Multidimensional Index Research Articles

A multidimensional financial attractiveness index: Scoring and classification of financial systems

This research paper aims to benchmark the characteristics of financial systems for 102 countries worldwide from the period of 2005 to 2017. The financial systems’ database encompasses four main dimensions, each consisting of several variables for every indicator: (a) financial depth, (b) financial efficiency, (c) financial access, and (d) financial stability. The objective is to closely analyse the different factors that contribute to the attractiveness of financial and economic systems globally. Furthermore, this paper employs a literature review and an empirical modelling and classification of financial systems worldwide to assess their attractiveness. The modelling process utilizes two statistical analysis methods: discriminant analysis (PCA) and neural analysis. By doing so, this research paper aims to identify the most appropriate measures to strengthen these systems and economies. The main conclusion of the research is to establish a ranking of the world’s best countries and also the validation of the hypothesis that macroeconomic conditions are the effective determinants of the classification dimensions of financial systems.

Digital and traditional financial inclusion: Trends and drivers

This study examines the drivers of traditional and digital financial inclusion, emphasizing the catalytic role of digital inclusion in propelling traditional financial inclusion. Utilizing global data from 2004 to 2020, we construct two multidimensional financial inclusion indices: digital and traditional, and observe time trends. We then explore country-level determinants of traditional and digital financial inclusion with a special focus on banking system conditions. Finally, we investigate the role of digital financial inclusion in driving the traditional financial inclusion. Results show a significant improvement in financial inclusion over the period studied. The top countries for digital financial inclusion are primarily African developing nations, while high-income European countries lead in traditional inclusion. In terms of determinants, our main findings reveal that banking system conditions, mobile penetration, and urbanization significantly impact the level of digital and traditional financial inclusion. Moreover, we demonstrate that digital financial inclusion paves the way towards traditional financial inclusion in the medium term.

Target trial emulation using new comorbidity indices provided risk estimates comparable to a randomized trial

ObjectivesTo quantify the ability of two new comorbidity indices to adjust for confounding, by benchmarking a target trial emulation against the randomized controlled trial (RCT) result. Study Design and SettingObservational study including 18,316 men from Prostate Cancer data Base Sweden 5.0, diagnosed with prostate cancer between 2008 and 2019 and treated with primary radical prostatectomy (RP, n = 14,379) or radiotherapy (RT, n = 3,937). The effect on adjusted risk of death from any cause after adjustment for comorbidity by use of two new comorbidity indices, the multidimensional diagnosis-based comorbidity index and the drug comorbidity index, were compared to adjustment for the Charlson comorbidity index (CCI). ResultsRisk of death was higher after RT than RP (hazard ratio [HR] = 1.94; 95% confidence interval [CI]: 1.70–2.21). The difference decreased when adjusting for age, cancer characteristics, and CCI (HR = 1.32, 95% CI: 1.06–1.66). Adjustment for the two new comorbidity indices further attenuated the difference (HR 1.14, 95% CI 0.91–1.44). Emulation of a hypothetical pragmatic trial where also older men with any type of baseline comorbidity were included, largely confirmed these results (HR 1.10; 95% CI 0.95–1.26). ConclusionAdjustment for comorbidity using two new indices provided comparable risk of death from any cause in line with results of a RCT. Similar results were seen in a broader study population, more representative of clinical practice.

Does Wives’ Empowerment Moderate the Association Between Husbands’ Drunkenness and Their Intimate Partner Violence Toward Their Wives?—A Study in India

Although the impacts of husband alcohol consumption and wife empowerment on intimate partner violence (IPV) have been researched independently, there is scanty evidence on the moderating role of empowerment in reducing IPV among wives whose husbands drink alcohol in India. Using the data from the fifth round of the National Family Health Survey (2019–2021), this study examines the intermediating role of wives’ empowerment in the association between husbands’ drunkenness and IPV among currently married women (n= 16,360). Multidimensional women empowerment indices were constructed, and logistic regression was used to show the association between husbands’ drunkenness and the type of IPV among their wives with different empowerment levels. The IPV prevalence ranged from 12% for sexual assault to 47% for physical violence. The odds of physical violence (odds ratio (OR) = 3.83, 95% confidence interval (CI) [3.29–4.44]), sexual violence (OR = 3.88, 95% CI [ 2.98–5.05]), and emotional violence (OR = 4.94, 95% CI [4.02–6.05]) were higher among wives whose husbands were often drunk compared to those with never drunk husbands. However, with an increasing degree of husband drunkenness, the odds of emotional and sexual violence significantly reduced among wives with high empowerment indices. The results suggest the urgent need to curb alcohol consumption among men and the simultaneous socio-economic upliftment of women to reduce the prevalence of IPV. To further safeguard the welfare of women, it is also necessary to raise women’s understanding of current laws and their stringent enforcement.

Does the Small-Scale Trade in Border Areas Contribute to Poverty Reduction?

With the vigorous development of strategies such as the "Belt and Road" Initiative, the Border Areas and Minorities Development Program, and the Western Development Program, China's border trade has flourished. Under the strong leadership of the Communist Party of China and the People's Government, China's poverty alleviation efforts have achieved remarkable results that have attracted worldwide attention, successfully eradicating the absolute poverty that has plagued the Chinese nation for many years. In this context, to investigate the impact of border trade on multidimensional poverty in border areas, this paper constructs a multidimensional poverty index from five dimensions: economy, healthcare, employment, education, and living conditions. A fixed-effects model is employed to empirically examine the impact of border trade on multidimensional poverty in border areas and its underlying mechanisms. The research findings are as follows: (1) Border trade significantly reduces the level of multidimensional poverty in border areas. This conclusion remains valid after conducting a series of robustness tests and using lagged variables as instrumental variables to address endogeneity issues. (2) Mechanism analysis reveals that border trade primarily improves multidimensional poverty through employment promotion effects, infrastructure effects, and fiscal expenditure effects. (3) Heterogeneity analysis shows that the poverty reduction effect of border trade is more pronounced in areas with lower population densities, lower urbanization rates, and in the southwestern and northwestern regions. This paper provides a new perspective for building common prosperity in the new era and offers academic support and policy guidance for further promoting border trade and strengthening friendly regional exchanges.

Health-Oriented Evaluation and Optimization of Urban Square Space Elderly Suitability: A Case Study of Yiyang City Center

Urban squares are the main place for older adults’ daily activities. The spatial layout of the urban square is not only related to the construction of a healthy city and the equitable distribution of public resources, but also of great significance in enhancing the sense of gain, security, and happiness of older adults. This essay takes “health needs of older adults” as the perspective, and constructs a multi-dimensional evaluation index system of urban square space for elderly suitability in terms of physiological, psychological, and social health. It also takes six urban squares in Yiyang city center as an example and analyzes and evaluates them using GIS network analysis and the entropy weight method. The results of the study show that (1) Yiyang city center squares show the distribution characteristics of “dense in the middle and scattered around”, and the spatial distribution is not balanced, which leads to a small service area. (2) The overall intervention value of urban square space is mainly distributed between 0.291 and 0.710, with an average value of 0.481, and the overall intervention effect of the square is good. The reasons for differences in health interventions for older adults in urban square spaces centered on six aspects: the quality of the square space, the variety and convenience of travel lines, the tidiness of the environment, physical activities, safety, and social interaction. Based on this, this essay proposes three optimization strategies for the renewal of urban squares, the construction of characteristic squares, and the reconstruction of the urban square space system. The aim is to provide a reference for the future design and renewal of the elderly suitability of urban squares, so as to promote the construction of a healthy city.

Analysis of the Impact of Urban Infrastructure on Urbanization Processes at Different Levels from a Spatiotemporal Perspective

A comprehensive understanding of the heterogeneity of urbanization development at different levels and its influencing factors is crucial for promoting global urbanization and advancing China’s new urbanization. Using indicators related to urbanization development, a multidimensional index system was constructed based on five dimensions: population, economy, space, society, and ecology. Employing methods such as the Mann–Kendall test, Sen’s trend analysis, multiple linear regression, and spatial autocorrelation analysis, the spatiotemporal evolution characteristics of urbanization from 2000 to 2019 were analyzed comprehensively at national, economic zone, provincial, and prefectural city scales. The results indicate the following. (1) From 2000 to 2019, urbanization levels at all levels showed an overall upward trend, with the national urbanization rate increasing most rapidly at 5.39%. (2) Trend analysis reveals rapid and significant growth trends in urbanization at the national and economic zone scales, while urban-level changes exhibit greater diversity and spatiotemporal heterogeneity. (3) Spatial distribution patterns show that urbanization levels in the eastern coastal economic zones are significantly higher than those in the northeastern economic zones, highlighting pronounced regional disparities in development and agglomeration effects in economically advanced regions and provinces. (4) Regression analysis demonstrates that spatial urbanization significantly influences urbanization development in China, with urban infrastructure playing a crucial role across different levels.

Construction of Multidimensional Deprivation Index of a Small Sample of TB Patient in Ranchi District by Using Alkire-Foster Method

Construction of Multidimensional Deprivation Index of a Small Sample of TB Patient in Ranchi District by Using Alkire-Foster Method

2319 Deficits comprising multi-dimensional frailty indices based on routine data: sub-analysis of a scoping review

Abstract Introduction The frailty index (FI) is a frailty assessment tool calculated as the proportion of the number of deficits, or ‘things that individuals have wrong with them’, to the total number of variables in the index. Routine health and administrative databases are valuable sources of deficits to automatically calculate FIs. There is large heterogeneity in the deficits used in FIs. This sub-analysis of a scoping review on routine data-based FIs aimed to describe and map the deficits used in multi-dimensional FIs. Methods Seven databases were searched to find literature published between 2013 and 2023. The main inclusion criterion was multi-dimensional FIs constructed from routinely collected data. Multi-dimensional FIs should have deficits in at least two of the following categories: ‘symptoms/signs’, ‘laboratory values’, ‘disease classification’, ‘disabilities’, and ‘other’. Results Of the 7526 publications screened, 57 distinct FIs were identified from 56 included studies. Most FIs were developed in the USA (n = 15) and in hospital settings (n = 19). The most dominant data source of deficits to calculate the FIs was hospital records (n = 21). Twenty-five FIs were developed for specific conditions and populations, for example, cancer, HIV, dementia, organ transplant recipients, and veterans. The median number of deficits used in the FIs was 36 (range = 5–72). Almost all the FIs (n = 56) had at least one deficit in the ‘symptoms/signs’ category, followed by ‘disease classification’ (n = 55) and ‘disabilities’ (n = 50). Approximately two-thirds of all the deficits were ‘symptoms/signs’ and ‘disease classifications’. Conclusion These findings highlight the reactive approach to frailty assessment, as most of these FIs were calculated from hospital data and used symptoms/signs and diseases as deficits. Given the heterogenous manifestations and long-term impacts of frailty, using a more proactive approach that leverages non-clinical routine data is warranted to prevent frailty development and progression.

A transitional care program in a technologically monitored in‐hospital facility reduces the length of hospital stay and improves multidimensional frailty in older patients: a Randomized Clinical Trial

BackgroundLonger length of hospital stay (LOS) negatively affects the organizational efficiency of public health systems and both clinical and functional aspects of older patients. Data on the effects of transitional care programs based on multicomponent interventions to reduce LOS of older patients are scarce and controversial.AimsThe PRO‐HOME study aimed to assess the efficacy in reducing LOS of a transitional care program involving a multicomponent intervention inside a technologically monitored in‐hospital discharge facility.MethodsThis is a Randomized Clinical Trial on 60 patients (≥65 years), deemed stable and dischargeable from the Acute Geriatrics Unit, equally assigned to the Control Group (CG) or Intervention Group (IG). The latter underwent a multicomponent intervention including lifestyle educational program, cognitive and physical training. At baseline, multidimensional frailty according to the Multidimensional Prognostic Index (MPI), and Health‐Related Quality of Life (HRQOL) were assessed in both groups, along with physical capacities for the IG. Enrolled subjects were evaluated after 6 months of follow‐up to assess multidimensional frailty, HRQOL, and re‐hospitalization, institutionalization, and death rates.ResultsThe IG showed a significant 2‐day reduction in LOS (median days IG = 2 (2–3) vs. CG = 4 (3–6); p < 0.001) and an improvement in multidimensional frailty at 6 months compared to CG (median score IG = 0.25(0.25–0.36) vs. CG = 0.38(0.31–0.45); p = 0.040). No differences were found between the two groups in HRQOL, and re‐hospitalization, institutionalization, and death rates.DiscussionMultidimensional frailty is a reversible condition that can be improved by reduced LOS.ConclusionsThe PRO‐HOME transitional care program reduces LOS and multidimensional frailty in hospitalized older patients.Trial registration: ClinicalTrials.gov n. NCT06227923 (retrospectively registered on 29/01/2024).

Multidimensional Financial Metrics for Corporate Financial Risk Assessment and Early Warning Mechanisms

By constructing a comprehensive multi-dimensional financial index evaluation system, this study effectively identifies, evaluates, and forewarns the financial risks of enterprises. Utilizing public financial data from S&amp;P Global and Alpha Vantage, a BP neural network-based enterprise financial risk assessment framework is built, and the KMO method analyzes different risk factors. This comprehensive evaluation model, paired with a risk warning mechanism, assesses financial situations through financial leverage, profitability, solvency, operating efficiency, growth indicators, and cash flow indicators. The example analysis shows: 1) The RMSE value ranges from 46.71 to 64.94, indicating high accuracy and stability in predicting financial risk; 2) The R value is very close to 1, demonstrating high stability.

Climate change exposure, financial development, and the cost of debt: Evidence from EU countries

Utilising climate-related narratives in conference call transcripts to measure firm-level exposure to climate risks, we examine the association between such exposure and the corporate cost of debt financing. Using a sample of 21 European countries from 2001 to 2020, we find that firms exposed to greater climate change experience higher debt costs. The impact is even more extreme when using climate-related opportunity and regulatory exposure measures. We further find critical economic channels through which the higher debt costs occur: financial development and credit supplies. Specifically, our findings hold only for firms in weakly developed financial markets and institutions as measured by the new broad-based multi-dimensional financial development indices. We also find some other conditioning factors. Firstly, the higher the carbon intensity level, the greater the debt cost a firm with more climate change exposure must pay. Secondly, debtholders appear to punish firms with high environmental and social disclosure that are exposed to more climate change. Thirdly, the findings are more pronounced in financially constrained firms.

MULTIDIMENSIONAL POVERTY MODELING IN CENTRAL JAVA, DI YOGYAKARTA, AND EAST JAVA PROVINCES

Indonesia has a National Medium-Term Development Plan (RPJMN) for 2020-2024 to reduce the poverty rate to 6 to 7 percent. However, the poverty rate has only declined by less than one percent in the past year, indicating the need for optimization to achieve the goal. Despite being located in Java, the center of development and economy in Indonesia, the poverty rate in Central Java, DI Yogyakarta, and East Java still exceeds the national average. This study used SUSENAS 2023 March KOR data to address this challenge and examine the multidimensional indicators affecting poverty. The Alkire-Foster method was used to obtain the Multidimensional Poverty Index (MPI) number, which was then analyzed using the Structural Equation Model (SEM) with the Asymptotically Distribution-free (AD-f) method approach. SEM is used to observe latent variables that cannot be measured and the relationship between variables that form a multidimensional poverty index. AD-f method approach is used to overcome data non-normality in SEM processing. The study revealed that the percentage of multidimensional poverty in the three provinces is higher than monetary poverty due to the household unit of analysis used. The standard of living dimension was the most deprived in most households, followed by the health dimension. To tackle this issue, the study recommends optimizing access to the Internet, assets, preschool participation, and nutrition.

Decomposition of intra-household disparity sensitive fuzzy multi-dimensional poverty index: A study of vulnerability through Shapley machine learning

The well accepted multi-dimensional measures have failed to properly project the vulnerability of human-beings towards poverty. Some of the reasons behind this inability may be the failure of the existing measures to consider the graduality within the concept of poverty and the disparities within the household in wealth distribution. So, this work wants to develop a measure to estimate the vulnerability of households in becoming poor through incorporating the intra-household disparities through the factors which suffer from graduality. The decomposition of the grade of vulnerability on the causal factors is also under the purview of this work. To that respect the idea of fuzzy logic and decomposition through artificial intelligence has been used to develop a mathematical framework. So, the idea of Shapley Value Decomposition method has been used extensively. This decomposition is implemented here with the help of Shapley Machine Learning. This decomposition will help the planners to locate the role of different dimensions behind the vulnerability of human beings to become poor more efficiently.

Estimating vulnerability to income and multidimensional poverty: a new methodological approach

Purpose The purpose of this paper is to propose a new vulnerability to income and multidimensional poverty estimation index (VIMPI). This index is designed to measure the likelihood of individuals falling into and remaining in poverty. Design/methodology/approach The paper introduces a new methodology that integrates the concepts of the well-being gap, individual and indicator-specific weighting, and vulnerability. This approach is simple to apply and accurately measures vulnerability with less susceptibility to measurement error and outliers. The index satisfies all poverty and vulnerability axioms, including transferability and monotonicity. The newly proposed method has been applied to Namibian and Ghanaian data and compared with similar techniques. Findings The results showed that Ghana's vulnerability to income and multidimensional poverty was 37.9% and 56%, respectively. Of the 37.9% of vulnerable individuals, 23.4% were at risk of falling into poverty, while 14.57% were at risk of remaining in poverty. These findings demonstrate the effectiveness of VIMPI in accurately estimating vulnerability to poverty and its potential to inform targeted policies to alleviate poverty. Originality/value This paper proposes a new methodology to estimate vulnerability to income and multidimensional poverty.

A multi-dimensional access index: Exploring emergency food assistance in New York City

Access to resources and services needs to be considered from a multi-dimensional perspective to capture the complex landscape of human experience. The robust body of research exploring food access has multiple limitations that create an incomplete view of food access, like studies limited to only one mode of transit and inconsistent methods across studies which limit generalizability. This study proposes a framework to formulate a multi-dimensional access index that considers travel time, operating hours, and availability of transit infrastructure across space. We use food pantries in New York City as a case study since there are relatively few of them and they have limited opening hours. We propose an index that quantifies spatiotemporal access by different modes of transportation and takes operating hours of food pantries into account during a one week time period. We compare our results to two traditional access measures and demonstrate that our index provides a significantly different measure of access. We utilize this index to highlight areas of high need but low resources, which shows the importance of this tool to policy makers and service providers. We use our experience of developing this index to highlight the challenges with quantitative analysis of human experience. Our tool is reproducible through an open-access software, which allows researchers and policy-makers to utilize it with parameters that reflect their communities.

Energy poverty and health in Turkey: Evidence from Longitudinal data

This study critically examines the effect of energy poverty on health in Turkey between 2018 and 2021 using the Income and Living Conditions Survey Longitudinal Micro Data Set. It considers the multidimensional structure of household energy poverty and its individual effects on health, resulting in several significant findings. Firstly, the multidimensional energy poverty index at the household level is obtained as a continuous variable using principal component analysis, considering subjective and objective indicators of energy poverty. Secondly, the impact of energy poverty on health at the individual level and the effect of several socioeconomic variables, including unobserved heterogeneity, are estimated with the random effects ordered logit model. Finally, commonly used measures of energy poverty in the literature and their health impacts are compared. The robustness analysis results show that the model's goodness of fit is highest when the multidimensional energy poverty index is constructed using principal component analysis. In addition, the analysis results show that unobserved heterogeneity across individuals significantly impacts health. These results indicate that decentralized policies should be implemented to increase policy effectiveness in combating energy poverty.

Spatial prediction of poverty in Gauteng province (South Africa) in‐between Censuses using land use datasets

AbstractTo realize the first sustainable development goal of ending “poverty in all its forms everywhere,” local governments in South Africa need to implement informed targeted policy interventions based on up‐to‐date data and sound analytics. Statistics South Africa (Stats SA) Censuses reveal the socioeconomic circumstances of people living in South Africa but are only conducted every 10 years. As a result, most analytical studies done in‐between Censuses rely on outdated socioeconomic data. This study demonstrates how poverty levels in one of the provinces of South Africa, Gauteng, can be predicted when up‐to‐date Census datasets are not available. The spatial lag model is used to explain the relationship between the South African Multidimensional Poverty Index (SAMPI) and statistically significant variables extracted from land use datasets (i.e., land areas classified as built‐up, informal, residential, township, and non‐urban), and to ultimately predict the levels of poverty. Out‐of‐sample predicted poverty levels obtained based on the spatial lag model correlate with the actual levels of poverty thereby reflecting known spatial patterns of the levels of poverty in Gauteng province.

Financial stability and financial inclusion: a non-linear nexus

PurposeThe purposes of the paper are as follows: (1) Analysing the effect of financial inclusion on financial stability. (2) Examining whether financial inclusion non-linearily impacts financial stability. (3) Analysing whether the effect of financial inclusion varies across quantiles of financial stability. (4) Investigating whether dimensions of financial inclusion affect financial stability differently. (5) Examining whether the effect of financial inclusion on financial stability depends on competitiveness of the banking industry.Design/methodology/approachUsing panel data for 19 African countries for the period 2006–2022, we first developed multidimensional index of financial inclusion using two-stage indexing approach. Then employing panel semiparametric regression, we analyse the non-linear nexus between financial stability and financial inclusion. We further employ panel quantile regression to investigate the differential effect of financial inclusion at different quantiles of financial stability. We also employed two-stage least squires, and alternative measurement of financial stability as robustness checks.FindingsEmploying panel semiparametric regression, we demonstrate that the financial inclusion-stability nexus exhibits non-linearity: below (above) threshold level financial inclusion promotes (reduces) financial stability. Employing panel quantile regression, we find that the effect of financial inclusion increases at higher quantiles of financial stability. We further demonstrate that the effect of financial inclusion on financial stability is pronounced in a more competitive bank industry. The findings are robust to two-stage least squares estimation, and alternative measurement of financial stability. The results suggest that keeping a balance between achieving stable and inclusive financial system, and ensuring a competitive banking industry are essential to achieve bank soundness while promoting financial inclusion.Originality/valueThe study incrementally contributes to the literature related to the financial inclusion – stability nexus in four-fold. First, unlike studies that relied on some indicators of financial inclusion, we employed the effect of multidimensional financial inclusion on financial stability and further examined whether or not the effect varies across financial inclusion dimensions. Second, unlike studies that assumed a linear nexus between financial inclusion and stability, employing panel semiparametric regression, we investigated for non-linear relationship between the two. Employing a novel panel quantile estimation approach, we further scrutinised whether the effect of financial inclusion varies across quantiles of financial stability. Third, to our knowledge, our study is the first to examine the effect of multidimensional financial inclusion on bank soundness in Africa.HighlightsWe find a non-linear nexus between financial inclusion and financial stability.Financial inclusion below (above) threshold enhances (reduces) financial stability.The effect of financial inclusion is pronounced at higher quantiles of financial stability.The effect of financial inclusion on financial stability depends on bank competition.The results hold across different dimensions of financial inclusion.

Multidimensional Poverty and its Determinants in Somalia: A Household Level Analysis

Purpose: Poverty is widespread and problematic in least developed countries such as Somalia. The main objective of this study is to analyze the extent and determinants of multidimensional poverty in Somalia. Methodology: To achieve this objective, this study used the Somali Health and Demographic Survey carried out by the Somali National Statistics Bureau in 2020. Alkire and Foster’s methodology was used to measure the extent of multidimensional poverty, and an ordered logistic regression model was employed to identify its determinants at the household level. Findings: The results of the descriptive analysis show that 84.2 percent of the sampled households are multidimensionally poor, while the intensity of poverty and adjusted headcount ratio were 56.8 percent, and 0.479 respectively. Meanwhile, the study found that the living standard dimension was the major contributor (45 percent) to the overall multidimensional poverty index, followed by education and health dimensions, contributing 28.2 percent, and 26.8 percent, respectively. The ordered logit results indicate that household size significantly increases the likelihood of a household’s status being multidimensionally poor. In contrast, household education, employment of at least one household member, livestock ownership, ownership of agricultural land, and having a bank account significantly reduce the probability of being multidimensionally poor. Unique Contribution to Theory, Practice and Policy: Based on these findings, the study recommends that the government and international partners target the living standards dimension to reduce multidimensional poverty, improve quality and quantity of education, expand employment opportunities, promote financial inclusion, and foster the livelihoods of households involved in agriculture and livestock.