
system-based reduced rank regression approach (Johansen, 1991; Johansen, 1995). These
testing procedures require that all variables are to be integrated of order 1. As this is not
valid for our variables, we use an autoregressive distributed lag (ARDL) procedure
developed by Pesaran and Shin (1999) and Pesaran and et al (2001). The main advantage
of ARDL procedure is that it tests the existence of cointegrating relationship when
variables are of different order of integration through the bound test.
The ARDL framework for a three variable model can be expressed as follows:
p
p
p
i 0
i 0
i 0
ΔYt=α+   i ΔYt-i+  i ΔXt-i +   i ΔZt-i +λ1Yt-1 +λ2Xt-1+ λ3Zt-1 +u1t
Where, α is a constant, Yt is the endogenous variable, Xt and Zt are the
explanatory variables, p is the maximum number of lag to be used, βi, γi, µi, λ1,λ2, and λ3
are parameters. The variables with differences represent the short-term relationships
whereas the variables with one period lag represent the long-term relationships. The
ARDL method estimates (p+1)k regressions to find out optimum lag (of differences) for
each variable, where p is the maximum number of lags and k is the number of regressors
in the equation. The optimal model can be determined using the model selection criteria
such as The Akaike Information Criterion (AIC) and Schwartz-Baysian Criterion (SBC).
The null hypothesis of the cointegration test is that all estimated coefficients of lagged
level variables equal to zero, that is, there is no long-run relationship. In case of the above
three variable model, the null hypothesis is λ1=λ2=λ3=0. If the calculated F-statistic
exceeds the upper bound of the critical value reported in Pesaran and Pesaran (1997), the
null hypothesis of no long-run relationship can be rejected irrespective of the order of
integration. On the other hand, if the test statistic is less that the lower bound of the
critical value, the null hypothesis cannot be rejected. However, if the test statistic falls
between the upper and lower critical values, the conclusion is not clear. The diagnostic
(serial correlation, normality, functional form, heteroscedasticity) and stability
(cumulative sum of recursive residuals, cumulative sum of squares of recursive residuals,
recursive residuals) tests are conducted to ascertain the appropriateness of the ARDL
model.
V. Results
We first checked the correlation coefficient (r) between explanatory variables to
avoid multicollinearty problems. It was found that the per capita income is highly
correlated with dependency ratio (r = -0.99) and corporate saving (r = 0.96). The
correlation coefficient between corporate saving and dependency ratio was found to be 0.95. Therefore, dependency ratio and corporate savings have been dropped from the
saving function. We have included one dummy variable (D91) in total household saving
13
function as an additional explanatory variable for a sharp change in household saving
bahaviour in 1990-1991. The D91 takes the value of 1 for 1990-1991 and 0 for the
remaining years. We have also included two dummy variables (D78 and D96) in bank
deposit function as additional explanatory variables for a sharp change in bank deposits
in 1977-1978 and 1995-1996. The variables D78 and D96 take the value of 1 for 19771978 and for 1995-1996, respectively, and 0 for the remaining years.
Computed FStatistic
Critical
Values (%)
1
5
10
Table 2: Bound Test for Cointegration Analysis
Household Total Saving Household Financial
Bank Fixed
(HTS)
Saving (HFS)
Deposits (BFD)
2.81
4.58***
11.55***
Lower
Bound
(K=6)
3.26
2.47
2.14
Upper
Bound
(K=6)
4.54
3.64
3.25
Lower
Bound
(K=6)
3.26
2.47
2.14
Upper
Bound
(K=6)
4.54
3.64
3.25
Lower
Bound
(K=7)
3.02
2.36
2.03
Upper
Bound
(K=7)
4.29
3.55
3.15
Notes: The critical values are obtained from Pesaran, M. H. and Pesaran, B(1997), page no.478, Table F,
Case II: Intercept and No Trend.
*** denotes rejection of null hypothesis at 1 % level of significance
The next step is to test whether there is a long-run relationship between the
variables. The cointegration test result is presented in table 2. As the calculated Fstatistics exceed the upper bound critical values in household financial saving and bank
deposit functions, there exists a long run relationship between household financial
savings and bank deposits on the one hand and the explanatory variables on the other. In
the case of household total saving, the computed F –statistic lies between the upper and
lower critical values. This may be due to the uncertainty with regard to the order of
integration of household total saving. Therefore, the unit root test was carried out for the
household total saving using ADF t-statistic by increasing the order up to 3. It was found
that whether the household total saving has a unit root is mixed (for both with and
without trend specifications). For example, the household saving was found to have I(0)
for the order 0 and I(1) for the order of 1, 2, and 3 (see notes for table 1). The uncertainty
of the order of integration of the household saving might have led to the lack of long run
relationship between the household total saving and its explanatory variables. We,
therefore, decided to treat household saving as non-stationary variable and accordingly
estimated its long-run coefficients. The existence of long-run relationship in household
total saving function is also confirmed by a statistically significant coefficient of the error
correction term with a correct sign (see table 4).
14
The next step is to estimate the long run estimates for three saving functions. The
lags in the models are selected on the basis of Akaike Information Criterion (AIC). In the
total household saving function, inflation, growth rate of per capita income, public saving
are found to be insignificant. Variables such as public saving and growth rate of per
capita income are found to be insignificant in time deposit equation. In household
financial saving function, public saving and bank credit are found to be insignificant.
Hence, these variables are dropped to avoid specification bias problem. The key
regression test statistics and the diagnostic test statistics of the ARDL models are shown
in Table No 3. The high values of R2 for all the models show that the goodness of fit is
satisfactory. The F- statistics which measures the joint significance of all regressors in the
model are also significant at 1 percent level of significance. The Durbin-Watson statistics
are also close to 2.
Constant
IR
INF
BDN
PCY
GR
BC
FS
D78
D96
D91
R Bar Square
F Statistic
DW Statistic
Serial Correlation
Functional Form
Normality ᵪ2(2)
Heteroscedasticity
Table 3: ARDL Model Long run Results
HTS
HFS
ARDL(1,1,0,0,1,0 )
ARDL(1,1,0,1,1,1,0 )
-5.76 (-3.70)****
-0.68 (-0.39)
0.05 (1.44)
0.03(0.65)
---0.08(-1.90)**
-0.24 (-3.23)****
-0.63 (-4.50)****
0.60 (5.42)****
0.44 (4.38)****
--0.04 (1.01)
-0.09 (-1.19)
----0.10 (-3.22)****
-0.06 (-1.56)*
--------0.29 (2.07)***
---
BFD
ARDL(1,0,0,1,0,1 )
-1.15(-0.62)
0.05 (1.51)*
--0.69 (-7.12)****
0.65 (5.10)****
--0.27 (3.06)
0.07 (1.39)
1.39 (5.72)****
-0.42 (-2.37)***
---
0.95
F(9, 32)= 96.39
(p-value=0.000)
1.77
F(1, 31)= 0.06
(p-value=0.80)
F(1, 31)= 6.01
(p-value=0.02)
1.05(p-value=0.58)
F(1, 40)= 0.005
(p-value=0.94)
0.96
F(10, 31)=108.02
(p-value=0.00)
1.95
F(1, 30)= 0.021
(p-value=0.88)
F(1, 30)= 5.20
(p-value=0.03)
1.33(p-value=0.51)
F(1, 40)= 1.22
(p-value=0.27)
0.93
F(11, 30)=52.91
(p-value=0.000)
2.14
F(1, 29)= 2.10
(p-value=0.15)
F(1, 29)= 5.79
(p-value=0.02)
0.15(p-value=0.92)
F(1, 40)= 2.37
(p-value=0.13)
Note: 1. ****, ***, **, and * denote the level of significance at 1%, 5%, 10%, and 15%, respectively, for
two-tailed test.
2. Figures in parentheses are calculated t-ratios.
15
ΔConstant
ΔIR
ΔINF
ΔBDN
ΔPCY
ΔGR
ΔBC
ΔFS
D78
D96
D91
ECMt-1
Table 4: ARDL Model ECM Results
HTS
HFS
ARDL(1,1,0,0,1,0 ) ARDL (1,1,0,1,1,1,0 )
-2.85 (-3.08)****
-0.49 (-0.38)
0.0002 (0.02)
-0.07 (-2.42)***
---0.06 (-2.61)***
-0.12 (-2.52)***
0.74 (0.95)
0.29 (3.61)****
-1.33 (-1.15)
--0.06 (2.22)***
0.04 (1.22)
---0.05 (-3.14)****
-0.04 (-1.48)*
--------0.14 (2.13)**
---0.49 (-4.53)****
-0.72 (-4.20)****
BFD
ARDL(1,0,0,1,0,1 )
0.80 (-0.61)
0.04 (1.56)*
---0.48 (-4.52)****
-0.85 (-1.16)
--0.19 (3.13)
-0.04 (-1.63)*
0.96 (7.83)****
-0.29 (-2.52)***
---0.69 (-7.47)****
Note: 1. ****, ***, **, and * denote the level of significance at 1%, 5%, 10%, and 15%, respectively, for
two-tailed test.
2. Figures in parentheses are calculated t-ratios.
The diagnostic test statistics show that all the models pass the tests for serial
correlation, functional form, normality, and heteroscedasticity. The plot of the CUSUM
and CUMSUMSQ of all the three models against the critical bound of the 5% level of
significance show that all models are stable over time (see appendix).
The long run and short run estimation results are reported in table 3 and 4. As
expected, bank density, that is, population per bank branch (BDN) is an important
determinant of all types of saving rates in India. It suggests that a decrease in population
per bank branch by 1 percent leads to an increase in total saving rate by 0.24 percent,
financial saving rate by 0.63 percent, and bank deposit rate by 0.70 percent in the longrun. It also affects savings rate in the short-run. For instance, a decrease in population per
bank branch by 1 percent leads to an increase in total saving rate by 0.12 percent and
bank deposit rate by 0.48 percent in the short-run. This implies that an increase in access
to banking facilities leads to an increase in savings in India. This finding is consistent
with some of the previous studies findings (Athukorala, 1998; Athukorala and Sen, 2004;
Agarwal and et al., 2010).
The per capita income (PCY) has significant effect on all types of saving in the
long-run. An increase in per capita income by 1 percent leads to an increase in total
saving rate, financial saving rate, and bank deposit rate by 0.60, 0.44, and 0.65 percent,
respectively in the long run. This supports the Keynesian absolute income hypothesis for
the saving behavior in India. Our results are also consistent with some other studies
(Modigliani, 1993; Loayza et al, 2000; Athukorala and Tsai, 2003; Athukorala and Sen,
16
2004; Jongwanich, 2010). However, the per capita income affects total household saving
but not financial saving and bank deposit in the short-run.
The growth rate of real per capita income (GR) does not have significant effect on
any types saving in the long-run. It is consistent with some other studies (Agarwal, et al,
2010). But it has significant effect on household financial saving rate in the short run. For
instance, an increase in growth rate of real per capita income by one percent leads to an
increase in saving rate by 0.06 percent in the short-run. Some other studies have reported
similar findings (Athukorala and Sen, 2004; Jongwanich, 2010).
The coefficient of inflation (INF) is significant in financial saving function and
insignificant in household total saving and bank fixed deposits. Rate of inflation affects
financial saving negatively. For instance, one percent increase in inflation results in 0.08
and 0.06 percent fall in household financial saving rate in the long-run and short run,
respectively. These above results suggest two things. Firstly, although rate of inflation
does not affect the level of total household saving, it affects its composition. An increase
in rate of inflation leads to a decrease in financial saving and an increase in physical
assets such as gold, silver, and other precious stones. In other words, an increase in rate
of inflation encourages people to substitute physical assets for financial assets. For
example, an increase in rate of inflation from 3.80 percent during 2000-2005 to 6.22
percent during 2005-2010, and to 8.40 percent in 2010-2011 might have increased the
purchases of gold and silver, which, in turn, might have caused the valuables (as percent
of GDP) to increase from 0.79 percent to 1.29 percent and to 2.12 percent, respectively,
in the time period just-mentioned. Secondly, an increase in rate of inflation reduces the
real purchasing power of disposable income, which, in turn, decreases financial saving to
maintain a given consumption expenditure. This result contradicts with some previous
studies (Athukorala and Sen, 2004; Jongwanich, 2010).
The coefficient of real interest rate (IR) has positive sign in all equations except in
the error correction equation of the financial saving. But it is significant only in the bank
deposit equation. An increase in real interest rate by 1 percent leads to an increase in
bank deposits by 0.05 and 0.04 percent in the long-run and short-run, respectively. This
result is consistent with some of the previous studies (Athukorala and Tsai, 2003). The
above results suggest that although a decrease in real deposit rate does not lead to a
decrease in household total saving and household financial saving but it leads to a
decrease in bank’s fixed deposits. In other words, a decrease in real interest rates affects
the composition.
As expected, foreign saving (FS) is an important determinant of household total
and financial savings. It suggests that an increase in foreign saving by 1 percent leads to a
17
decrease household total saving and financial saving by 0.10 percent and 0.06 percent in
the long-run and 0.05 and 0.04 percent in the short-run, respectively. Similarly, an
increase in foreign saving by 1 percent leads to a decrease in bank deposit by 0.04
percent in the short-run. This is consistent with Agarwal and et al, (2010). This implies
that an increase in foreign saving reduces household saving by stimulating consumption.
This is also supported by the actual consumption and saving behavior of the households
in India. For example, the increase in foreign saving from -0.67 percent during 20002005 to 1.76 percent during 2005-2012 might have caused the net foreign assets held by
the RBI to increase from 14.5 percent to 21.6 percent on account of intervention, which,
in turn, might have caused in part the high powered money to increase from 15.1 percent
to 17.7 percent and, hence, the M3 to increase from 68.5 percent to 83 percent in the
above-mentioned time period. This increase in money supply might have caused the
growth rate of private final expenditure and gross fixed capital formation to increase from
7.88 percent to 14.10 percent and 25 to 31.70 percent during 2000-2005 and 2005-2010,
respectively (see table 5). This implies that an increase in foreign saving increases
(decreases) not only consumption spending (household saving) but also investment
spending in India.
Table 5: Some Macroeconomic aggregates
Variables
1990200020052000
2005
2010
Net Foreign Currency Assets held by the RBI 5.8
14.5
21.6
(% of GDP at MP)
High Powered Money (% of GDP at MP)
15.3
15.1
17.7
M3 (% of GDP at MP)
51.3
68.5
83.0
Private Final Consumption Expenditure (% of 65.93
63.13
57.61
GDP at MP)
Private Final Consumption Expenditure
14.25
7.88
14.10
(annual average growth rate)
Gross Fixed Capital Formation (% of GDP at 23.14
25.00
31.70
MP)
Gross Fixed Capital Formation (growth Rate) 15.73
14.54
17.10
Valuables (% of GDP at MP)
--0.79
1.29
20102011
17.8
18.5
87.3
56.53
17.00
30.38
14.19
2.12
Note: All variables are measured in terms of current prices.
Bank credit (BC) does not affect household total and financial saving rates, as its
coefficient has insignificant t-ratio. On the contrary, the coefficient of bank credit in bank
deposit function is significant in long-run and short-run but it has an unexpected sign.
The coefficients of error correction term (ECMt-1) are found to be large in
magnitude and are statistically significant. For instance, the coefficients of ECMt-1 are
0.49, 0.72 and 0.69 in total saving, financial saving, and bank deposit functions,
18
respectively. This implies that around 50 to 70 percent of the disequilibrium (deviation of
saving rate from their respective equilibrium level) of the previous year’s shock gets
corrected this year. In other words, this suggests that disequilibrium in saving function
occurring due to various shocks get adjusted completely between 1.5 and 2 years.
Table 6: Summary data on variables used in econometric analysis (annual averages)
Variables
Unit
1970198019902000200520101980
1990
2000
2005
2010
2011
HTS
%
11.43
13.51
17.70
22.71
22.83
22.03
HFS
%
4.51
6.71
9.94
10.57
11.53
9.97
BFD
%
2.63
4.12
5.17
6.46
10.26
10.07
IR
%
-1.07
0.01
1.83
2.58
1.36
0.22
INF
%
7.73
8.79
8.84
3.80
6.22
8.40
BDN
Thousand 30.67
15.42
14.64
15.79
14.93
13.23
PCY
Thousand 8.92
10.77
14.93
19.87
27.32
33.06
GR
%
0.63
3.35
3.62
4.23
7.08
7.08
BC
%
2.04
2.75
2.79
5.00
9.09
9.35
FS
%
0.09
1.83
1.27
-0.67
1.76
2.72
Gold & Silver %
1.1
1.7
2.6
On the basis of the above empirical results, it can be argued that the increase in
household total saving, financial saving, and bank fixed deposit rates from 1990-2000 to
2000-2005 can be attributed to increase in per capita income and decrease in foreign
saving rate. Besides these factors, a decrease in inflation rate and an increase in growth
rate of GDP might have led to the increase in household financial saving rate and an
increase in real interest rate might have caused the increase in bank fixed deposit rate
during the above-mentioned time period (see table 6).
However, in spite of the improvement in bank density and significant increase in
per capita income (that is, from Rs 19.87 thousand in 2000-2005 to Rs 27.32 thousand in
2005-2010), the household total and financial saving rates remained either nearly
stagnant or showed little improvement during 2005-2010 (see table 6). It is so because
the favorable impact of these variables might have been offset by the increase in capital
imports, which, in turn, might have increased to consumption of importables. However,
the time deposit rate increased as it unaffected by the foreign saving.
Both household total saving and financial saving rate declined in 2010-2011.It is
so because the negative effect of the increase in foreign saving might have offset the
positive effect of the increase in per capita income and of improvement in bank density.
An increase in rate of inflation further might have contributed to the fall in financial
saving and a decrease in real deposit rate might have caused the fall in time deposit rate
in 2010-2011.
19
VI.
Conclusions
The study attempts to find out the determinants of household total saving rate,
financial saving rate and bank fixed deposit rate in India during 1970 to 2011 using
ARDL procedure developed by Pesaran and Shin (1999) and Pesaran and et al (2001). It
finds that there is a long-run equilibrium relationship between these above-mentioned
saving rates on the one hand and explanatory variables such as per capita income, bank
density, foreign saving rate, rate of inflation, and interest rates on the other. It finds that
changes in real interest rate do not affect either household total or financial savings.
However, it affects bank fixed deposit positively. This suggests that although an increase
in deposit rate does not affect the level of household total saving and financial saving
rates but it affects the composition of household financial saving rate. In other words, an
increase in real deposit rate encourages people to substitute fixed deposits for other form
of financial and physical savings.
It also finds that an increase in per capita income, an improvement in bank density
increases all types of household saving rates. A decrease in foreign saving increases both
household total saving and household financial saving rates but not bank deposit. An
increase in rate of inflation does not affect household total saving rate but it affects
financial saving negatively. This implies that an increase in inflation does not affect the
level of household total saving rate but affects its composition. In other words, an
increase in inflation reduces household financial saving and increases household physical
saving.
The results suggest that the stagnation in household total saving rates in 20052010 and 2010-2011 on the face of increase of per capita income and improvement in
bank density can be attributed to increase in foreign saving or capital imports. The
decrease in financial saving rate in the recent past can be attributed to both increase in
foreign saving and inflation. Further, the decrease in bank deposit can be due to decrease
in real deposit rate.
Our results have some important policy implications. Firstly, policies should be
made to reduce foreign savings to increase domestic household savings as the former
does not supplement but substitutes household saving. Secondly, policy should be taken
to encourage expansion of commercial bank branches in rural areas. Thirdly, the Reserve
Bank of India should tighten its interest rate policy which would not only increase the
bank deposits by increasing the real interest rate but also would increase total financial
saving by decreasing the rate of inflation. Fourthly, monetization of budget deficit should
also be reduced to reduce the rate of inflation, which, in turn, would increase household
financial saving.
20
References
Agarwal, Pradeep, Sahoo, Prabhakar, and Dash, Ranjan Kumar, 2010, ‘Savings
Behaviour in India: Co-integration and Causality Evidence’, The Singapore Economic
Review, Vol.55, No.2, pp.273-295.
Athukorala, Prema-Chandra, 1998, ‘Interest Rates, Saving and Investment: Evidence
from India’, Oxford Development Studies, Vol.26, No.2, pp.153-169.
Athukorala, Prema-Chandra, and Tsai, Pang-Long, 2003, ‘Determinants of Household
Saving in Taiwan: growth, Demography, and Public Policy, Journal of Development
Studies, Vol.39, pp.65-88.
Athukorala, Prema-Chandra, and Sen, Kunal, 2004, ‘The Determinants of Private Saving
in India’, World Development, Vol.32, No.3, pp.491-503.
Barro, Robert J., 1974, ‘Are government bonds net worth?’, Journal of Political
Economy, Vol. 82, No.6, pp.1095-1117.
Business Standard (BS), 2012, ‘High Inflation further pushes household savings down’
August, 24. http://www.business-standard.com/article/finance/high-inflation-furtherpushes-household-savings-down-112082400064_1.html
Carroll, Christopher D., and Weil, David N., 1994, ‘Saving and Growth: A
Reinterpretation’, Carnegie-Rochester Series Conference on Public Policy, Vol. 40,
pp.133-192.
Deaton, Angus, 1989, ‘Saving in Developing Countries: Theory and Review’,
Proceedings of the World Bank Annual Conference on Development Economics, pp.6196, Washington DC.
Edwards, Sebastian, 1996, ‘Why Are Latin America’s Saving Rates so Low? An
International Comparative Analysis’, Journal of Development Economics, Vol.51, No.,
pp.5-44.
Engle, Robert F., and Granger, C. W. J., 1987, ‘Co-integration and Error Correction:
Representation, Estimation, and Testing,’ Econometrica, Vol.55, No.2, pp.251-276.
Friedman, Milton, 1957, A Theory of the Consumption, Princeton: Princeton University
Press.
Fuller, Wayne A., 1976, Introduction to Statistical Time Series, New York: Wiley &
Sons.
Granger, C. W. J, and Newbold, P., 1974, ‘Spurious Regressions in Econometrics’,
Journal of Econometrics, 2, pp.111-120.
21
Griffin, Keith, 1970, ‘Foreign Capital, Domestic Savings and Economic Development’,
Bulletin of the Oxford University Institute of Economics and Statistics, Vol.32, Issue.2,
pp. 99-112.
Jaffee, Dwight, and Stiglitz, Joseph, 1990, ‘Credit Rationing’ in Handbook of Monetary
Economics, Vol. II, Benjamin M. Friedman and Frank Hahn, eds. Amsterdam: NorthHolland.
Jappelli, Tullio, and Pagano, Marco, 1994, ‘Saving, Growth, and Liquidity Constraints’,
The Quarterly Journal of Economics, Vol. 109, No.1, pp.83-104.
Johansen, Soren, 1991, ‘Estimation and Hypothesis Testing of Cointegration Vectors in
Gaussian Vector Autoregressive Models’, Econometrica, Vol.59, No.6, pp.1551-1580.
Johansen, Soren, 1995, Likelihood Based Inference in Co-integrated Vector
Autoregressive Models, Oxford: Oxford University Press.
Jongwanich, Juthathip, 2010, ‘The Determinants of Household and Private Savings in
Thailand’, Applied Economics, Vol. 42, pp. 965-976.
Keynes, John Maynard, 1936, The General Theory of Employment, Interest, and Money.
Cambridge: Harcourt Brace and Company.
Loayza, Norman, Schmidt-Hebbel, Klaus, and Serven, Luis, 2000, ‘What Drives Private
Saving Across the World?’ The Review of Economics and Statistics, Vol. 82, pp. 165181.
Loayza, Norman. and Shankar, Rashmi, 2000, ‘Private Saving in India’, The World Bank
Economic Review, Vol.14, No.3, pp.571-94.
Mackinon, Ronald I., 1973, Money and Capital in Economic Development, Washington
DC: Brookings Institutions.
Modigliani, Franco, 1970, ‘The Life Cycle Hypothesis of Saving and Inter-country
Differences in the Saving Ratio’ in W. A. Eltis, M. F. Scott, & J. N. Wolfe (Eds)
Induction, Growth and Trade, London: Macmillan.
Modigliani, Franco, 1993, ‘Recent Declines in the Saving Rate: A Life Cycle
Perspective’, in World Saving, Prosperity and Growth (Eds) M. Baldassarri, L. Pagenetto
and E.S Phelps, pp.249-86, London: Macmillan
Mohan, Rakesh, 2008, ‘Capital Flows to India’, RBI Bulletin, December, pp.2047-2079.
22
Ministry of Finance (MOF), 2011-12, Economic Survey, Government of India, New
Delhi.
Narasimhan, C. R. L., 2012, ‘When household savings evaporate…’ The Hindu,
September, 9. http://www.thehindu.com/opinion/columns/C_R_L__Narasimhan/whenhousehold-savings-evaporate/article3878073.ece
Pesaran, M. Hashem, and Pesaran, Bahram, 1997, Working with Microfit 4.0:Interactive
Econometric Analysis, Oxford: Oxford University Press.
Pesaran, M. Hashem, and Shin, Yongcheol, 1999, ‘An Autoregressive Distributed Lag
Modeling Approach to Cointegration Analysis’, in Econometrics and Economic Theory
in the 20th Century: The Ragnar Frisch Centennial Symposium (Eds) Strom, Steinor,
Cambridge: Cambridge University Press.
Pesaran M. Hashem, Shin, Yongcheol, and Smith, Richard, J., 2001, ‘Bound Testing
Approaches to the Analysis of Level Relationships’, Journal of Applied Econometrics,
Vol.16, No.3, pp.289-326.
Reserve Bank of India (RBI), 2012, Annual Report, Mumbai.
Sachs, Jeffery, Tornell, Aaron, and Velasco, Andres, 1995, ‘The Collapse of the Mexican
Peso: What have we Leared?’ NBER Working Paper Series, No.5142.
Santosh Kumar, M. V. S., 2012, ‘Household savings hit 21-year low’ The Hindu Business
Line,
August,
24.
http://www.thehindubusinessline.com/industry-andeconomy/economy/article3817126.ece
Shaw, Edward S., 1973, Financial Deepening in Economic Development, New York:
Oxford University Press.
The Times of India (TOI), 2012, ‘Household savings lowest in 22 years’, August 25.
http://timesofindia.indiatimes.com/business/india-business/Household-savings-lowest-in22-years/articleshow/15650813.cms
Yamini, Shruti, and Deokar, Bipin, 2012, ‘Declining Household Savings’ Economic and
Political Weekly, Vol.XLVII, No.50, pp.75-77.
23
Appendix
Panel A: Household Total Saving
Plot of Cumulative Sum of Recursive Residuals
15
10
5
0
-5
-10
-15
1970
1975
1980
1985
1990
1995
2000
2005
2010
2011
The straight lines represent critical bounds at 5% significance level
Plot of Cumulative Sum of Squares of Recursive Residuals
1.5
1.0
0.5
0.0
-0.5
1970
1975
1980
1985
1990
1995
2000
2005
2010
2011
The straight lines represent critical bounds at 5% significance level
Panel B: Household Financial Saving
Plot of Cumulative Sum of Recursive Residuals
20
15
10
5
0
-5
-10
-15
-20
1970
1975
1980
1985
1990
1995
2000
2005
2010
The straight lines represent critical bounds at 5% significance level
24
2011
Plot of Cumulative Sum of Squares of Recursive Residuals
1.5
1.0
0.5
0.0
-0.5
1970
1975
1980
1985
1990
1995
2000
2005
2010
2011
The straight lines represent critical bounds at 5% significance level
Panel C: Bank Deposits
Plot of Cumulative Sum of Recursive Residuals
15
10
5
0
-5
-10
-15
1970
1975
1980
1985
1990
1995
2000
2005
2010
2011
The straight lines represent critical bounds at 5% significance level
Plot of Cumulative Sum of Squares of Recursive Residuals
1.5
1.0
0.5
0.0
-0.5
1970
1975
1980
1985
1990
1995
2000
2005
2010
The straight lines represent critical bounds at 5% significance level
25
2011