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Shift-share analysis on international tourism competitiveness—A case of Jiangsu Province


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Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds of extended shift-share models have been advanced and put into practice in economic studies, but few have hitherto been introduced and applied to the tourism research in China. Moreover understanding the spatially competitive relationship is of paramount importance for marketers, developers, and planners involved in tourism strategy development. Based on international tourism receipts from 1995 to 2004, this study aims at probing into the spatial competitiveness of international tourism in Jiangsu Province in comparison with its neighbors by applying a spatially extended shift-share model and a modified dynamic shift-share model. The empirical results illustrate that exceptional years may exist in the application of dynamic shift-share models. To solve this issue, modifications to dynamic shift-share model are put forward. The analytical results are not only presented but also explained by the comparison of background conditions of tourism development between Jiangsu and its key competitors. The conclusions can be drawn that the growth of international tourism receipts in Jiangsu mainly attributes to the national component and the competitive component and Zhejiang is the most important rival to Jiangsu during the period of 1995–2004. In order to upgrade the tourism competitiveness, it is indispensable for Jiangsu to take proper positioning, promoting and marketing strategies and to cooperate and integrate with its main rivals.
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Chinese Geographical Science 2007 17(2) 173–178
DOI: 10.1007/s11769-007-0173-2
Shift-share Analysis on International Tourism Competitiveness
—A Case of Jiangsu Province
SHI Chunyun1, 2, ZHANG Jie1, YANG Yang3, ZHOU Zhang4
(1. School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing 210093, China;
2. School of Urban and Environmental Sciences, Xuzhou Normal University, Xuzhou 221116, China;
3. Center for Recreation and Tourism Research, Peking University, Beijing 100871, China;
4. Jiangsu Tourism Administration, Nanjing 210003, China)
Abstract: Shift-share analysis has been confirmed a useful approach in the study of regional economics and many kinds
of extended shift-share models have been advanced and put into practice in economic studies, but few have hitherto been
introduced and applied to the tourism research in China. Moreover understanding the spatially competitive relationship is
of paramount importance for marketers, developers, and planners involved in tourism strategy development. Based on
international tourism receipts from 1995 to 2004, this study aims at probing into the spatial competitiveness of interna-
tional tourism in Jiangsu Province in comparison with its neighbors by applying a spatially extended shift-share model
and a modified dynamic shift-share model. The empirical results illustrate that exceptional years may exist in the ap-
plication of dynamic shift-share models. To solve this issue, modifications to dynamic shift-share model are put forward.
The analytical results are not only presented but also explained by the comparison of background conditions of tourism
development between Jiangsu and its key competitors. The conclusions can be drawn that the growth of international
tourism receipts in Jiangsu mainly attributes to the national component and the competitive component and Zhejiang is
the most important rival to Jiangsu during the period of 19952004. In order to upgrade the tourism competitiveness, it is
indispensable for Jiangsu to take proper positioning, promoting and marketing strategies and to cooperate and integrate
with its main rivals.
Keywords: international tourism competitiveness; spatially extended shift-share analysis model; comparative static
shift-share analysis; modified dynamic shift-share analysis; Jiangsu Province
1 Introduction
The original formulation of shift-share method was first
advanced by Creamer (1943) and then was summarized
and induced by Dunn (1960). Shift-share analysis has
generally been used for describing regional and industrial
economic growth and examining the structural effect and
regional or industrial competitiveness underlining the
changes. It has been a very powerful tool for measuring
the changes over time in regional economics, marketing,
urban studies, etc. Stevens and Moore (1980) put forward
that the factor to account for its popularity is the techni-
cally simple procedure. Shift-share analysis requires only
less data that are generally accessible. Despite its sim-
plicity, it does well in capturing the underlining changes
in the variables under consideration (Nazara and Hew-
ings, 2004) and making the analysis fast and reasonably
Shift-share analysis has just been applied to tourism
industry in recent years (Sirakaya et al., 1995; Fuchs et
al., 2000; Alavi and Yasin, 2000; Sirakaya et al., 2002;
Toh et al., 2001; 2003; 2004; Yasin et al., 2004). Em-
ployment, arrivals and receipts are usually used as vari-
ables to measure changes in the shift-share models (Shi et
al., 2007). Only in recent years have a few studies using
shift-share analysis focused on sectoral structure and
competitiveness within tourism industry in China (Yang
et al., 2005; Wen and Wang, 2005; Chu, 2005; Li and
Cheng, 2004; Wang et al., 2004). Moreover, although
many kinds of extended shift-share models have been
Received date: 2006-06-25; accepted date: 2007-03-18
Foundation item: Under the auspices of the National Natural Science Foundation of China (No. 40371030)
Biography: SHI Chunyun. E-mail:
SHI Chunyun, ZHANG Jie, YANG Yang et al.
advanced, the application of the models to tourism re-
search in China mainly limits to the comparative static
model of shift-share analysis. Thus this study aimed at
applying both spatially extended shift-share analytical
approach and modified dynamic model to tourism spatial
competitiveness of Jiangsu Province based on interna-
tional tourism receipts from 1995 to 2004.
2 Methodology
2.1 Comparative static model and its extended models
Traditional shift-share analysis is used to decompose
regional growth over time into three effects: the national
growth effect, the industry-mixed effect, and the com-
petitive effect, which is generally applied in a study pe-
riod of several years and examines conditions only at the
beginning and the end years. In such a comparative static
approach, the subtle swings during the study periods
were lost (Sirakaya et al., 1995). The inherent limitations
of this comparative static method, however, can be
overcome by calculating the shift-share effects on a
multiple-year data on the basis of creating dynamic,
time-series-like data (Sirakaya et al., 2002). The dynamic
shift-share analysis is an extension of Thirlwall’s (1967)
suggestion that the study period be divided into two or
more sub-periods to reduce the severity of the changes. It
provides a more accurate allocation of changes to cal-
culate the national growth effect, the industry-mixed
effect, and the competitive effect on an annual basis and
then sum the results over the study period. One of the key
advantages of this dynamic shift-share analysis is that it
enables changes to be tracked over the years without
losing information in those periods and allows unusual
years and years of economic transition to be identified
(Sirakaya et al., 2002).
The current decomposition posits a strictly hierarchi-
cal view of influence—the nation influences the regions,
but regions do not influence each other. However, the
general idea is that the decomposed effects are not spa-
tially independent, and the performance of surrounding
regions, of regions with similar structures, or of regions
that are dominant trading partners will all have an in-
fluence on the growth performance of a particular region
(Nazara and Hewings, 2004). Here an extended
shift-share model newly put forward by Nazara and
Hewings should be emphasized on for the further study.
They first incorporate spatial structure within shift-share
analysis, to take interregional interaction into account in
the decomposition analysis. Given the relatively recent
conceptualization of such an extended model, it is not
surprising that the approach is needed to be tested em-
2.2 Methods in this study
Nazara-Hewings’ extended industrial model of the
shift-share formulation can be represented into three
distinct effects as Equation (1).
ii ii ii
GgG gg=+ − + − (1)
where gi denotes the actual growth of international tour-
ism receipts in the study region, i
, that in the
neighboring regions, i
G denotes the national counterpart,
and i, the peculiar sector, i.e. international tourism re-
ceipts in the study. Here, Jiangsu Province is the study
unit while its neighboring regions are used as benchmark
areas. Compared with the traditional shift-share analysis,
Equation (1) reflects two changes. First, only sectoral
growth over time is measured here, which can be called
purely industrial model. Nazara-Hewings’ extended
model offers varied forms so that appropriate form may
be selected in terms of the study’s requirement. Second,
besides the whole nation and peculiar area, neighboring
region is included here, which can reflect how the
study unit interacts with its neighbor and which region
is on earth the most important competitor to the study
Figure 1 denotes the formulation and explanation of
shift-share analysis in this study, where 0,
represent international tourism receipts of neighbor-
ing region, nation and the study region at the beginning
year respectively, while ,
Rdenote the am-
ount at the end of the period. Actual growth of interna-
tional tourism receipts in Jiangsu Province from 1995 to
2004 can be decomposed into three components, the
national sectoral shift effect, neighbor-national sectoral
shift effect and regional-neighbor sectoral shift effect
(also called competitive effect).
Fig. 1 Shift-share formulation
Shift-share Analysis on International Tourism Competitiveness
3 Case Study
3.1 Study area and data
The eastern coastal China has always been considered an
interesting and relevant location. Six provinces in the
eastern coastal China are chosen as samples for this study.
The study area includes Jiangsu, the target region, and its
neighboring regions, such as Shanghai, Zhejiang, Anhui,
Shandong and Henan. The study period spans ten years
from 1995 to 2004 during which continuous time series
data are available for all the provinces under examination.
In 2004 Jiangsu positioned the fourth international des-
tination in China. The persistent augment trend of inter-
national tourism receipts in both Jiangsu and its
neighbors can be clearly seen from Table 1. Jiangsu’s
share of Chinese international tourism receipts increased
from 3.15% to 8.39% during 1995–2004 while the whole
study area’s share from 20.37% to 33.15%. Thus Jiangsu
and its neighbors are regarded as one of the most sig-
nificant international destinations and foreign exchange
earners in China.
3.2 Results
Table 2 provides the analytical results of the comparative
static and dynamic shift-share models. Due to the ab-
normal severity resulted from typical exogenous factor of
Severe Acute Respiratory Syndrome (SARS) outbreak in
2003, such abnormal changes, which are extraordinarily
different from those in normal milieu, will be not as the
basis of measuring the swings of industries accurately
and truly. In order to eliminate such abnormal impact, the
data of 2003 are omitted and a two-year interval from
2002 to 2004, associated with other annual interval, is
included. We term it modified dynamic model, which can
not only overcome the inherent limitations of the tradi-
tional comparative static method, but also avoid the se-
vere and abnormal changes of individual year in general
dynamic model.
3.2.1 National sectoral shift effect
From the results in Table 2, 26.7% of the actual growth in
comparative static model is attributable to the national
sectoral shift effect while 41.3% in modified dynamic
model, 50.9% in general dynamic model. Such findings
Table 1 International tourism receipts in 1995–2004 (US$106)
1995 1996 1997 1998 1999 2000 2001 2002 2003 2004
Jiangsu 260
317 408 529 620 724 822 1050 1132 1763
Shanghai 939
1171 1317 1218 1364 1613 1808 2275 2053 3041
Zhejiang 236
292 345 361 410 514 699 928 873 1300
Anhui 31
42 64 51 67 86 106 124 83 141
Shandong 154
196 204 220 265 315 382 472 3703 567
Henan 60
73 95 101 114 124 133 145 64 160
China 8249
9476 10428 10760 12009 14327 15968 18531 14410 21031
Source: National Bureau of Statistics of China, 2006
Table 2 Results comparisons of dynamic and comparative static shift-share models in 1995–2004 (US$106)
are identical to conclusions advanced by Barff and Knight
(1988) that the national growth effect is underestimated
by the comparative static approach during periods when
regional growth rate exceeds the national rate. Both the
dynamic and comparative static approaches show that
Jiangsu Province has an actual growth of US$1503.44×106
Dynamic shift-share
model Static shift-share
model Modified shift-share
Actual growth 1503.44 1503.44 1503.44
National sectoral shift effect 765.98 402.86 621.09
Net shift 737.46 1100.58 882.35
Shanghai-nation 226.11 179.23 282.14
Zhejiang-nation 623.59 769.86 697.51
Anhui-nation 493.02 517.12 345.65
Shandong-nation 296.62 293.65 277.80
Neighbor-national sectoral
shift effect
Henan-nation 793.04 30.52 -85.74
Jiangsu-Shanghai 511.35 921.35 600.20
Jiangsu-Zhejiang 113.88 330.72 184.84
Jiangsu-Anhui 244.44 583.46 536.70
Jiangsu-Shandong 440.84 806.93 604.55
Regional-neighbor sectoral
shift effect
Jiangsu-Henan -55.57 1070.06 968.09
SHI Chunyun, ZHANG Jie, YANG Yang et al.
and a positive net shift, which means that Jiangsu performs
better relative to the whole country in the development of
the international tourism industry. Indeed, the national
sectoral shift effect has been positive, which indicates that
the international tourism receipts are secured due to the
national growth trend and implies that tourism industry is
one of the most flourishing sectors in China.
3.2.2 Neighbor-national sectoral shift effect
From the neighbor-national sectoral shift effect, all the
neighbors of Jiangsu except Henan have positive value
both in dynamic model and in static model, which sug-
gests that the development of international tourism in-
dustry is faster than the average of national growth and
tourism industry has been becoming one of the industries
with a high propensity to grow in the study area. Par-
ticularly, the growth of Zhejiang and Anhui are well in
excess of that of the others. Despite a relative uniformity
in most of neighbor-national sectoral shift effects, He-
nan’s effect by general dynamic approach reveals a much
stronger growth (US$793.04×106) than those (US$
30.52×106, US$–85.74×106) do by static approach and
modified model because of the exceptional year of 2003
in Henan.
3.2.3 Competitive effect
Jiangsu also has positive competitive effects over all its
neighboring regions, meaning that it is more competi-
tively positioned with respect to its neighbors. However,
Zhejiang has the least value both in comparative static
and in modified dynamic model, which means Zhejiang
develops much proximate to Jiangsu in international
tourism industry. Furthermore, it can be inferred that
Zhejiang is the most important competitor to Jiangsu.
Detailed results are listed in Table 3. In fact, changes
during these ten years have greatly fluctuated. The net
shifts seemed to be too modestly positive in 1999 and
2001 and even displayed negative in 2000, meaning that
the performance of international tourism industry in Ji-
angsu was so poor that its actual growth was even less
than that of the whole China in 2000. Nevertheless, in-
ternational tourism receipts in Jiangsu has been increas-
ing explosively since 2002, even in the exceptional year
of 2003 when the SARS outbreak almost devastated the
whole national tourism and the study area, all but Jiangsu
have endured a negative gain. Jiangsu has experienced
fluctuations, but still followed a rising trend.
International tourism industrial competitiveness in
Jiangsu has been expected to increase gradually with the
time by the spatial-temporal series analysis of the de-
Table 3 Dynamic shift-share analysis of
Jiangsu Province (US$106)
Actual growth
National sec-
toral shift effect Net shift
1995–1996 57.05 38.68 18.37
1996–1997 90.95 31.84 59.11
1997–1998 121.00 12.99 108.01
1998–1999 91.00 61.41 29.59
1999–2000 104.00 119.67 –15.67
2000–2001 98.02 82.90 15.12
2001–2002 227.98 131.97 96.01
2002–2003 81.87 –233.49 315.36
2003–2004 631.57 520.00 111.57
velopment trend of regional competitive component
(Table 4). Compared with Anhui Province, most of the
annual regional competitive components before 2001 are
negative, which indicates that Anhui has done a better job
in attracting more international tourists or encouraging
much more consumption than Jiangsu has. However,
Jiangsu has been experiencing a much swifter increase
than Anhui since 2002 and has well exceeded Anhui
since then. Compared with Zhejiang Province, regional
competitive components were negative from 2000 to
2002. The international tourism industry in Zhejiang has
been one of those industries with a high propensity to
grow since 2000 and displays advantages both in the
whole country and in the prosperous eastern China. Due
to the swift development of international tourism in Ji-
angsu in 2003 and 2004, Zhejiang failed to exhibit any
more competitive advantage. In conclusion, Jiangsu has
greater competitiveness in the international tourism in-
dustry in terms of the analytical results, while Zhejiang
Province can be viewed as the most important rival for
Jiangsu among all the neighboring regions both by
comparative static and by modified dynamic shift-share
models. Although dynamic shift-share model can over-
come some temporal limitations of comparative static
model, it cannot answer the perplexing questions and
make any judgments about the likely causes for com-
petitive advantages or disadvantages (Yasin et al., 2004).
To seek explanations for this, we would need to carry out
further analysis in comparison with local conditions.
3.3 Discussion
It is of very importance for marketers, developers, and
planners involved in strategy development to clearly
understand the spatially competitive situation for the
development of international tourism industry in Jiangsu.
Shift-share Analysis on International Tourism Competitiveness
Table 4 Regional competitive effect by modified dynamic model
1995-1996 1996-1997 1997-1998 1998-1999 1999-2000 2000-2001 2001-2002 2002-2004
Jiangsu-Shanghai –7.08 51.30 151.67 27.59 –9.18 10.62 15.49 359.79
Jiangsu-Zhejiang –4.47 33.20 102.08 19.20 –53.27 –162.41 –41.49 292.00
Jiangsu-Anhui –35.21 –75.12 203.88 –74.96 –71.82 –67.66 85.48 572.11
Jiangsu-Shandong –14.69 78.83 89.00 –17.20 –12.98 –56.92 35.40 503.11
Jiangsu-Henan 1.15 –5.17 95.23 22.91 49.61 43.37 156.23 604.75
Actually, further analysis of the background and condi-
tions in Jiangsu and Zhejiang can not only empirically
prove the findings of the above shift-share analytical
results, but also lay a foundation for making strategies for
the development of the tourism industry.
3.3.1 Competition analysis
(1) Similar location conditions. Both these two regions
are situated at the Changjiang River estuary and facing
with the sea, being important belt of eastern China’s
reform and opening up. They are also close to and de-
pendent on Shanghai, China’s biggest industrial base and
trade port. Findings from Zhang et al. (2000) indicated
that Shanghai is the significant distributing center for the
inbound and outbound tourists flow for both Jiangsu and
(2) Same culture milieu. These two regions belong to
the WU and YUE cultural districts well known as the
“first prosperous cultural relic” (Hu and Sun, 2005).
Elites in politics, culture, science, technology, art, etc.,
have come forth in great number since the Tang Dynasty,
which is profound and is responsible for the whole Chi-
nese history and for the prosperity nowadays in the fa-
mous Changjiang River Delta to a large degree.
(3) Similar tourism resources and characteristics.
These two regions are famous for the “water country”
because of its numerous lakes, rivers, streams and estu-
aries, and are usually referred to as “lands of fish and
rice” because of abounding in rice paddies, mulberry
orchards and fishponds. An old saying in China, “There
is a paradise in the heaven and Suzhou, Hangzhou on the
earth”, tells us both the scenic beauty and the similar
resources and tourism image in the two regions.
(4) Similar tourism development impetus. About
39.3% of the inbound tourists in Jiangsu and 47.1% in
Zhejiang were for business-related activities in 2004,
which particularly illustrates the importance of the eco-
nomic impetus to tourism development in the two regions.
The remarkable economic and trade growth have been
accompanied by persistent inbound tourist flows to the
study region.
(5) Similar origin market structure. The main tourists
origin markets in two provinces are much alike. Their
Spearman’s correlation coefficient is 0.829 and signifi-
cant at the 0.01 level (2-tailed). However, they all have
lower correlation coefficient with the whole China, 0.521
and 0.648 respectively and only significant at the 0.05
level, which means the two regions have similar structure
of tourist origins though they are some different from the
whole China.
3.3.2 Strategy development
As described above, there is little doubt that Jiangsu and
Zhejiang share much similarity in terms of the devel-
opment of tourism industry and exhibit close competitive
relation to each other. Therefore, understanding com-
petitors and taking proper strategies are of great signifi-
cance. With these considerations in mind, we can make
several recommendations. First, from supply-side per-
spective comes recognition of the importance of the
tourism product positioning in carrying out differentia-
tion and individuation strategies due to so many simi-
larities in the two provinces. Second, from market-side
perspective comes recognition of the importance of
promoting and marketing in attracting more inbound
tourists to retain the sustainable development of the in-
ternational tourism industry. Finally, cooperation will be
an effective way to improve the competitiveness for each
other. Starting with exploiting the tourism market, con-
stituting tourism policy and laws and constructing tour-
ism standardization, Jiangsu, Zhejiang and Shanghai
have set out to build the cooperation framework of re-
gional tourism, reinforce the policy auspice for regional
economic integration, eliminate regional administrative
and policy wall, and establish regional non-obstacle
travel pattern among Jiangsu, Zhejiang and Shanghai.
4 Conclusions
In the empirical application, the findings reflect that
tourism industry is booming and has continuous pros-
perity in China. Both Jiangsu and most of its neighboring
SHI Chunyun, ZHANG Jie, YANG Yang et al.
regions have positive growth rates in comparison with
the whole China, which suggests that these regions are
becoming one of the fastest-growing markets in China.
Moreover, Jiangsu has positive competitive effect over
all its neighboring regions, meaning that it was more
competitively positioned with respect to its entire
neighbor. Zhejiang is the most important rival to Jiangsu
as far as international tourism industry is concerned since
these two provinces share many similarities with each
other, which can be considered as the key reasons for
competitive relationship.
However, the shift-share analysis has its inherent
limitations. The model can not give the mechanism and
explanation underlying the changes but describe and
reflect the spatially competitive state. In order to make it
clear, further study and analysis should be needed.
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... Furthermore, in the basic model, the interaction of the industrial mix effect by the competitive effect cannot eliminate (Firgo & Fritz, 2016;Polyzos, 2019;Toh et al., 2004), and this weakness has led to the development of Homothetic Employment and Allocative Effect model (Matlaba et al., 2014;Toh et al., 2004). Lastly, shows a strictly hierarchical term of influence which declares that the nation affects the regions, but regions do not influence one another (Shi et al., 2007), and this weakness contributed on the development of Spatial Shift-Share Analysis (Nazara & Hewings, 2004). ...
... Toh et al. (2004) assessed the purpose of the journey for the visitors of the 15 main countries of the inbound tourism in Singapore, using the extension of the Esteban-Marquillas for Homothetic Employment and Allocative-Effect model. Shi et al. (2007) implemented the Spatial Shift-Share Analysis and a modification of the Dynamic Shift-Share Analysis to investigate the tourism competitiveness of the province Jiangsu of China concerning neighbouring provinces. Dogru et al. (2020) applying the Dynamic Shift-Share Analysis, ranked 150 countries for the period 2000-2017 to identify their tourism development or tourism competitiveness as measured by tourist arrivals, tourism receipts, and per-tourist-dollar. ...
... Almost a year later, Dogru and Sirikaya-Turk (2017), with a similar methodology, examined the change in the number of workers in the tourist industry in South Carolina. Whenever applied the Shift-Share analysis in the international literature to study tourism issues, the number of employees, the number of tourist arrivals, the tourist night spent, and tourism receipts are the principal studied regional sizes (Dogru et al., 2020;Shi et al., 2007;Shi & Yang, 2008). Firgo and Fritz (2016) Homothetic Employment and Allocative Effect Esteban -Marquillas (1972) Matlaba et al. (2014) Spatial Shift-Share Analysis Nazara and Hewings (2004) ...
This paper builds on Shift-Share analysis and aims to provide a methodological framework for studying the inequalities of Greek regions in the tourism industry. The method applied to yearly data of overnight stays, both for foreigners, and domestic visitors, for the periods 2003–2008, 2008–2013, 2013–2018, and results showing that disparities among Greek regions are generally very large for the tourism industry. The overall analysis is a useful tool for tourism management and regional policy. The paper advances Shift-Share analysis to be used as a tool of region classification, and it incorporates one-way ANOVA to examine the relationship between the inequalities concerns the policies from the last development law and tourism specialization in Greek regions.
... The results showed that the object variables, the number of hotels and the GRDP had a positive and significant effect on retribution revenues. Furthermore, some research discussed the relationship between tourism and regional growth (Garrigós-Simón et al., 2015;Lionetti and Gonzalez, 2012;Budiharseno, 2017;Liu et al., 2017;Surugiu and Surugiu, 2013), also the study of Li et al. (2015), Lau et al. (2017), and Shi et al. (2007) used dynamic method to analyze tourism sector with Regional Origin Revenue. ...
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The regional autonomy policy is expected to promote the government in carrying out fiscal management independently. However, local own-source revenue was the only source of revenue and measurement of independence and it has not been achieved optimally. Many provinces failed to fully support regional needs, even though it has the highest tourism sector revenue contributors. The purpose of this study is to analyze the influence of the tourism sector in terms of supply and demand on regional independence. To analyze the regional income model seen from the contribution of the tourism sector by including the lag variable, this study used a Dynamic Panel method which is also known as Partial Adjusted in Central Java Province, Indonesia from 2013-2018. The result showed that the increase in local own-source revenue from the demand side is more elastic than the supply contribution. The addition of new tourist objects without proper management tends not to have a significant impact on regional income. Also, the attraction of people visiting the tourist in Central Java Province is still high because of a significant positive number of tourist arrivals. The government needs to focus on managerial improvements, development, and innovation of existing tourist objects
... Dogru and Sirakaya-Turk (2017) improved the SSA method with the Shift-share regression for measuring the tourism industry's performance in a South Carolina in the USA. Shi et al. (2007) in Jiangsu Province and Firgo and Fritz (2017) in Australia applies a spatially extended SSA and a modified dynamic shift-share model to analyse the spatial competitiveness of international tourism in in comparison with its neighbours. Traistaru and Wolff (2002) apply SSA on employment data at county level, counting 89 regions for Bulgaria, Hungary and Romania for the period 1990-1999. ...
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World Tourism Organisation, declares the Tour Operators as tourism engine of strategically importance to support jobs and inclusive growth in all regions. Tour operators emerges following the 2008 crises, as a global job engine. Its atypical profile of highest human capital concentrator in tourism, attract and retain talents, works digital with a high-intensity information use. Is a rapid adopter of technological innovation, generate high value added in highly competitive global markets. We look in this paper to understand why employment is growing or declining in a regional tourism tour operator sector during 2008–2018, in some EU28 regions? We use Exploratory Spatial Data Analysis to map the indicator ‘tour operator’s employment growth’ components decomposed by the Shift Share Analysis Method. Analysed Eurostat data for 266 regions (281 regions) indicates that for the average regional tour operators employment growth heterogeneity is driven almost at half by region-specific factors. The main contributions are: identifying this indicator as appropriate to be a core one in OECD (2013) tourism competitiveness framework & redefine tour operator sector as a core sector of tourism in the Global model of tourism of Harrison.
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El objetivo de este artículo es estudiar los efectos que, a nivel regional, ha tenido la pandemia relacionada con la COVID-19 en el mercado peer-to-peer (p2p) de alojamientos turísticos en España. Nos basaremos en los datos que publica el INE a partir de la información suministrada por plataformas digitales que operan en dicho mercado. La técnica utilizada será el análisis shift-share que, en nuestro caso, consistirá en la descomposición del decremento que han experimentado las pernoctaciones en 2020, respecto a 2019, en las distintas Comunidades Autónomas españolas. Los resultados confirman que las pernoctaciones disminuyeron en primer año de la pandemia casi un 60 % en el mercado p2p en España con un impacto desigual a nivel regional. Aunque en todas las Comunidades Autónomas disminuyó el número de pernoctaciones las diferencias son apreciables. Asimismo, en todas las Comunidades ha aumentado el peso que representan las pernoctaciones de los residentes en España respecto al total, lo cual es compatible con la hipótesis de que los turistas españoles han sustituido sus viajes al extranjero por los realizados dentro del país. Por otro lado, la especialización en determinados mercados emisores no ha tenido influencia en la capacidad de una región para decrecer menos en ese mercado que la media nacional, es decir, no existe, con carácter general, una relación entre especialización y ventaja competitiva. No obstante, Galicia, Cantabria y Madrid sí han decrecido menos que la media nacional en los mercados en los que estaban especializadas y más en los que no estaban especializadas.
Purpose The objective of this study was to analyze the evolution of tourism competitiveness over the years, ascertaining the state of the art and the degree of consensus among scholars on its constituent elements to propose an integrative and updated concept. Design/methodology/approach A set of 130 definitions on tourism competitiveness formulated between 1999–2018 was analyzed and segmented into three periods, allowing its historical evolution to be ascertained. It is a qualitative and quantitative exploratory research that uses a combination of techniques, namely, content analysis, analysis of co-words and consensus analysis. Findings The results indicated a low use of elements such as the quality of life and the environment in the authors' definitions during 1999–2018, although these elements were present in the first concept of tourism competitiveness by Crouch and Ritchie (1999, 2003). Another finding of this study shows a reduction in the analysis of tourism competitiveness based on the supply and demand side. Nowadays, the research tends to turn on the basis of the population directly affected. It also reveals the enrichment of the theoretical corpus with new lines of research arising and new groups of scholars of the subject, consequently a new frontier in tourism competitiveness. Research limitations/implications The authors recommend deepening the analysis in each category of conceptual elements of tourism competitiveness to identify the origins of the low consensus. The authors also suggest conducting further research on the largest invisible schools of thought on this subject to understand their relations and perspectives, and thus to advance in the theoretical streams of the field. Finally, it is imperative to develop research on new models and monitors of tourism competitiveness that meet its renewed concept and integrate dimensions to consider the perspective of supply, demand, tourists and residents, as well as not excluding the economic bias but including the social side. Practical implications Owing to the fact that monitors of tourism competitiveness have practically no variables related to the social, most of the surveys are carried out from the supply or demand perspective, leaving the resident distant from the process. In this way, the results allow authors to indicate that new models of competitiveness measurement should be formulated based on the vision of the community impacted by tourism, i.e. a new version of tourism competitiveness not based on productivity but rather on the social aspect. Originality/value The findings of this study contribute to the field literature by offering an integrative concept of tourism competitiveness based on the elements with a higher level of consensus among researchers. Furthermore, the results accentuate a worrying fact regarding the operationalization of this concept, as the theoretical basis is not expressed in the monitors of competitiveness. Thus, nor it is possible in the management of the tourism industry.
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Hospitality and Tourism are among the fastest-growing sectors and the source of foreign exchange with indirect-direct employments for quite an appreciable number of economies worldwide. The nature of the sector provides an avenue towards regional development through entrepreneurship venture creations, value addition to the abandoned resources, and regeneration of abandon natural resources with new themes as a novel approach. Tourism currently in a paradigm shift as a comparative advantage of destination is becoming less important than a competitive advantage. The traditional destinations are diminishing while creating novel destinations more relaxation-oriented while leading to residents' economically enriched livelihood. The paper critically analyzes the current tourism competitive position of Sri Lanka with a panel of five rival destinations by adopting shift-share analysis by developing two propositions. Regional Tourism arrivals in rival tourism destinations have been used to perform Shift-share analysis. Findings revealed (a) Sri Lanka as a destination is gaining the competitive advantage of four tourism regions out of six markets. The competitive strategies proposed as recommendations to gain market specialization to the regions with a competitive advantage; (a) market specialization by targeting the markets with a competitive advantage, (b) new marketing programs for markets with competitive disadvantage, and (c) collaborative programs among Asian tourism destinations. The results would be beneficial to Asian region tourism decision-makers trusted with the growth and application of competitive strategies.
The Corona Virus pandemic threatens the fabric of people's lives in all fields, including in the economic field. This study aims to analyze the shift in the economic sector during the Corona Virus pandemic. This research is a descriptive type of research with a mathematical quantitative approach using the Shift Share analysis tool to analyze sectoral shifts due to the outbreak of the Corona Virus Pandemic. Specifically related to the elements of the shift share analysis, it is found that: overall, all sectors in the five provinces, the value of the effect of national growth is negative; the value of the industrial mix, in the five provinces of Indonesia has the same sectoral characteristics. The industrial mix has a positive value in some sectors and the industrial mix is negative in other sectors; the value of Regional Shares in the five provinces varies considerably. This of course is influenced by the ability of each province to produce; the characteristics of the total effect value vary in each sector in each province. The total effect value is positive, this means that a sector in a province is classified as progressive. The total effect value is negative, this means that a sector in a province is classified as conservative. Suggestions for future researchers are that this research does not include elements of economic agglomeration. If there are researchers who are interested in further research, maybe it can be added related to the elements of economic agglomeration.
The paper proposes a new version of spatial shift‐share decomposition to improve on the various approaches to conventional shift‐share analysis found in the literature. The novelty of our proposal is that it enables researchers to assess spatial competitiveness effects controlling for the influence of industrial specialization at both regional and neighborhood level. This new version is applied to inbound tourism in Italian regions and enables us to identify the best and worst performers. Our empirical results identify favorable scenarios in some areas of the country, such as Sardinia as well as regional advantage in a sizeable number of well‐known destinations.
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The purpose of this study was twofold: to illustrate the utility of shift-share analysis in examining the performance of the tourism industry in South Carolina and to compare industry performance with other South Atlantic states of the United States. The study assessed competitive advantages of tourism industry sectors. Results suggest that South Carolina's core tourism industries have not kept pace with the nation's core tourism industries, mainly because the air passenger transportation sector lacks regional advantage. The total tour ism industry improved, however, primarily due to the competitive advantage of supportive tourism industries. The contribution of supportive tourism industries (Tier 2) to the core tourism industries (Tier 1) revealed a paradox.
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The purpose of this paper is to present and demonstrate the usefulness of the dynamic shift-share method in examining the performance of the tourism industry using time-series employment data for the State of Texas and the USA, and to compare its results with those of the traditional accounting based shift-share analysis. The findings show that, compared to the US average, the change in employment in Texas was mainly due to the strong national economy and not to the region's competitiveness or sectoral make-up. According to the findings, the use of a dynamic shift-share model eliminated one theoretical problem inherent in the classical static method.
The growth in the global tourism market place presents many countries with great opportunities to capitalize on their natural competitive advantages. However, achieving the economic potential of global tourism remains elusive for many countries despite their natural advantages. In this context, Portugal is a case in point. The Portuguese tourism industry is facing some serious challenges that are limiting its potential. This study utilizes the shift-share technique in order to offer Portuguese policy-makers systematic and practical insights into the characteristics of the Portuguese tourism industry in the context of the challenges and opportunities of the global tourism market place. Understanding the dynamics and characteristics of the Portuguese tourism industry in a comparative context is a crucial first step toward formulating a strategy aimed at improving Portuguese competitive standing in the growing tourism market.
This article first uses the Esteban-Marquillas extension of the shift-share approach to analyze the growth of visitors to Singapore, measured against the benchmark countries of Thailand, Malaysia, and Hong Kong. Second, it modifies the extended model to analyze the growth of four of Singapore’s tourism sectors (holiday travel, business visits, business and pleasure visits, and transit stops). This two-stage shift-share approach allows the authors to determine how it is performing relative to its benchmark competitors, where Singapore’s tourism industry is specializing, and where it is competitive. The authors found that Singapore is very much like Hong Kong and is becoming less competitive relative to Thailand and Malaysia. Also, growth appears to be slower in the holiday and business and pleasure markets and faster for business visits and transit stops.
This article reviews the product life cycle and its extension to the tourist area life cycle (TALC) concept and its operationalization, proposes an alternative travel balance approach (TBA) based on changes in net travel balances, and then calibrates the TALC and TBA models with tourism statistics from Singapore. Using the TBA as a preferred alternative to the TALC, the proposed model suggests that Singapore is about to enter the decline stage, based on computations of income and price elasticities of demand, as well as the level of economic development. The authors then outline the advantages of the TBA model over the TALC and conclude with the hope that their model will be empirically validated with tourism statistics from other countries.
The product life cycle (PLC) has long been used as a planning and management tool to identify customers, make strategic marketing decisions, and plan ahead. The PLC concept was modified by Butler to apply to tourism destinations by way of the tourism area life cycle (TALC), which was then operationalized by Haywood to apply to countries. Recently, Toh, Khan, and Koh proposed a travel balance approach (TBA) as a superior alternative to the TALC in terms of predictive ability and completeness. The present article attempts to empirically validate and extend the TBA with tourism statistics from 18 countries, and then characterizes each of the stages of the modified tourism life cycle for predictive and planning purposes, all within the context of sustainable tourism.
This paper provides insights into the relative competitive advantage of Asian regions in tourism. The study employs the shift‐share technique which decomposes the growth in tourist arrivals to selected receiving regions from different generating regions of the world over a prescribed time period. Each receiving region's performance will be compared to the overall performance of the area (i.e., aggregated benchmark). As a result of this comparing decomposition, the relative competitive advantage of each receiving region in attracting tourists can be determined. The results could be helpful to Asian decision makers trusted with the development and implementation of tourism strategies.
Most applications of shift-share analysis to regional employment change have used a study period of several years and have examined conditions only at the beginning and end years. This comparative static approach does not take into account the continuous changes in both industrial mix and size of total employment of the region over the study period. Calculating the national growth effect, the industrial mix effect, and the competitive effect on an annual basis and then summing the results over the study period provides a more accurate allocation of job changes among the three shift-share effects. This approach, which we term dynamic shift-share analysis, also allows unusual years and years of economic transition to he identified. We illustrate the use of dynamic shift-share by presenting results of an analysis of New England employment growth from 1939 to 1984, using U.S. Bureau of Labor Statistics data. The use of the dynamic form of shift-share is important when the study period is characterized by either large changes in regional industrial mix or major differences between regional and national growth rates.