Abstract

Economic time series are considered to be the economic barometers of the health of an economy. They provide information about the performance and stability of key economic indicators, such as inflation. Therefore, it is wise to try and forecast these variables.

In this paper, we implement time series modeling techniques using R programming language to analyze economic time series related to inflation and interest rates. Specifically, we apply simple and ARIMA models to forecast the Consumer Price Index (CPI).

To focus our analysis, we will apply our method of analysis to one CPI series, and then use the same method for the remaining CPI series. This will allow us to compare the accuracy of our models across different CPI series and identify any patterns or trends in the data.

The goal of this analysis is to compare the accuracy of simple and ARIMA models for forecasting CPI. We will use evaluation measures such as Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) to compare the performance of the two models. Our analysis will provide insights into which modeling technique is better suited for forecasting these economic variables.

1. Historical Time Series of Inflation in the US

1.1 Overview of Series

Our research analyses time series on consumer prices in the U.S. maintained by the St. Louis Federal Reserve. Specifically, we analyse three series:

• Consumer Price Index for All Urban Consumers: All Items in U.S. City Average (CPIAUCSL)
• Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average (CPILFESL)
• Consumer Price Index for All Urban Consumers: All Items Less Food and Energy in U.S. City Average (CPILFENS)

CPILFENS and CPIFESL are both CPI series produced by the BLS, but they differ in terms of their geographic coverage, scope of coverage, and methodology. Also, CPILFESL is seasonally adjusted and CPILFENS is not. These are all monthly time series from January 2011 to November 2022. The next section provides descriptions of the four series.

1.2 Series Descriptions

Reference : https://fred.stlouisfed.org/

1.2.1 Description of CPIAUCSL

Source: U.S. Bureau of Labor Statistics
Units:Index 1982-1984=100, Seasonally Adjusted Frequency: Monthly

CPIAUCSL stands for Consumer Price Index for All Urban Consumers: All Items. It is a measure of the average change over time in the prices paid by urban consumers for a basket of goods and services, including food, housing, clothing, transportation, and medical care, among others. The CPIAUCSL is produced by the United States Bureau of Labor Statistics and is used as an indicator of inflation in the US economy.

1.32 Description of CPILFESL

Source: U.S. Bureau of Labor Statistics Release: Consumer Price Index

Units: Index 1982-1984=100, Seasonally Adjusted Frequency: Monthly

CPILFESL stands for Consumer Price Index for All Urban Consumers: All Items Less Food and Energy. It is a measure of the average change over time in the prices paid by urban consumers for a basket of goods and services, excluding food and energy prices. This measure is used to gauge the underlying inflation trend in the US economy, as food and energy prices can be highly volatile and subject to seasonal changes that can obscure the overall inflation trend. CPIFESL focuses on a narrow range of goods and services, including food, housing, apparel, and transportation.

1.35 Description of CPILFENS

Source: U.S. Bureau of Labor Statistics Release: Consumer Price Index

Units: Index 1982-1984=100, Not Seasonally Adjusted Frequency: Monthly

The “Consumer Price Index for All Urban Consumers: All Items Less Food & Energy” is an aggregate of prices paid by urban consumers for a typical basket of goods, excluding food and energy. This measurement, known as “Core CPI,” is widely used by economists because food and energy have very volatile prices.CPILFENS covers a broad range of goods and services, including food, housing, clothing, transportation, and medical care.

1.4 Extract symbol list

To conduct our analysis, we used an R time series object named “economic_data2.” This quarterly time series spans from 2011-01-01 to 2022-11-30 and contains multiple columns representing the three Consumer Price Index (CPI) series: CPILFENS (Consumer Price Index for All Urban Consumers: All Items Less Food & Energy), CPIAUCSL (Consumer Price Index for All Urban Consumers: All Item), and CPILFESL (Consumer Price Index for All Urban Consumers: All Items Less Food and Energy). The R objects were created by importing data directly from the Federal Reserve Economic Data (https: //fred.stlouisfed.org/).

We can extract the individual time series from economic-data2 using the following code

list_symbols<- unique(economic_data2$symbol)

2. Methodology

In this research paper, we investigated the time series properties of 3 CPI series : CPILFENS, CPIAUCSL, and CPILFESL.

Our approach was guided by the principles outlined in the book “Forecasting: Principles and Practice” by Rob J. Hyndman and George Athanasopoulos (Hyndman and Athanasopoulos, 2018). To perform the data analysis and modeling, we employed the functionality provided by the “fpp2” package in R. By applying the methodologies outlined in Hyndman’s book and implementing the “fpp2” package, we were able to conduct rigorous and robust time series analysis on the economic data.

To conduct our study, we first performed a series of exploratory data analysis by visually observing the time series plots and their seasonal plots, and the autocorrelation function (ACF). Subsequently, we used simple models , namely naive and drift, to forecast the data. Upon further investigation, we found that we can reject the null hypothesis that the time series are stationary for all three series, meaning their statistical properties change over time.

To transform the data into stationary time series, we applied first and second differencing. Next, we applied the auto.arima function from the forecast package in R to find the best fit model for each variable. For CPIAUCSL, the best model is ARIMA(0,1,1) with drift, indicating that the variable is influenced by its own one lagged value and has a linear trend over time. For CPILFENS, the best model is found to be ARIMA(2,1,0) with drift, indicating that the variable is influenced by its own two lagged values and has a linear trend over time. For CPILFESL, the best model is ARIMA(1,2,1), indicating that the variable is influenced by its own one lagged value , has a quadratic trend over time and is affected by a lagged moving average of order one.

We then applied root mean squared error (RMSE) , mean absolute error (MAE) , and mean absolute percentage error (MAPE). The results showed us that the ARIMA models provide forecasts for all three variables with relatively low error measures.

In the following section, we will delve into the detailed analyses of the three economic time series, namely CPIAUCSL, CPILFENS, and CPILFESL. This section will provide a comprehensive overview of the methodologies and the R code employed to generate the results. By including the R code, we aim to facilitate our readers to replicate the analyses presented in this paper or apply them to other time series data. We provide a practical resource for anyone who wishes to explore our methodologies further or extend the analyses to different time series datasets.

Through this detailed examination of the analyses and the inclusion of the R code, we hope that the insights gained from our research can be readily applied and built upon by the wider community.

3. Analysis of Consumer Price Index for All Urban Con- sumers: All Items Less Food and Energy in U.S. City Aver- age (CPILFESL)

3.1 Extract time series and plot the time plot, seasonal plot and auto- correlation function

First, extract series and create a ts object:

symbol1<- list_symbols[2]
data.symbol1<- filter(economic_data2, symbol==symbol1)
symbol1.ts<- ts(data.symbol1$price, frequency=12, start=c(2011,1))

Time Plot

autoplot(log((symbol1.ts)))+
  ggtitle("Time Plot")

Upon examining the data, we have made several observations about the trends in CPILFESL:

Firstly, we noted that CPILFESL fell in the year 2020. This indicates a decrease in inflation, as a falling CPI suggests that prices for goods and services are decreasing.

Secondly, we observed a considerable increase in CPILFESL during the years 2021 and 2022. This indicates an increase in inflation, as the CPI is rising.

Thirdly, we noticed that before the year 2020, the index increased at a constant linear rate. This suggests a relatively stable inflation rate during this time period, with prices for goods and services increasing at a steady rate.

Finally, we observed that after 2020, the index rose at a much faster rate. This indicates a sudden increase in inflation during this time period, which could be due to a variety of factors such as changes in supply and demand, shifts in government policies, or other external factors.

The main objective of predicting the index is to evaluate the sensitivity of our forecasting models to the use of data prior to the year 2020. Basically, we want to understand how the accuracy and reliability of our predictions are affected by the inclusion of data from and before 2020.

autoplot(window(symbol1.ts,start=c(2019,1)))+
  ggtitle("Time Plot") +
  ylab("CPILFESL")+
  xlab("Time")

Seasonal Time Plot :

A seasonal plot is a graphical representation of a time series that provides insights into its seasonal patterns. It helps identify repeating patterns or cycles within the data that occur over fixed time intervals, such as daily, monthly, or yearly patterns.

We use the following R code to plot the seasonal plot

ggseasonplot(symbol1.ts, polar= TRUE)+
  ylab("CPILFESL")+
ggtitle("Seasonal Plot: CPILFESL")

The seasonal plot is implemented by the “fpp2” package. It is a one-of-a-kind data visualisation technique that represents time series values in polar coordinates. By using polar coordinates, the seasonal plot provides a compact and simple representation of the seasonal patterns in the data. In this plot, the angle corresponds to the month-of-year, while the radius represents the time from the beginning of the series.

we have made the following observations:

Firstly, we noted that there is a large jump in the CPILFESL variable every year. This indicates that there is a significant change in the rate of inflation from one year to the next. This could be due to a variety of factors.

Secondly, we observed that there is no overlap in the data of the seasons. This means that the data points for each season are distinct and do not overlap with each other. This pattern could have important implications for our analysis, as it suggests that there may be seasonal trends or patterns that are unique to each season.

Autocorrelation and white noise :

ggAcf(symbol1.ts)+
  ggtitle(symbol0)

Based on our observations of the data, we have identified several important patterns that provide insight into the nature of the time series:

Firstly, we noted that there was a slow decrease in the autocorrelation function (ACF) as the lags increased.

Secondly, we compared the spikes in the ACF to the expected values for a white noise series. Specifically, we expected 95% of the spikes in the ACF to lie within the range of ±2/ T, where T is the length

of the time series. In this case, T is equal to 11*12, or 132. This means that we would expect the spikes in the ACF to lie within the range of ±0.17

However, we found that the spikes in the ACF actually lie above +0.17 and cross the blue bounds. This indicates that the series is not white noise, as there is significant correlation between the data points that cannot be explained by random chance alone.

The observed ACF pattern, where the autocorrelation gradually decreases as the lag increases, is typical of non-stationary time series. This pattern is often observed in time series models such as random walks. In section 3.2, we investigate the characteristics of non-stationary time series, particularly random walks, and analyze their behavior using the naive and drift models. Further, section 3.2 provides insights into the suitability of these models for capturing the dynamics of non-stationary time series, such as random walks, and sheds light on their capabilities to forecast in such situations.

3.2 Evaluate simple forecast models

3.2.1 Random walk with naive

Using the R package “fpp2”, let’s apply the naive method

symbol1.ts.naive=naive(symbol1.ts)
autoplot(symbol1.ts.naive)+
  ylab(symbol1)+
  ggtitle("Naive Method")

checkresiduals(symbol1.ts.naive)

##
##  Ljung-Box test
##
## data:  Residuals from Naive method
## Q* = 340.41, df = 24, p-value < 2.2e-16
##
## Model df: 0.   Total lags used: 24

The naive method of forecasting assumes that all future forecast values will be the same as the previous or last observation. However, this method is not useful

Regarding the seasonal naive method, this approach is only useful when the data is seasonal in nature, which is not the case for this particular dataset. Instead, the data appears to exhibit more of a trend-like pattern over time. Therefore, utilizing the seasonal naive method would not provide any meaningful insights or forecasts.

To ensure the adequacy of the naive model in capturing the underlying patterns of the non-stationary time series, we conducted a residual analysis. Residuals are the differences between the observed values and the corresponding model predictions. Analyzing the residuals allows us to assess the model’s ability to capture the remaining variation in the data.

In this analysis, we utilized the checkresiduals() function which provides various plots and tests to evaluate the model’s residuals. These plots and tests help in determining whether the model adequately captures the inherent patterns and randomness in the time series data.

By examining the plots, such as the autocorrelation function (ACF) and the histogram of residuals, we can assess if the residuals exhibit any remaining patterns or systematic behavior. Ideally, the residuals should be uncorrelated, normally distributed, and have constant variance.

In addition to visual inspection, the checkresiduals() function also performs statistical tests, including the Ljung-Box test, to check for significant autocorrelation in the residuals. A significant autocorrelation indicates that the model fails to capture some underlying structure in the data.

Upon analyzing the data, we have observed that the variability of the residuals is relatively low in the first half of the series, but it increases in the second half. This pattern indicates that taking the logarithms of the series is appropriate for applying stationary time series models. This approach can help to stabilize the variance of the data and make it easier to apply various statistical techniques.

After examining the data, we have found that taking logarithms does not seem to improve the stationarity of the series. However, we found that taking the second differences of the series, as opposed to the first differences, does lead to greater stationarity. This suggests that the trend in the data is not linear and may require more complex modeling techniques.

Now, we will check the errors of the naive method

e <- tsCV(symbol1.ts, forecastfunction=naive, h=8) 
# Compute the RMSE values and remove missing values 
rmse <- sqrt(colMeans(eˆ2, na.rm = T))
# Plot the RMSE values against the forecast horizon data.frame(h = 1:8, RMSE = rmse) %>%

ggplot(aes(x = h, y = RMSE)) + geom_point()

Throughout our analysis, we will provide RMSE values that are directly comparable to the original time series. This enables us to gauge the level of accuracy achieved by the forecasting models and make informed assessments about their suitability for practical applications and decision-making processes.

Upon examining the forecasting results, it becomes clear that the forecast error tends to increase as the forecast horizon increases. This means that the accuracy of our predictions tends to decrease as we try to forecast further into the future.

This is quite common because as we predict further into the future, forecasting becomes increasingly difficult due to uncertainty and variability of the outcomes.

e <- tsCV(symbol1.ts, forecastfunction = naive, h=1)
sqrt(mean(eˆ2, na.rm=TRUE))
## [1] 0.7189534
sqrt(mean(residuals(naive(symbol0.ts))ˆ2, na.rm=TRUE))
## [1] 0.9239518

The “tsCV” function is a useful tool for evaluating the accuracy of a time series forecasting model. It stands for “time series cross-validation” and is used to estimate the forecast error of a model. The “tsCV” function computes the forecast errors by dividing the time series data into training and testing sets. It fits the model to the training set and generates forecasts for the corresponding test set. The forecast errors are then calculated by comparing the predicted values to the actual values in the test set.

Comparing the cross-validation error to the RMSE of the residuals provides a way of assessing the model’s performance. The residuals are the differences between the observed values and the corresponding one-step predictions generated by the model. These residuals represent the in-sample prediction errors.

Ideally, the cross-validation error should be smaller than the RMSE of the residuals. This suggests that the model performs well not only on the data it was trained on but also on unseen data. If the cross- validation error is larger than the RMSE of the residuals, it indicates that the model may be overfitting the training data.

In this case, the RMSE of the cross-validation error is smaller than the RMSE of the residuals, suggesting the model performs quite well.

3.2.2 Random walk with drift

We now apply the drift method

autoplot(rwf(symbol1.ts, drift=TRUE),
    series="Drift")+
ylab(symbol1)+
  ggtitle("Drift Method")

Here, we are referring to a forecasting method known as the drift method. The drift method involves taking the last observation of the data and adding to it the amount of change over time (i.e., the drift) of the data. The resulting value is then used as a forecast for future time periods.

The drift component of the data is a measure of how much the data tends to increase or decrease over time. By using this drift into the forecast, we are able to account for the overall trend in the data and make more accurate predictions about future values.

fit0 = rwf(symbol1.ts, drift=TRUE)
summary(fit0)

##
## Forecast method: Random walk with drift
##
## Model Information:
## Call: rwf(y = symbol1.ts, drift = TRUE)
##
## Drift: 0.5455  (se 0.0394)
## Residual sd: 0.47
##
## Error measures:
##                         ME      RMSE       MAE
## Training set -6.605036e-15 0.4683447 0.2966886 -0.007742377 0.1129022
##                    MASE      ACF1
## Training set 0.04767245 0.7353021
##
## Forecasts:
##
## Dec 2022
## Jan 2023
## Feb 2023
## Mar 2023
## Apr 2023
## May 2023
## Jun 2023
## Jul 2023
## Aug 2023
## Sep 2023
Point Forecast    Lo 80    Hi 80    Lo 95    Hi 95
      300.8065 300.2020 301.4109 299.8821 301.7309
      301.3520 300.4942 302.2098 300.0401 302.6639
      301.8974 300.8432 302.9517 300.2851 303.5097
      302.4429 301.2214 303.6644 300.5748 304.3111
      302.9884 301.6180 304.3588 300.8926 305.0842
      303.5339 302.0276 305.0401 301.2303 305.8375
      304.0794 302.4469 305.7118 301.5828 306.5759
      304.6248 302.8738 306.3758 301.9469 307.3027
      305.1703 303.3069 307.0337 302.3205 308.0201
      305.7158 303.7451 307.6865 302.7019 308.7297
checkresiduals(fit0)

##
##  Ljung-Box test
##
## data:  Residuals from Random walk with drift
## Q* = 340.41, df = 23, p-value < 2.2e-16
##
## Model df: 1.   Total lags used: 24

accuracy(fit0)

##                         ME      RMSE       MAE          MPE      MAPE
## Training set -6.605036e-15 0.4683447 0.2966886 -0.007742377 0.1129022
##                    MASE      ACF1
## Training set 0.04767245 0.7353021

The output from the “fit0” model provides us with point forecasts and prediction intervals using the “rwf” function. The “rwf” function calculates point forecasts based on the random walk with drift model and provides prediction intervals that capture the uncertainty of the forecasts. We utilized the checkresiduals() function which provides various plots and statistical tests to evaluate the new model’s residuals. These plots and tests help us determine whether the model adequately fits the data.

We have made the following important observations :

Firstly, we can see that as the forecast horizon (h) increases, the width of the prediction intervals also increases. This widening of the prediction intervals is expected because forecasting becomes more challenging as we project further into the future. The increased uncertainty over longer time horizons results in wider intervals to account for the potential variability in the forecasted values.

Secondly, the estimated drift coefficient is 0.5455 with a standard error of 0.0394. This indicates that the time series has a linear trend over time. Also, the residual standard deviation is 0.47, which represents the average magnitude of the differences between the observed values and the forecasted values.

Thirdly, the RMSE (root mean squared error) is 0.4683447, indicating the average magnitude of the forecast errors. The MAE (mean absolute error) is 0.2966886, representing the average absolute magnitude of the errors. The MPE (mean percentage error) is -0.007742377. The MAPE (mean absolute percentage error) is 0.1129022, reflecting the average percentage deviation of the forecasts. The MASE (mean absolute scaled error) is 0.0476724. The ACF1 (first-order autocorrelation coefficient) is 0.7353021, suggesting a relatively high autocorrelation.

Finally, the test statistic (Q*) is 340.41, indicating significant evidence of autocorrelation.

Overall, the output suggests that while the random walk with drift model captures the linear trend in the time series, there are still autocorrelations that the model does not account for. Development of ARIMA models will address this issue below.

Now let’s check the errors in drift method

e <- tsCV(symbol1.ts, forecastfunction=naive, h=8) 
# Compute the RMSE values and remove missing values 
rmse <- sqrt(colMeans(eˆ2, na.rm = T))
# Plot the RMSE values against the forecast horizon 
data.frame(h = 1:8, RMSE = rmse) %>%

ggplot(aes(x = h, y = RMSE)) + geom_point()

e <- tsCV(symbol1.ts, rwf, drift=TRUE, h=1)
sqrt(mean(eˆ2, na.rm=TRUE))
## [1] 0.4723834
sqrt(mean(residuals(rwf(symbol1.ts, drift=TRUE))ˆ2, na.rm=TRUE))
## [1] 0.4683447

Comparing the RMSE values of drift and naive, we can see that the RMSE for the drift model (0.4683447) is lower than the RMSE for the naive model (0.9239518). This indicates that the drift model performs better in terms of forecast accuracy, as it has a lower average magnitude of errors compared to the naive model.

The lower RMSE in the drift model suggests that incorporating the linear trend (drift) in the forecast model improves its forecasting capability. The drift model takes into account the systematic change in the time series over time, resulting in more accurate forecasts compared to the naive model, which assumes no drift.

Therefore, based on the RMSE values, the drift model is preferred over the naive model for forecasting this time series.

3.3 Fit ARIMA models

The null hypothesis for our analysis is that the time series data is stationary. This means that the statistical properties of the data, such as the mean and variance, remain constant over time. To analyze this time series data, we will use an auto-regressive integrated moving average (ARIMA) model that will be used to identify trends and patterns in the data, and make predictions about future values.

In the R programming language, we can use the auto.arima() function to identify and specify a fitted ARIMA model for our data. This function automates the process of selecting the appropriate model by searching for the best-fitting model based on a variety of statistical criteria.

library(urca)
goog %>% ur.kpss() %>% summary()
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 7 lags.
##
## Value of test-statistic is: 10.7223
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

Based on our statistical analysis, we have found that we can reject the null hypothesis that the time series or data is stationary. This conclusion is based on the fact that the value of our test statistic was found to be higher than the critical values.

A stationary time series or data is one in which the statistical properties such as mean and variance do not change over time. In contrast, a non-stationary time series or data has statistical properties that change over time. The fact that we have rejected the null hypothesis indicates that the time series or data under consideration is not stationary, meaning that its statistical properties change over time.

We will now apply the technique of differencing. By doing so, we can improve the accuracy and reliability of our analysis, and make more informed decisions based on modeling a transformation of the time series that satisfies a test for consistency with being stationary.

The KPSS (Kwiatkowski-Phillips-Schmidt-Shin) test is a statistical test used to assess the stationarity of a time series. Stationarity refers to the property of a time series where its statistical properties (such as mean and variance) remain constant over time.

In the context of the KPSS test, the null hypothesis assumes that the time series is stationary. If the test statistic exceeds the critical value, it indicates evidence against the null hypothesis. This implies that the series is likely non-stationary.

Now let’s examine the output of the KPSS test

library(urca)
symbol1.ts %>% ur.kpss() %>% summary()
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.8269
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

ndiffs(symbol1.ts)
## [1] 2
# for whole series, ndiffs() suggests second order differencing.

The test statistic value is 2.8269, and it is compared against critical values at different significance levels. The critical values at significance levels of 10%, 5%, 2.5%, and 1% are 0.347, 0.463, 0.574, and 0.739, respectively.

In this case, the test statistic value of 2.8269 exceeds all the critical values. Therefore, we reject the null hypothesis of stationarity, suggesting that the time series may not be stationary.

The ndiffs() function is also applied to determine the number of differences required to achieve station- arity for the whole series. In this case, ndiffs(symbol1.ts) suggests second-order differencing to achieve stationarity.

library(urca)
symbol1.ts[1:108] %>% ur.kpss() %>% summary()
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.2539
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
ndiffs(symbol1.ts[1:108])
## [1] 2
# for whole series, ndiffs() suggests second order differencing.

Above, the subset symbol1.ts[1:108] is used, which means only the first 108 observations are consid- ered. Previously, the entire symbol1.ts time series was analyzed.

The time frame of 1:108 was specifically selected to fit the time series data, which includes inflation rates up until the year 2020. We identified a kink in the Consumer Price Index (CPI) at a certain point within this time frame, and we used this point as a cutoff for our analysis. By doing so, we were able to focus our analysis on a relevant and informative subset of the data, which allowed us to make more accurate and meaningful observations about inflation trends over time.

library(urca)
symbol1.ts[1:108] %>% ur.kpss() %>% summary()

The output shows that in both cases, the test statistic exceeds the critical values, indicating evidence against the null hypothesis of stationarity. The ndiffs() function suggests that 2 differences may be needed to achieve stationarity in both cases.

Overall, the difference in the output is primarily due to the subset of the time series under consider- ation, while the interpretation of the test results remains the same.

Let’s now apply the kpss test for the original series, for the first difference series and second difference series to compare them side by side

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.8269
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 1.2285
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

# KPSS test for the first-difference series
symbol1_diff1 <- diff(symbol1.ts)
kpss_diff1 <- ur.kpss(symbol1_diff1)
summary(kpss_diff1)

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 0.0269
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

# KPSS test for the second-difference series
symbol1_diff2 <- diff(symbol1.ts, differences = 2)
kpss_diff2 <- ur.kpss(symbol1_diff2)
summary(kpss_diff2)

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 0.0269
##
## Critical value for a significance level of
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

In the first case, we can observe that the test statistic for the original series (2.8269) is greater than the critical values at all significance levels. This suggests that the original series is non-stationary.

However, when we take the first difference of the series, the test statistic decreases to 1.2285. Although it is still higher than the critical values, it indicates a lower degree of non-stationarity compared to the original series.

Finally, the second-difference series exhibits a significantly lower test statistic of 0.0269, which is much smaller than the critical values. This indicates that the second-difference series can be considered stationary.

Overall, the KPSS test results suggest that the second-difference series shows the closest approxima- tion to stationarity among the three tested series.

library(urca)
# KPSS test for the original time series
kpss_original <- ur.kpss(symbol1.ts[1:108])
summary(kpss_original)
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.2539
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

# KPSS test for the first-difference time series
symbol1_diff1 <- diff(symbol1.ts[1:108])
kpss_diff1 <- ur.kpss(symbol1_diff1)
summary(kpss_diff1)

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 0.54
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

# KPSS test for the second-difference time series
symbol1_diff2 <- diff(symbol1.ts[1:108], differences = 2)
kpss_diff2 <- ur.kpss(symbol1_diff2)
summary(kpss_diff2)

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 0.0241
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739

In the first case, the test statistic (2.2539) is larger than the critical values at all significance levels. This indicates that we can reject the null hypothesis, suggesting that the original series is non-stationary. In the second case, The test statistic (0.54) is smaller than the critical values at all significance levels. This suggests that we cannot reject the null hypothesis of stationarity for the first-difference series. Thus,

the first-difference series is considered stationary.
In the third case, The test statistic (0.0241) is significantly smaller than the critical values at all

significance levels. This suggests that we cannot reject the null hypothesis of stationarity for the second- difference series.

The first differenced series is consistent with stationarity when only the first 108 values are included in the series. The apparent evidence for second differences with the entire series is , perhaps, due to the high volatility of the series after period 108.

Now, let’s apply the auto.arima() function and the checkresiduals() function

symbol1.ts.arima<- auto.arima(symbol1.ts)
summary(symbol1.ts.arima)
## Series: symbol1.ts
## ARIMA(2,2,2)(0,0,2)[12]
##
## Coefficients:
##          ar1      ar2      ma1     ma2     sma1     sma2
##       1.3288  -0.7466  -1.7074  0.8814  -0.2547  -0.1773
## s.e.  0.0785   0.0702   0.0625  0.0780   0.0988   0.0937
##
## sigma^2 = 0.07624:  log likelihood = -17.12
## AIC=48.24   AICc=49.08   BIC=68.88
##
## Training set error measures:
##                      ME      RMSE       MAE         MPE       MAPE       MASE
## Training set 0.01957902 0.2682821 0.1744252 0.006985389 0.06737252 0.02802695
##                   ACF1
## Training set 0.0259118
checkresiduals(symbol1.ts.arima)

##
##  Ljung-Box test
##
## data:  Residuals from ARIMA(2,2,2)(0,0,2)[12]
## Q* = 22.222, df = 18, p-value = 0.2223
##
## Model df: 6.   Total lags used: 24

Seasonal ARIMA models are denoted by the notation ARIMA(p, d, q)(P, D, Q)[h], where the seasonal differences of order D are incorporated using the ARMA(P, Q) terms.

In this notation, the lowercase letters (p, d, q) represent the non-seasonal components of the model. The parameter ‘p’ denotes the order of the autoregressive (AR) component, which captures the de- pendence of the current observation on its previous values. The parameter ‘d’ represents the order of differencing required to achieve stationarity in the non-seasonal part of the series. The parameter ‘q’ indicates the order of the moving average (MA) component, which models the dependence of the current observation on the residual errors from previous observations.

On the other hand, the uppercase letters (P, D, Q) represent the seasonal components of the model. The parameter ‘P’ denotes the order of the seasonal autoregressive (SAR) component, which captures the dependence of the current observation on its previous seasonal values. The parameter ‘D’ represents the order of differencing required to achieve stationarity in the seasonal part of the series. The parameter ‘Q’ indicates the order of the seasonal moving average (SMA) component, which models the dependence of the current observation on the residual errors from previous seasonal observations.

The parameter ‘h’ represents the forecast horizon, indicating the number of future time steps for which we want to generate forecasts.

The output of the auto.arima() function suggests an ARIMA(2,2,2)(0,0,2)[12] model for the time series data. In this notation, the first set of numbers (2,2,2) represents the non-seasonal components of the ARIMA model. Specifically, it indicates that the model includes an autoregressive (AR) term of order 2, a differencing (I) term of order 2, and a moving average (MA) term of order 2. The second set of numbers (0,0,2) represents the seasonal components of the ARIMA model. In this case, the model includes a seasonal moving average (SMA) term of order 2. The seasonal terms account for the patterns and fluctuations that occur at regular intervals (in this case, with a period of 12).

Additionally, the p-value of the Ljung Box test is larger than 0.05. Therefore, we fail to reject the null hypothesis of no autocorrelation. This suggests that the residuals of the ARIMA model do not exhibit significant autocorrelation, supporting the adequacy of the model in capturing the remaining patterns in the data. Also, ACF does not cross the blue lines, suggesting that there is no significant autocorrelation in the residuals. It indicates that the model has adequately captured the underlying patterns in the data.

Now, let’s apply the auto.arima() function and the checkresiduals() function for the subset of the original series

symbol1.ts.arima<- auto.arima(symbol1.ts[1:108])
summary(symbol1.ts.arima)

## Series: symbol1.ts[1:108]
## ARIMA(1,2,1)
##
## Coefficients:
##          ar1      ma1
##       0.1956  -0.9525
## s.e.  0.1014   0.0344
##
## sigma^2 = 0.02133:  log likelihood = 55.94
## AIC=-105.88   AICc=-105.64   BIC=-97.89
##
## Training set error measures:
##                       ME      RMSE       MAE         MPE       MAPE    MASE
## Training set 0.008633132 0.1433159 0.1138458 0.003132918 0.04679805 0.28179
##                      ACF1
## Training set -0.009711438

checkresiduals(symbol1.ts.arima)

##
##  Ljung-Box test
##
## data:  Residuals from ARIMA(1,2,1)
## Q* = 3.3287, df = 8, p-value = 0.9121
##
## Model df: 2.   Total lags used: 10

The analysis above suggests that the data follows an ARIMA(1,2,1) model.The ARIMA notation consists of three numbers representing the number of autoregressive, integrated, and moving average terms, respectively. For this data, the model is ARIMA(1,2,1), meaning there is one autoregressive term, two integrated terms, and one moving average term. The seasonal components (P, D, Q) are not present, suggesting that the model does not capture any seasonal patterns.

This ARIMA(1,2,1) model is appropriate for the data based on the patterns that we observed in the data. These patterns suggest that there is a significant amount of correlation between the data points, and that this correlation decreases over time. By using this model, we can effectively capture these patterns and make accurate predictions about future trends in the data.

Comparison of the output of the symbol1.ts model with the symbol1.ts [1:108] model :

In terms of the RMSE, the second model (symbol1.ts [1:108]) has a lower value (0.1433159) compared to the first model (symbol1.ts) with a higher RMSE (0.2682821). This suggests that the second model provides a better fit to the data in the subset symbol1.ts [1:108] than the first model does to the entire symbol1.ts series.

Additionally, we can consider the Ljung-Box test results. The second model has a lower Ljung-Box test statistic (3.3287) compared to the first model (22.222), suggesting a better fit in terms of residual autocorrelation. In both the models, the p value is quite high, indicating that there is no evidence of significant autocorreltion.

Taking into account the lower RMSE, lower Ljung-Box test statistic, and the fact that the second model captures the main characteristics of the data with fewer parameters, we can conclude that the ARIMA(1,2,1) model fitted to symbol1.ts [1:108] is better than the ARIMA(2,2,2)(0,0,2)[12] model fitted to the entire symbol1.ts series. These results also illustrate how including the volatile data at the end of the time series can result in sensitivity of which model is identified.

4. Discussion of results

In this section, I will be summarising the modelling results for three of the CPI series: CPILFENS, CPIAUCSL, CPILFESL.

For CPILFENS, the optimal model is ARIMA(2,1,0) with drift. This means that there are two autoregressive terms, one difference, and no moving average terms. The presence of drift means that the series has a non-zero mean. The model has a log-likelihood of -15.3, AIC of 38.6, AICc of 38.99, and BIC of 49.29. The residuals do not show significant autocorrelation based on the Ljung-Box test (p-value = 0.4659).

For CPIAUCSL, the optimal model is ARIMA(0,1,1) with drift. This means that there are no autoregressive terms, one difference, and one moving average term. The presence of drift means that the series has a non-zero mean. The model has a log-likelihood of -60.6, AIC of 127.2, AICc of 127.43, and BIC of 135.22. The residuals do not show significant autocorrelation based on the Ljung-Box test (p-value = 0.5011).

For CPILFESL, the optimal model is ARIMA(1,2,1). This means that there is one autoregressive term, two differences, and one moving average term. The model has a log-likelihood of 55.94, AIC of -105.88, AICc of -105.64, and BIC of -97.89. The residuals do not show significant autocorrelation based on the Ljung-Box test (p-value = 0.9121).

By referring to the coefficient tables in the Appendix, readers can gain a clearer picture of how these parameter estimates contribute to the predictive power of the chosen models and it supports the logic behind the selection of specific ARIMA models for each series.

Comparing the three models, we can see that they have different orders and coefficients, reflecting the different characteristics of the data. The models also have different AIC, AICc, and BIC values, which can be used to compare the relative accuracy of the model. In general, a lower value for these criteria indicates a better fit, so the ARIMA(1,2,1) model for CPILFESL has the best fit among the three models. Additionally, the residuals of all models do not exhibit any significant autocorrelation based on the Ljung-Box test, suggesting that all models fit the data reasonably well.

5. Conclusion

In this research paper, we aimed to model time series data of inflation using simple time series models and ARIMA models, and apply these models to forecast future values of the time series data. We collected data on three different economic indicators: Consumer Price Index for All Urban Consumers: All Items Less Food and Energy (CPILFENS), Consumer Price Index for All Urban Consumers: All Items (CPIAUCSL), and Consumer Price Index for All Urban Consumers: All Items Less Shelter (CPILFESL), and modeled them using both simple time series models and ARIMA models.

Our results showed that the ARIMA models generally performed better than the simple time series models in terms of their ability to capture the patterns and trends in the data and make accurate predic- tions. Specifically, the ARIMA(2,1,0) model with drift was found to be the best model for CPILFENS (Consumer Price Index for All Urban Consumers: All Items Less Food and Energy ), the ARIMA(0,1,1) model with drift was found to be the best model for CPIAUCSL (Consumer Price Index for All Urban Consumers: All Items), and the ARIMA(1,2,1) model was found to be the best model for CPILFESL (Consumer Price Index for All Urban Consumers: All Items Less Shelter). These models were able to capture the patterns in the data and make accurate predictions for future values of the time series.

In terms of the possible directions for future research, one direction could be to explore the use of other time series models beyond ARIMA. Another direction could be to incorporate external factors that may influence the time series data, such as changes in government policies and global events. Overall, the findings of this research suggest that ARIMA models can be effective in modeling and forecasting economic time series data, and further research could build on this foundation to improve the accuracy and precision of these models.

Appendix

Results of the other two CPI models using similar approach :

1.1 Extract time series of CPIAUCSL and CPILFENS

Extract series and create a ts object : CPIAUCSL :

CPILFENS :

symbol4<- list_symbols[5]
data.symbol4<- filter(economic_data2, symbol==symbol4)
symbol4.ts<- ts(data.symbol4$price, frequency=12, start=c(2011,1))

1.2 ARIMA models

1.2.1 CPIAUCSL’s ARIMA Model:

Null hypothesis: The time series/ data is stationary
We can use the R function auto.arima() to identify and specify a fitted auto-regressive integrated moving average (ARIMA) model.

In this case, the test statistic value is greater than the critical value at all levels of significance, which suggests that the symbol0.ts time series is non-stationary.

library(urca)
symbol0.ts %>% ur.kpss() %>% summary()
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.6058
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
ndiffs(symbol0.ts)
## [1] 2
# for whole series, ndiffs() suggests second order differencing.
library(urca)
symbol0.ts[1:108] %>% ur.kpss() %>% summary()
##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.1774
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
ndiffs(symbol0.ts[1:108])
## [1] 1
# for whole series, ndiffs() suggests second order differencing.
symbol0.ts.arima<- auto.arima(symbol0.ts)
summary(symbol0.ts.arima)
## Series: symbol0.ts
## ARIMA(0,2,2)
##
## Coefficients:
library(urca)
##
##
## s.e.   0.0794   0.0795
##
## sigma^2 = 0.3385:  log likelihood = -123.02
## AIC=252.05   AICc=252.22   BIC=260.89
##
## Training set error measures:
##                      ME      RMSE       MAE         MPE      MAPE       MASE
## Training set 0.02149972 0.5736192 0.4093012 0.007226791 0.1626624 0.06733796
##                      ACF1
## Training set -0.007051954
autoplot(symbol0.ts.arima)

checkresiduals(symbol0.ts.arima)

##
##  Ljung-Box test
##
## data:  Residuals from ARIMA(0,2,2)
## Q* = 16.696, df = 22, p-value = 0.7799
##
## Model df: 2.   Total lags used: 24
symbol0.ts.arima<- auto.arima(symbol0.ts[1:108])
summary(symbol0.ts.arima)
## Series: symbol0.ts[1:108]
## ARIMA(0,1,1) with drift
##
## Coefficients:
##          ma1   drift
##       0.3809  0.3518
## s.e.  0.0817  0.0567
##
## sigma^2 = 0.1854:  log likelihood = -60.6
## AIC=127.2   AICc=127.43   BIC=135.22
##
## Training set error measures:
##                      ME     RMSE       MAE          MPE      MAPE      MASE
## Training set 0.00159638 0.424554 0.3228736 0.0003542257 0.1355093 0.6720907
##                    ACF1
## Training set 0.03332243
autoplot(symbol0.ts.arima)

checkresiduals(symbol0.ts.arima)

##
##  Ljung-Box test
##
## data:  Residuals from ARIMA(0,1,1) with drift
## Q* = 8.3315, df = 9, p-value = 0.5011
##
## Model df: 1.   Total lags used: 10

Based on the patterns observed in the data, we have determined that it follows an ARIMA(0,1,1) model. Two specific patterns in the data support this conclusion.

Firstly, the partial autocorrelation function (PACF) is either exponentially decaying or sinusoidal. This indicates that the data is dependent on its own past values and that the current value is related to the previous value with a certain degree of correlation. Secondly, there is a significant spike at lag 1 in the autocorrelation function (ACF), but none beyond lag 1. This implies that the data has a strong correlation with its immediate past value, but this correlation decreases rapidly as we move further away from the current value.

Overall, these observations suggest that the data has a certain degree of autocorrelation and season- ality, and that a time series model like ARIMA(0,1,1) would be appropriate to accurately capture these patterns and make meaningful predictions.

1.2.2 CPILFENS’s ARIMA Model :

Null hypothesis: The time series/ data is stationary.

We can use the R function auto.arima() to identify and specify a fitted auto-regressive integrated moving average (ARIMA) model.

In this case, the test statistic value is greater than the critical value at all levels of significance, which suggests that the symbol4.ts time series is non-stationary.

library(urca)
symbol4.ts %>% ur.kpss() %>% summary()
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.8246
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
ndiffs(symbol4.ts)
## [1] 2
# for whole series, ndiffs() suggests second order differencing.
library(urca)
symbol4.ts[1:108] %>% ur.kpss() %>% summary()

##
## #######################
## # KPSS Unit Root Test #
## #######################
##
## Test is of type: mu with 4 lags.
##
## Value of test-statistic is: 2.2526
##
## Critical value for a significance level of:
##                 10pct  5pct 2.5pct  1pct
## critical values 0.347 0.463  0.574 0.739
ndiffs(symbol4.ts[1:108])
## [1] 1
# for whole series, ndiffs() suggests second order differencing.
symbol4.ts.arima<- auto.arima(symbol4.ts)
summary(symbol4.ts.arima)
## Series: symbol4.ts
## ARIMA(2,2,2)(2,0,0)[12]
##
## Coefficients:
##          ar1      ar2      ma1     ma2    sar1    sar2
##       1.2534  -0.6942  -1.5742  0.7216  0.3195  0.3412
## s.e.  0.1310   0.0873   0.1515  0.1519  0.0848  0.0907
##
## sigma^2 = 0.1333:  log likelihood = -58.34
## AIC=130.68   AICc=131.52   BIC=151.32
##
## Training set error measures:
##                       ME      RMSE       MAE         MPE       MAPE       MASE
## Training set 0.002739018 0.3546955 0.2447811 0.001036818 0.09470889 0.03932631
##                    ACF1
## Training set 0.02117099
autoplot(symbol4.ts.arima)

checkresiduals(symbol4.ts.arima)

##  Ljung-Box test
##
## data:  Residuals from ARIMA(2,2,2)(2,0,0)[12]
## Q* = 25.447, df = 18, p-value = 0.1131
##
## Model df: 6.   Total lags used: 24
symbol4.ts.arima<- auto.arima(symbol4.ts[1:108])
summary(symbol4.ts.arima)
## Series: symbol4.ts[1:108]
## ARIMA(2,1,0) with drift
##
## Coefficients:
##          ar1      ar2   drift
##       0.5217  -0.4906  0.3999
## s.e.  0.0853   0.0850  0.0279
##
## sigma^2 = 0.08015:  log likelihood = -15.3
## AIC=38.6   AICc=38.99   BIC=49.29
##
## Training set error measures:
##                       ME     RMSE       MAE         MPE       MAPE      MASE
## Training set 0.002997304 0.277811 0.2278571 0.001007415 0.09316369 0.5187606
##                     ACF1
## Training set -0.02481104

autoplot(symbol4.ts.arima)

checkresiduals(symbol4.ts.arima)

##
##  Ljung-Box test
##
## data:  Residuals from ARIMA(2,1,0) with drift
## Q* = 7.6742, df = 8, p-value = 0.4659
##
## Model df: 2.   Total lags used: 10

So, ARIMA(2,1,0) with drift means that the model has an autoregressive component of order 2, a degree of differencing of 1, no moving average component, and a constant term. The AR and MA components capture the autocorrelation and moving average patterns in the data, respectively, while the drift term captures the overall trend or level of the data. The drift term in the ARIMA model suggests that there may be a trend or a systematic change in the mean of the data over time that is not explained by the AR or MA components.

The ACF plot shows one line crossing the blue critical lines between the lags of 10 and 15, then it suggests that there is some significant autocorrelation still present in the residuals of the model. This means that the model may not be fully capturing all the relevant information in the data. It may be necessary to either modify the existing model or try alternative approaches to improve the fit.

1.3 Discussion of ARIMA models based on the coefficients obtained

1. Coefficient Table for CPILFENS Model – ARIMA(2,1,0) with Drift :
Coefficient Estimate Standard Error AR1 0.5217 0.0853 AR2 -0.4906 0.0850 Drift 0.3999 0.0279

2. Coefficient Table for CPIAUCSL Model – ARIMA(0,1,1) with drift :

Coefficient Estimate Standard Error MA1 0.3809 0.0817 Drift 0.3518 0.0567

3. Coefficient Table for CPILFESL Model – ARIMA(1,2,1) :

Coefficient Estimate Standard Error AR1 0.1956 0.1014 MA1 -0.9525 0.0344

Interpreting these parameter estimates can offer valuable insights into the underlying dynamics of the time series. For instance, positive autoregressive (AR) parameters often correspond to momentum or persistence in the series. This implies that the series tends to continue moving in the same direction as its past values. On the other hand, negative AR parameters suggest mean reversion, indicating that the series tends to revert back towards its mean over time.

Additionally, positive moving average (MA) parameters indicate that recent forecast errors have a momentum-like effect on the series. This means that deviations from the forecast tend to be corrected over subsequent time periods. Such insights obtained from the parameter estimates provide a deeper understanding of the time series data.

About the author

Ruthvik Reddy Bommireddy

Ruthvik is a 12th grade student at the Delhi Public School Bangalore North, India. His academic focus lies at the intersection of Science and Economics, driven by a desire to create positive change in the world through future ventures. Ruthvik enjoys basketball, tennis, and roller skating, as well as drawing and sketching. He also serves as the president and founder of his school’s business club.

The post Analyzing Inflation : A Comprehensive Study of Three CPI series using R Code appeared first on Exploratio Journal.

Abstract

As companies look for the most efficient ways to deliver products to customers, the hub and spoke model of supply chain management has emerged as a determinant of success for companies operating in the retail and distribution and logistics sectors. This paper examines the supply chains of Walmart, Kmart, Target, Amazon, FedEx, and DHL, and argues that there is a direct correlation between the hub and spoke model of supply chain management and profitability. Walmart, the largest retailer in the world, has one of the largest and most efficient hub and spoke supply chains globally. When combined with its technological investments to improve efficiency, Walmart’s supply chain is the main contributor to its consistent profits. Furthermore, through the use of their hub and spoke networks, both FedEx and DHL have been able to maintain significant profits. Supply chain management is an important aspect of a company’s business model due to its role in ensuring operational efficiency and cost effectiveness by managing inventory and streamlining distribution; particularly the hub and spoke model of supply chain management is a determinant of the ultimate profitability for successful companies such as Walmart, FedEx, and DHL.

Introduction to Supply Chains

As e-commerce and consumer demand for physical products grow, companies look for the most efficient ways to deliver products to customers. This means that supply chain management has emerged as a critical determinant of success for many companies, especially those operating in the distribution and logistics, retail, and e-commerce sectors. A supply chain is characterized as the movement of goods and products along the stages of production and distribution. A good and effective supply chain often involves complicated planning and coordinating to ensure that these goods are produced and transported efficiently to meet customer demand. As a result, supply chain management, due to its role in ensuring operational efficiency and cost effectiveness by managing inventory and streamlining distribution, is an important aspect of the business model for these companies. Overall, a well-managed supply chain leads to greater customer satisfaction, higher profit margins, and in general, leads to successful performance for a company. 

The Hub and Spoke Model 

One of the most common and successful models of supply chain management used within the package delivery and retail sectors is the hub and spoke model. The hub and spoke model relies on a centralized “hub” location, which serves as the consolidation point within the supply chain. Products and packages come from their manufacturers, or senders, and arrive at consolidation centers which are the hub locations for the companies that utilize this method. At these hubs, advanced technological solutions and manpower are employed to sort, organize, and prepare packages to be sent to their destinations, usually by truck or plane. Once a package has passed through the central hub, it is loaded with other packages headed for the same geographical area; each individual geographic region is considered the “spoke.” There are numerous spokes, and each is a secondary location which is connected to the hub location and generally serves one geographic region. These spokes can take the form of fulfillment centers, distribution centers, or retail stores. In the case of package delivery companies, the packages are taken from these spokes and loaded onto trucks to be delivered on their “last mile” to people’s homes. 

Thesis

Throughout this paper, we will examine the supply chains of some of the most successful and unsuccessful companies in the retail, e-commerce, and logistics industries, to answer the following question: Which is the most effective and efficient supply chain management model for companies in the retail, e-commerce, or logistics industries? From our analysis of these companies, we will argue that there is a direct correlation between the hub and spoke model of supply chain management and profitability. 

Case Study 1: Walmart

Walmart was founded as a discount goods store by Sam Walton in 1962 in Rogers, Arkansas as ‘Walton’s 5 & 10’. Walton’s goal with his store was to provide the lowest possible prices for his customers. By 1967, there were 24 Walmart stores bringing in over $12 million in revenue, and in 1970, Walmart went public on the New York Stock Exchange at $16.50 per share. During the 1970s and 1980s, Walmart underwent massive expansion, operating in small towns throughout the United States, mostly catering to the low and middle class families who could benefit from Walmart’s low prices. In 1988, Walmart opened the first Walmart Supercenter which operated as a supermarket and sold general merchandise, providing customers with a one-stop-shop for anything they could need. More supercenters opened and were a key factor in Walmart’s rapid expansion. Today, in 2023, Walmart is the largest retailer in the world, employing over two million people and operating 10,545 stores in 20 different countries, 4,684 of which are in the United States. 

The greatest contributor to Walmart’s success is its low pricing. Since its founding, Walmart has always provided its customers with the lowest prices available, and Walmart always took every step it could to ensure its customers got the best deal. The cornerstone of Walmart has been what it calls “Everyday Low Prices.”  “Everyday low prices” means that Walmart consistently gives customers the lowest prices without having to worry about or rely on sales or coupons like other large retailers. As a consequence of its low prices, Walmart has an extremely low profit margin, causing the company to rely on the volume of its sales to ensure profitability. This means that an efficient supply chain is even more important for Walmart than for other companies.

One of the most significant reasons Walmart is able to keep the prices on all of its goods so low is due to its efficient supply chain. Having a productive and effective supply chain allows Walmart to save as much money as possible. Walmart has accomplished this by using the hub and spoke system. Walmart utilizes a centralized hub in each region of the United States, where products, shipped by their suppliers, are brought to the centralized distribution center. From the centralized hub, products are consolidated and organized by employees and machines and brought to a more regional distribution center, where packages are once again organized and loaded onto trucks. From these distribution centers, Walmart utilizes its fleet of thousands of trucks to transport products to Walmart’s supercenter stores, where they are available for purchase by the customer. Walmart stores and distribution centers are strategically located so that every store is within a maximum of one day’s driving distance from a distribution center, which allows for maximum efficiency. 

The hub and spoke system, along with the company’s heavy investments in their distribution centers and new technologies, allow it to manage its inventory with precision, minimizing excess stock and reducing costs associated with carrying extra inventory. These technological investments include the Retail Link System, which shares live inventory data with suppliers, allowing them to make more accurate and informed decisions related to restocking and inventory planning. Another technological investment made by Walmart to improve its supply chain was the adoption of RFID (radio frequency identification) tags, which track products through their distribution network and make sure the network works as efficiently as possible. It is estimated that Walmart saved $500 million per year due to the RFID system. In addition, Walmart is able to leverage its vast network of stores and their efficient and centralized system of distribution, leading to better deals with suppliers and reduced transportation costs. The hub and spoke model also enables swift replenishment of products, maintaining well-stocked shelves. The Walmart distribution system and supply chain, backed by data-driven decision-making from Walmart’s technological investments, is able to optimize inventory and respond to demand shifts. These benefits, which result in greater customer satisfaction and decreased expenses, ultimately lead to Walmart’s ability to make a profit (Mark & Johnson, 2019).

Case Study 2: Distribution, Delivery, and Logistics Companies – FedEx, DHL

FedEx and DHL are two of the largest transportation and logistics companies in the world. Together, the two companies control 65% of the American market with FedEx controlling 44% and DHL controlling 21%. These companies provide many shipping and logistics services to both businesses and individuals. One of their most important services is their package shipping and delivery service. Given that their main business is shipping packages, the most important aspect of their business model is an efficient supply chain. These companies use similar styles of supply chain, both using the hub and spoke model. 

Fedex Express uses a large network of hubs and distribution centers around the United States to deliver packages between customers. When the package is received, it is brought to one of Fedex’s local hubs and sortation centers, where the packages are organized and flown to one of Fedex’s eight central hubs, the biggest and most important of which is located in Memphis, Tennessee. From these large hubs, equipped with technology and hundreds of employees, the packages are flown to their destinations on one of Fedex’s over 700 cargo aircraft, where they are delivered by truck or van for the last mile to the final destination (FedEx 2022).

DHL Express, the DHL service that delivers packages between people, uses a hub and spoke system of distribution, primarily through its use of airplanes and airports. When DHL first receives a package from a customer, it goes to one of DHL’s 111 US sortation facilities for organization, and then is brought to one of DHL’s hubs in either New York, Miami, Los Angeles, Chicago, or Cincinnati. At these hubs, there are larger and more efficient sortation centers, and from here, packages are flown to the facility closest to the intended destination. From these facilities, they are loaded onto trucks and driven the last mile to their final destination (DHL 2018). 

Both FedEx and DHL’s core businesses are based on package delivery; consequently, the supply chain strategy, distribution, and delivery networks are the basis of their business operations. Their use of the hub and spoke model in their delivery system allows FedEx and DHL to be two of the largest delivery companies in the world. Thus, the efficiency and effectiveness of their hub and spoke-based supply chain strategy directly contributes to the success and profitability of these two companies, as shown in the tables below. 

Case Study 3: Kmart 

Kmart, founded by Sebastian S. Kresge in 1899 as the S.S. Kresge Company, started as a small variety store in Detroit and soon expanded into a national discount goods chain. As the store gained popularity, it changed its name to Kmart and the first Kmart-named store was opened in 1962. Kmart continued to expand, and within the first year, had more than $483 million in corporate sales; by 1981, Kmart had opened 2000 stores (The New York Times 2002). Despite its success, Kmart began to face a decline in the late 1980s, and was overtaken by Walmart which became the largest retailer in the country. As Walmart continued to expand and have consistently lower prices than Kmart, Kmart continued to decline, closing hundreds of stores throughout the 1990s and early 2000s and declaring bankruptcy in 2002. Kmart then merged with Sears, another declining American retailer, however they faced continuous financial difficulties and were never able to revive their business. 

Kmart was once the most popular discount retail store in the United States. During its height, Kmart was known for its Blue Light Special, which was the retailer’s way of implementing its low pricing strategy. This is when a company initially has a product listed at a higher price, but lowers the price through sales and promotions; in contrast, the EDLP strategy that retailers like Walmart use always lists every item at its lowest possible price. During the “Blue Light Special,” Kmart used a blue light in the store to inform customers which products would be going on sale (Porter 2022). 

Ultimately, however, Kmart failed because it was unable to compete with Walmart’s low prices. This was largely due to a lack of a real distribution system and strategy. While both companies were in the process of transitioning to a supercenter-style store, Walmart with its supercenters and Kmart with its Super Kmarts, Walmart was able to organize its stores in clusters around one of its distribution centers in the hub and spoke system, allowing Walmart to create an efficient distribution process. Kmart, however, not only had far fewer Super Kmarts, but these Super Kmarts were scattered throughout the country, making it difficult to create a distribution network. In addition, in Kmart’s supermarket/grocery business, which was the most significant part of both Walmart and Kmart’s supercenters, Kmart tried to compete against Walmart on both price and quality through a partnership with Fleming Foods. This caused Kmart to not build a private distribution network, something that was a necessity for a company trying to become a national leader (Graff 2006). As a result of Kmart’s lack of a distribution network and strategy, the retailer was unable to compete with the low Walmart prices that resulted from its efficient hub and spoke supply chain and distribution model.  This ultimately led Kmart to lose money and eventually declare bankruptcy. 

Counterargument Case Study 1: Amazon

Amazon was founded in 1994 by Jeff Bezos as an online bookstore to offer an alternative to traditional brick and mortar bookstores such as Barnes & Noble. In 1997, Amazon went public on the NASDAQ and in 1998, Amazon expanded from selling books, to selling everything online. In 1999, Amazon reached a high stock price of $113 at the height of the dot com era, however it would soon crash down to a low of $5.50 per share. Despite this, Amazon was able to survive the crash and continued to expand; in 2005, Amazon introduced Amazon Prime, a subscription service which promised free two-day shipping, and further expanded Amazon’s growing market share. As of 2023, Amazon dominates the e-commerce market with a 37.8% market share. 

Until recently, Amazon had also used the hub and spoke supply chain management strategy. In this system, after a package was ordered online, it was picked by the combination of Amazon employees assisted by high tech robots in the Amazon distribution center closest to the location of the order. After the product was picked, it was packaged and shipped to an Amazon fulfillment center where products were organized, sorted, and prepared for shipment. These large fulfillment centers were Amazon’s hubs. Packages were then transported by truck or airplane to smaller regional distribution centers, which were the spokes in Amazon’s supply chain. From here, Amazon used multiple different methods to deliver packages to a customer’s house, including using their own trucks as well as using independent contractors through the Amazon Flex program. 

Amazon Logistics Process:

Despite continuing to use much of the already established infrastructure from their hub and spoke distribution system, in 2022 and 2023, Amazon shifted away from the hub and spoke system and toward a more local approach, dividing the country into eight regions, each with its own local distribution facilities. Since this switch, Amazon has experienced a dramatic increase in its delivery speeds due to a 15% reduction in distance driven between the distribution center and the customer, and a 12% reduction in the amount of times the package is handled. Since this shift, Amazon says that it has delivered the majority of Prime packages within one day or less in the large metro areas Amazon serves. In addition, Amazon says that these increased delivery speeds are causing customers to increase the amount they choose to buy from Amazon over another retailer (Palmer 2023).

Prior to its shift in supply chain strategy, Amazon’s online retail business did not directly contribute to its profit. In fact, Amazon loses money on most Amazon Prime orders due to the high cost of shipping (Byrnes 2020). Since this switch in strategy, Amazon continues to be unprofitable; the entire company reported a negative net income in 2022 (Amazon Inc. 2022). However, Amazon’s retail business is extremely useful to the retailer because it allows the company to run advertisements on its website, which is one of the most profitable parts of its business (Dennis 2022). Despite having a complicated and efficient supply chain in both its use of the hub and spoke method and its shift to a more regional system, the supply chain has not had a direct impact on Amazon’s profitability and success as a company. However, its regional approach has shown to be a more effective option for Amazon’s business model, as it produces faster delivery speeds, causing more customers to return to Amazon, and thus increasing the size of Amazon’s advertising business. 

Counterargument Case Study 2: Target

Target was founded in 1962 as a subsidiary of the Dayton’s Department Store empire. During this time, Dayton’s was expanding into many different sectors such as jewelry and bookstores, and was constantly opening new stores within malls. Target was opened as a discount goods retail store and unlike other discount stores like Walmart, Target always had access to capital since Target was backed by Dayton’s. Target quickly proved to be the most successful of Dayton’s subsidiaries, so Dayton’s began to buy more and more department and general stores while divesting from the other subsidiaries. Eventually, Target surpassed Dayton’s and the company changed its name to the Target Corporation and sold all of its other stores, allowing it to have more capital to invest in the Target stores. In 1979, Target had its first year with over $1 billion in sales and by 1989, it had 400 stores across the United States (Target).

In 1995, Target expanded into selling everything including groceries, and shifted to adopting a supercenter-style store similar to Walmart and Kmart called SuperTargets. Despite the fact that Target experienced slower growth than Walmart, Target still had 100 SuperTargets opened by 2006. With fewer retail locations, Target did not have the concentration of stores to build its own distribution system the way that Walmart did (Graff 2006). Instead, Target offered higher quality discount goods compared to Walmart and Kmart, and focused more on middle to upper class consumers. Even without a distribution system, by targeting a different customer demographic from that of Walmart and Kmart, Target was still able to remain profitable. 

More recently, Target has invested heavily in its distribution network due to the rise of the internet and Target’s expanding e-commerce business. Over the next three years, Target said it would invest $100 million into improving its next day delivery service by building more sortation centers and warehouses. Part of Target’s strategy also includes using its stores as distribution centers, with employees picking out the products from the store to be delivered to the customer (Young 2023).  Despite Target’s use of different distribution and supply chain strategies compared to Walmart’s hub and spoke system, Target has been able to remain profitable and competitive because it targets a different demographic customer base and continues to provide a higher quality of discount goods. 

Conclusions

This study and exploration of the supply chain and distribution networks of numerous companies concludes that the hub and spoke system is the most reliable and efficient method. Walmart, the largest retailer in the world, has one of the largest and most efficient hub and spoke supply chains in the world. When combined with its technological investments to improve efficiency, Walmart’s supply chain is the main contributor to its consistent profits as illustrated by its profitability data. Walmart’s supply chain is also superior to that of other large retailers such as Target, or failed retailers such as Kmart. During Kmart’s expansion, it was unable to build a network of supercenters around a common distribution center the way that Walmart has successfully done. Due to its lack of an organized supply chain network, Kmart was unable to compete with the low prices offered by Walmart. In the case of Target, the retailer has been able to maintain profitability despite not using a hub and spoke distribution model. However, this is not because of the superiority of its network. Target’s success is a result of the fact that it is not actually in direct competition with Walmart and Walmart’s supply chain. Target has been able to avoid direct competition by marketing its products as a higher quality product, attracting more middle to upper class customers who would be less interested in Walmart’s cheaper products; this allows Target to charge slightly higher prices but still maintain its consumer base. 

In the case of Amazon and the delivery companies FedEx and DHL, while FedEx and DHL may not be companies that are as large as Amazon, the supply chains of these package delivery companies have contributed more directly to their success and profitability. Because they do not have an online platform the way Amazon does, FedEx and DHL are almost completely reliant on their supply chain and distribution networks to make money. Through the use of their hub and spoke networks, both FedEx and DHL have been able to make significant and consistent profit. Amazon, on the other hand, loses money on most orders it fulfills through Amazon Prime. While Amazon’s delivery system has improved since it has moved toward a more local and regional supply chain approach, Amazon still continues to rely completely on advertisements on its e-commerce website to make up for the money lost by its supply chain and delivery costs. Supply chain management is an important aspect of a company’s business model due to its role in ensuring operational efficiency and cost effectiveness by managing inventory and streamlining distribution; particularly the hub and spoke model of supply chain management is a determinant of a company’s ultimate profitability as evidenced by the financials of successful companies such as Walmart, FedEx, and DHL.

Bibliography / Sources Cited

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About the author

Stefanos Salas

Stefanos is a senior at The Bronx High School of Science in New York City. He is interested in economics and entrepreneurship, a competitive swimmer, and captain of the Bronx Science swim team.

The post The Hub and Spoke Supply Chain Model: Determinant of a Company’s Success appeared first on Exploratio Journal.

Abstract

This paper explores the economic and national security implications of United States (US) trade policy with China. It first describes the current US-China relationship, with a focus on China’s ambitions in the South China Sea.  It then explores the benefits and downsides of restricting trade with China, specifically focusing on advanced technologies such as semiconductors. Further, the paper explores how the United States’ trade policies with other countries in the region impact US national security with respect to China. Throughout the paper, multiple historical case studies are used to analyze the economic and security implications of the proposed policies. The paper concludes with the recommendation that the United States should use its power to restrict the development of advanced Chinese technology while continuing to trade in other goods with China. It also recommends that the US reduce barriers to trade with other nations, both to strengthen its economy and build relationships beneficial to national security. 

Introduction 

Rivalry between states is a concept as old as the concept of states themselves. Before the invention of the atomic weapon, great power rivalry involved large-scale warfare, economic intimidation, and overt threats. Since the Cuban Missile Crisis and the collapse of the USSR in 1991, states have recognized that such tensions must be limited to certain spheres and in ways that ultimately prevent rivalries from turning into wars. However, rivalries still exist.  This paper focuses on the increasing tensions between China and the United States (US) as China acts in ways contrary to US interests and security. Economic pressure is increasingly being utilized by the US in order to confront China and decrease the threat it poses to US interests. However, because of the complex nature of global supply chains, any actions the US takes against rivals negatively impacts the US. Recognizing that China is the largest trading partner of the US, the goal of this paper is to articulate the economic policies capable of limiting the threat posed by China while avoiding unnecessary collateral damage; the goal of these policies is to ultimately strengthen the US economy in the long-term, rendering it capable of flourishing despite any damage caused by a confrontation with China. 

Status of US-China Relations

The United States has an interest in creating a world where its citizens can participate in global trade based on fair rules. Its main goal in this respect should be creating and maintaining a system based on rules and values that are conducive to trade flourishing. These include ensuring the freedom of navigation, encouraging the free and fair exchange of goods and people between countries, and assisting in the protection of the territorial integrity of other nations so that they can engage in trade. The United States has for many decades worked to implement these goals through international institutions and should continue to further implement these goals. These goals are not shared by all countries, and China in particular has sought to undermine them. 

The region of Asia which China seeks to influence is vital for the global economy and for securing the US policy goals mentioned above. The South China Sea, a body of water more than double the size of the Mediterranean Sea, is crucial to the world economy and thus crucial for the health of the US economy. It is estimated that 3.4 trillion dollars of goods pass through the South China Sea every year.  In addition, the South China Sea is critical for US allies and trading partners’ supply of energy since more than 80% of the crude oil that goes to South Korea, Japan, and Taiwan passes through the South China Sea.¹ Furthermore, 12% of the world’s fish are caught in the sea and there are estimated to be more than 11 billion barrels of oil and 190 trillion cubic feet of natural gas available in the sea.² One-third of the world’s crude oil and half of its liquefied natural gas passes through this region.³ The island of Taiwan located within the South China Sea produces 65% of the world’s semiconductor chips including over 90% of the most advanced chips crucial for the technology the US and its partners rely on for their economies and militaries. ⁴

China, in its official public statements and in its actions, has shown itself to be in opposition to US interests. For example, the Chinese Government has openly interfered with the stability of this important trading region vital to US interests and security. China has acted in ways that actively interfere with the US goals mentioned above in order to increase its own regional influence. China has claimed large parts of the South China Sea as its own territorial waters and has taken aggressive measures to interfere with the freedom of the seas. China has built multiple artificial islands in disputed sections of the South China Sea and has built military bases on them as a means to further expand its power in this disputed region. The Chinese military has also sent its forces into the South China Sea to harass and intimidate its neighbors. They have seized islands claimed by other nations, for example, the Scarborough Shoal from the Philippines, in an attempt to assert their dominance in the region. The Chinese have used armed civilian ships, as part of the People’s Armed Forces Maritime Militia (PAFMM), along with other military units to interfere with non-Chinese fishing operations.⁵ China has also repeatedly attempted to prevent other nations from exploiting oil reserves and other natural resources inside the South China Sea.⁶   In addition, China does not recognize the sovereignty of Taiwan and claims it as a part of its own country. It has claimed Taiwan is “indispensable for the realization of China’s rejuvenation” and has promised to use force to incorporate Taiwan into China if necessary. China has routinely conducted military drills in Taiwan’s air and water territory and has begun to modernize its military, one of its explicit goals being to force Taiwan under Chinese rule. ⁷

If China is allowed to grow its power unchecked, it could become a real threat to the United States. However, the United States’ relationship with China is not entirely negative. China is the third largest export market for the United States behind only Mexico and Canada.⁸ The large Chinese population offers US companies access to one of the largest markets in the world making the US hundreds of billions of dollars every year.  The United States also imports hundreds of billions of dollars worth of goods from China including over $120 billion in electronics, over $100 billion in machinery and appliances, and tens of billions more in industries such as textiles, chemicals, metals, rubber, and other products on which the US economy thrives. In 2022, US imports of goods and services from China totaled over $560 billion and S&P 500 companies received more revenue in China than the next three countries combined.⁹

US Policy Options

The economic and political goals of the United States with respect to China should be to balance the economic opportunities that support large sections of the US economy with the need to preserve a free and open East Asia based on fair rules. The clearest area where China needs to be limited is the technological growth and development of its armed forces. As mentioned in the preceding paragraphs, the Chinese have goals to dominate the larger South Asian region and impose an authoritarian system on it. They have already taken action toward this end. If China’s military is allowed to further strengthen and develop, the Chinese government can take any number of negative actions. The most drastic action China could initiate would be invading the island of Taiwan. If China is unwilling to launch an invasion it could use its military to enforce a naval blockade or no-fly zone surrounding Taiwan. Chinese forces could use their power to further exert control over commercial activities in the South China Sea including oil and gas exploration, internet and communication cables, and fishing. The government could also use its power to pressure or coerce nations into actions that are contrary to the interests of the United States. The United States also has an interest in preventing the growth of Chinese control over its own semi-autonomous regions, such as Hong Kong and Xinjiang, since a stronger internal government allows China to project its power outward.  

The United States has many tools available to counter the threats posed by China and to achieve its goal of furthering economic growth and regional stability. Traditional methods like building up US and allied militaries are, to some extent, clearly necessary.  However, these actions are expensive and may lead to misinterpretation and unnecessary military escalation. Instead, by utilizing economic tools, the United States can find a middle ground between doing nothing and preparing for war. A two-path approach to economic confrontation is necessary in this case. The first is to take the necessary actions to prevent the growth of the Chinese security apparatus. This can be accomplished primarily by restricting China’s access to advanced technologies. The goal is to ensure that for as long as China remains a threat to US interests, the US and its allies have a strategic advantage over China. While the primary goal is to deter China, in a worst-case scenario that leads to war (especially with a nation that does not have a mutual defense pact with the US), US allies would be able to stand their ground with minimal US involvement. Restricting advanced technology to China will also prevent China from having access to the most advanced tools necessary to control its own population (such as artificial intelligence-powered surveillance). This will make China less internally stable and less confident in its ability to project power abroad. In this respect, general trade sanctions may also be effective, for example, a law passed by the US Congress preventing most imports from the Xinjiang region of China.¹⁰ What needs to be analyzed is the economic consequences of these actions and whether they are justified to achieve the stated political ends. China is far from a friend to the US and the US should not seek to appease it, but it would be counterproductive for the US to unnecessarily damage the Chinese economy and by extension its own. 

The actions mentioned in the preceding paragraph are existing or potential punitive actions against China.  However, there is another side to this coin. Positive relations with other countries, particularly in South East Asia are essential to deterring Chinese aggression against US interests. Creating a freer exchange of goods and people between the United States and other nations can be an effective way to limit Chinese influence. This can include actions such as the expansion of trading blocs such as the aborted Trans-Pacific Partnership (TPP) which did not include China. Unfortunately, the nationalist views of the Trump administration meant the TPP goals were short-lived. Strengthening the economic bonds between the US and other nations is a way to give others a stake in US interests and thus make them more amenable to helping the US defend them. Demonstrating the superiority of a system based on liberal values through free trade deals and loosening immigration restrictions between nations may incentivize them to turn away from the much more authoritarian Chinese model. 

Deciding how the United States should work to counter Chinese influence is just as important as determining the areas where the United States should choose to not counter Chinese influence. For instance, as part of China’s Belt and Road initiative, large amounts of public money are being transferred from China into infrastructure projects in many other, mainly underdeveloped, nations. A strong argument can be made that these policies are not in China’s public interest as they necessitate large amounts of debt and taxes for projects that do not provide the necessary returns to cover these expenses. They are therefore detrimental to the health of the economy. China, being a nation strongly influenced by Marxist ideas and a desire for political power, will oftentimes implement policies that are detrimental to its economy. It is important for the US to identify these policies and not take similar actions in the name of countering China. 

Analysis of Punitive Actions against China

Actions aimed at crippling the Chinese economy may be necessary to achieve US national security goals, but they need to be targeted and not cause unnecessary damage to the US economy. During the Trump administration, the US began heavily increasing tariffs on multiple countries and industries, with the government specifically targeting China. In large part due to these increased tariffs and Chinese retaliation, multiple US industries and the economy as a whole suffered. One clear negative effect was the decreased US agricultural exports to China. American agricultural exports dropped by over 60% between 2014 and the end of 2018 which led the US government to spend 28 billion dollars of tax money to subsidize the farming industry.¹¹ In addition between 2018 and 2019, US exports to China of petroleum and coal products dropped by 75% and metal exports dropped by over 50%.¹² Contrary to the policies implemented above, future actions against China should be limited and serve specific defined goals. 

Limiting China’s Access to Semiconductors 

The semiconductor industry has been a particular target for US actions. This is because semiconductors are essential in military and security technology. Consequently, in order to weaken China’s ability to effectively exert control over disputed territory, limiting access to semiconductors is essential. The Trump administration began restricting access to semiconductors to major Chinese companies as punishment for violating US national security sanctions on countries such as Iran and North Korea. The Biden administration escalated this effort on October 7th, 2022 by authorizing sweeping restrictions designed to limit Chinese access to foreign advanced semiconductors and stifle Chinese efforts to design and manufacture them themselves. The Biden administration’s policies include expanding the foreign direct product rule which allows the US to control the transfer of technology made with US technology regardless of the country of origin. Given that so many products within the global supply chain are developed in the United States, the new rule prohibits the transfer of a multitude of advanced technologies necessary to manufacture advanced semiconductors. Furthermore, US nationals are now prohibited from assisting Chinese companies in the manufacturing of these semiconductors.¹³ There are clear national security benefits to these actions. Chinese entities will now have a harder time acquiring advanced technology to transfer to powers much more hostile toward the United States, most notably North Korea and Iran. China itself will no longer have the ability to use Western technology to threaten Western interests and nations. These moves will limit the advancement of Chinese military technology and temporarily, if not permanently, secure the US and its allies’ military superiority in relation to China.¹⁴ 

It is clear that these export restrictions set China and its armed forces back technologically. While for many years it has been a goal for China to increase its domestic production, the government had to balance the goal of producing semiconductors domestically with the fact that foreign semiconductors were by and large more economical.¹⁵ The question that remains is whether this setback is permanent or temporary. Even before the October 7th, 2022 export controls, China was investing heavily in onshoring its semiconductor manufacturing, including in 2021 when it is estimated China invested over $50 billion in the industry. Following the Biden administration’s controls, China no longer has to make that choice: the choice has been made for them – China will need to produce all semiconductors domestically. 

Following the American sanctions, Chinese intentions are increasingly clear: devote as many resources as possible to creating domestic capacity to design and manufacture advanced semiconductors. However, assuming the US is able to continue to convince its allies that the export controls are in their self-interest which so far it seems to be doing, the Chinese goal is easier said than done. The financial cost itself is staggering. One estimate of the cost to create a fully localized and self-sufficient supply chain for the manufacture of semiconductors is at least one trillion dollars in upfront investments and an increase of semiconductor prices between 35 and 65 percent.¹⁶ The financial cost is not the only obstacle in the path of China’s goal. 

The global semiconductor industry that has developed over at least the last 70 years is a complex system that includes a multitude of trade secrets, research institutions, and manufacturing facilities spread across many countries and companies. The difficulty of recreating the supply chain, no matter how much investment or corporate espionage, cannot be overstated. As of 2021, the US contributes 39% of the value in the semiconductor supply chain, and US allies in Asia and Europe contribute an additional 53% of the value while China contributes 6% of the value in the supply chain.¹⁷ With regard to the most advanced semiconductors, China’s position is even worse; three companies based in the US, South Korea, and Taiwan control virtually all of the world’s advanced semiconductor manufacturing capacity.¹⁸ China is advancing in the semiconductor industry, but given the above facts combined with the systematic cutting off of information and technology to China by the US and its allies, it is difficult to imagine a situation where China will obtain superiority over the West in this field.

The Chinese could defeat the export controls by separating the US from its allies. The export controls rely on the cooperation of multiple European and Asian countries such as the Netherlands, Germany, South Korea, and Japan. While all these countries have been victims to varying extents of Chinese economic and political aggression, if China can convince some of these countries to allow the export of their technology, the export control’s effectiveness will be limited.  It is important to note that the US need not hold China to the technological level it currently possesses for the controls to be successful. All that needs to occur is that the US and its allies restrict China enough so that by the time it achieves the current technology, continued R&D by Western nations means they will already have more advanced semiconductors. Thus, so long as China remains a threat, the West will remain dominant with regard to advanced semiconductors. 

Downside of Export Controls 

With the semiconductor example, a compelling argument can be built suggesting export controls are a strong and effective tool for the US to achieve its foreign policy goals; however, they are not consequence-free. Specialization caused by international trade has led to the creation of trillions of dollars in wealth; the disruption of the free flow of goods leads to a decrease in wealth in the same way. It is estimated that every $1 billion of exported US goods supports approximately 6,000 jobs in the United States.¹⁹ Approximately 4,500 US jobs are supported by every billion dollars of exported US services.²⁰ The loss of jobs is not the only consequence that should cause policymakers to hesitate before expanding export controls. 

The loss of American technological leadership is a potential risk with sanctions: with others developing semiconductor technology, they could create a competitor to American firms by making a better chip. In the late 1990s, the US Congress passed export controls on satellite technology which directly resulted in a loss of $2.4 billion, 19% of the market share, for US companies operating in the commercial communication satellite industry over a three-year period.²¹ Furthermore, almost one-third of US technology companies divested resources from the research of new satellites subject to US sanctions.²² It is not unreasonable to assume that if the US continues to expand export controls, a similar fate could await the much larger and strategically important US semiconductor industry. The more regulations which are placed on US products, the less favorably they will be looked at by consumers. Companies will naturally begin moving away from US products and toward foreign products that are less controlled. This is not to say that export controls are inherently wrong, and there are ways to limit the damage caused by these regulations. Most importantly, the US needs to ensure that the export controls will actually be effective. The reason that controls on highly advanced and sophisticated Western products and labor can be so effective is that no economically viable alternative exists. As export controls are applied to less sophisticated goods, viable alternatives begin to exist. If export controls restrict the ability of US companies to provide goods to China, which the country could obtain anyway, it actively harms the US while not holding back China. Thus, export controls have to be continuously updated and specifically focused on unique products to ensure they do not cause damage while providing no strategic advantage. 

In addition to the economic damage directly caused by sanctions, additional damage is caused by the retaliation they invite. The US sanctions against China have instigated retaliatory measures which will only increase in intensity as the US continues to confront China. One of the more overt acts of retaliation since the October 7th, 2022 export controls were instituted, is the ban of equipment produced by Micron, a US-based semiconductor manufacturer, in a wide range of Chinese products. The ostensible reason given for the ban was that the company failed a cyber security review, but it is widely accepted that the Micron ban is retaliation for US sanctions. Micron sales in China totaled $3.3 billion in 2022, a number which will decrease dramatically in future years as the ban takes effect.²³

As tensions escalate with China, more companies will become collateral damage and it will leave US policymakers with a choice: should these firms be compensated as bystanders? A policy could be pursued similar to the billions of tax dollars given to farmers as compensation for the damage the Trump administration’s trade war caused. Alternatively, the US could use the Micron ban and other similar actions as an opportunity to push a narrative that China is an authoritarian regime with arbitrary laws, and companies that engage in business under those conditions do so at their own risk. ²⁴

Possible Retaliation by China 

Any US policy has to be viewed in terms of Chinese retaliation: on the spectrum of retaliatory measures China could take is restricting exports of rare earth minerals. China currently controls over 60% of rare earth metal mining and 80% of refining capacity globally; however, China’s position here may not be as strong as it originally may appear.²⁵ Part of the reason China is so dominant in this industry is that other nations have chosen to artificially limit their capabilities for political reasons. However, political priorities can change; between 2010 and 2015, Japan cut its reliance on Chinese rare metals by over 20%  in response to China boycotting sales of minerals to Japan over a fishing dispute.²⁶ The United States and its allies have the mineral reserves necessary to weaken Chinese dominance in the industry should they ever make that choice.  

There is also a clear risk of runaway retaliation: more radical actions that China can take than those described in the previous paragraph in response to escalating US pressure. One action that could be taken is the voiding of intellectual property rights of foreign entities in China. While the specifics of such an action are difficult to predict, multiple laws and draft laws in Russia provide some clarity into what the Chinese could implement. Following the Russian invasion of Ukraine, the Russian government issued an order eliminating the royalty owed to patent holders from nations Russia viewed as unfriendly.²⁷ Another law waived trademarks for various consumer products heavily targeting computers, clothing, and automobiles. In addition, Russian courts have taken actions to nullify the intellectual property of foreign firms. There have also been other laws drafted weakening intellectual property, for example an order that would legalize piracy of foreign software, a law that would withdraw Russia from the World Trade Organization, and multiple laws limiting or voiding IP protections for firms based in “unfriendly countries.” These laws are at various stages of implementation and some have hit roadblocks within the Russian government, but if China were to pursue actions similar to this, it would have incredibly destabilizing effects for the global economy. There would be a breakdown in international trade cooperation: if such laws were enacted, it would deprive foreign firms of billions of dollars. Furthermore, China uses more intellectual property than it produces; in 2017, over 2.7 times more patents were filed in China by foreign actors than Chinese actors filed in foreign countries.²⁸ In the long-term, this strategy would harm China as much as it harms the rest of the world. China’s expenditure in R&D is increasing rapidly; however, if intellectual property is voided, and assuming nations would not respect the IP of companies that didn’t respect theirs, China will never be able to become a net exporter of intellectual property. Furthermore, nations where China was a net exporter of IP would have little incentive to protect Chinese intellectual property which would further deprive China of funds. Lastly, foreign companies will be deprived of revenue to invest further in intellectual property thus harming China in the long run. 

Another form of retaliation China could implement is to restrict the shipping of foreign goods on Chinese ships. China controls approximately 30% of the world’s shipping, and any restrictions placed on it could lead to a global shipping crisis. China could announce a large increase in the fees companies must pay in order to ship foreign goods on Chinese ships or a total ban on the shipment of foreign goods. Both actions would have similar effects varying only in degree. The immediate effects of such an action would be a shortage of ships transporting non-Chinese goods thus resulting in a price increase, and an increase in the supply of ships transporting Chinese goods resulting in a price decrease. The effects of a law like this on China are complicated. The artificially decreased prices of Chinese shipping would lead to an increase in sales of Chinese goods. This would lead to massive short-term gains for the Chinese economy. However, beneath the surface problems begin to arise. The market for shipping Chinese goods would become oversaturated and as shipping prices decreased, multiple Chinese shipping firms would risk bankruptcy. Companies could, in theory, leave China and provide shipping to the rest of the world. However, due to the nature of the Chinese economy and the fact that the shipping firms are owned or strongly influenced by the government, it is unlikely that shipping companies would pursue this action. Ultimately, China would need to significantly subsidize its shipping industry with state funds. In the long run, the rest of the world would build more shipping capacity and prices would begin to drop for non-Chinese goods. China ultimately would be under economic pressure to allow their shipping firms to compete as subsidies become unsustainable and a drag on the rest of the economy. 

In sum, there is a wide range of retaliatory actions which China can take. While some initial actions have already been taken, others are more probable and some are much less probable. It is clear that US regulation on semiconductors or Chinese limits on shipping cause economic damage whenever these regulations are promulgated. Both the US and China recognize this to some extent; however, there comes a moment where rational long-term judgment may be abandoned in favor of short-term gain. It is unclear when the actions taken by the US, combined with internal political pressure, will cause the Chinese government to reach that point. The US should enact the necessary regulations to defend its interests and security at the same time; however, the US should not needlessly antagonize the Chinese. Whenever possible, the free exchange of goods and services should be encouraged, and when it is not possible, other positive actions to strengthen trade should be enacted. 

Analysis of Positive Actions to Strengthen Trade

The actions discussed above are designed to hold China back in strategically important ways. However, the United States must also take steps to strengthen its economy to ensure it remains an important player in the global system and to mitigate any economic damage caused by the imposition of sanctions and subsequent Chinese retaliation. 

Trade is essential to the economy of the United States and strengthening trade with other nations. Attempting where possible to find common ground with China is necessary even as the US moves to economically confront the Chinese government. The main instrument to strengthen trade is trade deals with other nations. The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is a trade agreement among multiple countries mainly in Asia, but also including Canada, Mexico, and South American countries. The agreement was created after the US withdrew from the prior proposed agreement, the Trans-Pacific Partnership (TTP) which was designed to limit Chinese influence. The TTP was created to further integrate the US with Asia thus increasing US influence relative to China. The US not participating in a trade deal with three out of its top four trading partners in a crucial region of the world does not bode well for US political or economic influence. Contrary to the actions of the US, the Canadian government participated in the trade deal and has seen substantial benefits. Total merchandise trade between Canada and the five signatories that do not have additional trade deals with Canada (Australia, Japan, New Zealand, Singapore, and Vietnam) increased by 10% in the first three years, representing nearly $5 billion.²⁹ Canadian exports to those five countries increased by 8.3% in the same period, and taxable imports increased by 11.2% mainly in consumer products such as clothing and furniture.³⁰ While the agreement has increased trade and wealth, there are some legitimate issues that make the US reluctant to join, including issues relating to intellectual property and state control. The US, as the world’s largest economy, does have leverage in negotiating a favorable entry into the agreement. The US can insist on strengthening protections for intellectual property and currency manipulation similar to those included in the United States-Mexico-Canada trade agreement (USMCA) that was ratified by Congress in 2019. It can also insist on strengthening protections for companies against government interference and increase regulations on state-owned enterprises.³¹  The US should participate in agreements like CPTPP and others to strengthen its economy and influence, and thus better protect its national interests. 

It is important that the US cements its research and development leadership so that it can continue to have control over the most advanced and best technology. For the US to compete and outperform in the global economy, it needs to increase the number of workers in advanced technology areas. By 2030, it is estimated there will be a shortage of 1.4 million workers, such as skilled technicians, engineers, and computer scientists, in technology fields.³² If current trends continue, 58% of semiconductor jobs risk going unfilled by 2030.³³ Multiple policies have been proposed, such as increasing the number of resources invested in science education; while these policies warrant debate, it is worth noting that, in addition to causing an increase in taxes and debt, attempting to reform the education system is a long term solution to a current problem. 

While some government investment may be necessary, one of the best ways to assist the semiconductor and broader technology industry without sacrificing the rest of the economy is through immigration: at US colleges and universities, over half of the graduates with a master’s degree in engineering are foreigners and over 60% of PhD engineering graduates are foreigners. Approximately 80% of foreigners with a master’s degree and 25% of PhDs in engineering leave the US after graduating. The plurality of foreign students in US universities are Chinese. Instigating an exodus of Chinese tech workers from China would help fill the US worker shortage while depriving China of skilled engineers necessary for the development of strategic technology. A model can be found in the US approach to Cuba during the reign of Fidel Castro. Several factors increased immigration from Cuba: an authoritarian regime had already incentivized many to flee the country, the US put economic pressure on an already fragile communist economy, and the US incentivized the migration of Cubans by making it easier for them to obtain permanent residency, among other actions.³⁴ A more limited, but still effective, policy can be pursued with regard to China. China too is an authoritarian regime that naturally makes many people predisposed to leaving. In the case of Cuba, sanctions were implemented on goods from a variety of sectors further fueling the exodus. In the case of China, limited sanctions on the technology sector would restrict the growth of that industry and thus further incentivize an exodus of Chinese tech workers to the US. This can be done with an action similar to the Cuban Medical Professional Parole Program that specifically incentivized a wide range of Cuban medical professionals and their families to come to the US. Another option would be to announce increased incentives for tech workers to enter the US from any nation, understanding that a large portion of those would be Chinese for the reasons mentioned above. While other actions such as export controls would harm China, such actions also deal damage to the US, while increasing immigration from China would actually be a benefit to the US and should be pursued more aggressively. 

While the United States should join multilateral trade agreements and increase immigration, long-term strategy suggests that the United States should also engage with China directly on issues where both sides can find some common ground. It is worth reiterating that China is the largest trading partner of the US and both countries benefit from the relationship. If the US limits its coercive actions to measures which are necessary to defend national security and the property of US companies, there is still billions of dollars of trade to be done between China and the US. In 2022, US exports to China rose by 12.4% compared to the previous year, to a total of $35 billion. Between the years 2018 and 2022, there was an over 400% increase in US agricultural exports to China.  In addition, US imports of everyday consumer items such as furniture, toys, paint, and other manufactured products increased to a total of $69.4 billion in 2022 constituting 50.7% of total US imports of those product categories. Demand for Chinese textiles in the US also increased by 6.7% to $53.7 billion representing almost 30% of total US imports in that sector.³⁵

The US will also become less dependent on China in time. As more countries are industrializing, the ratio of Chinese exports to the US compared to total exports has been declining even as the amount of Chinese exports to the US has risen. For instance, the total amount of miscellaneous manufactured goods imported from China between 2018 and 2022 increased by $4.5 billion even as the share of US imports from China in that sector decreased by nearly ten percent. The world is diversifying away from China in some ways but the evidence is unequivocal that there is still demand for Chinese products in the United States and there is also Chinese demand for American exports. ³⁶

Political Obstacles to Policy Options

The United States government ultimately relies on the consent of its citizens in order to carry out its foreign policy. Many of the policies proposed in this paper lead to increased economic growth and prosperity for large numbers of Americans and thus can, in all likelihood, be implemented without substantial backlash. However, some of the actions proposed will inevitably result in negative economic consequences for Americans. Currently, China has not taken actions that seriously threaten the way of life of most Americans, and thus targeted sanctions against the Chinese regime can be instituted. These sanctions limit the Chinese’s ability to commit acts of aggression and, due to their targeted nature, allow most Americans to suffer relatively minor economic damage. However, should China choose to escalate matters through further aggressive actions, for example through an incursion into Taiwan’s outlying islands or a blockade of that nation, further US action may be necessary. An effective response to such an escalation would need to involve broad sanctions aimed at crippling the entire economy of China. Such a response would result in significant economic damage to US citizens. 

The question is, in such a circumstance, how much damage should the US be willing to accept in order to limit China’s aggression? If, as soon as such an escalation is made against Taiwan, a forceful response is not implemented, China will be further emboldened which will only fuel its aggression. Given the stakes involved, the ideal response would be a complete cessation of all trade with China thus breaking its economy and its war machine. Such a response would not be undertaken for altruistic purposes; on the contrary, as mentioned before, the US has a strong self-interest in deterring Chinese aggression. The only limitation to a policy of complete secession of trade would be the will of the American people; it is unclear if Americans would be willing to endure higher prices and declining living standards. This is one of the most important reasons to take positive actions to increase the wealth of the US economy; a wealthier population will be more amenable to accepting short-term economic losses in order to preserve their nation’s national security. 

Conclusion 

While there will be some rivalry between the US and China due to their strong economic and political differences, as this paper has suggested, the goal is to confront China and strengthen the United States to prevent unnecessary damage in both the economic and military realms. As this paper has detailed, in order to defend US interests from Chinese aggression, two simultaneous approaches should be taken. The first is punitive measures against Chinese strategic technology: most notably, advanced semiconductors for military technologies such as advanced surveillance technology for warships and hypersonic missiles. The second is increasing positive actions which can strengthen the US economy. This includes increasing trade with other nations and increasing immigration, especially in important sectors such as semiconductors and the wider technology sector. It is necessary to take both actions to maintain economic pressure on China. In order to limit the damage to the US economy, the US should trade with China in fields not related to national security while limiting its growth in the field of advanced technology.  

Footnotes

1. O’Rourke, Ronald. “U.S.-China Strategic Competition in South and East China Seas .” Congressional Research Service , 2023. https://sgp.fas.org/crs/row/R42784.pdf 
2.  “Territorial Disputes in the South China Sea | Global Conflict Tracker,” Council on Foreign Relations, 2023, https://www.cfr.org/globa “The South China Sea Is an Important World Energy Trade Route.” Homepage – U.S. Energy l-conflict-tracker/conflict/territorial-disputes-south-china-sea. 
3. Information Administration (EIA), 2013. https://www.eia.gov/todayinenergy/detail.php?id=10671. 
4.   “Territorial Disputes in the South China Sea | Global Conflict Tracker,” Council on Foreign Relations, 2023, https://www.cfr.org/global-conflict-tracker/conflict/territorial-disputes-south-china-sea. 
5.  Francis, Robert, and Roswell Lary . “Winning the Public Diplomacy Battle in the South China Sea.” Council on Foreign Relations, 2021. https://www.cfr.org/blog/winning-public-diplomacy-battle-south-china-sea.   
6.  “Timeline: China’s Maritime Disputes.” Council on Foreign Relations. Accessed August 14, 2023. https://www.cfr.org/timeline/chinas-maritime-disputes. 
7.  “Territorial Disputes in the South China Sea | Global Conflict Tracker,” Council on Foreign Relations, 2023, https://www.cfr.org/global-conflict-tracker/conflict/territorial-disputes-south-china-sea. 
8.  Berman, Noah. “The Contentious u.s.-China Trade Relationship.” Council on Foreign Relations, 2022. https://www.cfr.org/backgrounder/contentious-us-china-trade-relationship. 
9.  Swanson, Ana. “The Contentious u.s.-China Relationship, by the Numbers.” The New York Times, July 7, 2023. https://www.nytimes.com/2023/07/07/business/economy/us-china-relationship-facts.html.  
10.  “Biden Signs Bill Banning Goods from China’s Xinjiang over Forced Labor | CNN Business.” CNN, December 24, 2021. https://www.cnn.com/2021/12/23/business/biden-bans-xinjiang-imports-china-intl-hnk/index.html
11.  Rappeport, Alan. “Farmers’ Frustration with Trump Grows as U.S. Escalates China Fight.” The New York Times, August 27, 2019. https://www.nytimes.com/2019/08/27/us/politics/trump-farmers-china-trade.html
12.  Bekkers, Eddy. “An Economic Analysis of the US-China Trade Conflict.” World Trade Organization, 2020. https://www.wto.org/english/res_e/reser_e/ersd202004_e.pdf.  
13.  Shivakumar, Sujai, Charles Wessner, and Thomas Howell. “A Seismic Shift: The New U.S. Semiconductor Export Controls and the Implications for U.S. Firms, Allies, and the Innovation Ecosystem.” CSIS, 2023. https://www.csis.org/analysis/seismic-shift-new-us-semiconductor-export-controls-and-implications-us-firms-allies-and. 
14.  Ibid 
15.  Ibid 
16.  Varas, Antonio, Raj Varadarajan, Ramiro Palma, Jimmy Goodrich, and Falan Yinug. “Strengthening the Global Semiconductor Supply Chain in an Uncertain Era.” BCG Global, April 25, 2023. https://www.bcg.com/publications/2021/strengthening-the-global-semiconductor-supply-chain. 
17.  Khan, Saif. “The Semiconductor Supply Chain.” Center for Security and Emerging Technology, June 9, 2023. https://cset.georgetown.edu/publication/the-semiconductor-supply-chain/.  
18.  Ibid 
19.  Ryan Hass, David Dollar, and Vera Songwe Witney Schneidman. “New Trade Agreements Lead to More, and Better, Jobs.” Brookings, July 29, 2016. https://www.brookings.edu/articles/new-trade-agreements-lead-to-more-and-better-jobs/.  
20.  Trade Agreements and Jobs.” United States Trade Representative, 2011. https://ustr.gov/about-us/policy-offices/press-office/fact-sheets/2011/october/trade-agreements-and-jobs#:~:text=Every%20billion%2 0dollars%20of%20services%20exports%20supports%20more,American%20workers%20who%20produce%20Made-in-the-USA%20goods%20and%20services. 
21.  Ezell, Stephen. “How Stringent Export Controls on Emerging Technologies Would Harm the US” information technology and innovation foundation, 2019. https://www2.itif.org/2019-export-controls.pdf. 
22.  Tomoshige, Hideki. “The Unintended Impacts of the U.S. Export Control Regime on U.S. Innovation: Perspectives on Innovation.” CSIS, 2022. https://www.csis.org/blogs/perspectives-innovation/unintended-impacts-us-export-control-regime-us-innovation. 
23.  Mozur, Paul, and John Liu. “With Ban on Micron, China Escalates Microchip Clash with U.S.” The New York Times, May 22, 2023. https://www.nytimes.com/2023/05/22/business/micron-technology-china-ban.html. 
24.  Allen, Gregory C. “China’s New Strategy for Waging the Microchip Tech War.” CSIS, 2023. https://www.csis.org/analysis/chinas-new-strategy-waging-microchip-tech-war.  
25.  Ibid
26.  Hui, Mary. “Japan’s Global Rare Earths Quest Holds Lessons for the US and Europe.” Quartz, April 23, 2021. https://qz.com/1998773/japans-rare-earths-strategy-has-lessons-for-us-europe.  
27.  USPTO OPIA Bulletin Russia – United States Patent and Trademark Office.” United States Patent and Trade Office, June 2022. https://www.uspto.gov/sites/default/files/documents/OPIA-Bulletin-Russia.pdf. 
28.  Schneider-Petsinger, Marianne. “US–China Strategic Competition – Chatham House.” US–China Strategic  Competition The Quest for Global  Technological Leadership, 2019. https://www.chathamhouse.org/2019/11/us-china-strategic-competition.   
29.  “Trade Increases under CPTPP.” Government of Canada, March 27, 2023. https://www.international.gc.ca/trade-commerce/trade-agreements-accords-commerciaux/agr-acc/cptpp-ptpgp/access_trade_statistics-accedez_statistiques_commerciales.aspx?lang=eng. 
30.  Ibid 
31.  Cutler, Wendy. “Asia Society.” REIMAGINING the TTP: Revisions That Could  Facilitate U.S. Reentry. Accessed August 18, 2023. https://asiasociety.org/sites/default/files/2022-12/ASPI_CPTPP3_rev.pdf.  
32.  Martin, Dan. “Www.Semiconductors.Org.” semiconductor industry association  , 2023. https://www.semiconductors.org/wp-content/uploads/2023/07/SIA_July2023_ChippingAway_website.pdf.  
33. Ibid
34.  Lopez, Ernesto. (PDF) migration, Brain Drain, and Cuba-U.S. relations – researchgate, 2018. https://www.researchgate.net/publication/324876766_Migration_Brain_Drain_and_Cuba-US_Relations 
35.  “2022 U.S. Trade with China – Bureau of Industry and Security.” US Department of Commerce, 2022. https://www.bis.doc.gov/index.php/country-papers/3268-2022-statistical-analysis-of-u-s-trade-with-china/file. 
36. Ibid

About the author

Alejandro Salas

Alejandro is a senior at The Bronx High School of Science in New York City. He is interested in economics and international trade, and is a Boy Scout who enjoys competitive swimming and debate.

The post United States Policy Options Relating to Trade with China: Economic and National Security Implications appeared first on Exploratio Journal.

Abstract

This paper looks at recent trends in the employment of recently graduated college students and proposes an experiment to increase the likelihood of employment success. While analyzing the employment process that a recent college graduate must go through, certain behavioral biases revealed themselves: college students don’t know what jobs to apply for their major, they don’t know how many and for how long they should be applying for jobs, and they are also emotional fragile and are prone to discouragement due to rejection. To fix these problems, students need to be shown a survey with the employment data from the previous year’s class so that current students know how long to apply for and where they should apply based on their major. In addition to the survey, students would be given advice from a mentor who recently graduated and had employment success. This mentor would provide emotional encouragement, as well as tips on how to get employed. Students will also be provided with short videos of the mentor as well so that they can watch the videos on their own time. Interventions like these have been proven to increase the likelihood of employment success. To determine if and how well these interventions work, a sample size of college students would be given the resources, and another group of students would not be given the resources. Comparing the results will give an accurate conclusion.

Intro

College seniors and recent graduates face the challenge of obtaining a job before or shortly after they graduate. However, a set of behavioral biases often hinder them from getting the job they desire. This paper identifies these biases and proposes proven behavioral interventions that can increase the probability of recent college graduate employment success.

A behavioral “journey map” for employment consists of several steps. A person has to meet the education and experience requirements, have an adequate resume, meet for interviews, give a compelling reason why they should be hired, and so much more. During this process, there are many points during which behavioral biases can create bottlenecks. This paper focuses on two. First, recent college graduates must identify job openings that are a good match. Second, recent college graduates must persist in the face of rejection. Recent college graduates are an important part of the pool of willing and able workers because of their newly acquired skills from their degree. They have a college education and are willing to fill all of the entry-level positions in companies of all industries. However, recent college graduates still decide to give up too soon in their search for a job and are instead accepting positions that require no college education at all. In 2020, The Federal Reserve Bank of New York reported that 41% of recent college graduates are working at a job that doesn’t require a college degree. The Federal Reserve Bank also found that, in 2022, unemployment rates for recent graduates are higher than the national average of all workers.

After the pandemic hit in 2020, unemployment rose to an all-time high because companies could no longer afford to hire or even keep the workers that they had. The result of this was a staggeringly low amount of open positions for recent graduates. Unemployment for adults aged 20-24 rose to 23% in May of 2020, which is double what it was the year before. This led to extreme job-seeking anxiety as recent college graduates continued to lose hope of being employed. “I’m honestly kinda scared,” said recent USC graduate Hannah Grogin when she was asked about beginning to look for a job in May of 2021. Hannah knew that “there’s just a lot more to fear about not being able to get a job because of the pandemic.” This interview occurred when the unemployment rate for recent graduates dropped to 10% from 23%. Recent college graduates haven’t updated their view of the job market– this means the next class of graduates may face a similar level of anxiety due to the persistence of the older model of the job market. While it may seem like the market is not in these graduates’ favor, the job market for college graduates has become stronger. According to the National Association of Colleges and Employers, “This is an even hotter period for recruiters than they thought it was going to be…,” said Josh Kahn, assistant director of research and public policy, in May of 2022. As of June 2022, ZipRecruiter reported that there are around two job openings for every one person and that companies are lowering their experience requirements. If the market is strong, why are some college graduates not finding jobs and are becoming discouraged?

It is especially important that recent college graduates do not get discouraged when looking for a job because of the consequences that it can cause in their future search for employment. Studies have found that “The earnings of displaced workers do not catch up to those of their nondisplaced counterparts for nearly 20 years.” This unemployment spell has also been found to affect future home ownership and the total lifetime earnings of an individual.

In the next section of the paper, I am going to lay out what the job search process looks like for a recent college graduate, and then I am going to highlight the main behavioral biases that are hindering their success. Job search persistence is absolutely crucial for recent college graduates, and there are nudges that can provide them with the help they need.

Student Job Search

After the pandemic, which “was unusual in the severity and speed with which people lost jobs and entire sectors diminished,” the economy adapted, and sectoral shifts occurred, which grew industries that were less hurt. This means that these graduates now have to navigate “a complex labor market…that individual workers have to make countless decisions—big and small—about what kind of work they want to do and what they need to learn to achieve their career goals and earn a living.” It’s useful to break down the job search process into steps in order to account “for the complexity of human decision-making, we can design programs that work with human nature, not against it, benefiting countless lives and livelihoods.”

The first step students usually take when they first start looking for a job is to create their resume, which has a detailed list of their accomplishments and work experiences. For students who don’t know how to create an effective resume, many colleges offer free resources that provide students with effective guidance. However, students may not always be aware that these resources exist and can become discouraged by their failure to create a resume. Despite this possibility, this basic step is usually not the root of the students’ problems.

The next step is to develop and adopt a job search strategy. This includes finding open positions, applying to enough of them given the chances of success, and identifying the ones for which one is a good match. In terms of finding open positions, events such as career fairs and resources such as the career office can be extremely helpful. If students choose not to use these resources, this is where problems start to arise. Second, one of the main reasons why students stop looking for a job is because they are applying for the wrong positions. In a survey conducted by employment firm GradStaff that asked what the recent graduates’ top two concerns were when finding a job, more than 75% of respondents answered, “I don’t know what positions are for me,” and the second most common response at 46% was “I don’t know what to do with my major.” This survey is an indication that students need to be educated as to which specific positions they should be applying for, given their major. Even if they are applying for the right positions, they need to be reassured that they are doing the right thing.

Third, applying to the right number of jobs involves understanding the probability of success, which students might not know. According to the survey from GradStaff, respondents looked for a job for an average of 3.6 months and applied to just 23 jobs in total. That means that the respondents were applying for fewer than two positions per week. In order to increase the likelihood of getting a job, these graduates need to be sending out way more applications. Indeed.com’s editorial team published an article in 2021 recommending that seniors in college should be looking for positions for up to 20 hours per week. They recommend applying to up to 15 jobs throughout the week as well. Therefore, students need to be spending more time applying for the right jobs given their major.

Assuming the job search strategy is good, a person still has to have the persistence to continue applying until success is achieved. Graduates still need to be persistent despite facing rejection or simply having too much confidence to believe they will receive a job offer very quickly. Rejection can be difficult and harmful to job seekers because it can shatter their confidence levels and reduce their willingness to remain persistent. Rejection can especially be difficult for those who are overconfident because they did not expect any form of rejection.

We can assume that rejection of a job a person thought they would get is similar to that of losing a job a person already had because, in both situations, the people thought that they lost something that was theirs. Two professors at the University of British Columbia found that people who lose their job often face “denial, anger, bargaining, [and] depression” before they can accept what they lost.

In the next section of this paper, I prove interventions for the main behavioral biases of the student job search process: students applying for the wrong positions, students not applying for a long enough period of time, and students letting their emotions get the best of them.

Proposed Interventions

To help graduates apply for positions that are relevant to their major, a simple survey can nudge them in the right direction. Using data from the previous year’s class, we can attempt to predict what the process will look like for the new class; If we actively market and promote this data to them, the new class will be able to have an idea of where they should be applying. The survey that would be sent out to the previous year’s graduates would ask them what their major was and where they got a job. The survey would also show the number of people who got jobs and where, to make it clear which major has a higher likelihood of getting a job at a certain company. This survey from a recent graduating class should be encouraging to the current year class. A behavioral intervention experiment found that “Summarizing the data from 47 experimentally or quasi-experimentally evaluated job search interventions, we found that the odds of obtaining employment were 2.67 times higher for job seekers participating in job search interventions compared to job seekers in the control group, who did not participate in such intervention programs.” While all job search interventions are different, this intervention should increase the likelihood of obtaining employment. This intervention should also ease anxiety among the students because their job success becomes less uncertain to them: they know they aren’t facing significant trouble because the previous class could have faced the same thing and were still successful; they know if the job market for them is strong or not because of this. If the market is bad and the survey results do happen to be discouraging to them, the intervention for emotional encouragement becomes even more important.

To encourage graduates to keep being persistent in their job search and not give up over a short period of time, this survey would also ask the last class how long it took them to get their jobs. A study found that “unemployed youngsters who possess high levels of psychological capital also perceive more control over job search, which is directly connected with their job search intention. Notably, psychological capital seems to be a more beneficial resource for keeping unemployed youngsters engaged in job search in an adverse economic context.” This additional information that the survey provides is important psychological capital because it addresses the issue of students giving up their search after a certain period of time. In other words, the survey’s information would provide more control to the students in their search because they know what to expect. This would then be shown to the new class so that they can also draw conclusions on what they should do next based on how long they have been applying for the job they want. This year’s class may be shocked at how long it took some people with certain majors to get the jobs they wanted and therefore keep applying for jobs instead of giving up.

Alongside providing these students with statistics on how the previous class did, these students also need some form of emotional encouragement. A study by professors from universities in Hong Kong shows that “career-oriented mentoring was…significantly positively related to a number of promotions received and career satisfaction.” The intervention that these colleges would provide for their students would be bringing back a former student that would empathize with the students, and the class would be able to relate to him or her. This mentor would also provide their own personal guidance and how they were able to get a job by the time they graduated. Instead of a physical or live mentor to guide students, videos recorded by previous graduates could also be more effective because of the convenience of being able to watch them anytime. A survey that evaluated the help of online contacts in obtaining employment found that “based on 1322 telephone interviews with unemployed individuals in Germany, we [found] that online social support drives job search behavior. Our results show[ed] that social support derived from new information and communication technology counteract[ed] the adverse effect of being unemployed to a certain degree.”

Potential Experiment

A potential experiment needs to be outlined to see if the proposed interventions would work. In this experiment, a randomized control and experimental group of students need to be used in order to reduce room for error: a non-randomized control and experimental group can yield people with characteristics that may drive the outcome of the experiment instead of the intervention itself.

The control group would be 500 current students who would not receive any form of intervention, and the experimental group would be 500 current students who would. Both of these groups of students are going to be asked follow-up questions throughout their job search process to determine the progression of their success. The sample size of students should be based on the overall class size of the college where the experiment is being performed at. For example, at a college like Case Western Reserve University, where the class size is just over 1400, a sample size of 500 people each for the control and experimental group would be significant. Of this sample size, a small percentage of these people can be unaccounted for in the total experiment’s sample size because of attrition: people deciding not to participate, people getting sick for a long period of time, people lying when following up to see if they have gotten job offers yet, and more reasons why they don’t end up following the all the rules of the experiment that they should. This should be a very small percentage of people due to the appropriate sample size and shouldn’t affect the results in a way where the conclusion would be skewed. Note that these are estimates only. In reality, the appropriate sample size for this experimental design would require power calculations based on the estimated size of the experimental effect.

The next step in this experiment is determining the key questions that need to be asked in the follow-up and how often this follow-up should occur. The key variables of this experiment that should be asked of these students are: What is your major, how many job offers have you gotten, what type of job is it, how long did it take you to get an offer, and how long did it take you to get an offer that you are going to accept. This follow-up will be asked of the students every two weeks to get an update on their progress in finding a job. If we don’t ask this frequently, the experiment would be vulnerable to recall bias: students forgetting information that needs to be recorded in the follow-up. This survey requires that we trust that their answers to the questions are truthful. Asking to send proof of job offers can cause problems as some people may find it tedious or just simply not want to. They also may not be legally allowed to share certain information in that job offer as well.

Conclusion

In this paper, we determined the main behavioral biases that are hindering college students from getting a job right out of college. With the proposed interventions of providing data and a mentor to these students, we can significantly increase the likelihood of employment success. This is really important because we can apply these interventions to colleges all across the United States, which would have a significant impact on reducing employment for young adults with higher education. These interventions could even be applied to graduate school students who are also about to finish school and are looking for a job.

In the future, I hope to collaborate with the University I attend, Case Western Reserve University, to conduct the proposed experiment for my graduating class to see just how helpful our behavioral interventions are. If successful, this study could be applied in a much larger context of societal development; mentors having an emotional impact on students and possibly increasing their productivity is very useful information because mentors can also be used– in contexts other than employment–to increase efficiency in other areas. Similarly to providing the data to students, providing data to workers in a company that shows how others in their positions were able to be promoted and earn more may increase worker productivity, morale, and more. In conclusion, there are many applications for research like this where we can behavioral influence people to make better choices and be more productive.

Works Cited

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About the author

Aryan Mishra

Aryan is a first-year Economics student at Case Western Reserve University. He is interested in concentrating in Behavioral Economics so he can learn more about how psychology and human behavior can be used to solve economic problems.

The post Increasing College Graduate Employment Success appeared first on Exploratio Journal.

1. Introduction and Inspiration

This project is about improving the coffee shop (called Pinghe Coffee) in my high school. I came up with this idea in tenth grade. There are several inspirations that leads to this project. The first inspiration is I am planning to major in business so I would like to enroll an actual business and gain experience through it. The second is I like visiting different cafés because the atmosphere and the vibes it gives to me. I love the orange dim light and jazzy music in the café. Third, there is a coffee shop on campus, so I decide to start off a project which can improve the environment of the coffee shop and the quality of campus life for teachers and students. But, I am a 11^th grade student so I also need to pay close attention to my GPA and academic requirements. I need to balance my time between the school work and the activity.

2a. Marketing Mix

2.a.i Product

The Pinghe Coffee serves 13 different drinks in total. 

There are Americano, mocha, latte, vanilla or hazelnut latte, caramel macchiato, flat white, cappuccino, espresso, mandelin-indonesia, yirgacheffe, the quarter of selected, Osmanthus latte, and lemonade. The coffee shop serves both hot and iced drinks for all of the coffee drinks. The lemonade only have the choice of cold drink. The coffee beans are the same as the ones %Arabic and See Saw used. Other ingredients like milk or syrups are also delivered directly from factories.

2.a.ii Price

The price of the drinks in the coffee shop range from 16 RMB to 22 RMB. The average price for all of the drinks is more likely to be 17 RMB, because there are only a few drinks are a few RMB expensive than other drinks.

2.a.iii Place & Location

The coffee shop is located in the middle of the campus. The school has five main buildings. The high school students mainly work in building four. It usually take a 3-5 minutes’ walk from building four to the coffee shop. 

The coffee shop has two floors.

For the first floor, the entrance is located at the left corner of the coffee shop (Number 1 labeled in pic 2). The take away window is where high school students order coffee (Number 3 labeled in pic 2). The second floor is labeled as number 2 in pic 2.

Then on the right side of the coffee shop, a curve, glass-made alley is connected to the coffee shop. 

The first floor also contain both indoors and outdoors. When you enter the main entrance of the coffee shop, there is an open space. There are some chairs and table stacked at the corner. Then you can enter the actual door of the coffee shop. 

The coffee are all made on the counter on the right side. Milk and other ingredients are stocked on the side of the counter. The tables and the glass ally are located on the left side. Teachers often drink coffee and have chat at the tables. 

The second floor is not directly built on top of the first floor. There is no stairs in interior of the coffee shop to reach the second floor. There is a spiral stair case in the empty space on first floor. The second floor is entirely open-aired and there are no covering on the top. There are only two sets of wooden chairs and tables which are basically unused. 

2.a.iv Promotion

There are little promotion from the coffee shop because the customer base of it is relatively fixed. The coffee shop is not open to the public. It only offer drinks for high school students and teachers on campus.

2.b Regulations

2.b.i Food Policy

Because the coffee shop is operated in a school, so food security is essential. The school is responsible for students’ health condition. The products that are added to the menu need to be from the factory. Mr. Sun is the teacher that is in charge of importing all the raw materials or half-finished product. 

2.b.ii Operational Policy

There are two main operational policies. The first one is students are not allowed to run the coffee shop. Even though eleven and twelve grade students were allowed to make coffee for teachers and students, but due to some conflicts that happened in the coffee shop, students are not allowed to enter the coffee shop anymore. The second restriction is the school won’t hire an additional barista to help the present one in the coffee shop. 

2.b.iii Space Policy

The interior space of the coffee shop is not open to students. It is only open to teachers. The second floor is also not open for students. Except on Thursday, there is a coffee shop club after school. Students can learn how to make coffee in the club. This is the only time that students can enter the coffee shop and they have to sign up in the group chat for attending the club. 

2.c Cost

2.c.i Fixed cost

The fixed cost include several aspects. First, the coffee shop owns a set of coffee machine. Second, the cost of furnishing the interior space of the coffee shop. Third, there are 6 tables and 12 chairs in the coffee shop. Fourth, there are a lot of tools for making the coffee. For example, like iron cups or tools to mix the coffee and such. There is also a shelf that is used to hang the aprons which are used when people are making coffee. 

2.c.ii Variable cost

Variable cost mainly includes the cost of raw materials such as coffee beans, milk and sugar syrups. Besides raw materials, the coffee cups and straws that is provided in the coffee shop is also included in the variable cost. 

We have been unable to collect any data about the amount of these costs.

2.d Staff

2.d.i Manager

The coffee shop is managed by one of teachers called Mr. Sun who is in charge of the food security of the entire campus. Mr. Sun came up with this initial idea of starting a coffee shop in school. The coffee shop only served the teachers and faculty members at first, but the high school students also want to buy coffee, so it is also open to them now. The motivating reason is not to earn profit from students and teachers but is to serve students and faculties coffee and enhance the quality of campus life. 

2.d.ii Employee

The coffee shop is run by a barista every day. He is in charge to make the coffee every day. He gets paid regularly because he is an officially hired employee for the Pinghe Coffee shop. 

2.e Operation

The coffee shop starts serving at 9:00 AM and closes at 4:30 PM in the afternoon when the school ends. The customers walk to the coffee shop and order drinks there. The customers will tell the barista their order and pay the money by scanning the code at the take-away window. After paying, the customers will wait for their drinks at the left of the window. Because the coffee machines can only make two cups of coffee at a time, so customers sometimes have to wait for a long time.

When the barista receive the orders from the customers, he will start to make coffee on the machine. He will make two cups at a time. After the coffee shop is closed, the barista will clean the table and all of the machines. 

3. Proposal of Coffee Shop

3.a Survey

In order to better enhance the campus life quality of both students and teachers, we conduct two surveys to know the customers’ expectations for the coffee shop. The big proportion of the questions in the two set of surveys are generally the same, but they are still some questions that are varied.

There are 208 students in total answered the questionnaire. 90.08% of the people says there are rather limited choices for the drinks served in the coffee shop. The main products are coffee but non-coffee drinkers have limited choices. Also, students would like other choices like juice, bubble tea, and so on. 

Also, the average consumption of the coffee is two cups per week for an individual. There are more than 600 high school students and more than 200 teachers in the entire campus, so the consumption of coffee is really huge. 

3.a.i Student survey

There are 75.13% of the students say they would like to use the second floor of the coffee shop if it’s opened to them for holding different activities. 

3.a.ii Teacher Survey

For the teacher survey, we asked the teachers if they would like to use the first floor for holding meetings with parents or other teachers, 78.38% of the teachers say yes.

Second, we made two reports for students and teachers respectively. Then we collect the data of these surveys and we get support of our classmates and teachers. Some really like the idea of improving the coffee shop. According to the data I collected from 208 people (including high school students and teachers) on campus, the average consumption of the coffee shop is two cups per week for an individual. There are more than 600 high school students and the teacher in the entire campus, so the consumption of coffee is really huge. But there also people who prefer other beverages (like juice or soda) over coffee drinks. 

3.b.i Pros of current situation

1. Large and stable customer base. 

We have a fixed customer base because the shop is only open for Pinghe teachers and students. The coffee shop don’t need to worry about how to attract customers to purchase products. 

2. No rent fee

This saves the total cost for the coffee shop. 

3. The quality of the coffee served is acceptable 

There is only a small proportion of people who complained about the taste of the coffee. 

3.b.ii Cons of current situation

1. Relatively narrow menu 

The menu is unable to meet the demand of some students and teachers. Since there is limited varieties of drinks, some people may not buy beverages from the shop. 

2. Long waiting time

There is always a long line outside the coffee shop during the peak hour (lunch break). Students sometimes need to wait up to 15 minutes to get one cup of coffee. This also cause the problem that people are cutting through lines which make the waiting time even longer. 

3. Low usage of space

There are too many unused space in the area of coffee shop. The space on the first floor outside the coffee shop and the second floor are unused. 

4. Low quality of customer service

Some of the students complain about the attitude of the barista. The barista seem unfriendly to most of the students. 

3.c Proposals for Coffee shop

There are several struggles with the current situation of coffee shop. First, it is hard to convince Mr. Sun to improve the coffee shop with different strategies. He is unwilling to make any improvements with the coffee shop because there are no big problems of the coffee shop and it is running smoothly. So, he believes these improvements are not necessary. Second, there are a lot of restrictions and policies for running the coffee shop. A lot of ideas are impossible to carry out. I gave several proposals to Mr. Sun of the coffee shop. 

3.c.i Products Portfolio

The current situation of this proposal is rather limited, but Mr. Sun already agreed to add cake into the product portfolio of the coffee shop. He says he can ask factories to import the cakes directly to school and start to sell them in the coffee shop. But the range of drinks is still limited. Besides coffee, they only have the choice of lemonade. So, if other drinks like juice or bubble tea are added to the product portfolio, the coffee shop may increase its sales revenue even more.

3.c.ii Online Platform Reservation

The second proposal is to have an online reservation platform for ordering coffee. I wrote out the proposal with one of my classmates. 

“We are very willing to create an official account and mini program for Pinghe Coffee or Coffee Company. When you need to buy coffee, you can open the applet between classes, select the coffee you want, temperature, sweetness, and pick-up time, etc., and note your name. The barista can make coffee based on these in advance and wait for students or teachers to come receive. If a classmate or teacher still chooses to buy on-site, we can provide a QR code or a number machine (similar to Starbucks) at the ordering window, so that we will not delay the appointment due to the priority of making coffee for classmates on-site It’s time to get the coffee classmates at a specific time. For example, student A came to the cafe at 12:43 and bought a cup of coffee, but at the same time classmate B made an online reservation and will pick up the coffee at 12:45. So, the barista may make student A’s coffee first but after making his or her coffee, he may delay the order of student B. The barista may only start to make student B’s coffee at 12:45. 

This also solves another problem: some students or teachers accidentally forget to pay when buying coffee. For example, if student A wants to buy a glass of ice caramel macchiato at 12:25 noon, then he can open the mini program when the second class in the morning is over and choose caramel macchiato-ice-12:25PM , And pay directly in the applet, and then wait until 12:25, go to Pinghe Coffee to receive it. In this way, the coffee making time during the peak period will be evenly allocated to other periods. Students no longer need to wait a very long time, but only spend 10 seconds to show their proof of purchase and receive coffee. In this way, the three problems mentioned above will be effectively solved.

Of course, some people may worry about whether the coffee will deteriorate after a long period of time. Most of the takeaways in the market can choose the delivery time, and the merchant will not make it the moment the order is placed. Therefore, Pinghe Coffee can also use this system to complete coffee production within 10-20 minutes before the time of collection (different delays depending on the variety and temperature, depending on the specific situation). There are also people who are worried about whether it will be wrong, so after the user pays, we will provide a meal code with information such as the type of coffee purchased, and the barista can provide the correct coffee to the students based on this information. And we can use the Starbucks method to fill in a paper label on each cup, showing the name of the student or teacher, the name of the drink, the order time and other information.

Mobile phone scan code ordering is already an ordering mode that many milk tea shops or coffee shops like to use. Its characteristic is that it is convenient. Users only need to choose the variety they want and pay, which can solve a series of problems such as queuing. Wait for a long time. Therefore, Pinghe Coffee can imitate this model in order to better achieve its goals and serve every teacher and student of Pinghe.”

3.c.iii Use of Space

The third proposal is to increase the use of the space of the coffee shop. As I mentioned above, no one use the second floor and the open space on the first floor of the coffee shop. I think we can make some changes and decorations of the second floor of the coffee shop. There should be an entire cleaning for the balcony too. 

3.c.iv Internal Renovations

The fourth proposal is renovate the interior of the coffee shop. I think the coffee shop should create an atmosphere of warmth to the customers who visit and sit in there. There can be an adjustment to the lighting, the floor, and a rearrangement of the furniture of the coffee shop. 

3.c.v Birthday Parties

The fifth proposal is about celebrating birthday parties in school. I want to set up an online reservation and people can order small cakes in advance from that platform. So, we can prepare and throw a little party for the people who order the service on the platform. 

3.c.vi Employee Training

The last proposal is to have an employee training for the barista. I also receive a lot of comments on the services of the barista. The students complain about the barista’s attitude when he serves them. So, I believe an employee training is necessary. 

Conclusion

In conclusion, this is the process I did from coming up with the idea to giving proposals and talking with the teachers. This process is actually applicable to similar situation when you want you want to improve the operation of a coffee shop or start one in you school. This is a methodology that you can use to enhance the operation of coffee shops specifically in school. 

Step 1: Get excited!

If you have this idea or similar ideas in mind, get started! The most important step is to get the idea to the ground. Or else, the initial passion you have might disappear.  

Step 2: Data

Get all the information and data you need! Go to the coffee shop on your campus (if there is one) and see how it looks like. Take a lot of photos and videos of the store and make some notes or drawings. Make a list of products they offer and record their prices. Ask their employees or manager about the daily operation in coffee shop or how they import their raw materials such as coffee bean. Then, you can start making surveys for the customers of the coffee shop and ask them about their demand and suggestion for the coffee shop. Make analysis based on the survey and make your next move. If you want to start a new coffee shop, you may need to conduct a survey first and then settle rest of the things after the analyzing the surveys. 

Step 3: Proposals

Came up with your proposal and start to write a plan of how to execute the plans out. You need to include details of the proposals you want and also the reasons why you came up with these proposals. 

Step 4: Talk

Be prepared to talk or convince the managers who are in charge of the coffee shop for the improvements. You need to be ready to have several rounds of communication with the manager to settle things like the timelines, operations, and all sorts of details of how the coffee shop is going to work. 

About the author

Sijia Zhang

Sijia is currently an 11th grader at the Shanghai Pinghe school. She has studied business for about 2 school years and has also participated in business competitions, such as FBLA. Many of her family members have started their own business, so Sijia is interested in starting one too. She also likes Latin dance, singing, and drawing.

The post On-Campus Coffee Shop Business Proposal Plan appeared first on Exploratio Journal.

Introduction

The United States is at a critical point in its history. Communities and societies are falling apart. Relationships across America are fraught at best and downright violent at worst. Poor education and infrastructure are impeding progress and hastening the nation’s economic backslide. Meanwhile, a deadly virus has put lives on hold and the looming specter of climate change has made its presence known across the globe. Vast systemic changes are not only important, they are necessary for survival. The Brookings Institution put forward the idea of “recoupling,” pointing out that the economic future of the nation is inextricably linked to its societal future. Both are important to the future of the U.S. as a great power. However, when implementing economic policies, the potential societal or environmental impacts of said policies are often ignored. The Brookings study, “How COVID-19 changed the world: G-7 evidence on a recalibrated relationship between market, state, and society,” points out that, in theory, the result of economic progress should be better quality of life for all citizens of a nation, but policymakers ignore social success in favor of purely economic gain. Using this Brookings study as a basis for analysis, this paper looks in depth at the challenges the U.S. faces with the inward and outward solidarity of its citizens, economic agency, and the environment, both as they have resulted from the COVID-19 pandemic and from more ingrained, long-standing issues with United States policy. It then goes further, explicitly laying out the economic consequences for continued decline in these crucial factors of social prosperity. Having strong solidarity among a nation’s citizens, whether directed inwardly towards those in their own community or country or outwardly towards those outside of their nation is important for policy implementation. Providing agency, freedom of life choice, and opportunities for advancement to citizens allows for economic growth; failing to do this and to provide the necessary public goods for success can inhibit development and progress. Environmental deterioration not only harms future generations through the loss of land and culture, it will also be economically devastating. There is no easy way to solve the many problems the United States is encountering. This paper explores the challenges the U.S. faces and explains how continued inaction is damaging. These issues require innovative, manifold solutions which will involve valuing long-term benefit over individualistic short-term gains.

Inward Solidarity

Inward solidarity is defined by the Brookings Institution Recoupling Index as “the need for social belonging and embeddedness” as it is directed “to one’s national, religious, ethnic, racial, or class groups” (Miranda & Snower, 2021, p.4). Inward solidarity can undoubtedly be harnessed for good, but at the same time it can contribute to nationalism and tribalism. This is particularly dangerous in an event like a global pandemic, when cooperation among social groups and nations is exceedingly important. Inward solidarity can be estimated in a variety of ways using different metrics. The World Happiness Report’s metric of social support, which is based on citizens’ perceptions of the care they receive from their community, paints a picture of how inward solidarity has changed throughout the pandemic and may continue to change in the future. How does the United States shape up relative to its G7 peers according to this metric? The Sustainable Development Solutions Network (SDSN) reports that, in 2020, despite being a world leader in GDP per capita, the U.S. was exactly in the middle of the group for social support. 2021 was the same story, though, interestingly, the U.S. saw a moderate increase in the number. This seems to indicate that the pandemic was a centripetal force which brought social groups together on a relative basis.

Based solely upon the social support metric, one could conclude that the U.S. has fairly strong (though not excellent) inward solidarity. However, a brief glance at the daily headlines in the United States would seem to rebut this analysis. The January 6^th, 2021 riot at the U.S. Capitol, for example, portrayed a deeply unsatisfied and distrustful mob attempting to overturn the result of an election decided by their fellow citizens (Barry et al., 2021). It revealed a fundamental division among Americans seemingly in direct contradiction to the feeling of “social belonging and embeddedness” which inward solidarity refers to. Thus, perhaps it is better to look at other indicators to measure inward solidarity. Inclusiveness is also a key factor in inward solidarity as it pertains to the nation as a whole, and the U.S. sees its numbers trail far behind all of its G7 counterparts in inclusiveness metrics. According to the Social Progress Imperative, an international data collection not-for-profit, the United States ranks 27^th globally in inclusiveness with its score of 65.04/100. This metric factors in equality in positions of political power with regards to gender, social group, and socioeconomic status, and discrimination and violence against minorities and members of the LGBTQ+ community. Shockingly, the United States, with its claims of “liberty and justice for all” ranks 103^th globally for its performance regarding violence and discrimination against minorities (Social Progress Imperative, 2021).

The inclusiveness metric shows a very different side to inward solidarity in the United States. So why is there such a disconnect between Americans’ opinions of the social support they receive and the reality of life in the U.S.? The nature of the way inward solidarity is measured may have something to do with it. Having unity among “religious, ethnic, racial, or class groups” doesn’t necessarily correspond to strong national unity. The question then becomes whether these two ideas are inherently incompatible in a massive multinational state like the U.S. It is true that many of the nations with the highest marks for social support (e.g., Finland and Denmark) have a high degree of ethnic and religious homogeneity (Nokkentved, n.d.; SDSN, 2021; World Population Review, 2021a). At the same time, Switzerland, ranked third in social support in 2021, is a multilingual state with a variety of regional dialects and distinct ethnicities that still manages to foster a strong national identity (Helbling & Stojanović, 2011). This indicates that while the diversity of the U.S. may make it more difficult to encourage national unity, it is still quite possible to have an inclusive and supportive national climate.

A lack of national identity can have a harmful impact from an economic perspective as well. In the context of the COVID-19 pandemic, this has manifested itself in a high rate of vaccine hesitancy. An April 2020 study found that only 56.9% of U.S. adults planned to get vaccinated if a vaccine became available, compared to similar studies showing the same number at 79% in the United Kingdom, 77.3% in Italy, 76% in France, and 70% in Germany (Sallam, 2021). Americans reported much higher levels of mistrust of the vaccine itself, as well as general antivaccine beliefs (Bloomstone et al., 2020). In addition, the high levels of vaccine hesitancy again point to the lack of national solidarity in the United States. For young, low-risk individuals, getting vaccinated serves largely to help high-risk citizens by curbing the spread of the virus; it also boosts the nation’s economy as a whole as it allows businesses to reopen safely with fewer restrictions (Yan, 2021). A nation with higher inward solidarity among its citizens will be more likely to have high vaccination rates. The U.S., on the other hand, has the lowest vaccination rate of any G7 nation and one of the lowest among the G20, as of summer 2021, which could imperil its ability to recover and lead to yet another COVID surge (Oxford Martin Programme, 2021).

Nor will the end of the pandemic result in an end to the U.S.’ struggles with national unity. If Americans don’t feel a sense of connection with and responsibility toward their fellow citizens, they will be more resistant to making sacrifices for the greater good. Paying taxes provides a good example of this. While the United States ranks fourth-to-last in tax rates among OECD countries (almost 10 percentage points below OECD average), and taxes are used in large part for services which benefit Americans in their day-to-day lives, American citizens are disproportionately resistant to paying taxes (Mann, 2010; Tax Policy Center, 2020). Free enterprise and tax resistance have been a part of American history since the nation’s inception, but this cannot account for all of today’s anti-tax movements (Mercantini, 2015). The U.S.’ strong culture of individualism (the nation is often ranked the most individualistic country in the world) is a significant contributor to this and is harmful to the nation’s future cohesion and economic performance (Marsh, 2021). From 2011-2013, the IRS found that the federal tax gap (i.e., the disconnect between taxes owed versus taxes paid) was nearly $400 billion, accounting for about 2% of the nation’s GDP at the time (U.S. Internal Revenue Service, 2021). However, the issue extends beyond just the collection of taxes. The U.S. also has one of the most unequal tax systems among the OECD nations, particularly after recent corporate tax cuts (Chatzky, 2019). A recent book found that the richest 400 U.S. households pay a total tax rate of only 23%, while the bottom half of households pay a rate of 24% (Picchi, 2019). This inequity results in the U.S. having the fourth highest wealth gap among OECD nations (Chatzky, 2019). From 1980-2013, the top 0.1% of earners saw their average real income increase by 236%, while the median U.S. household actually saw their income decline between 1989 and 2013 (Stiglitz, 2015). The U.S.’ Gini coefficient is significantly higher than any other G7 nation, indicating wealth is much more concentrated in the hands of the rich (World Population Review, 2021b). All of these factors serve to harm both the economy and inward solidarity among U.S. citizens.

This money which is not collected from taxes is not being spent by governments on roads, security, infrastructure, education, and many other public goods crucial to a strong, developed economy. Among the OECD, the United States has the eighth lowest overall government spending as a percentage (38.1%), which is also the lowest number among G7 nations (OECD, 2020). In recent times, this unwillingness to spend taxpayer money for the greater good has led to long, drawn-out political battles over healthcare, education, and infrastructure bills. A 2021 study by the American Society for Civil Engineers rated U.S. infrastructure at a C-, well below its peers in the developed world. By 2040, the U.S. will most likely rank second to last in terms of spending on infrastructure among the G20 nations. Meanwhile, investments in infrastructure may increase GDP by as much as three times their value (McBride & Siriparapu, 2021). A U.S. House Committee on the Budget (2019) report found if infrastructure conditions are not improved by 2025, $3.9 trillion in GDP and 2.5 million jobs could be lost. Education is another key public resource that governments provide. The U.S. also ranks around the OECD average in terms of educational attainment and test scores (OECD Better Life Index, 2021). The boost in human capital provided by a strong education system has significant economic benefits that cannot be overlooked (Grant, 2017). If the United States wants to remain one of the world’s strongest economies, it must improve on these numbers, but it will be unable to do this if its citizens do not feel unity with their fellow Americans.

Outward Solidarity

The concept of outward solidarity (the feeling of connection with or responsibility toward those outside one’s own nation or social group) is similarly important. This is true not only in the time of COVID-19 when globally coordinated efforts are necessary, but also for the future of the world as nations must work together to tackle issues like climate change and international conflict. Miranda and Snower (2021) found that while Canada, France, Germany, and the U.K. saw significant declines in outward solidarity, and Italy and Japan saw increases, the U.S.’ outward solidarity score remained about the same from 2019 to 2020. Additionally, the U.S. had the second highest overall score among the G7 nations, after Canada. In this number, another disconnect is seen between public opinion and the actual realities of U.S. policy. Around two-thirds of the U.S. population is in favor of the U.S. being involved on a global scale and providing aid to developing nations, but as of 2019 the U.S. dedicated only 0.2% of its budget to foreign aid, one of the lowest numbers among wealthy nations (Ingram, 2019). According to the Recoupling Index, a relatively high “percentage of [Americans] believe that the city or area where they live is a good place for minority groups,” contradicting the U.S.’ poor rankings in inclusiveness shown in the previous section (Miranda & Snower, 2021). Meanwhile, anti-Asian hate crimes increased by 150% from 2019 to 2020 and police killings of Black Americans continued to shock the nation (Meier, 2020; Yam, 2021). So public opinion doesn’t really reflect reality. 

The U.S. struggles with inclusivity and minority rights, but what effect does this have on the nation’s ability to cooperate outwardly with other nations and be a part of the global community? During COVID, the U.S.’ outward solidarity changed in a variety of ways. One initial reaction seen in the United States was that of blaming and lashing out against other nations. For example, people began to refer to the pandemic as the “China Virus” as a way of placing the blame on another nation instead of putting aside differences and cooperating to curb the spread of the virus. Additionally, the arrival of the coronavirus coincided with an increase in anti-Asian violence (Vazquez, 2020). U.S.-China tensions defined the global response to the pandemic and put countries in a difficult position of trying to appease and not anger both nations. Around the globe, the U.S.’ failure to adequately combat the pandemic and coordinate its response with other nations are seen as a potential harbinger of the nation’s fall from global supremacy (Ba et al., 2020). Anti-China tensions were not the only indicator of a decline in outward solidarity in the United States. In the early months of 2021, the U.S. faced global criticism for not sharing its stockpile of vaccines with poorer nations, as the pandemic continued to rage. This failure threatened to fuel the development of more COVID variants in low-income nations with high population densities (González and Miller, 2021). Eventually, the U.S. did pledge to share its surplus doses, but only to try to counter similar programs of nations like China and Russia and improve its global relations (Keith, 2021). The U.S.’ response to the pandemic, while not all bad, may have greatly harmed its outward solidarity and global standing.

The future of outward solidarity in the United States is complex. Donald Trump’s election in 2016 was seen as a clear sign of anti-globalist sentiment in the U.S (Ip, 2020). Under the Trump administration, the United States took steps towards a more nationalistic identity, which most likely caused Americans to feel a decrease in outward solidarity. For example, the U.S. pulled out of the Paris Climate Accords (ratified by 189 other nations) in 2017, undoing a global step to prevent climate change. This action by the U.S. represented a lack of trust in the agreement (McGrath, 2020). By pulling out of an agreement like this, the U.S., a global leader, gave other nations the green light to not take responsibility and not meet the outlined climate goals, and this will have a devastating impact for years to come (Leahy, 2021). Trump also jettisoned the Trans-Pacific Partnership, signaling a more confrontational policy toward international trade and creating friction with its allies in the region (Mauldin, 2021). President Biden promised to reverse some of Trump’s policies, but he faces resistance from a nationalist movement of the American public (Ip, 2020). Biden has signaled the U.S. wants to improve its outward solidarity by re-entering the Paris climate agreement and the World Health Organization, and around the world there is hope that Biden will revive U.S. leadership (Huiyao, 2021). It remains to be seen whether Joe Biden, described as the “last, best hope for globalists” will be able to save American solidarity with the rest of the globe, but it is clear that U.S. engagement and cooperation is needed for accomplishments to occur on a global scale (Babones, 2020). Maintaining high outward solidarity will remain crucially important for the United States in the coming years in order to face future challenges and maintain the nation’s global standing.

Agency and Institutions

Agency, or a person’s ability to achieve advancement in society, is vital to a growing economy. A society with strong agency has the capability to match more people with good, innovative jobs that will add value to the economy and facilitate progress. The Brookings Institution’s agency index factors in life expectancy, confidence in empowering institutions, freedom of life choice, and vulnerable employment. By that measure, the United States ranks 6^th out of the G7 countries, though it saw a 3.0% increase from 2019 to 2020. This indicates that the U.S. was rebounding, but the increase in agency is at least in part a result of the fact that the U.S.’ numbers were very low to begin with. So, Americans generally do not feel as though they can get ahead to the same extent that citizens in other G7 nations do (Miranda & Snower, 2021). How does the U.S. measure up to other nations across the globe in other agency metrics? One way of estimating agency is to look at employment data. Interestingly, 2020 employment data (in terms of the percentage of population aged 15-24 working) corresponds closely to the agency index itself among the G7. Italy is lowest, France and the U.S. are fairly close in second/third to last, and Canada, Germany, and the U.K. rank near the top (OECD, 2021). Employment data is clearly correlated to agency, because if fewer people are employed or have opportunities for employment, they will feel less empowered and less able take control of their lives. Further, the U.S. has been found to have particularly low opportunity for economic advancement. Stanford economist Raj Chetty finds that those born into households in the bottom fifth of the U.S. in terms of income have only a 7.5% chance to advance to the top fifth of the income distribution, compared to 9.0% in the United Kingdom and 13.5% in Canada. This number declines further for those born in neighborhoods with particularly high concentrations of low-income residents (Chetty, 2015).

Access to quality education, overall health of the population, and many other factors affect the potential citizens will be able to reach in their nation as well. Having strong human capital (the knowledge and experience that people gain over time from their job and education) is one of the key drivers of economic growth. Improving human capital not only makes the nation more efficient, it also makes citizens feel a stronger sense of agency and freedom of life choice, as more job pathways for advancement are available to them (World Bank, 2020). COVID-19 had a detrimental impact on human capital in a number of ways. First, children lost significant time in school, and the decline in quality of education due to the lockdown will have a lasting impact. More than one billion children were out of school for at least some time during the pandemic. This loss of learning could account for a $10 trillion decrease in future earnings. As COVID overwhelmed health care systems, particularly in developing nations, access to quality medical services decreased. People dealing with illnesses were unable to receive proper care, so they ended up losing valuable time. Overall life expectancy decreased as well. The pandemic also exacerbated domestic violence, particularly against women, a phenomenon which, in addition to causing significant trauma to those who experience it, continues to worsen inequalities in human capital. Generally, the time not spent working and gaining job skills due to lockdowns or lay-offs will take a lasting toll on human capital and agency in the United States and across the globe (World Bank, 2021). Pre-pandemic numbers help demonstrate how the U.S. stacks up in human capital. According to the Human Capital Index, the next generation of workers will attain only 71% of their potential future earnings in the United States in 2018 (World Bank, 2018). In 2020 (before the impacts of COVID-19 were accounted for), this number was down to 70% (World Bank, 2020). The U.S. ranked 35^th out of the 174 nations accounted for in the HCI, lower than any other G7 nation. This indicates that workers in the U.S. are less likely to reach their full potential relative to other highly developed democracies. 

The Human Development Index (HDI) is closely related to agency and the concept of human capital; here too the US fares poorly. The HDI measures health, knowledge, and standard of living to demonstrate a nation’s ability to achieve progress and advancement for its citizens (WHO, 2021). The overall HDI declined for the first time since data collection began during COVID-19. Pre-pandemic, however, in 2019, the U.S. fared much better in the HDI compared to the Human Capital Index. It ranks 17^th overall and 4^th out of the G7 nations. However, the U.S.’ position is 7 places lower than its global rank in GDP per capita. Further, the United States sees its position fall 11 places further down if inequality in income, life expectancy, and education are factored in. The top 10% controls 30.5% of wealth, placing the U.S. second to only Chile among nations in the top 50 in HDI. The U.S. also ranks 46^th in the Gender Inequality Index, closer to nations like Bahrain and Kazakhstan than its highly-developed peers (Conceição, 2020). Its progress towards gender equality has essentially stopped, with major issues arising from a lack of opportunities for economic and political participation, as well as a continuing wage and income gap (World Economic Forum, 2020). All of this inequity in human development is devastating to agency in the U.S. It means significant portions of the population are less likely to become well-educated, experienced, and healthy members of the labor force, and thus less able to contribute to the overall well-being of the economy and society.

Institutions are crucial to sustaining agency and freedom of life choice in a nation. The strength and equality of governments, justice systems, and laws have been shown to have significant effects on economic performance in nations. Institutions in a nation have the capability to establish, secure, and regulate industries to varying degrees, depending on their quality, efficacy, and power. A study by economists Dani Rodrik and Arvind Subramanian (2003) found a clear, direct relationship between institutional quality and GDP per capita which was not seen with a variety of other indicators. They proposed the “primacy of institutions in economic development,” directly tying agency and empowerment as it is received from powerful institutions in a nation to that country’s economic performance. Americans specifically have shockingly low confidence in many of the nation’s most powerful institutions. As of 2021, only 12% of Americans have high confidence in Congress, compared to 51% who have very little or none. Only 32% of Americans have good confidence in public schools, and 38% feel a high degree of confidence in the presidency. Similarly low numbers have been seen over the past 5-10 years, showing a clear lack of faith in institutions in the nation (Gallup, 2021). 

The World Justice Project’s Rule of Law Index (2021) finds that while compared to the world as a whole, the U.S.’ institutions are fairly strong, it lags behind its regional counterparts and other highly developed nations. It ranks 27^th out of 139 countries surveyed globally, 20^th out of the 31 nations in its EU, EFTA, and North American regional group, and 27^th out of 46 nations under the high-income designation. U.S. citizens feel low confidence in their government, and the nation struggles with Open Government and Absence of Corruption, both of which declined from 2020-2021. Furthermore, the justice system sees severe discrimination and bias. The U.S. civil justice system ranks 122/139 globally in absence of discrimination, and 126^th in access and affordability. The U.S. also has one of the least impartial criminal justice systems, well below the global average. Numerous studies show bias against Black Americans and other minorities in policing (Balko, 2020). For example, 1 in 23 Black adults are on parole or probation, compared to 1 in 55 adults in America as a whole, and Black males receive, on average, 19.1% longer sentences than white males who commit the same crime in similar situations (Horowitz & Utada, 2018; U.S. Sentencing Commission, 2017). When the justice system works only for some and denies many fair treatment, people lose confidence in the institution. This is dangerous because the strength of society depends on people trusting and buying into institutions. The justice system has the potential to increase agency, but when it works in a biased and discriminatory manner, it takes away agency from certain groups. Having empowering institutions which work to help people get jobs and have healthy and productive lives provides a boost to both agency and the economy as a whole. 

Environmental Sustainability

Environmental sustainability is key to maintaining economic growth in the long run. As a major economy and a large center of population, the United States consistently ranks among the nations with the highest total greenhouse gas emissions (Social Progress Imperative, 2021). More telling, however, is the U.S.’ performance on more comprehensive environmental metrics relative to other highly developed nations. In 2019, the United States emitted 16.06 tons of CO₂ per capita, more than any other G7 nation, and third most among G20 nations (Ritchie & Roser, 2021). Yale’s Environmental Performance Index, which measures nations’ relative completion of their sustainability goals, places the United States second-to-last among the nations of the global west. Point-by-point, the U.S. ranks dead last in the western group in Environmental Health and Waste Management, third-to-last in Sanitation and Drinking Water, and in the bottom third for almost all other indicators (de Sherbinin et al., 2020). Past data demonstrates that this is not just a Trump-era trend, either. According to the 2014 EPI report, the U.S. would have ranked third-to-last in that same group (de Sherbinin et al., 2014).

The COVID-19 pandemic has had a variety of impacts on the environment across the globe. For example, lockdown orders significantly reduced the use of greenhouse gas emitting transportation, and outbreaks have forced supply-chain shutdowns that have put large, polluting factories out of commission either for a short time, or for good. In the United States, production of greenhouse gas nitrogen dioxide fell over 25%, while overall air pollution in New York City fell by 50% compared to the 2019 mark. Water and noise pollution have similarly fallen as major polluters have been limited by the pandemic. At the same time, the coronavirus pandemic has brought about negative environmental effects, particularly related to waste, from the increased production of medical equipment like masks and gloves to the shutdown of recycling programs that were crucial to helping reduce waste from trash deposited in landfills (Islam & Rume, 2020). 

It remains to be seen whether the trends initiated by the pandemic will have any lasting, long-term effect or if they simply reflect a short-term disruption. At first glance it may not seem as though the pandemic would cause long-term environmental change. However, a Global Workplace Analytics (2021) survey shows that, particularly as the pandemic continues to drag on, workers who have adapted to the remote work environment will be increasingly likely to continue this practice. They estimate that 25-30% of workers will still work digitally for at least half of the week by the end of 2021, up from approximately 3.6% pre-pandemic. The subsequent reduction in commuter traffic (and therefore pollution) could have an effect comparable to eliminating the entirety of commuter traffic from the state of New York. At the same time, however, COVID caused electricity use to increase across the entirety of the United States due to people working from home, anywhere from 12.7% in New York to 3.9% in Florida (Nikolewski, 2021). As 40% of energy production is used for electricity generation and a significant percentage of this energy production still uses non-renewable, fossil fuel-emitting resources, this has the potential to offset some of the pandemic’s positives (EPA, n.d.a). Also, fear of spreading the virus has led to a decline in the number of those riding public transit, with 75% of transit riders decreasing their usage of such services according to a study in Transport Policy (Bouzaghrane et al., 2021). Mass transit is an important way to reduce emissions, and the pandemic has disrupted its regular usage. A hybrid environment in which workers stay home for part of the time and go in to work for the other part of the time could be even worse, because it will maintain the higher energy usage at home while also increasing emissions from commuting and workplace energy consumption.

The pandemic will leave a complex environmental future, but regardless of the outcome it is clear that we cannot delay taking strong action to prevent climate change. Overwhelming evidence shows that continued inaction will soon have devastating consequences (Stern & Oreskes, 2019). Thus the question remains, why does the United States continue to lag behind its counterparts in its pursuit of environmental sustainability and what does this mean from an economic perspective? Why do we sacrifice future gain for short-lived happiness? Economist Paul Krugman (2020) refers to a “cult of selfishness” in American society which is endangering our future economic well-being. This is rooted in the widespread belief that the effects of climate change are far off in the future, so sacrifices do not have to be made in the present. So far, most Americans have not had to grapple with severe consequences of the climate crisis, though climate change-induced disasters are ever on the rise (Kluger, 2018). 

Another impediment to U.S. environmental sustainability efforts is that policymakers are uninformed of the wide-ranging potential economic costs of climate change. A London School of Economics (2019) study points out that due to the unprecedented nature of this event in human history, potential impacts are often understated or not fully understood. Economic changes are often referred to in GDP or dollar value, which makes it difficult to quantify effects like ecosystem destruction, habitat loss, and loss of human lives. It is impossible to know what the exact magnitude of the damage will be, but statistics paint an increasingly clear picture of where the world is headed. 

For example, conservative estimates place the number of coastal inhabitants who may be affected by flooding at anywhere from 35 to 125 million, and one billion people may live at elevations less than 10 meters above sea level by 2050. Given current warming patterns, the sea level will most likely rise 2 meters by 2100, making massive infrastructure investments critical (deFries et al., 2019). In the Netherlands, for example, where more than half of the population lives below sea level, $5 billion worth of flood prevention infrastructure has already been built out of necessity, and even more will have to be built in the future. American cities like New Orleans, Louisiana, and Charleston, South Carolina have spent hundreds of millions of dollars on infrastructure as well in response to damaging floods and storms (Morrison, 2019). As major hubs of transportation and trade are often located in coastal locations, damage from flooding or major weather incidents will be incredibly disruptive to economies. One-third of the world’s population will most likely experience severe heat waves with some regularity if temperatures rise by 2°C, according to the most conservative estimates. Some effects are already here: the United States has seen the number of heatwaves triple nationally in recent decades, and from 1996-2016, they accounted for 10,000 U.S. deaths (Gawthrop et al., 2020). A heat wave in Europe in 2003 accounted for 30,000 deaths, loss of crops and livestock, and the shutdown of nuclear power plants providing key electricity (Britannica, n.d.). More frequent and severe future heat waves will cause food scarcity and widespread economic disruption.

 The United States needs to take immediate action on climate change in order to avoid significant economic consequences. The U.S. continues to trail the rest of the world in its environmental sustainability goals. While the COVID-19 pandemic may have had a moderate impact on the environment globally, it will not be significant enough to stem the tide of climate change. It is important for policymakers and citizens to realize the economic issues that will arise if no action is taken to prepare for the coming problems.

Conclusions

The United States’ social and economic troubles are far from over. There is no easy solution to the variety of issues that the U.S. faces. How can the U.S. foster national unity and bring its citizens together, while avoiding nationalism and restoring citizens’ feelings of solidarity with the rest of the world? What is the best way to empower all Americans and deal with entrenched institutional problems? Is it too late to reverse the trends of climate change and stave off the economic disaster that could arise? The U.S. must now confront these crucial questions and work to reverse the global decline it has faced in recent years. It remains to be seen whether Americans will be willing to make necessary sacrifices and change their mindsets for the greater good. In the meantime, fundamental changes must be made on the national level, whether in education, public service, or environmental policy.

There is no silver bullet, but strong evidence suggests changes to the education system are a key component of building a better future. The impacts of improving education are wide-ranging. First, providing programs that would allow for stronger and more equitable schools would undoubtedly improve agency, particularly the human capital aspect of it. Better, more equal, education makes many more life paths available to each individual (Vilorio, 2016). A 2017 report by Mark Dynarski of Brookings finds that the U.S.’ education system continues to hold back economic growth and future potential, and limits opportunities for advancement for lower-income Americans. Ramping up investment in education would boost the economy from the most fundamental level, and help the U.S. become a center for innovation and growth once again. While short-term educational investment (e.g., one-time loans to struggling schools) tends to only replace the spending that would have otherwise been undertaken, long-term investment (e.g., changes to a state’s education finance system) tends to increase years of schooling and lead to more successful adults. Effecting these long-term changes will require considerable spending, but this spending can yield substantial economic benefit (Dynarski, 2017). An increase in spending of $3,400 total from kindergarten through 8^th was shown to translate into an additional $5,350 in increased earnings. Not even factoring in any other non-earnings-based gains, the benefit-cost ratio of educational spending exceeds 1.5; this demonstrates the potential for a huge economic impact if spending is more widely distributed (Lafortune et al., 2018). Ensuring that everyone has the same opportunities can help build national connectedness. As people branch out and interact with new people through school, inward solidarity could improve. Education and solidarity have been linked, as better educated individuals are more willing to make contributions to society, such as paying taxes to support less-fortunate Americans (“Educated voters’ leftward shift is surprisingly old and international,” 2021).

On a deeper level, the U.S. should invest in education which helps students learn how to think critically. In an education system that still emphasizes the memorization and test scores, the importance of critical thinking can easily be overlooked. However, having upcoming generations learn how to be aware of the world around them and make educated, pragmatic decisions could positively influence the future of United States economically and societally. The agency benefits are clear. People will be able to analyze their lives and careers to maximize both their own satisfaction and their contribution to the United States as a whole (Towler, 2014). It also has significant potential to reinvigorate the spirit of national unity and counter the current divisions in the United States. Instead of believing and propagating all of the divisive, reactionary rhetoric and unverified information circling the internet, Americans who are taught to think critically may reject the first narrative presented to them and look at both sides of an issue. It will promote positive dialogue and be a first step towards a more united America. A study for the World Inequality Database points out those with lower levels of education tend toward the far right of the political spectrum, while those who are able to attain a college degree are more centrist. Education, and the critical thinking skills that come with it, would lead to a population less likely to blindly follow the agenda of one major party, and more likely to form their own opinions and find common ground with other Americans (Gethin et al., 2021). At the same time, education can boost outward solidarity by teaching people about how their lives relate to and depend upon others around the world. More people might even become leaders and innovators thinking about solutions to the world’s biggest problems, such as climate change.

While education has the potential to alleviate many of the recent struggles the United States has endured, it is a long-term solution that only affects the youngest cohort of Americans. One of the U.S.’ most imminent challenges is bringing together Americans from all walks of life to re-establish national solidarity. This requires something that binds all Americans and encourages them to meet new people outside of their social group. One way to do this would be to inspire Americans to participate in public service in some capacity during their lifetimes. Mandating this would be highly controversial and likely generate backlash. However, incentivizing service in some way (e.g., through student loan forgiveness, payment/cash incentives, tax benefits, or school/work related programs) could be highly effective, given that for private sector workers incentives provide a significant boost to performance (Stolovich, 2010). This would have wide-ranging impacts on the national level. First, it provides more workers who can complete infrastructure projects, serve as educators, perform government-sponsored healthcare services, and fill many other jobs which are vital to the economy but often critically understaffed. During the coronavirus pandemic, when many have lost their jobs and are feeling disconnected from society, this could be a crucial opportunity. It also sets up workers for future success, possibly through offering them education benefits, or at the very least by giving them valuable work experience. From an economic perspective, money spent on national service companies like AmeriCorps may be as much as doubled in terms of societal benefit. Given the multitudinous advantages, increasing spending for public service programs and incentivizing participation in those programs would be highly advisable. Such a program has the potential to provide unity and opportunity to American citizens and a valuable workforce for a nation struggling with infrastructure, environmental, and educational issues (Bateman & Ross, 2020).

Both education and public service can be used for the benefit of the environment as well. Education will generate more people aware of the issue of climate change and the magnitude of the response needed to change its course, as well as more people qualified to innovate and engineer environmentally friendly solutions. Public service can be used to build more green and efficient infrastructure. There are many other solutions to help combat the effects of climate change that require only minor sacrifices, but they will require an open mindset from Americans who can take such proposed changes as affronts on their personal freedom. The ideal situation would see Americans making these life choices themselves without any inducement. However, as the crisis continues to escalate, it may become necessary for lawmakers to make the tough choice of legislating various aspects of life that are contributing to climate change, such as energy consumption. Hopefully, this will be eased by the widening availability of alternative sources of energy and fuel. At the same time, however, the EPA has proposed economic incentives for environmentally friendly companies, as well as limits on pollution across industries (EPA, n.d.b). 

This study has used the Brookings Institution Recoupling index to analyze and understand America’s current position. If the U.S. hopes to continue in its role as a global superpower, it must address both its societal and economic struggles. If the nation’s solidarity continues to plummet, it will see its populace unwilling to come together to tackle the issues of the future, and it will be unable to unite the rest of the world to solve issues like the coronavirus or climate change. If the U.S. does not empower its citizens and provide them with agency and opportunities for advancement, it will continue to fall behind and not make economic progress. Ignoring problems such as climate change has already placed the country in an unfortunate position where environmental degradation will harm future generations. Failure to make serious gains in education are also at the root of problems in advancement. The U.S. has the distinct potential to make extensive changes for the better right now. Delaying, however, will cause both America and Americans to suffer.

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About the author

Jaden Welch-Jani

Jaden is a junior at Glenbrook South High School, located near Chicago. He is particularly interested in the intersection between economics and international relations. Outside of school, Jaden is a club leader for Model United Nations, Academic Bowl, and Science Olympiad, and he enjoys Congressional Debate, Math Team, volunteer tutoring, and playing basketball.

The post A New Perspective: The Economics of Recent Social and Environmental Concerns in the United States appeared first on Exploratio Journal.

Abstract

Moore’s Law observes that the number of transistors on integrated circuits doubles approximately every two years. The accelerating pace of technological development brings about the advent of AI and automation on an unprecedented scale- It is estimated that 40-50% of workers with low education are at risk of automation by the mid-2030s (Hawksworth, John et al. 2018). This report explores the economic conflicts and consequences for Malaysia with a large-scale rollout of automation. I argue that: (i)Large firms will profit the most from automation and will need to be taxed accordingly; (ii) most low-skilled Malaysian workers will be rendered unemployed, thereby inflaming social tensions; (iii) the Malaysian government must play an active role in upskilling and supporting workers in an equitable manner through the introduction of a capital gains tax and stricter anti-corruption laws; (iv) Malaysians ought to establish a worker-cooperative system, much like the Mondragon Corporation so that workers will be able to engage in decisions on technological advancements. This report aims to bring to light the benefits and drawbacks of automation and policy changes with how Malaysia could benefit from them ultimately.

1. Introduction

Being the fourth-largest economy in Southeast Asia, Malaysia’s economy has proven to be incredibly robust. Due to the high concentration of knowledge-based enterprises and the adoption of advanced manufacturing technology, Malaysia enjoys relatively high labor productivity, with labor productivity per hour increasing by 3.4% in 2020 (Department of Statistics, Malaysia, 2020). In addition, it is worth mentioning that a big part of Malaysia’s manual workforce consists of foreign workers, mainly from neighbouring countries like Indonesia, Bangladesh, Nepal and the Philippines. Furthermore, while possessing a strong middle class, Malaysia has a globally recognized friendly business environment, ranked twelfth globally by the World Bank in 2020 (Eknath, 2020). All these points to Malaysia having a healthy economy to adapt to the ever-changing global environment often disrupted by advancements in technologies.

As of 2021, Malaysia is still heavily reliant on its manufacturing and agricultural industries, which accounted for 44.7% of the GDP in 2019 (O’Neill, 2021). The automotive industry alone contributed to more than 4% of GDP and employed 700,000 workers in 2016 (Malaysia Automotive Institute, 2017). However, this is changing as Malaysia transitions to a post-industrial economy. The tertiary sector accounted for 48.84% of GDP in 2009 and has grown to 54.17% in 2019 (O’Neill, 2021). As a result, the tertiary and quaternary sectors will only grow in importance, and traditional manufacturing, mining and agricultural jobs will be, by and large, replaced with machinery. 

Moore’s Law (Fig.2) observes that the number of transistors on integrated circuits doubles approximately every two years. This aspect of technological progress is essential as the capabilities of many digital electronic devices are strongly associated with Moore’s Law (Max Roser and Hannah Ritchie, 2013). This progress in technology is increasingly replacing labor. In an estimation by McKinsey & Company in 2020, automation could displace up to 25 per cent of hours, or 4.5 million equivalent workers, in Malaysia by 2030. Automation will have profound effects on the Malaysian economy, and the government will need to implement policies to ensure the welfare of the people. Increasing spending on education to produce a more highly skilled and adaptable workforce-as of 2019, only 21.3% of the working-age population are graduates with tertiary education. The demand for this skilled labor is only rising and will be a necessity in the digital age.

2. Benefits

Large firms who control the capital would benefit the most, should they replace half of their labor with technology. Technology requires an initial investment and regular maintenance. Still, it will cut production costs and increase overall productivity as machines can work 24 hours a day with little to no interruptions. As a result, firms’ revenues and profit margins will increase substantially. As firms’ profits increase, they can invest in training new workers and increase the wages of their employed workers, increasing their disposable income. This increase in disposable income would increase spending and drive more economic growth in the Malaysian economy as consumption makes up around 60% of nominal GDP in Malaysia(CEIC, 2021).With this rise in profits, firms will also have more capital to invest in research & development, further accelerating the advancement of technology and providing even more opportunities to expand by creating new products and exploring new markets.

Does it mean that 50% of the Malaysian labor force will be rendered unemployed if technology replaces 50% of the current jobs in the country? I believe that this will not be the case. Whilst old jobs will be displaced by automation, opportunities for new jobs and industries will also open up. We have seen this trend in the rise of the internet, with computer scientists in high demand as they have the skills and knowledge to maintain and operate the new technology. Of course, complete automation is not a process that will happen overnight. Displaced workers, who are willing and able to learn, will inevitably have to upskill their labor to adapt to the evolving labor market. Moreover, there will be opportunities for individuals who are ready and able to exploit new industries and markets built upon these new technologies.

Workers may stand to benefit as technology plays a more significant role in their jobs. It can help increase their productivity, raising their income and shortening their working hours as a result. Malaysian exports could become more competitive globally due to a fall in price thanks to the increase in productivity from technology. Lower production costs in Malaysia would incentivize multinational companies to raise their foreign direct investment, stimulate the economy, encourage more job growth, and improve the workforce’s skills.

Another consequence of automation is that it protects workers from dangerous jobs. Sarawak, Johor and Selangor alone account for around 15%(Ayob, 2018) of fatalities from occupational accidents, from which industries such as manufacturing, construction and agriculture contribute significantly. In 2013 alone, 26 fell to their deaths working in Malaysia’s construction sector. Accidents are disastrous for all parties involved and are likely to halt production and increase costs to the firm. In addition, organizations can be forced to pay for compensation to the affected family, and even OSHA fines spanning upwards of RM200 000 (Kamel, 2021)if an inspection deems a regulation to be broken in the course of the fatality. However, Malaysia has been adopting the use of machinery in production over the past few decades. With 2019 marking the official use of 373 000 industrial robots (International Federation of Robotics, 2020) installed in manufacturing facilities, there is no doubt that such workplace incidents will continue to fall. Cobots or collaborative robots designed to work directly with humans have seen an 11% increase in demand than their traditional counterparts, assisting workers in carrying out tasks more safely while keeping them employed. The move to introduce technology to take on more hazardous tasks is likely to result in a net social gain to society, preventing such tragedies while preserving able workers who can contribute to the economy. 

3. Implications

Due to the lack of tertiary education credentials, low-paying and low-skilled work are often taken up by the less educated. Advanced technologies quickly substitute such workers, and the tragic yet inevitable result is structural unemployment, meaning unemployment caused by changes in industrial structures and innovation of production technologies in this context. This would result in the reduction of income for lower-income groups, hence worsening the income disparity. Most workers in primary and secondary sectors, which compose 37% of the Malaysian workforce (Fig.1), could face unemployment due to automation in industries. Structural unemployment will prove an immense challenge for Malaysia’s limited welfare system. As of 2021, Malaysian citizens working in the private sector who have been made redundant can claim benefits through the Employment Insurance System Act 10, in which the unemployed can receive financial assistance for up to 6 months while continuing to look for a job . The Malaysian government will need to widen the scope of the unemployment benefit system and provide retraining schemes to maintain an active workforce. 

Technological advancement is guaranteed to face much resistance from displaced workers who lack the ability and willingness to adapt to the changes. Facing a lack of labor market competencies, this phenomenon creates a series of problems for the unemployed:

1. The unemployed would struggle to meet their everyday expenses such as food and accommodation costs due to not having a stable income, leading to a decrease in their living standards.
2. A rise in the unemployment rate would worsen social problems and cause a rise in crime rate, eroding social cohesion and discouraging investors.
3. Unemployment would mean that Malaysia’s labor resources are not being fully used in production, limiting national income.
4. Unemployment significantly stifles economic development while decreasing effective demand at the same time. The unemployed, who have little to no purchasing power due to insufficient disposable income, would subsequently prove ineffective in contributing to the national aggregate demand in Malaysia, in which consumption plays a significant role.

Having millions of workers requiring government assistance would almost certainly trigger a recession. It will be essential to introduce the automation of industries in phases to ensure that there will not be an unsustainable spike in unemployment. The planned restructuring of production and service can open up new avenues for employing those whose work has been automated. Rather than working unnecessarily long hours, workers could work fewer hours and let the new technologies carry the heavy workload under this system. Increased unemployment from automation will be juxtaposed with the increase in profits and revenue for owners of firms, leading to higher levels of income inequality. In addition, the Malaysian government ought to increase taxes to fund its welfare system and improve its infrastructure. This could be achieved by introducing a capital gains tax, which Malaysia currently does not implement (Deloitte, 2017), raising the tax on the upper tax brackets, and introducing heavier penalties for corruption- a serious issue amongst the political and social elite.

4. Conclusion

So would Malaysians be better off ultimately? As with most things in economics, the answer is rarely blunt. I may have painted a grim future for the socioeconomic impacts should technology replace half of the current jobs in Malaysia. There are feasible solutions to all the problems mentioned above. Rather than having machines put large swaths of the population out of work, Malaysia ought to build a system in which the technological advancements would be used as intended, which is to reduce the amount of work that workers need to do without dropping people from the workforce. I believe that such a reality could only be made true if Malaysia can establish a truly democratic economy, one in which the workers control some of the production of goods and services, as well as the implementation of technologies through worker cooperatives, unlike under a completely free-market economic system in which the industrialists do. Only then can technological advancements be used to serve the interests of the broader Malaysian populace rather than for the profit of a select few industrialists. Under this system, Malaysian workers would be able to democratically engage in their corporation’s decision-making process, which places the welfare of the workers and the nation at the centre of its mission. To establish such a system,  Malaysian policy-makers and workers can try to emulate the Mondragon Corporation, a highly successful federation of worker cooperatives employing over 70,000 in the Basque region of Spain.

There is a possibility that Malaysian workers could be freed from mundane work with technologies such as artificial intelligence, machine learning, and automation becoming universal. Instead of being an appendage to the machines, workers would now have the work of designing and maintaining the machines, a form of work that is more versatile and requires education and creativity. On top of this, Malaysians could devote more time to their creative development and leisure, allowing everyone to maximize their creative potential for arts, innovation or literature. Technology ought to be used as a liberating force instead of a divisive force in society. 

All in all, there remains no doubt that technology replacing half of the current jobs would result in many Malaysian workers being displaced, creating and leaving jobs only for those who are sufficiently skilled. This would certainly result in various social problems like unrest, much like the First Industrial Revolution did. In the short term, it would appear that the industrialists have the most to benefit while the ordinary worker has the most to lose. However, observing current trends, it is likely that technological advancements would culminate in a positive scenario for all parties involved.

References

Max Roser and Hannah Ritchie. (2013). “Technological Progress”. Published online at OurWorldInData.org. Retrieved from: https://ourworldindata.org/technological-progress

Ayob, Afizah et al. (2018). Fatal occupational injuries in the Malaysian construction sector–causes and accidental agents. IOP Conference Series: Earth and Environmental Science. Retrieved from: https://www.researchgate.net/publication/324443457_Fatal_occupational_injuries
_in_the_Malaysian_construction_sector-causes_and_accidental_agents

O’Neill, Aaron (2021).  Malaysia: Distribution of employment by economic sector from 2009 to 2019. Retrieved from: https://www.statista.com/statistics/319036/employment-by-economic-sector-in-malaysia/

Ee Huei Koh and Nimal Manuel. (2020). Automation and adaptability: How Malaysia can navigate the future of work. McKinsey & Company. Retrieved from: https://www.mckinsey.com/featured-insights/asia-pacific/automation-and-adaptability-how-malaysia-can-navigate-the-future-of-work

Kamel, Harizah (2021). “DOSH prosecutes 47 cases worth RM740,000 in 1Q”. The Malaysian Reserve. Retrieved from: https://themalaysianreserve.com/2021/04/06/dosh-prosecutes-47-cases-worth-rm740000-in-1q/#:~:text=THE%20Department%20of%20Occupational%20Safety,to%20March%2028%20this%20year.

Malaysian Automotive Institute. (2017). “The Automotive Industry”. Retrieved from: http://www.mai.org.my/index.php?option=com_content&view=article&id=32&Itemid=111&lang=e

Hooi Beng, Tan (2017). “International Tax: Malaysia Highlights 2017”. Deloitte. Retrieved from: https://www2.deloitte.com/content/dam/Deloitte/cn/Documents/international-business-support/deloitte-cn-ibs-malaysia-int-tax-en-2017.pdf

International Federation of Robotics. (2020) “Executive Summary World Robotics 2020 Industrial Robots” . Retrieved from:https://ifr.org/img/worldrobotics/Executive_Summary_WR_2020_Industrial_Robots_1.pdf

Department of Statistics, Malaysia. (2020). “Graduates Statistics 2019” Retrieved from: https://www.dosm.gov.my/v1/index.php

CEIC. (2021). “Malaysia Private Consumption: % of GDP”. Retrieved from: https://www.ceicdata.com/en/indicator/malaysia/private-consumption–of-nominal-gdp#:~:text=Malaysia%20Private%20Consumption%20accounted%20for,an%20average
%20share%20of%2047.4%20%25.

Hawksworth, John et al. (2018). “Will robots really steal our jobs?” PricewaterCoopers. Retrieved from: https://www.pwc.com/hu/hu/kiadvanyok/assets/pdf/impact_of_automation_on_jobs.pdf

Eknath, Varun. (2020). “Malaysia Doing Business 2020: Pursuing reforms at the top” The World Bank”. Retrieved from: https://blogs.worldbank.org/eastasiapacific/malaysia-doing-business-2020-pursuing-reforms-top

About the author

Jordan Tan

Jordan is currently a Year 13 (Grade 12) student at the Alice Smith School in Kuala Lumpur, Malaysia. His academic interests are in Economics, History, and Politics. Jordan’s experiences in Malaysia’s multicultural society have inspired him to learn more about how sociopolitical factors influence everyday life in Malaysia. Outside of academics, Jordan’s biggest hobby is soccer, having played the sport all his life; he is also involved in his school’s student council as the treasurer.

The post Automation in the Malaysian Workforce: The Future Challenges and Consequences of Replacing Labor appeared first on Exploratio Journal.

Abstract

The main goal of this essay is to try linking the effects of the traditional values and ideals of both the People’s Republic of China and the United States of America to their contemporary economic policies. The methods used to conduct this research use both historical and contemporary sources as well as data to justify some present day economic policies and reforms in said countries. For the United States, since there are vastly different ideals that branch off the same foundation within individuals and presidents. I would be using Donald Trump’s ideals, arguably one of the more popular presidents in the last decade, as the main American ideology. Overall, through this research I gained insight on the United States’ stress on individualism and liberalism that allowed it to push for policies that limited the government’s ability to influence the economy and tried to keep individual’s ability as the main factor in market competition. While China’s promotion towards collective, stable, and authoritarian society creates room for the government to limit individual’s capital, especially the ones who are wealthier, and restricting certain industries that is deemed to give unfair advantages.

Introduction

For decades, the People’s Republic of China and the United States of America have been competing for first and second place in terms of their GDP. However, one might be curious about how a socialist country and a capitalist country come toe to toe in terms of their economic might. Both countries, with completely different ideologies, cultures, and traditions are economic giants globally. With this in mind, this paper argues that traditional cultures and values do have a distinctive effect on the makings of contemporary economic decisions, and do provide reasonings for economic policies and reforms for the respective country.

What are Chinese Ideals:

Ever since the communist takeover of China in 1949, the definition of being a Chinese has changed. In half a century, China has transformed from an agrarian based, shattered feudal society to a communist, economic powerhouse. Even though the large economic boost puts China right behind the United States in terms of GDP, the average Chinese citizen has a completely different definition of what a successful society is than the average American citizen.

How would a Chinese citizen define a successful society? One of the main things that
Chinese culture stresses is stability. In Chinese history

The traditional moral-political model was able to withstand or absorb outside influences. Buddhism came from India, Mongol and Manchu invaders swept in from inner Asia, and traders from the Near East arrived along the Silk Road and by sea, but the system held fast. Chineseness was too powerful to be dislodged; it was the invaders who adapted. (Link, 2015)

With Chinese culture and traditions being kept static for such a long duration of time, it is not surprising that the Chinese would go out of their way to see a stabilized society. This is another reason why the Chinese seem to prefer more authoritarian governments. Ever since the Xia dynasty, absolute monarchies have held the reins in China, whether the ruler was Han, Mongol, Manchu, or even the self-proclaimed “democratic” leaders in the early twentieth century. Under authoritarian governments, politics is one factor that the public would not have to worry about. Even in Taoism, one of the leading philosophies throughout Chinese history, states, “those who followed the way did not try to give people knowledge, but kept them ignorant. People are difficult to rule because of their knowledge. To rule by knowledge ravages the country. To rule not by knowledge blesses the country” (Stendudd, Tzu, n.d.). With his reasoning being:

Not praising the deserving, prevents envy. Not valuing wealth, prevents theft. Not displaying what’s desirable, prevents confusion of the senses. Therefore: the sage governs by emptying senses and filling bellies, curbing strife and strengthening backs, keeping the people ignorant and without desire, making the learned afraid to act. If he acts without action, order will prevail. (Stenudd, Tzu, n.d.)

Lao Tzu’s ideals are still widespread in contemporary China, and one could definitely see why. With the stress for stability in Chinese society, the citizens believe that the government would do the best for the country, even if it means that some are sacrificed for the greater good, and that they are being good citizens simply by not interfering with the government. With the mentality of pursuing stability, this also requires the factor of collectivism, which is another strong trait found in Chinese ideologies. An additional factor for collectivism has been stated in Wong’s work, which they identified as the since relationship’s in Chinese society start with family but further extends to their extended family and village, and that any honor or face “bestowed upon a family member is shared by the family and beyond” (Wong, 2001). This further stresses the importance of collectivism in society, since if an individual
brings disgrace onto themselves, they would also affect their entire family (and possibly more). This ideology would not stop at the familial level, but also nationally and ethnically. In the end, the pursuit of stability and collectivism in contemporary China has largely influenced the current government’s economic policies and goals.

Contemporary Chinese Economic Policies

After the takeover of mainland China by the Communist Party of China (CPC), the party has taken advantage of the collective identity and utilized it to strive for a more egalitarian society, beginning with the Land Reform in 1948. Though the Economic Reform pioneered by Deng Xiaoping has altered the path to a more individualistic approach, famously saying, “Let some people get rich first” (Deng, n.d.). This method is slowly being adjusted back to its roots by recent policies rolled out by President Xi Jinping. According to Inman, Xi, in his recent meeting with the Financial and Economic Affairs Commision has stressed that the government should “regulate excessively high incomes and encourage high-income groups and enterprises to return more to society” (Inman, 2021).The policies that Xi has pushed out in order to “encourage high-income groups and enterprises to return more to society” (Inman, 2021)7 have shown to line up with the contemporary and historical Chinese ideals of a collective, stable, and egalitarian society.

Some of the policies that were pushed out with the intent of redistributing wealth and achieving wealth and education equality were heavily imposed restrictions on both the online gaming industry and the private education industry. The restrictions imposed on the private education industry were said to be made in order to “reduce the cost of child-rearing”, due to the high competitiveness of the higher education system (Goh, 2021). The guideline,
according to Koty, states that “regional governments are no longer permitted to approve new off-campus tutoring centers providing core/compulsory education. Existing ones must become registered as non-profit institutions” as well as an excessive restriction on online classes (Koty 2021). Knowing this, it could be inferred that the “unfair” advantage that certain students with high-income families would be heavily limited. Needless to say, this policy economically impacted the education industry in China significantly, causing one of the leading private education enterprises, New Oriental Education, to suffer a seven billion dollar loss in market value, about 50 percent of their market worth in Hong Kong (CNN, 2021). In similar fashion, the online gaming industry has also been thoroughly restricted, though this time with the aim “to help prevent young people becoming addicted to video games” (CNN, 2021). This policy prevented anyone under the age of 18 from partaking in any forms of online gaming during weekdays, and only allowed a one hour session per da from Friday till Sunday. Although this policy could be seen as trying to rid the younger population of this so-called “spiritual opium” (CNN, 2021), it could also be interpreted as a means to further restrict the elite class from gaining more capital. As there are over 110 million minors that game regularly in China, there would surely be a decline in game time and money spent gaming as Niko Partners senior analyst Niko Ahmed believed according to a report by CNBC (Feiner, 2021). In the end, these policies have shown to promote, or at least used traditional and contemporary Chinese ideals to justify their means.

In addition, President Xi has also been adopting alternative ways to make sure that the top entrepreneurs in China get the memo on the country’s new campaign to recreate an ideal society. And as Standaert puts it, “many of the activities these entrepreneurs had become accustomed to […] expressing their individuality, starting charitable foundations and educational institutions in their name – are increasingly unlikely to be tolerated in President
Xi Jinping’s China in the coming years” (Standaert, 2021)14 Using the “common prosperity agenda” as a vehicle, the country has already received donations from some of the richest individuals in China. Based on the reports of Buckly, Stevenson, and Li, both Alibaba and Tencent would donate $15.5 billion towards that goal, which includes but not limited to, “rural health care and insurance for delivery workers” (Buckley, 2021). Though that is not all. An article from the New York Times claims that some of the more extreme reforms could include higher taxes on capital gains, inheritance and property, and higher public sector wages to limit corruption and bribery -which has been rampant during the post Deng era- (Inman, 2021). These reforms and pressure towards the ultra-wealthy using the goal of common prosperity could be linked back to the ideals to create the Party’s and the people’s view on creating an utopian society.

Defining American Ideals

The United States, though still a young country compared to its counterpart in Asia, has quickly solidified its position as a world superpower after the second World War. Compared to China, the United States’ national ideology could be identified as liberalism, which pushes for democracy, limited government, republicanism, the rule of law, equality. According to the reports of Tierney, “Americans without liberalism have no idea who they are” (Tierney, 2011). With liberalism stressing for more autonomy for citizens and restrictions on the government, and a protest-heavy culture, it could be inferred that the United States also has a strong individualistic value. This could be backed up by Barone’s article “American Dream”, in which he defines the American Dream as “the belief that anyone, regardless of where they were born or what class they were born into, can attain their own version of success in a society in which upward mobility is possible for everyone. The American dream is believed to be achieved through sacrifice, risk-taking, and hard work, rather than by chance” (Barone, 2021). Its individualistic approach is even more clear in the Declaration of Independence, which states, “we hold these truths to be self-evident, that all men are created equal, that they are endowed by their Creator with certain unalienable Rights,
that among these are Life, Liberty, and the pursuit of Happiness” (Jefferson, 1776). Notice how in the definition of the American Dream, and present in the Declaration of Independence, there is a strong emphasis on individualism qualities, whether it is to restrict the power of the government, to promote individual economic achievements and fair competition, or the right of equality towards all regardless of ethnicity, social status, or economic situations.

American Economic Policies

For the past 40 years, United States presidents have used such ideologies to push for a Neoliberal economic policy. Neoliberalism, which pushes for deregulation of the market, celebrated cosmopolitanism (the idea that all human beings are members of a single community), and pushed for globalization (Grestle, 2021). This closely relates back to the ideals that founded the United States: deregulation of the market allows more autonomy for the citizens, cosmopolitanism relates back to values that were present in the American Dream, such as the right to give everyone a chance to succeed, and globalism also allows more autonomy for the citizens, allowing for more choices for citizens to choose and trade from. After being kick-started by president Ronald Reagan, it has been the primary economic ideology of presidents in the United States. This, with most of the Neoliberal ideals matching
with traditional American ideologies mentioned in the previous section, makes its policies a prime source for identifying how Neoliberal policies are impacted by current American beliefs.

In recent times, the Trump administration, one of the most significant and controversial administrations, a man which 25% of the nation (as of 2021) still sees as the “true president” of the United States, has pushed out policies that are easily justifiable with traditional American ideals (Brown, 2021). To demonstrate, the Tax Cut and Job Act, which allowed “significant and permanent tax cuts to corporate profits, investment income, estate tax, and more” (Floyd, 2021). At the same time, personal tax bracket rates (percentage of tax based on income level) were also being lowered. According to Investopedia, the top bracket dropped from 39.6% to 37%, while the lowest bracket dropped from 15% to 12%. As seen in Figure 1, the tax percent of the total GDP for both individuals and corporations notably dropped after President Trump was elected into office.

This policy disallowed financial restrictions that the government could utilise against corporations and individuals, which could be argued that it also limited the government’s financial abilities, which could be related back to Americans’ uneasiness with governments which have more authority and resources. Additionally, the reforms towards welfare and medicare programs, which claimed to already drop five-million citizens from its food stamp program, have similar reasoning as the tax reforms (Tankersley, 2020). These welfare program reforms raise requirements that are needed to apply to medicare, food stamps, checks for the disabled, and utilities for households (Kogan 2019). These reforms, justified by the fact that some do not think these social welfare programs are effective at creating equal opportunities, but rather wasting tax money on freeloaders. By these means, it would be easy to spot why president Trump made more restrictions for applying to these programs, which tries to create an equal opportunity for those in need and to keep out those who just want to take advantage of the system

In the end, though it is generally hard to define certain policies with a single ideology, with the evidence and justification, it could be said that these policies are definitely motivated by Neoliberal ideologies. From the impactful tax and welfare policies, it has shown to be used to limit the government’s financial abilities and as well as create a more equal and just competitive market for individuals as well as corporations.

Conclusion

Overall, using contemporary and historical ideologies to try and define economic policies and their goals has created a distinct but reasonable logic for both countries. For China, the belief of working towards a common goal (and under CPC’s rule, this has gotten even more apparent), the stress of stability, and the distinction between the common citizens and government created policies that aimed to even out individuals who have gotten too
much capital, to eliminate private education, which was seen as a mean for the wealthy to try and gain an upper hand in life, and also trying to create “common prosperity agenda” for everyone. On the other hand, the United States, which pushes for a limited government, autonomy for citizens, and equality (though it is for social equality, while China strives for economic equality), pushed Neoliberalist principles onto its economic policies. With some of the biggest examples being the tax and welfare reforms, which tries to limit the government’s participation in the market and as well as creating unfair advantages for certain individuals. These policies push the agenda that aims to promote an individual’s entrepreneurial abilities instead of working collectively to pursue state set goals.

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About the author

Qitian Huang

Qitian is an 11th-grade student at The Houde Academy in Shenzhen, China. Having lived both in China and the United States, he has a unique insight into the daily life and cultural differences between both countries. Qitian has taken great interest in both politics and history, and is seeking to learn more about political and cultural differences around the world. When he is not pursuing his academic interests in politics and history he likes to indulge himself in classic horror movies.

The post Cultural Ties with Contemporary Economic Policies and Reform: A Case Study Comparing China and the U.S. appeared first on Exploratio Journal.

Abstract

The purpose of this paper is to provide a finite understanding of how our world would adapt to a new form of a digital economy. We as humans have digitalizing most of our interactions, whether it’s befriending people on Facebook, speaking your words on Twitter, or even learning through Google. Therefore, Understanding the next step of our structured economies is crucial. Nothing comes close in terms of technological implications like blockchain, whom many have stated is the best technological advancement since the internet! My goal is to set the scene of how blockchain will play a huge role in how we perceive our functioning financial world. I describe its role and how it is already tied to implications in our national and commercial banks and how it will even provide a bigger role in the future. The research then shifts to how a central bank is already using blockchain and fully digital economy on full throttle in a thorough case study of the digital Yuan case. I go back home to identify and review the FinTech scene in my home country of Saudi Arabia, by assessing how big giga projects shaped up the region as a hub of technological developments for upcoming years. 

Keywords: Digital Economy, Fintech, Digital Yuan, Central Bank Digital Currency

1: Introduction

In any modern economy, the role of financing infrastructure is a crucial one. The current innovative efforts towards digitizing of finance are full of promise though they are still at a relatively early stage of development. One such effort concerns the application of blockchain algorithms and the platform in general to financing functions. These functions could be at the overall economy level as well as specifically focused on certain sectors, in particular the banking sector. The purpose of this paper is to describe the nature of Blockchain technology, its impact on financing with a special emphasis on the Central Banking Policy, budding sovereign digital currencies. The paper rounds this discussion off with a detailed case study of Fin Tech in Saudi Arabia – an important developing country. 

Complete digitalization of a whole functioning economy remains a distant and a difficult goal. Innovative efforts have been undertaken to overcome this challenge, the main effort being the Block-Chain Technology. In general, an effort to digitalize an economy must factor in the business need along with the human perspective regarding these transactions. Blockchain promises to be such an initiative. 

Simply put, Blockchain is an advanced database, where data are stored as chained chunks or ‘blocks’ of information hence the name. Having a chained structure of information gives the advanced technology a huge advantage of having a set timeline that is irreversible and secure. Blockchain and its many applications has been the best effort accomplished by communities to fully digitalize daily applications while making them organically verifiable without a central regulator or auditor. Companies, banks, and even governments have already made significant progress by achieving technological maturity that take into factor human preferences. Applications ranging from daily transaction of peer-to-peer transactions to large organizations such as corporations and governments are starting to use such structure to improve transaction verifiability and its execution. 

The effects of blockchain and other digital factors on the worldwide commercial banking scene have been crucial to developing a healthy financial ecosystem. Smaller businesses receive online payments with Stripe, which means that small businesses get paid 14 days faster on average versus businesses that do not use an online payment service. FinTech daily applications have grown incredibly in such a short period, companies like Robinhood, Square, Stripe are improving the daily experience for millions at a time.  PwC in a recent study have suggested that 83% of traditional banks think that they are at risk of profitable retailer failure. 

Financing is instrumental in building a healthy economy, and a technological approach has helped widen the scope for ways that an entrepreneur can go on financing a revolutionary idea. For years, economists have expressed their concern that rates of bank financing of smaller businesses are declining. This trend has hurt many economies especially those in developing countries where bank financing may be a major source of business financing. The new promising technology such as Blockchain can fix that. As Deutsche bank reported in 2016 new models of finance in the reduction of systemic risk and more diversified lending options. Established industry players are confronting the reality of alternative online platforms are improving the profitability of small business lending. 

Saudi Arabia an expanding economy, shows a promising adaption towards a more digitalized economy. The Saudi experience really underlines the effect of technology towards building a healthier more innovative economy with promising projects like NEOM.Enumerating and documenting such efforts while giving a suggesting outlook for how the future state of the world economy would be set like are two of goals of such a research paper. Emphasizing on the role of central banks is also quite crucial in identifying how a country sets itself up for the future. Central bank adoption can make or break a country’s economy destroying all economical and entrepreneur infrastructure which can take a toll on the said country future for generations to come, Venezuela comes to mind regarding an unfortunate case on the other hand a country like Vietnam has taken a more futuristic role which allowed its economy to be one of the fastest growing. 

The rest of the paper is organized as follows: the second section describes the nature of Blockchain, followed by section 3 which characterizes its application to Commercial Banking Systems. Section 4 focuses on Blockchain and Central Banking Policy. Finally, section describes the case study of Saudi Arabia in terms of the recent developments regarding the FinTech, Blockchain, and NEOM. The paper ends  with Concluding Remarks followed by References. 

2: Nature of Blockchain 

The nature of blockchain, gifts us a new way of re-organizing the financial sector, in the same way that the Internet and emails to be exact transformed how we communicate. While the Internet has organized input information, blockchain will help organize at least in its first step personal, peer-to-peer finance. Let us elaborate on the functionality of Blockchain Trust. 

The first application of Blockchain Trust  is to perform such fundamental functions of ‘money’ as acting as a ‘medium of exchange’. For that to happen, Blockchain needs to be ‘trustworthy’ and thus ‘generally acceptable’. Blockchain generates trust because of the data it contains, eliminating the participation of a third party in trust of the system. The role of these intermediaries is to give confidence to both the parties that wish to make a transaction or enter into an agreement, but do not trust each other because they do not know each other. The software of blockchain is open source; therefore, no one should pay licenses or ask permission to use it. In the same way, anyone can participate in the network by downloading a copy of the blockchain and use it according to the previously established consensus rules. It is no longer about nation-states as trusted intermediaries. That is, it must be based on computing, on software code, on the accuracy of mathematics.

The network must not have other parties. The network is trust per se, considering that all data that goes through it is publicly known through a block explorer. The network operates distributed in different geographical locations around the world in nodes that run an exact replica of the blockchain, thus preserving, the same unanimous state, the same truth that cannot be modified by any of the nodes without there being a consensus. It does not serve the purposes of any State, or organization. Each member of the network – called a node – follows the consensus rules neutrally and, if not followed, is simply expelled.

3.  Application of Blockchain to Commercial Banking Systems

In this section, we are going to focus on the nature of online banking services at the national level (3.1) with the emphasis on the U. S., followed by a case study of China in terms of global commercial banking and concluding this section with an overview of some of the challenges related to blockchain application to commercial banks (3.3).

3.1 Online banking services at the national level 

Blockchains as state is decentralized and permissionless, which can lead to major disruptions in the financial sector, especially in payment clearing. Blockchain can prove crucial to the new face of FinTech, facilitating technological factors as well as being renounced as a very safe and secure technology.  

Since 2015, several major international financial institutions have begun to formulate plans for the blockchain sector at the national level. Here are some of the examples of such efforts. Goldman Sachs, J.P. Morgan, UBS, and other banking giants have all established their own blockchain laboratories, working in close collaboration with blockchain platforms, and published a series of studies on this topic. Goldman Sachs even filed a patent for transaction settlement based on blockchain technology. Additionally, various national stock exchanges, such as the Nasdaq Stock Market and the New York Stock Exchange have also conducted in-depth research on blockchain technology. On December 30, 2015, Nasdaq announced that it had completed its first securities transaction using the blockchain transaction platform LINQ. Furthermore, the US Depository Trust & Clearing Corporation, Visa, the Society for Worldwide Interbank Financial Telecommunications. There has also been widespread optimism regarding the application of blockchain in the banking industry.

  In May 2016, McKinsey conducted a survey on global banking executives, finding that approximately half of executives believe that blockchain will have a substantial impact within 3 years, with some even considering that this will happen within 18 months.1 Another survey of 200 global banks predicted that, by the following year, blockchain technology will be extensively implemented by 15% of banks. Furthermore, IBM has stated that, in 4 years, 66% of banks will have commercial blockchain at scale. As such, an increasing number of banks have begun to be pay greater attention and place emphasis on blockchains. Therefore, why is the current banking industry focusing on the deployment of blockchain strategies? Which specific scenarios can blockchains be applied to? What are the existing problems in the implementation process of blockchain technology? This paper discusses these questions in turn in the following chapters. 

Blockchain is expected to formulate and change the banking future, in China’s case the internet finance and other aspects of FinTech have crippled the profitability of said banks, in consequence this led traditional banks to develop and cope with ongoing change. In recent years, booming Internet finance has accelerated the marketization of the financial sector. The endless emergence of Internet financial products has led to the substantial diversion of household saving deposits and increased banks’ cost of debt. According to monitoring data for national Internet FinTech platforms, as of the end of August 2016, the number of Internet financial platforms had reached 8490 and the number of active users 618 million. The latest data released by the China Internet Network Information Center indicated that, as of June 2016, the number of Internet users in China was 710 million. As such, the percentage of Internet finance users accounts for 87% of total Internet users in China. The great success of Internet finance within a short period of time is due to four vantages: infrastructure, platforms, channels, Internet finance companies are aware that finance is not an independent entity, but is embedded within numerous real-life scenarios. This goes with the mentioned human preferences that blockchain and the internet finance delicacy follow. Break the above into at least 2 maybe 3 smaller paragraphs – especially when you switch from the U. S. to China. 

  Since 2015, China’s macroeconomy has entered a “new normal,” wherein economic growth continues to decelerate, while interest rate liberalization is essentially complete, and their combined effects are becoming increasingly apparent. On one hand, there has been a trend towards narrowing interest-rate spread and declining the profitability of commercial banks. On the other hand, there has been increasing credit risk and nonperforming assets (Wang Wei 2016). The changes are shown in Figs. 1 and 2. Moreover, following a series of changes, including easing market access for foreign banks and the internationalization of the RMB, the survival environment of China’s commercial banks has become particularly harsh.

3.2 Implications of Blockchain to Global Commercial Banking

Aspects of blockchain technology which have been applied as forms of FinTech have hendred the profitability of massive commercial banks, the case study for this section would be China the reasoning for choosing said country is very straight forward. China has the most advanced FinTech ecosystem there is. The digital yuan and other state backed projects are also a key in determining Chinas advancement. 

In recent years, there has been a boom in the financial markets, increased product varieties, and continuous financial innovations. The focus of capital markets ranges from derivatives to asset securitization, and then to P2P, crowdfunding, and others. According to statistics by the Central Depository & Clearing Co. Ltd., in 2015, the total asset securitization products issued nationwide were worth RMB 603.24 billion, equivalent to a growth of 84%; their market stock was RMB 770.395, equivalent to a growth of 129%. In 2016, the asset securitization market will progress toward the scale of trillion RMBs. The crowdfunding industry has also achieved excellent results. According to incomplete statistics, as of the end of 2015, there were 283 normally operating crowdfunding platforms. For the entire 2015, the national crowdfunding industry successfully raised RMB 11.424 billion. This was the first year in history to exceed RMB 10 billion, registering an increase by 429.38% compared to 2014. Additionally, the Securities Regulatory Commission has also announced that an equity crowdfunding pilot will be carried out this year. Financial innovations can offer capital market participants, especially small and medium sized enterprises (SMEs), more options, thus providing more convenient and personalized services. This creates significant competition with the traditional banking industry.

Basis for strong internet finance in China

3.3 Challenges of blockchain application to commercial banks.

This section will take note of the challenges that might face any Technological Financial scene, multiple noticed challenges that faced said Chinese case study.  

• Social: From the perspective of the overall technical environment, all sectors of society still hold a cautious and even negative attitude towards blockchain technology. On the one hand, due to the lack of professional knowledge of blockchain and the news of bitcoin “roller coaster”, it is easy for the outside world to equate blockchain with encrypted digital currency. On the other hand, there is a phenomenon of overstating the function of blockchain, and it is believed that blockchain will subvert all the existing Internet. These are all biased views. In terms of technology itself, blockchain is the supplement and innovation of Internet technology application level, but it is not subversion.

• Technical; As a new technology, blockchain has some problems, such as lack of practical data, unknown market tolerance, and market education needs a certain time. As a blockchain structure, all information participants in the blockchain have a “public information account book” to ensure that the information cannot be tampered with and traceable. With the development of time, there are more and more nodes, which will lead to the increasingly serious problem of redundant storage, the dramatic increase of cost and directly lead to the problems of too long blockchain structure, slow data update and response. In addition to the performance may not be able to meet the high-frequency transactions, blockchain technology is still immature, and there are certain security risks, including whether the algorithm is reasonable, whether there is the possibility of cracking. 

• High costs; The development of blockchain finance in the banking industry will consume huge human and material resources. For banks, financial innovation should not only ensure economic benefits, but also comply with regulatory requirements. At present, blockchain technology is in the research and development period, and the relevant technology and use standards are not unified. The current infrastructure of financial institutions has spent a lot of manpower and material resources after years of construction and maintenance, which is relatively perfect. The use of blockchain system will completely subvert this traditional mode and create a new transaction settlement  and management system. 

4: Blockchain and Central Banking Policy 

Central banking policies effect the banking and financing ecosystem of said country, it effects mainly the scene of national banks and in this section, chapters will vary from discussing certain effect to discussing new central banking policies that are adept to the changing ecosystem.

4.1 Central Bank-Issued Digital Currency

What is a traditional Central Bank? Nowadays there are two forms of money issued by central banks, i.e., central bank money: cash and reserve deposits held with the central bank, where the depositor is usually a commercial bank. No currency area in the world is known to have issued any other type of central bank money, such as electronic scriptural money used by the general public. Both types of money are entered as liabilities on the central bank’s balance sheet. Most money is, however, scriptural currency created as a result of lending by deposit banks and could, in practice, be fully converted into central bank money. Central bank money is held by the general public only in the form of banknotes. It is also held by credit institutions and government in the form of reserve deposits. The central bank’s counterparties have the right to exchange reserve deposits for banknotes and vice versa. Credit institutions forward banknotes to customers, i.e., ordinary citizens, companies and entities. New central bank money is always created via central bank accounts and based on monetary policy decisions. The central bank creates new money in the central bank accounts via open market operations or refinancing operations. Deposits at commercial, savings and cooperative banks are commercial bank money. Credit institutions create commercial bank money by granting loans to the public. The bank granting a loan records a deposit on the customer’s account and on its receivables a loan of the same amount. The deposit recorded on the customer’s account is a valid payment instrument, for example as a credit transfer. The majority of consumer credit granted in Finland is in the form of housing loans. The majority of payment transactions by the general public are executed with commercial bank money created in the banking system, by transferring money between deposit accounts, e.g., when purchases in a shop are paid with a debit or credit card. 

In terms of CBDC this is how a projected outlook would look so: 

• The central bank issues it in digital form.

• Anyone has the right to hold it. It is not a privilege reserved to e.g., credit institutions. 

• It is the same currency as banknotes and central bank deposits. The conversion rate of banknotes and zero-interest bearing digital cash would always be one-to-one, and at least some economic entities, e.g., banks, could convert it freely into other types of central bank money.

• It can be used as a payment instrument in retail payments. 

• When two parties engage in a transaction, there is no third party – at least not a private one – that verifies or executes the payment as a central counterparty. The same principle applies to banknote payments.

For the new payment instrument to be a substitute for cash, it should share many of its properties. Cash, in its current form, has many characteristics that users may find important but cannot be found in current electronic payment instruments. These features include anonymity, immediate finality and transaction clearing without third parties. To be able to examine the characteristics of the various payment instruments and the need for such instruments in future, we must first classify and analyze the structural characteristics of the payment instruments. Only then can we systematically analyze what characteristics digital cash issued by central banks should have. As the first criterion, we examine real time settlement. One of the benefits of cash is that as soon as the authenticity of a banknote or coin is established and the banknote or coin is accepted, the execution of the payment transaction is validated as such. It is important because immediate settlement minimizes counterparty risk and funds are transferred to the payee in real time. Payment validation is, in principle, also independent of electronic systems, even though retailers’ point-of-sale terminals record cash and card payments into the same system. In card payments, the payment terminal checks availability of funds (validation of funds) for the required payment. The actual settlement takes place only in batch runs between banks, typically overnight. The new instant payment systems provide real-time settlement of payment, in a couple of seconds. This type of system has been launched at least in the Nordic countries.

Another natural characteristic of cash is anonymity of payment. In many payments situations identification of the parties to the payment transaction is neither necessary nor important. However, a payment transaction rarely requires anonymity. Cash payment machines, parking meters and most retail payments operate without identification, in cash. In Bank of Finland surveys, only a few consumers (some 2% of respondents) cited anonymity as a criterion for selecting a payment method. For example, in Germany, anonymity of payment is of significantly larger importance to the consumer. In a recent euro area payments survey, 13% of consumers cited anonymity as an important reason for using cash in retail payments.  In electronic payment methods, the user is usually identified via their bank account. With loyalty cards, the payer’s identity may also be revealed to the merchant.

The third factor distinguishing between payment methods is whether the payment instrument itself is also an asset. Cash is a liquid asset, and it is often used in saving ‘for a rainy day’. Card payments, credit transfers and direct debits are not assets as such; they only provide access to the payment system where settlement takes place. Like card payments, credit transfers and instant payments, in turn, are only tools for controlling and using the consumer’s purchasing power which is deposited on bank accounts, in the electronic systems of deposit banks. In the case of cash, anonymity, immediate settlement and independence of central counterparties are all based on the fact that cash is a bearer instrument, i.e., the person who is physically in possession of the banknotes and coins is legally their owner. In addition, banknotes and coins include all the information required for authentication and settlement finality. In practice, settlement means entering the transaction into a ledger, and in the case of cash payments, it also involves transferring the payment instrument to its next owner. In the case of scriptural money, all transactions and balances are recorded in the account bank on an ongoing basis. In the case of cash, there is no central authority responsible for bookkeeping; each holder of cash essentially keeps their own records. In such a situation, the ledger is distributed. The digital implementation of a bearer instrument does not fully correspond with cash money. For a digital currency to fulfill the criteria of a bearer instrument, it should be convertible into a sequence of number, the holding of which would be necessary and adequate verification of ownership. For example, in the case of Bitcoin and other cryptocurrencies, this type of sequence of number that is a sign of ownership is called a private key. The distributed ledger of the Bitcoin system, in turn, includes real-time information on the amount of purchasing power owned by the holders of each private key. To make this type of a system independent of a central authority, the amount of purchasing power held by each private key is recorded in a distributed ledger, instead of a centralized ledger. Another important characteristic of money is that it cannot be counterfeited. In the case of cash, non-counterfeit ability is tangibly based on security features that can been seen and felt. Banknotes also have several machine-readable optical and electromagnetic security features that ensure that they are checked for authenticity in a mechanical sorting process. In electronic record-keeping, non-counterfeit ability of money is based on verified transaction records and double-entry bookkeeping. 

In the case of Bitcoin, non-counterfeit ability is based on the openness and transparency of the distributed ledger (blockchain). For both cash and Bitcoin, production is technically so difficult that counterfeiting would be very expensive. One of the keys required features of a payment instrument is confidence in the acceptability and continuity of the instrument. In the euro area, only euro cash has the status of legal tender, but the scriptural money of commercial banks is fully convertible into cash. According to recommendations adopted by the European Commission in 2010, euro cash must be accepted for its full-face value for payment of debt, without surcharge. Some euro area countries have, however, set limits on the size of payment, with the aim of preventing such things as the grey economy ‘selling under the counter’, as it were. The continuity and acceptability of cryptocurrencies as payment instruments are not as certain as with currencies issued by central banks. 

Cryptocurrencies have also been more vulnerable to fraud and stealing of funds, e.g., if a private key is lost or in connection with hacking. Money always involves the issue of stability of value. Key national central banks have inflation targets that have an impact on the value of their currency over time. The inflation target applies particularly to cash money, because account deposits are often subject to an interest rate which, at least in principle, is higher than the rate of inflation. Moreover, cash is not dependent on technology or equipment; in other words, anyone can use cash, irrespective of the size or color of their wallet. Digital money as an intangible phenomenon can never be fully independent of technology or devices, even though this should be the ultimate goal. Digital cash would thus not be in the form of deposits in the central bank’s account system; it would be located in the counterparties’ own systems. In theory, households and companies could be offered deposit accounts in the central bank, but this alternative would not introduce any new technical features into the monetary system, and it is therefore excluded from our examination for now. In contrast, a system based on a distributed ledger could be interpreted as digital cash. There are a number of ways to implement central bank digital currency. The central bank could provide a system in which the digital currency is stored, transferred and authenticated. Another possibility is to create a standard for digital currency in which the private sector would be responsible for creating the storage and transaction applications. The third alternative is to limit the central bank’s role to the creation of money in respect to balance sheet and debt relationships. The private sector would be responsible for the technical arrangements. A private entity would open an account with the central bank, and the funds on the account would be earmarked as funds of the holders of payment instruments issued by a private issuer. The amount of funds on the account should be equivalent to the amount of payment instrument issued by the service provider. A private entity would issue certificates of ownership in deposits with the central bank, which would be used as payment instruments. The arrangement could also be compared to a bank deposit, which is subject to a 100% reserve requirement. To ensure neutral treatment of the different payment instruments, the central bank should impose on the providers of digital money conditions that do not give banknotes or digital money an artificial cost advantage or other competitive advantage. This type of arrangement in which the deposit is made with a commercial bank has been discussed above in the research paper.

4.2 The future of Sovereign Digital Currency and the Digital Yuan 

The Central Bank of China is testing its Digital Currency Electronic Payment in the cities of Shenzhen, Suzhou, Chengdu and Xuan with the involvement of four large state-owned banks in the issuing of the digital asset including, the Agricultural Bank of China, Industrial and Commercial Bank of China, Bank of China and China Construction Bank. China’s DCEP could be launched as early as next year. Already mobile transactions accounted for four of every five payments in China during 2019 and the Chinese population habitually use Alibaba’s Alipay and Tencent’s WeChat Pay as a preferred method of payment. The Central Bank, DCEP is one of latest FinTech developments, technology-enabled innovation in financial services that include the application of cloud computing and Big Data analytics to the finance economy and to the process of financialization. The World Bank Group’s (2020) report ‘Payment aspects of financial inclusion in the fintech era’ from the Committee on Payments and Market Infrastructures emphasizes a balance of challenges and opportunities:

‘Fintech can be leveraged to improve the design of transaction accounts and payment products, make them ubiquitously accessible, enhance user experience and awareness, and achieve efficiency gains and lower market entry barriers. At the same time, these benefits come with certain risks in terms of operational and cyber resilience, the protection of customer funds, data protection and privacy, digital exclusion and market concentration’. The report also makes clear that a fifth of world banks are likely to introduce DCEP systems within the next five or so years. Some commentators have suggested that the Chinese digital currency system is more about control over the cash economy than a progressive decentralization first promised by visions of the introduction of cryptocurrencies. Wired’s Barclay Bram (2020) indicates the original techno-utopian vision: Bitcoin, which was released into the world by the mysterious Satoshi Nakamoto in 2009, is based on a techno-utopian vision of a decentralized global currency that would provide anonymity and security, while allowing users to subvert the established financial system and its gatekeepers. Underpinning this technology is the blockchain. There are likely to be several fundamental differences between Bitcoin and the Chinese system because the blockchain ledger will be controlled by the government rather than distributed across the system giving government unprecedented control over the cash economy. The goal has been to protect our monetary sovereignty and legal currency status. Yet the biggest, and possibly the most important, objective of e-RMB could be a shot in the arm to RMB internationalization which has stalled since the China–US trade war started two years ago. One of the major implications is whether the current US–China tensions will spread to a FinTech race and the weaponization of currency wars. during times when there is less willingness to lend in safe-haven currencies like the US dollar, risk-off environments (such as those created by global pandemics) lead to greater demand for US dollars. Moreover, shock and uncertainty have led to risk aversion within key funding markets that have exacerbated dollar shortages within the international monetary system (IMS). Within the framework of an international dollar shortage, the age of Covid-19 has represented a boon to China’s decades long efforts to avoid the ‘dollar-trap’ on its quest towards RMB internationalization. DCEP is the cornerstone of the development of digital economy. Digital economy in the future will definitely be an encrypted digital economy rather than a plaintext digital economy. In this regard, DCEP should be crypto-currency from a technical perspective. Cryptographic technology is the key to the technical security and credibility of DCEP. To be specific, cryptography should be used in the design of DCEP presentation format to ensure that it can be circulated and stored and cannot be forged, double-spent or repudiated. The transaction of DCEP requires the use of cryptography, DLT, trusted cloud computing and secure element (SE) to ensure peer-to-peer security in order to prevent the DCEP from being stolen, tampered or imitated. In terms of DCEP user experience, while peer-to-peer payment service different from traditional electronic payment is provided to users, privacy protection technology and DLT should be integrated to ensure safety of user data and to prevent leakage of sensitive information without compromising usability. In terms of DCEP regulation, since DCEP adopts the mechanism of “voluntary anonymity at front-end and real-name at back-end”, security and privacy protection technologies can be used to manage data access, so as to make best use of RegTech such as big data analysis. Theoretical innovation and practice of crypto-currency have burgeoned in recent decades with relevant theories becoming increasing mature. Chaum was the first to propose an anonymous and untraceable e-cash system. Dai presented an anonymous distributed e-cash system called B-money, and Jakobsson and Jules brought up the concept of POW. Szabo invented Bit-Gold. In 2008, Nakamoto published the classic paper of Bitcoin: a peer-to-peer electronic cash system where he proposed a decentralized e-cash system completely based on peer-to-peer technology. In fact, the DLT adopted by Bitcoin was an integration of the latest achievements of various crypto-technologies at that time. The digital currency sector abounds with practical achievements. Ever since Bitcoin, alternative cryptocurrencies have emerged one after another. By 2016, there have been all together more than 600 types of digital currencies. Those crypto-currencies have evolved based on Bitcoin by making further use of crypto-technologies, and many of those attempts have made impressive academic innovation.

5: Saudi Arabia’s venture with FinTech, Blockchain, and NEOM

In this section, as a case study for an emerging economy, I will highlight Saudi Arabia’s experiment of  Internet Finance. In this respect, I will address, in general terms, Saudi Arabia’s venture involving FinTech and Blockchain and point out how this exercise has that affected the country’s financial ecosystem.

5.1 FinTech Expansion in Saudi Arabia

In Saudi Arabia, the development of FinTech is literally at a nascent stage. As can be imagined, starting ventures involving FinTech requires an ecosystem involving innovation, training and institutional support. To Saudi Arabia’s credit, as part of its Vision 2030 initiative, Saudi Arabia have set ambitious goals to tackle such a challenge. However, it is widely recognized that building a FinTech ecosystem from scratch with a limited number of FinTechs and with first time efforts to define clear paths for regulation as well as training and education presents a major challenge.

So, what are the first steps they have taken to build a FinTech industry from scratch.

For an industry to thrive, it needs a supportive ecosystem to help it grow. Saudi Arabia looked to establish this by creating a network of government organizations, universities, corporates, banks, venture capitalists and local FinTech companies, all working together to drive innovation, investment and to create a support system. This network is overseen by a new organization: FinTech Saudi.

By targeted action in education, regulation and building a supportive ecosystem, FinTech Saudi believes it can deliver the necessary knowledge, skills and support for a competitive FinTech sector.

Education
For a FinTech start-up to succeed, a person or group of people need an initial business idea. That idea most often arises from a frustration with a system or product combined with the knowledge and know-how to create a better alternative.

Without a pre-existing FinTech sector to draw upon, Saudi Arabia has lacked insight and knowledge on how to take ideas and turn them into a reality in the region. On top of this, those who have the inspiration and drive to start their own FinTech business have been faced with a lack of technical and commercial talent to drive local business growth.

There has therefore been a big focus on how Saudi Arabia can begin to develop these skills, starting with the education system. Initiatives have initially been centered around creating a FinTech-style curriculum, targeting university career fairs and offering internship opportunities. FinTech clubs and general co-working spaces have also been established, designed to inspire potential entrepreneurs and build a robust talent pool FinTech’s can access.

Regulation
London has over 4,000 FinTech start-ups. Saudi Arabia has just 15. Why?

Historically, Saudi Arabia has been challenged by FinTech start-ups relocating operations elsewhere once they need to scale. A key driver of this has been lack of infrastructure and regulation to support them.

Therefore, a huge focus on creating the right environment for the nascent FinTech industry in Saudi Arabia is to not only encourage more start-ups to be created in the first place, but to encourage them to stay.

Establishing a regulatory framework, including two sandboxes with the Saudi Arabian Monetary Authority (SAMA) and the Capital Markets Authority (CMA) – drawing lessons from the UK FCA’s sandbox established in 2016 – has been seen as paramount in creating an environment in which FinTechs could stay, thrive, and scale.

What does success look like?
Saudi Arabia has high aspirations for what it wants to achieve over the next five years. The strategic vision is categorized into three elements: community, education and Small-Medium Enterprises (SMEs).

The government is integrating the vision firmly in the community, through running a number of events and creating significant new jobs within the sector. Education will be embedded in the plans, with targets to reach thousands of students across the country. For SMEs, there are initiatives underway to grow the number of Saudi Arabian FinTech companies significantly – generating a substantial amount of new funding.

The initial expectation is that the majority of FinTech start-ups that emerge from this program will focus on the growing domestic market, but in time Saudi Arabia could ultimately be a great point of entry for international FinTechs looking to scale in the country.

Across the world, we’ve seen a diverse forest of FinTech sectors grow in different shapes and sizes, with occasional nurture and support from governments. In contrast, Saudi Arabia wants to prepare the soil, plant the seed and spur its growth.

5.2: Local FinTech expansion

Despite the challenges of COVID-19 in the first half of this year, 2019 / 2020 has been a pivotal period for the fintech industry in Saudi Arabia. Significant progress has been made in payments with the launch of Apple Pay, establishment of Saudi Payments and the release of the payments regulation; SAMA and CMA have continued to issue regulatory testing licenses and develop regulations to support fintech activities; Fintech Saudi has launched a number of initiatives to support the fintech industry including the Fintech Ecosystem Directory and Fintech Jobs Portal to support fintech companies, the Fintech Regulatory Assessment Tool to provide greater regulation clarity and the Fintech Data & Research Initiative to support data driven innovation in fintech. The industry in the kingdom is growing at a rapid rate. ‘’Between 2017 and 2019, the value of fintech transactions increased at a rate of over 18%each year, reaching over USD 20 billionin 2019’’. Payments account for two-third of the market and almost 98%2 of the userbase. It is followed by personal finance which comprise over 30%3 of the fintech transaction values. The number of payments made through SADAD has increased by a CAGR of 11% between 2016-19 to reach 270 million4 transactions in 2019. The value of these transactions has grown by a CAGR of 24% in the corresponding period to reach SAR 445 billion.5 ‘’The number of smart phone payment transactions in Saudi Arabia increased by 352% to 19.7 million in April 2020’’,6 compared to 4.4 million in April 20197. ‘’The fintech market in Saudi Arabia is expectedtoreachtransactionvaluesofover USD33billionby2023’’.8Payments are likely to continue to lead the market share followed by personal finance. Online insurance sales account for a relatively small proportion of the market in Saudi Arabia. However, with new regulations on insurance aggregation coming in force, the insurrect sector is poised for higher growth in Saudi Arabia.

Fintech Saudi Annual Report 

  There is an active community of banks, universities, corporates, government agencies and investors working for the development of the fintech sector in Saudi Arabia. From e-wallets to lending and insurance aggregation to investing, the services of fintech’s have redefined the way in which businesses and consumers carry out routine transactions. The increasing adoption of these trends bodes well for Saudi Arabia, especially in the post-Covid world where physical contact is likely to be minimized for financial transactions. Going forward, there is a keen interest from fintech’s to use   more   technology innovation in their solutions. FinTech’s in Saudi Arabia are interested in utilizing Application Program Interface (API), machine learning, artificial intelligence and blockchain technologies. Fintech companies are changing the financial landscape globally and in Saudi Arabia.  They are increasing competition and driving innovation for traditional banks, insurers and asset managers. The rise of fintech activity is also spurring regulatory change. Regulatory bodies in countries around the world are investigating and implementing banking policy modifications in order to attract established fintech players and encourage the growth of local fintech’s. In order to help transform Saudi Arabia into a smart financial hub, SAMA launched its regulatory sandbox environment in 2018 that allows local and international financial technology firms to test new digital solutions they intend to launch in the Kingdom. Services and products currently being tested or have been successfully tested include e-wallets, peer-to-peer (P2P) transfers, lending and direct international transfers. There are currently 26 applicants that are undergoing sandbox testing with SAMA. In January 2020, SAMA launched the regulations governing the provision of payment services in Saudi Arabia. The proposed regulations would allow payment service providers or licensed banks to offer payment services and electronic money issuance. The regulations are aimed at establishing a supervisory and oversight framework that ensures the safety and efficiency of transactions in the payments sector. Additionally, SAMA announced the issuance of the first license for an Electronic Wallet Company and the first license for a Payment Services Company in January 2020, after the successful completion of their testing in the regulatory sandbox environment.

5.3: ABER 

The Saudi Central Bank (SAMA) and Central Bank of United Arab Emirates (CBUAE) are pleased to present this report on Project Aber: Joint Digital Currency and Distributed Ledger proof of concept Project. This report reflects our journey in the project that aimed to contribute in the body of knowledge in CBDC and DLT technologies filed. In the light of the new experiments and researches both central banks led this project as an innovative driven initiative. The initiative sought to explore whether distributed ledger technology could enable cross-border payments between the two countries to be reimagined: using a new, dual-issued digital currency as a unit of settlement between commercial banks in the two countries and domestically. The name Aber was selected because, as the Arabic word, for “crossing boundaries”, it both captures the cross-border nature of the project as well as our hope that it would also cross boundaries in terms of the use of the technology. Over the course of a year, use cases were designed, implemented, and operated; with the solution, results, and key lessons learned documented in this report. The project confirmed that Distributed Ledger Technology (DLT) can provide central banks with the ability to reimagine both domestic and cross-border payment systems in new ways. We believe that it represents a significant contribution to the body of knowledge in this field and lays the foundation for future work that we plan to explore in the future. We are pleased by the promising results, insights, and learnings described in this report and trust that they will benefit the central banking community and broader financial ecosystem in visualizing the potential of this new technology to transform the GCC financial markets and indeed our industry.

Project Aber was an initiative launched by the central banks of Saudi Arabia and United Arab Emirates to explore the viability of a single dual-issued digital currency as an instrument of domestic and cross-border settlement between the two countries. The high-level objectives of the initiative were: 

• To explore, experiment, and gain a deeper understanding of distributed ledger technology (DLT) and analyze its maturity;
• To explore an alternative DLT-based cross-border payment solution that can overcome inefficiencies in existing cross-border interbank payment approaches;
• To understand and experiment with the dual issuance of a central-bank digital currency;
• Benchmark findings against those of other central banks.

The project was structured into three distinct phases or use cases: – Use case one to explore cross-border settlement between the two central banks; – Use case two to explore domestic settlement between three commercial banks in each country – Use case three to explore cross-border transactions between the commercial banks using the digital currency. Following an extensive assessment of the current payment systems, prior work in the application of DLT in the field, and informed by the current state of the art in DLT technologies, a number of key principles were agreed by all participants to guide the execution of the project. Firstly, commercial banks must be active participants, running local nodes on the network and engaging the fullest from a technical and business perspective throughout the lifecycle of the project. This was to ensure that the employees of both central and commercial banks would benefit from the acquisition of knowledge around this new technology and also so that the project could be better informed as to challenges, risks, or improvements, from a commercial bank perspective, that would need to be addressed if the full value of the technology was to be realized in this context.

Secondly, real money would be used in the project. This was important because it forced greater consideration of the non-functional aspects, such as security, that Project Aber Final Report 9 would need to be addressed moving forward; and also, how the system would interact with existing payment systems, such as the domestic RTGS system. Thirdly, rather than simply replicate the way in which conventional payment systems work, the project sought to explore how such systems can leverage the unique characteristics of DLT to drive greater levels of distribution. By doing that, it sought to develop a system that was more resilient to single points of failure. The project confirmed that a cross-border dual issued currency was technically viable and that it was possible to design a distributed payment system that offers the two countries significant improvement over centralized payment systems in terms of architectural resilience. The key requirements that were identified were all met, including complex requirements around privacy and decentralization, as well as requirements related to mitigating economics risks, such as central bank visibility of money supply and traceability of issued currency. 

The performance objectives that were originally set for the project were exceeded, proving that DLT technologies could offer high levels of performance whilst not compromising safety or privacy. As such, the project has confirmed the viability of DLT as a mechanism for both domestic and cross-border settlement and confirmed the technical viability of a single digital currency issued by both central banks. 

The project has also identified further areas that need to be explored in the future if the approach of a single digital currency is to be implemented: key amongst these is the need to understand impacts to the monetary policy of participating states and to address, in particular, the means by which interest is calculated and disbursed to the commercial banks in each jurisdiction and how this can be applied with a single digital currency. In terms of future work, there are many directions that this project can evolve towards. Firstly, it could provide the basis for a backup to domestic and regional RTGS; providing a more distributed and potentially resilient alternative to the centralized systems that are implemented or being implemented today. Secondly, by offering a DLT-based payments rails, there is the possibility to expand to Delivery versus Payment (DvP) scenarios such as using the Aber network as a means of settlement for other forms of transaction, such as the sale of bonds or other dematerialized assets. Thirdly, there is the possibility of extending it geographically to include regional or other international central banks or linking heterogeneous networks together. In summary, the project was successful in meeting its objectives, demonstrated possible incremental benefits of this new approach to payments, identified important Project Aber Final Report 10 lessons learned that can benefit other central banks exploring the field, and has identified several areas of future expansion that can be considered by either the participants in this project or other central banks. As such, we believe this project has made a material impact on industry understanding of the field and is a substantial contribution to the body of knowledge in how the emerging technology of DLT can be applied to cross-border and domestic payments.

Background Saudi Central Bank (SAMA) and Central Bank of the UAE (CBUAE) announced a joint digital currency initiative in January 2019. It was named Project Aber, which literally translates to ‘one who crosses boundaries’ highlighting the cross-border focus of the project. The unique aspects of Aber the following key objectives were outlined before the start of the project:

• To explore, experiment, and gain a deeper understanding of distributed ledger technology and analyze its maturity;
• To explore an alternative DLT based cross border payment solution that can overcome inefficiencies in existing cross-border interbank payment approaches; 
• To understand and experiment how to dually issue a central-bank digital currency;
• Benchmark findings against those of other central banks.

The central banks laid out several principles which formed the basis for deriving the objectives and requirements of the project. Whilst there have been other central bank digital currency experiments around the world, the unique elements below made Project Aber a “first of a kind”. Active commercial bank participation an important aspect of the project was active business and technical involvement of commercial banks from a very early stage. A total of six commercial banks (three from each jurisdiction) participated in all phases of the project. This allowed an opportunity for commercial banks — key stakeholders of any wholesale CBDC project — to have a first-hand experience of using and operating a distributed ledger based interbank payment solution. The deployment architecture was highly distributed and provided complete flexibility to banks in hosting their environments.

Simplify Cross Border Payments The vision of Project Aber was to create a central bank digital currency that could be used for settlement of cross-border payment obligations between commercial banks. Such an instrument could be especially useful in a region like the Gulf Coordination Council (GCC) states where there is substantial intra-region trade and movement of citizens and residents. This could address an inefficiency in the existing correspondent banking-based payment systems that often results in delays and required commercial banks to maintain substantial Nostro accounts with their correspondent banks. This has been characterized as “trapped liquidity” in some studies [McKinsey, 2016] as, for some commercial banks, there is an opportunity cost in maintaining these balances and it creates material compliance overheads. In contrast, in Project Aber, the movement of funds would be done in real time, without the need for the commercial banks to have correspondent bank Nostro account in each country. While there have been other CBDC experiments involving two central banks, the requirement of having a single network and single digital currency for settlement of cross-border payments was a unique aspect of Project Aber. Real Money in Operations Phase Another unique aspect was the use of “real money” in the pilot project. This was achieved by commercial banks pledging real money from the deposits that they held with the central bank; using these funds to then fund their digital currency accounts on the distributed ledger. These funds would then be subsequently returned to the commercial banks’ accounts once the project had completed. Having a three-month operations phase and the use of real money had the following tangible benefits: 

• Use of real money motivated both commercial and central banks to think about how the digital currency will be managed in their books and which core banking systems would be impacted if such a system was to be implemented in production and scaled. This led to a series of lessons learned and observations that would have been unlikely to have been made if real money had not been pledged and used;

• Having three months of operations phase, including several knowledge transfer sessions for banks, provided business and IT operations teams of the banks with invaluable hands-on experience in managing a DLT based payment system;
• The project team received valuable feedback from the banks on additional features and tooling that would be required or would be useful in managing and using a CBDC of this type in the future; The experience gained and lessons learned from the operations phase is expected to help streamline issues in the eventual rollout of a DLT based interbank payment system as an alternative to traditional RTGS and international wire transfers.

5.3: NEOM 

NEOM ( ‘city of the future’) is a new vision of what the future could be. It’s an attempt to do something that’s never been done before and it’s coming at a time when the world needs fresh thinking and new solutions. Put simply, NEOM will not only be a destination, but a home for people who dream big and who want to be part of building a new model for sustainable living. NEOM will be a hub for innovation, where established global businesses and emerging players can research, incubate and commercialize groundbreaking technologies to accelerate human progress.

Commerce and industry will be integrated into community centers to build supply chains that facilitate all human needs, making it a fully functioning area. The Financial Services sector aims for universal access to financial services by achieving seamless end-to-end solutions, fully digital payments, advanced interfaces, biometric identification systems and other cutting-edge technologies. 

Goals of NEOM:

1. Aim for 100 percent financial inclusion through seamless technologies and services

The Financial Services sector will aim for universal access to financial services by achieving seamless end-to-end solutions, digital payments, advanced interfaces, biometric identification systems and other cutting-edge technologies.

2. Create a robust financial hub that encourages innovation of ideas and products

The Financial Services sector aims to infuse the best practices with pioneering technologies to make NEOM a hub for entrepreneurial talent, creative financial solutions and new business models.

3. Become the global champion for green and ESG financing by creating the world’s leading green finance trading platform.

The Financial Services sector aims to infuse the best practices with pioneering technologies to make NEOM a leading facilitator and accelerator for emerging markets and a hub for entrepreneurial talent, creative financial solutions, fintech, and new business models.

6. Concluding Remarks 

The research paper started with identifying blockchain and how much of an impact it could make to the financial ecosystem as a well-developed technology. 

Case studies of the Chinese market as a major FinTech adapter and how its adaptation and government supported paved the way for it to become a mature market. CBDC and a case study for the most impressive well implemented project of the digital Yuan and its major shadowing of the retail Chinese market followed by a deep dive into how other major countries would try and implement the same methodology at some time. 

Saudi Arabia has identified itself through the last couple of years as a heavy FinTech adapter due to the banking scene and its financing issues, as FinTech tries to solve the markets issue by being more lucrative towards individuals. Saudis have been expanding locally at a slow yet promising rate we took to analyze the numbers at greater depths. Aber-Bridge- an outstanding achievement between Saudi Arabia and UAE and a certified solution to free-trade aches.

NEOM-the city of future- but this time at a deep economical point of view, as the city seems to have must-see solutions to all modern-day troubles. The South Asian and MENA market are defiantly ones to watch for the five upcoming years as the growth rate economically looks to continue so does the financial technological solutions scheme.    

References: 

Reference 1:  

https://link.springer.com/article/10.1186/s40854-016-0034-9

Reference 2:

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=6910&context=lkcsb_research

Reference 3: 

http://dstore.alazhar.edu.ps/xmlui/handle/123456789/96

Reference 4:

http://dstore.alazhar.edu.ps/xmlui/handle/123456789/96

Reference 5: 

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3528068

Reference 7:

https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3528068

Reference 8: 

https://helda.helsinki.fi/bof/handle/123456789/14952

Reference 9: 

https://fintechsaudi.com/wp-content/uploads/2020/03/Fintech_Saudi_Annual_Report-Eng.pdf

About the author

Khaled Aljuraywi

Khaled is the valedictorian to the graduating class of 2021 at Al-Rowad International Schools. He prides himself on being a hands-on learner. Specializing in Finance, he has worked in various financial positions – Investments Analyst at Annova, an Investments desk intern at Saudi Investment Bank, as well as a costing analyst at Mayyar. He has written various articles for publications like Elaph and Sada and he is now pursuing his own personal blog directed towards everything economics and business.

The post Implications of Blockchain for Financing with special emphasis on Central Banking Policy appeared first on Exploratio Journal.

Educational inequality is an extremely crucial and complex problem due to the fact that there are actually various factors that might lead to the inequality of education in society such as a lack of educational resources in urban and rural areas as well as regional inequality. However, the most significant origin of the cause to the educational inequality in China is the feudal ideologies and policies from a thousand years ago. In 1978, undoubtedly, China achieved tremendous economic success due to its economic reform (Henan), but the educational inequality was hard to solve or eliminate since the radical Chinese culture had passed from the generation to generation. The feudal ideology really had an affect on unequal allocation of resources and opportunities for education. However, as time elapsed, the educational system also improved a lot compared to the previous ones. The essay will mainly focus on the changes of educational inequality in China caused by feudal ideology and the differences between rural and urban areas regarding making the better the educational system. 

Gender inequality

Gender inequality is definitely the reflection of both social and economic inequality in China. The radical ideology of gender inequality has already been present in China for a long time and gradually became the predetermined arrangement in society, which even appears in the system of language in Chinese culture. There are also many different phrases and sayings for the son and the daughter, for instance: wangzi cheng long (“Expect one’s son to have a bright future/ Expect one’s son to be a dragon”) ; shengge nǚer peiqianhuo (“The birth of a daughter means the loss of money)” (Ganzhi Di). According to the conventional thoughts that formed throughout history, in patriarchal world, there is a stereotypical assumption that men are more valuable than women that men are not only more powerful but intrinsically more worthwhile than women since only men can deserve power and privilege.

A vast majority of families still expect to rely on sons for old-age support, and are supported with by the traditional saying, “Sending girls to school is useless since they will get married and leave the home” (Hannum12). Compared to boys, girls face lower educational expectations, which measures commonly used to capture educational ambitions— specifically, they indicate the level of formal schooling that one would like to complete, and a greater likelihood of doing household chores due to social acceptance. Even though some girls outperform some boys in some academic areas, they still do not have the opportunity or chance to go school to attain advanced education. Consequently, most families only send the boys to school while the girls mainly stay at home learning how to do the housework due to the fact that most parents have higher expectations of boys than of girls that girls are unlikely to succeed in the labor market in the future. “In China, Li and Tsang (2005) suggest that, because a good marriage is more important than a good job for rural girls’ long-term welfare, some parents may think more about maximizing the chances of a good marriage than about investing in long-term career options”(Emily Hannum). These examples also suggest that the cultural norms in China have lead to the different socialization of boys and girls. 

The conventional ideology mainly exists in the rural areas in China since more children have to compete with more siblings within a family. Especially large gender gaps can be found in rural Jiangxi, Anhui, and Sichuan provinces. For instance, according to the study by Li and Tsang, in Gansu and Hebei provinces, there are 400 hundred households who revealed that they had lower educational expectations for girls than for boys (Li and Tsang). Thus, in these rural areas, most girls stay in the family to help their parents do some farm work and wait to marry a man in the future. Most educational opportunities will be given to their sons due to the cultural norms. Another case study of Gansu province shows that 84 percent of the poorest boys and 90 percent of all other boys in the survey were enrolled in school. About 81 percent of the poorest girls and 85 percent of all other girls were enrolled. Poor girls are the most disadvantaged because the boys practically absorb all the educational resources (Emily Hannum). 

As a result, men have a greater opportunity to attain educational resources due to the feudal ideology(traditional ideology in China: people favor boys over girls)in place, which absolutely affects the trend of the education since a lot of girls do not even have the chance to contribute to the society or pursue their dreams. Inevitably, families still apply the feudal ideology when making decisions on education, and girls’ dreams continue to be buried in their own hearts.

Educational inequality in urban and rural areas

Furthermore, educational inequality occurs obviously in the urban and rural areas due to the unequal allocation of resources and faculties. The hukou system has the direct relation to the separation of urban and rural areas. The hukou system was established in 1958, and was initially used by the Communist government as a means of separating rural and urban populations and restricting rural-to-urban movement (Solinger, 1999; Liang et al, 2007; Kwong, 2006). Consequently, the hukou system not only makes it difficult for rural populations to gain permanent status of residence in urban ares, but also it results in residents in rural areas starting to lack in sufficient state provisions (Henan Cheng). The rural Chinese had to be self-reliant and contribute to the society without any support from society. The migrated workers from rural areas to urban areas did not have rights to enjoy the same benefits as their urban counterparts (Qiang Ren and Qiang Fu), which caused the huge differences between urban and rural areas in China. 

The substantial urban-rural disparities are mainly due to disparities of the teaching resources and school facilities. According to the study by Wang,  in 2001,  40.9% of primary school teachers in urban areas had finished secondary education, while only 20.3% of their rural colleagues had done so; while 23.5% of middle-school teachers in urban areas had at least graduated from tertiary schools, only 9.4% of their counterparts in rural areas had achieved that level of education (Qiang Ren and Qiang Fu). The advanced human capital is actually limited and scarce in rural areas since most qualified human capitals are more incentivized to stay in the city to teach since they can earn higher income. In addition, the rural areas will mainly hire some Minban teachers(community sponsored teachers) who have lower levels of education due to the scarcity of qualified teachers. It shows the striking disparity of the levels of human capital in rural and urban areas, which indicates that students in rural areas cannot possibly attain the accessibility of the advanced resources of teaching. In rural areas, they do not have enough money to buy the teaching equipment, which also leads to the subordinate level of education to students. According to the China Human Development Report, the illiteracy rate was about 11.6 percent in rural areas, which was three times than the illiteracy rate in urban areas. From the evidence, people can clearly see that the illiteracy rate in rural areas is high due to an unequal allocation of resources and human capital. Consequently, it even drags the educational gap to become larger and larger. 

Educational inputs such as school financing or funding also cause the crucial inequalities. According to the study by Yuan,  “the average urban primary per-student expenditure was 1.86 times that of rural per-student expenditure in 2001. The urban-rural gap in educational finance was even greater at the secondary level: per-student expenditure of lower secondary education in urban areas was almost double that of per-student expenditure in rural areas” (Henan Cheng). The government policy of previous times in China also allocated disproportioned resources of education in two areas and neglected the special needs in rural areas such as textbooks, tables, etc. The rural areas during that time did not have enough wealth and because of the lack of support from the government, they could not even have the opportunity to improve the educational resources. Most students did not even have the chance to graduate under that poor educational system and even some families forced their children to quit school due to the fact that they could not continue to afford for their children to study in school. The low levels of resources also could not attract a lot of students in the rural areas to study and thus led to lower school enrollment. As a result, the disparity between rural and urban areas became even wider due to the huge inequalities of the teaching resources and human capital.

Better trend in education inequality

Obviously, the educational inequality was extremely hard to eliminate, but it gradually became better and better in China.

As time elapsed, political priorities and education policies dramatically shifted and changed throughout the history of the People’s Republic of China. The purpose during the China’s period was to emphasize economic development and social equality by implementing various policies in order to reduce the educational inequality. 

In 1949, the central government tried to arrange the new educational system within China, which was totally different from the previous one. Firstly, the system should would have the right political nature and be led by the Chinese Communist Party. Secondly, it would serve the needs of rapid economic development. Many universities were reduced due to the fact that, in this case, it helped the government ease burdens(Ouyang Kang). “In 1951, one-third of all primary and general secondary-school teachers were employed by Minban schools, which refer to the private educational institution. Adult-education programs also contributed substantially to the expansion of educational opportunities in rural areas. Winter schools and spare time literacy programs in the early and mid-1950s affected tens of millions of peasants; worker-peasant schools enrolled smaller numbers of students, but many of these students continued on to college. The effectiveness of these efforts in improving literacy among peasants is well documented” (Emily Hannum).  

In 1958, the Great Leap Forward movement created the new educational policy to motivate the equality of education. Many policies during the movement focused on basic education and many universities were moved into the rural areas. For instance, more work-study opportunities and part time-schools were opened in the rural areas in order to expand the opportunities for poorer people. Thus, the rural students had the chance to attain better educational resources. In 1977, the national standardized examination, called gaokao, was highlighted, something extremely meaningful for the the Chinese society. According to Wang and Ross (2010), the gaokao brings about opportunities for success and social mobility, which is especially attractive to the students in rural areas where opportunities to economically improve their well-being and lives are scarce. Every student in every area of China has the opportunity to sit the gaokao, no matter from the rural areas or urban areas, which can even be the turning point for rural students. There was a huge expansion in university students immediately after the change of political systems in education. “The number of university students increased from 89% in 2001; the percentage of those who could enter university increased from 9.8% in 1998 to 13.2% in 2003; Master’s degree students increased 93%; and Ph.D. students increased from 45,000 in 2001 to 77,000 in 2003, an increase of 71%. It is more meaningful if you think that all of these increases happened in three years and the numbers of university lecturers did not significantly increase. The proportion of university lecturers to university students changed from 1:8 before 2001 to 1:16 in 2003”(Ouyang Kang). Moreover, the government started paying more attention on the educational system and resources in the rural areas, and increasingly provided more funds in order to ensure the rural students’ sufficient resources. 

During 1985 to 1990, China started to promote the nine year compulsory education. The People’s Congress passed the decision of the new educational system and that “all the children at age of six should have the right for schooling regardless of gender, ethnicity, or race.” The law also requires that “the state, the community, schools, and families should guarantee the right of all children for schooling” (Zhao Tiedao). The ideology of “education for the people and by the people” promoted China to construct a fair society and let every child has the right to access education. Consequently, massive publicity campaigns raised funds to improve school facilities. Therefore, the proportion of dilapidated school building reduced from 17 percent to 2 percent, which meant that the conditions of schools were improving and provided solid groundwork for universalizing compulsory education in rural areas (Zhao Tiedao). A lot of institutions also raised funds and donated money to rural areas in order to help students in the impoverished areas get the balanced educational resources. From the 1996 to 2000, communities including some overseas contributors donated almost 31 billion RMB to support the rural compulsory education program. For instance, the Project of Hope raised 2 billion RMB for construction of 8,300 new schools in rural villages, which attracted 2.3 million dropout children back to school. Besides, 10,000 sets of library books were provided to rural schools and 2,300 rural teachers were trained. The Spring Bud Project, implemented by the All-China Women’s Federation, raised 500 million RMB which assisted getting 1.3 million girls from poor rural and ethnic families access to schooling”. (Zhao Tiedao). By changing the educational system into the nine year compulsory educational system, China reduced the illiteracy rate and more people could enjoy the equal rights of education. The situation of educational inequality became better and better. 

Furthermore, the feudal ideology of gender inequality was gradually eliminated in mind and practice, and women started to have more rights. In 1949, newly introduced marriage and labor laws improved the social statuses of women and peasants. It was the turning point to turn the situation for women. During the time of the cotton textile industry, women proved their power to earn a lot of income and changed the perception of women being less capable than men. “For women to earn high incomes in today’s world, however, it is not necessary to have identical conditions to those in Imperial China. With advances in technology and changes in institutions, economic opportunities favoring women provide a richer variety of ways for women to earn high incomes today” (Xue, Melanie Meng). Women also had the same strength to complete the tasks as the men. As more people get educated, the ideology of gender inequality started to get adjusted and alternated toward an equal and fair path. Although the cultural norms still exist nowadays, women have equal rights, and have the ability to express their thoughts, and can gain the same level of education. 

In China, educational inequality was an extremely serious problem due to the social norms of the ideology called gender inequality and unequal resources in rural and urban areas. However, after  the long term of changes, the educational inequality started to eliminated and everyone has the rights to attain the same level of education in China. 

Citations

Cheng, Henan. Inequality in Basic Education in China: A Comprehensive Review. 17 Aug. 2015.

‌“Gender Inequality of Classical Chinese: A Study of Gender-Based Words – ProQuest.”

www.proquest.com, www.proquest.com/openview/eb1426c7b73fa36727c7f66616a90aa8/1?pq-origsite=gscholar&cbl=18750&diss=y. Accessed 20 Sept. 2021.

Qiang, Fu. Educational Inequality under China’s Rural ^ Urban Divide: The Hukou System and Return to Education. 2010.

‌Hannum, Emily, et al. Family Sources of Educational Gender Inequality in Rural China: A Critical Assessment. Elsevier, Sept. 2009.

‌book118.com. “《浅谈我国公民受教育机会不平等的原因及对策.Pdf》-支持高清全文免费浏览-Max文档.” Book118.com, 2015, max.book118.com/html/2015/0729/22212459.shtm. (Chinese)

‌“Study: Gender Inequality Serious in Rural Areas.” www.china.org.cn, www.china.org.cn/english/China/141286.htm. Accessed 20 Sept. 2021.

‌Wang D 2003, “China’s rural compulsory education: current situation, problems and policy alternatives”, WP 36, Institute of Population and Labor Economics, Chinese Academy of Social Sciences, Beijing, http://iple.cass.cn/file/36.pdf 

Hannum, Emily, and Yu Xie. Trends in Educational Gender Inequality in China: 1945-1985. 1994.

‌Adams, Jennifer. Girls in Gansu, China: Expectations and Aspirations for Secondary Schooling. ScholarlyCommons, 2008.

‌Kang, Ouyang. Higher Education Reform in China Today. 2004.

‌TSANG, MUN C. INTERGOVERNMENTAL GRANTS and the FINANCING of COMPULSORY EDUCATION in CHINA. June 2001.

‌Zhang, Tiedao, et al. Universalizing Nine-Year Compulsory Education for Poverty Reduction in Rural China. 25 May 2004.

‌Xue, Melanie Meng. High-Value Work and the Rise of Women: The Cotton Revolution and Gender Equality in China. Dec. 2018.

About the author

Ana Mao

Ana is currently a Senior at the Shanghai High School, International Division.

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