Asymmetric Trade Liberalizations and Current Account Dynamics

Asymmetric Trade Liberalizations and Current Account Dynamics Alessandro Barattieri January 15, 2015 Abstract Te current account deficits of Spain, Portugal and Greece are te result of large deficits in goods trade and modest surpluses in service trade. Germany, instead, displays a large surplus in goods trade, but a deficit in service trade. Starting from tis motivating evidence, I propose in tis paper a simple model tat rationalizes ow te asymmetric timing of trade liberalizations can affect current account dynamics. I solve analytically a log-linear version of te model and derive an expression were te current account depends on present and future relative canges in te exogenous trade costs. Second, I sow tat trade costs dynamics and productivity dynamics ave been igly asymmetric in te manufacturing and services sectors in Germany in te period 2000-2007 and I propose a quantitative analysis based on a standard 2-country international real business cycle model augmented wit trade costs. Wen fed wit te actual asymmetric trends found in te German data, te model can generate a trade surplus of about 6% of GDP. Te model delivers large trade surpluses also wen considering only asymmetric trade liberalizations, but fails to generate surpluses consistent wit te data wen considering only asymmetric productivity dynamics. Finally, I provide empirical evidence broadly supporting te key predictions of te simple model using bot data from 24 OECD countries plus te BRICS and data from a sample of developing countries specialized in te export of agricultural goods. JEL classification: F1, F32, F40 Keywords: Current Account Dynamics, Relative Trade Liberalization Measures I would like to tank James Anderson, Matteo Cacciatore, Fabio Gironi, Julien Martin, Susanto Basu and seminar participants to ESG UQAM, and Mid West Macro Fall 2014 for useful comments and suggestions. A special tank to te D.C. International Trade Worksop participants for teir detailed comments and suggestions. All errors are mine. University of Quebec at Montreal (UQAM) Economics Department. Mail: Case Postale 8888, sucursale Centre-ville Montreal (Quebec) H3C 3P8. Tel: +1-514-987-3000 (0850#). Fax: 514-987-8494. E-mail: barattieri.alessandro@uqam.ca 1

1 Introduction Te topic of external imbalances as always been an important source of concern for economists and policy makers. In te context of te European Union, for instance, te European Commission as recently introduced a mecanism to monitor te formation of excessive macroeconomic imbalances tat can trigger an excessive imbalance procedure. Te current account is one of te key indicators monitored. Figure 1 reports te evolution of te current account over GDP for four European countries, were we can see ow te increasing current account surplus of Germany is accompanied by increasing current account deficits in te soutern European countries (Spain, Greece, Portugal). Te motivation for tis paper is te uncovering of some new facts. Figures 2 to 5 propose a simple decomposition of te trade balance into te goods balance and service balance components for te same four European countries included in Figure 1. Wile for Germany (Figure 2) te large trade surplus emerges from a trade surplus in goods, accompanied by a trade deficit in services, te opposite is true for te oter countries. Spain, Portugal, and Greece, in fact, exibit increasing trade deficits in goods, but surpluses in services (Figures 3, 4, and 5). 1 Moreover, also looking at te bilateral trade relationsips between Germany and Spain, Portugal, and Greece we observe te same pattern: tese soutern European countries display trade surpluses in services and trade deficits in goods (Figures 6, 7 and 8). Starting from tis motivating evidence, te contribution of tis paper is to propose a teory of ow asymmetric trade liberalization processes can affect current account dynamics. I start by outlining a simple teoretical model were I sow ow asymmetric trade liberalizations can affect current account dynamics. I ten propose a quantitative analysis of te German surplus using a standard 2 country international real business cycle model wit trade costs, and I sow ow te asymmetry in te liberalization of manufacturing versus service trade documented in Barattieri (2014), can explain a significant fraction of it. Finally, I propose some empirical evidence tat broadly support te main predictions of te teory bot using data from 24 OECD countries and te BRICS and using a sample of developing 1 A very similar picture could be drawn also for te UK. 2

countries specialized in te export of agricultural goods. I proceed in tree steps. First, I sow ow asymmetric trade liberalizations can affect current account dynamics using a simple model: an environment wit two periods, two countries, no production, complete specialization, and exogenous trade costs. I propose a log-linear version of te model around a symmetric equilibrium and I sow ow te evolution of Home s current account depends purely on consumption smooting. I solve explicitly for te current account only as a function of te exogenous endowments and trade costs, and I sow ow te relevant sock for current account dynamics is te cange in te trade cost te ome good relative to te cange of te trade cost of te foreign good. Any symmetric trade liberalization in wic te trade costs for te ome and te foreign good move in te same way would not ave any impact on te current account. On te oter and, asymmetric trade liberalization processes were te timing of trade liberalization is different for te ome and foreign goods affect current account dynamics. 2 Second, I propose a quantitative investigation of te German trade surplus. I first sow tat in Germany, in te period 2000-2007, te dynamics of trade costs and productivity in te service and manufacturing sectors ave been igly asymmetric. I use te constructed ome bias index (CHB), first proposed by Anderson and Yotov (2010), as a way of describing te timing of liberalization in manufacturing trade and service trade. Te CHB, derived by te structural gravity model, is a pure number tat indicates ow muc more a country trade wit itself in a given sector relative to wat it would do if te world were completely frictionless. I sow tat wile tis indicator is declining in te manufacturing sector in Germany over te period 2000-2007, it is essentially flat in services. Moreover, using data from EU-KLEMS, I sow tat in Germany in te period 2000-2007 productivity growt in te manufacturing sector was muc iger tan in services. I ten use a standard 2- country 2-sector international real business cycle model, augmented wit trade costs, to assess wat fraction of te te German trade surplus can be reproduced by te asymmetric 2 Present and future relative endowments in te two countries are also determinants of te current account. A relative increase in ome output in period one leads ome toward current account surpluses, wile a relative increase in output at time two leads te ome country towards current account deficits. 3

trade liberalization and te asymmetric productivity growt processes. I solve te model under perfect foresigt. Wen I feed te model wit te asymmetric trends of te trade costs and te productivity found for Germany, te model produces a trade surplus of around 6% of GDP at its peak, rougly te same order of wat observed in te data (7.1%). Interestingly, if te model is fed only wit asymmetric trade costs dynamics and no canges in productivity it still delivers a sizeable trade surplus. To te contrary, wen te model is fed only wit asymmetric productivity dynamics and no canged in trade costs it is unable to deliver a pat for te trade balance consistent wit wat observed in te German data. Tird, I propose an empirical analysis tat broadly support te main predictions of te simple model. I use two different dataset. First, I use a sample of 24 OECD countries plus te BRICS and focus on te asymmetric trade liberalization in goods and service trade. I use te CHB to proxy for te trade costs in manufacturing and services. I divide te sample of countries into tose relatively specialized in te export of manufacturing and tose relatively specialized in te export of services. I build relative trade liberalization measures, defined as te differences in te cange of te average CHB faced in te export sector and te cange of te CHB in te import sector. I sow ow, on average, Spain, Portugal and Greece were caracterized by ig relative trade liberalizations during te period 1995-2009. Tis means tat te fall in te barriers to trade in te sector tey tend to import (manufacturing) were on average larger tan te fall of te trade barriers in te sector were tey tend to export (services). Germany, on te contrary, exibits, on average a low relative trade liberalization, meaning tat te barriers to trade in te German export sector decreased by more tan te barriers to trade in its import sector. I ten explore te role of relative trade liberalizations as determinants of current account dynamics. Following te specification of te key equation of te model, I regress te cange in te ratio of current account as a sare of GDP on bot te contemporaneous measure of relative trade liberalization and on some of its leads. Consistently wit te teory, I find a negative a statistically significant coefficient on te contemporaneous relative trade liberalization measure (a country tends to experience a deficit wen te restrictions to trade in its import sector fall by more tan 4

tose in its export sector) wile te coefficients on te leads of te same measure are positive and statistically significant (a country tends to experience a deficit if in te future it expects te impediments to trade in its export sector to fall by more tan te impediments to trade in its import sector). Tese correlations are robust to te inclusion of several controls, including growt, openness, gdp and gdp per capita, as well as year and country fixed effects. Moreover, I formally test te equality of te coefficients on te contemporaneous and forward relative trade liberalization measures, as predicted by te model, and I am unable to reject it at any reasonable confidence level. I also repeat te same exercise, but focusing on a different set of developing countries, caracterized by being igly relatively specialized in te exports of agricultural goods, and relatively specialized in te imports of manufacturing goods. I build relative trade liberalization measures using tariffs data for manufacturing and agricultural goods, and verify ow contemporaneous ig relative trade liberalizations are correlated wit deteriorations of te current account, wile ig future relative trade liberalizations are correlated wit current account improvements. Also in tis case, I cannot reject te ypotesis tat te coefficients on present and future relative trade liberalizations are te same. Tus, I conclude tat asymmetric trade liberalizations are indeed a driver of current account dynamics, wic was previously at least partly overlooked. Tis paper is linked to several strands of te literature. First, it is broadly linked to te literature on global imbalances. Wile te literature on global imbalances is extremely vast 3, a subset of papers ave tried to specifically link trade reforms and industrial structures to current account dynamics. Ju and Wei (2012) presents a model were te interaction of Hescker-Olin forces and trade liberalization can affect current account dynamics. Wile te teoretical cannels proposed by Ju and Wei (2012) are operating on te production side, te only force operating in te model proposed in tis paper is consumption smooting. Jin (2012) links industrial structure to capital flows (and ence to current account dynamics) in a model were te specialization in capital intensive sectors rises te demand for capital, and tus explain te emergence of current account deficits. However, Jin (2012) abstracts 3 See for instance Blancard and Milesi-Ferretti (2009), Caballero, Fari and Gourincas (2008), Engels and Rogers (2006), Hausman and Sturzenegger (2006), Mendoza, Quadrini and Rios-Rull (2009). 5

from trade cost, considering a world wit no trade frictions. Second, te paper is linked to te literature on structural gravity and te construction of trade restrictiveness measures (Anderson and Van Wincoop, 2003, Anderson and Yotov, 2010). Finally, tis paper is linked to empirical literature on te current account dynamics (see for instance Gruber and Kamin, 2003), wit particular empasis on te studies related to te external imbalances witin Europe (Blancard and Giavazzi, 2002; Lane, 2013; Kollmann et al, 2015). None of tese studies as proposed asymmetries in trade liberalizations as potential drivers of current account dynamics. Most closely to tis paper, in Barattieri (2014) I examine te extent to wic te asymmetry in te liberalization of service trade and manufacturing trade of te last decades can explain te current account dynamics of te U.S. Tere are similarities but also important differences between Barattieri (2014) and tis paper. First, unlike in Barattieri (2014) I now take into account also te dynamics of productivity wen calibrating a DGE model to matc te pat of te trade surplus of a specific country (Germany in tis case), and sow tat asymmetric trade liberalizations are indeed more important in order to explain te data tan asymmetric productivity dynamics. 4 Second, differently from Barattieri (2014), in tis paper te CHB index is used in order to build relative liberalization measures and ten (and more importantly) to test te key prediction of te model. Tird, I propose ere a new empirical analysis based on a sample of developing countries specialized in te export of agricultural goods. Te paper is structured as follows. Te next Section introduces te simple teoretical model. Section 3 contains te quantitative analysis of te German trade surplus. Section 4 contains te two empirical analyzes wile Section 5 concludes and includes some policy implications. 4 See Burka, Devereux and Engel (2014) for an analysis of te real excange rates and productivity dynamics in te Eurozone. 6

2 A Simple Two-Period Model In tis Section I lay out a simple model aimed at sowing ow asymmetric trade liberalizations can affect te dynamics of te current account. Te world consists of two countries: Home and Foreign (wit foreign variables denoted by ). Eac country is populated by a representative ouseold tat lives for two periods. Two goods are consumed: a ome good () and a foreign good (f). Te endowment of te ome good is Y t endowment of te foreign good is Y f t wit t = {1, 2}. Te wit t = {1, 2}. Te price of te ome good at Home is p t. Te price of te ome good in Foreign is p t = τ t p t, were τ t > 1 is an iceberg trade cost. Te foreign good f is imported in Home from Foreign. Te Home price of te foreign good is p f t iceberg trade cost. 5 = τ f t p f t, were p f t is te price of te foreign good in Foreign and τ f t > 1 is an In bot countries, ouseolds maximize lifetime utility, given by: X 1 1 σ 1 1 1 1 σ + β X1 1 σ 2 1 1 1 σ were X = C or C depending on te country. Te asset menu features only an international bond denominated in units of a common world currency. Te first-period and second-period budget constraints are, respectively: B 1 = p 1Y 1 P 1 C 1, B 1 = p f 1 Y f 1 P 1 C 1, (1) P 2 C 2 = p 2Y 2 + (1 + r 1 )B 1, P 2 C 2 = p f 2 Y f 2 + (1 + r 1 )B 1, (2) were B 1 and B 1 are te net bond positions of Home and Foreign and r 1 is te riskless net rate of return in units of te numeraire. Te consumption basket aggregates ome and foreign goods. I assume a C.E.S. aggregate wit elasticity of substitution different from 1. Te reason is tat, as sown by Cole and Obstfeld (1991) and Corsetti and Pesenti (2001), in te presence of unitary elasticity of 5 I set a world price index P W = P 1/2 P 1/2 = 1 to be te numeraire. 7

substitution between ome and foreign goods, tere are no intertemporal transfers of wealt across countries (i.e., no current account movements). Terefore, te consumption basket in te Home country is defined to be: C t = [ (C ) θ 1 ( θ t + C f t ) θ 1 ] θ 1 θ θ, were θ is te elasticity of substitution between goods and services, assumed to be larger tan 1. C t represents te consumption of ome goods in Home at time t, wile C f t is te consumption of foreign good in Home at time t. Ct, Ct, and C f t are defined in analogous fasion. Te price indexes in Home and Foreign are respectively: P t = [ (p t ) 1 θ ) ] 1 1 θ + (τ f t p f 1 θ t, [ P (τ t = t p t ) 1 θ ) ] 1 1 θ + (p f 1 θ t. (3) Te inter-temporal optimization problem yields standard Euler equations for bot Home and Foreign: {1, 2}: C 1 = β σ ((1 + r 1 ) ( P1 P 2 )) σ ( )) P C 2 C1 = β ((1 σ σ + r 1 ) 1 C P 2. (4) 2 Te intra-temporal optimization decision gives te following demand equations for t = ( p Ct s = t C f t = ( P t ) θ ( ) τ C t, Ct = p θ t C Pt t, (5) ) θ ( τ f t p f t C t, C f t = P t p f t P t ) θ C t. (6) To close te model, we must impose goods and bond market clearing conditions. Te nature of te iceberg trade costs implies te following goods market clearing conditions: Y t = C t + τ t C t, (7) Y f t = τ f t C f t + C f t. (8) 8

Finally, bond market clearing requires: B 1 + B 1 = 0. (9) We tus ave 21 endogenous variables (C t, Ct, P t, Pt, Ct, Ct, C f t, C f t, p t, B 1, B1, r 1 ) wit t = {1, 2}. Te 21 equations (1)-(9), togeter wit te evolution of te exogenous variables Y j t and τ j t (wit t = {1, 2} and j =, f) completely caracterize te equilibrium of tis economy. Unfortunately, one cannot obtain closed-form solutions witout unitary elasticity of substitution between ome and foreign goods. To make te results transparent, instead of relying on numerical examples, I will present analytical results based on te log-linearized version of te model around a symmetric equilibrium. 2.1 A Symmetric Equilibrium Te analysis below is based on a log-linearization of te model around a symmetric equilibrium were p = p f = 1, B1 = B 1 = 0, Y = Y f = Ȳ, and τ = τ f = τ. In tis symmetric equilibrium, price indexes are equal: P = P = ( 1 + τ 1 θ) 1 1 θ. (10) Moreover, we ave: C = C = Ȳ P, (11) C = C = P θ C, (12) C f = C f = τ θ θ P C. (13) Finally te Home sare of consumption of te ome good is equal to te Foreign sare of consumption of te foreign good: 9

C C + τc = C f τ C f + C = s f = s f = 1. (14) 1 + τ 1 θ Notice tat te foreign sare of consumption of te ome good includes also te amounts lost to trade costs. On te oter and, te Home sare of consumption of te foreign good is: τ C f τ C f + C = f τ C C + τc = s f = s = Consistent wit intuition, it is straigtforward to ceck tat s τ τ 1 θ 1 + τ 1 θ (15) > 0 and s f τ < 0. In oter words, te introduction of te trade costs creates ome bias in tis setting even in absence of ome bias in preferences. 6 extremely useful in te process of log-linearization. Finally, symmetry implies tat s = 1 s f. Tis property is 2.2 Te Log-Linear Model I denote percentage deviations from te symmetric equilibrium wit a at. So ˆx = log ( x x), were x is te value of x at te symmetric equilibrium. Te details of te log-linerarization and te solution of te model are described in te appendix. Te Euler equations take te log-linear form: Ĉ 1 = σ(1 β) ˆr 1 σ ˆP 1 + σ ˆP 2 + Ĉ2, (16) Ĉ 1 = σ(1 β) ˆr 1 σ ˆ P 1 + σ ˆ P 2 + Ĉ 2. (17) Te log-linear versions of te period-1 budget constraint in Home and Foreign are: 6 A point already made by Obstfeld and Rogoff (2001). ˆB 1 = ˆp 1 + Y ˆ 1 ˆP 1 Ĉ1, (18) ˆB 1 = p ˆf 1 + Y ˆf 1 P ˆ 1 Ĉ 1, (19) 10

were importantly te current account te percentage deviation from te equilibrium output Ȳ. 7 Te budget constraints for period 2 are: Ĉ 2 = ˆp 2 + ˆ Y 2 ˆP 2 + 1 β ˆB 1, (20) Ĉ2 = p ˆf 2 + Y ˆf 2 P ˆ 2 + 1 β ˆB 1. (21) Taking te difference between (18) and (19) and imposing te bond market clearing condition, we get te following expression for te current account of te Home country in period 1 (equivalent to te country s net foreign asset at te end of te period): 2 ˆB ( 1 = ˆp 1 p ˆ ) f 1 ( ˆP1 P ˆ ) 1 (Ĉ1 Ĉ 1 ) ( + Yˆ 1 ˆ Y f 1 ). (22) Equation (22) expresses te current account of te Home country as a function of te terms of trade ( ˆp 1 p ˆf 1 ), te real excange rate, te consumption differential and te endowments differential, wic we can tink of reflecting difference in te dynamics of productivity. Everyting else equal, an improvement of te terms of trade would lead to a current account surplus and a real appreciation to a current account deficit. An increased consumption differential between te Home and te Foreign country would lead to a current account deficit at Home, and an increase in productivity at Home would lead to a surplus. Using te difference between (16) and (17) and te difference between (20) and (21), we can rewrite (22) as 2 (1 + β) β ( ˆB 1 = ˆp 1 p ˆ ) ( f 1 ˆp 2 p ˆ ) f 2 ( + (σ 1) ˆP1 ˆP ) 2 + ( ˆ Y Y f 1 ˆ 1 ) (σ 1) ( ˆ P 1 ˆ P 2 ( Yˆ 2 ˆ 2 Y f ) ). (23) Equation (23) allows us to interpret te evolution of Home s current account as depending on six factors. Te first four represent a wealt effect. All else equal, consumption smooting tends to pus te Home current account toward surplus (deficit) in case of an increase of te 7 Tis is necessary because net foreign asset are zero in te symmetric equilibrium 11

ome endowment (or its price) relative to te foreign endowment in period 1 (period 2). Te next two terms represent a substitution effect. All else equal, if te inter-temporal elasticity of substitution is larger tan 1, an increase of te ome price index in period 2 relative to period 1 tends to pus Home s current account toward deficit, as would a decrease in te foreign price index in period 2 relative to period 1. Obviously, one must solve fully te model to ave te impact of te different exogenous variables on te current account. Wile te appendix explains te procedure in detail, I will give only a quick sketc ere. For bot periods, I substitute te budget constraints into te demand functions for te ome good, and ten I use te goods market clearing conditions to solve for ˆp t p ˆf t as function of te trade costs, te endowments and ˆB 1 (imposing te bonds market clearing condition eliminates ˆB 1 from te system). I ten express all te six elements of equation (23) as functions of te trade costs, te endowments and ˆB 1. Finally, I substitute tese functions back into equation (23). Tis allows me to express Home s current account only as function of te exogenous endowments and trade costs: ( ˆB 1 = η τˆ 1 τ ˆ ) ( f 1 + η τˆ 2 τ ˆ ) f 2 + ν ( ˆ Y Y f 1 ˆ 1 ) ( ν Yˆ 2 ˆ 2 Y f ) (24) were η is a function of te structural parameters of te model (β, θ, σ, τ). η is a positive number as long as θ > 1 and te elasticity of intertemporal substitution is sufficiently large. ν is also a parameter depending on (β, θ, σ, τ). 8 values of te parameters. It is positive for a large range of plausible Equation (24) is te key equation. It is important to notice tat te relevant sock is te cange in te trade cost te ome good relative to te cange of te transport cost of te foreign good. Any symmetric trade liberalization in wic te trade costs for te ome and te foreign good move in te same way would not ave any impact on te current account. ( On te oter and, asymmetric trade liberalization processes for wic τˆ 1 τ ˆ ) f 1 > 0 ( and/or τˆ 2 τ ˆ ) f 2 < 0 pus te current account of te Home country into deficit. More generally, equation (24) callenges te view tat trade policies cannot influence te 8 See te appendix for details. 12

trade balance because tey cannot affect savings and investment decisions. 9 Wile tis is certainly true in static settings, tings can be different in dynamic settings were te timing of te trade policy potentially matters for saving and investment (wic are inter-temporal decisions). 10 Moreover, also te endowment dynamics affects te current account, and in te usual way. Everyting else equal, an increase in te endowment of te Home country relative to te foreign one in period 1 (period 2) puses te Home country current account toward surplus (deficit). Tis points also to te potential importance of productivity dynamics in determining te current account. 3 A Quantitative Investigation of te German Surplus Wile equation (24) provides a clear qualitative insigt, a first order question is weter tis insigt is also quantitatively relevant. Te aim of tis Section is first documenting tat in Germany bot trade costs and productivity dynamics in te manufacturing and service sectors ave been igly asymmetric in te period 2000-2007. 11 Second, I outline a standard 2-country international real business cycle model augmented wit trade costs. Finally, I explore te ability of te model, fed wit te asymmetric trends found in te data, to reproduce te dynamics of te German trade surplus. 3.1 Asymmetric Trade Costs and Productivity Dynamics Trade Costs Dynamics. I follow ere Barattieri (2014), were I propose te use of te constructed ome bias index (CHB) 12 as a convenient way to capture te dynamics of trade costs in services and manufacturing. Te CHB index is a pure number and it express ow muc more a country is trading wit itself in a given sector, relative to wat it would do if 9 See for instance Lamy (2010). 10 Obviously ere te point is made only for savings. 11 Te coice of te period is partly motivated by Figure 1, were te German surplus starts growing in 2000 and peaks in 2007, partly motivated by some data limitations: productivity figures are available just until 2007 and service trade costs data are more reliable after 1999. 12 First proposed by Anderson and Yotov (2010). 13

te world were frictionless. Obviously, tis definition requires to define a bencmark of wat would be trade in te case of a frictionless world. Te structural gravity model contains suc a prediction. Following Anderson and Van Wincoop (2003), let X k ij be te total sipment from te origin country i to te destination country j in sector k, Y k i in te origin country i and E k j te total output of sector k te total expenditure in sector k in te destination country j (defined as output minus total exports plus total imports of country j in sector k). Te structural gravity model can be expressed as follows: X k ij = Y k i E k j Y k ( t k ij P k j Πk i ) 1 θk (25) were Y k represents te world output of sector k and t k ij represents te bilateral trade cost of sipping a unit of sector k good from country i to country j. P k j and Π k i are te inward and outward multilateral resistance terms, wic are in turn weigted averages of te bilateral trade costs t k ij. 13 Te equivalent expression for te internal trade would be: X k ii = Y k i E k i Y k ( ) t k 1 θk ii. (26) Pi kπk i were X k ii is defined as output minus total exports. Equations (25) and (26) can be used to get a prediction of te amount of trade tat would prevail in te absence of trade frictions. If t k ij = 1 for every country pair ij, in fact, ten Π k i = P k j of internal trade, we get X k ii = Y k i Ek i Y k. = 1, and Xij k = Y i kek j. In te case Y k Using (26), it is possible ten to express te ratio of realized internal trade to te trade 13 Defined as follows: ( ( ) Π k 1 θk t k ij i = j P k j ( ( ) P k 1 θk t k ij j = i Π k i ) 1 θk E k j Y k, ) 1 θk Y k i Y k. 14

tat would prevail in absence of friction as functions of observable variables: CHB ik = Y k i Ek i Y k ( t k ii P i Π i ) 1 θk Y k i Ek i Y k = ( ) t k 1 θk ii = Xk iiy k. (27) P i Π i Y k i Ek i In tis paper I use (27) to calculate te CHB index, differently from Barattieri (2014). 14 Te index as several advantages and some disadvantages. First, it is time varying. Second, te index allows te separation of te effects of canges in productivity (captured by te production data) from tose determined by oter frictions (suc as transport costs and legal barriers). Tird, te index is a number and tus invariant to te elasticity of substitution θ k. 15 On te oter and, te index relies on te gravity model to determine te bencmark trade in case of no friction. Figure 9 reports te evolution of te CHB in manufacturing and services for Germany for te period 2000-2007, normalized to 1 in 2000. As te Figure clearly sows, tere is a clear trend of te CHB in te manufacturing sector, wile te CHB in te services sector first decline and ten increases again, wit no clear trend. Productivity Dynamics. I use EU-KLEMS data to describe te dynamics of te total factor productivity in te manufacturing and service sectors in Germany over te period 2000-2007. Figure 10 reports te results for TFP evolution in tese sector, normalizing tem to 1 in 2000. Te Figure clearly sows an asymmetric dynamics. Productivity grew in te manufacturing sector of abut 18% in te period considered wile productivity in te service sector grew by only about 3%. 16 14 Tis metod of calculating te CHB includes te measurement error in te data, so it does not ave te virtue of te fitted gravity regression metod. It is similar in spirit to te tetrads metod of Head, Mayer and Ries (2010). Te two way of measuring te CHB, owever, gives similar results. Te correlation between te CHB in Manufacturing computed under te alternative metodologies (wic in bot cases was constructed for te period 1994-2009) is 0.90. 15 I m not aware of reliable estimates of θ k te for te service sector. 16 Te dynamics of productivity in Services is an average of te dynamics in construction, retail, otels, transport and communication, finance, insurance and real estate, personal services, education, ealt services. Restricting te attention only to transport, finance and communication services gives a sligtly iger but similar result. 15

3.2 A 2-Country International Real Business Cycle Model I develop in tis Section a standard 2-sector 2-country model in te spirit of Backus, Keoe and Kydland (1994). I assume away uncertainty, wile adding exogenous trade costs. Te environment. Tere are two countries, 1 and 2. Under perfect foresigt, in eac country i, a representative consumer maximizes er lifetime utility function: Max t=0 βt U(c it, 1 n it ), were U(c, 1 n) = [cµ (1 n) 1 µ ] γ. c it and n it are consumption and ours worked in country γ i. Tere is complete specialization in one intermediate good. Country 1 produces good a, wile country 2 produces good b. Te goods are produced wit capital, k, and labor, n, according to te following tecnology: y 1t = A 1t k α 1tn 1 α 1t y 2t = A 2t k α 2tn 1 α 2t (28) were A it is te total factor productivity of country i. Importantly, I m ere assuming tat productivity is non-stocastic. Bot consumption and investments are composite of foreign and domestic goods: were c 1t + i 1t = 1t c 2t + i 2t = 2t (29) 1t = [ ω 1 (a 1t ) θ 1 θ ] + ω 2 (b 1t ) θ 1 θ θ 1 θ 2t = [ ω 1 (b 2t ) θ 1 θ ] + ω 2 (a 2t ) θ 1 θ θ 1 θ (30) Were a 1t is te use of good a in te production of te composite consumption and investment good in country 1, wile b 2t is te usage of good b in te production of te composite consumption and investment good in country 2. θ represents te intra-temporal elasticity of substitution. Te law of motion for pysical capital are standard: k 1t+1 = (1 δ)k 1t + i 1t k 2t+1 = (1 δ)k 2t + i 2t (31) 16

Tere are common iceberg-type trade costs for te two goods: τ 1t and τ 2t. τ 1t units of goods a ave to be sipped from country 1 to country 2 in order to ave one unit of usable input in country 2. Hence, te resource constraints can be expressed as: y 1t = a 1t + τ 1t a 2t y 2t = b 2t + τ 2t b 1t (32) Te Planner s problem. bot countries 17 : A world planner maximizes a weigted sum of te utility of Max t=0 βt {Ω 1 U(c 1t, 1 n 1t ) + Ω 2 U(c 2t, 1 n 2t )}, Substituting equations (18) into (14) and equations (16) and (17) into (15), it is possible to express te constraints faced by te planner in te following way: [ ω 1 (a 1t ) θ 1 θ ] + ω 2 (b 1t ) θ 1 θ θ 1 θ = c 1t + k 1t+1 (1 δ)k 1t (33) A 1t k α 1tn 1 α 1t = a 1t + τ 1t a 2t (34) [ ω 1 (b 2t ) θ 1 θ ] + ω 2 (a 2t ) θ 1 θ θ 1 θ = c 2t + k 2t+1 (1 δ)k 2t (35) A 2t k α 2tn 1 α 2t = b 2t + τ 2t b 1t (36) Let λ 1, q 1, λ 2 and q 2 be te multipliers on constraints (33) to (36). {c i, n i, k i, a i, b i }, wit i [1, 2], are te coice variables for te planner. Te first ten order conditions for te planning problem, 18 togeter wit equations (33)-(36), constitute a system of 14 equations and 14 unknowns (10 coice variables and 4 multipliers), wic togeter wit te four exogenous variables (τ 1, τ 2, A 1, A 2 ) complete te description of te model. In te absence 17 I will assume equal weigts. 18 Listed in te appendix. 17

of aggregate uncertainty, te model can be solved as a nonlinear forward looking deterministic system using a Newton metod. Tis metod simultaneously solves all equations for eac period, witout relying on local approximations. Trade Balance. I measure te trade balance in te model as presented in te data, wic is expressed as te ratio of net exports to output: NX 1t = τ 1ta 2t T OT 1t b 1t y 1t (37) In (37), T OT 1t represent te terms of trade of country 1, defined as te domestic price of imports over te price of exports. Since te multipliers q 1 and q 2 represent te sadow prices of goods a and b, te terms of trade of country 1 can be defined as: T OT 1t = τ 2tq 2t q 1t (38) Importantly, te terms of trade now feature te presence of te trade costs on te imported good. A similar expression can also be found by computing te terms of trade as te marginal rate of transformation between te two goods in eac country (see te Appendix for details). Calibration. I use standard parameter values to calibrate te model. Following BKK (1994), I set γ = 1, corresponding to an inter-temporal elasticity of substitution of 0.5. α is set to 0.36, wile µ = 0.34, and gives a steady state level of ours equal to rougly one tird of available time. θ is set to 1.5, wile β is 0.96 and δ 0.1 to calibrate te model to annual frequencies. Te trade costs are assumed to be initially equal to 2.7, wic is te value suggested by Anderson and Van Wincoop (2004). ω 1 and ω 2 are calibrated to give an initial ratio between imports to output of about 0.33, as observed in Germany in 2000. 18

3.3 Te German Surplus: Data vs. Models I solve te model under two alternative assumptions: i) a symmetric reduction in trade costs and a symmetric increase in productivity and ii) an asymmetric reduction in trade costs and an asymmetric increases in productivity. In te case of te symmetric trade costs and productivity dynamics, I assume tat bot τ 1 and τ 2 decline for 8 years at a constant rate, equal to te trend observed for te CHB in manufacturing in Figure 9 and bot A 1 and A 2 grow at te a constant rate, equal to te trend found for German manufacturing TFP in Figure 10. In te asymmetric case, I assume tat τ 1, te trade cost for good a, declines for 8 years at te same trend as te manufacturing CHB and ten remains flat, wile τ 2, te trade cost for good b, remains flat for te first 8 years and ten declines at te same trend of te CHB in manufacturing until it reaces te level of τ 1. Moreover, in tis case A 1 grows for te first 8 years at te same rate as German TFP in manufacturing, and ten it stays flat, wile A 2 stays flat for te first 8 years, and ten start growing at te same rate of A 1. In te experiment, ence, country 1 would represent Germany, wose productivity grows faster tan te oter country in te first 8 years and wose trade cost falls first. Figure 11 reports te pat of some of te endogenous variables of country 1 in te case of a symmetric trade liberalization and productivity growt processes. Intuitively, te symmetric reduction of impediments to trade does not affect te trade balance. A symmetric reduction of te import prices in country 1 and 2 leads to an equal increase in exports and imports in eac country. As a result, te trade balance does not move in eiter country. On te oter and, asymmetric trade liberalization and productivity growt do affect te trade balance, as reported in Figure 12. Te country wose good becomes liberalized first (country 1 in te experiment) experiences a decrease in te relative price of imports later tat te country wose goods becomes liberalized second (country 2). As a result, te exports in country 1 rise more in te first years, and ten decline wen te imported good price is decreasing. As a consequence, te trade balance of country 1 goes into surplus and ten starts declining only wen te liberalization of good b takes off. Figure 13 reports on te same grap te German trade balance over GDP and te 19

corresponding object obtained wit te model simulations for te years 2000-2015. Wile te timing of te increasing of te German trade balance is not well captured by te model under te asymmetric trade liberalization experiment, te correlation between te two series is considerably ig (0.84). Moreover, te model is able to generate a peak of trade surplus of about 6% of GDP, fairly close to wat was observed in Germany in 2007 (7.1%). Furtermore, Figure 14 reports te trade balance dynamics obtained under two oter alternative experiments: i) an asymmetric reduction in te trade costs, wit no canges in productivity in eiter sector (solid line), and ii) an asymmetric increase in productivity, wit no canges to te trade costs in bot sectors (das-dotted line). For ease of comparison, I also report te bencmark result wit asymmetric dynamics in bot productivity and trade costs (dased line). Te peak of te trade surplus obtained under te scenario wit no cange in productivity in eiter sector is in fact now iger tan wat obtained under productivity growt only due to a denominator effect (growing GDP). More interestingly, toug, te model is unable to reproduce a dynamic consistent wit te German data, wen only fed wit an asymmetric productivity process, wit no canges to te trade costs. Te key to understand te difference in te two cases, is te dynamics of investment, wic initially decreases in te first case but increase in te second. In te case of an asymmetric reduction of trade costs, in fact, a consumption smooting motives makes te agents only wait to consume in te future to wait enjoying lower prices. In te case of a present increase in productivity, instead, on te one and tere is te same tendency to smoot consumption, wic would tend to an increase in savings, but on te oter and tere is also an investment motive, wic as an opposite effect on te trade balance. Quantitatively tese two effects balance out, as we see in Figure 14. I conclude tat indeed te asymmetric trade liberalization process between manufacturing and services migt potentially explain an important part of te dynamics of te German surplus in te period 2000-2007. 20

4 Empirical Evidence Te aim of tis Section is to provide empirical support to te teory presented in Section 2. Since te insigt of equation (24) is a general one, I will apply it ere to two different contexts. First, I ll propose an analysis based on te asymmetry between te liberalization of trade in manufacturing versus services tat I explored in Barattieri (2014). Second, I ll analyze te current account dynamics of a sample of developing countries igly specialized in te export of agricultural goods and te import of manufacturing. 4.1 Manufacturing versus Services Te first empirical analysis I propose exploit te asymmetry in te liberalization of manufacturing and service trade for a sample of 24 OECD countries plus te BRICS. 19 Te empirical strategy ere can be tougt of consisting of two stages. Stage 1: Relative Trade Liberalization Measures. In te first stage, I need to construct proxies for te terms τˆ ( t τ ˆ ) f t tat appear in equation (24). I use te CHB introduced in Section 3.1 to proxy for ˆτ in bot services and manufacturing. Table 1 contains te results obtained by using (27) to build CHB indexes for service and manufacturing for 24 OECD countries and te BRICS. I report te level of te CHB in manufacturing and services in 1995 and 2008, as well as teir percentage cange over te period. Two main observations stand out. First, bot services and manufacturing CHB indexes decline over time in most countries. Notable exceptions are te U.S., wic owever as te lower level of CHB in bot sectors, Japan and Germany. Second, te decline of CHB in manufacturing is greater tan tat of services in most countries. I ten use te CHB to build relative trade liberalization measures. In order to compute tese measures, I first divide te countries of my sample in two groups: te goods-oriented 19 Te countries included are Austria, Brazil, Canada, Switzerland, Cina, Czec Republic, Germany, Denmark, Spain, Finland, France, Greece, Hungary, India, Ireland, Israel, Italy, Japan, Korea, Mexico, Norway, New Zealand, Poland, Portugal, Russia, Sout Africa, Sweden, UK, US. See te appendix for details 21

and te service-oriented countries. In order to do so, I use an average of te Revealed Comparative Advantage in Services (RCA SERV ). RCA SERV is simply an measure of relative export specialization, computed as te ratio of te service sare in total export in a given country i divided by te service sare in total export for te world as a wole. Clearly, an RCA SERV > 1 reveals a relative specialization in te export of services, wile an RCA SERV < 1 would signal te contrary. Table 2 reports te average RCA SERV for all te countries in te sample. Countries like Greece, Spain, Portugal or te UK display ig levels of revealed comparative advantage in te export of services, wile countries like Mexico, Germany and Canada exibit levels of RCA SERV far below one. Wile te figures reported in Table 2 are averages over te entire period, it is important also to look at te dynamics of tis indicator, wic migt in fact be endogenous. Figure 15 reports te evolution of RCA SERV over te period 1994-2009 for five selected European Countries. Wile Germany displays a RCA SERV consistently below one, Spain, Portugal and Greece s RCA SERV is always above one. Ireland, instead, displays a rising RCA SERV, wic range from being below one in te mid nineties to be well above one in te mid two-tousands. I control ow te results of te empirical analysis cange wen I exclude from te regression te countries tat switced from an RCA SERV > 1 to an RCA SERV < 1 or viceversa. Te results turn out to be stronger wen excluding tese countries. I ten compute for eac country i a relative liberalization measure as te difference between te cange in an average CHB of te sector were country i exports are concentrated and te cange in country i CHB in te sector were it concentrates its imports: [ ] (ˆτ t ˆτ f t ) = ω i CHBi CHB f it (39) i t were and f are respectively te sectors were exports and imports are concentrated. For instance for Germany, a goods-oriented country, would be manufacturing wile f would be services. For Spain, instead, a service-oriented country, would be services and f would be manufacturing. ω i are weigts computed as te output sares of country i in total world output. Notice tat tis indicator is a difference between two canges. For a 22

country i, it is te difference between te cange of te trading partners CHB in te sector of export specialization of country i and te cange in te country i CHB in its importing sector. Hence, a positive number can reflect eiter tat te CHB of te trading countries in te export sector of country i increased by more tan country i own CHB in its importing sector, or tat te country i own CHB in te importing sector decreased by more tan te CHB of te trading countries in te export sector of country i. In bot cases, a positive number signal a ig relative trade liberalization. Conversely, a negative number indicates a low relative trade liberalization. 20 Figure 16 reports te average relative trade liberalization for te countries in te sample. Interestingly, Spain, Portugal and Greece all features on average a ig relative trade liberalization, wile Germany display, on average, a low relative trade liberalization. Moreover, we observe from figure 16 ow all te BRICS countries are caracterized by ig relative trade liberalizations, wile countries like te U.S., Japan, and Germany are caracterized by low relative trade liberalizations. Stage 2: Current Account Dynamics. After aving obtained an estimate of te (ˆτ t ˆτ f t ), I ten use it to explore te relation expressed by equation (24) between current account dynamics and asymmetric trade liberalization. I use te following econometric specification (in its more complete form): CA = η 0 + η 1 (ˆτ t ˆτ f t ) + GDP it were I use te current relative trade liberalization indexes (ˆτ t S η s+1 (ˆτ t+s ˆτ t+s) f + ψz it + δ i + δ t + ɛ it (40) s=1 ˆτ f t ) and S of its leads. Z it is a set of time varying country level controls including growt (to take into account of te oter elements in equation 24), openness, GDP and per capita GDP, and a proxy for financial development. δ i and δ t are country and time fixed effects, aimed at controlling for fixed unobserved caracteristics at country level and common trends over time. Finally, ɛ it 20 A negative number can reflect eiter tat te CHB of te trading countries in te export sector of country i increased by less tan country i own CHB in its importing sector, or tat te country i own CHB in te importing sector decreased by less tan te CHB of te trading countries in te export sector of country i 23

is an error term, wic can be interpreted as measurement error in te dependent variable, supposed to be i.i.d. normally distributed wit mean zero and variance σ 2 ɛ. Te empirical prediction of te model outlined in Section 2 would be to find η 1 < 0 and η s > 0. Moreover, te model as a precise testable implication, namely tat η 1 + S s=1 η s+1 = 0. Table 3 reports te results obtained using equation (40). In te first column, I regress te cange in te ratio of te current account over GDP on te contemporaneous relative trade liberalization measure. Te coefficient, as predicted by te model, is negative, and igly statistically significant: a country tends to experience a deficit wen te restrictions to trade in its import sector fall by more tan tose in its export sector. In te second column I use as a regressor only one leads of te relative trade liberalization, and as expected te coefficient is positive and statistically significant: a country tends to experience a deficit if in te future it expects te impediments to trade in its export sector to fall by more tan te impediments to trade in its import sector. In te tird column, I include bot te current and up to tree leads of te relative trade liberalization measure. Te coefficient on te current measure is negative, wile te coefficients on all te tree leads are positive. However, only te first two leads display statistically significant coefficients. In te spirit of te model, I test weter I can reject te ypotesis tat η 1 + η 2 + η 3 = 0, and I cannot reject it. Te overall R-squared of te regression is modest (0.127), but non-negligible. In te fourt column, I insert time varying country level control, and te main results do not cange substantially. Te degree of openness displays a positive and statistically significant coefficient, wile te coefficient on te te per capita gdp growt is negative and statistically significant. Once controlling for tese two factors, te GDP, te GDP per capita and a measure of financial development do not seem to be strongly correlated wit te cange in te ratio of current account over GDP. In te fift column I present te results obtained by inserting also time and country fixed effects. Again, tere are no major canges to te core result. Finally, in te last column, I run te regression excluding tose countries wose specialization in export canged significantly over te period considered. 21 Interestingly, te coefficients on te current and future relative 21 Te switcers countries are Czec Republic, Finland, Hungary, India, Ireland, Italy, Poland and Sweden. 24

trade liberalizations appears to be larger in tis case. Tis is not surprising, since we are now focusing on te countries for wic our division into goods-oriented and service-oriented is better targeted. Even in tis last case, owever, we cannot reject te ypotesis tat te coefficients of te leads of te relative trade liberalization measures sum up to te coefficient of te current relative trade liberalization measure. Wile igly suggestive, te results reported in Table 3 are not immune by concerns about te truly exogenous nature of te proxies used to build te relative trade liberalization measures. For tis reason, I propose also a second empirical analysis, were by relying directly on tariff data, te concern about endogeneity is mostly overcome. 4.2 Agriculture versus Manufacturing Since noting in te model presented in Section 2 sarply caracterizes te ome and foreign goods to be represented by a specific industry, I propose ere an analysis of te current account dynamics of a sample of developing countries wo sares te following caracteristics: 1) tey all are igly relatively specialized in te export of agricultural goods, 2) tey are all specialized in te import of manufacturing products, 3) tey report data on teir custom duties for te period 1995-2012. Tese tree criteria limit te countries available to tirteen. 22 In tis case it is simpler to build relative liberalization measures ( ˆ τ t τ ˆ ) f t by using directly tariff data for agricultural goods ( ˆ τ t ) and manufacturing goods ( ˆ τ f t ). 23 Once obtained te relative trade liberalization measures, I can use te specification expressed in equation (40). Figure 17 reports te evolution of te current account over GDP ratio in te countries of te sample. As te Figure sows, tere are cases were te current account is fairly constant (as in Costa Rica) and cases were it presents large fluctuations (as in Zambia). Figure 18 reports instead te evolution of te tariffs on Agricultural goods and tose on Manufacturing 22 Te countries included are Argentina, Brazil, Cile, Colombia, Ecuador, Guatemala, Honduras, Malawi, Nicaragua, Paraguay, Peru, Uruguay, Zambia. See te appendix for details. 23 See te appendix for details. 25

goods. Here as well, we see cases were te two tariffs ave very similar beaviors (like in Cile) and cases were te pat differ significantly (like in Malawi). Figure 19 reports a scatterplot of te average current account over GDP for te period ( 1995-2009 (vertical axis), against te average relative trade liberalization measure τˆ t τ ˆ ) f t over te same period (orizontal axis). Te Figure present a stark negative relation between te two variables (a regression wit an R 2 of 0.6), tus sowing tat, on average, tose countries wo tended to liberalize more in teir import sector tan in teir export sector were caracterized by lower current account balances. Table 4 reports te results obtained wen using te specification (40). Te first column reports only te contemporaneous relative trade liberalization measure, and as expected te coefficient is negative and statistically significant. Te second column includes also tree leads of te relative trade liberalization measure, and te coefficients attaced to tem are all positive. In te case of te second lead, te coefficient is also igly statistically significant. A formal test of equality to zero of te sum of te coefficient cannot reject te ypotesis tat indeed te coefficients are equal, as predicted by te model. Columns tree and four add as additional control variables te real GDP growt (3), and country and year fixed effects (4). Te results do not cange, wile te fit of te regression unsurprisingly increases. Albeit te overall explanatory power of te relative trade liberalization is modest, I conclude from te results presented in te Tables 3 and 4 tat asymmetric trade liberalizations are indeed a driver of current account dynamics, wic was at least partly neglected to date. 5 Conclusion In tis paper, I propose a teoretical model were asymmetric trade liberalization can affect current account dynamics. Using te case of te asymmetric trade liberalization in manufacturing and service trade I sow ow tis cannel is potentially relevant in explaining te dynamics of te German trade surplus over te period 2000-2007. Finally, I sow empirical evidence tat broadly support te main predictions of te model, bot using a sample of OECD countries and BRICS, concentrating mode on te asymmetries between manufac- 26

turing and services, and using a sample of developing countries, concentrating more on te asymmetries between manufacturing and agriculture. Tis paper as some policy implications for te process of global rebalancing in general, and for te rebalancing in Europe in particular. A furter liberalization of trade in services migt elp countries like Spain, Portugal, Greece and te UK, to fully exploit teir comparative advantage in te provision of services and tus elping teir rebalancing process witout te need to resort solely on draconian austerity measures, as te one implemented in te period 2010-2013. A service directive aimed at liberalizing te cross-border provision of services witin te EU as indeed entered into force in te period 2006-2009. However, tere is still a large scope for furter and deeper liberalizations in te trade of services in Europe (see Monteagudo et al, 2012). Wile tis is a fairly general insigt, more researc is clearly needed to clarify wic particular services sectors migt elp te rebalancing process of deficit countries. Tese are likely to be different for te UK tan for Portugal, Spain or Greece. An important point to stress is tat tere is not necessarily a direct link between te relative trade liberalization measures computed in Section 5.1 and specific policy action of specific countries. Take for example Germany and te case of tourism, were Germany exibit a deficit wit Greece, Spain and Portugal. Lowering te barriers to export of services in tis particular case (every dollar of spending of a German tourist in Greece is an export of services from Greece to Germany) is not likely to involve more action from te German autorities tan it could require it from te Greek autorities (for instance, in terms of fostering te learning of German in te operators of te tourism sector in Greece). Tis particular example makes clear ow te passage from te teory and te evidence proposed in tis paper to te reality of economic policies migt be more subtle tan it could at first appear. Tis paper leaves open several researc questions. First, it would be interesting to be able to move to a fully bilateral specification of te testable equation of te model proposed in tis paper. Te limit ere is te relative scarcity of data on bilateral current account balances. Tis limit, owever, migt be overcome in te future. Second, it is important to 27

study more te evidence for relative trade liberalization using finer disaggregated data, tus moving beyond te aggregate approac used in tis paper. Lastly, it would be important to incorporate in te analysis also te study of foreign direct investments and foreign affiliate sales. I plan to pursue tese venues of researc in te future. 28

A Model Appendix A.1 Two-Period Model: Te Complete Log Linearized Model I denote wit aˆte percentage deviations from te symmetric steady state. So ˆx = log ( x x ), were x is te value of x at te symmetric equilibrium. Log-linearizing te model around te symmetric equilibrium described in te main texts gives us: Ĉ 2 = σ(1 β) ˆr 1 + σ ˆP 1 σ ˆP 2 + Ĉ1 ˆB 1 = ˆp 1 + ˆ Y 1 ˆP 1 Ĉ1 Ĉ 2 = σ(1 β) ˆr 1 + σ ˆ P 1 σ ˆ P 2 + Ĉ 1 (41) ˆB 1 = p ˆf 1 + Y ˆf 1 P ˆ 1 Ĉ 1 (42) Ĉ 2 = ˆp 2 + Y ˆ 2 ˆP 2 + 1 β ˆB 1 Ĉ2 = p ˆf 2 + Y ˆf 2 P ˆ 2 + 1 β ˆB 1 (43) ˆ C t = θ ( ˆp t ˆP t ) + Ĉt ˆ C f t = θ ( ˆ pf t + τ ˆf t ˆP ) t + Ĉt ( ˆ ˆP t = s ˆp t + s f pf t + τ ˆ ) f t ( s ˆ C t + (1 s ) τˆ t + ˆ ( Cˆ t = θ ˆp t + τ ˆ t ˆ C f t = θ ( ˆ pf t ( Pˆ t = (1 s ) ˆp t + τ ˆ ) t Ct ) P ˆ ) + Ĉ t (44) t P ˆ ) + Ĉ t (45) t + (1 s f ) ˆ p f t (46) = ˆ Y t (47) s f ( C ˆf t + τ ˆf t ) + (1 s f ) C ˆf t = Y ˆf t (48) ˆB 1 + ˆB 1 = 0 (49) A.2 Two-Period Model Solution In order to solve te model, te strategy is to derive all te elements tat appear in equation (23) as functions of relative trade costs, te endowments, and ˆB 1, and ten find an explicit solution for ˆB 1. I plug into equation (47) te ome and foreign version of equation (44) for period one. I ten substitute in te resulting equation te Price indexes and te aggregate consumption levels using te period 1 budget constraints (42) and te Price index definitions (46). Tis allows me to express: ˆp 1 ˆ p f 1 = α 1 α 2 ( ˆ τ 1 ˆ τ f 1 ) β α 0 ˆB1 s ( f Yˆ α 2 α 1 ˆ 1 2 Were I defined te following parameters (some of te signs are valid only under te restriction θ > 1): Y f ) (50) 29

α 0 = s s f > 0 β α 1 = s f s (θ 1) > 0 α 2 = 2α 1 + s f > 0 Moreover, It is easy to sow ow: ˆP 1 P ˆ 1 = (s s f )( ˆp 1 p ˆ ( f 1 ) s ˆ f τ 1 τ ˆ ) f 1 Repeating te same procedure for period two, I get a very similar expressions: ˆp 2 p ˆf 2 = α ( 1 τˆ α 2 ˆ ) τ f 2 + α 0 ˆB1 s ( f Yˆ 2 α 2 α 2 ˆ ) Y f 2 2 and ˆP 2 P ˆ 2 = (s s f )( ˆp 2 p ˆ ( f 2 ) s ˆ f τ 2 τ ˆ ) f 2 Plugging back equations (50)-(53) into equation (23) after rearranging and defining [ ] α 0 α 2 + (σ 1) (s s f ) α 1 α 2 + s f η = [ ] [ ] > 0 (54) 2 (1 + β) + α 0β β α 2 + (1 + β) (σ 1)(s s f ) α 0 α 2 and ν = (1 + β) (51) (52) (53) 1 s f α 2 (σ 1)(s s f ) s f α [ ] [ 2 ] > 0 (55) 2 + α 0β β α 2 + (1 + β) (σ 1)(s s f ) α 0 α 2 gives equation (24) in te main text. From Equation (54) is possible to derive te restriction on te intertemporal elasticity of substitution tat makes η a positive number α 1 α (given θ > 1). In particular, a sufficient condition for η > 0 is σ > 1 2 (s s f ) α 1. From +s α f 2 equation (55) we see tat ν is instead positive as long as σ < 1 + 2s (θ 1). (s s f ) 30

A.3 Te Two-Country Multi-period Model I list ere te first order conditions of te planner s problem for te two-country model illustrated in Section 3.2. λ i and q i, i [1, 2] are te multipliers of te planner s constraints (33)-(36). Te F.O.C. wit respect to c 1 and c 2 read: [ Ω 1 µc µ 1 1t c µ Ω 2 µc µ 1 2t 1t(1 n 1t ) 1 µ] γ 1 [ c µ 2t(1 n 2t ) 1 µ] γ 1 = λ 1t (56) = λ 2t (57) Te F.O.C. wit respect to n 1 and n 2 read: Ω 1 (1 µ)(1 n 1t ) µ [ c µ 1t(1 n 1t ) 1 µ] γ 1 Ω 2 (1 µ)(1 n 2t ) µ [ c µ 2t(1 n 2t ) 1 µ] γ 1 = q 1t (1 α)a 1t k α 1tn α 1t (58) = q 2t (1 α)a 2t k α 2tn α 2t (59) Tese are te F.O.C. wit respect to k 1 and k 2 : λ 1t = βe [ (1 δ)λ 1t+1 + q 1t+1 αa 1t+1 k α 1 λ 2t = βe [ (1 δ)λ 2t+1 + q 2t+1 αa 2t+1 k α 1 1t+1n1t+1 1 α 2t+1n2t+1 1 α ] ] (60) (61) Te F.O.C. wit respect to a 1 and b 1 are: [ q 1t = λ 1t ω 1 (a 1 ) θ 1 θ [ τ 2t q 2t = λ 1t ω 1 (a 1 ) θ 1 θ ] + ω 2 (b 1 ) θ 1 1 θ 1 θ ω 1 a 1 θ + ω 2 (b 1 ) θ 1 θ Finally, te F.O.C. wit respect to a 2 and b 2 are: [ τ 1t q 1t = λ 2t ω 1 (b 2 ) θ 1 θ [ q 2t = λ 2t ω 1 (b 2 ) θ 1 θ ] + ω 2 (a 2 ) θ 1 1 θ 1 θ ω 2 a 1 θ + ω 2 (a 2 ) θ 1 θ 1 (62) ] 1 θ 1 ω 2 b 1 θ 1 (63) 2 (64) ] 1 θ 1 ω 1 b 1 θ 2 (65) Taking te ratio of equations (63) to (62) one gets te T OT 1t as in equation (38) in te main text. 31

B Data Appendix B.1 Manufacturing versus Services Te data sources used for tis part of te paper are several. Te data used for Figures 1-5 are taken from te World Bank World Development Indicators (WDI). WDI is te source also for te controls used in te empirical analysis: te GDP, te real gdp per capita, te gdp per capita growt, te real gdp te private credit over GDP. In order to build te CHB indicators, I used production as well as trade data for services and manufacturing. Te data on trade in services (and ence te bilateral data used in Figures 6-8) come from te Trade in Service Database, developed by Francois and Pindyuk (2013) using OECD, Eurostat and IMF data. Te data for trade in manufacturing are taken from te UN-Comtrade database. Te data on gross output at te sectoral level, from te OECD-STAN database, is available only for few countries. Te output data at te sectoral level for te BRICS are obtained using OECD-STAN input output matrices in order to convert value added into output values for manufacturing and total services. Using te same procedure for Germany, Japan and te United States, I obtained estimates of te output values wose correlation wit te raw data is of te order of 0.98. Tese data constraints limit te sample of countries to 24 OECD countries plus te BRICS reported in footnote (18). B.2 Agriculture versus Manufacturing Te data source for tis part of te paper are also several. I used WTO trade statistics data to compute Relative export specialization indexes in manufacturing, agricultural goods and services for a large sample of over 180 countries for te period 1980-2012. Te same data ave been used to compute relative import specialization in manufacturing. Tariffs data are taken from TRAINS. I ten selected te countries belonging to te first quartile of te distribution of agricultural exporters, wo were also above te median relative import specialization in manufacturing and ad valid tariffs data in bot manufacturing and agricultural goods for te period 1995-2012. Applying tese criteria leaves me wit a sample of tirteen countries, eleven Latin American and two African countries listed in footnote (21). τˆ t is extracted by TRAINS using te program WITS. For eac reporting country, it corresponds to te cange in te weigted average of te applied ad valorem custom duty for te Agricultural, Forestry and Fisery products (Category 0 in te SIC) were te partner ˆ country considered is te World. τ f t is te cange in te weigted average of te applied ad valorem custom duty for te Manufactured products (Category 2 in te SIC) were te partner country considered is te World. 32

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Table 1: CHB, Manufacturing and Services Sector MANUF MANUF MANUF SERV SERV SERV Time 1995 2008 % 1995 2008 % AUT 63.01 40.91-0.35 99.86 112.08 0.12 BRA 33.87 27.02-0.20 32.09 31.44-0.02 CAN 24.46 23.57-0.04 43.42 35.26-0.19 CHE 47.55 37.56-0.21 74.91 76.19 0.02 CHN 13.78 4.00-0.71 41.78 10.58-0.75 CZE 200.51 56.64-0.72 417.64 203.34-0.51 DEU 7.33 6.74-0.08 10.32 13.89 0.35 DNK 84.81 52.38-0.38 130.80 113.09-0.14 ESP 30.24 23.28-0.23 42.86 32.14-0.25 FIN 110.14 91.45-0.17 198.65 175.50-0.12 FRA 12.66 13.03 0.03 15.55 16.13 0.04 GBR 14.95 16.20 0.08 20.02 13.72-0.31 GRC 192.71 157.32-0.18 199.27 141.30-0.29 HUN 278.33 52.28-0.81 538.44 300.42-0.44 IND 49.00 25.29-0.48 82.94 36.43-0.56 IRL 87.40 76.97-0.12 101.15 ISR 182.56 143.13-0.22 206.42 ITA 14.77 14.30-0.03 21.83 20.32-0.07 JPN 4.13 7.71 0.87 4.96 11.66 1.35 KOR 22.46 18.45-0.18 57.02 55.38-0.03 MEX 49.54 28.30-0.43 96.46 61.68-0.36 NOR 142.09 107.53-0.24 165.69 120.60-0.27 NZL 283.67 281.00-0.01 336.92 344.93 0.02 POL 125.12 44.11-0.65 204.08 96.94-0.53 PRT 130.43 122.36-0.06 220.62 214.17-0.03 RUS 29.78 62.18 30.12-0.52 SWE 58.18 48.99-0.16 90.85 88.68-0.02 USA 3.37 3.93 0.16 2.93 3.00 0.02 ZAF 128.28 118.54-0.08 155.14 142.92-0.08 35

Table 2: Average Reveled Comparative Advantage in Services Country AV. RCA. SERV. GRC 3.175 GBR 1.575 ESP 1.563 DNK 1.526 ISR 1.501 AUT 1.436 USA 1.390 IND 1.378 PRT 1.360 IRL 1.310 NZL 1.249 NOR 1.170 CHE 1.154 FRA 1.090 SWE 1.072 POL 0.992 ITA 0.978 HUN 0.956 FIN 0.889 CZE 0.889 JPN 0.732 ZAF 0.730 KOR 0.729 DEU 0.696 CAN 0.638 BRA 0.611 RUS 0.538 CHN 0.529 MEX 0.399 36

Table 3: Relative Trade Liberalization and Current Account, 24 OECD countries + BRICS Dep. var: CA (1) (2) (3) (4) (5) (6) GDP t (ˆτ ˆτ f ) t -4.906*** -5.826*** -4.478*** -4.103*** -6.153*** (0.918) (0.960) (1.025) (1.173) (1.561) (ˆτ ˆτ f ) t+1 2.477*** 3.666*** 3.912*** 3.864*** 5.169*** (0.953) (1.039) (1.024) (1.107) (1.410) (ˆτ ˆτ f ) t+2 1.928* 2.524*** 2.274** 2.688** (1.024) (0.955) (1.023) (1.289) (ˆτ ˆτ f ) t+3 0.609 (0.966) OPENNESS 0.906** 3.949*** 5.476** (0.430) (1.497) (2.258) Real P.C. GDP -0.096-5.050-0.843 (0.117) (4.287) (5.428) Real GDP 0.233** 5.123 1.356 (0.106) (4.299) (5.504) Real P.C. GDP Growt -0.162*** -0.272*** -0.250*** (0.047) (0.068) (0.084) CREDIT -0.004-0.009-0.009 (0.002) (0.006) (0.006) Time + Country FE No No No No Yes Yes R-squared 0.062 0.017 0.127 0.157 0.247 0.327 N 433 404 346 369 369 265 P-value of Test η 1 + η 2 + η 3 = 0 0.87 0.24 0.31 0.48 Standard Errors in Parentesis *,**,*** Statistically Significant at 10%, 5% and 1% 37

Table 4: Relative Trade Liberalization and Current Account, 13 Developing Countries Dep. var: CA (1) (2) (3) (4) GDP t (ˆτ ˆτ f ) t -0.372** -0.268* -0.244* -0.345** (0.144) (0.138) (0.137) (0.153) (ˆτ ˆτ f ) t+1 0.070 0.083 0.018 (0.143) (0.142) (0.160) (ˆτ ˆτ f ) t+2 0.374** 0.385** 0.409** (0.151) (0.149) (0.168) (ˆτ ˆτ f ) t+3-0.193-0.203-0.000 (0.162) (0.160) (0.181) Real GDP growt -0.160** -0.123 (0.066) (0.084) Country FE No No No Yes Year FE No No No Yes R-squared 0.032 0.071 0.099 0.247 N 201 193 193 193 P-value of Test η 1 + η 3 = 0 0.61 0.50 0.84 Standard Errors in Parentesis *,**,*** Statistically Significant at 10%, 5% and 1% 38

Figure 1: Current Account Balance Divergence in Europe Current Account Divergence in Europe (source: WDI) Current account balance (% of GDP) 15 10 5 0 5 10 1990 1995 2000 2005 2010 Year Germany Greece Spain Portugal Figure 2: Decomposition of te Trade Surplus of Germany Decomposition of German Trade Surplus (source: WDI).02 0.02.04.06.08 1990 1995 2000 2005 2010 Year GOODS_GDP SERV_GDP 39

.1.05 0.05 Figure 3: Decomposition of te Trade Deficit of Spain Decomposition of Spanis Trade Deficit 1990 1995 2000 2005 2010 Year GOODS_GDP SERV_GDP.15.1.05 0.05 Figure 4: Decomposition of te Trade Deficit of Portugal Decomposition of Portuguese Trade Deficit (source: WDI) 1990 1995 2000 2005 2010 Year GOODS_GDP SERV_GDP 40

Figure 5: Decomposition of te Trade Deficit of Greece Decomposition of Greek Trade Deficits (source: WDI).2.1 0.1 1990 1995 2000 2005 2010 Year GOODS_GDP SERV_GDP 40000 30000 20000 10000 0 10000 Figure 6: Bilateral Net Trade Between Spain and Germany Spain bilateral net trade flows wit Germany 1995 2000 2005 2010 year MANUF SERV 41

Figure 7: Bilateral Net Trade Between Portugal and Germany 6000 4000 2000 0 2000 Portugal bilateral net trade flows wit Germany 1995 2000 2005 2010 year MANUF SERV 10000 5000 0 5000 Figure 8: Bilateral Net Trade Between Greece and Germany Greece bilateral net trade flows wit Germany 1995 2000 2005 2010 year MANUF SERV 42

Figure 9: Constructed Home Bias Index, Germany, Manufacturing and Services (2000=1) CHB Index (2000=1), Germany.8.85.9.95 1 2000 2002 2004 2006 2008 Time MANUFACTURING SERVICES Figure 10: Productivity, Germany, Manufacturing and Services (2000=1) Productivity Dynamics (TFP) Germany (2000=1) 1 1.05 1.1 1.15 1.2 2000 2002 2004 2006 2008 year MANUFACTURING TOTAL SERVICES 43

Figure 11: Selected Endogenous Variable Dynamics, Symmetric Trade Liberalization and Productivity Increase 1 Nat Exports over GDP 3.5 Import Prices 0.5 3 0 2.5-0.5 2-1 1995 2000 2005 2010 2015 2020 2025 2030 1.5 1995 2000 2005 2010 2015 2020 2025 2030 0.45 Exports 0.45 Imports 0.4 0.4 0.35 0.35 0.3 0.3 0.25 1995 2000 2005 2010 2015 2020 2025 2030 0.25 1995 2000 2005 2010 2015 2020 2025 2030 2.8 t 1 and t 2 1.2 A 1 and A 2 2.6 2.4 2.2 1995 2000 2005 2010 2015 2020 2025 2030 1.15 1.1 1.05 1 1995 2000 2005 2010 2015 2020 2025 2030 Figure 12: Selected Endogenous Variable Dynamics, Asymmetric Trade Liberalization and Productivity Increase 0.1 Net Export over GDP 4 Import Prices 0 2-0.1 1990 2000 2010 2020 2030 0.6 Exports 0 1990 2000 2010 2020 2030 0.6 Imports 0.4 0.4 0.2 1990 2000 2010 2020 2030 0.2 1990 2000 2010 2020 2030 3 t 1 and t 2 1.2 A 1 and A 2 2.5 1.1 2 1990 2000 2010 2020 2030 1 1990 2000 2010 2020 2030 44

Figure 13: Trade Balance, German Data and Model Net Exports over GDP, Germany, Data and Models.05 0.05.1 2000 2005 2010 2015 year DATA SYMMETRIC ASYMMETRIC Figure 14: Trade Balance, Different Models Asymmetries in Trade Costs vs. Productivity: NX/GDP.05 0.05.1 2000 2005 2010 2015 year ASYMM CHB, NO PROD ASYMM PROD, NO CHB ASYMM CHB & PROD 45

Figure 15: Revealed Comparative Advantage in Services, Selected Countries RCA_SERV.5 1 1.5 2 2.5 3 3.5 Relative Specialization in Services 1995 2000 2005 2010 Time DEU PRT IRL ESP GRC Figure 16: Cange in Relative Protection, Average 1995-2009 Average Relative Trade Liberalization, 1994 2009 CHN POL IND CZE ESP DNK RUS HUN NOR AUT BRA GRC CHE IRL SWE CAN FIN PRT ZAF ISR FRA NZL GBR ITA KOR USA MEX DEU JPN mean of RELATIVE_CHB.05 0.05.1 46

Figure 17: Current Account over GDP, Selected Developing Countries ARG BRA CHL COL 20 10 0 10 20 10 0 10 20 10 0 10 CRI ECU HND MWI ca_gdp 20 10 0 10 NIC PER PRY URY 1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010 ZMB 1995 2000 2005 2010 Graps by countrycode year Figure 18: Tariffs in Agricultural Goods and Manufacturing, Selected Developing Countries 0 102030 Weigted Average of Tariff Rates (source: TRAINS). ARG BRA CHL COL CRI ECU HND MWI 0 10 2030 0 102030 0 102030 NIC PER PRY URY ZMB 1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010 1995 2000 2005 2010 Graps by countrycode AGRICULTURE year MANUFACTURING 47

Figure 19: Relative Trade Liberalization and Current Account, Selected Developing Countries Average Current Account over GDP 15 10 5 0 PRY CA over GDP vs. Relative Liberalization ARG CHL ECU BRA URY COL PER CRI ZMB HND NIC y= 2 10x, R2=0.60.5 0.5 1 Average Relative Liberalization 1995 2009 MWI AV_CA_GDP Fitted values 48