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Analysis of hospitality efficiency in main Mexican touristic centers

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Analysis of hospitality efficiency in main Mexican touristic centers

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Tourism plays an important role in the Mexican economy, representing approximately 8.8% of the Mexican GDP and producing 4.1 million of direct and 6.5 million of indirect jobs. Although a positive trend in arrivals of international tourists to the country has been reported, the whole industry can be quickly negatively affected by the level of insecurity, lower economy performance, as well as by insufficient infrastructure. Therefore, it is important to search possible areas for improvements. In this article, hospitality efficiency of 67 main touristic centers in Mexico is analyzed for the period from 1992 to 2017. The results reveal low efficiency of inland touristic centers in case of foreign tourists, as the foreign tourism is concentrated in limited number of on-cost centers. On the other hand, national tourism is less centered, although on-coast centers remain within the most efficient. Therefore, there is clear opportunity for inland tourism in Mexico, which would stimulate the whole industry, as well as the Mexican economy.
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RELAIS, vol. 3, no. 2
Analysis of hospitality eciencyinmainMexican
touristic centers
Martin Flegl
La Salle University in Mexico City
Mexico
Brenda Rangel
La Salle University in Mexico City
Mexico
Daphne Mendoza
La Salle University in Mexico City
Mexico
Research article
Received: February 21, 2020
Accepted: June 30, 2020
Available online: August 31, 2020
Abstract
Tourism plays an important role in the Mexican economy, representing approximately 8.8% of the
Mexican GDP and producing 4.1 million of direct and 6.5 million of indirect jobs. Although a positive
trend in arrivals of international tourists to the country has been reported, the whole industry can
be quickly negatively aected by the level of insecurity, lower economy performance, as well as by
insucient infrastructure. Therefore, it is important to search possible areas for improvements. In
this article, hospitality eciency of 67 main touristic centers in Mexico is analyzed for the period
from 1992 to 2017. The results reveal low eciency of in-land touristic centers in case of foreign
tourists, as the foreign tourism is concentrated in limited number of on-cost centers. On the other
hand, national tourism is less centered, although on-coast centers remain within the most ecient.
Therefore, there is clear opportunity for in-land tourism in Mexico, which would stimulate the whole
industry, as well as the Mexican economy.
Key words: Data Envelopment Analysis, Development strategy, Eciency, Mexico, Tourism, Window
Analysis.
Análisis de eciencia de hospitalidadlosprincipalescentros turísticos mexicanos
Resumen
El turismo juega un papel importante para la economía mexicana, representando aproximada-
mente 8.8% del PIB Mexicano y produciendo 4.1 millones de empleos directos y 6.5 millones de
empleos indirectos. A pesar de que se ha repor tado una tendencia positiva en las llegadas de turistas
internacionales al país, la industria podría ser afectada negativamente por el nivel de inseguridad, el
bajo desempeño económico, así como la insuciencia en la infraestructura. Por lo tanto, es impor-
tante buscar posibles áreas de mejora. En este artículo, se analiza la eciencia del alojamiento de 67
principales centros turísticos de México para el periodo de 1992 a 2017. Los resultados revelan que
la actividad turística extranjera en el país se ve concentrada en un número limitado de centros turís-
ticos costeros, por lo que la eciencia del turismo extranjero en los demás centros turísticos es baja.
Por otro lado, el turismo nacional está menos centralizado, sin embargo, los centros turísticos
costeros siguen teniendo una eciencia mayor. Por lo tanto, hay una clara oportunidad para el
turismo territorial en México, la cual estimularía a toda la industria, así como a la economía mexicana.
Palabras claves: Análisis envolvente de datos, Estrategia de desarrollo, Eciencia, México, Turismo,
Análisis por ventanas.
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RELAIS, vol. 3, no. 2
1 Introduction
Tourism is one of the fastest growing industries in the world and an important piece in economic growth
and socio-economic progress, not only for many developing countries, but also for developed countries.
Tourism has become one of the principal performers of the international trade and represents one of the
main sources of income for many countries. Currently, 9% of the worldwide Gross Domestic Product (GDP) is
provided by tourism. What is more, tourism produces one of every 11 jobs in the world and generates USD
1.3 trillion in exports, which is responsible for 6% of international trade and for 6% of the exports of the less
developed countries (UNWTO, 2019a). “The United Nations World Tourism Organization (UNWTO) projects
that total international tourist arrivals will grow by 3.3% a year to reach 1.8 billion by 2030” (Hussain Shahzad
et al., 2017: 223).
Mexico is one of the most popular places for tourists. In 2010, Mexico was ranked in the 10th place in the
world in terms of international arrivals, as 21.3 million of tourists visited Mexico in 2010. What is more, there
has been a signicant positive tendency in the international arrivals UNWTO (2019b) ranked Mexico in the
6th place among the countries that received most international tourists, reaching a total of 39.3 million of
international tourists, leaving United Kingdom and Germany behind. The top 5 countries are France with 86.9
million of tourists, Spain with 81.8 million, United States with 75.9 million, China with 60.7 million and Italy
with 58.3 million (de la Rosa, 2018). Tourism is the second largest industry in Mexico, as this sector contrib-
uted MXN 11.8 billion to the country’s economy in 2013 (Elly, 2013). Secretaría de Turismo (SECTUR), armed
that touristic activity in Mexico represents approximately 8.8% of the Mexican GDP and produces 4.1 million
direct jobs and 6.5 million of indirect jobs (SECTUR, 2019a). The arrival of national tourists to Mexico in January
2018 reached 4.383 million tourists (70.4% from the total amount); meanwhile 1.842 million were international
tourists (29.6%) (SECTUR, 2019a). Moreover, these numbers (as well as its increasing tendency) attracts more
foreign investments in the sector. For example, 14 national and international hotel groups, such as Posadas,
City Express, Marriott, AM Resorts, IHG, among others, announced plans to open more than 350 new hotels in
Mexico between 2019 and 2022 (Valle, 2018).
Tourism is an economic activity that can be inuenced by many situations, in which tourists can be targets
of robberies, murders, crimes or others acts of this nature. According to Sánchez Mendoza (2015), the prox-
imity of these violent or dangerous events for tourists determines the perception of fear of a threat or danger.
As a result, such dangerous situations negatively aect the perception about touristic destinations. Therefore,
one of the most important factors in tourism industry is the reputation of each touristic destination. According
Coelho and Gosling (2015), the reputation of a touristic destination is inuenced by four main factors: 1)
communication (social media, internet, touristic guides), 2) individual consumers’ evaluations, 3) local specic
experiences, and 4) time that creates reputation over longer period. The time factor is considered as the most
fragile one, as it can be signicantly inuenced by one unique occasion, such as natural disaster, terrorism,
violence, etc. (Aula and Hermaakorpi, 2008; Lexow and Edelheim, 2004). The reputation is then constructed
by diverse set of elements, involving human resources of the destination, organization of human resources,
local infrastructure, touristic attractions, sociocultural environment and local business activities (Coelho and
Gosling, 2015).
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RELAIS, vol. 3, no. 2
One of the biggest problems that tourism in Mexico is facing are the increasing levels of delinquency and
violence, especially in certain touristic points. In 2017, OECD (2017) dened security problems as a one of the
main diculties for the tourism sector in Mexico. On the one hand, INEGI (2019) reported that the percep-
tion of insecurity of the Mexican population has decreased by 2.6%, from 73.9% in June 2018 to 71.3% in
June 2019. However, in other matters, Expansión Política (2019) reported increasing trend of homicides during
the rst seven months of the year 2019 by 4.14%. Therefore, the problem of insecurity in Mexico remains
and can further negatively aect the whole touristic sector and, consequently, the whole Mexican economy.
Tourism is an important determinant of overall long-term economic growth (Balaguer and Cantavella-Jordá,
2002; Perles-Ribes et al., 2017). What is more, there is a clear consensus about the positive reinforcing synergy
between tourism and economic growth (Chen and Chiou-Wei, 2009). This positive eect is larger in countries
where the tourism share on the GDP is bigger (Holzner, 2011), such as the case of Mexico.
Eciency in tourism is an important index for measuring the level and quality of tourism development.
Many quantitative methods can be applied to measure the performance and/or eciency in tourism.
Non-parametric approach based on Data Envelopment Analysis (DEA) is one of the most common method-
ologies. In this case, we can identify analyses linked on regional dierences, analyses of the hotel industry,
or analyses related to determination of inuential factors in the tourism industry. For example, Corne (2015)
applied Data Envelopment Analysis to analyze eciency in French hospitality sector in 16 conurbations to
identify possible improvement in the sector. Similarly, Liu, Zhang and Fu (2017) evaluated eciency of 53
Chinese coastal cities from 2003 to 2013 to explore regional dierences, whereas Chaabouni (2019) investi-
gated tourism eciency and its determinants in 31 provinces in China over the period 2008–2013. Song and
Li (2019) estimated the eciency of Chinese tourism industry from the sustainability point of view to increase
a touristic attractivity. At the hospitality level, Oukil, Channouf and Al-Zaidi (2016) applied DEA methodology
to examine the eciency in hotel industry in Oman in order to identify variables explaining the ineciency in
the industry. Further, Oliveira, Pedro, and Marques (2013) analyzed the impact of hotel quality (star rating) on
the eciency of 84 hotels in Algarve, Portugal.
Although tourism generates signicant revenues, a large percentage of these revenues is sent to the
hotels’ foreign investors or gained by the local rich individuals, but only a few revenues belong to poor neigh-
bors (Blake et al., 2008; Ely, 2013). Therefore, it is important for government to optimize resource allocation
to tourism development, i.e. to foster tourism activities. Although positive trend of international arrivals to
Mexico has been identied in recent years, it is of a high importance to identify areas for possible improve-
ments in the touristic industry. Therefore, the objective of the article is to evaluate the hospitality eciency in
Mexico based on the information of 26-year-long period from 1992 to 2017. Secondary objective is to identify
whether dierences in the eciency exist regarding foreign and national tourists.
Materials and Methods
Data Envelopment Analysis
The Data envelopment Analysis (DEA) allows to evaluate several decision-making units (DMU) regarding
their capabilities to convert multiple inputs into multiple outputs (Cooper, Seiford and Zhu, 2011). Each DMU
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RELAIS, vol. 3, no. 2
can have several dierent
m
input quantities to produce dierent outputs. If the model assumes consistent
yields at scale, you can use the so-called CCR model (Charnes, Cooper and Rhodes, 1978). The CCR output-ori-
ented model for 0
DMU
is formulated as follows:
Minimize
0
1
m
ii
i
q vx
=
=(1)
subject to
11
0, 1, 2, , .
ms
i ij r rj
ir
vx y j n
µ
= =
− ≥=
∑∑
0
1
1,
s
rr
r
y
µ
=
=
ri
(2)
Where
ij
x
is the quantity of the input
i
of the
DMU j
,
rj
y
is the amount of the output
r
of the
DMU j
,
and
r
µ
and
i
v
are the weights of the inputs and outputs
1, 2, ,im=
,
1, 2, ,jn=
,
1, 2, ,rs=
and
ε
is the so-called non-Archimedean element necessary to eliminate zero weights of the inputs and outputs.
DMU is 100% ecient if
1q=
, i.e., there is no other DMU that produces more outputs with the same combi-
nation of inputs. Whereas, DMU is inecient if
1q<
.
To measure DMUs productivity over a longer period, the Windows Analysis (WA) approach can be used. This
approach works on the principle of moving averages to detect DMUs performance trends over time (Cooper,
Seiford and Tone, 2007). In this case, each DMU in a dierent period is treated as if it were a dierent unit.
The performance of a DMU in a particular period is compared to its performance in other periods, in addition
to the performance of other DMUs. Therefore, there is
nk
DMU in each window, where
n
is the number of
DMUs in a given period (it must be the same in all periods) and
k
is the width of each window (same for all
windows). This feature increases the discriminatory capacity of the DEA model, as the total number of
T
periods is divided into overlapping period series (windows), each with a width
( )
kk T<
leading to
nk
DMUs. The rst window has
nk
DMUs for periods
{ }
1, , k
, the second period has
nk
DMUs and periods
{ }
2, , 1k+
, and so on, until the last window has
nk
DMUs and periods
{ }
1, ,Tk T−+
. In total, there
are
1Tk−+
separate analyses where each analysis examines
nk
DMUs.
An important factor is the determination of the size of the window. If the window is too narrow, there may
not be enough DMUs in the analysis that lead to a low power of model discrimination. Conversely, a too wide
window can yield misleading results due to signicant changes occurring during periods covered by each
window (Cooper, Seiford and Zhu, 2011). Therefore, the size of the window should consider the structure of
the DEA model (mainly with respect to the number of DMUs [Dyson et al., 2001]) and the characteristics of the
analyzed area. The attractivity of a touristic destination can be signicantly aected by negative reports by
media (Aula and Hermaakorpi, 2008; Coelho and Gosling, 2015, Hall, 2002). Negative reputation reported by
media can be linked to international conicts, acts of terrorism, criminality acts, natural disasters or to health
concerns (Lexow and Edelheim, 2004). There is no consensus about the length of the recovery time from each
reported case. This recovery can range from several months to several years depending the magnitude of
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RELAIS, vol. 3, no. 2
each incident and the tourist’s personality type (Kapuściński and Richards, 2016; Lexow and Edelheim, 2004).
To minimize the eects of the short-term negative events that would cause high volatility in the obtained
results, in the case of this article, the length of the window was selected as
3k=
(3-year window).
1 For more details address http://www.datatur.sectur.gob.mx
2 There are other variables that aect tourism eciency, such as the natural (ecological) characteristics of the
touristic centers (archeological zones, beaches, etc.), regional economic level, governmental contribution, as well
as the local infrastructure (bars, restaurants, museums, etc.). However, the objective of the article is to analyze the
hospitality eciency in the main touristic centers and, thus, the DEA model only includes variables related to
hospitality quality.
3 In this case we talk about the input side of the DEA model as there is only one output and its importance is 100%.
Data
The data obtained for the analysis comes from the database of DATATUR (Secretariat of Tourism, 20181). For
the purpose of the analysis, data related to touristic activities of the 67 main touristic centers (cities) in Mexico
were collected for the period from 1992 to 2017. In accordance with Formica and Uysal (2006), Lee, Huang and
Yeh (2010) and Oliani, Rossi and Gervassoni (2011), quality and capacity of hotels infrastructure (among others)
plays important role in tourism2. To express the level of hotel quality, their star rating is commonly used (Corne,
2015; Oliveira, Pedro and Marques, 2013). Therefore, for each touristic center, we selected following variables
as inputs: Number of one-star hotel rooms, number of two-stars hotel rooms, number of three-stars hotel
rooms, number of four-stars hotels room and number of ve-stars hotel rooms. These variables represent the
capacity of every touristic center to receive tourists.
The objective of the hospitality sector is usually to maximize the occupancy rate and, consequently, their
revenues. That is why, the DEA analysis usually includes occupancy rate, tourists’ arrivals and related revenues
per available room as outputs (Chaabouni, 2019; Corne, 2015; Liu, Song and Li, 2019; Zhang and Fu, 2017).
However, the absolute number of tourists’ arrivals avoids reecting the number of nights tourists stay in each
touristic center. Instead, the output part of the constructed DEA model is represented by tourists’ nights (TN),
which can be expressed as
TN = tourists’ arrivals * average number of nights.
Including the average number of nights stayed by each tourist into the model corresponds to the approach
presented by Oukil, Channouf and Al-Zaidi (2016).
Three dierent models were constructed: 1)
overall model
where the output side of the DEA model includes
the total number of tourists; 2)
foreign model
, which only includes data for the foreign tourists’ arrivals to
Mexico; and 3)
national model
, which includes data for the national tourists’ arrivals.
To secure correct representativity of all variables in the model3, we selected
.1
ε
=
. As a result, the impor-
tance of one-star hotels capacity was 10.28%, two-star hotels 9.63%, three-star hotels 22.84%, four-star hotels
17.16% and ve-star hotels 40.10% (regarding the overall model). Considering the basic requirements for DEA
model, this distribution is satisfactory. The distribution for the foreign model (considering the same order)
was 6.06%, 8.60%, 14.87%, 13.57% and 56.90%, whereas for the national model the distribution was 10.13%,
10.67%, 26.88%, 23.68% and 28.64% respectively.
The advantage of the DEA methodology is the possibility to make a benchmarking of DMUs of dierent
sizes and locations if the homogeneity requirement is not violated (Cooper, Seiford and Zhu, 2011; Dyson et
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RELAIS, vol. 3, no. 2
al. 2001). Although we evaluated touristic centers of dierent sizes and locations, the homogeneity is not
violated as all operate on the same market (Mexico) and use same type of inputs. Our approach is similar to
Chaabouni (2019), Corne (2015) or Liu, Zhang and Fu (2017). Considering the operation of the Window Analysis
method, 201 DMUs were available in each window, resulting in 4,824 analysis in total. This ensured sucient
discriminatory ability of the model (Dyson et al., 2001). Finally, rst, the output-oriented DEA model was used
as the analysis aims on providing the optimal number of arrivals (TN) based on the input structure of the
model. Second, CCR model was selected as there is no competition among the 67 touristic centers. We should
rather understand these centers as complementary to each other.
Results
The results are presented in three main parts. First, the overall eciency model is presented for all 67 tour-
istic centers. Second, the analysis is divided into eciency model considering only foreign tourists and, third,
considering only national tourists. In all three cases, the eciency is discussed from the average point of view
to have overall perspective, as well as regarding nine dierent periods with respect to the selected 3-year
long window in the DEA model to detect possible volatilities in the eciency.
Overall model
The average eciency of all 67 touristic centers for the entire period (1992-2017) was 69.07% with the
standard deviation (SD) of 15.28%. 29 touristic centers (representing 43.28%) are evaluated above the national
average (Table 1). The best evaluated touristic center is Playacar in Quintana Roo with average eciency of
97.40% throughout the evaluated period. What is more, Playacar reported very low year-to-year uctuation
as the SD is only 3.54%. The second-best evaluated center is Akumal, also from Quintana Roo with average
eciency of 96.44% (SD 5.77%), followed by Tonalá-Puerto Arista in Chiapas with average eciency of 95.82%
(SD 5.93%). In all three cases, we talk about small touristic centers on the coast. However, in the top 10 most
ecient centers, we can also observe in-land centers, such as Tecate in Baja California on 4th place (95.68%,
SD 5.40%), Comitán de Domínguez in Chiapas on 8th place (92.53%, SD 10.85%) and Salamanca in Guanajuato
on 10th place (89.90%, SD 11.08%).
Contrary, the worst evaluated center is San Miguel de Allende (67th position) in Guanajuato with an
average eciency of 42.08% (SD 15.61%). This result may be surprising as the old section of the town is part of
a proclaimed World Heritage Site of the UNESCO. However, there is a huge disproportion between the oer of
hotels and number of nights the tourists stay. For example, tourists stay in average 3.65 nights in the 10 best
evaluated centers compare to only 1.69 nights in San Miguel de Allende. Chihuahua city in Chihuahua is eval-
uated as the second worst (66th) with an eciency of 42.72% (SD 5.77%), followed by Toluca on 65th position
(44.11%, SD 5.93%). As we can observe in Table 1, the majority of the least evaluated touristic centers are big
in-land cities, such as Aguascalientes (62nd position, 46.17%, SD 5.77%), León (61st, 48.37%, SD 12.60%), Valle
de Bravo (59th, 51.24%, SD 23.19%), and Monterrey (58th, 52.30%, SD 11.18%).
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RELAIS, vol. 3, no. 2
Touristic center Efciency Rank Touristic center Efciency Rank Touristic center Efciency Rank
Acapulco 64.19% 39 Ixtapa-Zihuatanejo 83.18% 14 Querétaro 53.50% 57
Aguascalientes 46.17% 62 La Paz 57.93% 52 Salamanca 89.90% 10
Akumal 96.44% 2 León 48.37% 61 San Cristóbal de las
Casas
74.37% 22
Bahías de
Huatulco
86.10% 12 Loreto 68.18% 32 San Felipe 62.43% 42
Cabo San Lucas 90.94% 9 Los Mochis 59.13% 50 San José del Cabo 77.21% 20
Campeche 79.52% 19 Manzanillo 73.67% 25 San Juan de los
Lagos
68.78% 30
Cancún 92.63% 7 Mazatlán 74.01% 23 San Juan del Río 50.48% 60
Celaya 80.75% 17 Mérida 56.32% 53 San Luis Potosí 53.51% 56
Chihuahua 42.72% 66 Mexicali 73.78% 24 San Miguel de
Allende
42.08% 67
Ciudad de México 67.15% 36 Monterrey 52.30% 58 Taxco 45.90% 63
Ciudad Juárez 80.98% 16 Morelia 59.91% 48 Tecate 95.68% 4
Coatzacoalcos 65.45% 38 Nuevo Vallarta 95.22% 5 Tequisquiapan 58.76% 51
Colima 67.71% 34 Oaxaca 67.42% 35 Tijuana 61.53% 46
Comitán de
Domínguez
92.53% 8 Pachuca 76.48% 21 Tlaxcala 83.28% 13
Cozumel 70.76% 29 Palenque 67.73% 33 Toluca 44.11% 65
Culiacán 53.60% 55 Piedras Negras 88.39% 11 Tonalá-Puerto Arista 95.82% 3
Durango 59.64% 49 Playa del Carmen 94.68% 6 Tuxtla Gutiérrez 61.56% 45
El Fuerte 80.21% 18 Playacar 97.40% 1 Valle de Bravo 51.24% 59
Guadalajara 56.10% 54 Playas de Rosarito 44.45% 64 Veracruz 62.29% 43
Guanajuato 65.97% 37 Puebla 72.42% 27 Villahermosa 60.36% 47
Hermosillo 81.71% 15 Puerto Escondido 62.02% 44 Xalapa 71.06% 28
Irapuato 64.02% 40 Puerto Vallarta 73.55% 26 Zacatecas 63.52% 41
Isla Mujeres 68.36% 31
Table 1: Eciency of touristic centers, overall model 1992-2017
Average eciency presented in Table 1 might not be fully representative as the analysis includes 26 years
and the eciency of the touristic centers may vary, as tourists’ preferences can change in long-term perspec-
tive. Therefore, Figure 1 presents the evolution of the eciency since 1992 until 2017. We can observe two
signicant drops in the overall eciency between years 1996-1999 and 2002-2005. In both cases, these periods
are followed by a signicant four-year growth that diminished the previous drop. In the rst case, the average
eciency dropped from 74.35% in 1996 to 32.78% in 1999, so the eciency dropped by 13.85% annually. The
touristic industry recovered withing the following three years. In the second case, the eciency dropped from
75.46% in 2002 to 42.51% in 2005, resulting in an average decrease of 10.98%. Similarly, this drop was fully
recovered in 2008. In 2009, there was a beginning of the same pattern as the eciency dropped by 6.59%
in 2009. However, since this year, the eciency of the touristic centers remained more less stable around an
average eciency of 71.33% with SD of 2.95%.
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RELAIS, vol. 3, no. 2
Figure 1: Average eciency of the touristic centers, 1992-2017
Table 4 (in appendix) divides the average eciency of all 67 touristic centers presented in Table 1 into
nine 3-year-long periods4 to capture the eciency change. First, we can observe that there are variations
from period to period. For example, the best evaluated centers in the rst period were Cancún (99.75%), Valle
de Bravo (99.65%) and Pachuca (98.10%). However, Valle de Bravo has been losing its eciency constantly
that resulted in the worst eciency at the end of the analyzed period (67th position, 37.19%). Pachuca in
general remains within top 20 evaluated centers (with an exception in 2007-2009 when Pachuca was ranked
at position 56). The most stable evaluation can be observed in case of Ciudad de Juárez (12.55th average
position with SD 4.85), Manzanillo (17.22th, SD 5.12), Guadalajara (34.33th, SD 6.56), Cancún (7.77th, SD 6.76)
and Veracruz (27.66th, SD 7.04). It is also important to mention that the new- entry touristic centers during
2007-2009 demonstrates even higher stability than those centers represented in the analysis across the whole
analysis (in both cases, for the best evaluated, as well as for the worst evaluated). In these cases, the latest-
entry centers are usually smaller touristic destinations with limited hotel spaces, which makes them less
vulnerable for year-to-year changes.
4 The 3-year-long periods were chosen considering the size of the analyzed window in the DEA WA analysis.
Foreign tourists
As the results of the analysis show dierences across the analyzed period, we can also assume that similar
dierences can be observed considering the tourists’ origin. Therefore, we calculated other two models for
foreign and national tourists. In case of the foreign tourists, the average eciency of all 67 touristic centers
for the entire period (1992-2017) was 29.81% with the standard deviation of 28.27%. Only 24 touristic centers
(representing 35.82%) are evaluated above the average. What is more, if we consider the average from the
overall model (43.28%), then only 20 centers (29.85%) crossed this level. Further, we can observe many centers
with an average eciency around 10% and below. This indicates that foreign tourism is concentrated in
limited number of main centers. The best evaluated touristic center for foreigners is Playacar with average
eciency of 97.51% with very low year-to-year uctuation as the SD is only 3.55%. The second-best evaluated
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RELAIS, vol. 3, no. 2
center is Akumal (96.36%, SD 5.95%), followed by Playa del Carmen (95.70%, SD 3.59%), Tecate (91.89%, SD
8.45%) and Cancún (85.07%, SD 16.24%). Table 2 summarizes the eciency for all centers.
Touristic center Efciency Rank Touristic center Efciency Rank Touristic center Efciency Rank
Acapulco 13.27% 39 Ixtapa-Zihuatanejo 46.76% 18 Querétaro 4.39% 60
Aguascalientes 7.38% 50 La Paz 21.84% 32 Salamanca 32.30% 24
Akumal 96.36% 2 León 2.94% 65 San Cristóbal de las
Casas
71.75% 7
Bahías de
Huatulco
49.18% 17 Loreto 57.07% 13 San Felipe 56.59% 15
Cabo San Lucas 84.31% 6 Los Mochis 5.12% 54 San José del Cabo 62.08% 12
Campeche 46.62% 19 Manzanillo 15.66% 37 San Juan de los
Lagos
0.02% 67
Cancún 85.07% 5 Mazatlán 35.18% 21 San Juan del Río 11.69% 41
Celaya 10.75% 43 Mérida 22.64% 31 San Luis Potosí 6.30% 53
Chihuahua 8.73% 47 Mexicali 24.03% 29 San Miguel de
Allende
18.11% 35
Ciudad de México 26.72% 26 Monterrey 11.12% 42 Taxco 19.00% 34
Ciudad Juárez 29.22% 25 Morelia 4.78% 58 Tecate 91.89% 4
Coatzacoalcos 2.33% 66 Nuevo Vallarta 68.14% 9 Tequisquiapan 9.70% 46
Colima 7.02% 51 Oaxaca 33.81% 23 Tijuana 22.67% 30
Comitán de
Domínguez
56.84% 14 Pachuca 3.95% 61 Tlaxcala 24.93% 28
Cozumel 67.69% 10 Palenque 45.22% 20 Toluca 8.01% 48
Culiacán 2.99% 64 Piedras Negras 13.02% 40 Tonalá-Puerto Arista 25.89% 27
Durango 3.29% 62 Playa del Carmen 95.70% 3 Tuxtla Gutiérrez 4.79% 57
El Fuerte 71.47% 8 Playacar 97.51% 1 Valle de Bravo 4.87% 56
Guadalajara 10.38% 44 Playas de Rosarito 34.76% 22 Veracruz 3.18% 63
Guanajuato 10.04% 45 Puebla 20.17% 33 Villahermosa 6.77% 52
Hermosillo 14.29% 38 Puerto Escondido 16.85% 36 Xalapa 4.91% 55
Irapuato 4.71% 59 Puerto Vallarta 52.49% 16 Zacatecas 7.55% 49
Isla Mujeres 62.62% 11
Table 2: Eciency of touristic centers, foreign tourists 1992-2017
Clear pattern can be observed regarding the foreign tourists. Within top 10 most ecient destinations
eight are costal centers, with the main touristic destination in Riviera Maya in Yucatan (Figure 2). The only two
in-land destinations within the 10 best evaluated cities are San Cristobal de las Casas (7th position, 71.75%, SD
26.97) and El Fuerte (8th position, 71.47%, SD 28.95%). In the other matters, through analysis we can observe
that the center of Mexico is not that much attractive or well-known for foreigners as the main coast touristic
destinations. Within the 10 least ecient centers are San Juan de los Lagos in Jalisco (67th, 0.02%, SD 0.02%),
Coatzacoalcos in Veracruz (66th, 2.33%, SD 0.86%), León in León (65th, 2.94%, SD 2.14%), Culiacán in Sinaloa
(64th, 2.99%, SD 1.58%) and Veracruz in Veracruz (63rd, 3.18%, SD 1.37%). What is more, there is a huge stability
within the least evaluated touristic centers as the average SD is 1.76% across the whole analyzed period. In
addition, there is a big dierence between the top 10 and the least 10 centers regarding the length of stays
25
RELAIS, vol. 3, no. 2
per night. Foreign tourists stay in average 3.80 nights in the best evaluated centers compare to only 1.45
nights in the least evaluated centers. If we eliminate El Fuerte, San Cristobal de las Casas and Tecate, then the
average of nights increases up to 4.93 nights.
Figure 2: The most (yellow) and the least ecient (gray) touristic centers for foreign tourists 1992-2017
As in the overall model, it is important to analyze the variance of the eciency across the analyzed period
(Figure 2). We can obser ve similar drop between 1996-1999 as in the overall model. As the average eciency is
much lower in this model (29.81%), the decrease was not that dramatic. The eciency decreased from 33.52%
in 1996 to 18.13% in 1999 (falling by 3.85% annually), which means a total decrease by 45.91% (compare to a
decrease of 55.91% in the overall model). The second drop in the eciency had dierent pattern as there was
not only decrease between 2002 and 2005, but the eciency began to decrease in 2001 and lasted until 2006.
Since 2007, the average eciency uctuated around 26.16% with SD 2.27%. Table 5 (in appendix) presents the
evolution of the eciency for each touristic center regarding foreign tourists. Similarly, as in the overall model,
many uctuations can be observed, but several stable touristic centers can be observed. For example, Cancún
remains within the best evaluated centers with an average position 4.55 with SD 3.91. Puerto Vallarta in Jalisco
has an average position as 11.89th best (SD 5.16), Mazatlán in Sinaloa evaluated as 15.11th best in average (SD
4.70) and La Paz in Baja California Sur as 20.66th best (SD 4.72).
National Tourists
The average eciency of the touristic centers for the entire period (1992-2017) in case of national tourists
was 70.74% with the standard deviation of 20.53%. The level of eciency is much higher compare to the
foreign tourists. In this case, national tourists are not concentrated in several main touristic destinations. As a
result, 42 touristic centers (representing 62.69%) are evaluated above the average (Table 3). The best evaluated
touristic center for national tourists is Tonalá-Puerto Arista in Chiapas with an average eciency of 96.01% (SD
5.71%). The second-best evaluated center is Bahías de Huatulco in Oaxaca (95.96%, SD 4.00%), followed by
Piedras Negras in Coahuila (95.53%, SD 4.48%), Hermosillo in Sonora (95.27%, SD 4.11%) and Salamanca in
26
RELAIS, vol. 3, no. 2
Guanajuato (94.26%, SD 6.17%). In all ve cases, the eciency is around 95% or above, and the standard devi-
ations indicate very high stability of their score.
What is important to mention, national tourism presents completely dierent pattern compare to foreign
tourism. Similarly, as in the case of foreign tourism, many cost centers are registered within the highest eval-
uated. However, as Figure 3 highlights, these centers are not located in Riviera Maya, but are located in the
South-west coast of Mexico. Centers in Riviera Maya (Akumal 67th, Cancún 56th, Cozumel 64th, Isla de Mujeres
63rd, Playa del Carmen 66th, Playacar 58th) are evaluated as the least ecient. Alike, favorite touristic desti-
nations for foreigners in Baja California and Baja California Sur are not on the top list for Mexicans. Further,
in-land centers are visited more by Mexicans as none of the in-land center is evaluated among the ten least
ecient.
Touristic center Efciency Rank Touristic center Efciency Rank Touristic center Efciency Rank
Acapulco 84.70% 16 Ixtapa-Zihuatanejo 93.27% 8 Querétaro 82.91% 20
Aguascalientes 59.46% 55 La Paz 64.95% 47 Salamanca 94.26% 5
Akumal 2.51% 67 León 68.01% 44 San Cristóbal de las
Casas
64.72% 48
Bahías de
Huatulco
95.96% 2 Loreto 46.84% 60 San Felipe 47.24% 59
Cabo San Lucas 30.99% 62 Los Mochis 66.34% 46 San José del Cabo 44.18% 61
Campeche 84.23% 17 Manzanillo 88.58% 10 San Juan de los
Lagos
81.55% 22
Cancún 59.24% 56 Mazatlán 77.10% 31 San Juan del Río 71.38% 41
Celaya 87.00% 12 Mérida 64.22% 50 San Luis Potosí 74.67% 34
Chihuahua 60.60% 53 Mexicali 72.52% 38 San Miguel de
Allende
54.17% 57
Ciudad de México 78.73% 25 Monterrey 74.21% 35 Taxco 60.14% 54
Ciudad Juárez 85.35% 14 Morelia 77.84% 27 Tecate 94.06% 6
Coatzacoalcos 77.26% 29 Nuevo Vallarta 93.97% 7 Tequisquiapan 77.75% 28
Colima 85.08% 15 Oaxaca 69.64% 42 Tijuana 67.34% 45
Comitán de
Domínguez
92.76% 9 Pachuca 87.62% 11 Tlaxcala 86.43% 13
Cozumel 28.06% 64 Palenque 64.23% 49 Toluca 60.80% 52
Culiacán 72.55% 37 Piedras Negras 95.53% 3 Tonalá-Puerto Arista 96.01% 1
Durango 71.71% 40 Playa del Carmen 6.29% 66 Tuxtla Gutiérrez 76.94% 32
El Fuerte 81.96% 21 Playacar 47.96% 58 Valle de Bravo 69.12% 43
Guadalajara 73.85% 36 Playas de Rosarito 24.74% 65 Veracruz 83.06% 19
Guanajuato 81.28% 23 Puebla 83.72% 18 Villahermosa 78.66% 26
Hermosillo 95.27% 4 Puerto Escondido 75.24% 33 Xalapa 80.71% 24
Irapuato 71.90% 39 Puerto Vallarta 62.57% 51 Zacatecas 77.12% 30
Isla Mujeres 30.47% 63
Table 3: Eciency of touristic centers, national tourists 1992-2017
27
RELAIS, vol. 3, no. 2
Figure 3: The most (yellow) and the least (gray) ecient touristic centers for national tourists 1992-2017
As the national tourism is not dependent on only several main touristic zones, the evolution of the eciency
is more stable (Figure 1). As in both previous cases, the drop in the eciency between 1996 and 1999 can be
observed (from 79.73% to 47.55%). However, the second drop in 2002-2005 is not observable at all. There was,
however, a constant decreasing trend by approximately 1.61% annually since 2005 until 2014 (from 79.79% to
65.32%), which was partially recovered during the last three years. The evolution of the eciency regarding
the national tourists is presented in Table 6 (in appendix). Similarly, as in the overall model, many uctuations
can be observed, but several stable touristic centers can be observed. For example, Bahías de Huatulco is
evaluated with an average position 6.44 with SD 5.17 positions, Ixtapa-Zihuatanejo with average position 8.33
(SD 3.64) and Hermosillo with position 6.44 (SD 4.06). In these three cases, all record data for the whole period
1992-2017. We can observe other touristic centers with similar (or better) evaluation. Such as Nuevo Vallarta
(8.50, SD 7.22), Piedras Negras (5.75, SD 3.94), Salamanca (8.75, SD 4.80) and Tonalá-Puerto Arista (7.25, SD 5.14).
However, in these cases, they record does not include the whole period as the rst reported data are linked to
2007, respectively to 2008. This inequality can slightly bias their average result.
Discussion
One of the main signicant observations revealed by the analysis is the centralization of the tourism
in limited number of touristic centers. This observation corresponds with the results from dierent coun-
tries. For example, Oukil, Channouf and Al-Zaidi (2016) found high level of eciency concentrated mainly in
Muscat, the capital of Oman, Corne (2015) showed that Paris is a benchmark for the French hospitality sector,
while Liu, Zhang and Fu (2017) observed that highly ecient Chinese coastal cities are concentrated in two
main regions. In addition, the results indicate signicant dierences between the priorities of the foreign and
national tourism. The foreign tourism is concentrated in the coast areas, mainly in Riviera Maya in Yucatan.
This centralization leads to very low overall eciency of the tourism. Thus, there is need to prepare strategies
that would attract the in-land touristic areas for the foreign tourists. This goes along with the National touristic
28
RELAIS, vol. 3, no. 2
plan for the period of 2019-2024 of the Mexican government (SECTUR, 2019b). One of the ve main projects
for this period aims on regionalization of the tourism, to strengthen the tourism within the whole country
to make it more equal5. As foreign tourists generally stay more nights in one place, the regionalization eort
would lead to higher incomes in low ecient touristic places. Tourism contributes approximately by 8.8% to
the Mexican GDP. As Holzner (2011) pointed out, it is recommendable to invest apart from tourism specic also
into traditional infrastructure, which can be used by both the tourism sector and by the manufacturing sector.
In this sense, the project of Mayan train (El Tren Maya), which proposal is to connect main touristic places
in Yucatán in a 1,525-kilometre railroad circuit (SECTUR, 2019b) may be a project that would help increase
local infrastructure to leading to improvements in tourism industry. However, the results indicate already
high eciency in case of foreign tourism in Riviera Maya. Furthermore, the results also indicate variability of
the eciency across the analyzed period as tourists’ preferences are not stable. Therefore, the investments
in such huge projects must be carefully planned with long-term perspective, i.e. not to create projects based
on current local touristic trends. The impact on the tourism in the whole country must be taken into consid-
eration. Similarly, an environmental aspect of the tourism should not be forgotten. Liu, Zhang and Fu (2017)
recommend that improving environmental regulations and consciousness, driving regional tourism environ-
mental protection and ecological balance can have signicant eect on the eciency in the tourism industry.
Tourists satisfaction is one of the important factors in tourism marketing. The consequences of satisfac-
tion with a tourist experience are basically an increase in the intention to return to the destination, as well
as recommending it to third parties (Kim and Perdue, 2011). This satisfaction is aected by many factors:
human resources in the destination, organization of the human resources, local infrastructure, touristic
attractions, sociocultural environment, and local business activities (Coelho and Gosling, 2015). Thus, there
are many areas of opportunities to attract tourists into less ecient touristic centers. Oukil, Channouf and
Al-Zaidi (2016) investigated that the eect of culture attractions appears as the most inuential factors. The
culture factor includes traditional villages, world heritage, museums, archaeological and religious sites, crafts,
among others. Therefore, SECTUR should target their marketing operations on less ecient touristic centers
(including small touristic centers) to promote their culture attractions. Such promotions can include diverse
set of operations. For example, Olvera Mejía, Gea Pérez and González Silva (2018) developed a virtual reality
to promote tourist attractions in small municipality of Tepeapulco in Hidalgo. The objective of such tools is
to publicize the tourist attractions of a region, resulting in more arrivals of tourists. Such strategies aim on
both the national and foreign tourists, as each strategy can be adjusted to a specic market. For example,
promotion of the in-land destinations for foreign tourists and promotion of small municipalities for national
tourism. Promotion of small local municipalities complies with Mexican government strategy to consolidate
the internal touristic market (SECTUR, 2019b). However, local problems in such small municipalities can put
barriers in promoting local touristic activities (Méndez Méndez et al., 2016).
Similarly, these promotive activities can be ineective due to the national insecurity. That might be the
other reason why foreign tourism is concentrated in few main centers. Building better reputation in new desti-
nations can be easily ruined by worse security situation (Aula and Hermaakorpi, 2008). Satisfaction, perceived
value and the destination’s image are factors of the destination loyalty (Cossío-Silva, Revilla-Camacho and
Vega-Vázquez, 2019; Sun, Geng-Qing and Xu, 2013), which is a key element in marketing strategies, as well
5 “Proyecto está relacionado con la política de regionalización que se hará extensiva a todo el país, para que la
actividad turística sea más equilibrada” (SECTUR, 2019b).
29
RELAIS, vol. 3, no. 2
as the best predictor of post-visit behavior (Chen and Chen, 2010). Therefore, the investment into local infra-
structure should also consider investments in security, which would generate safer image of each touristic
destination.
Limitations of the analysis
The presented analysis has several limitations that must be considered. The obtained results cannot be
understood as the analysis of the tourism in Mexico as the article analyses the hospitality eciency. Including
hotel characteristics into the DEA model is only one of the possible variables. To cover the whole perspective
of the Mexican tourism, it would be necessary to include variables that reects the attractivity of the tour-
istic center: Nature (reserves, valleys, caves, beaches, islands, water springs, lagoons, etc.), Culture (traditional,
world heritage, museums, archaeological and religious sites, etc.) and Activities (physical activities: trekking,
hiking, climbing; shopping; and watching activities: whales, birds, turtles, dolphins), as well as the local
infrastructure (bars, restaurants, convention centers, etc.). Similarly, as the eciency measures the ration of
weighted inputs to weighted outputs, nancial inputs (governmental investments) should be considered in
the overall tourism model. However, such variables are not available for majority of analyzed touristic centers.
Finally, the study aimed to include the highest possible number of the most important touristic centers
in Mexico. The metrics and number of touristic centers published by SECTUR is constantly developing. We
included touristic centers for which we were able to get data for at least 9 out of the 26 years. That is why, the
results do not report eciency scores for several centers for the period from 1992 to 2006. This may cause
higher volatility of the results during the rst years and, vice versa, higher stability after 2006 (Figure 1). This
volatility could be minimized by a bigger length of the selected window (for example, extending the 3-year
window to 6-year window). However, the analysis must respect the total length of the period (26 years) and
the impact of the negative incidents’ magnitude in tourism.
Conclusions
This article aimed to evaluate the hospitality eciency of 67 main touristic centers in the dierent states
of Mexico for the period 1992-2017. For this purpose, annual information from DATATUR was used, with which
3 DEA models were built (overall model, foreign model and national model). The results indicate that the
overall eciency across the whole period was 69.07% (SD 15.28%) with two major drops between 1996-1999
and 2002-2005. Similar pattern was identied in the foreign model, but due to the lower average eciency
(29.01%, SD 28.27%), these drops were not that signicant. The main conclusion from the analysis can be
linked to signicant dierences between eciency in foreign and national tourism. Touristic centers with the
highest eciency for foreign tourists are greatly concentrated in Quintana Roo, and the rest of the country
is evaluated very low. On the other hand, the best evaluated centers for national tourists are also on-coast
centers (mainly in the South-West coast of Mexico), but the tourism activity is more decentralized. This result
suggests that there is a huge opportunity for improvements to regionalize the tourism and to strengthen
the tourism within the whole country. Mexican government should consider better promotion of the in-land
touristic centers for foreigners. As foreign tourists usually stay more nights in one place, this would generate
higher incomes for the local economies, resulting in higher income for Mexican economy. Finally, enlarging
the concentration of the foreign tourism would minimize the impact of local insecurity and/or the eect of
30
RELAIS, vol. 3, no. 2
natural disasters in the main touristic centers. As a result, this would stabilize the industry, as well as secure
future steady growth.
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RELAIS, vol. 3, no. 2
Appendix
1992-1994 1995-1997 1998-2000 2001-2003 2004-2006 2007-2009 2010-2012 2013-2015 2016-2017
Acapulco 77.50% 56.21% 38.92% 57.87% 50.11% 70.92% 71.36% 64.89% 77.49%
Aguascalientes 47.13% 42.84% 24.64% 60.72% 30.40% 54.04% 50.40% 50.26% 59.58%
Akumal - - - - - 93.29% 95.74% 97.92% 100.00%
Bahías de Huatulco 95.66% 98.93% 98.12% 98.64% 85.09% 73.32% 72.13% 70.85% 80.19%
Cabo San Lucas - - - - - 99.92% 83.82% 83.46% 99.37%
Campeche 97.98% 95.55% 36.80% 86.72% 49.40% 85.87% 95.52% 71.26% 83.71%
Cancún 99.75% 99.35% 93.27% 94.85% 90.70% 83.62% 84.00% 93.41% 96.13%
Celaya - - - - - 72.85% 82.86% 79.63% 91.09%
Chihuahua - - 25.81% 51.24% 35.08% 50.68% 44.04% 38.10% 51.36%
Ciudad de México 73.67% 74.24% 32.63% 76.74% 49.36% 76.78% 86.82% 67.75% 65.98%
Ciudad Juárez 94.74% 81.19% 41.81% 77.05% 80.22% 94.20% 91.76% 84.33% 84.76%
Coatzacoalcos - - - - - 70.92% 74.62% 60.90% 50.32%
Colima - - 68.10% 68.30% 42.19% 78.32% 76.68% 61.33% 84.89%
Comitán de Domínguez - - - - - 99.32% 96.67% 95.41% 75.19%
Cozumel 85.90% 84.23% 78.23% - 63.43% 65.11% 60.27% 59.30% 68.98%
Culiacán - - - - - 51.32% 56.85% 51.14% 55.81%
Durango 93.22% 85.50% 35.12% 37.80% 25.33% 63.23% 70.90% 57.72% 59.87%
El Fuerte - - - - - 89.95% 68.20% 84.63% 86.71%
Guadalajara 60.75% 54.15% 31.43% 55.27% 41.64% 70.40% 69.13% 57.13% 69.50%
Guanajuato 82.65% 61.88% 33.15% 79.42% 56.66% 67.54% 81.90% 62.38% 69.27%
Hermosillo 44.51% 75.54% 68.37% 81.57% 94.17% 92.36% 93.43% 92.90% 97.96%
Irapuato - - - - - 64.85% 66.52% 60.81% 64.25%
Isla Mujeres - - - - - 66.98% 65.87% 66.89% 76.39%
Ixtapa-Zihuatanejo 84.96% 85.02% 83.06% 96.80% 93.27% 76.45% 71.70% 69.80% 89.70%
La Paz 54.48% 65.63% 34.94% 58.02% 29.73% 79.69% 79.43% 55.55% 66.92%
León - - 34.94% 53.78% 28.76% 52.15% 58.27% 49.78% 60.48%
Loreto 58.34% 70.12% 99.13% 99.57% 94.06% 86.41% 40.82% 32.35% 59.49%
Los Mochis - - - - - 61.63% 61.14% 50.60% 65.13%
Manzanillo 93.25% 73.69% 40.14% 77.21% 51.34% 84.85% 89.84% 69.31% 88.30%
Mazatlán 95.47% 62.85% 38.77% 54.60% 46.14% 91.60% 94.96% 91.60% 98.13%
Mérida 61.20% 57.36% 46.28% - 38.82% 65.03% 63.26% 55.45% 56.54%
Mexicali - - - - - 79.77% 65.38% 80.63% 70.12%
Monterrey 50.59% 45.96% 36.85% 63.83% 44.48% 60.18% 48.70% 57.62% 67.52%
Morelia 86.51% 57.86% 30.26% 61.59% 38.67% 74.70% 64.68% 45.86% 73.78%
Nuevo Vallarta - - - - - 90.59% 94.62% 97.26% 100.00%
Oaxaca 76.65% 81.89% 41.44% 67.70% 42.66% 70.39% 84.27% 65.72% 80.34%
Pachuca 98.10% 57.48% 38.71% 98.03% 75.69% 56.02% 86.50% 96.68% 83.49%
Palenque - - - - - 72.05% 72.82% 62.47% 61.53%
Piedras Negras - - - - - 81.82% 83.66% 91.91% 96.78%
Playa del Carmen - - - - - 92.70% 95.28% 96.88% 93.48%
Playacar - - - - - 96.87% 96.91% 97.27% 99.13%
Playas de Rosarito - - - - - 39.24% 31.78% 49.64% 67.42%
Puebla 93.14% 58.39% 38.96% 84.98% 42.15% 73.38% 83.29% 89.89% 95.13%
Puerto Escondido - - 77.52% 85.51% 31.58% 76.42% 60.96% 53.09% 58.06%
Puerto Vallarta 53.87% 81.27% 63.81% 80.51% 63.62% 88.11% 78.69% 73.83% 80.61%
Querétaro 56.85% 46.70% 32.35% 64.13% 43.72% 61.57% 67.65% 54.87% 53.71%
Salamanca - - - - - 95.66% 90.76% 83.90% 88.97%
San Cristóbal de las Casas 96.38% 90.40% 76.23% 70.31% 37.83% 80.96% 81.59% 62.96% 71.76%
San Felipe - - - - - 69.61% 51.41% 54.29% 84.02%
San José del Cabo - - - - - 75.90% 73.59% 78.81% 82.20%
San Juan de los Lagos - - - - - 65.40% 68.03% 70.58% 72.28%
San Juan del Río 93.75% 53.76% 29.45% 49.66% 31.47% 49.23% 49.93% 45.53% 52.08%
San Luis Potosí 40.40% 50.31% 22.11% 56.54% 34.56% 56.96% 64.82% 75.32% 94.16%
San Miguel de Allende 42.80% 68.85% 22.78% 41.60% 27.92% 40.38% 44.69% 43.23% 48.63%
Taxco 37.42% 56.09% 51.99% 46.83% 31.42% 48.38% 44.06% 42.22% 59.09%
Tecate - - - - - 94.40% 99.37% 91.46% 98.43%
Tequisquiapan 98.09% 63.29% 53.75% 79.81% 34.07% 52.46% 52.98% 48.88% 38.88%
Tijuana 96.69% 73.76% 30.47% 70.18% 52.74% 62.63% 57.54% 53.33% 53.87%
Tlaxcala 93.41% 49.45% 50.62% 85.17% 96.35% 94.02% 94.43% 93.36% 97.40%
Toluca 39.12% 49.41% 21.07% 39.50% 25.89% 54.84% 59.78% 55.28% 56.11%
Tonalá-Puerto Arista - - - - - 93.38% 99.93% 94.15% 94.59%
Tuxtla Gutiérrez 71.44% 61.10% 34.13% 81.90% 56.87% 65.78% 67.77% 57.09% 56.08%
Valle de Bravo 99.65% 80.29% 39.20% 50.90% 32.22% 40.13% 41.38% 35.53% 37.19%
Veracruz 70.31% 74.54% 27.34% 55.81% 36.39% 78.53% 76.86% 71.52% 72.85%
Villahermosa 47.47% 63.62% 30.81% 64.13% 43.55% 74.26% 79.09% 80.21% 60.01%
Xalapa - - - - - 80.01% 83.13% 61.77% 53.47%
Zacatecas 88.78% 72.52% 34.30% 55.20% 39.77% 69.43% 68.82% 62.57% 74.00%
Average 75.19% 68.24% 45.76% 68.78% 49.76% 72.98% 72.60% 68.10% 74.04%
Table 4: Touristic center eciency by period, overall model
34
RELAIS, vol. 3, no. 2
1992-1994 1995-1997 1998-2000 2001-2003 2004-2006 2007-2009 2010-2012 2013-2015 2016-2017
Acapulco 33.31% 23.23% 18.88% 22.54% 8.03% 4.10% 3.60% 3.37% 2.57%
Aguascalientes 4.19% 3.46% 2.60% 11.10% 4.60% 4.21% 6.66% 16.75% 15.59%
Akumal - - - - - 92.97% 95.51% 98.18% 100.00%
Bahías de Huatulco 81.01% 95.92% 80.03% 60.85% 43.20% 20.34% 13.84% 17.69% 20.01%
Cabo San Lucas - - - - - 99.74% 71.55% 75.41% 93.65%
Campeche 85.69% 85.87% 21.82% 69.13% 14.85% 20.68% 29.06% 37.59% 46.56%
Cancún 99.34% 99.82% 98.74% 95.38% 88.95% 66.13% 58.14% 76.00% 88.98%
Celaya - - - - - 5.81% 5.20% 20.37% 12.05%
Chihuahua - - - - - 9.50% 6.40% 3.96% 5.46%
Ciudad de México 34.83% 31.01% 15.96% 44.36% 18.86% 25.64% 24.00% 21.07% 23.79%
Ciudad Juárez 53.94% 18.47% 11.56% 26.32% 57.35% 27.57% 34.27% 15.57% 12.28%
Coatzacoalcos - - - - - 1.68% 2.27% 3.18% 2.13%
Colima - - 13.28% 9.14% 3.04% 5.55% 3.80% 3.66% 15.61%
Comitán de Domínguez - - - - - 54.04% 67.17% 53.96% 48.45%
Cozumel 87.47% 87.46% 84.14% - 65.34% 54.41% 47.05% 49.34% 65.68%
Culiacán - - - - - 1.66% 2.81% 3.50% 4.48%
Durango 6.05% 2.34% 2.64% 3.47% 1.55% 3.29% 2.03% 5.81% 1.65%
El Fuerte - - - - - 89.67% 58.70% 80.55% 67.89%
Guadalajara 7.97% 8.98% 6.79% 14.81% 7.70% 10.23% 9.97% 11.84% 17.54%
Guanajuato 17.42% 10.92% 9.22% 19.88% 9.61% 6.52% 5.94% 4.33% 4.74%
Hermosillo 5.94% 12.11% 14.07% 20.09% 41.06% 8.03% 10.35% 9.18% 4.56%
Irapuato - - - - - 2.60% 4.81% 6.28% 4.33%
Isla Mujeres - - - - - 73.19% 58.44% 50.83% 70.74%
Ixtapa-Zihuatanejo 57.13% 64.51% 67.59% 80.28% 76.78% 24.01% 14.49% 15.25% 18.26%
La Paz 21.80% 30.70% 16.57% 26.27% 9.16% 25.67% 22.39% 20.15% 24.84%
León - - 4.23% 6.13% 1.32% 2.04% 1.45% 1.32% 5.34%
Loreto 57.75% 68.13% 97.03% 100.00% 83.71% 55.98% 22.47% 20.10% 40.38%
Los Mochis - - - - - 8.46% 4.90% 3.30% 3.16%
Manzanillo 44.52% 21.42% 12.12% 22.52% 7.71% 11.70% 6.06% 5.87% 5.76%
Mazatlán 68.52% 40.30% 27.47% 29.21% 20.34% 35.74% 32.30% 27.26% 35.63%
Mérida 50.03% 36.29% 28.02% - 13.59% 15.97% 11.60% 12.25% 14.12%
Mexicali - - - - - 16.23% 11.65% 33.82% 35.73%
Monterrey 10.04% 9.76% 9.52% 17.87% 11.12% 11.06% 6.85% 10.90% 13.91%
Morelia 7.20% 4.48% 2.51% 7.62% 4.14% 5.35% 2.48% 2.29% 6.87%
Nuevo Vallarta - - - - - 64.64% 45.01% 76.29% 95.87%
Oaxaca 60.84% 68.12% 28.11% 38.34% 14.26% 22.78% 21.52% 23.95% 22.68%
Pachuca 6.48% 1.48% 0.40% 3.64% 5.28% 5.12% 7.84% 2.54% 2.19%
Palenque - - - - - 80.55% 42.22% 24.50% 27.79%
Piedras Negras - - - - - 15.07% 12.89% 11.53% 13.39%
Playa del Carmen - - - - - 94.61% 95.15% 96.83% 96.46%
Playacar - - - - - 96.81% 97.24% 97.08% 99.59%
Playas de Rosarito - - - - 40.78% 26.00% 22.88% 55.24%
Puebla 21.67% 12.22% 9.68% 30.02% 9.26% 12.75% 14.25% 44.84% 30.18%
Puerto Escondido - - 41.83% 39.88% 7.99% 15.02% 8.44% 8.37% 11.19%
Puerto Vallarta 37.83% 70.24% 63.93% 69.10% 50.77% 52.38% 40.30% 39.62% 46.13%
Querétaro 1.86% 2.22% 2.80% 5.56% 6.39% 5.36% 5.22% 5.25% 5.12%
Salamanca - - - - - 19.85% 57.25% 27.68% 14.24%
San Cristóbal de las Casas 92.23% 92.24% 99.83% 84.55% 30.24% 79.99% 76.08% 43.75% 34.33%
San Felipe - - - - - 56.51% 36.34% 59.86% 82.14%
San José del Cabo - - - - - 57.31% 58.05% 69.01% 64.88%
San Juan de los Lagos - - - - - 0.04% 0.01% 0.00% -
San Juan del Río 84.09% 4.83% 2.25% 4.27% 0.79% 1.09% 1.45% 1.69% 1.31%
San Luis Potosí 2.95% 5.88% 1.07% 3.65% 3.76% 4.81% 7.22% 11.98% 19.91%
San Miguel de Allende 34.10% 42.28% 13.38% 22.50% 11.73% 10.59% 7.00% 8.53% 10.22%
Taxco 24.20% 29.69% 43.32% 26.18% 7.87% 1.37% 0.55% - 0.00%
Tecate - - - - - 89.08% 94.34% 95.64% 86.81%
Tequisquiapan 72.57% 2.27% 1.64% 4.66% 0.60% 0.38% 0.50% 1.22% 0.31%
Tijuana 32.06% 29.00% 13.57% 32.64% 18.47% 15.99% 20.34% 18.25% 24.23%
Tlaxcala 8.16% 5.51% 10.59% 45.01% 64.52% 25.17% 18.10% 23.52% 23.29%
Toluca 6.75% 9.61% 4.79% 7.18% 3.51% 7.69% 17.96% 7.66% 6.46%
Tonalá-Puerto Arista - - - - - 4.95% 11.87% 35.73% 100.00%
Tuxtla Gutiérrez 9.55% 6.67% 2.49% 4.93% 2.03% 3.66% 4.51% 4.81% 4.25%
Valle de Bravo 22.70% 11.97% 2.23% 1.76% 1.06% 0.32% 0.97% 0.64% 0.83%
Veracruz 3.92% 5.35% 2.29% 4.46% 2.39% 2.45% 1.75% 3.76% 1.80%
Villahermosa 5.17% 8.42% 3.47% 8.92% 3.57% 5.06% 7.52% 11.36% 7.71%
Xalapa - - - - - 3.68% 3.89% 8.76% 2.53%
Zacatecas 14.21% 4.58% 4.18% 7.34% 6.48% 8.24% 6.19% 6.54% 9.78%
Average 35.27% 29.94% 23.73% 28.29% 20.48% 26.92% 23.97% 25.98% 29.30%
Table 5: Touristic center eciency by period, foreign tourists
35
RELAIS, vol. 3, no. 2
1992-1994 1995-1997 1998-2000 2001-2003 2004-2006 2007-2009 2010-2012 2013-2015 2016-2017
Acapulco 95.44% 83.72% 46.38% 61.60% 83.75% 95.56% 94.31% 85.23% 93.75%
Aguascalientes 63.21% 58.15% 37.84% 72.56% 69.98% 66.25% 55.39% 50.66% 61.87%
Akumal - - - - - 2.66% 2.09% 2.55% 2.88%
Bahías de Huatulco 98.75% 98.32% 98.92% 100.00% 93.68% 94.86% 95.30% 90.42% 92.15%
Cabo San Lucas - - - - - 50.11% 28.60% 24.64% 15.44%
Campeche 96.57% 95.98% 51.14% 92.43% 93.04% 85.06% 90.57% 62.39% 77.63%
Cancún 95.78% 60.02% 36.89% 47.96% 70.09% 56.16% 48.30% 54.92% 53.71%
Celaya - - - - - 85.36% 87.26% 84.30% 93.10%
Chihuahua - - 37.93% 63.63% 72.35% 72.90% 54.27% 51.53% 65.72%
Ciudad de México 93.09% 82.93% 39.93% 76.45% 94.25% 84.25% 90.09% 75.72% 68.46%
Ciudad Juárez 93.18% 92.83% 60.81% 86.29% 82.84% 92.80% 90.00% 84.86% 84.10%
Coatzacoalcos - - - - - 89.32% 84.40% 71.53% 57.04%
Colima - - 87.34% 84.85% 93.46% 89.29% 82.91% 70.85% 88.85%
Comitán de Domínguez - - - - - 99.58% 98.02% 95.68% 73.65%
Cozumel 33.29% 27.70% 9.91% - 46.32% 23.73% 24.13% 30.06% 29.96%
Culiacán - - - - - 81.71% 72.29% 67.48% 66.81%
Durango 93.44% 96.49% 56.90% 45.38% 57.96% 77.72% 80.50% 63.11% 67.57%
El Fuerte - - - - - 90.05% 70.75% 85.61% 89.24%
Guadalajara 83.47% 76.79% 49.26% 67.27% 83.66% 86.54% 76.29% 66.80% 74.96%
Guanajuato 89.87% 84.59% 45.12% 88.58% 95.53% 81.51% 92.12% 74.09% 79.55%
Hermosillo 95.36% 92.75% 96.95% 91.46% 100.00% 96.12% 94.62% 92.89% 98.30%
Irapuato - - - - - 74.51% 72.90% 70.21% 70.35%
Isla Mujeres - - - - - 14.34% 29.55% 41.25% 39.86%
Ixtapa-Zihuatanejo 96.57% 87.37% 89.78% 91.80% 95.69% 95.78% 94.30% 89.89% 99.07%
La Paz 64.79% 64.14% 41.95% 62.45% 64.12% 85.08% 77.08% 56.64% 69.96%
León - - 60.52% 65.28% 70.42% 70.94% 71.92% 63.53% 72.42%
Loreto 35.52% 34.26% 52.33% 33.18% 89.80% 80.24% 34.56% 25.61% 38.54%
Los Mochis - - - - - 71.93% 65.96% 57.39% 71.93%
Manzanillo 94.16% 92.78% 56.93% 88.49% 98.22% 93.79% 97.91% 81.62% 95.71%
Mazatlán 92.69% 64.90% 44.99% 60.51% 72.48% 90.11% 85.12% 92.67% 97.12%
Mérida 51.92% 50.27% 45.10% - 71.57% 78.80% 71.80% 65.71% 66.64%
Mexicali - - - - - 79.93% 69.71% 79.47% 58.91%
Monterrey 96.88% 80.20% 62.87% 73.91% 69.27% 76.38% 58.17% 71.00% 81.76%
Morelia 97.27% 73.75% 52.67% 81.09% 92.53% 90.94% 74.08% 52.74% 76.74%
Nuevo Vallarta - - - - - 88.58% 95.57% 93.73% 100.00%
Oaxaca 63.25% 62.15% 46.29% 72.21% 80.91% 73.35% 84.07% 67.27% 81.05%
Pachuca 98.79% 79.19% 61.01% 99.24% 88.63% 85.41% 88.89% 99.21% 88.56%
Palenque - - - - - 64.92% 68.56% 63.13% 58.36%
Piedras Negras - - - - - 96.02% 94.53% 93.22% 100.00%
Playa del Carmen - - - - - 7.43% 8.55% 5.42% 2.52%
Playacar - - - - - 35.32% 66.25% 37.49% 55.19%
Playas de Rosarito - - - - 9.63% 6.90% 14.66% 44.69% 44.25%
Puebla 93.10% 69.28% 56.56% 93.65% 81.76% 85.14% 88.04% 93.10% 97.46%
Puerto Escondido - - 82.68% 92.98% 80.06% 86.14% 65.44% 58.16% 61.63%
Puerto Vallarta 73.27% 67.67% 38.56% 52.89% 60.23% 66.13% 62.90% 68.64% 77.93%
Querétaro 95.86% 83.11% 57.26% 89.50% 96.94% 92.01% 85.44% 70.78% 71.41%
Salamanca - - - - - 95.92% 95.19% 90.14% 96.58%
San Cristóbal de las Casas 56.95% 67.95% 42.05% 62.86% 80.13% 69.79% 73.70% 61.11% 69.54%
San Felipe - - - - - 46.86% 37.39% 40.04% 73.21%
San José del Cabo - - - - - 53.03% 31.82% 45.32% 47.75%
San Juan de los Lagos - - - - - 83.22% 78.86% 82.24% 82.07%
San Juan del Río 94.59% 69.93% 48.42% 63.88% 82.21% 82.24% 68.77% 60.67% 71.91%
San Luis Potosí 77.41% 83.64% 34.99% 69.55% 79.52% 83.09% 74.48% 79.69% 97.17%
San Miguel de Allende 46.68% 92.58% 25.97% 41.25% 48.41% 52.71% 56.26% 58.91% 70.03%
Taxco 44.71% 65.89% 52.79% 44.37% 63.48% 75.26% 59.29% 59.34% 84.06%
Tecate - - - - - 93.81% 96.37% 89.69% 97.55%
Tequisquiapan 98.47% 77.16% 91.73% 93.19% 87.89% 72.88% 65.60% 56.55% 45.50%
Tijuana 97.09% 91.83% 39.26% 69.66% 71.99% 74.28% 54.97% 52.93% 47.35%
Tlaxcala 94.72% 59.50% 59.92% 86.15% 96.91% 95.22% 96.08% 94.73% 98.76%
Toluca 75.51% 87.25% 33.69% 51.53% 54.15% 61.25% 53.17% 65.12% 67.96%
Tonalá-Puerto Arista - - - - - 94.20% 99.96% 94.33% 94.39%
Tuxtla Gutiérrez 71.43% 75.62% 52.63% 90.39% 97.07% 82.58% 81.62% 71.34% 66.18%
Valle de Bravo 99.15% 92.05% 64.91% 66.14% 64.39% 63.25% 57.90% 53.84% 56.14%
Veracruz 97.30% 87.96% 42.05% 73.71% 89.44% 98.95% 90.01% 84.45% 83.98%
Villahermosa 79.86% 82.16% 47.78% 75.04% 78.61% 95.80% 92.60% 86.91% 64.47%
Xalapa - - - - - 96.26% 94.88% 64.84% 59.95%
Zacatecas 92.55% 87.38% 56.70% 67.48% 76.22% 80.67% 74.02% 71.22% 83.00%
Average 82.36% 76.42% 53.44% 72.95% 77.94% 75.27% 71.21% 67.03% 71.19%
Table 6: Touristic center eciency by period, national tourists
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