Colonial origins and child poverty inequalities in Sub-Saharan Africa

Colonial origins and child poverty inequalities in Sub-Saharan Africa | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Colonial origins and child poverty inequalities in Sub-Saharan Africa Dr Cynthia Lum Fonta, Professor David Gordon, Dr Zoi Toumpakari This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8609099/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Persistent underdevelopment and widening inequalities in basic material needs in Sub-Saharan Africa (SSA) may reflect the administrative legacies of British and French colonial rule. This study examines within-group child poverty inequalities across Francophone and Anglophone countries using pooled Demographic and Health Survey (DHS) data from 2000–2019 and deprivation indicators developed by Gordon et al. (2003). Geographical and socioeconomic inequalities were assessed by colonial origin, alongside regression models adjusting for urbanisation and governance quality. Francophone countries showed wider socioeconomic inequalities in limited medical access and formal education than Anglophone countries, although disparities in incomplete primary schooling were narrower. Higher urbanisation correlated with greater inequality in incomplete schooling and child undernutrition. s. Across SSA, pronounced regional inequalities persisted in water, sanitation, and housing, irrespective of colonial origin, while health and nutrition showed comparatively smaller regional disparities. Policy priorities should combine universal education and healthcare reforms with sustained investment in basic household infrastructure. Figures Figure 1 Figure 2 Figure 3 Background As researchers and policymakers grapple with developing effective poverty alleviation strategies, it is essential to examine the structural environment that persistently constrains the poor’s access to an adequate standard of living (Barrett et al. 2006). This structural environment includes institutional frameworks that perpetuate marginalisation and inequality across segments of the population. A growing body of scholarship explores the profound influence of colonial legacies on the development trajectories of African nations, highlighting multiple mechanisms by which these historical effects endure. Acemoglu et al. ( 2001 ) argue that the type of institutions established during colonial times significantly affects current development outcomes. For example, in regions such as the Americas, Australia, and New Zealand, Europeans established inclusive institutions grounded in the rule of law, which promoted broad-based development. In contrast, many African colonies inherited extractive institutions designed to exploit resources rather than foster inclusive growth. Milanovic et al. (2011) argues that the extraction ratio, the share of potential resources elites are able to extract, was substantially higher in the pre-industrial period due to concentrated power and highly extractive institutions. They suggest economic growth can lower extraction ratios even as Gini-measured inequality rises, whereas in low-growth contexts, extraction pressures remain high. Nunn ( 2007 ) similarly attributes persistent underdevelopment in Africa to its historical legacy. He identifies three phases in this process: first, pre-colonial Africa was characterised by relatively secure property rights and productive economic systems; second, the slave trade and colonial rule intensified extractive tendencies, undermining productivity; and third, post-independence periods saw a continuation of these extractive institutions, further entrenching poverty and inequality. Njoh ( 2015 ) adds that, while more extended periods of colonial rule and greater colonial investment were associated with lower slum incidence in some African countries, these patterns showed no clear association with specific colonial strategies. Milanovic ( 2018 ) found that resource-rich colonies were more urbanised and extractive, resulting in higher inequality rates than less extractive colonies. Contrary to some prevailing views, researchers like Gilley ( 2018 ) argue that Africa's development is influenced less by the legacies of the slave trade and colonial extractive institutions, and more by how post-colonial governments chose to respond to and manage these legacies. One study found that high malaria incidence in African countries, irrespective of colonial ruling styles, showed a strong correlation with lower levels of development (Bhattacharyya 2009 ). The author noted that malaria endemicity leads to high morbidity and mortality rates, which ultimately reduce productivity over time. However, this study relied solely on GDP as a measure of a country's development, which is problematic as it does not account for wealth distribution or other aspects of human capital development. Development extends beyond economic growth to include access to essential social services (Stiglitz et al. 2009; Gordon 2015 ; Waddington 2020 ). This study, therefore, examines how colonial origins influence key SDG indicators: water, sanitation, housing, information, health, education, and nutrition, hypothesising that differences in material deprivation across SSA may reflect varying inherent colonial governance styles and institutions. Miles ( 2015 ) argues that British and French colonialism reshaped social, economic, and political structures in ways that continue to influence inequalities in SSA. British rule, based on indirect rule, granted local authorities executive and judicial powers, laying the foundation for decentralisation (Lange 2004 ). This approach yielded several advantages, including stronger education systems (Grier 1999 ), judicial systems (Joireman 2001 ), and fiscal transparency (Sarr 2016 ), yet it also deepened ethnic divisions and political instability in several British ex-colonies (Richens 2009 ). French colonialism, by contrast, imposed highly centralised administrative systems with politically motivated decentralisation that concentrates decision-making power and resources, often reinforcing regional inequality (Pinto 2004 ). Their former colonies are inherently connected to the French trade, military, political, and legal systems, fostering dependence and persistent underdevelopment (Yates 2018 ). Furthermore, Tusalem ( 2016 ) found that prolonged British and Spanish rule was associated with a lower likelihood of state failure than French and Portuguese rule. Against this backdrop, this study disaggregates child poverty indicators across administrative and socioeconomic groups to examine how past colonial rule may have influenced the unequal distribution of present-day material need. Research indicates that various factors, including marginalisation, influence inequality in SSA (Dupraz 2019 ), conflict (Howell et al. 2020 ), rapid urbanisation (Milanovic 2018 ), and poor governance (Halleröd et al. 2013 ). This study will control for these factors. The wealthiest 10% in SSA account for approximately 55% of national income and 70% of the wealth while the poorest half of the population have 10% of the national income and 1% of the wealth (Chancel et al. 2026 ). South Africa, Namibia, and Mozambique are among the most unequal countries in the region. Although economic growth policies promoted by international financial institutions have helped reduce poverty in some contexts, the benefits have often not reached substantial segments of the population (Chancel et al. 2019 ). Growth alone has rarely led to structural transformation. The relationship between poverty and inequality varies across contexts. Fosu ( 2015 ) warns that in settings with widespread poverty and low average incomes, reducing inequality can paradoxically increase the low income poverty rate, highlighting the need for context-specific policy approaches. Our research thus focuses on within-country inequalities, since within-group differences account for approximately 70% of inequality patterns in Africa (David et al. 2025 ). This heterogeneity is evident in progress toward the SDGs. For example, Hasan and Alam ( 2020 ) found that wealth inequalities showed the widest variation in access to improved water services in low- and middle-income countries than regional inequalities. The widest gaps were in Congo DRC, Madagascar and Mozambique, with more than 0.58 proportional variability between the rich and the poor. JMP ( 2025 ) estimates indicate that, in 2024, 52% of urban residents in SSA had access to safely managed water services, whereas only about 16% in rural areas did. Only a 6% improvement in urban access to safely managed water services was observed between 2015 and 2024. Nutritional inequalities persist, with extremely high stunting rates in Burundi, Niger, Eritrea, and the DRC, compared with relatively lower rates in countries such as Ghana and Kenya (SDG 2023 ). The most significant inequalities existed in wealth, followed by education and location (Global Nutrition Report 2020 ). Nigeria had the largest wealth inequality gap in stunting, with estimates of 63% for the poorest quintile and 18% for the richest quintile. Addressing inequality requires the need for strong social protection systems and pro-poor growth strategies, including subsidised essential services (Paul et al. 2018 ), effective inflation control (Kingsford and Richmond Silvanus 2024 ) and sustained agricultural development (Pasha and Palanivel 2004 ). These studies highlight the central role of governance quality and robust safety nets in promoting inclusive growth. This is the reason for adding explanatory governance indicators in this analysis. Despite these insights, limited attention has been paid to how historical institutional contexts, particularly colonial legacies, continue to shape patterns of inequality within SSA countries. This study addresses this gap by examining regional and socioeconomic disparities in material deprivation across countries with different colonial origins, assessing whether these inequalities are wider in Francophone or Anglophone ex-colonies. By controlling for key structural and contextual factors, it investigates whether and how historic colonial administrative structures and rules contribute to persistent within-country disparities. This approach is novel in that it explicitly situates inequalities in access to material needs within a colonial framework, an area that remains underexplored in existing SSA scholarship. Educational inequalities are explored not only in attainment but also across the entire attainment spectrum, including primary school completion rates. Methods Data source and study design Secondary data from the DHS were utilised for each country between 2000 and 2019, depending on data availability. These surveys are typically conducted on a representative sample of households and individuals within each country to enable valid inferences about the entire population (ICF 2020). The number of survey rounds per country, the survey years, and the sample sizes for each survey are set out below in Table 1 . All countries included in the study participated in at least two DHS rounds, with some participating in three. A smaller number of countries had data from four survey rounds. Data from the fourth survey were excluded from the analysis to prevent oversampling and to ensure even representation across countries. This decision helped maintain consistency in the dataset and avoided potential bias in trend assessments and cross-country comparisons. Table 1 shows the post-stratified weighted population sizes for Francophone states, ranging from the smallest, Gabon, to the most populated, Benin. For Anglophone states, the list includes Namibia, the least populous, and Nigeria, the most populous. The pooled DHS data encompass 1,845,638 children aged 0 to 17, including 1,064,566 (58%) Anglophone and 781,072 (42%) Francophone children. The surveys were then regrouped into four rounds to facilitate the applications of PSPWs; that is, 2000–2004 (represented by 2002), 2005–2009 (represented by 2007), 2010–2014 (represented by 2012), and 2015–2019 (represented by 2017). These are shown in Appendix 1which also includes the DHS and UN population estimates and proportion of children aged under 18 (U18) for each survey round. This study covers 43% of individuals under 18 years old in Francophone Africa, compared with only 17% in Anglophone countries. Overall, the DHS sample used in this study represents 60% of the UN U18 population across Francophone and Anglophone regions. Finally, the syvset command was utilised with the applied PSPWs. This command (recommended by the DHS) applies the specified weights to the survey data for subsequent analysis. It adjusts for the disproportionate sampling (design effects) to provide nationally representative data and enables complex survey data analysis, including estimations of deprivation and poverty levels. Table 1 Child poverty measurement This study assesses child poverty using the UNICEF-based ‘Bristol Approach’ or ‘Rights-based Approach’ to poverty, developed by Gordon and colleagues (Gordon et al. 2003 ). The authors utilised internationally recognised indicators from the United Nations Convention on the Rights of the Child (UN CRC) and the first World Social Summit in Copenhagen (United Nations 1995 ). The Bristol Approach categorised children by degree of deprivation of material needs, ranging from not deprived to mild, moderate, severe, and extreme. Children were in multidimensional poverty if they experienced severe deprivation in two or more dimensions of material needs. Table 2 presents the indicators used in this thesis, building on the Bristol Approach (Gordon et al, 2003 b )for assessing poverty. Although the Bristol Approach was introduced over twenty years ago, its indicators remain highly relevant as they are monitored globally and prioritised within the SDGs. However, modifications were made to align the indicators with the modern SDG framework, ensuring they reflect current global priorities and considering data availability and response rates in the DHS dataset used for this analysis. Unlike the original Gordon et al. ( 2003 b) methodology, this revised version includes children experiencing moderate and severe deprivation as poor, asserting that even moderate deprivations jeopardise children's health. In this study, child poverty was defined as those with moderate and severe deprivations, coded as ‘1’, and otherwise as ‘0’. The data was linearised and weights applied to all tabulations and mean child poverty estimations to account for the complex sample design and the disproportionate sample sizes across countries. Table 2 thus presents child poverty measurement for all dimensions and indicators. The data was disaggregated into household dimensions (water, sanitation, dwelling, and information) and child-specific indicators (anthropometric failure, minimum dietary diversity, medical access, vaccine uptake and education access). Table 2 Statistical analysis All analyses were performed using Stata version 18 and the World Health Organization health equity toolkit software (HEAT plus) (WHO 2021). Data were weighted to account for the complex DHS clustering and stratification sampling design (Hosseinpoor et al. 2018 ). Geographical inequalities in child poverty were assessed by disaggregating estimates into subnational and administrative regions. Child poverty prevalence was calculated for each regional group, and summary datasets were prepared in Excel and uploaded into HEAT Plus for validation and inequality analysis. Absolute geographical inequality was quantified using the Difference (D) summary measure, defined as the difference in child poverty prevalence between subnational and administrative regions: . $$\:Difference\:\left(D\right)={Y}_{Subnational\:regions}-{Y}_{Administrative\:regions}$$ 1 ……. Values ranged from 0 to 100. Estimates close to 100 indicated high levels of inequality, and near 0 implied narrow disparities. Socioeconomic inequalities were assessed using the Relative Index of Inequality (RII), a relative measure and the Slope Index of Inequality (SII), an absolute measure. Children were ranked into wealth quintiles, from the least advantaged (rank 1) to the most advantaged (rank 5). Each quintile was weighted by its population share, and the midpoint of the cumulative population distribution was used in the analysis. HEAT Plus estimates RII using a generalised linear model with a logit link, regressing the child poverty indicator on the ranked socioeconomic position. The RII was calculated as the ratio of the predicted child poverty prevalence among the most advantaged group (V₁) to that among the least advantaged group (V₀): $$\:RII=\raisebox{1ex}{${V}_{1}$}\!\left/\:\!\raisebox{-1ex}{${V}_{0}$}\right.$$ An RII value of 100 indicates no socioeconomic inequality; values below 100 indicate greater child poverty concentration among the disadvantaged, whereas values above 100 indicate greater concentration among the advantaged. Estimates closer to zero indicate greater socioeconomic inequalities in child poverty between children from rich and poor households. The Slope Index of Inequality (SII) measures absolute socioeconomic inequality and represents the difference in predicted child poverty prevalence between the most advantaged and least advantaged groups: $$\:SII={V}_{1}-{V}_{0}$$ Values close to zero indicate narrow socioeconomic inequalities, while larger absolute values indicate wider disparities. Negative SII values indicate a concentration of child poverty among disadvantaged children. The RII and SII of the respective child poverty-specific indicators on colonial origin were regressed to assess if there were significant differences in inequality across colonial backgrounds, while controlling for urbanisation and governance indicators. The dependent variable was each indicator's inequality estimate, and the independent variables included colonial origin, urbanisation, and the governance indicator. Urbanisation is defined here as the proportion of a country’s total population living in urban areas (UN Population Division 2014 ). The World Governance Indicators rank countries worldwide by percentiles based on their ability to control corruption, achieve political stability, prevent violence, and ensure government efficiency (World Bank 2025 ). Higher percentiles indicate better performance, whereas lower percentiles indicate poorer outcomes. These indicators were compiled to correspond with the years during which the DHS surveys were conducted. The corruption control variable was removed because its variance inflation factor (VIF) exceeded 5, showing significant multicollinearity. Results Table 3 summarises the unweighted study sample characteristics. The sample comprised children aged 0–17 years, of whom 31% were under five years of age. The gender distribution was balanced, with boys and girls each accounting for 50% of the sample. Approximately 70% of children resided in rural areas, about 12% in administrative capitals, and the remainder in other peripheral cities. Most children lived in male-headed households (78%), and just over half of household heads were younger than 45 years (54%). Literacy among mothers' children under five remains a concern: 46% of mothers had no formal education, while only 3% had attained tertiary education. Household wealth was almost evenly distributed across quintiles, with 24% of children in the poorest quintile and 17% in the wealthiest quintile. Table 3 Figure 1 presents post-ANOVA t -test mean estimates, illustrating the direction and magnitude of mean differences in child poverty indicators by colonial origin. Across both colonial settings, child poverty was most prevalent in the sanitation (M = 85%, SD = 10) and dwelling dimensions (M = 75%, SD = 9) and in the indicator of low minimum dietary diversity (MDD) (M = 64%, SD = 8%). However, these differences were not statistically significant. By contrast, low medical access was significantly more prevalent in Francophone countries (M = 67%, SD = 8%) than in Anglophone countries (M = 48%, SD = 7%), p < 0.001. Francophone countries also exhibited, on average, significantly higher rates of low vaccine uptake M = 41%, SD = 18%) compared with Anglophone countries (M = 25%, SD = 15%), p < 0.037, as well as higher levels of children lacking formal schooling (M = 29%, SD = 14%) than in Anglophone countries (M = 8%, SD = 8%), p < 0.01. All remaining dimensions and indicators of child poverty did not differ significantly by colonial origin. Figure 1 Figures 2 and 3 present weighted intraregional inequality gaps for household-related dimensions and child-specific indicators, disaggregated by countries. All estimates were obtained from the WHO 2023 health equity assessment toolkit outputs. The blue colours represent Francophone children, and the orange colours represent Anglophone children. Figure 2 , Graph A, illustrates intraregional inequalities in water poverty across African countries, measured by the average difference (D) in child poverty means between subnational and administrative regions. The widest disparities in water poverty were observed in Francophone Niger (D = 52%), Chad (D = 50%), and Anglophone Uganda and Tanzania (D = 48%). In contrast, the smallest regional water poverty gaps (D = 20% or less) were observed in Francophone Cameroon, Benin, and Anglophone Malawi, Nigeria, and the Gambia. Both Francophone and Anglophone countries exhibit similar patterns of intraregional inequality in water poverty. Graph B highlights Namibia (D = 58%), Zimbabwe (D = 51%), and Gambia (D = 43%) as the most unequal Anglophone states in sanitation poverty, while Burkina Faso (D = 54%) and Senegal (D = 40%) represent the most unequal Francophone states. These countries with the widest regional sanitation inequality gaps (Gambia, Namibia, and Zimbabwe) have achieved significant reductions in sanitation poverty within their capital cities compared to peripheral regions. Notably, nearly all countries reported sanitation poverty rates above 70%, even in countries like Benin and Malawi, where inequality gaps were narrow. Graph C reveals comparable colonial-era patterns in intraregional gaps in the prevalence of dwelling poverty across Francophone and Anglophone countries. The widest disparities were found in Francophone Guinea (D = 59%) and Mali (D = 51%), as well as in Anglophone Namibia (D = 49%), Zimbabwe (D = 48%), Tanzania (D = 47%), and Sierra Leone (D = 41%). Note that Namibia and Zimbabwe exhibit the widest regional inequality gaps in both sanitation and dwelling poverty. The least unequal countries in dwelling poverty were Anglophone Malawi (D = 3%) and Lesotho (D = 11%), and, Francophone Benin (D = 3%). No significant colonial differences were observed in intraregional inequalities for dwelling poverty. Turning to information poverty, Graph D also shows regional disparities by colonial origin. The widest gaps were noted in Francophone Chad (D = 52%) and Niger (D = 43%), and in Anglophone Tanzania (D = 42%), Uganda (D = 40%), and Sierra Leone (D = 40%). Chad, Niger, and Uganda consistently rank among the worst-performing states in intraregional inequalities in water, sanitation, and dwelling poverty. Figure 2 Figure 3 (Graphs A-E) presents disaggregated country data on intraregional inequalities in child-specific poverty indicators for nutrition and health. These graphs reveal much narrower regional inequality gaps for child-focused indicators compared to household-related estimates. In most countries, intraregional inequality was 20% or less, with values close to zero regardless of colonial origins. Such narrow gaps suggest improvements in health and nutrition indicators across all regions. In contrast, large regional disparities in educational access were observed in Francophone Niger (D = 41%), Burkina Faso (D = 35%), Guinea (D = 28%), and Chad (D = 27%). Most Anglophone states, as well as Francophone Congo and Benin, exhibited narrow education inequality gaps. In Zimbabwe, the proportion of children without formal schooling was too small to create summary measures. When examining regional inequalities in incomplete education, Sierra Leone (D = 41%) and Guinea (D = 34%) showed the widest gaps. Figure 3 Table 4 presents regression results assessing whether regional inequalities in household-related aspects and education services differed significantly by colonial origin, controlling for urbanisation, political stability, and government efficiency. The analysis reveals that intra-regional disparities in the lack of formal schooling are significantly wider in Francophone states than in Anglophone states (Coeff = 13.8, p < 0.05). Nevertheless, colonial origin accounts for only a moderate proportion of the variation in educational access inequalities (adjusted R 2 = 0.3). Urbanisation and governance indicators did not significantly affect intra-regional inequalities in household-related dimensions. The regression-based analyses in this section focused on household and educational dimensions, given the prominent regional gaps observed in Figs. 2 (A to D), in contrast to the narrower disparities in health and nutrition (Figs. 3 , A-B). Table 4 Tables 5 and 6 present regression models examining the association between colonial origin and socioeconomic inequalities in child-related indicators, measured using both relative (RII) and absolute (SII) indices (See Appendix 2 for raw estimates). The RII results indicate that living in a Francophone state is significantly linked to lower relative inequality in medical access (Coeff = − 18.8, p < 0.05) and higher relative inequality in incomplete primary schooling (Coeff = 48, p < 0.05). Practically, this suggests that gaps in medical access between wealthy and poor households are slightly wider in Francophone countries. Although the RII values for medical access appear concentrated among disadvantaged households across Francophone and Anglophone countries, closer analysis reveals consistent, though modest, differences (see Appendix 2). Conversely, inequalities in incomplete primary schooling are narrower in Francophone countries compared with Anglophone countries. Thus, Anglophone countries experience deeper economic inequalities in school completion rates. Urbanisation is also a significant correlate of inequality. Higher levels of urbanisation are associated with reduced relative inequality in incomplete primary schooling (Coeff = − 1.5, p < 0.01) and in nutritional outcomes, including severe anthropometric failure (SAF; Coeff = − 0.6, p < 0.05) and moderate anthropometric failure (MAF; Coeff = − 0.8, p < 0.05). These patterns imply that urbanisation may foster non-inclusive development, with the poorest children experiencing disproportionately higher risks of incomplete schooling and undernutrition compared with their wealthier counterparts. Table 5 Regarding absolute inequality measures, Table 6 indicates that residing in a Francophone state is associated with higher SII values for health access (Coeff = 11.3, p < 0.05), and for access to formal education (Coeff = 24, p < 0.05). This reflects wider absolute socioeconomic disparities in healthcare utilisation, consistent with the relative inequality results, as well as larger gaps in formal education between rich and poor households in Francophone countries. The models exhibit moderate explanatory power for health but remain weak for educational access, suggesting that unexplored factors beyond colonial origins may account for the observed inequality patterns. Political stability/the absence of violence, to a weak extent, was associated with lower socioeconomic inequalities in low vaccine uptake. All other governance indicators remained insignificant. Table 6 Discussion Few studies have examined material inequality through the lens of colonial origin. Our analysis shows that although overall regional inequalities in child poverty do not differ substantially by colonial origins, education-related poverty gaps are notably wider in Francophone regions than in Anglophone ones. Regional inequalities in household-level deprivations such as access to water, sanitation, and adequate housing are far more severe than those observed for child-specific indicators. The largest intraregional disparities in these sectors were found in Francophone countries such as Chad, Mali, and Niger, as well as in the Anglophone case of Uganda. This pattern suggests a broader policy failure across Sub-Saharan Africa, in which water, sanitation, and housing have historically received limited governmental investment, regardless of colonial heritage. As Werchota ( 2020 ) argues, insufficient state commitment has left poor households to “suffer in silence.” In contrast, the relatively narrow regional gaps in health and nutrition indicate numerous grassroots and donor-supported programmes that have targeted these sectors over the past decade. Strengthening water, sanitation, and housing systems, particularly in peripheral and underserved regions, should therefore be prioritised within pro-poor development strategies. The case of Namibia illustrates the consequences of persistent neglect in these basic service sectors. Despite being an upper-middle-income country, Namibia faces substantial sanitation and housing deficits in its peripheral regions. This finding aligns with earlier evidence documenting persistent inequalities in sanitation and dwelling conditions (Gold and Melkisedek 2013 ; UN 2013 ). Even amid sustained economic growth and political stability, inequality remains entrenched, with approximately 38% of informal settlements lacking basic sanitation (Gold & Melkisedek, 2013 ). These results reinforce the argument that national economic expansion alone does not translate into improved living conditions for marginalised communities unless accompanied by targeted and regionally equitable investments. Education inequalities also emerge as a critical concern, reflecting deeper systemic and historical dynamics within Francophone and Anglophone contexts. Although moderate, the wider education gaps observed in Francophone states may be rooted in long-standing governance structures. Jean-Yves ( 1980 ) notes that centralised administrative traditions in Francophone countries have historically hindered the development of peripheral regions, restricting their economic opportunities and financial autonomy. Families in such areas often struggle to afford schooling, reinforcing spatial and socioeconomic disparities. These patterns reflect broader forms of social exclusion, whereby marginalised regions, already limited in access to basic services, wealth, and political influence, remain trapped in a cycle of underdevelopment. Our finding of wide socioeconomic disparities in incomplete primary schooling in Anglophone countries is consistent with earlier DHS-based evidence from former British African colonies. Filmer and Pritchett ( 1999 ) and Lloyd and Hewett ( 2009 ) document large wealth-related differentials in primary completion in countries such as Ghana, Kenya, Tanzania, Uganda, Zambia and Zimbabwe, where children from the poorest quintiles are much less likely to complete grade 5 than their wealthier peers. Although socioeconomic inequalities in health were only weakly correlated with colonial origin, the association remains significant. We also observed significantly higher prevalence of medical access deprivation in Francophone than in Anglophone countries. Supporting findings have shown that Francophone children are twice as likely to face challenges in accessing healthcare compared with Anglophone children (Fonta et al. 2025 ). While it may be difficult to directly trace the pathways linking colonialism to poor healthcare performance, some scholars attribute health disparities between these two groups of ex-colonies to differing approaches to Universal Health Coverage (Azevedo 2017 ; Paul et al. 2018 ). They suggest that many Francophone countries struggle with fragmented policy implementation, high administrative costs, limited government commitment, and unclear rollout strategies, which hinder progress towards providing free healthcare for children under five. These challenges often lead to stalled or abandoned reforms, leaving low-income households without adequate financial protection. Empirical evidence further shows that in Francophone settings such as Burkina Faso, Guinea, Benin, and Senegal, poorer households shoulder a disproportionately high share of healthcare expenses relative to their higher-income counterparts (Cissé et al. 2007 ). By contrast, Anglophone countries, including Tanzania, Malawi, Uganda, Kenya, and Ghana, have made significant progress in reducing barriers to healthcare access (Azevedo 2017 ). Removing user fees for children under five and, in some cases, for entire populations has increased utilisation among the poorest households and helped to diminish health disparities. These policy differences result in measurable outcomes: studies by Boum and Mburu ( 2020 ) and El Bcheraoui et al. ( 2020 ) consistently report higher burdens of under-five and neonatal morbidity in Francophone countries, primarily due to weaker healthcare infrastructure and less effective financing models. This study thus lays the groundwork for comparing health systems across Francophone and Anglophone African countries. We also note that good health outcomes, such as optimal vaccine uptake, are driven by political stability and the absence of violence, similar to Arsenault et al. ( 2017 ). Additional findings indicate that although socioeconomic gaps in anthropometric failure are relatively small, nutrition remains a growing challenge amid rapid urbanisation. Research suggests that urbanisation tends to widen nutritional inequalities: wealthier households experience rising rates of obesity due to lifestyle changes and access to calorie-dense foods (Kuddus et al. 2020 ). In comparison, poorer households face higher rates of stunting linked to limited access to nutritious diets. Urban expansion can also displace agricultural land, reducing opportunities for subsistence farming among low-income families (Pouw and Humblot 2020 ), thereby heightening their dependence on costly market-based foods. Historically, income inequalities tend to rise in rapidly urbanising, resource-rich countries, with benefits accruing to already advantaged populations (Milanovic 2018 ). These dynamics reinforce socio-spatial divides and contribute to uneven nutritional outcomes. However, the weak statistical relationship observed in this study indicates that further empirical investigation is needed to better understand the mechanisms linking urbanisation and nutritional inequality in SSA. Study limitations This study faced certain limitations in directly tracing how colonial origins may have influenced socioeconomic inequalities in health. Nonetheless, historical literature was drawn on to link colonial policies with disparities in health financing, providing contextual explanations for the observed differences. In the absence of income data, wealth quintiles were used as a proxy for socioeconomic status. Consequently, it was not possible to assess inequalities in household-specific dimensions of child poverty, as some of these indicators were used to construct the wealth index. Furthermore, the analysis relied on simple regression-based associations rather than causal inference. The wide confidence intervals observed in the assessment of colonial heritage and material poverty disparities can be attributed to the relatively small sample of countries included (23 in total). Despite these limitations, this study provides a valuable baseline for future research utilising Multiple Indicator Cluster Surveys (MICS), particularly as they cover a larger representation of Anglophone countries, only 17% of which were included in the present analysis. Conclusion This study contributes to a small but growing body of work examining material inequalities associated with colonial origins, which continue to shape contemporary well-being in uneven ways. Although overall regional disparities in child poverty do not differ markedly between Francophone and Anglophone states, important sector-specific patterns emerge. Inequalities in water, sanitation, and housing are consistently the most severe across Sub-Saharan Africa, reflecting decades of limited state investment and weak institutional commitment. Even countries with higher income levels, such as Namibia, continue to experience stark regional disparities in sanitation and housing, implying that economic growth alone does not guarantee improved living conditions without targeted, regionally inclusive public spending. Education inequalities also remain a significant concern. Wider regional and socioeconomic disparities in Francophone regions, in the absence of formal schooling, appear to be rooted in long-standing centralised administrative systems that have constrained opportunities in peripheral areas. Nonetheless, substantial wealth-related educational disparities in incomplete schooling documented in many ex-British colonies indicate that the colonial legacy alone cannot account for contemporary educational inequalities. Instead, persistent socioeconomic exclusion and uneven public investment continue to place marginalised regions at a structural disadvantage. Health-related inequalities show a more straightforward but weak relationship with colonial origin, plausibly shaped mainly by divergent policy approaches to Universal Health Coverage. Lower socioeconomic inequality in many Anglophone systems highlights the importance of sustained political commitment, fee removal strategies, and effective financing models in reducing barriers to care. Overall, the findings emphasise that narrowing child material inequalities requires targeted, multisectoral interventions, particularly in water, sanitation, housing, education, and health financing, combined with governance reforms that prioritise equity across regions. Declarations Author Contribution CLF conceived the study, performed the analysis, and drafted the manuscript. DG and ZT contributed to the study conception and supervised the analysis and manuscript drafting. All authors reviewed and approved the final manuscript. Acknowledgement I gratefully acknowledge the South West Doctoral Training Partnership for awarding the funding that supported the completion of my PhD at the University of Bristol. I also thank Helen Gordon and Dr Mary Zhang for their invaluable inputs. References ACEMOGLU, D., S. JOHNSON, and J. A. ROBINSON. 2001. The colonial origins of comparative development: an empirical investigation. American Economic Review 91(5):1369–1401. ARSENAULT, C., M. JOHRI, A. NANDI, J. M. MENDOZA RODRÍGUEZ, P. M. HANSEN, and S. HARPER. 2017. Country-level predictors of vaccination coverage and inequalities in Gavi-supported countries. Vaccine 35(18):2479–2488. AZEVEDO, M. J. 2017. The State of Health System(s) in Africa: Challenges and Opportunities. 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Tables Table 1: Sub-Saharan African countries by colonial origin (2000-2019) Francophone states Sample size Survey years Sample size 1 survey 1 Sample size survey 2 Sample size survey 3 Gabon 31803 2000;2012 14359 17444 Congo 37590 2005;2011 14152 23438 Cameroon 53889 2004;2018 25591 28298 Niger 62349 2006;2012 26391 35958 Madagascar 62953 2004;2009 19413 43541 Guinea 66636 2005;2012;2018 19564 22601 24471 Chad 70894 2004;2015 16211 54683 B. Faso 77862 2003;2010 31904 45957 Senegal 90190 2005:2011;2019 33731 37021 19438 Mali 99707 2006;2012;2018 39321 31041 29345 Benin 127200 2006;2012;2018 45706 44653 36841 Total 781072 Anglophone states Sample size Survey years Sample size survey 1 Sample size survey 2 Sample size survey 3 Namibia 36457 2007;2013 18795 17662 Zimbabwe 41920 2005;2015 20556 21364 Lesotho 49581 2004;2009;2014 17186 17136 15259 Ghana 52926 2003;2008;2014 12764 20809 19352 Gambia 53195 2013;2020 26300 26895 Tanzania 82266 2004;2010;2015 24862 25555 31850 S. Leone 92039 2008;2013;2019 20671 37039 34329 Zambia 94493 2007;2010;2018 42872 32794 Uganda 98986 2006;2011;2016 37021 19438 Kenya 116322 2003;2009;2014 19315 19191 77816 Malawi 156016 2004;2010;2015 31477 62102 62437 Nigeria 190365 2003;2008;2018 17755 78381 94229 Total 1,064,566 Source: DHS data, PSPWs applied Table 2: Child poverty dimensions and indicators: an SDG-updated Gordon et al. (2003b) Poverty dimensions Child poverty=Moderate to severe deprivations. Mild deprivations Moderate deprivations Severe deprivations Water (SDG 6) a Basic water source: Improved source and collection time is 30 minutes or below from the source Limited water source: improved, and collection time is more than 30 minutes from the source. Unimproved water sources Drinking water directly from a river, dam, lake, pond, stream, canal, or irrigation canal Bristol Approach b Insufficient water occasionally due to lack of sufficient money Sanitation (SDG 6) Basic sanitation: Improved facilities that separate excreta from human contact (pit latrines with slabs, ventilated improved pit latrines, and composting toilets) and that are not shared with other households. Limited sanitation: Improved facilities yet shared with two or more households. Unimproved toilet facilities, including latrines with no slabs, buckets or hanging toilets Open defaecation in fields, forests, bushes, open bodies of water, beaches, and other open spaces or with solid waste Bristol Approach Having to share facilities with other households Sanitation facilities outside the dwelling Dwelling (SDG 11) At least one of the following items (floor, wall, or roof) is constructed using non-durable materials. Two of the following items (floor, wall, or roof) were constructed using non-durable materials. All three items (floor, wall, and roof) were constructed using non-durable housing materials. Dwelling lacks electricity Overcrowding with three persons per room Four persons per room Five or above persons per room. Bristol Approach Dwelling in poor repair/two persons per room Few facilities in dwelling/lack of heating/structural problems Information (SDG 17) Households lack one out of three items (mobile phone, radio or TV set Households lack two out of three items Households lack all three items. Bristol Approach Cannot afford newspapers/books Nutrition and food security (SDG 2) Minimum dietary diversity: Consumed four out of eight different food groups the previous day Consumed three of the eight food groups the previous day Consumed two or fewer of the eight food groups (starvation) Suffered at least one form of anthropometry failure Suffered multiple forms of anthropometry failure Bristol Approach Mild food insecurity Moderate food insecurity Severe food insecurity Bland diet of poor nutritional quality Going hungry occasionally Malnutrition Health (SDG 3) Missed out on one of the eight vaccines Missed two-six vaccines Received none of the vaccines. Received no medical treatment for diarrhoea, fever and cough Bristol Approach Occasionally, lack access to healthcare due to insufficient funds Inadequate medical care Education (SDG 4) Incomplete secondary schooling Incomplete primary schooling No formal schooling Bristol Approach Inadequate teaching due to a lack of resources Source: Gordon et al. (2003a )/Bristol Approach updated to reflect current SDGs, a SDG indicators, b Bristol Approach indicators that were modified Table 3: Unweighted study characteristics by colonial origins Variables N Percentage Child age 0-4 years 593,418 31.26 5-17 years 1,304,772 68.74 Sex Male 960,179 50.58 Female 938,011 49.42 Place of residence Urban 562,263 29.62 Rural 1,335,927 70.38 Locality Administrative region 227,172 11.97 Subnational region 1,671,018 88.03 Age of household head Below 45 1,042,887 54.95 45 and above 854,925 45.05 Sex of household head Males 1,489,790 78.48 Females 408,400 21.52 Maternal literacy of under-5s No education 179,583 46.91 Primary education 120,716 31.53 Secondary education 72,291 18.88 Tertiary education 10,245 2.68 Household wealth status Poorest 442,463 23.49 Poorer 395,228 20.98 Middle 381,606 20.26 Richer 352,868 18.74 Richest 311,279 16.53 Table 4: Coefficients of regression models showing associations between intraregional inequalities in child-specific indicators and colonial origins (at 95% CI). Water Sanitation Dwelling Information No formal schooling Incomplete schooling a Francophone countries 9.7 (-3, 23) -0.5 (-16, 15) 4.7 (-10, 20) 6 (-7, 19) 13.8* (4, 24) 1.39 (-7, 10) Urbanisation -0.3 (-.7, .1) 0.03 (-0.5, .5) -0.15 (-.6, .3) -0.2 (-.7, .2) -0.31 (-.7, -.01) -0 .03 (-.3, .3) b P_stability/ absviolence -0.1 (-.5, .3) 0.11 (-.4, .6) -0.01 (-.5, .5) -0.2 (-.8, .5) 0.10 (-.4, .4) -0.1 (-.4, .2) c G_efficiency 0.09 (-.4, .5) 0.2 (-.4, .6) 0.03 (-.4, .5) -0.2 (-.8, .4) -0.09 (-.2, .4) -.18 (-.5, .1) Adjusted R 2 0.04 -0.2 -0.2 -0.1 0.3 0.01 a Anglophone countries as base case, b political stability and absence of violence, c government efficiency, *p<0.05, **p<0.01, *p<0.001 Table 5: Coefficients of regression models showing socioeconomic inequalities in child-specific indicators (relative measures) and colonial origins (95% CIs). Low vaccine access Low medical access No formal schooling Incomplete schooling SAF MAF Low MDD a Francophone countries -19.7 (-47, 8) -18.8* (-32, -5) 13.7 (-0.3, 30) 44** (17.4, 70) 5.7 (-7, 18) 6.7 (-7, 20) 10.1 (-2, 23) Urbanisation 0.3 (-.6, 1.2) -0.02 (-.5, .4) 0.33 (-.2, .9) -1.5** (-2.3, -.6) -0.6* (-1.0, -.2) -0.8* (-1.2, -.3) 0.15 (-.3, .6) b P_stability/ absviolence -0.01 (-.4, 1.4) 0.55 (.09, 1.0) 0.4 (-.1, 1.0) -0.01 (-.9, .9) 0.1 (-.3, .5) 0.49 (.04, .9) 0.22 (-.2, .6) c G_efficiency -0.29 (-1.2, .6) -0.28 (-.7, .2) -0.26 (-.8, .3) 0.18 (-.7, 1.1) -0.4 (-.8, .03) -0.42 (-.9, .03) -0.39 (-.8, .01) Adjusted R 2 0.02 0.3 0.2 0.4 0.3 0.4 0.2 * p<0.05, **p<0.01, *p<0.00, SAF-Single Anthropometry Failure, MAF-Multiple Anthropometry failure, MDD- Minimum Dietary Diversity, Table 6: Coefficients of regression models showing associations between socioeconomic inequalities in child-specific indicators (absolute measures) and colonial origins Low vaccine access Low medical access No formal schooling Incomplete schooling SAF MAF Low MDD a Francophone countries 12.4 (-.2, 25) 11.3* (4, 18) 24* (6.7, 41) -18 (-36, -41) -2.5 (-7, 2) -.65 (-6, 4.0) -4.6 (-14, 5) Urbanisation 0.17 (-.2, .6) 0.08 (-.1, .3) -0.22 (-.8, .3) 0.22 (-.4, .8) 0.1 (-.05, .3) 0.13 (-.03, .3) -0.18 (-.5, .1) b P_stability/ absviolence -0.51* (-.9, -.1) -0.25 (-.5, -.01) -0.3 (-.9, .3) 0.19 (-.4, .8) -0.02 (-.2, .1) -0.2 (-.3, .01) -0.09 (-.4, .2) c G_efficiency 0.04 (-.4, .5) 0.04 (-.2, .3) -0.08 (-.5, .7) -0.09 (-.7, .6) -0.06 (-.1, .2) 0.1 (-.1, .3) 0.25 (-.1, .6) Adjusted R 2 0.2 0.4 0.2 0.1 -0.01 0.1 0.1 * p<0.05, **p<0.01, ***p<0.001 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8609099","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":624547240,"identity":"07f4a391-59c2-4fdd-a7ac-77a8270856f0","order_by":0,"name":"Dr Cynthia Lum Fonta","email":"data:image/png;base64,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","orcid":"","institution":"University of Cape Town","correspondingAuthor":true,"prefix":"Dr","firstName":"Cynthia","middleName":"Lum","lastName":"Fonta","suffix":""},{"id":624547241,"identity":"be2bf834-cbdd-4a06-9035-025310bca54b","order_by":1,"name":"Professor David Gordon","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"","firstName":"Professor","middleName":"David","lastName":"Gordon","suffix":""},{"id":624547242,"identity":"9630191a-469a-4807-b74a-8ce4bafe57a2","order_by":2,"name":"Dr Zoi Toumpakari","email":"","orcid":"","institution":"University of Bristol","correspondingAuthor":false,"prefix":"Dr","firstName":"Zoi","middleName":"","lastName":"Toumpakari","suffix":""}],"badges":[],"createdAt":"2026-01-15 09:38:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8609099/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8609099/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107616784,"identity":"a43cc695-0633-4c85-8a4d-c79b32684388","added_by":"auto","created_at":"2026-04-23 09:15:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":104222,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAverage child poverty estimates by colonial origins\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/77620481c01cb872901c958e.png"},{"id":107706024,"identity":"ac002dae-27e2-4902-8797-044e50176d89","added_by":"auto","created_at":"2026-04-24 09:17:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":351545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntra-regional inequalities in child poverty related to household dimensions: A. Water poverty, B. Sanitation poverty, C. Information poverty and D. Dwelling poverty\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/4d6d7743646240d1556ef62d.png"},{"id":107616787,"identity":"e488c2dc-de84-4f19-8a96-eb43bc7ddbbd","added_by":"auto","created_at":"2026-04-23 09:15:58","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":988953,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIntra-regional inequalities in child poverty related to child-specific indicators: A. Nutrition B. Health C. Education\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage10.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/8ce9674e3477c0a44ffbda6d.jpeg"},{"id":107709052,"identity":"29922e97-8058-40a3-a375-b1e8dc66fc11","added_by":"auto","created_at":"2026-04-24 09:34:28","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2040455,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/9dc2eb7c-0b25-4b1f-8654-d08ecb393f28.pdf"},{"id":107707040,"identity":"b04dcd50-86b5-4258-afbd-2bb505d924f1","added_by":"auto","created_at":"2026-04-24 09:19:19","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":70889,"visible":true,"origin":"","legend":"","description":"","filename":"EJDRSupplemtarymaterials.docx","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/1985e15fcf235e8693ec3ea8.docx"},{"id":107705919,"identity":"cd84c26d-03c5-40e6-a6af-03404b27d3ed","added_by":"auto","created_at":"2026-04-24 09:15:43","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":37210,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix13.docx","url":"https://assets-eu.researchsquare.com/files/rs-8609099/v1/0d6b6599482978c784eb9767.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eColonial origins and child poverty inequalities in Sub-Saharan Africa\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eAs researchers and policymakers grapple with developing effective poverty alleviation strategies, it is essential to examine the structural environment that persistently constrains the poor\u0026rsquo;s access to an adequate standard of living (Barrett et al. 2006). This structural environment includes institutional frameworks that perpetuate marginalisation and inequality across segments of the population. A growing body of scholarship explores the profound influence of colonial legacies on the development trajectories of African nations, highlighting multiple mechanisms by which these historical effects endure. Acemoglu et al. (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2001\u003c/span\u003e) argue that the type of institutions established during colonial times significantly affects current development outcomes. For example, in regions such as the Americas, Australia, and New Zealand, Europeans established inclusive institutions grounded in the rule of law, which promoted broad-based development. In contrast, many African colonies inherited extractive institutions designed to exploit resources rather than foster inclusive growth. Milanovic et al. (2011) argues that the extraction ratio, the share of potential resources elites are able to extract, was substantially higher in the pre-industrial period due to concentrated power and highly extractive institutions. They suggest economic growth can lower extraction ratios even as Gini-measured inequality rises, whereas in low-growth contexts, extraction pressures remain high.\u003c/p\u003e \u003cp\u003eNunn (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) similarly attributes persistent underdevelopment in Africa to its historical legacy. He identifies three phases in this process: first, pre-colonial Africa was characterised by relatively secure property rights and productive economic systems; second, the slave trade and colonial rule intensified extractive tendencies, undermining productivity; and third, post-independence periods saw a continuation of these extractive institutions, further entrenching poverty and inequality. Njoh (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) adds that, while more extended periods of colonial rule and greater colonial investment were associated with lower slum incidence in some African countries, these patterns showed no clear association with specific colonial strategies. Milanovic (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) found that resource-rich colonies were more urbanised and extractive, resulting in higher inequality rates than less extractive colonies.\u003c/p\u003e \u003cp\u003eContrary to some prevailing views, researchers like Gilley (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) argue that Africa's development is influenced less by the legacies of the slave trade and colonial extractive institutions, and more by how post-colonial governments chose to respond to and manage these legacies. One study found that high malaria incidence in African countries, irrespective of colonial ruling styles, showed a strong correlation with lower levels of development (Bhattacharyya \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The author noted that malaria endemicity leads to high morbidity and mortality rates, which ultimately reduce productivity over time. However, this study relied solely on GDP as a measure of a country's development, which is problematic as it does not account for wealth distribution or other aspects of human capital development. Development extends beyond economic growth to include access to essential social services (Stiglitz et al. 2009; Gordon \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Waddington \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). This study, therefore, examines how colonial origins influence key SDG indicators: water, sanitation, housing, information, health, education, and nutrition, hypothesising that differences in material deprivation across SSA may reflect varying inherent colonial governance styles and institutions.\u003c/p\u003e \u003cp\u003eMiles (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) argues that British and French colonialism reshaped social, economic, and political structures in ways that continue to influence inequalities in SSA. British rule, based on indirect rule, granted local authorities executive and judicial powers, laying the foundation for decentralisation (Lange \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). This approach yielded several advantages, including stronger education systems (Grier \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), judicial systems (Joireman \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), and fiscal transparency (Sarr \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e), yet it also deepened ethnic divisions and political instability in several British ex-colonies (Richens \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). French colonialism, by contrast, imposed highly centralised administrative systems with politically motivated decentralisation that concentrates decision-making power and resources, often reinforcing regional inequality (Pinto \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Their former colonies are inherently connected to the French trade, military, political, and legal systems, fostering dependence and persistent underdevelopment (Yates \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, Tusalem (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) found that prolonged British and Spanish rule was associated with a lower likelihood of state failure than French and Portuguese rule. Against this backdrop, this study disaggregates child poverty indicators across administrative and socioeconomic groups to examine how past colonial rule may have influenced the unequal distribution of present-day material need.\u003c/p\u003e \u003cp\u003eResearch indicates that various factors, including marginalisation, influence inequality in SSA (Dupraz \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), conflict (Howell et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), rapid urbanisation (Milanovic \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and poor governance (Haller\u0026ouml;d et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). This study will control for these factors. The wealthiest 10% in SSA account for approximately 55% of national income and 70% of the wealth while the poorest half of the population have 10% of the national income and 1% of the wealth (Chancel et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). South Africa, Namibia, and Mozambique are among the most unequal countries in the region. Although economic growth policies promoted by international financial institutions have helped reduce poverty in some contexts, the benefits have often not reached substantial segments of the population (Chancel et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Growth alone has rarely led to structural transformation.\u003c/p\u003e \u003cp\u003eThe relationship between poverty and inequality varies across contexts. Fosu (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) warns that in settings with widespread poverty and low average incomes, reducing inequality can paradoxically increase the low income poverty rate, highlighting the need for context-specific policy approaches. Our research thus focuses on within-country inequalities, since within-group differences account for approximately 70% of inequality patterns in Africa (David et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). This heterogeneity is evident in progress toward the SDGs. For example, Hasan and Alam (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) found that wealth inequalities showed the widest variation in access to improved water services in low- and middle-income countries than regional inequalities. The widest gaps were in Congo DRC, Madagascar and Mozambique, with more than 0.58 proportional variability between the rich and the poor. JMP (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2025\u003c/span\u003e) estimates indicate that, in 2024, 52% of urban residents in SSA had access to safely managed water services, whereas only about 16% in rural areas did. Only a 6% improvement in urban access to safely managed water services was observed between 2015 and 2024. Nutritional inequalities persist, with extremely high stunting rates in Burundi, Niger, Eritrea, and the DRC, compared with relatively lower rates in countries such as Ghana and Kenya (SDG \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The most significant inequalities existed in wealth, followed by education and location (Global Nutrition Report \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Nigeria had the largest wealth inequality gap in stunting, with estimates of 63% for the poorest quintile and 18% for the richest quintile.\u003c/p\u003e \u003cp\u003eAddressing inequality requires the need for strong social protection systems and pro-poor growth strategies, including subsidised essential services (Paul et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), effective inflation control (Kingsford and Richmond Silvanus \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and sustained agricultural development (Pasha and Palanivel \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). These studies highlight the central role of governance quality and robust safety nets in promoting inclusive growth. This is the reason for adding explanatory governance indicators in this analysis. Despite these insights, limited attention has been paid to how historical institutional contexts, particularly colonial legacies, continue to shape patterns of inequality within SSA countries. This study addresses this gap by examining regional and socioeconomic disparities in material deprivation across countries with different colonial origins, assessing whether these inequalities are wider in Francophone or Anglophone ex-colonies. By controlling for key structural and contextual factors, it investigates whether and how historic colonial administrative structures and rules contribute to persistent within-country disparities. This approach is novel in that it explicitly situates inequalities in access to material needs within a colonial framework, an area that remains underexplored in existing SSA scholarship. Educational inequalities are explored not only in attainment but also across the entire attainment spectrum, including primary school completion rates.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData source and study design\u003c/h2\u003e \u003cp\u003eSecondary data from the DHS were utilised for each country between 2000 and 2019, depending on data availability. These surveys are typically conducted on a representative sample of households and individuals within each country to enable valid inferences about the entire population (ICF 2020). The number of survey rounds per country, the survey years, and the sample sizes for each survey are set out below in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e. All countries included in the study participated in at least two DHS rounds, with some participating in three. A smaller number of countries had data from four survey rounds. Data from the fourth survey were excluded from the analysis to prevent oversampling and to ensure even representation across countries. This decision helped maintain consistency in the dataset and avoided potential bias in trend assessments and cross-country comparisons.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the post-stratified weighted population sizes for Francophone states, ranging from the smallest, Gabon, to the most populated, Benin. For Anglophone states, the list includes Namibia, the least populous, and Nigeria, the most populous. The pooled DHS data encompass 1,845,638 children aged 0 to 17, including 1,064,566 (58%) Anglophone and 781,072 (42%) Francophone children. The surveys were then regrouped into four rounds to facilitate the applications of PSPWs; that is, 2000\u0026ndash;2004 (represented by 2002), 2005\u0026ndash;2009 (represented by 2007), 2010\u0026ndash;2014 (represented by 2012), and 2015\u0026ndash;2019 (represented by 2017). These are shown in Appendix 1which also includes the DHS and UN population estimates and proportion of children aged under 18 (U18) for each survey round. This study covers 43% of individuals under 18 years old in Francophone Africa, compared with only 17% in Anglophone countries. Overall, the DHS sample used in this study represents 60% of the UN U18 population across Francophone and Anglophone regions.\u003c/p\u003e \u003cp\u003eFinally, the syvset command was utilised with the applied PSPWs. This command (recommended by the DHS) applies the specified weights to the survey data for subsequent analysis. It adjusts for the disproportionate sampling (design effects) to provide nationally representative data and enables complex survey data analysis, including estimations of deprivation and poverty levels.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eChild poverty measurement\u003c/h3\u003e\n\u003cp\u003eThis study assesses child poverty using the UNICEF-based \u0026lsquo;Bristol Approach\u0026rsquo; or \u0026lsquo;Rights-based Approach\u0026rsquo; to poverty, developed by Gordon and colleagues (Gordon et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). The authors utilised internationally recognised indicators from the United Nations Convention on the Rights of the Child (UN CRC) and the first World Social Summit in Copenhagen (United Nations \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). The Bristol Approach categorised children by degree of deprivation of material needs, ranging from not deprived to mild, moderate, severe, and extreme. Children were in multidimensional poverty if they experienced severe deprivation in two or more dimensions of material needs.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e presents the indicators used in this thesis, building on the Bristol Approach (Gordon et al, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003eb )for assessing poverty. Although the Bristol Approach was introduced over twenty years ago, its indicators remain highly relevant as they are monitored globally and prioritised within the SDGs. However, modifications were made to align the indicators with the modern SDG framework, ensuring they reflect current global priorities and considering data availability and response rates in the DHS dataset used for this analysis. Unlike the original Gordon et al. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2003\u003c/span\u003eb) methodology, this revised version includes children experiencing moderate and severe deprivation as poor, asserting that even moderate deprivations jeopardise children's health.\u003c/p\u003e \u003cp\u003eIn this study, child poverty was defined as those with moderate and severe deprivations, coded as \u0026lsquo;1\u0026rsquo;, and otherwise as \u0026lsquo;0\u0026rsquo;. The data was linearised and weights applied to all tabulations and mean child poverty estimations to account for the complex sample design and the disproportionate sample sizes across countries. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e thus presents child poverty measurement for all dimensions and indicators. The data was disaggregated into household dimensions (water, sanitation, dwelling, and information) and child-specific indicators (anthropometric failure, minimum dietary diversity, medical access, vaccine uptake and education access).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll analyses were performed using Stata version 18 and the World Health Organization health equity toolkit software (HEAT plus) (WHO 2021). Data were weighted to account for the complex DHS clustering and stratification sampling design (Hosseinpoor et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGeographical inequalities in child poverty were assessed by disaggregating estimates into subnational and administrative regions. Child poverty prevalence was calculated for each regional group, and summary datasets were prepared in Excel and uploaded into HEAT Plus for validation and inequality analysis. Absolute geographical inequality was quantified using the Difference (D) summary measure, defined as the difference in child poverty prevalence between subnational and administrative regions:\u003c/p\u003e \u003cp\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:Difference\\:\\left(D\\right)={Y}_{Subnational\\:regions}-{Y}_{Administrative\\:regions}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u0026hellip;\u0026hellip;.\u003c/p\u003e \u003cp\u003eValues ranged from 0 to 100. Estimates close to 100 indicated high levels of inequality, and near 0 implied narrow disparities.\u003c/p\u003e \u003cp\u003eSocioeconomic inequalities were assessed using the Relative Index of Inequality (RII), a relative measure and the Slope Index of Inequality (SII), an absolute measure. Children were ranked into wealth quintiles, from the least advantaged (rank 1) to the most advantaged (rank 5). Each quintile was weighted by its population share, and the midpoint of the cumulative population distribution was used in the analysis. HEAT Plus estimates RII using a generalised linear model with a logit link, regressing the child poverty indicator on the ranked socioeconomic position.\u003c/p\u003e \u003cp\u003eThe RII was calculated as the ratio of the predicted child poverty prevalence among the most advantaged group (V₁) to that among the least advantaged group (V₀):\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:RII=\\raisebox{1ex}{${V}_{1}$}\\!\\left/\\:\\!\\raisebox{-1ex}{${V}_{0}$}\\right.$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAn RII value of 100 indicates no socioeconomic inequality; values below 100 indicate greater child poverty concentration among the disadvantaged, whereas values above 100 indicate greater concentration among the advantaged. Estimates closer to zero indicate greater socioeconomic inequalities in child poverty between children from rich and poor households.\u003c/p\u003e \u003cp\u003eThe Slope Index of Inequality (SII) measures absolute socioeconomic inequality and represents the difference in predicted child poverty prevalence between the most advantaged and least advantaged groups:\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$\\:SII={V}_{1}-{V}_{0}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eValues close to zero indicate narrow socioeconomic inequalities, while larger absolute values indicate wider disparities. Negative SII values indicate a concentration of child poverty among disadvantaged children.\u003c/p\u003e \u003cp\u003eThe RII and SII of the respective child poverty-specific indicators on colonial origin were regressed to assess if there were significant differences in inequality across colonial backgrounds, while controlling for urbanisation and governance indicators. The dependent variable was each indicator's inequality estimate, and the independent variables included colonial origin, urbanisation, and the governance indicator. Urbanisation is defined here as the proportion of a country\u0026rsquo;s total population living in urban areas (UN Population Division \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The World Governance Indicators rank countries worldwide by percentiles based on their ability to control corruption, achieve political stability, prevent violence, and ensure government efficiency (World Bank \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Higher percentiles indicate better performance, whereas lower percentiles indicate poorer outcomes. These indicators were compiled to correspond with the years during which the DHS surveys were conducted. The corruption control variable was removed because its variance inflation factor (VIF) exceeded 5, showing significant multicollinearity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarises the unweighted study sample characteristics. The sample comprised children aged 0\u0026ndash;17 years, of whom 31% were under five years of age. The gender distribution was balanced, with boys and girls each accounting for 50% of the sample. Approximately 70% of children resided in rural areas, about 12% in administrative capitals, and the remainder in other peripheral cities. Most children lived in male-headed households (78%), and just over half of household heads were younger than 45 years (54%). Literacy among mothers' children under five remains a concern: 46% of mothers had no formal education, while only 3% had attained tertiary education. Household wealth was almost evenly distributed across quintiles, with 24% of children in the poorest quintile and 17% in the wealthiest quintile.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents post-ANOVA \u003cem\u003et\u003c/em\u003e-test mean estimates, illustrating the direction and magnitude of mean differences in child poverty indicators by colonial origin. Across both colonial settings, child poverty was most prevalent in the sanitation (M\u0026thinsp;=\u0026thinsp;85%, SD\u0026thinsp;=\u0026thinsp;10) and dwelling dimensions (M\u0026thinsp;=\u0026thinsp;75%, SD\u0026thinsp;=\u0026thinsp;9) and in the indicator of low minimum dietary diversity (MDD) (M\u0026thinsp;=\u0026thinsp;64%, SD\u0026thinsp;=\u0026thinsp;8%). However, these differences were not statistically significant. By contrast, low medical access was significantly more prevalent in Francophone countries (M\u0026thinsp;=\u0026thinsp;67%, SD\u0026thinsp;=\u0026thinsp;8%) than in Anglophone countries (M\u0026thinsp;=\u0026thinsp;48%, SD\u0026thinsp;=\u0026thinsp;7%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001. Francophone countries also exhibited, on average, significantly higher rates of low vaccine uptake M\u0026thinsp;=\u0026thinsp;41%, SD\u0026thinsp;=\u0026thinsp;18%) compared with Anglophone countries (M\u0026thinsp;=\u0026thinsp;25%, SD\u0026thinsp;=\u0026thinsp;15%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.037, as well as higher levels of children lacking formal schooling (M\u0026thinsp;=\u0026thinsp;29%, SD\u0026thinsp;=\u0026thinsp;14%) than in Anglophone countries (M\u0026thinsp;=\u0026thinsp;8%, SD\u0026thinsp;=\u0026thinsp;8%), p\u0026thinsp;\u0026lt;\u0026thinsp;0.01. All remaining dimensions and indicators of child poverty did not differ significantly by colonial origin.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFigures \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e present weighted intraregional inequality gaps for household-related dimensions and child-specific indicators, disaggregated by countries. All estimates were obtained from the WHO 2023 health equity assessment toolkit outputs. The blue colours represent Francophone children, and the orange colours represent Anglophone children.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Graph A, illustrates intraregional inequalities in water poverty across African countries, measured by the average difference (D) in child poverty means between subnational and administrative regions. The widest disparities in water poverty were observed in Francophone Niger (D\u0026thinsp;=\u0026thinsp;52%), Chad (D\u0026thinsp;=\u0026thinsp;50%), and Anglophone Uganda and Tanzania (D\u0026thinsp;=\u0026thinsp;48%). In contrast, the smallest regional water poverty gaps (D\u0026thinsp;=\u0026thinsp;20% or less) were observed in Francophone Cameroon, Benin, and Anglophone Malawi, Nigeria, and the Gambia. Both Francophone and Anglophone countries exhibit similar patterns of intraregional inequality in water poverty. Graph B highlights Namibia (D\u0026thinsp;=\u0026thinsp;58%), Zimbabwe (D\u0026thinsp;=\u0026thinsp;51%), and Gambia (D\u0026thinsp;=\u0026thinsp;43%) as the most unequal Anglophone states in sanitation poverty, while Burkina Faso (D\u0026thinsp;=\u0026thinsp;54%) and Senegal (D\u0026thinsp;=\u0026thinsp;40%) represent the most unequal Francophone states. These countries with the widest regional sanitation inequality gaps (Gambia, Namibia, and Zimbabwe) have achieved significant reductions in sanitation poverty within their capital cities compared to peripheral regions. Notably, nearly all countries reported sanitation poverty rates above 70%, even in countries like Benin and Malawi, where inequality gaps were narrow.\u003c/p\u003e \u003cp\u003eGraph C reveals comparable colonial-era patterns in intraregional gaps in the prevalence of dwelling poverty across Francophone and Anglophone countries. The widest disparities were found in Francophone Guinea (D\u0026thinsp;=\u0026thinsp;59%) and Mali (D\u0026thinsp;=\u0026thinsp;51%), as well as in Anglophone Namibia (D\u0026thinsp;=\u0026thinsp;49%), Zimbabwe (D\u0026thinsp;=\u0026thinsp;48%), Tanzania (D\u0026thinsp;=\u0026thinsp;47%), and Sierra Leone (D\u0026thinsp;=\u0026thinsp;41%). Note that Namibia and Zimbabwe exhibit the widest regional inequality gaps in both sanitation and dwelling poverty. The least unequal countries in dwelling poverty were Anglophone Malawi (D\u0026thinsp;=\u0026thinsp;3%) and Lesotho (D\u0026thinsp;=\u0026thinsp;11%), and, Francophone Benin (D\u0026thinsp;=\u0026thinsp;3%). No significant colonial differences were observed in intraregional inequalities for dwelling poverty. Turning to information poverty, Graph D also shows regional disparities by colonial origin. The widest gaps were noted in Francophone Chad (D\u0026thinsp;=\u0026thinsp;52%) and Niger (D\u0026thinsp;=\u0026thinsp;43%), and in Anglophone Tanzania (D\u0026thinsp;=\u0026thinsp;42%), Uganda (D\u0026thinsp;=\u0026thinsp;40%), and Sierra Leone (D\u0026thinsp;=\u0026thinsp;40%). Chad, Niger, and Uganda consistently rank among the worst-performing states in intraregional inequalities in water, sanitation, and dwelling poverty.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e (Graphs A-E) presents disaggregated country data on intraregional inequalities in child-specific poverty indicators for nutrition and health. These graphs reveal much narrower regional inequality gaps for child-focused indicators compared to household-related estimates. In most countries, intraregional inequality was 20% or less, with values close to zero regardless of colonial origins. Such narrow gaps suggest improvements in health and nutrition indicators across all regions. In contrast, large regional disparities in educational access were observed in Francophone Niger (D\u0026thinsp;=\u0026thinsp;41%), Burkina Faso (D\u0026thinsp;=\u0026thinsp;35%), Guinea (D\u0026thinsp;=\u0026thinsp;28%), and Chad (D\u0026thinsp;=\u0026thinsp;27%). Most Anglophone states, as well as Francophone Congo and Benin, exhibited narrow education inequality gaps. In Zimbabwe, the proportion of children without formal schooling was too small to create summary measures. When examining regional inequalities in incomplete education, Sierra Leone (D\u0026thinsp;=\u0026thinsp;41%) and Guinea (D\u0026thinsp;=\u0026thinsp;34%) showed the widest gaps.\u003c/p\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e presents regression results assessing whether regional inequalities in household-related aspects and education services differed significantly by colonial origin, controlling for urbanisation, political stability, and government efficiency. The analysis reveals that intra-regional disparities in the lack of formal schooling are significantly wider in Francophone states than in Anglophone states (Coeff\u0026thinsp;=\u0026thinsp;13.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Nevertheless, colonial origin accounts for only a moderate proportion of the variation in educational access inequalities (adjusted R\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.3). Urbanisation and governance indicators did not significantly affect intra-regional inequalities in household-related dimensions. The regression-based analyses in this section focused on household and educational dimensions, given the prominent regional gaps observed in Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e (A to D), in contrast to the narrower disparities in health and nutrition (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, A-B).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003c/p\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e6\u003c/span\u003e present regression models examining the association between colonial origin and socioeconomic inequalities in child-related indicators, measured using both relative (RII) and absolute (SII) indices (See Appendix 2 for raw estimates). The RII results indicate that living in a Francophone state is significantly linked to lower relative inequality in medical access (Coeff = \u0026minus;\u0026thinsp;18.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and higher relative inequality in incomplete primary schooling (Coeff\u0026thinsp;=\u0026thinsp;48, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Practically, this suggests that gaps in medical access between wealthy and poor households are slightly wider in Francophone countries. Although the RII values for medical access appear concentrated among disadvantaged households across Francophone and Anglophone countries, closer analysis reveals consistent, though modest, differences (see Appendix 2). Conversely, inequalities in incomplete primary schooling are narrower in Francophone countries compared with Anglophone countries. Thus, Anglophone countries experience deeper economic inequalities in school completion rates.\u003c/p\u003e \u003cp\u003eUrbanisation is also a significant correlate of inequality. Higher levels of urbanisation are associated with reduced relative inequality in incomplete primary schooling (Coeff = \u0026minus;\u0026thinsp;1.5, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and in nutritional outcomes, including severe anthropometric failure (SAF; Coeff = \u0026minus;\u0026thinsp;0.6, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and moderate anthropometric failure (MAF; Coeff = \u0026minus;\u0026thinsp;0.8, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). These patterns imply that urbanisation may foster non-inclusive development, with the poorest children experiencing disproportionately higher risks of incomplete schooling and undernutrition compared with their wealthier counterparts.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e5\u003c/span\u003e\u003c/p\u003e \u003cp\u003eRegarding absolute inequality measures, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e6\u003c/span\u003e indicates that residing in a Francophone state is associated with higher SII values for health access (Coeff\u0026thinsp;=\u0026thinsp;11.3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and for access to formal education (Coeff\u0026thinsp;=\u0026thinsp;24, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This reflects wider absolute socioeconomic disparities in healthcare utilisation, consistent with the relative inequality results, as well as larger gaps in formal education between rich and poor households in Francophone countries. The models exhibit moderate explanatory power for health but remain weak for educational access, suggesting that unexplored factors beyond colonial origins may account for the observed inequality patterns. Political stability/the absence of violence, to a weak extent, was associated with lower socioeconomic inequalities in low vaccine uptake. All other governance indicators remained insignificant.\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eFew studies have examined material inequality through the lens of colonial origin. Our analysis shows that although overall regional inequalities in child poverty do not differ substantially by colonial origins, education-related poverty gaps are notably wider in Francophone regions than in Anglophone ones. Regional inequalities in household-level deprivations such as access to water, sanitation, and adequate housing are far more severe than those observed for child-specific indicators. The largest intraregional disparities in these sectors were found in Francophone countries such as Chad, Mali, and Niger, as well as in the Anglophone case of Uganda. This pattern suggests a broader policy failure across Sub-Saharan Africa, in which water, sanitation, and housing have historically received limited governmental investment, regardless of colonial heritage. As Werchota (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) argues, insufficient state commitment has left poor households to \u0026ldquo;suffer in silence.\u0026rdquo; In contrast, the relatively narrow regional gaps in health and nutrition indicate numerous grassroots and donor-supported programmes that have targeted these sectors over the past decade. Strengthening water, sanitation, and housing systems, particularly in peripheral and underserved regions, should therefore be prioritised within pro-poor development strategies.\u003c/p\u003e \u003cp\u003eThe case of Namibia illustrates the consequences of persistent neglect in these basic service sectors. Despite being an upper-middle-income country, Namibia faces substantial sanitation and housing deficits in its peripheral regions. This finding aligns with earlier evidence documenting persistent inequalities in sanitation and dwelling conditions (Gold and Melkisedek \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; UN \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Even amid sustained economic growth and political stability, inequality remains entrenched, with approximately 38% of informal settlements lacking basic sanitation (Gold \u0026amp; Melkisedek, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). These results reinforce the argument that national economic expansion alone does not translate into improved living conditions for marginalised communities unless accompanied by targeted and regionally equitable investments.\u003c/p\u003e \u003cp\u003eEducation inequalities also emerge as a critical concern, reflecting deeper systemic and historical dynamics within Francophone and Anglophone contexts. Although moderate, the wider education gaps observed in Francophone states may be rooted in long-standing governance structures. Jean-Yves (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) notes that centralised administrative traditions in Francophone countries have historically hindered the development of peripheral regions, restricting their economic opportunities and financial autonomy. Families in such areas often struggle to afford schooling, reinforcing spatial and socioeconomic disparities. These patterns reflect broader forms of social exclusion, whereby marginalised regions, already limited in access to basic services, wealth, and political influence, remain trapped in a cycle of underdevelopment. Our finding of wide socioeconomic disparities in incomplete primary schooling in Anglophone countries is consistent with earlier DHS-based evidence from former British African colonies. Filmer and Pritchett (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and Lloyd and Hewett (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) document large wealth-related differentials in primary completion in countries such as Ghana, Kenya, Tanzania, Uganda, Zambia and Zimbabwe, where children from the poorest quintiles are much less likely to complete grade 5 than their wealthier peers.\u003c/p\u003e \u003cp\u003eAlthough socioeconomic inequalities in health were only weakly correlated with colonial origin, the association remains significant. We also observed significantly higher prevalence of medical access deprivation in Francophone than in Anglophone countries. Supporting findings have shown that Francophone children are twice as likely to face challenges in accessing healthcare compared with Anglophone children (Fonta et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While it may be difficult to directly trace the pathways linking colonialism to poor healthcare performance, some scholars attribute health disparities between these two groups of ex-colonies to differing approaches to Universal Health Coverage (Azevedo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Paul et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). They suggest that many Francophone countries struggle with fragmented policy implementation, high administrative costs, limited government commitment, and unclear rollout strategies, which hinder progress towards providing free healthcare for children under five. These challenges often lead to stalled or abandoned reforms, leaving low-income households without adequate financial protection. Empirical evidence further shows that in Francophone settings such as Burkina Faso, Guinea, Benin, and Senegal, poorer households shoulder a disproportionately high share of healthcare expenses relative to their higher-income counterparts (Ciss\u0026eacute; et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBy contrast, Anglophone countries, including Tanzania, Malawi, Uganda, Kenya, and Ghana, have made significant progress in reducing barriers to healthcare access (Azevedo \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Removing user fees for children under five and, in some cases, for entire populations has increased utilisation among the poorest households and helped to diminish health disparities. These policy differences result in measurable outcomes: studies by Boum and Mburu (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) and El Bcheraoui et al. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) consistently report higher burdens of under-five and neonatal morbidity in Francophone countries, primarily due to weaker healthcare infrastructure and less effective financing models. This study thus lays the groundwork for comparing health systems across Francophone and Anglophone African countries. We also note that good health outcomes, such as optimal vaccine uptake, are driven by political stability and the absence of violence, similar to Arsenault et al. (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditional findings indicate that although socioeconomic gaps in anthropometric failure are relatively small, nutrition remains a growing challenge amid rapid urbanisation. Research suggests that urbanisation tends to widen nutritional inequalities: wealthier households experience rising rates of obesity due to lifestyle changes and access to calorie-dense foods (Kuddus et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In comparison, poorer households face higher rates of stunting linked to limited access to nutritious diets. Urban expansion can also displace agricultural land, reducing opportunities for subsistence farming among low-income families (Pouw and Humblot \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), thereby heightening their dependence on costly market-based foods. Historically, income inequalities tend to rise in rapidly urbanising, resource-rich countries, with benefits accruing to already advantaged populations (Milanovic \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These dynamics reinforce socio-spatial divides and contribute to uneven nutritional outcomes. However, the weak statistical relationship observed in this study indicates that further empirical investigation is needed to better understand the mechanisms linking urbanisation and nutritional inequality in SSA.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStudy limitations\u003c/h2\u003e \u003cp\u003eThis study faced certain limitations in directly tracing how colonial origins may have influenced socioeconomic inequalities in health. Nonetheless, historical literature was drawn on to link colonial policies with disparities in health financing, providing contextual explanations for the observed differences. In the absence of income data, wealth quintiles were used as a proxy for socioeconomic status. Consequently, it was not possible to assess inequalities in household-specific dimensions of child poverty, as some of these indicators were used to construct the wealth index. Furthermore, the analysis relied on simple regression-based associations rather than causal inference. The wide confidence intervals observed in the assessment of colonial heritage and material poverty disparities can be attributed to the relatively small sample of countries included (23 in total). Despite these limitations, this study provides a valuable baseline for future research utilising Multiple Indicator Cluster Surveys (MICS), particularly as they cover a larger representation of Anglophone countries, only 17% of which were included in the present analysis.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study contributes to a small but growing body of work examining material inequalities associated with colonial origins, which continue to shape contemporary well-being in uneven ways. Although overall regional disparities in child poverty do not differ markedly between Francophone and Anglophone states, important sector-specific patterns emerge. Inequalities in water, sanitation, and housing are consistently the most severe across Sub-Saharan Africa, reflecting decades of limited state investment and weak institutional commitment. Even countries with higher income levels, such as Namibia, continue to experience stark regional disparities in sanitation and housing, implying that economic growth alone does not guarantee improved living conditions without targeted, regionally inclusive public spending.\u003c/p\u003e \u003cp\u003eEducation inequalities also remain a significant concern. Wider regional and socioeconomic disparities in Francophone regions, in the absence of formal schooling, appear to be rooted in long-standing centralised administrative systems that have constrained opportunities in peripheral areas. Nonetheless, substantial wealth-related educational disparities in incomplete schooling documented in many ex-British colonies indicate that the colonial legacy alone cannot account for contemporary educational inequalities. Instead, persistent socioeconomic exclusion and uneven public investment continue to place marginalised regions at a structural disadvantage. Health-related inequalities show a more straightforward but weak relationship with colonial origin, plausibly shaped mainly by divergent policy approaches to Universal Health Coverage. Lower socioeconomic inequality in many Anglophone systems highlights the importance of sustained political commitment, fee removal strategies, and effective financing models in reducing barriers to care.\u003c/p\u003e \u003cp\u003eOverall, the findings emphasise that narrowing child material inequalities requires targeted, multisectoral interventions, particularly in water, sanitation, housing, education, and health financing, combined with governance reforms that prioritise equity across regions.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eCLF conceived the study, performed the analysis, and drafted the manuscript. DG and ZT contributed to the study conception and supervised the analysis and manuscript drafting. All authors reviewed and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eI gratefully acknowledge the South West Doctoral Training Partnership for awarding the funding that supported the completion of my PhD at the University of Bristol. I also thank Helen Gordon and Dr Mary Zhang for their invaluable inputs.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eACEMOGLU, D., S. JOHNSON, and J. A. ROBINSON. 2001. The colonial origins of comparative development: an empirical investigation. \u003cem\u003eAmerican Economic Review\u003c/em\u003e 91(5):1369\u0026ndash;1401.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eARSENAULT, C., M. JOHRI, A. NANDI, J. M. MENDOZA RODR\u0026Iacute;GUEZ, P. M. HANSEN, and S. HARPER. 2017. 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Health Equity Assessment Toolkit (HEAT): Software for exploring and comparing health inequalities in.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ecountries. \u003cem\u003eBuilt-in database edition\u003c/em\u003e. Geneva: World Health Organization.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWORLD BANK. 2025. \u003cem\u003eWorld Governance Indicators\u003c/em\u003e [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access\u003c/span\u003e\u003cspan address=\"https://www.worldbank.org/en/publication/worldwide-governance-indicators/interactive-data-access\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [Accessed 18.10. 2025].\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYATES, D. A. 2018. Paradoxes of predation in Francophone Africa. \u003cem\u003eInternational Journal of Political Economy\u003c/em\u003e 47(2):130\u0026ndash;150.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1: Sub-Saharan African countries by colonial origin (2000-2019)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFrancophone states\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size \u003csup\u003e1\u003c/sup\u003esurvey 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size survey 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size survey 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eGabon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e31803\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2000;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e14359\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eCongo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e37590\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2005;2011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e14152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e23438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eCameroon\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e53889\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e25591\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e28298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNiger\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e62349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2006;2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e26391\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e35958\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMadagascar\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e62953\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19413\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e43541\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eGuinea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e66636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2005;2012;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19564\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e22601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e24471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eChad\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e70894\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e16211\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e54683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eB. Faso\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e77862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2003;2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e31904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e45957\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSenegal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e90190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2005:2011;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e33731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e37021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e19438\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMali\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e99707\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2006;2012;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e39321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e31041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e29345\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eBenin\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e127200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2006;2012;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e45706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e44653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e36841\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e781072\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnglophone states\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSurvey years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size survey 1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size survey 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size survey 3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNamibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e36457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2007;2013\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e18795\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17662\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eZimbabwe\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e41920\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2005;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e20556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e21364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eLesotho\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e49581\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2009;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17186\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e15259\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eGhana\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e52926\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2003;2008;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e12764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e20809\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e19352\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eGambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e53195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2013;2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e26300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e26895\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eTanzania\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e82266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2010;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e24862\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e25555\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e31850\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eS. Leone\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e92039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2008;2013;2019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e20671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e37039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e34329\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eZambia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e94493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2007;2010;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e42872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e32794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eUganda\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e98986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2006;2011;2016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e37021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19438\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eKenya\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e116322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2003;2009;2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e19191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e77816\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMalawi\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e156016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2004;2010;2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e31477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e62102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e62437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eNigeria\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e190365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e2003;2008;2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e17755\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e78381\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e94229\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 12px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1,064,566\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: DHS data, PSPWs applied\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2: Child poverty dimensions and indicators: an SDG-updated Gordon et al. (2003b)\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"580\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoverty dimensions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 291px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild poverty=Moderate to severe deprivations.\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\u003ctd\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMild deprivations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModerate deprivations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSevere deprivations\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater (SDG 6)\u003csup\u003ea\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eBasic water source: Improved source and collection time is 30 minutes or below from the source\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLimited water source: improved, and collection time is more than 30 minutes from the source. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unimproved water sources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eDrinking water directly from a river, dam, lake, pond, stream, canal, or irrigation canal\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u003csup\u003eb\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eInsufficient water occasionally due to lack of sufficient money\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanitation (SDG 6)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eBasic sanitation: Improved facilities that separate excreta from human contact (pit latrines with slabs, ventilated improved pit latrines, and composting toilets) and that are not shared with other households.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eLimited sanitation: Improved facilities yet shared with two or more households. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Unimproved toilet facilities, including latrines with no slabs, buckets or hanging toilets\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eOpen defaecation in fields, forests, bushes, open bodies of water, beaches, and other open spaces or with solid waste\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eHaving to share facilities with other households\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eSanitation facilities outside the dwelling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDwelling (SDG 11)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eAt least one of the following items (floor, wall, or roof) is constructed using non-durable materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eTwo of the following items (floor, wall, or roof) were constructed using non-durable materials.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eAll three items (floor, wall, and roof) were constructed using non-durable housing materials. Dwelling lacks electricity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eOvercrowding with three persons per room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eFour persons per room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eFive or above persons per room.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eDwelling in poor repair/two persons per room\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eFew facilities in dwelling/lack of heating/structural problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformation (SDG 17)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eHouseholds lack one out of three items (mobile phone, radio or TV set\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eHouseholds lack two out of three items\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eHouseholds lack all three items.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eCannot afford newspapers/books\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\u0026nbsp;\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNutrition and food security (SDG 2)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eMinimum dietary diversity: Consumed four out of eight different food groups the previous day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eConsumed three of the eight food groups the previous day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eConsumed two or fewer of the eight food groups (starvation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eSuffered at least one form of anthropometry failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSuffered multiple forms of anthropometry failure\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;Bristol Approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eMild food insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eModerate food insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eSevere food insecurity\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eBland diet of poor nutritional quality\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eGoing hungry occasionally\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eMalnutrition\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth (SDG 3)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eMissed out on one of the eight vaccines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eMissed two-six vaccines\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eReceived none of the vaccines. Received no medical treatment for diarrhoea, fever and cough \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eOccasionally, lack access to healthcare due to insufficient funds\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eInadequate medical care\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation (SDG 4)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eIncomplete secondary schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003eIncomplete primary schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003eNo formal schooling\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 124px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBristol Approach\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 165px;\"\u003e\n \u003cp\u003eInadequate teaching due to a lack of resources\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 155px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003eSource: Gordon et al. (2003a\u003c/em\u003e\u003cem\u003e)/Bristol Approach updated to reflect current SDGs, \u003csup\u003ea\u003c/sup\u003eSDG indicators, \u003csup\u003eb\u003c/sup\u003eBristol Approach indicators that were modified\u0026nbsp;\u003c/em\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc162081496\"\u003e\u003cstrong\u003eTable 3: Unweighted study characteristics by colonial origins\u003c/strong\u003e\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"329\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVariables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePercentage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChild age\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; 0-4 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e593,418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e31.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; 5-17 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,304,772\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e68.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e960,179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e50.58\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Female\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e938,011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e49.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlace of residence\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Urban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e562,263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e29.62\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Rural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,335,927\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e70.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocality\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Administrative region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e227,172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e11.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Subnational region\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,671,018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e88.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge of household head\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Below 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,042,887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e54.95\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; 45 and above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e854,925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e45.05\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex of household head\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Males\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1,489,790\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e78.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Females\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e408,400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e21.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaternal literacy of under-5s\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; No education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e179,583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e46.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Primary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e120,716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e31.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Secondary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e72,291\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18.88\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Tertiary education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e10,245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e2.68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHousehold\u003c/strong\u003e \u003cstrong\u003ewealth\u003c/strong\u003e \u003cstrong\u003estatus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e442,463\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e23.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Poorer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e395,228\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Middle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e381,606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e20.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e352,868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e18.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 189px;\"\u003e\n \u003cp\u003e\u0026nbsp; Richest\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 64px;\"\u003e\n \u003cp\u003e311,279\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e16.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Table 4: Coefficients of regression models showing associations between intraregional inequalities in child-specific indicators and colonial origins (at 95% CI).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSanitation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDwelling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo formal schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncomplete schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eFrancophone countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e9.7\u003c/p\u003e\n \u003cp\u003e(-3, 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.5\u003c/p\u003e\n \u003cp\u003e(-16, 15)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e4.7\u003c/p\u003e\n \u003cp\u003e(-10, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003cp\u003e(-7, 19)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.8*\u003c/p\u003e\n \u003cp\u003e(4, 24)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1.39\u003c/p\u003e\n \u003cp\u003e(-7, 10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUrbanisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003cp\u003e(-.7, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(-0.5, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.15\u003c/p\u003e\n \u003cp\u003e(-.6, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(-.7, .2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.31\u003c/p\u003e\n \u003cp\u003e(-.7, -.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0 .03\u003c/p\u003e\n \u003cp\u003e(-.3, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eP_stability/\u003c/p\u003e\n \u003cp\u003eabsviolence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e(-.5, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.11\u003c/p\u003e\n \u003cp\u003e(-.4, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-.5, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(-.8, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.10\u003c/p\u003e\n \u003cp\u003e(-.4, .4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003cp\u003e(-.4, .2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eG_efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003cp\u003e(-.4, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003cp\u003e(-.4, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003cp\u003e(-.4, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003cp\u003e(-.8, .4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.09\u003c/p\u003e\n \u003cp\u003e(-.2, .4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-.18\u003c/p\u003e\n \u003cp\u003e(-.5, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 105px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 113px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 94px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 116px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003ea\u003c/sup\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;Anglophone countries as base case, \u003csup\u003e\u0026nbsp;b\u003c/sup\u003epolitical stability and absence of violence, \u003csup\u003ec\u003c/sup\u003egovernment efficiency, *p\u0026lt;0.05, **p\u0026lt;0.01, *p\u0026lt;0.001\u003c/em\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTable 5: Coefficients of regression models showing socioeconomic inequalities in child-specific indicators (relative measures) and colonial origins (95% CIs).\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"662\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow vaccine access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow medical access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo formal schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncomplete schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow MDD\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eFrancophone countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-19.7\u003c/p\u003e\n \u003cp\u003e(-47, 8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-18.8*\u003c/p\u003e\n \u003cp\u003e(-32, -5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e13.7\u003c/p\u003e\n \u003cp\u003e(-0.3, 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e44**\u003c/p\u003e\n \u003cp\u003e(17.4, 70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e5.7\u003c/p\u003e\n \u003cp\u003e(-7, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e6.7\u003c/p\u003e\n \u003cp\u003e(-7, 20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e10.1\u003c/p\u003e\n \u003cp\u003e(-2, 23)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUrbanisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003cp\u003e(-.6, 1.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003cp\u003e(-.5, .4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(-.2, .9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-1.5**\u003c/p\u003e\n \u003cp\u003e(-2.3, -.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.6*\u003c/p\u003e\n \u003cp\u003e(-1.0, -.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.8*\u003c/p\u003e\n \u003cp\u003e(-1.2, -.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(-.3, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eP_stability/\u003c/p\u003e\n \u003cp\u003eabsviolence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-.4, 1.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003cp\u003e(.09, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003cp\u003e(-.1, 1.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003cp\u003e(-.9, .9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(-.3, .5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003cp\u003e(.04, .9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e(-.2, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ec\u003c/sup\u003eG_efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.29\u003c/p\u003e\n \u003cp\u003e(-1.2, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.28\u003c/p\u003e\n \u003cp\u003e(-.7, .2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.26\u003c/p\u003e\n \u003cp\u003e(-.8, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.18\u003c/p\u003e\n \u003cp\u003e(-.7, 1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.4\u003c/p\u003e\n \u003cp\u003e(-.8, .03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.42\u003c/p\u003e\n \u003cp\u003e(-.9, .03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.39\u003c/p\u003e\n \u003cp\u003e(-.8, .01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 97px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 80px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 95px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/em\u003e\u003cem\u003ep\u0026lt;0.05, **p\u0026lt;0.01, *p\u0026lt;0.00, SAF-Single Anthropometry Failure, MAF-Multiple Anthropometry failure, MDD- Minimum Dietary Diversity,\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan id=\"_Toc191917263\"\u003eTable 6: Coefficients of regression models showing associations between socioeconomic inequalities in child-specific indicators (absolute measures) and colonial origins\u003c/span\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"642\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow vaccine access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow medical access\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo formal schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIncomplete schooling\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMAF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u0026nbsp;\u003c/strong\u003eMDD\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003eFrancophone countries\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e12.4\u003c/p\u003e\n \u003cp\u003e(-.2, 25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e11.3*\u003c/p\u003e\n \u003cp\u003e(4, 18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e24*\u003c/p\u003e\n \u003cp\u003e(6.7, 41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-18\u003c/p\u003e\n \u003cp\u003e(-36, -41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-2.5\u003c/p\u003e\n \u003cp\u003e(-7, 2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-.65\u003c/p\u003e\n \u003cp\u003e(-6, 4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-4.6\u003c/p\u003e\n \u003cp\u003e(-14, 5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eUrbanisation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(-.2, .6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003cp\u003e(-.1, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.22\u003c/p\u003e\n \u003cp\u003e(-.8, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003cp\u003e(-.4, .8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003cp\u003e(-.05, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.13\u003c/p\u003e\n \u003cp\u003e(-.03, .3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.18\u003c/p\u003e\n \u003cp\u003e(-.5, .1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 98px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u003csup\u003eb\u003c/sup\u003eP_stability/\u003c/p\u003e\n \u003cp\u003eabsviolence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 83px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.51*\u003c/p\u003e\n \u003cp\u003e(-.9, -.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 75px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.25\u003c/p\u003e\n \u003cp\u003e(-.5, -.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.3\u003c/p\u003e\n \u003cp\u003e(-.9, 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valign=\"top\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e-0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd nowrap=\"\" valign=\"top\" style=\"width: 76px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e\u003csup\u003e*\u003c/sup\u003e\u003c/em\u003e\u003cem\u003ep\u0026lt;0.05, **p\u0026lt;0.01, ***p\u0026lt;0.001\u003c/em\u003e\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":false,"email":"","identity":"the-european-journal-of-development-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"The European Journal of Development Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8609099/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8609099/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Persistent underdevelopment and widening inequalities in basic material needs in Sub-Saharan Africa (SSA) may reflect the administrative legacies of British and French colonial rule. This study examines within-group child poverty inequalities across Francophone and Anglophone countries using pooled Demographic and Health Survey (DHS) data from 2000–2019 and deprivation indicators developed by Gordon et al. (2003). Geographical and socioeconomic inequalities were assessed by colonial origin, alongside regression models adjusting for urbanisation and governance quality. Francophone countries showed wider socioeconomic inequalities in limited medical access and formal education than Anglophone countries, although disparities in incomplete primary schooling were narrower. Higher urbanisation correlated with greater inequality in incomplete schooling and child undernutrition. s. Across SSA, pronounced regional inequalities persisted in water, sanitation, and housing, irrespective of colonial origin, while health and nutrition showed comparatively smaller regional disparities. Policy priorities should combine universal education and healthcare reforms with sustained investment in basic household infrastructure.","manuscriptTitle":"Colonial origins and child poverty inequalities in Sub-Saharan Africa","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-23 09:15:53","doi":"10.21203/rs.3.rs-8609099/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"273234713828132687773382863597005980280","date":"2026-04-16T15:00:14+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-14T19:58:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-17T05:55:05+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-17T05:54:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"The European Journal of Development Research","date":"2026-01-15T09:26:15+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":false,"email":"","identity":"the-european-journal-of-development-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"The European Journal of Development Research","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"VoR Journals","inReviewEnabled":false,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"897a654a-eca3-46e0-8a67-d2f875ba5996","owner":[],"postedDate":"April 23rd, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-23T09:15:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-23 09:15:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8609099","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8609099","identity":"rs-8609099","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}