Warmer weather conditions have a negative effect on most income activities. Three exceptions are to be noted. First, the weather impacts for staple crops are not significant or even positive for maximum temperatures. This finding indicates that these crops – mostly grown in the colder regions – are less sensitive to warmer temperatures or could even benefit. Second, hotter conditions and even drought events are related to higher forestry incomes. This finding may indicate that the extraction of forest resources is a copying mechanism applied to compensate shortfalls from other activities under heat stress. Similarly, fishing incomes go up under heat extremes expressed by the number of abnormally hot months and the maximum temperature in the hottest months. These results corresponds to other studies from rural Vietnam showing that incomes from the extraction of environmental resources tend to go up during weather shocks (Völker and Waibel, 2010).

Interestingly, weather variables do not only relate to agricultural and other ecosystem‐production related incomes, but also to supposedly less sensitive activities, such as non‐agricultural wages and business activities. This result may imply that in rural areas there are strong inter‐sectoral (demand and supply) linkages through which all income activities are negatively affected when weather shocks hit the agricultural sector. Moreover, some weather extremes may also affect non‐agricultural activities through their impacts on labor availability and productivity. Interestingly, all wage‐related activities are negatively affected by higher temperature, but are less sensitive to rainfall variation. This finding is in line with Park et al. (2015) who find that heat extremes can have a negative impact on labor productivity.

Some note of caution is needed. The predictive power of the models –especially those with the self‐reported weather events – become weak for some income activities. This finding mainly applies for income activities undertaken by a small sub‐sample of households and with limited income variation over time, such as forestry and fishing. Possibly these activities are less weather sensitive, so that the weather variables combined with the set of households controls cannot sufficiently explain income variation across time.

This finding may also be due to the dependency of effects on the specific locations within climate zonesand on specific activities within each income category. To further test this dependency, additional regressions were run for each income activity at the regional level. The results for the regional models with the highest predictive power are summarized in Tables A.18‐A.23. For example, in the Mekong River Delta less rain in the dry season _S1_ reduces income from summer‐autumn rice, while less rain during season _S3_ increases income from winter‐ spring rice. Unusually dry months are harmful for incomes from both winter‐spring and summer‐autumn rice (Tables A.18‐A.19). Coffee incomes in the Central Highlands suffer from high temperatures in _S1_ and temperature and rainfall extremes, as well as flood and drought events (Table A.20). In the North West region forestry incomes decrease in drier years with more cold months, while fishing incomes increase in warmer years with more hot months (Table A.21). Moreover, weather variation can explain a larger extent of the variation in wage and business‐related incomes for some regions (Tables A.22‐A.23). These findings suggest that a further disaggregation by income activities and regions would allow to refine the results.

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