midterm report topic the impact of tourism on income inequality in developing economies 2000 2022 does the kuznets curve hypothesis exist

In this context, attention to the impact of income inequality is not only an issue within the scope of politics and economics but also opens up many aspects to other fields, including th

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UNIVERSITY OF ECONOMICS AND LAW

Lecturer: PhD Pham Hoang Uyén

Group 2 Membership performed:

CHAPTER 3 RESFEARCH RESULTS AND DISCUSSION 7

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REFERENCE 20

INTRODUCTION Income inequality is a global problem that constantly poses serious challenges to social balance and sustainability In today's diverse and complex environment, the ever-widening gaps between the rich and the poor are contributing to significant disparities in many aspects of life In this context, attention to the impact of income inequality is not only an issue within the scope of politics and economics but also opens up many aspects to other fields, including the tourism industry calendar The tourism industry, as an important part of the global economy, is not only a source of income but also a bridge of culture, trade, and exchange between countries and peoples However, can income inequality affect the sustainability and equity of the tourism industry, and can the industry play a role in reducing income disparities between classes? These questions are not only the responsibility of researchers and managers but also pose a great challenge to the international community and our own society

In an effort to gain insight into the link between income inequality and the tourism industry, one of the key concepts that comes into focus is the Kuznets curve The Kuznets curve is a model of social thinking that describes the complex relationship between income and life satisfaction, asking whether people with high incomes have different travel experiences than those with lower incomes or not More than just a theoretical concept, the Kuznets curve could be the key to better understanding the impact of income inequality on travel behavior and decisions

This article will delve into the relationship between income inequality and the tourism industry and explore the hidden aspects behind the Kuznets curve, with the hope of providing new insights and knowledge insightful, helping us face the growing challenges of a rapidly changing world

CHAPTER 1 LITERATURE REVIEW 1.1 Definition of variables

A regression equation is an important mathematical tool in statistics and machine learning that helps show the relationship between a dependent variable (target variable) and one or more independent variables (explanatory variables) The main goal of a regression equation is to find a formula or model that predicts the value of the dependent variable based on the values of the independent variables Regression equations are widely used in many fields, from economics to medicine and social sciences

We have the regression equation: VWVgVWyy 0 Vy E VyVVVgVVVVyy E VyVVVgVVyy E VyVVVgVVVyy E Vyy E Vyy Based on the above model, we include factors affecting tourism into the variables in the model to show the relationship between them, appropriate predictions and conclusions through this method

The independent variable we show in the model is income inequality Income inequality, in economic terms, is a significant difference in the distribution of income between individuals, groups, populations, social classes or countries This means that the majority of an economy's total income is concentrated in the hands of a small percentage of the total population When income inequality occurs, there is a large gap between the assets and wealth of one segment of the population compared to the remaining segment of the population Income inequality can be analyzed through many ways of segmentation, including income distribution by gender, ethnicity, geographic location and occupation

We indicate the dependent variables through factors First of all is foreign direct investment FDI is the process by which an organization or individualfrom one country invests in another country and engages in business activities in that country FDI frequently occurs when a company wants to expand its operations into a foreign market or take advantage of that country's economic and legal benefits In addition, GDP per capita is also considered a dependent variable It is considered an important economic indicator that measures the average economic output generated by each individual in a country It is calculated by dividing a country's Gross Domestic Product (GDP) by its total population GDP per capita serves as an essential tool to understand the economic well-being of a country and its people Finally, tourism revenue is the last dependent variable mentioned in this model Tourism revenue refers to the income generated from the activities and expenditures of tourists visiting

a particular destination It includes the amount of money tourists spend on accommodations, transportation, food and drinks, shopping, entertainment, and other travel-related services

1.2 Meaning In the regression model, the relationship between the independent variable and the dependent variable have a close impact on each other In linear regression, the regression line is straight Any change in the independent variable has a direct effect on the dependent variable This is clearly shown through each variable First, income inequality can impact foreign direct investment (FDI) in many ways The wave of FDI after Vietnam's accession to the WTO may increase income inequality not only in urban areas, but also between urban and rural areas In addition, regions with favorable natural and socio-economic conditions and a level of FDI attraction in industrial and service sectors should have high growth rates and job opportunities more and thus the income level is also much higher than in areas that attract little FDI capital Finally, FDI can create growing income inequality among Vietnam's economic sectors Regions with more favorable conditions to attract FDI often have larger revenues than other regions

Second, income inequality impacts GDP per capita in many ways On the one hand, if income inequality is too high, it can cause social and political instability, reduce investment and increase education costs, leading to a decline in GDP per capita On the other hand, a certain level of income inequality can encourage competition and promote economic growth

Finally, income inequality can have a significant impact on tourism revenues When the income of a large portion of the population is limited, the ability to spend on activities such as tourism is also reduced This could lead to a reduction in tourist arrivals and corresponding revenue On the contrary, people with higher incomes may spend more on travel, but their number is usually less than the number of people with lower incomes Therefore, income inequality can create a large gap between the number of potential tourists and those who are actually able to travel This could also impact pricing and service structures in the tourism industry, as businesses try to attract customers from different income segments In short, income inequality can have a strong impact on tourism revenues and the structure of the industry

To better understand the effects of the dependent variable of total savings on the independent variable, the study will analyze the relationship through empirical analysis of the Arima equation model From there, check and compare the results of

the variables and constants therein, to make conclusions and trends for each of the above impacts

1.3 Research gap Although there have been many studies surrounding the link between income inequality and tourism, most of them focus on developed countries in Europe or countries with strong economies Therefore, we chose to do research in some Asian countries Asian countries not only have income disparities but also face many different regional, cultural, and economic characteristics Previous studies may not have been profound enough in considering the interaction between income inequality and other economic factors such as GDP and foreign investment This can lead to ambiguity as to whether these factors can influence each other and impact the tourism industry simultaneously Taking advantage of research opportunities in the East Asia region helps fill in gaps in our understanding of the complex relationship between Income inequality and tourism and provides useful information for making policy and business decisions in this area

1.4 Kuznets curve hypothesis 1.4.1 Definition

The Kuznets Curve Hypothesis, introduced by economist Simon Kuznets, presents a compelling perspective on the intricate relationship between economic development and income inequality Kuznets' theory, established in the mid-20th century, posits a distinctive inverted U-shaped curve, illustrating that as a society undergoes economic growth, income inequality initially rises and subsequently diminishes This hypothesis encapsulates a nuanced narrative of two developmental stages: the initial phase marked by widening income gaps due to factors such as rural-urban migration, and a subsequent stage characterized by a decline in inequality propelled by improved education, social policies, and economic diversification

While the Kuznets Curve has provided a foundational framework for understanding income distribution dynamics, criticisms highlight oversimplifications and the failure to account for diverse contextual influences such as political institutions and globalization Empirical evidence has shown mixed results across different countries and time periods, challenging the universality of the curve's applicability Contemporary challenges, including globalization and rapid technological advancements, further question the continued relevance of this hypothesis in the face of evolving economic landscapes

In light of these considerations, policymakers must approach the Kuznets Curve with caution and recognize its limitations Rather than relying solely on economic development as a panacea for income inequality, a comprehensive approach addressing education, healthcare, social safety nets, and labor market structures is imperative By acknowledging the multifaceted nature of income inequality, policymakers can design more effective and targeted interventions, fostering a more inclusive and sustainable economic environment Consequently, further research is essential to refine our understanding of the intricate dynamics between economic development and income inequality, ensuring that policy decisions are rooted in a nuanced and contemporary perspective

1.4.2 The relationship between Kuznet’s theory and tourism In the realm of tourism, the application of the Kuznets Curve hypothesis to income inequality presents a unique lens through which to analyze the dynamics of economic development within the sector Initially, as a destination experiences an influx of tourists and tourism-related activities, income disparities among various stakeholders may widen This can be attributed to factors such as uneven distribution of tourism benefits, unequal access to opportunities, and disparities in the development of tourism-related infrastructure

During the early stages of tourism development, local communities might witness an Increase in income inequality as certain segments benefit more from tourism revenues than others This phenomenon may be exacerbated by factors such as inadequate skill development, limited access to entrepreneurial opportunities, and the concentration of tourism benefits in specific regions

However, as the tourism sector matures and local economies adapt, there is potential for the Kuznets Curve to manifest Investments in education, skill development, and community engagement can contribute to a more inclusive distribution of tourism benefits Additionally, the diversification of tourism offerings and the promotion of sustainable practices can play a role in mitigating income inequality by ensuring that a broader spectrum of the local population participates in and benefits from the tourism industry

Nevertheless, the application of the Kuznets Curve to tourism income inequality is not universally applicable, as contextual factors, governance structures, and policy Interventions play crucial roles To harness the positive aspects of tourism development and address income inequality effectively, destination governments and stakeholders must adopt a comprehensive approach This involves implementing policies that prioritize local community engagement, skill development, and

sustainable tourism practices to ensure that the benefits of tourism are equitably distributed across society

CHAPTER 2 DATA AND METHODS

2.1 Data

Data from the World Bank plays an important role in analyzing the relationship between income inequality and the tourism industry This source of information not only provides credibility but also opens the door to the granularization of income disparities across both country and time scales Indicators such as the Gini coefficient, per capita GDP, direct investment capital flows from foreign countries, international tourism revenues, etc provide insight into the extent of income disparities in each country and allow for international comparisons This helps us better define the disprity and place it in the context of the challenges and opportunities that income inequality presents for the tourism industry Obtaining data from reliable sources aids research in better understanding the link between a country's economic strength and its tourism industry, especially in the context of income inequality The availability of this data facilitates detailed analyses of the Impact of income on development and opportunities in the tourism sector

2.2 Pooled OLS method The Pooled Ordinary Least Squares (Pooled OLS) method is a linear regression technique used to study the relationship between income inequality and tourism industry development In this context, we want to examine how the level of income inequality within countries affects the development of the tourism industry, taking into account independent variables such as GDP, inflation, unemployment, international travel, personal remittances, and foreign direct investment

The coefficients are estimated to evaluate the impact of each independent variable on the development of the tourism industry If V is positive and statistically significant, this may indicate that increased income inequality has a positive impact on tourism development

We have the regression equation: VVVgVVyy 0 Vy E VyVVVgVVVVyy E Vy VWWVgVVyy E VyVVVgVVV yy E Vyy E Vyy

In which:

- VVVgVVyy is the dependent variable and represents the logarithm of the income inequality variable in country 1 and time t

- VWWVgVVVWV yy- VVVEVVyy- VWVVgVVVyy are independent variables and represent the logarithmic values of GDP, Tourism revenue, and corresponding foreign direct investment in country 1 at time t These variables can be used to measure the influence of major economic factors on the dependent variable

- Vyy is a random error component in country i at time t, which may represent unobserved and unmeasured factors in the model

- Vyy is also a random error component, often assumed to be independent and normally distributed These are often used to describe random variations that are not explained by the independent variables

- Vy Vy Vy-Vy are the estimated coefficients in the model These coefficients describe the magnitude of the log-ieit change in response to changes in the respective independent variables

The logarithmic equation helps us model the relationship according to the rate of change (elasticity) between the dependent variable and the independent variables This can be useful for assessing the sensitivity of the dependent variable to fluctuations in the independent variables

CHAPTER 3 RESEARCH RESULTS AND DISCUSSION Using statistical software like Stata or any other software capable of performing regression analysis, we can estimate the specified regression model The results from the regression analysis will provide us with an insight into the impact of tourism on income inequality At the same time, it also helps us consider influences and show the relationship between them and the Kuznets curve

By examining the coefficients of the independent variables, we can determine which variables have a statistically significant impact on total savings This approach allows us to identify variables that play an important role in influencing tourism factors Regression analysis will include the estimation of coefficients (B0, B1, B2, B3, and B4) to optimize the model and provide insight into the quantitative relationship between variables This method is a tool to understand the level and direction of the impact of GDP, FDI, and tourism revenue on income inequality

With these results, we can draw accurate conclusions about the factors that impact tourism on inequality and the existence of the Kuznets curve This not only helps us better understand the current situation but also helps us come up with appropriate strategies and directions to optimize the tourism industry and reduce income inequality

Thus, through the use of regression analysis, we can learn more deeply about the relationship between tourism and income inequality, thereby providing effective solutions to improve the economic situation economy and society

3.1 Statistical description summarize YEAR lgdpa ltr lfdi liie

ee Pr rrr rr rrr rrr rrr rrr

lgdpa | 115 19.62388 3.034073 9.229598 22.95468 1tr | 115 23.25317 3.211289 18.48834 33.36436 lfdi | 115 21.63778 1.196689 17.4172 23.06507 liie | 115 3.690939 -663801 3.401197 10.71608

Table 1: Descriptive analysis of data of some Asia countries 2000-2022 The meaning of the parameters appears in table 1:

- Number of observations: 115; - Minimum: The smallest value among the observed values of the variable; - Maximum: The largest value among the observed values of the variable; - Mean: The average value of a variable, calculated by adding all values of that variable and then dividing the number of observations;

- Std.Dev: Standard deviation 3.2 Correlation between variables

corr liie lgdpa ltr lfdi

.—.—————==—=—===—==K== +~=~~T~T~T~~~~~~~~~~ >>> ——~

liie | 1.0096 lgdpa | 0.2275 1.0000

ltr | @.3426 -9.0016 1.ee00 lfdi | @.2264 -9.0697 -0.0079 1.86000

From the table above we see that:

- The coefficient correlation between liie and Igdpa is 0.2275 > 0, meaning positive relationship between liie and lgdpa

- The correlation coefficient between liie and Itr is 0.3426 > 0, meaning positive relationship between lite and Itr

- The correlation coefficient between liie and Ifdi is 0.2264 > 0, meaning positive relationship between liie and lfdi

Based on the research results, we can see that the lite variable is closely correlated with independent variables including lgdpa, Itr, lfdi This means income inequality has a significant impact on gross domestic product, foreign direct investment, and tourism revenue By pointing out this influence, we can better understand the relationship between tourism factors and income inequality This not only helps us see the current situation but also helps us draw economic conclusions and identify market trends With this information, we can find the right solutions and directions to overcome current errors and take full advantage of the advantages of the tourism industry and economy This not only helps improve the economic situation but also contributes to reducing income inequality, creating a more equitable and developed society Thus, through research and analysis, we can see that reducing income inequality and developing tourism can go together This opens up a new direction for economic and tourism policies in the future

3.3 Estimate regression equation

reg liie lgdpa ltr lfdi

` {Hck Ee: (EM, '1Ð7) = 10.58

Model | 084528938 3 028176313 Prob > F = 9.0009 Residual | 285073261 107 002664236 R-squared = 9.2287

cc=======~=~ tooo 2-2 eo eo 2-2 - Adj R-squared = 9.2971

Total | 369602199 119 99336992 Root MSE = 95162

liie | Coefficient Std err t P>|t| [95% conf interval]

-———————————~ parr rrr rrr rrr

lgdpa | - 0046425 - 08016256 2.86 9.995 99142 9097865 1tr | 996124 - 98015073 4.06 9.999 - 9831359 - 9091121 lfdi | 9118157 9941293 2.87 1212) - 0836478 9199836 cons | 3.143772 1030787 39.59 9.009 2.93943 3.348113

This model fits the data at the 5% significance level because F = 10.18 and Prob > F = 0.0000