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CHAPTER ONE

INTROUDCTION

1.1 BACKGROUND OF STUDY

The current period in the world economy is regarded as period of globalization and

trade liberalization. In this period, one the crucial issues in development and international

economics is to know whether trade openness indeed promotes growth. With globalization,

two major trends are noticeable: first is the emergence of multinational firms with strong

presence in different, strategically located markets; and secondly, convergence of consumer

tastes for the most competitive products, irrespective of where they are made. In this con-

text of the world as a global village, regional integration constitutes an effective means

of not only improving the level of participation of countries in the sub-region in world

trade, but also their integration into the borderless and interlinked global economy.

(NEEDS, 2005).

Since 1950, the world economy has experienced a massive liberalization of world

trade, initially under the auspices of the General Agreement on Tariffs and trade (GATT),

established in 1947, and currently under the auspices of the World Trade Organization

(WTO) which replaced the GATT in 1993. Tariff levels in both developed and developing

countries have reduced drastically, averaging approximately 4% and 20% respectively,

even though the latter is relatively high. Also, non-tariff barriers to trade, such as quotas, li-

censes and technical specifications, are also being gradually dismantled, but at a slower

rate when compared with tariffs.

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The liberalization of trade has led to a massive expansion in the growth of world trade rela-

tive to world output. While world output (or GDP) has expanded fivefold, the volume of

world trade has grown 16 times at average compound rate of just over 7% per annum. In

fact, it is difficult, if not impossible, to understand the growth and development process of

countries without reference to their trading performance. (Thirlwall, 2000).

Likewise, Fontagn and Mimouni (2000) noted that since the end of the European recovery

after World War II, tariff rates have been divided by 10 at the world level, international

trade has been multiplied by 17, world income has quadrupled, and income per capita has

doubled. Incidentally, it is well known that periods of openness have generally been associ-

ated with prosperity, whereas protectionism has been the companion of recessions. In addi-

tion, the trade performance of individual countries tends to be good indicator of economic

performance since well performing countries tend to record higher rates of GDP growth. In

total, there is a common perception that even if imperfect competition and second best situ-

ations offer the possibility of welfare improving trade policies, on average free trade is bet-

ter than no trade.

From the ongoing discussion, it is evident that trade is very important in promoting and

sustaining the growth and development of an economy. No economy can isolate itself from

trading with the rest of the world because trade act as a catalyst of growth. Thus Nigeria,

being part of the world, is no exemption. For this reason, there is a need to thoroughly ex-

amine the nature of relationship between trade openness and output growth in Nigeria.

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1.1.2 TRADE OPENNESS AND OUTPUT GROWTH: HISTORICAL EX-

PERIENCE OF THE NIGERIA ECONOMY

Today, Nigeria is regarded to have the largest economy in sub-Saharan Africa, excluding

South Africa. In the last four decades there has been little or no progress realized in allevi-

ating poverty despite the massive effort made and the many programmes established for

that purpose. Indeed, as in many other sub-Saharan Africa countries, both the number of

poor and the proportion of poor have been increasing in Nigeria. In particular, the 1998

United Nations human development report declares that 48% of Nigerias population lives

below the poverty line. According to the report (UNDP, 1998). The bitter reality of the

Nigerian situation is not just that the poverty level is getting worse by the day but more

than four in ten Nigerians live in conditions of extreme poverty of less than N320 per capi-

ta per month, which barely provides for a quarter of the nutritional requirements of healthy

living. This is approximately US 8.2 per month or US 27 cents per day.

Doug Addison (unpublished) further explained that the Nigeria economy is not merely

volatile; it is one of the most volatile economies in the world. There is evidence that this

volatility is adversely affecting the real growth rate of Nigerias gross domestic product

(GDP) by inhibiting investment and reducing the productivity of investment, both public

and private. Economic theory and empirical evidence suggest that sustained high future

growth and poverty reduction are unlikely without a significant reduction in volatility. Oil

price fluctuations drive only part of Nigerias volatility policy choices have also contrib-

uted to the problem. Yet policy choices are available that can help accelerate growth and

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thus help reduce the percentage of people living in poverty, despite the severity of Nige-

rias problems.

During the period 1960-1997, Nigerias growth rate of per capital GDP of 1.45% compares

unfavorably with that reported by other countries, especially those posted by china and the

Asian Tigers such as Hong Kong, Singapore, Taiwan, and south Korea, viewed in this

comparative perspective, Nigerias per capita income growth has been woefully low and

needs to be improved upon. (Iyoha and Oriakhi, 2002). In like manner, Ogujiuba, Oji and

Adenuga (2004) wrote that the Nigerian economy has severally been described as a diffi-

cult environment for business with a population growth of about 3%, it has been acknowl-

edged that the current average output growth rate of less than 4% will see the country being

poorer in the next decade.

A study conducted by Iyoha and Oriakhi (2002) on Nigerias per capita GNP from 1964 to

1997 show that it rose steadily from US$120 to US$780 in 1981. Thereafter, it fell almost

steadily to US$280 in 1997. Thus, between 1964 and 1981, income per capita increased by

550% or at an annual average rate of 32.3% while between 1981 and 1997, it fell by 64.1%

or at an annual average rate of 4%. It is worth noting that if income per capita had contin-

ued to increase beyond 1981 as it did before then, Nigerias GDP per capita would have

equaled US$1,279 in 1997. The difference between US $280 and US$1,279, i.e., approxi-

mately, US$1,000.00, is a rough measure of the cost to the average Nigerian of domestic

macro economic policy mistakes and adverse international economic shocks. Likewise in

1960 agricultural exports accounted for only 2.6%. Exports of other commodities like tin

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and processed goods amounted to 26.6% of total exports. By 1970 agricultural exports only

accounted for 33% of total exports while petroleum exports had started to establish domi-

nance by exceeding 58% of total exports. By the time the oil boom began in earnest in

1974, petroleum exports accounted for approximately 93% of all exports. The relative

share of agricultural exports in total exports had shrunk to 5.4% while other products ac-

counted for the remaining 1.9%. Since 1974, with the exception of 1978 when the relative

share of petroleum in total exports has exceeded 90%. In deed, since 1990, the relative

share of petroleum in total exports has exceeded 96%. Agricultures contribution has fluctu-

ated between 0.5% and 2.3% while the share of other products has fluctuated between

0.5% and 1.7%. Thus petroleum exportation has totally dominated the economy and indeed

government finances since the mid-1970s.

Meanwhile, a puzzling and disturbing aspect of Nigeria export boom is that the growth it

generated did not seem to be lasting or to have had a significant effect in changing the

structure of the economy. For instance, in the 1970s there was a major increase in mea-

sured GDP but the structure of the economy remained basically unchanged (see figure 2

below). This led professor Yesufu (1995) to describe the Nigerian economy as one that had

experienced growth without development.

Figures 1: trend of real GDP

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Year

During the period of 1970 1985, import substitution industrialization (ISI) strate-

gy was a dominant feature of trade policy in Nigeria. The trade policy was generally in-

ward oriented. Under this ISI strategy, Infant manufacturing industries were protected

using high tariffs, import quotas, and other trade restrictions like import licensing. Non-tar-

iff barriers to trade such as import prohibitions were also utilized. During this period, trade

policy was also adjusted in response to the exigencies of the balance of payments.

Also, Nigeria was operating a fixed exchange rate regime under which the value of the

Naira was essentially tied to US dollar and gold. It is worth noting that the trade policy pur-

sued during this period resulted in a rapid increase in manufacturing production and em-

ployment, particularly during the era of the oil boom (1975 -1980) and that led to a rise in

6

RealGDP

300000

250000

200000

150000

100000

500000

1965 1970 1975 1980 1985 1990 1995 2000 2005 2010

-.-RGDPlinear(RGDP)


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the share of manufacturing in Gross Domestic product (GDP) from 5.6% in 1962/63 to

8.7% in 1986. (Iyoha and Oriakhi, 2002).

In 1986, Nigeria adopted the structural adjustment programme (SAP) of the IMF/World

Bank. With the adoption of SAP in 1986, there was a radical shift from inward-oriented

trade policies to out ward oriented trade policies in Nigeria.

These are policy measures that emphasize production and trade along the lines dictated by

a countrys comparative advantage such as export promotion and export diversification,

reduction or elimination of import tariffs, and the adoption of market-determined exchange

rates some of the aims of the structural adjustment programme adopted in 1986 were diver-

sification of the structure of exports, diversification of the structure of production, reduc-

tion in the over-dependence on imports, and reduction in the over-dependence on petrole-

um exports. The major policy measures of the SAP were:

Deregulation of the exchange rate

Trade liberalization

Deregulation of the financial sector

Adoption of appropriate pricing policies especially for petroleum products.

Rationalization and privatization of public sector enterprises and

Abolition of commodity marketing boards.

However, as a result of trade liberalization gospel of the SAP, the Nigeria external sector

really experience dramatic growth. For instance, the total domestic exports of Nigeria in

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31.54% in 1970, to 46.91% 1980, 57.23% in 1990, 88.16% in 1995, 85.26% in 2003 and

57.63% in 2007 (see figure 4 below) indeed, in the 1990s the ratio of trade to GDP has av-

eraged 70%. This extreme openness of the economy could be disadvantageous in that it

makes the country highly susceptible to internationally transmitted business cycles, and, in

particular international transmitted shocks (like commodity price collapse). A good exam-

ple of this effect on the Nigerian economy is that of the global food crisis of 2007 and the

current global economic/financial crisis.

FIGURES 4: THE DEGREE OF OPENNESS

NIGERIA IMPORT AND EXPORT

1.2 STATEMENT OF THE RESEARCH PROBLEM

Nwafor Manson (unpublished) not that the Nigerias trade policy over the years

has been determined by one/ more of the following.

9

100

90

80

70

60

50

40

30

20

10

Degreetrade

openness(5)

0 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010


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Need to protect and stimulate domestic production (import capital goods at low

prices etc)

Need to ameliorate/prevent balance of payment problems.

Need to boost the value of the naira

Need to be competitive and enjoy the benefits of openness.

Need to increase revenue and

International agreements

Today, as part of moving with the trend of globalization and trade liberalization in

the global economic system, Nigeria is a member of and signatory to many international

and regional trade agreements such as international monetary fund (IMF), world trade or-

ganization (WTO), economic community of West African States (ECOWAS), and so many

others. The policy response of such economic partnership on trade has been to remove

trade barriers, reduce tariffs, and embark on outward-oriented trade policies. Despite all her

effort to meet up with the demands to these economic partnerships in terms of opening up

her border, according to the 2007 assessment of the trade policy review, Nigerias trade

freedom was rate 56% making her the worlds 131st freest economy while in 2009, it was

ranked 117th freest economy, the countrys GDP was also ranked 161st in the world in Feb-

ruary, 2009. The economy has struggled vigorously to stimulate growth through openness

to trade, In fact, it seems that as the country put greater effort to boost her economic growth

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by opening up to trade with the global economy the more she becomes worse-off relative to

her trading partners in terms of country output growth.

Having reviewed the related literatures and considering the structure of the Nigerian econo-

my as related to trade openness and output growth, we may then ask the following ques-

tions.

Does trade openness have any significant impact on out put growth in Nigeria?

Is there any other macroeconomic variable that has significant impact on output

growth in Nigeria?

Is there any linear association (correlation) between trade openness and outputgrowth in Nigeria?

Is there long run relationship between trade Openness and output growth in Nige-

ria?

Has there been any significant structural change in output growth between the pre-

SAP and post-SAP period in Nigeria?

1.3 OBJECTIVES OF THE STUDY

The broad objective of this research work is to study, in its entirely, the relationship be-

tween trade openness and output growth in Nigeria. This broad objective can be subdivided

into the following smaller objectives:

To examine the impact of trade openness on output growth in Nigeria.

To identify other internal and external macroeconomic shocks that determines out-

put growth in Nigeria.

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To identify other international and external macro economic shocks that determines

output growth in Nigeria.

To determine the linear association (correlation) between trade openness and output

growth in Nigeria.

To ascertain the possibility of long run relationship between trade openness and

output growth in Nigeria.

To determine the possibility of structural changes (if any) in output growth between

the pre-SAP and post-SAP period.

1.4 STATEMENT OF THE RESEARCH HYPOTHESES

In view of the foregoing study, with respect to trade openness and output growth in

Nigeria, the following null hypothesis will be tested:

Ho: Trade openness does not have any significant impact on output growth in Nigeria.

Ho: There is no other macroeconomic variable (internal and external) that have signifi-

cant impact on output growth in Nigeria.

Ho: There is no linear association (correlation) between trade openness and output

growth in Nigeria.

Ho: There is no long run relationship between trade openness and output growth in

Nigeria.

Ho: There is no significant structural change in output growth between the pre-SAP and

post-SAP period.

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1.5 JUSTIFICATION OF THE STUDY

Nigeria is currently undergoing a series of transformation in every sector of the economy,

including the external sector of the economy. The countrys economic policy in the last two

decades had one dominating theme which is an integral part of the structural Adjustment

programme (SAP) trade liberalization. This policy was espoused on the argument that it

enhances the welfare of consumers and reduces poverty as it offers wider platform for

choice from among wider variety of quality goods and cheaper imports. Today, there are

many existing literature on the topical issue of trade openness and growth of which some

support the axiom that openness is directly correlated to greater economic growth with the

main operational implication being that governments should dismantle the barriers to trade.

The focal point of this research work is to identify the short comings and benefits of this ar-

gument as well as check the validity of this mainstream axiom I Nigeria in the presence of

various internal and external shocks.

1.6 SIGNIFICANCE OF THE STUDY

The role of international trade in the developmental journey of an economy can not be over

emphasized, especially with the current trend of globalization. Nigeria. Being part of the

global village, is not left out of this world development. This research work is carried out to

study how trade openness has influenced the performance of the Nigeria economy through

output growth in the presence of other internal and external shocks. The findings of this re-

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search work transcend beyond mere academic brainstorming, but will be of immense bene-

fit to federal agencies, policy makers, intellectual researcher and international trade think

tanks that occasionally prescribe and suggest policy options to the government on trade re-

lated issues. It will also help the government to see the effectiveness of trade liberalization

policy on the economic growth of the nation over the years. This research work will further

serve as a guide and provide insight for future research on this topic and related field for

students who are willing to improve it. It will also educate the public on various govern-

ment policies as related to trade issues.

1.7 SCOPE AND LIMITATION OF THE STUDY

This research work span through the period of 1970-2007 (38 years), and is within the geo-

graphical zone of Nigeria. Thus, it is a country-specific research. This research exercise,

like every other research work, is really a rigorous one that consumes much time and ener-

gy especially in the area of data sourcing, data computation and modeling. This work is rel-

atively limited base on time and financial constraints, data availability precision of data anddata range, and methodology adopted which could further be verified by future research.

Nevertheless, the researchers have properly organized the research so as to present depend-

able results which can aid effective policy making and implementation at least for the time

being.

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CHAPTER TWO

LITERATURE REVIEW

Openness refers to the degree of dependence of an economy on international trade and fi-

nancial flows. Trade openness measures the international competitiveness of a country in

the global marked. Thus, we may talk of trade openness and financial openness. Trade

openness is often measured by the ratio of import to GDP or alternatively, the ratio of trade

to GDP. It is now generally accepted that increase openness with respect to both trade and

capital flows will be beneficial to a country. Increased openness facilitates greater integra-

tion into global markets. Integration and globalization are beneficial to developing coun-

tries although there are also some potential risks. (Iyoha and Oriakhi, 2002). Trade open-

ness is interpreted to include import and export taxes, as well as explicit non tariff distor-

tions of trade or in varying degrees of broadness to cover such matters as exchange-rate

policies, domestic taxes and subsides, competition and other regulatory policies, education

policies, the nature of the legal system, the form of government, and the general nature of

institution and culture (Baldwin, 2002).

2 EMPIRICAL LITERATURE

The relationship between trade openness and growth is a highly debated topic in the growth

and development literature, yet this issue is far from being resolved. There is a long history

of research, both theoretical and empirical, that provides at least an answer to the question:

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does openness to trade result in the growth of output (say, GDP)? But currently there is no

consensus, either empirically to theoretically, on the nature of the relationship between

trade openness and output growth. In fact, this is because the mechanisms behind it are not

well understood. The existing empirical literature however does not provide clear evidence

on relationship between trade openness and growth. Many studies provide evidence that in-

creasing openness has a positive effect on GDP growth. On the other hand some studies re-

port that it is difficult to find robust positive relationships or even that there is negative re-

lationship between openness and growth. Some studies, among other Rodriguez and Rodrik

(1999) and Rodriguez (2006), critically argue that trade policy variables are mostly uncor-

related with growth, while the trade shares can correlate with income levels and growth

rates. But the complexity of links of causality and endogeneity among trade shares, growth

and other sources of growth make a difficulty to define a strong effect of openness on eco-

nomic growth. Theoretical growth studies suggest very complex and different relationships

between openness and growth and the empirical evidence is not unambiguous. The growth

theory supposes that a countrys openness to world trade improves domestic technology,

and hence an open economy grows faster than a closed economy through its impact on

technological enhancement (Jin, 2006). Harrison (1996) asserted that openness to trade

provides access to imported inputs, which embody new technology, increases the size of

the market faced by the domestic producers, which raises the return to innovation, and fa-

cilitates a countrys specialization in research intensive production.

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In line with potential dynamic gains of trade openness, most early empirical studies have

examined a set of trade openness measures and with their correlation with each other and

with economic growth and found a clear positive link. For example, Harrison (1996)

looked at a number of openness indicators that turned out to have a positive association

with economic growth and produced evidence in support of bi-directional casualty between

openness (trade share) and economic growth. Recent research, however, has questioned the

robustness of the relationship. For instance, Harrison and Hanson (1999) show that the of-

ten quoted Sachs and Warner (1995) openness and growth link as claimed.

Rodriguez and Rodrik (1999) confirm the Harrison Hanson critique and argued that much

of the work to correlate trade openness and economic growth has been plagued with sub-

jective and collinear measures of openness that, though positively related with economic

growth, arrive at their conclusion through problematic econometric methodologies. Harri-

son (1996) and Pritchett (1996) show that the various measure of trade openness tend to be

only weakly correlated and are often of the wrong sign.

In general, empirical studies suffer from a number of short comings, and as a result they

have not resolved the questions surrounding the correlation between openness and growth.

Baldwin (2000) offers explanation for the differences among researchers of the openness

growth nexus. According to him, while econometric analyses based on quantitative data are

limited by the scope and comparability of available quantitative data, differences in what

investigators regard as appropriate econometric models and tests for sensitivity of the re-

sults to alternative specifications that may be based in part on the personal policy predilec-

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tions of authors and can also result in significant differences in the conclusions reached un-

der such quantitative approaches. If these studies used measures that were even slightly

correlated, then empirical literature together could be taken as proof of a positive relation

between openness and growth. Baliamoune-lutz and Ndikumana (2007) observed that,

from a methodological stand point, the weak link between trade liberalization and growth

may be attributed to measurement imperfections: the indicators used in empirical analysis

may not capture the true essence of openness. Indeed, due to lack of data on indicators of

trade openness as a policy empirical studies (as this one does) resort to measures of trade

outcomes i.e. trade volume, as proxies for trade openness it is assumed that positive trade

outcomes are an indication of a policy environment that is at least not anti-trade. Moreover,

a high trade volume indicates exposure to international markets with the associated benefits

(e.g. technological transfer) which openness policies seek to achieve. Thus, to some extent

trade outcomes do carry some indication of the effects of trade liberalization. Nonetheless,

results from analyses using trade volume as a measure of trade openness have to be inter-

preted consciously. Indeed, variations in the volume of trade do not always reflect actual

government policies that promote or hinder trade. For instance, fluctuations in commodity

prices result in changes in trade flows even in the absence of shifts in trade policy.

The weak empirical evidence on the link-between trade liberalization and growth can also

be due to problems of misspecification. In particular, the effects of trade liberalization may

materialize only with a lag. In the short run, liberalization may have negative effects, espe-

cially by undermining domestic production because of competitive import, retarding

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growth (Mukhopadhyay 1999). Hence, to the extent that these negative short-run effects

and the expected delayed positive effects occur consecutively, growth would exhibit a J-

curve of response to trade openness (Greenaway etal 2002). Therefore, empirical studies

may yield inconclusive and even misreading results if these dynamic and counter balancing

effects are not fully taken into account.

Another explanation relates to the structure of trade. Whether a country benefits from trade

liberalization or not in terms of growth depends on the composition of trade. Mazumdar

(1996) hypothesized that the composition of trade determines the strength of the engine of

growth. Indeed lower and Van Den Berg (2003) final evidence supporting the view that

countries that import capital goods and export consumer goods growth faster than those

that export capital goods. The evidence suggests that African countries and developing

countries in general would benefit from trade most by promoting exports of labour-inten-

sive goods and services while encouraging imports of capital goods. (Lopez 1991). This

implies that the current export boom which is driven by capital-intensive growth that is sus-

tainable, especially because of the low gains in employment creation and limited spill over

effects on non-oil sectors.

Dollar (1992) brought an important contribution to the trade and growth debate. The author

defines openness as the combination of two diversions:

i. A low level of protection, hence of trade distortions and

ii. A stable real exchange rate so that incentives remain constant over time.

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and estimates both fixed-effects and random-effects models. He reports that the coefficient

on openness is still significant and positive, but its point estimate is much lower than in the

OLS specification. In a fourth set of regressions, the author also considers growth in open-

ness instead of openness itself.

The author interprets this as reflecting the fact that static efficiency effects of trade liberal-

ization are negligible for countries with well-developed markets. Finally, in its conclu-

sions, the author cautions that his results, showing the beneficial effects of increased open-

ness, hold on average, but are not a universal truth, valid always and every where.

In particular, he stresses that trade liberalization can indeed stimulate growth in the aggre-

gate world economy. Whilst trade may have such positive effects for some countries, it

may conversely lock in other countries into a pattern of specialization in low-skill, low-

growth activities.

Sachs and Warner (1995) brought a seminal contribution to that literature. Their central hy-

pothesis is that some developing countries fail to grow rapidly enough as to converge be-

cause they are simply not open to trade. In their own wards: convergence can be achieved

by all countries, even those with low initial level of skill, as long as they are open and inte-

grated in the world economy. To check their hypothesis, the author first carefully, build

and discuss an openness measure. Building upon a sample of 135 countries over the period

1970-1990, they construct and openness dummy variable that is zero if any of the 5 follow-

ing conditions is true:

Non-tariff barriers covering 40% or more of trade

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Average tariff rate above 40%

Black market premium above 20%

The economy is ruled by a socialist system, or

There is a state monopoly on exports.

Otherwise, if none of these 5 conditions is fulfilled, the openness dummy is one.

The authors first divide their countries sample into open ones and closed ones, and show

that closed countries have grown at about the same rate (essentially about 0.7% a year), no

matter whether they are developed or not. By contrast, open developing countries havegrown much faster than their developed counter parts (4.49% versus 2.29%). Going beyond

these stylized facts the authors re-do the same regressions as in Barro (1991) and add their

openness dummy to them without the dummy, the results are sensibly the same as in Barro

(1991). After adding the openness dummy in the regresses list, it appears its coefficient is

highly significant. The points estimates suggest that open economies grow on average

2.45% faster than closed ones.

Moreover, educational attainment variables become even less significant than in Barro

(1991), which leads the authors to think that .growth rate over this period was deter-

mined less by initial human capital levels than by policy choices. They also address a spe-

cialization-related issue. Specifically, they test whether trade openness condemns raw ma-

terials exporters to non-industrialization and whether closed trade promotes industrial ex-

ports in the long run. To do this, they regress the change in the share of primary exports

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more rapidly from being primary-intensive to manufactures-intensive exporters. The differ-

ence in speed of adjustment is statistically significant.

Harrison (1996) starts from the judgment that it should be evident that no independent

measures of so-called openness is free from methodological problem. Therefore, to

make her point, she collects as many different openness indicators as she can, about 7 of

them, and she checks the consistency of the results across all these indicators. She uses var-

ious samples, whose time spans range from 1960-1998 to 1978-1987,and the country cov-

erage varies from 51 to 17.she first runs typical cross-country growth regressions. It ap-

pears that only one measure of openness out of 7, namely the black market premium, has a

significant impact on growth. To explain this weak result the author argues that a pure

cross-section specification, based upon long-run averages, is not an adequate one. Indeed,

though the use of long run averages appears as the most natural way to capture the determi-

nants of long-run growth, they may also hide significant variations in individual countries

performances and policies over time. To test this idea, the author re-does her regressions

using annual data for the same variables. She uses a panel fixed-effects specification to

take into account unobserved country specific differences in growth rates. Results show a

stronger link between openness and growth since 3 indicators become significant at the

conventional 5% level. The author next argues that such a yearly frequency is too high if

one is interested in long-run growth, since results may be affected by short-term conjec-

tural, variations. She therefore considers a third- intermediate- specification, based on

five-year averages and reports that, again 3 indicators come out with a significant coeffi-

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cient. The message from these results, as the author states, is that the choice of the time

period for analysis is critical. However, an interesting regularity appears across all specifi-

cations: When openness is significant; it is always in the sense that greater openness is as-

sociated with higher growth.

Edwards (1998) also uses an important number of openness indexes to investigate the trade

and growth relationship. He considers a sample of 93 advanced and developing countries,

and estimates a growth equation with a panel data random effects model. From that model,

he computes factor shaves, which are then used to get TFP estimates. Concentrating on a

cross-section of 1980s averages, TFP growth is finally regressed upon initial income level,

initial human capital level, and no less than 9 openness indicators, each one of them in turn.

The author reports that in all but one of the 18 equations the estimated coefficient on the

openness indicator has the expected sign and in the vast majority of cases it is significant.

Moreover, the coefficient on initial human capital is always significant and positive. Re-

garding the initial income level, the coefficient is always negative and in 16 cases out of

18, it is significant though very low, which can be interpreted as evidence in favor of con-

ditional convergence. To summarize, the authors concludes that his results are quite re-

markable, suggesting with tremendous consistency that there is a significantly positive rela-

tionship between trade openness and growth.

An important paper that is able to cast serious doubts about the consistency of the trade-

growth relationship is the one by Rodriguez and Rodrik (1999). These authors consider a

series of previous research results, among which Dollar (1998). Sachs and Warner (1995),

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and Edwards (1998). The re-do the computations in these papers, but slightly change the

specifications (through the addition of some dummies, e.g.), add newly available data to

the sample, or slightly change the estimation methods. They are able to demonstrate a fun-

damental lack of robustness of the results in the paper they reviewed.

Frankel and Romer (1999) claim that openness, as measured by the ratio of total trade to

GDP, should not be used as explanatory variable in the growth regressions. The trade ratio,

the authors argue, is endogenous, and needs to be instrumented. To construct their instru-

ment, the authors first argue that as the literature on the gravity model of trade demon-

strates, geography is a powerful determinant of bilateral trade. And they claim this is also

true for total trade. Moreover, geography is completely exogenous. Therefore, the authors

consider a database of bilateral trade between 63 countries for 1985 and they regress bilat-

eral trade upon purely geographical indicators. For each country, the fitted values of trade

are aggregate over all partners, and this aggregate is finally turned into an ideal trade

share that can be used as an instrument for the observed one. The authors then estimate

growth equations for a cross-section of 150 countries in 1985. They report a substantial im-

pact of trade openness on income growth: increasing the trade share by 1% should raise in-

come by between 0.5% and 2%. These findings are robust to various changes in specifica-

tions. The results also suggest that, controlling for openness; larger countries tend to expe-

rience higher growth rates, which could simply reflect that citizens living in larger coun-

tries engage more in within country trade.

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Baldwin and Sbergani (2000) argue that the reason why researchers failed to find a robust

relationship between trade and openness is because that relationship is fundamentally non

linear and non-monotonic. They raise the point that the fundamental engine of growth is

human and physical accumulation, and that the link between capital accumulation and trade

barriers is, in nearly all models, non linear and often even non-monotonic. They provide a

formal 2x2x2 dynamic model with imperfect competition that gives rise to (i) all-shaped

relationship between ad-valorem tariffs and growth and (ii) a bell-shaped relationship be-

tween specific tariffs and growth. This model is then confronted to the data, i.e. for a vari-

ety of openness indicators (actually, 10 of them are considered), a quadratic model is esti-

mated. It turns out that, in this new specification, for 6 of the 10 proxies both the linear and

the quadratic terms are significant individually. The authors conclude that: allowing for

non-linearity does have a big empirical impact.

A number of other studies have looked at the relationship between average tariff rates and

growth. Lee (1993), Harrison (1996) and Edwards (1998) found negative relationship be-

tween the tariff rates and growth. The studies of Edwards (1992), Sala-i- Martin (1997) and

Clemens and Williamson (2001) conclude that the relationship is weak. Rodriguez and Ro-

drick (1999) tried to replicate the result of Edwards (1998) and found that average tariff

rates had a positive and significant relationship with total factor productivity (TFP) growth

for a sample of 43 countries over the period 1980-1990.

In a recent study Vanikkaya (2003) used a large number of openness measure for a cross-

section of countries over the last three decades. His analysis found a significant positive

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correlation between trade shares and growth. However, this study observed that different

measures of trade barriers are positively associated with growth in the less developed coun-

tries. In recent empirical studies, one or more of the following indicators of openness in the

table below are used:

Measure DefinitionTrade dependency ratio The ratio of exports and import to GDPGrowth rate of exports The growth rate of exports over the specified periodTariff Averages A simple or trade-weighted average of tariff levels.Collected Tariff Ratios The ratio of tariff revenues to imports.Coverage of QuantitativeRestrictions

The percentage of goods covered by quantitative restric-tions.

Black market premium The black market premium for Foreign exchange, a proxyfor the overall degree of external sector distortions.

Trade Bias Index The extent to which policy increases the ratio of im-portable goods prices relative to exportable goods pricescompared to the same ratio in world markets.

Sachs And Warner Index A composite index that uses several trade-related indica-tors tariffs, quota coverage, black market premiums, so-cial organization and the existence of export marketing

boards.Learners Openness Index An index that estimates the difference between the actual

trade flows and those that was expected from a theoreticaltrade model.

Table1: openness indicators.

(Rodriguez and Rodrik, 2000; Ogujiuba, Oji and Adenuga, 2004).

Gross man and Helpman (991) and Matsuyama (1992) provide theoretical models where a

technological backward country specializes in a non-dynamic sector as result of openness,

thus losing out from the benefits of increasing returns. Underlying this result, there is an

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imperfection in contracts or in financial markets that makes people obey a myopic notion

of comparative advantage.

Dollar and Kraay (2004) and Loayza, Fajnzylber, and Calderon (2005) run growth regres-

sions on panel data of large samples of countries. Both papers use openness indicators

based on trade on trade volumes and control for their joint endogeneity and correlation with

country-specific factors through GMM methods that involve taking differences of data and

instruments. This implies that, although they continue to use cross country data, these pa-

pers favors within-country changes as the main sources of relevant variation. Both papers

conclude that opening the economy to international trade brings about significant growth

improvements. Wacziarg and Welch (2003) arrive to a similar, though more nuanced, con-

clusion from a methodological different stand point. Using an event-study methodology

where the event is defined as the year of substantial trade policy liberalization--, they find

that liberalizing countries tend to experience significantly higher volume of trade, invest-

ment rates, and most importantly, growth rates. However, in an examination of 13 country-

case studies Wacziarg and Welch find noticeable heterogeneity in the growth response to

trade liberalization. Although their small sample does not allow for definite conclusions, it

appears that the growth response after liberalization is positively related to conditions of

political stability.

Also, various empirical literatures offer some examples of non-linear specifications consid-

ering interaction effects. On the related topic of foreign direct investment, Borensztein, De

Gregorio and Lee (1998) find that the growth effect of FDI is significantly positive only

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when the host country has, respectively, sufficiently high human capital and financial

depth. Specifically in the analysis of grow effects of trade openness, an important an-

tecedent of our work is the empirical study by Bolaky and Freund (2004). Using cross-

country regressions in levels and changes of per capita GDP and controlling for simultane-

ity via external instruments, they find that trade opening promotes economic growth only in

countrys that are not excessively regulated. They argue that in highly regulated countries,

growth does not accompany trade openness because resources are prevented from flowing

to the most productive sectors and firms, and trade is likely to occur in goods where com-

parative advantage is actually missing.

Calderon, Loayza, and Schmidt Hebbel (2004) interact in their panel growth regressions a

measure of openness (volume of trade /GDP) with linear and quadratic terms of GDP per

capita, which they regard as proxy for overall development. They find that the growth ef-

fect of trade opening is nearly zero for low levels of per capita GDP, increases at a decreas-

ing rate as income rises, and reaches a maximum at high levels of income.

Chang, Kaltani and Loayza (2005) study how the effect of trade openness on economic

growth depends on complementary reforms that help a country take advantage of interna-

tional competition. They presented some panel evidence on how the growth effect of open-

ness depends on a variety of structural characteristics. They use non-linear growth regres-

sion specification that interacts a proxy of trade openness with proxies of educational in-

vestment, financial depth, inflation, stabilization, public infrastructure, governance, labour-

market flexibility, ease of firm entry, and ease of firm exit. They find that the growth ef-

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fects of openness are positive and economically significant if certain complementary re-

forms are undertaken.

Giles and Stroomer (2005) develop flexible techniques for measuring the speed of output

convergence between countries when such convergence may be of an unknown non-linear

form. They then calculate these convergence speeds for various countries, in terms of half

lives, using a time-series data-set for 88 countries. These calculations are based on both

non parametric kernel regression and fuzzy regression and the results are compared with

more restrictive estimates based on the assumption of linear convergence. The calculated

half-lives are regressed, again in various flexible ways, on cross-section data for the degree

of openness to trade. They find evidence that favors the hypothesis that increased trade

openness is associated with a faster rate of convergence in output between countries.

Joffrey (2003) in his work tries to clarify a number of issues related to the trade openness

and growth debate. He considers a number of sector specialization indicators and examine

whether they indeed affect the link between openness and growth. Using both cross-section

and panel data techniques, he finds that both its pattern are likely to affect significantly the

link between openness and growth.

On research studies that relate to Africa and Nigeria in specific, Sarkar (2007) examines

the relationship between openness (trade-GDP ratio) and growth. The cross-country panel

data analysis of a sample 51 countries of the South during 1981-2002 shows that for only

11 rich and highly trade-dependent countries a higher real growth is associated with a high-

er trade share. Time series study of individual country experiences shows that the majority

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of the countries covered in the sample including the East Asian countries experienced no

positive long-term relationship between openness and growth during 1961-2002. He finds

that the experience of various regions and groups shows that only the middle income group

exhibited a positive long-term relationship.

Also, Baliamoune-Lutz and Ndikumana (2007) explore the argument that one of the causes

of the limited growth effects of trade openness in Africa maybe the weakness of institu-

tions. They also control for several major factors and, in particular, for export diversifica-

tion, using a newly developed data set on Africa. Results from Arellano-Bond GMM esti-

mations on panel data from African countries show that institutions play an important role

in enhancing the growth effects of trade. They find that the joint effect of institutions and

trade has U-shape, suggesting that as openness to trade reaches high levels, institution play

a critical role in harnessing the trade-led engine of growth. The results from this paper are

informative about the missing link between trade liberalization and growth in the case of

African countries. Likewise, Ogujiuba, Oji and Adenuga (2004) test the validity of trade

openness for Nigerias long-run growth using a co-integration approach. They preferred the

VAR approach for some reasons and their econometric results show that there is no signifi-

cant relationship between openness and economic growth, and that unbridled openness

could have deleterious implications for growth of local industries, the real sector and gov-

ernment revenue.

Moreover, Addison and Wodon (2007) study the macroeconomic volatility, private invest-

ment growth, and poverty in Nigeria. Using cross-sectional data for 87 countries, they

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openness to world trade is conducive to TFP in SSA region only if issues related to supply

conditions such as poor transport and communication infrastructure, erratic supply of elec-

tric energy. Corruption and bad governance, insufficient education of the labour force etc

are adequately addressed, (ii) physical capital accumulation is important for TFP, (iii) thesize of the financial sector mattes for TFP, in some SSA countries and negative for TFP in

other SSA countries.

2.3 LIMITATION OF PREVIOUS STUDIES

The literatures of previous studies are plagued with a lot of problems. First of all, it is

worthwhile to note that the theoretical growth literature has given more attention to the re-

lationship between trade policies and growth rather than the relationship between trade vol-

umes and growth. Therefore the conclusion about the relationship between trade barriers

and growth cannot be directly applied to the effects of changes in trade volumes on growth.

There was also no consensus on the nature of the relationship and nature of linear associa-

tion (correlation) between openness and growth. Likewise, there is no generally accepted

measure of openness indicators as it suit and please the researcher(s). Moreover, many of

the existing empirical literature are not country-specific, that is they deal with cross-sec-

tional analysis, thus they did not provide for differential in nature and structure of various

economies. Hence, developing countries like Nigeria are recommended policies which are

based on research conducted for industrially advanced countries or even mixture of both.

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CHAPTER THREE

METHODOLOGY

3.1 ANALYTICAL FRAMEWORK

The primary aim of every economic research is to arrive at a conjunction of economic theo-

ry, actual measurement using the theory and techniques of statistical inferences as the

matching bridge (Haavelmo, 1994). The economic theory makes statement or postulates

hypothesis that are mostly quantitative (and some cases qualitative) in nature and as such, it

is the choice of the modeler or the researcher to validate these hypothesis using appropriate

models in line with current development and betting method of estimation and inference.

Economic theory and some empirical research argue that openness (trade or financial) will

definitely increase output growth while others opened that the relationship between the two

is ambiguous. In order to contribute empirically to this argument, this study will employ

econometric method as the research technique. The choice of method is necessitated by the

nature of the study which in this case is an analysis of relationship among variables.

3.2 MODEL SPECIFICATION

An economic model is a representation of the basic features of an economic phenomenon;

it is an abstraction of the real world (Fonta, Ichoku and Anumudu, 2003). The specification

of a model is based on the available information relevant to the study in question. This is to

say, the formulation of an economic model is dependent on available information on the

study as embedded in standard economic theory and other major empirical works, or else,

the model would be theoretical. Two models are postulated in this research work; the first

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is a non-monotonic model to capture the first and second objective of the study, while the

second is an analysis of covariance (ANCOVA) model. The functional form of these mod-

els can be specified as follows:

Model I:

RGDPt = (TPNt,TPNt2, RERt, RIRt, UNEMPt, TREND)..(i)

Mode II:

RGPt = f (DUMt, TREND, (DUMt*, TREND)).(ii)

The mathematical form of the model can be expressed as:

Model I:

RGDPt: o + 1TPNt + 2TPNt2 + 3RERt + 4RIRt +5UNEMPt + 6TREND

Model II:

RGDPt = o + iDUMt + 2TREND + 3(DUM*t TREND) ------------------(iv)

But equations (iii) and (iv) above are exact or deterministic in nature. In order to allow for

the inexact relationship among the variables as in the case of most economic variables sto-

chastic error term t is added to both equations. Thus, we can express the econometric

form of the models as:

Model I:

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RGDPt = o + 1TPNt + 2TPn2t + 3RERt + 4RIRt + 5UNEMPt + 6TREND + it

-------------------------------------(v)

Model II:

RGDPt = 0 + 1DUMt + 2TREND + 3(DUM*t TREND) +2t

------------------------------------------------------------------(vi)

Where RGDP = Real Gross Domestic Product which is a proxy for the real output of

the economy.

TPN = The Degree of openness measured as trade GDP

ratio i.e. (import + Export)/GDP

TPN2 =Real exchange rate

RIR = Real Interest Rate

UNEMP = Unemployment Rate

DUM = O for pre-SAP period observations

I for post SAP period observations

TREND = The chronological arrangement of time

= The stochastic error term

In order to properly estimate the parameters of the postulated models, we rescale

the dependent variable by logging it, thus, transforming them into a log-line models as fol-

low:

Model I:

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LOG (RGDPt) = o+ 1TPNt + 2TPN2t +3RERt + 4RIRt + 5UNEMPt

+6TREND + it -------------------- --------------(viii)

Model II:

LOG (RGDPt) = 0+1DUMt+2TREND +3 (DUM*tTREND) +2t

------------------------------------------------- (viii)

Also, in order to avoid a spurious regression, we subject each of the variables used to unit

root (or stationary) test so as to determine their orders of integration, since unit root prob-

lem is a common feature of most time-series data.

3.3 JUSTIFICATION OF THE MODEL

The choice of the model in this work is triggered by the fact that, though, trade openness at

the early stage of introduction into a developing country like Nigeria would certain have a

different structure and pattern where compared with its long run effect on the economys

output performance. This fact is embedded in the standard development economic theory

and buttress by Baliamoune- Lutz and Ndikumana (2007). The explanation follows suit: at

the early stage, when a developing country like Nigeria open up its economy for trade, its

domestics firms will face intense competition with the tiger foreign firms as the entire

market will be flooded with imported products which are cheaper and relatively better in

terms of qualify than the domestically produced products. This will make some of the in-

fant industries to loss their sales with less revenue along side with high cost of production.

This is unlike the foreign firms that enjoy low cost of production either economies of large

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scale production. As a result, many domestic firms will be forced out of the market. This

will surely have a negative effect on the economy.

Nevertheless, as the economy is acquiring new technologies from abroad via openness as

well as improving on its domestic infrastructure and capacity utilization of resources in the

long run, this will lead to low cost of production for the domestic infant industries and en-

able them to compete favorably with the foreign products in the marked. This will certainly

have positive effect on the economy as its domestic production capacity will increase

which will further lead to increase in export products, thus, having favorable balance of

payment. Another argument also suggests that developing countries should look inward to

achieve its development in the long run.

From the on going discussion, it is evident that fitting a monotonic model for such a situa-

tion would either over estimate or under estimate the actual potential of the economy;

hence the need for a non-monotonic model. The postulated model is a real model as its

variables are all in real form except unemployment. While the degree of openness and real

exchange rate represent the external shocks to the economy, the real interest rate and unem-

ployment rate represent the internal shocks to the economy.

Meanwhile, the second model is constructed to test for structural charge in the growth out-

put before and after the introduction of the structural Adjustment Program (SAP) in 1986.

Finally, the Error Correlation Model (ECM) is postulated so as to capture the linkage be-

tween the short run dynamics of the economy and the long run equilibrium of the economy.

3.4 ESTIMATION TECHNIQUES

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In order to develop strong, robust and reliable models that capture the relationship before

trade openness and output growth, the research work adopts the econometric techniques of

the Non-Monotonic modeling and the Analysis of Covariance (ANCOVA) modeling. In

building these models, the Ordinary Least Square (OLS) is used as the estimation tech-

nique. The method of OLS is extensively used in regression analysis primarily because it is

initiatively appealing and mathematically much simpler than nay other econometric tech-

nique (Gujarati, 2003).

The OLS method is based on some assumptions (see Gujarati, 2003) which make the OLS

estimators to become Blue (Best linear Unbiased Estimator). Some of the short comings of

the OLS method include the fact that while some of its assumptions are unrealistic (such as

no autocorrelation, homoscedasticity and no multicollinearity); a single model as well can

not fully satisfy all the assumptions at a time. Also, no single test can solve all the prob-

lems of this method at a time. Moreover, the OLS method can not be applied to purely non-

linear models such as ones that are non-linear in parameter. As a result of some of these

short-comings, we use the OLS method but correct the stand errors for autocorrelation by a

Newey-West method. The corrected standard errors are known as HAC (Heteroscedasticity

and autocorrelation-Consistent) standard errors or simply as Newey-West standard errors.

Hence, we have to apply individual initiative along side with the empirical rules and tests

so as to obtain tenable and robust results. Thus, an econometric modeling is said to be more

of an art than a science.

3.5 EVALUATION PROCEDURE

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3.5.1 ECONOMIC TEST (A Priori Expectation)

Tests shall be conducted to ascertain the a priori expectations which examine magnitude

and signs of the parameter estimates. This evaluation is guided by economic theory. The

aim of this test is to conform whether the parameter estimates conform to a priori expecta-

tion. The variables used in the model and their a priori expectations are analyzed below in

table (2).

Variables Definition Expected sign

RGDPt This is Real Gross Domestic product whichrepresent the real output represent the real out-

put of the economy. Its natural logarithm istaken

It is the dependent variableand considered to be stochas-tic.

TPNt This is the degree of trade openness in aneconomy it measures the international compet-itiveness of an economy in the global marked.It is an external stock to the economy.

It is expected to be positive.

TPNt2 This is the squared term of the degree of tradeopenness. It shows the structural pattern ofopenness relative to output growth

Since the structural patterncould be of any type, it can be

positive or negative.RERt This is the real exchange rate. It is the rate at

which the domestic currency is being ex-changed for the foreign currency with adjust-ment for relative price index; it is an externalshock to the economy.

It is expected to be negative.

RIRt This is the real interest rate. It is the real costof borrowing fund in the financial market. It isan internal shock.

It is expected be negative

UNEMPt This is the unemployment rate in the economy.

It is an internal shock.

It is expected to be negative.

DUMt This is a dummy variable introduced to capturethe effect of the structural Adjustment Pro-gramme (SAP) trade deregulation and liberal-

Since the effect of a policycould be favorable or adverse,its signs can be positive or

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ization policies. negative.TREND This is the chronological arrangement of time

which captures the incremental growth of out-put over time. It also serves the purpose of de-trending the fluctuations among the exogenousvariables.

It is expected to be positive.

ECMt-1 This is the error correction mechanism. It is expected to be negative.Table 2: a priori expectations

3.5.2 STATISTICAL (FIRST ORDER) TEST

Here, various statistical tests will be carried out so as to verify the acceptability, reliability,

and robustness of the estimated regression result. The tests include:

Student t-Test

This is used to test the statistical significance of the individual parameter estimates in the

regression models. This work will use the t-distribution to test the statistical significance of

these parameter estimates.

F-Test

This is used to test for the overall significance of the model. It tests the simultaneous nullhypothesis of all the parameter to be equal to zero in the regression model.

R2-Coefficient of Determination

This test is used to measure the goodness of fit of a regression line. It measures the propor-

tion of the total variation in the dependent variable explain by the repressors in the model.

r- Coefficient of Correlation

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This measures the significance of the strength of linear association (correlation). The corre-

lation coefficient measure the degree of association between two variables (such as TPN

and RGDP in the case of this work). It is obtained through the product moment correlation

coefficient.

3.5.3 ECONOMETRIC (SECOND ORDER) TEST

Here, various tests will be carried out in order to verify whether the estimated regression

results conform to the classical (normal) linear regression model assumption. This test in-

cludes:

Test of Normality

This test is used to verify whether the error term is normally distributed. The Jacque-Beva

(JB) test will be used to verify this assumption.

Test of Heteroscedasticity

This test is used to verify the assumption of equal spread of the error variance (ho-

moscedastic) between members of the same series of observations. The whites het-

eroscedasticity test (with no cross term) will be employed in the test.

Test of Autocorrelation

This test is used to verify the randomness of the error term between members of the same

series of observations. As a result of the numerous assumptions and problems associated

with the conventional Durbin-Watson (DW) test, the Breusch-Godfrey (LM) test will be

employed to verify this hypothesis.

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Test of Specification Error

This test is used to verify whether the econometric regression model being estimated is cor-

rectly specified. The Ramseys RESET (Regression Specification Error Test) will be em-

ployed.

Forecast Test

This test is used to verify the reliability of the estimated regression model in forecasting fu-

ture values. The Henry Theils in equality coefficient will be used to evaluate the forecast-

ing performance of the model.

3.6 SOURCES OF DATA AND SOFTWARE FOR ESTIMATION

Data is the most important materials for any economic research or analysis, and very much

indispensable to the field of econometrics indeed. Gugarati (2003 asserted that the success

of any econometric analysis ultimately depends on the availability of appropriate and accu-

rate data. In other words, the researcher should always keep in mind that the results of re-

search are only as good as the quality of the data.

The research study makes use of secondary data. The data used are obtained from the Cen-

tral Bank of Nigeria (CBN) statistical bulletin 2007 and National Bureau of Statistics

(NBS) online publication for 2006.

The percentage ratio and real values are computes by the researcher in order to capture the

objective of the study and in congruence with economic theory.

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The econometric software packages used for the analysis of this work are the Reviews 3.1

and SPSS 14 versions, white the Microsoft Excel 2003 is used to enter the data.

CHAPTER FOUR

PRESENTATION AND ANALYSIS OF RESULTS

4.1 INTRODUCTION

The purpose of this chapter is for presentation, evaluation and analysis of the regression re-

sults of the models postulated as well as verification of thevarious working hypothesis of

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this research which are drawn from the objective of the study. The results of the OLS re-

gressions of the first and second models are presented below. The parameter estimates are

also subjected to various economic, statistical and econometric tests.

Finally, we analyze our results in order to verify whether they conform to the working hy-

pothesis of this study.

4.2 PRESENTATIONS OF REGRESSION RESULTS

The ordinary least square (OLS) regression results for first and second models are present-

ed below:

Model 1:

Dependent variable: LOG (RGDP)

Variable Coefficient Std. Error t-statistic ProbabilityC 10.18152 0.134217 75.86839 0.0000TPN 0.025450 0.006052 4.205461 0.0002TPN2 0.000190 5.00E-05 -3.811704 0.0006RER 5.67E-05 1.51E-05 3.757570 0.0007

RIR -0.006919 0.003754 -1.842949 0.0749UNEMP 0.012686 0.004581 2.769490 0.0094TREND 0.019105 0.002605 7.333272 0.0000

Table 3: results of model 1

R2 = 0.924212 F-statistic = 63.00557

Adjusted R2 =0.909543 D-W statistic = 1.790166

For full detail of model 1 results see appendix (1)

Model II:

Dependent variable: LOG (RGDP)

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Variable Coefficient Std. Error t-statistic ProbabilityC 11.21105 0.136498 82.13329 0.0000DUM -0.772697 0.172093 -4.489996 0.0001TREND 0.002437 0.013885 0.175541 0.8617DUM* TREND 0.040715 0 .014603 2.788147 0.0086Table 4: results of model II

R2 = 0.847398 F-statistic = 62.93409

Adjusted R2 = 0.833333 D-W statistics =0.556859

Consult appendix (II) for complete results of

4.2.3 THE ERROR CORRECTION MODEL (ECM)

The existence of cointegration among the variable of the model which we verified above

necessitates the need for the postulation of the error correction model (ECM). This model

aims to link the short run dynamics with the longrun equilibrium the result of the ECM is

presented below.

Dependent variable: DLOG (RGDP).

Variable Coefficient Std. Error t-statistic ProbabilityC 0.031238 0.015978 1.955105 0.0599D(TPN) 0.013803 0.005890 2.343573 0.0259D(TPN2) -9.41E-05 4.40E-05 -2.140235 0.0406D(RER) -1.08E-05 1.58E-05 -0.682625 0.5001D(RIR) -0.003360 0.001588 -2.115853 0.0428D(UNEMP) -0.002548 0.003612 0.705384 0.4860ECMt- 1 -0.1785507 0.72896 -4.543232 0.0001

Table 7: Result of the Error correction modelR2 = 0.502682 F-statistic = 5.053938

Adjusted R2=0.403219 D-W statistic =1.407372

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see appendix (v) for comprehensive result of the error correction model.

4.3 INTERPRETATION AND EVALUATION OF RESULTS

4.3.1 EVALUATION BASED ON ECONOMIC CRITERIA

In this section, we present the economic interpretation of the regression results and verify

whether parameter estimates in each model conform to a priori expectation.

Model 1:

In the first model, the dependent variable is the log of real GDP while the independent vari-

ables are: Degree of openness to trade, the squared term of the openness to trade real ex-

change rate real interest rate, unemployment rate and the trend value.

CONSTANT(C): In the first model, the constant coefficient of 10.18152 represent the val-

ue of log of RGDP at the beginning of the study period i.e. LOG (RGDP) = 10.18152. By

taking the antilog, we obtain that the value of real GDP at the beginning of the study period

(i.e. 1969) is N26.410.58 million

(=e 10.18152), other factors held constant.

DEGREE OF OPENNESS TO TRADE (TPN): The sign of its coefficient is positive.

This conforms to the standard economic theory which postulates that trade openness en-

hance economic growth. The coefficient of 0.025450 implies that over the study period, on

average, a one percentage (1%) increase in the degree of trade openness leads to approxi-

mately 2.55% (0.025450 x100%) increase in output growth. This early stage increase in

output growth as a result of openness to trade may be due to internal vibrancy of govern-

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ment

objectives, development of infrastructure and, indeed, the oil boom of the 1970s.

REAL EXCHANGE RATE (RER): The sign of the real exchange rate coefficient is posi-

tive. This does not conform to the theoretical postulation which stressed that as foreign cur-

rency say (dollar) appreciate (negative) against the domestic currency (say Naira), exports

will become cheaper while imports will be more expensive, hence, greater net export which

turn means increase in GDP (output). Thus there should be a negative relationship between

RER and RGDP. The coefficient of 5.67E-05 means that over the period of study, a 1% in-

crease in real exchange rate, on average, leads to approximately 0.006% (=0.00000567

x100%) increase in the output growth (RGDP). Although the economic impact of RER on

RGDP in Nigeria is very small, it is however statistically significant. This result indirectly

shows the magnitude of the impact of the foreign exchange market to the growth of the

economy. The foreign exchange market is regarded as the largest market in the world.

However, the result shows that it has little impact on the Nigerian economy. This may be as

a result of the fact that the exchange market in Nigerian is largely dominated by the parallel

market (fondly known as black market) which is regarded as part of underground economy

and not accounted in the national income computation. Also, the sign of real exchange rate

is positive. This may be as a result of inconsistency in government policies with regard to

exchange rate.

REAL INTEREST RATE (RIR): The sign of the real interest rate coefficient is negative.

This conforms to a priori expectation as increase in rate of interest leads to rise in cost of

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borrowing which discourages investors from borrowing for investment purpose, thus, re-

ducing investment level; hence, reducing productivity and output. The result further shows

that during the study period 1% increase in real rate of interest will on average lead to ap-

proximately 0.69% (=0.006919 x 100%) decrease in real GDP. But this result is not statisti-

cally significant. This reflects the low level of development of the financial sector in the

economy especially the money and capital markets. The result further implies that the fi-

nancial sector instrument (interest rate) does not have significant economic impact in deter-

mining the level of output growth in Nigeria.

UNEMPLOYMENT RATE (UNEMP): The coefficient of unemployment rate has a posi-

tive sign. This does not conform to economic theory which postulates that a rise in unem-

ployment level will reduce productivity, hence output growth.

The unemployment rate coefficient of 0.012686 indicates that a 1% increase in unemploy-

ment rate, on average, leads to approximately 1.27% increase (0.012686 x 100%) in real

GDP. This kind of result is only possible when the impact of a few highly skill labour, em-

ployed at the expense of much unskilled labour that is laid-off is economically significant.

One of the arguments in favour of openness to trade is that new technologies and skills in

the production process may require more of capital and little labour. Hence, a branch of un-

developed, inept and sluggish labour is retired to the labour market causing high rate of un-

employment. Hence, the simultaneous coexistence of increased growth of output as a result

of improved skill and technology on one hand, and high unemployment rate on the other

hand in the economy. Nevertheless, the proportional relationship between unemployment

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and real GDP also indicate the explosive nature of the unemployment rate which has a sig-

nificant impact on industrial development in the nation. Moreover, the result reflects the

impotence of various government policies to curb unemployment in the face of stagnation

and fluctuation of macroeconomic indicators in the economy.

TREND: The sign of the trend value is positive which conforms to a priori expectation.

The 0.019105 trend coefficient indicates that, on average, output growth (proxy by RGDP)

increased at the rate of 1.91% (0.019105 x100%) per annum, other variables held constant.

But the compound rate of growth of output over the study period is approximately 1.93%

i.e. [(e0.019105-1) 100%].0.02% difference between the actual and compound rate of growth

is due to the compounding effect. Moreover, the trend coefficient is highly significant to

detrend the time relationship among the explanatory variables.

THE ERROR CORRECTION MODEL

In the ECM, the coefficient of the differenced variables reflects the short run dynamics. In

this model, all the variables conform to the priori expectation. Also all the variables are sta-

tistically significant except for the RER and UNEMP. The Error Correction Mechanism

(ECMt-1) is also negative which conform to a priori expectation. The negative value of the

ECM implies that output growth (proxy by RGDP) is above equilibrium and will start fall-

ing in the next period to correct the equilibrium error. The coefficient of -0.785507 implies

that about 79% (0.785507 x100%) of the equilibrium error will be corrected in the next pe-

riod. That is, RGDP will adjust to equilibrium by about 79% in the next period. The speed

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of adjustment of the ECM is sufficiently high to correct the imbalance in the macro eco-

nomic fluctuation.

The 0.013803 coefficient of the differenced TPN implies that in the short run, a 1% rise in

degree of openness will lead to about 1.38% (=0.013803 x100%) increase in RGDP. The

differenced TPN2 coefficient of -9.41E-05 indicates that a further 1% increase in squared

term of the degree of openness will lead to a minute decline in RGDP by about 0.009%

(see fig.5 above).

Also, a 1% increase in real interest rate (RIR) will lead to about 0.001% decline in RGDP

in the short run. Nevertheless, this result is not statistically significant. Moreover, on the

short run a 1% increase in the real interest rate (RIR) will lead to about 0.34% decline in

real GDP. Furthermore, a 1% rise in unemployment rate will cause output growth fall by

about 0.25% in the short run. However, this result does not have much economic impact.

4.3.2 EVALUATION BASED ON STATISTICAL CRITERIA

Here, the t-test, f-test and R2 (coefficient of determination) are carried out to test the statisti-

cal reliability to the estimated parameters and the regression results in general. In order to

avoid repetition, these tests are carried out on the regression results of the first model only.

We will also test for the degree of association between RGDP and TPN using the product

moment correction coefficient .

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Ho significantUNEMP 2.769 2.042 /t/>*: Reject Ho Statistically sig-

nificantTREND 7.333 2.042 /t/>*:Reject Ho Statistically sig-

nificantTable 8: Summary of the t-test

From the results displayed in the table above, we conclude that all the parameter estimates,

are statistically at 5% level of significance except for real interest rate which is statistically

significant at 10% critical level.

F-Test

This measures the overall significance of the regression model. The F-value provides a test

of the null hypothesis that the true slope coefficients are simultaneously zero. That is:

Ho: x1 =2 =3 =4 =5 =6= 0 (the model is statistically insignificant)

Hi: 23 4 56 0 (the model is statically significant)

The test statistic is given 25:

F = ESS/(k-1)~ F

(k-1, n-k)dfRSS/(n-k)Where ESS=estimated sum of square

RSS = Residual sum of square

The critical value is obtained from the F-distribution table at level of significance and

(k-1, n-k) degree of freedom.

Decision Rule: Reject Ho if F-statistic>f (k-1, n-k)df ;

Otherwise, do not reject Ho

From the regression result

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F-statistic = 63.00557

At =0.05, n=38, k=7

F(k-1, n-k)df = F0.05(6,31) 2.42.

Conclusion: Since f-statistic =63.00557 is greater than the critical F=2.42, we thereby re-

ject Ho and conclude that the model has a robust fit and it is statistical significant. That

means there exist a true relationship between the regression and the regresses.

R2 (Coefficient of Determination)

This measures the goodness of fit of the estimated model. The R2 measure the proportion of

total variation in the regress and explained by the regression model. From the regression re-

sult the R2 is 0.924212 while the adjusted R2 is 0.909543. This means that the model ex-

plain about 92% of the total variation in real GDP (output growth). This high R2 cannot be

said to be statistically significant for the true goodness of fit in a model unless subjected to

test. The hypothesis to be verified here is:

Ho: R 2 = 0 (the R2 is statistically insignificant)

H1: R2 0 (the R2 is statistically significant)

The test statistic for the critical R2 is given as

R2 = (k-1)f

(k-1)f + (n-k)

Where f is the critical-f value at level of significance, k is the number of parameters in

the model and n is the number of observations.

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Decision Rule: Reject Ho if observed R2 is greater than the critical R2: otherwise do not re-

ject Ho. From the regression result;

Observed R2 0.924

At = 0.05, k=7, n=38

R2 = 0.319.

Conclusion: Since observed R2 = 0.924 is greater than the critical R2 = 0.319, we thereby

reject Ho and conclude that the coefficient of determination (R2) is statistically significant

and a true goodness of fit for the model.

r (coefficient of correlation)

This is a measure of the degree of association between two variables. It measures the

strength of degree of linear association between two variables. The product moment corre-

lation coefficient is given as

r = nx1y1-(y1)(nx12 (x1)2][ny12-(y1)2)

When the coefficient of correlation (r) between real GDP (x1) and degree of openness (yi)

is computed, it is observed that:

r = 0.594989

This result shows that there is positive relationship (or linear association) between output

growth and trade openness. The correlation of about +0.6 implies that there is a positive

modern linear association or correlation between openness and output growth in Nigeria.

But is this correlation coefficient statistically significant?

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To answer this question, we subject the correlation result to test. The hypothesis to verify

is.

Ho: 0 = 0 (true linear association does not exist between the two variables)

H1: 0 0 (true linear association exist between the two variables).

The test statistics is given as;

Z = x ~N(0,1)

Where x =1/2 ln 1-r

1-r

= ln 1-0

1-0

1 = n-3

The critical value is obtained from the standardized normal distribution (/2) level of sig-

nificance.

Decision Rule: Reject Ho if /Zcal/> Z 12, otherwise do not reject Ho.

After simple computation, we observed that

X =0.68535,. =0, =0.16093

:. Zcal = 4.056

Also, zt=0.05

Z12=Z0.025 = 1.96

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Conclusion: Since Zcal =4.0546 is greater than Z/2 =1.96, we therefore reject Ho and con-

clude that the correlation coefficient of trade open and real GDP is statistically significant.

This means that linear association (correlation) truly exists between the two variables at 5%

level of significance.

CHAPTER FIVE

SUMMARY, POLICY PRESCRIPTION AND CONCLUSION

5.1 SUMMARY

This research works pursuit is to unravel the structural relationship between trade open-

ness and output growth in Nigeria from 1970 to 2007. It aims to determine the structural

impact of trade openness on output growth in the presence of other internal and external

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macroeconomic shocks using a non-monotonic approach. It further used an Analysis of co-

variance (ANCOVA) model to determine the possibility of structural change in output

growth so as to understudy the policy effect of the Structural Adjustment Programme

(SAP) of 1986 which tends to liberalize trade.

The regression results of the first model show that there indeed exist an inverted U-Shape

quadratic relationship between trade openness and output growth in Nigerian. This is re-

flected in the negative sign of the squared term of degree of openness. Also, all other macro

economic variables in the model including real exchange rate and unemployment rate are

interest rate which is statistically significant at 10%. These variables do not only exert sig-

nificant impact on output growth in the short run but also in the long run, and various diag-

nostic and performance test have been conducted to verify the reliability of these results.

Also, in the regression results obtained from the second model, we observed that the policy

thrust of the IMF/World Bank-Sponsored Structural Adjustment Programme (SAP) does

not only have a significant impact on the output growth in Nigeria but in fact have positive-

ly changed the trend of growth of output in the economy. This implies that the trade liberal-

ization policy of SAP has a significant positive effect on output growth in Nigeria. This re-

sult is further buttressed by the positive moderate correlation coefficient of about 0.6 ob-

tained. This indicates that trade openness and output growth are as well linearly associated.

That is, an increased openness to trade could as well mean a rise in output growth.

5.2 POLICY RECOMMENDATIONS

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Based on the finding of this research work discussed above, we hereby proffer the follow-

ing policy measures for long term sustenance of output growth in the economy.

First of all, there should be optimal control of trade through the borders of the economy.

The underground economic activities of bunkering, smuggling, child and drug trafficking,

edited copy for abrahim otioyebo

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