Impact of Non-Oil Export on the economic growth of Nigeria

Disclaimer: This is to guide undergraduate research only.

Cite as: Tonuchi, E, J. (2019). Impact of non-oil export on the economic growth of Nigeria. Available [Online] https://thesismind.com/non-oil-export-economic-growth-nigeria

Title: Impact of Non-Oil Export on the economic growth of Nigeria

ABSTRACT

This research work examined the impact of Non-Oil Export on the economic growth of Nigeria within the sample period of 1985-2016. The research work employed multiple regression analysis using Vector Error Correction Model (VECM) to estimate the relationship. The unit root test revealed that none of the variable examined was stationary at level, but at first difference all the variables examined became stationary given the 5% level of significance. The Johansson cointegration test revealed that there is presence of cointegration with at least two cointegrating equation among the variables examined. The research further revealed that 93.53% of the variations in economic growth of Nigeria was influenced by the changes in the Nigeria Non-Oil Export variables. In conclusion, the research revealed that there was sustainable relationship between Nigeria Non-Oil Export and economic growth in Nigeria. The researcher therefore recommend among others that, government should endeavor to support various export promotion programmes and institutions. This could be achieved by encouraging financial institutions, both formal and informal, to make loans available at reduced rates of interest for investors as to increase the level of investment in this country.

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

Exportation is required by any economy to enhance revenue and usher in economic growth and development. Itis therefore, crucial for economic progress sand this has informed the idea of export-led growth. Export is a catalyst necessary for the overall development of an economy (Abou-Strait, 2005). It was also noted that foreign trade creates an avenue for foreign capital to flow into a country (Kubalu & Hanif, 2016). This increases the earnings of the country thereby creating an avenue for growth by raising the national (Kromtit, Kanadi, Ndangra & Suleiman, 2017). In a country like Nigeria where the level of investment is low, foreign capital is very much needed in order to accelerate the creeping rate of economic growth. The Nigerian economy is one that depends largely on foreign trade for growth and is also one which depends majorly on one export commodity at a time. For instance, at independence, the major export commodity was cocoa and the leading sector in the economy was the agricultural sector but today, the major export commodity is crude oil and the leading sector is now the petroleum sector (Eze Onyebuchi Michael, 2017).

Export promotion has been argued by many economist as a major driver of economic growth in any country of the world. In Nigeria, the export sector is characterized by the dominance of a single export commodity. In the decades of the 1960’s and 1970’s the Nigerian economy was dominated by agricultural commodity exports. Such commodities included Cocoa, groundnut, cotton and palm produces. From the mid-1970s crude oil became the main export product of the Nigerian economy (Ifeacho, Omoniyi, and Olufemi, 2014; Abogan, Akinola and Baruwa, 2014). The economy is said to be suffering from the Dutch disease resulting from her mono export of oil. Of course, Nigerian crude oil is of the light and sweet type and is highly sought after in the international oil market.

The export of crude oil now constitutes about 96% of total exports. The performance of the non-oil exports in the past two decades leaves little or nothing to be desired. The policy concern over the years has therefore been to expand non-oil export in a bid to diversify the nation’s export base. The diversification of the Nigerian economy is necessary for important reasons, first the volatility of the international oil market with the attendant volatility of government revenue gives credence to any argument for diversification of exports (Ifeacho, Omoniyi, and Olufemi, 2014; Kawai, 2017). Secondly, the importance of export to a nation’s economic growth and development cannot be over-emphasized.

Exports are goods and services produced domestically and purchased by foreigners. Net exports are the difference between total exports and total imports. According to Afolabi (2011) Export can be defined as surplus goods and services of a country that are sent to other countries in the world for sale. Samuelson and Nordhaus (2010) see exports as the mirror image of imports. That one countries export is another’s imports. However, export is any goods or commodity transported from one country to another country in a legitimate fashion typically for use in trade (Oluchi, 2007).

Export is a catalyst necessary for the overall development of an economy. The primary objective of export policies in any economy is to increase the level of economic activities. It follows, therefore that export policies should be directed to the sector in which the impact of an increase in export demand will be both desirable and large. It is a source of foreign exchange earnings and since trade transaction followed by the ―oil boom period which arose from oil glut in the world oil market since 1981 only led to the neglect of non-oil export productive base (Noko, 2016).

This has also led to panic measures by successive government from the economic stabilization Act of 1982. Counter trade policy of Buhari/Idiagbon regime and the introduction of structural adjustment programme (SAP) by the Babangida Administration hence the need to diversify the export base of the economy. Prior to the phenomena emergence of the oil sector, Agriculture is one of oldest occupations in Nigeria and has been the main slay of the Nigerian economy contributing 80% of the export earnings and 75% of the Gross Domestic Product (GDP) (Eze Onyebuchi Michael, 2017; Kromtit et al, 2017; Kawai, 2017).

Consequently, this position has fallen consistently to date, the attendant fluctuation in the non-oil export promotion, the world prices of agriculture and manufacture products and the emergence of oil have helped in no small measure in diverging the role of agriculture in the nation’s development. This situation is worsened by the almost total neglect of the agricultural sector. The Nigerian economy has not recovered from the resultant disequilibria in both domestic and external sectors, this has therefore brought about the need for adjustment in Nigeria to diversify and restructure the productive base of the economy in order to reduce its dependence on oil export. It is this concerns the country non-oil exports (Kawai, 2017).

Furthermore, a well-developed sector will provide employment opportunity for the people with the attendant reduction in social costs of unemployment. Earnings from export reduce the strains on the balance of payment position and even improve it. A rewarding export drive can turn hitherto under developed economy into a prosperous economy. Export help in increasing the level of aggregate economic activities through its multipliers effects on the level of natural income. Income earned through exporting will help in increasing the level of demand within the economy. The Nigerian external sector has always been dominated by primary commodities which have the well-known basic characteristic of low price and income elasticity of demand, low growth of demand, terms of trade and instability of export earnings. This monoculture situation brought untold hardship on the people of the country (Kawai, 2017; Noko, 2016).

For instance, from 1970 to date oil exporting has constituted on the average of 90% of the total foreign exchange earnings. The adversity of the fluctuation in oil prices has in no small measure slated the developmental efforts of the various governments. This has made the Nigerian economy to scoring from the ―oil boom era‖, as exemplified by the buoyant economy of the period with massive infrastructural development and the Udoji award (Ifeacho, Omoniyi, and Olufemi, 2014; Noko, 2016)

A robust and strong export trade is indicative of how competitive the commodities and services are, and how large the scale of the industrial base of an economy is, this is reflected by the comparative advantages possessed by the country. Also, exports of commodities are possible when domestic demand for such are satisfied and surpluses exist in commercial quantities. Thus, the non-oil export sector serves as the hub for exporting these surpluses produces by the non-oil base of the country’s economy. Okoh (2004) observed that global integration had positive but not significant relationship in explaining the behavior of non-oil exports in the long-run. Since the aggregate non-oil exports data used by previous studies may biased their conclusion and the need to correct the existing cultural distortions and put the economy on the path of sustainable growth is therefore compelling. This raises the question of what need to be done in order to diversify the economy and develop the non-oil sector to realize the potentials of the sector at large.

1.2 Statement to the Problem

Prior to the discovery of oil in Nigeria in commercial quantities, agriculture sector dominates the economy in terms of export earnings, contribution to gross domestic product, and employment generation. Government earnings also depended heavily on taxes on export. Thus, during the period, the current account and fiscal balances depended on the agricultural sector. Until the early 1970’s where reliance was shifted to crude oil with the discovery of oil and rise in the price of oil in the 1970’s (Noko, 2016; Kawai, 2017).

The challenges of non-oil export sector is not that it is being over shadowed by the oil export trade, but traceable to declining non-oil export and loss of market share in the non-oil trade globally is a clear evidence of how the non-oil sector competitiveness of the Nigerian economy has been consistently eroded over the last three decades. A robust and strong export trade is indicative of how competitive the commodities and services are, and how large the scale of the industrial base of an economy is, this is reflected by the comparative advantages possessed by the country (Kawai, 2017). Also, exports of commodities are possible when domestic demand for such are satisfied and surpluses exist in commercial quantities. Thus, the non-oil export sector serves as the hub for exporting these surpluses produces by the non-oil base of the country’s economy (Eze et al, 2017). There has been several research works which have examined the relationship between non-oil export and economic growth. Another important problem was the poor implementation of policy measures by the various government agencies. Most of the institutions involved in policies implementation were very ineffective and were not particularly oriented to the needs of majority of the small farmers. Such inadequacies were common in key institution like credit agencies, research institution, commodity board, river basin development authority and institution which handled input procurement and distribution. Many of them either did not have adequate facilities and funds or competent staff to enable them to work efficiently. Inappropriate (Kawai, 2017; Kromtit et al, 2017).

Some previous studies showed the relationship between non-oil exports and Nigeria economic growth while other showed the extent at which non-oil export individually affect the economic growth of Nigeria (Kromtit et al, 2017). This study, however, failed to analyze clearly the effect of Bank credit on non-oil export earning cum economic growth of Nigeria. This research work will attempt at verifying the effect of this variable and hence closed the gap in knowledge inherent in other studies.

1.3 Research Question

In attempt to address the various problems prominent with Nigeria non-oil export, various questions has been raised by the researcher. The following questions will guide the research work:

  1. To what extent does non-oil export impact on Nigeria economic growth?
  2. Is there any observed long-run relationship between non-oil export and economic growth of Nigeria?

1.4 Objectives of the Study

The objective of this study is to evaluate; the significant relationship between non-oil export and economic growth in Nigeria. Specifically, the objective of this study include to:

1. Examine the impact of non-oil export on the economic growth of Nigeria.

2. Investigate the long run relationship between non-oil export and economic growth of Nigeria.

1.5 Statement of Hypothesis

Ho: Non-oil export has no significant impact on the economic growth of Nigeria.

Ho: Non-oil export has no long run relationship with economic growth in Nigeria.

1.6 Significance of the Study

The impact of non-oil export on the sustainable growth of any nation cannot be over-emphasized; since increase in export earnings (over its counterpart, import) would make any Nation better-off in trade with other countries. Therefore, this work will be of immense importance to government and its agencies, and the general public. Also, it will be of great importance to ministry of trade and industry, investors as well as financial intermediaries or institutions. Above all, it will be a stream of knowledge for economist, students and researchers who have interest on issues relating to non-oil export in Nigeria.

1.7 Scope and Limitation of Study

This research analyze the impact of non-oil export in Nigeria economy, taking proper analysis on various ways and means put forward by the government of Nigeria to improve non-oil export earnings since 1981-2016.

The research work, however, is not void of constraints as the researcher encountered a number of constraints in the cause of this work. The constraints include data sourcing as well as data inconsistency due to poor nature of information management in Nigeria. However, host of other constraints that prevent the researcher to present a better work than this abound. Prominent among them are time factor, financial constraints and lack of electricity. In spite of the aforementioned constraint above, the researcher made adequate efforts to present a clear and well-articulated research work.

 

CHAPTER TWO

LITERATURE REVIEW

2.1 Theoretical Literature

Export-Led Growth Theories

The literature on international trade which suggests that exports have a positive impact on economic growth is known as the export–led-growth (Giles and Williams, 2000). The present literature presents several plausible theoretical arguments supporting the view that exporting activities and overall economic growth are positively associated. On the one hand, exporting implies that a country gain access to the wider external demand, which act as a stimulus to domestic output and hence economic growth. Second, it is frequently argued that small domestic markets may not grow continuously and that any positive economic shock leading to the expansion of the domestic market is more likely to decay quickly. On the other hand, large external markets do not always encompass growth restrictions on the demand side, and this leads to the exploitation of economies of scale (Kromtit et al, 2017; Baale and Mutenuo, 2001).

The notion of trade as an engine of growth is given much emphasis by many economists. The idea that international trade brings economic growth and also increases the welfare of a nation started during the 17th century by a group of merchants, government officials and philosophers who advocated on economic philosophy known as mercantilism. For a nation to become and also powerful, it has to export more than it imports where the resulting export surplus is used to purchase precious metals like gold and silver. The Government in its power has control on imports and stimulates the nation’s export (Afolabi, 2011). As known, mercantilists prohibited the ultimate good import (for it causes valuable mine output) and aimed to increase the import of valuable mine by increasing export. In contemporary phrasing, this situation necessarily means running the balance of payments surplus and this aim states the essential intend of mercantilism (Kucukasoy, 2001; Kromtit et al, 2017).

Adam smith, as he briefly wrote in the “wealth of Nations stated that foreign trade will cause welfare gain in this way by the sentence that” if a foreign country can supply a commodity to us cheaper than use produce, buying this commodity from that country is useful for the country’. This option is based on a simple and intuitive logic. The country itself must produce the products it produces with less cost but he mustn’t produce goods it produces with high cost compared to the other country and must buy goods its self produces by given them to that country. With this, both the two countries together will get welfare gain (Ravenhill, 2005; Noko, 2016). In the words of Adam Smith, “between whatever place foreign trade is carried out, they all drive two district benefits from it. It carried out the surplus part of the produce of their land and labour for which there is demand for the said product. It gives value to their superfluities by exchanging them for something else which may satisfy a part of their wants and increase their enjoyment. By means of it, the narrowness of home 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 many exceed the home consumption. It encourages them to improve their production capacities as well augment its annual produce to the utmost thereby increase the revenue and wealth of the society”.

If getting goods form a foreign country is cheaper than those a domestic economy can accomplish is possible, it is more suitable to buy these goods in return for some part of goods in which domestic economy is advantageous. A country’s natural advantage to another can be relatively huge especially in the production of certain goods and so all other countries think that struggling with the given country will be a futile effort (smith, 2002; Kromtit et al, 2017).

According to the traditional Keynesian theory, an increase in exports is one of the factors that can cause increase in demand and thus will surely bring about increase in outputs, all other things being equal. Indeed, most people believe that the major constraints of modern economic growth lie on the supply side instead of on the demand side. In other words, they believe that only increase in factor input and improvements in economic efficiency can stimulate economic growth (Lin and li, 2007; Kromtit et al, 2017).

Huckscher-Ohlin export theory

Another theory on export-led growth is the Huckster-Ohlin theory. According to Souderton and Reed (1994) The Huckster-Ohlin theory postulates that international trade- of which exports are expected to constitute the major component will significantly reduce the gap between the rich and poor countries. The theory contends that inner-country differences in factor endowments are the basis for foreign trade (Noko, 2016; Kawai, 2017). Comparative cost advantage comes as a result of different factor intensities in the production of various commodities. The Heckscher-Ohlin theory also implies that free trade specialization in production based on relative factor endowments will tend to bring about factor price equalization and thus will increase the return to labour in poor countries to the levels in rich countries; this suggests that international trade in general and export in particular have the ability to mitigate inequality in income and wealth distribution between and within nations as well as the ability to bring about a convergence in absolute poverty incidence between the rich and poor countries (ozughahu and Ajiayi, 2004)

John Stuart, mill (1848, as cited in Oluchi, 2007) in his principle of political economy: in addition to static growth of trade and dynamic gain from trade which include:

  1. Widen the extent of the market, inducer innovation and increase productivity.
  2. Have educative effect in instilling new ideas, wants, taste, and transfer of technology, skill and entrepreneurship.
  3. Increase savings and capital accumulation. Therefore, trade offers a poor nations the opportunity of removing domestic shortage to overcome the economics of the small size of its domestic market.

According to Lee and Huang (2002) export growth are vice versa. The theoretical justification for these hypotheses is discussed as follows: From the growth-theory literature point of view, export expansion is the key factor promoting economic growth. There are various explanations that have been put forward to relate these two variables to each other. First, the growth through its impact on higher rates of capital formation. Second, the growth of export helps release the foreign exchange constraints, thereby facilitating import of capital goods and hence faster growth. Third, competition from overseas ensures an efficient price mechanism that fosters optimum resource allocation and increase the pressure on industries that export goods to keep cost relatively low and to improve technological change, thereby promoting economic growth. Clearly, these arguments lead us to hypothesis that exports contribute to economic growth (Kromtit et al, 2017).

The literature on exogenous growth theory also buttresses the export driven economic growth nexus. This theory posits that log-run economic growth due to increased exports allows for specialization in the sectors with economies of scale. Economics of scale may also arise from human capital accumulation, research and development. Increased export over imports also harness terms of trade and improve foreign exchange earnings. Relative to the non-export sectors, enhanced export also has a derived effect as external economies could also lead to improved management styles and achieved efficient production techniques (technological transfers). This can be achieved as producers gain knowledge of best practice production through their contacts with buyers on the international market (Paul’s Reinhardt and Robinson, 2003; Lin and li, 2007; Kromtit et al, 2017). As noted by Giles and Williams (2000) that different reason have been proposed for explaining the evidence found in previous studies dealing with this issue on export-led growth. The simplest explanation is that as the contribution to growth made by domestic consumption is limited to the size of regional (or national) markets, sales to foreign markets represents an additional consumption demand which increases the amount of real output produced in the economy.

The Export Crop Sector In Nigeria

In 1977 the commodity marketing boards were established by the federal military Government with the purpose of talking care of specific crop such as cocoa rubber roots and tuber, etc. food imports were limited but crop production for export was intensified during the period of liberalization. poor to liberalization, the overall objectives of trade policy in Nigeria include a marketing Board Policy (1960- 1977) through which all exportable agricultural products were purchased by the Government at prices far lower than world prices, and incentive were given to farmers to increase their acreage and adopt some important technologies (Lin and li, 2007; Kromtit et al, 2017).

The liberalization and diversification of the economy of Nigeria was a major aim of the structural adjustment programmed of 1986. The diversification of export was focused on moving the export base away from oil and the expansion of non-oil exports, especially agricultural exports cocoa increasingly accounted for the largest percentage of non-oil exports in Nigeria. In general, average figures for the period 1993- 1995 show that cocoa rubber, fish and shrimps, and cotton were the major agricultural commodities being export from Nigeria.

However, between 1962 and 1968, Nigeria major foreign exchange earner was the agricultural sector. While the period 1976- 1978 and 1978-1980 was characterized with restrictive trade policies. Such policies are as follows.

  • General Ban on non-essential imports, especially food imports
  • Tariff increases on some items
  • New duties on certain items not hitherto taxed.
  • Imposition of compulsory advance deposit on some classes of imports.
  • Industrial raw materials which were previously under open general license were placed under specific import license.
  • Export bans were imposed on certain items
  • Export tariffs were revived upward for some other items.
  • Centralized marketing of agricultural products was reinforced through the formation of commodity Boards which handled specific crops.

The Structure of Non-Oil Export during the Pre and Post SAP Era

Pre Sap Era

It was observed that most contribution of the non-oil sector was from agriculture whose largest contribution was in 1998 with 92.8% and the lowest in 1981 with 19.6%. The contribution of agriculture to total export is not something to be proud of, none of the years under review made a percentage of 10%. Before the introduction of SAP that is the year within 1981 and 1985, one would examine that there was a negative growth rate in agricultural export. It is generally known that agricultural performance was particularly unsatisfactory and this tend to increase the burden of the whole economic. Many factors responsible for this, the major frequent problem of agricultural production is its high propensity to weather changes. Whenever there was unfavourable weather, output decline substantially with adverse consequences of the economy. This in itself is a symptoms of an inefficient agriculture system, which is unable the economy unlike a developed agricultural system (Noko, 2016).

Another important problem was the poor implementation of policy measures by the various government agencies. Most of the institutions involved in policies implementation were very ineffective and were not particularly oriented to the needs of majority of the small farmers. Such inadequacies were common in key institution like credit agencies, research institution, commodity board, river basin development authority and institution which handled input procurement and distribution. Many of them either did not have adequate facilities and funds or competent staff to enable them to work efficiently.

Sap Era

According to Itegbe (2015), between 1984 to September 1986, successive military administrations started giving serious consideration to the need to urgently find other methods of sourcing foreign exchange, in addition to measures adopted to conserve what was already earned. This situation arose as a result of mounting obligation on the country to settle trade arrears and for debts servicing as well as to meet current trade bills. He further stated that by 1984, Nigeria had found herself in huge foreign debts in addition to being in serious arrears in settlement of foreign trade bills mainly on irrevocable letters of credit. Thus, it became clear to policy makers in Nigeria that additional effort had to be made by the nation to earn foreign exchange. It was for this reason that the government in 1986 adopted export oriented development strategy as a major comer stone of the structural adjustment programme.

SAP involved the formulation and adoption of a comprehensive export incentive legislation known as the export incentives and miscellaneous provision decree No.18 of 1986. The provisions of this decree were subsequently strengthened by the provision of the second tier foreign exchange market (SFEM) decree No.26 of September, 1986. The introduction of the export decree and SFEM decree could be described as ‘Watershed’ in the history of non-oil export policy development in Nigeria, according to Itegbe1989, pointing out for the first time, in the history of the country, export expansion and diversification strategy became a national policy objective. The removal of all bureaucracies and additional incentives through SAP did not however make any significant impact on the volume non-oil exports. Experts and academicians in the area of export promotion have tried to figure out why after 20 years of this export policy regime there is little significant positive results.

Post Sap Era

It is in the area of agriculture export that recent policy measures have produced the most visible impact so far. The growth rate of agriculture exports grew from negative figure apart from 1992 which was -10.8, all other years were positive. The share of agriculture in non-oil also grew with an average of 74.6. The highest contribution was in 1998 with 92.8%, the agricultural export from the total exports also increased making about 4.5% within 1986 to 1988, which was an improvement of 2.5% in the pre SAP period (Noko, 2016).

Apart from the significant rise in the agricultural export noted above, the upsurge in the sharp increase in local currency prices of the sharp export product, following the large depreciation in naira exchange rate and the removal of marketing and price control after the abolition of the commodity boards. Another source of increase was the new package incentives given to the non-oil exporters.

    1. Empirical Literature

As the world has become a global village, exports have been considered as growth-enhancing within the traditional development literature. It has been affirmed that an increase of exports could be advantageous in terms of alleviating the foreign exchange constraint, ultimately exacting a positive effect on growth. It is Cristal clear that there is not a single country that can attain economic growth and development in isolation of trade with other nation. The speed at which less Developed countries (LDCs) attains economic growth and development greatly depends on how they manage their gains from trade with other countries.

Eze et al (2017) examined the causality between agricultural sector and economic growth, as well as the impact of the sector on the growth of the Nigerian domestic economy. Cointegration test, Vector Error Correction Model (VECM) and Granger causality test were utilized in the analysis. The variables employed in the investigation include real gross domestic product (RGDP), value of agricultural output (VAO), foreign private investment (FPI) and financial development (FD). A stationarity test was conducted through the application of the Augmented Dickey-Fuller (ADF) stationarity test, and the result showed that all the variables except RGDP were non-stationary at level; however, the variables such as VAO, FPI and FD became stationary after first differencing. The cointegration result indicated long run equilibrium relationship among the variables under study. The VECM result on the other hand, showed that value of agricultural output (VAO) has positive and insignificant contribution to real GDP. Thus, it is estimated on average that 1% increase in the value of agricultural sector output (VAO) would lead to 1.9% increase in real GDP. Furthermore, the Pairwise Granger causality result showed that significant causality exist between the two variables, with causality running from agricultural output to RGDP. It therefore, implies that agricultural sector output contributed positively and insignificantly to the growth of Nigerian domestic economy. They recommends that government should increase its budgetary allocation on agriculture in order to boost the growth performance of the sector. Similarly, the study recommends that government should strengthen agricultural credit agencies to enable them monitor and ensure efficient disbursement of fund disbursed to farmers in the country.

Alimi and Muse (2013) examined the role of export in the economic growth process in Nigeria for a period of 39 years and discovered that economic growth and export are integrated of order one, i.e.1(1) and as well are co-integrated, indicating an existence of long run equilibrium relationship between the two. This result was achieved by employing unit root test, co-integration analysis and VAR Granger causality/Erogeneity Wald tests.

Ifeacho, Omoniyi, and Olufemi (2014), investigated the effect of non-oil export on the economic development of Nigeria. The study used per capita income as proxy for economic development and expressed it as a function of non-oil export volume, trade openness, exchange rate capital formation and inflation rate. The study applied ordinary least square estimating technique and the result show that non-oil export exhibits a significant positive relationship with per capita income. However, other variables do not have individual significant impact of economic development but jointly they can significantly influence economic development. In addition, the result shows that the coefficient of trade openness is negative thus, indicating that Nigeria might not be benefiting enough by trading with outside countries. This calls for review of trade policy of Nigeria if the positive effect of non-oil export on Nigerian economic development is to be promoted.

Kawai (2017) evaluates the impact of Nigeria’s non-oil exports as to whether they have been effective in diversifying the productive base of the Nigerian Economy from Crude oil as the major source of foreign exchange. Expectedly, attention of scholars had shifted towards non-oil exports as a remedial forth is quagmire. This study investigates the specific impact of the non-oil exports to the growth of Nigerian economy using annual data between 1980-to-2016. The study adopted the Phillip Perron (PP), the EngelGranger Model (EGM) for co-integration were employed in its analysis. Findings revealed a strong evidence of co-integration relationship of non-oil exports in influencing rate of change in the level of economic growth in Nigeria. The study, apart from empirically providing information that has failed to give backing to recent claims of non-oil exports led growth in Nigeria, has also make some recommendations which include government should re-emphasized and strengthen industrial revolution plan with a clear strategy to develop sectoral plan so that the planned should be working sector by sector for better outcome of these sectors.

According to Ogunkola et al., (2008), in the 1960’s Nigeria’s export trade was largely dominated by non-oil products such as groundnuts, palm kernel, palm oil, cocoa, rubber, cotton, coffee, copra, beniseed and others. Other non-oil exports of significant value then were tin ore, columbite, hides, skin and cattle. Over 66% of total exports on the average were accounted for by these commodities.

As a matter of fact, cocoa was the dominant export product at that time contributing about 15% of total exports in 1970. However, oil’s dominance of the country’s export basket began in 1973/74 and was greatly magnified during the 1980s. The crux of the problem was that while oil export was growing, non-oil exports were declining making the dominance much more rapid and pervasive.

Ogbonna (2010) emphasize that the contributions of the non – sector export to the GDP is still marginal and almost insignificant. What this implies is that all the export promotion strategies adopted failed to achieve the desired results, which is to improve the performance of the sector. In her research on “the impact of export promotion policies on Nigeria’s non –oil export” using ordinary least square (OLS) regression technique she noted that there is general need for policy frame work, otherwise, the non – oil sector will continue to make less contribution to the country’s balance of payments their research work however covered the period from 1981 – 2000.

Abogan et al (2014) investigated impact of non-oil export on economic growth in Nigeria between 1980 and 2010. The study examines the significant role of non-oil export on economic growth which the previous studies might have ignored and the aggregate non-oil exports data used by them might bias their conclusions. In achieving the objectives of the study, Ordinary Least Square Methods involving Error correction mechanism, over-parametization and parsimonious were adopted. In testing for the time series properties, the evidence from estimated economic models suggests that all the variables examined are stationary at first difference I(Is) using the Augmented Dickey- Fuller (ADF) and PhillipsPerron. Besides, Johansen Co integration test reveals that the variables are co integrated which confirms the existence of long-run equilibrium relationship between the variables. The study reveals that the impact of non-oil export on the economic growth was moderate and not all that heartening as a unit increase in non-oil export impacted positively by 26% on the productive capacity of goods and services in Nigeria during the period. This was evident in the study that the policies on non-oil sectors during the period in Nigerian do not sufficiently encourage non-oil export, thus reduce their contributions to growth. This study therefore predicts an imminent collapse of the Nigerian non-oil sector in the nearest future if immediate remedial measures are not taken to strengthen the sector. The study among other things encourages the government to strengthen the legislative and supervisory framework of the non-oil sectors in Nigeria and diversify the economy to ensure maximum contributions from all faces of the sectors to economic growth of Nigeria.

To Ajakaiye and Fakiyesi (2009), earnings from non-oil exports, such as finished leather products, cocoa and its products, sesame seeds and manufactured products like cosmetics and toiletries, rose to about US$1.38 billion in 2007. By the end of 2008, this value rose to $1.8 billion, the highest in the country’s history.

Ozoudo (2010) also discovered using econometric method that the dominance of petroleum / crude oil in the export sector’s export. He as well recorded that the inefficient performance of the non – oil marketing of board deterred progress the non – oil sector. His research covered the period from 1991 – 2008. Ezirim et al., (2010), observed that the economy, which was largely at a rudimentary stage of development at the first half of the last century, started experiencing some structural transformation immediately after the country’s independence in 1960.

Akinole (2001) in his study, he adopted the ordinary least square (OLS) regression technique. He investigated the prospects for Nigerian petroleum, groundnut, coca and palm oil in the expanded economic commodity. He discovered that the demand for Nigeria oil by the common market countries is price elastic. But the membership of Nigeria in the organization of petroleum exporting countries, a collective bargaining organization makes the exploitation of the high price elasticity of demand unlikely. He said that there exist an effective competition between Nigeria’s

Helleiner (2002) carried out a study using the Keynesian export multiplier approach and two variants of the two – gap frame work, incorporating, and the Harrod Domar model, which shows that only a small part if total agricultural out part of the developing countries receive elaborates local processing, since the bulk is usually sent abroad. He points out that the agriculture normally better in the supply of intermediate inputs to other rectors than in the use of other intermediate inputs.

Asanebi (2007) carried out a research using linear correlation co – efficient analysis and observed that the performance of non – oil sectors exports was below expectation in aggregate terms and so, has not made significant impact on the GNP of the country, cannot sustain the country in terms of economic growth and development. He also came up with the following findings;

– That primary commodities dominates Nigeria’s basket of non-oil export

– That introduction of the structural adjustment programme (SAP) came with export promotion policy that saw some improvement in the proportion of semi – manufactures and manufactures.

– Though the performance of non-oil exports below expectation in terms of market diversification, it however, recorded some success in terms of a gradual growth in the proportion of value added exports.

Okoro (2009), in his work on the impact of non – oil export the Nigeria economy” using econometric growth without the industrial, agricultural and manufacturing sectors improving from their present state. He states that a very strong link exists between these three sectors and other sectors of the economy. His period of study covered 1995 – 2005.

Limitation of Previous Studies

It is a common knowledge that on research work exists in a competence that is devoid of flaw and lapses, but the ability to reduce or make those lapses within the limit of this research work.

However, other works (period’s studies) are limited in the area of including the contribution of oil and the performance of other developed countries in compares with less developed countries especially Nigeria.

This study therefore intends to cover these lapses by focusing on Nigeria only and the impact of non – oil export earnings on the national’s cross domestic product from the period 1986 2010.

Agenri, this work seeks to advocate the policies or measures that would boast non – oil sector to the economic growth of Nigeria which previous studies neglected.

However, it is crystal clear from the analysis so far that there are some inheritable weaknesses in many works carried earlier on export earning relationship with economic growth in Nigeria as reviewed. The reason for this may not be farfetched as majority of the works did not subject the estimate parameters to some advance econometric test, like test of stationarity of the regression data, cointegration, VEM, etc., but only make use of ordinary least square (OLS).

Hence, this work will adopt various advance econometric test to ensure the reliability of the result and also prevent spurious result.

CHAPTER THREE

RESEARCH METHODOLOGY

3.1 Research Design

This chapter focuses on the research method that will be adopted, procedures employed in data collection as well as statistical and econometric analysis of the data for the purpose of examining the role of export earning on the economic growth of Nigeria.

The methodology to be adopted is the ordinary least square (OLS) method of regression. Ordinary least square (OLS) is adopted because of its simplicity and estimates obtained from this procedure have optimal properties including linearity, Unbiasedness, minimum variance, zero mean value of the random term, etc. (Gujarati, 2004).

3.2 Model Specification

Hypothesis has earlier been stated in this study with the view of evaluating the impact of export earnings fluctuation on economic growth of Nigeria. In capturing the study, we use the neoclassical growth model, otherwise referred to as the growth accounting framework to explain the source of growth in an economy. The national accounts form the basis of the economies to be analyzed and it is used in conjunction with aggregate production function. This application has been widely used (for example, Abogan et al, 2014, Akinlo and Odusola, 2003; Obstfeld, (1994; Ifeacho, Omoniyi, and Olufemi (2014). Using a production function approach, it states that the growth rate of output (GDP) is principally determined by the following factors: The rate of growth of gross labour/or the rate of growth of its quality, multiplied by the labour income share; the rate of growth of gross capital input and/or the rate of growth of its quality, multiplied by the capital income share; and change in technology or total factor productivity (TFP). This is given is:

g = f (L, K, T) ———————————-3.0

Where: g = growth of GDP; L = Labour; K= capital formation/ investment; and T = technology.

In the highest of the above, the model for this study is shown by incorporating other determinants of economic activities which include the key variables to be considered in this study. These include, Non-Oil export, Inflation rate and Exchange rate, Thus, the model is symbolically represented in its functional form as:

GDP = f (NOXP, INF, EXR) 3.1

Where;

GDP = Gross Domestic Product

NOXP = Nigeria Non-Oil Export

INF = Nigeria Inflation Rate

EXR = Exchange Rate.

 

The linearized model specification for the analysis is given as GDP = bO+ b1NOXP + b2 INF + b3 EXR + Ut 3.2

Where;

bO = Constant term/ parameter intercept

b1, b2, and b3 = Coefficients of the parameters estimates.

Ut = Error Term

As efforts will be made to rescale the data, and ensure consistence, the data will be further differenced as expressed as follow:

D(GDP) = bO+b1 D(NOXP) + b2 D(INF) + b3 D(EXR) + Ut

3.3 Estimation Procedure

At this level of research using time series data; the researcher estimates the model with Ordinary Least Square (OLS) method. This method is preferred to others as it is best linear unbiased estimator, minimum variance, zero mean value of the random terms, etc (koutsoyiannis,2003).

In the preliminary test, following tests shall be conducted. They include:

  • Unit Root Test
  • Co-Integration Test
  • Error Mechanism Test (ECM)
  • Granger Causality test

Unit Root Test: It is used to test for the stationary of the time series data. This involves testing of the order of integration of the individual time series under consideration. These test are initially performed at levels and then in first difference form. Three different models with varying deterministic components are considered while performing the tests. These are (1) model with an intercept which assumes that there are no linear trends in the data such that the first differenced series has zero mean (2) model with a linear trend which includes a trend stationary variable to take account of unknown exogenous growth and (3) a model which neither includes a trend nor a constant. The most popular ones are Augmented Dickey-Fuller (ADF) test due to Dickey and Fuller (1979, 1981).Augmented Dickey Fuller (ADF) test statistics shall be compared with the critical value at 5% level of significance. A situation whereby the ADF test statistics is greater than the critical value with consideration on absolute values, the data at the tested order will be said be stationary. Augmented Dickey-Fuller test relies on rejecting a null hypothesis of unit roots (the series are non-stationary in favor of the alternative hypotheses of stationary. The test conducted with and without a deterministic trend (t) for each of the series.

The general form of ADF test is estimated by the following regression:

∆ yt = β0 + β1 yt-1+∑ β∆ yt +et– 3.4

∆ yt = β0+ β1 yt-1+∑ β∆ yt1+et 3.5

Where: y is a time series, t is a linear time trend, ∆ is the first difference operator, β0 is a constant, n is the optimum number of lags in the dependent variable and e is the random error term.

The null hypothesis is that β1=0. If the null hypothesis β1=1, then we conclude that the series under consideration ∆ (yt) has unit root and is therefore non-stationary.

If the ADF test fails to reject the test in levels but reject the test in first differences then the series contain one unit root and is of integrated order one 1(1). If the test fails to reject the test in level and first differences but rejects the test in second differences, then the series contains two unit root and is integrated order two 1(2). The Phillip-Perron (pp) unit root test is implementing to justify the results of ADF test.

The equation thus:

∆ yt = β0 + β1yt -1 + et 3.6

Error Correction Mechanism (ECM)

The purpose of the error correction model is to indicate the speed of adjustment from the short-run equilibrium state. However, the greater the coefficients of the error term (ECM), the higher the seed of adjustment of the model form the short-run to the long-run equilibrium.

The ECM (p) form is written as:

∆ yt = ∂+pyt-1 +∑ø ∆ yt -1 + £t – 3.7

Where, ∆ is the differencing operator, such that ∆ yt-1=yt=yt-1

Coefficient of Multiple Determinations (R2): It is used to measure the proportion of variations in the dependent variables. The higher the (R2), the greater the proportion in the dependent variable as brought about by the independent variable and vice-versa.

Standard Error Test (S.E): It isused to test for the reliability of the coefficient estimates.

Decision Rule:

IfS.E<1/2 bi or p-value is less than 5%level significance reject null hypothesis and conclude that the coefficient estimate of the parameter is statistically significant. Otherwise accept null hypothesis.

Z- TEST: Itis used to test for statistical significance of individual estimate parameter. In this research, Z- test is chosen because the population variance is unknown and the sample side is more than 30.

Decision Rule

If Z-cal.> Z- tab, or P-value is less than 5% level of significance reject the null hypothesis and conclude that the regression coefficient is statically significant. Otherwise accept the null hypothesis.

3.4 Source of Data

The data for this research projects obtained from the following source:

  • Central Bank of Nigeria (CBN) Statically Bulletin. Volume 27, 2016

CHAPTER FOUR

PRESENTATION AND ANALYSIS OF RESULTS

Having estimated the model, the variables considered are gross domestic product (dependent variable), Nigeria Non-oil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) will all be used as the independent variables. The result covers the period of year 1985 – 2016.

4.1 Unit Root Test

In other to test for the presence or absence of unit root in the data used for the empirical analysis, Augmented Dickey-Fuller (ADF) test was employed and the test result is as presented below:

TABLE 1: UNIT ROOT

Augmented Dickey Fuller Result at Level and First Difference, Trend only

Variables

ADF @ Level

1st difference

Critical value (5%)

Order of

integration

Remarks

D(GDP)

 1.200016

-5.383955

-3.557759

I(1)

Stationary

D(NOXP)

-0.298585

-5.989962

-3.557759

I(1)

Stationary

D(INF)

-3.048442

-5.737084

-3.557759

I(1)

Stationary

D(EXR)

-0.314150

-5.665275

-3.557759

I(1)

Stationary

Source: Own Computation (See Appendix)

From the table 1 above, the result revealed that none of the variables were stationary at level while at first difference all the variables become stationary given the 5% level of significance, since the absolute value of the calculated ADF exceeds the absolute value of 5% and 1% critical value of the ADF. Hence, since all the variables are not stationary at the level, co-integration analysis is justified. We there proceed to conduct the long run relationship of the variables and their short term speed of adjustment to equilibrium.

4.2 Tests for Cointegration

This test is used to test for the long run relationship between the variables; it was carried out using the augmented eagle – Granger test on the residuals under the following hypothesis:

H0 : δ = 0 (Not- cointegrated)

H1 : δ ≠ 0 (cointegrated)

Decision Rule:

Reject H0 if t*.Adf (LR) > t-Adf (CV), accept if otherwise

COINTEGRATION TEST: TABLE 2

Series: GDP NOXP INF EXR 

   

Lags interval (in first differences): 1 to 1

 

Unrestricted Cointegration Rank Test (Trace)

 
         
         

Hypothesized

 

Trace

0.05

 

No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**

         
         

None *

 0.837726

 111.1755

 47.85613

 0.0000

At most 1 *

 0.712347

 52.98453

 29.79707

 0.0000

At most 2

 0.306263

 13.11250

 15.49471

 0.1107

At most 3

 0.043145

 1.411298

 3.841466

 0.2348

         
         

 Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

 * denotes rejection of the hypothesis at the 0.05 level

 **MacKinnon-Haug-Michelis (1999) p-values

 

Source: Own Computation (See Appendix)

From table 2 above, since the computed trace statistic i.e. (111.1755 and 52.98453) is greater than their respective T-Adf . i.e. the critical value (47.85613, and 29.79707) at 5% levels of significance or since the probability value (  0000< 0.05) are less than 5% level of significance, we reject Ho and conclude that there is at least two co-integrating equation and that all the variables are cointegrated. Put differently, there is a sustainable long-run relationship (i.e. steady-stated path) between gross domestic products (GDP), Nigeria Non-oil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR).

The Longrun Equation Nigeria Non-Oil export

GDP = 3340.874 – 10.10148NOXP -22.52152INF + 120.8039EXR

(0.57639) (12.0763) (51.8030)

The result above is the coefficient of the explanatory variables which indicate the direction of strength of the relationship between explanatory variables and economic growth in the long run. The figures in the parenthesis were the asymptotic standard error. The result reveal that one million increase Nigeria Non-oil export will bring about N10101480 decrease on the gross domestic product, at the same time one million increase in Nigeria Inflation rate will bring about N22521520 decrease on gross domestic product, again one million increase in exchange rate will bring about N1208039 increase on the gross domestic product, all other factors affecting gross domestic product remaining constant.

Vector Error Correction Mechanism

The existence of a long- run co-integrating equilibrium provides for short-term fluctuations. In order to strengthen out or absolve these fluctuations, an attempt was made to apply the Vector Error Correction Mechanism (VECM). As noted, the VECM is meant to tie the short-run dynamics of the co-integrating equations to their long-run static dispositions. Table 4 below shows the Vector error correction mechanism result.

TABLE 3

VECTOR ERROR CORRECTION MECHANISM RESULT

Sample (adjusted): 1986 2016

     

Included observations: 29 after adjustments

   
           
           

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

Remark

           
           

C

 5889.562

599.987

9.81615

0.0000

Reject

D(GDP(-2))

 2.315599

0.32156

7.20115

0.0000

Reject

D(NOXP(-1))

 -17.81252

2.30280

-11.0615

0.0000

Reject

D(INF(-1))

 2.601630

19.4607

0.13369

0.8971

Accept

D(EXR(-1))

4.514974

1.36332

3.31176

0.0000

Reject

VECM(-1)

-2.181188

0.18680

-11.6763

0.0000

Reject

           

Source: Own Computation (See Appendix)

R2 = 0.935255 D-W = 1.90

F (3, 25) = 33.70, F*(P-value) = 0.0000

From the result the coefficient of vector error correction term is -2.181188. This shows that 218% of the errors in the short run are corrected each year. Thus, the coefficient captures the speed for adjustment at which the short-run of GDP ties with its long-run. The result is significant since the coefficient of multiple determination (0.9353) is greater than zero. And also, the vector error correction coefficient has negative sign which indicate that there is feedback from the previous year’s disequilibrium or that the explanatory variables have power to correct the disequilibrium each year.

Coefficient of Multiple determinations: Also the computed R2 value (0.9353) of which is the coefficient of multiple determinations indicates that our model satisfies the requirement for goodness of fit. The value showed that 93.53% the variation in the gross domestic product (GDP) are explained by the variation of the explanatory variables namely; Nigeria Non-oil export (NOXP), Nigeria Inflation rate (INF), and Exchange Rate (EXR) while the remaining 6.47% is explained by variable not included in the model.

T-test: A mere observation of the individual’s parameters will reveal that all the variables used in the regression were statistically significant at 5% level of significance, since their P-value is less than the 5% level of significance i.e.(0.0000 < 0.05), except for that of inflation rate.

F-test: Furthermore, the joint influence of the explanatory variables on the dependent variable is statistically significant. This is also confirmed by the F-probability which is statistically zero i.e. the P-value of F-statistics is less than 5%

Durbin-Watson Test: At the same time the Durbin-Watson is 1.90 approximately. Using 5% level of significance, 3 explanatory variables and 34 observations, the tabulated Durbin-Watson statistics for lower and upper limit are 1.23 and 1.67, since the calculated Durbin-Watson is greater than upper limit of Durbin-Watson but less than 4-du (2.33), we conclude that there is no evidence of first order serial correlation.

This was further confirmed by the LM serial correlation test. The result is presented below.

VEC Residual Serial Correlation LM Tests

     
     

Lags

LM-Stat

Prob

     
     

1

 10.01800

 0.8657

2

 19.07625

 0.2647

3

 14.75249

 0.5428

     
     
     

Probs from chi-square with 16 df.

Given the optimal lag length, we accept the null hypothesis of no serial correlation. The result clearly revealed the absence of no serial correlation as the LM probability value at second lag level (0.2647) is greater than 5% level of significance. The implication is that the result can be fully relied on to make sound policies.

4.3 Test of Hypothesis

Hypothesis I: The main objective of this study is to examine the impact of Non-oil export on Nigeria economic growth. With respect to this, the null hypothesis and alternative hypothesis are stated as fellows;

HO: Non-oil Export has no significant impact on Economic Growth of Nigeria.

H1: Non-oil Export has significant impact on Economic Growth of Nigeria.

F- Test: Is employed in testing the hypothesis. This test will help to capture the joint influence of the explanatory variables on the dependent variable.

Decision Rule;

If F-cal. > F-tab reject the null hypothesis or if the P-value is less than 5% level of significance, otherwise accept the null hypothesis. Using 5% level of significance at 4 and 29 degree of freedom, the tabulated F-value is 2.76. Since, the calculated F-value (65) is greater than the tabulated F-value at 5% level of significance; we reject the null hypothesis and conclude that Non-oil export has significant impact on Economic Growth of Nigeria within the sample period.

Hypothesis II: The second objective of study is to determine the long-run relationship between Non-oil export and economic growth in Nigeria. In testing this, co-integration test was employed to determine the nature of the relationship. The null and alternative hypothesis is presented below;

H0: There is no long-run relationship between Non-oil export and economic growth in Nigeria.

H1: There is long-run relationship between Non-oil export and economic growth in Nigeria.

Decision Rule: Reject H0 if t*.Adf (LR) > t-Adf (CV), accept if otherwise

Reject H0 if t*.Adf (LR) > t-Adf (CV), accept if otherwise

From data in table 2, the computed trace statistic i.e. . (111.1755 and 52.98453) is greater than their respective T-Adf . i.e. the critical value (47.85613, and 29.79707) at 5% levels of significance or since the probability value (  0000< 0.05) are less than 5% level of significance, we reject Ho and conclude that there is at least two co-integrating equation and that all the variables are cointegrated. Put differently, there is a sustainable long-run relationship (i.e. steady-stated path) between gross domestic products (GDP), Nigeria Non-oil export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR).

4.4 Implication of the Result

Economic theory imposes a restriction on the signs and magnitudes of economic relationships. In view of this, the coefficients of the explanatory variables in the estimated model presented above all conform to the a priori expectations except for that of NOXP as analyzed below.

From the regression result presented in table 4 other factors (affecting: GDP) remaining constant, the researcher deduced as follows:

As Nigeria Non-Oil Export (NOXP) increases by, say, one percent, Gross Domestic Product (GDP) on the average decreases by 17.8 percent. As Nigeria Inflation Rate (INF) increases by, say, one percent, Gross Domestic Product (GDP) on the average increases by 26 percent. And as Exchange rate (EXR) increases by, say one naira Gross Domestic Product (GDP) on the average increases by N451497400.

From the analysis above, Nigeria Non-oil export has significant impact on Nigeria economic growth within the sample period. The reason for this may not be far from the fact that oil export revenue contribute to over 80% of Nigeria revenue. It should be noted that Non-oil export has negative relationship with economic growth both in the short run and longrun. The reason may not be far-fetched as earning from the non-oil export are often not properly utilized.

Another reason for this negative trends may be due to poor investment in the country, high inflation and exchange rate making the export not profitable. A conducive business environment should encourage business sectors to thrive.

CHAPTER FIVE

SUMMARY, CONCLUSION AND RECOMMENDATION

5.1 Summary of findings

The paper investigated the impact of Non-Oil Export on economic growth in Nigeria from1985 to 2016. Vector error Correction Mechanism was used to estimate the regression result. Cointegration test and Unit root test was also conducted to determine the stationarity and long-run relationship between the variables.

The result of the Cointegration test revealed that there is a sustainable long-run relationship (i.e. steady-stated path) between gross domestic product (GDP) and the explanatory variables (NOXP, INF, and EXR).

The Vector error Correction Mechanism result indicates that the coefficient of error correction term is -2.18. This revealed that 218% of the errors in the short run are corrected each year. Thus, the coefficient captures the speed for adjustment at which the short-run of GDP ties with its long-run dynamics.

The Unit Root Test result revealed that none of the variables used in this research work was stationary at level. But after differencing the variables all of them became at first difference given the 5% and 1% level of significance. Hence, the result of the regression can be fully relied on to make policy analysis and recommendation.

The entire regression plane was statistically significant; this means that the joint influence of the explanatory variables (NOXP, INF, and EXR), on the dependent variable (GDP) is statistically significant.

The result of the coefficient of multiple determination indicates that our model satisfies the requirement for goodness of fit. The value shows that 93.53% the variation in the gross domestic product (GDP) are explained by the variation of the explanatory variables namely; Nigeria Non-Oil Export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) while the remaining 6.47% is explained by variable not included in the model.

5.2 Conclusion

The empirical research reveals that Non-Oil Export captured by changes in Nigeria Non-Oil Export (NOXP), Nigeria Inflation Rate (INF), and Exchange rate (EXR) had significant influence on Nigeria economic growth during the period under review. The result showed that Non-Oil export changes have negative relationship in the short-run as well as longrun with economic growth in Nigeria.

The implication is that Nigeria Non-Oil Export has not been fully harnessed and have been grossly neglected by the successive government such that instead of Non-Oil export to be an agent of economic growth and development in Nigeria has become agent of economic instability in the country.

Nigeria needs to improve her trade policies with the rest of the world for the country to stabilize balance of payment equilibrium and sustained level of economic growth. Attention should be paid on how funds realized from Non-Oil export are utilized to ensure sustainable economic growth in the country. The negative sign of the VECM indicates that the dependent variable has power to adjust to short term fluctuations of the explanatory variables in the long-run. This sign is necessary giving the inconsistent nature of export earning in Nigeria, which are often distorted by the social-economic, political condition, regional policies and prevailing economic condition in the country.

5.3 Policy Recommendation

The research work recommends that for Nigeria industrial sector to take substantial benefits of broad participation in globalization, the following conditions need to be fulfilled.

  • The Federal Government of Nigeria should revamp both local industries and agriculture through subsidies, concessions, uninterrupted power supply, technical assistance, improving security of lives and properties and the creation of enabling business operating environment.
  • Also sound macroeconomic policies are needed to reinforce the globalization exercise for a better result. The positive sign is an indicator that Nigeria is benefitting from globalization; this could be a product of the oil export in Nigeria which makes Nigeria to enjoy a favourable balance of payment.
  • Encouragement of Export Promotion: The government should endeavor to support various export promotion programmes and institutions. This could be achieved by encouraging financial institutions, both formal and informal, to make loans available at reduced rates of interest for investors as to increase the level of investment in this country.
  • Reduction or Removal of Import Tariffs: Tariffs paid on imports of equipment necessary to boost non-oil production in Nigeria are so much that productions are averse to risk their resources. So there should be a down-ward review of tariff/tax structures to reduce the cost of production in Nigeria.
  • Nigeria must look beyond the mono-product type of business (oil sector) and research into other sectors for new products of international standard.
  • The Federal Ministries of Commerce and Industries (FMCI) should focus more attention on the development of the home industry with a view to increasing the county’s share of non-oil trade.

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Osuntogun B. K (1997). Potentials for diversifying Nigeria’s non-oil Exports to non-traditional markets. Nairobi, African Economic Research consortium Paper 68 (1997).

Papageorgiou, D., Michaely, M., & Choski, A. (1991). Liberalizing foreign trade: Lessons of Experience in the Developing World. Cambridge, MA: Basil Blackwell.

Pritzker, S. P., Arnold, K., & Moyer, C. (2015). Measuring the economy: A primer on GDP and the national Income and product accounts. Washington: Bureau of economic analysis (bea).

Soludo, C. (2002). Repositioning Nigeria for regional economic power: A paper presented by the cbn governor at the seminar organized by Nigeria Export Position Council, August 4, 2002. Lagos.

Subasat, T. (2002). Does export promotion increase economic growth? Some cross-sector evidence. Economic Policy Review, (2), 315-349.

Sylvester, J., & Aiyelabola, O. O. (2012). Foreign trade and economic growth: Evidence from Nigeria. Arabian Journal of Business and Management Review (OMAN Chapter), 2(1).

Todera, M.P. (1985). Economic Development in the Third World Edition. Longman: New York.

Usman, A. O. (2010). Non-oil export determinants and economic growth in Nigeria (1985-2008). European Journal of Business and Management Sciences, 3(3), 124.

APPENDIX I

REGRESSION DATA

YEARS

NOXP

(N’Billion)

INF

GDP

N’Billion)

EXR

(N’$)

1985

7.500000

3.226000

134.5900

0.629900

1986

5.600000

6.250000

134.6000

0.615900

1987

16.80000

11.76500

193.1300

0.626500

1988

20.40000

34.29100

263.2900

0.646600

1989

29.10000

49.02000

382.2600

0.606000

1990

42.90000

7.895000

472.6500

0.595700

1991

86.40000

12.19500

545.6700

0.546400

1992

127.8000

44.56500

875.3400

0.610000

1993

129.5000

57.14300

1089.680

0.672900

1994

125.8000

57.41600

1399.700

0.724100

1995

622.4000

72.72900

2907.360

0.764900

1996

423.8000

29.29200

4032.300

0.893800

1997

708.0000

10.67300

4189.250

2.020600

1998

695.6000

7.862000

3989.450

4.017900

1999

670.3000

6.618000

4679.210

4.536700

2000

789.0000

6.938000

6713.570

7.391600

2001

1149.100

18.86900

6895.200

8.037800

2002

1245.700

12.88300

7795.760

9.909500

2003

1776.100

14.03300

9913.520

17.29840

2004

1782.200

15.00100

11411.07

22.05110

2005

2109.500

17.85600

14610.88

21.88610

2006

2531.400

8.218000

18564.59

21.88610

2007

3343.000

5.413000

20657.32

21.88610

2008

4803.500

11.88100

24296.33

21.88610

2009

4912.800

12.54300

24794.24

21.88610

2010

7117.800

13.72000

54612.26

92.69340

2011

8865.800

10.80000

62980.40

102.1052

2012

7581.600

9.200000

71713.94

111.9433

2013

8140.200

7.900000

80092.56

120.9702

2014

9277.300

8.700000

89043.62

129.3565

2015

9527.800

9.200000

94,144.96

197.4566

2016

10134.68

16.85000

101,453.13

253.8800

SOURCE: CENTRAL BANK OF NIGERIA STATTISTICAL BULLETINE, VOL 26

APPENDIX II

REGRESSION RESULT

UNIT ROOT TEST

GDP AT LEVEL

Null Hypothesis: GDP has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

 1.200016

 0.9999

Test critical values:

1% level

 

-4.262735

 
 

5% level

 

-3.552973

 
 

10% level

 

-3.209642

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(GDP)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:11

   

Sample (adjusted): 1985 2016

   

Included observations: 32 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

GDP(-1)

0.070671

0.058892

1.200016

0.2395

C

-1761.044

1912.265

-0.920921

0.3644

@TREND(“1985”)

206.6215

133.0712

1.552714

0.1310

         
         

R-squared

0.353777

    Mean dependent var

2695.433

Adjusted R-squared

0.310695

    S.D. dependent var

5606.351

S.E. of regression

4654.638

    Akaike info criterion

19.81562

Sum squared resid

6.50E+08

    Schwarz criterion

19.95167

Log likelihood

-323.9578

    Hannan-Quinn criter.

19.86140

F-statistic

8.211798

    Durbin-Watson stat

2.225206

Prob(F-statistic)

0.001431

     
         
         

GDP AT FIRST DIFFERENCE

Null Hypothesis: D(GDP) has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-5.383955

 0.0006

Test critical values:

1% level

 

-4.273277

 
 

5% level

 

-3.557759

 
 

10% level

 

-3.212361

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(GDP,2)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:12

   

Sample (adjusted): 1986 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

D(GDP(-1))

-0.995018

0.184812

-5.383955

0.0000

C

-3242.399

1916.922

-1.691462

0.1015

@TREND(“1985”)

343.3944

109.9437

3.123365

0.0040

         
         

R-squared

0.500359

    Mean dependent var

279.5119

Adjusted R-squared

0.465901

    S.D. dependent var

6594.710

S.E. of regression

4819.552

    Akaike info criterion

19.88781

Sum squared resid

6.74E+08

    Schwarz criterion

20.02522

Log likelihood

-315.2050

    Hannan-Quinn criter.

19.93336

F-statistic

14.52082

    Durbin-Watson stat

2.012503

Prob(F-statistic)

0.000043

     
         
         

NOXP AT LEVEL

Null Hypothesis: NOXP has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-0.298585

 0.9873

Test critical values:

1% level

 

-4.262735

 
 

5% level

 

-3.552973

 
 

10% level

 

-3.209642

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(NOXP)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:12

   

Sample (adjusted): 1986 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

NOXP(-1)

-0.020180

0.067585

-0.298585

0.7673

C

-264.7531

245.9221

-1.076573

0.2903

@TREND(“1985”)

34.24210

18.55070

1.845866

0.0748

         
         

R-squared

0.213564

    Mean dependent var

280.7333

Adjusted R-squared

0.161135

    S.D. dependent var

624.1469

S.E. of regression

571.6535

    Akaike info criterion

15.62145

Sum squared resid

9803632.

    Schwarz criterion

15.75750

Log likelihood

-254.7539

    Hannan-Quinn criter.

15.66723

F-statistic

4.073391

    Durbin-Watson stat

2.167308

Prob(F-statistic)

0.027224

     
         
         

NOXP AT FIRST DIFFERENCE

Null Hypothesis: D(NOXP) has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-5.989962

 0.0001

Test critical values:

1% level

 

-4.273277

 
 

5% level

 

-3.557759

 
 

10% level

 

-3.212361

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(NOXP,2)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:14

   

Sample (adjusted): 1986 2016

   

Included observations: 29 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

D(NOXP(-1))

-1.112378

0.185707

-5.989962

0.0000

C

-275.1014

222.6929

-1.235340

0.2266

@TREND(“1985”)

33.89849

12.16683

2.786139

0.0093

         
         

R-squared

0.553695

    Mean dependent var

35.60937

Adjusted R-squared

0.522915

    S.D. dependent var

835.9846

S.E. of regression

577.4255

    Akaike info criterion

15.64410

Sum squared resid

9669185.

    Schwarz criterion

15.78151

Log likelihood

-247.3055

    Hannan-Quinn criter.

15.68964

F-statistic

17.98900

    Durbin-Watson stat

2.062313

Prob(F-statistic)

0.000008

     
         
         

INF AT LEVEL

Null Hypothesis: INF has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-3.048442

 0.1351

Test critical values:

1% level

 

-4.262735

 
 

5% level

 

-3.552973

 
 

10% level

 

-3.209642

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(INF)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:15

   

Sample (adjusted): 1986 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

INF(-1)

-0.469178

0.153907

-3.048442

0.0048

C

14.36339

6.963617

2.062633

0.0479

@TREND(“1985”)

-0.309286

0.284782

-1.086047

0.2861

         
         

R-squared

0.237413

    Mean dependent var

-0.360606

Adjusted R-squared

0.186573

    S.D. dependent var

16.53804

S.E. of regression

14.91569

    Akaike info criterion

8.329212

Sum squared resid

6674.332

    Schwarz criterion

8.465258

Log likelihood

-134.4320

    Hannan-Quinn criter.

8.374987

F-statistic

4.669875

    Durbin-Watson stat

1.712869

Prob(F-statistic)

0.017153

     
         
         

INF AT FIRST DIFFERENCE

Null Hypothesis: D(INF) has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-5.737084

 0.0003

Test critical values:

1% level

 

-4.273277

 
 

5% level

 

-3.557759

 
 

10% level

 

-3.212361

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(INF,2)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:15

   

Sample (adjusted): 1986 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

D(INF(-1))

-1.048137

0.182695

-5.737084

0.0000

C

2.780680

6.471047

0.429711

0.6706

@TREND(“1985”)

-0.154956

0.327212

-0.473563

0.6394

         
         

R-squared

0.532060

    Mean dependent var

0.484937

Adjusted R-squared

0.499788

    S.D. dependent var

24.14496

S.E. of regression

17.07668

    Akaike info criterion

8.602365

Sum squared resid

8456.780

    Schwarz criterion

8.739778

Log likelihood

-134.6378

    Hannan-Quinn criter.

8.647913

F-statistic

16.48687

    Durbin-Watson stat

1.959848

Prob(F-statistic)

0.000017

     
         
         

EXR AT LEVEL

Null Hypothesis: EXR has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-0.314150

 0.9868

Test critical values:

1% level

 

-4.262735

 
 

5% level

 

-3.552973

 
 

10% level

 

-3.209642

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(EXR)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:09

   

Sample (adjusted): 1985 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

EXR(-1)

-0.027459

0.087408

-0.314150

0.7556

C

-5.062242

4.735090

-1.069091

0.2936

@TREND(“1985”)

0.557585

0.310947

1.793182

0.0830

         
         

R-squared

0.146661

    Mean dependent var

3.898248

Adjusted R-squared

0.089772

    S.D. dependent var

12.43009

S.E. of regression

11.85904

    Akaike info criterion

7.870565

Sum squared resid

4219.101

    Schwarz criterion

8.006611

Log likelihood

-126.8643

    Hannan-Quinn criter.

7.916341

F-statistic

2.578008

    Durbin-Watson stat

2.043392

Prob(F-statistic)

0.092645

     
         
         

EXR AT FIRST DIFFERENCE

Null Hypothesis: D(EXR) has a unit root

 

Exogenous: Constant, Linear Trend

 

Lag Length: 0 (Fixed)

   
         
         
     

t-Statistic

  Prob.*

         
         

Augmented Dickey-Fuller test statistic

-5.665275

 0.0003

Test critical values:

1% level

 

-4.273277

 
 

5% level

 

-3.557759

 
 

10% level

 

-3.212361

 
         
         

*MacKinnon (1996) one-sided p-values.

 
         
         

Augmented Dickey-Fuller Test Equation

 

Dependent Variable: D(EXR,2)

   

Method: Least Squares

   

Date: 11/10/18 Time: 22:16

   

Sample (adjusted): 1985 2016

   

Included observations: 30 after adjustments

 
         
         

Variable

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

D(EXR(-1))

-1.050113

0.185360

-5.665275

0.0000

C

-5.179592

4.659744

-1.111561

0.2755

@TREND(“1985”)

0.536490

0.249016

2.154436

0.0396

         
         

R-squared

0.525334

    Mean dependent var

0.262659

Adjusted R-squared

0.492598

    S.D. dependent var

16.90536

S.E. of regression

12.04205

    Akaike info criterion

7.903747

Sum squared resid

4205.320

    Schwarz criterion

8.041160

Log likelihood

-123.4599

    Hannan-Quinn criter.

7.949295

F-statistic

16.04778

    Durbin-Watson stat

2.008015

Prob(F-statistic)

0.000020

     
         
         

COINTEGRATION TEST

Date: 11/10/18 Time: 22:17

   

Sample (adjusted): 1986 2016

   

Included observations: 30 after adjustments

 

Trend assumption: Linear deterministic trend

 

Series: GDP NOXP INF EXR 

   

Lags interval (in first differences): 1 to 1

 
         

Unrestricted Cointegration Rank Test (Trace)

 
         
         

Hypothesized

 

Trace

0.05

 

No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**

         
         

None *

 0.837726

 111.1755

 47.85613

 0.0000

At most 1 *

 0.712347

 52.98453

 29.79707

 0.0000

At most 2

 0.306263

 13.11250

 15.49471

 0.1107

At most 3

 0.043145

 1.411298

 3.841466

 0.2348

         
         

 Trace test indicates 2 cointegrating eqn(s) at the 0.05 level

 * denotes rejection of the hypothesis at the 0.05 level

 **MacKinnon-Haug-Michelis (1999) p-values

 
         

Unrestricted Cointegration Rank Test (Maximum Eigenvalue)

         
         

Hypothesized

 

Max-Eigen

0.05

 

No. of CE(s)

Eigenvalue

Statistic

Critical Value

Prob.**

         
         

None *

 0.837726

 58.19099

 27.58434

 0.0000

At most 1 *

 0.712347

 39.87203

 21.13162

 0.0001

At most 2

 0.306263

 11.70120

 14.26460

 0.1224

At most 3

 0.043145

 1.411298

 3.841466

 0.2348

         
         

 Max-eigenvalue test indicates 2 cointegrating eqn(s) at the 0.05 level

 * denotes rejection of the hypothesis at the 0.05 level

 **MacKinnon-Haug-Michelis (1999) p-values

 
         

 Unrestricted Cointegrating Coefficients (normalized by b’*S11*b=I): 

         
         

GDP

NOXP

INF

EXR

 

 0.000475

-0.000914

-0.010995

-0.263683

 

 4.22E-05

-0.001475

-0.013597

 0.040200

 

-1.93E-05

 0.000294

 0.066503

-0.007567

 

 0.000466

-0.001862

 0.012421

-0.111979

 
         
         
         

 Unrestricted Adjustment Coefficients (alpha): 

 
         
         

D(GDP)

 513.6431

-4305.178

-208.2857

 128.2741

D(NOXP)

 381.3364

-218.4302

-43.78510

 52.66628

D(INF)

-0.020062

 0.467248

-8.912722

-0.302783

D(EXR)

 2.273915

-10.16434

-0.268121

-0.115379

         
         
         

1 Cointegrating Equation(s): 

Log likelihood

-737.6035

 
         
         

Normalized cointegrating coefficients (standard error in parentheses)

GDP

NOXP

INF

EXR

 

 1.000000

-1.925712

-23.15976

-555.4117

 
 

 (0.27854)

 (12.6994)

 (21.8735)

 
         

Adjustment coefficients (standard error in parentheses)

 

D(GDP)

 0.243853

     
 

 (0.48014)

     

D(NOXP)

 0.181040

     
 

 (0.03790)

     

D(INF)

-9.52E-06

     
 

 (0.00151)

     

D(EXR)

 0.001080

     
 

 (0.00113)

     
         
         
         

2 Cointegrating Equation(s): 

Log likelihood

-717.6675

 
         
         

Normalized cointegrating coefficients (standard error in parentheses)

GDP

NOXP

INF

EXR

 

 1.000000

 0.000000

-5.727406

-643.3336

 
   

 (17.6284)

 (17.4226)

 

 0.000000

 1.000000

 9.052417

-45.65680

 
   

 (6.11549)

 (6.04410)

 
         

Adjustment coefficients (standard error in parentheses)

 

D(GDP)

 0.062108

 5.881917

   
 

 (0.26535)

 (0.96627)

   

D(NOXP)

 0.171819

-0.026377

   
 

 (0.03211)

 (0.11692)

   

D(INF)

 1.02E-05

-0.000671

   
 

 (0.00151)

 (0.00551)

   

D(EXR)

 0.000650

 0.012917

   
 

 (0.00061)

 (0.00224)

   
         
         
         

3 Cointegrating Equation(s): 

Log likelihood

-711.8169

 
         
         

Normalized cointegrating coefficients (standard error in parentheses)

GDP

NOXP

INF

EXR

 

 1.000000

 0.000000

 0.000000

-643.9219

 
     

 (16.5845)

 

 0.000000

 1.000000

 0.000000

-44.72685

 
     

 (6.53809)

 

 0.000000

 0.000000

 1.000000

-0.102730

 
     

 (0.29825)

 
         

Adjustment coefficients (standard error in parentheses)

 

D(GDP)

 0.066125

 5.820666

 39.03823

 
 

 (0.26485)

 (0.97740)

 (38.1793)

 

D(NOXP)

 0.172663

-0.039253

-4.134689

 
 

 (0.03187)

 (0.11762)

 (4.59436)

 

D(INF)

 0.000182

-0.003292

-0.598855

 
 

 (0.00126)

 (0.00466)

 (0.18201)

 

D(EXR)

 0.000656

 0.012838

 0.095372

 
 

 (0.00061)

 (0.00227)

 (0.08863)

 
         
         

VECM RESULT

 Vector Error Correction Estimates

   

 Date: 11/10/18 Time: 22:18

   

 Sample (adjusted): 1987 2016

   

 Included observations: 29 after adjustments

 

 Standard errors in ( ) & t-statistics in [ ]

 
         
         

Cointegrating Eq: 

CointEq1

     
         
         

GDP(-1)

 1.000000

     
         

NOXP(-1)

-10.10148

     
 

 (0.57639)

     
 

[-17.5255]

     
         

INF(-1)

-22.52152

     
 

 (12.0763)

     
 

[-1.86493]

     
         

EXR(-1)

 120.8039

     
 

 (51.8030)

     
 

[ 2.33199]

     
         

C

 3340.874

     
         
         

Error Correction:

D(GDP)

D(NOXP)

D(INF)

D(EXR)

         
         

CointEq1

-2.181188

-0.143267

 0.000189

-0.004917

 

 (0.18680)

 (0.02980)

 (0.00196)

 (0.00052)

 

[-11.6763]

[-4.80820]

[ 0.09665]

[-9.42695]

         

D(GDP(-1))

 0.686035

 0.060126

-0.001850

-0.000207

 

 (0.36799)

 (0.05870)

 (0.00385)

 (0.00103)

 

[ 1.86428]

[ 1.02435]

[-0.48025]

[-0.20162]

         

D(GDP(-2))

 2.315599

 0.261510

 0.000972

 0.005379

 

 (0.32156)

 (0.05129)

 (0.00337)

 (0.00090)

 

[ 7.20115]

[ 5.09856]

[ 0.28861]

[ 5.99193]

         

D(NOXP(-1))

-25.47231

-1.569795

-0.000393

-0.055732

 

 (2.30280)

 (0.36731)

 (0.02411)

 (0.00643)

 

[-11.0615]

[-4.27374]

[-0.01630]

[-8.66856]

         

D(NOXP(-2))

-17.81252

-1.261995

 0.001403

-0.038382

 

 (1.91014)

 (0.30468)

 (0.02000)

 (0.00533)

 

[-9.32522]

[-4.14202]

[ 0.07014]

[-7.19726]

         

D(INF(-1))

 2.601630

-2.106076

-0.019720

-0.010967

 

 (19.4607)

 (3.10411)

 (0.20374)

 (0.05433)

 

[ 0.13369]

[-0.67848]

[-0.09679]

[-0.20184]

         

D(INF(-2))

 13.42127

 2.334033

-0.342961

 0.037052

 

 (19.1192)

 (3.04963)

 (0.20016)

 (0.05338)

 

[ 0.70198]

[ 0.76535]

[-1.71341]

[ 0.69414]

         

D(EXR(-1))

 451.4974

 35.23067

 0.750173

 1.572958

 

 (136.332)

 (21.7458)

 (1.42728)

 (0.38062)

 

[ 3.31176]

[ 1.62011]

[ 0.52560]

[ 4.13259]

         

D(EXR(-2))

-239.3876

-90.97295

-0.318327

-0.714159

 

 (136.810)

 (21.8221)

 (1.43229)

 (0.38196)

 

[-1.74978]

[-4.16884]

[-0.22225]

[-1.86973]

         

C

 5889.562

 450.1588

-0.039259

 12.71818

 

 (599.987)

 (95.7018)

 (6.28138)

 (1.67510)

 

[ 9.81615]

[ 4.70376]

[-0.00625]

[ 7.59250]

         
         

 R-squared

 0.935255

 0.867312

 0.149829

 0.898230

 Adj. R-squared

 0.907507

 0.810445

-0.214530

 0.854615

 Sum sq. resids

 64124948

 1631489.

 7028.363

 499.8319

 S.E. equation

 1747.446

 278.7292

 18.29437

 4.878680

 F-statistic

 33.70535

 15.25173

 0.411212

 20.59426

 Log likelihood

-269.3936

-212.4879

-128.0548

-87.08151

 Akaike AIC

 18.02540

 14.35405

 8.906760

 6.263323

 Schwarz SC

 18.48797

 14.81663

 9.369337

 6.725899

 Mean dependent

 2868.825

 298.9774

-0.436194

 4.151568

 S.D. dependent

 5745.779

 640.1992

 16.60020

 12.79506

         
         

 Determinant resid covariance (dof adj.)

 2.61E+14

   

 Determinant resid covariance

 5.50E+13

   

 Log likelihood

-666.3402

   

 Akaike information criterion

 45.82840

   

 Schwarz criterion

 47.86374

   
         
         

SYSTEM EQUATION

System: UNTITLED

   

Estimation Method: Least Squares

 

Date: 11/10/18 Time: 22:19

   

Sample: 1988 2016

   

Included observations: 28

   

Total system (balanced) observations 124

 
         
         
 

Coefficient

Std. Error

t-Statistic

Prob.  

         
         

C(1)

-2.182380

0.186941

-11.67418

0.0000

C(2)

0.684889

0.368021

1.861005

0.0662

C(3)

2.314353

0.321650

7.195245

0.0000

C(4)

-25.46779

2.302949

-11.05877

0.0000

C(5)

-17.81158

1.910506

-9.322967

0.0000

C(6)

2.525136

19.46359

0.129736

0.8971

C(7)

13.46233

19.12049

0.704079

0.4833

C(8)

452.8883

136.4231

3.319733

0.0013

C(9)

-239.3099

136.8592

-1.748584

0.0840

C(10)

5887.669

600.0152

9.812534

0.0000

C(11)

-0.143267

0.029797

-4.808196

0.0000

C(12)

0.060126

0.058697

1.024348

0.3086

C(13)

0.261510

0.051291

5.098557

0.0000

C(14)

-1.569795

0.367311

-4.273745

0.0001

C(15)

-1.261995

0.304681

-4.142024

0.0001

C(16)

-2.106076

3.104108

-0.678480

0.4993

C(17)

2.334033

3.049632

0.765349

0.4462

C(18)

35.23067

21.74579

1.620115

0.1090

C(19)

-90.97295

21.82214

-4.168838

0.0001

C(20)

450.1588

95.70183

4.703764

0.0000

C(21)

0.000189

0.001956

0.096654

0.9232

C(22)

-0.001850

0.003853

-0.480253

0.6323

C(23)

0.000972

0.003366

0.288606

0.7736

C(24)

-0.000393

0.024108

-0.016300

0.9870

C(25)

0.001403

0.019998

0.070138

0.9443

C(26)

-0.019720

0.203738

-0.096789

0.9231

C(27)

-0.342961

0.200162

-1.713412

0.0903

C(28)

0.750173

1.427283

0.525595

0.6006

C(29)

-0.318327

1.432294

-0.222249

0.8247

C(30)

-0.039259

6.281383

-0.006250

0.9950

C(31)

-0.004917

0.000522

-9.426955

0.0000

C(32)

-0.000207

0.001027

-0.201619

0.8407

C(33)

0.005379

0.000898

5.991933

0.0000

C(34)

-0.055732

0.006429

-8.668563

0.0000

C(35)

-0.038382

0.005333

-7.197265

0.0000

C(36)

-0.010967

0.054332

-0.201843

0.8405

C(37)

0.037052

0.053379

0.694139

0.4895

C(38)

1.572958

0.380623

4.132587

0.0001

C(39)

-0.714159

0.381959

-1.869726

0.0650

C(40)

12.71818

1.675098

7.592498

0.0000

         
         

Determinant residual covariance

5.50E+13

   
         
         
         

Equation: D(GDP) = C(1)*( GDP(-1) – 10.101480713*NOXP(-1) –

        22.5215248963*INF(-1) + 120.803853033*EXR(-1) + 3340.87393996 ) 

        + C(2)*D(GDP(-1)) + C(3)*D(GDP(-2)) + C(4)*D(NOXP(-1)) + C(5)

        *D(NOXP(-2)) + C(6)*D(INF(-1)) + C(7)*D(INF(-2)) + C(8)*D(EXR(-1)) +

        C(9)*D(EXR(-2)) + C(10)

   

Observations: 31

   

R-squared

0.935246

    Mean dependent var

2868.824

Adjusted R-squared

0.907495

    S.D. dependent var

5745.779

S.E. of regression

1747.562

    Sum squared resid

64133436

Durbin-Watson stat

1.900738

     
         

Equation: D(NOXP) = C(11)*( GDP(-1) – 10.101480713*NOXP(-1) –

        22.5215248963*INF(-1) + 120.803853033*EXR(-1) + 3340.87393996 ) 

        + C(12)*D(GDP(-1)) + C(13)*D(GDP(-2)) + C(14)*D(NOXP(-1)) + C(15)

        *D(NOXP(-2)) + C(16)*D(INF(-1)) + C(17)*D(INF(-2)) + C(18)*D(EXR(

        -1)) + C(19)*D(EXR(-2)) + C(20)

 

Observations: 31

   

R-squared

0.867312

    Mean dependent var

298.9774

Adjusted R-squared

0.810445

    S.D. dependent var

640.1992

S.E. of regression

278.7292

    Sum squared resid

1631489.

Durbin-Watson stat

2.549426

     
         

Equation: D(INF) = C(21)*( GDP(-1) – 10.101480713*NOXP(-1) –

        22.5215248963*INF(-1) + 120.803853033*EXR(-1) + 3340.87393996 ) 

        + C(22)*D(GDP(-1)) + C(23)*D(GDP(-2)) + C(24)*D(NOXP(-1)) + C(25)

        *D(NOXP(-2)) + C(26)*D(INF(-1)) + C(27)*D(INF(-2)) + C(28)*D(EXR(

        -1)) + C(29)*D(EXR(-2)) + C(30)

 

Observations: 31

   

R-squared

0.149829

    Mean dependent var

-0.436194

Adjusted R-squared

-0.214530

    S.D. dependent var

16.60020

S.E. of regression

18.29437

    Sum squared resid

7028.363

Durbin-Watson stat

2.083356

     
         

Equation: D(EXR) = C(31)*( GDP(-1) – 10.101480713*NOXP(-1) –

        22.5215248963*INF(-1) + 120.803853033*EXR(-1) + 3340.87393996 ) 

        + C(32)*D(GDP(-1)) + C(33)*D(GDP(-2)) + C(34)*D(NOXP(-1)) + C(35)

        *D(NOXP(-2)) + C(36)*D(INF(-1)) + C(37)*D(INF(-2)) + C(38)*D(EXR(

        -1)) + C(39)*D(EXR(-2)) + C(40)

 

Observations: 31

   

R-squared

0.898230

    Mean dependent var

4.151568

Adjusted R-squared

0.854615

    S.D. dependent var

12.79506

S.E. of regression

4.878680

    Sum squared resid

499.8319

Durbin-Watson stat

1.637621

     
         
         

LM SERIAL CORRELATION TEST

VEC Residual Serial Correlation LM Tests

Null Hypothesis: no serial correlation at lag order h

Date: 11/10/18 Time: 22:20

Sample: 1988 2016

 

Included observations: 28

     
     

Lags

LM-Stat

Prob

     
     

1

 10.01800

 0.8657

2

 19.07625

 0.2647

3

 14.75249

 0.5428

4

 13.11967

 0.6640

5

 20.41634

 0.2021

6

 13.92353

 0.6044

7

 17.10099

 0.3791

8

 10.67969

 0.8288

9

 31.38871

 0.0120

10

 10.63728

 0.8313

11

 14.48802

 0.5624

12

 20.56763

 0.1957

     
     

Probs from chi-square with 16 df.

GRANGER CAUSALITY

Pairwise Granger Causality Tests

Date: 11/10/18 Time: 22:22

Sample: 1985 2016

 

Lags: 2

   
       
       

 Null Hypothesis:

Obs

F-Statistic

Prob. 

       
       

 NOXP does not Granger Cause GDP

 32

 12.6243

0.0001

 GDP does not Granger Cause NOXP

 3.12283

0.0603

       
       
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