How the money supply affected by the change of paying method ---from physical money to credit card

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 2, the year that the Internet started to be used broadly. So the transaction amount of the credit card raised significantly these years in Taiwan.

22 23 24 25 26 27 28 29 21 211 212 The transaction amount through credit card 4 3 2 1 In million Issue amount of Easy Card 6 4 2 In million 26 27 28 29 21 211 212 Easy card Average transaction amount per day

2 21 22 23 24 25 26 27 28 29 21 211 212 25 one millions one millions NT 2 15 1 5 1411 2156 3318 49443 62178 74171 152253 132576 11418 143116 9741 87635 122193 B2B online commerce 6 4 2 173 27625929232256 478114 395136 28 29 21 211 212 213 one millions NT B2C online shopping

2M1 2M7 21M1 21M7 22M1 22M7 23M1 23M7 24M1 24M7 25M1 25M7 26M1 26M7 27M1 27M7 28M1 28M7 29M1 29M7 21M1 21M7 211M1 211M7 212M1 212M7 213M1 The GDP of Taiwan 4,, 35,, 3,, 25,, 2,, 15,, 1,, 5,, Money supply (M2)

2M1 2M7 21M1 21M7 22M1 22M7 23M1 23M7 24M1 24M7 25M1 25M7 26M1 26M7 27M1 27M7 28M1 28M7 29M1 29M7 21M1 21M7 211M1 211M7 212M1 212M7 213M1 4,, 35,, 3,, 25,, 2,, 15,, 1,, 5,, 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 M s 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

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 M1A B( m1a ), then let both the denominator and numerator be divided by D. So we B got the equation: m 1A C k D k 1 where k rd rt * t rs * s e, t= T D, s= S D, e= ER D. 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.

19881211 19981 1991921 199219 1995812 1995117 1996824 1997116 1998929 199922 211 21229 21118 27622 2871 2111 19881211 19981 1991921 199219 1995812 1995117 1996824 1997116 1998929 199922 211 21229 21118 27622 2871 2111 198 2 2 2 2 2 21 ii. The growth trend of r d /r t /r s /E 3 25 2 15 1 5 14 12 1 8 6 4 2 25 2 15 1 5

These show that the multiplier is increasing. iii. And the following diagram is the trend of money multiplier. 3.% 25.% 2.% 15.% 1.% 5.%.%

2M1 2M8 21M3 21M1 22M5 22M12 23M7 24M2 24M9 25M4 25M11 26M6 27M1 27M8 28M3 28M1 29M5 29M12 21M7 211M2 211M9 212M4 212M11 2M1 2M8 21M3 21M1 22M5 22M12 23M7 24M2 24M9 25M4 25M11 26M6 27M1 27M8 28M3 28M1 29M5 29M12 21M7 211M2 211M9 212M4 212M11 iv.this is the growth of M1A 6,, 5,, 4,, 3,, 2,, 1,, 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 4,, 35,, 3,, 25,, 2,, 15,, 1,, 5,,

2M1 2M8 21M3 21M1 22M5 22M12 23M7 24M2 24M9 25M4 25M11 26M6 27M1 27M8 28M3 28M1 29M5 29M12 21M7 211M2 211M9 212M4 212M11 vi. Proportion of M1A to M2.16.14.12.1.8.6.4.2 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, R 2 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.

According to random walk, the model is When the null hypothesis, H: α =, 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.5, 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 2 to Q4 of year 212 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.