Slum infrastructure: Quantitative measures and scenarios for universal access to basic services in 2030
Introduction
During the 20th Century, urban areas became global platforms of change in production, trade, and social interaction. Urbanization became a pathway out of poverty through increased productivity, employment, and quality of life for urban dwellers. However, industrialization in the Global South also led to rapid urban growth, which generated cascading effects that fueled urban poverty bubbles. Rapid and unplanned urban growth outpaced the ability of city authorities to plan and provide affordable housing for low-income sections of the population, driving them to settle in slums (Ooi & Phua, 2007). Thus, although cities can be a catalyst for socioeconomic change, they still face persistent issues, such as the challenge of providing universal and adequate shelter and access to basic services (UN-Habitat, 2016).
In 2018, 23.5% of the world population lived in slums, representing an estimated total of more than billion slum dwellers (United Nations, 2019). They represent an assortment of vulnerable communities that fall within the United National Human Settlements Programme (UN-Habitat) classification that defines slums as a group of households, in which the dwellers live with one or more of the following ‘household deprivations’: (i) lack of access to improved water source; (ii) lack of access to improved sanitation facilities; (iii) lack of sufficient living area; (iv) lack of housing durability; and (v) lack of security of tenure (PSUP, 2016).
“Although the proportion of the urban population living in slums has shown a decreasing trend, the absolute number of slum dwellers is increasing (UN-Habitat, 2016). In the next decades, the highest population growth is projected to occur in less developed regions (i.e., East Asia, South Asia, and sub-Saharan Africa), which also have the largest slum populations. By 2030, a total of 3 billion people are estimated to lack adequate and affordable housing (United Nations, 2019). Consequently, the widespread growth of slums has become a core policy issue, as reflected in the UN Sustainable Development Goals (SDGs). In particular SDG 11 seeks to ensure access for all urban residents to adequate, safe, and affordable housing, and basic urban services by 2030 (United Nations, 2018).
Although there are common characteristics shared across all slums, they also represent a set of communities that are as diverse as the terminology used to describe them: favelas in Brazil, barrios urbanos marginales in Peru, slums in India, and informal settlements in South Africa (see Supplemental Material - SM, Section A.1). Whereas effective policy requires attention to the individual conditions/challenges of different slums (Krishna et al., 2014), there are crucial gaps in our understanding, especially concerning the variation in poverty and living conditions across these communities (Gulyani et al., 2014; Wang et al., 2019). Slums are often described with a focus on data gaps and uncertainty, so their governing is tightly related to the challenge of governing uncertainty (Kovacic, 2018). The general term “slum” thus does not capture the heterogeneity of these settlements, creating a need to define and monitor clear quantitative indicators on their infrastructure and living conditions. These indicators could promote more informed analysis, facilitate governing uncertainty, and guide policies and upgrading projects.
Slums in urban areas are usually characterized based on administrative definitions or income-based indicators, and the most traditional official data collection methods are census-based, failing to provide detailed spatial information (Kohli et al., 2012). Existing studies on slums tend to focus either on slum characterization (Kohli et al., 2012; Marques & Saraiva, 2017), or slum upgrading/intervention assessment (Bardhan et al., 2018; Degert et al., 2016; El Menshawy et al., 2011; Meredith & MacDonald, 2017). However, more quantitative and comparative research is needed to better understand what determines settlement conditions and to promote more efficient strategies to improve the lives of all urban residents (Gulyani et al., 2014).
The focus of this paper is to analyse which quantitative indicators are usually used to assess slum infrastructure. Here we interpret slum infrastructure as the physical and organizational structures and facilities needed for the basic living conditions of slum communities. Given the diversity of communities classified as slums, and ambiguity of the term (as discussed in SM Section A.1), it is challenging to use slum population as an urban metric for comparative studies (Amit Patel et al., 2020). Thus, well defined quantitative indicators on slum infrastructure, with less subjective metrics that are easier to compare, can facilitate the monitoring of living conditions in informal settlements. Quantitative indicators can also be used in multi-dimensional analyses, which provide a holistic view of the challenges that slum communities face, identifying hotspots for policy action (Amit Patel et al., 2014; Baud et al., 2010; Roy et al., 2020; Weeks et al., 2007).
Characterization of living conditions in slums goes beyond physical infrastructure and some dimensions cannot be easily quantified. Intangible assets, such as social capital and household relations (Moser, 1998), well-being (Biswas-Diener & Diener, 2001), and community engagement (Kumaran et al., 2015; Saigal, 2008) are among these. These aspects are usually analyzed using qualitative indicators derived from field surveys and interviews. Qualitative indicators can be more subjective and case-specific, but also provide valuable insights to the slum characterization discussion. They are also an alternative for studies in data deprived environments (Matous & Ozawa, 2010).
This paper builds on prior work that reviewed aspects of existing urban slum infrastructure indicators. Butera et al. (2016) reviewed urban development and energy access in informal settlements for countries in Latin America and Africa, and Martínez et al. (2008) reviewed trends in urban and slum indicators across developing world cities. The studies revealed that data on slum infrastructure, in particular on energy consumption and energy efficiency, are usually missing or out-dated, but that there has been a general improvement in various slum indicators in the past decades, such as in durable structures and access to basic services. However, to our knowledge, no recent research has systematically reviewed what has been quantitatively studied in the literature regarding slum infrastructure across the globe. Moreover, the challenges of providing marginalized communities with universal access to basic services are often discussed qualitatively, but seldom scoped quantitatively.
This paper aims to answer the following research questions:
- (i)
Overall, which quantitative indicators have been the focus of existing studies in the broad literature on slum infrastructure?
- (ii)
How can these indicators be used to increase understanding of infrastructure gaps in slum settlements? What are their limitations?
- (iii)
How can these indicators assist in evaluating the challenges of achieving universal access to basic services (as per SDG 11)?
Thus, to present policy-makers with data to facilitate more efficient strategies to improve the lives of marginalized urban residents, this paper aims to provide quantitative information and insights on slum infrastructure based on a review of quantitative indicators, along with estimating the scope of the challenges of providing underserved communities with universal access to basic services by 2030.
First, we develop a systematic literature review of comparative quantitative indicators on global slums. Then, based on the review findings, we determine the baseline for access to urban services in six large Global South cities (e.g. Sao Paulo, Rio, Lima, Johannesburg, Mumbai, and Hyderabad). Comparing the baseline per capita and percentage indicators, we analyse the relationship between access to urban services and inequality among distribution of basic resources for residents. The results for each city are plotted using inequality webs that demonstrate differentials between slum and non-slum populations. We then use the baselines to project four future scenarios of access/use of urban services (i.e. waste collection, treated water, and electricity) for 2030, focusing on future deficits and potential pathways to universal provision.
Section snippets
Indicators occurrence
To assess the quantitative literature on slum urban infrastructure, we review 122 peer-reviewed and grey literature studies published from 1998 to 2018, and written in English, French, Portuguese, and Spanish, which analyzed quantitative indicators on informal settlements. First, we search the Web of Science using the keywords: “slum”/”favela”/”informal settlement”/”barrios” + “urban” + “infrastructure”. Slum infrastructure here is seen as organizational structures and facilities needed for the
Indicators occurrence
The main characteristics of the reviewed literature are summarized in Fig. 1 (SM, Table A.1) provides more details on the studies). There has been a significant increase in publications on slum infrastructure in the past 20 years (Fig. 1a), which might indicate and/or be a consequence of an increase in data availability and awareness of the importance of these settlements to urban sustainability. SM, Section A.3, presents a comparison between the increase in slum-specific literature and the
Conclusions
Addressing the “slum challenge” is essential for urban sustainability and the capacity of cities to provide a minimum quality of life to their residents. To addresses this challenge, this paper reviews how slums are currently characterized by quantitative studies and scopes out the challenge of providing them with universal basic services by 2030. The paper answers the following questions:
- (i)
Overall, which quantitative indicators have been the focus of existing studies in the broad literature on
Data availability
All data, models, and code generated or used during the study appear in the submitted article or the SM.
CRediT authorship contribution statement
Tatiana C.G. Trindade: Conceptualization, Methodology, Investigation, Formal analysis, Writing - original draft. Heather L. MacLean: Conceptualization, Writing - review & editing, Supervision. I. Daniel Posen: Conceptualization, Writing - review & editing, Supervision.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
This research was supported by funds from the Natural Sciences and Engineering Research Council, the University of Toronto, and the Brikh Bhan Goyal Award.
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