The Effect of Technology Growth on Money Supply and Demand: A Cointegration Approach

The Effect of Technology Growth on
Money Supply and Demand:
A Cointegration Approach
By Patrick Holly, Jr.
I. INTRODUCTION
payment and purchase have developed that are
The 1990’s has been a prosperous decade
eclipsing older, more paper based forms. In fact,
economically, characterized by notable surges in
this “financial innovation” led the Financial Services
technological innovation and adaptation. Certain
Policy Committee of the Federal Reserve System
economic historians, Mokyr in particular, believe
to form a taskforce in 1996 to further research
we are experiencing growth that is parallel—though
emerging payment technologies. Specifically, the
not quite as substantial—to that of the Industrial
taskforce is concerned with the added liquidity these
Revolution, placing late 20th century America at the
technologies bring to current money storage options
forefront of a new “Technological Revolution.”
and the impacts they have had and will continue to
(Mokyr, 1996) Only time will dictate the accuracy
have on money supply and demand. (Marjanovic,
of that designation. However, there is no doubt that
1996)
substantial technological development has had a
The purpose of this paper will be to analyze
profound impact on U.S. economic evolution over
the significance of the effects current payment
the last 10-15 years. More specifically, significant
technologies have had on money supply and
technology growth has placed the nation’s monetary
demand, and their determinants (the interest rate and
structure at a dynamic crossroads; new methods of
income). Specific attention will be given to M1 and
Figure 1: U.S. Liquid Asset Holdings
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The Effects of Technology Growth on Money Supply, Demand and their Determinants
Figure 2: U.S. EFT Volume Growth
M2 stocks and velocities, the Fed Funds Rate and
Automated Teller Machine (ATM), an Electronic
National Income, and how their interaction with
Fund Transfer (EFT) from one account to another,
each other has been affected by technology
or an instant computer credit check for loan
development. Using Electronic Funds Transfer and
purposes, technology’s effects on the way we do
Automated Teller Machine introduction as a proxy
business through the banking structure is highly
for current technological development in a
significant. What is more startling is that most of
cointegration test model, it is found that current
the widely used technological payment mechanisms
payment technologies have had mixed effects on
have been around for a relatively short period of
money supply and demand, and the interaction
time.
between their associated factors within the IS-LM
When the ATM made its national,
framework. Section II summarizes the existing
commercial debut in 1980, there were 18,500
literature concerning payments technologies and
machines online nationwide. By the close of 1996,
their impacts on the economy. Section III details
there were 140,000 of these machines. There are
the resulting theory and hypothesis. Section IV
currently 140 million ATM cardholders in the U.S.
introduces the empirical model used to test the
and 210 million ATM cards in circulation. More
hypothesis and Section V presents the results of these
noteworthy, according to a study by an AT&T
models. Section VI concludes the study, presenting
Global Information Solutions team, the typical ATM
possible implications and directions for further
customer spends 20-25% more of his/her income
research.
than a non-ATM customer. (AT&T, 1997)
In addition, EFT volume has almost tripled
II. BACKGROUND & LITERATURE
in the last ten years (see Figure 2). Visa branded
REVIEW
debit cards alone accounted for $37.3 billion in
The emergence of computer technology in
transaction volume in 1996 whereas in 1990 they
banking and financial services is well documented
only accounted for $7.5 billion. Other companies
and observed. All it takes is a trip to your local
have experienced similar results. In fact, debit card
bank to witness the ease in transfer of money that
issuance as a whole has experienced a 500% growth
technology affords. Whether it be through an
rate in the 1990’s. (Faulkner & Gray, 1998) There
64
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Holly
is little doubt that these developments have played
of its use. Because the monetary system is used
a significant role in the shaping of our current
more, money demand and supply must increase to
banking and purchasing behaviors. In fact, there
a certain degree; their interaction with the interest
are few if any that refute that technology growth in
rate and income levels becomes more precise
payment systems has had an effect on the behavior
because transaction costs are lower and the existing
of money and the monetary system.
“monetary infrastructure” is made more efficient.
T. M. Podolski has offered much of the
This leads to either possible shifts in the LM curve
theoretical economic analysis concerning these
or movements along the LM curve, depending upon
effects. Citing a 1971 study by Laidler that observed
the type of innovation and its effects.
a slow shift over time in the demand-for-money
Basic to this interpretation, however, is the
function, he hypothesizes that this shift is one “that
idea that the representation of financial influences
has yet to be explained, but which may well be the
through IS-LM at the hands of technological
result of the increasing
innovation can be of value
financial sophistication of
only if one assumes that
the American economy.”
money supply in this state
The representation of finan-
(Podolski, 1986) He asserts
of change is demand
cial influences through IS-
that technological advances
determined rather than
used in modern finance
“exogenously proscribed.”
LM at the hands of techno-
have been the common
(Podolski, 1986)
logical innovation can be of
denominator in all major
Basically speaking, he
financial innovations and
states that money supply is
value only if one assumes
have a strong consequential
not constant as it is
that the money supply in this
impact on macroeconomic
normally assumed under
demand for “narrowly
the IS-LM model because
state of change is demand
defined money,” (M1, M2,
technological innovations
determined rather than ex-
MZM) mostly by reducing
in payment systems have a
transactions costs.
tendency to increase
ogenously proscribed.
(Podolski, 1986)
liquidity preferences. As
He uses the IS-LM
such, Podolski concludes
framework to outline the possible changes in money
that technology growth in payment systems induces
demand and its elasticity at the hands of
a positive and more pronounced co-movement
technological sophistication. More specifically, he
between money supply, demand and their
adopts a view that LM within the IS-LM framework
determinants (income and the interest rate) within
represents the monetary system rather than just
the IS-LM framework. Because technology
money as an asset. “Hicks, the principal creator of
improvements allow money supply to move freely
the IS-LM model, did not necessarily interpret M
within the IS-LM framework (to be endogenous)
as a single asset, money…but rather as representing
its interaction with and among money demand, the
the monetary system and the activities of the
interest rate and income is more dynamic.
monetary sector.” (Podolski, 1986) He further
Valerie A. Ramey argues along similar lines,
explains that since all modern money emanates from
dictating that money treated as a factor of production
the monetary system, “narrowly defined” money
“responds passively to fluctuations in production
supply and demand at various incomes and interest
induced by technological shocks and innovations”
rates was intended by Hicks to be a quasi-proxy for
rather than being an exogenous, static factor in
monetary system activity and resulting LM
economic and technological growth. Furthermore,
derivation. As such, he presents technological
she asserts that money demand and supply are
innovation in payment systems as an improvement
positively correlated to technological output.
to the monetary system that results in an increase
Consequently, the economic output of all industries
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The Effects of Technology Growth on Money Supply, Demand and their Determinants
Figure 3: Traditional IS-LM View
Interest
Interest
LM
Rate
Money Supply
Rate
B
R1
A
R0
MD2
MD1
M
Money Stock
GDP
GDP
Real GDP
0
1
2
collectively may be loosely determined by
the government.” (Dorn, 1997) Again, he states
technology advancement through its influence on
that the eventual changeover to electronic methods
the availability (liquidity) of trade credit and other
of payment is merely an evolution in monetary
very short-term loan/discount vehicles that provide
system development, not a cause of dramatic shifts
quick financing. (Ramey, 1992)
in the measurement of its aggregates or the real
However, Lawrence H. White disagrees
amounts of these aggregates.
with the above ideas that improvements in payment
technology have been revolutionary and have had
III. THEORY & HYPOTHESIS
profound effects on the levels of money supply and
Borrowing from the basis of Podolski’s
demand. He states that we are merely witnessing a
assertions, the IS-LM framework is used to analyze
period of monetary evolution rather than revolution,
the effects of payment technologies on the supply
characterized by a superficial transfer from one
and demand for money. The traditional premise of
transactions vehicle to another. “What happens
the IS-LM framework as it relates to the demand
behind the scenes—deposit transfer—remains the
for real money balances shows that an advancement
same, and has existed for hundreds of years.” (Dorn,
in technology can lead to a corresponding shift in
1997) In essence White argues that money supply
money demand and upward pressure on interest
and demand is not created or destroyed in this
rates, holding money supply constant. This
process; it is just changing in form.
movement creates an upward-sloping LM curve and
Furthermore, he criticizes the idea that the
displays the dynamic relationship between GDP,
movement towards electronic currency and
LM, money demand and the interest rate. These
transaction vehicles will radically change the
movements are shown in Figure 3. As we move from
monetary landscape, allowing the potential for
point A to point B on the LM curve, real GDP
money velocity (demand) and supply growth to go
increases and vice versa. Money demand increases
unchecked and unregulated. To White this
in response, moving from MD to MD , thus putting
1
2
development represents nothing more than the loss
upward pressure on interest rates. This is the
of government’s monopoly on currency
traditional sequence of events that the IS-LM
manufacture. “The transition from analog to digital
framework outlines to explain a shift in money
currency does not change the monetary standard:
demand.
the base money remains fiat money controlled by
However, as previously discussed, Podolski
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Holly
states that the representation of financial influences
This does not necessarily mean that GDP
through IS-LM at the hands of technological
loses its significance within the IS-LM framework
innovation can be of value only if one assumes that
or is unaffected by improvements in payment
money supply in this state of change is demand
technology and corresponding changes in money
determined. This implies that money supply (as part
supply and demand. As evidenced by the graphs in
of the proxy for monetary system activity) must be
Figure 4, these movements merely dictate that
assumed to be endogenous within the model rather
money supply and demand are not dependent upon
than exogenously proscribed in order to take
GDP movements when taking into account
technological improvement into account. If money
improvements to payment technologies, nor does
supply is made endogenous, rapid improvements in
GDP necessarily increase with increases in money
payment technologies have a tendency to affect
demand if money supply is endogenous.
money demand and supply outright through
Continuing with the Super-NOW example
reductions in transactions costs and increases in
on the previous page, money demand (as defined
liquidity preferences—independent of increases in
within the context of the IS-LM framework)
GDP—within the IS-LM model.
increases from MD to MD due to payment
1
2
For example, Super-NOW accounts offer
technology advances that make these accounts more
the liquidity of cash (due to electronic transfer
liquid and accessible. Simultaneously, money
capabilities) and the advantage of interest
supply increases from M to M as people move their
1
2
accumulation. It can be hypothesized that people
asset holdings to these accounts. Since these
will demand these savings mechanisms more and
movements occur simultaneously, GDP is
move their asset holdings from less liquid
unaffected.
mechanisms (that are not included within “narrowly
This is just a specific example. Different
defined” monetary system aggregate measurements
technological innovations will cause different
like M2) towards these Super-NOW accounts
movements within the IS-LM model, perhaps
(which are included within “narrowly defined”
increasing either money supply or demand more
monetary system aggregate measurements). As a
than the other, thus increasing or decreasing real
result, money supply and demand in the context of
GDP. The point is that technological innovation in
the IS-LM framework increase without necessarily
payment systems has an effect on money demand
affecting GDP or being affected by GDP.
and supply that is independent of movements in
Figure 4: Modified IS-LM View
Interest
Interest
Rate
Rate
LM
M
M
1
2
R0
MD2
MD1
M
M
Money Stock
GDP
Real GDP
1
2
1
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The Effects of Technology Growth on Money Supply, Demand and their Determinants
GDP within the IS-LM framework. These
outlined theory. Since these time-series variables
independent movements allow for a more dynamic
are somewhat unique in structure (they are non-
and pronounced interaction between money supply,
stationary), they require the use of an empirical
demand, the interest rate and income.
model other than OLS regression to uncover their
Given the evidence presented in Section II
explanatory power (cointegration model). An
by Podolski and Ramey combined with the above
explanation of their structure and the resulting
theory explanations, it is hypothesized that recent
empirical method follows the descriptions below.
technological innovation in payment systems has
effectively increased the supply and demand for
A. Variable Descriptions
“narrowly defined money” and provided for a more
Velocity—measured and calculated as the
integrated interaction between money supply,
ratio of nominal expenditure to money supply.
demand and their determinants.
According to the equation MV = PY within the IS-
LM framework, velocity represents the demand for
IV. EMPIRICAL MODEL
money. (Petersen, 1995) Theory states that various
The primary determinants of money supply
interest rate and income levels dictate velocity
and demand (velocity) according to the IS-LM
(money demand). It is also hypothesized that
framework are income and the interest rate. Income
technology has an effect on the measurement of
is a shift parameter for money supply and demand,
velocity and its interaction with money supply, the
and the interest rate is simply the cost of this money
interest rate and income. Data is obtained through
supply and demand as dictated by Figures 3 and 4.
the Federal Reserve Economic Data (FRED)
As a result of the hypothesis presented in section
database found on the Federal Reserve Bank of St.
III, a technology variable should also be included
Louis Internet site.
and/or controlled for as a participant in money
Money Stock—the level of a particular
supply and demand interaction with income and the
monetary aggregate dictated by the Federal Reserve
interest rate. Each of these factor variables is
System. Data used are M1 and M2 levels. IS-LM
discussed below in accordance with the previously
theory states that various interest rate and income
Table 1: Variable Descriptions
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68
The Park Place Economist / vol. VII

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Holly
levels dictate money stock volume. In addition, it
in any of the current monetary aggregates. (Ford,
is hypothesized that technology has an effect on
1992)
money stock and its interaction with the above
Given the shortage of accurate technology
factors. Data is obtained through the FRED
representations, EFT volume appears to be a proxy
database.
of choice among those studying technology’s effects
Income—the National Income
on the economy. Therefore it is used here. It is
measurement calculated by the Federal Reserve is
hypothesized that increases in payment technology
used to approximate the Income variable. IS-LM
use and innovation, specifically after 1980, have
theory states that as the income level increases,
had a positive effect on the co-movement of money
money demand and supply also increase. Data is
supply, demand, the interest rate and income.
obtained through the FRED database.
Interest Rate—the Fed Funds Rate is a
B. Stationarity Conditions and Cointegration
logical choice to represent interest rate movements.
Models
It is this rate that is used by the Federal Reserve to
The above variables cannot be used in a simple
dictate monetary policy and is most likely to have a
Ordinary Least Squares regression to determine
direct effect on money supply and demand. IS-LM
technology’s effects on money supply, demand, interest
theory states that as the interest rate decreases,
rate and income co-movement because they are not
supply and demand for money increases, and vice
structurally consistent. More specifically, some of these
versa. Data is obtained through the FRED database.
variables are not stationary time series measurements,
Technology—Electronic Fund Transfer
while others are.
(EFT) volume measured in dollar terms is used as a
A stationary variable is one that has a
proxy for technology use and innovation. EFT
tendency to return to an equilibrium level or trend
volume mostly comprises ATM transaction volume,
over a period of time. As such, the mean, variance,
but also includes the minor usage volume of other
autocorrelation and coefficients of such independent
electronic means of transferring money from one
variables regressed against a dependent variable in
account to the other. Since their national,
an OLS estimation can be approximated well by
commercial debut in 1980, ATM and other EFT
sufficiently long time-series data. (Enders, 1995)
transaction mechanisms have taken center stage in
An example of a stationary time-series would be
the payment system, experiencing large growth
seasonally adjusted quarterly sales figures for a
rates. (Daniels, 1994)
large, stable company.
EFT volume has been relied upon in a
Conversely, a non-stationary variable is one
number of other studies addressing payment
that meanders in value without any tendency to
technology issues. Kenneth N. Daniels and Neil B.
return to a long run level or trend. An OLS
Murphy of Virginia Commonwealth University used
regression estimation that incorporates either all
ATM volume as a proxy for technology’s effects on
non-stationary time-series variables, or non-
household transaction account balances. They
stationary variables in conjunction with stationary
further asserted that the national, commercial debut
variables, is not BLUE (Best Linear Unbiased
of EFT and the ATM in 1980 represented a
Estimate). The unknown variable errors in such a
significant technological shock to the monetary
regression will not have a zero mean and won’t
structure that has permanently changed the way
always be independent (autocorrelation), and the
consumers interact with the monetary system and
variance of the unknown variable errors will not
the overall economy. (Daniels, 1994) J. L. Ford,
always be constant. Simply correcting for
W. S. Peng and A. W. Mullineaux blamed EFT
autocorrelation (through Cochrane-Orcutt or Prais-
transaction volume increases in the U.K. for the poor
Winsten estimates) is not a reliable solution to
performance of its Divisia monetary aggregate in
stationarity problems because the unknown variable
indicating economic growth, leading them to
errors can still be independent with non-stationary
conclude that technology growth is not reflected well
variables in OLS regressions. Correcting for
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The Effects of Technology Growth on Money Supply, Demand and their Determinants
autocorrelation requires unknown variable errors to
make sense, however, any deviation in the demand
be dependent upon the independent variables in OLS
for money (or another dependent variable) must be
regressions. (Ramanthan, 1997)
temporary. A key assumption of a normal OLS
Coincidentally, money demand studies have
regression is that the error term (? ) is stationary. If
t
stimulated much of the literature concerning
? has a stochastic trend (the unknown variable errors
t
stationarity problems and cointegration solutions—
are not random or independent) the errors in the
the hypothesis presented in Section III serves as a
model will be cumulative so that deviations from
great example of an economic situation that contains
equilibrium will not be eliminated through OLS
stationarity and cointegration conditions in its
regression. The error term will have a stochastic
empirical framework. Take the simple money
trend if one or more of independent variables is non-
demand function:
stationary.
Interest rate and money demand (velocity)
MD = ? + ? Inc + ? Rate + ? Tech + ?
have always been traditionally characterized as non-
1
2
3
4
t
stationary time series measurements. In fact, after
where MD
= long run money demand (proxied
performing Augmented Dickey-Fuller tests for
by velocity), or money supply (as
stationarity on the variables presented in Table 1, it
supported by Podolski)
was found that M1 and M2 velocity, the Fed Funds
Inc
= real income
Rate and National Income were all non-stationary
Rate
= interest rate
variables. M1 and M2 stock were also found to be
Tech
= technology
non-stationary, but of a “weaker order” (see
?
= stationary disturbance term
Appendix A). Basic OLS estimates of the above
t
?
= coefficients to be determined
regression won’t be BLUE and the error term (? )
t
won’t be stationary with these variables.
The hypothesis that a third variable can be used to
However, the empirical theory presented
measure technology’s effects on money demand,
here suggests that there still exists a linear
supply and their determinants allows me to collect
combination of these non-stationary variables that
time series data on the above variables and run an
is stationary—this combination just cannot be
OLS regression to determine the effects. For this to
estimated through OLS regression. (Enders, 1995)
Figure 5: Generic Cointegration Diagram
Time
Series
??
?
?
?
?
?
?
A
?
?
?
?
?
?
?
?
?
?
?
? ? ? ? ? ?? ?
?
?
?
?
?
?
??
?
B
?
?
?
??
? ? ? ? ? ? ??
C
1980
Time
70
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Holly
Solving for the error term, we can rewrite the above
increases in monetary system efficiency.
equation as:
In addition, separate pairs of cointegration
test will be performed, as follows:
? = MD - ? - ? Inc - ? Rate - ? Tech
t
1
2
3
4
Pair 1
Since ? must be stationary, it makes sense that the
1961-1979
M1 Stock, M1 Velocity, Fed
t
linear combination of the integrated variables
Funds Rate, Income
shown by the right side of the above equation must
1980-1998
M1 Stock, M1 Velocity, Fed
also be stationary; the time paths of these variables
Funds Rate, Income
must be linked, even though they don’t return to
equilibrium levels. Simply put, “equilibrium
Pair 2
theories involving nonstationary variables require
1961-1979
M2 Stock, M2 Velocity, Fed
the existence of a combination of the variables that
Funds Rate, Income
is stationary.” (Enders, 1995)
1980-1998
M2 Stock, M2 Velocity, Fed
As a result, the beta coefficients of an OLS
Funds Rate, Income
regression analysis involving the money demand
function and its components would not be
Pair 3
statistically valid, but they could be through the use
1961-1979
M1 Stock, M1 Velocity, M2
of a cointegration model. A cointegration model is
Stock, M2 Velocity, Fed Funds
a variant of the ARMA model whereby one can test
Rate, Income
for linear cointegrating relationships among non-
1980-1998
M1 Stock, M1 Velocity, M2
stationary variables. We see from Figure 5 three
Stock, M2 Velocity, Fed Funds
variables (A, B and C). Each of these variables is
Rate, Income
meant to represent a nonstationary variable. A
cointegration model will test for linear relationships
M1 measurements and M2 measurements are first
between and among these nonstationary variables
tested separately to compare how each has been
by combining their movements in an econometric
affected by technology growth when combined with
test. In accordance with the previously mentioned
the other two variables. It is quite possible that M1
IS-LM analysis by Podolski, a cointegration model
and M2 measurements could react very differently
will test the strength of the co-movement between
to technology growth, which would in turn dictate
money supply, demand, the interest rate and income
for economic policy purposes how to emphasize or
at the hands of a defined technology parameter. That
de-emphasize each measurement when analyzing
parameter is the national, commercial debut of the
periods of rapid technology growth. Indeed, less
ATM and EFT in 1980.
liquid aggregates like M2 may have a more
As such, two cointegration tests will be
pronounced affect on their co-movement with the
performed to measure technology’s effects on money
interest rate and income after a technology shock
supply, demand, the interest rate and income. The
because of the added liquidity that technology adds
first will test co-movement among the above
to them at the hands of reduced transaction costs.
variables in the 19 years previous to the ATM and
Conversely, liquid aggregates like M1 may have
EFT introduction (1961-1979). The second will
less of an affect because they are already as liquid
test co-movement among these variables in the 19
as can be. If there is a significant difference between
years after the ATM and EFT introduction (1980-
M1 and M2 measurements and their relationship to
1998). It is hypothesized that this co-movement
the interest rate and income before and after a
will be stronger (there will be more cointegrating
technology shock, a cointegration test that includes
equations among the variables) in the 1980-1998
both measurements will not be able to separate that
cointegration tests due to technology growth and
difference.
the resulting reductions in transaction costs and
At the same time, however, it is also likely
The Park Place Economist / vol. VII
71

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The Effects of Technology Growth on Money Supply, Demand and their Determinants
that the 1980 ATM/EFT technology shock could
by the software program Econometric Views.®
have also caused increased co-movement among M1
The results of the paired cointegration tests
and M2 measurements when tested with the interest
performed for this study, as outlined in the previous
rate and income. If technology growth in the
section, are presented in Table 2.
monetary system has indeed reduced transaction
As seen, the results from test pair #1 and
costs significantly, it is possible that less liquid
test pair #3 contradict the hypothesis presented in
measurements (like M2) could start to mimic more
this study. The number of cointegrating equations
liquid measurements and strengthen the “bond”
found in these test pairs decreased from the first time
between them, the interest rate, income and the more
period (1961-1979) to the second time period
liquid aggregates. Therefore, a third pair of
(1980-1998), suggesting that the 1980 ATM/EFT
cointegration tests is performed to account for this
technology shock had a negative effect on the co-
possibility.
movement between money supply, demand, the
interest rate and income since its occurrence.
V. RESULTS
In the case of test pair #1, the decreased
There are no designated dependent or
number of cointegrating equations indicates that
independent variables in a cointegration test. The
technology growth since 1980 has had negative
model simply tests for linear trends among a set of
effects on M1 measurements and their co-interaction
non-stationary variables. The stronger the linear
with the interest rate and income. In the case of test
trend between the variables, the more “cointegrating
pair #3, the decreased number of cointegrating
equations” will be found by the model, as dictated
equations (from 6 to 3) indicates that the ATM/EFT
Table 2: Cointegration Test Results
N
e
b
m
u
r of
e
T st a
P r
i
e
m
i
T
e
P
o
i
r d
a
V
a
i
r
e
l
b s
e
d
u
l
c
n
I
d
o
C
e
t
n
i
g a
r
g
n
i
t
a
u
q
E
o
i
t
s
n
9
1 6 1
:
1
- 9
1 79 4
:
1
M S o
t
k
c ,
1
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
2
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
P r
i
a
1
#
9
1 80 1
: - 9
1 98 4
:
1
M S o
t
,
k
c
1
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
1
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
9
1 6 1
:
1
- 9
1 79 4
:
2
M S o
t
k
c ,
2
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
1
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
P r
i
a #2
9
1 80 1
: - 9
1 98 4
:
2
M S o
t
k
c ,
2
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
2
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
9
1 6 1
:
1
- 9
1 79 4
:
1
M S o
t
,
k
c
1
M
o
l
e
V
,
y
t
i
c
2
M S o
t
,
k
c
6
2
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
P r
i
a
3
#
9
1 80 1
: - 9
1 98 4
:
1
M S o
t
,
k
c
1
M
o
l
e
V
,
y
t
i
c
2
M S o
t
,
k
c
3
2
M
o
l
e
V
,
y
t
i
c
F d
e F d
n
u s
,
e
t
a
R
N
o
i
t
a
l
a
n
o
c
n
I
e
m
72
The Park Place Economist / vol. VII

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