The Trade Potential of the COMESA-EAC-SADC Tripartite: A Comparative Analysis * Preliminary, do not cite without the authors’ permission. Jana Riedel Anja Slany August 7, 2014 Abstract In 2008 the member states of the three major trading blocs in southern and eastern Africa - the Common Market for Eastern and Southern Africa (COMESA), the East African Commu- nity (EAC) and the Southern African Development Community (SADC) - agreed on establish- ing a common Free Trade Area (FTA). This so-called COMESA-EAC-SADC Tripartite is an important milestone towards Africa’s continental trade integration. This study analyzes the impact of regional integration among the Tripartite countries on bilateral exports and evaluates the latest integration efforts with respect to future trade po- tential. Within a panel framework bilateral export data in between 1995 and 2010 are used to estimate an extended gravity model for 24 member states. Doing so, we also account for multi- membership in regional trade agreements. The potential endogeneity of free trade agreements and the issue of zero trade flows are treated carefully by means of a systematic comparison between (instrumental variable) panel and Poisson pseudo-maximum-likelihood (PPML) esti- mation. The findings suggest a robust and significantly positive impact of the COMESA FTA. Coefficient estimates are about 0.8 using panel estimation techniques and about 0.2 for the PPML model including country-specific effects. The data on average tariff barriers have a sig- nificantly negative effect in most model specifications. The EAC and SADC FTAs do not show any positive effect on exports. Thus, to a certain extent our study confirms the pessimistic view of the effectiveness of African FTAs. Keywords: trade union, Africa, Hausman-Taylor, panel data, PPML JEL classification: F13, F14, F15, C23 * Jana Riedel: Westf¨ alische Wilhelms-Universit¨ at M ¨ unster, Wirtschaftswissenschaftliche Fakult¨ at, Institut f ¨ ur Inter- nationale ¨ Okonomie, Universit¨ atsstraße 14-16, 48143 M¨ unster, Germany, email: [email protected]; Anja Slany: Ruhr-Universit¨ at Bochum, Lehrstuhl f¨ ur Internationale Wirtschaftsbeziehungen, Universit ¨ ’atstraße 150, 44801 Bochum, Germany, email: [email protected].
Transcript
The Trade Potential of the COMESA-EAC-SADCTripartite: A Comparative Analysis∗
Preliminary, do not cite without the authors’ permission.
Jana Riedel Anja Slany
August 7, 2014
Abstract
In 2008 the member states of the three major trading blocs in southern and eastern Africa -the Common Market for Eastern and Southern Africa (COMESA), the East African Commu-nity (EAC) and the Southern African Development Community (SADC) - agreed on establish-ing a common Free Trade Area (FTA). This so-called COMESA-EAC-SADC Tripartite is animportant milestone towards Africa’s continental trade integration.
This study analyzes the impact of regional integration among the Tripartite countries onbilateral exports and evaluates the latest integration efforts with respect to future trade po-tential. Within a panel framework bilateral export data in between 1995 and 2010 are used toestimate an extended gravity model for 24 member states. Doing so, we also account for multi-membership in regional trade agreements. The potential endogeneity of free trade agreementsand the issue of zero trade flows are treated carefully by means of a systematic comparisonbetween (instrumental variable) panel and Poisson pseudo-maximum-likelihood (PPML) esti-mation. The findings suggest a robust and significantly positive impact of the COMESA FTA.Coefficient estimates are about 0.8 using panel estimation techniques and about 0.2 for thePPML model including country-specific effects. The data on average tariff barriers have a sig-nificantly negative effect in most model specifications. The EAC and SADC FTAs do not showany positive effect on exports. Thus, to a certain extent our study confirms the pessimistic viewof the effectiveness of African FTAs.
Over the last two decades world merchandise trade has more than tripled, accelerated by a largeincrease of South-South and North-South trade. Today, developing countries’ make up 45 percentof world trade whereas this trade growth has been generated to a large extent by Asian countriesand developing America (UNCTAD (2013)). Despite an annual growth rate of African exportsand imports of roughly 30% in 2010 and 17% in 2011, the share of African trade in world trade isstill low at 3.4% (2012). However, there is a strong believe that regional integration in Africa willeffectively promote independence from developed countries. The Abuja Treaty (1991) layed thefoundation for a so-called African Economic Community with the aim of establishing a continentalFTA in 2017 as well as a customs union in 2028. Currently, there are eight recognized regional eco-nomic communities (RECs): the Arab Maghreb Union, the Community of Sahel-Saharan States,the Common Market for Eastern and Southern Africa (COMESA), the East African Community(EAC), the Economic Community of Central African States, the Economic Community of WestAfrican States (ECOWAS), the Intergovernmental Authority on Development, and the SouthernAfrican Development Community (SADC). The process of African regional integration received abig impulse in October 2008 when the Tripartite of the COMESA, the EAC and the SADC agreedon forming a trading bloc free of tariffs, quotas and exemptions that combines the already existingfree trade agreements. On the second summit of the Tripartite in 2011, the declaration launchingthe negotiations on the FTA was signed by all members except Ethiopia, Eritrea and Madagascar.Of course, the agreement promises to increase intra-regional trade in south and east Africa by gen-erating a larger market and overcoming the problem of multi-membership. By now the 26 memberstates of the Tripartite already make up more than 50% of GDP of the total African Union.
However, the three RECs are at different stages of their integration which influences the nego-tiation process. The COMESA was formed in 1994 as a successor organization of the so-calledPreferential Trade Area. The organization aims at realizing a large economic trade bloc in order toovercome individual country’s barriers to trade.1 After the implementation of an FTA in 2000, theCOMESA launched a customs union in 2009. Parallel to negotiations within the Tripartite, thereis still an ongoing negotiation process within the COMESA in joining the FTA and the customsunion.2 The member states agreed on a list of sensitive products where current tariff rates arealigned to the so-called Common External Tariff within a transition period of three years whichcan be extended to five years. In general, the rules of origin in the COMESA agreement are lessstringent than those for many other FTAs, such as the SADC (Korinek & Melatos (2009)). More-over, the Common External Tariff of the COMESA is already harmonized with the tariff rate of theEAC (Othieno & Shinyekwa (2011)). The Treaty for Establishment of the EAC entered into forcein 2000. In 2005, the member states Tanzania, Kenya and Uganda formed a customs union thatwas transformed into a common market in 2010. Rwanda and Burundi who joined EAC in 2007
1Current member states are Burundi, Comoros, the Democratic Republic of the Congo, Djibouti, Egypt (since1999), Eritrea (since 1994), Ethiopia, Kenya, Libya (since 2005), Madagascar, Malawi, Mauritius, Rwanda, Seychelles(since 2001), Sudan, South Sudan (since 2011) Swaziland, Uganda, Zambia and Zimbabwe. Lesotho and Mozambiqueleft the organization in 1997. Due to an overlap with the REC SADC Tanzania (2000), Namibia (2004) and Angola(2007) also left the COMESA.
2Uganda and Ethiopia agreed to join COMESA FTA in December 2014.
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are also part of the customs union and the common market since 2009. The members have fullyliberalized the goods sector and only face remaining tariffs in a few service sectors. Today negotia-tions are still ongoing with Somalia, South Sudan, and Sudan (Othieno & Shinyekwa (2011)). TheSADC was formed in 1992, based on the Southern African Development Coordination Confer-ence, and today consists of 15 south African countries.3 In 2008 an FTA was established includingthe Southern African Customs Union (SACU) members who allow tariff-free imports from otherSADC members. Angola, the Democratic Republic of the Congo and Seychelles refused to par-ticipate in the FTA. Moreover, the agreement is based on minimum conditions where full tariffliberalization is only provided on 85% of intra-regional trade within the SADC. The objectivemaximum tariff liberalization in 2012 has not yet been achieved. Mozambique still imposes tariffson imports from South Africa. Malawi, Zimbabwe and Tanzania are allowed to set a 25% importduty on sugar and paper products until 2015 (Sandrey (2013)).
Since we observe an increasing role of South-South trade and new FTAs with different levelsof integration among the three REC, the question for the role of regional trading bloc creation ineast and south Africa on intra-regional trade naturally arises. If there is some evidence in favor ofa positive effect of recent trade integration efforts, the COMESA-EAC-SADC Tripartite may be asuccessful instrument to further enhance bilateral regional trade.
Regional trade agreements (RTAs) have received a lot of attention in the literature during thelast decade. Several econometric questions are discussed and solved exemplified within the analy-sis of effects of FTAs on North-North trade. Significantly positive effects of free trade agreementsare revealed in e.g. Baier & Bergstrand (2007), and Santos Silva & Tenreyro (2006). The empir-ical findings on African RTAs vary from a skeptical view (e.g. Yang & Gupta (2005)) to a ratheroptimistic one (e.g. Korinek & Melatos (2009), and Afesorgbor & van Bergeijk (2013)). Afesorg-bor & van Bergeijk (2013) conduct a meta-analysis in order to summarize empirical studies onAfrican regional integration and conclude that there might be a general upward estimation bias ofRTA effects in standard panel regressions. By comparing the trade effects of the five major recog-nized regional economic communities, among them the COMESA and the SADC, the authors finda significantly positive effect on trade within SADC countries, but not within COMESA memberstates.
To the best of our knowledge the effect of the FTAs within these RTA have not been examined.We will close this gap. Moreover, a number of previous studies on African trade do not includetariff measures, maybe mainly due to the poor data availability. We try to address to this by using asimple proxy for general market barriers that a country faces and show that the average of bilateraltariff rates still maintains important information about existing market barriers.
From extensive empirical work on the gravity equation it is widely known that bilateral tradeincreases by the countries’ GDPs and the distance between them. This is also confirmed by variousstudies for African countries. However, the panel dimension is still largely unexplored althoughcountry(-pair) specific and time effects may account for several unobserved determinants of intra-African trade. We account for this and provide well-founded results based on a sound estimationstrategy including several estimators and diagnostic tests not at least because we observe a high
3Current SADC members are Angola, Botswana, Democratic Republic of the Congo, Lesotho, Madagascar,Malawi, Mauritius, Mozambique, Namibia, Seychelles, South Africa, Swaziland, Tanzania, Zambia and Zimbabwe.
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sensitivity of results to the chosen estimation technique in the literature. We deal with heterogene-ity using a panel framework and robust standard errors. Moreover, zero trade flows especially occurwhen considering bilateral trade of small economies. (See Afesorgbor & van Bergeijk (2013), andHerrera (2012) for a detailed discussion.) Thus, we apply two different strategies. First, zero ex-port values are replaced by a small number within the panel framework (see e.g. Carrere (2004)).Second, the Poisson pseudo-maximum-likelihood (PPML) estimator handles zero trade flows (seee.g. Santos Silva & Tenreyro (2006), and Afesorgbor & van Bergeijk (2011)). Since zeros may alsoreflect missing values we control for a selection bias by means of the Heckman selection model(see e.g. Martin & Pham (2008), and Disdier & Marette (2009)). Within the panel framework wefollow Baier & Bergstrand (2007) and use the Hausman-Taylor model to account for a potentialendogeneity bias of the FTA dummies and tariff measures.
We estimate a panel of 24 member states of the Tripartite on bilateral exports in 1000 US$obtained from UNCTADstat. In order to observe the effects of regional integration over a longhorizon including the important phase of tariff liberalization in east and south Africa at the end ofthe 1990s and the beginning of the 2000s the panel consists of annual data from 1995 to 2010.4 Weshow that bilateral trade is driven by the traditional gravity variables whereas distance between twocountries and a remoteness variable have the largest negative effect on bilateral exports. Moreover,since the growing importance of North-South trade and South-South trade the role of comparativeadvantage have become more evident in trade. For instance, Hanson (2012) state that low-incomecountries’ exports concentrate in the sectors agriculture, raw-materials and apparel while middle-income economies often specialize in manufacturing exports. Even though most countries of theTripartite are low-income countries the members are highly heterogeneous in terms of GDP andthe level of social development. Thus we also include measures of factor endowment to the gravityequation (see e.g. Egger (2004)).
In the baseline two-way error component fixed effects estimation as well as in the Hausman-Taylor estimation we observe the striking result that the direction and size of the effects of limitedmarket access and a COMESA FTA dummy are robust to the underlying setup. Market barriershave a significantly negative effect on trade and the COMESA FTA accelerates bilateral exports.The PPML estimates do not support these findings, which is in line with previous studies.
The remainder of the paper is organized as follows. Section 2 introduces the model setup andestimation techniques. Section 3 presents the dataset and details on the estimation procedure, anddiscusses the main regression results. A sensitivity analysis is conducted in section 4. In section 5an economic interpretation as well as a comparison to recent literature is given. Finally, Section 6concludes.
4The downside of the broad time span is the lack of non-tariff barriers and transportation cost data, but non-tariffmeasures are recognized to be an important hindrance to trade. Therefore, we allow for country-pair as well asexporter- and importer-specific effects to account for unobservable variables. In order to give a first insight we includeadministrative cost measures from the Doing business indicators and mobile phone use and re-estimate our models fora subsample starting in 2004.
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2 The modeling framework and estimation strategy
The baseline gravity model was and still is a workhorse in international economics. Introduced toeconomics by Tinbergen (1962) and theoretically motivated by Anderson & Van Wincoop (2003),the model describes trade flows between two entities as the product of their economic sizes di-vided by the distance between them. Intuitively, trade between two regions i and j increases withtheir trade potentials reflected in national incomes, and decreases with its transportation costs ap-proximated by the distance between economic centers of the two entities. Transportation costs areadditionally proxied by common language and common currency indicator variables, tariffs etc. Inour setup we also account for country-pair and country-specific unobserved effects such as tradi-tion or preferences that may enhance or impede trade integration. Further we do not only considercross-country evidence but also the time dimension in the regional integration. Therefore we setup the following linear panel regression model stemming from the baseline gravity equation.
lnYijt = β1 lnX1ijt + β2 lnX2ij + uijt (2.1)
in which X1ijt are K1 explanatory time-varying variables and X2ij are K2 time-invariant explana-tory variables. β1 and β2 denote the corresponding unknown coefficient vectors, and uijt being
uijt = µij + λt + εijt (2.2)
Thus, our the two-way error component model allows for unobservable country-pair specific time-invariant effects, µij , and λt are time effects which may reflect common trends and/or specificevents such as global shocks or natural disasters. The remainder denotes the i.i.d. error term,εijt ∼ (0, σ2
ε).Since the research question directly addresses to a fixed set of countries, we use the fixed effects
regression approach to estimate the model parameters. We estimate the country-pair specific inter-cepts assuming that the unobserved country-pair effects are most likely correlated with the regres-sors. If the entities ij are randomly chosen among a large population we would benefit in terms ofefficiency using a random effects model, but random effects estimation is biased and inconsistent incase of correlation between u and the regressors (E(εjit|X1ijt, X2ij) = 0;E(µij|X1ijt, X2ij) 6=0). Thus, we do not consider this approach in our estimation strategy. We use the within estimatorto obtain consistent estimates for the time-varying coefficients. This estimator transforms the datainto the difference between the original value of a variable and its indivdual mean.
It is most likely that some of our regressors such as the tariff and free trade area variables, andin part also the GDP data, are endogenous to exports. Therefore we consider the Hausman-Taylormodel that makes use of instruments to account for potential endogeneity (Hausman & Taylor(1981)). The Hausman-Taylor model can be specified as follows
X1ijt and X2ij refer to the exogenous time-varying and time-invariant variables, and Z1ijt andZ2ij denote endogenous variables. lnZ1ijt is instrumented by the deviations from the individual
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mean, i.e. lnZ1ijt − lnZ1ij·; lnZ2ij is instrumented by the individual mean of lnX1ijt, i.e.lnX1ij·. Note, we add country-specific effects µi and µj to better address to multilateral resistance(see Herrera (2012)). The model is estimated using a two-stage least squares (2SLS) procedure.
In their recent research agenda Santos Silva & Tenreyro (2006) show that the parameters ofloglinear gravity models estimated by OLS may be upward biased compared to the true elasticitiesunder the presence of heteroskedasticity and misleading conclusions may be drawn. The expectedvalue of the ln of a random variable depends on the higher order moments of the distribution ofthat variable. Additionally loglinearizing is not possible for zero trade flows which we assumewhenever no trade flows are reported.5 To account for this the authors propose to use the Poissonpseudo-maximum likelihood (PPML) estimator. The PPML estimator just considers trade flows asbeing zero. Thus, Santos Silva & Tenreyro (2006) propose to estimate a regression equation usingthe following form
Yt = exp(xtβ)ηt, ηt = 1 + εt/ exp(xtβ)
OLS would be inappropriate and inconsistent as it is not feasible for Yt = 0 and the expectedvalue of the log-linearized error usually depends on the xts. Note the dependent variable is exportsinstead of ln exports. However, the coefficients can be interpreted in terms of elasticities, still.Trade flows do not need to follow a Poisson distribution to obtain consistent estimates. The PPMLestimator provides unbiased estimates in the presence of heteroskedasticity.6
From a methodological point of view, the analysis on the three different methods describedabove may suffer from a selection bias. Zero trade flows and missisng data maybe due to thefact that some countries do not trade within a certain period with some other countries. However,missings may also be present due to rather small trade flows which are just not reported or roundeddown. This is most likely to happen for small or distant countries, i.e. the probability of roundingdown depends on the realizations of the explanatories. The same holds true for a case of missingobservations being wrongly recorded as 0. Truncating countries may lead to a selection bias aswell as recoding data as being zeros although they may be just small or missings values. Thus weapply the Heckman sample selection model in a sensitivity analysis in section 4. We first determinethe probability that there is trade between two regions before considering the determinants of theamount of bilateral trade. Therefore we use a different set of variables and coefficients to determinethe probability of nonzero trade (selection equation) and the value (outcome equation). It may bedifficult to find appropriate selection variables. Zero entries are recorded as missings to conduct theHeckman estimation. To test for a selection bias we conduct a Wald test of independent equations.In case of a rejection of the hypothesis of independence, zero trade flows/missing trade is linkedsystematically to the realizations of the explanatory variables.
The HT, PPML and Heckman selection models are in estimated Stata version 13 using country-pair and/or country-specific effects. The dataset and the regression results of our main models aredescribed in the following section 3.
5The problem of zero trade flows occurs frequently for developing countries or in very large samples.6The authors offer supplementary material and recent findings on a webpage:
The panel consists of annual bilateral export data of 24 member countries of the COMESA, theEAC and the SADC from 1995 to 2010. We exclude Seychelles and Libya from the country list dueto missing datapoints for almost all years. Still, we work on a dataset with roughly 28% missingexport data. Nominal bilateral export data in thousands US$ are obtained from the UNCTAD-stat database. The data are scaled by the export value index provided by the World Bank WorldDevelopment Indicators (WDI) in order to have real export figures.
The explanatory variables are also given in constant US$. The GDP data in constant 2005 pricesand 2005 exchange rates, and population data are taken from the UNCTADstat database. Distancebetween countries’ capitals in kilometers, and data on whether or not countries share a commonborder and a common language are obtained from the CEPII database.
Besides the traditional explanatories, we include additional variables which may influence thecountries’ competitiveness and their value of exports. The share of urban population is taken fromthe World Bank WDI and can be also interpreted as a measure of different factor endowments.7
Exporter’s and importer’s human capital are proxied using data from the Barro & Lee (2013)schooling dataset. We use the sum of the shares of population over the age of 14 who attainprimary, secondary or tertiary schools and account for double-counting. Since the data are onlyobserved at a 5-year basis we use a linear trend to interpolate the remaining years. Unfortunatelywe have to cope with missing data for six countries: Angola, Djibuti, Eritrea, Ethiopia, Comorosand Madagascar. These datapoints are replaced by the averages from similar countries with respectto size and trends in terms of real GDP per capita and then compared with the relative size andtrends in the Cline Center schooling data.
We additionally include the countries’ ranks in the control of corruption from the World Bank,Worldwide Governance Indicators.8 Doing so we hope to proxy either transport costs or socio-economic and political factors influencing trade flows.
Moreover, we want to measure the effect of tariff rates on bilateral exports in this region, butbilateral tariff rates are not available for a sufficient number of countries and years. Similarly,data on the effectively applied tariff rate of a country on its imports from the world contains about50% missings which makes the data not usable. Hayakawa (2011) recommends to include time-invariant pair fixed effects and time-variant exporter- and importer-specific effects. Moreover, weinclude some proxy for formal trade barriers using the average bilateral tariff rate each exportingcountry has to pay on its exports to all countries in the world.9 In what follows, we interpret thisvariable as a measure for limited market access. In certain model specifications we additionallymake use of the Sub-Saharan average of the effectively applied tariff rates on total imports from theworld. All data on tariffs are obtained from the World Integrated Trade Solution (WITS) UNCTAD
7The larger the share of urban population the more diversified the products would be in comparison to an agricul-tural economy.
8We use the percentile rank among all countries (ranges from 0 (lowest) to 100 (highest) rank). A higher rank inindex means less corruption.
9Doing so we implicitly assume that a country with on average low market access to countries worldwide alsofaces low market access to eastern and southern African countries. This is of course not necessarily the case.
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Trade Analysis Information System (TRAINS) database.
3.2 Estimation procedure and results
Estimation procedure
We estimate two main different specifications of the loglinearized gravity equation. In the firstspecification we introduce dummy variables being equal to one whenever two trading partners aremembers of an FTA ratified either within two COMESA countries, two EAC or two SADC/SACUcountries. In what follows we denote this model by I. In the second approach we generate a newvariable to detect the impact of remaining tariffs when the trading countries are not in the sameFTA. Since only some datapoints on bilateral tariff rates are avaliable for the underlying sample ofour study, we use average tariff rates. We control for the country-pair specific elimination of tariffswhenever being in a common FTA.10 We denote this setup by II.
The dummy variables for the three FTAs are constructed such that they are one whenever theexporter and importer are both member of an FTA in a certain year and zero otherwise. Hence,our FTA dummies are time-varying. The variable lnmarketbarrierit measures the average of thebilateral tariff rates each exporting country has to pay on its exports to the world. Hence, thisvariable captures an exporter-specific effect and thereby a country’s lack of integration in the worldmarket.
In the second setup we define the variable Appliedtariff as effectively applied tariff rate country ihas to pay on its exports whenever the two countries are not a member of the same trade agreement.For this purpose we generate an auxiliary dummy variable, AUXijt, which is one if the two tradingpartners are at least in one common FTA. The interaction term between this indicator variable andthe tariff rate allows us to measure the actual effect of the applied tariff rate. lnTarifft denotes theln of the Sub-Saharan average of the effectively applied tariff rate on total imports (simple average)from the world.11
lnAppliedtariffijt = (1− AUXijt) lnTarifft (3.1)
10The complete elimination of tariffs when being in an FTA is a simplifying assumption that holds true for the EACFTA, but for the COMESA FTA and the SADC FTA still exist many exemptions.
11Due to a lack of data we asume that the average Sub-Saharan tariff rate is approximately equal to the tariff ratecountry j is applying on its imports from country i.
being the tariff term.lnEXijt denotes the ln of real exports from country i to country j. Further we include real GDP
data for the importing and exporting country as a measure for income, lnGDPit and lnGDPjt.Bilateral transport costs and formal barriers to trade are proxied by a distance term lnDistij . Anindicator variable for a common border between two countries, CBij , and another dummy beingone whenever the two countries have the same official language, CLij , indicate reduced trade costs.We also include the absolute value of exporter’s and importer’s difference in the ln real GDP percapita values to quantify economic differences between countries
lnSizeDiffijt =∣∣∣∣ln(GDPit
POPit
)− ln
(GDPjt
POPjt
)∣∣∣∣ (3.2)
Additionally a remoteness term is included to account for multilateral resistance within the sam-
ple: lnRemoteit =∑j
distij(gdpjt/gdpROW,t)
. Herrera (2012) emphasizes that this term in combina-
tion with the (country-pair and) country-specific effects should account for multilateral resistancediscussed in Anderson & Van Wincoop (2003). The share of urban population in the exportingcountry, lnUrbanit, act as an indicator of the speed of economic development. The larger the shareof urban population, the more diversified the products would be in comparison to an agriculturaleconomy. The schooling terms lnSchoolit and lnSchooljt described to a greater extent in the datasection 3.1 are used to measure technological developments. This is in line with Egger (2004).Note, Egger (2004) examines the export patterns for developed countries and includes the school-ing terms only as a distance in high-skilled to low-skilled labor ratios between trade partners. Weargue that intra-African trade depends on exporters factor endowments rather than the differencein schooling rates between trading countries as most of the traded products are agricultural prod-ucts and labor-intensive manufactured goods. Besides we add exporter’s and importer’s corruptionindices, lnExCoit and ln ImCojt, to control for effects not observed in the common aggregates.The effect of corruption on trade is not clear-cut, it can either enhance trade due to the possibilityof blackmailing customs officials, or lower exports due to higher uncertainty and costs. The lattercan be interpreted as a non-tariff barrier to trade. Except the dummy variables, all variables aregiven in natural logarithms.
The error term in the panel setup is given by uijt = µij + λt + εijt. The country-pair specificeffect is different for each direction of trade, i.e. µij 6= µji. The common time trend in increasingSouth-South trade is reflected by the time dummies λt, t = 1, . . . , T .
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First we estimate models I and II using fixed effects regressions. Since we use the log-logspecification in these setups, we have to get rid of zero trade flows. Thus we recode the zeros andthe missings in our sample to 0.001 before taking the natural logarithm of real exports.12
The choice of the fixed effects (FE) regression is statistically tested by the Hausman specifica-tion test that compares the estimates of the consistent estimator (fixed effects model, βWithin) withthe efficient, but only consistent under the null hypothesis that E(µij|Xijt) = 0, estimator (randomeffects model, βGLS). The test statistic of the Hausman specification test is formally written asq1 = βGLS − βWithin with H0 : plim q1 = 0. If the null hypothesis is rejected, i.e. if there is asignificant difference between the estimates, the fixed effects model should be used.
As described above, the Hausman-Taylor (HT) procedure addresses to potential endogeneityproblems. In the HT framework, the Sargan-Hansen test is used to test whether the chosen in-struments are exogenous. Moreover, since Santos Silva & Tenreyro (2006) show that FE and HTcoefficient estimates are biased upwards in case of heteroskedasticity, we use zero trade flows in-stead of our ad hoc choice of 0.001, and use the PPML estimation as an elegant way to to copewith zero trade data. We perform the estimation allowing for country-specific effects and then testfor their joint significance.
Results
The fixed effects results are presented in Table 1. The coefficients for the baseline gravitymodels I and II are given in columns (1) and (2). As expected the coefficients for importer’s andexporter’s real GDP are significantly positive and close to unity in both models I and II. Moreover,the remoteness term is strongly negative. Concerning to the tariffs we find that a restricted marketaccess reflected in the significantly negative impact of the average export tariff on real exports suchthat a one percent decrease in the tariff would result in an increase in exports by about 0.2 percent.Using the dummy variable approach (model I) and not accounting for applied tariff barriers wefind a significantly positive effect on bilateral trade if both the exporting and importing countriesjoin the COMESA Free Trade Agreement (see column (1)). In case of considering average appliedtariff rate on imports we detect a significantly negative relationship between these costs of tradingand real exports, a one percent increase of the average tariff rate the exporter has to pay, results ina decrease in trade of about 0.24 percent.
The baseline model is extended by the degree of urbanization and the schooling rates, andexporter and importer corruption as a measure of trade costs and non-tariff barriers (NTBs). Theresults for the models I and II are presented in Table 1, columns (3)-(8).
The baseline results are robust to these changes, except the dummy variable for the EAC FTAin model I. Remarkably, the effect of both trading partners being in the EAC FTA becomes sig-nificantly negative. Hence, the predicted positive effects of the EAC agreement documented inprevious studies might be misleading.
Since the coefficients only change slightly between the different specifications we directly con-sider columns (7) and (8).13 Urbanization has no significant effect on real exports. The exporter’s
12Since the data are given in thousands US$, this reflects real exports of 1 US$ a year, which is a negligible number.13In our empirical approach wie subsequently added each regressor separately and continued by increasing the
amount of regressors. Exemplarily we present models in columns (3)-(6). The results are robust to the order of choice
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schooling rate has a remarkably high effect on the exports. A one percent increase of the shareof population attained primary, secondary or tertiary schooling significantly increases bilateral ex-ports by more than three percent. Introducing the schooling rates results in a slight decrease of theexporter’s real GDP coefficient such that it is almost one, as predicted by the original gravity equa-tion, and also decreases the coefficient of our remoteness measure. The importer’s schooling ratedoes not significantly influence export flows as well as the exporters and the importers corruption.The overall picture concerning our tariff barriers do not change, the coefficients on the proxy forlimited market access change within an interval of (−0.219, −0.194) and on the applied tariff ratecoefficient lie in between −0.227 to −0.221.
As expected the within R2 is quite low in all fixed effects models. The Hausman test resultsunambigously support our choice of modeling, i.e. the error terms and thereby the country-pairfixed effects are strongly correlated with the explanatory variables. A random effects model wouldyield biased and inconsistent estimates.
As discussed in section 2 in a next step we control for endogeneity of the free trade agreeementsand tariff rates, and also real GDP. Furthermore, the within estimator naturally drops the timeinvariant variables, which are also of interest in our analysis. Thus Table 2 presents the result of theHausman-Taylor model including only time effects and country-pair effects in the first setup, andadditionally including country-specific effect in the second setup. According to Hausman & Taylor(1981) the endogenous variables are instrumented by the individual mean of the exogenous time-varying variables and the deviation from the mean of the endogenous time-varying variables. Thefirst two columns of the table address to the baseline specification I and II including only country-pair and time effects. Additionally to the fixed effects model results, the distance coefficient issignificantly negative in both model specifications I and II, and a common border between twocountries as well as a common language have the expected significantly positive influence on realexports. Not surprisingly, whenever distance is included as a regressor the remoteness coefficientdecreases in absolute values, but is still significant. Despite this, the significant coefficients for thedummy for the COMESA FTA, and the tariff terms are robust in terms of direction and size. TheEAC FTA coefficient is no longer significant. The tariff data and the FTA dummies as well as thereal GDP terms are considered to be endogenous and thus instrumented as described above. TheSargan-Hansen test of overidentification provides evidence that our model is well specified and thechoice of intruments is appropriate (p-value of the Sargan Hansen test about 0.177).
In what follows we extend our model by allowing for other exogenous regressors. The resultsfor the extended HT setup including country-pair and time effects are presented in Table 2, columns(3)-(8). In columns (3) and (4) urbanization is included as a regressor. The significantly positiveeffect of urbanization is visible in both specifications, I and II. The results of the other coefficientestimates are rather robust, only the remoteness coefficient increases in absolute terms and theexporter’s GDP coefficient decreases slightly. More interestingly, the EAC FTA dummy has asignifcant negative effect on exports. In columns (5) and (6) schooling is added to the model. Thehighest level of schooling attained in the importing as well as exporting country has a positiveand surprisingly large effects on exports. Besides, not surprisingly the coefficients the commonlanguage dummy, the urbanization rate, and the remoteness measure decrease in absolute values
of regressors, and available on request.
10
Tabl
e1:
Fixe
def
fect
spa
nelr
egre
ssio
nre
sults
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
III
III
III
III
lnex
port
er’s
real
GD
P1.
5050
∗∗∗
1.42
79∗∗
∗1.
3335
∗∗∗
1.25
89∗∗
∗1.
0957
∗∗1.
0230
∗∗1.
0887
∗∗1.
0206
∗∗
(0.4
632)
(0.4
561)
(0.4
752)
(0.4
690)
(0.4
922)
(0.4
859)
(0.4
924)
(0.4
863)
lnim
port
er’s
real
GD
P0.
9774
∗0.
9182
∗0.
9828
∗0.
9172
∗0.
9651
∗0.
9049
∗0.
9462
∗0.
8892
∗
(0.5
162)
(0.5
110)
(0.5
151)
(0.5
105)
(0.5
355)
(0.5
315)
(0.5
350)
(0.5
310)
lnD
iffer
ence
inpe
rcap
itaG
DP
0.14
830.
1768
0.14
340.
1730
0.15
000.
1779
0.19
130.
2233
(0.3
880)
(0.3
881)
(0.3
871)
(0.3
872)
(0.3
864)
(0.3
864)
(0.3
817)
(0.3
818)
Rem
oten
ess
−4.
8864
∗∗−
4.93
00∗∗
−4.
3719
∗∗−
4.43
44∗∗
−3.
7657
∗−
3.83
27∗
−3.
7949
∗−
3.86
97∗
(2.0
166)
(2.0
113)
(2.0
651)
(2.0
619)
(1.8
980)
(2.0
731)
(2.0
598)
(2.0
581)
lnex
port
er’s
urba
npo
pula
tion
1.51
821.
4445
1.37
741.
2980
1.38
861.
3190
(1.2
128)
(1.2
094)
(1.2
103)
(1.2
089)
(1.2
063)
(1.2
047)
CO
ME
SAFT
A0.
8613
∗∗∗
0.86
06∗∗
∗0.
8247
∗∗∗
0.78
51∗∗
(0.2
564)
(0.2
544)
(0.2
515)
(0.2
525)
EA
CFT
A−
0.29
24−
0.39
13∗
−0.
4317
∗∗−
0.40
90∗∗
(0.2
085)
(0.2
111)
(0.2
090)
(0.2
062)
SAD
CFT
A0.
1003
0.12
020.
1238
0.10
38(0
.246
4)(0
.248
9)(0
.251
5)(0
.250
9)m
arke
tbar
rier
−0.
2035
∗∗∗−
0.20
51∗∗
∗−
0.21
82∗∗
∗−
0.21
87∗∗
∗−
0.19
37∗∗
∗−
0.19
43∗∗
∗−
0.19
43∗∗
∗−
0.19
50∗∗
∗
(0.0
556)
(0.0
558)
(0.0
525)
(0.0
527)
(0.0
512)
(0.0
514)
(0.0
511)
(0.0
513)
appl
iedt
ariff
−0.
2481
∗∗∗
−0.
2468
∗∗∗
−0.
2332
∗∗∗
−0.
2212
∗∗∗
(0.0
799)
(0.0
795)
(0.0
785)
(0.0
786)
lnex
port
er’s
scho
olin
gra
te3.
3364
∗∗3.
3016
∗∗3.
2914
∗∗3.
2440
∗∗
(1.6
636)
(1.6
584)
(1.6
511)
(1.6
452)
lnim
port
er’s
scho
olin
gra
te0.
1926
0.09
68−
0.01
07−
0.11
80(1
.664
9)(1
.661
8)(1
.642
0)(1
.638
8)ln
expo
rter
’sco
rrup
tion
−0.
0186
−0.
0234
(0.0
790)
(0.0
789)
lnim
port
er’s
corr
uptio
n−
0.08
30−
0.09
02(0
.073
8)(0
.073
7)
with
inR
20.
024
0.02
40.
025
0.02
40.
027
0.02
60.
027
0.02
6ov
eral
lR2
0.34
00.
348
0.33
20.
342
0.35
10.
356
0.34
50.
349
corr
(ui,
Xb)
0.27
00.
301
0.25
60.
294
0.24
60.
276
0.24
20.
269
Not
e:**
*si
gnifi
cant
atth
e1%
,**
5%,*
10%
leve
l.co
nsta
nt,t
ime
dum
mie
san
dco
untr
y-pa
irfix
edef
fect
sin
clud
ed,r
obus
tsta
ndar
der
rors
.
11
to a large extent, but are still significant. Additionally there is an increase in the common bordercoefficient and a slight decrease of the limited market access coefficient, suggesting a smallereffect of tariff rate changes on exports. Similarly, the absolute value of the COMESA FTA dummydecreases slightly after including schooling, but remains significant at a 1% level. Adding thecorruption index to the models I and II does not change the results much. The correspondingcoefficient estimates are insignificant and all other estimates are robust to this change (see Table 2,columns (7), (8)). The Sargan-Hansen test always supports our choice of instruments.
Besides the use of country-pair fixed effects we also want to account for country-specific fea-tures such as specialization in a raw material export structure, transportation costs and multilateralresistance. Therefore we consider the HT model with country-pair and time effects, adding addi-tional country-specific terms to the set of regressors. The output is presented in Table 2, columns(9)-(16). Compared to the HT model in columns (1)-(8) we find some changes in the distance vari-ables’ coefficients indicating the importance of country-specific infrastructural measures: highercoefficients in absolute values for the distance in kilometers and the remoteness term, and lowercoefficient estimates on the common border dummy. The effects on exporter’s schooling are some-what smaller now, and those for the importing countries become insignificant. Furthermore, thereis no longer a significant effect of the share of urban population on exports. Besides two ex-ceptions, the exporters country-specific effects are individually and jointly significant. Moreover,changes in the results may be due to the fact that whenever using country-specific effects as ad-ditional regressors we only include the FTA dummies and the two tariff variables in the list ofendogenous regressors. This choice of endogenous variables is supported by the Sargan-Hansentest of overidentification.
Comparing the results from the HT specifications to those obtained from simple fixed effectsregression we find that most coefficient estimates do not vary substantially. Only the coefficienton urban population and on the importer’s schooling level are significant in columns (1)-(8) ab-stracting from country-specific fixed effects. Most important, we observe the striking result thatthe coefficients direction and size on the market access and the applied tariffs barriers are robust tothe underlying estimation method.
However, as discussed above, our analysis may be driven by the fact that we have 28 percentmissing observations on exports. Recoding this as being a small number to be able to work withinthe log-log notation might be subject to criticism. Thus, in a next step the analysis is conductedusing PPML estimation as proposed by Santos Silva & Tenreyro (2006). We recode all missingsby zeros and estimate regressions on both model specifications I and II including (i) time effects,(ii) in accordance to the panel estimation models we also include country-fixed effects. A thirdsepcification including country-pair specific effects instead of country-specific terms raises mul-ticollinearity issues. The results are presented in Table 3 and Table 4 using clustered standarderrors.
The results including (i) time effects only are outlined in Table 3, columns (1)-(8). In general,the outcomes of the traditional gravity variables are in line with our previous findings. But thereare some important changes. First, the difference in real GDP is positive and significant. Second,urban population and remoteness play a minor role and the estimated coefficient are insignificantin model I, colums (5) and (7), and in model I and II in columns (5)-(8), respectively, after includ-
12
Tabl
e2:
Hau
sman
-Tay
lorm
odel
regr
essi
onre
sults
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
III
III
III
III
III
III
III
III
lnre
alG
DP i
1.67
51∗∗∗
1.59
56∗∗∗
1.37
57∗∗∗
1.30
27∗∗∗
1.12
93∗∗
1.05
74∗∗
1.02
57∗∗
0.96
30∗∗
1.49
44∗∗∗
1.41
81∗∗∗
1.32
33∗∗∗
1.24
94∗∗
1.08
50∗∗
1.01
30∗
1.07
33∗∗
1.00
54∗∗
(0.4
461)
(0.4
080)
(0.4
436)
(0.4
845)
(0.4
460)
(0.4
254)
(0.4
551)
(0.4
559)
(0.5
322)
(0.2
645)
(0.4
803)
(0.5
282)
(0.5
021)
(0.5
561)
(0.4
610)
(0.4
594)
lnre
alG
DP j
0.95
65∗
0.89
64∗
0.97
62∗
0.90
81∗
0.93
06∗
0.86
49∗
0.86
40∗
0.80
59∗
0.96
98∗∗
0.91
11∗
0.97
56∗
0.91
04∗
0.95
78∗
0.89
80∗
0.93
59∗
0.87
88(0
.551
0)(0
.520
1)(0
.579
0)(0
.503
6)(0
.499
3)(0
.498
2)(0
.485
6)(0
.474
2)(0
.456
3)(0
.483
4)(0
.505
0)(0
.470
1)(0
.508
2)(0
.529
2)(0
.540
8)(0
.536
4)ln
dist
ance
−2.
2817
∗∗∗
−2.
1475
∗∗∗
−2.
3767
∗∗∗
−2.
2422
∗∗∗
−2.
2623
∗∗∗
−2.
1345
∗∗∗
−2.
1648
∗∗∗
−2.
0472
∗∗∗
−3.
2228
∗∗∗
−3.
1533
∗∗∗
−3.
2222
∗∗∗
−3.
1538
∗∗∗
−3.
2246
∗∗∗
−3.
1606
∗∗∗
−3.
2298
∗∗∗
−3.
1678
∗∗∗
(0.5
732)
(0.6
512)
(0.6
720)
(0.6
335)
(0.5
877)
(0.6
059)
(0.5
829)
(0.6
109)
(0.4
846)
(0.3
571)
(0.3
979)
(0.4
098)
(0.3
347)
(0.3
603)
(0.4
376)
(0.3
757)
CB
2.87
19∗∗∗
2.91
78∗∗∗
2.81
52∗∗∗
2.85
86∗∗
3.33
30∗∗∗
3.37
37∗∗∗
3.51
31∗∗∗
3.54
51∗∗∗
1.15
75∗
1.09
901.
1633
∗1.
0984
∗∗
1.16
42∗∗
1.09
81∗
1.16
43∗
1.10
25∗∗
(1.0
231)
(0.9
491)
(1.0
706)
(1.1
191)
(1.0
655)
(1.0
809)
(0.9
835)
(0.9
657)
(0.6
137)
(0.6
751)
(0.6
163)
(0.4
709)
(0.5
475)
(0.6
404)
(0.6
715)
(0.5
267)
CL
1.75
67∗∗∗
1.69
24∗∗∗
1.83
22∗∗∗
1.76
78∗∗∗
0.87
22∗
0.82
32∗
0.86
79∗∗
0.82
94∗∗
0.91
29∗∗
0.88
99∗∗
0.91
25∗∗
0.89
03∗∗
0.91
36∗∗∗
0.89
32∗∗
0.91
57∗∗∗
0.89
58∗∗
(0.4
294)
(0.3
580)
(0.3
807)
(0.4
440)
(0.4
628)
(0.4
535)
(0.4
129)
(0.4
016)
(0.3
614)
(0.4
012)
(0.3
580)
(0.3
804)
(0.2
995)
(0.4
505)
(0.3
385)
(0.3
479)
lnD
iffG
DPp
c0.
2028
0.23
090.
1618
0.19
240.
1964
0.22
490.
2227
0.25
630.
0826
0.11
660.
0815
0.11
510.
0838
0.11
540.
0968
0.12
91(0
.311
9)(0
.362
8)(0
.413
8)(0
.407
1)(0
.428
1)(0
.397
4)(0
.320
2)(0
.411
1)(0
.181
2)(0
.175
9)(0
.208
1)(0
.175
8)(0
.179
8)(0
.176
0)(0
.157
6)(0
.158
9)ln
Rem
ote
−2.
6034
∗∗∗
−2.
6647
∗∗
−3.
5305
∗∗∗
−3.
5870
∗∗∗
−2.
2120
∗∗
−2.
2710
∗∗
−2.
2008
∗∗
−2.
2645
∗∗∗
−4.
9189
∗∗
−4.
9594
∗∗∗
−4.
4019
∗−
4.46
23∗∗
−3.
7981
∗∗
−3.
8633
−3.
8352
∗−
3.90
97∗∗∗
(0.9
276)
(1.0
448)
(0.9
912)
(0.8
641)
(1.0
722)
(1.0
725)
(1.0
265)
(1.0
441)
(1.9
864)
(1.7
246)
(2.3
516)
(1.8
951)
(1.8
206)
(2.5
013)
(1.9
926)
(1.3
879)
lnur
ban i
1.65
40∗∗
1.65
93∗∗∗
0.95
09∗
0.96
58∗
1.00
53∗
1.01
81∗
1.52
011.
4456
1.37
931.
2993
1.39
011.
3193
(0.7
396)
(0.5
811)
(0.5
170)
(0.5
073)
(0.6
122)
(0.5
376)
(1.1
264)
(1.0
532)
(1.4
382)
(1.3
571)
(1.3
065)
(1.1
140)
CO
ME
SAFT
A0.
8696
∗∗∗
0.86
33∗∗∗
0.81
20∗∗∗
0.77
89∗∗∗
0.86
00∗∗∗
0.85
95∗∗∗
0.82
35∗∗∗
0.78
21∗∗∗
(0.2
409)
(0.2
694)
(0.2
259)
(0.2
306)
(0.2
266)
(0.2
920)
(0.2
402)
(0.2
017)
EA
CFT
A−
0.29
32−
0.40
02∗
−0.
4277
∗∗
−0.
4147
∗∗
−0.
2958
−0.
3945
∗−
0.43
51∗∗
−0.
4144
∗∗
(0.2
085)
(0.2
102)
(0.2
071)
(0.1
922)
(0.2
219)
(0.2
188)
(0.1
813)
(0.2
029)
SAD
CFT
A0.
1119
0.12
590.
1349
0.13
920.
1012
0.12
100.
1246
0.10
66(0
.212
1)(0
.220
4)(0
.279
5)(0
.217
1)(0
.251
7)(0
.249
5)(0
.225
5)(0
.254
0)m
arke
tbar
rier
−0.
2003
∗∗∗
−0.
2019
∗∗∗
−0.
2184
∗∗∗
−0.
2197
∗∗∗
−0.
1822
4∗∗∗
−0.
1837
∗∗∗
−0.
1818
∗∗∗
−0.
1831
∗∗∗
−0.
2032
∗∗∗
−0.
2048
∗∗∗
−0.
2179
∗∗∗
−0.
2184
∗∗∗
−0.
1934
∗∗∗
−0.
1940
∗∗∗
−0.
1939
∗∗∗
−0.
1946
∗∗∗
(0.0
551)
(0.0
569)
(0.0
480)
(0.0
505)
(0.0
508)
(0.7
108)
(0.0
537)
(0.0
558)
(0.0
492)
(0.0
630)
(0.0
498)
(0.0
560)
(0.0
397)
(0.0
478)
(0.0
446)
(0.0
467)
appl
iedt
ariff
−0.
2514
∗∗∗
−0.
2481
∗∗
−0.
2300
∗∗∗
−0.
2222
∗∗∗
−0.
2478
∗∗∗
−0.
2465
∗∗
−0.
2330
∗∗∗
−0.
2207
∗∗∗
(0.0
800)
(0.1
010)
(0.0
859)
(0.0
706)
(0.0
791)
(0.0
963)
(0.0
714)
(0.0
795)
lnsc
hooli
4.05
94∗∗∗
4.04
37∗∗∗
4.10
82∗∗∗
4.08
57∗∗∗
3.33
363.
2984
∗∗
3.29
57∗
3.24
70(0
.685
4)(0
.710
8)(0
.747
1)(0
.727
0)(2
.187
2)(1
.461
0)(1
.714
6)(1
.980
6)ln
scho
olj
1.19
87∗∗
1.16
06∗
1.24
42∗∗
1.20
11∗∗
0.18
800.
0918
−0.
0120
−0.
1208
(0.5
465)
(0.6
895)
(0.6
171)
(0.8
361)
(1.4
123)
(1.7
587)
(1.8
234)
(1.8
349)
lnE
xCo
0.01
290.
0067
−0.
0164
−0.
0212
(0.0
696)
(0.0
703)
(0.0
977)
(0.0
752)
lnIm
Co
−0.
0760
−0.
0836
−0.
0813
−0.
0884
(0.0
685)
(0.0
741)
(0.0
756)
(0.0
752)
coun
try
dum
mie
sno
nono
nono
nono
noye
sye
sye
sye
sye
sye
sye
sye
srh
o(f
rac
varu
)0.
640
0.64
60.
635
0.64
10.
626
0.63
30.
629
0.63
60.
427
0.42
80.
427
0.42
90.
427
0.42
90.
427
0.42
9SH
p-v
alue
0.17
70.
177
0.89
10.
8872
0.60
150.
621
0.26
900.
2975
0.81
140.
827
0.82
170.
8333
0.38
680.
339
0.41
00.
369
Not
e:**
*si
gnifi
cant
atth
e1%
,**
5%,*
10%
leve
l.co
nsta
nt,t
ime-
dum
mie
san
dco
untr
y-pa
iref
fect
sin
clud
ed,r
obus
tsta
ndar
der
rors
.SH
deno
tes
Sarg
an-H
anse
nte
st.
13
ing schooling and control for corruption. Third and most important, we do not find evidence onany significant effect of tariffs and the FTAs on real exports. In general, except the difference inreal GDP, the estimates in the HT and FE model seem to be upward biased compared to those inthe PPML framework. This is in line with the theoretical findings discussed above. Our full speci-fication of the PPML model in column (7) and (8) suggests that real exports increase by exportersand importers GDP, the absolute difference in per capita GDP, in exporters and importers school-ing rate and if the trading countries share a common border. Bilateral exports only significantlydecrease by the geographical distance.
Note, in columns (1)-(8) we completely disregard the panel structure of our dataset using thePPML estimator. Therefore, in the second approach (ii) we allow for exporter- and importer-specific effects. Doing so changes the results (see columns (9)-(16)). We find that real GDP dataare no longer determinants of exports, as well as the difference in real GDP per capita and theremoteness index. In contrast to columns (1)-(4) the common language dummy is insignificant inall model specifications including exporter- and importer-specific fixed effects and thus does notdetermine the size of exports. The distance and common border coefficients are significant at a1% level and are about −1.15 and 0.96, respectively. Considering directly the models includingschooling (columns (5)-(8) cp. to columns (13)-(16)), the importer’s schooling share enters theregression with a significantly negative coefficient. This may reflect the negative relationship be-tween the level of education in a country and its import demand for more sophisticated products(for detail see section 5). Concerning to our main research question we find that average tariffrates have a significantly negative effect on exports (coefficients of −0.043 to −0.045), but beingin one or more FTAs does not matter at all.14 To sum it up, our favorite specification of the PPMLmodels I and II in column (15) and (16) suggests that real exports increase if the trading countriesshare a common border, and with the exporter school attainment and importer corruption. Exportssignificantly decrease with distance, importers school attainment and limited market access.
Although we cannot directly compare the coefficient estimates of the PPML estimator and thosefrom the HT and FE regressions, we can comment on differences in sign and significance. Mostimportant, comparing the impact of a limited market access and of the on average applied tariffrate in the PPML approach to the HT model result (Table 2) again we find an upward bias in thepanel setup (in absolute values). The same holds true for the coefficients of the COMESA andEAC FTAs.
4 Sensitivity Analysis
This section deals with several robustness checks. The Heckman selection regressions are con-ducted with the common language dummy as a selection variable, i.e. we assume that countrieswith a common (official) language are more likely to trade at all, but linguistic abilities do notdetermine the amount of trade between two countries. Our choice of a common language as selec-tion variable is in line with the literature (see e.g. Martin & Pham (2008) and Disdier & Marette
14Including country-pair effects in the PPML setup presented in Table 4 we find a significantly positive effect of theCOMESA FTA and a significantly negative but small effect of the applied tariff rate for some model specifications.We refer to the role of these effects in the interpretation section 5
14
Tabl
e3:
Pois
son
pseu
do-m
axim
umlik
elih
ood
regr
essi
onre
sults
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
(10)
(11)
(12)
(13)
(14)
(15)
(16)
III
III
III
III
III
III
III
III
lnre
alG
DP i
1.11
73∗∗∗
1.12
27∗∗∗
0.90
82∗∗∗
0.89
39∗∗∗
0.97
35∗∗∗
0.95
69∗∗∗
0.97
56∗∗∗
0.96
12∗∗∗
−0.
3087
−0.
2376
−0.
3312
−0.
2701
−0.
3614
−0.
2861
−0.
3904
−0.
3250
(0.0
926)
(0.0
916)
(0.0
839)
(0.0
822)
(0.0
800)
(0.0
788)
(0.0
808)
(0.0
794)
(0.5
466)
(0.5
184)
(0.5
149)
(0.4
873)
(0.4
987)
(0.4
730)
(0.5
697)
(0.5
494)
lnre
alG
DP j
0.74
94∗∗∗
0.73
93∗∗∗
0.74
47∗∗∗
0.73
81∗∗∗
0.74
11∗∗∗
0.73
23∗∗∗
0.73
90∗∗∗
0.73
14∗∗∗
0.09
830.
1426
0.09
720.
1311
0.29
970.
3402
∗0.
2364
0.27
45(0
.072
4)(0
.072
8)(0
.067
6)(0
.066
6)(0
.067
0)(0
.065
6)(0
.066
1)(0
.064
5)(0
.200
7)(0
.185
4)(0
.197
9)(0
.182
8)(0
.190
5)(0
.182
8)(0
.182
1)(0
.176
)ln
dist
ance
−1.
2780
∗∗∗
−1.
2070
∗∗∗
−1.
2794
∗∗∗
−1.
2472
∗∗∗
−1.
4940
∗∗∗
−1.
4611
∗∗∗
−1.
4772
∗∗∗
−1.
4466
∗∗∗
−1.
1486
∗∗∗
−1.
1526
∗∗∗
−1.
1550
∗∗∗
−1.
1531
∗∗∗
−1.
1566
∗∗∗
−1.
1483
∗∗∗
−1.
1546
∗∗∗
−1.
1466
∗∗∗
(0.2
507)
(0.2
570)
(0.2
358)
(0.2
379)
(0.2
235)
(0.2
220)
(0.2
268)
(0.2
236)
(0.1
985)
(0.1
963)
(0.1
987)
(0.1
964)
(0.1
982)
(0.1
971)
(0.1
981)
(0.1
971)
CB
0.76
78∗∗
0.84
02∗∗
0.77
20∗∗∗
0.79
11∗∗
0.75
03∗∗
0.75
57∗∗
0.75
56∗∗
0.76
48∗∗
0.95
54∗∗
0.96
47∗∗∗
0.95
91∗∗∗
0.96
52∗∗∗
0.95
06∗∗∗
0.95
93∗∗∗
0.95
04∗∗∗
0.95
83∗∗∗
(0.3
736)
(0.3
795)
(0.3
250)
(0.3
278)
(0.3
035)
(0.3
089)
(0.3
064)
(0.3
117)
(0.2
661)
(0.2
616)
(0.2
669)
(0.2
618)
(0.2
672)
(0.2
629)
(0.2
675)
(0.2
629)
CL
0.62
82∗∗∗
0.65
40∗∗∗
0.61
53∗∗∗
0.62
22∗∗∗
−0.
0166
−0.
0147
0.00
790.
0090
0.23
040.
2137
0.22
610.
2147
0.22
430.
2145
0.22
260.
2131
(0.2
334)
(0.2
347)
(0.2
166)
(0.2
171)
(0.2
524)
(0.2
535)
(0.2
528)
(0.2
552)
(0.3
562)
(0.3
559)
(0.3
570)
(0.3
557)
(0.3
571)
(0.3
549)
(0.3
578)
(0.3
557)
lnD
iffG
DPp
c0.
2711
∗∗
0.28
82∗∗
0.23
42∗∗
0.23
31∗∗
0.25
24∗∗
0.24
79∗∗
0.24
75∗∗
0.24
42∗∗
0.04
530.
0584
0.04
520.
0567
0.04
480.
0565
0.04
570.
0567
(0.1
087)
(0.1
127)
(0.1
023)
(0.1
039)
(0.1
029)
(0.1
034)
(0.1
041)
(0.1
041)
(0.1
016)
(0.1
001)
(0.1
015)
(0.1
003)
(0.1
011)
(0.1
000)
(0.1
000)
(0.0
993)
lnR
emot
e−
1.08
26∗∗
−0.
8157
−1.
9828
∗∗∗
−1.
8715
∗∗∗
−0.
5580
−0.
4559
−0.
5026
−0.
3916
−1.
1508
−1.
1513
−0.
9926
−0.
9508
−0.
3254
−0.
3055
−0.
3122
−0.
2931
(0.5
174)
(0.5
786)
(0.5
848)
(0.6
014)
(0.5
687)
(0.5
768)
(0.5
608)
(0.5
730)
(0.7
321)
(0.7
139)
(0.6
729)
(0.6
555)
(0.7
096)
(0.7
062)
(0.7
144)
(0.7
115)
lnur
ban i
1.21
00∗∗∗
1.27
40∗∗∗
0.45
270.
5008
∗0.
4570
0.51
19∗
1.68
80∗
1.80
20∗∗
1.42
691.
4974
1.32
551.
3934
(0.2
655)
(0.2
615)
(0.2
928)
(0.2
902)
(0.2
939)
(0.2
914)
(1.0
208)
(0.9
816)
(1.0
684)
(1.0
142)
(1.0
523)
(1.0
024)
CO
ME
SAFT
A0.
0081
0.08
61−.0
3891
−0.
0697
−0.
1083
−0.
1195
−0.
1544
−0.
1441
(0.3
198)
(0.2
633)
(0.2
251)
(0.2
397)
(0.2
566)
(0.2
607)
(0.2
699)
(0.2
673)
EA
CFT
A−
0.56
19−
0.32
00−
0.47
93−
0.46
880.
1288
0.05
680.
0448
0.04
75(0
.346
5)(0
.342
0)(0
.327
3)(0
.325
6)(0
.233
8)(0
.246
6)(0
.246
2)(0
.247
9)SA
DC
FTA
0.25
460.
0477
−0.
1999
−0.
1683
−0.
0893
−0.
0807
−0.
0400
−0.
0332
(0.2
149)
(0.2
070)
(0.1
904)
(0.1
964)
(0.1
682)
(0.1
683)
(0.1
745)
(0.1
745)
lnm
arke
tbar
rier
0.04
270.
0581
0.04
220.
0500
−0.
0226
−0.
0174
−0.
0135
−0.
0080
−0.
007
−0.
0079
−0.
0142
−0.
0150
−0.
0429
∗−
0.04
34∗
−0.
0446
∗−
0.04
51∗∗
(0.0
939)
(0.0
997)
(0.0
950)
(0.0
961)
(0.0
664)
(0.0
674)
(0.0
629)
(0.0
644)
(0.0
181)
(0.0
175)
(0.0
207)
(0.0
200)
(0.0
243)
(0.0
235)
(0.0
228)
(0.0
220)
lnap
plie
dtar
iff−
0.04
84−.0
060
0.07
560.
0685
0.00
650.
0090
0.00
08−.0
013
(0.0
686)
(0.0
686)
(0.0
668)
(0.0
670)
(0.0
500)
(0.0
506)
(0.0
563)
(0.0
560)
lnsc
hooli
2.59
70∗∗∗
2.61
16∗∗∗
2.67
88∗∗∗
2.71
13∗∗∗
1.53
77∗
1.36
68∗
1.53
32∗∗
1.37
20∗
(0.6
517)
(0.6
590)
(0.7
000)
(0.7
142)
(0.7
886)
(0.7
946)
(0.7
825)
(0.7
865)
lnsc
hoolj
0.77
38∗∗
0.76
24∗∗
0.75
38∗∗
0.74
63∗∗
−1.
6470
∗∗∗
−1.
7234
∗∗∗
−1.
5055
∗∗∗
−1.
5813
∗∗
(0.2
992)
(0.2
978)
(0.3
061)
(0.3
044)
(0.6
033)
(0.6
524)
(0.5
834)
(0.6
356)
lnE
xCo
−0.
0509
−0.
0558
0.01
430.
0176
(0.0
751)
(0.0
715)
(0.0
847)
(0.0
845)
lnIm
Co
−0.
0166
−0.
0138
0.06
50∗∗∗
0.06
62∗∗∗
(0.0
432)
(0.0
396)
(0.0
219)
(0.0
226)
coun
try
dum
mie
sno
nono
nono
nono
noye
sye
sye
sye
sye
sye
sye
sye
sR
20.
718
0.71
00.
744
0.74
50.
760
0.76
00.
763
0.76
30.
861
0.86
10.
864
0.86
30.
869
0.86
80.
869
0.86
7
Not
e:**
*si
gnifi
cant
atth
e1%
,**
5%,*
10%
leve
l.co
nsta
ntan
dtim
edu
mm
ies
incl
uded
,clu
ster
edst
anda
rder
rors
.
15
Tabl
e4:
Pois
son
pseu
do-m
axim
umlik
elih
ood
regr
essi
onre
sults
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
III
III
III
III
lnex
port
er’s
real
GD
P−.1
807
−0.
1630
−0.
2049
−0.
1929
−0.
2223
−0.
2049
−0.
2189
−0.
1933
(0.1
953)
(0.1
930)
(0.1
866)
(0.1
853)
(0.1
815)
(0.1
815)
(0.2
004)
(0.2
021)
lnim
port
er’s
real
GD
P.2
167
0.23
06∗
.203
60.
2137
0.42
93∗∗
∗0.
4529
∗∗∗
0.38
54∗∗
∗0.
4076
∗∗∗
(0.1
386)
(0.1
365)
(0.1
364)
(0.1
349)
(0.1
341)
(0.1
323)
(0.1
317)
(0.1
290)
lndi
stan
ce−
2.93
68∗∗
∗−
2.82
95∗∗
∗−
2.94
38∗∗
∗−
2.85
43∗∗
∗−
2.39
78∗∗
∗−
2.28
47∗∗
∗−
2.42
82∗∗
∗−
2.32
03∗∗
∗
(0.6
450)
(0.6
543)
(0.6
713)
(0.6
732)
(0.6
773)
(0.6
737)
(0.6
656)
(0.6
639)
Com
mon
bord
er−.6
243
−0.
3721
−0.
6568
−0.
4082
0.36
610.
6474
0.27
030.
5568
(0.9
635)
(0.9
784)
(1.0
008)
(1.0
042)
(1.0
112)
(1.0
048)
(0.9
987)
(0.9
942)
Com
mon
lang
uage
5.36
33∗∗
∗5.
2794
∗∗∗
6.15
32∗∗
∗6.
0588
∗∗∗
5.81
57∗∗
∗5.
8420
∗∗∗
5.80
51∗∗
∗5.
8420
∗∗∗
(1.0
507)
(1.0
456)
(1.0
589)
(1.0
564)
(1.0
988)
(1.0
901)
(1.1
101)
(1.1
031)
lnD
iffer
ence
inpe
rcap
itaG
DP
.179
020.
1568
0.16
860.
1638
0.12
960.
1279
0.12
820.
1284
(0.1
479)
(0.1
474)
(0.1
444)
(0.1
434)
(0.1
374)
(0.1
367)
(0.1
275)
(0.1
268)
lnR
emot
enes
s−
1.06
04∗∗
−1.
1289
∗∗−
0.89
86∗∗
−0.
9325
∗∗−
0.25
11−
0.26
69−
0.22
91−
0.25
02(0
.465
8)(0
.447
7)(0
.444
6)(0
.432
2)(0
.454
7)(0
.457
0)(0
.457
7)(0
.460
4)ln
expo
rter
’sur
ban
popu
latio
n1.
9220
∗∗∗
1.76
99∗∗
∗1.
7802
∗∗∗
1.55
63∗∗
∗1.
6737
∗∗∗
1.42
83∗∗
∗
(0.4
268)
(0.4
002)
(0.4
356)
(0.4
121)
(0.4
322)
(0.4
089)
CO
ME
SAFT
A0.
1914
∗∗∗
0.20
24∗∗
∗0.
1982
∗∗∗
0.22
32∗∗
∗
(0.0
669)
(0.0
707)
(0.0
709)
(0.0
710)
EA
CFT
A−
0.00
02−.1
242∗
−0.
1286
∗−
0.12
66∗
(0.0
654)
(0.0
752)
(0.3
273)
(0.0
706)
SAD
CFT
A0.
0997
-0.0
871
−0.
0275
−0.
0129
(0.0
773)
(0.0
765)
(0.0
769)
(0.0
765)
lnm
arke
tbar
rier
-0.0
103
−0.
0082
−0.
0194
-0.0
152
−0.
0459
∗−
0.04
17∗
−0.
0444
∗−
0.04
00∗
(0.0
222)
(0.0
222)
(0.0
950)
(0.0
222)
(0.0
238)
(0.0
236)
(0.0
238)
(0.0
236)
lnap
plie
dtar
iff−
0.02
02−
0.01
58-0
.033
8−
0.03
85∗
(0.0
211)
(0.0
212)
(0.0
227)
(0.0
228)
lnex
port
er’s
scho
olin
gra
te1.
3301
∗∗∗
1.24
78∗∗
∗1.
2632
∗∗∗
1.17
66∗∗
∗
(0.3
973)
(0.4
057)
(0.4
003)
(0.4
113)
lnim
port
er’s
scho
olin
gra
te−
1.81
41∗∗
∗−
1.96
53∗∗
−1.
7062
∗∗∗−
1.85
28∗∗
∗
(0.3
239)
(0.3
296)
(0.3
166)
(0.3
246)
lnex
port
er’s
corr
uptio
n−
0.01
870.
0133
(0.0
414)
(0.0
413)
lnim
port
er’s
corr
uptio
n0.
0638
∗∗∗
0.06
38∗∗
∗
(0.0
120)
(0.0
121)
R2
0.92
40.
925
0.92
70.
927
0.93
20.
933
0.93
20.
933
Not
e:**
*si
gnifi
cant
atth
e1%
,**
5%,*
10%
leve
l.co
nsta
nt.t
ime
dum
mie
san
dco
untr
y-pa
irsp
ecifi
cef
fect
sin
clud
ed,c
lust
ered
stan
dard
erro
rs.
16
(2009)). The PPML results presented in Table 3 columns (5)-(16) also confirm the variable choice.The Heckman selection regression results for the observation equations are summarized in Table 5.We do not find any evidence for a potential selection bias in our dataset. Thus, the fixed effects,HT and PPML models presented above are robust with respect to sample selection problems. At afirst glance the parameter significances and signs are comparable to those presented for the PPMLestimation in Table 3. Exceptions of interest are the tariff rates’ coefficients and the COMESAFTA dummy. Moreover, also exporter’s corruption drives trade flows. However, we do not want togo too much into the detailed analysis of individual coefficient estimates because the choice of theselection variable may drive the results and is always subject to concerns.15
In general, an index of the political stability could also be a proper selection variable. We usethe products of the indices for the corresponding importing and exporting countries and re-estimatethe models. The political stability variable enters insignificantly negative in the selection equation.This raises the impression that the variable is no good choice for our sample. Results are availableon request.
To underpin our results we re-estimate the models in Table 2 allowing for the 28 percent missingdata on real exports. Our results are qualitatively and to a greater extent also quantitatively in linewith those including zero values or small numbers for the missing data. Regression tables areavailabe on request.
Complementary to our set of explanatory variables we address to the literature on estimatinggravity equations and include a landlocked dummy into our models. The variable is often foundto be important in trade regressions as we know that a major amount of products are shipped.However looking only at intra-regional trade in Sub-Saharan Africa one may doubt its relevance.This is reflected in the insignificance of coefficient estimates in most of our specifications.
5 Interpretation of Results
In order to evaluate the effect of FTAs on trade flows in the COMESA-EAC-SADC Tripartiteeconomies we use two different approaches. The first approach (model I) is a common procedurethat includes a dummy variable coded as being one if both trading countries are members of thesame FTA. While we estimate a positive effect for the COMESA FTA in a panel framework, theeffect is not robust to PPML techniques. Previous studies on the trade effects of African regionaltrade agreements applying the dummy variable approach also find mixed evidence. Carrere (2004)estimate a HT model and find a positive impact for four African regional agreements, among themthe ECOWAS and the SADC, on trade, but not for the COMESA RTA. In contrast to this, withina comparative analysis Korinek & Melatos (2009) show that the three RTAs AFTA, COMESAand MERCOSUR promote trade in agricultural products. Coulibaly (2009) estimates the impactof the number of years of a RTA membership instead of the usual dummy variable approach.The authors find this effect for the ECOWAS and the SADC by using a panel consisting of 56exporter and 90 importer countries and 39 years (1960-1999). The trade effect of the ECOWASRTA was positive for the first ten years, but was reversed afterwards. The SADC RTA is found
15Note, we use the common language dummy although the dummy has a significantly positive effect on real exportswithin the HT framework which suggests that a common language determines also the size of real exports.
17
Tabl
e5:
Hec
kman
regr
essi
onre
sults
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
III
III
III
III
lnex
port
er’s
real
GD
P0.
1656
0.10
460.
1571
80.
1014
.005
8−.0
536
.007
1−.0
597
(0.2
420)
(0.2
396)
(0.2
451)
(0.2
432)
(0.2
593)
(0.2
576)
(0.2
597)
(0.2
581)
lnim
port
er’s
real
GD
P0.
3900
∗0.
3556
0.39
040.
3555
0.53
80∗∗
0.50
23∗∗
0.56
35∗∗
0.52
10∗∗
(0.2
281)
(0.2
256)
(0.2
279)
(0.2
255)
(0.2
415)
(0.2
396)
(0.2
399)
(0.2
380)
lndi
stan
ce−
2.22
42∗∗
∗−
2.20
23∗∗
∗−
2.20
0∗∗∗
−2.
2005
∗∗∗−
2.22
23∗∗
∗−
2.20
09∗∗
∗−
2.21
62∗∗
∗−
2.19
34∗∗
∗
(0.0
754)
(0.0
737)
(0.0
755)
(0.0
737)
(0.0
757)
(0.0
739)
(0.0
756)
(0.0
739)
Com
mon
bord
er1.
5758
∗∗∗
1.54
72∗∗
∗1.
5479
∗∗∗
1.54
79∗∗
∗1.
5742
∗∗∗
1.54
62∗∗
∗1.
5797
∗∗∗
1.54
74∗∗
∗
(0.0
954)
(0.0
946)
(0.0
954)
(0.0
946)
(0.0
955)
(0.0
946)
(0.0
955)
(0.0
947)
lnD
iffer
ence
inpe
rcap
itaG
DP
0.08
24∗∗
0.09
38∗∗
∗0.
0825
∗∗0.
0938
∗∗∗
0.08
13∗∗
0.09
27∗∗
∗0.
0816
∗∗0.
0942
∗∗∗
(0.0
335)
(0.0
328)
(0.0
335)
(0.0
328)
(0.0
335)
(0.0
328)
(0.0
336)
(0.0
328)
lnR
emot
enes
s0.
0837
0.07
990.
1146
0.09
120.
4587
0.44
500.
6493
0.61
76(1
.013
9)(1
.015
0)(1
.026
5)(1
.028
3)(1
.048
7)(1
.050
4)(1
.061
4)(1
.062
8)ln
expo
rter
’sur
ban
popu
latio
n0.
0749
0.01
65-0
.020
7-0
.076
5-0
.137
0−
0.19
53(0
.585
6)(0
.576
7)(0
.586
9)(0
.577
9)(0
.593
5)(0
.584
3)C
OM
ESA
FTA
0.30
20∗∗
∗0.
3020
∗∗∗
0.30
22∗∗
∗0.
3543
∗∗∗
(0.1
062)
(0.1
062)
(0.1
070)
(0.1
071)
EA
CFT
A−
0.19
57−
0.19
76−
0.18
41−
0.19
86(0
.154
3)(0
.156
2)(0
.157
5)(0
.157
0)SA
DC
FTA
-0.0
436
-0.0
421
−0.
0417
−0.
0252
(0.1
200)
(0.1
202)
(0.0
120)
(0.1
203)
lnm
arke
tbar
rier
0.00
210.
0016
0.04
22-0
.000
50.
0152
0.01
324
0.01
930.
0171
(0.0
454)
(0.0
453)
(0.0
456)
(0.0
454)
(0.0
459)
(0.0
457)
(0.0
458)
(0.0
456)
lnap
plie
dtar
iff−
0.04
57−
0.04
59-0
.046
4−
0.05
82∗
(0.0
304)
(0.0
304)
(0.0
308)
(0.0
308)
lnex
port
er’s
scho
olin
gra
te1.
573∗
∗1.
6229
∗∗1.
7274
∗∗1.
7689
∗∗
(0.7
845)
(0.7
840)
(0.7
000)
(0.7
839)
lnim
port
er’s
scho
olin
gra
te−
1.76
13∗∗
−1.
7426
∗∗−
1.48
81∗
−1.
4829
∗
(0.7
694)
(0.7
645)
(0.7
6368
)(0
.758
5)ln
expo
rter
’sco
ntro
lofc
orru
ptio
n.0
704∗
.066
0∗(0
.035
7)(0
.037
7)ln
impo
rter
’sco
ntro
lofc
orru
ptio
n0.
1484
∗∗∗
0.14
25∗∗
∗
(0.0
432)
(0.0
356)
Not
e:**
*si
gnifi
cant
atth
e1%
,**
5%,*
10%
leve
l.co
nsta
ntin
clud
ed,r
obus
tsta
ndar
der
rors
.con
stan
t,tim
edu
mm
ies
and
coun
try-
spec
ific
effe
cts
incl
uded
.ro
bust
stan
dard
erro
rs.
18
to have a significant and increasing positive effect on bilateral trade over time. This might be anexplanation for the insignificance of our SADC FTA dummy. Given that our analysis only coversthree years of the SADC FTA (2008-2010), the expected positive effect might not be observable inour sample. Our finding on the missing robustness of RTA effects to different estimators is similarto the most recent literature on African RTAs published by Afesorgbor & van Bergeijk (2013).Their COMESA RTA dummy is positive and significant in most estimations, except those using aTobit and a PPML setup. Besides they find that if both trading countries are members of the SADCRTA, exports will be up to three times higher than if not. In their meta-analysis Afesorgbor & vanBergeijk (2013) integrates 14 individual empirical studies with 139 results. 40% of the estimatedcoefficients are larger than one which they interpret as an upward bias. While 35% of the resultsare between zero and one, 25% predicted a negative effect on exports. Since we look at the effectsof the ratified FTAs and not of the membership in regional organizations our results are comparableto the literature only to some extent. While the significantly positive of the COMESA FTA andthe missing positive effect of the SADC FTA are according to our expectations based on recentliterature, the in part negative effect of EAC FTA requires a deeper look. This trade agreementbetween Kenya, Tanzania, Uganda, Rwanda (joined 2007) and Burundi (joined 2007) promised tohave significantly positive effects on regional trade, although Kenya is the largest exporting countryand gains most of the profit (see Busse & Shams (2003)). Similarly, Buigut (2012) shows thatintra-EAC imports have largely increased for all countries, while intra-EAC exports were mainlydriven by Kenya and Uganda. Thus looking only at export data this may be at least in line withan insignificant coefficient of the EAC Dummy. Comparing the baseline model with the extendedmodel in our panel setup (see Tables 2 and 1) the negative effect of the EAC becomes significantonce we include two measures of factor endowments/technology, i.e. the shares of education andurban population, which are both positive and significant in most specifications. Increasing exportsare driven by the level of economic development, and tariff liberalization within this region maybeeven decrease the amount of exports due to higher competition.
FTA dummies suggests an immediate effect of the tariff liberalization and cannot fully capturethe effect of a stepwise tariff reduction as it happens in east and south Africa. (See again Coulibaly(2009) for a first attempt to account for this shortcoming.) Due to poor data availability only a fewpanel studies include tariff rates such as Iwanow & Kirkpatrick (2008) and Hayakawa (2011), andalso find a significantly negative effect on bilateral trade. Hayakawa (2011) supports our view thatthe omission of bilateral tariff rates does not raise an estimation bias. In his study the coefficientsof the traditional gravity variables do not change and time-variant importer and exporter fixedeffects account for unobserved variables. This is in accordance to our findings, that the traditionalvariables do not change when we include the proxy for limited market access.
In summary, we conclude that there is a positive effect of the COMESA FTA dummy andhigher market access when we consider the panel structure. Similar to Afesorgbor & van Bergeijk(2013) we find that the positive effect of the COMESA FTAs as well as the effect of the tariffrates becomes insignificant in most PPML specifications. In other words, only if we control forcountry-pair specific effects, a reduction in tariffs has a positive effect on bilateral trade.
Even though our tariff measure is only a rough proxy of bilateral tariff rates, our findings aresupported by a general equilibrium analysis of the COMESA-EAC-SADC Tripartite FTA done by
19
Willenbockel (2013). Within a trade policy simulation for eight different scenarios of the TripartiteFTA on an aggregated and on a country-specific level they show that all countries will largelybenefit in terms of welfare gain and increase in total exports. This holds for different szenarios:elimination of remaining intra-COMESA, intra-SADC tariffs, and a complete elimination of allintra-Tripartite tariffs in combination with a reduction of non-tariff barriers. In the latter scenariothe authors obtain a rise in bilateral trade which is five times larger than currently observed.
The sign and the value of the traditional gravity variables estimated by the panel estimators andthe PPML estimator without country-specific effects are in line with the literature on African trade,but some points are worth to discuss in greater detail. The effect of GDP becomes insignificantin the PPML estimation once we include either country-specific or country-pair specific effects.Previous studies (Afesorgbor & van Bergeijk (2013); Santos Silva & Tenreyro (2006)) did notcontrol for these specific effects and also estimated a positive effect of exporters and importersGDP close to unity. The inclusion of unobservable effects has received marginal attention. Herrera(2012) pointed at this specification problem and suggested the exclusion of the GDP variableswhen including time-varying country-specific effects. Since we only control for time-invariantcountry-specific characteristics, we still include GDP measures. However, since exporters GDPdoes not have an effect on exports anymore and the importers GDP effect fell in absolute terms, thecountry-specific effects capture a lot of information. As reported above in section 3, the coefficientfor the importer’s schooling rate does not change in the Hausman-Taylor setting after controllingfor all importer specific characteristics, but becomes significantly negative in the PPML setup. Westrongly suggest that this difference is driven by the panel structure. We argue that considering across section, a country which has a lower level of economic and social development has a highershare of imports relative to GDP as the production of more sophisticated products is less likely inthose economies. Despite the continuous reduction of bilateral tariffs we find that intra-Africantrade is still very low.
Our analysis leaves a high percentage of variance in exports unexplained as it is limited toobservable data. Non-tariff barriers that lower exports are difficult to measure, and difficult tobe provided for a broad time period. So far we can only approximate them by country-pair andcountry-specific fixed effects. The importance of non-tariff barriers and trade facilitation have al-ready been examined in several studies. Iwanow & Kirkpatrick (2008) examines the determinantsof manufacturing exports for 124 developing countries for the years 2003 and 2004 including atrade facilitation index that consists of the number of all documents required, the time necessaryto comply with all procedures and the cost associated with all the procedures to export/importgoods, as well as an infrastructure index containing a measure of paved roads, rail density, num-ber of telephone and mobile phone subscribers. Including these variables the ”African dummy”becomes insignificant. In absolute terms the infrastructure index has the largest effect and the ef-fect increased significantly within the African subsample. Considering the time period 2004-2007,Portugal-Perez (2012) examines the effect of a set of trade facilitation variables for 101 developingcountries. They retain a pool of 18 variables obtained from the WEF’s Global Competitiveness Re-port, the Doing Business Report and Transarency International containing and summarized themto four indicators: Physical infrastructure; information and communication technology; border andtransport efficiency; business and regulatory environment.They confirm the findings of Iwanow &
20
Kirkpatrick (2008), physical infrastructure has a large effect on bilateral exports, a 1% increaseof physical infrastructure results in an 0.2-0.5% increase of exports. Karugia (2009) conduct ananalysis for EAC - the FTA with the highest level of integration among the three FTAs. Theydetermine the trade and welfare impacts in the maize and beef sector within a spatial equilibriummodel based on data from a regional survey in 2007. According to their findings the low levelof intra-EAC trade of maize and beef can be increased by improving administrative proceduresat border points, reducing the extent of road blocs and by implementing an efficient monitoringsystem.
During our analysis we show that the proxy for limited global market access is robust to theunderlying estimation technique and model setup. This suggests that trade with the rest of theworld is still subject to barriers. Intra-African exports increased over the years but are still lowerthan exports to e.g. Europe. Maybe the integration to the world market also drives regional exports.In a next step the analysis of possible dependencies/ interactions between the development ofregional and North-South trade may yield important insight.16 The purpose of our analysis is theanalysis of the determinants of intra-African trade with a strong focus on the role of a commonFTA and regional integration efforts.
6 Conclusions
In 2008 the member states of the three major trading blocs in southern and eastern Africa -the Common Market for Eastern and Southern Africa (COMESA), the East African Community(EAC) and the Southern African Development Community (SADC) - agreed on establishing acommon Free Trade Area (FTA). This so-called COMESA-EAC-SADC Tripartite is an importantmilestone towards Africa’s continental trade integration.
This study analyzes the impact of regional integration among the Tripartite countries on bilateralexports and evaluates the latest integration efforts with respect to future trade potential. Within apanel framework bilateral export data in between 1995 and 2010 are used to estimate an extendedgravity model for 24 member states. Doing so, we also account for multi-membership in regionaltrade agreements. The potential endogeneity of free trade agreements and the issue of zero tradeflows are treated carefully by means of a systematic comparison between (instrumental variable)panel and Poisson pseudo-maximum-likelihood (PPML) estimation. The findings suggest a robustand significantly positive impact of the COMESA FTA. Coefficient estimates are about 0.8 usingpanel estimation techniques and about 0.2 for the PPML model including and country pair. Thedata on average tariff barriers have a significantly negative effect in most model specifications. TheEAC and SADC FTAs do not show any positive effect on exports. Thus, to a certain extent ourstudy confirms the pessimistic view of the effectiveness of African FTAs.
Besides, the traditional gravity variables such as several income and distance measures all havea significant effect on exports. Mixed results are reported for education and corruption variables.
16This is best done using disaggregated export data as African exports still depend on a country’s natural resources.
21
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