apa model

THE UCLA UNDERGRADUATE JOURNAL OF ECONOMICS

The Impact of Tourism on Income Inequality: An Econometric Assessment

Suraj Pant Advisor: Jeffrey Parker

Reed College Abstract Current data show that tourism is one of the fastest growing sectors in the world economy. The economic literature

explains that in line with an outward-oriented growth strategy, tourism was promoted as a means of development

during the 1970’s by many international agencies. Empirical investigations on the impact of tourism show that the

greater focus on tourism has been beneficial, as tourism has had a significant positive impact on growth. However,

the distributional consequences of the growth through tourism have not been investigated. This paper presents

original research tourism’s impact on income inequality using cross-country and panel data regressions. Results

from the regression analyses show that the tourism sector has decreased gross income inequality in the sample of

countries used in this study. The results also demonstrate that domestic tourism contributes more to decreasing

income inequality than international tourism does and weakly support the hypothesis that the tourism sector

decreases income inequality more than other sectors linked to tourism.

UCLA UNDERGRADUATE JOURNAL OF ECONOMICS 48

I. Introduction to Tourism

In the more distant past, tourism was an activity mainly enjoyed by the very few who had

significant wealth. However, due to increased per-capita incomes and shorter working hours,

many middle-class workers in the developed and newly industrialized countries increasingly had

enough discretionary income and leisure time. This raised the prevalence of domestic and

international tourism. Moreover, the decline in transportation costs and the increase in speed of

traveling further contributed to the attractiveness of traveling for recreational purposes.

In this paper, tourism will be understood according to the definition given by the United

Nations World Tourism Organization (UNWTO), a specialized agency composed of the member

nations of the United Nations (UN) that focuses exclusively on tourism development and

analysis. As international travel became a more mainstream activity, a precise definition of

tourism was necessary to accurately measure the impact of this sector. Therefore, in 1991, at the

UNWTO Ottawa Conference on Travel and Tourism Statistics, tourism was defined as – “the

activities of persons traveling to and staying in places outside their usual environment for not

more than one consecutive year for leisure, business and other purposes.” (UNWTO, 1995)

With regards to international tourism, this means the activities of people in countries other than

their own in accordance with the previous definition.

The preceding definition illustrates the difference between tourism and other economic

activities. Unlike other industries, in tourism, both the production and consumption take place at

the destination; therefore, while other industries are defined from a supply perspective, tourism is

defined solely in terms of the activities of its customers and not in terms of the particular

properties of goods or services. To further illustrate this, any good or service consumed by a

49 THE IMPACT OF TOURISM ON INCOME INEQUALITY

tourist is a tourism product, whereas if non-tourists consume the same product, it is excluded

from being classified as a tourism product (UNWTO, 1995).

As has been pointed out earlier, tourism is one of the growing sectors in the world even

though it is not withdrawn from the effects of the global financial system. Using the data on the

direct contribution of the tourism industry, which includes the contribution of domestic tourists

in addition to the contribution of international tourists, a graph is plotted to see the general trend

since 1989, which is presented below.

Figure 1.1. Travel and tourism receipts from 1989 to 2010 in 2011 constant USD

The graph reveals that the global travel and tourism receipts increased from 1013.9

billion constant 2011 USD in 1989 to 1768.8 billion constant 2011 USD in 2010. It is this

growth and the hope of the continued growth of tourism that has led to the development of a

greater focus on tourism in many countries of the world. A brief historical explanation of the

increasing importance of tourism is discussed below.


It was in the 1960’s that development theorists shifted from inward-oriented development

strategies to preferring outward-oriented development strategies. Since it was found that the

outward-oriented growth strategies were more aligned with the principle of comparative

advantage and were more advantageous, they were preferable to many inward-oriented

strategies, including autarky and/or import-substitution industrialization. Brohman (1996)

reports it was during this period that among many economists, the consensus developed that

developing countries should not attempt to develop industrial sectors not in alignment with the

principle of comparative advantage. These economists further recommended that the developing

countries adopt outward-oriented models by uniformly specializing in primary exports, such as

agricultural, fishing, or mining products, that were areas in which most of the developing

countries enjoyed a comparative advantage. Tourists visited the destination countries mostly for

recreation and as such consumed disproportionately more goods provided by the primary sector

of the economy, such as the natural amenities of the destination countries. The shift of focus to

expand the tourism sector was therefore a step towards outward-oriented development strategy.

The UN’s declaration in 1963 that tourism was a major contributor to the economic

growth of developing countries also had an impact on the adoption of many tourism-led

economic development policies by many countries. Tourism was also highly recommended,

especially for developing countries that had adequate tourist attractions, as tourism helped in the

economic diversification of those countries by freeing them from excessive reliance on a few

traditional exports. Brohman (1996) points out that by 1987, tourism was argued to have been

the world’s third largest industry, after oil and vehicle production. He further points out that

developing countries like Thailand, the Commonwealth of the Bahamas, Jamaica, Egypt and


Kenya, that had adequate tourism resources and infrastructures, had obtained a positive trade

balance largely because of the large surplus in the tourism balance by 1990.

Furthermore with programs like the structural adjustment lending, the World Bank and

the International Monetary Fund (IMF) also pushed many of the developing countries towards

outward-oriented strategies, especially tourism. Lafant (1980) maintains the view that the World

Bank provided a significant impetus for tourism friendly policies in many developing countries

with its financing programs in those countries. He further mentions that the World Bank

encouraged developing countries to “open their borders to tourists, work on policies to attract

FDI, and concede and guarantee tax advantages.” Loans were conditional on these criteria

because the World Bank maintained the view that promoting tourism would be instrumental in

solving the problem of the prevalence of high poverty in most of the developing countries.

I.1. The Case for Tourism

The argument for focusing on tourism stands up to the scrutiny of economic theories.

The Heskscher-Ohlin (1933) theorem states that the relative abundance of a country’s factors of

production will provide the country a comparative advantage in the production of the good that

uses the relatively abundant factor extensively. According to the theory, tourism is relatively

more labor-intensive so it enjoys a comparative advantage due to the abundance of cheap and

abundant labor. The theory also predicts that tourism becomes more prevalent due to the

abundance of exclusive properties of a country or a region like climate, sceneries, and

mountains, amongst others. Similarly, it also converts non-tradable goods into tradables because

both the production and consumption of tourism take place at the destination. The consumption

of non-tradables by international tourists enables inter-country export of non-tradables. This


helps to increase not only the tourism receipts, but also employment because of the greater

number of labor employed for providing services and goods to the tourists.

The literature on tourism demand analysis shows that the relative price difference

between the origin and the destination country is a significant variable affecting tourism demand.

Lim (2006), in her meta-analysis of 124 studies that model tourism demand, finds that relative

prices are one of the most frequently used explanatory variables, second only to the income of

the country of tourism origin. Relative price differences are caused by a number of different

factors that include the exchange rates and the provision of cheaper goods and services in the

destination. In these studies, Lim finds a great variation in the elasticity of tourism demand to

relative prices that range from -0.15 to -7.01 but finds that tourism is relative price elastic in

most studies while controlling for factors such as airfare costs, and the distance between the

origin and destination. This means that destinations that have lower relative prices have an extra

comparative advantage in attracting more tourists; therefore they are able to receive more

earnings through tourism.

All these factors contribute positively to the growth of tourism demand. It is also seen

that many developing countries possess the characteristics discussed above that boost their

comparative advantage in tourism. Therefore, the assessment of international organizations that

international tourism is a mechanism that links wealth and leisure in industrial and developed

countries to opportunities for economic growth and development in developing countries finds

merit because many developing countries possess the characteristics that contribute to a

comparative advantage in tourism.


I.2. The Weaknesses of Tourism

Tourism cannot be thought of as a trade in services only. In pursuing a systems analysis of

tourism, Sessa (1988) points out that tourism, unlike the rest of other services, appears to be also

strongly connected to the building of infrastructures, amongst other things, that have the highest

marginal capital to output ratio. This implies that to promote tourism, the development and

promotion of many other sectors would also be simultaneously necessary. On the one hand, the

construction of new infrastructures would be beneficial for the development of other sectors of

the economy. As an example, through the construction of roads to a rural tourist destination,

many other economic opportunities for the people in those regions will spring up. However, on

the other hand there would also be a concern about raising the capital for infrastructural

development necessary to develop the tourism sector. Recognition of this inherent problem

should have led to the structural lending programs of the World Bank and the IMF.

Another problem with tourism-led growth is associated with the consequence of tourism

activities in an area. Application of the product-life-cycle concept to tourism shows that

increased tourism can result in the deterioration of the tourism area and finally a decline in

tourism with time, if tourism is not managed with a focus on sustainability. Tourism sales, like

the sales of any product, pass through different stages in as tourism in a certain area evolves.

The first stage is marked by exploration and development, in which the sales of the product

increase slowly at first and then with a rapid growth. After that the sales stabilize and

subsequently decline, ceteris paribus.

Butler (2006) argues that only a small number of visitors come to a tourism destination

when the area is initially opened to tourists. With the provision of more facilities in the


destination and more marketing, the attractiveness of the tourism product increases and the

tourism sales will grow rapidly. However, when the environmental factors, physical plant, and

social factors reach their carrying capacity due to a large number of tourists coming in, the

attractiveness of the area declines as the qualities that initially brought in tourists degrade due to

tourism. In such a scenario, the attractiveness of the area declines both absolutely and in relation

to other areas that do not have such problems. Butler argues that unless a complete change in

tourism product at the destination occurs, in which case the area may undergo rejuvenation, the

destination will see a decline in the number of tourists.

Equally lethal for countries focusing on a tourism-led growth strategy is the problem

associated with the Dutch Disease. Initially formulated to explain the decline in Netherland’s

manufacturing sector after the discovery and then the subsequent reliance on natural gas for

exports, the concept applies to tourism, too. Along with the rapid expansion of the tourism

industry, there is also an inflow of foreign currency that leads to an appreciation of the exchange

rate. This causes a decline in the demand for tourism through an increase in the relative prices of

tourism in that country relative to other countries as relative prices have a significant effect on

tourism demand. The manufacturing sectors could also have declined because of the movement

of capital and labor into the booming tourism industry due to the initial rise in the demand, and

therefore higher prices, in tourism. In this case, if the infrastructures and facilities built for the

promotion of tourism are not useful for the development of other sectors or that there is no

substantial local demand for them, then the capital invested in tourism becomes a sunk cost.

Another major weakness of tourism is that in many cases it has low wages and little

career opportunity, and provides only seasonal and part-time jobs (Berrett 1987, cited in

Christensen and Nickerson 1995). Christensen and Nickerson (1995) further point out that in the


case of the state of Montana of the United States, the wage levels in tourism are 78% of the

average hourly wage across all sectors. However, these qualities do not make tourism a weak

sector.

Marcouiller (2007) points out that first-time, retired and inexperienced workers take up

the majority of low wage jobs in tourism. The seasonal employment in tourism, on the other

hand, provides economic opportunities to many of those, who have few other options,

particularly in rural areas in developing countries. As such, tourism is actually found to be

beneficial with a deeper analysis. Furthermore, he also explains that lucrative career

opportunities exist in tourism for relatively the higher skilled workers like chefs, hotel managers,

and professional entertainers. In addition to these, Wanhill (2002) also argues that the incentive

for entrepreneurialism is high in tourism, which is explained by the involvement of a large

number of, mostly family-owned, small and medium enterprises, in the tourism sector.

II. Income Inequality

The concept of inequality is closely related to poverty. The World Bank (2000) defines poverty

as the “pronounced deprivation in well-being.” In addition to the traditional concept of income

poverty in understanding poverty, the World Bank definition also includes low levels of

education, health, powerlessness and exposure to risk. The rationale for such a broad measure,

the report argues, is because of the interactions with and reinforcements on one another of these

different forms of poverty.

Inequality is a distinct and broader measure than poverty since it includes the whole

population and not just particular segments of the population. Merriam-Webster Dictionary

defines inequality in the most general sense as “the disparity of distribution or opportunity.” In


an economic sense, inequality is a measure of the extent of dispersion of wages, income, land,

assets, or overall wealth among individuals or groups of individuals in a locality, country or a

region. Income inequality is a much narrower measure of inequality that shows how the income

is distributed in the population, thereby enabling the study of relative poverty.

Sen (1992) criticizes the concentration on income inequality of most studies on

inequality, as income is just a means to the end. There are interpersonal differences in the

relationship between means and ends. Moreover, there are also other important means that affect

well-being, apart from income. Since the extent of the real inequality of opportunity or

comparative deprivation depends also on “the variety of physical and social characteristics that

affect our lives and make us what we are,” a focus only on income inequality dilutes the actual

study of the disparity of opportunity. However, for an empirical research on inequality using

cross-country data, many of the interpersonal differences are hard to quantify and are not

available in data. Furthermore, the data on the other means are also not available. Finally, there

is not any theoretical basis for tourism to have a direct impact on the other forms of inequality;

whatever the relationship there might be would be through the changes in the income

distribution.

II.1. Measures of Income Inequality

Most measures of inequality are constructed using mathematical formulations. The simplest

measure of income inequality is the range that is the difference between the highest and the

lowest income values for a population sample. However, this measure is extremely limited as it

relies only on two observations, does not take into account other underlying factors such as the

population of a region, and is not sensitive to inflationary pressure. This inequality measure fails


to provide the information one needs in an extensive research on income inequality; therefore,

studies on inequality do not use simplistic measures like the range and often rely on complex

measures of inequality that have a much higher informational content.

Haughton et al. (2009) mention a list of criteria necessary for an inequality measure to be

classified as good. Firstly, the measure should have the property of mean independence,

whereby a change in the mean income through a proportionate change in all incomes should not

affect the inequality measure. The measure also should have population size independence,

whereby a change in the population, ceteris paribus, should not have any impact on the

inequality measure. Thirdly, a good measure of inequality should also have the symmetry

property, which means that the inequality measure should remain unchanged in the case that

incomes are swapped across people. The fourth criterion sets forth the condition of Pigou-Dalton

transfer sensitivity that implies that income transfers from the rich to the poor should reduce the

value of the inequality measure. They also mention that decomposability, whereby a measure of

inequality can be broken down in sectors of occupation, region, or population groups, and

statistical testability are further desirable criteria for a good inequality measure.

Two of the most sophisticated measures of economic inequality used in the construction

of cross-country datasets are the Gini coefficient and the Theil’s T statistic. The Gini coefficient

is calculated using a mathematic formulation that relates intuitively to a graphical illustration.

The Lorenz curve is a plot that shows the relationship between cumulative incomes with respect

to the cumulative population for a particular economy. When cumulative shares in income are

plotted against the cumulative percentage of a population, the 45-degree line gives a line of

perfect equality. The Lorenz curve for a population will be different depending on the country or

on time. The Gini coefficient is then a measure of the deviation from perfect equality where the


Lorenz curve is superposed with the line of perfect equality, and consequently income inequality

is 0.

The curve in Figure 1.3 represents the cumulative share of income for any country and

the solid line represents the line of perfect equality. The Gini coefficient of inequality measures

the deviation of the dashed curve from the straight line, and can be mathematically calculated by

the formula A/(A+B), where A and B are the areas shown in the diagram above. For perfect

equality, the area A needs to be 0, in which case the Gini would be 0. For complete inequality, B

needs to be 0, in which case the Gini coefficient becomes 1 (or 100 in percentage terms).

Therefore, the value given by the Gini coefficient is always between 0 and 1 (or 0 and 100 in

percentage terms). The Gini coefficient passes the four required criteria enlisted by Haughton

and Khandker (2009), and there have been recent developments that enable to decompose the

Gini (Mussard et al., 2003), but it is not decomposable to the same extent as is the Theil’s T

Statistic.

Figure 1.2. Lorentz Curve

Though not as intuitive as the Gini coefficient either in representation or interpretation,

Theil’s T stastistic, which was developd by Theil in 1967 using information theory, is a more


flexible measure of economic inequality. This measure meets all the six criteria listed above for

being a good measure of economic inequality. The advantage of Theil’s T Statistic is that in

comparison to the other measures of income inequality, especially the Gini, it is additive across

different sub-groups or regions (World Bank, 2011). However, in analyzing the impact of

tourism on overall income inequality, the empirical methodology employed in this study does not

require the decomposition of the inequality variable, so the use of the data on Gini coefficient

that is readily available for a large number of countries and time-periods is more relevant for this

study.

III. Literature Review of Tourism, Growth and Income Inequality

The discussions in the preceding sections have shown that there is a theoretical basis based on

the Heskcher-Olin theorem for promoting tourism as a development strategy for countries with a

comparative advantage in tourism. Nevertheless, there are also concerns about the sustainability

of the tourism sector due to the problems associated with Dutch Disease and tourism product-

life-cycle. On an empirical level, most studies on tourism have been primarily concerned with

investigating the determinants of international tourism demand. The relatively few studies

analyzing the impact of international tourism on economic growth mostly point to the conclusion

that tourism has statistically significant positive impact on economic growth. Furthermore,

there have not been any cross-country studies that investigate the impact of domestic tourism on

economic growth; the studies on domestic tourism largely focus on modeling the trends in

domestic tourism demand.

Figini and Vici (2009), using a cross-section data of over 150 countries from 1980-2005,

fail to find a significant relationship between tourism specialization, which is the share of


international tourism receipts expressed as a percentage of the GDP, and economic growth for

the overall period. However, when they restrict the sample to include only the 1980’s, they find

a positive and significant impact of tourism on growth. Therefore, their study fails to provide a

robust conclusion of the effect of tourism on economic growth.

Other studies find a positive impact of tourism on growth. Fayissa et al. (2009) find a

positive and significant impact of international tourism earnings on the growth rates of GDP per

capita by using a panel data for a sample of 17 Latin American countries from 1995 to 2004.

After using a fixed-effects estimation method, they find that a 10% increase in the level of

tourism earnings increases the GDP per capita by 0.4% from its level. They also find the impact

of tourism on the growth of GDP per capita is higher when human and physical capital

improvements occur simultaneously that explains the interrelation of tourism with the other

sectors of the economy. However, as a study that recommends countries in Latin America to

work on policies to foster tourism, it does provide any evidence of the basis for focusing on

tourism with regards to other sectors. If the study had included the contribution from other

economic sectors as well, then the relative contribution of international tourism to economic

growth of these countries would have shed more light on the desirability of tourism.

Another empirical study by Di Liberto (2010) that uses a larger sample of countries and

a longer time-series, specifically the data on 72 countries from 1980 to 2000, also finds a

statistically significant positive impact of tourism on growth. The study also finds that for all the

economies in the sample, tourism is less technologically advanced and uses less skilled labor,

which is in line with the theoretical insights derived in the previous section.

Lee and Kang (1998), using the data on wages of South Korea from 1985 to 1995,

analyze the impact of earnings inequality in the South Korean tourism industry in comparison to


other industries. Using the Gini coefficient of inequality, and the Lorenz curve for income

distribution across different industries, they find that tourism generates a relatively more equal

distribution of earnings, and performs better than the secondary and tertiary industries, which

include mining, manufacturing, construction, finance, and social services. This means that the

workers in the low-income class gain disproportionately more from the tourism sector than they

would from employment in the secondary and tertiary industries. Their analysis of median

earnings across different industries again confirms that tourism is a low-wage sector.

Blake et al. (2009) confirm this effect of tourism in decreasing income inequality in the

case of Brazil. They develop a computable general equilibrium (CGE) model of tourism that

includes earnings by different types of labor in the tourism industry, households with different

income levels, and the channels through which tourism alters the income distribution between

the households with different income levels. Through their study, they find that tourism benefits

the lowest income segments of Brazil and leads to a more equal distribution of income through

changes in earnings, prices, and government transfers. They also mention other CGE analyses

that have been employed in other countries, and report that tourism is found to reduce income

inequality in Australia and Spain.

However, no cross-country studies that analyze the impact of tourism on income

inequality are found. Next, I will discuss the dataset and develop an empirical model to examine

the effect that tourism has on income inequality and will use cross-country and panel datasets to

implement the analysis.


IV. Data

Building a dataset for inequality analysis is challenging because of the limited availability

of cross-country measures of income inequality spread over time. This problem is further

compounded by the lack of availability of enough time-series data on tourism. When the already

scarce data for the two variables is used for analysis while controlling for several other

development indicators that have missing values for different years and/or different countries,

the final dataset ends up having fewer observations. These problems lead the final panel dataset

available for the analysis to be slightly unbalanced with 1001 observations for 93 countries.

IV.1. Income Inequality

The most widely cited dataset on income inequality is the Deininger and Squire dataset

(1996) compiled by Klaus Deininger and Lyn Squire for the World Bank.1 This dataset, which

was published in 1996, has extensive cross-country estimates for multiple years but has a

substantial number of missing observations. Furthermore, due to the unavailability of the data

for the pre-1995 period on the tourism variables, reported by the United Nations World Tourism

Organization (UNWTO) and compiled in the World Development Indicators (WDI), the

Deininger and Squire dataset is found to be inadequate for a pure cross-country or a panel

analysis. After exploring the World Income Inequality Database (WIID2) complied by UNU-

WIDER (United Nations University World Institute for Development Economics Research), the

Estimated Household Income Inequality Data Set (EHII) compiled by the University of Texas

Inequality Project and United Nations Industrial Development Organization (UTIP-UNIDO), and

1 Google Scholar reports a total of 2263 citations for the Deininger and Squire paper on their dataset.


the Standardized World Income Inequality Database (SWIID), it is decided that SWIID has the

most relevance for the analysis because of its extensive coverage. The SWIID has 4340 Gini

coefficients for 153 countries in the sample. The missing values for the Gini in the SWIID have

been generated using a custom missing-data algorithm to standardize the United Nations

University’s World Income Inequality Database 2008, (UNU-WIID) by using the data from the

Luxembourg Income Study (LIS) as the standard. LIS data on income inequality is pointed out

as high-quality data (Solt, 2010).

Furthermore, there are two measures of the Gini-coefficient in the SWIID. One is the

gross income inequality in which the Gini is calculated over gross income, and the other is net

income inequality in which the Gini is calculated over net income. The benefit of using both of

these measures of the Gini is that the difference between the impacts of tourism on these two

variables enables the analysis of the effects of the welfare policies in reducing income inequality

through the tourism sector.

IV.2. Tourism Variables

The main tourism variable used in this study to estimate the impact of tourism on

inequality is the share of international tourism receipts as a percentage of the GDP, tourismGDP.

To calculate this variable, the annual data for international tourism receipts at current prices is

divided by the GDP of that year at current prices and multiplied by 100. Both of these variables

are obtained from World Development Indicators (WDI) published by the World Bank (2011).

The data for international tourism receipts exists only from 1995 onwards and the data for the

Gini coefficient is available for only until 2005. Therefore, the period from 1995 to 2005 is used

for the econometric analysis in this study.


There is not only one-way of measuring the impact of tourism in an economy. The World

Travel and Tourism Council (WTTC) estimates the impact of the tourism industry as a

percentage of the GDP by also including the contribution from domestic tourism. TourismGDP2

is the variable included in this study that includes the impact of the overall tourism industry as a

percentage of the GDP.

The WTTC also has another measure that includes the direct, indirect and induced

contributions including the contribution of capital investment of the tourism industry. This

measure, which is indicated by tourismGDP3, includes the overall contribution of the tourism

industry through its backward and forward linkages with the other industries.

With these different measures of the tourism variable in the analysis, different insights

can be drawn from the results about the relative contributions of international and domestic

tourism, the welfare policies, and the industries interrelated with tourism in affecting the income

inequality.

IV.3. Control Variables

To estimate an accurate impact of the tourism variables on income inequality, it is

necessary to control for the variables that can have an effect on income inequality. Therefore,

additional variables are introduced as controls based on a theoretical rationale and the empirical

findings of previous studies that have analyzed inequality. The prominent control variables and

the rationale for including them in this study are discussed in this section.

The first important control variable is education, which is also a good proxy for human

capital stock. De Gregorio and Lee (2002), using the Deininger and Squire (1996) dataset on

income inequality and the Barro-Lee (2001) dataset on educational attainment, find that higher


educational attainment leads to a more equal distribution of income for a broad panel of

countries. They further find that more equal distribution of education also tends to make the

income distribution more equal. Another important analysis by Gottschalk et al. (1997) that uses

the OECD countries finds that increased education levels leads to a greater inequality in their

sample, except for a few outlier countries, due to increasing returns to education. This shows

that the impact of education on income inequality depends on the measure of the education

variable that is used, and the sample of countries that are analyzed. This study uses four

measures of educational attainment from the Barro-Lee Data set (2010) as controls. The

education variables are measured as the percentage of population 15 and over with noschool,

measuring the percentage of those with no schooling, primaryschool, those that have completed

primary schooling, secondary school, those that have attained secondary schooling, and

yearschool, the average number of years of schooling. However, this dataset has values in five-

year intervals, so with the data for 1995, 2000, and 2005, the data for the education variables are

interpolated using Stata2.

Another significant set of variables is comprised of the economic variables. These

variables enable the controlling of the effects of economic growth on income inequality. The

real per capita income variable, realincome, is included from the Penn World Tables (PWT)

Version 7.0 (2011). Following Kuznet (1955), a squared value of the real per capita income,

realincome-squared, is also included in the analysis to control for the effect of Kuznets

hypothesis. Kuzents hypothesis (1955) stated an inverse U-shaped relationship between

economic development and income inequality, which implied that inequality rose up and then

fell down as the levels of per-capita incomes increased.

2 Appendix B lists the Stata commands that were used to interpolate the education variables.


Two socio-political variables are included as additional controls in this study. First,

warintensity, which is a variable coded in three categories to show the intensity of violent wars

in a country, is included in the analysis. This variable is obtained from the World Development

Report (WDR) database (2011) published by the World Bank. In societies exhibiting more

violent rebellions, there could be a weakening of institutions, including that of property rights,

which affects income inequality. Mursed (2002) points out that civil wars can increase income

inequality as they divert resources from infrastructure development and social services that help

reduce income inequality to military spending to fight the wars.

Polity2 variable compiled by the Center for Systemic Peace (2011) is also used as an

additional control. This variable is a continuous measurement that ranges from -10 to 10 that

indicates the political form of governance with -10 being hereditary monarchy and 10 being

consolidated democracy. Even though a recent study by Timmons (2010) using the latest data on

income inequality does not find a statistically significant impact of democracy on income

inequality, some previous researches such as those of Reuveny and Li (2003) found a negative

impact of democracy on income inequality.

Previous studies also indicate a difference between the wages of males and females with

similar skill levels for similar jobs. Weichselbaumer et al. (2005), in their meta-analysis of 260

papers published between 1960-1990 covering 63 countries, find that the international gender

wage differential has been decreasing slowly but that it still persists. Their meta-regression

shows that the raw wage differentials have decreased from 65% in the 1960s to 30% in the

1990s. To control for the impact of the lower female wages on income inequality, the percentage

of females employed in the labor force, femalelabor, is used from the WDI (2011) in this study.


The additional variables, along with the variables discussed above with their description

and variable name used in the analysis, are listed in Table 2.1.

Table 2.1. Variable descriptions

Variable Description and Source Grossgini The Gini coefficient of income inequality calculated over gross income. Obtained

from the Standardized World Income Inequality Database (SWIID 2010). Netgini The Gini coefficient of income inequality calculated over net income. Obtained

from SWIID (2010). tourismGDP International tourism receipts as a percentage of the GDP. Both the tourism

receipts and the GDP are obtained from the World Development Indicators (WDI 2011) database of World Bank.

tourismGDP2 Alternative estimate of the travel and tourism industry’s direct contribution as a percentage of the GDP that includes domestic tourism. Data obtained from World Travel and Tourism Council (WTTC 2011).

tourismGDP3 An estimate of the total impact of the travel and tourism industry as a percentage of the GDP that includes the contribution from industries with backward and forward linkages to tourism. Data from WTTC (2011).

laborrate Proportion of the population over 15 that is economically active. Data obtained from WDI database (2011).

languagefrac Linguistic fractionalization variable obtained from Fractionalization dataset published by Harvard Institute Research (2002).

ethnicfrac Ethnic fractionalization variable obtained from Fractionalization dataset published by Harvard Institute Research (2002).

religiousfrac Religious fractionalization variable obtained from Fractionalization dataset published by Harvard Institute Research (2002).

agedependency Proportion of the population younger than 15 and older than 64 obtained from WDI (2011).

polity2 Variable that quantitatively shows the political form of governance published by the Center for Systemic Peace (2011).

warintensity Dummy variable that measures the intensity of the civil wars in a country is obtained from the World Development Report (WDR 2011) published by World Bank.

Variable Description and Source realincome Per-capita real income adjusted for purchasing power parity obtained from the

Penn World Table (PWT) 7.0 (2011). realincome-squared Squared value of the realincome variable.

openk Measure of openness of an economy, which is the ratio of the sum of imports and exports at constant prices to real per capita GDP. Obtained from PWT 7.0 (2011).

noschooling Percentage of population over 15 without schooling. Obtained from Barro-Lee Data set (2011).

yearschool The average years of schooling attained in a given population taken from the Barro-Lee Data set (2011).

colony A dummy variable that measures whether a country was colonized or not. Most of the values were obtained from the BACE Dataset (2011) and the missing values were filled using information from the CIA World Factbook(2011).

femalelabor Percentage of women employed in the labor force. Obtained from WDI (2011). corruption A variable measuring the corruption that measures the perceptions of the extent to

which public power is used for private gain. Obtained from World Governance Indicators (WGI) 2011 Dataset.

urbanpop Percentage of population residing in urban areas. Obtained from WDI (2011).


urbanprimacy Percentage of urban population residing in the largest city. Obtained from WDI (2011).

primarysch Percentage of population over 15 with primary schooling. Obtained from the Barro-Lee Data set (2011).

secondarysch Percentage of population over 15 with secondary schooling. Obtained from the Barro-Lee Data set (2011).

eurcenasia Regional dummy for Europe and Central Asia. Obtained from WDI (2011). subsaharanAf Regional dummy for Sub-Saharan Africa. Obtained from WDI (2011).

southasia Regional Dummy for South Asia. Obtained from WDI (2011). LatinAmCari Regional Dummy for Latin America and Caribbean. Obtained from WDI (2011). EastAsiaPaci Regional dummy for East Asia and the Pacific. Obtained from WDI (2011). Grossgini70 The Gini coefficient calculated over gross income of the year 1970. Obtained from

the SWIID (2010) dataset. Netgini70 The Gini coefficient calculated over net income of the year 1970. Obtained from

the SWIID (2010) dataset.

V. The Model

Following the empirical literature on income inequality, the Gini coefficients of

inequality are included in their original specification for the analysis. All the other variables are

also used in their normal specification. The model for the regression is estimated to be:

Grossgini (or Netgini) = f (tourismGDP or tourismGDP2 or tourismGDP3, laborrate,

languagefrac, ethnicfrac, religiousfrac, polity2, warintensity, agedependency, realincome,

realincome-squared, yearschool, colony, corruption, femalelabor, urbanpop, urbanprimacy,

primarysch, secondarysch, eurcenasia, subsaharanAf, southasia, LatinAmCari, EastAsiaPaci,

Grossgini70, Netgini70)

The control variables, which are the variables on the right-hand side of the functional

specification mentioned above, are included or removed in the each of the regressions in the

following sections based on the need of the analysis. The inclusion or exclusion of any of the

explanatory variables is mentioned in either the text or in the regression tables.


V.1. Cross-country Regression

To examine the relationship between tourism and income inequality, a cross-country

regression is initially used for the analysis. A cross-country analysis enables the inclusion of

variables that cannot be included in our panel analysis because of the unavailability of the data

on some of the explanatory variables for different countries and/or for different time-periods.

The cross-country analysis thereby enables the gathering of a preliminary understanding on the

impact of tourism on income inequality, while controlling for a greater number of explanatory

variables.

Table 2.2 presents a summary of the variables, along with the number of total

observations, mean, standard deviation, and minimum and maximum values for each of the

explanatory variables used in the cross-country regressions for the year 2000. The year 2000 is

chosen because it has the largest number of observations for the variables that were discussed

earlier to have an impact on income inequality.

Table 2.2. Summary statistics of variables used in cross-country regressions

V.1.1. OLS results using all variables

To test the relationship between tourism and income inequality, regressions are

run on the Gini coefficient calculated over gross income, Grossgini, and the Gini

coefficient calculated over net income, Netgini, as the dependent variables, while

controlling for all other possible variables influencing income inequality that were

discussed earlier. The results of the OLS regressions, using robust standard errors, are

presented in the Table 2.3.

Variable Observations Mean Std. Dev. Min Max tourismGDP 92 3.931996 3.430529 0.106101 17.50544 laborrate 93 63.78817 9.429796 47.6 90.2 languagefrac 90 0.3665978 0.2796897 0.0124 0.9227 ethnicfrac 93 0.3943366 0.2406398 0.0119 0.9302 religiousfrac 93 0.4191925 0.2310736 0.0035 0.8603 polity2 93 5.655914 5.163616 -9 10 warintensity 93 0.2258065 0.5734911 0 2 agedependency 93 64.1022 16.9707 40.4887 105.971 realincome 93 10734.56 11892.13 395.3928 44827.97

realincome-squared

93 2.55E+08 4.43E+08 156335.5 2.01E+09

openk 93 78.47849 45.65232 18.07178 335.939 noschooling 93 16.91398 19.63772 0.1 78.9 yearschool 93 7.548903 2.749564 1.05 12.7056 colony 93 0.6129032 0.4897261 0 1 corruption 93 0.1389931 1.061162 -1.385243 2.576003 femalelabor 93 52.39785 14.56218 16.1 90.5 urbanpop 93 54.32473 23.04999 8.3 100 urbanprimacy 92 29.91655 15.89224 2.92555 99.7626 primarysch 93 19.44516 10.93512 1.9 50.4 secondarysch 93 21.62903 14.06673 0.6 61.8 eurcenasia 93 0.3333333 0.4739596 0 1 subsaharanAf 93 0.2150538 0.4130865 0 1 southasia 93 0.0537634 0.2267728 0 1 LatinAmCari 93 0.1935484 0.3972204 0 1 EastAsiaPaci 93 0.1397849 0.3486433 0 1 Grossgini70 43 49.26409 13.1964 18.46805 72.28944 Netgini70 45 39.01829 12.17838 16.40425 65.13837 tourismGDP2 93 3.658065 1.927959 0.9 11.1 tourismGDP3 93 9.692473 4.978481 2 25.5


Regressions (1) and (2) use Grossgini and Netgini as the dependent variable

respectively. Similarly, regressions (3) and (4) also use Grossgini and Netgini as the main

dependent variables respectively, but also include the respective values of the Gini

coefficient for the year 1970. Because income inequality changes slowly across time for a

country, the income inequality in that specific country at an earlier period can have a

significant impact on the present value of the Gini coefficient of income inequality.

Therefore, the respective values of Grossgini and Netgini in 1970 are included as

additional controls in regressions (3) and (4). However, because the sample size is

reduced by more than half, it was also necessary to run regressions (1) and (2) that do not

include these variables.

The regressions above show that the R-squared value increases significantly when

the Gini values from 1970 are used as additional regressors. This, along with a

statistically significant positive coefficient on the Gini coefficients of 1970, further shows

that the income inequality of a country 30 years earlier still accounts for the income

inequality at the later period. Furthermore, when Netgini is used as the dependent

variable instead of Grossgini, the R-squared value increases from 0.5813 to 0.7844 in

models (1) and (2) and from 0.8308 to 0.9365 in models (3) and (4). This suggests that

the explanatory power of the models increases when Netgini is used as the dependent

variable.

The coefficient on tourismGDP is significant at the 1% level when Netgini and

Grossgini are regressed by the explanatory variables without including the respective

values of the Gini from 1970. However, when the Gini coefficients of 1970 are used as

additional regressors, the coefficient becomes statistically significant only at the 10%


level. Furthermore, the results also show that the coefficient on tourismGDP is lower

when Grossgini is used as the dependent variable instead of Netgini across all the

regressions in this set thereby suggesting that international tourism decreases gross

income inequality more than it decreases net income inequality.

Specifically, models (1) and (2) predict that an increase in international tourism

receipts as a percentage of the GDP, tourismGDP, for Albania by one standard deviation,

a 3.430 percentage points increase from its value of 10.796% of GDP for the year 2000,

leads to a 2.339 percentage points decrease in the Grossgini, and to a 2.068 percentage

points decrease in Netgini. Though the result becomes statistically less significant, the

inclusions of the Gini coefficients in 1970 further increase the discrepancy between the

impact of international tourism on gross and net income inequality. A one-standard

deviation increase in tourismGDP decreases Grossgini by 2.350 percentage points to

29.422 and Netgini by 1.616 percentage points to 26.986. The increased difference could

be a result of the selection bias in the sub-sample of the countries models (3) and (4) that

has less than half the observations than is available for models (1) and (2). Nonetheless,

given the statistically significant results, all these models lead to the conclusion that

tourism’s impact on net income inequality is less than its impact on gross income

inequality.

Table 2.3. Cross-country regression results using all the variables

Variables (1) Grossgini

(2) Netgini

(3) Grossgini

(4) Netgini

tourismGDP -0.6820709*** (0.2068008)

-0.602928*** (0.2224999)

-0.6849155* (0.3699837)

-0.4707832* (0.235447)

laborrate -0.5094311* (0.2902451)

-0.5975362* (0.3014187)

-0.4713704 (0.9124791)

0.0010797 (0.639231)

languagefrac -3.457582 (4.099094)

-2.951878 (3.429292)

7.650316 (6.854851)

-3.642713 (6.736323)

ethnicfrac 5.483865 (4.450285)

6.287187* (3.574549)

-3.986995 (6.014563)

4.353916 (5.983374)



(2) Netgini

(3) Grossgini

(4) Netgini

religiousfrac 5.462174 (3.766013)

3.576254 (3.627719)

6.859766 (6.586569)

4.374807 (4.970891)

polity2 0.2535447* (0.1333801)

0.1584258 (0.143256)

0.0742966 (0.3976081)

0.0255813 (0.2723533)

warintensity -0.3725765 (1.165099)

-0.451785 (1.14561)

0.3558966 (1.881745)

-0.9401958 (2.324928)

agedependency 0.0809642 (0.0666697)

-0.003749 (0.0652983)

0.065563 (0.1593234)

-0.0738132 (0.1312796)

realincome -0.000249 (0.0004035)

-0.0004904 (0.0003331)

-0.0004684 (0.0005115)

-0.0006827 (0.0004049)

realincome-squared 2.60E-09 (8.25E-09)

5.41E-09 (6.69E-09)

5.50E-09 (8.95E-09)

7.20E-09 (6.92E-09)

openk 0.0381905* (0.0129871)

0.0248579* (0.0126584)

0.0101056 (0.0409486)

-0.0113099 (0.0260945)

noschooling -0.1415575 (0.0931647)

-0.1596037* (0.0829198)

-0.0076388 (0.2607757)

-0.4106849* (0.2082651)

yearschool -0.940591 (0.8848564)

-0.6249884 (0.7597403)

0.683837 (1.333106)

-0.7351482 (1.211633)

colony -3.577502 (3.001804)

-2.255865 (3.156317)

-4.915251 (4.044111)

0.2267365 (2.810767)

corruption 3.987957*** (1.458104)

1.786262 (1.368426)

6.736228* (3.399634)

3.812449 (2.229341)

femalelabor 1.84E-01

(1.60E-01) 0.1925775

(0.1703908) 0.1042745 (0.6181155)

-0.2306777 (0.4326313)

urbanpop -0.1159794 (0.0717043)

-0.1519767*** (0.0639879)

-0.1784135 (0.1722682)

-0.1117354 (0.1129026)

urbanprimacy 0.0412328

(0.0506549) 0.0348546

(0.0443801) 0.0256997 (0.1186994)

0.0360082 (0.0790126)

primarysch 0.0515865

(0.0900467) -0.0413845 (0.0832274)

0.1641644 (0.1340714)

-0.1031521 (0.1246628)

secondarysch 0.0622099

(0.0823198) -0.0395788 (0.0857383)

0.0357339 (0.1107798)

-0.086098 (0.0997048)

eurcenasia -9.647785***

(3.518414) -13.17883***

(3.791172) -8.95257

(6.579279) -6.304318 (4.761203)

subsaharanAf -0.5952801 (3.369313)

1.400007 (3.716023)

-4.295949 (9.954309)

1.833066 (7.290684)

southasia -9.736409***

(3.498497) -8.00346*** (3.731771)

-14.95611 (10.2768)

-4.497323 (8.18245)

LatinAmCari 5.725807* (3.389126)

8.460838*** (3.074173)

11.75777 (7.742315)

4.229332 (5.610975)

EastAsiaPaci -4.926896 (3.186313)

-3.144773 (2.970114)

-4.982798 (3.383732)

-3.104213 (3.597963)

Grossgini70/ Netgini70

0.224913** (0.0936462)

0.4257383*** (0.1068849)

Constant 77.45955*** (14.87252)

89.51945*** (15.18502)

60.00265*** (31.54427)

66.76007*** (17.47585)

Number of observations

88 88 42 44

R-squared 0.5813 0.7844 0.8308 0.9365 Robust standard errors in parentheses. *** p<0.01, ** p<0.05, * p<0.1


V.2.2. OLS results using only main variables

More regressions are run on the Netgini and Grossgini variables by only using the

main variables that have a strong theoretical association with income inequality. The two

models that do this are reported in Table 2.4. Both the models use 89 cross-country

observations, but the R-squared value when using the Gini calculated over gross income,

Grossgini, is 0.4261, which increases to 0.6399 when the Gini calculated over net

income, Netgini, is used. This increase again reinforces the greater predictive ability of

the model when the income inequality after taxation and redistribution is analyzed.

In model (5), tourismGDP, tourism’s contribution to the GDP, is statistically

significant at the 1% level and has a coefficient of -0.632. Using the data of Albania for

2000, this result means that, all else constant, if tourismGDP of Albania were to increase

by 3.430 percentage points, then the Gini coefficient of income inequality calculated over

gross income, Grossgini, of Albania would decrease by 2.168 percentage points from

31.772 to 29.604. In model (6), where the Gini coefficient is calculated over net income,

the coefficient on tourismGDP is -0.587 and is statistically significant only at the 5%

level. This means that the same increase of a one standard deviation in tourismGDP of

Albania would decrease the Gini coefficient of income inequality calculated over net

income, Netgini, by only 2.013 percentage points and would result in a Gini coefficient of

26.588 from 28.602.

The results from Table 2.4 demonstrate that even though international tourism

contributes to decreasing the gross and net income inequalities, the decrease in Netgini is

less than the decrease in Grossgini for a given increase in tourismGDP. This suggests


that the effective contribution of international tourism in decreasing the income

inequality is diluted by the redistribution policies across the sample of 89 countries in

models (1) and (2).

Table 2.4. Cross-country regression using only the main variables


(6) Netgini

tourismGDP -0.6322687*** (0.2173793)

-0.5869047** (0.2723522)

laborrate -0.1507046 (0.1143213)

-0.1984209 (0.1395123)

languagefrac -8.942638** (3.76719)

-8.599423** (3.607381)

ethnicfrac 10.51298 (4.241608)

11.94419*** (3.954278)

religiousfrac 5.632714 (3.944152)

5.877969 (4.303161)

polity2 0.4080151*** (0.1517743)

0.3963797** (0.1707111)

warintensity -1.41281 (1.138957)

-1.479526 (1.253781)

agedependency 0.1770966*** (0.0661042)

0.0903834 (0.0802941)

realincome -0.0003068 (0.0003776)

-0.0007462** (0.0003463)

realincome-squared 1.40E-09 (7.72E-09)

6.88E-09 (6.35E-09)

openk 0.0331849** (0.0127992)

0.0200669 (0.0134647)

noschooling -0.2661507*** (0.0757445)

-0.280098*** (0.0803097)

yearschool -1.679278*** (0.6260234)

-1.919914*** (0.6536232)

colony 2.343977 (1.82365)

7.497197*** (2.080458)

corruption 3.95631** (1.569261)

2.237169 (1.871803)

Constant 56.11284*** (11.97585)

61.83856*** (14.10176)

Number of observations 89 89 R-squared 0.4261 0.6399

Robust standard errors in parentheses,

*** p<0.01, ** p<0.05, * p<0.1

On a side note, the statistically significant controls across both models are the

linguistic fractionalization, polity and the education variables. Of these, the variable


measuring the average years of schooling attained by the population over 15 of a country,

yearschool, has the largest coefficient of -1.679 for Grossgini, and -1.920 for Netgini.

Contrary to the case with international tourism, these results suggest that an increase in

the years of schooling attained leads to a greater decrease of net income inequality than

gross domestic inequality.

In the rest of the analysis, the results of the significance of the control variables,

apart from the income variables, will not be discussed. No evidence is found for Kuznet’s

hypothesis from the above results as per capita income is statistically significant for only

Netgini at the 5% level, and the squared-value of per capita income is not statistically

significant across both the models.

V.1.3. OLS results using alternate measures of tourism

In models (7), (8), (9) and (10) additional regressions are run using different

measures of the tourism variable. TourismGDP2, which denotes the direct contribution of

the overall tourism sector as a percentage of the GDP, and TourismGDP3, which is the

total contribution of tourism and sectors interrelated with tourism as a percentage of the

GDP, replace the international tourism variable in these analyses. The results of these

regressions are presented in Table 2.5.

Table 2.5 shows that all the coefficients on the tourism variable are statistically

significant across all the models from (7) to (10) at the 1% level when Grossgini is used

as the dependent variable and at the 5% level when Netgini is used as the dependent

variable. As was the case with the previous models, the R-squared value increases when


Netgini is used instead of Grossgini, suggesting that using net income increases the

predictive capacity of the models.

The coefficients of -1.230 and -0.933 on the tourismGDP2 variable in models (7)

and (8) indicate that, all else constant, an increase of 3.43 percentage points in the value

of the contribution of the tourism sector, including domestic and international tourism,

leads to a 4.219 percentage points decrease in Grossgini and to a 3.2 percentage points

decrease in Netgini, resulting in a Grossgini of 27.552 and a Netgini of 25.402. Both of

these resulting values demonstrate that tourismGDP2 contributes to a greater decrease in

both Grossgini and Netgini in comparison to the tourismGDP variable, but as was the

case earlier the decrease in Grossgini is larger than the decrease in Netgini. This greater

decrease in the value Grossgini than Netgini holds true for the tourismGDP3 variable as

demonstrated by models (9) and (10).

Table 2. Cross-country regression using alternate measures of tourism


(8) Netgini

(9) Grossgini

(10) Netgini

tourismGDP2 -1.230029*** (0.3474617)

-0.9329229** (0.3957952)

tourismGDP3 -0.4638298*** (0.1559241)

-0.4022322** (0.173879)

laborrate -0.1668465 (0.1114766)

-0.2078428 (0.1399437)

-0.1577695 (0.1106085)

-0.2039689 (0.1384964)

languagefrac -7.658667** (3.813715)

-7.278118* (3.807339)

-7.414669* (3.899806)

-7.154515* (3.820247)

ethnicfrac 10.9293** (4.213169)

12.09327*** (4.003384)

10.30588** (4.35474)

11.77814*** (4.083886)

religiousfrac 3.696424 (3.918673)

4.227494 (4.444954)

4.847862 (3.825912)

5.083983 (4.329507)

polity2 0.3945297** (0.1627278)

0.4029676** (0.1878728)

0.4185372** (0.1594214)

0.4204358** (0.1858746)

warintensity -1.345288 (1.131817)

-1.309159 (1.276671)

-1.175235 (1.148117)

-1.210846 (1.236375)

agedependency 0.1606367** (0.0716221)

0.0874031 (0.085496)

0.1701131** (0.0691618)

0.0909096 (0.0830424)

realincome -0.000161 (0.0003655)

-0.0006151* (0.0003422)

-0.0001296 (0.0003682)

-0.0005823* (0.0003309)

realincome-squared

-1.13E-09 (7.61E-09)

4.88E-09 (6.38E-09)

-1.90E-09 (7.68E-09)

3.91E-09 (6.26E-09)



(8) Netgini

(9) Grossgini

(10) Netgini

openk 0.0223145* (0.0129696)

0.009363 (0.0144311)

0.0207749 (0.0131873)

0.0085818 (0.0143824)

noschooling -0.2736888*** (0.0739203)

-0.2741499*** (0.0809045)

-0.2634952*** (0.0740762)

-0.2697611*** (0.0803078)

yearschool -1.876778*** (0.6297093)

-1.976689*** (0.6832767)

-1.640478** (0.6198328)

-1.818109*** (0.6569187)

colony 2.872726 (1.887933)

7.843733*** (2.11491)

2.703933 (1.83761)

7.747813*** (2.0567)

corruption 4.135211*** (1.474582)

2.252837 (1.818782)

4.038299*** (1.509008)

2.264313 (1.822309)

Constant 61.58968*** (12.26678)

63.77648*** (14.64844)

58.04544*** (11.85071)

62.13916*** (14.21308)


90 90 90 90

R-squared 0.4374 0.6259 0.4321 0.6313 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The result that tourismGDP2 leads to a greater decrease in both Netgini and

Grossgini than the tourismGDP variable means that domestic tourism contributes to a

greater decrease in income inequality than international tourism at both the gross and net

income levels.

The table above also shows that the coefficients on tourismGDP2, that of -1.23

and -0.933 when Grossgini and Netgini are used as dependent variables respectively, are

more than twice the value of the coefficients on the tourismGDP3 variable, that of -0.464

and -0.402. This suggests that tourism’s direct contribution leads to a greater decrease in

income inequality than the total contribution of tourism, which shows that tourism

decreases income inequality more than the other sectors associated with it.

V.1.4. Robustness tests for cross-country OLS Results

By removing the different control variables turn by turn regressions (3) and (4), the

statistical significance of the tourism variable is tested. The resulting coefficients on the

tourism variable with robust standard errors and the variables that are removed are listed


in the Table 2.6. The tabulated results show that the results from the regressions in

models (3) and (4) are robust across almost all the variable removals and only in the case

of the removal of the ethnic, linguistic and religious fractionalization variables all at once

does the coefficient for tourismGDP, while using Netgini as the dependent variable,

become statistically insignificant. Otherwise, the coefficients for the tourismGDP

variable are statistically significant either at the 1% or the 5% significance levels in all

the cases where Grossgini is the dependent variable, and at either the 5% or 10%

significance levels in all the cases where Netgini is the dependent variable.

Table 2.6. Robustness tests for cross-country regressions

Variables excluded (1) Grossgini

(2) Netgini

Dependencyratio, laborrate -.63004*** (.2138147)

-.5574342** (.2552735)

Fractionalization -.4782455** (.2173698)

-.4343041 (.2688207)

Polity2, warintensity -.5711876** (.2205033)

-.525762* (.2671062)

Realincome, realincome2 -.5204153** (.1990275)

-.3878716* (.2219641)

Openk -.4835754** (.2114057)

-.49699* (.2574669)

Noschooling, yearschool -.5911036*** (.2214581)

-.5461861** (.2589597)

Colony -.6493099*** (.2326017)

-.641411** (.3209404)

Corruption -.5343819** (.2112052)

-.5315528** (.2477593)

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

V.1.5. Discussion

The results from the cross-country regression analysis have shown that tourism

decreases income inequality. The results are robust across different measurements of the

tourism variables, and the income inequality variables. The results also hold across a

wide range of models, where many of the other control variables are removed or added.


Furthermore, the results also show that the impact of tourism is greater in

reducing the gross income inequality than in reducing net income inequality that suggests

that the redistribution and taxation policies contribute to a lesser decrease in income

inequality than would be the case without those policies.

Finally, the results have also demonstrated that domestic tourism is more

favorable to reducing income inequality than is international tourism. Likewise, the

impact of the tourism sector is greater in reducing income inequality than the impact of

the other sectors that have linkages to the tourism sector.

To further explore the relationship between tourism and income inequality more

deeply, the panel dataset discussed earlier will be used. The panel dataset will be

analyzed using pooled OLS and fixed-effects estimation in the next section. These panel

data analyses will enable to account for the omitted-variable bias, which produces biased

estimates in cross-country regressions, and hence will provide a strong evidence for the

results that emerge.

V.2. Panel Data Analysis

For the panel data analysis, an unbalanced panel dataset with 1001 observations for 94

countries is built. Table 2.7 summarizes the variables used in the regression for the

different panel data analyses performed in this section.

In a pooled OLS regression, the default standard errors produced by Stata, the

statistical software used for data analysis in this study, are calculated on the assumption

that the error terms are independently and identically distributed. However, this is not the

case with a time-series data on income inequality. Income inequality changes very slowly


over time and the covariance between any two Gini coefficients for a specific country is

large. Therefore, country-clustered standard errors, which are robust to the correlation

between any two Gini coefficients of any country at different time periods in our data, are

used in the analysis. To do this, an assumption has to be made that the correlation of the

error terms between the countries is zero. This assumption is innocuous; therefore, a

pooled OLS regression with cluster-robust standard errors is employed for analysis.

Fixed-effects is another estimation method in panel data analysis that enables to

control for the problem associated with omitted-variable bias. Fixed-effects estimation

relies on the within country variation over time to explain the relationship among

variables. This estimation removes the effects of the unobserved time-invariant

characteristics that are unique to each country by controlling for those factors. For fixed-

effects, the main assumption necessary to get unbiased estimators is that the error terms

are not correlated across countries – same as the assumption for using cluster-robust

standard errors. In our data, there is not any plausible reason as to why the error terms

would be correlated across countries; therefore, the fixed-effects estimation is suitable for

our analysis. Furthermore, since fixed-effects estimation allows for the capturing of

robust effects of the explanatory variables across time for the countries in the sample, it

can provide, in our case, conclusive evidence on the impact of tourism on income

inequality.

Table 2.7. Summary statistics of variables used in panel analysis

Variable Observations Mean Std. Dev. Min Max tourismGDP 990 3.933276 3.535175 0.058118 20.62451

laborrate 1001 63.46344 9.202402 46.9 90.8


Variable Observations Mean Std. Dev. Min Max languagefrac 1001 0.4182486 0.2315628 0.0035 0.8603

ethnicfrac 1001 0.3934833 0.2382937 0.0119 0.9302 religiousfrac 1001 0.4182486 0.2315628 0.0035 0.8603

polity2 1001 5.781219 5.206063 -9 10 warintensity 1000 0.209 0.5287708 0 2

agedependency 1001 63.77576 16.9612 38.9886 105.971 realincome 1001 10835.37 11656.29 321.2442 48701.21

Realincome-squared

1001 2.53E+08 4.32E+08 103197.8 2.37E+09

openk 1001 78.79656 47.12138 14.26907 429.7646 noschooling 995 16.53146 19.43508 0 81.2 yearschool 995 7.623637 2.726909 0.94984 12.9105

colony 1001 0.6083916 0.4883539 0 1 femalelabor 1001 51.93916 14.2944 12.5 90.8 urbanpop 1001 54.89163 22.78139 7.2 100

urbanprimacy 990 30.0138 15.98491 2.68838 100.956 primarysch 995 19.37504 10.51641 0.7 50.5

secondarysch 995 21.89626 13.90439 0.6 69.8 eurcenasia 1001 0.3396603 0.4738306 0 1

subsaharanAf 1001 0.2057942 0.4044829 0 1 southasia 1001 0.0509491 0.2200036 0 1

LatinAmCari 1001 0.1978022 0.3985413 0 1 EastAsiaPaci 1001 0.1398601 0.3470152 0 1 tourismGDP2 999 3.554054 2.033276 0.1 13.8 tourismGDP3 1001 9.543756 5.127555 0.2 33.3

V.2.1. Pooled OLS with cluster-robust standard errors

First, it is necessary to decide on whether to use the time-dummies in the

regressions. For this, regressions are run on the Grossgini and the Netgini variables with

only the main variables, in models (3) and (4) of section as explanatory variables. The

results of the pooled OLS regressions on including the time dummies do not produce

statistically significant coefficients for the time dummies. Thereafter, a joint test is

performed to analyze whether the time dummies are jointly significant. The p-value for

this test is 0.4104, which indicates that we fail to reject the null hypothesis that the time

dummies are not jointly significant, so the pooled OLS regressions are run without the

time-dummies.


Table 2.8 presents the coefficients on all the three different tourism variables

when using the variables used in models (3) and (4) in section 2.3.2 for the pooled OLS

analysis. The results of these regressions further reinforce the finding from the earlier

cross-country regressions that tourism decreases income inequality. Similarly, larger R-

squared values when Netgini is used as the dependent variable instead of Grossgini in

these pooled OLS regressions continue to demonstrate that the predictive capabilities of

the models increase when Netgini is used, which is again in line with the cross-country

regressions.

Furthermore, these results also show that domestic tourism has a greater impact

than international tourism in decreasing income inequality as demonstrated by the higher

negative coefficients on the tourismGDP2 variable in comparison to the tourismGDP

variable. Another similarity with the cross-country analysis results is the lower negative

coefficient on the tourismGDP3 variable in comparison to the tourismGDP2 variable.

This later result again strengthens the conclusion derived from the cross-country analysis

that the direct contribution of the tourism sector in decreasing income inequality is higher

than the total contribution of the tourism in which sectors that are linked to the tourism

sector are also taken into account.


Table 2.8. Pooled OLS regression results

Model Tourism variable

Grossgini (A)

Netgini (B)


R-squared

(1) tourismGDP -0.2428461* (0.1453484)

949 0.4470

tourismGDP -0.4005042*** (0.1304331)

949 0.7444

(2) tourismGDP2 -0.6991695** (0.2717837)

958 0.464

tourismGDP2 -0.8682261*** (0.2588153)

958 0.7493

(3) tourismGDP3 -0.2009649* (0.1147719)

960 0.454

tourismGDP3 -0.2828099*** (0.0977872)

960 0.7431

Cluster-robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

The main contradiction with the earlier cross-country regression results are the

lower values on the coefficients of all three tourism variables when Grossgini is used as

the dependent variable instead of Netgini. This result leads to a direct contradiction of the

OLS conclusion, and enables us to conclude that, holding other variables constant, as

tourism grows, the taxation and redistributive policies in our sample of countries actually

help in decreasing income inequality.

V.2.2. Robustness tests for pooled OLS

By removing some of the variables used in the above analysis, the robustness of

the model is tested. These different regressions still result in statistically significant

negative coefficients for the tourism variable across a wide range of variable exclusions.

Table 2.9 summarizes the results for the tourismGDP2 variable when specific variables,

mentioned alongside in the table, are excluded from the analysis.


The table shows that only in the case when all the regional dummies are excluded

from the model does the coefficient on only tourismGDP2 cease to be significant.

Otherwise, the results, and therefore the conclusions, from the main model hold true in all

the cases.

Table 2.9. Robustness tests on pooled OLS results

Variables excluded (1) Grossgini

(2) Netgini

Fractionalization variables -0.6991695** (0.2717837)

-0.789537*** (0.2512147)

laborrate, dependencyratio -0.8457086*** (0.2748114)

-0.9232949*** (0.2758948)

realincome, realincome2 -0.6956149*** (0.260723)

-0.8551839*** (0.253109)

Education variables -0.5331232* (0.2700183)

-0.6533142** (0.2543854)

Urbanization variables -0.6143441** (0.2710238)

-0.7626496*** (.2618871)

Colony dummy -0.5797551** (0.2661097)

-0.7330737*** (.0267913)

Regional dummies -0.4230423 (0.2863953)

-0.4279472 (0.3044586)

Cluster-robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

V.2.2. Fixed-effects regression with robust standard errors

The results of the fixed-effects estimation establish that the coefficients on the

tourismGDP2 variable are statistically significant, whereas the coefficients on the

tourismGDP and tourismGDP3 variables are not. Therefore, only the results of the fixed-

effects estimations that use the tourismGDP2 variable are used in the analysis.

Nonetheless, all three tourism variables continued to have a negative coefficient despite

being statistically not significant even at the 10% significance level.

Even while using only the tourismGDP2 variable, the p-value for the overall

regression is statistically insignificant when Netgini is used as the dependent variable.


However, the p-value for the regression as a whole, when using Grossgini as the

dependent variable, is 0.041; therefore the model is statistically significant at the 5%

level of significance. Even then the value of rho for this fixed-effect estimation is 0.921,

which means that greater than 92% of the variation in income inequality is due to the

difference across the countries in the sample. Table 2.10 summarizes the results of the

fixed-effect estimation, where Grossgini is used as the dependent variable.

The results from the fixed-effects estimation lead us to conclude that most of the

effect on the income inequality of a country comes from the country’s own unobservable

characteristics. This conclusion is in line with the cross-country OLS estimates that had

shown earlier that even the income inequality values from 30 years ago still had a

statistically significant positive effect on current income inequality values for a country.

There are some unobservable characteristics of countries that determine their income

distributions, which changes slowly through time.

However, despite this large degree of time-invariance of income inequality, the

fixed-effects regression estimates a negative coefficient, which is statistically significant

at the 5% level of significance, on the tourismGDP2 variable. Specifically, the negative

coefficient of -0.530 on the tourismGDP2 variable implies that, all else equal, if the share

in the GDP of the tourism industry were to increase by 3.430 percentage points, then the

gross income inequality would decrease by 1.82 percentage points. Using the same

context of Albania as in the previous cases, this results in a decline from 31.772 to 29.952

in the value of the Gini coefficient of income inequality calculated over gross income.

This decrease in value of gross income inequality is still less than the decreases in gross

income inequality that were predicted by the cross-country OLS and pooled OLS models.


This suggests that the presence of fixed unobserved characteristics of the countries in our

sample led to biased estimates of the coefficients on the tourismGDP2 variable in the

OLS model.

Table 2.10. Fixed-effects estimation results

Variable Grossgini

tourismGDP2 -0.5296756** (0.2656891)

Laborrate -0.2402909 (0.4179115)

polity2 -0.1048715* (0.0715491)

agedependency 0.0109372 (0.1031292)

Realincome 0.0006374** (0.0003097)

realincome-squared -7.06E-09 (4.63E-09)

Openk 0.0059657 (0.0127741)

Noschooling -0.0030135 (0.2753732)

Yearschool -2.461494 (1.731404)

Femalelabor 0.1507968 (0.298227)

Urbanpop 0.1517551 (0.2265738)

Urbanprimacy 0.1636785 (0.1850176)

primaryschool -0.0303423 (0.1421737)

secondaryschool 0.0079903 (0.1082803)

Constant 55.03712** (24.45512)

R-squared 0.0301 Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1


V.2.4. Robustness tests for fixed-effects estimations

For additional robustness checks, the fixed-effect estimation with robust standard

errors is performed on the previous model by removing and adding some of the control

variables. The results are summarized in Table 2.11.

The results show that the coefficient on the tourismGDP2 variable is negative in

all the cases and is statistically significant in all the cases apart from the one case, where

the education variables are removed. Hence, the fixed-effects estimation conclusively

shows that the direct contribution of the tourism sector, composed of both domestic and

international tourism, decreases gross income inequality. No evidence is found for the

other conclusions reached from the previous models.

Table 2.11. Robustness tests on fixed-effects results

Variables removed (-) Variables added (+)

Grossgini

(-)Laborrate, dependency ratio -.5110415** (.2367765)

(-)Income variables -.5314957* (.2678483)

(-)Polity2 -.5642853** (.2701684)

(-)Education variables -.4789049 (.2888473)

(-)Urbanization variables -.5025265* (.2609225)

(+) warintensity -.5100329* (.265849)

Robust standard errors in parentheses

*** p<0.01, ** p<0.05, * p<0.1

VI. Conclusion

The results of the empirical analysis in this study show that increases in the

overall contribution of the tourism sector reduce the income inequality calculated over


gross income across all the models used in this analysis. Additionally, the cross-country

OLS with robust-standard errors and pooled OLS with country-clustered standard errors

also show that tourism decreases income inequality calculated over net income but the

fixed-effects estimation does not show evidence for this.

International tourism, without the contribution of domestic tourism, is found to

decrease income inequality in the cross-country OLS and the pooled OLS regressions,

but no statistically significant results are obtained in the fixed-effects estimation.

Therefore, it is concluded that international tourism alone has a weak link in decreasing

income inequality. To generate more statistically significant results, a longer time-period

analysis than the one used in this study would be necessary because of the earlier finding

that income inequality changes very slowly over time for any given country. However,

this would be unlikely to accomplish in the near future given the lack of the availability

of adequate time-series data on international tourism.

Another conclusion supported by the cross-country and pooled OLS regressions is

that the contribution of domestic tourism is greater in reducing income inequality than the

contribution of international tourism. Perhaps this is because domestic tourists consume

disproportionately more goods and services provided by small and medium enterprises,

whereas international tourists consume more sophisticated goods and services, the

spending of which is goes disproportionately more to the well-off labor force and the

owners of capital in comparison to the case with domestic tourism. However, the fixed-

effects estimation failed to provide any evidence for this; therefore, this conclusion is

only weakly supported and needs to be investigated further.


A further conclusion that emerges from this study is that the tourism sector

contributes more to decreasing income inequality in comparison to the other sectors that

are linked to the tourism sector through forward or backward linkages. This means that

industries such as travel, hotels, and restaurants, amongst others that directly deal with

tourists, either domestic or international, contribute to decreasing income inequality more

than sectors such as the handicraft and the manufacturing industries that deal only

indirectly with tourists.

The study also contributes to generating some preliminary findings on the impact

of the taxation and redistribution policies for the sample of countries investigated in this

study though with a caveat that the cross-country OLS regressions and the pooled OLS

regressions led to contradictory results. Specifically, the cross-country OLS regressions

showed that tourism’s contribution in decreasing net income inequality was less than its

contribution in decreasing gross income inequality. However, the pooled OLS regressions

showed the reverse. A robust conclusion could have been drawn from the fixed-effects

estimation but the fixed-effects regression models that used net income inequality as the

dependent variable were not statistically significant, and even then, the coefficients on the

tourism variables were not statistically significant. In absence of robust evidence through

fixed-effects estimation, the results from the pooled OLS regression are preferred over

the cross-country regression results. Therefore, the study weakly supports the conclusion

that the taxation and redistribution policies of the countries in the sample help in

decreasing income inequality through tourism’s contributions. In other words, controlling

for most of the other imaginable factors that can have an impact on income inequality, it


is found that the welfare policies of the countries used in this study are strengthened due

to tourism and thus they help the poor disproportionately more.

Future research on tourism and inequality should try to incorporate not only

tourism’s distributional impact on income, but also the cause of this impact. Since

tourism is a low-wage sector, it is possible that increased tourism decreases income

inequality by decreasing absolute income levels. This study is limited because of its

failure to illustrate the dynamics that decrease income inequality through tourism. It is

essential that studies be undertaken to understand the processes through which tourism

decreases income inequality.

Appendix A

Table A.1 – Countries in the sample List of countries in the sample

Albania Algeria Argentina Armenia Australia Austria Bangladesh Belgium Bolivia Botswana Brazil Bulgaria Burundi Cambodia Cameroon Chile China Colombia Costa Rica Croatia Czech Republic Denmark Ecuador El Salvador Estonia Finland France Ghana Greece Guatemala Haiti Honduras Hungary India Indonesia Ireland Israel Italy Jamaica Japan Jordan Kazakhstan Kenya Latvia Lesotho Lithuania Malawi Malaysia Mali Mauritius Mexico Moldova Mongolia Morocco Mozambique Namibia Nepal Netherlands New Zealand Nicaragua Niger Norway Pakistan Panama Papua New Guinea Paraguay Peru Philippines Poland Portugal Rwanda Senegal Sierra Leone Singapore Slovak Republic Slovenia South Africa Spain Sri Lanka Swaziland Sweden Switzerland Tanzania Thailand Tunisia Turkey Uganda Ukraine United Kingdom United States Uruguay Vietnam Zambia


Appendix B Stata commands used for linearly interpolating the education variables.

. by country1: ipolate lu year, gen(noschool)

. by country1: ipolate yr_sch year, gen(yearschool)

. by country1: ipolate lpc year, gen(primaryschool)

. by country1: ipolate lsc year, gen(secondaryschool)

where,

country1 is the country identifier in the panel dataset,

lu, yr_sch, lpc, and lsc are education variables before interpolation, and

noschool, yearschool, primaryschool, secondaryschool, are the respective

interpolated variables.

List of Tables

Table 2.1 – Variable descriptions ..................................................................................... 67

Table 2.2 – Summary statistics of variables used in cross-country regressions ............... 70

Table 2.3 – Cross-country regression results using all the variables ................................ 72

Table 2.4 – Cross-country regression using only the main variables ............................... 75

Table 2.5 – Cross-country regression using alternate measures of tourism ..................... 77

Table 2.6- Robustness tests for cross-country regressions ............................................... 79

Table 2.7 – Summary statistics of variables used in panel analysis ................................. 81

Table 2.8 – Pooled OLS regression results ....................................................................... 84

Table 2.9 – Robustness tests on pooled OLS results ........................................................ 85

Table 2.10 – Fixed-effects estimation results ................................................................... 87

Table 2.11 – Robustness tests on fixed-effects results ..................................................... 88

Table A.1 – Countries in the sample ................................................................................. 92

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