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November 2004
On the Role of Non-Traded Inputs and Local Market Conditions in
Price Dispersion:Large Sample Evidence on Borders
Michael A. AndersonWashington and Lee University
Lexington, VA 24550540-463-8971
[email protected]
&
Stephen L. S. SmithGordon College
Wenham, MA 01984978-867-4421
[email protected]
Prepared for the Mid-West International Economics Meetings,
November 5-7, 2004Washington University, St. Louis
Acknowledgements. We thank our home institutions for generous
financial support and HughYeomans (Washington and Lee University)
for able and energetic research assistance over severalyears. Dave
Richardson and Raymond Robertson provided valuable comments on the
earliestversion of this research but should not be implicated in
any errors in the present paper. In a data-intensive project such
as this one we are also grateful to series of research assistants,
including DianeKashapova, Robyn Konkel, Christine McAnulty and
Norman Senior (of Washington and LeeUniversity), and Donald Sanya
of (of Gordon College).
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1
McCallum (1995) began an earnest exploration of how borders
affect international trade. In
his gravity-model analysis of Canadian provinces’ trade with
U.S. states he found that inter-
provincial trade was 22 times larger than province-state trade,
controlling for economic size and
distance. Other papers have found similar results for North
America1 and for other countries and
regions.2 Taken together there is good reason to believe that
the amount of trade inside of a country
far exceeds the amount the country will trade internationally,
after controlling for distance, income
and other relevant variables. This finding has been labeled the
“border effect.”
More recently borders research has moved in several different
directions. Various potential
explanations for the border effect have been offered3, while
other researchers have looked at prices
rather than quantity flows for evidence on whether borders truly
separate markets. Engel and Rogers
(1996), using consumer price indices, found that the variance in
prices inside of the U.S. and inside
of Canada is substantially smaller than the variance in prices
between the two countries, an implicit
border effect.
Studies of international price differences have the potential to
provide a great deal of clarity
on how borders affect markets. These studies can sidestep
problems of gravity model specification
to look at whether arbitrage is functioning internationally as
vigorously as it does domestically. But
until very recently such studies were hampered by the
unavailability of high quality, detailed good-
level absolute price data, forcing researchers to resort to
price index data. Tests of purchasing power
parity (PPP) had to be content with tests of relative PPP, and
Engel and Rogers (1996) had to be
content to test the excess variability in prices attributable to
the border as opposed to actual
underlying price differentials. This is an important matter for
properly understanding the border
1 Anderson and Smith (1999a, 1999b).2 See, among many, Wei
(1996), Parsley and Wei (2000).3 For instance, Anderson and Van
Wincoop (2003) argue for a proper measure of remoteness, or
“traderesistance,” as the correct gravity specification. Hillberry
and Hummels (2003) argue that co-location of finaland intermediate
goods producers accounts for much of the observed border
effect.
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2
effect. Suppose, for instance, that price differentials for all
goods across an international city pair
exceed those across a domestic city pair by a constant
proportion, reflecting a substantial border
effect. The variance of goods price ratios would be the same for
both city pairs, though the border’s
effect on prices is substantial.
In just the past few years, however, a number of studies have
used absolute price data to test
the law of one price, and find tantalizing evidence that it
fails substantially and persistently. For
instance, in a particularly striking case, Asplund and Friberg
(2001) find that even products at the
same location (duty free shops in Scandinavia) sell for
different prices once currency conversions are
made.
To date, however, studies have lacked sufficient generality to
allow any strong conclusions
about the size of border effects across many countries and
regions. Some research has a narrow
geographic focus; Engel, Rogers and Wang (2003), for example,
consider only Canada and the
United States. Some work on domestic versus international price
arbitrage does not try to explain
why domestic prices are more similar than prices across borders.
Hufbauer et al (2002), for
example, which helpfully explores the welfare consequences of
removing existing price differences,
attempts neither to explain the persistent failure of arbitrage
to remove international price differences
nor to measure the size of border effects.4 Parsley and Wei
(2001) focus on the role of currency
volatility in influencing market integration and do not estimate
border effects by region or by nation.
In this paper we address these gaps. Several things distinguish
this work from what has been
accomplished to date. The first is the detail of the data. We
use data from the Economist
Intelligence Unit (an increasingly popular source and, in fact,
relied on by the authors of the studies
cited in the paragraph above). This provides prices on detailed
goods and services like 60-watt light
bulbs, Coca-Cola, rice, men’s raincoats, razor blades, and
laundering a shirt. In this paper we use
4 See also Crucini, Telmer and Zachariadis (2000)
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data on 115 tradable goods across 74 cities in 35 countries
spread over three continents. Our annual
data spans 1990-2003 for a total (in principle) of more than
four million city-pair price comparisons.
But even more useful for our purposes is that this data contains
intra-national price information,
based on four or more cities, for five nations (the United
States, China, Australia, Canada, and
Germany). We thus can estimate border effects with greater
precision, consistency and geographical
generality than previous work, as well as estimate changes in
international integration across time.5
The second contribution is that we test whether both domestic
and international price
dispersion can be explained by local market conditions. These
conditions, reflected in part in local
prices of nontraded inputs, have long been thought to be
important in explaining failures of the law
of one price. The Balassa-Samuelson hypothesis, for instance,
gives the price of non-traded inputs
the central place in explaining international price
differences.6 In our model we control for the
prices of non-traded inputs, specifically labor and land. In
addition, we account for the effects on
price dispersion of local value-added taxes (VAT) and sales
taxes, country-specific tariffs, the
existence of a common language, common currency, and
preferential trading agreements. Overall,
this group of controls should approximate the full set of
relevant local market conditions that may
affect arbitrage. Thus this paper offers the most detailed
analysis to date of the sources of price
dispersion as well as of the remaining “border effect,” that is,
the cross border price dispersion not
otherwise explained by the model.
Here, in brief, are our findings. First, price differentials,
though large and persistent, are
significantly smaller within North America and the European
Union (EU) than in the rest of the
world, and significantly higher in cross-border city pairs than
in city pairs within a country. Second,
after applying the full set of appropriate controls, the
individual countries in our sample have
5 Many researchers use EIU data. Engel, Rogers and Wang (2003)
estimate the U.S.-Canada bordercoefficient over a sub-sample of
goods and services; Parsley and Wei (2001) estimate an overall U.S.
bordereffect, for 1990-2000.6 Froot and Rogoff (1995) and Rogoff
(1996) offer a modern discussion of the Balassa-Samuelson
hypothesis.
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“overall” border effects—additional international price
dispersion over and above within-country
price dispersion—in the range of 12 percent for the entire
sample. This is, perhaps, our most
important result. We confirm the results of other studies which
find high levels of price dispersion,
but in controlling for local market conditions our results
contrast with those who have found
international borders to substantially diminish market
arbitrage. For instance, estimates which
control only for factor prices but not for other local market
conditions predict border effects on the
order of 50 percent larger than reported here.7 By contrast, we
find here that the full set of local
market conditions, and not borders per se, explain the bulk of
international price dispersion. It is
therefore essential to control for factor prices and other local
conditions in estimating border effects
and measuring the extent of international economic
integration.
Third, and contrary to theoretical expectations, distance is not
an economically significant
determinant of price differentials across most city pairs. The
world may not be borderless, but
distance appears to have disappeared as a contributor to price
dispersion. Finally, in a provisional
analysis of the effects of foreign exchange variability to
inhibit arbitrage, we find that a common
currency does not have an important effect on price
dispersion.
In what follows we first discuss in a descriptive way some of
the main features of price
differentials by city pair and region. We also describe the data
in greater detail. In section II we
explain our model and estimating equations. Section III
discusses empirical results, and Section IV
concludes.
7 See Anderson and Smith (2004).
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I. Data and Descriptive Analysis
We use data from the Economist Intelligence Unit (EIU). The full
data set comprises detailed local
currency annual data on close to 200 goods and services prices
and wages and salaries for 122 cities
across all continents, though not all series are available for
all years in all locations. From this data
we select for this present study 74 cities from North America,
Europe (both EU and non-EU
members), Australia, New Zealand, and East Asia including China.
We include all countries for
which four or more cities are available, in order to maximize
the number of potential “internal” or
within-country price differentials. 8 (Cities and countries are
listed in Appendix Table 2.)
We selected 115 goods commonly understood to be tradable.9 After
converting all prices to
U.S. dollar equivalents at date-of-survey market exchange rates
(from the same EIU source), we
calculated all possible relative price permutations across city
pairs for each good and each time
period. Letting i, j, k, and t subscript city i, city j, product
k, and time t, respectively, each U.S.
dollar price of a product in location i or j can be defined as
Pikt or Pjkt. We (arbitrarily) choose to
calculate price differentials (for product k in period t) as
Pikt /Pjkt. The absolute value of the log of
this ratio provides a good approximation of the percentage price
differential (an especially good
approximation for small price differentials). Therefore our main
dependent variable, the city pair
price differential for product k in time period t, suppressing
time and location subscripts, is
(1) lnPi – lnPj
8 This is in effect the OECD plus Russia, China, Taiwan and Hong
Kong.9 The division between “tradable” and “nontradable” is
somewhat ad hoc but relies on distinctions typicallymade in
economic analysis, so that, for instance, men’s haircuts are
considered nontradables while men’sshirts are tradable. Our list is
in Appendix Table 1.)
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where the data are so arranged that the i – j difference above
is a non-negative value. We will refer
to this logged price differential as a price “wedge” and use
price “differential” to refer to the ratio of
actual prices.
Table 1 presents the summary descriptive statistics for price
wedges (mean and standard
deviation) by region, country, and type of city pair. Price
wedges are substantially higher in across-
region city pairs than in within-region city pairs.
Within-region means are in the range of 0.29 to
0.53 while the wedges across regions range from 0.47 to 0.59.
Notice also that there is a large
dispersion around the mean value: standard deviations are almost
always about the same size as the
mean price wedge.10 Overall, the average price wedge is 0.47,
which implies an actual price
differential of 60 percent (e0.47 – 1). Finally, it is notable
that the within-nation mean price wedges
for the United States, Canada, Germany and Australia all lie in
the range between 0.20 and 0.28.
(China’s within-nation mean price wedge is an outlier at 0.37.)
Each of these countries in this time
period has been characterized by a single currency regime,
internal capital mobility, and (with the
possible exception of Germany) full labor mobility. In short,
they have been highly integrated
national markets. That even here price wedges remain at 20 to 28
percent suggests that price
dispersion is firmly rooted in local market characteristics of
various kinds and may not ever be
completely removed by arbitrage.11
10 Anderson and Smith (2004) reports price wedges for
nontradables, which have almost uniformly highermeans than those
reported here for tradables. But the gap is not as large as one
might expect. The overallnontradables wedge is 0.61 for a price
differential of 84 percent (i.e. e0.61 - 1).11 Further descriptive
tables are available from the authors, including price wedges in
tradables broken out byproduct type and region and by individual
products
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II. The Model
We adopt a simple arbitrage model based on the model used by
Engel and Rogers (1996). All final
goods are produced locally and are nontraded. But production of
final goods for local sale requires
combining a traded intermediate input with non-traded factors.
Thus, for instance, the final good
may be Cornflakes sold in Hong Kong in 2003; the traded input is
the box of Cornflakes while the
nontraded inputs are the factors necessary for retailing
Cornflakes there at that time. Suppressing k
(good) and t (time) subscripts, define Ii to be the price of the
traded intermediate input and Wi to be
the price of the nontraded factors used in producing good k.
Assuming Cobb-Douglas technology,
the price of good k in location i can then be written as
(2) )1()()( ααµ −= iiii WIP
where µi is the markup and _ is the share of the traded input in
production cost. Several nontraded
factors may need to be combined to produce the final product. We
assume a nested Cobb-Douglas
formulation in which
(3) )1(i2i1 )w()w(γγφ −= iiW ,
where wi1 and wi2 are the prices of non-traded factors 1 and 2,
respectively. Taking the log of
relative prices in locations i and j, we obtain
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(4) )/ln()/ln()/ln()1(lnln j jijiiji IIPP αµµφφα ++−=−
)w/wln()1)(1()w/wln()1( j2i2j1i1 γαγα −−+−+ .
Consider the terms on the right hand side, from left to right.
The first term reflects the
relative productivity of nontraded inputs in producing goods i
and j. The second term, the relative
markup, can be expected to vary with firms’ ability to
price-to-market (itself related to the elasticity
of demand) and (since Pi and Pj are final sales prices) with
relative local sales taxes.
The price of the traded input in location i relative to j
(Ii/Ij), assuming frictionless trade,
should in principle be unity. However, in practice, we expect
this differential to be related to
differentials in transport costs, to differentials in national
trade taxes (or tax equivalents), and to
differences in language, as well as to the “pure” border effect
relating to the non-pecuniary costs of
transacting across international borders per se. The traded
goods price differential may also be
related to exchange rate variation, which may force costly
hedging and raise the price of arbitrage,
allowing larger traded goods price differentials to emerge than
would otherwise occur. In sum,
arbitrage may occur in traded products/inputs but is inhibited
by all of these factors.
Relative non-traded factor prices can be measured directly and
are expected to show
considerable variation across city pairs precisely because they
are nontraded and reflect all local
supply and demand conditions. We expect that large positive
factor price differentials between
locations should generate, all else equal, large final goods
price differentials. Low land prices in
Lexington should let lemons cost less there than in London. This
is a key implication of the Balassa-
Samuelson hypothesis. In our formulation above we assume that
all goods in any one location face
the same non-traded factor prices.
Our basic estimating equation is this:
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(5) lnPi – lnPj = β0 + β1(national border)ij + β2ln(distance)ij
+ β3ln(wage ratioij)
+ β4ln(rent ratioij) + β5ln(tariff ratioij)+ β6ln(VAT
ratioij)
+ β7(languageij)+ β8ln(common currencyij)+ β9(time trend) +
εij.
The border effect is measured by the dummy variable “national
borderij” which equals 1 if the two
locations lie across an international border. Its expected sign
is positive – all else equal there should
be larger goods price wedges between countries than inside
countries. “distanceij” measures the
great-circle distance, in miles, between locations i and j, and
is the standard proxy for transport costs.
We take labor wages and land rents as the factor prices, and
calculate relative factor price wedges
between locations i and j as the natural log of the factor price
ratios.12 The wage differential variable
ln(wage ratio)ij is the natural log of the hourly labor cost of
domestic cleaning services, in U.S. dollar
terms, in city i relative to city j (using EIU data), while the
rent differential variable ln(rent ratio)ij
proxies the overall land price differential between cities as
the natural log of the city i over city j
ratio of monthly rent on an unfurnished two-bedroom apartment
(again with EIU data).13 Likewise
we include the natural log of the ratio of tariffs between
locations, and of VAT or sales taxes
between locations (“tariff ratioij.” and “VAT ratioij”).14 In
the same spirit of attempting to net out
from the measured border effect all of the local conditions
which might affect arbitragers’ ability to
act, we include dummies for a shared official language and for a
common currency. The latter
equals one for within-country pairs and, starting in 1999, for
all city pairs in the Euro zone. Though
12 For city pairs where Pj > Pi the factor price ratios are
calculated as (wj1/wi1) and (wj2/wi2), and all otherratios are
similarly “flipped” for these i, j pairs.13 Our results are
invariant to using other EIU wage and rent series. This particular
labor price series seems agood index of the cost of low-skilled
labor.14 Country-specific tariff data is taken from various issues
of the World Bank’s annual World DevelopmentIndicators. We used the
weighted average tariff rates for primary products and manufactured
goods for allfood and manufactured goods, respectively, in our
sample, adjusted for known bilateral liberalization (such aswithin
the EU and NAFTA). Current VAT data is taken from OECD (2001) while
historical VAT and salestax data was taken from a variety of state
and national official sources.
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highly collinear with the national border dummy, the large
sample size here should allow us
distinguish between the two effects. We also include a time
trend to test for possible trends in goods
price wedges over time.
III. Results
Because of the massive sample sizes at play in these regressions
almost all of the estimated
coefficients in our results are statistically significant.
t-ratios in excess of 100 are common. Rather
than noting when a variable is significant, we put asterisks by
those few estimated coefficients which
have t-ratios less than 10 in absolute value, which we take as a
rough indicator of questionable
statistical significance in this sample. Our discussion focuses
on the economic significance of the
coefficient estimates.
Looking in Table 2 at the overall regression (column (1)) we see
that the border coefficient is
0.11, suggesting that price differentials across borders are 12
percentage points higher than price
differentials inside a country after controlling for
theoretically important factors. By contrast, this
coefficient is 0.20 when estimated in a regression that controls
only for distance, and is 0.16 in a
regression that controls for distance and factor prices. In
short, the full set of controls makes a
difference, and apparently addresses an omitted variables bias:
while still large, the border
coefficient has fallen by half.
Factor price, tariff and VAT wedges show positive coefficients
as expected in regression (1).
The shared language coefficient, at -0.11, predicts a reduction
in price dispersion by 12 percent. The
internal EU and US-Canada FTA dummies are -0.13 and -0.06,
respectively, indicating lower price
dispersion in these groupings as predicted. The common currency
coefficient, at approximately
0.01, is (unexpectedly) positive but indicates a very small
effect on price dispersion. Finally, and
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strikingly, the estimated sign on distance is negative, is very
small, and has low statistical
significance.
Of the separate types of goods broken out in Table 2, clothing
and personal care products
stand out with border coefficients of around 0.05, roughly half
of the value in other products. But
distance remains strikingly insignificant across all product
categories. Even in automobiles, where
the coefficient has its expected positive sign, its value is
only 0.011. In the double-log form used
here this coefficient is an elasticity; a value of 0.011 implies
a very inelastic response of the price
differential to distance. For instance, a 500 percent increase
in distance between city pairs is
predicted to raise the price wedge by just 5.5 percent (500 x
0.011). That is, all else equal, a price
wedge of 47 percent (the mean wedge in the sample) would rise to
49.6 percent. The economic
significance of distance for price wedges, therefore, appears to
be trivial.
Table 3 reports results of estimating equation (5) on four
distinct regions in our data: OECD
members (column(1)), Europe as a whole (2), the European Union
(3), and North America (4).15 In
each we consider only city pairs within the relevant region or
group. The patterns noted in Table 2
are evident here. The border coefficients are small, ranging
from 0.04 (Europe) to 0.08 (OECD).
For the EU and North America they are both approximately 0.05.
That is, within these regions
crossing a border is associated with an increase of 5% in the
price wedge. This is evidence that the
integration in these regions, assiduously pursued over many
decades, has in fact succeeded to some
extent. It is also interesting that, despite its attempts at
regulatory harmonization, the overall level of
integration in the EU is not appreciably smaller than achieved
between the US and Canada.16
15 The OECD and North America categories exclude Mexico, for
lack of proper tariff data.16 This border effect for North America
is smaller than that found by Engel, Rogers and Wang (2003)
using1990-2000 EIU data, namely, 0.073. Engel, Rogers and Wang use
a smaller number of United States cities,and a smaller group of
products (including both what we call tradables and nontradables)
than used in thepresent study, and use city population as (what
seems) a rough proxy for local factors.
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Distance coefficients are again notable by their small size,
ranging from 0.006 to 0.034.
While all positive as expected, they reveal vanishingly small
effects of distance on price wedges.
The common currency variable offers a mixed result. It is
negative for the EU, as expected, but
small and, by the standards of this study, shows marginal
statistical significance. In addition, the
variable was small and of the wrong sign for the OECD and Europe
as a whole.
Table 4 estimates equation (5) by individual country (or region)
including all possible partner
cities at home and abroad. (These are thus larger and more
variegated samples than used for the
within-region regressions reported in Table 3.) The US (column
(1)) and the EU (6)) show small
border effects again, 0.065 and 0.039, respectively. Amazingly,
Canada (2) and Germany (3) show
negative border coefficients, very close to zero at -0.03 and
-0.02. China and Australia have the
largest border effects at about 0.12 and 0.16. Distance is again
small and not always statistically
significant across these estimates. The common language dummy is
always negative and around 0.1
in absolute value, except for China for which it jumps to -0.16,
offering tantalizing evidence of the
importance of ethnic Chinese trading networks in transactions
between the mainland and Taiwan and
Hong Kong.
In Table 4 the common currency dummy again shows a puzzling
positive sign, though its
size is small. On its face this result implies that the
introduction of the Euro has not contributed to
the narrowing of price differentials in the EU and Europe more
generally. This appears sharply to
contradict the findings of Parsley and Wei (2001) and Rose
(2000) that exchange rate variability
decreases market integration. But it is consistent with Engel
and Rogers (2004) which, though using
EIU data for the EU exclusively and a different methodology than
employed here, is also unable to
find a positive impact of the Euro on price integration.
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IV. Conclusion
Taken together, what do these results suggest? Gravity models
have shown an astonishingly large
affect of borders on international trade. In the gravity model
debate, discussion continues about
whether border effects arise because borders really do separate
markets in profound ways, or
whether these quantity regressions reflect mis-specified models
(Anderson and VanWincoop) or an
unwise confounding of intermediate and final goods trade data
(Hillberry and Hummels), or some
other problem. The literature using aggregated price data,
including much of Engel and Roger’s
work, also suggests that borders may indeed create large market
separations. But the influence of
non-traded factors and the inability to look at individual goods
prices has hitherto made clear
conclusions difficult.
Here, with an alternative methodology using price data that
allow estimation of border
effects, we confirm that border effects are large and
persistent. But our work, using very
disaggregated price data across a broad range of international
cities, supplies evidence that after
accounting for the prices of non-traded inputs and a host of
other local market factors, borders
disrupt markets’ ability to perform price arbitrage functions to
a much smaller extent than shown in
previous work. To put this in perspective, after properly
controlling for local market factors the
border effect appears only to introduce a price differential of
12 percent, a relatively small
contributor to the average price differential of 60 percent.
Surprisingly, furthermore, and in sharp
contrast to gravity approaches, distance appears not to make
much difference for the existence of
arbitrage and integration. And we have preliminary evidence that
common currencies do not
promote international integration, again in contrast to previous
findings.
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Table 1. Mean Price Wedges in Tradable Goods, 1990-2003
Region/Group or Country Mean WedgeStandardDeviation
Number ofobservations
Regions/GroupNorth America (all partners) 0.44 0.38
1,638,308
within region 0.29 0.26 257,895between region and rest of world
0.47 0.39 1,380,413European Union (all partners) 0.45 0.38
1,830,739
within region 0.34 0.29 341,301between region and rest of world
0.48 0.40 1,489,438
Europe (all partners) 0.49 0.43 2,515,895within region 0.46 0.44
814,175
between region and rest of world 0.50 0.42 1,701,720East Asia
(all partners) 0.59 0.48 732,700
within region 0.53 0.46 41,297between region and rest of world
0.59 0.48 691,403
Within OECD 0.43 0.37 2,444,406
CountryUnited States (all partners) 0.44 0.38 1,334,850
within U.S. 0.28 0.24 157,336between U.S. and rest of world 0.46
0.39 1,177,514
Canada (all partners) 0.43 0.37 394,706within Canada 0.25 0.23
9,311
between Canada and rest of world 0.43 0.37 385,395Germany (all
partners) 0.44 0.38 490,910
within Germany 0.26 0.25 15,644between Germany and rest of world
0.44 0.38 475,266
Australia (all partners) 0.44 0.38 490,608within Australia 0.20
0.20 15,620
between Australia and rest of world 0.45 0.38 474,988China (all
partners) 0.58 0.44 290,014
within China 0.37 0.33 5,933between China and rest of world 0.58
0.45 284,081
Overall 0.47 0.42 3,456,494
Source: Authors’ calculations from EIU CityData.
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Table 2. Estimation Results, Overall and by Selected Product
Type. Dependent variable = lnPikt – lnPjkt115 tradable goods (see
Appendix 1 for complete list).
Note: sample size is so large that standard errors are extremely
low and almost all coefficients are highlystatistically
significant. Therefore only exceptions are noted, with asterisks,
as follows: * |t-statistic| ≤ 10;**not significant at 1%, i.e.
|t-statistic| ≤ 2.576.
Independent VariablesOverall
(1)
FoodProducts
(2)Clothing
(3)
HouseholdProducts
(4)
PersonalCare
Products(5)
Autos (6)
national border dummy 0.113 0.116 0.056 0.109 0.049*
0.100ln(distance) -0.001* -0.003* -0.007* -0.004* 0.008*
0.011ln(rent ratio) 0.088 0.135 0.080 0.053 0.060 0.057ln(wage
ratio) 0.107 0.132 0.085 0.084 0.076 0.025ln(tariff ratio) 0.269
0.084* -0.676 0.245 0.354 2.546ln(VAT ratio) 0.071 0.076 -0.133
0.228 0.294 -0.044*common language dummy -0.108 -0.130 -0.077
-0.045 -0.104 -0.026common currency 0.014 0.035 -0.002** 0.029*
-0.011** -0.011*internal EU dummy -0.134 -0.156 -0.081 -0.050
-0.069 -0.058internal US-Canada FTA dummy -0.063 -0.061 -0.030*
-0.059 -0.012** -0.089time trend -0.004 -0.004 -0.006 -0.003 -0.004
-0.003n 2,666,208 1,261,246 325,481 217,070 164,665 196,500Adj.
R-sq. 0.126 0.182 0.132 0.059 0.076 0.174s.e. 0.374 0.390 0.332
0.343 0.345 0.266
Constant term and standard errors not reported.
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Table 3. Estimation Results – Within Major Regions/Groupings
Dependent variable = lnPikt – lnPjkt115 tradable goods (see
Appendix 1 for complete list).
Note: sample size is so large that standard errors are extremely
low and almost all coefficients are highlystatistically
significant. Therefore only exceptions are noted, with asterisks,
as follows: * |t-statistic| ≤ 10;**not significant at 1%, i.e.
|t-statistic| ≤ 2.576.
Independent VariablesOECD
(1)Europe
(2)EU(3)
NorthAmerica
(4)national border dummy 0.081 0.041 0.048 0.053ln(distance)
0.009 0.039 0.034 0.006ln(rent ratio) 0.096 0.074 0.023
0.033ln(wage ratio) 0.078 0.113 0.036 0.021ln(tariff ratio) 0.540
-1.387 X 0.215*ln(VAT ratio) 0.152 0.046* 0.111 0.468common
language dummy -0.100 -0.079 -0.019* Xcommon currency 0.003* 0.042
-0.012* Xinternal EU dummy -0.093 -0.140 X Xinternal US-Canada FTA
dummy -0.034 X X Xtime trend -0.002 -0.008 0.001* -0.001*n
2,210,110 552,371 318,634 248,777Adj. R-sq. 0.115 0.183 0.024
0.024s.e. 0.343 0.370 0.280 0.253
Constant term and standard errors not reported.
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Table 4. Estimation Results by Country/Region Across all City
Pairs.
Dependent variable = lnPikt – lnPjkt
Note: sample size is so large that standard errors are extremely
low and almost all coefficients are highlystatistically
significant. Therefore only exceptions are noted, with asterisks,
as follows: * |t-statistic| ≤ 10;**not significant at 1%, i.e.
|t-statistic| ≤ 2.576.
IndependentVariables
UnitedStates
(1)Canada
(2)Germany
(3)China (4)
Australia(5)
EU(6)
Europe(7)
NorthAmerica
(8)national borderdummy 0.065 -0.029 -0.018 0.116 0.162 0.039
0.047 0.057ln(distance) 0.024 0.006* -0.023 -0.037 -0.005* -0.008
-0.004 0.025ln(rent ratio) 0.080 0.083 0.088 0.028 0.078 0.084
0.090 0.082ln(wage ratio) 0.063 0.068 0.091 0.039 0.100 0.079 0.118
0.068ln(tariff ratio) 0.050* -0.011** 0.368 0.028 0.651 0.052*
0.150 0.037*ln(VAT ratio) 0.175 0.098 0.050* -0.138* -0.255 0.113
0.078 0.167common languagedummy -0.090 -0.109 -0.120 -0.156 -0.118
-0.092 -0.103 -0.096common currency X X 0.015* X X 0.016 0.028
Xinternal EU dummy X X -0.166 X X -0.131 -0.137 XUS-Can FTA dummy
-0.022 X X X X X X -0.014*time trend -0.005 -0.004 -0.002 0.000*
-0.004 -0.003 -0.005 -0.005n 1161345 333127 434514 182577 434919
1549872 1896237 1408622Adj. R-sq. 0.089 0.113 0.095 0.021 0.119
0.079 0.113 0.094s.e. 0.349 0.339 0.347 0.425 0.347 0.357 0.384
0.350
Constant term and standard errors not reported.
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18
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Appendix Table 1. Tradable items
Food Food (cont.) Household GoodsApples (1 kg) Spaghetti (1 kg)
Batteries, D-LR20 (2)Bacon (1 kg) Sugar, white (1 kg) Dishwashing
soap (750 ml)Bananas (1 kg) Tea bags (25) Frying pan, TeflonBeef,
entrecote (1 kg) Tomatoes (1 kg) Insecticide spray (330 g)Beef,
filet mignon (1 kg) Tomatoes, canned (250 g) Laundry detergent (3
l)Beef, ground (1 kg) Veal chops (1 kg) Light bulb, 60 watt
(2)Beef, roast (1 kg) Veal fillet (1 kg) Soap (100 g)Beef, shoulder
(1 kg) Veal roast (1 kg) Toaster, electricBread, white (1 kg)
Water, mineral (1 l) Toilet tissue (2 rolls)Butter (500 g) Water,
tonic (1 l)Carrots (1 kg) Yogurt, natural (150 g) Personal Care
GoodsCheese, imported (500 g) Aspirin (100 tablets)Chicken, fresh
(1 kg) Alcohol Lipstick, deluxeChicken, frozen (1 kg) Beer, local
(1 l) Lotion, hand (125 ml)Coca-Cola (1 l) Beer, quality (1 l)
Razor blades (5)Cocoa (250 g) Cognac, French (700 ml) Shampoo (400
ml)Cocoa, beverage (500 g) Gin (700 ml) Tissues, facial
(100)Coffee, ground (500 g) Liqueur (700 ml) Toothpaste (120
g)Coffee, instant (125 g) Scotch whisky (700 ml)Cornflakes (375 g)
Vermouth (1 l) RecreationEggs (12) Wine, common (750 ml) Compact
disc albumFish, fresh (1 kg) Wine, fine (750 ml) Film, Kodak color
(36 exposure)Fish, frozen (1 kg) Wine, superior (750 ml) News
magazine, TimeFlour, white (1 kg) Newspaper, daily localHam, whole
(1 kg) Clothing Newspaper, internationalLamb, chops (1 kg) Boys
dress trousers Novel, paperback (bookstore)Lamb, leg (1 kg) Boys
jacket Personal computer (64 MB)Lamb, stewing (1 kg) Childs jeans
Television, color (66 cm)Lemons (1 kg) Childs shoes, dressLettuce
(one) Childs shoes, sport TobaccoMargarine (500 g) Girls dress
Cigarettes, local (20)Milk, pasteurized (1 l) Mens raincoat
Cigarettes, Marlboro (20)Mushrooms (1 kg) Mens shirt Pipe tobacco
(50 g)Olive oil (1 l) Mens shoesOnions (1 kg) Mens suit
AutomobilesOrange juice (1 l) Socks, wool Car, compact
(high)Oranges (1 kg) Womens dress Car, compact (low)Peaches, canned
(500 g) Womens panty hose Car, deluxe (high)Peanut or corn oil (1
l) Womens raincoat Car, deluxe (low)Peas, canned (250 g) Womens
shoes Car, family (high)Pineapples, sliced (500 g) Womens sweater
Car, family (low)Pork chops (1 kg) Car, low-priced (high)Pork loin
(1 kg) Social Car, low-priced (low)Potatoes (2 kg) Tennis balls
(6)Rice, white (1 kg) Gas
Gas, regular unleaded (1 l)
Source: EIU CityData, authors’ characterization as “tradable.”
Where applicable, all items are chosenfrom the “supermarket” or
“chain store” category.
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21
Appendix Table 2. Cities and Nations Included in this Study
Australia Greece Canada HungaryAdelaide Athens Calgary
BudapestBrisbane MontrealMelbourne Ireland Toronto IcelandPerth
Dublin Vancouver ReykjavikSydney
Italy United States NorwayChina Milan Atlanta OsloBeijing Rome
BostonGuangzhou Chicago PolandHong Kong Luxembourg Cleveland
WarsawShanghai Luxembourg DetroitShenzhen Honolulu RomaniaTianjin
Netherlands Houston Bucharest
Amsterdam LexingtonBelgium Los Angeles RussiaBrussels Portugal
Miami Moscow
Lisbon Minneapolis St. PetersburgDenmark New YorkCopenhagen
Spain Pittsburgh Serbia
Barcelona San Francisco BelgradeFinland Madrid SeattleHelsinki
Washington D.C. Switzerland
Sweden GenevaFrance Stockholm Austria ZurichLyon ViennaParis
United Kingdom Turkey
London Croatia IstanbulGermany Manchester ZagrebBerlin
AustriaDusseldorf Japan Czech Republic ViennaFrankfurt Tokyo
PragueHamburg Osaka/Kobe New ZealandMunich South Korea Auckland
Taiwan Seoul WellingtonTaipei
Source: EIU CityData.
