Ranjini Thaver Department of Economics, Stetson UniversityInternational Business: Research, Teaching and Practice 2012 6(1) 62 Congress (ANC). At the same time, to counter international

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International Business: Research, Teaching and Practice 2012 6(1)

WHAT DRIVES SOUTH AFRICA’S DISAGGREGATED IMPORT DEMAND FUNCTION WITH TANZANIA?

AN EMPIRICAL ANALYSIS

Ranjini Thaver*∗

Department of Economics, Stetson University 421 N Woodland Blvd, Deland, FL 32720

This empirical study investigates the behavior of South African imports from Tanzania during the period 1980-2010 utilizing cointegration analysis and the Error Correction Model developed by Pesaran, Shin, and Smith (2001). Results indicate that a long run stable relationship between imports and its independent variables exists. Estimates of the long-run and short-run partial elasticities of imports with respect to relative prices, real foreign reserves, exchange rate volatility, consumption expenditure, investment, and exports meet theoretical expectations and are mostly significant. The results of two dummy variables employed to capture the impact of apartheid (1980-1994) and the post-apartheid commitment to increase trade with other African countries (1996-2010) reveal that apartheid negatively drove imports, while the policy to increase trade has had a positive but inelastic impact on imports from Tanzania. To increase south-south trade and strengthen its own economy and that of Tanzania, we suggest that South Africa increases trade with Tanzania.

∗ E-mail: [email protected] Telephone: (386) 822-7573

Thaver South Afr i ca ’ s Impor t Demand Funct ion wi th Tanzania

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Key Words: South Africa, Tanzania, disaggregated import demand, apartheid, intra-African trade, volatility, elasticity, cointegration

INTRODUCTION South Africa has successfully climbed the ranks of other growth-dynamic emerging economies such as Brazil and India in the global economy. As Africa’s economic giant, it has been characterized as a middle power in international economics with a significant trade and growth impact on surrounding economies. As the economy works towards increasing its economic footprint on the African continent, an understanding of its trade functions with other African countries is imperative. So far, however, such research is sparse at best, with estimates of only a few African countries’ import and export demand functions existing. This study seeks to fill this void in the literature. Our objective is to investigate South Africa’s disaggregated import demand function and its associated long run and short run dynamics with Tanzania for the period 1980-2010. This import demand function is estimated using the bounds testing approach to cointegration and the unrestricted error-correction model. To our knowledge, this study is the only one of its kind to date, and will provide fodder for further research. We proceed in the next section with a brief history of South Africa, after which we review the literature on import demand functions. We then specify our theoretical model and explain the data used for estimation. Thereafter we explain and discuss the empirical results, and the final section concludes the paper with suggestions for future studies.

A BRIEF HISTORY OF SOUTH AFRICA South Africa, like most other developing countries, suffers from serious economic problems associated with its dependence on the imports of capital goods, declining exports, increased imports from the west, high unemployment rates, falling foreign reserves, and balance of payments constrictions (Ngandu, 2008, 2009; Saayman, 2010; Truett & Truett, 2003). However, while other developing countries have suffered these problems either because of their colonial heritage or a lack of resources (Gumede, 2000; Razafimahefa & Hamori, 2005), South Africa’s problems emanate primarily from the rigidities imposed by the apartheid state (Liu & Saal, 2001; Thompson, 2000; Truett & Truett, 2003). The apartheid era spanned the period 1948-1994, and the mineral-rich economy thrived at first. However, by the 1970s the economy began to stagnate mostly because of the inefficiencies resulting from the distorted allocation of its resources to service the racialized socio-economic structures of accumulation (Edwards, 2001; Truett & Truett, 2003) to benefit one race group at the expense of the other race groups. In this racialized economy, South Africa stymied trade relations with most of its African neighbors, in particular Tanzania, not least because the latter supported South Africa’s then outlawed African National

Internat iona l Bus iness : Research , Teach ing and Prac t i c e 2012 6(1)

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Congress (ANC). At the same time, to counter international sanctions against the country, the apartheid government reinforced its economic stagnation (Thompson, 2000) by creating further economic rigidities through import substitution industries (ISI), high import tariffs, and subsidies for export promoting industries (Liu & Saal, 2001; Ngandu, 2009; Truett & Truett, 2003). Thus by the 1990s, ISI accounted for 9.7% of GDP, gross domestic private investment grew negatively at -12.5%, GDP growth rates were anemic, and inflation rates were in the double-digits (World Bank, 2011). The end of the apartheid era brought with it a change in South Africa’s political and economic structures. The new post-apartheid government began to recreate an open economy with the help of international institutions and governments (Department of Trade and Industry, 2011; Edwards, 2001; Truett & Truett, 2003; World Bank, 2011). The new government implemented a series of strategic trade liberalization policies, among them, promoting privatization, loosening exchange controls, reducing tariffs and export subsidies, and encouraging intra-African trade (Edwards, 2001; Kabundi, 2009; Saayman, 2010). By 2007, real GDP growth reached 5.6%, and by 2010 inflation decreased to 4.3%, private investment dramatically increased to 22.6%, and exports increased to 27% of GDP (International Monetary Fund, 2011; World Bank, 2011). Imports grew remarkably at a growth rate of 8.6% between 1995 and 2008 and by 2010 comprised 28% of GDP (World Bank, 2011). South Africa also recorded its first ever budget surplus in history, helping it contain its external debt to 26% of GDP, which was lower than other similarly developing countries (Statistics South Africa, 2011). Trade with other African countries has become a part of post-apartheid South Africa’s commitment to economic growth and African development beyond poverty. However, as seen in Table 1, imports from other African countries comprised only 7.5% of total imports in 2010.

Table 1. Major sources of South African imports, 2010.

Region Value of Imports (Millions of Rands)

Share of Total Imports (%)

Asia 260,023 44.5 Europe 199,273 34.1 Americas 69,839 12.0 Africa 43,931 7.5 Pacific 11,124 1.9

Tanzania is South Africa’s fourth largest African trading partner, so it is critical to estimate its import demand function with Tanzania. By understanding the different sectoral contributions to imports, South Africa will develop a greater analytical insight into its imports from other countries, and its exports to Tanzania


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and to other countries, and make appropriate policy recommendations that affect its balance of payments. For the same reason, Tanzania, as it emerges as one of the fastest growing emerging economies in Africa (Read & Parton, 2009; Yedder, 2010), will better understand its trade relations with South Africa and thereby conduct policies to benefit from this trade in the short and long runs.

LITERATURE REVIEW Given the importance of understanding how import demand changes with varying economic and political conditions, numerous studies have estimated the import demand functions of countries on all continents. Earlier studies, focusing on developed countries, used ordinary least squares (OLS) methods to estimate a country’s import demand function (Gafar, 1995; Giovanneti, 1989; Thursby, 1988). For example, Giovanneti showed that consumption, investments, and exports significantly affected Italy’s import demand. Gafar, in estimating the demand for imports of Jamaica, Guyana, and Trinidad, using OLS also found that income elasticity was positive and price elasticity was negative. However, researchers have questioned the use of OLS methods in analyzing import demand functions precisely because this method assumes that the time series data used in such estimates are stationary (Thursby, 1988). However, because macroeconomic time series are typically non-stationary, as observed by the high serial correlation between successive observations, results from OLS studies may cause serious spurious regression problems (Modeste, 2011) leading to unreliable results. To overcome these problems, the last decade has witnessed a surge of studies employing refined econometrics methods, among them cointegration analysis, to estimate the import demand function for different countries. Razafimahefa and Hamori (2005) for example, in comparing the aggregate import demand function of Madagascar with Mauritius for the period 1960-2000, found that Madagascar’s long-run income elasticity is higher than for Mauritius. At the same time, their long-run relative price elasticities were almost equal and highly elastic, demonstrating that Madagascar was more import-dependent than Mauritius. Hibbert, Thaver, and Hutchinson (2012) investigated Jamaica’s aggregate import demand function with the United States and the United Kingdom from 1996 to 2010. In examining Jamaica’s imports from the US, they found that income had a lower negative elasticity in the short run than the long run, whereas relative prices were more elastic in the short run. In Jamaica’s trade with the UK, GDP was less elastic in the short run than in the long run, while relative prices adjusted much faster in the short run. Their study concluded that tight monetary policy significantly affected Jamaica’s import demand function with the UK, but not with the US. Narayan and Narayan (2005) approximated a disaggregated import demand model for Fiji using relative prices, consumption, investment, and export variables for the period 1970 to 2000. They found that in the long- and short-run, consumption, investment, and exports had an inelastic and positive impact on import demand. However, relative price had an inelastic impact on imports,


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reflecting a dependence on imports. Dutta and Ahmed (2004) determined the long-run aggregate import demand function for Bangladesh from 1974 to 1994. Drawing on two different error correction models, they found a unique long-run relationship among quantities of imports, import prices, GDP, and foreign reserves. However, while both models conveyed statistically significant results, the second model revealed a slower rate of adjustment and hence a prolonged period of disequilibrium in the markets before attaining long-run equilibrium. Tsionas and Christopoulos (2004) examined the import demand function of five industrial countries including France, Italy, the Netherlands, the UK, and the US. Their results revealed significant effects from relative prices and incomes, as well as significant short-run effects from temporary shocks. Matsubayashi and Hamori (2003), using quarterly data for different G7 countries in different periods under the flexible exchange rate system, analyzed the stability of the aggregate import demand function for these countries. Results indicated no stable cointegrating relation between real import, real GDP and relative import price for all G7 countries. Upon modifying their study to factor structural changes, results became significant for France and Germany, but not for the other countries. Very few efforts have been made to estimate aggregate import demand functions for Sub-Saharan African countries. Among them, Akinlo (2008) employed a translog cost function to examine the substitution relations among capital, labor, and imports in Nigeria. Results reveal that domestic capital served as a substitute for both labor and imports. However, capital elasticity values decreased over time making a reduction in import prices less significant on capital demand than in the past. Gumede (2000) studied the import demand function for South Africa from 1972-1997. His results revealed a long-run significant income elasticity of import demand, but short-run elasticities were less significant. Thaver and Ekanayake (2010) estimated South Africa’s aggregate import demand function from 1950 to 2008, explicitly investigating the impact of apartheid, in particular international sanctions against the apartheid government, on South Africa’s aggregate imports. Their results revealed that imports depended positively on the levels of domestic economic activity and foreign exchange reserves but negatively on relative prices. However, contrary to expectations, apartheid influenced its import demand function negatively in the short run only, while international sanctions affected imports positively in the short-run, but negatively in the long-run. The objective of the current study is to estimate South Africa’s disaggregated import demand function with a specific African country, Tanzania.

THEORETICAL MODEL AND DATA SOURCES South Africa’s long-run disaggregated import demand function with Tanzania, using the single-equation technique appropriate for its status as a small price-taking economy, may be specified as:


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𝑙𝑛𝑀! = 𝛽! + 𝛽!𝑙𝑛𝑅𝑃! + 𝛽!𝑙𝑛𝐹𝑅! + 𝛽!𝑙𝑛𝑉𝑂𝐿! + 𝛽!𝑙𝑛𝐶𝐺! + 𝛽!𝑙𝑛𝐼𝑁𝑉 + 𝛽!𝑙𝑛𝐸𝑋𝑃! + 𝛽!𝐷!! + 𝛽!𝐷!! + 𝜀! (1) In Equation (1), ln, M, t, RP, FR, VOL, CG, INV, EXP, and ε denote respectively, the natural logarithm, real import volume, time, relative price of imports, real foreign reserves, exchange rate volatility, the sum of real government and private consumption expenditures, real investment, real exports, and the white noise. Two dummy variables are included in the model to capture political events and trade-conducive policy during the period under study. D1t represents trade during South Africa’s apartheid era (1980-1994) to capture the effects of apartheid on import demand, and D2t captures the impact of post-apartheid South Africa’s policy (1996-2010) to increase trade with other African countries, in this case, Tanzania. In Equation (1), RP is calculated as the ratio of import price to domestic price as measured by each country’s CPI. According to economic theory, β1 is predicted to be negative. While FR does not appear in the traditional import demand function, more recent studies have concluded that it is an important determinant of imports for developing countries (Hoque & Yusop, 2010; Thaver & Ekanayake, 2010), so we include it in our model. Since higher foreign reserves encourage imports, we expect that β2 > 0. On the other hand, the effect of VOL

on imports has been found to be empirically and theoretically ambiguous (e.g., Bredin, Fontas & Murphy, 2003), so β3 could be either positive or negative. To convert CG, INV, and EXP into real terms, we divide each by South Africa’s GDP deflator (2005 = 100). In accord with economic theory, we assume that each has a positive impact on import demand, so that β4, β5, and β6 are predicted to be positive. The expected signs of β1, β2, β4, β5, and β6 are borne out in empirical results by numerous studies, among them, Akinlo (2008), Gumede (2000), Hoque and Yusop (2010), Matsubayashi and Hamori (2003), Mwega (1993), Narayan and Narayan (2005), Razafimahefa and Hamori (2005), Senhadji (1998); Tang (2002; 2004) and Thaver and Ekanayake (2010). The last two explanatory variables are dummy variables. The first dummy variable, D1t, represents the era of apartheid in South Africa and is defined to take the value 1 between 1980 and 1994 and 0 otherwise. The second dummy variable, D2t, represents the impact of South Africa’s policy of increasing intra-African trade on trade with Tanzania and takes the value 0 for years between 1980 and 1996 and 1 otherwise. We expect β7 to be negative and β8 to be positive and elastic. We employ the bounds test model developed by Pesaran, Shin and Smith (2001) for our cointegration analysis because of its three advantages over other models. First, it can be applied whether the regressors are purely I(0), purely I(1), or mutually cointegrated. Second, the model bypasses the process of determining the order of integration of the underlying regressors prior to testing the existence


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of a level relationship between two variables (Pesaran et al., 2001). Third, this method is robust for small and finite samples (Tang, 2002; 2003; 2004). In estimating the long-run model outlined by Equation (1), the model will distinguish short-run dynamics from the long-run effects. Therefore, Equation (1), following Pesaran et al. (2001), must be specified in an error-correction model (ECM) format, as in Equation (2) below:

∆𝑙𝑛𝑀! = 𝛼! + 𝛽!∆𝑙𝑛𝑀!!! +!

!!!

𝛿!∆𝑙𝑛𝑅𝑃!!! +!

!!!

𝜂!∆𝑙𝑛𝐹𝑅!!! +!

!!!

𝛾!∆𝑙𝑛𝑉𝑂𝐿!!! +!

!!!

𝜓!∆𝑙𝑛𝐶𝐺!!! +!

!!!

𝜗!∆𝑙𝑛𝐼𝑁𝑉!!! +!

!!!

𝜉!∆𝑙𝑛𝐸𝑋𝑃!!! +!

!!!

𝛼!𝐷!! + 𝛼!𝐷!! + 𝜆!𝑀!!! + 𝜆!𝑅𝑃!!! + 𝜆!𝐹𝑅!!! + 𝜆!𝑉𝑂𝐿!!! + 𝜆!𝐶𝐺!!! + 𝜆!𝐼𝑁𝑉!!! + 𝜆!𝐸𝑋𝑃!!! + 𝜔! (2) Except for the first difference operator, �, in Equation (2), all variables have been defined as previously. Equation (2) undergoes two procedural steps, the first employing the Wald test for the lagged level variables to test for the joint significance of the no cointegration hypothesis, all 𝜆=0, against the alternative hypothesis of cointegration, all 𝜆≠0. Pesaran, Shin and Smith (2001) provide two sets of critical values for a given significance level with and without a time trend. One assumes that the variables are I(0), and the other assumes that the variables are I(1). If the computed F-value exceeds the upper critical bounds value, H0 is rejected, signaling cointegration among the variables, whereas if the computed F-value falls below the critical bounds value, we fail to reject H0. If the computed F-statistic falls within the bounds, the results are inconclusive. Once a cointegration relationship has been established, the second step involves estimating the long-run coefficients of the cointegrated model and the corresponding short-run dynamics, or ECM. The lagged error correction term (ECMt-1) in the ECM model is important for the cointegrated system as it allows for adjustment back to long run equilibrium after a shock in the system in the previous time period. Since we use quarterly data, the maximum number of lags equals 4. Equation (2) specifies that real imports are influenced and explained by its past values. From the estimation of ECMs, the long-run elasticities are the negative of the coefficient of one lagged explanatory variable divided by the coefficient of one lagged dependent variable (Bardsen, 1989). Thus for example, the long-run relative price and foreign reserves elasticity are (𝜆2/𝜆1) and (𝜆3/𝜆1)


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respectively. The short-run effects are captured by the coefficients of the first-differenced variables in Equation (2). To estimate our model, we used the International Monetary Fund’s International Financial Statistics Yearbook (2011), and Direction of Trade Statistics (2011). We collected quarterly data on nominal imports, the import price index, real GDP, foreign exchange reserves, and the domestic price index from January 1980 to December 2010. Nominal imports in Rands were deflated by South Africa’s import price index (2005 = 100) to obtain the real import variable. To convert CGt, INVt,, and EXPt into real terms, we divided each by South Africa’s GDP deflator (2005 = 100). The relative price of imports series was calculated as the ratio of Tanzania’s to South Africa’s consumer price index, CPI (2005 = 100). To obtain the real foreign reserves series, we deflated the nominal foreign reserves series by the GDP deflator.

EMPIRICAL RESULTS COINTEGRATION AMONG VARIABLES

Table 2 presents the bounds test results of cointegration for the disaggregated imports of South Africa from Tanzania. The computed F-statistic (264.35) is higher than Pesaran, Shin and Smith’s (2001) upper bound critical value of 4.26 at the 1% level, so that the null hypothesis of no cointegration is not accepted, and a unique cointegration relationship between real imports and its determinants exists in our model. That is, import demand is a function of relative prices, foreign reserves, exchange rate volatility, private and public consumption, investment, and exports. The rejection of the null hypothesis allows us to progress to our next procedural step, namely, to estimate the associated long- and short-run partial elasticities of the independent variables.

Table 2. F-test results for cointegration between import demand and its determinants.

Critical value bounds of the F-‐statistic: Intercept and no trend

10% level 5% level 1% level

k I(0) I(1) I(0) I(1) I(0) I(1)

7 2.03 3.13 2.32 3.50 2.96 4.26

Calculated F-‐statistic: 𝐹!(𝑀|𝐹𝑅,𝑅𝑃,𝑉𝑂𝐿,𝐶𝐺, 𝐼𝑁𝑉,𝐸𝑋𝑃) 264.353***

Note: This table shows the results of the ARDL bounds testing for cointegration. The Critical values are taken from Pesaran, Shin and Smith (2001), Table CI(iii) Case III, p. 300). k is the number of regressors. *** indicates the statistical significance at the 1% level.


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LONG-RUN PARTIAL ELASTICITIES OF REGRESSORS TO IMPORT DEMAND Having observed a long-run cointegrated relationship between import demand and its regressors exist, we next estimate the long run elasticities of the explanatory variables based on the ARDL model:

𝑙𝑛𝑀! = 𝛼! + 𝛽!𝑙𝑛𝑀!!! +!

!!!

𝛿!𝑙𝑛𝑅𝑃!!! +!

!!!

𝜂!𝑙𝑛𝐹𝑅!!! +!

!!!

𝛾!𝑙𝑛𝑉𝑂𝐿!!! +!

!!!

𝜓!𝑙𝑛𝐶𝐺!!! +!

!!!

𝜗!𝑙𝑛𝐼𝑁𝑉!!! +!

!!!

𝜉!𝑙𝑛𝐸𝑋𝑃!!! +!

!!!

𝛼!𝐷!! + 𝛼!𝐷!! + 𝜔! (3) Table 3 presents long run partial elasticities of imports with respect to each independent variable. Table 3. Long-run elasticities for South Africa’s import function with Tanzania.

Dependent variable: ln Mt Explanatory Variables Coefficient/Partial Elasticity T-‐Statistic

constant -‐34.99*** -‐5.47

ln FRt 0.22** 2.31

ln RPt -‐1.00*** -‐3.50

ln VOLt 0.05 0.75

ln CGt 2.49*** 3.58

ln INVt -‐0.54* -‐1.91

ln EXPt 0.94** 3.25

D1t -‐14.24*** -‐42.08

D2t 0.39** 2.00

Adjusted R2 .95 Note: This table shows the long-run elasticities of South Africa’s estimated import demand function with Tanzania. ***, ** and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.


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The estimated equation derived from Table 3 is:

𝑙𝑛𝑀!

(𝑡 − 𝑟𝑎𝑡𝑖𝑜𝑠) = −34.99 + . 22𝑙𝑛𝐹𝑅(2.31) − 1.0𝑙𝑛𝑅𝑃(−3.50) +. 05𝑙𝑛𝑉𝑂𝐿0.75 + 2.49𝑙𝑛𝐶𝐺3.58 − . 54𝑙𝑛𝐼𝑁𝑉−1.91

+ . 94𝑙𝑛𝐸𝑋𝑃3.25 −

14.24𝐷!−42.08 +

. 38𝐷!(2.00)

Except for investment, the entire long-run estimated elasticities exhibit theoretically expected signs. Foreign reserves (.22), volatility (.05), and D2 (.39) display relative inelasticity indicating that a 1% change in any of these variables will lead to less than a 1% change in import demand. For example, a 1% increase in foreign reserves will yield a 0.22% increase in import demand. Relative prices (-1.00) and exports (.94) yield elasticity values that are at or near unity, so that for example, a 1% increase in exports will yield a 0.94% increase in import demand. However, import demand is highly responsive to apartheid (-14.24), illustrating that a small change in apartheid negatively affects import demand 14-fold. This finding contrasts with Thaver and Ekanayake (2010), who found that apartheid had an insignificant effect on its aggregate imports in the long run. Clearly, South Africa’s ideology of racism and apartheid and its confrontational relationship with Tanzania between 1980 and 1994 steered it away from trading with Tanzania to a greater degree than with other countries in general. Of interest also is the estimate (.39) on the policy of post-apartheid intra-African trade revealing that this policy has affected South Africa’s trade with Tanzania in a positive yet inelastic manner. Table 3 also reflects an adjusted R2 that is high at .95, indicating that 95% of South Africa’s imports from Tanzania are influenced by its determinants.

SHORT-RUN BEHAVIOR OF SOUTH AFRICA’S DISAGGREGATED IMPORT DEMAND FUNCTION WITH TANZANIA We next examine the dynamic short-run causality among the relevant variables by estimating the correction model specified in Equation (2). The results are shown in Table 4. Using Hendry’s general-to-specific method, four lags of each regressor are at first included, and then in subsequent step-by-step iterations, insignificant variables are eliminated. The resulting goodness of fit of the model is very high with an adjusted R2 of .948. Diagnostic test results such as Breusch–Godfrey serial correlation LM, augmented Dickey-Fuller, and Ramsey RESET in Table 4 are not significant, indicating that the estimated model is robust. It displays correct functional form, is serially uncorrelated, normally distributed and homoskedastic. Consequently, the results reported in Table 4 are valid for reliable interpretation. As seen in Table 4, the error correction term, ECMt-1, which gauges the rate at which import demand adapts to changes in the regressors in the short run before returning to its long run equilibrium level, conforms to theoretical


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expectations; it is statistically significant at the 1% level with the expected negative sign. The coefficient for ECMt-1 is -0.95 so that once the model in Equation (2) is shocked by changes in one of the import demand determinants, convergence to equilibrium is very rapid with 95% of the adjustment occurring in the first year. Since the apartheid dummy variable is the most influential of all independent variables in Table 4, we can conclude that South Africa’s discordant political relationship with Tanzania mostly drove the short-run adjustment process and the long run import demand relationship between the two countries. This result also contradicts previous findings that apartheid in general did not have a significant impact on South Africa’s aggregate import demand function in the long run (Thaver and Ekanayake, 2010). Table 4. Estimated Error-Correction Model and diagnostic tests for South Africa’s import demand function with Tanzania.

Dependent variable: Δ ln Mt

Explanatory variable Coefficient Std. Error t-‐ statistic

Intercept 0.00 0.08 0.00 Δln Mt-‐2 -‐0.04* 0.02 -‐1.97 Δln FR 0.15 0.09 1.61 Δln VOLt-‐1 -‐0.15** 0.06 -‐2.58 Δln RPt-‐2 -‐0.43 0.63 -‐0.67 Δln CGt-‐1 -‐3.09* 1.63 1.90 Δln INVt-‐1 0.66** 0.29 2.30 Δln EXP 1.32*** 0.39 3.39 D1t -‐13.47*** 0.30 -‐45.41 D2t 0.37*** 0.09 3.92 ECMt-‐1 -‐0.95*** 0.02 -‐44.28

Diagnostics:

R2 0.9528

Adjusted R2 0.9479

Augmented Dickey Fuller Test -‐1.65 p-‐value: 0.72

Breusch-‐Godfrey Test 1.63 p-‐value: 0.17

RESET test 2.19 p-‐value: 0.12

***, **and * indicate statistical significance at the 1%, 5%, and 10% level, respectively.

Further, as can be gleaned from Table 4, most partial elasticities are significant in the short run, as they are in the long run. Moreover, the second import lag (-.04), foreign reserves (.15), volatility (-.15), relative prices (-.43), and


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investment (.66) all affect South Africa’s imports from Tanzania, but in an inelastic manner; or in other words, less than proportionally. However, exports (1.32) and consumption (-3.09) have an elastic response to import demand. In the short-run, most elasticities meet theoretical expectations, except for consumption, and this may be due to South Africa’s increasing ability to supply consumption goods domestically, yielding a negative relationship between consumption and import demand. The results in Table 4 indicate very clearly that South Africa is not as integrated with the Tanzanian economy, as it is with other economies. The short run estimates for the dummy variables are both significant at the 1% level. D1t parallels the long-run estimates, showing that the strong inverse relationship between imports and apartheid is a steady one in the case of trade between South Africa and Tanzania. And this is a reasonable conjecture, given that Tanzania served as the host country for the outlawed African National Congress (ANC) during the apartheid era. Interestingly, D2t while significant and positive, as expected, is inelastic—imports have changed less than proportionally to the policy of enhancing intra-African trade, in this case, with Tanzania. This is somewhat surprising, given that the ANC became post-apartheid South Africa’s governing political body, but may underscore the limits of imports from a country that is one of the poorest and least developed in the world. Yet, because Tanzania is now one of the fastest growing emerging economies in the world, these results point to the potential for greater trade between the two countries.

CONCLUSIONS, LIMITATIONS, AND SUGGESTIONS FOR FUTURE RESEARCH

In this paper, we estimated South Africa’s disaggregated import demand function with Tanzania between 1980 and 2010 using the bounds testing approach to cointegration and the error correction model. Our results suggest that a cointegration relationship between imports and its regressors, namely relative prices, foreign reserves, exchange rate volatility, consumption, investment, and exports, exists. This cointegrated relationship enabled us to study the short-run and long run partial elasticities of South Africa’s import demand function with Tanzania. Our results reveal that most of the long run and short-run estimated elasticities exhibit theoretically expected signs, and they are mostly significant at the 1% level. Further, as revealed in the long-run and short-run, adjusted R2, foreign reserves, relative prices, volatility, consumption, investment, and exports, apartheid, and the policy of increasing intra-African trade explain South Africa’s import demand function very well. However, apartheid is the most dominant force driving its import function. At the same time, the policy of enhancing intra-African trade with Tanzania seems to influence the import demand function less substantially than hypothesized. This may be due to South Africa’s trade agreements with the Southern African Customs Union (SACU), the Southern African Development Community (SADC), the Common Market for Eastern and Southern Africa (COMESA), the East African Community (EAC), and the


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Economic Community of West African States (ECOWAS). Given our results, we suggest that South Africa, an African economic giant, and Tanzania, one of the fastest newly emerging economies, develop trade-enhancing policies that will mutually benefit both countries in terms of economic growth, macroeconomic stability, and their balance of trade. This may also contribute multiplicatively to the strengthening of regional trade, reducing their dependence on advanced economies for economic growth and development. Diagnostic tests reveal that the estimated import demand model is robust, so we may conclude that our results are valid and reliable. Further, the error correction model reflects a very rapid adjustment to equilibrium in the first year after shocks that disturb the function. To our knowledge, this study is the first attempt to estimate apartheid and post-apartheid South Africa’s disaggregated import demand function with Tanzania. The study is also consistent with other studies that demonstrate the superiority of a model that disaggregates GDP components because not all of its components are equally weighted in the import demand function. Our results demonstrate that this is indeed the case. Future studies may wish to emphasize an import demand function of specific commodities to understand South Africa’s comparative advantage in its import demand function with Tanzania and inform appropriate policy. In addition, studies focusing on South Africa’s import demand functions with intra-African free trade blocs such as SACU, SADC, COMESA, EAC, and ECOWAS, are critical to addressing how trade with these blocs have affected its trade with Tanzania. It may further inform scholars on the success of post-apartheid South Africa’s policies of fostering greater intra-African trade.

ACKNOWLEDGEMENTS I express my gratitude to Dr. Daniel Plante at Stetson University for providing access to his computer program in the open software, R, to run the econometrics model discussed in the paper. Access to this program allowed me to test the econometrics model with greater ease than if other commercially available econometrics packages were used. Thanks also to the three anonymous referees that reviewed this paper at the conference level and made constructive comments on it.


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