Energy-use statistics in Tanzania reflect the country's low level of industrialization and development. In 2016, only 16.9% of rural and 65.3% of urban inhabitants in mainland Tanzania were connected to some form of electricity. We use a nationally representative three-wave panel dataset (2008-2013) to contribute to the literature on household energy use decisions in Tanzania in the context of the stacking and energy ladder hypotheses. We firstly adopt a panel multinomial-logit approach to model the determinants of household cooking-and lighting-fuel choices. Secondly, we focus explicitly on energy stacking behaviour, proposing various ways of measuring what is inferred when stacking behaviour is thought of in the context of the energy transition and presenting household level correlates of energy stacking behaviour. Thirdly, since fuel uses have gender-differentiated impacts, we investigate women's bargaining power in the decision-making process of household fuel choices. We find that whilst higher household incomes are strongly associated with a transition towards the adoption of more modern fuels, especially lighting fuels, this transition takes place in a context of significant fuel stacking. In Tanzania, government policy has been aimed mostly at connecting households to the electric grid. However, the public health, environmental and social benefits of access to modern energy sources are likely to be diminished in a context of significant fuel stacking. Lastly, we present evidence that the educational attainment of women in the household is an important aspect of household fuel choices.

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In this article, we explore the role of biofuel production on deforestation in developing and emerging countries. Since the 2000s biofuel production has been rapidly developing to address issues of economic development, energy poverty and reduction of greenhouse gas (GHG) emissions. However, the sustainability of biofuels is being challenged in recent research, particularly at the environmental level, due to their impact on deforestation and the GHG emissions they can generate as a result of land use changes. In order to isolate the impact of bioethanol and biodiesel production among classic determinants of deforestation, we use a fixed effects panel model on biofuel production in 112 developing and emerging countries between 2001 and 2012. We find a positive relationship between bioethanol production and deforestation in these countries, among which we highlight the specificity of Upper-Middle-Income Countries (UMICs). An acceleration of incentives for the production of biofuels, linked to a desire to strengthen energy security from 2006 onwards, enables us to highlight higher marginal impacts for the production of bioethanol in the case of developing countries and UMICs. However, these results are not significant before 2006 for developing countries, and biodiesel production appears to have an impact on deforestation before 2006 on both subsamples. These last two results seem

Data are a key component in the design, implementation, and evaluation of economic and social policies. Monitoring data quality is an essential part of any serious, large‐scale data collection process. The purpose of this article is to show how paradata should be used before, during, and after data collection to monitor and improve data quality. To do this we use timestamps, global positioning system (GPS) coordinates, and other paradata collected from an 800‐household survey conducted in Tanzania in 2016. We demonstrate how key paradata can be used during each phase of a research project to identify and prevent issues in the data and the methods used to collect it. Our results corroborate the importance of collecting and analyzing paradata to monitor fieldwork and ensuring data quality for micro data collection in developing countries. Based on these findings we also make recommendations as to how researchers can make better use of paradata in the future to manage and improve data quality. We argue for an expansion in the understanding and use of varied paradata among researchers, and a greater focus on its use for improving data quality.