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The increased adoption of fertilizer and improved seeds are two key aspects to raising the level of land productivity in Ethiopian agriculture. However, the adoption and diffusion of such technologies has been slow. We use data from Ethiopia between 1999–2009 to examine the role of learning from extension agents versus learning from neighbors for both improved seeds and fertilizer adoption. We combine farmers' spatial networks with panel data to identify these influences, and find that while the initial impact of extension agents was high, the effect wore off after some time, in contrast to learning from neighbors.

This paper investigates an empirical puzzle in technology adoption for developing countries: the low adoption rates of technologies like hybrid maize that increase average farm profits dramatically. I offer a simple explanation for this: benefits and costs of technologies are heterogeneous, so that farmers with low net returns do not adopt the technology. I examine this hypothesis by estimating a correlated random coefficient model of yields and the corresponding distribution of returns to hybrid maize. This distribution indicates that the group of farmers with the highest estimated gross returns does not use hybrid, but their returns are correlated with high costs of acquiring the technology (due to poor infrastructure). Another group of farmers has lower returns and adopts, while the marginal farmers have zero returns and switch in and out of use over the sample period. Overall, adoption decisions appear to be rational and well explained by (observed and unobserved) variation in heterogeneous net benefits to the technology.

Despite widespread adoption of genetically modified crops in many countries, heated controversies about their advantages and disadvantages continue. Especially for developing countries, there are concerns that genetically modified crops fail to benefit smallholder farmers and contribute to social and economic hardship. Many economic studies contradict this view, but most of them look at short-term impacts only, so that uncertainty about longer-term effects prevails. We address this shortcoming by analyzing economic impacts and impact dynamics of Bt cotton in India. Building on unique panel data collected between 2002 and 2008, and controlling for nonrandom selection bias in technology adoption, we show that Bt has caused a 24% increase in cotton yield per acre through reduced pest damage and a 50% gain in cotton profit among smallholders. These benefits are stable; there are even indications that they have increased over time. We further show that Bt cotton adoption has raised consumption expenditures, a common measure of household living standard, by 18% during the 2006–2008 period. We conclude that Bt cotton has created large and sustainable benefits, which contribute to positive economic and social development in India.

The authors explore the role of hubs (people with an exceptionally large number of social ties) in diffusion and adoption. Using data on a large network with multiple adoptions, they identify two types of hubs—innovative and follower hubs. Contrary to recent arguments, hubs tend to adopt earlier in the diffusion process, even though they are not necessarily innovative. Although innovative hubs have a greater impact on the speed of the adoption process, follower hubs have a greater impact on market size (total number of adoptions). Importantly, a small sample of hubs offers accurate success versus failure predictions early in the diffusion process.

In a social network, adoption probability refers to the probability that a social entity will adopt a product, service, or opinion in the foreseeable future. Such probabilities are central to fundamental issues in social network analysis, including the influence maximization problem. In practice, adoption probabilities have significant implications for applications ranging from social network-based target marketing to political campaigns, yet predicting adoption probabilities has not received sufficient research attention. Building on relevant social network theories, we identify and operationalize key factors that affect adoption decisions: social influence, structural equivalence, entity similarity, and confounding factors. We then develop the locally weighted expectation-maximization method for Naïve Bayesian learning to predict adoption probabilities on the basis of these factors. The principal challenge addressed in this study is how to predict adoption probabilities in the presence of confounding factors that are generally unobserved. Using data from two large-scale social networks, we demonstrate the effectiveness of the proposed method. The empirical results also suggest that cascade methods primarily using social influence to predict adoption probabilities offer limited predictive power and that confounding factors are critical to adoption probability predictions.

Biomass combustion with traditional cookstoves causes substantial environmental and health harm. Nontraditional cookstove technologies can be efficacious in reducing this adverse impact, but they are adopted and used at puzzlingly low rates. This study analyzes the determinants of low demand for nontraditional cookstoves in rural Bangladesh by using both stated preference (from a nationally representative survey of rural women) and revealed preference (assessed by conducting a cluster-randomized trial of cookstove prices) approaches. We find consistent evidence across both analyses suggesting that the women in rural Bangladesh do not perceive indoor air pollution as a significant health hazard, prioritize other basic developmental needs over nontraditional cookstoves, and overwhelmingly rely on a free traditional cookstove technology and are therefore not willing to pay much for a new nontraditional cookstove. Efforts to improve health and abate environmental harm by promoting nontraditional cookstoves may be more successful by designing and disseminating nontraditional cookstoves with features valued more highly by users, such as reduction of operating costs, even when those features are not directly related to the cookstoves’ health and environmental impacts.

Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks.

There is an ongoing, revolutionary transformation occurring across the globe. This transformation is altering established processes, disrupting traditional business models and changing how people live their lives. The power sector is no exception and is going through a radical transformation of its own. Renewable energy, distributed energy sources, electric vehicles, advanced metering and communication infrastructure, management algorithms, energy efficiency programs and new digital solutions drive change in the power sector. These changes are fundamentally altering energy supply chains, shifting geopolitical powers and revising energy landscapes. Underlying infrastructural components are expected to generate enormous amounts of data to support these applications. Facilitating a flow of information coming from the system′s components is a prerequisite for applying Artificial Intelligence (AI) solutions in the power sector. New components, data flows and AI techniques will play a key role in demand forecasting, system optimisation, fault detection, predictive maintenance and a whole string of other areas. In this context, digitalisation is becoming one of the most important factors in the power sector′s transformation process. Digital solutions possess significant potential in resolving multiple issues across the power supply chain. Considering the growing importance of AI, this paper explores the current status of the technology’s adoption rate in the power sector. The review is conducted by analysing academic literature but also by analysing several hundred companies around the world that are developing and implementing AI solutions on the grid’s edge.

Over the postwar period, many industrialized countries have experienced significant medium-frequency oscillations between periods of robust growth versus relative stagnation. Conventional business cycle filters, however, tend to sweep these oscillations into the trend. In this paper we explore whether they may, instead, reflect a persistent response of economic activity to the high-frequency fluctuations normally associated with the cycle. We define as the medium-term cycle the sum of the high-and medium-frequency variation in the data, and then show that these kinds of fluctuations are substantially more volatile and persistent than are the conventional measures. These fluctuations, further, feature significant procyclical movements in both embodied and disembodied technological change, and research and development (R&D), as well as the efficiency and intensity of resource utilization. We then develop a model of medium-term business cycles. A virtue of the framework is that, in addition to offering a unified approach to explaining the high- and mediumfrequency variation in the data, it fully endogenizes the movements in productivity that appear central to the persistence of these fluctuations. For comparison, we also explore how well an exogenous productivity model can explain the facts.

We document some early effects of how mobile devices might change Internet and retail commerce. We present three main findings based on an analysis of eBay's mobile shopping application and core Internet platform. First, early adopters of mobile e-commerce applications appear to be people who already were relatively heavy Internet commerce users. Second, adoption of the mobile shopping application is associated with both an immediate and sustained increase in total platform purchasing, with little evidence of substitution from the core platform. Third, differences in user behavior across the mobile applications and the regular Internet site are not yet so dramatic.