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- 1. 1 Abstract There is no consensus among economists about how export affects productivity. Some argue that export oriented economies are opened to foreign competition which may lead to closure of local industries once they cannot withstand the competition. Others argue that export brings about competition which improves efficiency and productivity. This paper tries to establish and test the relation between these two variables using Nigeria as a case study. In view of this both stationarity and cointegration tests are conducted and the results portray that the two have a long term relationship. It goes further to test for OLS where the result shows that export is a positive determinant of GDP. Again, the ECM test is conducted and the Error Mechanism Coefficient is found to be significant. 1.1 BACKGROUND OF THE STUDY Nigeria like many other Developing African countries started as agrarian economy. The agricultural produce of the early Nigeria include groundnuts, rubber, timber, cocoa, beans, palm kernel, hides and skin, to mention just few. These products as declared by Rano and Tsauni (2006) accounted for over 50 percent of Gross Domestic Product(GDP) and was the main source of export earning and public revenue. With the crude oil discovery in 1956 and its exploration in commercial quantity in 1958 however, the oil sector gradually became the dominant sector in the economy, and almost the sole source of export earning. For instance in 1970’s petroleum constituted of about 78 percent of Federal Government revenue and more than95 percent of export earning (World Bank, 2002). With the oil boom in the mid – 1970s (1973) however, the country’s foreign exchange earning raised immensely, which translated into higher economic growth, to the extent that there was no fear of expenditure in the part of government even on necessary issues. With the fall in oil prices in the late 1970s and early 1980s, there ware enormous macroeconomic problems which include Balance payment deficit high rate of unemployment, budget deficits, price instability and more importantly less, or even negative growth. These were the products of the overdependence on oil sector, to the extent that the economy had to borrow externally to sustain the huge deficit in government expenditure. These developments came at the stage when the manufacturing share in GDP was relatively
- 2. 2 very small at an average 5.6 percent for the last half decade, while its share in total export translated to merely non-noticeable figure of less than 1 percent for the same period. In response to these enormous problems Structural Adjustment Program (SAP) was introduced in 1986 in the country. This was to liberalize and diversify the economy. With SAP in place, several export promotion strategies and policies especially on manufacturing export were formulated, which include various incentives on export,Research and Development (R&D) etc. Despite this effort to improve and diversify export the outcomes were not recommended. This was because the share of manufacturing export remains so low in the total export earning as compared to the oil sector in particular or primary goods in general. Evidence shows that the share of manufacturing export as percentage of total export remains less than 1 percent up to year 2000, as compared to average level of other sub- Saharan African countries of 6.2 percent of more than 70 percent of Eastern Asian countries. This is the nature and trend of Nigeria’s export over decades as well as how, from experience, the fluctuations in the volume of the export affect the level of economic growth. This paper is divided into three parts. The first part presents the background of the study, followed by the objectives of the study. In the second part review of theoretical literatures is presented, after which the empirical test is presented. The last part concludes the paper and possible recommendations are made. 1.2 OBJECTIVES OF THE STUDY The objectives of this paper are as follows: - To test empirically the relationship between export of commodities and economic growth in Nigeria. In this case empirical data is collected and relationship between manufacturing export and the GDP is analyzed. - To analyze the problems facing export of commodities in Nigeria. - To propose solutions to the problems as my recommendations. 2.0
- 3. 3 3.0 LITERATURE REVIEW Exports are the goods and services produced in one country and sell to earn foreign exchange which can be used to purchase goods and services from another country, thus leading to specialization (Jafiya, 2004). Exports are of two broads categories. First, primary commodity exports comprising mainly agricultural produce and minerals. Second, manufacturing export, which include industrial finished and semi-finished goods. In the present day of growing interdependence among the world economies through the process of globalization and trade liberalization, no country can stand alone or live in isolation. This is because most, if not all, of trade and development theories show the certainty of increased productivity and welfare improvement once an economy engages in bilateral or multilateral trade. Equally important is the nature of the trade as well as the type of commodities that are traded. This is because as emphasized by Todaro and Smith (2009), African countries that engage mostly in the export of primary products (what they called primary-product export dependence) carries with it a degree of risk and uncertainty that few nations desire. This is important issue because despite strength since 2002, the long-term trend for prices of primary goods is downward, with the exception of mineral, ores and metals which witnessed a slide rise in 2003. Hence there is the need to diversify the export based of their economies to manufacturing export if they are to flourish. Evidence from Newly Industrialized Economies (NIEs) shows that the export of non- traditional products, semi-manufactured and manufactured goods are behind the success of such country like South Korea, Taiwan, Singapore, Hong Kong, Thailand, Brazil and Turkey. In spite, the recognized importance of export of manufactured goods in achieving economic growth, Nigeria like many other African countries still depends heavily on the export of primary goods which stands at 98 percent of the total export earnings in 2005 (Todaro and Smith, 2009). This menace coupled with her heavy reliance on the importation of manufactured consumer and capital goods to satisfy her rising consumption aspirations of the increasing population, and raw materials as well as machineries for its local industries results in Balance of Payment problem in the country, whereby, the payment made on imports is increasing as compared to the export receipts for goods and services.Being net export (Export
- 4. 4 less Import) one of the determinants of National Income, this tragedy of higher import with fluctuations in the volume of export affects income (GDP) adversely. Consider the table below: Selected Domestic and External Macroeconomic Indicators (1986-2003). YEAR % GROWTH IN GDP MANUFAC. GOODS (AS % OF GDP) MANUFAC. EXPORT(AS % OF TOTAL EXPORT) MANUFAC. IMPORT (AS % OF TOTAL IMPORT) CAPACITY UTILIZATION 1986 -3.70 9.00 0.40 19.3 38.8 1987 4.00 9.66 0.20 25.1 40.4 1988 13.9 9.79 0.29 23.1 42.4 1989 2.20 8.24 0.19 20.1 43.8 1990 4.90 8.19 0.20 22.0 40.3 1991 9.40 8.26 0.10 23.5 42.0 1992 -4.50 7.86 0.10 23.0 38.1 1993 -3.70 7.34 0.20 24.0 37.2 1994 -1.30 6.90 0.20 22.2 30.4 1995 -5.20 6.65 0.20 23.2 29.3 1996 0.80 6.48 0.20 28.1 32.5 1997 0.40 6.29 0.40 29.2 30.4 1998 -6.90 5.92 0.53 29.7 32.4 1999 3.40 4.73 0.34 29.4 35.9 2000 3.40 5.95 0.30 29.0 36.1 2001 7.00 5.95 0.84 29.0 39.6 2002 10.10 4.59 2.34 28.9 44.3 2003 5.7 4.08 1.38 23.8 46.2 Source: Rano and Tsauni 2006.
- 5. 5 The figures above reflect the weak nature of the Nigerian manufacturing export, which stand at less than 1 percent of total export throughout the period with exception of 2002 and 2003, despite the various measures introduced by the government to improve the export of the manufactured goods. These policies include minimum local raw materials utilization, Export Expansion Grant, establishing export processing zones, duty drawback scheme, to mention just few. For Nigeria not to be marginalized in the ongoing globalization process there is the need to develop the manufacturing sector towards increasing production, not only for domestic consumption but for export. Let us now look at the theoretical framework of the study, and later the empirical facts. 2.1-THEORETICAL LITERATURES: Most, if not all, international trade and development theories portray a positive relationship between the volume of trade and economic growth, right from classical comparative advantage model of David Ricardo, the neoclassical model of Heckscher and Ohlin, to the contemporary endogenous growth models. Although the various models assume that different factors cause the trade, but the end result portrays improvement in theoutput and welfare. Let us now examine some of these models to have solid theoretical framework. The Ricardian Model This model as developed by David Ricardo (1817) is based on some simplified assumptions. First, the models assumes that each country involve in the trade has a fixed endowment of resources, and all units of each particular resource are identical. Also, the factors of production are completely mobile between alternative uses within a country, thus, the prices of factors are also the same among these alternative uses. However, factors are immobile externally, that is, they do not move between countries.
- 6. 6 This model further employs labor theory of value, thus, the relative value of a commodity is based solely on its relative labor content. This implies that either other factors are not used in the production process or they are measured in terms of labor hours. It also assumes fixed level of technology for the country and full employment of resources, with constant cost of production, and there is no transportation cost both internally and externally. Again, the model assumes differences in the production function (Labor Productivity) in different countries that are involved in trade, with each production function depicting constant return to scale. And there is perfect competition in the countries so no government- imposed obstacles to economic activity. The model of Comparative Advantage as it is called asserts that “a country should specialize in the export of the commodities that it can produce at the lowest relative cost”. Germany may be able to produce cameras and cars as well as fruits and vegetables at lower absolute unit costs than Kenya, but because the commodity cost differences between countries are greater for the manufactured goods than for agricultural products, it will be to Germany’s advantage to specialized in the production of manufactured goods and exchange them for Kenya’s agricultural products, whereas Kenya which has absolute disadvantage in theproduction of both goods in relation to Germany may still benefit from trade with Germany if it will specialize in the production of agricultural produce which the absolute disadvantage is less than that of manufactured goods (Todaro 2009). It is this phenomenon of differences in comparative advantage that gives rise to beneficial trade even among the most unequal trading partners. However, there are contradicting views on the relationship between exports and productivity. Some argue that increase in export increases foreign competition, and this may have detrimental effect on growth of GDP, as it may lead to marginalization or even closureof factories (Van Biesbrock, 2003). On the other hand, some argue that growth of export brings about higher growth of GDP through educative process. For example, higher contact with foreign competitors as a result of export growth can motivate rapid technological changes and managerial know-how, and enhance efficiency. For instance, Nashimizu and Robinson
- 7. 7 (1994), accepted the hypothesis that export growth causes productivity growth in Japan, Turkey, Yugoslavia, and South Korea. They concluded that the larger the share of output that goes into exports the higher the productivity growth. These contradicting views are the reasons for conducting the empirical test using Nigeria as a case study. 2.2 EMPERICAL ANALYSIS: This part presents the empirical evaluation of the effect of manufacturing export on the Gross Domestic Product in Nigeria. METHODOLOGY: The study collects the time series annual data of the Nigerian GDP and the volume of exports from secondary source, for a period of 31 years (1979-2010). With this data a relationship is established between GDP and manufacturing exports using linear regression model. However, using OLS when variables are not stationary at level will result into spurious regression. This problem can be overcome by cointegration, which imply that even if the variables are not stationary at level, there may be a linear combination of them which is stationary. To avoid spurious analysis a unit root test is conducted on the individual data and then followed by cointegration test to see if there exists a long run relationship between the two. Although there are many approaches to cointegration , in this research , Engel-granger two step algorithm is used as follows: I. Conduct testing for integration (if the variables are integrated of the same order) ii. Conduct a cointegration test. The first thing before conducting any test is to show graphically how the variables behave:
- 8. 8 From the above graphs we can see that in both variables (export and GDP) there are time trends and drifts. ECONOMETRIC SPECIFICATION: The model is presented below: GDPt =a1 + a2EXPt + Ut Where GDP = Gross Domestic Product EXP = Total volume of export U = Random error term t = time period To linearize the data the natural logarithms of both GDP and exports are used. Our model is now LnGDPt = a1 + a2lnEXPt To avoid spurious analysis the Augmented Dickey Fuller Unit Root test is conducted here on the individual series. 0 40,000 80,000 120,000 160,000 200,000 240,000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 GDP 0 20,000 40,000 60,000 80,000 100,000 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 EXPORT
- 9. 9 Summary of ADF unit root tests output: LnEXP: 1. H0: lnEXP has a unit root (at level) 2. H0: lnEXP has a unit root (at 1st difference) Lag length=1, Unitroot test (ADF) for export VARIABLE AT LEVEL AT FIRST DIFFERENCE ADF test statistic -3.45649 -3.77563 critical value at 5% level -3.568379 -2.967767 Prob. 0.0628 0.0079 R-squared 0.414363 0.491133 D-stat. 2.287609 2.047781 LnGDP: 1. H0: lnGDP has a unit root (at level) 2. H0: lnGDP has a unit root (at first difference) Lag length=1, with constant, automatic based on AIC. Unitroot test (ADF) GDP Variables At level At first difference ADF test statistic -0.971195 -4.391258 Critical value at 5% level -3.562882 -2.963972 Prob. 0.9338 0.0016 R-squared 0.240701 0.407823 D-stat. 1.887325 1.997254 It is evident from the above that both lnGDP and lnEXP are not stationary at level, even if we include trend. However, both series are stationary at first difference (I (1)). That is, the first
- 10. 10 difference of the two series is integrated. This can be seen from the lower value of probability and increasing value of R2 as a measure of fitness. The variables are conitegrated of the same order which satisfied the condition for cointegration. Although we found the series to be integrated of the same order is important to further test for Cointegation between the two, to see whether they have any long term relationship. Summary of Cointegration test: We first obtain our residual series as: Ut= lnGDPt – a1 - a2lnEXPt And then we run ADF test on Residuals as our Augmented Engle-Granger test for cointegration: The result: H0: Ut has a unit test (not cointegrated). Lag length=1, Cointegration test VARIABLE STATISTIC ADF test statistic -2.132067 Critical value at 5% -1.952473 R-squared 0.153977 D-stat. 1.976642 To some extent at 5% level, we can accept that the series are cointegrated in the long term. So, although the series are stationary at level, however, we can conduct our simple OLS method, but the parameters explain long term relation not short term, because there is Random Walk in the short term.
- 11. 11 OLS estimates result summary: Let us now estimate: lnGDPt = a1 + a2lnEXPt H0: a1, a2 = 0 Longrun relationship of the variables VARIABLE COEFFICIENT STD. ERROR t-STAT. PROB. LnEXP 0.7178 0.0561 12.7879 0.00 a1 3.8362 0.5460 7.0266 0.00 R-SQURED 0.8450 D- stat 0.2344 From the data above we can see that all the parameters are statistically significant. Also we can easily see that the export elasticity of GDP is 0.63, showing the rate at which export determines GDP in Nigeria in the long term. The R2 value is 84% which shows the good of fitness of the estimated values of GDP. Error Correction Mechanism: It is also important to test for ECM to see whether or not a shock in GDP as a result of change in export in Nigeria could be restored to equilibrium. Summary of ECM result: LnGDPt= a1 + a2 lnEXPt + a3 RESIDt-1 Where a3 is the Error Correction coefficient. Ho: a3=0
- 12. 12 ECM shortrun relationship VARIABLES COEFFICIENT STD. ERROR t-STAT. PROB. RESIDt-1 -0.1499 0.0716 -2.0934 0.0455 lnEXP 0.4567 0.06311 7.2371 0.00 a1 0.0187 0.0208 0.9006 0.5293 R-SQUARED 0.6864 D-STAT 1.51065 It is evident that although at 5% level the error correction coefficient is not significant, but taking its 10% counter value the coefficient is significant. What it tells here is that if there is shock in GDP that results from change in export the process that the system will go back to equilibrium is only 14%. This is to say that the correction process is very slow. Besides, the coefficient is negative as it is expected. This implies that if there is negative shock the total mechanism will positive, and vice versa. 2.3- PROBLEMS OF EXPORT IN NIGERIA: Below are some major problems facing exports of commodities in Nigeria. 1. Overdependence on primary goods as the major source of export earning at the expense of manufactured goods. 2. Closely related to above problem is the vulnerability of the prices of primary exports as compared to its manufactured counter part. This as pointed by Prebisch-Singer thesis, that the terms of trade of primary exports has been declining. 3. Poor institutional settings is another problem. The political and economic institutions are weak. For instance, the banking institutions could provide the required capital for investment and support exports. 4. Poor infrastructures needed for the production of exportable goods are insufficient. Amenities such as good roads, stable electricity supply, to mention just two, are lacking. 5. Technological backwardness. Nigeria like many other poor African countries adopts obsolescent technology that could not support higher productivity.
- 13. 13 6. Low capacity utilization of the industries as highlighted in the figures presented in literature review is another problem. 7. High cost of production. This is because of two reasons. One, is the physical distance from cheaper foreign suppliers. Secondly, the domestic substitutes are more expensive. 4.0 CONCLUSION: The analysis in this study uses the Ordinary Least Square method to test whether export determines productivity in Nigeria. To avoid spurious analysis both unit roots and cointegration tests are used and found that although the individual series are stationary at first difference, but there exists a long term relationship between the two. The ECM test is also conducted to see how past, if there is shock in the system, equilibrium will be restored. The final result shows there is significant positive relationship between the two. Besides, the problems of exports in Nigeria are presented. Below are the policy recommendations of the paper. RECOMMENDATIONS: Below are the policy recommendations of the paper: 1. The export base has to be diversified to give emphasis to manufactured goods that have more or less stable terms of trade. 2. The basic infrastructures such as electricity have to be provided sufficiently, either by the government or private firms. 3. Financial institutions such as banks have to be strengthened through vibrantmonetary policies. This is to ensure enough investible capital. 4. Nigeria is rich both in terms of resources and agricultural produce. As such the locally based sources of raw materials should be strengthened, to avoid the use of relatively expensive foreign raw materials. 5. The industries should import and adopt a relatively modern technology, this is to improve efficiencyand capacity utilization. 6. Lastly, the primary goods such as crude oil should be processed within the country as oppose to the current situation, whereby the crude is exported. This will add value to it.
- 14. 14 REFERENCES Appleyard, D.R. and Field, Jr. A.J. (1998), International Economics: Theory and Policy, 3th Edition, Irwin McGraw-Hill, Boston. Elbadawi, Ibrahim et al (2006), Market Access, Suppliers Access, and Africa’s Manufactured Exports: A Firm Level Analysis, Journal of International Trade and Economic Development, Vol. 15, No. 4, 493-523, December 2006. Gujirati, D. N. and Porter, D.C. (2009), Basic Econometrics, 5th Edition, McGraw- Hill, Singapore. Jafiya, A. (2004), ‘Financing Export of Goods and Services and Ensuring Prompt Payment’, Paper delivered at National Seminar on Export, Organized by Africa Project Consult Kano. Jhingan, M. L. (2003), Economic Development and Planning, 35th Edition, Vrinda Publications. Kali, Raja et al (2007), Trade structure and Economic Growth, Journal of International Trade and Economic Development, Vol. 16, No. 2, 245-269, June 2007. Markusen, J. R. et al (1995), International Trade: Theory and Evidence, McGraw- Hill, Singapore. Ogunleye, E. O. and Ayeni, R. K. (2008), The link Between Export and Total Productivity: Evidence from Nigeria, International Research Journal of Finance and Economics, ISSN 1450-2887, Issue 22. Rano, S. A. and Tsauni, A. M. (2006), Topics on the Nigerian Economy, Department of Economics, Bayero University, Kano. Reinhart, C. M. and Wickham, p. (1994), Commodity Prices: Cyclical Weakness or Secular Decline? IMF Staff Papers 41. Sutcliff, T. (1997), Industrial Development, cited in Rano and Tsauni (2006). Todaro, M. P. and Smith, S. C. (2009), Economic Development, 10th Edition, Pearson Addison Wesley, Boston. World Bank (2000), Can Africa Claim the 21st Century, Washington DC, World Bank. World Bank (2002), ‘Group Interim Strategy Update on the Federal Republic of Nigeria’.
- 15. 15 APPENDIX 1: Nigerian Export and GDP (1979-2010). OBS GDP EXPORT 1979 47259.91 11728.08 1980 64201.79 18859.39 1981 59918.54 13499.65 1982 49763.41 8132.95 1983 34950.46 4757.553 1984 28182.54 4185.334 1985 28407.93 4573.223 1986 20210.79 3455.049 1987 23441.33 6706.566 1988 22847.73 5282.838 1989 23843.51 7795.46 1990 28472.47 12365.87 1991 27313.35 10165.15 1992 32710.37 13816.46 1993 21352.76 10061.68 1994 23663.39 9880.815 1995 28108.83 12448.76 1996 35299.15 16994.78 1997 36229.37 16285.65 1998 32143.82 10776.22 1999 34776.04 12831.53 2000 45983.6 24820.55 2001 47999.78 20637.14 2002 59116.85 18839.02 2003 67656.02 28890.97 2004 87845.42 38609.37 2005 112248.6 52237.63 2006 146869 63403.73 2007 165920.9 66616.55 2008 212079.7 92200.79 2009 226892.9 96536.43 2010 232079.7 98240.69 Source: World bank WDI data base 2010
- 16. 16 APPENDIX 2 Unit root gdp Null Hypothesis:LNGDP has a unit root Exogenous:Constant,Linear Trend Lag Length: 0 (Automatic - based on SIC, maxlag=7) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -0.971195 0.9338 Test critical values: 1% level -4.284580 5% level -3.562882 10% level -3.215267 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation DependentVariable:D(LNGDP) Method: LeastSquares Date: 12/15/11 Time:20:27 Sample (adjusted):2 32 Included observations:31 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNGDP(-1) -0.058674 0.060414 -0.971195 0.3398 C 0.476304 0.605329 0.786852 0.4380 @TREND(1) 0.012809 0.004590 2.790413 0.0094 R-squared 0.240701 Mean dependentvar 0.051336 Adjusted R-squared 0.186466 S.D. dependent var 0.195462 S.E. of regression 0.176299 Akaike info criterion -0.541505 Sum squared resid 0.870277 Schwarz criterion -0.402732 Log likelihood 11.39333 Hannan-Quinn criter. -0.496269 F-statistic 4.438067 Durbin-Watson stat 1.887325 Prob(F-statistic) 0.021173 Null Hypothesis:D(LNGDP) has a unit root Exogenous:Constant Lag Length: 0 (Automatic - based on SIC, maxlag=7) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -4.391258 0.0016 Test critical values: 1% level -3.670170 5% level -2.963972 10% level -2.621007 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation DependentVariable:D(LNGDP,2)
- 17. 17 Method: LeastSquares Date: 12/15/11 Time:20:28 Sample (adjusted):3 32 Included observations:30 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNGDP(-1)) -0.785567 0.178894 -4.391258 0.0001 C 0.031621 0.036184 0.873906 0.3896 R-squared 0.407823 Mean dependentvar -0.009459 Adjusted R-squared 0.386674 S.D. dependentvar 0.244461 S.E. of regression 0.191450 Akaike info criterion -0.404040 Sum squared resid 1.026287 Schwarz criterion -0.310627 Log likelihood 8.060598 Hannan-Quinn criter. -0.374156 F-statistic 19.28314 Durbin-Watson stat 1.997254 Prob(F-statistic) 0.000146 APPENDIX 3 export unit root Null Hypothesis:LNEXPORT has a unit root Exogenous:Constant,Linear Trend Lag Length: 1 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.456493 0.0628 Test critical values: 1% level -4.296729 5% level -3.568379 10% level -3.218382 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation DependentVariable:D(LNEXPORT) Method: LeastSquares Date: 12/15/11 Time:21:31 Sample (adjusted):3 32 Included observations:30 after adjustments Variable Coefficient Std. Error t-Statistic Prob. LNEXPORT(-1) -0.351910 0.101811 -3.456493 0.0019 D(LNEXPORT(-1)) 0.026000 0.158267 0.164282 0.8708 C 2.720825 0.846006 3.216082 0.0035 @TREND(1) 0.043758 0.010206 4.287406 0.0002 R-squared 0.414363 Mean dependentvar 0.055014 Adjusted R-squared 0.346790 S.D. dependentvar 0.326943 S.E. of regression 0.264240 Akaike info criterion 0.299648 Sum squared resid 1.815393 Schwarz criterion 0.486474 Log likelihood -0.494722 Hannan-Quinn criter. 0.359415 F-statistic 6.132037 Durbin-Watson stat 2.287609 Prob(F-statistic) 0.002692
- 18. 18 Null Hypothesis:D(LNEXPORT) has a unit root Exogenous:Constant Lag Length: 1 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -3.775613 0.0079 Test critical values: 1% level -3.679322 5% level -2.967767 10% level -2.622989 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation DependentVariable:D(LNEXPORT,2) Method: LeastSquares Date: 12/15/11 Time:20:29 Sample (adjusted):4 32 Included observations:29 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(LNEXPORT(-1)) -1.007515 0.266848 -3.775613 0.0008 D(LNEXPORT(-1),2) 0.053980 0.185618 0.290811 0.7735 C 0.069662 0.064596 1.078418 0.2908 R-squared 0.491133 Mean dependentvar 0.012133 Adjusted R-squared 0.451989 S.D. dependentvar 0.453221 S.E. of regression 0.335509 Akaike info criterion 0.751362 Sum squared resid 2.926726 Schwarz criterion 0.892807 Log likelihood -7.894752 Hannan-Quinn criter. 0.795661 F-statistic 12.54693 Durbin-Watson stat 2.047781 Prob(F-statistic) 0.000153 APPENDIX 4 long run rship coingration testand shortrun ECM test DependentVariable:LNGDP Method: LeastSquares Date: 12/15/11 Time:20:30 Sample:1 32 Included observations:32 Variable Coefficient Std. Error t-Statistic Prob. C 3.836223 0.545992 7.026151 0.0000 LNEXPORT 0.717831 0.056134 12.78791 0.0000 R-squared 0.844986 Mean dependentvar 10.78637 Adjusted R-squared 0.839819 S.D. dependentvar 0.737404 S.E. of regression 0.295129 Akaike info criterion 0.457652
- 19. 19 Sum squared resid 2.613031 Schwarz criterion 0.549260 Log likelihood -5.322429 Hannan-Quinn criter. 0.488017 F-statistic 163.5306 Durbin-Watson stat 0.234422 Prob(F-statistic) 0.000000 cointegration test Null Hypothesis:RESID02 has a unit root Exogenous:None Lag Length: 1 (Fixed) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -2.132067 0.0338 Test critical values: 1% level -2.644302 5% level -1.952473 10% level -1.610211 *MacKinnon (1996) one-sided p-values. Augmented Dickey-Fuller Test Equation DependentVariable:D(RESID02) Method: LeastSquares Date: 12/15/11 Time:20:34 Sample (adjusted):3 32 Included observations:30 after adjustments Variable Coefficient Std. Error t-Statistic Prob. RESID02(-1) -0.286061 0.134171 -2.132067 0.0419 D(RESID02(-1)) 0.000128 0.184573 0.000691 0.9995 R-squared 0.153977 Mean dependentvar -220.6994 Adjusted R-squared 0.123762 S.D. dependentvar 7295.385 S.E. of regression 6829.034 Akaike info criterion 20.56009 Sum squared resid 1.31E+09 Schwarz criterion 20.65351 Log likelihood -306.4014 Hannan-Quinn criter. 20.58998 Durbin-Watson stat 1.976642 E ECM (shortrun) DependentVariable:D(LNGDP) Method: LeastSquares Date: 12/15/11 Time:21:25 Sample (adjusted):2 32 Included observations:31 after adjustments Variable Coefficient Std. Error t-Statistic Prob. C 0.018731 0.020798 0.900605 0.3755 D(LNEXPORT) 0.456716 0.063108 7.237062 0.0000 RESID01(-1) -0.149928 0.071619 -2.093413 0.0455
- 20. 20 R-squared 0.686389 Mean dependentvar 0.051336 Adjusted R-squared 0.663988 S.D. dependentvar 0.195462 S.E. of regression 0.113302 Akaike info criterion -1.425746 Sum squared resid 0.359449 Schwarz criterion -1.286973 Log likelihood 25.09906 Hannan-Quinn criter. -1.380510 F-statistic 30.64124 Durbin-Watson stat 1.510645 Prob(F-statistic) 0.000000 APPENDIX 5 descriptive statisticsof the variables EXPORT GDP Mean 25488.62 65868.44 Median 13165.59 35764.26 Maximum 98240.69 232079.7 Minimum 3455.049 20210.79 Std. Dev. 27925.55 62195.57 Skewness 1.645346 1.711519 Kurtosis 4.405698 4.625285 Jarque-Bera 17.07286 19.14498 Probability 0.000196 0.000070 Sum 815635.9 2107790. Sum Sq. Dev. 2.42E+10 1.20E+11 Observations 32 32

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