markups and the structure of international...
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Markups and the Structure ofInternational Specialization
Ahmad Lashkaripour∗
Indiana University
September 2016
Abstract
I develop a novel view of international specialization that reconciles the
gravity equation with salient facts about changes in trade composition across
space and over time. Guided by micro-level evidence, I argue that differ-
ences in geography and income induce within-industry specialization across
low- and high-markup product varieties. International specialization, in turn,
gives rise to international price disparities that govern the varying structure
of international consumption. I estimate a trade model that accommodates
these patterns and compare it to a standard gravity trade model. With only
two extra parameters, the new model displays a 43% improved (in-sample)
fit and a greatly enhanced out-of-sample predictive power. Furthermore, the
distinct patterns of specialization highlighted in this paper magnify the gains
from trade (by a factor of 4) and shift them in favor of poor and remote nations.
∗Many people have provided helpful comments and suggestions that improved this paper. Iam especially thankful to Jonathan Eaton, James Tybout, and Stephen Yeaple for their advice andencouragement. I also thank seminar participants at the University of British Columbia, Universityof California Santa Cruz, Chicago Fed, Drexel University, Indiana University, Pennsylvania StateUniversity, the New Faces in International Economics conference, the Midwest Trade Meetings, theInsTed workshop, and UECE Lisbon Meetings for helpful comments and suggestions. All errorsare my own. Correspondence: [email protected].
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1 Introduction
Gravity trade models have come a long way in explaining aggregate trade valuesacross countries and over space. However, despite their empirical success, gravitymodels have not come to grips with a number of basic facts about variations intrade composition among exporters, across space and over time:
i. North-North trade is conducted more intensively than South-South trade,but the difference has diminished considerably over time.
ii. Export unit values vary systematically with geographical distance, i.e. coun-tries selectively export higher-price product varieties to faraway markets.
iii. Income per capita is systematically correlated with the price composition ofexports, i.e. high-income exporters tend to specialize in higher-priced prod-uct varieties.
Explaining the above facts requires amending the standard gravity models in atleast one dimension. More often than not, amending the standard models to matchone fact generates inconsistency with respect to the other facts. To tackle this is-sue, I develop a distinct theory of international specialization that is motivated bymicro-level evidence. The theory tractably combines the role of geography withincome and delivers sharp predictions about variations in export composition (i)across space, (ii) between rich and poor countries, and (iii) over the course of tradeliberalization.
In principal, this paper develops a tractable model that in addition to standardtrade value moments matches spatial and longitudinal variations in trade compo-sition. At its core, though, the paper seeks to answer the following. How doesaccounting for these additional variations in the data modify our understandingof the gains from trade? To answer this question, I estimate the model with bi-lateral trade data on 100 countries, and compare it to a standard gravity modelthat is fitted to the same data. The new model implies gains from trade that are 4-times greater than the standard gravity model. The gains are magnified by within-industry specialization and are larger than those implied by standard multi-sectorextensions of gravity models. Furthermore, compared to the standard model, the
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gains from trade are distributed in favor of disadvantaged (i.e. remote and low-income) nations.
The genesis of my theory is the observation that quality and quantity are not nec-essarily isomorphic. There is exhaustive evidence that product quality matters rel-atively more for some products than others. I accommodate this regularity by con-structing a trade model where industries feature two types of goods: (i) a quality-intensive good and (ii) a quantity-intensive good for which demand is relativelymore price-elastic. In fact, this is my only point of departure from a standard grav-ity model. If I force quality and quantity to be isomorphic, goods become uniformwithin industries, and the model reduces to a standard gravity model that neststhe Krugman and Armington models as a special case.
In the trading equilibrium, quality-intensive goods feature higher markups, greaterprofit margins and are less affected by trade costs. Importantly, the endogenouslink between quality-intensity, markup and tradability is robust to various exten-sions of the model.1 The composition of trade (both at the national and industrylevel) is determined by how countries concentrate production and consumptionon the high-markup, high-value-added, highly tradable goods versus the low-markup, low-value-added, less-tradable goods.
Production specialization is regulated by international differences in labor cost.As in a standard (Armington) trade model, nations are characterized by their la-bor endowment, national product quality, and geography. Higher national prod-uct quality and bilateral distance, both contribute to a higher labor cost of pro-duction/transportation. Within industries, remote and high-wage countries haverevealed comparative advantage in quality-intensive, high-markup goods that areless sensitive to the high cost of production in these countries. Low-wage coun-tries, meanwhile, have revealed comparative advantage in low-markup goods thatare relatively more price-elastic. These forces induce international specialization,even in the absence of technical comparative advantage. More importantly, thesepredictions find broad support in product-level trade data.
1Relaxing the quality-quantity isomorphism creates a link between markup and trade elasticitythat is robust to adding even firm heterogeneity. In comparison, Chaney (2008) shows that imposingisomorphism between quality and quantity results in a weak link between trade elasticity andmarkup, which disappears under firm heterogeneity.
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Given that trade is costly, production specialization creates international price dis-parity. That is, locally abundant goods are relatively cheaper in every country —e.g. quality-intensive goods (that are more tradable and feature higher markups)are relatively cheaper in high-income countries. As a result of these price dispar-ities, the consumption of a country mirrors its production abilities. I show thatspecialization-induced price disparities can go a long way in explaining the struc-ture of international consumption. More importantly, a model where internationalconsumption is regulated by price disparities delivers distinct welfare implicationscompared to a model featuring international taste differences or non-homotheticpreferences.
Altogether, production and consumption in high-wage countries is concentratedon quality-intensive (highly-tradable, high-markup) goods, whereas poor coun-tries specialize in quantity-intensive (less-tradable, low-markup) goods. Each coun-try also engages in two types of trade: (i) two-way trade motivated by productdifferentiation and (ii) trade motivated by within-industry specialization. In equi-librium, rich countries are net exporters of highly-tradable, high-markup goods,while poor countries are net importers of these goods. As a result, not only richcountries export higher-price tradables within industries, they also trade a highershare of their GDP. Specifically, rich countries are more inclined to engage in two-way exchange of their comparative advantage good with other rich nations. Poorcountries, meanwhile, source their comparative advantage good (which is less-tradable) predominantly from local firms. These predictions align with the empir-ical observation that North-North trade is conducted more intensively than South-South trade (this well-established regularity eludes standard gravity models asthey do not distinguish between North-North and South-South trade).
Geography also affects the price composition of trade within industries. Quality-intensive (high-markup) goods that are (i) more tradable and (ii) feature higherprofit margins, are exported relatively more to faraway (harder-to-penetrate) mar-kets. This behavior captures a systemic relationship between bilateral distanceand export price composition, commonly know as the “Washington apples” effect.Importantly, the forces that drive the “Washington apples” effect, in the presentmodel, are both novel and independent of how one models trade costs. In com-parison (as Baldwin and Harrigan (2011) point out) all mainstream trade modelsfail to capture this well-documented regularity.
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Another empirical observation that eludes standard gravity models is the recentrise of North-South and South-South trade (Krugman (2009); Hanson (2012)). Specif-ically, the past two decades have brought a sharp decline in the relative importanceof North-North trade (to North-South/South-South trade) even though nominalincome levels have diverged across rich and poor countries (Milanovic (2011)). Inthe present model, these patterns emerge naturally in face of trade liberalization.As trade costs diminish, international prices converge. Rich and poor countries di-versify their consumption and consume relatively more of their comparative dis-advantage good, which is sourced from dissimilar partners. Additionally (withlower trade costs) poor countries become relatively more inclined to engage intwo-way exchange of less-tradable, low-markup goods with other low-income na-tions. This fuels the growth of South-South trade relative to North-North trade. Ina standard gravity models such patterns would emerge only if trade costs decreaseat a faster rate in low-income regions (which we lack reliable evidence for).
To demonstrate the quantitative merits of the model, I estimate it using bilateraltrade data on 100 countries. The data covers countries that are vastly dissimilarin per capita income. Standard gravity models usually have difficulty matchingbilateral trade values when both rich and poor countries are included in the anal-ysis. One way to tackle this issue is to add importer/exporter fixed effects, whichamounts to introducing 100 extra free parameters. This approach enhances the in-sample fit, but not the out-of-sample predictive power. The present model intro-duces only 2 extra free parameters, but improves the in-sample fit of the standardgravity model by 43%. Furthermore, the new model displays a considerably im-proved out-of sample predictive power. The estimated model, which is fitted todata in 2000, could reproduce the transformation of trade composition in responseto trade liberalization from 1980 to 2006. The standard gravity model, in com-parison, performs poorly in predicting these out-of-sample patterns. Additionally,in sharp contrast to standard models, the estimated model correctly predicts bothnational and spatial variations in export price composition.
I use the estimated model to answer a focal question: how does internalizing theseauxiliary variations in the data modify the predicted gains from trade? Overall, for theaverage country, the gains magnify by a factor of 4. Furthermore, compared tothe standard gravity model, the gains from trade are distributed systemically infavor of poor and remote nations. These distinct welfare effects are driven by
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within-industry specialization, and are identified using international variations intrade-to-GDP ratios and export elasticities. In many African nations, for example,within-industry specialization accounts for nearly 100% of the gains from trade.Furthermore, the new model predicts that liberalizing trade could reduce inter-country income inequality by more than 10%. Intuitively, poor countries specializein low-markup goods that are more affected by trade costs. Eliminating these costswould, therefore, have a disproportionally larger effect on poor countries.
To put the above results in perspective, note the both the size of the estimatedgains and the forces that govern them are distinct from existing multi-sector trademodels. Non-homothetic models would generally predict smaller gains from tradegiven the underlying assertion that consumers favor locally abundant goods (Markusen(1986); Fieler (2011)). Under this assertion, trade always increases the relative priceof the locally preferred goods, which effectively contracts the gains from trade.Homothetic trade models, meanwhile, typically use aggregate (national or industrylevel) data to estimate the gains from trade (Costinot and Rodríguez-Clare (2014);Ossa (2015)). Usually, though, no structure is imposed on aggregate data to extractinformation about within-industry specialization. My approach, by contrast, usesauxiliary variations in the data to infer patterns of within-industry specialization.Accounting for these variations modifies both the size and the distribution of theestimated gains from trade.2
These arguments contribute to a contemporary literature that studies the composi-tion of aggregate trade flows. Several studies account for the role of geography byintroducing non-iceberg trade costs or firm-sorting into gravity models (Baldwinand Harrigan (2011); Crozet, Head, and Mayer (2012); Irarrazabal, Moxnes, andOpromolla (2015)). Additionally, a vibrant literature studies the role per capitaincome from the perspective of international factor differences or non-homotheticpreferences (Markusen (1986); Flam and Helpman (1987); Matsuyama (2000); Ro-malis (2004); Fajgelbaum, Grossman, and Helpman (2011); Fieler (2011); Fajgel-baum and Khandelwal (2014); Feenstra and Romalis (2014)).3 I contribute to thisliterature by offering a new and unifying perspective that integrates the role of ge-
2Multi-sector gravity models (most notably Costinot, Donaldson, and Komunjer (2012)) esti-mate relatively smaller gains from across-industry specialization. Note that the gains from across-industry specialization are estimated directly using crude aggregate industry level data.
3Feenstra and Romalis (2014) develop a model of quality specialization that integrates the effectof geography with income through non-homothetic preferences and non-iceberg trade costs.
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ography with income. While my approach has its limitations, it retains the basicstructure of a gravity trade model, making it amenable to straightforward estima-tion. Moreover, my theory highlights a distinct force of international specializa-tion, which sheds new light on the welfare consequences of international trade.
The notion of international specialization developed here, complements traditionaltheories of within-industry specialization. Traditionally, the literature has empha-sized across-quality specialization driven by international differences in taste andfactor abundance, or by non-iceberg trade costs (Schott (2004); Hummels and Skiba(2004); Hallak (2006); Fajgelbaum et al. (2011); Feenstra and Romalis (2014); Suttonand Trefler (2014); Dingel (2015)). This paper highlights the role of across-markupspecialization, which is both novel and empirically relevant. In Lashkaripour(2015), I use detailed trade data to show that across-markup specialization con-tributes significantly to spatial variations in export unit values. Here, I provideproduct-level evidence on the link between the markup content of exports and in-come per capita. This finding sheds new light on the deep-rooted link betweenexport price and exporter income.4
Finally, at a broader level, this paper contributes to a literature that examines struc-tural disparities between rich and poor countries. The new model highlights therole of comparative advantage as a of driver of international consumption differ-ences. Furthermore, the theory of international specialization, develop here, im-plies that trade barriers have disproportionally larger effects on poor countries.These effects shed light on widespread evidence that poor countries face largerexport frictions (Rodrik (1998); Limao and Venables (2001); Waugh (2010)). Addi-tionally, the model delivers a rich set of predictions about structural transformationand has key implications for industrial policy in developing nations.
4My view of international specialization is also closely related to theories that highlight spe-cialization across goods with different degrees of differentiation (Helpman and Krugman (1985);Hanson and Xiang (2004); Fajgelbaum et al. (2011); Fieler (2011)). The forces that generate produc-tion specialization and consumption dissimilarity in the present model are, however, distinct fromthe aforementioned studies.
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2 Theory
The new model introduces within-industry specialization into a multi-country,general equilibrium gravity trade model. Without international specialization, themodel reduces to standard gravity model that nests the Krugman and Armingtonmodels as a special case. Trade within industries is driven by two distinct forces:comparative advantage (across-goods) and product differentiation (within-goods).Together, these two forces fully determine both the volume and the composition ofaggregate trade flows.
2.1 The Environment
There are N countries, with C = 1, ..., N denoting the set of countries. As in astandard Armington trade model, country i ∈ C is characterized by populationLi, and national product quality αi. There are K industries. A typical industry kconsists of two (class of) goods: Hk and Lk. Each good comes in a continuum offirm-specific varieties that are horizontally differentiated both at the firm level andat the national level.
Demand. Demand across industries is regulated by a Cobb-Douglas utility ag-gregator. Specifically, consumers in country i maximize the following utility
Ui = ΠKk=1 (Ui,k)
βk ,
where Ui,k denotes the utility derived from industry k, with ∑kβk = 1. Withinindustries, the demand structure accommodates the possibility that quality andquantity are not isomorphic. The break-down of the quality-quantity isomorphismis motivated by micro-level evidence in Baldwin and Ito (2008), Rodrik (1994), andBils and Klenow (2001).5 This feature is introduced using a standard, homothetic
5Isomorphism between quantity and quality implies that the relative importance of productquality to price is uniform across goods and industries. Several studies have empirically rejectedthis assertion. Baldwin and Ito (2008) and Aiginger (1997) show that some industries are charac-terized by quality-competition, whereas others operate on the basis of price competition. Similarly,Rodrik (1994) argues that some goods are more quality-intensive than others, and that this featureis quantitatively important in explaining growth patterns across developing countries. Fan, Li, and
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nested-CES utility function. Specifically, there are two types of goods (Hk and Lk)within each industry k. The utility derived from industry k is a CES aggregateacross the two good-specific sub-utilities:
Ui,k =
[∑
z=Hk ,Lk
(Ui,z)ε−1ε
] εε−1
,
where Ui,z denotes the sub-utility corresponding to good z = Hk, Lk, which aggre-gates across all national varieties of that good
Ui,z =
[N
∑j=1α
1σzj Q
1− 1σz
ji,z
] σzσz−1
(1)
The utility attained from country j varieties of good z depends on the aggregatequantity, Q ji,z, and quality,α j. National product qualityα j is an estimated parame-ter assigned exogenously to all product varieties produced in country j. Aggregatequantity, Q ji,z, is determined by aggregating across all quantities purchased fromindividual firms in country j:
Q ji,z =
[ˆω∈Ω ji,k
qσz−1σz
ji,z dω
] σzσz−1
= Mσzσz−1ji,k q ji,z,
where q ji,z denotes the quantity purchased of good z from a typical firm ω fromcountry j in industry k. Firms are symmetric, and M ji,k denote the industry-specificmass of firms that sell from country j to i. Note that in the above utility struc-ture, quantity (Q ji,z) and quality (α j) are not isomorphic.6 Further (within a typical
Yeaple (2015) show that quality upgrading and tariff reductions have differential effects on rev-enues across different products. Bils and Klenow (2001) show that the elasticity of demand withrespect to product quality and price could diverge depending on income and the product class.
6Product quality,α j, and quantity are assumed to be Cobb-Douglas complements. Alternatively,one could assume that quality and quantity are isomorphic, which requires the following utilityspecification:
Ui,z =
[N
∑j=1
(α jQ ji,z
)1− 1σz
] σzσz−1
The above specification is generally employed in settings where the scope of product differentia-tion is uniform across goods. When applied to environments with multiple product categories, itimplies that the importance of product quality (relative to price) is the same across all categories,
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industry k) ε is the elasticity of substitution between the two class of goods (Hk
and Lk); σz is the elasticity of substitution between aggregated national varietiesof good z; σz is the intra-national elasticity of substitution between firm-specificvarieties of good z.
The above demand structure nests both the Krugman and Armington models.When σz = σz there is no scope for national product differentiation, and the de-mand structure (within goods) reduces to that of Krugman (1980). If σz → ∞, thescope for national product differentiation is complete (à la Armington). Here, in-stead of imposing external restrictions, I allow for an estimated parameter η > 1to regulate the the degree of national product differentiation across all goods:
σz − 1σz − 1
≡ η , ∀z
Intuitively, the above specification allows for firm-specific varieties produced inthe same country to be closer substitutes. A higher η implies a greater degree ofnational product differentiation (The limiting case, η → ∞, corresponds to thestandard Armington specification that varieties from the same country are perfectsubstitutes).7
Finally, each industry is composed of two distinct class of goods. Specifically (in atypical industry k) good Hk offers a greater scope for product differentiation than
which is inconsistent with the findings of Baldwin and Ito (2008), Rodrik (1994), and Fan et al.(2015). Moreover, the above specification would imply that high-quality producers (like low-costproducers) sell relatively more in high-price-elasticity categories, which contradicts the findings ofHausman, Leonard, and Zona (1994), Berry, Levinsohn, and Pakes (1995), and Goldberg (1995) —these studies find that within a narrowly defined markets, high-quality producers face a lower priceelasticity. Additionally, from a theoretical perspective, Sutton (2007) and Hallak and Sivadasan(2013) show that the isomorphism between quality and productivity breaks down under a set of re-alistic assumptions. Hallak and Sivadasan (2013) argue that models with quality-productivity iso-morphism explain the exporter premia, but fail to account for the conditional exporter premia. Roberts,Xu, Fan, and Zhang (2012) too highlight the distinction between cost-shifters and demand-shiftersusing firm-level evidence. At at broader level, though, I am not the first to adopt this specifica-tion. It has been used in earlier gravity models (Anderson (1979) and Deardorff (1998)) and morerecently in Eaton, Kortum, and Kramarz (2011). Furthermore, the gravity equation that emergesfrom the above specification is identical to that implied by the technology-driven model in Eatonand Kortum (2002). I am, however, the first to apply this specification and study it in a setting withmultiple categories of goods.
7In section 3, I estimate η without imposing any restrictions, which delivers η = 3.16. When Iimpose the Armington assumption that η→ ∞, the fit of the model decreases by 20%.
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good Lk:8
σHk < σLk ⇐⇒ σHk < σLk
Hence, demand for good Lk is (by definition) quantity-intensive, whereas demandfor good Hk is quality-intensive. This follows immediately from 1
σHK> 1
σLkin equa-
tion 1. Later, I will show that this basic distinction translates into differences inequilibrium markups, profit margins, and degrees of tradability across the twogoods.
Supply. In the benchmark model, I assume that firms are monopolistically com-petitive and symmetric (à la Krugman (1980)). The main predictions of the modelare, however, robust to adding firm-level heterogeneity (see appendix A). Unlikethe Krugman model, however, I allow for per-market entry costs (i.e. an entry costsis paid separately for each national market). This feature is introduced to avoidthe counter-factually strong scale effects associated with aggregate economies ofscale.9
Labor is the only factor of production. The unit labor cost is one for all goods, inall countries. Exports from country j to i are subject to an iceberg trade cost, τ ji.Altogether, the marginal cost of producing good z in country j and selling it incountry i is
mc ji,z = τ jiw j
where w j denotes wage in country j. Similarly, let p ji,z denote the price of good z,produced in country j, and sold in country i. A typical firm exporting q ji,z units ofgood z from country j to i, therefore, collects a variable profit equal to
π ji,z =(
p ji,z − τ jiw j)
q ji,z
In any given industry, firms use the combined variable profits from both types ofgoods to pay the local entry cost, which is f e units of Home labor. The free entry
8Note that while σz regulates the within-category degree of product differentiation, η regulatesthe economy-wide degree of national product differentiation.
9As Ramondo, Rodríguez-Clare, and Saborío-Rodríguez (2012) point the standard treatment ofaggregate scale economies in trade models lead to counterfactually strong scale effects. That is,domestic trade shares and relative income levels increase too steeply with population size.
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condition (for market i, industry k) is, therefore, given by
∑z∈Hk ,Lk
π ji,z = w j f e ∀k
The free entry condition determines M ji,k: the mass of firms exporting from coun-try j to i in industry k.
Equilibrium. Consider some industry k. Let X ji,z ≡ M ji,k p ji,zq ji,z denote the totalamount spent by country i on varieties of good z = Hk, Lk imported from countryj. Utility maximization implies that:
X ji,z = α j
(Pji,z
Pi,z
)1−σz (Pi,z
Pi,k
)1−εXi,k , (2)
where Xi,k = βkwiLi, is total spending on industry k, and Pji,z denotes the good-specific price index of country j exports to i:
Pji,z ≡[ˆω∈Ω ji
p1−σzji,z dω
] 11−σz
= M1
1−σzji,k p ji,z
Pi,z denotes the price index of good z in country i:
Pi,z ≡[
∑k∈C
αkP1−σzki,z
] 11−σz
Pki is the aggregate price index of industry k in country i:
Pi,k ≡
∑z∈Hk ,Lk
P1−εi,z
11−ε
In equation 2,(
Pi,zPi,k
)1−εis the share spent on good z within industry k;α j
(Pji,zPi,z
)1−σzis
the share spent on country j varieties conditional on buying good z. A typicalfirm from country j in industry k, therefore, sells x ji,z ≡ p ji,zq ji,z =
X ji,zM ji,k
dollars ofgood z. Facing the above demand function, the monopolistically competitive firms
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charge a good-specific markup over the marginal cost:
p ji,z =σz
σz − 1τ jiw j
Importantly, within each industry, a higher markup is charged on the quality-
intensive good:σHkσHk−1 >
σLkσLk−1 . Plugging the equilibrium price into the demand
function (equation 2) delivers a good-specific gravity equation:
X ji,z =α jM
1η
ji,k
(τ jiw j
)1−σz
∑Nh=1αhM
1η
hi,k (τhiwh)1−σz
Xi,z ; z = Hk, Lk (3)
where Xi,z ≡ (Pi,z/Pi,k)1−ε
βkwiLi, denotes total spending in country i on good zfrom industry k. The above equation indicates that (within industry k) the tradeshares associated with the quantity-intensive good (Lk) are relatively more sen-sitive to price. Trade in the quality-intensive good (Hk), however, is relativelymore sensitive to variations in national product quality. Additionally, good Hk
is subject to a lower trade elasticity, and is less affected by iceberg trade costs:
τ1−σHkji < τ
1−σLkji .
The (industry-specific) mass of firms entering market i from country j, M ji,k, ispinned down by the free entry condition:
M ji,k =1
w j f e
[X ji,Hk
σHk
+X ji,Lk
σLk
](4)
Note that upon entry, firms collect higher profits from selling high-markup goods.Therefore, within industry k, countries that export relatively more of good Hk arerepresented by relatively more firms. Finally, balanced trade insures that for anycountry j, total spending equals total sales:
w jL j =K
∑k=1
N
∑i=1
X ji,Hk + X ji,Lk (5)
In the above framework, the endogenous link between quality-intensity, markupand tradability (i.e. trade elasticity) is robust to introducing firm-level hetero-geneity or input-output linkages (see appendix A). This universal link is driven
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by relaxing the isomorphism between quality and quantity. To give perspective,Chaney (2008) shows that when quality and quantity are isomorphic the link be-tween markup and trade elasticity disappears under firm heterogeneity. However,when quantity and quality are not isomorphic the link between markup and tradeelasticity is stable across various modeling specifications. In light of these stablerelations, I will henceforth refer to quality-intensive goods (i.e. good Hk in indus-try k) as the highly-tradable, high-markup good. Similarly, I will refer to quantity-intensive goods (i.e. good Lk in industry k) as the less-tradable, low-markup goods.
Finally, the above framework makes two simplifying assumptions: (i) I assumethat unit labor costs are uniform across goods and countries (i.e. I assume awaytechnical comparative advantage) and (ii) I allow for multi-product firms. Bothassumptions are conservative and adopted in the interest of clarity. Specifically,the first focal distinction between goods Hk and Lk is their price. Price differ-ences (under my conservative assumption) are solely driven by markups. There is,however, direct evidence that high-markup goods require higher production costs(Atkin, Chaudhry, Chaudhry, Khandelwal, and Verhoogen (2015)). Introducingthis feature into the model is straightforward and would magnify the predictedprice variations. Second, high-markup goods are less affected by iceberg tradecosts and are, therefore, more tradable. Allowing firms to be single product wouldmagnify the differences in tradability. In particular, single-product exporters of thehigh-markup goods will enjoy higher profit-margins and will be less-affected by,not only the iceberg trade costs, but also the market-specific entry costs. I conser-vatively rule out this channel by letting firms subsidize entry (within an industry)with profits collected on both types of goods.
2.2 Core Equilibrium Outcomes
Before estimating the model, I will briefly outline the workings of the theory. Tothis end, I first highlight four equilibrium outcomes that characterize the structureof within-industry production and consumption.
Outcome 1. In the trade equilibrium, all else equal, countries with higher na-tional product quality pay higher national wages. This outcome follows immedi-
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ately from the balanced trade condition (equation 5). In particular, consider twocountries: North (n) and South (s). Both countries share the same population sizeand face similar trade costs. The North, however, is endowed with a higher na-tional product quality, which implies more global demand for Northern varieties.Hence, for trade to be balanced, the North should pay higher equilibrium wagesthan the South:
αn > αs =⇒ wn > ws
Outcome 2. Within industries, high-wage countries have revealed comparativeadvantage in the highly-tradable, high-markup goods. In particular, the gravityequation 3 implies that
X ji,Hk/X ji,Lk
Xki,Hk/Xki,Lk
=
(τ jiw j
τkiwk
)σLk−σHk
∀k. (6)
Since σLk > σHk , the above equation indicates that (within industry k) high-costsuppliers sell relatively more of good Hk. For example, consider Northern andSouthern exports to country i, which is located at equal distance from both (τni =
τsi). The higher cost of labor in the North ensures that (in any given industry k) itexports relatively more of the highly-tradable, high-markup good Hk:
Xni,Hk/Xni,Lk
Xsi,Hk/Xsi,Lk
=
(wn
ws
)σLk−σHk
> 1 ∀k. (7)
Intuitively, the North has absolute quality-advantage in all goods: αn > αs. Thisresults in more demand for Northern varieties, which drags up the relative cost oflabor: wn > ws. The higher cost of labor would leave the North comparatively dis-advantaged in good Lk that is price-sensitive. Consequently, within industry k, theNorth would have (revealed) comparative advantage in good Hk. Such patternsare regulated by demand and emerge in the absence of technical comparative ad-vantage. Moreover, these patterns of comparative advantage are robust to addingfirm-level heterogeneity and are supported by micro-level evidence (see section 5and appendix A).
The above notion of comparative advantage fits into the conventional definitionthat countries have comparative advantage in a good for which they have a lower
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autarky relative price (Deardorff (1980)). Here, comparative advantage is deter-mined by the relative price index, which embodies production cost, product qualityand product variety. In a given industry k in country i, the autarky relative priceindex of good Hk is given by
(Pi,Hk
Pi,Lk
)Autarky
= φk
[αi
(βkLi
σi,k
) 1η
] 1σLk−1−
1σHk−1
∀k, (8)
where 1σi,k≡ ρi,Hk
σHk+ρi,LkσL
(with ρi,z denoting the within-industry autarky expenditure
share on good z), andφk ≡σHk
(σLk−1)
σLk(σHk
−1) . Sinceαn > αs, equation 8 entails that in anygiven industry the autarky relative price index of the high-markup good (Hk) islower in the North:10
(Pn,Hk
Pn,Lk
)Autarky
<
(Ps,Hk
Ps,Lk
)Autarky
∀k. (9)
North’s (revealed) comparative advantage in good Hk, therefore, coincides with alower autarky relative price index for that good.11
Outcome 3. In the presence of trade frictions, high-markup (quality-intensive)goods are relatively cheaper in high-income countries: ∂
∂αi
Pi,HkPi,Lk
< 0, ∀k. Similarto a Neoclassical trade model, when North (n) and South (s) engage in trade, therelative price index of good Hk rises in the North and Falls in the South. However,in the presence of trade costs, prices are not equalized across countries. Within
10Note that∂ρi,Hk
∂αi> 0, which implies that ∂
∂αi
βk Liσk
i> 0, for all k. More specifically, an increase in
national product quality induces consumption reallocation (within industry k) from category Lk toHk, creating a greater scope for firm entry. Inequality 9 then follows immediately from the fact that
1σLk−1 < 1
σHk−1 andαn > αs:
αn > αs =⇒ φk
[αn
(βkLn
σi,k
) 1η
] 1σLk−1−
1σHk−1
< φk
[αs
(βkLs
σs,k
) 1η
] 1σLk−1−
1σHk−1
∀k
11Equation 9 also implies that larger economies have comparative advantage in the highly-differentiated category, a pattern highlighted in Helpman and Krugman (1985). In fact, if I shutdown differences in product quality, eliminate national product differentiation (i.e. η = 1), andallow for global economies of scale ( f e is paid once and for all market), the present model wouldbecome a generalized case of Helpman and Krugman (1985).
16
industry k, the relative price index of good Hk remains to be lower in the North:
(Pn,Hk
Pn,Lk
)Autarky
<Pn,Hk
Pn,Lk
<Ps,Hk
Ps,Lk
<
(Ps,Hk
Ps,Lk
)Autarky
∀k.
The above result follows from the fact that price indexes under costly trade area weighted CES average of all international prices, with more weight assigned tolocal prices.12
Outcome 4. All else equal, high-wage countries consume relatively more of thehighly tradable, high-markup goods within a given industry. Specifically, in faceof international price disparities within industy k, the North (n) spends relativelymore on good Hk than the South (s):
Xn,Hk
Xn,Lk
=
(Pn,Hk
Pn,Lk
)1−ε>
(Ps,Hk
Ps,Lk
)1−ε=
Xs,Hk
Xs,Lk
∀k.
Trade costs, therefore, induce countries (with identical, homothetic preferences)to adopt systematically different consumption structures within industries. Morespecifically, in face of costly trade, the consumption of a country mirrors its pro-duction abilities.13 When estimating the model I will show that international pricedisparities can indeed go a long way in explaining the varying structure of inter-national consumption — if ε is sufficiently large (ε → ∞) consumption patternsacross rich and poor countries could diverge to any arbitrary degree. Note how-ever that without direct measures on international prices, the effect of price dis-parity on international consumption cannot be separately identified from that ofnon-homothetic preferences (see appendix B). The two channels nevertheless, de-liver systematically different welfare implications and are not isomorphic.14
12Note that under free trade (τ ji = 1, ∀i, j), international prices equalize:
(Pn,Hk
Pn,Lk
)Free Trade=
(Ps,Hk
Ps,Lk
)Free Trade∀k.
13These effects has a flavor similar to the “home-market effect” in Krugman (1980). The “home-market effect” implies that local demand determines patterns of local production, whereas here (inface of costly trade) local production determines the structure of local consumption.
14In non-homothetic models consumers typically favor locally abundant goods. Meanwhile,trade always increases the relative price of locally abundant goods that are strictly preferred lo-
17
2.3 The Composition of Aggregate Trade Flows
The four equilibrium outcomes, highlighted in section 2.2 regulate the composi-tion of aggregate trade flows at the national level and within industries. Whilethe above framework has its own clear limitations, it is the first to collectivelycapture three well-established facts about variations in trade composition acrossexporters and over space. Below, I briefly discuss how the model captures thesefacts. Further, when I estimate the model I will demonstrate that (in addition to ac-commodating the below facts) the estimated model could reproduce out-of-samplevariations in trade composition over time.
Income per capita × trade intensity. It is well-established that rich countrieshave significantly higher trade-to-GDP ratios. In 2013, low-income countries tradedon average 24% of their GDP compared to 42% by high-income countries. Thepresent model captures this regularity as follows: Within each industry, high-wagecountries produce and consume relatively more of high-markup goods (Hk) that
are subject to effectively lower trade costs, i.e. τσHk−1
ji < τσLk−1
ji (note that good Hk
also exhibits a greater profit margin, which make it more resilient to fixed export-ing costs). Let λii,z ≡
Xii,zXi,z
, denote the domestic expenditure share on good z inindustry k. Country i’s trade-to-GDP can be written as
(TradeGDP
)i=
K
∑k=1βk
(1− λii,Lk
) Xi,Lk
Xi,k+(1− λii,Hk
) Xi,Hk
Xi,k
In the South (when ε is sufficiently large) consumption in each industry is dom-inated by less-tradable goods (i.e.
Xs,LkXs,k≈ 1, ∀k) that are sourced predominantly
from local firms (i.e. λss,Lk ≈ 1, ∀k). This implies a relatively small trade-to-GDPratio in South: (
TradeGDP
)s≈∑
kβk(1− λss,Lk
)≈ 0
In the North, consumption within industries is dominated by highly-tradable goods(
Xn,HkXn,k≈ 1) that are subject to sizable two-way intra-good trade (e.g. the two-way
exchange of designer apparel between high-income nations). The North, therefore,
cally. As a result, the predicted gains from trade are smaller than a homothetic model with flexiblepreferences.
18
imports a larger fraction of its total expenditure:15
(TradeGDP
)n≈∑
kβk(1− λnn,Hk
)≈ 1−
(α−ηn wn
) 11−η
∑Nj=1
(α−ηj w j
) 11−η
Overall, The new model predicts that trade-to-GDP ratios should be systematicallylower in the South. Further, North-North trade (which involves highly-tradablegoods) should be conducted more intensively than South-South trade (which in-volves less-tradable goods) both at the national level and industry level.
Income per capita × the price composition of exports. The within-industry pat-terns of comparative advantage (outlined in the previous section) imply that high-wage countries export relatively more of high-markup goods. The higher markup-content of exports from rich countries results in higher export unit values. In par-ticular, the average price of country j exports to i in industry k is
p ji,k =
[(X ji,Hk
X ji,k
)σHk
σHk − 1+
(X ji,Lk
X ji,k
)σLk
σLk − 1
]︸ ︷︷ ︸
ρ ji,k
τ jiw j
where ρ ji,k denotes the average markup-content of exports from country j to i inindustry k. As established earlier (see equation 7) the North (n) exports relativelymore of high-markup goods within each industry:
Xni,HkXni,k
>Xsi,HkXsi,k
, ∀k. 16 Hence, allelse equal, Northern exports contain a higher markup-content and a higher price:
Xni,Hk
Xni,k>
Xsi,Lk
Xsi,k=⇒ ρni,k > ρsi,k =⇒ pni,k > psi,k ∀k.
15The above equation follows from the fact that (∀k)X jn,HkXnn,Hk
≈ α jαn
( M jn,kMnn,k
) 1η
andM jn,kMnn,k
≈ X jn,Hk/w j
Xnn,Hk/wn
,
when σHk approaches unity.16For example, consider auto exports from Korea and Germany. Suppose there are two categories
of cars: Luxury (high-markup) cars, and Economy (low-markup) cars. The present model predictsthat Germany sells both categories of cars at a higher price point due to its higher national productquality. Additionally, Germany sells relatively more luxury cars and Korea sells relatively moreEconomy cars. This additional composition effect contributes to Germany’s higher export pricesrelative to Korea.
19
Note that the effect of across-markup specialization on export prices will be mag-nified if we allow for high-markup goods to have higher production costs (Atkinet al. (2015) show that this assertion is backed by micro-level trade data). Ad-ditionally, the above result implies that as countries move up the quality-ladder(i.e. attain a higher αi) they shift production away from low value-added, low-markup goods, within each industry, to high-value-added, high-markup goods:∂ρ ji,k∂α j
> 0, ∀k.
Geography× the price composition of exports Gravity models predict that tradevalues decrease with bilateral distance. However, if one decomposes value intoquantity and price, export-quantity decreases with distance whereas export-priceincreases (Bernard, Jensen, Redding, and Schott (2007)). The positive relation be-tween export price and bilateral distance is a well-documented regularity knowas the “Washington apples” effect. Surprisingly, despite being one of the best-documented regularities in trade, the “Washington apples” effect is inconsistentwith mainstream gravity models (see Baldwin and Harrigan (2011)). Across-markupspecialization reconciles the gravity equation with the “Washington apples” effectunder any arbitrary specification of trade costs.17
Intra-industry specialization induces the “Washington apples” effect through twodistinct channels. First (as equation 6 implies) trade costs regulate the compositionof exports within industries. When facing larger iceberg trade costs, countries ex-port relatively more (quality-intensive) high-markup goods that are less affectedby these costs. This, creates a positive relationship between distance, the markup-content of exports and export prices within industries:
∂
∂τ ji
X ji,Hk
X ji,Lk
> 0 =⇒∂ρ ji,k
∂τ ji> 0 =⇒
∂p ji,k
∂τ ji> 0 ∀k.
Additionally, high-markup goods are also less-affected by fixed exporting costs asthey feature higher profit-margins. The benchmark model shuts down this channelin the interest of clarity. Activating this implicit channel, however, is straightfor-ward. If we allow for single-product firms, high-markup exporters will be more
17The standard explanation is founded on additive (non-iceberg) trade costs, and is due to Alchianand Allen (1983). Similarly, Irarrazabal et al. (2015) introduce non-iceberg costs into a gravitymodel.
20
likely to penetrate tougher, distant markets. As a result, trade between two distantpartners in industry k would revolve mostly around firms exporting the (quality-intensive) high-markup good Hk.18
2.4 A Special Case: The Standard Gravity Model
If we enforce isomorphism between quality and quantity and assume that goodsare uniform within industries (i.e. σHk = σLk = σk) the model reduces to a standardmulti-sector gravity model. In particular, aggregate trade values within industry kwill be characterized by:
X ji,k =M
1η
ji,k
(τ jiw jα j
)1−σk
∑Nh=1 M
1η
hi,k
(τhiwhαh
)1−σkβkwiLi (10)
The Armington gravity model is the perfectly competitive case of the standardgravity model. Specifically, if firms are perfectly competitive (η→ ∞, and f e = 0),equation 10 reduces to the familiar Armington gravity equation:
X ji,k =
(τ jiw jα j
)1−σk
∑Nh=1
(τhiwhαh
)1−σkβkwiLi
The standard gravity model describes aggregate trade values without systemati-cally pinning down the composition of these flows within industries. The standardmodel, therefore, cannot capture the effect of bilateral distance on the price compo-sition of exports. It also predicts no scope for within-industry specialization acrosslow and high-price product varieties, which is inconsistent with micro-level evi-dence. Additionally, the standard gravity model generates several counter-factualpredictions at the national level. First, it counter-factually predicts lower trade-to-GDP ratios for high-income countries (the standard gravity predicts that high-
18A real world example that corresponds to this effect, is auto exports from Europe. Europeexports the luxury, high-markup brands (e.g. Audi, BMW, Volvo) to the US, whereas the economy,low-markup brands (Opel, Renault, Peugeot) are not exported to the US market, but sold mostly inthe local European market. Further, in Lashkaripour (2015) I provide micro-level evidence that thetheory highlighted in this paper is a major driver of the “Washington apples” effect in US imports.
21
income countries have a lower equilibrium effective wage, wi/αi, which makesthem more likely the buy locally in all industries and less likely to import). Sec-ond, in a single-sector gravity model, all aggregate export flows are subject to thesame trade elasticity,σ − 1. In a multi-sector gravity model high-income countries(that have lower effective wages) export relatively more in high-σ industries. As aresult, export flows from rich countries would be subject to a higher average tradeelasticity, which contradicts existing evidence.
3 Mapping the Model to Data
The new model delivers distinct predictions about (i) variations in trade valuesat the national level and (ii) variations in export prices at the industry level. Theparameters of the model could be, therefore, identified using variations in eithernational trade values or within-industry export prices. Since data on trade val-ues are less noisy, I adopt the standard approach of fitting the model to bilateraltrade values. I match national trade data (which are available for a wider range ofcountries) by treating the economy as one integrated industry, i.e. K = 1. I demon-strate the merits of the model by comparing it to a standard gravity model fitted tothe same data — this strategy is similar to those adopted by Waugh (2010), Fieler(2011), and Di Giovanni and Levchenko (2013). Finally, I contrast the gains fromtrade implied by these two separately estimated models.
Data . I use data on bilateral merchandise trade flows in 2000 from the U.N.COMTRADE database (Comtrade (2010)). The data on population and GDP, andthe price of tradables are from the World Bank database (World-Bank (2012)). Thesample consists of the 100 largest economies (in terms of real GDP), which ac-count for more than 95% of world trade in 2000. Data corresponding to countrypairs (distance, common official language, and borders) are compiled by Mayerand Zignago (2011).
22
3.1 Estimation Strategy
The single-industry case of the model is characterized by universal parameters ε,σH, σL, and η and national parameters αi and τ ji. The new model, therefore, con-tains only two extra free parameters (σH
σLand ε) compared to the standard gravity
model. I use variations in aggregate bilateral trade flows and income per capitato estimate these parameters. Specifically, given the number of exporting firms
Mi j
i, j∈C, population size Lii∈C, national wage wii∈C, national product qual-ity α ≡ αii∈C, iceberg trade costs τ ≡
τ ji
j,i∈C, and demand parameters σL,
σH, ε, and η, one can calculate the aggregate export flows from country j to i as19
X ji = X ji,H + X ji,L , (11)
where X ji,H and X ji,L are given by equation 3. I use data on populations size (Li)and national wage (wi) and solve for a vector of national product qualities (α) thatimply wages consistent with the data; solve for the equilibrium mass of firms M ji
that satisfy the free entry condition; and estimate τ , σL, σH, ε, and η. Below, Iformally describe the estimation procedure:
i. I parametrize the iceberg trade costs as follows:
τ ji = 1 +[κconst +κdistdist ji
]κborderκlangκagreement
where dist ji denotes the distance (in thousands of kilometers) between coun-tries j and i. κborder, is one if countries do not share a border, and an estimatedparameter otherwise.20 Similarly,κagreement andκlang are one if a country pairdo not have a trade agreement or a common-language, and estimated other-wise. Altogether,κ ≡
κborder,κlang,κagreement,κconst,κdist
denotes the vector
of parameters describing the iceberg trade costs. For a given κ, and data ondistance, trade agreements, common-language and borders, I can construct amatrix of iceberg trade costs.
ii. Given parameters κ,α,σL,σH ,ε, η; and data (D) on the wages, population,
19The entry cost parameter, f e, governs the scale of entry and is normalized to one. The normal-ization does not affect trade flows, as it normalizes the mass of firms across all sources.
20For example, if κborder is, say, 0.9, sharing a border reduces τ ji − 1 by 10%.
23
distance, trade agreements, common languages and borders I can solve forthe mass of firms using the free entry condition (equation 4):
M ji = M ji(D;κ,α,σL,σH ,ε, η), i, j ∈ C
iii. Given M ≡ M jii, j∈C from the previous step, parameters κ,σL,σH ,ε, η,and data (D), I solve for a vector of National Product Qualities, α, that satisfythe balanced trade condition (equation 5):
α j = α j(D; M,κ,σL,σH ,ε, η), j ∈ C
That is,α j is chosen so that the market clearing wage equals data on GDP percapita.
iv. For any set of parameters, κ,σL,σH ,ε, η, and data, D, I iterate over steps 2and 3 to find an implicit solution forα j and M ji. Using the implicit solution,I calculate bilateral trade flows X ji from equation 11, and the matrix of trade
shares: λ ji =X jiXi
. The gravity equation in stochastic form becomes
ln λ ji = g(D;κ,σL,σH ,ε, η) +ε ji (12)
The above equation indicates that trade shares (λ ji) are a function of data, D,the estimated parameters,
κborder,κlang,κagreement,κconst,κdist,σL,σH ,ε, η
, and
the error term ε ji. I estimate equation 12 by minimizing the residual sumof squares (Non-linear Least Squares (NLLS)). Anderson and Van Wincoop(2003) argue that the NLLS estimator is unbiased if ε is uncorrelated with thederivative of g(.) with respect to D. This would be the case if ε representsmeasurement errors.
Identification of parameters. Parametersκ =κborder,κlang,κagreement,κconst,κdist
and η are common to both the new model and the standard gravity model. η gov-erns the scope for national product differentiation, and could be identified fromiceberg trade costs given our proxy for national wage.21 More specifically, A larger
21η is analogous to the trade elasticity in a perfectly competitive gravity model. As noted inHead and Mayer (2014) (chapter 4.2), we could separately identify the trade elasticity with data
24
η induces countries to diversify their imports, whereas a lower η induces countriesto concentrate imports on more competitive partners with lower quality-adjustedwages. The new model has two additional parameters: σH
σLand ε. Note that the
good-specific trade elasticities are not jointly identified from η. However, if weset σL = 6, we can separately identify σH. In particular, the spread σH
σLgoverns
the degree of international specialization, and regulates inter-country variations inthe distance elasticity of exports. Parameter ε regulates the effects of internationalprice disparities on consumption. As a result, it governs inter-country differencesin consumption and trade-to-GDP ratios.
3.2 Estimation Results
The estimation results are presented in table 1. The first column reports the esti-mation results for the new (benchmark) model. Column two reports estimationresults corresponding to the standard gravity model, and column three reports es-timation results for the Armington model (i.e. the perfectly competitive case ofthe standard gravity model).22 The fit of the standard gravity model is relativelypoor, given that both low and high-wage countries are included in the analysis.The new model, meanwhile, has an R2 that is 43 percent higher than the standardgravity model (note that this improvement does not fully manifest the merits ofthe new model as it overlooks the model’s predictive power with respect to exportprices). Below, I will discuss the two quantitatively important data-margins thatcontribute the improved in-sample fit of the new model.
Income per capita × trade intesity: In the data South-South trade (two-waytrade between poor countries) is conducted less intensively than North-North trade.The standard gravity model, however, could not distinguish between these differ-ent types of trade. Instead, in an attempt to match this regularity, it predicts a uni-versally large degree of home-bias and understates the trade-to-GDP ratio for allcountries. The new model, however, distinguishes between different types of trade
on wage or productivity. Alternatively, one could normalize the trade elasticity, and infer theimporter/exporter fixed effects (i.e. price indexes) from the structural model — this approach isadopted by Anderson and Van Wincoop (2003).
22When estimating the standard and Armington gravity models, I normalize the trade elasticityto 4.97, which is the trade-weighted average of σH and σL from the benchmark estimation.
25
Table 1: Estimation Results
Parameters New Model Standard Gravity Armington(Perfect Comeptition)
σHσL
0.54(0.004) ... ...
ε2.78
(0.011) ... ...
η3.16
(0.024)2.43
(0.015) ...
κconst2.16
(0.017)1.71
(0.017)0.44
(0.002)
κdist0.11
(0.002)0.13
(0.002)0.80
(0.004)
κborder0.57
(0.01)0.79
(0.013)0.60
(0.015)
κlang0.87
(0.007)0.81
(0.006)0.73
(0.008)
κagreement0.71
(0.013)0.84
(0.012)1.09
(0.017)
R2 (Goodness of fit) 0.43 0.30 0.21
Note: Standard errors are reported in parenthesis.
by (i) accommodating production specialization and (ii) internalizing the effect ofspecialization on consumption. In fact, as evident from figure 1, specialization-induced price disparities are quite pronounced and go a long way in explainingthe structure of international consumption.
With these adjustments, the new model distinguishes between South-South trade(which involves less-tradable goods) and North-North trade (which involves highly-tradable goods). As a result, the estimated model easily captures the relativelysmall volume of South-South trade to North-North trade (see figure 2).23 Similarly,unlike the standard gravity model, the new model captures the systemically higher
23North consists of the 21-richest countries in the sample and has roughly the same GDP as theSouth
26
Figure 1: International price disparities and the structure of international consumption.
USA
JPN
DEU
GBR
FRA
CHN
ITA
CAN
BRA
MEX
ESP
KOR
IND
AUS
NLD
TWN
ARG
RUS
CHE
SWE
BEL
TUR
AUT
SAU
POL
HKG
NOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRC
PRT
IRN
EGY
IRL
SGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGD
NGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
0.5
11.5
2R
elat
ive
pric
e in
dex
of g
ood
H (
log)
-6 -4 -2 0GDP per worker (log, US=1)
International Price Disparities
USA
JPN
DEU
GBR
FRA
CHN
ITA
CAN
BRA
MEX
ESP
KOR
IND
AUS
NLD
TWN
ARG
RUS
CHE
SWE
BEL
TUR
AUT
SAU
POL
HKG
NOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRC
PRT
IRN
EGY
IRL
SGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGD
NGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
-3-2
-10
1R
elat
ive
spen
din
g on
goo
d H
(lo
g)
-6 -4 -2 0GDP per worker (log, US=1)
International Consumption Differences
Note: The left panel shows that the relative price index of good H is lower in rich countries. Theright panel illustrates how international price disparities induce inter-country differences in con-sumption shares.
trade-to-GDP ratio of rich countries: N-N + N-SGDPN
> S-S + N-SGDPS
. Figure 3 illustrates thisresult. Notice that, trade-to-GDP ratios are an important metric to match, as theygovern the size of the gains from trade.
Income per capita× trade elasticity: A basic analysis of the data, reveals that ex-port flows from poor countries are more sensitive to distance.24 In fact, this effectis not limited to the present data. Disdier and Head (2008) show that the distanceelasticity has increased (over-time) with poor countries increased involvement intrade. Figure 4 displays the new model’s ability in capturing these effects. Specif-ically, it plots the (factual and predicted) normalized export flows
X jiXiX j
against bi-
24To formally illustrate the effect of income per capita on export elasticities, I can run a conven-tional gravity regression on my sample of 100 countries. Specifically, I allow for interaction betweenbilateral distance and the exporter’s income per capita:
ln X ji =
(−3.26(0.15)
+ 0.20(0.02)
ln w j
)ln DIST ji + S j + Mi +ε ji ,
where S j and Mi are exporter and importer fixed effects. The robust standard errors are reportedin the parenthesis, and the R2 is 0.47. The results confirm that export flows from rich countries aresignificantly less sensitive to distance.
27
Figure 2: The predicted trade composition versus data.
0
.1
.2
.3
.4
.5
New Model Data Standard Gravity Model
South−South North−South North−North
Note: The North corresponds to the 21 richest countries in the sample.
lateral distance dist ji. The new model correctly predicts that the 50-richest coun-tries have less distance-elastic exports than the 50-poorest countries. The standardgravity model, by contrast, does not distinguish between the effect of distance onSouthern versus Northern exports. Note that trade elasticities, like trade-to-GDPratios, are an important metric that govern the size of the gains from trade. Forcingthem to be uniform across countries is, therefore, not innocuous.
3.3 Out-of-sample Predictive Power
The out-of-sample predictive power of the standard gravity model has been calledinto question by several studies. Specifically, a standard gravity model fitted tocross-sectional data performs poorly in reproducing out-of-sample changes in tradevalues (Lai and Trefler (2002)). Below, I illustrate how my view of internationalspecialization enhances the out-sample-predictive power of the gravity model. Tothis end, I confront one of the most remarkable transformations in international
28
trade. Starting in the 1980s, North-South trade grew in relative importance and,in less than two decades, overtook North-North as the most dominant form oftrade. This transformation is highlighted extensively in Krugman (2009) and Han-son (2012). What makes this transformation notable is that (i) it coincides with the(weak) divergence of nominal per capita income-levels across rich and poor coun-tries (see Milanovic (2011) and figure 11), and (ii) trade flows from the South grewclose to two-times faster than the size of the Southern economies.25
Figure 5 illustrates this transformation in the data. Specifically, in 1985, the richest21 countries were sourcing most of their imports from other rich countries. Thispattern reverses over time and by 2006 more than 60% of rich countries’ importsare sourced from poor and middle-income nations. The standard gravity modelcannot reproduce this transformation. Specifically, trade liberalization in the stan-dard gravity model brings along more trade, but does not alter the composition ofinternational trade (figure 5, bottom panel).
In the new model, by contrast, the rise of North-South trade is the natural conse-quence of trade liberalization. As international trade costs are lowered, two pat-terns emerge. First, trade costs have asymmetric effects on goods and countries.Specifically, lower trade costs, increase trade disproportionally more in quantity-intensive (low-markup) goods. Put differently, poor countries and their compara-tive advantage good become relatively more competitive in international markets.Second, lower trade costs cause international prices to converge, inducing coun-tries to diversify their consumption. Rich countries substitute consumption awayfrom good H (their comparative advantage good) towards good L. This inducesthem to import relatively more from poor countries who have comparative ad-vantage in good L. Overall, trade liberalization in the new model not only bringsalong more trade, but also transforms the composition of international trade flows.Figure 5 illustrates these predicted transformations.
25The 2013 WTO world trade report (WTO, 2013) indicates that from 1980 to 2011 the share ofdeveloping countries in global trade grew by 45%, while their GDP-share grew by only 27%.
29
4 The Gains from Trade
Now that I have outlined the theory and estimated the model, let me take stock.I have developed a theory that is guided by within-industry and national varia-tions in trade composition. Using the theory I can decompose aggregate (eithernational or industry-level) trade values to identify and estimate the gains fromwithin-industry specialization. To put these estimated gains in perspective, I com-pare them to the typical gains implied by the standard gravity model. Overall,I find that: (i) the gains from trade are more than four-times larger in the newmodel, (ii) the gains from specialization systematically favor poor and remote na-tions, and (iii) trade liberalization could reduce international income inequality byaround 10%.
4.1 The realized gains from trade
Let Wi ≡ wiPi
denote welfare in country i. The industry-wide gains from trade are aCES average across the good-specific gains:
Wi/WAutarkyi = ΠK
k=1
[ρi,Hk
(λii,Hk
)− ε−1σHk−1
+ ρi,Lk
(λii,Lk
)− ε−1σLk−1] βkε−1
(13)
where ρi,z ≡αε−1/σz−1i
∑z′=Hk ,Lkαε−1/σz′−1i
denotes the autarky expenditure share on good z
in country i. Assuming goods are uniform within industries reduces the above
equation to Wi/WAutarkyi = ΠK
k=1λ− βk
1−σkii , which describes the gains from trade in
a wide range of mainstream gravity models (Arkolakis, Costinot, and Rodriguez(2012)). My estimation analysis, above, restricted attention to a one-industry case(i.e. K = 1) fitted to national data that are available for a wider range of countries.The arguments and procedures outlined below are, nevertheless, applicable to anymulti-industry industry analysis.
Typically, the literature relies on aggregate industry-level or national trade valuesto infer the aggregate gains from trade (Costinot and Rodríguez-Clare (2014); Ossa(2015)). This approach captures the gains from both product differentiation andacross-industry specialization. Here, however, I take an alternative approach. First,
30
I use my theory to decompose aggregate trade values into highly-differentiated(high-markup) and less-differentiated (low-markup) sub-categories — this decom-position could be applied to either national or sectoral trade values. I then use thedecomposed trade values to compute the aggregate gains from trade. This ap-proach essentially quantifies the gains from within-industry specialization. Figure6 reports the gains computed using the estimated model, and compares them tothose implied by the standard gravity model that relies on crude trade values. Asummary of the estimated gains are also reported in table 2.
The gains from trade are more than four-times larger in the new model and moreunequally distributed across countries. The larger gains are simply driven bywithin-industry specialization across low and high-markup (or quality and quantity-intensive) product varieties. These gains are identified from international varia-tions in trade-to-GDP ratios and import elasticities, i.e. the gains are driven by thehigh trade-to-GDP ratios in rich countries and the low import elasticity in poorcountries.
Table 2: Summary of the estimated gains from trade.
Mean Coefficient of variation
New Model 4.27% 95.58%
Standard gravity model 1.05% 69.6%
Note: The gains from trade correspond to percentage changes in real wage when moving from thecounter-factual autarky equilibrium to the factual trade equilibrium.
To put these results in perspective, note that both the size and the structure of theseimplied gains are different from those highlighted in the literature. For example,Costinot et al. (2012) estimate that the gains from across-industry specialization arerelatively smaller. Costinot and Rodríguez-Clare (2014) and Ossa (2015) show thatmulti-sector gravity models predict large gains only when the across-sector elastic-ity of substitution is relatively low. The estimated gains from within-industry spe-cialization, however, are large even with a high elasticity of substitution (ε = 2.78)across goods (to compare, Costinot and Rodríguez-Clare (2014) report that whenthe elasticity of substitution across sectors is 2.78, the gains from multi-sector tradeare very similar to that of a single-sector model).
31
The estimated gains are also larger than those implied by non-homothetic gravitymodels. Intuitively, trade always lowers the relative price of locally scarce goods,but increases the relative price of locally abundant goods. To match empirical reg-ularities, non-homotehtic models typically assume that tastes are biased towardslocally abundant goods. As a result, trade-induced changes in prices could ad-versely affect consumer welfare (this point is both highlighted and documentedin Atkin (2013)). In the present framework, however, preferences are homothetic,allowing consumers to diversify their consumption in response to trade shocks.
4.2 The gains from within-industry specialization
As pointed earlier, specialization across low and high-markup product varieties iswithin-industry in nature. Further, the gains from trade are four-times larger ina model that accommodates this type of specialization. My previous arguments,however, relied on the gains computed using two separately estimated models.These estimated gains are contaminated with estimation issues. To quantify thepure contribution of within-industry specialization, I counter-factually eliminatecomparative advantage from the estimated (benchmark) model.26 Eliminatingwithin-industry comparative advantage decreases average welfare by 28%, whichis considerably larger than those estimated for across-industry comparative ad-vantage (Costinot et al. (2012)).
Furthermore, the gains from specialization systematically favor poor and remotenations (figures 7 and 8). within-industry specialization, for example, accountsfor 50% of the real income in several African nations. Intuitively, within-industryspecialization opens up a new window of opportunity for countries with poorendowments. Low-income nations could concentrate economic activity on price-sensitive goods to benefit from the locally low labor cost. Remote nations, couldmitigate the burden of trade costs by shifting economic activity within industriestowards quality-intensive (highly-tradable) goods that are less-affected by thesecosts. Real income in many high-income countries would, nevertheless, fall as aresult of these adjustments (see figure 7).
26I eliminate comparative advantage by counter-factually forcing both categories to provide thesame scope for product differentiation, which is equal to the trade-weighted average ofσL andσH .
32
4.3 Trade and International Income Inequality
A key implication of the new model is that trade liberalization could reduce inter-national income inequality. The inequality-reducing effects of trade are driven bycomparative advantage. Specifically, even though trade costs are symmetric, theyhave asymmetric effects on countries. Poor countries are affected disproportion-ally more by trade costs as they specialize in the quantity-intensive, low-markupgoods that feature low degrees of tradability. Eliminating international trade costswill, therefore, make poor countries relatively more competitive, dragging up theirrelative wage. Figure 9 illustrates these inequality-reducing effects. The stan-dard gravity model, in comparison, predicts that trade liberalization has only aweak effect on inter-country income inequality. Note that inequality-reducing ef-fects would be magnified if we introduce fixed exporting costs into the benchmarkmodel. Fixed exporting costs (like variable trade costs) would affect low-markupgoods and, thus, poor countries relatively more. Eliminating them would thuscontribute to inter-country equality.
5 Micro-level Evidence
Thus far, I have motivated my theory with data on aggregate trade values. Themodel also correctly captures spatial and national variations in export unit values,and is broadly consistent with the fact that rich countries have higher value-addedexports within manufacturing industries (Johnson and Noguera (2012)). In thissection I dig even deeper and directly contrast the predictions of the model withmicro-level data. The model predicts that, all else equal, high-income countriesexport relatively more in high-markup (highly-differentiated) categories. That is,the export-mix from high-income countries should have a higher markup contentthan that of low-income countries. I verify this prediction using product-level USimport data.27 Broda and Weinstein (2006) have estimated the scope for productdifferentiation (σz) for various 10-digit product categories in the data.28 Using theirestimates and product-level import values, I can infer the average markup ( σz
σz−1 )
27The product-level US import data is compiled by Schott (2008), and is publicly available28See Soderbery (2015) for updated estimates.
33
embedded in the exports of a country to the US. Figure 10 plots the markup con-tent of exports against the (average) income per capita of an exporter during theperiod of 1989 to 2011. The graph supports the prediction that high-income coun-tries exports are composed of relatively more high-markup goods. The second testI perform is similar to the one conducted in Hanson and Xiang (2004). Specifically, Ilook at the relative export share of Northern countries to Southern countries acrossvarious product categories.29 Consistent with my theory, I find that Northern ex-port shares are systematically higher in the high-markup (highly-differentiated)product categories ( table 3).
Table 3: Patterns of specialization in product level import data.
Dependent variable: ln XNorth,zXSouth,z
(North’s export share in category z)
Markup in category z (logs) 0.10*** 0.03***(0.003) (0.003)
Constant 0.75*** 0.74***(0.005) (0.004)
Observations (product×years) 261,021 252,856Industry fixed-effect No YesR2 0.004 0.004
Note: The table estimates that the export share of high-income countries to the US is significantlyhigher in high-markup (low-σ) product categories. The high versus low-income categorizationis taken from Romalis (2004). The export shares are constructed using 10-digit product-level USimport data from 1989 to 2011. The standard errors are reported in parenthesis.
6 Conclusion
I relaxed a modeling assumption that is widely used in mainstream trade models,but is nevertheless inconsistent with micro-level evidence. Relaxing this assump-tion enabled me to highlight and formulate a novel view of within-industry spe-cialization. I argued that my view of international specialization goes a long way
29I use the North-South categorization employed in Romalis (2004).
34
in explaining not only export patterns, but the structure of international consump-tion. The framework that emerges from my theory captures salient features of thedata that elude standard trade models. Naturally, I asked how accommodatingthese additional features modifies the estimated gains from trade. To answer, Istructurally estimated the new model and compared it to a standard gravity trademodel. I found profound differences in both the size and the distribution of thegains from trade. These findings shed new light on the role of multilateral tradeagreements and have direct implications for industrial policy in developing na-tions.
Two aspects of the new model merit further research. First, in the model, inter-national consumption is regulated by trade-cost-induced home bias. Similar pat-terns could alternatively emerge under non-homothetic preferences. The gainsfrom trade depend crucially on which force we side with. Separately identifyingthese forces with micro-data opens an new avenue for future research. Second,extending the new model one could build a tractable framework to study input-output linkages and multi-national production. Existing models of input-outputlinkages or multi-national production are generally implemented within standardgravity frameworks and, therefore, miss out on principal moments when appliedto trade between rich and poor countries.
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Appendix
A Firm-level heterogeneity
Below, I show that the main predictions of the model are robust to adding firm-level heterogeneity. Without loss of generality and in the interest of clarity I restrictattention to the single industry case. The analysis closely follows Chaney (2008).Specifically, suppose that the mass of firms in country i is fixed to Mi. Firms are het-erogeneous in quality,ϕ, which is drawn (independently) from a country-specificFréchet distribution:
Gi(φ) = 1−αiϕ−γ
40
Here, αi denotes the overall quality of firms/varieties from country i. Moreover,firms should pay a local entry cost f e for each category they want to sell in a givenmarket.30 Within-category bilateral trade flows are, therefore, given by
X ji,z = M1η
j
(σz
σz − 1τ jiw j
Pi,z
)1−σz(ˆ ∞
ϕ∗ji,z
ϕdG j(ϕ)
)Xi,z (14)
whereϕ∗ji,z denotes the lowest quality in category z that would be exported fromcountry j to i. ϕ∗ji,z is pinned down by the zero cut-off profit condition:
ϕ∗ji,z
(σz
σz − 1τ jiw j
Pi,z
)1−σz Xi,z
σz= wi f e =⇒ ϕ∗ji,z =
(σz
σz − 1τ jiw j
Pi,z
)σz−1σz f e
λi,zLi
The average quality in category z exports from country j to i would, therefore, be:(ˆ ∞ϕ∗ji,z
ϕdG j(ϕ)
)=
γ
γ − 1
(ϕ∗ji,z
)1−γ=
γ
γ − 1α j
(σz
σz − 1τ jiw j
Pi,z
)−(σz−1)(γ−1) ( σz f e
λi,zLi
)1−γ
Plugging the above expression into the trade flow equation (14) would give us:
X ji,z = α jM1η
jγ
γ − 1
(σz
σz − 1τ jiw j
Pzi
)γ(1−σz) ( σz f e
λi,zLi
)1−γXi,z
The above equation combined with the market clearing condition implies that
X ji,z =α jM
1η
j(τ jiw j
)γ(1−σz)
∑Nk=1αk M
1η
k (τkiwk)γ(1−σz)
Xi,z (15)
The above gravity equation, like the benchmark case, implies that trade flows inthe the highly-markup category are less sensitive to trade costs. Further, equation
30The entry cost could also be paid for both categories and the main outcomes would still be thesame — see footnote 31.
41
15 implies that exports in category H relative to L are: 31
Xni,H/Xni,L
Xsi,H/Xsi,L=
(τniwn
τsiws
)γ(σL−σH)
The above equation implies that high-income countries has revealed comparativeadvantage in the highly-tradable, high-markup category, H:
αn > αs, τni = τsi ∀i =⇒
wn > wsXni,HXni,L
>Xsi,HXsi,L∀i
The other claims of the paper will follow once the above pattern of comparativeadvantage is established.
B Home Bias versus Non-homotheticity
In the present model, international consumption differences are driven by trade-cost-induced home bias. Alternatively, one could assume that consumption dif-ferences are driven by non-homothetic preferences. I purposely abstracted fromnon-homotheticity, since it cannot be separately identified from home bias in ag-gregate cross-sectional trade data. To illustrate this, suppose that preferences arenon-homothetic and have a formulation similar to that assumed in Fieler (2011):
Ui =
∑z∈H,L
εz − 1εz
Ui,zεz−1εz
31If entry costs were paid per category, then we would have a common quality cut-off,ϕ∗ji, and a
common average quality,´∞ϕ∗jiϕdG j(ϕ), for both categories. This would imply that for any country
j exporting to i:
X ji,H
X ji,L=
(Pi,H/ρH)σH−1
(Pi,L/ρL)σH−1
(τ jiw j
)σL−σH Xi,H
Xi,L=⇒ Xni,H/Xni,L
Xsi,H/Xsi,L=
(τniwn
τsiws
)σL−σH
The above equation would give rise to the same revealed pattern of comparative advantage. Thatis, high-wage countries would export relatively more in category H.
42
Consumption shares in country i would then be given by
Xi,H
Xi,L= λεL−εH
(P1−εH
i,H
P1−εLi,L
)(16)
where λ > 0 is the Lagrange multiplier associated with the utility maximizationproblem, and is strictly decreasing in the consumer’s total income. I will make mypoint using the North (i = n) as an example. Data on trade values suggest thatthe North spends relatively more on their comparative advantage (locally abun-dant) good, H (i.e. Xn,H > Xn,L). Moreover, the price index of the locally abundantgood is relatively lower in the North (i.e. Pn.H > Pn.L). Factual consumption pat-terns, therefore, could be driven by either the scale ofεH (which regulates the forceof home bias) or the spread, εL
εH(which governs the degree of non-homotheticity;
where good H is income-elastic: εH > εL).
One cannot separately identify home bias from non-homotheticity using aggregatetrade values. Non-homothetic models typically normalize εH and estimates εL
εH.32
My estimation strategy, by contrast, is analogous to setting εLεH
= 1 (shutting downnon-homotheticity) and estimating εH = εL ∼ ε, which delivers ε = 2.78. The re-lation between non-homotheticity and home bias has deep roots, and resembles ageneral identification issue faced by the gravity literature. In cross-sectional data,taste cannot be separately identified from trade costs (note that non-homotheticity isdriven by taste, whereas home bias is driven by trade costs). Recently, several stud-ies have employed richer data to disentangle these two forces (see Cosar, Grieco,Li, and Tintelnot (2015); Head and Mayer (2015)).
32Fieler (2011) sets εH = 5. Similarly, Caron, Fally, and Markusen (2014) normalize εTexteile =1, and control for the normalized price effects by constructing price indexes from the first-stageestimation.
43
Figure 3: Trade-to-GDP × income per capita
USA
JPN
DEU
GBR
FRA
CHN
ITA
CAN
BRA
MEX
ESP
KOR
IND
AUS
NLD
TWN
ARG
RUS
CHE
SWE
BEL
TUR
AUT
SAU
POL
HKG
NOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRC
PRT
IRN
EGY
IRL
SGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGD
NGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
-3-2
-10
Tra
de-t
o-G
DP
(lo
g)
-6 -4 -2 0 GDP per worker (log, US=1)
Data
USA
JPN
DEU
GBR
FRA
CHN
ITA
CAN
BRA
MEX
ESP
KOR
IND
AUS
NLD
TWNARG
RUS
CHE
SWE
BEL
TUR
AUT
SAU
POL
HKG
NOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRC
PRT
IRN
EGY
IRL
SGP
MYS
COLPHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGD
NGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
-4-3
-2-1
0 T
rade
-to-
GD
P (
log)
-6 -4 -2 0GDP per worker (log, US=1)
New model
USA
JPN
DEU
GBR
FRA
CHN
ITA
CAN
BRA
MEX
ESP
KOR
IND
AUS
NLD
TWN
ARG
RUS
CHE
SWE
BEL
TUR
AUT
SAU
POL
HKG
NOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRC
PRT
IRN
EGY
IRL
SGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGD
NGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
-6-4
-20
Tra
de-t
o-G
DP
(lo
g)
-6 -4 -2 0GDP per worker (log, US=1)
Standard Gravity Model
44
Figure 4: The distance elasticity of exports × income per capita
−3 −2 −1 0 1 2 3−35
−30
−25
−20
−15
−10
ln(dist i j)
lnX
ij
XiX
j
Data
North
South
South
North
−3 −2 −1 0 1 2 3−26
−24
−22
−20
−18
−16
−14
−12
ln(dist i j)
lnX
ij
XiX
j
New model
North
South
South
North
−3 −2 −1 0 1 2 3−45
−40
−35
−30
−25
−20
−15
ln(dist i j)
lnX
ij
XiX
j
Standard gravity model
North
South
South
North
Note: Normalized export flows (X ji
X jXi) from the the richest 50 countries (North) are less sensitive
to distance than export flows of the poorest 50 countries (South). The new (benchmark) modelcaptures this pattern, whereas the standard gravity model does not.45
Figure 5: Out-of-sample predictive power: New model vs. standard gravity.
estimation year
.4
.45
.5
.55
.6
Sh
are
in
Nort
hern
tra
de
1985 1990 1995 2000 2005
North−South trade
North−North trade
Data
.45
.5
.55
Sh
are i
n N
orth
ern
Tra
de
2468
International Trade Costs (mean)
New Model
.4
.45
.5
.55
.6
Sh
are
in
Nort
hern
Tra
de
2468
International Trade Costs (mean)
Standard Gravity Model
Note: The new model could reproduce the out-of-sample rise of North-South trade relative toNorth-North trade. The data used to construct the top panel is is from Head, Mayer, and Ries(2010)). The figure decomposes the overall trade of the 21 richest countries into: (i) trade with otherrich countries (North-North trade) and (ii) trade with middle-income and poor countries (North-South trade). The predictive power of the models are assessed by counter-factually lowering theestimated trade costs to replicate trade liberalization.
46
Figure 6: The estimated gains from trade relative to autarky.
Note: The gains from trade are both systematically larger and more dispersed in the new modelrelative to the standard gravity model. Unlike the standard gravity model, the new model accom-modates within-industry specialization. 47
Figure 7: The gains from specialization× GDP per capita
USA
JPN
DEUGBRFRA
CHN
ITACAN
BRA MEX
ESPKOR
IND
AUSNLD
TWN
ARG
RUS
CHESWEBEL
TUR
AUT
SAU
POL
HKGNOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRCPRT
IRNEGY
IRLSGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGDNGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
05
01
00
% G
ain
s fr
om
Sp
eci
ali
zati
on
−6 −4 −2 0
GDP per worker (log, US=1)
Figure 8: The gains from specialization× remoteness.
USA
JPN
DEU GBRFRA
CHN
ITACAN
BRAMEX
ESP KOR
IND
AUSNLD
TWN
ARG
RUS
CHESWEBEL
TUR
AUT
SAU
POL
HKGNOR
IDN
DNK
ZAF
THA
FIN
VEN
ISR
GRCPRT
IRNEGY
IRLSGP
MYS
COL
PHL
CHL
PAK
ARE
CZE
DZA
PER
NZL
HUN
BGDNGA
KWT
ROM
LBY
MAR
UKR
VNM
URY
SVK
LUX
OMN
DOM
TUN
SYR
SVN
HRV
KAZ
QAT
LBN
LKA
CRI
ECU
UZB
SLV
BLR
KEN
BGR
SDN
LTU
CIV
CMR
YEM
CYP
AGO
TZA
ISL
YUG
JOR
BOL
TTO
JAM
BHR
ETH
LVA
ZWE
PRY
BWA
UGA
05
01
00
% G
ain
s fr
om
Sp
eci
ali
zati
on
2 2.5 3 3.5 4 4.5
Remoteness
Note: Remoteness is calculated as the trade-weighted distance (in thousands of kilometers) to alltrading partners.
48
Figure 9: The effect of trade liberalization on international income inequality.
1 1.5 2 2.5 3 3.5 4
2.04
2.06
2.08
2.1
2.12
2.14
2.16
2.18
2.2
2.22
2.24
International trade costs (average)
Inte
rna
tio
nal
inco
me
ineq
ua
lity
Standard Gravity Model
New Model
Free Trade
(counter−factual)
Estimated Model
(factual)
Note: The above graph is generated by counter-factually lowering the estimated trade costs. Inter-national income inequality is calculated as the inter-country variance of ln wi.
Figure 10: The composition of exports to the US×Income per capita
AFG
AGO
ALBARE
ARG
ARM
ATG
AUS
AUT
AZE
BDI
BEL
BEN
BFA
BGD
BGR
BHRBHS
BIH
BLR
BLZ
BMU
BOL BRA
BRB
BRN
BTN
BWA
CAF
CAN
CHE
CHL
CHNCIV
CMR
COG
COL
COM
CPV CRI
CUB
CYP
CZE
DEU
DJI
DMA
DNK
DOM
DZA
ECU
EGY
ERI
ESP
EST
ETH
FIN
FJI
FRA
FSM
GAB
GBR
GEO
GHA
GIN
GMBGNB
GNQ
GRC
GRD
GRL
GTM
GUY HKG
HND
HRV
HTI
HUN
IDN
IND
IRL
IRN
IRQ
ISL
ISR
ITA
JAM
JOR
JPN
KAZKEN
KGZ
KHM
KIRKNA
KOR
KWT
LAO
LBN
LBR
LCA
LKA
LSO
LTU
LUXLVA
MAC
MAR
MDA
MDG
MDV
MEX
MHL
MKD
MLIMLT
MNG
MOZ
MRT
MUS
MWI
MYS
NAM NCL
NER
NGA
NIC
NLD
NOR
NPL
NZL
OMN
PAK
PAN
PER
PHL
PNG
POL
PRK
PRT
PRY
PYF
QAT
ROM
RUS
RWA
SAU
SEN
SGP
SLB
SLE
SLV
SOM
STP
SURSVK
SVNSWE
SWZ
SYC
SYR
TCD
TGO
THA
TJK
TKM
TON
TTO
TUNTUR
TWN
TZA
UGA
UKR
URY
UZB
VCT
VEN
VNM
VUT
WSM
YEM
ZAF
ZMB
ZWE
01
23
Mark
up
−co
nte
nt
of
exp
ort
s to
th
e U
S (
log)
4 6 8 10 12
GDP per worker (log)
49
Figure 11: The relative rise of North-South trade in face of North-South divergence.
estimation year
.4
.45
.5
.55
.6
Sh
are
in
Nort
hern
tra
de
1985 1990 1995 2000 2005
North−South trade
North−North trade
Data
8
9
10
11
Avg p
er
cap
ita
in
com
e (
log)
1985 1990 1995 2000 2005
Year
North
South
Note: This graph illustrates how the relative rise of North-South trade coincides with (weak) diver-gence of per capita income levels across Northern and Southern countries. The data is from Headet al. (2010). The 21-richest countries in 2000 are labeled as Northern countries, and the remainingcountries in the sample labeled as South.
50