Pakistan s International Trade: the Potential for Expansion. Towards East and West

Transcription

1 Pakisan s Inernaional Trade: he Poenial for Expansion Towards Eas and Wes (July, 2016) Ehsan Choudhri, Carleon Universiy Anonio Marasco, Lahore Universiy of Managemen Sciences Ijaz Nabi, Inernaional Growh Cenre 1

2 1. Inroducion Pakisan faces high barriers o inernaional rade across is easern as well as wesern and norh-wesern borders. Hisorically imporan eas-wes rade roues connecing India wih cenral Asia and China passed hough Pakisan. However, poor relaions wih India, war and poliical insabiliy in Afghanisan, lack of developmen of ransporaion infrasrucure for land roues o China, and srife in ribal areas and Baluchisan have impeded Pakisan s rade owards eas and wes. Expansion of eas-wes rade presens a grea opporuniy for Pakisan o increase is economic growh (Nabi, 2013). There has been much ineres in exploring he effec of rade liberalizaion wih India. The recen China-Pakisan Economic Corridor (CPEC) projec has also simulaed ineres in he poenial for rade expansion wih China. The objec of his research projec is o idenify he exen o which exising barriers influence Pakisan s eas-wes rade, and explore he poenial for rade expansion available from reducion in hese barriers. The projec is divided ino hree pars. The firs par assembled a large daa se required o esimae he modern versions of he Graviy model of inernaional rade. The second par esimaes hese versions and idenifies he effec of eas-wes rade barriers on Pakisan s bilaeral rade flows wih is neighbors, in paricular India and China. These esimaes will be uilized in he hird and final par o examine he poenial for rade expansion available from a number policy acions for lowering rade barriers. This par will examine he impac of CPEC on rade wih China, and of reducion of ariff and nonariff barriers on rade wih India. The second par of he projec has now been compleed. This progress repor will mainly focus on explaining he mehodology and discussing he resuls of he research in his phase of he projec. Secions 2 of his repor discusses he heoreical srucure and he empirical 2

3 implemenaion of he graviy model. Secion 3 briefly describes our daa se and Pakisan s inernaional rade. Secion 4 explains our mehodology and repors he resuls of our empirical analysis. Secion 5 briefly discusses fuure work o be done o complee he projec. 2. Graviy Model 2.1 Theoreical Srucure The Graviy model of inernaional rade is now regarded as he main empirical ool for explaining bilaeral rade flows. The radiional version of he Graviy model explains bilaeral rade flows beween a pair of counries as a posiive funcion of each counry s GDP and a negaive funcion of he disance beween he wo counries, and can be expressed as X AY Y D, a 0, a 0, a 0, (1) a1 a2 a3 i, j, ij where X is he value of expor from counry i o j, Y i, and Yj, and j in period ; A is a period-specific consan erm; and are he GDP s of counries i D ij represens a measure of bilaeral disance beween he exporing and imporing counries. 1 The disance measure is considered an index of he ransporaion cos beween he counry pair. Addiional indexes of rade coss are ofen included in he above equaion. Noe ha leing M ji, denoe he value of impors of j from i, X = M ji, impor or expor flows.. 2 Thus, he graviy equaion can be used o explain eiher 1 Here we discuss he form suiable for panel daa esimaion. The ime subscrip is no needed for cross-secion esimaion (and is ofen dropped for exposiional simpliciy). 2 In daa, X ji, and M ji, can differ because of discrepencies in he measeuremen of rade flows by he imporing and exporing counries. 3

4 Alhough he radiional graviy equaion did no have clear-cu heoreical underpinnings, recen developmens have derived graviy equaions ha can be relaed o newsyle srucural models of inernaional rade. One popular srucural graviy model developed by Anderson and van Wincoop (2003) can be derived from an inernaional rade model wih eiher perfec compeiion and he Armingon (1969) assumpion ha goods are differeniaed according o he counry of origin or wih monopolisic compeiion and differeniaed varieies produced by homogeneous firms (Krugman, 1980). Leing ( 1) denoe he elasiciy of subsiuion beween differeniaed goods or varieies (in a CES uiliy funcion) and (>1) a bilaeral iceberg rade cos index (i.e., unis of a good or variey have o be shipped from i for one uni o arrive in j ), he Anderson-van Wincoop model is expessed as X Y E i, j, ij, Y P i, j, 1, (2) where 1 E j,, j P j, Y 1 i, 1 Yi,, and j, i i, Y 1 Pj, E is counry j s aggregae expendiure and Y is world income in period. Under balanced rade, E j, Yj,. The iceberg rade cos index capures variable rade coss (i.e., rade coss per uni of rade flow) and can be defined broadly o depend on ransporaion coss (a funcion of bilaeral disance) as well as ariff and non-ariff barriers. The ariff equivalen of rade coss is given by 1. Boh he radiional and he above srucural graviy relaions imply ha bilaeral rade flows can be expressed in he following muliplicaive form: X GS U, (3) i, j, 4

5 where S i,, U j, and represen, respecively, he effec of facors specific o he exporing counry i, imporing counry j, and he pair i, j in period ; and G is a erm ha can vary over ime bu does no depend on counry characerisics. The componens specific o imporers and exporers in he srucural graviy model include variables and 1 i, 1 P j,, and hus are differen from he corresponding componens in he radiional model. 3 Anderson and van Wincoop (2003) call hese variables inward and ouward mulilaeral resisance erms. An imporan implicaion of heir model is ha he omission of hese variables from he graviy equaion can lead o a significan bias in he esimaes of is coefficiens. The rade model wih monopolisic compeiion has been exended by Meliz (2003) o allow for firm heerogeneiy and fixed coss of exporing.the Meliz model has been successful in explaining some key feaures of inernaional rade daa (such as why only a proporion of firms in an indusry are ypically engaged in exporing ) and is now widely used for rade policy analysis. Helpman e al. (2008) show ha a graviy equaion can also be derived from his model. Moreover, he graviy equaion based on he Meliz model under balanced rade can be expressed in he following form ha is similar o Anderson-van Wincoop model bu incorporaes an addiional erm: X YY 1 V i, j, ij, Y i, P j,, (4) 3 Equaion 2 also has a differen specificaion of rade fricions han (1). However, noe ha he bilaeral componen,, can be he same in he wo equaions if is assumed o be an appropriae funcion of he disance index and addiional (possibly ime-varian) rade cos variables are included in he radiional model. 5

6 where 1 1 Y j, i, V ij,, and V j P is an index based on weighed average uni inpu j, Y requiremen (inverse of facor produciviy) for i s firms exporing o j. 4 This index conrols for he proporion of exporing firms and equals zero if no firms in i expor o j. A firm in i will expor o j if is inpu requiremen is below he hreshold needed for profiable expor operaion. Thus some firms in i will expor o j if he hreshold for i and j is above he minimum value of he inpu requiremen; oherwise no firm in i will expor o j. The hreshold for a counry pair depends on boh fixed and variable coss of exporing and can vary from one pair o anoher. To deermine he bilaeral hreshold levels and he index V, a ypical assumpion is ha he disribuion of inpu requiremen across firms is characerized by he Pareo disribuion. If he Pareo disribuion is unruncaed (lower bound for he inpu requiremen equals zero), V 0 since some firms in i will have a low enough inpu requiremen o expor o j. The nonzero V erm can be decomposed muliplicaively ino hree componens depending on, respecively, he exporer, imporer and rading pair characerisics. The graviy equaion (4) for his version of he Meliz model can be expressed in he general form (3). The expression for he componen, however, is differen in he Meliz model. In he Anderson-van Wincoop model, 1. In conras, leing denoe he shape parameer in he Pareo disribuion and 1 1 he fixed inpu requiremens for i exporing o j, in he general form of he 4 * Leing denoe he inpu requiremen hreshold for i s expors o j for period, and G( ) represen he cumulaive disribuion funcion for inpu requiremen wih lower suppor equal o ; oherwise V, 0. * ij L ij 6 L, * ij, 1 L V dg( ) if

7 Meliz model wih unruncaed Pareo disribuion (see Head and Mayer, 2014). This expression implies ha he elasiciy of bilaeral expors wih respec o variable rade cos depend only on he shape parameer of he disribuion while he elasiciy wih respec o fixed coss depends boh on he shape paameer and he subsiuion elasiciy. One limiaion of he above version of he Meliz model is ha like he Anderson-van Wincoop model, i does no explain he fac ha zero expors are observed for many counry pairs. To explain his fac, Helpman e al. (2008) use a model which assumes a runcaed Pareo disribuion wih non-zero lower suppor. In his version, V 0 and here is zero rade if he hreshold for he i, j pair is below he lower suppor. This version of he model, moreover, canno be expressed in he general form (3) because his form does no allow for zero rade. Esimaing he model excluding observaions wih zero rade flows would inroduce a selecion bias. 2.2 Empirical Implemenaon Graviy models are genereally esimaed in a log-linear form. For example, afer adding a mulipliciive error erm, e, he radiional graviy model can be expressed as ln X ln A ay a Y a ln D ln e. (5) 1 i, 2 j, 3 ij This equaion can be esimaed by OLS (ime dummies can be used o esimae ln A ). The loglinear form of he Anderson-van Wincoop graviy model (wih balanced rade and mulipliciive error) is: ln Xij, b0lny b1 lnyi, b2 lnyj, b3 ln i, b4 ln Pj, b5 ln ij, ln eij,, where b, b1 b2 1, b3 b4 1 and b5 1. Trade coss are ypically modeled as 0 1 7

8 D ij, where is he elasiciy of rade coss wih respec o disance and is he effec of oher deerminans of rade coss generally represened by binary variables. 5 The mulilaeral resisance erms, i, and P j,, are no direcly observable. Alhough some proxies for hese variables can be derived, a ypical approach is o esimae his model in he log-linear version of he general form (3): ln X ln G ln S lnu ln ln e. (6) i, j, In his form, ln Si, 1 ln Yi, ( 1) ln i, and lnu j, ln Yj, ( 1) ln Pj,, and ime-varian dummy variables for exporing and imporing counries can be used o esimae hese componens. The componen ln G lny can be esimaed using ime dummies, bu if here is no need o isolae he effec of his componen, i could be dropped and is effec would hen be included in he exporer and imporer ime-varian dummy variables. Finally, ln ij, would depend on ln D and indicaor variables for oher deerminans of rade coss. OLS can also be used o esimae (6). ij The Meliz model wih unruncaed Pareo disribuion could also be represened by (6) wih a differen inerpreaion of he componen, ln ij,. One basic problem wih he log-linear form (6) is ha i canno include or explain zero bilaeral rade flows. One approach is o esimae (6) excluding zero rade observaions. This approach, however would inroduce a selecion bias because of he omission of observaions wih zero rade. 5 For example, oher deerminans of rade coss include dummy variables for wheher a counry pair shares a common border, language or colonial hisory or belongs o a regional rade agreemen. Noe ha some of hese variables like he membership of a regional rade agrremens could vary over ime. 8

9 The Meliz model wih runcaed Pareo disribuion explains zero rade. For his model, Helpman e al. (2008) sugges a wo-sage Heckman esimaion procedure where he firs sage model explains he choice of a pair of counries o rade or no o rade, and he second sage model explains bilaeral rade flows for counry pairs wih non-zero rade. A limiaion of his procedure is ha i requires an exclusion resricion, ha is, a leas one variable ha is included in he firs-sage model bu no he second-sage model. Such a variable is hard o find since he facors deermining a firm s decision wheher o expor o a desinaion or no are also likely o influence he decision of how much o expor if i chooses o expor. An alernaive approach is suggesed by a mehod proposed by Eaon and Korum (2001) based on he Tobi model. According o his mehod, here is a criical value of expors for each L counry (in a given period), X i,, such ha if ideal rade falls below his level, zero expors are observed, oherwise observed expors equal ideal expors. This mehod can be relaed o he heerogeneous firm model which implies ha a counry will no expor o a desinaion if he hreshold levels for he desinaion falls below he lower suppor of he runcaed Pareo disribuion. Threshold inpu requiremens (varying across desinaions) would deermine counry L i s expors o each desinaion. The criical value, X i,, can be inerpreed as he lower bound for counry i s expors o differen desinaions in period, and can be esimaed from observed Min bilaeral expors as X L i, Xij, j. This mehod can be implemened for esimaing (6), by using inerval regression wih he dependen variable represening poin daa (ln X, ln X ) for L observaions wih non-zero rade and lef-censored daa (,ln X i, ) for observaions wih zero rade. 9

10 Sanos Silva and Tenreyro (2006) poin ou anoher problem wih esimaing (6), which arises if e is heeroskedasic and is variance depends on one or more of he explanaory variables. In his case, since he expeced value of ln e ij, depends on he variance of e, OLS esimaor would be biased and inconsisen. They show ha under weak assumpions, he poisson pseudo-maximum likelihood (PPML) esimaor provides consisen esimaes of he original nonlinear model. As his procedure does no require logarihmic ransformaion of he dependen variable, i can accommodae zero rade flows. Thus PPML could be used o esimae a model ha uses he same explanaory variables as in (6) o explain bilaeral rade flows including zero flows. 3. Daa ij, he In his secion we briefly describe our sample and discuss he evoluion and he disribuion of Pakisan s rade in our sample period. 3.1 Sample We have assembled a panel daa se ha indcludes bilaeral rade flows of 183 reporing counries wih 253 parner counries from 2004 o For each year and reporing counry, he bilaeral daa se includes daa on he US dollar value of all expors o and all impors from each parner. The daa se includes observaions. The source of he daa is he U.N. Comrade Daabase. One feaure of he daa ha sands ou and is worh menioning is he large proporion of counry pairs ha repor zero rade flows. As Figure 1 shows, he proporion of counry pairs wih no rade is around he 60% mark in 2004 and edges seadily upwards hereafer. We have also colleced daa on a number of indexes of rade coss. These indexes are in line wih sandard pracice in he graviy lieraure and include, among ohers, he log of disance, 10

11 measured as he disance in kms beween mos populaed ciies. This ranges beween 1.88 and kms, wih a mean value of 7325 kms. We also measure rade coss by including dummies ha ake value 1 for common borders, common official or primary language and common colonizer afer These daa come from he CEPII graviy daase. Finally we include a dummy ha akes value 1 in case of membership o regional rade agreemens (RTAs). 3.2 Pakisan s Inernaional Trade Figure 2 shows ha Pakisan s impors have grown from less han 20 billion in 2004 o o nearly 45 billion US dollars in 2013 while expors have increased increased a a slower pace from less han 15 billion o abou 25 billion US dollars over he same period. The share of impors in GDP has flucuaed, bu has no changed much beween 2004 and The share of expors in GDP has declined from 2004 o Pakisan rades wih over 175 counries. However, he bulk of Pakisan s rade akes place wih a much smaller number of counries. Figures 3 shows he percenage of Pakisan s impors originaing from he op en counries in 2004 and Figure 4 shows he proporion of Pakisan s expors desined o he op en counries in he sames years. The 10 larges rading parners for impors are differen han for expors. As well, he lis has changed from 2004 o USA is he larges imporer of Palisan s goods in boh years, alhough is share of Pakisan s oal expors declined from 23% o 15%. Saudi Arabia in 2004 and UAE in 2013 were he larges exporers o Pakisan. A large counry is likely o have large rade flows wih Pakisan. To conrol for he size of he rade parner, we also examine Pakisan s bilaeral impors and expors as a percenage of parner s GDP. According o his measure, Figures 5 shows he op en counries in 2004 and 11

12 2013 for impors and Figure 6 he op 10 counries for expors. 6 The leading rading parner lis based on he size adjused impors and expors is quie differen. The wo large neighbors of Pakisan, China and India, do no appear in his lis. 4. Empirical Analysis 4.1 Mehodology Our main objecive is o esimae he effec of eas-wes rade barriers on Pakisan s rade wih neighboring counries. To conrol for oher facors influencing Pakisan s bilaeral rade, we use our panel daa se o esimae graviy models. We use he bilaeral rade daa of all reporing counries o esimae he effec of oher facors. Bilaeral dummy variables for Pakisan and seleced counries are hen used o isolae he effec of eas-wes barriers for Pakisan. We iniially consider four counries ha share a common border wih Pakisan: India, China, Afghanisan and Iran. We define bilaeral dummy variables for Pakisan and each of hese counries which ake he value of 1 for observaions for bilaeral rade beween Pakisan and he given neighbor and value of zero for all oher observaions. In esimaing he model, we focus on he general form (6). For esimaing he effecs of componens S i, and U j,, we consider ime-varian dummy variables (i.e, a separae dummy variable for each year) for reporers and parners. As his formulaion involves a very large number of dummy variables, we also use a simpler form which assumes ha ime effecs are he same for all counries. In his form, fixed (ime-invarian) reporer and parnner dummies and dummies for years are used o esimae he effec of he monadic componens S i, and U j,. The 6 In Figure 6, we have excluded Afghanisan, as repored expors from Pakisan could include US supplies shipped hough Pakisan and may have a considerable upward bias. 12

13 dyadic componen capures he effec of rade coss. We use he convenional indexes of rade coss represened by ln D and dummy variables for common border, common official ij language, shared colonial hisory and membership in RTA s. 7 In addiion, we incorporae bilaeral dummies for Pakisan and is neighbors o idenify exra coss of eas-wes rade barriers for Pakisan. For esimaing he model, we use a balanced sample where he number of parner counries is reduced o make he parner counry se he same as he reporer counry se. As i is no clear wheher repored impors or expors are a more accurae measure of bilaeral rade flows, we esimae wo models, one for explaining expors and he oher for explaining impors. We consider hree mehods for esimaing he srucural graviy model. 8 Firs, we use OLS o esimae a model for non-zero rade flows. This model suffers from a poenial selecion bias since zero rade observaions are omied. Second, we use EK Tobi procedure o esimae he model. This procedure includes all observaions and is relaed o heory explaining zero rade, bu can produce biased esimaes because of heeroskedasiciy. Finally, we employ PPML echnique for esimaion. This echnique addresses he heeroskedasiciy issue. I can also handle zero rade flows, bu would use a model no designed o explain zero rade Resuls We esimae he hree srucural graviy models discussed above. For comparison, we also esimae a radiional graviy model. Tables 1 and 2 presen he key resuls for hese models. 7 In his se, only he dummy variable for RTA is poenially ime varian (if a counry joins or leaves an RTA during he sample period). 8 We did no use he 2-sage Heckman procedure because of a lack of asaisfacory variable saisfying he exclusion resricion. 9 Anoher limiaion of his procedure is ha a number of counry dummy variables and observaions need o be dropped o ensure convergence in esimaion. 13

14 Table 1 shows resuls for regressions explaining bilaeral impors and Table 2 for regressions explaining bilaeral expors. In each able, we presen esimaes of he radiional OLS regression in column (1), he srucural OLS regression excluding zero rade observaions in column (2), he EK Tobi regression including zero rade observaions in column (3); and he PPML regression including zero rade observaions in column (4). 10 In he radiional regression, explanaory variables include he GDP of boh he reporer and parner counries, bu do no include any counry- or ime-specific dummy variables. All hree srucural regression include dummy variables for reporer and parner counries and for years (reporer and parner GDP is excluded). We also ried ime-varian reporer and parner dummy variables in he srucural regressions, bu he resuls were no very differen, and he ables focus on he simpler versions wih a much smaller number of fixed effecs. We use he same se of rade cos variables for all regressions. This se include radiional indexes represened by ln D and bilaeral dummy variables labelled conig (coniguous counry ij pair), com_langoff (common official language), comcol (common colonial hisory) and ra (par of an RTA). In addiion, he se includes he dummy variables for Pakisan and each of is immediae neighbors, which are of special ineres for his sudy. These variables are labelled pak_india, pak_china, pak_afg and pak_iran. In each regression, he coefficeins of a variable represens he elasiciy of he bilaeral rade index wih respec o he variable. As he ables show, resuls for he srucural regressions are differen han he radiional regressions. Moreover, he coefficiens of rade cos indexes vary considerably across he hree srucural regressions. Wih a few excepions, he coefficiens (in absolue values) are he highes 10 For reporer counry i, parner counry j and year, he dependen variables in Tables 1 and 2 are M and X for PPML regressions and ln M and ln X ij, for oher regressions. 14

15 in he EK Tobi regression and he lowes in he PPML regression. 11 As would be expeced, he coefficiens for he pak-india dummy are significanly negaive for all regressions in boh ables. We find, moreover, ha he coefficiens for he pak-china dummy are also significanly negaive in all cases. This resul shows ha boh Pakisan s impors from and expors o China are significanly less han he values prediced by he radiional as well as srucural graviy models. Pakisan has good relaions wih China and here are no apparen exra rade policy barriers beween he wo counries. Thus our findings provide srong suppor o he view ha inacessible land roues have diminished Pakisan s rade wih China. The coefficiens for pak-afg dummy are posiive and significan in all cases, and hose for pak-iran dummy are significanly posiive in some and insignifican in oher cases. These resuls may seem surprising as war in Afganisan and conflic in ribal areas and Baluchisan would be expeced o impede Pakisan s rade wih Afghanisan and Iran. Pakisan s repored rade wih Afghanisan, however, could be significanly biased because i likely includes movemen of good hrough Pakisan ha are relaed o war operaions. Moreover, oher counries rade wih Afganisan and Iran was hindered by Afghanisan war and sancions on Iran, perhaps o a larger exen han Pakisan s rade wih hese counries. These facors could accoun for he surprising resuls. 5. Fuure Work 11 One excepion is he coefficiens of he common border index (conig), which are insignifican for EK Tobl and significan and posiive for he oher srucural regressions in boh ables. The ordering of he coefficiens for he pak-afg dummy is also differen han for oher variables. 15

16 Our empirical analysis above suggess ha eas-wes barries have significanly decreased Pakisan s rade wih India and China.The nex phase of he projec will explore he poenial for he expansion of Pakisan s rade wih hese counries. In srucural graviy models, esimaes of he coefficiens of Pakisan-India and Pakisan-China dummy variables can be linked o variable and fixed rade coss. We will develop a model ha would relae hese coss o differen policy acions. This model and esimaes from he srucural graviy equaion will be used o consruc a number of policy scenarios. These scenarios will include examining he effec of specific reducions of ariff and nonariff barriers on rade wih India, and he impac of reducion in ransporaion coss via CPEC on rade wih China. 16

17 References Anderson, James E. and Eric van Wincoop, 2003, Graviy wih Gravias," American Economic Review, 93, Armingon, P.S., 1969, A heory of demand for producs disinguished by place of producion,, Inernaional Moneary Fund Saff Papers, 16 (1), Eaon, J. and S. Korum, 2001, Trade in capial goods, European Economic Review, 45 (7), Head, Keih and Thierry Mayer, 2014, Graviy Equaions: Workhorse,Toolki, and Cookbook, in G. Gopinah, E. Helpman and K.Rogoff (eds.), Handbook of Inernaional Economics (Elseveir), vol.4, chap. 3. Helpman, E., M. Meliz and Y. Rubinsein, 2008, Esimaing rade flows: rading parners and rading volumes, Quarerly Journal of Economics, 123 (2), Krugman, Paul, 1980, Scale Economies, Produc Differeniaion, and he Paern of Trade, American Economic Review, 70, Meliz, M.J., 2003, The impac of rade on inra-indusry reallocaions and aggregae indusry produciviy, Economerica, 71 (6), Nabi, Ijaz, 2013, A Growh Ven Anchored in Hisory and Geography, in Rashid Amjad and Shahid Javed Burki (eds.), Pakisan: Moving he Economy Forward, Lahore School of Economics, Chaper 14. Sanos Silva, J., and S. Tenreyro, 2006, The log of graviy, The Review of Economics and Saisics, 88 (4),

18 Figure 1 Proporion of counry pairs reporing zero rade, mean of Zero_Trade

19 Figure 2 Pakisan's Aggregae Impors and Expors, ,00E+10 4,50E+10 4,00E+10 3,50E+10 3,00E+10 2,50E+10 2,00E+10 1,50E+10 1,00E+10 5,00E+09 0,00E Aggregae Impor Aggregae Expor Share of Pakisan's Impors and Expors in GDP, ,3 0,25 0,2 0,15 0,1 0, Impors Raio Expors Raio 19

20 Figure 3 Impor Proporion of Parners (2004) 12% 35% 10% 10% 2% 3% 4% 4% 6% 6% 8% Saudi Arabia UAE USA China Japan Kuwai Germany Malaysia Indonesia India Oher counries Impor Proporion of Parners (2013) 26% 18% 3% 3% 4% 4% 4% 5% 9% 9% 15% UAE China Kuwai Saudi Arabia Japan Malaysia India USA Germany Indonesia Oher counries 20

21 Figure 4 Expor Proporion of Parners in % 23% 8% 7% 3% 3% 3% 5% 4%4%4% USA Germany Afghanisan Saudi Arabia UAE China, Hong Kong SAR France Oher counries UK Ialy Neherlands Expor Proporion of Parners in % 38% 11% 8% 7% 2% 3% 3% 3%4% 6% USA China Afghanisan UAE UK Germany Bangladesh Ialy Neherlands Spain Oher Counries 21

22 Figure 5 Impors relaive o parners GDP in ,8 1,6 1,4 1,2 1 0,8 0,6 0,4 0,2 0 Impors relaive o parners GDP in ,5 2 1,5 1 0,5 0 22

23 Figure 6 Expors relaive o parner GDP in ,4 1,2 1 0,8 0,6 0,4 0,2 0 Expors relaive o parner GDP in ,5 4 3,5 3 2,5 2 1,5 1 0,5 0 23

24 Table 1. Regressions Explaining Bilaeral Impors VARIABLES OLS OLS EK Tobi PPML ln_dis 1.093*** 1.505*** 1.917*** 0.597*** ( ) ( ) (0.0137) (0.0115) pak_india 4.043*** 5.035*** 7.064*** 1.747*** (0.143) (0.194) (0.194) (0.143) pak_china 0.914*** 1.869*** 3.068*** 1.063*** (0.203) (0.0919) (0.138) (0.0749) pak_afg 1.414*** 1.971*** 3.068*** 2.976*** (0.105) (0.127) (0.388) (0.151) pak_iran *** 1.351*** (0.242) (0.261) (0.253) (0.208) conig 0.817*** 0.490*** *** (0.0369) (0.0390) (0.0606) (0.0268) comlang_off 0.878*** 0.824*** 1.362*** *** (0.0170) (0.0177) (0.0258) (0.0267) comcol 0.892*** 0.822*** 0.906*** 0.558*** (0.0243) (0.0236) (0.0352) (0.0539) ra 0.867*** 0.549*** 0.818*** 0.493*** (0.0163) (0.0169) (0.0237) (0.0216) ln_reporergdp 1.044*** ( ) ln_parnergdp 1.264*** ( ) reporer dummies No Yes Yes Yes parner dummies No Yes Yes Yes year dummies No Yes Yes Yes Observaions 188, , , ,156 R squared Robus sandard errors in parenheses *** p<0.01, ** p<0.05, * p<0.1 24

25 Table 2. Regressions Explaining Bilaeral Expors VARIABLES OLS OLS EK Tobi PPML ln_dis 1.146*** 1.597*** 2.193*** 0.629*** ( ) ( ) (0.0140) (0.0105) pak_india 4.081*** 4.818*** 7.078*** 1.469*** (0.137) (0.189) (0.231) (0.151) pak_china 0.905*** 1.744*** 2.821*** 0.674*** (0.172) (0.143) (0.218) (0.0997) pak_afg 1.810*** 1.745*** 2.186*** 2.951*** (0.212) (0.212) (0.369) (0.157) pak_iran 0.733*** ** (0.213) (0.257) (0.241) (0.194) conig 1.132*** 0.669*** *** (0.0351) (0.0382) (0.0643) (0.0265) comlang_off 0.882*** 0.867*** 1.491*** 0.136*** (0.0172) (0.0177) (0.0261) (0.0267) comcol 0.824*** 0.674*** 0.978*** 0.408*** (0.0249) (0.0237) (0.0351) (0.0512) ra 0.745*** 0.531*** 0.952*** 0.590*** (0.0167) (0.0170) (0.0247) (0.0216) ln_reporergdp 1.300*** ( ) ln_parnergdp 0.901*** ( ) reporer dummies No Yes Yes Yes parner dummies No Yes Yes Yes year dummies No Yes Yes Yes Observaions 165, , , ,445 R squared Robus sandard errors in parenheses *** p<0.01, ** p<0.05, * p<0.1 25