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ABSTRACT A key challenge in conducting comparative analyses across social units, such as religions, ethnicities, or cultures, is that data on these units is often encoded in distinct and incompatible formats across diverse datasets. This can involve simple differences in the variables and values used to encode these units (e.g., Roman Catholic is V130 = 1 vs. Q98A = 2 in two different datasets) or differences in the resolutions at which units are encoded (Maya vs. Kaqchikel Maya). These disparate encodings can create substantial challenges for the efficiency and transparency of data syntheses across diverse datasets. We introduce a user‐friendly set of tools to help users translate four kinds of categories (religion, ethnicity, language, and subdistrict) across multiple, external datasets. We outline the platform's key functions and current progress, as well as long‐range goals for the platform. 

Abstract ObjectivesStudies suggest that living at high altitude decreases obesity risk, but this research is limited to single‐country analyses. We examine the relationship between altitude and body mass index (BMI) among women living in a diverse sample of low‐ and middle‐income countries. Materials and MethodsUsing Demographic and Health Survey data from 1 583 456 reproductive age women (20–49 years) in 54 countries, we fit regression models predicting BMI and obesity by altitude controlling for a range of demographic factors—age, parity, breastfeeding status, wealth, and education. ResultsA mixed‐effects model with country‐level random intercepts and slopes predicts an overall −0.162 kg/m²(95% CI −0.220, −0.104) reduction in BMI and lower odds of obesity (OR 0.90, 95% CI 0.87, 0.95) for every 200 m increase in altitude. However, countries vary dramatically in whether they exhibit a negative or positive association between altitude and BMI (34 countries negative, 20 positive). Mixed findings also arise when examining odds of obesity. DiscussionWe show that past findings of declining obesity risk with altitude are not universal. Increasing altitude predicts slightly lower BMIs at the global level, but the relationship within individual countries varies in both strength and direction. 

Scientists and policymakers are increasingly leveraging complex, multi-scale data from diverse, worldwide sources to understand the causes and consequences of economic development, social stratification, climate change, cultural diversity, and violent conflict. This work frequently requires integrating data across diverse datasets by complex, dynamic categories (e.g., ethnicities, languages, religions, subdistricts). However, different datasets encode corresponding categories in disparate formats and at different resolutions (e.g., Guatemala Indigenous vs. Maya vs. K’iche’). These diverse encodings must be translated across datasets before bringing them together for analysis. At global scales across thousands of categories, the combinatorial complexity creates thorny challenges for manual reconciliation and for transparent documentation and sharing of researcher decisions. There is a need to investigate direct and uncomplicated ways to support search and explore the semantics for complex and diverse datasets.We design and deploy such a tool, CatMapper, to support semantic discovery through exploration and manipulation for large, complex and diverse datasets. CatMapper enables exploring contextual information about specific categories, translating new sets of categories from existing datasets and published studies, identify and integrating novel combinations of datasets for researchers’ custom needs, including automatically generated syntax to merge datasets of interest, and publishing and sharing merging templates for public re-use and open science. CatMapper does not store observational data. Rather, it is a dynamic, interactive dictionary of keys to help users integrate observational data from diverse external datasets in disparate formats, thereby complementing and leveraging a fast-growing ecology of datasets storing observational data. We have conducted heuristic evaluation on CatMapper usability. Results shed lights on enriching semantic data discovery. 

Social scientists have developed numerous asset-based wealth indices to assess and target socioeconomic inequalities globally. However, there are no systematic studies of the relative performance of these different measures as proxies for socioeconomic position. In this study, we compare how five asset-based wealth indices—the International Wealth Index (IWI), the Standard of Living portion of the Multi-Dimensional Poverty Index (MPI-SL), the Poverty Probability Index (PPI), the Absolute Wealth Estimate (AWE), and the DHS Wealth Index (DHS)—predict benchmarks of socioeconomic position across 11 communities in rural Bangladesh. All indices were highly correlated. The IWI best explained variation in individual and community ranking of economic well-being, while the PPI best explained variation both between and within communities for total household wealth and a general measure of subjective social status. 

Abstract Social scientists have increasingly used asset‐based wealth scores, like the Demographic and Health Survey (DHS) wealth index, to assess economic disparities. However, current indices primarily capture wealth in globalized market economies, thus ignoring other forms of prosperity, such as success in agricultural activities. Using a simple extension to the standard estimation of the DHS wealth index, we describe procedures for estimating an agricultural wealth index (AWI) that complements market‐based wealth indices by capturing household success in agricultural activities. We apply this procedure to household data from 129 DHS surveys from over 40 countries with sufficient land and livestock data to estimate a reliable and consistent AWI. We assess the construct validity of the AWI using benchmarks of growth in both adults and children. This alternative measure of wealth provides new opportunities for understanding the causes and consequences of wealth inequality, and how success along different dimensions of wealth creates different social opportunities and constraints for health and well‐being. 

Abstract Studies have shown mixed associations between wealth and fertility, a finding that has posed ongoing puzzles for evolutionary theories of human reproduction. However, measures of wealth do not simply capture economic capacity, which is expected to increase fertility. They can also serve as a proxy for market opportunities available to a household, which may reduce fertility. The multifaceted meaning of many wealth measures obscures our ability to draw inferences about the relationship between wealth and fertility. Here, we disentangle economic capacity and market opportunities using wealth measures that do not carry the same market-oriented biases as commonly used asset-based measures. Using measures of agricultural and market-based wealth for 562,324 women across 111,724 sampling clusters from 151 DHS surveys in 64 countries, we employ a latent variable structural equation model to estimate (a) latent variables capturing economic capacity and market opportunity and (b) their effects on completed fertility. Market opportunities had a consistent negative effect on fertility, while economic capacity had a weaker but generally positive effect on fertility. The results show that the confusion between operational measures of wealth and the concepts of economic capacity can impede our understanding of how material resources and market contexts shape reproduction.