For _malaria_ , we increase the number of malaria cases in 2030 following (Caminade et al. 2014). As with stunting, we calculate the fraction of people who are affected by malaria, based on current prevalence, and we vary this fraction using estimates of future change due to climate change in various regions. Then, based on the literature reviewed in chapter 4, we assume that these people are affected between 0.1 and 2 times per year, and lose income through the cost of treatment (between $0.7 and $6 per occurrence) and lost days of work (by the sick or caregivers, between 1 and 5 days per occurrence). Note that we consider only the monetary expenses due to the disease and do not model non‐monetary effects (like the cost of life or loss in well‐being from being sick), which would be important in a multidimensional analysis of poverty.

In Vietnam, (Caminade et al. 2014) actually project a decrease of malaria because of climate change. So we increase the income of people who would have been affected by Malaria in the absence of climate change.

For _diarrhea_ , we start from data on the number of cases per country today, the cost of treatment (between $2 and $4 per episode), and the number of days out of work (between 3 and 7 days for the sick and caregivers) (Hutton, Haller, and others 2004). We also assume that the prevalence of diarrhea will increase by 10 percent by 2030 because of climate change, using results from (Kolstad and Johansson 2010). In Vietnam, the number of cases per year is around 80% of the population, so it is increased to nearly 90% of the population in our scenarios.

To account for development, we use DHS data to explore the relationship between household income and exposure to diarrhea. We find a threshold at $15,600 per year, and we assume that only households with income below this level are affected by impact of climate change on diarrhea.

Further, we assume that fast progress in access to water and sanitation in the _prosperity_ scenario would halve the number of cases, which is consistent with a recent assessment in India (Andres et al. 2014). Of course, this assumes that the new water and sanitation infrastructure can continue to perform well in 2030 and beyond – in other words, that development has been climate‐informed. For that to occur, the uncertainty in climate projections would need to be accounted for in the design phase, as would the extra funds needed to invest in more resilient infrastructure (possibly factoring in safety margins and retrofit options) (Kalra et al. 2014).

We find that with high climate change impacts, in Vietnam, in the high‐impact case, 350,000 people would be pushed into poverty in 2030 in the _poverty_ scenario and 200,000 would be pushed below 4usd per day ( Figure 4). However, in the _prosperity_ scenario and in the low‐impact case, impacts are very small because of the positive impact of malaria (actually, the average income of the bottom 40% slightly increases in the low‐impact case, see Table 5).

As for the impact of high temperatures on labor productivity (also based on results presented in chapter 4 in _Shock Waves_ ), we assume that in hot countries, people working outside or without air conditioning will lose between 1 and 3 percent in labor productivity due to this change of climate, compared with a baseline with no climate change. To assess the number of people affected, we estimate the shares of people working outside or without air conditioning in the two socioeconomic scenarios. Our results show 14