3. MATERIALS AND METHODS
3.3 Sampling Procedure
For this study, 139 farm households were sampled and interviewed from the District. A two-stage sampling technique was used to draw sample cotton producer farmers. First, 14 kebeles from the District were selected through purposive approaches. During the selection, the kebele’s potential for cotton production and the accessibility of the areas to travel were taken into consideration. In the second stage, using the population list of cotton grower farmers from sample kebeles, the intended sample size was determined proportionally to population size of cotton grower farmer. Then the predetermined size of the sample farmers from each kebele were randomly selected using systematic random sampling technique.
Prior to formal survey, a rapid market appraisal (RMA) was conducted in order to get the overall picture of cotton marketing chain. The sample size of cotton traders was 23. Since the number of cotton traders in each locality of the District was few, almost all of them were interviewed. Both licensed and unlicensed traders were included in the traders’
survey.
21
Table 3. Number of traders interviewed and their location Address of
The cooperatives involved in cotton marketing in the year 2005/06 were six out of 18 cooperatives in the District. The cooperatives that were involved in cotton marketing were used as data source. The cooperatives involved in cotton marketing in the year were Gende Wuha, Kokit, Das, Tumet, Shinfa, and Kumer Aftet primary farmers’ cooperatives. The Metema Farmers’ Cooperatives Union was also one of the sources of data. In addition,
Dess and Gondar Ginneries found in Gondar town were data sources from Ginneries.
Bahir Dar Textile Factory was used to represent textile factories as a source of data. This factory is the major purchaser of lint cotton from ginneries found in Gondar whose source of seed cotton is Metema District and the vicinity. The Gondar Oil Milling Factory was also used as the other source of data.
3.4. Method of Data Analysis
For analyzing factors affecting marketable supply of cotton at farm level, an econometric model was used. To describe the characteristics of market players’ descriptive statistics like mean, standard deviation and percentage were employed.
22 3.4.1. Descriptive statistics
To describe the characteristics of market players and to identify key constraints in cotton production and marketing descriptive statistics was used.
3.4. 2. Cotton marketable supply function
In this study, multiple linear regression model was used to analyze factors affecting farm level cotton supply in Metema District.
Model Specification
The economic model specification of the variables is as follows.
Y = )i F(X1,X2,X3,X4,X5,X6,X7,X8,X9,X10,X11,X12,X13 where: Y = quantity of seed cotton supplied to market i
X1= Owned oxen number by household X2= Access to credit for cotton
X = Land allocated to cotton in hectare by a household 3
X4= Productivity of cotton in 2005/ 06
X = Distance from main purchasers in the District 5
X = Price of cotton in the year 2003/04 6
X = Price of cotton in the year 2004/05 7
X = Access to market information 8
X = Access to extension service 9
X = Ownership of corrugated iron house 10
X11= Educational level of household
12
X = Number of male family members aged 14 to 64 years X = Years of experience of a household in cotton production 13
23
Econometric model specification of supply function in matrix notation is the following.
Y = β'X +U
where: Y = quantity of seed cotton supplied to market X = a vector of explanatory variables
β' =a vector of estimated coefficient of theexplanatory variables u = disturbance term i
When some of the assumptions of the Classical Linear Regression (CLR) model are violated, the parameter estimates of the above model may not be Best Linear Unbiased Estimator (BLUE). Thus, it is important to check the presence of heteroscedasticity and multicollinearity among the variables that affect supply of cotton in the area.
Test for heteroscedasticity: there are a number of test statistics for detecting heteroscedasticity. Among them are Park, Breusch-Pagan, Godfrey, White’s testes, Koenker-Bassett (KB) test of heteroscedasticity. However, according to Gujarati (2003), there is no ground to say that one test statistics of heteroscedasticity is better than the other test statistics. Due to its simplicity, Koenker-Bassett (KB) test of heteroscedasticity was employed in this study. Like other test statistics of heteroscedasticity, KB test is based on the squared residualsui2. However, instead of being regressed on one or more regressors, the squared residuals are regressed on the squared estimated values of the regressand.
Specifically, if the original model is
i
The null hypothesis is thatα2 =0. If this is not rejected, then, one can conclude that there is no heteroscedasticity. The null hypothesis can be tested by the usual t- test or the F- test
24
(Gujarati, 2003). In the presence of heteroscedasticity, ordinary least squares (OLS) estimates are unbiased. However, the usual tests of significance are generally inappropriate and their use can lead to incorrect inferences. Tests based on a heteroscedasticity consistent covariance matrix (HCCM), however, are consistent even in the presence of the heteroscedasticity of an unknown form (Long and Ervin, 2000).
Test for multicollinearity: to detect multicollinearity problem for continuous variables, Variance inflation factor
( )
21 1
Rj
VIF = − , for each coefficient in a regression as a
diagnostic statistic is used. Here, R represents a coefficient for determining the j2 subsidiary or auxiliary regression of each independent continuous variable X. As a rule of thumb, Gujarati (2003) stated that if the VIF value of a variable exceeds 10, which will happen if R exceeds 0.90, then, that variable is said to be highly collinear. Therefore, for j2 this study, Variance inflation factor
( )
VIF was used to detect multicollinearity problem for continuous variables. On the other hand, for dummy variables contingency coefficient was used.Determinants of marketable supply of cotton in Metema District
According to Branson and Norvell (1983), the supply offered by farmers is a function of:
• price of the commodity to be supplied;
• cost of all the inputs necessary to produce the commodity;
• net income or profit that could be obtained from alternative crops
• state of technology that affects potential yields;
• total acreage available, expectations about future price change and
• risk of production (weather, insects).
The factors that influence a person’s decision on how much to keep, how much and when to sell are determined by the following. These are the price, the size of production, the availability of alternatives for household consumption, the storage capacity, the amount of cash required (paying tax debts, and purchasing non-farm production), the availability of time and labor during harvest period, the availability of transportation and the condition of the weather (Chung, 1975; cited in Wolday, 1994). Therefore, it is not possible to include
25
an entire list of variables that could affect the household level marketable supply of a product since it varies according to the type or kind of the product and according to the location of the production. This study, thus, attempts to estimate factors affecting farm level marketable supply of cotton in Metema District. It attempts to do this using the cross-sectional data of the following variables.
Dependant Variable:
Quantity Supplied to Market: It is a continuous variable representing dependant variable.
It was amount of seed cotton supplied by households to market and measured in quintal.
The Independent variables are:
1. Owned oxen number (OX_NU): This variable is a continuous variable that has been