To what extent has electronic payment affected the domestic
consumption in China during 2005-2016?
Bachelor Thesis
Yuting Cui
09-06-2018
Supervisor: Daniel Dimitrov
Statement of Originality
This document is written by Student Yuting Cui who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents
.
Table of Contents
1. Introduction 5
2. Literature Review 7
3. Methodology and Hypothesis 11
4. Data 14
5. Results and Analysis 16
5.1 Descriptive Analysis 16
5.2 Statistical Output of the Hypothetical Modal 18
6. Conclusion 22
7. References 24
8. Appendix 25
I. Description table of variables and data collection 25
II. Household consumption per capita and real disposable income over time 28
III. Household consumption per capita and real disposable income growth over time 28
Abstract
The central question to this research is to what extent has electronic payment affected the domestic consumption in China during 2005-2016. How electronic payment penetration has developed in China is examined by applying both the descriptive analysis and a multiple-linear-regression. As a result, electronic payment in China has a leading usage and acceptance level over the world. Also, electronic payment penetration in China has developed rapidly. The growth of penetration has a significant influence to the growth of household consumption. The growing technology and usage of this payment method boosts the economy of China. Apart from that, the growth real disposable income is also determinant for rising the consumption in China.
1. Introduction
Electronic payment in recent years has been a heated topic because of its rapid growth and various instruments. Zandi, Singh, and Irving (2013) focus on the impact of electronic payment on economic growth. They took card usage (including debit and credit cards) as the leading indicator of the electronic payment, and found that card usage positively correlates with the economic growth, and the card payment contribution to consumption of China was increasing rapidly and reached its highest among 70 countries they observed for an average of 4.89% from 2008 to 2012. This contribution implies a high acceptance and usage of electronic payment in China. However, the primary instrument of electronic payment in China is not card payment, but namely QR code payment. Consumers using this payment method use mobile devices to scan the QR code with information of transactions to finish the payment process. This method makes consumers benefit from convenience and easiness; sellers benefit from the quickness of purchases made. Realization of this two-sided benefit has attracted more and more public concerns. According to the annual reports of two main electronic payment service providers: Alibaba Inc. and Tencent Inc., Kapron and Meertens found in their research (Kapron and Meertens, 2017) that the transaction values through electronic payment has increased 20-fold from 2012 to 2016, and most remarkably in 2015, these cashless payment transactions have accounted for almost 60% of the all retail operations. About existing findings of the encouraging results, I am motivated to start further research to see whether the electronic payment is a significant driver for the increase in domestic consumption in this country. Because Chinese government legally introduced this service in 2011, I consider six years before and after 2011 for observations. Within this time range, I could not only see the influence of this payment service
but also look at the impacts of its existence to deviate the growth of consumption. Therefore my central question of this research is addressed as to what extent has electronic payment affected the domestic household consumption expenditure in China during 2005-2016.
I present two main hypotheses in this research:
The first hypothesis is that the electronic payment is expected to grow faster and more developed than other countries, accounted for more and more proportions of the domestic consumption in the future. Descriptive analysis will examine this hypothesis.
The second hypothesis is that electronic payment has influences on domestic consumption per capita, and further this influence is expected to be positive. The statistical analysis will be in use to test this hypothesis.
The structure of the paper is as the following; the second section will be the literature review of more motivations of why I decided to build up the central question of this research. Moreover, this section will include some existing results from other articles concerning different countries but stand on the same topic of electronic payment. In part three, you will see the methodology of this research with a hypothetical model which includes one dependent variable and four independent variables, and a more detailed explanation of the hypothesis. In section four, I will explain what the resources of data I used to regress my hypothetical model are. A detailed description and mathematical process are in the Appendix I. Followed by that in section five; I placed the final results and my analysis concerning the outputs. In the end, the conclusion of this research will include a brief answer towards the central question, limitations of this research, and few suggestions for further studies.
2. Literature Review
This literature review focuses on the existing results from three articles about the development of electronic payment worldwide. Usage and acceptance of electronic payment within different countries vary. According to various reports, China is appealed to be in the lead on the usage of electronic payment during recent years.
Zandi et al. (2013) focus on the usage of cards. They consider debit and credit cards as the primary instruments of electronic payment to study in their research. According to their analysis cards usage could increase the efficiency of the economy in a circular flow. Firstly card usage could improve consumers’ utility by reducing transaction costs. Also, it brings convenience and easiness for transactions. Therefore card usage could boost consumptions and create more flows of goods and services. Then, as responses to the rising expenditures, more productions would be necessary for the economy to resolve the disequilibrium between demand and supply of goods and services. Furthermore, this increase in productions will reduce the unemployment rate in the labor market and increase the household income. Finally, as household income increases, it is predicted that households would consume more, the flow will sustain in this process. However, this indicates the problem of endogeneity. Electronic payments may have influences on consumption, but also electronic payment growth is affected by the change in expenditures. To answer their research question statistically, they developed a hypothetical model with the logarithm of real consumption per capita as a dependent variable and card penetration, accompanied with other four variables as independent variables. They run a cross-country panel regression with 350 observations during 2008-2012. As a result, they found their model with a high fitness as they hypothesized, that is the reason why I use their model as a base of my
hypothetical model. They realized that among 70 countries they observed, when usage of electronic payment increases, then consumption and GDP would be boosted. Also, if electronic payment penetration increases, more expansion will appeal to the growth of consumption and GDP. Moreover, in their research, they found that China has the highest usage of cards and the contribution of cards transactions to total household expenditures is the highest compared with other countries. Consumption elasticity concerning card usage is also the highest at 0.129%. These results motivate my further concern about the change when electronic payment has developed rapidly in recent years. Zandi et al. also made comparisons among countries on the card usage contribution to consumption, the results of 10 highest are shown below in Figure 1.
Figure 1. Usage Credit and Debit Cards’ contribution to Consumption, % (Zandi et al. 2013)
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In their research, they collected data from 2008 to 2012, which is the period included in my research observations as well. It is evident that China among all the countries has taken the lead
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for a percentage of 4.89% whereas the proportion of other countries were much falling behind. This result indicates that Chinese society if more adapted to the use of electronic payment, it could be a reason to explain why electronic payment development in China in the future is in rapid growth. Although as illustrated in the introduction that the primary instrument of electronic payment in China is QR code payment, but this payment method also requires consumers to connect their bank account with the providers’ account. Therefore the high usage of cards could also explain why people in China get adoptive to electronic payment easily and this gives reason to expect more significant growth of electronic payment in the future.
In addition to that, Humphrey, Kim, and Vale also studied the impacts of electronic payment (2001). They focused on the realized gains concerning the social costs, pricing, and consumers choices of payment method. Take consideration of social utilities; they noticed that electronic payment could reduce transaction costs by almost two-thirds of other cashless payment. This conclusion is corresponding with the findings of Zandi et al. (2013) which also believe that this costs reduction increases economic efficiency. Concerning the payment method, they found that the governing instrument of electronic payment varies among countries so when they compare among different nations, they took the average value of transactions to reduce the instrumental influences. In my research, comparisons of electronic payment development in China and other countries also suffer from the same problem because the dominant electronic payment method is QR code payment, which is seldom used by other countries. Therefore comparison in my research is made through literature review instead of statistical analysis. Considering pricing of electronic payment, Humphrey et al. found that compared with the pricing of paper payments, it has risen less electronically than that of paper during 1989-1995.
Consumers are quite sensitive with this payment pricing. This fact is the main reason why users’ preference for payment transfers to electronic payments. As realized in China, before 1st March 2016, users are completely free of using QR code payment, in the combination of the promotions given by the providers, transactions through electronic payment have proliferated over last few years. After the government introduced a service charge, this was only asked for users when they need to withdraw balance from their accounts but still no service charge with ordinary transactions. Therefore the increase in pricing of electronic payment is expected to slow the development for quite limited extents in China.
Last but not least, Tee and Ong (2016) focused their research in five European countries during 2000-2012 and studied the cashless payment impacts on economic growth by applying a panel regression. Different from the analysis in this paper, they used real GDP as dependent variable whereas this research only focuses on consumption which is one part of GDP indicators. For further studies related to my research, I also concern to study the influences of electronic payment on GDP. They found that in a short run, there is a causal transformation from cheque payment to card payment. Also similar to other works of literature, Tee and Ong realized that the convenience of electronic payment and the low cost are the main attractions to consumers’ selections to cashless payment method. More consumers transfer from traditional payment method to electronic payment. When the economy is more adaptive to the electronic payment, in the long run, the impacts to economic growth is expected to become significant. This result is advisable to explain the output of the research in this paper, and this will be addressed in section 5.1.
3. Methodology and Hypothesis
Followed by the contribution of Zandi et al. (2013), they used the logarithm of real household consumption per capita as the dependent variable. However, I think it is not necessary to use the logarithm to explain the influences of independent variables. I realize an increased trending pattern with real household consumption per capita in China during 2005-2016, for which the figure is in Appendix II. It is doubtful whether this increase is stationary so that I should use the change of this variable. To decide how this dependent variable should be, I apply an augmented Dickey-Fuller unit root test to this. The result of the ADF test is in Appendix V. It indicates that there is enough statistical evidence to prove that the data o household consumption per capita is stationary. Therefore, it is reasonable to use percentage change of this variable in my hypothetical model.
Concerning the independent variables, followed the model of Zandi et al., I consider electronic penetration, real disposable income per capita, and real interest rate to be determinants in this hypothetical model. Electronic penetration is at this moment in definition as the proportion of electronic payment values divided by the total household consumption nationally. However, for the same reason as that of the dependent variable, I also tested the stationarity of these three independent variables by using the ADF test. Also, I found the trending patterns of real disposable income per capita and electronic penetration shown in Appendix II and IV, respectively. But there is no apparent increase or decrease pattern concerning the real interest rate over time, so the ADF test of real interest rate does not take trend into account. The result of these independent variables are in Table 2.
Table 2. ADF test results for household consumption per capita and real disposable income H0: The variable is not stationary or has a unit root
H1: The variable is stationary
It shows that the growth of electronic penetration and that of real disposable income are stationary in China during 2005-2016. According to the result, the change of real interest rate is not statistically stationary, so it is not necessary to take percentage change of real interest rate as an independent variable in the model.
These results provide statistically significant evidence for me to make decisions on the model. The empirical model of this research is using household consumption per capita growth as the dependent variable, and the independent variables are the growth of electronic payment penetration, the growth of real disposable income per capita, and real interest rate.
Different from the model of Zandi et at. there are two quarter dummy variables added, because I use quarterly data, there is a potential seasonal effect. In my data investigations, I realize both real disposable income and electronic payment transaction values reach peaks periodically in quarter one and four. The analysis of the causes will be addressed later in section five. During the research, I realized that in fact, providers launched the electronic payment
dfuller results
Test statistic Significance level
Indicators Z(t) 1% 5% 10% Household_Consumption_Per_Capita -5.908* -4.178 -3.512 -3.187 Electronic_Penetration -5.681* -4.178 -3.512 -3.187 Real_Disposable_Income -8.485* -4.178 -3.512 -3.187 Real_Interest_Rate, no trend -1.931 -3.600 -2.938 2.604 Number of observations: 47 *statistically stationary
service earlier than the time of the earliest data I found for the transactions volume, this is due to Chinese government’s legal introduction of the electronic payment service in the third quarter of 2010, therefore data could be captured from the providers only after this period.
The hypothetical model I developed in my research is thus as following:
GrowthHouseholdConsumptionPerCapi = α + β1GrowthElecPt + β2GrowthRealDIt + β3rt +
β4Dummy_Q1 + β5Dummy_Q4 + εt
α: constant
GrowthHouseholdConsumptionPerCap: dependent variable, the growth of household consumption per capita
GrowthElecP: the growth of electronic payment penetration, calculated as the percentage change of electronic payment values divided by the total household consumption expenditure
GrowthRealDI: the percentage change in real disposable income per capita r: real interest rate
Dummy_Q1: if the time is the first quarter of the year, then the dummy is 1, otherwise 0. Dummy_Q4: if the time is the fourth quarter of the year, then the dummy is 1, otherwise 0. ε: error term
According to the literature review, it is expected to be easier and more convenient to pay with electronic payment. In addition to that, electronic payment can create a more efficient economy and boost consumptions. Therefore, I develop one hypothesis to examine my central question statistically. To test whether the effectiveness of electronic payment on domestic consumption, the hypothesis is addressed as follows:
H0: If electronic payment penetration changes, then real consumption per capita is not affected.
(β1 = 0)
Η1: If electronic payment penetration changes, then real consumption per capita would deviate.
(β1 ≠ 0)
4. Data
To analysis the central question, this research will use the data of electronic payment within 12 years range from 2005 till 2016, the amount of observations on this topic is quite limited, therefore instead of using annual data, I use quarterly data to extend the number of observations to 48. Also, this research will not attempt to answer the central question solely with statistical analysis but also with descriptive analysis.
To investigate statistically using the empirical model stated:
GrowthHouseholdConsumptionPerCapi = α + β1GrowthElecPt + β2GrowthRealDIt + β3rt +
β4Dummy_Q1 + β5Dummy_Q4 + εt
I collected data from various sources. I found household final consumption expenditure per capita from National Bureau of Statistics of China (NBSC), which is the official national database of China (2017). But on nationwide data could only be found directly through 2013-2016, so the data of 2005-2012 was calculated by the weighted average according to the demographic distribution between rural and urban areas, this data is from WorldBank (2018). Then to the independent variables during 2005-2016, real interest rate is calculated by the difference between nominal interest rates (Quandl, 2018) and inflation rates (OECD, 2018), then I can find real disposable income per capita nationwide for each quarter in NBSC from 2013 first
quarter to 2016 fourth quarter (2018). However, nationwide real disposable income data was not collected by this data during 2005 first quarter till 2012 fourth quarter, but the real disposable income of urban households is reachable in NBSC. Rural disposable income of 2005-2012 in this research is assumed to equal the per capita cash income which could be found in NBSC. Then the national disposable income is also calculated by the weighted average as that of the dependent variable. The electronic payment penetration is calculated by the division of electronic payment transaction values and the total household consumption during 2010-2016. As mentioned previously, electronic payment transaction values before 2010 quarter three will be assumed to be zero. After that, I collect the total transaction values of electronic payment through the annual reports of two main service providers: Alipay and Tencent, and the total household consumption expenditures for each quarter cannot be found directly through any database, so I multiply the quarterly per capita household consumption data with total population (WorldBank, 2018). The most remarkable point of my data collection is that data collected varies between accumulated data and quarterly data only. To avoid this error, I correct the accumulated data into quarterly data only because for real interest rate I cannot apply accumulated data, to match all the variables it is easier to use subtraction and reach the quarterly data instead.
To process the descriptive analysis, I collect data of China from the same resources as the statistical analysis. In this result no new comparisons among countries will be made because electronic payment instruments differ in different countries, it is impossible to collect the data of electronic payment with the same device, and comparing the penetration of varying payment tools among nations statistically is not meaningful for this research. In the descriptive analysis, to
understand the value more explicitly, I convert currency unit of data from Chinese Yuan to US dollars. Quarterly exchange rates are collected from Federal Reserve Bank of St. Louis.
The details of data sources and explanations are attached in Appendix I.
5. Results and Analysis 5.1 Descriptive Analysis
Before I regress the model statistically, I first investigate the trends of electronic payment development in China starts from 2010 third quarter till 2016, and focus on the transactions values weights account for household consumption expenditure within nationwide, namely electronic penetration. The results are in Figure 3 and Figure 4.
As shown in Figure 3, the transaction values increased slowly after the end of 2010 until 2012 but this increase does not last for long, in 2013 consumption made through electronic payment decreases dramatically in 2013. However, followed by that is a continuous boost since 2014 and this rapid growth boosts the transaction values to reach more than 1.88 trillion US dollars in 2016. One reason can explain this phenomenon is that providers, such as Alibaba Inc. and Tencent Inc. implemented various promotions to encourage consumers to pay with their electronic payment instruments. For example, the leading payment system of Alibaba Inc. Alipay gives extra discounts to consumers if they pay through their application, or provides cash back and this cash return to consumers’ account immediately after they finish the transaction. Before 1st March 2016, users of Alipay and Tencent payment system can withdraw any amount of their balance in the payment system without paying any service charge. This fact implies that consumers benefits with the convenience and cheapness with almost no transaction costs. After
1st March 2016, a service charge of 0.1% to the value withdrawn has been charging from the users, but this cost is not applicable to transactions, only if people withdraw balance from their account, then this service fee will be deducted. Therefore it is expected that this service charge cannot affect the growth of electronic payment transaction volume for great extents.
Figure 3. Yearly Transaction Values (US$) Figure 4. EP Penetration (%)
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In Figure 4, in general, electronic payment penetration is increasing over time and is expected to keep a high proportion in the future. Also, there is a periodic pattern of electronic penetration, electronic payment transaction values reach a peak every fourth quarter of the year and decrease rapidly after. In 2016 the fourth quarter, almost 95% of total household consumptions transactions were made through electronic payment method. This phenomenon may result from few reasons; firstly there are more festivals at the end of each year, e.g., Christmas, New Year Eve, traditional Chinese festivals, etc. The most noticeable festival is the “Double 11” shopping day, indicating the 11th, November of each year, this is a nationwide festival promoted by electronic commerce. The main idea is to give great discounts on products, and the only possible payment method is through electronic payments. Sellers always make advertisements earlier than the day. Therefore most consumers wait until the shopping day to purchase the products they want; this is the main reason why the transaction values are very high
0.00E+00 5.00E+11 1.00E+12 1.50E+12 2.00E+12 2010 2011 2012 2013 2014 2015 2016 0% 25% 50% 75% 100% 2010 2011 2012 2013 2014 2015 2016
in each fourth quarter. For example, the transaction values on the day of 11th November in 2015 and 2016 are 912.2 billion and 1207 billion Chinese Yuan respectively. In addition to “Double 11” shopping day, providers nowadays promoted also “Double 12”, indicating the 12th of December which is also in the fourth quarter of each year. Another reason for this periodic pattern is that most Chinese companies give employees ‘red pocket’, which is a form of end year bonus to reward their efforts. It gives people more income to spend therefore leads to higher possibilities to consume either through cash payments or non-cash payments.
5.2 Statistical Analysis of the Hypothetical Modal
Before I address further the analysis of the model, I test whether the assumptions of multiple-regression model hold in this research. Firstly I examine the unbiasedness of this by dropping regressors (Stock and Watson, 2011) and then compare the estimated coefficients of the full regression with those of the reduced regressions. I found omitted variable unbiasedness of all the independent variables which verifies this assumption of the model. To the assumption of multicollinearity (Stock and Watson, 2011), I predicted that real disposable income and electronic payment penetration are closely correlated. To confirm whether this correlation is statistically significant, I applied variance inflation factor with STATA, and the result is in Table 5. From the result, I realized that all the VIF of the variables is higher than one but lower than 5. This result implies that all the variables are moderately correlated. These correlations are not statistically significant enough to be taken into account within the statistical analysis.
Table 5. Variance Inflation Factor to Test the Multicollinearity
In addition to that, I also tested the assumption of homoscedasticity, which is an essential assumption of linear regression models to assume the error term of a model has a constant variance (Stock and Watson, 2011). The result is shown in Table 6. This result reveals that the problem heteroskedasticity is significant in the data. It means the error term varies when the independent variables are changing. One reason could explain this result is that when people have more real disposable income, some may not consume too much of them, some may consume an enormous amount of them. Then it could vary the error term under this situation.
Table 6. Test result of heteroskedasticity
To figure out whether electronic payment has a statistically significant effect on the growth of household consumption per capita in China, I applied a multiple linear regression. Because I found heteroskedasticity in the data, I used robust standard error when I run the regression, and the results are shown below in Table 7.
Variable VIF 1/VIF
Electronic Penetration Growth 1.21 0.8276 Real Disposable Income Growth 1.71 0.5851
Real Interest Rate 1.07 0.9344
Dummy_Q1 1.85 0.5399
Dummy_Q4 1.43 0.6975
Breusch-Pagan / Cook-Weisberg test for heteroskedasticity H0: Constant variance
Variables: fitted values of Household_consumption_per_capita_growth chi2(1) = 5.57
Table 7. Estimates of the Determinants for the Household Consumption Per Capita Growth
According to the regression result given in Table 7, in general, it is noticeable that the R-squared implies that the independent variables can explain 84.33% of the dependent variables. The hypothetical model is reasonable to China during 2005-2016. Considering the independent variables individually, the result of electronic payment penetration growth is consistent with the intuitive expectations. The features of convenience, low transaction costs, and easiness of electronic payment can boost the domestic consumption per capita. The growth of electronic payment penetration affects the consumption per capita growth positively. This result is consistent with most literature as I addressed in the literature review section. It is estimated in the model that every one percent increase in electronic penetration leads to 0.05% increase in household consumption per capita in China. Moreover, the t-value of this variable implies that this influence is also statistically significant with a significance level of 5%. China is in the considerably rapid growth of developing electronic payment penetration, so the effect of this
Variables Coefficient Robust
Std. Err. t P > | z |
Constant 0.0110 0.0095 1.16 0.251
Electronic Penetration Growth 0.0005 0.0001 4.39 0.000
Real Disposable Income Growth 0.4849 0.0501 9.67 0.000
Real Interest Rate -0.3154 0.2824 -1.12 0.270
Dummy_Q1 -0.0529 0.0162 -3.25 0.002 Dummy_Q4 0.0989 0.0182 5.43 0.000 Number of observations: 48 F (5, 42) = 76.39 Prob > F = 0.0000 R-squared = 0.8433 Root MSE = 0.0431
payment instrument is expected to reinforce in the future. This result is necessary to make a recall in the future with extended observations. Concerning the growing contribution of electronic payment expenditures, I expect national household consumption in China to increase more rapidly than other countries because the Chinese economy is more adaptive to this new instruments as illustrated previously. If the growth of electronic payment penetration could lead to an expedient increase of household consumption, then the increased use of electronic payment will result in more economic benefits than other countries.
In this research, data is limited to 12 years (48 observations), quarterly data is used instead of yearly. Taking the dummy variable into account, I realize that both dummy variables have significant influences on the change in household consumption per capita. However, two dummy variables have different signs of coefficients. One explanation for this result is similar to the reason addressed in the descriptive analysis. Numerous shopping events and festivals are in the fourth quarter which motivates households’ consumptions. If the time is in the fourth quarter, then household consumption per capita tends to increase by 9.9%. First quarter dummy is negative may because Chinese new year according to lunar calendar is in this period, people tend to save more after they consume much in the last period. The first quarter dummy coefficient indicates it has an adverse effect of 5.3% on the dependent variable.
In addition to the regressors of electronic payment, the results of real disposable income and real interest rate are also noticeable. I realized that real disposable income growth is statistically significant with the t-value of 9.67 and every one percent increase in real disposable income leads to an increase in consumption per capita by 48.5%. This effect implies that real disposable income is a dominant factor in Chinese households’ consumption behaviors. The
influence of this variable is the most effective among all independent variables. Contradicted to the effect of real disposable income, the effect of the real interest rate is adverse to the dependent variable. People tend to save more when the real interest rate increases, and consume less. The regression result also suggests for this idea: every one percent rise in real interest rate leads to a reduction of household consumption per capita by 0.32%. However, the t-value indicates that this effect is not statistically significant. Therefore the real interest rate is not determinant for Chinese citizens’ consumption behaviors.
In general, concerning the hypothetical model as shown below:
GrowthHouseholdConsumptionPerCapi = α + β1GrowthElecPt + β2GrowthRealDIt + β3rt +
β4Dummy_Q1 + β5Dummy_Q4 + εt
the significantly determinant variables are the growth of electronic penetration, the growth of real disposable income, and two dummy variables. However, the influence of real interest rates is not effective for Chinese domestic consumption development within the observational periods.
6. Conclusion
This research investigated the impacts of electronic payment penetration on domestic consumption per capita of China during 2005-2016. Both statistical and descriptive analyses are used in the explanation to answer the central question. Comparison of electronic payment development between China and other countries is made through a literature review of Zandi et al. (2013). Consistent with findings of existing literature, the results of this research also admits that Chinese people are quite acquainted with the electronic payment. The acceptance and penetration of this payment system have proliferated over the past decade. Within this research, I
observed if there is a rise in electronic payment penetration, then there will be a minute increase in domestic consumption per capita. There is enough statistical evidence to prove this impacts of electronic payment to be significant. As addressed by Tee and Ong (2016), the economy takes time to adapt the innovation of the payment method, so within a short-run, the influence is not significant in most countries. Contradicted to that, due to the rapid development and nationwide usage of electronic payment in China, this payment method brings significantly positive influence to the domestic consumption level during the 12 years observed. However, the research based on a twelve-year observation is a short-run. It is difficult to make any long-run predictions upon this model. The time restriction drives out one of the limitations of my research: the number of observations is very limited although I used the quarter instead of the annual data, there are only 48 observations which is not enough to observe a long-run effect. Also, in this research making comparisons among countries is problematic with the internal validity because electronic payment instruments vary in different countries. Usage, convenience, and service costs are different in various devices, but they are elements dominate users’ preference of whether pay with that electronic payment instrument (Humphrey et al., 2001). Moreover, this research may suffer from the endogeneity problem because in general electronic payments may have influences on consumption, but also electronic payment growth is affected by the change of consumption. It is noticeable that in the hypothetical model, electronic payment penetration is in definition as the proportion of electronic payment values divided by the total household consumption nationwide. The circular model illustrated in the paper of Zandi et al. could also explain this potential problem which I also suggest for further study in this aspect.
In addition to the suggestion on the study of endogeneity problem, I also suggest further research to focus on the expected influences on money supply in China. Because electronic payment, as a tool of non-cash transactions, it is expected that with the rapid boom of electronic payment penetration less cash is demanded in goods and service market but the question whether this expectation is significant attracts my further concern. Also consumption as one indication of GDP, it is suggested to study further this influence of electronic payment on GDP in China during the same or more extended period.
7. References
Humphrey, D. B., Kim, M., & Vale, B. (2001). Realizing the gains from electronic payments: Costs, pricing, and payment choice. Journal of Money, Credit and Banking, 216-234.
Kapron, Z., & Meertens, M. (2017). Social Networks, e-Commerce Platforms, and the Growth of Digital Payment Ecosystems in China: What It Means for Other Countries. United Nations
Capital Development Fund: Better Than Cash Alliance.
Stock, J., & Watson, Mark W. (2011). Introduction to Econometrics / (3rd ed., Addison-Wesley series in economics). Boston: Addison-Wesley.
Tee, H. H., & Ong, H. B. (2016). Cashless payment and economic growth. Financial Innovation,
2(1), 4.
Zandi, M., Singh, V., & Irving, J. (2013). The impact of electronic payments on economic growth. Moody’s Analytics: Economic and Consumer Credit Analytics, 1-16.
8. Appendix
I. Description table of variables and data collection Variable
Name Description Source
Name from the database Calculate for Country China / Time (t)
The quarter of the year, e.g., 2005 Q1 represents as the first quarter of 2005. Dependent Variable The Growth of Household consumption per capita percentage change in per capita household consumption expenditure nationwide. Calculation National Bureau of Statistics of China
Per Capita Expenditure Nationwide, Accumulated (yuan) / Independent Variables The Growth of Electronic payment penetration defined as the percentage change in the proportion of electronic payment volumes divided by the total household consumption nationally
The Growth of Real Disposable Income percentage change in real disposable income per capita nationwide, nationwide data before the fourth quarter of 2012 is calculated by the weighted average of rural and urban real disposable income Calculation National Bureau of Statistics of China
Per Capita Disposable Income Nationwide, Accumulated(yuan) / Real interest rate defined as the difference between the nominal interest rate and the inflation rate Calculation Dummy variable of quarter 1 of the year
if the data is from the first quarter of the year, then the dummy is 1, otherwise 0. Dummy
variable of quarter 4 of
the year
if the data is from the fourth quarter of the year, then the dummy is 1, otherwise 01
Data use for calculation
Population
the total population of the year used to calculate the total household consumption quarterly The World Bank (IBRD, IDA) Population, total Electronic payment penetration
Electronic payment value sum of main providers’ values of electronic payment transactions, accumulated Annual report 2005-2016, Alibaba Inc. and Tencent Inc. Electronic payment penetration Real disposable income urban real disposable income of urban households quarterly National Bureau of Statistics of China
Per capita Disposable Income of Urban Households, Accumulated (yuan) Real disposable income Real disposable income rural it is not collected by the database that how much the real
disposable income is in the rural area, in this research it is assumed equal the cash income
National Bureau of Statistics of China
Per capita Cash income of Rural Households Real disposable income Rural Population
used to calculate the weighted average of real disposable income nationwide in 2005-2012 The World Bank (IBRD, IDA) Rural population (% of total population) Real disposable income Nominal interest rates
used to calculate the real interest rates in China quarterly
quandl.com The official interest rate, China Real interest rate Inflation rates
used to calculate the real interest rates in China quarterly
OECD
database Inflation (CPI)
Real interest rate
Foreign exchange rate
used to convert data differences in currency Federal Reserve Bank of St. Louis China/U.S. Foreign Exchange Rate, Chinese Yuan to One U.S. Dollar, Quarterly, Not Seasonally Adjusted
descriptive analysis
II. Household consumption per capita and real disposable income over time
III. Household consumption per capita and real disposable income growth over time 0.0 1750.0 3500.0 5250.0 7000.0 2005 Q1 2006 Q1 2007 Q1 2008 Q1 2009 Q1 2010 Q1 2011 Q1 2012 Q1 2013 Q1 2014 Q1 2015 Q1 2016 Q1
Household Consumption Expenditure per capita, each quarter (Yuan) Real Disposable Income, each quarter (Yuan)
-0.225 -0.150 -0.075 0.000 0.075 0.150 0.225 0.300 2005 Q1 2006 Q2 2007 Q3 2008 Q4 2010 Q1 2011 Q2 2012 Q3 2013 Q4 2015 Q1 2016 Q2
IV. Electronic payment penetration (in %) over time -0.3000 -0.2250 -0.1500 -0.0750 0.0000 0.0750 0.1500 0.2250 0.3000 2005 Q1 2006 Q12007 Q1 2008 Q1 2009 Q1 2010 Q1 2011 Q12012 Q1 2013 Q1 2014 Q12015 Q1 2016 Q1 % Change of Real Disposable Income
0% 25% 50% 75% 100% 2010 2011 2012 2013 2014 2015 2016
