THREE ESSAYS IN TOURISM, TRADE, AND ECONOMIC GROWTH
Except where reference is made to the work of others, the work described in this thesis is
my own or was done in collaboration with my advisory committee. This thesis does not
include proprietary or classified information.
_____________________________________
Ka Ming Cheng
Certificate of Approval:
_______________________ _______________________
Hyeongwoo Kim, Co-Chair Henry Thompson, Co-Chair
Assistant Professor Professor
Economics Agricultural Economics
and Rural Sociology
_______________________ ______________________
Valentina Hartarska George T. Flowers
Associate Professor Dean
Agricultural Economics Graduate School
and Rural Sociology
THREE ESSAYS IN TOURISM, TRADE, AND ECONOMIC GROWTH
Ka Ming Cheng
A Dissertation
Submitted to
the Graduate Faculty of
Auburn University
in Partial Fulfillment of the
Requirements for the
Degree of
Doctor of Philosophy
Auburn, Alabama
December 18, 2009
iii
THREE ESSAYS IN TOURISM, TRADE, AND ECONOMIC GROWTH
Ka Ming Cheng
Permission is granted to Auburn University to make copies of this dissertation at its
discretion, upon the request of individuals or institutions and at their expense.
The author reserves all publication rights.
__________________________
Signature of Author
__________________________
Date of Graduation
iv
DISSERTATION ABSTRACT
THREE ESSAYS IN TOURISM, TRADE, AND ECONOMIC GROWTH
Ka Ming Cheng
Doctor of Philosophy, December 18, 2009
(M.S., The Chinese University of Hong Kong, 2004)
(M.Ed., The Chinese University of Hong Kong, 1998)
(B.S., The University of Alabama, 1985)
126 Typed Pages
Directed by Henry Thompson and Hyeongwoo Kim
This dissertation consists of three chapters in tourism, trade, and economic growth.
Chapter one investigates the short and long run effects of the change in the nominal
exchange rate on the US tourism trade balance, focusing on the J-curve effect following
currency devaluation. The export revenue and import expenditure functions are
estimated separately to capture the dynamics of the time path of each individual function
to an exchange rate shock with structural vector autoregressive methodology. Although
empirical results cannot statistically confirm a J-curve effect in tourism trade of the
United States, the approach utilizing disaggregated trade data avoids the aggregation bias
of data across all industries. There is a paucity of empirical studies on the balance of
trade in tourism in the literature, and the present study fills this gap by providing an
v
economic model to analyze the effect of the nominal exchange rate on the US trade
balance in tourism.
Chapter 2 examines the determinants on Hong Kong tourism demand for the top
three major tourist arrival countries, namely Mainland China, Taiwan and Japan; with an
error correction model. Specifically, this chapter will examine the effects of relaxing of
the visa requirement, the launch of Individual Visit Scheme, for Mainland Chinese
tourists in 2003. Empirical results show that tourists are income elastic and consider
international tourism a luxury good. Tourists are more sensitive to the change of the
nominal exchange rate than the change in the foreign price level. The positive effect of
the launch of Individual Visit Scheme for Mainland Chinese tourists outweighs the
adverse impact of the Severe Acute Respiratory Syndrome (SARS) on tourism demand
for Hong Kong.
Chapter 3 analyzes the impacts of trade openness, tourism, investment, and
human capital investment on economic growth in Mauritius. Aggregate and
disaggregated measures of these determinants examine their effects on economic growth.
The use of the error correction methodology can capture the dynamics of the output
growth to the specific determinants of growth. Empirical results indicate positive effects
of the Export Processing Zone, tourism, investment, and human capital investment. The
strategic tourism marketing policy aimed at high spending tourists has led to economic
growth.
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ACKNOWLEDGMENTS
I would like to thank God for the grace and blessings He has bestowed on me
during the three years PhD study at Auburn University. I would like to express deep
appreciation to my co-chairs, Dr. Henry Thompson and Dr. Hyeongwoo Kim, for their
intellectual guidance and support in the process of writing my dissertation. I also would
like to thank Dr. Valentina Hartarska for providing many insightful ideas. Special thanks
go to my wife, Lydia Lai Ying Wong, for her love and support throughout my graduate
studies.
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Style manual or journal used Review of International Economics.
Computer software used Microsoft Word and Excel 2003, GAUSS light 7.0 and
EVIEWS 6 student version.
viii
TABLE OF CONTENTS
LISTS OF TABLES ???????????????????????......??. x
LISTS OF FIGURES ?????????????????????..????.. xi
CHAPTER 1: THE NOMINAL EXCHANGE RATE AND TRADE BALANCE
IN US TOURISM: IS THERE A J-CURVE EFFECT? ????? ................................ 1
1. INTRODUCTION ?????????????????????...???...... 1
2. THE THEORETICAL FRAMEWORK ????????..?????????.. 2
3. THE LITERATURE ???????????????.?????????... 14
4. THE ECONOMETRIC MODEL ??????????????.?????? 19
5. EMPIRICAL RESULTS ???? ???????????.????????. 20
6. CONCLUSION ...??????????????????????????. 28
CHAPTER 2: TOURISM DEMAND IN HONG KONG: INCOME, PRICES
AND VISA RESTRICTIONS ??????????????????????. 43
1. INTRODUCTION ?????????????????????????... 43
2. THE THEORETICAL FRAMEWORK ?????????????????.. 45
3. STATIONARITY ANALYSIS AND ECONOMETRIC MODELS ??????.. 54
4. EMPIRICAL RESULTS ???????????????????????.. 58
5. CONCLUSION ...??????????????????????????. 61
CHAPTER 3: TRADE, TOURISM, EDUCATION, AND GROWTH
IN MAURITIUS ?????? ????.???????.????..????... 73
1. INTRODUCTION ?????????????????????????... 73
2. A BRIEF HISTORY OF ECONOMIC DEVELOPMENT IN MAURITIUS ?..?. 74
ix
3. THE THEORETICAL FRAMEWORK ?????????????????.. 79
4. DATA AND THE CHOICE OF ECONOMETRIC MODELS ????????.. 86
5. EMPIRICAL RESULTS ???????????????????????. 90
6. CONCLUSION ...?????????????????????????..... 92
CONCLUDING CHAPTER ??????????????????..???? 101
REFERENCES ?????????????????????????..? ? 105
x
LISTS OF TABLES
Table 1.1: Definitions of Data Variables ?????????????????? 31
Table 1.2: Unit Root Test Results ????????????????????.. 32
Table 1.3: Short-run and Long-run Price (or Exchange rate) and
Income Elasticities ???????????????????..??... 33
Table 2.1: Definitions of Data Variables ?????????????????? 63
Table 2.2: Unit Root Test Results ????????????????????.. 64
Table 2.3: OLS Regression for Tourism Demand ??????????????.. 65
Table 2.4: Error Correction Model ????.???..???..??????..??.. 66
Table 2.5: Derived Results on Hong Kong Tourism Demand ?????????... 67
Table 2.6: Difference Model ?..??????????????.??????... 68
Table 3.1: Definitions of Data Variables ?????????????????... 94
Table 3.2: Unit Root Test Results ??????????????? ?????.. 95
Table 3.3: OLS Regressions for Per Capita Real GDP ????????????.. 96
Table 3.4: Error Correction Model
(Regressions for Difference in Per Capita Real GDP) ????????.. 97
Table 3.5: Derived Results on Per Capita Real GDP ? ????????????. 98
xi
LISTS OF FIGURES
Figure 1.1: Export Revenue of Tourism Model ???????????????.. 34
Figure 1.2: Import Expenditure of Tourism Model ?????????????? 35
Figure 1.3: Variable Series ???????????????????????. 36
Figure 1.4a: Differences of Variable Series ????????????????? 37
Figure 1.4b: Differences of Variable Series ????????? ?? ?????... 37
Figure 1.4c: Differences of Variable Series ??????? ? ????????? 38
Figure 1.4d: Differences of Variable Series ?????? ? ??????.?..?? 38
Figure 1.4e: Differences of Variable Series ????????????????? 39
Figure 1.5: Impulse-Response Function Estimates of
the Export Revenue of Tourism ?............................................................... 40
Figure 1.6: Impulse-Response Function Estimates of
the Import Expenditure of Tourism ???????????????. 41
Figure 1.7: Impulse-Response Function Estimates of
the Trade Balance of Tourism ?????????????????.. 42
Figure 2.1a: Variable Series (Mainland China) ?.? ? ?????..??????... 69
Figure 2.1b: Variable Series (Japan) ?????? ?.. ????????????. 69
Figure 2.1c: Variable Series (Taiwan) ???????? ?.. ?????????.. 70
Figure 2.2a: Differences of Variable Series (Mainland China) ?????.????. 7 1
Figure 2.2b: Differences of Variable Series (Japan) ?????????????. 71
Figure 2.2c: Differences of Variable Series (Taiwan) ????????????... 72
Figure 3.1a: Variable Series ??????????????????????? 99
xii
Figure 3.1b: Variable Series ????.?..???????????? ????? 99
Figure 3.2a: Differences of Variable Series ??????????..?.??..?.... 100
Figure 3.2b: Differences of Variable Series ??????????????...?.. 100
1
CHAPTER 1
THE NOMINAL EXCHANGE RATE AND TRADE BALANCE IN US TOURISM:
IS THERE A J-CURVE EFFECT?
1. Introduction
The response of the trade balance following currency devaluation may exhibit a J-
curve pattern that has been examined with aggregate trade data, bilateral trade data, and
disaggregated trade data at the industry or commodity levels. Bahmani-Oskooee and
Ratha (2004a) review the previous studies of the existence of J-curve effect. Results are
mixed.
More recent studies investigate the J-curve with disaggregated trade data in
specific industries or commodities. No study has examined the tourism sector. The US
ranks first in international tourism receipts and second in tourism spending (UNWTO
World Tourism Barometer, 2008). There has been a significant growth in international
tourism over the last three decades and international tourism receipts have become a
major income source of some countries.
Most studies on trade adopt either the elasticity approach or trade balance
approach. Studies adopting elasticity approach estimate the price elasticities directly
from the export and import demand functions (Houthakker and Magee, 1969; Goldstein
2
and Khan, 1978; Rosenweig and Koch, 1988; Senhadji, 1998a; Senhadji and Montenegro,
1999). Studies using trade balance approach estimate the trade balance function to
examine the J-curve phenomenon and an improvement of trade balance that satisfies the
Marshall-Lerner condition (Magee, 1973; Bahmani-Oskooee, 1985; Rose and Yellen,
1989; Onafowora, 2003).
The purpose of this chapter is to study the short- and long-run effects of the
change of the nominal exchange rate on the US tourism trade balance, specifically the J-
curve effect following currency devaluation. Instead of estimating the export and import
demand functions, or the trade balance function, this chapter will estimate the export
revenue and import expenditure functions separately with structural vector autoregressive
methodology. The strength of estimating the two functions separately is that it can
capture the short-run and long-run dynamics of the time-path of each individual function
to an exchange rate shock instead of only examining the net change of the trade balance.
The outline of this chapter is as follows. Section 2 discusses the theoretical
framework of the export revenue and import expenditure models in tourism. Section 3
reviews the literature on the short-run J-curve effect on the trade balance. Section 4
discusses the econometric model. Section 5 discusses stationarity analysis of the
variables and reports empirical results of the econometric model. Section 6 concludes the
chapter.
2. The Theoretical Framework
International tourism is a luxury good with income elasticity thought to exceed
unity (Harrop, 1973; Rosensweig, 1988; Crouch, 1994; Song, Witt and Li, 2009)
3
implying that people would spend an increasing share of income on international tourism
when income increases. International and domestic tourism are imperfect substitutes
since domestic tourist attractions may not be a perfect substitute for tourist attractions
abroad, especially for cultural heritage, food, and natural resource attractions. Decisions
to travel abroad are affected by income and the cost of international relative to domestic
tourism.
A representative consumer in the home country maximizes utility subject to the
income constraint Y. A consumer chooses to consume international tourism, domestic
tourism, and other goods according to preferences and constrained by income. The home
consumer maximizes U(th*, th, g) subject to Y = Eph*th* + phth + pgg where th*, th, and g
are the quantities of international tourism, domestic tourism, and other goods, Eph*, ph,
and pg are their prices, and Y is income. Note that ph* is the price of international
tourism in the foreign currency and E is the nominal exchange rate of foreign currency in
terms of domestic currency.
Assume the price of all other goods, pg, is constant and does not affect the
demand for international and domestic tourism. The demand for international tourism
would then depend on income and prices of international and domestic tourism.
The demand for international tourism in general functional form is written
th* = th*(Y, Eph*, ph) (1.1)
If international tourism is a luxury good, home consumers have a non-homothetic
utility preference for international tourism. When income increases, people would spend
an increasing share of income on international tourism. A depreciation of domestic
currency or a fall in the price of international tourism will lead to a higher quantity
4
demanded for international tourism. The demand for international tourism rises when the
price of domestic tourism increases since international and domestic tourism are
substitutes.
The present trade model adopts a two-country partial equilibrium model with
imperfect substitutes that assumes imports and domestic goods are imperfect substitutes
(Goldstein and Khan, 1985; Rose and Yellen, 1989). There are two arguments
supporting for this assumption. First, it is observed that there are two-way trading
between countries and the imports and domestic goods coexist in the domestic market
(Goldstein and Khan, 1985). Rhomberg (1973) argues that if the hypothesis of perfect
substitutes is true, each country will be either an importer or exporter of a good but not
both. Magee (1975) further argues that either the imports or domestic goods will
swallow up the whole domestic market when the cost of producing each good is constant
or decreasing. Second, there is a significant and persistent price differential for the same
good in different countries which implies that the ?law of one price? does not seem to
hold (Goldstein and Khan, 1985). The home country is the United States (US) and the
foreign country is the Rest of the World (ROW).
On the supply side, assume an infinitely elastic supply of international tourism in
a perfectly competitive market of international tourism at home (the US) and abroad (the
ROW). The supply of import tourism to the US is infinitely price elastic since there are
numerous tourist attractions around the world and the market is competitive. Goldstein
and Khan (1978) argue that this assumption may not be valid for the supply of export in a
single country unless idle capacity exists in the export sector. However, this assumption
still can apply for the supply of export tourism to the ROW since the US is a large
5
country with many tourist attractions and idle capacity always exists to meet an increase
in world tourism demand. Indeed, this infinitely elastic supply assumption is invalid only
for those small open economies with limited tourism resources. Based on this
assumption, the price of international tourism to the ROW (ph*) faced by home tourists
and the price of international tourism to the US (pf) faced by foreign tourists are constant
and the only price variation is due to the nominal exchange rate (E). The price of
domestic tourism abroad (pf*) faced by foreign tourists and the price of domestic tourism
at home (ph) faced by home tourists are also determined in perfectly competitive markets
based on the same rationale as above. Therefore prices ph*, ph, pf* and pf are assumed
exogenous in the present model.
The supply of international tourism in each country is
Home country (US): Sx = Sx(pf) (1.2)
Foreign country (ROW): Sx* = Sx*(Eph*) (1.3)
On the demand side, national demand for international tourism in each of the two
countries is:
Home country (US): Dm = Dm(Y, Eph*, ph) (1.4)
Foreign country (ROW): Dm* = Dm*(EY*, pf, Epf*) (1.5)
For the home country, the quantity demanded for international tourism (Dm) is
positively related to home income (Y) and negatively related to the price of international
tourism in the domestic currency (Eph*). For the foreign country, the quantity demanded
for international tourism (Dm*) is positively related to foreign income (EY*) and
negatively related to the price of international tourism in the foreign currency (pf).
6
In equilibrium, the prices and the quantities of international tourism in the two
countries are determined by the supply and demand equilibrium,
Dm = Sx* and Dm* = Sx (1.6)
An export revenue model of the home country in general functional form can then
be derived from (1.5) as:
X = X(Y*, E) (1.7)
Export revenue in tourism (X) is in the home currency. Prices pf and pf* are exogenous.
The export revenue in tourism (X) is positively related with foreign income in the home
currency (Y*). A depreciation of the home currency, or an increase in the nominal
exchange rate (E), raises the foreign income in the home currency and results in a higher
demand for international tourism to the home country. An increase in the nominal
exchange rate causes a rise in export revenue in home currency given the export supply
of tourism is infinitely elastic. Figure 1.1 illustrates the change of the export revenue due
to a rise of the nominal exchange rate.
The import expenditure model of the home country in general functional form can
be derived from (1.4) as:
M = M(Y) = M(Y, E) (1.8)
Import expenditure on tourism (M) is in the home currency. Given that ph and ph* are
exogenous, home tourists with higher income spend more income in international tourism.
The import expenditure on tourism (M) is positively related with home income in the
home currency (Y). A depreciation of the home currency, that is a rise in E, lowers
effective home income in the foreign currency and deteriorates the purchasing power for
international tourism. In other words, a weaker home currency implies a higher price of
7
international tourism and results in a lower quantity demanded for international tourism.
The effect of the nominal exchange rate on the import expenditure depends on the price
elasticity of import demand. Figure 1.2 illustrates the change of the import expenditure
due to a rise of the nominal exchange rate.
The export revenue and import expenditure models are estimated with the
following log-linear equations:
lnXt = ?0 + ?1lnY*t + ?2lnEt + ?t (1.9)
lnMt = ?0 + ?1lnYt + ?2lnEt + ?t (1.10)
The definitions of the variables are summarized in Table 1.1.
In the export revenue model, a rise in the ROW income or a rise in the nominal
exchange rate (depreciation of US dollars) will raise the value of exports in tourism (in
US dollars). In the import expenditure model, an increase in the US income or
depreciation of US dollars will raise the value of imports in tourism (in US dollars) given
a price inelastic import demand. However, it should be noted that depreciation will lower
the value of imports in tourism in the ROW currency. Since the value of imports in
tourism in the ROW currency equals EM , a rise in E will lead to a fall in EM but M
might rise.
For a devaluation to raise the trade balance, the Marshall-Lerner condition must
hold. The condition is that the sum of the absolute values of the elasticities of export
demand and import demand exceed unity given balanced trade initially. The Marshall-
Lerner condition is
??x? ?
?????MX
+ ??m? > 1 (1.11)
8
where ?x is the elasticity of export demand and ?m is the elasticity of import demand.
Export revenue in tourism (X) is the product of the quantity of export in tourism
(tf) and the price of domestic tourism (pf). The import expenditure on tourism (M) is the
product of the quantity of import in tourism (th*) and the price of international tourism in
term of home currency (Eph*). The export revenue of the home country is defined as:
X = pftf (1.12)
Import expenditure of the home country is defined as:
M = Eph*th* (1.13)
To derive the Marshall-Lerner condition for the present model, totally
differentiate the export revenue equation (1.12) and the import expenditure equation
(1.13) as
dX = pfdtf + tfdpf (1.14)
dM = Eph*dth* + ph*th*dE + Eth*dph* (1.15)
Assume that the supply prices (pf and ph*) of international tourism do not change
given the supply curves are perfectly elastic over the range of quantity change. Therefore
both dpf and dph* equal zero.
Utilize the definitions of the elasticity of export and import demand as
? ?? ? ? ?EpEpd tdt
ff
ffx ?? (1.16)
? ?? ? ? ?** **
hh
hhm EpEpd tdt?? (1.17)
where (pf / E) is the price of international tourism to US in ROW currency and (Eph*) is
the price of international tourism to ROW in US dollars.
9
Equation (1.16) can be expanded as ? ?? ?? ? ? ?
EpEdEpE d p tdt fff ffx 2???
.
Since dpf = 0, we have
? ?? ?EdEtdt ffx ??? (1.18)
Equation (1.18) can be rearranged as
fxf tEdEdt ?????????
(1.19)
Substitute (1.19) into (1.14) to rewrite dX in terms of export demand elasticity ?x, to find
ffx tpEdEdX ?????????
XEdEdX x????????? (1.20)
Next, equation (1.17) can be expanded as ? ?? ? ? ?
***
**
hhh
hhm EpdEpEdp tdt??? .
Since dph* = 0, we have
? ?? ?EdE tdt hhm **?? (1.21)
Equation (1.21) can be rearranged as
** hmh tEdEdt ???????? (1.22)
Substitute (1.22) into (1.15) to rewrite dM in terms of import demand elasticity ?m, to
find
?????????????? EdEtEptEpEdEdM hhhhm ****?
10
? ?1???????? mMEdEdM ? (1.23)
The balance of trade in tourism, B, is defined as B = X ? M and the change in the trade
balance, dB, is defined as
dB = dX ? dM. (1.24)
Substitute (1.20) and (1.23) into (1.24), to find
? ?1???????????????? mx MEdEXEdEdB ??
For devaluation to improve trade balance, dB > 0, the condition is
? ? 01 ????????????????? mx MEdEXEdEdB ?? , which implies
? ? 01 ??????????????? mx MEdEXEdE ?? ,
? ? 01 ??? mx MX ?? ,
MMX mx ??? ?? , and finally
1????????? mx MX ?? (1.25)
We may restate the elasticities in absolute value to get the Marshall-Lerner
condition (1.11) as below:
??x? ?
?????MX
+ ??m? > 1
The advantage of estimating a log-linear equation is that the coefficients are
elasticities of the relevant variables. The estimated coefficient ?2 of the nominal
exchange rate in (1.9) is the elasticity of export tourism revenue while the estimated
11
coefficient ?2 in (1.10) is the elasticity of import tourism expenditure. From (1.20) and
(1.23), we can derive the elasticity of export tourism revenue and the elasticity of import
tourism expenditure as
? ?? ? xEdE XdX ?? ???2 (1.26)
? ?? ? ? ?12 ??? mEdE MdM ?? (1.27)
Restate the Marshall-Lerner condition (1.11) in terms of ?2 and ?2 from (1.26) and
(1.27) as below:
???2? ?
?????MX
? ??2? > 0 (1.28)
In earlier studies, the balance of trade is measured by the difference between
export revenue and import expenditure, that is (X ? M) (Rose, 1991; Bahmani-Oskoosee
and Malixi, 1992) or the ratio of net exports to national income, ? ?GDPMX? (Demirden and
Pastine, 1995; Senhadji, 1998b). Haynes and Stone (1982) propose the trade balance as
the ratio of a country?s imports to exports (or exports to imports). Some researchers
follow this definition of trade balance in their recent trade studies (Bahmani-Oskoosee
and Brooks, 1999; Boyd, Caporale and Smith, 2001; Onafowora, 2003). The advantage
of using the ratio of exports to imports is that it is a unit-free measure and also the ratio
can be in real terms or nominal terms. The trade balance is then defined as MXB? . Take
logarithms on both sides, we get
lnB = lnX ? lnM (1.29)
12
Substitute (1.9) and (1.10) into (1.29),
lnB = (?0 ? ?0) + ?1lnY*t ? ?1lnYt + (?2 ? ?2)lnEt + (?t ? ?t) (1.30)
Then we may estimate the trade balance model with the following equation:
lnBt = ?0 + ?1lnY*t + ?2lnYt + ?3lnEt + ?t (1.31)
where ?0 = (?0 ? ?0), ?1 = ?1, ?2 = ? ?1, ?3 = (?2 ? ?2) and ?t = (?t ? ?t). In this trade
balance model, a rise in the ROW income will improve the trade balance while a rise in
the US income will deteriorate the trade balance. A depreciation will raise the trade
balance given the sum of the absolute value of the elasticities of export and import
demand exceed unity. If ?3 is positive (?3 > 0) and statistically significant, it satisfies the
Marshall-Lerner condition.
In the present study, instead of estimating a trade balance model, the export
revenue and import expenditure models are estimated individually. Since the trade
balance is a ratio of exports to imports, an improvement in trade balance due to
devaluation may be the result of the following five different scenarios:
(1) a rise in exports with a fall in imports,
(2) a large rise in exports with a smaller rise in imports,
(3) a rise in exports with no change in imports,
(4) no change in exports with a fall in imports,
(5) a smaller fall in exports with a large fall in imports.
The weakness of the trade balance model is that the net change of the trade
balance cannot provide detailed adjustment dynamics of individual export revenue and
import expenditure functions to an exchange rate shock. Besides, the income effect on
13
the export revenue (X) or the import expenditure (M) in the trade balance model can be a
result of the change of both the foreign income (Y*) and the domestic income (Y).
By estimating export revenue and import expenditure model separately, we can
trace the change of the time-path of the export revenue and import expenditure after
devaluation and derive the trade balance afterward. We can also separate the income
effects from the US and the ROW.
Some trade studies estimate the export and import demand elasticities directly
from the export and import demand functions by using aggregate trade data. Export and
import volume indices (Goldstein and Khan, 1978; Rosenweig and Koch, 1988) or real
exports and imports (Houthakker and Magee, 1969; Senhadji, 1998a; Senhadji and
Montenegro, 1999) are used as proxy variables for the quantity of exports and imports.
The real exports (imports) is derived by deflating the value of exports (imports) with
aggregate price indices, such as unit value indices, wholesale price indices or export
(import) price indices. The major shortcoming of using volume indices or deflating the
value of exports (imports) with price indices is the aggregation bias across different
goods and results in unreliable estimates of the export and import demand elasticities
(Goldstein and Khan, 1985). Utilizing the disaggregated trade data in specific industries
or commodities, that is the quantity of a particular commodity, may avoid this
aggregation bias. Indeed, this is a common practice in consumer demand studies of a
specific good if the quantity data is available. However tourism is a good in various
qualities and attributes. No actual quantity data of tourism is available.
Since the data of export tourism revenue and import tourism expenditure is
available in the present study, the elasticities of export tourism revenue (?2) and import
14
tourism expenditure (?2) can be obtained by estimating the export revenue and import
expenditure functions separately. Then the elasticities of export tourism demand (?x) and
import tourism demand (?m) can be derived afterward from equations (1.26) and (1.27).
3. The Literature
The J-curve effect is a phenomenon that the trade balance is worsened at the
beginning of currency devaluation but improved after some adjustment lag. The US trade
balance turn around from a surplus in 1970 to a deficit in 1971. US officials determined
to correct the situation by devaluing the dollar in 1971. The situation had not improved
and even became worse the following year.
Magee (1973) argues that there exists a J-curve effect due to lag structure
following currency devaluation. He attributes the effect to (1) the currency-contracts
period: a deterioration of trade balance occurs when the share of import contract is larger
than the share of the export contract dominated in foreign currencies, (2) the pass-through
period: consumers are willing to change their purchases of foreign goods only when the
price of the imported goods change in terms of domestic currency after a devaluation, and
(3) the quantity adjustment period: a successful ?pass-through? leads to the quantity
adjustment and results an improvement of trade balance.
Junz and Rhomberg (1973) also argue that the trade effect in response to the
devaluation is composed of five lags. First, consumers and sellers have a recognition lag
of devaluation. Second, business have a decision lags in placing new orders. Third, there
is a delivery lag of payments being recorded when the goods are actually delivered.
Fourth, there is a replacement lag until inventories of materials are used up. Fifth, there
15
is a production lag for producers to be convinced by the profit incentives to expand the
supply of existing products or new products. All these business decisions affect the
demand for imports and exports. Magee (1973) and Junz and Rhomberg (1973) provide
reasonable answers to the short-run deterioration and long-run improvement in the trade
balance following currency devaluation.
According to the Marshall-Lerner condition, the necessary and sufficient
condition for an improvement of the trade balance following currency devaluation
depends on whether the sum of the absolute value of the elasticities of export and import
demand exceed unity. The J-curve phenomenon proposed by Magee (1973) sparks a vast
amount of studies on the short-run and long-run dynamics that trace the time-path of the
trade balance after devaluation. In the short-run, both export and import demand
elasticities are inelastic and the sum is less than unity which deteriorates the trade balance.
However, in the long-run, the elasticities become more elastic and the sum exceeds unity
which improves the trade balance.
The earlier studies of J-curve effect are usually employing aggregate trade data
(Laffer, 1976; Salant, 1976; Miles, 1979; Himarios, 1985; Bahmani-Oskooee, 1985;
Rosensweig and Koch, 1988; Flemingham, 1988; Noland, 1989; Wassink and Carbaugh,
1989; Mahdavi and Sohrabian, 1993; Backus, Kehoe and Kydland, 1994; Hoque, 1995,
Sehadji, 1998b; Gupta-Kapoor and Ramakrishnan, 1999; Lal and lowinger, 2002; Hacker
and Abdulnasser, 2003). Later studies employ bilateral trade data (Rose and Yellen,
1989; Marwah and Klein, 1996; Shirvani and Wilbratte, 1997; Bahmani-Oskooee and
Brooks, 1999; Wilson, 2001; Bahmani-Oskooee and Goswami, 2003; Bahmani-Oskooee
and Ratha, 2004b) and more recent studies investigate sector-specific responses to
16
devaluation (Meade, 1988; Doroodian, Jung and Boyd, 1999; Yazici, 2006; Ardalani and
Bahmani-Oskooee, 2007).
Researchers have used different econometric modeling to test the exchange rate
effect on the trade balance; however, the empirical results on J-curve effect are mixed.
Bahmani-Oskooee and Brooks (1999) examine the bilateral trade data of the US and six
major trading partners by using autoregressive distributed lag (ARDL) approach and find
there is no J-curve effect in the short run. Boyd, Caporale and Smith (2001) adopt
structural cointegrating vector autoregressive distributed lag (VARDL) model and the
generalized impulse response functions to investigate eight OECD countries and the
results show evidence of J-curve effect. Onafowora (2003) examines the bilateral trade
of three ASEAN countries to the US and Japan by the methodology of vector error
correction model (VECM) and the generalized impulse functions. He suggests that
Marshall-Lerner condition holds in the long-run and a short run J-curve effect.
Akbostanci (2004) investigates the J-curve hypothesis of the Turkish data by an error
correction model and the generalized impulse response methodology. The results do not
support the J-curve hypothesis. Narayan (2004) applies ARDL approach and the impulse
response analysis to capture the J-curve effect. The result indicates New Zealand?s trade
balance exhibits a J-curve pattern. Gomes and Paz (2005) use VECM model to examine
the J-curve effect and Marshall-Lerner condition and find the condition holds and the J-
curve effect exists in the Brazilian trade balance.
Instead of a J-curve, Backus, Kehoe, and Kydland (1994) find an asymmetric
shape of the cross-correlation function for net exports and the term of trade for a set of
OECD countries and report this finding the S-curve. Senhadji (1998b) confirms the S-
17
curve effect with a large set of less developed countries (LDCs). Bahmani-Oskooee and
Ratha (2007) argue that the use of aggregate US trade data and the terms of trade do not
provide strong empirical support of the S-curve. However, by using disaggregated US
bilateral trade data and the cross-correlation function, they find stronger results in support
of the S-curve.
Doroodian, Jung and Boyd (1999) investigate the J-curve effect for US
agricultural and manufactured sector using the Schiller lag model. They report a J-curve
effect for agricultural sector but not for manufactured sector. Yazici (2006) examines the
J-curve hypothesis in Turkish agricultural sector by multiplier-based model with a lag
structure and reports that agricultural trade balance improves initially, then deteriorates,
and then improves again. He concludes that there is no J-curve effect and devaluation
even worsens the trade balance of agricultural sector in the long-run. Ardalani and
Bahmani-oskooee (2007) use export and import data at sixty six industries in the US and
find the J-curve effect only in six industries by using error correction mechanism. The
use of disaggregated trade data in industry level or commodity level is proposed by
Doroodian, Jung and Boyd (1999) and Ardalani and Bahmani-Oskooee (2007). The
advantage of using disaggregated trade data can avoid the aggregation bias of data that
combine all traded goods across all industries.
In the present paper, we investigate the short- and long-run dynamics of the
export revenue and import expenditure in tourism of the US after devaluation. Although
the US has had a huge trade deficit in the last three decades, it has enjoyed a trade surplus
in tourism over the last twenty years. Many studies have explored the dynamics of the
US trade balance to dollar devaluation (Magee, 1973, Rosensweig and Koch, 1988;
18
Wassink and Carbaugh, 1989; Rose and Yellen, 1989; Moffett, 1989; Mahdavi and
Sohrabian, 1993; Demirden and Pastine, 1995; Bahmani-Oskooee and Brooks, 1999;
Bahmani-Oskooee and Ratha, 2004b). To the best of my knowledge, no study has
investigated the US trade balance in tourism sector. Socher (1986) mentions that tourism
as a trading service has not been explicitly discussed in the theory of international trade.
The major difference between traditional trade and tourism is that tourists have to
visit the importing destination country. Rising tourism receipts have had a significant
effect on the balance of trade of many developing and developed countries over the last
three decades. A series of studies (Hazari and Ng, 1993; Hazari, 1995; Hazari and
Nowak, 2003; Hazari and Sgro, 2004) incorporate tourism into the traditional trade
theory to explore the effects of the development of tourism on domestic welfare and
economic growth.
Tourism receipts and payments are components of the balance of goods and
services in the international current accounts. Tourism receipts are the major source of
income in some countries, such as Mauritius, Spain, and Turkey. In the US, tourism
receipts contributed 5% of export revenue in 2007 and the country ranks first in the
international tourism receipts and second in tourism spending.
In earlier studies, many researchers adopt unrestricted VAR or VECM
methodology that assumes interdependence of the variables. That is the trade balance,
home income, foreign income and exchange rate will affect each other. In the present
paper, we use Structural Vector Autoregressive (SVAR) model and the impulse-response
functions to estimate and trace the time-path of the export revenue and import
expenditure in tourism sector after devaluation. Since tourism trade is a small fraction of
19
international transaction, its contemporaneous effect on the exchange rate is negligible.
Though foreign income may affect the exchange rate in the long-run, its short-run effect,
in one or two quarters, is small. Besides, tourism trade is not contemporaneously
affected by foreign income growth since most tourists usually plan for international travel
at least one quarter ahead. Restrictions are imposed in the SVAR modeling based on the
above prior knowledge.
4. The Econometric Model
Consider the following Structural Vector Autoregressive (SVAR) process of
integrated variables of interest.
ttt L uyBAy ?? ?1)( , (1.32)
where A is an mm? square matrix, ty is an 1?m vector of m difference stationary
variables, )(LB denotes a matrix lag polynomial, and tu is 1?m vector of m structural
shocks. We assume that each shock has zero mean, unit variance, and mutually
independent each other. That is,
0u?tE and Iuu ?'ttE , (1.33)
where 0 is an 1?m null vector and I is an mm? identity matrix.
The structural form system of equations (1.32) can be represented by the
following reduced form system of equations.
ttt L ?yCy ?? ?1)( , (1.34)
where
)()( LL DBC ? , tt Du? ? , and 1??AD (1.35)
20
Combining (1.33) and (1.35), we obtain the following relation.
?DDDuDu?? ??? ''' 'tttt EE , (1.36)
where ? is a variance-covariance matrix from the reduced form VAR (1.34).
Note that to just-identify the system, we need 2/)1( ?mm identifying
assumptions. We employ a conventional approach proposed by Sims (1980) and utilize
the Choleski decomposition of ? to obtain D . This approach can be useful when we
have a certain prior knowledge on short-run relations between the variables of interest.
Once we obtain the least squares estimates )(LC and ? from the reduced form
(1.34), we recover the structural form VAR (1.32) using the identified contemporaneous
matrix D . Then, we implement the impulse-response analysis for the structural shocks in
the system.
5. Empirical Results
Data of the export revenue and import expenditure in tourism, including travel
spending and air fare, are from the US International Transactions Accounts of the Bureau
of Economic Analysis. The nominal exchange rate index is the Federal Reserve nominal
major currencies index. The Federal Reserve Board constructs the nominal major
currencies index which is trade weighted index including seven currencies, the euro,
Canadian dollar, Japanese yen, British pound, Swiss franc, Australian dollar, and
Swedish krona.
US income is the US nominal GDP and the ROW income is the sum of the
nominal GDP of the five major trading countries including United Kingdom, Canada,
Japan, France and Germany. The five countries are also the major tourist arrival
21
countries to the US. The nominal GDPs are extracted from the International Financial
Statistics of International Monetary Fund. The period of study is 1973 Q1 ? 2007 Q4
(quarterly data).
Stationarity of variables is pretested to check whether the variables are stationary
series converging to steady state levels. The results of the unit root test from
conventional augmented Dickey-Fuller (ADF) tests of the variables are summarized in
Table 1.2. The number of lags is chosen by the Schwarz Information Criterion (BIC).
The ADF test with an intercept fails to reject the null hypothesis of a unit root for
all level variables except the US income (Y). With the intercept and time trend, the ADF
test does not reject the null hypothesis of a unit root for all level variables. However if
more lags are added to the US income variable, the ADF test fails to reject the unit root
null hypothesis. Besides, the Y series in level appear non-stationary by visual inspection
of the plots of the variables series in Figure 1.3.
The ADF tests reject the unit root null hypothesis for all differenced variables.
The results indicate all variables are integrated in the first order. Plots of differences of
the variable series in Figure 1.4 (a, b, c, d, e) appear stationary.
Since all variables are I(1) series, first differencing can remove nonstationarity of
the variables. Then we proceed to construct a SVAR with differenced variables for
estimation.
We first consider an export revenue model of tourism, that is,
]'[ *tttt YXE ????y . The order of ty is chosen by the following. First, we assume that
the nominal exchange rate is not contemporaneously affected by either tourism export or
foreign income (demand) shocks. We believe that this is a reasonable assumption
22
because tourism takes a small fraction of total foreign exchange transaction volume.
Foreign income growth may affect the nominal exchange rate when it is related with
productivity differentials. Even if it is the case, such causality may arise only in the long-
run. Because we use quarterly data, we believe that our assumption is not crucially
problematic. Second, we assume that tourism export is not contemporaneously affected
by foreign income (demand) growth. This should not be a problem if most tourism
demand is predetermined at least one quarter in advance.
From our D estimates, we have the following contemporaneous relations of each
innovation and structural shock.
)0008.0( 0146.0
EtEt u??
)0056.0()0044.0( 0558.00030.0
XtEtXt uu ???
)0022.0()0029.0()0038.0( 0381.00034.00162.0
** YtXtEtYt uuu ?????
Standard errors are reported in brackets and obtained from 10,000 nonparametric
bootstrap simulations from empirical distribution. The choice of k = 4 is determined by
the Akaike information criterion (AIC).
Next we estimate D with the diagonal element estimates being normalized to one
with 'ttuEu becomes a diagonal matrix with non-unity variances. Then the
contemporaneous relations of each innovation and one percent structural shock are
obtained. Estimated export revenue response functions are reported in Figure 1.5. The
23
estimated response functions to one percent structural shock and the confidence intervals
are obtained by taking 5% and 95% percentiles from 10,000 bootstrap simulations.
When there is a one percent positive exchange rate shock, export tourism revenue
decreases contemporaneously, then increases after one quarter and converging to
equilibrium after six quarters, which exhibits a lagged effect to the exchange shock. The
short-run exchange rate elasticity of export tourism revenue is statistically insignificant
while the long-run exchange rate elasticity is marginally insignificant. Export tourism
revenue exhibits a robust positive response to a positive foreign income shock and its
own shock.
Next, we consider an import expenditure model of tourism, that is,
]'[ tttt YME ????y . The order of the variables can be similarly justified as above. Both
Akaike information criterion (AIC) and Schwarz Information Criterion (BIC) choose k =
1 but to remove any remaining serial correlation, we choose k = 4 as in export revenue
model.
From the D estimate, we obtain the following relations.
)0017.0( 0319.0
EtEt u??
)0053.0()0035.0( 0442.00032.0
MtEtMt uu ???
)0008.0()0005.0()0006.0( 0068.00016.00002.0
YtMtEtYt uuu ????
Then we estimate D with the diagonal element estimates being normalized to one
to obtain the contemporaneous relations of each innovation and one percent structural
24
shock. Estimated import expenditure response functions are reported in Figure 1.6. The
estimated response functions to one percent structural shock and the confidence intervals
are obtained by taking 5% and 95% percentiles from 10,000 bootstrap simulations.
Import tourism expenditure decreases contemporaneously when there is a one
percent positive exchange rate shock, then increases after four quarters and converging to
equilibrium after ten quarters. However the responses are statistically insignificant in
both the short-run and the long-run. Import tourism expenditure also exhibits a robust
positive response to a positive home income shock and its own shock.
Consolidating the results of the impulse-response functions of the export revenue
and import expenditure models, the trade balance in tourism deteriorates initially after
dollar devaluation, then improves after one quarter, and converges to the steady state
after ten quarters. The short-run deterioration of the trade balance in tourism is
statistically insignificant while the long-run improvement of the trade balance is
marginally insignificant within the 90% confidence interval. Although a significant J-
curve phenomenon is not found, a lagged effect of an exchange rate shock on the export
revenue is observed.
For comparison purposes, we further estimate a trade balance model of tourism,
that is, ]'[ *ttttt YYBE ?????y where MXB? . The order of ty is chosen by the
following. First, we assume that the nominal exchange rate is not contemporaneously
affected by the trade balance of tourism, home income or foreign income shocks. Second,
we assume that trade balance of tourism is not contemporaneously affected by the home
income or foreign income shocks. The rationale of these two assumptions is the same as
25
above. Third, we assume that the home income is not contemporaneously affected by the
foreign income growth (demand) shock. Foreign income growth may lead to a higher
demand for the exports of goods and services of the home country and results a home
income growth in the long-run. However its effect on the home income in one or two
quarters is negligible.
From the D estimate, we obtain the following relations.
)0008.0( 0147.0
EtEt u??
)0026.0()0041.0( 0418.00008.0
BtEtBt uu ???
)0020.0()0032.0()0036.0( 0383.00012.00169.0
YtBtEtYt uuu ????
? ? ? ? ? ? ? ?0007.00006.00005.00006.0 0066.00000.00010.00002.0 ** YtYtBtEtYt uuuu ??????
Standard errors are reported in brackets and obtained from 10,000 nonparametric
bootstrap simulations from empirical distribution. The choice of k = 4 is determined by
the Akaike information criterion (AIC).
Then D with the diagonal element being normalized to one is estimated to obtain
the contemporaneous relations of each innovation and one percent structural shock.
Estimated trade balance response functions are reported in Figure 1.7. The estimated
response functions to one percent structural shock and the confidence intervals are
obtained by taking 5% and 95% percentiles from 10,000 bootstrap simulations.
Trade balance of tourism increases contemporaneously when there is a one
percent positive exchange rate shock. However the short-run positive response is
26
statistically insignificant. The response becomes statistically significant after four
quarters and converges to long-run equilibrium after eight quarters. There is no evidence
of a J-curve effect. Trade balance of tourism exhibits a robust positive response to a
positive home income shock contemporaneously and the effect becomes statistically
insignificant after four quarters. The trade balance of tourism also exhibits a robust
positive response to a positive foreign income shock and its own shock over time.
The trade balance model cannot reflect the individual impulse responses of export
revenue and import expenditure functions that exhibit a lagged effect to an exchange rate
shock. Both export revenue and import expenditure functions show significant long-run
income effects to their respective income shocks. However, the trade balance model only
exhibits a significant long-run income effect to the foreign income shock and shows an
insignificant long-run income effect to the home income shock. Estimating export
revenue and import expenditure functions separately provides more detailed dynamics of
the responses to the respective shocks.
The estimated long-run price (or exchange rate) elasticities of export tourism
revenue and import tourism expenditure are ?2 = 0.875 and ?2 = 0.122 respectively.
Given the initial trade balance ?
?????MX
of the first quarter in 1973 is 0.591, the Marshall-
Lerner condition (1.28) is satisfied since ???2? ?
?????MX
? ??2? = 0.395 > 0. The price (or
exchange rate) elasticities of export tourism demand and import tourism demand can be
derived from equations (1.26) and (1.27), and their values are ?x = ?0.875 and ?m = ?
0.878 respectively. Since the effect of the nominal exchange rate on the import tourism
27
expenditure depends on the price elasticity of import tourism demand (Figure 1.2), the
import tourism expenditure will rise when there is an increase in price (or exchange rate)
given the import tourism demand (?m) is price inelastic. However the estimated long-run
price elasticity of export tourism revenue is marginally statistically insignificant while the
price elasticity of import tourism expenditure is statistically insignificant within the 90%
confidence intervals.
The short-run and long-run price (or exchange rate) elasticities and the long-run
income elasticities of the three models are summarized in Table 1.3.
Finally, we also estimate a trade balance model of tourism with the trade balance
defined as the excess of exports to imports that is, B = (X ? M). Studies by Rose (1991)
and Bahmani-Oskoosee and Malixi (1992) also adopt this definition of trade balance and
take logarithms for (X ? M). If there is a trade deficit, the value of the (X ? M) is
negative. Taking logarithms for a negative value is mathematically impossible. However,
the authors have not explicitly explained how they handle this problem in their empirical
works. Wilson (2001) recognizes this problem and estimates a lin-log trade balance
model. The (X ? M) in level is regressed with a set of logarithmic explanatory variables.
One disadvantage of estimating a lin-log model is that the estimated coefficients are not
the elasticities. To derive the elasticities, the estimated coefficients can be multiplied
with ? ?
MX?1
. It should be noted that the values of the elasticities vary with the values
of (X ? M). In practice, the elasticities are computed by using the mean value of (X ? M).
However if the mean value of (X ? M) is close to zero, the derived elasticities are not
reliable.
28
One of the possible solutions for this problem is to add a positive constant to the
(X ? M) to make the (X ? M) series positive. The positive constant can be any values
larger than the absolute value of the smallest negative (X ? M). We use several different
values of positive constant for estimations. The estimated exchange rate elasticities vary
with the values of the positive constant. A small positive constant results a relatively
larger exchange rate elasticity while a large positive constant results a relatively smaller
exchange rate elasticity. It shows that the results are not reliable and this methodology is
problematic.
6. Conclusion
A structural vector autoregressive model is used to examine the effect of the
nominal exchange rate innovations on the trade balance in tourism of the United States.
There is no evidence of a significant J-curve pattern of trade balance in tourism following
currency devaluation between the United States and the Rest of the World.
A lagged effect on export revenue is observed, however, it is marginally
statistically insignificant within a 90% confidence interval. The initial deterioration of
export revenue lasts for one quarter and is followed by an improvement afterward. The
finding is consistent with the assumption that tourists usually plan travel abroad at least
one quarter ahead. Payments on tour package, air tickets and hotel reservation are made
in advance. As a result, the change in the exchange rate may not have significant
immediate effect on tourism spending in the short-run. Tourists and tourism providers
have recognition lag and quantity-adjustment lag following a change in exchange rate.
29
These adjustment lags result in a worsening of export revenue in tourism initially and
then improve after an adjustment lag.
Although the findings in the present study cannot statistically confirm a J-curve
effect in tourism trade between the United States and the Rest of the World, the use of
disaggregated trade data in a specific industry does avoid the aggregation bias of data
across all industries.
In some recent trade studies, researchers are tempted to investigate sector-specific
responses to devaluation. However there is no single study on the balance of trade in
tourism. The present study fills this gap by providing an economic model to analyze the
effect of the nominal exchange rate on the US trade balance in tourism.
In comparison with the elasticity approach and trade balance approach, the
methodology of estimating the export revenue and import expenditure functions
separately have at least three appealing features. First, the methodology provides a better
picture of the dynamics of the time-path of each individual function to an exchange rate
shock rather than only focusing on the net change of the trade balance. Second, it avoids
the combined income effect of foreign income and domestic income on the export
revenue or the import expenditure. Third, the elasticities of export revenue and import
expenditure are estimated first and then the elasticities of export and import demand can
be derived afterward.
For future trade studies, the methodology advocated in the present paper can be
extended to investigate the bilateral trade in specific industries or commodities. Instead
of using aggregate trade data and exchange rate indices, disaggregated trade data on a
30
particular commodity and bilateral exchange rate can be utilized. Indeed, tourism trade
between two countries can be examined if data is available.
31
Table 1.1: Definitions of Data Variables
Variable
Explanatory Notes
X the nominal value of the export revenue in tourism in the US dollar
M the nominal value of the import expenditure in tourism in the US dollar
Y* the nominal ROW income in the US dollar
Y the nominal US income in the US dollar
E the nominal exchange rate (US dollar per ROW currency)
? the error term for export revenue equation
? the error term for import expenditure equation
32
Table 1.2: Unit Root Test Results
Variable Specification ADFc ADFc,t
X Level -2.16 -1.15
Differenced -12.80*** -13.22***
M Level -1.98 -0.91
Differenced -14.36*** -14.65***
E Level -1.06 -1.86
Differenced -10.86*** -10.93***
Y Level -4.91*** -2.09
Differenced -4.88*** -9.28***
Y* Level -2.12 -1.69
Differenced -5.30*** -5.48***
Note: The number of lags is chosen by the Schwarz Information Criterion (BIC). ADFc and ADFc,t refer to
ADF-t statistics when an intercept is included and when an intercept and time trend are included. *, ** and
*** indicate the null hypothesis of unit root is rejected at 10%, 5% and 1% level. Asymptotic critical values
are from Harris (1992).
33
Table 1.3: Short-run and Long-run Price (or Exchange rate) and Income Elasticities
Elasticitites Export Revenue
X
Import Expenditure
M
Trade Balance
B = (X/M)
E (short-run)
90% CI
-0.204
[-0.769, 0.346]
-0.101
[-0.309, 0.094]
0.051
[-0.445, 0.523]
E (long-run)
90% CI
0.875
[-0.038, 1.900]
0.122
[-0.319, 0.530]
1.007*
[0.085, 2.212]
Y (long-run)
90% CI
--- 1.988*
[0.547, 3.725]
0.710
[-1.642, 3.128]
Y* (long-run)
90% CI
0.633*
[0.190, 1.092]
--- 0.746*
[0.307, 1.293]
Note: 90% confidence intervals (CI) are obtained by taking 5% and 95% percentiles from 10,000 bootstrap
simulations. * represents the coefficients are significant within 90% confidence intervals.
34
Figure 1.1: Export Revenue of Tourism Model
Note: A rise in the nominal exchange rate (E) raises the foreign income in the home currency and results a
higher demand for international travel to the home country. It causes a rise in export revenue from Pftf1 to
Pftf2 in home currency given the export supply of tourism is infinitely elastic.
Pf (US $)
tf
Sx Pf
tf1 tf2
Dm1*(E1Y*, Pf)
Dm2*(E2Y*, Pf)
35
Figure 1.2: Import Expenditure of Tourism Model
Note: A rise in the nominal exchange rate (E) raises the price of international travel abroad in the home
currency and results an upward shift of the export supply of tourism of the foreign country. The rise of the
price of international travel reduces the quantity demanded for international travel from th1* to th2*. The
effect of the nominal exchange rate on the import expenditure depends on the price elasticity of import
demand.
EPh* (US $)
th*
Sx1* E1Ph*
th2* th1*
Dm(Y, EPh*)
E2Ph* Sx2*
36
Figure 1.3: Variable Series
0
1
2
3
4
5
6
7
8
9
1
9
7
3
Q
1
1
9
7
4
Q
3
1
9
7
6
Q
1
1
9
7
7
Q
3
1
9
7
9
Q
1
1
9
8
0
Q
3
1
9
8
2
Q
1
1
9
8
3
Q
3
1
9
8
5
Q
1
1
9
8
6
Q
3
1
9
8
8
Q
1
1
9
8
9
Q
3
1
9
9
1
Q
1
1
9
9
2
Q
3
1
9
9
4
Q
1
1
9
9
5
Q
3
1
9
9
7
Q
1
1
9
9
8
Q
3
2
0
0
0
Q
1
2
0
0
1
Q
3
2
0
0
3
Q
1
2
0
0
4
Q
3
2
0
0
6
Q
1
2
0
0
7
Q
3
l n X l n M l n E l n Y l n Y*
37
Figure 1.4a: Differences of Variable Series
d l n X
- 0 . 3
- 0 . 2
- 0 . 1
0
0 . 1
0 . 2
0 . 3
0 . 4
1
9
7
3
Q
2
1
9
7
4
Q
2
1
9
7
5
Q
2
1
9
7
6
Q
2
1
9
7
7
Q
2
1
9
7
8
Q
2
1
9
7
9
Q
2
1
9
8
0
Q
2
1
9
8
1
Q
2
1
9
8
2
Q
2
1
9
8
3
Q
2
1
9
8
4
Q
2
1
9
8
5
Q
2
1
9
8
6
Q
2
1
9
8
7
Q
2
1
9
8
8
Q
2
1
9
8
9
Q
2
1
9
9
0
Q
2
1
9
9
1
Q
2
1
9
9
2
Q
2
1
9
9
3
Q
2
1
9
9
4
Q
2
1
9
9
5
Q
2
1
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6
Q
2
1
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9
7
Q
2
1
9
9
8
Q
2
1
9
9
9
Q
2
2
0
0
0
Q
2
2
0
0
1
Q
2
2
0
0
2
Q
2
2
0
0
3
Q
2
2
0
0
4
Q
2
2
0
0
5
Q
2
2
0
0
6
Q
2
2
0
0
7
Q
2
d l n X
Figure 1.4b: Differences of Variable Series
d l n M
- 0 . 2
- 0 . 1 5
- 0 . 1
- 0 . 0 5
0
0 . 0 5
0 . 1
0 . 1 5
0 . 2
0 . 2 5
0 . 3
0 . 3 5
1
9
7
3
Q
2
1
9
7
4
Q
2
1
9
7
5
Q
2
1
9
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38
Figure 1.4c: Differences of Variable Series
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Figure 1.4e: Differences of Variable Series
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Figure 1.5: Impulse-Response Function Estimates of the Export Revenue of
Tourism
Note: 90% confidence intervals are obtained by taking 5% and 95% percentiles from 10,000 bootstrap
simulations.
41
Figure 1.6: Impulse-Response Function Estimates of the Import Expenditure of
Tourism
Note: 90% confidence intervals are obtained by taking 5% and 95% percentiles from 10,000 bootstrap
simulations.
42
Figure 1.7: Impulse-Response Function Estimates of the Trade Balance of Tourism
Note: 90% confidence intervals are obtained by taking 5% and 95% percentiles from 10,000 bootstrap
simulations.
43
CHAPTER 2
TOURISM DEMAND IN HONG KONG:
INCOME, PRICES, AND VISA RESTRICTIONS
1. Introduction
Hong Kong is one of the top destination cities for tourists in Asia. International
tourism demand for Hong Kong has had significant growth in the last two decades.
Tourism is one of the four key industries in Hong Kong. Tourism contributed around
3.2% to Hong Kong?s GDP and generated 5.2% of the total employment in 2006.
Japan was the number one tourist arrivals country to Hong Kong in the 1980s
with more than 20% of the total Hong Kong market. In 2006, Mainland China and
Taiwan became the first (53%) and second (9%) largest source countries for Hong Kong
tourism followed by Japan (5%). Mainland China became the first source market
because of the relaxation of visa requirement to visit Hong Kong.
Before 1983, Chinese citizens were highly restricted to travel outside Mainland
China. Traveling abroad only applied for businessmen and government officials.
However there was significant increase in outbound tourism to Hong Kong in the early
1980s. The strong growth of outbound tourism is a result of the Open Door Policy and
the introduction of ?visit friend and relatives travel?. Chinese citizens were issued two-
way permits and allowed to join ?7-night or 8-day package Hong Kong Tours? organized
44
by the China National Tourism Administration (CNTA) approved tour operators with the
sponsor of their friends and relatives in Hong Kong (Qu and Lam, 1997).
International travel by Chinese citizens became a reality in 1990 under the
Approved Destination Status (ADS) system when the bilateral tourism agreements
between China and overseas destinations, such as Singapore, Thailand, and Malaysia,
were signed. In 1991, the Hong Kong government also allowed Chinese tourists with a
valid visa to travel a third country to have free transit for seven days.
In May 2002, a new multiple entry permit was issued to Chinese citizens to
replace the quota system of Hong Kong Tours. The permit was good for traveling Hong
Kong for the purposes of leisure, business, and visiting relatives. In July 2003, the
government of Mainland China announced an Individual Visit Scheme (IVS) for Hong
Kong travel. Residents from selected cities or provinces were allowed to visit Hong
Kong in their personal expenses. The IVS was first applied to five pilot cities and then
gradually extended to residents in many cities and provinces in 2007 (Hong Kong
Tourism Board, 2007).
The purpose of the present chapter is to examine the determinants on Hong Kong
tourism demand for the top three major tourist arrival countries, namely Mainland China,
Taiwan and Japan, with an error correction model (ECM). The use of the ECM provides
detailed adjustment dynamics of tourism demand to change in its determinants.
Specifically, this chapter will examine the effects of relaxing of the visa requirement, the
launch of Individual Visit Scheme, for Mainland Chinese tourists in 2003.
The outline of this chapter is as follows. Section 2 discusses the theoretical
framework and the major determinants of tourism demand in the literature. Section 3
45
discusses the stationarity analysis of the variables and the econometric model. Section 4
reports the empirical results. Section 5 concludes the chapter with a discussion of the
effect to the tourism demand for Hong Kong focusing on relaxation of the visa
requirement for Mainland Chinese tourists relative to the other two countries.
2. The Theoretical Framework
The present paper adopts the disequilibrium export model of Goldstein and Khan
(1978). The model allows the lagged adjustment of the prices and quantities of exports in
response to the excess demand and excess supply of the market. The international
tourism of the destination country and the domestic tourism of the origin country are
assumed to be imperfect substitutes (Goldstein and Khan, 1985) since foreign countries
provide tourist attraction sites such as cultural heritage, food, and natural resources that
cannot be provided in the home country.
A typical tourist chooses to consume tourism (international or domestic tourism)
and other goods according to preferences and constrained by income. The tourist will
maximize U(q*, q, g) subject to y = pfq* + pq + pgg where q*, q, and g are the quantities
of international tourism, domestic tourism, and other goods, and pf, p, and pg are their
prices in domestic currency, and y is income.
Assume the price of all other goods pg faced by tourists is constant. Travel
decisions are then affected by income and the prices of tourism. If international tourism
is a luxury good, then a tourist has a nonhomothetic utility preference for international
tourism. A tourist will spend an increasing share of income on international tourism
relative to domestic tourism when income increases. Empirical studies show that the
46
income elasticity for international tourism exceeds unity (Harrop, 1973; Rosensweig,
1988; Crouch, 1994; Song, Witt and Li, 2009). The travel decision among international
and domestic destinations is made based on the prices of tourism.
The demand for international tourism in log-linear form follows the export
demand equation of Goldstein and Khan (1978) modified for the context of this chapter is
specified as
????????????? ppayaaq f210* (2.1)
where international tourism demand (q*) is a function of real income (y) and the relative
price of international and domestic tourism ??
??????p
pf .
It should be noted that pf equates to ep* where p* is the price of international
tourism in the foreign currency and e is the nominal exchange rate of foreign currency in
terms of domestic currency. Assuming the domestic tourism is a substitute for the
international tourism, the relative price of international and domestic tourism is defined
as
pep
* . To capture the nominal exchange rate effect on tourism demand,
pep
* is
decomposed into e and
pp*
where
pp*
is the price ratio of international and domestic
tourism. For the same goods, the price difference between two countries will provide the
opportunity for arbitrage. However the price differential of international and domestic
tourism might not be eliminated through arbitrage since they are imperfect substitutes.
Besides the international tourist products require the consumers to travel abroad to
consume the goods.
47
Then (2.1) can be rewritten as
?????????????? ppaeayaaq *3210*
?????? ipaeayaaq 3210* (2.2)
where ??
??????? pppi
* which represents the price ratio of international and domestic tourism of
country i. Finally the relative price of international and domestic tourism ??
??????p
pf in (2.1)
can be decomposed into e and ip as in (2.2).
In tourism demand literature, the dependent variable is the number of tourist
arrivals (Gunadhi and Boey, 1986; Chadee and Mieczkowski, 1987; Patsoratis, Frangouli
and Anastasopoulos, 2005), per capita holiday visits (Martin and Witt, 1987, 1988) or
tourist expenditure (Gonzalez and Moral, 1995; Papatheodorou, 1999; Li, Wong, Song
and Witt, 2006; Thompson and Thompson, 2009).
The choice of dependent variables in the present study is based on the theoretical
equation (2.2). Since q* in (2.2) is the quantity of international tourism, the number of
tourist arrivals is adopted as the proxy for q*. The tourist expenditure is not used since it
is inconsistent with the theoretical equation (2.2) and there is no reliable data available.
The number of tourist arrivals from three origin countries (Mainland China, Taiwan,
Japan) is employed since they are the top three tourist arrival countries to Hong Kong.
Li, Song, and Witt (2005) note that the major explanatory variables in tourism
demand studies include the income of tourists, the own-price of the tourist products, the
substitute-price of the tourist products, the exchange rate, transportation cost, and
48
investment in tourism. In addition, several dummy variables have been used to take into
consideration one-off events such as the oil crises, economic recession, terrorist events,
and border closures.
The income of tourists usually measured in terms of origin country income in
level form (Patsoratis, Frangouli and Anastasopoulos, 2005) or per capita (Lim and
Mcaleer, 2001). The estimated income elasticities typically exceed unity and the normal
range is from 1.0 to 2.0 (Harrop, 1973; Rosensweig, 1988). The empirical literature then
suggests international tourism is a luxury good. In the present study, the real gross
domestic product of the origin countries is employed as the proxy of the tourist income.
Residents of those countries with higher real gross domestic product tend to have higher
demand for international tourism since they have more income to consume luxury goods.
Demand theory requires the consideration of both own-price and substitute-price
effect on consumer demand. However, the own-price (ep*) and substitute-price (p) of
tourist product is difficult to measure since tourist product comprises different
commodities with a variety of quality. There exists no single price for tourist products.
Many tourism demand studies employ the consumer price index ratio ??
??????pp
*
adjusted with the bilateral exchange rate (ei) of the destination country and the origin
country i, ??
??????? ppeP ii
* as the relative price of tourism (Martin and Witt, 1987; Kulendran
and King, 1997; Witt, Song and Wanhill, 2004; Li, Wong, Song and Witt, 2006).
However it has been argued that Pi should be separated into ei and ??
??????pp
* (Crouch, 1994;
49
Witt and Witt, 1995; Webber, 2001). The justification for separating the two variables is
that tourists usually have better information about nominal exchange rates than foreign
prices. It is also noted that tourists are more sensitive to the change of exchange rate than
the cost of living (Harrop, 1973; Webber, 2001; Patsoratis, Frangouli and Anastasopoulos,
2005). In practice, the cross-price effects can be captured by using the consumer price
index ratio of the destination country and the origin country as the proxy for the price
ratio of international and domestic tourism (Crouch, 1994; Witt and Witt, 1995; Dritsakis,
2004; Wang, 2009).
Alternatively, some researchers use the price ratio of the destination and a
weighted average of prices in a set of alternative destinations (Song, Witt, and Jensen,
2003) or a weighted average of prices of the alternative destination adjusted with the
exchange rate (Wong, Song, Witt, and Wu, 2007; Ouerfelli, 2008) as the proxy for the
substitute-price. The disadvantage of using a weighted average price index is that the
cross-price effects among alternative destinations may cancel each other out due to the
aggregation. Therefore a weighted average price index is inappropriate to serve as a
proxy for the substitute-price.
In the present study, the relative price of tourism (Pi) is separated into ei and ??
??????pp
* .
The nominal exchange rate (ei) is a measure of the price of tourist products that is used
to capture the response of tourists to the change of the nominal exchange rate. The
consumer price index ratio ??
??????pp
* of the destination country and the origin country is a
50
proxy for the price ratio of international and domestic tourism. The choice of the
explanatory variables, ei and ??
??????pp
* , is consistent with the theoretical equation (2.2).
Since some special events occur during the period of the present study, several
dummy variables (D) are included to capture the effects of the events. Then the tourism
demand function (2.2) can be restated as
??????? Dapaeayaaq i 43210* (2.3)
A dummy variable (D1) captures the effect of 1997 for Japanese tourists. The
Japanese believed that Hong Kong might become politically unstable and might have a
drastic change of visa requirement for foreign tourists after 1997. Therefore they were
interested to visit Hong Kong in 1996 before the British government handed over the
country to the Mainland China government on July 1, 1997. As a result, Hong Kong
recorded a remarkable increase of Japanese tourist arrivals during 1996.
A dummy variable (D2) captures a new travel policy for Taiwanese to visit
Mainland China for family reunions through a third country after 1987. Since Hong
Kong served as the most convenient transit country to enter Mainland China for
Taiwanese tourists, there was a significant increase of Taiwanese tourists to Hong Kong
in 1988 and the years after.
A dummy variable (D3) takes into consideration the effect of introducing the
Individual Visit Scheme for Mainland Chinese tourists.
Another dummy variable (D4) is used to capture the effect of the outbreak of a
deadly contagious disease, Severe Acute Respiratory Syndrome (SARS) in Asian
countries in early 2003. The number of cases and deaths from SARS in Hong Kong were
51
1755 and 299 respectively in 2003 (SARS Expert Committee, 2003). To avoid the spread
of this highly infectious disease, the World Health Organization issued warning to
tourists to postpone visiting SARS affected countries such as Hong Kong. It was
observed that the international tourist arrivals from the major origin countries such as
Taiwan and Japan dropped by 24% and 38% respectively in 2003 (Hong Kong Tourism
Board 2003). In June 2003, the Hong Kong Special Administration Region Government
announced a HK$ 1 billion stimulus package to boost the local economy and entice back
tourists. The package included free airline tickets and special hotel and restaurant
discounts. From June, tourist arrivals started to recover and by August they had returned
to their original levels (Siu and Wong, 2004). A full recovery of the international tourist
arrivals from some major source countries, such as Japan and Taiwan, was achieved only
in 2004.
The final specification of the three tourism demand equations is as follows:
Mainland China:
ctctctctct Daeapayaaq ??????? 343210 (2.4)
t = 1984 - 2006 for Mainland China
Japan:
jtjtjtjtjt DbDbebpbybbq ???????? 45143210 (2.5)
Taiwan:
tttttttttt DcDcecpcyccq ???????? 45243210 (2.6)
t = 1973 - 2006 for Japan and Taiwan
The definitions of the variables are summarized in Table 2.1.
52
The relationships among the relevant variables are expressed in logarithmic form
and parameters are elasticities of the respective variables. A positive sign for real GDP is
expected since a higher level of tourist income will lead to a higher demand for
international tourism (a normal good). A negative sign of the price ratio of international
and domestic tourism is expected since they are substitutes. A negative sign of bilateral
nominal exchange rate is expected since a depreciation of the origin country?s currency
will increase the price to visit Hong Kong.
A positive sign of the dummy variable (D1) is expected since Japanese are
interested to visit Hong Kong in 1996 before the handover of the country to Mainland
China in 1997. A positive sign of the dummy variable (D2) is expected since Taiwanese
tourists use Hong Kong as a transit country to enter Mainland China under the new visa
policy. A positive sign of the dummy variable (D3) is expected since the relaxation of
visa requirement allows more freedom for Chinese citizens to visit Hong Kong.
The dummy variable (D4) is assigned 1 for both 2003 and 2004 since people
might believe the SARS disease might recur in 2004. Though there is no single cases
recur in Hong Kong, there are several cases reported in Mainland China in 2004. A
negative sign of D4 is expected since tourists are risk averse and prefer to choose safer
destinations to travel.
In early tourism demand studies, econometric modeling is restricted to single
equation static models which usually suffer from the problem of spurious regression and
later studies employ dynamic models such as the autoregressive distributed lag model
(ADLM), cointegrated analysis (CI) and error correction model (ECM), and vector
autoregressive (VAR) model (Li, Song, and Witt, 2005; Song and Li, 2008).
53
The ADLM was used in tourism demand analysis and forecasting (Song, Witt,
and Jensen, 2003; Song, Wong, and Chon, 2003; Wang, 2009). The ECM was employed
to examine the short-run and long-run effects of the determinants on tourism demand
(Kulendran and King, 1997; Song, Romilly and Liu, 2000; Kulendran and Witt, 2001;
Ouerfelli, 2008). The VAR approach treats all variables as endogenous in a system of
equations that can avoid possible bias to predetermine some explanatory variables as
exogenous. Many studies utilize the VAR approach, such as the cointegrated VAR and
VECM for tourism demand estimation (Lim and Mcaleer, 2001; Webber, 2001; Dritsakis,
2004; Mello and Nell, 2005; Bonham, Edmonds and Mak, 2006). However the major
practical challenge of the VAR approach is the choice of the appropriate lag length. If a
large number of lags of each variable is included in each equation, there will be a large
number of parameters for estimation. If the sample size is small, it will consume a lot of
degrees of freedom and results in unreliable regression results (Gujarati, 2003). Since the
sample size in the present study is small, the VAR approach becomes inappropriate.
In the present study, the cointegrated analysis (CI) and error correction model
(ECM) are adopted. The strength of this methodology is that it can capture both the
short-run dynamics and long-run equilibrium relationship of tourism demand to the
change of its determinants. Besides, the explanatory variables in the ECM are almost
orthogonal and that can avoid the issue of multicollinearity (Engle and Granger, 1987).
Indeed, estimating an ADLM involves a large number of explanatory variables that may
have the possibility of suffering from the problem of multicollinearity and causes
unreliable estimated results (Song, Witt and Li, 2009).
54
3. Stationarity Analysis and Econometric Models
Data on the number of tourist arrivals in Hong Kong are from Statistics Review of
Hong Kong Tourism (various years) from the Hong Kong Tourism Board. Data of the
bilateral nominal exchange rate are from Hong Kong Monetary Authority. Data of
consumer price index and implicit GDP deflator of Hong Kong, Mainland China, Japan,
and Taiwan are extracted from International Financial Statistics, International Monetary
Fund. Data of Nominal GDP of Mainland China, Japan and Taiwan are from the
corresponding National Statistics. The period of study is 1973 ? 2006 (annual data for
Taiwan and Japan) and 1984 ? 2006 (annual data for Mainland China since Hong Kong
Tourism Board only released the number of tourist arrivals from Mainland China starting
from 1984). All data are transformed to natural logarithms.
I first test the stationarity of variables by implementing the conventional
augmented Dickey-Fuller (ADF) tests. The results are reported in Table 2.2.
The ADF test with an intercept fails to reject the null hypothesis of a unit root for
all level log variables. With the intercept and the time trend, the ADF test does not reject
the null hypothesis of a unit root for all level log variables. The ADF tests reject the unit
root null hypothesis for all differenced log variables. The results indicate that all log
variables are I(1) series. By visual inspection of the plots of the level variable series in
Figure 2.1(a, b, c) and the differences of variable series in Figure 2.2(a, b, c), the level
variable series are non-stationary and the differences of variable series appear stationary.
Since all variables are difference stationary, a two-stage error correction model
(Engle and Granger, 1987) can be estimated. The first stage is to estimate the log-linear
models for each country. The diagnostics provided for the models are R2, adj. R2, F-test,
55
Durbin-Watson (DW) statistic, and the ARCH (1) test on the residuals. Diagnostic
statistics show that the residuals of the regressions have no autocorrelation or
heteroskedasticity.
A requirement to estimate an ECM is that the variables of the static long-run
equilibrium regression are cointegrated with the same order. To examine the
cointegration relationship among the variables, the Engle-Granger test is employed to
check whether the residual ?t of the OLS equation is stationary based on:
tpi ttt ????? ????? ? ? ?? 1 11 (2.7)
For Japan and Taiwan, the t statistics of the coefficients of ?t-1 reject the null
hypothesis of non-stationary based on the Engle-Granger critical values (MacKinnon,
1991). This result implies that the variables of the respective equations are cointegrated
in the first order. For Mainland China, the t statistic does not reject the null hypothesis of
non-stationary. It shows that there is no cointegration relationship among the variables.
The regression results and the cointegration tests are summarized in Table 2.3.
Although there is no cointegration relationship among the variables of the
Mainland China equation (2.4), the estimation of its ECM is still conducted for
comparison purposes. The second stage is to incorporate the lag estimated residuals (?t-1)
from the cointegration regressions into the corresponding ECMs as follows:
titkitpk kjitpj jiit qxq ?????? ???????? ????? ?? 110
(2.8)
where ? is the first difference operator and xi = [yi, pi, ei]? is a vector of the explanatory
variables of the origin countries, i = Mainland China, Japan, and Taiwan. The ?j are the
impact coefficients which represent the transitory effects of the vector of the explanatory
56
variables on the dependent variable. The ?it-1 is the estimated residual from the
cointegration regression (2.4), (2.5) and (2.6). The coefficient (?) of the ?it-1 is expected
to be negative and the value of ? is greater than -1 and less than 0. The dynamics of the
system will adjust towards equilibrium by ? from the error of the previous period.
Then a general-to-specific approach is adopted to identify the appropriate ECM
for each of the countries. Insignificant variables are dropped and the models are re-
estimated until the most appropriate specification of the ECMs is obtained. The lag
structure and the model specification are selected based on the Akaike Information
Criterion (AIC), the Schwarz Bayesian Criterion (SBC), and the Ramsey RESET test.
The appropriate lag structure of each model is obtained based on the smallest value of
AIC and SBC. Finally the most appropriate ECM models are obtained as follows:
Mainland China: tctctctct yqq ?????? ???????? ?? 12110 (2.9)
Japan: tjtjtjt yq ????? ?????? ? 110 (2.10)
Taiwan: ttttttt yq ????? ?????? ? 110 (2.11)
The diagnostics provided for the error correction models are R2, adj. R2, F-test,
DW statistic, and the ARCH (1) test on the residuals. Residuals of the three models have
no autocorrelation and no heteroskedasticity. There are no model misspecifications based
on the Ramsey RESET statistic. The regression results and the diagnostic statistics of the
ECMs are summarized in Table 2.4.
The transitory and the dynamic effects of the explanatory variables (the ?xi, xit-1
and the dummies) on Hong Kong tourism demand are derived by multiplying the
significant error correction coefficients (?) of ?it-1 in Table 2.4 by each of the significant
57
coefficients (as, bs and cs) in Table 2.3. The standard errors of the derived coefficients are
derived from the rules of error propagation as functions of the standard errors of as, bs, cs,
and ?. The derived coefficients and the t-statistics of ?xi, xit-1 and the dummies on Hong
Kong tourism demand are summarized in Table 2.5.
Finally, the difference models of the three countries are estimated for comparison
purposes. Although the difference model overcomes the problem of spurious regression
as the ECM, the major shortcoming of this methodology is that it can only provide short-
run effects and fails to capture the long-run dynamics of the dependent variable to the
change of the explanatory variables.
The difference models of the three countries are estimated based on:
ititititit epyq ????? ????????? 3210 (2.12)
where ?yit, ?pit, and ?eit are the first differenced of the real GDP, the price ratio of
international and domestic tourism, and the bilateral nominal exchange rate respectively.
The diagnostics provided for the difference models are R2, adj. R2, F-test, DW statistic,
and the ARCH (1) test on the residuals. The model specification is checked by the
Ramsey RESET test. The regression results and the diagnostic statistics are summarized
in Table 2.6.
Regression results show that Mainland China equation has a statistically
significant positive short-run income effect. However the low F statistic indicates that
the overall significance of the equation is statistically insignificant. Taiwan equation has
a statistically significant negative short-run exchange rate effect but there exists the
problem of model misspecification by the Ramsey RESET test. As a result, the
regression results in both Mainland China and Taiwan equations are unreliable. Indeed,
58
only the Japan equation passes all diagnostic tests and produces a statistically significant
positive short-run income effect.
In comparison with the difference model, the ECM produces more reliable
regression results and has no problem of model misspecification. In addition, the ECM
can also capture the long-run effects of the tourism demand to the change of its
determinants. Therefore only the regression results of the ECMs are discussed below.
4. Empirical Results
4.1. Mainland China
For Mainland China, the statistically significant lag estimated residual (?t-1) in the
ECM equation implies the existence of a cointegration relationship among the variables
of the Mainland China equation (2.4) based on Granger Representative Theorem (Engle
and Granger, 1987). The tourist arrivals in the present year will adjust towards
equilibrium by -0.43 of the error in the previous year.
The transitory income effect on Hong Kong tourism demand is 2.11. The lag-
income effect each period is 0.65 and adjusts dynamically towards long-run equilibrium
(1.52). Chinese tourists are income elastic for Hong Kong tourism. International tourism
is considered as a luxury good for Chinese tourists. This result confirms previous studies
with income elasticities usually above unity.
The Individual Visit Scheme (D3) has a significant positive impact on Chinese
tourism demand for Hong Kong. The positive sign (0.41) of D3 represents a 51%
increase in tourism demand. The percentage change is computed by using the formula
(exp (0.41) ? 1) x 100% = 51%. Since the IVS is launched at the same year of the SARS
59
outbreak, the 51% is a net rise in tourism demand resulting from outweighing the
negative effect of the SARS by the positive effect of the IVS.
The price ratio of international and domestic tourism is statistically insignificant
in both short- and long-run. The nominal exchange rate is statistically insignificant in the
short-run while it is statistically significant but with a wrong sign in the long-run. The
price ratio and exchange rate in the present study do not exhibit significant impacts on the
Chinese tourism demand for Hong Kong.
4.2. Japan
For Japan, the statistically significant lag estimated residual in the ECM equation
implies the tourist arrivals in the current year will adjust towards the underlying long
term equilibrium by -0.64 of the error in the previous year. There is a significant
transitory income effect with a short-run income elasticity (4.73) but the long-run
elasticity is insignificant and with a wrong sign. Japanese tourists are income elastic for
Hong Kong tourism and consider international tourism a luxury good.
The nominal exchange rate is an additional measure of the price of tourism.
There is insignificant transitory exchange rate effect on Hong Kong tourism demand.
However there is significant lag-exchange rate effect (-0.52) each period that converges
towards equilibrium (-0.81) in the long-run that adds to an inelastic tourism demand.
The price ratio of international and domestic tourism is significant but with a
wrong sign. Japanese tourists are more sensitive to the change of the nominal exchange
rate than the change in the price ratio of tourism.
60
Japanese has a special interest to visit Hong Kong in 1996 before the return of the
country to the Chinese government on July 1, 1997. The dummy (D1) is statistically
significant with the expected sign (0.48). The incidence of the SARS outbreak has a
significant negative impact on Japanese tourism demand. The negative coefficient (-
0.34) of D4 represents an approximately 40% drop in tourism demand after the SARS
outbreak.
4.3. Taiwan
For Taiwan, the statistically significant lag estimated residual in the ECM
equation implies the tourist arrivals in the current year will dynamically adjust towards
steady state by -0.50 of the error in the previous year. The transitory income effect on
Hong Kong tourism demand is 2.80. The lag-income effect is 1.00 and converges to
equilibrium with long-run income elasticity being 2.02. Taiwanese are income elastic
and consider international tourism a luxury good.
The price ratio of international and domestic tourism is significant but with a
wrong sign. The nominal exchange rate is statistically insignificant.
The new visa policy for Taiwanese to visit Mainland China for the purpose of
family reunion through a third country in 1988 has a significant beneficial impact for
Hong Kong tourism sector. Since there were no direct flights between Taiwan and
Mainland China until 2008, Hong Kong became the most convenient transit country for
Taiwanese to enter Mainland China. The dummy (D2) is statistically significant and with
the expected sign (0.75). However the SARS outbreak in 2003 has a significant adverse
61
effect for Taiwanese tourism demand. The negative coefficient (-0.40) of D4 represents
an approximately 49% drop in tourist arrivals during the SARS period.
5. Conclusion
The purpose of this chapter is to examine the determinants on Hong Kong tourism
demand from Mainland China, Taiwan, and Japan, and the effect of the relaxation of visa
requirement for Mainland Chinese tourists relative to the other two countries.
The error correction model investigates the short- and long-run dynamics of the
change of the tourist arrivals to Hong Kong from the change of the tourist income, the
price ratio of tourism, the nominal exchange rate and special events. The empirical
results confirm that tourists are income elastic. The short-run and long-run income
elasticities exceed unity. The finding is consistent with most of the previous studies that
consumers consider international tourism a luxury product.
The empirical results of the price ratio of tourism are either insignificant or
significant with a wrong sign in all models. The consumer price index ratios of Hong
Kong and the origin country might not be good proxy variables for the price ratio of
tourism since they represent the price level of the basket of goods and services of a
representative household and that might not be the typical basket of goods and services of
a tourist. A better alternate proxy or a more direct measure variable for the price of
tourist products might be used for further studies if data becomes available.
The effect of the nominal exchange rate on tourism demand is significant for
Japan. The Japanese are more sensitive to the change of the nominal exchange rate while
there is no evidence of any exchange rate effects on the Chinese and Taiwanese tourists.
62
Since the Chinese currency is managed by the Chinese officials under the period of study,
its effects on the international tourism demand may not be fully captured in the present
estimation. Taiwanese tourists consider Hong Kong is a transit country to enter Mainland
China. For those transit tourists, they will stay in Hong Kong for one or two days. As a
result, the exchange rate may not exhibit significant impact on their visits to Hong Kong.
The return of Hong Kong to the Chinese government in 1997 attracted a lot of
Japanese to visit Hong Kong during 1996. The new visa requirement for Taiwanese to
visit Mainland China through a third country in 1988 had a significant beneficial impact
on Hong Kong tourism industry. The external shock of the Severe Acute Respiratory
Syndrome outbreak has a detrimental effect on tourism demand confirmed by the
Japanese and Taiwanese tourists. Although Chinese tourists face the same risk of Severe
Acute Respiratory Syndrome, the favorable effect of freedom to visit Hong Kong under
the Individual Visit Scheme outweighs the adverse impact of the Severe Acute
Respiratory Syndrome.
Since residents from more cities and provinces are allowed to visit Hong Kong
under the Individual Visit Scheme in the coming years, the tourism demand for Hong
Kong will continue to grow. With the sustainable economic growth of Mainland China
in the current years, more and more Chinese citizens can afford to travel abroad for
sightseeing and shopping. Hong Kong is one of the favorable holiday destinations for
Chinese tourists in Asia. As a result, the tourism receipts from Chinese tourists will
continue to be one of the major income sources of Hong Kong in the coming years.
63
Table 2.1: Definitions of Data Variables
Variable
Explanatory Notes
qc , qj , qt The number of tourist arrivals to Hong Kong from Mainland China, Japan,
and Taiwan
yc , yj , yt
The real Gross Domestic Product in the national currency of Mainland
China, Japan, and Taiwan
ec , ej , et The bilateral nominal exchange rate is defined as the origin country?s
currency per Hong Kong dollar (Mainland China, Japan and Taiwan).
pc, pj, pt
The price ratio of international and domestic tourism (the price of tourism
in Hong Kong relative to the price of tourism in the origin country), i.e.
phk/pi where i = Mainland China, Japan, and Taiwan
D1 The dummy variable represents the 1997 effect for Japanese tourists (1 for
year 1996 and 0 for the other years)
D2 The dummy variable represents a new visa policy for Taiwanese tourists to
visit Mainland China for family reunion through a third country (1 for year
1988 to 1994 and 0 for the other years)
D3 The dummy variable represents the launch of Individual Visit Scheme
(IVS) in 2003 (0 for years before the launch of IVS and 1 for year 2003
onward). Since IVS and SARS occur in the same year so that D3 represents
the joint events.
D4 The dummy variable represents the year of Severe Acute Respiratory
Syndrome (SARS) outbreak in 2003.
Japan model: 1 for year 2003 and 2004 and 0 for the other years. The SARS
effect lasts for two years for Japanese tourists.
Taiwan model: 1 for year 2003 to 2006 and 0 for the other years. The
SARS has a longer adverse effect for Taiwanese tourists
64
Table 2.2: Unit Root Test Results
Variable Specification ADFc ADFc,t
qc Level -0.97 -2.08
Differenced -4.05** -3.71**
qj Level -1.21 -1.68
Differenced -5.42*** -5.40***
qt Level -1.59 -0.99
Differenced -4.79*** -4.96***
yc Level 1.66 -2.14
Differenced -4.10*** -4.63***
yj Level -1.81 -1.70
Differenced -3.37** -4.43***
yt Level -2.60 0.15
Differenced -3.97*** -5.37***
ec Level -3.51** -1.68
Differenced -4.30*** -5.13***
ej Level -1.29 -0.98
Differenced -5.40*** -4.65***
et Level -1.17 -0.82
Differenced -5.67*** -5.80***
pc Level -0.95 -3.00
Differenced -5.68*** -5.47***
pj Level -1.84 -1.25
Differenced -4.30*** -3.65**
pt Level -1.02 -0.93
Differenced -6.67*** -6.12***
Note: the number of lags is chosen by the Akaike Information Criterion (AIC). ADFc and ADFc,t refer to
ADF-t statistics when an intercept is included and when an intercept and time trend are included. *, ** and
*** indicate the null hypothesis of unit root is rejected at 10%, 5% and 1% level. Asymptotic critical
values are from MacKinnon (1996).
65
Table 2.3: OLS Regression for Tourism Demand
Independent
Variable
Mainland China
qc
Japan
qj
Taiwan
qt
constant -5.71*** 12.07*** -11.56***
(-3.03) (2.52) (-6.94)
yi 1.52*** -0.20 2.02***
(7.28) (-0.60) (14.41)
pi -0.07 0.26** 0.59***
(-0.11) (2.44) (3.55)
ei 0.37* -0.81*** 0.51
(1.94) (-5.35) (1.58)
D1 --- 0.48*** ---
(3.90)
D2 --- --- 0.75***
(6.20)
D3 0.41*** --- ---
(3.06)
D4 --- -0.34*** -0.40***
(-3.78) (-3.25)
F 418.78 118.68 397.31
R2 0.99 0.95 0.99
Adjusted R2 0.99 0.95 0.98
DW statistic 1.30 1.65 1.61
ARCH(1) -0.75 -0.23 0.74
Engle-Granger test
EG?,0.5 (-4.56) -3.35 -4.74** -5.02**
DW statistic 2.10 2.05 1.87
ARCH(1) 0.05 -0.09 1.16
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses. Critical value at the 5% level of Engle-Granger test is
calculated from MacKinnon (1991). i = Mainland China, Japan and Taiwan
66
Table 2.4: Error Correction Model
Independent
Variable
Mainland China
?qc
Japan
?qj
Taiwan
?qt
constant -0.07 -0.09* -0.07
(-0.69) (-2.00) (-0.71)
?qi-1 0.33 --- ---
(1.37)
?yi 2.11* 4.73*** 2.80**
(1.93) (3.30) (2.14)
?it-1 -0.43* -0.64** -0.50*
(1.76) (-2.67) (-2.02)
F 2.55 8.80 4.33
R2 0.31 0.37 0.22
Adjusted R2 0.19 0.33 0.17
DW statistic 1.66 2.21 1.98
ARCH(1) -0.59 1.85 -0.31
AIC -1.32 -0.87 0.01
SBC -1.12 -0.73 0.14
Ramsey RESET test
F statistic 1.26 0.61 0.32
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses. i = Mainland China, Japan and Taiwan
67
Table 2.5: Derived Results on Hong Kong Tourism Demand
Independent
Variable
Mainland China
?qc
Japan
?qj
Taiwan
?qt
constant -2.45 7.63*** -5.74*
(-1.52) (3.31) (-1.94)
?yi 2.11* 4.73*** 2.80**
(1.93) (3.30) (2.14)
yi-1 0.65* --- 1.00**
(1.71) (1.99)
ei-1 --- -0.52** ---
(2.39)
D1 --- 0.48*** ---
(3.90)
D2 --- --- 0.75***
(6.20)
D3 0.41*** --- ---
(3.06)
D4 --- -0.34*** -0.40***
(-3.78) (-3.25)
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses. i = Mainland China, Japan and Taiwan
68
Table 2.6: Difference Model
Independent
Variable
Mainland China
?qc
Japan
?qj
Taiwan
?qt
constant -0.03 -0.08 -0.03
(-0.28) (-1.64) (-0.29)
?yi 2.28* 3.85** 1.96
(1.96) (2.26) (1.31)
?pi -0.48 0.32 0.11
(-0.88) (0.63) (0.21)
?ei -0.06 -0.24 -0.97*
(-0.27) (-0.95) (-1.77)
F 2.07 3.40 2.51
R2 0.26 0.26 0.21
Adjusted R2 0.13 0.18 0.12
DW statistic 1.64 2.35 2.05
ARCH(1) 0.02 1.58 0.13
AIC -1.21 -0.65 0.09
SBC -1.01 -0.47 0.27
Ramsey RESET test
F statistic 0.03 0.39 5.17**
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses. i = Mainland China, Japan and Taiwan
69
Figure 2.1a: Variable Series (Mainland China)
-2
0
2
4
6
8
10
12
1
9
8
4
1
9
8
5
1
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8
6
1
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1
2
0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
2
0
0
6
qc yc ec pc
Figure 2.1b: Variable Series (Japan)
-2
0
2
4
6
8
10
12
14
1
9
7
3
1
9
7
5
1
9
7
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1
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1
1
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1
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1
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1
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1
1
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1
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1
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1
9
9
9
2
0
0
1
2
0
0
3
2
0
0
5
qj yj ej pj
70
Figure 2.1c: Variable Series (Taiwan)
-2
0
2
4
6
8
10
1
9
7
3
1
9
7
5
1
9
7
7
1
9
7
9
1
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1
1
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3
1
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5
1
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1
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1
1
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1
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1
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7
1
9
9
9
2
0
0
1
2
0
0
3
2
0
0
5
qt yt et pt
71
Figure 2.2a: Differences of Variable Series (Mainland China)
- 0 . 2
- 0 . 1
0
0 . 1
0 . 2
0 . 3
0 . 4
0 . 5
1
9
8
5
1
9
8
6
1
9
8
7
1
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1
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0
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0
0
2
2
0
0
3
2
0
0
4
2
0
0
5
2
0
0
6
dqc d y c dec dpc
Figure 2.2b: Differences of Variable Series (Japan)
- 0 . 6
- 0 . 5
- 0 . 4
3
- 0 . 2
- 0 . 1
0
0 . 1
0 . 2
0 . 3
0 . 4
1
9
7
4
1
9
7
6
1
9
7
8
1
9
8
0
1
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0
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2
0
0
2
2
0
0
4
2
0
0
6
dqj d y j dej dpj
72
Figure 2.2c: Differences of Variable Series (Taiwan)
- 0 . 6
- 0 . 4
- 0 . 2
0
0 . 2
0 . 4
0 . 6
0 . 8
1
1 . 2
1 . 4
1
9
7
4
1
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2
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2
0
0
6
dqt d y t det dpt
73
CHAPTER 3
TRADE, TOURISM, EDUCATION, AND GROWTH IN MAURITIUS
1. Introduction
Mauritius is an island economy in the Indian Ocean. The country had been an
underdeveloped country with low per capita income and high unemployment before its
independence in 1968. In late 1970, the economy suffered a sugar bust and faced a large
trade deficit. Mauritius then experienced significant economic growth starting from 1984
under a newly restructured Export Processing Zone (EPZ). Income per capita ranks
second among Sub-Sahara African countries in 2006 (World Bank Development
Indicators, 2008). Its global economic freedom ranks 18 among 157 countries based on
the index of economic freedom published by the Heritage Foundation (2008).
This chapter aims to analyze the impacts of trade openness, tourism, investment,
and human capital investment on economic growth in Mauritius. Aggregate and
disaggregated measures of these determinants examine their effects on economic growth.
An error correction model (ECM) indicates positive effects of the EPZ, tourism,
investment, and human capital investment. The strategic tourism marketing policy aimed
at high spending tourists has led to economic growth. The advantage of the
disaggregated measures is that they provide more information about the effects of the
specific measures on growth. The use of the ECM methodology can capture the
74
dynamics of the output growth relative to the specific determinants of growth. The
findings may shed light on policy implications for other similar economies.
The outline of this chapter is as follows. Section 2 reviews the economic history
of Mauritius with a focus on the Export Processing Zone, the tourism industry, and the
human capital accumulation through education. Section 3 discusses the theoretical
framework. Section 4 discusses the stationarity analysis of the variables and the choice
of econometric models. Section 5 reports the empirical results. Section 6 concludes the
chapter with a discussion of policy implications.
2. A Brief History of Economic Development in Mauritius
Mauritius is a small island economy in the Indian Ocean with a land area of 2040
sq km and a population of 1.2 million in 2007. A country is classified as ?small? if its
population is less than 1.5 million and the major characteristics of small island economies
are their deficiencies in natural resources, isolation, and remoteness from the other
countries (Prasad, 2003). Given these unfavorable resources endowment, the Nobel
laureate James Meade claims that the economic future of Mauritius will be potentially a
failure due to its heavy dependence on mono-crop production (sugar) and rapid
population growth (Meade, 1961). His pessimistic view about economic development in
Mauritius has proven to be a mistake by the rapid and sustained growth of Mauritius in
the past three decades. The country is praised as a ?Tiger of the Indian Ocean? since the
1980s. It has outperformed the other Sub-Sahara African countries due to its success in
turning around its economy by restructuring and strengthening the Export Processing
Zone (EPZ) and success in developing its tourism sector.
75
With a small domestic market, to achieve any economies of scale or any
sustainable production level requires trading with foreign countries. It is observed that
small island economies rely heavily on trade (exports and imports) to a greater extent
than larger countries (Prasad, 2003).
Trade can enhance growth. A developing country can obtain advanced
technology through trade with developed countries to enhance productivity. However,
the endogenous growth literature has diverse conclusions in which trade restrictions or
trade openness can increase or decrease the economic growth rate of the trading countries
(Romer, 1990; Grossman and Helpman, 1990; Matsuyama, 1992, Sachs and Warner,
1997). A country could liberalize its export sector and yet employ highly restricted trade
policies for imports to encourage development of import substitution industries (Krueger,
1978).
Mauritius was a typical country adopting highly open policy in exports but highly
restrictive policy in imports during the 1970s and 1980s. In the 1980s, the average tariff
was more than 100% and there are quotas on around 60% of imports (Subramanian,
2001). From the 2000s, the country adopted a more relaxed policy in imports. In 2005,
the weighted average tariff rate was about 4.7% though there are still some quantity
restrictions on imports.
2.1. Export Processing Zone
The Export Processing Zone (EPZ) in Mauritius is first established in 1970. It
was restructured and strengthened in 1984 with the introduction of many effective new
policy instruments (Subramanian and Roy, 2001). The government provides a variety of
76
tax incentives for firms operating in the EPZ. The major incentive is a 10 year tax break
on firms? retained earnings and later the tax break was extended to 20 years to retain the
interests of the foreign investors in EPZ.
The corporate tax is reduced to 15% in the EPZ providing incentives for foreign
direct investment in Mauritius. In addition, in order to increase the EPZ exporter
competitiveness, duty-free is applied to all imported inputs.
The Mauritius government separates the labor market of the EPZ from the other
sectors in the economy. Employers in the EPZ have greater flexibility in employing and
discharging workers, and workers are allowed to work longer hours each day. As a wage
policy, the minimum wage for females is relatively lower than males in the EPZ. The
lower labor cost provides incentives for firms to absorb unemployed women into EPZ.
Eventually the minimum wage law for men was also abolished in the EPZ in 1984. The
new labor policy largely increases the employment within the EPZ, especially the
employment for men.
The EPZ in Mauritius is mainly textile-based Export Processing Zone with about
90% textile and clothing exports. The country?s EPZ textile exports benefited from the
Multi-Fiber Agreement (MFA) during the 1980s and 1990s. The majority of textile
exports are shipped to European countries such as France and the United Kingdom, and
to the United States. The EPZ contributed over 25% to GDP during the period from 1986
to 2001. However, the EPZ textile exports are strongly affected due to the expiration of
the quotas under the World Trade Organization Agreement on Textile and Clothing
(ATC) at the end of 2004. Despite the declining trend of EPZ exports in recent years, the
77
restructured EPZ with the effective policies has been regarded as the engine of the rapid
economic growth in 1980s and 1990s.
2.2. Tourism
An increasing amount of literature has been analyzing the causal relationship
between tourism and economic growth. Some studies focus on tourism impacts on
growth in specific countries, such as Mauritius, Greece and South Korea (Durbarry, 2004;
Dritaskis, 2004; Oh, 2005) while other studies compare the relative growth performance
with a sample of tourism countries (Lea, 1988; Sinclair, 1998; Brau, Lanza and Pigliaru
2003; Leea and Chang, 2008).
Mauritius is a holiday destination beach resort in the coastal area. Tourism has
been a growing industry in Mauritius over the last three decades. The contribution of the
tourism sector to Gross Domestic Product in Mauritius has made it one of the three pillars
of the economy along with sugar production and the Export Processing Zone.
In 2006, tourism has contributed to about 16% of the GDP and 26% of the exports.
There is a significant increase in gross tourist receipts from US $69 million in 1980 to US
$1065 million in 2006. The major source of tourists is from European countries with
majority from France (23 %) and United Kingdom (13%). The second source is from the
nearby Reunion Island (11%) and South Africa (9%). The number of tourist arrivals rose
from 115,080 to 788,216 between 1980 and 2006.
The national tourism marketing policy is to target high end tourist products, such
as luxurious hotels and beach resorts, and to attract high spending tourists from Europe.
Most prestige resort hotels are operated and managed by large hotel groups that are
78
recognized internationally for providing high quality accommodation service. The
market positioning at high spending tourist is proven to be successful and evidenced by
the rising trend of the average tourist spending in real term over the period from 1980 to
2006.
2.3. Human Capital
The importance of human capital for economic growth is investigated in many
empirical studies (Barro, 1991; Mankiw, Romer and Weil, 1992) and the studies find that
human capital accumulation through schooling has a significantly positive impact on
economic growth. Barro and Sala-i-Martin (2004) further investigate the impact of
educational expenditures by governments and conclude that there is a strong positive
impact of male education on growth. On the contrary, Caselli, Esquivel and Lefort (1996)
and Knowles, Lorgelly and Owen (2002) find that female secondary education rather
than male secondary education leads to economic growth.
For more than 30 years, human capital in Mauritius has played a prominent role in
the country?s development since 1970s. However, Mauritius is facing severe budget
constraints to engage in human investment by developing an effective educational system.
A major achievement of the educational system so far is that it has provided the
manpower requirements for the Export Processing Zone and the tourism industry.
Secondary school enrollment rate in Mauritius rose from 44% to 69% from 1980
to 2006. Specifically, male secondary school enrollment rate increased from 46% to 66%
and female enrollment rate increased from 43% to 72%. However, the educational
system is lagging behind in manpower requirements for the economic transformation of
79
the economy (Bunwaree, 2001; Sacerdoti, El-Masry, Khandelwal, and Yao, 2005). The
relatively poor quality of scientific and technological education cannot equip enough
skilled labor to meet the demand for technical workers for the future economic
development in the high technology sector.
3. The Theoretical Framework
The output growth of an economy is due to growth in inputs including labor,
physical and human capital, or productivity growth (Feenstra, 2004). The source of
productivity growth does not just come from technology; it also includes resource
endowments, climate, institution, and some other variables (Mankiw, Romer, and Weil,
1992). Assume the aggregate output function as
Yt = AtF(Kt, Ht, Lt)
where Yt is the output at period t, Kt is the stock of physical capital, Ht is the stock of
human capital, Lt is the labor force, and At is a measure of Hicks-neutral technological
progress that also represents multifactor productivity.
Suppose the production function is defined as:
Yt = AtKt?Ht?Lt(1-?-?) (3.1)
The production function exhibits constant return to scale. Assume the values of ? and ?
are positive and (? + ?) is less than 1, which implies that labor, physical and human
capital exhibit diminishing returns.
To derive the output per labor or the productivity of a labor, (3.1) is divided by
the labor force on both sides to obtain
80
??
????????????????? ttttttt LHLKALY
yt = Atkt?ht? (3.2)
where yt is the output per labor, kt is the physical capital per labor and ht is human
capital per labor.
To capture the effect of the trade on the output per labor (yt), a vector of trade
variables, T, are incorporated into (3.2) for estimations (Frankel and Romer, 1999;
Yanikkaya, 2003). In general form, the model is as follows,
yt = Atf (kt, ht; T) (3.3)
where T includes the trade measures, such as the ratio of exports and imports to GDP
(oy), the import penetration ratio (mp) and the exports share in GDP (xy), the exports
share of Export-Processing Zone in GDP (ey), and the tourist receipts per tourist (t).
For the dependent variable, per capita real GDP is used as a proxy for the output
per labor (Lea, 1988; Mankiw, Romer, and Weil, 1992; Sinclair, 1998; Barro and Sala-i-
Martin, 2004; Durbarry, 2004).
The physical capital per labor is the critical determinant in the neo-classical
growth model. The derivation of the ratio of physical capital to labor requires an
estimation of the capital stock. By using the perpetual-inventory method, the net capital
stock in period t, Kt, is the accumulation of the weighted investment series of all
surviving vintages. That is Kt = ?0It + ?1It-1 + ? + ? TIt-T, where ?0 = 1 and (t-T) is the
year of the oldest surviving vintages (Hulten, 1990). The value of the efficiency weights,
?s, is between zero and one, and the current capital is more productive (or efficient) than
the older capital, i.e. ?0 > ?1 > ? > ?T. Assume the efficiency weights follow a ?one-
81
hoss shay? pattern, that is most of the assets have the same productivity regardless of
their age and their productivities will drop to zero when they are retired. Then the
efficiency weights, ?s, of the assets are equal to one in their life-time, i.e. ?0 = ?1 = ? =
?T = 1.
As a result, the estimation of the net capital stock is essentially the same as the
estimation of the gross capital stock (Hulten, 1990). The gross capital stock in period t,
KGt, is derived by adding the new gross fixed capital formation (It) of each year to the
existing capital stock, that is KGt = It + It-1 + ? + It-T .
Barro and Sala-i-Martin (2004) point out a practical problem of estimating the
initial capital stock, K0. They argue that the estimation of the capital stocks of the first
few years is unreliable and depends heavily on the accuracy of the guess of the initial
capital stock. However, the estimated capital stocks will become more and more accurate
after a few years. Therefore, in my present study, the initial capital stock (K0 = It-T) is the
sum of the gross fixed capital formation of the initial year (1980) and the previous four
years (1976 - 1979). By using this technique, the guess of the initial capital stock
becomes more accurate. Then KGt is divided by the labor force (Lt) to obtained the
physical capital per labor, i.e. kt = (It + It-1 + It-2 + ? + It-T ) / Lt.
In growth theory, human capital is a source of economic growth. A vast amount
of research investigates how human capital accumulation will sustain economic growth
(Lucas, 1988; Jones and Manuelli, 1990; Rebelo, 1991; Stokey, 1991, Barro, 2001). The
secondary school enrollment rate is a proxy for human capital accumulation (Barro, 1991;
Mankiw, Romer, and Weil, 1992). In addition, many researchers also interested in
whether there is a significant difference between male and female education on economic
82
growth (Caselli, Esquivel, and Lefort, 1996; Knowles, Lorgelly, and Owen, 2002; Barro
and Sala-i-Martin, 2004). In the present study, the aggregate secondary school
enrollment rate is used in the first model and then two disaggregated variables, male and
female school enrollment rates, are used for the second model.
Trade openness as an explanatory variable has been used in many studies (Romer,
1990; Young, 1991; Stokey, 1991; Frankel and Romer, 1999; Rodr?guez, F. and D.
Rodrik, 2001). Some studies report that trade openness can speed up growth (Romer,
1990) while some other studies argue that trade openness can slow down growth (Young,
1991; Stokey, 1991). Yanikkaya (2003) had explored the effects of a set of trade
openness measures on economic growth in a cross-country study and found ambiguous
results.
The typical proxy for trade openness is the ratio of the sum of exports and imports
to GDP. In addition, disaggregated variables, such as exports share of GDP and imports
penetration ratio, are also proxies for trade openness. In the present study, the ratio of the
sum of exports and imports to GDP (oy) is used in one model. Next, the import
penetration ratio (mp) and the exports share in GDP (xy) are used to explore the specific
effects of exports and imports on economic growth.
Rodr?guez and Rodrik (2001) argue that the indicators of ?openness? in many
studies are poor measures of trade barriers. Therefore, in my present study, two
additional disaggregated trade variables, the export share of Export-Processing Zone in
GDP (ey) and the tourist receipts per tourist (t), are used to investigate their effects on
growth.
83
In a recent study, Durbarry (2004) uses the EPZ exports as an explanatory
variable. The problem is that EPZ exports are parts of the total exports and enter directly
to GDP leading to the problem of endogeneity in regression estimates. Instead, the
exports share of EPZ in GDP (ey) is used to capture the effect of the restructured EPZ on
economic growth after 1984.
Tourism as a source of economic growth and development has been recently
studied (Sinclair, 1998; Durbarry, 2004; Dritaskis, 2004; Oh, 2005; Kim, Chen, and Jang,
2006; Brida, Carrera, and Risso, 2008) and the empirical results are mixed. Tourism
revenue, the number of tourist arrivals, and tourist receipts per tourist are the potential
candidates of the explanatory variable. Tourism revenue is a component of exports in
national income accounting and will lead to endogeneity problem in regression estimates.
In the present study, the appropriate proxy is the tourist receipts per tourist instead
of the number of tourist arrivals. Since Mauritius tourism marketing tactic is aiming at
high spending tourists, the tourist receipts per tourist as an explanatory variable tests the
hypothesis that high spending tourism contributes to economic growth.
Although rising tourist arrivals may contribute to economic growth, it is not
appropriate to test Mauritius tourism marketing policy. More tourist arrivals may lead to
economic growth while higher per tourist spending leads to higher economic growth.
The number of tourist arrivals has been included as an explanatory variable in the
preliminary estimation for comparison purposes.
To determine the responsiveness of income growth to the sources of economic
growth such as physical and human capital accumulation, trade openness, the exports of
EPZ and the tourist receipts per tourist, a number of log-linear production functions based
84
on equation (3.3) are derived for estimations. First, per capita real GDP (y) is regressed
by a set of explanatory variables including the physical and human capital per labor (k
and h) and a trade measure: the ratio of the sum of exports and imports to GDP (oy).
Second, to capture the specific effects of imports and exports on economic growth,
two separate disaggregated trade measures, the import penetration ratio (mp) and the
exports share in GDP (xy), replace the ratio of the sum of exports and imports to GDP
(oy) for estimation.
Third, to capture the specific effects of exports of EPZ and tourism on growth, the
EPZ export share in GDP (ey) and tourist receipts per tourist (t) are used for estimation.
Finally, to capture the specific effects of male and female education on economic
growth, two separate disaggregated human capital measures, the male and female
secondary school enrollment rates (mh and fh) replace the aggregate secondary school
enrollment rate (h) for estimation.
Since the relationships among the relevant variables are expressed in logarithmic
form, parameters are the elasticities of the respective variables. The expected sign of the
physical capital per labor (k) is positive since physical capital is an essential determinant
for growth in neoclassical growth theory.
The expected sign of the ratio of the sum of exports and imports to GDP (oy) is
ambiguous. It is reported in many previous studies that the sign of the variable can be
positive or negative (Romer, 1990; Grossman and Helpman, 1990; Matsuyama, 1992).
The expected sign of the exports share in GDP (xy) is positive while the import
penetration ratio (mp) can be positive or negative. If the major imports are consumer
goods, it may cause a negative effect on growth. However if the imports are essentially
85
capital goods or production inputs that help to produce final goods and services, it may
have a positive impact on domestic growth.
Positive signs of education, male education and female education (h, fh and mh)
are expected since education and training increase the productivities of the labor force.
A positive sign of per tourist spending (t) is expected since tourism is the exports
of services which lead to tourism receipts from foreign tourists.
The expected sign of the restructured EPZ (ey) is positive since it provides
incentives for investment in EPZ which leads to increasing exports.
Preliminary regression results from estimating the production functions show that
the import penetration ratio (mp) is statistically insignificant and the inclusion of the
export share in GDP (xy) in the model causes the problem of model misspecification.
For comparison purposes, the number of tourist arrivals replaces the tourist receipts per
tourist. Regression results show that although the number of tourist arrivals has a
positive impact on growth, the inclusion of the variable in the models causes the problem
of model misspecification.
As a result, three production functions are chosen for estimation:
Model 1: yt = a0 + a1kt + a2ht + a3oyt + ?t (3.4)
Model 2: yt = a0 + a1kt + a2ht + a3tt + a4eyt + ?t (3.5)
Model 3: yt = a0 + a1kt + a2fht + a3mht + a4tt + a5eyt + ?t (3.6)
The definitions of the variables are summarized in Table 3.1.
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4. Data and the Choice of Econometric Models
Data on per capita real GDP, gross fixed capital formation, exports and imports of
goods and services, secondary school enrollment rate, and tourist receipts are from the
Central Statistic Office of Mauritius. GDP implicit price deflator is from the World Bank
Development Indicators compiled by the World Bank. All nominal data are deflated by
the GDP implicit price deflator at the base year 1990 constant price. The period of study
is 1980 to 2006 (annual data).
Stationarity of variables is pretested to check whether the variables are stationary
series converging to steady state levels. The results of the unit root test from
conventional augmented Dickey-Fuller (ADF) tests of the variables are summarized in
Table 3.2. The number of lags is chosen by the Akaike Information Criterion (AIC).
The ADF test with an intercept fails to reject the null hypothesis of a unit root for
all level log variables. With the intercept and time trend, the ADF test does not reject the
null hypothesis of a unit root for all level log variables. By visual inspection, the plotted
variables series in Figure 3.1(a, b) appear non-stationary.
The ADF tests with an intercept reject the unit root null hypothesis for all
differenced log variables except the physical capital per labor (k). The ADF tests with an
intercept and trend reject the unit root null hypothesis for all differenced log variables
except the import penetration ratio (mp). The plots of the differences of the k and my
series in Figure 3.2(a, b) appear stationary. The results indicate all log variables are I(1)
series.
Since all log variables are difference stationary, a two-stage error correction
model (ECM) (Engle and Granger, 1987) may be estimated. The first stage is to estimate
87
the log-linear models 1, 2 and 3. The diagnostics provided for the models are R2, adj. R2,
F-test, Durbin-Watson (DW) statistic, and the ARCH (1) test on the residuals. Diagnostic
statistics show that the residuals of the regressions have no autocorrelation or
heteroskedasticity for models 1 and 3. For model 2, the residuals have the problem of
heteroskedasticity by the ARCH (1) test.
A requirement to estimate an ECM is that the variables of the static long-run
equilibrium regression are cointegrated with the same order. To examine the
cointegration relationship among the variables, the Engle-Granger test is employed to
check whether the residual ?t of the OLS equation is stationary based on:
tpi ttt ????? ????? ? ? ?? 1 11 (3.7)
For models 1 and 3, the t statistics of the coefficients of ?t-1 reject the null
hypothesis of non-stationary based on the Engle-Granger critical values (MacKinnon,
1991). The test results imply that the variables of the respective equations are
cointegrated in the first order. For model 2, the t statistic does not reject the null
hypothesis of non-stationary. This result shows that there is no cointegration relationship
among the variables. The regression results, the diagnostic statistics and the
cointegration tests are summarized in Table 3.3.
Although there is no cointegration relationship among the variables of model 2,
the estimation of its ECM is conducted for comparison purposes. The second stage is to
incorporate the lag estimated residuals (?t-1) from the cointegration regressions into the
corresponding ECMs as follows:
ttktpk kjtpj jt ycxbby ??? ???????? ????? ?? 1100
(3.8)
88
where ? is the first difference operator and x = [k, h; T]? is a vector of the explanatory
variables of the log-linear models 1, 2, and 3. The bj are the impact coefficients which
represent the transitory effects of the vector of the explanatory variables on the dependent
variable. The ?t-1 is the estimated residual from the cointegration regression (3.4), (3.5)
and (3.6). The coefficient (?) of the ?t-1 is expected to be negative and the value of ? is
greater than -1 and less than 0. The system will adjust dynamically and converge towards
equilibrium by ? from the error of the previous period.
A general-to-specific approach is used to find the most appropriate specification
of the ECM. In estimating the general equation (3.8), lagged dependent and explanatory
variables are included and a ?test-down? procedure is used to achieve a specific
specification of the ECM. The insignificant variables are eliminated and the model is re-
estimated until the most parsimonious specification of the ECM is achieved. The lag
structure is selected based on the Akaike Information Criterion (AIC) and the Schwarz
Bayesian Criterion (SBC). The appropriate lag structure of each model is obtained based
on the smallest values of AIC and SBC. The model specification of the ECM is checked
by the Ramsey RESET test. Finally the most appropriate ECM models are obtained as
follows:
Model 4: ?yt = b0 + b1?kt + b2?ht + b3?oyt + ??t-1 + ut (3.9)
Model 5: ?yt = b0 + b1?kt + b2?tt + ??t-1 + ut (3.10)
Model 6: ?yt = b0 + b1?kt + b2?tt + ??t-1 + ut (3.11)
The diagnostics provided for the error correction models are R2, adj. R2, F-test,
DW statistic, and the ARCH (1) test on the residuals. Residuals of the three models have
no autocorrelation and no heteroskedasticity. There are no model misspecifications for
89
models 5 and 6 based on the Ramsey RESET statistics. However there is model
misspecification for model 4. The regression results and the diagnostic statistics of the
ECMs are summarized in Table 3.4.
The transitory and the dynamic effects of the explanatory variables (the ?x and xt-
1) on per capita real GDP are derived by multiplying the significant error correction
coefficients (?) of ?t-1 in Table 3.4 by each of the significant coefficients (as) in Table 3.3.
Standard errors of the derived coefficients are derived from the rules of error propagation
as functions of the standard errors of as, and ?. The derived coefficients and the t-
statistics of ?x and xt-1 on per capita real GDP are summarized in Table 3.5.
Finally, three difference models (growth rate models) are estimated for
comparison purposes. The three difference models are as follows:
Model 7: ?yt = c0 + c1?kt + c2?ht + c3?oyt + ut (3.12)
Model 8: ?yt = c0 + c1?kt + c2?ht + c3?tt + c4?eyt + ut (3.13)
Model 9: ?yt = c0 + c1?kt + c2?fht + c3?mht + c4?tt + c5?eyt + ut (3.14)
The diagnostics provided for the difference models are R2, adj. R2, F-test, DW statistic,
and the ARCH (1) test on the residuals. The model specification is checked by the
Ramsey RESET test.
Regression results show that all coefficients of the three equations are statistically
insignificant by t-test and the overall significance of the equations are also statistically
insignificant by F-test. Therefore the regression results of the three difference models are
not reported here.
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5. Empirical Results
In models 4, 5 and 6, the significant negative lag estimated residuals (?t-1) in the
ECM equations imply the existence of an error correction relationship among the
variables of each of the OLS equations (3.4), (3.5) and (3.6) based on the Granger
Representative Theorem (Engle and Granger, 1987).
The regression result of model 4 shows that the growth rate of the per capita real
GDP in the current year will adjust towards equilibrium by -0.76 of the error term in the
previous year. The transitory effects of physical capital per labor (k) and the human
capital per labor (h) on economic growth are 0.51 and 0.49 respectively. The trade
openness (oy) has a positive transitory effect (0.16) on economic growth. The lagged
effects of physical capital, human capital, and trade openness on economic growth each
period are 0.29, 0.33 and 0.19 respectively. The lagged effects will adjust dynamically
towards long-run equilibrium.
However, model 4 has the problem of model misspecification based on the
Ramsey RESET test. As a result, model 4 may not be an appropriate model for
estimation.
In model 5, two disaggregated trade measures, the tourist receipts per tourist (t)
and the EPZ export share in GDP (ey), are adopted to replace the aggregate trade measure
(oy). The regression result shows that the economic growth rate in the present year will
adjust towards long-run equilibrium by -0.92 of the error in the last year. The transitory
effect and lagged effect of physical capital per labor (k) on economic growth are 0.39 and
0.29. The lagged effects will add to the transitory effects on economic growth
dynamically and converge towards long-run equilibrium. A 10% increase in physical
91
capital per labor leads to 3.9% economic growth in the current year and contributes to
2.9% growth for the next year. The positive effect of physical capital accumulation on
economic growth is consistent with the neo-classical growth theory.
There is no significant transitory effect of human capital (h) on economic growth.
However there is a significant lagged effect (0.59) that converges dynamically to steady
state with a long-run impact (0.64) on growth. Investment in schooling and training
may not have immediate impact on the productivity of labor. It takes time to convert
unskilled to skilled labor and raise their productivities. However the lagged effect of
human capital implies that education has a long-term impact on growth. The positive
impact of human capital investment is also consistent with theory proposed by Mankiw,
Romer, and Weil (1992). A 10% increase in secondary school enrollment rate results a
6.4% economic growth.
The transitory effect of the tourist receipts per tourist (t) on economic growth is
insignificant. However tourist receipts have a significant long-term effect (0.14) on
growth. A 10% increase of per tourist spending raises the economic growth rate by 1.4%.
Tourism plays an important role in economic growth in Mauritius. Tourism policy
targeting at high-spending tourists serves as a very successful tourism development
strategy in Mauritius.
There is no significant transitory effect of the EPZ exports (ey) on economic
growth. However there is still a small lagged effect (0.05) on growth.
In model 6, the male and female school enrollment rates replace the aggregate
secondary enrollment rate for estimation. The regression result shows that the economic
growth rate in the current year will converge towards long-run equilibrium by -0.88 of
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the error in the previous year. The transitory effect of physical capital per labor (k) on
economic growth is 0.33 while the lagged effect is 0.24.
The male capital investment is statistically insignificant while female capital
investment has significant impact on economic growth. Although there is no transitory
effect of female education, there exists a significant lagged effect (0.63) on economic
growth. The lagged effect adjusts dynamically towards equilibrium and contributes a
long-run impact (0.72) on growth. A 10% increase of the female school enrollment rate
leads to 7.2% economic growth in the long-run.
Indeed, when women receive more education and training, their opportunity costs
of staying home increase. Women are willing to leave home and enter the labor market,
increasing the supply of more productive and skilled labor for the EPZ and the tourism
sector.
The transitory effects and lagged effects of the tourist receipts per tourist (t) and
EPZ exports (ey) on economic growth are essentially the same as model 5. With the
rapid development of EPZ and tourism sectors since 1984, EPZ exports and tourism
contribute to the economic growth in Mauritius.
Since both model 5 and 6 have no problem of model misspecification based on
the Ramsey RESET test, the two models are appropriate.
6. Conclusion
This chapter aims at analyzing the impact of trade openness, tourism, physical
capital, and human capital on economic growth in Mauritius. Regression results of the
error correction models confirm the positive effects of the Export Processing Zone,
93
tourism, physical capital investment, and human capital investment on economic growth
in Mauritius. The error correction models capture the short-run and long-run dynamics of
the determinants on growth.
For policy implications, tourism can be considered as a development strategy for
developing small open economies. Higher tourist arrivals lead to economic growth while
higher per tourist spending results a higher growth rate. The strategic tourism marketing
policy aimed at high spending tourists has proven to be successful and contribute
significantly to the economic growth of the country.
The Export Processing Zone in Mauritius has been an impetus of economic
growth. The restructured and strengthened Export Process Zone after 1984 with the
introduction of many effective new policy instruments provides incentives for foreign
direct investment in the country.
The accumulation of physical capital through the development of Export Process
Zone and tourism industry has proved to be an important source of growth.
Education investment is essential for supplying skilled labor for both an Export
Process Zone and tourism. Higher school enrollment today implies a higher supply of
skilled labor a few years later. Education is a sustainable development strategy for all
developing countries. For the future development of high technology industry in
Mauritius, education reform aimed at improving the quality of education is advised.
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Table 3.1: Definitions of Data Variables
Variables Explanatory Notes
y Per capita real GDP
k Physical capital per labor
oy The ratio of the sum of exports and imports to GDP
xy The export share in GDP
mp The import penetration ratio (i.e. the ratio of imports to GDP)
h Aggregate secondary school enrollment rate
mh Male secondary school enrollment rate
fh Female secondary school enrollment rate
ey The exports share of Export Processing Zone in GDP
t Real tourist receipts per tourist
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Table 3.2: Unit Root Test Results
Variable Specification ADFc ADFc,t
y Level -1.67 -0.67
Differenced -3.44** -3.69**
k Level -1.60 -1.06
Differenced -2.03 -3.65**
h Level 0.87 -2.38
Differenced -2.97* -3.33*
fh Level 1.06 -1.28
Differenced -3.48** -3.99**
mh Level 0.80 -1.96
Differenced -2.75* -3.32*
oy Level -2.58 -2.67
Differenced -3.76*** -3.70**
xy Level -1.70 -1.67
Differenced -4.92*** -5.13***
mp Level -2.46 -2.80
Differenced -3.24** -3.18
t Level -2.08 -0.89
Differenced -2.68* -3.39*
ey Level -1.78 -2.25
Differenced -5.39*** -5.23***
Note: the number of lags is chosen by the Akaike Information Criterion (AIC). ADFc and ADFc,t refer to
ADF-t statistic when an intercept is included and when an intercept and time trend are included. *, ** and
*** indicate the null hypothesis of unit root is rejected at 10%, 5% and 1% level. Asymptotic critical
values are from MacKinnon (1996).
96
Table 3.3: OLS Regressions for Per Capita Real GDP
Independent
Variable
Model 1
y
Model 2
y
Model 3
y
constant 11.67*** 11.45*** 11.33***
(496.33) (60.10) (62.26)
k 0.39*** 0.31*** 0.27***
(17.82) (9.73) (8.05)
h 0.43*** 0.64*** ---
(5.10) (4.97)
fh --- --- 0.72***
(3.81)
mh --- --- -0.09
(-0.49)
oy 0.25*** --- ---
(4.88)
t --- 0.14** 0.13**
(2.53) (2.73)
ey --- 0.06** 0.05*
(2.28) (1.91)
F 2276.39 1928.48 1829.32
R2 0.99 0.99 0.99
Adjusted R2 0.99 0.99 0.99
DW statistic 1.51 1.60 2.24
ARCH (1) -0.38 1.99* -0.36
Engle-Granger test
EG? -4.56** -4.24 -5.67*
DW statistic 2.07 1.91 2.06
ARCH (1) 0.78 1.50 -0.58
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses. Critical values at the 5% and 10% level of Engle-Granger test
are calculated from MacKinnon (1991).
97
Table 3.4: Error Correction Model
(Regressions for Difference in Per Capita Real GDP)
Independent
Variable
Model 4
?y
Model 5
?y
Model 6
?y
constant -0.01 0.01 0.01
(-0.69) (0.57) (0.80)
?k 0.51*** 0.39*** 0.33**
(3.33) (2.91) (2.27)
?h 0.49* --- ---
(1.79)
?oy 0.16** --- ---
(2.46)
?t --- 0.06 0.09
(1.31) (1.69)
?t-1 -0.76*** -0.92*** -0.88***
(-3.41) (-4.48) (-3.49)
F 5.50 9.57 6.39
R2 0.51 0.57 0.47
Adjusted R2 0.42 0.51 0.39
DW statistic 1.83 1.54 1.51
ARCH (1) -0.32 -0.24 0.12
AIC -4.80 -4.99 -4.78
SBC -4.55 -4.80 -4.59
Ramsey RESET test
F statistic 4.03** 0.09 0.17
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses.
98
Table 3.5: Derived Results on Per Capita Real GDP
Independent
Variable
Model 4
?y
Model 5
?y
Model 6
?y
constant 8.87*** 10.53*** 9.97***
(3.41) (4.47) (3.48)
?k 0.51*** 0.39*** 0.33**
(3.33) (2.91) (2.27)
?h 0.49* --- ---
(1.79)
?oy 0.16** --- ---
(2.46)
kt-1 0.29*** 0.29*** 0.24***
(3.35) (4.07) (3.20)
ht-1 0.33*** 0.59*** ---
(2.83) (3.32)
fht-1 --- --- 0.63***
(2.57)
oyt-1 0.19*** --- ---
(2.80)
tt-1 --- 0.13** 0.11**
(2.20) (2.15)
eyt-1 --- 0.05** 0.04*
(2.03) (1.67)
Note: *, ** and *** represents the significance of the t-test at the 10%, 5% and 1% level respectively, and t
statistic is given underneath in parentheses.
99
Figure 3.1a: Variable Series
-6
-4
-2
0
2
4
6
8
10
12
1
9
8
0
1
9
8
2
1
9
8
4
1
9
8
6
1
9
8
8
1
9
9
0
1
9
9
2
1
9
9
4
1
9
9
6
1
9
9
8
2
0
0
0
2
0
0
2
2
0
0
4
2
0
0
6
y k t oy ey
Figure 3.1b: Variable Series
-1
- 0 . 9
- 0 . 8
- 0 . 7
- 0 . 6
- 0 . 5
- 0 . 4
- 0 . 3
- 0 . 2
- 0 . 1
0
1
9
8
0
1
9
8
2
1
9
8
4
1
9
8
6
1
9
8
8
1
9
9
0
1
9
9
2
1
9
9
4
1
9
9
6
1
9
9
8
2
0
0
0
2
0
0
2
2
0
0
4
2
0
0
6
mp fh h xy my
100
Figure 3.2a: Differences of Variable Series
- 0 . 0 6
- 0 . 0 4
- 0 . 0 2
0
0 . 0 2
0 . 0 4
0 . 0 6
0 . 0 8
0 . 1
0 . 1 2
0 . 1 4
0 . 1 6
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
dy dk d m h d f h dh
Figure 3.2b: Differences of Variable Series
- 0 . 2
- 0 . 1
0
0 . 1
0 . 2
0 . 3
0 . 4
0 . 5
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
dt d x y d m p doy dey
101
CONCLUDING CHAPTER
Tourism is the theme of this dissertation. Time series econometrics is the tool to
investigate tourism related economic issues such as the trade balance in tourism, demand
for international tourism, and tourism-led growth. Aggregate and disaggregated variables
have been employed in econometric models to compare their strength in estimations.
Tourism
Tourism as a trading service has not been extensively discussed in the theory of
international trade. There is a paucity of empirical studies on the balance of trade in
tourism in the literature. Chapter 1 fills this gap by providing an economic model to
analyze the effect of the nominal exchange rate on the US tourism in trade balance.
Chapter 2 confirms that international tourism is a luxury good. Instead of
examining a combined effect, Chapter 2 decomposes the relative price of tourism (or real
exchange rate) into nominal exchange rate and price ratio of tourism to capture their
separate effects on international tourism demand. Tourists are found to be more sensitive
to the change of nominal exchange rate than the change in the foreign price level.
Chapter 3 confirms that tourism development has led to economic growth in
Mauritius. The national tourism strategy aimed at high spending tourists is proven to be a
successful development strategy for that country.
102
The choice of aggregate or disaggregated variables
The choice of aggregate or disaggregated variables is an important issue in
empirical studies. Indeed, the choice of appropriate variables depends on the model,
estimating equation, and availability of reliable data.
Chapter 1 employs disaggregated trade data in the tourism industry to avoid the
aggregation bias of data that combine all traded goods across all industries. Chapter 1
also discusses the shortcoming of using aggregate volume indices or price indices to
derive proxy variables for the quantity of exports and imports that will cause the
aggregation bias and results in unreliable estimates. One limitation of Chapter 1 is that a
trade weighted currencies index has to be used in the two-country partial equilibrium
model. Indeed, tourism trade between two countries can be examined in future studies if
data becomes available. Then instead of using a trade weighted currencies index, a
bilateral exchange rate can be utilized.
Chapter 2 criticizes the use of a weighted average price index that may cause the
cross-price effects among alternative destinations to be cancelled due to the aggregation
resulting in unreliable estimates.
Chapter 2 also argues that the consumer price index as a proxy for the price of
tourist products is inappropriate since it might not be the typical basket of goods and
services of a tourist. Since tourist products comprise different commodities with a
variety of quality, there exists no single price for tourist products. Unless a better
alternate proxy or a more direct measure variable for the price of tourist products
becomes available, the empirical results based on the aggregate price index are unreliable.
103
Chapter 3 employs both aggregate and disaggregated measures to examine their
effects on economic growth. The advantage of the disaggregated measures is that they
provide more information about the effects of the specific measures on growth.
The choice of time series econometric models
In early empirical studies, econometric modeling was restricted to single equation
static models which usually suffer from the problem of spurious regression. More recent
studies employ dynamic models such as the autoregressive distributed lag model,
cointegrated analysis and error correction model and vector autoregressive model.
Chapters 2 and 3 employ cointegrated analysis and error correction models to
overcome the problem of spurious regression. In addition, this methodology can capture
both the short-run dynamics and long-run equilibrium relationship of the dependent
variable to the change of its explanatory variables.
Chapter 1 chooses the structural vector autoregressive (SVAR) model and the
impulse-response functions to study the dynamic behavior of trade tourism in response to
an exchange rate shock. A general vector autoregressive (VAR) model assumes all
variables are endogenous and is criticized for not having economic content. The SVAR
model requires imposing assumptions based on certain prior knowledge or economic
theory on short-run relations between the variables of interest. This technique can
capture the contemporaneous and long-run response of the dependent variable to a
structural shock.
Chapter 1 also advocates the methodology of estimating the export revenue and
import expenditure functions separately to provide a better picture of the dynamics of the
time-path of each individual function to an exchange rate shock. This methodology
104
outperforms the trade balance approach since the later approach focuses only on the net
change of the trade balance. Besides, it also avoids the combined income effect of
foreign income and domestic income on the export revenue or the import expenditure.
For the elasticity approach, if there are no actual quantity data available, the
estimation of the price elasticities by using aggregate trade data may lead to unreliable
results. Then estimating the export revenue and import expenditure functions separately
can provide better estimates for the price elasticities of export and import demand.
Conclusion
The trade theory of tourism is not well-developed and more empirical studies
should be done to test their validity. The choice of aggregate or disaggregated variables
should be carefully considered in empirical studies. If data is available, disaggregated
variables should always be employed to avoid the aggregation bias. The choice of time
series econometric models depends on the strength and weakness of the technique and its
ability to produce reliable results.
105
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