Beyond Growth: A Methodological Framework for Assessing Urban Quality of Life Disparities in Peru

Beyond Growth: A Methodological Framework for Assessing Urban Quality of Life Disparities in Peru | 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 Article Beyond Growth: A Methodological Framework for Assessing Urban Quality of Life Disparities in Peru Dana Hübelová, Lenka Hromková, Alice Kozumplíková This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8259461/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 4 You are reading this latest preprint version Abstract This article develops and applies a multidimensional framework to assess urban quality of life (QoL) disparities in Peru during a decade of economic growth (2007–2017). Moving beyond income-based approaches, the study is grounded in the capability approach, which conceptualises well-being as real opportunities and functionings, and in theories of regional inequality derived from New Economic Geography. A composite QoL index (IQoL) is constructed from five dimensions—income, living standards, education, fertility, and legal identity—using census microdata. Legal identity is conceptualised as a “gateway capability” conditioning access to other resources and overall QoL. Methodologically, we apply the Weighted Sum Approach with equal weights, address the modifiable areal unit problem, and capture spatial dependence through Local Indicators of Spatial Autocorrelation (LISA). The results show that while income poverty declined, income inequality remained stagnant and spatial disparities widened. This pattern reflects agglomeration effects predicted by New Economic Geography, the Williamson inverted-U hypothesis, and the Harris–Todaro expected wage model, which explain why migration and urbanisation sustain disparities despite poverty reduction. Fertility and documentation contributed most to improvements in IQoL, whereas living standards and education produced heterogeneous effects. The findings confirm the non-substitutability of capabilities: gains in one domain cannot compensate for deficits elsewhere. The study contributes by operationalising the capability approach in a Latin American context and linking multidimensional poverty analysis with spatial economic theories. It also offers a replicable methodological tool for monitoring QoL disparities across regions and over time. Social science/Development studies Earth and environmental sciences/Environmental social sciences Scientific community and society/Geography Social science/Geography Multidimensional Poverty Spatial Inequality Capability Approach Agglomeration Effects Well-being Indicators Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction The quality of life (QoL) significantly influences a wide range of societal domains, including economic productivity and public health to environmental sustainability and social development. Understanding and evaluating QoL is therefore essential for guiding targeted policies, especially in regions facing persistent socio-economic inequalities. While economic growth is often assumed to lead to improved living conditions, numerous studies in the Global South, including Latin America, show that this relationship is neither linear nor uniform across social groups or geographical areas (Gasparini et al., 2010 ; Sekkat, 2017 ). The need for multidimensional and spatially sensitive assessments of QoL has become particularly pressing in rapidly urbanising countries such as Peru, where aggregate economic progress coexists with deeply rooted regional disparities. Building on this, our approach is theoretically grounded in two complementary strands of literature. First, the capability approach (Sen, 1999 ; Nussbaum, 2011 ) and related work on multidimensional poverty measurement (Alkire and Foster, 2011 ) provide the conceptual foundation for moving beyond income-based indicators. These frameworks conceptualise well-being as a set of real opportunities and functionings, not merely as monetary resources, and are thus directly aligned with our construction of a five-dimensional QoL index (A.1–A.5). Importantly, we interpret legal identity (A.5 Undocumented) as a “gateway capability” conditioning access to other essential capacities such as health, education, and financial inclusion. Second, our spatial perspective is anchored in the literature on New Economic Geography (NEG) (Krugman, 1991 ) and regional inequality theories (Myrdal, 1957 ; Williamson, 1965 ; Harris and Todaro, 1970 ). These approaches emphasise the endogenous formation of core–periphery structures through agglomeration effects, transport costs, and increasing returns in key services. For urban Peru, these dynamics imply that access to jobs, services, and infrastructure in metropolitan cores may reinforce inequality even during periods of aggregate economic growth. The Harris–Todaro expected wage model explains rural–urban migration as a function of anticipated returns, often sustaining urban poverty despite national poverty reduction, while the Williamson inverted-U hypothesis highlights the possibility of widening disparities during early and intermediate phases of development. By embedding our empirical analysis within these frameworks, we provide a theoretically robust interpretation of the observed spatial heterogeneity in QoL. Latin America is among the most urbanised regions globally, with over 80% of the population living in urban areas. In Peru, the process of urbanisation intensified during the second half of the 20th century and accelerated further after the turn of the millennium, driven by internal migration and demographic pressures (Morley, 2017 ). While cities have traditionally been seen as engines of growth, innovation, and upward mobility (Glaeser, 2011 ), unplanned urbanisation in developing contexts has often exacerbated inequality, environmental degradation, and spatial marginalisation (Jedwab, Christiansen and Gindelsky, 2017 ; Glaeser, Kahn and Rappaport, 2008 ). In Peru, up to one-third of the population resides in the capital Lima, including large numbers in informal settlements known as pueblos jóvenes , where residents face systemic challenges in housing, infrastructure, and access to public services (Azpitarte, Gallegos and Yalonetzky, 2020 ). Despite significant reductions in national poverty rates during the 2000s—largely due to favourable export conditions and social spending—income inequality remains high. The Gini index in Peru decreased from 53.7 in 1997 to 40.2 in 2021, yet still indicates a significant concentration of wealth (World Bank, 2023 ). More importantly, monetary measures alone fail to capture disparities in access to health, education, and other non-income dimensions of well-being. Empirical studies show that human development losses due to inequality are substantial in Peru, particularly in education (Lopez-Calva and Ortiz-Juarez, 2012). Ethnic and geographic disparities further compound these inequalities. Up to 45% of indigenous populations such as the Aymara and Quechua live in multidimensional poverty, lacking access to adequate nutrition, schooling, or identification documents (INEI, 2018a , UNDP and OPHI, 2021 , Carranza Ko, 2020 ). Documentation status is a major determinant of access to rights and services. Without national identification documents or birth certificates, individuals are effectively invisible to the state. They are excluded from social programmes, unable to obtain loans or title deeds, and systematically disadvantaged in the labour market (INEI, 2008 ; DGPP MEF, 2015 ). In rural and marginalised urban areas, undocumented status is frequently intergenerational, perpetuating cycles of exclusion. Recognising this, the Peruvian government implemented national plans (2005–2015) to reduce undocumented populations, yet their reach and effectiveness remain limited (RENIEC, 2012 ; Peters and Mawson, 2016 ). Understanding the complexity of urban inequality thus requires more than GDP or income metrics. Multidimensional poverty measures such as the Global Multidimensional Poverty Index (MPI), which includes health, education, and living standard indicators, offer a broader lens. However, existing metrics often lack spatial granularity or the ability to track long-term changes at regional or municipal levels (Torado and Smith, 2011; Santos and Villatoro, 2018 ). In response to this gap, the present study proposes a methodological tool that builds composite indices of QoL using Peruvian census data from 2007 and 2017. These indices reflect five key dimensions—aligned with international frameworks—enabling the identification of patterns and persistence of regional disparities. QoL assessment can follow either subjective or objective approaches. While subjective well-being data (such as life satisfaction surveys) have gained traction in global research (Yonk, Smith and Wardle, 2017 ), they are often unavailable in national censuses. Therefore, objective measurement based on available indicators—such as housing conditions, access to utilities, school attendance, and healthcare infrastructure—remains the most viable path for longitudinal and large-scale studies in Peru. Our index-based approach draws on this tradition, creating a replicable and scalable tool for identifying vulnerable regions and informing policy priorities. Urbanised ecosystems present unique challenges for well-being. Factors such as air and noise pollution, green space availability, transport accessibility, and population density significantly affect residents health and quality of life (Lakes, Brückner and Krämer, 2014 ; Dizdaroglu, 2015 ; Chen, Land and Li, 2019 ; Floková et al., 2023 ). In Peru, infrastructure deficits and historical patterns of exclusion—exemplified by the colonial “hub and spoke” transport design—further exacerbate spatial inequalities (Czerny, Córdova-Aguilar and Rzucidło, 2015 ). The goal of this study is twofold. First, we aim to present and validate a methodological framework for assessing QoL disparities using a composite index approach based on national census data. Second, we apply this method to evaluate spatial and temporal changes in QoL across urban areas of Peru between 2007 and 2017. The research is guided by four sub-objectives: (1) assessing the spatial shift in income poverty; (2) comparing changes in sub-index values across QoL dimensions; (3) constructing an aggregate Quality of Life Index; and (4) interpreting the relationships between the sub-dimensions and overall index values across time and space. By developing and applying this methodological tool, we aim to contribute both empirically and conceptually to the ongoing debate on urban inequality, multidimensional poverty, and sustainable development in Latin America. Our approach allows for consistent cross-temporal comparisons and the identification of long-term trends that may otherwise remain hidden in aggregated national statistics. The findings can support decision-makers in designing place-based interventions, allocating resources more equitably, and ensuring that the benefits of urban development reach all segments of the population—not just those at the top. Methodologically, the construction of our indices and the overall IQoL is consistent with OECD ( 2008 ) recommendations for composite indicators, while also explicitly reflecting two key spatial concerns: the modifiable areal unit problem (MAUP) (Openshaw, 1984 ) and spatial autocorrelation (Anselin, 1995 ). These considerations strengthen the reliability and replicability of our results. This study addresses the following research question: did the period of economic growth between 2007 and 2017 reduce multidimensional inequalities in quality of life across urban regions of Peru? Building on the capability approach and New Economic Geography, we test two hypotheses: (H1) that aggregate economic growth does not automatically translate into reduced spatial inequalities of QoL; and (H2) that some capabilities, particularly legal identity, act as gateway conditions enabling access to other resources, while their absence reinforces disparities. By articulating these hypotheses, the study goes beyond descriptive analysis and contributes to the theoretical debate on the interplay between economic growth, spatial inequality, and multidimensional well-being. Data and methodological procedures This study is based on data from the 2007 and 2017 Peruvian Population and Housing Censuses provided by the National Institute of Statistics and Informatics (INEI). These datasets were chosen due to their consistent methodology, objectivity, and accessibility (Carr-Hill, 2013 ). Additional data on income poverty were obtained from the Peruvian Institute of Economics (IPE, 2023 ). The analysis was conducted at the level of Perus 25 administrative regions (24 departments and the Constitutional Province of Callao), with a focus exclusively on the urban population. Urban areas were defined by the urbanisation rate – the share of people living in urbanised zones. To analyse disparities in income and wealth distribution among the urban population, we recalculated the proportions of people living in income poverty (INEI) relative to the total urban population (IPE) (INEI, 2008 ; INEI, 2018b ; INEI, 2018c ; IPE, 2023 ). We visualised inequality using the Lorenz curve and interpreted results using the Gini index, which ranges from 0 (perfect equality) to 1 (maximum inequality). Income poverty was assessed both in terms of total and extreme poverty. To explore changes in regional disparities over time, we applied the concepts of beta and sigma convergence. Beta convergence examines whether regions with initially lower values (e.g. lower income levels) experience faster improvement over time. Sigma convergence assesses whether the variability among regions decreases. Both approaches used logarithmic transformations of the data. For beta convergence, we computed the average growth rate between the two periods; for sigma convergence, we analysed changes in standard deviation. The construction of the Quality of Life Summary Index (IQoL) was guided by a multidimensional approach covering five dimensions: income poverty, living standards, education, fertility, and documentation status (see Fig. 1 ). Each dimension consists of specific indicators (10 in total), which were standardised by expressing them as a proportion of the urban population. To facilitate interpretation, individual indices were created for each dimension. All indicators reflect deprivation – i.e. lower values indicate better conditions. Therefore, we applied a normalisation technique suitable for minimisation criteria. Specifically, we used the Weighted Sum Approach (WSA), a method rooted in utility theory assuming linear utility functions (Hübelová et al., 2021). In the first step, each indicator was normalised, and a weighted matrix was created with equal weights assigned to all indicators. We then aggregated the values within each dimension to obtain dimension-specific indices. In the second step, we repeated the WSA procedure using the five-dimension indices as inputs to calculate the overall IQoL. Again, all dimensions were equally weighted. To provide a spatial interpretation, z-scores were calculated for each dimension (A1–A5) using linear transformation. This expresses how many standard deviations each region differs from the national mean. Positive z-scores indicate better-than-average conditions, while negative scores reflect worse-than-average performance. Finally, we employed Local Indicators of Spatial Association (LISA; Anselin, 1995 ) to visualise spatial patterns. This technique identifies: Hot spots (HIGH-HIGH) – clusters of regions with high index values, Cold spots (LOW-LOW) – clusters of regions with low index values, Spatial outliers (HIGH-LOW or LOW-HIGH) – regions that contrast with their surrounding areas. Results Between 2007 and 2017, the total population of Peru increased by 10.7%, primarily driven by a 12% rise in the urban population. In contrast, the rural population declined by 8.1%, highlighting the ongoing process of urbanization. Notably, this demographic shift was not evenly distributed across the countrys geographical regions. In 2007, the Costa region accounted for 15 million inhabitants—58% of the national population—rising to 17 million by 2017 (a 13.8% increase). In contrast, the Sierra region experienced a population decline from 8.76 to 8.27 million (-5.7%), while the Selva region, despite being the least populous, saw a growth from 3.7 to 4 million (+ 10.9%) (INEI, 2008 ; INEI, 2018c ). Marked differences are also evident in the administrative distribution of the population. In 2017, nearly one-third (29.2%) of the population resided in the Lima administrative region, which overlaps geographically with the Costa. Additional concentrations of population were found in Piura and La Libertad (Costa), as well as in Arequipa and Cajamarca (Sierra). In contrast, the lowest population densities were recorded in Madre de Dios (Selva), and in Moquegua, Tumbes, and Tacna (Costa), as well as Pasco (Sierra). Census data from 2007 and 2017 further reveal that most administrative regions are predominantly urban. However, four regions—Huancavelica and Cajamarca (Sierra), and Amazonas (Selva)—remained majority rural. The highest urban population growth was recorded in Madre de Dios (+ 45%), while Amazonas, Pasco, and especially Huancavelica experienced a decline, with Huancavelicas urban population falling by over 25%. Spatial Variation in Quality of Life Dimensions The indices for each of the five QoL dimensions (A.1 to A.5) were analysed in terms of spatial distribution and temporal change between 2007 and 2017. Higher index values (particularly those indicated in dark green) were generally concentrated in the Costa region. In the Sierra, relatively high values were observed for dimension A.5 (Undocumented), while in Selva, improvements were most visible in A.4 (Fertility) (see Fig. 2 ). The aggregated IQoL score, based on all five dimensions, was calculated for each urban administrative region. Despite persistent spatial disparities, improvements were recorded in 76% of the regions, with a modest overall increase in IQoL (mean change: +0.060; median change: +0.066). The most notable positive changes occurred in Ayacucho and Apurímac (Sierra), which shifted from significantly below-average values in 2007 to near-average or higher levels in 2017. Conversely, 24% of regions recorded a decline, with the most pronounced negative shifts observed in Tacna (-0.01), Moquegua (-0.03), Callao (-0.05), Ucayali (-0.05), Lima (-0.07), and Loreto (-0.11) (see Fig. 3 ). A breakdown of the individual dimension indices suggests that improvements in IQoL were most strongly influenced by changes in A.4 (Fertility) and A.5 (Undocumented), followed by A.1 (Poverty). However, some regions experienced declines in A.2 (Living Standards) and A.3 (Education), contributing to stagnating or deteriorating IQoL (see Fig. 4 ). Regional Discrepancies and Composition Effects The contribution of each dimension to the overall IQoL reveals spatial inconsistency. In 2007, significant discrepancies were observed, particularly where a high value in A.5 (Undocumented) coincided with a low overall IQoL (e.g., Apurímac, Ayacucho, and Huancavelica). Similarly, in regions such as Tacna, Arequipa, and Moquegua, a lower A.3 (Education) index did not prevent an above-average IQoL. These mismatches persisted in 2017: for example, Ucayali showed high A.4 (Fertility) scores but low IQoL, while Lima and Tacna achieved high IQoL despite lower A.5 (Undocumented) scores (see Fig. 5 and Fig. 6 ). The relative significance of each dimension varied by both geography and year. In Costa, all dimensions except A.5 (Undocumented) were consistently above average in 2017. Conversely, all dimensions in the Sierra remained below average, with the exception of A.5. In Selva, dimensions A.1 (Poverty) and A.5 (Undocumented) were particularly weak, while A.3 (Education) showed the steepest decline over time. Dimension A.2 (Living Standards) remained below average, although A.4 (Fertility) reached the national average in 2017. Summary of Key Findings In alignment with the research objectives, the main findings are as follows: Between 2007 and 2017, the proportion of income-poor individuals in urban areas declined, while income inequality remained largely unchanged. A comparison of temporal and spatial changes in QoL dimension indices revealed regional disparities. Although the share of the population with low Living Standard scores declined overall, the pace of improvement varied across administrative regions. Spatial inequalities in Education persisted, with the Selva region (particularly Ucayali, San Martín, and Madre de Dios) seeing an increase in the number of individuals over age 15 without primary education or who were illiterate. Fertility rates improved nationally, particularly among young adolescents (aged 12–14), while high fertility (four or more children) remained concentrated in the Selva (notably Huancavelica and Apurímac). There was an overall improvement in documentation, as fewer individuals lacked identity cards or birth certificates, although progress was limited in Lima and Callao. The central and southern Costa regions exhibited consistently high IQoL in both years, though some (e.g., Lima and Callao) showed no further improvement between 2007 and 2017. Ayacucho and Apurímac improved markedly, transitioning from below-average to average IQoL, whereas Huancavelica (Sierra) and Ucayali (Selva) remained stagnant. Importantly, a single high or low value in one QoL dimension did not necessarily correspond to the final IQoL. For instance, Ayacucho and Huancavelica had high A.5 scores but low IQoL in both years. Similarly, Ucayali improved in A.4 but did not see a rise in IQoL, while Tacna, Arequipa, and Moquegua maintained high IQoL despite lower A.3 scores. Discussion A comparison of census data from 2007 and 2017 reveals that the administrative regions of Huancavelica, Pasco, and Amazonas experienced the most significant population decline, whereas Madre de Dios, Ica, and Arequipa recorded the highest population growth. These demographic shifts align with the observed values of IQoL—regions with low IQoL tend to be population losers, while those with above-average IQoL are income losers. In general, rural areas have seen a population decline, driven by a combination of declining fertility and outward migration (Morley, 2017 ). Migration has particularly affected the Sierra and Selva regions more than the Costa, reflecting disparities in employment opportunities and access to services (Todaro and Smith, 2011 ). One of the consequences of migration is a deepening of poverty, often associated with the separation of women from their families due to economic migration (Crivello, 2015 ; Morley, 2017 ). These results resonate with the theoretical expectations of the NEG framework and the Williamson hypothesis. The persistence of high disparities despite overall poverty reduction reflects strong agglomeration externalities in metropolitan cores such as Lima, which continue to attract resources and skilled labour, thereby reinforcing spatial concentration. The Harris–Todaro expected wage mechanism further explains why migration towards urban centres may stabilise income inequality despite poverty reduction, as migrants enter the informal sector where underemployment prevails. Together, these theoretical perspectives illuminate why income inequality (as measured by the Gini index) remains largely unchanged, while spatial disparities diverge. In parallel, the findings also confirm the analytical relevance of the capability approach. Dimensions A.4 (Fertility) and A.5 (Undocumented) emerged as decisive for improving IQoL, while A.2 (Living Standards) and A.3 (Education) weakened outcomes in some regions. The uneven contribution of dimensions illustrates the non-substitutability of capabilities: high performance in one area (e.g. legal identity) does not automatically compensate for deficits in another (e.g. education). The case of Ayacucho and Huancavelica, where high documentation scores coexist with low overall IQoL, exemplifies this principle. Legal identity functions as an enabling mechanism, but without complementary improvements in education or living standards, its impact remains limited. Methodologically, our adoption of the Weighted Sum Approach with equal weights, the reporting of spatial dependence through LISA, and explicit consideration of MAUP strengthen the robustness of these findings. By combining these statistical safeguards with theoretically informed interpretation, our analysis integrates both empirical and conceptual contributions to the study of urban inequality in Peru. In Latin America, including Peru, multidimensional poverty presents a more complex challenge than income poverty alone (Todaro and Smith, 2011 ). Indigenous populations face a disproportionately high risk of income poverty—up to half of their population is affected, with particularly high rates in the Amazon region. Although income inequality as measured by the Gini index has declined—primarily due to national economic growth—regional disparities persist. In some urban areas, inequality has even increased slightly (IPE, 2023 ; Winkelried and Escobar, 2022 ), which may relate to the growing presence of the middle class (López-Calva and Ortiz-Juarez, 2014 ). The quality of life is further reflected in the incidence of absolute poverty, which in 2017 was approximately three times higher in rural areas than in urban ones. For extreme poverty, this ratio reached up to eleven times. Nonetheless, urban poverty is often underestimated in official statistics (Parnell, 2005 ; Tacoli, 2007 ; Mitlin and Satterthwaite, 2013 ; Thanh, Anh and Phuong, 2013 ), particularly because informal settlements are frequently excluded. In addition, standard deprivation indicators often fail to provide decision-makers with sufficient information for effective intervention (Lucci and Bhatkal, 2014 ). In contrast, the positive spillover effects of urban development on surrounding rural areas are well documented (Ke and Feser, 2010 ; Chen and Partridge, 2011 ). These effects are associated with larger urban populations and reduced rural poverty, demonstrating that urbanisation can stimulate economic development in adjacent rural zones (Calì and Menon, 2012 ). Our findings partially support those of Calì and Menon ( 2012 ), who analysed six indicators—female-headed households, primary education completion, unemployment, electricity access, sanitation, and malnutrition—and found a poverty-reducing effect in Pasco, Lima, and Cajamarca in 2007, and in Junín, Huancavelica, and Cajamarca in 2017. The present study, using a broader set of indicators, similarly identifies Junín and Cajamarca as regions with the greatest improvement in IQoL, while also highlighting Cusco, Amazonas, Arequipa, San Martín, Apurímac, and Ayacucho as regions with significant gains. Moreover, our analysis confirms that regional poverty disparities persist, with Selva and Sierra regions exhibiting higher concentrations of poverty compared to the Costa. Between 2006 and 2016, the Multidimensional Poverty Index showed that among its three dimensions, education was the most affected in Peru (Morley, 2017 ; Borga and DAmbrosio, 2021). Our results confirm this, especially the impact of dimension A.3 (Education) on IQoL in the Sierra. In practice, schooling often ends after six years of primary education, and access to secondary education remains limited (Czerny, Córdova-Aguilar and Rzucidło, 2015 ). As a response, initiatives have been launched to improve teacher training and evaluation, alongside financial support for higher education institutions (Prado, 2022 ; Boitano and Abanto Aranda, 2020 ). According to Morley ( 2017 ), investments in education, combined with economic growth, serve as an effective anti-poverty strategy for both the Sierra and Selva. The National Institute of Statistics and Informatics (INEI) used the Unmet Basic Needs (UBN) methodology to identify regions with above-average absolute and extreme poverty, including Loreto, Ucayali, Amazonas, San Martín, and Pasco, while the best-performing regions were Callao, Lima, Tacna, Arequipa, and La Libertad (INEI, 2018c ). Our results corroborate these findings: the greatest deterioration in dimension A.1 (Poverty) between 2007 and 2017 occurred in Loreto (-0.15), Ucayali (-1.10), and Amazonas (-0.08). Conversely, the most notable improvements in this dimension were observed in Apurímac (0.45), Ayacucho (0.25), and Huancavelica (0.24), partially differing from INEIs data (INEI, 2018b ; INEI, 2018c ). Conclusion The analysis of poverty drivers and conditions conducive to development has enabled the identification of key developmental challenges in Peru, consistent with previous findings (Plasencia, 2016 ). These challenges include: Limited access to infrastructure and basic services, which are fundamental prerequisites for poverty alleviation. Moreover, the quality of these services is frequently insufficient, further impeding progress. Persistent inequality in educational access, despite evidence that households with members over the age of 14 who have completed primary education demonstrate higher chances of socio-economic advancement. Household composition and gender dynamics play a crucial role in determining living standards. Family size and the gender of the household head are significant predictors of well-being, with female-headed households often exhibiting more progressive development trajectories. Linguistic marginalisation continues to be a critical issue, as populations speaking only indigenous languages are significantly more likely to live in poverty. Inequitable access to productive assets, such as housing, hinders the ability of poor households to formalise ownership. Legal recognition of assets substantially increases access to financial instruments, such as loans and credit. Entrepreneurial underdevelopment represents a further consequence of structural inequalities. The absence of enabling conditions stifles business activity, although evidence suggests that even small-scale commercial use of household space can improve living standards. Another under-addressed issue is the prevalence of undocumented individuals. In many countries, the lack of civil registration—particularly the absence of a birth certificate—renders individuals effectively stateless, excluding them from access to education, healthcare, banking, and other essential services. As UNICEF ( 1998 ) asserts, a birth certificate confers the first rights of name and nationality, enabling participation in civic life. In Peru, the challenge of undocumented status persists on a national scale, disproportionately affecting rural populations, women, and children (INEI, 2008 ; Woodhead, Dornan and Murray, 2013 ). In the poorest regions, up to 75% of the population lacks access to civil registration systems (Mennen, 2015 ). Barriers include geographic inaccessibility, dysfunctional administrative institutions, and high associated costs (Czerny, Córdova-Aguilar and Rzucidło, 2015 ; Cai, Selod and Steinbuks, 2018 ). Children without official identification face increased risks of exclusion, illiteracy, and health vulnerability (Peters and Mawson, 2016 ). A high degree of poverty is typically associated with a higher prevalence of undocumented individuals, particularly in the Selva region and in the departments of Lima and Arequipa (Azpitarte, Gallegos and Yalonetzky, 2020 ). Our results confirm these patterns. Although the Lima and Arequipa regions achieved above-average IQoL scores in both 2007 and 2017, the Lima region experienced a decline from 0.83 to 0.76. This was largely attributable to a significant decrease in the index for dimension A.5 (Undocumented), which dropped from 0.81 to 0.43—representing the largest decline among all departments studied. It is acknowledged that spatial assessments of quality of life are subject to methodological variability, depending on the selection of indicators and indices. This can hinder comparability across studies and regions. While this study is not exempt from such limitations, several measures were taken to mitigate them. The IQoL was constructed from a relatively comprehensive set of indicators, and census data were used to ensure consistency and methodological rigor in data collection and reporting. We also recognise that objective indicators may not fully align with subjective well-being. Empirical studies conducted in Peru have shown that increases in income or material security do not necessarily correspond with higher subjective life satisfaction. In some cases, individuals experiencing higher material deprivation report greater subjective well-being (Copestake et al., 2009 ). Guillen-Royo ( 2008 ) similarly notes that in the Peruvian context, consumption assumes symbolic and aspirational dimensions, influencing happiness through factors such as social status, peer comparison, and the desire to overcome marginalisation. The principal strength of this study lies in its longitudinal and spatial comparative approach, employing data from two census periods to capture regional disparities in quality of life over time. Although the data pertain to an earlier period, the methodology offers a solid foundation for future longitudinal analyses, including forthcoming census data. This paves the way for long-term monitoring of development outcomes and targeted interventions in the most vulnerable regions of Peru. The findings of this study highlight the necessity for integrated, territorially sensitive development policies in Peru that go beyond income-based measures of poverty. First, targeted investments in infrastructure and public services—particularly in rural and indigenous areas—are essential to enable social mobility and reduce spatial inequality. Second, there is a pressing need to ensure universal access to quality education, with a focus on overcoming linguistic and gender-based barriers. Special attention should be given to strengthening civil registration systems, especially in remote regions, to reduce the number of undocumented individuals. Policies aimed at promoting asset formalisation—such as property titling programs—must be linked with access to financial services and entrepreneurial support. Furthermore, social protection frameworks should explicitly include subjective dimensions of well-being, recognizing the social and psychological implications of poverty and exclusion. By implementing evidence-based and inclusive policies grounded in regional disparities, Peru can better respond to structural inequalities and support long-term improvements in the quality of life for its most vulnerable populations. Beyond empirical findings, this research contributes to theory in two ways. First, it demonstrates that the capability approach requires context-specific operationalisation: in Peru, legal identity emerges as a critical “gateway capability”, extending the original framework of Sen ( 1999 ) and Nussbaum ( 2011 ). Second, by applying NEG and inequality theories to longitudinal census data, our results nuance Williamsons inverted-U hypothesis (Williamson, 1965 ) and the Harris–Todaro model (Harris and Todaro, 1970 ), showing that spatial disparities may persist even under conditions of declining poverty. These contributions underline the need to integrate multidimensional poverty frameworks with spatial economic theories when analysing quality of life in rapidly urbanising societies. The proposed methodological framework, based on multidimensional indices and spatial econometrics, is transferable to other national contexts, thereby expanding the comparative study of quality-of-life disparities. Declarations Author Contribution DH was responsible for conceptualisation, methodology, investigation, formal analysis, and data curation, in addition to writing the original draft, reviewing, validation, and supervision. LH contributed to visualisation, data curation, and formal analysis. AK was involved in conceptualisation, formal analysis, validation, and visualisation, as well as writing the original draft, reviewing, and editing. All authors reviewed the manuscript. Acknowledgement This research was supported by the Internal Grant Agency FRRMS MENDELU (Grant number: GA-FRRMS-22-015). Data Availability Hübelová, Dana; Kozumplíková, Alice (2025), “Peruvian regions: demographic and socioeconomic data from the 2007 and 2017 censuses”, Mendeley Data, V1, doi: 10.17632/r74syfxwxj.1. References Alkire, S., and Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics 95(7–8): 476–487. https://doi.org/10.1016/j.jpubeco.2010.11.006 Anselin, L. (1995). Local Indicators of Spatial Association-LISA. Geographical Analysis 27: 93–115. https://doi.org/10.1111/j.1538-4632.1995.tb00338.x Azpitarte, F., Gallegos, J., and Yalonetzky, G. (2020). On the Robustness of Multidimensional Counting Poverty Orders. 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1","display":"","copyAsset":false,"role":"figure","size":154758,"visible":true,"origin":"","legend":"\u003cp\u003eDimensions and indicators of quality of life\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/3bc4c9fc3b4258aa753b34f2.png"},{"id":98396800,"identity":"25abe1aa-56f4-415b-8c96-532790a860c4","added_by":"auto","created_at":"2025-12-17 10:46:02","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":175878,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial variation of the indices of dimensions A.1 to A.5 in the administrative regions of Peru (2007 and 2017)\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/041928f1588cc211285b16da.png"},{"id":98396801,"identity":"59e52c00-56eb-4ed7-9122-1f342eeebede","added_by":"auto","created_at":"2025-12-17 10:46:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":54616,"visible":true,"origin":"","legend":"\u003cp\u003eIQoL of urban population in administrative regions of Peru (2007 and 2017)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/530495545754d197b0d9432f.png"},{"id":98440430,"identity":"c425ed72-7d90-408f-9339-8ea5dd88f733","added_by":"auto","created_at":"2025-12-17 17:03:50","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":29153,"visible":true,"origin":"","legend":"\u003cp\u003eChange in IQoL values and dimension indices in the urban population in the administrative regions of Peru (2007 and 2017)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/165d92381770e9045793375f.png"},{"id":98441512,"identity":"2e7a720b-c420-49e9-86bc-7623b288799c","added_by":"auto","created_at":"2025-12-17 17:05:31","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":104868,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the index values of dimensions A.1 to A.5 with the IQoL value in the urban population in the administrative regions of Peru (2007)\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/dc5ab306ff2bb2be68fd2b1d.png"},{"id":98440530,"identity":"6f66d846-baf6-46a4-b487-a7350e799754","added_by":"auto","created_at":"2025-12-17 17:03:57","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":113794,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of the index values of dimensions A.1 to A.5 with the IQoL value in the urban population in the administrative regions of Peru (2017)\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/0e0df430c1798050b19588de.png"},{"id":98622481,"identity":"20d40790-7872-412a-ad54-3d1833de5af0","added_by":"auto","created_at":"2025-12-19 16:55:56","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1004474,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8259461/v1/195aa474-52af-44f0-af1e-ac8a32bceb94.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Beyond Growth: A Methodological Framework for Assessing Urban Quality of Life Disparities in Peru","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe quality of life (QoL) significantly influences a wide range of societal domains, including economic productivity and public health to environmental sustainability and social development. Understanding and evaluating QoL is therefore essential for guiding targeted policies, especially in regions facing persistent socio-economic inequalities. While economic growth is often assumed to lead to improved living conditions, numerous studies in the Global South, including Latin America, show that this relationship is neither linear nor uniform across social groups or geographical areas (Gasparini et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sekkat, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The need for multidimensional and spatially sensitive assessments of QoL has become particularly pressing in rapidly urbanising countries such as Peru, where aggregate economic progress coexists with deeply rooted regional disparities.\u003c/p\u003e \u003cp\u003eBuilding on this, our approach is theoretically grounded in two complementary strands of literature. First, the capability approach (Sen, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Nussbaum, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and related work on multidimensional poverty measurement (Alkire and Foster, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) provide the conceptual foundation for moving beyond income-based indicators. These frameworks conceptualise well-being as a set of real opportunities and functionings, not merely as monetary resources, and are thus directly aligned with our construction of a five-dimensional QoL index (A.1\u0026ndash;A.5). Importantly, we interpret legal identity (A.5 Undocumented) as a \u0026ldquo;gateway capability\u0026rdquo; conditioning access to other essential capacities such as health, education, and financial inclusion.\u003c/p\u003e \u003cp\u003eSecond, our spatial perspective is anchored in the literature on New Economic Geography (NEG) (Krugman, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) and regional inequality theories (Myrdal, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1957\u003c/span\u003e; Williamson, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1965\u003c/span\u003e; Harris and Todaro, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). These approaches emphasise the endogenous formation of core\u0026ndash;periphery structures through agglomeration effects, transport costs, and increasing returns in key services. For urban Peru, these dynamics imply that access to jobs, services, and infrastructure in metropolitan cores may reinforce inequality even during periods of aggregate economic growth. The Harris\u0026ndash;Todaro expected wage model explains rural\u0026ndash;urban migration as a function of anticipated returns, often sustaining urban poverty despite national poverty reduction, while the Williamson inverted-U hypothesis highlights the possibility of widening disparities during early and intermediate phases of development. By embedding our empirical analysis within these frameworks, we provide a theoretically robust interpretation of the observed spatial heterogeneity in QoL.\u003c/p\u003e \u003cp\u003eLatin America is among the most urbanised regions globally, with over 80% of the population living in urban areas. In Peru, the process of urbanisation intensified during the second half of the 20th century and accelerated further after the turn of the millennium, driven by internal migration and demographic pressures (Morley, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). While cities have traditionally been seen as engines of growth, innovation, and upward mobility (Glaeser, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), unplanned urbanisation in developing contexts has often exacerbated inequality, environmental degradation, and spatial marginalisation (Jedwab, Christiansen and Gindelsky, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Glaeser, Kahn and Rappaport, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). In Peru, up to one-third of the population resides in the capital Lima, including large numbers in informal settlements known as \u003cem\u003epueblos j\u0026oacute;venes\u003c/em\u003e, where residents face systemic challenges in housing, infrastructure, and access to public services (Azpitarte, Gallegos and Yalonetzky, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite significant reductions in national poverty rates during the 2000s\u0026mdash;largely due to favourable export conditions and social spending\u0026mdash;income inequality remains high. The Gini index in Peru decreased from 53.7 in 1997 to 40.2 in 2021, yet still indicates a significant concentration of wealth (World Bank, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). More importantly, monetary measures alone fail to capture disparities in access to health, education, and other non-income dimensions of well-being. Empirical studies show that human development losses due to inequality are substantial in Peru, particularly in education (Lopez-Calva and Ortiz-Juarez, 2012). Ethnic and geographic disparities further compound these inequalities. Up to 45% of indigenous populations such as the Aymara and Quechua live in multidimensional poverty, lacking access to adequate nutrition, schooling, or identification documents (INEI, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018a\u003c/span\u003e, UNDP and OPHI, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, Carranza Ko, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDocumentation status is a major determinant of access to rights and services. Without national identification documents or birth certificates, individuals are effectively invisible to the state. They are excluded from social programmes, unable to obtain loans or title deeds, and systematically disadvantaged in the labour market (INEI, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; DGPP MEF, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In rural and marginalised urban areas, undocumented status is frequently intergenerational, perpetuating cycles of exclusion. Recognising this, the Peruvian government implemented national plans (2005\u0026ndash;2015) to reduce undocumented populations, yet their reach and effectiveness remain limited (RENIEC, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Peters and Mawson, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eUnderstanding the complexity of urban inequality thus requires more than GDP or income metrics. Multidimensional poverty measures such as the Global Multidimensional Poverty Index (MPI), which includes health, education, and living standard indicators, offer a broader lens. However, existing metrics often lack spatial granularity or the ability to track long-term changes at regional or municipal levels (Torado and Smith, 2011; Santos and Villatoro, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In response to this gap, the present study proposes a methodological tool that builds composite indices of QoL using Peruvian census data from 2007 and 2017. These indices reflect five key dimensions\u0026mdash;aligned with international frameworks\u0026mdash;enabling the identification of patterns and persistence of regional disparities.\u003c/p\u003e \u003cp\u003eQoL assessment can follow either subjective or objective approaches. While subjective well-being data (such as life satisfaction surveys) have gained traction in global research (Yonk, Smith and Wardle, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), they are often unavailable in national censuses. Therefore, objective measurement based on available indicators\u0026mdash;such as housing conditions, access to utilities, school attendance, and healthcare infrastructure\u0026mdash;remains the most viable path for longitudinal and large-scale studies in Peru. Our index-based approach draws on this tradition, creating a replicable and scalable tool for identifying vulnerable regions and informing policy priorities.\u003c/p\u003e \u003cp\u003eUrbanised ecosystems present unique challenges for well-being. Factors such as air and noise pollution, green space availability, transport accessibility, and population density significantly affect residents health and quality of life (Lakes, Br\u0026uuml;ckner and Kr\u0026auml;mer, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Dizdaroglu, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Chen, Land and Li, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Flokov\u0026aacute; et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In Peru, infrastructure deficits and historical patterns of exclusion\u0026mdash;exemplified by the colonial \u0026ldquo;hub and spoke\u0026rdquo; transport design\u0026mdash;further exacerbate spatial inequalities (Czerny, C\u0026oacute;rdova-Aguilar and Rzucidło, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe goal of this study is twofold. First, we aim to present and validate a methodological framework for assessing QoL disparities using a composite index approach based on national census data. Second, we apply this method to evaluate spatial and temporal changes in QoL across urban areas of Peru between 2007 and 2017. The research is guided by four sub-objectives: (1) assessing the spatial shift in income poverty; (2) comparing changes in sub-index values across QoL dimensions; (3) constructing an aggregate Quality of Life Index; and (4) interpreting the relationships between the sub-dimensions and overall index values across time and space.\u003c/p\u003e \u003cp\u003eBy developing and applying this methodological tool, we aim to contribute both empirically and conceptually to the ongoing debate on urban inequality, multidimensional poverty, and sustainable development in Latin America. Our approach allows for consistent cross-temporal comparisons and the identification of long-term trends that may otherwise remain hidden in aggregated national statistics. The findings can support decision-makers in designing place-based interventions, allocating resources more equitably, and ensuring that the benefits of urban development reach all segments of the population\u0026mdash;not just those at the top.\u003c/p\u003e \u003cp\u003eMethodologically, the construction of our indices and the overall IQoL is consistent with OECD (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) recommendations for composite indicators, while also explicitly reflecting two key spatial concerns: the modifiable areal unit problem (MAUP) (Openshaw, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e1984\u003c/span\u003e) and spatial autocorrelation (Anselin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). These considerations strengthen the reliability and replicability of our results.\u003c/p\u003e \u003cp\u003eThis study addresses the following research question: did the period of economic growth between 2007 and 2017 reduce multidimensional inequalities in quality of life across urban regions of Peru? Building on the capability approach and New Economic Geography, we test two hypotheses: (H1) that aggregate economic growth does not automatically translate into reduced spatial inequalities of QoL; and (H2) that some capabilities, particularly legal identity, act as gateway conditions enabling access to other resources, while their absence reinforces disparities. By articulating these hypotheses, the study goes beyond descriptive analysis and contributes to the theoretical debate on the interplay between economic growth, spatial inequality, and multidimensional well-being.\u003c/p\u003e"},{"header":"Data and methodological procedures","content":"\u003cp\u003eThis study is based on data from the 2007 and 2017 Peruvian Population and Housing Censuses provided by the National Institute of Statistics and Informatics (INEI). These datasets were chosen due to their consistent methodology, objectivity, and accessibility (Carr-Hill, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Additional data on income poverty were obtained from the Peruvian Institute of Economics (IPE, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The analysis was conducted at the level of Perus 25 administrative regions (24 departments and the Constitutional Province of Callao), with a focus exclusively on the urban population. Urban areas were defined by the urbanisation rate \u0026ndash; the share of people living in urbanised zones.\u003c/p\u003e \u003cp\u003eTo analyse disparities in income and wealth distribution among the urban population, we recalculated the proportions of people living in income poverty (INEI) relative to the total urban population (IPE) (INEI, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; INEI, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; INEI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018c\u003c/span\u003e; IPE, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We visualised inequality using the Lorenz curve and interpreted results using the Gini index, which ranges from 0 (perfect equality) to 1 (maximum inequality). Income poverty was assessed both in terms of total and extreme poverty.\u003c/p\u003e \u003cp\u003eTo explore changes in regional disparities over time, we applied the concepts of beta and sigma convergence. Beta convergence examines whether regions with initially lower values (e.g. lower income levels) experience faster improvement over time. Sigma convergence assesses whether the variability among regions decreases. Both approaches used logarithmic transformations of the data. For beta convergence, we computed the average growth rate between the two periods; for sigma convergence, we analysed changes in standard deviation.\u003c/p\u003e \u003cp\u003eThe construction of the Quality of Life Summary Index (IQoL) was guided by a multidimensional approach covering five dimensions: income poverty, living standards, education, fertility, and documentation status (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach dimension consists of specific indicators (10 in total), which were standardised by expressing them as a proportion of the urban population.\u003c/p\u003e \u003cp\u003eTo facilitate interpretation, individual indices were created for each dimension. All indicators reflect deprivation \u0026ndash; i.e. lower values indicate better conditions. Therefore, we applied a normalisation technique suitable for minimisation criteria. Specifically, we used the Weighted Sum Approach (WSA), a method rooted in utility theory assuming linear utility functions (H\u0026uuml;belov\u0026aacute; et al., 2021). In the first step, each indicator was normalised, and a weighted matrix was created with equal weights assigned to all indicators. We then aggregated the values within each dimension to obtain dimension-specific indices. In the second step, we repeated the WSA procedure using the five-dimension indices as inputs to calculate the overall IQoL. Again, all dimensions were equally weighted. To provide a spatial interpretation, z-scores were calculated for each dimension (A1\u0026ndash;A5) using linear transformation. This expresses how many standard deviations each region differs from the national mean. Positive z-scores indicate better-than-average conditions, while negative scores reflect worse-than-average performance. Finally, we employed Local Indicators of Spatial Association (LISA; Anselin, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e1995\u003c/span\u003e) to visualise spatial patterns. This technique identifies:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eHot spots (HIGH-HIGH) \u0026ndash; clusters of regions with high index values,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCold spots (LOW-LOW) \u0026ndash; clusters of regions with low index values,\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSpatial outliers (HIGH-LOW or LOW-HIGH) \u0026ndash; regions that contrast with their surrounding areas.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eBetween 2007 and 2017, the total population of Peru increased by 10.7%, primarily driven by a 12% rise in the urban population. In contrast, the rural population declined by 8.1%, highlighting the ongoing process of urbanization. Notably, this demographic shift was not evenly distributed across the countrys geographical regions. In 2007, the Costa region accounted for 15\u0026nbsp;million inhabitants\u0026mdash;58% of the national population\u0026mdash;rising to 17\u0026nbsp;million by 2017 (a 13.8% increase). In contrast, the Sierra region experienced a population decline from 8.76 to 8.27\u0026nbsp;million (-5.7%), while the Selva region, despite being the least populous, saw a growth from 3.7 to 4\u0026nbsp;million (+\u0026thinsp;10.9%) (INEI, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; INEI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018c\u003c/span\u003e). Marked differences are also evident in the administrative distribution of the population. In 2017, nearly one-third (29.2%) of the population resided in the Lima administrative region, which overlaps geographically with the Costa. Additional concentrations of population were found in Piura and La Libertad (Costa), as well as in Arequipa and Cajamarca (Sierra). In contrast, the lowest population densities were recorded in Madre de Dios (Selva), and in Moquegua, Tumbes, and Tacna (Costa), as well as Pasco (Sierra). Census data from 2007 and 2017 further reveal that most administrative regions are predominantly urban. However, four regions\u0026mdash;Huancavelica and Cajamarca (Sierra), and Amazonas (Selva)\u0026mdash;remained majority rural. The highest urban population growth was recorded in Madre de Dios (+\u0026thinsp;45%), while Amazonas, Pasco, and especially Huancavelica experienced a decline, with Huancavelicas urban population falling by over 25%.\u003c/p\u003e\n\u003ch3\u003eSpatial Variation in Quality of Life Dimensions\u003c/h3\u003e\n\u003cp\u003eThe indices for each of the five QoL dimensions (A.1 to A.5) were analysed in terms of spatial distribution and temporal change between 2007 and 2017. Higher index values (particularly those indicated in dark green) were generally concentrated in the Costa region. In the Sierra, relatively high values were observed for dimension A.5 (Undocumented), while in Selva, improvements were most visible in A.4 (Fertility) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe aggregated IQoL score, based on all five dimensions, was calculated for each urban administrative region. Despite persistent spatial disparities, improvements were recorded in 76% of the regions, with a modest overall increase in IQoL (mean change: +0.060; median change: +0.066). The most notable positive changes occurred in Ayacucho and Apur\u0026iacute;mac (Sierra), which shifted from significantly below-average values in 2007 to near-average or higher levels in 2017. Conversely, 24% of regions recorded a decline, with the most pronounced negative shifts observed in Tacna (-0.01), Moquegua (-0.03), Callao (-0.05), Ucayali (-0.05), Lima (-0.07), and Loreto (-0.11) (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA breakdown of the individual dimension indices suggests that improvements in IQoL were most strongly influenced by changes in A.4 (Fertility) and A.5 (Undocumented), followed by A.1 (Poverty). However, some regions experienced declines in A.2 (Living Standards) and A.3 (Education), contributing to stagnating or deteriorating IQoL (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eRegional Discrepancies and Composition Effects\u003c/h3\u003e\n\u003cp\u003eThe contribution of each dimension to the overall IQoL reveals spatial inconsistency. In 2007, significant discrepancies were observed, particularly where a high value in A.5 (Undocumented) coincided with a low overall IQoL (e.g., Apur\u0026iacute;mac, Ayacucho, and Huancavelica). Similarly, in regions such as Tacna, Arequipa, and Moquegua, a lower A.3 (Education) index did not prevent an above-average IQoL. These mismatches persisted in 2017: for example, Ucayali showed high A.4 (Fertility) scores but low IQoL, while Lima and Tacna achieved high IQoL despite lower A.5 (Undocumented) scores (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe relative significance of each dimension varied by both geography and year. In Costa, all dimensions except A.5 (Undocumented) were consistently above average in 2017. Conversely, all dimensions in the Sierra remained below average, with the exception of A.5. In Selva, dimensions A.1 (Poverty) and A.5 (Undocumented) were particularly weak, while A.3 (Education) showed the steepest decline over time. Dimension A.2 (Living Standards) remained below average, although A.4 (Fertility) reached the national average in 2017.\u003c/p\u003e\n\u003ch3\u003eSummary of Key Findings\u003c/h3\u003e\n\u003cp\u003eIn alignment with the research objectives, the main findings are as follows:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eBetween 2007 and 2017, the proportion of income-poor individuals in urban areas declined, while income inequality remained largely unchanged.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eA comparison of temporal and spatial changes in QoL dimension indices revealed regional disparities. Although the share of the population with low Living Standard scores declined overall, the pace of improvement varied across administrative regions. Spatial inequalities in Education persisted, with the Selva region (particularly Ucayali, San Mart\u0026iacute;n, and Madre de Dios) seeing an increase in the number of individuals over age 15 without primary education or who were illiterate.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eFertility rates improved nationally, particularly among young adolescents (aged 12\u0026ndash;14), while high fertility (four or more children) remained concentrated in the Selva (notably Huancavelica and Apur\u0026iacute;mac).\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThere was an overall improvement in documentation, as fewer individuals lacked identity cards or birth certificates, although progress was limited in Lima and Callao.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eThe central and southern Costa regions exhibited consistently high IQoL in both years, though some (e.g., Lima and Callao) showed no further improvement between 2007 and 2017. Ayacucho and Apur\u0026iacute;mac improved markedly, transitioning from below-average to average IQoL, whereas Huancavelica (Sierra) and Ucayali (Selva) remained stagnant.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eImportantly, a single high or low value in one QoL dimension did not necessarily correspond to the final IQoL. For instance, Ayacucho and Huancavelica had high A.5 scores but low IQoL in both years. Similarly, Ucayali improved in A.4 but did not see a rise in IQoL, while Tacna, Arequipa, and Moquegua maintained high IQoL despite lower A.3 scores.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eA comparison of census data from 2007 and 2017 reveals that the administrative regions of Huancavelica, Pasco, and Amazonas experienced the most significant population decline, whereas Madre de Dios, Ica, and Arequipa recorded the highest population growth. These demographic shifts align with the observed values of IQoL\u0026mdash;regions with low IQoL tend to be population losers, while those with above-average IQoL are income losers. In general, rural areas have seen a population decline, driven by a combination of declining fertility and outward migration (Morley, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Migration has particularly affected the Sierra and Selva regions more than the Costa, reflecting disparities in employment opportunities and access to services (Todaro and Smith, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). One of the consequences of migration is a deepening of poverty, often associated with the separation of women from their families due to economic migration (Crivello, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Morley, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese results resonate with the theoretical expectations of the NEG framework and the Williamson hypothesis. The persistence of high disparities despite overall poverty reduction reflects strong agglomeration externalities in metropolitan cores such as Lima, which continue to attract resources and skilled labour, thereby reinforcing spatial concentration. The Harris\u0026ndash;Todaro expected wage mechanism further explains why migration towards urban centres may stabilise income inequality despite poverty reduction, as migrants enter the informal sector where underemployment prevails. Together, these theoretical perspectives illuminate why income inequality (as measured by the Gini index) remains largely unchanged, while spatial disparities diverge.\u003c/p\u003e \u003cp\u003eIn parallel, the findings also confirm the analytical relevance of the capability approach. Dimensions A.4 (Fertility) and A.5 (Undocumented) emerged as decisive for improving IQoL, while A.2 (Living Standards) and A.3 (Education) weakened outcomes in some regions. The uneven contribution of dimensions illustrates the non-substitutability of capabilities: high performance in one area (e.g. legal identity) does not automatically compensate for deficits in another (e.g. education). The case of Ayacucho and Huancavelica, where high documentation scores coexist with low overall IQoL, exemplifies this principle. Legal identity functions as an enabling mechanism, but without complementary improvements in education or living standards, its impact remains limited.\u003c/p\u003e \u003cp\u003eMethodologically, our adoption of the Weighted Sum Approach with equal weights, the reporting of spatial dependence through LISA, and explicit consideration of MAUP strengthen the robustness of these findings. By combining these statistical safeguards with theoretically informed interpretation, our analysis integrates both empirical and conceptual contributions to the study of urban inequality in Peru.\u003c/p\u003e \u003cp\u003eIn Latin America, including Peru, multidimensional poverty presents a more complex challenge than income poverty alone (Todaro and Smith, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Indigenous populations face a disproportionately high risk of income poverty\u0026mdash;up to half of their population is affected, with particularly high rates in the Amazon region. Although income inequality as measured by the Gini index has declined\u0026mdash;primarily due to national economic growth\u0026mdash;regional disparities persist. In some urban areas, inequality has even increased slightly (IPE, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Winkelried and Escobar, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), which may relate to the growing presence of the middle class (L\u0026oacute;pez-Calva and Ortiz-Juarez, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe quality of life is further reflected in the incidence of absolute poverty, which in 2017 was approximately three times higher in rural areas than in urban ones. For extreme poverty, this ratio reached up to eleven times. Nonetheless, urban poverty is often underestimated in official statistics (Parnell, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tacoli, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Mitlin and Satterthwaite, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Thanh, Anh and Phuong, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), particularly because informal settlements are frequently excluded. In addition, standard deprivation indicators often fail to provide decision-makers with sufficient information for effective intervention (Lucci and Bhatkal, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the positive spillover effects of urban development on surrounding rural areas are well documented (Ke and Feser, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Chen and Partridge, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). These effects are associated with larger urban populations and reduced rural poverty, demonstrating that urbanisation can stimulate economic development in adjacent rural zones (Cal\u0026igrave; and Menon, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur findings partially support those of Cal\u0026igrave; and Menon (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2012\u003c/span\u003e), who analysed six indicators\u0026mdash;female-headed households, primary education completion, unemployment, electricity access, sanitation, and malnutrition\u0026mdash;and found a poverty-reducing effect in Pasco, Lima, and Cajamarca in 2007, and in Jun\u0026iacute;n, Huancavelica, and Cajamarca in 2017. The present study, using a broader set of indicators, similarly identifies Jun\u0026iacute;n and Cajamarca as regions with the greatest improvement in IQoL, while also highlighting Cusco, Amazonas, Arequipa, San Mart\u0026iacute;n, Apur\u0026iacute;mac, and Ayacucho as regions with significant gains. Moreover, our analysis confirms that regional poverty disparities persist, with Selva and Sierra regions exhibiting higher concentrations of poverty compared to the Costa.\u003c/p\u003e \u003cp\u003eBetween 2006 and 2016, the Multidimensional Poverty Index showed that among its three dimensions, education was the most affected in Peru (Morley, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Borga and DAmbrosio, 2021). Our results confirm this, especially the impact of dimension A.3 (Education) on IQoL in the Sierra. In practice, schooling often ends after six years of primary education, and access to secondary education remains limited (Czerny, C\u0026oacute;rdova-Aguilar and Rzucidło, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As a response, initiatives have been launched to improve teacher training and evaluation, alongside financial support for higher education institutions (Prado, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Boitano and Abanto Aranda, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). According to Morley (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), investments in education, combined with economic growth, serve as an effective anti-poverty strategy for both the Sierra and Selva.\u003c/p\u003e \u003cp\u003eThe National Institute of Statistics and Informatics (INEI) used the Unmet Basic Needs (UBN) methodology to identify regions with above-average absolute and extreme poverty, including Loreto, Ucayali, Amazonas, San Mart\u0026iacute;n, and Pasco, while the best-performing regions were Callao, Lima, Tacna, Arequipa, and La Libertad (INEI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018c\u003c/span\u003e). Our results corroborate these findings: the greatest deterioration in dimension A.1 (Poverty) between 2007 and 2017 occurred in Loreto (-0.15), Ucayali (-1.10), and Amazonas (-0.08). Conversely, the most notable improvements in this dimension were observed in Apur\u0026iacute;mac (0.45), Ayacucho (0.25), and Huancavelica (0.24), partially differing from INEIs data (INEI, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2018b\u003c/span\u003e; INEI, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018c\u003c/span\u003e).\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThe analysis of poverty drivers and conditions conducive to development has enabled the identification of key developmental challenges in Peru, consistent with previous findings (Plasencia, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These challenges include:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eLimited access to infrastructure and basic services, which are fundamental prerequisites for poverty alleviation. Moreover, the quality of these services is frequently insufficient, further impeding progress.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003ePersistent inequality in educational access, despite evidence that households with members over the age of 14 who have completed primary education demonstrate higher chances of socio-economic advancement.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eHousehold composition and gender dynamics play a crucial role in determining living standards. Family size and the gender of the household head are significant predictors of well-being, with female-headed households often exhibiting more progressive development trajectories.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eLinguistic marginalisation continues to be a critical issue, as populations speaking only indigenous languages are significantly more likely to live in poverty.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eInequitable access to productive assets, such as housing, hinders the ability of poor households to formalise ownership. Legal recognition of assets substantially increases access to financial instruments, such as loans and credit.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eEntrepreneurial underdevelopment represents a further consequence of structural inequalities. The absence of enabling conditions stifles business activity, although evidence suggests that even small-scale commercial use of household space can improve living standards.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eAnother under-addressed issue is the prevalence of undocumented individuals. In many countries, the lack of civil registration\u0026mdash;particularly the absence of a birth certificate\u0026mdash;renders individuals effectively stateless, excluding them from access to education, healthcare, banking, and other essential services. As UNICEF (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e) asserts, a birth certificate confers the first rights of name and nationality, enabling participation in civic life.\u003c/p\u003e \u003cp\u003eIn Peru, the challenge of undocumented status persists on a national scale, disproportionately affecting rural populations, women, and children (INEI, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Woodhead, Dornan and Murray, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). In the poorest regions, up to 75% of the population lacks access to civil registration systems (Mennen, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Barriers include geographic inaccessibility, dysfunctional administrative institutions, and high associated costs (Czerny, C\u0026oacute;rdova-Aguilar and Rzucidło, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e; Cai, Selod and Steinbuks, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Children without official identification face increased risks of exclusion, illiteracy, and health vulnerability (Peters and Mawson, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A high degree of poverty is typically associated with a higher prevalence of undocumented individuals, particularly in the Selva region and in the departments of Lima and Arequipa (Azpitarte, Gallegos and Yalonetzky, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOur results confirm these patterns. Although the Lima and Arequipa regions achieved above-average IQoL scores in both 2007 and 2017, the Lima region experienced a decline from 0.83 to 0.76. This was largely attributable to a significant decrease in the index for dimension A.5 (Undocumented), which dropped from 0.81 to 0.43\u0026mdash;representing the largest decline among all departments studied.\u003c/p\u003e \u003cp\u003eIt is acknowledged that spatial assessments of quality of life are subject to methodological variability, depending on the selection of indicators and indices. This can hinder comparability across studies and regions. While this study is not exempt from such limitations, several measures were taken to mitigate them. The IQoL was constructed from a relatively comprehensive set of indicators, and census data were used to ensure consistency and methodological rigor in data collection and reporting.\u003c/p\u003e \u003cp\u003eWe also recognise that objective indicators may not fully align with subjective well-being. Empirical studies conducted in Peru have shown that increases in income or material security do not necessarily correspond with higher subjective life satisfaction. In some cases, individuals experiencing higher material deprivation report greater subjective well-being (Copestake et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Guillen-Royo (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) similarly notes that in the Peruvian context, consumption assumes symbolic and aspirational dimensions, influencing happiness through factors such as social status, peer comparison, and the desire to overcome marginalisation.\u003c/p\u003e \u003cp\u003eThe principal strength of this study lies in its longitudinal and spatial comparative approach, employing data from two census periods to capture regional disparities in quality of life over time. Although the data pertain to an earlier period, the methodology offers a solid foundation for future longitudinal analyses, including forthcoming census data. This paves the way for long-term monitoring of development outcomes and targeted interventions in the most vulnerable regions of Peru.\u003c/p\u003e \u003cp\u003eThe findings of this study highlight the necessity for integrated, territorially sensitive development policies in Peru that go beyond income-based measures of poverty. First, targeted investments in infrastructure and public services\u0026mdash;particularly in rural and indigenous areas\u0026mdash;are essential to enable social mobility and reduce spatial inequality. Second, there is a pressing need to ensure universal access to quality education, with a focus on overcoming linguistic and gender-based barriers. Special attention should be given to strengthening civil registration systems, especially in remote regions, to reduce the number of undocumented individuals. Policies aimed at promoting asset formalisation\u0026mdash;such as property titling programs\u0026mdash;must be linked with access to financial services and entrepreneurial support. Furthermore, social protection frameworks should explicitly include subjective dimensions of well-being, recognizing the social and psychological implications of poverty and exclusion. By implementing evidence-based and inclusive policies grounded in regional disparities, Peru can better respond to structural inequalities and support long-term improvements in the quality of life for its most vulnerable populations.\u003c/p\u003e \u003cp\u003eBeyond empirical findings, this research contributes to theory in two ways. First, it demonstrates that the capability approach requires context-specific operationalisation: in Peru, legal identity emerges as a critical \u0026ldquo;gateway capability\u0026rdquo;, extending the original framework of Sen (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1999\u003c/span\u003e) and Nussbaum (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Second, by applying NEG and inequality theories to longitudinal census data, our results nuance Williamsons inverted-U hypothesis (Williamson, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e1965\u003c/span\u003e) and the Harris\u0026ndash;Todaro model (Harris and Todaro, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1970\u003c/span\u003e), showing that spatial disparities may persist even under conditions of declining poverty. These contributions underline the need to integrate multidimensional poverty frameworks with spatial economic theories when analysing quality of life in rapidly urbanising societies. The proposed methodological framework, based on multidimensional indices and spatial econometrics, is transferable to other national contexts, thereby expanding the comparative study of quality-of-life disparities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eDH was responsible for conceptualisation, methodology, investigation, formal analysis, and data curation, in addition to writing the original draft, reviewing, validation, and supervision. LH contributed to visualisation, data curation, and formal analysis. AK was involved in conceptualisation, formal analysis, validation, and visualisation, as well as writing the original draft, reviewing, and editing. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThis research was supported by the Internal Grant Agency FRRMS MENDELU (Grant number: GA-FRRMS-22-015).\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eH\u0026uuml;belov\u0026aacute;, Dana; Kozumpl\u0026iacute;kov\u0026aacute;, Alice (2025), \u0026ldquo;Peruvian regions: demographic and socioeconomic data from the 2007 and 2017 censuses\u0026rdquo;, Mendeley Data, V1, doi: 10.17632/r74syfxwxj.1.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlkire, S., and Foster, J. (2011). Counting and multidimensional poverty measurement. Journal of Public Economics 95(7\u0026ndash;8): 476\u0026ndash;487. https://doi.org/10.1016/j.jpubeco.2010.11.006 \u003c/li\u003e\n\u003cli\u003eAnselin, L. (1995). 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InTech, 71\u0026ndash;95.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Multidimensional Poverty, Spatial Inequality, Capability Approach, Agglomeration Effects, Well-being Indicators","lastPublishedDoi":"10.21203/rs.3.rs-8259461/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8259461/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis article develops and applies a multidimensional framework to assess urban quality of life (QoL) disparities in Peru during a decade of economic growth (2007\u0026ndash;2017). Moving beyond income-based approaches, the study is grounded in the capability approach, which conceptualises well-being as real opportunities and functionings, and in theories of regional inequality derived from New Economic Geography. A composite QoL index (IQoL) is constructed from five dimensions\u0026mdash;income, living standards, education, fertility, and legal identity\u0026mdash;using census microdata. Legal identity is conceptualised as a \u0026ldquo;gateway capability\u0026rdquo; conditioning access to other resources and overall QoL. Methodologically, we apply the Weighted Sum Approach with equal weights, address the modifiable areal unit problem, and capture spatial dependence through Local Indicators of Spatial Autocorrelation (LISA). The results show that while income poverty declined, income inequality remained stagnant and spatial disparities widened. This pattern reflects agglomeration effects predicted by New Economic Geography, the Williamson inverted-U hypothesis, and the Harris\u0026ndash;Todaro expected wage model, which explain why migration and urbanisation sustain disparities despite poverty reduction. Fertility and documentation contributed most to improvements in IQoL, whereas living standards and education produced heterogeneous effects. The findings confirm the non-substitutability of capabilities: gains in one domain cannot compensate for deficits elsewhere. The study contributes by operationalising the capability approach in a Latin American context and linking multidimensional poverty analysis with spatial economic theories. It also offers a replicable methodological tool for monitoring QoL disparities across regions and over time.\u003c/p\u003e","manuscriptTitle":"Beyond Growth: A Methodological Framework for Assessing Urban Quality of Life Disparities in Peru","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 10:45:58","doi":"10.21203/rs.3.rs-8259461/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-15T04:40:35+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-12-15T04:30:37+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-12-08T04:25:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Humanities and Social Sciences Communications","date":"2025-12-02T10:31:58+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"humanities-and-social-sciences-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"palcomms","sideBox":"Learn more about [Humanities \u0026 Social Sciences Communications](http://www.nature.com/palcomms/)","snPcode":"41599","submissionUrl":"https://submission.springernature.com/new-submission/41599/3","title":"Humanities and Social Sciences Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"8fe771d4-778a-4fac-ab1e-807dba2c2b17","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":59638051,"name":"Social science/Development studies"},{"id":59638052,"name":"Earth and environmental sciences/Environmental social sciences"},{"id":59638053,"name":"Scientific community and society/Geography"},{"id":59638054,"name":"Social science/Geography"}],"tags":[],"updatedAt":"2026-05-13T10:53:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 10:45:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8259461","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8259461","identity":"rs-8259461","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}