November 2011

On the Trade and Price Effects ofPreferential Trade Agreements
Rod Falvey*
School of Business, Bond University

Neil Foster**
Vienna Institute for International Economics

Abstract
In this paper we extend recent work using the Gravity Model to estimate the trade and priceeffects of Preferential Trading Arrangements (PTAs), by explicitly taking into account the extent
of the preferential access being provided by the importer. This involves specifying a trade model,
deriving appropriate PTA variables, appending them to a gravity-type equation and estimating it.
Estimates of the relative price effects of PTAs are derived and a range of PTAs are considered.

WORK IN PROGRESSJEL Classification Numbers: F13, F14
Key Words: Preferential Trading Arrangements, Gravity, Trade Diversion*Corresponding author: Rod Falvey, Bond Business School, Bond University, Gold Coast QLD 4229 Australia.
Ph 61755951258; Fax 61755951160. Email: rfalvey@bond.edu.au**
Neil Foster, Vienna Institute for International Economics, Rahlgasse 3, Vienna, A-1060. Email:foster@wiiw.ac.at

11. Introduction
With the apparent dormancy of the Doha Round, Preferential Trade Agreements (PTAs) now
appear to many policy makers to be the major practical route for obtaining increased market
access for their nation’s exports. Many countries are now involved in numerous agreements1,
expanding the geographical scope of the predominantly regional trading arrangements of thepast. The economic analysis of discriminatory trade of this type has always been a challenge, anddiscussions of the trade effects of PTAs are still often cast in terms of Viner’s concepts of tradecreation and trade diversion. Trade creation emerges because partners can now compete withdomestic producers free of trade barriers. Trade diversion occurs because partners now havepreferential access to the domestic market relative to third parties. Both effects generate increased
trade between partners: the former at the expense of domestic producers; the latter at theexpense of trade with non-members. But trade between members are not the only trade flows
affected by a PTA.
Estimating the magnitude of the trade effects of PTAs has typically relied on the gravity equation,with total bilateral trade or total exports as the variable of interest. In the absence of a theorybased alternative, the significance and magnitude of the estimated coefficients on dummies
indicating the presence (or absence) of a PTA between two countries have provided the evidence.
Here we build upon this pragmatic approach, taking as our starting point the observation thatwhat usually motivates the formation of a PTA is the prospect of preferential access for exports.
Yet the extent to which the access thus obtained is truly preferential will not be captured by adummy variable since it will depend on whether competitors also have preferential access to thesame market through this or other PTAs. If preferences are available to all exporters, then none
have preferential access. In the past when countries were typically members of one PTA at most,this was perhaps a secondary issue. But now with most countries members of multiple
agreements, the extent of preferential access is likely to be important for the trade effects of PTA
membership.2 This is the issue that we investigate below.
The empirical literature using the gravity equation to analyse the trade creation effects of PTAs
goes back to the 1970s. Since then a huge literature has accumulated, producing what appears to
be a mixed bag of results on whether PTAs significantly increase trade among members. These
1 The number of PTAs reported to the WTO was 25 in 1990, 91 in 2000 and 194 in 2007 (Urata and Okabe, 2007).

2 Our dataset consists of 184 countries. Of these 150 were at some point in more than one PTA. EU countries tend

to be in the most PTAs. In addition to their own PTA, they have agreements with other regional blocs such as theEuropean Economic Area, European Free Trade Association, the Overseas Countries and Territories, and the
CARIFORUM, as well as a number of bilateral agreements with countries such as Chile, Cote d’Ivoire, Croatia,
Egypt, Faroe Islands, Israel, Jordan, Lebanon, Macedonia, Mexico, Montenegro, Morocco, South Africa, Syria,
Turkey, Tunisia, and the Palestinian Authority.

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outcomes have taken on greater coherence as more attention has been paid to the theory
underlying the gravity equation and the econometrics of its estimation. Several issues are
involved. First, the importance of including controls for what Anderson and van Wincoop (2003)
refer to as ‘multilateral resistances’ (MR). Because the average trade costs of a country will affectits bilateral trade, failure to control for these induces omitted variables bias. Second, therecognition that PTA membership is endogenous. Allowance should be made for unobserved
bilateral heterogeneity that influences trade and extends beyond the factors explicitly included in
the empirical analysis. Countries that trade more for these unobserved reasons may be more likely
to join a PTA, and the omission of these factors will bias the PTA estimate upwards3. Third, thePTA coefficient estimate itself is subject to omitted variable bias if a single PTA dummy isincluded but the impacts of preferences on bilateral trade flows vary across individual PTAs.
Fourth, as noted earlier, by focussing only on a dummy variable based on joint PTA membership,
the analysis ignores the general equilibrium effects that PTAs have on all trading countries (Egger
et. al. 2010). A main objective of this paper is to continue the process of explicitly bringing thesegeneral equilibrium effects into the analysis. Finally, ideally both the intensive margin (changes tothe volume of existing trade flows) and the extensive margin (opening of new trade flows orcomplete closing of existing trade flows) should be encompassed by the empirical analysis.
The remainder of the paper is organised as follows. In Section 2 we use a standard model toderive our new PTA variables, which take account of PTA membership, the degree ofpreferential access into the importing country’s market and the relative price effects of the PTAs
currently in force. Section 3 discusses the estimating method and data used, while Section 4reports some preliminary results. Section 5 concludes.

2. Preferential AccessTo illustrate the potential importance of preferential access in explaining trade flows in a gravity
equation context, we adapt the familiar general equilibrium model of Anderson and van Wincoop(2003). Consider a world of n+1 countries where each country is specialised in the production of
its own good and has a fixed real output. Countries have identical, homothetic CES preferences.
Consumers in county j maximise
3

This is a point emphasised by Baier and Bergstrand (2002, 2004). They find that two countries are more likely tohave a PTA the larger and more similar their GDPs, the closer they are to each other but the more remote the pairare from the rest of the world, and the wider (narrower) the difference in their relative factor endowments withrespect to each other (rest of world). Baier & Berstrand (2007) suggest using bilateral fixed effects in a panel datasetting, which also controls for the time-invariant component of unobservable MR.

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𝜎 𝜎−1𝑛𝑐𝑖𝑗𝜎−1 𝜎𝑖=0 𝛽𝑖

𝜎 𝜎−1

s.t.

𝑖 𝑝𝑖𝑗 𝑐𝑖𝑗 = 𝑝𝑗 𝑦𝑗(1)

Where 𝜎 is the elasticity of substitution between goods, 𝛽𝑖 is a positive demand parameter
relating to product i, 𝑦𝑗 is the real output of country j, 𝑝𝑗 is the relative price of the output ofcountry j (the output of country 0 is chosen as the numeraire), 𝑐𝑖𝑗 is the consumption of and 𝑝𝑖𝑗
is the price of country i’s output in country j. Trade costs imply that prices differ between
countries. Let 𝑡𝑖𝑗 ≥ 1 be the trade cost factor between country i and j, implying that 𝑝𝑖𝑗 = 𝑝𝑖 𝑡𝑖𝑗 .
From (1), the value of the demand for country i’s goods in country j can be derived as
𝛽 𝑖 𝑝 𝑖 𝑡 𝑖𝑗

𝑋𝑖𝑗 =

1−𝜎

𝑅𝑗

𝑝𝑗 𝑦𝑗

(2)

1−𝜎
𝑛
is a measure of aggregate consumer prices in country j. In order
𝑘=0 𝛽𝑘 𝑝𝑘 𝑡𝑘𝑗

where 𝑅𝑗 =to convert (2) into a gravity equation, Anderson and van Wincoop then use the market clearing
conditions to solve for 𝛽𝑖 𝑝𝑖 1−𝜎 . The market clearing condition for the output of country i
requires that the value of its output equals the value of demand for it – i.e. from (2) that𝑝𝑖 𝑦𝑖 =

𝑛
𝑘=0 𝑋𝑖𝑘 =𝛽𝑖 𝑝𝑖 1−𝜎1−𝜎 𝑝 𝑘𝑛
𝑘=0 𝑡𝑖𝑘 𝑅 𝑦𝑘𝑘(3)

When the solution for 𝛽𝑖 𝑝𝑖 1−𝜎 is substituted in (2) we obtain a gravity equation
𝑌𝑌 𝑡

1−𝜎

𝑊

𝑖 𝑗

𝑋𝑖𝑗 = 𝑌𝑖 𝑗 Π𝑖𝑗 𝑃(4)where 𝑌𝑖 , 𝑌𝑊 are the value of income (and expenditure) in country i and the world respectively,Π𝑖 ≡1−𝜎 𝜃 𝑘𝑛
𝑘=0 𝑡𝑖𝑘 𝑃 and 𝑃𝑗 ≡𝑘1−𝜎 𝜃 𝑘𝑛
𝑘=0 𝑡𝑘𝑗 Π are the ‘multilateral resistance’ (MR) terms, and 𝜃𝑘 is𝑘the share of country k in world income. Bilateral trade costs therefore appear directly in thegravity equation but also indirectly through the MR terms. When estimating this equation, thestandard practice is to sweep the MR terms into (time varying if appropriate) country fixed
effects, and to focus only on the direct trade costs effect (i.e. the 𝑡𝑖𝑗1−𝜎 term), which is proxiedwith the usual variables – geographical distance and dummies for common languages andborders, being landlocked, former colonial status etc. Also included are dummy variables for PTA
status, if preferential trade is of interest.

The coefficients on the PTA dummies are theninterpreted as indicating the net trade creating effects of the relevant PTA, though it is recognised4that the presence of PTAs will also affect bilateral trade flows through the trade costs andinduced product price changes in the MR terms4.
Our objective in this section is to examine the implications of PTA membership on trade flows
in a little more detail, with the aim of extracting variables that better capture these effects for theempirical analysis. We begin by deriving the effects of an arbitrary set of changes in trade costs
on bilateral export flows. Let 𝑧 denote a proportional change (𝑑𝑧 𝑧) for any variable z. Then
using (2) and assuming no induced changes in real outputs, we have
𝑋𝑖𝑗 = − 𝜎 − 1 𝑝𝑖 + 𝑡𝑖𝑗 + 𝑝𝑗 − 𝑅𝑗

and 𝑅𝑗 = − 𝜎 − 1𝑛𝑘=0 𝑚𝑘𝑗

𝑝𝑘 + 𝑡𝑘𝑗(5)This allows us to write
𝑋𝑖𝑗 = − 𝜎 − 1

𝑝𝑖 + 𝑡𝑖𝑗 −𝑛
𝑘=0 𝑚𝑘𝑗

𝑝𝑘 + 𝑡𝑘𝑗

+ 𝑝𝑗

(5A)

𝑋

where 𝑚𝑖𝑗 ≡ 𝑝 𝑖𝑗𝑦 denotes the (import) market share of country i in j, with 𝑚𝑖𝑗 ≥ 0 and𝑗 𝑗𝑛𝑖=0 𝑚𝑖𝑗 = 1, where the latter includes home sales of j. The term in brackets on the right of (5A)

indicates that the value of exports from i to j falls if the cost of i’s product rises in the j market,
relative to a market-share weighted average of cost changes for all suppliers, including j itself. The
second term indicates that an increase in the relative price of j’s output tends to increase the valueof imports from i by increasing numeraire income in j. As (5A) reveals, changes in trade costs willaffect trade flows through three channels – a direct effect, a relative price effect and an expenditure effect –as laid out in equation (5B).
𝑋𝑖𝑗 =− 𝜎 − 1 𝑡𝑖𝑗 −𝑛
𝑘=0 𝑚𝑘𝑗 𝑡𝑘𝑗

𝑑𝑖𝑟𝑒𝑐𝑡 𝑒𝑓𝑓𝑒𝑐𝑡− 𝜎 − 1 𝑝𝑖 −𝑛
𝑘=1 𝑚𝑘𝑗 𝑝𝑘

𝑟𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑝𝑟𝑖𝑐𝑒 𝑒𝑓𝑓𝑒𝑐𝑡

+𝑝𝑗

(5B)

𝑒𝑥𝑝𝑒𝑛𝑑𝑖𝑡𝑢𝑟𝑒𝑒𝑓𝑓𝑒𝑐𝑡

The latter two effects depend on the induced changes in relative prices. To determine these wetotally differentiate the market clearing conditions (3) which gives us
𝑝𝑖 = 1 − 𝜎 𝑝𝑖 +𝑛𝑘=0 𝑒𝑖𝑘

1 − 𝜎 𝑡𝑖𝑘 + 𝑝𝑘 − 𝑅𝑘(6)

4

Anderson and Yotov (2011) explicitly use these relationships to measure the terms of trade and global
efficiency effects of PTAs, using data on 2 digit manufacturing sectors.

5

𝑋

where 𝑒𝑖𝑗 ≡ 𝑝 𝑖𝑗𝑦 denotes the (export) share of country j in the output of country i, with 𝑒𝑖𝑗 ≥ 0𝑖 𝑖

𝑋 𝑖𝑗 𝑝 𝑗 𝑦 𝑗
𝑗 𝑒𝑖𝑗 = 1, where the latter includes home sales of i. Note further that 𝑒𝑖𝑗 = 𝑝 𝑦 𝑝 𝑦 =𝑗 𝑗 𝑖 𝑖and
𝜃

𝑚𝑖𝑗 𝜃𝑗 . Using (5) to substitute for 𝑅𝑘 and rearranging leads to
𝑖

𝜎𝑝𝑖 −

𝑛
𝑘=0 𝑒𝑖𝑘 𝑝𝑘 +𝑛𝑗 =1 𝑚𝑗𝑘 𝑝𝑗

𝜎−1=− 𝜎−1𝑛𝑘=0 𝑒𝑖𝑘𝑡𝑖𝑘 −𝑛𝑗 =0 𝑚𝑗𝑘 𝑡𝑗𝑘(7)

The right side of this equation shows how changes in trade costs affect the demand for i’s output.
Let 𝑡𝑘𝑚 ≡𝑛
𝑗 =0 𝑚𝑗𝑘 𝑡𝑗𝑘

be the (import) market-share weighted average change in trade costs for

all goods sold in country k. Then 𝑡𝑖𝑘 − 𝑡𝑘𝑚 gives the change in i’s trade costs of selling in k
relative to this average. If this term is positive (negative), then k’s demand for i’s product will fall(rise) other things equal, with the magnitude of this change depending on the elasticity of productsubstitution. Let 𝑡𝑖𝑒 ≡𝑛𝑗 =0 𝑒𝑖𝑗 𝑡𝑖𝑗 denote the export-share weighted average change in trade costs

for country i. Then we can write (7) as𝜎𝑝𝑖 −𝑛
𝑘=0 𝑒𝑖𝑘 𝑝𝑘 +𝜎−1𝑛𝑗 =1 𝑚𝑗𝑘 𝑝𝑗= − 𝜎 − 1 𝑡𝑖𝑒 −𝑛𝑚
𝑘=0 𝑒𝑖𝑘 𝑡𝑘(8)

Demand for product i increases if the average change in its export trade costs is lower than the
average trade cost change in its export markets.
Taking these equations for all the non-numeraire goods, gives us a system of n equations to solve
for the n relative output price changes. This system can be written as
𝐷(𝜎) − 𝑆 𝑝 = −𝐷(𝜎 − 1) 𝑡 𝑒 − 𝐸𝑡 𝑚

(9)Where 𝑝 is the nx1 vector of proportional changes in relative output prices; 𝐷(𝜎) is an nxn
diagonal matrix with 𝜎 as its diagonal elements; S is a nxn matrix whose ijth term is 𝑒𝑖𝑗 +
𝜎−1

𝑛
𝑒
𝑚
are,
𝑘=0 𝑒𝑖𝑘 𝑚𝑗𝑘 > 0; E is an nx(n+1) matrix of export market shares and 𝑡 and 𝑡

respectively, the nx1 and (n+1)x1 vectors of export and import weighted average trade costchanges defined above. Since the off-diagonal elements of 𝐷(𝜎) − 𝑆 are all negative and thesum of the coefficients in the ith row is 𝑒𝑖0 + 𝜎 − 1𝑛𝑘=0 𝑒𝑖𝑘 𝑚0𝑗 > 0 ; then the diagonal

elements are all positive and 𝐷(𝜎) − 𝑆 has a dominant diagonal. Thus 𝐷(𝜎) − 𝑆 is nonsingular. In principle we can then solve for the induced changes in relative prices as functions of
the changes in trade costs from𝑝 = −𝐷(𝜎 − 1) 𝐷(𝜎) − 𝑆 −1 𝑡 𝑒 − 𝐸𝑡 𝑚

(10)

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and these solutions can be substituted in (5A) to obtain the total effects of trade costs changes onbilateral trade flows.
Our interest is in identifying the effects of PTA membership amongst the trading parties on thevalue of their bilateral exports. We start from a pre-PTA equilibrium (i.e. where there are noPTAs), and then suppose countries form PTAs with an exogenously chosen subset of theirtrading partners. Initially we assume PTA membership has a uniform effect on bilateral trade
costs and suppose that if country 𝑗 grants preferential access to exports from a set of trading
partners 𝑖 ∈ 𝑆𝑗 , this corresponds to an equi-proportionate reduction in the trade costs for thecorresponding exports – i.e. these costs become (1 − 𝛾)𝑡𝑖𝑗 ; 0 < 𝛾 < 1, 𝑖𝜖𝑆𝑗 , implying that𝑡𝑖𝑗 = −𝛾. It then follows that 𝑡𝑘𝑚 = −𝑗 ∈𝑆𝑘 𝑚𝑗𝑘 𝛾 = −𝑚𝑘 𝛾, where 𝑚𝑘 ≡𝑗 ∈𝑆𝑘 𝑚𝑗𝑘 is the pre-PTA market share of its PTA partners in country k, a measure of the extent of preferential accessoffered by k; and 𝑡𝑘𝑒 = −𝑗 ∈𝑆𝑘 𝑒𝑘𝑗 𝛾 = −𝑒𝑘 𝛾 , where 𝑒𝑘 ≡𝑗 ∈𝑆𝑘 𝑒𝑘𝑗 is the pre-PTA share of its

PTA partners in country k’s exports, a measure of the extent of preferential access offered to k.
Equation (8) then becomes𝜎𝑝𝑖 −𝑛
𝑘=0 𝑒𝑖𝑘 𝑝𝑘 +𝜎−1𝑛𝑗 =1 𝑚𝑗𝑘 𝑝𝑗= 𝜎 − 1 𝑒𝑖 −𝑛𝑘=0 𝑒𝑖𝑘 𝑚𝑘 𝛾

(11)

The impact of PTA formation on the demand for i’s output thus depends on the extent of i’spreferential access to its trading partners’ markets (𝑒𝑖 ) relative to the (export-share weighted)
average of preferential access to those markets in general (𝑛
𝑘=0 𝑒𝑖𝑘 𝑚𝑘 ).

Given the pre-PTA market shares and a value of 𝜎, we can use (10) to solve for the relative pricechanges in the form
𝑝𝑖 = 𝑏𝑖 𝛾; 𝑖 = 1, … , 𝑛(12)

Where the numerical values of the 𝑏𝑖 will depend on all the parameters in (10) and (11). These
solutions can then be substituted in (5B) to get expressions for the proportionate changes intrade flows as a function of 𝛾.
This leads us to the estimating equation for the effects of PTAs on trade flows. Let 𝑋𝑖𝑗 denotethe pre-PTA value of exports of i to j. Then we can write
𝑋𝑖𝑗 = 𝑋𝑖𝑗 + 𝑑𝑋𝑖𝑗 = 𝑋𝑖𝑗 1 + 𝑋𝑖𝑗
Which, using (5A) and the solutions for the price changes, gives us

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𝑙𝑛𝑋𝑖𝑗 ≅ 𝑙𝑛𝑋𝑖𝑗 + 𝑋𝑖𝑗= 𝑙𝑛𝑋𝑖𝑗 + 𝜎 − 1 𝐼𝑖𝑗 − 𝑚𝑗 𝛾 − 𝜎 − 1 𝑏𝑖 −𝑛
𝑘=0 𝑚𝑘𝑗 𝑏𝑘+ 𝑏𝑗 𝛾(13)where 𝐼𝑖𝑗 = 1 if countries i and j are in the same PTA and 0 otherwise – i.e. 𝐼𝑖𝑗 is a standard
PTA dummy variable. The first term is what the value of exports from i to j would be in theabsence of PTAs (and will be captured by standard gravity variables below). The other terms
capture the effects of PTAs on this trade. The first of these captures the direct effects of the
trade cost changes on this trade flow – the effect normally attributed to the PTA dummy alone.
If i and j are not members of a PTA (𝐼𝑖𝑗 = 0) this term reduces to the direct trade diversion
effects of the PTAs that the importing partner belongs to (− 𝜎 − 1 𝑚𝑗 𝛾). If i and j are membersof the same PTA, then this term becomes + 𝜎 − 1 1 − 𝑚𝑗 𝛾. The standard trade expansion
effect is weakened to the extent that the importing country grants preferential access through thisand other PTAs. Note that 𝑚𝑗 will vary over time if j varies its membership of PTAs. The
remaining term captures the indirect (relative price and expenditure) effects of the PTAs working
through changes in output prices. The solutions from (12) can be inserted in (13) to provide a‘comprehensive’ variable capturing the full effects of PTAs on bilateral trade flows. The
estimated coefficient on this variable then provides an estimate of 𝛾.

3. Method and Data
Since our focus is on market access, we follow the majority of the literature in using the value ofexports as our dependent variable. We take a fairly standard specification of the gravity equation
and augment it with our PTA related variables, obtaining
𝑙𝑛𝐸𝑋𝑃𝑖𝑗𝑡 = 𝛼0 + 𝛼1 𝑙𝑛𝐺𝐷𝑃𝑖𝑡 + 𝛼2 𝑙𝑛𝐺𝐷𝑃𝑗𝑡 + 𝛼3 𝑙𝑛𝑃𝑂𝑃𝑖𝑡 + 𝛼4 𝑙𝑛𝑃𝑂𝑃𝑗𝑡 + 𝛼5 𝑙𝑛𝐷𝐼𝑆𝑇𝑖𝑗 +𝛼6 𝐿𝐴𝑁𝐺𝑖𝑗 + 𝛼7 𝐴𝐷𝐽𝑖𝑗 +𝛼8 𝐿𝑂𝐶𝐾𝑖𝑗 + Γ𝑍𝑡 + 𝛿𝑖(𝑡) + 𝜔𝑗 (𝑡) + 𝜏𝑡 + 𝜗𝑖𝑗 + 𝑢𝑖𝑗𝑡 (14)where 𝐸𝑋𝑃𝑖𝑗 is the value of merchandise trade flow imported by country 𝑗 from exporter 𝑖, 𝐺𝐷𝑃𝑖
(𝐺𝐷𝑃𝑗 ) is the level of nominal gross domestic product of country 𝑖 (𝑗), 𝑃𝑂𝑃𝑖 (𝑃𝑂𝑃𝑗 ) is the
population of country 𝑖 (𝑗), 𝐷𝐼𝑆𝑇𝑖𝑗 is the distance between economic centres of countries 𝑖 and 𝑗,
𝐿𝐴𝑁𝐺𝑖𝑗 is a binary variable equal to one if countries 𝑖 and 𝑗 share a common language, 𝐴𝐷𝐽𝑖𝑗 is abinary variable equal to one if countries 𝑖 an 𝑗 share a common border, 𝐿𝑂𝐶𝐾𝑖𝑗 is a variableaccounting for whether none, one or both countries are landlocked5, 𝛿𝑖(𝑡) , 𝜔𝑗 (𝑡) , and 𝜏𝑡 areexporter, importer and time fixed effects respectively, where the former two may also be time
5 𝐿𝑂𝐶𝐾𝑖𝑗 takes on the value 0, 1 or 2 depending on whether none, one or both countries are landlocked respectively.

8dependent6, and 𝜗𝑖𝑗 is a bilateral pair dummy. When we include the bilateral pair dummy, thedistance and other geographical variables, which are bilateral-pair time-invariant variables have to
be excluded from (14). All variables are included in log form, with the exception of the PTA
variables and the dummy variables. We include the PTA effects, denoted by vector 𝑍𝑡 in (14)above, both individually and collectively. While the direct effect 𝐼𝑖𝑗𝑡 − 𝑚𝑗𝑡 can be included
straightforwardly, calculating the price effects requires a value for the elasticity of substitution in
consumption. We illustrate results for 𝜎 = 8, the values ‘preferred’ by Anderson and van
Wincoop. It is then convenient to define all three PTA effects so that their coefficients eachprovide an estimate of 𝛾. We therefore define thedirect effect𝐷𝐸𝑖𝑗𝑡 = 𝜎 − 1 𝐼𝑖𝑗𝑡 − 𝑚𝑗𝑡 ;relative price effect

𝑅𝐸𝑖𝑗𝑡 = − 𝜎 − 1 𝑏𝑖𝑡 −expenditure effect

𝐸𝐸𝑗𝑡 = 𝑏𝑗𝑡 .

𝑛
𝑘=1 𝑚𝑘𝑗𝑡 𝑏𝑘𝑡 ; andIf we combine the two price effects and then all three effects we have the
combined price effects

𝐶𝐸𝑖𝑗𝑡 = 𝑅𝐸𝑖𝑗𝑡 + 𝐸𝐸𝑗𝑡 ; andtotal PTA effects

𝑇𝐸𝑖𝑗𝑡 = 𝐷𝐸𝑖𝑗𝑡 +𝐶𝐸𝑖𝑗𝑡

Data on the GDP and population of the importer and exporter are from the World Development
Indicators (2008) dataset. Data on distance, common language and adjacency are from CEPII7.
The landlocked variable is constructed based on information from Wikipedia8. The trade data istaken from COMTRADE via WITS, and in our analysis we consider total exports of the reporter
country 𝑖. The dataset includes up to 183 countries over the period 1976-2006. Finally, data on
PTAs is taken from the WTO website9 (accessed at various dates) and complemented withinformation from Baier et al (2008) and Wikipedia.10
So far we have modelled the case where all PTAs generate the same proportionate reduction intrade costs among their members. In reality this uniformity is unlikely given the flexibility that6

As discussed above the inclusion of country-pair fixed effects is used to account for the multilateral resistanceterms. In a panel context however we may need to take account of the time varying nature of these terms throughthe inclusion of importer-time and exporter-time fixed effects (see Baldwin and Taglioni, 2006).
7 http://www.cepii.fr/anglaisgraph/bdd/distances.htm
8 http://en.wikipedia.org/wiki/Landlocked
9 http://rtais.wto.org/UI/PublicAllRTAList.aspx
10 The reason for considering alternative sources is that the WTO dataset only includes PTAs in force, thus excluding
a number of PTAs that are no longer in force, but that would have been in the period of interest – e.g. the PTAs
agreed between the EU-15 and Romania, Bulgaria and others in the 1990s, but which are no longer in force now thatthese countries are members of the EU.

9countries have in the depths of their liberalisations while still meeting the WTO obligations under
Article XXIV. So in the empirical analysis we also allow that different PTAs might involve
different levels of trade cost reduction and introduce individual 𝛾s for the major PTAs. The
adjustments that consideration of multiple PTAs requires to our modelling specification are
straightforward. Suppose that there are H separate PTAs, indexed as 𝑕 ∈ 𝐻 with associated trade𝑕cost reduction 𝛾𝑕 . Define index 𝐼𝑖𝑗𝑡
= 1 if countries i and j are members of PTA h at time t and= 0 otherwise. Then we can define preferential access measures analogous to those used above –i.e. 𝑚𝑗𝑡𝑕 =𝑕𝑛
𝑘=0 𝑚𝑘𝑗𝑡 𝐼𝑘𝑗𝑡 , which measures the preferential access to j’s market given to membersof PTA h, and 𝑒𝑖𝑡𝑕 =𝑕𝑛𝑘=0 𝑒𝑖𝑘𝑡 𝐼𝑖𝑘𝑡 , which measures the preferential access that i receives throughits membership of PTA h. To determine the changes in relative prices we use a modified versionof (11) (ignoring the time dimension):𝜎𝑝𝑖 −𝑛𝑘=0 𝑒𝑖𝑘 𝑝𝑘 +𝜎−1𝑛𝑗 =1 𝑚𝑗𝑘 𝑝𝑗

= 𝜎−1

𝑕
𝐻
𝑕=1 𝑒𝑖 −𝑛𝑕
𝑘=0 𝑒𝑖𝑘 𝑚𝑘𝛾𝑕 (15)The solution to this system gives us expressions for the relative price changes in terms of the
PTA cost reductions, i.e.

𝑝𝑖 =

𝑕
𝐻
𝑕=1 𝑏𝑖 𝛾𝑕 .

These in turn can be used to generate the expression for the proportionate change in the value ofexports from i to j giving
𝑋𝑖𝑗 = 𝜎 − 1𝑕𝑕𝐻
𝑕=1 𝐼𝑖𝑗 − 𝑚𝑗

𝛾𝑕 −

𝐻
𝑕=1

𝜎 − 1 𝑏𝑖𝑕 −𝑛𝑕
𝑘=1 𝑚𝑘𝑗 𝑏𝑘− 𝑏𝑗𝑕 𝛾𝑕(16)

Again, we insert the relevant PTA-specific direct, relative price and expenditure effects in thestandard gravity equation.

4. Preliminary Results
In this section we present and discuss some preliminary estimates for the modelling discussed
above. We begin by simply including a dummy variable for PTA membership as has beenstandard in the literature, and provide estimates with and without time, importer, exporter andtrading-pair fixed effects. The results are given in Table 1. The coefficients on the gravity
variables are as expected, with the only anomaly being the changing sign on population in theimporter. The coefficient on the PTA dummy is positive and significant, but declines inmagnitude when we take account of unobserved heterogeneity (through bilateral pair dummies).
It predicts an increase in bilateral trade of between 25% and 44% for PTA members.
The direct effect derived in Section 2 modifies the PTA dummy by subtracting a term that
captures the degree of preferential access offered by the importer (i.e. 𝐼𝑖𝑗 − 𝑚𝑗 ). The results of10

replacing the PTA dummy by this modified version are given in Table 2. The outcomes for thegravity variables are not different from Table 1. But the estimated coefficient on the PTA variable
is at first negative and significant, becoming insignificant once we take account of unobserved
heterogeneity. Taking even partial account of the trade diverting potential of PTAs, leads to aconclusion that PTAs have had no significant effect on bilateral trade.
The following two tables show that estimates based on the different components of the full PTA
effect can be quite varied. Table 3 includes the direct, relative price and expenditure effects
separately. These variables have been defined so that all are expected to have positivecoefficients, and indeed all coefficients should provide an estimate of the proportional trade cost
reduction due to PTA membership. Unsurprisingly these expectations are not meet in full. The
direct effect and expenditure effects have positive and significant coefficients, although they arequite different in magnitude, but the relative price variable has a negative and significant
coefficient. Combining the ‘price’ effects gives us the outcomes reported in Table 4. Here theestimated coefficients on the direct effects are quite similar to the corresponding equations inTable 3. The estimated coefficient on the combined price effects varies in sign and significance,
however.
When we move to our preferred specification, which includes a single variable combining the full
effects of PTAs in the gravity equation, we get the results reported in Table 5. As noted in theprevious section, the coefficient on this variable provides an estimate of the proportional
reduction in trade costs implied by PTA membership on average. In Table 5 these estimates are
statistically significant and range from 4.16% to 1.17% in terms of their estimated trade cost
reductions. Our variable is constructed so as to capture the full effects of PTA on the multilateralresistance terms, so the inclusion of time varying importer and exporter fixed effects will be
capturing any other influences on trade costs. The country-pair fixed effects should take accountof unobserved heterogeneity that might promote PTA membership, and their inclusion indicatestrade cost reductions and the lower end of the range (a little over 1%).
Some information on the price effects are provided in Tables 7 and 8. The data used for thesecalculations is for the year 2006 and the variables listed are: 𝑏𝑖 the coefficient on 𝛾 in the
proportional price change equation (12), which captures the change in the relative price ofcountry i’s output; 𝑐𝑖 ≡country i; 𝑔𝑐𝑖 ≡𝑛
𝑘=0 𝑚𝑘𝑖 𝑏𝑘 capturing the proportional change in the cost-of-living in𝑛𝑘=0 𝑒𝑖𝑘 𝑏𝑖 − 𝑐𝑘

capturing the change in country i’s global competitiveness(𝑔𝑐𝑖 measures the change in the price of i’s output relative to the change in the cost-of-living ineach of its markets, where each market is weighted by its share in i’s sales); 𝜃𝑖 which is country i’s
11share of world income; 𝑚𝑖 which captures the extent of the preferences offered by country i and𝑒𝑖 which captures the extent of preferences offered to country i. We draw several conclusions
from Table 7. First, that the price effect is highly correlated with the cost of living effect, and that
all three price effects are reasonably highly correlated (above 0.63). Second, that all the priceeffects are more highly correlated with the preferences received than with the preferences
offered. Third, country size is not highly correlated with any price outcome, and is not
significantly correlated with preferences received or granted.

Finally, while the preferences

received and granted are significantly positively correlated, the correlation of 0.68 is not as high as
one might have imagined.
Table 8 reports the variables from Table 7 at the country level. The countries are listed
alphabetically, but their rank in terms of highest to lowest relative price changes are given incolumn two - i.e. the Dominican Republic has the largest relative price increase and Lebanon has
the smallest relative price increase (in fact a reduction). If the numbers in column three are
multiplied by the relevant estimate of 𝛾, then they will give the estimated relative price changes.
Some very preliminary results for the total effects of PTAs when we separate out the major
individual PTAs are presented in Table 6. Here we find considerable variation in the sign andsignificance of the estimated coefficients on the PTA effects, depending on whether time,
importer and exporter or bilateral pair fixed effects are included. We will focus on the last twocolumns, which include importer/exporter and country pair fixed effects respectively. Only fourindividual PTAs have positive and significant coefficients in both columns - the European
Economic Area (EEA), the European Free Trade Agreement (EFTA), the Latin American
Integration Association (LAIA) and ‘Other’, our residual of non-major PTAs. Five others havepositive and significant estimated coefficients in one case and an insignificant coefficient in theother – ANZCERTA, the Andean Pact (AP), the Central American Common Market (CACM),
the Caribbean Community (CARICOM), and the Southern Cone Common Market(MERCOSUR). The North America Free Trade Agreement (NAFTA) is not significant in either
equation, while the Association of South East Asian Nations (ASEAN), Asian Pacific Economic
Cooperation (APEC) and the European Union (EU) are positive and significant in one equationand negative and significant in the other.

5. Conclusions
Our objective in this paper has been to extend the empirical analysis of the effects of PTA
membership on bilateral trade flows, by deriving ‘better’ indicators of the likely impact on tradeof PTA membership. In particular, we sought to capture the potential trade diversion effects that12

are missed by the standard membership dummy. We began by considering a standard trademodel that generates a gravity equation to explain bilateral trade. Taking the view that PTA
formation leads to a reduction in bilateral trade costs amongst PTA members, we were able toderive a variable capturing the ‘direct’ effects of PTA membership that involved subtracting an
indicator of the degree of preferential access offered by the importing country to all its trading
partners from the standard membership dummy. Thus this variable took a positive value forbilateral trade between PTA members and a negative value for bilateral trade where the importer
was a member and the exporter was a non-member.
We also derived two other price-related PTA effects – a relative price effect and an expenditureeffect. Applying the model and under the assumptions that a PTA results in an unknown but
common proportional reduction in trade costs among members (𝛾), we could solve for thecorresponding multipliers on 𝛾 for these effects and thereby include them in the estimating
equation. Our preliminary estimates of the relative price effects show them to be much more
highly correlated with preferences received than preferences given, consistent with a focus onmarket access. When all PTA effects were combined and included we estimated that PTA
membership resulted in a trade cost reduction between 1% and 4% on average. Of course thisaverage could hide a wide range of outcomes on individual PTAs, and we provided somepreliminary evidence that this might indeed be the case.

13

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16

Table 1: A single PTA Dummy
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lock
PTA Dummy(1)
lexports(2)
lexports(3)
lexports(4)
lexports(5)
lexports(6)
lexports

1.157***(0.00270)
0.952***(0.00288)-0.0749***(0.00343)
0.00468(0.00354)-1.195***(0.00570)
0.781***(0.0285)0.956***(0.0125)-0.408***(0.00913)
0.350***(0.0122)1.192***(0.00270)
0.973***(0.00287)-0.105***(0.00340)-0.0139***(0.00351)-1.183***(0.00565)
0.754***(0.0288)0.959***(0.0123)-0.311***(0.00904)
0.441***(0.0122)0.379***(0.0202)0.809***(0.0236)-0.850***(0.0745)0.587***(0.0769)-1.393***(0.00678)
0.552***(0.0293)0.932***(0.0135)-0.764(3922.8)
0.421***(0.0124)

0.419***(0.0130)
0.892***(0.0155)-0.273***(0.0510)0.768***(0.0540)0.237***(0.0233)
0.707***(0.0287)-0.224(0.167)0.412**(0.182)0.297***(0.0188)

0.223***(0.0363)0.649***(0.0443)-0.860***(0.255)0.449*(0.263)-1.402***(0.00676)
0.548***(0.0294)
0.932***(0.0134)-1.957(5032.7)
0.408***(0.0126)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Yes
No
Yes
Yes

265,0540.087
1002***

265,0540.745
632.5***

265,054
0.139
41.52***

0.245***(0.0197)

Fixed Effects:
Time
Importer / Exporter
Country-Pair.
Importer-Time and Exporter-Time

Observations
R-squared
F-Test265,0540.663
68470***

265,054
265,0540.672
0.738
23310***
2051***
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

17

Table 2: Modified Direct effect for a Single PTA
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lock
𝐼−𝑚

(1)
lexports(2)
lexports(3)
lexports(4)
lexports(5)
lexports(6)
lexports

1.156***(0.00271)
0.941***(0.00309)-0.0668***(0.00342)
0.0223***(0.00371)-1.241***(0.00552)
0.856***(0.0285)
0.986***(0.0125)-0.445***(0.00928)-0.401***(0.0634)

1.190***(0.00270)
0.938***(0.00307)-0.0939***(0.00338)
0.0306***(0.00367)-1.234***(0.00548)
0.859***(0.0288)
1.004***(0.0124)-0.388***(0.00915)-1.747***(0.0647)

0.386***(0.0203)
0.819***(0.0237)-0.919***(0.0745)0.536***(0.0777)-1.473***(0.00639)
0.609***(0.0295)
0.945***(0.0136)-0.774(186.2)-0.111(0.106)0.425***(0.0130)0.900***(0.0155)-0.345***(0.0508)0.717***(0.0545)0.235***(0.0233)
0.704***(0.0287)-0.266(0.167)0.365**(0.182)0.0442(0.0748)0.219***(0.0364)0.645***(0.0445)-0.903***(0.255)0.392(0.264)-1.479***(0.00637)
0.604***(0.0296)0.944***(0.0135)-1.999(5470.3)
0.00762(0.193)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Yes
No
Yes
Yes

265,0540.086
990.0***

265,0540.744
606.9***

265,0540.139
41.33***

0.131(0.134)

Fixed Effects
Time
Importer / Exporter
Country-Pair
Importer-Time & Exporter-Time

Observations
R-squared
F-Test265,0540.662
67882***

265,054
265,0540.672
0.737
23076***
2039.6***
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

18

Table 3: Individual Effects for a single PTA: 𝝈 = 8)
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lock
DE
RE
EE

(1)
lexports(2)
lexports(3)
lexports(4)
lexports(5)
lexports(6)
lexports

1.154***(0.00279)
0.950***(0.00297)-0.0730***(0.00348)
0.00559(0.00362)-1.182***(0.00585)
0.794***(0.0285)0.975***(0.0126)-0.412***(0.00920)
0.0496***(0.00178)-0.0134***(0.00212)
0.308***(0.0233)

1.188***(0.00279)
0.971***(0.00296)-0.102***(0.00345)-0.0124***(0.00358)-1.178***(0.00580)
0.777***(0.0288)0.980***(0.0124)-0.319***(0.00911)
0.0550***(0.00177)-0.0128***(0.00210)0.284***(0.0231)0.380***(0.0203)
0.818***(0.0236)-0.835***(0.0761)
0.586***(0.0796)-1.393***(0.00679)
0.552***(0.0293)0.932***(0.0135)-0.419(127.3)0.0603***(0.00179)-0.00301(0.00273)
0.0723**(0.0288)

0.415***(0.0130)
0.896***(0.0155)-0.302***(0.0517)0.723***(0.0555)

0.0448***(0.00274)
0.00763***(0.00188)-0.0675***(0.0204)

0.228***
0.239***(0.0363)(0.0233)
0.647***
0.707***(0.0443)(0.0287)-0.784***-0.191(0.256)(0.167)0.442*
0.413**(0.263)(0.182)-1.401***(0.00676)
0.548***(0.0294)
0.932***(0.0134)
1.420(203.3)0.0585***0.0360***(0.00181)(0.00284)-0.0186*** -0.00772***(0.00403)(0.00273)
0.192***
0.106***(0.0440)(0.0298)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Yes
No
Yes
Yes

265,0540.087
923.2***

265,0540.745
697.6***

265,0540.139
41.45***

Fixed Effects
Time
Importer / Exporter
Country-Pair
Country-Time

Observations
R-squared
F-Test265,054
265,054
265,054
0.663
0.672
0.738
55950***
21686***
2011.5***
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

19

Table 4. Combined Price Effects for a single PTA: 𝜎 = 8)
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lock
DE
CE
Fixed Effects
Time
Importer /Exporter
Country-Pair
Importer-Time &Exporter-Time
Observations
R-squared
F-Test(1)
lexports(2)
lexports(3)
lexports(4)
lexports(5)
lexports(6)
lexports

1.159***(0.00275)
0.956***(0.00293)-0.0764***(0.00345)
0.000389(0.00360)-1.198***(0.00569)
0.780***(0.0285)0.955***(0.0125)-0.399***(0.00917)
0.0485***(0.00177)
0.00547***(0.00155)

1.193***(0.00275)
0.977***(0.00292)-0.105***(0.00342)-0.0173***(0.00356)-1.193***(0.00563)
0.764***(0.0288)
0.962***(0.0123)-0.307***(0.00908)
0.0540***(0.00177)
0.00466***(0.00154)

0.378***(0.0203)
0.816***(0.0236)-0.853***(0.0756)
0.551***(0.0781)-1.393***(0.00679)
0.552***(0.0293)0.932***(0.0135)-0.7500.417***(0.0130)
0.898***(0.0155)-0.284***(0.0515)0.758***(0.0546)

0.237***(0.0233)
0.706***(0.0287)-0.217(0.167)0.386**(0.182)0.0603***(0.00179)0.00107(0.00205)

0.0446***(0.00274)
0.00341**(0.00143)

0.225***(0.0363)0.646***(0.0443)-0.833***(0.256)0.393(0.263)-1.402***(0.00676)
0.548***(0.0294)
0.931***(0.0134)-1.884(578.7)0.0585***(0.00181)-0.00699**(0.00303)

0.0360***(0.00284)-0.00133(0.00208)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Yes
No
Yes
Yes

265,0540.745
692.8***

265,0540.139
41.48***

265,0540.663
61567***

265,054
265,054
265,054
0.672
0.738
0.087
22440***
2025.6***961.2***
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

20

Table 5: Total Effects for a single PTA: 𝜎= 8
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lockTEFixed Effects
Time
Importer / Exporter
Country-Pair
Importer-Time &Exporter-Time
Observations
R-squared
F-Test(1)
lexports(2)
lexports(3)
lexports(4)
lexports(5)
lexports(6)
lexports

1.164***(0.00273)
0.946***(0.00290)-0.0762***(0.00345)
0.0140***(0.00354)-1.220***(0.00557)
0.818***(0.0285)0.972***(0.0125)-0.418***(0.00915)
0.0240***(0.00119)1.198***(0.00273)
0.965***(0.00289)-0.105***(0.00342)-0.00156(0.00350)-1.218***(0.00553)
0.808***(0.0289)
0.981***(0.0124)-0.329***(0.00906)
0.0259***(0.00118)

0.367***(0.0203)
0.828***(0.0236)-1.044***(0.0748)0.730***(0.0777)-1.428***(0.00663)
0.577***(0.0294)
0.938***(0.0135)-0.603(125.1)0.0342***(0.00139)

0.418***(0.0130)
0.902***(0.0155)-0.383***(0.0509)
0.796***(0.0545)

0.212***(0.0364)0.659***(0.0444)-1.037***(0.255)0.505*(0.263)-1.424***(0.00666)
0.565***(0.0294)
0.935***(0.0134)
1.573

0.233***(0.0233)
0.708***(0.0287)-0.298*(0.167)0.410**(0.182)0.0123***(0.00127)

0.0416***(0.00156)

0.0117***(0.00168)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

Yes
No
No
Yes

Yes
No
Yes
Yes

265,0540.745
608.42***265,0540.139
41.39***

265,0540.662
68078***

265,054
265,054
265,054
0.671
0.737
0.087
23141***
2046.3***994.5***
Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

21

Table 6: Total Effects for Individual PTAs: 𝜎= 8
VARIABLES
lgdp_exp
lgdp_imp
lpop_exp
lpop_imp
ldist
contigcomlang_off
lock
asean
anzcerta
ap
apec
cacm
caricom
eea
efta
eu
laia
mercosur
nafta
other
Constant
Fixed Effects
Time
Importer / Exporter
Country-Pair
Importer-Time &Exporter-Time
Observations
R-squared
F-Test(1)
lexports(2)
lexports(3)
lexports(4)
lexports

1.162***(0.00273)
0.955***(0.00292)-0.0616***(0.00347)
0.0155***(0.00357)-1.173***(0.00569)
0.803***(0.0293)0.885***(0.0128)-0.391***(0.00920)-0.0985***(0.0206)
0.247***(0.0443)0.0572***(0.0156)
0.00620(0.00449)
0.436***(0.0142)0.679***(0.0148)0.0908***(0.0105)0.170***(0.0154)-0.0150***(0.00531)
0.219***(0.00848)
0.186***(0.0302)0.457***(0.0272)
0.0303***(0.00310)-32.60***(0.0900)

1.198***(0.00274)
0.975***(0.00292)-0.0906***(0.00344)-0.00158(0.00354)-1.169***(0.00564)
0.805***(0.0296)0.897***(0.0126)-0.299***(0.00910)-0.116***(0.0210)0.245***(0.0387)
0.0568***(0.0166)
0.00572(0.00452)
0.421***(0.0142)0.691***(0.0145)0.0936***(0.0102)0.123***(0.0163)-0.00235(0.00538)
0.190***(0.00870)
0.184***(0.0303)0.420***(0.0264)0.0416***(0.00309)-32.54***(0.0925)0.370***(0.0202)
0.816***(0.0235)-0.844***(0.0743)
0.529***(0.0768)-1.366***(0.00691)
0.633***(0.0295)
0.861***(0.0137)-0.748(3966.9)-0.0356**(0.0169)
0.120***(0.0209)
0.133***(0.0188)0.0399***(0.00502)
0.365***(0.0161)
0.659***(0.0140)0.0492***(0.0106)
0.0837***(0.0185)-0.0999***(0.00606)
0.246***(0.00964)-0.0626**(0.0258)-0.0346(0.0261)
0.0751***(0.00323)
3.364*(1.835)0.426***(0.0130)
0.891***(0.0155)-0.333***(0.0509)0.753***(0.0541)0.0295*(0.0158)-0.0555(0.320)-0.152(0.0960)-0.0127***(0.00382)-0.207(0.146)-0.0186(0.0280)
0.0356**(0.0151)
0.0530*(0.0313)0.0676***(0.00762)
0.227***(0.0574)
0.00589(0.0552)0.000464(0.0246)
0.0222***(0.00312)-30.96***(1.259)

No
No
No
No

Yes
No
No
No

Yes
Yes
No
No

Yes
No
Yes
No

265,054
265,054
265,054
0.666
0.675
0.740
30309***
16643***
2020.9***Robust standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

265,0540.087
655.6***

22

Table 7: Correlation Matrix for price effects 2006

Preferences Offered (𝑚𝑖 )Preferences Received (𝑒𝑖 )COL Change (𝑐𝑖 )Income Share (𝜃𝑖 )Competitiveness Change (𝑔𝑐𝑖 )PriceChange(𝑏𝑖 )0.02190.549
0.99150.04450.7148Preferences
Offered(𝑚 𝑖 )
1
0.68680.063-0.0609-0.1569

Preferences COL
Received
Change(𝑒𝑖 )(𝑐𝑖 )Income
Share
(𝜃𝑖 )

1
0.5379-0.0155
0.42641
0.03890.63931
0.0634

Table 8: Country Price Effects in 2006

Exporter
Albania
Algeria
AngolaAntigua and Barbuda
Argentina
Armenia
Australia
Austria
Azerbaijan
Bahamas, The
Bahrain
Bangladesh
BelarusBelgium
BelizeBenin
BermudaBhutanBolivia
Botswana
BrazilBrunei Darussalam
Bulgaria
Burkina Faso
BurundiCameroon
CanadaCape VerdeCentral African
Republic

Rank
163
16
129
48
64
77
164
25
47
70
157
123
26
17
95
92
58
96
100
167
84
88
42
172
102
103
22
83

Price
Change(𝑏𝑖 )-0.30797
0.276049-0.11575
0.089105
0.034630.004865-0.36364
0.217788
0.092019
0.019509-0.25504-0.10596
0.215661
0.265391-0.03126-0.01819
0.054437-0.03849-0.04645-0.41012-0.0071-0.01614
0.111842-0.86899-0.04812-0.04871
0.229706-0.00675

Preferences
Offered(𝑚 𝑖 )
0.1776
0.096404
00.051224
0.033209
0.05859
0.045357
0.215882
0.087326
0.003935
0.209749
0.075392
0.284082
0.324957
0.033209
0.059982
000.093123
0.209281
0.02527600.166122
0.06369
0.022799
0.036516
0.170591
0.003398

Preferences
Received(𝑒𝑖 )0.067469
0.1844300.005889
0.047466
0.038436
0.029407
0.191641
0.068033
0.007673
0.074094
0.008412
0.233083
0.315231
0.009782
0.075731
0.015346
0.043828
0.060786
0.002139
0.024234
0.086594
0.17520.01757
0.000111
0.009331
0.235375
0.000965

COL
Change(𝑐𝑖 )-0.27124
0.225386-0.08564
0.149328
0.035410.029342-0.32415
0.228729
0.126599
0.040532-0.19464-0.05938
0.236130.245022-0.00624-0.02666
0.057876-0.06696-0.03428-0.2998
0.000986-0.06734
0.123911-0.82478-0.03563-0.01206
0.178934-0.00083

IncomeShare(𝜃𝑖 )0.000116
0.001731
0.000288
0.000021
0.008977
6.04E-05
0.013169
0.006039
0.000167
0.000175
0.000252
0.001488
0.000402
0.00734
2.63E-05
7.12E-05
0.000111
1.35E-05
0.0002650.0001780.020363
0.000190.000408
8.25E-05
2.24E-05
0.000318
0.022897
1.68E-05

Comp
Change(𝑔𝑐𝑖 )-0.11636
0.068282-0.03543-0.03434-0.00125-0.05586-0.08648-0.02403-0.0491-0.01836-0.09278-0.0569-0.05470.034938-0.0449-0.00393-0.001250.019696-0.0209-0.06693-0.01245
0.045785-0.04015-0.04525-0.02818-0.05114
0.082432-0.02722

137-0.14982

0.010696

3.36E-05-0.12765

3.03E-05-0.02213

23

Chad
Chile
China
Colombia
Comoros
Congo, Dem. Rep
Congo, Rep
Costa Rica
Cote d'Ivoire
Croatia
Cuba
CyprusCzech Republic
DenmarkDjibouti
Dominica
Dominican Republic
EcuadorEgypt
El Salvador
Equatorial Guinea
Estonia
Ethiopia
Fiji
FinlandFranceGabon
Gambia, The
Georgia
Germany
Ghana
GreeceGreenland
GrenadaGuatemalaGuineaGuinea-Bissau
GuyanaHaiti
HondurasHong Kong
Hungary
Iceland
India
Indonesia
Iran
Ireland

91
50
133
69
108
120
119
39
59
131
162
144
10
32
18
97
1
55
107
43
117
6
89
125
15
21
122
126
98
2
150
158
62
143
35
65
140
105
79
9
155
12
46
148
111
106
8

-0.01783
0.080965-0.13508
0.019993-0.06636-0.09811-0.09631
0.127413
0.050769-0.1229-0.30405-0.171330.333746
0.168824
0.265154-0.04290.910094
0.05973-0.06617
0.111828-0.08484
0.371597-0.01633-0.10962
0.2831110.232231-0.10578-0.11308-0.04332
0.524562-0.21013-0.25857
0.040909-0.16620.150049
0.03048-0.16092-0.05001
0.003453
0.333813-0.24018
0.3231
0.099389-0.19945-0.07161-0.0569
0.35154600.165228
0.041360.03270.022062000.18097
0.055611
0.199222
0.052862
0.184
0.283878
0.17711
00.099651
00.07797
0.063521
0.15219400.205405
0.004456
0.101337
0.140219
0.130569
0.004431
0.044227
0.060871
0.145253
0.101173
0.127678
0.195983
0.084260.130420.050146
00.16459700.181529
0.250169
0.274866
0.155521
0.020273
0.063313
0.028624
0.206367

0.003144
0.1651550.079484
0.0301010.000154
00.001969
0.201858
0.054559
0.110268
0.004163
0.066156
0.298689
0.165848
0.072407
0.075501
0.155272
0.076358
0.040142
0.168835
0.008498
0.359034
0.0011
0.099544
0.192417
0.135432
0.000250.00565
0.035851
0.158507
0.024875
0.060635
0.171258
0.017965
0.159221
0.033407
0.003749
0.072352
0.0001550.310793
0.042448
0.306873
0.148648
0.017924
0.07694
0.060666
0.301524-0.01621
0.068393-0.09197
0.026027-0.04511-0.07008-0.06456
0.110555
0.079736-0.08049-0.28406-0.11392
0.320254
0.179156
0.204755-0.03272
0.779874
0.060438-0.04436
0.105546-0.0520.311206-0.01144-0.10863
0.256269
0.222626-0.08235-0.1072-0.02469
0.424046-0.17076-0.23004
0.060057-0.15453
0.135180.06042-0.16078
0.006985
0.0051090.240035-0.17116
0.302163
0.119516-0.17509-0.06427-0.0625
0.279741

4.37E-05
0.002376
0.037854
0.00317
6.38E-06
0.000136
0.0001020.000504
0.000329
0.000673
0.000965
0.000294
0.001792
0.005056
1.74E-05
8.56E-06
0.000758
0.000504
0.003153
0.000415
3.96E-05
0.000179
0.000258
5.32E-05
0.003844
0.041944
0.00016
1.33E-05
9.66E-05
0.060019
0.000157
0.003966
3.37E-05
1.36E-05
0.000609
9.83E-05
6.81E-06
2.25E-05
0.0001160.000224
0.005342
0.001513
0.000275
0.014535
0.005212
0.003199
0.003051
24-0.00421
0.020881-0.06249-0.00839-0.01442-0.02809-0.03719
0.036414-0.03459-0.13573-0.05936-0.11204
0.038998-0.02111
0.059511-0.03004
0.129632
0.003806-0.03716
0.020975-0.03854
0.097274-0.00682-0.00201
0.038712
0.017738-0.05256-0.06551-0.07998
0.164779-0.08255-0.10699-0.04851-0.07385
0.036737-0.02736-0.00212-0.07387-0.00226
0.154274-0.14457
0.041023-0.03626-0.049-0.01163-0.00137
0.109069

IsraelItaly
JamaicaJapan
JordanKazakhstan
Kenya
Kiribati
Korea, Rep.
Kyrgyz Republic
Lao PDR
LatviaLebanonLesothoLiberia
Libya
Lithuania
Luxembourg
Macao
Macedonia, FYR
Madagascar
MalawiMalaysia
Maldives
Mali
Malta
Marshall Islands
Mauritania
Mauritius
MexicoMicronesia, Fed. Sts.
Moldova
Mongolia
MoroccoMozambique
Namibia
Nepal
Netherlands
New Zealand
Nicaragua
Niger
Nigeria
NorwayOman
Pakistan
Palau
Panama

52
23
112
154
147
31
56
127
90
99
20
37
174
173
57
44
33
45
153
86
71
156
54
134
161
93
34
73
104
36
138
19
124
81
139
166
170
7
146
74
114
51
4
149
113
159
66

0.073042
0.220683-0.07257-0.23949-0.19776
0.175934
0.059686-0.11383-0.01691-0.04595
0.2362320.130876-1.27115-0.87809
0.058635
0.111182
0.167444
0.107596-0.2276-0.01421
0.019273-0.24256
0.065381-0.14173-0.28444-0.02018
0.153987
0.011002-0.04931
0.135287-0.15294
0.251074-0.10694-0.00184-0.15568-0.39989-0.60693
0.360018-0.19177
0.010974-0.07781
0.073709
0.415194-0.20536-0.07597-0.261090.029937

0.162675
0.124039
0.046534
0.004647
0.184077
0.120207
0.004038
00.0579780.17382700.225855
0.137373
0.381541
000.184723
0.294939
0.114033
0.259273
0.013224
0.126976
0.267928
0.088531
0.094862
0.298556
00.014161
0.055747
0.22154600.126453
00.2002320.108015
0.233602
0.084171
0.218662
0.0551250.229856
0.051305
0.018734
0.119784
0.067067
0.028617
00.002153

0.165531
0.120586
0.004905
0.0091170.054461
0.130888
0.038070.002972
0.077813
0.095654
0.090807
0.205215
0.025034
0.001334
0.004296
0.011148
0.190722
0.224488
0.010983
0.172553
0.007140.029097
0.282657
0.000446
0.003482
0.17306
0.000230.057663
0.014616
0.216982
0.000242
0.1908100.161303
0.025612
0.002965
0.041803
0.285467
0.051031
0.164190.011803
0.063810.205693
0.007106
0.013711
00.010299

0.067476
0.209437-0.0424-0.20718-0.1657
0.181247
0.05793-0.11313-0.01729-0.00738
0.183013
0.155111-1.17526-0.62325
0.116426
0.137604
0.185659
0.147599-0.18398
0.028774
0.040085-0.20668
0.017737-0.11528-0.25949
0.034382
0.195875
0.014399-0.01202
0.111902-0.14160.254999-0.08728
0.020894-0.08598-0.28913-0.57147
0.317036-0.17002
0.02119-0.05503
0.054136
0.367905-0.1649-0.06479-0.25433
0.043288

0.00394
0.03466
0.000285
0.147422
0.000267
0.000578
0.000401
2.16E-06
0.016847
4.33E-05
5.48E-05
0.000247
0.000545
2.36E-05
1.77E-05
0.001071
0.000361
0.000640.000193
0.000113
0.000123
5.51E-05
0.002962
1.97E-05
7.65E-05
0.000123
3.40E-06
3.41E-05
0.000145
0.018364
7.14E-06
4.07E-05
3.44E-05
0.001169
0.000134
0.000124
0.000174
0.012163
0.001625
0.000124
5.68E-05
0.001452
0.005315
0.000628
0.002336
3.79E-06
0.00036725-0.00253
0.016268-0.05328-0.05153-0.10076-0.01635
0.02995-0.00158
0.006513-0.12749
0.054457-0.06342-0.09726-0.25573-0.05142-0.02443-0.05665-0.07697-0.07514-0.09554
0.000887-0.01705
0.066432-0.06546-0.0857-0.09548-0.02198-0.01496-0.03935
0.042496-0.011080.012312-0.07819-0.04718-0.04533-0.06272-0.03607
0.088052-0.04227-0.01955-0.03704
0.020362
0.078562-0.06349-0.01906-0.01111-0.01115

Papua New Guinea
Paraguay
Peru
Philippines
PolandPortugal
Romania
Russian Federation
RwandaSamoa
Saudi Arabia
Senegal
Seychelles
Sierra Leone
Singapore
Slovak Republic
Slovenia
South Africa
Spain
Sri Lanka
St. Kitts and Nevis
St. Lucia
St. Vincent and the
Grenadines
Sudan
Suriname
Swaziland
SwedenSwitzerland
Syrian Arab Republic
Tajikistan
Tanzania
Thailand
Togo
Tonga
Trinidad and Tobago
Tunisia
TurkeyTurkmenistan
UgandaUkraineUnited Arab Emirates
United Kingdom
USA
Uruguay
Uzbekistan
Vanuatu

116
76
109
110
27
87
28
3
72
30
141
132
94
63
67
14
29
171
49
60
128
115

-0.081090.005674-0.06712-0.06917
0.201065-0.01465
0.188655
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0.036191
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0.298293
0.188064-0.79913
0.089012
0.050359-0.11476-0.07961

0.135846
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0.104793
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000.005866
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00.44641
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0.288621
0.069702
0.150922
00.065021
0.0692140.157184
0.101548
0.026254
0.093334
0.128954
0.150475
0.185304
0.039644
0.000977
0.143182
0.018025
0.022773
0.005917
0.005377
0.348711
0.260063
0.263892
0.113089
0.13104
0.023819
0.006165
0.0114-0.12112
0.022899-0.04434-0.05737
0.220867
0.019215
0.194301
0.398583
0.017689
0.113232-0.12851-0.11882
0.014360.06366-0.01047
0.295407
0.204441-0.63422
0.101205
0.055084-0.09499-0.04796

0.000111
0.000223
0.001683
0.002398
0.005410.003696
0.001170.008203
5.48E-05
7.76E-06
0.005952
0.000148
1.94E-05
2.01E-05
0.002929
0.000907
0.000628
0.004197
0.018341
0.000516
1.03E-05
2.23E-05

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0.100528-0.00018
0.171046-0.05965-0.06676-0.031-0.02566
0.046459
0.01406-0.03073-0.30123-0.02749
0.014639-0.05647-0.06404

118
142
68
168
24
41
13
135
130
101
75
169
85
38
82
11
136
53
145
40
80
78
5
160

-0.09054-0.1650.027128-0.56242
0.217922
0.119144
0.300941-0.14358-0.11816-0.0472
0.009057-0.57149-0.00810.129297-0.00577
0.330828-0.1450.070535-0.17898
0.127163
00.004116
0.398474-0.27707

0.091103
0.052637
0.079910.401418
0.184723
0.195801
0.054559
0.371288
0.04951
0.106354
0.041216
0.208318
0.10720.265453
0.104016
0.15168
0.044854
0.17790.013318
0.111734
0.038839
0.070596
00.159933

0.039887
0.029329
0.034113
0.028927
0.183907
0.176537
0.135624
0.212932
0.022810.114937
0.065404
0.007196
0.08582
0.228388
0.065371
0.241824
0.003366
0.104006
0.025024
0.101344
0.031442
0.058094
0.085216
0.008183-0.06997-0.16499
0.097051-0.4234
0.208555
0.129018
0.283995-0.05949-0.10796-0.04344
2.81E-05-0.53717
0.002407
0.151850.018588
0.248587-0.12784
0.103909-0.15470.133444
0.006931
0.017854
0.339418-0.20296

1.07E-05
0.000391
2.82E-05
0.000047
0.007810.007894
0.00061
2.72E-05
0.000287
0.003876
0.000042
5.96E-06
0.000258
0.000614
0.00842
9.17E-05
0.000196
0.000987
0.00223
0.046670.312654
0.000721
0.000435
8.87E-06-0.05512-0.0228-0.07353-0.11559
0.005422-0.03495
0.066085-0.08346-0.00723-0.00140.005369-0.1436-0.01801-0.03236-0.05368
0.066894-0.03154-0.07815-0.06586-0.0116-0.01381-0.01693
0.057508-0.08668

26

Venezuela RB
Vietnam
Yemen, Rep.
ZambiaZimbabwe

61
121
152
165
151

0.046827-0.10053-0.21882-0.37146-0.21868

0.029985
0.176632
0.060555
0.146267
00.041714
0.080535
0.006355
0.020633
0.057953

0.041604-0.06234-0.19329-0.31983-0.24178

0.00370.000985
0.000298
0.0001020.000234

27

0.00528-0.05352-0.05867-0.05227
0.053382