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Climate Variability in the Origin Countriesas a “Push” Factor on Tourist Arrivals in thePhilippinesVivienne Saverimuttuab & Maria Estela Varuaa

a School of Business, University of Western Sydney, Parramatta, Australiab Australian Institute of Higher Education P/L, Level 4, 451 Pitt Street,Sydney, NSW 2000, AustraliaPublished online: 26 Jun 2013.

To cite this article: Vivienne Saverimuttu & Maria Estela Varua (2014) Climate Variability in the OriginCountries as a “Push” Factor on Tourist Arrivals in the Philippines, Asia Pacific Journal of Tourism Research,19:7, 846-857, DOI: 10.1080/10941665.2013.806940

To link to this article: http://dx.doi.org/10.1080/10941665.2013.806940

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Climate Variability in the Origin Countries as a“Push” Factor on Tourist Arrivals in the Philippines

Vivienne Saverimuttu1,2∗ and Maria Estela Varua1

1School of Business, University of Western Sydney, Parramatta, Australia2Australian Institute of Higher Education P/L, Level 4, 451 Pitt Street, Sydney, NSW 2000,

Australia

The objective of this paper is to test the impact of climate variability in origin countries as a“push factor” on tourist arrivals, specifically in the Philippines, and to select a suitableproxy to measure climate variability. This paper uses the Southern Oscillation Index(SOI) constructed by the Australian Bureau of Meteorology. Climate variability is stronglylinked to the El Nino Southern Oscillation (ENSO) and this link is used by meteorologiststo forecast changes in weather globally. SOI is a widely used indicator of the ENSO and itsbest known extremes are the El Nino (warm phase) and La Nina (cold phase) effects. Thestudy proves to some extent that there is a significant increase in US tourist arrivals inthe Philippines when La Nina-like weather conditions prevail in the USA. Moreimportantly, the SOI proved to be a good measure of climate variability.

Key words: climate variability, tourism, El Nino Southern Oscillation, Philippines

Introduction

Developing nations are increasingly relying on

tourism to finance growth and development

(United Nations World Tourism Organisation

[UNWTO], 2009) and the Republic of the Phi-

lippines is no exception. According to the

UNWTO, between January and June 2012,

international tourist arrivals increased in all

regions of the world despite increasing econ-

omic uncertainty. Comparing growth in

tourist arrivals by region in the same period,

the best results were exhibited by “desti-

nations in South Asia and South-East Asia

(both +9%)”, with arrivals in the Philippines

growing by 12% (2012, p. 4). With tourism

revenues rising in the Philippines since 2002,

the industry has greatly improved the coun-

try’s economic landscape by generating jobs

and business opportunities for Filipinos.

Thus, identifying the significant determinants

of demand for inbound tourism and estimating

Asia Pacific Journal of Tourism Research, 2014Vol. 19, No. 7, 846–857, http://dx.doi.org/10.1080/10941665.2013.806940

∗Corresponding author. Email: v.saverimuttu@aih.nsw.edu.au

# 2013 Asia Pacific Tourism Association

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their magnitude would be of particular interest

to decision-makers within this industry in the

Philippines.

In estimating tourism demand for a particu-

lar destination, studies generally focus on econ-

omic explanatory variables such as the income

of tourists, destination costs, travel costs, sub-

stitute prices and exchange rates (Crouch,

1994; Song & Li, 2008; Witt & Witt, 1995).

Although economic explanatory variables

play a key role in determining demand for

travel to a particular destination, some

studies (Cho, 2010; Hamilton, Maddison, &

Tol, 2005a; Hsu, Tsai, & Wu, 2009; Preben-

sen, Skallerud, & Chen, 2010; Stepchenkova

& Eales, 2011; Varua & Saverimuttu, 2012;

Witt & Martin, 1987) have extended their

models to include non-economic qualitative

variables, which affect a tourist’s choice of a

particular destination. These variables include

marketing promotions, political instability,

prior travel to a particular destination and the

impact of climate or weather among others.

Goh, Law, and Mok (2008) in a study of

long-haul tourism demand for Hong Kong

proved that non-economic factors such as

climate and leisure time were stronger determi-

nants of travel to a particular destination com-

pared with economic factors.

In considering non-economic factors,

climate is of particular interest to the tourism

industry as it gives rise to “seasonality” in

tourist arrivals in destination countries.

Recognizing the causes of seasonality is impor-

tant to planners within the tourism industry

because of its “significant implications for

employment and capital investment” (Nadal,

Font, & Rosello, 2004, p. 698). Hylleberg in

Cho (2009, p. 466), exploring seasonality,

identified three causes for tourism demand,

namely the weather, festival and calendar

events. The latter two reflect social norms

and practices and the impact of various holi-

days. However, weather and climate not only

impact the decision to travel to a particular

destination (Kozak, Uysal, & Birkan, 2008),

but are also key factors that affect the travel

experience (Scott & Lemieux, 2010) and as a

result determine subsequent visitation

(Wilson & Becken, 2011). Climate refers to

the meteorological long-term average con-

ditions that are characteristic of a location,

while weather is the state of the atmosphere

in a given climate at a particular point in

time and determines the participation rate

(Moreno, 2010). In addition to choice of des-

tination, climate, in the country of origin as

well as in the destination, can determine the

timing of travel, giving rise to seasonality or

intra-year fluctuations in tourist arrivals at a

particular destination (Goh, 2012; Lim &

McAleer, 2001). Consequently, climate is

both a “push” and “pull” factor in the

tourism and travel industry (Hamilton et al.,

2005a; Scott, McBoyle, & Schwartzentruber,

2004). Hamilton et al. (2005a) found that

people from very hot or very cold countries

travel more (push factor) and tropical

countries attract more tourists (pull factor).

Studies that analyse the impact of weather or

climate on travel (Bigano, Hamilton, & Tol,

2006; Hamilton et al., 2005a; Hamilton,

Maddison, & Tol, 2005b; Wietze & Tol,

2002) mostly focus on destination choice,

recognizing that tourist destinations are

“climate sensitive” (Scott & Lemieux, 2010).

Among these some focus on the impact

climate change would have on previously pre-

ferred destinations (Maddison, 2001; Scott

et al., 2004). According to Eugenio-Martin

and Campos-Soria (2010, p. 745) few studies

take into account the impact of climate in

the country of origin as a determinant to

travel domestically or abroad.

Tourism in the Philippines is “climate

dependent”, as it is the climate itself that

Climate Variability in the Origin Countries 847

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attracts certain visitors. It is also “weather sen-

sitive” (Amelung, Nicholls, & Viner, 2007), in

that the Philippines has distinct seasons in

climate. Travel warnings generally feature

the risk of flooding during the south-western

monsoon season with moderate to heavy

rains, which peak in the period July–Septem-

ber, during which time most typhoons also

occur. However, the rainfall distribution

during this season is uneven and tourists con-

tinue to arrive even during the monsoon

season, giving rise to steadily increasing

quarter 3 arrivals as depicted in Figure 1.

This is not to say that the prevailing weather

conditions have had little or no impact on

tourist arrivals as it is evident that the

number of tourist arrivals in the Philippines

during quarter 3 (July–September) is generally

lower than during the other quarters and the

reason could be that some tourists, especially

first-time visitors, do take heed of the travel

weather warnings. However, this paper,

while accepting that climate and weather in a

particular destination do contribute to intra-

year fluctuations in tourist arrivals, explores

the possibility that climate and weather in

the origin country are also at play in determin-

ing the seasonal nature of arrivals.

Thus, the purpose of this paper is to test the

impact of climate variability in origin

countries as a “push” factor on tourist arri-

vals, specifically in the Philippines rather

than considering the tropical climate in the

Philippines as a “pull” factor. Traditionally,

the US (20–25%) and Japanese citizens (15–

20%) accounted for the highest number of

visitors to the Philippines. South Korean visi-

tors usually ranked third (10–15%). The

period under review in this study is from

1994 Quarter 1 to 2011 Quarter 2, during

which time the USA ranked first in terms of

visitor arrivals in all years except for the

periods 2006–2008 and 2010–2011 when

South Korea outranked the USA (Philippines

Figure 1. Seasonality in Tourist Arrivals in the Philippines, 1994–2011.

Source: Generated using data from the Philippines Department of Tourism (DOT, 2011).

848 Vivienne Saverimuttu and Maria Estela Varua

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Department of Tourism [DOT]). Hence, the

selected “origin” country for the study is the

USA.

An additional contribution to existing lit-

erature is the choice of the variable selected

to represent climate variability in the country

of origin, namely the Southern Oscillation

Index (SOI). Climate variability has been

associated by many with the El Nino Southern

Oscillation (ENSO). The ENSO and its con-

nection to climate are used by many meteoro-

logical agencies to produce monthly weather

forecasts. Swings in the SOI are associated

with El Nino (sustained negative values of

the SOI) and La Nina (sustained positive

values of the SOI) events. When El Nino con-

ditions (warmer winter/spring) prevail in the

USA, residents are expected to travel less to

warmer climates and when La Nina conditions

(colder winter/spring) prevail, they travel more

(Chiew, Piechota, Dracup, & McMahon,

1998; Kiem & Franks, 2001; McPhaden,

Zebiak, & Glantz, 2006).

The next section is a discussion of the

impact of tourism on the Philippines

economy, especially in the last few years, fol-

lowed by an explanation of the significance

of the ENSO on climate anomalies especially

in the west. The section on the model justifies

and explains other independent (control) vari-

ables considered to provide a comparison of

the significance and strength of the impact of

the climate variable. Variables included in

the model, such as the “Word of Mouth”

(WOM) effect, habit persistence and return

visits by Philippines nationals living abroad

all represented by the lagged dependent vari-

able (push factor), income in the USA (push

factor), and internal conflict (in the Philip-

pines), were identified from the literature

reviewed as having been tested and found to

be significant in other studies (Crouch, 1994;

Prebensen et al., 2010; Song & Li, 2008;

Varua & Saverimuttu, 2012; Witt &

Witt, 1995). This is followed by an analysis

of the results, limitations of the model and

conclusion.

Tourism in the Philippines

The Philippines, with its long sandy beaches,

especially White Beach on Boracay Island,

and its rich natural and cultural heritage is

an ideal destination for those seeking “sun

and sand” holidays or even socio-cultural

experiences (Hendersen, 2011; Smith, Hen-

derson, Chong, Tay, & Jingwen, 2011).

Tourist arrivals have been steadily increasing

since 2002 (Figure 1), after experiencing a

low point during the presidency of Mrs

Gloria Arroyo (2001–2010) due to various

political and internal disturbances. Following

the 2008 global financial crisis (GFC) there

was another dip in tourist arrivals in the Phi-

lippines though it did not fall to previous

lows. This was partly due to the Philippine

DOT’s initiative in cultivating new markets

in China and Taiwan and an increase in the

number of visitors from Russia and France,

which partially offset the decline in numbers

arising from the GFC. Since then there has

been strong growth in the industry.

Tourism is of paramount importance to

individuals, households, the private sector

and governments. According to Stabler,

Papatheodorou, and Sinclair spending on

tourism and tourism-related products has

risen considerably in the world and is an

important component in people’s expenditure

budgets. An increase in tourism expenditure

has a positive impact on the welfare of the

tourists as well as the welfare of the residents

of the destination area, affecting its “income,

employment, government revenue and

balance of payments” (2010, p. 22).

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However, with increased dependence comes

increased vulnerability to any event that

decreases the demand for tourism which

could result in a decrease in living standards

and higher unemployment. An increase in

demand for tourism too could have negative

consequences such as environmental degra-

dation (Gossling, 2002).

The World Travel and Tourism Council

(WTTC) reported that the total spending

within the travel and tourism industry in

2011, by residents and non-residents, was

“PHP (Philippine Peso) 194.7 bn (2% of

gross domestic product, GDP) and is expected

to rise by 9.9% in 2012”. When the “wider

impacts on the economy” are included the

total contribution of the industry increases to

PHP 830.8 bn (8.5% of GDP) in 2011, and

“is expected to grow by 7.8% in 2012”.

These “wider impacts” include the GDP and

jobs supported by travel and tourism invest-

ment spending, government spending on

tourism marketing, aviation administration,

security services, etc., “domestic purchases of

goods and services by sectors directly dealing

with tourists” and the “the GDP and jobs sup-

ported by” the induced spending “of those

who are directly or indirectly employed” by

the tourism industry (WTTC, 2012, pp. 2–

3). The contribution of the industry to employ-

ment in the Philippines too is encouraging in

current terms and future trends as can be

seen from Figure 2.

The Philippines is not the only country in

the Asia Pacific region that is targeting and

encouraging growth in its tourism industry.

In 2011, in Southeast Asia alone, countries

such as Cambodia, Thailand, Malaysia, Singa-

pore, Vietnam and Indonesia all outranked the

Philippines in terms of their tourism industry’s

Figure 2. Philippines: Total Contribution of Travel and Tourism to Employment.

Source: World Travel & Tourism Council (WTTC, 2012, p. 4).

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percentage share of total contribution to GDP.

In addition, the industry’s percentage share of

total contribution in the Philippines ranked

below the world average. The industry’s total

contribution to employment too was less

than the world average and the Philippines

industry ranked below Cambodia, Malaysia

and Thailand. However, the industry’s long-

term percentage growth rate per annum

between 2012 and 2022 is encouraging in

terms of its total contribution to GDP, where

the Philippines, with a growth rate of 5.2%

per annum is ranked above the world

average growth rate and is outranked only

by Indonesia, Thailand, Cambodia and

Vietnam of the countries previously men-

tioned. All these countries in the Southeast

Asian region as well as others in the Asia

Pacific region compete with each other for

“sun and sand” seeking tourists as well as

those seeking a socio-cultural experience.

Thus, climate variability as a “push factor”

as well as a “pull factor” will have an impact

on future growth.

The ENSO Effect on Climate andTourism

Climate is a “natural resource” in terms of the

tourism industry, but at the same time it poses

a risk in that climate variability in a particular

destination could result in a decline in tourist

arrivals as it interferes with tourism activities

related to that particular destination. This

point is recognized in studies that have

explored the impact of climate change on

specific tourist destinations (Aguilo, Alegre,

& Sard, 2005; Falk, 2013; Hamilton & Tol,

2007). However, climate variability in the

country of origin could also induce residents

to travel to warmer locations. Consequently,

climate variability could potentially result in

a win for some tourism-related locations that

market themselves as “sun and sand”

locations. From either perspective, the impact

of climate variability on the tourism industry

is of relevance and therefore the issue is

worth further research.

Climate variability has been associated by

many with the ENSO and the “teleconnec-

tion” between this phenomenon and climate

forms the scientific basis for worldwide long-

range weather forecasts by meteorological

agencies and researchers. In the equatorial

Pacific Ocean, ocean and atmospheric circula-

tion processes interact on a large scale (Chiew

et al., 1998, pp. 138–139), giving rise to unu-

sually warm (El Nino), cold (La Nina) and

neutral phases of the ENSO (Kiem & Franks,

2001). El Nino and La Nina “are associated

with swings in the Southern Oscillation” and

result in changes in precipitation patterns in

the Pacific, which in turn result in changes in

atmospheric circulation and weather patterns

outside of the tropical Pacific, and its effects,

especially those of strong events, are felt glob-

ally (McPhaden et al., 2006, p. 1741). From

Figure 1 it can be seen that the number of

visitor arrivals in the Philippines generally

peaks in quarter 1 (January–March),

coinciding with winter/early spring months in

the USA. Although, the impact of El Nino

and La Nina can vary across the USA, in

general El Nino episodes are associated with

warmer winters in cold climates and La Nina

episodes with colder winters. In areas prone

to hurricanes, with a moderate to strong El

Nino event “hurricanes tend to be reduced in

number and intensity, but are more numerous

and stronger during a La Nina event” (McPha-

den et al., 2006, p. 1741). As a consequence of

the above-described effects of El Nino and La

Nina events on origin countries and the desti-

nation country, the expectation would be for a

significant increase in tourist arrivals in the

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Philippines, especially in quarter 1 during La

Nina events. An El Nino event would have

the opposite effect. In the Philippines, El

Nino events are associated with drought con-

ditions (Roberts, Dawe, Falcon, & Naylor,

2009) while La Nina events bring torrential

rain. Although the focus of this paper is not

on the weather in the Philippines, it is interest-

ing to note that the increased rainfall during

La Nina events generally coincides with the

monsoon season in quarter 3.

The Model

An international tourist is a person travelling

to a foreign destination and staying in places

for more than 24 h but less than one consecu-

tive year for leisure, business and other pur-

poses. Tourism demand is measured by the

number of visitor arrivals as it is still the

most popular measure (Song & Li, 2008).

Quarterly time series data, of visitor arrivals

in the Philippines from the USA, which

includes Filipino nationals employed and

residing abroad who return to visit family

and friends, were obtained from the Philip-

pines DOT. Causal relationships initially

explored in this paper include the impact of

the lagged dependent variable, tourist

incomes, inflation calculated as the rate of

change in the Philippines consumer price

index (CPI), internal conflict in the Philippines

and climate variability in the country of origin.

The lagged dependent variable, which is an

auto-regressive term, was included to rep-

resent habit persistence based on the assump-

tion that a positive travel experience is likely

to trigger a return visit, as the uncertainty

element associated with a particular destina-

tion has been removed (Witt & Witt, 1995).

The likelihood of an increase in visits is also

based on the “Word of Mouth (WOM)”

effect where travel experiences spread

through “blogs” (Prebensen et al., 2010) and

remove the “uncertainty” element for first-

time visitors. Thus, the lagged dependent vari-

able also captures the impact of return visits,

by Philippines nationals residing abroad, to

visit family and friends (Witt & Witt, 1995).

The theory of inter-temporal choice allows

consumption of a good (such as tourism) to

depend on any combination of current,

future and or past income. Tourism purchase

decisions are usually made in advance of

their actual consumption date and thus past

income is assumed to determine the demand

for travel. Therefore, past income is rep-

resented by US real GDP lagged by one

quarter. Unit root tests for tourist arrivals

and US real GDP were non-stationary and

therefore the log of these two variables was

utilized to transform the data, allowing the

use of “multiple least-squares regression”,

which is appropriate for stationary time

series data (Kulendran & Witt, 2001). The

CPI data were non-stationary. Therefore, its

rate of change, the inflation rate, is included

in the model to test the sensitivity of tourists

to prices. A second model omits inflation and

includes all other variables. The conflict vari-

able is an index compiled by the International

Country Risk Guide and represents the nega-

tive publicity and the impact of travel warn-

ings based on internal conflict in the

Philippines during the period under review.

The log of the conflict variable is included in

all three models to be tested.

Finally, there is the issue of seasonal pat-

terns in tourist flows and expenditures,

which are well-known characteristics of inter-

national tourism demand. Climate variability

is one factor that produces these intra-year

fluctuations. Most studies that have estimated

the impact of climate or climate variability on

tourism demand, as either a push factor or a

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pull factor, have used temperature or rainfall

as indicators, whereas this paper uses the

SOI as a proxy for climate variability in

origin countries. The SOI and the sea surface

temperature are widely used indicators of

ENSO (Chiew et al., 1998) and the best

known extremes of the SOI are the El Nino

and La Nina effects. As mentioned previously,

the ENSO and its connection to climate are

used by many meteorological agencies to

produce monthly weather forecasts (Kawa-

mura, McKerchar, Spigel, & Jinno, 1998).

Monthly data on the SOI were obtained

from the Australian Bureau of Meteorology.

The Bureau uses the Troup method to calcu-

late SOI. The monthly data were averaged

over three months to compile the quarterly

data. Chiew et al. found that in general the

best results were obtained using “SOI values

averaged over two or three months” (1998,

p. 146). The Augmented Dicky Fuller con-

firmed that the SOI data are stationary.

Model 1

ln(tours)t = b0 + b1 ln (tours)t−1

+ b2 ln (USRGDP)t−1 + b3(PHINF)t

+ b4(CONFL)t + b5(SOI)t + 1t.

Model 2

ln(tours)t = b0 + b1 ln (tours)t−1

+ b2 ln (USRGDP)t−1

+ b4(CONFL)t + b5(SOI)t + 1t,

where ln(tourists)t ¼ log of the number of

foreign tourists arrivals in quarter t,

ln(tourists)t21 ¼ the log of the number of

foreign tourists in quarter (t 2 1),

ln USRGDPt21 ¼ US real GDP in quarter

(t 2 1), PHIINFt ¼ inflation rate in the Philip-

pines in quarter t, CONFLt ¼ index of internal

stability in the Philippines in quarter t and

SOIt ¼ Southern Oscillation Index.

The two models are illustrated above. The

expected sign is positive for the lagged depen-

dent variable which represents the WOM

effect and returning Filipino and foreign tour-

ists. Real US GDP representing income of tour-

ists is also expected to be positive based on

consumption theory. Higher inflation and there-

fore higher prices in the Philippines would act as

a deterrent to inbound tourists and thus the

expected sign is negative. The conflict index is

constructed such that the higher this index, the

lower is the internal conflict and political

unrest resulting in increased tourist arrivals

(Varua & Saverimuttu, 2012). Hence, the

expected sign is positive. For the variable repre-

senting climate variability, sustained negative

values of the SOI are associated with El Nino

episodes and sustained positive values of the

SOI with La Nina episodes. As explained pre-

viously, colder than usual winters and more

numerous and stronger hurricanes in origin

countries are all associated with La Nina and

should result in an increase in the number of

tourist arrivals while the opposite is hypoth-

esized as true for El Nino episodes. Thus, the

expected sign for the SOI variable is positive.

Results and Analysis

A summary of the results is present in Table 1.

The diagnostic tests carried out indicate that

both models are robust with no serial corre-

lation or omitted variable bias. The main

purpose of this paper was to test the impact

of climate variability in origin countries on

tourist arrivals in the Philippines using a suit-

able proxy to represent climate variability.

The climate change variable, represented by

the SOI, has proven to be more than adequate

in that the quarterly data on SOI when tested

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proved to be stationary. More importantly the

expected sign of the variable was positive in

both models as hypothesized and statistically

significant at 5%, indicating an increase in

US visitors to the Philippines when La Nina

events occurred in the USA during the period

1994–2011. This result implies that climate

variability in origin countries is an important

“push” factor to motivate tourists towards

travel and tourism activities.

The results of the control variables con-

sidered indicate that they are consistent with

the literature reviewed. The coefficients of

the lagged dependent variable were significant

at 1% and positive in both models, indicating

the strong possibility of return visits and the

positive impact of the WOM effect. The

income variable (ln USRGDPt21) was also

positive and statistically significant at 5% in

both models. In general, the income results

suggest that tourists’ anticipated income and

the decision to travel are influenced by their

income in the previous quarter.

The coefficients in both models indicate that

although the climate variable had a statisti-

cally significant impact on US tourist arrivals

in the Philippines, the impact of the WOM

effect and tourist income was stronger. The

conflict variable confirmed the expected posi-

tive sign but was significant only at 10% in

both models. This paper uses the multiple

least-squares regression technique to estimate

the coefficients whereas a state space model

was employed by Varua and Saverimuttu

(2012), and the level of significance for the

conflict variable was higher.

Although the model only explains approxi-

mately 72% (adjusted R2) of the variation in

US tourist arrivals in the Philippines, the

Ramsay reset test confirms that the model

has the right functional form and that there

is no bias from omitted variables. It must be

noted however that tourists are also sensitive

to prices, either in the form of transportation

costs (airfares) or the cost of living (accommo-

dation, meals, etc.) in the destination country.

Table 1 Multiple Regression Analysis on Tourist Arrivals

Model 1 Model 2

Constant 20.7105 (0.686) 20.6794 (0.693)

ln(tourists)t21 0.7630 (0.000)∗∗∗ 0.7618 (0.000)∗∗∗

ln(USRGDP)t21 0.3521 (0.046)∗∗ 0.3504 (0.044)∗∗

PHINFt 20.1100 (0.910)

ln CONFLt 0.0245 (0.095)∗ 0.0241 (0.089)∗

SOIt 0.0027 (0.027)∗∗ 0.0027 (0.025)∗∗

Adj. R2 0.71 0.72

F-stat 35.08 44.53

BG-LM test (p_value) 0.3623 0.3693

Ramsay RESET test (p_value) 0.2900 0.2778

( ) represents the p_value.∗Significant at 10%.∗∗Significant at 5%.∗∗∗Significant at 1%.

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In the absence of reliable historical data on air-

fares the travel cost was omitted. The inflation

rate was used as a proxy for the cost of

tourism-related goods. The inflation rate had

the correct sign but was not significant and

was therefore omitted in model 2. Exchange

rates are also sometimes used “as the sole rep-

resentation of tourists’ living costs” (Witt &

Witt, 1995). The exchange rate itself (the

amount of pesos per dollar) was non-station-

ary. Therefore, the change in exchange rate

was substituted for the inflation rate. Increases

in the exchange rate resulting in more pesos

per US dollar should have a positive influence

on tourist arrivals. However, the result was

negative and not significant. The Philippines

is a cheaper location than the USA. In

addition, the peso is weaker than the dollar.

Perhaps this accounts for the reduced sensi-

tivity of demand for tourism and tourism-

related goods to prices at this particular

location. Other possible variables that were

excluded were “pull” factors such as market-

ing and promotions, not “push” factors,

which may account for the unexplained

variation.

Conclusion

The primary contribution of this paper was to

test the impact of climate variability in origin

countries as a “push” factor on tourist arri-

vals, specifically in the Philippines. Both

models indicate that climate variability in

origin countries was a significant determinant

of tourism demand for the Philippines during

the period 1994–2011. From the literature

reviewed, significant explanatory variables

were included as control variables to test the

strength of the climate variable. These vari-

ables, the WOM effect and tourists’ income

also proved significant determinants of US

tourist arrivals in the Philippines during this

period. Furthermore, the coefficients indicate

that the impact of these variables on US

tourist arrivals in the Philippines was stronger

than that of the climate variable. However,

weather forecasting techniques are becoming

more sophisticated and increasing in accuracy.

Thus, estimating the impact of colder winters

(which at least in Europe appear to be occur-

ring more frequently) on tourist arrivals in

“sun and sand” locations such as the Philip-

pines would be of importance to planners in

the tourism industry. Tourism literature may

need to focus more (than in the past) on

climate variability in the country of origin as

the necessary impetus for residents of an

affected location to travel to another location

to avoid extreme weather patterns, especially

if it was predictable. Theoretically, the model

does have its limitations due to the exclusion

of some explanatory variables. However, the

Ramsay reset test confirms that the model

has the right functional form and that there

is no bias from omitted variables.

Additionally, this study uses the SOI as

proxy to represent climate variability in

origin countries. Other studies that have

tested the impact of climate variability on

tourism either as a “push” or “pull” factor

have used proxies such as rainfall and temp-

erature. Climate variability is strongly linked

to the ENSO and this link is widely used by

meteorologists to forecast the changes in

weather globally. The SOI, constructed by

the Australian Bureau of Meteorology, is a

widely used indicator of the ENSO and the

best known extremes of the SOI are the El

Nino (warm phase) and La Nina (cold phase)

effects and historical data are readily available

for researchers. In conclusion, this paper

proves to some extent that there is a significant

increase in tourist arrivals in the Philippines

when La Nina (colder) winters prevail in the

Climate Variability in the Origin Countries 855

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USA. More importantly, the SOI proved to be

a more than adequate proxy for measuring the

impact of climate variability on tourism

demand.

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