International climate negotiations occur against the backdrop of increasing collective risk: the likelihood of catastrophic economic loss due to climate change will continue to increase unless and until global mitigation efforts are sufficient to prevent it. We introduce a novel alternating-offers bargaining model that incorporates this characteristic feature of climate change. We test the model using an incentivized experiment. We manipulate two important distributional equity principles: capacity to pay for mitigation of climate change and vulnerability to its potentially catastrophic effects. Our results show that less vulnerable parties do not exploit the greater vulnerability of their bargaining partners. They are, rather, more generous. Conversely, parties with greater capacity are less generous in their offers. Both collective risk itself and its importance in light of the recent Intergovernmental Panel on Climate Change report make it all the more urgent to better understand this crucial strategic feature of climate change bargaining.
Do legislative voting rules affect the diversity of policies observed across structurally similar political economies, and if so, to what extent? To what degree do these rules affect legislative compromise and the stability of the social optimum? Using a spatial model of political competition with single-peaked preferences, we examine these questions in static and dynamic political economies where changing proposed policies requires supermajority consensus. We develop three findings pertaining to equilibrium policies that are immune to change by any supermajority coalition. First, we find the number of equilibrium policies that exist as a function of the supermajority’s size. This result implies that under supermajority rules, structurally identical political economies may implement very different policies. Second, we find the optimal level of compromise needed by a leader to ensure that her proposed policy is not defeated and establish that compromise decreases in the supermajority’s size. Third, we identify the minimal supermajority rule that ensures the stability of the social optimum. We derive implications for political design, policy diversity, and political inclusiveness.
Scientists predict higher global temperatures over this century. While this may benefit some countries, most will face varying degrees of damage. This has motivated research on solar geoengineering , a technology that allows countries to unilaterally and temporarily lower global temperatures. To better understand the security implications of this technology, we develop a simple theory that incorporates solar geoengineering, countergeoengineering to reverse its effects, and the use of military force to prevent others from modifying temperatures. We find that when countries’ temperature preferences diverge, applications of geoengineering and countergeoengineering can be highly wasteful due to deployment in opposite directions. Under certain conditions, countries may prefer military interventions over peaceful ones. Cooperation that avoids costs or waste of resources can emerge in repeated settings, but difficulties in monitoring or attributing interventions make such arrangements less attractive.
How beneficial is basic energy access – typically lighting and mobile charging – for rural households? Despite research on the economic impacts of basic energy access, few studies have investigated how it changes household behavior. Here we report results from a randomized controlled trial in rural Uttar Pradesh, India, which identifies the behavioral impacts of providing solar lanterns to households that normally rely on kerosene as their primary source of lighting. Eighty-nine of the 184 households partici- pating in the study were given a free, high-quality solar lantern. Comparing changes in responses from the baseline questionnaire and an endline questionnaires administered six months later, we find that the lanterns reduced energy expenditures, improved lighting, improved satisfaction with lighting, more use of lighting for domestic activities (e.g., reading), and improved satisfaction with lighting for domestic activities. Overall, our results show that basic energy access can offer substantial benefits within the households, even if broader rural economic transformation is not plausible.
To reach the United Nations Sustainable Development Goal of universal household electrification by 2030, developing countries are spending billions of dollars to expand access. India, for example, recently undertook an audacious expansion plan which aimed to electrify every household by December 2018. However, there is little academic study of strategies to increase electrification rates. We argue that significant transaction costs inhibit household applications for connections. As evidence, we report the results of a randomized controlled trial (in 200 communities and 2000 households) in the Indian state of Uttar Pradesh, with a treatment consisting of an informational campaign about the costs and procedure of applying. We found that households exposed to the campaign were three times as likely to apply for a connection. Yet actual connection rates remained unchanged. The results suggest that transaction costs are an important barrier to electrification, but limited capacity and incentive to expand connections are equally important.
Measuring energy access in developing countries involves much more than simply recording whether or not households are connected to the grid. Both international organizations and scholars now recognize the importance of reliable electricity supply for achieving positive development outcomes. Yet, measuring reliability is much more difficult than measuring the existence of connections. We propose an economical croudsourcing method for measuring reliability, and compare this method to energy monitor data for 122 households over 12 months. The results suggest that, while far from perfect, crowdsourcing provides a reasonably accurate method for monitoring the reliability of access over time, especially when modeled as a non-linear relationship. We apply these findings to model energy reliability in a broader group of villages across Uttar Pradesh, India, demonstrating the existence of disparities between urban and rural reliability and seasonal fluctuations in reliability. The system laid out in this study can be utilized by government and non-government organizations to quickly and cheaply monitor energy reliability.
Even as India pursues universal electricity access, household electricity consumption remains poorly understood. Studies have investigated residential electricity consumption, but most focus on urban consumers, even though a majority of the newly electrified households are in rural areas. Using primary data from 10,000 households, we investigate rural electricity consumption in 200 villages in Uttar Pradesh, Bihar, Odisha, and Rajasthan. We rely on energy use surveys that capture appliance use and multiple energy sources. We find that the surveyed households typically consume 39.3 kWh per month during the summer months, which is half of the country’s average residential consumption. We also find that hours of grid-electricity supply predicts consumption: every 1% increase in supply hours is associated with a 1.245% increase in consumption. Our findings suggest that improved supply can lead to significant welfare gains for consumers, and allow distribution companies to tap into unmet electricity demand in rural areas.
How can demand for electricity be estimated without fine-grained usage data? Employing an original and large dataset, we develop a novel method for determining drivers of demand without electricity meter data. We first segment Indian consumers by their willingness to pay for electricity service, their level of usage, and their satisfaction with lighting, and then use cluster membership as a dependent variable in order to determine which household-level factors predict electricity usage. Our approach employs machine-learning and more traditional regression techniques to determine the optimal number of segments, generate the segments, and determine the predictors of segment membership. The dataset consists of more than 10,000 households in more than 200 villages in the states of Bihar, Odisha, Rajasthan, and Uttar Pradesh. We find that the rural Indian electricity market can be segmented into three clusters based on households' willingness to pay, satisfaction with lighting, and appliance wattage. The clusters consist of potential customers, low-demand customers, and high-use customers. We then determine the predictors of membership in these clusters. We show that different types of consumers can be identified along easily observable measures. Moreover, we show that there are clear groups of consumers that vary along their satisfaction, willingness to pay, and existing appliance usage.
While rural electrification has been a high priority for many governments in the developing world, the factors that make individual households more likely to pay for a connection have received insufficient attention. In particular, many studies have dealt with the role of affordability of grid connections, but they have generally avoided studying the effects of service quality. Estimating the effect of quality on willingness of potential customers to pay is a difficult task because of self-selection – if quality is important, those in higher quality service areas are more likely to have a connection. Using household data from rural India, we estimate a Heckman selection model to deal with this issue and find a substantial impact of quality on willingness to pay for a connection in India. The results suggest that improving the quality of connections is critical to improving access.