Measuring the preferences towards a market sharing option: - PowerPoint Presentation

1. Measuring the preferences towards a market sharing option:an application of DCA and LCAResearch advance presented for the EUSMEX 2018 meeting Tlaxcala, Mexico. August 2018BySergio Colin Castillo, Naim Manríquez García (CISE-UADEC) Adan L. Martínez Cruz (CIDE)

2. Summary By means of a DCE, we explore the willingness of a group of Mexican farmers to adopt the organic production, a collaborative arrangement (economia solidaria) based on four characteristics: i) to implement chemical/pesticide-free farming practices, ii) to receive management support from the municipality, and iii) to set up a communal insurance schemeiv) Price marketAs a result, a two-class LCL delivers the following insights:More experience and more education are associated with the probability of being a type of organic farmerAlso, more group cohesion and willingness to adopt a communal insurance scheme is also associated with being classified as a type of organic farmer. The willingness to receive support and work with the municipal government is not clear for both type of farmersImportantly, traditional type of farmers are unwilling to give up chemical pesticides use while organic farmers significantly reject the use of such inputLast, on one hand, It looks like organic farmers do not care for a price increase, but they are willing to accept a slight price reduction. On the other hand, non organic farmers really cares for price movements, but mainly look for a price increase

3. IntroductionMost farmers worldwide practice conventional farming in the sense that their production is based on synthetic chemical inputs. Indeed, the chemical inputs increase productivity at the expenses of:Soil quality (Virto et al, 2014; Cox, 2013; Gorniero, 2013), Biodiversity (Takeshi-Aida, 2016; Shakhramanyan et al., 2013), Human health (Sparling et al., 2017), and Social equity (FAO, 2013).Instances in Mexico are plenty:Chiapas: particles of organochlorine pesticides have been detected in pygmy owls (Arrona-Rivera et al., 2016). Mexico City have found presence of organochlorine pesticide residues in bottled drinking water (Diaz et al., 2009)Sinaloa, contaminated fish by pesticides, that puts in risk the people who consume them (Granados-Galvan, 2015).

4. Collaborative arrangements (named here as: Sharing Economy or “Economia Solidaria” in Spanish) have been documented to decrease negative externalities from conventional farming practices. An instance, the role of government policies in influencing maize diversity in Chiapas, Mexico (Keleman et al., 2008), Farmers’ social arrangements in Oaxaca, Mexico where farmers’ collective action play an important role in local seed supply (Badstue et al., 2009). Organic agriculture, as a case of collaborative arrangement (sharing economy: economía solidaria: ES), offers a number of societal benefits including the protecting nature, maintaining biodiversity, improving scenery, and supporting communities

5. In a broader perspective the authors who have studied organic farming practices through the discrete choice experiments (DCE) have focused: Pesticides free: Aslam, (2017); Chèze and Martinet., (2017); Christensen et al., (2011); Espinosa-Goded et al., (2010); Ruto and Garrod, (2009); Birol et al., 2009Cover crops Buffer zonesBut, what about the main drivers or motivations to adopt such practices?

6. Related Literature: Collaborative arrangementsPartnerships arrangements and collaboration among farmers has been studied in agriecological economy:Collaboration may involve one or several activities at the farm such as cultivation, planting, fertilizer application, pesticide application and harvest but also the role of social norms.One of the probably most important potential gains from collaboration is reduced cost of capital and risks.

7. For the case of empirical evidence, particularly the DCE; most of the literature focuses on the possible economic advantages for their members: Andersson et al., (2005); Artz et al., (2010); Aurbacher et al., (2011), Asai and Langer (2014), Feil et al., (2015).

8. Empirical ApproachRandom utility (RU) models are methods for describing discrete choice behavior. Accordingly, it is possible to determine a utility function. (1)where, the individual chooses the alternative in a choice situation which gives his maximum utility, i.e., (Train, 2003).  

9. An extension on this type of utility models to analyze DCE are conditional logit, random parameter logit and latent class logit model.The CL choice probability that individual chooses alternative : (2)The random parameter logit choice probability that individual chooses alternative : (3) 

10. The latent class logit choice probability that individual chooses alternative j belonging to class g is: (4)Then, in accordance with Swait (2007), the probability of individual in class can be expressed as: (5) 

11. Combining conditional choice equation (4) and membership classification equation (5), the unconditional probability of choosing alternative is given as: (6)Therefore, the central issue in the LC model is how to determine. The most common criteria to determine G are AIC and BIC criterion: (7) 

12. Data: Choice of the attributes and their levelsA first step for the DCE was to choose the attributes and their associated levels. For collecting data, we have made a questionnaire:The first part was dedicated to general questions regarding the farmer’s context. The rest of the survey was the discrete choice experiment using illustrated slides and describing in detail the attributes. Table 1. Attributes and attribute levels used in the choice experimentAttributeLevelsUse of agrochemicals No use2. UseManagement with government aid Not management2. ManagementCooperative Scheme Cooperative2. No cooperativePrice increase of a 30% Increase2. No increasePrice reduction of a 10% Reduction2. No reduction

13. Table 6. Estimates of the conditional logit modelVariablesEntireTraditionalOrganic farmersUse of agrochemicals (1 = No use, 0 = Use)0.964*** (0.121) -0.335 (0.236)2.847*** (0.278)Management with government aid (1 = Not management, 0 = management) 0.356** (0.118)1.206*** (0.287)0.991***(0.166)Cooperative Scheme (1 = Cooperative, 0 = no cooperative)1.009*** (0.124)0.262 (0.237)2.439***(0.276)Price increase of a 30% (1= Increase, 0 = No increase)0.572*** (0.150)1.083***(0.253)0.0048 (0.227)Price reduction of a 10% (1 = Reduction, 0 = no reduction)-0.336* (0.138)-2.053*** (0.348)-0.108 (0.180)N1314504810Log likelihood-705.59-370.61-221.54adj. R-sq0.10430.26680.2368Standard errors in parenthesesNote: *, ** and *** indicate statistical significance at the 90%, 95% and 99% levels, respectively.

14. ResultsTo compare LC models and determine the number of classes, we used: AIC and BIC criterions. Table 2. AIC and BIC values for different numbers of classes (without membership)Number of classesTraditional farmersOrganic farmersBICAICBICAIC2231.95219.96166.13146.253245.38226.73185.58154.87Table 3. AIC and BIC values for different numbers of classes (with membership)Number of classesTraditional farmersOrganic farmersBICAICBICAIC2211.77190.45186.06157.153304.73268.76211.77162.99

15. Tabla 4. LC Membresia Conventional Table 4. Estimates of the latent class modelVariable Class 1Class 2Use of agrochemicals (1 = No use, 0 = Use)-1.535*** (0.243)8.631*** (1.332)Management w/government aid (1 = Not management, 0 = management)1.473*** (0.196)-15.918*** (2.304)Cooperative Scheme (1 = Cooperative, 0 = no cooperative)-0.881*** (0.333)1.041** (0.544)Price increase of a 30% (1= Increase, 0 = No increase)0.254 (0.280)7.991** (1.164)Price reduction of a 10% (1 = Reduction, 0 = no reduction)-6.508*** (1.011)-78.185 (74.320)Gender0.283 (0.656) Highschool-1.772** (0.945) Experience-0.130*** (0.042) 

16. Social cohesion measuresTimes respondent invites people from the market to his/her house-2.097*** (0.781) Other economic activity besides market-1.192 (0.736) C 5.187 (1.572) Class share0.6010.399Note: *, ** and *** indicate statistical significance at the 90%, 95% and 99% levels, respectively.

17. Table 5. Marginal effects latent classVariableLC1LC2Use of agrochemicals (1 = No use, 0 = Use)-1.535*** (.243)-.011** (.046)Management with government aid (1 = Not management, 0 = management)1.473*** (.196)1.450***(.492)Cooperative Scheme (1 = Cooperative, 0 = no cooperative)-0.881*** (.333).003*** (.031)Price increase of a 30% (1= Increase, 0 = No increase)0.254 (.280).234 (.755)Price reduction of a 10% (1 = Reduction, 0 = no reduction)-6.508*** (1.011)-1.82*** (.945)Standard errors in parenthesesNote: *, ** and *** indicate statistical significance at the 90%, 95% and 99% levels, respectively.

18. ConclusionsExploring the farmers’ choice-decision to switch from conventional to organic farming, or to adopt SE scheme, our analysis reports that: More experience and more education are associated with the probability of being of the first type of farmer (SE)Also, more group cohesion is also associated with being classified as the first type of farmerGeneral critique (warning) to DCE: Whether people understand and/or pay attention to all attributes. In our case, readers might get suspucious of the fact that our respondets do not react to pesticide-free attribute

19. AppendixTable 2. AIC and BIC values for different numbers of classes (without membership)Number of classesEntireTraditional farmersOrganic farmersBICAICBICAICBICAIC2470.93445.73231.95219.96166.13146.253457.56418.62245.38226.73185.58154.874452.69400.01252.71227.40208.42166.875477.20410.77260.64228.67228.14175.75

20. Table 3. AIC and BIC values for different numbers of classes (with membership)Number of classesEntireTraditional farmersOrganic farmersBICAICBICAICBICAIC2404.57365.63211.77190.45186.06157.153427.11360.69304.73268.76211.77162.994486.2392.3314.01263.38250.49181.845-261.39196.11349.12260.6