FRBSF Economic Letter
2007-24; August 10, 2007
Are Global Prices Converging or Diverging?
Pacific Basin Notes. This series
appears on an occasional basis. It is prepared under the auspices
of the Center for Pacific
Basin Studies within the FRBSF's Economic Research Department.
Most people barely think twice anymore when they discover
that their toothbrush was made in China, their tee-shirt
was made in Honduras, and their car was made in Germany.
With an increasing volume of goods and services flowing
around the world, it is natural to assume that the marketplace
has become "global," which is to say, much more
integrated. One implication of greater integration among
the world's markets is that prices for equivalent goods
and services from country to country should tend to converge.
This Economic Letter reports on recent research that
analyzes trends in global prices over the past decade and
a half
(Bergin and Glick 2007). It finds that, in fact, according
to one measure, there was a trend of convergence from
1990 through 1997, which is consistent with the view that
the
world has become increasingly more trade-integrated over
time, due to fewer governmental barriers and declining
costs for transportation and communication. Somewhat
surprisingly, however, it also finds that this trend was
interrupted
and then reversed in subsequent years, implying a general
U-shaped pattern over the past one and a half decades.
In exploring possible factors accounting for this reversal,
a likely suspect turns out to be the hike in oil prices
in recent years, which has raised transportation costs.
Data Our measure of price dispersion is constructed from data
on actual price levels obtained from the Worldwide
Cost of Living Survey conducted by the Economist
Intelligence Unit (EIU), which records local prices
for over 160
individual
goods and services in more than 120 cities worldwide.
The goods are narrowly defined—for example, "apples (1
kg)," "men's raincoats (Burberry type)," and "light
bulbs (2, 60 watt)." All prices are recorded in
local currency and converted into dollars.
The EIU database does not contain a price quote for
all goods and cities in every year. Since we are interested
in how prices vary both from country to country and
over
time, we assembled data for the same set of tradable
products for cities where generally no more than 30%
of the observations
were missing in any given year. The resulting panel
consists of price data on 101 tradable products in
108 cities
in 70 countries for the period 1990 to 2005, the last
year
for which we have data.
We use these data to compare price level differences
across cities in different locations and countries.
For example,
for a given pair of cities, say, London and New York,
we compute the difference in the dollar price level
of each
good, such as tomatoes (specifically, the log of the
ratio of the price levels of tomatoes in the two locations).
We then define price dispersion for each pair of cities
as the mean of squared price differences across all
101 traded goods. (We square the price differences
before
calculating
the mean because we care only about the magnitude of
price differences, not whether prices are higher or
lower in
one country than in another.)
 The solid line in Figure 1 presents our measure of
price dispersion averaged over all city-pairs on a
year-by-year
basis over the period 1990 to 2005; a rough U-shaped
pattern is apparent, with dispersion falling from 1990
to 1997
and then gradually rising through 2005. The dotted
line plots results that exclude city pairs within the
same
country, and clearly, the pattern is little changed.
The fact that
this line is somewhat higher implies that price dispersion
is less among cities within the same borders.
Further analysis (not shown) indicates that the U-shape
applies broadly across various subgroupings of countries,
that is, for city pairs where both are in industrial
countries, both are in developing countries, and one
city is in an
industrial country and the other is in a developing
country. For city pairs within the eurozone alone and
for U.S.
pairs, the degree of "U-ness" is shallower,
but it still holds. In addition, as shown in Figure
2, the U-pattern
is present when we break the data into particular commodity
groups. (For comparison, we also show the pattern of
price dispersion for a set of nontraded goods.) While
there are
clear differences between commodity groups in terms
of average levels of price dispersion—high dispersion
among
perishable food items, and low dispersion among household
supplies—the U-shaped pattern over time is consistent
across almost all of the commodity groups, with falling
levels of dispersion until 1997 and rising dispersion
afterward.
Determinants of price dispersion
To explore further the determinants of the pattern
of price dispersion in our sample, we conducted a
formal statistical
analysis. Specifically, we modeled price dispersion
between
any two cities as a function of standard trade friction
determinants, such as the distance between the cities,
language differences, adjacent national borders,
tariff barriers, and institutional arrangements, including
membership in regional trade agreements or currency
unions. Also
included are city and year dummy variables to capture
factors that
may affect the dispersion in prices between cities
and over time that are not otherwise modeled.
 Regression results for the full world data set indicate
that price dispersion increases with distance and
tariffs, and it decreases when the cities are in
the same country,
when they share a border, and when they share a language.
In addition, price dispersion declines when countries
participate in a regional trade agreement or in a
currency union. However,
the U-shaped pattern over time remains, indicating
that it is not attributable to the "usual suspect" variables
studied in past research. (The U-shaped time pattern
is captured in the regression by the estimated coefficients
for the time variable dummies.)
Therefore, we considered several other factors. One
is exchange rate variability, as measured by the
nominal exchange rate volatility or the occurrence
of a currency
crisis.
If exchange rate variability has increased in recent
years, this might have contributed to the greater
price dispersion
observed in the data. In fact, we found that neither
measure of exchange rate variability is time-varying
in a way that
is helpful in explaining the U-shaped pattern of
price dispersion. The yearly means of exchange
rate volatility
and the occurrence of currency crises across the
pairs in the sample take on large values in several
points
during our sample period (1992, 1997-1998, and
2002), but no U-shape
is evident. This is confirmed by regressions, which
find
that greater price dispersion is associated with
greater exchange rate variability, but the latter
does not
account for the U-shaped time variation in price
dispersion.
Another avenue we explored is whether the relationship
between our explanatory factors and price dispersion
has shifted over time. For example, there is
some evidence that the pass-through of exchange rate
movements to
import prices has fallen over time, so that a
given depreciation
of a country's currency leads to a smaller increase
in import prices in the local currency. Lower
pass-through implies that price changes in one country
are less
likely
to lead to price changes in other countries,
and therefore to more price divergence. While we do
find statistically
significant variation over time in the sensitivity
of price
dispersion to exchange rate volatility, this
variation does not appear to correspond much with the U-shaped
pattern in price dispersion.
However, in a similar exercise examining the
variation in the sensitivity of price dispersion
to distance,
we find a rough U-shape is apparent, with the
sensitivity to distance first declining and
then rising. This
suggests that some common factor affecting
the cost of transporting
goods a given distance may be at work.
Oil
and transport costs
Pursuing this possibility, we search for additional
variables that vary over time and that
are related to distance.
An obvious candidate is the price of oil,
which has varied significantly over time and affects
transportation
costs.
If oil prices raise transportation costs,
they should increase
differences in the prices of imported goods,
raising average price dispersion. In addition,
they should
raise costs
proportionately more for countries farther
apart.
 In fact, a plot of oil prices (relative
to the U.S. Consumer Price Index, CPI)
in Figure
3 shows
that
it is time-varying
in a manner that roughly coincides with
the pattern in price dispersion, in that
the
price of oil
reached a
low point in the sample shortly after
1997 and rose gradually in subsequent years.
For comparison,
the
figure also
plots
measures of U.S. air and freight transport
costs, which also rose at the end of
the sample. In
the absence
of a general measure of world transport
costs, we use oil
prices
to proxy for trends in such costs in
regressions. We find this variable is highly significant
and associated with
increased price dispersion. We also find
that a rise in the price of oil has a
bigger
impact
on
price
dispersion
if two countries are far apart. This
clearly lends support to the transportation cost
story.
The basic findings of our analysis hold
under a variety of alternative formulations,
namely,
omitting
any
city without data for all 15 years
of the sample period,
including only one city per country,
using different measures of
price dispersion, and considering additional
control variables, such as international
differences in
GDP per capita, GDP,
or wages.
Conclusion
Our analysis finds significant variation
over time in the degree of global
price convergence over
the past
15 years.
In particular, there appears to
be a general U-shaped pattern with rising
price dispersion
in recent
years, a pattern
which is remarkably robust across
country groupings
and commodity groups. This time-variation
is difficult to
explain in terms of the explanatory
factors
common in the literature,
as these tend not to vary much
over time. However, regression analysis
indicates
that this time-varying
pattern coincides
well with oil price and transport
cost fluctuations, which clearly vary over
time and have risen
in recent years.
Reuven Glick
Group Vice President
Reference
Bergin, Paul, and Reuven Glick. 2007. "Global Price
Dispersion: Are Prices Converging or Diverging?" Journal
of International Money and Finance 26(5) pp. 703-729
(September).
Opinions expressed in this newsletter
do not necessarily reflect the views of the management
of the Federal Reserve Bank of San Francisco or of the
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Questions? Contact
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