Randomization bias in field trials to evaluate targeting methods

Potash, Eric

doi:10.1016/j.econlet.2018.03.012

Cited by 2 publications

(2 citation statements)

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“…There are also practical problems that arise from the experimental design that bias the estimates. The implementation of the experiment itself alters the framework within which the program operates, resulting in a “randomization bias” (Potash, 2018; Sianesi, 2017). Additionally, experiments are generally costly to perform and need close supervision to maintain successful administration.…”

Section: Improving Adoption-impact Research In Wheat Systemsmentioning

confidence: 99%

Assessing technological change in agri-food systems of the Global South: A review of adoption-impact studies in wheat

Krishna

Yigezu²,

Karimov³

et al. 2020

Outlook Agric

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Agricultural research-and-development (R&D) systems generate and disseminate new technologies, aiming to enhance crop and livestock production sustainably. Adoption-impact studies analyze farmer acceptance of these innovations and their impacts to identify what was taken up, how it works in farmers’ fields, and the implications thereof. In this paper, we focus on adoption-impact studies in wheat agri-food systems in the Global South and review 47 empirical studies published during 2008–2017. The review focuses on the assessment of technological changes rather than the nature of these changes per se. We document the breadth and adequacy of empirical approaches and methodologies, as well as the diversity and coverage of thematic areas for adoption-impact analyses. The coverage and scope do not fully capture the complex and dynamic technological changes taking place in wheat systems. The reviewed studies typically conceive the adoption process as a dichotomous choice representing the replacement of old inferior practices with new superior ones. Most reviewed impact studies address only the short-term changes associated with innovation adoption on yield and profitability, often assuming that improvements in the living standards of farm households follow de facto. Labor productivity and the ecological and social impacts of technological innovations were largely ignored. We propose several directions to enhance both data availability and analytical rigor, and to reorient future research toward the less-explored but socially, economically, and ecologically relevant topics.

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Section: Improving Adoption-impact Research In Wheat Systemsmentioning

confidence: 99%

Assessing technological change in agri-food systems of the Global South: A review of adoption-impact studies in wheat

Krishna

Yigezu²,

Karimov³

et al. 2020

Outlook Agric

View full text Add to dashboard Cite

show abstract

“…The data from these studies, if publicly available, would be ideal for the personalized intervention as described here [5]. The trials come with their own set of challenges and biases that need to be evaluated [83]. Alternatively survey data can be used as well.…”

Section: Challenges a Data Availability And Augmentationmentioning

confidence: 99%

Machine learning and behavioral economics for personalized choice architecture

Hrnjic,

Tomczak

2019

Preprint

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Behavioral economics changed the way we think about market participants and revolutionized policy-making by introducing the concept of choice architecture. However, even though effective on the level of a population, interventions from behavioral economics, nudges, are often characterized by weak generalisation as they struggle on the level of individuals. Recent developments in data science, artificial intelligence (AI) and machine learning (ML) have shown ability to alleviate some of the problems of weak generalisation by providing tools and methods that result in models with stronger predictive power. This paper aims to describe how ML and AI can work with behavioral economics to support and augment decision-making and inform policy decisions by designing personalized interventions, assuming that enough personalized traits and psychological variables can be sampled.

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Randomization bias in field trials to evaluate targeting methods

Cited by 2 publications

References 10 publications

Assessing technological change in agri-food systems of the Global South: A review of adoption-impact studies in wheat

Assessing technological change in agri-food systems of the Global South: A review of adoption-impact studies in wheat

Machine learning and behavioral economics for personalized choice architecture

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