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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).
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.4, 2011
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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)
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.4, 2011
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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
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.4, 2011
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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%,
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
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.
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
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
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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%
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Vol.2, No.4, 2011
9
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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Vol.2, No.4, 2011
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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
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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).
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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
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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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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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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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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.
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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,
University of Ilorin, Ilorin, Nigeria
Email: [email protected]
Joshua Adeyemi Olaoye
Department of Accounting and Finance,
University of Ilorin, Ilorin, Nigeria
Email: [email protected], [email protected]
Atanda Olanrewaju Aliu
Department of Accounting and Finance,
University of Ilorin, Ilorin, Nigeria
Email: [email protected], [email protected]
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
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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
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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
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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.
Alternative hypothesis
Compliance with Standards that promote sustainable development improves net-asset.
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3.2.3 Hypothesis 3
Null hypothesis
Compliance with Standards that promote sustainable development is not associated with improve market
value.
Alternative hypothesis
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
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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.
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Accounting WEB, www.accountingweb.co.uk
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Nigerian Quoted Companies”, Proceedings of the 16th Annual International Sustainable Development
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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
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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
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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)
Company Names 2002 N 2004 N 2006 N
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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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
Source: Nigerian Stock Exchange Fact Book 2003, 2005 and 2007
Table 3: Market Value data
Company Names 2002 N 2004 N 2006 N
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
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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
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
Vol.2, No.4, 2011
32
Source: Nigerian Stock Exchange Fact Book 2003, 2005 and 2007
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
Journal of Economics and Sustainable Development www.iiste.org
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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.
Journal of Economics and Sustainable Development www.iiste.org
ISSN 2222-1700 (Paper) ISSN 2222-2855 (Online)
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