The database from which health, wealth, and some of the matching covariates were extracted is more fully described in (17). Briefly, data from all DHS surveys (18) that have been conducted in 39 developing countries since 2000 were included in an initial database. The DHS program conducts nationally and subnationally representative surveys, implemented using a stratified two-stage cluster sampling design, across the developing world. These publicly accessible surveys contain detailed demographic and socioeconomic data at both the individual and household level, obtained by interviewing women and men aged 15 to 49 on a variety of issues related to livelihoods, household assets, reproductive health, family planning, and child health. After eliminating instances where relevant DHS data for our analyses were missing, this resulted in 312,727 observations across 33 countries for early childhood growth and 190,794 observations across 34 countries for household wealth. Key blocks of missing data occurred in Indonesia, Peru, and the Philippines, where questions on stunting were not asked in some or all DHS survey years.

We used global spatial data layers on elevation, annual precipitation, tree cover, roads, anthropogenic land transformation, and human population density to characterize the biophysical environment of DHS sampling clusters within which the households in our database were contained. For elevation and annual precipitation, we extracted the corresponding value at each cluster, while for roads, we calculated the distance to the nearest road. For tree cover, anthropogenic land transformation, and human population density, we calculated the average value in a 10-km buffer around each DHS cluster.

We used the 2013 version of the World Database on Protected Areas (22) to assess whether DHS sampling clusters were located within or outside a 10-km radius to a PA. We restricted our analysis to PAs in IUCN categories I to VI, removing those PAs that were unclassified since we could not be sure of their management objectives. We also merged previously developed databases on the prevalence of tourism at PAs, classifying PAs with any demonstrable level of visitation as having associated tourism (33, 34).

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