This study models the purchasing behavior of specialty coffee by 114 coffee shops across 15 cities in nine states in Mexico. Simple and multilevel mixed-effects logistic models are tested. Our models extend the framework used in prior research. We model the purchase of specialty coffee as a function of: (a) material attributes, (b) symbolic attributes, (c) coffee shop characteristics, (d) profile of the coffee shop’s owner, and (e) socio-economic variables of the cities where the coffee shops were located. Overall, our results are consistent with expectations developed from the coffee literature. That is, the likelihood of purchasing specialty coffee increases when: coffee’s aroma drives the purchase, coffee purchased is from the state of Oaxaca, the coffee shop has a value-added business model, the coffee shop is diversified selling both ground coffee and coffee drinks, the coffee shop owner’s knowledge on coffee supply chain activities is high, and the coffee shop is located in a city with a higher education index. In contrast, the likelihood of purchasing specialty coffee decreases when a coffee professional tastes the coffee before the purchase, when coffee shops are not given the opportunity to roast their own coffee, and in coffee shops located in larger cities. Overall, our research suggests that the specialty coffee niche in Mexico has some elements required for this segment to transition from a supply chain approach to a value-based supply chain approach. This might be particularly beneficial for smallholder coffee growers, who despite several constraints contribute to the sustainability of coffee supply chains.

This study investigates what factors relate to the coffee farmer’s cooperative affiliation decision and whether this decision impacts the farmer’s cash holdings. First, we propose a cooperative affiliation model based on transaction cost economics theory. There is a lack of consensus in the literature on what factors explain the farmer’s cooperative affiliation decision in the coffee sector. Overall, we find that the more specialized coffee farmers are, the more likely they will become cooperative affiliates. This is consistent with transaction cost economics predicting that cooperatives are business structures that can reduce transaction costs and safeguard specialized assets from opportunistic behavior. Specifically, logit regression models suggest that shade-grown coffee plantations, off-farm income, coffee farming experience, low-level market competition, farmland size, altitude, and private farmland are statistically related to the farmer’s decision to affiliate with cooperatives. Results on farmland size and shade-grown coffee plantations can be particularly relevant for scholars, policymakers, cooperative leaders, and extension professionals in the region. Second, based on the affiliation model, we employ propensity score matching to evaluate the impact of the farmer’s cooperative affiliation decision on cash holdings, particularly on cash shortness. It is often claimed that farmers do not affiliate with cooperatives because these organizations cannot pay them in full at harvest and coffee collection time. It is believed that cooperatives’ inability to pay farmers early increases the likelihood of farmers’ cash shortness and their need for additional financing to operate or cover household needs. However, this study finds no evidence that the affiliation decision is related to the likelihood of the farmer experiencing cash shortness around harvesting and selling time.

PurposeThe aim of this study is to estimate the technical efficiency of the massive and economically important crop of rice in Ecuador, and then conduct a comparison between groups of farmers with and without insurance.Design/methodology/approachThe authors use an input-oriented data envelopment analysis approach (DEA) to estimate technical efficiency scores. The DEA is combined with the double bootstrap approach in Simar and Wilson (2007) to study factors that may affect technical efficiency. This method overcomes the traditional two-stage DEA approach frequently used in the efficiency literature. The authors thus research the role of insurance on rice efficiency production using this technique and sizeable field-level survey data from 376 rice farmers distributed in five provinces during the 2019 winter cycle in Ecuador.FindingsMost uninsured rice farmers operate with increasing returns to scale, which means that farms improve their resource use efficiency by increasing their size. However, since scale efficiencies are relatively high, it appears that inefficiencies are explained by inadequate input use. Also, the authors find evidence that insured farmers have a negative relationship with technical efficiency in rice production. In other results, when exploring the influence of additional variables on efficiency, the authors find that parameters related to transplanting, high education, farm size and some locations are positive and statistically significant.Social implicationsThe results of this work are relevant for policymakers interested in evaluating technology performance, risk management instruments and farm efficiency in an industry in a developing country such as rice production in Ecuador.Originality/valueThis paper is the first attempt to estimate farm-level technical efficiency employing the double bootstrap approach to assess the efficiency and its determinants of Ecuadorian rice producers.