Trade Frictions and Market Access of Developing Countries: A Product-Level Empirical Investigation

Transcription

1 Trade Frctons and Market Access of Developng Countres: A Product-Level Emprcal Investgaton Eugene Bempong-Nyantaky, Steven Husted, and Shuchro Nshoka November 13, 2013 Abstract Ths paper examnes the effects of trade frctons, ncludng tarffs and a varety of factors that rase trade costs, on export market access at the product level and, n partcular, the role these frctons have on the ablty of developng countres to access world markets. We fnd that a varety of trade frctons do serve to lmt market access. We fnd dstance and eff cency n trade facltaton are sgnfcant determnants of the probablty of success n enterng foregn markets. We examne whether there are any systematc development-related bases from these frctons that further lmt market access for exporters from developng countres. Our results suggest that developng countres are not dfferentally mpacted by these factors. In the sprt of an earler study by Markusen and Wgle (1990), we also conduct a seres of counterfactual exercses to see the mpact of sgnfcant reductons n trade frctons on developng country market access. In contrast to ther results, our fndngs show that reductons n tarffs do not greatly mprove the number of new markets for developng countres. Our results suggest a tradtonal recommendaton to resolve the market access problem for developng countres: expanson and dversfcaton of the ndustral base and productvty mprovements n the handlng of exports. Both are vtal precondtons to ncreasng the number of export markets. Keywords: Trade Frctons, Market Access, Extensve Margn, Economc Development JEL Classfcaton: F12, F14, O19. School of Busness, Whtworth Unversty, 300 W. Hawthorne Rd. Spokane WA USA, Tel: +1(509) , Emal: ebempongnyantaky@whtworth.edu Department of Economcs, Unversty of Pttsburgh, 4508 WW Posvar Hall Pttsburgh PA 15216, Tel: +1(412) , Emal: husted1@ptt.edu (correspondng author) Department of Economcs, West Vrgna Unversty, 1601 Unversty Avenue Morgantown WV , Tel: +1(304) , Emal: shuchro.nshoka@mal.wvu.edu

2 1 Introducton One of persstent features of nternatonal trade patterns s the predomnance of ndustralzed countres n nternatonal trade. Industralzed countres, as a group, are responsble for a large majorty of world exports and represent prncpal markets for world mports. Indeed, well over half of all world trade s accounted for by trade among developed economy countres. To llustrate ths pont, Fgure 1 provdes detal on world market shares for developed countres (North) and developng countres (South) over the years As shown n the fgure, North-North trade has consstently accounted for well more than half of all world commerce, although ths share has been fallng n recent years due, n partcular, to the rse of Chna as a leadng world exporter. 1 The low partcpaton of developng countres n nternatonal trade has been studed for some tme. Over twenty years ago Markusen and Wgle (1990, hereafter MW) reported fndngs from an emprcal study amed at understandng why trade between South and North accounts for such a small share of trade. They focused on two partcular explanatons for exstng trade patterns, dfferences n protecton levels n the North and South and dfferences n the szes of Northern and Southern economes. They worked wth a computatonal general equlbrum (CGE) model that allowed them to consder the mpact of several hypothetcal experments. In the frst, estmates from ther model showed that trade would double between North and South f all tarff and non-tarff barrers to trade were elmnated. 2 In the second they consdered the mpact of expandng resources n the South so that the Southern economes would have roughly the same purchasng power as Northern economes. In that case, ther model predcted that South-South and North-South trade would both more than double. More recently a varety of studes, mostly usng gravty equatons to explan trade at the aggregate level, have focused on effects of trade frctons on the extensve and ntensve margns of export (e.g., Helpman et al. (2008)). One ssue that has receved very lttle attenton n the lterature s whether the effects of market access barrers as observed n the data are dfferent for exporters from developng versus developed countres accessng the same destnaton market. In a recent paper, Waugh (2010) argues that to reconcle blateral export and prce data across countres, trade 1 Husted and Nshoka (2013) document the growth of Chnese exports and show that t has largely come at the cost of reductons n export market shares of the Unted States, Japan, and several EU countres. 2 Recent studes that examned non-tarff barrers nclude Kee et al. (2009). Whle ther paper focuses on the measurement ssues of trade barrers, we examne the role of trade frctons on the number of trade partners, that s, the extensve margn of trade. 1

3 frctons for developng countres must be systematcally dfferent from developed countres, wth developng countres facng hgher frctons than developed countres. Studes that use gravty equatons necessarly focus on the volume of trade. Whether or not developng countres can overcome the hurdles to enter foregn markets s a necessary precondton for trade to take place. Over the years, multlateral trade talks at the World Trade Organzaton (WTO) have focused on reducng government mposed barrers to market access. But even as barrers have been reduced, developng country partcpaton n trade contnues to langush. In ths paper, we focus on market access for a broad set of countres and goods. Our focus s on the roles that varous trade frctons play n reducng market access. In our emprcal analyss we consder both tarffs as well as a varety of measures that proxy for varous types of trade costs. To our knowledge, ths paper s the frst to look at ths broad a set of frctons at the product level. We then ask f these frctons adversely affect the abltes of developng countres to access foregn markets more so than the abltes of developed countres. We also study how reductons n trade frctons mpact on the number of destnaton markets. To carry out our analyss, we employ a heterogeneous frm model of trade developed by Helpman, Meltz, and Rubnsten (2008, hereafter HMR) that has some advantages suted for our purposes. Focusng on the frst stage of ther framework, we are able to embed fxed and varable trade costs nto the HMR model and test for ther effects on market access. 3 Accordng to the nature of the heterogeneous frm model of Meltz (2003), whether producers from a country succeed to export to a destnaton market depends only on the most productve frm n that country. Gven ts productvty, that frm s success n enterng a certan market depends on fxed and varable costs of trade. We use the frst stage equaton of the HMR model but apply to the product-level data of market access. The product-level estmaton enables us to nvestgate f developng countres face more dff culty than developed country counterparts n exportng certan products to foregn markets. Our results show that exporters n developng countres suffer, n partcular, from neff cent trade facltaton that lmts ther ablty to shp ther products to foregn markets. Our results are consstent wth Lmao and Venables (2001) and Djankov et al. (2010) who fnd a crucal role of trade nfrastructure for trade. In contrast to the prevous smulaton results that emphasze the role 3 We use market access and extensve margn of export nterchangeably. Market access n ths paper s dentcal to product-level extensve margn n HMR. 2

4 commercal polces have n reducng the volume of trade (e.g., MW), the counterfactual exercses n our paper suggest that tarff elmnaton does not greatly mprove the number of new markets for developng countres, although we cannot determne to what extent the volume of trade mght expand n exstng markets. Snce producers n developng countres are not productve enough to cover fxed costs of global trade, the elmnaton of tarffs has only a mnor effect on market access. 4 Our results suggest a tradtonal set of remedes to resolve the market access problem for developng countres: expanson and dversfcaton of the ndustral base, and productvty mprovements n the handlng of exports are the crtcal precondtons to ncreasng the number of trade partners for developng countres. The remnder of the paper proceeds as follows. Secton 2 outlnes the model for estmatng export partcpaton. Secton 3 provdes an n-depth dscusson of our trade data and varous measures of fxed and varable costs. Secton 4 presents the emprcal results of our estmates from product-level data and provdes a dscusson of our counterfactual results. Secton 5 concludes. 2 Frm-Heterogenety and Market Access 2.1 Frm-Level Decson to Enter Global Markets In ths secton we provde a model of the decson by a frm to enter an export market. The emprcal approach we take s essentally that proposed by HMR n ther study of blateral aggregate export flows. We modfy t to the product-level so that we can study the product-level characterstcs of success n market access. Demand n each country l s obtaned from a two-ter utlty functon of a representatve consumer (.e., Hallak, 2006; Chaney, 2008). The upper ter of ths functon s separable nto subutltes defned for each product = 1,..., G l : U l = U[u l 1,..., ul,..., ul G l ]. The representatve consumer uses a two-stage budgetng process. The frst stage nvolves the allocaton of expendture across products. In the second stage, the representatve consumer determnes the demand for each varety ω n product subject to the optmal expendture (Y l ) obtaned from the frst stage. The sub-utlty ndex s a standard CES (Constant Elastcty of Substtuton) utlty func- 4 Clerdes et al. (1998) and Das et al. (2007) show that frms are concerned about export costs that are fxed n nature n addton to per unt costs. Frms decde to snk an ntal nvestment to enter export markets only f expected future profts wll be suff cent to cover these costs. 3

5 ton: u l = [ [ ω B q l l (ω) ] ] α 1/α. dω Here, q l (ω) s the consumpton of varety ω n product chosen by consumers n country l, B l s the set of varetes n product avalable for consumers n country l, and the product-specfc parameter α determnes the elastcty of substtuton across varetes so that ε = 1/(1 α ) > 1. From the utlty maxmzaton problem of a representatve consumer, we can fnd the demand functon for each varety: q l(ω) = [pl (ω)] ε Y l where (P l ) 1 ε [ P l = ( ω B p l l (ω) ) ] 1 ε 1/(1 ε ) dω. A frm n country k produces one unt of output wth a cost mnmzng combnaton of nputs that costs c k, whch s the country and ndustry specfc cost for unt producton. 1/ak s the frm-specfc productvty measure (.e. a frm wth a lower value of a k s more productve and that wth a hgher value of a k s less productve) whose product-specfc cumulatve dstrbuton functon G (a k ) has a country specfc support [āk, + ].5 We assume that each varety ω s produced by a frm wth productvty a k. If ths frm sells n ts own market, t ncurs no transportaton costs. If ths frm seeks to sell the same varety n country l, t faces two addtonal costs: one s the fxed cost of servng country l (f kl a varable transport cost (τ kl >0) and the other s > 1). Snce the market s characterzed by monopolstc competton, a frm n country k wth a productvty measure of a k maxmzes profts by chargng the standard mark-up prce: p k (ak ) = ck ak /α. If the frm exports ts good to country l, the delvery prce s p k (ak ) = τ kl ck ak /α. As a result, the assocated operatng proft from sales to country l s ( ) π kl a k ( ) = (1 α ) Y l τ kl c k /α P l 1 ε (a k ) 1 ε f kl (1) where proft s a monotoncally ncreasng functon wth respect to 1/a k for any par of an exporter country k and an mporter country l. Snce profts are postve n the domestc market for survvng frms, all frms are proftable n home country k. However, sales to an export market such as country l are postve only when a frm s productve enough to cover both the fxed and varable costs of exportng. Moreover, the postve observaton of trade n product from country k to l depends solely on entry of the most productve frm snce the expected proft from equaton (1) vares only wth the frm-specfc 5 Tarasov (2012) also ntroduces a country-specfc dstrbuton of productvtes. 4

6 productvty (1/a k ) n each ndustry. Now, we defne the latent varable for the most productve frm n country k n product whose productvty level s ā k : Z kl (ā k ) = (1 α ) Y l ( τ kl ck /α P l ) 1 ε (ā k )1 ε f kl. (2) Equaton (2) s the rato of export profts for the most productve frm to the fxed cost of exportng product to market l. Postve exports are observed f and only f the expected profts of the most productve frms n ndustres are postve: Z kl (āk ) > 1. Equaton (2) provdes the foundaton for our emprcal nvestgaton on market access. To estmate ths equaton, we defne f kl any country-par specfc fxed trade costs, 6 and e kl = exp(λ ϕ kl e kl ) where ϕkl s an observed measure of s a random varable. Usng ths specfcaton together wth the emprcal specfcaton of varable trade costs: (1 ε ) ln(τ kl ) = γ1 dkl γ 2 tkl γ 3 νkl + u kl where d kl s the log of dstance between countres k and l, t kl s the tarff rate, νkl s the other blateral varable costs of trade, and u kl z kl(āk ) = ln(zkl (āk )) can be expressed as s a random error, the log of the latent varable z kl (ā k ) = β + β k + β l γ 1 d kl γ 2 t kl γ 3 ν kl λ ϕ kl + η kl (3) where β k s an exporter fxed effect that captures (1 ε ) ln(c k ) and (1 ε ) ln(ā k ); βl s an mporter fxed effect that captures (ε 1) ln(p l ) and ln(y l ); ηkl = u kl +ekl s random error; and the remanng varables are captured n a constant term (β ). 7 We now defne the ndcator varable T kl l and to be 0 when t does not. Let ρ kl to be 1 when country k exports product to country be the probablty that country k exports product to country l condtonal on the observed varables. Then, we can specfy the followng logt equaton: 6 Emprcally, we do not have the product- and mporter-exporter-specfc varables for trade costs except for tarff. Thus, we use the country-level blateral varables and estmate product-specfc margnal effects. As n Hummels (2007), modes of transportaton dffer due to the characterstcs of products. For example, the varable to capture the days to handle contaners may not be a perfect ndcator for products that shp by ar. Although some of the trade varables are not deal for product-level estmatons, the varables we employ are the best avalable for our purpose. 7 Although Kee et al. (2009) emphasze the sgnfcant mpacts of non-tarff trade barrers, most of ther measures are mporter- and product-specfc varables, whch should be captured by mporter fxed effects for the product-level estmatons. 5

7 ( = Pr T kl = 1 β, β k, β l, d kl, t kl, ν kl, ϕ kl) (4) ( ) = Λ β + β k + β l γ 1 d kl γ 2 t kl γ 3 ν kl λ ϕ kl + η kl ρ kl where Λ s the logstc dstrbuton functon wth standard error σ η. Note that equaton (4) depends only on the market access decson of the most productve frm. Fxed and varable costs for trade are prmary factors of the probablty of successful entry nto a foregn market after controllng for exporter and mporter fxed effects that capture the market sze, the most productve frms productvty levels, unt producton costs, and the average prce n mportng country l. 3 Overvew of Data We obtan blateral mport data for the year 2004 for 95 mporter and 169 exporter countres from the UN Comtrade Database at the 4-dgt product level of the harmonzed system (HS) commodty classfcaton (1996). 8 For the purpose of our emprcal analyss, 93 exporter countres wth nomnal GDP per capta less than $2,935 are classfed as developng countres (.e., low-ncome countres n World Development Report (the World Bank, 2006)) otherwse they are consdered as developed (mddle-ncome and hgh-ncome) countres. 9 The developng countres n the current study account for 24.0% of trade value and 24.5% of the number of non-zero observatons n trade (Table 1). There are 1,240 products, of whch 313 are agrculture and mnng products. 10 Based 8 The choce of the countres n our data and the trade year was dctated entrely by the avalablty of data on tarffs. The data are based on the 4-dgt level data of "mports" from the UN Comtrade data base. Although the data on tarffs are avalable at the 6-dgt level, we use the 4-dgt for the followng two reasons. Frst, several countres do not report the 6-dgt values of mports n Comtrade. Second, some of tarff quota records are developed from the 4-dgt data. The results from the 2-dgt data are smlar to those from the 4-dgt data and are avalable upon request. 9 The results of the current paper do not change due to the alternatve cut-off GDP per capta values from the World Bank. For the robustness checks, we defne developng countres wth the cut-off GDP per capta value of $3,500 or less, and also we estmate for the subsets of low-ncome countres ($2,935 or less) and mddle-ncome countres ($2,935-$9,076) as n World Development Report (The World Bank, 2006). See the Appendx for the lst of countres. Table 1 reports the average GDP per capta for these three subsets of countres. Sub-Saharan Afrca forms the hghest share of low-ncome countres followed by countres n the Western Hemsphere and Asa. For hgh-ncome countres, European countres form the hghest share. 10 Agrculture and mnng products consst of homogeneous and reference goods as dentfed n Rauch (1999) and Hallak (2006). 6

8 on the fndngs reported by MW, the data on tarffs are an essental part of our study. 11 We use HS 6-dgt tarff data from 95 mporter countres from the World Bank Blateral Tarff Data, and calculate average blateral tarff rates at the HS 4-dgt level. After ncorporatng the tarff data, we have 8,866,607 possble blateral trade relatonshps, 12 of whch 18.4% exhbt postve (non-zero) trades. Thus, approxmately 80% of the observatons are zeros. Ths hghlghts the hgh presence of zeros n trade data at a detaled level of product dsaggregaton. 13 The proporton of zero trade s even hgher for exporters from developng countres. For these countres, we have 4,015,111 observatons, of whch 399,788 (10.0%) have non-zero values, whereas the sample wth exporters from hgh-ncome countres has 2,955,380 observatons for whch 928,209 (31.4%) have non-zero values. The reason why developng countres have hgh presence of zeros depends on the number of products they produce and export. On average low-ncome countres export 37.3% of the products n our data set to at least one partner country, whereas hgh-ncome countres export 75.7% of these products. Industralzaton and the dversfcaton of ndustres are the key factors to understand the proportons of zero-trade observatons. 14 For each blateral par of countres, we construct correspondng measures of trade frctons that allow us to capture the effects of varable and fxed trade costs on market access. Frst, as s standard n gravty equaton estmaton, we use geographcal dstance as a proxy for transport costs. Second, we ntroduce the ad-valorem tarff rate, a dsncentve to trade whch has been the man theme of past WTO dscussons on expandng market access. Thrd, we develop a measure of busness-related trade frctons, whch s obtaned from the World 11 On trade negotaton tables at the World Trade Organzaton (WTO), developng countres often express ther frustraton about the problems faced by ther producers n accessng markets n developed countres. In 2001, the WTO publshed a report ttled Market Access: Unfnshed Busness-Post Uruguay Round Inventory and Issues, n whch the structure of tarffs across rch and poor countres s examned. 12 We have 15,264 exporter-mporter country pars n the data. The data cover 85.2% of total blateral trade value (.e., mport-bass) of these country pars. For 83.0% of 15,264 country pars, the data cover more than 90% of the correspondng total value of blateral mports. 13 See the recent lterature on zero trade (e.g., Slva and Tenreyro (2006)). It s well noted that zero observatons are pervasve n developng country export flows. Asde from the problem of sample selecton bas, gravty models that use only postve trade are not able to account for the extensve margn of exportng. Thus, the coeff cent on dstance or any other measures of trade costs only captures shppng costs. Exceptons nclude Baldwn and Harrgan (2011). Usng dsaggregated US blateral data and a modfed verson of the Meltz (2003) model that ncorporates a preference for qualty, they examned how an mporter s market sze and blateral dstance explans the spatal patterns of zeros n blateral trade data. 14 Denns and Shepherd (2011) studed the assocaton between the product dversfcaton and the trade facltaton. They estmate the number of eght-dgt HS product lnes exported n every two-dgt category by assumng that the count data follows Posson dstrbuton. To obtan the detaled product lnes, they focus ther research on mports of EU countres from developng countres. Whle they examne the role of trade facltaton n the product dversfcaton, we examne the role of trade frctons on the product-level extensve margns from a broad set of developed and developng countres. 7

9 Bank s Dong Busness Database (World Bank). Recent academc studes show that busness costs play an essental role n explanng the success and falure of foregn market access. Our measure of these costs s the sum of an mporter s and an exporter s days to shp a product. Ths measure captures the days requred for document preparaton, nland transport, customs and other clearance, and port and termnal handlng. Importantly, nternatonal transport tme s not ncluded. Ths measure s desgned to capture the eff cency of trade nfrastructure. Clearly, shppng tme vares wth the volume of trade and should lkely nfluence the probablty of success n enterng markets. 15 Fourth, we use dummy varables for common legal orgn, common language, colonal tes, and common border to capture varous fxed costs of trade. These varables are from Head et al. (2010). Snce t s dff cult to fnd the exact fxed cost varables, 16 we hope that these measures would capture the resources spent on admnstratve and regulaton procedures, establshng dstrbutve, and contract networks, and followng legal requrements n both the mportng and exportng countres. 3.1 Development Stages and Trade Frctons Table 2 provdes the average tarff rates for agrculture and manufacturng products for certan combnatons of exporter and mporter countres as well as specfc examples from several mportng countres n each ncome group. Whle average tarff rates of hgh-ncome countres (as mporters) are less than 5% for both agrculture and manufacturng products across all ncome groups of exporters (e.g., 2.1% for agrculture goods from low-ncome exporter countres), those of low-ncome countres are around 10% (e.g., 9.9% for manufacturng goods from hgh-ncome countres). Lowncome mporter countres tend to mpose hgher tarff rates regardless of product groups, whereas hgh-ncome mporter countres mpose lower tarff rates snce these countres tend to have more FTA partners (Table 1). As s well known, tarff rates for agrculture and mnng products are hgher than those for manufactures. Fnally, hgh-ncome exporter countres, not low-ncome exporter countres, face slghtly hgher tarff rates to access the same destnaton markets. For example, the average US tarff rate on manufactured goods s 2.1% for hgh-ncome exporter countres, whch 15 As a robustness check, we also develop a varable cost of trade usng the sum of an mporter s mport and an exporter s export cost of handlng a contaner. These costs capture the varable costs wthn a country to shp a 20-foot contaner, whch nclude port charges, the resources requred for preparng and storng cargo, and the loadng and unloadng of cargo. Snce these varables are hghly correlated wth each other, we use one of these varables to capture busness related trade frctons. The results do not change wth the ncluson of both trade costs and trade eff cency varables. 16 We also estmate wth the busness related varables such as busness startup costs from the World Bank s Dong Busness Database (the World Bank) as another robustness check. Results avalable on demand. 8

10 s more than twce the rate charged on manufactured goods mported from low-ncome exporter countres (0.8%). 17 Fgure 2 examnes the correlaton between the log of days requred to prepare exports for shppng and the log of GDP per capta for 169 exporter countres. As countres develop, the days needed to export declne. Developed countres such as Sngapore (SGP), Denmark (DNK), Hong Kong (HKG), the Netherlands (NLD), and the Unted States (USA) are clustered at the lower rght corners, whereas developng countres such as Kazakhstan (KAZ) and Uzbekstan (UZB) are clustered at the upper left corners. Ths fgure suggests that developng countres appear to face hgher trade frctons n shppng relatve to developed countres. 18 Comparng the evdence presented n Table 2, Fgure 2 suggests that developng countres face tougher market access condtons not because of the global structure of tarffs but because of ther own neff cent trade nfrastructures. We turn now to a more formal test of ths hypothess usng the theory we bult n the prevous secton. 4 Estmaton Results 4.1 Trade Costs and Market Access We use equaton (4) to study the determnants of market access. We estmate two versons of the model. The frst s a pooled study where all of the products and exporter-mporter trade relatonshps are used n a sngle estmaton n order to obtan sngle parameter estmates for each of the exogenous varables. In the second study we break the data along ndvdual product lnes 17 In general, developed countres allow preferental market accesses for certan developng countres. One notable example s the law that the Unted States enacted called the Afrcan Growth and Opportunty Act (AGOA). It provdes U.S. trade preferences to the sub-saharan Afrcan countres. Table 2 also shows that average tarff rates on agrcultural goods set by hgh-ncome mporter countres are hgher for hgh-ncome exporter countres (3.4%) than low-ncome exporter countres (2.1%). The hgh level of the observed average tarff among hgh-ncome countres s drven by hgh tarff rates on agrcultural goods mposed by Swtzerland, Norway and Iceland. Whle these three countres are members of the European Free Trade Assocaton (EFTA), they do not partcpate n the European Economc Area (EEA). Hence, relatve to other countres n the European Unon, they keep hgh tarff rates on agrcultural goods rrespectve of the mporter, but under the Generalzed System of Preference (GSP) scheme and other blateral agreements, they provde low tarff rates for developng countres. For nstance, Swtzerland reduces tarff rates substantally on mports of tropcal fruts, vegetables, seafood and fsh from developng countres. Lowncome countres are enttled to addtonal preferences for some agrcultural products n the form of 55-75% reductons of the most favored naton (MFN) tarffs (WTO, 2004). 18 Ths measure ncludes the days requred for nland transport. The strong correlaton partally reflects the remoteness and ncome dfferences. Reddng and Venables (2004) argue that the geography of market access plays an mportant role n explanng cross-country dfferences n ncome. Very dstant countres pay hgher export costs as well as hgher mport costs for nputs and captal equpment. 9

11 to see how varous frctons mpact market access at the product level. The dependent varable for each equaton s a bnary varable that takes on the value of 1 when trade s observed n that product between an exporter-mporter par. Accordng to our model, trade wll occur between two countres f at least one frm n the exportng country s eff cent enough to overcome the fxed and varable costs of entry nto a partcular market. Consequently, we nclude the followng explanatory varables n our logt model: (1) the log of dstance as a proxy for transportaton costs; (2) productlevel data on the ad-valorem tarff rate measured as ln(1 + t kl ); (3) a measure of trade eff cency, whch s the log of the sum of days requred to export n an exporter country and days requred to mport n an mporter country; and (4) four varables as proxes for fxed costs: dummy varables for common legal orgn, common border, common language, and colonal tes. 19 In Table 3, we report the estmaton results of equaton (4) for the pooled sample 20 and summary statstcs for the parameter estmates obtaned from ndvdual logt regressons for each of the 1,190 products. For each product, we have at most 12,248 observatons. The medan value of observatons s 5,057, of whch around 15.7% are non-zeros. Although we have 15,264 possble country pars for each product, we have to drop the observatons f a country exports that product to all 95 mporter countres, mports that product from all 168 countres, or does not export or mport the product at all. For example, snce low-ncome countres do not produce and export 62.7% of products on average (Table 1), we cannot estmate the probablty of exports for these products snce the observed probablty s 0%. In fact, we are forced to drop 42.8% of observatons for low-ncome countres (Table 4), whch s much hgher than the percent of dropped observatons for exporters n the other ncome groups (.e., 24.8% for mddle-ncome countres and 14.3% for hgh-ncome countres). Gven the large number of estmates we have for each product, we do not report all the results. Instead, n the table we provde summary statstcs for the estmated margnal effects, 21 the proporton of margnal effects that have the expected sgn, and the proporton of those that are statstcally sgnfcant at the 5% level (one-tal test as the expected sgns descrbed n the table). 19 We also estmated equaton (4) as a probt model. The results from ths were qualtatvely dentcal to those reported here and are avalable on request. 20 In partcular, we estmate ρ kl ( = Λ β s + β k + β l γ 1 d kl γ 2 t kl ) γ 3 ν kl λϕ kl + η kl where s s the ndex for 2-dgt HS groups. Snce the exporter- and mporter-dummy varables are product-specfc and the pooled estmatons cannot report the product-specfc margnal effects, we dscuss the product-level results ntensvely. 21 We report the margnal effects for the coeff cents at the average values of the ndependent varables. 10

12 As dscussed extensvely by Baldwn and Harrgan (2011), geographc separaton helps us to explan the zero export observatons. We fnd that the probablty of successful exports from country k to l (ρ kl ) s negatvely related to the log of dstance between them. From the pooled estmaton, we obtan a margnal effect of That s, a one standard devaton ncrease n the log of dstance wll reduce the probablty of market access by 14%. The negatve mpact of dstance s remarkably robust. In the product-level estmatons, 100% of the product-level estmates carry negatve sgns and 99.7% are statstcally sgnfcant. Smlar strong and consstent results are found for common legal orgn, common border, common language, and a former colonal relatonshp. 22 Estmated margnal effects on tarff rates are expected to be negatve and we found some evdence that tarffs adversely mpact market access. The margnal effect of tarffs n the pooled estmaton was For ndvdual product regressons, 80.0% carry negatve sgns but only 37.2% are statstcally sgnfcant at the 5% level. The mprecson n these estmates suggests that the exstng tarff structure across mportng countres only plays a lmted role n preventng market entry. That s, because tarff rates have already been reduced sgnfcantly as a result of the Tokyo and Uruguay Rounds of trade talks, our results are consstent wth the concluson that further tarff reductons would lkely have only mnor mpacts on market access. Fnally, we turn to consder the role that trade eff cency plays n affordng market access. Recall that our proxy for eff cency s the sum of the number of days that t takes to shp a product n an exportng country and an mportng country. We fnd that ths varable has a sgnfcant mpact on market access; 78.0% carry negatve sgns and 52.1% are statstcally sgnfcant at the 5% level. Thus far, our dscusson has concentrated on estmates for varous trade frctons ncluded n our model. However, t s also mportant to examne the margnal effects on exporter-dummy varables (β k ), whch would capture (1 ε ) ln(ā k ck ) as suggested by the theory. In other words, the most mportant varable of Meltz s (2003) model, frm-specfc productvty (ā k ), s reflected n our estmate of the coeff cent on the exporter fxed effect varable. Fgure 3.1 shows the scatter plot of the value of estmated margnal effect of exporter fxed effect aganst the log of the GDP per capta for each country for the pooled estmate. Snce the coeff cent on the exporter fxed effect manly captures the productvty of the most eff cent frm, and productvty typcally ncreases as 22 Although we have smlar results from the 2-dgt data for most varables, the margnal effects on tarff dffer between the 2-dgt and 4-dgt data. For the 2-dgt level estmatons, 93.6% carry negatve sgns and 62.8% are statstcally sgnfcant at the 5% level. 11

13 countres develop, there s a postve assocaton. 23 On the other hand, Fgure 3.2 examnes the correlaton for margnal effect of an mporter fxed effect (β l ) and the log of GDP for the pooled estmate. As n our theoretcal dscussons, the mporter fxed effects captures the sum of the product-level market sze (ln(y l)) and the domestc prce level of the product ((ε 1) ln(p l )). As the theory suggests, there s a strong correlaton between an mporter s fxed effect estmate and the sze of that country s market (.e., the log of GDP). 24 Overall, the margnal effects on exporters and mporters fxed effects are consstent wth the theory presented n the prevous secton. Fnally, we report the goodness of fts for equaton (4) n terms of actual and predcted success and falure of market access n Table 4. Here, we defne the predcted success f the ftted value of equaton (3), ẑ kl(āk ), s greater than 0 (.e., ˆT kl = 1) and the predcted falure f that value s less than 0 (.e., ˆT kl = 0). Equaton (4) succeeds to predct 88.9% of the actual market access outcomes, of whch 19.0% are actual and predcted successes and 69.9% are actual and predcted falures. The results also ndcate that the model correctly predcts market access outcomes for roughly 90% of the observatons regardless of whether potental exporters came from developed or developng countres. 4.2 Market Access for Agrculture and Manufacturng Products In order to understand better the roles that trade frctons and productvty levels play on market access especally for countres n the South, we dvde our product sample nto two classes of goods: agrculture and mnng, and manufactures. 25 Typcally, developng countres specalze n producng and exportng agrculture and mnng products. For a varety of reasons trade barrers on these products tend to be hgher than those on manufactured goods, and that s the case wth the products n our sample. 26 Based on ther HS commodty classfcatons, we desgnate 313 goods 23 In Fgure 3.1, there s a sgnfcant varaton n the estmates for exporter fxed effects. Snce the fgure uses the estmates from the pooled estmaton exercse, the exporter fxed effects appear to capture the average level of productvty across ndustres. Thus, smaller countres that do not have some ndustres tend to have smaller values of margnal effects on the exporter fxed effects. For 92.2% of 1,190 product-level estmatons, we fnd a postve relatonshp between the estmated coeff cent on exporter fxed effects (.e., ˆβk ) and the correspondng exporter country log GDP per capta. Moreover, 75.6% of the coeff cent estmates are statstcally sgnfcant at the 5% level. 24 For 93.8% of 1,190 product-level estmatons, we fnd the postve assocatons between the estmated margnal effects on mporter fxed effects (.e., ˆβ l ) and the correspondng mporter countres log GDP. Moreover, 85.7% are statstcally sgnfcant at the 5% level. 25 See Arvs et al. (2013) who examne the development-related bas n trade costs wth a gravty approach for an aggregate of agrculture and that of manufacturng. 26 The average tarff rate on the agrculture and mnng products s 8.1% and that of the manufacturng products s 5.9%. 12

14 n our sample as agrculture and mnng products, and 927 goods as manufactured products. We then re-estmate equaton (4) for each class of goods. Table 5 summarzes our results. As was the case wth our entre pooled sample, dstance plays a sgnfcant role n reducng market access. For both types of goods, all of the products carry negatve sgns and vrtually all of the estmates are statstcally sgnfcant. We also fnd that regardless of commodty type, lower fxed costs of trade, as proxed by common legal orgn, common border, common language, and past colonal relatonshps, act to enhance trade. On average, about 97% of the coeff cents on these varables had the correct sgn wth typcally 75% of the coeff cents statstcally sgnfcant. However, when we look at the mpact of tarff protecton on these goods some dfferences across product type emerge. Tarffs carry statstcally sgnfcant negatve sgns mostly for agrculture and mnng products. From the pooled estmaton, tarffs are statstcally sgnfcant for the subset of agrculture and mnng products (margnal effect s ), whereas they carry an unexpected postve sgn for manufactured products (margnal effect s 0.022). For product-level estmatons, 86.5% of the agrculture and mnng products have negatve sgns and 46.7% are statstcally sgnfcant at the 5% level (Panel 5.1), whereas 77.9% for manufactured products have negatve sgns but only 34.2% are statstcally sgnfcant at the 5% level (Panel 5.2). The results suggest that agrculture products are senstve to prces snce they are not purely dfferentated goods but are close to homogeneous goods. Although we do not have the nformaton requred to desgnate certan goods as tme senstve as s done n Djankov et al. (2010), trade eff cency matters for market access for a broad range of manufacturng products. These results are not surprsng snce tmelness s crtcal for nternatonal producton and manufacturng networks of manufacturng sector. Multnatonal frms nvest n dstrbuton technques, requrng eff cent and tmely delvery of ntermedate and fnal goods. In our logt regressons for manufactured goods, we fnd that the trade eff cency varable has an expected mpact manly on market access for manufacturng products (margnal effect of -0.09). Ths pattern of results also holds n the product-level analyss. Almost 90% of the estmates for the trade eff cency parameter carry the correct sgn n product-level analyss for manufactured goods; 60.8% of these are statstcally sgnfcant at the 5% level (Panel 5.2). In contrast, n the pooled regresson for agrculture and mnng products, decreases n trade eff cency have a perverse effect on market access. Accordng to the pooled estmaton, decreases n 13

15 trade eff cency lead to greater market access (margnal effect of 0.05). In the ndvdual product regressons, only about half of the product-level trade eff cency coeff cent estmates for agrculture and mnng goods carry the predcted sgn, and only 24.9% of these estmates are statstcally sgnfcant at the 5% level (Panel 5.1). 4.3 Dfferental Effects for Developng Countres Whle our prmary nterest s how trade frctons affect success n enterng foregn markets, we are also nterested n whether there s any systematc bas aganst the ablty of developng countres to be able to access foregn export markets. We turn now to examne f the coeff cents on the trade costs (.e., d kl, t kl, and νkl ) dffer across developng country and developed country access to the same destnaton markets. We modfy equaton (4) by nteractng our trade costs measures (.e., dstance, tarff, and trade eff cency) wth the dummy varable of exporters from developng countres (D k where D k = 1 f an exporter country k belongs to the subset of developng countres, otherwse D k = 0): ρ kl = Pr(T kl = 1 β, β k, β l, d kl, D k d kl, t kl, D k t kl, ν kl, D k ν kl, ϕ kl ) (5) = Λ(β + β k + β l γ 1 d kl γ 1 D k d kl γ 2 t kl γ 2 D k t kl γ 3 ν kl γ 3 D k ν kl λ ϕ kl + η kl ). We also created a dummy varable to dentfy mporters from developng countres (D l where D l = 1 f an mporter country l belongs to the subset of developng countres, otherwse D l = 0). Then, we examne the nteractons n a smlar manner as n equaton (5). Agan, we are nterested n seeng whether exporters face extraordnary hgher costs when sellng goods to customers n developng countres. Table 6.1 shows the logt estmaton results for equaton (5). Most of the margnal effects do not change from those shown n Table 3. We fnd no apparent evdence that the margnal effects of developng countres are larger n negatve sgns for dstance, tarff, and trade eff cency. In partcular, the probablty of negatve sgns on the dummy varables s no better than a con toss: 37.9% for the log of dstance, 54.6% for tarff, and 55.8% for the log of trade eff cency. Moreover, most of the estmated coeff cents are statstcally nsgnfcant. Thus, both developed 14

16 and developng countres face the comparable margnal effects n export markets. The stuaton s slghtly dfferent for the nteracton of dstance and the dummy varable for developng countres as mporters (Table 6.2). 92.4% of product-level margnal effects carry negatve sgns and 62.8% are statstcally sgnfcant at the 5% level, ndcatng that the probablty of success n accessng developng country markets declnes more wth dstance than that n accessng those n developed countres. 4.4 Quantfyng the Effects of Trade Frctons on Extensve Margns In ths secton, we dscuss the effects of the reductons n trade frctons on the ablty of countres to expand at the extensve margn of trade. Usng the estmaton results, we wll examne how many more markets exporter countres would gan f trade frctons could be reduced. Our ntenton s smlar to that found n MW, who smulated the percentage ncrease n nternatonal trade due to the elmnaton of all tarffs and non-tarff barrers to trade. The focus of our counterfactual exercses s on how the probabltes of further market access would change n response to a reducton n a varety of trade frctons. We use the estmated coeff cents from equaton (4) and examne how the predcted probabltes vary wth a change n ndependent varables. In partcular, we examne four counterfactual cases: (1) blateral dstances between each par of countres falls by 50%, (2) all countres adopt the same language, (3) all countres ncrease ther export eff cency to the most eff cent level (.e., 5 days to export for Denmark and Sngapore) by replacng ln(eday k + day l ) to ln(5 + day l ), 27 and (4) all countres move to free trade by settng t kl to 0. We examne the ftted probabltes of success n market access for the four scenaros mentoned above and compare them wth the ftted probabltes of equaton (4) wth the orgnal values of ndependent varables. We are nterested n those case where the reductons n trade frctons alter the predcted values from zero ( ˆT kl = 0) to one ( T kl = 1). In other words, we are lookng for those cases where exporters are able to enter new markets when trade frctons are elmnated or reduced. Table 7 summarzes the results from these counterfactual exercses. Overall, we fnd that large and sgnfcant reductons n trade frctons do not greatly mprove market access at the extensve margn for developng country exporters. In spte of the ntense poltcal debates on market access, 27 eday k s exporter country k s days to shp a product and day l s mporter country l s days to handle a product. We keep constant the number of days for mport eff cency to focus on the exporter-sde factor. 15

17 even f all countres elmnate tarffs, the success n market access for developng countres ncreases only for 1.3% of the cases, whch s even smaller than our estmate of 1.9% growth n export markets for mddle-ncome countres and 3.3% growth for hgh-ncome countres. To be concrete, 1.3% s the percentage of estmated observatons for developng countres (.e., 2,297,292) that swtch from zero ( ˆT kl = 0) to one ( T kl = 1) due to the elmnaton of tarffs. The actual magntude s probably even smaller than 1.3% for developng countres; we fal to estmate 42.8% of observatons because they nvolve products not exported anywhere by developng countres. Smlarly, we fnd that the reducton n dstance and the adopton of a common language beneft developed countres more than developng countres. One counterfactual change that would have a sgnfcant mpact on ncreasng market access for countres n the South would be mprovng export eff cency. As shown n Fgure 2, eff cency n exportng s tghtly assocated to the development level of countres. Thus, f all the countres obtan the eff cency level of Denmark and Sngapore n handlng exports, developng countres can mprove ther market access more than developed countres. Our estmates suggest that, on average, developng countres can obtan 9.4% more new markets although the potental growth n market access vares across countres: 2.5% for Honduras and 27.2% for Chad. Our fndngs on the mportance of mprovng trade eff cency to promote export growth are consstent wth Waugh (2010) and Tarasov (2012) who analyze development-related market access frctons. Although polcy debates on market access at the WTO concentrate on tarff levels, mprovements n trade nfrastructure mght be a more powerful tool to solve the market access problem of developng countres. The results from our counterfactual exercses are somewhat counter-ntutve for polcy dscussons n nternatonal trade. However, they are consstent wth the prevous lterature on market access and global trade. Frst, because producers from developng countres are not productve enough to cover fxed costs of trade, reductons n trade frctons alone have only a mnor mpact on market access. In other words, even f trade frctons are greatly reduced, there are only lmted opportuntes for producers from developng countres n foregn markets. Therefore, mprovement n productvty s a crtcal precondton to mprove the extensve margn of trade. Second, countres n the South tend to be hghly specalzed n a small number of export products (Hanson, 2012). As n Table 1, on average only 40% of products are produced n and exported from devel- 16

18 opng countres. Wthout the expanson and dversfcaton of the producton base, the benefts of reductons n trade frctons would be mnor n terms of expandng the extensve margns of trade. 5 Concluson Ths paper has examned the effects of trade frctons on market access at the product level and, n partcular, the role these frctons have on the ablty of developng countres to access world export markets. We fnd that a varety of trade frctons do serve to lmt market access. Consstent wth many prevous papers, we fnd dstance and eff cency n trade facltaton are sgnfcant determnants of the probablty of success n enterng foregn markets for a broad range of products. Our results are consstent wth Lmao and Venables (2001) and Djankov et al. (2010) who fnd the crucal role of trade nfrastructures for trade. We tested and rejected the hypothess that there are any systematc development-related bases from these frctons that further lmt market access for exporters from developng countres. Our results suggest that developng countres are not dfferentally mpacted by these frctons. In the sprt of an early study by MW, we then conducted a seres of counterfactual exercses to see the mpact of sgnfcant reductons n trade frctons on developng country market access. In contrast to the results n MW, our fndngs suggest that reductons n trade frctons do not greatly mprove the number of new markets for developng countres. Snce producers n developng countres are not productve enough to cover costs of global trade, reductons n trade frctons, partcularly the elmnaton of tarffs, have a mnor effect on market access. In other words, even f trade frctons are reduced, there are lmted opportuntes for developng countres n accessng foregn markets. Our results suggest a tradtonal recommendaton to resolve the market access problem for developng countres: expanson and dversfcaton of the ndustral base and productvty mprovements n the handlng of exports. Our study suggests that both are vtal precondtons to ncreasng the number of trade partners for developng countres. Acknowledgement We thank partcpants n semnars at the European Trade Study Group (ETSG) at Copenhagen, the Brxen Workshop on Internatonal Trade and Fnance, the Yokohama Conference on Emprcal Issues of Internatonal Trade and Frms, and the Western Economc Assocaton Internatonal 88th 17

19 Conference. Ths research s supported by a West Vrgna Unversty Faculty Senate Research Grant (2013). Appendx We mark * for 76 developed countres (.e., mddle- and hgh-ncome countres n the World Development Report 2006) and " for 95 mporter countres. The 169 exporter countres are Afghanstan (AFG), Albana (ALB"), Algera (DZA"), Angola (AGO), Antgua and Barbuda (ATG*), Argentna (ARG*"), Armena (ARM), Australa (AUS*"), Austra (AUT*"), Azerbajan (AZE), Bahamas (BHS*), Bahran (BHR*"), Bangladesh (BGD"), Belarus (BLR"), Belgum (BEL*"), Belze (BLZ*), Benn (BEN), Bhutan (BTN), Bolva (BOL"), Bosna-Herzegovna (BIH), Botswana (BWA*), Brazl (BRA*"), Brune (BRN*"), Bulgara (BGR*"), Burkna Faso (BFA"), Burund (BDI), Camboda (KHM), Cameroon (CMR"), Canada (CAN*"), Cape Verde (CPV), Central Afrcan Republc (CAF), Chad (TCD), Chle (CHL*"), Chna (CHN"), Colomba (COL"), Comoros (COM), Congo (COG), Costa Rca (CRI*"), Cote d Lvore (CIV"), Croata (HRV*), Czech Republc (CZE*"), Denmark (DNK*"), Djbout (DJI), Domnca (DMA*), Domncan Republc (DOM), Ecuador (ECU"), Egypt (EGY), El Salvador (SLV"), Equatoral Gunea (GNQ*), Ertrea (ERI), Estona (EST*"), Ethopa (ETH"), Fj (FJI*), Fnland (FIN*"), France (FRA*"), Djbout (DJI), Gabon (GAB*), Georga (GEO), Gamba (GMB), Germany (GER*"), Ghana (GHA"), Greece (GRC*"), Grenada (GRD*), Guatemala (GTM"), Gunea (GIN), Gunea-Bssau (GNB), Guyana (GUY), Hat (HTI), Honduras (HND"), Hong Kong, Chna (HKG*"), Hungary (HUN*"), Iceland (ISL*"), Inda (IND"), Indonesa (IDN"), Iran (IRN), Iraq (IRQ), Ireland (IRL*"), Israel (ISR*), Italy (ITA*"), Jamaca (JAM*), Japan (JPN*"), Jordan (JOR"), Kazakhstan (KAZ"), Krbat (KIR), South Korea (KOR*), Kuwat (KWT*), Kyrgyz (KGZ"), Lao Republc (LAO), Latva (LVA*"), Lebanon (LBN*"), Lesotho (LSO), Lbera (LBR), Lthuana (LTU*"), Luxembourg (LUX*), Macedona (MKD), Madagascar (MDG"), Malaw (MWI"), Malaysa (MYS*"), Maldves (MDV*), Mal (MLI"), Marshall Islands (MHL), Maurtana (MRT), Maurtus (MUS*"), Mexco (MEX*"), Mcronesa (FSM), Moldova (MDA"), Mongola (MNG), Morocco (MAR"), Mozambque (MOZ"), Namba (NAM*), Nepal (NPL), the Netherlands (NLD*"), New Zealand (NZL*"), Ncaragua (NIC"), Nger (NER), Ngera (NGA), Norway (NOR*"), Oman (OMN*"), Pakstan (PAK"), Palau (PLW*), Papua New Gunea (PNG"), Paraguay (PRY"), Peru (PER"), the Phlp- 18