1 34.whole paper

Journal of Economics and Sustainable Development www.iiste.org

ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)

Vol.2, No.4, 2011

1

Analysis of Profitability of Fish Farming Among Women in

Osun State, Nigeria

Awoyemi, Taiwo Timothy

Department of Agricultural Economics,

University of Ibadan, P. O. Box 20583, Ibadan, Nigeria.

Tel: +2348029541320 Email: [email protected]

Ajiboye, Akinyele John (Corresponding Author)

Department of Agricultural Education,

Osun State College of Education,

PMB 5089, Ilesa, Nigeria.

Tel: +2348034885815 Email: [email protected]

Abstract

The simple random sampling technique was employed in selecting 62 farmers drawn from the sampling

frame obtained from the list of Agricultural Development Programme (ADP) contact farmers in the four

Local Governments Areas (LGAs) of Egbedore, Olorunda, Ede South and Ife Central, which made up the

study area. The main instrument for collecting the primary data was structured questionnaire. It is evident

from the result is that an average total cost of N371486.35 was incurred per annum by fish farmers while

gross revenue of N791242.52 was realized with a gross margin of N 574314 and a profit of N 419756.17. The

rate of return on investment of 0.58 implies that for every one naira invested in Fish production by farmers, a

return of N1.5 and a profit of 58k were obtained. The multiple regression result revealed that fish output was

significantly determined by pond size, labour used, cost of feeds, cost of lime and cost of fingerlings. The

study concluded that fish production in the study area is economically rewarding and profitable.

Keywords: Women, Profitability, Fish Farming, Gross Margin, Elasticity.

1. Introduction

The Nigerian fishing industry consists of three major sub –sectors, namely the artisanal, industrial and

aquaculture. The awareness on the potential of aquaculture to contribute to domestic fish production has

continued to increase in the country. This stems from the need to meet the much needed fish for domestic

production and export. Fish species which are commonly cultured include Tilapia spp, Heterobranchus

bodorsalis, Clarias gariepinus, Mugie spp, Chrysichthys nigrodigitatus, Heterotis niloticus,

Ophiocephalus obscure, Cyprinus carpio and Megalo spp. Fish culture is done in enclosures such as tanks.

The aquaculture sub sector contributes between 0.5% and 1% to Nigeria’s domestic fish production.

The rapid increase in population of the world has resulted in a huge increase in the demand for animal protein

(which is essentially higher in quality than plant protein). The average protein intake in Nigeria which is

about 19.38/output/ day is low and far below FAO requirement of 65g/ output/day. The nutritional

requirement is particularly crucial in a developing country such as Nigeria where malnutrition and starvation

are the major problems faced by million of rural dwellers .The low protein intake is an indication of shortage

of high quality protein food in the diet of Nigerians. The consumption has been estimated to be 1.56267metric

tonnes. Tabor (1990).

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Although fish farming started over 40 years ago, aquaculture has not significantly contributed to domestic

fish production. Equally estimated was the possible creation of 30000 jobs and generation of revenue of

US$160 million per annum by the aquaculture industry.

Fish has been recognized to contribute 55% to the protein intake in Nigeria. However, local fish production

has been below consumption with imports accounting for aboutUS$48.8m in 2002 (Central Bank of Nigeria

2004).Despite the increase in the major sources of animal protein such as livestock and poultry industries, the

problem of protein deficiency still continues unabated. The protein deficiency in diet is equally associated

with the inability of fish farming industry to supply the required quantity of fish.

The situation causes poor health, low efficiency, low productivity and poor standard of living and decline in

the contribution of fishery industry’s contribution to the Gross Domestic Product (GDP).The industry now

contributes only2.0% of the GDP and accounts for 0.2% of the total global fish production. Nigeria is one of

the largest importers of fish with a per capita consumption of 7.52kg and a total consumption of 1.2million

metric tonnes with imports making up about 2/3 of the total consumption. This indicates the large deficit in

fish supply in Nigeria Olapade and Oladokun (2005). It is therefore expedient to examine the profitability of

fish farming in the study area to identify possible areas that require improvement. The development of the

fish industry will increase local production of fish and save much of the foreign exchange being used for fish

importation. Specifically, it has a special role of ensuring food security, alleviating poverty and provision of

animal protein.

It is generally accepted that women participate actively in the rural economy due to their social and economic

roles. According to Ani (2004), women are the backbone of agriculture labour force producing 40% of the

gross domestic product (GDP) and over 50% of food in developing nations. The rural economy in Nigeria is

dominated by women through their participation in crop and animal production, marketing as well as

processing (Adeyokunnu 1981). Women have important roles as producers of food, managers of resources

and as income earners (Angers et al 1995). Women are the mainstay of small scale agriculture. They supply

the farm labour and are responsible for the family subsistence.

The participation of women in aquaculture extends to every aspect of fish farming like preparing fish, feeding

the feed, cleaning of nets/cages and general maintenance and upkeep of the pond or cages (FAO 1985).

Homestead fish farming is the most suitable option for women to be involved in, since it does not require

them to be away from their homes for long periods which might force them to neglect their household or

domestic responsibilities (FAO 1985). It is particularly suitable for women Nigeria where women seclusion

is practiced. The home base fishery establishments are usually operated by the family or household members.

They are characterized by small-scale operation, low capital investment, simple labour-intensive

technology.

The study will therefore describe the socioeconomic status of female fish farmers, determine the profitability

of fish farming and examine the determinants of fish output in the study area.

2.0 Research Methodology

This study was conducted in Osun state, Nigeria and made use of primary data. The main instrument for

collecting the primary data was structured questionnaire. Information were collected on input and output in

fish farming and socio-economic characteristics of fish farmers through personal interview. A total sample of

62 female fish farmers were randomly selected from the list of fish farmers with the assistance of extension

agents from Osun State Agricultural Development Programme (OSADEP) for the study. Data analysis was

done using the descriptive statistics, budgetary technique and multiple regression technique.

2.1 Budgetary Technique

The budgetary technique which involves the cost and return analysis was used to determine the profitability

of fish farming in the study area. The model specification is given as:

= TR- TC………………………..Equation 1

TR= PQ………………………...…. Equation 2. Where

= Total Profit (N)




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TR=Total revenue (N)

TC= total Cost (N)

P= Unit price of output (N)

Q= Total quantity of output (N)

2.2 The Regression Model

The multiple regression model was employed to determine the influence of socioeconomic factors on the fish

output level. The model is specified as follows

Q=f(X1, X2, X3, X4, X5, X6, X7, e) ....Equation 3

Q is the value of fish output in naira

X1 represents the pond size measured in square metres

X2 is the quantity of labour used in fish production in mandays

X3 is the cost of feeds measured in naira

X4 represents the cost of fertilizer in naira

X5 stands for the cost of lime in naira

X6 represents the cost of fixed inputs in naira

X7 is the cost of fingerlings measured in naira

e= Error term

Following Olayemi (1998) the relationship between the endogenous variable and each of the exogenous

variables were examined using linear, exponential, logarithm and quadratic functional forms. Based on the

value of the coefficient of determination (R2), statistical significance and economic theory that support fish

production, the lead was chosen.

3.0 Results and Discussion

3.1 Descriptive Analysis

Evidence from the descriptive analysis of socio-economic characteristics of respondents in the study area in

Table 1 shows that the fish farmers whose ages fall between 31 – 40 years constituted the majority.

On the whole, 80.0% fall into the economically active group of 20 – 50 years. The result of the marital status

shows that majority 67.7% of the fish farmers were married. It is also evident that most of the respondents

(66.1%) were part time fish farmers. A large proportion (54.8%) of them fish farmer had no formal training.

A large proportion (77.5%) finances their fish production through personal savings. The result compares

favourably with Aromolaran (2000) .The distribution of the household size indicates that the household size

ranged from 2 to 13 while the average fish pond size was found to be 355m2. The study also revealed poor

extension visits to fish farmers who mostly operated on part-time basis. Also 74 (90.3%) of them obtained

their fingerlings from farm gate while 84.2% purchased the feeds and 10.5% used household wastes. The

descriptive analysis also indicates that most fish farmers (56.5%) feed their fish twice daily to achieve high

yield. The most common breeds of fingerlings utilized by fish farmers were Claris, Heteroclarias and Tilapia.

3.3 Profitability Analysis

The study examines the profitability of fish production in the study area. To determine the profit level,

attempts were made to estimate the cost and return from fish farming. The input used, cost, yield or output

data generated from the farmers were used to undertake the cost and return analysis for assessing the

profitability of fish production in the study area.

The cost and return analysis is presented in the table 2. The result reveals that the cost of feeds accounted for

the largest proportion (17.7%) of the total cost of fish production. This is followed by cost of fingerlings

(12.4%).The lime cost and labour cost accounted for 3.2% and 3.9% of the total cost respectively. This

clearly shows that large amount of money is spent by fish farmers in the study area for the purchase of




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fingerlings and feeds. The fixed cost of production consists of cost of fixed assets such as pump, vehicles,

aerators and pond which accounted for 56.5% of total production cost. Consistent with the findings of

Ashaolu et al. (2005) from their studies on profitability on fish farming, the rate of return per capital invested

(RORCI) is the ratio of profit to total cost of production .It indicates what is earned by the business by capital

outlay Awotide and Adejobi (2007). The result revealed that the RORCI of 83% is greater than the prevailing

bank lending rate, 17% implying that fish farming in the study area is profitable. If a farmer takes loan from

the bank to finance fish farming, he will be 58k better off on every one naira spent after paying back the loan

at the prevailing interest rate.

3.4 Multiple Regression Result

The regression analysis was carried out to examine the determinants of factors effecting fish output in the

study area. Based on the econometric and statistical criterion, the double logarithm was chosen as the lead

equation and the results as presented in the table 3. The multiple regression result revealed that fish output is

significantly determined by pond size, labour used, cost of feeds, cost of lime and cost of fingerlings. The

coefficients are in line with the a priori expectation. Hence, the more the amount expended on labour, lime

and feeds, the more the amount that will be realized from fish farms in the study area. The result is consistent

with the finding of Emokaro and Ekunwe (2009). The result equally suggests the need for fish farmers to

purchase more of these inputs to increase their revenue from fish production. Similarly, policies that will

ensure availability of these inputs to fish farmers at affordable price should be put in place. The positive

relationship between value of fish and pond size indicates that with increase in the size not surprising because

all things being equal the

Equally evident from the result an average total cost of N371486.35 was incurred per annum by the

respondents while gross revenue of N 791242.52 was realized thereby returning gross margin of N574, 314

and a profit of N419756.17. The rate of return on investment of 0.58 implies that for every one naira invested

in fish production by farmers, a return of N1.58 and a profit of 58k were obtained. The implication of this is

that there is a considerable level of profitability in fish farming in the study findings area. This result is

quantity of fish produced is directly proportional to the pond size.

The coefficient of determination, R2 values of 0.52 indicates that 52% of the variation in the value of fish

output is explained by pond size, quantity of labour used, cost of feed, cost of lime and cost of fingerlings.

Also, 48% of the variation in the value of fish is determined by other factors not considered. Table 4 shows

that the regression coefficient, standard error, F ratio and the level at which the ratio was significant for each

of the independent variables. The performance of the analysis of variance in table 4 shows that F ratio of

9.110 was significant at 0.01 alpha level. This provided the evidence that a combination of pond size, cost of

labour, cost of feeds, lime, fertilizer, fixed inputs and cost of fingerlings had joint impact on the fish output in

the study area. The beta weight ranged from 0.056 to 0.316. The result implies that out of seven independent

variables considered, fingerling is the most important input. It has the highest value of 0.316. This is followed

by the quantity of lime while fertilizer is the least. This is not surprising because irrespective of the efforts and

management practices, the output from a fish farm will be determined by the quantity and quality of

fingerlings used.

3.5 Elasticity of Production and Return to Scale

The magnitude of elasticity of production is one of the economic concepts of measuring efficiency in

resource-use Oladeebo, Ambe-Lamidi (2007). The total sum of elasticity of production of the significant

variables, 0.787 as shown in table 5 was less than unity. This suggests that fish production in the study area

had a decreasing return. The implication is that each additional unit of the inputs will results in a small

increase in the value of fish output than the preceding unit. This shows that production occurred among fish

farmers in the study in stage 2, a rational stage of production. In stage 2, the sum of elasticity of production is

greater than zero but less than one. The implication is that the more the inputs used, the higher will be the

value of fish even though at a decreasing rate. This finding is consistent with that of Olagunju et al. (2007) in

their study on economic viability of cat fish production in Oyo state, Nigeria. The degree of responsiveness of

the value of fish output to changes in the independent variables shows that a percent increase in the values of

pond size, labour, feeds, fertilizer, lime, fixed input and fingerlings will lead to 20.1%, 26.3%, 27.6%,




Vol.2, No.4, 2011

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2.7%, 6% , 14.1% and 0.1% change in the value of fish produced respectively. With the production result,

increase in the utilization of labour and feeds is likely to boost the fish output substantially.

4. Conclusion and Recommendations

It was shown in this study area that fish production among women is economically rewarding and profitable.

It is capable of creating employment, augmenting income and improving the standard of living of the women.

The result also shows that the positive decreasing return to scale as evidence by the return to scale estimate,

indicating that fish production in the study is still in stage 2 of the production process. This suggests the

existence of intervention points by relevant stakeholders in the current production technology of fish among

women farmers in the study area.

To ensure sustainability in homestead fish production and provide substantial income for women, there may

be the need to develop an extension system is gender specific and tailored towards women. This can be

achieved if the level of women’s involvement in homestead fish production in Nigeria is determined and in

addition, if the constraints they face and their training needs are identified. If the identified needs of women

involved in homestead fish production are used in the design of the training content, then the training

becomes more effective in enhancing the skills and competence of women.

References

Adeyokunnu T. O. (1981). Women in Agriculture in Nigeria. ST/ECA/ARCN/81/11: Economic Commission

for Africa, Addis Ababa, Ethiopia.

Agnes R., Lynn R., Christine P. (2005). Women: The key to food security, food policy report. The

international food policy research institute, Washington, D.C. pp1-14.

Ani A. O. (2004). Women in Agricultural and Rural Development. Priscaquilla Publishers, Maiduguri,

Nigeria.

Awotide D.O., Adejobi AO (2007). Technical efficiency and cost of production among plantain farmers in

Oyo State Nigeria, Moor Journal of Agricultural Science, 7(2), 107-113.

Aromolaran A.B. (2000). Analyzing Resources use Efficiency on fish farms: A case Study of Abeokuta zone

Ogun-State, Nigeria. Aquafield, 1(1), 12-21.

Ashaolu O.F., Akinyemi, A.A., Nzekwe LSO (2006). Economic Viability of homestead Fish Production in

Abeokuta Metropolis of Ogun State, Nigeria. Asset Series A, 6(2), 209-220. Central Bank of Nigeria 2004.

Statistical Bulletin, 264- 267.

Emokaro C. O., Ekunwe P.A. (2009). Efficiency of resource-use and elasticity of production among catfish

farmers in Kaduna, Nigeria. African Journal of Bio-technology 8(2), pp 7249-7252

Food and Agricultural Organization (1985). A Review Study of the Sungai Merbok flooting Cago culture

project. Project Code TCP/MAI./403 Technical Report 2, Rome.

Oladeebo J.O., Ambe-Lamidi A. l. (2007). Profitability, input elasticities and economic efficiency of poultry

production among youth farmers in Osun state, Nigeria. International Journal Poultry Science. 6(12), 994

–998.

Olagunju F.I., Adesinyan I.O., Ezekiel A.A. (2007). Economic viability of catfish production in Oyo state.

Journal of Human Ecology, 21(2): 121-124. Olapade A.O., Adeokun O.A. 2005. Fisheries Extension

Services in Ogun State. Africa Journal of Livestock Extension, 3, 78-81.

Olayemi J.K. (1998). Elements of Applied Econometrics. A Publication of the Department of Agricultural

Economics, Ibadan, Nigeria: University of Ibadan.




Vol.2, No.4, 2011

6

Tabor J.G. (1990). The Fishing Industry in Nigeria: Status and Potential for Self-sufficiency in Production.

National Institute of Oceanography and Marine Research Technical Paper 22, 1-8.

Table 1. The capitals, assets and revenue in listed banks

Socio-economic characteristics Categories Frequency Percentage (%)

Education Primary

Secondary

Tertiary

Total

2

49

11

62

3.2

79.1

17.7

100.0

Age 10 – 20

21 – 30

31 – 40

41 – 50

>50

Total

2

19

31

7

3

62

3.2

30.0

50.0

12.0

4.8

100. 0

Marital Status Married

Widow

Single

Total

42

11

09

62

67.7

18.8

14.5

100.0

Household Size 1 – 4person

5 – 8

>8

No response

Total

25

21

3

13

62

40.3

33.9

4.8

21.0

100.0

Farming Experience

(Years)

<5 years

5 – 10years

11 – 15years

>15years

Total

24

32

3

3

62

38.8

51.6

4.8

4.8

100.0

Times of Feeding 1 time

2 times

3 times

4 times

5 times

Total

7

35

16

2

2

62

11.3

56.5

25.8

3.2

3.2

100.0

Contact with Extension

Workers

0 time

1 time

2 times

3 times

5 times

Total

49

5

5

2

1

62

79.0

8.1

8.1

3.2

1.6

100.0

Training in Fish Farming Formal training

No formal training

28

34

45.2

54.8




Vol.2, No.4, 2011

7

Total 62 100.0

Mode of Farming Part time

Full time

Total

41

21

62

66.1

33.9

100.0

Main Source of Finance Personal Savings

Friends

Relatives

Cooperatives

Bank loans

Total

48

1

2

9

2

62

77.5

1.6

3.2

14.5

3.2

100.0

Source: Computed from Field survey data 2009

Table 2: Average cost and return of fish production

Source: Computed from Field survey data 2009

Item (Annual) Amount (#) % of total cost

Fertilizer

Feeds Feeds

Lime

Fingerlinks

Labour

Total variable cost

Fixed inputs

Total cost

Total returns

Profit

ROI

ROIC

23560.21

10541.34

1374.22

53452.03

15529.11

14742.44

252287

371486.35

791242.52

419756.17

0.58

0.83

6.34

17.7

3.2

12.4

3.9




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Table 3: The regression results of the determinants of fish outputs in the study area

Variable Coefficient Beta T Significant

Constant 7.328 - 4.882 .000*

Pond size 0.201 .204 2.234 .029**

Labour 0.263 .174 1.934 0.57

Feed 0.276 .263 2.888 0.005*

Fertilizer 0.027 .056 0.625 0.534

Lime 0.006 0.248 2.780 0.007*

Fixed input 0.141 0.163 1.783 0.79

Fingerling 1.471E-05 0.316 3.33 0.001*

R2 = 0.52; F stat = 9.110

*variable significant @1% ** Variable significant @5%

Source: Computed from Field survey data 2009.

Table 4: Analysis of variance

.

Source of Variation Sum of Square Df Mean Square F-ratio Sig.

Due to regression 40.260 7 5.866 9.110 0.01

Due to Residual 49.637 74 0.646

Total 89.897 81

Source: Computed from field survey data 2009.

*Significant 1%

Table 5: Elasticity of production and return to scale of fish farmers

Independent Variables Elasticities of Production

Pond size* 0.201

Labour* 0.263

Feed* 0.276

Fertilizer 0.027

Lime* 0.060

Fixed input 0.141

Fingerlings* 1.471E-05

Source: Computed from field survey data 2009.

*Significant Variable@5%




Vol.2, No.4, 2011

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Interaction between Real And Financial Sectors In Nigeria:

A Causality Test

Adaramola, Anthony Olugbenga

Banking and Finance Department, Faculty of Management Sciences, University of Ado Ekiti, Nigeria.

E-mail: [email protected]

Owoeye Taiwo

Economics Department, Faculty of Social Sciences,

University of Ado Ekiti, Nigeria.

E-mail: [email protected]

The research is financed by Asian Development Bank. No. 2006-A171

Abstract

This study investigates the interrelationship between industrial productivity and money supply as

proxies for the real and financial sectors by testing for causality under a Vector Auto-Regression (VAR)

structure. In the study, it was revealed that Nigeria over the 35-year period between 1970 and 2005

like many other LDC’s has a unidirectional causality running from the financial sector to the real

sector growth. This indicates that the country still operates in the short-run and to take advantage of

long-run changes, such variables as technology and factor productivity should to be taken into

cognizance.

Keywords: Industrial Productivity; Money Supply; Vector Auto-Regression; Causality.

1.1 Introduction

Nigeria like every other economy in the world seeks to maximise her macro-economic objectives by

introducing appropriate policies to channel her economy in the path of growth and stability. Prominent

among the issues of concern are industrialisation and the bid to tackle inflation and hence the control

of money supply. The industrial sector has always been recognized as the main sector to speed up the

rate of development such that in Rostow’s (1960) theory of economic development, also known as the

stage theory. He recognised the industrial sector as the leading sector to economic development path

calling it the “core sector”, to lead the economy to development in the “take-off” stage while citing

Britain’s leading sector in her take-off period as the cotton textile industry. Thus, the state of

industrialisation or development consist of having accumulated established efficient and economic

mechanism for maintaining and increasing large stock of capital per head in the various firms,

similarly, the condition of underdevelopment is characterised by possession of relatively small stack of

various kinds of capital (Chete, 1995). Monetary authorities on another hand seek to control the

amount of money in circulation and, hence, money supply, since it is exogenously determined, it is

generally accepted in the quantity theory of money that if there is an increase in money supply, the

price level would raise, if however some resources were idle the output could increase, as classified

into three categories: factors that give rise to productivity of existing factors; an increase in the

available stock of factors of productivity; and technological change. In recent years, Nigeria like other

less-developed nations has been experiencing substantial slack in the use of her productive potential such that output/growth had remained disquietingly low. In order to redress this undesirable state of

affairs, Nigeria has been and particularly under the Structural Adjustment Programme (SAP), using


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and emphasising monetary policy, this is in line with the financial literature made popular by

Mackinnon (1973) and Shaw (1973), which suggest that financial liberalisation is what is needed to

release the finance necessary to promote growth. Unfortunately, the proceeding economic problem

persists and even in some cases seemingly worsened. In the light of this development, public

confidence in the ability of government to manage the economy has waned and belief in the likelihood

of continuing economic growth weakened. In effect, questions are being raised as to the effectiveness

of monetary policies adopted by government over these years. The need however arises to understand

the direction of the relationship between finance and growth as was highlighted by Patrick (1960)

when he posed the question, “Is it financial sector or the real sector that leads to other?”The

deregulation of the financial sector under the SAP which gave way to liberal interest rates and

licensing of banks together with the recent recapitalisation process which left in its trail the emergence

of 25 mega banks and other non-bank financial institutions show a belief in the “supply leading

hypothesis”. Reversal of deregulation in January 1994 with return to what the government called

“managed deregulation”, that is, administratively determined interest rate and a halt to liberal bank

licensing could suggest a weakening in earlier belief. Could that reflect a belief in the “demand

following hypothesis”? This study intends to use data for Nigerian economy to establish the direction

of relationship between industrial productivity and money supply in Nigeria and verify previous

studies from other countries.This paper is concerned with investigating the interrelationship between

industrial productivity and money supply using Nigerian data and it is organised in this sequence.

What follows this introductory section is the literature review, section three reviews industrial

productivity and money supply in Nigeria. Section four discusses the estimation techniques and model

specification while section five discusses the result of data analysis and section six concludes.

2.1 Literature Review

In a bid to raise the standard of living and quality of life of her people, the primary focus of economic

management, particularly in developing countries, becomes effective economics development

transformation. According to Todaro (1971), “raising people’s living levels so much so that their

incomes and consumption levels of food, medical services, education, utilities and social services

expand through relevant economic growth process is the focus of economic management.” In other

words, therefore, to expedite the pace of the process of this attainment, He proposes the need for

government to provide for the prevalence of some socio-economic transformation conditions which

involve “increasing people’s freedom to choose by enlarging the range of their choice variables, for

example, increasing the varieties of consumer goods and services of reasonable costs.” This view

presupposes increased industrial productivity which is generally accepted by economic planners,

researchers, policy makers irrespective of their desirable means of raising the standard of living of the

populace. In a supportive mood, Lewis (1967) opined that “in any economy one or more sectors serve

as a prime mover, driving the rest of the economy forward. This role of “engine of growth” or leading

sector has usually been played by the industrial sector under the industrialization process”. Though

small in relative sizes as compared to GDP, especially in developing countries, nonetheless, the

industrial sector is seen as potential leading sector with latent resources and expansions that could pull

u the rest of the economy through backward and forward linkages. Therefore, it is considered as a

leading paradigm grossly because of its dynamism in technological transmission and organisational

stimuli. However, the economic regulatory approach under which industrialisation strategies were

adopted in Nigeria up to the mid-1980s did not yield any remarkable result the near total collapse of

the global crude oil prices in the early 1980’s and the subsequent economic crisis that followed it

coupled with some internal factors such as economic mismanagement of natural resources, resulted in

accumulation of huge external and internal debts, chronic budget deficits with the attendant

inflationary pressures and resources economic declines in all its ramifications as well as high

unemployment rates. These created some transformation challenges which prompted Nigeria to adopt the World Bank/IMF endorsed Structural Adjustment Programme (SAP) in July 1986, in order to

among several objectives: achieve fiscal and balance of payment viability; evolve a private sector-led




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economic development process; lessen the dominance of unproductive investments; and restructure

and diversify the productive base of the economy (Philips, 1987). Towards these ends, there was a

reversal of Nigeria development approach from economic regulation to economic deregulation and

liberalisation-relying on market forces to allocate available resources. Within this new paradigm are

such policies as: adoption of appropriate pricing policies for products; adoption of measure to

stimulate production and broaden the supply base of the economy; deregulation and greater reliance

on market forces; rationalisation and privatisation of public enterprises; strengthening of existing

demand management policies; trade and payment mobilization; tariff reform and rationalisation to

promote industrial diversification (Philips, 1987). According to Ajakaiye and Ayodele (2001), in spite

of these elaborate strategies which would have favoured effective industrialisation process in an

economically conducive environment, most of the results were socio-economically undesirable. This

is not unconnected with some SAP associated development problems such as chronic budget deficit,

huge external debt burden and serious economic decline.” Against the background of this

disappointment, Nigeria’s Vision 2010 Report (1998) aims at creating a stable macroeconomic

environment that will provide a conducive atmosphere for dynamic, long-term self-sustaining growth

and development within the sustainable economic development paradigm as proposed in the 1980

Lagos Plan of Action. Towards this and economic planning and policy instruments seem to be

currently directed at the development of the key productive sectors of the economy such as agriculture,

industry and particularly manufacturing and commerce for the promotion of the pace of

industrialisation in Nigeria. In this regard, there is an urgent need for policy instruments to be properly

focused on energising the past executed industrial transformation process in the country.

3.1 Nigeria in Perspective

Before the discovery of crude oil in commercial quantity in Nigeria, the country was grossly

dependent on the proceeds of agricultural (primary) products for foreign exchange. However, at

independence, the government saw need for import-substitution and thus reduce the level of reliance

on the external sector for the supplies of manufactured products and equipment. In essence, through

the lure of incentives foreign investors were technically and strategically invited to champion

Nigeria’s industrialisation because of the scarcity of investible funds in the country. The incentives

adopted by the government were broadly classified into five (5) groups which are: effective protection

with import tariff; export-promotion of products produced in Nigeria; fiscal measures of taxation and

interest rates to make for cheap production costs; foreign currency facility for international trade; and

the evolution of development banks for resource mobilization. However, by '72/'73, oil price had a

consistent increase from $2pb to close at $40pb and crude oil production to 2.5mpd in 1980 signifying

an increase of about $76million per day in the nation’s capacity to spend, which of course, gave rise to

the declining emphasises on agricultural sector and thus, the reduction in her contribution to total GDP

from 65% in 1960s to 20% in the late 1970s.

In the early 1970s, the manufacturing sector had depended mainly on the external sector for foreign

exchange to purchase equipment, spare parts and intermediate input and there was phenomena

increase in the performance of the sector in the mid-1970s and 1980s occasioned principally by the

massive inflow of foreign exchange from crude oil sales. However, the near total collapse of the

economy’s driving force (crude oil prices) which started in 1981 reversed the phenomena increase in

the performance of the manufacturing sector in Nigeria. As from 1975, the sector witnessed a

persistent decline due to discovery and subsequent reliance on crude oil. For example, the

manufacturing sector grew at 4.8percent in 1960s, this rose to 7.2percent in 1970s but declined in

1975 and 1980 to 5.6 and 5.4percent respectively and further rose again in 1985 at about 10.5% before

it entered into a period of steady decline (Ajakaiye and Ayodele, 2001). The decline in this

performance can rightly according to them be attributed to three major factors, which are: a weak demand due to the sharp fall in real income arising from the economic recession and high product

prices; low export market production due to poor quality control and the high cost of production due




Vol.2, No.4, 2011

12

to the high cost of imported inputs; and the sector’s dependence on the external sector for the supply

of inputs. In recent years, manufacturing as a percentage of GDP has declined to as low as 2% (CBN,

2005).

4.1 Method of Analysis

This study employs the econometric technique of Co-integration and Vector Auto Regression (VAR)

which most analysts have found to be very adequate for handling economic data especially for less

developed countries LDC’s like Nigeria. Core to the values of this analysis is the examination of the

variables in the econometric model for stationarity. Basically, the idea is to ascertain the order of

integration of the variables and the number of time the variables have to be differentiated to arrive at

stationarity. This enables us to avoid the problems of spuriousity on inconsistent regression that are

associated with non-stationary time series models; particularly, ordinary least square (OLS) (Engel and

Yoo, 1987). The traditional econometric method only assumes stationary data around a deterministic

trend by including a time trend in the regression equation. It is however known that many economic

variables have tendencies to trend through time, so that the level of these variables can be

characterised as non-stationary. The independent variable cannot act significantly on the dependent

variables individually but collectively, the relationship between the dependent and independent

variables acting collectively may be insignificant. This problem is generated due to the fact that the

data has not been tested to confirm its satisfaction of the condition for OLS lists and needs to be

resolved. The mean variance computed from variable that have series that are stationary will be

unbiased estimates of the unknown population mean and variance (Eguwaikhide, 1999). However,

economic variables that are non-stationary series in a regression equation would generate estimates

that are biased.

4.2 Causality (VAR)

Since the objective of the study includes examining the direction of relation between industrial

productivity index (Indpx) and money supply (Ms). The co-integration says nothing about the

direction of the causal relationship between the two variables are co-integrated, it follows that there

must be causality in at least one direction. In this study, VAR causality test was employed to examine

the causal linkage between industrial productivity and money supply.

Granger (1969) test regresses a variable Y. If X is significant; it means that it explains some of the

variance of Y that is not explained by lagged values of Y itself. This indicates that X is causally prior

to Y and is said to dynamically cause or Granger cause Y cases of unilateral, bilateral and independent

causalities are explained in chapter one of this work and therefore are not repeated in this chapter.

However, when two variables are both co-integrated, the joint process as indicated in Engel and

Granger (1987) and restated by Keke, Olomola and Saibu (2005) can be written in the error-correction

mechanism from given by:

1.4...111 311 21 tt

n

it

n

itttt XbAYbECMbY

2.4...211 311 21 tt

n

it

n

itttt XdAYdECMdX

Equation (4.1) and (4.2) were used for testing the causality between the variables of interest. The ECM

term shows the size of error in the preceding term. Keke et al (2003) has cautioned against the

exclusion of the ECM term from equation 4.1 and 4.2. He opined that if the ECM term is neglected, an

important error is induced in the empirical analysis and the F-test are no longer valid (Keke et al, 2003).




Vol.2, No.4, 2011

13

4.3 Model Specification

This paper uses Granger causality model in which two variables, Ms and Indpx are taken to represent

money supply and industrial productivity index respectively.

Let At; t=1,2,3,… be the set of given information including at least (Mst, Indpxt) the bi-variate process

of interest. Also, let At=(As:s<t). Mst and Indpxt are defined similarly, for example, Mst represents all

past values of Mst and Indpxt represents all past values of Indpxt.

Granger’s definition of causal relationship between Ms and Indpx are as follow:

1. Ms causes Indpx if iMA

MsA

Indpx

s

...22

Where ___ (Indpx/Z) represents the minimum prediction error variance of Indpx, given an

information period Z, a reduction in the minimum prediction error variance when past values

of Ms are included in the information set on which the prediction of Indpx is conditioned,

signifies Ms causes Indpx.

2. Similarly, for Indpx causes Ms we have:

iiIndpxA

MsA

Ms ...22

Bi-directional causality (feedback) occurs when Indpx causes Ms and Ms causes Indpx. That

is:

3. iiiMsA

MsA

IndpxandIndpx

AMs

AMs ...2222

Ms and Indpx are independent of one another, if neither causes the other, that is inclusion of

values of past data set does not reduce the minimum prediction error variance of the other; thus:

4.

ivIndpx

AMs

AMsandMs

A

Indpx

A

Indpx...2222

In addition, on undergoing the unit root test for stationary, A=(Indpx, Ms) and Indpx and Ms

are taken as a pair of linear covariance stationary time series, thus, the Granger causality

between industrial productivity (Indpx) and money supply (Ms) can be modeled as follows:

vMsbIndpxbECMbIndpx tt

n

it

n

itttt ...111 311 21

viMsdIndpxdECMdMs tt

n

it

n

itttt ...211 311 21

Where t1 and 2t are serially uncorrelated with zero mean and finite covariance matrix.

The decision rule for i, ii , iii and iv will be the test of the null hypothesis that the estimates

coefficients are equal to zero at an appropriate level of significance; thus:

A. Ms causes Indpx if HO2:b3=0, i=1,2,3, … n is rejected

B. Indpx causes Ms if HO1:d2=0, i=1,2,3, … n is rejected

C. Ms and Indpx are dependent if a and b above holds

D. Ms and Indpx are independent if both a and b are not rejected




Vol.2, No.4, 2011

14

5.1 Presentation and Analysis of Result

Being a time series data with the usual flow of spurious result, any successful research on such must

commence on the test for stationarity on the data. On the recommendation of Hamilton (1994) and

Hayashi (2000) a stated in Dauda (2005), it was accepted to investigate carefully the nature of any

probable non-stationarity, testing each series individually for unit root and then testing for possible

co-integration among the series, thus, analysis of causality using the typical VAR model is preceded

by the unit root and co-integration test.

5.2 Unit Root Test

To avoid spurious rejection or acceptance of no causality in the results, it was necessary to confirm

stationarity of the variables of interest as investigated using the ADF (Augumented Dickey-Fuller)

tests. The result is presented in table 1.

Table 1: Unit Root Test (ADF)

Variables (With intercept only) Lag length Order of integration

Levels 1st difference 2

nd difference

Indpx -1.58527 -3.188599 -4.076669 2 I (0)

Ms 1.043672 0.936757 -2.208719 2 I (0)

(with intercept and trend)

Level 1st difference 2

nd difference

Indpx -2.890306 -4.337425 -7.545803 1 I (1)

Ms 1.834180 -0.962222 -4.851390 1 I (0)

Using intercept only, Indpx was stationary at 5% critical level on the first and second difference while

Ms was not stationary at either levels or first and second differencing. However, since the two series

were trended, the analysis of ADF using intercept and trend showed Indpx to be stationary at 5%

critical levels on the first and second differencing while Ms was stationary only after the second

difference at 5% critical values. Since the stationary of the variables had been confirmed, a simple

co-integration test was conducted using the Johansen’s technique (Johansen and Juselius, 1990). As

stated in Dauda (2006), Hamilton (1994) and Hayashi (2000), argues that testing and analysing

co-integration in a VAR model is superior to the Engle-Granger simple equation.

5.2 Johansen’s Co-integration Test

Table 2: Series: Indpx, Ms




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15

Lags interval: 1-4

Eigen values Likelihood ratio 5% critical value 1% critical value Hypothesised N0

of CE(S)

0.637625 39.75192 25.32 30.45 None**

.234513 8.284543 12.25 16.26 At most 1

(**) denote projection of the hypothesis at 5% (1%) significant level. L.R. test indicates one

co-integration equation at 5% significant level.

The co-integration test indicates one co-integrating equation at 1-4 lags suggesting the existence of

long-run relationship between money supply and industrial productivity. The choice of appropriate lag

length for the VAR model plays a critical role in determining causality, thus, using the Akaike

information criterion (AIC), and Schwarz information criterion (SIC) the optimal lag length of ten (10)

lags was chosen. The Granger causality equation was estimated using ordinary least square technique

within a VAR structure in E-views version 3.1. The results are presented below:

Table 3: Result of causality running from Ms to Indpx

Included observation: 26 after adjusting end points

Independent variable Coefficients Standard error t-statistics

Indpx (1-) 0.218732 0.3335 0.65617

Indpx (2-) 0.005200 0.30634 -0.01698

Indpx (3-) -0.344753 0.32503 -1.06068

Indpx (4-) -0.649665 0.35033 -1.85444

Indpx (5-) -0.026547 0.38127 -0.6963

Indpx (6-) 0.368365 0.37550 0.98101

Indpx (7-) -0.382565 0.36096 -1.05985

Indpx (8-) -0.161459 0.35693 -0.45236

Indpx (9-) -0.205445 0.45196 -0.45456




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16

Indpx (10-) 0.851862 0.45196 1.93761

Ms (-1) 0.016109 0.00622 2.58896

Ms (-2) -0.27721 0.01073 -2.58420

Ms (-3) 0.008779 0.00344 2.54999

Ms (-4) -0.004961 0.00196 -2.52958

Ms (-5) 0.014646 0.00569 2.57503

Ms (-6) -0.005964 0.00234 -2.54490

Ms (-7) -0.001112 0.00129 -0.86317

Ms (-8) -0.000655 0.00114 -0.57609

Ms (-9) -0.001100 0.00143 -0.77113

Ms (-10) 0.001491 0.00100 1.49354

ECM (-1) -0.015817 0.00612 -2.58494

R2=0.928065; R

2=0.640323; F-statistic=3.925340

From analysing the Indpx regression, we discovered that only the first to sixth lags of Ms were

significant judging by their respective standard error which was less than half of the coefficient of

their respective cases. Also, of the six significant lagged values, 3 conformed to the apriori

expectations of a positive relationship while the second fourth and sixth lag period yielded a negative

relationship which means an adverse effect of money supply on industrial productivity. The R2 was

93% and the adjusted R2 was 64% which shows that 93% of the variations in Indpx are explained by

the variables in the model. Correspondingly, the F-statistic that checks the significance of the R2 was

significant at 5% level of significance. The negative sign in the ECM shows that it was currently

signed though not prompting adequate feedback from long-run trend as indicated by its low value

(2%).

Table 4: Result of causality Indpx to Ms

Included observation: 26 after adjusting end points

Independent variable Coefficients t-statistics




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17

Indpx (1-) 493.1702 0.90022

Indpx (2-) 740.9661 1.47178

Indpx (3-) 520.6822 0.97476

Indpx (4-) -431.9268 -0.75020

Indpx (5-) -404.7310 -0.64592

Indpx (6-) 998.6761 1.61832

Indpx (7-) 329.5950 0.55560

Indpx (8-) -172.7511 -0.29450

Indpx (9-) -966.4992 -1.30120

Indpx (10-) 238.5283 0.392411

Ms (-1) -15.37683 -1.50377

Ms (-2) 28.22666 1.60112

Ms (-3) -7.911610 -1.39831

Ms (-4) 3.058081 0.94873

Ms (-5) -16.46770 -1.76180

Ms (-6) 11.6360 3.02156

Ms (-7) 1.036140 0.48953

Ms (-8) -1.345473 -0.72060

Ms (-9) -3.929832 -1.67652

Ms (-10) 4.116349 2.50843

ECM (-1) 15.92098 1.58325

R2=0.999905; R

2=0.999523; F-statistic=2622.320

In the money supply regression result presented in table 4, however, we see that none of the lagged




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values of Indpx was significant judging by the standard error test. Though, some of the lagged values

conform to apriori expectation, their statistical insignificance condemn their relevance in this analysis.

The R2 and adjusted R

2 surprisingly show a 100% relevance of the variables in the model in

explaining changes in money supply. The large F-statistic (2622.326) also reveals the significance of

the R2 while the positively signed ECM shows that correction of past disequilibria is not possible in

the model. The gross insignificance in the individual parameter estimates and high significance of the

F-statistic is consistent with the submission of Gujarati who says “with several lags of the same

variable, each estimated coefficient will not be statistically significant, possibly because of

multi-collinearity. But collectively, they may be significant on the basis of standard F-test (Gujarati,

2004:850).

6.1 Summary and Concluding Remarks

The study has examined the degree of inter-relationship and independence between industrial

productivity and money supply in Nigeria. The empirical analysis has led to the discovery that the

Nigerian economy shows a uni-directional causality that runs from money supply to industrial

productivity. This conforms to the quantity theory of money that an increase in money supply either by

mobilizing savings, increase in government expenditure or through foreign private investment (FPI),

causes an increase in price level which also lead to an increase in output if there are some idle

resources.

It also affirms the postulation of Mackinnon (1973) and Shaw (1975) which suggest that financial

liberalisation is what is needed to release the finance necessary for growth. Expressed in another way,

Porter (1966) agrees that development and expansion of the financial sector precede the demand for its

services. This evidence is consistent with the conclusion of Aigbokhan (1995) who states a causality

running from financial to real sector growth (demand following hypothesis). The importance of

managing money supply, that is, inflation control is however shown in section five where two of the

six significant lags of money supply yielded a negative result, indicating a disincentive to industrial

productivity caused by adverse inflationary spirals. This is adequately explained by the near

hyper-inflationary trends recorded in the country between the late 70s and the late 90s which arose

from the oil boom, more recently termed “oil money”. Question however arises on the inability of the

industrial sector to yield adequate feedback by simultaneously increasing money supply and thus

creating a circle of perpetual growth in both the financial and real sectors. This slack in industrial

productivity is seen to be caused by a myriad of factors ranging from the inflationary spiral indicated

by the near zero contribution of money supply to industrial productivity as shown in table 3. The

market attitude towards home-made goods and the rampant corruption in the system which has

hampered the results of the policies which would have brought desired results.

References

Aigbokhan, B. E. (1995). Financial development and economic growth: a test of hypothesis on supply

leading and demand following finance, with evidence in Nigeria. In Nigerian Economic and Financial Review. 1(2) 49-75.

Ajakaiye, D. O. and Ayodele, A. D. (2005). Industrial transformation efforts in Nigeria: some

reflections. Ibadan: NISER occasional paper. 1.

Chete, L. N. (1995). The dynamics of productivity performance in Nigerian manufacturing. In

Nigerian Economic and Financial Review. 1(2) 43-58.

Dauda, O. Y. (2006). Dollarisation and exchange rate volatility in Nigeria: exploring causal relationships. In Journal for Economics and Social Studies. 5,1596—4256.




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Egwaikhide, F. O. (1999). Import substitution industrialisation in Nigeria. A selected review. The

Nigerian Journal of Economics and Social Studies. 35(1), 64—77.

Engel, R. F. and Granger, C. W. (1987). Co-integration and error-correction: representation, estimation

and testing econometrics. 55, 251—276.

Engle, R. F. and Yoo, K. (1987). Spurious regression in econometrics. Journal of Economics. 2,

111—120.

Granger, J. C. W. (1969). Investigating causal relations by econometric models and cross special

methods. Econometricia, 37(3) 424-438 .

Gujarati, D. N. (2004). Basic econometrics. New York: McGraw-Hill.

Hamilton, J. D. (1994). Time series analysis. Princeton University Press, Princeton, New Jersey, USA.

Hayashi, F. (2000). “Econometrics”. Princeton University Press, Princeton, New Jersey, USA.

Juselius, K. (1990). Maximum likelihood, estimation and inference on co-integration with application

to the demand for money. Oxford Bulletin of Economics and Statistics, 52, 169-210.

Keke, N. A.; Olomola, P. A. and Saibu, M. O. (2003). Foreign direct investment and economic growth

in Nigeria: a causality test. In S. A. Olaiya (eds). Journal of Economics and Social Studies.

3:1596—4256.

Mackinnon, R. (1973). Money and capital in economic development. Washington, DC: The Brooklyn

Institution.

Philip, O. A. (1987). Structural adjustment programme in a developing economy: the case of Nigeria.

Ibadan: Nigerian Institute of Social and Economic Research (NISER).

Rostow, W. W. (1960). The stages of economic growth: a non-communist manifesto. London:

Cambridge Press.

Shaw, E. S. (1973). Financial deepening in economic development. New York: Oxford University

Press.

Todaro, M. P. (1971). Economic development. 2nd

Edition. London: Pearson Education Limited.




Vol.2, No.4, 2011

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Sustainable Development and Performance, Financial Position

and Market Value of Nigerian Quoted Companies

Abubakar Sadiq Kasum (Corresponding Author)

Department of Accounting and Finance

University of Ilorin, Ilorin, Nigeria

Email: [email protected], [email protected]

Olubunmi Florence Osemene

Department of Accounting and Finance,


Email: [email protected]

Joshua Adeyemi Olaoye




Atanda Olanrewaju Aliu




Tunde Saka Abdulsalam

Department of Management Sciences,

Kwara State University, Malete, Nigeria

Email: [email protected]

Abstract The study is against the background that sustainable development practices may involve financial outflows

and hence, may be an unattractive investment to managers. This study evaluated the impact of corporate

compliance with accounting standards that are deemed to enforce sustainable development practices and

can, therefore, imply sustainable development practices by companies, on profitability, financial position

and market value of companies. Forty-four companies that have existed since standardization began in

Nigeria in 1984 were studied over five years, using Pearson product moment and spearman’s rank

correlation statistical techniques. The correlations compared compliance to financial reporting standards on

the one hand with financial performance, financial position and market value on the other. Results showed




mailto:olaoyejoshua@




Vol.2, No.4, 2011

21

that sustainable development practices of companies are rarely associated with profitability. The practices

are, however, shown to associate a little with better asset worth and improved market values.

Keywords: Sustainable Development, Profitability, Financial Position, Market Value, Standardization

1. Introduction

Businesses, like all other communal stakeholders, are faced with dual sustainable development challenges.

The first challenge is internal sustainability while the second is external or global. Internal sustainability

could be referred to as the going concern sustainability, which can also be referred to as the internal economic

sustainable development. It is concerned with ensuring that current activities of an organization are

conducted in a manner that will not hinder future economic activities. Global sustainability can be divergent

in scope. It can be communal, national or universally focused. The essence of sustainable development here is

that activities of business organizations are conducted in such a manner that both the current and future needs

of the society are not compromised.

This places many responsibilities on the managements of an organization, who are required to strike a

balance between corporate goals and communal interests. The most likely happening is that management, as

a service to their employers, will focus more on internal sustainability against the communal sustainable

development needs. ‘In contrast to the above, many governments are pinning their hopes of economic growth

and technological innovation on strong private sector growth (Fourie, 2009).

For good corporate governance that especially takes care of the interests of all stakeholders, the issue of

standardization comes as a handy tool. Standardization is the mechanism by which procedures of activities

are being regulated, so that common interests, rather than self-interests are promoted. Standardization is

adopted in many aspects of life globally, which include provisions for the control of business activities.

The aim of this study is to investigate the impact of compliance to accounting standards with sustainable

development provisions, issued in Nigeria, on the result of activities of Nigerian companies.

2. Review of Related Literature

2.1 Sustainable Development in the Business Sector

According to Middleton (1995:240), there could only be theoretical justification for the removal of

resources from environment in the comparative benefit of the removed resources, and in the ability to

ensure that, the environment is, generally, not worse-off. Corporate governance is the concept

that best describes the responsibility of business in sustainable development. According to Brundtland

report of the United Nations, sustainable development is the ‘development which meets the needs of the

present without compromising the ability of future generation to meet their own needs’. The 2005 world

summit of the United Nation referred to economic development, social development and environmental

protection as the interdependent and mutually reinforcing pillars of sustainable development. Davis (2009)

explained it as the economic development and the consumptive use of world’s natural resources in ways

that are sustainable. In other words, it is realized that resources are finite and that part of our job as human

beings is to preserve the human future on this planet into limitless future.

On the other hand, Newton-King (2009) stated that ‘economic sustainability evaluates whether a company




Vol.2, No.4, 2011

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has positioned itself for long-term growth rather than only short-term performance’. According to her, a

company ‘must be able to adapt to macro-economic trends and act in such a way that the long-term

viability of the business is assured’. These are the two sustainable development issues for business.

Corporate governance already incorporated these as it is said to be ‘concerned with holding the balance

between economic and social goals and between individual and communal goals, with the aim of aligning

as nearly as possible, the interest of individuals, corporations and society (Dixon, 2009). Additional to this

is the fact that ‘many governments are pinning their hopes of economic growth and technological

innovation on strong private sector growth’ (Fourie, 2009).

2.2 Business Procedure Standardization

According to Russell (2007), standardization involves inspection, assurance and certification services aimed

at regulating businesses, enforcing contracts and assurance for acceptable social and environmental behavior

expectations. Standardization that affects business exists as far back as the eighteen century, for weight and

measure by French scientists. Several standards exists, today that have impacts on businesses worldwide. The

most familiar and well - established set of standards are those on financial reporting. The standards usually

prescribe what information to make available to stakeholders and the form in which the information should

be prepared and presented. Accounting standards were developed as a guiding tool which defined how

companies should display transactions and events in their financial statements, ensure the needed uniformity

of practices, enlighten users of financial reports, provide a framework for preparation, presents and interprets

financial statement (Kasum, 2009; Kantudu, 2005; Blair, Williams and Lin, 2008; Oghuma and Iyoha,

2005).

Business accounting standardization, therefore, could be said to centre on financial reporting

standardization, in a manner that stakeholders in business are adequately provided for. The standards made

some provisions that facilitate the two sustainable development concerns of business. Dixon (2009)

therefore opined that the move towards sustainable reporting is a welcome one in that it encourages a more

positive response to sustainable development issues.

2.3 Sustainable Development Related Issues in Nigeria Accounting Standards

The Nigerian Accounting Standard Board has issued thirty accounting standards covering various business

issues to date. Five of the standards are considered favorable to sustainable development.

2.3.1 Statement of Accounting Standard No. 3 on Accounting for Property Plant and Equipment

This standard could be linked to internal sustainability of businesses. “Property plant and equipment are

tangible assets that have been acquired or constructed and held for use in the production or supply of goods

and services and may include those held for maintenance or repairs of such assets; and are not intended for

sale in the ordinary course of business”. Most popular examples of property plant and equipment as contained

in the standard include land and improvements, building and plants and equipments (Statement of

Accounting Standard No. 3: 1984).

2.3.2 Statement of Accounting Standard No. 8 on Accounting for Employee’s Retirement Benefits

Contract is a fundamental principle in employee retirement benefit (Gold, 2005). The kind of contract

needed, he posited, is that which may extend over a long period of time that will have force even after one




Vol.2, No.4, 2011

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party would rather no longer be bound. “Economists expect contracts to be rational and efficient” (Gold,

2005). The two methods usually adopted for funding retirement plan are the advance financing method and

the pay-as-you-go-system. For advance financing, “Funds are provided on a regular basis during the working

life of employees”, while for pay-as-you-go “the active working generation provides the funds for pensions

of those who have retired”. Retirement benefit scheme could be administered by the employer’s organization

or by a third party (Statement of Accounting Standard No. 8: 1990).

2.3.3 Statement of Accounting Standard No. 9 on Accounting for Depreciation

Like the standard on Property Plant and Equipment, the standard could be linked to internal sustainability of

businesses, because of the importance of assets to income generation. Depreciation is a systematic and

rational process of distributing the cost of tangible asset over the life of assets. ‘It is the process by which a

company gradually records the loss in value of fixed assets… to spread the initial purchase price of the fixed

asset over its useful life’. It as the periodic, systematic expiration of the cost of a company’s fixed assets

(except for land) (Lopes, 2006).

Various methods exist for calculating depreciation; two broad classifications could be made of the methods,

as time based or usage based. Whatever method to use should consider:

-the cost or revalued amount of the asset,

-the estimated economic life, and

-the estimated residual value of the asset (Dunn, 2004).

Depreciation is in respect of items of property plant and equipment otherwise referred to as fixed assets.

Depreciation “represents an estimate of the portion of the historical cost or revalued amount of a fixed asset

chargeable to operation, during an accounting period” (Statement of Accounting Standard No. 9: 1989).

2.3.4 Statement of Accounting Standard No. 12 on Accounting for Investments

Investment decisions of businesses have both internal and global implications and consequently the standard

will have both internal and external sustainable development consequences. Assets held by an enterprise for

the purposes of capital appreciation or income generation rather than production, trade or provisions of

service qualify as investment. Investment, therefore, generates return to investing company and will among

other, create more employment. Investments are classified as short term if they are readily realizable and

otherwise, classified as long term (Statement of Accounting Standard No. 12: 1992).

2.3.5 Statement of Accounting Standard No. 19 on Accounting for taxes

Taxation practices have more external sustainable development implications. Tax could be defined as a

compulsory levy imposed by the government on income, expenditure or properties of an individual or a

concern, that is viewed like contribution to government administration and/or payment for the use of public

goods. It is also described as a compulsory levy imposed on a subject or upon his property by the government

to provide security, social amenities and create conditions for the economic well-being of the society. Profit

of any company, which accrued in, derived from, brought into or received in Nigeria are chargeable to tax

(Ola, 1999; 350 - 362).




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Taxes that affects a company include those paid directly by the company and those paid by the company on

behalf of others. Tax should be recognized as expense or income and should be included in the profit and loss

account of the period, as a separate line item (Statement of Accounting Standard No. 19: 2001).

The Nigerian Accounting Standard Board has issued thirty accounting standards covering various business

issues, five of which are considered favorable to sustainable development.

3. Research Methodology

3.1 Research Design

This study is an exploratory type that is seeking understanding of a phenomenon. Samples for this study were

drawn from The Nigerian Stock Exchange. Forty-four companies that have filed report with The Nigerian

Stock Exchange from the commencement of standardization in Nigeria to date, out of the current 218 listed,

are the samples for the study. The study was carried out over five years range using three years data.

Consequently, profit, net-asset and market value record of the companies for 2002, 2004 and 2006 were

collected from the Nigerian Stock Exchange. The financial statements of the 44 companies for 2002 were

collected from Stock Exchange library in Lagos, Nigeria. For compliance statistics, the standards were

subjected to content analysis, with the aim of, on a point-by-point basis, determining what the provisions

therein are and consequently the requirement of the standards from companies. By this, each point of

compliance was identified and scores were assigned to each of the points. The financial statements are then

examined for the extent to which they comply with the provisions on points, as set up in the above. The

degree of compliance index was, thereafter, computed as:

Compliance score = point scored ……………(1)

Maximum possible score

Summation of score per standard divided by number of standards applicable to the companies produced the

aggregate compliance score for individual companies.

Pearson product moment and Spearman ranked correlation statistical methods were used to investigate if

compliance associates with the three variables.

3.2 Statement of Hypothesis

3.2.1 Hypothesis 1

Null hypothesis

Compliance with Standards that promote sustainable development is not associated with improved

profitability.

Alternative hypothesis

Compliance with Standards that promote sustainable development improves profitability.

3.2.2 Hypothesis 2

Null hypothesis

Compliance with Standards that promote sustainable development is not associated with improved net-asset.


Compliance with Standards that promote sustainable development improves net-asset.




Vol.2, No.4, 2011

25

3.2.3 Hypothesis 3

Null hypothesis

Compliance with Standards that promote sustainable development is not associated with improve market

value.


Compliance with Standards that promote sustainable development improves market value.

3.3 Decision Rule

The results will be positive or negative and will between ‘zero’ and ‘one’. Positive result indicates favourable

association and the closer to one the result is, the stronger the degree of association between compliance and

each of the dependent variables and vice versa. Consequently, only statistically significant results shall be

used for testing our hypotheses. Alternative hypothesis, therefore, shall be accepted if the study’s statistically

significant result is positive and shall be rejected if it is negative.

4. Results

4.1 Data Presentation and Analyses

First, both the total and per share values of the relevant data to this study are presented in tables 1 - 3. The

data, which are for the forty-four companies under study are presented in Naira(N), the national and reporting

currency for Nigeria. The compliance score earned from each identified compliance item in the considered

standards, by the companies are in table ‘4’ that followed Naira data.

The result of statistical analyses presented in tables 5 and 6 are both total and per share analyses of the

correlation between the extent of compliance with those standards that are sustainable development related

and profitability, financial position and market value as presented in tables 1, 2 and 3 above respectively.

Pearson moment correlation for impact on profitability as presented in table ‘5’ shows that all total value

analyses gave positive result, while per share analyses gave negative results. All the outcomes are, however,

not statistically significant. Similarly, table ‘6’ shows that 2002 and 2004 results are positive, while 2006

results are negative. The results too are not significant. This profitability result is similar to Kasum and

Osemene (2010). In table ‘5’, analyses for net-asset shows that all the results are positive and the results for

2002 are statistically significant at 5% level of significant. Spearman’s rank correlation statistics for

net-assets in table ‘6’ shows, also, that all results are positive, but are not statistically significant.

Pearson moment correlation analyses to test impact on market value in table ‘5’ show that all the computed

Rs are positive. Spearman’s correlations too are positive in all the six cases in the three years. Total value’s

Pearson analysis of 2002 is statistically significant at 10% level, while all other results are not statistically

significant. Overall, profitability analyses provided results that may suggest that sustainable development

practices are not in business interest. On the other hand, both net-asset and market value analyses indicate

that sustainable development practices are in the interest of business. The last two variables are considered

to be long-term focused and are of interest than short-termed accounting profit. This suggests that the result

here is not bad for business.

4.2 Testing of the Hypothesis




Vol.2, No.4, 2011

26

For hypothesis ‘1’, a combination of ‘12’ items in both tables ‘5’ and ‘6’ are relevant. Five items are

positive and seven negative. All the result items, however, are not statistically significant and not useful for

hypothesis testing. The study, therefore, failed to accept alternative hypothesis ‘1’. For hypothesis 2, the

combination of 12 items in both tables ‘5’ and ‘6’ that are relevant are positive. Importantly, two items are

statistically significant and are useful for testing hypothesis. Since the statistically significant results are

positive, we accept the alternative hypothesis that ‘Compliance with Standards that promote sustainable

development improves net-asset’. For hypothesis ‘3’, the ‘12’ items that are relevant are also positive.

One item is useful for testing hypothesis being statistically significant at 10% level of significance. Since

the statistically significant result is positive, we accept alternative hypothesis ‘3’ that ‘Compliance with

Standards that promote sustainable development improves market value’.

The meaning of these results is that compliance to standards that promotes sustainable development by

Nigerian companies has nothing significantly to do with their profitability. Implying that whether they

comply or not to those standards, their profitability situation is not really affected. Net-Asset and Market

value, are however, improved as companies comply with sustainable development related accounting

standards.

5. Conclusion

Based on the findings of this study, we conclude that compliance to those accounting standards that this

study adjudged to promote sustainable development, by the companies listed on Nigerian Stock Exchange,

does not affect their profitability. The study also, concludes that long-term enhancing variables like asset

and market value improve as companies comply with the standards. These results are informative in so

many senses. If truly the standards promote sustainable development that fulfills the basics of sustainable

development, long-term sustainable profitability will be more an appropriate measure than short-term

results.

In line with the same thinking, rather than building immediate profits, economic sustainability should

actually target building business assets that would be positioned to produce long-term sustainable future

profits for the concerns. All these relate to internal sustainability, which also aids global sustainability.

Sustainable development from the point of view of the society, of course, may involve investment in the

society and meeting obligations. These will usually involve resources outflow from the otherwise retainable

incomes of businesses. The goodwill of these kinds of activity will in turn bring patronage to the

businesses.

References

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Services in Global Business’, Comparative Research in Law and Political Economics Research

Paper No 12/2008, 4(3), www.ssrn.com.

Davis, T. (2009), ‘What is Sustainable Development?’, Enviropedia, www.enviropedia.com.

Dixon, T. (2009), ‘Sustainable Development: A Corporate Responsibility’, Enviropedia,

www.enviropedia.com.

Dunn, P. E. (2004), ‘Accounting for Depreciation and the Concept of Revenue and Capital Expenditure’,




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Accounting WEB, www.accountingweb.co.uk

Fourie, A. (2009), ‘Strategic Considerations for the Business Community to Shape a Sustainable Future’,

Enviropedia, www.enviropedia.com.

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Design’, North American Actuarial Journal, 9(2), www.google.com.

Kantudu, A. S. (2005), ‘The Degree of Compliance with the Requirement of Information to be Disclosed in

Financial Statement by Listed Firms in Nigeria’, Abuja management Review, 3(1), 26-46.

Kasum, A. S. (2009), ‘The Need for ‘General Business Procedures and Practices Standards’ to Holistically

Guide and Regulate Businesses Worldwide and to, Importantly, Prevent Failure: An Empirical Based

Proposition’, Proceedings of The 27th Standing Conference on Organizational Symbolism,

Copenhagen Business School, Denmark, 8th

– 11th

of July.

Kasum, A. S. and Osemene, O. F. (2010), “Sustainable Development and Financial Performance of

Nigerian Quoted Companies”, Proceedings of the 16th Annual International Sustainable Development

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May – 1st June.

Lopes, R. (2006), Depreciation in Accounting, Accounting Resources, www.cnx.org.

Middleton, N. (1995), The Global Casino An Introduction To Environmental Issues, New York: John Wiley

and Sons Inc.

Newton-King, N. (2009), ‘Sustainable Development: Investment’, Enviropedia, www.enviropedia.com.

Oghuma, R. and Iyoha, F. (2006), ‘Compliance with Accounting Standards by Quoted Insurance

Companies in Nigeria: An Empirical Investigation’, Nigerian Journal of Education Research, 7(2),

18 – 27.

Ola, C. S. (1997), Income Tax Law in Nigeria, Revised Ed, Ibadan: Heinemann Educational Books Ltd.

Russell, A. L. (2007), ‘The America System: A Schumpterian History of Standardization’, Progress on Point

Release 14.4, The Progress and Freedom Foundation.

-Statement of Accounting Standard No.3: Nigeria Accounting Standard Board (1984)





-Sustainable Development, Wikipedia: The free Encyclopedia, www.wikipedia.org.

Table 1: Profitability data (Profit After Tax)

Company Names 2002 N 2004 N 2006 N

Total Pr Sh. Total Pr Sh. Total Pr Sh.

A.G LEVENTIS 59,565,000 0.06 240,992,000 0.12 468,000,000 0.21

AFPRINT 65,633,000 0.12 -618,407,000 -1.1 11,974,000 0.02


http://www.accountingweb.co/

http://www.wikipedia.org/



Vol.2, No.4, 2011

28

AFRICAN PET 2,156,893,000 9.99 890,120,000 2.06 2,161,530,000 2.74

BERGER PAINTS 85,941,000 0.4 101,542,000 0.47 81,678,000 0.38

CADBURY 2,249,078,000 3 2,812,623,000 2.81 -4,665,459,000 -4.66

CAP PLC 140,806,000 0.84 161,455,000 0.77 312,748,000 1.49

CAPPA & D'ALBER 25,509,000 0.26 126,114,000 1.28 127,946,000 0.65

CFAO 689,957,000 1.66 -1,123,119,000 -2.7 -1,225,053,000 -2.94

CHELLARAMS 31,305,000 0.26 56,127,000 0.31 72,500,000 0.2

COSTAIN(W. AF) 20,048,000 0.13 -469,010,000 -2.93 -1,488,639,000 -9.31

DN MEYER 75,333,000 0.52 62,680,000 0.32 60,753,000 0.21

DUNLOP 96,580,000 0.16 -316,027,000 -0.42 -667,356,000 -0.88

FIRST BANK 4,776,000,000 1.88 14,853,000,000 4.24 21,833,000,000 4.17

GLAXO

SMITHKLINE 497,053,000 0.62 955,261,000 1.2 1,082,290,000 1.13

GUINNESS 4,149,536,000 5.86 7,913,503,000 6.71 7,440,102,000 6.31

INCAR NIGERIA

PLC -18,422,000 -0.22 -33,960,000 -0.41 1,008,000 0

JOHN HOLT 179,000,000 0.46 70,000,000 0.18 -476,000,000 -1.22

LEVER BROTHERS 1,571,918,000 0.52 2,167,249,000 0.72 -1,617,615,000 -0.53

LIVESTOCK FEEDS -66,364,000 -2.68 -237,114,000 -9.58 748,424,000 0.62

MOBIL OIL 474,230,000 2.47 1,759,468,000 7.32 1,716,208,000 7.14

MORISON IND. 6,341,000 0.07 9,667,000 0.11 8,147,000 0.09

NIG. BOTTLING

COY 4,170,544,000 4.28 3,032,322,000 2.33 766,248,000 0.59

NIG. BREWERIES 9,218,954,000 2.44 5,086,403,000 0.67 10,900,524,000 1.44

NIG.

ENAMELWARE 15,966,000 0.55 15,970,000 0.55 6,343,000 0.22

NIGERIAN ROPES 9,804,000 0.3 14,355,000 0.05 22,754,000 0.09

NIG WIRE IND. 36,202,369 2.41 -39,856,000 -2.66 -18,969,000 -1.26

NORTH. NIG

FLOUR 149,640,000 2.02 138,499,000 1.24 55,071,000 0.37

P.S MANDRIES 31,804,000 0.8 10,557,000 0.26 8,427,000 0.21

P.Z INDUSTRIES 1,685,918,000 1.16 3,303,662,000 1.9 3,235,587,000 1.27

PHARMA DEKO

PLC 42,304,000 1.06 30,619,000 0.36 8,216,000 0.09

POLY PRODUCTS 21,053,000 0.09 12,209,000 0.05 725,000 0

R.T BRISCOE PLC 166,418,000 1.39 155,445,000 0.43 531,776,000 1.46

ROADS NIG. PLC -19,780,000 -0.99 -4,783,000 -0.24 11,957,000 0.6

S.C.O.A NIG. PLC 104,000,000 0.21 -327,000,000 -0.5 733,000,000 1.49

STUDIO PRESS PLC -47,629,000 -0.6 30,044,000 0.38 55,095,000 0.69

TOTAL NIGERIA 2,514,087,000 8.46 2,778,904,000 8.18 2,516,693,000 7.41




Vol.2, No.4, 2011

29

PLC

U.A.C 1,166,200,000 1.28 1,570,100,000 1.37 3,203,600,000 1.25

U.B.A 1,566,000,000 0.92 4,525,000,000 1.77 11,550,000,000 1.64

U.T.C -370,565,000 -0.33 -74,115,000 -0.07 52,561,000 0.05

UNION BANK 5,633,000,000 2.24 8,341,000,000 3.31 10,802,000,000 1.2

UNITED NIG. TEXT 1,074,344,000 1.27 132,087,000 0.16 -756,502,000 -0.9

VITAFOAM 258,401,000 0.59 272,234,000 0.42 275,118,000 0.42

VONO 15,072,000 0.31 -218,862,000 -4.53 134,000 0

W. AFRICA. P. CEM -1,348,000,000 -0.79 -3,401,000,000 -1.98 10,678,000,000 3.56

Source: Nigerian Stock Exchange Fact Book 2003, 2005 and 2007

Table 2: Net-Asset data (Net-Asset as per Balance Sheet)


Total NA/Sh. Total NA/Sh. Total NA/Sh.

A.G LEVENTIS 2,357,769,000 2.49 3,438,429,000 1.67 4,046,651,000 1.83

AFPRINT 2,756,216,000 4.91 1,939,956,000 3.46 832,370,000 1.48

AFRICAN PET -20.159739b -93.33 -7,568,785 -0.02 2,455,230,000 3.11

BERGER PAINTS 439,323,000 2.02 496,385,000 2.28 965,293,000 4.44

CADBURY 6,859,572,000 9.14 10,848,768,000 10.84 2,181,121,000 2.18

CAP PLC 481,009,000 2.86 594,747,000 2.83 857,065,000 4.08

CAPPA & D'ALBER 651,848,000 6.62 840,132,000 8.53 1,057,169,000 5.37

CFAO 2,463,541,000 5.92 1,653,913,000 3.98 328,187,000 0.79

CHELLARAMS 1,009,330,000 8.38 1,435,520,000 7.94 2,015,407,000 5.58

COSTAIN(W. AF) 70,815,000 0.44 110,490,000 0.69 -1,349,945,000 -8.44

DN MEYER 288,364,000 1.98 313,148,000 1.61 163,357,000 0.56

DUNLOP 1,526,235,000 2.52 587,948,000 0.78 6,900,327,000 9.13

FIRST BANK 19,406,000,000 7.64 41,605,000,000 11.88 64,277,000,000 12.27

GLAXO SMITHK 1,396,347,000 1.75 2,517,722,000 3.16 4,193,075,000 4.38

GUINNESS 14,157,810,000 20.00 16,908,244,000 14.33 20,947,782,000 17.75

INCAR NIGERIA

PLC 102,380,000 1.22 56,721,000 0.68 323,879,000 1.04

JOHN HOLT 1,942,000,000 4.98 2,603,000,000 6.67 2,311,000,000 5.93

LEVER BROTHERS 4,167,664,000 1.38 6,072,800,000 2.01 3,953,348,000 1.31

LIVESTOCK

FEEDS 250,812,000 10.13 -830,728,000 -33.55 -343,406,000 -0.29

MOBIL OIL 686,083,000 3.57 882,551,000 3.67 2,833,678,000 11.79

MORISON IND. 106,967,000 1.17 110,177,000 1.21 119,955,000 1.31

NIG. BOTTLING CO 14,915,193,000 15.31 18,699,659,000 14.39 20,047,083,000 15.32




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30

NIG. BREWERIES 26,425,983,000 6.99 31,278,969,000 4.14 36,249,393,000 4.79

NIG. ENAMELWAR 94,112,000 3.27 102,835,000 3.57 118,088,000 4.10

NIGERIAN ROPES 36,467,000 1.10 249,278,000 0.95 286,269,000 1.09

NIG WIRE IND. 563,460,224 37.56 247,901,000 16.53 223,175,000 14.88

NORTH. NIG

FLOUR 505,147,000 6.80 725,565,000 6.51 846,220,000 5.70

P.S MANDRIES 156,350,000 3.91 202,034,000 5.05 219,224,000 5.48

P.Z INDUSTRIES 14,349,551,000 9.88 18,701,185,000 10.73 27,055,099,000 10.65

PHARMA DEKO

PLC 68,877,000 1.72 144,988,000 1.71 423,288,000 4.46

POLY PRODUCTS 222,357,000 0.93 245,732,000 1.02 240,169,000 1.00

R.T BRISCOE PLC 547,443,000 4.56 1,785,118,000 4.92 2,207,970,000 6.08

ROADS NIG PLC 45,349,000 2.27 26,647,000 1.33 42,280,000 2.11

S.C.O.A NIG PLC 947,000,000 1.92 929,000,000 1.43 787,000,000 1.60

STUDIO PRESS

PLC 236,079,000 2.95 294,901,000 3.69 944,447,000 11.81

TOTAL NIG. PLC 4,008,510,000 13.49 3,742,235,000 11.02 5,765,754,000 16.98

U.A.C 6,428,600,000 7.08 11,150,000,000 9.76 16,099,200,000 6.27

U.B.A 10,627,000,000 6.25 19,533,000,000 7.66 48,535,000,000 6.87

U.T.C 430,543,000 0.38 119,276,000 0.11 688,828,000 0.61

UNION BANK 32,240,000,000 12.81 39,732,000,000 15.79 100,500,000,000 11.14

UNIT NIG.

TEXTILES 10,003,955,000 11.86 9,717,363,000 11.52 9,016,410,000 5.26

VITAFOAM 585,905,000 1.34 772,069,000 1.18 962,274,000 1.47

VONO 206,659,000 4.27 -21,530,000 -0.45 268,209,000 0.89

W. AFRICA. P.CEM. 9,213,000,000 5.37 2,637,000,000 1.54 25,015,000,000 8.33


Table 3: Market Value data


Total MV/Sh. Total MV/Sh. Total MV/Sh.

A.G LEVENTIS 711,189,000 0.75 2,917,430,571 1.42 3,242,930,250 1.47

AFPRINT 420,899,549 0.75 420,899,549 0.75 364,779,609 0.65

AFRICAN PET. 3,166,560,000 14.66 27,514,080,000 63.69 33,263,189,960 42.17

BERGER PAINTS 556,462,080 2.56 891,208,800 4.10 734,703,840 3.38

CADBURY 23,554,769,400 31.38 77,985,452,800 77.92 51,273,033,200 51.23

CAP PLC 549,360,000 3.27 1,428,000,000 6.80 4,139,100,000 19.71

CAPPA & D'ALBER 767,816,400 7.80 713,675,500 7.25 2,067,198,000 10.50




Vol.2, No.4, 2011

31

CFAO 1,426,880,000 3.43 2,945,280,000 7.08 1,522,560,000 3.66

CHELLARAMS 250,615,040 2.08 354,234,720 1.96 437,371,440 1.21

COSTAIN(W. AF) 100,749,600 0.63 255,872,000 1.60 289,455,200 1.81

DN MEYER 1,114,948,638 7.65 1,220,367,280 6.28 1,046,449,100 3.59

DUNLOP 1,639,008,000 2.71 2,600,640,000 3.44 2,305,800,000 3.05

FIRST BANK 59,817,000,000 23.55 92,748,335,480 26.48 266,543,498,461 50.88

GLAXO SMITHK 2,040,960,000 2.56 7,653,600,000 9.60 12,858,063,994 13.44

GUINNESS 29,557,507,438 41.75 155,162,110,000 131.50 152,802,230,000 129.50

INCAR NIG. PLC 142,375,000 1.70 129,812,500 1.55 1,209,587,720 3.89

JOHN HOLT 631,800,000 1.62 522,600,000 1.34 468,000,000 1.20

LEVER BROTHERS 82,748,282,920 27.34 56,749,462,500 18.75 52,270,038,260 17.27

LIVESTOCK

FEEDS 85,422,000 3.45 70,070,800 2.83 2,267,998,900 1.89

MOBIL OIL 12,525,671,340 65.13 39,377,192,400 163.80 41,588,854,000 173.00

MORISON IND. 235,572,705 2.58 127,830,150 1.40 81,263,453 0.89

NIG BOTTLING CO 26,066,754,416 26.75 95,367,005,200 73.40 64,625,284,920 49.38

NIG. BREWERIES 159,113,064,320 42.08 639,792,914,400 84.60 299,931,288,240 39.66

NIG.ENAMELWAR 96,192,000 3.34 82,368,000 2.86 114,336,000 3.97

NIGERIAN ROPES 66,684,082 2.01 485,149,120 1.84 574,796,240 2.18

NIG WIRE IND. 36,600,000 2.44 33,600,000 2.24 33,600,000 2.24

NORT. NIG FLOUR 617,760,000 8.32 2,300,986,840 20.66 3,636,765,000 24.49

P.S MANDRIES 206,400,000 5.16 322,400,000 8.06 312,000,000 7.80

P.Z INDUSTRIES 14,520,601,770 10.00 25,387,817,040 14.57 57,352,762,420 22.57

PHAR DEKO PLC 103,200,000 2.58 401,860,800 4.73 347,553,600 3.66

POLY PRODUCTS 100,800,000 0.42 148,800,000 0.62 240,000,000 1.00

R.T BRISCOE PLC 308,400,000 2.57 4,023,436,080 11.08 3,489,640,860 9.61

ROADS NIG.PLC 20,600,000 1.03 21,600,000 1.08 20,200,000 1.01

S.C.O.A NIG PLC 1,180,800,000 2.40 1,280,500,000 1.97 359,890,000 0.73

STUDIO PRESS

PLC 126,400,000 1.58 132,800,000 1.66 126,400,000 1.58

TOTAL NIG. PLC 19,031,072,920 64.06 72,827,469,000 214.50 65,860,477,560 193.98

U.A.C 3,588,857,145 3.95 15,730,199,998 13.77 61,173,794,880 23.81

U.B.A 15,164,000,000 8.92 29,325,000,000 11.50 141,623,600,000 20.06

U.T.C 897,000,000 0.80 2,130,375,000 1.90 1,110,037,500 0.99

UNION BANK 52,752,128,000 20.96 78,146,640,000 31.05 246,052,400,881 27.27

UNITED NIG. TEXT 2,698,508,502 3.20 3,027,389,226 3.59 1,370,336,349 0.80

VITAFOAM 1,987,440,000 4.55 2,660,112,000 4.06 2,692,872,000 4.11

VONO 90,909,280 1.88 89,458,600 1.85 480,000,000 1.60

W. AFRICA. P.CEM 39,672,576,000 23.13 29,209,856,000 17.03 124,596,416,166 41.51




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Table 4: Compliance indices

Company Names SAS3 SAS8 SAS9 SAS 13 SAS 19 Avg score

A.G LEVENTIS 1 0.67 1 0.75 0.67 0.82

AFPRINT 0.8 0.33 0.85 0.5 0.5 0.6

AFR. PET 1 0.8 1 1 0.69 0.9

BERGER PAINT 1 0.5 1 0.5 0.69 0.74

CADBURY 1 0.83 1 1 0.92 0.95

CAP PLC 0.9 0.83 0.85 0.5 0.77 0.77

CAPPA & D'ALB 0.9 0.67 0.92 1 0.85 0.87

CFAO 1 0.83 1 0.5 0.77 0.82

CHELLARAMS 0.8 0.83 0.85 0.83 0.75 0.81

COSTAIN W.AF 1 0.67 1 0.8 0.62 0.82

DN MEYER 1 0.67 1 NA 0.92 0.9

DUNLOP 1 0.71 1 0.5 0.77 0.8

FIRST BANK 1 0.67 1 0.5 0.85 0.8

GLAXO SMITH 1 0.83 1 1 0.77 0.92

GUINNESS 1 0.83 1 NA 0.92 0.94

INCAR NIGERIA 1 0.83 1 0.4 0.85 0.82

JOHN HOLT 0.9 0.83 0.85 1 0.77 0.87

UNILEVER 0.9 0.78 0.92 NA 0.62 0.81

L/STOCK FEEDS 1 0.83 1 NA 0.77 0.9

MOBIL OIL 1 0.67 1 1 0.93 0.92

MORISON INDS 1 1 1 NA 0.77 0.94

NIG BOTTLING 1 0.83 NA 1 0.92 0.94

NIG.BREWERIES 1 0.88 1 0.67 0.92 0.89

NIGENAMELWARE 0.6 0.83 0.69 NA 0.92 0.76

NIGERIAN ROPES 1 0.83 1 NA 0.92 0.94

NIG WIRE INDS 1 0.83 1 NA 0.54 0.84

N. N FLOUR MILLS 1 0.67 1 0.5 0.92 0.82

P.S MANDRIES 0.8 0.5 0.85 1 0.92 0.81

P.Z INDUSTRIES 1 0.83 1 1 0.92 0.95

PHARMA DEKO 1 0.83 1 NA 0.85 0.92

POLY PRODUCTS 1 0.83 1 1 0.92 0.95

R.T BRISCOE NIG. 1 0.67 1 0.5 0.92 0.82

ROADS NIGERIA 1 0.67 1 0.8 0.85 0.86




Vol.2, No.4, 2011

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S.C.O.A NIGERIA 0.9 0.67 0.92 0.25 0.5 0.65

STUDIO PRESS 0.8 0.67 0.85 0.5 0.85 0.73

TOTAL NIGERIA 1 0.92 0.83 0.5 0.79 0.81

U.A.C 0.9 0.67 0.92 0.83 0.62 0.79

U.B.A 1 0.67 1 0.5 0.77 0.79

U.T.C 1 0.83 1 0.5 0.62 0.79

UNION BANK 1 0.67 1 0.5 0.71 0.78

UNI. NIG. TEXTI 1 0.67 1 1 0.92 0.92

VITAFOAM 1 0.67 1 0.67 0.92 0.85

VONO 0.9 0.83 0.92 NA 0.92 0.89

W. AFR.P. CEM 0.9 1 0.92 0.8 0.93 0.91

Source: Authors’ Computations, 2010, based on Financial statements of 2006.

Table 5: Correlation Statistics

Measurement Items R. Pearson R. Spearman

2002 2004 2006 2002 2004 2006

S.D. Compliance and

Profitability

Total 0.163 0.198 0.158 0.052 0.103 -0.056

Per Share -0.256 -0.044 -0.047 0.085 0.037 -0.121

S.D. Compliance and Net

Asset

Total 0.413** 0.252 0.211 0.162 0.098 0.004

Per Share 0.501** 0.042 0.066 0.173 0.122 0.008

S.D. Compliance and

Market Value

Total 0.318* 0.22 0.26 0.056 0.021 0.076

Per Share 0.196 0.042 0.067 0.184 0.108 0.187

Source: Authors' Computations, 2010.




Vol.2, No.4, 2011

34

Factors Influencing Real Estate Property Prices

A Survey of Real Estates in Meru Municipality, Kenya

Omboi Bernard Messah (corresponding author)

School of Business & Management Studies

Kenya Methodist University, Tel: +254 724770275 E-mail: [email protected]

Anderson .M. Kigige

P.O. Box 111 - 60200, Meru- Kenya

Tel 0721401289 E-mail: [email protected]

Abstract

Real estate is often used to refer to things that are not movable such as land and improvements

permanently attached to the land. Different types of real estate can have very different cyclic properties.

Real estates go through bubbles followed by slumps in Meru municipality and some real estate

properties take shorter time while others take longer to sell despite that the prevailing conditions seem

similar. Several studies done especially on changes in prices of real estates revealed that real estate

prices go through bubbles and slumps.

The study therefore, investigated factors at play in determining real estate property prices in Meru

Munincipality in Kenya. The study investigated factors such as incomes of real estate investors, the

influence of location on the price, demand and realtors influence on the price.

The study adopted descriptive research design to obtain information on the current status of the

phenomenon. Structured questionnaires were used in data collection to obtain the required information

needed for the study. The population consisted of all 15,844 registered real estate owners in the 5 (five)

selected areas of Meru municipality from which a sample of 390 real estate owners were selected by

stratifying the population and then selecting the respondents by use of simple random sampling.

The data obtained was analyzed by use of available statistical packages for social sciences to obtain

descriptive statistics and a regression model. Findings indicated that incomes alone contributed almost

70% of the variations in prices. Demand alone contributed 20% of the changes in prices of real estate.

Location and Realtors were found insignificant in determining real estate prices.

A summary regresion showed that the variables consindered could explain up to about 70% of variations

in prices. The study recommends that further investigation be done on reasons why location and

realtors were not significat in determining real estate property prices in Meru municipality.

Keywords: Real Estate, Property Prices, realtors, Demand, Meru Municipality

ACRONYMS AND ABBRREVIATIONS



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