A linear probability model (LPM) analysis of seller decisions selling cassava on-farm (farm-gate) or off-farm (market) was derived and estimated consistent with a sample 216 farm households. Variables of proportional transaction costs have different effects on off-farm and on-farm market participation decisions. The seller type decision revealed that coefficients for have a personal means of transport, road conditions to the nearest town and marketing experience were positive and significantly related to increase in off-farm cassava sellers at 5% level of probability. The coefficient for distance from the house to the farm was negatively and significantly related to increase in farmers selling off-farm at 1% level of probability. The coefficients for crop transportation costs, distance to the nearest town, distance from the house to the farm and yield were negative and significant at 5% level of probability. These decisions to participate as an off-farm or on-farm seller were as a result of proportional transaction costs associated with participating in the market. It was found that unexpected transaction costs inhibit farm households from selling off-farm. The results therefore calls for policies aimed at infrastructural development especially on roads and establishment of bulking centers to mitigate transportation costs.Transaction Costs, Seller Decisions and Cassava Farmers

Okoye, B.C

Onyenweaku, C.E

Ukoha, O.O

English

Research Papers in Economics

EFFECT OF TRANSACTION COSTS ON SELLER DECISIONS AMONG SMALL-HOLDER CASSAVA 
FARMERS IN SOUTH-EASTERN NIGERIA 
 
Okoye, B.C
1(okoyebenjamen@yahoo.com)., Onyenweaku, C.E
2 and Ukoha, O.O
2 
1National Root Crops Research Institute, Umudike, Abia State.  
2Dept. of Agricultural Economics, Micheal Okpara University of Agriculture, Umudike, Abia State. Nigeria. 
 
ABSTRACT 
A  linear  probability  model  (LPM)  analysis  of  seller  decisions  selling  cassava  on-farm  (farm-gate)  or  off-farm 
(market)  was  derived  and  estimated  consistent  with  a  sample  216  farm  households.  Variables  of  proportional 
transaction costs have different effects on off-farm and on-farm market participation decisions. The seller type 
decision revealed that coefficients for have a personal means of transport, road conditions to the nearest town and 
marketing experience were positive and significantly related to increase in off-farm cassava sellers at 5% level of 
probability. The coefficient for distance from the house to the farm was negatively and significantly related to 
increase  in  farmers  selling  off-farm  at  1%  level  of  probability.  The  coefficients  for  crop  transportation  costs, 
distance to the nearest town, distance from the house to the farm and yield were negative and significant at 5% level 
of probability. These decisions to participate as an off-farm or on-farm seller were as a result of proportional 
transaction costs associated with participating in the market. It was found that unexpected transaction costs inhibit 
farm households from selling off-farm. The results therefore calls for policies aimed at infrastructural development 
especially on roads and establishment of bulking centers to mitigate transportation costs. 
 
Keywords 
Transaction Costs, Seller Decisions and Cassava Farmers 
 
INTRODUCTION 
Fresh cassava roots, with about 70% water content, are bulky and therefore expensive to transport especially over 
long distances. The roots are also perishable, and begin to deteriorate soon after harvest. These features are expected 
to have profound bearing on the trade network for the roots. For instance, the bulky nature of the roots makes the 
market for fresh roots more localized around the producing areas than the market for processed cassava products. 
Also the perishability factor makes the marketing process considerably shorter for fresh roots than other cassava 
products (Ezedinma et al., 2007). 
 
Marketing can be a problem for poor farmers especially those living in villages with poor feeder roads who may not 
have resources to transport their commodities to the market. Typically, farmers transport cassava as Head loads, on 
Bicycles, or in Lorries. With poor market access, marketing cassava can be particularly problematic because of its 
bulky nature, especially unprocessed roots. Poor access also makes the movement of goods and people difficult; 
particularly during the rainy season. This has significant implications for marketing (IITA, 2004) To date, the 
literature has typically assumed that transaction costs are exogenously determined and has focused on the various 
ways that self-sufficiency affects behavior, especially with respect to production, insurance and credit (de Janvry et 
al., 1991). Paradoxically, little research has been devoted to the study of transactions costs themselves (Fafchamps 
and Hills, 2005).  
 
By examining how small cassava farmers sell their output, we hope to throw some light on the nature of transactions 
costs affecting farmers in poor countries. In contrast with farmers in developed countries who often have large farms 
and enjoy good institutions and infrastructure, most farmers in developing countries are very small, geographically 
isolated, and outside the reach of formal market institutions (Fafchamps and Hills, ibid). For them, interacting with 
the market is fraught with difficulty and danger, so much so that many opt for self-subsistence altogether (Key et al., 
2000). The objectives of the study therefore are to describe the socio-economic characteristics of the on-farm and 
off-farm  sellers  as  well  as  estimate  the  determinants  of  selling  (on-farm  and  off-farm)  for  cassava  producing 
households.  
 
 
METHODOLOGY 
Three out of the 5 states in the South-East Geo-Political zone were randomly selected for the study. They were 
Anambra, Abia and Enugu States. A multi-stage randomised sampling procedure was used to collect cross sectional 
data to identify factors of market participation among cassava producing households. Since each state is made up   2 
of three agricultural zones each except Anambra State with four Zones, at such the second stage, two agricultural 
zones per state were randomly selected giving a total of six agricultural zones. In the third stage, two LGAs were 
randomly selected from each zone giving a sample of 12 LGAs.  In the fourth stage, three communities were selected 
randomly from each Local Government Area giving a sample of 36 communities. In the last stage 10 household 
producers were randomly selected, giving a total of 360 respondents. Among the 360 cassava producing households 
were 216 net sellers (166off-farm and 50on-farm), 95 autarkic (neither buying nor selling) households and 49 net 
buyers (37off-farm and 12on-farm). This study dropped 144 households that are autarkic and buyers. 
 
The Linear Probability Model (LPM) looks like a typical linear regression model but because the regressand is 
binary, or dichotomous, it is called a linear probability model. This is because the conditional expectation of Yi 
given Xi, E(Yi/Xi), can be interpreted as the conditional probability that the event will occur given Xi, that is, Pr (Yi 
= 1/Xi) (Gujarati, 2004). Thus in our data, E (Yi/Xi) gives the probability of being an off-farm seller or on-farm 
seller and whose Xi’s are the independent variables.  
 
The  LPM  poses  several  problems,  which  are  as  follows:  non-normality  of  the  disturbances  ui,  heteroscedastic 
variances  of  the  disturbances  and  questionable  R
2  as  a  measure  of  goodness  of  fit.  These  problems  can  be 
surmounted by (a) the use of Weighted Least Squares (WLS) to resolve the heteroscedasticity problem or increase 
the sample size to minimize the non-normality problem, (b) use of the Logit model, (c) and the Probit model (see 
Gujarati, 2004).The LPM was used because of the nature of the data set used in this study. 
 
The LPM, which tests factors affecting the incidence of being an off-farm or on-farm seller of cassava, can be 
specified as follows implicitly: 
Yi
sellertype = bo + biXi + e ………………………… (1) 
Where: 
Y
sellertype is dummy variable for seller decision (1 = off-farm, 0= on-farm), i is number of observations, Xi is vector of 
independent variables, bi is vector of unknown coefficients and e is independently distributed error term. 
 
Explicitly the seller type equation for cassava selling households was also modeled as follows; 
Yi
sellertype= b0 + b1X1 + b2X2 + b3X3 + b4X4 + b5X5 + b6X6 + b7X7
 + b8X8
 + b9X9 + b10X10 + b11X11
 + b12X12 + b13X13 + 
b14X14 + e …………….. (2)  
 
Where; 
X1 is personal means of transportation (dummy variable; 1=yes, 0=no), X2 is distance to nearest town (km), X3 is 
distance from the farm to the market (km), X4 is distance from the house to the market (km), X5 is distance from the 
house to the farm (km), X6 is amount of credit borrowed in Naira, X7 is crop transportation costs (Naira/tonne), X8 is 
household size, X9 is dependency ratio (the number of dependents below 18 and above 60 per household of working 
age), X10 is road conditions to nearest town (dummy variable; 1=good, 0=bad), X11 is marketing experience (in 
years), X12 is farm income in naira, X13 is non farm income in Naira, X14 is Cassava yield (kg/ha), b1 – b14  are 
coefficients to be estimated and e
 is error term. 
 
RESULTS AND DISCUSSION 
The data in table 1 show the characteristics of cassava producers in South-Eastern Zone of Nigeria. Among the 216 
respondents were 166off-farm and 50on-farm sellers. The size of cassava harvest and farm size varied considerably 
across households. All the respondents had small land holdings (2.3ha and 3.26ha for off-farm and on-farm sellers 
respectively). The average off-farm seller had an output of about 18.6t, sold 13.6t and consumed 4t. The on-farm 
seller harvested about 31t of cassava, sold 24t and consumed about 6t. The on-farm seller had the highest land 
holdings and may prefer to sell cassava at the farm gate as a result of huge transaction costs emanating from its 
bulky nature. Since most of the households were located in the rural areas, the nearest passable road on the average 
to the nearest town were 4.12km and 9.84km away for the off-farm and on-farm seller respectively. On-farm seller 
households were distantly (12.66km) located from their homes to their farms to 4.55km for their off-farm seller 
counterparts.  
 
 
 
   3 
Table 1: Characteristics of Survey Households, by Market participation Status in South-East Agricultural 
Zone of Nigeria, 2010  
Variable Description  Seller 
  Off-Farm  On-Farm 
Proportional Transaction Costs Related     
% that have personal means of transport  33.85  16.00 
Distance from the farm to market  6.19 
(2.34) 
12.48 
(5.61) 
Distance to the nearest town (km)  4.12 
(1.51) 
9.84 
(3.07) 
Distance from house to the market  6.48 
(2.08) 
10.47 
(4.24) 
Distance from house to the farm  4.55 
(1.13) 
12.66 
(3.94) 
Volume of Credit (N)  37,333.33 
(48,549.63) 
102,013.23 
(137,092.71) 
Cost of transportation (kg)  3.74 
(3.43) 
 
Household Size  5.62 
(1.44) 
5.51 
(1.57) 
Dependency ratio  0.63 
(0.49) 
0.58 
(0.15) 
Cassava Marketing experience in Years  20.95 
(15.44) 
7.13 
(4.41) 
Farm income (N)  218,024.04 
(285,371.2) 
275,060.60 
(378,783.70) 
% of Road conditions to the nearest town  46.98  48.00 
Farm size (ha)  2.30 
(3.78) 
3.26 
(4.16) 
Cassava Output (kg)  18,600.0 
(30,980.73) 
30,769.39 
(42,003.94) 
Yield (kg/ha)  4,520.39 
(962.76) 
9,295.84 
(2,067.46) 
Quantity Sold (kg)  13,491.70 
(4,858.65) 
24,288.95 
(6,448.58) 
Non-farm Income (N)  110,074 
(123,161.20) 
213,448.30 
(197,688.00) 
Survey Results, 2010; Numbers in parentheses are standard deviations. 
 
On-farm seller households were also distantly (12.48km) located from their farms to the markets to 6.19km for off-
farm sellers. This might also partly explain the longer travel and assembly time during the wet season. It also 
illustrates the von Thunen hypothesis. The more perishable and the higher value the products the less distance they 
are transported (Minten and Kyle, 1999). However, the main reason for the longer time was probably the fact that 
roads were worse and often impassable in the rainy season. It also took about N3, 740 to transport a tonne of cassava 
to the market. About 46.98% and 48% of the off-farm and on-farm sellers indicated that the road conditions were 
good respectively. About 33.85% of the off-farm sellers also had a means of transport. 
 
Most off-farm (66.45%) and on-farm (56.31%) households relied on cassava sales for their major source of income. 
On average the income from cassava sales accounted for 55.15% of the total income. The respondents also borrowed 
about N51,000 being volume of credit for production activities with the on-farm sellers having the highest volume 
of N102,013.23. The off-farm sellers had more years (20.9) of marketing experience compared to the farmers selling 
on-farm (7.13years). This seems to be an indication of the high influx into this activity. This might be a reflection of 
the overall economic situation as entry into the market seems often driven by excess labor supply created by falling 
living standards and increasing formal sector unemployment. 
 
   4 
Decision on Market Participation by Seller type (LPM) 
The empirical results of the linear probabiliy regression estimates for market participation among on-farm sellers 
and off-farm sellers are shown in table 2. The chi
2 was highly significant at 1.0% level of probability indicating 
regression of best fit. The likelihood ratio tests indicate that the slope coefficient was significantly different from 
zero for seller decision. The coefficients for distance to the nearest town, crop transportation costs and distance from 
the house to the market were negative and significant at 5.0% level of probability. The coefficient of distance from 
the house to the farm was also negative and significant at 1.0% level of probability respectively. This indicates that 
increase  in  distance  to  the  nearest  town,  from  the  house  to  the  market,  from  the  house  to  the  farm  and  crop 
transportation costs will increase the number of on-farm sellers and decrease the number of sellers’ off-farm. 
 
Table 2: Results of Linear Probability Regression for Market Participation 
Variable  Parameter  Coefficient  Std. Error  t-value 
Have personal means of transport  a11   0.4068     0.1601   2.5414** 
Distance to the nearest town (km)  a12  -0.0990     0.0495  -2.0162** 
Distance from the farm to the market 
(km) 
a13   -0.0302    0.0433  -0.6974 
Distance from the house to the market 
(km) 
a14  -0.114703     0.0579  -1.9782** 
Distance from the house to the farm 
(km) 
a15  -0.2655     0.0721  -3.6803*** 
Volume of Credit (N)  a16  8.73e-06     6.54e-06  1.3348 
Cost of transportation (N/kg)  a17  -0.0003      0.0001  -2.3382** 
Household Size  a18  -0.0286     0.0635  -0.4576 
Dependency ratio  a19  -0.2360   0.5926  -0.3982 
Road conditions to the nearest town  a20  0.8895    0.4079  2.1806** 
Marketing experience  a21  0.0205  0.0081  2.5148** 
Farm income (N)  a22  3.72e-06     2.99e-06  1.2409 
Non farm Income (N)  a23  -7.82e-06     7.33e-06  -1.0666 
Yield (kg/ha)  a24  -0.0036    0.0014  -2.5556** 
Constant  a0  1.7881     0.1872  9.5543*** 
Log likelihood     -79.8119     
chi
2             0.0002     
Pseudo R
2             0.5720     
LPM; on farm seller = 0, off farm seller = 1. *, ** and *** = Significant at 10%, 5% and 1% respectively 
 
As expected, the results suggest that those households located far away to the nearest town, from the house to the 
market and to the farm were more likely to sell their cassava on-farm. This contention is plausible since farmers in 
the rural areas do not have easy access and up to-date information about the markets, for the simple reason that 
functional extension offices and some marketing institutions were located in these towns. Increased distance to the 
market and farm also pre-disposes the farmer towards on-farm sales because of huge transactions costs involved in 
traveling long distances emanating from in-accessible roads. This also tends to increased crop transportation costs. 
When the infrastructure is poor, farmers are generally discouraged to use it. And those who do use the infrastructure 
experience high costs (Makhura et al., 2001). Binswanger et al., (1993) conclude that ‘‘the major effect of roads is 
not via their impact on private agricultural investment but rather on marketing opportunities and reduced transaction 
costs of all sorts’’. The coefficients for personal means of transport and marketing experience were positive and 
significant at 5.0% level of probability. These imply that increase in personal means of transport and marketing 
experience will increase the number of off-farm sellers. This is expected because farmers with more marketing 
experience  will  prefer  to  take  their  produce  to  the  market  where  they  may  make  better  price  bargains.  The 
coefficient for yield was negative and significant at 5.0% level of probability. This implies that increase in yield will 
lead to a corresponding decrease in off-farm sales and increase in on-farm sales. This may be because of the huge 
PTCs associated with traveling to the market and house from the farm by the farmers. The on-farm sellers had long 
traveling distances from the farm to the market (12.48km), nearest town (9.84km), from the house to the market 
(10.47km) and from the house to the farm (12.66km).  The farmer may therefore prefer to sell in the farm to mitigate 
these costs. The coefficients for distance from the farm to the market, volume of credit and farm income were 
positive  but  not  significant.  The  coefficients  for  household  size,  dependency  ratio  and  non-farm  income  were 
negative but also not significant.   5 
CONCLUSION 
This article shows that reductions in transport and/or transaction costs provoke changes selling decisions among 
cassava  farmers  in  South-Eastern  Nigeria.  Policy  efforts  should  enhance  the  production  capacity  through  the 
provision of land.  The development infrastructure especially roads would make a difference in the economics of the 
marketing behaviour of these farmers. Surfacing unpaved routes makes the most critical period for food availability 
in the shortage regions by reduction in travel time and easy accessibility especially during the rainy season. Such 
developments could further provide opportunities for private sector development in the rural areas. 
 
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Selling at the Farmgate or Traveling to

Effect of Transaction Costs on Seller Decisions among Small-Holder Cassava Farmers in South-Eastern Nigeria

Effect of Transaction Costs on Seller Decisions among Small-Holder Cassava Farmers in South-Eastern Nigeria

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