temperature and precipitation events accurately. In addition to climate data, we incorporate monthly pollution data from the Atmospheric Composition Analysis Group at Washington University in St. Louis (Van Donkelaar et al., 2021), which has a spatial resolution of 0.1 degrees and provides annual and monthly estimates of ground-level fine particulate matter ( _PM_ 2 _._ 5).

To establish hazard exposure, we leverage the geographic coordinates collected by the DHS. We create 2km and 5km buffers around urban and rural households, respectively, and then extract the average temperature, precipitation, and pollution levels from the satellite data within these predefined radii.<sup>4</sup> We employ larger buffers for rural households, taking into account the random displacement of coordinates conducted by the DHS to ensure the privacy of respondents in sparsely populated areas. Our comprehensive dataset for analysis encompasses household-level covariates and health outcomes spanning the years 1998, 2000, 2005, 2014, and 2021, along with pollution exposure data covering the period from 1997 to 2021. We merge the DHS data with the temperature and precipitation data at the cluster, day, month, and year levels. The associated weather variables represent the average temperature and precipitation in the month leading to the interview. Air pollution data is at the monthly level. We merge it at the cluster, month, and year levels. The resulting variable measures air pollution in the month of the survey.<sup>5</sup> Table 1 furnishes an extensive overview of key variables essential to our empirical analysis. In Panel A, environmental data, including mean daily maximum temperature, average monthly precipitation, and average monthly ground-level fine particulate matter ( _PM_ 2 _._ 5), are presented. Panel B sheds light on health outcomes, specifically, diarrhea and cough prevalence. Notably, data on these health indicators were exclusively available for children, constituting 25.28% of the overall sample. For children aged 0-4, data on diarrhea was accessible for nearly 99.81% of the 30,003 children in the sample, while data on cough was available for approximately 99.84% of the same group. Panel C offers insights into relevant covariates and household characteristics, encompassing toilet access (available for 100% of the sample), refrigerator ownership (accessible for approximately 99.97% of the sample), education level, household size, and urban residence. Education in Cambodia DHS is defined for six levels: people with no, incomplete primary, complete primary, incomplete secondary, complete secondary, and higher education. Our dummy variable encompasses the last three.

> 4The DHS includes a displacement of coordinates to preserve anonymity. It is standard practice in the literature to include buffer sizes corresponding to the DHS displacement to account for it. We apply this method in our study and set the size of the buffers to equal the size of the maximum displacement.

> 5We focus on average air pollution in this paper. Air pollution peaks could also have acute consequences on health, but their study requires temporally disaggregated data that we do not have.

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