




































 

 

 
32 

† Corresponding author 
© 2017 Conscientia Beam. All Rights Reserved. 

 

 

AN ASSESSMENT OF THE MARKET STRUCTURE OF SESAME AMONG 
FARMERS,WHOLESALERS AND RETAILERS IN NASARAWA STATE,NIGERIA 

 

 

Anzaku T.A.K1+ 
Chidebelu S.A.N.D2 
Achike  A.I3 

 

1College of Agriculture, Department of Agricultural Extension and 
Management, Lafia , Nigeria 
2,3Department of Agricultural Economics, University of Nigeria, Nsukka, 
Nigeria 
 

 
(+ Corresponding author) 

 ABSTRACT 
 
Article History 
Received: 9 December 2016 
Revised: 3 January 2017 
Accepted: 8 February 2017 
Published: 17 March 2017 
 
 
 

Keywords 
Farmers 
Wholesalers 
Retailers 
Market 
Lorenz curve 
Gini coefficient 
Herfindahl index. 

 

 
The study assessed principally the level of equality/inequality in the sales distribution 
of sesame among farmers, wholesalers and retailers. The sampling procedure used 
involved a random selection of 120 sesame farmers, 40 wholesalers, and 60 retailers 
proportionately drawn from the three agricultural zones of Nasarawa State. The 
instrument for data collection was a structured questionnaire. Tools of data analysis 
were descriptive statistics, regression technique and measures of market concentration 
such as Lorenz curve, Gini coefficient and Herfindahl index. The results which showed 
Gini coefficient index values of 0.686, 0.331 and 0.589 and Herfindahl index values of 
0.115, 0.442 and 0.118 for farmers, wholesalers, and retailers, respectively, were 
indications of varying levels of inequality in the distribution of sesame sales income and 
the existence of some monopolistic forces in sesame market. Socio-economic variables 
which affected sales income (the main determinant of market concentration) positively 
and significantly (p≤0.01) were education, total value of investment and non-farm 
sources of income. As farmers and middlemen are indispensably interdependent, 
deliberate policy intervention is required in areas such as adequate formal credit, 
diversification of enterprises and human capital development to make them (farmers 
and middlemen) more competitive in the rapidly evolving global economy. 
 

Contribution/ Originality: The study is one of very few studies which have investigated the existence or 

otherwise of monopolistic forces in the marketing of farm products using a combination of analytical tools such as 

Gini coefficient, Herfindahl index, and Lorenz curve. 

 

1. INTRODUCTION 

Sesame (Sesamun indicumL), also called beniseed, is believed to have originated from tropical Africa. Major 

producing areas worldwide include India, China, Malayar, Sudan, Mexico, Pakistan, Venezuela, Uganda and 

Nigeria. Japan, U.S.A., Italy, Israel and Venezuela are the major importers [1]. In Nigeria, the crop is widely grown 

in the northern and central zones of the country as one of the major export crops [2]. Average seed yields, ranging 

from 500-800kg/ha obtained from farmers’ fields, are considered relatively low compared to average yield of 1000 

kg/ha obtained from research farms [3]. Annual output figures increased from 56000 metric tonnes in 1994 to over 

93,250.7 metric tonnes in 2007 [1, 4, 5]. Generally, sesame seed is used in food preparation such as stew and 

Current Research in Agricultural Sciences 
Vol. 4, No. 2, 32-42. 
ISSN(e): 2312-6418 
ISSN(p): 2313-3716 
DOI:  10.18488/journal.68.2017.42.32.42 
© 2017 Conscientia Beam. All Rights Reserved. 

 

 
 

http://crossmark.crossref.org/dialog/?doi=10.18488/journal.68.2017.42.32.42


Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
33 

© 2017 Conscientia Beam. All Rights Reserved. 

confectioneries. The oil is used in manufacturing industries as well as substitute for olive oil in salads and cooking 

oil. Sesame is an important commercial crop, and one of the major crops produced in different locations of Nasarawa 

State. With its estimated output of over 41570 metric tonnes (about 40% of the national output) from about 46710 

hectares under cultivation in the state, an annual estimated foreign exchange earning of US $12.3 million can be 

generated [2, 6]. Sesame is marketed mostly in its primary form in the State. The oil extracted by traditional 

methods and the cakes resulting from the process are used mainly for local consumption.  Sesame is a source of 

income to farmers, middlemen, and service providers involved in its production and marketing. Thus, sesame has 

the potential to generate foreign exchange earnings for the country, reduce poverty, especially among rural 

dwellers in the producing areas, and facilitate agro-industrialization and rural development in Nigeria. Market 

structure defines the nature of competition and pricing within the market [7]. It deals primarily with  the number 

of buyers and sellers of a product, the degree of buyer and seller concentration, the condition of entry to the market 

and product differentiation [8]. An assessment of market structure reflects market performance and reveals the 

degree of the existence of competitive forces and how imperfect a market is [9, 10]. A situation of perfect equality 

in the size distribution of market participants, implying very low market concentration is required for the ideal 

perfect competition to thrive. Cases of the existence of monopolistic forces and varying levels of inequality in the 

distribution of sales revenue among market participants have been reported for different commodities other than 

sesame in Nigeria [11-14]. Study of the market structure of sesame is necessary for policy decisions or initiatives 

geared towards boosting its production, processing and consequently the earnings of farmers and middlemen. The 

specific objectives of the study were to: 1) describe the socio-economic characteristics of farmers, wholesalers and 

retailers, 2) determine the level of equality/inequality in sales distribution of sesame among farmers, wholesalers 

and retailers, and 3) identify important socio-economic factors that affect sales income, the main determinant of 

market concentration.  

 

2. MATERIALS AND METHODS        

2.1. Study Area 

The study area is Nasarawa State, created out of former Plateau State in October,1996.It lies between latitudes 

7o 45´ and 9o 25´ N and longitudes  7o and 9o    37´ E of the Greenwich meridian, and has a land area of 27,137.8 

square kilometres with a population of about  1,863,275 people [15]. Mean annual rainfall in the State ranges from 

1100 – 1600 mm and temperature could rise as high as 39oC and fall as low as 17oC [16]. The thirteen Local 

Government areas that make up the state are Akwanga, Awe, Doma, Karu, Keana. Keffi, Kokona, Lafia, Nasarawa, 

Nasarawa-Eggon, Obi, Toto and Wamba. Major crops produced in the State are rice, yam, cassava, sesame, egusi, 

groundnut and cowpea. Sesame has been a major cash crop produced in commercial quantities in Doma, Lafia and 

Nasarawa Local Government Areas of the State. 

 

2.2. Sampling Procedure 

Sampling was preceded by a reconnaissance survey to determine the sampling frame. The sampling   procedure   

involved a simple random selection of 108 sesame  farmers, 40 wholesalers and 60 retailers of the produce. The 

farmers were proportionately drawn from the main and minor producing areas, and wholesalers and retailers from 

urban and rural sesame markets in the three agricultural zones of Nasarawa State. Each sample size was determined 

from a sampling frame of 300 sesame farmers, 112 wholesalers and 213  retailers  provided by Nasarawa 

Agricultural Development Programme (NADP) and listed during the reconnaissance survey.  

 

2.3. Data Collection 

Data for the study were collected from primary sources using two sets of questionnaires, one set for farmers 

and the other for middlemen. Trained enumerators were used to administer them so that information on sales and 



Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
34 

© 2017 Conscientia Beam. All Rights Reserved. 

the socio-economic characteristics of farmers and middlemen were elicited for sesame produced in 2013, in a 

duration of about three months. 

 

2.4. Data Analysis 

Descriptive statistics were employed to realize the objective on the socio-economic characteristics of the 

farmers and middlemen. Measures of concentration used in determining levels of inequality/equality among 

farmers, wholesalers and retailers are Lorenz curve, Gini coefficient and Herfindahl index. Lorenz curve relates the 

cumulative proportion of income to the cumulative proportion of population, after ordering the population 

according to increasing level of income [17]. Lorenz curve is obtained by plotting the cumulative proportion of 

each category of population arranged in order from the smallest number to the largest, against cumulative 

proportion of their income [9, 11]. The extent to which a Lorenz curve swings away from the line of equal 

distribution is a measure of inequality of the variables of interest [9]. The degree of inequality in sales revenue is 

estimated by reading the curve at the point where it lies furthest from line of equal distribution [11]. If the 

distribution is totally equitable, the curve will fall on the 45-degree line. The greater the inequality the greater the 

departure from the 45-degree line [9, 14]. Gini coefficient is based on Lorenz curve and is 1 minus the sum of the 

product of the proportion of market participants of interest and the cumulative proportion of their sales earnings 

arranged in class intervals from the lowest to the highest [9, 11]. Values of Gini coefficient, which range between 0 

and 1 express the degree to which the market is concentrated. Herfindahl index represents the sum of squared 

ratios of the sales of each individual trader to the total sales of all traders [10]. Lorenz curve, Gini coefficient and 

Herfindahl index are interconnected as measures of concentration. The interpretation of Gini coefficient is based on 

the Lorenz curve. The interpretation of Herfindahl index and Gini coefficient are the same but Gini coefficient is 

particularly sensitive to inequalities in the middle range of the distributions [10].  

(a)    Gini coefficient    

     . . . (1) 

where: 

GC = Gini coefficient  

Pi= proportion of market participant of sesame under consideration in the ith class  

Ci=cumulative proportion of the sales of the market participants of sesame under consideration in the ith  class. Gini 

coefficient has a value ranging from 0 to 1. It expresses the degree to which a market is concentrated. If the Gini 

coefficient value is 0, it implies perfect equality in the size distribution of participants. If it is 1, it implies perfect 

inequality (imperfect market) [9-11]. The middle values can be similarly interpreted depending on the direction 

they tilt to.   

(b) Herfindahl index (HI) 

. . . (2) 

where: 

xi= volume of sales in the ith class of middlemen  

x = total volume of sales in the sample  

n =  total number of classes         

HI tends to be more accurate as the size of the data increases. The highest possible value of HI is 1 which implies 

that the market is perfectly concentrated (imperfect market). The determinants of sesame sales income was achieved 

through the use of regression analysis, expressed mathematically in its explicit form as: 

Y = a + b1X1 + … + b5X5  + e  . . . (3) 



Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
35 

© 2017 Conscientia Beam. All Rights Reserved. 

Where:               

X1 = level of education in years   

X2 = experience  in sesame marketing in years 

 X3 = credit accessed by market participants in naira 

X4 = total value of investment in sesame marketing in naira  

X5= other sources of income in naira                                                                                                                                     

a   = constant                                                                                                                                                                        

b1-b5= coefficients of independent variables                                                                                                                      

e = error term 

 

3. RESULTS AND DISCUSSION 

3.1. Socio-Economic Characteristics of the Farmers 

The socio-economic characteristics of farmers are shown in Table 1. All the farmers (100%) were married and 

88.9% of them were males, while only 11.1% were females. Although the ages of the farmers were between 20 and 

75 years, the mean age of 46.7 years depicted an active farming population. Formal education by farmers was 

limited to an average schooling duration of 12.4 years. These included farmers who did not go to school and those 

who spent a maximum duration of 20 years schooling. Education is important in facilitating the response of farmers 

to the adoption of available technologies that increase productivity. Marketing and production experience spanned 

for a period of 1 to 35 years. The mean marketing experience of 11.8 years could be a good asset if properly 

harnessed. Statistics of average distance to the nearest market centre and output, farm size, household size, sales 

volume, price, quantity sold and yield per farmer provide tools for decision-making by the farmers, marketers and 

policy-makers. 

 

3.2. Socio-Economic Characteristics of Wholesalers and Retailers  

Education, marketing experience and age represent the human capital acquired by the wholesalers and retailers. 

Table 2 shows the age of 41.1 years, 9.1 years in school, 13.9 years of marketing experience, value of equipment of 

N8472.5, value of investment of N877622.0 and sales income of N1025096.1, all in mean values for wholesalers. These 

were in all cases higher than the age of 34.9 years, 3.9 years in school, 10.3 years of marketing experience, value of 

equipment of N1961.7, value of investment of N 219171.8 and sales income of N 276829.3, all in mean values for retailers. 

These variables, consisting of human capital (education, experience and age), physical capital (equipment, investment 

funds) and earnings (income) affect market performance and partly explain the level of efficiency and inequality reported 

in the study. 

Table-1. Statistics of socio-economic variables of the farmers 

  Statistics     

Variables Minimum Maximum Percent Mean Standard Deviation                  

Gender(% male)   88.9   
% married   100.0   
Age (years) 30.0 75.0  46.7 0.8 
Education (years) 0.0 20.0  4.8 0.6 
Production experience (years) 1.0 35.0  12.4 0.8 
Marketing experience (years) 1.0 35.0  11.8 0.7 
Distance to the nearest market centre 2.0 40.0  10.3 0.8 
Output of sesame (Kg/farmer) 50.0 6500.0  1005.6 123.2 
Farm size (Ha) 0.2 12.0  2.5 0.3 
Price (N/Kg) 110.0 210.0  160.8 2.0 
Sales income from sesame (N/farmer) 8500.0 1170000.0  161381.3 21640.8 
Quantity sold (Kg/farmer) 50.0 6500.0  979.9 122.5 
Family size 3.0 37.0  12.8 0.5 
Yield (Kg/ha) 100.0 720.0  363.8 11.7 

Source: Field Data    

 

 



Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
36 

© 2017 Conscientia Beam. All Rights Reserved. 

Table-2. Socio-economic characteristics of wholesalers and retailers 

 Wholesalers Retailers 
Variables Mean Standard deviation Mean Standard deviation 

Education (years) 9.1 4.4 3.9 3.9 

Marketing experience    
(years) 

13.9 5.9 10.3 4.9 

Value of equipment used(N) 8472.5 8220.5 1961.7 958.3 
Age in years 41.1 5.9 34.9 4.7 
Value of investment (N) 877622.0 656113.9 219171.8 145145.8 
Sales income (N) 1025096.1 736593.7 276829.3 218139.2 

Source: Field Data 

 

3.3. Distribution of Sales Income of Sesame among Farmers, Wholesalers and Retailers 

Tables 3, 4 and 5, show prominent features of the distribution of sales income of sesame among farmers, 

wholesalers and retailers. Most farmers, wholesalers and retailers fell into lower income classes. With the exception 

of the first class, distribution of sales income in the other four classes was fairly even among wholesalers. Combined 

sales income of two farmers or firms in the last two classes was about 66% of the total sales income of 66 farmers in 

the first class. This is an example of the disparity of income among farmers. The distribution among retailers was 

characterized by sales income per retailer ranging from N 25,689 in one class to N 900,000 in another. 

The Lorenz curves of the distribution of income from the sales of sesame among farmers, wholesalers and 

retailers are presented in Figures 1, 2 and 3, respectively. The further away the divergence of the Lorenz curve is 

from the line of equal distribution, the less equally the commodity involved is distributed. Thus, the curves depicted 

that the inequality in the distribution of sales income of sesame was highest among farmers and lowest among 

wholesalers. The level of inequality among retailers falls in between the two. 

The estimated Gini coefficients of 0.686, 0.331 and 0.589 among farmers, wholesalers and retailers, 

respectively, are shown in Tables 3, 4 and 5. These indicate varying levels of inequality, competition or imperfection 

and consequently inefficiency in sesame markets in Nasarawa State. The rule of thumb suggested by Dillon and 

Hardaker [18] is to regard Gini coefficient value greater than 0.35 as an indication of inequitable distribution. The 

high Gini coefficient values among farmers and retailers are indications of high levels of concentration and 

inequality in size distribution, while the relatively low Gini coefficient value among wholesalers tended more to 

equality in size distribution. The highest level of inequality was found among sesame farmers. Similar patterns were 

portrayed by the Lorenz curves. The large number of resource-poor and predominantly small-scale producers 

cannot produce and sell as much quantity to earn as much income as their large-scale commercial counterparts. 

The Herfindahl index values also exhibited the existence of some inequality in size distribution. Interestingly, 

the Herfindahl index values of 0.115, 0.442 and 0.188 in Tables 3, 4 and 5 for farmers, wholesalers and retailers, 

respectively, were different from those of Gini coefficient in size and pattern. The Herfindahl index values were 

smaller than the Gini coefficient values in size in the cases of the farmers and retailers. It is possible that these Gini 

coefficient values were exaggerated as a result of being sensitive to inequalities in the middle range of their 

distributions. For the wholesalers, the Herfindahl index value was higher than the Gini coefficient value. The 

relatively small sample size of wholesalers could have affected the accuracy of its Herfindahl index value. The 

lowest point of equality (perfect equality) of the Herfindahl index decreased with sample size and as the sample size 

increased, the accuracy of the Herfindahl index value also increased. The lowest, middle and highest values of the 

Herfindahl index are the reverse of the Gini coefficients. The interpretation of the Herfindahl index depends on the 

lowest point of equality, which is defined as , n being the sample size. Comparative analysis of the values of the 

Herfindahl index of elements of differing sample sizes can therefore be a futile exercise in determining the degree of 

inequality in size distribution. 



Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
37 

© 2017 Conscientia Beam. All Rights Reserved. 

 
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Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
38 

© 2017 Conscientia Beam. All Rights Reserved. 

 

T
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Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
39 

© 2017 Conscientia Beam. All Rights Reserved. 

 

 

 

 

 

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Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
40 

© 2017 Conscientia Beam. All Rights Reserved. 

0

20

40

60

80

100

120

0 10 20 30 40 50 60 70 80 90 100

Cu
m

ul
at

iv
e 

%
 o

f s
al

es
 in

co
m

e(
N

)

Cumulative % of farmers

Line of equal
distribution

Lorenz curve

 
Figure-1. Lorenz curve of the distribution of sales income of sesame among farmers. 

                                        Source: Field Data                                                                                                                                                                                                                                

 

 
Figure-2. Lorenz curve of the distribution of sales income of sesame among wholesalers. 

Source: Field Data                                                                                                                                                                                                                   

 

 

Figure-3. Lorenz curve of the distribution of sales income of sesame among retailers. 
    Source: Field Data                                                                                                                                                                                                                         

 



Current Research in Agricultural Sciences, 2017, 4(2): 32-42 
 

 
41 

© 2017 Conscientia Beam. All Rights Reserved. 

3.4. Socio-Economic Factors Affecting Sales Income, the Main Determinant of Market Concentration 

Result of the regression between socio-economic characteristics and sales earning of middlemen is presented in 

Table 6. Socio-economic characteristics used were education, experience, credit accessed, total value of 

investmentand other sources of income. Relationship therefore existed between sales earning and the socio-

economic characteristics. Education, total value of investment and other sources of income were the socio-economic 

variables that affected sales income positively and significantly at 0.01 level of probability. In the circumstance, 

these variables are important in efforts aimed at improving the market concentration of sesame. 

  

Table-6. Estimated regression coefficients of the relationship between socio-economic variables and sales income of middlemen 

Independent Variable Coefficients 

Constant -13406.543 

Education   30399.579* 

Experience         -15.170 

Credit accessed           -0.392 

Total value of investment            0.567* 

Other sources of income            0.102* 

R2             0.661 

Adjusted R2             0.642 

Durbin-Watson             1.936 

Dependent variable – sales income; significant at 1% level of probability   

 

4. CONCLUSION AND RECOMMENDATION 

For the study, the marketing system of sesame was not perfectly competitive, evidently as a result of disparity 

in sales income of sesame among farmers, wholesalers, and retailers. Wide differences in the size and value of output 

of each farmer and the volume and value of sales of each middleman accounted for the disparity. Unlimited access to 

adequate formal credit, diversification into other enterprises for free interflow of resources and continual human 

capital development are measures recommended for making market participants-farmers, wholesalers and retailers, 

more competitive  in the area. 

 

Funding: This study received no specific financial support. 
Competing Interests: The authors declare that they have no competing interests. 
Contributors/Acknowledgement: All authors contributed equally to the conception and design of the 
study. 

 

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Views and opinions expressed in this article are the views and opinions of the author(s), Current Research in Agricultural Sciences shall not be responsible or 
answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. 

 

https://scholar.google.com/scholar?hl=en&q=Efficiency%20measurement%20of%20cashew%20nut%20marketing%20in%20Enugu%20State,%20Nigeria
https://scholar.google.com/scholar?hl=en&q=Price%20communication%20and%20market%20integration:%20The%20case%20of%20banana%20and%20plantain%20in%20Nigeria
https://scholar.google.com/scholar?hl=en&q=Estimating%20Lorenz%20curves%20using%20a%20Dirichlet%20distribution
http://dx.doi.org/10.1198/073500102317352029

