Tourism Economics, 2013, 19 (4), 823–834 doi: 10.5367/te.2013.0226

The impact of wage rate growth on
tourism competitiveness
KAHLIL PHILANDER

University of Nevada, Las Vegas, 4505 Maryland Parkway, Box 456017, Las Vegas,
NV 89154–6107, USA. E-mail: philande@unlv.nevada.edu.
(Corresponding author.)
SUSAN J. ROE

Department of Hospitality and Tourism Management, College of Business, San Francisco
State University, 1600 Holloway Avenue, San Francisco, CA 94132, USA.
E-mail: susanroe@sfsu.edu.
This study tests whether national labour costs affect international
competitiveness in the tourism industry. Using a fixed effect model
design and two-stage least squares (2SLS) estimation, a set of 40
countries is examined during the period 1990–2010. The study
reveals that labour costs may be an important supply-side determinant
of tourism expenditures, and supports the hypothesis of a negative
relationship between these two variables. Implications of the reported
findings for industry and for government macroeconomic policy are
provided. Potential extensions of these findings to the tourism area
life cycle model are also discussed.
Keywords: tourism competitiveness; wages; labour costs; instrumental
variable; visitor expenditures

Tourism is a common driver of economic and societal change. As countries
evolve into more wealthy and developed nations, tourism and economic growth
typically coincide with increases in wage rates. Wages and labour costs have
been noted to be effective measures of international competitiveness in other
sectors (Turner and Golub, 1997; Turner and Van’t dack, 1993), but little
research has explored how labour costs affect tourism industry competitiveness.
To date, it has remained empirically untested whether increasing wages in
these developing and transitional countries have affected the ability of their
tourism sectors to compete effectively with less developed nations, where
labour costs remain low. As a nation improves its welfare, in part through
development of the tourism sector; does the corresponding increase in labour
market wages affect the ability for that sector to remain competitive with other
jurisdictions?
In this study, a discussion of the critical determinants considered when
tourists select a destination is followed by a dialogue regarding the evolution

TOURISM ECONOMICS

of a tourism destination, and a review of how business costs, like labour, impact
destination competition. A macroeconomic panel data set of predominantly
European countries from 1990 to 2010 is then tested, estimating the impact
of employee wage growth on countries’ tourism industry. A fixed effect model
design with 2SLS estimates is used to correct for potential model endogeneity.
The article then concludes with a discussion of the findings, and potential
extensions of the model.

Literature review
Destination life cycle theory
When examining tourism sector competitiveness, it is helpful to understand the
life cycle of a tourism destination. Butler (1980) proposed the tourism area life
cycle model (TALC), which is an often cited description of the evolution of a
tourist area. Butler’s TALC model is depicted by an s-shaped curve including
the following stages: exploration, involvement, development, consolidation,
stagnation, rejuvenation and decline (Butler, 1980). The model explains that
a destination moves from one stage to the next based on a variety of variables
including accessibility, political support, resource availability, competition and
visitor preference. Butler suggests that the development stage might include
growth leading a destination to import labour to complete tourism-related
work, suggesting that labour demands increase as a destination grows in
popularity; however, it does not discuss how this increase would impact the
destinations competitiveness. Similarly, Butler (1980) defines the decline stage
as a period where the destination will not be able to compete as the
attractiveness of the destination decreases; yet, there is little economic discussion
of the reasons a location may enter the decline stage.
Getz (1992) suggests that the status of the economic market in the
destination area may be an important impact related indicator of a destination’s
designation in one of the TALC stages. Prideaux (2000) describes several
challenges of the TALC model as an analytical tool, including the lack of
consideration in the model for significant economic factors. Identifying specific
economic indicators, like wage rate growth, can continue to advance the TALC
model by including more tangible variables.

Determinants of destination choice
Several items are often considered when tourists decide country destinations.
Destinations often are analysed on qualitative factors like image, quality of
tourist services, and destination marketing as well as service factors like the cost
of transport services and ground content, which includes accommodations, food
and beverage, and entertainment (Dwyer et al, 2000). Price has repeatedly been
identified as a critical factor for tourists when selecting a destination (Crouch,
1992; Crouch, 1995; Crouch and Ritchie, 1999). Low cost destinations may lure
travellers to visit while higher cost locations may drive potential tourists away,
given similar destination characteristics.
As rational consumer decision making would suggest, given all other
attractiveness features for a trip with the same characteristics, a tourist will
select to visit a country in which the personal expenditure will be lower (Dwyer

Wage rate growth and tourism competitiveness

et al, 2000; Haroutunian and Pashardes, 2005; Mangion et al, 2005;
Papatheodorou, 2002; Zhang and Jensen, 2007; Azzoni and de Menezes, 2009).
Crouch (1992) suggests that for international tourism as a product, the cost of
goods and services are a major portion of the total price. Factors that cause
prices to increase need to be identified and examined to determine how those
variables impact tourism competitiveness. This study examines one supplier cost
factor: employee wages.

Labour cost impact on destination competitiveness
Tourism destinations compete for visitors. Researchers indicate that visitors
often substitute one destination for another. Bornhorst et al (2010) suggest that
substitution among destinations is persistent and that destinations need to
effectively control all components of the tourism system in order to maintain
a competitive advantage.
Enright and Newton (2004) find that business-related factors as well as more
conventional attractiveness factors, like price, should be included in studies of
tourism competiveness. Based on the ‘evidence of price sensitivity on the
demand for travel, destinations need to monitor their price competitiveness
relative to alternate locations’ (Dwyer et al, 2000, p 21). Edwards (1995)
investigated the cost competitiveness of chosen countries in the Asia Pacific
area. He suggests that an increase in costs can be associated with a decrease
in travel market share from every origin country. He further suggests that
decreases in destination comparative costs are linked to an increase in market
share.
This idea is demonstrated by several specific countries or areas, which have
been the focus of investigation. A case study of the British Channel Island of
Jersey states that increases in labour costs ‘inevitably undermine the island’s
competitiveness’ (Hampton and Christensen, 2007, p 1004). Further, they
suggest that Jersey has been ‘crowded-out by high labour costs relative to
comparable destinations’ (Hampton and Christensen, 2007, p 1004). Similarly,
a study that investigated the competitiveness of Slovenian tourism found that
prices were considered to be too high, specifically stating that ‘Slovenian
tourism is uncompetitive in human resource management’ (Gomezelj and Mihalic,
2008, p 296). Similarly, Keane’s (1997) investigation of quality and pricing in
tourism destinations uses the examples of Bermuda and Belize to demonstrate
the impact of price on competitiveness. Keane describes Bermuda as having
taken on features of a high-cost destination, including high labour costs. These
factors contributed to intensifying competition in Bermuda, whereas Belize had
not experienced similar competitive threats due in part to its small scale,
flexible operations and lack of labour market problems.

Wages and industry competitiveness
Under the conditions of a perfectly competitive labour market, workers can
expect to receive a wage that is equal to their opportunity cost. That is, firms
will offer a wage that leads to a level of utility equal to that which could be
received from the next best alternative in the labour market (including leisure).
If skills are not entirely industry specific, the wage will therefore reflect the
wage that could be obtained in an alternative industry. This supposition is

TOURISM ECONOMICS

supported by the findings of Kambourov and Manovskii (2009), who find that
wage inequality and occupational mobility are highly interrelated. This does
not suggest that labour markets will demonstrate wage parity; indeed large
inter-industry wage differentials have been found in the USA that cannot be
attributed to unobserved differences in ability or to compensating differentials
for working condition (Krueger and Summers, 1988). However, the result
suggests that the labour component of a firm’s cost structure may be influenced
in part by the wages offered in other industries where their human capital may
be productive. As a proxy for the opportunity cost of tourism workers, national
wage rates are proposed in this study.
Other industries have previously been the focus of studies that explore the
effect of rising wages on international competitiveness. Increasing wages in the
manufacturing sector have been identified as affecting the ability to compete
with countries whose comparative advantage did not erode (Kreinin, 1984).
Some researchers distinguish the importance of comparative advantages as being
determined by the wage difference between high-skilled workers and lowskilled workers (Sachs et al, 1994). More recently, studies have examined China’s
competitive advantage in the manufactured goods sector, as economic prosperity
has led to increased labour costs, which must be balanced against increases in
labour productivity (Ceglowski and Golub, 2007; Houjun and Danli, 2011).
Assuming that the supply of tourism is not perfectly inelastic to labour costs,
an increase in the wage rate will cause an upward shift in the supply curve,
and reduce consumption in a competitive international market. Dwyer et al
(2010) have described the tourism market as competitive, but not perfectly
competitive due to product differentiation and inseparability of production and
consumption. To measure the extent of tourism consumption decreases that
result from an increase in the national wage rate, this study examines tourism
expenditures by foreign visitors.

Methodology
Data
Through econometric causal modelling, the primary determinants of tourism
demand have been noted in a handful of meta-studies. Generally, these have
been noted as price (Crouch, 1995; Witt and Witt, 1995; Lim, 1999), income
(Crouch, 1995; Witt and Witt, 1995; Lim, 1999), exchange rates (Crouch,
1995; Witt and Witt, 1995; Uysal and El Roubi, 1999), transportation cost
(Crouch, 1995; Lim, 1999), marketing expenditures (Crouch, 1995; Witt and
Witt, 1995), population size (Witt and Witt, 1995) and time (Crouch, 1995;
Witt and Witt, 1995). Some support for non-economic determinants has also
been noted (Cho, 2010). Qualitative effects, such as ‘one-off’ events like a health
pandemic or terrorism attack have also been noted to have effects on tourism
demand (Witt and Witt, 1995; Pizam and Smith, 2000; Goodrich, 2002;
Eisendrath et al, 2008; Pine and McKercher, 2004).
Since it is not possible to compile all of these determinants for each country
over the period of study, traditional ordinary least squares (OLS) estimation
methods would lead to biased coefficient estimates in the final model. Therefore, a panel data method with 2SLS is used to provide cross-sectional and

Wage rate growth and tourism competitiveness

temporal variation, and to account for gaps in reported information for
respective countries. The data set examines 40 countries in the period from
1990 to 2010. The estimation model summary statistics are provided in the
Appendix.
The dependent variable, tourism expenditures within the country by foreign
visitors, was obtained from the United Nations World Tourism Organization
(2011). The variable of interest – gross average monthly wages – was obtained
from the United Nations Economic Commission for Europe (2011). The
instrumental variable – years of school life expectancy from primary to tertiary
education – was obtained from the UNESCO Institute for Statistics (2011). The
other explanatory variables that could be controlled in this study were compiled
from several sources (ERS International Macroeconomic Data Set, 2010; International Monetary Fund, 2010; International Labour Organization, 2011; World
Travel and Tourism Council, 2011). The selection of these variables was
influenced by the findings noted in the literature review. Among the possible
control variables were several permutations of exchange rates, population size,
gross domestic product (GDP), hotel and restaurant employment, and an ease
of doing business proxy – average the number of days required to start a
business. The final model specification is discussed further below.

Estimation framework
This study uses a fixed effect model design with 2SLS estimates to address
potential endogeneity in the variable of interest, the wage rate. The fixed effect
model design allows for the removal of potential inter-country endogenous error
that is constant over time. Fixed-effect models are commonly used in panel data
sets when there is an immeasurable unobserved effect in each section (Wooldridge,
2006). In this model, the fixed effects design is used to account for stable
country-specific factors such as climate, historical visitor attractions, language,
and time invariant attitude or image components. As opposed to estimating
the parameter coefficients over a pooled sample, fixed-effect regression instead
looks at variation across time, within each country to eliminate bias.
That is, the proposed model for each country ‘i’ may be similar to:
yit = xitβ + vi + εit, t = 1,2,...,T,
where xit denotes the explanatory variables that can be thought of in the same
manner as in a typical OLS regression model, the yit term denotes the dependent
variable, and the vi term contains the stable characteristics that are unique to
each country. If the average of this equation is taken over time, and subtracted
from the equation above, the time demeaned equation that can be estimated
by pooled OLS (or 2SLS) is defined by:
yit – y– i = (xit – x– i)β + vi – vi + εit – ε– i, t = 1,2,...,T
or
yit = xitβ + εit, t = 1,2,...,T.

••

••

••

TOURISM ECONOMICS

In order to account for other potential endogeneity in the wage term, this study
also implements a 2SLS instrumental variable approach. Since there may be nonconstant factors in the model error term that affect both tourism expenditures
and wages (for example, popularity of the destination, political stability or
public infrastructure), OLS estimates of wage impacts on tourism expenditures
may produce biased coefficient estimates. The 2SLS estimator consistently
estimates the wage regression parameter by using an instrument to purge out
the correlation between the explanatory variable and the model error term. Twostage least squares estimation produces a consistent coefficient estimate through
the use of an exogenous instrumental variable, education (Wooldridge,
2006).
In instrumental variable estimation, two conditions need to be satisfied by
the instrument. The first condition is a correlation of zero between the
instrument and the structural model error term. In this model, that would
be characterized by Cov(Educationit, εit) = 0. Second, it must be the case that
the instrument and the endogenous variable be correlated, Cov(Educationit,Wagesit)
≠ 0.
The first condition is not empirically testable in this model, though it seems
intuitively plausible that education will not provide any additional explanatory
power to tourism expenditures beyond what is already addressed by the model
through the wage, the control and the fixed effect variables. For example, if
the presence of a well-established higher education system encourages visitation
to a country’s universities, the fixed effect variables would likely capture most,
if not all, of this variation.
The second condition of a non-zero correlation between education and wages
has been empirically established in labour economics literature (Ashenfelter and
Krueger, 1994; Card, 2001; Klein and Vella, 2009). The assumption is also
tested for this particular set of data in the subsequent results section. The
reduced form (first stage) model consists of all of the exogenous variables in
the second stage model and the instrumental variable, education. The estimates
from this first stage model are then used to replace the potentially endogenous
wage variable in the second stage model. The first stage model equation can
be expressed as follows:
Wagesit = Π0 + Π1 • Educationit + Π2 • Jobsit + Π3 • Populationit + Π4 •
GDPit + Π5 • MarketSizet + vi + µit,
where Wagesit = gross average monthly wages, in USD; Educationit = school life
expectancy from primary to tertiary education, in years; Jobsit = total hotel and
restaurant employment; Populationit = national population, in millions; GDPit
= the natural log of real gross domestic product per capita, in IMF international
dollars; MarketSizet = the world leisure and business travel & tourism spending,
in billions of real USD; vi = fixed structural characteristics that are unique to
each country; µit = the model error term.
The structural model, which uses the estimate of wages from the first stage
model as an exogenous independent variable, can be expressed as follows:
Tourismit = Π0 + β1 • Wagesit + β2 • Jobsit + β3 • Populationit + β4 •
GDPit + β5 • MarketSizet + vi + εit,

Wage rate growth and tourism competitiveness

where Tourismit = tourism expenditure in the country, in millions of USD;
Wagesit = the first stage estimate of gross monthly wages, in USD; Jobsit = total
hotel and restaurant employment; Populationit = national population, in millions;
GDPit = the natural log of real gross domestic product per capita, in IMF
international dollars; MarketSizet = the world leisure and business travel &
tourism spending, in billions of real USD; vi = fixed structural characteristics
that are unique to each country; εit = the model error term.
After the fixed effect adjustment, the variables in the equation above can be
replaced with time-demeaned variables and the vi term is dropped. Although
other model specifications were estimated based on variables identified in the
literature, the specification outlined above provided the highest adjusted R2
value and eliminated any non-significant variables from the model specification.
Some alternative model specifications are provided in the results section to
illustrate the robustness of the findings.

Results
First stage tests
As shown in Table 1, education is a statistically significant predictor of the
market wage rate; t(232) = 3.10, p = 0.002. The F-stat value for the instrumental
variable was F(1, 232) = 9.61, p = 0.002, indicating that education is a reasonable
instrument for the wage rate, and satisfies the Cov(Educationit,Wagesit) ≠ 0
condition.
The structural model findings are provided in Table 2. Standard errors robust
to any arbitrary forms of heteroscedasticity were estimated using the robust
procedure in Stata (StataCorp LP, 2009). The structural model revealed that
labour costs may be an important determinant of tourism expenditures, and
supports the theory of a negative relationship between these two variables. In
the final model identified in Table 2, a US$1 increase in gross monthly wages
is estimated to decrease annual tourism expenditures by US$188,000.

Table 1. Reduced form full model summary.
N

F(5, 232)

Centred R2

Uncentred R2

3.91

0.17

0.17

Coefficients – dependent variable: wages

Education
Jobs
Population
GDP
Market size

B

Standard error

t-stat

p-value

2,815.34
–5.83
–675.90
988.93
15.57

908.04
8.07
550.93
258.17
3.97

3.10
–0.72
–1.23
3.83
3.92

0.002
0.471
0.221
<0.001
<0.001

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Table 2. Structural model summary.
N

F(5, 232)

Centred R2

Uncentred R2

74.36

0.65

0.65

Coefficients – dependent variable: tourism expenditures
Model 1

Fixed effects Yes
Monthly wages –

Model 2

Model 3

Yes
Yes
–0.380** –0.244*
(–2.65)
(–2.42)
57.996**
–
(8.98)
–
3,556.817**
(9.98)
–
–

Jobs

–

Yes
–0.473*
(–2.53)
–

Population

–

–

GDP

–

–

Market size

–

–

Time

–

Model 4

–

601.400** 1230.192** 685.682** 739.028**
(3.63)
(4.78)
(3.45)
(4.78)

Model 5

Model 6

Final
Model 7

Yes
Yes
Yes
–0.280**
–0.238**
–0.188**
(–2.78)
(–2.89)
(–2.81)
46.706**
45.716**
46.412**
(7.49)
(7.70)
(7.97)
1,094.028* 1,139.499** 1,157.764**
(2.38)
(2.69)
(2.93)
293.690**
383.751** 350.732**
(3.55)
(3.69)
(3.64)
–
4.289*
6.355**
(2.26)
(4.69)
635.268**
262.099
–
(4.09)
(1.51)

Notes: *Significant at the 0.05 level; **significant at the 0.01 level. Models 2–7 are estimated using
two-stage procedures; z-statistics are in parentheses.

Not surprisingly, GDP per capita and market size are both found to have
positive effects on tourism expenditures – as people become more wealthy and
as an increasing amount of income is spent on the travel and tourism industry,
the higher the tourism expenditures are observed in our sample. Population also
is noted to have an impact on tourism expenditures. The positive coefficient
may reflect a larger national infrastructure to serve foreign visitors. The finding
of hotel and restaurant job variable significance likely reflects the importance
of those hospitality-related sectors to the tourism industry. For example, in the
UK, hotels and restaurants comprise 46% of the tourism satellite account
employment (Eurostat, 2009).

Conclusion
These results suggest that increases in labour costs might be an important
consideration in the tourism levels of a country. This finding seems to be a novel
factor previously held but not empirically tested as a negative force to tourism.
The findings of this study have multiple practical implications for both
policymakers and business operators.
Policymakers should utilize these findings to evaluate the labour market in
their respective countries. Estimation of the labour market size and expected
levels of compensation could provide assistance in the country’s tourism
marketing strategy. For instance, a country with a tight labour market (or

Wage rate growth and tourism competitiveness

anticipated labour shortage) might find itself unable to compete on price for
the marginal leisure traveller due to increases in wage rates. This country may
need to re-evaluate its marketing strategy to focus on less price-sensitive
travellers, such as business tourists or high-end vacationers (Garin-Munoz,
2006). In addition, countries experiencing wage rate growth at particularly high
levels may consider increasing the amount of labour available to control wage
rate growth. Governments could provide training for indigenous residents to
increase labour supply or encourage immigrant workers through increased work
permits or visa offerings.
Operators’ investment decisions may also change as a result of a higher cost
structure due to wage rate growth. Tourism industry operators seeking
expansion destinations may look to lesser developed markets with lower relative
wage rates as opposed to markets where wage rate growth has occurred or is
expected to occur. Companies choosing to develop or operate in new
destinations may want to examine the history of wage changes in the market
and develop strategic compensation and human resource strategies in an effort
to offset wage rate growth. Long-term forecasting and investment decisions can
also be impacted by recognizing changes in labour costs through national
statistics. Tourism providers like tour operators, airlines, and other leisure
service organizations will be able to anticipate changes in tourist spending
levels, and focus marketing efforts on other low cost destinations.
A final implication of this study is the addition of the variable of labour cost
to the classic model of tourism development; the TALC. TALC claims to predict
that as tourism increases, so do its associated impacts (Butler, 1980; Butler,
2009). Butler also describes a final step in the development stage where
negative impacts surpass the positive, which leads to a decline in the tourism
market. Negative forces discussed by Butler include environmental factors,
changing socio-demographics of the world’s population, and increasing fuel
costs. The inclusion of increased labour costs could also be considered as a
negative force to those previously posed in the life cycle model. Similarly, the
increase in labour costs could be contributing to the stagnation or decline
phases described by Prideaux (2000) and Prideaux (2004) in the resort
development spectrum (RDS). Those phases are noted to occur after the international tourism phase, and may partially reflect a wage-related reduction in
international competitiveness.
It is important to point out that this study does not address what impacted
the wage rate growth. Future investigations could be conducted to determine
how increases in wages reflect productivity levels, increases in skill, and number
of jobs in tourism destinations. This macro-level test does not fully capture
changes in worker productivity, which is also important in determining international competitiveness. As noted by Smeral (2003, p 81), ‘… highly
developed countries and regions can still achieve relatively high tourism growth
rates and productivity increases by focusing investment on human capital’.
Similarly, additional studies of the impact of union development, minimum
wage levels, or other labour market regulations might be relevant variables of
interest to consider. For example, exogenous changes in the wage rate from
collective bargaining negotiation or government policy changes in minimum
wages may affect the national wage rate differently. This study also only
examined one aspect of tourism output, total foreign expenditures. Extensions

TOURISM ECONOMICS

of this analysis could be made with visitor levels, average spend per visitor, and
with competitiveness for domestic tourist spending.
This study finds wage rate growth affects destination competitiveness and
suggests how policy-makers and organizations could utilize this information to
alter development, marketing, and labour supply decisions. While wage rate
growth is one factor, it is important to note there are many alternative and
complex factors that impact destination competitiveness that should also be
considered. Further investigation, which identifies items that impact a
destination’s ability to compete, will provide insightful information in decision
making. Empirical investigations of this nature will also help researchers to
further validate and expand theoretical models of tourism visitation, such as the
TALC and the RDS. Although this study used a macro level secondary data
analysis approach, researchers could consider examining the same problem using
an experimental or survey design at a more micro level. This would allow
researchers to determine the extent to which the impacts discussed in this study
are considered by managers and policy-makers.
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Appendix

Estimation model summary statistics.

Tourism expenditures
Monthly wages
Jobs
Population
GDP
Market size
Education

Mean

SD

Min

Max

9,387.50
14,793.73
390.71
20.57
24.03
3,369.57
14.64

19,436.99
38,868.86
1,258.55
44.22
2.20
201.10
1.91

9.60
36.90
2.00
0.27
20.63
3,081.61
10.61

145,231.00
264,028.20
9,582.00
301.90
32.06
3,724.01
19.14