Data sources for the indicators featured in the Socio-economic & political data explorer

Gender Inequality Index

Source: UNDP (historical data) and Andrijevic et al., 2021 (projections)

The Gender Inequality Index (GII) an annually published index by the United Nations Development Program. It measures gender inequality in three areas: reproductive health (measured by maternal mortality ratio and adolescent birth rate), empowerment (proportion of parliamentary seats held by women and share of women in education) and labor force participation rate. It ranges from 0 (complete equality) to 1 (complete inequality).

The projections derived in Andrijevic et al. (2021) are based on the GII indicator, which is modeled as a function of GDP, education and gender gap in education, which allows for internally-consistent projections along the five SSP scenarios.

Governance

Source: Kaufmann and Kraay/World Bank (historical data) and Andrijevic et al., 2019 (projections)

Based on the Worldwide Governance Indicators (WGI) and using an econometric approach for panel data, this paper estimates projections of governance indicator for the SSP scenarios.

Governance is described as “the traditions and institutions by which authority in a country is exercised. This includes the process by which governments are selected, monitored and replaced; the capacity of the government to effectively formulate and implement sound policies; and the respect of citizens and the state for the institutions that govern economic and social interactions among them.” The aggregate indicator consists of six underlying dimensions: voice and accountability, political stability and absence of violence/terrorism, government effectiveness, regulatory quality, rule of law, and control of corruption. The indicators are based on more than 30 data sources from different research and non-governmental and international organizations. The original range of the indicators goes from -2.5 to 2.5 but for the purposes of the analysis underlying the projections has been rescaled to the range from 0 to 1.

The model is expresses governance as a function of GDP per capita, share of population with secondary education and gender gap in mean years of schooling. The WGI indicator can be used as a composite indicator with six underlying dimensions, or the dimensions can be used as standalone indicators. This paper delivers projections of the composite indicator and of two underlying components, namely government effectiveness and control of corruption described below.

Government effectiveness

Source: Kaufmann and Kraay/World Bank (historical data) and Andrijevic et al., 2019 (projections)

Government effectiveness is one of the six dimensions of the composite governance index from the Worldwide Governance Indicators. It “captures perceptions of the quality of public services, the quality of the civil service and the degree of its independence from political pressures, the quality of policy formulation and implementation, and the credibility of the government’s commitment to such policies.”

Control of corruption

Source: Kaufmann and Kraay/World Bank (historical data) and Andrijevic et al., 2019 (projections)

Control of corruption is one of the six dimensions of the composite governance index from the Worldwide Governance Indicators. It “captures perceptions of the extent to which public power is exercised for private gain, including both petty and grand forms of corruption, as well as “capture” of the state by elites and private interests.”

Rule of law

Source: Coppedge et al., 2022, Pemstein et al., 2022 (historical data) and Soergel et al, 2021 (projections by Ruhe, Leininger, and Wingens, updated and expanded in 2022)

The Rule of law-projections calculated in Soergel et al. (2021) are part of a larger modelling effort to map out a comprehensive pathway towards a sustainable future (Sustainable Development Pathway, SDP) that covers the full Sustainable Development Goals (SDGs) space. As a proxy to capture the institutional goals enshrined in SDG16, in particular accountability, political equality and participation, Soergel et al. (2021) draw on V-Dem’s “Equality before the law and individual liberty index”. The index ranges from 0 to 1, with high values indicating stronger institutions / a higher level of rule of law.

The projection model incorporates different drivers around GDP per capita growth, primary education, population growth, and previous rule of law values as well as interactions. A detailed description of the modelling approach is provided in the Supplementary Material of the Soergel et al. (2021) paper (see pp. 73ff.). The projections were updated and expanded for SSP1, SSP2, and SSP3 in 2022 by Ruhe, Leininger, and Wingens, who also performed the initial modelling for the Soergel et al. (2021) paper.

Extreme poverty headcount

Source: Crespo Cuaresma et al., 2018

The paper calculates poverty pathways within the SSP scenarios up to the year 2030. The estimation model combines estimates of the worldwide distribution of income, projections of population by age and education and income per capita to calculate the absolute population affected by extreme poverty (defined as living on less than $1.90 a day, measured in 2011 PPP prices).

Human Development Index

Source: UNDP (historical data)/Crespo Cuaresma and Lutz, 2015 (projections)

This paper uses demographic and economic projections in the SSPs, to combine the future pathways of life expectancy, education and income into a projected version of the Human Development Index (HDI) of the UNDP. The HDI has been computed following the same formula as used by the UNDP, namely the geometric mean of the three dimensions: health (life expectancy), education (geometric average of actual and expected educational attainment measured by years of schooling) and income (gross national income (GNI) per capita).

Income inequality

Source: Rao et al., 2019

This study combines projections of GDP with assumptions of trajectories of within-country Gini coefficients to derive possible trajectories of income distributions in the SSPs. The econometric model used here is a linear combination of the total factor productivity, educational attainment, non-resource trade, the labor share of income, and policy variables. The sum-product of the coefficient estimates and the projections of their corresponding explanatory variables from the model are then used to project the income Gini.

Migration Flows

Benveniste et al., 2021

Estimates of migration flows are part of the same study by Benveniste et al. (2021) as the remittances flows suggested here as an indicator relevant for the economic dimension of adaptive capacity. Migration flows are implicit parts of the SSP population projections (KC and Lutz, 2017), while in this stud they are made explicit with the use of a gravity model (Jones and O’Neill, 2016) that summarizes the quantitative effects of push and pull factors on migration flows. The data included in the migration_flows.csv file is available on the country level, expressed as the net number of migrants per country (in ’000).

Remittances

Source: Benveniste et al., 2021

Flows of remittances are estimated as part of the effort to highlight where and to what extent migration affects projections of income and inequality. The model uses the data on the share of income sent as remittances (which is kept constant over time), the cots of sending remittances, bilateral estimates of migrant stocks and per capita GDP, to estimate remittances for origin/destination pairs or on the country level.The value shown here is the net amount of remittances in a given country, expressed in USD.

Structural change

Source: Leimbach et al., forthcoming

In this study, the authors have developed scenarios of economic structural change within the SSP framework. The explorer lists sectoral shares of employment in agriculture, manufacturing and services, as well as the value added (as a share of GDP) of the three sectors.

This Explorer was conceived as an accompanying tool to a publication entitled ‘Towards scenario representation of adaptive capacity for global climate change assessments’ by Marina Andrijevic, Carl-Friedrich Schleussner, Jesus Crespo Cuaresma, Tabea Lissner, Raya Muttarak, Keywan Riahi, Emily Theokritoff, Adelle Thomas, Nicole van Maanen and Edward Byers (2023).

The Explorer hosts the data listed in Table 1 of the publication, which is not already part of a data tool elsewhere (such as the Wittgenstein Centre’s Human Capital Data Explorer that hosts all the demographic data).

We would like to sincerely thank the authors of the publications containing the data featured in this Explorer, who kindly provided us with data files and guidance: Helene Benveniste, Marian Leimbach, Julia Leininger, Narasimha Rao and Christopher Wingens.

When using this data, please ensure to cite the underlying publications. For citations and links to the papers, please consult the “Detailed description” page.