metadata: authors: 0: author: Ballon Fernandez,Paola Marcela
1: author: Cuesta Leiva,Jose Antonio
count: Peru,South Africa
docna: 0: docna: Measuring Social Sustainability : A
            Multidimensional Approach
docty: Policy Research Working Paper
abstracts: cdata!: While the concept of social
            sustainability is growing in salience, there is little
            consensus on how to measure it. This lack of an accepted
            measure makes it harder to monitor progress toward
            sustainable development goals, honor political commitments
            to leave no one behind, and design effective social
            development and protection programs. This study proposes an
            original measure of social sustainability and its associated
            fragilities in the form of multidimensional social gaps. The
            measure is anchored conceptually in the new social
            sustainability in development framework and applied
            empirically using a counting approach. The study calls this
            metric the Social Sustainability Index. It was piloted in
            Peru and South Africa, country contexts with low levels of
            trust, deep social tensions, and stark inequality. The
            measure comprises four dimensions&#x2014;inclusion,
            resilience, social cohesion, and process
            legitimacy&#x2014;measured by 16 indicators. The study
            finds that roughly two-thirds of the population in Peru and
            South Africa experience overlapping social gaps in the space
            of social sustainability. On average, these populations
            exhibit intensity rates of 47 and 53 percent, respectively,
            equivalent to experiencing multiple social gaps in seven and
            eight indicators. Women and ethnic minorities are
            disproportionally fragile. Weak process legitimacy is the
            main driver of multidimensional social gaps in both
            countries. In South Africa, low satisfaction with the way
            corruption is fought and deficits in government
            effectiveness are the principal indicators driving
            multidimensional social gaps. In Peru, inequality before the
            law and deficits in government effectiveness are the two
            indicators contributing the most to overall gaps in social
            sustainability. These findings call for strategies to boost
            accountability and inclusion beyond access to markets,
            services, and social benefits.
display_title: Measuring Social Sustainability : A
            Multidimensional Approach
listing_relative_url: /research/2024/08/34379448/measuring-social-sustainability-multidimensional-approach
topicv3: Social Inclusion m1326031 421,Social Analysis m1326036 744,Social Accountability m1326920 733,Social Cohesion m1327189 738,Social Resilience and Climate Change
            m1327192 747
publishtoextweb_dt: 2024-08-20T00:00:00Z
dois: 10.1596/1813-9450-10875
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 69
author_keys: 0: author_key: 1278368
1: author_key: 434969
model_extractions: Policy Research Working Paper 10875 Measuring Social Sustainability A Multidimensional Approach _Paola Ballon Jose Cuesta_ Social Sustainability and Inclusion Global Practice August 2024
# **Measuring Social Sustainability: A Multidimensional Approach**

Paola Ballon and Jose Cuesta World Bank Social Sustainability and Inclusion Global Practice

## **Acknowledgement**

This paper has benefited from comments from Richard Damania, Louise Cord, Nikolas Myint, Alexandru Cojocaru, Ana Maria Munoz, Ezgi Canpolat, and participants of the Quality Enhancement Review that took place in August 2023. Paola Ballon is grateful to Omar Santos Alburqueque for his excellent research assistance.

**Keywords:** Social Sustainability, Multidimensional Social Gaps, Intersectionality, Counting Approach, Peru, South Africa

**JEL codes** : D63, J15, I30
Policy Research Working Paper 10875

# **Abstract**

While the concept of social sustainability is growing in salience, there is little consensus on how to measure it. This lack of an accepted measure makes it harder to monitor progress toward sustainable development goals, honor political commitments to leave no one behind, and design effective social development and protection programs. This study proposes an original measure of social sustainability and its associated fragilities in the form of multidimensional social gaps. The measure is anchored conceptually in the new social sustainability in development framework and applied empirically using a counting approach. The study calls this metric the Social Sustainability Index. It was piloted in Peru and South Africa, country contexts with low levels of trust, deep social tensions, and stark inequality. The measure comprises four dimensions—inclusion, resilience, social cohesion, and process legitimacy—measured by 16 indicators. The study finds that roughly two-thirds of the population in Peru and South Africa experience overlapping social gaps in the space of social sustainability. On average, these populations exhibit intensity rates of 47 and 53 percent, respectively, equivalent to experiencing multiple social gaps in seven and eight indicators. Women and ethnic minorities are disproportionally fragile. Weak process legitimacy is the main driver of multidimensional social gaps in both countries. In South Africa, low satisfaction with the way corruption is fought and deficits in government effectiveness are the principal indicators driving multidimensional social gaps. In Peru, inequality before the law and deficits in government effectiveness are the two indicators contributing the most to overall gaps in social sustainability. These findings call for strategies to boost accountability and inclusion beyond access to markets, services, and social benefits.

This paper is a product of the Social Sustainability and Inclusion Global Practice. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at pballon@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **1. Introduction**

Scholars, policy makers and the public around the world are paying increasing attention to social sustainability as social, economic and environmental crises mount and intersect. The number of the extremely poor grew from 648 million to 719 million globally during the COVID-19 pandemic, reversing decades-long declines in income poverty (World Bank 2022). The fallout from the pandemic has driven the largest increase in global inequality since the Second World War (Yonzan et al. 2022). Interpersonal trust is at its lowest since measurement started in the 1980s and social unrest is rising globally (ACLED 2022). Yet in contrast to monetary poverty or income inequality, social sustainability lacks a consensus empirical measure. This limits its broader adoption as a concept, monitoring, and the effectiveness of policies designed to tackle it. Without such a measure, international and national institutions cannot effectively monitor progress towards sustainable development goals, design effective social development and protection programs, or honor their political commitments to leave no one behind. Addressing deteriorating levels of social sustainability effectively requires precise estimates and agreed-upon methodologies, drawing on sources available across different country contexts that can be monitored frequently.

This paper addresses this gap by proposing an original measure of social sustainability and its associated fragilities in the form of multidimensional social gaps. This measure is conceptually embedded in a framework recently developed by Barron et al (2023) and empirically anchored in the Counting Approach Methodology (Alkire and Foster 2011). The proposed measure provides an estimate of the incidence (number of individuals) and intensity (number of indicators) of simultaneous social gaps experienced by citizens in a given country in the dimensions of inclusion, resilience, social cohesion, and process legitimacy. This metric complements existing measures of national (and subnational) poverty, inequality, human capital and human development. Second, the proposed measure can be disaggregated to capture multiple social gaps across specific vulnerable groups and quantify the contribution of each dimension (and indicator) to the observed levels of social sustainability in a society. Our measuring framework thus captures the incidence, depth and composition of multidimensional social gaps in the space of social sustainability; precisely quantifies the main drivers of multidimensional social gaps; and identifies the most-excluded population groups that is, those that fail to satisfy minimum levels of inclusion, cohesion, resilience and legitimacy, and their gaps with other groups. These insights are relevant not only for measurement and monitoring, but also for policy making.

We provide a proof of concept for our proposed measure in two countries, Peru and South Africa. They are good candidates for piloting our measure: they are highly unequal countries, with longstanding social tensions, low levels of trust, and a history of protracted, intergenerational vulnerabilities associated with ethnicity and race. Both countries also collect data that integrate multiple social dimensions, allowing us to assess social sustainability from a multidimensional angle. As such, Peru and South Africa provide ample context for the analysis of complex, multiple and intersecting social vulnerabilities.

The paper is organized as follows. Section 2 presents the conceptual and measurement framework of social sustainability used in this paper. Section 3 describes the methodology of our proposed measure. Section 4 illustrates the construction of our new Social Sustainability Index in practice. Section 5 applies the proposed index in Peru and South Africa. Section 6 concludes, reflecting on the relevance of these results for policy design, and pathways to overcome the limitations of the proposed measure.

2
# **2. Conceptual and Measurement Framework of Social Sustainability**<sup>**1**</sup>

<mark>The social sustainability literature spans multiple decades across academic and professional disciplines, with diverse applications, definitions, and connotations across the public and private sectors and the global, national, and local levels.</mark><sup>2</sup> <mark>The initial literature on sustainability often treated the social pillar as secondary to or subsumed within environmental and economic sustainability.</mark><sup>3</sup> <mark>A more contemporary view is that no pillar can be understood in isolation and that all three must be considered relationally (World Bank 2013, Ballet Bazin, and Mahieu 2020).</mark> <mark>Specific to social sustainability, this concept was initially defined as “development that meets the needs of the present without compromising the ability of future generations to meet their own needs” (World Commission on Environment and Development, 1987). At other times, social sustainability has been equated with social inclusion (World Bank, 2013b). It has also been described as some combination of community and national dynamics, contemporary and intergenerational equity, social justice, voice, inclusion, participation, and citizenship (Giddings, Hopwood and O’Brien, 2002; McKenzie, 2004; Cuthill, 2010; Dempsey et al, 2011; Bostrom, 2012; Eizenberg and Jabareen, 2017; Ballet, Bazin and Mahieu, 2020). The complexity of social sustainability in terms of its components, interactions and goals has often led to unworkably long lists of attributes. In fact, the list of features employed in the literature is long and open-ended. Dempsey et al. (2011) proposed a list of 27 elements, whereas Weingaertner and Moberg (2014) identified 17 dimensions.</mark> <mark>Despite the absence of a consensus around the concept or measurement of social sustainability, there is sufficient common ground to discard some elements and include others in our proposed framework. Ballet, Bazin and Mahieu (2020) identify just three recurring aspects of social sustainability in the literature: social cohesion (coherence in the attitudes and behaviors of members of a given society), equity (lack of inequalities), and safety (protection from economic shocks). They also show that each of these components is closely connected with environmental sustainability. Littig and Griessler (2005) define social sustainability as interactions between individuals and related institutional arrangements. Those links help satisfy an extended set of human needs and fulfill the normative claims of social justice, human dignity and participation. World Bank (2005) provides a conceptually similar definition of socially sustainable development. According to the World Bank, development is socially sustainable when it promotes inclusive, resilient, cohesive and accountable institutions. More recently, Barron et al (2023) construct a social sustainability framework out of components spanning inclusion, resilience, social cohesion and process legitimacy.</mark> <mark>Barron et al (2023), arguably provide the definition and conceptual framework for social sustainability that is more rigorously grounded in the existing academic literature but that are also aligned with the key objectives, strategic priorities, and operational frameworks common in international development. Their definition is the following:</mark>

> 1 This section draws from Barron et al (2023), Cuesta et al (2022).

> 2 See, for instance, Åhman (2013); Barron et al (2023); Boström (2012); Boyer et al. (2016); Colantonio (2007, 2009); Eizenberg and Jabareen (2017); Griessler and Littig (2005); James et al. (2013); Koning (2001); McKenzie (2004); Sachs (1999).

> 3 See, for instance, Daly (1996); Sachs (1999); Kunz (2006); Locke and Dearden (2005); Partridge (2005); Vifell and Soneryd (2012).

3
_<mark>Social sustainability is when all people feel part of the development process and believe that they and their descendants will benefit from it. Socially sustainable communities and societies are willing and able to work together to overcome challenges, deliver public goods, and allocate scarce resources in ways that are perceived as legitimate and fair by all so that all people may thrive over time.</mark>_ <mark>(Barron et al 2023: 30)</mark> <mark>This definition highlights four critical components of social sustainability: social cohesion, inclusion, resilience, and process legitimacy. A cohesive society has a shared purpose and high levels of trust, allowing communities and groups to work together toward a common good, respond to challenges, and drive real solutions and sustainable compromises. An inclusive society is one where all people have access to markets and services as well as political, social, and cultural spaces, which allows all members of society to thrive. A resilient society has the ability, capacity, and flexibility to avoid conflicts (including inter-personal violence) and to withstand, bounce back from, or absorb the impacts of exogenous shocks over time. Process legitimacy captures the processes by which policies or programs are designed and implemented within the context of existing norms and values, such that the decisions made and carried out are considered fair, credible, and acceptable by all members and groups of a given community or society.</mark> <mark>Barron et al.’s (2023) definition of social inclusion aligns closely with World Bank (2005, 2013b) and Das and Espinoza (2019) and shares commonalities with the definitions presented in Ballet, Bazin and Mahieu (2020). While similar Barron et al (2023) differ from Ballet et al (2020) in the following: i) they use social inclusion rather than equity; ii)focus on equal access to economic, political, civic and physical spaces instead of inequalities; iii)employ resilience instead of safety so that the framework can capture readiness to all kind of shocks; and iv) add empowerment to social cohesion, agency and participation. A particular feature of Barron et al (2023) is their inclusion of process legitimacy, feature emphasized by Pawlowski (2008) and Dempsey et al (2011) as necessary to maintain social sustainability.</mark> <mark>As such, Barron et al.’s (2023) definitions and framework unite the social sustainability literature emphases on connected communities, well-being for all, durability or resilience over time, and meaningful participation and engagement, and a strong social contract within an intertemporal horizon (each emphasized in works by Dempsey et al. 2011, Pierson 2002; Ratcliffe 2000). Durability and resilience focus on the stability and security of communities over time. Some literature characterizes these principles as safety, resembling but going further than resilience by emphasizing reduced vulnerability before shocks occur (Adger 2000). Meaningful participation and engagement reinforce the importance of connected and cohesive communities, underscoring the value of a strong social contract.</mark> <mark>Figure 1 shows how Barron et al.’s (2023) components interact. Despite the framework’s simplicity, the interactions it portrays are, in practice, highly complex, nonlinear, and context-dependent, reflecting the rich dynamics at play in all communities and societies. The framework functions within a conceptual space known as the “policy arena”: the institutions and forums where public resources are allocated and decisions are made among individuals, government, and stakeholder groups through debate, negotiation, and compromise, with ample potential for disagreement, tensions, or even conflict (World Bank 2017). Expanding access to the policy arena, especially for marginal and vulnerable groups, as well as sharing information and building in feedback loops and other social accountability measures, are important for resolving tensions.</mark> 4
# Process Legitimacy

<!-- Start of picture text --> Inclusion > > > .<br>nied : . , . Social<br>or es on Design Implementation Sustainability<br>Resilience > > ><br>Context _____________ Policy Arena.——————___! ‘Outcomes<br><!-- End of picture text -->
<mark>Furthermore, existing indicators mainly focus on social norms related to gender, overlooking issues concerning discrimination of minorities and the integration of displaced populations. To bridge this gap, we advocate for a broader approach, encompassing various forms of discrimination and social integration in our assessment.</mark> <mark>Additionally, there are indicators for which suitable metrics are yet to be established. These include variables associated with thriving, dignity, interventions tailored to conflict prevention, and perceptions of fairness regarding specific policies, programs, or issues. To address these gaps, socioeconomic status and the ability to express acceptance or dissent concerning policies through individual and collective voices, accountability, and participation could serve as valuable indicators.</mark>

# **<mark>Table 1: From Concepts to Indicators of Social Sustainability</mark>**

6
|**Definition**|**Key issues to be**<br>**captured**|<br>**Ideal measurement**|**Measures available in the**<br>**literature**|<br>**Proposed measure**|
|---|---|---|---|---|
|A cohesive society<br>has a shared<br>purpose and high<br>levels of trust,<br>allowing|Shared purpose,<br>common good<br>|Measure contains sufficiently<br>relevant common goals that<br>resonate across large shares of<br>population, instead of narrow,<br>partizan aims|Work on issues relevant to the<br>community; sign a petition;<br>participate in demostrations; vote<br>to elect representatives|Share of population that feels insecure in the<br>neighborhood; share of people that have ever felt<br>unsafe from crime in their community; share of<br>population that was victim of a crime; share of<br>population for which racist behavior is frequent in<br>their neighborhood|
|communities and<br>groups to work<br>together toward a<br>common good,<br>respond to<br>challenges, and<br>drive real|Trust|Trust understood as interpersonal<br>trust but also trust in authories and<br>institutions|<br>Interpersonal trust, trust in<br>government, police, Congress,<br>judiciary, and other relevant<br>institutions|Share of population that say most people can be<br>trusted; Share of population that would not like to<br>have "homosexual" neighbors Share of population<br>that has confidence in government; share of<br>population that has confidence in the Police|
|solutions and<br>sustainable<br>compromises|Work togethet|Cooperation, participation in<br>groups, whether organized<br>formally or not.|Member of clubs or organizations;<br>active participants in clubs or<br>organizations of diverse nature|Share of population that participates in voluntary<br>associations or community groups.<br>|
|An inclusive<br>society is one<br>where all people<br>have access to|Access to all kind of<br>spaces, that is,<br>markets, services,<br>political, social,<br>culutral|Access to a wide range of markets<br>(labor, financial, land), services<br>(heath, eduation, social protection),<br>political spaces (voting, political<br>parties, local authority positions),<br>social (in the sense of physical, that<br>is, streets, neighborhoods), cultural<br>(including internet, digital), ideally<br>with some notion of quality, such<br>as for example, access to decent<br>jobs, access to credit in not abusive<br>conditions, quality education, good<br>digital content, or access to streets<br>safely and without fear|<br> <br> <br> <br> <br>Access to labor markets, financial<br>resources, ownership of land,<br>access to education, healthcare<br>when needed, coverage of social<br>protection programs, benefiting<br>from public or private transfers,<br>possession of ID and/or birth<br>certificates, perception of safety in<br>the community, access to internet.<br>Only a few of these variables<br>contain some notion of quality,<br>such as assistance vs contributory<br>pensions or transfers, private vs<br>public education, access to bank<br>account vs credit card. piped water<br>vs latrine, while others do not (eg<br>access to quality content in<br>internet).|<br> <br>**Access to Markets**: labor force participation rate;<br>unemployment rate; self-employment rate;<br>**Access to Financial Services**: share of population<br>with a bank account;<br>**Access to Basic Public Services**: share of<br>households with access to improved water; access to<br>adequate sanitation; access to electricity; internet<br>connection at home;<br>**Access to Human Capital Services**: primary<br>enrolment rate; secondary enrolement rate; share of<br>households with health insurance.|
|markets and<br>services as well as<br>political, social,<br>and cultural<br>spaces, which<br>allows all members<br>of society to<br>thrive.|Everyone, no<br>exceptions<br>|No gaps across individuals due to<br>considerations of age, gender,<br>ethnicity, disability, displacement,<br>SOGI considerations|Questions are typically aged<br>appropriate, eg, labor access only<br>at individuals at working age.<br>There are however gaps in terms<br>of ethnicity and disability (self<br>reported in the case of ethnicity<br>and issues of functionality not<br>always capturing impairing<br>disability), while little or nothing<br>on irregular migration and SOGI--<br>making it difficult to unpack by<br>those groups|<br>n/a|
||Thrive|Notion of inclusion beyond<br>survival and escaping poverty<br>towards capturing each individual<br>potential|Possible to capture subjective<br>wellbeing, happiness, satisfaction<br>and perceptions or projections of<br>past, current and future living<br>standards, as well as monetary<br>poverty, but not trully the concept<br>of dignity|<br>n/a|

7
|**T**<br>**Definition**|**able 1: From**<br>**Key issues to be**<br>**captured**|**Concepts to Indicat**<br> <br>**Ideal measurement**|**ors of Social Sustaina**<br>**Measures available in the**<br>**literature**|**bility (continued)**<br> <br>**Proposed measure**|
|---|---|---|---|---|
|A resilient society<br>has the ability,<br>capacity and|Ability, capacity and<br>flexibility to avoid<br>coflict|Resilience captures efforts<br>dedicated to keep peace, avoid<br>conflict, keep crime and insecurity,<br>or victimization perceptions low,<br>which could be approached with<br>questions about perceptions and<br>satisfaction with specific policies or<br>programs by government, judiciary<br>or legislative powers, as well as<br>police, among others.|<br> <br> <br>There are typically variables that<br>capture satisfaction with policies,<br>goverments and, depending on<br>context, CSO or international<br>organizations, but usually not<br>referring to peacekeeping activities<br>conflict prevention, or crime<br>policies. The questions are<br>therefore unable to specifially refer<br>to these aspects|,<br> <br>n/a|
|,<br>flexibility to avoid<br>conflicts<br>(including inter-<br>personal violence)<br>and to withstand,<br>bounce back from,<br>or absorb the<br>impacts of<br>exogenous shocks<br>over time|Withstand, bounce<br>back and absorb<br>impacs of shocks over<br>time<br>|Variables capturing the availability<br>of resources and strategies to cope<br>with different types of shocks<br>(savings, migration, selling of<br>assets, transfers, and so forth)<br>Variables reflecting the outcomes<br>of preventive and coping strategies<br>like experiences of food insecurity,<br>mortality/morbidity, forced<br>displacement as the result of the<br>shock<br>|<br>Most surveys will ask for<br>individual and household coping<br>strategies as a specific module,<br>asking for both idiosincratic<br>shocks as well as systematic shocks<br>Strategies also asked for positive as<br>well as negative coping strategies<br>(for example, hazardous work or<br>dropping children out of school).<br> <br> <br>Food insecurity is typically asked<br>and depending on the shock, for<br>example, COVID, issues of<br>mortality and morbidity.<br>Displacement is also typically<br>asked although unclear whether<br>voluntary or forced, or rather,<br>preventive or coping strategies.|.<br> <br>Share of households with computer, mobile phone,<br>washing machine, motorcycle; Received domestic<br>remittances in the past year; share of households<br>with several sources of incomes<br>Share of population that has gone without enough<br>food|
|Process legitimacy|Acceptance of<br>r diin|Individuals are asked for specific<br>lii rrm r tm|Generally speaking there are no<br>variables asking for perceptions of<br>fairness, except for some rare<br>questions on whether the||
|<br>captures the<br>processes by which<br>policies or<br>programs are<br>designed and<br>implemented<br>within the context<br>of existing norms<br>and values, such<br>that the decisions<br>made and carried<br>|pocesses, ecsos<br>and outcomes<br>regardless of specific<br>benefits to an<br>indiviudal or group.<br>Acceptance is based<br>fundamentally on the<br>notion of being fair<br> <br>|poces, pogas, o oucoes,<br>not whether they are satisfied or<br>trust those responsible for them,<br>but whether they feel they are fair.<br>Crticially, it should refer to specific<br>outcomes or policies and not<br>simply a broad acceptance of an<br>authority|<br>individual think the distribution of<br>incomes is fair. This is more of an<br>outcome resulting from many<br>policies than a particular policy<br>itself. More frequently, there are<br>indicators about the general<br>satisfaction with government<br>effectiveness or, in some cases,<br>with some sectoral disaggregation,<br>although much more rare.|<br> <br>**Voice**: Share of people who voted in most recent<br>national elections; share of population that thinks<br>they have freedom of speech;<br>**Accountability**: World Governance Indicators;<br>**Citizen engagement**: World Governance<br>Indicators.|
|out are considered|||||
|fair, credible, and<br>|||||
|acceptable by all<br>members and<br>groups of a given<br>community or<br>society.<br>Source: Own e|Legitimacy is<br>anchored on current<br>norms and values so<br>policies are carried<br>out in a concrete<br>context<br>laboration|Indicators of legitimacy capture the<br>social norms and values that<br>defines a particular society in a<br>given time|<br>Indicators avaialble typically<br>capture gendered norms and<br>values such as gender<br>discrimination, and much less<br>norms related to other vulnerable<br>groups such as (integration of)<br>migrants and displaced<br>populations, (discrimination of)<br>ethnic, religious or sexual<br>minorities|**Social norms**: Share of people who agrees is a<br>problem if women have more income than men;<br>when jobs are scare, men should have more rights<br>than women; share of women who are chief wage<br>earner in the household;|

8
# **3. A New Methodology to Measure Social Sustainability Multidimensionally**

<mark>To measure social sustainability multidimensionally, we revisit and repurpose the Counting Approach to multidimensional poverty measurement, proposed by Alkire and Foster (2011) and Alkire</mark> _<mark>et. al</mark>_ <mark>. (2015). This approach offers an assessment that captures overlapping social gaps across dimensions and indicators. As in the Counting Approach, our method similarly consists of two steps: identifying those who experience multiple social gaps and aggregating their status into a synthetic index. The identification step examines social gaps unidimensionally (by indicator/dimension) and multidimensionally (across indicators/dimensions). To do so it uses two forms of thresholds: a set of thresholds per dimension or indicator (denoted by vector z ), and a threshold across dimensions (denoted by k). The unidimensional assessment contrasts the achievements per indicator with the</mark><sup>4</sup> <mark>corresponding thresholds. As such, a person is considered to experience a social gap in an indicator if she falls below a certain cut-off. The multidimensional assessment goes one step further and examines the joint social gaps experienced by a person, by comparing the number of social gaps (score) he experiences with a cross-dimensional cut-off, that represents the societal tolerance of multiple social gaps (number of weighted indicators) that a person can experience simultaneously.</mark> More formally, to describe the identification process, consider a population of individuals with attainments in dimensions/indicators. This information is represented by , an achievement matrix of size . A typical element of this matrix is i (∈R≥0), which denotes the attainment NN of the individual in dimension/indicator DD . Let and denote the social-gap threshold and XX NN × DD xx weight specific to indicator , respectively.<sup>5</sup> Thus, a person is deemed to experience a social gap in ii ii indicator ii<sup>tth</sup> if: i . The count of social gaps experienced by a person at the same time is computed jj zz ww by weighting the social gaps, such that: jj ∈R+ and ∑ Hence the weighted number of ii social gaps experienced by individual jj xx < zz is: = ∑ iDD, where i i denotes the weighted ii ii=1 ii social-gap in indicator , and i = 1 if ww i , and 0 otherwise. For a given cross-dimensional DD ww = DD. ii ii=1 ii threshold , a person experiences multiple social gaps if ii cc gg gg = ww. This is represented by the ee ii multidimensional identification function jj ee xx < zz that takes a value of 1 if , and 0 otherwise. ii Figure 2 presents the sequence of steps required for identifying those who experience social gaps kk∈[0, DD] cc ≥kk ii ii multidimensionally. First, we select the achievement set an individual is going to be assessed against. ρρ (kk) cc ≥kk Thresholds for each of those achievements are then selected, below which an individual is identified as experiencing a social gap from that achievement (unidimensional social gaps). Next, each of the dimensions and indicators the individual is found to experience social gaps from are weighted, counted and aggregated. The aggregation leads to a score of social gaps which is contrasted with a cross-dimensional threshold that determines whether the individual is regarded as experiencing social gaps multidimensionally. This is reflected by the identification function.

The aggregation step then synthesizes each person’s status of multidimensional social gaps into an index. The Social Sustainability Index (SSI) is defined as:

> 4 Vectors and matrices are denoted by bold lower- and upper-case letters, respectively.

> 5 From a vector of social-gaps thresholds, 1 � and a vector of weights, 1 �. ii DD ii DD zz: �zz , ... , zz , ... , zz9 ww: �ww , ... , ww , ... , ww
<!-- Start of picture text --> Achievements Unidimensional Weighted Score of weighted ; Multidimensional<br>social gaps social gaps social gaps identification function<br>Xwep:ie%j} EvanNEE ee0 otherwise?hee [mmm] 22 {g:;Ft = &;W,}FF | Crvset?oe(64 = /D Hj} Puxt*(0,Pi == 19pil otherwiseter |<br>Count of j=1<br>exclusions<br>Zpx1:{Z;} Woxi: {Wj} k € (0,D)<br>Dimensional Weights<br>thresholds<br><!-- End of picture text -->
indicators; choice of deprivation thresholds per indicator; choice of weights to reflect the importance of each indicator/dimension; and choice of cross-dimensional cut-off. This section focuses on the first of these aspects: selection of dimensions and indicators.

<!-- Start of picture text --> Figure 3: Counting those who experience social gaps multidimensionally: Intuitive<br>Representation by Cross-dimensional Threshold<br>Unidimensional Social gaps<br>(Excluded if: xij j ) Multidimensional Social gaps (if: ci ≥k )<br>< zz<br>Achievement in Yes<br>Indicator E1 E1<br>Achievement in Indicator E2 zz E2 No If the cross-dimensional threshold is:<br>Dimension “E” zz<br>Achievement in No<br>Indicator E3 E3 k = 33%<br>zz ( 5 + out of 16 Yes<br>indicators)<br>Achievement Yes<br>in Indicator E4 E4<br>zz<br>Achievement in No<br>Indicator C1 C1 k = 50%<br>Number of ( 8+out of 16 Yes<br>Achievement in zz Yes joint social gaps indicators)<br>Indicator C2 C2 (score ci)<br>Dimension “C”<br>Achievement in zz<br>Indicator C3 C3 No<br>Achievement zz Yes k = 75%<br>in Indicator C4 C4 ( 12+ out of No<br>16 indicators)<br>zz<br>Achievement in<br>Indicator R1 R1 No<br>zz<br>Achievement in<br>Indicator R2 R2 No<br>Dimension “R”<br>zz<br>Achievement<br>in Indicator R3 R3 Yes<br>zz<br>Achievement in<br>Indicator R4 zR4 Yes<br>Achievement in No<br>Indicator L1 L1<br>zz<br>Achievement in<br>Dimension “L” Indicator L2 L2 No<br>zz<br>Achievement in<br>Indicator L3 L3 No<br>zz<br>Achievement in Indicator L4 L4 No<br>zz<br><!-- End of picture text -->

**<mark>Figure 3: Counting those who experience social gaps multidimensionally: Intuitive Representation by Cross-dimensional Threshold</mark>**

<mark>Source: authors. Note: Components “E”, “C”, “R” and “L” are generic names capturing any possible dimension of social sustainability. In our proposed measure, we use inclusion, social cohesion, resilience and process legitimacy. The size of the box denotes the maximum achievement a person can have in an indicator, the colored area denotes the person's achievement, and the dashed line denotes the indicator threshold.</mark> 11
As discussed above, the literature on sustainability contains a wide range of concepts and definitions of its numerous social dimensions, among which social sustainability is often considered. Commonly discussed outcomes associated with inclusion include equity, intra- and intergenerational well-being, quality of life, and the satisfaction of basic needs (see Barron et al 2023 for a recent review). Other analyses emphasize instead processes, including social interaction, interconnectedness, social integration, and participation; as well as alternative outcomes such as freedom, safety and security, and access to basic infrastructure and services as part of an integrated concept of social sustainability (see Littig and Griessler 2005; Cuthill 2010; Dempsey _et al._ 2011; Boström 2012; Purvis, Mao, and Robinson 2019; Ballet, Bazin and Mahieu, 2020).

While extensive lists of outcomes and processes help to establish the complexity and multidimensional nature of social sustainability, they are less useful in delivering a definition that can be understood, agreed upon, and operationalized. There is, however, some convergence and overlap in the literature encompassing a narrower set of outcomes and processes as discussed in the previous section and summarized in Table 1. Building on this evidence, we conducted a systematic review of available data sources to identify the dimensions and indicators widely used in empirical studies of social sustainability. We find that the construction of social exclusion metrics typically follows measurement frameworks that cluster dimensions to conceptualize one specific construct (Table 2). As such, it finds that access to markets, services and political, civic, cultural and physical spaces are grouped to conceptualize social inclusion (World Bank 2013, Das and Espinoza 2020). Absorptive, adaptive, and transformative capacities are used to conceptualize resilience (Walker _et al_ 2004, DFID 2011, IPCC 2012, Oxfam 2017).<sup>6</sup> Meanwhile empowerment, voice, agency, citizen engagement, and social accountability are grouped in one concept, namely, process legitimacy (e.g., Kabeer 1999, Fox 2007, Joshi 2008, IFPRI 2020, IDS 2020). This synthesis illustrates, both the indicators often used in terms of their global coverage, and the possibility to be unpacked by vulnerable groups and populations. This snapshot of indicators, though not comprehensive, first confirms that there are numerous indicators available from a relatively small amount of reputable data sources. Second, it shows that indicators notably vary in terms of their country coverage. This is partly a reflection of their methodologies: experts’ opinion-based sources usually have a global coverage with frequent updates, while household surveys vary in the data collected and may be realized far less frequently. Disaggregation-wise, there are also trade-offs: data based on expert opinion is national in scope and is often not designed to explore subnational, inter- and intragroup heterogeneity, including among vulnerable groups. Other sources like household surveys can unpack results across groups and location, although the extent to which those disaggregations are considered in the sample design to provide statistically significant results varies from source to source.

> 6 _Absorptive capacity_ is the capacity to take intentional protective action and adopt coping strategies to bounce back after a shock, ensuring stability as it limits the negative impact of shocks. _Adaptive capacity_ is the capacity to make intentional incremental adjustments in anticipation of or response to change, to create more flexibility in the future. _Transformative capacity_ is the capacity to make intentional change to stop or reduce the causes of risk, vulnerability and ensure a more equitable sharing of risk. It is about fundamental changes in the structural causes and aggravators of vulnerability and risk.

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**Table 2: A Systematic Review of Dimensions and Indicators Used in the Analysis of Social Sustainability**

|**Dimensions and Indicators**|**Number of**||**Group tha**|**t the indicat**|**or can be disa**|**ggregated by**|**:**||
|---|---|---|---|---|---|---|---|---|
||<br>**Countries**|**Age**<br>**A**<br>|**rea: urban/rur**<br>|**al**<br>**Gender**|**Disability**|**Ethnicity**|**Religion**|**Source**|
|**Access to Labour Markets**||**Social Incl**|**usion**||||||
|<br>Labor force participation rate|105<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Unemployment rate (%)<br>Self-employed|104<br>71|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|**Access to Financial Services**<br>Share of population with a bank account|34|X|X|X|X|X||Afrobarometer|
|**Access to Basic Public Services**|||||||||
|Share of households without access to adequate sanitation<br>Share of households with access to improved water|130<br>129|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|<br>Share of households with access to electricity<br>|93<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Internet connection at home|45|X|X|X||||GMD|
|**Access to Human Capital Services**|||||||||
|Primary enrollment rate<br>ll|100<br>|X<br>|X<br>|X<br>||||GMD<br>|
|Secondary enroment rate<br>Share of households with health insurance|104<br>24|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|Share of households with mobile<br>Share of households with computer|93<br>87|X<br>X<br>**Resilien**|X<br>X<br>**ce**|X<br>X||||GMD<br>GMD|
|<br>Share of households with washing machine<br>Share of households with car or motorcycle|57<br>67/57|X<br>X|X<br>X|X<br>X||||GMD<br>GMD|
|<br>Received domestic remittances in the past year|34|X|X|X|X|X|X|Barometers|
|Share of population that saved any money during past year|85|X|X|X||X|X|Barometers/WVS|
|<br>Share of population that has gone without enough food|110<br>|X<br>|X<br>|X<br>||X|X|Barometers/WVS<br>|
|Share of households with several sources of income|105<br>**So**|X<br>**cial Cohesio**|X<br>**n/Capital**|X||||GMD|
|Share of population that feels in insecure in their neighborhood|85|X|X|X||X|X|WVS|
|Share of population that have somewhat often felt unsafe from<br>crime in their own home|75|X|X|X||X|X|WVS|
|Share of population that was victim of a crime|82|X|X|X||X|X|Barometers/WVS|
|Share of population for which racist behavior is frequent in their<br>neighborhood|74|X|X|X||X|X|WVS|
|Share of population that participates in voluntary associations or<br>community groups|43|X|X|X|X|X|X|Barometers/WVS|
|Share of population that says that most people can be trusted|86|X|X|X||X|X|Barometers/WVS|
|Share of population that mentions they would NOT like to have as<br>neighbors: "Homosexuals"|73|X|X|X||X|X|WVS|
|Share of population that has confidence in the government|84|X|X|X||X|X|Barometers/WVS|
|Share of population that has confidence in the police|85|X<br>|X<br>|X||X|X|Barometers/WVS|
|**Voice and Agency**||**Process Legi**|**timacy**||||||
|Share of population that disagrees it is a problem if women have<br>more income than husband|75|X|X|X||X|X|WVS|
|Share of population that disagrees when jobs are scarce: men should<br>have more right to a job than women|76|X|X|X||X|X|WVS|
|Share of women respondents that are the chief wage earner in your<br>house|18|X|X|X||X|X|Barometers/WVS|
|Share of population who voted in the most recent national elections|106|X|X|X|X|X|X|Barometers/WVS|
|Share of population that attended a demonstration or protest|102|X|X|X|X|X|X|Barometers/WVS|
|Share of population that thinks they have freedom of speech|42|X|X|X|X|X|X|Barometers|
|**Social accountability**|||||||||
|<br>Voice and accountability|204|||Nati|onal level|||WGI|
|<br> <br>Government effectiveness|209<br>|||<br>Nati|<br>onal level|||WGI<br>|
|Control of corruption<br>Rule of law|209<br>209|||Nati<br>Nat|onal level<br>onal level|||WGI<br>WGI|
|**Citizen engagement**||||Nati|onal level||||
|Civil societ participation (0-10)<br>|137|||Nati|onal level|||BTI|
|Civil rights (0-10)<br> <br>y|137<br>|||Nati<br> <br>|onal level<br> <br>|||BTI<br>|
|Civil society participation index<br>CSO women’s participation|179<br>179|||Nati<br>Nati|onal level<br>onal level|||VDEM<br>VDEM|

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Source: authors. Note: The sources listed above are: Bertelsmann Stiftung’s Transformation Index (BTI); Global Monitoring Database (GMD); Barometers which comprise Afrobarometer (AF), Arab Barometer, Asian Barometer; and Latinobarómetro; World Values Survey (WVS); Varieties of Democracy (V-DEM); World Governance Indicators (WGI).

# **5. Application to Peru and South Africa**

We now present an empirical application of our measure of social sustainability in the context of Peru and South Africa. These two countries are good candidates for piloting an appraisal of social sustainability and its related fragilities in the form of multiple social gaps: both are upper middleincome economies, with high levels of income poverty (30 and 57 percent, respectively). South Africa is meanwhile the most unequal country in the world—with race playing a significant role—while Peru is one of the most unequal countries in Latin America, itself a highly unequal region. Poverty in Peru is disproportionally high among Indigenous peoples (Busso and Messina 2020, IMF 2020). Discrimination, lack of societal cohesion (including extreme hostility towards immigrants), regular episodes of social unrest, and lack of accountability are notorious features of both countries (World Bank 2018, 2022a).

To assess social sustainability multidimensionally, we use Peru’s 2019 National Household Survey (ENAHO) and South Africa’s 2018 Social Attitudes Survey (SASAS). Both are nationally representative at the department and province levels, respectively. SASAS uses face-to-face, threestage-stratification data collection to gather information on relevant demographic, behavioral and attitudinal characteristics of a representative sample of 3,500 adult individuals aged 16 and older in households spread across the country’s nine provinces.<sup>7</sup> The 2018 survey collects information on democracy and governance, intergroup relations, education, crime and security, poverty, the labor market, household characteristics and assets. The ENAHO likewise uses face-to-face interviews and a probabilistic three-stage sampling method to collect information on personal and household characteristics of all household members and their living conditions. While its primary aim has been to monitor the evolution of monetary poverty, ENAHO has become a key source of information for wider policy uses. The 2019 survey, which encompassed 23,347 households, included data collection modules on citizen participation, governance, democracy, transparency, discrimination, perception of insecurity, and access to justice.<sup>8</sup> As such, SASAS and ENAHO provide a unique, long-term account of the speed and direction of changes in the underlying public perceptions, values and social fabric of South Africa and Peru, making them an ideal tool to inform policy-making focused on enhancing social sustainability and reducing fragilities of exclusion.

Our empirical application seeks a common denominator consistent with all the reported dimensions across both surveys and proposes to assess social sustainability across four dimensional pillars: inclusion, resilience, social cohesion, and legitimacy. We opt for inclusion, instead of equity, to analyze equal access to economic, political, civic, and physical spaces. Inclusion thus refers to the process of creating opportunities for all people and addressing deep systemic inequalities. It involves improving the ability of all people to access basic services like running water, human capital services like schools, and markets (including the labor market) regardless of their personal or community characteristics. We select resilience and social cohesion, , to capture readiness for all kinds of shocks while accounting for the strength of inter-personal relationships and the broader sense of solidarity among members of a society. Resilience thus refers to the ability of communities and groups in both fragile and nonfragile environments to cope with shocks such as climate change, pandemics,

> 7 <mark>Small area layers (SALs) were used as primary sampling units, from urban formal, urban informal, rural formal and rural informal settlements.</mark>

> 8 <mark>The modules on opinions and perceptions are collected on adults aged 18 years or older.</mark>

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interpersonal violence, and conflict. Social cohesion captures shared purpose and high levels of trust, the ability of communities and groups to work together toward a common good, respond to challenges, and drive real solutions and sustainable compromises. Lastly, we opt for process legitimacy to capture empowerment, voice and accountability, as well as, aspects of trust, citizen participation, democracy, and corruption. Process legitimacy can thus be understood as expanding vulnerable groups’ voices and influence. This increased voice helps them shape development solutions, influence public policy, and foster accountable service delivery (Table 3).

These four dimensions are operationalized through 16 indicators.<sup>9</sup> Inclusion considers metrics capturing access to labor markets, basic services, and human capital services. Resilience is measured by indicators denoting asset ownership, quality of housing, public assistance (government transfers) and capacity for saving. These indicators are consistent with those frequently used by the literature on resilience to natural disasters (see Kusumati et al 2014; and Marzi et al 2019). They emphasize both exposure to hazards and the ability to resist, absorb, accommodate, and quickly recover from them. Social cohesion considers indicators denoting confidence in government, experience of discrimination, perception of safety, and being a victim of crime. Process legitimacy consists of indicators denoting civil participation, satisfaction with democracy, government effectiveness, and faith in anti-corruption measures. All indicators are equally weighted across dimensions. Table 2 describes the thresholds that identify a person as excluded, following the criteria set out in the sustainable development goals.

# **5.1. Profiles of social sustainability**

In what follows we describe the experience and extent of multidimensional social gaps in Peru and South Africa. Of particular interest is the appraisal of profiles by location and vulnerable group. Table 4 reports the profiling of multidimensional exclusion in these two countries for a cross-dimensional threshold of 33 percent (experiencing multiple social gaps in 4 indicators or more).<sup>10</sup> South Africa shows a higher Social Sustainability Index of 0.34, compared to 0.31 in Peru, explained by a 2- percentage point incidence gap (67 percent compared to 65 percent in Peru), and a 6-percentage point intensity gap (53 percent in South Africa, and 47 percent in Peru). This means that, on average, people in South Africa who experience multiple social gaps do so in eight indicators simultaneously, and in Peru in seven indicators.

However, this pattern is not uniform across vulnerable populations or locations within each country. By gender, we find that women vis-à-vis men in South Africa fare worse in their experiences of social gaps compared to women vis-à-vis men in Peru: something reflected by a 12-percentage point gap in social-gap rates in South Africa, and a 4-percentage point gap in Peru (Table 4). By ethnicity, Indigenous from the Amazonian regions (referred to as other indigenous) Quechuas and Aymaras in Peru and Black South Africans are especially fragile compared to non-native populations and Whites, respectively.<sup>11</sup> Yet the incidence gap between Black South Africans and White populations reaches 43 percentage points: far greater than any of the incidence gaps across ethnic groups in Peru (Table

> 9 For a discussion on the selection of indicators using statistical methods, see Ballon (2023).

> 10 For robustness purposes, we have also analyzed profiles of multidimensional exclusion for cross dimensional thresholds of 50 percent of joint exclusions. These are reported in section 4.3. The results presented here are robust to the choice of cross-dimensional threshold.

> 11 The ENAHO survey uses the main native language spoken by respondents as the criteria for ethnic classification. The survey includes the following groups: Aymara, Quechua, other Indigenous (Ashaninka, Awajún, Bora y Shipibo-Konibo) and Non-Native (Spanish, Portuguese, other foreign language).

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4). By location, we observe a greater heterogeneity within South African provinces compared to the different departments in Peru. Western Cape has the lowest incidence and intensity rates for both South Africa and Peru, where not even Lima, the capital, comes close. Lima instead reports

|**Dim**|**nsion**|**Table 3: No**<br>**Idi**<sup>**(a)**</sup>|**rmative Considerations**<br>**Peru**|**South Africa**|
|---|---|---|---|---|
|||**ncator**|**Excluded if theperson...**|**Excluded if theperson...**|
||**Access to**<br>**labour**<br>**markets**|Quality of employment|is unemployed or informally<br>employed|is employed part-time or less|
|**Social**<br>**Inclusion**|**Access to**<br>**basic services**|Access to water,<br>sanitation, electricity &<br>internet|has two or fewer than two services<br>at home|has two or less out of four<br>services at home|
||**Access to**<br>**human capital**|Level of education<br>|does not have complete secondary<br>education|does not have complete<br>secondary education|
||<br>**services**|Medical attention<br>|did not get medical attention when ill|has inadequate access to health<br>care|
|||Quality of housing<br>Possession of assets|lives in a household that has<br>inadequate floor/roof/walls<sup>(b)</sup><br>is in the bottom third of the asset<br>ownership distribution|lives with inadequate housing<br>conditions<br>is in the bottom third of the asset<br>ownership distribution<br>|
|**Resilience**||Public assistance|receives public assistance (in-kind and<br>cash transfers from government)|receives public assistance (in-kind<br>and cash transfers from<br>government)|
|||Capacity for saving|is not able to make savings out of<br>income|is not able to make savings out of<br>income|
|||Confidence in<br>government institutions|lives in a household where at least<br>one member has low confidence in<br>government institutions|distrusts the national government|
|**Social**<br>||Experience of<br>discrimination|lives in a household where at least<br>one member has been discriminated<br>against|feels they are in a group that is<br>discriminated against|
|**Cohesion**||Perception of safety<br>Victim of crime|thinks security is a main issue<br>lives in a household where at least<br>one member has been victim of a<br>crime in the past year|feels unsafe most of the days<br>lives in a household where at least<br>one member has experienced<br>burglary or assault in the past 5<br>years|
||**Agency and**<br>**voice**|Civil participation|lives in a household where no<br>member has participated in a group,<br>organization and/or association|has not participated in march,<br>and/or contacted traditional<br>leader and/or government official|
|**Process**<br>**Legitimacy**|**Social**<br>**accountability**|Satisfaction with<br>democracy<br>Government<br>effectiveness<br>Equality before the law|lives in a household where at least<br>one member thinks democracy is not<br>working in Peru<br>lives in a household where at least<br>one member thinks the government<br>is performing poorly|is dissatisfied with the way<br>democracy is working in South<br>Africa<br>is dissatisfied with the local<br>government basic service<br>provision in the neighborhood|
|||<br>(PE)/<br>Satisfaction with the<br>way corruption is<br>combatted (SA)|lives in a household where at least<br>one member thinks there is no<br>equality before the law in society|is dissatisfied with the way<br>corruption is combatted in their<br>neighborhood|

(a) All indicators are equally weighted within a dimension. Each indicator has a weight of 1/16 or 6.25% .

(b) Inadequate floor/walls/roof is defined per the SDG guidelines. Assets includes all durables listed in the survey of each country (i.e TV, radio, washing machine, refrigerator, fan, stove). Possession of assets is measured as a score that counts the number of assets that a person owns.

16
<mark>Source: authors</mark> <mark>similar rates of social-gaps to Gauteng in South Africa. The incidence of multidimensional social gaps in South Africa ranges from 46 to 82 percent and its intensity from 45 to 56 percent. In Peru this spread is narrower for incidence (56-81 percent) and for intensity (44-50 percent). Puno in Peru and North West in South Africa are the regional units with highest social-gap rates (Figure 4).</mark>

**<mark>Table 4: Social Sustainability Index (SSI), Incidence (H) and Intensity (A)</mark>**<sup>**12**</sup>

# **<mark>Panel A: Peru</mark>**

|<sup>**(a)**</sup>|**Nil**||**Gende**|**r**||||**Ethnici**|**ty**<br>|||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**Metric**|**atona**|**Male**|**Female **|**T-test**<sup>**(b)**</sup>|**Quechua**|**T-test**<sup>**(b) **</sup>|<sup>**Aymara **</sup>|**T-test**<sup>**(b)**</sup>|**Other**<br>**Indigenous**|<sup>**T-test(b)**</sup>|**Non-**<br>**Native**|
|**Incidence rate (H)**|67%|65%|<br>69%|<br>***|80%|<br>***|83%|<br>***|92%|<br>***|63%|
||_(0.05)_|_(0.06)_|_(0.05)_||_(0.05)_||_(0.06)_||_(0.02)_||_(0.05)_|
|**Intensit rate (A)**|47%|47%|<br>47%|<br>***|49%|<br>***|51%|<br>***|50%|<br>***|46%|
|**y**|_(0.01)_|_(0.01)_|_(0.01)_||_(0.01)_||_(0.01)_||_(0.01)_||_(0.01)_|
|**Social Sustainability**|0.31|0.31|0.32|<br>***|0.39|<br>***|0.42|***|0.46|<br>***|0.29|
|**index(SSI)**<br>(a) For each metric we report<br>(b) To assess difference betw<br>at 1%, 5% and 10% levels re|_(0.03)_<br>the point estim<br>een groups we<br>spectively. n.s d|_(0.03)_<br>ate and it<br>perfomed<br>enotes non|_(0.03)_<br>s standard e<br>t-tests. The<br>-statistical|rrors(in parent<br>baseline catego<br>significance.|_(0.03)_<br>heses).<br>ry for ethnicity|is non-native.|_(0.04)_<br>***, **, * d|enote statis|_(0.02)_<br>tically significant|differences bet|_(0.03)_<br>ween groups|

# **<mark>Panel B: South Africa</mark>**

|**Mtri**<sup>**(a)**</sup>|**National**||**Gende**<br>|**r**<br>|**Black**|||**Ethnicity**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**ec**||**Male**|**Female **|**T-test**<sup>**(b)**</sup>|<br>**African**|**T-test**<sup>**(**</sup>|<sup>**b) Coloured**</sup>|**T-test**<sup>**(b)**</sup>|**Indian**|**T-test**<sup>**(b)**</sup>|**White**|
|**Incidence rate (H)**|65%|58%|70%|<br>***|72%|<br>***|51%|<br>***|42%|<br>***|29%|
||_(0.02)_|_(0.02)_|_(0.02)_||_(0.02)_||_(0.04)_||_(0.02)_||_(0.04)_|
|**Intensit rate (A)**|53%|51%|54%|<br>***|54%|<br>***|49%|<br>***|46%|<br>ns|45%|
|**y**|_(0.01)_|_(0.01)_|_(0.01)_||_(0.01)_||_(0.01)_||_(0.01)_|..|_(0.02)_|
|**Social Sustainability**|0.34|0.3|0.37|<br>***|0.39|<br>***|0.25|<br>***|0.19|<br>***|0.13|
|**index(SSI)**<br>(a) For each metric we repor<br>(b) To assess difference bet<br>5% and 10% levels respectiv|_(0.01)_<br>t the point estim<br>ween groups we<br>ely. n.s denotes|_(0.01)_<br>ate and it<br>perfomed<br>non-statist|_(0.01)_<br>s standard<br>t-tests. Th<br>ical signifi|errors(in parenth<br>e baseline catego<br>cance.|_(0.01)_<br>eses).<br>ry for ethnicity|is white. *|_(0.02)_<br>**, **, * denot|e statistically si|_(0.01)_<br>gnificant diffe|rences between|_(0.02)_<br>groups at 1%,|

<mark>Source: authors</mark>

# **5.2. Drivers of multidimensional social gaps**

Here we describe the composition of multidimensional social gaps. Understanding which indicators (or dimensions) contribute most to overall gaps in social sustainability can provide the basis for policy design aiming at alleviating the negative experiences of those who suffer multiple social gaps. Figure 5 presents the composition for both countries. Overall, we see in both countries that multidimensional social gaps are consistently driven by weak process legitimacy. This single dimension explains 40 percent of the overall social gaps in Peru, and 30 percent in South Africa. Limited social inclusion contributes 30 percent in Peru, while resilience accounts for 27 percent in South Africa. Within dimensions, low satisfaction with the way corruption is fought in South Africa, and inequality before the law in Peru, are the principal indicators driving multidimensional gaps in

> 12 Results correspond to a 33% cross-dimensional threshold of joint exclusions.

17
<!-- Start of picture text --> Intensity (A)<br>59%<br>Kwa-Zulu Natal<br>57%<br>55%<br>53%<br>Northern Cape<br>51% Cusco & > Puno<br>° Free State e Q .<br>Ou Western Cape % _e<br>47% Mom f eo 3i)<br>45%43% ey<br>40% 45% 50% 55% 60% 65% 70% 75% 80% 85%<br>@ South Africa @ Peru Tnealense CY<br><!-- End of picture text --> <!-- Start of picture text --> South Africa Peru<br>Equality before the law (PE)/ 10%<br>Satisfaction with the way corruption is combatted (SA) as<br>0<br>Satisfaction Legitimacy 1% °<br> with democracy Se °° fo<br>CwaVienparticipation termewoe 4%‘Ls.9 7<br>PerceptionA of safety 5% ri 2%<br>Experience Cohesion 5%<br> ofdiscrimination jy 2%<br>Confidence . ee TM%<br>in gov. institutions 4%<br>: . Oe mh<br>Capacity for saving |. ea<br>Public assistance %9<br>iti CSE<br>. Resilience 6%<br>Possession of assets a Ew<br>. A 5%<br>Quality of housing a<br>. 5 5%<br>tevetoteaweaton. . . | soci 8% *.<br>Access to basic services Inclusion 4%<br>Cutty. ofemploymen TM% SS<br><!-- End of picture text -->
<!-- Start of picture text --> 14% @ Quechua @Aymara @ Other indigenous @ Non-Native<br>12% q Ps<br>e e e e<br>10% e 8 e<br>e ° ®<br>° 3 e e<br>8% e e e e<br>6% e e 3 ry<br>¢<br>e ° e<br>%e8<br>ee ° e 3<br>2% é e e e<br>0% e 8 8<br>Quality of Access to Levelofedu. Medical Qualityof Possession of Public Savings Confidence Exp.of —Perceptionof Victim of Civil Satisfaction Gov. Equality<br>employment services attention _housing assets assistance gov, discrimination _safety crime participation with _ effectiveness _before the law<br>institutions democracy<br>Social inclusion Resilience Social Cohesion Process Legitimacy<br><!-- End of picture text --> <!-- Start of picture text --> A Black African AColoured Alndian AWhite<br>14%<br>ry<br>12% a‘ .<br>ry ry<br>10% ry k t<br>a ry<br>a<br>aa4<br>4rN A 4 A<br>A a a ry 4<br>6% rN nN A 4<br>4% a a 4A AA AA ryA Aa aA aA<br>ry A<br>A A A<br>2% 4 4<br>A<br>0% 4 4<br>Quality of Access to Level of edu. Medical Quality of — Possession of Public Savings Confidence Exp. of Perception of Victim of Civil Satisfaction Gov. Satisfaction<br>employment services attention housing assets assistance gov. discrimination _ safety crime participation with effectiveness how corruption<br>institutions democracy in dealt with<br>Social inclusion Resilience Social Cohesion Process Legitimacy<br><!-- End of picture text -->
# **Table 5: Reliability Analysis**

# **Cronbach Alpha Coefficient**

|**Dimension**|**Average**<br>**interitem**<br>**covariance**<br>**Per**|**Alpha**<br>**u**|**Average**<br>**interitem**<br>**covariance**<br>**South A**|**Alpha**<br>**frica**|
|---|---|---|---|---|
|**Social Inclusion**|0.08|0.87|0.03|0.81|
|**Resilience**|0.08|0.85|0.04|0.80|
|**Social Cohesion**|0.02|0.83|0.02|0.85|
|**Process Legitimacy**|0.05|0.79|0.02|0.88|

# **Factor analysis: loadings**

20
|**Dimension**|**Indicators**|**Peru**|**South Africa**|
|---|---|---|---|
|||0.17***|<br>0.35***|
||Quality of employment|(0)|(0)|
||Access to water, sanitation,<br>|0.78***<br>|<br>0.6***<br>|
|**Social Inclusion**|electricity & internet|(0)|(0)|
||Level of education|0.76***<br>(0)|<br>0.46***<br>(0)|
||Medical attention|0.71***<br>(0)|<br>0.58***<br>(0)|
||Quality of housing|0.79***<br>(0)|<br>0.69***<br>(0)|
|**Resilience**|Posession of assets|-0.05***<br>(0)|<br>0.79***<br>(0)|
||Public assistance|0.86***<br>(0)|<br>0.39***<br>(0)|
||Capacity for saving|0.53***<br>(0)|<br>0.47***<br>(0)|
||Confidence in government|0.52***|<br>0.66***|
||institutions|(0.01)|(0)|
|**Social Cohesion**|Experience of discrimination|-0.19***<br>(0.01)|<br>0.12***<br>(0.01)|
|||0.19***|<br>0.35***|
||Perception of safety|(0.01)|(0)|
|||0.12***|<br>0.16***|
||Victim of crime|(0.01)|(0.01)|
||Civil participation|0.56***<br>(0)|<br>0.08***<br>(0.01)|
|**Process**|Satisfaction with democracy|0.42***<br>(0.01)|<br>0.74***<br>(0)|
|<br>**Legitimacy**|Government effectiveness|0.7***<br>(0.01)|<br>0.52***<br>(0)|
||Equality before the law (PE) /<br>Sifi ih h|0.27***|<br>0.52***|
||atsacton wt te way<br>corruption is combatted|(0.01)|(0)|
|**Number of obser**|**vations**|45881|2885|
|**Log-likelihood**||-966870.93|-61074.971|
|**LR test of model**|**vs. Saturated**|32107.30***|5222.04***|

The robustness analysis aims to assess the sensitivity of results to changes in parameters. We consider four scenarios: i) changes in the cross-dimensional threshold, ii) changes in the definition of indicators, iii) changes in weights, and iv) changes in the number of indicators per dimension, and analyze the changes using dominance analysis and rank correlation.

# **Scenario 1: Changes in the cross-dimensional threshold**

Figure 7 reports the incidence, intensity, and social sustainability rates for each possible cutoff point in Peru and South Africa. As expected, incidence rates decrease, intensity rates increase, and the Social Sustainability Index decreases as cutoff points become more stringent. That is, the higher is the number of indicators required to identify a person as experiencing multiple social gaps, the lower the incidence, while its intensity will increase. The robustness analysis also shows that such changes are not linear. In Peru, incidence declines slowly for cutoff points lower than 40 percent, declines more markedly up to a 60 percent cutoff point, and then stabilizes regardless of increases in cutoff points (Figure 7, panel A). Intensity in Peru also follows a nonlinear pattern. It increases slowly up to the 45 percent cutoff point to then increase markedly across the rest of the cutoff points. The resulting Social Sustainability Index rates in Peru follow a nonlinear pattern as 21
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<!-- Start of picture text --> Cross-dinensional cutoff (&) (in %) Cross-dimensional cutoff (4) (in Cross-dimensional cutoff (&) (im %)<br>Black African White Black African White Black African White<br><!-- End of picture text -->
||**Scenario**|**H**<br>**Rank co**|**A**<br>**rrelation (**|**M0**<br>**k=33%)**|**Partial**<br>**Dom**|**Full**<br>**inance**|
|---|---|---|---|---|---|---|
|**Change in the definition of**<br>**indicators**|Indicator considered "Level of education".<br>Baseline is complete secondary education.<br>Alternative is completeprimaryeducation.|0.98***|0.98***|0.99***||X|
||Weight of Social Inclusion 40%, all others 20%.|0.93***|0.95***|0.96***|X||
|**Changes in weights: 1**<br>**dimension gets 40% all**|Weight of Resilience 40%, all others 20%.|0.92***|0.85***|0.9***|X||
|<br>**others 20%**|Weight of Social Cohesion 40%, all others 20%.|0.85***|0.65***|0.85***|X||
||Weight of Process Legitimacy 40%, all others<br>20%.|0.24|0.35*|0.29|X||
||Quality of employment dropped; weights<br>reassigned from 1/16 to 1/12.|0.94***|0.97***|0.98***|X||
||Access to water, sanitation, electricity & internet<br>dropped; weights reassigned from 1/16 to 1/12.|0.91***|0.89***|0.95***|X||
||Level of education dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.93***|0.98***|X||
||Medical attention dropped; weights reassigned<br>from 1/16 to 1/12.|0.93***|0.98***|0.96***|X||
||Quality of housing dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.77***|0.98***|X||
||Possession of assets dropped; weights reassigned<br>from 1/16 to 1/12.|0.93***|0.82***|0.97***|X||
||Public assistance dropped; weights reassigned<br>from 1/16 to 1/12.|0.89***|0.93***|0.94***|X||
|**Changes in the number of**<br>**indicators per dimension.**|Capacity for saving dropped; weights reassigned<br>from 1/16 to 1/12.|0.92***|0.86***|0.94***|X||
|**We drop one indicator at a**<br>**time: from 4 to 3 indicators.**|Confidence in government institutions dropped;<br>weights reassigned from 1/16 to 1/12.|0.96***|0.94***|0.97***|X||
||Experience of discrimination dropped; weights<br>reassigned from 1/16 to 1/12.|0.96***|0.98***|0.97***|X||
||Perception of safety dropped; weights reassigned<br>from 1/16 to 1/12.|0.95***|0.98***|0.97***|X||
||Victim of crime dropped; weights reassigned<br>from 1/16 to 1/12.|0.94***|0.98***|0.97***|X||
||Civil participation dropped; weights reassigned<br>from 1/16 to 1/12.|0.94***|0.92***|0.93***|X||
||Satisfaction with democracy dropped; weights<br>reassigned from 1/16 to 1/12.|0.95***|0.9***|0.95***|X||
||Government effectiveness dropped; weights<br>reassigned from 1/16 to 1/12.|0.95***|0.91***|0.96***|X||
||Equality before the law dropped; weights<br>reassigned from 1/16 to 1/12.|0.96***|0.88***|0.95***|X||

1Statistical significance levels: *p<0.1, **p<0.05, ***p<0.01. Spearman's rank correlation coefficients are used throughout.

25
# **Panel B: South Africa**

||**Scenario**|**H**<br>**Rank co**|**A**<br>**rrelation**|**M0**<br>**(k=33%)**|**Partial**<br>**Full**<br>**Dominance**|
|---|---|---|---|---|---|
|**Change in the definition of**<br>**indicators**|Indicator considered "Level of education".<br>Baseline is complete secondary education.<br>Alternative is completeprimaryeducation.|0.93***|1***|0.97***|X|
||Weight of Social Inclusion 40%, all others 20%.|0.98***|0.9***|0.93***|X|
|**Changes in weights: 1**<br>**dimension gets 40% all**|Weight of Resilience 40%, all others 20%.|0.97***|0.95***|0.93***|X|
|<br>**others 20%**|Weight of Social Cohesion 40%, all others 20%.|0.85***|0.98***|0.97***|X|
||Weight of Process Legitimacy 40%, all others<br>20%.|0.95***|0.93***|0.98***|X|
||Quality of employment dropped; weights<br>reassigned from 1/16 to 1/12.|0.98***|0.98***|0.97***|X|
||Access to water, sanitation, electricity & internet<br>dropped; weights reassigned from 1/16 to 1/12.|0.97***|0.92***|0.95***|X|
||Level of education dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|1***|0.95***|X|
||Medical attention dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|0.93***|0.93***|X|
||Quality of housing dropped; weights reassigned<br>from 1/16 to 1/12.|0.92***|0.97***|0.93***|X|
||Possession of assets dropped; weights reassigned<br>from 1/16 to 1/12.|0.92***|0.93***|1***|X|
||Public assistance dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|1***|0.98***|X|
|**Changes in the number of**<br>**indicators per dimension.**|Capacity for saving dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|0.97***|0.92***|X|
|**We drop one indicator at a**<br>**time: from 4 to 3 indicators.**|Confidence in government institutions dropped;<br>weights reassigned from 1/16 to 1/12.|0.97***|0.98***|0.98***|X|
||Experience of discrimination dropped; weights<br>reassigned from 1/16 to 1/12.|0.92***|0.93***|0.98***|X|
||Perception of safety dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|0.95***|0.98***|X|
||Victim of crime dropped; weights reassigned<br>from 1/16 to 1/12.|0.97***|0.92***|0.98***|X|
||Civil participation dropped; weights reassigned<br>from 1/16 to 1/12.|0.92***|0.95***|0.98***|X|
||Satisfaction with democracy dropped; weights<br>reassigned from 1/16 to 1/12.|0.92***|0.98***|0.93***|X|
||Government effectiveness dropped; weights<br>reassigned from 1/16 to 1/12.|0.93***|0.92***|0.98***|X|
||Equality before the law dropped; weights<br>reassigned from 1/16 to 1/12.|1***|1***|0.98***|X|

1Statistical significance levels: *p<0.1, **p<0.05, ***p<0.01. Spearman's rank correlation coefficients are used throughout.

# **6. Discussion and Conclusions**

26
Our multidimensional assessment of social sustainability in Peru and South Africa shows that on average 67 percent and 65 percent of the Peruvian and South African population, respectively, experience overlapping social gaps—defined as experiencing gaps in least a third of the social dimensions considered. These rates are much higher than their official income poverty rates of 30 and 57 percent, respectively.<sup>15</sup> These people experience an intensity rate of 47 percent in Peru and 53 percent in South Africa, equivalent to 7 and 8 indicators, respectively. Women are especially affected in South Africa, but much less so in Peru. Ethnic populations experience greater levels of social gaps in both countries. Geographically, we find that South African provinces show greater disparities compared to departments in Peru. Our analysis also shows that these findings are robust to changes in the cross-dimensional threshold, confirming patterns of multiple social gaps by gender and ethnicity as well as the drivers found with the 33 percent threshold.

Our measurement approach has several limitations. First, current microdata sources at the individual and household levels fail to provide rich evidence for all of the dimensions of social sustainability. While most household surveys provide a comprehensive diagnostic of access to markets and services, they fail to simultaneously capture issues of trust, satisfaction, participation, and accountability; South Africa and Peru are rare exceptions where comprehensive datasets are collected. Hence, even though the mechanics of constructing the index are replicable across countries, the choice of indicators and the composition of the Social Sustainability Index will inevitably vary based on data availability. Second, some of the most vulnerable population groups remain virtually invisible to household surveys. This is notoriously the case of LGBTI people and irregular migrants. Other disadvantaged population groups in terms of race, ethnicity or disability are more frequently identified in standard household surveys, but their sampling is typically not designed to be statistically representative. Addressing such challenges requires huge efforts in harmonizing national household surveys worldwide; expanding the number and scope of questions typically collected by censuses; and improving sampling methods for household surveys.

Yet challenges in defining social sustainability do not automatically mean that measuring multiple social gaps is impossible. Moves towards more inclusive and harmonized data are already underway. Some examples include the <mark>Integrated Public Use Microdata Series managed by the Minnesota Population Center; the development of disability statistics suitable for census and national surveys by the Washington Group; efforts by the World Bank’s Global Monitoring Database to produce comparable poverty statistics worldwide; or the Inclusive Data Charter sponsored by the UN. S</mark> imilar methodological challenges have been overcome in the past: witness systems that monitor global food prices and warn of acute food insecurity; the internationally agreed system of national accounts; definitions and measurement of decent work; or international statistics on crime and justice. <mark>Clearly, greater a</mark> nd better-coordinated efforts are still needed before we can measure social sustainability at a granular level and on a global scale. But both previous and current experiences offer lessons about the value of concerted action, arriving at technical agreements, operationalizing monitoring, and using data for effective policy making.

In the meantime, national estimates of multidimensional social sustainability and its fragilities in the form of social gaps, as produced here for South Africa and Peru, still provide impactful findings for policy makers. Threats to social sustainability are driven primarily by process legitimacy in both countries. These results not only confirm the multidimensional nature of social sustainability, but also the need for intervention packages separate from other policies related to poverty reduction,

> 15 Official reports in Peru come from INEI (2022) and in South Africa from STATSSA (2022).

27
consumption smoothing or human capital accumulation. For example, policies aimed at alleviating poverty might overlook the non-poor who are nevertheless multiply excluded. Such groups can be large: 21 and 57 percent of the entire population in South Africa and Peru, respectively, are nonmonetarily poor yet experiencing social gaps multidimensionally. While some interventions might reduce poverty and social gaps simultaneously (such as broadening access to labor, financial and land markets and the coverage and quality of basic services), others will not do so automatically or to the same extent.

For example, cash or in-kind transfers proven to successfully increase consumption and smooth its volatility among the extremely poor will probably be less effective in reducing multiple social gaps due to lack of voice. Conversely, improving access to political, civic, physical and digital spaces might reduce social gaps and improve social sustainability without necessarily reducing monetary poverty immediately. Making political institutions more familiar and closer to citizens, reducing the bureaucratic costs of participation in civic or political events, or strengthening crime prevention might all boost political and civic participation, help control corruption and increase vulnerable groups’ empowerment, while not directly leading to an immediate reduction in monetary poverty.

Moreover, our findings underscore that distinctive intervention packages are needed to address distinctive sources of social sustainability While some drivers of eroding social sustainability—such as discriminatory laws, social norms, weak institutions, and recurrent crises—may be familiar to all excluded groups, some excluded populations might disproportionally suffer from different dominant drivers. For example, exclusion due to GBV or forced displacement requires a package of interventions that might not prove effective in tackling exclusion due to long-term unemployment, or lack of access to health or financial services. Well-designed residence permits might be a powerful policy instrument for integrating refugees: as suggested by proponents of full residence permits for Venezuelan migrants in Colombia (Bahar, Ibañez, and Rozo 2021). Yet when addressing the exclusion of vulnerable groups from political spaces, quotas might be more effective: as argued by advocates of Canada’s 50-30 Challenge to ensure gender parity and at least 30 percent representation of under-represented groups in senior management positions (Government of Canada 2023). Put simply, the mantra of “one size does <u>not fit all” should guide the design of policies to combat</u> multidimensional social gaps in all its forms: a diversity and complexity that can be revealed by a multidimensional, rigorous, adaptable and widely applicable Social Sustainability Index.

28
# **References**

<mark>ACLED (2022). “Armed Conflict Location & Event Data Project (ACLED) Codebook”.</mark> <mark>Alkire, S., & Foster, J. (2011). Counting and multidimensional poverty measurement.</mark> _<mark>Journal of public economics</mark>_ <mark>,</mark> _<mark>95</mark>_ <mark>(7-8), 476-487.</mark> <mark>Alkire, S., Roche, J. M., Ballon, P., Foster, J., Santos, M. E., & Seth, S. (2015).</mark> _<mark>Multidimensional poverty measurement and analysis</mark>_ <mark>. Oxford University Press, USA.</mark> Atkinson, A.B. (2003). Developing Comparable indicators for monitoring social inclusion in the European Union. In: Hauser, R., Becker, I. (eds.) _Reporting on income distribution and poverty_ . Springer, Berlin, Heidelberg.

Bahar, D., A.M. Ibañez and S. Rozo. (2021). Give me your tired and your poor: Impact of a largescale amnesty program for undocumented refugees. _Journal of Development Economics_ , 151: 102652.

Ballet, J., D. Bazin, F. Mahieu (2020). A policy framework for social sustainability: Social cohesion, equity and safety. _Sustainable Development_ 28(5): 1388–1394.

<mark>Ballon, P. (2023). Statistical issues in multidimensional poverty measurement: redundancy analysis. In: Silber, J. (ed.)</mark> _<mark>Research Handbook on Measuring Poverty and Deprivation</mark>_ <mark>(pp. 463-474). Edward Elgar Publishing.</mark> Barron, P., L. Cord, J. Cuesta, S. Espinoza, G. Larson and M. Woolcock (2023). Social sustainability in development. World Bank: Washington DC.

Basso, M. and J. Messina, eds. (2020). _The inequality crisis: Latin America and the Caribbean at the crossroads._ IAD: Washington DC.

Boström, M. (2012). A missing pillar? Challenges in theorizing and practicing social sustainability: Introduction to the special issue. _Sustainability: Science, Practice and Policy_ , 8(1), 3–14.

Cuesta, J., López-Noval, B., and M. Niño-Zarazúa (2022). Social sustainability, poverty, and income an empirical exploration. _World Bank Policy Research Working Paper 10085_ . Washington DC: World Bank.

Cuthill, M. (2010). Strengthening the ‘social’ in sustainable development: Developing a conceptual framework for social sustainability in a rapid urban growth region in Australia. _Sustainable Development, 18_ , 362–373.

Das, M. B. and S. A. Espinoza (2020). _Inclusion matters in Africa._ Washington, DC: World Bank.

Dempsey, N., G. Bramley, S. Power, and C. Brown (2011). The social dimension of sustainable development: Defining urban social sustainability. _Sustainable Development_ 19: 289–300.

Eurostat (2022). Over 1 in 5 at risk of poverty or social exclusion, Eurostat online news, available at: https://ec.europa.eu/eurostat/web/products-eurostat-news/-/ddn-20220915-1.

29
Government of Canada (2023). The 50-30 Challenge: Your diversity advantage. Available online: https://ised-isde.canada.ca/site/ised/en/50-30-challenge-your-diversity-advantage.

Griessler, E., and B. Littig (2005). Social sustainability: a catchword between political pragmatism and social theory. _International Journal for Sustainable Development_ 8(1/2): 65-79.

IMF (2020). South Africa: 2019 Article IV Consultation—Press Release; Staff Report; and Statement by the Executive Director for South Africa. IMF Country Report No. 20/33, January 2020, IMF: Washington, DC.

INEI (2022). Evolución de la Pobreza Monetaria 2010-2021, Informe Técnico. Instituto Nacional de Estadística e Informática del Perú, Lima, May 2022.

Kusumastuti, R.D., Z. Viverita, L. Husodo and D. Danarsari (2014). Developing a resilience index towards natural disasters in Indonesia. _International Journal of. Disaster Risk Reduction_ , 10, 327–340.

Levitas, R., Pantazis C., Fahmy, E., Gordon, D., Lloyd, E., EHRR and D. Patsios (2007). The Multidimensional Analysis of Social Exclusion. (246 plus additional Appendix 7 ed.) Department for Communities and Local Government (DCLG), London: UK Cabinet Office, Social Exclusion Task Force.

Littig, B. and E. Griessler (2005). Social sustainability: a catchword between political pragmatism and social theory. _International Journal of Sustainable Development_ , 8, 65–79.

Marzi, S., J. Mysiak, A. Essenfelder, M. Amadio, S. Give and A. Fekete, (2019). Constructing a Comprehensive Disaster Resilience Index: The case of Italy. _PLoS ONE,_ 14, e0221585.

Nussbaum, M. (2006). _Frontiers of Justice_ . Cambridge, MA: The Belknap Press.

Pawłowski, A. (2008). How many dimensions does sustainable development have? _Sustainable Development_ , 16(2), 81–90.

Purvis, B., Y. Mao and D. Robinson (2019). Three pillars of sustainability: in search of conceptual origins. _Sustainability Science,_ 14 _:_ 681–695.

<mark>Sen, A. (1976). Poverty: an ordinal approach to measurement.</mark> _<mark>Econometrica: Journal of the Econometric Society</mark>_ <mark>, 219-231.</mark> Sen, A. (1985). “A sociological approach to the measurement of poverty: A reply to Professor Peter Townsend.” _Oxford Economic Papers_ 37 (4): 669–76.

Sen, A. (2000). “Social exclusion: concept, application and scrutiny.” _Social Development Papers_ No. 1. Manila, Philippines: Asian Development Bank.

Silver, H. (1995). Reconceptualizing social disadvantage: Three paradigms of social exclusion. In _Social Exclusion: Rhetoric, Reality, Responses_ , eds. G. Rodgers, C. Gore, and J. Figueiredo. Geneva: International Institute for Labour Studies.

30
Smith, A. (1776). _An inquiry into the nature and causes of the wealth of nations_ (republished, eds. R.H. Campbell and A.S. Skinner. Oxford: Clarendon Press, 1976).

STATSSA (2022). Available at: https://www.statssa.gov.za/?page_id=739&id=1 United Nations (2016). “Leaving no one behind: The imperative of inclusive development.” _Report on the world social situation 2016_ . New York: United Nations.

World Bank (2005). “Empowering people by transforming institutions: Social development in World Bank Operations.” _Social development strategy paper_ . Washington, DC: World Bank.

World Bank (2013). “Inclusion matters: The foundation for shared prosperity.” Washington, D.C.: The World Bank.

<mark>World Bank (2018).</mark> _<mark>South Africa - Systematic country diagnostic: An incomplete transition - Overcoming the legacy of exclusion in South Africa</mark>_ <mark>(English). Washington, D.C.: The World Bank.</mark> <mark>World Bank (2022). World Bank Poverty and shared prosperity Report 2022: Correcting course. Washington DC: The World Bank</mark> **<mark>.</mark>** <mark>World Bank (2022a).</mark> _<mark>Peru Systematic Country Diagnostic Update (English)</mark>_ <mark>. Washington, D.C: The World Bank.</mark> Yonzan, N., A. Cojocaru, C. Lakner, D. Gerszon Mahler, and A. Narayan. A. (2022). “The Impact of COVID-19 on poverty and inequality: Evidence from phone surveys.” _World Bank Data_ (blog), January 18, 2022. https://blogs.worldbank.org/opendata/impact -covid-19-poverty-and-inequalityevidence-phone-surveys 31
# **Appendices**

## **Appendix I.1: Social Sustainability Index, Incidence and Intensity rates Cross-dimensional Cutoff at 50%**

## **Panel A: Peru**

|**Mi**<sup>**(a)**</sup>|**National**||**Gender**|||||**Ethnici**|**ty**||**Nn**|
|---|---|---|---|---|---|---|---|---|---|---|---|
|**etrc**||**Male**|**Female **|**T-test**<sup>**(b)**</sup>|**Quechua**|**T-test**<sup>**(b)**</sup>|**Aymara **|**T-test**<sup>**(b) **</sup>|**Indigenous **|**T-test**<sup>**(b)**</sup>|**o-**<br>**Native**|
|**Incidence rate (H)**|29%|28%|31%|<br>***|43%|<br>***|51%|<br>***|55%|<br>***|25%|
||_(0.07)_|_(0.07)_|_(0.07)_||_(0.07)_||_(0.09)_||_(0.03)_||_(0.05)_|
|**Intensit rate (A)**|55%|55%|55%|<br>ns|56%|<br>***|58%|<br>***|55%|<br>ns|55%|
|**y**|_(0.01)_|_(0.01)_|_(0.01)_|..|_(0.01)_||_(0.01)_||_(0.01)_|..|_(0.01)_|
|**Social Sustainability**|0.16|0.15|0.17|<br>***|0.24|<br>***|0.29|***|0.3|<br>***|0.14|
|**index(SSI)**|_(0.04)_|_(0.04)_|_(0.04)_||_(0.04)_||_(0.05)_||_(0.02)_||_(0.03)_|

(a) For each metric we report the point estimate and its standard errors(in parentheses).

(b) To assess difference between groups we perfomed t-tests. The baseline category for ethnicity is non-native. ***, **, * denote statistically significant differences between groups at 1%, 5% and 10% levels respectively. n.s denotes non-statistical significance.

## **Panel B: South Africa**

|<sup>**(a)**</sup>|**Nil**||**Gender**|||||**Ethnicity**||||
|---|---|---|---|---|---|---|---|---|---|---|---|
|**erc**|**aona**||||**ac**|||||||
|||**Male**|**Female **|**T-test**<sup>**(b)**</sup>|<br>**African**|**T-test**<sup>**(b**</sup>|<sup>**) Coloured **</sup>|**T-test**<sup>**(b)**</sup>|**Indian**|**T-test**<sup>**(b)**</sup>|**White**|
|**Incidence rate (H)**|40%|32%|45%|<br>***|46%|<br>***|24%|***|15%|<br>***|7%|
||_(0.02)_|_(0.02)_|_(0.02)_||_(0.02)_||_(0.04)_||_(0.02)_||_(0.02)_|
|**Intensit rate (A)**|61%|60%|61%|<br>ns|61%|<br>***|59%|***|56%|<br>***|59%|
|**y**|_(0)_|_(0.01)_|_(0.01)_|..|_(0.01)_||_(0.01)_||_(0.01)_||_(0.04)_|
|**Social Sustainability**|0.24|0.19|0.27|<br>***|0.28|<br>***|0.14|***|0.08|<br>***|0.04|
|**index(SSI)**|_(0.01)_|_(0.01)_|_(0.01)_||_(0.01)_||_(0.02)_||_(0.01)_||_(0.02)_|

(a) For each metric we report the point estimate and its standard errors(in parentheses).

(b) To assess difference between groups we perfomed t-tests. The baseline category for ethnicity is white. ***, **, * denote statistically significant differences between groups at 1%, 5% and 10% levels respectively. n.s denotes non-statistical significance.

32
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# **Appendix II : Composition of Multidimensional Social-Gaps**

# **Panel A: Peru**

# **A.1: Cross-dimensional cutoff at 33%**

|**Gr**|**oup**|**Quality of**<br>**employment**|**Access to bas**<br>**services**|**ic**<br>**Level of**<br>**education**|**Medical**<br>**attention**|**Quality of**<br>**housing**|**Possession of**<br>**assets**|**Public**<br>**assistance**|**Capacity for**<br>**saving**<br>**In**|**Confidence on**<br>**government**<br>**institutions**<br>**dicators**|<br>**Experience of**<br>**discrimination**|<br><br>**Perception of**<br>**safety**|**Victim of crim**|**e **<sup>**Civil**</sup><br>**participation**|**Satisfaction**<br>**with**<br>**democracy**|**Government**<br>**effectiveness**|**Equality before**<br>**the law**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Na**|**ional**|11.8%|<br>3.9%|<br>7.2%|<br>7.7%|<br>4.9%|<br>4.3%|<br>8.8%|<br>3.1%|<br>4.5%|<br>2.1%|<br>1.9%|<br>0.5%|<br>7.3%|<br>8.9%|<br>11%|<br>12.2%|
|||_(0.1%)_|_(1.2%)_|_(0.9%)_|_(0.2%)_|_(1.4%)_|_(1.4%)_|_(0.8%)_|_(0.4%)_|_(0.2%)_|_(0.5%)_|_(0.6%)_|_(0.1%)_|_(1.7%)_|_(0.9%)_|_(0.8%)_|_(0.5%)_|
||**Male**|11.5%|<br>4.1%|<br>6.7%|<br>7.9%|<br>5%|<br>4.5%|<br>8.7%|<br>3.1%|<br>4.5%|<br>2%|<br>1.9%|<br>0.5%|<br>7.2%|<br>9%|<br>11%|<br>12.3%|
|**Gender**||_(0.2%)_|_(1.2%)_|_(0.9%)_|_(0.2%)_|_(1.4%)_|_(1.4%)_|_(0.8%)_|_(0.4%)_|_(0.2%)_|_(0.4%)_|_(0.6%)_|_(0.1%)_|_(1.7%)_|_(1%)_|_(0.8%)_|_(0.5%)_|
||**Female**|12%|<br>3.6%|<br>7.8%|<br>7.5%|<br>4.7%|<br>4.2%|<br>8.9%|<br>3.1%|<br>4.4%|<br>2.2%|<br>1.9%|<br>0.5%|<br>7.3%|<br>8.8%|<br>11%|<br>12.1%|
|||_(0.1%)_|_(1.1%)_|_(0.9%)_|_(0.2%)_|_(1.4%)_|_(1.4%)_|_(0.8%)_|_(0.4%)_|_(0.2%)_|_(0.5%)_|_(0.6%)_|_(0.1%)_|_(1.6%)_|_(0.9%)_|_(0.7%)_|_(0.5%)_|
||**Quechua**|12%|<br>4.8%|<br>9.2%|<br>7.6%|<br>6.7%|<br>6.3%|<br>9.5%|<br>1.5%|<br>5.1%|<br>1.9%|<br>1.2%|<br>0.5%|<br>3.8%|<br>8%|<br>10.3%|<br>11.8%|
|||_(0.1%)_|_(1.3%)_|_(0.3%)_|_(0.3%)_|_(1.3%)_|_(1.2%)_|_(0.6%)_|_(0.3%)_|_(0.4%)_|_(0.4%)_|_(0.5%)_|_(0.1%)_|_(1.5%)_|_(0.8%)_|_(0.6%)_|_(0.4%)_|
||**Amara**|11.4%|<br>4.2%|<br>8.1%|<br>8.8%|<br>5.2%|<br>6.7%|<br>8.9%|<br>1.8%|<br>6.5%|<br>3.4%|<br>0.9%|<br>0.5%|<br>3.4%|<br>8.7%|<br>10%|<br>11.5%|
|**Ethnicit**|**y**|_(0.2%)_|_(1.4%)_|_(0.3%)_|_(0.3%)_|_(1.7%)_|_(1.1%)_|_(0.5%)_|_(0.5%)_|_(0.4%)_|_(0.2%)_|_(0.4%)_|_(0.2%)_|_(1.7%)_|_(0.5%)_|_(0.5%)_|_(0.4%)_|
|**y**|**Other**|12.3%|<br>11.1%|<br>9.9%|<br>7.6%|<br>5.6%|<br>11%|<br>11.3%|<br>1.6%|<br>3.9%|<br>0.5%|<br>0.5%|<br>0.3%|<br>1.3%|<br>5%|<br>7.9%|<br>10.2%|
||**Indigenous**|_(0.2%)_|_(0.5%)_|_(0.4%)_|_(0.3%)_|_(0.5%)_|_(0.6%)_|_(0.1%)_|_(0.5%)_|_(0.4%)_|_(0.1%)_|_(0.2%)_|_(0.2%)_|_(0.5%)_|_(0.2%)_|_(0.2%)_|_(0.1%)_|
||**Non-native**|11.7%|<br>3.4%|<br>6.6%|<br>7.7%|<br>4.3%|<br>3.5%|<br>8.5%|<br>3.7%|<br>4.2%|<br>2.1%|<br>2.2%|<br>0.5%|<br>8.7%|<br>9.3%|<br>11.3%|<br>12.4%|
|||_(0.1%)_|_(1%)_|_(0.9%)_|_(0.2%)_|_(1.2%)_|_(1.2%)_|_(0.8%)_|_(0.3%)_|_(0.2%)_|_(0.5%)_|_(0.6%)_|_(0.1%)_|_(1.4%)_|_(0.9%)_|_(0.7%)_|_(0.5%)_|

# **A.2: Cross-dimensional cutoff at 50%**

|**Group**|**Quality of**<br>**employment**|**Access to bas**<br>**services**|**ic**<br>**Level of**<br>**education**|**Medical**<br>**attention**|**Quality of**<br>**housing**|**Possession of**<br>**assets**|**Public**<br>**assistance**|**Capacity for**<br>**saving**<br>**In**|**Confidence on**<br>**government**<br>**institutions**<br>**dicators**|<br>**Experience of**<br>**discrimination**|<br><br>**Perception of**<br>**safety**|**Victim of crim**|**e **<sup>**Civil**</sup><br>**participation**|**Satisfaction**<br>**with**<br>**democracy**|**Government**<br>**effectiveness**|**Equality before**<br>**the law**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**National**|10.9%|<br>5.2%|<br>8.2%|<br>7.9%|<br>6.4%|<br>6%|<br>8.9%|<br>3.1%|<br>4.9%|<br>2.2%|<br>1.5%|<br>0.5%|<br>5.7%|<br>8%|<br>9.8%|<br>10.7%|
||_(0%)_|_(1.1%)_|_(0.6%)_|_(0.1%)_|_(1.2%)_|_(1.3%)_|_(0.4%)_|_(0.5%)_|_(0.2%)_|_(0.6%)_|_(0.5%)_|_(0.1%)_|_(1.4%)_|_(0.6%)_|_(0.4%)_|_(0.2%)_|
|**Ml**|10.8%|<br>5.5%|<br>7.8%|<br>8%|<br>6.4%|<br>6.2%|<br>8.8%|<br>3.1%|<br>4.9%|<br>2%|<br>1.5%|<br>0.5%|<br>5.7%|<br>8.1%|<br>9.8%|<br>10.8%|
|**ae**<br>**Gender**|_(0.1%)_|_(1.1%)_|_(0.6%)_|_(0.1%)_|_(1.1%)_|_(1.2%)_|_(0.5%)_|_(0.5%)_|_(0.2%)_|_(0.5%)_|_(0.5%)_|_(0.1%)_|_(1.5%)_|_(0.6%)_|_(0.4%)_|_(0.2%)_|
|**Female**<br>|11%|<br>4.9%|<br>8.6%|<br>7.7%|<br>6.3%|<br>5.7%|<br>9.1%|<br>3.2%|<br>4.8%|<br>2.4%|<br>1.5%|<br>0.5%|<br>5.8%|<br>8%|<br>9.8%|<br>10.7%|
||_(0%)_|_(1.1%)_|_(0.6%)_|_(0.2%)_|_(1.2%)_|_(1.3%)_|_(0.3%)_|_(0.5%)_|_(0.2%)_|_(0.6%)_|_(0.4%)_|_(0.1%)_|_(1.4%)_|_(0.6%)_|_(0.4%)_|_(0.2%)_|
|**Quechua**|11%|<br>5.9%|<br>9.2%|<br>8.1%|<br>7.5%|<br>7.3%|<br>9.2%|<br>1.5%|<br>5.4%|<br>2.1%|<br>1%|<br>0.5%|<br>3.1%|<br>7.8%|<br>9.7%|<br>10.7%|
||_(0.1%)_|_(1.2%)_|_(0.3%)_|_(0.1%)_|_(1.1%)_|_(1%)_|_(0.5%)_|_(0.4%)_|_(0.4%)_|_(0.5%)_|_(0.4%)_|_(0.1%)_|_(1.3%)_|_(0.4%)_|_(0.4%)_|_(0.2%)_|
|**Amara**|10.7%|<br>5.1%|<br>8.2%|<br>8.7%|<br>6.2%|<br>7.7%|<br>8.8%|<br>1.8%|<br>7.1%|<br>3.5%|<br>0.6%|<br>0.4%|<br>2.6%|<br>8.4%|<br>9.6%|<br>10.4%|
|**y**<br>**Ethnicit**|_(0.1%)_|_(1.2%)_|_(0.1%)_|_(0.4%)_|_(1.6%)_|_(1%)_|_(0.3%)_|_(0.5%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.1%)_|_(1.5%)_|_(0.4%)_|_(0.4%)_|_(0.2%)_|
|**Other**<br>**y**|11.2%|<br>10.5%|<br>9.6%|<br>7.7%|<br>5.7%|<br>10.2%|<br>10.7%|<br>1.8%|<br>4.8%|<br>0.6%|<br>0.4%|<br>0.3%|<br>1.4%|<br>6%|<br>8.8%|<br>10.3%|
|**Indigenous**|_(0.1%)_|_(0.3%)_|_(0.3%)_|_(0.1%)_|_(0.6%)_|_(0.5%)_|_(0.1%)_|_(0.5%)_|_(0.5%)_|_(0.2%)_|_(0.1%)_|_(0.2%)_|_(0.4%)_|_(0.1%)_|_(0.4%)_|_(0.3%)_|
|**Non-native**|10.9%|<br>4.8%|<br>7.8%|<br>7.8%|<br>5.9%|<br>5.2%|<br>8.8%|<br>3.9%|<br>4.5%|<br>2.2%|<br>1.8%|<br>0.6%|<br>7.1%|<br>8.2%|<br>9.9%|<br>10.8%|
||_(0%)_|_(1%)_|_(0.6%)_|_(0.2%)_|_(1.2%)_|_(1.3%)_|_(0.4%)_|_(0.4%)_|_(0.3%)_|_(0.6%)_|_(0.5%)_|_(0.1%)_|_(1.2%)_|_(0.7%)_|_(0.4%)_|_(0.2%)_|

34
# **Panel B: South Africa B.1: Cross-dimensional cutoff at 33%**

|**Group**|**Quality of**<br>|**Access to**<br>**basic**|**Level of**<br>|**Medical**<br>|**Quality of**<br>|**Possession of**|<br>**Public**<br>|**Capacity for**<br>|**Confidence on**<br>**government**<br>**Indicators**|<br>**Experience o**<br>|**f**<br><br>**Perception o**<br>|**f**<br>**Victim of**<br>|**Civil**<br>|**Satisfaction**<br>**with**|<br>**Government**<br>|**Satisfaction with**<br>**the way**<br>|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
||**employment**|**services**|**education**|**attention**|**housing**|**assets**|**assistance**|**saving**|<br>**institutions**|**discriminatio**|**n**<br>**safety**|**crime**|**participation**|**democracy**|**effectiveness**|**corruption is**<br>**combatted**|
|**National**|6.7%|<br>4.4%|<br>7.7%|<br>4.5%|<br>4.6%|<br>5.7%|<br>9.4%|<br>6.9%|<br>6.9%|<br>4.6%|<br>5%|<br>3.8%|<br>4%|<br>7.2%|<br>8.7%|<br>10.1%|
||_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|
|**Male**|6.2%|<br>4.1%|<br>7.3%|<br>4.7%|<br>4.7%|<br>5.2%|<br>8.7%|<br>6.6%|<br>7.3%|<br>5.4%|<br>4.7%|<br>3.6%|<br>4.7%|<br>7.3%|<br>8.9%|<br>10.4%|
|**Gender**|_(0.4%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.4%)_|_(0.3%)_|_(0.4%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.4%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|
|**Female**<br>|6.9%|<br>4.5%|<br>7.9%|<br>4.4%|<br>4.5%|<br>6%|<br>9.7%|<br>7.1%|<br>6.7%|<br>4.1%|<br>5.2%|<br>3.9%|<br>3.6%|<br>7.1%|<br>8.5%|<br>9.9%|
||_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|
|**Black African**|6.8%|<br>4.8%|<br>7.7%|<br>4.5%|<br>4.8%|<br>6.2%|<br>9.4%|<br>6.9%|<br>6.5%|<br>4.4%|<br>4.9%|<br>3.6%|<br>4%|<br>7%|<br>8.6%|<br>10%|
||_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|
|**Coloured**|6.4%|<br>1.2%|<br>7.6%|<br>5.9%|<br>3.8%|<br>2.7%|<br>10.1%|<br>7.7%|<br>9.7%|<br>6.6%|<br>5.6%|<br>5.1%|<br>2.9%|<br>6.8%|<br>7.6%|<br>10.2%|
|**Ethnicity**|_(0.8%)_|_(0.4%)_|_(0.7%)_|_(0.7%)_|_(0.7%)_|_(0.5%)_|_(0.5%)_|_(0.6%)_|_(0.6%)_|_(0.6%)_|_(0.7%)_|_(0.8%)_|_(0.5%)_|_(0.7%)_|_(0.5%)_|_(0.6%)_|
|**Indian**<br>|4.3%|<br>0.5%|<br>8.4%|<br>2.4%|<br>2.3%|<br>0.7%|<br>9%|<br>5.2%|<br>12.2%|<br>5.5%|<br>7.5%|<br>4.3%|<br>3.3%|<br>11.8%|<br>9.5%|<br>13.1%|
||_(0.5%)_|_(0.2%)_|_(0.6%)_|_(0.4%)_|_(0.4%)_|_(0.2%)_|_(0.5%)_|_(0.5%)_|_(0.4%)_|_(0.5%)_|_(0.5%)_|_(0.5%)_|_(0.5%)_|_(0.4%)_|_(0.5%)_|_(0.3%)_|
|**White**|4.4%|<br>0.3%|<br>6.9%|<br>2.8%|<br>2%|<br>0.4%|<br>8.4%|<br>5.7%|<br>11.4%|<br>5.9%|<br>6.8%|<br>7.8%|<br>4.8%|<br>11%|<br>10.7%|<br>10.5%|
||_(0.8%)_|_(0.3%)_|_(1.2%)_|_(0.7%)_|_(0.8%)_|_(0.3%)_|_(1.1%)_|_(1.2%)_|_(1.1%)_|_(1%)_|_(1.1%)_|_(1%)_|_(1.4%)_|_(0.7%)_|_(0.8%)_|_(1%)_|

# **B.2: Cross-dimensional cutoff – 50%**

|**Group**|**Quality of**<br>**employment**|**Access to**<br>**basic**<br>**services**|**Level of**<br>**education**|**Medical**<br>**attention**|**Quality of**<br>**housing**|**Possession of**<br>**assets**|<br>**Public**<br>**assistance**|**Capacity for**<br>**saving**|**Confidence o**<br>**government**<br>**institutions**<br>**Indicators**|**n**<br>**Experience o**<br>**discriminatio**|**f**<br>**n**<br>**Perception o**<br>**safety**|**f**<br>**Victim of**<br>**crime**|**Civil**<br>**participation**|**Satisfaction**<br>**with**<br>**democracy**|<br>**Government**<br>**effectiveness**|<br><br>**Satisfaction with**<br>**the way**<br>**corruption is**<br>**combatted**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**National**|6.8%|<br>4.7%|<br>7.1%|<br>4.8%|<br>5.2%|<br>6.2%|<br>8.7%|<br>7.1%|<br>6.8%|<br>4.4%|<br>5%|<br>3.6%|<br>4.2%|<br>7.5%|<br>8.5%|<br>9.4%|
||_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.1%)_|
|**Male**|6.2%|<br>4.6%|<br>6.7%|<br>5.2%|<br>5.4%|<br>5.7%|<br>8.1%|<br>6.7%|<br>7%|<br>5.3%|<br>4.9%|<br>3.5%|<br>4.9%|<br>7.5%|<br>8.9%|<br>9.4%|
|**Gender**|_(0.4%)_|_(0.5%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.4%)_|_(0.3%)_|_(0.2%)_|
|**Female**<br>|7.1%|<br>4.8%|<br>7.4%|<br>4.6%|<br>5.1%|<br>6.5%|<br>9%|<br>7.2%|<br>6.7%|<br>4%|<br>5%|<br>3.7%|<br>3.8%|<br>7.4%|<br>8.3%|<br>9.4%|
||_(0.3%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.4%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.4%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|
|**Black African**|6.8%|<br>5%|<br>7.1%|<br>4.8%|<br>5.2%|<br>6.5%|<br>8.7%|<br>7%|<br>6.6%|<br>4.3%|<br>4.8%|<br>3.5%|<br>4.2%|<br>7.4%|<br>8.5%|<br>9.4%|
||(0.2%)|_(0.4%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.3%)_|_(0.2%)_|_(0.3%)_|_(0.2%)_|_(0.2%)_|_(0.2%)_|
|**Coloured**|5.9%|<br>1.2%|<br>7.2%|<br>5.4%|<br>5.1%|<br>3.1%|<br>9.3%|<br>8.5%|<br>8.3%|<br>5.9%|<br>6.8%|<br>5.1%|<br>3.3%|<br>7.1%|<br>8.6%|<br>9%|
|**Ethnicity**|_(1.1%)_|_(0.5%)_|_(0.8%)_|_(1%)_|_(1%)_|_(0.7%)_|_(0.5%)_|_(0.5%)_|_(0.6%)_|_(0.9%)_|_(0.7%)_|_(1%)_|_(0.9%)_|_(0.9%)_|_(0.5%)_|_(0.5%)_|
|**Indian**<br>|4.8%|<br>0.4%|<br>7.9%|<br>2.9%|<br>2.9%|<br>0.8%|<br>9.1%|<br>6%|<br>10.1%|<br>6.2%|<br>8.9%|<br>5.4%|<br>4.5%|<br>10.1%|<br>8.9%|<br>11.2%|
||_(0.7%)_|_(0.2%)_|_(0.7%)_|_(0.7%)_|_(0.7%)_|_(0.4%)_|_(0.6%)_|_(0.8%)_|_(0.4%)_|_(0.7%)_|_(0.6%)_|_(0.8%)_|_(0.8%)_|_(0.5%)_|_(0.6%)_|_(0.2%)_|
|**White**|6.9%|<br>0.8%|<br>8.3%|<br>4%|<br>5.2%|<br>1%|<br>7.7%|<br>7.2%|<br>10.3%|<br>5.1%|<br>7.1%|<br>7.5%|<br>2.3%|<br>9%|<br>8%|<br>9.6%|
||_(0.8%)_|_(0.8%)_|_(0.8%)_|_(0.9%)_|_(1.3%)_|_(0.9%)_|_(1%)_|_(1.1%)_|_(0.6%)_|_(1%)_|_(1.2%)_|_(1%)_|_(1.3%)_|_(0.8%)_|_(1%)_|_(0.8%)_|

35
**Appendix III: Composition of Multidimensional Social-Gaps,Tests of Statistical Significance between Ethnic Groups**

# **Panel A: Peru**

**4**

|||||**Quechua**<sup>**(a)**</sup>|||||**Aymara**<sup>**(a)**</sup>||||**Ot**|**her Inidgeno**|**us**<sup>**(a)**</sup>|||||**Non-Nati**|**ve**|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Dimension**|**Indicator**|||**Cutoff**|||||**Cutoff**|||||**Cutoff**|||||**Cutoff**|||
|||**33%**<br>||**50%**<br><br>|**T**|**75%**<br>|**T**|**33%**<br><br>**T**|**50%**<br>**T**<br>|**75%**<br>|**T**|**33%**<br>|**T**|**50%**<br>|**T**|**75%**<br>||**T**|**33%**<br>|**50%**|**75%**|
|||**Est./SE**<br>|**-tes**|**Est./SE**<br><br>|**-tes**|**Est./SE**<br>|**-tes**|**Est./SE**<br><br>**-tes**|**Est./SE**<br>**-**<br>|**Est./SE**<br>**est**|**-tes**|**Est./SE**<br>|**-te**|**Est./SE**<br>**t**|**-te**|**Est./S**<br>**t**|**E**|**-te**|**Est./SE**<br>**t**|**Est./SE**|**Est./SE**|
|**Access to labour**|**Quality of**|12%|***|11%|***|8.3%|<br>***|11.4%<br>***|10.7%<br>*|**<br>8.2%|<br>***|12.3%|***|11.2%|<br>***|8.3%||<br>***|11.7%|<br>10.9%|<br>8.2%|
|**markets**|**employment**|(0.1%)||(0.1%)||(0%)||(0.2%)<br>|(0.1%)<br>|(0.1%)||(0.2%)||(0.1%)||(0%)|||(0.1%)|(0%)|(0.1%)|
|**Access to basic**|**Access to basic**|4.8%|***|5.9%|***|5.8%|<br>***|4.2%<br>***|5.1%<br>*|**<br>6%|<br>***|11.1%|***|10.5%|<br>***|8.3%||<br>***|3.4%|<br>4.8%|<br>7.4%|
|**services**<br>**Social Inclusion**|**services**|(1.3%)||(1.2%)||(1.2%)||(1.4%)<br>|(1.2%)<br>|(1.4%)||(0.5%)||(0.3%)||(0%)|||(1%)|(1%)|(0.2%)|
|||9.2%|***|9.2%|***|8.1%|<br>***|8.1%<br>***|8.2%<br>*|**<br>6.5%|<br>***|9.9%|***|9.6%|<br>***|8.3%||<br>***|6.6%|<br>7.8%|<br>7.9%|
|**Access to human**|**Level of education**|(0.3%)||(0.3%)||(0.1%)||(0.3%)<br>|(0.1%)<br>|(0.3%)||(0.4%)||(0.3%)||(0%)|||(0.9%)|(0.6%)|(0.2%)|
|**capital services**||7.6%|***|8.1%|***|8.2%|<br>***|8.8%<br>***|8.7%<br>*|**<br>6.5%|<br>***|7.6%|***|7.7%|<br>**|8.3%||<br>***|7.7%|<br>7.8%|<br>7.4%|
||**Medical attention**|(0.3%)||(0.1%)||(0.1%)||(0.3%)<br>|(0.4%)<br>|(1.2%)||(0.3%)||(0.1%)||(0%)|||(0.2%)|(0.2%)|(0.2%)|
|||6.7%|***|7.5%|***|8%|<br>***|5.2%<br>***|6.2%<br>*|**<br>7.8%|<br>***|5.6%|***|5.7%|<br>***|8.3%||<br>***|4.3%|<br>5.9%|<br>7.2%|
||**Quality of housing**|(1.3%)||(1.1%)||(0.3%)||(1.7%)<br>|(1.6%)<br>|(0.4%)||(0.5%)||(0.6%)||(0%)|||(1.2%)|(1.2%)|(0.6%)|
|||6.3%|***|7.3%|***|7%|<br>***|6.7%<br>***|7.7%<br>|**<br>8%|<br>***|11%|***|10.2%|<br>***|8.3%||<br>***|3.5%|<br>5.2%|<br>7.8%|
|**Rili**|**Possession of assets**|(1.2%)||(1%)||(1.2%)||(1.1%)<br>|(1%)<br>|(0.3%)||(0.6%)||(0.5%)||(0%)|||(1.2%)|(1.3%)|(0.2%)|
|**esence**||9.5%||9.2%||7.8%|<br>|8.9%<br>|8.8%<br>|8%|<br>|11.3%||10.7%|<br>|8.3%||<br>|8.5%|<br>8.8%|<br>7.6%|
||**Public assistance**|(0.6%)|***|(0.5%)|***|(0.5%)|***|(0.5%)<br>***|(0.3%)<br>*|**<br>(0.1%)|***|(0.1%)|***|(0.1%)|***|(0%)||***|(0.8%)|(0.4%)|(0.2%)|
||**Capacit for savin**|1.5%|***|1.5%|***|3.8%|<br>***|1.8%<br>***|1.8%<br>*|**<br>3.8%|<br>***|1.6%|***|1.8%|<br>***|8.3%||<br>***|3.7%|<br>3.9%|<br>5.6%|
||**y g**|(0.3%)||(0.4%)||(0.8%)||(0.5%)|(0.5%)|(0.7%)||(0.5%)||(0.5%)||(0%)|||(0.3%)|(0.4%)|(0.4%)|
||**Confidence on**<br>|5.1%|***|5.4%|***|8.1%|<br>***|6.5%<br>***|7.1%<br>*|**<br>8.2%|<br>***|3.9%|***|4.8%|<br>***|8.3%||<br>***|4.2%|<br>4.5%|<br>5.1%|
||**government**<br>**institutions**|(0.4%)||(0.4%)||(0.1%)||(0.4%)<br>|(0.4%)<br>|(0.1%)||(0.4%)||(0.5%)||(0%)|||(0.2%)|(0.3%)|(1%)|
||**Experience of**|1.9%|***|2.1%|***|4.6%|<br>***|3.4%<br>***|3.5%<br>*|**<br>7.4%|<br>***|0.5%|***|0.6%|<br>***|0%||<br>***|2.1%|<br>2.2%|<br>3.8%|
|**Social Cohesion**|**discrimination**|(0.4%)||(0.5%)||(0.6%)||(0.2%)<br>|(0.3%)<br>|(0.3%)||(0.1%)||(0.2%)||(0%)|||(0.5%)|(0.6%)|(0.6%)|
|||12%||1%||21%||09%|06%|1%||05%||04%||0%|||22%|<br>18%|<br>15%|
|||.|***||***|.|<br>***|.<br>***|.<br>*|**<br>|<br>***|.|***|.|<br>***|||<br>***|.|<br>.|<br>.|
||**Perception of safety**|(0.5%)||(0.4%)||(1.6%)||(0.4%)<br>|(0.3%)<br>|(0.7%)||(0.2%)||(0.1%)||(0%)|||(0.6%)|(0.5%)|(0.5%)|
|||0.5%|***|0.5%|***|1.6%|<br>***|0.5%<br>***|0.4%<br>*|**<br>2.4%|<br>***|0.3%|***|0.3%|<br>***|0%||<br>***|0.5%|<br>0.6%|<br>0.6%|
||**Victim of crime**|(0.1%)||(0.1%)||(1.1%)||(0.2%)<br>|(0.1%)<br>|(1%)||(0.2%)||(0.2%)||(0%)|||(0.1%)|(0.1%)|(0.2%)|
|**Agency and**||3.8%|***|3.1%|***|3.5%|<br>***|3.4%<br>***|2.6%<br>|**<br>3.4%|<br>***|1.3%|***|1.4%|<br>***|0%||<br>***|8.7%|<br>7.1%|<br>6.6%|
|<br>**voice**|**Civil participation**|(1.5%)||(1.3%)||(1%)||(1.7%)<br>|(1.5%)<br>|(1.2%)||(0.5%)||(0.4%)||(0%)|||(1.4%)|(1.2%)|(0.4%)|
||**Satisfaction with**|8%||7.8%||7.6%|<br>|8.7%<br>|8.4%<br>|8.2%|<br>|5%||6%||8.3%||<br>|9.3%|<br>8.2%|<br>7%|
|**Pr**|**democracy**|(0.8%)|***|(0.4%)|***|(0.4%)|***|(0.5%)<br>***|(0.4%)<br>*|**<br>(0.1%)|***|(0.2%)|***|(0.1%)|***|(0%)||***|(0.9%)|(0.7%)|(0.5%)|
|**ocess**<br><br>|**Government**|10.3%||9.7%||8.1%|<br>|10%<br>|9.6%<br>|6.4%|<br>|7.9%||8.8%|<br>|8.3%||<br>|11.3%|<br>9.9%|<br>8.2%|
|**Legitimacy**<br>**Social**<br>**accountability**|**effectiveness**|(0.6%)|***|(0.4%)|***|(0.1%)|***|(0.5%)<br>***|(0.4%)<br>*|**<br>(1.2%)|***|(0.2%)|***|(0.4%)|***|(0%)||***|(0.7%)|(0.4%)|(0.1%)|
||**Equality before the**|11.8%|***|10.7%|***|7.2%|<br>***|11.5%<br>***|10.4%<br>*|**<br>8.2%|<br>***|10.2%|***|10.3%|<br>***|8.3%||<br>***|12.4%|<br>10.8%|<br>8.1%|
||**l**|04%||02%||12%||04%|02%|01%||01%||03%||0%|||05%|02%|01%|
||**aw**|(.)||(.)||(.)||(.)|(.)|(.)||(.)||(.)||()|||(.)|(.)|(.)|
|(a) To assess difference betweengro|ups we perfomed t-tes|ts. The baseli|ne ca|tegoryforeth|nicity|isnon-nat|ive.***|,**,*denote stati|stically significa|nt differences|betwee|ngroups at1|%, 5%|and10%lev|elsre|spectively.|n.|s den|otesnon-st|atisticalsigni|ficance.|

36
# **Panel B: South Africa**

|||||**Black A**|**frican**|<sup>**(a)**</sup>|||||**Colou**|**red**<sup>**(a)**</sup>|||||**Indi**|**an**<sup>**(a)**</sup>||||**White**||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
|**Dimension**|**Indicator**|||**Cutoff**|||||||**Cutoff**||||||**Cutoff**|||||**Cutoff**||
|||**33%**<br>**Est./SE**|**T-test**|**50%**<br>**Est./SE**|**T-**|**test**|**75%**<br>**Est./SE**|**T-test**|**33%**<br>**Est./SE**|**T-test**|**50%**<br>**Est./SE**|**T-test**|**75%**<br>**Est./SE**|**T-test**|**33%**<br>**Est./SE**|**T-test**|**50%**<br>**Est./SE**|**T-test**|**75%**<br>**Est./SE***|**T-test**|**33%**<br>**Est./SE**|**50%**<br>**Est./SE**|**75%**<br>**Est./SE**|
|**Access to**<br>|<sup>**Qualit of emploment**</sup>|6.8%|***|6.8%|<br>n|s|5.3%|***|6.4%|***|5.9%|<br>***|6.3%|***|4.3%|***|4.8%|***|0%|nd|4.4%|<br>6.9%|<br>8.3%|
|**labour markets **|<sup>**y y**</sup>|(0.2%)||(0.2%)||..|(0.4%)||(0.8%)||(1.1%)||(0.9%)||(0.5%)||(0.7%)||(0%)|..|(0.8%)|(0.8%)|(0%)|
|**Access to basic**|**Access to basic services**|4.8%|***|5%|<br>*|**|6.2%|***|1.2%|***|1.2%|<br>***|4.8%|***|0.5%|***|0.4%|***|0%|nd|0.3%|<br>0.8%|<br>0%|
|**services**<br>**Social Inclusion**||(0.3%)||(0.4%)|||(0.4%)||(0.4%)||(0.5%)||(2.1%)||(0.2%)||(0.2%)||(0%)|..|(0.3%)|(0.8%)|(0%)|
|||77%||71%|||66%||76%||72%||63%||84%||79%||0%||69%|<br>83%|<br>83%|
|**Access to**|**Ll f di**|.|***|.|<br>*|**|.|***|.|***|.|<br>***|.|***|.|***|.|***||d|.|<br>.|<br>.|
|<br>**human caital**|**eve o eucaton**|(0.2%)||(0.2%)|||(0.4%)||(0.7%)||(0.8%)||(0.9%)||(0.6%)||(0.7%)||(0%)|n..|(1.2%)|(0.8%)|(0%)|
|**p**||45%||48%|||57%||59%||54%||79%||24%||29%||0%||28%|<br>4%|<br>83%|
|**i**||.|***|.|<br>*|**|.|***|.|***|.|<br>***|.|***|.|***|.|***|||.|<br>|<br>.|
|**servces**|**Medical attention**|(0.2%)||(0.3%)|||(0.4%)||(0.7%)||(1%)||(0.3%)||(0.4%)||(0.7%)||(0%)|n.d.|(0.7%)|(0.9%)|(0%)|
|||4.8%|***|5.2%|<br>||6.4%|***|3.8%|***|5.1%|<br>|7.9%|***|2.3%|***|2.9%|***|0%||2%|<br>5.2%|<br>8.3%|
||**Quality of housing**|(0.2%)||(0.3%)|n|.s.|(0.3%)||(0.7%)||(1%)|n.s.|(0.3%)||(0.4%)||(0.7%)||(0%)|n.d.|(0.8%)|(1.3%)|(0%)|
||**Possession of assets**|6.2%|***|6.5%|<br>*|**|7%|***|2.7%|***|3.1%|<br>***|7.9%|***|0.7%|***|0.8%|***|0%|nd|0.4%|<br>1%|<br>0%|
|||(03%)||(03%)|||(03%)||(05%)||(07%)||(03%)||(02%)||(04%)||(0%)|..|(03%)|(09%)|(0%)|
|**Resilience**||.||.|||.||.||.||.||.||.||||.|.||
||**Public assistance**|9.4%|***|8.7%|<br>*|**|7.3%|***|10.1%|***|9.3%|<br>***|7.9%|***|9%|**|9.1%|***|0%|nd|8.4%|<br>7.7%|<br>8.3%|
|||(0.2%)||(0.2%)|||(0.2%)||(0.5%)||(0.5%)||(0.3%)||(0.5%)||(0.6%)||(0%)|..|(1.1%)|(1%)|(0%)|
||**Cit f i**|6.9%|***|7%|<br>*|**|7%|***|7.7%|***|8.5%|<br>***|5.9%|***|5.2%|***|6%|***|0%|d|5.7%|<br>7.2%|<br>8.3%|
||**apacy or savng**|(0.3%)||(0.3%)|||(0.2%)||(0.6%)||(0.5%)||(1.6%)||(0.5%)||(0.8%)||(0%)|n..|(1.2%)|(1.1%)|(0%)|
||**Confidence on**|6.5%|***|6.6%|<br>*|**|6.8%|***|9.7%|***|8.3%|<br>***|7.9%|***|12.2%|***|10.1%|***|0%||11.4%|<br>10.3%|<br>8.3%|
||**government institutions**|(0.2%)||(0.3%)|||(0.3%)||(0.6%)||(0.6%)||(0.3%)||(0.4%)||(0.4%)||(0%)|n.d.|(1.1%)|(0.6%)|(0%)|
||**Experience of**|4.4%|***|4.3%|<br>*|**|4.7%|***|6.6%|***|5.9%|<br>***|3.5%|***|5.5%|***|6.2%|***|0%|nd|5.9%|<br>5.1%|<br>0%|
|**Social Cohesion**|**discrimination**|(0.2%)||(0.3%)|||(0.4%)||(0.6%)||(0.9%)||(1.7%)||(0.5%)||(0.7%)||(0%)|..|(1%)|(1%)|(0%)|
||**Pi f f**|4.9%|***|4.8%|<br>*|**|5.9%|***|5.6%|***|6.8%|<br>***|4.7%|***|7.5%|***|8.9%|***|0%|d|6.8%|<br>7.1%|<br>8.3%|
||**ercepton o saety**|(0.3%)||(0.3%)|||(0.4%)||(0.7%)||(0.7%)||(1.4%)||(0.5%)||(0.6%)||(0%)|n..|(1.1%)|(1.2%)|(0%)|
||**Victim of crime**|3.6%|***|3.5%|<br>*|**|4%|***|5.1%|***|5.1%|<br>***|2.8%|***|4.3%|***|5.4%|***|0%|n.d.|7.8%|<br>7.5%|<br>8.3%|
|||(0.3%)||(0.2%)|||(0.4%)||(0.8%)||(1%)||(1.8%)||(0.5%)||(0.8%)||(0%)||(1%)|(1%)|(0%)|
|**Agency and**|**Civil participation**|4%|***|4.2%|<br>*|**|5%|***|2.9%|***|3.3%|<br>***|2.8%|***|3.3%|***|4.5%|***|0%|nd|4.8%|<br>2.3%|<br>0%|
|**voice**||(0.2%)||(0.3%)|||(0.5%)||(0.5%)||(0.9%)||(1.8%)||(0.5%)||(0.8%)||(0%)|..|(1.4%)|(1.3%)|(0%)|
||**Satisfaction with**|7%|***|7.4%|<br>*|**|7.2%|***|6.8%|***|7.1%|<br>***|7.9%|***|11.8%|***|10.1%|***|0%|nd|11%|<br>9%|<br>8.3%|
||**democracy**|(0.2%)||(0.2%)|||(0.3%)||(0.7%)||(0.9%)||(0.3%)||(0.4%)||(0.5%)||(0%)|..|(0.7%)|(0.8%)|(0%)|
|**Process**|**Government**|86%||85%|||73%||76%||86%||79%||95%||89%||0%||107%|<br>8%|<br>83%|
|**Legitimacy**<br>**Social**|<br>|.<br>|***|.<br>|<br>*|**|.<br>|***|.<br>|***|.<br>|<br>***|.<br>|***|.<br>|***|.<br>|***|<br>|n.d.|.<br>|<br> <br>|<br>.<br>|
|**accountability**|**effectiveness**|(0.2%)||(0.2%)|||(0.2%)||(0.5%)||(0.5%)||(0.3%)||(0.5%)||(0.6%)||(0%)||(0.8%)|(1%)|(0%)|
||**Satisfaction with the**<br>|10%|***|9.4%|<br>*|**|7.6%|***|10.2%|***|9%|<br>***|7.9%|***|13.1%|***|11.2%|***|0%||10.5%|<br>9.6%|<br>8.3%|
||**way corruption is**<br>**combatted**|(0.2%)||(0.2%)|||(0.1%)||(0.6%)||(0.5%)||(0.3%)||(0.3%)||(0.2%)||(0%)|n.d.|(1%)|(0.8%)|(0%)|

(a) To assess difference between groups we perfomed t-tests. The baseline category for ethnicity is white. ***, **, * denote statistically significant differences between groups at 1%, 5% and 10% levels respectively. n.s denotes non-statistical significance.

37
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<!-- Start of picture text --> 2s<br>Bo3 \ — g58SS ee 322PsPi | .<br>29 z y 3 \<br>- : a4 an ;<br>2 ee & ° - =<br>10 20 30 Cross-dimensional40 50 cutoff (6)60(i %) 70 80 90 10 20 30 Cross-dimensional40 50 cutoff (&)60(in %) 70 80 90 10 20 30 Cross-dimensional40 30 cutoff (4)60fin %) 70 80 90<br>| Coloured White —___} Coloured | White () Coloured White<br>J. g” ~<br>- J 3 ——<br>aspBe 22~ / &g | TM ~\<br>3. Z 3 :<br>\ = ——, 7 3 . \<br>2 — Rg = oo<br>10 20 30 Cross-dimensional40 50 cutoff (8)60(in %) 70 80 90 10 20 30 Cxoss-dimensional40 30 cutoff (&) (ia60 %) 70 80 90 10 20 30 Cross-dimensional4050 cutoff (&)60{in %) 70 80 90<br>(= —] Indian [[") White (J) Indian [| White () Indian White<br><!-- End of picture text -->
<!-- Start of picture text --> Pem South Africa<br>Diimension. Indicator. Ttem-test Item-rest |Average. Ttem-test Ttem-rmest |,Average<br>correlation correlation tntentern Alpha eormelation correlation mntentera Alpha<br>coVvanance covanance<br>Quality of employment 046 0.28 013 097 0.59 034 0.04 0.73<br>Aecess to water, saniiton eleticity = 9 0.89 0.06 076 0.82 061 002 0.58<br>Social Inchsion “=<br>Level of ecucation 095 0.89 0.06. 076 0.79 058 0.03 0.60<br>Medical attention 095 0.89 0.06. O76 0.72 048 0.03 0.66<br>Test scale 0.08 O87 0.03 0.81<br>Quality of hoveing 096 091 0.05 O71 0.78 0.60 0.04 0.75<br>Posession of assets 043 0.23 ol4 095 0.39 O76 003 0.66<br>Resilience = Public assistance 096 091 0.05 o71 0.69 047 0.05 0.81<br>Capacity for saving O88 0.78 0.07 077 0.79 063 0.04 0.74<br>Test scale 0.08 0.83 0.04 0.80<br>Confidence in government 072 0.38 001 039 0.58 0.19 0.02 0.60<br>mshifuhons<br>Social Cohesion ESP Ssience of discsimintion 056 0.23 002 052 0.53 0.20 002 0.58<br>Pesception of safety 0.66. 054 0.02 043 0.70 045. 001 0.39<br>Victim of crime 062 O31 0.02 046 0.79 O54 001 0.28<br>Test scale 0.02 0.83 0.02 0.85<br>Civil participation 0.83 0.69 0.04 069 0.59 0.30 0.03 O71<br>Satisfaction with democracy O71 0.52 0.05 O77 0.78 054 002 0.55<br>Process Government effectvensss 0.88 O76 O04 0.64 0.64 038 0.05 0.66<br>Satisfaction with the way cornption O72 0.45 005 082 0.83 O64 001 048<br>is comiba ted<br>Test scale 0.05 O79 0.02 0.88<br><!-- End of picture text -->

|||Pem<br>|||South|Africa<br>||
|---|---|---|---|---|---|---|---|
|Di<br>.<br>.<br>imension<br>Indicator<br>c|Ttem-test<br>orrelation|Average<br>Item-rest<br>|<br>.<br> correlation<br>tntentern<br>coVvanance|Alpha|Ttem-test<br>eormelation|Ttem-rmest<br> correlation|Average<br>|,<br>mntentera<br>covanance|Alpha|
|Quality ofemployment<br>|046<br>|0.28<br>013<br><br>|097<br>|0.59<br>|034<br>|0.04<br>|0.73<br>|
|Aecesstowater,saniiton eleticity <br>SocialInchsion“=|=9|0.89<br>0.06|076|0.82|061|002|0.58|
|<br>Levelofecucation<br>|095<br>|0.89<br>0.06.<br><br>|076<br>|0.79<br>|058<br>|0.03<br>|0.60<br>|
|Medicalattention<br>|095|0.89<br>0.06.<br>|O76<br>|0.72|048|0.03<br>|0.66<br>|
|Testscale<br>||0.08<br><br>|O87<br>|||0.03<br>|0.81<br>|
|<br>Qualityof hoveing|096|091<br>0.05|O71|0.78|0.60|0.04|0.75|
|<br>Posessionofassets<br><br>|043<br>|0.23<br>ol4<br><br>|095<br>|0.39<br>|O76<br>|003<br>|0.66<br>|
|<br>Resilience<br>= Public assistance<br>|096<br>|091<br>0.05<br><br>|o71<br>|0.69<br>|047<br>|0.05<br>|0.81<br>|
|Capacity for saving<br>|O88|0.78<br>0.07<br>|077<br>|0.79|063|0.04<br>|0.74<br>|
|Testscale||0.08|0.83|||0.04|0.80|
|Confidence ingovernment<br>mshifuhons|072|0.38<br>001|039|0.58|0.19|0.02|0.60|
|SocialCohesionESPSsienceofdiscsimintion<br>|056<br>|0.23<br>002<br><br>|052<br>|0.53<br>|0.20<br>|002<br>|0.58<br>|
|<br>Pesceptionof safety|0.66.|054<br>0.02|043|0.70|045.|001|0.39|
|Victimofcrime<br>|062|O31<br>0.02<br>|046<br>|0.79|O54|001<br>|0.28<br>|
|Testscale<br>||0.02<br><br>|0.83<br>|||0.02<br>|0.85<br>|
|Civilparticipation|0.83|0.69<br>0.04|069|0.59|0.30|0.03|O71|
|Satisfactionwithdemocracy|O71|0.52<br>0.05|O77|0.78|054|002|0.55|
|Governmenteffectvensss<br>Process|0.88|O76<br>O04|0.64|0.64|038|0.05|0.66|
|<br><br>Satisfactionwiththeway cornption<br>is comiba ted|O72|0.45<br>005|082|0.83|O64|001|048|
|Test scale||0.05|O79|||0.02|0.88|
|Dimension<br>Indicators<br>Coe|°Mt|Pent<br>2<br>P>|z|_<br>[95Conf.|Imewall|Coef, saee|South <br><br>z|Afnca<br>P=|z|<br>[95Con|f, Interval]|
|‘Quality ofemployment<br>017|000|35.70<br>0<br>0.16|018|035<br>000|© 8529|0<br>054|036|
|.<br>___<br>Moonee towater, sanitation deeminty&:<br>Social Inclusion internet<br> <br>|000<br><br>|© 39851<br>0<br>078<br><br><br>|07<br>|90<br>000<br><br>|«160<br>|760<br>059<br><br>|= 0.60<br>|
|Levelofeducation<br>0.76|000|30453<br>0<br>076|077|0.46<br>000|©11811|0<br>045|047|
|<br><br>Medicalateation<br>O71<br><br>|0.00<br>|254.85<br>0<br>070<br><br><br>|o71<br>|058<br>000<br><br>|<br>15197<br>|0<br>057<br><br>|059<br>|
|Qualityofhousing<br>079|000|351.92<br>0<br>0768|079|06<br>000|22052|0<br>068|O70|
|<br><br>Retikence<br>PosMOnofates<br>00<br> <br>|000<br>|-1046<br>0<br>006<br><br><br>|OM<br>|oF<br>000<br><br>|28656<br>|0<br>079)<br><br>|oD<br><br>|
|<br><br>Publicassistance<br>0.86<br><br>|000<br>|4720<br>0<br>0.86<br><br><br>|086<br>|0.39<br>000<br><br>||«(2B<br>|0<br>058<br><br>|8<br>8©60.40<br>|
|<br><br>Capacity for saving<br>0.55<br>Confidenceingovernmentinstimion<br>0.52|000<br>001|141.79<br>0<br>052<br>57.60<br>0<br>050|054<br>054|047<br>000<br>0.66<br>000|<br>11516<br>14047|0<br>04<br>0<br>0.65|60.48<br>0.66|
|.<br>_<br>Expetisare ofdiceimimton<br>019<br> <br>|O01<br>|24.82<br>0<br>020<br><br><br>|07<br>|OL<br>ool<br><br>|2233<br>|0<br>O11<br><br>|0.15<br>|
|SocialCohesionPesveptionofsafety<br>019<br><br>|001<br>|25.38<br>0<br>017<br><br><br>|020<br>|080.35<br>000<br><br>|271.28<br>|0<br>034<br><br>|036<br>|
|Vietim ofcome<br>Oo<br>Crdpacicipation<br>0.56<br> <br>|0.01<br>000<br><br>|19.60<br>0<br>O11<br>11290<br>0<br>055<br><br><br>|014<br>057<br>|016<br>O01<br>0.08<br>O01<br><br>|7854<br>1529<br>|0<br>O15<br>0<br>007<br><br>|017<br>0.09<br>|
|<br><br>Satisfaetionwithdemoceacy<br>042|001<br>|=.B.01<br>0<br>041|045|O74<br>000||21915|0<br>073|075|
|<br><br>Pmeess<br>Goverumenteffectiveness<br>0.70|001|6135.44<br>0<br>0.69|o71|<br>02<br>ooo|<br>8911948|0<br>O51|055|
|<br><br>Legitimacy<br>Equality before thehw(PE) /<br>Satisfietionwiththewayeormption is = 0.27<br>combated|ool|94173<br>0<br>0.26.|028|052<br>000|812568|0<br>051|0.55|
|Numberofobservation:<br>||45881<br>|||28<br>|85<br>||
|Log-Uselhood||-966870.93|||6107|971||
|LRee:tofmodel<br>vs.Satwated||o21073|||5222|046||

<!-- Start of picture text --> Dimension Pent South Afnca<br>Indicators Coe °Mt 2 P>|z|_ [95Conf. Imewall Coef, saee z P= |z| [95 Conf, Interval]<br>‘Quality of employment 017 000 35.70 0 0.16 018 035 000 © 8529 0 054 036<br>Social. Inclusion___ Mooneeinternetto water, sanitation deeminty&: 000 © 39851 0 078 0790 000 «160760 059 = 0.60<br>Level of education 0.76 000 30453 0 076 077 0.46 000 © 11811 0 045 047<br>Medical ateation O71 0.00 254.85 0 070 o71 058 000 15197 0 057 059<br>Quality of housing 079 000 351.92 0 0768 079 06 000 22052 0 068 O70<br>Retikence PosMOn of ates 00 000 -1046 0 006 OM oF 000 28656 0 079) oD<br>Public assistance 0.86 000 4720 0 0.86 086 0.39 000 | «(2B 0 058 8 8©60.40<br>Capacity for saving 0.55 000 141.79 0 052 054 047 00011516 0 0460.48<br>Confidenceingovernmentinstimion 0.52 001 57.60 0 050 054 0.66 000 14047 0 0.65 0.66<br>Social. Cohesion_ PesveptionExpetisareofof safety diceimimton 019019 O01001 24.8225.38 00 020017 07020 OL080.35 ool000 2233271.28 00 O11034 0.15036<br>Vietim of come Oo 0.01 19.60 0 O11 014 016 O01 7854 0 O15 017<br>Crd pacicipation 0.56 000 11290 0 055 057 0.08 O01 1529 0 007 0.09<br>Satisfaetion with democeacy 042 001 =.B.01 0 041 045 O74 000 | 21915 0 073 075<br>Pmeess Goverument effectiveness 0.70 001 6135.44 0 0.69 o71 02 ooo 8911948 0 O51 055<br>Legitimacy Equality before the hw (PE) /<br>Satisfietion withthe way eormption is = 0.27 ool 94173 0 0.26. 028 052 000 812568 0 051 0.55<br>combated<br>Number of observation: 45881 2885<br>Log-Uselhood -966870.93 61074971<br>LRee:t of modelvs. Satwated o2107 3 5222046<br><!-- End of picture text -->
A1.1. FOSD: Counting Vector

# A1.2. Dominance Analysis

<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>A1.3. SOSD: Social Sustainability Index A1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.98*** 0.96***<br>H<br>Kendall tau-b 0.89*** 0.92*** 0.85***<br>Spearman 0.98*** 0.98*** 0.89***<br>A<br>Kendall tau-b 0.92*** 0.91*** 0.81***<br>Spearman 0.99*** 0.98*** 0.96***<br>M0<br>Kendall tau-b 0.94*** 0.92*** 0.85***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>A1.1. FOSD: Counting Vector A1.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>A1.4. Rank correlations<br>1 .08<br>.8 .06<br>.6<br>.04<br>CCDF<br>.4 Difference<br>.2 .02<br>0<br>0<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .08<br>.8 .06<br>CCDF .6 .04<br>.4 Difference .02<br>.2 0<br>0 -.02<br><!-- End of picture text --> A1.3. SOSD: Social Sustainability Index 40
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.93*** 0.92***<br>H<br>Kendall tau-b 0.83*** 0.83*** 0.78***<br>Spearman 1*** 0.87*** 0.88***<br>A<br>Kendall tau-b 1*** 0.72*** 0.7**<br>Spearman 0.97*** 0.98*** 0.92***<br>M0<br>Kendall tau-b 0.89*** 0.94*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>B. Sensitivity Analysis: Changes in weights.<br>B1. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators<br>(25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Inclusion dimension (40% for Social<br>Inclusion, and 20% for the rest of dimensions).<br>Peru<br>B1.1. FOSD: Counting Vector B1.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B1.3. SOSD: Social Sustainability Index B1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.96*** 0.77***<br>H<br>Kendall tau-b 0.79*** 0.84*** 0.57***<br>Spearman 0.95*** 0.88*** 0.65***<br>A<br>Kendall tau-b 0.84*** 0.75*** 0.52***<br>Spearman 0.96*** 0.95*** 0.77***<br>M0<br>Kendall tau-b 0.85*** 0.83*** 0.56***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B1. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Inclusion dimension (40% for Social Inclusion, and 20% for the rest of dimensions)._ South Africa B1.1. FOSD: Counting Vector B1.2. Dominance Analysis 41
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B1.3. SOSD: Social Sustainability Index B1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.95*** 0.93***<br>H<br>Kendall tau-b 0.94*** 0.89*** 0.83***<br>Spearman 0.9*** 0.52 0.98***<br>A<br>Kendall tau-b 0.78*** 0.39 0.94***<br>Spearman 0.93*** 0.97*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B2. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Resilience dimension (40% for Resilience, and 20% for the rest of dimensions)._

# Peru

<!-- Start of picture text --> B2.1. FOSD: Counting Vector B2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.4<br>1<br>.8 .3<br>.6 .2<br>CCDF<br>.4 Difference .1<br>.2 0<br>0<br>-.1<br><!-- End of picture text --> B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations 42
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.89*** 0.91***<br>H<br>Kendall tau-b 0.78*** 0.75*** 0.77***<br>Spearman 0.85*** 0.87*** 0.76***<br>A<br>Kendall tau-b 0.69*** 0.72*** 0.64***<br>Spearman 0.9*** 0.9*** 0.91***<br>M0<br>Kendall tau-b 0.76*** 0.77*** 0.76***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>B2.1. FOSD: Counting Vector B2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B2.3. SOSD: Social Sustainability Index B2.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.9***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.72***<br>Spearman 0.95*** 0.92*** 0.68**<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.56**<br>Spearman 0.93*** 0.93*** 0.9***<br>M0<br>Kendall tau-b 0.83*** 0.83*** 0.72***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>-.1<br>0<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _B3. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Social Cohesion dimension (40% for Social Cohesion, and 20% for the rest of dimensions)._ Peru B3.1. FOSD: Counting Vector B3.2. Dominance Analysis 43
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B3.3. SOSD: Social Sustainability Index B3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.85*** 0.71*** 0.55***<br>H<br>Kendall tau-b 0.67*** 0.54*** 0.45***<br>Spearman 0.65*** 0.62*** 0.72***<br>A<br>Kendall tau-b 0.49*** 0.44*** 0.63***<br>Spearman 0.85*** 0.72*** 0.56***<br>M0<br>Kendall tau-b 0.67*** 0.54*** 0.46***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>B3.1. FOSD: Counting Vector B3.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.4<br>1<br>.8 .3<br>.6<br>.2<br>CCDF<br>.4 Difference<br>.2 .1<br>0 0<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.2<br>1<br>.8 .1<br>.6<br>0<br>CCDF<br>.4 Difference<br>.2 -.1<br>0<br>-.2<br><!-- End of picture text --> B3.3. SOSD: Social Sustainability Index <!-- Start of picture text --> B3.4. Rank correlations<br><!-- End of picture text --> 44
|Second-order stochastic dominance<br>**Social Sustainability Index by cutoff and scenario**<br>**Social gaps**<br>**Metrics**<br>**Rank correlation**<br>**coefficient**|**k = 33%**<br>**k = 50%**<br>**Cutoff value**|**k = 75%**<br>**s**|
|---|---|---|
|.4<br>Spearman<br>**H**|0.85***<br>0.87***|0.77**|
|.3<br>**Index**<br>Kendall tau-b<br>|0.72***<br>0.67**|0.61**|
|**nability**<br>Spearman<br>**A**|0.98***<br>0.85***|0.58*|
|.2<br>**l Sustai**<br>Kendall tau-b<br>|0.94***<br>0.67**|0.5*|
|.1<br>**Socia**<br>Spearman<br>|0.97***<br>0.88***|0.77**|
|0<br>Kendall tau-b<br>**M0**|0.89***<br>0.72***|0.61**|
|0<br>20<br>40<br>60<br>80<br>100<br>**Cutoff**_k_**(relative scale)**<br>Baseline<br>Alternative<br>***p<0.01, **p<0.05, *p<0.1|||
|_B4. Change in the weights assigned to each dimension. Baseline scenario: Ev_<br>_(25% each). Alternative scenario: Skewed weighting scheme in favor of th_<br>_Process Legitimacy, and 20% for the rest of dimensions)._<br>Peru<br>B4.1. FOSD: Counting Vector<br>B4.2. Domi<br>First-order stochastic dominance<br>**Counting vector CCDF by scenario**<br>**Difference between**|_enly distributed weig_<br>_e Process Legitima_<br>nance Analysis<br>(α=0)<br>**counting vector CDF c**|_hts across_<br>_cy dimensi_<br>**urves**|
|.2<br>.4<br>.6<br>.8<br>1<br>**CCDF**<br>-.4<br>-.3<br>-.2<br>-.1<br>0<br>**Difference**|||
|0<br>0<br>.2<br>.4<br>.6<br>.8<br>1<br>**Counting vector (relative scale)**<br>Baseline<br>Alternative<br>-.5<br>0<br>.2<br>.4<br>**Counting**<br>Confidence interval|.6<br>.8<br>**vector (relative scale)**<br>(95 %)<br>Estimated di|1<br>fference|
|B4.3. SOSD: Social Sustainability Index<br>B4.4. Ran<br>Second-order stochastic dominance<br>**Social Sustainability Index by cutoff and scenario**<br>**Social gaps**<br>**Metrics**<br>**Rank correlation**<br>**coefficient**|k correlations<br>**k = 33%**<br>**k = 50%**<br>**Cutoff value**|**k = 75%**<br>**s**|
|.5<br>Spearman<br>|0.24<br>0.25|0.8***|
|.4<br>**Index**<br>Kendall tau-b<br>**H**|0.18<br>0.18|0.63***|
|.3<br>**ability**<br>Spearman<br>|0.35*<br>0.76***|0.7***|
|.2<br>**Sustain**<br>Kendall tau-b<br>**A**|0.23<br>0.57***|0.57***|
|.1<br>**Social**<br>Spearman|0.29<br>0.28|0.8***|
|0<br>Kendall tau-b<br>**M0**|0.19<br>0.21|0.63***|
|0<br>20<br>40<br>60<br>80<br>100<br>**Cutoff**_k_**(relative scale)**<br>Baseline<br>Alternative<br>***p<0.01, **p<0.05, *p<0.1|||

_B4. Change in the weights assigned to each dimension. Baseline scenario: Evenly distributed weights across all indicators (25% each). Alternative scenario: Skewed weighting scheme in favor of the Process Legitimacy dimension (40% for Process Legitimacy, and 20% for the rest of dimensions)._ South Africa B4.1. FOSD: Counting Vector B4.2. Dominance Analysis 45
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>B4.3. SOSD: Social Sustainability Index B4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 1*** 0.98***<br>H<br>Kendall tau-b 0.83*** 1*** 0.94***<br>Spearman 0.93*** 0.97*** 0.65*<br>A<br>Kendall tau-b 0.83*** 0.89*** 0.5*<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8 0<br>.6<br>-.1<br>CCDF<br>.4 Difference<br>.2 -.2<br>0<br>-.3<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text -->

# **C. Sensitivity Analysis: Number of indicators per dimension, 3 instead of 4.**

_C1. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Quality of employment indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C1.1. FOSD: Counting Vector

<!-- Start of picture text --> C1.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.2<br>1<br>.8 .15<br>.6<br>CCDF .1<br>.4 Difference<br>.05<br>.2<br>0<br>0<br><!-- End of picture text --> C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations 46
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 0.96*** 0.95***<br>H<br>Kendall tau-b 0.79*** 0.84*** 0.84***<br>Spearman 0.97*** 0.88*** 0.92***<br>A<br>Kendall tau-b 0.89*** 0.75*** 0.86***<br>Spearman 0.98*** 0.96*** 0.95***<br>M0<br>Kendall tau-b 0.89*** 0.87*** 0.84***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C1.1. FOSD: Counting Vector C1.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C1.3. SOSD: Social Sustainability Index C1.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.98*** 0.92*** 0.92***<br>H<br>Kendall tau-b 0.94*** 0.78*** 0.78***<br>Spearman 0.98*** 0.73** 0.77**<br>A<br>Kendall tau-b 0.94*** 0.56** 0.61**<br>Spearman 0.97*** 0.9*** 0.92***<br>M0<br>Kendall tau-b 0.89*** 0.72*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.3<br>1<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C2. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Access to water, sanitation, electricity & internet indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C2.1. FOSD: Counting Vector C2.2. Dominance Analysis 47
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C2.3. SOSD: Social Sustainability Index C2.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.91*** 0.92*** 0.96***<br>H<br>Kendall tau-b 0.77*** 0.79*** 0.86***<br>Spearman 0.89*** 0.81*** 0.72***<br>A<br>Kendall tau-b 0.77*** 0.59*** 0.6***<br>Spearman 0.95*** 0.92*** 0.96***<br>M0<br>Kendall tau-b 0.85*** 0.79*** 0.84***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C2.1. FOSD: Counting Vector C2.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1<br>0<br>.8<br>-.05<br>.6<br>CCDF -.1<br>.4 Difference<br>.2 -.15<br>0<br>-.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6 -.05<br>CCDF .4 Difference -.1<br>.2 -.15<br>0 -.2<br><!-- End of picture text --> C2.3. SOSD: Social Sustainability Index C2.4. Rank correlations 48
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.93*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.83*** 0.83***<br>Spearman 0.92*** 0.8*** 0.87***<br>A<br>Kendall tau-b 0.78*** 0.67** 0.72***<br>Spearman 0.95*** 0.93*** 0.93***<br>M0<br>Kendall tau-b 0.89*** 0.83*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C3. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Level of education indicator removed (weights within the Social Inclusion<br>dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C3.1. FOSD: Counting Vector C3.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C3.3. SOSD: Social Sustainability Index C3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.95*** 0.88***<br>H<br>Kendall tau-b 0.81*** 0.82*** 0.75***<br>Spearman 0.93*** 0.83*** 0.86***<br>A<br>Kendall tau-b 0.81*** 0.65*** 0.76***<br>Spearman 0.98*** 0.96*** 0.88***<br>M0<br>Kendall tau-b 0.89*** 0.86*** 0.74***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .1<br>.8 .05<br>.6 0<br>CCDF<br>.4 Difference -.05<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C3. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Level of education indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C3.1. FOSD: Counting Vector C3.2. Dominance Analysis 49
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C3.3. SOSD: Social Sustainability Index C3.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.92*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.78*** 0.83***<br>Spearman 1*** 0.95*** 0.98***<br>A<br>Kendall tau-b 1*** 0.89*** 0.94***<br>Spearman 0.95*** 0.95*** 0.93***<br>M0<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C4. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Medical attention indicator removed (weights within the Social Inclusion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._

# Peru

# C4.1. FOSD: Counting Vector

# C4.2. Dominance Analysis

<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>.2 -.05<br>0<br>-.1<br><!-- End of picture text --> C4.3. SOSD: Social Sustainability Index C4.4. Rank correlations 50
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.95*** 0.93***<br>H<br>Kendall tau-b 0.79*** 0.83*** 0.8***<br>Spearman 0.98*** 0.87*** 0.83***<br>A<br>Kendall tau-b 0.91*** 0.71*** 0.72***<br>Spearman 0.96*** 0.98*** 0.94***<br>M0<br>Kendall tau-b 0.84*** 0.89*** 0.81***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C4.1. FOSD: Counting Vector C4.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C4.3. SOSD: Social Sustainability Index C4.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.97*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>Spearman 0.93*** 0.9*** 0.58*<br>A<br>Kendall tau-b 0.83*** 0.83*** 0.56**<br>Spearman 0.93*** 0.98*** 0.93***<br>M0<br>Kendall tau-b 0.83*** 0.94*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference 0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C5. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Quality of housing indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C5.1. FOSD: Counting Vector C5.2. Dominance Analysis 51
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C5.3. SOSD: Social Sustainability Index C5.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.82*** 0.65***<br>H<br>Kendall tau-b 0.81*** 0.63*** 0.52***<br>Spearman 0.77*** 0.89*** 0.71***<br>A<br>Kendall tau-b 0.6*** 0.71*** 0.61***<br>Spearman 0.98*** 0.85*** 0.64***<br>M0<br>Kendall tau-b 0.89*** 0.68*** 0.5***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C5.1. FOSD: Counting Vector C5.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>CCDF 0<br>.4 Difference<br>-.05<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6 -.05<br>CCDF .4 Difference -.1<br>.2 -.15<br>0 -.2<br><!-- End of picture text --> C5.3. SOSD: Social Sustainability Index C5.4. Rank correlations 52
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.92*** 0.95***<br>H<br>Kendall tau-b 0.78*** 0.78*** 0.83***<br>Spearman 0.97*** 0.95*** 0.9***<br>A<br>Kendall tau-b 0.89*** 0.89*** 0.78***<br>Spearman 0.93*** 0.95*** 0.95***<br>M0<br>Kendall tau-b 0.83*** 0.89*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C6. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Possession of assets indicator removed (weights within the Resilience dimension<br>were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C6.1. FOSD: Counting Vector C6.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C6.3. SOSD: Social Sustainability Index C6.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.88*** 0.67***<br>H<br>Kendall tau-b 0.81*** 0.73*** 0.54***<br>Spearman 0.82*** 0.84*** 0.83***<br>A<br>Kendall tau-b 0.65*** 0.68*** 0.76***<br>Spearman 0.97*** 0.88*** 0.66***<br>M0<br>Kendall tau-b 0.87*** 0.73*** 0.53***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8<br>0<br>.6<br>CCDF -.05<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C6. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Possession of assets indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C6.1. FOSD: Counting Vector C6.2. Dominance Analysis 53
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C6.3. SOSD: Social Sustainability Index C6.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.95*** 1***<br>H<br>Kendall tau-b 0.78*** 0.89*** 1***<br>Spearman 0.93*** 0.8*** 0.63*<br>A<br>Kendall tau-b 0.83*** 0.67** 0.5*<br>Spearman 1*** 0.92*** 1***<br>M0<br>Kendall tau-b 1*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.1<br>1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.1<br>.2<br>0 -.2<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C7. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Public assistance indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C7.1. FOSD: Counting Vector

<!-- Start of picture text --> C7.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> C7.3. SOSD: Social Sustainability Index <!-- Start of picture text --> C7.4. Rank correlations<br><!-- End of picture text --> 54
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.89*** 0.92*** 0.78***<br>H<br>Kendall tau-b 0.73*** 0.8*** 0.69***<br>Spearman 0.93*** 0.89*** 0.78***<br>A<br>Kendall tau-b 0.81*** 0.72*** 0.69***<br>Spearman 0.94*** 0.93*** 0.78***<br>M0<br>Kendall tau-b 0.81*** 0.81*** 0.68***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C7.1. FOSD: Counting Vector C7.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C7.3. SOSD: Social Sustainability Index C7.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.72*** 1***<br>Spearman 1*** 0.82*** 0.83***<br>A<br>Kendall tau-b 1*** 0.72*** 0.67**<br>Spearman 0.98*** 0.93*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .3<br>.8 .2<br>.6<br>.1<br>CCDF<br>.4 Difference<br>.2 0<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C8. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Capacity for saving indicator removed (weights within the Resilience dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C8.1. FOSD: Counting Vector C8.2. Dominance Analysis 55
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C8.3. SOSD: Social Sustainability Index C8.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.91*** 0.67***<br>H<br>Kendall tau-b 0.77*** 0.76*** 0.53***<br>Spearman 0.86*** 0.87*** 0.67***<br>A<br>Kendall tau-b 0.72*** 0.71*** 0.55***<br>Spearman 0.94*** 0.91*** 0.67***<br>M0<br>Kendall tau-b 0.81*** 0.76*** 0.53***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C8.1. FOSD: Counting Vector C8.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C8.4. Rank correlations<br>1 .05<br>.8<br>0<br>.6<br>CCDF -.05<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br><!-- End of picture text --> C8.3. SOSD: Social Sustainability Index 56
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.98*** 0.93***<br>H<br>Kendall tau-b 0.89*** 0.94*** 0.78***<br>Spearman 0.97*** 0.8*** 0.45<br>A<br>Kendall tau-b 0.89*** 0.61** 0.33<br>Spearman 0.92*** 0.98*** 0.93***<br>M0<br>Kendall tau-b 0.78*** 0.94*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C9. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Confidence in government institutions indicator removed (weights within the<br>Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C9.1. FOSD: Counting Vector C9.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.96*** 0.96*** 0.78***<br>H<br>Kendall tau-b 0.85*** 0.87*** 0.66***<br>Spearman 0.94*** 0.83*** 0.82***<br>A<br>Kendall tau-b 0.82*** 0.65*** 0.7***<br>Spearman 0.97*** 0.96*** 0.78***<br>M0<br>Kendall tau-b 0.87*** 0.87*** 0.66***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.1<br>1<br>.8<br>.05<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C9. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Confidence in government institutions indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C9.1. FOSD: Counting Vector C9.2. Dominance Analysis 57
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C9.3. SOSD: Social Sustainability Index C9.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 0.9*** 1***<br>H<br>Kendall tau-b 0.89*** 0.78*** 1***<br>Spearman 0.98*** 0.95*** 0.78**<br>A<br>Kendall tau-b 0.94*** 0.89*** 0.67**<br>Spearman 0.98*** 0.92*** 1***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 1***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8 .15<br>CCDF .6 .1<br>.4 Difference .05<br>.2 0<br>0 -.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C10. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Experience of discrimination indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C10.1. FOSD: Counting Vector

<!-- Start of picture text --> C10.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>1 .05<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0<br>-.1<br><!-- End of picture text --> C10.3. SOSD: Social Sustainability Index C10.4. Rank correlations 58
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.96*** 0.96*** 0.95***<br>H<br>Kendall tau-b 0.87*** 0.86*** 0.84***<br>Spearman 0.98*** 0.95*** 0.64***<br>A<br>Kendall tau-b 0.93*** 0.82*** 0.52***<br>Spearman 0.97*** 0.97*** 0.95***<br>M0<br>Kendall tau-b 0.89*** 0.9*** 0.84***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C10.1. FOSD: Counting Vector C10.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C10.3. SOSD: Social Sustainability Index C10.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.97*** 0.95***<br>H<br>Kendall tau-b 0.78*** 0.89*** 0.83***<br>Spearman 0.93*** 0.92*** 0.9***<br>A<br>Kendall tau-b 0.83*** 0.78*** 0.72***<br>Spearman 0.98*** 0.93*** 0.95***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.2<br>1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C11. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Perception of safety indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C11.1. FOSD: Counting Vector C11.2. Dominance Analysis 59
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C11.3. SOSD: Social Sustainability Index C11.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.98*** 0.98***<br>H<br>Kendall tau-b 0.84*** 0.91*** 0.91***<br>Spearman 0.98*** 0.8*** 0.71***<br>A<br>Kendall tau-b 0.92*** 0.6*** 0.59***<br>Spearman 0.97*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.89*** 0.91*** 0.9***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C11.1. FOSD: Counting Vector C11.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C11.3. SOSD: Social Sustainability Index C11.4. Rank correlations<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>.2 -.05<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>.1<br>1<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference -.1<br>.2<br>0 -.2<br><!-- End of picture text --> 60
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 1*** 0.97***<br>H<br>Kendall tau-b 0.89*** 1*** 0.89***<br>Spearman 0.95*** 0.97*** 0.93***<br>A<br>Kendall tau-b 0.89*** 0.89*** 0.83***<br>Spearman 0.98*** 0.98*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>C12. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All<br>indicators included. Alternative scenario: Victim of crime indicator removed (weights within the Social Cohesion<br>dimension were scaled from 6.25% to 8.33% so all weights add up to 1).<br>Peru<br>C12.1. FOSD: Counting Vector C12.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 1*** 0.98***<br>H<br>Kendall tau-b 0.82*** 0.99*** 0.9***<br>Spearman 0.98*** 0.86*** 0.82***<br>A<br>Kendall tau-b 0.9*** 0.67*** 0.7***<br>Spearman 0.97*** 0.99*** 0.97***<br>M0<br>Kendall tau-b 0.89*** 0.96*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6<br>-.05<br>CCDF<br>.4 Difference<br>.2 -.1<br>0 -.15<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C12. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Victim of crime indicator removed (weights within the Social Cohesion dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ South Africa C12.1. FOSD: Counting Vector C12.2. Dominance Analysis 61
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C12.3. SOSD: Social Sustainability Index C12.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.97*** 1*** 0.92***<br>H<br>Kendall tau-b 0.89*** 1*** 0.78***<br>Spearman 0.92*** 0.87*** 0.88***<br>A<br>Kendall tau-b 0.83*** 0.78*** 0.72***<br>Spearman 0.98*** 1*** 0.93***<br>M0<br>Kendall tau-b 0.94*** 1*** 0.83***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.2<br>1<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0 -.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C13. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Civil participation indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru

# C13.1. FOSD: Counting Vector

<!-- Start of picture text --> C13.2. Dominance Analysis<br><!-- End of picture text --> <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>.1<br>1<br>.8 .05<br>.6<br>0<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0 -.1<br><!-- End of picture text --> C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations 62
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.94*** 0.92*** 0.68***<br>H<br>Kendall tau-b 0.81*** 0.78*** 0.53***<br>Spearman 0.92*** 0.78*** 0.74***<br>A<br>Kendall tau-b 0.81*** 0.63*** 0.64***<br>Spearman 0.93*** 0.92*** 0.68***<br>M0<br>Kendall tau-b 0.79*** 0.77*** 0.54***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C13.1. FOSD: Counting Vector C13.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C13.3. SOSD: Social Sustainability Index C13.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.92*** 0.98*** 0.93***<br>H<br>Kendall tau-b 0.78*** 0.94*** 0.83***<br>Spearman 0.95*** 0.9*** 0.93***<br>A<br>Kendall tau-b 0.89*** 0.78*** 0.83***<br>Spearman 0.98*** 0.98*** 0.98***<br>M0<br>Kendall tau-b 0.94*** 0.94*** 0.94***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .05<br>.8 0<br>.6 -.05<br>CCDF .4 Difference -.1<br>.2 -.15<br>0 -.2<br>.5<br>.4<br>.3<br>.2<br>Social Sustainability Index .1<br>0<br><!-- End of picture text --> _C14. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Satisfaction with democracy indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ Peru C14.1. FOSD: Counting Vector C14.2. Dominance Analysis 63
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C14.3. SOSD: Social Sustainability Index C14.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.87*** 0.82***<br>H<br>Kendall tau-b 0.84*** 0.71*** 0.64***<br>Spearman 0.9*** 0.86*** 0.8***<br>A<br>Kendall tau-b 0.77*** 0.71*** 0.71***<br>Spearman 0.95*** 0.87*** 0.81***<br>M0<br>Kendall tau-b 0.84*** 0.73*** 0.63***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C14.1. FOSD: Counting Vector C14.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C14.4. Rank correlations<br>1 .05<br>.8<br>0<br>.6<br>CCDF<br>.4 Difference<br>-.05<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1 .2<br>.8<br>.1<br>.6<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br><!-- End of picture text --> C14.3. SOSD: Social Sustainability Index 64
||**Social Sustainability Index by cutoff and scenario**|**Social gaps**|<br>**Rank correlation**|**C**|**utoff value**|**s**|
|---|---|---|---|---|---|---|
||Second-order stochastic dominance<br>|**Metrics**|**coefficient**|**k = 33%**|**k = 50%**|**k = 75%**|
|.3<br>.4<br>**Index**||**H**|Spearman<br>Kendall tau-b|0.92***<br>0.78***|0.95***<br>0.89***|0.98***<br>0.94***|
|.2<br>**al Sustainability**||**A**|Spearman<br>Kendall tau-b|0.98***<br>0.94***|0.93***<br>0.83***|0.78**<br>0.67**|
|0<br>.1<br>**Soci**||**M0**|Spearman<br>Kendall tau-b|0.93***<br>0.83***|0.93***<br>0.83***|0.98***<br>0.94***|
|0|20<br>40<br>60<br>80<br>100<br>**Cutoff**_k_**(relative scale)**<br>Baseline<br>Alternative|***p<0.01, *|*p<0.05, *p<0.1||||

_C15. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Government effectiveness indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._

# Peru

# C15.1. FOSD: Counting Vector

# C15.2. Dominance Analysis

<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8<br>Counting vector (relative scale) 0 .2 .4 .6 .8 1<br>Baseline Alternative Counting vector (relative scale)<br>Confidence interval (95 %) Estimated difference<br>C15.3. SOSD: Social Sustainability Index C15.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.95*** 0.82*** 0.92***<br>H<br>Kendall tau-b 0.83*** 0.65*** 0.77***<br>Spearman 0.91*** 0.8*** 0.93***<br>A<br>Kendall tau-b 0.78*** 0.64*** 0.87***<br>Spearman 0.96*** 0.85*** 0.93***<br>M0<br>Kendall tau-b 0.86*** 0.71*** 0.78***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1 .1<br>.8<br>.6 .05<br>CCDF<br>.4<br>Difference<br>0<br>.2<br>0<br>-.05<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> C15.1. FOSD: Counting Vector South Africa C15.2. Dominance Analysis 65
<!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C15.3. SOSD: Social Sustainability Index C15.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.93*** 0.92*** 0.97***<br>H<br>Kendall tau-b 0.83*** 0.78*** 0.89***<br>Spearman 0.92*** 0.85*** 0.78**<br>A<br>Kendall tau-b 0.78*** 0.72*** 0.67**<br>Spearman 0.98*** 0.92*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.78*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> _C16. Change in the indicators included for each dimension, one indicator removed at a time. Baseline scenario: All indicators included. Alternative scenario: Equality before the law (Peru)/Satisfaction with the way corruption is combatted (South Africa) indicator removed (weights within the Process Legitimacy dimension were scaled from 6.25% to 8.33% so all weights add up to 1)._ <!-- Start of picture text --> Peru<br><!-- End of picture text --> A1.1. FOSD: Counting Vector A1.2. Dominance Analysis <!-- Start of picture text --> Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>A1.3. SOSD: Social Sustainability Index A1.4. Rank correlations<br>1 .15<br>.8 .1<br>.6<br>.05<br>CCDF<br>.4 Difference<br>.2 0<br>0 -.05<br><!-- End of picture text --> 66
<!-- Start of picture text --> Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 0.96*** 0.77*** 0.9***<br>H<br>Kendall tau-b 0.85*** 0.59*** 0.75***<br>Spearman 0.88*** 0.86*** 0.88***<br>A<br>Kendall tau-b 0.73*** 0.71*** 0.8***<br>Spearman 0.95*** 0.79*** 0.89***<br>M0<br>Kendall tau-b 0.84*** 0.61*** 0.73***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>South Africa<br>C16.1. FOSD: Counting Vector C16.2. Dominance Analysis<br>Counting vector CCDF by scenario Difference between counting vector CDF curves<br>First-order stochastic dominance (α=0)<br>0 .2 .4 .6 .8 1 0 .2 .4 .6 .8 1<br>Counting vector (relative scale) Counting vector (relative scale)<br>Baseline Alternative Confidence interval (95 %) Estimated difference<br>C16.3. SOSD: Social Sustainability Index C16.4. Rank correlations<br>Social gaps Rank correlation Cutoff values<br>Social Sustainability Index by cutoff and scenario<br>Second-order stochastic dominance Metrics coefficient k = 33% k = 50% k = 75%<br>Spearman 1*** 0.9*** 0.98***<br>H<br>Kendall tau-b 1*** 0.72*** 0.94***<br>Spearman 1*** 0.87*** 0.9***<br>A<br>Kendall tau-b 1*** 0.72*** 0.78***<br>Spearman 0.98*** 0.93*** 0.97***<br>M0<br>Kendall tau-b 0.94*** 0.83*** 0.89***<br>0 20 40 60 80 100<br> Cutoff k (relative scale) ***p<0.01, **p<0.05, *p<0.1<br>Baseline Alternative<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br>1<br>.2<br>.8<br>.6 .1<br>CCDF<br>.4 Difference<br>0<br>.2<br>0<br>-.1<br>.4<br>.3<br>.2<br>.1<br>Social Sustainability Index<br>0<br><!-- End of picture text --> 67