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Can targeting information to network-central farmers induce more adoption of a new agricultural technology? By combining social network data and a field experiment in 200 villages in Malawi, we find that targeting central farmers is important to spur the diffusion process. We also provide evidence of one explanation for why centrality matters: a diffusion process governed by complex contagion. Our results are consistent with a model in which many farmers need to learn from multiple people before they adopt themselves. This means that without proper targeting of information, the diffusion process can stall and technology adoption remains perpetually low.

Background Health literacy concerns the ability of citizens to meet the complex demands of health in modern society. Data on the distribution of health literacy in general populations and how health literacy impacts health behavior and general health remains scarce. The present study aims to investigate the prevalence of health literacy levels and associations of health literacy with socioeconomic position, health risk behavior, and health status at a population level. Methods A nationwide cross-sectional survey linked to administrative registry data was applied to a randomly selected sample of 15,728 Danish individuals aged ≥25 years. By the short form HLS-EU-Q16 health literacy was measured for the domains of healthcare, disease prevention, and health promotion. Adjusted multinomial logistic regression analyses were used to estimate associations of health literacy with demographic and socioeconomic characteristics, health risk behavior (physical activity, smoking, alcohol consumption, body weight), and health status (sickness benefits, self-assessed health). Results Overall, 9007 (57.3%) individuals responded to the survey. Nearly 4 in 10 respondents faced difficulties in accessing, understanding, appraising, and applying health information. Notably, 8.18% presented with inadequate health literacy and 30.94% with problematic health literacy. Adjusted for potential confounders, regression analyses showed that males, younger individuals, immigrants, individuals with basic education or income below the national average, and individuals receiving social benefits had substantially higher odds of inadequate health literacy. Among health behavior factors (smoking, high alcohol consumption, and inactivity), only physical behavior [sedentary: OR: 2.31 (95% CI: 1.81; 2.95)] was associated with inadequate health literacy in the adjusted models. The long-term health risk indicator body-weight showed that individuals with obesity [OR: 1.78 (95% CI: 1.39; 2.28)] had significantly higher odds of lower health literacy scores. Poor self-assessed health [OR: 4.03 (95% CI: 3.26; 5.00)] and payments of sickness absence compensation benefits [OR: 1.74 (95% CI: 1.35; 2.23)] were associated with lower health literacy scores. Conclusions Despite a relatively highly educated population, the prevalence of inadequate health literacy is high. Inadequate health literacy is strongly associated with a low socioeconomic position, poor health status, inactivity, and overweight, but to a lesser extent with health behavior factors such as smoking and high alcohol consumption.

Background Health Literacy (HL) is the knowledge and competence to access, understand, appraise, and apply health information for health judgment. We analyze for the first time HL level of Catalonia’s population. Our objective was to assess HL of population in our area and to identify social determinants of HL in order to improve the strategies of the Healthcare Plan, aimed at establishing a person-centered system and reducing social inequalities in health. Methods This was a cross-sectional study based on the Health Survey for Catalonia (ESCA, Enquesta de Salut de Catalunya ), which included the 16 items of the European Health Literacy Survey Questionnaire (HLS-EU-Q16). The statements in the questionnaire cover three different health literacy domains: Health Care, Disease Prevention, and Health Promotion. HL was categorized in three levels: Sufficient, Problematic and Inadequate. Chi-square tests were performed to compare the percentages of subjects with adequate or inadequate HL across sociodemographic and health-related variables. Variables showing significant differences were included in a stepwise logistic regression to predict inadequate HL level. Results The questionnaire was administered to 2433 subjects aged between 15 and 98 years old (mean of 45.9 years, SD 18.0). Overall, 2059 subjects (84.6%) showed sufficient HL, 250 (10.3%) inadequate HL, and 124 (5.1%) problematic HL, with no significant differences between men and women ( p  = 0.070). A logistic regression analysis showed that low health literacy is associated with a lower level of education (OR 2.08, CI 95% 1.32–3.28, p  = 0.002), low socioeconomic status (OR 2.11, CI 95% 1.42–3.15, p  <  0.001) and a physical limitation to perform everyday activities (OR 2.50, CI 95% 1.34–4.66, p  = 0.004). We also found a more modest association with low physical activity, having a self-perceived chronic disorder and performing preventive activities. Conclusions Catalonia has a high percentage of subjects with sufficient HL. Education level, socioeconomic status and physical limitations were the factors with the strongest contribution to inadequate or problematic health literacy. Although these results are likely to be country-specific, the factors identified will allow policymakers of areas with similar socioeconomic profiles to identify groups with high risk of problematic or inadequate HL, which is essential for a successful patient-centered model of care.

We discuss recent trends in agricultural productivity in Africa and highlight how technological progress in agriculture has stagnated on the continent. We briefly review the literature that tries to explain this stagnation through the lens of particular constraints to technology adoption. Ultimately, none of these constraints alone can explain these trends. New research highlights pervasive heterogeneity in the gross and net returns to agricultural technologies across Africa. We argue that this heterogeneity makes the adoption process more challenging, limits the scope of many innovations, and contributes to the stagnation in technology use. We conclude with directions for policy and what we feel are still important, unanswered research questions.

The panel approach with fixed effects and nonlinear weather effects has become a popular method to uncover weather impacts on economic outcomes, but its ability to capture long-run climatic adaptation remains unclear. Building upon a framework proposed by McIntosh and Schlenker (2006), this paper identifies empirical conditions under which the nonlinear panel approach can approximate a long-run response to climate. When these conditions fail, the obtained relationship may still be interpretable as a weighted average of underlying short-run and long-run responses. We use this decomposition to revisit recently published climate impact estimates. For spatially large panels, the estimated temperature-outcome relationship mostly reflects the long-run climatic response; this is not so for precipitation. We find some evidence of long-run climatic adaptation for crop yield outcomes in the United States and France.

Low adoption of agricultural technologies holds large productivity consequences for developing countries. Many countries hire agricultural extension agents to communicate with farmers about new technologies, even though a large academic literature has established that information from social networks is a key determinant of product adoption. We incorporate social learning in extension policy using a large-scale field experiment in which we communicate to farmers using different members of social networks. We show that communicator own adoption and effort are susceptible to small performance incentives, and the social identity of the communicator influences others’ learning and adoption. Farmers appear most convinced by communicators who share a group identity with them, or who face comparable agricultural conditions. Exploring the incentives for injection points in social networks to experiment with and communicate about new technologies can take the influential social learning literature in a more policy-relevant direction.

Standard experimental designs are geared toward point estimation and hypothesis testing, while bandit algorithms are geared toward in-sample outcomes. Here, we instead consider treatment assignment in an experiment with several waves for choosing the best among a set of possible policies (treatments) at the end of the experiment. We propose a computationally tractable assignment algorithm that we call “exploration sampling,” where assignment probabilities in each wave are an increasing concave function of the posterior probabilities that each treatment is optimal. We prove an asymptotic optimality result for this algorithm and demonstrate improvements in welfare in calibrated simulations over both non-adaptive designs and bandit algorithms. An application to selecting between six different recruitment strategies for an agricultural extension service in India demonstrates practical feasibility.

Most infectious diseases in humans originate from animals. In this paper, we explore the role of animal farming and meat consumption in the emergence and amplification of infectious diseases. First, we discuss how meat production increases epidemic risks, either directly through increased contact with wild and farmed animals or indirectly through its impact on the environment (e.g., biodiversity loss, water use, climate change). Traditional food systems such as bushmeat and backyard farming increase the risks of disease transmission from wild animals, while intensive farming amplifies the impact of the disease due to the high density, genetic proximity, increased immunodeficiency, and live transport of farmed animals. Second, we describe the various direct and indirect costs of animal-based infectious diseases, and in particular, how these diseases can negatively impact the economy and the environment. Last, we discuss policies to reduce the social costs of infectious diseases. While existing regulatory frameworks such as the “One Health” approach focus on increasing farms’ biosecurity and emergency preparedness, we emphasize the need to better align stakeholders’ incentives and to reduce meat consumption. We discuss in particular the implementation of a “zoonotic” Pigouvian tax, and innovations such as insect-based food or cultured meat.

Agricultural extension programs have changed significantly over the past four decades. What has changed and why? Have these changes affected adoption of innovations by farmers? What if anything should policy makers and extension agencies do differently, particularly in developing countries? Structural changes in agriculture, new types of agricultural technologies, tight public budgets, efforts to decentralize government, and emerging information and communication technologies (ICT) have led to pluralistic and, in some cases, lower-cost extension and advisory services that combine public and private mechanisms for financing and implementing extension activities. Farmer groups and virtual networks play a growing role in technology diffusion, and extension services can exploit these networks using the latest ICT approaches.

We use a randomized experiment in India to show that improved technology enhances agricultural productivity by crowding in modern inputs and cultivation practices. Specifically, we show that a new rice variety that reduces downside risk by providing flood tolerance has positive effects on adoption of a more labor-intensive planting method, area cultivated, fertilizer usage, and credit utilization. We find that a large share of the expected gains from the technology comes from crowding in of other investments. Therefore, improved technologies that reduce risk by protecting production in bad years have the potential to increase agricultural productivity in normal years.