DEMIDE, OYIZA GRACE PG/M.SC/12/62440

RELATIVE IMPACT OF FINANCIAL SECTOR REFORMS ON AGRICULTURAL AND MANUFACTURING SECTOR GROWTH IN NIGERIA

Faculty of Social Sciences

Economics

Chukwueloka.O. Uzowulu

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TITLE PAGE

RELATIVE IMPACT OF FINANCIAL SECTOR REFORMS ON AGRICULTURAL AND MANUFACTURING SECTOR GROWTH IN NIGERIA

BY

DEMIDE, OYIZA GRACE PG/M.SC/12/62440

Department of Economics University of Nigeria, Nsukka

Supervisor: Dr (Mrs) G. C. Aneke

SEPTEMBER, 2015

APPROVAL PAGE

This thesis has been approved as meeting the requirements for the award of the degree of Master of sciences (M.Sc) of the Department of Economics, University of Nigeria, Nsukka.

___________________ _________________

Dr (Mrs) G.C. Aneke Date

(Supervisor)

____________________ _________________

Prof (Mrs) I.S Madueme Date

(Head of Department)

__________________________ _________________

Prof C.O.T.Ugwu Date

(Dean Faculty of Social Sciences)

____________________________ _________________

External Examiner Date

CERTIFICATION

Demide, Oyiza Grace, a postgraduate student in the department of social science, with registration number PG/M.Sc/12/62440, has satisfactorily completed the research required for the Award of Masters of Science in Economics in the University of Nigeria Nsukka.

_______________________ ________________________

Demide, Oyiza Grace

(Researcher) Date

________________________ ________________________

Dr. (Mrs) G. C. Aneke

(Suprvisor) Date

DEDICATION

This research work is dedicated to my parent, Mr and Mrs Samuel Sule Demide who gave me my

Values and the best upbringing any one could wish for.

ACKNOWLEDGEMENT

I tender my unreserved gratitude to God almighty who laid the foundation of my education and gave me the sufficient grace to ride through the twists and turns of academic journey successfully.

I am particularly grateful to my project supervisor, Dr (Mrs) G.C Aneke, whose suggestions and criticisms were invaluable to the successful completion of this research work.

My profound gratitude also goes to my Lecturers and student of the department of economics for their support and encouragement in the execution of this research work.

A home away from home is what I found amongst my roommate, Dr Dorothy Paskhot, Dr Bola Ogunsola, Dr Mbanefo , Josephine, who God positioned to bless me, your good will and support are highly appreciated.

I wish to acknowledge the contributions of all those who in various ways identified with me especially in time of need. I remain grateful to the various sources I consulted during this research whose works were used as instructional materials.

Finally, I thank in a special way my parents, Mr and Mrs Samuel Sule Demide for being the pillar of my life, thanks for solidifying my aspirations with your support. I am proud to have you as parents.

TABLE OF CONTENTS

Title page i

Approval page ii

Certification iii

Dedication iv

Acknowledgement v

Table of contents vi

List of Tables x

Abstract xi

CHAPTER ONE

INTRODUCTION 1

1.1 Background to the study 1

1.2 State of the problem 4

1.3 Research Questions 6

1.4 Research Objectives 6

1.5 Research Hypotheses 6

1.6 Significance of the Study 6

1.7 Scope of the Study 7

CHAPTER TWO

2.1 Financial Sector Reforms in Nigeria 8

2.2 Reform of Financial Sector and Links with Agricultural and Manufacturing

Sector in Nigeria 8

2.2.1 Trend in Agricultural Sector Growth and contribution to GDP 8

2.2.2 Trend in Manufacturing Sub-Sector Growth and contribution to GDP 13

CHAPTER THREE

LITERATURE REVIEW 15

3.1 Conceptual Literature 15

3.2 Theoretical Literature 16

3.2.1 Financial Repression Theory 16

3.2.2 Financial Liberalization Theory 17

3.2.3 The Post Keynesian Theory 17

3.2.3 Neo –Structuralist Theory 18

3.3 Empirical Literature 19

3.4 Limitation of Previous Studies 26

CHAPTER FOUR

METHODOLOGY

4.1 Theoretical Framework 28

4.2 Model Specification 29

4.3 Variable Description 31

4.4 Estimation Procedure 32

4.5 Justification of the Model 33

4.6 Data Sources 33

CHAPTER FIVE

DATA PRESENTATION AND ANALYSIS

5.1 Introduction 35

5.2 Descriptive Analysis 35

5.3 Stationarity Test 36

5.4 Co-integration Test 36

5.5 Model One : OLS result for impact of financial sector reforms on

agricultural sector output 37

5.5.1 Economic Criteria (Apriori expectation) 37

5.5.2 Gross Capital Formation 38

5.53 Ratio of credit to Private sector (CPSGDGDP) 38

5.5.4 Average annual rainfall (ARR) 38

5.5.5 Real interest rate (RINR) 39

5.5.6 Interest Rate spread (SINT) 39

5.5.7 Real exchange rate (EXRATE) 39

5.5.8 Financial Sector reform (DUM) 40

5.5.9 Effect of financial sector reforms on financial indicators 40

5.5.10 Agricultural Sector contribution to GDP (AGGDP) 42

5.5.11 Model fit and diagnostic check 42

5.6 Model Two: OLS for impact of financial sector reforms on

manufacturing sector output 45

5.6.1 Gross capital formation (GCF) 45

5.6.2 Ratio of Credit to Private sector (CPSGDP) 46

5.6.3 Manufacturing capacity Utilization (MANCU) 46

5.6.4 Ratio of broad money supply to GDP (M2GDP) 46

5.6.5 Real interest rate (RINR) 47

5.6.6 Interest rate spread (SINR) 47

5.6.7 Real exchange rate (EXRATE) 47

5.6.8 Financial sector reform (DUM) 48

5.6.9 Manufacturing sector contribution to GDP (MANGDP) 49

5.6.10 Model fit and diagnostic check 49

5.7 Comparison of impact of key financial indicators on agricultural and

Manufacturing sector output 51

5.8 Evaluation of research hypotheses 52

CHAPTER SIX

SUMMARY, CONCLUSION AND POLICY RECOMMNDATION

6.1 Summary 54

6.2 Conclusion 55

6.3 Policy recommendation 56

REFERENCE 57

APPENDIX 64

LIST OF TABLES

Table 5.1 Descriptive statistic 34

Table 5.2 Augmented Dickey-fuller test statistics 36

Table 5.3 Impact of financial sector reforms on agricultural sector 36

Table 5.4 Model fit and diagnostic check for impact of financial sector reforms on

Agricultural sector output 43

Table 5.5 Impact of financial reforms on manufacturing sector 45

Table 5.6 Model fit and diagnostic check for impact of financial sector reforms on

Manufacturing sector output 49

Table 5.7 Wald coefficient restriction test 51

ABSTRACT

The study investigates relative impact of financial sector reforms on agricultural and manufacturing

sector growth in Nigeria. To guide the study, Ordinary Least Square technique was adopted and

Eviews 8.0 econometric software was utilized for the analysis. A time series quarterly data sourced

from Central Bank of Nigeria Statistical Bulletin 2009 and 2013and it covered the period 1970-2013

was used for the analysis. After carrying out necessary pre- and post diagnostic test, the result shows

that gross fixed capital formation and credit to private sector ratio to GDP has positive but

insignificant relationship with agricultural and manufacturing sector output. While real interest rate,

manufacturing capacity utilization and financial sector reform dummy were positive and significant,

interest rate spread, real exchange, average annual rainfall and money supply ratio to GDP displayed

negative relationship with agricultural and manufacturing sector output. Upon comparison of impact

of key financial indictors on agricultural and manufacturing sector output, the result revealed that

impact of real interest rate and financial sector profitability index (SINR) in the pre- and post-financial

sector reform were significant in each sector. In contrast, while impact of real exchange rate does not

significantly influence agricultural sector output, it subsequently became significant in the model for

manufacturing sector output. The study however concludes that domestic investment on

infrastructure and credit facility to the sectors was sub-optimal. Secondly, participants in the sectors

were made worse-off by the reform. Extensive review of existing policies, provision of incentives,

accessible and affordable funding was recommended by the study.

CHAPTER ONE

INTRODUCTION

1.1 Background to the Study

The financial sector is central to any economy of the world, and the ripples of the sector’s downturn

are usually felt in all other sectors of the economy. Lin, Sun, and Jiang (2009) hinted that the

structure of the financial sector reveals the nature of the productive activities in such economy. It is

therefore not surprising that Nigeria like most developing economies, has adopted various forms of

policy and institutional reforms since independence to ensure that the sector remains in good

health. The success story is not the same everywhere though, while some countries have been

successful in eliminating underlying distortions and restructuring their financial sectors in the

beginning of the new millennium, in some cases financial sectors remains underdeveloped (Dileep,

Rambabu, & Bhisma, 2007). Financial sector reforms, especially a comprehensive one, would be a

turnaround approach to cope up with the threats of global competitiveness in carrying out the

financial services. The country has witnessed a wave of reform in the financial sector. It is pertinent

to point out at this juncture that financial sector is comprised of banks and non-bank financial

institutions (money and capital markets) along with other financial system that supports them.

As the financial reform phenomena advances, so do the understandings of it advance. Financial

reform as Gencalo (2011) puts it “is a multifaceted phenomenon”. According to Ebong (2006), they

are deliberate policy response to correct perceived or impending financial crises and subsequent

failure. In other words, the different interventions of the federal government through the central

bank of Nigeria and other financial institutions regulators to enable the financial sector and the

economy recover from actual or impending disaster is what is here referred to as financial reform.

On the expectations on financial reforms, Edirisuriya (2008) reported that financial sector reforms

are expected to promote a more efficient allocation of resources and ensure that financial

intermediation occurs as efficiently as possible. By implication, financial sector reforms brings

competition in the financial markets, raises interest rate to encourage savings, thereby making funds

available for investment, and hence lead to economic growth (Asamoah, 2008). The different ways

in which these competitiveness have been kindled includes deregulation of interest rates, exchange

rate, entry/exit into the banking business, establishment of the Nigeria Deposit Insurance

Corporation (NDIC), strengthening the regulatory and supervisory institutions, upward review of

capital adequacy, sectoral credit guidelines, capital market deregulation and the introduction of

direct monetary policies instruments (Nnanna, Englama, & Odoko, 2004, and Iganiga, 2010).

Specifically, financial sector reforms began in Nigeria with the deregulation of interest rates in

1987 (Ikhide & Alawode, 2001). The reforms were initiated to enhance competition, reduce

distortion in investment decisions and evolve a sound and more efficient financial system. The

reforms which focused on structural changes, monetary policy, interest rate administration and

foreign exchange management, encompass both financial market liberalization and institutional

building in the financial sector (CBN June 2009).

Since the deregulation of interest rate, far reaching policy measures had been initiated and

implemented, amongst these measures includes licensing of new banks, the capital market reforms,

and the direct to indirect monetary controls have been undertaken (Mike & Lawal, 2012).

Economists believe that the link between financial sector and the real sector of the economy can be

explored from two perspectives, namely: the intermediation role of the financial institutions and the

monetary policy perspective (i.e. the transmission mechanism of monetary policy impulse) (Levine,

2005). In whichever case, Harvey (1993) believes that the motive for the establishment of financial

institutions is primarily to extend credit access to local businesses. In the words of Sanusi (2012) on

the imperative of reforms, he said “there is a need for periodic reforms in order to foster financial

stability and confidence in the system. In line with this primary objective, Enang and Francis (2011)

observed that the banking system’s credit to the private sector improved significantly during the first

few years of the reforms, although bulk of the credit was mainly a short term investment. According

to Mbutor (2007), the impetus for the reforms follows from the understanding that a sound financial

system will render monetary policy more effective and also support growth in the real sector of the

economy. He therefore suggests that if the financial sector is healthy, it will certainly reflect in the

activities of the real sector of the economy. Prominent among these sectors where the effect of

these reforms could be manifest is the manufacturing and the agricultural sectors.

A vibrant agricultural and manufacturing sectors activity creates more linkages in the economy than

any other sector and thus would reduce the economic pressures on the external sector. One may

ask, why these sectors? According to Ogwuma (1995), the manufacturing sector has a wider and

more effective linkage among different sectors. Loto, (2012) refers to manufacturing sector as an

avenue for increasing productivity in relation to import replacement and export expansion, creating

foreign exchange earning capacity, raising employment and per capita income which causes

unrepeatable consumption pattern. This sector also creates investment capital at a faster rate than

any other sector of the economy. The manufacturing sector is therefore an important component of

the real sector. In terms of its contribution to GDP, manufacturing in Nigeria however is still at an

infant stage. It accounted for only about 5.02 percent of the Gross Domestic Products in 1998 in

Nigeria, and the subsector is responsible for an average of about 10 percent of total GDP annually.

Manufacturing in Nigeria includes: cement, oil refining and other manufacturing, although the

industrial base is small, there is great scope for expansion which is believed could have been possible

if there were a level playground for the industries to compete given a reliable financial system (NBS,

2010).

The percentage changes in the agricultural and manufacturing sector share of the GDP indicates that

both sectors has not been performing well for close to two decades now, which is very alarming

(CBN Statistical Bulletin 2010). As a result it has attracted the attention of researchers in recent

times (Adam, 2008, Eze & Ogiji 2013) among others. On the other hand, agriculture is the dominant

sector of the Nigerian economy. As a matter of fact, over 60% of the population is engaged in this

sector with an average of 41% contribution to the GDP between 2006 and 2010. Despite the

dominance of agriculture, the crude petroleum sub sector contributes over 80% of Nigeria’s foreign

exchange. As a result governments, over the decades, initiated numerous policies and programs

aimed at restoring the agricultural sector to its pride of place in the economy. This sector comprises

crops, livestock, fishing and forestry (Adedepo, 2004 & Ezirim, 2010).

Furthermore, in the word of Binswanger, Townsend and Tshibaka (1999) agriculture has a backward

and forward linkage with itself and other sectors of the economy. It supplies raw materials to the

agro allied industries which enhance the provision of foods, job opportunities and income to those

engaged in the sector as well as the government. Like the manufacturing sector, percentage growth

rate of the agricultural sector contribution to GDP has also been relatively low for close to two

decades now compared to its performance during the periods before the Structural Adjustment

Programme (SAP).

It is again imperative to point out that period 1986-1988 correspond with the SAP during which

austerity measures were introduced to remove all the bottlenecks impeding the growth of the

economy. The growth in GDP responded favorably during the period when financial sector reform

and economic liberalization were embarked upon. Interest rate structure was employed principally

to direct cheap credit to specific sectors such as agricultural and manufacturing sector. This was

done by consistently stipulating relatively lower interest rates for loans and advances of the sectors.

With the liberalization of interest rates in 1987, coupled with the abolition of the administrative

sectoral allocation of bank credit, market forces were then allowed to determine the appropriate

interest rate, exchange rate and credit allocation. Experience has shown that since the post-SAP

market reforms, lending rate has been on the upward trend. Lending rates at present vary between

15.0 percent and 25.0 percent (excluding other ancillary charges) and are too excessive for a

developing economy like ours (CBN, 2012). This is contrary to what is obtainable in developed

economies of the world where lending rates are usually single digit and even tilted towards zero per

cent at the peak of the global financial crisis in those countries (Mike, 2010).

To further establish a fact that finance is very crucial for these sectors, in a study conducted in mid-

2001, Nigerian Manufacturing Enterprise survey (NMES), the study covered three main regions in

Nigeria: The western region (Lagos & Ibadan), the eastern region (Enugu, Onitsha, Nnnewi & Aba),

and the northern region (Kaduna & Kano), (Soderbon & Teal 2002). Based on the responds

gathered, the perceived main problem facing the sector were nine, out of which access to credit was

identified as the second main factors militating against the sector. Also, based on the number of

programmes initiated in the agricultural sector since the SAP, one plausible assumption that could

be made is that finance is also identified as the major factor militating against the sector, it therefore

provide opportunity to access the efficacy of these financial reforms in the agricultural and

manufacturing sectors.

1.2 Statement of the Problem

Over the years, the financial sector had undergone reforms with the aim of positioning it to play a

catalytic role, thereby stimulating the real sector. However, opinions abound that the period of the

financial sectors reforms coincided with the economic reforms (structural adjustment programme).

Again, the results arrived at by Taiwo and Anthony (2011) suggests that financial sector reform has

not actually improved the performance of the Nigerian economy. Considering the contributions of

the manufacturing and agricultural sectors to the country’s GDP, it becomes imperative that this

relationship is investigated to ascertain the impact of the financial sector reforms on the

manufacturing and agricultural sectors in Nigeria. Moreover, statistics has confirmed the low and

decreasing contribution of both sectors under study to the economy’s GDP (CBN, 2012). Even the

recent publication by NBS showed that the manufacturing sector’s contribution to the economy is

minimal with an average of 3% which is an indication of poor performance (NBS, 2010).

It’s no gainsaying to argue that the near total neglect of agriculture in the country has denied many

manufacturers the primary source of raw materials needed in production. It has also been

postulated that the financial sector had not done well enough in providing credit to both the

manufacturing and agricultural sectors. Ultimately, the rate of interest at which loans are granted to

the firms in the manufacturing sector makes it almost impossible for them to access fund for

expansion and entry. Consequently, a series of programmes have been drawn up to ensure that

credit are made available to both sectors, among these programmes include: the Agricultural credit

guarantee scheme fund, (ACGSF) 1978, interest drawback programme (IDP) 2003, agricultural credit

support scheme (ACSS) 2006, Commercial Agriculture Credit Scheme (CACS) 2009 in the agricultural

sector. As a way of encouragement to firms in the manufacturing sector, the Small and Medium

Scale enterprise Credit Guarantee Scheme (SMECGS) was empowered with loan to the tune of N 200

billion.

Granted that the real cost of small loans is very high, the approach thus far has been to deregulate

interest rates for financial activities in order to stimulate credit provision. Since the influence of

monetary policy operates through the interest rate and credit, the question is then: has financial

sector reforms really helped to make credit available to the manufacturing and agricultural sectors?

Recent analyses of the Nigerian reforms have focused on specific reforms that emerged and its

myriad consequences. The evidence leads to mixed conclusion across time periods and across

reform and policy areas. And also, less attention has been given to the issue of whether; reforms

notwithstanding the dramatic switch in regulatory regime generated any of the efficiency and

growth benefits predicted by the literature on financial reforms.

Although it may be argued that financial sector reforms (with respect to its impact on real interest

rate, interest rate spread and real exchange rate) have engendered a well developed, healthy and

competitive financial system, which in turn as would be expected, impacted on the manufacturing

and agricultural sectors of the economy. However, it is not convincing how these reforms have hit

the real sector, specifically the manufacturing and the agricultural sectors, considering the fact that

most manufacturing firm have closed up and others moved out of the country while agriculture

remained at subsistence level. Nigeria, though an agrarian economy still rely on the importation of

agricultural finished goods despite the plethora of financing schemes. Against this backdrop, the

researcher in this work has raised a number of critical questions.

1.3 Research Questions

1. What is the effect of financial sector reforms on the agricultural sector growth in

Nigeria?

2. What is the effect of financial sector reforms on the manufacturing sector growth in

Nigeria?

3. Is there any difference between the impact of financial sector reforms on agricultural

and that of manufacturing sector growth in Nigeria?

1.4 Objectives of the Study

The purpose of this study is to draw lessons from reforms on the financial sector in Nigeria and

how the reforms have affected agricultural and manufacturing sectors in Nigeria. Specifically

the study seeks to:

1. ascertain the effect of financial sector reforms on the agricultural sector growth in

Nigeria

2. ascertain the effect of financial sector reforms on the manufacturing sector growth in

Nigeria

3. compare the impact of financial sector reforms on agricultural and manufacturing

sector growth in Nigeria

1.5 Research Hypotheses

In line with the study objectives, the following testable hypotheses emanate:

H01: The financial sector reforms do not exert positively on the agricultural sector growth in

Nigeria.

H02: The financial sector reforms do not exert positively on the manufacturing sector output

growth in Nigeria.

H03: The financial sector reforms do not impact differently to the agricultural and manufacturing

sector growth in Nigeria.

1.6 Significance of the Study

Agriculture continues to remain the mainstay of the Nigerian economy, contributing about 40% of

the GDP and main source of employment for about two-thirds of the population (CBN, 2013). On the

other hand, the manufacturing sector has been identified as one of the key sectors that have growth

potentials in the near future. And where there are growths potentials, finance is very crucial for goal

actualization. Whereas both sectoral growth and the supporting finance are made possible by a

strong financial system, and therefore reforms of the financial sector is also important.

This research work when completed will be useful to the government as it will enable it channel the

available financial resources to key sectors in the economy, and also through the central bank set an

appropriate interest rate for improved credit accessibility to the entrepreneurs in agricultural as well

as manufacturing sectors. Another group of individuals that will benefit from this work are the

entrepreneurs in the agricultural and manufacturing sectors.

On examination of individual reform policy variables will shed more light on the impact of specific

policies on the manufacturing and agricultural sector and thus result in important policy

implications. This work when completed will be valuable to students and future researchers

interested in knowing how the financial sector reforms have affected the agricultural and

manufacturing sectors of the Nigerian Economy for the period under investigation.

1.7 Scope of the Study

This study investigates the financial sector reforms as it relates to the agricultural and the

manufacturing sectors of the real sector in Nigeria. Annual time series data for this study will be

generated from the Central Bank of Nigeria Statistical Bulletin of 2009 and 2013, and it will cover the

period 1970-2013. The variables that will be employed in the analysis will include: ratio of

Agricultural output in total GDP (as a proxy for Agricultural output), ratio of manufacturing output in

total GDP (as a proxy for manufacturing output), Broad money supply (M2) to GDP ratio (Proxy for

financial depth), interest rate, interest rate spread, credit to private sector, exchange rate and

dummy to capture the effect of the financial reforms.

CHAPTER TWO

2.1 Financial Sector Reforms in Nigeria

The Nigerian financial sector grew stronger in 2007 in all the segments, as indicated by the various

performance measures (CBN 2007). The depth of the financial sector increased, as broad money

supply to nominal GDP ratio rose to 21.1 percent from 19.8 percent at the end of 2006. The

banking sector also showed a stronger capacity to finance real sector activities with substantial

credit flow to the core private sector as the credit to private sector (Cp/GDP) ratio increased from

13.0 to 21.7 percent at end-2007. In addition, the increased use of the various electronic money

products reflected the shift away from cash transactions and, thus, an improvement in the efficiency

of funds intermediation. Consequently, the ratio of currency outside banks to broad money supply

fell further to 15.2 per cent from 18.8 per cent at end-2006. The money market was further

deepened with the introduction of the ten-year government bond, while liquidity was enhanced

through the trading of the bonds in the secondary market, using the two-way quote system. Thus,

the ratio of money market assets outstanding to GDP at end-2007 rose to 9.9 per cent, from 8.8 per

cent at end-2006. The performance of the capital market improved substantially as the ratio of

market capitalization to GDP increased to 58.2 percent from 27.6 percent in 2006, (CBN 2007).

One of the major characteristics of the financial sector reforms over the years is the disorderly

manner in which the reforms have been implemented in Nigeria (Ebong, 2006). Different regimes in

Nigeria mean different reforms strategies and the abandonment of existing ones. This discontinuity

leads to non-smooth sailing reform process and thus, obscured the appraisal and outcome of these

reforms. Attempts at reforming the financial sector in Nigeria have fallen under five main headings -

reform of the financial structure, monetary policy reforms, foreign exchange reforms, liberalization

of capital movement and capital market reforms.

a) Reform of the financial structure: Generally, measures undertaken here are

designed to increase competition, strengthen the supervisory role of the regulatory authorities

and strengthen public sector relationship with the financial sector. In this direction, some

measures undertaken include:

Enhancing bank efficiency through increased competition and management by granting

licenses to more banks to operate. Conditions for the licensing of new banks were

relaxed. In response, the number of banks increased dramatically from 40 in 1986 to 120

in 1992. A comparable increase in the number of non-bank financial institutions occurred.

Strengthening banks supervision and increasing their viability through adequate

regulations regarding minimum capital requirements, specifying the range of assets and

liabilities they can acquire, introduction of uniform accounting standards for banks to

ensure accuracy, reliability and comparability. Two banking laws were promulgated with

effect from June 1991, the CBN Decree No. 24 of 1991 and the banks and other financial

institutions Decree (BOFID), No. 25, 1991. In addition, the Nigerian Deposit Insurance

Corporation (NDIC) charged with the responsibility of insuring banks deposit against

bank failures and ensuring safe and sound banking practices through effective monitoring

and supervision of banks in collaboration with the CBN was established whereby banks

granted domestic loans on the security of foreign exchange deposits held abroad or on

domiciliary accounts. There was also the introduction of an auction-based system for the

issuance of treasury certificates aimed at promoting a greater reliance on market forces in

the determination of yields on government debts instrument through market determined

interest rates and the decision by the Federal government to sell its share-holdings in

some commercial and market banks thereby reverting such banks to private ownership,

(Ikhide, 1996).

b) Monetary policy reforms: Designed mainly to stabilize the economy in the short run

and to induce the emergence of a market-oriented financial sector. Such included:

Rationalization of credit controls: Although credit ceilings on banks were not completely

removed, the sector specific credit distributions target were compressed from 18 in 1985

to 2 in 1987 - Priority (agriculture and manufacturing) and non-priority (others). Other

credit measures enacted were the elimination of exceptions within the ceiling on bank

credit expansion, giving similar treatment to commercial and merchant banks in relation

to required liquidity ratios and credit ceiling, the modification of cash reserve

requirements which is now based on the total deposit (demand, savings, and time

deposits), rather than on time deposits only, and the reintroduction of stabilization

securities. These are non-negotiable and non-transferable debt instruments of the Central

Bank which banks are mandated to purchase at intervals in order to control their excess

reserves. It was designed to mop-up the excess liquidity of the banking system.

Deregulation of interest rates: In January 1987, a partial deregulation of interest rates was

attempted, but by August, all rates became market determined. The CBN adopted the

system of fixing only its minimum rediscount rate to indicate the desired direction of

interest rates changes. Interest rate liberalization was aimed at enhancing the ability of

banks to charge market-based loans rates and also guarantee the efficient allocation of

scarce resources. In 1989, banks were encouraged to pay interest on current account

deposits. The rate to be paid was to be negotiated between banks and their customers.

The shift from direct to indirect system of monetary control: in June 1993, Open-market

Operations (OMO) was introduced. Under the scheme, OMO was to be conducted

exclusively through licensed discount houses, which are supposed to constitute the open

market for government securities. The introduction of OMO was meant to replace the use

of direct controls for managing liquidity in the economy, (Anthony & Lekan 2013).

c) Foreign exchange market reforms: Transactions in foreign exchange constitute an

important aspect of financial sector activities. A second-tier foreign exchange market was

established in 1986 as an auction forum for the sale and purchase of foreign exchange.

Previously, the sale and purchase of foreign exchange was rigidly controlled through the use

of import licenses and the exchange rate was fixed by fiat. This resulted in an overvaluation

of the Naira with its attendant consequences. In order to restore appropriate exchange rates,

the authorities began the auction sales of foreign exchange to licensed dealers. A first-tier

market was retained to take care of transactions related to government debt-servicing,

contributions to international organizations and transfers to Nigerian missions abroad. In

1988, the government permitted the establishment of private foreign exchange and to accord

recognition to small dealers in foreign exchange.

d) Liberalization of capital movement: With the deregulation of the foreign exchange,

all existing restrictions on capital transfers were abolished. All that was needed was for

evidence of importation and exportation to be provided to the Federal Ministry of Finance. In

addition, all applications for capital transfer abroad were to be backed by appropriate

documents and settled at the appropriate exchange rate.

e) Capital market reforms: capital market reforms are in two parts. First, we have

measures undertaken as part of the structural adjustment program which had some impacts on

the capital market. Among such measures are:

Interest rate deregulation

Privatization: the privatization of erstwhile public institutions which started under the

reform program

Debt conversion program: debt swaps were first developed as part of the restructuring

program of Nigeria’s external debt which reached a crisis proportion with the structural

adjustment program. Debts to equity swaps have had some impact on the capital market

since they are a form of securitization.

Second, they are reform measures aimed principally at the capital market. These include:

Deregulation of the capital market: an inter-ministerial committee was set up in 1991 to

examine ways of carrying out the proposed deregulation. The main focus is the on-going

deregulation of securities pricing with the intention of stimulating competition and

enhancing investment in the market

The reconstitution of the Securities and Exchange Commission.

Tax policies: the reduction of the withholding tax on dividend, and the reduction of the

fiscal burden with respect to the proceeds and yields from debt and equity, although much

still needs to be done with regards to the latter.

Regulatory measures: these include measures aimed specifically at alleviating the

difficulties involved in listing, disclosures and checking insider trading. Since the concern

of this work is mainly with monetary policy, we now devote the following section to the

examination of monetary policy in Nigeria before the reform programme.

2.2 Reform of Financial Sector and links with Agricultural and Manufacturing Sector in Nigeria

The financial sector was reformed in order to enhance its competitiveness and capacity to play a

fundamental role in financial investment. Basically the financial sector reforms were components of

the Structural Adjustment Programme (SAP), which kicked off in 1986. The major financial sector

reform policies implemented at that time were the deregulation of interest rates, exchange rate and

the liberalization of entry/exit into banking business. The reform also saw the establishment of the

National Deposit Insurance Corporation (NDIC). In this section the researcher reviews the trend in

the growth of the agricultural and manufacturing sector following these reforms.

2.2.1 Trend in Agricultural Sector Growth and contribution to GDP

During the period of the financial sector reforms, investments in agriculture increased progressively

from ₦2556 million in 1987 to ₦34473.1 million in 1996 with fluctuating growth rates of 31.24

percent to 30.15 percent. The available information on agricultural performance revealed a decline

of 0.9 per cent in the growth rate of agricultural production for the period 1970-1985. Livestock and

fishery output fell by 2.4 and 2.0 per cent, respectively. Crop and forestry production, however, rose

by 0.3 and 2.5 per cent, respectively. During the period, the GDP registered an average growth of 2.6

per cent, while agriculture’s GDP rose by 3.4 per cent. The share of agriculture in total GDP averaged

29.7 percent. The value of agricultural exports fluctuated from N265.2 million in 1970 to

N192.1million in 1985, representing a growth rate of 7.8 per cent and 3.4 per cent of the value of

total exports during the period. As a result of the decline in agricultural output, domestic food

supply had to be augmented with large imports. The food import bill rose from a mere N57.7 million

in 1970 to a peak of N1,819.6 million in 1981 before declining to N940.6million in 1985, representing

an average of N750.2million per annum during the period and a growth rate of 25.4 percent. It

accounted for11.4 percent of total imports and 1.6 per cent of total GDP. In spite of the importation

of food, domestic price for food remained high as the increase in consumer price index for food

averaged 43.4 percent, during the period. The value of imported food/agricultural products more

than outweighs the value of agricultural exports (CBN, 2004).

The performance of the agricultural sector during this period was undermined mainly by

disincentives created by the macro-economic environment. Specific policy measures targeted at the

agricultural sector under SAP included institutional reforms, improved pricing policy and specific

production schemes for local staples.

Between 1986 and 1993, the performance of the agricultural sector under SAP was improved over

the preceding period, for instance, aggregate agricultural production grew at an average annual rate

of 9.0 per cent. This was an impressive performance when compared with a negative growth of 0.9

per cent recorded between 1970 and 1985. All the sub-sectors of agriculture (crops, livestock,

fishery, other crops and forestry) contributed to this improvement as they all recorded positive

growth rates, unlike in the previous period when only two sub-sectors recorded positive growth

rates. The GDP grew at an annual average of 4.8 percent and the share of agricultural output was

40.0 per cent, compared with 29.7 per cent in the preceding period, (Ukeje, 2003).

As a reflection of the increase in agricultural output and the trade liberalization policy of SAP, the

value of agricultural exports rose on the average by 52.0 per cent while its share in total exports

stood at 3.1 per cent. Also, the value of food imports during the period rose by 45.5 per cent, while

the value of food import as a ratio of total imports was 10.2 per cent, reflecting largely the

depreciation in the naira exchange rate. The profitability of some agricultural enterprises increased

considerably resulting in expansion in their scale of operation. Others with high foreign components

in their inputs became less profitable, owing to high cost of these inputs.

Finally, between the period 1994 and1999 the financial sector experienced a shift in policy from

deregulation to guided deregulation. The growth in the output of agricultural products during this

period was slower, as aggregate output rose on the average by 3.6 percent and agriculture’s GDP

grew by 3.4 per cent. Its share of total GDP was 39.3 percent. All the sub-sectors of agriculture

recorded lower growth rates except fishery which grew by 9.7 per cent. Available data from the

Federal Ministry of Agriculture indicated that productivity of grains and roots and tubers farmers

increased to 1.6 and 10.2 tons per hectare, respectively. The productivity of pulses farmers

remained at the SAP level while that of industrial crops farmers declined to 1.0 tons per hectare

compared with 1.3 tons per hectares achieved during the SAP era. Food imports rose to an average

of N70, 484.1 million, representing an annual growth of 78.1 per cent and 11.9 per cent of total

import, reflecting the continued depreciation in the naira exchange rate, the lower output in the

agricultural sector, and the reduced capital expenditure on the sector, (Ukeje, 2003).

2.2.2 Trend in Manufacturing Sub-Sector Growth and contribution to GDP

In the absence of data on productivity in the sub-sector, data on other indicators of performance can

be reviewed. These include manufacturing production annual growth rate, capacity utilization rate

and the sub-sectors share in the gross domestic product (GDP). Available statistics on the sectors

performance showed that the growth rate in the sub-sector was relatively high in the period 1966-

1975 at an annual average of 12.9 percent. This reflected the importance which the government

attached to manufacturing activities and the adoption of import substitution industrialization

strategy from independence which resulted in the establishment of many consumer goods

industries, including soft drinks, cement, paints, soap and detergents. Growth in the sector

expanded in the period 1976-1985 with the establishment of more import substitution industries,

with an annual average growth of 18.5 per cent. The oil boom of the era which provided enough

foreign exchange for the importation of needed inputs: raw materials, spare parts and machinery,

provided the incentive for this incident growth. However, with the collapse of the world oil market

from the early 1980s and drastically reduced foreign exchange earning capacity, the sub-sector was

no longer able to import needed inputs. Hence, manufacturing output growth fell drastically to an

annual average of about 2.6 percent during the period 1986-1998, even with the introduction of SAP

in 1986. In fact, for the period 1993-1998, growth in the sub-sector was negative, (Allision, 2010).

Capacity utilization rate followed the same downward trend, from an annual average of 53.6 per

cent in the period 1981-1985 to 41.1, 35.4 and 31.8 per cent during the periods 1986-1990, 1991-

1995 and 1996-98. In addition, the sector’s share in the gross domestic product fell persistently,

from 9.2 per cent in 1981-1985 to 8.3 per cent for period 1986-90, 7.5 percent in 1991-1995 and 6.3

per cent in 1996-1998. These negative trends in the performance of manufacturing production

cannot but indicate falling productivity. The average growth of 2.6 percent during the SAP period fell

short of the expected rate of at least 8 per cent needed to put the sector on the path of recovery. Its

stunted growth constrained the capacity of the reform process to pull the economy out of recession.

In addition, capacity utilization rate at about 30 percent is low to make for profitable operations

estimated at about 50 percent. Its share of about 6 percent of GDP is also poor when compared with

between 20 and 40 percent in many industrialized and industrializing nations. Worst still, it is not

encouraging when it is recognized that over 60 percent of the nation’s foreign exchange earnings is

allocated to a sub-sector that contributes only about 6 percent of the GDP, (Anyanwu, Offor,

Adesope, & Ibekwe, 2013).

CHAPTER THREE

LITERATURE REVIEW

3.1 Conceptual Literature

In a research work, it is common for readers to ask question about certain concepts, especially as

used by the researcher, as a result, the researcher explains the concept as used in this work.

Financial Sector Reform: The term “financial reform” is used interchangeably with the terms

“financial liberalization”, “financial deregulation and “financial deepening”. Financial reform is the

process of moving or substantially reducing financial market distortions created by government

intervention in setting interest rates and allocating credit. Financial reform may be a gradual process

or sudden dismantling of all repressive regulations (Wilbert, 1994). As pointed out in Baliamoune

and Chowdhury (2003), financial reform involves the elimination of credit control, deregulation of

interest rates, easing of entry into the financial services industry, development of capital market,

increased prudential regulation and supervision, and liberalization of international capital flows.

However Financial sector reform policies complement financial liberalization and includes a broad

range of measures aimed at improving the regulatory and supervisory environment in the financial

sector and at the restructuring and development of financial sector institutions (Baden, 1996). On

his own part, Inanga (1995) conceived financial sector reforms to include policies and actions geared

towards (a) reducing direct and indirect taxation of financial institutions through reserve

requirements, mandatory credit ceilings and credit allocation guidelines (b) reducing barriers to

competition in the financial sector by scaling down government ownership through privatization,

and facilitating entry into the sector by domestic and foreign firms, and (c) restructuring and

liquidation of solvent banks. Subjective literature indicates that financial sector reforms are

propelled by the need to deepen the financial sector and reposition it for growth to become

integrated into the global financial architecture and evolve a banking sector that is consistent with

regional integration requirement, savings mobilization and international best practices (Nnanna,

Englama & Odoko 2004). Financial sector reform is indeed a complex phenomenon, making it hard

to isolate the effects of the different policies. For instance, targeted policies have consequences

across the board as they affect all the other dimensions of financial sector reform.

3.2 Theoretical Literature

The different speculations on how financial sector reform affects growth in the real sector and the

economy at large includes: Financial repression theory, financial liberalization theory, the post

Keynesian theory and the neo-structuralists theory. These will be discussed briefly.

3.2.1 Financial Repression Theory

The original theory of financial repression was developed by McKinnon (1973) and Shaw (1973).

Their models focus on the issue of controls or ceilings on interest rates for loans and/or deposits.

Real interest rates are thought to influence growth through their effect on savings and thus

investment. In the McKinnon Shaw model, investment, a negative function of real interest rates and

savings are influenced by interest rate and rate of growth. If the interest rate ceiling applies to

deposit rates only, banks can charge what they like for loans and the margin will be used in non-

price competition. However, it is more likely that ceilings will be on loan as well as deposit rates,

leading to unsatisfied demand for investment, in other words, credit rationing. This will leave some

profitable projects which do not have access to finance. Those projects which do get finance will

tend to be those with rates of return only just above the interest rate, since, it is argued, banks are

not able to charge a risk premium and therefore will be unwilling to invest in risky projects with

higher returns.

It is thus argued, interest rate ceilings stifle savings by promoting current consumption (and also

reduce the demand for financial versus real assets) and thus reduce the quantity of investment

below its optimal level. They also reduce the quality of investment by encouraging banks to invest in

relatively low return projects. The assumption underlying this model is that savings determine

investment that savings are necessary prior to investment and will be channeled into productive

investment. The effect of financial liberalization (i.e. removal of controls on interest rates) will be to

increase both the overall funds available for investment and the efficiency of that investment, since

riskier projects with higher rates of return are now being undertaken, leading to increased growth.

McKinnon and Shaw have different views on the transmission mechanism whereby savings affects

investment and growth. McKinnon’s complementary hypothesis states that since economic agents

have to accumulate money balances before investment can take place (this assumes that all

investment is self-financed), money and physical capital are complementary. Since a high deposit

rate positively influences the accumulation of savings, a high deposit rate will encourage investment.

In Shaw’s debt intermediation view, liberalization leads to a greater role for financial intermediaries,

who can offer more attractive deposit accounts and lower loan rates, thus encouraging savings,

investment and growth (Gibson & Tsakalotos, 1994).

3.2.2 Financial Liberalization Theory

Extensions of the basic McKinnon-Shaw hypothesis have been made to look at financial liberalization

in the context of macroeconomic stabilization programmes in both closed and open economies. In a

closed economy with surplus capacity (capital and labour), the real supply of credit affects capital

accumulation through its role as the sole source of working capital. The supply of credit is in turn

determined by the demand for broad money, which is a function of inflation and the deposit rate.

Under liberalization, the deposit rate and reserve requirements are removed, acting to increase

capital accumulation and thus growth. A reduction in the reserve requirement directly increases the

supply of credit and thus enables firms to borrow more working capital, which, given spare capacity,

will allow output and growth to increase. Raising the deposit rate increases the demand for broad

money and thus savings accounts, allowing banks to supply more loans. The issue of the quality of

investment was introduced into the model, with a two-sector model which comprises a traditional,

low-growth sector, where investment is self-financed, and a modern, high growth sector, with

investment financed through the banking system. An increase in the deposit rate reduces

investment in the traditional sector, since firms find it more profitable to invest their surplus in a

bank. This makes more funds available to invest in the modern sector, raising the overall quality of

investment and hence growth (Gibson & Tsakalotos, 1994).

3.2.3 The Post Keynesian Theory

In the post-Keynesian scenario, investment is dependent not only on the interest rate, as in the

McKinnon model, but also on expectations of future demand. The role of effective demand is

emphasized which is in turn influenced by income distribution. Financial liberalization as part of the

financial sector reform may lead to fall in output, growth and financial instability. A rise in interest

rates increases the marginal propensity to save, leading to a reduction in demand which may

outweigh the increased supply of credit, investment and growth caused by the interest rate rise

(Gibson & Tsakalotos, 1994). Other effects which reinforce this and may lead to financial instability

include:

the effect on exchange rates which can lead to overvaluation and squeeze on

tradable/aggregate demand,

large losses for banks if they have contracted a high proportion of long term loans at fixed

rates but have to pay higher rates,

increased government deficit due to increase in debt service payments on domestic debt

and possibly reduction in revenue if taxes on financial sector are reduced,

danger of upward financial repression since market clearing interest rates may be negative

(people may still save because of uncertainty and the need for credit to make large

purchases) and government attempts to push them up may be damaging, undermining the

stability of the banking system (Davidson 1986, Dutt 1990, Diaz-Alejandro, 1985).

3.2.4 Neo-Structuralists Theory

Neo-Structuralists views on financial liberalization are similar to post-Keynesian in that price rigidity

means that adjustment in output rather than prices occurs. However, they differ in their detailed

specification of the supply of credit and in their emphasis on the importance of the stock market.

The overall assessment is that liberalization can result in stagflation, as increased desire to save

results in decline in aggregate demand and possibly in increased inflation (Gibson & Tsakalotos, 1994

and Fry, 1997).

In the neo-structuralists approach, households hold their assets in a variety of forms, including

physical assets, time and currency deposits, direct loans to business through stock market,

depending on interest rates, inflation, income and other factors. The impact of rises in the deposit

rate depends on where the increased savings induced come from. If they originate from non-

productive assets such as jewellery, then there is likely to be an increase in the supply of credit

overall. However, if they come from the stock market, this is likely to reduce the overall credit supply

since the stock market does not have reserve requirements whereas official markets do. According

to neo-structuralists, rising interest rates will tend to shift funds from the stock market into time

deposits, so reducing the overall availability of credit. This in turn leads to a fall in output and

investment. It may also lead to increased inflation, if the effect of the rise in costs of working capital

on output is greater than effect of fall in aggregate demand (Gibson & Tsakalotos, 1994).

3.3 Empirical Literature

Financial sector reforms have been widely researched in the financial system, As Gerard, Atiyas, and

Hansen (1996) noted, “The type of financial reforms undertaken in developing countries subsumes a

diversity of phenomena. Various expression such as liberalization, innovation, privatization,

capitalization and so forth, has been employed to describe different parts of the process.” Among

the literatures reviewed on the effect of the financial sector reforms on the real sector includes:

Evidences from Nigeria

Empirical research on the effect of financial sector reform on the economy of Nigeria, Fadare (2010)

investigated the effect of banking sector reforms on economic growth in Nigeria over the period

1999 to 2009. Using the ordinary least squares regression technique, it was established that

interest rate margins, parallel market premiums, total banking sector credit to private sector,

inflation rate, inflation rate lagged by one year, size of banking sector capital and cash reserve ratio

account for a very high proportion of the variation in economic growth. Except total banking sector

capital, other exogenous variables revealed wrong signs with economic growth.

In a similar but more extensive study, Obamuyi and Olorunfemi (2011) examined the implications of

financial reform and interest rate behaviour on economic growth in Nigeria. Using the cointegration

and error correction model on time series data from 1970-2006. Their result suggested that financial

reform and interest rates have significant impact on economic growth in Nigeria. Their findings

implied that the behaviour of interest rate is important for economic growth considering the

empirical nexus between interest rates and investment, and investment and growth. They therefore

recommended that government should embark on growth enhancing financial reform and be

sensitive to the behaviour of interest rates for overall economic growth in the country.

In a related study also, Odeniran and Udeaja (2010) examined the relationship between financial

sector development and economic growth in Nigeria. The study investigated the competing finance-

growth nexus hypothesis using Granger causality tests in a VAR framework over the period

1960-2009. They made use of four variables, namely, ratios of broad money stock to GDP,

growth in net domestic credit to GDP, growth in private sector credit to GDP and growth in

banks deposit liability to GDP, to proxy financial sector development. Their empirical results

suggest bidirectional causality between some of the proxies of financial development and economic

growth variable. They found that the various measures of financial development granger cause

output even at 1 percent level of significance with the exception of ratio of broad money to GDP.

The variance decomposition result indicated that shock to deposit does not significantly affect net

domestic credit. They recommended that attention should be given to the complementary and

coordinated development of financial reforms and changes in the real sector of the economy.

Ogun and Akinlo (2011) also investigated the impact of financial sector reforms on the performance

of the Nigerian economy using descriptive statistics and Vector Autoregressive (VAR) Model. They

found that the means of performance indicators - saving rate, investment ratio and growth of real

GDP, were very low relative to pre-reform period and their correlation with financial indicators were

mostly low or negative under reform. Evidence from the VAR analysis also showed that shocks to

financial indicators either had negative or insignificant positive effect on the saving rate investment

and growth during reform. They recommended that to promote growth in Nigeria it is necessary to

complement financial reforms with structural reforms.

A contrary result was found by Omankhanlen (2012) who examined the financial sector reforms

and its effect on the Nigerian economy for the period 1980-2008. By employing the ordinary least

squares technique on gross domestic product with explanatory variables Interest rate, Credit

allocation to the private sector and Investment rate, the study found that an improvement in

financial intermediation was considered a necessary condition for stimulating investment, raising

productive capacity and fostering economic growth. He therefore recommended that there should

be macroeconomic and political stability as this also affects the effective operation of the financial

sector.

In a more narrow study, Oke and Adeusi (2012) examined the impact of capital market reforms on

the Nigerian economic growth between 1981 and 2010. The prevailing challenges in the world

financial markets especially the capital market justifies the various forms of reforms going on around

the world. The ordinary least squares method of regression and the Johansen co integration analysis

were employed to analyze the secondary data. Their findings showed that capital reforms positively

impact the economic growth. They therefore recommended that government should objectively

evaluate enacted laws and reforms agenda in a manner that will enhance economic growth rather

than considering political issues before embarking on reforms.

On the effect of the financial sector reforms at the sectoral level, Udah and Obafemi. (2011) studied

the impact of financial sector reforms on agricultural and manufacturing sectors in Nigeria. They

used the variance decomposition and impulse response paradigms on time series data to test

whether or not financial sector variables stimulate the growth of output in agricultural and

manufacturing sectors of the Nigeria economy. The results suggest that relaxing the financial

development constraints and deepening the financial sector is crucial to boost economic growth in

the Agricultural and Manufacturing sectors.

Akpaeti (2012) evaluates the effect of financial sector reforms on agricultural investments in Nigeria

from 1970-2009 using a cointegration and vector error correction model (VECM) and descriptive

analysis. The descriptive analysis shows that the mean agricultural investments during financial

sector reforms period was higher than the pre-financial sector reforms. The result also reveals that

financial sector reforms significantly affect agricultural investments in Nigeria both in the long and

short-run. It is recommended that the Nigerian government should adopt strong macroeconomic

policies, thereby encouraging investments in the agricultural sector of the country.

Similarly, Ogunleye and Saliu (2013) investigated the impact of financial institutional reforms on

the manufacturing performance in Nigeria. Applying the Co-integration and Error Correction Model

(ECM) techniques on annual time series from 1970 - 2005. They observed that, in general the

financial institutional reforms did not have any significant impact on the Nigerian manufacturing

sector performance during the period covered by the study. In particular financial reforms exhibited

an insignificant relationship with the share of manufacturing in GDP.

Anthony, Onu and Ajodo-Ohiemi (2012) examined the contributions of financial sector reforms and

credit supply to Nigerian agricultural sector (1978-2009). They analyzed the trends and pattern of

institutional credit supply to agriculture during pre and post financial reforms along with their

determinants. It then compared the effects of reform policies on access to institutional credits in

Nigerian agricultural sector before and after the reforms (1978–1985, and 1986 -2009). They used

the ordinary least squares technique (linear, semi-log and double log) to model the

determinants of banking sector lending to the agricultural sector. Chow test was used to verify

the presence of structural change in the selected equation before and after the reforms. Results

indicated that there was a significant difference between the credit supply function during the pre-

reform and post reform periods. They recommended that government must consider interest rate

regulation as a veritable tool for making credit accessible to farmers at affordable levels, increase

fund allocation to Agricultural credit guarantee scheme fund (ACGSF), boost monitoring capacity of

CBN on banks generally and strengthen the microfinance banks to be more responsive to agricultural

credit needs.

In a bid to contribute in the research on financial sector reform in Nigeria, Adulphus and Peterside

(2014) analyzed the role of banks in financing the agriculture and manufacturing sectors in

Nigeria from 1981 – 2010. They used both descriptive and inferential techniques to analyze

secondary data generated from CBN Bulletin. They estimated two multiple regression models using

the Software Package for social Sciences (SPSS). The tolerance values are greater than zero in the

estimated models. The inferential results show a significantly weak correlation between commercial

bank lending and the contribution of agriculture to GDP. Their result also showed a significantly

positive correlation between merchant bank lending and agricultural contribution to GDP. Their

findings, however, indicate that the role of banks in facilitating the contribution of the agriculture

and manufacturing sectors to economic growth is still significantly limited. The rise of numerous

public intervention funding programs in these sectors is evidence of the lagging banking

intermediation. They recommended that monetary policy should emphasize mandatory sectoral

allocation of credit with appropriate incentives to boost the flow of bank credit to these sectors.

Enoma and Isedu (2011) investigated the impact of financial sector reforms on non-oil export in

Nigeria for the period 1986-2009. Applying the Co-integration and Error Correction Model (ECM)

techniques, they observed that the Nigerian economy has been a mono- product economy relying

more on oil export and this has an adverse effect on non- oil export supply . The results obtained

both in terms of the time series properties and the estimated error correction model revealed that

the hypothesis of financial liberalization has continued to yield positive results in developing

countries. They therefore recommend that financial sector reforms should be improved upon and

sustained by the monetary authorities in order to fully optimize the gains so far achieved.

On the effect of financial sector reform on banking operations Ezirin and Muoghalu (2004) examined

the effect of financial sector reforms on commercial banks operations in Nigeria by comparing two

decades: 1976-1985 and 1986-1995. In the study, they used various indices of firms performance

such as return on equity investment (ROE), return on total assets (ROA), deposit assets ratio (DAR),

total deposit ratio (TDR), total investment ratio (TINVR). They found that the performance of

commercial banks was significantly different under deregulated regime compared with regulated

one. Specifically the study indicated that the period of financial reform (1986-1995) is more

favorable to commercial banks in Nigeria than the pre-reform period (1976-1985).

In a similar study but with focus on the capital market, Jegede and Mokuolu (2004) examined the

effect of financial sector reforms as a panacea to capital market growth in Nigeria. Two approaches

were employed in the study, the first approach involves the comparison of the capital market

variables before and after the adoption of financial sector reform and the second approach is a

regression analysis. Overall, the main findings indicated that the financial sector reform in Nigeria

has led to a significant improvement and growth of the capital market.

Assessing the impact of financial deregulation, Ikenna (2012) employed time series data from 1970-

2009. Using the Autoregressive Distributed Lag (ARDL) Based Test Model, he tested for the long and

short run impact of financial deregulation and the possibility of a credit crunch in the real sector. The

results suggested that deregulating the Nigerian financial system had an adverse boomerang effect

on the credits allocated to the real sectors in the long run, and in the short run financial liberalization

was in all insignificant and negative. He also concluded that Deposit Money Banks (DMBs) in Nigeria

have strong discriminatory credit behaviour towards the real sector (agriculture and manufacturing)

and the SMEs as credit crunch is found to be present in these sectors both in the short and long run.

Another empirical study on the effect of financial sector reform on SMEs was done. Mike and Lawal

(2012) accessed the impact of financial sector reforms on the growth of small scale enterprises in

Nigeria using primary data. The paper used Poisson regression and ordinary least squares estimation

modeling method to determine output performance of SMEs as a function of several inputs such as

firm’s characteristics, firm’s ownership and credit facilities through the financial sector. Their

results indicated that all these variables have positive and significant impact on the output

performance of SMEs in Nigeria. Based on the findings they concluded that financial sector reforms

have positive impact on the growth of SMEs in Nigeria. They as a result recommended that the

government should create an enabling environment by providing infrastructural facilities and

security to ease the cost of doing business and attract foreign investors and build confidence in the

economy.

Olomola (1997) empirically investigated the relationship between financial deepening (associated

with the deregulation of the financial sector in Nigeria) and real private sector investment for the

periods 1960 - 1996. Using Ordinary Least Squares (OLS) techniques, the study found that a positive

and significant relationship exists between real private sector investment and financial deepening.

The researcher concluded that improved financial intermediation would help bridge the gap

between domestic savings and investment in Nigeria.

Owolabi, Olanrewaju, and Okwu, (2013). In their study investigated the causal link between banking

sector reforms and output growth of manufacturing sector in Nigeria using annual data between

1970 and 2008. Cointegration and Granger-causality techniques were applied to ascertain evidence

regarding financial development and manufacturing output .The result of Granger causality analysis

showed that the MDGP and banking sector reforms indicators (BF) move differently with one not

predicting the other within the study period.

Udoh and Ogbuagu (2012) studied Financial Sector Development and Industrial Production in

Nigeria. Using an aggregate production framework and autoregressive distributed lag (ARDL)

cointegration technique for Nigerian time series data covering the period 1970 to 2009, they found a

cointegrating relationship between financial sector development and industrial production.

Furthermore, their study indicate that both the long run and short run dynamic coefficients of

financial sector development variables have negative and statistically significant impact on industrial

production. They advised the introduction of further financial sector reforms to improve the

efficiency of the domestic financial sector which is a pre-requisite for the achievement of industrial

development.

International and Cross Country Evidences

Bandiera, Caprio, Honohan, and Schiantarelli (2000) analyzed the impacts of financial liberalization in

Chile, Ghana, Indonesia, Korea, Malaysia, Mexico, Turkey, Zimbabwe by using data over 1970-1994.

They found no systematic and reliable real interest rate effect on savings, while the effects of

liberalization have a mixed record. Rather they found negative relationship in most of the cases.

Furthermore, their findings suggested the effects of financial liberalization index on savings are

mixed: negative and significant in Korea and Mexico, positive and significant in Turkey and Ghana.

Bertrand, Schoar and Thesmar (2004) investigated the impact of deregulation in 1985 that

eliminated government intervention in bank lending decisions and fostered greater competition in

the credit market. They found that after the deregulation, banks bailed out poorly performing firms

less frequently, increased the cost of capital to poorly performing firms, and induced an increase in

allocative efficiency across firms. This lowered industry concentration ratios and boosted both entry

and exit rates for firms. While not directly tied to growth, the paper suggests that better functioning

banks not only influence bank-firm relations rather they also exert a first-order impact on the

structure and dynamics of the product markets.

Chowdhury (2002) examined the financial sector reform experience of Bangladesh. While there have

been some improvements in competition and efficiency, loan defaults was identified as a significant

problem. The study found there was urban bias in loan allocation and shift of resources away from

the rural sector. According to him, the main obstruction in the area of loan recovery was political

interference. He argued that without moral norms donor agency-engineered formal institutional

reforms become meaningless, he concluded that effective implementation of an optimal policy mix

in the financial sector depends on complex political and institutional factors. He further suggest that

failure to address this, perhaps market-oriented policy reform may increase transactions cost.

Cooray (2003) reviewed the regulatory reforms in the financial sector of Sri Lanka and evaluated the

effects of policy reforms examining two phases of the financial reform (pre- and post- 1989 periods).

He measured the width of the financial sector by the presence of number of financial instruments

and markets, depth by the volume of deposits, and resilience by the ability to bounce back from a

price change. To him, though process of reforms is incomplete, financial reform has not only

increased the width and depth of the financial system, but also increased competition, and mitigated

constraints on resource allocation. Operational and allocative efficiency in the credit market has

increased and the financial sector has become resilient. Fiscal discipline is promoted and ratios of

investment, national savings, as well as domestic savings have been increased after adopting the

measures of reforms in the financial sector.

Damar et al. (2006) studied the link between financial development and economic growth using a

province-level data set for 1996-2001 on Turkey. Using both traditional OLS and dynamic panel

GMM techniques, their result showed that financial deepening (i.e. an increase in the total deposits

to GDP ratio) has a direct impact on the growth rate of real GDP per capita. Their findings suggested

that financial development has a negative relationship to economic growth. Their conclusion does fit

rather well with the state of the Turkish economy and banking sector during the late 1990s. Unlike

the traditional theories of financial intermediation, the Turkish banking sector during this period was

not mobilizing and pooling domestic savings in order to invest in productive capital. Rather, the

sector was engaged in channeling domestic resources to the government, which used the funds to

cover its budget deficit.

Reinhart and Toktladis (2001) compared the impact of financial liberalization in 50 countries over the

period 1970-1998 by using annual data series of gross national savings, gross investment, current

account balance, foreign direct investment, GDP growth, consumption, real interest rates, ratios of

narrow to broad money (M1/M2), credit to private sector and spread between lending and deposit

rates. They found that financial liberalization, as measured by M2/GDP and credit to the private

sector, would lead to financial deepening.

3.3 Limitation of Previous Studies

From the plethora of empirical literatures reviewed in Nigeria, research efforts in the area of

financial sector reforms and its impact on agricultural and manufacturing sectors are minimal, when

compared to the research efforts into the other aspect reforms as trade liberalization, bank reforms

on bank performance, capital market and the Nigerian economy.

The very few works that researched on manufacturing and agricultural sectors failed to investigate

the effect of the financial sector reforms (effect of interest rate, interest rate spread, and exchange

rate) rather they laid emphasis on financial development, without really investigating effect of the

key financial sector reform variables. One of such works reviewed in this regard is the study by

Adulphus and Peterside (2014) who used mainly descriptive and inferential statistics and the other

(Udah & Obafemi, 2011) who used VAR model. Both failed to include such variable as interest rate

spread which is an important financial reform variable (Abiad, 2008). In fact their work was more of

financial development than reform because of the variables they employed, and this have serious

implication for output growth of the agriculture and manufacturing sectors. Any analysis of financial

sector reforms that does not take full account of this plethora of policies will not provide useful

insight into how financial reforms have affected investment, and will suffer from omitted variable

bias (Gibson & Tsakalatos, 1994, Demetriades & Luintel, 1996).

As much as the literatures reviewed, none of the works concentrated on the financial sector reforms

variables, specifically, none of the previous works with respect to manufacturing and agricultural

sectors used interest rate spread which shows the quality and efficiency of the financial system

which of course result from reforms.

This research work therefore intend to fill this gap identified by firstly, using the Ordinary Least

Squares (OLS) on the variables with Financial reforms dummy interacted with the financial reform

variables to test whether or not financial sector reforms stimulate the growth of output in

agricultural and manufacturing sectors in Nigeria, and secondly, the study intends to employ both

the Wald test and descriptive analysis to compare and analyze how output growth in the agricultural

sector differs from that of manufacturing sector as a result of financial sector reforms.

CHAPTER FOUR

METHODOLOGY

4.1 Theoretical Framework

The theoretical underpinning behind the methodology adopted in this thesis is the endogenous

growth model. The Solow (1957) model considered a production function which has labour and

capital only as its inputs. The theory developed by Grossman, and Helpman, (1991), Lucas (1988) and

Romer (1986) identifies two main channels through which the financial sector might affect long-run

growth in a country. They include: through catalyzing the capital accumulation (including both

human and physical capital) and by increasing the rate of technological progress. A series of papers

on the benefits of financial sector reforms include Fry (1988) who argues that reforms enable

interest rates and exchange rates to reflect their relative scarcity, stimulate savings and lead to

efficient distribution between alternative investments. In order to examine the impact of financial

reforms on sectoral output growth, Abiad, Detragiache and Tressel (2008) pointed out some

variables such as interest rate spread, private sector credit, exchange rate and real interest rate

were introduced into the model to capture relationship between financial sector reform and

economic growth. Unfortunately, there is no data or any appropriate proxy for labour in Nigeria for

the period under investigation.

As a result, in accordance with the endogenous growth theory, and also following the McKinnon and

Shaw hypotheses the researcher modifies the model, and the functional form is thus:

Where:

Yt = Output (proxied by sectoral GDP)

GCF = Gross capital Formation

SINT = Interest Rate Spread

CPSGDP = Ratio of Credit to Private Sector GDP

RINT = Real Interest Rate

EXRATE = Exchange Rate

M2 GDP = M2 to GDP ratio (Proxy for financial depth)

ARR= Average annual rainfall

MANUCU= Manufacturing capacity utilization

t = time series subscript

The econometric form of equation (1) can be specified as equation (2) below

Where:

αi = parameters to be estimated, and

t = error term

)1(),,,,,( 2 GDPMEXRATERINTCPSGDPSINTCAPfY tttttt

)2(2543210 tttttt eGDPMEXRATERINTCPSGDPSINTCAPY

4.2 Model Specification

In line with the methodology explained above, According to Gujarati (2004), in finding the growth

rate of economic variables, a semi-log model (Log linear model) is recommended. In order to capture

the first objective, the researcher specifies a log-linear model as the base model, thus:

Model One

Where:

LAGGDP = log of Agricultural sector output

ARR = Average annual rainfall

ε = error term

While all other variables and parameter remains as explained previously in equation (1).

This study further introduced a dummy variable which is interacted with different financial reform

indicators to be able to capture the regime change from regulation to deregulation of both interest

rates and exchange rates (The fixed exchange rate regime adopted since independence was replaced

by floating exchange rate after the devaluation of the Nigerian Naira in 1986). Because a number of

other reforms later sprang up after the SAP which started with interest rate liberalization, it will

therefore be difficult to capture all of them, as such, this study will make use of just two periods

financial policy dummy, that is the Pre-reform (1970-1985) and the Post-reform (1986-2013)

periods. The interaction of shift in financial policy dummy with the variables of interest is shown in

equations 4 below

Where:

Dum = Financial sector Reform Dummy (0= 1970-1985, 1= 1986-2013)

Dum*[VAR] = Financial reform dummy interacted with the respective variables of interest, and

)3(726543210 tttttt eAARGDPMEXRATERINTCPSGDPSINTCAPLAGGDP

)4(12

*

11

*

1098

2765

*

4321

tttt

tttttt

eAAREXRATEDumRINTDumEXRATERINT

GDPMCPSGDPSINTSINTDumCAPDumLAGGDP

all variables remain as defined earlier.

The apriori expectation of the parameter of all the explanatory variables : Gross capital formation

(GCF), Ratio of credit to private sector GDP (CPSGDP), Manufacturing capacity Utilization (MANCU),

Ratio of broad money supply to GDP (M2GDP) are > 0

On the other hand Real interest Rate (RINR), Interest rate spread (SINR), Real exchange rate

(EXRATE) are < 0

Model Two

As in equation (3), to be able to capture the second objective, the researcher specifies a similar

model

(log-linear model) for the manufacturing sector output growth.

Where:

LMANGDP = log of Manufacturing sector output

MANCU = Manufacturing capacity utilization

ε = error term

βi = parameter to be estimated, and

all variables are as defined earlier

Again, in order to account for the financial reform as specified in the second objective

Where:

Dum = Financial sector Reform Dummy (0=1970-1985, 1=1986-2013)

)5(726543210 tttttt eMANUGDPMEXRATERINTCPSGDPSINTCAPLMANGDP

)6(12

*

11

*

1098

2765

*

4321

ttttt

ttttt

eMANCUEXRATEDumRINTDumEXRATERINT

GDPMCPSGDPSINTSINTDumCAPDumLMANGDP

Dum*[VAR] = Financial reform dummy interacted with the respective variables of interest, and

all variables remain as defined earlier.

The apriori expectation of the parameter of all the explanatory variables : Gross capital formation

(GCF), Ratio of credit to private sector GDP (CPSGDP), Manufacturing capacity Utilization (MANCU),

Ratio of broad money supply to GDP (M2GDP) are > 0

On the other hand Real interest Rate (RINR), Interest rate spread (SINR), Real exchange rate

(EXRATE) are < 0

For the third objective, this study will make use of Wald test which will depend on the estimated

coefficients of equation (4) and (6). Basically, Wald test will make it possible to impose constraint on

the financial reform indicators (which includes Real interest rate, Interest rate spread and Exchange

rate) in the two sectors to know if financial sector reform impacted differently on the output growth

of agricultural and manufacturing sectors.

4.3 Variable Description

AGGDP and MANGDP: is the ratio of Agricultural/Manufacturing GDP in total GDP. It is used to

measure the output in the agricultural and manufacturing sectors respectively.

The real interest rate demonstrates the allocative efficiency of financial resource use. Alternatively

real interest rate is a measure of financial liberalization.

M2GDP ratio: which is the ratio of broad money supply to gross domestic product is a measure of

financial depth. The rational for including it in the model is to ascertain if the deepening of the

financial system has contributed to the growth of the sectors under review.

Private sector credit to GDP ratio: is a measure of the ability of the banking system to provide

finance-led growth.

Interest rate spread (lending rate minus deposit rate): Interest rate spread is the interest rate

charged by banks on loans to private sector customers minus the interest rate paid by commercial or

similar banks for demand, time, or savings deposits.

Exchange rate: The Exchange rate employed in this work is the real exchange rate of naira to dollar

for the period under study.

4.4 Estimation Procedure

Empirical analysis using time series that the series that are non-stationary should be transformed

appropriately prior to estimation, otherwise finite-sample inferences may suffer serious distortions.

Following the general econometric techniques for unit roots testing, the standard Augmented

Dickey Fuller (ADF) test is employed. In testing for a unit root, we consider the possibility of a linear

trend in levels of the variables.

The Estimation technique that will be employed is the Ordinary Least Squares (OLS) Technique.

Choice of this approach is premised on the reasons that the Gauss-Markov theorem portends that

the least squares technique is the best linear unbiased estimator, with which straight line trend

equations could be estimated.

Since most time series variables are usually stationary only after first or second differencing,

applying the difference variable for regression would imply loss of valuable information about the

long run relationship among the variables. In order to correct for such loss of information, the error

correction estimation may be used so as to integrate short-run dynamics with long run relationship

(Maddala, 1992, Gujarati, 2004) and further diagnostic test of the stochastic properties of the

models shall be carried out.

For the Wald test, the researcher intends to impose constraints on the financial sector reform

indicators (RINR, SINR, EXRATE) in the two equations based on the estimated coefficients of models

one and two. Mainly, restrictions will be imposed on the interest rate, interest rate spread and

exchange rate in the two equations. Wald test is a good econometric test tool for comparisons.

4.5 Justification of the Model

The OLS method is chosen because it possesses some optimal properties. It is the Best Linear

Unbiased Estimator (BLUE), its computational procedure is fairly simple and it is also an essential

component of most other estimation techniques. Specifically, the introduction of dummy variable

and their interaction with key variables of interest does not distort the BLUE properties of the

ordinary least squares.

In finding the growth rate of economic variables, a semi-log model was used. In order to capture the

first and second objectives, the researcher specifies a log-linear model as the base model in which

only the GDP will be logged while other variables (regressor) remains at their ordinary form.

A log-linear model is a model in which the regressand is logarithmic.

4.6 Data Sources

The data in this piece of research work is sourced from the CBN statistical bulletin of 2009 and 2013.

It will cover the period 1970-2013.The data is further transformed to quarterly data. The use of

quarterly data was driven by the fact that no monthly data are available for the sectoral real GDP

variable and also annual data would make available time series too short. The EViews 8.0 and Stata

12 econometric software packages are used for the estimation.

CHAPTER FIVE

DATA PRESENTATION AND ANALYSIS

5.1 Introduction

In this section, the interest of the researcher is to present and analyze the data and also interpret

the result that will be presented based on the models specified in the work. In the analysis, two real

sectors were considered in investigation of impact of financial sector reform on output growth.

These include the manufacturing and the agricultural sectors in Nigeria. Before we proceed, it is

insightful to first of all present a descriptive statistic of the variables used in this research and also

perform a unit root test on each of the variables.

5.2 Descriptive Analysis

Table 5.1: Descriptive Statistics

Variable Obs Mean HAC Std. Error Min Max

AGGDP 176 29346.47 26922.94 411.4 93094.77

MANGDP 176 3561.762 2559.231 74.59922 10343.43

M2GDP 176 5.895705 1.91801 2.241261 9.833985

MANCU 176 13.3418 3.93564 7.257863 20.4625

GCF 176 177403 329208.2 288.8824 1246585

CPSGDP 176 4.007576 2.001545 1.113888 9.317089

AAR 176 12780.41 9009.688 1118.1 32504.2

RINR 176 3.739509 1.594561 1.460938 7.642578

SINR 176 2.015637 1.458093 -0.12891 5.328066

EXRATE 176 52.5517 62.13847 0.513974 163.7994

DUM 176 0.636364 0.482418 0 1

Out of 176 observations in the real sector components of agriculture and manufacturing

contribution to GDP, mean quarterly contribution of agriculture to GDP (AGGDP) is N29,346.47

million while that of manufacturing sector (MANGDP) is N3,561.762 million. Agriculture recorded its

minimum and maximum contribution of N411.4 million and N93,094.77 million in 1980 quarter 1

and 2013 quarter 4 respectively. Similarly, manufacturing sector recorded N74.59 million and

N10,343.43 million in 1971 quarter 1 and 2013 quarter 4 as minimum and maximum contribution to

GDP respectively. On the average, quarterly ratio of broad money supply to GDP (M2GDP) is 5.89%,

manufacturing capacity utilization (MANCU) is 13.34%, gross capital formation (GCF) is N177,403

million, ratio of credit to private sector to GDP (CPSGDP) is 4.% and annual rainfall (AAR) is 12,780.41

millilitre. Again, Table 5.1 shows that M2GDP increased from 2.24% in 1974 third quarter to 9.83% in

2012 fourth quarter, while MANCU decreased from 20.46% in 1970 first quarter to its lowest rate of

7.25% in 1995 second quarter. Other variables such as GCF, CPSGDP and AAR recorded their

respective minimum and maximum value of N288.88 million & N1246585 million, 1.11% & 9.31%,

and 1118.1mm & 32504.2mm in 1980 first quarter & 2013 fourth quarter, 1974 third quarter & 2009

third quarter, and 1973 first quarter & 2012 third quarter respectively.

The three financial indicator variables of real interest rate (RINR), interest rate spread (SINR) and real

exchange rate (EXRATE) maintained quarterly average value of 3.74%, 2.01% and N52.55 for a dollar.

While RINR and SINR recorded 1.46% & 7.64% and -0.12% & 5.32% in 1975 fourth quarter & 1992

second quarter and 1985 third quarter & 2002 third quarter as minimum and maximum value

respectively, EXRATE had its minimum value of N0.51 in 1979 second quarter and maximum value of

N163.79 in 2011 third quarter.

5.3 Stationarity Test

Table 5.2: Augmented Dickey-Fuller test statistic

Variable ADF test statistic P-value Order of integration

AGGDP -6894610 0.0000*** I(2)

MANGDP -6.771046 0.0000*** I(2)

M2GDP -3.556330 0.0004*** I(1)

MANCU -3.726678 0.0002*** I(1)

GCF -12.93232 0.0000*** I(2)

CPSGDP -3.137993 0.0019*** I(1)

AAR -3.649643 0.0287** I(0)

RINR -2.774610 0.0057*** I(1)

SINR -3.387373 0.0008*** I(1)

EXRATE -16.35341 0.0000*** I(1)

DUM -13.19091 0.0000*** I(1)

Note: *** p-value < 0.01; ** p-value < 0.05

Following Dickey and Fuller (1979), a variable is stationary if its ADF test statistic value is greater

than the critical value at a given percent either at 5% or at 1% level. From Table 5.2 above, only AAR

is stationary at level form but this is only at 5% level of significance. The Table shows that the two

real sector variables of AGGDP and MANGDP and GCF were stationary after second difference both

at 1% and 5% level of significance. Other variables such as M2GDP, MANCU, CPSGDP, RINR, SINR,

EXRATE, DUM were stationary at first difference at 1% and 5% level of significance.

5.4 Co-integration Test

Given above order of integration between AGGDP and MANGDP and their respective explanatory

variables, the chances of co-integration existed especially with GCF. Therefore, following Engle and

Granger (1987), the researcher estimated the linear combination of the variables at their stationary

level and obtained their residuals which were subjected to Augmented Dickey-Fuller unit root test.

The result from Appendix A1 and A2 shows that ADF test t-statistic of -5.590457 and -5.193308 for

AGGDP and MANGDP are simultaneously less than t-critical values at 1% and 5% levels of

significance. Since the residuals obtained from the linear combination of the non stationary variables

are stationary at level, the researcher concludes that there is co-integration between dependent and

independent variables in model one and two of this study. This means that there is need to

introduce an Error Correction Mechanism in order to capture the short-run relationship between the

variables.

5.5 Model One: OLS result for impact of financial sector reforms on agricultural sector output

Below is the OLS estimation of impact of financial sector reforms on quarterly agricultural sector

output in Nigeria from first quarter 1970 to fourth quarter 2013.

Table 5.3: Impact of financial sector reforms on agricultural sector

Variable Coefficient HAC Std Error t-statistic P-value

GCF 4.43E-07 3.15E-07 1.406675 0.1614

CPSGDP 0.041409 0.057772 0.716770 0.4745

AAR -9.11E-07 1.58E-06 -0.574475 0.5664

RINR 2.094430 0.275323 7.607176 0.0000***

SINR -2.870541 0.465820 -6.162335 0.0000***

EXRATE -0.724255 0.453750 -1.596154 0.1124

DUM 3.790910 0.757965 5.001431 0.0000***

DUM*RINR -2.125930 0.274140 -7.754920 0.0000***

DUM*SINR 2.963764 0.463912 6.388634 0.0000***

DUM*EXRATE 0.728358 0.453794 1.605040 0.1104

Constant 5.973801 0.687804 8.685322 0.0000***

Observations 175

R-squared 0.950229

DW-stat 0.265009

Note: *** p-value < 0.01; ** p-value < 0.05

5.5.1 Economic Criteria (Apriori expectation)

Since the above estimated output in Table 5.3 is a semi-elasticity model which seeks to determine

the impact of absolute change in regressors on relative change in dependent variable, estimated

coefficients will be interpreted on the basis of their impact on percentage change on agricultural

sector contribution to GDP. Again, the researcher employs exact (true) level of significance (p-value)

approach in testing hypotheses of the study. Thus, any coefficient with corresponding p-value less

than or equal to (<=) 0.05 is considered as being statistically significantly different from zero. This

implies that its effect on the dependent variable cannot be underestimated in the model.

5.5.2 Gross capital formation (GCF)

The coefficient of GCF is 0.000000443 which shows the percentage mean response of AGGDP per

unit change in GCF. Thus, holding the effect of other variables constant, a million naira increase in

gross fixed capital formation will increase agricultural sector contribution to GDP by about

0.0000443% per quarter on average. This effect is however statistically insignificant given its p-value

of 0.1614 which is greater than 0.05. The result suggests that increase in gross capital formation will

bring about increase in contribution of agricultural sector to GDP though this contribution is

considered statistically insignificant. This finding is consistent with Mankiw (2009) whose

explanation supports a positive relationship between gross capital formation and gross domestic

product.

5.5.3 Ratio of Credit to Private Sector GDP (CPSGDP)

Ratio of credit to private sector GDP also has a positive coefficient. The positive coefficient displayed

in the result conforms to economic apriori expectation since increase in ratio of credit to private

sector is expected to boost overall investment and output of the private sector and consequently

increase the country’s GDP. From the result above, holding other variables constant, one million

naira increase in credit to private sector GDP will on average increase agricultural sector contribution

to GDP by about 4.44%. This result validates theoretical linkage between credit to private sector

performance and a country's economic growth as generally supported by the empirical evidence in

the literature. However, the CPSGDP is statistically insignificant as evidenced from the p-value of

0.4745 which is greater than 0.05 or 5% level of significance.

5.5.4 Average annual rainfall (AAR)

The coefficient of average annual rainfall (-9.11E-07) indicated a negative relationship with

agricultural sector contribution to gross domestic product. The result suggests that on average, a

millimeter increase in mean quarterly rainfall will bring about 0.0000911% decreases in agricultural

sector contribution to GDP per quarter, all other variables held constant. This negative relationship

conforms to the findings of Ayinde, Muchie and Olatunji (2011) who held that the effect of climate

change has adversly affected agricultural output in Nigeria. This finding implies that changes in

climatic factor have been responsible for excessive, insufficient or late rainfall which could negatively

affect agricultural output and by extension, agricultural contribution to GDP. This effect is not

statistically significantly different from zero at 5% given its estimated p-value of 0.5664.

5.5.5 Real interest rate (RINR)

The result shows that real interest rate has positive relationship with agricultural sector contribution

to GDP. It suggests that when the effect of other variables are held constant, one percentage

increase in real interest rate will on average lead to about 2.09% increase in quarterly agricultural

sector contribution to GDP in Nigeria. This result indicates that increase in real interest rate at time t

will translate to increasing output (consumption) of economic agents in the agricultural sector due

to increase in income accrue from the rising interest rate. This relationship conforms to the

theoretical explanation of income effect of the real interest rate on savings which according to Abel,

Bernanke and Croushore (2011) leads to increase (decrease) in net worth of the saver when real

interest rate rises (falls). With corresponding p-value of 0.0000, the coefficient of RINR (2.094430) is

highly statistically significantly different from zero at 5%. It shows that real interest rate has real

impact on agricultural contribution to GDP in Nigeria within the period of study.

5.5.6 Interest rate spread (SINR)

Interest rate spread as a measure of financial sector profitability maintained negative relationship

with agricultural sector contribution to GDP in this study. From the result, the p-value of SINR is

0.0000. It shows that effect of the variable is statistically significantly different from zero at 5%. Its

coefficient of -2.870541 implies that when effect of all other variables are zero, one unit increase in

SINR will decrease the mean quarterly agricultural sector contribution to gross domestic product by

about 2.87 percent. The result validates the argument of Damar et al. (2006) and Udoh and Ogbuagu

(2012) who found significant negative relationship between coefficient of financial sector

development and output. It shows that as the net difference between lending and deposit rate

increases on average, financial intermediaries will increase their profitability at the expense of

depositors and hence leads to reduction in their contribution to GDP.

5.5.7 Real exchange rate (EXRATE)

The coefficient of real exchange rate displayed a negative trend with agricultural sector contribution

to gross domestic product. Given the coefficient value of -0.724255, one naira increase in exchange

rate will decrease the mean agricultural sector contribution to GDP by about 0.72% per quarter,

ceteris paribus. This result meets apriori economic expectation since Nigerian agricultural sector

significantly depends on importation of farm machineries, seedlings and technical services from

abroad. Hence, increasing cost of exchange implies increase in cost of production in the sector. It will

therefore translate into reduction in net output. Effect of this variable is however not significant at

5% as the p-value of its coefficient is 0.1124. The result shows that effect of real exchange rate does

not have meaningful impact on agricultural sector contribution to GDP in within the period of study.

5.5.8 Financial sector reform (DUM)

Evidence from Table 5.3 above shows that financial sector reform dummy coefficient positively

related with agricultural sector contribution to gross domestic product during the study period. This

implies that with estimated coefficient value of 3.790910, the post-financial reform era (1986-2013)

contributes more to growth of agricultural sector than pre-financial reform era (1970-1985) by

approximately 3.79%. This relationship is in line with apriori expectation since the reform embodied

large scale deregulation of exchange rate, introduction of market-determined interest rate and other

forms of financial liberalization in the economy. In addition to the sign and magnitude of estimated

coefficient, the variable (DUM) is statistically significantly different from zero at 5% level. Its

estimated p-value and t-statistic of 0.0000 and 5.001431 respectively validated the claim of

important effect of financial sector reform on output of agricultural sector contribution to GDP.

5.5.9 Effect of financial sector reform on financial indicators

This section measures the effect of financial sector reform or its interaction with financial sector

indicators. Here the difference between the pre-reform and post-reform will be presented on each

of the variables as follows.

Effect of financial reform on real interest rate (DUM*RINR). The coefficient of interaction of financial

sector reform on real interest rate yielded a negative coefficient value of -2.125930. The differential

slope coefficient of DUM*RINR suggests that the impact of real interest rate of post financial sector

reform on agricultural sector contribution to GDP is less than pre financial sector reform real interest

by about 2.13%. Estimate from Table 5.3 shows that while AGGDP-RINR regression for 1970-1985

has intercept and slope coefficients as follows;

LAGĜDPt = 5.973801 + 2.094430RINRt ………………………………………(5.1),

AGGDP -RINR regression for 1986-2013 has its different intercept and slope coefficient of

LAGĜDPt = (5.973801 + 3.790910) + (2.094430 - 2.125930)RINRt

= 9.764711 – 0.0315 RINRt …………………………………………….. (5.2).

Equations (5.1) and (5.2) suggest that when effect of all factors are held constant, a unit increase in

pre-reform RINR leads to about 2.09% rise in AGGDP per quarter on average, while a unit increase in

post-reform RINR brings about 0.03% quarterly decrease in AGGDP respectively. Interestingly

enough, the difference in the slope coefficient of equation (5.2) is statistically significantly different

from slope coefficient of equation (5.1) at 5% level given the DUM*RINR p-value of 0.0000 in Table

5.3. Implication of this result is that while investors in agricultural sector increased their current

consumption expenditure (output) as real interest rate increases during pre-financial sector reform,

the significant reduction in post-reform real interest rate forced them to cut down current

consumption expenditure as well as their output. Return on savings yielded less interest at post-

reform era and as such it led to decrease in their contribution to GDP. This finding also conforms to

above quoted Abel, Bernanke and Croushore (2011) who argued that income effect of the real

interest rate can be either positive or negative on savers’ net worth depending on whether the real

interest rate is rising or falling at a given point in time.

Effect of financial reform on interest rate spread (DUM*SINR). Going by estimated p-value of 0.0000

in Table 5.3, the coefficient of post-reform interest rate spread is statistically significantly different

from that of pre-reform at 5% level. The differential slope maintained a positive coefficient value of

2.963764 which indicated that all other variable held constant, post financial sector reform interest

rate spread is greater than pre financial sector reform interest rate spread by about 2.96%. On the

average, while one percentage increase in financial sector pre-reform (1970-1985) profitability index

(SINR) results to 2.870541% decrease in agricultural sector contribution to GDP, it instead leads to

about 0.093223% increase in agricultural sector contribution to GDP in the post-financial sector

reform era (1986-2013). That is (SINR + DUM*SINR) ≈ (-2.870541 + 2.963764). The result testified to

the fact that while financial institutions’ profitability significantly appreciated after deregulation of

the financial sector in Nigeria, agricultural sector experienced more funding from banks and

consequently expanded their investment and reap from large economics scale on their investment.

This long existed impact of large economics of scale accounted for the positive slope coefficient of

the interactive dummy (DUM*SINR) in Table 5.3 above.

Effect of financial reform on real exchange rate (DUM*EXRATE). The output in Table 5.3 shows that

post-financial sector reform (1970-1985) real exchange rate coefficient is greater than coefficient of

real exchange rate in pre-financial reform period (1986-2013) on average by 0.728358%. This

difference is however statistically insignificantly different from zero at 5% level as it recorded

estimated p-value of 0.1104. The result shows that when effect of other variables are held constant,

while a percentage point increase in real exchange rate in pre-financial reform period leads to mean

decrease of about 0.724255% in agricultural sector contribution to GDP, it brings about 0.004103% (-

0.724255 + 0.728358) increase in agricultural sector contribution to GDP in the post-financial sector

reform. The positive differential slope coefficient suggests that as the local currency (the naira)

further depreciated more by quarterly average of about 0.73% (i.e., as real exchange rate rises) after

the deregulation policy, agricultural sector contribution to GDP witnessed growth rate of about

0.004102% as against its previous decrease of about 0.724255% before the reform. The observed

rise in exchange rate was followed by rising demand for Nigerian agricultural sector product at the

foreign market due to its cheap prices. This consequently led to growth in agricultural sector

contribution to GDP in Nigeria during the study period.

5.5.10 Agricultural sector contribution to GDP (AGGDP)

The coefficient of the intercept term measures the growth rate of the dependent variable. From

Table 5.3 above coefficient of the intercept (constant) (5.973801) is positive and significant at 5%

level. This implies that when effect of all other variables in the model is held constant or assumed to

be insignificant, agricultural sector output will grow on average by about N597.38 million per

quarter. The significant impact of the constant term in the model suggests that some other variable

outside the model also contribute to the growth of agricultural sector output in Nigeria. These may

include the level of technology, introduction of hybrid crop and animals as well as improved

agricultural extension services.

5.5.11 Model fit and diagnostic check

Model fit and diagnostic check in this study has to do with evaluation of goodness of fit of the model

using estimated R-squared coefficient and other diagnostic test statistic to validate reliability of

estimated model. This measure is aimed at certification of the usefulness of the result for policy

prescription.

Table 5.4: Model fit and diagnostic check for impact of financial sector reforms

on agricultural Sector output

Test Model 1

R-squared 0.950229

DW-stat 0.265009

RESET F-stat Df p-value

30.48570 (1, 163) 0.0000***

Normality Test JB-statistic p-value

1053.157 0.0000***

ECM ECM parameter p-value

-0.051945 0.0486***

Note: *** p-value < 0.01; ** p-value < 0.05

Coefficient of determination (R-squared). This measures the proportion of variation (change) in the

dependent variable explained by the independent variables. In this case it measures the proportion

of change in agricultural sector output (AGGDP) explained by all the explanatory variables in Table

5.3 above. Estimated R-squared value of 0.950229 in Appendix B1 shows that about 95% of total

variation in AGGDP was explained by explanatory variables in the model. This higher coefficient

demonstrated the effectiveness of the explanatory variables in the estimated model.

Test for autocorrelation. In order to avoid the problems of serial correlation in the model the

researcher employed the estimated d-statistic after Durbin and Watson (1951). The null hypothesis

of ‘no positive autocorrelation’ was however rejected at both 5% and 1% level of significance as 0 <

d(0.265009) < dL(1.654) and (1.564) respectively. Where d is estimated d-statistic in Table 5.4 and

Appendix B1, and dL is lower boundary of d-critical value from Durbin-Watson statistic table. Hence,

the conclusion that the model is well specified but rather, it suffered from the problem of pure

autocorrelation. In a bid to resolve this problem the researcher instead employed Newey and West

(1987) heteroscedasticity- and autocorrelation-consistent (HAC) standard errors technique in

estimated OLS. This approach was in line with Gujarati and Porter (2009) who argued that Newey-

West HAC procedure to correct OLS standard errors is situable not only in situations of

autocorrelation but also in cases of heteroscedasticity.

Specification error test. To further investigate the remote cause of positive serial correlation in the

model the researcher adapted the Regression Specification Error Test (RESET) after Ramsey (1969).

This approach was aimed at verifying whether the positive serial correlation was as a result of wrong

model specification or that it is a case of higher-order serial correlation (pure autocorrelation).

Again, given the evidence of observed F-statistic in Table 5.4 and Appendix D1 the null hypothesis

that ‘the model was mis-specified’ was rejected at both 5% and 1% level of significance since

Fcal(30.48570) > Ftab(k-1/n-k) ≈ (1.88 and 2.41) respectively. Hence, the conclusion that the model is

well specified but rather, it suffered from the problem of pure autocorrelation. In a bid to resolve

this problem the researcher instead employed Newey and West (1987) heteroscedasticity- and

autocorrelation-consistent (HAC) standard errors technique in estimated OLS. This approach was in

line with Gujarati and Porter (2009) who argued that Newey-West HAC procedure to correct OLS

standard errors is situable not only in situations of autocorrelation but also in cases of

heteroscedasticity.

Normality test. In order to satisfy one of the key assumptions of Ordinary Least Squares estimation

technique the Jarque-Bera (JB) normality test carried out after Jarque and Bera (1987). The statistic

is an asymptotic test which follows Chi-square distribution with 2 df. From the output in Table 5.4

the researcher do not reject the null hypothesis that ‘the residuals are normally distributed’ at 5%

and 1% levels of significance as estimated p-value of JB-statistic (0.0000) is sufficently low. It

suggests that the probability of commiting Type I error is sufficently low in this case. Graphical

evidence of normality assumption in Appendix C1 also validated this conclusion.

Error correction mechanism. In line with above observation that explanatory and explained variable

in the model are co-integrated, the researcher choose to employ the error correction mechanism as

propounded by Sargan (1984). The entire variables were regressed in their stationary form including

the lagged value of the residual in order to reconcile the short-run behavior of above economic

variables with its long-run behavior. According to Engle and Granger (1987), for the assumptions of

ECM to be met, the coefficient of the lagged residual term (ECM parameter) must be significant and

at the same time negative. Evidence from Table 5.4 and Appendix E1 show that ECM parameter was

-0.051945. With its corresponding p-value of 0.0486, the output conformed to apriori expectation.

This suggest that for equilibrium to be achieved, the model will correct about 5.19% of temporary

discrepancy in short-run agricultural sector output every quarter. Therefore, the system can only

correct average of about 20.76% of the discrepancy per annum and consequently it requires

approximately 4 years and about 8 months (56 months) to fully achieve equilibrium or total

adjustment.

5.6 Model Two: OLS result for impact of financial sector reforms on manufacturing sector

Output

This section seeks to discuss OLS estimation of the impact of financial sector reforms on quarterly

output of the manufacturing sector in Nigeria from 1970 first quarter to 2013 fourth quarter.

Table 5.5: Impact of financial sector reforms on manufacturing sector

Variable Coefficient HAC Std Error t-statistic P-value

GCF 3.38E-07 3.06E-07 1.106355 0.2702

CPSGDP 0.120654 0.076975 1.567440 0.1189

MANCU 0.069725 0.034004 2.050468 0.0419**

M2GDP -0.072432 0.060466 -1.197890 0.2327

RINR 2.496479 0.426278 5.856458 0.0000***

SINR -3.532525 0.452686 -7.803484 0.0000***

EXRATE -1.096856 0.337871 -3.246373 0.0014***

DUM 4.014837 0.921118 4.358656 0.0000***

DUM*RINR -2.491054 0.437638 -5.692038 0.0000***

DUM*SINR 3.561659 0.473772 7.517668 0.0000***

DUM*EXRATE 1.096186 0.338051 3.242662 0.0014***

Constant 3.382059 1.082198 3.125175 0.0021***

Observations 176

R-squared 0.945140

DW-stat 0.313868

Note: *** p-value < 0.01; ** p-value < 0.05

5.6.1 Gross capital formation (GCF)

Gross capital formation is positively related with output of manufacturing sector in Nigeria. With

estimated coefficient value of 3.38E-7 it suggest that when the effect of all other explanatory

variables are held constant, a million naira increase in gross fixed capital formation will on average

increase manufacturing sector contribution to GDP by about 0.0000338% per quarter. Similar to its

impact on agricultural output above, the effect of GCF is not statistically insignificant different from

zero at 5% level given its estimated p-value of 0.2702. The result indicates that mean increase in

gross fixed capital formation will increase the contribution of manufacturing sector to gross

domestic product. However, the insignificant p-value suggests to the fact that quarterly national

expenditure on physical productive assets is relatively insufficient to bring about meaningful changes

in manufacturing sector output during the period under study.

5.6.2 Ratio of Credit to Private Sector GDP (CPSGDP)

The result shows that credit to private sector ratio to GDP is positively related to manufacturing

sector output. The positive estimated coefficient (0.120654) of the variable is also in concord with

economic aprior expectation because increase in ratio of credit to the private sector will translate to

increase in overall investment and output of the private sector. Holding other variables constant, a

million naira average increase in CPSGDP will lead to quarterly increase in manufacturing sector

contribution to GDP by about 12.06%. The result is very similar to the previous estimation on its

impact on agricultural sector output. With estimated p-value of 0.1189 the researcher considered

the effect of CPSGDP as not having significant effect in the model. The implication is that private

sector’s economic agents assess to finance is considerably low in Nigeria as was previously found by

(Fadare, 2010; Ogun and Akinlo, 2011).

5.6.3 Manufacturing capacity utilization (MANCU)

The coefficient of manufacturing capacity utilization is 0.069725 and suggests that the higher the

utilization of installed capacity of manufacturing firms, the more output they are likely to produce.

The above coefficient of MANCU in Table 5.5 shows that when effect of all other variables are

assumed to be insignificant, a percentage increase in manufacturing firms’ installed capacity

utilization will on average results to about 0.06% increase in manufacturing sector quarterly

contribution to GDP in Nigeria. Though this impact is considered significant at exactly 4.19% level in

the model, its minimal contribution to growth of manufacturing sector contribution to GDP testified

that MANCU is not a key growth driver in the sector. These may include factors such as

management, availability and cost inputs, infrastructural requirements and government policy

among others.

5.6.4 Ratio of broad money supply to GDP (M2GDP)

Money supply ratio to GDP displayed negative relationship (-0.072432) with manufacturing sector

contribution to gross domestic product. This concurs with theoretical explanation that net rise in

money supply in the long-run will cause output to fall due to general price effect. The result in Table

5.5 shows that ceteris paribus, a million naira increase in money supply ratio to GDP will cause

manufacturing sector output to decrease by about 7.24% in each quarter. In this study, the

coefficient of M2GDP has an estimated p-value of 0.2327 which indicated that the variable is not

significant at 5% level. The result is in line with Ekeocha and Oduh (2012) who found negative and

insignificant relationship between money supply ratio to GDP and output.

5.6.5 Real interest rate (RINR)

Like in the above result in Table 5.3, real interest rate in Table 5.3 displayed positive relationship

with manufacturing sector output growth rate. It implies that holding effect of other variables

constant, a percentage rise in real interest rate will lead to mean growth rate of about 2.49% in

manufacturing sector out in each quarter. Again, it shows that economic agents in the

manufacturing sector benefits from rising real interest rate which they plug back in form of direct

investment. From the result, the coefficient of RINR has an estimated p-value of 0.0000 which is the

same with result in Table 5.3. Hence, the researcher concludes that the coefficient of RINR

(2.496479) is statistically significantly different from zero at 5%. With this information, real interest

rate proved itself a core determinant of growth in above two real sectors of Nigerian economy.

5.6.6 Interest rate spread (SINR)

The coefficient of interest rate spread is negative and significant with the value of -3.532525 and p-

value of 0.0000 respectively. The variable as has been defined above measures the difference

between banks lending rate and their deposit rate. It implies that holding the effect of other

explanatory variables constant, a percentage increase in interest rate spread will on average cause

manufacturing sector contribution to gross domestic product to fall by approximately 3.53 percent

on quarterly basis. Similarly, effect of interest rate spread has the same sign and relative magnitude

in both Table 5.3 and Table 5.5. One important deduction from this result this that financial

institutions are making abnormal profit from their customers. The net import of this phenomenon

suggests that customers are made worse off and their investment (output) capacity reduced due to

the expansion in the difference between deposit and lending rate.

5.6.7 Real exchange rate (EXRATE)

Real exchange rate coefficient is negative and at the same time its estimated p-value of 0.0014 is

statistically significantly different from zero at 5% level. This suggests that on average, one naira

increase in exchange rate causes manufacturing sector output to fall by approximately 1.1% in each

quarter, holding effect of other variable constant. Again, the sign of the variable is in line with apriori

economic expectation as has been discussed in Table 5.3 above. As real exchange rate depreciates

over time, manufacturing firms will experience considerable rise in the cost of imported raw

materials. This situation often brings about cut in output. Real exchange rate is an important factor

that affects output of manufacturing sector in this study.

5.6.8 Financial sector reform (DUM)

The differential intercept coefficient of DUM is positive. It suggests that on average, post financial

sector reform (1986-2013) contributes more to growth of manufacturing sector output than pre-

financial sector reform (1970-1985) by about 4.01%. Similar to the output in Table 5.3, estimated p-

value of 0.0000 is also statistically significantly different from zero at 5% level. This result shows that

the manufacturing sector benefited more from the 1986 Structural Adjustment Programme.

Effect of financial sector reform on real interest rate (DUM*RINR). The coefficient of interaction of

financial sector reform on real interest rate is -2.491054. As was discussed in model one above,

DUM*RINR coefficient is significant at 5% level since its estimated p-value is 0.000 in Table 5.5. The

above coefficient (-2.491054) suggests that holding effect of other variables constant, a percentage

increase in post financial sector reform (1986-2013) will on average increase manufacturing sector

output by 0.005425% (2.496479 - 2.491054). The result shows that effect of real interest rate in post

financial sector reform on manufacturing sector output is less than pre financial sector reform real

interest by about 2.49%. This implies that a percentage rise in post-financial sector reform real

interest rate will on average decrease quarterly output of manufacturing sector by approximately

2.5%.

Effect of financial reform on interest rate spread (DUM*SINR). Having previously said that interest

rate spread as used in this study is a proxy for financial institutions’ profitability, it implies that the

coefficient of DUM*SINR will measure the difference between pre- and post-reform as regards their

contribution to output growth of the manufacturing sector. The estimated p-value of 0.0000 in Table

5.5 shows that the variable is statistically significantly different from zero at 5% level. The differential

slope is 3.561659 which imply that holding other variables constant, post financial sector reform

interest rate spread is greater than pre financial sector reform interest rate spread by about 3.56%.

On the average, while one percentage increase in financial sector pre-reform (1970-1985) SINR

causes manufacturing sector output to fall by -3.532525%, it however causes the sector’s output to

rise by 0.029134% in the post-financial sector reform period (1986-2013), that is (-3.532525 +

3.561659).

Effect of financial reform on real exchange rate (DUM*EXRATE). Table 5.5 shows that post-financial

sector reform (1970-1985) real exchange rate coefficient is greater than coefficient of real exchange

rate in pre-financial reform period (1986-2013) on average by 1.096186%. The coefficient of

DUM*EXRATE is statistically significantly different from zero at 5% level due to its estimated p-value

of 0.0014. It means that when effect of other variables are held constant, a percentage increase in

real exchange rate in pre-financial reform period leads to mean decrease of 1.096856% in

manufacturing sector output. But on the other hand the same magnitude of change will on average

cause manufacturing sector output to grow by 0.00067% (-1.096856 + 1.096186) during the post-

financial sector reform. This observation suggests that demand for local manufactured commodities

will meet rising demand at the international market as a result of its relative low price. This will

evidently lead to expansion of the sector’s output in order to meet both local and international

demand.

5.6.9 Manufacturing sector contribution to GDP (MANGDP)

The result from Table 5.5 has a positive intercept (constant) coefficient of 3.382059. It measures the

rate of growth in manufacturing sector output when effects of other explanatory variables were

assumed to be zero. Hence, the coefficient value suggests that holding effect of other variables

constant, manufacturing sector output will have mean quarterly growth rate of about N338.2

million. With estimated p-value of 0.0021 the variable is considered to be statistically significantly

different from zero at 5% level. This implies that some important factors different form explanatory

variables in the model also cause manufacturing sector output to grow over time.

5.6.10 Model fit and diagnostic check

Table 5.6: Model fit and diagnostic check for impact of financial sector reforms

on manufacturing sector output

Test Model 2

R-squared 0.945140

DW-stat 0.313868

RESET F-stat Df p-value

51.77230 (1, 163) 0.0000***

Normality Test JB-statistic p-value

77.80484 0.0000***

ECM ECM parameter p-value

-0.140120 0.0000***

Note: *** p-value < 0.01; ** p-value < 0.05

Coefficient of determination (R-squared). As an indicator for the goodness of fit of the model,

estimated coefficient of R-squared is 0.945140. It suggests that explanatory variables in the model

were able to explain about 94.5% of total variations in the manufacturing sector output during the

period of study. This result is considerably fair and justifies the fact that appropriate explanatory

variables were employed in the model estimation.

Test for autocorrelation. Thus, in line with above method of analysis of autocorrelation test in

Section 5.5.11 the researcher rejected the null hypothesis of ‘no positive autocorrelation’ at 5% and

1% level of significance because 0 < d(0.313868) < dL(1.643 and 1.550) respectively.

Specification error test. Once more, the researcher employed above Ramsey RESET test in Table 5.4

to investigate the model specification. From Table 5.6 the null hypothesis that ‘the model was mis-

specified’ was rejected at both 5% and 1% level of significance because Fcal(51.77230) > Ftab(k-1/n-k) ≈

(1.84 and 2.34). Similar to above conclusion from the result in Table 5.4, the model was well

specified but it suffered from the problem of pure autocorrelation. This problem was perceived by

the researcher who thereby adopted the Newey-West HAC standard error technique in estimation

of the model.

Normality test. The normality assumption of the model’s residual was tested using Jarque-Bera (JB)

statistic. From the estimation in Table 5.6 the null hypothesis that ‘the residuals are normally

distributed with zero mean and constant variance’ at 5% and 1% level of significance was rejected

given estimated p-value of the JB-statistic of 0.000. Hence, the researcher concluded that the

residuals are normally distributed.

Error correction mechanism. The error correction mechanism parameter in Table 5.6 satisfied aprori

expectation of negative and statically significant coefficient at 5% level. With estimated coefficient of

-0.140120 the result shows that on average, about 14.01% of temporary discrepancy between long-

run and short-run manufacturing sector output is corrected every quarter. Hence, the model will

correct about 56.04% of error (disequilibrium) short-run in a year. It will therefore achieve full

equilibrium (100% adjustment) in approximately 22 months (a year and 10 months). This duration is

the model’s speed of adjustment.

5.7 Comparison of impact of key financial indicators on agricultural and manufacturing sector output

In order to compare effect of financial indicators in above OLS estimate on impact of financial sector

reforms on agricultural and manufacturing sector output, the researcher employed Wald coefficient

restriction test following Wald (1940). The following value in Table 5.7 below was obtained.

Table 5.7: Wald coefficient restriction test

Financial indicators for model 1 Financial indicators for model 2

Variable Chi-square Df P-value Chi-square Df P-value

RINR 57.86912 1 0.0000*** 34.29810 1 0.0000***

SINR 37.97438 1 0.0000*** 60.89436 1 0.0000***

EXRATE 2.547707 1 0.1105 10.53893 1 0.0012***

Note: *** p-value < 0.01; ** p-value < 0.05

In accordance with Wooldrige (2013) the decision rule in this study is to reject the null hypothesis

that ‘coefficient of target variable is not equal to zero in models one and two’ (λ1 = λ2 = 0) if the p-

value of Chi-square statistic is less than 0.05. Do not reject otherwise. Based on the values in Table

5.7 the researcher rejected the null hypothesis that ‘real interest rate (RINR) and interest rate spread

(SINR) have the same impact on agricultural and manufacturing sector output given their

corresponding p-values (0.0000 & 0.0000) and (0.0000 & 0.0000) respectively. It implies that both

RINR and SINR impacted differently on output of the two sectors after the 1986 Structural

Adjustment Programme in Nigeria.

Unlike in the case of RINR and SINR, the null hypothesis that real exchange rate (EXRATE) has the

same impact on agricultural and manufacturing sector output was not rejected as the p-value of its

Chi-square statistic is (0.1105 & 0.0012). This shows that while there is no difference in real

exchange rate of the pre- and post- reform period on agricultural sector output, the impact of real

exchange rate of the pre- and post-reform period is different in output of the manufacturing sector.

The insignificance of EXRATE coefficient in the model for agricultural sector output can be explained

by the fact that Nigeria ceased to be a major exporter of agricultural product immediately after the

oil boom of 1973. For this reason exchange rate could not have meaningful impact on agricultural

sector output since a larger proportion of the product were consumed locally. Hence, it became

predominantly difficult for pre- and post-financial sector reform to bring about noticeable difference

on EXRATE-AGGDP relation in the model.

5.8 Evaluation of research hypotheses

The research hypotheses of the study will be evaluated based on regression result obtain from

estimated model in this work. It shall be based on statistical criteria (Significance of F- and t-statistic)

and econometric criteria (result of diagnostic checks).

The estimated p-values of corresponding t-statistic of explanatory variables gross fixed capital

formation (GCF), credit to private ratio to GDP (CPSGDP), average annual rainfall and money supply

ratio to GDP were not significant at 5% level. Except for real exchange rate (EXRATE) which was only

significant at 5% in result for manufacturing sector output but insignificant in regression for

agricultural sector output, others variables such as real interest rate (RINR), interest rate spread

(SINR), manufacturing capacity utilization (MANCU) and financial sector reform dummy (DUM) were

all significant at 5% level. In addition, the result was able to show that the F-statistic which measures

overall level of significance of the model was highly significant in both regressions for agricultural

and manufacturing sector output. Hence, the study shows that all financial indicator variables were

all significant. This pointed to the fact financial sector reform of Structural Adjustment Programme

policy of 1986 had noticeable impact on agricultural and manufacturing sector output during the

period of study in Nigeria.

Based on econometric analysis for model fit and diagnostic checks, it was observed that the models

produced considerably high coefficients of goodness of fit (R-squared) which were 95% and 94.5%

for the two sectors respectively. Similarly the result justified that normality assumption of residuals

in the model and at the same time rejected null hypothesis of model mis-specification for the

sectors. Having indicated the existence of long-run relationship between dependent and

independent variables in the model, it was able to estimate the speed of adjustment. The result

shows that while it takes about 56 months on average to correct temporary discrepancies in the

model for agricultural sector output, it only takes about 22 months to correct such discrepancies in

that of manufacturing sector (short-run estimate).

Limitations of the study

It is of great importance to note that despite the effort and success demonstrated in this study, it

has not been without constraints and limitations.

One of such constraints is the existence of scanty literature on impact of financial sector reform on

agricultural and manufacturing sector performance in Nigeria. The researcher found it difficult to

access very relevant literature as relates the interest of the study. Again, the study was constrained

by availability of scarce resources which included time allocation for M.Sc thesis report by the

university authority as well as cash needed for funding of the research work.

In view of above mention constraints, the researcher here considered it worthy to recommend the

following areas of study for future interested researchers;

Effect of fiscal policy on agricultural and manufacturing sector contribution to GDP

in Nigeria.

Evaluation of resource curse hypothesis on performance of agricultural and

manufacturing sector: the Nigerian experience.

CHAPTER SIX

SUMMARY, CONCLUSION AND POLICY RECOMMENDATIONS

6.1 Summary

In summary, the choice of the topic ‘Relative impact of financial sector reforms on agricultural and

manufacturing sector growth in Nigeria’ was made due to the relevance of the two sectors with

respect to job creation, income generation and the quest for national economic development. The

fact that agricultural sector lost its prime position to the oil sector in early 1970s as well as the

manufacturing sector which witnessed serious decline within the same period were among others

factors that motivated professionals to recommend structural adjustment policy change for the

economy. In order to move the economy forward, the financial sector was seriously reformed

following the military government implementation of SAP in 1986. In line with the spirit of

professional inquiry, this study took it upon itself as a main objective, to ascertain the effect of

financial sector reforms with particular reference to key financial indicators such as real interest

rate, interest rate spread and real exchange rate on the agricultural and manufacturing sector

growth in Nigeria. It equally proposed to compare the impact of financial sector reform on

agricultural and manufacturing sector growth in Nigeria given those indicators.

To achieve above mention objectives, the study employed quarterly time series data from the CBN

statistical bulletin 2009 and 2013. It covers observation from 1970 to 2013. A dummy for the

financial sector reform was generated and assume the value of 0 if observations from 1970-1985

(pre-reform period) and 1 if observations from 1986-2013. Above data was analyzed using Eviews

8.0 econometric software to estimate Ordinary Least Square technique. From the result in Chapter

Four, it was found that in model one (Relative impact of financial sector reform on agricultural sector

output), gross fixed capital formation and credit to private sector ratio to GDP were positively

related to agricultural sector output. However, their respective impacts were insignificant. On the

other hand, mean quarterly rainfall has negative relationship with agricultural sector output and at

the same time, insignificant. While interest rate spread and real exchange rate significantly causes

output to fall, real interest rate on the other hand causes output to rise. Finally, the result shows

that while financial sector reform brought about significant decline in mean real interest rate, it led

to the appreciation of financial sector profitability (interest rate spread) and depreciation of local

currency.

For model two (Relative impact of financial sector reform on manufacturing sector output), while

gross fixed capital formation and credit to private sector ratio to GDP were positive but insignificant,

manufacturing capacity utilization and real interest rate were positive and significant. In addition,

money supply ratio to GDP and real exchange rate were negative but significant. Result from model

two shows that the impact of financial sector reform was similar to that in model one but its

influence on economic activities was more on manufacturing sector output than it was on

agricultural sector output.

Lastly, the comparison of impact of key financial indictors on agricultural and manufacturing sector

output revealed that the impact of real interest rate and index of financial sector profitability (SINR)

in the pre- and post-financial sector reform is statistically significantly different from zero in each

sector. In contrast to above findings, it was shown that while impact of real exchange rate does not

significantly influence agricultural sector output, it became a major determinant of manufacturing

sector output.

6.2 Conclusion

From the already conducted analysis on the relative impact of financial sector reforms on

agricultural and financial sector output in chapter four, it was deducted that the level of gross fixed

capital formation which constituted investment in infrastructural need of the economy and credit to

private sector ratio to GDP which stands for fund availability and accessibility are below optimal.

Despite their positive relationship with output, they were insignificant in both models. From the

sign and magnitude of coefficients of real interest rate and interest rate spread in pre- and post-

financial sector reform, the study indicated that economic agents in both agricultural and

manufacturing sectors were made worse-off by the reform. While the reform caused a major

downturn on average returns on savings (captured by DUM*RINR in Tables 5.3 and 5.5), it lead to

noticeable increase in financial sector profitability index which suggests that bank lending rate

increased while deposit rate decreased.

Again, the study found that the financial sector reform led to further depreciation of the naira. This

adverse effect on real exchange rate unfortunately coincided with the problem of inadequate

funding and infrastructure. As such depreciation of the naira was unable to induce output growth in

the study. Similarly, the study shows that manufacturing sector depends more on foreign exchange

than agricultural sector. And this explains why the negative impact of financial sector reform was felt

more in the manufacturing sector than it was in the agricultural sector.

6.3 Policy recommendations

In line with findings of the study, the following recommendations were made;

Government should come up with more effective policies to address the problem of

inadequate infrastructure which militate against investment and growth in output of

agricultural and manufacturing sectors. Existing policies should also be reviewed and

made to tackle the present challenges.

Provision of affordable funds for investment in the sectors should be encouraged. This

will help to attract more investors and at the same time motivate existing investors in

both sectors.

There should be provision of incentives (subsidy or tax allowance) for investors in the

sectors. This could be in form of subsidy in lending rate or in the foreign exchange

market so as to minimize the impact of associated risk introduced in the sector by the

above financial sector reform.

Finally, government should look into possible ways to review existing policy options

so as to incorporate factors needed to achieve internally driven sustainable growth in

the economy.

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APPENDICES

Appendix A1: Co-integration for agricultural sector output

Null Hypothesis: SER01 has a unit root

Exogenous: None

Lag Length: 2 (Automatic - based on SIC, maxlag=13) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.590457 0.0000

Test critical values: 1% level -2.578476

5% level -1.942688

10% level -1.615474 *MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(SER01)

Method: Least Squares

Date: 08/11/15 Time: 10:15

Sample (adjusted): 1970Q4 2013Q3

Included observations: 172 after adjustments Variable Coefficient Std. Error t-Statistic Prob. SER01(-1) -0.281078 0.050278 -5.590457 0.0000

D(SER01(-1)) 0.158711 0.074590 2.127768 0.0348

D(SER01(-2)) 0.219703 0.074996 2.929531 0.0039 R-squared 0.161427 Mean dependent var 0.000439

Adjusted R-squared 0.151503 S.D. dependent var 0.232152

S.E. of regression 0.213845 Akaike info criterion -0.229846

Sum squared resid 7.728288 Schwarz criterion -0.174948

Log likelihood 22.76679 Hannan-Quinn criter. -0.207573

Durbin-Watson stat 1.972660

Appendix A2: Co-integration for manufacturing sector output

Null Hypothesis: RESID02 has a unit root

Exogenous: None

Lag Length: 7 (Automatic - based on SIC, maxlag=13) t-Statistic Prob.* Augmented Dickey-Fuller test statistic -5.193308 0.0000

Test critical values: 1% level -2.578799

5% level -1.942733

10% level -1.615446 *MacKinnon (1996) one-sided p-values.

Augmented Dickey-Fuller Test Equation

Dependent Variable: D(RESID02)

Method: Least Squares

Date: 08/11/15 Time: 10:31

Sample (adjusted): 1972Q1 2013Q4

Included observations: 168 after adjustments Variable Coefficient Std. Error t-Statistic Prob. RESID02(-1) -0.285962 0.055064 -5.193308 0.0000

D(RESID02(-1)) 0.385797 0.078320 4.925899 0.0000

D(RESID02(-2)) 0.199070 0.081919 2.430071 0.0162

D(RESID02(-3)) 0.349981 0.082852 4.224183 0.0000

D(RESID02(-4)) -0.386711 0.070142 -5.513271 0.0000

D(RESID02(-5)) 0.152328 0.076008 2.004103 0.0467

D(RESID02(-6)) 0.031202 0.076728 0.406649 0.6848

D(RESID02(-7)) 0.219192 0.076228 2.875479 0.0046 R-squared 0.401760 Mean dependent var 0.001777

Adjusted R-squared 0.375587 S.D. dependent var 0.180140

S.E. of regression 0.142346 Akaike info criterion -1.014661

Sum squared resid 3.241990 Schwarz criterion -0.865901

Log likelihood 93.23155 Hannan-Quinn criter. -0.954287

Durbin-Watson stat 1.959759

Appendix B1: Relative effect of financial sector reform on agricultural sector output

Dependent Variable: LOG(AGGDP)

Method: Least Squares

Date: 08/11/15 Time: 01:52

Sample: 1970Q1 2013Q4

Included observations: 175

HAC standard errors & covariance (Bartlett kernel, Newey-West fixed

bandwidth = 5.0000) Variable Coefficient Std. Error t-Statistic Prob. GCF 4.43E-07 3.15E-07 1.406675 0.1614

CPSGDP 0.041409 0.057772 0.716770 0.4745

AAR -9.11E-07 1.58E-06 -0.574475 0.5664

RINR 2.094430 0.275323 7.607176 0.0000

SINR -2.870541 0.465820 -6.162335 0.0000

EXRATE -0.724255 0.453750 -1.596154 0.1124

DUM 3.790910 0.757965 5.001431 0.0000

DUM*RINR -2.125930 0.274140 -7.754920 0.0000

DUM*SINR 2.963764 0.463912 6.388634 0.0000

DUM*EXRATE 0.728358 0.453794 1.605040 0.1104

C 5.973801 0.687804 8.685322 0.0000 R-squared 0.950229 Mean dependent var 9.519792

Adjusted R-squared 0.947194 S.D. dependent var 1.624546

S.E. of regression 0.373312 Akaike info criterion 0.927991

Sum squared resid 22.85535 Schwarz criterion 1.126920

Log likelihood -70.19919 Hannan-Quinn criter. 1.008682

F-statistic 313.1104 Durbin-Watson stat 0.265009

Prob(F-statistic) 0.000000 Wald F-statistic 231.5929

Prob(Wald F-statistic) 0.000000

Appendix B2: Relative effect of financial sector reform on manufacturing sector output

Dependent Variable: LOG(MANGDP)

Method: Least Squares

Date: 08/11/15 Time: 01:54

Sample: 1970Q1 2013Q4

Included observations: 176

HAC standard errors & covariance (Bartlett kernel, Newey-West fixed

bandwidth = 5.0000) Variable Coefficient Std. Error t-Statistic Prob. GCF 3.38E-07 3.06E-07 1.106355 0.2702

CPSGDP 0.120654 0.076975 1.567440 0.1189

MANCU 0.069725 0.034004 2.050468 0.0419

M2GDP -0.072432 0.060466 -1.197890 0.2327

RINR 2.496479 0.426278 5.856458 0.0000

SINR -3.532525 0.452686 -7.803484 0.0000

EXRATE -1.096856 0.337871 -3.246373 0.0014

DUM 4.014837 0.921118 4.358656 0.0000

DUM*RINR -2.491054 0.437638 -5.692038 0.0000

DUM*SINR 3.561659 0.473772 7.517668 0.0000

DUM*EXRATE 1.096186 0.338051 3.242662 0.0014

C 3.382059 1.082198 3.125175 0.0021 R-squared 0.945140 Mean dependent var 7.653862

Adjusted R-squared 0.941460 S.D. dependent var 1.343649

S.E. of regression 0.325095 Akaike info criterion 0.656350

Sum squared resid 17.33267 Schwarz criterion 0.872519

Log likelihood -45.75878 Hannan-Quinn criter. 0.744027

F-statistic 256.8570 Durbin-Watson stat 0.313868

Prob(F-statistic) 0.000000 Wald F-statistic 112.6041

Prob(Wald F-statistic) 0.000000

Appendix C1: Normality test for agricultural sector output estimation

0

5

10

15

20

25

30

35

-2.5 -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0

Series: ResidualsSample 1970Q1 2013Q4Observations 175

Mean -5.57e-15Median -0.029403Maximum 1.157422Minimum -2.424512Std. Dev. 0.362426Skewness -1.106000Kurtosis 14.81271

Jarque-Bera 1053.157Probability 0.000000

Appendix C2: Normality test for manufacturing sector output estimation

0

4

8

12

16

20

24

-1.0 -0.8 -0.6 -0.4 -0.2 0.0 0.2 0.4 0.6 0.8 1.0

Series: ResidualsSample 1970Q1 2013Q4Observations 176

Mean 1.29e-14Median 0.001911Maximum 0.977129Minimum -0.987032Std. Dev. 0.314712Skewness 0.743049Kurtosis 5.898493

Jarque-Bera 77.80484Probability 0.000000

Appendix D1: RESET test for agricultural sector output estimation

Ramsey RESET Test

Equation: OBJ_1

Specification: LOG(AGGDP) GCF CPSGDP AAR RINR SINR EXRATE DUM

DUM*RINR DUM*SINR DUM*EXRATE C

Omitted Variables: Squares of fitted values Value Df Probability

t-statistic 5.521385 163 0.0000

F-statistic 30.48570 (1, 163) 0.0000

Likelihood ratio 30.00434 1 0.0000 F-test summary:

Sum of Sq. Df Mean

Squares

Test SSR 3.601100 1 3.601100

Restricted SSR 22.85535 164 0.139362

Unrestricted SSR 19.25425 163 0.118124

Unrestricted SSR 19.25425 163 0.118124 LR test summary:

Value Df

Restricted LogL -70.19919 164

Unrestricted LogL -55.19702 163

Appendix D2: RESET test for manufacturing sector output estimation

Ramsey RESET Test

Equation: OBJ_2

Specification: LOG(MANGDP) GCF CPSGDP MANCU M2GDP RINR SINR

EXRATE DUM DUM*RINR DUM*SINR DUM*EXRATE C

Omitted Variables: Squares of fitted values Value Df Probability

t-statistic 7.195297 163 0.0000

F-statistic 51.77230 (1, 163) 0.0000

Likelihood ratio 48.54577 1 0.0000 F-test summary:

Sum of Sq. Df Mean

Squares

Test SSR 4.178156 1 4.178156

Restricted SSR 17.33267 164 0.105687

Unrestricted SSR 13.15451 163 0.080703

Unrestricted SSR 13.15451 163 0.080703 LR test summary:

Value Df

Restricted LogL -45.75878 164

Unrestricted LogL -21.48589 163

Appendix E1: Error correction mechanism model test for agricultural sector output

Dependent Variable: D(D(LOG(AGGDP)))

Method: Least Squares

Date: 08/11/15 Time: 10:09

Sample (adjusted): 1970Q3 2013Q4

Included observations: 171 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(D(GCF)) -8.73E-08 5.98E-07 -0.145912 0.8842

D(CPSGDP) -0.021284 0.028918 -0.736026 0.4628

AAR 1.83E-07 8.58E-07 0.213422 0.8313

D(RINR) -0.148507 0.201861 -0.735693 0.4630

D(SINR) -0.102409 0.289558 -0.353674 0.7241

D(EXRATE) -0.095234 0.108819 -0.875152 0.3828

D(DUM) -0.402294 0.530963 -0.757669 0.4498

D(DUM*RINR) 0.115532 0.210385 0.549144 0.5837

D(DUM*SINR) 0.136085 0.296598 0.458819 0.6470

D(DUM*EXRATE) 0.094900 0.108818 0.872101 0.3845

RESID01(-1) -0.051945 0.026134 -1.987620 0.0486

C -0.009158 0.013778 -0.664675 0.5072 R-squared 0.061249 Mean dependent var -0.009397

Adjusted R-squared -0.003696 S.D. dependent var 0.098700

S.E. of regression 0.098882 Akaike info criterion -1.722188

Sum squared resid 1.554645 Schwarz criterion -1.501721

Log likelihood 159.2471 Hannan-Quinn criter. -1.632732

F-statistic 0.943096 Durbin-Watson stat 2.152237

Prob(F-statistic) 0.500908

Appendix E2: Error correction mechanism model test for manufacturing sector output

Dependent Variable: D(D(LOG(MANGDP)))

Method: Least Squares

Date: 08/11/15 Time: 10:25

Sample (adjusted): 1970Q3 2013Q4

Included observations: 174 after adjustments Variable Coefficient Std. Error t-Statistic Prob. D(D(GCF)) -1.84E-08 6.13E-07 -0.029957 0.9761

D(CPSGDP) 0.017960 0.050537 0.355386 0.7228

D(MANCU) 0.003937 0.025751 0.152877 0.8787

D(M2GDP) -0.000153 0.042961 -0.003568 0.9972

D(RINR) -0.136243 0.205891 -0.661726 0.5091

D(SINR) 0.582871 0.283880 2.053234 0.0417

D(EXRATE) 0.175686 0.111772 1.571830 0.1180

D(DUM) -0.075138 0.547472 -0.137246 0.8910

D(DUM*RINR) 0.105480 0.218082 0.483672 0.6293

D(DUM*SINR) -0.549113 0.296888 -1.849567 0.0662

D(DUM*EXRATE) -0.176028 0.111809 -1.574361 0.1174

RESID02(-1) -0.140120 0.026473 -5.293045 0.0000

C 0.003540 0.008285 0.427226 0.6698 R-squared 0.199138 Mean dependent var 0.000425

Adjusted R-squared 0.139446 S.D. dependent var 0.108990

S.E. of regression 0.101106 Akaike info criterion -1.673523

Sum squared resid 1.645806 Schwarz criterion -1.437501

Log likelihood 158.5965 Hannan-Quinn criter. -1.577778

F-statistic 3.336113 Durbin-Watson stat 2.707993

Prob(F-statistic) 0.000238