Download How and why do prices of identical products vary across countries?

How and why do prices of identical
products vary across countries?
Bachelor Thesis within Economics
Authors:
Mercédesz Dani
Gabriele Noreikaite
Supervisors:
Sara Johansson
Sofia Wixe
Jönköping
May 2015
Acknowledgements
We would like to express our gratitude foremost to Sara Johansson and Sofia Wixe for
their outstanding tutoring, support and engagement throughout the process of this thesis.
Valuable feedback and guidance have been very relevant and stimulating to accomplish
this work. Additionally, we would like to thank all members of our thesis seminar group
for their important inputs and contribution.
Finally, we are really grateful to our parents and friends who have been supporting us in
every step of the thesis writing process.
Gabriele Noreikaite and Mercédesz Dani
ii
Bachelor’s Thesis in economics
Title:
How and why do prices of identical products vary across countries?
Authors:
Gabriele Noreikaite & Mercedesz Dani
Tutors:
Sara Johansson
Sofia Wixe
Date:
May 2015
Key terms:
Price differentials, the law of one price, purchasing power parity, exchange
rates, identical goods
Abstract
The validity of purchasing power parity and the law of one price have been constantly
questioned as it is usually assumed that, at least in the long-run, commodity prices are
perfectly arbitraged. The purpose of this paper is to investigate how and why do prices
of identical goods vary across countries even today, in the globalized world, when trade
costs are relatively low due to high mobility and the countless number of free-trade
agreements. In order to fulfill the purpose of the study, prices of identical products from
H&M are analyzed. The main factors taken into consideration when investigating the
nature of price deviations are: tariffs, productivity, trade volume of clothing and how
well the producing company is established within the sampled countries. The findings
of the regression analysis indicate that price disparities exist and the law of one price, as
well as purchasing power parity, fail to hold even when identical goods are compared.
iii
Table of Contents
1
Introduction ..................................................................................................... 1
1.1
2
Limitations ......................................................................................................... 2
Theoretical Framework ..................................................................................... 4
2.1
The Law of One Price .......................................................................................... 4
2.2
Purchasing Power Parity ..................................................................................... 5
2.3
Nominal Exchange Rate and Real Exchange Rate ................................................. 6
2.4
Why LOP (and thus PPP) does not hold? .............................................................. 7
2.4.1 Trade Barriers .............................................................................................................. 8
2.4.2 Transportation Costs ................................................................................................... 9
2.4.4 Productivity ................................................................................................................. 9
2.5
Demand-Based Pricing ...................................................................................... 12
2.6
Previous Studies ............................................................................................... 13
3
Methodology .................................................................................................. 15
3.1
3.2
3.3
3.4
3.5
3.6
3.7
4
Empirical Results ............................................................................................ 23
4.1
5
Primary and Secondary Data ............................................................................. 15
Data Collection and Measures ........................................................................... 15
Choice of the Dependent Variable and the Company ......................................... 19
Choice of Independent Variables ....................................................................... 19
Model Formation .............................................................................................. 21
Empirical Model................................................................................................ 21
Diagnostic Tests ................................................................................................ 22
Sensitivity Analysis ........................................................................................... 26
Conclusion ...................................................................................................... 28
5.1
Suggestions for Further Studies ......................................................................... 28
Appendix ............................................................................................................... 30
References ............................................................................................................. 33
iv
1 Introduction
The world we currently live in is characterized by globalization; a process by which
‘national and regional economies, societies, and cultures have become integrated
through the global network of trade, communication, immigration and transportation’
(Financial Times Lexicon). Thus, one would expect that the more globalized the world
becomes, the vast extensity of market conditions and prices of goods would be equalized. However, reality demonstrates different patterns.
The law of one price (LOP) states that when expressed in the same currency, the prices
of identical goods and services are equal across countries. The purchasing power parity
(PPP) supports this theory, however, it reflects the prices of basket of goods instead of
individual goods in order for the LOP to hold.
There is a lack of available research investigating the question of why do prices vary
across countries for standardized products even today, in the globalized world, when the
countries and markets are becoming increasingly integrated. The purpose of this paper
is to test if the LOP holds for a basket of identical and standardized goods, as well as to
analyze the determinants for the potential price differentials. Previous research has
demonstrated that prices in Japan in the 1980s and 1990s were 40% higher on average,
than in the other OECD member countries. In the Nordic countries and Switzerland,
prices were 15–25% higher. In comparison, prices in the United States were 10% lower
than the OECD averge, whereas in Portugal 20% lower (Lapse & Swedenborg, 2010).
In order to compare the prices of the same goods, exchange rates must be incorporated
into the analysis in order to get the price of the same good translated into one currency.
Another useful tool that is considered in the analysis is the widely used Big Mac Index,
that is intended to represent price deviations from the PPP with the help of the current
prices of a standardized Big Mac burger at McDonald’s. With the help of this index,
currency under- and overvaluations can be detected. However, the use of the index
alone is simply inefficient because of trading issues.
This paper is designed to contribute to the investigation of the nature of price differences in the clothing sector, as developed countries are becoming more service-oriented,
which implies that more emphasis is put on the retail industry. The reason why the
clothing industry is chosen as an example is due to the fact that it has played a decisive
1
role in the past economic development. Palpacuer, Gibbon and Thomsen (2004) claim
that the clothing sector has played a fourfold role in the development process: (1) it
helped reduce unemployment by targeting unskilled labour, (2) it satisfied the basic
needs of a large population by mass-production, (3) it created the capital for more technologically demanding production in other sectors, (4) the large volume of clothing exports financed imports of more advanced technologies. Furthermore, price data for apparel are easily accessible and highly transparent; clothes are relatively easy and cheap
to transport, and significantly important for employment and growth for the developing
countries.
In order to test price deviations empirically, the well-established international company, H&M is chosen as an example, whose prices are analyzed in this paper. The main
focus relies on the price levels of four chosen identical products in 53 countries incorporating four different regions: North and South America, Middle East and North Africa,
Asia Pacific and Europe. Sweden is chosen as a geographical benchmark, as H&M was
founded in Västerås and still has its parent company located there. When investigating
the prices of identical H&M products in different markets, it is clear that even after converting them to a common currency, the prices of identical products strongly differ from
each other across numerous countries. Thus the LOP does not hold and further research
is necessary.
1.1 Limitations
The four products used in the analysis are strictly identical in terms of size, color,
product ID and material from which it is produced. An identical shopping environment
(physical and online stores respectively) is assumed across the 53 chosen countries,
since H&M stores are standardized and all equipped with the ‘Basic’ products.
There are some limitations regarding the data collection, as there is no available information regarding the prices of the same products from the previous years, collections
have changed and catalogues are no longer available. Moreover, as H&M is only present in 53 markets, the sample size of this research is limited to these countries. It is also
hard to predict or collect data on transportation costs associated with these products,
since the place of origin and production costs are not stated in the catalogue or on the
company’s website. Furthermore, data on tariffs, trade volume and wages are not available for all sampled countries, which results in conclusions based on fewer observations
2
whilst incorporating all relevant information. Additionally, the results on the effect of
tariffs on price changes are limited, as the European Union member countries posess the
same tariff as Sweden.
All necessary measures for the analysis, such as: exchange rates, GDP per capita,
wages, tariffs, number of stores, exports and imports of clothes, and total population are
collected from various official databases, causing a differentiation throughout time horizons. For some variables the most recent information dates back to 2007, while others
provide data for 2015. Nevertheless, all data is presented as recent as possible.
The remainder of the paper is structured as follows: in Section 2, the theories used for
the analysis of differences in prices are presented. In Section 3, a description of the data
set and model formation are presented, as well as the choice of dependent and independent variables are introduced. In section 4, the empirical findings are analyzed and
interpreted. In section 5, the concluding thoughts are discussed and the possible further
developments of the paper are addressed.
3
2 Theoretical Framework
In this chapter, the theories of the LOP and PPP are discussed and as an implication
of the PPP, the real and nominal exchange rates are explained in order to see the possible reasons why the LOP and PPP do not hold. The relationship between demand and
pricing, the impact of total trade on price levels as well as previous studies are also included.
2.1 The Law of One Price
The law of one price is one of the most dated theories in economics. Krugman,
Obstfeld and Melitz (2012) define it as a condition, where competitive markets are free
of transportation costs and official barriers to trade (such as tariffs), and identical goods
in different countries are sold for the same price when prices are expressed in the same
currency.
If the law of one price does not hold, arbitrage opportunities exist. This means that a
good can be bought for a lower price in one country and then resold for a relatively
higher price in another country, making the transaction beneficial for both the supplier
and the customer.
Assumptions of the LOP:
-
The goods are identical;
-
No barriers to trade exist (costless and open trade);
-
The commodities’ value is expressed in the same currency.
The law of one price is modeled as follows:
PH=PF*E
(1)
Where PH represents the price of a good in the home country, PF is the price of the
same product in a foreign country, and E is the nominal spot exchange rate (price of
foreign currency in terms of domestic currency). Therefore, arbitrage opportunities exist
if PH ≠ PF*E. For example, if PH<PF*E, then it is cheaper to buy the commodity at home
and to resell it on the foreign market for a price between PH and PF. On the other hand,
if this occurs in a larger volume, the demand will increase in the local market and decrease in the foreign market. This will result in a new equilibrium condition in the shortrun with reduced foreign prices and increased domestic prices. However, in the long-
4
run, one would expect prices to converge.
Several researchers, such as Engel and Rogers (2001) and Fenestra and Kendall (1997)
find evidence on the fact that a significant portion of the observed deviations from the
law of one price is attributable to an incomplete exchange rate pass-through (responsiveness of prices to exchange rate changes) as a result of local currency pricing.
2.2 Purchasing Power Parity
The theory of PPP is one of the most fundamental economic concepts, firstly put into
practice by the economist, Gustav Cassel (Officer, 1976) in the early 20th century. Following, Krugman et al. (2012) define purchasing power parity as a condition where two
countries’ price level ratio equals the exchange rate of their currencies. Therefore, an
increase (decrease) in the domestic price level would result in a depreciation (appreciation) of the domestic currency in the foreign exchange market, indicating a decrease (increase) in purchasing power.
According to the theory of absolute PPP, the home price of a domestic commodity
basket of goods and services should equal the foreign price of a foreign basket in order
to reach the equilibrium condition. For the absolute PPP to hold, the baskets must meet
certain prerequisites:
1)
The baskets must contain identical goods;
2)
Prices of the goods have to be expressed in the same currency;
3)
The LOP has to hold for all the identical goods.
Every year since 1986, The Economist issues the alleged Big Mac Index, a set of data
that indicates the current prices of a Big Mac hamburger throughout different countries,
making a comparison between the price levels with the help of the actual exchange rate.
The reason why this index is used as a measure of PPP instead of the LOP is that the
hamburger is considered as a “basket” of its ingredients. ‘Most of the ingredients that go
into a Big Mac are individually traded on international markets, so we might expect that
the law of one price would hold, at least approximately’ (Pakko & Pollard, 2003, p. 11).
To determine if the Big Mac index can give an explanation related to tradable goods, the
correlation between the relative Big Mac prices and the selected pieces of H&M
clothes’ prices are incorporated into the analysis.
Moreover, if the PPP holds, the prices, when expressed in the same currency (adjusted
5
by the exchange rate), are the same. Since in reality this is not the case, the Big Mac Index is rather used as an indicator of how abscure price levels are set. Many economists
have studied the applicability of the Big Mac Index, as it raises the question of why
does a single product have different prices across the globe. Alessandria and Kaboski
(2008) find that the average price of the burger is lower in relatively poor countries because the labor cost is low. Almås (2012) finds that due to substitution and quality bias,
relatively poorer countries’ incomes are overestimated, causing PPP bias within the
food industry. On the other hand, since the production process of a hamburger is not
traded, Balassa and Samuelson’s idea, developed in 1964, of how non-traded goods systematically affect the deviation from PPP seems to be appropriate. The BalassaSamuelson effect claims that an increase in the relative productivity of tradables in a
country raises relative wage, resulting in the increase of relative average price (MacDonald & Ricci, 2001).
According to Sarno and Valente (2006), who study the deviations from PPP under different exchange rate regimes, ‘regardless of the great interest in this area of research,
manifested by the large number of papers on PPP published over the last few decades,
and despite the increasing quality of data sets utilized and the econometric techniques
employed, the validity of long-run PPP and the properties of PPP deviations remain the
subject of ongoing controversies’ (Sarno & Valente, 2006, p. 3148).
2.3 Nominal Exchange Rate and Real Exchange Rate
Broadly speaking, an exchange rate is the price of one currency in terms of another.
However, there is a difference between nominal and real exchange rates. As Krugman et
al. (2012) state, because of their strong influence on the current account and other macroeconomic variables, exchange rates are among the most important prices in an open
economy. The relationship between the exchange rates and the law of one price (thus
PPP) is of key importance in order to understand the nature of price differences.
Following Krugman et al. (2012), the nominal exchange rate is defined as the relative
price of two currencies:
E=
(2)
where E is the nominal exchange rate and P* and P are the domestic and foreign price
levels respectively.
6
One step to broaden the theory of PPP is to describe what real exchange rate, which is
the relative price of basket of goods between two countries. Since the real exchange rate
is defined in terms of both prices and the nominal exchange rate, the relationship is the
following:
q=
(3)
where q represents the real exchange rate.
The main prediction of PPP is that the real exchange rate must remain unchanged. As
equation (3) shows, the real exchange rate can never change when absolute PPP holds.
Thus, the real exchange rate plays a more important role in relation to this research as it
can tell when PPP fails to hold and it also takes the relative price differences between
countries into account.
Furthermore, the shifts in supply and demand in open economies cause nominal and
real exchange rates to move, causing PPP not to hold anymore. From equation (3) it can
be seen that with stable output prices, nominal depreciation (appreciation) implies real
depreciation (appreciation).
Krugman et al. (2012) conclude that exchange rates play a central role in international
trade because they allow us to compare the prices of goods and services produced in different countries.
Several economists have analyzed the impact of the nominal exchange rate against the
real exchange rate on various issues related to PPP. Sarno and Valente (2006) find that
the relative importance of exchange rates and relative prices in restoring the long-run
equilibrium level of the exchange rate varies over time and is affected by the nominal
exchange rate arrangement in operation. Empirical studies in the literature typically find
slow long-run convergence of the real exchange rate to PPP (Xu, 2003).
2.4 Why LOP (and thus PPP) does not hold?
Despite the fact that PPP and the LOP are well-established economic models, empirical research has demonstrated that they have several limitations and do not hold under
certain circumstances. One of the empirical analysis has shown that ‘a Big Mac devotee
could buy one and a half of the sandwich in the USA for every one he could purchase in
Switzerland’ (Pakko & Pollard, 2003, p. 16). According to the PPP, a Big Mac should
7
have a fixed price globally. It would be impractical to ship a burger from a cheaper
country to a more expensive one, even though the components of the sandwich are
traded on world markets and the prices should be equalized.
Ardeni (1989) states that where a distinction between traded and non-traded goods is
made in other models, deviations from the LOP are existing for non-traded goods only.
Other empirical research about the general price levels find PPP to hold in respect to the
long-run. However, there are an increasing amount of studies showing significant deviations even in the long-run. Isard (1977) states that ‘in reality the law of one price is flagrantly and systematically violated by empirical data’ (Isard, 1977, p. 942). Furthermore, Ardeni’s (1989) research on commodity trade demonstrates that the prices of
primary commodities are generally considered independent of the country of origin as
such commodities are expected to be identical or nearly perfect substitutes and therefore
perfectly arbitraged. However, some perversions may occur in these markets. Factors
which potentially create permanent price differentials across countries are import quotas, tariffs and various means of trade control. There are also some other reasons such as
institutional aspects, high cost of arbitraging and errors in data. All applicable when
evaluating the failure of the LOP.
In the following sections some of the reasons why LOP (and thus PPP) does not hold
are presented in more detail such as trade barriers, transportation costs and productivity.
2.4.1 Trade Barriers
There are many government restrictions, such as import/export licenses, import quotas
and subsidies which function as trade barriers. Tariffs play a crucial role in international
economics as a barrier to trade. This government intervention usually influences income
distribution within the country. For the purpose of the research problem, tariffs are considered as the most important means of trade barriers, as they can create a difference between prices for the goods which are traded between countries as well as the differences
in prices within a country. ‘The direct effect of a tariff is to make imported goods more
expensive inside a country than they are outside the country’ (Krugman et al., 2012, p.
124).
Tariffs are used in most countries as protective tools against competition originating
from other countries. If the government imposes a tariff, it means that the imported
8
goods become relatively more expensive than the domestically produced ones. On the
other hand, Ravn and Uribe (2007) argue that price differences among countries, thus
deviations from the LOP, hold even when tariffs or quotas are not existing.
There are several academic papers that analyze the trade restrictions in relation to differences in commodity prices. Parsley and Wei (1996) state that due to barriers to flows
of goods and services, minimal expectations exist that price differences will immediately disappear as they do, for example, in financial markets. In an empirical paper,
Pandit (2009) examines U.S.-Canada softwood lumber trade. One of the main findings
of this research shows that ‘U.S. demand factors and trade barriers on the import price
of Canadian softwood lumber suggests that these factors and barriers explain about 91%
of the variation in the import price’ (Pandit, 2009, p. 417). As it can be seen, barriers to
trade are relevant for price fluctuations and should be taken into account when analyzing the price differences of identical products.
2.4.2 Transportation Costs
The expenses encountered when changing the geographical locations of products are
one of the main determinants of price differences. Transportation costs arise mostly on
three occasions: (1) when raw materials are transported from the supplier to the customer, (2) when the products are transported to distribution centers or stores, and (3)
when the products are delivered to the end-customer.
Ravn and Mazzenga (2004) have studied the quantitative role of transportation costs in
the international business cycle. Their findings support the hypothesis according to
which ‘costs of transportation can have large welfare costs while their effects on the pattern of trade depend crucially on the degree of substitutability between domestic and
foreign goods’ (Ravn & Mazzenga, 2004, p. 668). Thus, transportation cost is a factor
contributing to the failure of PPP and the LOP as it causes prices to diverge across
countries.
2.4.4 Productivity
‘It’s a good bet that most of the clothing you are wearing as you read this came from a
country far poorer than the United States.’(Krugman et al., 2012, p. 279)
As it was already mentioned, the Balassa-Samuelson effect contributes to the explanation of how productivity affects price differences: an increase in the relative productivi-
9
ty of tradables increases the relative average price level in the country. Their notion is
based on non-traded goods: variation in prices for non-tradables contributes to the discrepancies between rich and poor nations. Krugman et al. (2012) state that ‘rich countries with higher labor productivity in the tradables sector will tend to have higher nontradables prices and higher price levels’ (Krugman et al., 2012, p. 402). In their study,
García-Solanes, Sancho-Portero and Torrejón-Flores (2008) give an empirical support
to the Balassa-Samuelson differential productivity postulate. The conclusions are that
the difference in relative prices of non-tradables leads to overall differences in price
levels and wealthier countries are associated with relatively elevated price levels.
Moreover, productivity differences explain variations in wages and GDP per capita.
Wages play an important role when examining price differences of goods as they take
the marginal productivity of labour (MPL) into account. MPL is defined as a unit
change in output due to one unit change in labour. Krugman et al. (2012) also define
wages as the function of marginal productivity of labour and price. Thus, wages are not
only related to productivity, but also to the actual price of a product:
MPL * P = w
(4)
thus
P= w / MPL
(5)
Due to lack of available data on wages, one can use GDP per capita as a proxy for
wage (hence productivity) differences. GDP is the most widely used measure of living
standards as well as an indicator of the overall economic performance of a country.
Compared to wages, GDP per capita is a boader measure of average income and standards of living, implying that GDP per capita may also reflect differences in consumption
patterns and preferences. Higher GDP per capita represents higher income and living
standards, while at the same time, real income per capita is positively related to the
price level of the country if expressed in a single currency. Causa, Araujo, Cavaciuti,
Ruiz & Smidova (2014) find a positive correlation between the levels of GDP and
household income. Thus, productivity and hence GDP per capita can partially explain
price differentials in different countries and that is why it is relevant to include this
measure as one of the explanatory variables in the empirical analysis.
10
GDP per capita variations across countries are demonstrated in Figure 1 where the data for 2013 is obtained from the World Bank1 database.
GDP per Capita SEK
1,000,000
800,000
600,000
400,000
200,000
Luxenbourgh
Norway (NOK)
Qatar (QAR)
Switzerland (CHF)
Australia (AUD)
Sweden (SEK)
Denmark (DKK)
Singapore (SGD)
USA
Kuwait (KWD)
Canada (CAD)
The Netherlands
Austria
Ireland
Finland
Belgium
Germany
UAE (AED)
France
UK (GBP)
Hong Kong (HKD)
Israel (ILS)
Italy
Spain
Saudi Arabia (SAR)
Bahrain (BHD)
Greece
Oman (OMR)
Portugal
Czech Republic (CZK)
Estonia
Slovakia
Chile (CLP)
Latvia
Russia (RUB)
Poland (PLN)
Croatia (HRK)
Hungary (HUF)
Turkey (TRY)
Malaysia (MYR)
Mexico (MXN)
Lebanon (LBP)
Romania (RON)
Bulgaria (BGN)
China (CNY)
Serbia (RSD)
Thailand (THB)
Jordan (JOD)
Indonesia (IDR)
Egypt (EGP)
Morocco (MAD)
Phillippines (PHP)
Taiwan (TWD)
0
Figure 1- GDP per Capita (SEK) 2013
In this paper, GDP per capita is used as an estimator of wages as there is a strong correlation (~92%) between the two variables by using 41 pairs of observations (Figure 2).
20,000
Wage (SEK)
16,000
12,000
8,000
4,000
0
0
200,000 400,000 600,000 800,000 1,000,000
GDP per Capita (SEK)
Figure 2 - Correlation between GDP per Capita and Wage (SEK)
When analyzing productivity, it is crucial to take the volume of trade, which is exports
plus imports, into consideration. Adam Smith and David Ricardo argue that overall
openness is very important – nations grow more prosperous through imports and exports (Ondrich & Richardson, 2004). Since changes in GDP also reflect price differ-
1
World Bank – https://www.worldbank.org/
11
ences across countries as it is discussed above, the impact of trade would affect changes
in price level through changes in GDP.
There are two possible explanations how trade could influence changes in income and
productivity and thus the price level. Firstly, GDP is affected positively by an increase
in net exports (exports minus imports). As Ondrich and Richardson’s (2004) research
indicates, countries with large exports (and low import penetration) have high income
per capita which would lead to an increase in the GDP and thus in the price level.
Therefore, relatively more exports than imports increases GDP. Secondly, many researchers have analyzed how trade volume (imports plus exports) affect GDP and income level in the country. Brunner (2003) has found that trade has a significant and
large effect on the level of income. Li, Chen and San’s (2010) result suggests that there
exists a long and short-term causality between GDP and total trade. In general, it could
be expected that the increase in the trade volume (exports plus imports) in a country increases the level of income and thus GDP leading to an increase in the price level.
2.5 Demand-Based Pricing
A widely used approach to generate profits when entering a new market is to charge a
relatively higher price to both high-price and low-price products, whilst gradually adjusting it to market-demand over time. Saturation is an essential factor that needs to be
considered during the setup of a pricing-strategy, that is: a downward shift of the demand function due to increased market penetration that results from the loss of potential
buyers (see Figure 3) (Raman & Chatterjee, 1995; Khouja & Robbins, 2005). The saturation effect will eventually result in the decrease of the price of a product. In other
words, the less common the product is in the market (either because it has not been present for long or the products are not supplied in the amount that would meet the demand) the higher the price can be set and maintained by ocmpanies. Different demand
conditions in different countries are reflected by pricing-to-market that is connected to
the elasticity of demand to the country’s consumers. Empirical studies have shown the
clear existence of this practice in manufacturing trade (Raman & Chatterjee, 1995).
12
Figure 3- The Saturation Effect
2.6 Previous Studies
Several previous researches have been conducted in order to examine the reasons behind the price differences across countries of the products of another Swedish giant,
IKEA. Baxter and Landry (2012) have differentiated high-price and low-price products
when examining the entire assortment of IKEA and found a remarkable difference between the price-deviations between the two categories. Furthermore, the difference between the deviations of new and continuing products have been studied. The results
show that the mean price differences of continuing goods differ more than in the case of
new goods.
The other study by Haskell and Wolf (2001) examines absolute prices for more than
100 identical goods sold in 25 countries by IKEA. They find significant common currency price divergences across countries for a given product and across products for a
given country pair.
Moreover, other studies have used the Big Mac Index to model price differences and
the results unanimously show that the LOP does not hold for identical goods. Alessandria and Kaboski (2008) find that the prices of Big Macs across countries provide a
clear example of the LOP failure. When converted into U.S. dollars, Big Macs sell for
up to 65 % more than in the U.S. and down to 57 % less than in the U.S.
A project on price comparisons in Europe was led by The European Consumer Centre
Network (ECC-Net) in 2009. 27 ECCs participated in the survey in Luxembourg, called
‘Price Research, Price Differences in Europe’. This survey was based on some of the
most common textile products of Zara, C&A and cosmetic products of Body Shop.
Prices of three items for women and two items for men from each company have been
compared and the result has demonstrated that the price differences of identical goods
13
exist between countries and those differences are not easily explained. The main findings clearly show that:

shopping in Portugal is cheaper than in other European countries;

Scandinavian countries such as Norway, Finland, Denmark and Sweden
appeared to be more expensive than the rest of Europe;

prices within the non-Euro zone deviate more from the average prices in
Europe than inside the Euro zone.
14
3 Methodology
3.1 Primary and Secondary Data
The research method chosen for the paper consists of the collection of both primary
and secondary data. As previous researchers on the topic use the Big Mac Index as the
main indicator of price differences, this paper makes a contribution to the literature,
since the data collected is about true tradables and is obtained by the authors themselves, providing a unique dataset.
3.2 Data Collection and Measures
Data are collected on exchange rates, tariffs, GDP per capita, wages, total population,
exports and imports of clothes, the PPP conversion factor and Big Mac Index from various databases such as World Bank or World Trade Organization (WTO)2. It ensures that
the research has enough information to be comparable and analyzable. The data retrieved from the observations are transcribed and evaluated by the authors themselves.
All data are converted to a relative form, taking the values for Sweden as a basis in order to see how the price difference is affected by the differences in the selected explanatory variables.
Data are also collected on the prices of the chosen sample products and the number of
stores in each country from H&M’s website for all sampled countries. The company´s
products are divided into several departments and in order to make easier comparisons,
only low-price and continuing - ‘Basic’ - products are selected and examined from
women’s and men’s departments respectively. Overall, 4 products are compared whose
short descriptions can be seen in Table 1.
2
World Trade Organization - https://www.wto.org/
15
Table 1- Description of Products
Name
Department
Description
Sweatpants with an elasticated
drawstring waist, side pockets, one
back pocket and ribbed hems. Brushed
inside.
Details
Sweatpants
Men - Basics - Bottoms
Polo shirt
Men - Basics - T-Shirts & Vests Short Sleeve
Polo shirt in stretch jersey with a collar, short sleeves and buttons at the
top.
95% cotton,
5% elastane
Jersey skirt
Ladies - Basics - Dresses & Skirts
Short skirt in jersey with an elasticated waist.
95% cotton,
5% elastane
Long jersey
top
Ladies - Tops - Vests
Long top in cotton jersey with a racer
back.
95% cotton,
5% elastane
60% cotton,
40% polyester
Prices are collected from 53 countries and all include value added tax (VAT). After
collecting prices in local currencies, the first step is to convert them into Swedish Krona
with the help of exchange rates. All exchanges rates were obtained on the 15th March
2015 as the most recent data to work with. The current date is important in order to see
the most recent price deviations possible. To avert from creating misleading calculations
and conclusions, relative price deviations have been calculated for each country for all
four chosen products. Sweden is chosen as a benchmark with the relative price of 1 (see
Appendix Table 1). Visual deviations of the mean prices can be seen in Figure 4 where
100 corresponds to the value of 1. As presented in the bar chart, relative prices deviate
from 1, indicating that further research is needed and it is appropriate to work with the
data not making biased estimates. A value higher (lower) than 1 indicates that it is less
(more) expensive to buy the selected products in Sweden. Moreover, it is clear that
prices heavily deviate in countries outside the EU, while among the EU member states
the prices are relatively constant.
16
Mean Price
1.6
1.5
1.4
1.3
1.2
1.1
1.0
0.9
0.8
Jordan (JOD)
Lebanon (LBP)
China (CNY)
Switzerland (CHF)
Egypt (EGP)
Israel (ILS)
Qatar (QAR)
UAE (AED)
Singapore (SGD)
Saudi Arabia (SAR)
Morocco (MAD)
Thailand (THB)
Bahrain (BHD)
Oman (OMR)
Kuwait (KWD)
Phillippines (PHP)
Norway (NOK)
Malaysia (MYR)
Taiwan (TWD)
Hong Kong (HKD)
Australia (AUD)
USA
UK (GBP)
Denmark (DKK)
Chile (CLP)
Sweden (SEK)
Indonesia (IDR)
Canada (CAD)
Mexico (MXN)
Austria
Belgium
Estonia
Finland
France
Germany
Greece
Ireland
Italy
Latvia
Luxenbourgh
Slovakia
The Netherlands
Croatia (HRK)
Russia (RUB)
Spain
Poland (PLN)
Bulgaria (BGN)
Serbia (RSD)
Czech Republic (CZK)
Romania (RON)
Portugal
Hungary (HUF)
Turkey (TRY)
0.7
Figure 4- Mean Price Distribution (SEK)
Furthermore, prices are also compared with the Big Mac Index. The index was obtained for the selected countries as of January 2015. Figure 5 indicates the relationship
between the relative prices of Big Mac and the relative prices of the selected H&M
products. It shows that the two variables are not strongly correlated, since there is no
observable pattern between the dots, as shown by the regression line. The reason for this
may be that clothes are tradable goods while the production process of the Big Mac is
non-tradable. It could also be argued that the raw ingredients of a burger are traded, although the whole value of the Big Mac is not comparable to the meat or cheese used in
preparation. Also, it is more reasonable to treat the product as a non-traded good. Therefore, for the further research of this paper the Big Mac Index is not considered as one of
the explanatory variables.
1.4
Relative Price H&M
1.3
1.2
1.1
1.0
0.9
0.8
0.7
0.2
0.4
0.6
0.8
1.0
1.2
1.4
1.6
Relative price BigMac
Figure 5- Correlation between relative Big Mac and H&M products price deviations (SEK)
17
The data on tariffs levied on clothes are collected from the websites of WTO and ITA3
and are expressed as the average tariff for HS chapter 62: ‘Articles of apparel and clothing accessories, not knitted or crocheted’ from the following subchapters:
 6203 – Men's or boys' suits, ensembles, jackets, blazers, trousers, bib and brace
overalls, breeches and shorts (other than swimwear);
 6204 – Women's or girls' suits, ensembles, jackets, blazers, dresses, skirts, divided skirts, trousers, bib and brace overalls, breeches and shorts (other than
swimwear);
 6205 – Men’s or boy’s shirt;
 6206 – Woman’s or girl’s blouses, shirts and shirt-blouses.
The rate of ad valorem tax is collected for these four categories and their average is
used in the regression model. As it can be seen from Appendix Table 1, all European
Union members hold the same tariff, which is equal to 10%. It implies that all member
countries can circulate their goods freely. The European Commission defines the
'Common Customs Tariff' (CCT) which applies to the import of goods across the external borders of the EU. Thus, tariffs are not explaining sufficiently the price differences
between the European Union members. Only tariffs associated with non-EU countries
can have a measurable influence on the price differences.
Sweden’s trade with EU countries is very important, because according to Statistics
Sweden4 it accounts for a large share of exports and imports respectively. In 2014, the
EU countries accounted for 73.7% of Sweden's exports and 84.6% of Sweden's imports.
Having free trade within the EU countries is sufficient, since the world demand outside
the EU is rapidly growing. This is a direct result of why opening markets with other
countries is increasing market opportunities for negotiating new businesses and other
benefits which can be obtained through free trade agreements. Currently, the European
Free Trade Association (EFTA) States hold 25 free trade agreements (covering 35 countries) with the partners shown in Appendix Table 2.
3
Iternational Trade Administration - http://www.trade.gov/
4
Statistics Sweden - http://www.scb.se/
18
3.3 Choice of the Dependent Variable and the Company
The relative average price of selected H&M clothes is chosen as the dependent variable. According to the most recent data, the company is represented in 53 markets with
more than 3,300 stores worldwide. Since the analysis requires identical products to be
compared, the company’s production scale complements the research well. During the
past decade, H&M has become one of the most famous clothing companies due to affordable price of its identical products. The company is growing at a fast pace (their targeted growth is 10-15 % per annum) and outsources to approximately 700 facilities, located mainly throughout Asia and Europe. These include particularly low wage countries such as Turkey, Bangladesh, Indonesia and India (H&M Corporate Website). As
one of the the company’s primary aims is to ensure the best price to its customers
through cost-consciousness and efficient logistics it is interesting to investigate how the
prices of the company’s products vary across countries worldwwide.
Lack of research regarding price differences of this company’s products combined
with Sweden being the parent country, are two of the underlying principles when choosing this company and its production as an example. Furthermore, deviations in prices of
identical products have interested many researchers that earlier mainly have previously
worked with comparable Swedish giants, such as IKEA, which is presented in the paper.
3.4 Choice of Independent Variables
In order to explain the average relative price in foreign countries compared to Sweden,
the following independent variables are chosen:
1)
Relative GDP per capita: as GDP per capita is highly correlated with the
level of minimum wages, it is presumed as a viable estimator of wages. Also,
GDP per capita can tell a lot about the tastes and preferences related to the living
standards of the residents of the country. Thus, relative GDP per capita can indicate if a country is wealthier or poorer than Sweden. A positive relationship is
expected between the relative average price and the relative GDP per capita. As
it increases, so does the wealthiness of the country, therefore the general price
level is expected to be higher;
19
2)
Relative PPP conversion factor5: In order to measure the purchasing
power conditions for the selected countries, the PPP conversion factor is collected. This analyzes how many units of the country´s currency have the same buying-power for a given basket of goods and services to the US dollar. To make
the measurement relevant to the study, the values are converted in relation to the
Swedish value. One would expect a positive relationship between the relative
average price and the relative PPP conversion factor, as a higher PPP conversion
rate reflects a higher general price level.
3)
Relative tariff (in %): In order to reflect how difficult it is to access a
country with the product, the ad valorem tariff is an appropriate measure. Relative tariff shows if it is more or less difficult to access a foreign market than to
access the Swedish market. The change in price is expected to be influenced
positively by the tariff, as the higher the tax imposed on the imported good is,
the more difficult it is to enter the market, thus more expensive the domestic
price of the good becomes.
4)
Relative trade volume: The relative volume of trade (exports plus im-
ports) can be used as a proxy for domestic productivity and competitiveness in
the clothing industry. The volume of exports reflect how productive a country is
in the given sector, while the volume of imports indicate the consumption patters
of a nation in relation to Sweden. One would expect that domestic prices on
clothes are relatively low in countries with large clothing industries due to increased competition.
5)
Relative total population per store: The population per store indicates
how unique H&M is within a country. The value would tell if the H&M store is
more or less accessible in the sampled country than in Sweden. If the value is
greater (smaller) than 1, it means that H&M stores are less (more) available for
the inhabitants of the country, showing lower (higher) market-penetration, creating a higher (lower) status and thus higher (lower) prices for the selected products. The relative average price is expected to be affected positively by the total
population per store, as the higher the share of population per store, the less the
saturation is apparent, resulting in potentially higher prices of the company´s
products.
5
The PPP conversion factor was planned to be included as an independent variable, but since it shows
84% correlation with the GDP per capita, it is being left out.
20
Table 2 - Expected Signs
Variable
Expected Sign
Relative GDP per capita
+
Relative PPP conversion rate
+
Relative tariff (in %)
+
Relative trade volume
+
Relative total population per store
+
3.5 Model Formation
Firstly, the econometric model is tested by the use of OLS estimation as the expected
relationship between the dependent variable and each of the independent variables is
linear. The dependent variable; the relative average price of the products is tested
against the independent variables, such as: relative GDP per capita, relative tariff, relative total trade and relative total population per store.
It is also important to test for normality, model misspecification, multicollinearity and
heteroscedasticity. After computing all estimations, the conclusion is drawn and the
relevance of the chosen variables is determined.
3.6 Empirical Model
The data is processed with the statistical software, EViews. During the procedure, the
linear regression model is used in order to analyze the relation between the dependent
and independent variables. The regression model is developed for quantitative analysis
based on the theories discussed previously and set up accordingly:
Yi=β0+β1ADVi+β2GDPCi+β3TTi +β4PSi+ε
(6)
where Y denotes the price relative to Sweden, ADV denotes the rate of ad valorem tariff compared to Sweden, GDPC denotes the GDP per capita relative to Sweden, TT denotes the total volume of trade of clothes relative to Sweden and PS denotes the population per store relative to Sweden. With the help of OLS estimation, the β-values are estimated in order to see how much each explanatory variable influence the extent of the
price differences.
21
3.7 Diagnostic Tests
Tests for normality, heteroscedasticity, misspecification and multicollinearity are used
in order to check the consistency and stability of the regression model.
Firstly, the Jarque-Bera test is used to check if residuals are normally distributed.
Since the probability (p-value) is lower than the chosen level of significance
(0.0011<0.05), the normality between the residuals is not present in the model (Appendix Table 3). The presence of non-normality can be due to the fact that the dataset contains too many outliers, resulting in a skewed distribution of each of the variables as
well as the residuals (Table 3). However, as the assumption of normality is not necessary in order for the OLS estimation to be unbiased. Secondly, White’s test is applied to
see if heteroscedasticity exists among the residuals. Since the probability associated
with the ‘Observations*R2’ is lower than the level of significance (0.008<0.05), it can
be concluded that there is heteroscedasticity in the model (Appendix Table 4). As a
remedy, White heteroskedasticity-consistent standard errors and covariance have been
applied. Thirdly, in order to test if the model is correctly specified, Ramsey’s RESET
test is made. The probability of the likelihood ratio, which is the RESET test statistic, is
higher than the significance level (0.59>0.05) meaning that the model is correctly specified (Appendix Table 5). Finally, variance inflation factor values are less than 10, meaning that there is no multicollinearity in the model. The rule of thumbs is that if VIF<10,
then there are no severe multicollinearity problems (Appendix Table 6). In addition to
this, the correlations between the variables have been checked and it can be concluded
that none of the variable pairs are reaching 50% correlation (Appendix Table 7).
Table 3 – Descriptive Statistics
Variable
Mean
Max
Min
Std. dev.
Price
0.9678
1.3530
0.7652
0.1268
Population per store
47.7184
601.2703
0.7893
22.1999
Total trade
2.3145
16.3344
0.0530
3.8661
Tariff
1.0037
2.1800
0.0000
0.4115
GDP per capita
0.5835
1.8318
0.0458
0.4448
22
4 Empirical Results
The regression is performed on the regressand, the relative average price of selected
products. The predictor variables are as follow: ad valorem tariff, GDP per capita, trade
volume and finally, the relation between total population and the number of H&M
stores in the country. As shown before, two of the selected explanatory variables, GDP
per capita and the PPP conversion rate are showing strong correlation. Thus, one of the
variables has to be excluded from the model. As GDP per capita captures the entire economic activity of a country, it is included in the regression. The results are displayed in
Table 4.
Table 4 - Regression Output
Dependent variable: Relative price of H&M clothes
Variables
Constant
Relative Ad valorem
Relative GDP per capita
Relative trade volume
Relative population per store
Regression
(1)
Regression
(2)
Regression
(3)
1.0454***
0.9027***
0.9999***
(15.7086)
(21.2722)
(19.7111)
-0.0266
0.0117
-0.1499***
(-0.6038)
(0.4375)
(-3.5216)
0.0035
0.0299
0.1022**
(0.056)
(0.7939)
(2.728)
0.0002
0.0001
0.0144***
(0.0309)
(0.0212)
(3.3877)
0.0002
-0.0001
0.0005***
(0.9853)
(-0.7572)
(3.7217)
0.2308***
Dummy Asia Pacific
(4.2148)
0.3478***
Dummy Middle East and North Africa
(8.9254)
0.0616
Dummy North and South America
(0.9709)
R2
0.0232
0.6778
0.4893
Adjusted R2
-0.0618
0.6253
0.4274
51
51
38
Number of observations
***p-value<0.01
**p-value<0.05 *p-value<0.1
The figures in parentheses indicate the t-statistics
When analyzing Regression (1), the intercept, β0, represents the relative price of H&M
for selected products in Sweden in comparison to the sampled 51 countries6. According6
Switzerland and Taiwan are excluded from the original set of 53 countries, as the data on tariffs and
trade volume are missing.
23
ly, this figure equaled 1.0454, which means that the spread of relative prices between
Sweden and the average is about 1.0454. Other than the intercept, no other variables are
significant, meaning that their effect cannot be differentiated from zero.
The estimated R2 of the model is 0.0232; therefore, 2.32% of the change in relative
price is explained by the change in independent variables. Since none of the selected independent variables are significant in the first model, and the R2 is extremely low, it is
concluded that this model is not correctly specified or that among the observed countries, the chosen variables do not have any significant effect on the price differences.
A possible explanation for the weakness of Regression (1) is that due to the fact that
H&M has recently endeavored to expand throughout Asian and Middle Eastern countries, it is assumed that the prices in these areas differ greatly from those areas, where
H&M has been present for several years. In addition to this, in most of these countries
H&M still exists in a form of franchise, so prices are assumed to be higher. For example, the highest relative prices are in Lebanon (1.4) and Morocco (1.5), whose markets
H&M has entered in 2012 via franchise, so it is assumed that the company has not penetrated the market entirely.7
In order to account for these general price differences, dummy variables are introduced in Regression (2). Dummy variables reflect the region to which each country belongs to. After collecting all the relevant data, Asian and Middle Eastern countries are
reasonably excluded from the scope of the study. However, the use of dummy variables
is a suitable remedy for this problem, making it possible to keep the original sample
size. As a result, a second regression has been set up:
Yi=β0+β1ADVi+β2GDPCi+β3TTi +β4PSi+DUMA+DUMM+DUMN+ε
(7)
Where DUMA represents the dummy variable for Asian and Pacific countries,
DUMM represents the dummy variable for Middle Eastern and North African countries
and DUMN represents the dummy variable for North American and South American
countries.
When comparing the results of Regression (2) to those of Regression (1), it is clear
that the inclusion of dummies have greatly improved the model. Less of the independent
7
Included countries: Bahrain, Egypt, Hong Kong, Indonesia, Jordan, Kuwait, Lebanon, Morocco, Oman,
Quatar, Saudi Arabia, Thailand, United Arab Emirates.
24
variables are considered insignificant and a great effect of geographical location on
price differences is visible, thus the assumption made earlier (that Middle Eastern and
Asian markets have not been penetrated entirely) is supported by the empirical results.
The R2 value of 0.68 means that 68% of the price changes are explained by the second
model. However, this increase compared to the value in Regression (1) is partly due to
the increase of explanatory variables from 4 to 7. Gayawan and Ipinyomi (2009) study
which criteria shows more reliable results when it comes to model selection. In their paper, ‘A Comparison of Akaike, Schwarz and R Square Criteria for Model Selection Using Some Fertility Models’, they find that eventually, R2 would indicate more complex
models to be more appropriate, but this can lead to over parameterization (the use of too
many parameters in the model).
Other than the intercept term, only two dummy variables are shown to be statistically
significant. The dummy ‘Asia Pacific’ implies that the countries in this region have
generally higher prices (0.23 unit increase) than European countries. Moreover, the
countries belonging to the ‘Middle East and North Africa’ region have even higher prices (0.38 unit increase).
As most of the variables are still insignificant in the Regression (2), it is found that the
chosen variables do not have a measurable effect on the prices even when the countries
are controlled for the geographical region which they belong to.
A new model, Regression (3) is set up with the exclusion of the countries whose markets have not been penetrated entirely by H&M, thus reducing the sample size from 51
to 38. However, this reduction results in the fact that the model can only be applied to
describe the prices in the European and American regions.
Regression (3) shows a relatively high R2 (0.49), which means that 49% of the price
variations are explained by the model.
In contrast to the expectations, the relative ad valorem tax shows a negative effect on
the relative price. This could be due to the fact that relative GDP per capita and relative
ad valorem tariff shows a negative relationship (the higher the GDP per capita, the lower the ad valorem is) (see Table 8 in Appendix). This implies that wealthier countries
levy lower taxes on imported clothes. It can also be noted that the main influence comes
from the volume of ad valorem tax and GDP per capita, which is supported by the theo-
25
ry, as these two components reflect the economy and trade between countries more accurately than the other variables.
In accordance with theory, the relative GDP per capita shows a positive affect on the
relative price. This is due to the fact that higher GDP per capita reflects higher productivity and standard of living that is resulting in higher price levels.
The relative trade volume (exports plus imports) also show a positive relationship to
prices according to the regression output, meaning that the more a country is involved
when concerning trade, the higher the relative prices become. An underlying assumption in this case is that exports exceed imports, resulting in a less competitive environment where higher prices can be set. According to the dataset obtained for exports and
imports, only 2 out of the 38 countries have more imports than exports confirming the
idea that trade volume positively affects the dependend variable.
Lastly, the total relative population per store has a positive impact on the dependent
variable. In other words, the less available the store is in a country, the higher the average price level is. This result is also in accordance with the underlying theoretical assumptions of the saturation effect: the higher population per store indicates less saturation, resulting in potentially higher prices of the company’s products.
To sum up, despite the fact that Regression (1) includes a full set of observations, the
insignificance of the independent variables and the extremely low R2 suggest that the
model is not performing well with the dataset. On the other hand, Regression (2) captures the effect of the geographical location and demonstrates significant results. Furthermore, the R2 is considerably high, meaning that the model performs well, however,
most of the independent variables are insignificant. Lastly, Regression (3) shows significant relationships between the dependent and the independent variables, although, this
result is obtained at the cost of excluding the countries that have demonstrated strong
deviations.
4.1 Sensitivity Analysis
As the PPP conversion rate is considered as a pure price reflecting value, a new model
is set up that includes this variable. In order to test the sensitivity of the regression
model, relative GDP per capita is replaced by the relative PPP conversion rate (as they
are 84% correlated) and the following model, Regression (4), is set up:
26
YI=β0+β1ADVi+β2PPPi+β3TTi +β4PSi+ε
(8)
Table 5 – Modified Model
Dependent variable: Relative price of H&M clothes
Variables
Constant
Relative Ad valorem
Relative PPP
Relative trade volume
Relative population per store
Regression
(4)
0.9696***
(15.2508)
-0.1639***
(-3.7712)
0.1583**
(2.3056)
0.0142***
(3.2493)
0.0005***
(3.5571)
R
0.4610
Adjusted R2
0.3956
Number of observations
38
***p-value<0.01 **p-value<0.05 *p-value<0.1
The figures in parentheses indicate the t-statistics
2
The results in Table 5 show that the inclusion of PPP instead of GDP results in a
model with a slightly lower R2 (0.46<0.49). Even though all the explanatory variables
are significant, they have slightly different coefficients in comparison to Regression (3).
Therefore, relying on the model-selection criteria, the R2 value, Regression (3) is preferred to Regression (4).
27
5 Conclusion
This paper assesses the possible impact of many legitimate reasons for price differences of identical goods across countries. Based on the economic theories of the law of
one price and purchasing power parity, variation in prices of selected H&M products
were examined. The analysis was based on data collection, previous research and findings; finally, the empirical model was constructed and results were analyzed.
The main findings for price differences can be attributed to the variations in tariffs,
GDP per capita, PPP, trade and total population per store within the European and
American countries. Firstly, the empirical findings questioned the relationship between
the dependent and the various independent variables, as none of the variables were statistically significant. Later, with the reduction of the sample size based on market penetration, new results showed significant relationships for all the variables. GDP per capita, PPP, trade volume and population per store were found to be directly proportional to
the relative price. On the other hand, contradicting the expectations, tariffs were shown
to have a negative effect on the relative average price of clothes, that can be explained
by the negative relationship between GDP per capita and those tariffs. Furthermore,
when introducing dummy variables for regions such as Asian Pacific, Middle Eastern
and North African countries, it was found that they have significantly higher price for
H&M clothes than European countries.
Since the products investigated are perfectly (and relatively cheaply) tradable goods,
and the prices are easily accessed online, one would suggest that the prices around the
world are set to be equal in order to eliminate arbitrage opportunities. On the other
hand, it seems that even a company offering ‘fashion and quality at the best price’ with
cost-consciousness in mind takes advantage of the fact that in some parts of the world
their brand is still considered as unique.
5.1 Suggestions for Further Studies
Even though this study presents an indication of the determinants of price differences
and their impact across countries, the research was highly limited due to the lack of
available data for specific countries and previous case studies on this matter. In addition, price data was collected only on one company for year, and the time horizons of
the other variables differ, so the data on the same time horizon would produce more
concrete and reliable results. Moreover, a study incorporating numerous countries into
28
consideration, and also applying truncated regression models would solve the problem
of missing values. Furthermore, the level of penetration of a company in the selected
markets could be further investigated within the sampled companies in addition to
mark-up pricing, as it would give a good estimate of the demand and pricing patterns.
As Sweden is a high-income country, further studies can be conducted with a different
benchmark, more preferably a middle-income country and a low-income country in order to reflect the diffences more accurately.
This is an important topic to be analyzed further, as nowadays, with the help of the
developed consumer legislation and the increasing importance of electronic commerce,
it is easy to compare the prices across countries and take the advantage of bargain with
confidence.
29
Appendix
Table 1. Ad valorem tax (%) and relative average price deviations (SEK)
Country
Australia
Austria
Bahrain
Belgium
Bulgaria
Canada
Chile
China
Croatia
Czech Republic
Denmark
Egypt
Estonia
Finland
France
Germany
Greece
Hong Kong
Hungary
Indonesia
Ireland
Israel
Italia
Jordan
Kuwait
Latvia
Lebanon
Luxenbourgh
Malaysia
Mexico
Morocco
Norway
Oman
Phillippines
Poland
Portugal
Qatar
Romania
Russia
Saudi Arabia
Serbia
Singapore
Slovakia
Spain
Sweden
Switzerland
Taiwan
Thailand
The Netherlands
Turkey
UAE
UK
USA
Average price deviations
1.03
0.92
1.18
0.92
0.88
0.94
1.00
1.35
0.92
0.86
1.02
1.30
0.92
0.92
0.92
0.92
0.92
1.04
0.85
1.00
0.92
1.30
0.92
1.51
1.15
0.92
1.40
0.92
1.09
0.93
1.22
1.13
3.66
1.13
0.89
0.85
1.27
0.86
0.91
1.24
0.86
1.25
0.92
0.90
1.00
1.33
1.07
1.19
0.92
0.77
1.26
1.02
1.02
30
Ad valorem (%)
10,0
12,0
5,0
10,0
10,0
17,5
6,0
16,5
10,0
10,0
10,0
30,0
10,0
10,0
10,0
10,0
10,0
0,0
10,0
14,1
10,0
6,0
10,0
5,0
5,0
10,0
5,0
10,0
0,0
21,8
25,0
10,7
5,0
15,0
10,0
10,0
5,0
10,0
5,0
5,0
13,5
0,0
10,0
10,0
10,0
11,3
30,6
10,0
12,0
5,0
10,0
11,4
Table 2. EFTA free trade agreements
Albania
Bosnia and Herzegovina
Canada
Central American States (Costa Rica and Panama)
Chile
Colombia
Egypt
Gulf Cooperation Council (GCC)
Hong Kong, China
Israel
Jordan
Korea, Republic of
Lebanon
Macedonia
Mexico
Montenegro
Morocco
Palestinian Authority
Peru
Serbia
Singapore
Southern African Customs Union (SACU)
Tunisia
Turkey
Ukraine
Table 3. Jarque-Bera Test
Normality Test:
Jarque-Bera
13.5839
Probability
0.0011
Table 4. White’s Test
Heteroskedasticity Test:White
F-statistic
46.3336
Prob. F(14,23)
0.0000
Obs*R-squared
35.6988
Prob.Chi-Square(14)
0.0008
31
Table 5. Ramsey’s RESET Test
Breusch-Godfrey Serial Correlation LM Test:
F-statistic
0.2887
Prob. F(1,32)
0.5948
Obs*R-squared
0.3398
Prob.Chi Square(1)
0.5600
Table 6. Variance Inflation Factors
Coefficient
Uncentered
Centered
Variance
VIF
VIF
Constant
0.0026
10.6169
NA
Relative Ad valorem
0.0018
8.7630
1.2323
Relative GDP per capita
0.0014
3.0855
1.1148
Relative Trade Volume
1.80E-05
1.4792
1.0812
Relative Population per Store
2.05E-08
1.4223
1.2297
Variance Inflation Factors:
Variable
Table 7. Correlation Matrix between variables
Correlation Matrix
Relative Trade
Volume
Relative
Ad valorem
Relative
GDP per
capita
Relative
price
Relative Trade Volume
1.000000
Relative Ad valorem
0.231989
1.000000
Relative GDP per capita
0.008245
-0.203143
1.000000
0.298802
-0.283868
0.305857
1.000000
-0.057548
0.338072
-0.301623
0.215706
Relative price
Relative population per
store
32
Relative
population per
store
1.000000
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