Our one-year income snapshot provides a static picture of the households’ economic status. While this fails to capture explicit mechanisms of poverty dynamics (e.g. Hulme & Shepherd 2003), asset holding and other household characteristics may offer clues regarding whether households’ poverty status is likely to be structural or stochastic (Carter & May 2001). A second, complementary report to the World Bank, drawing on the same household income data set, looks specifically at climate vulnerability from this angle (Dokken and Angelsen 2015). Instead, we will here describe the importance of environmental sources in rural household incomes. From scattered case study work done across the developing world over the last couple of decades, there are reasons to believe that environmental incomes occupy an important place in the household economy of many rural smallholders, especially as compared to official household surveys and national accounts, where these income sources tend to be heavily under-appreciated (Cavendish 2000; Vedeld et al. 2004). Vedeld et al (2004) in particular looked in their meta-study at 54 case studies where environmental income contributions had been quantified, and found an average environmental income share of 22%. The authors, however, also make various caveats about methodological inconsistencies within and between these studies, as well case selection biases that may have prevailed. These factors may make the calculated case average less representative of the rural developing world. The primary data presented below, gathered quarterly with a consistent methodology for household income accounting, should thus be able to consolidate that picture.

In Section 5, we will turn our attention to the second research question: _RQ2: How does climate variability affect the vulnerability of the rural poor through impacts on environmental and other incomes?_ We start by looking at household self-reported shocks, and responses to those, analyzing what household and contextual characteristics mattered in their decision whether or not to use environmental extraction as their principal coping response to the shock. We continue with an econometric analysis of how climate variability in our cross-sectional data may affect income levels and sector income composition. Previous studies have shown that agricultural income is particularly sensitive to changes in average temperatures and precipitation (Mendelsohn et al. 1994; 2007; Kurukulasuriya et al. 2006) and also to temperature and precipitation fluctuations (Deschênes & Greenstone, 2007). We combine these two strands of literature to ask how changing climate and variable degrees of climate shocks and anomalies likely affect rural household incomes from agriculture, the environment, and from other sectors, respectively.

In Section 6, we conclude by outlining our main findings, and discussing their scope. Notably, we will reflect on some important limitations in the extent to which we can reasonably interpret our cross-sectional results for a prediction of how climate change will affect rural production systems and livelihoods over time. On the other hand, as long as still no comprehensive time series data exist for how a changing climate affects the contribution of different sectors to livelihoods in developing countries, the cross-sectional multicountry pattern analyzed here can at least serve to refine our hypotheses regarding what impacts we may expect.

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