Tourism is one of the most important factors in the productivity of Mexican economy with significant multiplier effects on economic activity. This paper investigates possible causal relationships among tourism expenditure, real exchange rate and economic growth by using quarterly data. Johansen cointegration analysis shows the existence of one cointegrated vector among real GDP, tourism expenditure and real exchange rate where the corresponding elasticities are positive. The tourism-led growth hypothesis is confirmed through cointegration and causality testing. Tourism expenditure and Real Exchange Rate (RER) are weakly exogenous to real GDP. A modified version of the Granger Causality test shows that causality goes unidirectionally from tourism expenditure and RER to real GDP. Impulse response analysis shows that a shock in tourism expenditure produces a short fall and then a positive effect on growth.economic growth Johansen cointegration test Granger causality tourism-led growth hypothesis.

Edgar J Sanchez Carrera

Juan Gabriel Brida

W. Adrian Risso

English

Research Papers in Economics

Tourism’s Impact on Long-Run Mexican Economic Growth 
Juan Gabriel Brida
Free University of Bolzano, Italy.
Edgar J Sanchez Carrera W. Adrian Risso
University of Siena University of Siena
Abstract
Tourism is one of the most important factors in the productivity of Mexican economy with
significant multiplier effects on economic activity. This paper investigates possible causal
relationships among tourism expenditure, real exchange rate and economic growth by using
quarterly data. Johansen cointegration analysis shows the existence of one cointegrated
vector among real GDP, tourism expenditure and real exchange rate where the corresponding
elasticities are positive. The tourism-led growth hypothesis is confirmed through
cointegration and causality testing. Tourism expenditure and Real Exchange Rate (RER) are
weakly exogenous to real GDP. A modified version of the Granger Causality test shows that
causality goes unidirectionally from tourism expenditure and RER to real GDP. Impulse
response analysis shows that a shock in tourism expenditure produces a short fall and then a
positive effect on growth.
Comments by anonymous referees were particularly useful in improving the paper and they are gratefully acknowledged with
the usual caveats. Our research was supported by the Free University of Bolzano, project: “Market imperfections and tourism
policy”. An earlier draft of this paper was presented at the ATLAS International Conference 2007 Polytechnic Institute, Viana
do Castelo-Portugal, 5 - 7 September 2007.
Citation: Brida, Juan Gabriel, Edgar J Sanchez Carrera, and W. Adrian Risso, (2008) "Tourism’s Impact on Long-Run Mexican
Economic Growth." Economics Bulletin, Vol. 3, No. 21 pp. 1-8
Submitted: December 4, 2007.  Accepted: April 14, 2008.
URL: http://economicsbulletin.vanderbilt.edu/2008/volume3/EB-07C20155A.pdf1. Introduction 
 
The tourism-led growth hypothesis (TLGH) postulates that international tourism is 
considered as a potential strategic factor for economic growth. There are several empirical 
papers using time series techniques, analyzing the tourism industry's contribution to a 
country's economic growth. Some of the most remarkable works on this topic are Balaguer 
and Cantavella (2002) for Spain (suggesting the validity of TLGH), Dritsakis (2004) for 
Greece (showing the impact of tourism on long-run economic growth), Gunduz and Hatemi-J 
(2005) for Turkey (supporting the TLGH), Louca (2006), Noriko and Motosugu (2007), and 
Gani (1998) for small islands. Oh (2005) for Korea, Kim et al. (2006) for Taiwan. Similarly, 
Proença and Soukiazis (2005) examine the impact of tourism for Portuguese regions and 
Shan and Wilson (2001) study the causality between tourism and trade. 
The purpose of this paper is to investigate possible causal relationships among tourism 
expenditure (TourExp), real exchange rate (RER) and economic growth measured by real 
GDP. We shall provide a plausible answer to the question: "Does the tourism sector cause 
economic growth and/or can it be a key factor for the Mexican economy?" 
The hypotheses are tested empirically by using the cointegration test by Johansen (1988), 
Johansen and Joselius (1990) and Johansen (1995). In addition, a modified version of the 
Granger Causality test (see Granger (1988)) is applied by using methodology suggested by 
Toda and Yamamoto (1995).  
 
2.  Specification of the model and data set 
 
We specify a Vector Auto Regressive (VAR) model in order to test the causality among the 
variables: 
 
) , , ( RER TourExp GDP U = .                                                                     (1) 
 
We search for a long-run relationship among the three variables, but a Vector Error 
Correction (VEC) model is applied to model the short-run dynamics. The model is 
represented in a first-differenced error correction form: 
 
t i t i
k i
i
t t Y Y Y ε μ + Δ Γ + Π + = Δ −
− =
=
− ∑
1
1
1 ,                                                                   (2) 
 
where Y = (real GDP, TourExp, RER) is a vector containing the variables and μ is a vector of 
constant terms. The matrix Π conveys information about the long-run relationship between 
the Y variables. The rank of Π is the number of linearly independent and stationary linear 
combinations of the variables.  
Firstly, unit root tests are applied to study the stationarity of the series. In the case of non-
stationarity, we apply the Johansen cointegration test in order to detect long-run relationships 
in the data. Then, weak exogeneity is tested in the model. Finally, a modified version of the 
Granger causality test is applied in order to analyze causality between the variables. 
We use quarterly data from 1980 to second term 2007. Data of real GDP and real exchange 
rate (RER) are obtained from the Central Bank of Mexico. Tourism expenditure (TourExp) is 
a generated variable with the amount of foreign currency of international tourism, the 
nominal dollar-peso exchange rate and the consumer price index. The data source is the 
Central Bank of Mexico.  
 
  13.  Methodology and Results 
 
We have a spurious regression when trending or unit root time series produce non-stationary 
residuals, significant OLS parameter estimates, and a high R-square. Non-stationary residuals 
violate the standard assumption to apply OLS methodology. In this case, Phillips (1986) 
pointed out that cointegration techniques must be applied. A first step in cointegration 
analysis is to study the stationarity of the series by using unit root tests, such as the 
Augmented Dickey-Fuller (ADF) and the KPSS. The null hypothesis of the KPSS test is 
stationarity, complementing the ADF test. Remember that the ADF test has low power 
against stationary near unit root processes.  
Tables I and II show unit root tests for the variables in levels and in differences. Variables are 
expressed in logarithms form. 
 
Table I: Unit Root Test results: Levels 
Variable real  GDP  TourExp  RER 
Unit Root Test  ADF  KPSS  ADF  KPSS  ADF  KPSS 
Trend, Constant  -3.42  0.31*  -3.07  0.13  -2.03  0.19* 
Constant 0.89  2.25*  -1.81  1.71*  -1.40  2.00* 
Without Trend, Const.  2.34    1.41    0.18   
* Null Hypothesis Rejection at 5% 
 
Table II: Unit Root Test results: First Difference 
Variable  Δ(real GDP)  Δ(TourExp)  Δ(RER) 
Unit Root Test  ADF  KPSS  ADF  KPSS  ADF  KPSS 
Trend, Constant  -4.17*  0.02  -5.10*  0.03  -3.55*
  0.05 
Constant -3.85*  0.06  -5.08*
  0.03 -3.48*
  0.08 
Without Trend, Const.  -2.51*    -4.68*
   -3.29*   
* Null Hypothesis Rejection at 5% 
 
According to the tests, time series are integrated processes of first order, I(1). Hence, we have 
to study the existence of a cointegrating relationship. The two-step procedure by Engle and 
Granger (1987) assumes the existence of only one cointegrating relationship. The general 
procedure proposed by Johansen (1988) has the advantage of testing all the possible 
cointegrating relationships. Banerjee et al. (1993) highlight that searching for a cointegration 
relation is equivalent to searching for a statistical equilibrium between variables tending to 
grow over time. The discrepancy of this equilibrium can be modeled by a Vector Error 
Correction (VEC) model (equation (2)). The VEC shows how the variables come back to the 
equilibrium after suffering a shock. 
In order to obtain the optimal VEC model we applied the minimum AIC-criterion, suggesting 
  2a lag length of four. 
To determine the number of cointegrating equations, the Johansen maximum likelihood 
method provides both trace and maximum eigenvalue statistics. Note in Table III that both 
tests detect the existence of one cointegrating vector. 
 
Table III: Unrestricted Cointegration Rank Test 
Trend assumption:  No deterministic trend 
Series:  real GDP, TourExp and RER 
Trace 
Hypothesized No. of CE  Eigenvalue  Trace Stat.  Critical Value  Prob. 
None* 0.298  54.965  35.193  0.00 
At most 1  0.097  17.853  20.262  0.10 
At  most  2  0.066 7.142 9.164  0.12 
Maximum Eigenvalue 
Hypothesized No. of CE 
Eigenvalue 
Max-Eigen 
Statistic 
Critical Value  Prob. 
None* 0.298  37.112  22.299  0.00 
At most 1  0.097  10.710  15.892  0.27 
At  most  2  0.066 7.142 9.164  0.12 
Trace and Max.Eigen. test indicates 1 cointegrating eqn(s) at the 0.05 level 
* Denotes rejection of the hypothesis at the 0.05 level 
 
Table IV shows that, in the long-run, real GDP is positively related with tourism expenditure 
and RER. 
 
Table IV: Cointegrating Vector 
real GDP  TourExp  RER  const. 
1 -0.754  -0.005  -7.048 
 [-2.301]  [-0.917]  [-2.079] 
t-statistics  in  [].     
 
 
McCallum (1984) asserts that incorrect signs can be produced if exogeneity is not studied. To 
  3apply inference techniques we must test weak exogeneity. When we consider separately the 
weak exogeneity of the variables we obtain that tourism expenditure is exogenous producing 
a test statistic of 0.05 and a p-value of 0.82, and that RER is exogenous with test statistic of 
0.02 and p-value of 0.88. Table V presents the joint hypothesis of exogeneity for TourExp 
and RER (α2=α3=0). The test indicates a test statistic of 0.131 and the hypothesis cannot be 
rejected at 5% level (note a high p-value = 0.936). 
 
Table V: Weakly exogeneity test 
Cointegrating Restrictions: 
H0:  A(2,1)=0, A(3,1)=0 
Restrictions identifying all cointegrating vectors 
LR test for biding restrictions (rank=1) 
Chi-square(2): 0.131 
P-value: 0.936 
Cointegrating Vector after exogeneity 
real GDP  TourExp  RER  const. 
1 -0.696 -0.006  -7.645 
 [-2.137]  [-1.099]  [-2.270] 
t-statistics in []. A(2,1) and A(3,1) are the adjustment coefficients in the 
TourExpend and RER equations of the VEC, respectively.   
 
Cointegration by itself does not indicate the direction of the causal relationship. Granger 
(1988) proposes a test to study causality. Such causality can be captured from the VAR 
model. However, since the variables are integrated, application of the standard Granger 
causality test is invalid. Toda and Yamamoto (1995) suggest an alternative procedure. When 
the variables are integrated, they propose to estimate a VAR model with (k+dmax) lags, where 
k is the standard optimal number of lags and dmax is the maximal order of integration that we 
suspect might occur in the process. Once the VAR is estimated, we test Granger causality 
only using the first k lags. For instance, if we consider the following equation from a VAR 
model:   
 
t t t t t t t t TourExp GDP RER RER TourExp TourExp GDP ε γ γ γ γ γ γ γ + + + + + + + + + + = − − − − − − 5 15 1 11 5 10 1 6 5 5 1 1 0 ... ... ...
 
where k = 4 and dmax = 1, the null hypothesis of non-causality from TourExp to GDP should 
be: 
 
H0: γ1=γ2=γ3=γ4=0 
 
This means,  
H0: TourExpend does not Granger-cause real GDP. 
  4 
The hypothesis is tested using the Wald test. However, Toda and Yamamoto (1995) assert 
that Wald and LR tests are asymptotically equivalent in the present situation. Table VI shows 
the results for all the variables. 
 
Table VI: Granger Causality Test (by Toda & Yamamoto) 
Null Hypothesis  Wald-statistic  P-value 
TourExp does not Granger Cause real GDP  24.54  0.000* 
real GDP does not Granger Cause TourExp  8.936  0.063 
RER does not Granger Cause real GDP  24.80  0.000* 
real GDP does not Granger Cause RER  3.152  0.533 
RER does not Granger Cause TourExp  8.487  0.075 
TourExp does not Granger Cause RER  1.684  0.793 
* Indicates rejection of the null hypothesis at 5%. We used a VAR with k+dmax = 4+1. P-values 
correspond to the Chi-square distribution with 4 degree of freedom. 
 
Equation (3) shows the long-run equilibrium after testing weak exogeneity of tourism 
expenditure and RER: 
 
t t t RER GDP ) ( 006 . 0 ) TourExp ( 696 . 0 645 . 7 )   (real + + =                                                         (3) 
 
Summarizing, tourism expenditure and RER are weakly exogenous and in the long-run they 
Granger-cause real GDP. The elasticity of real GDP with respect to tourism expenditure is 
0.696. This means that increasing tourism expenditure by 100% produces an increment of 
almost 70% of the Mexican real product.  
 
-.01
.00
.01
.02
2 4 6 8 10 12 14 16 18 20
Response of GDP to TourExpend.
-.01
.00
.01
.02
2 4 6 8 10 12 14 16 18 20
Response of GDP to RER.
 
Figure 1: Impulse Response Functions of GDP 
 
Furthermore, we study the impulse response functions. Figure 1 shows that after a shock in 
tourism expenditure, there is a fall as initial response of real GDP but then it presents a 
sustained positive response. Moreover, a shock in RER first produces a negative response but 
  5then presents a positive reaction on the real GDP. 
Note that a positive shock in the tourism expenditure positively affects the long-run real 
GDP. A positive shock in RER (real devaluation) first produces a negative effect for 9 
quarters and then a high positive effect. This is the well-known J-curve
1 introduced by Magee 
(1973) and empirically modeled by Bahmani-Oskooee (1985). 
 
4. Comparing  results 
 
In this section, we compare our results with two similar papers: i) Dritsakis (2004) for the 
Greece case and, ii) Balaguer and Cantavella (2002) for the Spanish case.  
Dritsakis found bidirectional causality, whereas we obtained just one direction going from 
tourism to GDP. Moreover, we obtained a "Granger causal" relationship between tourism and 
economic growth. We, as Dritsakis, obtained the existence of a cointegration relationship 
among the three variables with similar signs and significance. 
Balaguer and Cantavella found a cointegration relationship indicating that tourism positively 
affects economic growth over time. We obtained similar results. They also found that the 
corresponding elasticity of tourism expenditure has a significant effect on economic growth. 
As in our case, tourism expenditure affects economic growth in a unidirectional way. This 
provides the necessary arguments to support the TLGH for Mexico. 
 
5. Conclusion 
 
International tourism expenditure positively impacts Mexican economic growth. The 
elasticity of real GDP to tourism expenditure (0.69) shows that an increment of 100% in 
tourism expenditure produces an increment of almost 70% of the real product. However, note 
that the real exchange rate produces an insignificant effect with elasticity 0.006.  
Then the tourism-led growth hypothesis applies to the Mexican economy suggesting that 
tourism is an important determinant of overall long-run economic growth. 
A policy implication which may be drawn from this study is that Mexico can improve its 
economic growth performance by strategically harnessing the contribution of the tourism 
industry and improving their governance performance. Since tourism is an important engine 
of local development, it is necessary to increase domestic tourism in order to have more 
decentralization of local development caused by such activity. 
 
 
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  8

A further step into the ELGH and TLGH for Spain and Italy,

An analysis of contributions international inbound tourism to the Turkish economy,

Causality between trade and tourism: empirical evidence from China,

Co-integration and error correction: representation, estimation and testing,

Currency contracts, pass-through, and devaluation,

Devaluation and the J-curve: some evidence from LDCs,

Impacts of tourism and fiscal expenditure to remote islands: the case of the Amami islands in

Income and expenditure in the tourism industry: time series evidence from Cyprus,

Is the tourism-led growth hypothesis valid for Turkey?,

Likelihood-based inference in cointegrated vector autorregressive models,

Macroeconomic determinants of growth in the South Pacific island economies,

Maximum likelihood estimation and inference on cointegration with applications to the demand for money&quot;,

On low-frequency estimates of long-run relationships in macroeconomics,

Some recent developments in a concept of causality,

Statistical Analysis of cointegration vectors,

Statistical inference in vector autorregressions with possibly integrated processes,

The contribution of tourism development to economic growth in the Korean economy,

Tourism as a long-run economic growth factor: an empirical investigation for Greece using causality analysis,

Tourism as a long-run economic growth factor: the Spanish case,

Tourism as an alternative source of regional growth in Portugal: a panel data analysis at NUTS II and III levels,

Tourism expansion and economic development: The case of Taiwan,

Understanding spurious regressions in econometrics,

Tourism's Impact on Long-Run Mexican Economic Growth

http://www.accessecon.com/pubs/EB/2008/Volume3/EB-07C20155A.pdf

Tourism's Impact on Long-Run Mexican Economic Growth

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