Midterm report topic the impact of inflation on savings in vietnam, israel and jordan

The interconnected relationship between inflation and savings is evident through factors such as gross savings % of GDP, unemployment rate % of total labor force, life expectancy at birt

Vietnam National University, Ho Chi Minh University of Economics and Law

Midterm Report

Topic: THE IMPACT OF INFLATION ON SAVINGS

IN VIETNAM, ISRAEL AND JORDAN

Subject: Panel data analysis Class: 231TO5603 Lecturer: PhD.Nguyễn Phúc Sơn

Group 2 Membership performed:

Student’s ID

Full name

Doan Quynh Anh K214130930

Mai Y Nhu K214132038

HCMC, 16/12/2023 se

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Table of Contents

T INTRODUCTION 1

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I INTRODUCTION

The global economic crisis has had profound implications for market economies, impacting inequality and widening the wealth gap between economic classes Moreover, the crisis has exacerbated poverty, leading to consequences that affect the trends in asset accumulation for a small segment of society In recent years, countries worldwide have been progressing towards economic recovery post-crisis Despite the implementation of recovery policies globally, the pace of growth varies significantly among nations, accompanied by differing economic differentials and inequalities This has contributed to an increase in inflation rates in many countries globally The interconnected relationship between inflation and savings is evident through factors such as gross savings (% of GDP), unemployment rate (% of total labor force), life expectancy at birth (years), inflation, consumer price index (%), and gross domestic product (GDP)

Among these, Asia stands out with its diverse and developing economies, each possessing unique characteristics and challenges Economic diversification implies reduced dependency on a single industry or sector In the case of Asia, this diversification helps minimize the risk of supply shocks affecting specific industries, potentially leading to inflationary pressures Additionally, a diversified economy provides individuals and businesses with various income sources and investment opportunities, potentially influencing the quantity and nature of total savings for the population

When inflation occurs, it erodes the purchasing power of money over time, with the general prices of goods and services increasing, leading to decreased purchasing power On the other hand, savings constitute the portion of income not used for consumption Therefore, total savings tend to increase People tend to accumulate and save money to cope with inflation Individuals try to maintain their budget by limiting unnecessary expenses, restricting spending on luxury items, and planning frugal spending to cope with inflation

In summary, inflation and savings are considered noteworthy issues Understanding how these two concepts are related is crucial for individuals, businesses, and policy planners in managing their finances and making informed decisions However, the relationship between inflation and savings in Asia is complex and influenced by various factors To comprehensively study and analyze all aspects, considering the macroscopic nature and broad scope of the topic, this research will narrow its focus to three countries in Asia, namely Israel, Jordan, and Vietnam The study will utilize observational data from the period 2012-2019 to explain specific characteristics of the relationship on multiple dimensions through inflation data, consumer characteristics, and savings trends In addition to factors such as gross savings, the unemployment rate, average life expectancy at birth, and GDP are particularly crucial as they interact

with each other to reveal the correlation between inflation and savings Finally, regression methods will be applied to draw significant conclusions about the relationship between inflation and savings in the market economies of the three countries during the specified period

Il LITERATURE

2.1 Literature theories

2.1.1 Linear Regression Model

The research overview relies primarily on the analytical method of regression modeling The linear regression model is a statistical approach used to model the relationship between a dependent variable and one or more independent variables It is one of the simplest and widely used approaches to model the relationship between two variables The basic idea behind linear regression is to find the best-fitting line through data points to minimize the total squared difference between observed values and values predicted by the model The linear regression model is a statistical technique used to study the relationship between a dependent variable and one or more independent variables In this context, the dependent variable is the explained or predicted variable, while the independent variable(s) are variables that help explain or predict the dependent variable

Linear regression finds extensive applications across various fields In economics, it is employed to analyze relationships between economic variables such as supply and demand, inflation and unemployment, and more In finance, linear regression is utilized in asset pricing models, risk management, and optimizing portfolio returns Additionally, linear regression is applied in medical research to study relationships between variables like dosage and drug response, disease progression, and others Beyond analyzing social phenomena like educational attainment, income distribution, and voting behavior, linear regression is instrumental in quality control, process optimization, and system modeling

2.1.2 Independent variable in linear regression model

In a linear regression model, the independent variable is the manipulated or altered variable used to observe its impact on the dependent variable It is also known as the predictor or explanatory variable The independent variable is employed to predict or explain the values of the dependent variable It plays a crucial role in the linear regression model, as it is used to predict or explain changes in the dependent variable

By manipulating or altering the values of the independent variable, one can observe how it influences the values of the dependent variable

2.1.3 Dependent variable in linear regression model

The dependent variable in a linear regression model is the variable that is explained or predicted, and it is typically a quantitative variable The primary objective of the linear regression model is to estimate the relationship between the dependent variable and the independent variable(s) by fitting a straight line (in simple regression) or a plane (in multiple regression) to the data points Understanding the role of the dependent variable is crucial in assessing the accuracy and significance of the model's predictions

In the linear regression model, the dependent variable plays a vital role in comprehending the relationships between variables and determining the accuracy of the model's predictions It is used to evaluate the adequacy of the model and assess the importance of the independent variables in predicting the dependent variable

2.2 Literature review

A regression model is often represented as a mathematical equation, where independent variables are used to predict the value of the dependent variable In the context of regression analysis, the model elucidates the relationship of how dependent variables impact independent variables Specifically, it clarifies the impact of factors such as total savings (% of GDP), unemployment (% of total labor force), life expectancy at birth (years), inflation, consumer price index (%), and gross domestic rate (GDP)

2.2.1 Definition

Equation:

Gross saving = B0 + BI Inflation + B2 GDP + B3 Unemployment + B4 Life expectancy at birth + ¢

First, we need to explain the definitions of the variables in the above model Total savings represent the dependent variable Total savings refer to the total income saved

by households, businesses, and the government before deducting depreciation This is

a crucial indicator of the financial health of the economy and its investment capacity Gross savings include personal savings, business savings, and government savings Personal savings indicate the portion of disposable income that households save instead of spending Business savings refer to the income or profit retained by businesses that reinvest in their operations Government savings are the difference between government income and expenditure, excluding interest payments Total savings play a vital role in the long-term growth and development of the economy It

provides the necessary capital for investment in infrastructure, education, research and development, as well as other production activities Additionally, a high level of total savings can contribute to reducing dependence on foreign borrowing, thereby enhancing economic sovereignty and stability

In addition to dependent variables, there are independent variables First is the inflation rate, which is the percentage change in the general price level of goods and services in an economy over a specific period It measures the extent of price increases or decreases in goods and services Alongside this is GDP Gross Domestic Product (GDP) is a measure of a country's economic performance It represents the total value of all goods and services produced within the borders of a country over a specific period, usually annually or quarterly GDP is widely used as an indicator of a nation's economic health and is a key determinant of overall economic efficiency and living standards Furthermore, the unemployment rate is a crucial economic indicator measuring the percentage of the labor force currently unemployed but actively seeking employment It is calculated by dividing the number of unemployed individuals by the total labor force, including both employed and unemployed individuals The unemployment rate is an essential measure of the economic health of a country, reflecting the availability of jobs and the overall welfare of the labor force Finally, life expectancy at birth is the average number of years a person is expected to live, calculated from birth, based on the current mortality rate in a specific population It 1s

a critical indicator of the overall health and well-being of the population and is influenced by various factors such as access to healthcare services, socio-economic conditions, lifestyle choices, and public health measures

2.2.2 Significance

In regression models, the relationship between the independent variable and the dependent variable closely interacts In linear regression, the regression line is straight Any changes in the independent variable directly impact the dependent variable, clearly demonstrated through each variable

Firstly, the inflation rate can significantly affect total savings Inflation refers to the increase in the prices of goods and services over time, reducing the purchasing power

of money When inflation is high, it erodes the value of money, resulting in a decrease

in the real value of savings This means that although the nominal amount of savings remains the same, its purchasing power decreases due to rising prices

Additionally, GDP also influences the dependent variable through various factors When GDP increases, it often leads to higher incomes for individuals and businesses With higher income, households and businesses are more likely to save This is because, when people earn more money, they tend to save a portion of their income

for future needs or investments As businesses expand and invest in new projects, they may allocate more capital for growth and future reserves, contributing to the overall savings Furthermore, robust GDP growth can boost consumer confidence When consumers are optimistic about the future economic situation, they are inclined to save more for future purchases or unforeseen circumstances

The impact of the unemployment rate on total savings is also a noteworthy concern During periods of high unemployment, individuals and households may experience reduced income or even job loss, leading to a decrease in the ability to save This is because unemployed individuals have less disposable income, making it difficult for them to allocate money for savings Consequently, a high unemployment rate may result in a lower overall savings rate in the economy Conversely, when the unemployment rate is low, many individuals may be employed and earning income This can lead to an increase in total savings as people have more disposable income to allocate for savings and investments Additionally, a lower unemployment rate can contribute to enhanced economic stability and confidence, encouraging individuals and businesses to save and invest for the future

Finally, the impact of life expectancy at birth on total savings is also a significant factor Life expectancy at birth plays a crucial role in shaping an individual's saving behavior With increased life expectancy, individuals are more likely to save over a longer period, anticipating an extended retirement As a result, individuals tend to save more to ensure a comfortable retirement and sustain their living standards Moreover, increased life expectancy can lead to a shift in an individual's perception of risk, as people become more concerned about financial stability during their retirement years This change in perception encourages individuals to save more, invest in long-term financial instruments, and contribute to retirement funds

To comprehensively understand the impacts of the dependent variables on independent variables, the study will analyze the relationship through experimental analysis of the linear equation model This involves examining and comparing the results of each variable and constant, providing concluding remarks and identifying trends for each observed effect

III Data and method

3.1 Data:

To establish the foundation for this research, data was gathered from reliable and official sources Drawing upon international economic databases and leading financial organizations, the data for this study was sourced from the World Bank—a vast and

reputable data repository that reflects precise measurement and monitoring of economic indicators across global nations

The data collection process was executed using statistical data analysis tools and methods We identified specific years to ensure that the time series under consideration is sufficiently long to comprehend trends and fluctuations in economic indicators The data values underwent careful examination to eliminate outliers and ensure consistency

3.2 Linear Regression Model Method

In the process of researching the impact of inflation on the savings rate of four Southeast Asian countries, the choice of a linear regression model is highly valued for several reasons Firstly, this model is selected because it reflects an ideal assumption about the linear relationship between inflation and savings, facilitating a clear understanding and explanation of the impact of inflation on the dependent variable The linear regression model also provides simplicity and high statistical significance The assumption of linearity helps make the analysis clear, reducing complexity in the interpretation of results Moreover, regression coefficients offer detailed information about the influence of each independent variable on savings, making the conclusion- generation process transparent and understandable

Furthermore, the linear regression model is a flexible and widely used predictive tool

in statistics This creates favorable conditions for applying the model to reality and utilizing the results to propose policy recommendations that can be applied in the economic management of Asian countries Thus, the choice of the linear regression model not only reflects careful theoretical consideration but also ensures the applicability and practicality of studying the impact of inflation on savings in the diverse economic context of the region

The Regression Model Equation:

Gross saving = B0 + BI Inflation + B2 GDP + B3 Unemployment + B4 Life expectancy at birth + ¢

In which:

- Gross saving represents the dependent variable

- Inflation, gross domestic product, unemployment rate, and life expectancy at birth are a set of independent variables

- BO represents the intercept term

- Bl, B2, B3, and B4 represent the coefficients for each independent variable They indicate the degree of change in "Gross Saving" when each independent variable increases by one unit, keeping the other variables constant

- e represents the error term

The goal of the model is to find the optimal values for BO, Bl, B2, B3, and B4 to simulate the relationship between independent and dependent variables This is typically achieved using the least squares method (linear regression method) Once the model is estimated, it can be used to predict the value of the dependent variable based

on the values of the independent variables

IV Result

Using Stata or any statistical software capable of performing regression analysis, it is possible to estimate the specified regression model The results of the regression analysis will provide a profound insight into the relationship between inflation and savings in Jordan, Israel, and Vietnam, while examining the influences of GDP, unemployment, and life expectancy at birth

By examining the coefficients of the independent variables, it can be determined which variables have statistically significant effects on gross savings This approach allows for the identification of the variables that play a substantial role in influencing gross savings in the context of inflation The regression analysis will involve estimating the coefficients (B0, B1, B2, B3, and B4) to optimize the model and provide insights into the quantitative relationships between the variables This method is instrumental in understanding the magnitude and direction of the effects of GDP, unemployment, and life expectancy at birth on gross savings in the selected countries 4.1 Vietnam

To examine the relationship between inflation and savings in Vietnam, we regressed with the command line in stata:

Source ss df MS Number of obs) = 8

F(4, 3) = 64.31

Model 42.8654648 4 190.7163662 Prob > F = 9.0031

Residual -499909864 3 166636621 R-squared = 6.9885

Adj R-squared = 9.9731

Total 443.3653746 7 6.19505352 Root MSE = 40821

GrosssavingsofGDP Coef Std Err t P>|t| [95% Conf Interval] Inflationconsumerpricesannu - 5969103 - 1083187 5.51 6.012 «2521919 9416286

Unemploymentoftotallaborf -4.101117 „6798917 -6.03 0,009 -6.264835 -1.937398 Lifeexpectancyatbirthyears 26.45223 4.837271 5.47 9.012 11.05787 41.84658

GDP 5.74e-11 1.06e-11 5.43 0.012 2.38e-11 9.10e-11 _cons -1936.952 360.4275 -5.37 6.013 -3083.993 -789.9107

This model fits the data at the 5% significance level because F = 64.31 and Prob > F = 0.0031

And R-squared = 0.9885: the model explains 98.85% of the total variation in Vietnam's gross saving

From the table above, we see that the P_value of inflation is 0.012 < 0.05 This shows that inflation is considered statistically significant in explaining fluctuations in gross savings in Vietnam And the coefficient of inflation is 0.5969103; this means that when inflation increases by | unit, the country's gross saving increases by 0.5969103 units, assuming other variables remain unchanged

In addition, we see that life expectancy at birth is also statistically significant in explaining fluctuations in gross savings in Vietnam Because P_ value = 0.012, less than the 5% significance level And the coefficient of life expectancy at birth is 26.45223, which means that with | unit increase in life expectancy at birth, the country's gross savings will increase by 26.45223 units, assuming other variables remain unchanged

Unemployment is also statistically significant with gross savings, with a P_ value of 0.009 And the coefficient of unemployment is -4.101117; this means that with | unit increase in life expectancy at birth, the country's gross savings will decrease by 4.101117 units, assuming other variables remain unchanged

Besides, GDP is also statistically significant with gross saving, with a P value of 0.012 And the coefficient of GDP = 5.74e-11, this means that with | unit increase in life expectancy at birth in Indonesia, the country's gross savings will increase by 5.74e-11 units, assuming other variables are equal change

4.2 Israel

To examine the relationship between inflation and savings in Israel, we regressed with the command line in Stata and got the following regression table:

Source ss df MS Number of obs) = 8

F(4, 3) = 21.72

Model 9.1349482 4 2.28373765 Prob > F = 8.0149

Residual „315594673 3 195168224 R-squared = 9.9666

Adj R-squared = 9.9221

Total 9.45045288 7 1.3500647 Root MSE = 3243

GrosssavingsofGDP Coef Std Err t P>|t| [95% Conf Interval]

Inflationconsumerpricesannu -1.491284 „2568521 -5.81 0.019 -2,308792 -.6738657

Unemploymentoftotallaborf 1.52591 6357481 2.40 0.096 - 4973246 3.549144 Lifeexpectancyatbirthyears -1716994 1.695946 90.19 0.926 -5,225557 5.568956

GDP 4.54e-11 1.47e-11 3.09 0.054 -1.42e-12 9.22e-11 _cons -9.774305 138.7274 -0.07 0.948 -451.2669 431.7183