A Guide to Measures of Trade Openness
and Policy
H Lane David*
Indiana University South Bend
July 31, 2007
*e-mail: hdavid@iusb.edu
The author wishes to thank Thomas Willett, Arthur Denzau, James Lehman, and
participants at WEA sessions in 2003, 2004, and 2005 for support and invaluable
comments. Any remaining errors are the sole responsibility of the author.
©2007 by H Lane David. All rights reserved. Short sections of text, not to exceed two
paragraphs, may be quoted without explicit permission provided that full credit, including ©
notice, is given to the source.
A Guide to Measures of Trade Openness and Policy
1
Introduction
To analysts and policymakers the issue of trade policy (or openness to trade) is
important. There is a large literature supporting the idea of a positive relationship
between trade openness and economic growth (Edwards [1992], Krueger [1997],
Wacziarg and Horn Welch [2003]). Trade openness is regarded by many, in both
academic and political spheres, as one being of the most important influences on
economic growth (along with the quality of institutions1 and tropical geography2). Given
that location is immutable and changing the quality of a nation's institutions is a longterm process, there has been a strong emphasis on trade openness (and trade
liberalization), particularly for developing countries.3
The findings on trade
liberalization, however, are contentious and the research and empirical findings not yet
conclusive (Rodriquez and Rodrik, 2001).
The focus of this paper, however, is not on the effects of trade policy
liberalization on economic growth but rather on how analysts measure trade openness
and policy. Beyond a general understanding that "openness" refers to trade barriers (and
how restrictive they may be) there is not a clear definition of the term. Empirical studies
have described openness in many ways and authors have used varied approaches in the
attempt to capture, via a summary measure, the multifaceted nature of trade policy. As a
result, a large number of measures of trade openness and policy have been created.
Notable examples include Leamer (1988), Dollar (1992), and Sachs and Warner (1995).
Harrison (1996) observed over a decade ago that, at that time, there was "... a dizzying
array of 'openness' measures, methodologies, and sample countries,...".4 The observation
holds true today, as there has been continued production of new measures of trade
openness and policy. As Greenaway et al (2002) noted:
"Even at the conceptual level, liberalisation is not unambiguous. In the
simple 2x2x2 trade model, one naturally thinks of it as tariff liberalisation.
In a more sophisticated setting with instruments affecting the domestic
prices of both importables and exportables, one can conceive of it as a
move towards relative price neutrality. Finally, one can think of second
best liberalisation, i.e. the substitution of more efficient for less efficient
instruments---typically tariffs for quotas. This ambiguity is reflected in
the range of measures used empirically."
Clearly, identifying an appropriate measure is a challenging exercise.5
1 Important contributions include Barro (1991), Knack and Keefer (1995), Easterly and Levine (2002), and
Rodrik, Subramanain, and Trebbi (2002).
2 See, for example, Hall and Jones (1999) and Sachs (2003).
3 It should be noted that there is an ongoing debate as to whether trade openness and trade policy are the
most important determinants of economic growth or whether the quality of institutions is more responsible
for economic growth. The debate is beyond the scope of this paper, but interested readers are referred to
Easterly and Levine (2002) and Sachs (2003).
4 p.420
5 Unless one decides to choose a measure based on the number of times it has been cited, which is a
mistake (as will be demonstrated).
2
Several authors (Harrison [1996], Edwards [1998], Rodriquez and Rodrik, [2001],
and Greenaway et [2002]) have tested the explanatory power of a number of these
measures to find which might be "best". Unfortunately, taken as a whole, the studies
provide little guidance for those wishing to use such measures. First, because the results
have been mixed, ranging from strong scepticism of the usefulness of such measures to
enthusiastic endorsement of them in general. Second, because it is difficult to compare
measures across the studies. Each study considers a relatively small (though generally
diverse) number of measures, ranging from three to nine, and there are few measures that
appear across studies. Finally, all the studies use different methodologies for testing the
measures, again making it difficult to compare results from one study to another even
where they contain common measures. It is safe to say that no single measure or group
of measures has emerged as a universally accepted indicator of trade openness and
policy, highlighting the need of analysts to carefully consider which will be the most
appropriate measure(s) for their work.
The purpose of this guide is to aid researchers faced with choosing among the
plethora of measures available. I have collected a large number of these measures into a
single dataset. Following Pritchett (1996) I also examine correlations between measures
to see if they appear to capture common trade policy elements. As the reader will see,
this does not resolve the discussion of what exactly constitutes "openness" and how best
to measure it. However, analysts who are familiar with the variety and construction of
these measures will be able to identify and use those best suited to their work.
Section 2 contains a brief review of the theory connecting trade and economic
growth, Section 3 reviews the literature on measures of trade openness, Section 4 puts
forth a taxonomy for the large number of measures now extant and then looks at the
strengths and weaknesses of each of the categories in the taxonomy, Section 5 examines
correlations between measures and across groups of measures for evidence that these
measures are capturing some common aspect(s) of trade policy, and Section 6 concludes.
2
Brief Theory of the Gains from Trade
Standard trade theory6 posits three channels through which trade liberalization
affects economic growth. First, there are gains from exchange. Consumers benefit
directly from lower prices of imports when trade barriers are reduced. Producers (and
ultimately consumers) benefit when they can import primary and intermediate inputs at
lower prices. Second, reducing trade barriers encourages firms to direct resources away
from previously protected sectors and towards those that have the greatest value added
(in both domestic and international markets). This results in gains from specialization as
sectors and industries which have comparative advantage in production expand their
output. Finally, there are gains from economies of scale. Lowering trade barriers has a
pro-competitive effect on firms. Previously marginal firms will be forced out of
6 This is the conventional (and dominant) theory of international trade, often referred to as the neoclassical
model of international trade. It has been challenged (primarily because of the assumption of perfect
competition) by models that incorporate imperfect competition, increasing returns, and learning effects.
Seminal contributions in this branch of the literature include Linder (1961), Posner (1961), Vernon (1966),
Krugman (1979), Caves (1985), Helpman and Krugman (1985), and Rodrik (1988).
3
business, allowing surviving firms to increase output and achieve lower average total
costs, allowing for greater efficiency in use of resources and, consequently, output.
These gains from trade can also be viewed inter-temporally. In the short-term
there are static, or efficiency, gains from trade liberalization. Trade restrictions create
price distortions that shift production between countries. Under domestic protection
against international competition, production is encouraged in sectors which are not
internationally competitive, and this forces consumers to pay higher prices. The removal
of these price distortions through the lowering of trade barriers leads to a more efficient
allocation of resources, as making domestic markets more open to competition from
foreign sources encourages production based on comparative advantage.
In the longer run there are numerous potential sources of dynamic gains. Reduced
trade barriers allow domestic industries greater access to intermediate goods, capital
goods, and technologies7 that foster economic growth. Industries that export can enjoy
the economies of scale that result from serving a larger market that includes both
domestic and foreign consumers. This leads to the specialization and economies of scale
mentioned previously, which in turn can lead to the growth of intra-industry trade.8
There will also be increased efficiency due to market competition reducing the degree of
monopoly power that existed prior to liberalization. For many developing countries this
is a significant step, as there are frequently state-run monopolies in important sectors of
these countries. Importantly, increased openness can lead to greater investment from
both domestic and foreign sources. Investment in the economy is crucial to economic
growth. For many developing countries with savings rates insufficient to develop the
capital markets which support economic growth, foreign direct investment (FDI) is
necessary for the economy to grow. Finally, increased openness imposes constraints on
government-induced distortions.9
Given that theory tells us greater openness to trade stimulates economic growth
and that casual observation suggests that countries which pursue more liberal trade
policies are more successful economically, the question that arises for researchers is
"How can we quantitatively capture a countries trade regime (or trade policy stance)?"
The next section begins to answer the question by reviewing the literature on the subject
of measures of trade openness and policy.
3
Some Background
There is a large (and still growing!) body of literature that documents a positive
correlation between trade openness and economic growth. Recent studies supporting a
7 Access to technological advances generated by developed countries can play a particularly important role
in the economic growth of developing countries. See, for example, Grossman and Helpman (1991) and
Barro and Sala-i-Martin (1995).
8 The majority of trade between industrialized countries is of an intra-industry, rather than an interindustry, nature.
9 See Grossman & Helpman (1991).
4
positive empirical relationship between openness and economic growth or growth in
income levels include those by Dollar (1992), Ben-David (1993), Sachs and Warner
(1995), and Edwards (1992, 1998). Up until the mid-1990s this finding was almost
unquestioned.10 However, the study of measures of outward openness published by
Pritchett (1996) brought to the field doubts that researchers were adequately capturing
openness. Pritchett examined the correlations between a number of measures of
openness to see if they were capturing some common aspect of trade policy or openness.
He found that
"Examination of the link between various empirical indicators used in the
literature to measure trade policy stance reveals that, with minor
exceptions, they are pairwise uncorrelated. This finding raises obvious
questions about their reliability in capturing some common aspect of trade
policy and the interpretation of the empirical evidence on economic
performance."
Thus, in an important way, the finding cast doubt on the interpretation of the
empirical evidence on openness and economic growth.
Since the publication of the Pritchett study there has been an active debate
concerning measures of trade openness. A study by Edwards (1998) used 9 measures to
test the relationship between openness and total factor productivity (TFP) growth as a
proxy for economic growth. His thesis was that, given the difficulties of creating
satisfactory summary indexes, it would be worthwhile to examine whether econometric
results were robust to the use of alternative measures. His finding, that TFP growth is
faster in more open economies, was robust to the choice of openness indicators,
providing support for the argument that a positive relationship between openness and
productivity growth exists.
However, the study by Edwards did not resolve the debate. Rodriguez and Rodrik
(2001), in a piece that reviewed extensively four measures (as well as three other studies),
argued that, owing to methodological problems with the empirical strategies used, the
relationship between trade policy (as reflected by openness) and economic growth had
not been well established. Their skepticism was driven by two concerns: first, the
measures used were either poor measures of trade policy or were highly correlated with
other factors affecting economic performance; and second, the mechanisms linking trade
policy and economic growth had not been well established. In their study they found
little evidence that lower trade barriers were associated with economic growth.
Greenaway et al (2002) postulated that both misspecification and the diversity
of the openness measures contribute to the inconclusiveness of the debate. They
addressed the misspecification issue by using a dynamic panel framework and found that
liberalization impacts economic growth with all of three measures of openness they
used.11
The problems of finding measures of trade openness that capture the relationship
between a liberal trade regime and economic growth are explored further in Winters et al.
(2004). They cite three sources of difficulty: 1) measuring trade stances is a difficult
exercise, 2) the direction of causation between openness and growth is difficult to
10 Leamer (1988) did find that the choice of measure was arbitrary.
11 It is important to note that the three measures used were very similar in nature, all of them being
dichotomous open/closed indicators. The weaknesses of dichotomous indicators are discussed further on.
5
establish, and 3) the interaction of trade policy with other policies has to be considered
when determining the effect on economic growth. With regards to the first issue, the
essential problem is that trade policy is multifaceted. Trade policy involves numerous
instruments, including (but not limited to) tariffs, non-tariff barriers (such as
administrative classification of goods, domestic content provisions, government
procurement provisions, restrictions on services trade, and trade-related investment
measures), and quantitative restrictions such as import quotas. There are problems with
the quality of data, assessment, and aggregation both within and across instruments.
The question of causality is an important issue. Trade policy itself may be a
function of other variables. For example, trade liberalization may be driven by income,
rather than the other way around. The push for greater trade liberalization through rounds
of trade negotiations since WWII has come largely from the industrialized countries,
which implies higher income may drive trade liberalization. However, on the other hand,
much trade liberalization has come about as a result of conditionality requirements
imposed by the International Monetary Fund when lending to countries undergoing crisis,
which implies a negative relationship in which liberalization drives income.
Macroeconomic policies as well as institutional and geographic variables can also
affect trade policies. It is thus likely that many of the measures of openness are
endogenously determined. Including an endogenous factor as an explanatory variable in
a regression model creates problems for understanding the relationships between the
explanatory variables (including trade openness) and economic growth. However, work
by Frankel and Romer (1999) and Frankel and Rose (2002) addressed this issue and
provided evidence of a positive causal relationship from trade (in the first) and openness
(in the second) to income, providing further support for a positive relationship from
openness to economic growth.
Finally, there is the observation that trade policy does not operate in a vacuum but
that its effects can be attenuated or ameliorated by other government policy actions.
Baldwin (2002) argues that to understand economic growth one must incorporate more
than just the effects of trade policies. He notes that restrictive trade policies are
"...usually associated with export subsidies to selected sectors, overvalued
exchange rates, large government deficits, extensive rent-seeking and
corruption, unstable governments, and so forth, but significant reductions
in trade barriers are also accompanied by important liberalization efforts in
these non-trade policy areas."
and he emphasizes the efforts of researchers who seek to combine various policy
measures into an index measure.
An approach that addresses the second of Rodriguez and Rodrik's criticisms (that
the mechanisms linking trade policy and economic growth have not been well
established) is to examine the channels by which openness could be linked to economic
growth. Wacziarg (2001) identified six channels through which trade policy can affect
growth: 1) macroeconomic policies, 2) the size of government, 3) price distortions, 4)
factor accumulation, 5) technology transmission, and 6) foreign direct investment. Using
a composite measure of trade openness the effect of trade policy on growth through
specific channels was estimated as the product of the effects of trade policy on the
channel and the effect of the channel on growth. The results support positive impacts of
openness on economic growth through these channels.
6
Despite the significant questions raised in recent years concerning both
methodology and the robustness of the conclusion of a positive correlation between
openness and economic growth, it is generally agreed that, at worst, the relationship
between openness and growth is bounded below by zero and that, more likely, it is the
case that increasing trade openness leads to increases in economic growth and income
levels. Researchers continue working to resolve the questions of whether trade
liberalization does stimulate economic growth in developing countries and on whether
measures of trade openness and policy can be successfully used, in particular for policymaking purposes. New measures of trade policy continue to be developed on a regular
basis and it is safe to say that economists and policy-makers are not about to give up
using such measures. Despite reservations, researchers continue to use existing measures
of trade policy and new measures continue to be developed.
4
Classification
I have collected data for 30 distinct measures of trade openness and policy.
Because a number of the measures have been calculated for more than one period (in the
case of some cross-sectional measures), have an overall measure for the economy along
with sectoral sub-measures (for the manufacturing, natural resources, and agricultural
sectors), or have a measure and some variant(s) of it, there are a total of 70 measures in
the dataset.12 Given the large number of measures of trade openness and policy available,
it is useful to group and then compare them. In this section I do so by presenting a
taxonomy in which the measures are divided into logical groupings. I then review the
strengths and weaknesses of each category. This is a particularly important exercise in
light of the lack of a universally agreed upon definition of openness. The taxonomy used
is adapted from Rose (2002).13 Under this version measures of trade openness and policy
are divided into six groups:
1. Trade ratios
2. Adjusted trade flows
3. Price-based
4. Tariffs
12 The data set is available upon request from the author. The data set contains both cross sectional and
panel data collected from a large number of studies. The measures cover a variety of periods (depending
on the source) from 1950-2000 and 168 countries. A large portion of the data was obtained from the data
set for Rose (2002). Interested readers may wish to consult his website at
http://faculty.haas.berkeley.edu/arose/.
13 A simpler taxonomy that draws the distinction between causes and effects of trade policy is from
Baldwin (1989), who suggested that measures of trade barriers could be divided into two types: incidence
and outcome. Incidence-based measures attempt to gauge trade policies by observation of policy
instruments (both trade and non-trade) while outcome-based measures deal with the levels of trade flows
or attempt to assess the difference between the actual outcome and what would have transpired in the
absence of the trade barriers. It should be noted that this use of the term incidence is in contradiction to the
way economists generally employ the term i.e. the incidence of a tax describes who bears the burden of the
tax.
Another taxonomy is from Wacziarg (1998) who proposed that there are three broad categories of
measures of trade openness: outcome measures, policy indicators, and measures of effective protection
based on deviations from the predicted free trade volume of trade.
7
5. Non-tariff barriers
6. Composite Indices
It is important to note that the first three categories contain measures that are
based on trade flows or levels of prices while the rest seek to assess trade restrictions
directly. Put another way, the first three categories focus on outcomes while the last
three focus on policies. This is an important distinction. Ideally, one would want to
measure trade restrictions directly to determine the level of protection of a country.
However, in general, it is easier to measure flows and prices than barriers. Flows are
observable and quantifiable. Data on trade flows are gathered and disseminated on a
regular basis. For many countries trade data is available extending back several decades
(to the 1950s for developed countries and at least back to 1970 for a large number of the
developing countries). This data is often rich in detail, frequently available to three- and
four-digit SITC levels, and easy to compare across countries. Information on prices of
internationally traded goods is also generally available.
The availability of these types of data, however, is not a sufficient condition to
create good measures of trade openness and policy. The trade ratios category examines
measures that focus solely on trade flows and finds them wanting. While these types
measures are popular because of data availability and ease of calculation it will be shown
that they capture neither trade policy nor the effects of it. Of great concern is the fact that
there is no theory, only data availability, driving the measures. The other two categories
of flow- and price-based measures attempt to avoid this problem by using theoretically
founded models to create free-trade counterfactuals and then use the difference between
observed trade flows and theoretically predicted free-trade flows to estimate trade flows.
Conversely, data based on the observation of trade restrictions themselves (which
is the focus of the final three categories) is much harder to collect and work with. As will
be seen, even with tariffs, where analysts can go directly to the tariff schedule of any
country for data, quantification and interpretation are difficult exercises.14 Quantifying
and aggregating non-tariff restrictions suffers from the same problems to a greater
degree, as the researcher must calculate and combine the effects of what are frequently
fundamentally different types of instruments as well as problems arising from the use of
qualitative data. The result of these data issues has been the creation of far more flowbased measures than direct measures of barriers. The final category, composite indices,
seeks to combine tariff and non-tariff indicators with other economic and political
indicators. These measures are subjective in both the choice of indicators to be included
and in much of the data used.
Another important issue to be addressed is whether particular measures are based
on theory. Many of the measures (particularly in the trade ratios category, which is the
largest) have been created primarily because of data availability of and not because of a
theoretical basis. This lack of theoretical foundations has to be of great concern in both
the construction of measures and, even more so, in justifying the decision to use a
particular measure.
Wacziarg (1998) emphasizes these issues that the reader should keep in mind: 1)
outcome measures may not be well correlated with actual trade policies (because trade
outcomes are the result of a multidimensional process) but researchers tend to confuse the
14 The problems of working with tariff data are detailed in section 4.4.
8
two, 2) policy indicators such as tariffs, non-tariff barriers, etc., capture different aspects
of a country's trade policy, such that the use of a single one of these indicators may not be
very informative, and 3) one cannot be sure that deviation measures (the second two
categories of the taxonomy) create correctly estimated predicted free trade flows for the
counterfactuals.15 Wacziarg also argued against the use of outcome measures on the
grounds that they may be more reflective of levels of integration than capturing the
effects of institutions that affect openness.
4.1 Measures of Trade Openness and Policy
This section contains a discussion of the nature, strengths, and weaknesses of the
measures in the categories of the taxonomy. For each category at least one example is
reviewed.
4.1.1
Trade Ratios
This category contains the most widely used measure of trade openness and policy, the
simple and intuitively appealing trade ratio measure of openness, most often calculated as
(Exports + Imports) / GDP. Commonly referred to as openness (which is, I think, a
misnomer) it is also known as trade share or trade intensity.16 The measure is popular
because data are readily available for many countries and, as it is quite commonly used, it
allows for comparability across studies.
A variant of the trade ratio measures are import penetration ratios (Leamer, 1988).
Not widely used, these ratios are calculated by dividing imports of a given commodity by
the total domestic supply of that commodity. Total domestic supply is defined as imports
plus gross output of the domestic producers minus exports of that commodity. These
ratios seek to provide a broad indication of the international competitiveness of domestic
producers. These measures have been calculated as overall aggregates for the entire
economy, as well as sectoral measures for the manufacturing, agricultural, and resource
sectors.
Despite the overwhelming popularity of the simple trade ratio measure,
researchers should be aware that this measure is a measure of country size and integration
into international markets rather than trade policy orientation. A few examples using
year 2000 data for the well-cited openness measure (in current prices) from the Penn
World Tables (PWT6.1) serve to illustrate the point. First, the five least open countries
are (in order) Japan, Argentina, Brazil, the United States, and India. While it is clear that
these countries have trade restrictions in varying degrees, it is difficult to believe that
they are the most restrictive countries in the world in terms of trade policies. It is most
likely that the large size of the economies relative to their volumes of trade is responsible
15 Reasons why counterfactuals may not be estimated properly include possible omitted variables in the
estimation of free trade flows, the possible correlation of some of the determinants used to estimate the free
trade flows with trade policies, and an increased downward bias associated with measurement error in the
observed volume of trade.
16 Calculated using either current prices or base year prices.
9
for these results rather than trade policies (China ranks 24th out of the 136 countries for
which the data is available).
Second, many of the comparisons across countries are not plausible with respect
to the degree of difference of openness between them and, for example, the U.S. One
should not be surprised to learn that Singapore and Hong Kong are, by this measure,
ranked as the most open countries. However, according to the data they are 11.2 and 9.7
times more open than the United States. What is surprising are comparisons involving
less developed countries still struggling with economic (and social and political)
development. Jamaica is almost 4 times more open than the U.S. and African nations
such as Ghana and Congo 4.5 and 5.1 times more open. Even many of the former
communist countries are listed as far more open than the U.S., for example, the Ukraine
and the Czech Republic at 4.5 and 5.6 times respectively.17 Given these examples, it is
hard to see that this measure produces satisfactory ordinal rankings much less the
cardinal ranking implied by the precision of the measures. What is obvious from
examining the data is that these measures show that small countries are relatively more
engaged in international trade than large countries, returning us to the contention that this
is a measure of size and not policy.18 This is reflective of smaller countries being
constrained by the size of their domestic markets and needing to trade to achieve
specialization and economies of scale much more than it is a measure of trade policy or
openness.
At best the trade ratio measure would be a highly imperfect proxy for trade
policy. It is difficult to substantiate the claim that trade ratios changes only in response to
trade policy changes. Trade is affected by many factors in addition to trade policy:
structural and environmental factors such as differences in geographical variables,
resource endowments, the size of the country, the level of economic development, the
state of the global economy, etc. Consider the impact of a sudden increase in oil prices.
The price increase would show up in the numerator of the measure, causing the country
to appear to have increased its openness when in fact no trade policy changes had
occurred. If the price increase was severe enough and of sufficient duration it could lead
to an economic recession, which would reduce the denominator in the measure, again
making the country appear to have become more open when that was not the case.
Most damning, there is no theory supporting the idea that trade ratios reflect trade
policies. In fact, trade ratios have been used to support theory tying openness to country
size rather than growth. Alesina, Spolaore, and Wacziarg (2000) develop a model of the
relationship between openness and the equilibrium number and size of nations. They
argue that trade liberalization and size are inversely related and use trade ratios in the
empirical test of their model. Their reasons for doing so are: 1) that they feel it is a
"broad" measure, capturing a policy component, a gravity component, and other
determinants of trade openness such as differences in political and legal systems,
languages, etc.; 2) the data is readily available; and 3) use of the measure allows for
comparability across studies.
17 The actual data for the countries cited are Japan 20.095, Argentina 22.215, Brazil 23.028, United States
26.197, India 30.449, Jamaica 99.332, Ukraine118.236, Ghana 118.75, Congo 132.505, Czech Republic
146.617, Hong Kong 295.186, and Singapore 341.591.
18 For a good exposition and a model of this idea see Alesina and Spolaore (2003), Ch. 6, pp.81-94.
10
Alesina et al reach two conclusions. First, that country size correlates less with
growth for countries that are more open to trade, and second, that the link between
openness and growth is stronger for small countries than large ones. Given the flawed
nature of the measure, I think they read too much into their findings.
I hope that I persuade my readers that none of Alesina et al’s points are good
reasons to use such a measure to capture trade openness or trade policy. By their own
admission it is too "broad" to be a measure of trade policy and is driven by data not
theory (they use the measure to "prove" their theory, not the other way around). As for
the last reason, comparability is not an issue if the measure does not capture what it is
supposed to.
A number of attempts to improve trade share measures have been undertaken.
However, empirical work to improve trade ratios has yet to prove that they serve as
measures of trade openness and policy. Pritchett (1996) noted that highly protectionist
policies should reduce the amount of economic activity that is traded. To estimate the
size of this reduction he used "structure adjusted trade intensity" measures, which are the
residuals from a regression of trade intensity on structural characteristics such as
population, land area, level of per capita GDP, and transport costs.19 The residual from
the equation implies by how much a country's openness differs from what would be
expected of a country with the same characteristics. This is not a large improvement,
however. As Pritchett himself notes "...the regression adjustment is ad hoc and
atheoretic."20 Changes in structural characteristics or omitted variables (such as foreign
demand) may cause changes in the residuals that would be interpreted as changes in trade
policy when policy has not changed. Nor does the measure establish a common
benchmark, such as a free trade or an average level of the residuals that would facilitate
comparisons across countries.
Frankel and Romer (1999) attempt to improve on the standard trade ratios
measure by creating one constructed using a number of geographic characteristics. Their
finding that a 1 percent increase in the trade to GDP per person ratio raises income per
capita by at least one-half percent has been widely seized upon. Yet they themselves
admit the measure is "clearly an imperfect measure of economic interactions with other
countries,...”. They show that the hypotheses that the impacts from trade and size are
only marginally rejected at standard levels of significance. Finally, they caution that their
results "...cannot be applied without qualification to the effects of trade policies." This is
far from convincing evidence.
More recently some authors such as Alcalá and Ciccone (2004) have argued that
it is theoretically preferable to use a measure of "real" openness rather than nominal trade
share measures. Real openness is defined in their work as imports plus exports in
19 A potential problem exists with any regression-based index that uses the residual from a regression to
proxy for an excluded variable: these indices accurately capture variations in the excluded variable only if
the model is correctly and fully specified. If any variables that should be included are excluded from the
estimated equation, the effects of all of them will be included in the index. This caveat, that the measures
themselves are sensitive to the specification of the underlying models that produce them, applies to many of
the measures that follow as well and should be borne in mind when deciding among measures.
Wacziarg (2001) maintains that simple trade ratio measures should be viewed as resulting from a
mixture of policy, factor endowments, and gravity determinant variables.
20 p. 313
11
exchange rate US$ divided by GDP in PPP US$.21 This is a constant price equivalent of
the simple trade ratio measures and is the total trade as a percentage of GDP measured in
constant prices.22 In their paper, which examines the relationship between trade and
productivity, they show that countries that are more productive due to trade may not
necessarily have higher trade ratios using nominal measures. This can occur because of
greater productivity gains in the tradable manufacturing sector than in the non-tradable
services sector, which leads to a rise in the relative price of services, which in turn leads
to a decrease in the nominal measure of openness. They contend that using a real
measure of openness rather than a nominal one eliminates distortions due to crosscountry differences in non-tradable goods relative prices.23 An extension of this
approach is to calculate the real growth rates of the GDP of each of the export partner
countries and compare these with the real growth rates of exports to those countries. If
exports to these partners are growing faster than the real GDPs of the partner countries,
then the export ratios (as opposed to overall trade ratios) would be growing. However,
while agreeing with their contention that a real measure is superior to a nominal one, it
does not change the fact that trade share is still a measure of size rather than trade policy.
Caution should also be exercised when considering the use of import penetration
ratios. While import penetration ratios have long been found to be positively correlated
with levels of import protection, in the late 1970s trade theorists realized that trade
protection should be considered as an endogenous policy (e.g., Brock and Magee 1978).
This interpretation of implies that the impact of trade liberalization tends to be
underestimated by the import penetration variable. The logic is that as import flows rise
domestic import-competing interests are likely to mobilize and lobby for higher
protection (Trefler 1993).24
However, one can also argue the opposite, that there could be a negative
correlation between import penetration and import protection. In an open economy,
while there may be resistance to liberalization from selected sectors, there is probably a
limit on how much the affected industries can influence trade policies. Most likely the
affected sectors can temporarily increase protection but it is very unlikely that they will
be able to get permanent (possibly prohibitive) import tariffs. Thus it may be the case
that we would find a negative correlation in more open economies and a positive
correlation in more protectionist economies.
Two final weaknesses of trade ratios measures (and these are criticisms of all
outcome-based measures in general) need to be addressed. First, there are timing lags
between when policy changes are decided upon and when a measure reflects that policy
change. It takes time to implement policy and it takes further time for producers,
importers, and consumers to react to the change. The length of the period is likely
21 They use log real openness instead of real openness because their theoretical framework does not
determine the functional form of the relationship between real openness and productivity.
22 For examples of other ways real openness has been calculated see Dollar and Kraay (2002) and Bolaky
and Freund (2004).
23 The main finding of their study is that trade has a positive effect on productivity. For a dissenting view
see Rodriguez and Rodrik (2001). A related issue is whether there is reverse causation from productivity to
trade. On this see Ades and Glaeser (1999), Frankel and Romer (1999), and Alesina, Spolaore, and
Wacziarg (2000).
24 Goldberg and Maggi (1999) estimate import penetration interactively with a dummy variable of political
organization but find mixed results.
12
variable, different across countries, dependant on other factors such as long term
contracts and thus it is difficult to separate out the effects of trade policy changes from
other structural or environmental factors that change during the period using these
measures.
Second, business cycles cause movements in these measures unrelated to changes
in trade policy. The measures will be contaminated by these movements, implying that
reform is more effective than it actually is should reform coincide with an upswing of the
business cycle or making reform look less effective should it occur during a downturn of
the business cycle.
I do not recommend the use of trade share measures as indicators of trade
openness and policy. As mentioned earlier, they serve well as measures of country size
or country integration into the global economy, but their interpretation should not be
taken beyond that. Researchers need to bear in mind that their wide usage is not proof of
their ability to capture a country's trade openness or trade policies.
The use of a data-driven, atheoretic measure is difficult to justify in the presence
of a large body of trade theory. Thus, to repeat, there is no basis for the argument that
this measure is capturing changes in trade policy and there is little, if any, justification for
using it. Even the claim of comparability across studies fails when the measure does not
capture either trade barriers or the effects of them.
4.2
Adjusted Trade Flows Measures
The Adjusted Trade Flows category uses deviations of actual trade flows from
predicted free-trade flows (the counterfactual) to form measures of trade policy. The
counterfactuals are assumed to represent what would have happened under different
policy choices, e.g. free trade policies. It is important to note that all such outcome
measures are sensitive to the model chosen to construct the counterfactual.25 Choosing
among the measures in this category thus requires consideration of the underlying
models. Users must think carefully about what explanatory variables they consider
important and the form of the relationship between those variables. Fortunately, the
models used to produce adjusted trade flow measures have theoretical foundations unlike
many measures whose creation is simply driven by data availability) to which users can
turn for guidance.
This section considers two methodologies.26 The first uses an empirical factor
proportions model (also known as the Hecksher-Ohlin factor model) in which trade flows
are determined primarily by resource endowments.27 Factors frequently included are
capital, labor, land, oil production, coal, minerals, GDP-weighted distance, and the trade
balance in terms of net exports. Leamer (1988) used a regression based methodology
25 Pritchett (1996), p. 312.
26 For the factor proportions models a useful overview is contained Appleyard & Field (2001, pp.118-139),
while for the gravity model important works include Anderson (1979), Deardorff (1998), and Anderson and
van Wincoop (2003).
27 The major conclusion of the Hecksher-Ohlin analysis, known as the Hecksher-Ohlin Theorem, is that
countries will export those products which use relatively intensively their relatively abundant factors of
production and will import products that use relatively intensively the relatively scarce factors of
production.
13
employing a factor model to estimate disaggregated net trade flows for 183 commodities
at the three digit SITC level, using 1982 data for 53 countries.28 He then used the
differences between the predicted trade flows and actual measured trade flows as adjusted
trade intensity ratios, using the difference for a country weighted by that country's GDP.29
Leamer (1988) also estimated measures which he termed "measures of trade
intervention". Based on the observation that trade policies do not always have a negative
impact on trade, but that some trade policies are designed to increase the flow of trade
from a country (export subsidies for example), his estimations of the rate of intervention
were an attempt to measure the degree to which trade was distorted by policy.
The second methodology involves use of the gravity model. Gravity models from
Newtonian physics have been widely used in a variety of applications. They have been
used to in the social sciences to explain a wide range of flows including labor migration,
commuting, customers, hospital patients, and international trade, and have also been used
to estimate the impact of policies in these areas. Gravity models of international trade
and integration have been used extensively and are widely accepted (so much so that
Eichengreen and Irwin (1998) have called the model the "workhorse for empirical studies
of [regional integration] to the virtual exclusion of other approaches."30). They are
popular because of their theoretical foundations and their strong fit to empirical data.
An important difference between empirical factor proportions models and the
gravity models is that the former focus on net trade flows while the latter focus on
bilateral trade flows. The use of gravity models is advantageous as bilateral trade flows
may more accurately capture the effects of trade policy changes, such as becoming a
member of a free trade area. Examination of bilateral flows also allows for estimation of
trade creation and trade diversion effects. Net flows, on the other hand, capture
aggregate changes and it is more difficult to distinguish the effects of trade policy
changes from broad movements in the international economy using aggregates.
In the basic form of the gravity model of international trade, trade flows between
two countries are assumed to be positively related to size (as measured by national
incomes) and negatively related in the distance between the two countries (which proxies
for the cost of transport between them). The basic gravity model is expressed in loglinear form as:
ln M ij = α + β ln Yi + γ ln Y j − δ ln Dij + ε ijt
(1)
where Mij is the total trade flow from country j to country i, Y’s are national incomes, and
D is a measure of the distance between the countries.
Extended versions of the gravity model have included population or per capita
income as additional measures, creating what is known as the augmented gravity model.
The use of one of these variables allows for non-homothetic preferences in the importing
28 As the reader has no doubt begun to note, many of these measures require large amounts of data for
estimation. I will not continue to point this out but recommend readers to bear this in mind (particularly if
they are considering constructing a new measure).
29 This measure is calculated (for country i) as TIRi = (
A
∑N
ij
− N ij* ) / GDPi , where N ij is the value
j
*
ij
of net exports and N is corresponding number predicted by the model.
30 P. 33.
14
country as well as serving as a proxy for the capital-labor ratio of the exporting country.31
Further explanatory variables, such as contiguity or a common language between a
country pair, have also been used.
An appealing advantage of gravity models is that they provide an approach to
deal with the problem of the endogeneity of trade. Frankel and Romer (1999) show this
by focusing on the component of trade that can be attributed to geographic factors, such
as distance. Distance between trading partners is not something that is determined
arbitrarily or that changes over time.32 Using instrumental variables based on
geographical characteristics (characteristics that are not affected by income or
government policy and are thus exogenous to the model) they find that trade has a
positive effect on income. This suggests that trade policy liberalization has a positive
effect on income, though they do caution that a:
".... limitation of the results is that they cannot be applied without
qualification to the effects of trade policies. There are many ways that
trade affects income, and variations in trade that are due to geography and
variations that are due to policy may not involve exactly the same mix of
the various mechanisms."33
A recent modification of the gravity model is found in Hiscox and Kastner (2002).
They use trade as a proportion of income for their dependent variable in the basic
equation:
ln( M ij / Yit ) = α it + β ln Y jt − δ ln Dij + ε ijt
(2)
They also use an augmented model, with relative factor endowment differentials
as additional variables to capture factor-proportions type effects. The augmented
equation is:
ln( M ij / Yit ) = α it + β ln Y jt − δ ln Dij + λ ln Lijt + κ ln Wijt + ε ijt
(3)
where Lij and Kij are measures of the differences in endowments of land and capital and
Wjt is the wealth of the exporting country.34 From these equations they find importingcountry-specific and time-specific effects, which they compare to the sample mean in
each year to evaluate the distorting effects of each country's policies. The reported
figures are the deviations of the estimates from a "free trade" benchmark to capture
implicit protection effects of other policies that act as barriers to trade (including
domestic policies concerning industrial policy, labor market policies, etc.).
As with all measures, the ones in the adjusted trade flows category have
disadvantages. The largest concern is that there is no way of assuring that the
counterfactual accurately produces the volume of trade that would occur under free trade.
As Hiscox and Kastner themselves note:
31 Bergstrand (1989).
32 It can be argued that the effects of distance have been reduced through improvements in transportation
and reductions in the cost of transportation. Baier and Bergstrand (2001) find that transport cost declines
account for approximately 8% of the growth in trade between OECD countries for the period encompassing
the late 1950s to the late 1980s. Whether this conclusion can be extended to include developing countries
is open to debate. Hummels (1999) finds that, for the post-WWII period ocean freight rates have increased
while air freight rates have decreased dramatically.
33 P.395.
34 See pp.9-14 of Hiscox and Kastner (2002) for further details.
15
"A key problem here is that we cannot distinguish between the effects of
changes in trade policies and other changes, specific to particular
importing countries in particular years, that also affect trade flows and are
not accounted for in the model."
Our confidence in the counterfactual depends crucially on both the model being correctly
specified and that there are no errors in the data, conditions that are unlikely to be
completely satisfied. A particular concern is that some determinants of trade will be
omitted from the model. However, given the strong theoretical underpinnings of these
models, combined with their robust empirically proven success in other applications, I
urge analysts to consider the use of measures from this category. There is much to be
said for using a measure having theoretical foundations, unlike many found in the other
categories. If the investigator is confident that the model producing the measure has been
well-specified, then justification for the choice of one of the measures in this category is
much clearer.
I believe that the use of gravity model-based measures may be the appropriate
choice for many applications that involve trade policy or trade openness as an
explanatory variable. This broad recommendation comes with a few caveats, mostly
concerning data. While not as data-intensive as the factor endowment approach these
measures still require relatively large amounts of data, as they focus on bilateral trade
flows rather than aggregate flows (across all trading partners). In addition, it is important
maintain in consideration is that these measures are highly sensitive to the data used. It is
often the case that simply changing the time period of, or the countries in, the sample can
have a large impact on the results of the estimation. Users of these types of measures will
need to examine the underlying data carefully. Data availability (as always) is
problematic. Data for industrialized countries may be available back to the 1950s while
that for developing countries may not begin until 1970 or later, potentially leading to a
small sample size. One would generally like to use the longest time period possible but
results are more easily compared when the data time periods are the same for the
countries in the sample. In some cases it will make sense to separate developing
countries from industrialized ones, to make for comparable series.
4.1.3
Price-Based Measures
Price-Based measures attempt to capture trade policy by seeking price distortions
in either goods markets (by comparison with international prices) or with currencies
(generally through the black market premium). Advocates of price-based measures claim
that they capture the effects of both tariff and non-tariff barriers and that economic
interpretation is easier than with the flow based measures, as countries with high price
levels over time would be seen as countries with a relatively high levels of protection.
A widely-cited example of a measure using goods price distortions comes from
Dollar (1992), who constructed two indices, an "index of real exchange rate distortion"
and an "index of real exchange rate variability". For the first, Dollar took data on the
estimated real prices for a common basket of goods and removed the effect of systematic
16
differences arising from the presence of non-tradables.35 The index is supposed to
measure the extent to which the real exchange rate is distorted away from its free trade
level by trade policies. The index of real exchange rate variability is calculated by taking
the coefficient of variation of the annual observations of the index of real exchange rate
distortion for each country over the same period.
Simply looking at levels of prices of tradable goods across countries may be
misleading, however. Trade policies work by altering relative prices within an economy
but the effects of trade policies on the level of prices in one country relative to another
are not clear-cut. Rodriguez and Rodrik (2001) point out that for the index of real
exchange rate distortion to be theoretically appropriate three conditions must hold: 1)
countries are not using export taxes or subsidies,36 2) the law of one price holds,37 and 3)
transport costs and geographic factors do not create systematic differences in price levels
between countries.
It is quite unlikely that all three of these conditions hold at the same time. Indeed,
it is likely that none of them hold in many cases. Countries can and do use multiple trade
restrictions simultaneously on both imports and exports.
This holds true for
industrialized nations as well as developing countries. It is also, at best, dubious that the
law of one price holds continuously. Substantial evidence indicates that deviations from
the law of one price are frequent and that these deviations die out only in the very long
run (Rogoff 1996).38 Finally, it has been shown that geographic factors (and thus
transport costs) are important in explaining trade (see, for example, Frankel and Romer
[1999]). The index of real exchange rate variability measure undoubtedly reflects the
effects of geographic factors in addition to exchange rate and trade policies but has no
mechanism to differentiate between them. As well, and this is a criticism of price-based
measures in general, price-based measures are unable to differentiate between the effects
of domestic market distortions induced by macroeconomic policies and those induced by
trade policy interventions.
35 To do this, he regresses the real price level (RPLi) of the basket of goods on the level and
square of GDP per capita and on regional dummies for Latin America and Africa, as well as year
dummies, which gives him a predicted value, ( RPL i ) . The index of real exchange rate distortion
is then:
RPLi
RPL i
averaged over the ten-year period 1976-1985.
36 This condition is required because tariffs and NTBs, which fall on imports, are not the only tools of
trade policy. Other important tools include export subsidies and export taxes that, if they play more than a
minor role in the trade policy of a country, will affect the ranking of trade regimes. The effect of these
instruments combined with those of import barriers must be considered when using price-based measures.
Countries that try to offset the resource allocation distortion induced by import restrictions through the use
of export subsidies will appear to have higher levels of protection than countries that do not do so. More
perversely, economies that have both import restrictions and export taxes will be seen as less protectionist
than those that rely on import restrictions alone.
37 A consequence of the law of one price failing to hold is that nominal exchange-rate movements affect
the domestic price level of traded goods relative to foreign prices. Therefore a nominal appreciation will
make a country appear to have a higher level of protection while a depreciation make it seem to be more
open.
38 Rogoff concludes that the speed of convergence to purchasing-power parity (PPP) is extremely slow,
perhaps 15 percent per year.
17
Rodriguez and Rodrik demonstrate that Dollar's index of real exchange rate
distortion is neither a robust correlate of growth nor robust to alterations in the
specification of the estimating equation. They dismiss the second measure, the index of
real exchange rate variability, as "...a measure of instability more than anything else." In
addition to their critique, one must add the usual list of suspects that bedevil many of the
measures reviewed in this paper: these measures require large amounts of information on
domestic prices and border prices, as well as adjustments for transportation costs,
markups and quality differences, and the data are not always readily available, especially
for developing countries. Thus, overall, models based on finding distortions in goods
prices across countries have sufficient problems that one should feel uneasy using them.
The most popular currency price measure is the black market premium, which is
measured as the deviation of the black market exchange rate from the official exchange
rate. The black market premium is not a direct measure of trade openness but rather
measures the extent of rationing in the market for foreign currency. The argument for
using the black market premium as a measure of trade openness is that foreign exchange
restrictions act as a barrier to trade. Edwards (1992, 1998) used the black market
premium as a proxy for trade restrictions on the assumption that countries with more
restrictions on imports and fixed exchange rates would often have overvalued currencies;
thus it serves as a broad measure of the extent of distortions in the external sector (i.e. not
only distortions in trade but in capital flows and other markets as well).
Rodriguez and Rodrik have argued against the use of this measure as well.
They contend that it is not a good measure of trade policy stance, as it most likely reflects
a wide range of policy failures (poor macroeconomic policy, weak government, lack of
rule of law, and corruption) as well macroeconomic and political crises. The black
market premium is thus serving as a proxy for many variables which are unrelated to
trade policy. Crises and policy failures, rather than the trade-restricting effects of the
black market premium, would be the reasons for low growth in this case.
The appeal of price-based measures derives from their theoretical foundations.
Unfortunately, the constraints of both data and methodology at this time lead me to
believe that, for the present, price-based measures are inadequate for capturing trade
restrictions. It is most likely that price-based measures in general are affected by much
more than just trade policies. I do not recommend their use.
4.4
Tariffs Measures
Tariffs are highly visible restrictions of trade and can be viewed as the most direct
indicators of restrictions. They are popular because data is available.39 There are a
number of measures of tariffs that have been widely used by trade economists: simple
tariff averages, trade-weighted tariff averages, revenue from duties as a percentage of
39 Nominal tariff rates are either ad valorem, based on the value of the good, or specific, per unit of the
good. Specific tariffs can be converted to ad valorem tariffs by dividing the specific tariff amount per unit
of a good by the price of the good.
18
total trade (which is a shortcut method for estimating the import weighted average tariff),
and the effective rate of protection (ERP).40
Gathering data on tariffs, however, can be challenging. Countries do not report
their weighted average tariff rate or even their simple average tariff rate every year, so the
most recent data may be several years old. The quantity of data required for calculating
weighted tariffs and ERPs is daunting. The data for both tariff and non-tariff indicators
(discussed in the next section) are measured with some error due to weaknesses in the
underlying data (issues of both collection and coding) and there are frequently problems
with missing data due to activities outside the formal market such as smuggling.
Problems thus arise when attempting to aggregate data into these types of index measures
and this can make consistent cross-country comparisons a difficult task.
An example that illustrates the problems of constructing such a measure is the
trade policy component of the Index of Economic Freedom.41 This component is based
on a country's weighted average tariff rate (weighted by the imports from the country's
trading partners). As noted, many countries do not report their weighted average tariff
rate every year and for some of the countries in the 2005 Index the last reported weighted
average tariff data was as old as 1993. In these cases the authors of the Index were
forced to turn to less direct measures of tariff barriers. If the weighted average tariff rate
was not available, the authors then used the country's average applied tariff rate; if the
country's average applied tariff rate was not available, they used the weighted average or
the simple average of most favored nation (MFN) tariff rates. In the case where neither
the applied tariff rate nor MFN tariff data were available, the authors based their grading
on the revenue raised from tariffs and duties as a percentage of total imports of goods. If
data on duties and customs revenues are not available, they either used data on
international trade taxes or they analyzed the overall tariff structure of the country and
estimated an effective tariff rate.42 Obviously, the progression through the different
methodologies implies an increasing chance of error and, as the methodologies move
further from estimating weighted average tariff rates, a decreasing ability to make useful
comparisons across countries.43
Despite the fact that these measures are the most direct indicators of trade policy,
they are viewed as poor indicators of trade policy. First, as mentioned, tariff protection
affects producers and consumers differently. Second, import elasticities are likely to vary
across products and countries, thus implying that a tariff of a given magnitude may have
40 Also known as the effective tariff rate. The ERP for any industry j is commonly calculated as
ERPj =
t j − ∑ i aij ti
1 − ∑ i aij
, where aij is the free trade value of input i as a percentage of the free trade value of
the final good j and tj and ti are the tariff rates on the final good and any input i.
41 Published annually by The Heritage Foundation and Dow Jones & Company, Inc. See pp.57-77 of the
2005 edition for an explanation of the components of the Index.
42 Pp.60-61.
43 The authors of the Index recognize that tariff barriers are not the only impediment to trade and thus,
because the trade policy component also considers non-tariff barriers and corruption as well, the measure is
included in the informal or qualitative category rather than the tariff barriers category. It does, however,
serve to illustrate well the difficulties of constructing measures of tariff barriers. It is also a good example
of a measure that is used by policy makers, as it is one of the criteria considered by the Millennium
Challenge Corporation in selecting the developing countries that are eligible for Millenium Challenge
Account assistance (Millenium Challenge Corporation, 2005).
19
different effects both for differentiated products44 in a single country and for the same
product across different countries. Third, collecting comprehensive tariff data on all
product categories is a large undertaking not often carried out. Even when such data is
compiled, the researcher is still faced with the question of the appropriate weighting
scheme. Finally, the typical trade regime in developing countries also restricts imports
with other barriers as well, such as those discussed in section 4.5.
The list of problems with tariff measures goes on. It is well known that
unweighted average tariff rates tend to overstate the height of average tariffs because they
include very high and prohibitive tariffs whereas weighted average tariff rates tend to be
biased downwards because the import levels of high-tariff items tend to be low. There is
concern about the use of nominal tariff rates reported in tariff schedules. While nominal
tariff rates reflect the impact of trade barriers on consumers, they generally do not reflect
the effective protection granted to producers because of differing tariffs on imported
inputs and final products. Researchers must be clear that they are using the appropriate
measure given that the welfare effects of tariffs are different for the two groups. For
example, in assessing overall impact of trade policy changes it is best to examine the
effects through both nominal tariff rates and ERPs. A final potential conceptual problem
is that tariffs may not be linear in their effects, that is to say, there may be either declining
or (more likely) increasing marginal protection effects as tariff rates rise. In either case,
the effects of reducing tariffs will be nonlinear also and measures based on the levels of
tariffs will not capture this.
Thus, while these are the most direct measures of trade restriction available, I
caution against relying, at least solely, on them. As noted, the measures are far from
perfect and, in addition, other policy actions are important in determining the pattern of
trade. Tariff-based measures might work well in combination with other measures, but
this has yet to be shown.
4.5
Non-Tariff Barrier Measures
As tariff levels have declined (at least on manufactured goods) during the various
GATT rounds, non-tariff barriers (NTBs) have become increasingly important (Bhagwati
(1988), Edwards (1992)). These are policies other than tariffs that alter, directly or
indirectly, the prices and/or quantities of traded goods and services. Official (i.e.
mandated or legislated by the government) NTBs come in a wide variety of forms: import
quotas, voluntary export restraints, government procurement and domestic content
provisions, restrictions on services trade, trade-related investment measures,
administrative classification, etc. They also come in forms that do not appear to be
"barriers" to trade but rather serve to stimulate trade (at least from a domestic viewpoint)
such as export subsidies.
Not all forms of NTBs are "official" barriers, they may also arise from other
sources. Market structures vary across countries and national governments differ in how
much they promote competition. In some cases there is extensive government
involvement in industry, often allowing extensive collusion among firms or creating
government monopolies. This is viewed internally as domestic policy though it has
44 For example with imported beers, wines, cheeses, etc.
20
implications for the international trade policy of the country. Another, more contentious,
source of barriers are the cultural, social, or even political institutions operating within a
country. For example, countries such as Japan and Korea allow intricate relationships
among firms (the keiretsu and chaebol, respectively) across industries that would not be
found in a country such as the U.S. (indeed, would be illegal in the U.S.) and these in a
very real sense create trade policy on their own. The effects of these forms of trade
protection are more difficult to assess than the official NTBs.
The imperfections of NTBs as indicators of protection are well-known and are
larger than those of the tariff measures. To begin with, one has to identify or define the
barriers, which is difficult as NTBs are generally not transparent in either implementation
or operation. Deardorff and Stern (1997) note "A basic difficulty in approaching NTBs is
that they are defined by what they are not. That is, NTBs consist of all barriers to trade
that are not tariffs."45 The creation of a summary measure of NTBs would require the
inclusion of all of NTBs, otherwise the measure will make trade look freer than it is.
Unfortunately many NTBs, particularly the unofficial ones, are hard to identify or
measure and thus cannot be included. Furthermore, given the diversity of NTBs it is
difficult to put information about each of them in a format that is comparable across
them. Consider NTB coverage ratios, which are estimated as the percentage of imports
covered by these types of trade barriers. Counting the frequency of NTBs suggests the
existence of trade barriers but does not capture their effects. Different policy instruments
(say quotas, domestic content requirements, and customs regulations to name just a few)
will have different effects. The impact (intensity) of NTBs is not related to the number of
them but to the enforcement of them, which can vary from non- to strict enforcement.
Aggravating the problem is that the most effective barriers, which are prohibitive (or
close to), receive little weight in the estimations. Thus, coverage ratios for non-tariff
barriers are both difficult to calculate and understate effects of such barriers.46
To sum up, NTBs are poor indicators of trade restrictions both because broad
coverage by NTBs does not necessarily mean a higher distortion level and (as always)
there are difficulties of estimation because of data limitations. Edwards (1992) notes
"...NTB is likely to be one of the poorest indicator of trade orientation." Thus these
measures are often excluded from empirical studies. Ultimately, these measures are more
useful for identifying the existence of barriers to trade than for measuring them.47
4.6
Composite Indices
45 p.4.
46 An additional problem that likely occurs is that the effects of one NTB may affect the performance of
another NTB, which further weakens the idea that NTB measures can simply be added up to get a summary
measure.
47 It is worth noting that the exchange rate regime of a country can serve as a NTB. An overvalued
currency works against exports, as it has the same effect as an export tax, which in the case of developing
countries frequently implies both industrial protection and a bias against agriculture. Manipulating the
exchange rate directly is not the only way to use the exchange rate as a NTB. Capital controls, in the form
of foreign exchange allocations, can serve as a constraint on imports and governments can also ration
foreign currency, favoring certain industries or firms to the detriment of other sectors of the economy.
21
This category contains measures based on subjective evaluations of trade barriers,
structural characteristics, and institutional arrangements. As noted by Baldwin (2002,
p.27) high barriers to trade are very frequently found in conjunction with poor
macroeconomic policies, corruption, and unstable governments. In recognition of this a
number of indices that combine various indicators (such as macroeconomic, exchange
rate and educational indicators in addition to trade openness and policy indicators) into a
single variable have been developed. Researchers considering using composite indices
should become familiar with the components as well as the data on which the indices are
based prior to deciding to use one, in order to ascertain that the chosen measure captures
features important and not features immaterial to their models. Examples of two very
widely used indices are presented here.
The first of these is the World Bank's Outward Orientation Index.48 Examining
effective rates of protection, the use of direct trade controls and export incentives as well
as the degree of exchange rate overvaluation, the World Bank classified 41 LDCs for the
periods 1963-1973 and 1973-1985 into four categories: strongly-outward oriented
economies (rank = 1 in the data), moderately-outward oriented economies, moderatelyinward oriented economies, and strongly-inward oriented economies (rank = 4).
Strongly-outward oriented economies were characterized as having few controls on trade
and a currency that was neither over- or undervalued, moderately-outward oriented
economies had relatively low ERPs and bias in the exchange rate as well as only slight
biases against production for export, moderately-inward oriented economies favored
production for the domestic market through relatively high protection and an exchange
rate regime that discouraged exports, and strongly-inward oriented economies exhibited
comprehensive incentives toward import substitution.
The Bank's much-cited analysis found strong support for the claim that outward
oriented countries grew faster than inward oriented countries. Subsequent research
appeared to strengthen the conclusion. Greenaway and Nam (1988) showed that
economic performance progressively improved as countries moved from strongly inward
oriented stances to strongly outward oriented ones. Alam (1991) found the Bank's
measure of trade orientation to be positively associated with subsequent gains in real
GDP per capita.
As with all measures, there are concerns. Clearly there can be a large degree of
subjectivity in these assessments. Problems arise in coding data uniformly across
countries. Coding data often requires that judgments be made about the impacts of
different types of policies in different countries. This is a highly subjective exercise and
the results can be highly dependant upon who codes a particular country. Even with a
well-defined protocol applied by coders trained to assess data from different countries in
a consistent manner, judgment bias can occur and comparability of measures across
countries (and even for the same country over time) may be suspect.
In my judgment the orientation index is useful for ranking countries in relative
terms of trade regime (restrictiveness) and I do recommend them for that purpose.
However, the user must bear in mind that it will not be possible to distinguish the most
"open" country in a category, say the moderately-outward oriented economies, from the
"least open" country in that category or whether a country at the top of a category, such
48 World Development Report 1987 (chap. 5). The coding scheme was originally developed in Greenaway
(1986).
22
as a ranking of 2, can be said to be greatly different from the country at the bottom of the
rank 1 category.
The next example, which is also widely used in cross-national research on
economic growth, is the Sachs and Warner (1995) measure of openness. They examine
the linkage between openness and economic growth for 79 countries over the period
1970-1989. The openness indicator they construct is a zero-one dummy, which has the
value 0 (indicating the economy is closed) if any one of the following conditions is met:
• the country had average tariff rates higher than 40%,
• the country's non-tariff barriers covered more than 40% of imports,
• the country had a socialist economic system,
• the country had a state monopoly for major exports, or
• the country's black market premium exceeded 20% during either the 1970s or the
1980s.
There are a number of grounds on which a measure of this type can be criticized.
An obvious weakness is the binary nature of the measure. If one assumes that the effects
of trade liberalization do not occur instantaneously, but rather accrue over time, then a
continuous measure would better reflect the process. Another problem is that the choice
of numerical limits, as with the tariff, non-tariff, and black market premium components
of the measure, is arbitrary. The lack of any theoretical justification for the levels chosen
casts doubt on the results from using such a measure and may encourage data-mining in
order to find the "best fit" in measures of this type. Furthermore, it must be borne in
mind that for a given level of tariffs, NTBs, or foreign exchange market manipulation we
would not expect to find the same effects across countries.
With specific reference to the Sachs and Warner measure, a number of criticisms
have been raised. Harrison and Hanson (1999) and Rodriguez and Rodrik (2001) have
shown, through separating the index into its constituent components, that the effects of
the trade policy indicators included (tariffs and quotas) on the measure are small and not
significant. Furthermore, they find that the measure is mainly driven by the black market
premium with some impact by state monopoly of exports.49 Given that both theory and
much empirical evidence indicate that tariffs and quotas have significant impacts on
trade, this is a damning critique. Rodriguez and Rodrik conclude that the Sachs and
Warner measure serves as a proxy for a wide range of policy and institutional differences.
If it is indeed correlated with such other explanatory variables then it is not possible to
draw strong inferences about the influence of openness on growth.50
In sum, the Outward Orientation Index has some usefulness but I cannot
recommend the Sachs-Warner measure; in fact, I strongly urge researchers not to use the
latter. That having been said there are reasons not to write off these types of measures
forever. First, they focus on incidence not outcomes. This is desirable and preferable as
ultimately we should be measuring the instruments of trade policy, not inferring them
from observed outcomes. Second, these types of measures represent the opportunity to
49 Even the state monopoly result is open to criticism. Rodriquez and Rodrik point out that the latter is
essentially a Sub-Saharan Africa dummy with limited coverage both geographically and over time, as the
measure by Sachs and Warner uses an index of the degree of distortions caused by export marketing boards
that covers only the 29 African economies that were under World Bank structural adjustment programs
from 1987 to 1991.
50 They also suggest that it gives upward-biased estimates of the effects of trade restrictions.
23
combine multiple facets of trade policy, as well as other important policies and structural
characteristics, into a single measure. This is the goal of measures of trade openness and
policy, to successfully capture a multifaceted process with a summary measure.
5
Correlations
In order to make a judgment as to whether the various (and abundant) measures of
openness available are capturing some common aspects of trade policy such that
researchers could use these measures to rank (at least ordinally) countries, I follow the
lead of Pritchett (1996) and check to see if the measures are correlated. First I examine
correlations between measures within their own category and then look at correlations
across categories.
5.1 Within-Category Correlations
In this section I report on the correlations within categories, to see if measures
within a category seem to be capturing the same features or information. These results
are summarized below in Table 1.51
Prior to interpreting the results, a brief discussion of terminology is in order. The
magnitude of (the absolute value of) a correlation coefficient reflects the strength of the
correlation. Although there are no universally accepted rules for using words to describe
correlational strength, I propose the following definitions for the purposes of this paper:
0≤│r│<.3 weak correlation,
.3≤│r│<.7 moderate correlation,
.7≤│r│<1 strong correlation.
While these definitions are clearly arbitrary there is a need for them. Correlations that are
significant but low may not be informative. What does a (significant) correlation of .085
tell us? In terms of asking whether the two measures are capturing common aspects of
trade policy, the answer is that they do not seem to be. On the other hand, a correlation
of .85 provides a useful point to start from. Hopefully this approach is generally
acceptable to the reader.52
51 The complete within-category correlations are presented in the appendix in Tables A.1-A.7.
52 I remain open to alternative definitions, asking only that they have strong reasoning as to why they
would be preferred.
24
Category
Trade Shares
Adjusted Trade
Flows
Price Based
Tariffs
Non-Tariff
Barriers
Composite
Indices
TABLE 1- Correlations Within Categories
Significant at
Number of
Total
5% or better
Measures
Correlations
(%)
106
19
107
(.99)
86
18
153
(.56)
3
5
4
(.75)
18
9
24
(.75)
10
5
10
(1.0)
28
14
45
(.62)
Significant and
> or = to .7
(% of total)
57
(.53)
14
(.09)
0
(.00)
11
(.46)
5
(.50)
2
(.04)
The first column identifies the category, the second the number of measures
within the category, the third the total number of correlations within a category53, the
fourth reports the total number, across all magnitudes, of within category correlations that
are significant at the 5% level or better (percent reported in parentheses), and the fifth
reports the number of significant correlations which meet the definition of strong
correlation (percent reported in parentheses).
The results in the fourth column are striking. Ignoring the strength of the
correlations and just focusing on significance, there are high numbers of significant
pairwise correlations within all of the categories. Averaged across all the categories the
average percentage of significant correlations is 74%. It appears that the measures within
five of the categories are proxies for the same thing, which bolsters the case that existing
measures of trade openness and policy have succeeded in combining different aspects of
trade policy into summary measures.
However, as seen in the fifth column, which considers only significant
correlations of .7 or greater, the large majority of the correlations are weak to moderate.
Across all categories only 28% of all the significant correlations are equal to or greater
than .7. Furthermore, when the trade ratios category is dropped from the calculation,
which seems reasonable given that these measures are neither measures of openness or
policy, the percentage of strong, significant correlations drops to 10% of the total
observations.
This impression is reinforced through examination of Tables A2-A.6. Tables A.3
(price-based measures), A.5 (non-tariff barriers)54, and A.6 (composite indices)55, provide
53 Because some series do not intersect in time the number of reported correlations is less than the ((n²n)/2) potential observations for each category, except in the adjusted trade flows category.
54 All the correlations above .35 in this category involve the various aggregates of the Pritchett non-tariff
measure.
25
little support for the notion that measures within these categories are capturing common
aspects of trade policy. Examination of Table A.4 (tariff measures) reveals that the most
basic tariff measure (import duties as a percentage of total imports) has moderate
correlation with all of the other measures (except for an extremely high correlation with
the effective rate of protection). This gives some support to Rodriguez and Rodrik's
(2001) observation that simple averages of taxes on imports and exports and NTB
coverage ratios may do an adequate job at ranking openness.56 The other potentially
interesting case is in Table A.2 (adjusted trade flows). However, examination of the table
reveals that all of the correlations of interest involve cross-sectional measures for the year
1982 from Leamer (1988). None of these measures seems to have anything in common
with any measure that it is not directly related to.
Thus, with the exception of the trade ratios category, the indications from
examining the correlations within categories are that there is little support for the idea
that these measures capture some common aspect of trade policy. Overall, the lack of
correlation between measures within categories is in line with Pritchett's (1996) earlier
findings. However, given the much larger number and types of measures used in this
study, the warning to users of these measures is that the growing supply of measures has
in no way reduced the problems associated with using them and, indeed, has likely
exacerbated the situation by giving users an unwarranted sense of security.
5.2 Across-Category Correlations
The next step is to examine the cross-category correlations. This is a somewhat
daunting task, as there are a total of 1,153 cross-category correlations57, of which 605
(53%) are significant. In order to facilitate the analysis, I further simplify the definition
of correlational strength and focus only on cross-correlations that have an absolute value
of .5 or higher. This is (again) an admittedly arbitrary choice for the threshold, but should
provide a middle ground where the test cannot be roundly accused of being either too
stringent or too lax. Applying this rule leaves us with 179 cross category correlations to
consider (that is, 15.5% of the total observations and 30% of the significant observations
are .5 or greater). Table 2 below shows the breakdown of cross-category correlations that
meet this threshold. The number in parentheses is the total number of correlations
between the two categories and the number in square brackets shows the percentage of
correlations that meet or exceed the .5 threshold.
55 The only two correlations of interest in this category involve: 1) the overlap of the two World Bank
trade orientation indices (which in an ideal world should equal unity), and 2) the Index of Economic
Freedom and its subcomponent, Trade Policy.
56 All of the other correlations that might be of interest because of the size of the correlation involve
aggregates of the same measure and thus are not informative.
57 Because the data set contains both cross-sectional and panel data measures and because the data series
do not all have the same period coverage the total number of actual correlations is lower than the potential
upper bound of 1,992 cross-correlations.
26
TABLE 2- Correlations Across Categories
Adjusted
NonTrade
Price
Tariffs Tariff
Category
Trade
Shares
Based
Flows
Barriers
79
Adjusted
(238)
Trade Flows
[0.33]
0
6
Price Based
(61)
(64)
[0.0]
[0.09]
3
20
2
Tariffs
(107)
(82)
(29)
[0.03]
[0.24]
[0.07]
0
0
0
0
Non-Tariff
(55)
(25)
(15)
(35)
Barriers
[0.0]
[0.0]
[0.0]
[0.0]
24
27
2
7
0
Composite
(136)
(137)
(38)
(71)
(35)
Indices
[0.18]
[0.20]
[0.05] [0.10] [0.00]
Table 2 provides some interesting results. The first is that there are five
intersections where 10% or more of the correlations are significant and equal to or greater
than .5. Though not dramatic, this is in contrast to Pritchett (1996), who found little
evidence that different types of measures appeared to be correlated.
As with the within-category analysis, I will drop the trade ratios category. Doing
so leaves three intersections of interest. Interestingly, the non-tariff barriers category has
no strong correlations with any of the other categories. I believe that this reflects both the
small number of measures within the category (only five measures) as well as the
difficulties of creating such measures.
The adjusted trade flows and composite indices categories have strong
correlations with each other and with the tariffs category. Consider first the correlations
between adjusted trade flows and composite indices. Examination of the correlations
showed that three measures from the composite indices category that correlate broadly
across the adjusted trade flows category are driving the result. The first is the World
Bank Outward Orientation Index, which accounts for approximately half (13) of the
strong correlations, while indices created by Wacziarg (2001) and the Heritage
Foundation (various) account for the rest. Note that this intersection involves the two
categories that I have recommended for researchers to use. Strong correlations between
them suggest that composite indices can capture the effects of trade policy changes,
changes reflected in the outcomes captured by adjusted trade flow measures. This adds
support to the contention that research in developing new measures focus on composite
indices.
It is reassuring to see that both categories also have some strong correlations with
tariff measures. While tariff measures have not been shown to work well on their own,
they are the most direct measure of trade barriers available. Correlation between tariff
27
measures and the other two categories implies that the adjusted trade flows and
composite indices categories are picking up trade restrictions.
6
Conclusions
Much work remains to be done in this area. There is no consensus as to what is
(are) the "best" measure(s) of trade openness and policy. Many of the existing measures
lack theoretical foundations, are poorly specified, or are simply ad hoc measures driven
by data availability. The majority of the measures available focus on outcomes, while
what researchers really need are measures of policies and barriers to trade. Perhaps most
serious, the most popular measures (simple trade ratios and Sachs-Warner) are, I think,
among the worst measures a researcher could choose.
All of that said, readers should not give up hope. Gravity model-based measures,
although they focus on outcomes, do have solid theoretical foundations and fit the data
well. For the future, the development of composite indices holds much promise in the
quest to capture the multifaceted nature of openness in a single measure based on policy
instruments and other indicators found to be relevant. It is my belief that ultimately the
successful measures of trade policy and openness will emerge from work with composite
indices.
If the reader has been left feeling discomfited by what has been presented here, it
is probably a good thing. The words of Leamer (1988) are as applicable today as they
were almost two decades ago:
"The question is not whether a particular method produces perfect
measures of openness, since none will. The real question is which method
seems likely to produce the best measures."
28
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35
Appendix - Tables
openc
openk
trshare
impen85o
impen85m
impen85a
impen85r
impen82o
impen82m
impen82a
impen82r
tars85ov
tars85ma
tars85ag
tars85re
tars82ov
tars82ma
tars82ag
tars82re
openc
1
0.758179
0.955525
0.924257
0.922016
0.700484
0.736566
0.936964
0.903494
0.797236
0.837399
0.948044
0.874667
0.594079
0.656747
0.958899
0.906837
0.62129
0.757361
Table A.1 - Correlations of Measures of Openness
1
0.776996
0.648023
0.479044
0.578672
0.309503
0.54503
0.589255
0.085608
1
0.94709
1
0.802323 0.803016
1
0.849433 0.650732 0.490531
0.564677
0.627917
0.39898
0.456628
1
trshare impen85o impen85m impen85a impen85r impen82o impen82m impen82a impen82r tars85ov tars85ma tars85ag tars85re tars82ov tars82ma tars82ag tars82re
0.659852
0.632746
0.570516
0.305504
0.421461
0.716681
0.524719
0.178852
1
openk
0.916642
0.930284
0.479675
0.528567
1
0.95267
1
0.796481 0.729115
1
0.806127 0.619133 0.501041
0.941239
0.90129
0.554385
0.573116
0.515857
0.741483
0.548329
0.301902
1
0.894128
1
0.538929 0.383579
1
0.730793 0.429357 0.133461
1
0.954363
0.939098
0.730857
0.807378
0.957291
0.931384
0.784632
0.890579
0.978743
0.947264
0.530968
0.702088
0.977721
0.957467
0.645438
0.82594
1
0.886383
1
0.554001 0.550147
1
0.955513 0.729989 0.373044
1
0.659977
0.785418
0.760741
0.639866
0.645297
0.730826
0.735956
0.68725
0.572326
0.805591
0.634055
0.581129
0.701027
0.726726
0.628387
0.535365
0.626873
1
36
leamer82
leamer82
1
openover 0.363003
openmanu 0.449632
openag 0.161557
openres 0.219675
intv1nov -0.154823
intvn1man -0.029626
intvn1ag -0.427066
intvn1res 0.044714
intv2nov 0.43398
intvn2man 0.411804
intvn2ag 0.302856
intvn2res 0.101582
pctbcfe
-0.0441
pctacfe -0.065262
wacz
0.392126
waczsm 0.392126
logwaczsm 0.417381
1
0.085568
0.427507
0.362602
0.54425
0.146196
0.567764
0.398933
0.495954
0.249638
-0.263723
-0.296632
0.150788
0.150788
0.046757
1
0.452102
0.348376
0.097879
0.589594
0.109741
0.037185
-0.001703
0.257355
-0.284905
-0.296478
0.181349
0.181349
0.155387
1
0.867336
0.670346
0.823285
0.416079
0.31056
0.333016
0.406068
-0.15637
-0.16273
0.137283
0.137283
-0.027594
1
0.391989
0.59493
0.626839
0.621541
0.473555
0.333407
-0.509294
-0.522154
0.348536
0.348536
0.125106
1
0.31907
0.117682
0.021557
0.163505
0.14257
0.19601
0.188396
-0.195873
-0.195873
-0.328675
1
0.174961
0.012371
0.113858
0.464459
-0.144261
-0.136079
0.170619
0.170619
0.072947
1
0.88282
0.700557
0.59254
-0.597126
-0.622993
0.42903
0.42903
0.36547
1
0.735237
0.310542
-0.554638
-0.57222
0.380457
0.380457
0.341376
1
0.235176
-0.284501
-0.307038
0.240006
0.240006
0.044566
1
-0.363007
-0.357214
0.123401
0.123401
0.148473
wacz
1
waczsm logwaczs
1
0.990515
1
-0.533966 -0.542471
1
-0.552299 -0.56039 0.99569
1
-0.468703 -0.506502 0.826424 0.839246
pctacfe
Table A.2 - Correlations of Adjusted Trade Flows Measures
1
0.470917
0.464519
0.664654
0.825087
0.184755
0.479715
0.771211
0.750776
0.560454
0.365702
-0.447186
-0.470414
0.402087
0.402087
0.363531
openover openmanu openag openres intv1nov intvn1man intvn1ag intvn1res intv2nov intvn2man intvn2ag intvn2res pctbcfe
1
0.902611
0.629566
0.69926
0.696157
0.72103
0.324912
0.555799
0.659096
0.559813
0.468337
0.393768
-0.413535
-0.439358
0.316937
0.316937
0.246603
37
Table A.3 - Correlations of Price-Based Measures
distort
distort
variaby
mtip
dollar
black
variaby
1
0.275898
mtip
dollar
black
1
1
-0.095085
1
0.101844 0.139633
1
Table A.4 - Correlations of Measures of Tariffs
tariffs
tariffs
owti
txtrdg
totchrgo
totchrgm
totchrga
totchrgr
erp
stderp
owti
1
0.38861
0.619277
0.509738
0.524273
0.488719
0.367912
0.9741
0.322303
1
0.263683
0.865143
0.858855
0.818942
0.789058
txtrdg
totchrgo
totchrgm totchrga
1
-0.098605
1
-0.096902 0.993731
1
-0.095791 0.930161 0.896388
1
-0.10274 0.850224 0.807505 0.789235
totchrgr
erp
stderp
1
1
0.897983
1
Table A.5 - Correlations of Measures of Nontariff Barriers
owqi
owqi
nontarov
nontarma
nontarag
nontarre
nontarov nontarma nontarag nontarre
1
0.345924
1
0.35147 0.99059
1
0.320775 0.925055 0.898322
1
0.238436 0.797582 0.730159 0.665149
38
1
Table A.6 - Correlations of Informal or Qualitative Measures
torient6
torient6
torient7
torm75
torm85
herindex
nberc
ief
tradepol
1
0.667698
torient7
1
0.057235
-0.075299
torm75
torm85
herindex nberc
ief
tradepol
1
1
1
-0.404313 -0.62137
1
1
0.705805
1
Table A.7 - Correlations of Composite Measures
sw
sw
open70s
open80s
tr1
tr2
indirect
open70s open80s tr1
tr2
1
0.39539
1
0.431287
1
0.498503 0.250987 -0.373575
1
0.582458 -0.223607 -0.054888 0.809263
1
0.451738 0.041521 0.126494 0.390286 0.438766
39
indirect
1