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Determinants of East African Community Trade: A Gravity Model Analysis of Trade

Flows between Tanzania and Kenya

Mjema, G.*, Mahona, B.* and Bagumhe, P. **

*Institute of Finance Management, Tanzania

** Ministry of East African Cooperation, Tanzania

Paper to be presented at the 5th

International Conference organized by the Institute of Finance

Management, 23rd

-25th

August 2012

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1 Introduction

Economists have, for a long time been preoccupied with analyzing the determinants of trade

flows between nations. During the mercantilist era for example it was argued that since gold

and other precious metals were generally accepted as global currencies, a country which was

able to accumulate large reserves of such metals was able to pay for imports from other

countries using such a reserve. Thus, trade surplus countries were synonymous to countries

with large reserves of the precious metals. The reverse was also true, that is trade deficit

countries were countries which were deficient on such metals.

It was Adam Smith and later David Ricardo who laid the foundations for analyzing the

determinants of trade flows between nations using the absolute and comparative advantage

concepts (see for example Arthur and Steven, 2003). In a nutshell, while absolute advantage

refers to a country’s ability to produce certain commodities more effectively than another

country, comparative advantage refers to a country’s ability to produce a particular

commodity with a lower opportunity cost than another.

In subsequent models that followed including the Heckscher-Ohlin approach, trade flows

between nations are said to be determined by the respective country’s state of either capital or

labour abundance. In this setting, with the exception of where the ―Leontief paradox‖ holds,

a capital abundant country will tend to produce and export capita intensive goods. The same

country will import labour intensive goods from the rest of the world.

Several variants of the Heckscher- Ohlin model have been suggested including the Linder

hypothesis which has suggested that countries with similar demands tend to develop similar

industries and would then trade with each other in similar but differentiated goods (see

Soderstein, 1980).

Other researchers including Tinbergen (1962) have attributed the flow of trade between

nations to the gravity concept borrowed from natural sciences. According to this model

factors like the sizes of countries, distance as well as other cultural factors are important

determinants of trade flows between them. This paper uses a modified version of the gravity

model of trade to analyze the determinants of trade between Kenya and Tanzania which are

members of the East African Cooperation.

Trade between EAC member states is arguably as old as the history of these states. However,

trade between Kenya and Tanzania which is the focus of this paper is of particular interest for

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various reasons. For example, the volume of trade between the two states constitutes over

50% of the entire EAC trade. Studies including those by Ackello-Ogutu and Echessah (1989)

have revealed flourishing yet unrecorded cross border trade activities between Kenya and

Tanzania than in any other borders in the East African region. Furthermore, there is currently

more pronounced cross border investments between Kenya and Tanzania than in any other

EAC member states (EAC, 2010). Indeed, data from the Tanzania Investment Centre show

that investments from Kenya to Tanzania have increased from US $ 37.5m in 2005 to US

$ 67.25 m in 2010(an increase of over 50%). Similarly, Tanzania’s investments to Kenya

increased from US $ 0.31m in 2005 to US $ 2.54m in 2010 (an increase of over 500%).

There are indications that the on-going formalization of free movement of capital within the

auspices of the EAC Treaty trade will usher in a surge of investments between EAC member

states and more specifically between Kenya and Tanzania.

Currently EAC has five member states namely; Tanzania, Kenya, Uganda, Rwanda and

Burundi. There are potentials for other countries within the eastern and central Africa to join

this economic cooperation between nations. Historically there have been two phases in the

creation of EAC. The first phase stretches from 1967 to 1977. This period saw the signing of

the EACTreaty of cooperation between Kenya, Uganda and Tanzania in 1967 but also

witnessed its collapse in 1977 amid political and economic frictions between the three

countries.The objectives of the first phase of EAC were, inter alia, to achieve a balanced

economic growth within the EAC member states through the establishment of a common

market, common customs tariff, and common public services (see for example, Kirkpatrick

and Watanabe, 2005 and Ng’ang’a, 2006). With the demise of the first phase of the EAC in

1977, these objectives were hardly attained.

The collapse of the first phase of EAC had profound negative impacts on the economies of its

member states. Besides loosing the economies of scale advantage the region had prior to the

collapse of the scheme, each country had to re-start a costly programme of establishing

services and industries which were run somewhat efficiently at the EAC level.

Thus, in 1999 after a series of high level inter-state consultations a Treaty for establishing the

second phase of EAC was signedby the respective Heads of State of the member countries in

Arusha, Tanzania. One of the significant developments of EAC during the second phase has

been the inclusion of two new members; Rwanda and Burundi thus bringing the current

membership of EAC to five.Apart from the expansion of the EAC membership the other

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milestones so far registered in EAC include (i) the establishment of the customs union (ii) the

establishment of the common market (iii) convertibility of Kenya, Uganda and Tanzanian

currencies (iv) harmonization of goods produced in the region (v) reduction of tariff and non-

tariff barriers and (vi) free movement of stock (EAC, 2010).

In terms of size, EAC has a total area of 1,817,945 km2 and an estimated population of 131.9

million people. The total Gross Domestic Product (GDP) of the region was estimated (2007)

to be 61US $ bn. which translates to an average per capita income of over US $ 450 (EAC,

2010). As stated earlier, interest in this paper is to analyse factors determining the flow of

trade between Kenya and Tanzania. As the following sub-section reveals, the need to get a

better understanding of the determinants of trade flows between Kenya and Tanzania is

beneficial not only to the current trade relations in EAC but also to its future development.

2 Trade Flows between Tanzania and Kenya

Although the EAC sub-region has witnessed a surge in trade flows among member states

particularly after the establishment in 1st July, 2010 of the common market. The EAC

common market has provided for ―four freedoms‖ namely; free movement of goods, labour,

services and capital all of which are expected to boost trade and investment within the region.

Interest in analyzing the trade flows between Tanzania and Kenya is based on economic as

well as non economic factors. The essence of focusing on trade flows between Tanzania and

Kenya is that currently the two countries dominate the economy of the sub-region, in many

aspects. For example, Kenya and Tanzania share the largest border within EAC countries, an

important factor in facilitating cross border trade. While all the other EAC countries are

landlocked, Kenya and Tanzania have access to the Indian Ocean through their Mombasa and

Dar es Salaam ports respectively. This access has important implications on not only distance

but also in transport related trade aspects. The two countries have a large population of

people who can speak two languages (Kiswahili and English) and, to a certain extent, share a

common culture which is also important for trade facilitation.

Kenya and Tanzania can loosely be referred to as the economic ―power houses‖ of EAC. In

2010 for example, the combined GDP of the sub-region was estimated to be US $ 79.3 bn.

Kenya’s GDP during the same year was US $ 32.2 (equivalent to 40.6% of the total EAC

GDP). Tanzania’s GDP was US $ 22.9 bn. (equivalent to 28.9%) during the same year. This

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implies that the combined GDP of Kenya and Tanzania was equivalent to 69.5% of the total

EAC GDP.

The combined share of GDP of the three other EAC member states (Uganda, Rwanda and

Burundi) wasonly 30.5% of the total EAC GDP. According to data from the World Bank

(290) the two countries dominate the region where GDP per capita is concern. In 2010

Kenya had the highest GDP per capita (US $ 468.7) in the EAC region compared to

Tanzania’s per capita GDP of US $ 458.4. The per capita GDP of Rwanda, Uganda and

Burundi was lower (averaged just over US $ 380) than that recorded in Kenya and

Tanzania.Trade as a percentage of GDP was highest in Kenya (65.4%) but was closely

followed up by Tanzania whose ratio in the same year was 63.8%. The trade –GDP ratio in

Rwanda, Uganda and Burundi was lower (under 50%) compared to what was recorded in

Kenya and Tanzania. Recent trade trends between Tanzania and Kenya (Table 1) suggest that

apart from their relative large GDPs they (Kenya and Tanzania) dominate the region’s trade

activities.

Table 1: Trade Flows in EAC: 2009 – 2011 (US $ m)

Year Tanzania’s Exports to

Kenya (1) (US $m)

Tanzania’s Exports to Rest of

World (2) (US $m)

(1) as % of

(2)

2009 192,904 2,982,405 6.5

2010 324,888 4,050,546 8.0

2011 221,313 4,734,960 4.7

Avg.2009-2011 246,368 3,922,637 6.2

Year Tanzania’s Exports to

Uganda(1) (US $m)

Tanzania’s Exports to Rest of

World (2) (US $m)

(1) as % of

(2)

2009 51,651 2,982,405 1.7

2010 60,205 4,050,546 1.5

2011 52,634 4,734,960 1.1

Avg.2009-2011 54,830 3,922,637 1.4

Year Tanzania’s Exports to

Rwanda (1) (US $m)

Tanzania’s Exports to Rest of

World (2) (US $m)

(1) as % of

(2)

2009 15,805 2,982,405 0.5

2010 116,802 4,050,546 2.8

2011 95,160 4,734,960 2.0

Avg.2009-2011 75,922 3,922,637 1.7

Year Tanzania’s Exports to

Burundi (1) (US $m)

Tanzania’s Exports to Rest of

World (2) (US $m)

(1)as % of

(2)

2009 24,632 2,982,405 0.8

2010 56,132 4,050,546 1.3

2011 39,848 4,734,960 0.8

Avg.2009-2011 40,204 3,922,637 0.9

Source(s): World Bank 2010, EAC 2011

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In 2009 Tanzania’s exports to the rest of the World (ROW) was US $ 2,982,405 m compared

to the country’s exports to Kenya which were valued at US $ 192,904 m (equivalent to 6.5%).

This ratio increased to 8.0% in 2010 before leveling off to 4.7% in 2011. Tanzania’s exports

to Kenya during the 2009 – 2011 period averaged US $ 246,368 m compared to the value of

Tanzania’s exports to ROW which totaled US $ 3,922,637 m. The central message emerging

from Table 1 is that Kenya is an important destination for Tanzania’s exports than all the

other EAC countries combined. The economic trends (Table 2) indicate that the trade

scenario in EAC is not likely to change in the near future and hence Tanzania and Kenya will

continue to dominate trade activities in the sub region.

Table 2: Economic Growth in EAC Region 1990 - 2010

Region 1990 –

1999

2006 2007 2008 2009 2010

Burundi -1.3 5.2 3.6 4.3 3.5 3.9

Kenya 2.1 6.3 7.0 1.5 2.6 5.6

Rwanda -0.1 8.6 7.7 11.5 6.1 7.5

Tanzania 3.1 6.7 7.1 7.4 6 7.0

Uganda 6.3 7.0 8.1 10.4 3.9 5.6

Source: EAC (2011)1

In the following sub-section the paper presents the theoretical underpinnings of the gravity

model used to analyze the determinants of trade flows between Kenya and Tanzania.

Although different authors have included different variables in the gravity model, the interest

in the current paper is on four variables notably GDP, GDP per capita, distance and trade

openness.

1 From the EAC 4

thDevelopment Strategy document published on August,2011

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3 Modeling the Determinants of Trade Flows between Kenya and Tanzania: The

Gravity Model Approach

The gravity model used in the current paper to analyze the determinants of trade flows

between countries draws its theoretical foundations from gravity models used in the field of

natural sciences. The model predicts bilateral trade flows based on the economic sizes of

countries (using mainly GDP ) and distance between them. The model was first used in the

social sciences field by Tinbergen (1962)and p yh nen (1963) who postulated that trade

between two countries (i and j) takes the form of:

Where is the trade flow, are the economic masses of each country, is the distance

between them and G is a constant. Besides being deployed to analyze trade flows between

countries the model has also been used in international relations including evaluating the

impact of treaties and alliances on trade.

Since then (1963), the gravitymodel has become a popular instrument in empirical foreign

trade analysis such that different scholar has successfully appliedthe model to show flows of

varying types suchmigration, foreign direct investment and more specifically to

internationaltrade flows. According to this model, exports from country i to country j

areexplained by their economic sizes (GDP or GNP), their populations, directgeographical

distances and a set of dummies incorporating some kind ofinstitutional characteristics

common to specific flows.

Starting from the second half of 1970s several theoretical developments haveappeared in

support of the gravity model showing the theoretical support of the research in this fieldwhich

was originally very poor. Anderson (1979) made the first formalattempt to derive the gravity

equation from a model that assumed productdifferentiation. Bergstrand (1985, 1989) also

explored the theoreticaldetermination of bilateral trade in a series of papers in which gravity

equationswere associated with simple monopolistic competition models. Helpman and

Krugman (1985) used a differentiated product framework with increasingreturns to scale to

justify the gravity model. More recently Deardorff (1995) has proven that the gravity

equation characterizes many models and can bejustified from standard trade theories. Finally,

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Anderson and Wincoop (2001) derived an operational gravity model based on the

manipulation of the CESexpenditure system that can be easily estimated and helps to solve

the so-calledborder puzzle. The differences in these theories help to explain the

variousspecifications and some diversity in the results of the empirical applications.

There are a huge number of empirical applications in the literature ofinternational trade,

which have contributed to the improvement of performanceof the gravity equation. Some of

them are closer related to our work. First, papers of Mátyás (1997) and (1998), Chen and

Wall (1999), Breuss andEgger (1999) and Egger (2000) improved the econometric

specification ofthe gravity equation. Second, Berstrand (1985), Helpman (1987), Wei,

(1996),Soloaga and Winters (1999), Limao and Venables (1999), and Bougheas etal, (1999)

among others, contributed to the refinement of the explanatoryvariables considered in the

analysis and to the addition of new variables.In other occasions the model has been used to

test the effectiveness of trade agreements and organizations such as the North American Free

Trade Agreement (NAFTA) and the World Trade Organization (see for example Helman,

2003, Panagariya, 1999, and Krugman 2001 among others).

While the model’s basic form consists of factors that have more to do with geography and

spatiality, the gravity model has been used to test hypotheses rooted in purer economic

theories of trade as well. One such theory predicts that trade will be based on relative factor

abundances. One of the common relative factor abundance models is the Heckscher-Ohlin

model. This theory predicts that trade patterns would be based on relative factor abundance.

According to this theory, countries with a relative abundance of one factor would be expected

to produce goods that require a relatively large amount of that factor in their production.

While a generally accepted theory of trade, comparative advantage has suffered empirical

problems. Investigations into real world trading patterns have produced a number of results

that do not match the expectations of comparative advantage theories. Notably, a study by

Wassily Leontief found that the United States, the most capital endowed country in the world,

actually exports more labor intensive commodities. Comparative advantage in factor

endowments would suggest the opposite would occur. Other theories of trade and

explanations for this relationship were proposed in order to explain the discrepancy between

Leontief’s empirical findings and economic theory. The problem has become known as the

―Leontief paradox‖ (see for example (Soderstein, 1980).

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The gravity model has often been specified in econometric form as:

, ……………………..…………………………. (2)

Where represents volume of trade from country to country , and typically

represent the GDPs for countries and , denotes the distance between the two countries,

and represents an error term. The traditional approach to estimating this equation consists in

taking logs of both sides, leading to a log-log model of the form:

. ……… (3)

However, this approach has two major problems. First, it obviously cannot be used when

there are observations for which is equal to zero. Second, it has been argued by Santos

Silva and Tenreyro (2006) that estimating the log-linearized equation by least squares (OLS)

can lead to significant biases. As an alternative, these authors have suggested that the model

should be estimated in its multiplicative form, i.e.

, ………. (4)

One of the authors' more surprising findings was that, when controlling for factors like

sharing a common language, having past colonial ties does not increase trade—a finding

which contrasts with what more basic methods, such as OLS tend to indicate. In applied work,

the model is often extended by including variables to account for language relationships,

tariffs, access to sea, colonial history, exchange rate regimes, and other variables of interest.

The current paper uses a simplified version of the gravity model to analyze the determinants

of trade between Kenya and Tanzania and whether the creation of the EAC has benefitted

them. This simplified version of the gravity model examines the determinants (mainly GDP,

size of population, distance and other socio-economic factors) that could influence the flow

of trade between Tanzania and Kenya. Essentially, the gravity model traces geographical-

spatial relationship of the foreign trade between Kenya and Tanzania. Used in the economics

sense, the gravity model stipulates that the economic size of countries and geographical

distance are the two basic factors determining the bilateral trade flows between Kenya and

Tanzania.

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The stochastic form of the equation to estimate the determinants of trade of trade flow

between Kenya and Tanzania is given as:

ijijijjijioij ADNNYYTD 654321 ……………………………………………….. (5)

Where:

ijTD Value of total trade between Kenya and Tanzania

jI andYY GDPs of the Kenya and Tanzania respectively

jiandNN Population sizes of Kenya and Tanzania respectively

ijD = The distance between the capital cities of Kenya and Tanzania

ijA Other factors (including cultural) influencing trade between two countries

ij Error term

Linearising equation (5) by taking natural log of all variables results into equation (6) as

follows:

ijijijijiij ADNYYTD loglogloglogloglog 653210 …….. (6)

The current paper uses a stochastic form of the model to estimate the determinants of the

trade flows between Tanzania and Kenya as follows: The basic, standardized empirical

gravity equation takes the following form:

ijjiijjijiij PPLnLnDPYPYLnYYLnLnTD )*(]*)/(**)/[()*( 43210 .......(7

)

While if we want to understand the home market effect one could use the following equation

ijijtijij

ijijjijiij

PEITOTOPENEAC

EXRDNNYYEXP

logloglog8

loglogloglogloglog

11107

6543210

(8)

Where:

and = GDPs of Kenya and Tanzania respectively

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and = Population sizes in Kenya and Tanzania respectively

= Geographical distance between the respective countries’ capita cities.

= Exports of Kenya or Tanzania

= Exchange rate between Kenya and Tanzania

= Degree of openness of Kenya or Tanzania economies

= Terms of trade between Kenya and Tanzania

= per capita income of Tanzania or Kenya

The theoretical propositions of the gravity model which are tested in this paper are that

bilateral trade flows between Kenya and Tanzania tend to be directly proportional to the

product of their respective GDPs and inversely proportional to their distance (D). That is to

say, the higher the GDP ratio the higher is the trade volume and vice versa.

Similarly the geographical distance between the two countries is inversely proportional to the

trade flows between the two countries. The gist of this proposition is that the further away

integrating countries are from each other the less attractive it becomes for them to gain the

advantages of integration. Other variables which are likely to influence trade flows between

Kenya and Tanzania including exchange rate, terms of trade, per capita income and degree of

openness will also be tested. It is hoped that the outcomes of this paper will influence

positively ongoing efforts to create a stable regional integration scheme in East Africa.

This paper tests how significantly the gravity model is in explaining Tanzania’s or Kenya’s

bilateral trade flows in the EAC and tries to extract implication for Tanzania’s and Kenya’s

trade policies. The equation’s application first fixes Tanzania as i, but leaving the rest of EAC

as j=1, 2...N in a Nx1 setting, and then it does so for Kenya. In this kind of equation, all

variables are in naturallogarithm of real value terms expect dummy variables. Any variable

with relatively small numbers are usually exempted from taking the logarithm (inflation,

terms of trade and openness for this study).

In particular, since the EAC countries borders each other, use common language, share

colonial relationship or historical ties the dummy variable is not that much relevant in this

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model. Hence, the model starts with only three explanatory variables, namely the product of

GDPs, the product of per capita GDPs and distance.

The product of GDPs serves as a proxy for the two countries’ economic size, both in terms of

production capacity and size of market. Larger countrieswith great production capacity are

more likely to achieve economies of scale and increase their export based on comparative

advantage. They also possess large domestic market able to absorbed more imports.

Therefore an increase in the product of the two Countries’ GDPs is expected to increase

bilateral trade volume. Thus, we expect the estimated coefficient of production capacity to be

positive.

As explained above, the explanatory variables, the product of GDP2 serves as a proxy for the

two countries’ economy size, both in terms of production capacity and size of the market.

The per capita GDP, however, it serves as a proxy for the income level and/ or purchasing

power of the exporting and importing countries. When the fit of the gravity model for

Tanzania was run, the results turned out that, Tanzania’s trade depend much on GDP for its

bilateral trade flow and it is negatively related to the per capita patterns of trade.

Per capita GDP3 is an explanatory variable that serves as a proxy for the incomelevel and

indeed the purchasing power of the exporting and importing countries. Since, Tanzania’s or

Kenya’s per capita is fixed, this variable will serve to predict whether Tanzania’s orKenya’s

trade flows are dependent on its trading partners’ income level.

The distance variable is mainly a trade resistance factor that represents trade barrier such

transport costs, time, cultural unfamiliarity and market access barriers. Previous literature on

this subject has tended to interpret the coefficient of distance variable as elasticity with

respect to an absolute level of geographical distance. In the gravity model, trade volume will

be larger between countries pairs that are far from the rest of the world than between

countries pairs that are close to the rest of the world (Anderson and Van Wincoop 2003;

Harrigan 2003)4.

2Products of the GDP was used since the authors did not need the home market effect analysis.

3Bergstrand (1989), combining economic geography and factor proportional theory, derived the gravity equation at the

industry level which predicts that the export of a good in bilateral trade depend on income and per capital income as well,

assuming a constant elasticity of transformation of supplies among different markets.

4 A decrease of the distance coefficient indicates that trade with far away countries increases relative to the trade with closer

countries, whereas an increase represents trade with closer countries increases faster than that with far way countries. The

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4 Empirical Analysis

The objective of the paper was to analyze the determinants of trade flows between Kenya and

Tanzania. The gravity model of international trade was deployed for this purpose due to its

widespread acceptability and availability of data. Prior to the presentation and analysis of the

results obtained, it is important to assess the robustness of our model. For this purpose, we

have employed the Linktest to test for model specification. This command is based on the

notion that if a regression is properly specified, we should not be able to find any additional

explanatory variables that are significant except by chance. The test creates two new

variables, the variable of prediction, _, and the variable of squared prediction, _hatsq. In our

model hat is significant indicating that our model is correctly specified, and hence we have

no reason to find any additional variable (see appendix 1).

The Hausman test is used to test for the significance of the parameters. The Hausman

specification test compares the fixed versus random effects under the null hypothesis that the

individual effects are uncorrelated with the other regressors in the model. The trick is that

random effect estimator would be useful if some explanatory variable remain constant over

time. The random effect assume that group effect are uncorrelated with regressor , while

fixed effect estimator measures the relationship based on time variation within a cross section

unit. In our estimation the null hypothesis is that the preferred model is random effects V/S

the fixed effect effect. The Hausman test whether the unique error are correlated with the

regressors, the null hypothesis is, they are not correlated with the regressor. Since in our

estimation the ch2 is greater than 0.05 i.e is significant then random effect model is preferred, ,

and hence both time invariant and time invariant variable is estimated (see appendix 2).

Breusch pagan test is employed to test for the presence of heteroskedasticity, presence of

heteroskedasticity means there is constant variance which violate the assumption of OLS.

The interpretation is that you look for the p-value, if the p- value is 0.05 or small then it

means there is presence of heteroskedacity, is otherwise no potential problem of

heteroscadacity. In our case the p-value is large than 0.05 that means our model estimation is

not suffering from the potential problem of heteroskedaciticity and hence does not violate the

assumption of OLS. The point of departure was analyze empirically whether variables like

notion of relative distance remains significance at the NxN countries setting, whereas it is important drops sharply in our

Nx1 because all distances are measured absolutely from Tanzania or Kenya, we anticipate the coefficient greater than one,

but its magnitude matters.

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GDP, per capita GDP and distance between Kenya and Tanzania have any influence on trade

flows between them..

Table 3: OLS Results for the model (equation 7)

Explanatory variables Dependent Variable (Total

volume of trade)

OLS Coefficient

Constant 47.345***

(4.984)

Product of GDPs 1.119***

(0.267)

Product of per capita GDPs -1.923**

(0.530)

Distance -3.794***

(0.609)

R2 0.863

Adjusted R2 0.74

Hausman test 0.534

Notes: (i)) the numbers in parenthesis are standard errors

(ii) ) *** and ** and * indicate significance levels 1%, 5% and 10% level, respectively

The results obtained here (Table 3) show that with the exception of the per capita GDP

variable which has an unexpected negative sign (-1.9) the other variables have the expected

signs as predicted by the gravity model. The influence of the two countries’ GDP (as

indicated by the coefficient 1) is positive (1.19) as expected. This result is not only expected but

is also consistent with the basic hypothesis of the gravity model that the trade volume

between two countries tends to increase with the increase in the economic sizes of trading

partners. This result suggests that holding constant for other variables; a 1 percent point

increase in GDP will result in about 1.119 percent increase in Tanzania bilateral trade flows

with Kenya. The seemingly high value of this estimate is consistent with Heckscher-Ohlin

type5 hypothesis basing on inter-industry trade or differentiated type of trade basing on intra-

trade model of trade (Deardorff, 1998; Grossman 1998).

Cases where an increase in bilateral trade volume is less proportionate to the increase in GDP

can be explained in various ways. One is the existence of relatively large home-market effect

5 Frankel (1998) showed the coefficient lying in the range of 0.75-0.95. Our estimate more or less fit the range

but in a higher level.

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(Trifler 1995, McCallum6, 1995). The existence of a large home market for a particular good

tradable between two countries implies that only a small amount of goods can be available

for trade in the other markets7. The other possibility is the presence of lower level of intra-

industry trade, whereby the volume of trade is higher in sectors characterized by a

monopolisticcompetition or scale economies (Harrigan, 2003).Thus, a country enjoying a

lesser scale economies will trade a smaller volume. Finally, this situation can be explained by

the existence and extent of trade barriers. Accordingly, the higher and wider the trade

barriersare, the smaller will be the trade volume.

Theoretically the per capita GDP variable is supposed to be a significant factor in

determining Tanzania’s bilateral trade in not only Kenya but in theEAC bloc as well.

However, results in Table 3 indicate that this variable is negatively related to the bilateral

trade flows between Kenya and Tanzania. The estimated coefficient ß2 has a magnitude of -

1.923, suggesting that predicting that a 1 percent increase in per capita GDP leads to about 2

percent decrease in bilateral trade flows between Kenya and Tanzania. The empirical result

is different from the regression analyses of Frankel (1997) that predicted that a 1% increase

in per capita GDP leads to about 0.1% increase in bilateral trade flows. Our findings implies

that Tanzania trade patterns follow a GDP pattern rather than a per capita pattern, relying

more on its trading partners’ overall economic size than its income level. The implications

from our results suggests that Tanzania trade can be characterized asa country which depends

more on exporting of quantity-based standardized products that are sensitive to the overall

market size instead of exporting quality-based high value-added products that are sensitive to

the trading partners’ income levels.

The estimation of the distance is significant with the expected negative sign. The results show

that geographical distance is an important factor for Tanzania’s trade. The coefficient ß3is

estimated to be -3.794, the result that is higher compared to previous studies (Frankel 1997;

Wall 1999; Buch et al. 2003), but in the line of Grossman (1998) who pointed out that most

of empirical gravity studies show a surprising larger size of the estimated coefficient on the

6 Home-biased effect, such as localized taste of local distribution network, plays a greater role in trade.

7 This is true for a small market, theoretically the home-market effect can be interested as an elasticity of export

with respect to domestic income that exceeds the importing country’s income elasticity of export and import for

a country, thus engendering the gravity analysis utilizing Y1and Y2 as a separate terms more appropriate.

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distance variable. Furthermore, the coefficient implies higher cost of trading in the EAC bloc

because it reflects transport costs, time and market access barriers8and that the data used are

of nearby countries because a decrease in of the distance coefficient indicates that trade with

far away countries increase relative to the trade with closer countries, whereas an increase

represents trade with closer countriesincrease faster than that with far countries.

We also tested the influence of Tanzania’s trade openness (defined as total exports plus total

imports divided by GDP) on the trade flows to Kenya. The results indicated that the

estimatedcoefficient on the openness index is 0.99and is

robustwiththepresentspecificationofthegravityequation. This result implies that when

Tanzania tariff and policy liberalization change by 0.9% the country’s export tend to

increase by 1%. This suggests further that there is some correlation between Tanzania

tariff liberalization in EAC and her export performance. This finding is in line with

the standard international trade theories. In the presence of compensatory and

complimentary policies, openness to international trade tends to accelerates

development of poor countries (Dollar and Kraay, 2000).

Although high openness index is widely accepted an indicator of the degree of

trade liberalization it is certainly not a sufficient indicator of the welfare p art of

trade policy reform. The World Development Indicator (2006) for example show

that Sub Saharan Africa as a region managed to increase its openness index from

56% in 1975 to 66% in 2004. China on the other hand increased its openness index

from just 9% in 1975 to 65% in 2005. While Sub Saharan Africa has not managed

to eradicate object poverty, China has succeeded to do so.

This finding has important policy messages for Tanzania. It calls for policy makers

to focus more on the fundamentals of economic growth, investment,

macroeconomic stability, human resources and good governance as a strategy for

addressing poverty. Flexibility is highly needed while pursing this policy. Since

complete openness might expose a country to greater risk from external shocks.

Poor country like Tanzania may find it hard to buffer these shocks and to bear the

costs they incur.

5 Concluding Remarks

8Butch et al,. 2003 argue that changes in distance coefficient do not carry much information on changes in

distance costs over time. Change in distance costs are to a large extent picked up solely in the constant term of

the gravity models.

17

The purpose of this paper was to contribute to the on-going debate on the determinants of

trade between nations which began during the mercantilist era and was later shaped by the

introduction of absolute and comparative cost advantage theories. The Heckscher – Ohlin

and later the Linder hypotheses and other modern trade theories also contributed significantly

to the understanding of trade flows between nations.

The current paper has adopted a modified version of the gravity model used originally by

Tinbergen (1962) as improved by other researchers including Helman (2003) and Krugman

(2001) among others to analyze factors underpinning trade flows between Kenya and

Tanzania. Interest in analyzing the trade flows between the two countries has come as a

result of the observation that the two countries can loosely be referred as the ―economic

powerhouses‖ within the auspices of the East African Cooperation. The combined GDPs of

Kenya and Tanzania constitute over 60% of the total EAC GDP. Likewise there are

significant recorded and unrecorded trade activities between Kenya and Tanzania than in

other EAC countries. The importance of the two countries in influencing not only trade

activities but also economic development of the region is likely to increase with the

intensification of the EAC integration especially towards the formation of the common

market and monetary union. That is why a better understanding of the trade flows between

Kenya and Tanzania is essential in enabling the EAC countries to formulate appropriate

macroeconomic policies for the development the region.

Different forms of the gravity model were tested in the current paper. However, the most

plausible model for this paper was one where the total trade volume between Kenya and

Tanzania was influenced by four factors namely, the product of Kenya and Tanzania GDPs

( ), the product of their respective per capita incomes ( ) to degree of trade

openness and distance between Kenya and Tanzania.

The results obtained generally support the basic hypothesis of the gravity model that the

economic size of the respective countries (denoted as ) exerts a positive impact

on the trade between Kenya and Tanzania. This result is hardly surprising. GDP is a

reflection of not only the production capacity of a country but also the consumption and

export ability of countries. Thus the results indicate that the commodities produced in both

Kenya and Tanzania not only sustains consumption levels in the two countries but also is

exported to markets in the respective countries.

18

The results obtained with respect to the combined per capita income in Kenya and Tanzania

( ) are unexpected. The impact of this variable was supposed to ―mirror‖ that of

combine GDPs. Instead, the result shows this variable has a negative impact on the trade

flows between the two countries. One possible explanation for this result is that Tanzania

tends to depend more on exporting quantity products that satisfy the broad EAC market

instead of producing goods that are sensitive to the EAC partners’ income levels.

The distance variable was expected to have a negative impact on trade flows between Kenya

and Tanzania. The coefficient on this variable is – 3.794 and implies among other things the

existence of high costs of trading between Kenya and Tanzania. The costs of trading refereed

to here include transport costs (freight etc.), time related costs and costs related to market

access.

The results obtained with regard to trade openness showed that trade liberalization measures

have significantly improved trade flows between Kenya and Tanzania.The results obtained

herein have policy implications for both Kenya and Tanzania. They point to the importance

of the two countries focusing more on policies aimed at promoting economic growth with a

view to promoting trade flows between their countries and more significantly within the EAC

flows. Furthermore there is need to encourage the industries which cater specifically for the

needs of over 120 million people the region is a ready market for products from such

industries.

Efforts need to be made especially on measures aimed at reducing trade related costs in EAC

and specifically Kenya and Tanzania. The two countries have to come up with concrete

measures aimed at reducing tariff and non-tariff restrictions to trade.

Finally, the study has pointed at the need for EAC countries and specifically Kenya and

Tanzania to undertake further trade liberalization of their economies with a view to

promoting trade activities within the region.

19

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Appendix 1

(Appendix Table 1: Test for Model Specification)

_cons .0012679 .0332237 0.04 0.970 -.0666822 .0692181 _hatsq -.0065264 .0143904 -0.45 0.654 -.035958 .0229052 _hat 1.014454 .0371544 27.30 0.000 .9384647 1.090443 EXPTZA Coef. Std. Err. t P>|t| [95% Conf. Interval]

Total 45.0939847 31 1.45464467 Root MSE = .12757 Adj R-squared = 0.9888 Residual .471952524 29 .016274225 R-squared = 0.9895 Model 44.6220322 2 22.3110161 Prob > F = 0.0000 F( 2, 29) = 1370.94 Source SS df MS Number of obs = 32

Appendix 2

Appendix Table 2: Test for Heteroscekedasticity

Prob > chi2 = 0.1685 chi2(1) = 1.90

Variables: fitted values of EXPTZ Ho: Constant varianceBreusch-Pagan / Cook-Weisberg test for heteroskedasticity