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iv TITLE PAGE TRADE OPENNESS AND OUTPUT GROWTH IN NIGERIA: AN ECONOMETRIC ANALYSIS (1970-2007) A PROJECT SUBMITTED IN PARTIAL FULFILIMENT FOR THE COURSE REQUIREMENT FOR THE AWARD OF BACHELOR OF SCIENCE (B.Sc) DEGREE IN ECONOMICS BY ALUM EMEKA .J. EC/2006/259 DEPARTMENT OF ECONOMICS FACULTY OF MANAGEMENT AND SOCIAL SCIENCES CARITAS UNIVERSITY AMORJI-NIKE EMENE, ENUGU AUGUST 2010 iv APPROVAL PAGE This is to certify that this project was undertaken by Alum Emeka Japhet and duly supervised and approved as having met the requirement for the award of Bachelors of Science (B.Sc.) degree in the department of economics, Caritas University. Amorji-Nike, Enugu State. ……………………………. Ojike, R. O. Project Supervisor Date:…………………. ……………………….. Peter. C. Onwudinjo Esq Head of Department Date:………………… …………………………….. Dr. C.C. Umeh Dean, Faculty of Management And Social Science Date:………………… …………………………. External Examiner Date:……………….. iv DEDICATION To God Almighty the Giver of wisdom and the creator of heaven and earth. iv ACKNOWLEDGMENT To my father and mother Mr. and Mrs. Alex, F. Alum, To BABA God in Heaven. To my supervisor Mr. Ojike R.O. for his kind supervision throughout the course of this project. Thank you very much. To my H.O.D Peter C. Onwudinjo Esq, all my lecturers, Mrs. Okonkwo P.C, Mr. Agu S.V, Mr. Ojike R.O, Dr Umeadi C.C, Dr Asogwa, Mr. Ugwu, Prof. Udabah and Prof. Onah for their inspiration throughout the course of my studies. May God Almighty bless them. To my brothers and sister Eloka, Uchenna and Mercedes. To my siblings Brother Prince, Brother Dan, Brother Godwin, and to all my Aunties, And all the Alum Families. To my friends Aduaka Hilary (Pojas), Ejike Ezeorah (Madukanaya), Joseph. O. Faithful (Kpanko), David (Night Walker), David Eme Njokwu (H.O.D), Chibuike, Kala, Mgbade, K.c, and all those that I could not include their names due to circumstances. iv To my class mates, Femi, Titi, Nwankwo John, Emenike, John, Enesi, Goodness, Ezinne, Jenifer, Onyekachi Kalu, and all my course mates, it was fun learning together. Special thanks to all my room mates, Chidera, Richard, Funcho, Koja, Godwin and Pastor Methusela. Alum Emeka Japhet iv ABSTRACT This research work studies the international competitiveness of the Nigerian economy in the global market by analyzing the relationship between trade openness and output growth in Nigeria. Using timeseries data over the period 1970-2007, we show that output growth of the Nigeria economy is a function of two sets of shocks; (i) external shocks (openness and real exchange rate) and (ii) internal shocks (real interest rate and unemployment rate). A non-monotonic and an ANCOVA econometric models are postulated in order to capture the structural pattern of the relationship between openness and output growth as well as the policy effect of structural Adjustment program (SAP). The result shows that there is an inverted U-shape (nomonotonic) relationship between openness and output growth in Nigeria and the optimum degree of openness for the economy is estimated to be about 67%. Also, the liberalization policy of the SAP has positive economic effect on the output growth. The ECM reveals that 79% of the equilibrium error is being corrected in the next period. We concluded that unbridled openness may have deleterious effect on the real growth of output of the Nigerian economy. iv TABLE OF CONTENTS Title page i Approval page ii Dedication iii Acknowledgement iv Abstract v Table of contents vi List of tables and figures ix CHAPTER ONE: INTRODUCTION iv 1.1 Background of study 1 1.1.2 Trade openness and output growth Historical Experience of the Nigeria economy 3 1.2 Statement of the research problem 14 1.3 Objectives of the study 16 1.4 Statement of the research hypothesis 17 1.5 Justification of the study 17 1.6 Significance of the study 18 1.7 Scope and limitation of the study 19 CHAPTER TWO: LITERATURE REVIEW iv 2.1 Theoretical literature 21 2.1.2 Theory of customs union and free trade areas 37 2.1.3Models of export-led growth 40 2.2 Empirical literature 45 2.3 Limitation of previous studies 69 CHAPTER THREE: METHODOLOGY 3.1 Analytical framework 70 3.2 Model specification 71 3.2.1 Test of stationarity 74 iv 3.2.2 Test of co integration 75 3.2.3 Error correction model 76 3.3 Justification of the model 78 3.4 Estimation techniques 80 3.5 Evaluation Procedure 81 3.5.1 Economic test (a priori expectation) 81 3.5.2 Statistical (first order) test 83 3.5.3 Econometric (second order) test 84 3.6 Sources of data and software for estimation 85 iv CHAPTER FOUR: PRESENTATION AND ANALYSIS OF RESULTS 4.1 Introduction 87 4.2 Presentations of regression results 87 4.2.1Test of stationarity 89 4.2.2 Test of co integration 91 4.2.3 The Error correction model (ECM) 92 4.3 Interpretation and Evaluation of result 93 4.3.1Evaluation based on economic criteria 93 4.3.2Evaluation based on statistical criteria 103 iv 4.3.3 Evaluation based on econometric criteria 110 4.4 Evaluation of the working Hypotheses 118 CHAPTER FIVE: SUMMARY, POLICY PRESCRIPTION AND CONCLUSION 5.1 Summary 122 5.2 Policy Recommendations 123 APENDIX I APENDIX II APENDIX III (A) APENDIX III (B) APENDIX III(C) iv APENDIX III (D) APENDIX III (E) APENDIX III (F) APENDIX IV APENDIX V APENDIX VI APENDIX VII APENDIX VIII APENDIX IX APENDIX X APENDIX XI APENDIX XII LIST OF TABLES AND FIGURES Figure 1: Growth Rate of Real GDP iv Figure 2: Trend of Real GDP Figure 3: Growth of Export and Import Figure 4: The Degree of Openness Table 1: Openness Indicators Table 2: A Priori Expectation Table 3: Results of Model 1 Table 4: Results of Model 2 Table 5: Results of Stationarity test Table 6: Results of Co integration test Table 7: Results of the Error Correction Model Figure 5: Non- Monotonic Relationship between TPN and RGDP Table 8: Summary of the T-Test Table 9: Pair-Wise Correlation Matrix CHAPTER ONE INTROUDCTION iv 1.1 BACKGROUND OF STUDY The current period in the world economy is regarded as period of globalization and trade liberalization. In this period, one the crucial issues in development and international economics is to know whether trade openness indeed promotes growth. With globalization, two major trends are noticeable: first is the emergence of multinational firms with strong presence in different, strategically located markets; and secondly, convergence of consumer tastes for the most competitive products, irrespective of where they are made. In this context of the world as a “global village”, regional integration constitutes an effective means of not only improving the level of participation of countries in the sub-region in world trade, but also their integration into the borderless and interlinked global economy. (NEEDS, 2005). Since 1950, the world economy has experienced a massive liberalization of world trade, initially under the auspices of the General Agreement on Tariffs and trade (GATT), established in 1947, and currently under the auspices of the World Trade Organization iv (WTO) which replaced the GATT in 1993. Tariff levels in both developed and developing countries have reduced drastically, averaging approximately 4% and 20% respectively, even though the latter is relatively high. Also, non-tariff barriers to trade, such as quotas, licences and technical specifications, are also being gradually dismantled, but at a slower rate when compared with tariffs. The liberalization of trade has led to a massive expansion in the growth of world trade relative to world output. While world output (or GDP) has expanded fivefold, the volume of world trade has grown 16 times at average compound rate of just over 7% per annum. In fact, it is difficult, if not impossible, to understand the growth and development process of countries without reference to their trading performance. (Thirlwall, 2000). Likewise, Fontagné and Mimouni (2000) noted that since the end of the European recovery after World War II, tariff rates have been divided by 10 at the world level, international trade has been multiplied by 17, world income has quadrupled, and income per capita has doubled. Incidentally, it is well known that periods of openness iv have generally been associated with prosperity, whereas protectionism has been the companion of recessions. In addition, the trade performance of individual countries tends to be good indicator of economic performance since well performing countries tend to record higher rates of GDP growth. In total, there is a common perception that even if imperfect competition and second best situations offer the possibility of welfare improving trade policies, on average free trade is better than no trade. From the ongoing discussion, it is evident that trade is very important in promoting and sustaining the growth and development of an economy. No economy can isolate itself from trading with the rest of the world because trade act as a catalyst of growth. Thus Nigeria, being part of the world, is no exemption. For this reason, there is a need to thoroughly examine the nature of relationship between trade openness and output growth in Nigeria. 1.1.2 TRADE OPENNESS AND OUTPUT GROWTH: HISTORICAL ECONOMY EXPERIENCE OF THE NIGERIA iv Today, Nigeria is regarded to have the largest economy in subSaharan Africa, excluding South Africa. In the last four decades there has been little or no progress realized in alleviating poverty despite the massive effort made and the many programmes established for that purpose. Indeed, as in many other sub-Saharan Africa countries, both the number of poor and the proportion of poor have been increasing in Nigeria. In particular, the 1998 United Nations human development report declares that 48% of Nigeria’s population lives below the poverty line. According to the report (UNDP, 1998). The bitter reality of the Nigerian situation is not just that the poverty level is getting worse by the day but more than four in ten Nigerians live in conditions of extreme poverty of less than N320 per capita per month, which barely provides for a quarter of the nutritional requirements of healthy living. This is approximately US 8.2 per month or US 27 cents per day. Doug Addison (unpublished) further explained that the Nigeria economy is not merely volatile; it is one of the most volatile economies in the world (see figure 1 below). There is evidence that iv this volatility is adversely affecting the real growth rate of Nigeria’s gross domestic product (GDP) by inhibiting investment and reducing the productivity of investment, both public and private. Economic theory and empirical evidence suggest that sustained high future growth and poverty reduction are unlikely without a significant reduction in volatility. Oil price fluctuations drive only part of Nigeria’s volatility policy choices have also contributed to the problem. Yet policy choices are available that can help accelerate growth and thus help reduce the percentage of people living in poverty, despite the severity of Nigeria’s problems. Figure 1: growth rate of real GDP Nigeria real GDP Growth Rate Real GDP growth Rate (%) 50 40 30 20 10 0 10 iv Year During the period 1960-1997, Nigeria’s growth rate of per capital GDP of 1.45% compares unfavorably with that reported by other countries, especially those posted by china and the Asian Tigers such as Hong Kong, Singapore, Taiwan, and south Korea, viewed in this comparative perspective, Nigeria’s per capita income growth has been woefully low and needs to be improved upon. (Iyoha and Oriakhi, 2002). In like manner, ogujiuba, Oji and Adenuga (2004) wrote that the Nigerian economy has severally been described as a difficult environment for business with a population growth of about 3%, it has been acknowledged that the current average output growth rate of less than 4% will see the country being poorer in the next decade. iv A study conducted by Iyoha and Oriakhi (2002) on Nigeria’s per capita GNP from 1964 to 1997 show that it rose steadily from US$120 to US$780 in 1981. Thereafter, it fell almost steadily to US$280 in 1997. Thus, between 1964 and 1981, income per capita increased by 550% or at an annual average rate of 32.3% while between 1981 and 1997, it fell by 64.1% or at an annual average rate of 4%. It is worth noting that if income per capita had continued to increase beyond 1981 as it did before then, Nigeria’s GDP per capita would have equaled US$1,279 in 1997. The difference between US $280 and US$1,279, i.e, approximately, US$1,000.00, is a rough measure of the cost to the average Nigerian of domestic macro economic policy mistakes and adverse international economic shocks. Likewise in 1960 agricultural exports accounted for only 2.6%. Exports of other commodities like tin and processed goods amounted to 26.6% of total exports. By 1970 agricultural exports only accounted for 33% of total exports while petroleum exports had started to establish dominance by exceeding 58% of total exports. By the time the oil boom began in earnest in 1974, petroleum exports accounted iv for approximately 93% of all exports. The relative share of agricultural exports in total exports had shrunk to 5.4% while other products accounted for the remaining 1.9%. Since 1974, with the exception of 1978 when the relative share of petroleum in total exports has exceeded 90%. In deed, since 1990, the relative share of petroleum in total exports has exceeded 96%. Agricultures contribution has fluctuated between 0.5% and 2.3% while the share of other products has fluctuated between 0.5% and 1.7%. Thus petroleum exportation has totally dominated the economy and indeed government finances since the mid-1970s. Meanwhile, a puzzling and disturbing aspect of Nigeria export boom is that the growth it generated did not seem to be lasting or to have had a significant effect in changing the structure of the economy. For instance, in the 1970’s there was a major increase in measured GDP but the structure of the economy remained basically unchanged (see figure 2 below). This led professor Yesufu (1995) to describe the Nigerian economy as one that had experienced “growth without development’’. iv Figures 2: trend of real GDP -.-RGDP – linear (RGDP) Real GDP 300000 250000 200000 150000 100000 500000 Year 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 During the period of 1970 – 1985, import substitution industrialization (ISI) strategy was a dominant feature of trade policy in Nigeria. The trade policy was generally inward oriented. Under this ISI strategy, “Infant” manufacturing industries were protected using high tariffs, import quotas, and other trade restrictions like import licensing. Non-tariff barriers to trade such as import prohibitions were iv also utilized. During this period, trade policy was also adjusted in response to the exigencies of the balance of payments. Also, Nigeria was operating a fixed exchange rate regime under which the value of the Naira was essentially tied to US dollar and gold. It is worth noting that the trade policy pursued during this period resulted in a rapid increase in manufacturing production and employment, particularly during the era of the oil boom (1975 -1980) and that led to a rise in the share of manufacturing in Gross Domestic product (GDP) from 5.6% in 1962/63 to 8.7% in 1986. (Iyoha and Oriakhi, 2002). In 1986, Nigeria adopted the structural adjustment programme (SAP) of the IMF/World Bank. With the adoption of SAP in 1986, there was a radical shift from inward-oriented trade policies to out ward –oriented trade policies in Nigeria. These are policy measures that emphasize production and trade along the lines dictated by a country’s comparative advantage such as export promotion and export diversification, reduction or elimination of import tariffs, and the adoption of market-determined exchange iv rates some of the aims of the structural adjustment programme adopted in 1986 were diversification of the structure of exports, diversification of the structure of production, reduction in the overdependence on imports, and reduction in the over-dependence on petroleum exports. The major policy measures of the SAP were: Deregulation of the exchange rate Trade liberalization Deregulation of the financial sector Adoption of appropriate pricing policies especially for petroleum products. Rationalization and privatization of public sector enterprises and Abolition of commodity marketing boards. However, as a result of trade liberalization gospel of the SAP, the Nigeria external sector really experience dramatic growth. For instance, the total domestic exports of Nigeria in 2006 amounted to N755141.32 million against N6621303.64 million in 2005 showing an increase of 14.10%. Domestic exports recorded negative growth rates iv in 1993 (7.70%), 1994 (45.5%), 1997 (2.03%), 1998 (38.48%) and 2001 (27.06%); while it recorded positive growth rates in other periods. The largest increase in domestic exports was witnessed in 1995 (448.42%). Total imports (C.I.F) stood at N2922248.46 in 2006 as against N1779601.57 million in 2005 recording an increase of 64.20%. Total imports also recorded negative growth rates in 1994(45.72%),1998(9.41%) and 2004(18.07%) while it is positive all through other years. The value of total merchandise trade amounted to N10477389.78 million in 2006 as against N45272.24 recorded in 1987. External trade was dominated by domestic exports between 1987 and 2006 averaging 67.17% while imports (C.I.F) averaged 32.82% (see figure 3 below), consequently, the trade balance was positive between 1987 and 2006. Oil export remains the dominant of export trade in Nigeria between 1987 and 2006 accounting for about 93.33% of total domestic exports. On the other hand, non oil exports accounted for a small value of 6.67% over the same period. (NBS report, 2008). iv FIGURES 3: GROWTH OF EXPORT AND IMPORT NIGERIA IMPORT AND EXPORT Import and export 9000000 8000000 7000000 6000000 5000000 4000000 3000000 2000000 1000000 0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 iv Therefore, it could be understood that the SAP involved the deregulation and liberalization of the Nigerian economy. This policy thrust of this program dovetailed nicely with the emerging international orthodoxy to the effect that deregulation and economic liberalization would yield the optimal allocation of scarce resources, reduce waste, and promote rapid economic growth in developing countries. Unfortunately, there has been no significant progress made in the achievement of these objectives. The openness of the economy has significantly increased in the past four decades, with the tradeGDP ratio rising from 31.54% in 1970, to 46.91% 1980, 57.23% in 1990, 88.16% in 1995, 85.26% in 2003 and 57.63% in 2007 (see figure 4 below) indeed, in the 1990s the ratio of trade to GDP has averaged 70%. This extreme openness of the economy could be disadvantageous in that it makes the country highly susceptible to internationally transmitted business cycles, and, in particular international transmitted shocks (like commodity price collapse). A good example of this effect on the Nigerian economy is that of the iv global food crisis of 2007 and the current global economic/financial crisis. FIGURES 4: THE DEGREE OF OPENNESS NIGERIA IMPORT AND EXPORT 100 Degree trade openness (5) 90 80 70 60 50 40 30 20 1.2 STATEMENT OF THE RESEARCH PROBLEM 10 Nwafor Manson (unpublished) the 1995 Nigeria’s 0 1965 1970 1975 1980 not 1985that1990 2000 trade 2005 policy over the years has been determined by one/ more of the following. Need to protect and stimulate domestic production (import capital goods at low prices etc) Need to ameliorate/prevent balance of payment problems. 2010 iv Need to boost the value of the naira Need to be competitive and enjoy the benefits of openness. Need to increase revenue and International agreements Today, as part of moving with the trend of globalization and trade liberalization in the global economic system, Nigeria is a member of and sygnatory to many international and regional trade agreements such as international monetary fund (IMF), world trade organization (WTO), economic community of West African States (ECOWAS), and so many others. The policy response of such economic partnership on trade has been to remove trade barriers, reduce tariffs, and embark on outward-oriented trade policies. Despite all her effort to meet up with the demands to these economic partnerships in terms of opening up her border, according to the 2007 assessment of the trade policy review, Nigeria’s trade freedom was rate 56% making her the worlds 131st freest economy while in 2009, it was ranked 117th freest economy, the country’s GDP was also ranked 161st in the world in February, 2009. The economy has struggled iv vigorously to stimulate growth through openness to trade, In fact, it seems that as the country put greater effort to boost her economic growth by opening up to trade with the global economy the more she becomes worse-off relative to her trading partners in terms of country output growth. Having reviewed the related literatures and considering the structure of the Nigerian economy as related to trade openness and output growth, we may then ask the following questions. Does trade openness have any significant impact on out put growth in Nigeria? Is there any other macroeconomic variable that has significant impact on output growth in Nigeria? Is there any linear association (correlation) between trade openness and output growth in Nigeria? Is there long run relationship between trade Openness and output growth in Nigeria? Has there been any significant structural change in output growth between the pre-SAP and post-SAP period in Nigeria? 1.3OBJECTIVES OF THE STUDY iv The broad objective of this research work is to study, in its entirely, the relationship between trade openness and output growth in Nigeria. This broad objective can be subdivided into the following smaller objectives: To examine the impact of trade openness on output growth in Nigeria. To identify other internal and external macroeconomic shocks that determine output growth in Nigeria. To identify other international and external macro economic shocks that determine output growth in Nigeria. To determine the linear association (correlation) between trade openness and output growth in Nigeria. To ascertain the possibility of long run relationship between trade openness and output growth in Nigeria. To determine the possibility of structural changes (if any) in output growth between the pre-SAP and post-SAP period. 1.4 STATEMENT OF THE RESEARCH HYPOTHESES iv In view of the foregoing study, with respect to trade openness and output growth in Nigeria, the following null hypothesis will be tested: Ho: Trade openness does not have any significant impact on output growth in Nigeria. Ho: There is no other macroeconomic variable (internal and external) that have significant impact on output growth in Nigeria. Ho: There is no linear association (correlation) between trade openness and output growth in Nigeria. Ho: There is no long run relationship between trade openness and output growth in Nigeria. Ho: There is no significant structural change in output growth between the pre-SAP and post-SAP period. 1.5 JUSTIFICATION OF THE STUDY Nigeria is currently undergoing a series of transformation in every sector of the economy, including the external sector of the economy. The country’s economic policy in the last two decades had iv one dominating theme which is an integral part of the structural Adjustment programme (SAP) – trade liberalization. This policy was espoused on the argument that it enhances the welfare of consumers and reduces poverty as it offers wider platform for choice from among wider variety of quality goods and cheaper imports. Today, there are many existing literature on the topical issue of trade openness and growth of which some support the axiom that openness is directly correlated to greater economic growth with the main operational implication being that governments should dismantle the barriers to trade. The focal point of this research work is to identify the short comings and benefits of this argument as well as check the validity of this mainstream axiom I Nigeria in the presence of various internal and external shocks. 1.6 SIGNIFICANCE OF THE STUDY The role of international trade in the developmental journey of an economy can not be over emphasized, especially with the current trend of globalization. Nigeria. Being part of the global village, is not left out of this world development. This research work is carried out iv to study how trade openness has influenced the performance of the Nigeria economy through output growth in the presence of other internal and external shocks. The findings of this research work transcend beyond mere academic brainstorming, but will be of immense benefit to federal agencies, policy makers, intellectual researcher and international trade think tanks that occasionally prescribe and suggest policy options to the government on trade related issues. It will also help the government to see the effectiveness of trade liberalization policy on the economic growth of the nation over the years. This research work will further serve as a guide and provide insight for future research on this topic and related field for students who are willing to improve it. It will also educate the public on various government policies as related to trade issues. 1.7 SCOPE AND LIMITATION F THE STUDY This research work span through the period of 1970-2007 (38 years), and is within the geographical zone of Nigeria. Thus, it is a country-specific research. This research exercise, like every other research work, is really a rigorous one that consumes much time and iv energy especially in the area of data sourcing, data computation and modeling. This work is relatively limited base on time and financial constraints, data availability precision of data and data range, and methodology adopted which could further be verified by future research. Nevertheless, the researchers have properly organized the research so as to present dependable results which can aid effective policy making and implementation at least for the time being. CHAPTER TWO LITERATURE REVIEW “Openness” refers to the degree of dependence of an economy on international trade and financial flows. Trade openness measures the international competitiveness of a country in the global marked. Thus, we may talk of trade openness and financial openness. Trade openness is often measured by the ratio of import to GDP or alternatively, the ratio of trade to GDP. It is now generally accepted that increase openness with respect to both trade and capital flows will be beneficial to a country. Increased openness facilities greater integration into global markets. Integration and globalization are beneficial to developing countries although there are also some iv potential risks. (Iyoha and Oriakhi, 2002). Trade openness is interpreted to include import and export taxes, as well as explicit non tariff distortions of trade or in varying degrees of broadness to cover such matters as exchange-rate policies, domestic taxes and subsides, competition and other regulatory policies, education policies, the nature of the legal system, the form of government, and the general nature of institution and culture (Baldwin, 2002). 2.1 THEORETICAL LITERATURE The issue of whether trade and increased openness would lead to higher rate of economic growth is an age-old question which has sustained debate between pro-traders and protectionists over the years from classicalists like Adam Smith, John Stuart mill, to John Maynard Keynes, Raul Prebisch, Hans Singer, Paul Krugman and many others. Theorists from both theses have influenced policy many countries and at various stage of development there has also been a huge policy debate about what constitute “good” and “bad” policies for these countries, especially the developing countries such as Nigeria should iv these countries completely open up to international trade? Or should they instead, at least temporality, protect some or all of their industries from the world marked forces? Form arguments have been developed pro and con of both theses. These arguments were discussed extensively by Maskus (1998) thus: Argument One: Economies Will Grow Faster If They Protect Domestic Industry From Import Competition This is a general statement of the infant-industry hypothesis”, which states that manufacturing sectors in underdeveloped economics must be sheltered from competition in order to have the incentive to invest capital, learn how to produce goods efficiently, take advantage of scale economies through large scale production, and develop innovative or distinctive products that can be sold on world markets. The broadest application of the infant-industry argument for world markets. The broadest application of the infant-industry argument for isolation from global markets emerged in the widespread use of import substitution policies in developing countries. iv A policy of import substitution for industrialization purpose (ISI) involves extensively controlling virtually all components of the economy in order to direct resources into manufacturing. It is an old idea, but its modern origins come from economists writing in the 1950s and 1960s (Arthur Lewis, Raul Prebish, Hans singer, Gunnar Myrdal., and others), who claimed that developing economies faced two fundamental problems. First, their status as primary-commodity exporters left them vulnerable to world swings in commodity prices (e.g. oil, sugar, tin, copper, etc) and also that over a long-run, commodity prices would decline relative to manufacturing prices and costs of new technologies. Second, because developing countries have high population growth rates and abundant labor supplies, it would be difficult to absorb workers into primary production. Rather than waiting for comparative advantage to push resources into laborintensive manufacturing, it would be better to force industrialization through ISI policies. Such programmes become common in the 1950s throughout Latin America, Africa, the Middle East, South Asia and Southeast Asia; they are still much in evidence in many countries. iv Policies imposed in a thorough ISI programme include the following: Escalating tariffs, or tariff rates that rise with the stage of processing. Thus, low tariffs on primary goods, medium tariffs on industrial inputs and machinery, and high tariffs on final goods, particularly consumers goods such as food products, clothing, cosmetics, automobiles, and so on. Such tariff structures established very high effective rates of protection for final goods, meaning that auto factories and so on were strongly protected. Considerable taxes on production of primary commodities in other to push labor out of the country’s side and into the cities for developing manufacturing. Such taxes include tariffs on imported fertilizer price ceilings at very low rates for crops, export taxes on farm goods, and so on. For these reason ERPs in agriculture were often strongly negative, vastly reducing output and productivity in farming. iv Fixing exchange rates at expensive levels (i.e. “overvaluing” the domestic currency), again in order to discourage primary exports and production and also to reduce the cost of imported inputs for manufacturing sectors. Such exchange rates tended to generate large trade deficits, forcing governments to borrow from abroad and build up debt. It also required setting and controlling multiple exchange rates, so that capital and input transactions could take place at cheaper rates than goods imports in order to protect domestic industry. Extensive systems of quotas and licensing for imports and production. Rigorous controls on FDI coming into the country, requiring foreign firms to meet certain performance requirements. Also controls on imported technologies, with governments placing restrictions on costs of technology and under what terms it would be transferred to local firms. Extensive nationalization of industry to establish state-owned enterprises (SOEs) in key sectors, such as petroleum, steel, iv chemicals, construction, banking, and airlines. These “industry champions” received government subsides and were favored in the process of capital allocation, typically being allowed to borrow at very low rates from state banks (usually at negative real interest rates). To some degree these policies successfully pushed industrialization, but rarely of an efficient kind. Developing countries are full of large manufacturing operations that operate at inefficiently low scales because market sizes are small and product quality is not good enough to penetrate export markets, which is a costly activity. These operations are partly supported by government subsidies, generating rested interests in keeping them going and opposing liberalization. Relative prices of goods are heavily distorted by the various subsidies, trade restrictions, and licenses. Other unintended effects include massive shifts of workers into the cities and worsened sanitation and health problems. However, the question is whether such policies have limited growth. Evidently many other factors are at work. What seems clear is iv that such countries have not performed well in terms of acquiring and improving technologies, have lagged significantly behind in product innovation and adaptation, have inefficient and distorted agricultural and manufacturing sectors, and have not performed well in building human capital, physical capital, and infrastructures. Some relevant figures are given later. Thus, these sources of growth have likely been limited in countries pursing ISI program. ARGUMENT TWO: COUNTRIES WILL GROW FASTER IF THEY ARE OPEN TO INTERNATIONAL COMPETITION This is the basic hope underlying trade-reform programmes that involve extensive liberalization of trade and investment barriers, unification of tariff rates and domestic tax rate, removal of consumption and production subsidies, and deregulation of industry and privatization of state owned enterprises. It is the essential philosophy behind World Bank loans to facilitate restructuring and IMF lending packages that require microeconomic structural reforms. It is also a very old idea (going back to Adam Smith and David Ricardo at least) but its modern translation into trade liberalization iv largely began with the reforms in Chile in the 1970s advocated by the “Chicago School” of economists (e.g. Milton Friedman, George Stigler). A somewhat different version of this approach is (to contrast it with ISI) called export promotion, which is the policy followed largely by East Asian and Western countries. These approaches are not necessarily liberal in the sense of free competition. There are many examples of sheltered and subsidized domestic firms or industrial groupings; much of this protection was designed to encourage infant industries to mature and export. However, the key component of export promotion programs is not to discourage exports, as is done under ISI programs. The basic policies under export promotion include the following. Properly valued exchange rates, meaning exchange rates that do not discriminate between imports and exports. This is accomplished either through flexible rates or pegged exchange rates that are allowed to move gradually to account for inflation differences between the country in question and major iv export markets. In this sense the exchange rate did not impose any tax on exports. Remove taxes on export production and, indeed make the tax and tariff systems as neutral as possible across sectors of production. Thus, while in most of these nations agricultural production was protected from import competition, in manufacturing there were relatively little discrimination across types of goods. It is for this reason that export- promotion policies are far closer to open trade policies than are ISI policies. There were certainly major exceptions to this rule in many export- promotion countries, however. Rather than rely largely on import protection to promote infant industries, some active forms of export promotion in manufacturing and high-tech sector were taken, including favorable allocation of loans and subsides and rebates of import tariff paid on imported industrial inputs. Recognizing that exporting is harder than cutting off import because export require improving levels of quality and iv considerable foreign marketing cost, East Asia firms have emphasized quality control and access to foreign technology on favourable terms. Government have supported this by ensuring strong public educational effort, investments in infrastructure for export, and technology transfer policies that attempted to force inward technology flows at cheap price. Recent problems in some countries (especially Asian countries) indicate that while export promotion strategies may have contributed to growth, they ultimately cause serious problems of over production (excess capacity) relative to the economy’s ability to consume commodities. (Maskus, 1998). The World Bank favors lifting the protectionist measures that have locked low income countries out of rich- country export market infact most international bodies (WTO,IMF World Bank etc) strongly support the case for trade openness and financial liberalization when setting up programmes for developing countries or when multilateral meetings occur. Some of the arguments put forward in favor of increased openness to trade include the following: iv Specialization: Gains from specialization in the good in which the country has a comparative advantage such as productivity gains, lower costs of production etc. Variety: Greater variety of goods available to consumers thus increasing the consumer surplus and satisfying the consumers “demand of difference”. Increasing Returns: Economies of scale justify any market enlargement. However, conclusions are quite ambiguous. For instance the gain is by large dependent of the process of firms’ issue. A fixed market structure inhibits such gains in the many firms complete specialization scheme of monopolistic competition. In the same way, imposing consumers to buy a greater quantity of domestic products can be optimal. Domestic firms increase their output and achieve economies of scale, while the variety is not reduced. Notwithstanding such argument economies of scale are generally referred to as a key objective of integration policies. Pro-competitive effect: When an economy is open, there will be more intense competition which obliges local firms to operate iv more efficiently than under protection. There will also be drive for innovation and efficiency in production in a smaller of goods. The country Can thus compete internationally. Positive Externality: The technology is spread over the boundaries through trade and open countries benefit a better access to a world-wide basket of technology. Also, there will be Adoption of sound policies to make sure the country is attractive to investors: trade openness acts as a watch dog for politicians as bad economic performances are blamed on them. Good governance should thus be fostered. On the other hand, some arguments were put forward by protectionist against trade openness Apoteker and Crozet (2003) have put forward five reasons that can at least under line the potential risks in opened trade. Fluidity: All products do not have the same fluidity, meaning that some may be easily relocated while some others are stickier and cannot be as easily moved. iv Multiple Comparative Advantages: As many countries with similar resources are opening up to trade, they bring at the same time their same comparative advantage on the market. This will create an excess supply of that product and its world price will decrease, thus harming all the providers. Instability as regards Financial Volatility: Implementing financial liberalization implies allowing financial flows to freely move in or out of a country. However, in a country which lacks political credibility or monetary strength and power, capital mobility and volatility can make economic policies useless, thus preventing a country from using fiscal or monetary tools to try to solve domestic economic problems. This means greater dependence on the international environment. Investor Size versus Market Size: If concentration on a small market is high, the issue of market efficiency becomes critical. Indeed if one of the large and few investors on a small market withdraw its funds, it can threaten the whole market iv equilibrium, and even create panic among other investors without any fundamental” reasons behind it. Time Frame: A country’s development process is a long-term one, while financial investors usually have a short-term approach. Thus there are potential conflict of interest between public authorities and private investors/companies, which can be harmful for development goals. The unananimous agreement on the beneficial effects on growth and development of trade liberalization goes back to the emergence of the Washington consensus in the early 1980s. The consensus emerged in response to economic crisis affecting most developing countries at the time, triggered by the debt crisis. Nonetheless long-term economic growth is generally seen as being dependent on openness to trade. But, literature on trade theory and policy has since the time of Adam Smith debated whether openness and trade liberalization provide the necessary ingredient for economic growth (miller and Upadhya, 2000). Thus, in order to effectively understand the relationship between trade openness and output growth we need to review and iv understudy the trade theory, the theory of customs union and free trade areas and models of exported growth. THEORY OF TRADE The doctrine that trade enhances welfare and growth has a long and distinguished ancestry dating back to Adam Smith (1723-90). In his famous book, and inquiry into nature and causes of the wealth of nations (1776), Smith stressed the importance of trade as a vent for surplus production and as a means of widening the market thereby improving the division of labor and the level of productivity. He assert that “between whatever places foreign trade is carried on, they all of them derive two distinct benefits from it. It carries the surplus part of the produce of their land and labour for which there is no demand among them, and brings back in return something else for which there is a demand. It gives value to their superfluities, by exchanging them for something else, which may satisfy part of their wants and increase their enjoyments. iv By means of it, the narrowness of the labour market does not hinder the division of labour in any particular branch of art or manufacture from being carried to the highest perfection. By opening a more extensive market for whatever part of the produce of their labour may exceed the home consumption, it encourages them to improve its productive powers and to augment its annual produce to the utmost, and thereby to increase the real revenue of wealth and society”. (Thirl Wall, 2000). We may summarize the absolute advantage trade theory of Adam Smith, thus, countries should specialize in and export those commodities in which they had an absolute advantage and should import those commodities in which the trading partner had an absolute advantage. That is to say, each country should export those commodities it produced more efficiently because the absolute labour required per unit was less than that of the prospective trading partners. (Appleyard and Field, 1998). In the 19th century, the Smithian trade theory generated a lot of arguments. This led to David Ricardo (1772-1823) to develop the theory of comparative advantage and showed rigorously in his iv principles of political economy and taxation (1817) that on the assumptions of perfect competition and the full employment of resources, countries can reap welfare gains by specializing in the production of those goods with the lowest opportunity over domestic demand, provided that the international rate of exchange between commodities lies between the domestic opportunity cost ratios. These are essentially static gains that arise from the reallocation of resources from one sector to another as increased specialization, based on comparative advantage, takes place. These are the trade creation gains that arise within customs to trade are removed between members, but the gains are once-for-all. Once the tariff barriers have been removed, and no further reallocation takes place, the static gains are exhausted. The static gains from trade stem from the basic fact that countries are differently endowed with resources and because of this the opportunity cost of producing products varies from country to country. Opportunity cost is measured by the marginal rate of transformation between one good and another, as given by the slope of the production possibility curve; that is, by how much one good has iv to be sacrificed in order to produce another. The law of comparative advantage states that countries will benefit if they specialize in the production of those goods for which the opportunity cost is low and exchange those goods for other goods, the opportunity cost of which is higher. That is to say, the static gains from trade are measured by the resource gains to be obtained by exporting to obtain imports more cheaply in terms of resources given up, compared to producing the goods oneself. In other words, the static gains from trade are measured by the excess cost of import substitution, by what is saved by not producing the imported good domestically. The resource gains can then be used in a variety of ways including increased domestic consumption of both goods (Thirl Wall, 2000). On the other hand, the dynamic gains from trade continually shift outwards the whole production possibility frontier of countries if trade is associated with more investment and faster productivity growth based on scale economies, learning by doing and the acquisition of new knowledge from abroad, particular through foreign iv direct investment. The essence of dynamic gains is that the shift outwards the whole production possibility frontier by augmenting the availability of resources for production through increasing the productivity of resources and increasing their quantity. One of the major dynamic benefits of trade is that export markets widen the total market for a country’s producers. If production is subject to increasing returns, export growth becomes a continual source of productivity growth. There is also a close connection between increasing returns and the accumulation of capital. For a small country with no trade there is very little scope for large scale investment in advanced capital equipment; specialization is limited by the extent of the market. But if a poor country can trade, there is some prospect of industrialization and of dispensing with traditional methods of production. It is the dynamic gains from trade that are focused on in modern trade theory such as the Heckscher-Ohlin trade theory. 2.1.2 THEORY OF CUSTOMS UNIONS AND FREE TRADE AREAS iv Since the end of the World War II, there had been several attempts to promote trade through the creation of international and regional trade agreements in the form of customs unions and free trade areas. Free trade area is a form of economic union in which all members of the group remove tariffs on each others products, while at the same time each member retain its independence in establishing trading policies with non-members. In other words, the members of a free trade area can maintain individual tariffs and other trade barriers on the outside world. That is to say, in a free trade area, barriers to trade are brought down within the area, but there is no common external tariff. Also, free trade areas create trade, but the extent of trade diversion is likely to be much less, with the presumption that on narrow economic grounds free trade areas are superior. On the other hand, a customs union is a form of economic integration in which all tariffs are removed between members and the group adopts a common external commercial policy toward nonmembers. Furthermore, the group acts as one body in the negotiation iv of all trade agreements with non-members. The existence of the common external tariff takes away the possibility of transshipment by non-members. Customs unions create trade, but also divert it from lower cost suppliers to higher cost suppliers within the union. Thus, the question is whether the benefits of trade creation exceed the costs of trade diversion. Apart from trade creation and trade diversion, customs unions may also have other important effects associated with the enlargement of the market which are neglected by the static analysis. Firstly, the larger market may generate economies of scale. Secondly, integration is likely to promote increased competition which is likely to affect favorably prices and costs, and the growth of output. Thirdly, the widening of markets within a customs union is likely to attract international investment. Producers will prefer to produce within the union rather than face a common external tariff from outside. Finally if the world supply of output is not infinitely elastic, there are terms of trade effects to consider. Specifically if there is trade diversion, the world price of the good will fall, moving the terms of trade in favor of iv the customs union. This term of trade effect represents a welfare gain which may partly off set the welfare loss of trade diversion. The two forms of economic integration discussed above are likely to be interior to a policy of unilateral tariff reductions, and therefore need to be justified on other economic or non-economic grounds. Thus, De Melo, Panagariya and Rodick (1993) suggest three channels through which regional integration could alter economic outcomes for the better. Firstly, a regional trade agreement entails a larger political community which might lessen the scope for adverse discretionary actions by governments, and particular restrict the power of growthretarding political interest groups, unless politically powerful lobbies can form alliances across countries. Secondly, when a regional institution is set up ab nitio, better choices may be made than at the nation-state level, where policymakers have to contend with existing institutions that accommodate factional interests. Thirdly, when participating countries have different economic institutions, policy-making at the regional levels iv will entail a compromise between those institutions and may lead to a superior outcome for at least some member countries. For example, if a customs union adopts as its common external tariff, the average tariff of the union, at least some members must benefit. Nevertheless, the World Bank is generally hostile to regional trading blocs, despite the potential political –cum-economic benefits, because of their relatively inward-looking nature. (Thirlwall, 2000). 2.1.3 MODELS OF EXPORT –LED GROWTH The three main models of export-led growth that will be discussed are the neo classical supply –side model, the balance of payments constrained model which is also known as the Hicks supermultiplier model, and the virtuous circle model. The Neoclassical Supply-Side Model: This model shows the relationship between exports and growth, and assumes that the export sector confers externalities on the non export sector, because of its exposure to foreign competition; and secondly that the export sector has a higher level of productivity than the non export sector. Thus, the share of exports in GDP, and the growth of exports, matter for overall iv growth performance. Feder (1983) was the first to prove a formal model of this type to explain the relation between export growth and output growth. The output of the export growth sector is assumed to be a function of labour and capital in the sector, the output of the nonexport sector is assumed to be a function of labour, capital and the output of the export sector (so as to capture externalities), and the ratio of respective marginal factor productivities in the two sector is assumed to deviate from unity by a factor d. Feder tests the model taking a cross section of 19 semi industrialized countries and a larger sample of 31 countries over the period1964-73. He finds that there are substantial differences in productivity between the export and nonexport sector are also evidence of externalities. The externalities conferred are part of the dynamic gains from trade which are associated with the transmission and diffusion of new ideas from abroad relating to both production techniques and efficient management practices. The cross-section work on exports and growth assumes, however that all countries in a sample conform to the same model, with the same intercept and coefficient parameters linking iv exports and growth. In practice, this is highly unlikely to be the case; and it transpires, in fact, that when time series studies are conducted for individual countries, the relation between exports and growth is much weaker. BALANCE OF PAYMENTS CONSTRAINED GROWTH MODEL: No country can grow faster than rate consistent with balance of payments equilibrium on current account in the long run, unless it can finance ever-growing deficits which, in general, it cannot. Ratios of deficit to GDP of more than 2%-3% to make the international financial markets nervous and all borrowing eventually have to be repaid. A country’s balance of payments equilibrium growth rate can be modeled by stating the balance of payments equilibrium condition specifying multiplicative (constant elasticity) import and export demand functions in which imports and exports are a function of domestic and foreign income, respectively, and of relative prices, and substituting these functions in the equilibrium conditions. Since imports are a function of domestic income, the model can be easily solved for the growth of income consistent with balance of payments iv equilibrium. Nureldin-Hussain (1995) applied this model to Africa to contrast the experience of slow growing African countries with the faster growing countries of Asia over the period 1970-90. He uses an extended model which also includes terms of trade effects and the effects of capital flows. The major explanation of the difference in growth rates between Africa and Asia turns out to be the difference in the growth of exports. He finds that the average growth of the African countries, excluding oil exporters, was 3.4 percent per annum, and of the Asian countries 6.6 percent. The contribution of export growth in Africa was 1.99 percentage points and in Asia 5.91 percentage points. Differences in capital flows and terms of trade movements made only a minor contribution to growth rate differences. Thus, he concluded that exports are unique as a growth inducing force from the demand side because it is the only component of demand that provides foreign exchange to pay for the import requirements for growth. In this sense, it allows all other components of demand to grow faster in a way that consumption-led growth or investment-led growth does not. iv Virtuous Circle Models of Export-Led Growth There is need to recognize the fact that export and growth may be interrelated in a cumulative process. This raises the questions of casualty; but more importantly, such model provide and explanation of why growth and development through trade tends to be concentrated in particular areas of the world, while other regions and countries have been left behind. These models provide a challenge to both orthodox growth theory and trade theory which predict the long run convergence of living standards across the world. A simple cumulative model, driven by exports as the major component of autonomous demand, is to assume that (i) output growth is a function of export growth, (ii)export growth is a function of price competitiveness and foreign income growth, (iii) price competitiveness is a function of wage growth and productivity growth, and (iv) productivity growth is a function of output growth (this is referred to as verdoorn law which works through static and dynamic returns to scale, including learning by doing). It is this induced productivity growth that makes the model circular and iv cumulative’ since if fast output growth (caused by export growth) induces faster productivity growth this makes goods more competitive and therefore induces faster export growth. The verdoorn relation not only makes the model ‘circular and cumulative’; but also gives rise to the possibility that once an economy obtains a growth advantage it will tend to keep it. Suppose, for example, that an economy obtains an advantage in the production of goods with a high income elasticity of demand in world markets, such as high technology goods, which raises its growth rate above other countries. Through the verdoorn effect, productivity growth will be higher and the economy will retain its competitive advantage in these goods, making it difficult, without protection or exceptional industrial enterprise, to establish the same commodities. In such a cumulative model, it is the difference between the income elasticity characteristics of exports (and imports, if balance of payments equilibrium is a requirement as argued earlier) that is the essence of divergence between industrial and agricultural economies, or between centre and periphery. (Thirl Wall, 2000). iv From the ongoing, we can conclude that trade liberalization does not necessarily imply faster export growth, but impractical the two appear to be highly correlated. Impact of the liberalization on economic growth probably works mainly through improving efficiency and stimulating exports which have powerful effects on both supply and demand within an economy. There are several different measure of trade liberalization or trade orientation, and all studies seem to show a positive effect of liberalization on economic performance. Likewise there are several different studies of the relation between exports and growth and the evidence seem over whelming that the two are highly correlated in a causal sense, but the relative importance of the precise mechanisms by which export growth impacts on economic growth are not always easy to discern or qualify. 2.2 EMPIRICAL LITERATURE The relationship between trade openness and growth is a highly debated topic in the growth and development literature, yet this issue is far from being resolved. There is a long history of research, both iv theoretical and empirical, that provides at least an answer to the question: does openness to trade result in the growth of output (say, GDP)? But currently there is no consensus, either empirically to theoretically, on the nature of the relationship between trade openness and output growth. In fact, this is because the mechanisms behind it are not well understood. The existing empirical literature however does not provide clear evidence on relationship between trade openness and growth. Many studies provide evidence that increasing openness has a positive effect on GDP growth. On the other hand some studies report that it is difficult to find robust positive relationships or even that there is negative relationship between openness and growth. Some studies, among other Rodriguez and Rodrik (1999) and Rodriguez (2006), critically argue that trade policy variables are mostly uncorrelated with growth, while the trade shares can correlate with income levels and growth rates. But the complexity of links of causality and endogeneity among trade shares, growth and other sources of growth make a difficulty to define a strong effect of openness on economic growth. Theoretical growth studies suggest iv very complex and different relationships between openness and growth and the empirical evidence is not unambiguous. The growth theory supposes that “a country’s openness to world trade improves domestic technology, and hence an open economy grows faster than a closed economy through its impact on technological enhancement” (Jin, 2006). Harrison (1996) asserted that openness to trade provides access to imported inputs, which embody new technology, increases the size of the market faced by the domestic producers, which raises the return to innovation, and facilitates a country’s specialization in research intensive production. In line with potential dynamic gains of trade openness, most early empirical studies have examined a set of trade openness measures and with their correlation with each other and with economic growth and found a clear positive link. For example, Harrison (1996) looked at a number of openness indicators that turned out to have a positive ‘association’ with economic growth and produced evidence in support of bi-directional casualty between openness (trade share) and economic growth. Recent research, iv however, has questioned the robustness of the relationship. For instance, Harrison and Hanson (1999) show that the often quoted Sachs and Warner (1995) openness and growth link as claimed. Rodriguez and Rodrik (1999) confirm the Harrison –Hanson critique and argued that much of the work to correlate trade openness and economic growth has been plagued with subjective and collinear measures of openness that, though positively related with economic growth, arrive at their conclusion through problematic econometric methodologies. Harrison (1996) and Pritchett (1996) show that the various measure of trade openness tend to be only weakly correlated and are often of the wrong sign. In general, empirical studies suffer from a number of short comings, and as a result they have not resolved the questions surrounding the correlation between openness and growth. Baldwin (2000) offers explanation for the differences among researchers of the openness growth nexus. According to him, while econometric analyses based on quantitative data are limited by the scope and comparability of available quantitative data, differences in what iv investigators regard as appropriate econometric models and tests for sensitivity of the results to alternative specifications that may be based in part on the personal policy predilections of authors and can also result in significant differences in the conclusions reached under such quantitative approaches. If these studies used measures that were even slightly correlated, then empirical literature together could be taken as proof of a positive relation between openness and growth. Baliamoune-lutz and Ndikumana (2007) observed that, from a methodological stand point, the weak link between trade liberalization and growth may be attributed to measurement imperfections: the indicators used in empirical analysis may not capture the true essence of openness. Indeed, due to lack of data on indicators of trade openness as a policy empirical studies (as this one does) resort to measures of trade outcomes i.e. trade volume, as proxies for trade openness it is assumed that positive trade outcomes are an indication of a policy environment that is at least not anti-trade. Moreover, a high trade volume indicates exposure to international markets with the associated benefits (e.g. technological transfer) which openness iv policies seek to achieve. Thus, to some extent trade outcomes do carry some indication of the effects of trade liberalization. Nonetheless, results from analyses using trade volume as a measure of trade openness have to be interpreted consciously. Indeed, variations in the volume of trade do not always reflect actual government policies that promote or hinder trade. For instance, fluctuations in commodity prices result in changes in trade flows even in the absence of shifts in trade policy. The weak empirical evidence on the link-between trade liberalization and growth can also be due to problems of misspecification. In particular, the effects of trade liberalization may materialize only with a lag. In the short run, liberalization may have negative effects, especially by undermining domestic production because of competitive import, retarding growth (Mukhopadhyay 1999). Hence, to the extent that these negative short-run effects and the expected delayed positive effects occur consecutively, growth would exhibit a J-curve of response to trade openness (Greenaway etal 2002). Therefore, empirical studies may yield inconclusive and even iv misreading results if these dynamic and counter balancing effects are not fully taken into account. Another explanation relates to the structure of trade. Whether a country benefits from trade liberalization or not in terms of growth depends on the composition of trade. Mazumdar (1996) hypothesized that the composition of trade determines the strength of the engine of growth.” Indeed lower and Van Den Berg (2003) final evidence supporting the view that countries that import capital goods and export consumer goods growth faster than those that export capital goods. The evidence suggests that African countries and developing countries in general would benefit from trade most by promoting exports of labour-intensive goods and services while encouraging imports of capital goods. (Lopez 1991). This implies that the current export boom which is driven by capital-intensive growth that is sustainable, especially because of the low gains in employment creation and limited spill over effects on non-oil sectors. iv Dollar (1992) brought an important contribution to the trade and growth debate. The author defines openness as the combination of two diversions: i. A low level of protection, hence of trade distortions and ii. A stable real exchange rate so that incentives remain constant over time. From that very definition, follow two measures openness: a trade distortion index, and a real exchange rate variability index. The distortion index measures the deviation from the law of one price after controlling for the impact of non-tradable. The variability index captures the variance of the real exchange rate. The author considers a sample of 95 countries over the period 1976 -1985 and regresses average per capital growth upon his openness indexes and the average investment rate. Both the distortion index and the variability index are significantly negatively correlated with growth and the investment rate comes out with a significantly positive coefficient. Dow Rick (1994) tests whether trade openness affects output growth and /or investment. He considers a sample of 74 countries over iv the period 1960-1990. As openness indicator, the author considers the residuals of an OLS cross-country regression of the average trade intensity upon a constant and average population. In a second stage, the author runs cross-country OLS regressions of average per capita GDP growth upon the average investment rate, the initial GDP level and his openness indicator. The coefficient on openness is significant and positive. More over, dropping the investment rate considerably lowers the overall fit of the model but enhance the coefficient on openness, which according to the author “suggests that openness works partly through increased investment rates”. In a third stage, the author computes decade averages for his variables and turns to panel data techniques, gauging that such techniques “enable some control for time invariant country-specific factors such as institutional arrangements that might be correlated with the explanatory variables”. The author uses labour productivity growth as dependent variable and estimates both fixed-effects and random-effects models. He reports that the coefficient on openness is still significant and positive, but its point estimate is much lower than iv in the OLS specification. In a fourth set of regressions, the author also considers growth in openness instead of openness itself. The author interprets this as reflecting the fact that “static efficiency effects of trade liberalization are negligible for countries with well-developed markets”. Finally, in its conclusions, the author cautions that his results, showing the beneficial effects of increased openness, hold on average, but are not a universal truth, valid always and every where. In particular, he stresses that “trade liberalization can indeed stimulate growth in the aggregate world economy. Whilst trade may have such positive effects for some countries, it may conversely lock in other countries into a pattern of specialization in low-skill, lowgrowth activities.” Sachs and Warner (1995) brought a seminal contribution to that literature. Their central hypothesis is that some developing countries fail to grow rapidly enough as to converge because they are simply not open to trade. In their own wards: “convergence can be achieved by all countries, even those with low initial level of skill, as long as iv they are open and integrated in the world economy”. To check their hypothesis, the author first carefully, build and discuss an openness measure. Building upon a sample of 135 countries over the period 1970-1990, they construct and openness dummy variable that is zero if any of the 5 following conditions is true: Non-tariff barriers covering 40% or more of trade Average tariff rate above 40% Black market premium above 20% The economy is ruled by a socialist system, or There is a state monopoly on exports. Otherwise, if none of these 5 conditions is fulfilled, the openness dummy is one. The authors first divide their countries sample into open ones and closed ones, and show that closed countries have grown at about the same rate (essentially about 0.7% a year), no matter whether they are developed or not. By contrast, open developing countries have grown much faster than their developed counter parts (4.49% versus 2.29%). Going beyond these stylized facts the authors re-do the same regressions as in Barro (1991) and iv add their openness dummy to them without the dummy, the results are sensibly the same as in Barro (1991). After adding the openness dummy in the regresses list, it appears its coefficient is highly significant. The points estimates suggest that open economies grow on average 2.45% faster than closed ones. Moreover, educational attainment variables become even less significant than in Barro (1991), which leads the authors to think that “….growth rate over this period was determined less by initial human capital levels than by policy choices”. They also address a specialization-related issue. Specifically, they test whether trade openness condemns raw materials exporters to non-industrialization and whether closed trade promotes industrial exports in the long run. To do this, they regress the change in the share of primary exports more rapidly from being primary-intensive to manufactures-intensive exporters. The difference in speed of adjustment is statistically significant”. Harrison (1996) starts from the judgment that “it should be evident that no independent measures of so-called ‘openness’ is free iv from methodological problem”. Therefore, to make her point, she collects as many different openness indicators as she can, about 7 of them, and she checks the consistency of the results across all these indicators. She uses various samples, whose time spans range from 1960-1998 to 1978-1987,and the country coverage varies from 51 to 17.she first runs typical cross-country growth regressions. It appears that only one measure of openness out of 7, namely the black market premium, has a significant impact on growth. To explain this weak result the author argues that a pure cross-section specification, based upon long-run averages, is not an adequate one. Indeed, though the use of long run averages appears as the most natural way to capture the determinants of long-run growth, they may also hide significant variations in individual countries performances and policies over time. To test this idea, the author re-does her regressions using annual data for the same variables. She uses a panel fixed-effects specification to take into account unobserved country specific differences in growth rates. Results show a stronger link between openness and growth since 3 indicators become significant at the conventional 5% level. iv The author next argues that such a yearly frequency is too high if one is interested in long-run growth, since results may be affected by short-term conjectural, variations. She therefore considers a third“intermediate”- specification, based on five-year averages and reports that, again 3 indicators come out with a significant coefficient. The message from these results, as the author states, is that “the choice of the time period for analysis is critical”. However, an interesting regularity appears across all specifications: When openness is significant; it is always in the sense that greater openness is associated with higher growth. Edwards (1998) also uses an important number of openness indexes to investigate the trade and growth relationship. He considers a sample of 93 advanced and developing countries, and estimates a growth equation with a panel data random effects model. From that model, he computes factor shaves, which are then used to get TFP estimates. Concentrating on a cross-section of 1980s averages, TFP growth is finally regressed upon initial income level, initial human capital level, and no less than 9 openness indicators, each one of them iv in turn. The author reports that “in all but one of the 18 equations the estimated coefficient on the openness indicator has the expected sign and in the vast majority of cases it is significant”. Moreover, the coefficient on initial human capital is always significant and positive. Regarding the initial income level, the coefficient is always negative and in 16 cases out of 18, it is significant though very low, which can be interpreted as evidence in favor of conditional convergence. To summarize, the authors concludes that his results “are quite remarkable, suggesting with tremendous consistency that there is a significantly positive relationship between trade openness and growth”. An important paper that is able to cast serious doubts about the consistency of the trade-growth relationship is the one by Rodriguez and Rodrik (1999). These authors consider a series of previous research results, among which Dollar (1998). Sachs and Warner (1995), and Edwards (1998). The re-do the computations in these papers, but slightly change the specifications (through the addition of some dummies, e.g.), add newly available data to the sample, or iv slightly change the estimation methods. They are able to demonstrate a fundamental lack of robustness of the results in the paper they reviewed. Frankel and Romer (1999) claim that openness, as measured by the ratio of total trade to GDP, should not be used as explanatory variable in the growth regressions. The trade ratio, the authors argue, is endogenous, and needs to be instrumented. To construct their instrument, the authors first argue that “as the literature on the gravity model of trade demonstrates, geography is a powerful determinant of bilateral trade. And they claim this is also true for total trade. Moreover, geography is completely exogenous. Therefore, the authors consider a database of bilateral trade between 63 countries for 1985 and they regress bilateral trade upon purely geographical indicators. For each country, the fitted values of trade are aggregate over all partners, and this aggregate is finally turned into an “ideal” trade share that can be used as an instrument for the observed one. The authors then estimate growth equations for a cross-section of 150 countries in 1985. They report a substantial impact of trade openness on income iv growth: increasing the trade share by 1% should raise income by between 0.5% and 2%. These findings are robust to various changes in specifications. The results also suggest that, controlling for openness; larger countries tend to experience higher growth rates, which could simply reflect that citizens living in larger countries engage more in within country trade. Baldwin and Sbergani (2000) argue that the reason why researchers failed to find a robust relationship between trade and openness is because that relationship is fundamentally non linear and non-monotonic. They raise the point that the fundamental engine of growth is human and physical accumulation, and that the link between capital accumulation and trade barriers is, in nearly all models, non linear and often even non-monotonic. They provide a formal 2x2x2 dynamic model with imperfect competition that gives rise to (i) allshaped relationship between ad-valorem tariffs and growth and (ii) a bell-shaped relationship between specific tariffs and growth. This model is then confronted to the data, i.e. for a variety of openness indicators (actually, 10 of them are considered), a quadratic model is iv estimated. It turns out that, in this new specification, for 6 of the 10 proxies both the linear and the quadratic terms are significant individually. The authors conclude that: “allowing for non-linearity does have a big empirical impact”. A number of other studies have looked at the relationship between average tariff rates and growth. Lee (1993), Harrison (1996) and Edwards (1998) found negative relationship between the tariff rates and growth. The studies of Edwards (1992), Sala-i- Martin (1997) and Clemens and Williamson (2001) conclude that the relationship is weak. Rodriguez and Rodrick (1999) tried to replicate the result of Edwards (1998) and found that average tariff rates had a positive and significant relationship with total factor productivity (TFP) growth for a sample of 43 countries over the period 1980-1990. In a recent study Vanikkaya (2003) used a large number of openness measure for a cross-section of countries over the last three decades. His analysis found a significant positive correlation between trade shares and growth. However, this study observed that different measures of trade barriers are positively associated with growth in the iv less developed countries. In recent empirical studies, one or more of the following indicators of openness in the table below are used: Measure Definition Trade dependency The ratio of exports and import to GDP ratio Growth rate of exports The growth rate of exports over the specified period Tariff Averages A simple or trade-weighted average of tariff levels. Collected Tariff Ratios The ratio of tariff revenues to imports. Coverage of The percentage of goods covered by quantitative Quantitative restrictions. Restrictions Black market premium The black market premium for Foreign exchange, a proxy for the overall degree of external sector distortions. Trade Bias Index The extent to which policy increases the ratio of importable goods’ prices relative to exportable goods prices compared to the same ratio in world markets. Sachs And Warner A composite index that uses several trade-related Index indicators tariffs, quota coverage, black market premiums, social organization and the existence of export marketing boards. Learner’s Openness An index that estimates the difference between Index the actual trade flows and those that was expected from a theoretical trade model. Table1: openness indicators. iv (Rodriguez and Rodrik, 2000; Ogujiuba, Oji and Adenuga, 2004). Gross man and Helpman (991) and Matsuyama (1992) provide theoretical models where a technological backward country specializes in a non-dynamic sector as result of openness, thus losing out from the benefits of increasing returns. Underlying this result, there is an imperfection in contracts or in financial markets that makes people obey a myopic notion of comparative advantage. Dollar and Kraay (2004) and Loayza, Fajnzylber, and Calderon (2005) run growth regressions on panel data of large samples of countries. Both papers use openness indicators based on trade on trade volumes and control for their joint endogeneity and correlation with country-specific factors through GMM methods that involve taking differences of data and instruments. This implies that, although they continue to use cross –country data, these papers favors withincountry changes as the main sources of relevant variation. Both papers conclude that opening the economy to international trade brings about significant growth improvements. Wacziarg and Welch (2003) arrive iv to a similar, though more nuanced, conclusion from a methodological different stand point. Using an event-study methodology –where the event is defined as the year of substantial trade policy liberalization--, they find that liberalizing countries tend to experience significantly higher volume of trade, investment rates, and most importantly, growth rates. However, in an examination of 13 country-case studies Wacziarg and Welch find noticeable heterogeneity in the growth response to trade liberalization. Although their small sample does not allow for definite conclusions, it appears that the growth response after liberalization is positively related to conditions of political stability. Also, various empirical literatures offer some examples of nonlinear specifications considering interaction effects. On the related topic of foreign direct investment, Borensztein, De Gregorio and Lee (1998) find that the growth effect of FDI is significantly positive only when the host country has, respectively, sufficiently high human capital and financial depth. Specifically in the analysis of grow effects of trade openness, an important antecedent of our work is the iv empirical study by Bolaky and Freund (2004). Using cross-country regressions in levels and changes of per capita GDP and controlling for simultaneity via external instruments, they find that trade opening promotes economic growth only in country’s that are not excessively regulated. They argue that in highly regulated countries, growth does not accompany trade openness because resources are prevented from flowing to the most productive sectors and firms, and trade is likely to occur in goods where comparative advantage is actually missing. Calderon, Loayza, and Schmidt –Hebbel (2004) interact in their panel growth regressions a measure of openness (volume of trade /GDP) with linear and quadratic terms of GDP per capita, which they regard as proxy for overall development. They find that the growth effect of trade opening is nearly zero for low levels of per capita GDP, increases at a decreasing rate as income rises, and reaches a maximum at high levels of income. Chang, Kaltani and Loayza (2005) study how the effect of trade openness on economic growth depends on complementary reforms that help a country take advantage of international competition. They iv presented some panel evidence on how the growth effect of openness depends on a variety of structural characteristics. They use non-linear growth regression specification that interacts a proxy of trade openness with proxies of educational investment, financial depth, inflation, stabilization, public infrastructure, governance, labourmarket flexibility, ease of firm entry, and ease of firm exit. They find that the growth effects of openness are positive and economically significant if certain complementary reforms are undertaken. Giles and Stroomer (2005) develop flexible techniques for measuring the speed of output convergence between countries when such convergence may be of an unknown non-linear form. They then calculate these convergence speeds for various countries, in terms of half lives, using a time-series data-set for 88 countries. These calculations are based on both non parametric kernel regression and ‘fuzzy’ regression and the results are compared with more restrictive estimates based on the assumption of linear convergence. The calculated half-lives are regressed, again in various flexible ways, on cross-section data for the degree of openness to trade. They find iv evidence that favors the hypothesis that increased trade openness is associated with a faster rate of convergence in output between countries. Joffrey (2003) in his work, tries to clarify a number of issues related to the “trade openness and growth debate”. He considers a number of sector specialization indicators and examine whether they indeed affect the link between openness and growth. Using both cross-section and panel data techniques, he finds that both its pattern are likely to affect significantly the link between openness and growth. On research studies that relate to Africa and Nigeria in specific, Sarkar (2007) examines the relationship between openness (tradeGDP ratio) and growth. The cross-country panel data analysis of a sample 51 countries of the South during 1981-2002 shows that for only 11 rich and highly trade-dependent countries a higher real growth is associated with a higher trade share. Time series study of individual country experiences shows that the majority of the countries covered in the sample including the East Asian countries experienced no iv positive long-term relationship between openness and growth during 1961-2002. He finds that the experience of various regions and groups shows that only the middle income group exhibited a positive longterm relationship. Also, Baliamoune-Lutz and Ndikumana (2007) explore the argument that one of the causes of the limited growth effects of trade openness in Africa maybe the weakness of institutions. They also control for several major factors and, in particular, for export diversification, using a newly developed data set on Africa. Results from Arellano-Bond GMM estimations on panel data from African countries show that institutions play an important role in enhancing the growth effects of trade. They find that the joint effect of institutions and trade has U-shape, suggesting that as openness to trade reaches high levels, institution play a critical role in harnessing the trade-led engine of growth. The results from this paper are informative about the missing link between trade liberalization and growth in the case of African countries. Likewise, Ogujiuba, Oji and Adenuga (2004) test the validity of trade openness for Nigeria’s long- iv run growth using a co-integration approach. They preferred the VAR approach for some reasons and their econometric results show that there is no significant relationship between openness and economic growth, and that unbridled openness could have deleterious implications for growth of local industries, the real sector and government revenue. Moreover, Addison and Wodon (2007) study the macroeconomic volatility, private investment growth, and poverty in Nigeria. Using cross-sectional data for 87 countries, they show that real per-capita growth over the period 1980 -1994 was a function of productivity growth and investment rates, both of which were negatively affected by volatility (in terms of trade, real exchange rate, and public investments). When comparing Nigeria to high growth nations, they find that most of the growth differential can be attributed to Nigeria’s higher macroeconomic volatility. Simulations suggest that if Nigeria had lower levels of volatility and better macroeconomic policies, poverty would have been much lower than observed. iv Nwafor (unpublished) examines the effects reduction of import tariffs will have on poverty in Nigeria, using information on Nigeria’s past experience with trade liberalization he examined the possible impacts on the economy with a view to making the reductions propoor. Kandiero and Chitiga (2003) investigate the impact of openness to trade on the FDI inflow to Africa. Specifically, in addition to economy wide trade openness, they analyze the impact on FDI of openness and manufactured goods, primary commodities and services. The empirical work is conducted using cross-country data comprising of African countries observed over four periods: 1980-1985, 19851990, 1990-1995, and 1995-2001, they find that FDI to GDP ratio responds well to increased openness in the whole economy and in the services sector in particular. Finally, Njikam, Binam and Tachi (2006) assess the factors behind differences, in total factor productivity (TFP) across subSahara Africa (SSA) countries over the period 1965-2000. The crosssection, fixed effects using annual data, fixed-effects using data in 3year averages as well as the seemingly unrelated regression (SUR) iv results show that (i) openness to world trade is conducive to TFP in SSA region only if issues related to supply conditions such as poor transport and communication infrastructure, erratic supply of electric energy. Corruption and bad governance, insufficient education of the labour force etc are adequately addressed, (ii) physical capital accumulation is important for TFP, (iii) the size of the financial sector mattes for TFP, in some SSA countries and negative for TFP in other SSA countries. 2.3 LIMITATION OF PREVIOUS STUDIES The literatures of previous studies are plagued with a lot of problems. First of all, it is worthwhile to note that the theoretical growth literature has given more attention to the relationship between trade policies and growth rather than the relationship between trade volumes and growth. Therefore the conclusion about the relationship between trade barriers and growth cannot be directly applied to the effects of changes in trade volumes on growth. There was also no consensus on the nature of the relationship and nature of linear association (correlation) between openness and growth. Likewise, there is no generally accepted measure of openness indicators as it suit and please the researcher(s). Moreover, many of the existing empirical iv literature are not country-specific, that is they deal with crosssectional analysis, thus they did not provide for differential in nature and structure of various economies. Hence, developing countries like Nigeria are recommended policies which are based on research conducted for industrially advanced countries or even mixture of both. CHAPTER THREE METHODOLOGY 3.1 ANALYTICAL FRAMEWORK The primary aim of every economic research is to arrive at a conjunction of economic theory, actual measurement using the theory and techniques of statistical inferences as the matching bridge (Haavelmo, 1994). The economic theory makes statement or postulates hypothesis that are mostly quantitative (and some cases qualitative) in nature and as such, it is the choice of the modeler or the researcher to validate these hypothesis using appropriate models in line with current development and betting method of estimation and inference. iv Economic theory and some empirical research argue that openness (trade or financial) will definitely increase output growth while others opened that the relationship between the two is ambiguous. In order to contribute empirically to this argument, this study will employ econometric method as the research technique. The choice of method is necessitated by the nature of the study which in this case is an analysis of relationship among variables. 3.2 MODEL SPECIFICATION An economic model is a representation of the basic features of an economic phenomenon; it is an abstraction of the real world (Fonta, Ichoku and Anumudu, 2003). The specification of a model is based on the available information relevant to the study in question. This is to say, the formulation of an economic model is dependent on available information on the study as embedded in standard economic theory and other major empirical works, or else, the model would be theoretical. Two models are postulated in this research work; the first is a non-monotonic model to capture the first and second objective of iv the study, while the second is an analysis of covariance (ANCOVA) model. The functional form of these models can be specified as follows: Model I: RGDPt = (TPNt,TPNt2, RERt, RIRt, UNEMPt, TREND)…..(i) Mode II: RGPt = f (DUMt, TREND, (DUMt*, TREND))…….(ii) The mathematical form of the model can be expressed as: Model I: RGDPt: o + 1TPNt + 2TPNt2 + 3RERt + 4RIRt +5UNEMPt + 6TREND Model II: RGDPt = o + iDUMt + 2TREND + 3(DUM*t TREND) ------------------------------------------------------------------------(iv) But equations (iii) and (iv) above are exact or deterministic in nature. In order to allow for the inexact relationship among the variables as in the case of most economic variables stochastic error term “μ t” is iv added to both equations. Thus, we can express the econometric form of the models as: Model I: RGDPt = o + 1TPNt + 2TPn2t + 3RERt + 4RIRt + 5UNEMPt + 6TREND + μit -------------------------------------(v) Model II: RGDPt = β0 + β1DUMt + β2TREND + β3(DUM*t TREND) +µ2t ------------------------------------------------------------------(vi) Where RGDP = Real Gross Domestic Product which is a proxy for the real output of the economy. TPN = The Degree of openness measured as trade – GDP ratio i.e. (import + Export)/GDP TPN2 = Real exchange rate RIR = Real Interest Rate UNEMP = Unemployment Rate DUM = O for pre-SAP period observations I for post –SAP period observations TREND = The chronological arrangement of time iv µ = The stochastic error term In order to properly estimate the parameters of the postulated models, we rescale the dependent variable by logging it, thus, transforming them into a log-line models as follow: Model I: LOG (RGDPt) = o+ 1TPNt + 2TPN2t +3RERt + 4RIRt + 5UNEMPt +6TREND + µit -------------------- --------------(viii) Model II: LOG (RGDPt) = β0+β1DUMt+β2TREND +β3 (DUM*tTREND) +µ2t -------------------------------------------------(viii) Also, in order to avoid a spurious regression, we subject each of the variables used to unit root (or stationary) test so as to determine their orders of integration, since unit root problem is a common feature of most time-series data. 3.2.1 TEST OF STATIONARY A stochastic process is said to stationary if its mean and variance are constant overtime and the value are auto-covariance between the two time period depends only on the distance or lay iv between the two time periods and not the actual time at which the covariance is computed (Gujarati, 2003). In other word, a stationary stochastic process is one with constant mean, variance and covariance. Hence, stationarity test is carried out to verify whether a time series is stationary or time-invariant so as to avoid a spurious regression. The Phillips-perron (pp) unit roof test will be employed. The choice of this test is to correct for some anomalies associated with the conventional Augmented Dickey-Fuller (ADF) test. The Phillipsperron test use non-parametric statistical methods to take care of the serial correlation in the error terms without adding lagged difference terms. This test is specified thus: ∆Yt = +∆Yt-1+ μt Where ∆ = difference operator Yt = Time series μt =Pure white noise. Under the null hypothesis that = 1 for stationarity, we use the PP test statistics to verify the presence of unit root in the series. 3.2.2 TEST OF COINTEGRATION iv Economically, two (or more) variables will be co integrated if they have a long term, or equilibrium, relationship between (or among) them (Gujarati, 2003). Individual time – series in a model may be spurious but their linear combination may not. This is the purpose of co-integration test. The augmented Engle-Granger (AEG) test will be employed to validate this hypothesis. This hypothesis is of two stages: We will run the regression of equation (vii) and generate the residual. The residual generated to unit root test. If the generated residual is stationary at level form or integrated of order zero i.e. 1(0), then the variables of the model are cointegrated. The AEG test is specified as: ∆t = t -1-+i∑ ∆t-1 +ℓt Where t=errors generated from congregating regression t-1= one period lag of the co-integrating-digression error. ∑ = summation sign P = amount of lag used iv ℓt = white noise error. If is statistically significant at the chosen level, then the variables of model 11 in equation (vii) are co integrated. 3.2.3 ERROR CORRECTION MODEL An important issue in econometric is the need to integrate short run dynamics with long run equilibrium. The analysis of short run dynamics is often done by first eliminating trends in variables, usually differencing. But this differencing procedure, however, throws away potentials valuable information about long run relationship which economic theories have a lot to say about. The error correction model (ECM) is an extension of short run disequilibrium models, which also incorporates past period’s disequilibrium. The Granger representation theorem states that if two (or more) variables, Y and X (s), are co-integrated, the relationship between (or among) then can be expressed as error correction mechanism. The ECM requires that each time-series is included into the model to the model at its order of integration. Also added to the ECM is the one period lay of the error term generated from the co-integrating iv regression whose variables are integrated of the same order. The generated error term is treated as the equilibrium error” and can be used to link the short run dynamics with the long run equilibrium. The ECM for model I of equation (vii) is specified as: ∆ko LOG (RGDPt) =Ø0+Ø1∆k1TPNt+Ø2∆k2TPN2t+ Ø3∆k3RER+ Ø4∆k4RIRt+ Ø5∆k5UNEMPt+ECMt-1+∑t ---(ix) The variables are co-integrated if and only if K0 = k1, k2, k3, k4, k5 Where ∆ = difference operator Ki = order of integration of a particular series. ECMt-I= error correction mechanism which is the last Period equilibrium error. ∑ = pure white noise error. 3.3 JUSTIFICATION OF THE MODEL The choice of a non-monotonic model in this work is triggered by the fact that, though, trade openness at the early stage of iv introduction into a developing country like Nigeria would certain have a different structure and pattern where compared with its long run effect on the economy’s output performance. This fact is embedded in the standard development economic theory and buttress by Baliamoune- Lutz and Ndikumana (2007). The explanation follows suit: at the early stage, when a developing country like Nigeria open up its economy for trade, its domestics firms will face intense competition with the “tiger” foreign firms as the entire market will be flooded with imported products which are cheaper and relatively better in terms of qualify than the domestically produced products. This will make some of the infant industries to loss their sales with less revenue along side with high cost of production. This is unlike the foreign firms that enjoy low cost of production either economies of large scale production. As a result, many domestic firms will be forced out of the market. This will surely have a negative effect on the economy. Nevertheless, as the economy is acquiring new technologies from abroad via openness as well as improving on its domestic iv infrastructure and capacity utilization of resources in the long run, this will lead to low cost of production for the domestic infant industries and enable them to compete favorably with the foreign products in the marked. This will certainly have positive effect on the economy as its domestic production capacity will increase which will further lead to increase in export products, thus, having favorable balance of payment. Another argument also suggests that developing countries should look inward to achieve its development in the long run. From the on going discussion, it is evident that fitting a monotonic model for such a situation would either over estimate or under estimate the actual potential of the economy; hence the need for a non-monotonic model. The postulated model is a real model as its variables are all in real form except unemployment. While the degree of openness and real exchange rate represent the external shocks to the economy, the real interest rate and unemployment rate represent the internal shocks to the economy. Meanwhile, the second model is constructed to test for structural charge in the growth output before and after the introduction iv of the structural Adjustment Program (SAP) in 1986. Finally, the Error Correlation Model (ECM) is postulated so as to capture the linkage between the short run dynamics of the economy and the long run equilibrium of the economy. 3.4 ESTIMATION TECHNIQUES In order to develop strong, robust and reliable models that capture the relationship before trade openness and output growth, the research work adopts the econometric techniques of the NonMonotonic modeling and the Analysis of Covariance (ANCOVA) modeling. In building these models, the Ordinary Least Square (OLS) is used as the estimation technique. The method of OLS is extensively used in regression analysis primarily because it is initiatively appealing and mathematically much simpler than nay other econometric technique (Gujarati, 2003). The OLS method is based on some assumptions (see Gujarati, 2003) which make the OLS estimators to become Blue (Best linear Unbiased Estimator). Some of the short comings of the OLS method include the fact that while some of its assumptions are unrealistic iv (such as no autocorrelation, homoscedasticity and no multicollinearity); a single model as well can not fully satisfy all the assumptions at a time. Also, no single test can solve all the problems of this method at a time. Moreover, the OLS method can not be applied to purely non-linear models such as ones that are non-linear in parameter. As a result of some of these short-comings, we use the OLS method but correct the stand errors for autocorrelation by a Newey-West method. The corrected standard errors are known as HAC (Heteroscedasticity –and autocorrelation-Consistent) standard errors or simply as Newey-West standard errors. Hence, we have to apply individual initiative along side with the empirical rules and tests so as to obtain tenable and robust results. Thus, an econometric modeling is said to be more of an art than a science. 3.5 3.5.1 EVALUATION PROCEDURE ECONOMIC TEST (A Priori Expectation) Tests shall be conducted to ascertain the a priori expectations which examine magnitude and signs of the parameter estimates. This iv evaluation is guided by economic theory. The aim of this test is to conform whether the parameter estimates conform to a priori expectation. The variables used in the model and their a priori expectations are analyzed below in table (2). Variables Definition Expected sign RGDPt This is Real Gross Domestic product which represent the real output represent the real output of the economy. Its natural logarithm is taken This is the degree of trade openness in an economy it measures the international competitiveness of an economy in the global marked. It is an external stock to the economy. This is the squared term of the degree of trade openness. It shows the structural pattern of openness relative to output growth This is the real exchange rate. It is the rate at which the domestic currency is being exchanged for the foreign currency with adjustment for relative price index; it is an external shock to the economy. This is the real interest rate. It is the real cost of borrowing fund in the financial market. It is an internal shock. This is the unemployment rate in the It is the dependent variable and considered to be stochastic. economy. It is an internal shock. negative. TPNt TPNt2 RERt RIRt UNEMPt It is expected positive. to be Since the structural pattern could be of any type, it can be positive or negative. It is expected negative. to be It is expected be negative It is expected to be iv DUMt TREND ECMt-1 This is a dummy variable introduced to capture the effect of the structural Adjustment Programme (SAP) trade deregulation and liberalization policies. This is the chronological arrangement of time which captures the incremental growth of output over time. It also serves the purpose of detrending the fluctuations among the exogenous variables. This is the error correction mechanism. Since the effect of a policy could be favorable or adverse, its signs can be positive or negative. It is expected to be positive. It is expected negative. Table 2: a priori expectations 3.5.2 STATISTICAL (FIRST ORDER) TEST Here, various statistical tests will be carried out so as to verify the acceptability, reliability, and robustness of the estimated regression result. The tests include: Student t-Test This is used to test the statistical significance of the individual parameter estimates in the regression models. This work will use the tdistribution to test the statistical significance of these parameter estimates. F-Test to be iv This is used to test for the overall significance of the model. It tests the simultaneous null hypothesis of all the parameter to be equal to zero in the regression model. R2-Coefficient of Determination This test is used to measure the goodness of fit of a regression line. It measures the proportion of the total variation in the dependent variable explain by the repressors in the model. r-Coefficient of Correlation This measures the significance of the strength of linear association (correlation). The correlation coefficient measure the degree of association between two variables (such as TPN and RGDP in the case of this work). It is obtained through the product moment correlation coefficient. 3.5.3 ECONOMETRIC (SECOND ORDER) TEST Here, various tests will be carried out in order to verify whether the estimated regression results conform to the classical (normal) linear regression model assumption. This test includes: iv Test of Normality This test is used to verify whether the error term is normally distributed. The Jacque-Beva (JB) test will be used to verify this assumption. Test of Heteroscedasticity This test is used to verify the assumption of equal spread of the error variance (homoscedastic) between members of the same series of observations. The white’s heteroscedasticity test (with no cross term) will be employed in the test. Test of Autocorrelation This test is used to verify the randomness of the error term between members of the same series of observations. As a result of the numerous assumptions and problems associated with the conventional Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test will be employed to verify this hypothesis. Test of Specification Error iv This test is used to verify whether the econometric regression model being estimated is correctly specified. The Ramsey’s RESET (Regression Specification Error Test) will be employed. Forecast Test This test is used to verify the reliability of the estimated regression model in forecasting future values. The Henry Theil’s in equality coefficient will be used to evaluate the forecasting performance of the model. 3.6 SOURCES OF DATA AND SOFTWARE FOR ESTIMATION Data is the most important materials for any economic research or analysis, and very much indispensable to the field of econometrics indeed. Gugarati (2003 asserted that the success of any econometric analysis ultimately depends on the availability of appropriate and accurate data. In other words, the researcher should always keep in mind that the results of research are only as good as the quality of the data. iv The research study makes use of secondary data. The data used are obtained from the Central Bank of Nigeria (CBN) statistical bulletin 2007 and National Bureau of Statistics (NBS) online publication for 2006. The percentage ratio and real values are computes by the researcher in order to capture the objective of the study and in congruence with economic theory. The econometric software packages used for the analysis of this work are the Reviews 3.1 and SPSS 14 versions, white the Microsoft Excel 2003 is used to enter the data. CHAPTER FOUR PRESENTATION AND ANALYSIS OF RESULTS iv 4.1 INTRODUCTION The purpose of this chapter is for presentation, evaluation and analysis of the regression results of the models postulated as well as verification of the various working hypothesis of this research which are drawn from the objective of the study. The results of the OLS regressions of the first and second models are presented below. The parameter estimates are also subjected to various economic, statistical and econometric tests. Finally, we analyze our results in order to verify whether they conform to the working hypothesis of this study. 4.2 PRESENTATIONS OF REGRESSION RESULTS The ordinary least square (OLS) regression results for first and second models are presented below: Model 1: iv Dependent variable: LOG (RGDP) Variable Coefficient C 10.18152 TPN 0.025450 2 TPN 0.000190 RER 5.67E-05 RIR -0.006919 UNEMP 0.012686 TREND 0.019105 Table 3: results of model 1 Std. Error 0.134217 0.006052 5.00E-05 1.51E-05 0.003754 0.004581 0.002605 R2 = 0.924212 Adjusted R2 =0.909543 t-statistic 75.86839 4.205461 -3.811704 3.757570 -1.842949 2.769490 7.333272 Probability 0.0000 0.0002 0.0006 0.0007 0.0749 0.0094 0.0000 F-statistic = 63.00557 D-W statistic = 1.790166 For full detail of model 1 results see appendix (1) Model II: Dependent variable: LOG (RGDP) Variable Coefficient C 11.21105 DUM -0.772697 TREND 0.002437 DUM* 0.040715 TREND Table 4: results of model II R2 = 0.847398 Std. Error 0.136498 0.172093 0.013885 0 .014603 t-statistic 82.13329 -4.489996 0.175541 2.788147 F-statistic = 62.93409 Adjusted R2 = 0.833333 D-W statistics =0.556859 Consult appendix (II) for complete results of Probability 0.0000 0.0001 0.8617 0.0086 iv Mode II 4.2.1 TEST OF STATIONAIRY In order not be run a spurious regression, we subjected the variables of the above model to unit root test. The Phillips-Perron (PP0) unit root test is carried out to do this. The Phillips-Perron test is based on the OLS estimate ά of in the model. ∆Yt = + ∆Yt-1+ μt The hypothesis to be tested here is: Ho: = 1 (stationary) H1/ / <1 (non –stationary) Decision Rule: Reject the null hypothesis of stationarity if the absolute value of the calculated PP-statistic is less than the absolute value of the critical PP – statistic. Otherwise, do not reject Ho. The stationarity test results for the variables are presented in the table below: Level form First difference iv variable Critical values pp-statistic RGDP TPN TPN2 RER RIR UNEMP 1.306661 -2.749627 -3.346943 1.482691 -1.825652 -1.690459 critical values pp-statistic 1% NS NS NS NS NS NS 5% NS NS NS NS NS NS 10% NS S S NS NS NS -6.112691 -11.03146 -12.84645 -4.042057 -9.541773 -7.306097 1% S S S S S S 5% S S S S S S Table 5: Results of stationarity test Where NS = non –stationarity S = stationarity The Mackinnon critical values are: 1% critical value = -3.6228 5% critical value = -2.9446 10% critical value = -2.6105 From the above table, it easy to deduce that all the variables are only stationary after first difference at 1%, 5% and 10% critical levels respectively. That is to say they are all integrated of order one i.e 1(1). This means that the mean variance and autocovariance of each series 10% S S S S S S iv are costant overtime after difference at the chosen critical level. For comprehensive results of the stationarity test, consult appendix (III). 4.2.2 TEST OF COINTEGRATION Even though individual series in the models above seems not to be stationary at level form, it is possible that their linear combination may be stationary at level form. Hence, they will be co integrated. That is to say, there exist a long run relationship between the dependent variable and the independent variables. In order to verify this hypothesis, we use the Augmented Engle-Granger (AEG) cointegration test. This test makes use of the residuals generated from the cointegrating regression and subjects it to unit root test using the Augmented Dickey-Fuller (ADF) test. The AEG test is specified as ∆μt = μt -1 +∑ i∆μt-1 + ℓt Where μ = The generated residual series ℓt = pure white noise error. The hypothesis to be tested is: Ho: = 0 (non cointegration) iv H1: ≠ 0 (cointegraton) Decision Rule: Reject Ho if the absolute value of the ADF test statistic is greater than the absolute critical value at the chosen level of significance for the generated residual series; otherwise, do not reject Ho: The result is presented below: Variable Residual (μt) ADF statistic -4.145516 1% -3.6228 Critical values 5% 10% -2.9446 -2.6105 Table 6: Result of Cointegration Test From the result obtained, we therefore do reject Ho and conclude that there exist cointegration among the variables i.e there is a longrun relationship among the variables of the model at the chosen critical level. For detailed result of the cointegration test, see appendix (iv). 4.2.3 THE ERROR CORRECTION MODEL (ECM) The existence of cointegration among the variable of the model which we verified above necessitates the need for the postulation of iv the error correction model (ECM). This model aims to link the short run dynamics with the longrun equilibrium the result of the ECM is presented below. Dependent variable: DLOG (RGDP). Variable Coefficient Std. Error t-statistic Probability C D(TPN) D(TPN2) D(RER) D(RIR) D(UNEMP) ECMt- 1 0.031238 0.013803 -9.41E-05 -1.08E-05 -0.003360 -0.002548 -0.1785507 0.015978 0.005890 4.40E-05 1.58E-05 0.001588 0.003612 0.72896 1.955105 2.343573 -2.140235 -0.682625 -2.115853 0.705384 -4.543232 0.0599 0.0259 0.0406 0.5001 0.0428 0.4860 0.0001 Table 7: Result of the Error correction model R2 = 0.502682 F-statistic = 5.053938 Adjusted R2=0.403219 D-W statstic=1.407372 see appendix (v) for comprehensive result of the error correction model. 4.3 4.3.1 INTERPRETATION AND EVALUATION OF RESULTS EVALUATION BASED ON ECONOMIC CRITERIA iv In this section, we present the economic interpretation of the regression results and verify whether parameter estimates in each model conform to a priori expectation. Model 1: In the first model, the dependent variable is the log of real GDP while the independent variables are: Degree of openness to trade, the squared term of the openness to trade real exchange rate real interest rate, unemployment rate and the trend value. CONSTANT(C): In the first model, the constant coefficient of 10.18152 represent the value of log of RGDP at the beginning of the study period i.e. LOG (RGDP) = 10.18152. By taking the antilog, we obtain that the value of real GDP at the beginning of the study period (i.e. 1969) is N26.410.58 million (=e 10.18152), other factors held constant. DEGREE OF OPENNESS TO TRADE (TPN): The sign of its coefficient is positive. This conforms to the standard economic theory which postulates that trade openness enhance economic growth. The coefficient of 0.025450 implies that over the study period, on average, iv a one percentage (1%) increase in the degree of trade openness leads to approximately 2.55% (0.025450 x100%) increase in output growth. This early stage increase in output growth as a result of openness to trade may be due to internal vibrancy of government objectives, development of infrastructure and, indeed, the oil boom of the 1970’s. SQUARED TERM OF DEGREE OF OPENNESS (TPN2): The coefficient of the squared TPN of -0.0019 implies that as an economy continue to open its border to external trade over a longer time, output growth begins to decline at an approximate rate of 0.02% (=-0.00019 x 100%). This result shows the effect of unregulated openness to trade both in the short run and long run (see fig. 5 below). During the oil boom of the 1970’s, which led to greater output growth, huge income was generated. This realized income was further spent lavishly on luxuries which were mostly imported goods. Also the deregulation of the external sector during the IMF/Word Bank – Sponsored Structural Adjustment Programme (SAP) of 1986 is another point here. Hence, iv the international competitiveness of the economy continues to dwindle which further led to decline in output growth proxy by real GDP. Figure 5: Non-Monotonic relationship between TPN and RGDP. RGDP O observed Quadratic 200000.000000000000 150000.000000000000 100000.000000000000 50000.000000000000 20.00 40.00 TPN 60.00 80.00 iv REAL EXCHANGER RATE (RER): The sign of the real exchange rate coefficient is positive. This does not conform to the theoretical postulation which stressed that as foreign currency say (dollar) appreciate (negative) against the domestic currency (say Naira), exports will become cheaper while imports will be more expensive, hence, greater net export which turn means increase in GDP (output). Thus there should be a negative relationship between RER and RGDP. The coefficient of 5.67E-05 means that over the period of study, a 1% increase in real exchange rate, on average, leads to approximately 0.006% (=0.00000567 x100%) increase in the output growth (RGDP). Although the economic impact of RER on RGDP in Nigeria is very small, it is however statistically significant. This result indirectly shows the magnitude of the impact of the foreign exchange market to the growth of the economy. The foreign exchange market is regarded as the largest market in the world. However, the result shows that it has little impact on the Nigerian economy. This may be as a result of the fact that the exchange market in Nigerian is largely dominated by the parallel market (fondly known as black market) iv which is regarded as part of underground economy and not accounted in the national income computation. Also, the sign of real exchange rate is positive. This may be as a result of inconsistency in government policies with regard to exchange rate. REAL INTEREST RATE (RIR): The sign of the real interest rate coefficient is negative. This conforms to a priori expectation as increase in rate of interest leads to rise in cost of borrowing which discourages investors from borrowing for investment purpose, thus, reducing investment level; hence, reducing productivity and output. The result further shows that during the study period 1% increase in real rate of interest will on average lead to approximately 0.69% (=0.006919 x 100%) decrease in real GDP. But this result is not statistically significant. This reflects the low level of development of the financial sector in the economy especially the money and capital markets. The result further implies that the financial sector instrument (interest rate) does not have significant economic impact in determining the level of output growth in Nigeria. iv UNEMPLOYMENT RATE (UNEMP): The coefficient of unemployment rate has a positive sign. This does not conform to economic theory which postulates that a rise in unemployment level will reduce productivity, hence output growth. The unemployment rate coefficient of 0.012686 indicates that a 1% increase in unemployment rate, on average, leads to approximately 1.27% increase (0.012686 x 100%) in real GDP. This kind of result is only possible when the impact f a few highly skill labour, employed at the expense of much unskilled labour that is laidoff is economically significant. One of the arguments in favour of openness to trade is that new technologies and skills in the production process may require more of capital and little labour. Hence, a branch of undeveloped, inept and sluggish labour is retired to the labour market causing high rate of unemployment. Hence, the simultaneous coexistence of increased growth of output as a result of improved skill and technology on one hand, and high unemployment rate on the other hand in the economy. Nevertheless, the proportional relationship between unemployment and real GDP also indicate the explosive iv nature of the unemployment rate which has a significant impact on industrial development in the nation. Moreover, the result reflects the impotence of various government policies to curb unemployment in the face of stagnation and fluctuation of macroeconomic indicators in the economy. TREND: The sign of the trend value is positive which conforms to a priori expectation. The 0.019105 trend coefficient indicates that, on average, output growth (proxy by RGDP) increased at the rate of 1.91% (0.019105 x100%) per annum, other variables held constant. But the compound rate of growth of output over the study period is approximately 1.93% i.e. [(e0.019105-1) × 100%].0.02% difference between the actual and compound rate of growth is due to the compounding effect. Moreover, the trend coefficient is highly significant to detrend the time relationship among the explanatory variables. Model II: iv This is an ANCOVA model which comprises of both discrete and continuous variables at the same time. We observe that the dummy is assigned zero (0) for pre-SAP era and one (1) for post-SAP era. The expectations of the regression results for the pre-SAP and post-SAP era are given as: E [LOG (RGDPt)/DUMt = 0] = 11.21105 + 0.002437 TREND E [LOG (RGDPt)/DUMt = 1] =10.438353 +0.043152 TREND This shows that the median value of the LOG of real GDP for the preSAP era is N73,943.01 million (=e11.21105). Also, the 0.002437 trend coefficient indicates that the compound rate of growth of output for the pre-SAP period is approximately 0.244% [=(e0.002437-1) x 100%]. But this rate of output growth is not statistically significant i.e. the trend is flat. This implies that during the pre-SAP era, there is no significant increase in output growth for that period of time. This may as a result of impromptu oil boom of the 1970’s and the crowding-out effect that follows suit on the economy between late 1970’s and the early 1890’s. Another reason may be due to political instability and iv policy inconsistency exhibited by various government of the economy. The differential intercept dummy coefficient of 0.772697 implies that there is a decline in the median value of log of real GDP for the post-SAP era by 0.772697 when compared with that of preSAP era. This difference in median value of output growth between the two periods under study is highly significant. The median value of real GDP of post SAP era is N34,144.37 million i.e. (=e11.21105-0.772697). Meanwhile, the differential (dummy* trend) slope of 0.04715 implies that during the post –SAP era, the rate of growth of output (RGDP) has risen to approximately 4.41%[=(e(0.002437+0.040715))×100%] as against growth rate of about 0.244% during the pre-SAP era. This implies that during the period of post-SAP, output growth increase at a cumulative rate of 4.41%. This slope differential is statistically significant. Since both the differential intercept and differential slope are statistically significant. Since both the differential intercept and differential slope are statistically significant, we therefore conclude iv that there is structural break in the growth of output over the sample period. This implies that the trade liberalization policy of the SAP has a positive impact on Nigerian economy as it leads to greater output growth rate. THE ERROR CORRECTION MODEL In the ECM, the coefficient of the differenced variables reflects the short run dynamics. In this model, all the variables conform to the priori expectation. Also all the variables are statistically significant except for the RER and UNEMP. The Error Correction Mechanism (ECMt-1) is also negative which conform to a priori expectation. The negative value of the ECM implies that output growth (proxy by RGDP) is above equilibrium and will start falling in the next period to correct the equilibrium error. The coefficient of -0.785507 implies that about 79% (0.785507 x100%) of the equilibrium error will be corrected in the next period. That is, RGDP will adjust to equilibrium by about 79% in the next period. The speed of adjustment of the ECM iv is sufficiently high to correct the imbalance in the macro economic fluctuation. The 0.013803 coefficient of the differenced TPN implies that in the short run, a 1% rise in degree of openness will lead to about 1.38% (=0.013803 x100%) increase in RGDP. The differenced TPN2 coefficient of -9.41E-05 indicates that a further 1% increase in squared term of the degree of openness will lead to a minute decline in RGDP by about 0.009% (see fig.5 above). Also, a 1% increase in real interest rate (RIR) will lead to about 0.001% decline in RGDP in the short run. Nevertheless, this result is not statistically significant. Moreover, on the short run a 1% increase in the real interest rate (RIR) will lead to about 0.34% decline in real GDP. Furthermore, a 1% rise in unemployment rate will cause output growth fall by about 0.25% in the short run. However, this result does not have much economic impact. 4.3.2 CRITERIA EVALUATION BASED ON STATISTICAL iv Here, the t-test, f-test and R2 (coefficient of determination) are carried out to test the statistical reliability to the estimated parameters and the regression results in general. In order to avoid repetition, these tests are carried out on the regression results of the first model only. We will also test for the degree of association between RGDP and TPN using the product moment correction coefficient ®. t-Test This is used to determine the statistical significance of individual parameter in a model. The hypothesis to be tested is: Ho: βi = 0 (the estimated parameter is statistically insignificant). Hi: βi ≠ 0 (The estimated parameter is statistically significant). The test statistic is given as: T = β^i ~t /2 (n-k)df Se(β^i) The critical value is obtained from the students iv t-distribution table at ( /2) level of significance and (n-k) degree of freedom. Decision Rule: Reject Ho if/t-cal/>t /2(n-k) df, otherwise do not reject. Alternatively, using the rule of thumb, we could reject Ho if/t -cal/>2. At ∞ = 0.05, n-38, k=7 t /2 (n-k)df =t0.025(31)≈ 2.042. The calculated t-values are presented below on the result obtained from the regression on model 1. Variable C t-statistic 75.868 Critical 2.042 Decision /t/>t*:Reject H1 TPN 4.205 2.042 /t/>t*:Reject Ho TPN -3.812 2.042 /t/>t*:Reject Ho RER 3.758 2.042 /t/>t*:Reject Ho RIR -1.843 2.042 UNEMP 2.769 2.042 /t/<*: do not Reject Ho /t/>*: Reject Ho TREND 7.333 2.042 /t/>*:Reject Ho Table 8: Summary of the t-test Conclusion Statistically significant Statistically significant Statistically significant Statistically significant Statistically insignificant Statistically significant Statistically significant iv From the results displayed in the table above, we conclude that all the parameter estimates, are statistically at 5% level of significance except for real interest rate which is statistically significant at 10% critical level. F-Test This measures the overall significance of the regression model. The F-value provides a test of the null hypothesis that the true slope coefficients are simultaneously zero. That is: Ho: x1 = 2 = 3 = 4 = 5 = 6= 0 (the model is statistically insignificant) Hi: ≠ 2≠ 3 ≠ 4 ≠ 5≠ 6 ≠0 (the model is statically significant) The test statistic is given 25: F = ESS/(k-1)~ F (k-1, n-k)df RSS/(n-k) Where ESS=estimated sum of square RSS = Residual sum of square The critical value is obtained from the F-distribution table at significance and (k-1, n-k) degree of freedom. Decision Rule: Reject Ho if F-statistic>f (k-1, n-k)df ; level of iv Otherwise, do not reject Ho From the regression result F-statistic = 63.00557 At =0.05, n=38, k=7 F (k-1, n-k)df = F0.05(6,31) ≈ 2.42. Conclusion: Since f-statistic =63.00557 is greater than the critical F=2.42, we thereby reject Ho and conclude that the model has a robust fit and it is statistical significant. That means there exist a true relationship between the regression and the regresses. R2 (Coefficient of Determination) This measures the goodness of fit of the estimated model. The R2 measure the proportion of total variation in the regress and explained by the regression model. From the regression result the R2 is 0.924212 while the adjusted R2 is 0.909543. This means that the model explain about 92% of the total variation in real GDP (output growth). This high R2 cannot be said to be statistically significant for the true goodness of fit in a model unless subjected to test. The hypothesis to be verified here is: iv Ho: R2 = 0 (the R2 is statistically insignificant) H1: R2 ≠ 0 (the R2 is statistically significant) The test statistic for the critical R2 is given as R2 = (k-1)f (k-1)f + (n-k) Where f is the critical-f value at level of significance, k is the number of parameters in the model and n is the number of observations. Decision Rule: Reject Ho if observed R2 is greater than the critical R2: otherwise do not reject Ho. From the regression result; Observed R2 ≈ 0.924 At = 0.05, k=7, n=38 R2 = 0.319. Conclusion: Since observed R2 = 0.924 is greater than the critical R2 = 0.319, we thereby reject Ho and conclude that the coefficient of determination (R2) is statistically significant and a true goodness of fit for the model. r (coefficient of correlation) iv This is a measure of the degree of association between two variables. It measures the strength of degree of linear association between two variables. The product moment correlation coefficient is given as r= n∑x1y1-(∑y1) √(n∑x12 – (∑x1)2][n∑y12-(∑y1)2) When the coefficient of correlation (r) between real GDP (x1) and degree of openness (yi) is computed, it is observed that: r = 0.594989 This result shows that there is positive relationship (or linear association) between output growth and trade openness. The correlation of about +0.6 implies that there is a positive modern linear association or correlation between openness and output growth in Nigeria. But is this correlation coefficient statistically significant? To answer this question, we subject the correlation result to test. The hypothesis to verify is. Ho: 0 = 0 (true linear association does not exist between the two variables) H1: 0 ≠ 0 (true linear association exist between the two variables). iv The test statistics is given as; Z = x –µ ~N(0,1) б Where x =1/2 ln 1-r 1-r μ = ½ ln 1-0 1-0 1 =√ n-3 The critical value is obtained from the standardized normal distribution ( /2) level of significance. Decision Rule: Reject Ho if /Zcal/> Z ∞12, otherwise do not reject Ho. After simple computation, we observed that X =0.68535,. μ=0, б=0.16093 :. Zcal = 4.056 Also, zt =0.05 Z 12=Z0.025 = 1.96 Conclusion: Since Zcal =4.0546 is greater than Z /2 =1.96, we therefore reject Ho and conclude that the correlation coefficient of trade open and real GDP is statistically significant. This means that iv linear association (correlation) truly exists between the two variables at 5% level of significance. 4.3.3 EVALUATION BASED ON ECONOMETRIC CRITERIA Here, various tests are conducted in order to test for robustness and reliability of results obtained from the OLS regression. These tests will also help us to verify the fulfillment of the assumptions underlying the classical (normal) linear regression model (CLRM) as used within study. The tests to be conducted include. Normality test, multi-co linearity test, Heteroscedasticity test, Autocorrelation test, test of specification errors and forecast test. Normal Test One of the assumptions of the CLRM is that the error terms are normally distributed with zero mean and constant variance i.e. μt~N (0,б2) The normality test is conducted to verify whether the error terms are normally distributed. The Jacque – Bera (JB) test of normality is used to verify this assumption. This test is based on the iv residuals generated from OLS. Under the null hypothesis that the residuals are normally distributed, the JB statistic asymptotically follows the chi-square distribution with two (2) degrees of freedom. The test statistic given as: JB = n s2 +(k-3)2 Nx2 6 24 (2)df Decision Rule: Reject the null hypothesis of normality if JB –statistic exceeds critical JB (i.e.x2(2)df) at 5% level of significance; otherwise, do not reject the null hypothesis of normality. Form the result obtained, we observed that: JB –statistics =1.1863575 While, critical JB = (X2(2)df) = 5.99147 Conclusion: Since the JB-statistic =1.18675 is less, than the critical JB = 5.99147, we do not reject the null hypothesis of normality and therefore conclude that the error terms are normally distributed at 5% level of significance. Multicollinearity Test iv Another assumption of the CCRM is that there is no multiconlliearity among the explanatory variables induced in the regression model. Multicollinearity refers to the existence of more than one exact (or inexact) linear relationship. Multicollinearity test is carried in order to verify the possibility of this assumption. The test is carried out using the correlation matrix. It has been suggested that if the pair-wise correlation coefficient between two regression is highly present excess of 0.8, then multicollinearity is present and may pose serious estimation problem. The result of the correlation matrix is presented below: TPN TPN2 RER RIR UNEMP TREND TPN 1.0000 0.9831 0.3399 0.5811 -0.2529 0.6720 TPN2 0.9831 1.0000 0.3113 0.5536 -0.2769 0.6466 RER 0.3399 0.3113 1.0000 0.2764 -0.0068 0.6603 RIR 0.5811 0.5536 0.2764 1.0000 -0.6220 0.7576 UNEMP -0.2529 -0.2769 -0.0068 -0.6220 1.0000 -0.5196 Table 9: pair –wise correlation matrix From the correlation matrix above, we can confirm that there is no pair-wise correlation coefficient that is in excess of 0.8 except between TPN and TPN2 which is about 0.98. But the relationship TREND 0.6728 0.6466 0.6603 0.7576 0.5196 1.0000 iv between TPN and TPN2 is non-linear. Hence, they can not be said to be collinear. Therefore we conclude that there is no multicollinearity among the repressors. Heteroscedacity Test One important assumption of the CLRM is that the error terms are homoscedasticity variance i.e. E (μi2) = 02 i = 1,2, ----------n. The heteroscedasticity test is carried out in order to verify whether the disturbances μi actually exhibit the equal variance (homoscedasticity) assumption. The white’s heteroscedasticity test with (nocross term) is used to carry out this task. This test make use of the residuals generated from the original regression. It then runs each of the regressors and their squared terms against the generated residuals. The auxiliary model can be stated as: μt = o +1TPN +2TPN2 +3 (TPN2)2 + 4RER2 + 5RER2 +6RIR + 7RIR2 +8 UNEMP + 9UNEMP2 + 10TREND +11TREND2 +Vi Where Vi =pure white noise error iv This model is run and auxiliary R2 from its obtained. The hypothesis to be tested is Ho: 1 = 2 = 3 = --------- 11 = 0 (Homoscedasticity) Hi: 1≠ 2 ≠ 3 ≠ --------- 11 ≠ 0 (Hoteroscedasticity Under this hypothesis, it has been shown that the sample size (n) times the R2 obtained from the auxiliary regression asymptotically, follow the chi-square distribution with degree of freedom equal to the number of regressors (excluding constant term) in the auxiliary regression. That is: n.R2 ~ X2 df Decision Rule: Reject Ho if 2 cal > 2 tab at 5% level of significance; otherwise, do not reject Ho. From the result obtained; Calculated X2 = 11.43079 While, critical 2 0.05 (II) = 19.6751 iv Conclusion: Since calculated 2 = 11.43079 is less than critical 2 df = 19.651, we therefore do not reject the null hypothesis of homoscedasticity and conclude that the error terms have a constant variance. Autocorrelation (or serial correlation) Test The CLRM also assumes that autocorrelation does not exist in the disturbances. That is to say, the disturbance term relating to any observation is not influenced by the disturbance term relating to any other observation. Symbolically; E(μi μ;) = 0 i≠j The Breusch-Godfrey (BG) general test of autocorrelation also known as LM-test is used to verify this assumption. This test is better than the conventional Durbin-Watson test of autocorrelation in the sense that it allows for non-stochastic repressors such as the lagged values of the regress and, higher-order autoregressive schemes, and simple or higher order moving averages of the error terms. The test assumes that the error term generated from the original regression follows the pth-order autoregressive, AR(P) scheme. Then, the AR (P) iv scheme alongside the original regresses is run against the generated residual. For our model, we generated AR (2) for the residual. The auxiliary model to be tested is; μt =β0 + β1TPN + β2TPN2 + β3RER + β4RIR + β5 UNEMP + β6TREND +p,μt-1 +pμt-2 +et. The hypothesis to be tested is Ho: p1 = p2 = o (No autocorrelation) H1: p1 ≠p2 ≠0 (autocorrelation) The test statistic is given as; (n-p)R2 ~ x2 (p)df Where n = number of observation p = the order of the autoregressive scheme of μt R2 = R2 from auxiliary regression Decision Rule: Reject Ho if 2 and > 2(2) critical at 5% level of significance; otherwise do not reject Ho. From the result obtained, Calculated X2 = 0.299777 While, critical 2(2) = 5.99147 iv Conclusion: Since calculated 2 is less than critical 2 =5.99147, we do not reject the null hypothesis of no autocorrelation and therefore conclude that the error terms in the model are not serially correlated. Test of Specification Errors Another assumption that is fundamental to the CLRM is that the model must be correctly specified. A general test proposed by Ramsey called RESET (Regression specification Error Test) is used to verify this assumption. This test follows the F-distribution. Under the null hypothesis that the model is well-specified, our decision rule is to reject the null hypothesis if the calculated F exceeds the critical value F at a given level of significances. From the Ramsey RESET result, F-statistic = 1.57044 While, critical F0.05 (6,31) ≈ 2.42 Conclusion: Since F-statistic ≈ 1.57 does not exceed the critical F ≈ 2.42, we do not reject the null hypothesis of well-specification of model and therefore conclude that the estimated model is correctly specified; that is, there is no specification error. iv Forecast Test This test is carried out so as to evaluate the forecasting performance of the model. Henry Theil has suggested a systematic measure of the accuracy of the forecast obtained from an econometric model. The measure is called Theil inequality coefficient, which is defined as: μ = + ∑(pi –Ai)2/n √ ∑ A2i/n pi = predicted (forecast) change in the regress and Ai = Actual (Realized) change in the regress and The smaller the value of the inequality coefficient, the better is the forecast performance of the model. From the result obtained, it is observed that the inequality coefficient is about 0.0458. This shows that the model has a high prediction power. Also, the bias proportion (the difference between the predictive and the actual value of the regress and) in the model is about 0.0016. This also indicates higher forecasting strength of the model. Moreover, the covariance proportion, which measures the correlation between actual and fitted iv values, is approximately 96%. This is also a very good result for the forecast performance of the model. In summary, the predictive power of the model is fairly high, robust and reliable. 4.4 EVALUATION OF THE WORKING HYPOTHESES In order to recap, the working hypothesis of this study includes the following: Trade openness does not have any significant impact on output growth in Nigeria. There is no other macroeconomic variable (internal or external) that have significant impact output growth in Nigeria. There is no linear association (correlation) between trade openness and output growth in Nigeria. There is no long run relationship between trade openness and output growth Nigeria. There is no significant structural change in output growth between the pre-SAP and post-SAP period. iv After rigorously subjecting these hypothesis to various economic, statistical and econometric test, we hereby present the findings. The results obtained from model one invalidates the first hypothesis. Trade openness, in real sense; actually have significant impact on output growth. The t-values of degree of openness and its squared term are statistically significant both in short run and long run. Model one regression results also invalidate the second hypothesis as all other macroeconomic variables included in the model are individually statistical significant using the t-test. This implies that trade openness and other macroeconomic variables (both internal and external) do not really have significant impact on output growth in Nigeria. The F-statistic of the model is highly statistically significant which indicates that the overall performance of the model is pretty significant. Also, the result obtained from the coefficient of correlation indeed invalidate the third hypothesis as it shown that there truly exist a linear association (correlation) between trade openness and output growth in Nigeria. The result further shows that there exists a positive iv moderately high degree of correlation between trade openness and output growth in Nigeria over the same period. This suggests that trade openness is beneficial to the economy in general and to domestic producers in particular. The results obtained from the cointegration test, which was later verified by the Error correction model, provided the ground for the invalidation of the fourth hypothesis as it is shown that trade openness (with other macro economic variable in the model) and output growth are not only related in the short run, but have a long term equilibrium relationship. The error correction mechanism provides the basis for the adjustment of shortrun dynamics to long run equilibrium. This mechanism is statistically significant which indicates that the equilibrium error is not instantly corrected in the short run, rather it persist, and is being fully corrected in the long run. Further more, the regression results obtained from model two provide the basis for the invalidation of the fifth hypothesis. As the regression result shows, there exist statistically significant differences in the intercepts and slope of both periods in question. The intercept iv of the first period is large than that of the second period; while the slope of the post –SAP era is much larger than that of the pre-SAP era. Therefore, we can conclude that there is statistically significant structural change in the growth of output in Nigeria between the preSAP and post-SAP era. This structural changes in the growth of output is as a result of trade liberalization policy of the structural Adjustment programme (SAP), 1986. Finally, from the estimated regression result of model 1, the optimum degree of openness is estimated to be about 67% in Nigeria. This is calculated as taking the derivative of the non-monotonic model with respect to openness (TPN), equating it to zero and solving for openness. This result further buttress the fact that trade openness is quite high in Nigeria. iv CHAPTER FIVE SUMMARY, POLICY PRESCRIPTION AND CONCLUSION 5.1 SUMMARY This research work’s pursuit is to unravel the structural relationship between trade openness and output growth in Nigeria from 1970 to 2007. It aims to determine the structural impact of trade openness on output growth in the presence of other internal and external macroeconomic shocks using a non-monotonic approach. It further used an Analysis of covariance (ANCOVA) model to determine the possibility of structural change in output growth so as to understudy the policy effect of the Structural Adjustment Programme (SAP) of 1986 which tends to liberalize trade. The regression results of the first model show that there indeed exist an inverted U-Shape quadratic relationship between trade openness and output growth in Nigerian. This is reflected in the negative sign of the squared term of degree of openness. Also, all iv other macro economic variables in the model including real exchange rate and unemployment rate are interest rate which is statistically significant at 10%. These variables do not only exert significant impact on output growth in the short run but also in the long run, and various diagnostic and performance test have been conducted to verify the reliability of these results. Also, in the regression results obtained from the second model, we observed that the policy thrust of the IMF/World Bank-Sponsored Structural Adjustment Programme (SAP) does not only have a significant impact on the output growth in Nigeria but in fact have positively changed the trend of growth of output in the economy. This implies that the trade liberalization policy of SAP has a significant positive effect on output growth in Nigeria. This result is further buttressed by the positive moderate correlation coefficient of about 0.6 obtained. This indicates that trade openness and output growth are as well linearly associated. That is, an increased openness to trade could as well mean a rise in output growth. 5.2 POLICY RECOMMENDATIONS iv Based on the finding of this research work discussed above, we hereby proffer the following policy measures for long term sustenance of output growth in the economy. First of all, there should be optimal control of trade through the borders of the economy. The underground economic activities of bunkering, smuggling, child and drug trafficking,and other related illegal activities should be properly checked. This will help the economy to fully account for every trade/transaction through the border and determine its impact on the output growth of the economy. In order to achieve this, governments trade policy must be liberal. Also, government should properly regulate import tariff so that it will not be discouraging in such a way that it will aid illegal importation. Secondly, there is a dire need for adequate infrastructural development in the country. Nigeria has been recently noted to be one of those Africa countries that have difficult geographic condition. The country is the largest oil producer in Africa and the 6th largest oil producer in the world. Yet, the country’s infrastructure is at serious decay. iv This has negative effect on output growth as cost of production is high due to high due to high transportation, communication, and other services costs. Government should devote much of its resources to the development of infrastructure. Excess crude oil revenue should be properly allocated for infrastructural development such as good roads, potable waters, standard rail system, buildings and other public utilities. Thirdly, there is a need stable macroeconomic policy as regard to the exchange rate system of the country. Exchange rate is one of the most volatile macro economic variables. But this variable has not been properly handled in such a way that the country will derive optimum benefit from it. As a result, the growth of output in the economy has been thwarted. The government should put sound machinery in place to properly monitor the movement of exchange rate and regulate it indirectly through currency depreciation or directly through devaluation. This will make the country’s exports to become cheaper and imports more expensive; hence, a favourable balance of iv payment which will enhance output growth. The country’s reserve also needs proper management. Also, there is still need for greater development in the financial sector of the economy. The financial sector is said to be hub of every economy. The on going capital market as well as the money market reform are examples towards the development of the financial sector. The government needs o put in lace proper and non- partisan machineries for supervision and regulation of this sector so as to achieve optimum performance. Moreover, stagnation is one of the various macro-economic problems the economy has been facing for the past three decades. This is a situation where the economy is simultaneously experiencing rise in inflation and unemployment rate at the same time. Various government efforts towards this problem have proven to be economically insignificant and this has seriously inhibited the growth of productivity, hence output, in the economy. Government should redirect its priority and properly allocate its resources to combat these problems. This could be achieved by creating conducive environment iv for business; give special attention to education, proper control of government financial spending, effective expenditure switching and expenditure reducing polices, and sound fiscal and monetary policy objectives for the economy in a specified period. Furthermore, there is an urgent need for diversification of the economy. The Nigerian economy has been depending on crude oil exportation. But today, this strange dependency has really dampened the growth of the economy as the country is open to every international shock associated with the oil market. Government should look inward to seek for other fallow grounds where it can explore and generate resources. Government should explore other sector of the economy such as manufacturing, agriculture, mining and quarrying. Finally, the government should vigorously seek to improve the international stand of the economy with other economies of the world so as to enlarge the market for Nigerian exports. It should also reorient its policy towards the external sector and ensure that the sector contribute optimally to output growth. 5.3 CONCLUSION iv The contribution to be drawn from this study is that trade openness have a significant economic impact on output growth in the presence of other internal and external macro economic shock. Nevertheless, to achieve a high and sustainable output growth, we proffer some policy recommendation which when properly implemented will surely stimulate greater growth of output. Meanwhile, it is not all the areas that need proper treatment are adequately treated due to various limitations being faced by this research work. But we recommend that further studies be genuinely carried out using different and more sophiscated methodologies and choice variables in other to harmonise the structural relationship between trade openness and output growth in Nigeria. iv BIBLIOGRAPHY Addison, D. (1998). Managing Extreme Volatility for long run Growth. London: Oxford ,Clareden Publishers. Alexander, S.S. (1952). The Effects of Devaluation on a Trade Balance. New York: Sache City Publishers. Amadeo, E. (1994). Institution, Inflation and Unemployment. London: Aldershot Edword Elgar. Arida, P. and Resende, A. L. (1985). Inertial Inflation and Monetary Reform in Brazil. Washington: Iahiran publishers. Baldwin, R. and Sbergami, F. (2000). Non-linearity in Openness and Growth links. Geneva: Woodsheilf Publishers. Carolie, B, and Smith, B.D. (1989). Money, Banking and the International of Real and Normal Exchange Rates. Florida: Oxford Macmillan publishers. Chang, R., Kaltani, L. (2005). Openness can be good for growth. 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HR focus 31, 4-7. iv APPENDIX A: DATA PRESENTATION Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 BP CB 5223.3 63252.5 8712.6 76589.1 7565.2 79652.8 4606.4 86534.2 10067.6 89936.6 7453.5 98564.1 6767 159190.8 31192 226162.8 40444 295033.2 22695 385141.8 49751 458777.5 42661.5 584375 52993.8 694615.1 193412.9 1070020 285294.4 1568839 192731.8 2247100 435601 276680 434299 3047856 677957.4 3753278 834522.9 4741125 1651513 6400784 2028220.03 7612568.3 SM 13934.1 18676.3 23249 23601.3 29651.2 37738.2 55116.8 85027 100460.6 108490.3 134503.2 177648.7 200066.1 277667.5 385190.9 488045.4 592004 655739.7 797557.2 1316957 1739636 2226795.5 GDP 71859 108183 142618 220200 271908 316670 875342.5 1089680 1399703 2907358 4032300 4189250 3989450 4679212 6713575 6895198 7795758 9913518 11411067 14610882 18564595 22015709 Source: central Bank of Nigeria (CBN) Statistical Bulletin (2007)