Dataset on farmers’ perception of commodity futures market

The commodity futures market plays a major role in reducing the price risk for the participants. Unfortunately, the farmers’ participation in the futures market particularly from the Tamil Nadu region is very less. A survey was conducted using the interview method to identify the information sources used by farmers for taking pricing decisions, the awareness and perception of farmers towards the futures market, and its effect on preferred marketing alternatives. The data cleaning process was done using content validity, confirmatory factor analysis, and reliability test using Cronbach's alpha, and the assumptions of normality and multicollinearity were examined. The data will be of potential use to researchers who wish to explore farmers’ behavior towards hedging in the commodity futures market.


a b s t r a c t
The commodity futures market plays a major role in reducing the price risk for the participants. Unfortunately, the farmers' participation in the futures market particularly from the Tamil Nadu region is very less. A survey was conducted using the interview method to identify the information sources used by farmers for taking pricing decisions, the awareness and perception of farmers towards the futures market, and its effect on preferred marketing alternatives. The data cleaning process was done using content validity, confirmatory factor analysis, and reliability test using Cronbach's alpha, and the assumptions of normality and multicollinearity were examined. The data will be of potential use to researchers who wish to explore farmers' behavior towards hedging in the commodity futures market.

Value of the Data
• The data on Tamil Nadu farmers' participation in futures markets is unknown through any of the sources.
• It will answer the following questions: How many farmers are using the futures market?
What other marketing tools are used by farmers to sell their produce? • This data will provide useful insights into farmers' understanding of various aspects of commodity futures trading. • This data will add value to the policymakers in developing strategic measures for protecting the farmers from price risk and exploitation by the traders. • The researchers can make use of the data to develop models on farmers' hedging behavior in the futures market.

Data Description
The interview schedule includes demographic data which were recorded on a nominal scale, training and farming details noted on an ordinal scale, the cost details were collected as openended, the preference of marketing alternatives recorded on a three-point scale with "1" Not preferred, "2" Preferred and "3" Highly preferred. The farmers' awareness level of the futures market was collected using a Likert scale which includes "1" completely unaware and "5" being completely aware. Similarly, the perception of farmers towards the futures market was obtained using Likert scale responses which include "1" Strongly disagree and "5" Strongly agree. The data collection instrument included one section on the frequency of usage of information sources for taking pricing decisions which were recorded as "1" Never and "5" Always. The results of the reliability test are presented in Table 1 , confirmatory factor analysis (CFA) for awareness,     Tables 2 , 3 , and 4 , respectively. The assessment of normality of the data using the Kolmogorov Smirnov test is presented in Table 5 and the examination of multicollinearity in the data set was assessed using the Tolerance and Variance Inflation Factor which is presented in Table 6 . The alpha coefficients for Preferred Marketing Alternatives, Information Sources, used by farmers for taking pricing decisions, the Awareness level of farmers towards futures market, Perception level of farmers towards futures market [1] , and Selection of Marketing Alternatives were found to be 0.739, 0.775, 0.822, 0.943 and 0.794 respectively, suggesting that these items recorded relatively high internal consistency and hence the instrument was fit to collect data [2] . The results of confirmatory factor analysis are presented in Tables 2-4 . It is clear from Table  2 that the Critical Ratio (CR) was found to be 0.90, 0.874, and 0.911 for the constructs related to the awareness level of farmers towards the futures market, which includes Knowledge of Commodity Futures, Knowledge about Price, and Knowledge of Trading and Settlement. Similarly, the perception is measured through four constructs namely Expected Performance from futures, Entrepreneurial Freedom, Perceived Risk Exposure from futures, and the Risk Attitude [3][4][5][6] . Table  3 presents the CR values for the same which includes 0.946, 0.901, 0.878, 0.804, respectively. The marketing alternatives and information sources recorded CR values of 0.870 and 0.893, respectively. Since all the CR values were greater than 0.7, the constructs given in the model were valid. The AVE values for Knowledge on Commodity Futures, Knowledge on Price, Trading and Settlement were found to be 0.566, 0.635, and 0.6 respectively which met the required standard. The Convergent Validity was measured, through Average Variance Extracted (AVE), which should be greater than 0.5. The Average Variance Explained (AVE) was found to be 0.779, 0.646, 0.592, and 0.531 respectively, for Expected Performance from Futures, Entrepreneurial Freedom, Perceived Risk Exposure, and Risk Attitude of farmers respectively and this ensures that constructs were convergent valid. It is clear from Table 4 that the AVE value for Marketing Alternatives, and for the Information Sources used by farmers for taking pricing decisions, was found to be 0.513 and 0.808, respectively. The results of Divergent/Discriminant Validity were evaluated, using diagonal values of each construct, which should be greater than AVE values. From the results, the Maximum Shared Variance (MSV) and Average Shared Variance (ASV) were less than the Average Variance Explained (AVE) and hence the factors are distinct and uncorrelated. The item loading, for all the constructs under awareness, perception, information sources used, and marketing alternatives was greater than 0.5, hence all the items perfectly fit the constructs [2] .
It is to be noted that the Kolmogorov-Smirnov 'Z' Statistic from Table 5 was found to be 1.09, 1.06, 1.05, and 0.95, for Information sources used by farmers for taking pricing decisions, the Awareness level of farmers towards futures market, Perception level of farmers towards futures market and the Preference of farmers in the selection of Marketing Alternatives respectively. The p -value of Kolmogorov Smirnov 'Z' Statistic, was found to be greater than 0.05, indicating statistically insignificant results. Hence accept the null hypothesis, "The data follows normal distribution".
The Variance Inflation Factor of independent variables, namely, the awareness and perception level of farmers towards futures market, the information sources used by farmers for taking pricing decisions were found to be 1.186, 1.089, and 1.094, respectively from Table 6 . The VIF specifically gives the variances that are inflated in number which arises due to multicollinearity. The values, extracted in the model used in the research were well within the limit specified i.e. three, which indicates the absence of multicollinearity. Similarly, another indicator of multicollinearity is the Tolerance level. Generally, a higher level of tolerance is preferred as lower levels of tolerance can have adverse effects on the model. The Tolerance level, extracted for the present model, was found to be 0.843, 0.918, and 0.914 for awareness, perception level of farmers towards futures, and information sources used by farmers for taking pricing decisions respectively. These results confirmed that there was no multicollinearity in the data.

Experimental Design, Materials and Methods
Initially, it was decided to use the questionnaire method for collecting the data. During the pilot study, the farmers were contacted individually at the regulated markets where auctioning of turmeric, cardamom, and chilly are conducted. The informal discussions, with farmers', revealed that the majority were not able to read and understand the questions. Some of the choices, with respect to information sources used by farmers, did not find a place in the questionnaire. All the above issues were addressed after the first pilot study. During the second pilot study, with 125 farmers', additional marketing alternatives namely selling to the traders, co-operative societies, etc., were added. It was noticed during the interaction, that the farmers were not aware of different marketing alternatives for selling their produce. The only price risk instrument known to them was crop insurance and moreover, majority of the farmers were not enrolled in the crop insurance schemes. The outcome of the pilot study revealed the following: (a) Questionnaire cannot be used for data collection as the respondents could not understand a few terminologies (b) Additional explanations in the vernacular language needed to be provided for the farmers to get reliable answers. After scrutinizing all the above issues, it was decided to use the Structured Interview Schedule for data collection. The market for commodity futures helps the farmers to hedge against undesirable price changes at future periods. Hence, the data collection instrument was designed to capture the awareness and perception of farmers towards the futures market. Since majority use auction at the regulated markets as the primary tool for selling, it was decided to include other marketing alternatives such as local mandi, traders, cooperative societies, farmers' associations, forward contracts, and futures markets, to obtain the data on farmers' choice of selling.

Ethics Statements
The data does not involve any biological or scientific experiments. Participation in the research was voluntary and the farmers were briefed clearly about the study and its significance.