1. The Effect of Tourism on Income Inequality
Rafael Wu
Advisor: Prof. Piyush Chandra
Abstract
Several types of research have shown that the tourism sector is among the fast growing
sectors in the economies of several countries across the world. A good number of
international agencies have considered this factor considering the outward-oriented
growth strategy and promotes the sector as a means for economic development in
countries around the world. Statistics have proven that tourism has brought a positive
impact to the growth and development of several countries. On the other hand, little
research has been conducted to analyses the distributional consequences of the
development and growth of countries as a result of tourism. In our discussion, we
concentrate on the impact of tourism on the income inequality by applying panel data and
cross-country regression. According to Page & Connell (2010), the samples of countries
that were considered in the analysis, the data found have shown that tourism has been a
contributing factor towards the decrease in the gross income inequality.

2. I. Introduction
1. Tourism
Kulkarni (2010) defines tourism as an activity undertaken by a person that involves
travelling to a place outside their normal environment and staying over there for less than
a year. He further stresses that the motive behind the travel is leisure. The increase in the
per-capita income and reduce working hour has been a major factor contributing to the
increase in tourism accrues the world. It evident that all social classes currently
participate equally in the tourism sector thus the increase in both the domestic and
international tourism (Walker & Harding 2007). Other factors that have positively
contributed to the growth of the tourism sector include the low transport costs and fast
and affecting ways of moving from one region to another (Page & Connell, 2010).
The contribution of the tourism sector to the economies of several countries has been
positive, and the outcome has been seen in the increase levels of development. According
to Tourism and Poverty (2010), expansion of tourism sector in the developing countries
will be a step forward toward the outward-oriented development strategy. In our
document, we analyze the impact of tourism on the economy’s income inequality of
countries and also compare the impact that the domestic and international tourism have in
the income inequality.
2. Income Inequality
Tang, Selvanathan & Selvanathan (2008) explain that income inequality has a direct
relationship with poverty. The definition of poverty can be regarded as the pronounce
denial in the provision of a sustainable life for an individual. Other factors that can be
used as a measure of poverty include poor health, low levels of education, high exposure
to risk conditions and powerlessness. However, Cole & Morgan (2010) ranks income
inequality as the better measure of the economic state of a country than poverty. He
explains that inequality captures the entire population and gives a distinct and broader

3. measure of the economic state, unlike poverty which only entails a smaller section of the
economy.
In our we study, however, we concentrate of the income inequality which is in
contrary to the actual study of measures inequality since several other factors have the
almost same effect to the extent of inequality of income. Moreover, applying the concept
of cross-country data, it is difficult to incorporate other elements apart from income in the
study of inequality since they are difficult to quantify and almost impossible to collect
sufficient data in them (Cole & Morgan, 2010). On the other hand, tourism has little or no
direct relationship to the elements and any relationship that may exist might be due to the
variation in income distribution.
3. Measures of Income Inequality
Several measures that have been incorporated into many types of research to measure
the extent of inequality are based mathematical concepts. Initially, the extent of income
inequality was carried out by determining the highest and the lowest income in the
market in a given population sample. The limitation that this approached had was the fact
that it gives only two observations leaving out important factors such as the population of
a given regions. Therefore, there was the need to apply complex measuring approaches so
as to perform an efficient analysis.
According to Cole & Morgan (2010) the best measure of income inequality is
composed of the following characteristics; mean independence such that any variation in
the mean income due to change in some incomes should not have any effect on the
inequality measure. Secondly, must have a population size independences and symmetry
properties. Cole & Morgan (2010) add that measure should build on Pigou-Dalton
transfer sensitivity which implies that if the income of the rich is passed to the poor, there
will be a proportional decrease in the inequality measure. The most commonly used
measure of economic inequality in the cross-country datasets includes the Thiel’s T
statistics and Gini coefficient.

4. To determine the Gini coefficient, a mathematical formulation is applied. About
the Lorenz curve showing the relationship between aspects of income inequality, the Gini
coefficient is taken as the degree of deviation from perfect equality after superposition is
done on the Lorenz curve by a perfect equality line. With the recent development, Thiel’s
T statistic method has been used in place of the Gini coefficient method due to it great
flexibility when measuring income inequality. Also, OECD Factbook 2008 (2008) states
that the method incorporated all the good characteristics of a measurement element stated
above and even much more. However, in our analysis of the impact of tourism on the
income inequality, there is little decomposition of the inequality variable making the use
of the Gin coefficient more efficient. Also, several countries have this coefficient, and the
time-period makes more reliable for these study.
II. Literature Review
There are only a handful of studies regarding the impact of tourism on economic
growth, and even fewer papers on tourism and a specific economic issue: income
inequality. Furthermore, there have not been any cross-country studies that investigate

5. the impact of tourism on income inequality. There are only a few publications focusing
on the domestic impact of tourism industry on income inequality.
Blake et al. (2009), developing a Computable General Equilibrium (CGE) model of
tourism, including earnings by different types of labor in the tourism industry, examines
the issue of how tourism affects poverty in the context of its effects on an economy as a
whole and on particular sectors within it. With a dataset that is unique in the context of
developing countries, the CGE model shows tourism does benefit lower-class people in
brazil in lowering income inequality. With their model on this study, they expand the
research to some developed countries like Australia and Spain, tourism still generates an
impact on reducing income inequality. Actually, industry lowering income inequality
reduces government’s role in tax. They redistribute the resources from upper class to
lower class.
Lee and Kang (1998), using the data on wages of South Korea from 1985 to 1995,
measure the degree of earning inequality in tourism employees in South Korea. With Gini
coefficient and Lorenz Curve, they present the different income distribution across all the
industries. In this study, tourism performs more equal earning distribution than most of
traditional studies. However, the authors focus on the income inequality inside every
industry, and they do not figure out the reasons of the results. Their study focuses on the
disproportional distribution to lower-class rather than implying the identity. To modify
the study, it would be better off if they run the time-series with every industry’s scale and
income inequality. Some industry’s earning distribution might be more equal with the
industry’s expanding.
III. Data
1 Experimental Variables
As the topic showed in this paper, the experimental variables we set should be income
inequality variable and tourism variable.

6. As the dependent variable in the whole model, we choose Gini coefficient
representing the income inequality level. There are quite a few data sources of Gini
coefficient among various research institutes, and the most widely cited dataset is the
Deininger and Squire dataset (1996) compiled by Klaus Deininger and Lyn Squire for the
World bank. However, the D&S dataset is found to be inadequate for a pure cross-
country panel analysis. Beyond that, we also have World Income Inequality Database
(WIID2), Estimated Household Income Inequality Data Set (EHII) and so on. Among all
the databases, Standardized World Income Inequality works best in this studies since it
contains 4340 Gini coefficients for 153 countries in the sample.
SWIID has two methodologies to calculate Gini coefficients, gross income inequality
and net income inequality. As the term implies, gross/net income inequality is calculated
over gross income and net income, respectively. In the following model, we focus on the
gross income across countries, so we choose gross Gini coefficient as the dependent
variable.
As for tourism variable, what we want is the role of tourism industry plays among all
the economic activities. In other words, we should calculate how much tourism
contributes to GDP. World Travel and Tourism Council (WTTC) presents tourism
industry’s direct contribution as a percentage of the GDP, then we add the dataset into the
model.
2. Control Variables
To select control variables affecting income inequality, firstly we should classify
potential factors into different groups, such as education, economics and politics.
For education sector, we use Barro-Lee (2011) dataset, measuring the effect of no
schooling percentage, primary school percentage, secondary school percentage and
average years of schooling on income inequality.
When it comes to equality issue, politics is always crucial. In this study, “policy2”
variable indicates the political form of governance with rating from Center for Systemic

7. Peace (2011). CSP grades the political form of governance from -10 to 10 regarding the
institution.
As for economic sector, we pick real income per capita variable from Penn World
Table (2011). According to Kuzents Hypothesis (1955), income inequality has an inverse
U-shaped relationship between income inequality and economy. Based on the income
classifications by World Bank (2016), upper middle-income countries are expected to
have higher income inequality, and low-income and high-income countries are predicted
to be more “equal” economically. Therefore, realincome square should be included in the
regression. Unfortunately, the realincome is not discovered in Penn World Table (2011),
and we adopt real GDP PPP per capita instead. Another economic variable we use is the
openness of economics for a country.
In addition to education, politics and economic issues, we also need add some
sociologic issues including labor and urbanization, which seem to be important in all the
previous studies on income inequality.
3. Summary
Table 3.1 Variable sources
Variable Source ………………………………………………
Grossgini Standardized World Income Inequality Database (2011)
TourismGDP2 World Development Indicators (2011)
Laborrate World Development Indicators (2011)
agedependency World Development Indicators (2011)
femalelabor World Development Indicators (2011)
urbanpop World Development Indicators (2011)
urbanprimacy World Development Indicators (2011)
noschooling Barro-Lee Dataset (2011)
primarysch Barro-Lee Dataset (2011)
secondarysch Barro-Lee Dataset (2011)

8. yearschool Barro-Lee Dataset (2011)
RealGDP PPP per Capita Penn World Table (2011)
realincome-squared Penn World Table (2011)
openk Penn World Table (2011)
polity2 Center for Systematic Peace (2011)
Table 3.2.x Summary of variables

9. 3.3 Time-series analysis of experimental variables
Figure 3.3.1 Gini Coefficient-China

10. From Figure 3.3.1, the Gini coefficient of China perfectly meets Kuzents Hypothesis.
Before the economic reform happened in 1978, this country suffered from Anti-Rightest
Campaign, Great Leap Forward and Culture Revolution, and the Gini coefficient reduced
to a low level since the economic development of China was at the lowest level
throughout the world. After the reform, the Gini coefficient raised rapidly along with the
rapid development of this country as a result of marketing economy. The Gini coefficient
of China will keep rising as long as China is still a middle-income developing country.
Figure 3.3.2 Tourism’s Contribution to GDP (%) – World (From Koenma Database)
From 3.3.2, it seems that tourism’s contribution to GDP was rising rapidly before
1998. It steps down a little bit after 1998, perhaps it is due to the financial crises in 1998
and 2008. In general, tourism’s contribution to GDP will maintain at a certain level as
time goes by.

11. IV. Replication
Based on all the variables mentioned last part, we try to run the regression as the
original publication. A fixed-effect estimation is presented as follows:
Table 4.1 Fixed-effect Replication
Table 4.2 Fixed-effects estimation results:
Variable Grossgini
TourismGDP2 -.2664629
(.1268513)
Laborate -.2986885
(.2056305)

12. Polity2 .0260885
(.0938379)
Agedependency 0.264343
(0.736555)
Rgdpl2 .0004745
(.00035)
Rgdp2sq -3.82e-09
(-4.82e09)
Openk .0106121
(.0190147)
Noschooling .2679628
(.4316772)
Yearschool -1.645246
(2.350404)
Femalelabor .5769924
(.2812813)
Urbanpop .1045371
(.1748709)
Urbanprimacy .1643134
(.1660433)
Primarysch .1906528
(.2769215)
Secondarysch .1846698
(.181899)
Constant 19.46196
(40.87753)
R-squared 0.1661
Despite some coefficients seem to be a little bit far from the original paper
(Coefficient table presents in appendix B), most of the variables’ coefficients are closed
to the original results. Especially, the results for the experiment variable are well fitted.
The coefficient of tourismGDP2 is -.266, which implies the Gini coefficient will
reduce .266 with one percentile creeping-up of tourism’s contribution to GDP.
Furthermore, the p-value of its coefficient is less than 0.05, which indicates statistical
significance of tourism variable.
The measurement of “polity2” seems to be against expectation. In general, countries
with better political form are expected to have better social equality. The coefficient of
“polity2” is positive, which implies better politic-organized country has poorer income
inequality.
Similar with the results of original paper, the “rgdpl2” variable, as well as its square,
does not show significant effects in the regression. With the polynomial function of

13. income level, it would be hard to generate an inverse U-shape in the regression. We will
try to find a new variable instead of it next part.
For the education variables, all of them are statistically insignificant as the original
paper. These four variables seem to have multicolliearity.
The value of rho for this fixed-effect estimation is 0.894, which means that greater
than 89% of the variation in income inequality is due to the difference across countries in
the sample. Table 4.2 above summarizes the results without presenting the equation
mathematically.
V. Twist
Since the regression we replicated last part is concerned with Fixed-effects
estimation, we will try to figure out the feasibility using Random-effects estimation:
Table 5.1 Random-effect Estimation
Since the p-value is less than 0.05 in Hausman Test, we reject the null hypothesis.
Fixed-effect estimations should be the better methodology as the author used in the
original publication.
The result of Hausman Test implies the correlation between variables and
interception term and correlation between variables themselves. Since we use more than
one variable in education, economic and political sectors in the model, there is a potential
possibility of multicollinearity.:
Table 5.2 Collin Test (multicollinearity)

14. The Collin Test shows there is an extremely serious situation of multicollinearity
among the education variables. Obviously, year of schooling, and no schooling are
almost perfectly negative correlated based on our common sense. Moreover, primary
school and secondary school participation rate also seem to be closely related. We just
keep one of the variables, years of schooling, to indicate the education sector.
Another multicollinearity is under our expectation, rgdpl2 and rgdpl2sq are
absolutely correlated. However, when we replicate the data, since we cannot find “real
income” as the author did in the publication, this economic variable does not play a
significant role in the regression. The polynomial expression of real GDP cannot present
the Kuzents’s inverse U-shape perfectly. We consider adding a new economic variable,
inflation, to the regression from the World Bank database.
According to Kuzents Hypothesis, rich countries and poor countries are expected to
have low income inequality. Rich countries and extremely countries have similar features
with low inflation. Then, we add inflation as a new variable instead of rgdpl2, and run the
fixed-effect regression again:
5.3 New Fixed-effect Estimation

15. As expected, inflation is positively related to Gini coefficient with statistical
significance. Beyond that, the dropping of education variables and the adding of inflation
increases the variables which are statistically significant in the regression. TourismGDP2,
inflation, years of schooling and female labor are significant to gross Gini coefficient.
Since we have quite a few variables in this regression, the change of variables does not
really change the coefficients numerically.
Under ceteris puribus assumption, we could interpret the coefficients of all variables
as below:
1. One percentile increase in tourism’s contribution to GDP leads to .264 percentile
reduce in Gini coefficient;
2. One percentile increase in labor rate leads to .346 percentile reduce in Gini
coefficient;
3. One-point increase political form of governance rating leads to .036 percentile
increase in Gini coefficient, which is against the expectation;
4. One percentile increase in age dependency leads to .034 percentile increase in Gini
coefficient;

16. 5. One percentile increase in inflation leads to .015 percentile increase in Gini
coefficient;
6. One-point increase in economic openness index leads to .024 percentile increase in
Gini coefficient;
7. One more year increase of schooling leads to 1.839 percentile reduce in Gini
coefficient;
8. One percentile increase in female labor rate leads to .683 percentile increase in
Gini coefficient;
9. One percentile increase in urban population percentage leads to .083 percentile
increase in Gini coefficient;
10. One percentile increase in urban primacy leads to .239 percentile increase in Gini
coefficient.
In the process of modification, the coefficient on the tourismGDP2 variable is
negative in all cases and is statistically significant in all the cases. Hence, the fixed-effect
estimation conclusively shows the direct contribution of the tourism sector to income
inequality.
VI. Conclusion
The results in this study indicate the negative relationship between the contribution
of the tourism sector to GDP and income inequality. To conclude, tourism not only
contributes to the economic development, but makes sense to the equality of human
beings.
Based on microeconomic theory, tourism increases the marginal revenue to some
sub-industries, like restaurant, hotel, transportation and service. Along with the marginal
revenue and marginal profit, tourism benefits a large number of small business owners.
From macroeconomic side, tourism increases the job opportunities in the related sub-
industries, where lower-class people can find a job and earn money. It is how tourism
reduces the income inequality behind the studies with cross-country and panel data.
According to the “twist” model, this study also compares the effect on income
inequality between tourism and some traditional sectors like economics and politics.
Comparatively, tourism has a large impact on lowering income inequality among all the
sectors of the regression. For instance, inflation even has a positive relationship with
income inequality. When inflation occurs, rich people generally have better knowledge of

17. investment, as they always perform better skills to manage their wealth. In other words,
as traditional industry, finance seems to enlarge the income inequality in spite of their
contribution to economic growth.
As time goes by, more and more social research focuses on people’s life quality.
Researchers would like to evaluate something like “Happy Planet Index” with a
quantitative analysis methodology. Actually, equality is believed to affect people’s
happiness in daily lives. Similar to this empirical study, new industry could be involved
in such an econometric model with income inequality. Hopefully, we could evaluate its
impact to the equality of human beings in addition to its contribution to the economic
development.

18. Biobiography
Blake, A., Arbache, J. S., Sinclair, M. T., & Teles, V. (2008). Tourism and poverty
relief. Annals of Tourism Research, 35(1), 107-126.
Brohman, J. (1996). New directions in tourism for third world development.Annals of
tourism research, 23(1), 48-70.
Cameron, A. C., & Trivedi, P. K. (2010). Microeconometrics using stata (Vol. 2). College
Station, TX: Stata Press.
Cole, S., & Morgan, N. (Eds.). (2010). Tourism and inequality: Problems and prospects.
CABI.
Edwards, S. (1997). Trade policy, growth, and income distribution. The American
Economic Review, 87(2), 205-210.
Lee, Choong-Ki, and Seyoung Kang. "Measuring earnings inequality and median
earnings in the tourism industry." Tourism Management 19, no. 4 (1998): 341-348.
Lundberg, D. E., Krishnamoorthy, M., & Stavenga, M. H. (1995). Tourism economics.
John Wiley and sons.
Page, S., & Connell, J. (2006). Tourism: A modern synthesis. Cengage Learning EMEA.
Scheyvens, R. (2012). Tourism and poverty. Routledge.
Tang, S., Selvanathan, E. A., & Selvanathan, S. (2008). Foreign direct investment,
domestic investment and economic growth in China: a time series analysis. The World
Economy, 31(10), 1292-1309.

19. Appendix A
List of Countries:

20. Appendix B
Replicated Table from Original Publication:

21. Appendix C
Stata Code:
sort countryname
rename countryname country
drop country
rename countryname country
gen id=_n
reshape long y, i(id) j(year)
rename y tourismGDP2
rename country countryname
save "ClientH$Downloadsyuxiaoze.dta"
replace countryname="Slovak Republic" if countryname=="Slovakia"
merge 1:1 countryname year using "ClientH$Downloadsyuzeyu.dta"
drop _merge
merge 1:1 countryname year using "ClientH$Downloadsxiaoze.dta"
drop _merge
merge 1:1 countryname year using "ClientH$Downloadsjianrujing.dta"
drop _merge
merge 1:1 countryname year using "ClientH$Downloadshowlongwilliloveyou.dta"
rename y femalelabor
drop _merge
merge 1:1 countryname year using "ClientH$Downloadscristiana.dta"
drop _merge
merge 1:1 countryname year using "ClientH$Downloadsieu.dta"
rename countryname country
merge 1:1 country year using "ClientH$Downloadsieu.dta"
replace country="Slovakia" if country=="Slovak Republic"
save "ClientH$Downloadsyuxiaoze.dta", replace
drop _merge
merge 1:1 country year using "ClientH$DownloadsBL(2010)_MF1599_v1.2 (1).dta"
drop if id==.
save "ClientH$Downloadsteamo.dta"
drop _merge
merge 1:1 country year using "ClientH$Downloadswoaini.dta"
drop if id==.
save "ClientH$Downloadsteamo.dta", replace
xtset country year
merge 1:1 country year using "ClientH$Downloadsyuzeyushishabi.dta"
drop _merge

22. merge 1:1 country year using "ClientH$Downloadsyuzeyushishabi.dta"
drop if id==.
save "ClientH$Downloadsteamo.dta", replace
xtset gini_gross year
sort country time
sort country year
isid country year
rename femalelabor agedependency
drop _merge
merge 1:1 country year using "ClientH$Downloadsyuzeyuwoaini.dta"
rename y femalelabor
rename femalelabor y
drop _merge
merge 1:1 country year using "ClientH$Downloadsyuzeyuwoaini.dta"
isid country year
xtset id year
xtreg gini_gross tourismGDP2 laborrate polity2 agedependency openc lu yr_sch
femalelabor urbanpop urbanprimacy lp ls, fe robust
use "ClientH$Downloadsteamo.dta", clear
drop _merge
sum tourismGDP2
sum laborrate
sum agedependency
sum femalelabor
sum urbanpop
sum urbanprimacy
rename lu noschooling
rename lp primarysch
rename ls secondarysch
rename yr_sch yearschool
sum noschooling
sum primarysch
sum secondarysch
sum yearschool
rename openc openk
sum openk
sum policy2
sum polity2
rename gini_gross Grossgini
sum Grossgini

23. xtset id year
xtline Grossgini
twoway (tsline Grossgini)
gen rgdpttsq= rgdptt^2
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdptt rgdpttsq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, fe
robust
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdptt rgdpttsq openk
femalelabor urbanpop urbanprimacy , fe robust
gen rgdpl2sq= rgdpl2^2
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, fe
robust
estimates store fixed
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, re
robust
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, fe
robust
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, fe
estimates store fixed
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, re
estimates store random
hausman fixed random
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk
noschooling yearschool femalelabor urbanpop urbanprimacy primarysch secondarysch, fe
robust
help collin
findit collin
collin id tourismGDP2 laborrate polity2 agedependency rgdpl2 rgdpl2sq openk yearschool
femalelabor urbanpop urbanprimacy primarysch
save "ClientH$Downloadsteamo.dta", replace
merge 1:1 country year using "ClientH$Desktopainixiaoze.dta"
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency inflation openk yearschool
femalelabor urbanpop urbanprimacy primarysch, fe robust
xtreg Grossgini tourismGDP2 laborrate polity2 agedependency inflation openk yearschool
femalelabor urbanpop urbanprimacy, fe robust