Arbeitsbereich

Bevölkerungsdynamik und Nachhaltiges Wohlbefinden

Auf einen Blick Projekte Publikationen Team

Projekt

Methodological Improvements to the Study of Social Vulnerability

Carolina Coimbra Vieira, Rumi Chunara (New York University, Vereinigte Staaten); in Zusammenarbeit mit Ronak Paul (International Institute for Population Sciences, Mumbai, Indien), Sean Reid (University of California, Santa Barbara, Vereinigte Staaten), Christopher Wolfe (University of Nevada, Reno, Vereinigte Staaten), Yan Zhang (University of Southampton, Vereinigtes Königreich von Großbritannien und Nordirland), Yuan Zhao (New York University, Vereinigte Staaten)

Ausführliche Beschreibung

Social conditions shape individual responses to external environmental hazards, including the degree to which an individual or a community responds to natural disasters, economic changes, or global health crises. Social vulnerability is a multidimensional measure of the social conditions that define how such actors respond to hazards. A range of studies have demonstrated the numerous ways in which social vulnerability has significantly determined community outcomes following crises, such as floods (Hamidi, et al. 2022), earthquakes (Zhang, et al. 2017), and noncommunicable diseases (Khan, et al. 2021).

The factors that lead to social vulnerability are theoretically well defined, and include economic status, age, disability, language, ethnicity, and location. Theoretical knowledge has enabled the creation and validation of social vulnerability indices across many specific locations and outcomes. However, existing approaches generally assume structured, linear relationships among input variables, and they may not capture subtle nonlinear patterns that represent the multidimensionality of social vulnerability more precisely. Another potential methodological challenge is that the available data resources are often fairly limited. In addition, the variables included are often selected by hand (e.g., through expert opinion) to represent specific concepts, as opposed to being selected using a data-driven method. These data and methodological limitations affect the quality and comparability of a number of social vulnerability measures.

In our first study, we produced a standard and comparable social vulnerability index across eight countries. We used global harmonized data from the Integrated Public Use Microdata Series (IPUMS) project as well as novel methodologies in deep learning techniques, such as autoencoders, to delineate patterns of social vulnerability in Cambodia, Nepal, Costa Rica, Panama, the Dominican Republic, Senegal, Morocco, and the USA. Initial results have shown that wealth-related factors explain the largest variance in the data and that these factors are the most common elements of social vulnerability. A data-driven approach to variable selection was used for the internal and external validation of the indices that we developed. Variable selection also included the relationship between the constructed index and childhood mortality. Given the growth in the hazards that affect multiple environmental, social, and economic aspects of society, the consistent finding of the relevance of wealth reveals an important dimension of resilience to natural and anthropogenic risks.

This research line has a range of implications for both research and policy. Given the increasing relevance of social vulnerability associated with natural, anthropogenic, and socionatural hazards, our findings inform data collection and the development of indices for other places, and can address various needs of policymakers. Although IPUMS represents an important harmonized data resource, the basic data needed to compose the social vulnerability index were only available in seven countries. Accordingly, understanding the components that capture the most variance in social vulnerability can be helpful for prioritizing data collection in new places and for estimating social vulnerability in places where data covering all base concepts are not available.

Social Vulnerability Index scores across administrative units in eight countries - Cambodia, Nepal, Costa Rica, Panama, Dominican Republic, Senegal, Morocco, and USA

Maps of Social Vulnerability Index (SoVI) scores on each administrative unit. Each administrative unit is visualized by vulnerability using a standard deviation representation similar to Cutter et al. (2003). Places with SoVI values between -1 and 1 standard deviations from the mean are shown in gray and indicate neutral vulnerability. Scores greater than 1 standard deviation above the mean are shown in orange and red indicating higher vulnerability. Scores less than -1 standard deviation below the mean are shown in light and dark blue indicating lower vulnerability. All SoVI scores were computed using the same harmonized IPUMS variables, except for the United States, and the scale is standardized across all countries. Countries are displayed in the following order: A. Cambodia, B. Nepal, C. Costa Rica, D. Panama, E. Dominican Republic, F. Senegal, G. Morocco, and H. USA © Paul, R., et al. (2023). Methodological Improvements in Social Vulnerability Index Construction Reinforce Role of Wealth Across International Contexts.

Publikationen

Zhao, Y.; Paul, R.; Reid, S.; Coimbra Vieira, C.; Wolfe, C.; Zhang, Y.; Chunara, R.:

Constructing social vulnerability indexes with increased data and machine learning highlight the importance of wealth across global contexts

Social Indicators Research 175:2, 639–657. (2024)    

Paul, R.; Reid, S.; Coimbra Vieira, C.; Wolfe, C.; Zhao, Y.; Zhang, Y.; Chunara, R.:

Methodological improvements in social vulnerability index construction reinforce role of wealth across international contexts

MPIDR Working Paper WP-2023-017. (2023)