the exact wording of the question; the age, gender, and status (i.e., household head vs another household member) of the survey respondent; the unit of analysis (individual vs household) and the data source’s geographical representation (administrative levels and urban-rural). Small differences in the wording of some questions from different sources were assessed and categorized accordingly. For example, the indicator that captures freedom of expression, namely “ _share of population that agrees they are free to express what they think_ ”, derives from three different sources, with the original questions referring to freedom to say what you think; the extent to which freedom to express opinions is guaranteed; and agreement with the statement that people are free to say what they think without fear. In other cases, questions asked in different sources were judged to be too different to be harmonized. This is the case for the internet use indicator, which derived from several different sources, one of which was not possible to merge with the others given that it referred to using the internet as a source of information. As a result, two indicators were generated: internet use and internet use for information purposes. A codebook is included in online Annex A that shows the various definitions of variables and their differences in wording. Missing values is another issue that required harmonization. In the global data set, we assume that missing values for up to 5 percent of the sample are either random or do not cause a significant bias. When missing values exceed that cutoff (typically for sensitive questions in perception surveys), the global database does not include that variable and looks for a close alternative in another data source.

The final step is the _consolidation in a single data set_ of harmonized social sustainability indicators and other indicators of interest for our analysis. The consolidation exercise consists first of selecting a reference period. In order to maximize the number of variables and countries, we refer to a “most recent” period comprising 2016 to 2020, instead of a single baseline year. A single baseline year would require a degree of artificial alignment by updating data from a previous year or simply losing information that is not reported for the year of reference. Once we had processed all the indicators from the different sources within the 2016-20 period, we compiled them into two single data sets according to the source type (national vs regional and global). We next combined both data sets by country name into a single Stata file. Online Annex B contains a readme.file with instructions for creating the final version of the data set, and all do.files are available upon request. The final version of the database contains 71 harmonized social sustainability variables: 23 in the social inclusion component, 18 in the resilience component, 20 covering social cohesion, and 10 for process legitimacy. In addition, we include 21 indicators covering per capita GDP, growth rates, monetary and multidimensional poverty, inequality (of incomes through the Gini coefficient), the World Bank’s Human Capital Index, fertility rate, equality of opportunity and shared prosperity, among others (see list online Annex A), in order to analyze their linkages with social sustainability indicators.

The resulting Social Sustainability Global Database (SSGD) encompasses 193 countries and 37 territories across seven world regions, accounting for 98.7 percent of the global population (see Annex 1 for the list of countries and territories). Table 1 lists all the variables in the SSGD that correspond to each of the social sustainability components (for exact definitions, see the codebook reported in online Annex A), as well as the number of countries for which a value for each variable is observed (Column “Obs”).

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