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The purpose of this paper is to explore farmer acceptance of a biofortified staple food crop in a developing country prior to its commercialization. The paper focuses on the hypothetical introduction of a high-iron pearl millet variety in Maharashtra, India, where pearl millet is among the most important staple crops.

A choice experiment is used to investigate farmer preferences for and trade-offs among various production and consumption attributes of pearl millet. The key pearl millet attributes studied include days it takes pearl millet to mature, color of the roti (flat bread) the grain produces, the presence of high-iron content (nutritional attribute), and the price of the pearl millet seed. Choice data come from 630 pearl millet-producing households from three purposefully selected districts of Maharashtra. A latent class model is used to investigate the heterogeneity in farmers’ preferences for pearl millet attributes and to profile farmers who are more or less likely to choose high-iron varieties of pearl millet.

The results reveal that there are three distinct segments in the sample, and there is significant heterogeneity in farmer preferences across these segments. High-iron pearl millet is valued the most by larger households that produce mainly for household consumption and currently have lower quality diets. Households that mainly produce for market sales, on the other hand, derive lower benefits from consumption characteristics such as color and nutrition.

The main limitation of the study is that it uses a stated preference choice experiment method, which suffers from hypothetical bias. At the time of implementing this study biofortified high-iron pearl millet varieties were not yet developed, therefore the authors could not have implemented revealed preference elicitation methods with real products and payment.

The method used (stated preference choice experiment method) is commonly used to value non-market goods such as environmental goods and products that are not yet in the market. It’s application to agriculture and in developing countries is increasing. As far as the authors know this is the first choice experiment implemented to investigate farmer/consumer preferences for biofortified crops. The study presents valuable information for development and delivery of biofortified crops for reducing micronutrient deficiencies.

This paper aims to study low adoption of modern technology for pearl millet in Rajasthan, India, from the perspective of social networks. The state has the lowest adoption of modern pearl millet seeds among Indian states. . In particular, this paper tries to identify the limitations of channels with endogenous effects, thereby limiting large-scale adoption of modern varieties that would require social multipliers.

Defining the network/reference groups in terms of social identity and geographical proximity, this paper utilizes the intensity of interaction with different network nodes to identify the presence of endogenous effects. In particular, this paper uses the interaction of intensity of social exchange with the group level adoptions to establish the presence of endogenous effects. With adequate controls, greater intensity of interaction having a bearing on technology choice can only happen when there exists social learning (endogenous effect) and cannot be associated with other forms of social effects (namely, exogenous and correlated effects).

This paper finds evidence for the existence of endogenous social effects in adoption but largely from exclusionary channels. A comprehensively mapped network is used with its intensity to explain the extremely low rate of adoption. Only close-knit networks that, with social fragmentation, limit benefits to few, affect adoption significantly. The non-functionality of less exclusionary information sources and services can be a factor underlying low adoption.

The main limitation of the study is inability to control for unobserved individual heterogeneity because of the cross-sectional nature of data. Further, although an extensive mapping of individual networks has been done, it still cannot be guaranteed to be exhaustive.

With fragmentation, large-scale adoption programs would require networks, sources of information and services that are less exclusionary. Based on the survey data, media and non-religious organizations play a focal role here in the adoption of modern technology. This finding is extremely crucial for policy, as these channels comprise direct policy levers in a fragmented society like India. Indeed, several government programs in India have relied on these channels to run large-scale adoption programs. Their ineffectiveness could be a prime factor for such limited dissemination of technology in Rajasthan.

In different settings, social fragmentation could be an important factor determining technology adoption outcomes. The evolving consensus in the literature based on several studies is that ethnic fragmentation has potentially negative consequences on macro-economic performance (Alesina and Tabellini, 1989 and Collier, 2000). In the literature on technology adoption, the role of fractionalization is somewhat under-studied. With fragmentation, there can be significant micro-level impacts (for instance, low technology adoption of a crop) if channels that are inclusive are not well developed. The finding that channels like extension services, media or organizations are not effective in determining choice of technology does not mean that they should not be tapped. The empirical findings suggest that, in their current form in the state of Rajasthan, the roles played by these are limited. The policy implications would be to develop these systems in a way that there is a greater uptake. Recall that less than 4 per cent of the respondents got information on seeds from media sources, an extremely low number. There is certainly scope for increasing the outreach of these channels that are much more important for spread of agricultural technology in a fragmented society.

This paper is an attempt to come up with an empirical strategy to mitigate the issues related to reflection problem. In the cross-sectional data itself, we use the interaction of group choices with intensity of interaction within the group to introduce a non-linearity that tries to bypass the identification issues as in reflection problem. This method of introducing non-linearity in cross-sectional data is a novel attempt to achieve identification of endogenous effects.

The US fossil fuel industry is vulnerable to opposition from other sectors of the ruling class. Non-fossil fuel capitalists might conclude that climate breakdown jeopardizes their interests. State actors such as judges, regulators, and politicians may come to the same conclusion. However, these other elite actors are unlikely to take concerted collective action against fossil fuels in the absence of growing disruption by grassroots activists. Drawing from the history of the Obama, Trump, and Biden presidencies, I analyze the forces determining government climate policies and private-sector investments. I focus on how the climate and Indigenous movements have begun to force changes in the behavior of certain ruling-class interests. Of particular importance is these movements' progress in two areas: eroding the financial sector's willingness to fund and insure fossil fuels, and influencing judges and regulators to take actions that further undermine investors' confidence in fossil fuels. Our future hinges largely on whether the movements can build on these victories while expanding their base within labor unions and other strategically positioned sectors.