how the money supply affected by the change of paying...
TRANSCRIPT
How the money supply affected by the
change of paying method
---from physical money to credit card
Content
I. Motivation
--briefly introduce mega trend on e-commerce and how we’d like to conduct
the suggested model to prove our hypothesis.
II. Definition
--State the definition of credit card and easy card
--Show the graph of GDP and money supply for the past few years, and
indicated their relevance.
III. Methodology
--Introduce the methodology we use
--Show the whole model implementing process.
IV. Statistics
--Show the statistics we use to support our hypothesis
--including regression analysis, unit root test, VAR, and Granger causality.
V. Conclusion
I. Motivation
E-Commerce: A Ripe Growth Opportunity ----The world mega trend,
e-commerce, is growing
Global e-commerce is thriving as infrastructure, laws, and consumer preferences
evolve. Global e-commerce has grown 13 percent annually over the past five
years (see figure below).3 Retail expansion is increasingly occurring through
online channels as a way to tap into growth markets, build brands, and learn
about consumers while investing less capital than traditional formats. For
example, American luxury retailer Neiman Marcus acquired partial ownership in
a Chinese fashion website to test China's market, learn about Chinese consumers'
likes and dislikes, and capitalize on the country's increasing demand for luxury
goods. Neiman Marcus got all the information it needed without entering into
expensive real estate contracts or trying to navigate the complexity of tier 2 and
tier 3 cities. French luxury retailer Louis Vuitton Moet Hennessy (LVMH) used a
similar strategy, acquiring Sack's, Brazil's leading online beauty retailer, to
develop local recognition of its Sephora cosmetics line.
To complete e-commerce, credit card is the most common seen paying method.
This paying method has become very common in Taiwan, and with the rapid
development of smart phone, the market becomes even bigger.
The idea of paying with credit cards anytime and anywhere via smartphones
and tablets may soon become a reality in Taiwan as major local lenders join
with US credit card giant Visa Inc in launching a mobile point of sale service
(MPOS).
MPOS is a card payment acceptance service that turns a smartphone or a tablet
computer into a acceptance point of sale device via a card swipe and/or chip
reading, allowing merchants to accept card payments anytime and anywhere.
The service, which already in use in the US, Canada, Hong Kong, Malaysia,
Japan, Australia and South Korea, is a good fit for Taiwan given the
prevalence of mobile devices.
PARTNERS
Chinatrust Commercial Bank, Cathay United Bank, Taishin International Bank,
Taipei Fubon Commercial Bank, Bank SinoPac, EnTie Commercial Bank and
Union Bank of Taiwanare all in the final stage of preparation.
MPOS provides easier and quicker payment solutions that are particularly
good for small firms predominantly accepting cash, companies with delivery
services or lower volume outlets, companies with a need to extend their point
of sale presence economically and large corporations with a delivery service or
supply chain management.
For merchants, MPOS has the benefits of lower setup costs, operating
efficiency, payment convenience and cost reduction linked with e-receipt
issuance.
For cardholders, MPOS offers a more flexible and environmentally friendly
means of payment, although the new choice requires some trust on the part of
the consumer.
II. Definition
Why credit card affect the financial system and money supply?
Firstly, the definition of a credit card is that it’s a plastic card with magnetic strip,
issued by a bank or a business authorizing the holders to buy goods and services on
credit. It is also called plastic money. In Taiwan, there are two forms of plastic money
used broadly, the easy card and iCash. We can see from the graph that the transaction
amount and issue amount of the plastic money is raising.
Furthermore, in Taiwan we use the credit card to pay for online shopping, there is also
a trend that the scale of B2B and B2C online shopping increase from 2000, the year
that the Internet started to be used broadly. So the transaction amount of the credit
card raised significantly these years in Taiwan.
The transaction amount through credit card
Issue amount of Easy Card
Easy card Average transaction amount per day
0
1000
2000
3000
4000
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
10
20
11
20
12
In million
0
200
400
600
2006 2007 2008 2009 2010 2011 2012
In million
B2B online commerce
B2C online shopping
14110
21560
33180
49443
62178
74171
8763597401
110418
122193
132576
143116
152253
0
50000
100000
150000
200000
250000
20
00
20
01
20
02
20
03
20
04
20
05
20
06
20
07
20
08
20
09
20
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20
11
20
12
one millions NTone
millions…
170300207600259292
322560395136
478114
0
200000
400000
600000
2008 2009 2010 2011 2012 2013
one millions NT
The GDP of Taiwan
Money supply (M2)
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
20
09
M0
1
20
09
M0
7
20
10
M0
1
20
10
M0
7
20
11
M0
1
20
11
M0
7
20
12
M0
1
20
12
M0
7
20
13
M0
1
We observed that the spending on credit card and the B2B e-commerce rate is
positive related as well as the GDP of Taiwan and M2 money supply.
III. Methodology
In order to find out how spending by credit card would affect Money Supply, we used
Money Multiplier Theory to analyze how would the money multiplier change by the
variation of credit cards and VAR(Vector Autoregression) to see the correlation
between credit cards and money supply.
A. Money Multiplier Theory
(a)
First, let Ms be equal to Monetary Aggregate(M1A), as we know,
M1A=C+D
C=currency
D=demand deposits
Second, the monetary base, B, is equal to currency plus deposit reserve,
R=deposit reserve=RR+ER,
RR=required reserve
ER=excess reserve
After computations, we know that B should be equal to currency plus deposit reserve.
So, B=C+R, and according to the definition,
R=RR+ER=(rd*D+rs*S+rt*T)+ER., where
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
20
00
M0
1
20
00
M0
7
20
01
M0
1
20
01
M0
7
20
02
M0
1
20
02
M0
7
20
03
M0
1
20
03
M0
7
20
04
M0
1
20
04
M0
7
20
05
M0
1
20
05
M0
7
20
06
M0
1
20
06
M0
7
20
07
M0
1
20
07
M0
7
20
08
M0
1
20
08
M0
7
20
09
M0
1
20
09
M0
7
20
10
M0
1
20
10
M0
7
20
11
M0
1
20
11
M0
7
20
12
M0
1
20
12
M0
7
20
13
M0
1
rd/rs/rt: reserve ratio of demand deposit/savings deposit/time deposit
S: saving deposit
T: time deposit
Then B=C+(rd*D+rs*S+rt*T)+ER
(b)The money multiplier of M1A(m1A) is equal to M1A(money supply) divided by b
B(B
AMAm
11 ), then let both the denominator and numerator be divided by D. So we
got the equation:
es*rt*rrk
1km
std
1A
where
D
Ck , t=
D
T, s=
D
S, e=
D
ER.
Under normal condition, m1A will be larger than 1. And we assume that if the use of
credit cards increases, k will decrease. That’s because credit cards would replace part
of the currency, and people should save more money in their accounts. As a result, we
infer that k will decline.
(c) According to the Money Multiplier theory, we can know that if the multiplier rises,
the money supply will rise, too. Then we inference here: If the use of credit cards
increases, k will decrease, and the m1A increase, leading to the increase of money
supply.
(d) Condition of Taiwan
i. The following diagram is the growth trend of k
This shows that k decreases as we had inferred.
ii. The growth trend of rd/rt/rs/E
0
5
10
15
20
25
30
198…
199…
199…
199…
199…
199…
199…
199…
199…
199…
200…
200…
200…
200…
200…
201…
0
2
4
6
8
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19
88
12
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90
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91
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92
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95
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5
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19
88
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11
19
90
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91
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19
92
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95
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95
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19
96
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19
97
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98
09
29
19
99
02
20
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10
01
20
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12
29
20
01
11
08
20
07
06
22
20
08
07
01
20
10
01
01
These show that the multiplier is increasing.
iii. And the following diagram is the trend of money multiplier.
0.000000000%
50.000000000%
100.000000000%
150.000000000%
200.000000000%
250.000000000%
300.000000000%
iv.This is the growth of M1A
As a result, these data shows that the condition in Taiwan is consistent with the theory
we use: the money multiplier increases because of the decline of k, rd, rt, rs, E,
making the rise of M1A. However, what we want to know is that will the total money
supply,M2, be affected by the use of credit cards.
v. the growth of M2
0
1,000,000
2,000,000
3,000,000
4,000,000
5,000,000
6,000,000
20
00
M0
1
20
00
M0
8
20
01
M0
3
20
01
M1
0
20
02
M0
5
20
02
M1
2
20
03
M0
7
20
04
M0
2
20
04
M0
9
20
05
M0
4
20
05
M1
1
20
06
M0
6
20
07
M0
1
20
07
M0
8
20
08
M0
3
20
08
M1
0
20
09
M0
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20
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M1
2
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M0
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20
11
M0
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20
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M0
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20
12
M0
4
20
12
M1
1
0
5,000,000
10,000,000
15,000,000
20,000,000
25,000,000
30,000,000
35,000,000
40,000,000
20
00
M0
12
00
0M
08
20
01
M0
32
00
1M
10
20
02
M0
52
00
2M
12
20
03
M0
72
00
4M
02
20
04
M0
92
00
5M
04
20
05
M1
12
00
6M
06
20
07
M0
12
00
7M
08
20
08
M0
32
00
8M
10
20
09
M0
52
00
9M
12
20
10
M0
72
01
1M
02
20
11
M0
92
01
2M
04
20
12
M1
1
vi. Proportion of M1A to M2
The proportion also shows the increasing trend, so we simply infer that the increase
of M1A has effect on M2.
IV. Statistics
After we prove that the increasing usage of the credit card will have positive effect on
the money supply of Taiwan, we use the statistics data to run the regression analysis
between the credit card usage and the money supply. We define the regression model
as following:
Y=a1*CC+a2*GDP+a3*CPI +a4*DR+a5*LR
Where Y equals to M2, CC equals to transaction amount on credit card, DR and LR
refers to deposit rate and loan rate respectively. Note that the deposit rate and the loan
rate is the opportunity cost of holding currencies instead of deposit money into the
bank account. But the data should be time series data. If we put the time series data
into the regression computation directly, the coefficient of determination, R2 will be
very big, and the t-statistics of the variables will be very significant, too. The time
series data does not have stationarity, and will lead to an inaccurate outcome of the
regression analysis, so we have to test when the variables is suitable to run the
regression.
Here we use the unit root test.
Unit Root Test
The unit root test is to test whether the time series data is “random walk” or not.
0
0.02
0.04
0.06
0.08
0.1
0.12
0.14
0.16
20
00
M0
1
20
00
M0
8
20
01
M0
3
20
01
M1
0
20
02
M0
5
20
02
M1
2
20
03
M0
7
20
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M0
2
20
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M0
9
20
05
M0
4
20
05
M1
1
20
06
M0
6
20
07
M0
1
20
07
M0
8
20
08
M0
3
20
08
M1
0
20
09
M0
5
20
09
M1
2
20
10
M0
7
20
11
M0
2
20
11
M0
9
20
12
M0
4
20
12
M1
1
According to random walk, the model is
When the null hypothesis, H0: α =0 , that means the null hypothesis has unit root.
When the null hypothesis is rejected, the series has stationarity. If level data is not
rejected, we have to differentiate the data in the first order or the second order and
redo the unit root test until the null hypothesis is rejected. After we do the unit root
test on all the variables in the regression formula, we have the result that the variables
CPI and GDP has sationarity in the first order difference, the variables M2, CC, DR,
LR has sationarity in the second order difference. So we can rewrite the regression
model to:
Y=ddM2=a1*ddCC+a2*dGDP+a3*dCPI +a4*ddDR+a5*ddLR
Where d before variables means the variable has stationary in the first order, double d
before variables means the variable has stationary in the second order.
After Unit Root Test, we changed our formula to
Y=ddM2=a1*ddCC+a2*dGDP+a3*dCPI YY+a4*ddLR+a5*ddDR, which has
stationarity. Then we can do VAR model. We do VAR model to find the lag value of
these variables. Lag value means the period of the variables that have effect on those
variables on time. For example, if lag value is 1, the value of CC(credit card) at T
period will be affected by the value of these variables in T-1 period.
Therefore after we did VAR, we found the most suitable lag value and is 2. That is
Y=ddM2=a1*ddCC+a2*dGDP+a3*dCPI YY+a4*ddLR+a5*ddDR, these variables
will be affected by their previous 2 period value.
Then we start to do Granger Causality. This statistic model can make us find out the
relationship between these variables. Under lag value 2, if P-value is less than 0.05, it
means having cause and effect relationship.
The following is our results.
First, GDP and CPI both have cause and effect relationship to credit card, but credit
card doesn’t have. These two are both one-way relationship. Next, GDP and M2 have
two-way relationship. They both have cause and effect relationship to each other. Last
CPI has cause and effect relationship to M2 and loan rate, which both are one-way
relationship.
So after testing our theory by statistic model, we got these consequences, that is the
transaction amount of credit card mainly resulted from CPI and GDP. In addition, the
amount of M2, money supply, mainly resulted from CPI and GDP. Therefore, the
increase of payment by credit card cannot obviously cause the increase of M2.
V. Conclusion
In this paper we use the financial and macroeconomics data of Q1 of year 2000 to Q4
of year 2012 in Taiwan to do the empirical analysis, and we first use the money
multiplier theory to testify the accuracy of the assumption that the change of the
paying method will affect the money supply in Taiwan. In the money multiplier theory,
when the use of credit cards increases, k will decrease, and the m1A increase, leading
to the increase of money supply. But when we use the vector regression analysis to do
the test, we find that the result is not accordant with the assumption. The finding of
VAR regression is:
1.The amount of Credit Card mainly resulted from GDP and CPI. 2.The amount of M2(Money Supply) mainly resulted from GDP and CPI.
3.The increase of payment by credit card cannot obviously caused the increase of M2.
But increase of payment by credit card cannot obviously caused the increase of M2.
We investigate in the financial phenomenon in Taiwan and have two reasons to
explain the result of VAR analysis:
1. The growing of credit card use amount dose not have a very significant effect in
Taiwan now. So the influence of credit card use change doesn’t be reflected on the
economic data.
2. The authority of central bank in Taiwan is very powerful, so the financial policy
will weaken the market implementation effect. The money supply will not be
significantly change by the credit card amount change.