Major Waves and Critical Juncture in Sino-Indian Geopolitical Rivalry in Nepal

International Journal of Multidisciplinary and Innovative Research ISSN(print): 3050-6883, ISSN(online): 3050-6891 Volume 02 Issue 11 November 2025 DOI: 10.58806/ijmir.2025.v2i11n03 Page No: 343-357 343 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) Omobolaji Adedasola, Aremu1, Seun Andrew, Eyiolawi2, Johnson Sunday, Ojewumi (PhD)3 1,2University of Hull, Hull, United Kingdom 3Adeyemi Federal University of Education, Ondo, Nigeria ABSTRACT ARTICLE DETAILS This study investigates the socio-economic and demographic determinants of financial well-being (FWB) disparities in the United Kingdom using the 2018 Financial Capability Survey (𝑛 = 4,492). Financial well-being was operationalised as a three-level ordinal variable (“struggling”, “squeezed”, “cushioned”) derived from a composite satisfaction index. Employing a proportional odds model (POM), the analysis reveals that ethnicity, gender, income, education, employment, region, and marital status significantly predict financial well-being disparities, while age has no statistically significant effect. The findings showed that black respondents (AOR=0.47,95% CI:0.26−0.81) are more likely to belong to lower FWB categories relative to White counterparts and male respondents (AOR=1.40,95% CI:1.19−1.64) are more likely to have higher odds ratio of belonging to higher categories of FWB. Household income was revealed to have a significant progressive increase effect on FWB disparities as the odds ratio of belonging to higher categories of FWB increases as household income increases (𝑟𝑎𝑛𝑔𝑒 𝑜𝑓 AOR=1.41− 2.88,𝑟𝑎𝑛𝑔𝑒 𝑜𝑓 95% CI:1.12−3.90). Higher education, employment stability, and geographical locations determine FWB outcomes. The findings has revealed the persistent structural inequalities in financial well-being within the UK and suggests the need for targeted policy interventions to promote inclusive financial resilience. Published On: 16 November 2025 Available on: https://ijmir.com INTRODUCTION At a pivotal juncture stands our contemporary world, striving to recover from a series of economic disruptions, environmental degradation, a global pandemic (COVID-19) (McKibbin & Fernando, 2023; Gagnon, Kamin & Kearns, 2023), persistent geopolitical conflicts (Yan & Piao, 2025; Afonso, Alves, & Monteiro, 2024) and the recent trade wars that have profoundly reshaped both intraand international economic activities. Moreover, in an increasingly interdependent global economy, no nation remains insulated from the economic and social reverberations of these crises, even when geographically or politically distant from their origins. The consequences and spillovers are most acutely borne by ordinary citizens, who experience these disruptions through successive inflationary pressures, soaring housing costs, and stagnant real wages that impact cost of living, thereby deepening socioeconomic inequalities across and within nations. However, these global disruptions are not the sole determinants of hardships faced by the common man. Equally culpable is the adoption of economic policies that, while often not well-intentioned, prove ineffective in alleviating poverty and, in many instances, exacerbate financial vulnerability and social inequality (Walker, Druckman & Jackson, 2021). While these policies could have fostered inclusive growth, they seem to expand existing disparities and undermine the financial well-being of individuals already burdened by uncontrollable external shocks. For instance, the introduction of austerity programmes in the United Kingdom (UK) following the 2008-2009 global financial crisis disproportionately affected the economically vulnerable, deepening the financial precarity of people with disabilities, single parents, and low-income earners (McKee et al., 2012). Welfare retrenchments and benefit caps will not only constrain disposable incomes but also eroded the social safety nets upon which many households/individuals relied. This was noted to have led to a significant increase in the mortality rate in the UK (Walsh et al., 2022) and caused about sixmonth decline in average life expectancy, with women disproportionately affected (Berman & Hovland, 2025). Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 344 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Similarly, the implementation of the two-child limit on benefits has been widely criticised for its adverse implications for household welfare, as it effectively penalises larger families and exacerbates child poverty (Child Poverty Action Group, 2025). It was estimated that 1 in 9 children is negatively impacted by the policy (Child Poverty Action Group, 2025). These policy directions, though often justified as measures for fiscal discipline and macroeconomic stability, have in practice intensified socio-economic disparities and weakened the financial well-being of the most disadvantaged groups in society. With the identification of the joint role of global and national economic policies on the financial well-being of individuals, it is equally succinct to note that these may not be uniformly experienced. Rather, they may vary significantly across socio-economic and demographic groups, reflecting some level of disparities in income, education, gender, and age. Conceptually, the notion of financial well-being has been defined in diverse ways within the literature; however, this study adopts the convergence of most definitions, which is the ability of individuals to meet current financial obligations while maintaining sufficient resources for future needs. For instance, Brüggen et al. (2017) presented financial well-being as “the perception of being able to sustain current and anticipated desired living standards and financial freedom”, which is similar to the conceptualisation of Kempson, Finney and Poppe (2017). Nonetheless, certain strands of the literature viewed financial well-being through a unidimensional lens either negatively, in terms of financial threats or stress/hardship (Kassie et al., 2025; Nasir et al., 2025; Jackson et al., 2025) or positively, in terms of satisfaction or resilience (Rahadian, Mardian & Firli, 2020; Sahi, 2013; DePianto, 2011). Recent literature has conceptualised and established the multidimensional nature of financial well-being (FWB). It has been wellestablished that FWB encompasses both objective financial conditions (income, assets, debt) and subjective perceptions such as confidence in meeting obligations and perceived economic control (Nanda & Banerjee, 2021; Kumar et al., 2021; Brüggen et al., 2017). FWB is often conceptualised through behavioural/cognitive, psychological, and sociological/economic frameworks, highlighting the interaction between financial literacy, decision-making, and social context (Garg, Priyadashi & Malik, 2024; Sorgente, Totenhagen & Lanz, 2021; Sehrawat, Vij, & Talan, 2021; Ponchio, Cordeiro & Gonçalves, 2019). Psychological perspectives emphasise the role of subjective self-control and optimism (Strömbäck et al., 2020), while sociological and economic approaches focus on material resources, household income, and occupational stability (Sahi, 2013; Arber, Fenn, & Meadows, 2014). Moreover, the interdisciplinary nature of the approaches adopted in theorising and conceptualising FWB also introduces some form of discrepancies and inconsistencies in empirical findings in the literature (Garg et al., 2024). Although financial well-being is commonly conceptualised as a product of personal capabilities and individual agency, it is equally susceptible to exogenous socio-economic and institutional forces that can neutralise the effectiveness of even strong financial capabilities. For instance, macroeconomic shocks such as the COVID-19 pandemic exacerbated financial inequalities, with lowerincome and precarious workers experiencing greater declines in financial well-being (Botha et al., 2021). Similarly, Barrafrem, Tinghög and Västfjäll (2021) observed that trust in government mitigated financial insecurity during crises, suggesting that institutional confidence plays a mediating role in subjective economic stability. Prior empirical studies on financial well-being have investigated the correlates between socio-economic and demographic and perceived financial well-being in different context. Although a strand of the literature argues for the insignificance of income in determining FWB (Roslan et al., 2025; Rahadian et al., 2020), numerous independent findings from various context has reveals its significant impact on FWB (Prakash et al., 2022; Bufe et al., 2021; Suh, 2021; Rahman et al., 2021; Chatterjee, Kumar & Dayma, 2019; Ritsalu & Murakas, 2019; Arber et al., 2014; Sahi, 2013; DePianto, 2011). Moreso, income-FWB nexus was found to be moderate by demographic and socio-economic factors like gender, wealth, and social comparison among others (Zyphur et al., 2015; Chatterjee et al., 2019). Despite this, empirical evidence on broader socio-economic and demographic determinants of FWB remains relatively limited and inconclusive, as most existing studies tend to emphasise financial capability constructs like financial literacy, education, knowledge, socialisation, behaviour, and attitude over structural or demographic influences (Chetloul et al., 2025; Kumar et al., 2023; Rahadian et al., 2020; Utkarsh et al., 2020). Moreover, this is not to argue that socio-economic and demographic factors are viable enough to understanding FWB but the notion of this study is that FWB though largely explained by financial capability constructs, its distribution may differ based on socio-economic and demographic factors such as age, gender, education level, income level, and ethnicity. For instance, evidence from the United States reveals intersectional disparities in FWB, as white males have been reported to have higher levels of financial satisfaction relative to other gender–race groups (DePianto, 2011). Similar patterns of gender-based disparities in FWB in the United States were also observed by Zyphur et al (2015). Also, the relationship between wealth and FWB varies by gender and personality traits, with neuroticism reducing FWB and wealth associated lower FWB among women but not men in the United States (Chhatwani, 2025). This evidence the existence of a gender-based disparities in FWB and at the same time provided an insight into the possibility that FWB might be subjectively viewed between the two genders. While one might have concluded that the male gender generally has higher FWB compared to their female counterpart, findings from Japan demonstrated otherwise with women exhibiting higher FWB and financial security than men (Sawada et al., 2025). Nonetheless, gender gaps persist in financial literacy and access to financial resources, which may obscure the true distribution of FWB (Gonçalves, Ponchio Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 345 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu & Basilo, 2021; Nanda & Banerjee, 2021). Moreover, there is yet not enough empirical evidence to further elucidate gender disparities in FWB in the UK. Studies conducted across developed and emerging economies have consistently demonstrated significant association between financial satisfaction and key socio-demographic variables such as age, occupation, and marital status (Sahi, 2013; Prakash et al., 2022). For instance, findings from Canadian-focused research indicates that Canadian Armed Forces members with dependents and lower ranks experience greater financial strain and lower satisfaction, while members with marital status of ‘separated’ report the lowest levels of FWB (Peach, 2019). Marital status was also found to interact with income to influence perceived well-being (Roslan et al., 2025), while family responsibilities and dependents shape financial resilience (Sorgente & Margherita, 2017). This further buttress Peach (2019) that marital dissolution or single parenthood and larger household size are commonly related with lower perceived FWB. Findings from the United States and Australia studies have respectively confirmed that older adults tend to exhibit higher levels of FWB, mainly because of better financial literacy, accumulated asset, and reduced financial obligations (Xiao, Chen, & Sun, 2015; Xue et al., 2019). Also, Lelisho, Wogi and Tareke (2022) argued that socioeconomic status tends to improve among middle aged individuals and that older age-groups were more likely to have higher socio-economic status. In contrast, FWB among younger adults appears more strongly influenced by financial socialisation, attitude toward money, and early financial experiences (Utkarsh et al., 2020). This pattern was particularly evident in emerging economics like India (She et al., 2022) and among adolescents and young adults in the United States, where lower FWB is commonly reported among them (Xiao et al., 2015). Education and employment have also emerged as crucial socio-economic and demographic factors in understanding FWB, although empirical evidence remain mixed across contexts. Findings from Indonesia, for example, signifies that financial behaviour, financial knowledge, and age are significant predictors of financial satisfaction whereas education and income exert no significant impacts, suggesting the importance of behavioural and age factors (Rahadian et al., 2020). This finding partially corresponds with Kumar et al. (2023) who found the limited predictive capacity of socio-demographic factors on FWB of individuals in India. Conversely, in the Moroccan context, a strong moderating role was found for education, age, gender, and financial literacy in shaping perceived well-being of people in Morocco (Chetloul et al., 2025). Lelisho et al. (2022) also revealed that education status as a statistically significant predictor of socioeconomic status which is closely related to FWB. Employment stability has also been consistently recognised as a determinant of FWB with studies revealing that unemployment in India and insecure job contracts in Australia is associated with significant lower levels of FWB (Chatterjee et al., 2019; Botha et al., 2021). Indeed, during COVID-19 pandemic, job insecurity among Australians was linked to a 29% decline in FWB, a persistent reduction across the entire FWB distribution, and a widening of FWB inequalities (Botha et al., 2021). More so, very little has been revealed about FWB disparities based on ethnicity/race and regional. For instance, only Mahdzan et al. (2020; 2019) and DePianto (2011) studies considered race/ethnicity with race disparities significantly evident in FWB in Malaysia and the US respectively. Moreover, few studies focusing on the UK overlooked this aspect as Arber et al. (2014) focused on how health and income inequalities influenced subjective FWB. Similarly, Suh (2021) highlighted significant disparities in financial resilience among British adults, shaped by income, home ownership, and retirement planning behaviours. These findings resonate with broader evidence that social inequality and structural barriers in the UK labour and housing markets translate into persistent disparities in FWB. Although the global literature on FWB has increased substantially, few studies have systematically examined how FWB varies across socio-economic and demographic segments in the UK. Much of the existing evidence has concentrated on objective indicators such as income or debt while neglecting subjective dimensions that capture satisfaction, perceived security and confidence. Moreover, most existing studies used sample size that are not nationally representative except for a limited number of recent contributions (Kassie et al., 2025; Sawada et al., 2025; Botha et al., 2025; DePianto, 2011), hence, constraining the generalisability of their findings to national context and limiting their applicability across different socio-economic settings. This indicates that the focus and national context of prior studies are not applicable to the UK, hence, justify the need for this study. Furthermore, existing UK-based studies (Suh, 2021; Arber et al., 2014) provided limited insight into disparities in FWB across different population groups. While both studies (Suh, 2021; Arber et al., 2014) utilised nationally representative data, the datasets employed are outdated data which might not be suitable in informing contemporary policy. For instance, Abber et al. (2014) drew on 2006 General Household Survey data comprising 7,743 Britain households, whereas Suh (2021) analysed the fourth wave of Wealth and Asset Survey of 2012/2014, focusing solely on individuals between the age 30 and 49. This indicates a significant gap that needed to be filled for contemporary policy initiatives as it does not capture the entire working age population. Therefore, the present study advances the existing literature by providing an updated empirical analysis of socio-economic and demographic disparities in financial well-being (FWB), drawing on data from the 2018 Financial Capability Survey (FCS) for the United Kingdom. It extends existing knowledge by (1) operationalising financial well-being as a multidimensional, subjective construct; (2) applying survey-weighted ordinal logistic regression, and (3) examining heterogeneity across gender, age, ethnicity, income, education, employment, and marital status. Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 346 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu The remaining parts of this research are as follows: Section 2 presents the methods and materials and methods of data analysis providing clear explanation into the study design, data sourcing and model specification and diagnostics. Section 3 presents the results of the analysis encompassing the descriptive statistics and the major findings from the regression analysis. Section 4 is used to discuss the findings and policy implications, while Section 5 provides conclusion of the findings METHODS AND MATERIALS Study design A cross-sectional research design is adopted in examining the socio-economic and demographic determinants of financial wellbeing disparities in the United Kingdom. The analysis of this study was based on data collected from the Financial Capability Survey (FCS) which was originally commissioned by the Financial Services Authority in 2005. Since 2015, Money and Pensions Service (formerly the Money Advice Service) has thus been managing the survey (Money Advice Service, 2020). The FCS was structured as a nationally representative survey encompassing England and the three devolved nations (Scotland, Wales and Northern Ireland) in the UK. Given that the FCS follows a repeated cross-sectional data collection structure, where each survey constitutes independent and nationally representative survey (Money Advice Service, 2020), it provides an appropriate framework for the present study, allowing the assessment of financial well-being across different socio-economic and demographic groups within the UK. Data source and collection procedures This study adopts the 2018 Financial Capability Survey (FCS) which is the third survey of the series of interview conducted across the UK between March and May 2018. This dataset was accessed from UK Data Service portal. This dataset was chosen because it represents a more recent survey data collection relative to the 2015 survey, thus, offering recent insights into financial capability and well-being among UK residents. The 2018 FCS is specifically suitable for this study as it contains comprehensive variables necessary to operationalise the constructs of this study. Since the unit of analysis of this study is focused on individuals and requires nationally representative data, the FCS is deemed appropriate as it provides detailed information at both individual and household levels and is designed to be representative of the UK adult population (see Figure 1 for the sample spread). In designing the survey, study participants aged 18 above were targeted by the FCS across the entire UK using a mixed mode approach of balancing between online and offline interview to ensure higher representativeness of both group of participants. The online interview targeted heavy internet users while face-to-face interview targeted individuals and households with less than six hours weekly internet usage. The sample design followed an exclusivity approach rather than an overlap option as used in 2015. The exclusivity approach ensures strict compliance of interview based on internet usage criteria. Individuals with more than 7 hours weekly internet usage are strictly permitted into the online panel sample while those with less than 6 hours weekly internet usage were only allowed to complete the face-to-face sample. This ensures there is no overlap between the two samples and avoidance of complication of the process and the data collected. Consequently, a larger sample size of 5,974 adults was successfully interviewed in 2018 survey relative greater than 2015 sample size (Money Advice Service, 2020) as opposed to adopting an overlapping option. Specifically, the composition of the interviews achieved are 4,035 online interviews and 1,939 face-to-face interviews. Of the entire sample size, 2,774 individuals were interviewed in England, 1,143 in Wales, 1,036 in Scotland and the remaining 1,021 in Northern Ireland (Money Advice Service, 2020). However, this study utilised only 4,492 sample size after cleaning the data by excluding all cases with “Do Not Know”, “In Another Way”, and “Prefer Not to Say”. The questionnaire covered a range of topics like retirement planning, financial resilience, satisfaction, confidence, savings, finance tracking, credit usage, attitudes and motivations, skills and knowledge, and demographics and other characteristics. Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 347 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Figure 1: Sample spread of the Financial Capability Survey, 2018 Source: Money Advice Service (2020) Study Variables The dependent variable for this study is Financial Well-being (FWB), conceptualised as an ordinal categorical variable following Money and Pensions Service (MaPS) Financial Wellbeing Segmentation model. Based on the 2016 MaPS model, FWB is classified into three broad (‘macro’) segments (i.e., ‘struggling’, ‘squeezed’, and ‘cushioned’) derived from the FWB index. In this study, before constructing the FWB index, a reliability test was conducted on the items measuring financial satisfaction using Cronbach’s alpha. The alpha value of 0.88 obtained from the test indicates a high level of internal consistency which confirms that the items are reliable and measure the intended variable. Subsequently, the mean of the six items capturing financial satisfaction was computed to generate the FWB index. This index was then categorised into three groups using a quantile (tertile) method with cut-offs points at 0.33 and 0.67 in R software. The quantile method was chosen to ensure that the distribution of the categories is statistically balanced and free from distribution bias. Accordingly, Financial Well-being (FWB) is categorised as: 𝐹𝑊𝐵= {1,𝑆𝑡𝑟𝑢𝑔𝑔𝑙𝑖𝑛𝑔 2,𝑆𝑞𝑢𝑒𝑒𝑧𝑒𝑑 3,𝐶𝑢𝑠ℎ𝑖𝑜𝑛𝑒𝑑 Where: FWB = 1 (“struggling”), represents individuals whose FWB index falls within the lowest quantile (1 - 4), indicating poor FWB. FWB = 2 (“Squeezed”), corresponds to individuals within the middle quantile (5 - 8), reflecting a moderate level of FWB. FWB = 3 (“Cushioned”), represents individuals in the highest quantile (9 - 12), denoting relatively strong or better FWB. The independent variables in this study are categorical variables and were derived through recoding and collapsing of their original categories. These include: the age of respondents (18-24, 25-44, 45-64, 65 – 74, 75+), which was retained in its original form; gender (male, female), with participants who selected “prefer not to say” excluded; and ethnic group (White, Black, Asian, Mixed, Arab and others), where “Arab” and “Others” were merged into a single category. We collapsed marital status (single, married, divorced/separated, widowed), particularly the “divorced” and “separated” categories into one category, thereafter, re-coded into short form and meaning names. Education/qualification level (GCSE or below, A-level/vocational, Diplomas, University degree or higher, studying and other) was similarly re-coded and collapsed. Employment status (employed, unemployed, retired/semi-retired, student/others) was simplified through the merging of closely related categories under the final categories. Originally, there are seven household income brackets from the original dataset, moreover, we grouped these into four meaningful income brackets as follows: up to £11,499, £11,500 - £24,999, £25,000 - £49,999, £50,000+ by collapsing and recoding them. Finally, the geographical location variable “region” has the final output as England, Scotland, Wales, and Northern Ireland after grouping all the English regions into a single category labelled England. Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 348 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu METHODS OF DATA ANALYSIS Descriptive data analysis Prior to conducting the main analysis of this study, it is considered best practice to examine the descriptive statistics of the dataset through cross-tabulations between the dependent variable and each explanatory variable. This procedure allows for the presentation of count and percentages of study participants within each category of the explained variable across categories of the explanatory variables. It also encompasses the chi-square test p-value to analyse the relationship of the explained and explanatory variables in the study. The cross-tabulation, being at least a two-dimensional table, therefore, facilitates straightforward comparison and interpretation of distributions across variables and categories. This analytical technique is thus adopted in this study to show both the counts and the percentages of the respondents in each category of financial well-being (FWB). Proportional odds model (POM) analysis This study adopted a logistic regression model in examining the socio-economic and demographic determinants of financial wellbeing disparities. According to Kassie et al. (2025), logistic regression or logit model is a modelling technique that enables the estimation of the relationship that exists between a categorical independent variable and any form of explanatory variables. Although logistic regression exists in several forms, the core or classical forms of logits are namely binary logistic, multinomial logistic, and ordinal logistic models (also known as proportional odds model). Moreover, the apt model specification is largely determined by the dependent variable measurement level. Furthermore, logit models can be specifically classified into two based on dichotomous and polytomous types, with the latter subdivided into multinomial and ordered logistic regressions, depending on whether categories of the response variable are ordered or unordered, respectively. Since the response variable for our model is ordered categorical variable (1 – struggling, 2 – squeezed, 3 – cushioned), the ordinal logistic regression or proportional odds model is the most apposite model. However, empirical studies do not rely on conjecture but on statistical testing for confirmation. In conducting the ordinal logistic regression analysis, the key assumption is the proportional odds assumption, which posits that the relationship between each pair of outcome categories is identical across all thresholds of the dependent variable (Kassie et al., 2025; Liu et al., 2023; Lelisho et al., 2022). Consequent upon the result of the model diagnostic, a proportional odds model (POM) was specified for the analysis. Model specification Let the ordinal response variable “financial well-being (FWB)” be 𝑌 and its ordinal response levels labelled as 1,2,3 representing “struggling”, “squeezed”, and “cushioned”. The following model is thus specified for the study: 𝑃(𝑌≤𝑗)=1 1+𝑒−(𝛼𝑗+𝛽𝑖′𝑋), 𝑗=1,2,3 (1) Such that equation (1) becomes log[ 𝑃(𝑌≤𝑗|𝑋) 1−𝑃(𝑌≤𝑗|𝑋)]=𝛼𝑗+𝛽𝑗′𝑋, 𝑗=1,2,3 (2) Equation (2) can also be referred to as a model with parallel or equal slopes (Liu et al., 2023). Where: 𝑃(𝑌≤𝑗|𝑥) 1−𝑃(𝑌≤𝑗|𝑥) is the cumulative probability link of the response variable in 𝑗𝑡ℎ category or lower. 𝑗 stands for the thresholds/categories of the response variable. 𝛼𝑗 represents the intercept of 𝑗𝑡ℎ threshold. 𝛽𝑗 indicates the vector of the ordinal logit regression coefficients for the factors at the 𝑗𝑡ℎ category corresponding to each covariate. 𝑋 represents the covariates vector with dimension q(1,2,…q) containing the observations on all 𝑞 explanatory variables. To estimate this model, the study adopts R Statistical Language, utilising packages such as survey, MASS, brant, and dplyr, among others. The preference for R over other statistical software such as STATA, SAS, or STATA stems from its open-source nature and its well-established capacity to handle complex statistical analyses. Moreso, R benefits from an extensive ecosystem where usercontributed packages help enrich the programme and enables the provision of specialised and updated tools for statistical and empirical analysis that enhance reproducibility, flexibility, and efficiency in both academic and applied research. The results from the model were presented as adjusted odd ratios (AORs) with corresponding 95% confidence intervals. Model diagnostics In order to test the proportional odds assumption, an ordinal logistic regression model was fitted using the “polr” function in the MASS package. Afterwards, the proportional odds assumption was tested on this model using Brant test. The null hypothesis for testing the proportional odds assumption is given in equation (3) as 𝐻0:𝛽1=𝛽2=⋯ 𝛽𝐽−1 (3) Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 349 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Although several statistical procedures such as the Score, Wald, Likelihood Ratio (LR), and Wolfe-Gould tests can be employed to assess the proportional odds assumption, this study adopts the Brant test, similar to the Wolfe-Gould test, because it has been shown to control Type I error more effectively than the Wald, LR, and Score tests (Liu et al., 2023). Moreover, both the Brant and WolfeGould tests are particularly suitable for studies with relatively small sample sizes, as they maintain estimation efficiency under such conditions (Liu et al., 2023). Consequently, to reject 𝐻0 for unmet proportional odds assumption, significant p-values (p<0.05) of each variable and the overall model are required (Kassie et al., 2025). Therefore, if the Brant test fails to reject the 𝐻0 then the proportional odds assumption holds and the proportional odds models is adopted. RESULTS Description of the perceived financial well-being Out of the 5,974 responses in the 2018 Financial Capability Survey (FCS), a total of 4,492 valid responses were retained after excluding cases with responses such as “Don’t know”, “Prefer not to say”, and “In another way”. As shown in Figure 1, among the 4,492 study participants, 1,509 (33%) were categorised as struggling, 1,725 (37.7%) as squeezed, and 1,338 (29.3%) as cushioned, based on quantile cut-offs of 0.33 and 0.67 on the calculated FWB index. Although the distribution of FWB categories appears relatively balanced, the squeezed group represents largest proportion, while the cushioned group constitutes the smallest. Figure 1: Pie chart of perceived financial well-being distribution The perceived financial well-being across socio-economic and demographic characteristics Table 1 presents the distribution of perceived financial well-being (FWB) categories (struggling, squeezed, and cushioned) across socio-demographic factors. We examine the association between the independent variables and FWB using chi-square (𝝌𝟐) test. The results of the analysis suggested that all explanatory factors associate statistically significantly (p < 0.05) with FWB. This signifies that perceived FWB systematically varies across most socio-economic and demographic factors in the UK. The relationship between ethnicity and perceived FWB was revealed to be statistically significant (p = 0.017). Furthermore, White respondents were mostly evenly distributed across the three categories of FWB (31.9% struggling, 38.3% squeezed, 29.8% cushioned). Moreover, a larger percentage of ethnic minority groups, particularly Black (45.4%), Asian (49%), Mixed (47.2%), and Arab and other ethnic groups (33.3%), fall within the struggling category. However, Black ethnic minority has the lowest representation in the highest category level of FWB (cushioned) with just 12.7%, while other minority groups range between 23.7% and 24.3%. The implication of this that people with ethnic minority backgrounds are likely to fall within lower financial well-being categories. It also shows that a significant relationship between age and FWB (𝑝 < 0.01) exist. The highest proportion of individuals found to be struggling financially are the younger adults aged 18-24 years with 47.9% classified in this category. In contrast, only 17.1% of older adults aged 65-74 years and 18.2% aged 75 years and above, reported to struggle financially. A similar pattern is observed in the cushioned category, where at least 47.0% of older adults are classified as having strong FWB, compared to only 15.5% among younger adults. This gradient reflects that FWB tends to improve with age, likely reflecting lifecycle effects such as income accumulation, asset ownership, and reduced financial obligations in later life. A larger percentage of the female sample (37.9%) reported to be struggling financially compared to males (28.2%). Moreover, 33.4% of male sample is found within the cushioned category. This pattern suggests persistent gender disparities in FWB. In addendum, more than half of the respondents (50.4%) were reported to be in households with the lowest income bracket (up to £11,499) and were basically struggling, while those in households earning £50,000 or more are predominantly financially cushioned (42.9%). This implies that income a key economic determinant of financial well-being. Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 350 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Similarly, individuals with a university degree or higher report the highest proportion of cushioned well-being (35.3%). In contrast, participants with GCSE or below are more concentrated in the struggling group (38%). This indicates that higher educational attainment might enhance financial capability and well-being. More than half of the unemployed (56.6%) and students/others (52.5%) are struggling, compared to only 30.8% of those employed. Conversely, retired or semi-retired individuals are largely cushioned (47.6%), reflecting post-retirement stability or accumulated financial security. Regional results indicates that England and Scotland have marginally higher proportions of individuals in the squeezed category, while Wales and Northern Ireland have relatively more respondents in the struggling group. These differences, though modest, suggest the possibility of geographic disparities potentially tied to labour markets and cost-of-living differences across UK regions. Furthermore, the results show that single respondents are most likely to be struggling (46.5%), while widowed and married respondents constitute the largest proportions of those in the cushioned group (45.8%, 37.6% respectively). This pattern may be attributable to greater financial stability arising from shared household resources, asset accumulation, and pension income which are typically associated with these groups. Table 1: The perceived financial well-being across socio-economic and demographic characteristics Variable Category Perceived financial well-being 𝝌𝟐 p-value Struggling Squeezed Cushioned Count (%) Count (%) Count (%) Ethnicity White 1348 (31.9) 1617 (38.3) 1261 (29.8) 0.017 Asian 86 (49) 47 (26.7) 43 (24.3) Black 31 (45.4) 29 (41.9) 9 (12.7) Mixed 33 (47.2) 19 (26.8) 18 (26) Arab and others 10 (33.3) 13 (43) 7 (23.7) Age 18-24 276 (47.9) 211 (36.6) 89 (15.5) <0.001 25-44 638 (34) 695 (37) 544 (29) 45-64 474 (33.2) 584 (40.9) 369 (25.9) 65-74 73 (17.1) 155 (35.9) 203 (47) 75+ 47 (18.2) 80 (30.7) 133 (51.1) Gender Female 856 (37.9) 839 (37.1) 565 (25) <0.001 Male 653 (28.2) 886 (38.3) 774 (33.4) Household income Up to 11,499 578 (50.4) 354 (30.9) 214 (18.6) <0.001 £11,500 - £24,999 455 (36.7) 453 (36.5) 333 (26.8) £25,000 - £49,999 353 (25.7) 578 (42) 443 (32.3) £50,000+ 123 (15.1) 340 (41.9) 348 (42.9) Education GCSE or below 458 (38) 412 (34.2) 336 (27.9) <0.001 A-Level/vocational 427 (35.9) 477 (40.1) 285 (24) Diplomas (HNC/HND/BTEC) 164 (36.6) 176 (39.3) 107 (24.1) University degree or higher 430 (25.9) 643 (38.7) 586 (35.3) Studying and others 30 (41.8) 18 (25.1) 23 (33.1) Employment Employed 899 (30.8) 1175 (40.2) 846 (29) <0.001 Unemployed 346 (56.6) 177 (28.9) 89 (14.5) Retired/Semi-retired 126 (16.1) 282 (36.2) 370 (47.6) Student/Other 138 (52.5) 92 (34.9) 33 (12.6) Region England 1243 (32.5) 1430 (37.5) 1145 (30) 0.024 Scotland 140 (33.6) 168 (40.4) 108 (26) Wales 80 (37) 80 (37.1) 56 (25.8) Northern Ireland 46 (38) 47 (38.2) 29 (23.8) Marital status Single 782 (46.5) 626 (37.2) 274 (16.3) <0.001 Married 573 (25.2) 847 (37.2) 855 (37.6) Divorced/Separated 119 (28) 183 (43.2) 121 (28.7) Widowed 35 (18.2) 69 (36) 88 (45.8) Socio-Economic and Demographic Determinants of Financial Well-Being Disparities in The United Kingdom: Evidence from The Financial Capability Survey (2018) 351 Volume 02 Issue 11 November 2025 Corresponding Author: Omobolaji Adedasola, Aremu Brant test of parallel regression assumption As stated earlier, the Brant test is employed to examine the proportional odds (parallel regression) assumption for independent variables in the ordinal logistic regression model. The Brant test results presented in Table 2 show an overall 𝝌𝟐 value of 32.76 (df = 25,p= 0.137) indicating that the proportional odds assumption holds for the entire model (p > 0.05). The results also reveal that all individual covariates met the assumption of proportional odds, with p-values ranging between 0.113 and 0.676. This implies that the null hypothesis of a proportional odds cannot be rejected, hence, the justification of the adoption of the proportional odds model for this study maintained. Table 2: Brant test of proportional odds assumption Variable 𝝌𝟐 df 𝒑>𝝌𝟐 Ethnicity 6.706 4 0.152 Age 5.244 4 0.263 Gender 2.514 1 0.113 Household income 5.401 3 0.145 Education 5.436 4 0.245 Employment 1.526 3 0.676 Region 3.45 3 0.327 Marital status 1.801 3 0.615 Overall 32.76 25 0.137 The proportional odds model: major findings and estimates The results from the multivariable ordinal logistic regression model presented in Table 3 reveal how socio-economic and demographic characteristics are associated with the likelihood of being in a higher financial well-being (FWB) category (squeezed or cushioned relative to struggling). The table presents the adjusted odds ratio (AOR), the standard error (se), the 95% confidence interval (CI), and the chi-square p-value. Out of all the covariates, only age has no statistically significant effect on FWB (p>0.05) of the study participants. From Table 3, ethnicity has a statistically significant effect on perceived FWB (p = 0.01) after adjusting for other covariates. Relative to White respondents, Black individuals have significantly lower log odds (AOR = 0.46,95% CI: 0.26−0.81) of being in a higher FWB category, suggesting they are more likely to experience poor financial well-being. Although the other minority groups AORs (Asian = 0.67,Mixed = 0.86,Arab and others = 0.69) are greater than the Black group, these are not statistically significant (p > 0.05). Nonetheless, the log odds ratios attributed to are lower relative to White individuals. This suggests that ethnic disparities in financial well-being within the UK are particularly pronounced between White and Black ethnic groups. Furthermore, gender has a statistically significant influence on differences in financial well-being (p< 0.01). Specifically, males were 1.40 times more likely than females to belong to a higher financial well-being category (AOR=1.40,95% CI:1.19−1.64). Additionally, household income significantly determines financial wellbeing and its differences across different income group (p< 0.01). Compared with the reference income level of individuals earning up to £11,499 annually, the likelihood of being in a higher FWB category increased progressively across income brackets. Respondents earning between £11,500 and £24,999 per annum were 1.41 times more likely (AOR=1.41,95% CI:1.12−1.77) to fall within a higher financial well-being category, while those earning between £25,000 and £49,999 annually are 2.05 times more likely (AOR=2.05,95% CI:1.60−2.61). Individuals with annual incomes of £50,000 or above had the highest likelihood, being 2.88 times more likely (AOR=1.40,95% CI:2.13−3.90) than those earning up to £11,499 to attain higher financial well-being levels. Like ethnicity, education and region were statistically significant at less than 1% significance level for few categories. Individuals with university degrees or higher degrees are 1.56 times more likely to fall within higher financial well-being relative to hose with GCSE or no formal qualifications in the UK (AOR=1.56,95% CI:1.24−1.97). Although A-level or vocational qualifications and those currently studying have higher likelihoods of being in a higher financial well-being level relative to GCSE holders, these were not statistically significant (p>0.05). This implies that financial well-being disparities was only explained by university degree or higher degrees held by individuals in the UK. Additionally, the results showed that individuals residing in the devolved countries in the UK are less likely to have higher financial well-being compared to their counterparts in England as the AOR ranges between 0.70 and 0.93 suggesting lower log odds. Though insignificant for Scotland and Wales at the 0.05% significance level, individuals residing in Northern Ireland have lower log odds (AOR=0.70,95% CI:0.58−0.85) of achieving higher financial well-being relative to those in England.