This paper proposes to quantify the macroeconometric relationships among the variables broad money, lending by banks, price, and output in India using simultaneous equations system keeping in view the issue of endogeneity.Money, Output, Price, WPI, IIP, Credit, Commercial Bank, Endogeneity

Das, Rituparna

English

Research Papers in Economics

Working Paper March 2009
Endogenous Money, Output and Prices in India – Simultaneous Equations Model
Rituparna Das
I. Introduction
The current literature on endogenous money supply in India described how the issue of
endogeneity of money supply was born when RBI relied on credit control on banks in
1970s, but found it ineffective because banks managed to take advantage of loopholes in
the definition of deposits or credit, use cash credit, and mobilise non-deposit resources. It
also described how, with the acceptance of money multiplier approach the RBI moved
toward closer monitoring of reserve requirements, which were also blunt instruments. It
further  narrated  how  automatic  financing  of  the  budget  deficit  endogenized  the  base
money though the link was weakened by onset of financial reforms in 1991 but banks
demonstrated their ability induced by financial reforms to circumvent controls to satisfy
profit motive.
In the above literature
a.  endogeneity of money supply is attributed to the lending activities of the banks and
b.  the theoretical propositions by and large belong to the Post Keynesian school.
This paper proposes to quantify the macroeconometric relationships among the variables
broad money, lending by banks, price, and output in India using simultaneous equations
system keeping in view the issue of endogeneity.
II. Literature Review
a. Findings of Rangarajan and Mohanty (1997)
Rangarajan and Mohanty (1997) vividly dealt with endogenous money supply in India
relating it to fiscal deficit with the help of open economy model. The goal of their model
was to place emphasis on the role of deficit and money in the real economy by bringing
out the nexus between money, output and prices. Applying the order condition it was
seen that none of the equations had over-identification problem. The model was solvedby running deterministic simulation in both static and dynamic framework for the period
1975-76 to 1993-94. The main linkages in the model of Rangarajan and Mohanty (1997)
were as follows: Given the availability of borrowed resources from non-monetary and
external sources, a part of the deficit was financed by borrowing from RBI. The money
stock therefore evolved endogenously through the feedback from reserve money, which
varied  with  the  changes  in  the  fiscal  deficit.  The  money  stock  along  with  output
determined the price level in the economy, which in turn determined the relative prices of
exports and imports. To the extent that nominal exchange  rate deviated from its full
purchasing parity level, given the domestic and world price levels, fiscal deficit financed
by money creation led to appreciation of real exchange rate, leading to a rise in imports
and  decline  in  the  competitiveness  of  exports.  The  resulting  current  account  deficit
created a financing need and increases the stock of external debt and interest payment,
reinforcing the initial deterioration in the current account balance. Another source of
transmission  could  arise  from  the  financial  balance  of  the  private  sector,  which  was
behaviourally linked to the government deficit. In short they found that money supply
was determined by reserve money, and in turn determined the exchange rate.
b. Rath (1999)
Findings  of  Rath  (1999)  used  Granger-causality  framework  to  identify  the  relative
consistency of the three models with the data  at hand: pure  portfolio  approach,  loan
demand  approach  and  portfolio  loan  demand  approach.  The  following  were  his
conclusions:  There  might  exist  a  paradigm  of  mixed  portfolio-loan  model  for  India.
Within the post-Keynesian endogenous money framework, there was a reason to support
the structural approach over the accommodative endogeneity approach, since the non-
stationarity in the bank loan-reserve  ratio  and  its  corresponding  time  variance  would
indicate that loans had not grown proportionately over time relative to reserves. Some of
the reasons for absence of proportionality could be, among others, the practice of liability
management that banks did leading to a situation of growth in lending in excess of the
growth of reserves.
c. Dash and Goal (2001)Dash and Goal (2001) found that the money supply process in India lying between two
extreme beliefs of economists: (a) Money supply could be endogenous and (b) Money
supply could be controlled.
III. The model in this paper
Endogeneity of reserve money or bank credit means presence of causality from other
variables  to  these  variables.  A  model  of  endogenous  money  supply  is  a  variant  of
causality model. It seems necessary to explore their recent interrelationships especially in
the post reform period from 1996-97 to 2002-03 because conclusive data are available till
2003 in the RBI sources as in March 2009. In order to maintain consistency the data on
all variables are taken till 2002-03. Quarterly data is taken on gross domestic product
(GDP), wholesale price index (WPI) and broad money (M3). From the economic theory
of money supply it is known that money supply causes price level and in a less developed
country like India where development is credit financed, money supply causes output
also. So the following simultaneous equations model is proposed
1:
Y = α0 + α1P + α2M + u1t, Y represents GDP, P represents WPI, M represents M3.
Y = β0 + β1P + u2t
Here it is assumed that M is exogenous whereas Y and P are  endogenous. The  first
equation  is  not  identified  by  the  order  condition  which  is  a  necessary  condition  of
identification whereas the second equation is exactly identified by the order condition as
well as the rank condition. The reduced form equations corresponding to the preceding
structural equations are
Yt = Π0 + Π1M + wt
Pt = Π2 + Π3M + vt
Here the Πs are the reduced from coefficients and are the non-linear combinations of the
structural  coefficients  where  w  and  v  are  linear  combinations  of  the  structural
disturbances u1 and u2. Here each reduced form equation contains only one endogenous
variable  which  is  the  dependent  variable  and  is  a  function  solely  of  the  exogenous
variable and the stochastic disturbances. Hence the parameters of the preceding reduced
form equations may be estimated by the OLS. These estimates are Π1
# = {∑(Y-Y*)(M-
                                                          
1 Maddala (1989) has developed a similar model involving three time series variables. p 299M*)}/∑(M-M*)
2, Π0
# = Y* - Π1
# M*, Π3
# = {∑(P-P*)(M-M*)}/∑(M-M*)
2, Π2
# = P* -
Π3
#M*, Y* and M* are respectively mean Y and mean M whereas Π0
#, Π1
#, Π2
# and
  Π3
#
are estimates of Π0, Π1, Π2
 and
  Π3
 respectively. The computed values are Π1
# = 0.095 and
Π3
# = 4, Π0
# = 273864.1644, Π2
# = 101. Now the following estimates of reduced form
coefficients  are  obtained:  β1
#  =  Π3
#/Π1
# =  42.1,  β0  =  Π2
#  -  β1
#  Π0
#  =  101  –  (42.1  x
273864.1644) = -11531021.71. The second equation stands as GDP = -11531021.7 + 42
P. These are the indirect least square estimates. The negative intercept means that without
credit finance it is not feasible to obtain positive netput (net output) in the economy.
Here there is an effort to estimate the famous quantity equation M = kPY, where M is
money supply, PY is nominal GDP and k is income velocity of money. Here Y is taken at
1993-94 constant P. So Y is divided by the 1993-94 constant P in order to give real Y and
then multiplied by P in order to give true nominal Y with a view to making the model in
line with macroeconomic theory.  Since all the variables have deterministic trend one can
first estimate the equation and then test the residuals whether they are stationary. Thus
the equation M3 = k GDP is estimated. The estimated equation stands as
M3 = 0.484 GDP
          (1.35)            R
2 = 0.2348
Here  the  residuals  are  found  to  have  a  downtrend,  the  t  value  is  very  poor  and  the
regression coefficient is very week. So first one needs to stationarise the variables. This is
done by deducting their means from the respective variables. If the true nominal Y is
called TY, the mean of TY called TY* and the mean of M called M*, then TY-TY* can
be called ‘ty’ and M-M* called ‘m’. Now regress if m on ty is run without intercept.
The estimated equation is:
m = 7.8 ty
    (16.77)            R
2 = 0.9
In order to get back to the original M and TY the means are added with the respective
variables in order to give M = 7.8 TY, where M = m + 1187943 and TY = ty + 192024.1.
Here the residuals are purely stochastic, i.e. white noise. The economics of the above
result lies in the fact that true nominal GDP growth is backed by  real GDP growth.
Increase in volume of goods and services need be accompanied by increase in the volume
of money supply in order to lubricate the exchange processes and facilitate transactions.Again it is also true that endogenous money supply in form of bank credit increases in the
wake of increase in planned output. Further, rise in nominal GDP leads to rise in factor
payments which are made by checks or drawing upon the banks. In such a situation banks
often need knock the door of the central bank for more reserves so much so that money
supply rises. Another explanation can be in the context of exchange rate of rupee vis-à-
vis  other  currencies.  When  domestic  output  increases  transaction  demand  for  money
increases  and  hence  total  demand  for  money  increase.  This  makes  rupee  appreciate
ceteris  paribus  vis-à-vis  other  currencies  like  US  dollar  or  the  reference  basket  of
currencies. Too much of such appreciation may create undesirable quantum of foreign
exchange outflow. So the central bank might require to increase money supply to the
extent sufficient to make the value of rupee reduce to the target level or it has to buy US
dollars sufficient in exchange for rupee to make US dollar appreciate vis-à-vis rupee or
do both in order to maintain a the desired level of rupee/US dollar rate.
Next the relationship between money, output and prices is examined with the help of
partial  adjustment  model.  One  should  focus  here  on  how  output  (GDP)  responds  to
commercial bank credit (CBC) and prices (WPI). Here there are certain features of the
RBI data to be noted. The monthly CBC data from 1990-1 and 1999-2000, quarterly GDP
data at 1993-94 prices from 1996-97 to 2002-03 and the monthly WPI data at 1993-94
prices from 1994 to 2002-03 are taken from the RBI source. In order to bring the data on
all the three variables on a uniform footing one should choose monthly data from 1994-
95 to 1999-00 for CBC and for WPI and IIP at 1993-94 prices. Following Acharya and
Kamaiah (1998) index of industrial production (IIP) is substituted for GDP because IIP
data at 1993-94 prices are available1994-5 to 1999-2000 while GDP data are not. The
following models are proposed:
IIPt = α + β CBCt + δ IIPt-1 + υt
IIPt = α + γ WPIt + δ IIPt-1 + υt
IIPt = α + β CBCt + γ WPIt + δ IIPt-1 + υt
Before estimation one should check the stationarity status of all the variables. It was
found that IIP at first difference, and CBC and WPI at second difference are white noise.
The observation is verified by Augmented Dickey-Fuller Tests for the variables at their
first and second differences with intercept. So the models to be estimated areIIP_2t = α + β CBC_2t + δ IIP_2t-1 + υt
IIP_2t = α + δ IIP_2t-1 + γ WPI_2t  + υt
IIP_2t = α + β CBC_2t + γ WPI_2t + δ IIP_2t-1 + υt
‘_2’ means at the second difference e.g. IIP_2t means the value of IIP at the second
difference for the period t.
The estimated equations are respectively
IIP_2t = 0.24361 -0.5354 IIP_2t-1 -0.0003 CBC_2t + υt
              (0.26859) (-5.2299)         (-2.8774)            R
2 = 0.52093
IIP_2t = 0.14919 -0.6853 WPI_2t -0.7311 IIP_2t-1 + υt
             (0.15553)  (-0.5841)         (-6.9623)             R
2 = 0.4636
IIP_2t = 0.23931 -0.7917 CBC_2t -0.5706 WPI_2t -0.0003 IIP_2t-1
           (0.26285)  (-0.711)             (-5.0028)           (-2.8886)     R
2 = 0.51731
Now one need examine whether the error terms are white noise. So their graphs are
checked. All the errors are found white noise. Here cointegrating vector is not found
because  of  different  orders  of  integration  of  the  level  time  series  data  nor  is
multicollinearity problem detected such as to require application of principal component
analysis.
IV. Conclusion
Without  credit  finance  it  is  not  feasible  to  obtain  positive  netput  (net  output)  in  the
economy given the feedback from price to output via money. This result is in line with
Dash and Goal (2001). They found that during the credit liberalisation regime the banks
were circumventing the RBI control and expanding credit. Again Rangarajan and Arif
(1990) found price level to have been determined by money supply also. Again during
post liberalisation regime banks got more autonomy in extending credit. It seemed that
bank  credit  had  influenced  prices,  which  in  turn  had  influenced  output.  Thus  GDP
responded strongly to price.
In  the  relationship  between  nominal  GDP  and  broad  money  the  residuals  are  purely
stochastic, i.e. white noise. The economics of the above result lies in the fact that true
nominal GDP growth is backed by real GDP growth. Increase in volume of goods andservices need be accompanied by increase in the volume of money supply in order to
lubricate the exchange processes and  facilitate transactions. Again it is also true that
endogenous money supply in form of bank credit increases in the wake of increase in
planned output. Further, rise in nominal GDP leads to rise in factor payments which are
made by checks or drawing upon the banks. In such a situation banks often need knock
the door of the central bank  for more reserves so much so that money  supply  rises.
Another  explanation  can  be  in  the  context  of  exchange  rate  of  rupee  vis-à-vis  other
currencies. When domestic output increases transaction demand for money increases and
hence total demand for money increase. This makes rupee appreciate ceteris paribus vis-
à-vis other currencies like US dollar or the reference basket of currencies. Too much of
such appreciation may create undesirable quantum of foreign exchange outflow. So the
central bank might require to increase money supply to the extent sufficient to make the
value of rupee reduce to the target level or it has to buy US dollars sufficient in exchange
for rupee to make US dollar appreciate vis-à-vis rupee or do both in order to maintain a
the desired level of rupee/US dollar rate.
From the above equations involving commercial bank credit, industrial output index and
wholesale price index one finds that individually commercial bank credit and wholesale
price index have strong influences on industrial output. But jointly they do not influence
industrial output perhaps because wholesale price index is correlated to commercial bank
credit. Thus one can say that because their correlation coefficient between is as high as
0.976, commercial bank credit raises the real cost of production which has an adverse
effect on IIP. This goes against Rangarajan and Mohanty (1997).
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Endogenous Money, Output and Prices in India

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