long-run effects on climate means (proxying trends) and deviations in realized weather from those climate means in the particular survey year (weather anomalies). Households can _ex ante_ adapt to the former, but can only cope with the latter _ex post_ . Our analytical focus was on climate anomalies, analyzing how household incomes had changed in sites that had suffered a weather anomaly with likely negative impacts on crop production, controlling for other factors of variation. This analysis of anomalies could thus also become relevant to future climate change, which inter alia is predicted to cause greater climate variability and unpredictable weather patterns.

In our multiple regressions, only roughly half of the climate variables came out as significant determinants of income differences, no matter whether in the global or in regional samples, or whether we controlled for household assets (allowing for climate change adaptation) or only for other contextual variables (allowing just for coping). This is perhaps not surprising, especially given that much fewer weather stations exist in developing countries, forcing us to rely on data that is mostly based on spatial interpolations. This introduces imprecisions into the raw data underlying our analysis.

Notwithstanding these important caveats, we do find at least some plausible cross-cutting results. First, anomalies suffered during the survey year generally seem to be better predictors of income impacts than are (the so far modest) changes in climate means. However, these anomalies are causally often best combined in interaction variables with the means. For instance, a severe drought matters much more to people in a dry than in a wet area: the former will likely report it as a negative economic shock; the latter may not register any effect, or even see production gains. Climate shocks seem to matter most for people when they accentuate climatic conditions that are already fairly detached from mean temperatures and mean precipitation, respectively from the levels that are optimal for crop production.

Looking at how climate variables affected total household income and six of its sub-categories (crops, livestock, forest and non-forest extraction, wages and other incomes) gave us a more disaggregated picture. We found, as expected, that crop incomes were negatively affected by weather anomalies when the latter took temperature or precipitation further away from absolute levels that are favorable for crop production. In turn, animal husbandry seems to be more resilient to these fluctuations, being more dependent than crops upon biomass stocks that have been accumulated multi-annually. Especially rainfall anomaly effects prove to be both variable and often quite significant for sector production patterns. Although the differences between the estimations with and without household capital/ asset control variables were not large, the generally less significant results with asset controls did confirm our expectations that rural households seemingly with some success adapt their asset holdings to climate differences across our sample. Finally, we found surprisingly little significant effects from climate anomalies on total household incomes: the impacts on sector production seem to widely cancel each other out, indicating at this point fairly effective household compensatory strategies to cope with the weather anomalies.

If natural forests and other wildlands are important as income stabilizers (Hypothesis 3), why is it that only 8% of our households stated that they have used them as their primary safety net in response to economic shocks threatening their livelihoods (Hypothesis 2)? We believe that the differently perceived nature of shocks vs. weather variability may be a good potential explanation for this seeming paradox. Shocks were registered in our survey through one-year recall, and were perceived by households as discrete events with significant income shortfalls that have to be met by short-run compensatory (or other) emergency strategies: otherwise, the event would not have been flashed in the memory of the respondent. Conversely, most of the weather anomaly impacts were actually not perceived as shocks that called for emergency responses, but rather as tacit changes in production conditions that called for marginal adjustments in the household’s strategy of income generation. This interpretation is supported by the fact 36