This dataset is part of the Ecosystem Services and Poverty Alleviation (ESPA) programme. These data represent the central quantitative datasets from a mixed method, quasi-experimental study of the effects of an environmental intervention (national implementation of Wildlife Management Areas - WMAs) on Tanzania's rural population. The study focused on changes in wealth, livelihoods and wellbeing 2007-2014 for households in villages that are members of a WMA, compared to households in matched control villages that are not in WMAs, and causal attribution of those changes. The study covered 6 of the then 18 WMAs, (3 in northern rangelands and 3 in southern Miombo woodlands), and surveyed a total of 47 villages (both 'inside' WMAs and matched 'outside' non-WMA villages). In all, 13,578 households in these villages were wealth ranked on locally derived, village-specific criteria for both 2014/5 and (by recall) for 2007. From this sample frame we surveyed a stratified random sample of 1924 household heads (including 187 female heads of household) and 945 wives of household heads. Questions to household heads addressed household composition, land and livestock assets, resource use, income generating activities and income portfolios, participation in decision-making in natural resources management, and perceived benefits and costs of conservation, at the time of the 2014/5 survey and also by recall for 2007. Related questions addressed women’s perceptions of changes since 2007 in access to land and natural resources, production, income-generating activities, human-wildlife conflict, participation in WMA management; and overall costs and benefits of WMAs. Though there is also considerable qualitative data, much of this is politically sensitive and therefore not deposited here: interested researchers may contact the PI for partial access. Environmental data not already in the public domain are being deposited in the NERC Environment Information Data Service.Rural people across the global south are caught between competing land demands for large-scale cultivation, global conservation, and local needs. These can in theory be integrated locally through community-based natural resource management (CBNRM) and payments for ecosystem services (PES): where communities can decide on and benefit directly from natural resources, they may invest in and manage those resources in ways that are more socially and environmentally sustainable. CBNRM/PES initiatives are being rolled out across the global south, but there are conflicting views as to how well they work, for whom and under what circumstances. This is partly due to the complexity and multidimensionality of the ecosystem services (ES) and poverty alleviation (PA) outcomes involved, and the inevitable tradeoffs, but also to the hitherto limited use of either qualitatively or quantitatively rigorous impact evaluation approaches that are independent, control for confounding factors and ensure the voices of the most marginalized are heard. As well as being limited by generally weak research design, studies to date have often failed to account for the ways political sensitivities around changing access to and use of ecosystem services may compromise data quality and mask differentiated impacts. PIMA seizes a unique policy moment, with Tanzania's poverty reduction strategy Mkukuta driving nationwide implementation of CBNRM/PES-based Wildlife Management Areas (WMAs), and other countries in the region considering comparable initiatives. The WMAs comprise different ecosystems (rangeland, miombo), socio-political structures (long-established/ethnically uniform vs recent, heterogeneous constituent villages), and a broad range of ecosystem services (water-regulating and -supplying, provision of forest products, grazing, livestock, crop and wildlife production, cultural services both local and global (from locally significant social and ritual spaces, to heritage and tourism). Quasi-experimental comparison of social and ecological outcomes for established WMAs with statistically matched non-WMA areas (within the same ecosystems) offers an ideal opportunity for rigorous impact evaluation. PIMA combines analysis of remotely-sensed, public-domain MODIS and NDVI data, with cutting edge study of governance, and new data from qualitatively and quantitatively rigorous, differentiated survey of livelihoods and resource use histories, structured within a quasi-experimental research design. PIMA brings together a powerful international research team to work with strongly-rooted civil society organizations to ensure research excellence and development impact.

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