Socio Economic Factors Of Contract Farming: A Logistic .
IRA-International Journal of Management &Social SciencesISSN 2455-2267; Vol.03, Issue 03 (2016)Institute of Research MSSSocio Economic Factors of Contract Farming: ALogistic AnalysisProf. A. P. PandeyProfessor & Head, Department of Economics,Banaras Hindu University, Varanasi, U.P., India.DOI: http://dx.doi.org/10.21013/jmss.v3.n3.p31How to cite this paper:Pandey, A. P. (2016). Socio Economic Factors of Contract Farming: A LogisticAnalysis. IRA-International Journal of Management & Social Sciences (ISSN 24552267), 3(3). doi:http://dx.doi.org/10.21013/jmss.v3.n3.p31 Institute of Research AdvancesThis work is licensed under a Creative Commons Attribution-Non Commercial 4.0International License subject to proper citation to the publication source of the work.Disclaimer: The scholarly papers as reviewed and published by the Institute of ResearchAdvances (IRA) are the views and opinions of their respective authors and are not theviews or opinions of the IRA. The IRA disclaims of any harm or loss caused due to thepublished content to any party.749
IRA-International Journal of Management & Social SciencesINTRODUCTIONAccording to studies from Lajili et al. (1997), Rehber (2000), Sartwelle et al. (2000) and Key (2003), afarmer’s discrete choice to join contract farming scheme or not it is influenced by the household’scharacteristics, operational features, socio-economic characteristics, market attributes of product andunderlying agreement condition. Zhu found in a study of contract arrangement in China, that farmers’decisions to enter into contract with their sponsors were influenced by Economic influence, distance fromthe target market, specialization and commercialization of the production. In a study of contract farmingin transitional economies of Eastern Europe, Swinnen (2005), found that the most important factorswhich is more influenced farmers to enter into contracts or not, in order of importance were; guaranteedproduct sales, avoidance of price uncertainty, higher price offers, profitability, pre-payment offer inputsupply and technical assistance and some form of credit.In a detailed study of contract farming in poultry, chilly, Potato, banana, Wheat, Rice, maize, fruits andvegetables in Bali ,south Africa, India, America and Lombok province of Indonesia, it was revealed thatfactors that the important considerations and motivating factors for farmers were the increasing theproductivity of crops, and getting better Income & Price and less uncertainty; past experience in workingwith Contracting firm and agribusiness; education levels credit constraints and strong borrowing histories.The contracts were more appealing to less well-capitalization smallholders who were well educated, werecredit constrained but who had strong borrowing histories (Patrick, 2004)*.The main objective of this paper is to analyze the main socio-economic factors that motivate smallholderfarmers to engage in contract farming mechanism or not.Methodology:Theoretical Framework – Motivation to Participate in Contract Farming ArrangementTo analyze the socio-economic factors that influence farmer’s decision to enter into contract agreementswith processors, a logistical regression was used to determine the impact of those socio economic factorson farmers’ decision to accept contract farming system or not. Farmers decision to participate in anyproduction activity or not, are influenced in part by the perceived balanced of benefits, opportunities andconstraints. Discrete choice models are used to identify and quantify the factors that affect the likelihoodof a farmer participating in a production and/or marketing institutional arrangement. These modelsinclude the linear probability, Logit and Multinomial Logit models. This study opts for the logit modelbecause the sample size is sufficiently large for normality to be assured.The logit model - Analytical model and Model specificationsThe study focuses on farmer’s decision to adopt the farming method which is improve their farmingpattern by providing better income and employment, farmers decision depends upon economic and socialviability of the Contract farming system, it further quantifies the probability of the factors that maysignificantly constraint or influence the decision to adopt the contract farming method.The logistic model is the standard method of analysis when the outcome variable is dichotomous† (Hosmer and Lemeshow, 20evelop0), and the dependent variable was dichotomized with the value of 1 ifthe farmer’s decision to accept the contract farming and 0 not accepting contract farming method. Toassess the relative contribution of significant factor, binary logistic analyses was employed and predict amodel with simple indicators was developed. This model predicts the probability that and individual with*†Patrick I. (2003). Contract farming in Indonesia: Smallholders and agribusiness working together.Hosmer DW, Lemeshow S (2000).Applied Logistic Regression (Vol. 354 )750
IRA-International Journal of Management & Social Sciencescertain socio-economic characteristic choose one of the alternative (Gujrati, 2003) cording to the logisticmodel, the probability, Pi, represent the adaptation behavior of the farmers of Uttar Pradesh.Pi expZi / 1 expZi (1)Where Pi a random variable that predicts the probiability of the ith farmer is willing toparticipate in contract farming, Zi is an index that is linearly related to an array of socio-economic,demographic and other variables influencing farmers’ willing to contract. More specifically, therelationship between these variables and Zi may be specified as follow:Zi β0 β1x1i β2x2i β3x3i . βnxni .(2)The model is specification for the study can therefore be summarized in equation:Zi β0 β1x1 β2x2 β3x3 . βnxn ε .(3)The Empirical ModelQualitative response models, which are strongly linked to utility theory, have been widely used ineconomics to investigate factors affecting an individual’s choice from among two or more alternatives(Amemiya 1981; Greene, 2000). The model aims at determining the probability that, given a set ofattributes about the individual farmer and other demographic characteristics, the individual will chooseeither to enter into contract or not.Following the theoretical framework and the choice variables specified in studies by Lajili et al. ()1997),rehber (2000), Sartwelle et al. (2000) Zhu et al (2001), key (2003) and Gulati et al (2005), decision enterinto contract farming arrangement in this study could be described as a function of personalcharacteristics of the farmer, household’s characteristics, operation features, product categories, andmarket attributes‡. These factors have been decomposed in to the explanatory variables shown in theempirical model below. The model is specified as follow:Y β0 β1 Gender β2 Education β3Age β4 Family size β5 Loan β6 Electricity β7 Input β8Off Farm β9 Less Uncertainty β9 Expected Price of Product β9 Employability β9 Nature ofFarming β9 Earnings of Farmers .(4)Here qualitative dependent variable is willing to adopt the contract farming or not, which takes on thevalue of 1 if the farmers adopted the contract farming method and 0 otherwise not adaptation occurred.Where: Y adaptation level ( 1 adopters; 0 otherwise) or proportion of farmers adopting the contractfarming system for the particular value of the independent variable X 1, X2, .Xn that influencesthe adaptation of contract farming method, β1, β2, denoted the regression coefficients, ε is the errorterm.Statement of HypothesisThe following null hypotheses (Ho) were tested against the alternative (Ha).The specific a priori expectations on the estimated parameters of equation (6) are:(i) H0: β1 – β9 0, Ha: β1-9 0‡Key, N and D. Runsten., (1999). “Contract Farming, Smallholders, and Rural Development in Latin America:751
IRA-International Journal of Management & Social SciencesWhereH0: there is no effect of age on farmers’ decision to participate in contractHa: there is a positive effect of age on farmers’ decision to participate in contract.H0: there is no effect of gender on farmers’ decision to participant in contract farmingHa: there is a positive effect of gender on farmers’ decision to participate in contract.The hypothesis is repeated similarly forValidation of Hypothesis:The Z statistic is used to measure the level of significance for each of the estimated coefficients. Thegoodness of fit statistic is the McFadden R-squared. The likelihood ratio (LR) test is computed todetermine the joint significance of the independent variables in the model. The LR test follows a standardchi-square (χ2) distribution the degrees of freedom to the number of independent variables used in themodel. The higher the percentage prediction, the greater the predictive power of the model. Thediscussion of results is based on the log-odds ratio. The log-odds is given asβ [logYi / 1 - Yi ]/ β Xi M/ Xi βiThe marginal effects of the independent variables are also estimated. These are given as Yi/ Xi βi [Yi (1- Yi)]Where, Yi represents probabilitiesRESULTS AND DISCUSSIONSFor a logistic regression, the predicted dependent variable is a function of the probability that a particularsubject will be in one of the categories (for example, the probability that Suzie Cue has the disease, givenher set of scores on the predictor variables).Reliability Analysis:Cronbach’s alpha reliability coefficient normally ranges between 0 and 1. However, there is actually nolower limit to the coefficient. The closer Cronbach’s alpha coefficient is to 1.0 the greater the internalconsistency of the items in the scale. Based upon the formula rk / [1 (k -1)r] where k is the numberof items considered and r is the mean of the inter-item correlations the size of alpha is determined by boththe number of items in the scale and the mean inter-item correlations. George and Mallery§ (2003)provide the following rules of thumb: “ .9 – Excellent, .8 – Good, .7 – Acceptable, .6 –Questionable, .5 – Poor, and .5 – Unacceptable” (p. 231). While increasing the value of alpha ispartially dependent upon the number of items in the scale, it should be noted that this has diminishingreturns. It should also be noted that an alpha of .8 is probably a reasonable goal. It should also be notedthat while a high value for Cronbach’s alpha indicates good internal consistency of the items in the scale,it does not mean that the scale is one-dimensionalReliability StatisticsCronbach'sAlphaN of Items.91716§George, D., & Mallery, P. (2003). SPSS for Windows step by step: A simple guide and reference. 11.0 update (4thed.). Boston: Allyn & Bacon752
IRA-International Journal of Management & Social SciencesThis table represents the Reliability of the all variable which is 0.917; total numbers of items are sixteen.This value comes under the Excellence range that is greater than 0.9.Item-Total StatisticsScale Mean if Scale Variance if Corrected Item-Total Cronbach's Alpha ifItem DeletedItem DeletedCorrelationItem DeletedTypes of the Farmers8.212523.952.792.906Decision about 5.391.918Education8.132525.779.411.918Age of The Farmers8.140025.750.416.917Family Size8.200023.860.813.9068.197523.883.808.906Using Electricity8.187524.158.748.908Getting Input Timely8.185024.261.726.908Off Farm 135024.814.614.912Good Employbility8.200025.178.529.914Nature Of ficient For Family24.441.686.909All Children Go ormal)&Less UncertaintyCropsGettingPricesBetterofCropsThe values in the column labelled Alpha if Item is Deleted are the values of the overall alpha if that itemisn’t included in the calculation. As such, they reflect the change in Cronbach’s alpha that would be seenif a particular item were deleted. The overall alpha is .912, and so all values in this column should bearound that same value. We’re looking for values of alpha greater than the overall alpha because if thedeletion of an item increases Cronbach’s alpha then this means that the deletion of that item improvesreliability. None of the items here would substantially affect reliability if they were deleted.753
IRA-International Journal of Management & Social SciencesDecision about acceptance of contract farming by farmersTable 02Decision about farming System * Types of the Farmers Cross tabulationTypes of the FarmersNotGrowerDecisionSystemaboutfarming No CountTotal3913119.5%32.8%161269% within Types of the54.0%Farmers80.5%67.2%Count200400100.0%100.0%% within Types of the46.0%FarmersYes CountTotal92Contract ContractGrower108200% within Types of the100.0%FarmersTable 02 shows the results obtained from the 200 non contract farmers and 200 contract farmers in thesurvey with respect to their decision to participate or otherwise in contract farming arrangement. Thefarmers who are already in the contract farming arrangement, their decision to participate in contractarrangement is 161 (80.5%) When farmers with no contracts were asked if they were willing to engage incontract farming arrangements, an overwhelming 108 (54%) responded in the affirmative.Whereas the decision about not accepting the contract arrangement the answer of the contract growers are39 (19.5%) & answer from non contract growers are 92 (46 %). Its decision represent enough difference(46% - 19.5%) 26.5% that is considerable.The results suggest that most farmers tend to respond positively and have a strong desire to engage incontract arrangements if they were offered the opportunity. From the results it can be inferred that farmersin Uttar Pradesh generally have a positive or favourable attitude towards contract farming.Table 03 Chi-Square TestsValue df Asymp. Sig. (2-sided) Exact Sig. (2-sided) Exact Sig. (1-sided)Pearson Chi-Square31.885a 1.000bContinuity Correction30.693 1.000Likelihood Ratio32.581 1.000Fisher's Exact Test.000Linear-by-Linear Association 31.805 1.000bN of Valid Cases400a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 65.50.b. Computed only for a 2x2 table.000Finally, the table below provides the summary statistic info. The observed chi-square statistic is 31.851,which is associated with a 0.00 % risk of being good in rejecting the null hypothesis. This is no any risk,so we are able to accept the null. We therefore find support for the research hypothesis, and can conclude754
IRA-International Journal of Management & Social Sciencesthat contract and not contract growers’ and their decision to acceptance farming system in study.Logit Model:- Decision about the farming system of Contract Growers’Classification Tablea,bPredictedDecisionSystemObservedStep 0 Decision about farming 0161100.0Overall Percentagea. Constant is included in the model.b. The cut value is .50080.5Decision about the farming system of Non Contract Growers’Classification Tablea,bPredictedDecision about farming SystemObservedNoStep 0 Decision about farming System No 0Yes 0Overall Percentagea. Constant is included in the model.b. The cut value is .500YesPercentage Correct92.0108100.054.0In the Table 01 (Contract Growers) the Block 0 output is for a model that includes only the intercept.Given the base rates of the two decision options (39/200) 19.5 % decided to not accept the contractfarming system, 81.5% decided to continue working with contract farming system, and no otherinformation, the best strategy is to predict, for every case, that the subject decided to work with contractfarming system. Using this strategy, we would be correct 80% of the time.And another side in the table 02 – 46% of the non contract growers’ decided to work with contractfarming system 54% farmers want to work with contract farming system., that the subject decided to workwith contract farming system. Using this strategy, we would be correct 54% of the time. By thecomtrative analysis of the both tables we can say the percentage of working with contract farming systemis high of Contract growers.755
IRA-International Journal of Management & Social SciencesTable 03 (CF)Variables in the EquationStep able 04 (NCF)Variables in the EquationStep 0ConstantUnder Variables in the Equation you see that the intercept-only model is ln (odds) 1.418 for thecontract growers (CG) and 0.160 for non-contract Growers (NCG). If we exponentiate both sides ofthis expression we find that our predicted odds [Exp (B)] .4.128 for (CG) & 1.174 for (NCG). That is,the predicted odds of deciding to work contract farming system for CG is 4.128 and for NCG is 1.174.This means contract growers want to work with contract farming system near about four times to the noncontract growers.Omnibus Tests of Model Coefficients gives us a Chi-Square of (CG) 25.555 and for (NCG) 16.491 on 1df, significant beyond .000 and 0.258. This is a test of the null hypothesis that adding the size of farmer’svariable to the model has not significantly increased our ability to predict the decisions made by oursubjects.(CG)Omnibus Tests of Model CoefficientsStep 1(NCG)Omnibus Tests of Model CoefficientsChi-squaredf Sig.Step16.4911.0001 .000Block16.4911.0001 .000Model16.4911.000Chi-squaredf Sig.Step25.5551 .000Block25.555Model25.555Step 1Under Model Summary we see that the -2 Log Likelihood statistics are 171.801 & 259.487. This statisticmeasures how poorly both the model predicts the decisions about farming system by small & big farmers-- the smaller the statistic the better the model. After Adding the more variable as like gender, age of thefarmers, and gender etc. variable reduced the -2 Log Likelihood statistics by 197.356 – 171.801 25.555(CG) & 275.978 – 259.487 16.491 (NCG), both model is very weak but model of contractgrowers is more weak then non-contract grower model so we add some another variables, The Cox &Snell R2 can be interpreted like R2 in a multiple regression, but cannot reach a maximum value of 1. TheNagelkerke R2 can reach a maximum of 1 and in the both models its value is very low.756
IRA-International Journal of Management & Social SciencesModel Summary (CG)Model Summary(NCG)Cox-2Log SnellStep likelihood Square&R NagelkerkeR Squarea1171.801.120.191a. Estimation terminated at iterationnumber 5 because parameter estimateschanged by less than .001.Cox-2Log SnellStep likelihood Square&R NagelkerkeR Square1259.487a.079.106a. Estimation terminated at iteration number3 because parameter estimates changed byless than .001.The Variables in the Equation output shows us that the regression equation isIn (ODDS) -0.182 2.121 (Size of Farmers) .(CG)Variables in the Equation95.0% C.I.for EXP(B)BStep 1aSize of FarmersS.E. Wald2.121 .420 25.470Constant-.182 .350 .272a. Variable(s) entered on step 1: Size of the farmers.df Sig. Exp(B) LowerUpper1 .000 8.34319.0163.6601 .602 .833We can now use this model to predict the odds that a subject of a given gender will decide to continuethe research. The odds prediction equation is ODDS ea bxIf our subject is size of Farmers Small and Big farmers (Big farmers 0), then the ODDS e-0.182 2.121(0) e-0.809 0.4493 That is, big farmers is only .4493 as likely to decide to continue work with contractfarming system as They are to decide Not working the contract farming system.Ŷ ODDS/1 ODDSThe validity of the modelThe coefficients of the binary logistic regression model were estimated by maximum likelihood methods.The Hosmer and Lemeshow statistic is one of the most reliable tests of model fit for binary regression(Sidibe , 2005). The results of the model are given
The logistic model is the standard method of analysis when the outcome variable is dichotomous† ( Hosmer and Lemeshow, 20evelop0), and the dependent variable was dichotomized with the value of 1 if the farmer’s decision to accept the contract farming and 0 not accepting contract farming method. To
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