he Lnk between rade and Income: Export Effect, Import Effect, or Both? huo Zhang Department of Economcs tate Unversty of New York at Fredona Jan Ondrch J. Davd chardson 1 Department of Economcs yracuse Unversty February, 2004 Abstract hs paper presents a new framework to evaluate how cross-country dfferences n export openness and mport openness separately affect the real per capta ncome levels. Instrumental varable estmaton extracts the exogenous components of total trade and net exports, whch mply dstnct export and mport effects. We use countres geography as an nstrument for trade openness; and we buld on demography and cross-border transfers to develop a novel nstrument for net export openness. New estmates reveal that export alone correlates wth ncome, not mport. Ceters parbus, countres wth hgh export ntensty (but not hgh mport penetraton) have hgh ncome per capta. Keywords: Export; Import; rade; Income; Instrument JEL classfcaton: F41; F43 1 Zhang: Department of Economcs, E358 hompson Hall, Fredona, New York 14063; el.: 716-673 4671; Fax: 716-673 3332; zhangs@fredona.edu. Ondrch, chardson: Department of Economcs, 110 Eggers Hall, yracuse, New York 13244; el.: 315-443 2414; Fax: 315-443 3717; jondrch@maxwell.syr.edu; jdrchar@maxwell.syr.edu. All responsblty for errors remans wth the authors.
I. Introducton Mercantlsts beleved that trade surpluses enrched a country, and mplctly that ts government should encourage exports and restrct mports. Adam mth and Davd cardo challenged mercantlsm by argung that overall openness s what matters natons grow more prosperous through mports as well as exports. Many modern developng countres have replaced ther strategy of government-protected mport substtuton ndustralzaton wth an export orented strategy. hs paper re-poses the queston of whether trade openness rases a country s ncome per person (or per worker); and f t does, what are the channels and mechansms through whch trade affects ncome, n partcular through exports or through mports? 2 We buld on Frankel and omer (1999) and Irwn and ervö (2002). her papers allevated many of the conceptual and econometrc barrers to these ssues by showng how geographcal characterstcs provde an arguably good nstrument for a country s ntrnsc openness n a cross secton. Yet they admtted that ther approach could not separate the mport effect from the export effect. 3 No one to our knowledge has yet fgured a way to do what seems ntally the most natural next thng. hat s to construct a measure of export openness, then an arguably 2 Baldwn (2004) especally, but also Bosworth and Collns (2003) and OECD (2003) provded comprehensve lterature revews. ee also Zhang (2003). 3 We and Wu (2001) and We (2002a, 2002b) conscously followed smlar leads n a study of how globalzaton affects Chnese cty-level growth and nequalty. We conflated export and mport nfluences by selectng Chnese ctes dstance to two major Chnese ports as hs nstrument for a cty s natural openness. Hanson (2003), n somewhat the same sprt, found Mexcan wages hghest (ceters parbus) n regons that are geographcally and economcally closest to the Unted tates. 2
ndependent measure of mport openness, and nvestgate whether one has a dfferent effect on ncome across countres than the other, ceters parbus. hat s the prncpal objectve of ths paper to dentfy the separate nfluences of export openness and mport openness on ncome levels across countres n 1990 (as well as n 1985, 1980, and 1975). In partcular, we conceptualze and estmate an addtonal net trade effect on ncome levels. When combned wth Frankel and omer s total trade effect, the two effects together mply separable export and mport effects. 4 We fnd that export openness correlates much more closely and strongly wth a country s lvng standards cross-sectonally than does mport openness 5. he rest of the paper s as follows. ecton II descrbes the models and develops our approach, and ecton III reports the emprcal results. ecton IV tests the robustness of the fndngs. ecton V contans the conclusons of the paper. II. Emprcal pecfcaton We develop a smple model based on Frankel-omer (1999), separatng ther total trade share nto export share and mport share: ln( y ) η Z + ε (1) = 0 + η1x + η 2M + η3 where y represents the real per capta ncome for country. X and M are exports and mports scaled by real GDP. Z stands for other control varables. 4 Ondrch et al. (2002) explored the senstvty of the Frankel-omer approach to heteroscedastcty. Zhang (2003) examned the ssues of ths paper n a panel approach; see also Greenaway et al. (2002). 5 Unfortunately, the nstrumentng technques that we develop for net trade cannot be easly mplemented blaterally, so there remans some doubt about the exact conformty of our conclusons to Frankel and omer s. 3
here are two famlar problems wth smply estmatng equaton (1). One s the demandsde accountng relatonshp (Y = C + I + G + X M) coupled wth the supply-sde relaton between overall supply Y and export supply X. hus the key varables of nterest are mutually endogenous. he second problem s that export openness and mport openness are hghly correlated across countres, as every general-equlbrum thnker knows. And Country A s exports to country B are country B s mports from country A. o both measures of openness are endogenously related to each other and to ncome (per capta), the focus varable on whch they are thought to operate. 6 Confrontng these challenges, what s a researcher to do? Instrumental varable estmaton provdes a theoretcally appealng way to handle the endogenety problem. he mportant practcal queston posed n ths study s how to fnd two sets of nstruments that can not only capture the exogenous components of exports and mports but also dstngush the export effect from the mport effect. hs paper proposes an alternatve approach to dstngushng the effects of mports and exports n the sprt of nstrumental varables that meet the challenge of endogenety. Consder algebracally re-arrangng equaton (1). Let be total trade (exports plus mports) dvded by real GDP and let E be net trade (exports less mports) dvded by real GDP (to control for scale effects). Equaton (1) transforms to ln( y ) δ Z + ν. (2) = 0 + δ1 + δ 2E + δ 3 We argue that fndng good nstruments for and E n order to estmate equaton (2) across countres s much easer than fndng good nstruments for X and M n order to estmate 6 Focusng on the cases of Japan and Korea, Lawrence and Wensten (2001) found that mports rather than exports are the condut of faster productvty growth. However, we are not confdent that they adequately addressed the problem of reverse causaton. 4
equaton (1) cross-sectonally. Part of our approach rests on a comparson of Fgures 1 and 2. In Fgure 1, the average export share from 1970 to 1998 s plotted aganst the average mport share from 1970 to 1998. he World Development Indcator 2000 data cover 174 countres. he fgure exhbts an unsurprsng strong postve relatonshp between export share and mport share (wth a correlaton of 0.85). In Fgure 2, the sum of the export share and the mport share s plotted along the vertcal axs. Along the horzontal axs, we plot the dfference between the export share and the mport share. he chart shows no evdent relatonshp between the two varables n the long run (the correlaton s equal to -0.13.) Frankel and omer (1999) have already provded good canddates to nstrument for total trade share. he major contrbuton of ths paper s to propose nstruments for the net trade share varable that capture cross-country dfferences n remttances and n borrowng and lendng behavor, and the demographc and asset/portfolo varables underlyng such behavor. nce = X + M and E = X M, our estmaton approach wll shed mplct lght on the dstnctve export effect and the dstnctve mport effect. 7 A. Blateral rade egresson We adapt the approach and data of Frankel and omer (1999), as updated by Frankel and ose (2002), to employ geographcal characterstcs of countres as attractve cross-sectonal nstruments for total trade based on the gravty model of blateral trade. We accept ther demonstraton that countres geographcal features do affect ther trade, but are not affected by ther ncomes, by government polces, or by other factors that nfluence ncome. 7 We also further refne the Frankel-omer model wth a heteroscedastcty correcton descrbed brefly below and n more detal n Zhang (2003). 5
We follow ther specfcaton exactly, ncludng the usual gravty-equaton varables, as well as ndcators of landlocked status and common language and borders. We refne ther approach slghtly, due to the possblty of heteroscedastcty. We use weghted least squares estmaton based on the procedure proposed by Harvey (1976). As n Frankel and omer (1999), the predcted value of the overall trade share for country s aggregated from the predcted values of the blateral trade shares between country and all her tradng partners. 8 B. Net rade egresson Our man task n ths paper s to replace net trade (E) wth determnants n the sprt of nstrumental varables, then to nfer separate export and mport effects from ths and the Frankel- omer approach. Whle aspects of the tme-seres relatonshp between net exports and ncome are well-establshed, e.g., ts cyclcalty, the cross-secton relatonshp s not. What knd of natonal and nternatonal varables determne whch countres have overall trade surpluses and whch have overall trade defcts? What varables determne blateral net exports between two countres? And are those cross-sectonal determnants sutable for frst-stage nstruments n the current study, or do they too vary cross-sectonally wth ncome levels? hs sub-secton ams to answer. o begn, a country s current account balance conssts of three parts: (1) the balance of trade n goods and servces our E; (2) net rents, nterest, profts and dvdends represented as rb where B (-B) s the net foregn nvestment poston (a stock varable) that earns (pays) yearly nterest at some approprate nterest rate r; (3) transfer payments P such as foregn workers 8 ee some detals of the procedure n Appendx A1 and more n Zhang (2003). 6
remttances. In the open economy, the current account balance s also determned by the savngnvestment gap ( I), nclusve of government savngs/dssavngs. Changes n offcal reserves may offset any surplus or defct n the overall balance of the captal account KA and the current account CA. CA = E + rb + P = I = KA. (3) In prncple, equaton (3) descrbes both overall and blateral current-account balances. Blateral emprcal mplementaton of our approach unfortunately founders on data lmtatons. (Few natons or global data-collecton agences publsh more than a handful of the varables n equaton 3 on a blateral bass, ncludng the current account tself.) It s clear that we can re-wrte equaton (3) to conceve of E as determned crosssectonally by, I, r, B, remttances, and reserve changes. hough, I, and possbly r are also correlated wth ncome levels, and hence endogenous, transfer payments and reserve changes are less lkely to be. And there are at least some deeper determnants of and I themselves that mght qualfy as good nstrument canddates, especally n fnancally open economes. Wth respect to and I, n a closed economy wth zero nternatonal captal movement, savng s equal to nvestment. Economc growth depends on both savng and nvestment drectly. As countres open up, however, domestc nvestment s fnanced by the worldwde pool of savngs, whle domestc savng seeks the hghest returns n the global captal market. When domestc savng s not suffcent to fnance domestc nvestment, such as n the U.., the dfference s made up by foregn savngs. When domestc savng exceeds domestc nvestment, such as n Japan, the extra savngs are nvested abroad. Hgher savng s assocated wth captal outflow and lower savng ndcates captal nflow. hus, whle nvestment remans as a drect determnant of countres ncome level n the open market scenaro, global borrowng and 7
lendng mples that domestc savng alone s arguably detached from ts closed-economy nfluence on ncome. 9 hat n turn makes domestc savng or ts deeper determnants reasonably good canddates to nstrument for net trade (E). hough there s well-known skeptcsm over how open world captal markets really are, 10 almost all commentators agree that the degree of nternatonal captal moblty has ncreased sgnfcantly, especally durng the last 20-25 years. he wdespread removal of captal controls n the ndustral countres enhanced fnancal ntegraton. he fnancal sector reforms and the openng up of the captal account to prvate captal nflows help to create lower and lower correlaton between domestc savng and nvestment, even n developng countres. 11 Hgh captal moblty allows us to use savng or factors that determne savng as nstruments for net trade or current account balance. 12 Yet the relatonshp between savng and net trade or current account balance reflects a complex nteracton of households, frms, and governments both at home and overseas. A random shock specfc to the net trade balance could 9 ee Kraay and Ventura (2002) for an up-to-date treatment and correspondng lterature. 10 Feldsten and Horoka (1980) orgnated the skeptcsm. ee Zhang (2003) and Kraay and Ventura (2002) for a revew. 11 Wth less dversfed producton and export structures, ol-exportng countres and small countres tend to have an especally low savng-nvestment correlaton. 12 Glck and ogoff (1995) showed that country-specfc shocks rather than global productvty shocks are mportant determnants of current account fluctuatons. Emprcal work extended dynamc optmzng models proposed by Ghosh (1995) and Ghosh and Ostry (1995) to the open economy context (azn 1995; Obstfeld and ogoff 1996). From a savng-nvestment perspectve, Debelle and Faruqee (1996) nvestgated the determnants of current account usng the structural approach. Holdng level of nvestment constant, countres wth a hgher savng rate tend to be net exporters and net mporters had a lower savng rate. 8
potentally affect a country s savng rato. 13 In order to avod these new endogenety problems, we propose countres demographc characterstcs as more fundamental and cleaner nstruments for net trade recognzng that dfferences of savng between two countres can be very well explaned by ther dfferences n demography. Demography matters n both age structure and n steady-state populaton growth. Wth regard to the former, lfe cycle models n an ntertemporal approach suggest that net trade balance s the outcome of forward-lookng dynamc savng and nvestment decsons. People save durng the earnng span at productve ages and dssave when they are young or old. hus, aggregate natonal savngs wll be relatvely hgh f the sze of the dependent populaton s low compared to the sze of the workng-age populaton. Hgh savngs buld up domestc and foregn assets, reflected n a large net trade surplus. In the cross-sectonal context, the real growth of output (GDP) s expected to have a postve mpact on the net trade balance due to the fast growng supply n the global market. We use the populaton growth rate as the proxy for the real growth of output to capture the mpact. Urbanzaton s also a deeper fundamental determnant of savng, lke demography, and s less lkely than savng tself to be subject to feedback nfluences from exogenous shocks to ncome. he trend toward urbanzaton leads to a lower prvate savng rate because precautonary savng s less necessary. 14 An mportant determnant of the net trade balance s the net nterest and nvestment ncome from abroad. A country can run a steady-state trade defct equal to her nvestment 13 For nstance, governments tend to adopt contractonary fscal polcy to prevent sustaned large captal flows when ther countres are n trade defct. 14 Edwards (1996); Loayza et al. (2000). 9
ncome, ceters parbus. Any deteroraton n nvestment ncome requres mprovement n the trade balance. Lkewse, nternatonal reserves flow nto the home country when t exports and flow out of the home country when t mports. Ceters parbus, a country can run a steady-state trade defct equal to ts declne n offcal reserves. Fnally, net current transfers from abroad, such as workers remttances, are an mportant source of foregn exchange for many countres that can fnance trade mbalances. he net trade balance s also nfluenced by a country s relatve prce of tradeable output to non-tradeable output (compared n turn to the comparable world prce rato). he hgher s a naton s relatve tradeables prce (by world standards), the larger wll be ts relatve output of both exportables and mport substtutes, output that gets exported n the frst nstance and dsplaces mports n the second (Obstfeld and ogoff 1996, pp. 199-257.) Yet there s no obvous reason why a naton s ncome level should vary systematcally wth ths relatve prce, because every naton produces both tradeables and non-tradeables and earns ncome from both. 15 o summarze, we beleve the followng equaton to represent a strong, exogenous predcton of net exports: ˆ E = ˆ γ 0 + ˆ γ 1dep + ˆ γ 2 popg + ˆ γ 3ol + ˆ γ 4urpop ˆ + γ 5n ˆ + γ 6dres ˆ + γ 7relp ˆ + γ 8 nct. (4) 15 One could object that several of these varables, e.g., net remttances or returns on cross-border captal placement are co-determned endogenously wth net exports (E) as part of the deeper fundamentals of current account behavor how the current account sum of all of them, E, remttances, captal ncome, responds to shfts and shocks to output, nvestment prospects, and government spendng needs (Obstfeld and ogoff 1996, pp. 74-116). We agree, but can thnk of no feasble measures of global and country-specfc productvty and fscal shocks that could serve as deeper, more fundamental nstruments than those we choose. Furthermore, we thnk ths objecton relates more to net captal ncome and flows of offcal reserves than to remttances. 10
he dependent rato dep s calculated as the proporton of people under age 14 or over age 65. popg stands for the populaton growth rate. ol takes the value of 1 f country depends heavly on ol revenues as her man source of ncome. 16 urpop s defned as the percentage of the total populaton lvng n an urban locaton. n s the stock of net nvestment ncome from abroad as a rato of GDP, measured n year 1990. It captures the term rb n equaton (3). dres denotes changes n net offcal reserves as a rato of GDP. Prce relatve (relp) s measured as the rato of ratos of export prces to GDP deflators from 1985 to 1990. nct represents current transfers (from abroad), such as mgrants remttances, scaled by GDP. C. Income egresson he last step n re-askng the Frankel-omer queston, and n decomposng the effect of openness on per capta ncome nto an export and an mport effect, s to use the predcted values for the total trade share () and net export share (E) varables from sub-sectons A and B as nstruments n regresson equaton (5), and to estmate t across countres: ln( ) 0 + β1 + β 2E + β 3 ln( pop ) + β 4 y = β ln( area ) + u (5) where the dsturbance u represents all the uncertan factors that may also affect the level of the real per capta ncome. he sze of the coeffcents on and E determne f exports or mports are correlated more closely across countres wth per capta output. 16 he dummy varable for ol exportng countres (prmarly Gulf tates) s ncluded to capture several unque phenomena. One s the unusually low savngs-nvestment correlaton descrbed n note 12; another s strkngly large ntra-regonal and nternatonal mgraton. he dummy varable for ol exportng countres s expected to be postve snce these countres typcally have a more favorable current account poston on average (Chnn and Prasad 2003). 11
Λ ln( y ) ˆ ( ˆ ˆ ) ( ˆ ˆ ) ˆ ln( ) ˆ = β 0 + β1 + β 2 X + β1 β 2 M + β 3 pop + β 4 ln( area ) (6) where ˆ β ˆ 1 + β 2 measures the predcted partal effect of exports on per capta ncome and ˆ β ˆ 1 β 2 measures the predcted partal effect of mports on per capta ncome, holdng the sze of countres constant. he relatve mportance of exports and mports depends on the sgn and magntude of ˆ β 1 and ˆβ 2 estmated for equaton (6). he estmaton results are shown n ecton III (data are descrbed n the appendces). III. Estmaton esults A. Blateral rade egresson We start by testng for heteroscedastcty n the Frankel-omer (1999) blateral trade regresson by runnng the Breusch-Pagan test on a regresson of log squared OL resduals on the blateral trade regressors. he resultng test statstc of 88.38 s hghly sgnfcant statstcally (p value = 0.0001). Accordngly, the null hypothess of homoscedastcty s rejected. We therefore use the weghted least squares (WL) approach proposed by Harvey (1976), where the 2 square of the weght (error varance σ ) s constructed as the exponental functon of the predcted e ˆ. All the varables n the regresson are weghted by the nverse of the square root 2 ln of the varance. WL regresson results n able 1 reveal a postve and statstcally sgnfcant relatonshp between blateral trade and country s geographc characterstcs. All else beng equal, large dstance leads to less trade. If the offcal languages n country and country j are the same, trade rses by 55 percent. wo countres that share a border trade 62 percent more than countres pars whch do not. Landlocked countres tend to trade less due to hgh transportaton 12
costs. A sgnfcant porton of the varaton of the blateral trade share s explaned by the estmated equaton. he Frankel-omer results are thus confrmed, usng a larger sample. 17 B. Net rade egresson he results of ths paper rest crucally on our approach to net exports. he net exports regressons are estmated for eght dfferent sets of varables to control for countres demographc characterstcs and cross-border transfers features. able 2 provdes the results of the estmaton based on equaton (5). he results of the frst specfcaton ndcate that an ncrease n the age dependency rato leads to a reducton n net trade, as expected. he mpact of populaton growth rate s postve but not sgnfcant. Controllng for country s demography, ol-exportng countres do tend to have a hgher net trade share relatve to non-ol-exportng countres. he second specfcaton adds the urban populaton rato varable. he urban populaton rato s postvely related to the net trade balance at a 10% sgnfcance level, as opposed to our hypothess. Alternatve specfcatons exclude the urban populaton rato from the regresson; the results for other coeffcents reman largely unchanged. In specfcaton (3), we emphasze fnancal-transfer factors alone. As expected, net nvestment ncome from abroad has a sgnfcant and negatve mpact on net export share. Changes n reserves and the relatve prce of tradeables do not appear to play a major role n 17 Data on blateral trade are only avalable for 62 countres n the Frankel and omer (1999) study. Based on the coeffcents estmated from the gravty model, Frankel and omer mputed the blateral trade share for country pars whose recorded trade share s mssng. he qualty of the nstruments and the precson of the estmated effects are brought nto queston especally f the gravty relaton s systematcally dfferent for countres n the sample than for countres added through mputaton. 13
determnng the trade balance, possbly because of the non-market forces n some planned economes that make t dffcult for nternatonal trade flows to adjust to changes n market condtons. Net current transfers are statstcally sgnfcant n explanng the trade balance. A one percentage pont ncrease n net current transfers s assocated wth a 1.14 percentage pont decrease n the net trade share balance. In specfcatons (4) to (8), we combne the two sets of nstruments, demographc and fnancal. hese specfcatons dffer n two aspects: () whether or not the urban populaton rato ncluded; () the two alternatve measures of current transfers consdered (current transfers from abroad and net workers remttances.) In most cases, countres net exports balance sgnfcantly mproves wth the lower dependency rato, hgher urban populaton rato, lower net ncome from abroad, or lower net current transfers. hese fndngs are consstent wth our hypothess. Alternatve measures of net current transfers do not seem to alter the results. he bnary varable for ol-exportng countres s assocated wth the net trade balance at the 1% sgnfcance level, whle we do not fnd a sgnfcant relatonshp between countres trade balance and the populaton growth rate, changes n reserves, or the relatve prce of tradeables. he varous specfcatons predct between 14% and 80% of the overall varaton n the net export share. C. Income egresson In order to cope wth the smultanety between openness and ncome, the ncome equaton s estmated usng nstruments for total and net trade that come from geography, demography, and cross-border transfers, as descrbed n the prevous two sub-sectons. he objectve of ths sub-secton s to test whether, after controllng for the sze of the economy, total 14
trade openness and net trade openness contrbute to explanng cross-country dfferences n the level of real per capta ncome. o nvestgate the qualty of the nstruments, the scatter plot matrces n the eght panels of Fgure 3 vsually dentfy the relatonshp between the trade varables and ther predcted values. Next to the correlaton matrces, numercal values of the correlatons are dsplayed n the correlaton tables. Overall, Fgure 3 shows that total trade share s postvely lnked wth the aggregaton of the estmated blateral trade equatons; correlatons range from 0.53 to 0.77. egardng net exports, we fnd that the actual values are n general postvely correlated wth predcted values, wth correlatons rangng from 0.51 to 0.90. Consderable nformaton about countres overall trade and trade balance s provded by the predcted values wth no apparent outlers. he correlaton between the total trade share and the net trade share, as well as the correlaton between the predcted total trade share and the predcted net trade share, are weak and nonlnear, as desred, mplyng that collnearty s probably not a problem, agan as desred. able 3 presents eght two-stage least squares estmaton results for the lnk between openness and per capta ncome, one for each of the eght specfcatons of the frst stage net trade balance estmaton. he frst column reports OL estmaton results that serve as a benchmark. he prmary focus of ths table s the coeffcent estmates of the total trade share and net trade share. When controllng for the sze of the economy, we fnd that hgher levels of the net trade share only are typcally lnked wth more per capta ncome. he sze of countres, as measured by populaton and area, has an nsgnfcant effect. 18 able 3 shows that these regressons account for 28 to 60 percent of the cross-country varaton n per captal real GDP. 18 hs suggests that the man nfluence of geography on ncome s through the channel of nternatonal flows of goods and servces, rather than the wthn-country trade. 15
he man results on the coeffcents estmates for and E from able 3 are solated n able 4. An F-test s used to fnd the jont sgnfcance of the coeffcents on and E. he test statstcs reject the hypothess that both coeffcents n row 1 and 2 are zero, supportng that the per capta ncome s assocated wth both the total trade share and the net trade share. Impled coeffcents for the separate effects on per capta ncome of export openness and mport openness appear n the last two rows. Coeffcents of X n row 4 are the sum of the row 1 coeffcents on and the row 2 coeffcents on E. Coeffcents of M n row 5 are the dfference between the row 1 coeffcents on and the row 2 coeffcents on E. he correspondng standard errors for the coeffcent sums/dfferences are computed and recorded n the parenthess. he coeffcents for X change moderately across the eght specfcatons, but reman postve and hghly sgnfcant. he coeffcents for M are negatve and margnally sgnfcant. able 4 leads to three man conclusons. Frst, the more generally open a country, the hgher per capta ncome, ceters parbus (the Frankel-omer concluson). 19 econd, mport openness alone s ether uncorrelated wth per capta ncome, or negatvely correlated. 20 o put the pont quanttatvely, comparable and equally open countres by the Frankel-omer 19 he combned export-openness and mport-openness effect n the expanded model of ths study (approxmately 0.01) appears to be much smaller than the trade-openness effect estmated n Frankel and omer (1999) and Ondrch et al. (2002) (approxmately 1 to 2). Frankel and omer (1999), as well as Ondrch et al. (2002), dvded both the actual and the constructed trade share by 100, whch causes the dscrepancy. he nterpretaton of the coeffcent estmates remans consstent, even though t depends on how the trade share varable s scaled. An ncrease n fr,.e., (X + M) / 100, of one percentage pont s assocated wth a 1 percent ncrease n ncome per person. An ncrease n (X + M) of one percentage pont s assocated wth a 0.01 percent ncrease n ncome per person. 20 Very smlar results emerge mplctly from Mller and Upadhyay s (2000) s study of the effect of openness on a country s total factor productvty (ntmately related, of course, to ts ncome per person). her trade orentaton measure, based on the countres devatons from Purchasng-Power-Party exchange rates proxes for mport shares. 16
measure but that dffer between themselves n X-openness and M-openness have dfferent per capta GDP; those that are 1 percent more X-open than M-open have approxmately 0.1 hgher GDP per person. hrd, the postve export openness effect s greater than the negatve mport openness effect n all cases. IV. obustness ests o test the relablty of the emprcal fndngs, ecton III estmatons are carred out for year 1975, 1980, and 1985. he frst stage blateral trade regresson s specfed as outlned above and n Frankel and ose (2002). Instruments used n the frst stage net trade share regressons correspond to specfcatons (1) through (8) n able 2. Coeffcents and standard errors for all openness ndcators are summarzed n ables 5, 6, and 7. 21 Each table s n the style of able 4. he results demonstrated are mostly stable n sgnfcance and quanttatvely smlar across dfferent tme perods. Gven the excluson of numerous economes for whch data are not avalable at the blateral level, the 1975 cross-secton estmates appear to be less precse. he man results n ecton V are confrmed. he export share varable retans ts magntude, sgn, and strong sgnfcance. he fndng suggests that export s the prmary path through whch openness determnes ncome. hs s consstent wth typcal emprcal estmates n the export-led-growth lterature. he estmated mport share contnues to ndcate the weak assocaton between mport and ncome. he sgn of the coeffcent s negatve, suggestng that mports may undermne the overall effectveness of openness and globalzaton. 21 he frst stage regresson results for year 1975, 1980, and 1985 are avalable upon request. 17
V. Concludng emarks How does openness correlate wth per capta ncome? And does t matter whether ts ndcator s export openness or mport openness? he man contrbuton of ths study s the development of an ntegrated two-stage framework to these questons, as descrbed below: Exports Cross-border ransfers Geography otal rade Income Net rade Demography 1st stage 2nd stage 2nd stage 1st stage Imports We develop nstruments to estmate cross-sectonally both the export-ncome lnkage and the mport-ncome lnkage. o address the smultanety problem, we employ an nstrumental-varable approach to cross-country varatons n the overall trade share, relyng on geographc factors, as others have done, and to cross-country varatons n the net trade share (exports mnus mports), emphaszng demography as well as cross-border fnancal transfers. he nfluence of export openness and mport openness on ncome s nferred through the nteracton of the total trade effect and the net trade effect. Our cross-sectonal estmaton ndcates a postve correlaton between export openness and ncome levels. Import openness correlates negatvely wth countres ncomes, however, though sgnfcantly dfferent from zero n only half our runs. When sgnfcant, the negatve mport openness effect s always quanttatvely smaller than the postve export openness effect. aken together, ths leads the total-trade-openness effect export openness plus mport openness to be postve, whch s n lne wth the emprcal predctons from other research n ths genre. 18
In a provsonal panel extenson of ths paper, Zhang (2003) found that the exogenous components of export openness are postvely assocated wth ncome levels and are hghly sgnfcant after controllng for possble reverse causalty from ncome to exports. he mport openness effect s postve and nsgnfcant most of the tme (whereas t s often negatve n the cross-sectons). he favorable export effect s larger than the favorable mport effect, when sgnfcant. Appendx A1 wo-tep Procedures he nstrumental varable analyss for the total trade share n ths paper s generalzed nto the followng two steps: A1.1 tep I ---- Blateral trade regresson In step I, we regress the log blateral trade share (t ) on countres geographcal features represented by dstance (dst ), tradng partner s populaton (pop j ), product of areas (area area j ), common language dummy (langauge ), common border dummy (border ), and landlocked dummy (landlocked ). ln t = ln( τ / GDP ) = α ' X + e = a0 + a1 ln( dst ) + a2 ln( pop j ) + a3 ln( area area j ) + a4 language + a 5 border + a6landlocked + e. (a1) where e 2 = ln t α ' X, e ~ N(0, σ ). 19
Harvey s (1976) procedure s used to correct for heteroscedastcty. We assume that the varance of the dsturbance term e n equaton (a1) takes the followng form: σ 2 = exp(λ'x ). (a2) he estmator of λ s the OL estmator 1 ˆ = X ' X ln(ˆ e 2 j j λ X ), (a3) of the coeffcent vector n the regresson 2 eˆ ln ( ) = λ'x + η (a4) where 2 e ˆ s the square of the resdual resultng from the OL regresson of equaton (a1) and η s the dsturbance term. A1.2 tep II ---- otal trade constructon γ n In a heteroscedastc model, one should use nonlnear least squares regresson to estmate t = exp(γ ' X ) exp( aˆ' X ) + u, (a5) and set ˆ γ = exp( ˆ' γ X ). (a6) he predcted overall trade share for country s: ˆ = ˆ γ exp( aˆ' X ). (a7) j ee Zhang (2003). A2 Data Descrpton 20
A2.1 Blateral rade Data et he blateral trade data are obtaned from Andrew ose web ste. hs data set ncludes blateral trade shares, dstance between countres, populaton, area, a common-language dummy varable, a common-border dummy varable, and a landlocked dummy varable for 186 countres n year 1990. ose s orgnal source for the trade data s the World rade Database and Unted Naton s Internatonal rade tatstcs Yearbook. he populaton and real GDP per capta data come from Penn World able 5.6. Area data are from the World eference Atlas. he nformaton for the dstance and common language dummy varable s from the Central Intellgence Agency (CIA) s web ste. A2.2 Net rade Data et he net trade data ncludng exports, mports, GDP, populaton aged 0-14, populaton aged 65 and above, total populaton, populaton growth rate, net ncome, changes n net reserves, GDP at market prces, offcal exchange rate, and current transfers are taken from World Bank s World Development Indcator 2000. Net ncome from abroad ncludes the net labor ncome and net property entrepreneural ncome components of the system of natonal accounts (NA). Changes n net reserves are the net change n a country s holdngs of nternatonal reserves resultng from transactons on the current, captal, and fnancal accounts. he net current transfers take three forms. he net current transfers from abroad comprse transfers of ncome between resdents of the reportng country and the rest of the world that carry no provsons for repayments. he net current transfers are recorded n the balance of payments whenever an economy provdes or receves goods, servces, ncome, or fnancal tems wthout a qud pro quo. 21
he thrd measure comes from the net workers remttances as recorded by Internatonal Monetary Fund s Balance of Payments tatstcs. he relatve prce of tradeables s calculated based on the data obtaned from the World Bank s World able. We defne the prce relatve as the 1990 to 1985 rato of export-to-gdpdeflator prce ndces. nce the export prce ndex s n terms of the 1987 current U dollars whle the GDP deflator s n 1987 local currences, we create an exchange rate ndex for both year 1990 and year 1985 to adjust the prces nto the same currences. In partcular, (exchange rate ndex) t (exchange rate) 100 (exchange rate) t = (a8) 1987 where t refers to year 1985 or 1990. herefore, 1990 to 1985 rato of export-to-gdp-deflator prce ndces (export prce ndex) 1990 = (GDP deflator) 1990 /(exchange rate ndex) (export prce ndex) 1985 (GDP deflator) 1985 /(exchange rate ndex) 1990 1985. (a9) he data on the offcal exchange rate are taken from the World Development Indcator 2000. Ol-exportng country lst ncludes eleven OPEC member countres (Algera, Lbya, Ngera, Indonesa, Iran, Iraq, Kuwat, Qatar, aud Araba, the Unted Arab Emrates), addtonal Gulf Cooperaton Councl countres (Oman, Bahran), and other non-opec ol producng countres (ussa, Norway, Mexco). A2.3 Income Data et Data on trade, GDP, populaton, and area for the ncome regresson are also taken from the Andrew ose web ste. hs data set conssts of the year 1990 observatons for 210 22
countres from the World Development Indcators 2000, merged wth data from the Penn World able 5.6. A3 ummary tatstcs for the otal rade hare egresson A3.1 General tatstcs Varable Obs. Mean td. Dev. Mn Max blateral trade share 10940 0.005 0.025 5.31e-09 0.793 dstance 13264 4667.31 2735.97 19.43 12351.26 populaton j 11034 57986.14 168141.90 40 1133683 area area j 14746 8.86e+11 6.41e+12 1883 2.08e+14 language 14750 0.12 0.32 0 1 border 14746 0.02 0.14 0 1 landlocked 14746 0.19 0.41 0 2 A3.2 Varable Descrpton Varable Descrpton blateral trade share Blateral trade value dvded by real GDP dstance he great crcle dstance between the prncple ctes of country and j populaton j Country j s populaton n 000 s area area j Product of real areas. language 1 for common offcal language between country and j border 1 for common land border between country and j landlocked Number of landlocked countres n par (0, 1 or 2) Data source: Frankel and ose (2002). A4 ummary tatstcs for the Net rade hare egresson A4.1 General tatstcs Varable Obs. Mean td. Dev. Mn Max Net trade share 145-7.01 17.63-199.46 26.84 Age dependency rato 179 40.01 6.71 26.98 52.11 Populaton growth rate 196 1.89 1.64-4.87 14.12 Ol-exportng countres 206 0.08 0.27 0 1 Urban populaton rato 196 50.69 23.75 5.2 100 Net ncome from abroad 168-1.40 11.51-30.62 74.74 Changes n reserves 148-1.24 4.05-19.87 20.95 elatve prces 116 1.00 0.40 0.14 3.05 Net current transfers from abroad 96 3.57 10.78-22.82 81.29 Net current transfers 142 5.60 10.45-26.86 64.03 emttances 91 1.41 4.90-10.70 27.24 23
A4.2 Varable Descrpton Varable Descrpton Net trade share 1990 Net trade dvded by real GDP Age dependency rato 1990 Populaton aged 0-14 + populaton aged 65 and above (% of total) Populaton growth rate 1990 Populaton growth (annual %) Ol-exportng countres 1 for OPEC, Abab-OPEC, and non-opec countres Urban populaton rato 1990 Urban populaton (% of total) Net ncome from abroad 1990 net ncome from abroad (% of GDP) Changes n reserves Net changes n reserves (% of GDP) elatve prces 1990 to 1985 rato of export-to-gdp-deflator prce ndces Net current transfers 1990 net current transfers (% of GDP) Net current transfers from abroad 1990 net current transfers from abroad (% of GDP) emttances 1990 Workers remttances (% of GDP) Data source: Frankel and ose (2002). World Development Indcators 2000. Internatonal Monetary Fund Balance of Payments tatstcs World Bank able. A5 ummary tatstcs for the Income egresson A5.1 General tatstcs Varable Obs. Mean td. Dev. Mn Max Per capta GDP 115 4913.78 4945.33 399 18054 otal trade share 145 80.67 63.57 13.62 538.674 Net trade share 145-7.01 17.63-109.47 26.84 Populaton 115 39177.84 134104.40 40 1133683 Area 210 631418.7 1813066 2 1.71e+07 A5.2 Varable Descrpton Varable Descrpton Per capta GDP eal per capta GDP chan ndex otal trade share otal trade dvded by real GDP Net trade share Net trade dvded by real GDP Populaton Populaton n 000 s Area Area n sq. km. Data source: Frankel and ose (2002). World Development Indcators 2000. Bblography Baldwn,.E., 2004, Openness and Growth: What s the Emprcal elatonshp?, n: Baldwn,.E., Wnters, L.A. (Eds.), Challenges to Globalzaton: Analyzng the Economcs. he Unversty of Chcago Press, Chcago, pp. 499-521. 24
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able 1: Blateral rade hare and Geography, 1990 Dependent varable: ln (blateral trade share) Ln (dstance ) -1.21*** (0.03) Ln (populaton j ) 1.01*** (0.02) Ln (area area j ) -0.29*** (0.01) Language 0.55*** (0.08) Border 0.62*** (0.15) Landlocked -0.70*** (0.06) Constant -0.58* (0.33) Number of Observatons 8104 2 0.41 E of regresson 2.28 tandard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. 28
able 2: Net rade hare, Demography, and Cross-Border ransfers Characterstcs, 1990 Dependent varable: net export share (1) (2) (3) (4) (5) (6) (7) (8) Dependency rato -0.73** (0.28) -0.34 (0.36) -0.06 (0.15) 0.09 (0.18) -0.65*** (0.19) -0.43** (0.22) 0.13 (0.41) Populaton growth rate 0.93 (1.59) 0.60 (1.59) -0.89 (0.82) -1.02 (0.82) 1.52 (1.14) 1.51 (1.11) -2.23 (2.10) Ol-exportng countres 14.39*** (4.64) 12.16** (4.79) 4.26** (2.09) 3.56* (2.12) 8.10*** (2.39) 6.86*** (2.40) 9.28** (3.61) Urban populaton rato 0.13* (0.07) 0.05* (0.03) 0.08** (0.04) 0.15* (0.07) Net ncome from abroad -0.79*** (0.10) -0.64*** (0.14) -0.59*** (0.14) -0.69*** (0.19) -0.58*** (0.19) -0.67** (0.26) Changes n reserves -0.35* (0.19) -0.16 (0.17) -0.16 (0.17) -0.45* (0.24) -0.45* (0.24) 0.15 (0.34) elatve prce -0.43 (1.64) 0.23 (1.35) 0.47 (1.35) -2.36 (2.10) -2.10 (2.05) -0.52 (2.74) Net current transfers -1.14*** (0.10) -1.03*** (0.10) -1.02*** (0.10) Net transfers from abroad -0.70*** (0.13) -0.68*** (0.13) Net remttances -0.84*** (0.28) Constant 22.30** (9.94) -0.64 (16.08) -2.31 (1.73) 2.04 (5.34) -6.84 (7.49) 21.01*** (6.57) 7.91 (8.87) -13.85 (17.12) Number of Observatons 134 134 113 106 106 70 70 67 2 0.14 0.16 0.78 0.73 0.73 0.69 0.71 0.52 E of regresson 14.43 14.33 6.85 5.50 5.45 5.42 5.28 8.27 obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. 29
able 3: otal rade hare, Net rade hare, and Per Capta Income, 1990 Dependent varable: ln (per capta real GDP) OL IV (1) IV (2) IV (3) IV (4) IV (5) IV (6) IV (7) IV (8) otal trade share 0.005*** (0.002) 0.004 (0.007) 0.004 (0.007) 0.024 (0.017) 0.017 (0.012) 0.017 (0.011) 0.007 (0.010) 0.006 (0.010) 0.096* (0.054) Net trade share 0.029*** (0.009) 0.147*** (0.044) 0.150*** (0.035) 0.046** (0.016) 0.068*** (0.016) 0.072*** (0.016) 0.102*** (0.038) 0.112*** (0.035) 0.039 (0.058) Ln (populaton) 0.10 (0.08) 0.00 (0.10) 0.00 (0.11) 0.12 (0.18) 0.12 (0.14) 0.11 (0.14) 0.03 (0.12) 0.02 (0.12) 0.97* (0.50) Ln (area) -0.06 (0.07) -0.06 (0.13) -0.06 (0.13) 0.17 (0.14) 0.10 (0.20) 0.10 (0.12) -0.02 (0.18) -0.04 (0.17) 0.31 (0.36) Constant 7.57*** (0.77) 8.83*** (2.17) 8.93*** (2.04) 3.32 (3.42) 4.65* (2.59) 4.82* (2.57) 7.96** (3.16) 8.38*** (2.98) -10.82 (11.49) Number of Observatons 110 102 102 94 92 92 64 64 59 2 0.23 0.56 0.65 0.35 0.45 0.48 0.45 0.50 0.60 ME 0.95 0.74 0.66 0.87 0.81 0.79 0.80 0.76 0.69 obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. able 4: he Impact of Export hare and Import hare on Per Capta Income, 1990 Coeffcent estmates OL IV (1) IV (2) IV (3) IV (4) IV (5) IV (6) IV (7) IV (8) otal trade share 0.005*** (0.002) 0.004 (0.007) 0.004 (0.007) 0.024 (0.017) 0.017 (0.012) 0.017 (0.011) 0.007 (0.010) 0.006 (0.010) 0.096* (0.054) Net trade share 0.029*** (0.009) 0.147*** (0.044) 0.150*** (0.035) 0.046** (0.016) 0.068*** (0.016) 0.072*** (0.016) 0.102*** (0.038) 0.112*** (0.035) 0.039 (0.058) F test 11.57 19.37 25.88 6.31 (0.003) 12.43 13.40 13.19 13.79 6.11 (0.009) Export share 0.035*** (0.009) 0.151*** (0.037) 0.154*** (0.03) 0.076*** (0.021) 0.085*** (0.017) 0.089*** (0.018) 0.109*** (0.030) 0.117*** (0.029) 0.135*** (0.040) Import share -0.023** (0.009) -0.143*** (0.048) -0.146*** (0.040) -0.022 (0.026) -0.050** (0.022) -0.055** (0.022) -0.095** (0.046) -0.106** (0.043) 0.056 (0.104) obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. 30
able 5: he Impact of Export hare and Import hare on Per Capta Income, 1985 Coeffcent estmates OL IV (1) IV (2) IV (3) IV (4) IV (5) IV (6) IV (7) IV (8) otal trade share 0.009*** (0.002) 0.006 (0.019) 0.004 (0.014) 0.035** (0.016) 0.033** (0.015) 0.032** (0.014) 0.017* (0.010) 0.017* (0.009) 0.069* (0.037) Net trade share 0.024*** (0.006) 0.147*** (0.075) 0.157*** (0.050) 0.035* (0.019) 0.044** (0.020) 0.050*** (0.019) 0.135*** (0.022) 0.137*** (0.022) -0.010 (0.066) F test 19.61 14.32 15.06 6.73 (0.002) 10.44 11.56 26.57 26.55 8.09 Export share 0.033*** (0.006) 0.153*** (0.058) 0.161*** (0.040) 0.070*** (0.019) 0.076*** (0.017) 0.082*** (0.017) 0.152*** (0.021) 0.154*** (0.021) 0.059 (0.036) Import share -0.016** (0.007) -0.142 (0.092) -0.153** (0.062) -0.001 (0.029) -0.011 (0.030) -0.018 (0.028) -0.118*** (0.027) -0.120** (0.027) 0.079 (0.101) obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. Frst stage blateral trade regresson nstruments: ln (dstance), ln (populaton), ln (product of area), common language dummy, common border dummy, and landlocked dummy. Frst stage net trade regresson nstruments: age dependency rato, populaton growth rate, ol-exportng country dummy, urban populaton rato, net ncome from abroad, changes n offcal reserves, relatve prce of tradeables, net current transfers (from abroad), and net workers remttances. able 6: he Impact of Export hare and Import hare on Per Capta Income, 1980 Coeffcent estmates OL IV (1) IV (2) IV (3) IV (4) IV (5) IV (6) IV (7) IV (8) otal trade share 0.002*** (0.001) 0.013 (0.008) 0.013 (0.010) 0.014** (0.001) 0.017** (0.007) 0.016** (0.007) 0.015* (0.009) 0.015* (0.009) 0.013 (0.010) Net trade share 0.028*** (0.006) 0.076*** (0.020) 0.077*** (0.028) 0.046*** (0.009) 0.039*** (0.009) 0.043*** (0.009) 0.137*** (0.030) 0.140*** (0.029) 0.055*** (0.015) F test 17.63 19.11 20.75 15.50 11.63 13.33 12.26 13.26 23.42 Export share 0.031*** (0.006) 0.089*** (0.016) 0.090*** (0.020) 0.060*** (0.011) 0.057*** (0.012) 0.059*** (0.012) 0.152*** (0.021) 0.155*** (0.030) 0.069*** (0.010) Import share -0.026*** (0.007) -0.063** (0.026) -0.064* (0.037) -0.032*** (0.011) -0.022* (0.012) -0.026** (0.012) -0.122*** (0.032) -0.125*** (0.031) -0.042** (0.023) obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. Frst stage blateral trade regresson nstruments: ln (dstance), ln (populaton), ln (product of area), common language dummy, common border dummy, and landlocked dummy. Frst stage net trade regresson nstruments: age dependency rato, populaton growth rate, ol-exportng country dummy, urban populaton rato, net ncome from abroad, changes n offcal reserves, relatve prce of tradeables, net current transfers (from abroad), and net workers remttances. 31
able 7: he Impact of Export hare and Import hare on Per Capta Income, 1975 Coeffcent estmates OL IV (1) IV (2) IV (3) IV (4) IV (5) IV (6) IV (7) IV (8) otal trade share 0.007** (0.004) 0.016* (0.009) 0.014 (0.011) 0.023*** (0.007) 0.025*** (0.008) 0.025*** (0.008) 0.042 (0.030) 0.042 (0.031) 0.015* (0.007) Net trade share 0.027*** (0.007) 0.079* (0.043) 0.094* (0.058) 0.039 (0.024) 0.025 (0.021) 0.026 (0.021) 0.062 (0.135) 0.059 (0.136) 0.055*** (0.019) F test 10.06 7.68 (0.001) 6.81 (0.002) 6.96 (0.002) 6.86 (0.003) 6.92 (0.002) 2.38 (0.113) 2.32 (0.118) 7.40 (0.004) Export share 0.034*** (0.008) 0.095** (0.038) 0.108** (0.050) 0.062** (0.025) 0.050** (0.021) 0.051** (0.021) 0.104 (0.118) 0.102 (0.119) 0.070*** (0.019) Import share -0.020** (0.008) -0.063 (0.048) -0.080 (0.066) -0.016 (0.026) -0.000 (0.023) -0.001 (0.023) -0.020 (0.155) -0.017 (0.157) -0.041* (0.022) obust standard errors recorded n parentheses. he symbols *, **, *** ndcate statstcal sgnfcance at the 10%, 5%, and 1% levels, respectvely. Frst stage blateral trade regresson nstruments: ln (dstance), ln (populaton), ln (product of area), common language dummy, common border dummy, and landlocked dummy. Frst stage net trade regresson nstruments: age dependency rato, populaton growth rate, ol-exportng country dummy, urban populaton rato, net ncome from abroad, changes n offcal reserves, relatve prce of tradeables, net current transfers (from abroad), and net workers remttances. 32
200 350 BH 180 160 BH 300 Export (% of GDP) 140 120 100 80 60 40 20 0 BH AG ML GUY WZ MY LCA VK YC GAB KW VN VC IL BWA CZE BB MNG OMN COG MU BLZ AGO BGCYP E JAM LB NLD KNA FJI NAM GMB MKD GNQ IQAU DMA LBY LB CHE PNGVU HV LU O KM GD CIVHUN MDA CI DJI NGA NOLVA GO DNK IL AUUN M YEM ZMB JO HND CHL CAN FIN KO UK VNM WE I P DZA ECU BOL BL BN CM DEU IDNDOM EI FA GB NZL KAZ KEN PHL HA JK AZE EP GHA GIN CHN IN IA AFG CAF KGZ KHM LKA EN VEN ZAFPYP ONWM ZWE MA MWI NIC U POL OM Y LV MEX UY ZA EGY COL AUGC G MDG MNE PE LE GEO LAO ALB MLI AMCOMCPV OM AG JPNU UZB NPL BDI BA EH PAK HI CD ZA WAMOZ GNB LBN IND UA DN UGA ABW MDV U 0 20 40 60 80 100 120 140 160 180 200 Import (% of GDP) KI LO otal rade (% of GDP) MDV 250 U ABW 200 BH GUY AG ML KI WZ LO GNQ LCA 150 KNAVC YC MKD LB VK MY BLZ BB DJI DMA GMB NAM COG EI GD CYP E JAM MU MNG CZE VN BWA IL JO FJI WM LU BGNLD 100 YEMDA VU AGO KW GAB ON P M GOHV KM PNG OMN LBN BN CI HUN CHELB AU OM I UN LVA LBY IQ AUCIV CPV NIC LKA ENHND VNM NO O UK ZMB IL DNK COM AZE KHM MWI JK NGA P KO DOM PHL KEN HA AM EGY LV WE BL 50 ALB CAF BOL CHL CAN FIN GNB KGZ MA AFG CM DEU FA ECU GEO GHAGB GIN PY KAZ NZL ZA Y MLI OM ZWE IDN MOZ LAONE POL DZA ZAF LE VEN CD EP BDIHI MDG GC G MZA ME UY IA U WA EH PAK NPL AU IN X UGA U UZB PE COL CHN DN UA IND BA AG JPN 0-120 -110-100 -90-80 -70-60 -50-40 -30-20 -10 0 10 20 Net rade (% of GDP) Fgure 1: Export hare vs. Import hare, Mean 1970 1998 Fgure 2: otal rade hare vs. Net rade hare, Mean 1970 1998 (Fg 3-1) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA GC HI I GM GB IA DID KO HUN HND GHA GIN N ILIL KW N MDV UY NO MEX NLDMY KEN I JAM NPL GMB FJ I MDG LAO NE MA MWI NZL POL PE AU U UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD IL GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB EC CMCOG AU DZA GHA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI KI IN GNBGNQ DGIN FJ I IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM NZL POL P WA LE PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM N AU YG U CD ZAF ZA ZAZMB VN VEN YEM E M 20 KW BH VEN NO AU MEX DZA IIDN OMN 0 A AU CAN DEU NGA G JPN BH B EP GB KO CHL DNK IL I NLD ML UA UY NZL AAU COL GC FA IA W BG CHE E FIN LBN LBN PE UUN OM DOM POL HUN GAB CHN BOL ALB ECCI B CYP B CM CAFBW CPVBLZ A AGO CIV COG U IL EGYJAM IN FJ JO I GHA GM D MA MU MY PH P O ZAF L U P HND LKA EH HI MDG PAKM GNB COM GIN GNQ GMB NIC Y DOM LAO MOZ LE N LV NAM MLI EN Y BD KEN PN G LO NPL CD ZW HA NE ZA ZA VN MWI WM MDV WA BN GO E M ZMB YEM LBW Z UGAI VU LO -20 0 200 400 600 net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BGBLZ DEU D GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJ CAFCAN BOL CHL AU CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW IND I LAO LKA NPL U MA MY MDGMEX MWI KEN I JPN MOZMLI M B ID IL LB WA EN PAK PH W NNO NZL PE L P LE NE AGO DZA COG A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY D AU N GUG CDUA VN ZAF M E ZA VEN NGA BH KW AU NOVEN MEX IIDDZA N OMN ML I GB AU CAN DEU BH EP KO JPN NLD NGA UA AU CHL A G BG CHE W NZL IL DNK GC FIN FA B IA UY E COL A OM DOM CI CYP PE HUN POL GAB JO MU P U U ALB BOL B CHN MY I EC BL U EGY MA JAM NIC PH N UO PFJ CPVCAFBW BLZ Y A AGO BD BN BG CM COG LZAF LV I GNQ OM CIV COM GHA GMB LKA HND GNB MDG M GM IND MOZ NAM PAK MLI LAO D EN LB W EH HI NPL NE GIN IMWI KEN PNLE D NZ G Y CD GO HA WMZA ZMBZW VN M VU YEM MDV UGA ZAE WA predcted_total_trade QA A E KW BH AU VEN NO MEX I Q IDN DZA IN OMN AU NGA CAN A CUB CHL JPN IL GKO DEU BH B A EP GBI DNK NLD ML UA NZL UY W CZEAU COL FIN FA BG DJI CHE GAB GC IA E NCL U PE U DOM POL HUN MNG CHN BOEC L MY UN OM JO UCIICYP L B B COG EGY FJI CAF CM GUY MA YU PH BLZ AGO CIV IND GHA EH GIN GNB GNQ HND GMB JAM MU MD MM LB NIC L P G O ZAF P Y D O HA PAK BD BN HI GM MOZ PNLAO LE LKA NE MLI N KEN G EN Y LV CD ZA ZMB VN ZA ZW MWI LB M NPL COM E YEM GO UGA WA I 0 100 200 300 predcted_net_trade E ˆ Ê 1.0000 E 0.1344 1.0000 ˆ 0.5277 0.0844 1.0000 Ê 0.1683 0.5134 0.2866 1.0000 (Fg 3-2) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA GC HI I GM GB IA DID KO HUN HND GHA GIN N ILIL KW N MDV UY NO MEX NLDMY KEN I JAM NPL GMB FJ I MDG LAO NE MA MWI NZL POL PE AU U UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD IL GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB EC CMCOG AU DZA GHA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI KI IN GNBGNQ DGIN FJ I IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM NZL POL P WA LE PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM N AU YG U CD ZAF ZA ZAZMB VN VEN YEM E M 40 20 BH ID NO KW DZA MEX VEN NAU I NNGA OMN JPN CHN GC AU CANCHE DEU AU 0 EP A BALB FA GB IA FIN KO GA CHL DNK UA NZL NLD HUN BG ILB BHB COL DOM CI CYP ML INU OM UY POL W P HA BOL EC DLKALBN EGY GAB IE MU UGMB IL MY PEPH ZAF MA FJ I EH NPL MDG CMCOG HI LAO GHA CAFBW PAK GM BD GNBGNQ AGO CIV BN JAM COM CPV HND P O GIN I BLZ LO MOZ MWI MLI JO A KEN M PN UN L G DOM NAM Y U LBN WA NE LE NEN LV CDGO ZW VN M UGA ZA ZA Y WM E YEM NIC VU LB W Z LO ZMB MDV -20 0 200 400 600 net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BGBLZ DEU D GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJ CAFCAN BOL CHL AU CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW IND I LAO LKA NPL U MA MY MDGMEX MWI KEN I JPN MOZMLI M B ID IL LB WA EN PAK PH W NNO NZL PE L P LE NE AGO DZA COG A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY D AU N GUG CDUA VN ZAF M E ZA VEN NGA BH KW ID NO MEX AU N VEN DZA I CAN CHE CHN AU BG ALB CHL BH A BA CI CYP DEU N OMN NGA GC KO EP FIN JPN DNK ML GB FA EGY GMB DOM I HUN IA NLD OM MU P HA UA LKA W NZL COL G BDCAF BNBW FJ INDI MY IL MA BOL CPV COMI BLZ CM CIV D IECL GAB U GNQ GNBGHA EH JAM JO NAM LAO MDG MOZ NIC NPL P PAK MLI NE MWI PH HI HND GM GINAGO COG A KEN M PN PE E U UN UY OMLVY L GPOL LB D VN ZAF N WA EN LE U O WM M ZAUGA VU CD GO W Z ZMB ZW YEM ZA MDV E Y predcted_total_trade A E BH IDN NO KW AU MEX VEN NGA DZA IN OMN I Q AU UA CAN CHN CUB JPN KODEUAU CHE B CHL NZL EP FIN FA AA BG BH COLCI CZECYP DNK DOM B B GAB GB GC IA NLD HA NCL ILHUN GUY BOEGY MM IND MY LKA OM EC L AGO CAF COG CM CIV BD BN FJI UGMB I MU DJI L MNG MDMA EH LAO GHA GIN GNB G JAM MOZ PE NE MLI GNQ HI JO HND KEN MWI PH PAK LB NPL PN GPOLP D O P U W YU LE LB Y NIC GM L WA NEN E G ZAF U UY VN M UN O ICOM BLZ LV CD ZA ZMB YEM ZA UGA ZW Y E GO QA ML 0 100 200 300 predcted_net_trade E ˆ Ê 1.0000 E 0.1344 1.0000 ˆ 0.5277 0.0844 1.0000 Ê 0.2180 0.5579 0.3060 1.0000 33
(Fg 3-3) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM CD ZAF ZA N VN VEN YEM E ZAZMB AU M YG 50 BH COLCOG CIV A BAU BOL CAN GA CHL CHN CM DZA EC DNK AG 0 AU U EH EP CAF DOM FIN GAB FA GB CI CHE B CYP BLZ DMA GC GHA GM LE IN JPNKEN HI MEX DID FJI B BD GNB EGY HND HUN N GD I L I NE IA KO N IL JAM MDG MA MWI NZL NGA AU D PENO LKA KN LCA A MLI I M MU MY NPL NEN POL W Y O UGA UA UY ZAF ZA NLD PAK PH HA E UUN VEN P L Y YC G CD LV I GO ZMB ZA GMB ML NIC VU LB JO CPV WM -50 U U KI net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA BH CIV COL COG AU AG G BD I BOL CAN B CHL DMA CI CHE CHN CM DNK DOM CYPA CAF EH EP FJI FIN BLZ DZA EC GAB GDGM LCA GB FA B U GHA GNBEGY GC HND HUN ID N KN A KEN JAM KO IN D HI IJPN IA MDG LKA MA IL I L I MEX NGA NE MLI ML MU MWI JO GMB M MY NLD NO NPL NZL PAK PHPE LE AU HA P D EN W PNY N L GPOL E YC O Y LV GO UN U ZA UGAUY UA ZA ZAF ZMB VEN CD LB NICVU CPV WM predcted_total_trade BH COG GAB COL CIV NEMY ZA AU B CAN CHL CHN BOL NGA AU ID CM DZA EC U AU CAF BLZ CI DNK EH MEX IN DOM EP FIN DGB GHA FA FJI HND HUN I CYP L I N IL CHE B B EGY HI GM IAJAM M JPN KEN KO MDG PE NZL LE DNO PNHA W MWI MA NPL NLD LKA MU GC KN A MLI PPH NEN PAK G POL Y E O UA ZAF UGA VEN UY ZMB U YUN L P GO GNB I YC CD ZA BDJO GMB I LV ML LB NIC predcted_net_trade E ˆ Ê 1.0000 E -0.1820 1.0000 ˆ 0.7105-0.1185 1.0000 Ê -0.1445 0.9028-0.0775 1.0000-100 KI 0 200 400 600 KI KI 0 100 200 300 (Fg 3-4) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM CD ZAF ZA N VN VEN YEM E ZAZMB AU M YG BH 20 COLCOG CIV NGA AU ID NO GAB N AAU MEX DZA LE BCAN G CHL DNK A CHN CM EP FA GB IA EC FIN NZL VEN O HUN U 0 BOL AU CHE FJI IN JPN UY POL W ID KO N ILI NLD E UA PE ZAF ZA Y HA L GM DOM CI B JAM MY NECYP MU B D UGA EH NPL NEN PH GC KEN L UUN HI HND LKA PN G MDG GHA PAK CAF MA MWI P M Y BLZ EGY MLI I ML CD GNB LV GO ZMB ZA BD I GMB -20 NIC VU LB JO CPV U U KI net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA BH CIV AU COG ID COL NONGA NGAB GB AU CAN CHL DNK DZA EP FJI MEX FIN NZL LE FA EC AU A GU BOL CHE CHN CM HUN DOM CI CYP IN I IL BD I L O W IA JAM KO JPN MY NLD POL NE MU GMB NPL D PE EN GC LKA KEN PH EH HND CAF MDG GHA MA MWI HI BLZ MLI ML PAK M PNN L E VEN ZA HA UY UA ZAF Y UGA P UN U Y G ZMB GO ZAEGY I GN CD LV BDGMB I JO LB NICVU CPV predcted_total_trade BH COG NGA AU ID GAB COL CIV N NO AU B CAN MEX DZA CHL VEN NZL EC LE CHN CM AG FIN DNK EP GB FA FJI UHUNI L BOL IINN IL O ZAW JPN POL IA D AU DOM CI JAM CYPCHE GM B B EH KEN KO E MY NLD NE PE HA UY UA ZAFY D MU PNPH NPL L M P UGA G LKA HND GC CAF MDG GHA MWI U NEN MA PAK YUN P ZMBHI BLZ MLI GO EGY I ML CD ZA GNB LV BD GMB I LB NIC JO predcted_net_trade E ˆ Ê 1.0000 E 0.0114 1.0000 ˆ 0.7075 0.0111 1.0000 Ê 0.0257 0.8242 0.0246 1.0000-40 WM WM 0 200 400 600 0 100 200 300 (Fg 3-5) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM CD ZAF ZA N VN VEN YEM E ZAZMB AU M YG BH 20 AU COLCOG CIV NGA VEN NO AAUG CHL O GAB MEX DZA UY DNK B A CM EP FA GB LE CAN EC ID FIN NZL NU JPN PE IIA POL W HUN 0 CHN BOL N IL NLD E UA ZAF ZA I AU CHE FJI L INGM D KO Y JAM DOM CI B CYP B EH GC KEN HND HI MDG GHA BLZ CAF MA LKA I M MU MY NE D NEN PH HA UGA U L NPL PAK MWI P P PN UN Y G ML CD EGY LV MLI GO ZMB ZA GNB BD I NIC GMB JO -20 VU LB CPV U U KI net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA BH AU CIV COL COG GAB GB AU CHL DNK CAN DZA B G EP FJI FIN FA IDEC N BOL CM AU A U IHUN IL NONGA O VEN MEX W NZL LE IA I L JAM KO JPN MY NLD UY PE POL E UA ZA N CHE NE MU DOM CI GM CYP IN B CHN ZAF Y HAD UGA B BLZ CAF GHA GC KEN MLM NPL P D EN PH L P LKA MA EH HND MDG PAK MWI PNY N UN U ZMB G HI I ZA MLI LV EGY GN CD GO JO NICBDGMB I LB VU CPV predcted_total_trade BH AU COG NGA GAB VEN COL CIV NO AU B CAN IDZA CHL EC N A G DNK CHN BOL CM EP FIN GB U I N IL O ZA MEX NZL LE UY W PE JPN POL E UA ZAF FA FJI HUN IA I L AU INDOM D JAM GM CI CYPCHE KEN KO MY Y NLD NE DHA PH L MUB B M PN P UGA U NEN BLZ CAF EH HND GHA MA HI LKA GC MDG MWI PAK GNPL YUN ZMB P I ML CD MLI GO ZA EGY LV GNB BDNIC JO GMB I LB predcted_net_trade E ˆ Ê 1.0000 E 0.0114 1.0000 ˆ 0.7075 0.0111 1.0000 Ê 0.0407 0.8295 0.0376 1.0000-40 WM WM 0 200 400 600 0 100 200 300 34
(Fg 3-6) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU U UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM N AU YG U CD ZAF ZA ZAZMB VN VEN YEM E M 20 ID N COL NO VEN AU DZA MEX CAN FIN NZL CHL DNK 0 B JPN CHN I EP FA IA GB EC A KO W AU CM BOL CI CHE U GC HND HUN NIL HA PNLD E MY UA UY ZAF JAM I GMU L D IN DNEN PH FJI EH KEN LKA P NPL M L UGA U MDG PAK DOMNIC GM LV CD LB GNB -20 VU CPV WM -40 0 200 400 600 net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA ID N COL NO VEN MEX AU CAN FIN NZL CHL DNK AU DZA MU HA PN A BOL CHE CHN EC U CI GB EP FA GC HND JAM KO FJI CM HUN IN I W JPN MY IA NLD IL LKAD I G N E UA UY ZAF P D EN L PH KEN MDG DOM EH M N L NIC NPL PAK LB CD LV UGA U GM GNB VU CPV WM predcted_total_trade ID N COL NO VEN AU CAN MEX DZA CHL NZL FIN DNK B CHN PN I JPN HA EC W ANILIA GKO MY EP GB FA U E AU BOL CMCI CHE FJI HND HUN JAM NLD UA ZAF UY I L MU M DIN D GC EH KEN PH NEN LKA LP MDG UGA U PAK DOM NPL NIC CD LBGM LV GNB 0 100 200 300 predcted_net_trade E ˆ Ê 1.0000 E 0.0752 1.0000 ˆ 0.7719 0.0959 1.0000 Ê 0.2711 0.7534 0.1697 1.0000 (Fg 3-7) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM CD ZAF ZA N VN VEN YEM E ZAZMB AU M YG 20 COL AU B 0 CAN ID VEN CHL NO N MEX NZL DZA DNK JPN I EP UY EC FIN FA GB A AU U CHN CM BOL IA W KO N IL NLD E MY UA ZAF HND HUN CI CHE JAM I L INGC DKEN MFJI MU D NPH EN HA U PN L G EH DOM LKA PNIC UGA NPL MDG GM PAK LV CD LB GNB -20 VU CPV U U KI net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA ID AU CAN CHL COL NO N VEN MEX AU CI BOL CHE CHN CM DNK GB DZA EP FIN FA EC A U HND JAM W NZL KO I JPN MY HUN IA NLD IL UY E GC FJI IN D I N MU PN UA HAZAF G KEN M L NIC P D EN PH L LKA N DOM EH MDG NPL PAK LB LV U UGA GM CD GNB VU CPV predcted_total_trade ID VEN AU CAN CHL COL NO MEX NZL DNK B I DZA EC AN IL JPN UY W KO EP FIN MY E GB FA UIA NLD UA AU CHN PN BOL ZAF CM HA G CI CHE FJI HND HUN JAM I L MU M D KEN IN U PH N EN DNIC GC L EH DOM LKA P UGA MDG PAK NPL LB GM LV CD GNB predcted_net_trade E ˆ Ê 1.0000 E 0.0752 1.0000 ˆ 0.7719 0.0959 1.0000 Ê 0.2753 0.7712 0.1787 1.0000-40 WM 0 200 400 600 WM 0 100 200 300 (Fg 3-8) total_trade 50 NGA VEN Y O OMN GAB A B CHNCIV GAGO AU AG 0 ALB AU BH BH BG COL CM DZA EC BOL A CANCHE DEU CHL DNK COG U EH EP DOM BBLZ B BD BN CI BW CYP A CAF EGY FIN IN JPN FA HI I GC GM GB IA D GHA ID KO HUN HND GIN N ILI KW N I GMB FJI L MDV UY POL NO PE MEX NLDMY KENJAM NPL MDG LAO NE MA MWI NZL AU UA D WA U ZW ZAF LE ZA VN W PAK PH LKA MMU PN EM E L G UGA OM NEN P Y YC CDGO HA UN LV LCA COM MLI YEM W I ABWML Z ZMB GNB GD NAM VC OM ZA VU MOZ CPV PNIC LB DMA WM GNQ ON KN JO A -50 LBN -100 LOKI ML KN A B B BH MU CHE YC AUCYP DNK O COM DEU LV NLD I L GC GM BD BG BH CI BLZ EP DOM BN I A B CHNCIV COL FA GB GHA EC CMCOG AU DZA BOL CAF EGY CAN GAGO CHL U EH FIN HND HUN I GMB HI IA JAM KI IN GNBGNQ DGIN FJI IID A IL JO KO KW GO NPL OM JPNKEN LAO MA LKA NICMY MDG MEX MWI NO PE PAK MOZM POL P WA LE NZL PH EN W Y UN NE MLI P NGA GAB OMN PN L ELB D UGA U ZW UY HA UA OM CD ZAF ZA N VN VEN YEM E ZAZMB AU M YG 20 BH NO COL MEX VEN DZA CIV COG ID NZL NGA O AU BEP W FA 0 BOL A GAB N IA NLD E UA PE J AM LE KO CM GC CAF AU M ZMB MDG DOM CHE ML D GHA GM GNB INUUN CD NEN NE D PH KEN Y PL BLZ PAK MA JO EGY LV MLI LKA GO LB CPV VU -20 U U KI net_trade ML KN A B BHB MU YC CHE CYP AU NLD DNK I LO LV GOBH COMCI GMB BG BLZ DEU GNQ GC BDBN I DOM GM JAM I JO HND GB GNB HI EGY GHA EP FJI CAFCAN BOL CHL CHN CM CIV COL EC U EH GIN FIN FA HUN NIC OM P KO IA KW LAO LKA NPL UN MA MY KEN I JPN MOZMDGMEX B ID IL LB WA PAK EN MWI PHWNO AGO DZA COG D MLI NE M NZL PE L P LE A GAB OMN PNY POL Y HA U OM ZAUGA ZMBZW YEM UY DAU N GUG CDUA VN ZAF M E ZA VEN NGA BH NO MEX COL CIV AU COG VEN NZL IDDZA N EP FA BOL B A GAB O NGA W JAM IA CAFAU DOM CHE CM GNBGHA GC KO ML M NLD E ZMBUA LE PE MDG GM JO NE KEN CD P D EN PH IN BLZ N L Y LB D MLI LV GO UN U EGY LKA MA PAK CPV VU predcted_total_trade BH NO COG AU MEX VEN COL NGA ID DZA CIV GAB NZL N O W B BOL AEP E FA IAJAM NLD UA M CAF ZMB PE LE KO CM AU MDG KEN DOM CHE GHA GNB GM GC ML CD D NE IN U PH NEN D Y UN LP BLZ MLI MA EGY LB PAK LKA JO GO LV predcted_net_trade E ˆ Ê 1.0000 E -0.0641 1.0000 ˆ 0.6312-0.0595 1.0000 Ê 0.2058 0.6414 0.0752 1.0000-40 WM 0 200 400 600 WM 0 100 200 300 Fgure 3: Actual-Predcted rade Correlaton Matrces for Net-rade pecfcatons (1) to (8) 35